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As effective onchocerciasis control efforts in Africa transition to elimination efforts , different diagnostic tools are required to support country programs . Senegal , with its long standing , successful control program , is transitioning to using the SD BIOLINE Onchocerciasis IgG4 ( Ov16 ) rapid test over traditional skin snip microscopy . The aim of this study is to demonstrate the feasibility of integrating the Ov16 rapid test into onchocerciasis surveillance activities in Senegal , based on the following attributes of acceptability , usability , and cost . A cross-sectional study was conducted in 13 villages in southeastern Senegal in May 2016 . Individuals 5 years and older were invited to participate in a demographic questionnaire , an Ov16 rapid test , a skin snip biopsy , and an acceptability interview . Rapid test technicians were interviewed and a costing analysis was conducted . Of 1 , 173 participants , 1 , 169 ( 99 . 7% ) agreed to the rapid test while 383 ( 32 . 7% ) agreed to skin snip microscopy . The sero-positivity rate of the rapid test among those tested was 2 . 6% with zero positives 10 years and younger . None of the 383 skin snips were positive for Ov microfilaria . Community members appreciated that the rapid test was performed quickly , was not painful , and provided reliable results . The total costs for this surveillance activity was $22 , 272 . 83 , with a cost per test conducted at $3 . 14 for rapid test , $7 . 58 for skin snip microscopy , and $13 . 43 for shared costs . If no participants had refused skin snip microscopy , the total cost per method with shared costs would have been around $16 per person tested . In this area with low onchocerciasis sero-positivity , there was high acceptability and perceived value of the rapid test by community members and technicians . This study provides evidence of the feasibility of implementing the Ov16 rapid test in Senegal and may be informative to other country programs transitioning to Ov16 serologic tools .
Onchocerciasis , commonly known as river blindness , is caused by the filarial parasite O . volvulus ( Ov ) that affects an estimated 37 million people , with an estimated 187 million living in areas at risk of infection , primarily in Africa . [1 , 2] An estimated 1 . 1 million disability-adjusted life years ( DALYs ) were lost in 2015 due to onchocerciasis , as it can lead to severe and disfiguring skin disease , visual impairment , and eventually blindness . [3] Onchocerciasis especially affects poor rural communities and the risk of infection is substantially higher among socioeconomically disadvantaged groups . [4] In Africa , efforts to date have focused primarily on disease control through mass drug administration ( MDA ) with ivermectin , an antiparasitic drug donated by Merck . [5 , 6] . Recent evidence from Sudan , Senegal , Mali and Uganda suggests elimination is possible in Africa as it is in the Americas . [6–11] In response to this success , the global strategy has shifted from disease control to disease elimination . [6 , 12] The 2016 World Health Organization ( WHO ) guidelines on stopping MDA and verifying elimination describe three phases of onchocerciasis elimination programs that require different diagnostic tools: transmission suppression , transmission interruption , and transmission elimination . [13] The standard method is direct observation of the Ov microfilaria in a skin snip biopsy using microscopy . Skin snip microscopy is highly specific and able to detect active infections , but has diminished sensitivity in low-prevalence settings . As the prevalence of onchocerciasis in endemic communities decreases , more sensitive diagnostic tests are needed . [14] Ov16 serology , used to detect IgG antibodies to the Ov16 antigen in a sentinel population of children under ten years , is now recommended to determine if interruption of transmission of Ov has occurred . [13] Laboratory-based Ov16 ELISA ( enzyme-linked immunosorbent assay ) is one method to measure these markers , though it requires collecting samples in the field to transport to a laboratory setting for analysis . Currently , there is no standardized commercially available Ov16 ELISA so variations in protocols and procedures exist across labs . [15 , 16] In 2014 , a field deployable , rapid diagnostic tool that could be more easily integrated into current onchocerciases surveillance programs in endemic countries was developed and made commercially available ( SD BIOLINE Onchocerciasis IgG4 rapid test , referred to here as Ov16 rapid test ) . [17 , 18] Performance of the Ov16 rapid test continues to be evaluated in the field and current global research priorities focus on operational and implementation research to demonstrate utility and increase access of the Ov16 rapid test , particularly in low prevalence settings which have undergone multiple rounds of MDA . [14 , 19] In Senegal , MDA and surveillance has been ongoing since 1988 and has resulted in the successful control of onchocerciasis . [7–9] Additionally , MDA for onchocerciasis and lymphatic filariasis ( LF ) are now integrated . After over 25 years of control efforts , program managers require more clarity around whether transmission has been interrupted . Though skin snip microscopy has been used to this point , it is a painful and invasive procedure that may result in decreased participation in surveillance activities in communities where decades of testing have occurred . Implementation research on the Ov16 rapid test in Senegal was desired to support the program transition to elimination . In 2015 , a workshop was held with representatives from the Senegal Ministry of Health and Social Action ( MoH ) to discuss the current process for using skin snip microscopy and evaluate the potential process if they were to use the rapid test , to streamline introduction of the new test . Acceptability , usability and costing data was also needed to inform decisions on use of the tests in surveillance activities . The aim of this study is to demonstrate the feasibility of integrating the Ov16 rapid test into onchocerciasis surveillance activities in Senegal , based on the attributes of acceptability , usability , and cost . Quantitative and qualitative methods are used to evaluate the following outcomes: 1 ) the diagnostic results of the Ov16 rapid test compared to skin snip microscopy; 2 ) an assessment of the acceptability and usability of the rapid test among community members and health workers; and 3 ) an estimation of the economic costs to conduct a surveillance activity by diagnostic method from the government’s point of view . A recently developed comprehensive quality assurance ( QA ) program was also piloted to support proper use of the rapid tests . This implementation research is intended to build evidence to support the introduction of the Ov16 rapid test in Senegal , as well as to inform other settings that may be at a comparable phase of elimination programming .
A cross-sectional study using qualitative and quantitative methods was conducted to assess the feasibility of integrating the Ov16 rapid test into ongoing surveillance activities . The study was conducted along with the Senegal MoH , which currently utilizes skin snip microscopy for onchocerciasis diagnosis . The study was performed in 13 villages in the Kédougou and Saraya districts of southeastern Senegal in May 2016 . Villages were representative of the region endemic for onchocerciasis in Senegal , and are co-endemic for LF . These communities started MDA with ivermectin ( IV ) in 1988 . Albendazole was added to the MDA in 2015 and was last administered in these villages in March 2015 . Individuals 5 years and older were invited to participate in any or all components of the study , including a demographic and health history questionnaire , the Ov16 rapid test , two skin snip biopsies for microscopy , and an exit interview . Community sensitization was conducted in each village 2–3 days prior to the surveillance activity . This study was approved by the PATH Research Ethics Committee and the Senegal National Ethics Committee for Health Research . Informed consent or assent was obtained from all participants . All participants 18 years and older provided written informed consent , and all participants under 18 years provided assent in addition to their parent or guardian providing written informed consent . Prior to study start , a comprehensive quality assurance program was introduced and a training on proper use of the Ov16 rapid test was conducted . The QA program includes training resources such as videos and PowerPoint slides , as well as a quality assurance panel to verify a quality product was received , and daily quality controls to ensure proper functioning of the test throughout data collection . For more information: http://sites . path . org/dx/ntd/training-and-qaqc-materials/ . The Ov16 rapid test was performed per the product instructions , which involves transferring 10 μL of finger stick capillary blood to the cassette using a disposable capillary tube that is included with the test . After buffer is added to the cassette , the test runs for 20 minutes and then results are recorded . All rapid test results were read a second time the next day as a research activity to compare 20 minute and overnight results . Skin snip microscopy was performed by taking two skin snips from the iliac crests with a sterile 2 mm corneoscleral punch biopsy tool . The skin snips were incubated in distilled water for 30 minutes , then examined under a light microscope to detect the presence of Ov microfilaria . Skin snips that were negative at 30 minutes were incubated in saline for 24 hours and examined again by microscope to confirm the negative result . A demographic and health history questionnaire was completed for all participants . Data were entered directly into a mobile-phone based data collection application developed using the Open Data Kit ( ODK ) 2 . 0 software , and captured village-specific GPS coordinates as well . [20] Rapid test and skin snip microscopy data was also recorded in the data collection application , including any refusals to perform a test and reasons for that refusal . As rapid test results were not available for at least 20 minutes , individuals who refused the skin snip or rapid test did so prior to knowledge of their test results . Characteristics of participants were reported as proportions for dichotomous variables , and median ( interquartile range ) or mean ( standard deviation ) for continuous variables . Sero-positivity of Ov16 rapid test was evaluated using equally distributed age categories . Age was also evaluated as a confounder for continuous and dichotomous variables using linear and logistic regression , respectively . Logistic regression was used to determine associations between exposure characteristics and rapid test result , adjusted for age ( Table 1 ) . Participation rates for the two diagnostic methods were compared by McNemar test . Questionnaire data was analyzed using StataSE version 13 . 1 . Targeted members of the surveillance team and community members participating in surveillance activities were interviewed to provide feedback on the acceptability and usability of the rapid test . All rapid test technicians , were asked questions regarding their experience in using the tests . Community members were sampled purposively based on the diagnostic testing they participated in as well as their willingness to participate in an exit interview . A semi-structured interview guide was used to gather data on the user experience , how the test was received by participants , and how the test compared to experiences with skin snip microscopy . Interviews with community members and technicians were recorded as audio files , then transcribed and translated from local languages into French and then into English . Interview data were coded using content analysis based on key themes from the semi-structured interviews . [21] Refinements were made to the codebook in an iterative fashion during the analysis process and reviewed by two researchers who reached consensus on the findings . Interview data was analyzed using NVivo version 10 . A costing analysis was conducted to assess the costs related to the implementation of the onchocerciasis surveillance activity in Senegal by diagnostic test . The study focuses on the economic costs from the government’s perspective , therefore valuing volunteers’ time . Data was mainly gathered from secondary sources such as financial reports , consolidated budgets , and other secondary sources of financial information from PATH and from the Senegalese onchocerciasis surveillance team . A simple structured questionnaire was also used to identify resources used during surveillance activities that had been purchased in previous years . Where needed , costs were calculated using the ingredient approach , multiplying the input prices by the number of inputs used . [22 , 23] Key input prices for this analysis are: Ov16 rapid test ( $1 . 20 ) , skin snip tool ( $225 ) , and microscope ( $2 , 490 ) . Costs were captured for all activities conducted during the surveillance activity , including training , field work , and data reporting . Field work cost categories were further split into labor , supplies , devices and instruments , transport and lodging , and data reporting . Drugs costs were zero since none of the study participants tested positive by skin snip microscopy , which would indicate treatment according to standard of care . The identified resources used were then allocated to the rapid test , the skin snip microscopy , or to shared costs , which were costs that were incurred independent of the type of test used , such as data entry and analysis , or transport cost to the villages . Total costs were first calculated by cost category and then aggregated across categories . The costs per test performed were calculated by dividing the total costs by the number of participants evaluated with each test . We also estimated the costs assuming the same population size ( n = 1 , 169 ) for both tests by proportionally scaling up the variable costs for skin snip microscopy , while keeping the fixed costs constant . This was done because of the difference in the number of participants tested with the rapid test ( n = 1 , 169 ) compared to skin snip microscopy ( n = 383 ) and the presence of high fixed costs of devices and instruments , allowing a comparison of the costs without the volume effect . All cost estimates are presented in $US using an exchange rate of 591 . 45 XOF per $US ( World Bank Development Indicators , World Bank ) . Further details on the cost analysis is available as supporting information ( S1 File )
Of the 1 , 173 participants who agreed to participate in the study , the median age was 12 years and ranged from 5 to 92 years . The most common professions were farmer , student and housewife . Participation rates for the two diagnostic tests differed with a total of 1 , 169 participants ( 99 . 7% ) agreeing to be tested by the rapid test and 383 participants ( 32 . 7% ) agreeing to be tested by skin snip microscopy ( p<0 . 0001 ) . ( Table 2 ) The sero-positivity rate of the rapid test among those who performed the test was 2 . 6% ( 30/1 , 169 ) among all ages ( age range of positives , 11–81 ) , 0 . 4% ( 3/775 ) among 20 years and under , and 6 . 9% ( 27/394 ) among those over 20 years . The 20 year breakpoint was evaluated based on the distribution of the data . ( Fig 1 ) All results were either positive ( sero-positive ) or negative , as no invalid results were detected . There were zero positive skin snip results . Age was associated with the rapid test result ( p<0 . 001 ) and 13/17 exposure characteristics , though it was not associated with refusal to participate in either diagnostic test . In an age adjusted analysis , odds of having a rapid test positive result decreased the more recently IV was distributed in their village ( 0 . 83 , 95% CI: 0 . 74–0 . 93 ) . The 3 participants under 20 years who had a positive rapid test result had all lived in their villages their whole lives , frequently went to the stream near their village , and did not report experiencing any of the onchocerciasis symptoms that were included in the questionnaire . Two of the 3 individuals reported having taken IV in the last year . ( Table 1 ) Interviews were conducted with 4–5 community members from all 13 villages ( n = 61 ) . Over 90% of participants ( 57/61 ) reported that they valued or appreciated the rapid test . Community members liked that the test was performed quickly and was not painful , and they perceived it to provide reliable results . Community members noted that the test brought health knowledge to the community , enabled them to access follow-on care if needed , and could effectively be used to test children . Most participants ( 55/61 ) reported that they had no concerns about the rapid test while 10 percent of participants ( 6/61 ) disliked the finger stick component of the rapid test procedure . Moreover , many participants indicated that they would be more likely to participate in future surveillance activities if the rapid test was used and suggested that its use would spur broader participation within the community . The more common reasons for refusing the skin snip biopsy were that they “did not like the idea” and “thought it would be too painful” . Some community members discussed historical experiences with the biopsy procedure like it being painful and suggested that now , they are less willing to undergo the biopsy if the results are consistently negative . With regards to preferences for either diagnostic tool , 50% of respondents preferred the rapid test to skin snip microscopy , 39% expressed liking both tests , and 10% preferred the skin snip microscopy . The primary reasons were that the rapid test was less painful , quicker , and could provide individual test results . Some respondents , particularly those who were sensitized to the differences between the principles of each test , wanted to continue to use both tests or preferred the skin snip microscopy because it could provide a confirmation of infection . ( Fig 2 ) Community members also noted that the role of the surveillance team and the information they provided influenced their experience with the test . Nearly all of the participants indicated that the surveillance team was skilled and they trusted their abilities . For some , the skills of the surveillance team translated to credibility of the test itself and trust in the test result . The information that the surveillance team provided to community members regarding the test procedure and the test results varied among participants . Some felt like the test and their result were well-explained while others reported not learning their test results . Community members expressed a strong preference for understanding the test procedure and purpose as well as their results . Participants overwhelmingly preferred to receive their individual results though a minority of respondents also wanted to receive sero-prevalence results to understand the health status of the entire community . Community members also noted appreciation for the health services being made available in their village . Interviews were conducted with all rapid test technicians ( n = 7 ) . All technicians commented that they liked using the rapid test and most preferred it to skin snip microscopy as they perceived it to be more reliable and quicker to complete . They noted that it was less painful for participants , and thus made their jobs easier as community members were more willing to participate in surveillance . When prompted , one technician reported that the disposable capillary pipette was difficult to use; no other challenges were reported . Most of the rapid test technicians trusted the results of the test , in part due to emphasis on the quality assurance program throughout the study . However , these technicians noted some of the limitations of an antibody test and one technician stated a preference for skin snip microscopy to confirm infections . All technicians indicated that they would be willing to use the rapid test in future surveillance activities . The total costs for the onchocerciasis surveillance activities in the 13 villages was estimated at $22 , 272 . 83 . Costs were allocated to rapid test , skin snip microscopy , or shared costs . Shared costs were those incurred independent of the diagnostic test used and accounted for 70% of the total study costs ( $ 15 , 697 . 48 ) . Total test-specific costs were $3 , 671 . 76 for rapid test and $2 , 903 . 59 for skin snip microscopy , though the number of tests performed with each method differed ( 1169 and 383 , respectively ) . Most of the total study costs ( 87% ) were related to the field work activities , while training costs accounted for 10% of the total costs and data entry and reporting was 3% of the total costs . Of the field work costs , the main cost driver was transport costs ( 57% ) , followed by supplies , instruments , and devices ( 27% ) . In this surveillance activity , the total cost per test performed was $3 . 14 for rapid test , $7 . 58 for skin snip microscopy , and $13 . 43 for shared cost , giving a total cost per person tested of $16 . 57 for the rapid test , $21 . 01 for the skin snip microscopy , and $24 . 15 if both diagnostic tests were performed on the same participant . If no participants had refused the skin snip microscopy , so the same number of participants were tested with both diagnostic tests , the cost per person tested by skin snip microscopy would have decreased to $2 . 91 . Adding in shared costs , the total cost per participant in the surveillance activity would have been $16 . 57 for the rapid test and $16 . 34 for skin snip microscopy , a difference of $ 0 . 23 per method . ( Fig 3 )
This study demonstrates that the inclusion of the rapid test in surveillance activities is feasible based on acceptability , usability , and costs . The sero-positivity rate of the rapid test among those who performed the test was 2 . 6% among all ages with no positives detected under 10 years of age , and no invalid results . The 3 individuals under 20 years who had positive rapid test results may be positive due to exposure outside their community , or from residual transmission occurring over 10 years ago , however the possibility that these are false positives cannot be ruled out . While early prototypes of the Ov16 rapid test demonstrated a 97–98% specificity[18 , 24] , the product insert states the performance of the commercially available Ov16 rapid test compared to skin snip microscopy in a laboratory setting to be 81 . 1% ( 95% CI: 70 . 7–88 . 4% ) sensitive and 99 . 0% ( 95% CI: 94 . 8–99 . 8% ) specific using whole blood , or 85 . 3% ( 95% CI: 75 . 6–91 . 6% ) sensitive and 99 . 0% ( 95% CI: 94 . 7–99 . 8% ) specific using serum and plasma ( http://www . standardia . com/en/home/product/Rapid_Diagnostic_Test/Anti-Onchocerciasis_IgG4 . html ) . Evaluation of the performance of the tool in the field is ongoing . Currently more data evaluating the sensitivity and specificity of the Ov16 rapid test to skin snip microscopy and ELISA in field settings is needed . The difference in participation rates for Ov16 rapid test and skin snip microscopy suggests a greater willingness in these communities to undergo a rapid test with a finger prick compared to a more invasive skin snip procedure ( 99 . 7% and 32 . 7% respectively , p<0 . 0001 ) . Some individuals who initially refused the skin snip biopsy later changed their mind and had the skin snip biopsy performed after learning their rapid test was positive . This may have resulted in an increased participation rate for skin snip than would have been seen otherwise , though the difference was likely small as there were relatively few positives . The 2016 WHO guidelines call out a need to further investigate the acceptability of skin snip microscopy in low prevalence settings . These findings align with others that observed high refusal rates for skin snip microscopy in similar settings . [9 , 13] The refusal of the skin snip biopsy in our study was largely due to not liking the idea of the test and considering the test to be too painful . Moreover , some community members suggested that they are less willing to undergo the biopsy if the results are consistently negative . The value of the different diagnostic tests during the distinct phases of elimination is important , and community members and rapid test technicians noted that skin snip microscopy remains the primary method for assessing infection status and recommending treatment , while the rapid test is a screening tool to inform decisions regarding the continuation or culmination of MDA . Community members reported high levels of acceptability and willingness to participate in surveillance activities that included the rapid test . The role of the surveillance team and the information they provided influenced community members’ experience with the test . Clear communication about the test purpose , procedure , and result was appreciated by community members and increased their trust in the result and motivation to participate in other onchocerciasis control activities . The influence of the surveillance team should not be overlooked as they may be a valuable tool to encourage participation in future surveillance activities and greater compliance with mass drug administration . Community member feedback also showed that in areas endemic for onchocerciases and where consistent access to quality health services may be lacking , there is an appreciation for the delivery of health services through NTD control programs . Community members expressed a desire for greater knowledge of the health of their community , potential risk factors , and their achieved progress towards program goals . The Ov16 rapid test is intended for use in populations nearing elimination . In this setting , the population is predominately healthy and unaffected by onchocerciasis . Attributes such as invasiveness of the test may be more important in these settings , particularly when testing is focused on children . The costing analysis showed that the cost per person tested in this activity was $16 . 57 for the rapid test , $21 . 01 for the skin snip microscopy , and $24 . 15 for both methods . If no participants had refused the skin snip microscopy , the costs per participant using either method would have been comparable at around $16 . The labor and instrument costs for skin snip microscopy were largely fixed and independent of the number of participants tested . The skin snip microscopy team had to remain with the study team for the duration of the surveillance activity regardless of how many people they were testing . Multiple skin snip microscopy instruments were used for this activity due to the need to sterilize equipment after each use , and these instruments were assumed to not be shared with other programs . Additionally , the rapid test had slightly lower training costs due to shorter training , but higher costs for devices and instruments that were dependent on the number of people tested . Performing skin snip microscopy on a subset of participants who are all receiving rapid testing is costly , due to the high costs of instruments and the need for two teams of technicians ( rapid test technicians and skin snip technicians ) . This costing information may be useful when considering how to transition programs from skin snip to rapid testing . However , while comparable conclusions may arise from similar studies , the cost estimates from this study are specific to the Senegalese context . For example , costing results would vary based on country salary and per diem policies , the surveillance activity approach used such as number of days spent in the field and number of people tested per village . Similarly , different assumptions regarding the useful life of instruments for skin snipping would also affect the results . Additional implementation research is important in other locations to evaluate how results vary by setting . A comprehensive quality assurance ( QA ) program including training videos and materials , quality assurance panels and daily quality control standards , was implemented along with the Ov16 rapid test to facilitate proper use of the test in this study . The QA program resources , which are freely available to implementing programs , minimize improper handling of the tests and user errors , while ensuring consistent product quality . Findings from this study also suggest this QA program may provide surveillance teams with greater confidence in technician skills and validity of the results , which benefits the community members by supporting the surveillance team in their dissemination of information and results . More research is needed to understand the role QA practices have on influencing user and participant confidence , identify QA best practices , and drive adoption of these practices through integration into global guidelines . [25] A process map illustrating the use of one or both tools in surveillance activities was generated from the 2015 workshop , this study , and the 2016 WHO guidelines . ( Fig 4 ) The “current practice” uses skin snip microscopy only , a more relevant strategy in higher prevalence areas . The “parallel method” uses skin snip microscopy and Ov16 rapid test , which may be more appropriate when programs are transitioning to stopping MDA and implementing Ov16 serology such as in this study . However , in these transition areas , high refusal rates for skin snip may prevent programs from attaining a sufficient sample size to determine with certainty if program goals have been reached . [13] An alternative to the parallel method may be testing only Ov16 rapid test positives with skin snip microscopy , though in low prevalence settings few if any skin snip positives would likely be detected . The “final method” uses the rapid test only and “should be used in children under 10 years to demonstrate interruption of transmission” . [13] This study was not designed with a sampling methodology sufficient to determine prevalence or if transmission had been broken . According to guidelines , roughly 2000 children under 10 years of age would need to be tested to detect a prevalence of less than 0 . 1% with sufficient confidence , and only 368 children under 10 years were included in this study , all of whom were rapid test negative . [13] Finally , diagnostic tools play an important role in influencing health outcomes , usually through the intended benefits of enabling timely diagnosis , accurate disease surveillance , and proper treatment . These tools may also have the ability to influence individual and community behaviors , such as participation in surveillance activities and confidence in control program activities . Taking a broader perspective , the true value of diagnostic tools may go beyond the intended utility to include extended benefits such as increased utilization of health care services , individual agency over the health care experience , and confidence in provider abilities . These broader benefits should be identified and measured in future implementation research to better understand how to move technologies beyond innovation and validation , and into adoption and scale-up . [19] Ov16 as a biomarker has successfully moved from discovery and development at the bench , to evidence of effectiveness in the field . The remaining barriers are optimized and context-specific integration into systems and programs . As global focus shifts to the integration of onchocerciasis and lymphatic filariasis ( LF ) programs to reach elimination in Africa faster , a rapid assessment of Ov transmission through LF transmission assessment surveys ( TAS ) will be required . [26] A more appropriate tool for this work may be the SD BIOLINE Oncho/LF IgG4 biplex rapid test to detect ongoing onchocerciasis and LF transmission simultaneously ( http://www . standardia . com/en/home/product/Rapid_Diagnostic_Test/Oncho-LF_IgG4_biplex . html ) . [24] As more settings achieve success in control of either disease , integrated surveillance for transmission interruption may be a best-buy , and implementation research to support successful adoption and scale up is essential . In this area of Senegal with low onchocerciasis sero-positivity , there was high participation with the rapid test , while participation with skin snip microscopy was significantly lower . Acceptability and perceived value of the rapid test was high among community members and rapid test technicians . The role of the surveillance team and the information they provided influenced community members’ trust in the result and motivation to participate . This may be a valuable tool to encourage participation in future surveillance activities and greater compliance with mass drug administration . This study provides evidence of the feasibility of implementing the Ov16 rapid test and the associate costs , which may be informative to other country programs interested in adopting this new tool as they move from control to elimination of onchocerciasis . | As onchocerciasis control programs succeed and transition to elimination efforts , different diagnostic tools are needed . The goal of this study was to determine if integrating the Ov16 rapid test is feasible based on acceptability , usability , and cost . A study was conducted in 13 villages in southeastern Senegal in May 2016 . Community members were invited to participate in a demographic questionnaire , a rapid test , a skin snip biopsy , and an acceptability interview . Technicians were also interviewed and a costing analysis was conducted . Out of 1 , 173 participants , 1 , 169 ( 99 . 7% ) agreed to the rapid test while 383 ( 32 . 7% ) agreed to skin snip microscopy . The rapid test result was reactive in 2 . 6% of those tested , while none of the skin snips were positive . Community members thought the rapid test was performed quickly , was not painful , and provided reliable results . If no one had refused skin snip microscopy , the total cost would have been around $16 per person tested for either method . In this area with little if any remaining onchocerciasis , there was high acceptability and perceived value of the rapid test . This study suggests that implementing the Ov16 rapid test in Senegal is feasible and these findings may be informative to other country programs . | [
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] | 2017 | Feasibility of utilizing the SD BIOLINE Onchocerciasis IgG4 rapid test in onchocerciasis surveillance in Senegal |
Infants account for a small proportion of the overall dengue case burden in endemic countries but can be clinically more difficult to manage . The clinical and laboratory features in infants with dengue have not been extensively characterised . This prospective , cross-sectional descriptive study of infants hospitalized with dengue was conducted in Vietnam from November 2004 to December 2007 . More than two-thirds of 303 infants enrolled on clinical suspicion of dengue had a serologically confirmed dengue virus ( DENV ) infection . Almost all were primary dengue infections and 80% of the infants developed DHF/DSS . At the time of presentation and during hospitalization , the clinical signs and symptoms in infants with dengue were difficult to distinguish from those with other febrile illnesses , suggesting that in infants early laboratory confirmation could assist appropriate management . Detection of plasma NS1 antigen was found to be a sensitive marker of acute dengue in infants with primary infection , especially in the first few days of illness . Collectively , these results provide a systematic description of the clinical features of dengue in infants and highlight the value of NS1 detection for diagnosis .
Dengue represents a substantial disease burden in many tropical and sub-tropical countries , particularly in children and young adults [1] . Infection with any one of the four dengue virus ( DENV ) serotypes can lead to a sub-clinical infection , or to clinical disease ranging in severity from a non-specific febrile illness to classical dengue fever , dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . Severe dengue ( DHF/DSS ) is strongly associated with secondary heterotypic dengue virus infections in children and adults [2] , [3] , but can also occur in primary DENV infection of infants born to dengue-immune mothers [4] , [5] . Common to these two epidemiological populations are pre-existing DENV-reactive IgG antibodies , which are thought to be a factor in both serotype-specific immunity to infection as well as the pathogenesis of dengue , through a mechanism of antibody-dependent enhancement of infection [4] , [5] . Previous studies have indicated a low incidence of DENV exposure in infants [5] , [6] , however infants with DHF can be clinically challenging to manage and are at higher risk of mortality than older children [4] , [7] . In dengue-endemic areas infants under one year of age comprise between 1–5% of the dengue cases admitted to hospital each year [4] , [5] , however only a limited number of studies have documented clinical and laboratory findings in infants with dengue [8] , [9] . This evidence indicates that the clinical manifestations of dengue in infants may differ from older children and adults , with a greater frequency of low platelet count ( <50000 cells/mm3 ) , plasma leakage and shock and fewer haemorrhagic manifestations in infants compared with dengue in older children [8] , [9] . This paper describes the clinical , hematological and virological characteristics of infants hospitalized with dengue in southern Vietnam . These characteristics were compared to infants hospitalized with other acute febrile illnesses .
This prospective , descriptive study was conducted at Pediatric Hospitals Number 1 and Number 2 , Ho Chi Minh City , and at Dong Thap Hospital , Dong Thap province , Vietnam , from November 2004 to December 2007 . Infants under 18 months old with suspected dengue were eligible to be enrolled . Recruitment also took place in the outpatient department of Pediatric Hospital Number 1 from July to December 2005 . Written informed consent was obtained from a parent or guardian of each patient . The study was approved by the Scientific and Ethical Committees of the Hospital for Tropical Diseases , Children's Hospital No . 1 , Children's Hospital No . 2 , and Dong Thap Hospital and Oxford Tropical Research Ethical Committee . Daily venous or capillary blood samples were collected from infants for 4 consecutive days beginning on entry to the study ( study day 0 ) , and again 10–14 days after discharge from hospital . Venous blood samples were collected from the mothers of each infant on study day 0 and again 4–8 days later . The extent of hemoconcentration during symptomatic illness was determined by comparing the maximum hematocrit recorded during hospitalization with either the value recorded at follow-up in each patient ( 47% of cases ) or at hospital discharge for those who did not return for follow-up ( 53% of cases ) . Dengue antigen capture IgM and IgG ELISA assays used both inactivated DENV and Japanese encephalitis virus ( JEV ) antigens and monoclonal anti-DENV antibody provided by Venture Technologies ( Sarawak , Malaysia ) as previously described [10] . Acute DENV infection was defined as DENV-IgM seroconversion ( i . e . from negative to positive ) between paired specimens , or rising levels of DENV reactive IgM ( at least 20% increase in DENV-IgM ELISA Units from 1st to 2nd sample and 2nd sample has ≥20 ELISA Units ) . World Health Organization ( WHO ) classification criteria for disease severity [11] were applied to each case after reviewing study notes . DENV viraemia was measured using an internally-controlled , serotype-specific , real-time RT-PCR assay [12] . Results were expressed as cDNA equivalents per milliliter of plasma . DENV NS1 antigen in plasma was measured using the Platelia NS1 assay ( BioRad , Hercules , CA ) according to the manufacturers' instructions . The concentration of DENV NS1 protein in test plasma samples was quantified by reference to a standard curve , using serial dilutions of immune-affinity purified recombinant DENV-2 NS1 protein ( Hawaii Biotechnology Group , Inc ) . The limit of detection of this test was 5 ng/ml . DENV viraemia and NS1 antigenaemia were measured in samples collected on four consecutive days from study enrolment . Neutralizing antibody titres against four reference serotypes ( DENV-1 PR/94 , DENV-2 NGC , DENV-3 Sleman/76 , and DENV-4 814699 ) in maternal plasma samples were determined by a complement-enhanced PRNT assay as described previously [13] . Statistical analyses were performed with SPSS version 14 . To compare the distribution of categorical variables between patient groups , a chi-squared test for association was used , with p< . 05 considered to be statistically significant . For comparison of continuous variables between groups we used a non-parametric test ( Mann Whitney U ) or , where there were more than two comparison groups , Kruskal-Wallis one-way analysis of variance . We calculated Spearman's rank correlation coefficients ( rho ) to examine the strength of association between two continuous variables .
During the study period , we enrolled 293 inpatient infants and young children with suspected dengue and 10 infants treated as outpatients . Seventy-five of these infants have been described previously by our group [13] . The intention of this report is to summarize the main clinical and virological findings in a larger population of infants with dengue . Table 1 shows the demographic characteristics of study participants . Infants' mothers were aged between 19–45 years old ( median: 28 yrs ) , and the infants were aged between 2–18 months old ( median: 7 months ) . The male to female ratio of the infants was 173∶126 . The majority of inpatients were enrolled on day 4 or 5 of illness . Sixty-eight percent ( 204/303 ) of enrolled infants had serologically confirmed dengue , of which 201 were primary dengue cases and 3 secondary dengue cases . The remaining 99 patients ( 32% ) without serological evidence of dengue were classified as “other febrile illnesses” ( OFI ) . Almost 80% ( 162/204 ) of the infants with laboratory confirmed dengue were between 4–10 months old , with the peak at 7–8 months of age . Most of the primary dengue cases were classified as DHF grade II ( 141/201; 70% ) , with 17% ( 35/201 ) classified as DF and 9% ( 19/201 ) classified as DSS ( DHF III and IV ) . Five cases could not be classified by the WHO criteria because of insufficient investigations of vascular leakage . Clinical data was not available for one infant . The five infants in whom disease severity could not be classified were excluded from further analysis . The clinical features of participants at the time of enrolment are shown in Table 2 . Infants with dengue did not present with specific clinical signs compared to patients with other febrile illnesses ( OFI ) . Common features of upper respiratory tract viral infection , including running nose and cough , were observed with similar frequency in both groups , however diarrhea , vomiting and a petechial rash occurred more frequently in dengue patients than in infants with OFI . Fever was observed less frequently in dengue patients; however in some patients enrolled later in their illness , defervescence may have occurred prior to study enrolment . The hematological findings and clinical manifestations during hospitalization in infants with primary dengue or other febrile illness are shown in Table 3 and Table 4 . The median nadir in white blood cell ( WBC ) count in infants with dengue was significantly lower than that in infants with OFI ( 5600 vs 6100 cells/mm3 , P = 0 . 035 ) and was primarily due to neutropenia . Liver transaminase levels were significantly higher in dengue cases ( Table 3 ) . In comparison with OFI cases , infants with dengue had lower platelet nadirs , greater hemoconcentration , petechiae , bruising , hepatomegaly and clinical evidence of systemic vascular leak ( Table 4 ) . The case fatality ratio was approximately 1% among dengue cases . One patient in the OFI group had shock and died of meningitis and pneumonia ( Table 4 ) . RT-PCR was performed on plasma samples collected at enrolment . In plasma samples from the 201 serologically-confirmed primary dengue patients , DENV was detected in 161 ( 80% ) . All four serotypes were detected: DENV-1 ( 66/161 , 41% ) , DENV-2 ( 72/161 , 44% ) , DENV-3 ( 22/161 , 14% ) , and DENV-4 ( 2/161 , 1% ) ( Table 5 ) . The remaining 40/201 ( 20% ) cases were aviraemic at the time of study entry . DENV-1 and DENV-2 were the dominant serotypes in this study population and accounted for 85% ( 137/161 ) of viraemic patients . We compared disease severity in dengue patients infected with different DENV serotypes ( Table 5 ) . Although a greater proportion of DENV-2 infections resulted in DHF/DSS ( 91% ) compared to the other serotypes ( DENV-1 78% , p = 0 . 03; DENV-3 82% , p = 0 . 20; DENV-4 50% , p = 0 . 05 ) , overall there was no significant association between DENV serotype and severity of disease when DHF and DSS were considered separately ( p = 0 . 22 ) . Four of the 134 infants who developed DHF/DSS were aviraemic at admission . Data on viraemia at enrolment was available for 118/201 infants with primary dengue . We compared viraemia at the time of study enrolment in infants with DF , DHF and DSS and found a non-significant trend towards higher viraemia with more severe disease ( p = 0 . 1; Table 5 ) . However , a limitation of our study was that we did not have samples collected in the first 48–72 hours of illness to be able to measure peak viraemia levels . Instead , we were limited to measuring viraemia at a time when in most patients it was already declining . The concentration ( ng/ml ) of DENV NS1 antigen in infant plasma was measured at study enrolment ( Table 5 ) . At enrolment , 176/196 ( 89% ) of dengue cases had detectable plasma NS1 levels . The NS1 concentration was significantly higher in DENV-1 and DENV-3 infections than in DENV-2 infections ( p<0 . 001 ) , and did not correlate with viraemia levels , which were similar among DENV serotypes ( data not shown ) . We compared NS1 levels between primary dengue patients of different severities , stratified by DENV serotype ( Figure 1 ) . This showed a non-significant trend toward higher NS1 levels with increasing disease severity in those infants infected with DENV-3 or avireamic at admission ( Chi-square test: p = 0 . 2 , p = 0 . 1 respectively ) , but this trend was not observed in DENV-1 and DENV-2 infected patients . The sensitivity of different dengue diagnostic approaches at the time of enrolment were compared to laboratory confirmation by serology on paired samples . NS1 detection was a sensitive approach ( >80% ) to diagnosing dengue in enrolment plasma samples collected on day 3 , 4 or 5 of illness ( Figure 2 ) . The sensitivity of the NS1 test was significantly higher than real time RT-PCR in the first 6 days of illness ( Chi-square test: p = 0 . 001 ) . RT-PCR was sensitive early , but sensitivity declined with increasing illness duration . Conversely , IgM detection by MAC ELISA was only moderately sensitive on day 3 and 4 , but detection improved with increasing duration of illness . Plasma samples from 120 mothers of infants with primary dengue were available for PRNT50 assays , after excluding those women with serological evidence of recent DENV infection ( n = 33 ) , those with preterm babies ( n = 19 ) and those mothers who did not provide paired blood samples ( n = 29 ) . Recent DENV infection was defined as a four-fold change in IgG to recombinant E protein in paired plasma samples or when any maternal plasma sample was IgM positive . Amongst the evaluated maternal plasma samples , 100% had neutralizing antibodies ( PRNT50 assay ) to at least one DENV serotype and 96% of mothers possessed neutralizing antibodies to ≥3 serotypes . The median titer of neutralizing antibody against DENV-4 was much lower than to the other 3 serotypes . The maternal neutralizing antibody titre to DENV1-4 was independent of maternal age ( data not shown ) . Maternal neutralizing antibody titres against the serotype that infected each infant were compared to the infant's age at illness onset . There was only a limited correlation between maternal neutralizing antibody titres and the age at which infants present to hospital with symptomatic primary dengue ( Figure S1 ) .
This study describes the clinical and virological features of dengue in Vietnamese infants and is an extension of previous reports from our group [13] . This report highlights the value of NS1 detection in the diagnosis of dengue in infants who otherwise present with non-specific clinical symptoms . Interestingly , we found DENV-2 infections to be associated with significantly lower plasma NS1 levels relative to DENV-1 or DENV-3 infections . Collectively , these data provide new insights into the pathogenesis of severe dengue in an age-group where diagnosis and management can be challenging . The majority of infants hospitalized with dengue were between four and ten months of age and this is consistent with previous studies at this hospital and elsewhere in SE Asia [4] , [5] , [8] . Infants with dengue initially presented with non-specific symptoms that included coryza , cough and diarrhea . Subsequently , many infants developed pathognomonic signs of DHF including petechiae , bleeding , bruising and vascular leak . Although the World Health Organization classification criteria were originally developed for older children , 97% of the infants with acute dengue in this study were adequately classified [11] . Consistent with previous studies [8] , [9] , we demonstrate that DHF/DSS can occur during primary infection of infants . Surprisingly , the patients enrolled in our study had predominantly DHF grade II , with few cases having either DF or DSS . Consequently , we had little statistical power to detect differences in clinical or virological features between mild and severe cases . The limited breadth in our patient population was despite efforts to recruit patients with milder disease presentations by enrolling infants in the outpatients department of Children's Hospital No . 1 . The clinical signs and hematological manifestations in infants hospitalized with dengue were similar to those seen in children and adults [8] , [9] , [14] , [15] . Whole blood count results during hospitalization demonstrated leucopenia and elevated liver transaminases in infants with dengue compared to those with other febrile illnesses , however these differences are unlikely to be sufficient for differential diagnosis at the individual level . Infants with dengue were more likely to have hepatomegaly , petechiae and bruising than infants with other febrile illnesses . Clinical evidence of plasma leak ( e . g . ascites ) was observed in a minority of infants with DHF/DSS . The average time to study enrolment since illness onset was 4–5 days . A disadvantage of not enrolling patients into the study earlier in their illness is that it was not possible to identify early risk factors for more severe outcomes e . g . DSS . One of the reasons few infants were enrolled into the study early in their illness relates to the standard of care at the hospitals where the study was conducted . Infants with fevers of just 1–2 days duration are often seen in the outpatient department , but most are treated as outpatients only with daily follow-up . For infants with DHF , only when the characteristic thrombocytopenia and a rising hematocrit are observed , usually on day 3 or 4 , are those infants admitted to hospital . Specific dengue diagnostic tests such as PCR or NS1 assays are not routinely available . By RT-PCR we were able to detect viraemia in 80% of the dengue cases . As supported by previous studies [16] , we also found that the proportion of viraemic cases was highest in the first few days of illness . In this study , neither the viraemia nor antigenemia at enrolment correlated with disease severity , although a trend towards higher NS1 concentrations with increasing disease severity was observed in infants infected with DENV-3 or aviraemic at admission . However , our study participants were generally enrolled after 4–5 days of illness and therefore we probably have not measured the peak viraemia or NS1 antigenaemia . A greater proportion of DENV-2 infections resulted in DHF/DSS compared to other serotypes , however the number of DF and DSS cases was small and there was no significant association overall between infecting serotype and disease severity . NS1 detection may be helpful in diagnosing dengue [17] , [18] , [19] , [20] . In this paper , we illustrated the superior sensitivity of DENV NS1 detection in comparison with two other dengue diagnostic tests ( MAC ELISA and real-time RT-PCR ) , especially in the first few days after disease onset ( ≤4 days ) . Early diagnosis could assist clinical management by focusing attention on the clinically important features of dengue ( capillary leakage ) and minimizing unnecessary use of antibiotics . The sensitivity of NS1 detection was higher than RT-PCR during the first six days of illness , and many infants were still NS1 positive at the time of discharge from hospital . This is consistent with previous descriptions of the sensitivity of NS1 detection in children and adults with primary dengue [17] , [20] , [21] . One of the reasons NS1 detection might be more sensitive in primary dengue is that the IgM/IgG response does not become measurable until day 6 or later . Previous studies in Vietnamese children demonstrated that a measurable anti-DENV IgG level in the test sample significantly reduced the likelihood of NS1 detection , possibly because of the immune complex formation between anti-NS1 IgG and soluble NS1 in the blood [20] . Viraemia and NS1 antigenaemia appear to correlate closely early in infection , with divergence in the sensitivity of NS1 detection and RT-PCR from day four of illness ( Figure 2 ) . This may be due to more rapid degradation of viral RNA compared to NS1 protein . Our results support previous findings [20] of a lower concentration of NS1 antigen in DENV-2 infections , compared with other serotypes , and demonstrate that this is observed also in primary infections in infants as well as in secondary infections , with which DENV-2 is commonly associated . This could be due to the antibodies used in the NS1 ELISA having differential binding to DENV-2 NS1 protein compared with other serotypes and the limitation of using a DENV-2 NS1 protein standard for calibration of the quantitative assay used for all serotypes . This may have resulted in an overestimate of NS1 levels for DENV-1 and DENV-3 , however our analysis of the association between NS1 antigenaemia and disease severity was stratified by serotype so is robust to this limitation . Alternatively , DENV-2 infections may result in less soluble NS1 protein due either to a greater tendency for immune complex formation with pre-existing IgG or to differences in viral infection between serotypes . All of the mothers of infants with primary dengue in this study for whom plasma samples were available possessed DENV-neutralizing antibody , as measured by PRNT50 . Neutralizing antibody , acquired passively by infants born to dengue-immune mothers , is suggested to provide protective immunity against dengue during the early months of life , but sub-neutralizing levels of maternal antibody are thought to be a factor in the pathogenesis of dengue in infants [22] . We have recently demonstrated that the waning of maternal DENV-neutralizing antibody correlates temporally with the age of peak dengue burden in infants [23] . Building on previous analyses [13] in a small subset of the current study population , we examined the relationship between maternal serotype-specific neutralizing antibody titre and infants' age at infection in individual mother-infant pairs , with the hypothesis that a higher PRNT50 titre in the mother may confer a longer period of protection in the infant . A weak positive correlation between maternal anti-DENV-2 PRNT50 titre and age at time of disease onset in infants with DENV-2 infections suggested higher maternal neutralizing antibody titres may be associated with a longer window of passively acquired immunity for DENV-2 . However , no such correlation was found for DENV-1 or DENV-3 . Our findings differ from recently reported observations of a strong positive correlation between maternal DENV-3 PRNT50 titre and infant age at the time of symptomatic DENV-3 infection [24] . This difference may be partially attributable to several limitations to the current analysis . Maternal blood at infant admission is an imperfect proxy for infant's maternal antibody titre at birth as , although we excluded any mothers with serological evidence of recent dengue infection , we cannot exclude the possibility of DENV exposure and antibody boosting in the mothers since birth . For reasons of feasibility , and consistent with the work of others [24] , we measured PRNT50 titres using prototype dengue viruses from each serotype , not viruses isolated from individual infected infants . Although these titres may therefore be an imperfect measure of in vivo DENV neutralizing capacity , we believe this to be a reasonable methodology based on the amino acid identity of the DENV E gene between the prototype viruses and dengue viruses isolated in southern Vietnam during the study period ( mean 97 . 8% identity , range 96 . 3%–98 . 5% comparing prototype DENV of each serotype to 4 contemporary isolates of that serotype ) . An important consideration is the fact that the large majority ( 73% ) of dengue cases in our study occurred after 6 months of age . We have reported previously that maternal dengue neutralizing antibody was undetectable ( by PRNT50 ) in the majority of a healthy cohort of Vietnamese infants by 6 months of age , regardless of titre at birth [23] . This suggests that after 6 months , infant age at dengue onset may be independent of PRNT50 titre at birth if few or no infants have remaining protection from maternal antibody . Further prospective studies of the relationship between passively acquired antibody and the spectrum of dengue infection and disease in infants can contribute to an improved understanding of immune correlates of protection against dengue , and a large prospective birth cohort study is currently underway in southern Vietnam to address these questions . The data presented in this study highlight the challenges faced by clinicians in distinguishing infants with dengue from those with other febrile illnesses based on clinical and hematological signs alone , and thus identifying infants who may be at risk of developing severe dengue disease . Routine serology is an imperfect tool for the laboratory confirmation of dengue , particularly in the first 1–3 days of illness , and our results indicate that the addition of NS1 detection to the diagnostic algorithm has the potential to improve the early diagnosis of dengue in infants , as well as in older children and adults . | Dengue is a major public health problem in tropical and subtropical countries , including Vietnam . Dengue cases occur in children and young adults; however , severe dengue also occurs in infants less than 1 year of age . Prompt recognition of dengue is important for appropriate case management , particularly in infants in whom febrile illness from other causes is common . We describe the clinical picture , virological and immunological characteristics of infants with dengue admitted to three hospitals in southern Vietnam , compared with infants admitted with fever not due to dengue . We show that infants with dengue are difficult to distinguish from those with other febrile illnesses based on signs and symptoms at presentation , and so laboratory tests to confirm dengue virus infection may be useful for diagnosis and management . Conventional diagnostic methods for dengue have low sensitivity early in infection , and we show that an alternative antigen-detection assay that has demonstrated good sensitivity and specificity in older age groups also performs well in infants . This study will help to inform the diagnosis and management of dengue in infants . | [
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] | 2010 | Clinical and Virological Features of Dengue in Vietnamese Infants |
In the Peruvian Amazon , the dengue vector Aedes aegypti is abundant in large urban centers such as Iquitos . In recent years , it has also been found in a number of neighboring rural communities with similar climatic and socioeconomic conditions . To better understand Ae . aegypti spread , we compared characteristics of communities , houses , and containers in infested and uninfested communities . We conducted pupal-demographic surveys and deployed ovitraps in 34 communities surrounding the city of Iquitos . Communities surveyed were located along two transects: the Amazon River and a 95km highway . We calculated entomological indices , mapped Ae . aegypti presence , and developed univariable and multivariable logistic regression models to predict Ae . aegypti presence at the community , household , or container level . Large communities closer to Iquitos were more likely to be infested with Ae . aegypti . Within infested communities , houses with Ae . aegypti had more passively-filled containers and were more often infested with other mosquito genera than houses without Ae . aegypti . For containers , large water tanks/drums and containers with solar exposure were more likely to be infested with Ae . aegypti . Maps of Ae . aegypti presence revealed a linear pattern of infestation along the highway , and a scattered pattern along the Amazon River . We also identified the geographical limit of Ae . aegypti expansion along the highway at 19 . 3 km south of Iquitos . In the Peruvian Amazon , Ae . aegypti geographic spread is driven by human transportation networks along rivers and highways . Our results suggest that urban development and oviposition site availability drive Ae . aegypti colonization along roads . Along rivers , boat traffic is likely to drive long-distance dispersal via unintentional transport of mosquitoes on boats .
Aedes aegypti is the vector of several arboviruses of major global health importance , including dengue virus ( DENV ) , yellow fever virus , and chikungunya and Mayaro viruses . Of these , dengue virus is the most prevalent and geographically extensive , with approximately 2 . 5 billion people at risk worldwide [1] , and 390 million new dengue infections each year [2] . This mosquito vector is well-adapted to the urban environment: females feed almost exclusively on humans , prefer to rest in dark , cool areas ( usually indoors ) [3] , and adult female mosquitoes lay their eggs on the walls of water-filled artificial containers found in and around the home such as vases , plastic buckets , water storage tanks , and discarded refuse and tires [4] . These adaptations to human environments , coupled with the longevity and resistance of its eggs to desiccation [5] , [6] , contribute to the vector's passive spread to new areas via human transportation networks [7] . In the absence of a vaccine or cure for dengue , most dengue control programs rely on the suppression of vector populations to prevent human exposure to infected mosquitoes . Accordingly , understanding the geographic distribution and range expansion of this vector is of utmost importance for disease surveillance and control . Originally African in origin , it is thought that Ae . aegypti was transported inadvertently to the Americas via European ships used for trade , commerce , and slave transport in the 17th–19th centuries [8] . As urbanization continued and the shipping industry expanded , outbreaks of dengue-like illnesses became more common in port cities [8] . By the 20th century , Ae . aegypti was present throughout North and South America , probably first infesting port cities and then moving inland [8] , [9] . During the mid-20th century ( 1946-1963 ) , Ae . aegypti populations in the Americas were dramatically reduced as a result of a yellow fever control program led by the Pan American Health Organization [10] , [11] . The successful reduction of yellow fever led to the waning of control programs targeting the mosquito , and consequentially , since the 1980s , Ae . aegypti has become reestablished throughout the Americas [12] . In Latin America and in Peru , Ae . aegypti mosquitoes are in the process of expanding from urban to rural areas [13] , [14] . The Amazonian city of Iquitos was the first site of Ae . aegypti ( in 1984 ) and dengue ( in 1990 ) reestablishment in Peru [15] . Iquitos rests at the intersection of the Amazon , Nanay , and Itaya Rivers which connect it to a number of smaller settlements throughout the region . Despite apparently similar climatic and socioeconomic conditions shared by most communities in the region , Ae . aegypti is heterogeneously distributed among these rural settlements . Invasion ecologists describe the invasion process as a series of sequential steps that include transport to a new area , release , establishment , and spread [16] . Invasion success is determined by several factors including the number and frequency of introduction events ( propagule pressure ) [17] , [18] , abiotic and biotic properties of the receiving ecosystem [19]–[22] , and behavior of the invader ( e . g . , tolerance of or attraction to human environments , oviposition behavior ) [16] . In this study , we focus on introductory pressure ( by measuring the number of vehicles and trips traveling to and from each town ) , selected abiotic factors ( e . g . , container abundance ) , and biotic factors ( e . g . , presence of other mosquito species ) . Understanding the mechanisms underlying these invasion steps for Ae . aegypti is critical for predicting and mitigating future expansion of this mosquito species and the pathogens it transmits . The region surrounding Iquitos provides an ideal setting to study these questions , due to the presence of an interconnected network of settlements with different population sizes , varying degrees of urbanization , reliance on both river and road transit , and similar climatic conditions ( e . g . , temperature , rainfall ) at this ecological scale . In this context , we used two pupal-demographic datasets ( an existing historical dataset from 2008-11 and more in-depth data collected for this study from 2011-12 ) to address two questions: 1 ) Which factors are associated with Ae . aegypti establishment in rural areas ? ; and 2 ) How do different human transportation networks influence Ae . aegypti spread ( rivers vs . roads , primary vs . secondary transportation routes ) ?
No personal information was collected during interviews . Permission for this study was granted by the Loreto Regional Health Department , and the study protocol was approved by the NAMRU-6 Institutional Review Board in compliance with all applicable federal regulations governing the protection of human subjects ( protocol number NAMRU6 . 2012 . 0039 ) . In addition , the Emory University Institutional Review Board determined that this study does not represent human subjects based research . With approximately 380 , 000 inhabitants , Iquitos is the largest population center in the Department of Loreto , Peru . Transportation pathways , including new roads and river routes have been developed over the course of the past 30 years as a result of increased commerce and trade in natural resources ( e . g . , oil , timber , and coca ) . As a result , small settlements in the region have experienced rapid population growth and expansion [23] . Although river networks are the predominant mode of transit , a 95 km road connecting Iquitos to the smaller city of Nauta ( population: 17 , 000 ) facilitates terrestrial commerce and population movement . With its construction , the road has brought about the establishment of new settlements in areas that were previously inaccessible , human population growth ( growth rate approximately 4% greater than that of Iquitos ) , and deforestation due to farming [24] . Communities included in the present study ( Table S1 ) can largely be described as “rural” due to small population sizes ( ranging from ∼100 to 6 , 000 inhabitants ) , geographic isolation from large cities , and limited access to cellular networks and other communication channels . People living in these communities subsist on hunting , fishing , and small-scale agriculture , such as cassava root and plantain farming [25] . Water is derived from a variety of sources: piped water systems ( accessible in the home via a faucet but not potable ) , water collected in buckets directly from the river , rain water that is actively gathered in large drums from roofs either directly or from gutters , and well water from ground sources . Unmanaged or discarded containers may also be passively filled through the unintentional accumulation of rain water . A map of the study area is shown in Figure 1 . We used historical data available from the Peruvian Ministry of Health ( MOH ) and Naval Medical Research Unit No . 6 ( NAMRU-6 ) to characterize patterns of Ae . aegypti expansion at the community scale . NAMRU-6 personnel conducted Ae . aegypti pupal-demographic surveys as part of epidemiological studies on alphaviruses , while the Peruvian MOH independently carried out larval surveys as a component of normal surveillance activities . The MOH and NAMRU-6 data consisted of information about 31 communities and two cities in the region during 2008 , 2011 , and 2012 , and were obtained by surveying approximately 10% of houses in a community [26] . Each house was searched thoroughly for Ae . aegypti mosquito larvae and pupae . In addition , in NAMRU-6 surveys Prokopack aspirators were used to collect adult mosquitoes [27] . To supplement these historical data we selected communities for a more detailed analysis of Ae . aegypti presence . To determine mosquito presence in 2011-12 , we deployed ovitraps and simultaneously conducted a thorough survey of wet containers ( with water at the time they were surveyed ) within households ( described in further detail below ) . Communities were selected along two transects: one following the Iquitos- Nauta highway ( N = 22 ) and the other along the Amazon River ( N = 12 ) , for a total of 34 communities . The geographic limit of transects was defined by the network distance ( travel time ) from Iquitos , under the assumption that long-distance dispersal of Ae . aegypti is due to unintentional passive human transport of immature and adult mosquitoes . For the Iquitos-Nauta highway , two hours of travel time resulted in a path-distance from Iquitos of 76 . 4 km . For the Amazon River , two hours of travel time in the fastest vehicle ( a speed boat ) translated into an approximate 44 . 5 km path-distance from Iquitos . In all , we collected information about 48 rural communities ( Table S1 ) . Iquitos and Nauta were excluded from all analyses , as the purpose of this study was to understand Ae . aegypti expansion from urban to peri-urban and rural areas . We also collected information on the year of community incorporation , water system type , the number of inhabitants , and the number of houses ( obtained from the 2007 Peruvian National Census ) [28] . When census data were not available , population estimates were obtained from local authorities such as the mayor or the health center director . With the exception of one site , El Terminal , all of the study sites are towns that have been officially incorporated . While El Terminal ( a bus station and residential area ) is not politically separated from Iquitos , it is geographically far enough to be ecologically distinct . ( The distance between Iquitos and El Terminal , ∼400 m , exceeds that of the estimated Ae . aegypti flight range , ∼100 m [29]-[32] . ) Houses were systematically selected for Ae . aegypti sampling and ovitrap deployment: starting at a randomly selected household within the areas of highest housing density , every Nth house was sampled based on the total number of houses in the community to ensure a minimum coverage of 10% . This resulted in a minimum of 10 and a maximum of 78 houses sampled per community . During October through December of 2012 , ovitraps were deployed in all communities along our transects . Ovitraps were red plastic cups filled ¾ water ( volume = 56 . 5 in3 ) and lined with paper . Two ovitraps were placed within each home in a dark , secluded area where they would not be a nuisance to residents . Eight days after deployment , ovitraps were checked for immature mosquitoes and removed from the household . Immature mosquitoes were collected in sterile bags ( Whirlpak Co . ) and transported to the field laboratory for rearing and taxonomic identification to species for Ae . aegypti and to genus for other mosquitoes . Paper from the ovitraps was thoroughly examined under a microscope to count the number of eggs present . Ae . aegypti eggs are easily differentiated from other container-breeding mosquitoes due to their smooth texture , black color , and position above the water line along the sides of containers . There are no other common container breeding Aedes species in this region . Although a number of Ochleratatus ( formerly Aedes-genus mosquitoes ) have been documented in the area [33] , [34] , these mosquitoes predominate in natural water bodies in forested areas such as rain pools and swamps [34]–[38] . Culex genus mosquitoes are often found in containers but they lay their eggs in rafts on the water surface . Simultaneous with ovitrap deployment , household pupal-demographic surveys were conducted to determine the abundance of wet containers and the presence of other mosquito genera in each household . Wet containers within and around each household were exhaustively surveyed for the presence of mosquito larvae . Using previously established protocols for Ae . aegypti surveys in Iquitos , we recorded the following information for each container: container type and material , observed solar exposure ( if the container was exposed to direct sunlight at any time during the day; yes/no ) , degree of organic material present in the water ( ranked on a scale of one to three ) , container location , whether the container was inside or outside or underneath a roof , whether the container was filled manually ( including active collection of rain water ) or passively ( through unintentional rain water accumulation ) , and the presence of abate larvacide [39] , [40] . Mosquito eggs , larvae , and pupae were collected in Whirlpak bags and were transported to the field laboratory for rearing , counting , and taxonomic identification to species for Ae . aegypti and to genus for other mosquitoes . River networks are the primary mode of transportation in the region ( Figure 1 ) . There are a variety of boat types that carry both passengers and cargo throughout the Peruvian Amazon including; large barges ( for cargo and passengers ) , medium-sized barges , speed boats , and small water taxis . Terrestrial vehicle types include mini-buses taxis that travel the Iquitos-Nauta highway . To characterize the connectivity between Iquitos and surrounding communities , 140 vehicle drivers were interviewed across 11 sites throughout Iquitos: 9 different ports and 2 bus/taxi departure points . Each sampling location was visited twice , and route information was collected for 8 taxis , 14 mini-buses , 19 medium-sized barges , 22 large barges , 25 speed boats , and 52 water taxis ( a total of 140 vehicles ) . All available vehicle drivers were interviewed at each port or bus station . For each vehicle we collected information on the frequency and duration of travel , the final destination of the vehicle , and the number of trips to each community per month . All data analysis and graphs were produced using R statistical software [41] . GPS coordinates of each town were recorded with a Garmin GPSMAP 62sc and integrated with other information ( rivers , political boundaries ) to create study area maps in ArcMap 10 . 1 ( ESRI , Redlands , CA ) . Finalized maps were projected in Universal Transverse Mercator ( UTM ) , Zone 18S , WGS1984 datum . Descriptive maps of Ae . aegypti presence were developed by year ( 2008 and 2011 for the historical data , and 2011-12 for the data collected for this study ) . ( Table S2 shows datasets and data analyses employed . ) A community was considered positive for Ae . aegypti if the mosquito was found either via ovitraps or larval surveys . For the data collected for this study ( 2011-12 ) , we compared median values for community age , number of inhabitants , number of houses , and distance from the city of Iquitos ( assumed to be the source population ) between positive and negative communities using nonparametric Mann-Whitney Wilcoxon non-paired tests . We measured both Euclidean distance and path-distance – the latter was calculated by tracing the shortest routes from Iquitos ( fluvial , terrestrial , or some combination of the two ) . We calculated the following entomological indices in 34 communities surveyed; Container Index ( positive containers/containers surveyed *100 ) , House Index ( positive houses/houses surveyed*100 ) , and Breteau Index ( positive containers/houses surveyed *100 ) . For one community ( Varillal ) , we found a positive ovitrap but no positive containers from the pupal surveys . Logistic regression models were used to explore factors associated with Ae . aegypti presence . The variable population size was log-transformed to force normality prior to its inclusion in the models . Other variables tested included the total wet containers and passively-filled containers , the average number of wet and passively-filled containers per house , access to the highway vs . the river , water system type , community age , the number of vehicles traveling to each location , the number of high-risk vehicles traveling to each location , and the presence of other mosquito genera . “High risk” vehicles were defined as vehicles that were most likely to contain Ae . aegypti mosquitoes , including large river boats for riverine communities and buses for communities along the highway . All possible combinations of variables were explored in each of the models , and the final model was selected using backwards stepwise selection in the MASS package in R [41] , [42] , based on the Akaike Information Criterion . Only explanatory variables that were significant univariable predictors ( p<0 . 10 ) were included in the multivariable model . The independence of predictor variables was evaluated by testing regression models of all possible combinations of predictor variables . Residual plots were used to evaluate heteroscedasticity . For the household-level analysis , we explored variables thought to be predictive of Ae . aegypti presence/absence in univariable and multivariable logistic regression models . Predictor variables included; number of people per household , the number of wet and passively-filled containers , the presence of mosquito genera within the house , and the abundance of other mosquito genera within the house . Multivariable logistic models were developed using backwards stepwise selection in the MASS package in R [42] . The independence of predictor variables was evaluated by running regressions of all possible combinations of predictor variables . We calculated the proportion of positive containers by container type for each community positive for Ae . aegypti mosquitoes . To assess container productivity we calculated the proportion of pupae produced by each container type . Univariable and multivariable logistic regression models predicting Ae . aegypti presence/absence were developed using the following predictor variables; container material ( plastic , metal ) , container type ( plastic bucket/pans , large drums and tanks ) , solar exposure ( yes/no ) , presence or absence of a container cover , presence of other mosquito genera within the same container , the number of other mosquitoes within the same container , and fill method ( manually filled vs . passively filled ) . A multivariable model was developed using backwards stepwise selection in the MASS package in R [42] . Predictor variables that were correlated with one another were not included in the selection process .
Ae . aegypti was the most abundant species , followed by Culex-genus mosquitoes . Other mosquito genera included; Culex , Limatus , Toxorhynchites , Wyeomyia , Trichoprosopon , all of which have been previously reported from the Peruvian Amazon [33] , [34] . Among the 34 communities where entomological surveys were conducted for this study , 14 were positive for Ae . aegypti ( Figure 2 ) . Both Ae . aegypti and Culex-genus mosquitoes were found in 23 . 5% ( 8 of the 34 communities surveyed ) . In 17 . 6% ( 6/34 ) communities Ae . aegypti mosquitoes were found without Culex , and in 20 . 6% ( 7/34 ) of communities Culex mosquitoes were found without Ae . aegypti . We report here the presence of Ae . aegypti in three new communities , two along the highway , and one along the Amazon River . Descriptive maps of Ae . aegypti infestation based on MOH/NAMRU collections from 2011 showed clustering of Ae . aegypti positive towns near Iquitos ( Figure 2 ) . Data collected for this study in 2011-12 , however , showed a clear limit of Ae . aegypti expansion along the Iquitos-Nauta highway ( Euclidean distance to Iquitos of 19 . 3 km ) . River communities , in contrast , showed a more heterogeneous spatial pattern , and the farthest point of expansion from Iquitos was 37 . 1 km . Ae . aegypti entomological indices revealed differences in mosquito abundance across sites ( Table 1 ) . Ae . aegypti positive communities had larger population size ( Mann-Whitney , U = 56 , p<0 . 05 ) , were closer to Iquitos ( U = 208 , p<0 . 02 ) , and had more wet containers per household ( U = 79 , p<0 . 05 ) than Ae . aegypti negative communities ( Figure 3 ) . No significant differences were detected in terms of community age ( U = 114 , p>0 . 5 ) or the average number of passively-filled containers per household ( U = 96 , p>0 . 1 ) . Univariable logistic regression models ( Table S3 ) showed that increased human population ( odds ratio , OR = 1 . 004 , p<0 . 05 ) and log human population ( OR = 5 . 06 , p<0 . 01 ) increased the odds of Ae . aegypti establishment . Increased Euclidean and path distance from Iquitos were both negatively associated with Ae . aegypti presence ( OR = 0 . 94 , p<0 . 05 for both distance measures ) . A higher number of wet containers resulted in an increased probability of Ae . aegypti establishment in that community ( OR = 1 . 03 , p<0 . 05 ) . Since the number of wet containers per community is positively correlated with the population size ( R2 = 0 . 67 , p<0 . 0001 ) , we also used the average number of wet containers per household as a predictor variable . A greater number of wet containers per household increased the risk of Ae . aegypti establishment by 1 . 55 times ( p<0 . 05 ) . Communities relying predominantly on river/stream water were much less likely ( 0 . 18 times ) to have Ae . aegypti mosquitoes than those relying on other water sources ( e . g . , well water , piped water ) ( p<0 . 05 ) . Variables with no significant impact on Ae . aegypti presence included; community age , the number of vehicles ( and high risk vehicles ) traveling to each site per month , the average number of passively-filled containers/household , the absolute number of passively-filled containers per household , use of piped water , access to the Amazon River , and presence of other mosquito genera . For the multivariable model the log-transformed human population number ( which had a more powerful effect than the raw population number ) was used . Euclidean distance was used because it had a lower AIC value than path distance from Iquitos . The predictor variables included in the model selection process were; log human population , Euclidean distance from Iquitos , the average number of wet containers per household , and whether or not the community relied on river or stream water . The final multivariable logistic regression model ( Table 2 ) showed that the risk of Ae . aegypti establishment is increased 5 . 76 times per log population unit ( p<0 . 05 ) , when taking into account the Euclidean distance from Iquitos and the use of river/stream water . Communities farther away from Iquitos were less likely to have Ae . aegypti mosquitoes ( OR = 0 . 89 , p<0 . 05 ) when adjusting for the other variables included in the model . The use of river/stream water was not statistically significant . Of the 580 houses ( in 34 communities ) that we surveyed , 80 ( 13 . 8% ) were positive for Ae . aegypti . Among houses ( N = 380 ) in the 14 positive communities , 22 . 9% houses were positive ( 80/350 houses ) . House-level logistic regression models were restricted to communities in which Ae . aegypti was present . In houses in positive communities , Culex mosquitoes were found together with Ae . Aegypti in 4 . 2% ( 16/380 ) of houses . In 16 . 6% ( 63/380 ) of houses in positive communities , Ae . aegypti were found without Culex , and in 2 . 6% ( 10/380 ) houses Culex mosquitoes were present without Ae . aegypti . Univariable logistic regression ( Table S4 ) showed that the number of wet containers found within a household slightly increased the risk of Ae . aegypti presence ( OR = 1 . 05 , p<0 . 05 ) . Houses with higher number of passively-filled containers were 1 . 17 times more likely to have Ae . aegypti mosquitoes ( p<0 . 001 ) . Both the presence and number of other mosquitoes within the household increased the risk of Ae . aegypti presence ( OR = 5 . 85 , p<0 . 001 , and OR = 5 . 44 , p <0 . 001 , respectively ) . Given that the numbers of wet and passively-filled containers in a house are correlated , we chose to include only the number of passively-filled containers in the multivariable model , as the AIC and p-value were both lower . Similarly , the number of other mosquitoes was chosen over the presence/absence of other mosquitoes , based on the AIC and significance levels of the univariable predictors . The final multivariable logistic regression model ( Table 3 ) showed that the number of passively-filled containers increased risk of Ae . aegypti presence 1 . 16 times ( p<0 . 001 ) , while the presence mosquitoes of other genera present increased this risk 5 . 67 times ( p<0 . 001 ) . Among containers that were positive for Ae . aegypti , Culex genus mosquitoes were also found in 8 . 6% of containers ( 11/128 ) . The most common types of water-holding containers found ( regardless of infestation status ) were plastic buckets ( 61 . 2% of all containers ) and large water drums ( 10 . 5% of all containers ) . Although plastic containers were very common ( 1 , 977 found ) , the proportion infested was small ( 2 . 7% ) . Toilets and drains had the highest infestation level ( 17 . 0% positive of 23 ) , followed by tires ( 12 . 8% positive of 39 ) and large water storage tanks/drums ( 11 . 5% positive of 340 ) ( Table 4 ) . Productivity analysis by container type ( Figure 4 ) demonstrated that plastic containers and water storage tanks/drums produced 41 . 1% and 35 . 6% of all pupae , respectively , followed by animal watering pans ( 11 . 7% ) . A similar pattern held for larval productivity , with plastic containers and water tanks/drums accounting for 33 . 4% and 32 . 0% of the larvae , respectively . Univariable logistic regressions ( Table S5 ) showed that toilets/drains ( OR = 5 . 24 , p<0 . 01 ) and tires ( OR = 3 . 64 , p<0 . 01 ) were more likely to be infested with Ae . aegypti in comparison to other container types . Large water storage tanks and drums increased the probability of Ae . aegypti presence 4 . 04 times ( p<0 . 001 ) , as did the presence of other mosquito genera ( OR = 12 . 60 , p<0 . 001 ) . Containers with solar exposure were more likely to contain Ae . aegypti ( OR = 2 . 42 , p<0 . 001 ) . Containers with lids were 0 . 15 times less likely to have Ae . aegypti in comparison with containers without lids ( p<0 . 01 ) . Plastic containers were less likely to be infested with Ae . aegypti ( OR = 0 . 44 , p<0 . 001 ) , consistent with the observation that only 2 . 8% were positive for Ae . aegypti even though they comprise the most prevalent container type . Our multivariable model ( Table 5 ) showed that drums/tanks increased the risk of Ae . aegypti infestation by 4 . 22 times ( p<0 . 0001 ) , while solar exposure increased this risk by 2 . 53 times ( p<0 . 0001 ) . Because regressions of all possible combinations of predictor variables ( Table S6 ) revealed that container type categories were correlated with one another , we used only the value of type = drum/tank which had the lowest AIC among the univariable models . We also excluded presence/absence of container lids from the final multivariable model because this variable was correlated with both solar exposure and type = drum/tank . 10 . 1371/journal . pntd . 0003033 . t005 Table 5 . Multivariable logistic regressions: Ae . aegypti risk factors at the container scale . Variable OR 95% CI SE P Type = drum/tank 4 . 22 2 . 81 , 6 . 23 0 . 20 <0 . 0001 Solar Exposure = yes 2 . 53 1 . 72 , 3 . 81 0 . 20 <0 . 0001 Variables included in the multivariable model selection processes were; type = drums/tanks , presence of mosquitoes of other genera , solar exposure , and presence of a container lid . Statistically significant ( p<0 . 05 ) variables are shown in bold ( N = 3235 containers ) .
To our knowledge this is the first extensive , multi-scale analysis of Ae . aegypti geographic expansion from urban to peri-urban and rural areas . Of the three ecological scales , the most novel findings were based on our community-level data , as the house and container-level data simply confirmed previous findings about Ae . aegypti . At the container-level , for example , water tanks/drums have previously been shown to be important for Ae . aegypti production , and Ae . aegypti has been shown to co-exist with Culex genus mosquitoes [40] . ( The co-occurrence of Ae . aegypti and Culex mosquitoes in households is likely attributable to the abundance of suitable containers that are favorable to all container-breeding mosquitoes , and the availability of shade and sufficient organic material for larval feeding . ) Below we discuss further these effects of water use , population size , and distance from Iquitos on the invasion process . We also explore the spatial pattern of Ae . aegypti spread , and its implications for DENV transmission . Community use of river/stream water reduced the odds of Ae . aegypti establishment . While this variable was not statistically significant in our multivariable model , we suspect that this may be due to relatively small number of observations and low statistical power . The observed association may be a result one of three mechanisms 1 ) Water use may be correlated with other factors important for Ae . aegypti establishment and spread . ( For example , piped water systems are likely to be most abundant in larger settlements closer to Iquitos city . ) 2 ) River/stream water may be less attractive to Ae . aegypti mosquitoes for oviposition due to the chemical and organic composition of the water . 3 ) Containers filled with river/stream water may be frequently emptied and re-filled , thus reducing the probability of the accumulation of organic material and therefore oviposition . For the first mechanism , we were unable to identify a significant correlation between river/stream water usage and population size or distance to Iquitos ( Figure S1 ) . The second mechanism could be evaluated through simple oviposition experiments to test the hypothesis that river water is less suitable for Ae . aegypti oviposition and development , similar to that in [43] , [44] . Lastly , longitudinal studies could elucidate patterns of container use and quantify water turnover [45] by water source in rural areas , although this is likely to vary depending on local cultural and socioeconomic conditions . Human population size may influence Ae . aegypti invasion in two ways . First , large population centers are more likely to have an abundance of oviposition sites , thus contributing to greater habitat suitability . Secondly , population centers are also more “connected” to other places via vehicle traffic , thereby contributing to human-mediated introduction of immature mosquitoes ( introductory pressure ) . Thus , from an invasion ecology perspective , we cannot disentangle the effects of habitat suitability vs . introductory pressure , since both are correlated with population size . Regardless of which of these individual mechanisms ( or combination of the two ) is driving the observed associations , Ae . aegypti , control programs should address both habitat suitability ( wet container management/reduction , insecticide spraying ) and introductory pressure ( surveillance and control of mosquitoes on vehicles ) . Our analyses revealed that population and inverse distance increased risk of Ae . aegypti presence at a community scale . While beyond the scope of this paper , gravity models may be an appropriate way to predict the future spread of Ae . aegypti mosquitoes . Although gravity models have been used to model DENV dispersal [46] , [47] , to date , such an approach has not been applied to Ae . aegypti spread . Gravity models assume that connectivity between locations is a function of the inverse distance between them and of their ‘attractiveness’ based on population size . They have been applied to invasive organisms that spread through human-mediated activities [48]–[50] . A key advantage to this approach is that the required data are often easily accessible through public resources such as census data and public maps . The contrast between the spatial pattern of Ae . aegypti establishment along rivers vs . the highway is noteworthy . Genetic studies have suggested that Ae . aegypti spreads along transportation networks [51]–[53] , but few studies to date have used field collections to identify areas where Ae . aegypti has become successfully established following introduction from a known source [13] , [14] , [54] . We propose that urbanization is responsible for the linear pattern observed along the highway , due to the high density of settlements relatively close to Iquitos and immediately adjacent to the highway . Short-distance active dispersal of Ae . aegypti mosquitoes is driven by availability of oviposition sites [32] , and as urbanization continues southward , ovisposition sites become more abundant . The community 5 de Abril represents the geographic limit of Ae . aegypti along the highway , approximately 19 km south of Iquitos . This is most likely due to the ∼6 . 5 km gap between 5 de Abril and the next community to the south , San José ( Figure 5 ) . Prior to that point , each community is distanced <3 . 1 km from the next settlement along the highway . In contrast , it is probable that Ae . aegypti geographic expansion along fluvial routes is the result of longer-distance dispersal events that are mediated via the passive transport of mosquito eggs through human vehicles ( boats ) . Urbanization along the highway is more relevant for DENV transmission , owing to greater density of human host and vector populations [55] , [56] . Mahabir et al ( 2012 ) showed that arterial highways in Trinidad tended to have fewer dengue cases than did smaller , rural roads , as major highways may serve as barriers to transmission [57] . With only one lane in each direction , the Iquitos-Nauta highway would likely be classified as a smaller road leading to rural towns . Thus , we believe that as urbanization south of Iquitos continues , the conditions along the Iquitos-Nauta highway will grow increasingly suitable for DENV transmission . In contrast , while riverine communities may be susceptible to mosquito introductions , most of these towns currently lack enough human hosts to ensure sustained local DENV transmission . ( Realistic estimates of minimum human population required for local transmission range from 3 , 000 to 100 , 000 [58]–[61] . ) Our approach may be applicable to Ae . aegypti in other regions ( e . g . , Vietnam's Mekong Delta which relies heavily on fluvial transport ) , and to other insect vectors that are passively transported by humans ( e . g . , Aedes albopictus [62] , Culex quinquefasciatus [63]; and Triatoma infestans [64] , among others [65] ) . | Ae . aegypti mosquitoes carry a number of viruses that cause human disease , including dengue and yellow fever . Over the past 30 years , the burden of dengue has increased exponentially , due to urbanization , poor waste and water management , human transportation , and expanding mosquito populations . Although much research has been conducted on Ae . aegypti at the household and container levels , little is known about the mechanisms fueling the range expansion of this mosquito across longer distances . The goal of this study is to characterize Ae . aegypti spread along transportation networks and to identify risk factors associated with its establishment , thus improving our ability to predict future Ae . aegypti expansion . Characterizing current patterns of establishment will aid in understanding and preventing future invasions . Our approach is broadly applicable to other biological invasions associated with human activities . | [
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] | 2014 | Patterns of Geographic Expansion of Aedes aegypti in the Peruvian Amazon |
The mental contents of perception and imagery are thought to be encoded in hierarchical representations in the brain , but previous attempts to visualize perceptual contents have failed to capitalize on multiple levels of the hierarchy , leaving it challenging to reconstruct internal imagery . Recent work showed that visual cortical activity measured by functional magnetic resonance imaging ( fMRI ) can be decoded ( translated ) into the hierarchical features of a pre-trained deep neural network ( DNN ) for the same input image , providing a way to make use of the information from hierarchical visual features . Here , we present a novel image reconstruction method , in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers . We found that our method was able to reliably produce reconstructions that resembled the viewed natural images . A natural image prior introduced by a deep generator neural network effectively rendered semantically meaningful details to the reconstructions . Human judgment of the reconstructions supported the effectiveness of combining multiple DNN layers to enhance the visual quality of generated images . While our model was solely trained with natural images , it successfully generalized to artificial shapes , indicating that our model was not simply matching to exemplars . The same analysis applied to mental imagery demonstrated rudimentary reconstructions of the subjective content . Our results suggest that our method can effectively combine hierarchical neural representations to reconstruct perceptual and subjective images , providing a new window into the internal contents of the brain .
While the externalization of states of the mind is a long-standing theme in science fiction , it is only recently that the advent of machine learning-based analysis of functional magnetic resonance imaging ( fMRI ) data has expanded its potential in the real world . Although sophisticated decoding and encoding models have been developed to render human brain activity into images or movies , the methods are essentially limited to image reconstructions with low-level image bases [1 , 2] , or to matching to exemplar images or movies [3 , 4] , failing to combine the visual features of multiple hierarchical levels . While several recent approaches have introduced deep neural networks ( DNNs ) for the image reconstruction task , they have failed to fully utilize hierarchical information to reconstruct visual images [5 , 6] . Furthermore , whereas categorical decoding of imagery contents has been demonstrated [7 , 8] , the reconstruction of internally generated images has been challenging . The recent success of DNNs provides technical innovations to study the hierarchical visual processing in computational neuroscience [9] . Our recent study used DNN visual features as a proxy for the hierarchical neural representations of the human visual system and found that a brain activity pattern measured by fMRI could be decoded ( translated ) into the response patterns of DNN units in multiple layers representing the hierarchical visual features given the same input [10] . This finding revealed a homology between the hierarchical representations of the brain and the DNN , providing a new opportunity to utilize the information from hierarchical visual features . Here , we present a novel approach , named deep image reconstruction , to visualize perceptual content from human brain activity . This technique combines the DNN feature decoding from fMRI signals with recently developed methods for image generation from the machine learning field ( Fig 1 ) [11] . The reconstruction algorithm starts with a given initial image and iteratively optimizes the pixel values so that the DNN features of the current image become similar to those decoded from brain activity across multiple DNN layers . The resulting optimized image is considered as a reconstruction from the brain activity . We optionally introduced a deep generator network ( DGN ) [12] to constrain the reconstructed images to look similar to natural images by performing optimization in the input space of the DGN .
We trained the decoders that predicted the DNN features of viewed images from fMRI activity patterns following the procedures of Horikawa & Kamitani ( 2017 ) [10] . In the present study , we used the VGG19 DNN model [13] , which consisted of sixteen convolutional layers and three fully connected layers and was pre-trained with images in ImageNet [14] to classify images into 1 , 000 object categories ( see Materials and Methods: “Deep neural network features” for details ) . We constructed one decoder for a single DNN unit to predict outputs of the unit . We trained decoders corresponding to all the units in all the layers ( see Materials and Methods: “DNN feature decoding analysis” for details ) . The feature decoding analysis was performed with fMRI activity patterns in visual cortex ( VC ) measured while subjects viewed or imagined visual images . Our experiments consisted of the training sessions in which only natural images were presented and the test sessions in which independent sets of natural images , artificial shapes , and alphabetical letters were presented . In another test session , a mental imagery task was performed . The decoders were trained using the fMRI data from the training sessions , and the trained decoders were then used to predict DNN feature values from the fMRI data of the test sessions ( the accuracies are shown in S1 Fig ) . Decoded features were then forwarded to the reconstruction algorithm to generate an image using variants of gradient descent optimization ( see Material and Methods: “Reconstruction from a single DNN layer” and “Reconstruction from multiple DNN layers” for details ) . The optimization was performed to minimize the error between multi-layer DNN features decoded from brain activity patterns and those calculated from the input image by iteratively modifying the input image . For natural image reconstructions , to improve the “naturalness” of reconstructed images , we further introduced the constraint using a deep generator network ( DGN ) derived from the generative adversarial network algorithm ( GAN ) [15] , which is known to capture a latent space explaining natural images [16] ( see Material and Methods: “Natural image prior” for details ) . Examples of reconstructions for natural images are shown in Fig 2 ( see S2 Fig for more examples , and see S1 Movie for reconstructions through the optimization processes ) . The reconstructions obtained with the DGN capture the dominant structures of the objects within the images . Furthermore , fine structures reflecting semantic aspects like faces , eyes , and texture patterns were also generated in several images . Our extensive analysis on each of the individual subjects demonstrated replicable results across the subjects . Moreover , the same analysis on a previously published dataset [10] also replicated qualitatively similar reconstructions to those in the present study ( S3 Fig ) . To investigate the effect of the DGN , we evaluated the quality of reconstructions generated both with and without using it ( Fig 3A and 3B; see S4 Fig for individual subjects; see Material and Methods: “Evaluation of reconstruction quality” ) . While the reconstructions obtained without the DGN also successfully reproduced rough silhouettes of dominant objects , they did not show semantically meaningful appearances ( see S5 Fig for more examples; also see S6 Fig for reconstructions from different initial states for both with and without the DGN ) . Evaluations using pixel-wise spatial correlation and human judgment both showed almost comparable accuracy for reconstructions with and without the DGN ( accuracy of pixel-wise spatial correlation , with and without the DGN , 76 . 1% and 79 . 7%; accuracy of human judgment , with and without the DGN , 97 . 0% and 96 . 0% ) . However , reconstruction accuracy evaluated using pixel-wise spatial correlation showed slightly higher accuracy with reconstructions performed without the DGN than with the DGN ( two-sided signed-rank test , P < 0 . 01 ) , whereas the opposite was observed for evaluations by human judgment ( two-sided signed-rank test , P < 0 . 01 ) . These results suggest the utility of the DGN that enhances the perceptual similarity of reconstructed images to target images by rendering semantically meaningful details in the reconstructions . To characterize the ‘deep’ nature of our method , the effectiveness of combining multiple DNN layers was tested using both objective and subjective assessments [5 , 17 , 18] . For each of the 50 test natural images , reconstructed images were generated with a variable number of multiple layers ( Fig 4A; DNN1 only , DNN1–2 , DNN1–3 , … , DNN1–8; see S7 Fig for more examples ) . In the objective assessment , the pixel-wise spatial correlations to the original image were compared between two combinations of DNN layers . In the subjective assessment , an independent rater was presented with an original image and a pair of reconstructed images , both from the same original image but generated with different combinations of multiple layers , and was required to indicate which of the reconstructed images looked more similar to the original image . While the objective assessment showed higher winning percentages for the earliest layer ( DNN1 ) alone , the subjective assessment showed increasing winning percentages for a larger number of DNN layers ( Fig 4B ) . Our additional analysis showed poor reconstruction quality from individual layers especially from higher layers ( see S8 Fig for reconstructions from individual layers ) . These results suggest that combining multiple levels of visual features enhanced the perceptual reconstruction quality even though the pixel-wise accuracy is lost . Given the true DNN features , instead of decoded features , as the input , the reconstruction algorithm produces almost complete reconstructions of original images ( S8 Fig ) , indicating that the DNN feature decoding accuracy would determine the quality of reconstructed images . To further confirm this , we calculated the correlation between the feature decoding accuracy and the reconstruction quality for individual images ( S9 Fig ) . The analyses showed positive correlations for both the objective and subjective assessments , suggesting that improving feature decoding accuracy could improve reconstruction quality . We found that the luminance contrast of a reconstruction was often reversed ( e . g . , the stained-glass images in Fig 2 ) , presumably because of the lack of ( absolute ) luminance information in the fMRI signals , even in the early visual areas [19] . Additional analyses revealed that the feature values of filters with high luminance contrast in the earliest DNN layers ( conv1_1 in VGG19 ) were better decoded when they were converted to absolute values ( Fig 5A and 5B ) , demonstrating a clear discrepancy between the fMRI and raw DNN signals . The large improvement levels demonstrate the insensitivity of fMRI signals to pixel luminance , suggesting the linear-nonlinear discrepancy of DNN and fMRI responses to pixel luminance . This discrepancy may explain the reversal of luminance observed in several reconstructed images . While this may limit the potential for reconstructions from fMRI signals , the ambiguity might be resolved by modelling DNNs to fill the gaps between signals of DNNs and fMRI . Alternatively , further emphasis of the high-level visual information in hierarchical visual features may help to resolve the ambiguity of luminance by incorporating information on semantic context . To confirm that our method was not restricted to the specific image domain used for the model training , we tested whether it was possible to generalize the reconstruction to artificial images . This was challenging , because both the DNN and our decoding models were solely trained on natural images . The reconstructions of artificial shapes and alphabetical letters are shown in Fig 6A and 6B ( also see S10 Fig and S2 Movie for more examples of artificial shapes , and see S11 Fig for more examples of alphabetical letters ) . The results show that artificial shapes were successfully reconstructed with moderate accuracy ( Fig 6C left; 70 . 5% by pixel-wise spatial correlation , 91 . 0% by human judgment; see S12 Fig for individual subjects ) and alphabetical letters were also reconstructed with high accuracy ( Fig 6C right; 95 . 6% by pixel-wise spatial correlation , 99 . 6% by human judgment; see S13 Fig for individual subjects ) . These results indicate that our model did indeed ‘reconstruct’ or ‘generate’ images from brain activity , and that it was not simply making matches to exemplars . Furthermore , the successful reconstructions of alphabetical letters demonstrate that our method can expand the possible states of visualizations , with advance in resolution over reconstructions performed in previous studies [1 , 20] . To assess how the shapes and colors of the stimulus images were reconstructed , we separately evaluated the reconstruction quality of each of shape and color by comparing reconstructed images of the same colors and shapes . Analyses with different visual areas showed different trends in reconstruction quality for shapes and colors ( Fig 7A and see S14 Fig for more examples ) . Human judgment evaluations suggested that shapes were reconstructed better from early visual areas , whereas colors were reconstructed better from the mid-level visual area V4 ( Fig 7B and see S15 Fig for individual subjects; ANOVA , interaction between task type [shape vs . color] and brain areas [V1 vs . V4] , P < 0 . 01 ) , although the interaction effect was marginal when considering evaluations by pixel-wise spatial correlation ( P = 0 . 06 ) . These contrasting patterns further support the success of shape and color reconstructions and indicate that our method can be a useful tool to characterize the information content encoded in the activity patterns of individual brain areas by visualization . Finally , to explore the possibility of visually reconstructing subjective content , we performed an experiment in which participants were asked to produce mental imagery of natural and artificial images shown prior to the task session . The reconstructions generated from brain activity due to mental imagery are shown in Fig 8 ( see S16 Fig and S3 Movie for more examples ) . While the reconstruction quality varied across subjects and images , rudimentary reconstructions were obtained for some of the artificial shapes ( Fig 8A and 8B for high and low accuracy images , respectively ) . In contrast , imagined natural images were not well reconstructed , possibly because of the difficulty of imagining complex natural images ( Fig 8C; see S17 Fig for vividness scores of imagery ) . While the pixel-wise spatial correlation evaluations of reconstructed artificial images did not show high accuracy ( Fig 8D; 51 . 9%; see S18 Fig for individual subjects ) , this may have been due to the possible disagreements in positions , colors and luminance between target and reconstructed images . Meanwhile , the human judgment evaluations showed accuracy higher than the chance level , suggesting that imagined artificial images were recognizable from the reconstructed images ( Fig 8D; 83 . 2%; one-sided signed-rank test , P < 0 . 01; see S18 Fig for individual subjects ) . Furthermore , separate evaluations of color and shape reconstructions of artificial images suggested that shape rather than color had a major contribution to the high proportion of correct answers by human raters ( Fig 8E; color , 64 . 8%; shape , 87 . 0%; two-sided signed-rank test , P < 0 . 01; see S19 Fig for individual subjects ) . Additionally , poor but sufficiently recognizable reconstructions were obtained even from brain activity patterns in the primary visual area ( V1; 63 . 8%; three subjects pooled; one-sided signed-rank test , P < 0 . 01; see S20 Fig for reconstructed images and S21 Fig and S22 Fig for quantitative evaluations ) , possibly supporting the notion that low-level visual features are encoded in early visual cortical activity during mental imagery [21] . Taken together , these results provide evidence for the feasibility of visualizing imagined content from brain activity patterns .
We have presented a novel approach to reconstruct perceptual and mental content from human brain activity combining visual features from the multiple layers of a DNN . We successfully reconstructed viewed natural images , especially when combined with a DGN . The results from the extensive analysis on each subject were replicated across different subjects . Reconstruction of artificial shapes was also successful , even though the reconstruction models used were trained only on natural images . The same method was also applied to mental imagery , and revealed rudimentary reconstructions of mental content . Our method is capable of reconstructing various types of images , including natural images , colored artificial shapes , and alphabetical letters , even though each component of our reconstruction model , the DNN models and the DNN feature decoders , was solely trained with natural images . The results strongly demonstrated that our method was certainly able to ‘reconstruct’ or ‘generate’ images from brain activity , differentiating our method from the previous attempts to visualize perceptual contents using the exemplar matching approach , which suffers from restrictions derived from pre-selected image/movie sets [3 , 4] . We introduced the GAN-based constraint using the DGN for natural image reconstructions to enhance the naturalness of reconstructed images , rendering semantically meaningful details to the reconstructions . A variant of the GAN-based approach has demonstrated the utility in a previous face image reconstruction study , too [22] . GAN-derived feature space appears to provide efficient constraints on resultant images to enhance the perceptual resemblance to the image set on which a GAN is trained . While one of the strengths of the present method is its generalizability across image types , there remains room for substantial improvements in reconstruction performance . Because we used the models ( DNNs and decoders ) trained with natural ‘object’ images from the ImageNet database [14] , whose images contain objects around the center , it would not be optimal for the reconstruction of other types of images . Furthermore , because we used the DNN model trained to classify images into 1 , 000 object categories , the representations acquired in the DNN would be specifically suited to the particular objects . One could train the models with diverse types of images , such as scenes , textures , and artificial shapes , as well as object images , to improve general reconstruction performance . If the target image type is known in prior , one can use a specific set of images and a DNN model training task that are matched to it . Other DNN models with different architectures could also be used to improve general reconstruction performance . As the reconstruction quality is positively correlated with the feature decoding accuracy ( S9 Fig ) , DNNs with highly decodable units are likely to improve reconstructions . Recent studies evaluated different types of DNNs in term of the prediction accuracy of brain activity given their feature values ( or the encoding accuracy ) [23–25] . Although it remains to be seen how closely the encoding and decoding accuracies are linked , it is expected that more ‘brain-like’ DNN models would yield high-quality reconstructions . Our approach provides a unique window into our internal world by translating brain activity into images via hierarchical visual features . Our method can also be extended to decode mental contents other than visual perception and imagery . By choosing an appropriate DNN architecture with substantial homology with neural representations , brain-decoded DNN features could be rendered into movies , sounds , text , or other forms of sensory/mental representations . The externalization of mental contents by this approach might prove useful in communicating our internal world via brain–machine/computer interfaces .
All subjects provided written informed consent for participation in our experiments , in accordance with the Declaration of Helsinki , and the study protocol was approved by the Ethics Committee of ATR . Three healthy subjects with normal or corrected-to-normal vision participated in our experiments: Subject 1 ( male , age 33 ) , Subject 2 ( male , age 23 ) and Subject 3 ( female , age 23 ) . This sample size was chosen on the basis of previous fMRI studies with similar experimental designs [1 , 10] . Visual stimuli consisted of natural images , artificial shapes , and alphabetical letters . The natural images were identical to those used in Horikawa & Kamitani ( 2017 ) [10] , which were originally collected from the online image database ImageNet ( 2011 , fall release ) [14] . The images were cropped to the center and resized to 500 × 500 pixels . The artificial shapes consisted of a total of 40 combinations of 5 shapes and 8 colors ( red , green , blue , cyan , magenta , yellow , white , and black ) , in which the shapes were identical to those used in Miyawaki et al . ( 2008 ) [1] and the luminance was matched across colors except for white and black . The alphabetical letter images consisted of the 10 black letters , A , C , E , I , N , O , R , S , T , and U . We conducted two types of experiments: image presentation experiments and a mental imagery experiment . The image presentation experiments consisted of four distinct session types , in which different variants of visual images were presented ( training natural images , test natural images , artificial shapes , and alphabetical letters ) . All visual stimuli were rear-projected onto a screen in the fMRI scanner bore using a luminance-calibrated liquid crystal display projector . To minimize head movements during fMRI scanning , subjects were required to fix their heads using a custom-molded bite-bar individually made for each subject . Data from each subject were collected over multiple scanning sessions spanning approximately 10 months . On each experimental day , one consecutive session was conducted for a maximum of 2 hours . Subjects were given adequate time for rest between runs ( every 5–8 min ) and were allowed to take a break or stop the experiment at any time . The image presentation experiments consisted of four distinct types of sessions: training natural-image sessions , test natural-image sessions , artificial-shape sessions , and alphabetical-letter sessions . Each session consisted of 24 , 24 , 20 , and 12 separate runs , respectively . For these four sessions , each run comprised 55 , 55 , 44 , and 11 stimulus blocks , respectively , with these consisting of 50 , 50 , 40 , and 10 blocks with different images , and 5 , 5 , 4 , and 1 randomly interspersed repetition blocks where the same image as in the previous block was presented ( 7 min 58 s for the training and test natural-image sessions , 6 min 30 s for the artificial-shape sessions , and 5 min 2 s for the alphabetical-letter sessions , for each run ) . Each stimulus block was 8 s ( training natural-images , test natural-images , and artificial-shapes ) or 12 s ( alphabetical-letters ) long , and was followed by a 12-s rest period for the alphabetical-letters , while no rest period was used for the training natural-images , test natural-images , and artificial-shapes . Images were presented at the center of the display with a central fixation spot and were flashed at 2 Hz ( 12 × 12 and 0 . 3 × 0 . 3 degrees of visual angle for the visual images and fixation spot respectively ) . The color of the fixation spot changed from white to red for 0 . 5 s before each stimulus block began , to indicate the onset of the block . Additional 32- and 6-s rest periods were added to the beginning and end of each run respectively . Subjects were requested to maintain steady fixation throughout each run and performed a one-back repetition detection task on the images , responding with a button press for each repeated image , to ensure they maintained their attention on the presented images ( mean task performance across three subjects: sensitivity 0 . 9820; specificity 0 . 9995; pooled across sessions ) . In one set of training natural-image session , a total of 1 , 200 images were presented only once . This set of training natural-image session was repeated five times ( 1 , 200 × 5 = 6 , 000 samples for training ) . In the test natural-image , artificial-shape , and alphabetical-letter sessions , 50 , 40 , and 10 images were presented 24 , 20 , and 12 times each respectively . The presentation order of the images was randomized across runs . In the mental imagery experiment , subjects were required to visually imagine ( recall ) one of 25 images selected from those presented in the test natural image and artificial shape sessions of the image presentation experiment ( 10 natural images and 15 artificial images ) . Prior to the experiment , subjects were asked to relate words to visual images , so that they could recall the visual images from word cues . The imagery experiment consisted of 20 separate runs , with each run containing 26 blocks ( 7 min 34 s for each run ) . The 26 blocks consisted of 25 imagery trials and a fixation trial , in which subjects were required to maintained a steady fixation without any imagery . Each imagery block consisted of a 4-s cue period , an 8-s mental imagery period , a 3-s evaluation period , and a 1-s rest period . Additional 32- and 6-s rest periods were added to the beginning and end of each run respectively . During the rest periods , a white fixation spot was presented at the center of the display . At 0 . 8 s before each cue period , the color of the fixation spot changed from white to red for 0 . 5 s , to indicate the onset of the blocks . During the cue period , words specifying the visual images to be imagined were visually presented around the center of the display ( 1 target and 25 distractors ) . The position of each word was randomly changed across blocks to avoid cue-specific effects contaminating the fMRI response during mental imagery periods . The word corresponding to the image to be imagined was presented in red ( target ) and the other words were presented in black ( distractors ) . Subjects were required to start imagining a target image immediately after the cue words disappeared . The imagery period was followed by a 3-s evaluation period , in which the word corresponding to the target image and a scale bar was presented , to allow the subjects to evaluate the correctness and vividness of their mental imagery on a five-point scale ( very vivid , fairly vivid , rather vivid , not vivid , cannot correctly recognize the target ) . This was performed by pressing the left and right buttons of a button box placed in their right hand , to change the score from its random initial setting . As an aid for remembering the associations between words and images , the subjects were able to use control buttons to view the word and visual image pairs during every inter-run-rest period . fMRI data were collected using a 3 . 0-Tesla Siemens MAGNETOM Verio scanner located at the Kokoro Research Center , Kyoto University . An interleaved T2*-weighted gradient-echo echo planar imaging ( EPI ) scan was performed to acquire functional images covering the entire brain ( TR , 2000 ms; TE , 43 ms; flip angle , 80 deg; FOV , 192 × 192 mm; voxel size , 2 × 2 × 2 mm; slice gap , 0 mm; number of slices , 76; multiband factor , 4 ) . High-resolution anatomical images of the same slices obtained for the EPI were acquired using a T2-weighted turbo spin echo sequence ( TR , 11000 ms; TE , 59 ms; flip angle , 160 deg; FOV , 192 × 192 mm; voxel size , 0 . 75 × 0 . 75 × 2 . 0 mm ) . T1-weighted magnetization-prepared rapid acquisition gradient-echo ( MP-RAGE ) fine-structural images of the entire head were also acquired ( TR , 2250 ms; TE , 3 . 06 ms; TI , 900 ms; flip angle , 9 deg , FOV , 256 × 256 mm; voxel size , 1 . 0 × 1 . 0 × 1 . 0 mm ) . The first 8 s of scans from each run were discarded to avoid MRI scanner instability effects . We then used SPM ( http://www . fil . ion . ucl . ac . uk/spm ) to perform three-dimensional motion correction on the fMRI data . The motion-corrected data were then coregistered to the within-session high-resolution anatomical images with the same slices as the EPI , and then subsequently to the whole-head high-resolution anatomical images . The coregistered data were then re-interpolated to 2 × 2 × 2 mm voxels . Data samples were created by first regressing out nuisance parameters from each voxel amplitude for each run , including any linear trend and the temporal components proportional to the six motion parameters calculated during the motion correction procedure . After that , voxel amplitudes were normalized relative to the mean amplitude of the initial 24-s rest period of each run and were despiked to reduce extreme values ( beyond ± 3 SD for each run ) . The voxel amplitudes were then averaged within each 8-s ( training natural image-sessions ) or 12-s ( test natural-image , artificial-shape , and alphabetical-letter sessions ) stimulus block ( four or six volumes ) , and within the 16-s mental imagery block ( eight volumes , mental imagery experiment ) , after shifting the data by 4 s ( two volumes ) to compensate for hemodynamic delays . V1 , V2 , V3 , and V4 were delineated following the standard retinotopy experiment [26 , 27] . The lateral occipital complex ( LOC ) , fusiform face area ( FFA ) , and parahippocampal place area ( PPA ) were identified using conventional functional localizers [28–30] ( See S1 Supporting Information for details ) . A contiguous region covering the LOC , FFA , and PPA was manually delineated on the flattened cortical surfaces , and the region was defined as the higher visual cortex ( HVC ) . Voxels overlapping with V1–V3 were excluded from the HVC . Voxels from V1–V4 and the HVC were combined to define the visual cortex ( VC ) . In the regression analysis , voxels showing the highest correlation coefficient with the target variable in the training image session were selected to decode each feature ( with a maximum of 500 voxels ) . We used the Caffe implementation of the VGG19 deep neural network ( DNN ) model [13] , which was pre-trained with images in ImageNet [14] to classify 1 , 000 object categories ( the pre-trained model is available from https://github . com/BVLC/caffe/wiki/Model-Zoo ) . The VGG19 model consisted of a total of sixteen convolutional layers and three fully connected layers . To compute outputs by the VGG19 model , all visual images were resized to 224 × 224 pixels and provided to the model . The outputs from the units in each of the 19 layers ( immediately after convolutional or fully connected layers , before rectification ) were treated as a vector in the following decoding and reconstruction analysis . The number of units in each of the19 layers is the following: conv1_1 and conv1_2 , 3211264; conv2_1 and conv2_2 , 1605632; conv3_1 , conv3_2 , conv3_3 , and conv3_4 , 802816; conv4_1 , conv4_2 , conv4_3 , and conv4_4 , 401408; conv5_1 , conv5_2 , conv5_3 , and conv5_4 , 100352; fc6 and fc7 , 4096; and fc8 , 1000 . In this study , we named five groups of convolutional layers as DNN1–5 ( DNN1: conv1_1 , and conv1_2; DNN2: conv2_1 , and conv2_2; DNN3: conv3_1 , conv3_2 , conv3_3 , and conv3_4; DNN4: conv4_1 , conv4_2 , conv4_3 , and conv4_4; and DNN5: conv5_1 , conv5_2 , conv5_3 , and conv5_4 ) , and three fully-connected layers as DNN6–8 ( DNN6: fc6; DNN7: fc7; and DNN8: fc8 ) . We used the original pre-trained VGG19 model to compute the feature unit activities , but for analyses with fMRI data from the mental imagery experiment , we changed the DNN model so that the max pooling layers were replaced by average pooling layers , and the ReLU activation function was replaced by a leaky ReLU activation function with a negative slope of 0 . 2 ( see Simonyan & Zisserman ( 2015 ) [13] for the details of the original DNN architecture ) . We used a set of linear regression models to construct multivoxel decoders to decode the DNN feature vector of a seen image from the fMRI activity patterns obtained in the training natural-image sessions ( training dataset ) . In this study , we used the sparse linear regression algorithm ( SLR ) [31] , which can automatically select important voxels for decoding by introducing sparsity into a weight estimation through Bayesian estimation of parameters with the automatic relevance determination ( ARD ) prior ( see Horikawa & Kamitani ( 2017 ) [10] for a detailed description ) . The training dataset was used to train the decoders to decode the values of individual units in the feature vectors of all DNN layers ( one decoder for one DNN feature unit ) , and the trained decoders were then applied to the test datasets . For details of the general procedure of feature decoding , see Horikawa & Kamitani ( 2017 ) [10] . For the test datasets , fMRI samples corresponding to the same stimulus or mental imagery were averaged across trials to increase the signal-to-noise ratio of the fMRI signals . To compensate for possible differences in the signal-to-noise ratio between training and test samples , the decoded features of individual DNN layers were normalized by multiplying them by a single scalar , so that the norm of the decoded vectors of individual DNN layers matched with the mean norm of the true DNN feature vectors computed from independent 10 , 000 natural images . This norm-corrected vector was then subsequently provided to the reconstruction algorithm ( See Supporting Information for details of the norm-correction procedure ) . Given a DNN feature vector decoded from brain activity , an image was generated by solving the following optimization problem [11] . v*=argminv12∑i=1Il ( ϕi ( l ) ( v ) −yi ( l ) ) 2 ( 1 ) =argminv12‖Φ ( l ) ( v ) −y ( l ) ‖22 ( 2 ) where v∈R224×224×3 is a vector whose elements are pixel values of an image ( 224 × 224 × 3 corresponds to height × width × RGB color channel ) , and v* is the reconstructed image . ϕi ( l ) :R224×224×3→R is the feature extraction function of the i-th DNN feature in the l-th layer , with ϕi ( l ) ( v ) being the output value from the i-th DNN unit in the l-th layer for the image v . Il is the number of units in the l-th layer , and yi ( l ) is the value decoded from brain activity for the i-th feature in the l-th layer . For simplicity , the same cost function was rewritten with a vector function in the second line . Φ ( l ) :R224×224×3→RIl is the function whose i-th element is ϕi ( l ) and y ( l ) ∈RIl is the vector whose i-th element is yi ( l ) . The above cost function was minimized by either a limited-memory BFGS algorithm ( L-BFGS ) [32–34] or by a gradient descent with momentum algorithm [35] , with L-BFGS being used unless otherwise stated . The obtained solution was taken to be the image reconstructed from the brain activity ( see Supporting Information for details of optimization methods ) . To combine the DNN features from multiple layers , we took a weighted sum of the cost functions for individual DNN layers , given by v*=argminv12∑l∈Lβl‖Φ ( l ) ( v ) −y ( l ) ‖22 ( 3 ) where L is a set of DNN layers and βl is a parameter that determines the contribution of the l-th layer . We set βl to 1/‖y ( l ) ‖22 to balance the contributions of individual DNN layers . This cost function was minimized by the L-BFGS algorithm . The DNN layers included in L were combined . In the main analyses , we combined all convolutional ( DNN1–5 ) and fully connected layers ( DNN6–8 ) , unless otherwise stated . To improve the ‘naturalness’ of reconstructed images , we modified the reconstruction algorithm by introducing a constraint . To constrain the resulting images from all possible pixel contrast patterns , we reduced the degrees of freedom by introducing a generator network derived using the generative adversarial network algorithm ( GAN ) [15] , which has recently been shown to have good performance in capturing a latent space explaining natural images [16] . In the GAN framework , a set of two neural networks , which are called a generator and a discriminator , are trained . The generator is a function to map from a latent space to the data space ( i . e . pixel space ) , and the discriminator is a classifier that predicts whether a given image is a sample from real natural images or an output from the generator . The discriminator is trained to increase its predictive power , while the generator is trained to decrease it . We considered constraining our reconstructed images to be in the subspace consisting of the images that could be produced by a generator trained to produce natural images [12 , 36] . This is expressed by z*=argminz12∑l∈Lβl‖Φ ( l ) ( G ( z ) ) −y ( l ) ‖22 ( 4 ) and v*=G ( z* ) . ( 5 ) G is the generator , as the mapping function from the latent space to the image space , which we have called a deep generator network ( DGN ) . In our reconstruction analysis , we used a pre-trained DGN which was provided by Dosovitskiy & Brox ( 2016; available from https://github . com/dosovits/caffe-fr-chairs; trained model for fc7 ) [36] . The above cost function for the reconstruction with respect to z was minimized by gradient descent with momentum . We used the zero vector as the initial value . To keep z within a moderate range , we restricted the range of each element of z following the method of a previous study [36] . Reconstruction quality was evaluated by either objective or subjective assessment [5 , 17 , 18] . For the objective assessment , we performed a pairwise similarity comparison analysis , in which a reconstructed image was compared with two candidate images ( its original image and a randomly selected image ) , to test whether its pixel-wise spatial correlation coefficient ( Pearson correlation between vectorized pixel values ) with the original image was higher than that for a randomly selected image . For the subjective assessment , we conducted a behavioral experiment with a group of 13 raters ( 5 females and 8 males , aged between 19 and 37 years ) . On each trial of the experiment , the raters viewed a display presenting a reconstructed image ( at the bottom ) and two candidate images ( displayed at the top; the original image and a randomly selected image ) , and were asked to select the candidate image most similar to the reconstructed one presented at the bottom . Each trial continued until the raters made a response . For both types of assessments , the proportion of trials , in which the original image was selected as more similar was calculated as a quality measure . In both objective and subjective assessments , each reconstructed image was tested with all pairs of the images from the same types of images ( natural-images , artificial-shapes , and alphabetical-letters for images from the image presentation sessions , and natural-images and artificial-shapes for images from the mental imagery session; e . g . , for the test natural-images , one of the 50 reconstructions was tested with 49 pairs , with each one consisting of one original image and another image from the rest of 49 , resulting in 50 × 49 = 2 , 450 comparisons ) . The quality of an individual reconstructed image was evaluated by the percentage of correct answers that was calculated as the proportion of correct trials among all trials where the reconstructed image was tested ( i . e . , a total of 49 trials for each one of the test natural-images ) . The resultant percentages of correct answers were then used for the following statistical tests . To compare the reconstruction quality across different combinations of DNN layers , we also used objective and subjective assessments . For the subjective assessment , we conducted another behavioral experiment with another group of 7 raters ( 2 females and 5 males , aged between 20 and 37 years ) . On each trial of the experiment , the raters viewed a display presenting one original image ( at the top ) and two reconstructed images of the same original image ( at the bottom ) obtained from different combinations of the DNN layers , and were asked to judge which of the two reconstructed images was better . This pairwise comparison was conducted for all pairs of the combinations of DNN layers ( 28 pairs ) , and for all stimulus images presented in the test natural-image session ( 50 samples ) . Each trial continued until the raters made a response . We calculated the proportion of trials , in which the reconstructed image obtained from a specific combination of DNN layers was judged as the better one , and then this value was treated as the winning percentage of this combination of DNN layers . For the objective assessment , the same pairwise comparison was conducted using pixel-wise spatial correlations , in which pixel-wise spatial correlations to the original image were compared between two combinations of DNN layers to judge the better combination of DNN layers . The results obtained from all test samples ( 50 samples from the test natural-image dataset ) were used to calculate the winning percentage of each combination of DNN layers in the same manner with the subjective assessment . These assessments were performed individually for each set of reconstructions from the different subjects and datasets ( e . g . , test natural-images from Subject 1 ) . For the subjective assessments , one set of reconstructed images was tested with at least three raters . The evaluation results from different raters were averaged within the same set of reconstructions and were treated in the same manner as the evaluation results from the objective assessment . We used two-sided signed-rank tests to examine differences in assessed reconstruction quality according to the different conditions ( N = 150 , 120 , and 45 for the test-natural images , artificial shapes , and imagery images , respectively ) and used ANOVA to examine interaction effects between task types and brain areas for artificial shapes ( F ( 1 , 1 ) = 28 . 40 by human judgment; F ( 1 , 1 ) = 3 . 53 by pixel-wise spatial correlation ) . We used one-sided signed-rank tests to examine the significance of correct classification accuracy by the human judgment for evaluations of the imagery image reconstructions ( N = 45 ) . | Machine learning-based analysis of human functional magnetic resonance imaging ( fMRI ) patterns has enabled the visualization of perceptual content . However , prior work visualizing perceptual contents from brain activity has failed to combine visual information of multiple hierarchical levels . Here , we present a method for visual image reconstruction from the brain that can reveal both seen and imagined contents by capitalizing on multiple levels of visual cortical representations . We decoded brain activity into hierarchical visual features of a deep neural network ( DNN ) , and optimized an image to make its DNN features similar to the decoded features . Our method successfully produced perceptually similar images to viewed natural images and artificial images ( colored shapes and letters ) , whereas the decoder was trained only on an independent set of natural images . It also generalized to the reconstruction of mental imagery of remembered images . Our approach allows for studying subjective contents represented in hierarchical neural representations by objectifying them into images . | [
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] | 2019 | Deep image reconstruction from human brain activity |
Patients with obsessive-compulsive disorder ( OCD ) can be described as cautious and hesitant , manifesting an excessive indecisiveness that hinders efficient decision making . However , excess caution in decision making may also lead to better performance in specific situations where the cost of extended deliberation is small . We compared 16 juvenile OCD patients with 16 matched healthy controls whilst they performed a sequential information gathering task under different external cost conditions . We found that patients with OCD outperformed healthy controls , winning significantly more points . The groups also differed in the number of draws required prior to committing to a decision , but not in decision accuracy . A novel Bayesian computational model revealed that subjective sampling costs arose as a non-linear function of sampling , closely resembling an escalating urgency signal . Group difference in performance was best explained by a later emergence of these subjective costs in the OCD group , also evident in an increased decision threshold . Our findings present a novel computational model and suggest that enhanced information gathering in OCD can be accounted for by a higher decision threshold arising out of an altered perception of costs that , in some specific contexts , may be advantageous .
A core feature of psychiatric illness includes personal suffering and functional impairments in daily life [1 , 2] often coupled with a negative impact on a sufferer’s social environment [3] . Whilst this overall negative impact is well recognised , the possibility that some manifestations of psychopathology might be beneficial is rarely a focus of consideration . Anecdotally , bipolar disorder may be related to creativity [4] , while increased exploration in attention-deficit hyperactivity disorder can be beneficial in some limited settings [5 , 6] . Empirical accounts of the underpinnings of these benefits is sparse , and its deeper understanding could throw light on fundamental aspects of these conditions . Obsessive-compulsive disorder ( OCD ) is characterized by intrusive thoughts ( obsessions ) and/or repetitive behaviours ( compulsions ) [1 , 2] . In its first formal definition in the 19th century it was characterised as a disorder of doubt [7 , 8] . Phenomenologically , patients with OCD show a high level of indecisiveness that impairs efficiency of decision making , even for decisions of little relevance [2 , 9 , 10] . An increased intolerance of uncertainty [10–12] and a more cautious decision making style [13–15] are considered to be key features of OCD . For example , in sequential sampling tasks , which allow participants to sample additional information in performing a task , several studies suggest that patients with OCD sample more and are less certain about available options [13 , 14 , 16 , 17] , although not unequivocally so [18–20] . Although handicapping in general , it is interesting to conjecture whether these same features might be beneficial in specific contexts , for example where lengthy deliberation carries little cost relative to the cost of a wrong decision . To examine potential benefits that might arise out of excessive information gathering , and to understand the computational mechanisms underpinning such benefit , we compared performance of a modest sized group of 16 juvenile patients with OCD to that of 16 healthy matched adolescents during performance of a sequential information gathering task . We find that the OCD group outperformed controls in terms of their winnings , an advantage linked to increased information sampling behaviour . To capture the cognitive mechanisms driving this behavioural difference , we developed a novel Bayesian model and show that an elevated decision threshold was the driving factor in patients’ increased information gathering , and this in turn arose out of an altered structure of intrinsic sampling costs .
The study was approved by the ethics committee of the Canton of Zurich , Switzerland . All participants and their legal guardians provided oral and written informed consent . Thirty-two adolescent subjects between 13 and 17 years participated in the study . The OCD group consisted of 16 patients recruited from public and private psychiatric practices in the cantons of Zurich , Aarau and Bern ( Switzerland ) . All participants were seeing a clinician due to a primary diagnosis of OCD . Two of the patients were on ward at the time of the study , all others were in outpatient treatment . The controls were recruited from the general population and matched to patients for age and IQ ( Table 1 ) . All subjects underwent a structured clinical interview ( K-SADS-PL , German version [21] ) , conducted by experienced clinicians . The OCD group fulfilled the DSM-5 and ICD-10 criteria for OCD at least once in lifetime , and all but one fulfilled the ICD-10 criteria of OCD at the time of the experiment . In addition , self-reported symptom severity in patients was assessed using the CY-BOCS interview [22] . Behavioural results did not change when excluding the subject in remission . Nine patients with OCD were medicated ( medication details in S1 Table ) . No subject from the control group met criteria for major psychiatric disorder based on the clinical interview . A detailed list of comorbidities is provided in Table 1 . Some of the subjects participated in an fMRI experiment at a later time point , of which data is reported elsewhere [23] . Participants received vouchers for local stores as reimbursement for their participation ( CHF 60 ) . There was no additional reimbursement for actual performance on this task . The study was approved by the ethics committee of the Canton of Zurich , Switzerland . All participants and their legal guardians provided oral and written informed consent . The participants performed an information gathering task implemented by the CANTAB test system ( Fig 1 , ‘information sampling task’; Cambridge Cognition , Cambridge UK [18 , 24] ) , administered on a touch-screen tablet computer . In each game , subjects were presented with 25 covered cards . They were told each of these cards was either coloured with yellow ( y ) or blue ( b; sets of colours varied across games , y and b were chosen here for simplicity ) . The subjects had to infer on each iteration of the task whether the majority of the 25 cards was yellow or blue . Before declaring their decision as to the majority colour , subjects were free to reveal as many cards as they wished , until they felt certain enough to declare their chosen colour . The task involved two conditions . The first 10 games belonged to a ‘fixed’ condition , where there was no explicit cost for revealing cards . In this condition , the subject received 100 points upon declaring the correct colour , irrespective of how many cards were opened . A wrong declaration resulted in a loss of 100 points . The second 10 games belonged to a ‘decreasing’ condition , in which subjects could win 250 points for a correct declaration . However , in this iteration turning over of a card led to a reduction in the potential overall winnings amount by 10 points . Thus , if a subject correctly declared after turning 3 cards , then they won 220 points ( 250–3*10 points ) . The punishment for a wrong declaration was always 100 points irrespective of the number of cards that had been turned over in that game . After each game , a waiting period was interposed before subjects continued with the next game . This period was dynamically adjusted to approximately level out differences in timing due to varying response speed in the game . This means that deciding earlier did not lead to the task ending more quickly , i . e . subjects could not increase their reward rate using a fast responding strategy . The colour sequences presented to the subjects were predetermined and were independent of the spatial location of the opened card so that all subjects played with the exact same sequences . Based on an hypothesis that patients with OCD may perform better than controls , we first compared the total points won between groups . To further analyse any behavioural differences between groups , we then ran repeated-measures ANOVAs with factor condition ( ‘fixed’ , ‘decreasing’ ) and group ( ‘OCD’ , ‘controls’ ) , followed by post-hoc t-tests to test specific differences . Although there was no significant group difference in gender ( cf . Table 1 ) , there were more males in the OCD than in the control group . To account for any potential confound , we re-ran the behavioural analyses by adding ‘gender’ as a covariate . These analyses did not change any of the results reported below , which means that gender did not impact in any of the reported group differences . To examine whether our task findings were related to self-reported symptom characteristics , we assessed whether indecisiveness or symptom severity in the OCD group , as recorded using the CY-BOCS [22] , was related to the behavioural effects found in OCD using Spearman rank correlations . Moreover , we also tested whether medication or a comorbid diagnosis of anxiety ( current or lifetime ) in OCD patients had any impact on information gathering behaviour . To understand the processes generating any observed behavioural difference between the groups we developed a set of Bayesian generative models , where each model assumed that different characteristics accounted for participants’ behaviour . Models were compared using complexity-adjusted model-fits ( AIC and BIC ) , and the winning model was then used for further analyses . These models are described in full in the supplemental material . This winning model was based on principles we previously used to model a different sampling task ( the ‘Urns’ task ) [25] . At its heart is the idea of Bayesian belief formation about the generative probabilities that gave rise to the presented sequences . This belief is coupled to a decision-theoretic choice of action based on inferred subjective costs . At each stage of the game , a subject is assumed to compute long-run state-action or Q-values [26 , 27] for choosing colour y , colour b , as well as for continuing sampling ( ‘not-deciding’ , ND ) . The computation of the Q-value for the two colours is based on the probability that the visible evidence was derived from a board favouring the given colour , and the associated rewards/costs of making a correct/incorrect decision . The Q-value for not-deciding was computed as the expected value of the subsequent states , plus a subjective cost per step . The expected values were computed using backward induction based upon solving the Bellman equation [28] . The decision policy was determined using a softmax choice rule with an additional lapse rate parameter . To understand better the structure of the participants’ subjective costs we compared models with two different cost functions . The first model assumed a cost function that grows linearly with the number of samples , i . e . the subjective cost for continuing sampling is the same irrespective of how much one has already sampled . This would mean that the subjective urge to make a decision is stable over time . The alternative model assumes that the subjective costs increase nonlinearly over samples . This means that subjects become more impatient and feel greater urgency to make a decision as more cards are turned over . An alternative way to describe the policy arising from our model is via a threshold on the evidence favouring one or the other colour for making a choice . In simple cases of evidence accumulation and optional stopping , such as standard drift diffusion models [29–31] , this threshold is fixed . However , recent computational , behavioural and neurophysiological studies have focused on the possibility that the decision threshold decreases over time , associated with increasing urgency [32–38] . Our model attributes such a decreasing decision threshold to two factors: a ) an objective component of an approaching horizon , given knowledge that there is only a fixed number of cards , b ) an apparent growth in subjective costs per sampling step , incorporating both explicit and implicit costs . To understand the dynamic decision threshold in our winning model , we performed two different analyses . First , we calculated the predicted decision threshold based on each subject’s model parameters . At each stage , the decision threshold was computed as the indifference point between the Q-value of the best colour and the one for not-deciding ( relative to the total evidence ) . The differences in the decision thresholds were then compared between the groups by performing independent t-tests for each stage , and then run in a cluster-extent permutation test to assess the significance of threshold-differences ( height threshold t = 1 , 1000 iterations ) on the extent of these effects [39 , 40] . In a second analysis , we showed how actual choice behaviour had a different relationship to evidence in the two groups . To do that , we plotted decisions as a function of both samples and evidence difference , i . e . to illustrate how much evidence an agent needs for every given stage to make a decision [33] . However , due to few data points in this sample ( 10 per subject and condition ) , we could not perform such an analysis with the raw behaviour . We exploited our computational model by taking each subject’s best fitting parameters and then allowing 1000 simulated agents to perform the task . Based on the multitude of generated behaviours , we could then calculate the mean evidence difference for each stage and plot this as a behavioural decision threshold . We again compared the two groups using t-tests and cluster permutation tests to correct for multiple comparison [39 , 40] . To understand the aspects of the model that drive the decision threshold and behavioural differences , we compared the model parameters between the two groups using non-parametric Wilcoxon rank-sum tests and corrected for multiple comparisons using Bonferroni correction . A detailed description of model parameter estimation is provided in the supplement .
The OCD group won significantly more points than controls throughout the task ( OCD: 1929±268 points , controls: 1406±502 , t ( 30 ) = 3 . 67 , p = 0 . 001 , mean difference: 522 . 5 , 95%-confidence interval: 231–813 ) . When analyzing the number of points won in the two conditions separately , a repeated-measures ANOVA confirmed a difference in the main effect of group ( F ( 1 , 30 ) = 13 . 48 , p = . 001 , marginal means: OCD: 964 , CI: 861–1067 , controls: 703 , CI: 600–806 ) . Moreover , there was also a main effect of condition ( F ( 1 , 30 ) = 7 . 78 , p = . 009 ) , but no interaction ( F ( 1 , 30 ) = 2 . 32 , p = . 138 ) . Post-hoc t-tests revealed the group difference was primarily driven by the decreasing condition ( decreasing condition: OCD: 1104±182 , controls: 744±227 , t ( 30 ) = 4 . 94 , p<0 . 001 , mean difference: 360 , C . I . : 211–509; fixed condition: OCD: 825±229 , controls: 662±398 , t ( 30 ) = 1 . 41 , p = . 168 , mean difference: 114 , C . I . : -72-397; Fig 2A ) , indicating OCD patients were more successful in this task in terms of points won . To obtain a deeper understanding of this superior performance we analyzed the number of draws before making a decision and the accuracy of their decisions . OCD subjects turned over significantly more cards compared to controls , as revealed in a main effect of group ( F ( 1 , 30 ) = 8 . 3 , p = . 007 , marginal means: OCD: 13 . 4 , CI: 11 . 6–15 . 2 , controls: 9 . 9 , CI: 8 . 1–11 . 6 , Fig 2B ) . There was also a main effect of task condition ( F ( 1 , 30 ) = 51 . 64 , p < . 001 ) , but no interaction ( F ( 1 , 30 ) = 2 . 17 , p = . 151 ) . Post-hoc t-tests show that increased sampling in OCD was apparent in both conditions , but only significantly so in the fixed condition ( fixed condition: OCD: 18 . 1±5 . 4 , controls: 12 . 9±6 . 3 , t ( 30 ) = -2 . 47 , p = . 019 , mean difference 5 . 16 , CI: . 90–9 . 41; decreasing condition: OCD: 8 . 8±2 . 7 , controls: 6 . 8±3 . 0 , t ( 30 ) = -2 . 00 , p = . 054 , mean difference 2 . 02 , CI: - . 04–4 . 08 ) . We analysed participants’ accuracy by comparing how often a subject chose the colour that was more plentiful at the time of decision . There was no significant difference between groups ( F ( 1 , 30 ) = 1 . 2 , p = . 288 , marginal means: OCD: 95 . 6 , CI: 92 . 7–98 . 5 , controls: 93 . 4 , CI: 90 . 5–96 . 4 ) , no condition effect ( F ( 1 , 30 ) = 1 . 7 , p = . 197 ) and no interaction ( F ( 1 , 30 ) = . 54 , p = . 470 ) . This suggests that neither group was more random at the point of declaring . To gain further insight into the mechanism accounting for greater winnings in patients , ( win more in the decreasing condition , but sample more in the fixed condition ) , we analysed the sequences’ win probabilities as a function of stage . We found that the sequences were less likely to result in a win around stage 5 ( cf . supplementary material for detailed analysis; S4 and S5 Figs ) . To understand whether lower wins in controls were specifically due to this trough , we simulated balanced sequences and found that the superior performance of patients with OCD was not an artefact of the sequences . In fact the effect remained in other sequences and was best explained by patients’ increased sampling . It is noteworthy that the simulated agents’ increased wins were more prominent in the fixed than in the decreasing condition , in line with our hypothesis of a superior performance of compulsive subjects when the cost of sampling is low . The finding of an increased information gathering in OCD raises the question as to whether there is a relationship to behavioural patterns beyond a laboratory task . A general indecisiveness is often reported in OCD as assessed in a clinical interview ( CY-BOCS; [22] ) , but is not taken into account in providing a description of symptom severity . We correlated information gathering behaviour in our task with this self-reported indecisiveness in the OCD group and found a strong correlation with a total number of draws across both conditions ( Fig 2D , ρ = . 584 , p = . 018 ) . This was mainly due to the increased sampling in the fixed condition ( ρ = . 498 , p = . 049 ) , but was also evident in the decreasing condition ( ρ = . 441 , p = . 087 ) . Within the OCD group , we did not observe any relationship of OCD symptom severity with either total points won ( CY-BOCS total: ρ = . 143 , p = . 598; obsessions: ρ = . 007 , p = . 980; compulsions: ρ = . 143 , p = . 597 ) or draws to decision ( CY-BOCS total: ρ = . 157 , p = . 563; obsessions: ρ = . 348 , p = . 186; compulsions: ρ = - . 044 , p = . 870 ) . These findings suggest that an increased information gathering is closely linked to self-reported indecisiveness , but not to a symptom severity , among OCD patients . Draws to decision might conceivably reflect a decision trait inherent to OCD , rather than an indication of illness severity . However , the absence of a correlation with symptom severity could also be caused by an imprecise estimate of the OCD severity due to a lack in disorder insight in juvenile OCD [41] , or the modest size of our patient group that might not have the sensitivity to detect more subtle associations . Because many of our OCD patients received medications , we tested whether behavioural differences ( draws to decision , total points won ) were equally distributed across medicated and unmedicated patients . We found no significant differences for any of these variables ( all p’s > . 3 ) . Likewise , a comorbid current or lifetime diagnosis of an anxiety disorder did not affect patients’ behaviour ( p’s > . 05 ) . This also held true when we adopted a machine learning approach ( 5-fold cross-validation regression , cf supplemental information ) to evaluate whether an additional variable such as anxiety or medication would improve the prediction of the behavioural markers ( draws to decision fixed condition , total points won ) , over and above a mere OCD diagnosis . While the group regressor predicted both behavioural markers ( p’s < . 05 ) , neither medication status ( p’s> . 2 ) nor anxiety ( current and lifetime; p’s> . 1 ) improved the classification . However , given the relatively small patient sample , a replication of these effects is desirable . To understand behavioural differences at a deeper level , we developed several computational models of the task and compared their performance . The best model was then used to analyse model parameters and decision thresholds further . We used a two-part process in our model selection . In the first part , we compared three candidate models that embodied different premises . The winning model , ‘Mgenerative’ calculates the value for choosing yellow , blue , or continuing sampling . These action values are computed based on an agent’s belief as to whether the sequence-generator is more likely to deal cards of yellow or blue colour ( i . e . ‘what generative process is likely to cause this sequence’ ) . The second model , ‘Mmajority’ , estimates the action values in the same way as Mgenerative , except for assessing a belief as to whether colour yellow ( or blue ) is more plentiful across all 25 cards in the particular set of cards presented . In fact this latter model implements what participants were instructed to do ( i . e . ‘whether there are more cards of yellow or blue’ ) . The third is a heuristic model ( ‘Mheuristic’ ) that involves a simple stopping rule but does not consider the accumulated evidence . Model comparison showed ( S1A Fig ) that subjects’ behaviour is best reflected by the Mgenerative model . We used the winning model from the first part of our model selection ( Mgenerative; S1B Fig ) to compare in the second part whether a linear ( as used in part 1 ) or a nonlinear cost function performed better , and whether free parameters for the cost-functions were the same across both conditions . The final winning model had a nonlinear ( sigmoidal ) cost function ( S2 Fig ) , defined by three different parameters: a cost parameter c describing how costly sampling is in general ( i . e . scaling factor ) ; the patience parameter p which describes the stage at which a subject becomes impatient , i . e . at what time point the costs start to escalate ( i . e . indifference point ) ; the slope parameter k describing how quickly a subjects becomes impatient ( i . e . slope of the cost function ) . In the winning model , both c and k were shared across both conditions , whereas p differed for the decreasing and fixed condition . This model outperformed a model where the explicit costs per step ( winning 10 points less at every step ) in the decreasing condition were modelled in addition to the cost parameters . This suggests that subjects did not take these explicit costs into account accurately . The model predictions ( policy ) of the winning model are shown in Fig 3 . We also simulated behaviour using the best-fitting parameters for each subject . We found that these simulations produced very similar behaviour to the actual behaviour of our subjects ( S3 Fig ) . Additionally , the model fits did not differ between the two groups , meaning that the model reflected both groups equally well ( S3D Fig ) . Our model provides an implicit measure of a dynamic decision threshold determining the difference in cards at each stage at which subjects are more likely to declare than to continue sampling . The way these decision thresholds change over samples , and the way they differ between the groups , can reveal about factors such as caution and urgency . We compared the model-predicted decision thresholds at every stage of the game ( Fig 4A ) , and cluster-extent permutation tests revealed an extended increase of the decision threshold in OCD patients in the fixed condition ( p = . 019 ) and in the decreasing condition ( p = . 042 ) . To verify the effects of an altered decision threshold , we used subjects’ best-fitting parameters to generate simulated data from the model . Each subject’s model played the task 1000 times and we then computed the mean evidence difference for each stage and condition ( i . e . , evidence difference between the two colours at the stage where the agent chose; Fig 4B ) . This analysis also revealed an increased decision threshold in the OCD group for the fixed ( p = . 029 ) as well as the decreasing condition ( p = . 013 ) . To understand how the decision thresholds arose , we compared the model parameters between groups . The main difference was for the patience parameter p in the fixed condition ( p1: controls: 16 . 34±7 . 89 , OCD: 22 . 15±4 . 80; z ( 186 ) = -2 . 92 , p = 0 . 021 , Bonferroni corrected ) . This difference suggests that the intrinsic costs in patients with OCD arose later than in healthy controls ( S2 and S6 Figs ) . There was also a difference in the slope parameter k of the nonlinear cost-function , though this did not survive multiple comparison correction ( p = . 017 , uncorrected; S2 Fig ) . These findings show that the subjective costs for OCD patients are smaller and escalate later in time , as evident in S6 Fig . This less impatient behaviour in OCD helped them to outperform healthy controls in this task .
We show that an increased information gathering in juvenile OCD patients is driven by a higher dynamical decision threshold due to a delayed urgency to respond in OCD . In this specific sequential information sampling task more cautious decision making behaviour resulted in higher task winnings in OCD . | Patients with obsessive-compulsive disorder ( OCD ) report to suffer from indecisiveness and overly cautious decision making . Although many studies captured such a bias experimentally , little is known about the cognitive mechanisms driving such an indecisiveness . In this study , we investigated 16 juvenile OCD patients and compared their performance in a sequential information gathering task to healthy , matched controls . We found an increased information gathering behaviour in OCD . This was accompanied by increased winnings in the OCD group . A newly developed Bayesian computational model revealed that OCD patients outperformed controls in this task because subjective costs for gathering information arose significantly later in the decision making process . This was also reflected by a later collapse in decision boundaries as captured in a delayed urgency signal . | [
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] | 2017 | Increased decision thresholds enhance information gathering performance in juvenile Obsessive-Compulsive Disorder (OCD) |
Much debate has arisen from research on muscle synergies with respect to both limb impedance control and energy consumption . Studies of limb impedance control in the context of reaching movements and postural tasks have produced divergent findings , and this study explores whether the use of synergies by the central nervous system ( CNS ) can resolve these findings and also provide insights on mechanisms of energy consumption . In this study , we phrase these debates at the conceptual level of interactions between neural degrees of freedom and tasks constraints . This allows us to examine the ability of experimentally-observed synergies—correlated muscle activations—to control both energy consumption and the stiffness component of limb endpoint impedance . In our nominal 6-muscle planar arm model , muscle synergies and the desired size , shape , and orientation of endpoint stiffness ellipses , are expressed as linear constraints that define the set of feasible muscle activation patterns . Quadratic programming allows us to predict whether and how energy consumption can be minimized throughout the workspace of the limb given those linear constraints . We show that the presence of synergies drastically decreases the ability of the CNS to vary the properties of the endpoint stiffness and can even preclude the ability to minimize energy . Furthermore , the capacity to minimize energy consumption—when available—can be greatly affected by arm posture . Our computational approach helps reconcile divergent findings and conclusions about task-specific regulation of endpoint stiffness and energy consumption in the context of synergies . But more generally , these results provide further evidence that the benefits and disadvantages of muscle synergies go hand-in-hand with the structure of feasible muscle activation patterns afforded by the mechanics of the limb and task constraints . These insights will help design experiments to elucidate the interplay between synergies and the mechanisms of learning , plasticity , versatility and pathology in neuromuscular systems .
Limb impedance control by the central nervous system ( CNS ) has been a subject of much study and debate over the past three decades . Numerous experiments and theoretical analyses have studied the biomechanical and neuromuscular capabilities of the CNS to regulate the impedance of a limb ( e . g . , [1–22] ) . The preferred paradigm of many studies is to analyze the stiffness the human arm can produce at its endpoint ( i . e . , the hand ) in reaching-like postures in a horizontal plane in front of a seated subject . One set of experimental findings is that , after some training , the CNS can regulate to varying degrees the orientation and eccentricity of arm stiffness ellipses to perform a task more reliably and efficiently than before training [1 , 4 , 5 , 10] . Another set of experiments concludes that the CNS cannot arbitrarily regulate endpoint stiffness , and that it is only able to rotate the orientation of the stiffness ellipsoid around 30° [15 , 20 , 21] . Here we focus on reconciling some of these conflicting results by using novel computational analyses of tendon-driven systems to establish the neuromechanical capabilities of biological limbs in the context of muscle synergies . The existence and interpretation of muscle synergies is controversial and has received much attention in the recent literature [23–29] . Synergies—defined as the correlated activation of multiple muscles by using a small number of coordination patterns—are theoretically one way to simplify the control of movement in the highly redundant musculature of vertebrates . They have also been observed by EMG measurements during reaching movements with the arm [19] . Here we explore the restrictions synergies could impose on the ability of the CNS to synthesize arm endpoint stiffnesses with differing characteristics . There is extensive literature on the analysis and synthesis of endpoint stiffness in robotic limbs [7 , 30–33] . The theoretical contributions and conclusions of these robotics studies are independent of the mechanisms and limitations of sensorimotor control by the CNS , and hence form a good theoretical foundation to design and interpret experiments to study the neuromechanical capabilities of biological limbs both in the presence and absence of synergies . In [34] such an approach was used to compare theoretical predictions against experimental findings by recording from a few finger muscles . In this study , we investigate the effects of muscle synergies on endpoint stiffness synthesis and energy consumption ( Fig 1 ) . To this end , we apply principles of robotics in a novel computational formulation for tendon-driven systems that allows us to easily and efficiently analyze the range over which the stiffness of the endpoint of the limb can be modified . More specifically , we are referring to the magnitude of endpoint stiffness in a variety of directions which can be mathematically approximated by a stiffness ellipse . From an engineering perspective , we can call this the range of ‘stiffness realizations’ because each of them is an instance of the neuromechanical capabilities of the limb . By studying stiffness realizations in the presence and absence of muscle synergies throughout the workspace , we find that synergies drastically decrease the ability of the CNS to synthesize an arbitrary stiffness ellipse . Importantly , our work takes on the approach that we are interested in finding the families of feasible endpoint stiffness realizations throughout the workspace of the limb . That is , how much can the nervous system control the size , shape ( i . e . , eccentricity ) and orientation of endpoint stiffness ellipses for all limb postures ? Due to muscle redundancy , there may be multiple ways to achieve any one possible stiffness ellipse . That set of multiple neural commands that can achieve a given realization is its ‘feasible activation set’ [35 , 36] . We can therefore optimize over that set to find the muscle activation pattern that produces the desired endpoint stiffness while minimizing energy consumption . The question is , then , how do muscle synergies compromise—or even annihilate—the ability of the nervous system to control the properties of endpoint stiffness and minimize energy consumption ? As mentioned in the Discussion , this neuromechanical approach emphasizes the feasibility of neuromechanical actions and allows us to consider several potential confounds when comparing across studies . Such studies may include examination of the extent , efficacy and nature of training , the influence of limb postures on task goals specified by the experimenter , and the implicit neural strategies specified by the CNS with regard to stiffness regulation in health and disease . Our results also allow us to discuss how learning , experimental design , and neural strategies affect our ability to tune endpoint stiffness .
We use a simplified planar arm model with 6 muscles similar to those that have been used in other theoretical and computational studies [3 , 8 , 12 , 13 , 37] ( Fig 2 ) . In those studies as well as this study—as described below—the spatial distribution of the stiffness of the endpoint of the limb , Kend , is a matrix that is calculated as a function of individual musculotendon stiffnesses , which are the elements of the matrix Kmuscles . The individual musculotendon stiffness generated by each muscle is represented by a numerical variable that is , in effect , a lumped parameter model introduced by Hogan and Mussa-Ivaldi [8 , 13] that combines the active and passive components of muscle and the passive components of tendon . This approximation remains commonplace and valid in the computational literature whose goal is not to simulate the physiology of musculotendon stiffness , but rather use a mechanical analogue of musculotendons to allow the study of the feasible mechanical behavior of the limb . This lumped parameter approach is accepted in the computational literature to replicate the fact that musculotendons have stiffness , and that stiffness can be modulated by the individual neural commands to the muscles of a limb , the activation vector a → . The reader is referred to the literature for details [3 , 8 , 12 , 13 , 37] , but a brief description is presented below . We use workspace constraints identical to those used in [8] to produce the workspace of the limb ( i . e . , the locations that are reachable by the endpoint ) , also shown in Fig 2 . As is common , we use singular value decomposition ( SVD ) to transform the endpoint stiffness matrix ( Kend ) to an ellipse that represents the characteristics of this matrix—shape ( can also be termed eccentricity ) —measured by the matrix condition number , and orientation of the major axis with respect to the x-axis . We begin our formulation with the endpoint stiffness matrix , Kend , as a function of muscle active stiffnesses , Kmuscles . As a point of clarification , we focus on muscle active stiffness that results from the feedforward activation of the muscle such as during force production or co-contraction . We do not include passive stiffness , which normally refers to the inherent material properties of the limb or muscles with no muscle activation . This could arise , for example , from tendon properties . Similarly , as mentioned in the Discussion , we do not include the time-delayed stiffness resulting from reflexes , often called reflexive stiffness . In our formulation , Kend relates the vector of differential endpoint displacements to differential endpoint forces: ∂ F → = K e n d ∂ x → ( 1 ) where ∂ F → is the endpoint force vector resulting from a displacement vector ∂ x → . The joint stiffness matrix , Kjoint , relates the vector of differential joint angle displacements to differential joint torques: ∂ τ → = K j o i n t ∂ θ → ( 2 ) where ∂ τ → is the joint torque vector resulting from a joint angle displacement vector ∂ θ → . The endpoint stiffness matrix is dependent on the joint stiffness matrix as well as the manipulator Jacobian J ( which is posture dependent: a vector of joint angles θ → uniquely defines the posture ) : x → ˙ = J ( θ → ) θ → ˙ ( 3 ) where x → ˙ denotes the endpoint velocity vector and θ → ˙ denotes the joint angle velocity vector . The endpoint stiffness matrix , in the absence of an external tip force , is given by [8]: K e n d = J - T K j o i n t J - 1 ( 4 ) Furthermore , the joint stiffness matrix is given by [8]: K j o i n t = R K m u s c l e s R T ( 5 ) where Kmuscles is the diagonal matrix of muscle stiffnesses and R is the moment arm matrix relating joint angle changes to tendon displacements , ∂ s →: ∂ s → = R ∂ θ → ( 6 ) Combining Eqs 4 and 5 , we obtain the relationship of muscle stiffness to endpoint stiffness: K e n d = J - T R K m u s c l e s R T J - 1 ( 7 ) This is equivalent to other formulations , such as in [34] . The diagonal elements of Kmuscles are assumed to be linearly related to their corresponding muscle forces [38]: K m u s c l e s = α × d i a g ( F → m u s c l e s ) ( 8 ) For simplicity in this study , we assume the scaling factor α is equal to one . We can define a diagonal matrix of maximal muscle forces , Fmax , so that we can calculate F → m u s c l e s using the muscle activation vector a →: F → m u s c l e s = F m a x a → ( 9 ) The entries of a → are inside the interval [0 , 1] since muscle force can only be positive ( this constraint can also be expressed as the requirement that the activation vector lies in the positive orthant of the unit hypercube in activation space ) . We assume Fmax to be the identity matrix for simplicity in this study . Using Eqs 7 , 8 and 9 and reformulating the endpoint stiffness matrix , the moment arm matrix , and the Jacobian , we can make the endpoint stiffness K ˜ e n d a vector that is a linear function of the muscle activations . K ˜ e n d = J ˜ - T R ˜ F m a x a → ( 10 ) We show these reformulations in Fig 3 . ( • ) denotes element-by-element multiplication , and Ri is the ith row of R . The Jacobian reformulation is specific to the 2-link planar arm model , but similar expressions can be formulated for Jacobians of higher dimensions . The endpoint stiffness and the moment arm matrices have been previously reformulated in this way [33] . And [34] speaks of the equations defining iso-stiffness planes . But to the best of our knowledge , no study has yet reformulated the Jacobian in this way to allow for the simple set of linear equations found in Eq 10 relating muscle activations to endpoint stiffness . Each realization of a given endpoint stiffness matrix—and its associated ellipse—is produced by a given neural command , a → , as shown in Eq 10 . As per Eq 9 , the individual forces in each muscle contribute to the overall stiffness of the limb while producing zero net torque at each joint to maintain equilibrium . These isometric muscle forces have a metabolic cost , which we calculate as the sum of squares of muscle forces [39]: e n e r g y = ∑ k = 1 6 ( F m a x k * a k ) 2 ( 11 ) We simulate synergies that have been experimentally observed in a previous EMG study of static postures similar to those used during arm reaching tasks [19] . These synergies couple the bi-articular muscles with the mono-articular elbow muscles as shown in Fig 4 . Quantitatively , that study found that the elbow stiffness from co-contraction of the bi-articular muscles was approximately one half of the elbow stiffness from the mono-articular elbow muscles . They did not find mono-articular shoulder muscles to have synergies with the bi-articular muscles . Consequently , for our model , the activation of the shoulder synergy , ashoulder , activated the two mono-articular shoulder muscles with unity weight . The activation of the the elbow synergy , aelbow , activated the mono-articular elbow muscles with unity weight and the biarticular muscles with weights of one-half ( Fig 4 ) . In the presence of these synergies , one parameter suffices to change the orientation and shape of the endpoint stiffness ellipse: the ratio of elbow synergy activation to shoulder synergy activation , aelbow/ashoulder . Increasing the activation of both synergies simultaneously and proportionately only increases the size of the ellipse but not its shape or orientation ( i . e . , the angle from the x-axis to the major axis of the ellipse ) . As we will see in the results , this one-dimensional manifold in muscle activation space does not allow the realization of the arbitrary endpoint stiffness ellipses because the synergies , by coupling muscles , also couple two important stiffness characteristics: the stiffness ellipse’s shape and orientation . That is , in the presence of synergies , as just described , there is only one free parameter that can be varied to control these characteristics , aelbow/ashoulder . Therefore , changing orientation independently of shape is impossible . To further explore this coupling of task constraints by synergies , we vary the ratio of shoulder synergy activation to elbow synergy activation ( by varying aelbow/ashoulder ) over a range of 2 orders of magnitude ( 1/10 to 10 ) to see how much the orientation of the ellipse is able to change . In the absence of synergies in our model ( i . . e , all muscles can be activated independently ) , we can determine if the arm is able to meet the constraints that all activation vectors lie within a unit 6-dimensional cube in the positive octant of the activation space [35 , 40] ( i . . e , all activations lie between 0 and 1 ) 0 ≤ a i ≤ 1 ( 12 ) the net joint torque vector is zero ( i . e . , the posture is in equilibrium ) R F m a x a → = 0 ( 13 ) and the endpoint stiffness has a given desired shape and orientation , as in Eq 10 J ˜ - T R ˜ F m a x a → = K ˜ e n d , d e s i r e d ( 14 ) Then if ∃a → s . t . Eqs 14 , 13 and 12 , are satisfied , K ˜ e n d , d e s i r e d is realizable in the absence of synergies . An illustration of these constraints , the existence of a solution , and the potential for energy minimization in the absence of synergies is illustrated in Fig 5 for a simple 3-muscle model . We use three muscles because this allows us to visualize the feasible activation space in 3D , and each of the linear constraints can be shown as a plane , whose intersection is a line that still holds some redundancy . Since this example is for a manipulator with only one joint , the endpoint stiffness is only in the x-direction . Thus the feasible activation set begins as a 3-dimensional unit cube in the positive octant . The constraint of zero endpoint force is a 2-dimensional plane in activation space passing through the origin . This is because endpoint forces have a minimal value of 0 at zero activation ) . The constraint for desired endpoint stiffness of unity is also a 2-dimensional plane in activation space , but it does not pass through the origin . This is because muscle activation is required to produce stiffness: at the origin , there is no muscle contraction , therefore there is no muscle stiffness stiffness or endpoint stiffness . This geometric interpretation [34 , 35 , 43 , 44] helps us understand the effect of synergies as additional constraints on feasible activations . The intersection of the first two functional constraints is a one-dimensional linear subspace of solutions that mathematically satisfy Eqs 14 and 13 . Further constraining this subspace by the activation N-cube ( Eq 12 ) results in the muscle activation solution space to realize a unity stiffness . In this the feasible activation space , that has the structure of a one-dimensional subspace ( i . e . , a line ) , energy ( measured by the sum of the squares of the muscle forces , or concentric spheres ) can be minimized or maximized by varying the activation point in the feasible activation set ( i . e . , a point along the line ) . The presence of even a simple synergy for this model ( a1 = 2a2 ) results in an additional constraint plane that passes though the origin that will reduce the feasible activation set , reducing the dimensionality of the solution space . In this simple example , the dimensionality is reduced to zero—a unique solution [40 , 43 , 45] . But even in high dimensions [41] , synergies will reduce what is already a well-structured space . In our 6-muscle model , the activation hypercube is 6-dimensional . The constraint of zero endpoint force is a 4-dimensional hyperplane in activation space passing through the origin ( 6 dimensions − 2 equilibrium constraints = 4-dimensional solution space; Eq 12 is a system of two equations , one for each joint ) . The constraint for a desired endpoint stiffness is a 3-dimensional hyperplane in activation space ( 6 dimensions − 3 stiffness constraints = 3-dimensional solution space; as per Fig 3 , Eq 14 is a system of three equations , one for each unique element of the symmetric matrix K ˜ e n d , d e s i r e d ) . The intersection of these two hyperplanes is a one-dimensional linear subspace ( 6 dimensions − 2 equilibrium constraints − 3 stiffness constraints = 1-dimensional feasible activation space ) embedded in 6-dimensional space . It satisfies Eqs 14 and 13 . If any part of this solution subspace lies in the activation N-cube ( satisfying Eq 12 as well ) , then the desired stiffness is realizable . Furthermore , synergy constraints can reduce the dimensionality of the solution space to zero ( i . e . , there is a solution , it will be the unique solution of a point at the intersection of a line with a plane ) , or they can overconstrain the problem , making the desired stiffness unrealizable . Within this context , we can now explore the range of achievable endpoint stiffness ellipse orientations given the arm posture and a desired ellipse shape . To this end , we fixed both the condition number of the stiffness matrix and the posture , and then determined a set of desired endpoint stiffnesses , each corresponding to a different ellipse orientation . We formulated a constrained quadratic programming problem , with the optimization criteria being minimizing the sum of squares of muscle activations . If an optimum was found , then the orientation ( for that specific posture and ellipse shape ) is realizable . We did this every 5° around the full range of orientations ( i . e . , 180° ) and then checked the fraction of these orientations that are realizable . An example of the fraction of realizable orientations for all postures in the workspace is shown in Fig 6 . The constraints in the realizability tests have five equality constraints ( Eqs 14 and 13 ) . Since there are 6 muscles , if there is any solution which satisfies Eq 12 , in general there will be a one-dimensional feasible activation space for the desired endpoint stiffness embedded the 6-dimensional muscle activation space . Vertex enumeration algorithms can be used to determine the vertices of this one-dimensional manifold ( which is a convex set [35] ) . However , we and the available literature , are also interested in the maximal and minimal energy expenditures within this feasible activation space . Therefore , we can use opposite quadratic programming optimization criteria to determine both of these energy expenditures . For the minimal energy expenditure , as already described , our optimization criteria is to minimize the sum of squares of the muscle forces . For maximal energy expenditure , our optimization criteria is to maximize the sum of squares of the muscle forces . From these extreme values we can then determine the maximal amount of energy reduction that is possible . For example , if the maximal energy expenditure is 0 . 5 , say , and the minimal energy expenditure is 0 . 35 , then there is a maximum of 30% reduction in energy possible . Our rationale for quantifying these ratios is that , for given observed stiffness ellipsoid in human subjects experiments , we want to know whether or not the central nervous system could minimize energy expenditure . If there is a large possible range of energies expended for a same endpoint stiffness ellipse , then it may only be possible for experimental means such as EMG to reach strong conclusions about energy minimization . But if the range is low , then EMG measurements may not have the resolution to reveal much additional information about energy expenditure ( above the information obtained by only measuring the stiffness ellipse ) .
Fig 7 shows the fraction of realizable endpoint stiffness ellipse orientations for various ellipse shapes throughout the workspace . We can make a couple of observations from Fig 7 . First , posture has a very large effect on the range of realizable orientations ( also observed in [22] ) . Second , the range of realizable orientations decreases with increasing ellipse eccentricity . Thus a more uniform ellipse that is closer to a circle is easier to achieve throughout the workspace , but also arguably less able to set specific directions of higher or lower stiffness . Also , our computational results for ellipse eccentricity = 1 is identical to the theoretical result determined by [8] . To explore the effect of synergies in detail , we performed a more detailed analysis for a single posture . In that sample posture , Fig 8 shows the range of sizes and orientations of the stiffness ellipse achievable when varying the ratio of elbow to shoulder synergy activations from 10−1 to 10 . The arm endpoint is in a sample x − y position ( 0 , 1 ) , where each link of the arm has length of 1 . The area of the ellipses in Fig 8 are normalized to be equal to each other to highlight the covarying shape and orientation of the stiffness ellipses . The range of orientations is approximately 70° , which represents a realizable fraction of orientations of about 0 . 39 . In this posture , shown in Fig 8 , for all 3 ellipse shapes , the fraction of realizable orientations is 1 ( all orientations are achievable ) in the absence of synergies ( Fig 7 ) . In addition , we see that as the orientation of the ellipse in Fig 8 changes , the shape of the ellipse must also change . The range of physically-realizable ratios of elbow to shoulder synergy activation are likely much less extreme than two orders of magnitude , which would result in an even smaller range of possible ellipse orientations . Therefore , we see that using the synergies observed by Gomi and Osu [19] severely limits the ability of the CNS to control the shape and orientation of the endpoint stiffness of the arm . Fig 9 shows the greatest possible reduction in energy expenditure given a stiffness ellipse shape and arm posture for any orientation . Note the strong dependence on the posture of the arm ( i . e . , location in the workspace ) . In general , the maximal possible energy reduction for many of the workspace postures for these stiffness ellipse shapes is low ( 10–30% ) , but can increase significantly to around 50% for some specific postures . Fig 10 summarizes our findings , and compares them to prior work . We see that implementing fewer synergies ( i . e . , fewer muscle groupings , that reflect greater correlation among muscle activations ) reduces the independent controllability of the size , shape and orientation of the stiffness ellipses , as well as the energy consumption .
The literature on muscle synergies is large and growing . There are already several papers debating their origins , advantages , and disadvantages [24 , 28 , 29 , 42 , 46] . The goal of this study , however , is to speak to the need pointed out by several authors to investigate the relationship between muscle synergies and the neuromechanical constraints that define a task ( sometimes also called task variables ) [24 , 28 , 34 , 40 , 42 , 46] . We do so in the context of the neuromechanical consequences of using synergies while meeting the multiple and compounding constraints that define tasks in the ‘real world’ [34 , 41 , 45 , 47] , such as the well accepted need to regulate the stiffness of the endpoint of the arm ( e . g . , [1–22] ) . In the literature mentioned above , the origins of synergies as well as their specific structure and permanence continue to be debated . In their paper on static arm postures , Osu and Gomi ( 1999 ) mention that other arm synergies have been reported and that the regulation of muscle activation in static conditions seems to be quite different from that during movements . Nevertheless , this does not affect our main finding that synergies—regardless of their origin , structure or permanence—have important neuromechanical consequences in a variety of functional domains . This is because synergies imply a loss of control degrees of freedom ( i . e . , fewer independently controllable muscles ) . Therefore , the specifics of the synergies we chose to simulate as reported by Osu and Gomi do not affect the generality of our results . In fact , we went on to simulate five additional synergies as shown in Figs 11 and 12 , labeled Cases 2 through 6 . In all cases , synergies yield a reduction in the controllability of the size , shape and orientation of the stiffness ellipses . It is important to mention that comparing muscle coordination and stiffness regulation in static versus dynamic movement conditions may not be advisable—or even possible . This stems from the fact that the physics and neuromuscular physiology of the control of static force versus movement are inherently distinct and can even be incompatible; see [36 , 45 , 48–50] and references therein . In addition to the differences in their governing equations and the force-length properties of muscle , the control of movement at a neurological level additionally requires the careful and time-sensitive orchestration of alpha-gamma co-activation and reciprocal inhibition of eccentrically contracted muscles to prevent the disruption of the movement [51] . This stems from the fact that the control of tendon excursions is overdetermined ( few joint angles determine the necessary lengths of all musculotendons ) . This is the opposite of the underdetermined control of joint torques ( many combinations of muscle forces can equivalently produce a given net joint torque ) [36 , 41] . Therefore , orchestrating alpha-gamma co-activation and reciprocal inhibition to produce movement imposes additional time-varying constraints that distort and reduce the feasible activation set for a given endpoint stiffness ellipse compared to the static condition . From this perspective , our results for static endpoint stiffness are a best-case scenario as the additional constraints to produce movement will likely exacerbate the limitations imposed by synergies . Understanding muscle coordination and stiffness regulation in static versus dynamic movement conditions remains an important area in motor control in need of attention [36 , 48] . The feasible activation set—i . e . , all feasible neural commands to achieve a given task [34 , 35 , 41 , 43 , 44]—has a well defined structure given by the biomechanics of the limb and the constraints defining the task . Muscle synergies reduce the number of independent degrees of freedom for control from the ( usually large ) number of independently controlled muscles , to a smaller number of independently controlled groupings of muscle activations . The presence of synergies , by reducing the number of independent degrees of freedom for control , naturally reduces the size and affects the shape of the feasible activation set—and therefore the set of tasks that are possible [34 , 40] . This geometric approach uses a 6-muscle arm model with experimentally derived synergies to show that synergies severely constrain the ability to control the properties of the stiffness of the arm’s endpoint . Furthermore , it also shows reduction in the flexibility of energy consumption to implement them . That is , by reducing the dimensionality of the feasible activation set , synergies drastically limit the ability to orient the endpoint stiffness ellipse independently of its shape . The range of achievable orientations in the absence of synergies is already very sensitive to posture , but still allows significant energy minimization in some postures . Implementing synergies drastically reduces , and can even remove , the ability to minimize energy . We would like to point out an important difference in our formulation compared with other modeling studies for arm stiffness [3 , 5 , 7 , 8 , 11 , 15] . The general form of the joint stiffness matrix in these studies is ( for all equal moment arms ) : K j o i n t = K s + K b K b K b K e + K b ( 15 ) where Ks is the shoulder stiffness provided by co-contraction of the mono-articular shoulder muscles , Kb is the bi-articular joint stiffness provided by co-contraction of bi-articular muscles , and Ke is the elbow stiffness provided by co-contraction of mono-articular elbow muscles . This implies 3 constraints , and therefore 3 degrees of freedom for the system . However , our formulation without synergies has 4 degrees of freedom since only 2 constraints must be satisfied ( R F 0 a → = 0 ) . This study analyzes the extent to which the active stiffness ( i . e . , not including passive or reflexive muscle stiffness ) of the endpoint of a simulated arm can be controlled in the presence or absence of muscle synergies . That is , the extent to which the endpoint of the limb would displace passively in response to a force perturbation in every direction . That stiffness is the product of the level of activation of each muscle , and the anatomy and posture of the limb . We assumed , as others have in the past , that the active stiffness of a muscle is linear and proportional to the maximal force a muscle can produce and the level to which it is activated . While the linearity of active muscle stiffness with respect to muscle strength and activation is likely not entirely realistic , we focused on the effects of the presence or absence of muscle synergies . Using a nonlinear relationship would likely produce different numerical results for the precise shape , size and orientation of stiffness ellipses . However , it would not overcome the limitations that muscle synergies impose because those limitations come about from a reduction of the number of individually controllable muscles . That is a matter of scale rather than quality . Future work should naturally explore whether or not more realistic physiological mechanisms for muscle stiffness exacerbate the effects of synergies—particularly in neurological conditions . In addition , this model is limited in that it does not take into account passive muscle stiffness , reflexive stiffness , or feedback pathways , which can clearly be used to minimize energy further depending on the frequency content of a perturbation or motor noise during a task . It has been suggested [9] that some studies involving endpoint stiffness analysis may incorporate active reflex contributions [1 , 5 , 21] . If only active , neurally-driven , stiffness properties are considered and there is no net force at the endpoint , then the endpoint stiffness matrix is symmetric . It has been noted that any non-symmetric component of endpoint stiffness “can only be due to heteronymous inter-muscular feedback” [8] . Although future work is needed to explore these effects , our study is still able to help shed light on conflicting findings even if we only considered active stiffness without producing any net endpoint force or torque . A subtle but important issue is that studying symmetric endpoint stiffness does not take away from our findings , bur rather enhances our result about the functional limitations of synergies . Adding a net endpoint force ( or torque ) deforms the symmetry of endpoint stiffness , but it also further constrains the range of stiffness modulation . Balasubramanian and colleagues have made this point well by indicating that defining an endpoint force imposes an additional set of functional constraints that compromise the modulation of endpoint stiffness [34] . Similarly , our formulation presents a best-case scenario from the perspective that we do not consider the effects of signal-dependent noise . Selen and colleagues [52] studied the general case of endpoint stiffness modulation while producing a net endpoint force vector plus a non-zero endpoint torque under different stability conditions . They demonstrate the additional control trade-offs that arise when considering the potentially destabilizing effect of signal-dependent noise . Our model , like many others used to study arm stiffness [3 , 8 , 12 , 13 , 37] , assumes equal moment arms , equal maximal muscle forces , and a planar 6-muscle arm anatomy . While this work could be easily extended to 3-D modeling and utilizing physiological values for moment arms and maximal muscle forces ( as in [22] ) , our model includes both mono-articular and bi-articular muscles . These suffice to capture the gross capabilities of human arms since there are no bi-articular muscles that cross over from one side of the shoulder to the other side of the elbow . More importantly , the results and conclusions formed here about the effects of synergies , stiffness synthesis , and energy minimization remain the same . Our results suggest ways in which future high-dimensional models and arm stiffness experiments may be conducted to analyze stiffness synthesis strategies used by the CNS such as synergies , energy minimization , posture adjustment , and active reflex pathways . Reaching experiments could test stiffness ellipses in various postures during the reaching movement , since stiffness ellipsoid orientation flexibility is very sensitive to small changes in posture . Findings that conflict with the results of such a study could be analyzed in more detail as this would suggest significant feedback pathways that were developed as a result of motor learning and neural plasticity . More research into muscle synergies will help elucidate existing mysteries of neuromuscular control and empower improved mechanisms for therapeutic interventions for neuromuscular disorders in aging and disease . More generally , to our knowledge , this is the first study of the neuromechanical and energetic consequences of using synergies while meeting the multiple and compounding force and stiffness constraints that define tasks in the real world , particularly important for unstructured environments . As pointed out previously ( e . g . , [34 , 45 , 47] ) , we expected to see a natural reduction of task capabilities with the implementation of synergies . But understanding the specific task-level neuromechanical trade-offs in detail is critical to move our field forward . For example , Selen and colleagues [52] highlight that ( even in the absence of synergies ) observed stiffness geometries and their pattern of change with instability are the result of a tradeoff between maximizing the mechanical stability and minimizing the destabilizing effects of signal-dependent noise . In addition , as pointed out in [42] , understanding neuromechanical trade-offs require that we distinguish between synergies that are extracted descriptively from data vs . synergies that are implemented prescriptively by a controller . The work presented here is very much taking the latter approach . We asked what feasible activation sets result when meeting a given set of task constraints with and without synergies . We find that when synergies are implemented prescriptively , the trade-off is a drastically reduced , or lost , ability to control the details of the endpoint stiffness of the arm , and the energy used to produce it . An objection to the strength ( though not to the substance ) of this conclusion is that having many more muscles will naturally allow the implementation of synergies without such a drastic reduction of the feasible activation set . We agree with this interpretation as we have argued that thinking of vertebrates as having ‘too many’ muscles is paradoxical with evolutionary biology and clinical reality . That is , we have barely enough muscles for versatile and robust function in the real world [41 , 45 , 47] . Prescriptive synergies can and should be generalizable , flexible and learnable as correctly argued by several authors [26 , 29 , 46 , 53] , which is enabled by our many muscles . More generally , every modeling study must assess its generalizability to everyday life—especially models with a relatively small number of joints and muscles . In such simplified systems , a few constraints may suffice to artificially deplete the system of its control degrees of freedom . The question then becomes whether , in models with many more muscles , the achievable stiffnesses are ‘good enough’ for the usual , day-to-day operation of the limb; and if so , from the functional perspective this reduced flexibility may not really be a disadvantage at all . We and others have debated this important issue in the contexts of muscle redundancy for the production of static forces , unimpeded limb motion , and their combinations [41 , 45 , 48 , 54 , 55] . Importantly , real world tasks are the subject of neuroethology , which includes the evolutionary and comparative study of the mechanistic control of natural behavior by the nervous system [56–58] . In this context , having more ( or even many more ) muscles than joints would be an appropriate anatomical adaptation to satisfy multiple constraints . This is because natural behavior is defined by multiple and often competing constraints , which would naturally reduce the feasible activation set ( and therefore the feasible output sets ) much more than the reductionist experimental tasks we often study [35 , 47] . Therefore , the extent and quality of redundancy cannot be expressed simply as the number of muscles . It is the structure and dimensionality of the feasible activation set ( after all relevant constrains are taken into consideration ) that helps us see muscle redundancy from a neuroethological perspective . For a critical review of the classical concept of muscle redundancy see [36] . For example , in the case of producing active endpoint stiffness during limb movement while manipulating an object , the nervous system must issue neural commands , coordinated throughout the entire duration of the movement , to at the very least simultaneously: Set the necessary endpoint stiffness size , shape and orientation [19] Specify the direction , speed and duration of the movement [1] Control for the desired endpoint forces and torques [34] Consider the influence of motor noise [52] Regulate activity across the α-motoneuron pools to produce the necessary joint torques as per the classical muscle redundancy force-sharing problem ( e . g . , [39 , 43] ) Coordinate reciprocal inhibition of α-motoneuron pools across shortening and lengthening muscles [59] Tune γ-drive and/or inhibit the stretch reflex in muscles undergoing eccentric contractions ( e . g . , [51 , 60] ) Mediate interneuronal interactions [61] Satisfy the temporal constraints of conduction velocities , muscle excitation-contraction dynamics , and activation/deactivation time constants [62] to ensure the continuity of these neural commands as the motion progresses This compounding of multiple , potential conflicting , spatial and temporal constraints naturally leads to a dramatic shrinking of the set of feasible neural commands for natural movements even when we have many muscles . This line of thinking helps clarify the apparent and longstanding paradox between the classical concept of muscle redundancy and the clinical reality of motor development and dysfunction [36] . For example , clinicians have long been aware of how disorders of reflexes or the neural circuits of ‘afferented muscles’ lead to disruptions or failures of everyday movements and interactions with objects ( for an overview see [63 , 64] ) . Thus , these pathologies of everyday limb function may in fact be a natural consequence of the nervous system failing to meet the multiple and stringent spatio-temporal demands listed above—in spite of having many muscles . In spite of the simplified model used in the prior literature and here , our results nevertheless add to current thinking about endpoint stiffness in two critical ways . First , they enable us to explore the specific task-level trade-offs associated with specific synergies . And second , they show that there is a natural limit to how generalizable and flexible any synergy can be . Simply said , every synergy that is prescribed will reduce the feasible activation set ( and thus the set of feasible actions ) as strictly as the mechanics of the limb or the constraints of the task . Thus if one is to prescribe synergies to meet the multiple constraints of the many tasks we face in real life , how many synergies should one learn ? The idea that each prescribed synergy solves , by construction , a well-defined control problem is well studied in the control literature [42 , 65 , 66] . We propose that the approach presented here will enable future research to understand the extent to which organisms find the middle ground between prescribing synergies to simplify control ( at the expense of loss of functionality ) and retaining the independence of muscle control to enable the learning , execution and refinement of motor function that meets the multiple and competing demands of tasks in the real world . | The manner in which the nervous system coordinates the multiple muscles in the body is complex . It has been studied for decades , but a more full understanding is needed to enable the development of effective evaluation and treatment methods in disorders that cause neuromuscular disability such as cerebral palsy and stroke . In addition , the computational control of robots has and will continue to improve as the brain’s methods of muscular control are progressively reverse-engineered . Here , we study the capacity of arm muscles to regulate the stiffness of the hand for tasks such as using tools , stabilizing hand-held objects , and using doors . Using a simplified but generalizable model , we show that there will be necessary trade-offs in the functional capabilities of the limb if the nervous system chooses to control muscles in functional groups . This adds to our understanding of the consequences of different strategies to control muscles for real-world tasks with multiple and often competing demands . It enables future research and clinical experiments on the learning and execution of the multiple tasks of varying difficulty encountered in real life . It also sheds light on the design of control strategies for robots to operate in human and unstructured environments . | [
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] | 2016 | Muscle Synergies Heavily Influence the Neural Control of Arm Endpoint Stiffness and Energy Consumption |
Cell cycle progression is carefully coordinated with a cell’s intra- and extracellular environment . While some pathways have been identified that communicate information from the environment to the cell cycle , a systematic understanding of how this information is dynamically processed is lacking . We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae . We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions . For example , it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate . This prediction is validated by comparison to available literature data . Other consistent patterns emerge , such as widespread nonmonotonic changes in cell size down generations in response to parameter changes . We extend our analysis by investigating glucose signalling to the cell cycle , showing that known regulation of Cln3 translation and Cln1 , 2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations . Together , these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments .
The cell cycle is the process by which cells alternate replication of their DNA with cell division . As a central process in the life of a cell , it is subject to multiple forms of regulation . These range from hormonal and growth factor signals in higher organisms , down to nutrient and stress signals in micro-organisms . While there has been much progress in understanding the mechanisms driving cell cycle progression , a system-level understanding of how signals regulate this progression has been lacking . In this paper , we investigate the dynamic response of the cell cycle to perturbations . In particular , we apply a combination of computational and mathematical analyses to study how the cell cycle of a particular model organism—the budding yeast Saccharomyces cerevisiae—responds to changes in conditions . The progression of the cell cycle in S . cerevisiae , as in all eukaryotic cells , can be divided into four phases: the G1 , S , G2 , and M phases . The G1 and G2 ( “gap” ) phases mark the pauses between the essential processes of DNA duplication ( which occurs during S phase ) and segregation ( which occurs during M phase ) . Several checkpoint mechanisms regulate progression through the cell cycle . These checkpoints ensure that progression through the cell cycle occurs only when the cell is in a suitable environment , and has adequately completed the previous stages of its cell cycle . For example , cells in G1 ( with unreplicated DNA ) must pass a checkpoint , regulated by factors such as nutrient availability and cell size , to go into S phase and begin synthesising DNA [1 , 2] . Similarly , cells in the G2 phase must pass through checkpoints to enter mitosis ( e . g . the spindle assembly checkpoint ) . In S . cerevisiae , progression through the cell cycle is coupled to changes in cell morphology and growth , as depicted in Fig 1 . After birth , the cell grows isotropically during the G1 phase . The duration of this phase is strongly correlated with the size of the cell as a result of a “cell size checkpoint” [3] . Beyond this checkpoint , the cell is allowed to pass into S phase . Upon entry into S phase , DNA replication begins , the cytoskeleton is polarised , and a bud forms [4] . Cell growth continues , but with growth directed to the bud . The cell then passes through the G2 and M phases and begins the process of cytokinesis . This results in the bud splitting from the mother cell , producing a new daughter cell . The prevailing view of the molecular mechanisms underlying cell cycle progression is one of interlocking positive and negative feedback loops which trigger a cascade of transitions in the appropriate sequence [5] . One of the central components in the cell cycle network is cyclin-dependent kinase ( CDK ) , named Cdk1 in S . cerevisiae . A pre-requisite for the kinase activity of CDK during the cell cycle is the presence of cyclins . Different cyclins are expressed in different phases of the cell cycle and lend specificity to the CDK-cyclin complex , allowing regulation of many transcription factors and other processes [6 , 7] . These cyclins may be broadly divided into different classes depending on the timing of their expression . For example , G1 cyclins are responsible for the transition from G1 into S phase , while mitotic cyclins are responsible for the transition from G2 into M phase . The abundance of cyclins is regulated at the levels of transcription , translation , and degradation . In addition , the CDK-cyclin complex may be rendered inactive by binding to a stoichiometric inhibitors such as Sic1 [8] . Ultimately , the cell cycle completes when CDK activity is reduced by the degradation of cyclins by the Anaphase Promoting Complex ( APC ) [9] . This allows the cell to progress through anaphase and cytokinesis . An illustration of this dynamic progression is shown in Fig 1C and 1D . As shown , the cell is born with low but increasing levels of G1 cyclins . When the level of G1 cyclin reaches a threshold , S-phase is initiated . Levels of G1 cyclins then decrease , with a complementary increase in mitotic cyclins maintaining Cdk activity . After sufficient time for progression through mitosis and the satisfaction of additional checkpoints , CDK inhibitors and components responsible for cyclin degradation ( such as the APC ) become active , along with the phosphatase Cdc14 , which dephosphorylates Cdk substrates . This rapidly depletes CDK activity , allowing cytokinesis to occur and a new cell to be produced . The distinct morphology of S . cerevisiae—in particular the correspondence between the initiation of S-phase and the appearance of the bud—means that it has been a useful model organism for the study of the cell cycle . A number of environmental cues have been found to regulate cell cycle progression in S . cerevisiae . For example , addition of glucose to cells growing in ethanol increases the average size of the cells at bud initiation and reduces the duration of the cell cycle [10] . The cell cycle is also responsive to changes in other nutrient signals [11–16] , growth [1 , 17 , 18] , osmotic stress [19 , 20] , and temperature [21] . In addition , under certain conditions the cell cycles of a population of cells can spontaneously exhibit partial synchronisation with an oxidative metabolic cycle [22 , 23] . Finally , experiments in which cyclin expression is inducible by an external signal have demonstrated the possibility of mode locking the cell cycle to a periodic stimulus [24] . The responsiveness of the S . cerevisiae cell cycle to environmental conditions is a generic property of the eukaryotic cell cycle . Despite the rapid accumulation of knowledge of the molecular details of the cell cycle mechanism and its regulation , such are the number of pathways and the complexity of the cell cycle itself that it is difficult to predict a priori how the system will respond to changes in conditions . As a result , it is also difficult to evaluate and interpret experimental observations and determine whether an observed phenomenon can be accounted for by known regulatory mechanisms . To this end , mathematical modelling approaches are useful to investigate hypotheses about cell cycle regulation . Models describing the dynamics of essential cell cycle components have existed for some time [25] , and have reached high levels of molecular detail [26–31] . These models describe the interactions between key regulators of cell cycle progression , and formalise the understanding accumulated over decades of fundamental cell cycle research . In this paper , a framework is developed for the investigation of the dynamic regulatory capabilities of cell cycle models , and by extension the cell cycle itself . This framework consists of exhaustive computational sensitivity analysis , allowing evaluation of how the cell cycle might respond to changes in conditions , both dynamically and after a sustained change in conditions . While the cell cycle is a highly nonlinear system , we note that similar approaches using sensitivity analysis of complex biological systems have been applied successfully before , e . g . in the study of circadian clocks [32 , 33] . We apply this analysis to three models of the S . cerevisiae cell cycle [30 , 34 , 35] . This allows several key questions about cell cycle regulation to be addressed , focusing on understanding the interaction between the cell cycle and the key developmental transitions of S . cerevisaie ( Fig 1 ) . For example: to what extent can key cell cycle characteristics such as period and size at division be regulated independently ? What qualitative behaviours can be observed in the response of the cell cycle to a sudden change in conditions ? How flexible can this dynamic response be for a given eventual change in behaviour ?
All of the models considered here share the same basic behaviour , with a continuously growing cell alternating between division and budding . The volume of the cell at budding and division , and the duration of cell cycle phases , constitute a simple description of the dynamics . Following [24] , this model incorporates the assumptions that growth is exponential [3] ( growing at an exponential rate μ ) , that all growth after budding is localised to the bud , and that the daughter cell receives all of the volume of the bud . The variables of interest are the cell cycle period of a daughter cell ( i . e . the time from birth to division , denoted Tdiv ) , the time from birth to budding ( i . e . the duration of the G1 phase , denoted TG1 ) , the size of the cell at division ( denoted Vdiv ) , at budding ( denoted Vbud ) , and the initial size of the daughter cell ( i . e . the size of the daughter cell at birth , denoted Vdau ) , and the fraction of the cell volume given to the daughter cell after division ( denoted f ) . At constant growth rate , these variables are interrelated according to the following expressions: V d i v = V d a u e μ T d i v V b u d = V d a u e μ T G 1 f = V d a u / V d i v ( 1 ) Note that the underlying molecular models control the timing of budding and division events , with the result that the fraction f is an emergent property of the models rather than a parameter . Similarly , Tdiv is determined by the dynamics of the underlying models , and is in general be different from the mass doubling time ( MDT ) , TMDT , which depends only on μ ( TMDT = ln ( 2 ) /μ ) . All models considered here give a pattern of behaviour that can be related directly to this simple description , after slight alteration to include a budding event where appropriate . The differences between the cell cycle models thus arise from the quantitative details of their structure and their parameter values . While more detailed models of the coupling between cell cycle to growth and metabolism have been suggested [36] , the above description is an adequate minimal representation for the purposes of our investigation . In this section , the models analysed are described , with model equations presented in S1 Text . The number of variables and parameters used in each model are also given . For more complete descriptions of these models , reference should be made to the corresponding papers . The three models are presented in order of increasing complexity , from a minimal model due to Pfeuty and Kaneko [34] ( referred to here as the Pfeuty model ) , through a modified version of the Chen model [26 , 35] ( referred to here as the Chen model ) , and a more recent model incorporating detailed representations of multisite phosphorylation [30] ( referred to here as the Barik model ) . The simplified ( Pfeuty ) and detailed ( Chen , Barik ) molecular cell cycle models play complementary roles in the analysis . In particular , the simplified model demonstrates the range of behaviours possible with a minimal description of the molecular interactions . Thus , behaviours that are identified across all three models are unlikely to have arisen from a special combination of parameter choices and model structure . The detailed models , in turn , provide complementary insights as they contain explicit representations of important molecular regulators ( e . g . cyclins ) . This allows specific hypotheses about regulation to be investigated ( e . g . in the case of glucose signalling , below ) . The Chen and Barik models share several essential and well-established features with each other , for example the distinct roles of different cyclins in determining progression through different cell cycle checkpoints . In addition , the Barik model incorporates several additional mechanistic features that have been discovered more recently , such as the role played by Whi5 in progression through the G1/S transition [37–39] . All three models consist of systems of ODEs . This formalism is useful in this context as it provides a level of detail that allows investigation of how incremental changes in parameters change the system behaviour . Furthermore , a straightforward framework exists for the calculation and interpretation of sensitivity analysis of ODE models . It should further be noted that models that just consider particular phases of the cell cycle ( e . g . models of the G1-S transition [20 , 29] or mitosis [40 , 41] ) are not suitable for investigation here , since they cannot be run across multiple cell cycles . Sensitivity analysis provides a straightforward way of understanding how combinations of parameter perturbations change cell cycle behaviour . In particular , we can approximate changes in behaviour ( in the linear regime ) by the linear combination of changes elicited by each perturbation , following [42] . For example , in the case of changes in Vdau in generation i following a perturbation in parameter k at time t , we have: Δ V d a u , i ( t ) = Δ k k S k V d a u , i ( t ) ( 8 ) Thus , for changes in multiple parameters k1 , k2 , … , kn , we have: Δ V d a u , i = ∑ j = 1 n Δ k j k j S k j V d a u , i ( t ) ( 9 ) An assessment of the accuracy of this approximation to changes in model behaviour away from the basal parameter set is shown for 8 parameters in the Barik model in S2 Fig . While the approximations are generally good , the highly non-linear nature of the model dynamics means that the range of parameter values for which this approximation is accurate is limited in some cases . However , even in these cases the qualitative changes in behaviour are matched across a wide range of parameter values . This demonstrates the utility of sensitivity analysis for understanding changes in model behaviour in a wide regime of parameter space .
We begin by evaluating the steady-state parameter sensitivities of the models , focussing on the macroscopic observable quantities such as the cell cycle duration ( Tdiv ) and cell volume at division ( Vdiv ) . First , we note that , for a particular growth rate , the macroscopic cell cycle observables can be calculated in terms of only Tdiv and Vdiv . For example , for Vdau and TG1: V d a u = V d i v e - μ T d i v T G 1 = - 1 μ ln ( V d a u ( V d i v - V d a u ) ) ( 10 ) As a result , the sensitivity of the cell cycle to changes in parameters can be understood in terms of changes in Tdiv and Vdiv alone ( or , equivalently TG1 , Vdau ) . This makes it natural to visualise the distribution of sensitivities in 2-dimensional scatter plots for each model , with each parameter shown as a point with position ( C k T d i v , C k V d i v ) ( or , similarly , ( C k T G 1 , C k V d a u ) ) . This as shown in Fig 2A . This allows comparison across models of the properties of particular parameters , and identification of general trends across many parameters and models . Some parameters of particular interest are those representing the regulation of cyclin synthesis and degradation . For example , the G1/S-specific cyclin Cln3 controls the timing of Start . Cln3 activity has been hypothesised to increase with cell size , and to therefore communicate cell size information to the cell cycle [47 , 48] . Parameters representing the synthesis of Cln3 are present in both the Chen and Barik models , and an analogous parameter can be identified in the Pfeuty model ( see S1 Text for details ) . As can be seen in Fig 2A , increasing the rate of synthesis of Cln3 acts to reduce the cell size in all three models , consistent with its role in cell size sensing . While changes in Vdiv are consistent across models , Tdiv is sensitive to changes in this parameter only in the Chen model . Other species of interest are the mitotic cyclins . Mitotic cyclins increase through the G2-M transition , and are rapidly degraded by the APC upon exit from mitosis [49] . Parameters representing the synthesis of mitotic cyclins and the synthesis of APC subunits Cdc20 and Cdh1 are present in the Chen and Barik models ( analogous components are not present in the Pfeuty model; see S1 Text for details ) . As can be seen in Fig 2 , in both models these parameters act primarily to change Tdiv in opposing directions , with increased mitotic cyclin levels leading to a longer cell cycle period . While this is consistent across models , it should be noted that the changes in Vdiv predicted by the models are not . Apart from the molecular species represented in the models , all three models also naturally include a parameter that specifies the growth rate ( named μ by convention ) . In all three models , increasing the growth rate reduces the duration of the cell cycle , and increases the size of the daughter cell ( S3 Fig ) , in agreement with experimental observations [10 , 12 , 50 , 51] . This qualitative agreement has previously been noted for other cell cycle models [28] . In summary , it is clear that the models broadly agree on some , but not all , qualitative features of regulation by particular parameters . Beyond specific parameters , it is also interesting to look at patterns observed across all parameters . It is clear from Fig 2A that in all three models most parameters act to modulate Vdau and TG1 in opposite directions ( with a few clear exceptions in the case of the Chen model ) . This is quantified in Fig 2B . As a result , most combinations of parameter perturbations are expected to either increase Vdau and decrease TG1 , or vice versa . This suggests that , for cells growing at the same rate under different conditions ( i . e . with different environmental cues perturbing cell cycle components ) , Vdau and TG1 should be negatively correlated . A dataset that is useful for evaluating this model prediction was generated by Brauer et al . [12] . In their experiments , cells were grown in chemostats at 6 different growth rates ( 0 . 05 , 0 . 1 , 0 . 15 , 0 . 2 , 0 . 25 , and 0 . 3 h−1 ) under 6 different nutrient limitations ( glucose , nitrogen , phosphate , sulphur , leucine , and uracil ) . Average cell volume ( denoted V ¯ , proportional to Vdau ) and the fraction of unbudded cells ( denoted FG1 , proportional to TG1 ( see S1 Text for derivation ) ) were measured . Analysis of these data reveals a negative correlation between V ¯ and FG1 at all 6 growth rates , as shown in Fig 2C . Similarly , a recent study by Soma et al . measured Vdau , TG1 , and μ for various strains under different conditions [46] . Selecting those experiments for which μ was within a 0 . 02 h−1 window , a clear negative correlation between Vdau and TG1 is again observed ( Fig 2D ) . Finally , recently a high-throughput screen of cell cycle behaviour by Soifer et al . measured Vdau and TG1 in a range of mutants [52] . Considering only those mutants which were classified as having wild-type growth rates , this correlation was again observed ( S4 Fig ) . The consistency of the qualitative behaviour of all three models with these experimental data suggests that they share essential dynamics that correctly describe cell cycle progression . While the steady-state sensitivity analysis allows the characterisation of cell cycle models under constant conditions , it is also interesting to ask how the cell cycle responds to dynamic changes in parameters . Dynamic sensitivity analysis allows us to understand the complex dynamic behaviour which the cell cycle is capable of producing on its own . This provides a foundation for understanding how signalling networks with their own complex dynamics interface with the cell cycle . As detailed above , dynamic sensitivity can be characterised by the change in cell cycle characteristics down generations to a sustained step change in a parameter , starting at a particular time t . By way of example , the sensitivity of daughter cell size and the combined duration of the S/G2/M phases ( TS/G2/M ) to changes in the rate of synthesis of mitotic cyclin in the Barik model ( specified by the parameter ks , bM ) are shown in Fig 3 . In this example , the sensitivity functions S k V d a u ( t ) and S k T S / G 2 / M ( t ) are evaluated at two different times—one early ( t = 30 ) , and one late ( t = 104 ) in the cell cycle ( Fig 3B and 3C ) . This illustrates the changes in Vdau and TS/G2/M that follow step changes made at these times . Two characteristics are apparent in this example , and are seen frequently in many parameters across all models: the dependence of the response on the timing of the perturbation , and the non-monotonic dynamics of this response . This sensitivity can also be visualised as a continuous function of the time of perturbation , as shown in Fig 3D . As before , it is also instructive to consider the biological significance of this particular example . First , the qualitative characteristics of the response change depending on the time at which the perturbation is applied . Increasing mitotic cyclin synthesis early in the cell cycle reduces TS/G2/M and Vdau in all subsequent generations , as compared to the initial state ( Fig 3B ) . However , increasing mitotic cyclin synthesis at the end of the cell cycle increases TS/G2/M and Vdau in the short term ( Fig 3C ) . This can be understood by the role played by mitotic cyclins: their level must first increase to initiate mitosis , but must then decrease to allow the cell cycle to restart . Increasing mitotic cyclin synthesis at a time when cyclin levels need to decrease might be expected to temporarily delay cell cycle progression , as demonstrated by this sensitivity analysis . While this sensitivity analysis is qualitatively consistent with known biology , we note that an assessment of how mitotic cyclins drive the cell cycle in S . cerevisiae found that the models mis-predicted the quantitative extent of this sensitivity [53] . In summary , dynamic sensitivity analysis provides a useful tool for understanding the range of behaviours which the cell cycle is capable of producing . In all three models considered here , nontrivial dynamic behaviours were identified , including nonmonotonic changes in cell size down generations . It has been observed qualitatively in many studies that the duration of the G1 phase of the cell cycle is especially sensitive to changes in conditions . This manifests itself in a change in the fraction of unbudded cells in populations [10 , 50] . It has also been observed that cells subjected to stresses transiently arrest the cell cycle at the G1/S-phase transition , without undergoing budding [54–58] . As a result , there has naturally been significant interest in understanding how signals determine progression through this transition . In this section , we investigate how the duration of the G1 phase changes under parameter perturbations of the models . We begin by asking how changes in the duration of the pre-budded ( TG1 ) and post-budded ( TS/G2/M ) phases of the cell cycle are related to one another in the phenomenological model ( Eq 1 ) . From this , we identify the relationship: C k T S / G 2 / M = - f C k T G 1 ( 11 ) Where f denotes the fraction of cell mass taken by the daughter cell upon division ( see S1 Text for derivation ) . This demonstrates how parameter changes which alter the duration of the pre- and post-budded phases of the cell are fundamentally coupled to one another in the model . Furthermore , it shows that the magnitude of changes in the duration of the S/G2/M phases of the cell cycle are expected to be less than half the change in the duration of the G1 phase ( since f ≤ 0 . 5 , both in silico and in vivo [24] ) . This relationship is depicted for all three models in Fig 4A . One counter-intuitive consequence of this is that changes in parameters affecting cell cycle progression during S/G2/M will modify TG1 more strongly than they modify TS/G2/M . Therefore , at steady-state , the duration of a particular phase of the cell cycle may be altered by perturbations that act during other phases . In particular , perturbations affecting processes during the G2/S/M phases will alter the duration of G1 . As discussed above , it is also commonly observed that moving cells into a stress condition can result in a transient accumulation of cells in G1 before the cell population eventually returns to its original state . At the single-cell level , this corresponds to a transient increase in TG1 . One interpretation of this behaviour is that the cells take time adapt to the stress , during which cell cycle dynamics are perturbed , before the cells eventually return to their original state ( and their original cell cycle behaviour ) . In the context of the analysis presented here , this would be analogous to changing model parameters for some time ( while the cells are experiencing stress ) before returning them to their original values ( after the cells have adapted to the stress ) . However , we previously noted that a step-change in parameters can result in complex cell cycle dynamics , including transient changes away from the eventual behaviour . This was observed in the examples of S k T S / G 2 / M and S k V d a u given previously ( Fig 3 ) , and is also true of changes in TG1 . Since growth rate is held constant in these simulations , this behaviour is not the result of temporary changes in growth rate that might also be expected to accompany some changes in conditions . The prevalence of this behaviour can be quantified by calculating fraction of time which the sensitivity functions S k T G 1 , i ( t ) display a nonmonotonic sensitivity down generations , averaged across all parameters , with all models displaying at least 80% nonmontonic responses ( Fig 4D ) . This suggests that transient responses of the cell cycle to changes in conditions must be interpreted with some caution . There are cases in which transient signalling appears to give rise to transient changes in cell cycle dynamics ( e . g . [20] ) . However , the models suggest that transient signalling or changes in growth rate are not required for this behaviour to be observed . In conclusion , these results demonstrate two causes for caution in the interpretation of changes in cell cycle dynamics in different conditions . First , that in cases where cells are grown under constant conditions , it is difficult to identify the cause for a change in cell cycle timing . This is because the duration of one cell cycle phase might change significantly as a result of regulation occurring during a different phase . Second , that transient changes in the duration of the G1 phase are a generic property of these models , and do not imply that signalling to the cell cycle is itself transient . The core yeast cell cycle oscillator interacts with other cellular oscillators , including the yeast metabolic cycle ( YMC ) [22] , and is postulated to entrain slave oscillators such as oscillations in Cdc14 activity [59] and a transcriptional oscillator [60] . In addition , it is possible to partially mode-lock the cell cycle to an external periodic signal [24] . In other organisms , additional oscillator interactions have been identified , for example gating of cell cycle transitions by circadian clocks [61–63] . In this context , it is interesting to ask how dynamic perturbations alter the timing of cell cycle events . This has been investigated previously in the context of cell cycle responses to periodic forcing signals [64 , 65] . Here , we are able to link control of cell cycle timing to the modulation of macroscopic cell cycle variables . The phase shift , Δϕ , resulting from a perturbation applied between the times t1 and t2 is given by its resultant effect on Tdiv down generations: Δ ϕ = ∑ i = 1 ∞ ( T d i v , i - T d i v , 0 ) ( 12 ) This can also be calculated according to: Δ ϕ = Δ k k ( S k p h a s e ( t 1 ) - S k p h a s e ( t 2 ) ) ( 13 ) This enables us to predict the mode-locking behaviour of the cell cycle to periodic forcing . For example , for a given periodic perturbation of the parameter sx , 2 in the Pfeuty model , analogous to stimulating Cln3 synthesis , the phase response is predicted to mode-lock the cell cycle so that the stimulus occurs ∼39 minutes after cell birth ( see S5 Fig ) . This prediction is borne out by simulations , with some error ( ∼7 minutes , S5 Fig ) . The phase shift between two cells can be related to differences in the mass fraction donated to the daughter cell down generations . In particular , consider a perturbation which causes a temporary change in the fractions of mother cell volume donated to the daughter cell . Denote the initial fraction f0 , and denote the deviation from this in the ith generation Δfi . Then the phase shift is given by: Δ ϕ = 1 μ ln ( ∏ i = 1 ∞ f 0 + Δ f i f 0 ) ( 14 ) ( see S1 Text for derivation ) . In practice this limit converges rapidly ( within a few generations ) . This establishes a link between how a parameter changes the mass of daughter cells , and how it changes the phase of the cell cycle . This correspondence is demonstrated in Fig 5A and 5B . We note that this is independent of any details of the models considered here , and applies to any asymmetrically dividing cell growing exponentially at a constant rate . One notable qualitative feature of some of these phase response curves is that they are biphasic ( i . e . both phase advances and delays are possible , depending on the timing of a perturbation ) . This property can be quantified for a parameter k by the metric Bk: B k = 1 - | ∫ 0 T d i v Z k p h a s e ( t ) d t | ∫ 0 T d i v | Z k p h a s e ( t ) | d t ( 15 ) This gives values of Bk ranging between 0 and 1 . Bk is 0 for a completely monophasic pattern of sensitivity , as Z k p h a s e ( t ) is strictly positive or negative , so | ∫ 0 T d i v Z k p h a s e ( t ) d t | = ∫ 0 T d i v | Z k p h a s e ( t ) | d t . The distribution of Bk across the parameters of all models are shown in Fig 5E . From this , it is clear that many parameters in all models display this property . This is a property shared with other biological oscillators , for example circadian and neuronal oscillators [34 , 66] . Another observation that can be made is that the phase shifts are most pronounced when perturbations are applied later in the cell cycle ( from TG1 onwards ) . The distributions of the times of peak sensitivity of the parameters of all models are shown in Fig 5F . In all models there are two main groups of parameters—those peaking around TG1 and those peaking around Tdiv—with very few parameters displaying peak sensitivity before TG1 . This is somewhat counter-intuitive given the noted sensitivity of TG1 to parameter changes ( see above ) . The robustness of the cell cycle model behaviour to perturbations during G1 has been observed previously in the case of the Chen model [43] . In summary , these results show that the cell cycle models consistently predict a preponderance of biphasic phase response curves , and further illustrate the qualitative differences in sensitivity observed before and after the G1-S transition . The analysis presented above provides a framework for understanding the effects of perturbations on the dynamics of cell cycle progression . In order to demonstrate how the analysis presented can be applied to understanding signalling to the cell cycle , it is useful to consider a specific example . Here , we investigate how glucose-sensing signalling pathways might affect cell cycle progression . Glucose sensing is particularly important in this context , as the extra- and intracellular glucose levels are key determinants of nutrient availability . As such , several pathways have been identified through which glucose affects cell cycle components , both through direct sensing [13 , 14 , 16 , 67 , 68] , and indirect effects via metabolism and growth rate [15 , 16] . Here , we consider the effects of direct signalling pathways , and note that their effects can be separated from indirect , growth-rate-mediated effects in conditions where growth rate does not change in response to glucose levels . An example of this was recently demonstrated in experiments by Soma et . al in which changing glucose concentrations in the range of 0 . 05% to 2% had no effect on growth rate but did perturb the cell cycle [46] . We consider three particular forms of cell cycle regulation by glucose ( Fig 6A ) . The first mechanism of cell cycle regulation by glucose involves the control of translation of Cln3—a cyclin responsible for inducing G1-S transition . The regulation of Cln3 translation is mediated in part through the direct regulation of the translation initiation factor eIF4E [69] , and can also be controlled through the relief of competition for translation initiation factors ( e . g . due to rapid degradation of GAL1 transcripts in the transition from galactose- to glucose-driven growth [70] ) . The rate of translation of Cln3 is represented in the Barik model by the parameter ks , n3 . The second mechanism we consider is the repression of Cln2 expression by glucose [71] . In the Barik model , Cln2 falls within the class of G1 cyclins , denoted by ClbS . The rate of ClbS transcription is represented by the parameter ks , mbS . Finally , it is known that signalling through the TOR kinase complex is capable of modulating the activity of the PP2A phosphatase complex [72 , 73] . Upon phosphorylation by the TOR1C complex , this phosphatase dephosphorylates a wide range of targets , including Net1 [74] . Net1 , in turn , is responsible for sequestering the cell-cycle phosphatase Cdc14 , which is required for progression through mitosis . The dephosphorylation of Net1 in the Barik model is represented by the constitutive activity of a generic phosphatase , Ht1 . The model parameters representing this activity are kd , t1 and kd , nt , regulating free Net1 and Net1 in the RENT complex , respectively . A natural assumption is that regulation of this pair of parameters is coupled , and therefore that they are modulated proportionally to one another . The above summary of some regulatory mechanisms is by no means complete , partly as a result of some regulatory components not being present in this model ( e . g . the regulation of Cdk1 phosphorylation by Cdc25 and Swe1 [75] ) . However , since it includes components involved in regulating different cell cycle phases , it provides a useful starting point for understanding the range of behaviours that might be achieved by glucose regulation of the cell cycle . The effects of these regulatory mechanisms can be summarised by the following constraints on the parameter perturbations applied through this pathway ( where the “signal” , assumed to be proportional to the availability of glucose , is represented by G , and the sensitivity of a parameter k to changes in G is denoted R G k ) : R G k s , n 3 = 1 k s , n 3 d k s , n 3 d G > 0 R G k s , m b S = 1 k s , m b S d k s , m b S d G < 0 R G k d , n t = R G k d , t 1 = 1 k d , t 1 d k d , t 1 d G = 1 k d , n t d k d , n t d G > 0 ( 16 ) These constraints imply a certain attainable range of responses in Vdau and TG1 , meaning that only particular changes in Vdau and TG1 are possible in response to increases in G . For each parameter k , we can calculate contribution of that parameter to the changes in ΔVdau and ΔTG1 that result from a change ΔG: ( Δ T G 1 Δ V d a u ) = ( C k T G 1 C k V d a u ) R G k Δ G ( 17 ) The responses possible in response to increasing glucose ( ΔG > 0 ) can then be plotted as vectors in the ( ΔTG1 , ΔVdau ) space for each pathway , as shown in Fig 6B . The shaded region represents the space spanned by linear , positive sums of these vectors , which is the attainable range of responses . Here , the regulatory mechanisms that we consider are limited to speeding up the cell cycle with increasing glucose levels ( i . e . ΔTG1/ΔG < 0 ) . Additionally , while this form of regulation can freely decrease the cell size without having a significant impact on the cell cycle period , there must be a decrease in period in order to effect an increase in cell size . The consistency of the attainable region with experimental observations can be assessed by evaluating measured Vdau and TG1 values under different glucose concentrations and constant growth rate , as reported in [46] . The linear correlation between these values at three glucose concentrations ( 0 . 05 , 0 . 1 , and 2% ) suggest the following empirical relationship , as depicted in S6 Fig ( note that Vdau , TG1 are in units of fL and minutes , respectively ) : Δ V d a u = - 0 . 27 Δ T G 1 ( 18 ) Note that this makes no assumption about the explicit relationship between glucose levels and the magnitude of parameter perturbations . The corresponding sensitivity to changes in glucose is then given by: ( Δ V d a u Δ T G 1 ) ∝ ( - 0 . 27 1 ) ( 19 ) As shown in Fig 6B , this lies within the attainable region , confirming that this simple combination of regulations is consistent with the observed changes in behaviour . An interesting aspect of the attainable region is that it is bounded by the opposing effects of stimulation of Cln3 translation and inhibition of ClbS transcription by glucose . This means that regulation of Net1 dephosphorylation does not broaden the range of behaviours that can be brought about through the pathway under constant conditions . Additionally , we observe that any change in behaviour ( ΔTG1 , ΔVdau ) within the attainable region can be achieved in an infinite number of ways depending on the relative strengths of the three posited regulatory mechanisms ( see S1 Text and S7 Fig ) . These different combinations of parameter perturbations will , by construction , have identical cell cycle behaviour under constant conditions , but may have distinct behaviours under dynamic changes in conditions . In order to evaluate the potential for diverse dynamics in this system , we fix the change in behaviour achieved by parameter perturbations according to experimental observations ( Eq 19 ) , and consider three cases: no , weak , and strong up-regulation of Net1 dephosphorylation with increasing glucose levels . These changes are automatically balanced by changes in Cln3 and ClbS regulation by the constraint to achieve the specified ( ΔTG1 , ΔVdau ) . The resultant changes in the dynamic sensitivity ( S k V d a u , 1 ( t ) ) shown in Fig 6D are the result of differences in the timing of sensitivity of the cell cycle to the different parameters ( see Fig 6C for the individual sensitivity profiles ) . Regulation of Cln2 transcription and Cln3 translation alone is only capable of modulating cell cycle progression around the G1-S transition , while regulation of Net1 dephosphorylation modulates progression through mitosis . Therefore , regulation of Net1 in the model allows for a faster response to changes in glucose levels by extending the time window of responsiveness to glucose levels . It has been noted previously that glucose levels act predominantly to modulate duration of the G1 phase of the cell cycle [76] , as discussed above in the more general case . An important conclusion arising from the work presented here is that this form of regulation does not exclude active regulation of processes occurring during mitosis ( or other phases of the cell cycle ) . Indeed , as long as counteracting pathways can be modulated in tandem , regulation of processes occurring in mitosis may be a useful strategy for dynamic adjustment of cell cycle characteristics after a change in conditions . In the particular example of strong Net1 regulation shown in Fig 6D , this is seen to lead to a more rapid modulation of Vdau than would be possible if only Cln2 and Cln3 were regulated . As discussed above , observations of cell populations under constant conditions ( e . g . the chemostat experiments in [12 , 76] ) are not capable of distinguishing between these strategies of regulation . In summary , this analysis demonstrates that control of the G1/S transition is insufficient for rapid adjustment of the cell cycle to changing conditions . In order for rapid response to changing conditions , it is necessary for the components that are active during the S/G2/M phases of the cell cycle to be regulated by environmental signals . Furthermore , the effects of such perturbations may only be observable in experiments in which response dynamics are observed . Cell cycle sensitivity during mitosis has been observed experimentally in response to sudden nutrient starvation or application of rapamycin [77 , 78] , suggesting that investigation of nutrient signalling under constant conditions can indeed mask important regulation .
Cell cycle progression is a highly regulated process . This is a result of the importance of the processes it coordinates , and of the fine-tuned response required in changing conditions . A number of environmental stimuli have been observed to regulate cell cycle progression [16] , and in some cases regulatory components have been identified . While mathematical models have been able to provide insight into cell cycle responses to some particular environmental changes ( e . g . in the case of osmotic stress [20] ) , a broader view of how the cell cycle regulatory network might respond to environmental changes , and how that might affect subsequent growth patterns , has been lacking . It is clear that this is a problem of importance in both basic and applied contexts and that its analysis requires a systematic approach . Our analysis was focussed on three mathematical models spanning a range from the simple ( the Pfeuty model [34] ) , to the complex ( the Chen [26 , 35] and Barik [30] models ) . This revealed that some patterns of sensitivity were common to all models . For example , an anticorrelation between changes in G1 phase duration and daughter cell size was observed in all three models , and matched experimental observations [12 , 46 , 52] . These are also reminiscent of correlations observed at the single-cell level within populations of cells [3] . In addition , the models were shown to exhibit other qualitative behaviours that are observed experimentally , such as a sensitivity of G1 phase duration to perturbations . The consistency of model behaviours with experimental observations demonstrates that the models capture essential properties of cell cycle behaviour beyond those typically considered ( e . g . the behaviour of cell cycle mutants [26] ) . The fact that that these behaviours are observed even in the simplified Pfeuty model suggests that they are robust features of the cell cycle . This suggests that other aspects of model behaviours identified here , such as the prevalence of biphasic phase response curves , are good candidates for further investigation . Sensitivity analysis characterises changes in model behaviour in response to small perturbations , providing a platform for understanding their behaviour under large perturbations that may elicit nonlinear responses . However , it is important to recognise that changes that result in bifurcations which transform the qualitative behaviour must be analysed with tools from bifurcation theory . This constitutes an important class of cell cycle behaviours , including cell cycle arrest , meiosis , or the transition to endoreplication . Bifurcation analysis has been applied to understand these behaviours in a variety of cases ( e . g . [79] ) , and provides insights into system behaviour that are complementary to those obtained by sensitivity analysis . Bifurcation analysis has also been an vital tool for understanding the dramatic changes in cell cycle behaviour caused by loss of some cell cycle genes ( e . g . [28] ) . Through a combination of sensitivity analysis in the linear regime , and bifurcation analysis and simulations in the nonlinear regime , a comprehensive analysis of cell cycle behaviour in dynamic environments can be undertaken . While this study has focussed on S . cerevisiae as a model system in which to study the cell cycle , related questions arise in a range of contexts of both fundamental and applied interest . For example , the question of how environmental cues regulate cell cycle progression is a general one , and is of interest in other yeast species , as well as in plant and animal systems . Though the cell cycle mechanisms are somewhat different in these systems , a similar approach to that taken here can be used to address this class of questions . In an applied context , having a mechanistic basis for understanding the connections between extracellular conditions , growth , and cell cycle progression in yeast is an important practical tool , for example in maximising yield of a valuable product . The relevance of parameter sensitivity analysis to experimental studies of cell cycle behaviour depends on technology for accurate observation of cellular behaviour , fine control of cellular environment , and manipulation of cellular network structures . Rapid advances in microfluidic and imaging technology are addressing these issues [39 , 52 , 80–82] , with current methods capable of observing hundreds of cells over many generations under rapidly changing conditions [83] . The ability to measure coordinated changes in regulatory network and cell cycle dynamics in response to perturbations is allowing increasingly detailed understanding of molecular mechanisms ( e . g . [78] ) . This then makes it feasible to perform controlled changes in the environment and observe the resulting macroscopic changes in growth patterns . In addition , the burgeoning possibilities of synthetic approaches allow hypotheses about molecular mechanisms to be explored at unprecedented levels of detail [84] . The combination of quantitative modelling methodologies such as those employed here with these high-throughput , quantitative experimental approaches will allow for a significant improvement in our understanding of cell cycle and growth progression in varying environments . Overall , by performing systematic steady state and dynamic sensitivity analysis to a range of detailed and simplified models , we have established a methodological platform to investigate the effects of dynamically varying environments on the cell cycle . Future extensions may incorporate bifurcation analysis to understand qualitative transformations in behaviour in the nonlinear regime . While we have applied our analysis to understand characteristics such as cell size and cell cycle duration in this particular study , it can also be applied to understand other characteristics of cell cycle behaviour . In conjunction with experiments , this approach provides a sound basis for beginning to understand the roles of the different parts of the cell cycle machinery in generating these responses . Furthermore , it also provides a basis for developing simplified descriptions which combine biological realism and mathematical soundness . This may be important in application domains . Finally , this approach provides a new window into the cell cycle as a complex system , and a route into understanding how dynamic information processing is undertaken by the cell cycle control system . Mathematical models of the cell cycle have been useful in describing how known molecular interactions give rise to the observed complex dynamics [85] , and to predict the behaviour of cell cycle mutants [86] . Despite the fact that these models may contain many parameters , they exhibit a fairly limited range of behaviours . This arises from the fact that these models encode similar regulatory logic . As a result , while our understanding of the biochemical details may change substantially , if the regulatory logic is broadly the same , we expect future mathematical models to exhibit similar behaviours . We further note that , if we focus on particular macroscopic , experimentally observable features ( as is the case here ) , the range of behaviours for these features is especially restricted . A reduced effective dimensionality has been noted in a range of biological models , including models of the cell cycle [42 , 87 , 88] . Overall we find that the models are capable of reproducing a range of experimental observations . This consistency in the face of considerable molecular and dynamic complexity suggests that these models will be valuable tools for understanding how the cell cycle responds to changing environments and for utilizing this in multiple applications . | The cell cycle is an exquisitely tuned process , alternating between states of cell growth , DNA replication , mitosis , and cytokinesis . While this process is robust , it is also responsive to diverse environmental signals . For example , cell cycle events may be delayed or advanced in response to changes in temperature or nutrient availability . While the molecular mechanisms underlying cell cycle progression have been well-characterised , how these mechanisms are perturbed by a cell’s environment is still not well understood . This problem is made difficult by the dynamically changing nature of the cell cycle itself . In this paper , we tackle this issue by performing a meta-analysis of mathematical models and experimental data describing cell cycle progression in the budding yeast , Saccharomyces cerevisiae . This shows how the timing of perturbations relative to the cell cycle stage ( e . g . during DNA replication or mitosis ) can give rise to qualitatively different responses . By looking for consistent patterns across multiple models and experimental datasets , we demonstrate how known molecular mechanisms change cell cycle behaviour in different nutrient conditions . This also allows us to make predictions for novel behaviours that can be experimentally tested in the future . | [
"Abstract",
"Introduction",
"Models",
"Results",
"Discussion"
] | [] | 2016 | Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments |
While immunological memory has long been considered the province of T- and B- lymphocytes , it has recently been reported that innate cell populations are capable of mediating memory responses . We now show that an innate memory immune response is generated in mice following infection with vaccinia virus , a poxvirus for which no cognate germline-encoded receptor has been identified . This immune response results in viral clearance in the absence of classical adaptive T and B lymphocyte populations , and is mediated by a Thy1+ subset of natural killer ( NK ) cells . We demonstrate that immune protection against infection from a lethal dose of virus can be adoptively transferred with memory hepatic Thy1+ NK cells that were primed with live virus . Our results also indicate that , like classical immunological memory , stronger innate memory responses form in response to priming with live virus than a highly attenuated vector . These results demonstrate that a defined innate memory cell population alone can provide host protection against a lethal systemic infection through viral clearance .
Immunological memory allows the immune system to provide enhanced host protection upon secondary exposure to an infectious pathogen . Memory has long been considered the sole province of adaptive lymphocytes . Lymphocytes recognize pathogens via unique somatically-rearranged antigen receptors , expand clonally upon activation , and eventually give rise to a population of long-lived progeny . These progeny cells maintain their antigenic specificity and exhibit enhanced functional activity upon secondary exposure to a priming pathogen . Recent studies have suggested that a reconsideration of the classical paradigm of immunological memory is warranted . These studies have shown that innate cell populations have the capacity to generate enhanced responses upon secondary exposure to the priming immunogen[1] , [2] , [3] . O'Leary , et al . , demonstrated that NK cell-mediated delayed-type hypersensitivity ( DTH ) responses[1] can be generated upon secondary exposure to sensitizing compounds . Further , they showed that these compound-specific DTH responses were mediated by a liver-resident NK cell population expressing the Thy1 ( CD90 ) molecule . Recently , Sun , et al . demonstrated that an innate memory response forms to MCMV and is mediated by a population of NK cells expressing Ly49H . In mice containing the B6 haplotype NK complex , NK cells expressing the germline-encoded NK receptor Ly49H—an activating NK receptor identified that is capable of specifically recognizing a virally-encoded product ( the MCMV protein m157 ) [4] , [5]—are the predominant contributors to the innate response to a primary MCMV infection[2] , [6] , [7] , [8] , [9] , [10] , [11] . Sun et al . , made use of the cognate recognition of m157 by Ly49H+ to establish the specificity of the enhanced host protection provided by memory Ly49H+ NK cells upon re-exposure to MCMV[2] . Recently , a report from the von Andrian laboratory [3] showed that CXCR6+ NK cells primed with virus-like particles ( VLPs ) expressing viral transgenes were capable of mediating antigen-specific contact hypersensitivity ( CH ) in response to antigens not known to be recognized by germline-encoded receptors . These memory NK cells were also capable of providing partial host protection from infection with live virus expressing the priming antigens , as measured by a delay in mortality upon lethal viral challenge and the capacity to resolve localized , lower dose viral infection . Further , they demonstrated that signaling through CXCR6 , a chemokine receptor that binds to CXCL16 ( primarily expressed by hepatic sinusoidal epithelium ) , is critical for the maintenance of this hepatic NK memory population of cells . The present studies were initiated to determine whether innate memory could contribute to the control of viral pathogens for which no cognate germline-encoded receptor has been identified . Furthermore , we wanted to assess whether innate memory cells alone can provide protection against a viral challenge in the absence of classical adaptive immunity . In this series of experiments , we used live vaccinia virus to prime and challenge the memory NK cell population and demonstrate that memory NK cells alone are capable of providing sterilizing protection against a systemic challenge with a fatal dose of the priming pathogen . This protection was mediated by a Thy1+ subset of hepatic NK cells primed with live vaccinia virus; hepatic Thy1+ NK cells primed with a highly attenuated strain of vaccinia ( Modified Vaccinia Ankara ( MVA ) ) were unable to confer this protection upon adoptive transfer into naïve , immunodeficient hosts . This suggests that innate memory , like classical adaptive memory , is primed more efficiently with live , replication-competent organisms . We demonstrate that innate memory to vaccinia virus is extremely durable , as the memory NK cells that mediated clearance of the lethal pathogen were active greater than 6 months after priming with the live pathogen . We also show that on a cellular level , enhanced activation of hepatic NK cells from virally primed mice can be demonstrated not just in response to the pathogen , but in response to more generalized stimuli .
IgHko mice received an inoculation of either PBS ( control naïve IgHko ) , or 1×107 plaque forming units ( pfu ) of rVV-luc intraperitoneally ( ip ) ( primed IgHko ) ) . We then rested the mice for a minimum of six months after clearance of the rVV-luc ( as determined by IVIS imaging; Fig . S1 ) to ensure that any effects we observed during a secondary challenge would reflect the influence of a durable , long-lived memory immune response rather than the activity of residual effector cell populations that may have persisted following clearance of the virus . Ten days prior to a secondary vaccinia virus challenge we began administering isotype control antibodies or a cocktail of T cell- depleting monoclonal antibodies ( clones GK1 . 5 ( αCD4 ) [15] , H57–597 ( αTCRß ) [16] , and UC7-13D5 ( αTCRγδ ) [17] ) ip to groups of naïve and primed animals . We did not include a CD8α-depleting monoclonal antibody in this cocktail because we did not want the depletion of CD8α+ innate populations of cells to potentially confound the outcome of the experiments . The monoclonal antibody mixtures were administered every two weeks throughout the life of the experiment to maintain T cell depletion . The efficiency of T cell depletion was monitored by flow cytometric analysis of peripheral blood stained with a panel of monoclonal antibodies , including antibodies specific for CD3 , TCRβ , TCRγδ , and NK1 . 1 . Representative flow cytometry plots from control and T cell-depleted mice 1 week post-challenge showed that the efficiency of T cell depletion was extremely high , greater than 99% as measured by percentage of CD3+ within the lymphocyte gate ( Fig . 1a ) . Very few of those cells that fall within the CD3+ gate in the depleted mice showed staining for TCRβ , TCRγδ , or CD8α; there are no masking antibodies to CD3 or CD8α present in the cocktail . Moreover , pilot studies performed in control B6 mice showed that the extent of T cell depletion in the peripheral blood following administration of the depleting antibodies reflected the T cell depletion in spleen and lymph nodes ( data not shown ) . In repeated experiments , we observed that both primed ( data not shown ) and naïve IgHko mice treated with isotype control antibodies were capable of clearing vaccinia virus infection . However , in the T cell-depleted groups of mice , only the primed mice ( primed , T-depleted IgHko ) were capable of resolving the infection ( Fig 1 , b and c ) . Representative IVIS images show the kinetics of vaccinia virus clearance in each of the groups of mice ( Fig . 1b ) . The luminescence measurements on all mice demonstrated that while naïve , T-depleted IgHko were incapable of controlling vaccinia virus after challenge , the primed T-depleted IgHko mice resolved the infection ( Fig . 1c ) . The clearance of vaccinia virus infection occurred within 2–3 weeks in primed , T cell-depleted mice ( Fig . 1 , b and c ) , more slowly than virus clearance occurred in primed , control IgHko mice ( clearance in 1 week-data not shown ) or in naïve , control IgHko mice ( clearance in 10–14 days; Fig . 1 , b and c ) . Once the primed , T cell-depleted mice had resolved the vaccinia virus infection , there was no evidence of a later recrudescence of viral replication . In mice that succumbed to vaccinia ( naïve , T cell-depleted IgHko ) , we observed spread of vaccinia virus from the peritoneal cavity into hotspots located on the tails , feet , and mouths of infected mice . This distribution of vaccinia virus was consistent with previous reports[12] and was likely the result of transfer of infection to satellite sites via saliva . Together , these data suggest that the long-term resolution of vaccinia virus infections can occur in the absence of classical adaptive immune effector mechanisms . We next sought to identify the cell population ( s ) that mediated this non-T and non-B cell protective memory response in vivo . Since Thy1+ lymphokine-activated killer ( LAK ) cells have been implicated in protection against infection with vaccinia virus[18] , [19] , [20] , [21] , [22] , and O'Leary , et al . , have shown that the multiple compound-specific DTH responses they observed are mediated by a liver-resident Thy1+ NK cell population[1] , we hypothesized that Thy1+ NK cells may be the effector population providing protection against secondary vaccinia virus infection . To explore this possibility , we inoculated groups of IgHko mice with either 1×107 pfu rVVluc ( primed IgHko ) or PBS ( naïve IgHko ) ip . Eight months later , these mice were treated either with isotype control antibodies , the T cell-depleting antibody cocktail ( 300 μg each of clones GK1 . 5 , H57–597 , and UC7-13D5 ) , or a T- and Thy1-depleting antibody cocktail ( 300 μg each of clones GK1 . 5 , H57–597 , UC7-13D5 , and the Thy1 . 2 ( CD90 . 2 ) -specific clone 30H12[23] ) ; the monoclonal antibody mixtures were administered every 2 weeks throughout the course of the experiment to maintain T and Thy1+ cell depletion . One week after initiating monoclonal antibody administration , all groups of mice were challenged ip with 1×106 pfu rVV-luc . Representative flow cytometric plots of peripheral blood cells from groups of naïve and primed mice 2 weeks after challenge demonstrate that the depletion of the target cell populations by monoclonal antibody administration was extremely efficient ( >99% T cell depletion in all groups at 2 weeks post-challenge; Fig . 2a ) . Depletion of T cells had no significant impact on the number of NK cells present in the peripheral blood . We did observe a significant shift in the representation of Thy1 ( CD90 ) + NK cells in mice that had been depleted of T cells . Nevertheless , the proportion of Thy1+ NK cells was comparable in the naïve and primed groups ( Fig . 2b ) , suggesting that differences in clearance and survival between the naïve and primed T-depleted groups is not a consequence of differential effects of T cell depletion on the NK cell populations . As anticipated , the addition of the Thy1 . 2 ( CD90 . 2 ) -depleting monoclonal antibody 30H12 in the T&Thy1-depleting cocktail efficiently eliminated all Thy1+ NK cells in IgHko recipients ( Fig . 2b ) . Both the naïve and primed IgHko control ( T cell-competent ) groups of mice cleared the infection with kinetics consistent with primary ( naïve IgHko ) and secondary ( primed IgHko ) adaptive immune responses , respectively ( Fig . 2 , c and d ) . As observed in the initial series of experiments , the naïve , T-cell-depleted IgHko mice were unable to control the challenge infection while the primed , T cell-depleted IgHko mice resolved the vaccinia virus infection within 2–3 weeks . However , depletion of both T cells and Thy1+ non-T cells abrogated the protection that was evident in the primed T-cell-depleted IgHko group ( Fig . 2c and d ) . The naïve T- and Thy1-depleted mice also succumbed to infection . These data suggested that the cell population ( s ) mediating protection against secondary vaccinia virus challenge was a Thy1+ , non-T , non-B cell population . To examine the comparability of data generated using the luciferase/IVIS system and data generated using a traditional strategy for measuring vaccinia virus infection in vivo , we quantified vaccinia virus titers in ovaries of primed T-depleted and primed T- and Thy1-depleted groups of mice . The results established that the clearance of luciferase activity in primed T-depleted animals as visualized using the IVIS technology correlated with an inability to detect vaccinia virus in ovaries of these animals ( Fig . S2 and data not shown ) . This observation indicates that viral clearance as measured by IVIS is consistent with traditional methods of measuring viral clearance . One of the fundamental characteristics of a classical memory response is that memory cells exhibit enhanced activation upon secondary stimulation . Therefore , we wanted to determine if functional differences existed between NK cells from naïve and vaccinia virus-primed mice . To do so , we performed in vitro activation assays using isolated liver mononuclear cell preparations . We pooled liver mononuclear cells from groups of 8–12 age-matched naïve mice and mice that had been VV-primed 6 months earlier to ensure that we could isolate sufficient numbers of cells for stimulation and analysis . These cell preparations were isolated from enzymatically-dissociated livers via percoll gradient separation and 5×105 cells/well were aliquoted into 24 well plates . The cells were then incubated for 6 hours in the presence of fluorescently-labeled anti-CD107a/b antibodies in wells containing either no stimulus , PMA and ionomycin , 200 micrograms of plate-bound isotype control antibody ( clone CI . 8 ) , 200 micrograms of plate-bound anti-NK1 . 1 antibody ( clone PK-136 ) , or either 2×106 pfu of vaccinia virus or 2×106 viral particles ( vp ) of recombinant adenovirus that had been treated with 2% paraformaldehyde . The recovered cells were washed , stained for CD69 expression , and fixed prior to flow cytometric analysis . In each condition , analysis on gated NK cells ( NK1 . 1+CD3− ) showed that the NK cells from vaccinia virus-primed animals showed higher activation , as determined by CD69 ( activation ) and CD107a/b ( degranulation ) staining ( Fig . 3 ) . Primed NK cells showed significantly higher levels of activation and degranulation than naïve NK cells when stimulated with PMA and ionomycin ( 24 . 1% CD69+CD107+ for primed NK vs . 14 . 3% for unprimed; specific activation above unstimulated 13 . 5% for primed vs . 7 . 1% for unprimed NK ) , and plate-bound anti-NK1 . 1 antibody ( 40 . 8% primed vs . 22 . 1% unprimed; specific activation above isotype-stimulated 29 . 3% primed vs . 14 . 3% unprimed ) . This activation occurred in response to the indicated stimuli , as activation was well above control levels , and by stimuli that were not dependent on a presenting cell population or the presence of vaccinia virus antigens . The cells also showed an enhanced response to fixed , plate-bound vaccinia virus when compared to the unstimulated control cells ( 16 . 5% primed vs . 9 . 9% unprimed; specific activation above unstimulated 5 . 9% primed vs . 2 . 7% unprimed ) , although the response to vaccinia virus was only marginally higher than the response to an unrelated virus ( 16 . 5% primed vs . 9 . 9% unprimed responded to vaccinia virus , while responses to adenovirus were 14 . 2% primed vs . 8 . 6 unprimed; vaccinia-induced activation relative to adenovirus-induced activation 2 . 3% primed vs . 1 . 3% unprimed ) . These results showed that , as with the non-specific stimuli , primed NK cells exhibited significantly enhanced activation relative to unprimed NK cells in response to direct exposure to viral particles . However , the relatively small increases in activation seen in both primed and unprimed cells in response to vaccinia virus as compared to an unrelated virus ( adenovirus ) suggest that direct and specific recognition of vaccinia virus antigens plays a limited role in stimulating both vaccinia –primed and –unprimed NK cells . Collectively , these results show that NK cells from vaccinia virus-primed mice were more responsive than NK cells from naïve mice to a wide range of activating stimuli . We next characterized the dynamics of NK cell populations throughout the course of vaccinia virus infection in unmanipulated , wild type mice . B6 mice were administered PBS ( control ) or 1×107 pfu of rVV ( challenged ) ip . At various time points after infection , groups of control and challenged mice were sacrificed , and cells from the peripheral blood , spleen , and liver were isolated , counted , and analyzed by monoclonal antibody staining and flow cytometry . A rapid expansion was seen in the absolute number of NK cells in the spleen ( data not shown ) and liver ( Fig . 4a ) during the first week following challenge , followed by a contraction of this population as the infection resolved . By 7 days following infection , the absolute number of liver NK cells ( CD3−NK1 . 1+ ) had increased 5-fold . There was a preferential expansion of the Thy1+ NK cell population in the liver of infected mice , with the representation of Thy1+ cells increasing in the liver both as a percentage of total NK cells ( from 46% to 64% of total liver NK cells by day 4 post-challenge ) and in their absolute number ( a 10-fold increase over baseline by 7 days after infection ) . A preferential expansion of splenic Thy1+ NK cells was also observed , albeit to a lesser degree ( approximately 2–fold expansion in the total number of splenic NK cells , and a 5-fold expansion in the number of Thy1+ splenic NK cells; the percentage of total splenic NK cells that were Thy1+ rose from a baseline of 32% to a peak of 57% by 4 days post-infection; data not shown ) . We also undertook a detailed phenotypic analysis of the liver NK cells following vaccinia virus infection . These cells were stained with antibodies specific for NK-associated cell surface molecules , including CD94 , NKG2D , CD43 , Ly49H , Ly6C , KLRG1 , CD27 , CD11b , and Thy1 ( Fig . 4 , b and c ) . During the first week after vaccinia virus challenge , the absolute number of all NK subsets we analyzed had increased . However , we were able to identify several NK cell subsets that preferentially increased in number and percentage: the total Thy1+ NK cell population , as described above , and a subpopulation of Thy1+ NK cells that were also KLRG1hi . Most of these cells were phenotypically NKG2DbrightLy6ChiCD27loCD11bhiCD43hi ( Fig . 4 , b and c , and data not shown ) . The representation of this KLRG1hi population rose from approximately 5% to a peak of 20–25% of the liver Thy1+ NK cell population by days 4–7 after infection , and then contracted to baseline levels by 21 days after challenge ( Fig . 4b and c ) . However , by 118 days after challenge there were no phenotypically discrete subsets within the Thy1+ NK cell subset that we could identify as potential memory populations . We wanted to determine whether the expansion of Thy1+ NK cells observed in the liver and spleen during primary infection were a consequence of proliferation or an alteration in NK cell trafficking . Groups of B6 mice were infected with 1×107 pfu rVV ip , and at various time points administered the thymidine analogue ethynyl deoxyuridine ( EdU ) 12 hours prior to sacrifice . We designed the experiment to allow the assessment of the proliferative activity within the NK cell population at various time points rather than following continuous EdU administration . As shown in Fig . 5 , extensive proliferation within the NK cell population occurred early in infection ( 25% of total NK cells proliferating between days 3–4 post-challenge ) , and returned to baseline levels by 7 days post-challenge ( Fig . 5a ) . The preferential expansion of Thy1+ NK cells was seen at both day 4 and 7 post-challenge ( where the percentage of EdU+ NK cells that were also Thy1+ reached levels of 74 . 6% and 82 . 9% respectively , compared to 49% in naïve controls; Fig . 5b ) . These results are consistent with the population dynamics observed in the liver NK cell population ( Fig . 4a ) , and suggest that the large increases in NK cell number and Thy1+ NK cell percentage in liver are driven by in situ proliferation early in infection . In support of the mechanism of in situ proliferation , plaque assays performed on de novo infected animals showed that low levels of vaccinia virus could be recovered from the livers of animals receiving vaccinia virus ip beginning at 4 hours post-infection and through day 4 post-infection ( ranging from 4×102–5×104 pfu; data not shown ) . This finding established that pathogen is also present in situ and might directly stimulate the expansion of Thy1+ NK cells . Our initial experiments in the IgHko model system indicated that a VV-primed Thy1+ , non-T- non B- cell population was capable of providing host protection upon secondary exposure to the virus . However , despite the extreme efficiency of depletion ( >99%; Fig . 1a and Fig . 2 , and b ) achieved in the IgHko system , in vivo depletion via monoclonal antibody administration was not 100% efficient . Therefore , it remained a formal possibility that the protection we observed might be mediated by residual T cells . To address the possibility of residual T cell contamination , and confirm that a population of memory Thy1+ NK cells could confer protection against vaccinia virus , we evaluated the anti-viral activity of these cells in an adoptive transfer system . First , we wanted to establish that any protection we might observe in mice receiving these transferred cells was mediated by the transferred NK cell populations and not by contaminating T cells . To determine if we could adequately control for contaminating T lymphocytes in the transfers , we isolated NK cell-depleted liver mononuclear cell preparations from enzymatically-dissociated livers of naïve and vaccinia virus-primed mice by percoll gradient separation followed by magnetic separation of non-NK cells . We then transferred 2×106 of these NK-depleted mononuclear cells ( a mixture of T cells , B cells , macrophages , DCs , and granulocytes ) into naïve RAG1ko mice; one group received naïve NK-depleted cells , one group received VV-primed non-NK cells , and a third group received VV-primed non-NK cells in conjunction with the administration of the T cell-depleting monoclonal antibody cocktail utilized in the IgHko depletion experiments described above . Five days after the transfer of these cells , we challenged the recipients with 1×105 pfu of rVV-luc ip and monitored their ability to control infection using the IVIS system . Mice that received naïve or VV-primed NK-depleted cells cleared the infection with kinetics consistent with a primary ( naïve recipients ) or secondary ( primed recipients ) T cell response ( Fig . 6 ) . However , the mice that received the T cell-depleting monoclonal antibody with the VV-primed non-NK cells were unable to control infection and had to be sacrificed at d34 post-challenge . These results indicate that even when transferring large numbers of primed T lymphocytes into naïve RAG1ko hosts , administration of the T cell-depleting monoclonal antibody cocktail abrogated the protection afforded by the transferred cells . We then isolated NK cells from the collagenase-treated livers of B6 or B6 congenic mice a minimum of 6 months after these mice had received either PBS ( naïve ) or 1×107 pfu rVVluc ( primed ) ip . We utilized two congenic mouse models in these studies; B6 . PL mice , which express the CD90 . 1 isoform of CD90 , or B6 . SJL mice , which express the CD45 . 1 isoform of CD45 . The B6 , IgHko , and RAGko mouse models utilized in these studies express CD90 . 2 and CD45 . 2 . Blood mononuclear cells were isolated from livers after enzymatic digestion followed by Percoll gradient centrifugation , and NK cells were isolated from this cell fraction using the MACS NK cell isolation kit and autoMACS instrument . Purified NK cells were then separated into Thy1− and Thy1+ populations . Three distinct cell fractions were adoptively transferred into naïve RAG1ko mice: 5×106 NK-depleted cells ( a mixture of T cells , B cells , macrophages , DCs , and granulocytes ) , 1×105 Thy1− NK cells , and 1×105 Thy1+ NK cells . The groups of mice receiving the NK cell populations were treated with the T cell-depleting monoclonal antibody cocktail at the time of transfer to ensure that any contaminating T cells were eliminated . Thy1+ NK cells , but not T cells could be detected in the spleen or peripheral blood of RAG1ko hosts that received purified liver Thy1+ NK cell transfers 4 weeks after transfer and challenge ( Fig . S3 ) ; as expected , donor T cells were readily apparent in the peripheral blood of mice receiving NK-depleted liver cell transfers ( Fig . S3 ) , but were not detectable in mice receiving NK cell transfers . Five days after cell transfers ( and concurrent T cell-depleting antibody administration for recipients of purified NK cell population transfers ) , we challenged these RAG1ko mice with 1×105 pfu rVV-luc ip and monitored the mice for viral burdens using the IVIS system . Representative IVIS images of individual mice from each of the experimental groups are shown in Fig . 7a , and the persistence or clearance of virus in each mouse from each group in 4 separate experiments are summarized in Fig . 7 , b and c . As predicted , the RAG1ko mice that received naïve , NK cell-depleted liver lymphocytes ( 15/16 mice; 94% ) resolved the vaccinia virus challenge within 2 weeks , and the RAG1ko mice that received the VV-primed NK cell-depleted liver lymphocytes ( 15/15; 100% ) resolved the vaccinia virus challenge within 1 week ( Figs . 7b and c , and data not shown ) . However , the groups of RAG1ko mice that received naive Thy1− ( 1/16; 6 . 25% ) or Thy1+ ( 0/17; 0% ) NK cell populations , or VV-primed Thy1− ( 0/16; 0% ) NK cells were unable to resolve vaccinia virus infections . Strikingly , a significant proportion of RAG1ko mice that received VV-primed Thy1+ NK cell transfers ( 9/22; 41% ) were able to clear the challenge vaccinia virus infection by three weeks after challenge , with viral clearance provided by transferred VV-primed Thy1+ NK cells significantly enhanced over mice receiving VV-primed Thy1− NK cells ( two-tailed t-test; p<0 . 01 ) , naïve Thy1−NK cells ( p<0 . 05 ) , and naïve Thy1+ NK cells ( p<0 . 01 ) transfers ( Fig . 7b and c ) . Survival in these groups mirrored the clearance we observed by IVIS ( Fig . 7d ) . All mice that received NK cell-depleted cells in transfer ( cells that contained T lymphocytes ) survived; however , of NK cell transfer recipients , only those that received the VV-primed Thy1+ NK cells were afforded significant protection , as shown by a survival rate of greater than 40% ( 9/22 recipients ) . We also wanted to determine if a highly attenuated poxvirus strain , MVA , was as capable of priming for protective NK cell memory as replication competent VV ( Western Reserve strain ) . We transferred 1x105 Thy1− or Thy1+ NK cells isolated from the livers of naïve or MVA-primed RAG1ko mice into naïve RAG1ko recipients , then challenged the recipients with 1×105 pfu rVV-luc IP 5 days after NK transfer . As shown in Fig . 7e , no significant protection against a replication-competent VV was observed in any of the recipients . These data indicate that VV-primed , but not MVA-primed , Thy1+ NK cells can clear a lethal systemic challenge with VV . To verify that the protection observed in these studies is afforded by the transferred Thy1 ( CD90 ) + NK cells and not contaminating T lymphocytes , we isolated either NK-depleted liver lymphocytes ( 2×106/transfer ) or liver Thy1+NK cells ( 1×105/transfer ) from B6 . SJL ( CD45 . 1+CD90 . 2+ ) mice and transferred them into naïve RAG1KO mice ( CD45 . 2+CD90 . 2+ ) with simultaneous administration of the T depleting monoclonal antibody cocktail to the mice receiving the transferred NK cells . Five days after transfer , recipients were infected with 1×105 pfu rVV-luc ip The course of the vaccinia virus infection in these animals is indicated by both IVIS images and classical pfu assay ( Fig . 8 ) . We were able to establish that the control ( d7 post-challenge; Fig . 8a ) and resolution ( d14 post-challenge; Fig . 8b ) of challenge vaccinia virus infection observed in animals receiving primed B6 . SJL Thy1+ ( CD45 . 1+CD90 . 2+ ) NK cell transfers occurred in the absence of contaminating T cells ( CD45 . 1+CD3+ ) and in the presence of the transferred Thy1+ NK cells ( CD3−NK1 . 1+CD45 . 1+CD90+ ) . The protection and clearance of virus mediated in vivo by adoptively transferred B6 . SJL NK-depleted liver lymphocytes ( including T cells; CD3+CD45 . 1+CD90 . 2+ ) at d14 post-challenge is also shown ( Fig . 8b ) . These results indicate that the protection observed against vaccinia virus challenges in recipients of primed Thy1+ NK cells is mediated by the transferred NK cells and is not a consequence of contaminating T lymphocytes in the transferred cells .
In these studies , we show that innate memory develops in response to a viral infection in the absence of an identified cognate receptor-virus interaction . The enhanced control of vaccinia virus in primed , T-depleted IgHko mice was apparent by 4 days post-challenge , indicating that the innate responses were both potent and rapid . That we observed this innate protection in animals that had resolved primary infection greater than 6 months prior to the secondary challenge has two important implications . First , it suggests that the protection represents a ‘memory’ response , rather than the persistence of a residual population of activated effectors . Second , the innate memory that formed following exposure to vaccinia virus is extremely durable . We further established that the complete protection we observed in the primed , T- depleted IgHko mice was abrogated with the addition of the Thy1-depleting antibody to the T cell depletion cocktail , indicating that innate memory to vaccinia virus resides within a Thy1+ non-T and non-B cell population . Perhaps the most striking result we observed in these experiments was that the memory provided by this innate Thy1+ cell population was manifested not only in enhanced control of vaccinia virus infection during the early stages of infection , but proved to be sufficiently potent to protect the infected hosts against a systemic lethal dose of virus in the absence of classical adaptive immune cells . We performed experiments to determine if the memory Thy1+ NK cell that responded to vaccinia virus had a unique , persistent phenotype . In vitro stimulation assays established that the NK cells from primed animals responded more vigorously than NK cells from naïve animals , with increased CD69 upregulation and degranulation upon exposure to both priming antigens ( plate-bound vaccinia virus ) and a non- antigen-specific stimulus ( plate-bound anti-NK1 . 1 ) . These results indicate that primed hepatic NK cells develop and maintain an enhanced capacity for activation , not just enhanced pathogen-specific recall . These results also suggest that VV-specific memory NK cells may recognize subsequent exposure to vaccinia virus through a combination of signaling related to an induced self profile and direct recognition of virally-encoded proteins . That we observed only small differences in activation between NK cells stimulated with plate-bound vaccinia virus and the unrelated virus adenovirus suggests that the activation of NK cells is not driven predominantly by pathogen-encoded antigens , although it remains a formal possibility that formaldehyde fixation of the virus in these studies may have diminished the capacity of NK cells to interact directly with and recognize immunogenic portions of the virus . We further sought to define a durable cell surface marker expression profile acquired after a primary infection that might identify a memory cell subpopulation within the liver-resident Thy1+ NK cell compartment . We observed significant increases in total NK cell numbers in spleen ( data not shown ) and liver , consistent with previous reports of global expansions of NK cell populations after viral infections[7] . We also observed a preferential expansion of the Thy1+ NK cell population , as determined both by the percentage and the absolute numbers of Thy1+ NK cells . Our EdU incorporation experiments ( Fig . 5 ) established that the expansion in total NK cell numbers , and Thy1+ NK cells in particular , is driven by in situ proliferation of Thy1+ NK cells during the first week of infection , not the accumulation of effector cells migrating from peripheral sites . This expansion of Thy1+ NK cells was accompanied by a preferential increase in the representation of a KLRG1+ subset of cells that was predominantly CD27loCD11bhiLy6chi ( Fig . 4 , b and c ) , a profile consistent with these cells being mature , activated NK cell effectors[24] , [25] . Importantly , in accordance with an earlier report[7] , we observed no preferential expansion of the Ly49H+ NK cell subset that mediates innate memory against MCMV[2] . These data suggest that innate memory to vaccinia virus is mediated by a subset of NK cells that is distinct from those that form innate memory to MCMV . However , by 4 months post-challenge , the phenotypic differences between the Thy1+ liver NK cell population in control and vaccinia virus-infected mice observed during the acute phase of infection was no longer apparent . This observation is consistent with a recent report that previously stimulated NK cells retain enhanced functional capacity in the absence of a phenotypic profile that distinguishes these cells from naïve NK cells[26] . A significant concern that must be addressed when assessing the protection against infection described in the present studies is whether any residual functional T cell populations might be present that would be capable of mediating the protection . In the IgHko system , we were able to achieve extremely efficient depletion of T cells as measured by anti-CD3 staining ( approximately 99% at 1 week post-challenge ) . However , while this T cell depletion in the naïve and primed IgHko groups of mice was efficient , complete depletion of all T cells was not possible . Despite the fact that T cell depletion completely abrogated protection in naïve IgHko recipients , it therefore remained a formal possibility that residual T cells in the primed mice could contribute to host protection . We therefore addressed this issue by adoptively transferring highly purified NK cell subsets into naïve RAG1ko hosts and concurrently administering the same T cell-depleting cocktail ( Figs . 6-8; Fig . S3 ) . We were able to demonstrate that protection against vaccinia virus could be transmitted to naïve RAG1ko hosts by adoptive transfer of purified vaccinia virus-primed Thy1+ liver NK cells . Of the naive RAG1ko mice that received 1×105 primed Thy1+ liver NK cells , 41% ( 9/22 ) were able to resolve a systemic , lethal vaccinia virus challenge infection , while only 2% ( 1/49 ) of RAG1ko mice that received other purified NK cell subpopulations were able to resolve and survive infection ( Fig . 7 ) . Our inability to achieve protection in 100% of RAG1ko mice by transfer of primed Thy1+ liver NK cells may be a consequence of limiting numbers of Thy1+ liver NK cells and the inefficiency associated with the cell transfers . We administered the T cell-depleting monoclonal antibody cocktail to NK cell transfer recipients at the time of transfer to prevent contaminating T cells from contributing to the protection of these recipients . Indeed , as shown in Fig . 6 , even when transferring 20-fold more cells —cell preparations that contained a significant proportion of primed T cells , in contrast to the highly purified NK cell populations transferred in these experiments — we observed that the T cell depleting cocktail abolished protection ( as measured by both IVIS and survival ) . Further , when we looked for the presence of contaminating T cells at d7 , d14 , and d28 post-challenge ( d12 , d19 , and d33 post-transfer and antibody administration , respectively ) , we were able to identify transferred Thy1+ NK cells but were unable to detect T cells in the peripheral blood , livers , or spleens of NK cell transfer recipients ( Fig . 8; Fig . S3 ) . These data indicate that the protection we observed in mice that received VV-primed Thy1+ liver NK cell transfers was indeed conferred by this population of cells ( Figs . 7 and 8 ) . We also investigated whether MVA-primed Thy1+ NK cells from RAG1ko hosts were capable of conferring the same protection upon transfer into naïve immunodeficient hosts and determined that priming with the highly attenuated MVA virus was not sufficiently robust to generate protective memory comparable to that induced by priming with live , replication competent VV . There are several possible explanations for why MVA priming did not engender the same degree of protective innate memory . One possibility is that the viral product ( s ) necessary for NK memory recognition of VV were lost in the attenuation process . A recent study [3] was able to demonstrate antigen-specific memory NK cells induced by a range of virus-like particles ( VLPs ) , including VLPs containing antigens derived from pathogens not endemic to the mouse population ( like HIV-1 ) , suggesting that this mechanism is unlikely . Even the highly attenuated MVA strain has a rather large genome encoding an extremely diverse range of potential antigens , including a wide range of antigens shared with the vaccinia virus strain used in the challenges . A second possibility is that NK memory formation to VV requires conditioning from adaptive cell populations at some point during the priming response . However , the ability of RAG1ko hosts to generate protective , pathogen-specific NK memory upon exposure to a variety of VLPs also suggests that the presence of classical adaptive lymphocytes are not necessary during the priming phase for effective innate memory formation . A third possibility is that , as has been observed for classical memory [27] , innate memory is generated more potently in response to a live , replication-competent agent . If an ‘active’ infection stimulates a more robust response , it may do so is by stimulating enhanced expression of self-molecules in cells under the stress of active infection and replication . In such a scenario , NK cells might well represent the population best equipped to respond to those stressed-self indicators through their array of germline-encoded receptors . These data demonstrate that VV infection generates a liver-resident Thy1+ NK cell population capable of mediating a protective innate memory response . It is not clear how primed Thy1+ liver NK cells provide this protection against VV infection . It will be important to determine whether innate memory NK cells respond to pathogens more effectively than naïve memory NK cells because they have enhanced lytic capacity , increased stores of premade effector molecules ( such as cytokines , granzyme , and/or perforin ) , superior proliferative capacity , or an as yet undefined property that facilitates a particularly robust effector response . While the in vitro stimulation assay shown in this study indicates that the primed NK cells do possess enhanced effector capacity ( Fig . 3 ) , the precise mechanism by which this is mediated remains unclear . The mechanism by which primed Thy1+ liver NK cells recognize vaccinia virus upon secondary exposure is of great importance . There are several hypotheses , not mutually exclusive , that could account for pathogen-specific recognition of a previously encountered pathogen . First , there may exist a whole class of previously unidentified receptors expressed by innate cell populations that , like Ly49H , have co-evolved to specifically recognize the various pathogens endemic to the host population . Such molecules could act as highly pathogen-specific pattern recognition PRRs . Indeed , the murine homologue of the human NKp46 receptor ( also known as Ncr1 ) is critical for host defense against influenza[28] , and human NKp46 has been shown to bind viral hemagglutinins[29] , [30] , [31] . Moreover , previous studies have shown that genes within the NK complex on chromosome 6 in B6 mice contribute to NK cell-mediated immune responses to ectromelia[32] , [33] , an orthopoxvirus closely related to vaccinia virus . This finding suggests that an as-yet-unidentified receptor capable of recognizing a poxviral product may be encoded within this region . A second potential mechanism by which pathogen-specific recognition could be mediated through germline-encoded receptors could reflect alterations in the ability of NK receptors to interact with MHC class I and MHC class I-like ligands when binding and/or presenting viral epitopes . Alternatively , infection by pathogen could induce a state within the cell that results in the display of an ‘infected-self’- or ‘stressed-self’- associated profile of protein expression that can then be recognized by receptors on innate memory NK cells . One consequence of such an ‘infected-self’ mechanism of recognition would be that the innate memory formed in response to one pathogen might provide protection against challenge with heterologous pathogens that induce a similar molecular self-profile in infected cells . The phenomenon of NK cell recognition and response to induced-self molecules has previously been implicated in both infections and oncogenesis[34] , [35] , [36] , [37] , where ligands MULT1[38] , H60[39] , and Rae-1[39] , [40] ) for the activating receptor NKG2D are preferentially induced upon infection or cellular transformation[36] , [37] , [41] . Indeed , activation through NKG2D has been established as a critical component of the NK cell response to ectromelia virus infection[42] . That protective NK cell responses to poxvirus infections have been tied to both an as-yet unidentified gene in the B6 NK receptor complex[32] , [33] and to NKG2D activity[42] suggests that both the pathogen-specific PRR and ‘infected-self’ mechanisms may contribute to NK cell responses to poxviruses . Indeed , our in vitro assays showing enhanced responsiveness of vaccinia virus-primed NK cells to stimulation through the NK1 . 1 receptor pathway as well as direct exposure to vaccinia virus would be consistent with both mechanisms playing a significant role in NK memory cell recognition of poxviruses . These studies have established a model of innate memory to a pathogen in a system in which there is no known pathogen-specific receptor expressed by innate cell populations . This system will allow us to explore models of innate memory recognition further . Understanding the nature of that recognition will be critical for harnessing innate memory for prophylactic or therapeutic use . The stimulation of innate memory targeted to specific pathogens might represent a novel and powerful approach for the design of future vaccination strategies .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All animals are treated humanely and in accordance with the policies of the Beth Israel Deaconess Medical Center ( BIDMC ) , the regulations of the Animal Welfare Act , and other laws and policies of the federal government and other agencies . All mice were maintained under specific-pathogen-free conditions and research on mice was approved by the BIDMC Institutional Animal Care and Use Committee ( IACUC ) under protocol #095-2009 . All efforts were made to minimize suffering of animals in this study . Age-matched adult female C57Bl/6 , B6 . PL ( Thy1 . 1 congenic ) , B6 . SJL ( CD45 . 1 congenic ) , IgHμko ( μMT[13] , [14] ) , and RAG1ko were obtained from Jackson Laboratories ( Bar Harbor , ME ) . All mice were maintained in the Harvard Institute of Medicine and Beth Israel Deaconess Medical Center ( BIDMC ) Animal Research facilities and used in accordance with protocols approved by the Institutional Animal Care and Use Committees of BIDMC , Harvard Institutes of Medicine , and Harvard Medical School . Stocks of recombinant vaccinia were prepared as previously described[43] . Briefly , seed stocks of recombinant vaccinia virus ( Western Reserve strain ) expressing firefly luciferase[44] ( rVV-luc; kindly provided by Michael Seaman ( BIDMC; Boston , MA USA ) were added to HeLa cell monolayers at an MOI of 1 . Seeded monolayers were harvested and washed into 10 mM Tris pH 9 . 0 48–72 hours after seeding , and cell-associated virus was released from cells by 3 freeze-thaw-sonication cycles followed by centrifugation at 850 g for 10 min at 4°C . Supernatants ( containing free virus ) were then layered over a 36% sucrose cushion and centrifuged for 2 hours at 27 , 000 rpm ( 1 . 33×105 g ) at 4°C . After centrifugation , pellets containing purified cell-free virus were resuspended in 10 mM Tris pH 9 . 0 , aliquoted , and titered using a standard in vitro plaque assay using CV-1 cells[43] . Recombinant Modified Vaccinia Ankara virus expressing firefly luciferase [27] ( rMVA-luc; were provided by Michael Seaman ( BIDMC; Boston , MA USA ) . Sterile , certified low endotoxin preparations of monoclonal antibodies H57–597 ( hamster IgG anti-mouse TCRβ ) [16] , UC7-13D5 ( hamster IgG3 anti-mouse TCRγδ ) [17] , 30H12 ( rat IgG2b anti-mouse Thy1 . 2 ) [23] , GK1 . 5 ( rat IgG2b anti-mouse CD4 ) [15] , LTF-2 ( rat IgG2b anti-Keyhole Limpet Hemagglutinin ( KLH ) ; used as an isotype control ) , as well as a polyclonal preparation of purified low endotoxin hamster IgG , were purchased from Bio-X-cell cell culture services ( West Lebanon , NH ) , diluted in sterile PBS to the desired concentration , and administered intraperitoneally ( ip ) . Depleting or isotype control antibodies were administered intraperitoneally to groups of naïve and VV-primed groups of IgHko mice every 2 weeks starting at least 1 week prior to secondary challenge . For adoptive transfer experiments , appropriate depleting or isotype control antibodies were administered intraperitoneally concurrently with transfer of the indicated cell populations . Fluorescently conjugated antibodies specific for mouse CD3ε ( clones 145-2C11 , 17A2 , and/or 500A2 ) , CD11b ( clone M1/70 ) , CD16/32 ( clone 24G2; for blocking Fc receptor-mediated binding of fluorescent labeled antibodies ) , CD49b ( clone DX5 ) , CD27 ( clone LG . 7F9 ) , CD43 ( clone eBioR2/60 ) , CD45 . 1 ( clone A20 ) , CD45 . 2 ( clone 104 ) , CD62L ( clone MEL-14 ) , CD69 ( clone H1 . 2F3 ) , CD90 . 1 ( clone HIS51 ) , CD90 . 2 ( clone 53-2 . 1 ) , CD94 ( clone 18d3 ) , CD107a ( clone 1D4B ) , CD107b ( clone ABL-93 ) , CD127 ( clone A7R34 ) , CD244 . 2 ( B6 2b4 alloantigen; clone ebio244F4 ) , KLRG1/MAFA ( clone 2F1 ) , Ly6c ( clone AL-21 ) Ly49C/I/F/H ( clone 14B11 ) , Ly49H ( clone 3D10 ) , NKG2D ( clone CX5 ) , NK1 . 1 ( clone PK136 ) , TCRß ( clone H57–597 ) , and TCRγδ ( clone GL-3 ) were obtained from either BD Biosciences ( San Jose , CA USA ) or eBioscience , Inc . ( San Diego , CA USA ) . Livers were removed from naïve and vaccinia virus-primed B6 , B6 . SJL ( CD45 . 2 congenic ) , or B6 . PL ( CD90 . 1 congenic ) mice and diced in calcium-magnesium- free HBSS + 2% FBS prior to enzymatic dissociation . Diced livers were resuspended in a digestion buffer consisting of 500 µg/ml Collagenase D ( >0 . 15 U/mg; Roche Applied Science ) and 10 µg/ml DNAse I ( 2500 U/mg; Roche Applied Science ) in HBSS + 2% FBS and agitated for 1 hour at 37°C . The resulting cell suspension was passed through a 70 micron filter and added to the cell suspensions released by dicing the livers harvested prior to digestion . The remaining pieces were washed two additional times , and the resulting cell suspensions were passed through a 70 micron filter and added to the previously pooled cell suspensions . The pooled cell suspensions were then spun at 500 g for 10 minutes at 20°C . The supernatant was discarded and pelleted cells were resuspended and washed 2 more times in HBSS + 2% FBS . The pelleted cells were then resuspended in 40% Percoll in 1x HBSS , then underlaid with an equal volume of 67% Percoll in 1x HBSS prior to centrifugation at 900 g for 30 minutes at 20°C . Cells at the interface between the two Percoll layers were saved and washed twice in MACS buffer ( PBS +0 . 5% BSA +2 . 5 mM EDTA ) . Cells for phenotypic analysis were counted using Guava ExpressPlus software on a Guava easyCyte instrument ( Millipore; Billerica , MA ) prior to staining with fluorescently-conjugated monoclonal antibodies for analysis on an LSR II instrument in our laboratory . For preparation of adoptive cell transfers , two consecutive rounds of purification were performed: first the mouse NK cell isolation kit ( cat # 130-090-864 ) was used in accordance with the manufacturer's protocols ( miltenyi biotec; Bergisch Gladbach , Germany ) to deplete non-NK cells on an autoMACS instrument . The enriched NK population from B6 or B6 . SJL mice was then washed and separated via the AutoMACS ( program ‘possel’; non-labeled fraction is Thy1− NK , labeled fraction is Thy1+ NK ) using MACS beads specific for CD90 . 2 ( cat # 130-049-101 , miltenyi biotec ) . The enriched NK population from B6 . PL ( CD90 . 1+CD90 . 2− ) livers was incubated with antibodies specific for CD3ε , NK1 . 1 , and Thy1 . 1 prior to flow cytometric sorting into Thy1− NK ( Thy1−CD3−NK1 . 1+ ) and Thy1+ ( Thy1+CD3−NK1 . 1+ ) populations on a BD Vantage instrument in our laboratory . Purified populations of NK cell-depleted blood mononuclear cells ( control; 2–5×106 cells/mouse ) , Thy1− ( 1×105 cells/mouse ) , or Thy1+ NK cells ( 1×105 cells/mouse ) in 1x PBS from naïve and vaccinia virus-primed mice , or naïve RAG1ko and MVA-primed RAG1ko mice , were transferred into age-matched female RAG1ko recipients via tail vein injection using a 25 G needle . Within 5 days post-transfer , recipients were challenged with 1×105 pfu of rVV-luc ip and monitored over time using the IVIS Illumina-II imaging system ( Xenogen , inc . ; Alameda , CA USA ) . At numerous time points following challenge , rVV-luc burdens were monitored in vivo using the IVIS imaging system . Briefly , rVV-luc –infected and/or uninfected control animals were injected ip with 100 µl of a 30 mg/ml solution of firefly luciferin-D ( Caliper Life Sciences; Hopkinton , MA , USA ) in PBS , and 100 µl of a 20 mg/ml ketamine and 1 . 72 mg/ml xylazine mixture and imaged 14–16 minutes later in the IVIS series 100 imager ( IgHko experiments ) or IVIS- lumina II instrument ( adoptive transfer experiments ) . Typical exposure times were 60 seconds ( IVIS 100 series ) or 30 seconds ( IVIS-Lumina II ) ; however , when higher viral burdens were present exposure times were shortened to ensure that images were not oversaturated , and all measurements were normalized for one minute exposure . Overlay images and luminescence measurements were made using Living Image software ( version 2 . 50 . 1; Xenogen ) . Tissue culture plates were treated with either carbonate binding buffer alone ( unstimulated and PMA/ionomycin wells ) , 1 mg/ml control Ig ( clone CI . 8; Bio-X-cell ) in binding buffer , 1 mg/ml anti-NK1 . 1 ( clone PK-136; Bio-X-cell ) , 2×106 pfu/ml vaccinia virus , or 2×106 vp adenovirus in binding buffer overnight at 4°C . Wells were washed three times with HBSS + 2% FBS . Wells coated with vaccinia virus or adenovirus were treated with 2% formaldehyde in PBS for 20 minutes at room temperature , washed 4 additional times with complete DMEM + 10% FBS , and all wells were blocked for 2 hr at 4°C with cDMEM + 10% FBS . 5×105 liver mononuclear cell preparations from groups of 8–12 mice were incubated in DMEM alone ( unstimulated , antibody coated , and vaccinia coated wells ) or DMEM with PMA and ionomycin ( 1 µg/ml and 5 µg/ml respectively ) for 6 hours at 37°C . Cell suspensions were then recovered from the plate and processed for flow cytometric analysis . Groups of B6 mice were infected with 1×107 pfu rVV ip 12 hours prior to sacrifice , infected mice and a matching group of naïve mice were administered 250 µg of the thymidine analog 5-ethynyl-2′-deoxyuridine ( EdU ) in PBS ip . At sacrifice , mononuclear cells were isolated from livers and spleens , stained with monoclonal antibodies to NK1 . 1 , CD3ε , CD90 . 2 , and fixed . Fixed cells were then washed in a permeabilization buffer containing saponin , treated with Click-IT EdU staining buffer containing Alexa 647 azide to stain incorporated EdU , washed , and analyzed by flow cytometry . Ovaries were harvested in 10 mM Tris pH 9 . 0 and snap frozen on a dry ice-ethanol bath . Samples were then thawed , homogenized , and snap frozen again . The samples were then thawed , vortexed , and serially diluted stepwise 1∶10 in duplicate in cDMEM + 10% FCS . 500 µl of each dilution was placed on a confluent layer of CV-1 cells in 6 well plates and incubated at 37°C for one hour , prior to aspiration and addition of 3 ml cDMEM + 10%FCS to each well . Plates were incubated for an additional 48 hours at 37°C prior to aspiration and staining and fixing with 500 µl of 0 . 1% crystal violet in 20% ethanol for 5 min . After removing the staining solution , the plates were air-dried and then counted . Viral loads in the ovaries were calculated based on the number of plaques , the dilution factor , and the volume of homogenate . | Immunological memory is a hallmark of adaptive immunity and provides the basis for our ability to become ‘immune’ to pathogens to which we have previously been exposed , and provides the basis for vaccination . For decades , the paradigm held that only the classical adaptive lymphocytes were capable of forming and maintaining protective immunological memory . Recently , several papers have shown the capacity of an innate cell population , a subset of natural killer ( NK ) cells , to exhibit certain aspects of immunological memory . Here we show that innate memory forms in response to infection with vaccinia virus and resides in a discrete subset of NK cells . We further demonstrate that this innate memory provides significant host protection against a subsequent systemic infection with a lethal dose of vaccinia virus , in some cases resulting in the complete clearance of detectable virus . We also demonstrate that priming with live , replicating virus stimulates innate memory more robustly than a highly attenuated vector . These findings shed new light on this emergent area of immunology , and hold significant implications for harnessing innate memory as part of creating novel vaccination strategies . | [
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] | [
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"biology"
] | 2011 | Thy1+ Nk Cells from Vaccinia Virus-Primed Mice Confer Protection against Vaccinia Virus Challenge in the Absence of Adaptive Lymphocytes |
Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations . Details of this process are however unclear . While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets , the more general application for temporally varying signals has yet to be demonstrated . Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants . The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior . We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells . Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells , variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway . Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network .
Many models of the cerebellum assume that the granular layer recodes its mossy-fiber inputs into a more diverse set of granule-cell outputs [1–4] . It is further assumed that the recoded signals , which travel via granule-cell ascending axons and parallel fibers to Purkinje cells and molecular layer interneurons , are appropriately weighted using plastic synapses and then combined to produce the particular Purkinje cell outputs that are required for any given learning task . Recoding in these models thus enables a given set of mossy-fiber inputs to generate one of a very wide variety of Purkinje cell outputs , giving the model demonstrable computational power ( e . g . [5] ) . Although this framework is seen as plausible in broad outline ( e . g . [6 , 7] ) , the details of its workings are far from established [8] . Relatively simple top-down models have shown that theoretically well-understood recoding schemes such as tapped delay lines , spectral timing , Gaussians , sinusoids , and exponentials can be effective , but do not establish how they could be implemented biologically ( references in [8–10] ) . In contrast , more complex bottom-up models of recurrent inhibitory networks representing the connectivity between granule and Golgi cells are closer to biological plausibility , but have been used for very specific tasks such as eye-blink conditioning so that their general computational adequacy is unknown [11–20] . In part this is because eyeblink conditioning requires a response only at the time the unconditioned stimulus arrives . Eyelid ( or nictitating membrane ) position is not specified either for the period between the conditioned and unconditioned stimulus , or for the period ( possibly some hundreds of milliseconds ) after the unconditioned stimulus has been delivered . In contrast , for a task such as the vestibulo-ocular reflex eye-position is very precisely specified for as long as the head is moving , and afterwards for as long as gaze has to be held constant . Thus , cerebellar output—and hence granular-layer output—is more tightly constrained in motor-control tasks resembling the vestibulo-ocular reflex than in eyeblink conditioning [3] . Here we combine elements of top-down and bottom-up approaches , by investigating whether the outputs of neural networks that incorporate the recurrent inhibition observed in the granular layer can be linearly combined to generate continuous filter functions which are computationally useful for example in vestibulo-ocular reflex adaptation [9] . The split between a complex representation layer ( granular layer ) and a linear reconstruction layer ( perhaps corresponding to the plastic synapses between granule cells and Purkinje cells or molecular-layer interneurons ) is similar to the structure employed in reservoir computing [21] , and it is convenient to use terminology and methods from that field in analyzing these networks ( see Methods ) . We begin by analyzing the case of a one-layer network with recurrent inhibition [15] . This is simpler than the real granular layer in which feedback is provided via a second layer of Golgi cell interneurons , but is worth analyzing separately because it allows us to test the hypothesis , suggested by the reservoir computing metaphor , that the crucial parameter in determining the time extension of responses is the mean amount of feedback in the network , and how closely this parameter is tuned to the edge-of-chaos [22] . This degree of tuning can be measured by the Lyapunov exponent . Generally speaking , if there is very little recurrent feedback in a network , then responses will be highly stable and die away very quickly over time , while for large amounts of feedback the responses can be chaotic or even unstable . The Lyapunov exponent ( see Methods ) is a quantitative measure of stability because it captures the rate of growth or decay of small perturbations . In linear systems negative values imply stability , while positive values imply instability . In non-linear systems , small , negative values of Lyapunov exponents can be especially interesting , since they can signal the ‘edge-of-chaos’ , where there are long-lasting and possibly complex responses to transient inputs . We show that this is the interesting region for our reconstruction problem . One novel feature of this contribution is its use of generic colored noise inputs , rather than the stereotyped pulse or step inputs that are usually considered . These colored-noise inputs are essential for motor control applications such as the VOR , where they are needed to demonstrate that the filter can process generic vestibular signals . A second novel feature is the use of statistical techniques that allow us to evaluate the ability of the network to approximate the range of linear filters required for these applications . While previous work on reservoir networks focused on generic inhibitory and excitatory networks [22–30] this is the first work to systematically examine stability and reservoir performance in networks dominated by recurrent inhibition like the granular layer while also taking into account the effects of cerebellar network properties on filter approximations . To achieve this we extend the model to two populations in order to represent inhibition via Golgi cells . We also test the effect of other non-generic features of the cerebellum such as the newly discovered functional feature of Golgi cell inhibition by mGluR2 receptor activated GIRK channels [31 , 32] and Golgi cell afferent excitation often neglected in cerebellar simulations . Furthermore we also evaluate the effect of output-feedback to the granular layer through the nucleocortical pathway .
The one-population model used in this study ( Fig 1A ) was based on that of Yamazaki and Tanaka [15] . It consisted of Nz = 1000 granule cells , each receiving excitatory afferent inputs Ii ( t ) derived from the external signal x ( t ) , and recurrent inhibitory inputs from other cells . The model neurons were firing-rate ( i . e . non-spiking ) , and the output zi ( t ) of the i-th neuron at time t was given by zi ( t ) =[Ii ( t ) −∑jNzAijwij∑s=1texp ( −t−sτw ) zj ( s−1 ) +nNi ( t ) ]+ ( 1 ) ( here the bracket notation []+is used to set negative values to zero , preventing the firing-rate of a neuron from becoming negative ) . This equation describes ( see Fig 1A ) memory-less rate-neurons connected by single-exponential synaptic process with time constant τw so that neuron i sums past inputs zj ( s − 1 ) , 1≤ s ≤ t from other neurons , exponentially weighted by distance s − t into the past . Neuron j has synaptic weight Aij wij on neuron i where Aij was set to 1 with probability a and 0 otherwise , hence the parameter a controls the sparsity of the connectivity . The connectivity strengths wij were drawn from a normal distribution with mean w and standard deviation vww , normalized by population size Nz , and constrained to be positive , so that wij = 2Nz ( w±vww ) + . Each neuron received an excitatory input Ii ( t ) with additive noise nNi ( t ) ( here Ni ( t ) is a discrete white noise process with std ( N ) = 1/2 so that the added noise is smaller in magnitude than the noise amplitude n 95% of the time ) . In the simulations , unless otherwise specified , we used the following default values for the parameters above . The population size was Nz = 1000 . The probability of connectivity was a = 0 . 4 ( close to the value 0 . 5 in Yamazaki and Tanaka [15] ) , and synaptic variability was set to zero ( vw = 0 ) . The default input noise level was n = 0 . This model had a single time constant which Yamazaki and Tanaka [15] took to be equal to the membrane time constant of Golgi cells in their simulations of granular layer dynamics . However it is not clear that this is the relevant time constant for a firing-rate model since the dynamics of the sub-threshold domain cannot be easily carried over into the supra-threshold ( spiking ) domain and are often counter intuitive . While a still prevailing misconception is that long membrane time-constants are equal to a slow spike response , the exact opposite is the case: integrate-and-fire with an infinite time constant ( perfect integrators ) have the fastest response time to a current step and can respond almost instantaneously [33] . Since temporal dynamics of neurons in a network are primarily determined by the time course of the synaptic currents [33–36] we have ignored membrane time constants in this and following models and instead related τw to the synaptic time constant of recurrent inhibition in the network . We further want to note that the values for the synaptic time constants were not directly adjusted to replicate results for individual electrophysiological studies but rather kept at general values to study the effect on network output of interaction between different magnitudes of time constants . This issue is considered further in the Discussion . To allow for more realistic modeling of the dynamics of the granular layer we extended the one-population network of granule cells above to include inhibition via a population of interneurons corresponding to Golgi cells ( see Fig 1B ) . In this model the firing-rates zi ( t ) of granule cells and qi ( t ) of Golgi cells were given by zi ( t ) =[Ii ( t ) −∑jNqAijwij∑s=1texp ( −t−sτw ) qj ( s−1 ) +L ( t ) ]+ ( 2 ) qi ( t ) =[g⋅Ii ( t ) +∑jNzBij ( uij∑s=1texp ( −t−sτu ) zj ( s−1 ) −mij∑s=1texp ( −t−sτm ) zj ( s−1 ) ) +L ( t ) ]+ ( 3 ) The default sizes for the two populations were Nz = 1000 and Nq = 100 . As before , the excitatory afferent input into a granule cell i was given by Ii ( t ) , however the two-population model also had direct afferent excitation gIi ( t ) of Golgi cells . The factor g setting the level of excitation was set to 0 in the initial simulations , resulting in no afferent excitation for Golgi cells . The output-feedback L ( t ) was 0 until later simulations ( see below ) . The connectivity between the two populations was given by the random binary connection matrices W and U , however in this model the connectivity was not defined by a probability but by the convergence ratios cw = 4 between Golgi and granule cells and cu = 100 vice versa . Thus exactly 4 randomly selected Golgi cells inhibited each granule cell and 100 randomly chosen granule cells were connected to each Golgi cell . The weight of GABAergic inhibition between Golgi and granule cells was drawn from a normal distribution and normalized with wij = 2cw ( w±vww ) + ( default vw = 0 ) and the time constant of inhibition was given by τw . Besides the glutamatergic excitatory connections between granule and Golgi cells with weight uij = 2cu ( u±vuu ) + ( default vu = 0 ) and time constant τu the model was extended to emulate the inhibitory effect of mGluR2 activated GIRK channels [31] with mij = 2cb ( m±vmm ) + ( default m = 0 , vm = 0 ) and time constant τm = 50ms . Note that mGluR2 inhibition was not used until later simulations with m = 0 . 003 . Additional simulations were conducted with only half of the Golgi cells receiving mGluR2 inhibition i . e . Pr ( m = 0 ) = 0 . 5 . In all simulations u was set to 0 . 1 and normalized by the excitatory time constant resulting in u = 0 . 1/τu . All network simulations were written in C and were integrated into Python by transforming them into dynamically linked extensions with the package distutils . The stepsize in all simulations was dt = 1ms . All results were analyzed using Python . All models , methods and simulation results are available from the github repository https://github . com/croessert/ClosedLoopRoessertEtAl . A snapshot of the model code can also be found on ModelDB: https://senselab . med . yale . edu/modeldb/ShowModel . asp ? model=168950 . Computational resources for the simulations were partially provided by the ICEBERG cluster ( University of Sheffield; access granted by the INSIGNEO Institute for in silico Medicine ) . The modulated input to each cell was given by the excitatory input Ii ( t ) = [I0i + f ∙ 0 . 1 ∙ I0i ∙ x ( t ) ]+ . Unless noted otherwise the input I0i was chosen from a normal distribution with mean 1 and default standard deviation vI = 0 . 1 . To test increased input variability , standard deviation was increased to vI = 2 in a later experiment . The factor f , randomly picked as either 1 or -1 defined whether the input was inverted or not . This type of input coding , here termed “push-pull” coding can be routinely found for example in the vestibulo-cerebellum where half of the cells are ipsilateral preferring ( f = 1 , type I ) or contralateral preferring ( f = −1 , type II ) [37] . In order to test the ability of the network to construct a linear filter with a given impulse response it is not sufficient to use impulse inputs alone , since this does not test linearity ( for example the response to two successive impulse inputs may not be the sum of the individual responses ) . For this reason we also used random process inputs that mimic behavioral inputs . The input signal x ( t ) consisted of 3 parts ( see Fig 1C ) . The first part was a training sequence of a 5 second band-passed white noise signal ( low-passed with a maximum frequency of 20 Hz ) [38] chosen to mimic head velocity in the behaviorally relevant frequency range of 0–20 Hz [39] . Additionally a 5 second silent signal ( x ( t ) = 0 ) was added to the training sequence to train a stable response . Training with a segment of null data finds weights which not only give the appropriate impulse response but also produce zero output for zero input data , so that they reject spontaneous modulatory activity in the network . Consecutively the previous signals were repeated with a different realization of the noise signal to test the quality of the filter construction . The third part was an impulse test signal where x ( t ) = 0 apart from a brief pulse of 50 ms where x ( t ) = 1 . The colored noise signal was normalized to std ( x ) = 1/2 which ensured that the amplitude 0 . 1 ∙ I0i included the input 95% of the time . To assess the ability of the network to implement linear filters that depend on the past history of the inputs , the output signals zi ( t ) of all granule cells during the training sequence were used to construct exponential ( leaky integrator ) filters y ( t ) = F * x ( t ) of increasing time constants as linear sums y ( t ) = ∑βizi ( t ) of granule cell outputs . This can be regarded as the output of an artificial Purkinje cell that acts as a linear neuron . In matrix terms ( writing time series in columns ) this expression can be written y −Zβ where the undetermined coefficients β are usually fitted by the method of least squares to minimize root sum square fitting error ∥y−Zβ∥2=∑t ( y ( t ) −Σβizi ( t ) ) 2 ( 4 ) However over-fitting of the data , due to the large output population , can make this method misleading and give excessively high estimates of reconstruction accuracy . To avoid this problem we used the method of LASSO regression taken from the reservoir computing literature . This is a robust fitting procedure that includes a regularization term to keep the reconstruction weights small [40 , 41] . Here , the estimates are defined by β^ = argminβy-Zβ2+αβ1 which is the least-squares minimization above with the additional constraint that the L1-norm ||β||1 = ∑βi of the parameter vector is also kept small . In practice we find that up to about 90% of weights are effectively zero using this method . In contrast to ridge regression that employs a L2 -norm penalty and is commonly used to prevent over-fitting in reservoir computing [27] LASSO regression produces very sparse weight distributions . This corresponds well to the actual learning properties of the Purkinje cell , approximated as a linear neuron , in which optimality properties of the learning rule with respect to input noise force the majority of synapses to silence [42–45] . We fitted three responses yj ( t ) = x ( t ) * Fj ( t ) with j = 1 , 2 , 3 and with Fj ( t ) = exp ( −t/τj ) being one of three exponential filters τ1 = 10ms , τ2 = 100ms or τ3 = 500ms ( see Fig 1D ) . The regularization coefficient was set to α = 1e−4 which gave best maximum mean goodness-of-fit results for the one-population model with τw = 50ms ( not shown ) . LASSO regression was implemented using the function sklearn . linear_model . Lasso ( ) from the python package scikit-learn [46] . In general the estimated weights βi take both positive and negative values , which is not compatible with the interpretation of equation ( 4 ) above as parallel fiber synthesis by Purkinje cells . The use of negative weights is usually justified by assuming a relay through inhibitory molecular interneurons [42 , 44] . To test whether learning at parallel fibers alone is sufficient for the construction of filters from reservoir signals we additionally employed LASSO regression with only positive coefficients ( positive-LASSO ) as a comparison . As a measure of the quality of filter construction , the weights estimated from the training sequence were used to construct the filtered responses in the test sequence and the goodness-of-fit between expected output and constructed output was computed for each filter using the squared Pearson correlation coefficient ( R2 ) [47] ( see Fig 1D ) . For the final goodness-of-fit measure the mean of 10 networks with identical properties but with different random connections was computed . A convenient way to analyze the stability or chaoticity of a dynamic system is the Lyapunov exponent λ . It is a measure for the exponential deviation of a system resulting from a small disturbance [25] and a value larger than 0 indicates a chaotic system . The Lyapunov exponent was measured empirically , similar to Legenstein and Maass [22] by calculating the average Euclidian distance dt = ∑i = 1Nzzit-zi' ( t ) 2 between all granule cell rates zi ( t ) from a simulation where x ( t ) = 0 and the rates zi' ( t ) from a second simulation where the input was disturbed by a small amount at one time step , i . e . x ( 0 ) = 10−14 . This state separation simulation was repeated for 10 randomly connected networks but otherwise identical parameters and λ was estimated from the mean average Euclidian distance d-t with λ = log2mean ( d-t = 2 . 01s:2 . 11s ) /mean ( d-t = 0 . 01s:0 . 11s ) /2s . To estimate the transition between stability and chaos we were mainly interested in the sign of the Lyapunov exponent . Although taking the mean of a 100 ms period and using a relatively large Δt of 2s [24] decreases the accuracy of the Lyapunov estimation , it was used here to prevent errors in the estimation of the sign . The edge-of-chaos was defined as the point where λ crosses 0 for the first time when traversing in the direction of strong inhibition w to weak and therefore from high λ to low . To model putative output-feedback to the reservoir via the nucleocortical pathway the signal L ( t ) = f ∙ oi ∙ −∑βizi ( t ) was injected into 20% of all granule and Golgi cells in the last simulations . The factor f was randomly picked as either 1 or -1 to model 50% excitation and inhibition and the weight was drawn from a normal distribution with oi = [1e−4±1e−5]+ . In these simulations only the case for output-feedback of the slowest filter signal is shown . Thus βi are the weights needed to construct the filter with τ3 = 500ms . As noted in the reservoir computing literature [27 , 48 , 49] output-feedback in general is a very difficult task since it leads to instability . Therefore the weights βi were not learned online but a method called teacher forcing with noise was applied [27] . The weights βi were learned in a prior step by using the teacher signal L′ ( t ) = f ∙ oi ∙ −y3 ( t ) ∙ N ( t ) instead of the feedback signal L ( t ) to uncouple the instable learning . Here y3 ( t ) is the target response for the slowest filter ( Fig 1D ) and N ( t ) is a discrete white noise process that helps to increase the dynamical stability [27] . The quality of filter construction and the Lyapunov exponent were estimated in a second simulation using the previously learned weights βi for filter construction and the feedback signal L ( t ) .
In the first part of this study we focused on the one-population rate-neuron model previously published by Yamazaki and Tanaka [15] . While in this previous study the model was used to represent the passage of time , i . e . an internal clock , we now show that it is also possible to use its output to construct exponential filters with various time-constants . To illustrate the dependence of network stability regime on the amount of feedback we begin by presenting sample impulse responses ( Fig 2 , second row ) for a network ( Fig 2 , top ) with intermediate time constant τw = 50ms and with three values of the recurrent inhibition: w = 0 . 01 , lying in the highly stable region , w = 1 . 4 , close to the edge-of-chaos , and w = 3 , in the chaotic region . When the weight w was low , ( Fig 2A , w = 0 . 01 ) the network was highly stable to perturbations and showed no long lasting responses . Close to the edge-of-chaos ( Fig 2B , w = 1 . 4 ) complex , long lasting responses were present . For larger weights ( Fig 2C , w = 3 ) the network entered a chaotic state in which cells showed random activity without further input modulation . We further illustrate this dependence in the last two rows of Fig 2 which shows filter constructions ( see Methods ) for three target exponential filters with time constants τi of 10 ms ( Fig 2D1 and 2E1 ) , 100 ms ( Fig 2D2 and 2E2 ) and 500 ms ( Fig 2D3 and 2E3 ) ( chosen to cover the range of performance required for e . g . VOR plant compensation [9]; filter construction of intermediate time constants are not shown , but are generally of similar quality ) . It is clear that in the highly stable regime only fast and intermediate time constant responses could be reconstructed ( dotted light lines ) . Near the edge-of-chaos acceptable reconstructions were possible at all three time constants ( dark lines ) , and in the chaotic regime reconstruction was always inaccurate and showed oscillatory artifacts ( solid light lines ) . While Yamazaki and Tanaka [15] argued that this chaotic network state is the preferred network state to implement an internal clock ( compare Fig 2C with Fig 1 from [15] ) these results show that it is disadvantageous when a filter of a continuous signal has to be implemented ( see Discussion ) . We have noted above ( Methods ) that accurate reconstruction of the impulse response of a linear filter does not imply that the output for other inputs is correct; this requires linearity of the reconstructed filter . Linearity of the reconstructed filters is investigated in the second row of Fig 2 by comparing their effects on a band-passed noise signal with that of the exact filter ( plotted in black ) , again for time constants τj of 10 ms ( Fig 2D1 ) , 100 ms ( Fig 2D2 ) and 500 ms ( Fig 2D3 ) , It is clear that the reconstruction in the stable regime or the chaotic regime ( light lines ) were much less accurate than in the edge-of-chaos-regime ( dark lines ) . Note these plots show the response to a test input ( rather than the training input , see Methods ) . The regularized fitting method used ( LASSO regression , see Methods ) tends to use weights that are as small as possible . This property is clear in our example , to construct filters from granule cell signals at the edge-of-chaos only a small subset of granule cell responses were necessary . For the filters with 10 , 100 and 500 ms ( Fig 2D and 2E; w = 1 . 4 ) , the percentage of weights being equal to zero was 90% , 86% and 75% , respectively , and the mean of non-zero weights was 5 . 5 and 11 . 7 and 52 . 6 , respectively . The high proportion of silent synapses is consistent with experimental findings ( see Discussion ) As discussed previously , the value of w corresponding to the edge-of-chaos can be identified using the Lyapunov exponent ( see Methods ) . We illustrate this property by investigating the dependence of filter reconstruction accuracy on the Lyapunov exponent ( Fig 3 ) . Results are shown for three networks with different time constants for the recurrent inhibition: τw = 10ms ( column 1 ) , τw = 50ms ( column 2 ) Fig 2B and τw = 100ms ( column 3 ) approximately corresponding to the ranges of membrane and synaptic time constants present in the granular layer . The top row of Fig 3 shows the Lyapunov exponent of each network plotted against the amount of recurrent inhibition w . In each case there was a point at which the exponent crossed the zero axis , corresponding to the edge-of-chaos value for that network time constant . It can be seen that the amount of recurrent inhibition needed decreased as the time constant increased . The bottom row shows the effect of w on reconstruction accuracy ( measured by R2 goodness-of-fit ) for exponential filters with the three time constants considered previously: τj = 10ms ( blue lines ) , 100ms ( green lines ) and 500ms ( red lines ) for each network . Performance strongly depended on the weight of the recurrent inhibition . The goodness-of-fit was best , especially for filters with time constants longer than the internal inhibitory time-constant , for networks close to the edge-of-chaos , just before the transition from stable to chaotic behavior . Other observations were that while , as expected , the goodness-of-fit for slow filters , e . g . 500ms , increased with the ( inhibitory ) time constant , the performance for fast filters decreased slightly ( Fig 3C2 ) . Furthermore the performance was best if the inhibitory time constant was equal to the time-constant of the filter ( Fig 3A2 , τ1 = 10ms blue line; Fig 3C2 , τ2 = 100ms green line ) . Fig 4 investigates the robustness of the properties described above to moderate levels of additive noise and to variability in input signal levels and synaptic weights . While white noise with amplitude of a = 0 . 01 ( noise amplitude equal to 10% of the input modulation amplitude ) lead to a reduction in goodness-of-fit ( Fig 4A1 ) the principal mechanism of filter construction was not disrupted and the edge-of-chaos was only shifted to larger weights w ( Fig 4A2 ) . Increasing the between-neuron variability of the mean input excitation to a high value of e . g . vI = 2 ( i . e . 95% of constant input increased to 0–5 from 0 . 8–1 . 2 for default value vI = 0 . 1 ) ( Fig 4B1 solid dark lines ) had almost no benefit for the goodness-of-fit while shifting the edge-of-chaos to larger weight values . In contrast , imposing larger variability in the inhibitory weight with vw = 2 ( i . e . 95% of weights between 0 and w+4w ) shifted the edge-of-chaos in the opposite direction—towards lower weights ( Fig 4B2 , dotted lines ) , and the quality of filter construction was increased ( Fig 4B1 , dotted lines ) . This phenomena may be caused by a proportion of input signals or weights being driven to zero due to the positive cut-off which effectively leads to some cells receiving no input and a reduction of connectivity , respectively . To test the effect of reduced connectivity we examined the direct effect of increased sparseness on reservoir performance ( Fig 4C ) . Two methods were used to increase sparseness: the first was to decrease the convergence of inhibition to 40 cells ( Fig 4C1 , dotted lines ) by decreasing the network connectivity from a = 0 . 4 to a = 0 . 04 while keeping the network size at Nz = 1k . The second way was to increase the network size to Nz = 10k while keeping convergence constant at 400 cells ( Fig 4C2 , solid dark lines ) with a = 0 . 04 . While both cases resulted in an improvement of filter quality , a smaller convergence slightly outperformed an increased network size suggesting that a sampling from less cells is more beneficial since it leads to a higher diversity and variability . An important requirement for filter construction turned out to be push-pull coding , found for example in the vestibulo-cerebellum , where half of the input signals are inverted ( see Discussion ) . When the input did not include inverted signals the responses from individual granule cells showed almost no variety in damped oscillations in response to pulse input ( Fig 5A ) . This consequently lead to an impairment of filter construction performance especially for larger filter time-constants and a shift of the edge-of-chaos to lower weights w ( Fig 5B1 , dark lines ) when compared to the control case ( light lines ) . Although filter construction performance was only slightly reduced when using regression with positive coefficients only ( see Methods ) ( Fig 5C , light lines ) when push-pull input was present , without push-pull input filter construction quality was heavily reduced ( Fig 5C , dark lines ) . While the previous model was able to show the principles of filter construction from a simplified model of the granular layer with recurrent inhibition , it did not take into account the fact that inhibition in the granular layer is relayed via a second population of cells , i . e . Golgi cells . To investigate the effects of this arrangement we extended the one-population model to a two-population model . The connectivity of the extended model was based on plausible convergence ratios of cw = 4 between Golgi and granule cells and cu = 100 vice versa [50] . Additional parameters were excitatory time constant τu and the weight of excitation u ( Fig 6 , top ) . Increasing τu while keeping the inhibitory time constant at τw = 50ms showed that the performance of the two-population model was very similar to the one-population model if the excitation is fast ( Fig 6A1 ) . However , increasing the excitatory time constant improved the quality of the constructed slow filter ( τ = 500ms ) at the expense of the faster filters ( τ = 10ms and τ = 100ms ) ( Fig 6B1 and 6C1 ) . Additionally , this leads to a lowered gradient of the Lyapunov exponent ( Fig 6B2 and 6C2 ) . We therefore focus in the following on the best-case scenario of τu = 1ms and τw = 50ms . As in the one-population model before ( Fig 2B ) , responses of single granule and Golgi cells in networks close to the edge-of-chaos featured complex but stable , long lasting damped oscillations ( not shown ) . In Fig 6D we show that increased sparseness , achieved by reducing the convergence onto Golgi cells from cu = 100 to cu = 10 ( light lines ) increased the quality of constructed filters as in the previous model . However , this time , increasing the granular cell population size to Nz = 10k ( dotted lines ) has almost no beneficial effect , which can be attributed to the bottleneck effect of the small Golgi cell population of Nq = 100 ( compare to Fig 4C , dotted lines ) . Here , many granule cell responses converge onto a lower dimension of signals , which decreases the fidelity . On the contrary increasing the granule cell as well as the Golgi cell population size to Nq = Nz = 10k increased the filter construction performance similar to before ( results not shown ) . Equally , further reducing the Golgi cell population to Nq = 10 for the default case ( Nz = 1k , cu = 100 ) enforced the bottleneck effect and strongly decreased the construction quality of slow filters ( results not shown ) . The effects of synaptic-weight variability in the two-population model differed for excitatory and inhibitory weights ( Fig 6E ) . Adding a large variability to excitatory weights vu = 4 increased the goodness-of-fit ( light lines ) just as seen in the model before . However , adding variability to inhibitory weights vw = 4 decreased the quality of constructed filters ( dotted lines ) . This can be explained by the low number of connections between Golgi and granule cells of cw = 4 . Using equal convergence of cw = cu = 20 gave equal effects in increased filter quality with increased variability for excitatory and inhibitory weights ( results not shown ) .
The one-population model demonstrated the properties of a homogeneous reservoir dominated by recurrent inhibition . However in the cerebellum recurrent inhibition is implemented as a relay via a second population of Golgi cells . To test the effect of this layered recurrence we considered a two-population model and found that reservoir behavior was generally preserved . We did observe three differences between the two types of networks: 1 . A slow synaptic time constant for Golgi cell excitation decreases the quality of fast filter construction ( Fig 6C ) . 2 . The lower number of Golgi cells can create a bottleneck in filter fidelity when few Golgi cells sample input from a large number of granule cells ( Fig 4C ) . 3 . Direct afferent Golgi cell excitation can decrease chaotic behavior and is beneficial for filter construction by prolonging the edge-of-chaos regime . These issues are further discussed below . Early models of the cerebellar microcircuit focused on its ability to adaptively process static patterns . Subsequently Fujita [3] described a cerebellar model that could adaptively process time-varying analogue signals , based on the adaptive linear filter used in signal processing and control engineering [61] . In Fujita’s model the granular layer recurrent neuronal network ( RNN ) generated a set of parallel-fiber outputs for a given mossy-fiber input which by linear summation at the Purkinje cell allowed the microcircuit to adaptively transform time-varying inputs into the specific time-varying outputs required for any given signal-processing task . Fujita’s adaptive-filter model required the granular layer to generate computationally adequate sets of parallel-fiber outputs in a biologically realistic way [4] . Subsequent extensions of his work initially focused on computational adequacy , suggesting suitable basis functions for transforming mossy-fiber inputs that included tapped delay lines , spectral timing , and sinusoids ( references in [8–10] ) . However , it was not usually made clear how these functions could be generated by a plausible neuronal network , raising concerns that the approach was unrealistic [11] and excessively ‘top-down’ [14] . The preferred alternative was ‘bottom-up’ modeling in which temporal-processing properties emerged from biologically detailed models of the granular-layer RNN rather than being imposed by a priori theoretical considerations [13 , 14 , 62] . These models successfully learned an eyeblink-conditioning task by generating a suitably delayed output timed to coincide with the arrival of the unconditioned stimulus . Yamazaki and Tanaka [15] investigated the generic time-coding properties of simplified RNNs , with ( usually ) 1000 rate-coding neurons receiving excitatory afferent inputs and recurrent inhibitory inputs from other neurons ( cf . first model here ) . These RNNs could generate a sequence of activity patterns that never recurred , a sequence that could be triggered reliably by a strong transient input signal . Such networks could therefore be used to encode the passage of time for any task that required it . Related results were found for RNNs of integrate-and-fire model neurons [16] , and for a more realistic two-layer RNN embedded in a spiking model of the entire cerebellar microcircuit [63] . The main theoretical conclusion is that with appropriate network parameters the granular-layer RNN can in principle generate the outputs needed for an adaptive filter . It also indicates what those outputs might look like . We now consider experimental evidence bearing on these two points . For convenience , evidence from detailed models of the granular-layer that are concerned primarily with its electrophysiology ( as opposed to the functional models discussed above ) is also included in this experimental section . In all networks and configurations considered the single cell activity of granule and Golgi cells at the edge-of-chaos showed complex but stable , long lasting damped oscillations that were the effect of inhibition and dis-inhibition ( only shown for the one-population model , Fig 2B ) . The similarity between Golgi and granule cell responses is easily graspable when one considers that for the default two-population network Golgi cells merely relay signals from granule cells und thus have to show similar activity . On the other hand for Golgi cells with added mGluR2 dynamics they themselves become prone to inhibition and dis-inhibition . This study thus suggests that one indicator for the presence of reservoir computation in a certain area of the cerebellum would be the similarity in heterogeneity and timing of the responses for granule and Golgi cells . This comparison however must not be made based on the spike activity but on the modulated signals . One explanation for the often-reported bursting responses in granule cells compared to Golgi cells could be the lower baseline/background activity of the former cells [77] . While the signal is carried and hidden by the higher spike rate in Golgi cells , granule cells would ultimately only spike during phases of strong dis-inhibition , which would effectively resemble bursting behavior . This would be even further increased if the operation point of the network is not close to the edge-of-chaos but in the chaotic regime . A further evaluation of these properties will however require the inclusion of spiking neurons in future studies . While the present study only focuses on the interaction between granule and Golgi cells the inclusion of other identified neurons might improve filter construction properties . Glycinergic Lugaro cells which have been found to increase the long-lasting depression of Golgi cells [32] and various other non-traditional interneurons like globular [84] or perivascular neurons [85] might further improve the reservoir performance by contributing to the inhibitory circuit . Furthermore , in some areas of cerebellar cortex , particularly in the vestibulo-cerebellum ( e . g . [86] ) , a substantial proportion of mossy-fiber input is processed and relayed by unipolar brush cells ( UBC ) which are thought to prolong and diversify the input signals [87] . In addition a recently discovered timing mechanism intrinsic to Purkinje cells [88] would potentially further increase the heterogeneity of the granular layer reservoir signals . Although the edge-of-chaos criterion is not a universal predictor of maximal computational performance [22] we find that it applies for most of the configurations considered here . With this requirement however the question arises how the granular layer network can be adjusted to operate in this computationally powerful regime . While the easiest way to achieve this is to change properties inside the loop like weights at Golgi cell—granule cell synapses or the convergence ratio of Golgi cell excitation we show that also external mechanisms like noise , input variability and especially mossy fiber—Golgi cell exaction can shift the network state . The question however remains how and if the cerebellar granule layer network can be automatically tuned to operate close to the edge-of-chaos . Possible mechanism that could help achieve this are synaptic long or short-term plasticity . | The cerebellum plays an important role in the learning of precise movements , and in humans holds 80% of all the neurons in the brain , due to numerous small cells called “granule cells” embedded in the granular layer . It is widely thought that the granular layer receives , transforms and delays input signals coming from many different senses like touch , vision and balance , and that these transformed signals then serve as a basis to generate responses that help to control the muscles of the body . But how the granular layer carries out this important transformation is still obscure . While current models can explain how the granular layer network could produce specific outputs for particular reflexes , there is at present no general understanding of how it could generate outputs that were computationally adequate for general movement control . With the help of a simulated granular layer network we show here that a random recurrent network can in principle generate the necessary signal transformation as long as it operates in a state close to chaotic behavior , also termed the “edge-of-chaos” . | [
"Abstract",
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] | [] | 2015 | At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters |
New approaches to preventing chikungunya virus ( CHIKV ) are needed because current methods are limited to controlling mosquito populations , and they have not prevented the invasion of this virus into new locales , nor have they been sufficient to control the virus upon arrival . A promising candidate for arbovirus control and prevention relies on the introduction of the intracellular bacterium Wolbachia into Aedes aegypti mosquitoes . This primarily has been proposed as a tool to control dengue virus ( DENV ) transmission; however , evidence suggests Wolbachia infections confer protection for Ae . aegypti against CHIKV . Although this approach holds much promise for limiting virus transmission , at present our understanding of the ability of CHIKV to infect , disseminate , and be transmitted by wMel-infected Ae . aegypti currently being used at Wolbachia release sites is limited . Using Ae . aegypti infected with the wMel strain of Wolbachia that are being released in Medellin , Colombia , we report that these mosquitoes have reduced vector competence for CHIKV , even with extremely high viral titers in the bloodmeal . In addition , we examined the dynamics of CHIKV infection over the course of four to seven days post feeding . Wolbachia-infected mosquitoes remained non-infective over the duration of seven days , i . e . , no infectious virus was detected in the saliva when exposed to bloodmeals of moderate viremia , but CHIKV-exposed , wild type mosquitoes did have viral loads in the saliva consistent with what has been reported elsewhere . Finally , the presence of wMel infection had no impact on the lifespan of mosquitoes as compared to wild type mosquitoes following CHIKV infection . These results could have an impact on vector control strategies in areas where Ae . aegypti are transmitting both DENV and CHIKV; i . e . , they argue for further exploration , both in the laboratory and the field , on the feasibility of expanding this technology beyond DENV .
Chikungunya virus ( CHIKV; Togaviridae , Alphavirus ) has recently re-emerged out of Africa and caused explosive outbreaks of arthritic disease in Southeast Asia , India , Europe and currently the Americas [1–4] . The current outbreak in the Americas is cause for great concern because CHIKV is spreading nearly uncontrolled with at least 44 countries experiencing autochthonous spread [5] . Infection with CHIKV results in a severe febrile illness , called chikungunya fever . Clinically , it resembles dengue fever and several other arboviral diseases [6] , but it is more associated with joint pain , which in some patients can progress to chronic arthralgia that lasts for months to years [7] . CHIKV disease can be highly debilitating and has a pronounced economic impact on both the affected individual and the countries which experience the outbreaks , resulting in great losses in productivity [8–10] . CHIKV is transmitted to humans by the mosquitoes Aedes aegypti and Aedes albopictus . The distribution of these mosquitoes explains the recent global spread of the virus and invasion of the Americas [4 , 5 , 11] . Both mosquito species have demonstrated the capacity to sustain CHIKV transmission cycles and both have been associated with CHIKV outbreaks [1]; however , the etiologic strain of CHIKV , a member of the old Asian lineage [12] , causing the current outbreak does not efficiently infect Ae . albopictus , suggesting that most CHIKV transmission in the Americas will occur via Ae . aegypti [5] . Despite the continued spread of the virus , there remains no effective antiviral therapy or licensed vaccines . Therefore , new approaches to preventing CHIKV are needed because the endemic range of this virus is expanding and because current methods are limited to controlling mosquito populations . To date , mosquito control has not prevented invasion of this virus into new locales or controlled the virus when it arrives [13] . A promising candidate for arbovirus control and prevention relies on the introduction of the intracellular bacterium Wolbachia into Ae . aegypti mosquitoes . Wolbachia biocontrol has advanced from laboratory experiments demonstrating that certain strains of Wolbachia shorten the lifespan of the mosquito [14] while simultaneously reducing virus replication [15] to small-scale field trials demonstrating that Wolbachia are capable of spreading through wild Ae . aegypti populations [16–18] . This primarily has been proposed as a tool to control dengue virus ( DENV ) transmission [19–21]; however , Wolbachia infections confer protection for their insect hosts against a range of pathogens including for Ae . aegypti against CHIKV [22 , 23] and for Ae . albopictus against CHIKV [24] . As a result , this technology currently is being evaluated in five countries around the globe ( Australia , Brazil , Colombia , Indonesia , and Vietnam ) for its potential to control DENV transmission . The approach is well-established that Wolbachia infection confers protection against DENV transmission by Ae . aegypti . In contrast , the ability of CHIKV to infect , disseminate , and be transmitted by wMel-infected Ae . aegypti is far less established [23] . For example , van den Hurk et al . ( 2012 ) tested the wMel strain of Wolbachia , but they only assayed Ae . aegypti vector competence for CHIKV at a single time point ( 12 days post feeding ) with a single bloodmeal titer , and only could detect virus in the saliva via qRT-PCR [23] . And Moreira et al . ( 2009 ) tested the wMelPop strain of Wolbachia against CHIKV [22] , which no longer is being utilized by the Eliminate Dengue Program ( EDP ) because mosquitoes infected with this strain of Wolbachia displayed reduced fitness in small-scale field releases [18] . Therefore , we assessed vector competence for CHIKV in wMel-infected and wMel-free Ae . aegypti from Medellin , Colombia , because at present our understanding of the ability of CHIKV to infect , disseminate , and be transmitted by wMel-infected Ae . aegypti currently being used at Wolbachia release sites is limited . This becomes particularly important if one considers that vector competence of Ae . aegypti for certain viruses likely is governed to a large extent by vector genotype x virus genotype ( G x G ) interactions in genetically diverse , natural Ae . aegypti populations [25] . This challenges the general relevance of conclusions from laboratory systems that consist of a single combination of mosquito and virus genotypes [25 , 26] . These Wolbachia-infected mosquitoes were created as part of a collaboration with the EDP in Colombia and in the spring of last year ( 2015 ) , medium-scale deployments of these mosquitoes began in the DENV metropolitan area of Medellin [see www . eliminatedengue . com/colombia] . Our results suggest that Wolbachia effectively blocks the transmission potential of Colombian Ae . aegypti for CHIKV and wMel infection has no impact on the lifespan of mosquitoes as compared to wild type mosquitoes following CHIKV infection . To our knowledge , this is the first description of the effects of naturally acquired CHIKV infection ( i . e . , exposure to virus was accomplished by feeding on a viremic host ) on Wolbachia-infected mosquito vector competence . All previous studies ( including those mentioned for CHIKV , as well as the numerous studies described with DENV ) have relied on animal blood spiked with cultured virus or have relied on viremic human blood from a membrane feeder . These data argue for the expansion of this technology to CHIKV in South America and are useful and germane in the broader context of CHIKV-mosquito interactions . Additionally , knowledge about factors shaping vectorial capacity ( e . g . , probability of daily survival ) will be informative for a more accurate appraisal of CHIKV transmission and the likelihood of establishing Wolbachia infection in natural mosquito populations .
This study was carried out in strict accordance with recommendations set forth in the National Institutes of Health Guide for the Care and Use of Laboratory Animals . All animals and animal facilities were under the control of the School of Veterinary Medicine with oversight from the University of Wisconsin Research Animal Resource Center . The protocol was approved by the University of Wisconsin Animal Care and Use Committee ( Approval #V01380 ) . African Green Monkey kidney cells ( Vero; ATCC #CCL-81 ) were grown in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS; Hyclone , Logan , UT ) , 2 mM L-glutamine , 1 . 5 g/l sodium bicarbonate , 100 U/ml of penicillin , 100 μg/ml of streptomycin , and incubated at 37°C in 5% CO2 . Aedes albopictus mosquito cells , ( C6/36; ATCC #CRL-1660 ) were maintained in MEM supplemented with 10% FBS , 2 mM L-glutamine , 1 . 5 g/l sodium bicarbonate , 0 . 1 mM non-essential amino acids , 100 U/ml of penicillin , 100μg/ml of streptomycin , and incubated at 28°C in 5% CO2 . CHIKV isolate 99659 ( GenBank:KJ451624 ) , originally isolated from a 33 year old male in the British Virgin Islands with a single round of amplification on Vero cells , was obtained from Brandy Russell ( Centers for Disease Control and Prevention , Ft . Collins , CO , USA ) . Virus stocks were prepared by inoculation onto a confluent monolayer of C6/36 mosquito cells . This CHIKV strain is related phylogenetically to strains recently identified in Asia with most of them sharing a specific four amino-acid deletion in the nsP3 gene [3] , and is representative of CHIKV strains circulating in Colombia [27] . Ae . aegypti used in this study were maintained at the University of Wisconsin-Madison as previously described [26] . Two populations of mosquitoes were used in this study . Wild type ( WT ) mosquitoes ( not infected with Wolbachia ) were established from eggs collected from ovitraps placed around the municipality of Bello , a northwest suburb of Medellin , Colombia . The Wolbachia-infected ( wMelCOL; infected with the wMel strain of Wolbachia pipientis ) mosquito line was created by crossing uninfected field strains with a wMel-infected laboratory strain of Ae . aegypti essentially as described in [27] . The wMel-infected laboratory population of Ae . aegypti originated from eggs provided by Scott O’Neill ( Monash University , Victoria Australia ) . Wolbachia infection status was routinely verified using PCR with primers specific to the IS5 repeat element [19] . Mosquitoes were exposed to CHIKV by feeding on isoflurane anesthetized CHIKV-infected Ifnar-/- mice . These mice have abrogated type I interferon signaling and as a result develop lethal infection , with muscle , joint , and skin serving as primary sites of replication [28 , 29]; as well , as developing high viremia . Ifnar-/- mice on the C57BL/6 background were obtained from Eva Harris ( University California-Berkeley , Berkeley , CA ) and were bred in the pathogen-free animal facilities of the University of Wisconsin-Madison School of Veterinary Medicine . Groups of three and six week-old mixed sex mice were used for mosquito exposures because viremia varied with age . Mice were infected in the left , hind foot pad with either 103 plaque forming units ( PFU ) of CHIKV in 50 μl of animal diluent ( AD: 1% heat-inactivated FBS in Dulbecco’s PBS ) for three week-old mice or 104 . 5 PFU of CHIKV in 50 μl of AD for six week-old mice . Uninfected mosquitoes ( both WT and wMelCOL ) were allowed to feed on mice two days post infection at which time sub-mandibular blood draws were performed and serum was collected to verify viremia . Three week-old mice yielded an infectious bloodmeal concentration of 9 . 51 log10 PFU/ml ± 0 . 09 ( mean ± standard deviation; n = 6 ) and six week old mice yielded an infectious bloodmeal concentration of 6 . 90 log10 PFU/ml ± 0 . 14 . These bloodmeal titers were consistent with human viremias observed in the field [30–32] . Infection , dissemination , and transmission rates were determined using long established procedures [33 , 34] . Briefly , three- to six-day-old female mosquitoes were sucrose starved for 14 to 16 hours prior to exposure to mice . Mosquitoes that fed to repletion were separated into cartons and maintained on 0 . 3 M sucrose in an environmental chamber at 26 . 5°C ± 1°C , 75% ± 5% relative humidity , and with a 12 hour photoperiod within the Department of Pathobiological Sciences BSL3 Insectary facility at the University of Wisconsin-Madison . All samples were screened by plaque assay on Vero cells . Dissemination was indicated by virus-positive legs . Transmission was defined as release of infectious virus with salivary secretions , i . e . , the potential to infect another host , and was indicated by virus positive-salivary secretions . All CHIKV screens and titrations for virus quantification were completed by plaque assay on Vero cell cultures . Duplicate wells were infected with 0 . 1 ml aliquots from serial 10-fold dilutions in growth media and virus was adsorbed for one hour . Following incubation , the inoculum was removed , and monolayers were overlaid with 3 ml containing a 1:1 mixture of 1 . 2% oxoid agar and 2X DMEM ( Gibco , Carlsbad , CA ) with 10% ( vol/vol ) FBS and 2% ( vol/vol ) penicillin/streptomycin . Cells were incubated at 37°C in 5% CO2 for two days for plaque development . Cell monolayers then were stained with 3 ml of overlay containing a 1:1 mixture of 1 . 2% oxoid agar and 2X DMEM with 2% ( vol/vol ) FBS , 2% ( vol/vol ) penicillin/streptomycin , and 0 . 33% neutral red ( Gibco ) . Cells were incubated overnight at 37°C and plaques were counted . Infection , dissemination , and transmission rates were analyzed using an Exact unconditional test [35] . Saliva CHIKV titers were analyzed using a Bootstrap t-test and survival data were analyzed using Kaplan-Meir analysis and log-rank statistics .
In Colombia , all four DENV serotypes actively circulate in many parts of the country and there has been a significant increase in the number of severe dengue cases since re-emergence [36] . The rise in cases coincided with an increase in Ae . aegypti populations that also have expanded into new geographic locales . Similar to the country as a whole , Medellin , the second largest city in the country , also had a significant increase in dengue cases , despite the presence of a national integrated vector control strategy . This provided the impetus for new approaches to preventing DENV transmission . In fact , deployment of Wolbachia-infected Ae . aegypti began in Medellin early last year ( 2015 ) to assess the efficacy of this technology in reducing DENV transmission in endemic populations . Not surprisingly , CHIKV reached Colombia in August 2014 [25] , and since its introduction , there have been over 300 , 000 cases of CHIKV detected . Again , current vector control measures were insufficient in preventing invasion of this virus into the country or controlling it after invasion . Although primarily designed as a biocontrol tool for DENV , evidence suggests that Wolbachia can limit infection in Ae . aegypti with CHIKV [23]; therefore , Wolbachia-infected Ae . aegypti could potentially be used to simultaneously control DENV and CHIKV . As a result , we evaluated whether Colombian mosquitoes infected with the wMel strain of Wolbachia reduced CHIKV transmission potential . Here , we verified that the phenotype of reduced vector competence existed in Wolbachia-infected laboratory colonies of Colombian Ae . aegypti for CHIKV . Adult , female , mosquitoes were exposed to infectious bloodmeals containing CHIKV and mosquitoes that ingested blood containing virus were assayed for infection , dissemination , and transmission potential at 7 and 14 days ( d ) post feeding ( PF ) . As expected , infection , dissemination , and transmission rates were high for WT exposed to blood containing CHIKV at a concentration of 9 . 51 log10 PFU/ml ( Table 1 ) . Although viral titer in the bloodmeal was high , CHIKV viremia in humans can vary drastically ( ranging from 101−109 PFU/ml ) , and therefore was consistent with observations in the field [30–32] . Furthermore , evidence suggested that infection and dissemination rates were dose-dependent and rates increase with the titer of the ingested bloodmeal ( see [37] for review ) . Our first goal then was to determine if there was a threshold in which a high viral infectious dose could overwhelm the system and negate the protection conferred by Wolbachia . Interestingly , there was a significant reduction ( Exact Unconditional Test ) in infection , dissemination , and transmission rates for wMelCOL exposed to blood containing CHIKV; i . e . , Wolbachia infection in Colombian Ae . aegypti completely blocked CHIKV transmission at 7d PF and significantly reduced infection and dissemination rates at 14d PF ( Table 1 ) . These data were consistent with other strains of wMel-infected Ae . aegypti when exposed to CHIKV [23] or DENV [21 , 38]; i . e . , Wolbachia infection does not completely ablate transmission of virus , but rather delays the extrinsic incubation period ( EIP ) of the virus and reduces the transmission potential of CHIKV-infected mosquitoes . Recently , Ye et al . ( 2015 ) demonstrated that Wolbachia-infected mosquitoes exhibited fewer infective days compared to WT mosquitoes , and their data suggested that Wolbachia-infected mosquitoes were infective at earlier timepoints [38]; however , they relied on qRT-PCR to detect and quantify virus , which does not differentiate infectious from non-infectious virus [39] . The plaque assays used here quantified infectious particles . Furthermore , it also has been demonstrated that this strain of CHIKV could be detected in the saliva of Ae . aegypti as early as 3d PF , albeit at very low levels [40] . To ascertain if wMelCOL had the potential to transmit CHIKV at earlier time points , we assessed the dynamics of infection in WT and wMelCOL over time following an infectious bloodmeal more in agreement with viremias detected in Colombian patients ( 6 . 90 log10 PFU/ml ) [25] versus a high viremic infectious bloodmeal ( >9 . 0 log10 PFU/ml ) . After a CHIKV-infected bloodmeal of moderate viremia , WT mosquitoes quickly became infective ( Fig 1A–1C ) and peaked at 53% infective ( 10/19 ) at 5d PF ( Fig 1C ) . In contrast , wMelCOL remained non-infective over the duration of seven days ( Fig 1C ) , but a large proportion ( 39%-70% ) of wMelCOL had established infections ( Fig 1A ) and a moderate number ( 11%-29% ) also disseminated virus ( Fig 1B ) . Likewise , after a CHIKV-infected bloodmeal of high viremia , WT mosquitoes quickly became infective ( Fig 2A–2C ) and maintained infectivity ( Fig 2C ) . In contrast , wMelCOL remained non-infective over the duration of seven days ( Fig 2C ) , with the exception of a single mosquito with CHIKV-positive saliva on day six . A large proportion ( up to 95% at 4d PF ) of wMelCOL had established infections ( Fig 2A ) and a moderate number ( 21–70% ) also disseminated virus ( Fig 2B ) . Infectious virus also was detected in the saliva of wMelCOL on day 14 PF ( Table 1 ) . WT mosquitoes exposed to a bloodmeal of high viremia had viral titers in the saliva consistent with WT exposed to a bloodmeal of moderate viremia ( Fig 3 ) . We then investigated whether CHIKV had a negative effect on mosquito survival , because probability of daily survival is an important parameter in estimating vectorial capacity . It is critically important to understand how virus infection impacts vector survival if accurate predictions of transmission dynamics are to be made , because low mosquito survival will reduce the likelihood of onward transmission of the infecting virus to a new host . There has been inconsistency among reports of the effects of arboviruses on mosquito survival , and to our knowledge no reports on the impact of CHIKV infection on mosquito survival . A recent meta-analysis involving various vector-virus combinations found that , overall , arboviruses do reduce the survival of their mosquito vectors [41] . And , others have suggested that the presence of wMel infection can lengthen the lifespan of mosquitoes as compared to WT following DENV infection , suggesting that DENV infection is costly to mosquitoes and that Wolbachia is conferring some protection to the host [38] . Here , the presence of wMel infection had no impact on the lifespan of mosquitoes as compared to WT following CHIKV infection ( p = 0 . 369 and p = 0 . 429; Fig 4A and 4B , respectively ) , nor was there any indication that CHIKV infection was overly costly to WT mosquitoes ( Fig 4B ) . Certainly , mosquitoes survived the relatively short EIP of CHIKV ( Figs 1 and 2 ) . It also is important to note that we explored the effects of naturally acquired CHIKV infection ( i . e . , exposure to virus was accomplished by feeding on a viremic host ) on mosquito survival; whereas , most previous studies have relied on animal blood spiked with cultured virus , which may or may not have influenced the magnitude of the observed effect . Furthermore , recent studies suggested that viral titer in the bloodmeal might impact mosquito survival; i . e . , high viral titers in the blood lead to increased mosquito mortality [42] . Here , unusually high mortality was not observed in mosquitoes exposed to blood containing CHIKV at a concentration of >9 . 0 log10 PFU/ml , i . e . , a very high viral titer in the bloodmeal ( Fig 4 ) . These data are in concordance with a recent study by Carrington et al . ( 2015 ) that demonstrated that DENV infection adds minimal cost to Ae . aegypti when mosquitoes were exposed to DENV by feeding on infected humans , and there was no relationship between survival and human plasma viremia levels [43] . Although a direct comparison cannot be made , our data suggest that the relationship between CHIKV and Ae . aegypti is also relatively benign; but , we cannot rule out that CHIKV and/or Wolbachia infection may impart additional costs not measured here , e . g . , reduced fecundity [44] . Finally , Wolbachia biocontrol depends on Wolbachia infections being maintained stably at high levels within natural mosquito populations as well as continuing to exhibit virus interference . Wolbachia may not stably persist if there are changes in maternal transmission , cytoplasmic incompatibility , and/or fitness effects to the mosquito as a result of Wolbachia infection . Wolbachia infection did not shorten the lifespan of infected mosquitoes ( Fig 4B ) , which bodes well for the success of this strategy , but work still is needed to assess the long-term stability of infection and changes in host fitness effects following invasion in Colombia . In sum , Wolbachia biocontrol has been proposed primarily as a tool to control DENV transmission [19] , but Wolbachia infections also confer protection for Ae . aegypti against CHIKV and to some extent yellow fever virus ( YFV ) [23] as well . And , as a result of the explosive outbreak of CHIKV and now Zika virus in the Western hemisphere [12 , 45–47] , all four of these viruses co-circulate in many parts of the tropics . The possibility exists that Wolbachia biocontrol could be used as a multivalent strategy for all of these Ae . aegypti-transmitted arboviruses . At the very least , these results warrant further exploration , both in the laboratory and the field , on the feasibility of expanding this technology beyond DENV and informing whether Wolbachia biocontrol can be used to supplement or replace existing vector control strategies . | New approaches to preventing chikungunya virus ( CHIKV ) infection are needed because the endemic range of this virus is expanding and because current methods are limited to controlling mosquito populations , and this approach has not effectively controlled this virus . A promising candidate for arbovirus control and prevention relies on the introduction of the intracellular bacterium Wolbachia into Aedes aegypti mosquitoes . Wolbachia biocontrol has advanced from laboratory experiments demonstrating that Wolbachia reduces virus replication to small-scale field trials demonstrating that Wolbachia are capable of spreading through wild Ae . aegypti populations . This primarily has been proposed as a tool to control dengue virus ( DENV ) transmission; however , Wolbachia infections confer protection for their insect hosts against a range of pathogens including CHIKV in Ae . aegypti . Medium-scale Wolbachia deployments are imminent or in certain instances have commenced . Therefore , assessing whether or not Wolbachia-infected Ae . aegypti are effective against CHIKV will help inform the viability of Wolbachia biocontrol for CHIKV control . Our study provides valuable evidence that could justify expanding this type of control program to other Ae . aegypti-transmitted arboviruses , primarily CHIKV . | [
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] | 2016 | The wMel Strain of Wolbachia Reduces Transmission of Chikungunya Virus in Aedes aegypti |
Exposure to influenza viruses is necessary , but not sufficient , for healthy human hosts to develop symptomatic illness . The host response is an important determinant of disease progression . In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection , we inoculated 17 healthy adults with live influenza ( H3N2/Wisconsin ) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours . Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections . We show that symptomatic hosts invoke , simultaneously , multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress . In contrast , asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses . We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection . Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature , suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza .
Influenza viruses are highly infectious and can cause acute respiratory illness in human hosts . Infected hosts present a variety of clinical symptoms including fever , runny nose , sore throat , myalgias , and malaise with potentially more serious complications such as viral pneumonia [1] . Many hosts also withstand comparable level of viral insult with little or no overt symptoms , exhibiting a higher degree of tolerance [2] , [3] . Clearly , these asymptomatic infected hosts are able to control and eradicate viral threats more effectively than those who become symptomatic . Given the dynamic nature of viral infection , it is now recognized that interactions between hosts and viruses play a crucial role in determining the presence and absence of symptoms [4] . This leads to an interesting question ― what are the principal factors associated with such divergent disease outcome ? In recent years , seminal studies on the sensing of pathogens by pattern-recognition receptors ( PRRs ) and their related signaling cascades have advanced our understanding of innate immunity [5]–[10] . Many elegant experimental analyses have further elucidated the mechanistic activation and modulation of host response to invading pathogens [11]–[16] . By design , however , host responses in these experimental conditions are often characterized for individual cells via cell culture; or they represent a snapshot of the immune response pertaining to a limited number of time points . The components of the host immune system are diverse and they interact in a complicated manner . Owing to both technical and ethical difficulties , it has not been practical to experimentally determine the full course of immune responses leading to severe symptoms in otherwise healthy human hosts . Thus the time sequence and orchestration of host response events remain to be fully understood . The peripheral blood contains key elements of the immune system and the circulating immune cells recruited by the host in response to viral infection and virus-induced tissue damage provides a global view of the host immune response . Thus , we hypothesized that it can be used to monitor the temporal dynamics of host-virus interactions . Analyzing whole-genome gene expression profiles from healthy human subjects challenged with influenza H3N2/Wisconsin , we studied the full temporal spectrum of virus-mediated disease dynamics . Going beyond the peak symptom time analysis reported in Zaas et al . [17] , this report offers an hour-by-hour detailed view of host immune response as a continuum , spanning the time from exposure to peak symptom manifestation . Utilizing biological and mathematical models , we highlight key immune response events representing potential factors that determine the pathogenicity of influenza viral infection . We further present a statistical risk-stratification model for estimating current disease state with potential forward risk assessment capability . These results are concordant with findings reported by Zaas et al . that was limited to peak symptom time analysis .
A cohort of 17 healthy human volunteers ( Table S1 ) received intranasal inoculation of influenza H3N2/Wisconsin and 9 of these subjects developed mild to severe symptoms based on standardized symptom scoring [18] . Gene expression profiles were measured on whole peripheral blood drawn from all subjects at an interval of ∼8 hours post inoculation ( hpi ) through 108 hpi . A total of 267 gene expression profiles were obtained for all subjects at 16 time points including baseline ( −24 hpi ) . As outlined in Figure S16 , our analysis of the data consists of two parallel components: 1 ) clinically uninformed ( unsupervised ) factor analysis using Bayesian Linear Unmixing ( BLU ) [19]; 2 ) clinically informed ( supervised ) pathway analysis using EDGE [20] and self organizing maps ( SOM ) [21] that leverages clinical and temporal covariates for increased statistical power . The former establishes the existence of an ab initio molecular signature that strongly correlates to symptomatic clinical disease . The later further reveals important host factors that delineate time courses of designated symptomatic ( Sx ) and asymptomatic ( Asx ) subjects . Symptomatic infection exhibits a distinct time evolving molecular signature . This signature is sufficiently strong that a clinically uninformed factor analysis method is able to pick it up without using any clinical phenotype information such as disease outcome , subject or time labels . For this analysis we used the BLU factor analysis method described in the Methods section . Figure 1A shows a heatmap of the linear combination ( BLU factor score ) of genes in this signature , where for visualization we have arranged the samples in a matrix whose rows and columns are organized according to clinical phenotype of the subject and sample time . The image of the BLU factor score shown in Figure 1A bears striking resemblance to the standardized clinical symptom observation matrix in Figure 1B . The BLU factor score signature is sufficiently strong that application of a threshold to the post-inoculation part of the heatmap in Figure 1A perfectly divides the subjects into asymptomatic subjects ( Class 2 ) and symptomatic subjects before onset ( Class3 ) and after onset ( Class 4 ) of acute infection . The selection of the threshold was based on the pre-inoculation samples ( Class 1 ) and is described in the Methods section . Then , using logistic regression [22] as an association measure between class label and gene expression , we extracted sets of genes that are most associated with differences between pairs of classes ( Table S4 ) . When the expression profiles of these genes are plotted as heatmaps ( Figure 1C ) the contrasts in gene expression are striking . For example , the type-I interferon antiviral response related genes IFI44L , IFI27 , GBP1 , RTP4 , and OAS1 are among the most associated with differentiating acute infection ( class 4 ) from the other 3 classes . As another example , note the contrast between complement component 3a receptor ( C3AR1 ) between Classes 2 and 3 , exhibiting a marked change after inoculation in symptomatic subjects . These genes are well known for their critical function in host immunity [6] , [23] , [24] . This demonstrates both the strength of the genomic signature of acute infection and the utility of BLU factor analysis for ab initio discovery of this signature . When we add clinical and temporal information about the samples to the analysis we can identify clusters of genes whose temporal expression patterns differentiate immune response of clinically asymptomatic from clinically symptomatic subjects . Using EDGE with false discovery rate ( FDR ) significance level ( q-value ) <0 . 01 , we selected 5 , 076 genes whose temporal expression profiles differed significantly between Asx and Sx phenotypes . Heatmaps of these 5 , 076 EDGE genes are shown in Figure S18 . Next , these 5 , 076 gene expression profiles were grouped into clusters based on using SOM applied jointly to the Sx and Asx phenotypes . A total of eight clusters were identified and their associated centroids are shown in Figure 2A and 2C as polar and linear plots of expression over time . Heatmaps of gene expression are shown for the top 5 genes in each SOM cluster ( Figure 2B ) . These eight clusters decompose temporal host response into eight distinct classes of differential expression dynamics , revealing divergent trends in asymptomatic and symptomatic responses over time . The contrasts in expression patterns between phenotypes are all statistically significant ( q-value<0 . 01 ) ( Figure 2C ) . Most clusters show significant monotonic increase or decrease in expression over time in Asx or Sx phenotypes ( Table S3 ) . For Sx subjects we define three stages of infection: early ( 0–12 hpi ) , middle ( 12–45 hpi ) , and late ( >45 hpi ) . Collectively , clusters 2 , 3 , 4 , and 6 contain more than 78% of all significant genes and highlight the sharp contrasts in expression dynamics between phenotypes . Although the discussion below focuses on these four clusters , pathway enrichment analysis indicates that genes from all eight clusters are directly related to the activation and modulation of host immune and inflammatory responses ( Table 1 ) . Clusters 3 and 4 contain genes that are associated with equally strong Sx response but responded discordantly . Cluster 3 is denoted as ( ) where superscripts nc and up stands for no change and upregulation , respectively . The subscript mid ( middle stage ) indicates the onset time of the change . Cluster 3 is characterized by strong activation , in Sx phenotype , of genes responsible for antiviral and inflammatory responses . Cluster 4 , ( ) , contains genes that are continuously down-regulated in the Sx phenotype in contrast to nearly no change in the Asx phenotype . On the other hand , genes in clusters 2 and 6 are associated with strong but discordant responses in both Asx and Sx individuals , indicating an active physiological response in Asx hosts . Cluster 2 , ( ) , includes genes exhibiting sustained decrease unique to the Asx phenotype from early time onward . In Sx , the expression of cluster 2 genes increases to peak level at the middle of challenge ( 45–69 hpi ) , followed by a rescinding trend . Cluster 6 , ( ) , is populated by genes whose expression steadily increases in the asymptomatic phenotype over all time . In contrast , for the symptomatic subjects these genes exhibit a transient but significant decrease beginning at 29 hpi and return to baseline after 60 hpi . The eight clusters represent molecular signatures of unique and contrasting temporal dynamics . We evaluated whether these signatures are related to symptom development by correlating the expression of these signatures against standardized clinical symptom scores [17] , [18] . Both positive and negative correlations were observed ( Figure 3C ) . In particular , cluster 3 ( ) showed the strongest positive correlation with symptom scores ( = 0 . 77 ) followed by cluster 2 ( = 0 . 58 ) . The temporal expression pattern of cluster 3 genes closely resembled the disease progression trajectory of each individual Sx subject . It is noteworthy that luster 3 is most significantly enriched with 70% of the BLU factor genes ( p<0 . 05; Fisher's exact test ) . This is in strong concordance with the BLU gene expression signature being highly correlated with temporal disease progression ( Figure 3A and 3B ) . Furthermore , 90% of “acute respiratory viral” signature genes are found in cluster 3 ( Table S5 ) [17] . In comparison , the lack of symptoms in Asx subjects was consistent with their nearly-constant low-level expression of this same cluster of genes ( Figure 3B ) . Interestingly , the two largest clusters , cluster 4 ( ) and cluster 6 ( ) , were the most negatively correlated with the development of symptoms , ( = −0 . 54 ) and ( = −0 . 41 ) respectively ( Figure 3C ) . These demonstrate the close association between the host transcriptional signatures and the overt clinical disease development . A recent study identified 66 and 87 human proteins that physically interact with H3N2/Udorn and H1N1/A/PR/8/34 ( PR8 ) viruses , respectively [25] . We examined the distribution of genes corresponding to these proteins among the eight clusters identified in our analysis . Several interesting findings result from the comparison . A total of 27 ( 45% ) and 40 ( 46% ) , respectively , of genes overlap with the set of differentially expressed genes found in our study ( Figure 3D ) . The majority of these genes ( 67% ) are found in cluster 4 and 6 . Except for clusters 2 and 3 , the H3N2/Udorn and H1N1/PR8 genes are distributed in a similar proportion across the eight SOM clusters . Such similarity shows functional conservation between the two viral strains . Secondly , cluster 6 alone contains 44% of the 27 overlapping genes ( H3N2/Udorn ) . This is significantly disproportional to the size of cluster 6 ( p-value<0 . 05; Fisher exact test ) . Several of the overlapping genes such as PRKRA , MAPK9 , and NRF1 have been shown to play important roles in host immune or antioxidant function . Thirdly , cluster 2 and 3 showed a significantly lower proportion of overlapping genes ( p-value<0 . 05; Fisher's exact test ) . These results suggest that genes in these two clusters are more likely to be indirectly regulated by the viruses such as those involved in inflammatory responses . Taken together , the results independently validate the functional relevance of the molecular signatures identified in our challenge study and suggest that many cluster 6 genes might be directly regulated by viruses . An examination of the highest ranked genes in cluster 3 ( ) reveals strong activation of host antiviral defense program ( Table 1 ) . These genes include several PRR genes such as Toll-like receptor 7 ( TLR7 ) , the RNA helicases ( RIG-I ) , and interferon induced with helicase C domain 1 ( IFIH1 ) – genes encodes proteins that are key to innate immune responses by acting as viral RNA sensors [12] , [26]–[28] . These are among the most statistically significant ( q-value<0 . 0001; EDGE ) , exhibiting dramatic increase of expression starting at 45 hpi in Sx hosts ( Figure 4B , Figure S8 ) . Previous studies have demonstrated that the downstream signaling triggered by these PRRs converge at TANK-binding kinase 1 ( TBK1 ) , resulting in direct phosphorylation of interferon regulatory factor 7 ( IRF7 ) [29] . Both TBK1 and IRF7 ( Figure S1 ) have similar expression dynamics and are found in cluster 3 . In total , cluster 3 contains 11 genes from the TLR signaling pathway , including MyD88 , TRAF6 , and STAT1 . As a group , they showed an aggregated effect that is significantly associated with the symptomatic disease . This association reaches statistical significance ( p<0 . 05; Globaltest ) at 53 hpi with an increasing trend appearing as early as 36 hours before peak symptom time . By 93 hpi , the association attains its maximum level of significance with all 11 member genes significantly upregulated ( Figure 4A , Figure S1 ) . The activation of PRRs by viral ligands triggers downstream signaling cascades that include both antiviral and inflammatory responses . In line with this , cluster 3 contains many such downstream effector genes that were fully activated with similar dynamics . Several interferon-stimulated antiviral genes , such as MX1 , OAS1 , RSAD2 , PKR , exhibit Sx-specific significant temporal activation beginning at 36–45 hpi ( Figure 4C , Figure S3 , Figure S9 ) . This increase persists many hours beyond symptom peak time , suggesting non-rescinding efforts in viral resolution by the host . It is noteworthy that none of the type-I interferon genes themselves is differentially expressed between the Sx and Asx phenotypes . Similarly , cluster 3 also contains many elements of the inflammatory branch of TLR signaling , e . g . , the interferon regulatory factor 5 ( IRF5 ) . As a master regulator of the inflammatory arm of TLR7 signaling [9] , IRF5 directly activates proinflammatory cytokine tumor necrosis factor alpha ( TNF ) , which has been directly implicated in flu-like symptoms in many types of diseases with excessive inflammation . These and other mediators of inflammatory response such as IL15 and IL10 genes share similar Asx-specific increasing pattern ( Figure 4D , Figure S7 ) . Of interest , the sialic acid binding Ig-like lectin 1 ( SIGLEC1 or Sialoadhesin ) was strongly activated in Sx hosts at mid-to-late stage of infection ( Figure 4D ) . As a macrophage-specific adhesion molecule , SIGLEC1 has recently been related to pro-inflammatory function of macrophages in HIV infections [30] . These results show that the expression kinetics of cluster 3 genes constitutes a transcriptional signature of host antiviral program . This signature fully presents itself 36 hours before the peak symptom time and it is indicative of disease severity . Moreover , its activation intensity maintained high level through 108 hpi . Members of cytoplasmic Nod/NACHT-LRR ( NLR ) family have recently been linked to pathogen pattern recognition . Originally identified in bacterial infections , this family of molecules is important to the function of innate immunity [31]–[33] . A recent study showed that nucleotide-binding oligomerization domain 2 ( NOD2 ) recognizes ssRNA of both Influenza and respiratory syncytial viruses [34] . Furthermore , activated NODs were linked to the activation of receptor-interacting serine-threonine kinase 2 ( RIPK2 ) and subsequently nuclear factor kappa-B ( NFkB ) activation whereas activated NLPRs result in forming so-called inflammasome complexes . This process involves caspase-1 ( CASP1 ) and caspase-5 ( CASP5 ) and ultimately the release of pro-inflammatory and pro-oxidant cytokine interleukin 1-beta ( IL1B ) [35] , [36] . The NLR-related genes are among the most highly differentially expressed genes discovered in our study . These genes appear in two clusters , cluster 2 ( ) and cluster 3 ( ) , exhibiting markedly different temporal patterns ( Figure 2 ) . Residing in cluster 3 , NOD1 , RIPK2 and CASP1 showed no significant change in Asx subjects ( q-value>0 . 01; EDGE ) but highly increased among Sx individuals ( q-value<0 . 0001; EDGE ) ( Figure 5A , Figure S2 ) . On the other hand , NOD2 , NLPR3 , and CASP5 are found in cluster 2 . Their expression decreased in Asx but increased evidently in Sx ( Figure 5B , Figure S2 ) . In addition , the expression level of IL1B ( cluster 2 ) was evidently suppressed in the Asx phenotype while activated in the Sx phenotype ( Figure 5C ) . Given the importance of NOD2 and NLPR3 to the processing of IL1B , the Asx specific lower expression of IL1B may be contributed directly to the similar downregulation patterns of NOD2 and NLRP3 . This hypothesis is supported by a new study in which Nod2-deficient mice showed decreased levels of TNF and IL1B in PBMC [34] . Of relevance to the phenotypically different expression dynamics of NLR-mediated inflammasome activation , an opposite trend is observed in two cluster 6 ( ) genes that are related to cellular response to oxidative stress . The superoxide dismutase ( SOD1 ) and serine/threonine kinase 25 ( STK25 or SOK1 ) are markedly activated in Asx subjects , contrasting to the transient suppression pattern ( 45–60 hpi ) in Sx hosts ( Figure 5D , Figure S10 ) . As SOD1 and STK25 both have been linked to anti-oxidant/stress response and reduced concentration of ROS [37]–[39] , their sustained up-regulation in Asx hosts highlights a host response signature unique to the Asx phenotype . This signature may relate to the concomitant suppression of NLRP3 and IL1B in Asx individuals . Collectively , our data reveal a phenotypically divergent expression of NLR family genes and inflammasome signaling , which may be related to the host anti-oxidant response . A hallmark of host recognition of viral RNA is the activation of Janus kinase-signal transducer and activator of transcription ( JAK-STAT ) pathway , which is crucial for the antiviral function of interferons . However , such activation is tightly controlled to limit the possibility of over-stimulating inflammatory cytokine-receptor signals . As an integral component of the JAK-STAT pathway , the family of suppressor of cytokine signaling ( SOCS ) proteins have recently been reported to negatively regulate the response of immune cells to cytokine signals [40] . Using pathway analysis , we detected significantly distinct JAK-STAT signaling dynamics ( p-value<0 . 05; Globaltest ) , involving two different sets of SOCS genes . The first set included SOCS1 and SOCS3 from cluster 2 ( ) while the second group consists of SOCS2 and SOCS5 from cluster 6 ( ) . The expression of SOCS1 and SOCS3 declines at early time points among Asx but strongly increases among Sx ( Figure 6A , Figure S11 ) . Growing evidence suggests that SOCS1 and SOCS3 are important inhibitory modulators in limiting the inflammatory effect of interferon signaling during viral infection [41] , [42] . Our data supports such a protective role of SOCS1 and SOCS3 given their much higher levels of expression during late infection phase ( 45 hpi onward ) . Consistent with cluster 6 but contrasting with the cluster 2 expression pattern ( Figure 2 ) , SOCS2 and SOCS5 exhibits expression dynamics that clearly differ from that of SOCS1 and SOCS3 . Starting from the early infection stage ( 12 hpi ) , SOCS2 and SOCS5 show marked increasing trend in Asx and this trend persists throughout the entire infection period ( Figure 6B , Figure S11 ) . In contrast , their expression diminishes in Sx , especially between 45 hpi and 69 hpi . A recent study showed that the anti-inflammatory actions of aspirin-induced lipoxins depend upon the function of SOCS2 [43] . Highly expressed in lymphoid organs , SOCS5 was hypothesized to be important for the generation of Th1 responses by repressing IL-4-induced signals that promote Th2 differentiation [44] . In addition , we observed a significant positive association of interleukin 7 ( IL7 ) and STAT4 ( Figure 6B ) . Of these , STAT4 transduces IL12 and IFN-A cytokine signals in T cells and monocytes [45] whereas IL7 is critical for proper T cell response and expansion during viral infection [46]–[48] . Taken together , the distinct expression patterns of SOCS family genes and related JAK-STAT signaling suggest possible early involvement of Th1-type adaptive immune response in Asx hosts with no sign of excessive inflammation . In addition to expression changes in magnitude , genes in clusters 2 ( ) and 6 ( ) also exhibit directional contrast between two phenotypes . As the largest cluster with a total of 1 , 326 member genes , cluster 6 contains genes with expression profiles similar to those of SOCS2 and SOCS5 . Among them , we found an unusual saturation of genes related to ribosomal protein synthesis . Out of 47 significant genes in this pathway , 35 ( 76% ) of them are located in cluster 6 ( p-value<0 . 0001; test ) . Together , these 35 genes correlated positively with Asx phenotype ( p-value<0 . 05; Globaltest ) and their expression increased over the course of the study ( Figure 6C ) . Such association emerges at 45–53 hpi and peaks at 60 hpi , at which point every one of the 35 genes becomes highly expressed . Individually , all genes showed increased expression trend ( Figure S4 ) . This trend can be seen at as early as 5 hpi and as late as 108 hpi . In contrast , Sx subjects showed sustained down-regulation of the same set of genes , with lowest expression level at 60 hpi . This decreasing trend continues until ∼84 hpi , which coincides with the peak symptom time observed in symptomatic subjects ( Figure 3 ) . In addition , these 35 genes include 53% of 13 genes whose expression are characteristic of peripheral blood lymphocytes ( Figure 6D ) [49] , suggesting prominent presence of lymphocytes in the blood of Asx subjects during infection . This is further supported by the increased number of whole blood leukocytes in Asx subjects ( Figure S17 ) . Given the markedly contrasting trends observed between Asx and Sx phenotypes , we conclude that Asx hosts responded differently to the viral insult by inducing leukocyte response with enhanced cellular protein biosynthesis .
We showed that the viral sensing and inflammation in Sx hosts clearly correlate to clinical symptom development over time . As mounting evidence has established the role of various PRRs in sensing viral components of influenza viruses , our results confirm the concurrent activation of all known classes of PRRs and their signaling cascades by influenza viruses in human challenge models . In contrast , Asx hosts showed not only an absence of such activation , but also negative regulation of related inflammatory signals , especially in the case of NLRP3 and NOD2 . This corresponds to their lack of clinical apparent symptoms . It has long been postulated that multiple PRRs represent a functional redundancy of host defense and that there exists signaling crosstalk among them , stimulating similar cytokine profiles that are both pro-inflammatory and pro-oxidant [36] . Here we found simultaneous and continued activation of all known PRRs in Sx hosts with particular emphasis on NLR family genes . Of important relevance , two recent studies showed that H1N1 1918 pandemic virus induced upregulation of inflammasome components in a macaque model while avian H5N1 virus Vietnam/1203/04 caused increasing expression of NLR family genes in mice [50] , [51] . In both cases , the early and sustained upregulation of inflammasome component genes was directly associated with lethal or detrimental host responses . Abnormal expression of NOD2 has been implicated in inflammatory bowel disease [52] , [53] . Conversely , it was shown in a study on chronic arthritis that Nod2 gene-deficiency resulted in reduced joint inflammation and increased protection against early cartilage damage in mice [54] . Our results provide new evidence for a much broader role played by NLR-family genes during influenza viral infection that is likely to be shared by multiple viral strains and influenced by specific cellular context . Their contrasting expression dynamics in Sx versus Asx points to potential benefit in controlling inflammation by regulating NLRP3-mediated inflammasome activation or other inflammatory responses [55] . The inflammasome and pro-inflammatory cytokines have been linked to increased level of oxidative stress during viral infection [56]–[58] . A recent report showed in mouse model that Nlrp3 inflammasome activation depends on reactive oxygen species ( ROS ) and inhibition of ROS induction abolished IL1B production during influenza infection [59] . It is intriguing that our data shows a temporal Asx-specific upregulation versus Sx-specific suppression of SOD1 and SOK1 . This coincides with the observed negative correlation between these genes and NLRP3 . Since SOD1 and SOK1 are capable of reducing ROS and of suppressing oxidative stress [37] , their increased expression in Asx hosts may play a role in negatively regulating NLRP3 expression and inflammasome signaling . In support of this hypothesis is a study on the efficacy of antioxidant therapy found that pyran polymer-conjugated SOD1 protected mice against potentially lethal influenza virus infections [38] . Together , our results provide evidence for a protective role of antioxidants SOD1 and SOK1 . Their increased mRNA expression may constitute an effective antiviral mechanism by which aberrant immune responses are avoided in Asx hosts . It is estimated that Asx infections account for 30–50% of seasonal flu cases [2] , which is consistent with the attack rate in our study . Since both Asx and Sx subjects were challenged under the same protocol and displayed inoculation dosage-independent viral shedding , this raises a critical question concerning the nature of the observed Asx phenotype . We have strong evidence that the observed Asx molecular signatures are a consequence of rapid innate response rather than being due to failed inoculation . Firstly , 50% of Asx subjects had evident viral shedding . This is on par with that of “subclinical” or “secondary” infections reported in the literature . In addition , serum neutralizing antibody ( nAb ) titre were nearly identical in Asx and Sx subjects on day 0 and day 7 with pre-inoculation nAb independent of disease severity . Critically , the nAb titre increased over time in both Asx and Sx individuals ( Figure S12 ) . This indicates a boosting effect of immunity , and suggests that even if viral replication was inhibited , enough viruses were detected by the Asx host immune system to cause expansion of Ab producing cells . Secondly , there was no apparent dosage effect – subjects who received relatively lower amount of inoculation do not necessarily become more ill than individuals who received higher dose of virus . We found no statistically significant dependence between disease outcome and inoculation dosage ( Figure S13A ) . Furthermore , the amount of viral shedding from the site of infection did not appear to differ among groups who received varying inoculation doses ( Figure S13B ) . Thirdly , Asx subjects presented dramatic transcriptional responses towards inoculation . When their expression profiles were studied alone , more than 3 , 000 genes showed significant post-infection expression changes . These changes do not correlate with the amount of virus detected . Two subjects ( #3 and #17 ) who never yielded detectable virus ( <1 . 25 TCID50/mL ) in their nasal wash appeared to have the most significant temporal suppression of gene NLRP3 ( Table S2; Figure S14; Figure S15 ) . Additionally , the responses of two seroconverted Asx subjects ( #2 and #3 ) , according to haemagglutination inhibition ( HAI ) assay , are not different from those of other Asx individuals ( Table S2; Figure S14; Figure S15 ) . With all presented evidence supporting the activation of a robust Asx immune response , our findings point to an important host factor that may lead to such Asx subclinical infections . Shutting down protein synthesis helps control infection by inducing apoptosis of infected cells [60]–[62] . Consistent with this , we observed marked downregulation of protein biosynthesis and apoptosis related genes in Sx hosts at mid-to-late stages ( Figures S4 , S5 , S6 ) . A similar lowering expression of ribosomal proteins has been reported in measles-infected dendritic cells [63] . What is surprising is the sustained upregulation of as many as 35 ribosomal proteins in only Asx subjects ( Figure 6C , Figure S4 ) . The increased ribosomal gene expression has been associated with peripheral blood lymphocytes [49] and our data also showed significant increase of white blood cells in Asx subjects ( Figure S17 ) . Lacking strong PRRs activation , and hence an absence of adaptive immune response , these Asx hosts appeared to be capable of mounting a more potent cell-mediated innate immune response than the symptomatic subjects . As our study mainly focuses on gene expression in whole peripheral blood , it is possible that the changes observed in gene expression levels are at least partially due to changes in cell population . However , this is unlikely for two reasons . First , the maximum observed change in cell populations for both Asx and Sx hosts was no more than 80% from baseline ( Figure S17 ) . Second , the distribution of leukocyte subpopulations is not correlated with phenotype at baseline or throughout the time course of the study ( Table S6 ) . Thus , the dramatic changes in gene expression described here cannot be attributed greatly to cell population changes . Another uncontrolled factor is that certain subjects may have come into the study with related preconditions . While we cannot completely dismiss the possibility of previous exposure to other respiratory viruses , all subjects were healthy and tested negative for H3N2 influenza antibody at pre-inoculation time . None of the volunteers had been vaccinated for any influenza virus in the previous 3 years . Finally , while we did not observe subject demographics such as age , gender , or ethnicity to be influential of final disease outcome ( Table S1b ) , we cannot rule out the possibility of small sample bias . We have been careful to provide statistical safeguards against model overfitting by reporting significance measures ( p-values and q-values with qualifying confidence intervals ) that are associated with our findings . To our knowledge , this multi-institutional collaborative study presents the first systematic analysis of the full temporal spectrum of pathogen-elicited host responses during influenza viral infection . This work represents by far the most extensive in vivo human challenge study on influenza viruses . Combined with key clinical parameters , our results offer an opportunity to look beyond individual signaling events and into their collective effects on symptomatic disease pathogenicity . The detailed timing of various immune response events in vivo will advance our understanding of their biological and clinical relevance to influenza virus-mediated disease progression .
We performed a healthy volunteer dose-ranging intranasal challenge with influenza A A/Wisconsin/67/2005 ( H3N2 ) at Retroscreen Virology , LTD ( Brentwood , UK ) . We enrolled 17 pre-screened volunteers aged 18 to 45 years of age who provided informed consent . All volunteers were without recent influenza-like illness in the preceding 45 days , tested influenza A H3N2 antibody negative by HAI at pre-inoculation screening and had not been vaccinated with a seasonal influenza vaccine within the preceding 3 years . On day of inoculation , a dose of 106 TCID50 Influenza A manufactured and processed under current good manufacturing practices ( cGMP ) by Bayer Life Sciences ( Vienna , Austria ) was inoculated intranasally per standard protocol at a varying dose ( 1∶10 , 1∶100 , 1∶1000 , 1∶10000 ) with four to five subjects receiving each dose . Subjects were not released from quarantine until after the 216th hour . Blood and nasal lavage collection continued throughout the duration of the quarantine . All subjects received oral oseltamivir ( Roche Pharmaceuticals ) 75 mg by mouth twice daily prophylaxis at day 6 following inoculation . All patients were tested negative by rapid antigen detection ( BinaxNow Rapid Influenza Antigen; Inverness Medical Innovations , Inc ) at time of discharge . All exposures were approved by the relevant institutional review boards and conducted according to the Declaration of Helsinki . Symptoms were recorded twice daily using standardized symptom scoring [2] . The modified Jackson Score requires subjects to rank symptoms of upper respiratory infection ( stuffy nose , scratchy throat , headache , cough , etc ) on a scale of 0–3 of “no symptoms” , “just noticeable” , “bothersome but can still do activities” and “bothersome and cannot do daily activities” . For all cohorts , modified Jackson scores were tabulated to determine if subjects became symptomatic from the respiratory viral challenge . A modified Jackson score of > = 6 over the first five days period was the primary indicator of successful viral infection [18] , [64] and subjects with this score were denoted as “Symptomatic” ( Sx ) . Viral titers from daily nasopharyngeal washes were used as corroborative evidence of successful infection using quantitative PCR ( Table S2 ) [18] , [64] , [65] . Subjects were classified as “Asymptomatic” if the Jackson score was less than 6 over the first five days of observation and viral shedding was not documented after the first 24 hours subsequent to inoculation . Successful inoculation in Asx hosts was further validated by analysis of multimodal data including serum neutralizing antibody and haemagglutination inhibition titers . For additional evidence see discussion in Text S1 . Standardized symptom scores were tabulated at the end of each study to determine attack rate and time of maximal symptoms ( time “T” ) . The clinical disease is mild ( only a single fever was observed ) . Immune activation assays ( such as antibody response ) over the full time course of the challenge study were not available for our analysis . During the challenge study , subjects had samples taken 24 hours prior to inoculation with virus ( baseline ) , immediately prior to inoculation ( pre-challenge ) and at set intervals following challenge: peripheral blood for serum , peripheral blood for RNA PAXgene™ , nasal wash for viral culture/PCR , urine , and exhaled breath condensate . Peripheral blood was taken at baseline , then at 8 hour intervals for the initial 120 hours and then 24 hours for the remaining 2 days of the study . For all challenge cohorts , nasopharyngeal washes , urine and exhaled breath condensates were taken at baseline and every 24 hours . Samples were aliquoted and frozen at −80°C immediately . RNA was extracted at Expression Analysis ( Durham , NC ) from whole blood using the PAXgene™ 96 Blood RNA Kit ( PreAnalytiX , Valencia , CA ) employing the manufacturer's recommended protocol . While whole blood RNA is initially extracted , a secondary procedure ( B-globin reduction ) was then employed to remove the contribution of red blood cell ( RBC ) RNA to the total RNA . A set of four peptide nucleic acid ( PNA ) oligomers whose sequences are complementary to the 3′ portions of the alpha and beta hemoglobin RNA transcripts were added to reduce globin RNA transcription due to RBC . The inhibition of globin cDNA synthesis dramatically reduces the relative amount of anti-sense , biotin-labeled cRNA corresponding to the hemoglobin transcripts . Hybridization and microarray data collection was performed using the Human Genome U133A 2 . 0 Array ( Affymetrix , Santa Clara , CA ) and expression profiles were pre-processed using robust multi-array ( RMA ) method [66] ( Text S1 ) . Both raw and normalized gene expression data are available at GEO ( GSE30550 ) . Temporal gene expression was analyzed using EDGE [20] on RMA normalized intensities . A total of 5 , 076 genes were identified as most significantly differentially expression genes ( q-value<0 . 01 ) between Asx and Sx . Co-clustering of the significant genes found by EDGE was performed using Self-Organizing Map [21] ( Text S1 ) . We estimated the correlation between disease symptom scores and temporal expression values of clusters using a standard linear mixed model [67] , [68] . Specifically , for each individual symptom measured , we regressed the scores onto the expression value vector of each SOM cluster , separately , with a random-effects term accounting for within-subject temporal correlation . Biological pathway enrichment analysis was performed using Ingenuity Pathway Analysis ( IPA ) . We implemented the non-parametric Jonckheere-Terpstra ( JT ) method [69] to test monotonicity of the expression patterns of individual gene clusters . Briefly , the JT test was applied independently to each cluster and configured to test the null hypothesis that there exists no monotonic trend in the temporal change of gene expression . This test was performed separately for each one of two phenotypes separately . The resulted -values were adjusted for multiple comparisons with Benjamini-Hochberg method [70] . To identify canonical gene pathways in each SOM cluster that are highly associated with disease phenotypes , we applied Globaltest [71] using the pathway definition in MsigDB database ( v2 . 5 ) [72] that include both pathway components and targets . We assessed the correlation between clinically determined symptom scores and the temporal gene expression of SOM clusters using standard linear mixed model regression . The correlation ( R value ) was estimated using a signed coefficient of determination [67] , [68] . The BLU factor analysis was used to detect disease signature shown in Figure 1A . Unlike our implementation of EDGE , SOM and Globaltest , BLU is an unsupervised method requiring no prior class information . Like other unsupervised Bayesian factor analysis methods , BLU finds a decomposition of the data matrix Y , here a p by n matrix of abundances of the p mRNA transcripts for each of n gene expression profiles , into a matrix product MA where each column of M is a factor and each column of A is a set of factor loadings corresponding to individual factors in M for a given chip:In essence , BLU estimates two matrix valued latent variables M and A , whose product best approximates the most important information contained in the observation Y while minimizing the residual model fitting error ( denoted as N in the formula above ) with latent variable order selection according to an hierarchical Bayesian model . However , unlike other factor analysis , BLU decomposes the data into relative proportions such that the columns of M and the columns of A are non-negative and the columns of A sum to one . Intuitively , a BLU-discovered factor can be viewed as a gene expression profile , whose amplitudes represent the relative contribution of each gene present in that factor , and the factor loadings are the proportions of these factors that are present in each chip . Such positivity constraints aid in interpretation and are natural in gene microarray analysis as the expression intensity measurements of genes are always non-negative . BLU was run on all genes on the expression array and extracted a total of three major BLU factors . The factor scores of the samples were subsequently divided into two groups: samples taken before inoculation ( pre-inoculation samples ) and samples taken after inoculation ( post-inoculation samples ) . We then tested for significant difference between the scores of the pre-inoculation and post-inoculation samples ( t-test with p-value less than 0 . 01 ) . At this significance level only one of the factors passed this test – the acute respiratory factor shown in Figure 1A . Based on the score of this acute respiratory factor , we quantitatively determine the four regions by a threshold criterion using the pre-inoculation samples . The threshold was set to be more than 4 times the maximum pre-inoculation sample score ( corresponding to a t-test p-value less than 0 . 05 ) ( Text S1 ) . In this manner , all samples were labeled with one of four classes , namely classes 1–4 ( Figure 1A ) . The class designation of a sample indicates distinct risk levels of four intrinsic disease states – uninfected ( class 1 ) , infected with low-risk for symptom development ( class 2 ) , infected with high-risk for symptom development ( class 3 ) , and infected with overt symptoms ( class 4 ) . The genes exhibiting largest contrast between each pair of classes were extracted from all genes on the expression array using a LogitBoost classifier [73] as a contrast function . Note that our objective is not to obtain a classifier between regions but rather to use LogitBoost to identify groups of genes most associated with differences between a pair of classes . As it uses boosting algorithm to perform variable selection , our implementation of LogitBoost yields a set of genes in addition to a classifier function . To do this , we generated 200 bootstrap samples from each class [74] . We randomly selected 2/3 of each bootstrap sample to construct the boosting ensemble and the other 1/3 of data was used to evaluate the variability of the association between the largest contrast genes and each class pair . We defined the largest contrast genes as the set of genes that were selected by LogitBoost algorithm for each class pair more than 100 ( 50% ) of the 200 bootstrap samples . The average expression of these genes are shown in Figure 1C . | The transcriptional responses of human hosts towards influenza viral pathogens are important for understanding virus-mediated immunopathology . Despite great advances gained through studies using model organisms , the complete temporal host transcriptional responses in a natural human system are poorly understood . In a human challenge study using live influenza ( H3N2/Wisconsin ) viruses , we conducted a clinically uninformed ( unsupervised ) factor analysis on gene expression profiles and established an ab initio molecular signature that strongly correlates to symptomatic clinical disease . This is followed by the identification of 42 biomarkers whose expression patterns best differentiate early from late phases of infection . In parallel , a clinically informed ( supervised ) analysis revealed over-stimulation of multiple viral sensing pathways in symptomatic hosts and linked their temporal trajectory with development of diverse clinical signs and symptoms . The resultant inflammatory cytokine profiles were shown to contribute to the pathogenesis because their significant increase preceded disease manifestation by 36 hours . In subclinical asymptomatic hosts , we discovered strong transcriptional regulation of genes involved in inflammasome activation , genes encoding virus interacting proteins , and evidence of active anti-oxidant and cell-mediated innate immune response . Taken together , our findings offer insights into influenza virus-induced pathogenesis and provide a valuable tool for disease monitoring and management in natural environments . | [
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] | 2011 | Temporal Dynamics of Host Molecular Responses Differentiate Symptomatic and Asymptomatic Influenza A Infection |
Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control . Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests . Although recently introduced approaches represent substantial progress , for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population , capturing partially the joint epidemiological-evolutionary dynamics . Improved methods are needed to fully integrate such genetic data with epidemiological observations , for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods . Here , building on current literature , a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences . Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks . Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics , even when pathogen sequences and epidemiological data are incomplete , and when sequences are available for only a fraction of exposures . These results also characterise and quantify the value of incomplete and partial sequence data , which has important implications for sampling design , and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak . The framework is used to analyse an outbreak of foot-and-mouth disease in the UK , enhancing current understanding of its transmission dynamics and evolutionary process .
Epidemiological data for infectious disease , defined here as clinical observation , diagnostic test results and associated covariates such as location , only indirectly reflect underlying contact structures , exposure times , and other aspects of disease dynamics . Developments in Bayesian data-augmentation methodology for spatio-temporal processes over the last decade or so [1–4] allow key epidemiological quantities , e . g . contact rates and latent periods , that are critical to risk assessment and disease control , to be inferred from such data . These methods typically employ stochastic integration techniques such as Markov Chain Monte Carlo ( MCMC ) to infer the full history of the epidemic , including the transmission tree , from partial observations . Unfortunately , epidemiological data available for an epidemic outbreak typically do not typically allow very precise inference of detailed aspects of disease transmission dynamics [5] . However , a parallel development is the increasing availability of genetic data on pathogens collected , in particular , based on whole genome sequencing [6–8] . During an outbreak pathogen populations are subject to genetic change through mutation and selection . Genetic data on pathogens , sampled from exposed hosts within an outbreak , therefore carry information on relatedness of different infection events . When genetic change and disease transmission occur on comparable time scales joint analysis of epidemiological and genetic data can lead to valuable insights concerning epidemic outbreaks . For example , it can help us to identify the transmission network [9] which can be used to quantify superspreading events [10] , to study the evolutionary patterns of pathogens [11] and to design and evaluate of control measures [12] . Approaches that rely on reconstructing phylogenetic trees have been followed in several scenarios [13 , 14] . A number of limitations of these approaches are highlighted in [15] . For example , when the sampled sequences include donor-recipient pairs with respect to the infection process , a situation commonly arising during the early stages of an epidemic , these approaches may not capture adequately the direct ancestor-descendant relationship between them . This paper presents novel methodology which advances the joint analysis of epidemiological and genetic data , building on recent substantial progress of others [16–21] . These authors sought to overcome the limitations noted above of using phylogenetic trees as a proxy for transmission dynamics , developing approaches which explicitly construct transmission trees by combining genetic and epidemiological data [16 , 18–22] . These methods have proved to be very valuable in unravelling transmission paths during an epidemic outbreak . However , they employ various approximations/simplifications which either avoid explicit inference of the unobserved sequences from pathogens Transmitted from donors to recipients upon infection ( solid black circles in Fig 1 ) [16 , 18–21] or use approximate Bayesian inference to account for these sequences [22] . Thus they may not fully infer the entire epidemiological-evolutionary process and may not utilise the most appropriate likelihood function ( see section Complete-data Likelihood ) . For example , [19] considers sequence combinations that exhibit the minimum amount of mutation necessary to explain sub-trees of transmission connecting the observed pathogen strains; [16 , 20] consider a pseudo-likelihood computed for only observed sequences; and , as opposed to a genuine joint approach , [17] considers a two-step inference procedure , whereby a phylogeny is first constructed independently of the transmission network before conducting inference of the transmission network . These approximate approaches greatly reduce the computational challenges inherent in inferring the unobserved transmitted sequences , and facilitate statistical inference , particularly when the transmission tree is of primary interest . However , there is certainly scope for improving on their performance and better capturing the joint epidemiological-evolutionary dynamics . For example , it is already recognised that reconstruction of the transmission tree can be sensitive to the choice of prior for some epidemiological parameters [16] , suggesting that a more rigorous joint inference may yield improved inference . In addition , the latent period of a disease may be overestimated by ignoring the unobserved pathogen sequences transmitted upon infections [20] . Further research on the systematic integration of epidemiological and genetic data , in the context of inferring both the transmission tree and the epidemiological-evolutionary process , is therefore warranted . It is well-known , particularly within a Bayesian framework , that explicit imputation of unobserved processes is a beneficial strategy for addressing such issues . This enables the use of likelihood functions consistent with models that better represent the underlying processes e . g . reducing bias when quantifying disease dynamics from epidemiological data [23–26] . In this paper we therefore address the challenge of explicitly imputing transmitted sequences within the framework of data-augmented Bayesian analysis whereby unobserved processes are treated as supplementary unknown parameters . In the context of joint inference of epidemiological-evolutionary processes , the unobserved data include not only standard aspects related to epidemiological data , such as exposure times , but also unobserved genetic sequences transmitted during these events . Implementation of inference e . g . via MCMC , is accordingly more computationally challenging than for epidemic data only , due to the complexity of the data-augmented parameter space which comprises the model parameters and all potential transmission graphs and sequences consistent with the observed data . Within the Bayesian framework the result of inference is described by the posterior distribution over data-augmented parameter space . MCMC algorithms draw correlated samples from the posterior which are used to generate statistics of interest e . g . the marginal posterior distribution of transmission trees . In this context Markov chains which produce highly correlated samples are described as poorly mixing . Standard MCMC algorithms , such as the single-component Metropolis-Hastings algorithm , make updates to a single model parameter at any time . However , for the problem that we consider here , identifying well-designed proposal schemes for jointly updating components is challenging , but necessary for obtaining a well-mixing Markov chain that can efficiently explore the joint posterior distribution of model parameters , transmission graphs and transmitted pathogen sequences . Specifically , the challenge arises when proposing updates to the source of a given infection . A naive algorithm may update the source of infection leaving the corresponding transmitted sequence unchanged so that the downstream pathogen sequences would still belong to the previous branch of the infection tree . It is easy to see that this would lead to a very low acceptance probability for the proposed change and inefficient exploration of the domain of transmission trees and sequences . A crucial research challenge , and key aim of this paper is therefore , to devise a computationally tractable algorithm for the joint proposal of unobserved sequences and the transmission tree to be embeded within an MCMC algorithm . We also consider the general case of epidemics with arbitrary numbers of clusters ( where a cluster is a set of infections arising from a single primary infection ) , of which the one-cluster scenario considered in many practical applications ( e . g . [19 , 20] ) is a special case . In contrast to existing approaches [16 , 18] to the multi-cluster scenario , we model explicitly the process of generating sequences for background/primary infections ( see Models and Methods ) . Note that , when including multiple-cluster scenarios , a transmission tree , which is the term used routinely in the literature where typically a single cluster is assumed [19 , 20] , should be referred to as a transmission graph ( or sometimes transmission forest ) . In summary the main outcomes reported in the paper are as follows . We devise a statistically sound and computationally tractable Bayesian framework that facilitates systematic integration of epidemiological and genetic data . Specifically , we formulate Bayesian tools for imputing unobserved data , particularly for the joint proposal of the transmission graph and the sequences transmitted ( at times of infection ) , facilitating a more explicit representation and accurate recovery of the processes of epidemic transmission and pathogen evolution , even when only data on a subset of the infected population are available . Having enabled systematic integration of epidemiological and evolutionary process , we characterise and quantify systematically the importance of genetic data for the inference of some important aspects of epidemic dynamics: the inference of the transmission graph , epidemiological parameters and the identification of clusters . Moreover we demonstrate that genetic data may also facilitate model assessment using methods recently developed by the authors [27] . We demonstrate the reliability of these novel methods using simulated data and their practical utility by analysing a foot-and-mouth outbreak in the UK .
We consider a broad class of spatio-temporal stochastic models exemplified by the SEIR epidemic model with susceptible ( S ) , exposed ( E ) , infectious ( I ) and removed ( R ) compartments . Suppose that we have a spatially distributed population indexed by 1 , 2 , … . Denote by ξS ( t ) , ξE ( t ) , ξI ( t ) and ξR ( t ) the set of indices of individuals who are in class S , E , I and class R respectively at time t and let S ( t ) , E ( t ) , I ( t ) and R ( t ) be the respective numbers in these classes at time t . An individual j ∈ ξS ( t ) becomes exposed via primary infection with stochastic rate α and from an infection i ∈ ξI ( t ) with rate βK ( dij;κ ) . The term K ( dij;κ ) characterises the dependence of the infectious challenge from infective i to susceptible j as a function of distance between them dij and is known as the spatial kernel function[25 , 27] . Here , we assume K ( dij;κ ) = exp ( −κdij ) . Sources of infection are assumed to act independently of each other and combine so that the overall probability of j becoming infected during [t , t + dt ) is given by r ( j , t , d t ) = [ α + β ∑ i ∈ ξ I ( t ) K ( d i j ; κ ) ] d t + o ( d t ) . ( 1 ) We refer to α as the primary ( background ) transmission rate and β as the secondary transmission rate , and we note that the term α + β∑i ∈ ξI ( t ) K ( dij;κ ) represents the total hazard of infection . Note that the magnitude of primary infection rate α is the determining factor for the number of primary cases and hence the number of clusters in the transmission graph . Following exposure , the random times spent by individuals in classes E and I are modelled using an appropriate distribution such as a Gamma or a Weibull distribution [3 , 4] . Specifically , we use a Gamma ( a , b ) parameterized by the shape a and scale b for the random time x spent in class E with density function f E ( x ; a , b ) = 1 b a Γ ( a ) x a - 1 e - x b . For the random time x spent in class I we use a Weibull ( γ , η ) parameterized by the shape γ and scale η with density function fI ( x;γ , η ) = ( η/γ ) ( x/γ ) η−1 e− ( x/γ ) η . All sojourn times are assumed independent of each other given the model parameters . The various epidemic and ecological studies cited in the previous section make use of models that conform to this general framework . The evolutionary process of the pathogen is modelled at the level of nucleotide substitutions . It is assumed that the nucleotide substitution process is independent over infected sites , conditional on the transmission graph and infection times . We assume that there is a single dominating strain/lineage at each infectious site at any time point ( e . g . [16 , 19 , 20] ) so that , upon exposure , the newly exposed individual is infected with this single dominant strain from the source individual . The dominant strain at an infected site evolves according to the continuous-time evolutionary process described below . Nucleotide bases at different positions of a sequence are assumed to evolve independently . A nucleotide sequence is assembled from four nucleotide bases which can be classified into purines ( e . g . , adenine ( A ) and guanine ( G ) in both DNA and RNA viruses ) and pyrimidines ( i . e . , thymine ( T ) and cytosine ( C ) in DNA viruses and uracil ( U ) and C in RNA viruses ) . Substitution between bases in the same category is called transition ( not to be confused with the term transition in the context of a Markov process ) and the substitution between bases from different categories is called transversion . Generally speaking , transversion occurs less frequently than transition . In keeping with common practice we model the mutation process by a continuous-time Markov process . Specifically we adopt the two-parameter Kimura model [28] ( see also S1 Text :A Markov Process to Model the Evolutionary Process ) which allows for different rates of transition and transversion . Taking RNA viruses as an example , we let ωN = {A , C , G , U} be the set of nucleotide bases . Under the Kimura model , a nucleotide base x ∈ ωN mutates to a nucleotide base y ∈ ωN within an interval of arbitrary length △t with probability P μ 1 , μ 2 ( y | x , Δ t ) = 0 . 25 + 0 . 25 e - 4 μ 2 Δ t + 0 . 5 e - 2 ( μ 1 + μ 2 ) Δ t , for x = y , ( 2a ) P μ 1 , μ 2 ( y | x , △ t ) = { 0 . 25 + 0 . 25 e − 4 μ 2 △ t − 0 . 5 e − 2 ( μ 1 + μ 2 ) △ t , for x ≠ y specifying a transition , 0 . 25 − 0 . 25 e − 4 μ 2 △ t , for x ≠ y specifying a transversion , ( 2b ) where μ1 and μ2 are the rates of transition and transversion respectively . Note that △t is arbitrary and does not have to be small for the equations above to hold . Moreover , this process is quite general and not restricted to modelling only RNA virus mutations . The assumption of having only one single primary infection during an outbreak has been shown to be applicable in many scenarios [19 , 20] . This assumption has been more recently relaxed to allow for multiple initial infections – for example , [18] uses an ad hoc algorithm to detect genetic outliers and hence the imported cases , and [16] uses a sound post-processing algorithm to identify imported cases . To include multiple primary infections explicitly into our framework , we model the distribution of pathogen sequences from which the primary cases are drawn so that primary and secondary infections can be included and distinguished using the Bayesian computational procedures presented later . Background/primary sequences ( i . e . actual sequences passed to primary cases which initiated the clusters ) are stochastic variants of a population characterised by a universal master sequence , GM , with each nucleotide base of the background/primary sequences sequence having a probability p ( i . e . variation parameter ) of differing from the base at the corresponding site in GM , in which case the base is drawn uniformly from the three possible alternatives . For example , if the jth position of the universal master sequence GM is base A , the corresponding base passed to the background/primary sequence has probability p 3 of taking each of the values in the set ωN\A = {C , G , U} and has a probability 1 − p of being A . The completely drawn background/primary sequence may then evolve in time along the transmission in the initiated cluster . Also , deviations from GM are assumed to be independent over sites . The universal master sequence ( GM ) , the background/primary sequences that initiated clusters and the variation parameter ( p ) are all to be imputed ( see later ) . We note that , the background/primary sequences are largely constrained by the sampled sequences – an assumption made implicitly in [18] where genetic outliers are classified as imported cases . The universal master sequence GM and the variation parameter p are considered as nuisance parameters , accommodating other scenarios concerning the process generating the background/primary sequences . For example , when two background/primary sequences that initiate two different clusters are actually derived from two distinct master sequences , the variation parameter p would be estimated to be large under the constraint of having only one master sequence . One may , of course , consider the two master sequences explicitly in the model . Nevertheless , we stress that the primary goal of having a primary infection model is to include more explicitly the primary sequences into our framework . This multiple-cluster framework can be easily simplified to a single-cluster scenario considered in many practical problems ( e . g . [19 , 20] ) by assuming that the initial exposure is drawn uniformly from all possible sites , that the sequence of the ( initial ) infecting strain drawn uniformly from all possible sequences , and that all subsequent exposures arise through secondary infection . Note that , in this case we are not required to represent explicitly the master sequence and the process generating the background/primary sequences . As the inferential procedures that we propose make extensive use of data augmentation we first discuss the formulation of a complete-data likelihood for the integrated epidemic/genetic model , bearing in mind that some of the quantities required to calculate the likelihood will be observed directly while others will be imputed . Consider a population of N sites and assume that pathogen sequences comprise n bases . Suppose that we observe the epidemic between time t = 0 and t = tmax , during which period the precise times and locations of all transitions between compartments are observed . Moreover , assume that for any exposure , the source of infection is also recorded , this being either primary infection or infection by a specific infectious host . Let χS denote the set of individuals remaining in class S at tmax , and let χE ⊆ χI ⊆ χR denote the sets of individuals who have entered class E , class I and class R by tmax respectively . Also , let E = ( … , Ej , … ) denote the exposure times for j ∈ χE , I = ( … , Ij , … ) denote the times of becoming infectious for j ∈ χI and R = ( … , Rj , … ) denote the times of recovery or removal for j ∈ χR . The cumulative distribution functions corresponding to the sojourn times in class E and class I are denoted by FE and FI respectively . Note that we use the term exposure time to denote the time of any transition from S to E , preferring not to use infection time in order to avoid potential confusion with times of transition from E to I . Furthermore , to formulate the model it is necessary to allow recording of the sequences characterising the dominant pathogen strain at each exposed site j ∈ χE at potentially multiple times during the epidemic . Therefore , let G⋅j = ( G1 , j , … , Gmj , j ) denote mj sequences that characterise the dominant strain at site j ∈ χE at the corresponding ( increasing ) sequencing times t⋅j = ( t1 , j , … , tmj , j ) . Note that t⋅j includes the time of exposure for site j , t1 , j = Ej so that G1 , j characterises the strain transmitted to j . Also represented in t⋅j are any times at which j passes infection to a susceptible host , so that strains transmitted from j are captured in G⋅j . Finally t⋅j also includes the observed sampling time t j s at which the dominant strain is sequenced at site j . We denote by G = ( G⋅1 , … , G⋅j , … ) the complete set of nucleotide data formed . The transmission graph is specified by a vector ψ which records the source of infection ψj for each individual j ∈ χE . Some key notation is summarised in Table 1 . A sequence of events in which individual i infects individuals j and then k along with the sampling of sequences taken from these individuals is shown in Fig 1 to clarify the notation above . In practice , the observed data will only record the sampling times t i s , t j s , t k s and the corresponding sequence samples ( coloured grey ) with all other quantities needing to be imputed . We will also consider the more general sampling situation where some exposures may never be sampled so that no sequence is recorded for them . In the general multiple-cluster scenario , with complete data z = ( E , I , R , G , ψ ) and model parameters θ = ( α , β , a , b , γ , η , κ , μ1 , μ2 , p ) , we can express the likelihood as L ( θ ; z ) = ∏ j ∈ χ E - 1 P ( j , ψ j ) × exp { - q j ( E j ) } × ∏ j ∈ χ S exp { - q j ( t m a x ) } × ∏ j ∈ χ If E ( I j - E j ; a , b ) × ∏ j ∈ χ Rf I ( R j - I j ; γ , η ) × ∏ j ∈ χ E \ I { 1 - F E ( t m a x - E j ; a , b ) } × ∏ j ∈ χ I \ R { 1 - F I ( t m a x - I j ; γ , η ) } × ∏ j ∈ χ Eg ( G 2 , j , … , G m j , j | t · j , ψ j , G 1 , j ) × ∏ j ∈ χ E h ( G 1 , j | ψ j ) . ( 3 ) Here χ E - 1 denotes χE with the earliest exposure ( which must be a primary infection ) excluded . The contribution to the likelihood arising from the infection of j by the particular source ψj is given by P ( j , ψ j ) = α , if individual j is a primary case , β K ( d ψ j j ; κ ) , if ψ j ∈ χ I at time E j . ( 4 ) We define q j ( s ) = ∫ 0 s { α + ∑ i ∈ ξ I ( t ) β K ( d i j ; κ ) } d t , ( 5 ) so that the terms exp{−qj ( Ej ) } and exp{−qj ( tmax ) } give the contribution to the likelihood arising from the survival of each exposed individual until its respective exposure time or , in the case of non-exposed individuals , until tmax . The second and third lines in Eq 3 represent the contribution to the likelihood of the sojourn times in class E and I respectively . Terms in the last line in Eq 3 carry the contribution to the complete-data likelihood of the sequence data . The term g ( G 2 , j , … , G m j , j | t · j , ψ j , G 1 , j ) = ∏ i = 1 n ∏ k = 1 m j - 1 P μ 1 , μ 2 ( G k + 1 , j i | G k , j i , Δ t = t k + 1 , j - t k , j ) ( 6 ) gives the probability that , conditional on the infecting strain ( i . e . , G1 , j ) and the sampling times , a given sequence of mutations ( to be inferred ) occurs in the exposed individual j . The term pμ1 , μ2 ( ⋅ ) is defined in Equation 2 ( where G k , j i denotes the nucleotide base at position i of sequence k on individual j ) . The expression h ( G1 , j|ψj ) represents the contribution to the likelihood arising from the infecting strain , and is given by h ( G 1 , j | ψ j ) = ( p 3 ) l j ( 1 - p ) n - l j , if individual j is a primary case , 1 , if ψ j ∈ χ I , ( 7 ) where p ( the variation parameter ) is the probability that a base of G1 , j is different from the base at the corresponding position of the given master sequence GM and lj is the total number of differing bases . The term 1 3 reflects the assumption that a base is randomly chosen from a uniform distribution on the set ω N \ G M i , where G M i is the nucleotide base on ith position of the master sequence . The likelihood for the single-cluster scenario is obtained simply by discarding the factor ∏j ∈ χE h ( G1 , j|ψj ) . It is now standard practice to conduct Bayesian analyses of partially observed epidemics using the process of data augmentation supported by computational techniques such as Markov chain Monte Carlo methods [1 , 3 , 25 , 29] . Given observed partial data y , such as times of symptom onset or culling times , these approaches involve sampling from the joint posterior distribution π ( θ , z|y ) ∝ L ( θ;z ) π ( θ ) , where z represents the complete data and π ( θ ) represents the prior distribution of model quantities , such that the complete z is reconstructed , or ‘imputed’ . In our application , z involves both partially observed epidemic and sequence data . As discussed in Introduction , a crucial research challenge for the joint inference of epidemic and molecular evolution processes is to devise a statistically sound , and computationally efficient algorithm for the joint imputation of the unobserved sequences , the transmission graph ψ and the unobserved infection times E . In this section we describe how the unobserved ψ and the unobserved sequences in G may be updated along with the unobserved exposure times E , this being the key challenge in devising a suitable algorithm . The analysis takes about 2 to 17 hours to run on a single-core computer , depending on the amount of genomic data used ( see details in S1 Text :Computing Time and Other Benchmarks ) . Details of more standard elements of the MCMC algorithm are also described in S1 Text :Supplementary Details of the MCMC Algorithm . Beside using extensive simulations , our methods have also been tested and validated by mathematical arguments and specifically-designed computer experiments ( for details see S1 Text :Validation of the Methodology ) .
In this section we apply our algorithm to a localized FMDV outbreak that occurred in the UK ( Darlington , Durham County ) in 2001 , in which 12 infected premises ( indexed here by the letters C-P ) , forming the so-called “Darlington cluster” , were observed and sampled to obtain one virus sequence for each premises with sequence length n = 8176 [9 , 20] . The geographical locations , the sampling times and removal ( i . e . culling ) times of the infected premises were reported . Estimated onset dates of lesions were also provided by experts at the times of sampling . These data were previously analysed by [20] in one of the first important attempts , using a pseudo-likelihood approach , to jointly consider epidemiological and genetic data in an integrated framework . Note that , 3 additional premises were not included in previous analysis as these premises were believed not to be epidemiologically linked to the rest of the premises in the “Darlington cluster” . Here , for a more valid comparison , we analyse the same dataset using our methodology . As in the section Simulation Studies , where we have tested our methodology with a much larger number of sites N = 150 , we fit a spatial SEIR model to the data . In particular , we assume that sojourn times in classes E and I follow Gamma ( a , b ) characterized by the shape a and scale b and Exp ( μr ) characterized by the mean μr respectively . The spatial kernel is assumed to be an exponentially-bounded kernel exp ( −κdij ) ( Refs [20] ) . The model is fitted to the data using the methods as described in A Systematic Bayesian Integration Framework . A single-cluster scenario has been assumed in [20] . To validate this assumption and demonstrate the generality of our framework , we allow multiple clusters in our analysis . We consider whole genome sequencing in this section . The estimated onset dates of lesions provide important information on the starting dates of infectiousness for infected premises as these two dates were suggested to be close to each other [37] . To incorporate uncertainty in the estimated lesion onset dates , for each infected premises we allow the onset of infectiousness to vary within a 14-day interval centered at the estimated lesion onset date provided . It is noted that , given that the maximum of the estimated duration between lesion onset times and sampling times is 7 days , 14 days may represent a conservative upper bound of the estimation uncertainty .
In response to the increasing availability of genetic data from pathogens in epidemic outbreaks substantial progress has been made on the joint analysis of epidemiological and genetic data [9 , 13–21] . However , existing approaches make use of approximations in modelling the epidemiological-evolutionary process , which in particular avoid inferring the unobserved sequences transmitted from donors to recipients upon infections or use approximate Bayesian inference to account for these sequences . These approximate approaches greatly reduce the computational challenges inherent in inferring the unobserved transmitted sequences , but only partially capture the joint epidemiological-evolutionary dynamics ( Refs [23–26] ) and may lead to less robust and accurate inference – for instance , the reconstruction of the transmission tree can be sensitive to priors chosen for some epidemiological parameters [16] and the latent period of a disease may be overestimated [20] . There is therefore a need to extend current approaches and develop a more systematic framework for the joint inference of these two coupled processes . Such a framework is useful to better understand the epidemic dynamic and to systematically characterise the importance of genetic data , which may yield useful insights for predicting , managing and controlling the epidemics [12 , 25 , 26] . We show that it is feasible to systematically integrate epidemiological and genetic data by devising an algorithm for jointly imputing the transmission graph and the transmitted sequences in a statistically sound Bayesian framework . Our key innovation is the development of an MCMC algorithm that allows for explicit representation and imputation of unobserved , transmitted sequences which in turns facilitates the use of realistic likelihood functions in the analysis . We have tested and validated this methodology via specifically-designed computer experiments ( for details see S1 Text :Validation of the Methodology ) and demonstrated its utility in a range of scenarios . We have tested our methods on epidemics with moderate size ( n∼150 ) comparable to those used in practical applications [16 , 18 , 20] , which should also suffice for example , providing insights into decision support during the early stage of a major outbreak . Also , the run-time is greatly reduced when we consider partial genome sequencing , but that this resulted in no material difference in the estimates of epidemiological parameters compared to using full genome sequencing ( see S1 Text :Computing Time and Other Benchmarks ) . Our results also have important implications for future study design . Using our methods , we characterise and quantify the effect of using a subset of genetic data from a number of important perspectives . First , generally speaking , both the epidemiological and evolutionary model parameters , including the transmission graph , are more accurately estimated when more genetic data are available . In particular , we show that the spatial transmission mechanism ( i . e . the spatial kernel ) can be estimated more precisely . The identification of the clusters of transmission helps the identification of risk factors and yields useful insights into devising effective control strategies [30 , 31] . We show that , even if the transmission graph may not be well-identified at low levels of sub-sampling of sequences data , the clusters and the sites of primary infections can still be identified with good accuracy . We also show that the parameter values of mutation rates and latent period distributions can have some influence on the tolerance level of sub-sampling for achieving robust inference . Moreover , our results suggest that partial genome sequencing may be adequate if the epidemiological dynamic is of primary interest . Lastly , we demonstrate that genetic data can also facilitate model assessment using methods recently developed by the authors [27] . We show the practical usage of our framework by applying our methods to data on the FMD outbreak in 2001 in the UK , demonstrating both agreement with and improvement over previous findings . First , our results suggest a transmission graph broadly consistent with previous work [20] , supporting the use of specific pseudo-systematic approaches [16 , 20] when only the transmission graph is of primary interest . Also , our results validate the one-cluster assumption used in [20] , which also demonstrates the flexibility of our ( multiple-cluster ) framework . On the other hand , we show that more realistic estimates of the latent period can be obtained , and mutation rates can also be estimated . This highlights the importance of explicitly taking into account the transmitted sequences for constructing a more accurate and integrated representation of the transmission dynamics , with the proximate goal of reliable prediction and the ultimate aim of effective management of disease outbreaks . Our framework can readily accommodate more complicated models and be applied more generally , by relaxing a number of simplifying assumptions made in formulating the component models that we use in this paper . For instance , similar to many practical applications in the literature [16 , 18 , 20] , we assume a dominant strain on an exposure at any time point . In doing so , we have not considered the within-host dynamic of the pathogens . By considering a single dominant strain , we assume that the transmitted strain in an infection event is a direct descendant of the strain transmitted in a previous transmission event involving the same donor . This assumption simplifies the structure of the tree that we need to consider ( Ref [42] ) and facilitates the design of the proposal distributions used for the joint updating of donor and transmitted strain which is fundamental to our algorithm . However , a within-host diversity model component can be included naturally , by at the same time specifying a distribution for selecting a transmitted strain among the multiple strains in a host . Similarly the assumption of having one master sequence GM may be relaxed by treating p and GM as nuisance parameters ( see discussion in Models and Methods ) . For example , if suggested by empirical data or prior knowledge , one may allow for multiple distinct master sequences for different specified ranges/domains of time or space . We also note that the background/primary sequences are largely constrained by the sampled sequences , and the principal goal of including a primary infection model is to include more explicitly the primary sequences into our framework . Also , it is not required to assume a primary infection model when considering a single-cluster scenario . Nevertheless , we have successfully demonstrated in this paper the feasibility of integrating systematically epidemiological and evolutionary processes using a methodology that allows explicit inference of both . Moreover , application to a real world problem demonstrates not only the practicality of this approach but also the added-value which it brings in terms of extracting information from available data . | In the midst of increasingly available sequence data of pathogens , a key challenge is to better integrate these data with traditional epidemiological data , with the proximate goal of reliable prediction and the ultimate aim of effective management of disease outbreaks . Although substantial advances have been made for such an integration , and they have improved our understandings of many disease dynamics which are not available otherwise , current methods have relied on fast algorithms , rather than achieving a systematic integration and accurate inference of the joint epidemiological-evolutionary process . Building on methods in current literature , this paper describes a novel Bayesian approach for systematically integrating these two streams of data . We propose a computationally tractable Bayesian inferential algorithm which takes the full joint epidemiological-evolutionary process into account . Using this algorithm , we study systematically the value of genetic data , providing valuable insights into future sampling designs . The algorithm is subsequently applied to real-world dataset describing the spread of animal foot-and-mouth disease in the UK , demonstrating the importance of such a systematic integration achieved with our methodology . | [
"Abstract",
"Introduction",
"Models",
"Results",
"Discussion"
] | [] | 2015 | A Systematic Bayesian Integration of Epidemiological and Genetic Data |
Protein-protein interactions are regulated by a subtle balance of complicated atomic interactions and solvation at the interface . To understand such an elusive phenomenon , it is necessary to thoroughly survey the large configurational space from the stable complex structure to the dissociated states using the all-atom model in explicit solvent and to delineate the energy landscape of protein-protein interactions . In this study , we carried out a multiscale enhanced sampling ( MSES ) simulation of the formation of a barnase-barstar complex , which is a protein complex characterized by an extraordinary tight and fast binding , to determine the energy landscape of atomistic protein-protein interactions . The MSES adopts a multicopy and multiscale scheme to enable for the enhanced sampling of the all-atom model of large proteins including explicit solvent . During the 100-ns MSES simulation of the barnase-barstar system , we observed the association-dissociation processes of the atomistic protein complex in solution several times , which contained not only the native complex structure but also fully non-native configurations . The sampled distributions suggest that a large variety of non-native states went downhill to the stable complex structure , like a fast folding on a funnel-like potential . This funnel landscape is attributed to dominant configurations in the early stage of the association process characterized by near-native orientations , which will accelerate the native inter-molecular interactions . These configurations are guided mostly by the shape complementarity between barnase and barstar , and lead to the fast formation of the final complex structure along the downhill energy landscape .
Protein-protein interactions are the fundamental components in the interaction networks describing cellular processes such as metabolic reactions and signal transduction . When trying to acquire a more detailed understanding of the association and dissociation processes of protein complexes , however , we encounter some complicated physics involved in these protein-protein interactions , in which a subtle balance between the weak atomic interactions and solvation determines the marginal stability/affinity and the specificity [1]–[3] . Such a physical picture is reminiscent of the complexity in protein folding , which has been overviewed from the energy landscape picture linking the unfolded states to the folded state [4] , [5] . Likewise , an energy landscape of protein-protein interactions linking the dissociated states to the unique stable complex structure [6]–[8] is necessary . There are two stages in the process involved in the formation of a protein complex , the “diffusion-collision” process from the fully separated states to the encounter complex , and the “association” process from the encounter complex to the native complex structure . The formation of the encounter complex has been experimentally well studied by using the Förster resonance energy transfer [9] , [10] , atomic microscopy [11]–[13] , transferred NOE spectroscopy [14] , [15] , paramagnetic relaxation enhancement [16]–[18] , and computationally by conducting Brownian dynamics simulations [19]–[25] . On the other hand , study of the second stage , which is the formation of the tightly bound native complex structure , still remains a challenge both experimentally and theoretically due to the difficulties in detecting the atomic-detailed process of the formation of complicated interactions including the desolvation at the interface of protein complexes . In particular , the large scale configurational sampling by conventional equilibrium molecular dynamics ( MD ) simulations is a difficult task due to the slow kinetics and a large number of degrees of freedom in the sampling space . To solve this problem more elaborate simulation techniques have been used to calculate the free energy surface ( FES ) , such as steered MD [26] , constrained MD [27] , and the weighted histogram analysis method [28] . These simulations introduced a single dimensional reaction coordinate , such as the distance between the centers of mass for the two proteins , connecting the bound state and a dissociated state , to reduce the sampling space . However , the potential of mean force along a pre-fixed one-dimension is too simple for describing the FES of the complicated protein-protein interactions , just as in the protein folding problem that requires many dimensions for a proper description of the FES in the folding funnel landscape . The simplest and most direct way to solve the problem is thus a full configurational sampling of the protein-protein interactions . In this study , we try to directly obtain the energy landscape , or the FES , of the all-atom protein-protein interactions during the association process in explicit solvent by conducting a multiscale enhanced sampling ( MSES ) simulation [29]–[31] . The MSES enhances the sampling by using a multiscale scheme where the all-atom model ( MM ) is coupled with the accelerated dynamics of the coarse-grained ( CG ) degrees of freedom , together with the Hamiltonian replica exchange method to eliminate the bias of the coupling to the CG model [32]–[38] . The scalability in the Hamiltonian replica exchange for application to large protein systems is attained by setting the dimensionality of the CG model small enough to represent only the “essential subspace” . Our previous studies on the folding dynamics of chignolin [29] , [31] and on the ordering transition of an intrinsically disordered protein ( sortase ) [30] have demonstrated the outstanding capability of the all-atom conformational samplings of large proteins in explicit solvent . The use of multiscale scheme has also been aimed to develop the CG force fields from the MM simulations by bottom-up approach [32]–[34] , [38] , and applied for enhanced sampling such as resolution replica exchange [39] , [40] , adiabatic coupling [41] , [42] and temperature accelerated MD [43]–[46] . We chose the barnase-barstar complex , which is a bacterial RNase bound to its inhibitor [47]–[49] , as a model protein complex to study the association dynamics . This complex is characterized by its extraordinary tight binding ( Kd = 10−14 M ) [50] and fast binding kinetics ( kon = 108 s−1M−1 ) [51] . Comparative mutation studies revealed that the fast and tight binding is due to a significant electrostatic complementarity between the two protein interfaces [50]–[54] . Brownian dynamics simulations successfully reproduced the diffusion process of the mutant complexes under various environmental conditions [19]–[25] . Here , the FES of the barnase-barstar interaction during the association process after the encounter was calculated using the MSES simulation to investigate how the electrostatic and shape complementarity determined the energy landscape for the processes of the formation of the native intermolecular contacts and desolvation of the hydrated waters .
The MSES simulation of barnase and barstar in explicit solvent was performed to fully sample the all-atom configurations during the association process to form the native complex structure . Twelve replicas for the Hamiltonian exchange were sufficient for simulating the solvated system containing ∼35 , 000 atoms , owing to the high scalability of the MSES [29]–[31] . The energy distribution of the MM/CG coupling term ( see Eq . 1 in Methods ) significantly overlaps the distributions of the neighboring replicas ( Fig . 1A ) , guaranteeing a high exchange probability or a successful Hamiltonian exchange simulation; the average acceptance ratio of the exchange was 0 . 25 . The fluctuation of VMMCG in Eq . 2 shows sufficient swapping of kMMCG in all the replicas , indicating the successful simulation of the Hamiltonian replica exchange ( Fig . 1B and Fig . S1 ) . The enhanced sampling of the barnase-barstar system was achieved by using the following MSES procedure . The potential energy of the CG model was set as the Lennar-Jones type potential for the protein-protein interactions , which has a shallow minimum for the native complex structure and a broad potential of non-native states ( see Methods for details ) . This CG potential energy plays a role in leading the barstar to frequently move back and forth between the bound and unbound state , rather than favoring the bound state , as indicated in the FES exhibiting a single minimum at the intermediate distance ( Fig . S2 ) . The strong coupling with the CG models ( a large value of the coupling constant , kMMCG , see Eq . 1 in Methods ) drives the MM model to sample a large configurational space to provide a broad distribution , as shown in the FES for all the replicas showing close similarity to that for the CG force field ( Fig . S2 ) . We obtained the FES of the unbiased potential ( VMM in Eq . 1 ) by extrapolating kMMCG to zero , which is depicted as the configurational ensemble covering a much larger configurational space than that sampled by the conventional equilibrium MD simulation ( Fig . 1F and 1G , respectively; hereafter we call the latter the MM simulation ) . The MSES ensemble for the unbiased simulation with kMMCG = 0 shows that the barnase and barstar molecules experience the association and dissociation processes several times , thus traversing a large configurational space ( Figs . 1C–E ) , which is seen in RMSDbs ( Cα root-mean-square displacement ( RMSD ) of barstar after superimposing barnase ) = 1–15 Å , dCOM ( the center-of-mass ( COM ) distance between the two proteins ) = 22–30 Å ( dCOM = 23 . 2 Å for the complex structure in the Protein Data Bank ( PDB ) :1BRS [49] and dCOM<25 Å for the MM simulation ) , the number of inter-molecular polar contacts ( out of the eight contacts listed in Table 1 ) = 0–8 , and in representative structures ( Fig . 1F ) . The proteins in all the replicas maintained their stability during the MSES simulation ( Cα RMSD within barnase and barstar being <1 . 5 and 1 . 3 Å , respectively , for any replica with a finite value of kMMCG ) . The configurational sampling of the protein-protein interaction process when using the all-atom model in explicit solvent allows for a straightforward analysis of the energy landscape . We analyzed the polar contact network at the interface to examine the protein-protein interactions at the atomistic resolution . Fifteen inter-molecular polar contacts formed in the crystal structures [54] were chosen to calculate the contact probability in the MSES ensemble ( Table 1 ) . It is demonstrated in Table 1 that the probability of forming the polar contacts observed in the near-native structures of the MSES ensemble ( Cα RMSD of barstar after superimposing barnase is less than 4 Å ) have almost the same pattern of the probability observed in the MM simulation that started from the crystal structure ( Table 1 ) ; the correlation coefficient between the two columns was 0 . 83 . This indicates that the atomistic interactions on the interface were correctly reproduced during the large-scale association-dissociation process in the MSES simulation . In Fig . 2 , the process of the formation of inter-molecular interactions was illustrated in the distribution of the COM of barstar on the surface of barnase ( along the x-y plane and the x-z plane; see the legend of Fig . 2 for the definition of the axes ) for various ranges of Q , the fraction of the native inter-molecular contacts formed in the MSES ensemble ( 0≤Q≤1; the native contacts were defined as those having more than a 70% probability of occurrence in the MM simulation ) , as is used in the studies of protein folding . At a low Q range , barstar is positioned over a wide area on the surface of the barnase , where the distributions appear to largely spread in the x-direction compared to the y-direction . This is simply because there are two protrusions on barnase , one at Ser38 and the other at Glu60 and Gln104 , which are respectively located above and below the barstar binding site along the y-axis , and this significantly restricts the barstar's motion ( Figs . 2 and S3 ) . The broad distribution for an increasing Q-value gradually converges to a more restricted area centering on the complex structure . The same distributions were also shown in the occupancy maps of the barstar molecule , representing its translational and rotational motions relative to barnase ( Fig . 3 ) ; the space occupied by barstar is spread widely at Q<0 . 4 and becomes smaller with increasing Q . At Q>0 . 7 , the space shrinks to the level in the MM simulation , going downhill to the bottom of the FES . This monotonous contraction of the distribution suggests that the FES of the barnase and barstar interactions is funnel-like downhill . Using more quantitative statistics , we characterized the shape of the FES as a function of Q , i . e . , the average distance between the native contacts ( the inter-molecular contacts in the complex structure ) , dN , the number of inter-molecular contacts , NC ( two atoms within 4 Å ) , the amount of hydrated water at the interface , NW ( water within 4 Å from the protein interface ) , and the number of polar contacts , NPC ( out of the eight polar contacts listed in Table 1 ) . In Fig . 4 , we observed the association of the two proteins from the encounter forming the complex structure for the decrease of dN , which was accompanied by an increase in the number of protein-protein interactions ( NC and NPC ) and a decrease in the amount of the hydrated water at the interface ( NW ) . All of these values show gradual and smooth convergence to those of the complex structure with an increasing Q-value . The associated fluctuations , indicated by their standard deviations in the figure , also tend to converge to small values , implying a narrowing of the configurational space . The convergence of NC and NW with Q was also demonstrated on the x-y plane of the interface in Fig . 5: the two-dimensional energy landscape for the interfacial atoms again indicates the funnel-like downhill FES . The NC and NW distributions are complemental to each other; with an increased Q value , NC increases and NW decreases , indicating that the atom contacts are gradually formed and the solvents are excluded from the interface , yielding the complete complex structure . All these data indicate the funnel-like downhill FES of the association process heading to the native complex structure: Various kinds of structural characteristics converge to those of the native complex structure as the Q-value increases , or more native contacts are formed . This is the same as the funnel picture of a protein folding whose ideally smooth funnel is expressed by the Go-model , in which the low-dimensional reaction coordinates , e . g . , the native contacts , drive all the other reservoir variables to attain folding [55]–[57] . Similarly , in the association process after the encounter , the downhill FES or the funnel landscape was revealed . We further focused on the FES of the more localized interactions of the inter-molecular polar contacts . The barnase-barstar interface was divided into two regions according to the geometric location of the interacting residues in the complex structure ( see Table 1 and Fig . 6 ) [54] . The first group contains #7 , 8 , 11 , 12 and 13 , which form a network ( “interface 1” ) via relatively long side-chains ( arginine , tyrosine , and so on ) on the core of the interface , and the other consists of #3 , 4 and 6 , whose network ( “interface 2” ) is mostly via the main-chain atoms and located at the lower edge of the interface . Fig . 4D shows that each of these interfaces also exhibits funnel-like downhill FES . A more detailed picture is illustrated in Fig . 6A , in which the distribution of the interaction free energy expanded to two reaction coordinates , RMSD1 and RMSD2 , i . e . , the non-hydrogen atom RMSD's from the complex structure for interface 1 and for interface 2 , respectively . Upon the formation of all the polar contacts on interfaces 1 and 2 , the distribution converged to the restricted region of the complex structure ( Fig . 6B ) . When further decomposing the two-dimensional plot into each of the one-dimensional distributions ( Figs . 6C and 6D ) , we found that the increase in the number of native polar contacts in the interfaces progressively led to their native complex structures , respectively . These figures suggest that the inter-molecular interactions in the two localized interfaces appear to be formed almost independently along each funnel-like potential . This picture was confirmed in the projection of the probability distribution onto the x-y plane ( Fig . 7 ) : the positional fluctuations of the two interfaces are very large when no polar contacts are formed ( Fig . 7A ) , while the interfaces are finally stabilized when all the contacts are formed ( Fig . 7H ) . Figs . 7D and G demonstrate that the formation of interface 1 contributes more to the stability of the complex structure than that of interface 2 . This may be , however , only due to the difference in the number of polar contacts , i . e . , that the number of polar contacts at interface 1 ( 5 ) is larger than that of interface 2 ( 3 ) . The MM simulations of the wild-type complex and two additional simulations of barstar mutants , D39A and D35A ( reducing the number of polar contacts at interface 1 and interface 2 , respectively ) , yielded consistent results with the MSES simulation results ( Figs . 7I–K ) : the stability of D35A is comparable to that of the wild-type while D39A is much more destabilized than the wild type . This indicates a larger significance of Arg39 than Arg35 in the stabilization of the complex structure . When looking at the detailed interactions on the two interfaces shown in Figs . 6 , we noticed that these inter-molecular interactions were formed along a preferential pathway . As listed in Table 2 , interface 1 was formed in the sequence , #12 or #13 ( barnase ( br ) :His102 – barstar ( bs ) :Asn33 or Asp39 ) →#7 ( br:Arg83 – bs:Tyr29 ) →#8 or #11 ( br:Arg83 or Arg87 – bs:Asp39 ) , and interface 2 has the sequence , #4 ( br:Arg59 – bs:Glu76 ) →#3 ( br:Arg59 – bs:Asp35 ) →#6 ( br:Glu60 – bs:Leu34 ) . These preferential pathways of the formation of the inter-molecular polar contacts are consistent with the FES in Figs . 7B–G , revealing that the two interfaces are more stabilized with the increasing number of formed polar contacts . The early stages of the association process predominantly involved the two residues in barnase , His102 on interface 1 and Arg59 on interface 2 ( see Fig . S2 for the positions of the two residues ) . Since the configurational ensemble with nPC = 1 in Table 2 does not correspond to sufficiently small Q values , i . e . , <Q> = 0 . 21 at interface 1 and <Q> = 0 . 38 at interface 2 , the polar contacts at the very beginning of the association process , Q<0 . 1 , were further examined in Table 3 . Just as in Table 2 , we found that br:His102 with native contact #13 ( br:His102 – bs:Asp39 ) and br:Arg59 with a non-native contact ( br:Arg59Nη – bs:Asp35Oδ; note that native contact #3 is between br:Arg59N and bs:Asp35Oδ ) are the most probable polar contacts at Q<0 . 1 ( with the probability ≥0 . 2 ) . The barstar counterparts of the polar contacts are Asp35 and Asp39 on helix 3 ( residues 34–42 ) , which is the helix most deeply interacting with the binding groove of barnase ( see Fig . S2 ) . Moreover , the molecular recognition between Arg59/His102 in barnase and Asp35/Asp39 in barstar has been considered to be crucial for molecular recognition in the barnase-barstar complex structure [47]–[50] , [54] , [58] , [59] . This suggests that the inter-molecular interactions stabilizing the native complex structure are already formed at the very beginning of the association process after the encounter . A clue for understanding these native contacts formed in the early stages of association was found in the structural information for the transition state derived from the kinetic experiments by Schreiber et al . [57] . Schreiber et al . found that the transition-state structures had the binding surfaces of the two molecules correctly aligned as in the native complex structure . We found a similar feature in the early stages of the association process in the MSES simulation . At Q<0 . 1 , the barstar orientation is already restricted near the native alignment , although the barstar helix is “floating” above the binding surface of barnase ( Fig . 8A , showing only helix 3 for clarity ) . The later stages of the association process , including all the Q-ranges , have a similar distribution of the orientation of barstar ( Fig . 8B ) ; the orientation angle of barstar is within ∼50 deg , although this is a much wider range than that of the MM simulation ( Fig . 8C ) . The helix 3 of barstar appears to preferentially make contacts with a neighboring residue on barnase , either Arg59 or His102 , depending on its position on the binding surface ( see Table 3 ) . It is thus understood that the native-like polar contacts in the very early stages of the association process occur due to the near-native orientations of barstar . The restriction of the barnase/barstar orientation can be attributed to the extensive shape complementarity between the two molecules ( see Fig . S3 ) . The shape complementarity between concave barnase and convex barstar mainly comes from the protrusions at Ser38 , Glu60 , and Gln104 forming the binding site of barnase and strictly precludes barstar's motion . We found in the MSES simulation that the steric hindrance was frequently seen in the residue pairs , br:Ser38-bs:Tyr29 , br:Glu60-bs:Trp38 and br:Gln104-bs:Asp39 ( see Fig . S3 ) ; on barstar the interfacial residues with large side-chains appear in the collision . In principle , barstar would make a full rotation when it is fully separated from barnase . However , the MSES simulation sampled up to the rotation angle of 50 deg , and the range of dCOM = ∼30 Å ( Fig . 8B ) , and maintained NC>∼40 ( Fig . 4B ) . Beyond this range , the two molecules are completely separated ( NC = 0 ) , and cause energetically unfavorable states that were not easily sampled even by the MSES simulation . As an extrapolation of the landscape obtained above , we conducted a simple simulation in which the relative motion of the two molecules was restricted only to the rigid-body translation and rotation along the COM axis . The result shows that the accessible rotation angle decreased drastically when dCOM<27 Å and atomic clashes impeded the free rotation of barnase and barstar ( Fig . S4 ) . It means that strong geometrical complementarity of the complex structure already occurs at the COM distance of ∼5 Å away from the crystal structure whose dCOM = 22 . 3 Å . The geometrical complementarity is also seen in the sudden increase in NC at dCOM<26 Å . Note that the configurational space thus derived is very limited and different from the results in the MSES simulation including all degrees of freedom . However , this simple simulation may demonstrate the extensive influences from the shape complementarity to the energy landscape .
We have successfully simulated the association-dissociation processes of the barnase-barstar complex in atomic detail including explicit solvent by use of multiscale enhanced sampling . The following scenario of the association process of the barnase-barstar system can then be considered based on the above observations . In the encounter complex , the electrostatic complementarity determines the interacting surface ( Fig . S3 ) , and barstar retains rotational freedom in the encounter complex [20] , [23] , [27] , [52] . Once barstar approaches barnase closer than dCOM = ∼30 Å , or goes beyond the transition state , barstar is caught in the binding pocket of barnase and thus loses the rotational freedom due to the extensive shape complementarity . This native-like orientation allows the interface residues to make inter-molecular contacts , including the native and near-native contacts . The formation of these contacts successively induces the inter-molecular interactions listed in Table 1 to produce the downhill funnel-like landscape , yielding the final complex structure . The diffusion limited rate constant of the association process [51] can be attributed to this funnel-like landscape . Such an extremely smooth downhill landscape may be found exclusively in a barnase-barstar system exhibiting extraordinarily strong interactions and fast association kinetics [50] , [51] . This smooth landscape in the protein-protein interaction may correspond to the landscapes of the fast folding of small proteins , which also has smooth downhill landscapes [55]–[57] , [60] , [61] . Another class of protein complex systems with a lower affinity should have a more rugged landscape , as in the folding of larger proteins . The MSES simulation has opened up the possibility to delineate much more complex landscapes in the protein-protein interactions .
The MSES simulation is described in detail in the literature [29]–[31] . We provide a brief summary here . This introduces a multiscale system in which both an all-atom system , composed of protein molecules and surrounding solvents ( MM; rMM ) , and the associated coarse-grained system ( CG; rCG ) are simulated in the following method . Since a multiple CG method was used in this study , we describe the method using multiple CG systems [31] . The Hamiltonian , H , for the MSES simulation is given by ( 1 ) with ( 2 ) where VMM ( KMM ) and VCG ( KCG , i ) are the potential ( kinetic ) energy functions for MM and the i-th CG ( i = 1 , 2 , … , L; L is the number of CG models ) , respectively , and the number of degrees of freedom in each CG , M , is much smaller than that of MM , N . The CG models can be arbitrarily chosen according to prior knowledge or experimental information . In this study , a Cα model of barnase and barstar ( M = 199 atoms×3 ) was used with L = 2 . The term , VMMCG , i , defines the coupling ( harmonic constraint ) for K variables , χCG , determined by CG coordinates , with the force constants kMMCG , 1 = kMMCG , 2≡kMMCG , to drive the MM system by the accelerated dynamics of the two CG systems , where the K-dimensional vector χ ( rMM ) is a projection of rMM onto the K-dimensional space . Here , a set of K inter-molecular Cα distances between barnase and barstar was used as the variables χMM and χCG in VMMCG , i ( K = 104 was used in this study for Cα atom pairs with pairwise distances less than 10 Å in the crystal structure of the complex , and therefore K<M≪N∼105 ) . The details of the MM and CG parameters are given in the next section . The potential VCG , i/CG , j produces repulsive force between a pair of the CG systems to avoid the overlap of the CG systems and then to maintain the sampling efficiency . Here , the following function was used [31]; ( 3 ) where kCG , i/CG , j is a coupling constant and σ is a parameter to determine the correlation distance . The ultimate goal of the simulation is to derive the free energy surface solely from VMM without any bias due to the coupling VMMCG . Therefore , it is necessary to eliminate the coupling influence , or to extrapolate the system to the one with kMMCG = 0 . For this purpose , the Hamiltonian replica exchange method [62] , [63] is adopted , in which many replicated systems are assigned various values of kMMCG , from a large value to zero . The exchange probability between replicas m and n , satisfying the detailed balance condition , is given by ( 4 ) with ( 5 ) where β is the inverse temperature of the MM-CG coupled system . Eq . 3 indicates that the probability is determined by the difference between χMM ( rMM ) and χCG ( rCG , i ) defined in a small dimension ( K ) . Because of K≪N , Δmn can be kept small enough to provide a high exchange probability pmn irrespective of the size of the system N . This guarantees an excellent scalability highly superior to that of the conventional temperature replica exchange method , where the difference in the potential energy of MM ( scaling up with N2 ) determines the exchange probability Δmn . The energy functions , VMM+KMM , VCG , i+KCG , i and the coupling term in Eq . 1 were defined as follows . For the all-atom potential energy VMM , AMBER ff99SBildn was used [64] . The CG potential VCG was prepared as the sum of two terms representing the intra-molecular interactions ( VCG , intra ) and the inter-molecular interactions ( VCG , inter ) . For VCG , intra the potential function of the Cα elastic network model was used [65] . The force constant and the cut-off length in the elastic network model were set at 1 . 8 kcal/mol/Å2 and 12 Å , respectively . For VCG , inter the Lennard-Jones potential with a potential depth of 0 . 2 kcal/mol and a soft ( harmonic ) boundary with a force constant of 5 kcal/mol/Å2 at 10 Å apart from the minimum of the LJ potential was applied to the selected 104 Cα atom pairs . The 104 pairs were selected as those of the interfacial residues under the condition of a Cα atom distance less than 10 Å in the crystal structure of the complex ( PDB: 1BRS [49] ) . The LJ potential is used for the attraction between the two CG models , and the soft boundary potential is to avoid a too large separation . The mass of the CG model was set as mCG = 10 , 000 . The starting structure was taken from the X-ray structures in the PDB entry 1BRS [40] , in which Cys40 and Cys82 were mutated to Ala [48] , [49] . We used the C40/82A mutant for the simulations . Rectangular simulation box was constructed with a margin of 12 Å to the boundary of the simulation box , resulting in the dimension , 73 . 8 Å×71 . 8 Å×83 . 8 Å . The solution system contained ∼11 , 000 TIP3P water molecules [66] together with four sodium ions to neutralize the simulation system . There were a total of 35 , 656 atoms in the system . The MSES simulations were performed using the class library for multicopy and multiscale MD simulations . The MM simulations were under constant temperature and pressure ( NPT ) conditions at T = 300 K and P = 1 atm using Berendsen's thermostat and barostat [67] at a relaxation time of 1 ps , and using the particle mesh Ewald method [68] for the electrostatic interactions . The simulation time step ( dt ) was 2 fs using constraining bonds involving hydrogen atoms via the SHAKE algorithm [69] . The CG simulation was also performed by using a Berendsen's thermostat under a constant temperature ( NVT ) condition of T = 300 K with dt = 2 fs . The parameters , kCG1/CG2 and σ2 in Eq . 2 , were set at 15 kcal/mol and 10 Å2 , respectively . For the MSES simulations , 12 replicas were used with kMMCG≡kMMCG1 = kMMCG2 = 0 , 0 . 001 , 0 . 0024 , 0 . 0046 , 0 . 0084 , 0 . 015 , 0 . 022 , 0 . 03 , 0 . 042 , 0 . 056 , 0 . 072 and 0 . 09 kcal/mol/Å2 . The replica exchange was attempted every 20 ps . The total simulation time of MSES was 100 ns , extending 12×100 ns = 1 . 2 µs simulation time . The convergence of the simulation was confirmed by the dCOM distribution , which was calculated using several partial trajectories ( Fig . S5 ) . For comparison , the conventional equilibrium MD ( MM simulation ) was also performed starting at the complex structure during the same simulation time ( i . e . , 100 ns ) . The MM simulations of the wild-type and the two mutants , bs:D35A ( PDB: 1X1Y ) and bs:D39A ( PDB: 2ZA4 ) [54] , were also conducted under the same simulation conditions as that described above . | Dynamic nature of the protein-protein interactions is an important element of cellular processes such as metabolic reactions and signal transduction , but its atomistic details are still unclear . Computational survey using molecular dynamics simulation is a straightforward method to elucidate these atomistic protein-protein interaction processes . However , a sufficient configurational sampling of the large system containing the atomistic protein complex model and explicit solvent remains a great challenge due to the long timescale involved . Here , we demonstrate that the multiscale enhanced sampling ( MSES ) successfully captured the atomistic details of the association/dissociation processes of a barnase-barstar complex covering the sampled space from the native complex structure to fully non-native configurations . The landscape derived from the simulation indicates that the association process is funnel-like downhill , analogously to the funnel landscape of fast-folding proteins . The funnel was found to be originated from near-native orientations guided by the shape complementarity between barnase and barstar , accelerating the formation of native inter-molecular interactions to complete the final complex structure . | [
"Abstract",
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"Results",
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] | [
"biophysics",
"biology",
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] | 2014 | Energy Landscape of All-Atom Protein-Protein Interactions Revealed by Multiscale Enhanced Sampling |
Population structure in genotype data has been extensively studied , and is revealed by looking at the principal components of the genotype matrix . However , no similar analysis of population structure in gene expression data has been conducted , in part because a naïve principal components analysis of the gene expression matrix does not cluster by population . We identify a linear projection that reveals population structure in gene expression data . Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis . Our method is able to determine the significance of the variance in the canonical correlation projection explained by each gene . We identify 3 , 571 significant genes , only 837 of which had been previously reported to have an associated eQTL in the GEUVADIS results . We show that our projections are not primarily driven by differences in allele frequency at known cis-eQTLs and that similar projections can be recovered using only several hundred randomly selected genes and SNPs . Finally , we present preliminary work on the consequences for eQTL analysis . We observe that using our projection co-ordinates as covariates results in the discovery of slightly fewer genes with eQTLs , but that these genes replicate in GTEx matched tissue at a slightly higher rate .
Genes mirror geography to the extent that in global populations without admixture , individuals can be localized to within hundreds of kilometers purely on the basis of their genotype [1–3] . Population structure in genotypes is revealed via projection of single nucleotide polymorphism ( SNP ) data onto the first few principal components of the population-genotype matrix . The principal components space , which is a lower-dimensional distinguished subspace of the high-dimensional data , is computed by a procedure called principal components analysis ( PCA ) . While PCA has been successful in revealing population structure from SNP data , it does not identify such structure in some other genomic data types . For example , in the case of gene expression data , PCA has not revealed obvious population signatures ( Supporting Information Fig 1A , [4] ) . Here we show that although the first two principal components of expression data do not capture population structure , there are other projections that do . One approach to finding such a projection is the coupling of dimension reduction to correlation maximization . This approach , utilizing PCA and canonical correlation analysis ( CCA ) , has been used to effectively analyze the relationship between gene expression and copy number variation [5] . The method is implementable via singular value decomposition and is therefore also efficient . We apply it to finding population structure in expression data , thereby further highlighting the combination of PCA and CCA as a powerful approach to integrative analysis of genomics data . For convenience of notation , we refer to this method as principal component correlation analysis ( PCCA ) . As an optimization procedure , PCA can be viewed as the projection of data onto the lower-dimension subspace that minimizes the average distance of the data to its projection . This is algebraically equivalent to finding the lower-dimensional subspace that maximizes the variance of the projected data [6] . This statistical view of PCA helps to explain why PCA of expression data might not reveal population structure: even if such structure is present in the data , it may not lie on the directions of maximal variance ( Fig 1A ) . CCA is a widely used method for joint analysis of heterogeneous data and provides a linear-algebraic mechanism for identifying shared structure among a pair of datasets . Given a pair of data matrices , CCA finds maximally correlated linear combinations of the columns of each matrix [7] . We show that CCA applied to the PCA projections of expression and genotype data identifies a projection of the expression data that reveals population structure . To validate our method , we examined population structure in expression data from the Genetic European Variation in Health and Disease ( GEUVADIS ) project [8] , which consists of RNA-seq data obtained from lymphoblastoid cell lines derived from whole-genome sequenced individuals belonging to five distinct populations . From this data , we study 14 , 070 genes and 6 , 785 , 201 SNPs in the Great British ( GBR ) , Finnish ( FIN ) , Tuscan ( TSI ) ( collectively referred to as EUR ) and Yoruba ( YRI ) samples ( see Methods ) . We choose to use the first 30 principal components of expression , the first 5 components of genotype , and the first two canonical correlations ( see Methods ) . The GEUVADIS data has been extensively studied [8–11] , yet our analysis reveals structure not previously examined in this well-characterized dataset . In addition to presenting and cross-validating the PCCA projection , we also show that this projection can be constructed from only a small fraction of randomly selected genes and SNPs in the dataset , that it is not primarily driven by allele frequency differences at known cis-eQTLs , and we briefly explore the consequences for multi-population cis-eQTL analyses .
A naïve PCA analysis of the GEUVADIS expression data ( Fig 1A ) shows that unlike genotype data ( Fig 1B ) , there is no clear clustering of individuals by population . This result is consistent with other analyses of expression data , in which population structure is not detected by PCA [4] . To understand the sources of variation that could explain the first and second principal component axes , we labeled the individuals according to the lab where they were sequenced ( Fig 1C ) . This provides some insight into the sources of variation . For example , samples from Lab 3 are distinctly separated from Lab 1 . We therefore proceeded to correct for confounding by regressing the gene expression matrix on a matrix of potentially confounding variables and taking the residual ( see Methods ) . After correction for batch , the PCs of the expression matrix still fail to show obvious population structure ( Fig 1D ) . We note that it is also possible to correct for confounding using CCA by exploiting the relationship between CCA with categorical data and linear discriminant analysis [12] ( Supplementary Methods , S1 Fig ) . Next , we examined whether coupling of expression data to genotype data could identify a projection that reveals population structure . A naïve CCA analysis of genotype and expression again results in a projection that does not reveal population structure , while also suffering from extreme over-fitting since both datasets have many more features than samples ( S2 Fig ) . Instead , we performed PCA followed by CCA on the batch-corrected expression matrix and the genotype matrix . In brief , let X be the genotype matrix and Y be the expression matrix . Let UX , k be the first k = 5 genotype principal components and UY , j be the first j = 30 expression principal components . Let UMρVMT=UX , kTUY , k be the singular value decomposition of M=UX , kTUY , j . Then the coordinates of the expression data Y in PCCA space are CY = UY , jVM , l , where VM , l represents the first l = 2 right singular vectors of M . See Methods and Supplementary Methods for details . The resulting CCA projection of expression data ( Fig 2A ) , reveals distinct population patterns in the data , although not as clearly as the PCA of the genotype data ( Fig 1B ) . The first two canonical correlations are 0 . 963 and 0 . 766 . To ensure that we did not over-fit , we performed a leave-one-out cross-validation experiment , where we removed each individual from the dataset to confirm that the reconstruction error of the model on the held out point is close to the error in the training set , and that the principal components of the reconstructed gene expression matrix show similar population patterns ( Fig 2B ) . Notably , correction for batch effects , i . e . confounding that is induced by differential sample processing , may not be strictly necessary when applying this method since the batch effects should not be correlated with genotype . In this specific case , two of the seven labs processed 39 of 89 YRI samples , but an application of our method with no correction for lab id gave nearly identical results ( S3 Fig ) . Since population identity can often be determined using only a handful of SNPs [13] , we asked whether the same structure might be visible when using a small number of SNPs , genes , or both . First , we used all genes and randomly sampled SNPs with probability p = 0 . 00001 , leaving only 63 total SNPs . With this dataset , we still observe separation of the YRI and FIN populations ( S4A Fig ) . Next , we included all SNPs and randomly sampled genes with probability p = 0 . 01 , leaving 142 genes for analysis . In this case we again observe separation of the YRI and FIN populations ( S4B Fig ) . When sampling SNPs with probability p = 0 . 00001 and genes with probability p = 0 . 01 together ( 57 SNPs and 145 genes ) , we still observe separation of the YRI population , but not the FIN population ( S4C Fig ) . Remarkably , by increasing the sampling rate for SNPs and genes to p = 0 . 00002 and p = 0 . 02 , respectively ( 111 SNPs , 283 genes ) , we again recover the separation of YRI and FIN populations , demonstrating that population structure can be identified using only a small fraction of SNPs and genes ( S4D Fig ) . The CCA projection is indexed by linear combinations of genes , which can be understood to discriminate individuals based on expression signatures . That is , genes with high variance in the CCA expression projection ( see Supplementary Methods ) have expression distributions that segregate based on patterns in the genotype PCs , which we interpret to represent population structure [1–3] . After correction for correlated multiple testing using the Benjamini–Hochberg–Yekutieli procedure [14] , we identified 3 , 571 genes with significant scores at FDR 5% , indicating that population structure within gene expression data is pervasive . The three genes with largest z-score in this analysis were TCC9 , LATS-2 and UAP1 ( Fig 3 ) . The first two genes display increased expression in the YRI population , whereas the third displays increased expression In the FIN population . After identifying genes that significantly influence the PCCA projection , we sought to contextualize our result within the original GEUVADIS eQTL analysis . The GEUVADIS analysis identifies 3 , 377 genes with an eQTL ( eGenes ) in either the EUR or YRI populations . Of these , 2 , 539 are among the 14 , 070 genes used in our analysis . We found that 837 of these genes were determined to be significant in our analysis , and therefore that 2 , 734 of our significant genes were not reported as eGenes in the original GEUVADIS analysis ( Fig 4A ) . To further evaluate the effects of known eQTL variants on our analysis , we removed the population-level expected gene expression level from each gene with a GEUVADIS eQTL ( see Methods ) . We then re-normalized the expression values and re-calculated the PCCA projection with genotype ( Fig 4B ) . We observe no perceptible difference between the main projection and the projection after removing the population-level expected gene expression level , and little change to the canonical correlation values ( 0 . 966 and 0 . 803 ) . This indicates that genes with known eQTLs in the GEUVADIS analysis are not the primary drivers of the PCCA structure . Finally , we sought to understand the implications of the PCCA projections for eQTL studies involving multiple populations . We conducted two joint cis-eQTL analyses of the four GEUVADIS populations examined in this study . In both cases , we used the common strategy of correcting for the top 10 PEER factors [8 , 15] and regressing each gene level on every SNP with MAF > 5% within 1 MB of the transcription start site ( TSS ) , independently . In the first study , we included the first 5 principal components of the genotype matrix as covariates in the analysis ( the PC strategy ) , and in the second we used the first 5 components of both the gene and genotype PCCA projection as covariates ( the PCCA strategy ) . Interestingly , we found that using PEER rather than regression for batch correction also removed the separation between the YRI and EUR individuals , while leaving the structure within the EUR populations in tact ( Fig 5A ) . In addition , a Q-Q plot of the p-values resulting from the eQTL analysis shows reduced inflation at the top end of the distribution when using the PCCA strategy as opposed to the PC strategy ( Fig 5B ) . Next we compared the number of eGenes discovered using both methods of correction as a function of the nominal significance cutoff used . For all significance cutoffs analyzed , we found slightly fewer eGenes using PCCA coordinates as covariates ( Fig 5C ) . For example , at a nominal significance value of α = 1e-6 , we found 2 , 818 eGenes using the PC strategy and 2 , 732 eGenes using the PCCA strategy . The eGenes discovered using the PCCA strategy are not a strict subset of the eGenes discovered using the PC strategy ( Fig 5D ) . At the same nominal significance of α = 1e-6 , the PC strategy discovers 259 eGenes not discovered using the PCCA strategy , while the PCCA strategy discovers 173 eGenes not discovered using the PC strategy . To compare the accuracies of the two methods , we used GTEx EBV-transformed lymphocyte eGenes as a replication dataset ( Table 1 ) . Of the 2 , 818 eGenes discovered using the PC strategy , 1 , 407 were reported as significant at FDR 10% in the GTEx dataset ( 49 . 92% ) , while 1 , 394 of the 2 , 732 eGenes discovered using the PCCA strategy were reported as significant at FDR 10% ( 51 . 02% ) . We compared replication Q-value cutoffs from 0 . 05 to 0 . 50 , and found that at all cutoffs used , eGenes discovered using the PCCA strategy replicated at a slightly higher rate ( Table 1 ) .
A key feature of the PCCA approach is interpretability in the form of genes which significantly influence the projections , highlighting the possibility of directly relating population expression differences to disease as in [16] . One interesting example is the gene PSPH ( p<1e-7 ) , which was examined in [17] and was found to be the gene with the highest degree of differential allelic expression . This gene is reported as an eGene in the original GEUVADIS EUR analysis but , importantly , not the GEUVADIS YRI analysis . The reported eQTL in that analysis is rs34458430 . The SNP rs6700 , which has also been reported as an eQTL for that gene , is an ancestry informative marker [18] . In [19] authors show that PSPH plays an important role in breast tumor development , and in [20] , the authors note that elevated PSPH levels in breast tumors give poor prognosis , and that PSPH is elevated in tumor samples from African American women . We wondered whether any SNP in the region near rs6700 ( chr 7:55 , 773 , 495 ) was associated with breast cancer , and found that rs12718945 ( chr7:55 , 125 , 270 ) was reported as such in [21] . While rs12718945 is not in LD with rs6700 or rs34458430 , it does have different allele frequencies in the YRI and EUR GEUVADIS populations . Specifically , in YRI the effect allele T has a frequency of 68% , while in EUR this allele has a frequency of 48% . However , rs12718945 is nearly 1MB away from the PSPH transcription start site ( chr7:56 . 078 , 056 ) and therefore is excluded from most cis-eQTL analyses . While we view the identification of such genes as important , we caution that African-Americans also experience substantial structural inequality in healthcare , which confounds this analysis [22] . We also note that while genes such as PSPH must also have substantial genetic/epigenetic regulation that is linked to population differences , the projection-associated genes identified by our method does not produce that information . Indeed , its power to detect genes associated with population structure comes by virtue of requiring only one test per gene and is agnostic to the source of regulation . While a complete analysis of population-associated expression differences is beyond the scope of this paper , this example suggests that our method should be a powerful approach for directly identifying genes whose expression associates with population . With the observation that the directions of maximal variance in the gene expression data do not represent population structure or even technical variation , we wondered what they did represent . We calculated the variance in the first two PCs of every gene and searched for the top 100 using the Gene Ontology PANTHER Overrepresentation test for biological process in homo sapiens , database release 2018-10-08 [23 , 24] . Using Fishers exact test with a Bonferroni correction , we found significant results for the top-level categories “regulation of gene expression” ( p<0 . 0261 ) , “regulation of RNA metabolic process” ( p<0 . 0392 ) , and “regulation of cellular macromolecule biosynthetic process” ( p<0 . 032 ) . This indicates that the directions of maximal variance capture basic components of gene and metabolic process regulation ( see S5 Fig for full GO output ) . We have shown that many genes ( 3 , 571 ) contribute to population structure , and that the majority of these ( 2 , 734 ) were not reported as eGenes in the original GEUVADIS analysis . Moreover , we have demonstrated that removing the population-level expected expression due to these genes yields nearly identical visualizations . While some may view this as unexpected , there could be a number of reasons for this . First , we show that only a few hundred genes can be used to produce visualizations that separate out the FIN and YRI populations and therefore removing some signal from only 6% of the genes is unlikely to effect this . Second , most cis-eQTL analyses separate African populations and attempt to control for population structure as much as possible , whereas we explicitly look for genes that separate by population . Third , it has been shown that the genetic correlation of eQTL effect sizes between YRI and EUR populations in GEUVADIS is low [25] , and therefore applying the effect sizes learned from the EUR population to the YRI population and vice versa may be problematic . It is possible that with full knowledge of cis-genetic effects on gene expression , population-level differences in expression could be entirely explained by differences in allele frequency at these variants . It is also possible that genetic effects on gene expression are so pervasive , and gene networks so interconnected , that nearly every gene is affected by genetic variation in trans from thousands of variants . This is consistent with the recently described omnigenic model [26] , and many studies showing that the majority of heritability of gene expression is explained in trans [27–31] . Under this model , population-level expression differences could be explained by consistency in effect from many eQTLs acting in trans . We view exploration of the differential contributions of cis and trans eQTL effects on population structure in gene expression as an intriguing area for future research . However , methods that improve power to detect cis-eQTLs while handling data from multiple populations remain important . We have explored the consequences of our result for eQTL analyses in multiple populations by using the coefficients from our model as covariates in the analysis . While we discover fewer genes with this method , the genes we do discover replicate at a slightly higher rate in a matched GTEx tissue . We caution that the observed difference in replication rate is very small and that these results are preliminary . Further investigation including simulations and testing in additional , larger eQTL cohorts will be required before we can definitively say that this is a superior approach to eQTL analysis . The identification of population structure in expression data suggests that it should be interesting to extend population genetic methods such as [32] to population transcriptomics . The example of joint analysis of expression and genotype data can be extended to include other data types via an extension of CCA to more than two matrices [12 , 33–35] , and the coupling of PCA to CCA could also be extended to a hierarchical factor analysis method . Importantly , the coupling of PCA and CCA is not the only projection that reveals population structure . For example , connecting the principal components using linear regression gives similar visualizations ( Supplementary Methods , S6 Fig ) . The choice of model should reflect the variance structure of the data , which here we have deliberately remained agnostic to . Moreover , there are other variants of CCA that can be used to analyze genomic data , such as sparse and regularized CCA [5 , 34] . Ultimately , it is important to identify the optimal model for inference . While we believe the extensions described above will be interesting to pursue , our analysis and that in [5] show that PCCA is a useful and rapid approach to exploratory analysis of heterogeneous data . As the generation of large-scale , high-dimensional , multi-modal genomics datasets becomes more commonplace [35–37] , we expect the combination of PCA and CCA to become as common as PCA is today .
We obtained genotype data of the Phase 1 1000 genomes individuals in PLINK format [38] from cog-genomics [See Data and Software Availability] . GEUVADIS project RNA-seq reads were downloaded from the European Nucleotide Archive ( accession number ENA: ERP001942 ) . In the analyses performed we omitted the CEU population because it has been previously found to display biased expression patterns due to the age of the cell line [10] . Importantly , this bias affects every CEU sample and therefore cannot be corrected for traditional methods of handling confounding . There are 343 individuals with genotype data from 1000 genomes phase 1 and corresponding RNA-seq data from GEUVADIS in the FIN , GBR , TSI and YRI populations . We quantified the transcript abundances of these individuals using kallisto [39] with the GENCODE v27 protein coding transcript sequences annotation . The GENCODE v27 annotation contains 95 , 659 transcripts . We omitted all transcripts with mean transcripts per million ( TPM ) less than 0 . 1 across the quantified samples , leaving 58 , 012 transcripts . We then used the GENCODE v27 annotation to obtain gene level quantifications by summing transcript quantifications in TPM units . Finally , we removed genes in the MHC region and on non-autosomal chromosomes . This left 14 , 070 genes for analysis . The Phase 1 1000 genomes genotypes contain 39 , 728 , 178 variants . We filtered indels , variants with minor allele frequency ( MAF ) less than 5% , and non-biallelic SNPs leaving 6 , 785 , 201 SNPs for analysis . Finally , we quantile-normalized the expression matrix , and centered and scaled each gene quantification vector to have mean 0 and variance 1 . In the following analyses , we chose to keep 30 principal components of expression and 5 principal components of genotype , while analyzing the first two canonical components ( Fig 2 ) . We chose these numbers by inspecting a plot of the percentage of variance explained as the number of components is increased , also known as the elbow method ( S7 Fig ) . However we note that our results are stable under different choices of numbers of components ( S8 Fig , S1 Table ) . In that analysis , we choose to use a smaller number of PCs of genotype than expression due to the observation that the genotype data has a smaller number of large eigenvalue components than the expression data ( S7 Fig ) . Intuitively , one can imagine the population structure in the genotype data dominates the first few PCs , while it is spread out more among the top PCs of the expression data . To remove batch effects from the expression matrix , we one-hot encoded the lab identification vector , and then added a column for sample gender [40] , resulting in a 343 x 7 matrix of potentially confounding variables . We then regressed each gene expression vector on the confounding matrix and used the residual expression vector for all further analysis . Next , we computed principal components of the genotype matrix using PLINK and principal components of the corrected expression matrix using the eigendecomposition of the Gram matrix ( See Fig 1 for visualizations ) . Finally , we computed the canonical variables between the top principal components of the genotype and corrected expression matrices ( see the Supplementary Methods for details on the linear algebra ) . To verify that we did not over-fit in estimating coefficients using CCA , we performed leave-one-out cross validation . We removed each of the 343 individuals one-by-one from the dataset , re-calculated the principal components of the genotype and expression matrices , and re-estimated the canonical variables and bases . We then projected each held out individual into the resulting CCA gene expression subspace . After this process , for each individual , we plotted the first two principal components of the re-constructed expression matrix to verify the individual clusters by population ( Fig 2B , see also the Supplementary Methods for details of the how the projection was performed ) . Furthermore , we calculated the in-sample and out-of-sample reconstruction error as the squared Frobenius norm of the original and reconstructed data points , and verified that it was similar for both left-in and held-out samples . We asked which genes had significant variance in the CCA gene expression projection . We computed the variance of each gene in the projection , and calculated significance via a permutation test with 10 million permutations . In each iteration , we shuffled the genotype principal components and recomputed the variance explained . The p-value derived from this test is the number of times the permuted score is greater than the observed score , divided by the number of permutations ( see the Supplementary Methods for details of how the variance is computed ) . We further estimated a z-score for each gene as the difference between the estimated and mean permutation variance divided by the variance of the permuted variance . To remove the effects of known GEUVADIS eQTLs , we downloaded the YRI and EUR summary statistics [8] . The authors provide the correlation of genotype and expression for the top SNP identified at each gene determined to be significant at FDR 5% ( rg ) . For any e-gene reported in both the YRI and EUR datasets , we chose the larger rg value . From this correlation , we calculate the effect size as βg=rg2fa ( 1-fa ) where fa is the allele frequency of the associated variant in all populations considered . From this we computed the mean expected population expression level for gene g as YG , k = 2βgfk where fk is the frequency of the associated variant in population k . We subtracted this value from the empirical expression level for each e-gene for every individual in population k and recalculated the projection . To conduct our cis-eQTL analysis , we corrected for batch effects using the first 10 PEER [15] factors and used PLINK [38] to do the regression analysis . We used the plink “—linear” association method for every SNP with allele frequency above 5% in the combined EUR+YRI dataset within one megabase of the TSS of each gene . For the PC strategy we used the first 5 principal components of genotype as covariates and for the PCCA strategy we used the first 5 genotype and first 5 expression PCCA components as covariates .
The software used to produce the analyses is on GitHub . We provide a package of tools for computing the projections and estimating gene significance , as well as a Snakemake file [41] that can be used to completely reproduce the analysis , from data acquisition to figure generation . | Increasingly complex , high dimensional , multi-modal genomics datasets warrant investigation into analysis techniques that can reveal structure in the data without over-fitting . Here , we show that the coupling of principal component analysis to canonical correlation analysis offers an efficient approach to exploratory analysis of this kind of data . We apply this method to the GEUVADIS dataset of genotype and gene expression values of European and Yoruba individuals , finding as-of-yet unstudied population structure in gene expression abundances . We show that this structure is not driven by known eQTLs , and explore the consequences of our results for eQTL studies involving multiple populations . | [
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] | 2018 | Expression reflects population structure |
Vascular endothelial growth factor ( VEGF ) is a powerful regulator of neovascularization . VEGF binding to its cognate receptor , VEGFR2 , activates a number of signaling pathways including ERK1/2 . Activation of ERK1/2 is experimentally shown to involve sphingosine kinase 1 ( SphK1 ) activation and its calcium-dependent translocation downstream of ERK1/2 . Here we construct a rule-based computational model of signaling downstream of VEGFR2 , by including SphK1 and calcium positive feedback mechanisms , and investigate their consequences on ERK1/2 activation . The model predicts the existence of VEGF threshold in ERK1/2 activation that can be continuously tuned by cellular concentrations of SphK1 and sphingosine 1 phosphate ( S1P ) . The computer model also predicts powerful effects of perturbations in plasma and ER calcium pump rates and the current through the CRAC channels on ERK1/2 activation dynamics , highlighting the critical role of intracellular calcium in shaping the pERK1/2 signal . The model is then utilized to simulate anti-angiogenic therapeutic interventions targeting VEGFR2-ERK1/2 axis . Simulations indicate that monotherapies that exclusively target VEGFR2 phosphorylation , VEGF , or VEGFR2 are ineffective in shutting down signaling to ERK1/2 . By simulating therapeutic strategies that target multiple nodes of the pathway such as Raf and SphK1 , we conclude that combination therapy should be much more effective in blocking VEGF signaling to EKR1/2 . The model has important implications for interventions that target signaling pathways in angiogenesis relevant to cancer , vascular diseases , and wound healing .
Angiogenesis is the growth of new capillaries from the pre-existing vasculature . The process of angiogenesis involves increased proliferation , survival , and migration of the endothelial cells that form the foundation of a developing vascular bed [1] . This process is critically involved in both health and disease [2] . Physiologically , it is involved in placental vascularization during pregnancy and the growth of normal blood vessels during development . Pathological angiogenesis is crucial in vascularizing tumors , a critical step in transition to neoplasm and cancer [3] . Newly formed tumor vasculature also contributes to the process of metastasis by shedding tumor cells into the bloodstream that then travel throughout the body and provide seeds for new tumors in more distant tissues [4 , 5] . In diseases such as age-related macular degeneration and diabetic macular edema , angiogenesis contributes to the neovascularization of the retina and the leakiness of the ocular blood vessels that may eventually lead to blindness [6] . In other diseases such as peripheral arterial disease , the opposite occurs where the blood capillaries and vessels regress leading to the reduction and , in some cases , total cessation of the blood flow to lower extremities [7] . Left untreated , this condition may require amputation of the regions affected by the lack of blood flow . Considering the crucial role of angiogenesis in human health and disease it is no wonder that there is deep interest in understanding the mechanisms responsible for regulation and modulation of this phenomenon . Several endothelial cell growth factors have been identified as being critical for priming the endothelial cells to undergo the processes that would eventually lead to the generation of new blood vessels . One critical factor is the vascular endothelial growth factor A ( VEGF-A , hereby referred to as VEGF ) identified as a potent inducer and regulator of angiogenesis [8] . There are six different human isoforms of VEGF , with VEGF165 being by far the most intensely studied member of the group . VEGF165 sits at the helm of signaling pathways that prominently include VEGF receptor 2 ( VEGFR2 ) , VEGF receptor 1 ( VEGFR1 ) , and neuropilin-1 and 2 ( NRP1 and NRP2 ) co-receptors . VEGF signaling is initiated by the binding of VEGF to VEGFR2 with subsequent VEGFR2 auto-phosphorylation on several tyrosine residues , leading to pro-angiogenic phenotypes such as increased cell proliferation and motility . Binding of VEGF to VEGFR2 and subsequent dimerization and auto-phosphorylation of at least six tyrosine residues ( with Y1175 being the most widely studied ) on the receptor leading to the recruitment of various adaptor proteins that transduce the phospho-tyrosine signal to downstream pathways including PI3K/AKT , Nitric Oxide ( NO ) , and ERK1/2 that play crucial roles in determining and regulating vascular function [9] . Of different pathways , ERK1/2 activation has been shown to play a major role in VEGF-induced angiogenesis by inducing endothelial cell proliferation and motility [10–13] . The canonical MAPK pathway that leads to ERK1/2 phosphorylation , involves the recruitment and binding of the adaptor proteins Grb2 and SOS to the phospho-tyrosine sites on the receptors that activate small G protein Ras . Activated Ras then binds and activates Raf with subsequent activation of MEK1/2 and ERK1/2 , with the eventual nuclear translocation of activated ERK1/2 [14 , 15] . While this canonical pathway operates in the activation of ERK1/2 in response to other growth factors such as the fibroblast growth factor ( FGF ) and epidermal growth factor ( EGF ) , VEGF activation of this cascade seems to be fundamentally different involving the complex feedback mechanism initiated by calcium and ERK1/2-dependent activation of sphingosine kinase 1 ( SphK1 ) [9 , 11 , 16 , 17] . Given the enormous complexity of these signaling pathways , computational models have been developed to aid in the elucidation of basic mechanisms of signal transduction and identify nodes of the pathway that might act as hubs in modulating the strength and duration of the signal . There is a large literature of mathematical models focusing on the quantitative understanding of the canonical MAPK pathway initiated by the activation of ErbB [18 , 19] , EGFR [20 , 21] , and VEGFR2 [22 , 23] . While ErbB and EGFR do signal via the canonical MAPK pathway to ERK , current experimental data on the VEGFR2 signaling suggest that the signaling is through a mechanism involving SphK1 and calcium [11 , 24] . Shu et al . were the first to show that the inhibition of SphK1 and PKC completely abolished the pERK1/2 signal [11] . Subsequent evidence suggested that the activation of SphK1 is through phosphorylation by activated ERK1/2 [16] . The mechanism becomes more complicated considering that activated SphK1 needs to be translocated from the cytoplasm to the plasma membrane , and that this is mediated by calcium binding to calcium- and integrin binding protein 1 ( CIB1 ) [25] . Our intention here is to provide the first proof-of-principle simulations for the activation of ERK1/2 by SphK1 and calcium , and investigate the consequences of SphK1 positive feedback on VEGF signal transduction to ERK1/2 . In so doing , we also develop a model that includes the major VEGF binding receptors on the cell surface: VEGFR1 , VEGFR2 , and NRP1 . We also include the effect of internalization and degradation of the receptors by considering signaling from separate endocytic and membrane compartments . While internalization has been incorporated in recent computational models of VEGFR2 signaling [22 , 26] , our model is the first to explicitly incorporate multi-complex internalization and signaling to downstream targets such as ERK1/2 and calcium . Our basic assumption based on the available evidence is that SphK1 signaling is sufficient for activation and sustenance of ERK1/2 downstream of VEGFR2 . Moreover , by explicitly incorporating a mechanistic model of cytoplasmic and ER calcium dynamics in the VEGF model here , we highlight the important connections between calcium dynamics and ERK1/2 activation . Regarding the computational implementation of the model , a radical departure from the existing models of VEGF signaling , is the application of a rule-based modeling approach utilizing the programming language BioNetGen to accurately capture all the species and their interactions in the cell [27 , 28] . This method has been successfully applied to develop a detailed model of EGF/EGFR signaling taking into account the combinatorial complexity generated by multi-domain protein interactions [29] . BioNetGen automatically generates the biochemical network given the input rules operating on the seed species for domain interactions and phosphorylation reactions . This methodology has the added advantage of including receptor complexes and single or doubly phosphorylated species . While this can be done with conventional modeling where the reaction list is written down manually a priori , rule-based approach generates all the relevant species and bypasses the potential for errors inherent in manual construction of the pathway .
In the rule-based modeling approach , the protein domains and modification sites are explicitly included in the model design process and the rules for the modification and binding are implemented using a programming environment such as BioNetGen ( see the supplementary material for the BioNetGen file , the corresponding SBML file , and the list of parameters with their descriptions ) . The general framework for our rule-based model and the constructed signaling pathway are summarized in Fig 1 . Fig 1A shows different binding sites for the initial receptor species in the model . VEGF contains three binding sites: two that are capable of binding to single binding sites on VEGFR1 and VEGFR2 , and the third binding site is located on C-terminal domain that binds NRP1 [30] . VEGFR2 has two binding sites , one for VEGF and the other for ligand-independent coupling with another VEGFR2 or VEGFR1 molecule [31] . Also included , is the essential phosphotyrosine site that can be modified by phosphorylation and de-phosphorylation . VEGFR1 has a binding site for the ligand and a ligand-independent coupling site with VEGFR2 or VEGFR1 as shown . Ligand-independent receptor dimerization of VEGFR2 and VEGFR1 has been observed experimentally and have been incorporated in a recent computational model [32] . VEGFR1 also includes a ligand-independent binding site to NRP1 . NRP1 has a single binding site that can competitively bind either VEGF or VEGFR1 [33] . In the absence of the ligand and VEGFR1 , NRP1 is assumed to be in monomeric form . VEGF-dependent dimerization of VEGFR2 proceeds by the rule shown in Fig 1B . This rule is capable of generating multi-receptor complexes by including VEGFR2 and NRP1 in the complex . VEGF can also bind to NRP1 binding site directly from the solution . The interaction rule for the binding of VEGFR1 to NRP1 is illustrated by Fig 1C . VEGFR1 and VEGFR2 can also heterodimerize by VEGF according to the scheme shown . Ligand-independent dimerization of the receptors can also occur ( Fig 1D ) . This can generate not only homodimers of VEGFR2/VEGFR2 and VEGFR1/VEGFR1 , but also VEGFR1/VEGFR2 heterodimers . Note that all the species interact simultaneously according to the rules that take into account the combinatorial complexity inherent in the interacting multi-domain proteins . VEGFR2 receptors can undergo auto-phosphorylation if they are part of a homodimer complex with the ligand ( Fig 1D ) . We also include internalization of the receptor complexes as shown in Fig 1E . In the model , we also include internalization and degradation of the receptors in the absence of ligand , constrained by the condition that the number of receptors in the absence of ligand remain steady during simulations . Based on current evidence from the literature combined with the VEGF pathway information from the Reactome database [34 , 35] , a pathway from activated VEGFR2 receptors to ERK1/2 activation is constructed as shown in Fig 1F . According to the experimental evidence , SphK1 is phosphorylated and activated mainly by pERK2 [16] . Direct phosphorylation of SphK1 by active PKC is included in the diagram for completeness and can augment the effects through ERK1/2 , but the experimental evidence for direct activation of SphK1 by PKC within the context of VEGF signaling to ERK1/2 is not adequate and thus is not explicitly incorporated in the model [16] . Once activated , SphK1 is translocated to the plasma membrane by the calcium and integrin binding protein 1 ( CIB1 ) . CIB1 has a myristoyl switch that is activated upon calcium binding [25] . Calcium/CIB1/phospho-SphK1 complex is translocated to the plasma membrane employing the myristoylated CIB1 . SphK1 then phosphorylates its substrate , sphingosine ( Sph ) , generating the diffusible sphingosine 1 phosphate ( S1P ) . S1P then activates Ras in a process conjectured to involve the inhibition of a Ras GTPase activating protein ( RasGAP ) . A significant addition to the model is the inclusion of a detailed calcium cycling module illustrated in Fig 1G . This module includes the calcium release activated calcium channels ( CRAC channels ) that are crucial for VEGF-dependent rise in calcium [36 , 37] . Further details of the model are included in the supplementary section of this paper . To estimate the parameters for receptor dynamics , the model is fitted to the total VEGFR2 levels using the data from [38 , 39] . All the relevant parameters are simultaneously fitted to a consistent set of experimental data . The variables computed by the model and the corresponding data are normalized to the maximum values . After 180 min of VEGF application , 80% of the receptors in the cell are lost as shown in Fig 2A . We assume that VEGFR1 remains on the cell membrane and does not internalize . This is in accordance with experimental evidence indicating that VEGFR1 internalization requires VEGFR1 phosphorylation [46] , and given the weak VEGFR1 auto-phosphorylation under normal conditions , it is reasonable to assume a constant VEGFR1 surface level . To include the effects of NRP1 on VEGFR2 level , Fig 2B shows the result of fitting the total VEGFR2 in the absence of NRP1 ( NRP1 = 0 in the model ) to the experimental data [39] ( red , solid circles ) in endothelial cells . In the absence of NRP1 , levels of VEGFR2 decline to zero , while the control cell still retains 20% of its VEGFR2 content after 180 min . Surface VEGFR2 levels from the model ( Fig 2C , blue ) are fitted to two sets of experimental data that have utilized either flow cytometry [40] ( solid circles , black ) or western blot [38] ( solid circles , red ) . According to the model , the effect of NRP1 association with VEGFR2 is to substantially increase the internalization rate ( 0 . 404 s-1 vs . 6 . 1×10−2 s-1 ) and the recycling rate to the plasma membrane ( 0 . 756 s-1 vs . 1 . 24×10-3s-1 ) . While in this model the internalized receptors with NRP1 have higher degradation rate than receptors without NRP1 ( 1 . 18×10-2s-1 vs . 1 . 41×10−3 s-1 ) , the combined effect of receptor internalization , recycling , and degradation , is the rapid decline in receptor number in the absence of NRP1 compared to the control ( Fig 2B ) , consistent with the literature . Further , the recycling of the phosphorylated receptors is negligible in the model consistent with data in [39] . To constrain the signaling parameters we use the western blot measurements for pY1175 , pPLCγ , and pERK1/2 [41] . We also simultaneously apply the constraint that blocking SphK1 abolishes pERK1/2 consistent with the experimental data in endothelial cells demonstrating that ERK1/2 activation is blocked at t = 10 min following SphK1 inhibition [11] . The phosphorylated pY1175 ( normalized by total VEGFR2 ) from the model is fitted to the experimental data ( solid red circles ) ( Fig 2D ) . The predicted fractional phosphorylated receptor levels ( fraction of total receptors ) at the surface and endosomal compartment are shown in Fig 2E and 2F . The surface receptors show rapid transient activation that declines to zero in 10 min . The endosomal signaling is sustained for ~40 min demonstrating that the internalized receptors are the major contributors to the sustenance of the VEGFR2 signal consistent with the current experimental and computational evidence [26 , 47] . The predicted value for the dephosphorylation of the receptors at the surface is ~150 fold higher than for the internalized receptors . It is instructive to note that this differential signaling from internalized receptors is an emergent property of the model and is not assumed a priori . Phosphorylated PLCγ is fitted to the experimental data ( Fig 2G ) showing a transient activation and decline of the signal in ~40 min . To find the parameters for calcium cycling module in the model , Fig 2H shows the normalized calcium signal from the model fitted to the normalized experimental data in [36] . The amplitude of the calcium transient was constrained to be within 200–300 nM in accordance with the empirical measurements [48] . The raw calcium transient is shown in Fig 2I with maximum calcium concentration of ~280nM reached in ~1 . 5 min with duration of ~20 min . While there is variation in the amplitude and duration of the calcium transients in response to different VEGF dosing strategies [49] , the simulated calcium output from the model is consistent with the experimental data . There are several parameters ( S1 Table ) for the activation of ERK1/2 including the parameters that define the strength of the positive feedback loop from SphK1 . The data used to estimate these parameters are the time-course of pERK1/2 [41] , the experimental data in human umblical vein endothelial cells ( HUVEC ) indicating that SphK1 and PKC inhibition block ERK1/2 activation [11] , and the VEGF dose-response for ERK1/2 activation in HUVEC [44] and porcine aortic endothelial cells ( PAEC ) [45] . The fit of pERK1/2 from the model to the data is shown in Fig 2J , along with the constrain that blocking SphK1 blocks pERK1/2 ( Fig 2K ) . PKC inhibition in the model abolishes pERK1/2 consistent with data ( Fig 2L ) . To constrain receptor binding and coupling parameters , the binding curve for VEGF is computed under the condition that there is no receptor internalization ( solid blue curve , Fig 2M ) and is fitted to the data ( red circles , Fig 2M ) [42] . The VEGF dose response curve for pVEGFR2 is also constrained ( Fig 2N , solid red circles ) demonstrating half-maximal activation ( EC50 ) at 30 pM consistent with experimental observation [43] . We were able to constrain the dose response curve for pERK1/2 using the two sets of experimental data in PAEC [45] and HUVEC [44] . The data demonstrated in vitro ERK1/2 activation in response to soluble VEGF at concentrations as low as 0 . 25 ng/ml ( 6 pM ) in PAEC with ERK1/2 activation saturated at 1 ng/ml ( 24 pM ) ( Fig 2O , solid red circles ) . In HUVEC , the experimental data ( Fig 2O , solid black circles ) indicated that soluble VEGF is capable of activating ERK1/2 at concentrations as low as 0 . 5 ng/ml ( 12 pM ) . The maximum fractional pERK1/2 from the model is fitted to the data as shown in Fig 2O ( solid blue line ) . The model predicts a threshold behavior in ERK1/2 in response to VEGF ( 2O , inset ) . For concentrations of VEGF below 5 pM , the ERK1/2 is incapable of being activated , while for values above 5 pM , there is a gradual increase in maximum pERK1/2 versus VEGF . Two important observations regarding the pERK1/2 dose-response curve are worth noting . First , the EC50 for pERK1/2 is lower than pVEGFR2 ( ~5pM for pERK1/2 and ~30pM for pVEGFR2 ) . Second , constraining the pERK1/2 VEGF dose response using the available data predicts the existence of a threshold behavior at VEGF concentration of 5 pM that will be explored in more detail later . The threshold behavior ( or bi-stable behavior using the language of dynamic systems theory ) is expected in systems containing positive-feedback loops [50] . ERK1/2 signals are expected to exhibit wide range of durations and amplitudes [22] . An important aspect of the current study is to identify what parameters or mechanisms determine and modulate the duration and amplitude of the pERK1/2 signal given the positive feedback generated by SphK1 and calcium . The predicted S1P reaches 1 μM after t~2 . 3 min and declines to baseline in ~20 min . The activated SphK1 signal is more sustained even after 40 min retaining a ~92 nM concentration ( Fig 2P ) . The predicted curves for active PKC , active Ras , active Raf , and the dose-response curve for active Ras are included in S1 Fig . Active Ras also shows threshold behavior in response to VEGF at 5 pM . In the next section , we apply global sensitivity analysis to better understand the effect of Sphk1 in shaping the pERK1/2 signal . We will also investigate other parameters modulating the threshold behavior of ERK1/2 activation in response to VEGF . We next carried out global sensitivity analysis to identify the most sensitive parameters influencing ERK1/2 activation . We utilized partial rank correlation coefficient ( PRCC ) [51] for this task which determines positive or negative monotonic relationships between the input parameters and the output observable ( pERK1/2 in this case ) . Fig 3A shows the top 15 parameters with positive monotonic relationship with pERK1/2 at t = 15 minutes . PRCC coefficients were computed at 1 , 2 , 5 and 15 minutes . We selected t = 15 minutes to identify parameters affecting the decaying and the plateau phase of pERK1/2 . The top parameters include either the total protein levels or the parameters determining the kinetic rates . The partial rank correlation coefficient ( PRCC ) values for parameters with negative correlation to pERK1/2 are shown in Fig 3B . These parameters describe either the off kinetics of binding , the dephosphorylation rate , or the Michaelis-Menten-type constants for the phosphorylation reactions . We next applied the insights gained from sensitivity analysis to investigate the factors determining ERK1/2 response to VEGF ligand . Specifically , we investigated the possibility of modulating the threshold behavior observed by the model ( previously illustrated in Fig 2O ) . Fig 3C shows the maximum value of pERK1/2 ( normalized to the total ERK1/2 level ) plotted against the concentration of VEGF for various values of total SphK1 concentrations . As illustrated by the figure , maximum pERK1/2 exhibits a threshold response for sufficiently high values of SphK1 concentrations . The threshold value for VEGF here is defined as the concentration of VEGF below which pERK1/2 is zero . The baseline model with 100 nM SphK1 results in a threshold value of 5 pM for VEGF . The threshold level increases gradually as the SphK1 concentration is lowered progressively to 10 nM . The increase in threshold value of VEGF is monotonic as shown in Fig 3D , from 36 pM ( [SphK1_0] = 5 nM ) , to 3 pM ( [SphK1_0] = 150 nM ) . The striking model prediction here is that a single parameter , namely the total SphK1 concentration , can have a significant effect on the sensitivity of the cell to VEGF by setting the threshold for ERK1/2 activation . Total SphK1 concentration is also a strong determinant of the amplitude and duration of pERK1/2 as illustrated by the pERK1/2 versus time curves for various values of the total concentration of SphK1 ( Fig 3E ) . According to the model , SphK1 can modulate both the maximum and duration of the pERK1/2 signal . For sufficiently high SphK1 , pERK1/2 signal reaches a plateau with very slow rate of decay ( 3E , green ) . The strength of the SphK1 positive feedback is expected to be influenced by the catalytic rate of SphK1 . This parameter was also a top hit in our global sensitivity analysis . The catalytic rate of SphK1 powerfully modulates the threshold for ERK1/2 activation as demonstrated by Fig 3F . The VEGF threshold value monotonically decreases as the catalytic rate increases as shown by Fig 3G . The threshold value of VEGF decreases from 25 pM to 2 pM , as the catalytic rate of SphK1 is increased from 3 . 7 s-1 to 74 . 5 s-1 . The baseline fitted value of this parameter was kcatSK1 = 37 . 24 s-1 with the threshold value of VEGF = 5 pM . SphK1 catalytic rate also powerfully modulates the shape of the ERK1/2 activation signal as shown in Fig 3H . Once again , for sufficiently high catalytic rates , pERK1/2 signal reaches a plateau phase with no significant decay . The responses to variations in total level of Raf are similar and are summarized in S2 Fig . To investigate the effect of a top negatively correlated parameter within the SphK1 feedback on pERK1/2 dynamics , Fig 3 ( panels I and J ) shows the effect of variations in the dephosphorylation rate of S1P ( kdpS1P ) on the threshold value of VEGF . Increasing kdpS1P , increases the VEGF threshold from 1 pM ( kdpS1P = 0 . 059 s-1 ) to 14 pM ( kdpS1P = 4 . 75 s-1 ) . S1P dephosphorylation rate also crucially determines the maximum and duration of pERK1/2 signal ( Fig 3K ) . Fig 3K shows the striking effect of decreasing kdpS1P on pERK1/2 duration and plateau indicating that for sufficiently low values of this parameter , the pERK1/2 signal reaches a steady state with no decay ( red and blue curves ) . Put together , these results demonstrate that ERK1/2 activation in response to VEGF is critically dependent on parameters affecting the SphK1 and S1P feedback downstream of phosphorylated receptors . Calcium signaling plays a crucial role in the activation of SphK1 by regulating CIB1-dependent SphK1 translocation to the membrane . Sensitivity analysis also identified the rate of membrane calcium pump ( see Fig 2B ) as being significant . Here we consider perturbations of calcium dynamics and present concrete and experimentally testable predictions of the model . The plasma membrane calcium pump ( PMCA ) rate significantly perturbs the duration of the ERK1/2 activation and is a sensitive determinant of the plateau phase of the signal as shown in Fig 4A . Blocking PMCA is predicted to convert a transient pERK1/2 signal into a plateau-phase response ( Fig 4A , green to blue ) . Inhibition of ER calcium pump ( SERCA ) is considered next . Increasing the pump rate powerfully influences the shape of the pERK1/2 signal ( Fig 4B ) . Similar to PMCA , sufficient inhibition of the SERCA pump can transform a transient pERK1/2 signal into a plateau signal with no decay over time ( 4B , blue ) . Increasing the amplitude of the CRAC channel ( with baseline fitted value of 1 . 74x104 μM/s ) is also predicted to have a significant effect on the duration of the pERK1/2 signal as demonstrated in Fig 4C . Sufficient increase in CRAC channel current amplitude can lead to a sustained pERK1/2 signal with no decay ( Fig 4C , pink and green ) . We next consider the concentration of the calcium binding protein CIB1 that is critical for SphK1 translocation to the plasma membrane in the model . The concentration of CIB1 is varied and the effects on pERK1/2 are considered . ERK1/2 activation is insensitive to total concentration of CIB1 higher than 0 . 5 μM ( baseline value is 0 . 5 μM ) . However , pERK1/2 monotonically decreases as the CIB1 is reduced from 0 . 5 μM to 0 ( Fig 4D ) . Sample pERK1/2 versus time curves in response to variations in CIB1 concentration are shown in Fig 4E . For sufficiently small values of CIB1 concentrations ( 5nM ) , there is no ERK1/2 activation ( Fig 4E , blue ) . We next considered the effect on pERK1/2 of altering the dissociation constant for the binding of CIB1 to SphK1 . As shown in Fig 4F , the changes in the binding constant affect maximum pERK1/2 . Sample traces are shown in Fig 4G . The change in binding affects both the duration and the amplitude of the pERK1/2 signal . Another important parameter in SphK1 activation is CIB1/SphK1 translocation rate constant from the cytoplasm to the plasma membrane . The maximum pERK1/2 is relatively stable for translocation rates down to 0 . 1 s-1 ( Fig 4H ) . However , below this value , the maximum pERK1/2 rapidly and monotonically declines to zero . In fact , a lower bound of 0 . 044 s-1 ( time constant of ~22 s ) is predicted to be necessary for ERK1/2 activation . Sample traces are also shown in Fig 5I . The translocation time course of CIB1/SphK1 is thus predicted to be an important regulator of the pERK1/2 dynamics in response to VEGF . Overall , these simulations predict that pERK1/2 shape and dynamics can be strongly modulated by the perturbations in calcium dynamics including the PMCA rate , SERCA pump rate , and the current through the CRAC channels . ERK1/2 activation is also regulated by the concentration of CIB1 protein , the strength of CIB1 binding to SphK1 , and the translocation time constant of the activated CIB1 from the cytoplasm to the plasma membrane . We also carried out simulations to evaluate the effect of receptor-level perturbations on ERK1/2 activation . These included changes in VEGFR2 internalization rate ( S3 Fig ) , dephosphorylation rate ( S4 Fig ) , and the number of receptors ( S5 Fig ) . The main result from these simulations is that ERK1/2 activation is predicted be stable in response to a wide range of changes in parameters that determine receptor dynamics . Considering signal transduction from VEGFR2 to downstream species , it is tempting to assume that ERK1/2 activation is linearly correlated with the extent of phosphorylated VEGFR2 . Our hypothesis was that the effect of SphK1 feedback on ERK1/2 activation would result in a fundamentally different relation between pERK1/2 and pVEGFR2 . In fact , we already observed that the model predicted the existence of threshold behavior in ERK1/2 activation in response to VEGF concentration . In this section , we investigate the behavior of pERK1/2 versus pVEGFR2 predicted by the model . As shown in Fig 5A , pERK1/2 is relatively insensitive to changes in the phosphorylation rate down to 98 . 5% of the baseline phosphorylation rate . Further , pERK1/2 exhibits a threshold behavior in response to receptor phosphorylation . For values larger than 0 . 64 s-1 ( 1 . 5% of the control value ) , ERK1/2 activation is unscathed . Plotting maximum pERK1/2 versus maximum fractional pVEGFR2 ( fraction of total VEGFR2 ) results in a highly non-linear relation presented in Fig 5B . The model predicts that there is a threshold fraction of pVEGFR2 below which there is no ERK1/2 activation . This threshold value is ~4 . 6% , meaning that only 4 . 6% of the total receptors need be phosphorylated to induce a robust ERK1/2 activation . SphK1-dependent ERK1/2 activation is therefore robust to variations in phosphorylated VEGFR2 population , implying that above 4 . 6% fractional pVEGFR2 , ERK1/2 activation is not fundamentally altered by changes in pVEGFR2 fraction . For instance , reducing pVEGFR2 fraction from 30% to 10% , does not significantly affect ERK1/2 activation and the sole predicted effect is a reduction in maximum pERK1/2 from ~0 . 23 to ~0 . 15 ( Fig 5B ) . We next investigated parameters within the SphK1 feedback that might alter this threshold value . As shown in Fig 5C , pVEGFR2 threshold value in ERK1/2 activation is strongly modulated by the concentration of SphK1 in the cell . 50% reduction in SphK1 concentration ( baseline value of 0 . 1 μM ) , increased the threshold value from 4 . 6% to ~9% . Increasing SphK1 to 1 μM , reduced the threshold further to 0 . 8% , implying that under conditions where SphK1 is overexpressed in the cell , less than 1% of the receptors need be phosphorylated for robust ERK1/2 activation . Similar pattern held true for kdpS1P , a parameter determining the dephosphorylation rate of S1P ( Fig 5D ) . Increasing S1P dephosphorylation by 4-fold , increased the threshold value from 4 . 6% to 15% . Decreasing S1P 10-fold , reduced the threshold value from 4 . 6% to 0 . 8% , once again implying that under heightened S1P levels , less than 1% of the receptors are required to be phosphorylated for effective ERK1/2 activation . Threshold behavior is also predicted for the active SphK1 ( Fig 5E ) and S1P ( Fig 5F ) , implying the existence of intimate connection between SphK1 and ERK1/2 dynamics . The model thus proposes a radical hypothesis that SphK1 feedback endows the cells with the ability to effectively activate ERK1/2 in response to small fraction of active VEGFR2 on the cell and effectively shield downstream ERK1/2 activation from slight perturbations in receptor phosphorylation rate . The model was then utilized to simulate the anti-angiogenic strategies targeting VEGF pathway . Fig 5G simulates the effect of depleting VEGF ( with an agent such as bevacizumab ) . Model demonstrates that VEGF depletion alone is ineffective in blocking ERK1/2 activation . The figure shows that over 99% of the external VEGF need be depleted before any inhibitory effect on pERK1/2 is observed . Similarly , Fig 5H shows the result of inhibiting the phosphorylation rate with a generic tyrosine kinase inhibitor ( TKI ) such as sunitinib that primarily inhibits receptor phosphorylation . Once again over 99% inhibition is necessary . The model thus predicts that TKIs and antibodies that solely target VEGFR2 autophosphorylation and VEGF are not effective in shutting down signaling to ERK1/2 . Depleting of VEGFR2 by an anti-VEGFR2 mAb agent , e . g . , ramucirumab is predicted to have a lower threshold of inhibition at 66% , but ERK1/2 activation is robust until the threshold depletion level is achieved ( Fig 5I ) . This level of inhibition might still be very challenging to achieve in an in vivo setting where antibody delivery to tumors is a confounding effect . Moreover , ERK1/2 activation is robust up to 66% VEGFR2 depletion . Next we simulate a combined inhibition of receptor phosphorylation and Raf by a generic small molecule TKI such as sorafenib ( Fig 5J ) . This is much more effective in gradually inhibiting pERK1/2 amplitude until complete inhibition at 78% . This is in contrast with previous cases where drop in ERK1/2 amplitude to zero is very sudden ( switch-like ) occurring at a threshold inhibition level . Combining sorafenib and ramucirumab is even more effective achieving full blockade of pERK1/2 at 41% inhibition ( Fig 5K ) . Combining sorafenib and ramucirumab with an anti-SphK1 agent further improves the inhibitory potential reducing the threshold for inhibition to 36% . These simulations demonstrate that combination therapy is essential in achieving efficient and sustained ( pERK1/2 amplitude decreasing as a function of percent inhibition as in Fig 5J , 5K and 5L ) inhibition of VEGF signaling to ERK1/2 .
An important prediction of the model is the existence of threshold in VEGF activation of ERK1/2 . This implies that there is a critical value of VEGF concentration below which ERK1/2 activation ceases . The EC50 for the activation of VEGFR2 by VEGF is ~30 pM [43] . Soluble VEGF is capable of activating ERK1/2 in concentrations as low as ~12 pM in HUVEC [44] and ~6 pM in PAEC [45] . What emerges from Constraining the model with these experimental data is the existence of VEGF threshold of ~5 pM for ERK1/2 activation . An interesting observation in [44] was that in the absence of the heparin binding domain of fibronectin , the antibody to phosphorylated VEGFR2 did not detect a signal at 1 ng/ml ( 24 pM ) , while pERK1/2 signal was still detectable down to VEGF concentrations of 0 . 5 ng/ml ( 12pM ) . One experimentally verifiable prediction of the model is that by overexpressing SphK1 , one should be able to increase the lower bound for the activation of soluble VEGF . The pERK1/2 versus pVEGFR2 curve ( Fig 5B ) is also revealing , demonstrating that robust activation of ERK1/2 is achieved with only 4 . 6% of the receptors phosphorylated . The small population of phosphorylated receptors required for activation of ERK1/2 , might be the reason for the difficulty in detecting phosphorylated receptors at low VEGF concentrations while being able to detect the pERK1/2 signal . The model provides a rationale and motivation for performing further experiments at more physiological concentrations of VEGF , even in the absence of detectable phosphorylated receptor species . Calcium elevation is essential for angiogenic response to VEGF , and inhibition of VEGF-mediated calcium influx prevents endothelial cell proliferation [48 , 52 , 53] . Moreover , strong buffering of cytoplasmic calcium blocks ERK1/2 activation downstream of VEGF [54] . To our knowledge , the computational model developed here is the first of its kinds to incorporate specific calcium cycling mechanisms downstream of VEGFR2 , including the CRAC channels that have been experimentally shown to be critical for angiogenesis [36] . The calcium influx through the CRAC channels is predicted to alter the plateau phase and duration of the pERK1/2 signal . Indeed , complete inhibition of CRAC channels abolishes the pERK1/2 plateau phase . Experimentally , calcium channel inhibition has been explored as a viable target in cancer for inhibiting angiogenesis in solid tumors [55] and ovarian cancer [56] . Moreover , some types of cancer have been shown to down-regulate the expression PMCA and SERCA pumps [57] that are predicted by the model to increase the duration and amplitude of pERK1/2 signal ( Fig 4A and 4B ) . The model predicts that calcium signaling should not be overlooked when investigating the activation of angiogenic pathways by VEGF . Further experimental evidence is needed to elucidate and test the predicted link between calcium dynamics and ERK1/2 activation . Overall , the model predicts that changes in the amplitude and duration of the calcium transient by interventions such as changes in the activity of SERCA and PMCA pumps , and CRAC channels may modulate VEGF-dependent ERK1/2 signaling . Moreover , any therapeutic agent that interferes with calcium signaling might also have far-reaching effects on VEGF-mediated angiogenesis . In cancer , the goal is to inhibit angiogenesis and prevent tumor vascularization and growth . Our simulations show that antibodies such as bevacizumab [58 , 59] that target VEGF would not be effective at shutting down VEGF signaling to ERK1/2 unless 99% inhibition of VEGF is achieved . This high threshold for ERK1/2 inhibition would seriously hinder the applicability of the antibody as an effective anti-angiogenic agent and might explain the limited increase in overall survival ( usually less than 6 months ) in cancers responding to bevacizumab treatment such as metastatic colorectal cancers [60–62] , non-small-cell lung cancer ( NSCLC ) [63 , 64] , metastatic renal cell carcinoma [65] , and ovarian cancer [66] . In metastatic breast cancer , bevacizumab resulted in no improvements in overall survival [67] . While our model has focused on one specific pathway ( namely ERK1/2 ) , it does show that even within the context of a single pathway , monotherapy in the form of anti-VEGF antibody would not be effective unless the concentrations are sufficiently high so that 99% of the ligand molecules are sequestered . This is a very stringent requirement in any in vivo setting , especially that we are not even including the very real possibility of developing resistance to anti-VEGF therapies [68] . Similarly , simulating the inhibition of VEGFR2 autophosphorylation by a generic TKI such as sunitinib that primarily inhibits receptor phosphorylation indicates that over 99% inhibition is necessary before signaling to ERK1/2 is compromised . Sunitinib has been approved for use in advanced renal cell carcinoma and increases median overall survival from 21 . 8 to 26 . 4 months [69] . What is suggested by our modeling exercise is that the high threshold for inhibition of VEGF-ERK1/2 signaling might explain some of the difficulties in effective inhibition of VEGF-mediated angiogenesis with TKIs in various types of cancers . The model also emphasizes the absolute necessity of developing more efficient TKI drug delivery strategies to enhance local concentration of the drug in tumor microenvironment in order to overcome the inhibition threshold . An interesting prediction from the model is that compared to other mono-therapies ( agents targeting a single node of the VEGF pathway ) , depleting VEGFR2 with an antibody such as ramucirumab should be more effective , exhibiting lower inhibition threshold ( ~66% ) . Once again , ERK1/2 activation is robust up until the threshold level of inhibition is reached . Similar to other agents , ramucirumab has shown limited efficacy in certain types of tumors such as advanced gastric [70] and metastatic advanced non-small-cell lung carcinoma [71] . Our simulations suggests that when it comes to inhibiting VEGF signaling to downstream effectors such as ERK1/2 , combination therapy seems to be essential . In fact , according to the model , TKIs such as sorafenib are more effective because they inhibit signaling at both the receptor level ( VEGFR2 phosphorylation ) and a downstream effector node ( Raf ) . It is indeed interesting to note that sorafenib is the only FDA approved anti-angiogenic agent for hepatocellular carcinoma ( HCC ) and the only TKI ( in the list of inhibitors in clinical trials for HCC ) that targets two distinct nodes of the VEGF/VEGFR2 pathway [72] . Simulations demonstrate more effective combination strategies . For example , combining ramucirumab and sorafenib achieves a lower threshold of inhibition ( 41% according to the model ) with rapid decline in maximum pERK1/2 as a function of inhibition . Another example is triple combination involving sorafenib , ramucirumab , and an inhibitor for Sphk1 pathway ( such as sphingomab [73] ) that is predicted to further improve the inhibitory effects on VEGF-ERK1/2 axis . In all , the model developed here demonstrates some of the challenges in developing effective anti-angiogenic therapies targeting the VEGF pathway and highlights the need to consider specific pathway dynamics ( e . g . threshold behavior ) and structure ( e . g . positive and negative feedback loops ) when evaluating therapeutic interventions . The most clinically relevant prediction from the current model is that even in inhibiting a single pathway involving VEGF signaling to ERK1/2 and in the absence of any consideration of tumor resistance , combination therapeutic strategies seem to be essential . There are several aspects of the model that can be improved in the future . The model includes only a single phosphorylation site on VEGFR2 . The modular structure of BioNetGen allows for additional phosphorylation sites to be included and investigated . These can in principle be readily included and investigated in the future . Another limitation is the simplified description of receptor recycling that includes only two compartments , namely the surface receptors and the receptors within the signaling endosomes . Including Rab specific compartments similar to the model in [26] would significantly increase model complexity and molecular detail . The calcium cycling module includes a phenomenological description of the current through CRAC channels as a function of calcium concentration within the ER lumen . This model can be improved by including dynamic STIM oligomerization similar to the model in [74] . The model also does not include TRPC calcium channels that are regulated by DAG and are shown to be important in VEGF-mediated angiogenesis [52 , 75] . As additional data with specific inhibitors of TRPC channels become available , the relation between TRPC signaling and ERK1/2 activation downstream of VEGF would be a fruitful avenue of investigation in the model . Pertaining to SphK1 signaling , a confounding pathway is the activation of S1P receptors [76 , 77] ( S1PR1 and S1PR2 ) by the S1P generated downstream of VEGF . Including this receptor would go well beyond the scope of the current study; however , rule-based modeling would indeed be very suitable for studying the interaction between VEGFR2 and S1PRs at the receptor and downstream levels and the future versions of the model can include this important pathway .
The rules for the interaction of the receptors and downstream signaling details are incorporated into the BioNetGen text file and can be accessed with ease and is included in the supplementary material . We have also supplemented the SBML file associated with the model . The binding of VEGF to VEGFR1 , VEGFR2 , and NRP1 follows standard kinetic schemes similar to previous studies [32 , 78] . List of parameters with their descriptions is also includes in S1 Table . S2 Table contains the initial values for the seed species in the model . The rules generate 208 species and 932 reactions . The binding of PLCγ to pVEGFR2 and subsequent phosphorylation and dissociation of PLCγ from the receptor is described by a Michaelis-Menten type reaction as follows: pVEGFR2surface + PLCγ → pVEGFR2surface + pPLCγRate=kpPLCγ[pVEGFR2surface][PLCγ][PLCγ]+KmPLCγ/R2 ( 1 ) pVEGFR2membrane + PLCγ → pVEGFR2membrane + pPLCγRate=kpPLCγ[pVEGFR2membrane][PLCγ][PLCγ]+KmPLCγ/R2 ( 2 ) Note that BioNetGen accounts for all the VEGFR2 species that are phosphorylated . This approach significantly lowers the number of reactions generated by the rules and prevents combinatorial explosion in the model . Another aspect of the model is the generation of active Ras ( RasGTP ) by S1P . As the precise link between S1P and Ras activation is not entirely clear , we assume a Michaelis-Menten type reaction: RasGDP+S1P→RasGTP+S1PRate=kS1P/Ras[S1P][S1P]+kmS1P/Ras ( 3 ) with parameters kS1PRas and Km , S1PRas determining the strength of Ras activation by S1P . More details of calcium cycling and SphK1 activation module are given in the supplementary material . BioNetGen created the network as a set of reactions and the corresponding ordinary differential equations ( ODEs ) saved as a C file , readable with MEX functionality in MATLAB ( Mathworks , 2015 ) . The set of ODES was numerically integrated using SUNDIAL numerical solver suite [79] . For parameter fitting , we applied a direct search algorithm implemented in the MATLAB function patternsearch as part of the global optimization toolbox . All the pieces of data , including the surface and total receptor levels and downstream activation were fitted simultaneously in MATLAB . Data from the western blot images were extracted using the software imageJ [80] . Global sensitivity analysis was performed using the partial rank correlation coefficient ( PRCC ) algorithm described in [51] . The parameter values were randomly chosen from a uniform distribution within a range 0 . 01 × fitted_value ≤ p ≤ 20 × fitted_value . | Vascular endothelial growth factor ( VEGF ) signaling is a potent regulator of angiogenesis , the growth and development of new vessels out of a preexisting vascular network . Angiogenesis requires enhanced survival , proliferation , and motility of the vascular endothelial cells . Crucial signaling endpoints in VEGF-mediated angiogenic response include elevation in intracellular calcium and the activation of the proteins ERK1 and 2 ( ERK1/2 ) . In this study , we have developed a novel computer model for the activation of ERK1/2 and calcium downstream of VEGF receptor type 2 ( VEGFR2 ) . Our model is the first of its kind to incorporate and investigate the consequences of calcium elevation and the role of a cellular lipid modifier known as sphingosine kinase 1 ( SphK1 ) . We also utilize the model to simulate therapeutic strategies targeting VEGF signaling to ERK1/2 indicating inefficiency of single therapies known as tyrosine kinase inhibitors ( TKI ) that target receptor phosphorylation . Computer simulations indicate that combination therapy is essential for effective blockade of this important pathway . Our results have important implications for human diseases such as cancer where plethora of anti-VEGF therapies are currently employed . Overall , our computer model sheds new light on a complex feedback involving SphK1 and calcium that radically alters the response of cells to VEGF . | [
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] | 2017 | Computational investigation of sphingosine kinase 1 (SphK1) and calcium dependent ERK1/2 activation downstream of VEGFR2 in endothelial cells |
Photosynthesis is a crucial biological process that depends on the interplay of many components . This work analyzed the gene targets for 4 transcription factors: FnrL , PrrA , CrpK and MppG ( RSP_2888 ) , which are known or predicted to control photosynthesis in Rhodobacter sphaeroides . Chromatin immunoprecipitation followed by high-throughput sequencing ( ChIP-seq ) identified 52 operons under direct control of FnrL , illustrating its regulatory role in photosynthesis , iron homeostasis , nitrogen metabolism and regulation of sRNA synthesis . Using global gene expression analysis combined with ChIP-seq , we mapped the regulons of PrrA , CrpK and MppG . PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions , while CrpK , a previously uncharacterized Crp-family protein , regulates genes involved in photosynthesis and maintenance of iron homeostasis . Furthermore , CrpK and FnrL share similar DNA binding determinants , possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant . We show that the Rrf2 family protein , MppG , plays an important role in photopigment biosynthesis , as part of an incoherent feed-forward loop with PrrA . Our results reveal a previously unrealized , high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators , while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis .
Photosynthetic organisms are central to life on the planet . Their ability to harness solar energy and fix atmospheric carbon dioxide makes them integral parts of most ecosystems . Furthermore , many photosynthetic microbes , either naturally or via modifications , are capable of producing a variety of valuable commodities such as grain for food , hydrocarbons , hydrogen gas and valuable chemicals [1]–[4] . These properties will likely make them important in efforts to develop more sustainable societies . We are interested in obtaining new knowledge about the transcriptional networks of photosynthetic cells that underlie these important activities . Anoxygenic photosynthetic bacteria have and continue to provide significant insight into the networks that govern photosynthetic activities because of their ease of growth , genetic tractability , and prior knowledge about solar energy capture and other aspects of this lifestyle [3] , [5] . The advent of genomic approaches has allowed development of metabolic and transcriptional regulatory network ( TRN ) models for bacterial photosynthesis , the latter of which has led to predictions about regulatory networks in photosynthetic cells that extend beyond prior knowledge [6]–[10] . Thus , there is likely still much more to be learned about photosynthesis through testing the predictions of metabolic and TRN models in well-studied photosynthetic organisms . To obtain this new knowledge , we analyze Rhodobacter sphaeroides , the best studied member of the purple non-sulfur bacteria – a group of photosynthetic microbes displaying great metabolic versatility and having significant biotechnological potential [1] , [7] , [11]–[18] . R . sphaeroides is capable of growing by aerobic respiration , anaerobic respiration and anaerobic anoxygenic photosynthesis . Prior analysis indicates that transitions between aerobic respiratory and anaerobic photosynthetic growth is achieved , in part , via a TRN involving 3 global transcription factors ( TFs ) – PrrA , FnrL and PpsR – that act to activate or repress relevant operons depending on the presence of oxygen or other signals . For instance , PrrA ( the response regulator of the PrrAB two component system ) and FnrL ( the R . sphaeroides homolog of FNR ) directly activate transcription of photosynthesis related genes at low oxygen tensions [9] , [19]–[25] . On the other hand , PpsR represses the expression of photosynthesis related genes at high oxygen tensions [8] , [26] , [27] . In addition to these TFs , a small non-coding RNA , PcrZ has recently been implicated in the regulation of photosynthesis gene expression in R . sphaeroides [28] . While there is considerable information on how these regulators impact some photosynthesis genes , global information on their targets and how they act together to impact this lifestyle is lacking . Furthermore , a large-scale reconstruction of the R . sphaeroides TRN [10] , which combined comparative genomics analysis with global gene expression data , predicted that two previously uncharacterized TFs , CrpK and RSP_2888 ( hereafter referred to as modulator of photopigment genes , MppG ) , were involved in controlling the transcription of a number of operons that encode key functions involved in photosynthesis in R . sphaeroides , suggesting that the photosynthetic TRN of this organism is more complex than previously thought . In this work , we use a combination of genetic , genomic and physiological analysis to dissect the roles of 4 TFs known or predicted to be involved in the regulation of the photosynthetic lifestyle of R . sphaeroides . The regulons of the previously characterized TFs , PrrA and FnrL , were refined and extended , while those of CrpK and MppG were characterized for the first time . Our analysis confirmed many predictions of the large-scale R . sphaeroides TRN , revealed the existence of significant overlap in direct targets for these TFs , as well as the high degree of combinational regulation of key operons . We also identified how components in this photosynthetic TRN provide robustness and fine-tuned expression of target genes . Overall , this study provides a large amount of new insight into the photosynthetic TRN of R . sphaeroides that is likely to be conserved in other related photosynthetic bacteria .
Based on previous analysis in R . sphaeroides and related purple non-sulfur bacteria , FnrL , PrrA and PpsR have been identified as key regulators of the photosynthetic lifestyle [8] , [9] , [20] , [24] , [27] , [29] . We have previously characterized the genome-wide binding sites of PpsR via chromatin immunoprecipitation followed by sequencing ( ChIP-seq ) and gene expression analysis [10] . This analysis identified a total of 15 PpsR target operons , of which 13 had photosynthesis related functions . Here , we analyze the regulons of FnrL and PrrA using both ChIP-seq and global gene expression analysis . FnrL is an iron-sulfur cluster-containing Crp-family TF that has been reported to be essential for both photosynthetic and anaerobic respiratory growth in R . sphaeroides [9] , [25] . ChIP-chip analysis has previously been used to map genome-wide FnrL binding sites in vivo , identifying targets that indicate the direct involvement of this protein in a host of processes including those required for photosynthetic and anaerobic respiratory growth [24] . However , a large-scale reconstruction of R . sphaeroides TRN predicted that the FnrL regulon is significantly larger than previous analyses suggested . Thus , we re-examined the FnrL regulon using new and higher resolution complementary genomic approaches . PrrA is the response regulator of the two component PrrAB system that has previously been proposed to be a major global TF in R . sphaeroides and related purple non-sulfur bacteria [20] . PrrA is essential for photosynthetic growth in R . sphaeroides and direct control of photosynthesis-related operons by PrrA has been shown via the use of in vitro experiments [22] , [32] . To obtain a better understanding of the functional role of PrrA , we assessed PrrA activity using genome-wide gene expression data and ChIP-seq . A recent large-scale reconstruction of the TRN of R . sphaeroides predicted that there are additional regulators of photosynthesis . Among the highest scoring TFs that fell into this category were: ( i ) CrpK ( RSP_2572 ) , a Crp/Fnr-family regulator , and ( ii ) MppG , a BadM/Rrf2-family protein . Using a combination of physiological , genetic and genomic analysis , we investigated the contributions made by these proteins to regulation of photosynthesis in R . sphaeroides . CrpK is a Crp/Fnr-family TF , which based on Pfam analysis [34] , shares similar cyclic nucleotide-binding and Crp-like helix-turn-helix domains as FnrL . However , unlike FnrL , CrpK does not possess the 4 cysteine residues at its N-terminus required for coordination with iron-sulfur clusters , suggesting CrpK might not directly sense oxygen . Nevertheless , ectopic expression of CrpK in an ΔfnrL mutant from an IPTG-inducible plasmid restores photosynthetic growth on succinate ( S3A Figure ) , indicating CrpK might directly regulate a similar set of genes as FnrL . However , a markerless crpK deletion mutant is capable of photosynthetic growth on succinate ( S3B Figure ) , indicating that FnrL and CrpK might also have distinct targets . In addition , CrpK transcript levels are ∼2 to 3 fold higher in photosynthetic cells relative to cells grown under aerobic or anaerobic respiratory conditions ( indicating it may have a physiological role linked to photosynthesis ) , but the levels of CrpK-specific transcripts are lower than those coding for FnrL under all growth conditions that have so far been tested by global expression analysis . Another TF predicted by the large-scale TRN to play a role in the control of R . sphaeroides photosynthesis genes is the BadM/Rrf2 family TF , MppG . mppG transcript levels are increased under photosynthetic conditions in WT cells and mppG is predicted to be a direct target of PrrA ( Table 2 , Fig . 2B ) . Consistent with this , mppG transcript levels are more than 5 fold higher in WT cells relative to ΔprrA , being the most differentially expressed TF in that dataset ( S3 Table ) . To test the role of MppG in regulation of photosynthesis , we conducted a combination of physiological , gene expression and protein-DNA binding assays for this TF .
Previous analysis of photosynthetic gene expression in R . sphaeroides had established the importance of 3 global TFs , PpsR , PrrA and FnrL , in the regulation of this lifestyle [8] , [9] , [19] , [20] , [24] , [26] , [27] . Our global analysis of these previously identified members of this TRN extends prior knowledge by comprehensively identifying the direct targets for two of these proteins , PrrA and FnrL . The work in this paper also , complements parallel genome-wide analysis of a global repressor of photosynthesis , PpsR [10] . For example , our analysis indicated the total number of genes , directly or indirectly , controlled by R . sphaeroides PrrA is ∼4 times smaller than previously reported , providing a picture of the PrrA regulon that is not influenced by apparent growth-rate differences between wild type and the mutant used previously [20] . In addition , our data verified the major direct role played by this TF in photosynthesis gene expression , significantly expanding previous analyses that reported the ability of PrrA to bind DNA in vitro at a handful of sites [32] , [38] , [39] Our data show that PrrA , controls expression of genes required for light energy capture , as well as a number of operons encoding proteins involved in electron transport both directly ( e . g . , fbcFBC complex , cytochrome B561 and cycA [40] ) and indirectly ( S3 Table ) . Although we identified several new PrrA direct targets , we were unable to identify a strong consensus binding motif for this TF . While PrrA , and its analog in Rhodobacter capsulatus RegA , have been proposed to bind a degenerate GCG inverted repeat with a varying length spacer region , previous analyses have suggested that both DNA curvature and sequence specificity might contribute to target site recognition [23] , [32] . These potential features , together with the GC-rich nature of the R . sphaeroides genome and the Fis-like nature of the PrrA DNA binding domain [23] , possibly made it difficult to identify a shared motif among target genes from our analysis . Thus , despite identifying members of the PrrA regulon , it is not always possible to predict the consensus binding site for a given TF . A large-scale reconstruction of the R . sphaeroides TRN predicted that the regulon of a second mater regulator , FnrL , was larger than previously described [24] . Our studies verified several of these predictions , significantly extending the size and function of genes in the FnrL regulon to include nitrogen regulatory proteins , iron sulfur assembly proteins , ABC transporters , additional TFs and recently identified sRNAs , all of which significantly increase the scope of genes and functions that are controlled by FnrL ( see below ) . This illustrates how the predictions of a large-scale TRN can provide new insights into the functions that are regulated by even a previously well-studied TF . One of the previously uncharacterized TFs we tested for a role in the photosynthetic lifestyle was CrpK . Genome-wide analysis of CrpK targets revealed an overlapping but distinct regulon to that of FnrL , providing an explanation for the ability of CrpK to rescue the photosynthesis defect of an FnrL deletion strain . While bacterial TRNs are often tightly controlled , they also need to be robust to allow cells to adapt to potentially deleterious changes to these networks . Given the central role of FnrL in regulating photosynthesis and a large number of anaerobic processes , the redundancy observed between this TF and the CrpK regulon might function to provide robustness to the R . sphaeroides photosynthetic TRN . Alternatively , CrpK might have a broader function under different conditions from FnrL . For instance , FnrL contains an O2-sensitive iron sulfur cluster that controls it DNA binding activity [24] , so the absence of such a metal center in CrpK might allow this protein to function under conditions that would inactivate FnrL , facilitating photosynthesis or other metabolic functions during microaerophilic or semi-aerobic growth in nature . Interestingly , while FnrL binds upstream of dorS ( encoding the histidine kinase of the DorSR two-component system involved in regulation of anaerobic respiratory growth on DMSO [41] ) , CrpK binding was not observed at this promoter ( S4 Figure ) . This suggests that CrpK's ability to functionally replace FnrL might not extend to FnrL's role in regulation of anaerobic respiration . In order to assess predicted cases of redundancy in a TRN or overlapping target genes of TFs , it is important to identify promoter elements that may allow discrimination between binding sites . For instance , although the predicted consensus motifs derived from the FnrL and CrpK binding sites were similar , the observation that both proteins can recognize unique , as well as overlapping sites , indicates there must be some subtle but functionally significant differences in DNA recognition by these TFs . Closer inspection of these DNA sequence motifs , suggest there may be a greater tolerance by FnrL for deviations from the TGA N6 TCA consensus , while bases within the spacer region or outside the core target site might play a role . Under the anaerobic photosynthetic growth conditions typically used in the lab , the CrpK transcript is present at a significantly lower level than that of FnrL . While it is possible that both proteins might compete for some shared binding sites under laboratory conditions , our analysis of a few shared or unique promoters suggested this was likely not a major factor in our experiments ( S3E Figure ) . However , reproducibly greater FnrL enrichment was observed at the ccoN/RSP_0697 promoter in the absence of CrpK ( S3D Figure ) , so the possibility that multiple TFs can compete for binding at selected sites cannot be ruled out without additional genetic and biochemical studies . This illustrates the need to couple predictions of TRN function with experimental studies . The second photosynthesis-related TF characterized for the first time in this study was MppG . Our data showed that MppG functions as a direct transcriptional repressor of photopigment biosynthesis operons , including bchCXYZ and bchFNBHM , with high cellular levels of this protein inhibiting photosynthetic growth . In addition , transcripts from several other operons that encode photosynthesis-related functions were indirectly repressed by MppG . Our data predict that much of this indirect regulation of photosynthesis function is achieved through the direct regulation of the gene that encodes the anti-repressor , AppA , by MppG . Reduced cellular levels of AppA caused by the presence of MppG , would in principle cause accumulation of free PpsR under photosynthetic conditions , which would lead to repression of the photosynthesis-related genes that are PpsR targets ( Fig . 5 ) . Given that mppG transcript levels are significantly elevated during photosynthetic growth , its function in repressing photopigment synthesis would appear to be counterintuitive , similar to the observation for the sRNA , PcrZ [28] . Since no significant difference in photosynthetic growth was observed between WT and ΔMppG cells , the additional pigment produced in the ΔMppG mutant strain did provide increased fitness , potentially equating to a waste of cellular resources in the production of this extra pigment . In addition , the presence of excess photopigment could be a source of metabolic stress , especially since they can result in production of reactive oxygen species if light is present under microaerophilic conditions in the lab or in nature [42] . Thus , MppG may function as a negative modulator of pigment synthesis to ensure the optimal expression and tight coordination between expression of photopigment biosynthetic pathway genes and those for other components of the photosynthetic apparatus . In addition to its role in photosynthesis , MppG also regulates , either directly or indirectly , a variety genes encoding iron/heme dependent proteins ( AppA , RdxBHIS , BchX , BchL , RSP_2785 ) and iron/heme transporters ( RSP_2913 , HmuS ) . MppG shares a high degree of amino acid sequence similarity to RirA , which was previously shown to regulate iron-responsive genes in Rhizobium leguminosarum [43] , [44] . Thus , in addition to its role in regulation of photopigment synthesis , MppG appears to have a previously unidentified role in maintaining iron homeostasis during photosynthetic growth in R . sphaeroides . Furthermore , like RirA , MppG possesses a set of cysteine residues in its C-terminal region , which could coordinate an iron-sulfur cluster or some other metal , potentially allowing it to directly sense signals such as oxygen or metal availability . Our data also provide new evidence that both FnrL and CrpK directly regulate genes encoding iron-dependent , iron transport and iron-sulfur biogenesis proteins , as well as several proteins involved in tetrapyrrole biosynthesis . In addition , we showed that FnrL directly activates expression of another RirA-like protein , RSP_3341 , which has also been shown to directly regulate other iron dependent genes in R . sphaeroides [10] . Thus , we have provided new evidence that the TRNs and TFs controlling photosynthesis and iron homeostasis are tightly linked in R . sphaeroides . This link is likely , at least in part , due to the anaerobic anoxygenic mode of photosynthesis in R . sphaeroides , the sensitivity of Fe-S clusters to oxygen , and the involvement of a variety of iron-dependent proteins in light energy capture or other aspects of photosynthesis . For complex TRNs to function effectively , the components of the network often need to communicate with one another , and this is the case with the photosynthetic TRN . For example , our previous analysis of the PpsR regulon identified a PpsR binding site upstream of prrA , in an intra-operonic promoter shown to be occupied by σ70 under photosynthetic conditions [24] . If this PpsR binding site upstream of prrA is functional , it would provide an additional , previously unrecognized , mechanism to prevent aerobic expression of photosynthetic genes . We also found that PrrA directly activates both AppA and MppG , which in turn represses AppA , forming an incoherent feed-forward loop to control photosynthetic genes ( Fig . 5 ) similar to the situation proposed for PcrZ [28] . Thus , our data provide new support for the previous hypotheses that the control of appA transcription serves as a major point of integration of regulatory signals , integrating opposing regulatory inputs from PrrA , MppG , PcrZ , oxygen and possibly other as of yet unidentified factors . Type 1 incoherent feedforward loops can enable significant acceleration in the expression of a target gene in response to a signal compared to simple activation [45] . Thus we predict that this network architecture likely results in a rapid response of cells to small environmental perturbations and allows optimal expression of photosynthetic genes under anaerobic conditions . Bacteria and other cells use a myriad of TRNs to respond to different types of stimuli , with these TRNs varying in depth and complexity [46] , [47] . While the regulation of some cellular processes can be largely controlled by a TRN involving just one TF ( for instance , LexA regulation of DNA repair in some bacteria [48] ) , other cellular processes involve the coordinated activities of multiple globally and locally acting TFs ( e . g . , the regulation of amino acid metabolism by ArgR , Lrp and TrpR in E . coli [49] ) . Generally , cellular processes which result in significant physiological or morphological changes ( such as sporulation in Bacillus [50]–[53] ) or are central to cell survival ( such as central metabolism [54] or chemotaxis [55] ) require highly interconnected TRNs involving multiple TFs and sensory components . The TRN network controlling photosynthesis in R . sphaeroides is multi-faceted , involving the activities of at least 5 TFs ( including 4 global regulators ) and one sRNA ( Figs . 5 , 6 ) . It is not surprising to find that a photosynthetic TRN has these multiple elements given the number and types of functions that need to be coordinated to harvest and conserve the energy in sunlight . In addition , the transition to the photosynthetic lifestyle of R . sphaeroides involves profound changes gene expression [56] , resulting in major physiological changes that culminate in the accumulation of photopigments and formation of specialized intracytoplasmic membrane structures to house the photosynthetic apparatus [57] . Achieving such seamless transitions requires the use of a multi-faceted TRN , parts of which have been described in this work . Some aspects of the TRN controlling photosynthesis in R . sphaeroides show similarities to mechanisms employed in other photosynthetic cells , suggesting that design principles learned from this system will be applicable to other organisms . For instance , in the cyanobacterium Synechocystis sp . , regulation of photosynthetic gene expression has been shown to be under the control of bacterial two-component systems , which are proposed to sense the redox state of the cell and coordinate the expression of the two photosystems ( PSI and PSII ) to maintain redox poise [58]–[60] ( a process referred to as photosystem stoichiometry adjustment [61] ) . In this organism , loss of the response regulator RppA or its cognate sensor kinase RppB , has been shown to result in dysfunctional regulation of PSI and PSII in response to changes in the redox state of the plastoquinone pool [58] . In addition , the sensor kinase Hik2 has also been proposed to sense redox signals [60] and activate its cognate response regulator Rre1 in cyanobacteria [62] . Homologs of Rre1 have been shown to bind to and regulate the expression of photosynthesis related genes in other organisms [60] . Hik2 has also been shown to interact with RppA , potentially controlling its activities in regulation of photosystem stoichiometry [62] . These characteristics could make these systems functionally analogous to the PrrAB system in R . sphaeroides , which has also been proposed to regulate photosynthetic gene expression in response to redox state of the ubiquinone pool [63] . A third cyanobacterial sensor kinase Hik33 is proposed to function as global regulator integrating multiple inputs from different environmental stress conditions including cold and nutrient stress , and high light intensities [60] , [64] . Its cognate response regulator Rre26 has been shown to bind and/or regulate the expression of specific photosynthesis related genes [65] , [66] . Thus , through the use of two-component systems sensing the cell's redox state , cyanobacteria are able to control expression of their photosynthetic apparatus . Cyanobacteria are the ancestors of chloroplasts found in modern day algae and higher plants [61] . While the TRNs controlling photosynthetic gene expression are not as well characterized in plants , the nuclear-encoded homolog of Hik2 , CSK ( a chloroplast sensor kinase which has lost the conserved histidine residue that serves as the site of phosphorylation in bacterial sensor kinases ) , has been shown to be required for the normal expression of PSI and PSII in response to changes in the redox state of the chloroplast plastoquinone pool in Arabidopsis thaliana [61] , [67] . While no cognate response regulator has been identified for CSK , it has been shown to directly interact with the chloroplast-encoded sigma factor SIG1 and PTK ( plastid transcription kinase ) . CSK is proposed to phosphorylate SIG1 when the plastoquinone pool is oxidized resulting in transcriptional repression of PSI genes , while permitting expression of PSII genes [67] . Thus , aspects of the regulatory mechanisms that control transcription of photosynthetic genes appear to be conserved between purple bacteria , cyanobacteria and photosynthetic eukaryotes . One link between cellular redox state and gene expression is achieved through the two-component PrrAB system in R . sphaeroides . However , in addition to this TF , our studies show that R . sphaeroides employs at least 4 other TFs to control photosynthesis . The requirement for these additional TFs is likely , in part , due to the anaerobic nature of photosynthesis in R . sphaeroides , requiring its photosynthetic TRN to incorporate additional systems that sense oxygen tensions and modulate gene expression to minimize production of reactive oxygen species that can damage cellular components [42] . Thus , the R . sphaeroides photosynthetic TRN likely integrates the signals of cellular redox state , oxygen tension and possibly light intensity [68] to achieve optimal expression of photosynthesis genes . It should be noted that in general , the TRNs controlling photosynthesis in cyanobacteria and higher plants , are not as well characterized as that of R . sphaeroides and thus the complexity of these systems are probably yet to be fully appreciated . Historically , TFs were often identified while studying individual metabolic or developmental pathways . Our results illustrate how the ability to globally predict and analyze the roles of TFs provides insight into previously unknown roles and connections between these proteins . For example , in characterizing the regulons of global regulators involved in the transcriptional control of photosynthesis in R . sphaeroides: PrrA , FnrL and CrpK , we found that each of these TFs directly or indirectly regulates a broad range of other cellular processes . Of the total number of operons directly regulated by these TFs , only 18 ( ∼53% ) , 5 ( ∼6% ) and 3 ( ∼10% ) ( for PrrA , FnrL , and CrpK respectively ) , correspond to operons directly involved in photosynthesis , suggesting that these TFs may coordinate the control of photosynthesis with other processes that are beneficial or even required for the photosynthetic lifestyle . In addition to the previously noted example of iron homeostasis , FnrL is predicted to directly regulate the expression of the nitrogen regulatory proteins GlnB and GlnK ( homologs of nitrogen regulatory protein P-II ) , which modulate the synthesis and activity of glutamine synthetase , implicating FnrL in the regulation of nitrogen metabolism [69] , [70] . In addition , both FnrL and CrpK are predicted to directly regulate the expression of aspartate carbamoyltransferase ( RSP_1002 ) , which catalyzes the first step in the pyrimidine biosynthetic pathway . Furthermore , FnrL and CrpK also both directly regulate key enzymes in the electron transport chain including NuoA-N ( RSP_0100-12 ) and CcoNOQP ( Cbb3-type cytochrome c oxidase ) ( Table 3 , S1 Table ) . On the other hand , PrrA is predicted to directly control the expression genes encoding a variety of electron transport chain enzymes including nuoA-N ( RSP_0100-12 ) , fbcFBC , fbcQ and cytochrome B561 ( Table 2 ) . In addition to these metabolic functions , PrrA and FnrL are also predicted to directly regulate the expression of a number of other transcriptional regulators . For instance , PrrA directly activates both MppG and RSP_2854 ( a TetR family TF ) in addition to the anti-repressor of PpsR , AppA . On the other hand , FnrL is predicted to directly regulate 4 other TFs: DorS ( a direct regulator of DMSO reductase ) , RSP_3341 ( a direct regulator of nitrate reductase ) , RSP_4201 ( an ArsR family TF ) and RSP_1243 ( a LacI family TF ) , as well as 2 proposed small RNAs ( RSs0019 and RSs2461 ) . While the cellular roles of some of these putative transcriptional regulators are unknown , the expression of their target genes is likely to be indirectly affected by FnrL and PrrA under appropriate conditions . Consistent with this , over 85% of the 303 differentially expressed genes between WT and ΔFnrL cells did not correspond to direct targets of FnrL . Instead this set of indirectly regulated genes included known direct target of DorS and RSP_3341 , a variety iron transport and iron-dependent genes , genes involved in electron transport ( such as quinol oxidases ) , nitrogen metabolism , and several photosynthetic genes . The set of indirectly controlled PrrA genes included those encoding several enzymes involved in central carbon metabolism ( TCA cycle ) and electron transport . These data predict that , through direct and indirect mechanisms , FnrL and PrrA serve to integrate and coordinate of the processes of photosynthesis , central metabolism , nitrogen metabolism , anaerobic respiration , electron transport , pyrimidine biosynthesis , PHB biosynthesis , phosphate metabolism and iron homeostasis during anaerobiosis ( Fig . 6 ) . These findings provide insight into previously unrecognized processes controlled by these TFs that could potentially be conserved by homologues of these TFs in other organisms . In E . coli , the sRNA FnrS is directly regulated by FNR [71] and functions to repress several target RNAs under anaerobic conditions [72] . The two sRNAs ( RSs0019 and RSs2461 ) , which we found to be direct targets of FnrL , are yet to be functionally characterized [31] and do not share sequence similarities to FnrS . Additionally , the regulatory influence of FnrL on these target sRNAs could not be established in this study , as they are not represented on the R . sphaeroides Affymetrix gene chip used in our analyses . Nevertheless , one might also expect that the indirect targets of FnrL captured in our global expression analysis also includes downstream targets of these regulatory elements . Interestingly , only about 50% of the genes predicted to be directly regulated by FnrL and CrpK were observed to be differentially expressed when the expression profile of the cognate deletion mutants were compared to that of wild type cells . Similar observations have previously been reported for another Crp family protein , FNR , in E . coli [71] . Thus , these observations could reflect the requirement of as yet uncharacterized TFs , which could function as condition-dependent co-activators , for controlling the expression of their target genes [71] . Alternatively , these observation could be the result of condition-dependent repression of specific operons by alternative TFs that obscured the regulatory influence of FnrL and CrpK on their target promoters under the specific conditions used for our global gene expression assays . Our analyses have also shown that several of the photosynthetic operons are under the control of multiple TFs , raising the possibility that the regulatory effect of each of these TFs , could potentially be compensated for by others at some of these operons , as shown for FnrL and CrpK . If this is the case , it could equip the cells with increased robustness in the expression of specific operons , obscuring the regulatory influence of individual TFs . This feature of the TRN would better enable cells to adapt to potentially deleterious changes . Several of the genomic locations that were enriched for PrrA and MppG binding also did not show significant changes in gene expression under the conditions we tested and thus were not considered further as direct targets of these TFs ( see Results ) . In addition , we observed that the TF enrichment at target sites was much lower for PrrA and MppG , than at FnrL and CrpK sites . These observations could reflect fundamental differences in the binding properties of these TFs . For instance , the predicted NMR structure of the PrrA DNA binding domain indicates that it is a Fis-like protein [23] . Previous studies in E . coli have shown that Fis , a nucleoid associated protein , binds to DNA in both a sequence specific and non-specific manner [73] , [74] and that only about a fifth of bound sites are differentially expressed upon deletion of Fis [73] . If PrrA exhibits similar properties , it could account for the large number of bindings sites observed in our ChIP-seq data that do not correspond to genes that are differentially expressed in a PrrA-dependent manner . Using a combination of genetic , genomic and physiological approaches , guided in large part by computational predictions from a large-scale reconstruction of the R . sphaeroides TRN , we obtained a significant amount of new knowledge about regulation of photosynthesis in R . sphaeroides . Our analyses highlight the important role computational predictions can play in guiding biological discovery , as novel components of the photosynthetic TRN , not previously identified using traditional approaches , were identified computationally , with those predictions serving as the basis for this work . We expect that predictions from this large-scale TRN will continue to provide new insights into other aspects of R . sphaeroides diverse metabolic and energetic lifestyles , including those involved in production of high-value commodities such as biofuel precursors . In addition , given the ancestral relationship of R . sphaeroides to plants and other oxygenic phototrophs , we predict that knowledge of this photosynthetic TRN will help inform parallel or future studies in other photosynthetic organisms . Integration of the available large-scale network models of metabolism and transcriptional regulation for R . sphaeroides , will broaden the predictive capabilities of these models and further guide future experimental efforts .
R . sphaeroides 2 . 4 . 1 was used as the parental ( wild type ) strain and all mutants were constructed in this background except the ΔfnrLΔcrpK double deletion strain which was constructed in an existing ΔfnrL mutant background [9] ( S8 Table ) . E . coli DH5α was used as a plasmid host , and E . coli S17-1 was used to conjugate DNA into R . sphaeroides . R . sphaeroides cultures were incubated at 30°C in Sistrom's minimal medium ( SMM ) [75] . Anaerobic cultures were started from cells obtained from exponentially growing aerobic cultures . When required , the media was supplemented with 100 µM IPTG , 25 µg/mL kanamycin , 25 µg/mL spectinomycin , or 1 µg/mL tetracycline . E . coli cells were grown in Luria Bertani medium at 37°C , supplemented with 50 µg/mL kanamycin , 25 µg/mL spectinomycin , or 20 µg/mL tetracycline where needed [11] . Photosynthetic pigments were quantified in R . sphaeroides strains grown photosynthetically in screw cap tubes at a light intensity of ∼10 W/m2 as previously described [76] . Briefly , 5 mL of culture was centrifuged and supernatant discarded . Cells were resuspended in 100 µL of water , transferred to 15 mL glass corex centrifuge tubes held in centrifuge adaptors and covered with rubber stoppers to prevent exposure to light . 4 . 9 mL of a 7∶2 mixture of acetone and methanol was added to the cell suspension and vortexed thoroughly in the dark . Samples were centrifuged for 10 minutes at 10000 g . Absorbance of the supernatant was measured at 775 nm and total bacteriochlorophyll was determined as follows: Abs775 * total volume of sample ( 5 mL ) * ( bacteriochlorophyll molecular weight ( 914 g/mol ) /bacteriochlorophyll millimolar extinction coefficient ( 75 mM−1 cm−1 ) ) . Total bacteriochlorophyll in each sample was normalized to total protein content of samples determined using the Lowry assay [77] . All mutants constructed for this study contained in-frame markerless deletions , which were constructed as previously described [11] , [78] . Briefly , regions spanning ∼1500 bp upstream and downstream of the target gene were amplified using sequence-specific primers containing restriction sites for EcoRI , XbaI or HindIII . These fragments were digested with the appropriate restriction enzymes and ligated into pK18mobsacB [79] , which had been digested with EcoRI and HindIII , by three-way ligation to generate the various gene deletion constructs , which were confirmed by sequencing . The pK18mobsacB-based plasmids were separately mobilized from E . coli S17-1 into relevant R . sphaeroides strains . Cells in which the plasmid had successfully integrated into the genome via homologous recombination were identified by selection on SMM plates supplemented with kanamycin . These cells were then grown overnight in SMM without kanamycin [11] . Cells that had lost the sacB gene via a second recombination event were identified by growth on SMM plates supplemented with 10% sucrose [11] . Gene deletions were confirmed by PCR and sequencing with specific primers ( S8 Table ) . To construct plasmids for the ectopic expression of 3x Myc tagged proteins , we modified pIND5 [80] to include 3 copies of a codon optimized Myc tag ( EQKLISEEDL – GAGCAGAAGCTGATCTCGGAGGAGGACCTG ) within the plasmid's multiple cloning site . New multiple cloning sites were added to allowing tagging of proteins either C-terminally ( NdeI-PstI-NcoI ) or N-terminally ( BamHI-SalI-BglII-HindIII ) . Individual expression plasmids were made by amplifying the target genes from the genome using sequence specific primers ( S8 Table ) containing restriction sites for NdeI and BglII , HindIII or BamHI for cloning into pIND5 [11] and NdeI/NcoI or BamHI/HindIII for cloning into pIND5-3xMyc . These DNA fragments were digested with the appropriate enzymes and cloned into pIND5 or pIND5-3xMyc digested with the same enzymes . These plasmids were conjugated from E . coli S17-1 into the relevant R . sphaeroides strains . Cells which harbor the desired plasmid were identified by selection on SMM plates supplemented with kanamycin [11] . To assay the activity of FnrL and CrpK in vivo , β-galactosidase assays were conducted , as previously described [78] , [81] , in ΔfnrLΔcrpK deletion strains containing different promoter-lacZ fusions integrated into the genome . To construct these reporter strains , ∼200–300 bp regions upstream of putative target genes ( RSP_0281 ( bchE ) , RSP_0696 ( ccoN ) , RSP_0697 ( usp ) , RSP_2346 and RSP_3341 ) , were amplified from genomic DNA using specific primers having NcoI and XbaI restriction sites at their ends ( S8 Table ) . The amplified DNA fragments , as well as a pSUP202 suicide vector containing a promoterless lacZ gene [78] , were digested with NcoI-XbaI . DNA fragments containing the upstream regulatory sequences were cloned into pSUP202 . These promoter-lacZ fusion plasmids were then individually conjugated into the ΔfnrLΔcrpK strain , generating single copy promoter-lacZ fusions integrated in the genome after selecting for the plasmid-encoded tetracycline resistance activity . The fnrL and crpK genes cloned into pIND5 were conjugated into individual reporter strains and cells harboring the reporter construct and the ectopic expression plasmid were identified by selection with tetracycline and kanamycin . These strains were grown aerobically by shaking 10 mL of culture in 125 mL conical flasks until exponential phase , then were treated with 100 µM IPTG for 3 hrs to increase expression of the indicated TF before measuring β-galactosidase activity as previously described [81] . To assess the contribution of specific bases to FnrL and CrpK activity , β-galactosidase assays were conducted in ΔfnrLΔcrpK double deletion strains containing reporter gene fusions of the RSP_0281 ( bchE ) upstream regulatory region with individual point mutations ( see Results ) . These reporter strains were constructed as described above , with individual point mutations being generated by overlap extension PCR . β-galactosidase assays were conducted as described above [11] , [78] , [81] . RNA was isolated from exponential phase cultures of R . sphaeroides strains that were grown photosynthetically in 16 mL screw cap tubes or 500 ml cultures in roux bottles with bubbling ( 95% N2 , 5% CO2 ) [11] , [78] . RNA isolation and subsequent cDNA synthesis , labeling and hybridization to R . sphaeroides GeneChip microarrays ( Affymetrix , Santa Clara , CA ) were performed as previously described [82] . Microarray datasets were normalized by Robust Multichip Average ( RMA ) to log2 scale with background adjustment and quantile normalization [83] . Statistical analysis of normalized data to identify differentially expressed genes was done using the limma package [84] . Correction for multiple testing was done using Benjamini-Hochberg correction [85] . All analyses were conducted in the R statistical programming environment ( http://www . R-project . org ) . R . sphaeroides cells were grown photosynthetically in 500 ml cultures ( see above ) . For FnrL studies , 3 independent ChIP-seq experiments were conducted for WT cells grown photosynthetically with succinate ( 2 replicates ) or acetate as sole carbon source . For tagged TFs , plasmids expressing the tagged variant of the gene from an IPTG inducible promoter , were cloned into the appropriate mutants ( S8 Table ) . Protein expression was induced with IPTG concentrations ( MppG ( 5 µM ) , PrrA ( 10 µM ) and CrpK ( 10 µM ) ) , which restored WT-like growth or pigmentation phenotypes . Cells were harvested at mid-exponential phase and chromatin immunoprecipitation was conducted as previously described [86] , using polyclonal antibody against FnrL [24] or against the Myc epitope tag ( ab9132 , Abcam plc ) for all other TFs analyzed . Immunoprecipitated DNA samples were PCR-amplified , gel purified ( size selection ∼200 bp ) and sequenced at the UW Biotechnoloy Center using the HiSeq 2000 sequencing system ( Illumina , Inc ) . The initial 100 bp sequence tags were trimmed to 70 bp , to remove less reliable DNA sequences , and mapped to the R . sphaeroides strain 2 . 4 . 1 genome ( ftp://ftp . ncbi . nih . gov/genomes/Bacteria/Rhodobacter_sphaeroides_2_4_1_uid57653/ ) using SOAP version 2 . 21 [87] , allowing a maximum of 3 mismatches and no gaps . Peaks that represent potential TF binding sites were identified using MOSAiCS [88] at a false discovery rate of 0 . 05 . The MOSAiCS analysis was conducted as a two-sample analysis , with control ChIP-seq data generated from ΔfnrL grown on acetate ( for FnrL analysis ) , myc antibody ChIP in WT cells ( for myc-tagged proteins ) or input DNA . Only peaks that were called as significant using both input DNA and an appropriate ChIP control were considered as true peaks . Motifs were identified from within peak regions using MEME [89] . All microarray and ChIP-seq datasets generated for this study have been deposited in GEO under the accession GSE58717 . | Photosynthetic organisms are among the most abundant life forms on earth . Their unique ability to harvest solar energy and use it to fix atmospheric carbon dioxide is at the foundation of the global food chain . This paper reports the first comprehensive analysis of networks that control expression of photosynthesis genes using Rhodobacter sphaeroides , a microbe that has been studied for decades as a model of solar energy capture and other aspects of the photosynthetic lifestyle . We find a previously unappreciated complexity in the level of control of photosynthetic genes , while identifying new links between photosynthesis and central processes like iron availability . This organism is an ancestor of modern day plants , so our data can inform studies in other photosynthetic organisms and improve our ability to harness solar energy for food and industrial processes . | [
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] | 2014 | Global Analysis of Photosynthesis Transcriptional Regulatory Networks |
The heteropentameric condensin complexes have been shown to participate in mitotic chromosome condensation and to be required for unperturbed chromatid segregation in nuclear divisions . Vertebrates have two condensin complexes , condensin I and condensin II , which contain the same structural maintenance of chromosomes ( SMC ) subunits SMC2 and SMC4 , but differ in their composition of non–SMC subunits . While a clear biochemical and functional distinction between condensin I and condensin II has been established in vertebrates , the situation in Drosophila melanogaster is less defined . Since Drosophila lacks a clear homolog for the condensin II–specific subunit Cap-G2 , the condensin I subunit Cap-G has been hypothesized to be part of both complexes . In vivo microscopy revealed that a functional Cap-G-EGFP variant shows a distinct nuclear enrichment during interphase , which is reminiscent of condensin II localization in vertebrates and contrasts with the cytoplasmic enrichment observed for the other EGFP-fused condensin I subunits . However , we show that this nuclear localization is dispensable for Cap-G chromatin association , for its assembly into the condensin I complex and , importantly , for development into a viable and fertile adult animal . Immunoprecipitation analyses and complex formation studies provide evidence that Cap-G does not associate with condensin II–specific subunits , while it can be readily detected in complexes with condensin I–specific proteins in vitro and in vivo . Mass-spectrometric analyses of proteins associated with the condensin II–specific subunit Cap-H2 not only fail to identify Cap-G but also the other known condensin II–specific homolog Cap-D3 . As condensin II–specific subunits are also not found associated with SMC2 , our results question the existence of a soluble condensin II complex in Drosophila .
Chromosome condensation is a critical process ensuring faithful distribution of the replicated genetic information onto the daughter cells . While the exact mechanism underlying the longitudinal compaction of the dispersed interphase chromatin into the rod-like and sturdy metaphase chromosomes is still subject of intense research , the participation of the condensin complexes in this process has been thoroughly demonstrated ( for review see [1]–[3] ) . However , while condensin is clearly required and sufficient for compaction of sperm chromatin incubated in Xenopus laevis egg extracts [4] , [5] , the phenotypes observed after condensin depletion in other systems suggest the existence of alternative mechanisms mediating chromatin compaction . Condensin depletion in vertebrate cells , worms and flies does affect the structure of mitotic chromosomes , but compaction of chromatin is only slightly impaired . The extent of this compaction phenotype varies by the organism studied and the experimental system used ( for review see [3] ) . However , in all cases , persistent interconnections of chromatin fibres can be observed in anaphase ( so-called anaphase bridges ) , resulting in severe problems during chromatid segregation in mitosis . Thus , condensin has a role in resolving chromatin bridges present between the replicated chromatids . Plants and animals harbour two condensin complexes , both containing the structural maintenance of chromosomes ( SMC ) proteins SMC2 and SMC4 , but differing in their non-SMC regulatory subunits . Condensin I complexes contain the subunits Cap-D2 , Cap-G and Cap-H ( also called Barren in Drosophila ) , while condensin II complexes contain the related subunits Cap-D3 , Cap-G2 and Cap-H2 . Cap-H and Cap-H2 belong to the kleisin family of proteins which are characterized by their ability to bind to the head domains of SMC protein dimers [6] . Cap-G , Cap-G2 , Cap-D2 and Cap-D3 contain in their N-terminal parts extended regions of Huntingtin , elongation factor 3 , A-subunit of protein phosphatase 2A , TOR1 lipid kinase ( HEAT ) repeats , which are thought to mediate protein-protein interactions [7] . In vertebrates , both condensin complexes play essential roles and collaborate in structuring of mitotic chromosomes and in ensuring their unperturbed segregation . Interestingly , the two complexes fulfil non-overlapping functions as exemplified by distinct phenotypes upon depletion of either condensin I or condensin II-specific subunits [8]–[10] , by their alternating association with mitotic chromosomes [11] , [12] , or by their different localization in interphase cells: Condensin I-specific subunits are enriched in the cytoplasm , while condensin II-specific subunits can be found primarily in the nucleus [9]–[11] . Within the eukaryotic kingdom , the composition of the condensin complexes found in different species is not uniform . Fission and budding yeast harbour homologs only for condensin I , as do e . g . ciliates and kinetoplastids ( for review see [3] ) . C . elegans , on the other hand , contains three condensin complexes , one of which ( condensin IDC ) has specialized to function in dosage compensation in hermaphrodites [13] . In Drosophila melanogaster , condensin I is present , but for condensin II only the subunits Cap-H2 and Cap-D3 can be identified . No gene encoding the condensin II-specific subunit Cap-G2 is apparent in the genome . This has led to the speculation that Drosophila Cap-G might be a component of both complexes , just as SMC2 and SMC4 [14]–[16] . The essential role for all condensin I-specific subunits in mitotic proliferation is well established [14] , [17]–[22] . On the other hand , loss-of-function mutations of the Drosophila genes encoding Cap-H2 and Cap-D3 are viable , indicating that their function is dispensable for mitotic proliferation [18] , [23] , [24] . However , Cap-D3 and Cap-H2 mutant males are sterile , and cytological as well as genetic evidence clearly indicates a role during male meiosis for these two subunits [23] . Interestingly , mutations in Cap-H2 have also been shown to prevent the dispersal of nurse cell polytene chromosomes , which are present for a short developmental period during oogenesis , and to enhance transvection phenomena . Conversely , Cap-H2 overexpression leads to dispersal of the polytene chromosomes in larval salivary glands and in addition suppresses transvection [24] . These results suggest that Cap-H2 negatively regulates chromosome associations and additional genetic evidence indicates that this function is dependent on Cap-D3 [24] . Moreover , Cap-D3 has been shown to interact with the Drosophila Retinoblastoma ( Rb ) -protein homolog Rbf and the two proteins colocalize on the regulatory regions for transcription of the antimicrobial peptide ( AMP ) genes , thereby influencing innate immunity [16] , [25] . Thus , the Drosophila condensin II subunits Cap-H2 and Cap-D3 perform roles in regulating gene expression , as has been demonstrated for condensin complexes in other studies [20] , [21] , [26] , [27] . However , whether these functions are performed in the context of a physical protein complex containing SMC2 , SMC4 , Cap-H2 , Cap-D3 and possibly Cap-G is unknown . While biochemical evidence for the existence of a soluble condensin I complex has been published [18] , the existence and protein composition of a soluble condensin II-like complex in Drosophila is uncertain . Here , we have analyzed in detail the localization behaviour and complex formation capabilities of Drosophila Cap-G in vivo and in vitro to test the hypothesis , whether it might be a common component of both condensin complexes in Drosophila . The comparison of the localization and dynamics of various fluorescently tagged , functional condensin subunits highlights the fact that Cap-G indeed behaves differently from other condensin I-complex components . However , complex formation studies strongly argue against Cap-G being associated with condensin II-specific components . Furthermore , immunoprecipitation analyses consistently provide evidence for soluble condensin I complexes , but fail to support the presence of native soluble condensin II complexes in vivo and indicate a strongly reduced complex formation potential in vitro . Thus , while we cannot exclude the assembly of condensin II-like complexes specifically on chromatin in specialized cell types , our data argue against the existence of an abundant and stable soluble condensin II complex in Drosophila .
In interphase , vertebrate condensin I subunits are primarily cytoplasmic , while condensin II subunits are primarily nuclear [9]–[11] . Consistently , Drosophila Barren/Cap-H and Cap-H2 have also been found to be cytoplasmic or nuclear enriched , respectively [24] , [28] . Towards a comparative description of the localization behavior of Drosophila condensin subunits in the living organism , we have generated EGFP-fused variants of the condensin subunits Cap-D2 , SMC2 and Cap-G ( Figure S1A ) . EGFP-Cap-D2 should label exclusively condensin I-complexes , while SMC2h-EGFP is expected to occur in both condensin I and condensin II . As no condensin II-specific Cap-G2 subunit has been identified in Drosophila , Cap-G has been hypothesized to be also part of both condensin complexes [14]–[16] . Thus , Cap-G localization may provide a hint as to whether it is part of only condensin I or both condensin complexes in Drosophila . All three transgene constructs are expressed under control of the flanking genomic regulatory sequences and quantification of the expression levels in early embryogenesis reveal a ratio of transgene products of approximately 1∶4∶8 ( Cap-G-EGFP∶SMC2h-EGFP∶EGFP-Cap-D2; Figure S1B ) . Despite these differences , all transgenes encode biologically functional products as the presence of single copies of the transgenes can complement the lethality associated with loss-of-function-mutations in the respective genes ( Table S1 and data not shown ) . Analysis of living embryos progressing through the divisions of the syncytial blastoderm revealed that during interphase , SMC2h-EGFP and EGFP-Cap-D2 are enriched in the cytoplasm , as has been reported previously for the condensin I - specific subunit Cap-H/Barren ( Figure 1A; Videos S1 and S2; [28] ) . In contrast , Cap-G-EGFP is nuclear enriched in interphase , reminiscent of condensin II localization in vertebrates ( Figure 1A , Video S3 ) . All three EGFP-fused subunits rapidly associate with condensing chromatin at early stages of mitosis . However , Cap-G-EGFP associates with chromatin slightly earlier than EGFP-Cap-D2 and SMC2h-EGFP , which might be due to its preferential nuclear localization in interphase . All condensin subunits leave chromatin during late anaphase/early telophase ( Figure 1A; Videos S1 , S2 , S3 ) . As the different condensin subunits exhibit distinct localization patterns during interphase , and differ in their chromatin association kinetics , we scrutinized the dynamics of mitotic chromatin association of these subunits during cycle 12 of the syncytial divisions . To this end , we performed quantitative measurements of the EGFP fluorescence signals and normalized them to the simultaneously recorded fluorescence measurements of the mRFP1-fused histone variant His2Av , which was also expressed in these embryos . The data revealed that Cap-G-EGFP is loaded maximally already at nuclear envelope breakdown , a time-point when the EGFP-fused subunits Cap-D2 and Cap-H/Barren ( data from [28] ) are just beginning to associate with chromatin ( Figure 1B ) . Interestingly , SMC2h-EGFP loading appears even more delayed ( half-maximal association of SMC2 is −2 . 5 min before anaphase onset vs . −3 . 5 min for Cap-D2 and Cap-H/Barren; Figure 1B ) . Similar loading kinetics are observed , when SMC2h-EGFP chromatin association was determined in an SMC2 mutant background , ruling out the possibility that the presence of endogenous SMC2 significantly delays incorporation of the EGFP-fused variant ( Figure S2 ) . For all analyzed subunits , maximal chromatin association levels are achieved during late metaphase/early anaphase . During exit from mitosis , the four condensin subunits delocalize from chromatin with almost identical kinetics ( Figure 1B ) . To assess , which regions of Cap-G mediate the subcellular localization during the cell cycle , we expressed various EGFP-fused deletion constructs under GAL4/UAS-control in early embryos and analyzed the localization behavior of the fusion proteins while cells were progressing through epidermal mitosis 14 ( Figure 2A ) . Computational analyses predict nuclear localization signals ( NLS ) at positions 1072 , 1162 , and 1210 . Consistently , a C-terminal Cap-G fragment ( Cap-GC; aa 958–1351 ) encompassing these signals is strongly nuclear enriched in interphase . At nuclear envelope breakdown , the fusion protein distributes throughout the cell ( Figure 2B , Video S4 ) . During early to mid mitosis , Cap-GC-EGFP associates only very weakly with chromatin . However , beginning with late anaphase , Cap-GC-EGFP accumulates on the segregating chromatids ( Figure 2B , Video S4 ) . The construct Cap-GNM-EGFP ( aa 1–977 ) lacks the C-terminal region with the NLS , but retains an extended region predicted to form HEAT-repeats and it displays a complementary localization behavior when compared to Cap-GC-EGFP . In interphase , this Cap-G variant is primarily localized in the cytoplasm , but approximately 20–40 sec after nuclear envelope breakdown , it associates rapidly and efficiently with mitotic chromatin ( Figure S3 ) . Starting with anaphase , Cap-GNM-EGFP dissociates from chromatin similar to full length Cap-GFL-EGFP ( Figure 2B , Video S5 ) and as was observed for the other condensin subunits ( Figure 1B; [28] ) . To assess , whether the mitotic localization behavior of Cap-GNM-EGFP reflects its potential to form complexes with the other condensin subunits , we performed immunoprecipitation analyses . Extracts were prepared from embryos expressing various EGFP-fused Cap-G variants followed by precipitation using anti-EGFP antibodies . Proteins bound to the beads were eluted in two steps , with the second being more stringent . Four prominent protein bands in the high molecular weight range can be detected on silver stained gels in the first round eluates of both Cap-GFL-EGFP and Cap-GNM-EGFP-coupled beads ( Figure 2C ) . The identity of two of the bands was confirmed as Cap-D2 and Cap-H/Barren by immunoblot analysis ( Figure S4 ) . Based on their migration behavior , the first and third bands were suspected to correspond to SMC4 and SMC2 , respectively . This assignment was corroborated by mass-spectrometric analyses of Cap-GFL-EGFP immunoprecipitates ( see below ) . The antibody-bound EGFP-fused Cap-G variants were primarily eluted under more stringent conditions ( Figure 2C ) . Cap-GC-EGFP immunoprecipitates did not contain the other condensin I subunits in significant amounts , as did not the precipitates of a Cap-GNM-EGFP- variant with a further N-terminal truncation of 242 amino acids ( Cap-GNM4-EGFP ) . This latter variant does not localize to mitotic chromatin and it is distributed in interphase throughout the cell ( Figure S5 ) . The HEAT repeats predicted to form in the N-terminal region of Cap-G are implicated in protein-protein interactions [7] . Thus , binding of Cap-G to the condensin complex may be mediated via the HEAT-repeat motifs , since Cap-GNM4-EGFP lacking a large part of this domain is not able to precipitate Cap-D2 or Cap-H/Barren . However , the N-terminal 242 amino acids are not sufficient for efficient association with mitotic chromatin , since the variant Cap-GNM1-EGFP , which encompasses the region of aa 1–848 , is primarily cytoplasmic in interphase like Cap-GNM-EGFP , and associates only very weakly with chromatin during mitosis ( Figure S5 ) . We conclude that the C-terminal third of Cap-G contains nuclear localization sequences , but it is dispensable for mitotic chromatin association . Moreover , the HEAT-repeat region as well as the stretch encompassing aa 848–977 within the N-terminal two-thirds of Cap-G are required for binding to mitotic chromatin , most probably by virtue of their mediating the assembly into condensin complexes . We have noticed that during interphase , Cap-GFL-EGFP and Cap-GC-EGFP are not homogeneously distributed in the nucleoplasm . As the patchy appearance of Cap-G signals is reminiscent of heterochromatin distribution in these nuclei , we analyzed embryos expressing EGFP-fused Cap-GFL or Cap-GC concomitant with a red fluorescently labeled variant of heterochromatin protein 1 ( mRFP1-HP1 ) ( Figure 3A ) . HP1 binds to histone H3 methylated at lysine 9 and is thus a marker for heterochromatin distribution in interphase cells [29] . In vivo microscopy of embryos progressing through epidermal cycle 14 revealed that the two Cap-G variants indeed largely co-localize with mRFP1-HP1 during interphase , indicating heterochromatin association of Cap-G ( Figure 3A ) . During mitosis , mRFP1-HP1 dissociates from chromatin , as has been previously observed with fixed material ( arrowheads in Figure 3A; [30] ) . This observation , together with the fact that Cap-GC associates with chromatin in late mitosis when mRFP1-HP1 is still absent , indicates that Cap-G chromatin association does not depend on the presence of HP1 . While Cap-G clearly co-localizes with heterochromatin in interphase , it does not appear to be physically associated with HP1 in a common protein complex as HP1 cannot be co-precipitated with Cap-G ( Figure S6 ) . Embedded within the heterochromatin are the centromeres . As enrichment of other condensin subunits in centromeric regions has been demonstrated [11] , [17] , [31]–[33] and a genetic and physical interaction of Cap-G with the centromeric H3 variant Cid has been established [14] , we scrutinized the dynamics of Cap-G chromatin association . To this end , we analyzed the localization behavior of Cap-GFL-EGFP in comparison with Cid-mRFP1 in embryos progressing through cycle 14 . Indeed , early chromatin accumulation of Cap-GFL-EGFP occurs in nuclear regions where Cid-mRFP1 signals can be detected ( Figure 3B ) . Similar dynamics are observed when embryos progress through syncytial cycle 12 , and quantitation reveals an approximately twofold enrichment of Cap-GFL-EGFP in centromere-proximal vs . centromere-distal regions in early stages of Cap-G chromatin association ( Figure S7 ) . Thus , our observations are consistent with a model in which Cap-G first binds to centromeric regions and then spreads into the adjacent heterochromatin . The C-terminus of Cap-G is required for nuclear localization and sufficient to confer heterochromatic enrichment during interphase . The N-terminal two-thirds of Cap-G , on the other hand , are sufficient for efficient chromatin localization during mitosis and for assembly within the condensin I holocomplex . To assess the relevance of the functional features contributed by the Cap-G C-terminus , we generated individuals expressing Cap-GNM or Cap-GNM-EGFP as sole source for this condensin subunit in a Cap-G1/Cap-G6 trans-heterozygous mutant background . Loss-of-function mutations in Cap-G are embryonic lethal [14] , [20] . Expression of Cap-GFL-EGFP , either under control of the genomic regulatory sequences or under GAL4/UAS control using the ubiquitous da-GAL4 driver , gave rise to viable and fertile adults demonstrating the biological functionality of these constructs ( Table S1 ) . Surprisingly , adult flies were also obtained with high efficiency by ubiquitous expression of two independent pUAST-based UAS-Cap-GNM-EGFP-transgene insertions in the same trans-heterozygous Cap-G mutant background . As pUAST does not direct expression in the female germline , female fertility could not be assessed in these cases . However , expression from the Cap-GNM-EGFP transgene contained in a pUASP-based vector , which also allows expression in the female germline [34] , restored fertility in both sexes ( Table S1 ) . Immunoblot analysis confirmed that these animals lacked expression of endogenous Cap-G and survived solely due to the expression of the C-terminally truncated Cap-G variants ( Figure S8A ) . To assess , whether the C-terminally truncated Cap-GNM variant also fails to localize to interphase nuclei in the absence of competing full-length Cap-G , we analyzed Cap-GNM-EGFP localization in the rescue situation . Cap-GNM-EGFP is excluded from the nuclei in interphase also in a Cap-G mutant background , and it does not bind to chromatin in prophase , ruling out the possibility that the presence of competing full-length Cap-G might prevent early chromatin association of the Cap-GNM variant ( Figure S8B and see also Figure S10C ) . Not all Cap-GNM transgenes complemented the Cap-G mutant phenotype efficiently . Fertility was only observed after crosses of rescued individuals with wild type flies , and many eggs laid by Cap-GNM rescued mothers displayed developmental defects ( data not shown ) . Therefore , it was not possible to establish stable rescue stocks . We conclude nevertheless that the C-terminal 374 amino acids of Cap-G are not absolutely critical for condensin function required for development from the fertilized egg to a fertile adult . While the full-length protein rescues with higher efficiency than the C-terminal truncated version when expressed at comparable levels ( Table S1; genomic transgenes ) , the development of fertile adult animals is still possible when the C-terminal domain of Cap-G is lacking . As this C-terminal part contains the NLS , nuclear enrichment of Cap-G during interphase is dispensable for condensin function in the cell cycle and during development . Due to the lack of an obvious Cap-G2 homolog encoded in the Drosophila genome , Cap-G has been hypothesized to be part of both condensin subunits , just as SMC2 and SMC4 [14]–[16] . In the anti-Cap-GFL-EGFP immunoprecipitates shown in Figure 2C , four prominent high molecular weight bands are evident , which were assigned to the condensin I-specific subunits and the two SMC's . As the condensin II-specific subunits Cap-H2 and Cap-D3 might not have been abundant enough in the analyzed extracts to be detected by silver staining , we performed additional immunoprecipitation experiments followed by sensitive mass spectrometric ( MS ) analysis of the precipitates . We have used a variety of strains expressing condensin subunits fused with fluorescent proteins , which were precipitated with the appropriate antibodies ( Figure 4A ) . First , we prepared extracts from early embryos or from ovaries isolated from individuals expressing Cap-GFL-mRFP1 under the control of the genomic regulatory sequences . Like the EGFP-fused Cap-G variant , mRFP1-fused Cap-G is biologically functional as it rescues Cap-G mutants to vitality and fertility ( data not shown ) . After immunoprecipitation using anti-mRFP1 antibodies , aliquots of the eluates were separated on an SDS-polyacrylamide gel and stained with silver to visualize the precipitated proteins ( Figure 4B ) . In a parallel experiment , lanes with the eluates were stained with colloidal Coomassie Blue , cut into seven slices each and processed for MS . This procedure allowed a comprehensive evaluation of the proteins associated with the precipitated bait . As a negative control , an extract from w1-ovaries not containing mRFP1-fused proteins was treated identically . From the list of identified proteins all non-Drosophilid proteins were removed , and then sorted according to the cumulative intensities of the identified peptides . In both the ovary and the embryo extracts , among the top eleven most abundant proteins , SMC2 , SMC4 , as well as the condensin-I specific subunits Cap-H/Barren and Cap-D2 were identified ( Figure 4B ) . The majority of the peptides specific for SMC2 or SMC4 were detected in gel slices containing proteins of molecular weights corroborating our assignment of the SMCs in the silver stained IP-eluates shown in Figure 2C . However , in the complete list of identified proteins ( 189 for the embryonic extracts and 537 for the ovary extract ) , neither Cap-D3 nor Cap-H2 were found , not even represented by a single peptide ( Tables S2 and S3 ) . In a complementary approach , we expressed EGFP- and mCherry-fused variants of the condensin II-specific subunit Cap-H2 in ovaries using the GAL4/UAS-system . These variants were shown to be functional as they I ) rescue the phenotypic consequences described for Cap-H2 mutants in ovarian nurse cell nuclei and II ) trigger a dispersal of polytene chromatin when expressed in the nuclei of larval salivary glands ( Figure S9; [24] ) . Anti-EGFP-Cap-H2 and anti-mCherry-Cap-H2 precipitates from ovarian extracts were separated by SDS-PAGE , stained with colloidal Coomassie Blue , and analyzed by MS ( Figure 4C ) . Within the lists of identified proteins , SMC2 and SMC4 can be found in both experiments . However , the SMCs were ranked much lower in this experiment when compared to the Cap-G immunoprecipitates , indicating that they are of relatively low abundance in the Cap-H2-specific precipitates . Significantly , within the complete list of more than 1200 proteins in both cases , neither Cap-G nor Cap-D3 could be found ( Table S4 ) . The N-terminal EGFP- and mCherry-fusions in our Cap-H2 constructs may preclude efficient complex formation . Therefore , we also performed immunoprecipitations of SMC2 , from protein extracts of wild type or SMC2h-EGFP expressing individuals , using either anti-SMC2-antibodies or anti-EGFP antibodies , respectively . In these experiments , we would expect to precipitate both condensin I and condensin II complexes . Again , we could identify the components of the condensin I complex in all cases , but in none of the three experiments , the condensin II-specific components Cap-H2 or Cap-D3 were detected ( Figure 4D , Tables S5 , S6 , S7 , S8 ) . As Cap-GFL is nuclear during interphase , like condensin II subunits in other systems , one might expect condensin II-like phenotypes in Cap-G mutant animals rescued by Cap-GNM , which is cytoplasmic in interphase . A prominent phenotype in Drosophila Cap-D3 and Cap-H2 mutants is the perdurance of nurse cell chromosome polyteny in developing egg chambers [24] . However , in Cap-GNM rescued females , the nurse cell chromosomes disperse on time , arguing against nuclear Cap-G fulfilling a condensin II-like function ( compare Figure S10A and S10B ) . We have ascertained that in the rescue situation in this tissue , Cap-GNM is also excluded from the nuclei ( Figure S10C ) . Taken together , the phenotypic analysis of nurse cell chromosomes in Cap-GNM rescued females , as well as our immunoprecipitation analyses argue against Cap-G being incorporated into a soluble condensin II-like complex in Drosophila . Furthermore our MS results also speak against the presence of soluble condensin II-like complexes in the analyzed extracts in significant amounts . The analysis of condensin subunit associations described above involved immunoprecipitations from complexes present in soluble extracts from Drosophila tissues . To allow the assessment of direct protein-protein interactions in a more simple system , we analyzed complex formation of various condensin subunits produced in an in vitro transcription/translation ( IVT ) system . In case the molecular mass of the synthesized proteins was sufficiently different , they were co-translated in the presence of [35S]methionine , subjected to immunoprecipitation using antibodies against fused epitope-tags , separated by SDS-PAGE , and detected by autoradiography . Otherwise , proteins were translated in different reactions only one of which contained [35S]methionine . After mixing the extracts and subsequent immunoprecipitation , the components were detected after SDS-PAGE both by autoradiography and immunoblot . To validate our system , we first wanted to demonstrate the physical interactions between the condensin I-specific non-SMC subunits . We used a C-terminally His-FLAG-epitope-tagged Cap-H/Barren ( Barren-HFHF ) construct as bait . A C-terminally extended Cap-H/Barren variant has been shown to be biologically functional in the fly [28] . As a negative control , we prepared human securin analogously tagged at its C-terminus with His-FLAG ( hSecurin-HFHF ) . Both Cap-G and Cap-D2 can be specifically co-immunoprecipitated with Barren-HFHF , but not with hSecurin-HFHF ( Figure 5A ) . Thus , the Drosophila HEAT-repeat containing condensin I subunits interact with the kleisin subunit Cap-H/Barren like their human counterparts [35] . If Cap-G is also part of condensin II , one would expect that it forms a complex with the condensin II-specific kleisin subunit Cap-H2 . However , while Cap-G can be readily detected in immunoprecipitates of Barren-HFHF , it is not present in Cap-H2-HFHF immunoprecipitates ( Figure 5B ) . This result once more argues against Cap-G being a condensin II component . The human kleisin subunits were shown to interact with SMC4 [35] . Consistently , Drosophila SMC4 can be precipitated with Barren-HFHF , in low amounts with Cap-H2-HFHF , but not with hSecurin-HFHF ( Figure 5C ) . This result reveals on the one hand a reduced binding efficiency between Drosophila Cap-H2 and SMC4 , which is consistent with the results from our immunoprecipitation analysis of ovarian extracts containing ectopically expressed Cap-H2-variants ( Figure 4C ) . On the other hand , it demonstrates that in the IVT-system Cap-H2-HFHF is produced in a conformation competent for complex formation , ruling out the possibility that the lack of interaction between Cap-H2-HFHF and Cap-G is due to mis-folded Cap-H2-HFHF . Next we asked whether we could reconstitute the condensin II-specific interaction between Cap-D3 and Cap-H2 . To this end , we synthesized a Cap-D3 variant fused at its N-terminus with six copies of the human c-myc-epitope ( myc-Cap-D3 ) . In these experiments , we used as negative control the catalytic ( C ) -subunit of human protein phosphatase 2A , also with an N-terminal myc6-tag ( myc-hPP2 ( A ) C ) . Cap-H2 could be identified in myc-Cap-D3 immunoprecipitates , but not in myc-hPP2 ( A ) C precipitates ( Figure 5D ) . However , the co-precipitation efficiency was again very low . Cap-H/Barren was also detected in myc-Cap-D3 immunoprecipitates , but this protein was also precipitated by myc-hPP2 ( A ) C , arguing for non-specific associations . To underscore the biological relevance of these in vitro studies , we attempted to form ternary complexes . Based on the geometry of the human condensin complexes , Cap-D2 does not directly interact with the SMC subunits , but the kleisin subunit Cap-H/Barren is expected to bridge Cap-D2 and SMC4 . Indeed , SMC4 can be precipitated together with myc-Cap-D2 when Cap-H/Barren is present , but not in its absence ( Figure 5E , compare lanes 9 and 11 ) . When Cap-H2 was included in an analogous reaction instead of Cap-H/Barren , Cap-H2 was precipitated with low efficiency , but SMC4 could not be detected ( Figure 5E , lane 10 ) . In an effort to reconstitute an analogous condensin II subcomplex , we precipitated myc-Cap-D3 in the presence of both Cap-H2 and SMC4 or just SMC4 . In this case , no ternary complex could be detected and only inefficient co-precipitation of Cap-H2 with myc-Cap-D3 was observed ( Figure 5F , lane 10 ) . Cap-H/Barren did not co-precipitate with Cap-D3 above background . Taken together , our in vitro complex forming studies confirm the predicted interactions among the Drosophila condensin I-specific subunits . However , the complex forming potential between condensin II-specific subunits is limited and we find again no evidence for incorporation of Cap-G in a condensin II-like subcomplex .
We set out to test the hypothesis that in Drosophila , Cap-G might be part of both condensin I and condensin II . This hypothesis is based on the facts that i ) no condensin II-specific Cap-G2 homolog can be identified in the Drosophila genome and ii ) that SMC2 and SMC4 are also part of both condensin complexes . The localization pattern of Cap-G-EGFP in interphase initially suggested its participation in a condensin II-like complex since it was found to be nuclear like vertebrate condensin II subunits [9]–[11] . At least , a functional importance was suggested by the preferential nuclear localization of Cap-G and its different dynamics in chromatin association when compared to the other EGFP-fused condensin I subunits . However , the intriguing observation that flies are viable and fertile , when they exclusively express a C-terminal truncation variant of Cap-G , which is nuclear excluded in interphase and gains access to chromatin only around NEBD , suggests that its nuclear localization is dispensable for proliferation and development , at least under laboratory conditions . Furthermore , the observed heterochromatic enrichment of Cap-G and its initiation of loading at the centromeric regions are obviously not essential . It is possible that the Cap-G C-terminus , which contains many predicted phosphorylation sites in Drosophila and other organisms [36] may fine tune Cap-G activity . This fine-tuning is probably required for the restoration of full fertility in both sexes and early syncytial development , as shown by the defects when no full length Cap-G is provided by the mother . In this respect , the C-terminus might be required for full length Cap-G to be sequestered into the nucleus to avoid any dominant negative effects in the cytoplasm . SMC2h-EGFP and EGFP-Cap-D2 localize like Cap-H/Barren-EGFP [28] in the cytoplasm during interphase and rapidly associate with chromatin during early stages of mitosis . Intriguingly , these subunits associate significantly later with chromatin than Cap-G-EGFP , indicating that Cap-G has the potential to bind to chromatin in the absence of the other condensin subunits . This notion is supported by the observation that Cap-GC can associate with chromatin in late anaphase , at a time point when the other subunits dissociate . Recently , it has been shown in human tissue culture cells and fission yeast that Cap-H binds to the N-terminal tail of histone 2A and the variant histone 2A . Z . In vitro studies have revealed that this binding can occur independent of other condensin subunits [37] . While these results are consistent with chromatin targeting of condensin via Cap-H in these systems , our findings suggest that in Drosophila , Cap-G may direct chromatin targeting of condensin . The target molecule on chromatin , which is recognized by Drosophila Cap-G , remains to be identified . While our study is the first report on the dynamics of SMC2 localization in Drosophila during the cell cycle , our data on Cap-D2 appear to be at odds with studies on fixed S2 tissue culture cells using anti-Cap-D2-antibodies [18] . In this study , Cap-D2 was reported to be primarily nuclear . This discrepancy can be explained by the different tissues analyzed . Nuclear import may be slow for Cap-D2 , as , in fact , Savvidou et al . [18] observe increasing nuclear concentration of Cap-D2 when the cells progress through G1-S-G2 . During the rapid syncytial divisions , nuclear import of Cap-D2 may not be efficient . Analysis of other tissues of EGFP-Cap-D2 expressing animals indeed showed nuclear localization , for example in ovarian follicle cells ( data not shown ) . Interestingly , nuclear localization of Cap-H2 has also been described to progressively increase in more advanced ovarian nurse cell nuclei when compared with nuclei at younger stages [24] , own unpublished observation ) . Condensin complexes have been initially identified and characterized in the biochemically tractable Xenopus egg extract system [4] . In mitotic extracts , soluble 13S heteropentameric holocomplexes as well as 8S SMC2/SMC4 dimers were readily detected . Besides this initial identification of the complex later termed condensin I , condensin II was also detected in high-speed supernatants of Xenopus egg extracts [12] , as well as in HeLa cell lysates [12] , [38] . Quantification revealed that in the Xenopus egg extract system condensin I is present in roughly five-fold excess over condensin II while in HeLa cells both complexes occur in approximately equimolar amounts [12] . These differences in abundance are paralleled by a different appearance of condensed chromosomes . While in HeLa cells , metaphase chromosomes appear short and thick , the condensed chromosomes in the Xenopus egg extract system are rather long and thin . Intriguingly , experimentally shifting the ratio of condensin I∶condensin II in Xenopus egg extracts from ∼5∶1 to ∼1∶1 resulted in shorter and thicker chromosomes [31] . As metaphase chromosomes in Drosophila are also short and thick , one would expect a roughly balanced abundance of the two condensin complexes , if condensin I and II play comparable roles in the fly . As we did not detect any soluble endogenous condensin II complexes in our immunoprecipitation analyses , this is apparently not the case . We have analyzed extracts from ovaries and embryos . Cap-H2 mutants display a phenotype in ovarian nurse cell nuclei suggesting that Cap-H2 is expressed at this stage [24] . Also , the temporal expression data provided by the modENCODE project reveal expression of both Cap-H2 and Cap-D3 in ovaries and in early embryos , albeit at only low to moderate levels [39] . In fact , these levels are significantly lower than those reported for condensin I-specific subunits in most tissues indicating that condensin II-like complexes must be of low abundance . Our analysis of ovarian extracts derived from females overexpressing Cap-H2-fusion proteins circumvented the issue of low endogenous expression levels . Indeed , in these experiments , SMC2 and SMC4 were found to be associated with overexpressed Cap-H2 , but peptide intensities and unique peptide numbers were significantly lower than in the experiment , in which proteins in association with Cap-G-mRFP1 in ovaries were assessed . Also , as our in vitro interaction assays revealed only weak affinities of Cap-H2 towards Cap-D3 and SMC4 in solution , a condensin II-like holocomplex in Drosophila may be functionally assembled in an efficient manner only on chromatin , unlike the situation found in vertebrates . Published studies on the phenotypic consequences of the loss of Cap-D3 or Cap-H2 have shown that these phenotypes can be modified by mutations in other condensin subunit genes ( namely Cap-H2 , Cap-D3 and SMC4 ) , thus revealing genetic interactions [23] , [24] , [40] . However , it remains to be shown , whether these genetic interactions are based on a physical interaction of these subunits bound to the chromatin . Furthermore , such a chromatin-associated condensin II-like holocomplex is unlikely to play a mitotic role , given the absence of mitotic phenotypes in Cap-H2 and Cap-D3 mutants [18] , [23] , [24] , which is also consistent with the failure of EGFP-Cap-H2 to load onto mitotic chromatin ( data not shown ) . Cap-G was not found in association with overexpressed Cap-H2 , even though Cap-G would be expected to bind to the kleisin component if it was part of a condensin II-like complex [35] . The direct binding assays of in vitro translated proteins also did not produce any indication of an association of Cap-G with Cap-H2 , rendering the proposal of the participation of Cap-G in a condensin II-like complex highly unlikely . So the question remains whether a second HEAT-repeat containing protein besides Cap-D3 is part of a putative condensin II complex in Drosophila . BLAST analyses do not produce Cap-G2 homologs encoded in the D . melanogaster genome or in any of the sequenced genomes of dipterans . It is possible that a Cap-G2 homolog does exist in Drosophila , but has escaped detection using the BLAST algorithms because it might have diverged significantly during evolution . Therefore , we have scrutinized the list of proteins identified in the Cap-H2 immunoprecipitates for possible Cap-G2 candidates by the virtue of a size above 100 kDa , and an extended stretch of predicted HEAT repeats in the N-terminal region , but with dissimilarity to importins/exportins which also have blocks of HEAT repeats in their N-termini . However , none of the proteins contained in the list of immunoprecipitated proteins qualifies as a Cap-G2 homolog based on these criteria ( data not shown ) . Thus , the possibility remains that condensin II has diverged in dipterans to function as a mainly chromatin-bound heterotetrameric complex lacking a Cap-G2 subunit . Moreover , in combination with the facts that Cap-H2 and Cap-D3 loss-of-function mutants have no obvious mitotic phenotype [18] , [23] and that these two subunits have been shown to participate in such diverse processes as transvection , the regulation of AMP-expression or chromosome territory formation [23]–[25] , [40] , our results support a model in which a Drosophila condensin II-like complex has functionally specialized beyond regulation of chromatin structure during nuclear divisions .
Fly stocks were obtained from the Bloomington Drosophila Stock Center at Indiana University , unless indicated otherwise . Expression constructs for condensin subunits were generated by cloning genomic fragments isolated from bacterial artificial chromosomes ( BACs ) obtained from CHORI BacPac Resources into appropriate vectors , or cDNAs obtained from the Drosophila Genomic Resource Center ( DGRC ) into the vectors pUAST or pUASP1 [14] , [41] . Appropriate restriction sites for cloning were introduced by PCR with primers containing the recognition sequences for the respective enzymes . The integrity of coding regions amplified by PCR was verified by subsequent DNA sequence analysis . Transgenic flies were generated by using established germ line transformation protocols for microinjection into w1 embryos ( pUAST , pUASP1 and pBac-constructs ) or into embryos expressing the PhiC31 integrase and containing an attP landing site at specific genomic sites [42] . For the construction of fly stocks expressing an EGFP-fused variant of SMC2 , a 5 . 2 kb fragment containing SMC2 including its flanking genomic regions was amplified from the BAC clone CH321-59P12 as template and cloned into the pattB vector [42] . A 1370 bp internal PstI/MluI SMC2-fragment was subcloned into the pSLfa1180fa vector [43] and fused with the EGFP-coding sequence using a BspEI site introduced by inverse PCR . The EGFP tag was fused internally between amino acid residues G582 and S583 of SMC2 within the hinge region ( SMC2h-EGFP ) . Internal fusions within the hinge region of yeast SMC1 and SMC3 have been shown to be functionally tolerated [44] . The modified fragment was cloned back into the pattB-SMC2 vector . Transgenic flies were generated via injection of the pattB-SMC2h-EGFP plasmid into y1 , w1 , M[vas-int]ZH2A; M[3x3P-RFP , attP′]ZH96E embryos [42] . For the construction of fly stocks expressing an EGFP-fused variant of Cap-D2 under control of the genomic regulatory sequences , a 6 . 8 kb genomic fragment encompassing Cap-D2 and 600 bp upstream of the transcriptional start site as well as 1 , 600 bp downstream of the poly ( A ) site was cloned via recombineering [45] into pattB using the BAC CH321-26K05 as sequence source . A 1 . 5 kb NotI/Acc65I fragment of the 5′-terminal Cap-D2 region was isolated from pattB-Cap-D2 and subcloned into the pBluescriptSK vector ( Stratagene ) . The naturally occurring NcoI site at the Cap-D2 translational initiation codon was used to insert a PCR-amplified fragment encoding EGFP , flanked by PciI sites , which are compatible with NcoI . The 2 . 2 kb EGFP-fused NotI/Acc65I 5′-terminal Cap-D2 fragment was cloned back into the NotI/Acc65I cleaved pattB-Cap-D2 . Transgenic flies were generated via injection of the pattB-EGFP-Cap-D2 plasmid into y1 , w1 , M[vas-int]ZH2A; M[3x3P-RFP , attP′]ZH22A embryos [42] . For the construction of fly stocks expressing EGFP- and mRFP1-fused variants of Cap-G under control of the genomic regulatory sequences , a 1 . 2 kb XhoI fragment encompassing the 3′-terminal region of the Cap-G reading frame and downstream regulatory sequences was cloned from a genomic Cap-G pBac rescue construct [14] into the vector pLitmus 28 ( New England Biolabs ) . After introduction of a NotI restriction site immediately upstream of the translational stop codon by inverse PCR , PCR-amplified fragments encoding either EGFP or mRFP1 flanked by NotI sites were cloned into this newly generated site . The modified 1 . 9 kb XhoI fragments were excised from the pLitmus 28 constructs and cloned back into the pBac Cap-G rescue constructs . Transgenic flies were generated via injection of the pBac-Cap-G-mRFP1 and pBac-Cap-G-EGFP plasmids into w1 embryos using established procedures [43] . The genomic region encoding Cap-G-EGFP was also cloned into the pattB vector and transgenic lines were established after injection into y1 , w1 , M[vas-int]ZH2A; M[3x3P-RFP , attP′]ZH96E embryos . For the construction of pUAST-Cap-G-EGFP vectors containing various Cap-G fragments , the corresponding Cap-G coding regions were PCR-amplified from the cDNA clone SD10043 and cloned into pUAST-MCS-EGFP [46] . Fragments encoding the following Cap-G-variants were amplified: Cap-GFL ( full length , aa 1–1351 ) ; Cap-GNM ( aa 1- 977 ) ; Cap-GNM1 ( aa 1–848 ) ; Cap-GNM4 ( aa 243- 977 ) ; Cap-GC ( aa 958–1351 ) . For the construction of pUASP1-Cap-GNM-EGFP , the Cap-GNM-EGFP-fragment was transferred from pUAST-Cap-GNM-EGFP into pUASP1 [14] . The constructs were used for P-element-mediated germ line transformation by injection into w1 embryos following established procedures . For all experiments , the following established lines were used: UAST-Cap-GFL-EGFP II . 2 , UAST-Cap-GFL-EGFP III . 2 , UAST-Cap-GNM-EGFP III . 2 , UAST-Cap-GC-EGFP II . 3 , UAST- Cap-GC -EGFP III . 2 , UAST-Cap-GNM1-EGFP II . 1 , UAST-Cap-GNM4-EGFP II . 1 , UASP1-Cap-GNM-EGFP III . 4 , UASP1-Cap-GNM III . 2 . Cap-GNM –EGFP and Cap-GNM were also cloned into the pattB vector containing the flanking Cap-G genomic regulatory elements ensuring expression at physiological levels . Transgenic lines were established after injection into y1 , w1 , M[vas-int]ZH2A; M[3x3P-RFP , attP′]ZH96E embryos . For the construction of pUASP1-EGFP-Cap-H2 and pUASP1-mCherry-Cap-H2 , the Cap-H2 coding region ( based on the Cap-H2-RE annotation ) was isolated using NcoI/XhoI from the cDNA clone SD18322 and subcloned into pLitmus28 . The resulting plasmid pLitmus28-Cap-H2 was cleaved with AvrII/NcoI and PCR-fragments encoding mCherry and EGFP were inserted as AvrII/PagI fragments . The EGFP-Cap-H2 and mCherry-Cap-H2 cassettes were finally transferred as SpeI/Asp718-fragments into pUASP1 to generate pUASP1-EGFP-Cap-H2 and pUASP1-mCherry-Cap-H2 , respectively , which were used for P-element-mediated germ line transformation . For all experiments , the transgene insertions UASP1-EGFP-Cap-H2 II . 4 and UASP1-mCherry-Cap-H2 II . 1 were used . For expression of UAS-transgenes , we used da-GAL4 G32 [47] , F4-GAL4 [48] , maternal α4tub-GAL4-VP16 [49] and tubP-GAL4 . Rescue experiments were performed using trans-heterozygous mutant allele combinations of the respective genes , simultaneously expressing our transgenes either under control of the flanking genomic regulatory regions or under UAS-control driven by the ubiquitous active GAL4-driver da-GAL4 G32 or by α4tub-GAL4-VP16 in the case of Cap-H2 . The following alleles were used: Cap-G1 and Cap-G6 [14] , Cap-D2f03381 , Cap-D2 Df ( 3R ) 01215 , SMC2jsl2 , SMC2f06842 , SMC2Df ( 2R ) BSC429 , Cap-H2Df ( 3R ) Exel6159 , Cap-H2EY09979 and Cap-H2TH2 [24] . For Cap-G , Cap-D2 and SMC2 , complementation of the lethality associated with the trans-heterozygous mutant situation was assessed . For Cap-G , rescued trans-heterozygous individuals could be readily identified by the recessive markers al , b , c and sp present on the Cap-G1 and Cap-G6 chromosomes [14] . For Cap-H2 , suppression of the delayed dispersal of nurse cell chromatin observed in Cap-H2 mutant ovarioles [24] was monitored upon transgene expression . Furthermore , the phenotype upon overexpression of EGFP-Cap-H2 and mCherry-Cap-H2 in larval salivary glands was compared with the phenotype obtained after the GAL4 dependent Cap-H2 overexpression using the allele Cap-H2EY09979 , which is an UAS containing P-element inserted upstream of Cap-H2 . To drive expression of Cap-GNM1-EGFP or Cap-GNM4-EGFP together with His2Av-mRFP1 , individuals of the corresponding UAS-lines were crossed with w* , α-tub-GAL4-VP16 , gHis2Av-mRFP1 II . 2 flies ( generously provided by C . Lehner , University of Zurich ) . To express HP1-mRFP1 together with Cap-GFL-EGFP or Cap-GC-EGFP , we generated recombinant chromosomes containing either UAST-Cap-GFL-EGFP II . 2 or UAST-Cap-GC-EGFP II . 3 together with gmRFP1-HP1 II . 1 [50] using standard genetic techniques . To co-express Cap-GFL-EGFP with Cid-mRFP1 , both under control of the flanking genomic sequences , lines were generated by classical genetic techniques containing the gCap-GFL-EGFP III . 1 and gCid-mRFPII . 1 [28] transgenes . For chromatin loading analyses , chromosomes carrying a transgene allowing expression of His2Av fused with mRFP1 [51] were combined with gCap-G-EGFP III . 1 , or gSMC2h-EGFPΦX-96E or gCap-D2-EGFPΦX-22A . Antibodies against the human c-myc epitope [52] , Drosophila Cap-H/Barren [22] and Drosophila Cap-D2 [18] have been described previously . Rabbit-anti-Flag ( Sigma ) , mouse-anti α-Tubulin ( Sigma ) as well as secondary antibodies ( Jackson laboratories ) were obtained commercially . Antibodies against EGFP and mRFP1 were raised in rabbits using bacterially expressed full length proteins as antigen . The anti-mRFP1 antibodies also recognize and precipitate mCherry-fused proteins . Mouse monoclonal antibodies against EGFP were purchased from Roche Biochemicals or were a gift from D . van Essen and S . Saccani ( MPI Freiburg , Germany ) . Antibodies against Cap-G and SMC2 were raised in rabbits using bacterially expressed N-terminal protein fragments of Cap-G ( aa 1- 553 ) and SMC2 ( aa 1–313 ) , respectively . The antisera were affinity purified using standard procedures [53] . For immunoblotting , the antibodies were used at a 1∶3000 dilution . A mouse monoclonal antibody directed against HP1 was obtained from the Developmental Studies Hybridoma Bank ( clone C1A9; dilution 1∶1000 for immunoblotting ) . For in vivo microscopy , embryos at the desired developmental stage were collected and processed as previously described [54] . Single-stack confocal images were acquired every 18 or 20 sec using a Leica SP5 confocal microscope ( Leica Microsystems , Germany ) , equipped with a 63× oil-immersion objective , a 458–514 nm Ar laser and a 561 nm DPSS laser for the excitation of EGFP and mRFP1 , respectively . For fixed samples stained with Hoechst 33258 , a 405 nm UV- diode laser was used in addition , and confocal images were acquired with a 40× oil-immersion objective . Images were processed using ImageJ 1 . 46 ( National Institute of Health , USA ) and Adobe Photoshop CS4 ( Adobe Systems Inc . ) . In some images , shot noise was decreased with a Gaussian filter . Quantitative fluorescence measurements to determine chromatin association of the EGFP-fused condensin subunits was done as described in [28] with the exception that a Leica SP5 confocal system was used for analysis of EGFP-Cap-D2 and SMC2h-EGFP . The analyzed genotypes were gCap-G-EGFP III . 1 , gHis2Av-mRFP1 III . 1/TM3 , Ser or gHis2Av-mRFP1 II . 2; gSMC2h-EGFPΦX-96E or gCap-D2-EGFPΦX-22A; gHis2Av-mRFP1 III . 1 . or SMC2f06842/SMC2Df ( 2R ) BSC429; gSMC2h-EGFPΦX-96E . To quantify Cap-G-EGFP in centromeric regions , embryos co-expressing Cap-G-EGFP and Cid-mRFP were analyzed by laser scanning time lapse microscopy while progressing through the syncytial cycle 12 . Small circular regions of interest ( R . O . I . s ) were defined in the channel for Cid-mRFP fluorescence , one encircling a centromere ( cen-proximal ) and one of the same size in a region within the nucleus but not encircling a centromere . The identical R . O . I . s were applied to the channel for Cap-G-EGFP fluorescence and the ratio of the cen-proximal fluorescence intensity:cen-distal fluorescence intensity was calculated . For each time point , 62 pairs of R . O . I . s from three different embryos were evaluated . For immunoblotting experiments , ovaries of 4–5 days old females were dissected in 1× PBS and homogenized in sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) sample buffer . Protein samples corresponding to 5 ovaries were loaded on Tris-glycine based polyacrylamide gels and blotted onto nitrocellulose membranes . For the immunoprecipitation experiments , 5–8 h old embryos expressing fluorescently tagged Cap-G variants were collected on apple-juice agar plates and dechorionized . Alternatively , we dissected ovaries in 1×PBS from females expressing epitope-tagged condensin subunits . These tissues ( 150 µl embryos or 300 ovaries ) were homogenized in 4 volumes of lysis buffer ( 50 mM HEPES at pH 7 . 5 , 60 mM NaCl , 3 mM MgCl2 , 1 mM CaCl2 , 0 . 2% Triton X-100 , 0 . 2% Nonidet NP-40 , 10% glycerol ) including protease inhibitors ( 2 mM Pefabloc , 2 mM Benzamidin , 10 µg/ml Aprotinin , 2 µg/ml Pepstatin , A , 10 µg/ml Leupeptin ) . In the experiment shown in Figure S6 , aliquots of the raw extracts were treated with a mixture of DNaseI and nuclease S7 for 45 min at 4°C to solubilize chromatin . The extracts were cleared by centrifugation ( 20 min , 14000×g; 4°C ) and 200–400 µl of the supernatants were used for immunoprecipitation with anti-EGFP- , anti-mRFP1- , or anti-SMC2-antibodies bound and covalently cross-linked using dimethyl pimelimidate to Protein A-Sepharose ( Affi-Prep Protein A , BIORAD; 25 µg of affinity purified antibodies bound to 30 µl of Protein A-Sepharose slurry ) . In the experiment shown in Figure S6 , mouse monoclonal antibodies ( Roche ) were coupled to Protein G-Sepharose ( GE Healthcare ) . After 3–4 h incubation at 4°C with gentle agitation , the Sepharose was washed for four times with 1 ml of lysis buffer . Bound polypeptides were eluted by incubation with 40 µl of elution buffer ( 50 mM Tris/HCl at pH 6 . 8; 2% ( w/v ) SDS ) for 10 min at 37°C and/or by addition of 40 µl SDS-PAGE sample buffer and subsequent incubation at 95°C for 5 min ( “hot elution” ) . The immunoprecipitates were subjected to SDS–PAGE followed by silver staining ( “PageSilver Silver Staining Kit” , Fermentas ) or by western blot analysis . For mass spectrometric analysis , immunoprecipitates were separated by SDS-PAGE on precast gradient gels ( Serva , Heidelberg ) and the proteins were visualized by staining with colloidal Coomassie Blue according to [55] . Entire gel lanes containing immunoprecipitates were cut into slices . Proteins were extracted from the gel pieces , digested with trypsin , separated via on-line nanoLC and analyzed by electrospray tandem mass spectrometry at an LTQ Orbitrap mass spectrometer . The complete lists with the identified proteins are available in the supplementary information . DNA fragments encoding different regions of the condensin subunits were amplified by PCR and inserted into the vectors pCS2 ( F/A ) , pCS2 ( F/A ) -HFHF ( allowing a fusion of a C-terminal His6 Flag His6 Flag epitope tag ) , and/or pCS2-myc6 ( F/A ) ( allowing a fusion of an N-terminal myc6-epitope tag ) , which all contain FseI/AscI-restriction sites within their MCS . Condensin coding regions were amplified from the cDNA clones SD10043 ( Cap-G ) , LD40412 ( Cap-D2 ) , RE48802 ( Cap-H/Barren ) , SD18322 ( Cap-H2 , based on the Cap-H2-RE annotation ) and RE74832 ( Cap-D3 , based on the Cap-D3-RA annotation ) . To generate pCS2-Cap-G-EGFP , the Cap-G-EGFP fragment was transferred from UAST-Cap-GFL-EGFP into pCS2 ( F/A ) . To generate pCS2-SMC4 , the corresponding coding region was amplified using first strand cDNA derived from reverse transcription of mRNA extracted from w1-embryos , using the “RNeasy Mini Kit” and the “Omniscript RT Kit” ( Qiagen ) , and inserted into pCS2 ( F/A ) . For controls , the plasmids pCS2-hSecurin-HFHF and pCS2-myc6-hPP2A ( C ) ( generously provided by O . Stemmann ) were used , which contain the coding DNA sequences for human securin and the catalytic subunit of the human protein phosphatase 2A , respectively . Coupled in vitro transcription/translation reactions ( IVT ) were performed using the “TNT SP6 Coupled Reticulocyte Lysate System” or the “TNT SP6 Quick Coupled Transcription/Translation System” ( Promega ) according to the manufacturer's instructions . Up to 3 different plasmids ( final amount of 2 µg DNA total ) were included in 25 µl reaction mixtures . For radioactive labeling , 0 . 4 µM [35S]methionine ( 1000 Ci/mmol ) was added to the reaction mix . In some instances , the produced proteins migrated at almost the same position during SDS-PAGE . In these cases , only the components without an epitope tag were translated in the presence of [35S]methionine . The epitope tagged variants were translated in a separate reaction in the absence of radioactive label . Afterwards , the reactions were mixed and subjected to immunoprecipitation using 5 µl of mouse-anti-Flag-Agarose-slurry ( Sigma , A1080 ) or 5 µl Protein-A-Sepharose beads to which monoclonal mouse antibodies against the myc-epitope had been covalently crosslinked with dimethyl pimelimidate [53] . After 3 h incubation at 4°C with gentle agitation and a subsequent brief centrifugation , the supernatants were removed and immunoprecipitates were washed 3 times with 1 ml of lysis buffer . Bound polypeptides were eluted by addition of 40 µl SDS-PAGE sample buffer and subsequent incubation at 95°C for 5 min . Precipitated polypeptides as well as samples derived from the input and supernatant fractions were resolved by SDS-PAGE and analyzed by immunoblotting and/or autoradiography ( FLA 7000 Phosphoimager , Fuji Corp . ) | The accurate duplication and segregation of chromosomes during cell divisions are prerequisites for ensuring genetic stability within an individual organism and in entire populations . Among the many components involved in regulating these processes , a protein complex called condensin plays a crucial role in shaping mitotic chromosomes , so that they can be faithfully distributed . Many organisms contain two of these condensin complexes ( condensin I and II ) , which both have been shown to be required for accurate chromosome distribution . In the fly Drosophila melanogaster , condensin II appears to lack one of its components , called Cap-G2 . We have tested the hypothesis whether the corresponding component of condensin I ( Cap-G ) might also participate in the assembly of condensin II . Careful analyses of complexes formed in the living organism or in the test tube argue against Cap-G being part of condensin II . Moreover , our results question the very existence of a soluble condensin II complex in flies , as opposed to other organisms . Surprisingly , a substantially truncated variant of the essential Cap-G still supports development of living and fertile flies . As this variant localizes within the cell differently from full-length Cap-G , our results show that localization of a protein does not always determine its function . | [
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] | 2013 | Functional Dissection of the Drosophila melanogaster Condensin Subunit Cap-G Reveals Its Exclusive Association with Condensin I |
Synaptic vesicles dock to the plasma membrane at synapses to facilitate rapid exocytosis . Docking was originally proposed to require the soluble N-ethylmaleimide–sensitive fusion attachment protein receptor ( SNARE ) proteins; however , perturbation studies suggested that docking was independent of the SNARE proteins . We now find that the SNARE protein syntaxin is required for docking of all vesicles at synapses in the nematode Caenorhabditis elegans . The active zone protein UNC-13 , which interacts with syntaxin , is also required for docking in the active zone . The docking defects in unc-13 mutants can be fully rescued by overexpressing a constitutively open form of syntaxin , but not by wild-type syntaxin . These experiments support a model for docking in which UNC-13 converts syntaxin from the closed to the open state , and open syntaxin acts directly in docking vesicles to the plasma membrane . These data provide a molecular basis for synaptic vesicle docking .
Fusion of synaptic vesicles with the plasma membrane is thought to occur in three ordered steps: docking , priming , and fusion [1] . The biological state of a synaptic vesicle can be defined by three distinct parameters: morphology ( its location in the synapse ) ; physiology ( its release competence ) ; and molecular interactions . A goal of studies in neurotransmission is to define the state of the vesicle at each step in exocytosis using morphological , physiological , and molecular criteria . For example , the final step of vesicle fusion , in which vesicles fuse with the plasma membrane , is well defined by these three criteria . Fusing vesicles can be observed by electron microscopy [2 , 3] and by electrophysiological recordings [4] . The molecular basis of fusion is thought to be mediated by the soluble N-ethylmaleimide–sensitive fusion attachment protein receptors SNARE proteins . When reconstituted into liposomes under permissive conditions , the SNARE proteins have been demonstrated to be necessary and sufficient for membrane fusion [5–12] . Specific sets of complementary SNARE proteins are localized to each cargo vesicle and target compartment in the cell and thereby provide dedicated fusion proteins for each trafficking event [13] . For synaptic vesicle fusion , the vesicular SNARE protein synaptobrevin ( also called vesicle-associated membrane protein or VAMP ) interacts with the plasma membrane SNARE proteins syntaxin and SNAP-25 to form a four-helix bundle [14] . The formation of this tightly wound structure may provide the driving force for fusion [15–18] . Priming describes a molecular state in which a four-helix SNARE complex has formed between SNARE proteins on a synaptic vesicle and those on the plasma membrane [1] . It is believed that the SNARE proteins partially wind into a complex , but membrane fusion is arrested , and the vesicle is held in this state until triggered to fuse by an increase in calcium [19–24] . Thus , the SNARE proteins function both in priming and in fusion . These primed vesicles are likely to correspond to the physiologically defined readily releasable pool [25] . Docking precedes priming and at this point is defined solely by morphological criteria . Synaptic vesicle docking is observed in electron micrographs of the synapse and is defined as the attachment of vesicles to their target membranes [26–28] . However , the precise definition of docking is a muddle since morphologically docked vesicles are thought to include those in both the primed and unprimed pools [28 , 29] . Moreover , because standard fixation methods often introduce changes in membrane structure , docking is sometimes defined as including all vesicles near the membrane—usually specified as vesicles within about 30 nm of the membrane [30 , 31] . Thus , even the morphological definition of docked vesicles varies in the literature . In addition , the molecular basis for docking is unknown . It is recognized that protein interactions must specifically associate a vesicle to the correct target membrane . In the original SNARE hypothesis , contacts between the SNARE proteins were proposed to confer specificity during docking [32] . However , genetic and other perturbation experiments indicated that SNARE proteins were not required for docking . Disruption of syntaxin , either by mutation [30 , 33] or by proteolytic cleavage [31 , 34] , dramatically reduced synaptic vesicle fusion , but did not eliminate morphologically docked vesicles . Similarly , in a recent study proteolytic cleavage of syntaxin was found to result in no decrease in docked synaptic vesicles in neurons ( although docking of secretory vesicles in neurosecretory cells was reduced ) [35] . Thus , the current model for syntaxin function in neurons is that it acts during priming and fusion , after docking has been completed . Although many proteins have defined roles in synaptic transmission , few have been shown to play a role in docking , and none are essential for docking [36] . Here we study docking in the nematode C . elegans using a new fixation method that reduces artifacts [37–39] . We demonstrate that syntaxin is essential for all synaptic vesicle docking , that the syntaxin-binding protein UNC-13 is required for docking vesicles at the active zone , and finally that the docking defects observed in unc-13 mutants can be bypassed by expressing an open form of syntaxin . Together these data suggest that the open form of syntaxin mediates docking . Thus , all three steps of vesicle fusion—docking , priming , and fusion—depend on the SNARE protein syntaxin .
To study the ultrastructure of the synapses , we fixed worms using high-pressure freezing followed by substitution of ice by solvent-borne fixatives [38] . We analyzed sections from the ventral nerve cord containing neuromuscular junctions to determine the distribution of synaptic vesicles . In all cases in this study , the wild types were fixed on the same day as the mutant strains and analyzed in parallel , and all genotypes were scored blind . All numerical values and statistical tests are provided in Table S1 . In the worm , the acetylcholine neurons in the ventral cord stimulate muscle contraction , and the gamma-aminobutyric acid ( GABA ) neurons inhibit muscle contraction [40] . The target muscles receive input from numerous en passant synapses , which appear as varicosities containing large numbers of synaptic vesicles abutting the muscle . At each synapse , synaptic vesicles dock to the plasma membrane at sites of release called active zones [41] . Docked vesicles can be identified by visual inspection as vesicles forming a contact patch with the plasma membrane [28 , 42] . This patch distinguishes them from other vesicles within 30 nm of the membrane that are sometimes identified as “docked” ( Figure 1 ) . The active zone flanks an electron-dense specialization called the dense projection ( Figures 1 and 2A ) [43 , 44] . We determined the distribution of all docked vesicles relative to the nearest dense projection . In most cases , we defined a synapse as a set of contiguous profiles that contained a dense projection . In these profiles , we measured the distance from the edge of the dense projection to the docked vesicle ( Figure 2A and 2B , d1 ) . For the complete reconstruction of the wild-type animal , we also analyzed the adjacent profiles that did not contain a dense projection . In these profiles we calculated the distance between the docked vesicle and the dense projection based on section thickness ( Figure 2B , d2 ) . Most docked vesicles cluster tightly around the dense projection in the active zone pool . In fully reconstructed synapses there are on average 34 . 5 docked vesicles in the active zone pool of acetylcholine synapses and 32 . 6 docked vesicles in the active zone pool of GABA synapses ( Figure 2D ) . Vesicle docking is suppressed in regions lateral to the active zone ( Figure 2C and 2D; between 231 and 330 nm from the dense projection ) . This vesicle-free zone exhibits very little docking in all genotypes analyzed and can be quite pronounced in some datasets ( for example , Figure 8 ) . Similar docking-depleted regions have been identified in other synapses [45] . This domain probably corresponds to regions of adhesion [45–48] or endocytosis [3 , 49–54] . Outside of the vesicle-free zone , on the far side of the synapse , there is a second smaller pool of docked vesicles ( Figure 2C and 2D ) . Such docking is sometimes referred to as ectopic [55]; however since ectopic refers to an abnormal condition , we call this perisynaptic docking . The average number of vesicles in the perisynaptic pool in reconstructed synapses is 3 . 5 vesicles at acetylcholine synapses and 6 . 6 vesicles at GABA synapses ( Figure 2D ) . Vesicles in this perisynaptic pool are not oriented toward clear synaptic targets . Although we do not know if such vesicles contain or release neurotransmitter in C . elegans , in vertebrates ectopic release plays an important role in activation of extrasynaptic receptors [55–57] . In summary , vesicles dock to the plasma membrane in at least two domains separated by a docking-suppressed zone . Syntaxin null mutants arrest after hatching in the first larval stage [58 , 59] . To study the loss of syntaxin in adult neurons we generated mosaic strains in the syntaxin null background unc-64 ( js115 ) ( Figure S1 ) . These strains express wild-type syntaxin in the acetylcholine neurons of the head; this expression is required to rescue syntaxin null mutants to adulthood . In C . elegans , the ventral body muscles are innervated by the VA and VB acetylcholine motor neurons and the VD GABA motor neurons [60] . We made two mosaic strains: the first lacked expression of syntaxin in both the acetylcholine and GABA motor neurons , ( EG3278 ) ; the second lacked syntaxin in the GABA motor neurons but expressed syntaxin in the acetylcholine motor neurons ( EG3817 ) . The mosaic animals are viable but paralyzed . We confirmed that syntaxin was absent from the relevant motor neurons by immunostaining ( Figure S2B and S2C ) . Importantly , the syntaxin mosaic strains enable us to analyze neurons that lack syntaxin in viable adult animals . Loss of syntaxin function could result in abnormal development or cell death . To determine whether development was normal , we assayed the structure of the syntaxin mutant neurons by expressing green fluorescent protein ( GFP ) in the GABA neurons ( Figure 3A ) . The number of GABA neurons and arrangement of commissures is normal in the mosaic animals ( syntaxin mosaic: 16 . 8 GABA commissures/animal; wild type: 16 . 8 GABA commissures/animal; no abnormalities were observed; the large cells in the mosaic are coelomocytes that express GFP to mark the transgene ) . We also assayed the density of synaptic varicosities of syntaxin mutant neurons by tagging synaptic vesicles in the GABA neurons with synaptobrevin-GFP ( Figure 3B ) . The number of synapses in these cells is similar to the wild type ( syntaxin mosaic: 1 . 9 varicosities/10 μm; wild type: 2 . 3 varicosities/10 μm ) ( see Materials and Methods ) . Postsynaptic GABA receptors cluster normally on the muscle opposite GABA presynaptic varicosities in the syntaxin mosaic ( Figure 3B ) . The clustered postsynaptic GABA receptors are functionally indistinguishable from those in wild-type animals ( response to GABA application in syntaxin mosaic: 1 . 53 ± 0 . 33 nA; wild type: 1 . 31 ± 0 . 11 nA; p = 0 . 54 ) ( Figure 3C ) . Finally , we confirmed that these synaptic contacts are intact at the ultrastructural level , and that the interweaving of acetylcholine and GABA neuromuscular junctions is normal ( Figure 3D ) . These results differ from Drosophila in which syntaxin mutants exhibit developmental abnormalities [30 , 61–63] . In the fly there is a substantial maternal contribution of syntaxin to the embryo that provides important functions during cellularization [61 , 63] . In mutants lacking zygotic expression of syntaxin , fewer boutons are observed , and in late embryos the postsynaptic clusters of neurotransmitter receptors apparently dissipate [30 , 63 , 64–66] . In the fly studies , the entire embryo lacked syntaxin; thus , some of these defects may not be cell autonomous . In the mosaic worm , the absence of syntaxin in the GABA neurons does not lead to degeneration of presynaptic or postsynaptic elements . Previous experiments demonstrated that syntaxin is required for synaptic vesicle exocytosis [30 , 31 , 34 , 62] . Similarly , we observe that syntaxin is required for exocytosis in the nematode . In C . elegans , individual synaptic vesicle fusions can be observed by recording miniature postsynaptic currents ( minis ) in the postsynaptic muscles [67] . Under our recording conditions acetylcholine and GABA miniature currents are both inward and are of roughly the same amplitude ( combined rate: 42 . 8 ± 6 . 5 fusions per second ) ( Figure 4A ) [67] . By adding d-tubocurare we can block acetylcholine receptors and monitor synaptic vesicle exocytosis from only the GABA motor neurons ( GABA rate: 28 . 5 ± 4 . 8 fusions per second ) ( Figure 4A and 4D ) . d-tubocurare is completely effective at blocking all acetylcholine-induced currents , since it eliminates all minis in mutants lacking the muscle GABA receptor UNC-49 , unc-49 ( e407 ) ( 21 . 0 ± 5 . 8 fusions per second before treatment; 0 . 0 ± 0 . 0 fusions per second after treatment ) ( see d-tubocurare in Materials and Methods ) ( Figure 4D ) . To determine if syntaxin is required for synaptic vesicle exocytosis , we recorded from syntaxin mosaic animals . The EG3278 mosaic animals almost completely lack mini currents from both the acetylcholine and GABA neurons ( Figure 4B and 4D ) ( Acetylcholine 0 . 02 ± 0 . 01 fusions per second; GABA 0 . 00 ± 0 . 00 fusions per second ) . Thus , syntaxin is required for exocytosis at both excitatory acetylcholine synapses and inhibitory GABA synapses . The requirement for syntaxin in exocytosis could be cell intrinsic . Alternatively , unc-49 ( e407 ) syntaxin ( − ) motor neurons might fail to release synaptic vesicles because they are not excited by upstream neurons . To control for this possibility , we assayed transmission in the second syntaxin mosaic strain ( EG3817 ) that expresses syntaxin in the acetylcholine motor neurons but lacks syntaxin in the GABA motor neurons ( Figure S1 ) . These animals are viable and healthy but exhibit behavioral defects associated with loss of GABA neurotransmission . Specifically , EG3817 animals shrink when touched due to lack of GABA inhibition of the body muscles [68 , 69] and are constipated due to loss of activation of a GABA-gated cation channel during defecation [70] . The syntaxin-expressing acetylcholine neurons exhibit substantial levels of vesicle fusion ( Figure 4C and 4D ) ( 4 . 3 ± 1 . 1 fusions per second ) . Thus , the lack of exocytosis in syntaxin ( − ) cells is due to a cell-autonomous requirement for syntaxin rather than due to the paralysis of the mutant strain . By contrast , the mini rate in the syntaxin ( − ) GABA neurons is 1% of the rate in the syntaxin ( + ) acetylcholine neurons ( Fig 4C and 4D; 0 . 06 ± 0 . 03 fusions per second ) . GABA neurons receive inputs from the acetylcholine motor neurons . Restoring acetylcholine inputs into the GABA motor neurons did not rescue exocytosis; thus , the observed defects are not due to a lack of synaptic input into the motor neurons . Note that synaptic activity is not fully rescued in the acetylcholine neurons; mini frequency is only 20% compared to the wild type . There are two possible causes for the lack of complete rescue: either syntaxin is not expressed at high levels in these cells , or modulatory inputs from other neurons , which are missing in the mosaic , are required to obtain normal levels of activity from these synapses . Syntaxin is not thought to function in synaptic vesicle docking [30 , 31 , 34 , 35]; however , syntaxin is known to mediate interactions between the plasma membrane and synaptic vesicles that could in principle dock vesicles . To determine whether loss of syntaxin affects synaptic vesicle docking , we fixed the syntaxin mosaic strains by high-pressure freezing and analyzed them by serial section electron microscopy . An analysis of the distribution of vesicles at synaptic profiles in the mosaic animals demonstrated that syntaxin is required for synaptic vesicle docking . First , we analyzed docking in the EG3278 syntaxin mosaic , which lacks syntaxin in both acetylcholine and GABA neurons . These mosaic animals exhibit a severe reduction of docking in the acetylcholine neurons ( docked vesicles per acetylcholine synaptic profile: mosaic 0 . 12 ± 0 . 05; wild type 2 . 56 ± 0 . 22; p < 0 . 0001; see Table S1 for statistical methods and complete list of p-values ) ( Figure 5A ) and in the GABA neurons ( docked vesicles per profile: mosaic 0 . 27 ± 0 . 04; wild type 3 . 13 ± 0 . 33; p = 0 . 0001 ) ( Figure 5B ) . Thus , syntaxin is required for docking at both excitatory acetylcholine synapses and inhibitory GABA synapses . Second , to confirm that the docking defect in syntaxin ( − ) neurons is cell autonomous , we examined docking in the EG3817 syntaxin mosaic . In this strain , docking at acetylcholine synapses in mosaic animals is fully rescued compared to wild-type synapses ( docked vesicles per acetylcholine synaptic profile: syntaxin mosaic 3 . 09 ± 0 . 11; wild type 2 . 99 ± 0 . 15; p = 0 . 59 ) ( Figure 5C ) . By contrast , in the syntaxin ( − ) GABA neurons of the same strain , docked vesicles are reduced to 3% compared to wild-type synapses ( docked vesicles per GABA synaptic profile: syntaxin mosaic 0 . 09 ± 0 . 05; wild type 3 . 42 ± 0 . 15; p < 0 . 0001 ) ( Figure 5D ) . The full rescue of docking in acetylcholine synapses of the mosaic strain confirms that the docking defects are cell autonomous and do not result from general paralysis . In all syntaxin ( − ) neurons analyzed , docking was eliminated both in the active zone pool as well as the perisynaptic pool; thus , both of the docked pools require syntaxin . This defect in docking was not caused by a lack of vesicles at the synapse . In both mosaic strains , the total vesicle number was not reduced ( Figure 6 ) . In addition , the distribution of this reserve pool of vesicles was normal ( Figure S3 ) ; vesicles were clustered near the dense projection in the synaptic varicosity . These data suggest that the docking defect in syntaxin mutant neurons is not the result of a general trafficking defect such as synaptic vesicle biogenesis , transport , or clustering . UNC-13 is a syntaxin-binding protein that is required for synaptic vesicle priming [71–73] . To determine whether UNC-13 functions in docking at specific membrane domains , we analyzed the number of docked vesicles in the active zone and perisynaptic pools in unc-13 mutants . The number of docked vesicles in the active zone pool in unc-13 mutants is 16% that of the wild type ( docked vesicles in the active zone per profile: unc-13 = 0 . 31 ± 0 . 06; wild type = 1 . 91 ± 0 . 16; p < 0 . 0001 ) ( Figure 7A and 7B ) . Docking in the perisynaptic pool actually increases slightly in unc-13 ( docked vesicles in the perisynaptic zone per profile: unc-13 = 0 . 85 ± 0 . 15; wild type = 0 . 41 ± 0 . 10; p = 0 . 01 ) . These results differ from our previous results using ice-cold glutaraldehyde fixations [73] . In those experiments we combined active zone regions with peri-synaptic regions , which could obscure decreases in active zone docking . Moreover , we defined the docked pool as vesicles within 30 nm of the membrane . When we apply those criteria to the current dataset , we also do not observe a decrease in docking ( see Materials and Methods ) . In addition , our current results are in agreement with data from two independent laboratories [54 , 74] . To demonstrate that the docking defects were not caused by irrelevant background mutations we analyzed a second allele , unc-13 ( e1091 ) . Similar results were obtained with this mutant: decreased docking was observed in the active zone pool and increased docking in the perisynaptic pool ( active zone 28% , perisynaptic zone 145% compared to the wild type ) ( Figure 7C ) . The decrease in docking is restricted to the active zone and is most severe near the dense projection . The specific reduction in docking in the active zone pool is consistent with the localization of UNC-13 near the dense projection [54] . Surprisingly , we did not observe an increase in the number of cytoplasmic vesicles in unc-13 mutant animals ( Figure 6 ) , despite observing an increase using a different fixation protocol [73] . In the present study unc-13 and other release-defective genotypes generally do not display an increase in cytoplasmic vesicle number ( Figure 6 ) . This lack of increase in the number of cytoplasmic vesicles in unc-13 mutant animals was also found in an independent study using high pressure freezing [74] . Glutaraldehyde fixations used in previous studies can induce vesicle fusion [75] . Glutaraldehyde-induced fusion would result in a reduction of docked vesicles in the wild type relative to release-defective mutants and thus lead one to believe that there is an actual accumulation in the mutant . We have confirmed these differences by comparing glutaraldehyde and freeze-substituted fixations in parallel ( see Materials and Methods ) . It is still possible that synaptic vesicles accumulate in the reserve pool of unc-13 mutants . These data only analyze synaptic vesicles in profiles containing a dense projection; the reserve pool was not fully reconstructed . Syntaxin can adopt two configurations: a closed configuration in which the N-terminal Habc domain binds to the SNARE motif and an open configuration in which this cis interaction does not occur . Mutations in the linker between the Habc domain and the SNARE motif cause syntaxin to preferentially adopt the open conformation [76] . We found that the replacement of wild-type syntaxin with the open form of syntaxin does not lead to a redistribution of docked vesicles ( docked vesicles per profile: open syntaxin 3 . 27 ± 0 . 21; wild type 2 . 92 ± 0 . 21; p = 0 . 27 ) ( Figure 8A ) . It has previously been proposed that UNC-13 opens or maintains the open state of syntaxin at the active zone [77] . Since docking requires syntaxin , this suggests that the docking defects in unc-13 animals might be due to its failure to open syntaxin . To test this idea , we examined docking in unc-13 mutant animals in which wild-type syntaxin was replaced with open syntaxin . We found that expression of the open form of syntaxin fully rescues the docking defect in unc-13 ( s69 ) mutants ( docked vesicles per active zone profile: unc-13 open-syntaxin 2 . 18 ± 0 . 14; wild type 2 . 48 ± 0 . 17; p = 0 . 19 ) ( Figure 8B ) . To control for the possibility that this result was due to overexpression of the syntaxin protein rather than its conformation , we tested whether overexpression of wild-type syntaxin could restore docking to unc-13 mutants . First , overexpression of wild-type syntaxin had no effect on the distribution of docked vesicles in an otherwise wild-type background ( Figure 8C ) . Second , overexpression of wild-type syntaxin had no effect on docking in unc-13 animals ( docked vesicles per active zone profile: unc-13 syntaxin OE 0 . 19 ± 0 . 05; wild type 1 . 93 ± 0 . 23; p < 0 . 0001 ) ( Figure 8D ) . Thus , the function of UNC-13 in vesicle docking is specifically to promote the open state of syntaxin . Finally , the full bypass of unc-13 mutants by open syntaxin demonstrates that syntaxin functions in docking downstream of UNC-13 , further reinforcing the fact that syntaxin plays a direct role in docking rather than an indirect role in trafficking or development . Interestingly , the distribution of docked vesicles is normal in the presence of open syntaxin ( Figure 8A ) . Thus , it is likely that open syntaxin is involved in the mechanics of docking but not in the distribution of docked vesicles . Similarly , this distribution is independent of UNC-13 , since the distribution of docked vesicles is normal in the unc-13 open syntaxin genotype . Other proteins must therefore determine the distribution of docked vesicles relative to the dense projection . Mutants lacking tomosyn , for example , have a large increase in the number and distribution of docked synaptic vesicles [74] . As described above , normal vesicle docking occurs in the absence of UNC-13 when open syntaxin is present . We tested the release competence of these vesicles by comparing spontaneous and evoked release in unc-13 mutant animals in the presence and absence of open syntaxin . Experiments were performed at two different concentrations of external calcium . We found that open syntaxin restores vesicle fusion to approximately one-third wild-type levels . First , we examined vesicle fusion in external solutions containing 5 mM calcium . In unc-13 ( s69 ) animals evoked responses were essentially absent ( 0 . 015 ± 0 . 004 nA; n = 9 ) ( Figure 9A and 9C ) . However , expression of open syntaxin in unc-13 ( s69 ) animals partially rescued the evoked response ( Figure 9A ) . Peak amplitude was restored to 38% of wild type ( unc-13 open-syntaxin 0 . 75 ± 0 . 10 nA , n = 7; wild type 1 . 95 ± 0 . 21 , n = 7 ) ( Figure 9C ) , and total current transferred was restored to 35% of wild type ( unc-13 open-syntaxin 7 . 41 ± 1 . 26 pC , n = 7; wild type 20 . 98 ± 3 . 41 pC , n = 7 ) ( Figure 9D ) . In addition to the rescue of evoked responses , endogenous fusion events were restored to 26% of wild type ( unc-13 0 . 59 ± 0 . 13 Hz , n = 9; unc-13 open-syntaxin 14 . 42 ± 3 . 25 Hz , n = 6; wild type 54 . 47 ± 6 Hz , n = 6 ) ( Figure 9A and 9F ) . Thus , docking via open syntaxin in the absence of UNC-13 results in vesicles that can be released , although release is not restored to wild-type levels . Second , we examined vesicle fusion in external solutions containing 1 mM calcium . Again , the presence of open syntaxin partially rescued the unc-13 defects in both evoked and endogenous release ( Figure 9B–9D and 9G ) . However , this experiment revealed additional characteristics of vesicle fusion in unc-13 open-syntaxin animals . The evoked response in wild-type animals at 1 mM calcium was only 15% lower than the response at 5 mM calcium ( 1 . 95 ± 0 . 21 nA at 5 mM calcium , n = 7; 1 . 65 ± 0 . 22 nA at 1 mM calcium , n = 6 ) ( Figure 9B and 9C ) . By contrast , evoked responses in unc-13 open syntaxin at 1 mM calcium were 67% lower than the response at 5 mM calcium ( 0 . 75 ± 0 . 10 nA at 5mM calcium , n = 7; 0 . 25 ± 0 . 04 nA at 1 mM calcium , n = 6 ) ( Figure 9B and 9C ) . Further , release kinetics were altered in unc-13 open-syntaxin animals at 1 mM calcium: release was slower and more asynchronous in comparison to wild-type animals ( Figure 9B and 9E ) .
Previously , syntaxin was not thought to be required for docking . By contrast , our results demonstrate that syntaxin is required for docking synaptic vesicles at the C . elegans neuromuscular junction . Vesicles are docked in two pools: the active zone pool and the perisynaptic pool . We also find that UNC-13 is required for synaptic vesicle docking . However , while both pools of docked vesicles depend absolutely on syntaxin , UNC-13 only plays a role at the active zone . Finally , the docking function of UNC-13 is completely bypassed by open syntaxin . The observed docking defects in the syntaxin and unc-13 mutant synapses are likely to be caused by a direct role of these proteins in the docking pathway rather than by an indirect effect on neuronal health . First , syntaxin acts cell autonomously: expressing syntaxin in the acetylcholine neurons rescues docking in these cells but not in downstream neurons in the motor circuit . Second , chronic lack of syntaxin does not lead to developmental abnormalities in the cell . Synaptic vesicles and synaptic vesicle components such as synaptobrevin are transported to the synapse , vesicles are clustered , dense projections and adherens junctions appear normal at the ultrastructural level , the postsynaptic receptors cluster appropriately , and the receptors are functional: the synapses appear to be intact . Third , syntaxin appears to play a late role in docking . The syntaxin-binding protein UNC-13 is required for docking as well , and open syntaxin can rescue the docking defect in unc-13 mutants , suggesting that syntaxin acts downstream of UNC-13 during docking . These data are most consistent with a direct role for syntaxin in the docking of synaptic vesicles . A role for syntaxin in docking conflicts with previous studies [30 , 31 , 34 , 35] . It is unlikely that syntaxin function is not conserved among organisms; it is more likely that the conflicting results arise from the difficulties in studying docking . The different conclusions might be attributed to two causes: definitions for docking and the potential for residual syntaxin . First , different definitions for docking were used in these various studies . In the present study only vesicles contacting the membrane were considered docked ( Figure 1 ) . This definition was used in studies of vertebrate synapses comparing the docked and readily releasable pools [26 , 27 , 42] . By contrast , previous syntaxin studies , as well as our previous UNC-13 studies , defined docked vesicles as those near the plasma membrane ( less than 30 , 40 , or 50 nm , [30 , 31 , 73] ) . If we analyze our current data using the 30 nm definition , we also do not detect decreases in docking ( for example , vesicles within 30 nm per profile , matched wild-type GABA 5 . 6 ± 0 . 2; syntaxin ( − ) GABA from EG3817 5 . 4 ± 0 . 3; p = 0 . 49 ) . It was not possible to reanalyze our previous data with our current definition of docking , because the glutaraldehyde fixation used in the previous experiments did not preserve membranes well enough to distinguish between docked and undocked vesicles . Tethering proteins span larger distances than the SNARE proteins and thus are thought to function in those vesicles that are close to but not contacting the plasma membrane [78 , 79] . Our data thus suggest that syntaxin is not required to tether synaptic vesicles to the membrane . In contrast to synaptic vesicles , secretory vesicles require syntaxin for tethering [35 , 80] . The second possible explanation for the discrepancy is that residual syntaxin could have mediated docking in previous experiments . In the studies on squid and cultured hippocampal cells , syntaxin was acutely disrupted by protease digestion; nevertheless , about 10% of synaptic vesicle fusions remained , suggesting that some syntaxin was still present [31 , 34 , 35] . Further , syntaxin may itself be redundant , in agreement with the almost complete lack of a phenotype in syntaxin knockout mice [81] . Studies in Drosophila used mutation rather than protease cleavage to disrupt syntaxin . In fly syntaxin mutants , vesicle fusions were 5% the wild-type rate [30]; much greater than the fusion rate observed in the syntaxin mosaics in C . elegans ( less than 0 . 2% of the wild-type rate ) . In Drosophila , there is a significant maternal contribution of syntaxin [61 , 63] , and it has been suggested that syntaxin might perdure until late embryogenesis [30 , 33] . In our own data , although syntaxin is not detectable by antibody staining , we do observe a few docked vesicles and a few spontaneous fusions ( Figures 4 and 5 ) . These rare events are likely due to residual syntaxin , either as a result of read-through of the stop codon in unc-64 ( js115 ) or as a result of misexpression from our rescuing array . Thus , syntaxin is likely to be essential for all synaptic vesicle docking . In addition to syntaxin , docking in the active zone also relies on UNC-13 . The docking defect in unc-13 mutants is completely bypassed by open syntaxin but not by closed syntaxin . This observation suggests that UNC-13′s function in docking is to promote open syntaxin . However , open syntaxin does not completely restore exocytosis in unc-13 mutant animals . Specifically , in unc-13 mutants expressing open syntaxin evoked response is 38% of the wild type . Further , we find that the presence of open syntaxin only slightly improves locomotion in unc-13 mutants ( unpublished data ) . The simplest explanation is that UNC-13 has a second function after docking to increase the probability of fusion [82 , 83] . Alternatively , levels of open syntaxin might not be sufficient to support normal exocytosis in the absence of UNC-13 . It is worth noting that this strain has changed with time; previously the strain was more active and evoked responses were more robust [77] . By contrast , some recently derived strains have no evoked response [84] . It is possible that expression levels have declined in these strains . We propose that only a few molecules of open syntaxin suffice for docking a vesicle , but that multiple molecules of open syntaxin are required to mediate normal exocytosis . Thus , very high expression levels of open syntaxin might be required to bypass the function of UNC-13 . In a wild-type synapse , UNC-13 is specifically localized to active zones [54] , where it can locally generate the high levels of open syntaxin that are required for release . How does open syntaxin interact with synaptic vesicles during docking ? There are two regions of syntaxin that could be involved: the Habc domain and the SNARE motif . In the open state of syntaxin both of these regions are free to interact with vesicle proteins . It is possible that the Habc domain mediates docking independently of SNARE function . In this model , the other SNARE proteins would not be required for docking . In support of this idea , previous data suggest that genetic and toxin disruption of synaptobrevin and SNAP-25 does not disrupt docking [30 , 80 , 85–87] . However , these studies used differing definitions of vesicle docking , perhaps obscuring specific docking defects . Further , it has been suggested that redundant SNARE proteins compensate for the loss of the synaptic SNAREs in these experiments [81 , 85 , 87–90] . If the SNARE motif of syntaxin mediates docking then it is likely that the SNARE proteins synaptobrevin and SNAP-25 , which interact with the SNARE motif of syntaxin , will also be required for docking . In this case , formation of the SNARE complex would mediate docking , as originally predicted in the SNARE hypothesis [32] , and the distinction between morphological docking and priming would not exist .
A synapse is defined as the serial profiles containing a dense projection and usually comprised three to four adjacent profiles . The exception is the complete wild-type reconstruction described in Figure 1 , in which a synapse included all the profiles on either side of the dense projection up to the profile on either side where the synaptic vesicle number fell to the average intersynaptic vesicle density , as determined from all the profiles analyzed . The dense projection is defined as an electron dense structure in the center of the active zone [43 , 44] . In C . elegans , this structure is quite prominent compared to many vertebrate central nervous system synapses [91] . The active zone encompasses the region where synaptic vesicles are docked opposite the postsynaptic target [41] . In our micrographs , docked vesicles extended laterally an average of 230 nm from the dense projection . Docked vesicles are morphologically defined as those contacting the plasma membrane [27 , 42] . In this study , vesicles were considered docked if their membranes and those of the plasma membrane appeared to be in direct contact ( see Figure 1 ) . The perisynaptic docked pool includes any docked vesicles not in the active zone . These can be oriented away from the active zone and would presumably not be part of the physiologically defined readily releasable pool . To drive the expression of syntaxin/UNC-64 under exogenous promoters , a minigene cassette ( pMH421 ) was constructed that contains the endogenous unc-64 promoter , the ATG , an inserted SphI site , unc-64 cDNA up to the NheI site ( in exon 6 ) , followed by genomic sequence including the 3′ UTR ( Figure S1 ) . This construct was injected and rescued the unc-64 ( js115 ) null phenotype ( unpublished data ) . Next , the endogenous unc-64 promoter was replaced with the unc-17 , rab-3 , and glr-1 promoters , which were amplified by PCR . For unc-17 , the primers were unc-17 5′ and unc-17 3′ , which includes intron 1 , and the resulting construct was pMH425 . For rab-3 , the primers were rab-3 5′ and rab-3 3′ , and the resulting construct was pMH415 . For glr-1 , the primers were glr-1 5′ and glr-1 3′ , and the resulting construct was pMH427 . These constructs were injected and gave the expected expression except for the unc-17 promoter , which had very little expression and none outside the nerve ring . To improve expression in cholinergic neurons , a different version of the unc-17 promoter ( 3 , 656 bases in front of the ATG in exon 2 ) was used to generate pMH441 . Neither of the unc-17 promoter constructs , pMH441 , or pMH425 , include the motor neuron enhancer since this construct resulted in leaky expression in the GABA motor neurons as assayed by electrophysiology . Thus , expression in the acetylcholine motor neurons was achieved using the acr-2 promoter . For acr-2 , the primers were acr-2 5′ and acr-2 3′ , and the resulting construct was pMH417 . Wild type was Bristol N2 . All strains were obtained from the C . elegans Genetics Center ( http://www . cbs . umn . edu/CGC ) unless otherwise indicated and maintained at 22 °C on standard NGM media seeded with HB101 . Strains used were: BC168 , unc-13 ( s69 ) ; CB1091 , unc-13 ( e1091 ) ; EG1285 , lin-15 ( n765 ) and oxIs12[Punc-47:GFP; lin-15 ( + ) ]; EG1983 , unc-13 ( s69 ) , unc-64 ( js115 ) , and oxIs34[openSYX , Pmyo-2:GFP]; EG1985 , unc-64 ( js115 ) and oxIs34[openSYX; Pmyo-2:GFP]; EG2279 , unc-49 ( e407 ) ; EG2466 , unc-64 ( js115 ) and oxIs33[SYX; Punc-122:GFP]; EG3278 , unc-64 ( js115 ) and oxEx536[Punc-17:SYX; Pglr-1:SYX; Punc-122:GFP; lin-15 ( + ) ]; EG3817 , unc-64 ( js115 ) and oxEx705[Punc-17:SYX; Pglr-1:SYX; Pacr-2:SYX; Pmyo-2:GFP]; EN560 , krIs1[Punc-47:SNB:CFP; UNC-49::YFP; lin-15 ( + ) ] and lin-15 ( n765 ) ; MT8247 , lin-15 ( n765 ) and nIs52[Punc-25:SNB:GFP; lin-15 ( + ) ]; and NM959 , unc-64 ( js115 ) /bli-5 ( e518 ) . To generate the acetylcholine ( − ) GABA ( − ) syntaxin mosaic strain EG3278 , unc-64 ( js115 ) and oxEx536[Punc-17:SYX; Pglr-1:SYX] , the strain NM959 unc-64 ( js115 ) /bli-5 ( e518 ) was injected using standard techniques [92] with an injection mix containing 5 ng/μl each of pMH425 and pMH427 ( see Figure S1 ) , as well as unc-122::GFP at 20 ng/μl ( coelomocyte marker ) and lin-15 ( + ) at 80 ng/μl . These animals are very sick , and when maintained for long periods of time , these strains became less uncoordinated . Analysis of this derived strain demonstrated that docking was restored to 50% in the acetylcholine neurons ( unpublished data ) . Reported data are from animals that were freshly thawed from the original isolate . The GABA ( − ) syntaxin mosaic strain EG3817 , unc-64 ( js115 ) and oxEx705[Punc-17:SYX; Pglr-1:SYX; Pacr-2:SYX] was generated in a similar way , except the injection mix contained pMH441 , pMH417 , and pMH427 ( see Figure S1 ) , as well as myo-2::GFP at 2 ng/μl and 1 kb ladder at 100 ng/μl ( Gibco/Invitrogen , http://www . invitrogen . com ) . Multiple stable lines were obtained , and homozygous unc-64 animals were recovered from each line and found to have similar phenotypes . For fluorescence analysis of neuroanatomy in the syntaxin mosaic , strains carrying the appropriate fluorescent marker were crossed with EG3278 to generate the three strains EG3301 , unc-64 ( js115 ) /+ , oxIs12 , and oxEx536; EG3349 , unc-64 ( js115 ) /+ , nIs52 , and oxEx536; and EG3299 , unc-64 ( js115 ) /+ , krIs1 , and oxEx536 . Homozygous unc-64 animals were recovered from these strains , allowed to self , and their progeny used for analysis . Reconstruction was performed on a VA synapse from a wild-type animal . We converted 16-bit TIFFs to 8-bit using Graphic Converter ( Lemke Software GMBH , http://www . lemkesoft . com ) and manually aligned using Midas ( Boulder Laboratory for 3-D Electron Microscopy of Cells , University of Colorado , Boulder , Colorado , United States ) . The VA/VD relationship was used as a fiduciary mark during the alignment . Image segmentation was performed in 3dmod ( Boulder Laboratory for 3-D Electron Microscopy of Cells ) by manually tracing neuronal profiles and presynaptic specializations at 200% magnification . Synaptic vesicles were modeled as spheres with a diameter of 28 nm , and section thickness was set to 33 nm . For overall neuronal morphology , ten young adult animals of each genotype ( unc-64 ( js115 ) , oxIs12 , oxEx536 , and wild-type oxIs12 ) were imaged on a confocal microscope and scored blind to genotype for the number of commissures . oxIs12 expresses GFP in the GABA neurons under the control of the unc-47 promoter . For synapse density , five young adult animals of each genotype ( unc-64 ( js115 ) , nIs52 , oxEx536 , and wild-type nIs52 ) were imaged on a confocal microscope . nIs52 expresses synaptobrevin-GFP in the GABA neurons under the control of the unc-25 promoter . For each animal , ImageJ was used to measure a region of the dorsal nerve cord , and puncta within the region were counted . For pre- and postsynaptic colocalization , ten young adult animals of each genotype ( unc-64 ( js115 ) , krIs1 , oxEx536 , and wild-type krIs1 ) were imaged on a confocal microscope . krIs1 expresses synaptobrevin-CFP in the GABA neurons under the control of the unc-47 promoter and expresses GABAA-receptor-YFP in muscles under the unc-49 promoter . Colocalization of CFP and YFP was observed in all cases . Previously we used ice-cold glutaraldehyde fixations for electron microscopy [73] . We have switched to high-pressure freezing followed by substitution of solvent-based fixatives [38] . Although membranes tend to be less darkly stained in this preparation , this fixation is superior to that observed with slow fixation methods . First , glutaraldehyde fixation itself stimulates exocytosis of synaptic vesicles and will therefore affect the docked pool of vesicles [75] . Second , shrinkage in conventional fixations dislodges docked vesicles and the dense projection at C . elegans synapses ( our observations ) . Finally , changes in membrane trafficking in the coelomocytes can be observed using the slow fixation method ( our observations ) . For these reasons we defined docked vesicles in our previous study as those within 30 nm of the plasma membrane since identifying vesicles docked at the surface was unreliable . No docking defect was observed in unc-13 mutants using this definition [73] . Our current data using high-pressure freezing confirm this observation , since there is no significant docking defect defined by vesicles within 30 nm of the plasma membrane ( number of vesicles within 30 nm , the wild type: acetylcholine = 4 . 57 ± 1 . 41 , 108 profiles , GABA = 5 . 31 ± 1 . 67 , 91 profiles; unc-13 ( e1091 ) : acetylcholine = 4 . 45 ± 1 . 17 , 33 profiles , GABA = 4 . 53 ± 1 . 07 , 28 profiles; unc-13 ( s69 ) : acetylcholine = 3 . 35 ± 1 . 30 , 34 profiles , GABA = 4 . 16 ± 1 . 37 , 32 profiles ) . Using high-pressure freezing we can now subdivide pools of docked vesicles and reliably determine if vesicles are touching the membrane; using this definition we see differences in docking in unc-13 mutants compared to the wild type . Worms were prepared for transmission electron microscopy essentially as described [38 , 93] . Briefly , ten animals were placed onto a freeze chamber ( 100-μm well of type A specimen carrier ) containing space-filling bacteria , covered with a type B specimen carrier flat side down , and frozen instantaneously in the BAL-TEC HPM 010 ( BAL-TEC , http://www . bal-tec . com ) . Frozen animals were fixed in a Leica EM AFS system ( http://www . leica . com ) with 0 . 5% glutaraldehyde and 0 . 1% tannic acid in anhydrous acetone for 4 d at −90 °C , followed by 2% osmium tetroxide in anhydrous acetone for 38 . 9 h with gradual temperature increases ( constant temperature at −90 °C for 7 h , 5 °C/h to −25 °C over 13 h , constant temperature at −25°C for 16 h , and 10 °C/h to 4 °C over 2 . 9 h ) . Fixed animals were embedded in araldite resin ( 30% araldite/acetone for 4 h , 70% araldite/acetone for 5 h , 90% araldite/acetone overnight , and pure araldite for 8 h ) . Mutant and control blocks were blinded . Ribbons of ultrathin ( 33 nm ) serial sections were collected using an ultracut E microtome . Images were obtained on a Hitachi H-7100 electron microscope ( http://www . hitachi . com ) using a Gatan ( http://www . gatan . com ) slow=scan digital camera . A total of 250 ultrathin contiguous sections were cut and the ventral nerve cord reconstructed from two animals representing each genotype . Image analysis was performed using ImageJ software ( http://rsb . info . nih . gov/ij ) . All morphometry was conducted blind to genotype and included a matched wild-type worm that was fixed in parallel . The number of synaptic vesicles ( ∼30 nm in diameter ) in each synapse was counted , and their diameters and distances from the dense projection and plasma membrane were measured . Analysis included the acetylcholine neurons VA and VB and the GABA neuron VD . To compare freeze-substitution fixations with our previous methods using ice-cold glutaraldehyde [73] , we analyzed samples fixed previously ( by W . Davis ) and samples fixed recently ( by S . Watanabe ) and analyzed under current scoring conditions ( by S . Watanabe ) . We observed fewer vesicles in the ice-cold glutaraldehyde fixations ( average number of synaptic vesicles per profile with a dense projection , acetylcholine 7 . 8 SV , n = 28 profiles; GABA 27 . 7 SV , n = 16 profiles ) compared to freeze-substituted samples ( Acetylcholine 22 . 6 SV , n = 35 profiles; GABA 33 . 8 SV , n = 20 profiles ) . Electrophysiological methods were performed as previously described [67 , 73] with minor adjustments . Briefly , animals were immobilized in cyanoacrylic glue ( B . Braun , Aesculap , http://www . aesculapusa . com ) , and a lateral incision was made to expose the ventral medial body wall muscles . The preparation was then treated with collagenase ( type IV; Sigma , http://www . sigmaaldrich . com ) for 15 s at a concentration of 0 . 5 mg/ml . The muscle was then voltage clamped using the whole cell configuration at a holding potential of −60 mV . See Protocol S1 for electrophysiology solutions . GABA neuron activity was isolated by specifically blocking acetylcholine currents through the application of d-tubocurare ( 1 mM , Sigma ) from a perfusion system . Pressure ejection of GABA from pipets of 4–5 MΩ resistance was computer triggered . Evoked responses were elicited using a fire-polished electrode positioned along the ventral nerve cord . The stimulating electrode was placed at least half a muscle length away from the patched muscle to cleanly separate the stimulus artifact from the evoked response . A square wave depolarizing current of 1 ms was then delivered from an SIU5 stimulation isolation unit driven from an S48 stimulator ( Grass Telefactor , http://www . grasstechnologies . com ) . All recordings were made at room temperature ( 21 °C ) using an EPC-9 patch-clamp amplifier ( HEKA , http://www . heka . com ) run on an ITC-16 interface ( Instrutech , http://www . instrutech . com ) . Data were acquired using Pulse software ( HEKA ) . All data analysis and graph preparation were performed using Pulsefit ( HEKA ) , Mini Analysis ( Synaptosoft , http://www . synaptosoft . com ) , and Igor Pro ( Wavemetrics , http://www . wavemetrics . com ) . Bar graph data are presented as the mean ± S . E . M . To be confident about low mini rates we needed to be certain that d-tubocurare provided a complete block . d-tubocurare block was tested daily on unc-49 ( e407 ) to insure that the solution aliquot completely blocked acetylcholine neurotransmission . d-tubocurare was added after 2 min of recording; recordings in d-tubocurare were done for 1 min for each animal . Mini analysis was performed on the traces beginning 10 s after d-tubocurare application and on traces both before d-tubocurare application and after washout . Only those animals with full recovery after d-tubocurare washout were used . From the matched unc-49 controls , no minis were observed in unc-49 in 4 min of total recordings from four animals . Thus , the probability of seeing a rogue acetylcholine mini from the matched controls is less than 0 . 0041 fusions per second . In addition we have recorded from 103 nonmatched unc-49 animals covering greater than an hour in d-tubocurare without seeing a single fusion event . The 0 . 06 fusions per second observed in the syntaxin mosaic ( six minis observed ) are therefore likely to be fusions from the GABA neurons . However , we cannot claim that these six minis are syntaxin-independent , since we cannot exclude the possibility that there is a low level of syntaxin expression in the GABA neurons from our transgenic array . | Like Olympic swimmers crouched on their starting blocks , synaptic vesicles prepare for fusion with the neuronal plasma membrane long before the starting gun fires . This preparation enables vesicles to fuse rapidly , synchronously , and in the correct place when the signal finally arrives . A well-known but poorly understood part of vesicle preparation is docking , in which vesicles prepare for release by attaching to the plasma membrane at the eventual site of release . Here , we outline a molecular mechanism for docking . Using a combination of genetics and electron microscopy , we find that docking requires two proteins: the cytoplasmic protein UNC-13 and the plasma membrane protein syntaxin . Syntaxin is known to form two configurations , closed and open . We find that the open form of syntaxin can bypass the docking function of UNC-13 , while the closed form cannot . These experiments suggest that docking is the attachment of synaptic vesicles to syntaxin; that syntaxin must be open for this attachment to occur; and that UNC-13′s role in docking is to promote open syntaxin . | [
"Abstract",
"Introduction",
"Results",
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] | [
"cell",
"biology",
"caenorhabditis",
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] | 2007 | Open Syntaxin Docks Synaptic Vesicles |
We have previously shown that the physiological size of postsynaptic currents maximises energy efficiency rather than information transfer across the retinothalamic relay synapse . Here , we investigate information transmission and postsynaptic energy use at the next synapse along the visual pathway: from relay neurons in the thalamus to spiny stellate cells in layer 4 of the primary visual cortex ( L4SS ) . Using both multicompartment Hodgkin-Huxley-type simulations and electrophysiological recordings in rodent brain slices , we find that increasing or decreasing the postsynaptic conductance of the set of thalamocortical inputs to one L4SS cell decreases the energy efficiency of information transmission from a single thalamocortical input . This result is obtained in the presence of random background input to the L4SS cell from excitatory and inhibitory corticocortical connections , which were simulated ( both excitatory and inhibitory ) or injected experimentally using dynamic-clamp ( excitatory only ) . Thus , energy efficiency is not a unique property of strong relay synapses: even at the relatively weak thalamocortical synapse , each of which contributes minimally to the output firing of the L4SS cell , evolutionarily-selected postsynaptic properties appear to maximise the information transmitted per energy used .
Information transmission in the brain is energetically expensive [1–7] , yet has to satisfy demands of speed and signal-to-noise reliability [8 , 9] . In order to balance these competing demands , the brain may tend towards a design which prioritises energy efficiency at the expense of computational power . For instance , theoretical analysis has previously shown that the low mean firing rate of neurons [10] and the surprisingly low release probability of cortical synapses [4 , 11] can be explained if axons and presynaptic terminals operate to maximise the information transmitted per energy used . Experimentally , we have shown previously that the strong retinothalamic synapse , relaying information from the retina to the thalamus , also maximises energetic efficiency when transferring information [12] . At that synapse , the evolutionarily selected properties are not set to transmit the maximum amount of information possible—more information would be transmitted if larger excitatory postsynaptic currents ( EPSCs ) were evoked by presynaptic action potentials . However , EPSCs that are larger or smaller than physiological EPSCs decrease the information transmitted per energy used . The physiological EPSC size therefore maximises energy efficiency of information transfer rather than information transfer across the retinothalamic synapse . Crucially however , it is unclear whether energy efficiency at excitatory synapses is a special property of strong relay synapses , or a more general principle also governing synaptic inputs that contribute more weakly to determining the output of the postsynaptic cell . To address this question , we investigated information transmission and EPSC energy cost at the next synapse along the visual pathway , from relay neurons in the thalamus to spiny stellate ( SS ) cells in layer 4 ( L4 ) of the primary visual cortex ( V1 ) . This relatively weak thalamocortical synapse operates in the presence of many other synaptic inputs from the thalamus and from the cortex . Using a multicompartment Hodgkin-Huxley-type model of L4SS cells , we simulated random synaptic bombardment from thalamic and cortical synapses , while quantifying the energetic cost of information transmission across the synapses impinging on the cortical cell from a single thalamic axon . We assessed the energetic cost incurred by these postsynaptic cells in V1 by converting the ion flows across the membrane resulting from EPSCs or from action potentials into the corresponding amount of adenosine triphosphate ( ATP ) molecules necessary to pump these ions back out [1 , 4 , 12] . At the same time , we evaluated information transfer from an axon of interest to the output spike train of these cells using transfer entropy [13 , 14] . Similar to what we observed at retinothalamic synapses , our simulations suggested that the energetic efficiency of information transmission was maximal at the physiologically observed level of thalamocortical synapse conductance . Then , we tested this result experimentally on real L4SS cells patch-clamped in rat brain slices , evoking thalamocortical input while using dynamic-clamp to inject the random background synaptic conductance derived from our simulations . We found that increasing or decreasing the conductance at thalamocortical synapses decreased the energetic efficiency of information transmission from one , experimentally-stimulated , thalamic axon . Thus , both simulations and experiments suggest that , like at the retinothalamic synapse [12] , thalamocortical postsynaptic properties are evolutionarily set to be energy efficient .
P28 Sprague Dawley rats were killed by cervical dislocation following sedation with isoflurane . Animal procedures were carried out in accordance with the guidelines of the UK Animals ( Scientific Procedures ) Act 1986 and subsequent amendments . The mathematical model of L4SS cells was adapted from an earlier model by Lavzin and colleagues [15] available at ModelDB ( https://senselab . med . yale . edu/ModelDB/showmodel . cshtml ? model=146565 ) [16] . The multicompartment simulations were conducted using the NEURON 7 . 3 simulation platform ( http://neuron . yale . edu/ ) and will be available from our GitHub page ( https://github . com/JolivetLab ) . The cell was subdivided into 360 compartments , with a maximum length of 21 μm ( 14 . 6 μm on average ) . Following Lavzin et al . [15] , the soma area was 757 μm2 , the total dendritic area was 11 , 885 μm2 , the resting membrane potential was -70 mV , the membrane resistance was 16 , 000 Ω·cm2 , the axial resistivity was 100 Ω·cm and the membrane capacitance in all compartments was set to 1 . 5 μF/μm2 to account for the presence of spines . The model included Hodgkin-Huxley-type voltage-gated channels . Fast sodium channels ( reversal potential = 70 mV ) , and delayed rectifier and slow non-inactivating potassium channels ( reversal potential = -77 mV ) , were distributed with a higher density at the soma ( gNa = 300 mS/cm2 , gKdr = 30 mS/cm2 , gKs = 100 mS/cm2 ) than in the dendritic tree ( gNa = 3 mS/cm2 , gKdr = 1 mS/cm2 , gKs = 1 mS/cm2 ) . L-type voltage-gated calcium channels were distributed evenly across the cell ( gCa = 0 . 03 pS/μm2 ) . Calcium diffusion within and between compartments was modelled as described in Carnevale and Hines [17] with a diffusion coefficient of 0 . 6 μm2/ms , and an intracellular calcium buffer at 3 μM concentration with a dissociation constant of Kd = 1 μM . Calcium pumping across the surface membrane was modelled again as described in [17] with a plasma membrane calcium pump density of 10−13 mol/cm2 . The pump is modelled with a two-step reaction . The first step describes binding of intracellular calcium to the pump , with a binding rate constant of k1 = 1 mM-1ms-1 and an unbinding rate constant of k2 = 0 . 005 ms-1 ( giving an apparent dissociation constant of 5 μM ) . The second step describes loss of the calcium from the pump to the extracellular solution , with an unbinding rate constant of k3 = 1 ms-1 and a rebinding rate constant of k4 = 0 . 005 mM-1ms-1 ( giving an apparent dissociation constant of 200 mM ) . See Chapter 9 of Carnevale and Hines [17] for further details . To simulate cortical background input to the modelled cell , one lumped excitatory and one lumped inhibitory synapse ( each representing a number of synapses ) were placed on each compartment of the model ( Fig 1A ) . The frequency of activation of each lumped synapse was adjusted by the size of the compartment area so as to model monosynaptic inputs onto 1870 excitatory and 460 inhibitory spines homogeneously distributed throughout the dendritic tree at densities of 0 . 167/μm2 and 0 . 041/μm2 respectively , which were randomly activated at 0 . 45 Hz and 0 . 09 Hz respectively ( Fig 1B ) . Frequencies for the corticocortical background input were slightly adjusted from Waters and Helmchen [18] , so that after adding thalamocortical synapses ( see below ) , the modelled cell’s output frequency was ~4 Hz , the mean cortical firing rate in vivo [1] . Excitatory conductances were modelled with a time course of the form exp ( -t/τfall ) —exp ( -t/τrise ) with time constants τrise = 0 . 3 ms and τfall = 1 . 7 ms , and with the reversal potential at 0 mV . Inhibitory synapses were modelled with the same function with time constants τrise = 1 ms and τfall = 10 ms , and with the reversal potential at -75 mV . The conductance of these synapses was held constant at gcc = 0 . 0008 μS and ginh = 0 . 0005 μS . To test for the influence of inhibition , some simulations were run without inhibitory synapses . To simulate thalamocortical background input to the modelled cell , one excitatory synapse was placed on each compartment and the frequency of activation of each one of those synapses was adjusted according to the distance of that compartment to the soma ( Fig 1B ) . This is equivalent in the NEURON simulation environment to modelling 470 spines distributed as a Gaussian with respect to their position in the dendritic tree [19] and randomly activated at 4 Hz ( the average output frequency we measured in LGN cells [12] ) , but is less computationally intensive . Thalamocortical synapses were modelled with a conductance time course of the form exp ( -t/τfall ) —exp ( -t/τrise ) with τrise = 0 . 3 ms and τfall = 1 . 7 ms , and with the reversal potential at 0 mV . Their conductance was initially set at gtc = 0 . 001 μS . To evaluate the contribution of an individual thalamic cell projecting onto the modelled cell , three synapses were added , clustered onto the same dendritic branch , approximately 78 μm away from the soma [19] and typically spaced by 18 μm ( see Fig 1A and 1B for details; located on compartments a3_122 , a3_121 and a3_12 at positions 0 . 5 , 0 . 2 and 0 . 9 respectively , see file l4sscell . hoc in https://senselab . med . yale . edu/ModelDB/showmodel . cshtml ? model=146565 for details ) . These synapses were modelled using the same parameters as for other thalamocortical synapses . In particular , their conductance was initially set at gsyn = 0 . 001 μS . This is to approximate in the model three synaptic sites , which together produce an EPSC of a similar magnitude to that observed in experiments ( 3* ( conductance of 10-9S ) * ( driving force of -70mV ) ~ 210pA; see below ) . These synapses were synchronously activated using the same input spike train as was used for the experiments described below in the presence of both thalamocortical , and excitatory and inhibitory corticocortical background noise . To determine the optimal conductance value for all thalamocortical synapses , we modulated both gsyn and gtc by the same gain factor while maintaining gcc and ginh constant . None of the synapses exhibited plasticity , facilitation , depression or failures . Note however that these synapses typically express only mild depression ( see below ) . To test for the influence of the exact location of these three additional thalamocortical synapses on our results , we repeated all the simulations with a second set of such synapses ( located on compartments a5_1111 , a5_11111 and a5_11111 at positions 0 . 9 , 0 . 15 and 0 . 1 respectively , see file l4sscell . hoc in https://senselab . med . yale . edu/ModelDB/showmodel . cshtml ? model=146565 for details ) . These additional simulations revealed no qualitative differences from what had been obtained with the first set . The location of the first set of synapses is plotted in Fig 1A ( syn ) . Additionally , for experiments described below , we generated two synthetic “noise” conductance trains , one corresponding to the total excitatory corticocortical background input and one corresponding to the total thalamocortical background input . To generate these trains , simulations were run as described above with two exceptions . First , inhibitory input was ignored . Second , all synapses were relocated to the soma and their conductances were summed . To calculate the metabolic cost incurred by the modelled cell ( Figs 2 and 3 ) , the total synaptic current generated by gsyn , which is the largest signalling cost [1 , 4] , was integrated over the three synaptic locations and converted to the corresponding ATP consumption per unit time [1 , 12] . The energetic cost of other synapses and output action potentials was calculated in the same way . For the experiments described below , the ATP used to reverse the postsynaptic ion flux was calculated for voltage-clamp and dynamic-clamp recording modes as described in [12] . Using information theory in neuroscience has a long tradition [20] . In particular , it is common to use mutual information to quantify the flow of information from one neuron to another , or from a stimulus set to a recorded neuron [4 , 12 , 21 , 22] . Numerous methods have been devised to measure mutual information in various contexts , and to correct for its inherent biases ( see [23] for a review ) . However , mutual information is by construction a symmetric measure and thus does not strictly measure information being transferred from a sender to a receiver . Rather , when measuring mutual information between two random variables , one captures how much information can be inferred about one of those processes when observing the other one . Mutual information also suffers from significant estimation biases when the dataset is limited , a recurrent problem in experimental contexts like the one we will deal with here . More recently , a measure analogous to mutual information called transfer entropy , which seeks to capture the unidirectional flow of information from one variable to another , was introduced [13] , and has been increasingly applied to neuronal spike data ( for comprehensive reviews of the application of transfer entropy in neuroscience , see [24–26] ) . Transfer entropy is non-symmetric . Let us define two processes I and J with joint probability pIJ ( i , j ) . The transfer entropy from J to I is defined by: TEJ→I=∑p ( in+1 , in ( k ) , jn+1−u ( l ) ) logp ( in+1|in ( k ) , jn+1−u ( l ) ) p ( in+1|in ( k ) ) ( 1 ) where in ( k ) = ( in , … , in−k+1 ) is a shorthand notation for words of length k , and where n denotes the nth time bin and u an optional frame shift ( see below ) [13] . The sum runs over all possible combinations of in , in ( k ) and jn ( l ) . For words of length 1 , in simple cases where information flow is strictly unidirectional and when consecutive bins are both independent and conditionally independent given the source value , it is possible to show analytically that transfer entropy is equivalent to mutual information . Simulations also suggest that transfer entropy is largely superior to mutual information in that its estimate converges much faster when increasing the size of the dataset when analysing spike trains ( Conrad and Jolivet , in preparation ) . Transfer entropy is used extensively in fields outside neuroscience and in systems neuroscience but is used relatively little in cellular neuroscience . While we decided to use here transfer entropy instead of mutual information because of the technical reasons highlighted above , essentially the same results were obtained when we used mutual information . Similarly , there is no reason to think that we would have found different results in ref . [12] had we used transfer entropy instead of mutual information . Here , we binarized the 125 second input and output spike trains in 3 ms time bins ( approximately the refractory period of a neuron ) and measured the transfer entropy from the input axon to the output spike train using the package published by Ito and colleagues ( https://code . google . com/archive/p/transfer-entropy-toolbox/ ) [14] . The results of this procedure were divided by the time bin ( 3 ms ) to get information rates in bits/sec [27] . The package by Ito and colleagues allows consideration of a temporal frame shift ( u ) between the sender and the receiver . We systematically analysed the effect of this parameter on the results and observed that peak information transfer always occurs in the same time bin in our simulations , i . e . that transfer entropy is maximal without a frame shift between the input and output sequences when using a 3 ms temporal resolution ( S2 Fig ) . Using shorter bins yielded similar results ( see S3 Fig ) . Nevertheless , in all subsequent analysis , we allowed for temporal frame shifts of up to 10 time bins ( 30 ms ) . Changing this value had no significant impact on the results . Changing the word length however does have an effect on the measure of transfer entropy . ( We talk here about word length in the singular , as we systematically considered the same word length for both the input and output sequences , thus k = l . See refs . [26] and [28] for best practice in choosing word lengths ) . Specifically , increasing the value of k increases the measured transfer entropy , because of bias increase ( this is well documented [26–28] ) . With a finite dataset , random coincidences can lead to mis-estimating probabilities , which will add up in the final calculation of the transfer entropy product ( see Eq 6 in [14] ) . In order to remediate that problem , one solution is to subtract from our raw estimates of TE , the value of TEnoise , calculated between a random permutation of the input sequence and the actual output sequence . We carried out a systematic analysis of the effect of varying k on TE—TEnoise and observed no significant changes in the main results of this paper when k ≤ 10 . We thus carried out all of our analyses with k = l = 10 ( 30 ms ) . All values reported in the results section are of TE defined as: TE=TEraw−TEnoise ( 2 ) with TEraw calculated following Eq ( 1 ) above and TEnoise calculated following Eq ( 1 ) above but with randomly permuting the input sequence ( randomizing words instead of individual time bins produces almost identical results , see S1 Fig ) . In practice , for each condition , we generated multiple simulations with different seeds initialising the random number generator , averaging about 6 individual simulations for each condition . For each one of those , we calculated TEraw , 30 realisations of TEnoise over which we took an average , and TE . The values of TEnoise and TE reported in the Results section are the average over all these individual simulations . For all conditions ( simulations , and experiments with real stimulation and all dynamic-clamp gains described below ) , the information rate was divided by the rate of energy consumption on reversing the ion flux generating EPSCs and action potentials to get a measure of efficiency in bits/ ( ATP consumed ) . All data were analysed using custom-written MATLAB scripts ( The Mathworks Inc . ) . In the second part of the present study , we sought to replicate our simulation results in in vitro experiments using rat brain slices . The first part of each experiment was performed in whole-cell voltage-clamp mode , in order to calculate the energy cost per EPSC . L4SS cells were patch-clamped and thalamic axons were stimulated with a previously recorded thalamic relay neuron response train to retinal input ( see below ) . The second part of the experiment was performed by injecting the same stimulation pattern in current-clamp in order to measure information transmission across the synapse . From the voltage-clamp recording , a conductance trace was then generated and injected into the cell with dynamic-clamp . The conductance trace , to which noise was added to simulate the physiological background of additional synapses , was scaled up or down to measure information transmission at conductances above or below the physiological value . Energetic efficiency for each scale factor ( gain ) was calculated by dividing the transfer entropy rate by the rate of energy consumption [4 , 12] . This allowed us to assess whether , as in the LGN [12] , energetic efficiency at the thalamocortical synapse was maximised at the physiological gain . P28 Sprague Dawley rats were killed by cervical dislocation following sedation with isoflurane . The brain was rapidly removed and immersed in ice-cold , slicing solution containing ( in mM ) : 87 NaCl , 25 NaHCO3 , 7 MgCl2 , 2 . 5 KCl , 1 . 25 NaH2PO4 , 0 . 5 CaCl2 , 25 glucose , 75 sucrose , 1 kynurenic acid , saturated with 95% O2/5% CO2 ( modified from ref . [29] ) . The hemispheres were separated along the midline and an off-coronal cut ( 20° from vertical , heading anteriorly while cutting towards the dorsal surface , between the cerebrum and cerebellum ) was performed on each hemisphere to create an angled surface , which was glued onto the stage of the vibratome . Off-coronal 225 μm slices containing the thalamus and cortex including V1 were then made . Slices were placed in a storage chamber containing continuously oxygenated slicing solution at 35°C , which was allowed to come to room temperature naturally . During the experiment , slices were continuously perfused with artificial cerebrospinal fluid ( aCSF ) containing ( in mM ) : 124 NaCl , 26 NaHCO3 , 10 glucose , 2 . 5 KCl , 2 CaCl2 , 1 NaH2PO4 , 1 MgCl2 , 0 . 005 Gabazine ( to block disynaptic inhibition during stimulation ) . The aCSF was heated to 35°C and constantly bubbled with 95% O2/5% CO2 . Whole-cell recordings from cortical L4SS cells were obtained using 2–3 MΩ borosilicate glass electrodes filled with internal solution containing ( in mM ) : 130 K-gluconate , 10 EGTA , 10 HEPES , 4 NaCl , 4 MgATP , 1 CaCl2 , 0 . 5 Na2GTP , 0 . 4 K2-Lucifer yellow . Spiny stellate cells in layer 4 of cortical area V1 were identified according to their location , morphology , and electrophysiological characteristics ( Fig 4A and 4B ) . In contrast to the less-numerous pyramidal neurons also found in L4 , the round or ellipsoidal spiny stellate cells do not have a prominent apical dendrite or dendrites reaching across several layers , but have a broader dendritic tree confined to L4 [30 , 31] with a high density of spines , which could be seen once a cell was dye-filled ( Fig 4A ) . L4SS cells were recorded at their resting potential of approximately -70mV , at which they respond to current injection with sustained , regular spiking ( Fig 4B; [32] ) . Online corrections were made for the junction potential of -14 mV for the gluconate-based internal solution used ( e . g . neurons were held at an apparent potential of -56 mV to achieve a true potential of -70 mV ) . Recordings were made with an Axopatch 200B amplifier , filtered at 5 kHz and sampled at 20 kHz . Data were acquired using custom-made MATLAB software , kindly provided by Ho Ko and Tom Mrsic-Flogel ( UCL ) . The first part of the experiment was performed in voltage clamp . Upon seal formation , pipette capacitance was compensated . Once in whole-cell mode , the series resistance was compensated by up to 70% ( after which the mean series resistance was 6 . 4 ± 1 . 0 MΩ ) . The second part of the experiment was performed in current clamp , using the I-CLAMP FAST mode ( which was stable with the 2–3 MΩ pipettes used ) . In current clamp mode , series resistance compensation was set to 100% . Thalamic axons in the subcortical white matter were stimulated extracellularly with a borosilicate glass electrode ( gently broken to achieve a tip diameter of ~10–15 μm ) containing aCSF . In voltage clamp , stimulation strength was gradually increased to achieve the smallest reliable EPSC ( defined as an EPSC that , when it occurred , did not vary in size in response to a pulse delivered every 3 s ) . This stimulus intensity was then maintained throughout the experiment . The average minimal EPSC size was 356 ± 186 pA ( Fig 4C ) and paired thalamic stimulation elicited the mild EPSC depression characteristic of this synapse ( PPR = 0 . 70 ± 0 . 13 , Fig 4D and 4E; [31 , 33 , 34] ) . We stimulated thalamic axons making synapses onto whole-cell patch-clamped L4SS cells of V1 in rat brain slices ( Fig 4A ) . The spike train used for stimulation was recorded from a typical LGN relay neuron in response to retinal ganglion cell ( RGC ) input [12] , which in turn was recorded from RGCs in response to natural movies [35] . The spike train was 25 seconds long , with an average frequency of 4 . 3 Hz . The stimulation procedure followed that of our previous experiments in the LGN [12]: After a 5 second input train to habituate the synapse ( the data from this train were discarded ) , the 25 second spike train was repeatedly played five times , resulting in 125 seconds of stimulation in total . This procedure was followed once in voltage-clamp , once in current-clamp , and several times in dynamic-clamp with various conductance gains ( see below ) . A 125 second long conductance train ( gsyn ) was derived from the 125 second postsynaptic current recording obtained from each spiny stellate cell in voltage clamp . First , we removed the stimulation artefacts by setting the current value for the duration of the artefact to the current value immediately preceding the artefact . The resulting current trace ( Isyn ) was converted to a synaptic conductance trace ( gsyn ) via: gsyn ( t ) =Isyn ( t ) / ( Vm−Vrev ) ( 3 ) where Vm is the membrane potential of the cell ( the holding potential , -70 mV ) , and Vrev is the reversal potential of the synapse ( 0 mV , the reversal potential for glutamatergic ionotropic receptors ) . In addition to the synaptic conductance trace , we mimicked the effect of this synapse operating in the presence of hundreds of other synaptic inputs . We generated two synthetic “noise” conductance trains , designed to have the characteristics of ( 1 ) thalamocortical ( TC ) inputs ( 4 Hz input to 470 spines: gtc ) and ( 2 ) corticocortical ( CC ) inputs ( 0 . 45 Hz input to 1870 spines: gcc ) . These two trains were the result of simulations carried out before the experiment ( see above the section ‘Mathematical model of spiny stellate cells’ ) , and were the same for each cell . In subsequent dynamic-clamp experiments , the amplitude of the TC conductance noise was scaled with the conductance of the synapse being studied ( gsyn ) , since we assume that all the TC synapses will have their conductances set to the same optimal value . In contrast , the corticocortical noise was not scaled . The baseline amplitude of the noise conductance was set individually for each cell by combining both trains ( gtc+gcc ) and scaling them up or down ( mean scaling factor 0 . 4 ± 0 . 1 ) until a firing frequency of approximately 4 Hz [1] was triggered upon injection into the L4SS cell ( “pre-scaling” , Fig 4G ) . The pre-scaled thalamic noise train ( gtc ) and the thalamic single-input train recorded in that cell ( gsyn ) were then summed and scaled together ( by a factor of 0 . 1 , 0 . 3 , 0 . 5 , 0 . 75 , 1 , 1 . 5 , and 3 ) . In contrast to the LGN relay synapse [12] , for which only the synaptic conductance was scaled up , in this experiment the combined synaptic and noise conductances could not be scaled above 3 times the physiological value without inducing oscillations or a depolarising block , resulting in a lack of action potential firing . The pre-scaled corticocortical noise train ( gcc ) was then added to each of these scaled trains , to create 7 differently scaled composite conductance trains for each cell ( Fig 4G ) . These trains were injected into the cell using dynamic-clamp ( SM-1 , Cambridge Conductance [36] ) , which injects a time-varying current Iinj ( t ) , at time t , calculated from gsyn ( t ) and the instantaneous value of the cell membrane potential: Iinj ( t ) =gsyn ( t ) · ( Vm ( t ) –Vrev ) ( 4 ) Because of the liquid junction potential , the Vm received by the SM-1 was 14 mV more positive than the real membrane potential . We therefore set Vrev on the SM-1 to 14 mV ( rather than 0 mV ) , to account for this in the online calculation of Iinj . In this calculation , all of the synaptic current was assumed to scale linearly with membrane potential . The voltage response of the postsynaptic cell was simultaneously recorded . After injecting gsyn+tc / ( normal gsyn+tc ) *1 , the order of scaled conductances was randomized . This meant that not every cell experienced every scale factor , as it was not usually possible to maintain whole-cell recordings for long enough to carry out every conductance injection , after all initial steps were completed ( i . e . minimal stimulation protocol , paired pulse ratio characterisation , voltage clamp response to real stimulation , current clamp response to real stimulation , noise pre-scaling to achieve 4 Hz firing rate , and finally various dynamic clamp conductance injections ) . As such , gsyn+tc / ( normal gsyn+tc ) *1 was applied to all 6 cells , *0 . 3 to 3 cells , *0 . 5 to 4 cells , *0 . 75 to 5 cells , *1 . 5 to 5 cells , and *3 to 4 cells . The mean and SEM were calculated separately across these n numbers for each condition . Data were analysed using custom scripts written in MATLAB ( The Mathworks Inc . ) . Postsynaptic current traces were used to calculate ATP consumption at the synapse as described above . Postsynaptic voltage traces were converted to binarised sequences of 1s ( representing action potentials ) and 0s ( their absence ) by identifying events whose amplitude exceeded a threshold defining action potential occurrences ( set individually for each cell , between -15 mV and -30 mV ) . This output sequence could then be compared with the binary input spike train to look at simple transmission characteristics ( Fig 4F ) , or used to calculate the amount of information that would be propagated to the visual cortex by the postsynaptic cell ( Figs 5A and 6C ) . Spike transmission characteristics were calculated as in [12] . The percentage occurrence of each possible input-output relationship ( Fig 4F ) was calculated by searching for an output ( AP or EPSP ) in the 18 ms following an input spike ( top row ) or for an input ( AP ) in the 18 ms preceding an output spike ( bottom row ) , and summing each occurrence across all L4SS neurons studied . Data are presented as mean ± standard error of the mean ( s . e . m . ) , unless mentioned otherwise . Differences between means were assessed with Student’s t-tests ( Fig 5 ) . Note that we did not compare the means between different conductance scaling factors for the experimental results in Fig 6; we present ± s . e . m . for each point simply as a visual guide for the reader . This is because we want to be careful not to claim that the peak is precisely at 1 . We do not have a large enough dataset nor fine enough sampling around gsyn = 1 to claim that this is the exact value of the peak .
We have previously shown that the strong retinothalamic synapse relaying information from the retina to the thalamus maximises energetic efficiency ( information transmitted per ATP used ) when transferring information [12] . It is however unclear whether this principle of energy efficiency also applies at synapses that contribute more weakly to determining the output of the postsynaptic cell . To address this question , we adapted a Hodgkin-Huxley-type multicompartment model of a layer 4 spiny stellate ( L4SS ) cell [15] ( see Materials & Methods ) , the next step in the visual pathway after the thalamus . L4SS cells receive excitatory inputs from thalamocortical relay cells , and excitatory and inhibitory inputs from cortical neurons ( Fig 1A ) . While corticocortical synapse surface density is assumed to be homogeneous throughout the dendritic tree , thalamocortical synapse density follows a Gaussian distribution with respect to the distance from the soma ( Fig 1B ) [19] . To create background ‘noise’ , all thalamocortical synapses were randomly activated at 4 Hz [12] , while corticocortical excitatory and inhibitory synapses were randomly activated at 0 . 45 Hz and 0 . 09 Hz respectively [18] . Synaptic conductances were initially set to the same value for every synapse in a given category so as to generate on average an ~0 . 8 mV depolarisation at the soma for thalamocortical synapses , a ~0 . 6 mV depolarisation for corticocortical synapses and a ~0 . 1 mV hyperpolarisation for inhibitory synapses ( Fig 1C ) . This constant barrage of excitatory and inhibitory inputs generated random fluctuations of the membrane voltage at the soma and led to irregular spiking ( Fig 1D , black trace ) . We then modelled one extra thalamocortical axon . This axon of interest contacts the dendritic tree at three independent but relatively close synaptic locations ( ‘syn’ in Fig 1A ) . Activation of this single extra axon ( at ~4 Hz , the firing frequency of the thalamocortical neurons for physiological input [12] ) visibly affects the output of the neuron even in the presence of intense random background activity from all other synapses ( Fig 1D , light violet trace; activation times for that specific simulation are labelled with vertical light violet bars , the background synaptic input was identical for the black and light violet traces ) . Thus , some information about this input spike train is carried across the synapses to be represented in the output spike train of the postsynaptic L4SS neuron . We then evaluated the amount of information transmitted across thalamocortical synapses . Information transfer at synapses can be assessed in different ways . One commonly used method consists of computing the mutual information between the input and output spike trains across the synapse ( s ) of interest [20] . This method has been successfully applied in a number of studies ( e . g . [21 , 22] ) , including by ourselves [4 , 12] , and numerous studies have addressed the pitfalls and biases of using mutual information in that context ( see [23] for a review ) . We have recently used the so-called direct method of Strong et al . [21] to measure information transfer ( mutual information ) at the retinothalamic synapse [12] . That synapse differs significantly , however , from the one we study here , as it is a strong relay synapse , generating EPSCs sufficiently large that a single one of them is often sufficient to trigger an output action potential on its own . Indeed , the output of thalamic relay cells in the visual pathway is driven almost entirely by input from a single strong relay synapse impinging on their dendritic tree from the retina [37] . The case we study here is different as L4SS cells receive weak inputs from hundreds to thousands of thalamic and cortical synapses ( Fig 1A and 1C ) , each one of them generating small EPSCs that individually contribute little to the output of the cell ( Fig 1B–1D ) . Preliminary simulations revealed that using the direct method would be inappropriate because of the relative weakness of the synapse and the presence of background noise . At the thalamocortical synapse , in the presence of a significant barrage of background inputs , most output spikes are not related to input spikes in the axon of interest ( Fig 1D ) . In these circumstances , we could not feasibly run long enough simulations and experiments to estimate accurately the mutual information between input and output sequences , a recurrent problem in experiments using acute brain slices with a lifespan counted in hours [38] . The direct method we have used in ref . [12] computes the mutual information by subtracting the noise entropy ( Hnoise ) from the output’s total entropy ( Htotal ) . While Htotal is estimated by looking at the distribution of binary ‘words’ over time , Hnoise is computed as the entropy of these words’ distributions across repetitions , and then averaged over time . It is thus crucial for correctly estimating Hnoise to collect a sufficient number of repetitions . Htotal and Hnoise impose competing constraints over the time available for data collection as the best estimate of Htotal will be obtained with the longest possible non-repeating sequence , while the best estimate of Hnoise will be obtained with the largest number of repetitions of a repeating sequence . In Harris et al . [12] , in the case of thalamic relay cells , most output spikes were directly driven by an input spike and the noise was relatively small in comparison to Htotal . It was thus possible to reach a reasonable estimate of the mutual information with few repetitions . In the present case , only a few of the output spikes are related to an input spike from the thalamocortical axon of interest , and one expects that Hnoise becomes approximately equal to Htotal , necessitating a number of repetitions for proper estimation of the mutual information incompatible with the experiments presented here . Others have faced the same problem before . London and colleagues , for instance , addressed it by devising an alternative measure similar to mutual information called synaptic information efficacy [39] . However , another concern is that measures like the mutual information are symmetric , i . e . they measure how much information can be inferred about one process when observing the other one , but do not measure directional information flow . To deal with these issues , we decided to employ the measure termed transfer entropy ( see Eq 1 in Materials & Methods ) [13] . Transfer entropy is designed to measure directional information transfer from a sender to a receiver . If the information flow is unidirectional ( from the sender to the receiver ) , measuring transfer entropy from the receiver to the sender will return 0 . Thus , we binarized the 125 second input and output spike trains in 3 ms time bins and measured transfer entropy from the input axon to the output spike train using the package published by Ito and colleagues ( http://code . google . com/p/transfer-entropy-toolbox/ ) [14] . Due to the limited size of the dataset that can be realistically acquired , we additionally applied a correction for random coincidences that contribute to noise in the estimation of the transfer entropy ( see Materials & Methods for a detailed discussion of these issues ) . In the following , we usually report TEraw , the raw value of transfer entropy calculated from the input to the output using Eq ( 1 ) ( see Materials & Methods ) , TEnoise , the value of transfer entropy calculated using Eq ( 1 ) but using a scrambled input ( scrambling the sequence of words instead of the sequence of individual time bins produces almost identical results , see S1 Fig ) , and TE = TEraw−TEnoise , the effective information transfer after correcting for the effect of random coincidences ( Eq ( 2 ) in Materials & Methods ) . In multicompartment simulations of the thalamocortical synapse , using TE for our axon of interest , we found that a small amount of information is indeed carried across the simulated synapse of interest , from input action potentials , even in the presence of strong background noise generated by other synapses ( Fig 1A ) , yielding a transfer entropy of TE = 0 . 47 ± 0 . 06 bits/sec ( mean ± s . e . m . ) after subtraction of TEnoise ( TEraw = 1 . 0 ± 0 . 1 bits/sec; mean ± standard deviation ) , which is roughly 40 times less than at thalamic relay cells [12] . We then investigated whether modulating the strength of thalamocortical connections modulates information transfer , and how this relates to energy consumption by the postsynaptic cell . In order to assess how modulating the strength of thalamocortical connections affects the postsynaptic cell’s energy consumption and information transfer at our axon of interest , we scaled all thalamocortical synapses by a range of gain factors [0 , 0 . 4 , 0 . 6 , 0 . 8 , 1 , 1 . 2 , 1 . 4 , 1 . 6 , 2 . 4 , 4 . 8 , 7 . 2 , 9 . 6] while injecting excitatory and inhibitory corticocortical background noise at a constant level . Increasing the conductance of all the thalamocortical synapses increased both the EPSC size ( because more current is injected from the axon of interest ) and the energy consumption in the postsynaptic cell associated with the thalamic input ( Fig 2A ) , because more sodium ions need to be actively extruded from the cell via the Na , K-ATPase pump due to extra Na+ entry at all the thalamocortical synapses ( see [4] for a review of synaptic energetics ) . This increase in energy consumption is roughly proportional to the scaling factor for the thalamocortical synapse conductance ( Fig 2A ) . Altering the conductance of the set of thalamocortical inputs to the L4SS cell affects the output firing rate . The rate of information transmission across a single thalamocortical connection increases with the output firing rate as a sigmoid function , up to about 20 Hz , above which the information transmission plateaus ( Fig 2B ) . This is true for the raw value of transfer entropy ( TEraw ) or after subtraction of TEnoise ( TE ) . Increasing the conductance of the set of thalamocortical inputs by 20% increases the amount of information that is transmitted across a single thalamocortical connection by ~16% , showing that thalamocortical inputs to L4SS cells , as with retinothalamic inputs to relay neurons [12] , do not maximise information transmission across single connections ( Fig 2C ) . However , dividing the information transmitted by the energy used on this connection demonstrates that the physiological thalamocortical input characteristics are close to optimality for maximising the information transmitted per energy used for each individual connection ( Fig 2D ) . This was true when placing the three synapses of interest at the location depicted in Fig 1A ( syn ) or at a different location on another dendrite . Specifically , to test for the influence on our results of the location of the three thalamocortical synapses from our single axon of interest ( see Fig 1A ) , we repeated all the simulations with a second set of such synapses ( see Materials & Methods for details ) . These additional simulations revealed no qualitative differences to what had been obtained with the first set . Results for this second set of synapses are plotted in dark violet in Fig 2A–2C , with the average over both datasets plotted in blue . The ratio of information transmitted to energy used is approximately maximised for both datasets when looking at TEraw ( light and dark violet traces ) , averaging over both sets of locations ( blue trace ) , or when looking at TE after noise subtraction and averaging over both datasets ( red trace ) , either when considering the energetic cost associated with the synapses of interest only ( Fig 2D ) , or when considering the total signalling energy budget of the cell ( Fig 2E ) . While small differences are present between the results obtained with the two different synapse placements tested for the investigated thalamocortical axon , the average shows a sharp peak at gain = 1 , suggesting that these synapses are , on average , tuned for energetic optimality of information transmission . These results were obtained with words of length 10 time bins ( 30 ms ) and allowing for temporal frame shifts between the input and output sequences of up to 30 ms . Note however that transfer entropy is maximal without a frame shift between the input and output sequences when using a 3 ms temporal resolution and all results reported hereafter are for temporally aligned sequences with no frame shift . Systematic tests showed that changing the value of these parameters , while introducing small quantitative changes to the results , did not lead to significant changes in the conclusions reached above ( see Materials & Methods for a detailed discussion of these parameters ) . To test for the influence of inhibition at the thalamocortical synapse , we used the mathematical model of L4SS cells and ran a second set of simulations with the same synaptic placements as before but without inhibitory input ( setting ginh to 0; see Materials & Methods ) . The results obtained without inhibition are plotted in Fig 3 and overall , match those from Fig 2 ( with inhibition ) very closely . As for when inhibition was present , the increase in energy consumption was roughly proportional to the scaling factor of thalamocortical synapse conductance ( Fig 3A ) , the rate of information transmission across a single thalamocortical connection increased with the output firing rate as a sigmoid function ( Fig 3B ) and thalamocortical inputs to L4SS cells did not maximise information transmission across single connections ( Fig 3C ) , although now increasing the conductance of the set of thalamocortical inputs could only increase the amount of information that is transmitted across a single thalamocortical connection by ~8% . Interestingly , even though the changes between the results obtained with ( Fig 2 ) and without inhibition ( Fig 3 ) were small , they have an effect on the value of the energetically optimal conductance for information transmission . Specifically , removing inhibition slightly shifted the curve in Fig 3C to the left ( towards lower gain factors ) when compared to results obtained with inhibition ( Fig 2C ) . As a result , dividing the information transmitted by the energy used by all thalamocortical connections now yielded an optimum synaptic conductance value for energetic efficiency that was slightly lower than obtained previously ( Fig 2D ) . Energetic optimality of information transmission was obtained for TE for gain = 0 . 8 when considering the energetic cost associated with all thalamocortical synapses ( Fig 3D ) and again for gain = 0 . 8 when considering the total signalling energy budget of the cell ( Fig 3E ) . Overall , these results suggest that the finding that experimentally reported synaptic conductances are close to the energetic efficiency for information transmission is robust , even though the exact position of that optimum on the conductance scale can be slightly affected by the exact positioning and clustering of synapses on the dendritic tree , or by the fine balance between excitatory and inhibitory activity levels in the network . We next explored whether these results could be replicated experimentally . In order to experimentally test our simulation results , we stimulated thalamic axons making synapses onto whole-cell patch-clamped L4SS cells of V1 in rat brain slices ( Fig 4A and 4B ) . As for the retinothalamic synapse [12] , we used P28 animals , minimal stimulation to activate a single input , and gabazine to block GABAA receptors . The stimulus size in the experiments was adjusted to produce the smallest detectable EPSC ( mean value 356±186 pA ) . Single EPSCs were smaller ( Fig 4C ) and showed less paired pulse depression ( Fig 4D and 4E ) than at the retinothalamic synapse ( compare with S1 Fig in ref . [12] ) . The L4SS cells were held at -70 mV , and the input synapse stimulus trains used were thalamic relay neuron responses to retinal input obtained by [12] , which had a mean spike frequency of ~4 . 3 Hz . Upon presynaptic stimulation , fewer spikes were successfully transmitted and more spontaneous spikes occurred at the thalamocortical synapse ( 9% and 68% , respectively , Fig 4F ) than at the retinothalamic synapse described previously ( 19% and 7% respectively , [12] ) , confirming that one lateral geniculate nucleus ( LGN ) relay neuron has a weaker effect on L4SS cell firing than does one retinal ganglion cell on LGN relay neuron firing . In contrast to the strong one-to-one relationship between retinal ganglion cells ( RGCs ) and thalamic relay neurons , where RGC activation is sufficient to evoke a relay neuron spike 19% of the time [12] , L4SS cells receive weaker input from ~100–600 spines targeted by thalamic neurons ( cat V1: Peters and Payne [40]; rat barrel cortex: Bruno and Sakmann [41] ) , that individually evoke 0 . 5~2 mV EPSPs ( cat V1: Stratford et al . [33]; rat barrel cortex: Bruno and Sakmann [41] ) . These EPSPs are sufficient to evoke a spike only 9% of the time ( Fig 4F ) . The receptive field of a L4SS cell differs from those of its input relay neurons ( cat V1: Hubel and Wiesel [42 , 43] ) , and L4SS cell spiking is also generated by intracortical inputs ( mouse V1: Lien and Scanziani [44] ) with input strengths that tend to be slightly smaller ( ~1 mV in L4 and ~ 0 . 22 mV in L6 ) , but more numerous , than the thalamocortical inputs ( cat V1: Stratford et al . [33]; Tarczy-Hornoch et al . [45] ) . Is the ratio of information transmitted to energy used maximised at a single thalamocortical synapse , operating in the context of hundreds of similarly weighted synapses ? We recorded the sequence of L4SS cell EPSCs evoked by minimal stimulation with a physiological firing pattern ( i . e . the thalamic relay neuron responses to retinal input described above ) , and converted this to a synaptic conductance train ( gsyn; Fig 4G ) . To simulate the action of this synapse with background synaptic activity present , we used our computational model to synthetically generate two excitatory “noise” conductance trains ( see above and Materials & Methods ) . One train represented other thalamocortical input synapses , which make up ~20% ( 5% in cat V1: Peters and Payne [40]; 30% in mouse V1: Lien and Scanziani [44] ) of the excitatory synapses to L4SS cells ( gtc ) , while the other represented corticocortical inputs , which make up the remaining 80% of excitatory synapses to L4SS cells ( gcc ) ( Fig 4G ) . ( L4SS neurons also receive inhibitory input in vivo , which was not injected experimentally , but was simulated in our Hodgkin-Huxley-type multicompartment model , see above . ) The baseline levels of the two excitatory noise traces were scaled together for each cell ( mean scaling factor 0 . 4 ± 0 . 1 in 6 cells ) so that , when injected simultaneously using dynamic-clamp , the L4SS cell firing frequency was approximately 4 Hz , the mean cortical firing rate in vivo [1] . Then , gsyn was added to the noise trains and the resulting composite conductance time course was injected into the L4SS cell ( using dynamic-clamp to generate the appropriate membrane current from the conductance time course ) , while the voltage response was recorded ( Fig 4H ) . Adding gcc+gtc led to an increase in the proportion of spikes transmitted from the stimulated thalamocortical axon ( from 9% to 16% of input spikes ) , presumably because depolarisation by the synaptic noise conductances ( which would be occurring in vivo ) enabled more synaptic depolarisations to reach threshold ( Fig 4F ) . The input action potential train was derived from the output spike train recorded in thalamic relay cells , which had a frequency of 4 . 3 Hz , and had an entropy rate of about 32 bits/sec [12] . The output spike train recorded in cortical L4SS neurons ( without the simulated synaptic noise added ) had a frequency of 1 . 2 ± 0 . 8 Hz ( mean ± s . e . m . ) and a transfer entropy rate of 1 . 9 bits/sec ( 1 . 9 ± 1 . 6 bits/sec; mean ± s . e . m . ; Fig 5A ) encoding ~1 . 6 bits/spike ( 1 . 9 bits/sec at 1 . 2 Hz ) . Employing dynamic clamp to apply the recorded synaptic conductance at the cell soma , in addition to the thalamocortical and cortical noise conductances , gave a transfer entropy rate of 1 . 5 ± 1 . 1 bits/sec , which was not significantly different from that seen with real presynaptic stimulation ( paired Student t-test , p = 0 . 41; Fig 5A ) . These rates were slightly higher than the rates observed in simulations ( TE = 0 . 47 ± 0 . 06 bits/sec ) , probably due to the fact that unlike in simulations , in experiments , all input is injected at the soma . We calculated the energy used on a single thalamocortical connection–either evoked by presynaptic stimulation in voltage-clamp mode or scaled and injected using dynamic clamp in current-clamp mode–by calculating the total number of Na+ ions that must be actively extruded at the expense of 1 ATP molecule for every three Na+ ions [1 , 4] . When the postsynaptic conductance trace derived from presynaptic stimulation was not scaled ( but noise from the corticocortical and thalamocortical inputs was injected ) , the postsynaptic energy use was very slightly reduced by ~4% with respect to that occurring with real presynaptic stimulation in voltage-clamp mode ( p = 0 . 02; Fig 5B ) . This is because , in current-clamp mode , depolarisation of the cell by the injected conductances and any action potentials that are evoked reduce slightly the Na+ entry through postsynaptic channels compared to the stimulation condition in which the cell is voltage clamped at its resting potential . We next used dynamic clamp to investigate experimentally the effects on information transmission and energy use of scaling the thalamic input conductances up or down , while keeping the corticocortical input conductance constant . Since we are seeking the optimal conductance value for all thalamocortical synapses , the values of gsyn and gtc were scaled together . This results in the thalamic noise level increasing when gsyn is increased . As observed with the simulations ( Figs 2A and 3A ) , the energy use calculated to be associated with gsyn increased approximately linearly with the effective postsynaptic conductance but appears to tail off as the gain increases above 2 ( Fig 6A ) . The L4SS cell firing frequency increased with thalamocortical conductance ( with gsyn and gtc scaled up or down together as described for Fig 4G ) , although , like for thalamic relay cells [12] , this relationship begins to plateau at conductance gain values higher than 1 ( Fig 6B ) . Examining transfer entropy as a function of thalamocortical conductance showed that , on average , the physiological gain of the synapse is at , or very close to , maximizing information transmission from one LGN relay neuron to the cortex ( Fig 6C ) , unlike in simulations where a modest increase in information transmission was observed when increasing the physiological level of the thalamocortical conductance ( Figs 2C and 3C ) . This discrepancy is probably explained by the fact that , in experiments , the whole synaptic conductance had to be injected via the patch-pipette at the soma . We then assessed the relationship between information transmission and its associated energetic costs , as previously done for the thalamic relay synapse [12] and in simulations ( Figs 2D , 2E , 3D and 3E ) . We calculated the ratio of the information transmitted through one thalamocortical synapse to the energy used on its postsynaptic current , when dynamic clamp was employed to inject a thalamocortical conductance of different magnitudes ( Fig 6D ) . Like for multicompartment simulations ( Figs 2D , 2E , 3D and 3E ) , the resulting average peak energy efficiency for information transfer occurred at a conductance value close to the physiological value for the synapse . We cannot claim that the peak is precisely at the physiological value: this would require finer sampling of the conductance values around gsyn = 1 , and a larger experimental dataset to justify statistical comparison between conductance gain factors . We simply wish to show that the shape of the experimental curve is comparable to the simulated results , recapitulating and supporting the predictions made by the simulations in Figs 2 and 3 . Together , the simulation and experimental results suggest that , as for the much stronger retina-LGN synapse , the weak thalamocortical synapse displays an optimal value of postsynaptic conductance for maximizing the information transmitted per energy used on synaptic currents which is in the region of the physiologically observed value .
To test for the influence of inhibition at the thalamocortical synapse , we used the mathematical model of L4SS cells and ran simulations with and without inhibitory input ( see Materials & Methods ) . Results obtained in the Hodgkin-Huxley-type multicompartment model in the presence of inhibition recapitulate the experimental results of Fig 6C and 6D . Even though information transfer increases monotonically in the simulations ( Figs 2C and 3C ) while it eventually decreases when the gain is increased in experiments ( Fig 6C ) , this did not affect the excitatory conductance magnitude which produced the peak metabolic efficiency . Removing the inhibitory input in simulations did however change slightly the synaptic conductance value for the peak metabolic efficiency , shifting it to lower values ( Fig 3D and 3E ) , suggesting that the balance between excitation and inhibition in the network may play a role in fine-tuning synaptic conductance values for energetic optimality of information transfer . Specifically , we reason that , because increased inhibition would demand that more excitatory charge enter the postsynaptic cell to achieve the same voltage change , when inhibition does not have to be overcome , the peak position of energy efficiency shifts to a lower excitatory conductance value . The discrepancy observed between simulations ( Fig 2C ) and experiments ( Fig 6C ) in the relation between information transfer and gain , at gains higher than ~1 . 5 , can probably be attributed to the slightly different stimulation scenarios , as in experiments we were constrained to inject all input at the cell soma . In vivo , especially in sensory systems , it is likely that a postsynaptic neuron will receive inputs from cells that are transmitting correlated information . For instance , the receptive fields of “simple cells” in the visual cortex are thought be built from the receptive fields of spatially adjacent thalamic relay neurons [42 , 43] , and therefore the excitatory thalamic input to a cortical cell may be correlated not just in amplitude ( as we have mimicked here ) , but also in time , as a single object passes through the visual field . Additionally , inhibitory input may be correlated with excitatory input . In assessing how synaptic conductances are set to regulate information transfer and energy use , it will be interesting to investigate whether the conductance of each input to a postsynaptic cell is set independently , or whether account is taken of the correlations in information passing through spatially adjacent cells . Temporal correlations are also seen in the form of oscillatory activity throughout the thalamocortical loop in different states of arousal [46] . This activity may be important for putting the visual scene into context , for attention , or even for conscious “binding” of different attributes of a visual scene [47] . In the present paper , we have focused on the simple feed-forward connection from thalamus to cortex , but of course a feed-forward input could arrive at any stage of such temporal oscillation , thus impinging on a more or less hyperpolarized cortical neuron . It would be interesting to investigate how such network-level membrane potential rhythms affect the energy efficiency of input connections . However , we do not find it necessary to postulate that the same energetic principles should hold for all oscillation conditions . For instance , it is possible that the high amplitude , low frequency thalamocortical oscillations that are seen in deep sleep may play a role in synaptic renormalization , perhaps actively regulating synaptic strength with energy efficiency a prioritized parameter . Note that detailed investigation of these questions in the future might require the use of conditional transfer entropy . How can the preference of single synapses for an optimal information-to-energy ratio be reconciled with the need for strongly varying synaptic strengths for learning ? This is a question that we are very intrigued by , and our best explanation is that synapses are efficient on average . If each synapse were always set to its energetic optimum , then how could the relative weights of synapses store information ? From what is known about experience-driven and homeostatic mechanisms of synaptic plasticity , it is clear that we cannot consider energy to be the only determinant of neuronal organisation . Nor is information the only alternative competing pressure . For instance , brain-wide activity as a whole must be protected from entering dangerous regimes of recurrent excitation , and such stable network dynamics have recently been shown to be prioritised above optimal information transmission [48] . Many organisational constraints need to be considered to gain a full understanding of the evolutionary pressures that have guided brain design . We do not claim to have considered all such constraints here , but simply argue that energetic efficiency appears to be one important pressure on the strength of any synapse . Both our previous work ( retinogeniculate synapse [12] ) and the present study ( thalamocortical synapse ) , focus on relatively low-level feed-forward synapses in the adult visual system , examined after the critical period of development . We would predict that the distribution of synaptic strengths would be broader in a highly plastic brain region , or during development , with fewer synapses sitting at the energetically optimal point at any one time . We have found that postsynaptic properties maximise the information transmitted per energy used . While these properties are of course the result of evolutionary selection , how do we know that energy efficiency was the selective pressure for these properties ? That the evolved synapse properties facilitate energetically efficient information transfer could be a happy coincidence . One way to investigate this question would be examine the energy efficiency of synaptic communication in phylogenetically related species , to see if efficiency improves on an evolutionary time scale . Looking beyond synapses in the mammalian brain , there is evidence that many levels of neural organisation adhere to a principle of minimising energy use . At the most macroscopic level , the organisation of brain area localisation has been shaped by energy constraints by minimising the length of “wiring” required for signals to travel from one neuron to another [49] . The segregation of the brain into grey and white matter areas has also been suggested to promote energy efficiency [50] . At the single-cell level , surprising parameters such as the sparse firing [10] and low release probability [4] of many cortical neurons can also be explained when energy is viewed as a limiting factor . Even the dimensions of synaptic boutons and axons and the timescale of neuronal computation have apparently been shaped by the pressure to transmit information reliably given a limited energy supply [51 , 52] . It therefore does not seem surprising that the postsynaptic site , where most energy in the brain is used [1 , 2] , would also show evidence of evolving within strict energetic constraints . In conclusion , we have shown that weak , as well as strong , synapses in the visual system operate in the region of optimal information transmitted per energy used . This is consistent with energy use being a major constraint on the evolution of the CNS [53–55] . | Compared to other organs , the brain consumes a vast amount of energy for its size . Most of this energy is used to power the electrical and chemical processes that support neural computation . As the energy supply to the brain is limited , it follows that this computation should be energetically efficient . Previously , we showed that this is indeed the case for transmission of information between cells at synapses . Synapses transferring information from the retina to the brain do not maximise information transmission—some information is lost and does not reach the visual cortex . Instead , these synapses maximise the information transmitted per energy used . Here , we demonstrate that this principle of energetic efficiency also holds at the next synapse in the visual pathway , the thalamocortical synapse . This synapse is weaker and competes with hundreds of other inputs to influence the output firing of the next cell . Using detailed simulations of cortical neurons , and electrophysiological recordings in rodent brain slices , we found that this relatively weak synapse also does not maximise information transmission . Instead , it maximises the amount of information transmitted per energy used . This suggests that energy efficiency at synapses could be a common design principle in the brain . | [
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] | 2019 | Energy-efficient information transfer at thalamocortical synapses |
Leishmania parasites lack pathways for de novo purine biosynthesis . The depletion of purines induces differentiation into virulent metacyclic forms . In vitro , the parasites can survive prolonged periods of purine withdrawal changing their morphology to long and slender cells with an extended flagellum , and decreasing their translation rates . Reduced translation leads to the appearance of discrete granules that contain LeishIF4E-3 , one of the six eIF4E paralogs encoded by the Leishmania genome . We hypothesize that each is responsible for a different function during the life cycle . LeishIF4E-3 is a weak cap-binding protein paralog , but its involvement in translation under normal conditions cannot be excluded . However , in response to nutritional stress , LeishIF4E-3 concentrates in specific cytoplasmic granules . LeishIF4E-3 granulation can be induced by the independent elimination of purines , amino acids and glucose . As these granules contain mature mRNAs , we propose that these bodies store inactive transcripts until recovery from stress occurs . In attempt to examine the content of the nutritional stress-induced granules , they were concentrated over sucrose gradients and further pulled-down by targeting in vivo tagged LeishIF4E-3 . Proteomic analysis highlighted granule enrichment with multiple ribosomal proteins , suggesting that ribosome particles are abundant in these foci , as expected in case of translation inhibition . RNA-binding proteins , RNA helicases and metabolic enzymes were also enriched in the granules , whereas no degradation enzymes or P-body markers were detected . The starvation-induced LeishIF4E-3-containing granules , therefore , appear to store stalled ribosomes and ribosomal subunits , along with their associated mRNAs . Following nutritional stress , LeishIF4E-3 becomes phosphorylated at position S75 , located in its less-conserved N-terminal extension . The ability of the S75A mutant to form granules was reduced , indicating that cellular signaling regulates LeishIF4E-3 function .
Leishmania are heteroxenous pathogenic parasites that live in the alimentary tract of sandflies as extracellular flagellated promastigotes . Upon transmission to the mammalian host , the parasites enter macrophages and become obligatory intracellular and non-motile amastigotes , residing within phagolysosomes [1] . While cycling between vector and host , Leishmania parasites must overcome extreme physiological changes in their host milieu , experiencing temperature and pH switches , which serve as environmental signals to undergo differentiation from one life form to another [2–4] . In addition to the environmental changes , the parasites also experience changes in their nutritional supplies , based on the availability of nutrients from the vector or host [5 , 6] . The changes described are known to drive a developmental program of gene expression . Since processes that control gene expression in trypanosomatids are mostly post-transcriptional , translation regulation plays a key role in establishment of the stage-specific expression profiles [7 , 8] . Trypanosomatids lack enzymes for the de novo biosynthesis of purines and the parasites are entirely dependent on the host for purine supply [9–11] . Furthermore , the lack of purines was shown to induce metacyclogenesis [12] . The parasites are ingested with a blood drop that continues to provide essential nutrients such as purines , until their consumption . Although the sandfly's gut is co-inhabited by a rich repertoire of bacteria , these do not provide the parasites with nutrients , and were even shown to compete for food sources , generating nutrient stress and differentiation into metacyclics [13 , 14] . The pathway for metacyclogenesis in Leishmania can be triggered in vitro by depletion of purines , using adenosine receptor antagonists that inhibit purine uptake . Accordingly , replenishing the adenosine supply reduces the induction of metacyclogenesis [12] . Metacyclic promastigotes are growth-arrested , and equipped with a long flagellum . Nutrient stress requires the swift regulation of gene expression that includes control of translation [15] . As a consequence of impaired translation cytosolic mRNAs and stalled ribosomes are often stored in dedicated cytoplasmic RNA-protein granules [16] . These cytoplasmic granules are membrane-less aggregates of RNA and proteins [17] . Trypanosomatids harbor a wide repertoire of cytoplasmic granules that include P-bodies , stress granules , heat shock granules , nuclear periphery granules and tRNA halves [18–21] . In higher eukaryotes , P-bodies contribute to normal cellular physiology and are also claimed to be associated with mRNA decay [15 , 22] . Accumulation of mRNAs in granules occurs in response to cellular stresses when translation is inhibited [23 , 24] , and the mRNAs can be dynamically altered between the different granules [25] . Starvation-induced stress granules assemble in Trypanosoma cruzi [20] during the nutritional stress that also occurs within the insect vector . Such granules can store mRNAs and translation complexes [26] , yet can also lead to mRNA degradation [27] . Cap-dependent translation is the default mechanism for protein synthesis under normal conditions . It is based on assembly of a cap-binding complex onto the 5' cap structure through the cap-binding protein eIF4E , along with its binding partner and scaffold protein eIF4G . The latter serves as a hub for the DEAD-box RNA helicase eIF4A and for other subunits of the initiation complex . The initiation complex scans the 5' UTR until the first AUG is reached , where the large ribosomal subunit is recruited [28] . A variety of cellular stresses can lead to a global arrest of protein synthesis . In yeast and mammals , this can be achieved by inhibition of cap-dependent translation , although specific proteins may continue to be synthesized via cap-independent pathways [28] . While it is yet unclear whether stress-induced differential regulation of gene expression in Leishmania follows a similar pattern , we previously showed that the LeishIF4E-4 cap-binding complex in Leishmania is inactivated in axenic amastigotes [29] . The trypanosomatid genomes encode for a rather large number of cap-binding complexes that include six paralogs of eIF4E ( 1–6 ) and five identified paralogs of eIF4G [29–32] . The different eIF4E paralogs show different patterns of expression and cap-binding activity throughout the parasite’s life cycle , supporting the possibility that each paralog performs a different role under the changing environmental conditions . It is generally accepted that LeishIF4E-4 and LeishIF4G-3 form a canonical eIF4F complex as part of translation initiation , with LeishIF4E-1 seemingly also playing an additional , as yet unresolved role in axenic amastigotes at elevated temperatures [29] . In trypanosomes , the ortholog of LeishIF4E-3 is essential for viability [33] , and could be involved in translation initiation . However , its role in Leishmania remains unclear due to its relatively weak cap-binding activity [30 , 34] . Whereas LeishIF4E-3 interacts with LeishIF4G-4 under normal conditions , this interaction is impaired during nutritional stress , causing LeishIF4E-3 to concentrate within specific cytoplasmic granules [34] . Here , we studied the fate of LeishIF4E-3 during nutritional stress . We show that under such conditions whereby translation slows down , LeishIF4E-3 is phosphorylated and migrates into dedicated granules . We identified the site of its phosphorylation ( serine 75 ) and show that the S75A mutant fails to enter the starvation-induced granules . We further examined the effect of depleting specific nutrients on LeishIF4E-3 phosphorylation and its ability to enter granules in response to the depletion of purines , amino acids and glucose . We found that only purine starvation induced the morphological changes generating cells that resemble the nectomonad stage , prior to metacyclogenesis . To further examine the interactome of the LeishIF4E-3 containing granules induced following nutrient depletion , we collected the heavy fractions enriched for LeishIF4E-3 from sucrose gradients , affinity purified them through tagged LeishIF4E-3 and assessed their content . The LeishIF4E-3 containing granules contain mRNA and a multitude of ribosomal particles , along with other proteins that accompany mRNAs , supporting their suggested function as storage bodies .
Cutaneous infection-causing Leishmania amazonensis ( L . amazonensis , strain MHOM/BR/LTB0016 ) promastigotes were used in this study . Wild type L . amazonensis promastigotes were routinely cultured in Medium 199 ( pH 7 ) supplemented with 10% fetal calf serum ( FCS , European Grade; Biological Industries ) , 4 mM L-glutamine , 0 . 1 mM adenine , 5 μg/ml hemin , 40 mM Hepes , pH 7 . 5 , 100 U/ml penicillin and 100 μg/ml streptomycin and grown at 25°C . All nutritional starvation treatments were performed in Dulbecco’s modified eagle medium ( DMEM; Biological Industries ) . Purine starvation was performed in DMEM supplemented as above , except for adenine and FCS that were not included in the medium . Amino acid starvation was performed in custom-made DMEM lacking essential amino acids ( i . e . , Arg , His , Trp , Phe , Ser , Tyr , Thr , Val , Leu , Lys , Pro , Met , Cys and Ile ) [35] [36] and supplemented as described above , except for the absence of FCS . Glucose starvation was performed in DMEM lacking glucose , supplemented as described above , except for the absence of FCS . Control cells used in the starvation assays were also grown in DMEM supplemented as described above . All experiments were performed with logarithmically growing L . amazonensis cells at a cell density of 4–6 x 106 cells/ml . For FCS dialysis , 2000 MW cut-off dialysis tubing was used and the FCS was dialyzed against PBS overnight at 4ºC with stirring . All experiments with dialyzed FCS were performed with DMEM supplemented with 10% FCS . LeishIF4E-3 was tagged with streptavidin-binding protein ( SBP ) . Accordingly , the gene for LeishIF4E-3 was PCR amplified using LeishIF4E3 primers: Forward- ACTGGATCCATGAACCCGTCTGCCGCTGC , reverse- GCTCTAGAACAGAACGTGTGATCG and was cloned into the BamHI and XbaI sites of the pX H-SBP-H plasmid cassette [H represents the HSP83 intergenic regions required for processing of the transgene transcript] [29] . The pX H-SBP-H plasmid cassette is designed for cloning of the DNA coding region fused to a C-terminal SBP tag . L . amazonensis cells were transfected as previously described [37] . A stable cell line expressing the fusion protein was generated using 200 μg/ml G-418 selection . Wild type L . amazonensis promastigotes were cultured to a late log phase of growth ( 4–6 x 106 cells/ml ) and starved as described above for different times . The cells were harvested , washed twice in ice cold phosphate buffer saline ( PBS ) pH 7 . 4 , and once in PRS buffer ( 35 mM Hepes , pH 7 . 5 , 100 mM KCl , 10 mM MgCl2 , 1 mM DTT ) before being resuspended in PRS+ buffer . PRS+ buffer consisted of 2x cocktail of protease inhibitors ( Sigma ) , 4 mM iodoacetamide ( Sigma ) , 25 mM sodium fluoride , 55 mM β-glycerophosphate and 5 mM sodium orthovanadate . Cells were lysed in 5x sample buffer and heated at 95°C for 5 minutes . Cell lysates were stored at -80°C . Cell extracts ( equal protein loads ) were resolved on 12% SDS-polyacrylamide ( SDS-PAGE ) gels and probed using specific antibodies against LeishIF4E-3 or LeishIF4G-4 . Protein loads were verified using specific antibodies against LeishIF4A1 . All the primary antibodies were used at 1:5000 dilutions . Wild type log phase parasites were exposed to specific starvation conditions along with control non-starved cells for designated periods . The cells were washed twice in ice cold PBS and resuspended in λ-phosphatase buffer ( Sigma ) supplemented with 2 mM MnCl2 ( Sigma ) and 2x cocktail of protease inhibitors ( Sigma ) . The cells were lysed using 1% Triton X-100 and 10 units of λ-phosphatase were added to the lysates , with or without phosphatase inhibitors ( sodium fluoride , β-glycerophosphate and sodium orthovanadate ) . After incubation at 30°C for 30 min , the reaction was stopped by adding 5x Laemmli sample buffer and heating for 5 mins at 95°C . Protein mixtures were resolved on 12% low bis-acrylamide ( 30:0 . 2 ) containing SDS-PAGE and immunoblotted using antibodies against LeishIF4E-3 . Cell lysates from wild type L . amazonensis promastigotes starved for different periods were prepared as described above and resolved on 12% reduced bis-acrylamide SDS-PAGE gels . Protein mixtures were allowed to resolve for an extended duration of 4 h , followed by staining with Coomassie blue . Protein bands between 35 and 48 kDa ( the molecular mass of LeishIF4E-3 is ~37 kDa ) were sent for phospho-peptide mapping to the Smoler Proteomics Center at the Technion , Israel . For confocal microscopy , mid-log phase transgenic LeishIF4G4-GFP expressing L . amazonensis promastigotes ( ~1 x 107 ) were washed with PBS and fixed in 2% paraformaldehyde for 30 min . The cells were washed once with PBS , allowed to adhere to poly-L-lysine-coated slides and permeabilized with 0 . 1% Triton X-100 for 10 min , followed by blocking with 2% bovine serum albumin ( BSA ) for 1 h . LeishIF4E-3 was detected using anti-LeishIF4E-3 serum ( 1:50 ) , followed by incubation with secondary DyLight 550 goat anti-rabbit IgG ( 1:500 , KPL ) . DNA was stained with DAPI ( Sigma ) . Finally , the cells were washed thrice with PBS and an anti-bleach mounting solution ( DABCO ) was added prior to the placement of cover slips . The slides were examined in an inverted Zeiss LSM 800 spinning disc confocal microscope with Airyscan at a magnification of x63 . A single representative section is presented in all figures . Mid-log phase parasites were grown in complete medium or exposed to specific starvation conditions , for 24 h . The cells ( 1x107 ) were then counted , collected by centrifugation , washed once with PBS and fixed using 2% paraformaldehyde . After being pelleted by centrifugation , the cells were resuspended in PBS and allowed to stick to poly-L-lysine-coated slides , permeabilized with 0 . 1% Triton X-100 and washed in 25 mM ammonium chloride for 10 min . After blocking with 2% BSA for at least 1 hour , the slides were pre-hybridized in hybridization solution ( 2% BSA , 5x Denhart's solution , 4x SSC , 5% dextran sulphate , 35% deionized formamide , 0 . 5 mg/ml yeast tRNA and 20 units RNasin ( RNase inhibitor ) . The cells were then hybridized using PCR-amplified digoxigenin ( DIG ) -labeled DNA probes against HSP83 and the HSP83 intergenic region . The probes were synthesized using standard PCR conditions and the following forward and reverse primers for HSP83: 5'-AGGTGACGAAGGAGTACGAGG-3' , 5'-CCGAACTGCTCGTAGAACTGC-3' and for the HSP83 intergenic region: 5’-GAGGCACAAAGAGAGGGAAAAC-3’ , 5’- GAGGGCGACGGAGATGGAAG-3’ ( thus spanning positions 891–1120 of the intergenic region ) . The DIG-labelled probes ( 7 μl , following boiling ) was mixed with the hybridization solution and applied to cells . Hybridization took place overnight under humid conditions . The slides were then washed once with 4x SSC and 35% formamide in PBS , followed by washes with 4x and 2x SSC and with PBS . The slides were then treated with a standard confocal procedure for staining by antibodies , beginning with blocking , followed by incubation with anti-LeishIF4E-3 antibodies ( 1:50 ) and FITC-labeled anti-DIG antibodies ( 1:20 ) . Slides were then incubated with secondary DyLight 550 goat anti-rabbit IgG ( 1:500 ) and DAPI ( 1 μg/ml ) , washed in PBS and cover slips were mounted using DABCO . The slides were examined in an inverted Zeiss LSM 800 spinning disc confocal microscope with Airyscan at the magnification of x63 . Approximately 1 x 109 SBP-tagged LeishIF4E-3-expressing transgenic parasites , grown under normal ( control ) or under starvation conditions for 12 hours , were incubated with 100 μg/ml cycloheximide ( Sigma ) for 5 minutes . The cells were washed twice with ice cold PBS containing 100 μg/ml cycloheximide , lysed in lysis buffer ( 15 mM Tris-HCl , pH 8 , 150 mM KCl , 5 mM MgCl2 , 0 . 5 mM DTT , 100 μg/ml cycloheximide , 0 . 5 mg/ml heparin , 4 mM iodoacetamide , 2x protease inhibitors , 25 mM sodium fluoride , 55 mM β-glycerophosphate and 100 units RNase inhibitor ) containing 1% Triton X-100 for 5 min . Lysates were clarified by centrifugation at 20 , 000g at 4°C for 20 min . Cell lysates equivalent to 50 OD260 units were layered over a 12 ml 10–40% sucrose gradient prepared in gradient buffer ( 40 mM Tris-HCl , pH 8 , 280 mM KCl , 10 mM MgCl2 , 1 mM DTT , 200 μg/ml cycloheximide , 1 mg/ml heparin , 8 mM iodoacetamide , 4x protease inhibitors , 50 mM sodium fluoride , 110 mM β-glycerophosphate , 10 units RNasin and varying concentrations of sucrose solution ) . The gradients were centrifuged for 160 min at 154 , 000g in a SW 40Ti rotor ( Beckman Coulter ) . Fractions ( 300 μl ) were collected from the top and optical density was measured at 260 nm . Alternate fractions were diluted 1:1 with lysis buffer and 7 μl StrataClean Resin ( Agilent Technologies ) was added and the samples were mixed over overnight at 4°C in a rotator . The fractions were centrifuged at 1000g for 3 min and the supernatant was discarded . The washed resin was mixed with 5x Laemmli sample buffer and heated at 95°C for 5 minutes . Proteins bound to the beads were resolved by 12% SDS-PAGE and western analysis was performed using antibodies against LeishIF4E-3 or LeishIF4G-4 . The second set of collected fractions was diluted with gradient buffer to reduce sucrose concentration to less than 10% and aliquots were used in a pull-down assay . The aliquots were incubated with streptavidin-Sepharose beads ( GE Healthcare ) for 2 h on a rotator at 4°C . Following binding , the flow-through fraction was collected and the beads were washed with 0 . 1% NP-40 containing gradient buffer . Bound LeishIF4E-3-SBP was eluted in gradient buffer using 5 mM biotin . Proteins from the eluted fractions were tricholoroacetic acid ( TCA ) precipitated and washed with acetone before the addition of 5x Laemmli sample buffer . Aliquots of the eluted protein complexes were resolved by 12% SDS-PAGE and probed using antibodies against LeishIF4E-3 or LeishIF4G-4 . The remaining material was resolved by 12% SDS-PAGE and those heavy fractions containing LeishIF4E-3 were subjected to mass spectrometry analysis . Protein preparation from isolated granules: To characterize the proteins enriched in the starvation-induced LeishIF4E-3 containing granules in L . amazonensis , cells expressing SBP-tagged LeishIF4E-3 were starved , extracted and pulled-down over streptavidin-Sepharose beads followed by elution with biotin , as described above . A cell line expressing SBP-tagged luciferase was similarly treated and analyzed in parallel . The pulled-down proteins were precipitated using 10% TCA and the pellets were washed with acetone . Protein pellets were dissolved in 5x sample buffer , heated and resolved by 12% SDS-PAGE . Mass spectrometric analysis was performed by the Smoler Proteomics Center at the Technion , Israel . Mass Spectrometry: Proteins in the gel were reduced using 3 mM DTT ( 60°C for 30 min ) , followed by modification with 10 mM iodoacetamide in 100 mM ammonium bicarbonate for 30 min at room temperature . This was followed by overnight digestion in 10 mM ammonium bicarbonate in trypsin ( Promega ) at 37°C . Trypsin-digested peptides were desalted , dried , re-suspended in 0 . 1% formic acid and resolved by reverse phase chromatography over a 30 min linear gradient with 5% to 35% acetonitrile and 0 . 1% formic acid in water , a 15 min gradient with 35% to 95% acetonitrile and 0 . 1% formic acid in water and a 15 min gradient at 95% acetonitrile and 0 . 1% formic acid in water at a flow rate of 0 . 15 μl/min . Mass spectrometry was performed using a Q-Exactive plus mass spectrometer ( Thermo ) in positive mode set to conduct a repetitively full MS scan followed by high energy collision dissociation of the 10 dominant ions selected from the first MS scan . A mass tolerance of 10 ppm for precursor masses and 20 ppm for fragment ions was set . Translation was measured using the SUnSET ( Surface SEnsing of Translation ) method [43] . Wild type mid-log phase parasites were subjected to specific starvation treatments , while control cells were maintained under normal growth conditions . The cells were treated with 1 μg/ml puromycin ( Sigma ) for 20 min , washed twice with ice cold PBS , once with PRS buffer and finally resuspended in PRS+ buffer and lysed upon addition of 5x Laemmli sample buffer . Cycloheximide treated control parasites served as negative controls . Samples were resolved by 12% SDS-PAGE . Western analysis was performed using anti-puromycin antibodies . Wild type mid-log phase L . amazonensis promastigotes were exposed to different starvation conditions along with the control organisms for up to 5 days . Each day , 50 μl of cells were fixed using 4% paraformaldehyde and counted . Cell viability was measured using the trypan blue exclusion assay . Wild type mid-log phase L . amazonensis promastigotes were exposed to different starvation conditions for 24 h . Cells were then washed , fixed and coverslips were mounted . Phase contrast images of parasites were captured at 100x magnification with a Zeiss Axiovert 200M fluorescence microscope equipped with a AxioCam HRm CCD camera . Motility patterns of the parasites were examined by video-microscopy . Mid-log phase parasites were grown in either complete medium or specific nutrient-depleted medium for 24 h . Cells were then observed using the 40x objective of an Olympus IX73 inverted microscope . Videos were captured with a QImaging Retiga 6000 monochrome CCD camera .
The absence of specific nutrients occurs already in the sand fly , and is an integral part of the Leishmania life cycle . We , therefore , examined the effects of eliminating specific nutritional components on global protein synthesis . Active translation was followed using the 'surface sensing of translation' ( SUnSET ) technology , which is based on monitoring the incorporation of puromycin into de novo synthesized polypeptides [44] . L . amazonensis promastigotes were incubated in nutrient-free PBS , or in media deficient of glucose for 4 h , whereas removal of purines or amino acids was performed during longer time periods . The starved cells were then incubated with puromycin for 20 min and de novo translation was monitored on western blots using anti-puromycin antibodies . As shown in Fig 1A , translation was reduced already after 4 h of nutrient depletion , as compared to actively translating non-starved cells . The strongest effect was observed in cells incubated in PBS or cells depleted of glucose , as compared to the complete arrest observed in cells incubated with cycloheximide . The effects of eliminating purines and amino acids for longer periods ranging between 1–4 days ( no cells survived longer periods of glucose depletion ) were also examined ( Fig 1B ) . Depletion of purines and amino acids reduced translation as early as after 4 h , and the effect became more pronounced with time . The presence or absence of dialyzed fetal calf serum did not change the inhibitory effect ( S1A Fig ) , indicating that the absence of purines or amino acids was the cause of the reduced translation . The western blots were all quantified by densitometry ( S1B Fig ) . Impaired translation often leads to the formation of cytoplasmic RNA granules [20] . We previously examined granules induced by nutritional stress and showed that incubation in PBS caused LeishIF4E-3 to concentrate in specific cytoplasmic foci [34] . In view of the role played by purine starvation during metacyclogenesis pathways , we tested the effects of depleting purines , as well as other specific nutrients , on the formation of LeishIF4E-3-containing granules . In addition , the intracellular localization of LeishIF4G-4 was examined in a transgenic cell line expressing GFP-tagged LeishIF4G-4 , thus allowing for parallel visualization of both proteins . Fig 2 and S2A Fig ( for the broad field ) show that under normal growth conditions , LeishIF4E-3 was dispersed throughout the cytoplasm , co-localizing with LeishIF4G-4 . However , exposing the cells to different types of nutritional depletion caused LeishIF4E-3 to concentrate in specific cytoplasmic foci as early as 4 h after the onset of the stress . LeishIF4G-4 was not detected in the granules formed following purine starvation ( see the merge panel in Fig 2 ) . We excluded the possibility that granule formation could be affected by the high level of episome-derived expression of SBP-tagged LeishIF4E-3 , since starvation-induced LeishIF4E-3 containing granules were observed when wild type parasites were subjected to nutritional stress but not under normal conditions [S2B Fig and S2C Fig ( broad field ) ] . To analyze the dynamic nature of these granules , we tested whether they disassembled upon recovery from nutritional depletion . Starved parasites ( 4 h ) were replenished with complete medium containing all the supplements required for normal growth , and were allowed to grow for 24 h . Confocal microscopy using antibodies against LeishIF4E-3 monitored the abundance of LeishIF4E-3-containing granules . Notably , following recovery from nutritional stress , the starvation-induced LeishIF4E-3 containing granules disappeared [S2D Fig and S2E Fig ( broad field ) ] . Recovery was also observed if the cells were starved for purines during 4 days ( with or without dialyzed FCS , S2F Fig ) . The granules disappeared after the cells returned to complete media for 24 h with and without dialyzed FCS [S2F Fig and S2G Fig ( broad field ) ] . Thus , the absence of dialyzed FCS did not prevent recovery of the cells from the presence of LeishIF4E-3 containing granules . The mechanism that targets LeishIF4E-3 to starvation-induced cytoplasmic granules is poorly understood . One possibility is that LeishIF4E-3 escorts mRNAs for storage in granules , under conditions whereby translation is arrested due to nutrient deficiencies . Alternatively , if stalled ribosomes are targeted to these granules , associated mRNAs would accompany them . We , therefore , examined whether the starvation-induced LeishIF4E-3 containing granules indeed contain mRNA . Non-starved and starved parasites were examined by fluorescence in situ hybridization ( FISH ) analysis of mRNA , using DIG-labelled probes derived from HSP83 . While non-starved parasites showed homogeneous cytoplasmic distribution of the mRNA , following nutritional depletion ( 12 h ) , their staining was enriched within the starvation-induced LeishIF4E-3 containing granules [Fig 3 and S3A Fig ( broad field ) ] . The granules contained mature mRNAs , as no hybridization was observed with probes directed against the HSP83 intergenic region [IR , positions 891–1118 , S3B Fig and S3C Fig ( broad field ) ] . Finally , we cannot exclude the possibility that LeishIF4E-3 and the HSP83 transcript simply co-migrate into the granules , as we do not show a direct interaction between them . However , since the LeishIF4E-3 complex contains RNA binding proteins , these could be involved in targeting the HSP83 mRNA to the cytoplasmic granules , along with LeishIF4E-3 . Western analysis of protein lysates from starved and non-starved parasites demonstrated changes in the migration profile of LeishIF4E-3 following specific nutrient starvation ( 4 h ) , and highlighting a shift to slower migrating isoforms [Fig 4A ( I–III ) ] . All westerns of Fig 4A were subjected to densitometry quantification ( S4A Fig ) . The shift in migration was observed even after a shorter 1 h incubation , with or without dialyzed FCS ( S4B Fig ) . The effects of depleting purines and amino acids were observed only after 24 h [Fig 4A ( II ) ] and continued for 4 days ( S4C Fig ) . Following recovery from nutritional stress obtained by incubation of the parasites in fresh promastigote growth medium for 24 h , LeishIF4E-3 lost its differential migration pattern , as no change in the migration pattern of LeishIF4E-3 was observed , in comparison to non-starved parasites [Fig 4A ( IV ) ] . To verify that the LeishIF4E-3 modification during starvation originated from phosphorylation , protein lysates from starved ( 4h in PBS and 4 days for purine starvation ) and non-starved parasites were treated with λ-phosphatase and further analyzed in western blots . Fig 4B shows that following the phosphatase treatment , the LeishIF4E-3 once again resumed its original fast migrating pattern , reflecting the non-phosphorylated form of the protein . The phosphorylation site of LeishIF4E-3 following nutritional deprivation was identified using LC-MS/MS , performed with and without enrichment for the phospho-peptides . In both cases , a single phosphorylated peptide was identified in the enriched fraction of phospho-peptides , highlighting that phosphorylation occurred at position S75 , which is part of the extended amino terminus of LeishIF4E-3 ( S1 Table and S4D Fig ) . Peptides covering positions 84 and 105 , which are known to be phosphorylated in L . infantum [3] , were not phosphorylated in L . amazonensis LeishIF4E-3 . The T . brucei phosphorylation sites [45 , 46] are not conserved with L . amazonensis , or L . infantum ( S4D Fig ) . The percentage of the phosphorylated peptide in the cell extract was 74% in cells starved in PBS , whereas only 15% of this peptide were phosphorylated in non-starved cells ( S1 Table ) . To test the relevance of the serine 75 phosphorylation for the ability of LeishIF4E-3 to form granules , a LeishIF4E-3 ( S75A ) phospho-mutant was overexpressed in cells via stable transfection . Fig 5A shows the migration profile of the endogenous and mutant LeishIF4E-3 polypeptides . Using the antibodies against the SBP tag shows that the slower migrating band of the mutant protein was reduced , as compared to the wild type tagged protein ( S5A Fig ) . However , a faint band above the main interacting protein was still observed , and could be assigned to other modifications of the mutant S75A LeishIF4E-3 ( carbamidomethylation and oxidation ) , which were identified in the mass spectrometry analysis ( S1 Table ) . Fig 5B shows that the S75A mutation of the phosphorylation site also reduced the interaction between LeishIF4E-3 and LeishIF4G-4 ( Fig 5B and S5B Fig ) . Finally , the S75A mutation also caused a reduction in the ability of the mutated LeishIF4E-3 to granulate in response to a nutritional stress , as observed in Fig 5C and S5C Fig , whereas the formation of granules containing native LeishIF4E-3 was not interrupted . The proteomic content of the dense sucrose fractions following pull down by LeishIF4E-3 was analyzed . These fractions could represent the newly formed starvation-induced granules . Extracts of exponentially growing cells that expressed the tagged LeishIF4E-3 were fractionated over sucrose gradients and the heavy fractions were pooled . The OD260 profile is given in Fig 6A . The western analysis of the gradient fractions that is shown in Fig 6B ( middle panel ) further highlights that starvation conditions eliminated the polysomes . The western analysis of the different fractions shows that starvation conditions caused LeishIF4E-3 to migrate in the heavy fractions . These were pooled for further treatment . Parallel fractions from non-starved control cells contained only a small amount of LeishIF4E-3 , as expected , since under normal growth conditions LeishIF4E-3 is not abundant in the heavy fractions . The pooled heavy fractions from extracts of stressed cells were affinity-purified over streptavidin beads and the presence of LeishIF4E-3 in the eluted fractions was verified by western analysis ( Fig 6 bottom panel and S6A Fig ) . A control assay verified that the addition of cycloheximide to the cells prior to granule purification did not prevent the formation of granules during starvation . This was verified by confocal microscopy of starved and non-starved cells that were incubated with cycloheximide [S6B Fig and S6C Fig ( broad field ) ] . In accordance , LeishIF4E-3 migrated in the heavy fractions of the gradient in the absence of cycloheximide ( S6D Fig ) . The protein complexes that were eluted from the heavy fractions following pull-down of SBP-tagged LeishIF4E-3 were subjected to LC-MS/MS analysis . To exclude non-specific pulled-down products , we employed the same protocol with a control Leishmania cell line that over-expressed the luciferase reporter gene , to generate a non-related protein that was also SBP-tagged . Extracts of cells expressing SBP-tagged luciferase were separated over sucrose gradients , the heavy fractions were pooled and pulled-down via the SBP tag using streptavidin beads ( S6E Fig ) . The eluted material was analyzed by LC-MS/MS in parallel to the LeishIF4E-3 pulled-down products . Each pull-down experiment was performed in triplicates and all samples were analyzed in the same run . The peptides that were pulled down from SBP-tagged LeishIF4E-3 and from SBP-tagged luciferase fractions were identified using the MaxQuant software . The Perseus statistical analysis highlights the proteins that were significantly enriched by at least two fold in the LeishIF4E-3 pulled-down assay , as compared to the luciferase control pulled-down experiment , with p<0 . 05 . This comparison was based on average intensities , calculated from three similar but independent experiments . The proteins enriched in the LeishIF4E-3 containing granules were manually categorized into functional groups , as shown in Fig 7A and S2 Table . The manual classification highlighted the presence of multiple ribosomal proteins , along with RNA binding proteins , chaperones , signaling proteins and metabolic enzymes . The proteins were also analyzed for their GO term enrichment , using TriTrypDB; using a threshold of four fold enrichment as compared to the gene sets encoded in the genome , with a p< 0 . 01 . Results are shown in Fig 7B and S3 Table . The GO term analysis also highlighted the enrichment of a large number of ribosomal proteins , along with ribonucleoproteins , pre-ribosomal proteins and proteins involved in ribosome maturation . Altogether , our observations suggest that the granules could serve for storage of ribosomes and ribosomal subunits that were stalled due to the nutritional stress . The LeishIF4E-3 pulled-down granules also contained RNA helicases and RNA-binding proteins , in line with the potential role of these granules in RNA storage . The presence of a variety of metabolic enzymes is intriguing , but supported by a recent report indicating their ability to bind RNA , despite the absence of canonical RNA recognition motifs [47 , 48] . With respect to translation , while most initiation factors were not detected , the granules contained factors related to the elongation process , namely LeishEF1b and LeishIF5a . The latter has been implicated in elongation , rather than in initiation of translation [49] . The enriched fractions also contained LeishPABP2 , but not LeishPABP1 , which is part of the LeishIF4E-4 canonical complex [29 , 50] . The trypanosomatid ortholog TbPABP2 is also found in stress granules in T . brucei [51 , 52] . It is important to note that RNA degradation enzymes were not highlighted in the LeishIF4E-3 pulled-down granules . This distinguishes the LeishIF4E-3 pulled-down granules from the nutritional stress-induced granules of T . brucei , which were purified using a different approach [26] . Another notable difference is that classical P-body markers , such as DHH1 and Scd6 , were not found in the LeishIF4E-3 granule proteome . The absence of LeishIF4G-4 among the significantly enriched granular proteins is in line with our former report that nutritional stress disrupted the interaction between LeishIF4E-3 and LeishIF4G-4 [34] . As expected , the starvation-induced LeishIF4E-3 containing granules contained several chaperones , which usually accompany proteins that are over-expressed in cells . The presence of a few signaling proteins is not surprising , as are the few proteins related to the cytoskeleton . However , the presence of a larger repertoire of nuclear proteins is intriguing . Using confocal microscopy , we further validated the co-localization of two proteins that were identified in the LeishIF4E-3 starvation granule , namely LeishPABP2 and the Leishmania ribosomal protein S6 ( RPS6 ) [Fig 8 ( I ) and ( II ) and S7 Fig] . LeishPABP2 was tagged with SBP and its localization was monitored using anti-SBP monoclonal antibodies . LeishIF4E-3 was stained by rabbit-raised specific antibodies . Fig 8 shows that the two proteins co-localize , at least on a partial basis , which is not surprising , since LeishPABP2 could participate in multiple complexes on top of the granules . For testing co-localization of RPS6 and LeishIF4E-3 during a starvation stress , the protein was stained with rabbit antibodies against RPS6 , LeishIF4E-3 was monitored using mouse anti-SBP monoclonal antibodies . The results verify the presence of ribosomal proteins in the starvation-induced LeishIF4E-3 containing granules . Thus , the confocal analysis suggests both RPS6 and PABP2 co-localize , at least partially , with LeishIF4E-3 in the same nutritional stress-induced granules . The effects of specific nutrient stresses on parasite growth and viability were monitored following the depletion of purines , essential amino acids or glucose . S8 Fig shows that while removal of each nutrient arrested cell proliferation , their effects on cell viability varied . While prolonged deprivation of either glucose or amino acids was lethal to the cells , the absence of purines over the same period did not reduce viability . Furthermore , the deprivation of each nutrient had a different effect on cell morphology ( Fig 9 ) . Eliminating purines as well as amino acids induced the appearance of slender-shaped cells with a long flagellum , which are typical of nectomonad cells , a form that precedes differentiation into metacyclic cells [53] . Glucose deprivation resulted in the appearance of "fat" cells , which are usually associated with cell lethality . Starvation for purines and amino acids maintained and even increased parasite motility ( S1–S5 Videos ) . This mimics events in the fly , where starved parasites leave the hindgut and migrate to the front parts of the mouth [53] .
In this paper , we report a direct correlation between the availability of nutrients on translation , post translational modifications of LeishIF4E-3 and on morphological changes . We further propose that the parasites may have developed temporary means to store inactive mRNAs and stalled ribosomes in dedicated granules that also contain LeishIF4E-3 , possibly allowing their recycling upon renewal of nutrient availability . In trypanosomes , TbIF4E-3 is an essential protein , which could support its role in active translation . The Leishmania ortholog , LeishIF4E-3 , could be assigned a similar role , although it binds the mRNA cap structure rather inefficiently as compared to other LeishIF4Es [54] . Indeed , one of the three key aromatic residues in its cap-binding pocket is substituted by methionine , possibly accounting for its weak cap-binding activity [30] . We , therefore , searched for additional functions for LeishIF4E-3 that could coincide with being part of storage bodies for RNA , and possibly inactive ribosomes . Leishmanias and trypanosomes are known to harbor a multitude of RNA granules , which are formed in response to different stresses [19 , 21 , 55] . We previously reported that during nutritional stress , LeishIF4E-3 concentrated in specific granules [34] , but the content of these granules was not resolved . We verify here the presence of mature mRNAs in these starvation-induced LeishIF4E-3 containing granules , in line with our hypothesis that these granules serve as storage bodies for mature mRNAs , such as the HSP83 transcript . However , it is not clear whether the HSP83 mRNA concentrates in the granules through a direct association with LeishIF4E-3 , or through other RNA-binding proteins that were identified in our proteomic analysis . A partial co-localization of LeishIF4G-4 with the starvation-induced LeishIF4E-3 containing granules was observed , but mostly in cells starved for amino acids and glucose , and not in cells fully starved ( PBS ) or starved for purines . We further characterize the nutritional stress that led to the formation of LeishIF4E-3 containing granules . Depleting purines , amino acids or glucose led to a decrease in translation ( with variable kinetics ) , despite previous reports that mRNA abundance is not altered during purine starvation [11] . However , removal of each nutrient had a different effect on cell viability and morphology . Depletion of purines generated elongated slender cells with a long flagellum that maintained their viability and motility , whereas depletion of glucose generated "fat cells" that died within 24 h . The effect of depleting glucose for short periods ( 4 h ) could be reversed , while longer incubations were lethal . Unlike glucose depletion , elimination of purines for a relatively long time ( 4 days ) did not lead to cell death and the cells could easily recover . During recovery , the LeishIF4E-3 containing granules disappeared , and the protein resumed its homogenous cytosolic distribution . In attempt to examine the nature of the LeishIF4E-3 containing foci following a nutritional stress , we separated extracts of cells expression the SBP-tagged LeishIF4E-3 over sucrose gradients and isolated the dense fractions . These could include the LeishIF4E-3 containing granules , since the non-granulated LeishIF4E-3 remained in the top fractions . A subsequent pull-down step over streptavidin beads enriched the fraction of the putative starvation-induced LeishIF4E-3 containing granules , which was subjected to LC-MS/MS analysis . Parallel control cultures expressing SBP-tagged luciferase were treated and processed similarly , and analyzed in the same run , to control for non-specific binding to the streptavidin-Sepharose beads . The statistical analysis identified a significant increase of the bait protein , LeishIF4E-3 , along with a large group of ribosomal proteins . Co-localization of the ribosomal protein RPS6 in the LeishIF4E-3 containing granules was verified by confocal microscopy . Although a potential contamination of ribosomal proteins could be attributed to residual polysomes in these dense fractions , these were not enriched in the control experiment with the SBP-luciferase expressing cells . Thus , enrichment of ribosomal proteins in these granules can account for the mechanism by which the parasites store stalled ribosomes . LeishPABP2 was identified in the LeishIF4E-3 containing granules , and further verified by confocal microscopy . The PABP2 ortholog was also found in stress granules of trypanosomes [26 , 51] . In response to transcription inhibition by the addition of Actinomycin D [18] , LeishPABP2 and LeishPABP3 were also shown to migrate to the nucleus . It is interesting to note that the LeishIF4E-3 containing granules did not contain other translation initiation factors , except for two elongation factors , LeishEF1b and LeishIF5a , with the latter having been shown to be involved in elongation rather than in initiation [49] . The possibility that stress granules are active in translation elongation but not in initiation was recently proposed [15] . Although we have no evidence that translation elongation is performed in these granules , the significant enrichment of an elongation factor in the granules could support the possibility that a limited elongation process occurred within such bodies . Finally , enrichment with ribosomal proteins was not reported in T . brucei , excluding the possibility that translation elongation takes place in the T . brucei granules [26] . The presence of ribosomal proteins in the LeishIF4E-3 containing granules were also identified through the GO term enrichment analysis , along with several RNA helicases and RNA-binding proteins that usually accompany RNA molecules . The Leishmania and Trypanosoma granules [26] share several RNA-binding proteins , such as DRB2 and the Lupus La protein . La is an RNA chaperone that can promote translation of cellular RNAs through internal ribosome entry sites [56] and also appears in nutritional stress granules in T . brucei [26] . PUF proteins and UBP2 usually function as important post-transcriptional regulators [57–59] , both are found in T . brucei and Leishmania nutritional stress granules . A few other RNA-binding domain proteins were found exclusively in the LeishIF4E-3 granules , thereby distinguishing them from T . brucei granules [26] . The significant enrichment of metabolic enzymes in the LeishIF4E-3 granules is an intriguing observation . However , recent reports have highlighted a non-classical RNA-binding activity of such enzymes , despite the absence of conventional RNA-binding domains [47] . A non-canonical moonlighting RNA-binding activity was also recorded for several metabolic enzymes in T . brucei [48] , of which enolase ( Tb927 . 10 . 2890 ) was shown to be enriched in our analysis . Other granular polypeptides enriched in the Leishmania LeishIF4E-3 containing granules were chaperones , along with signaling proteins , and proteins that presently play unknown roles in the granules , such as nuclear proteins . Unlike the nutritional stress granules in T . brucei [26] , the LeishIF4E-3 granules described in this paper do not contain RNA degradation enzymes as described in [60] . We also could not find typical P-body markers , DHH1 or Scd6 , in accordance with our previous report that LeishIF4E-3-containing granules did not co-localize with DHH1 containing granules [34] . The absence of RNA degradation enzymes and P-body markers strengthens our conclusion that the LeishIF4E-3 containing granules described in this paper are dedicated mainly for storage of inactive mRNAs and stalled ribosomes , unlike the starvation granules described elsewhere [26] . Previous studies based on the content of P-bodies suggest that they are involved not only in storage of translationally repressed mRNAs , but possibly in their decay as well [17] . However , these reports were challenged by studies claiming that purified P-bodies are not involved in mRNA decay [61] . Altogether , it appears that P bodies could have a dynamic nature that is highly sensitive to the conditions that induce their formation and content . We can envision how different severities of nutritional stress in trypanosomatids could lead to the appearance of different subsets of granules and to dynamic changes between them . It is also possible that the different methodologies used to purify or enrich the granular fraction led to the identification of two granule sub-populations . Although both studies use a similar starvation protocol ( incubation in PBS ) , the purification processes vary . T . brucei granules were obtained by sequential lysis and centrifugations using the cytoskeletal network as a molecular sieve . However , the Leishmania granules were enriched over sucrose gradients followed by pull-down of SBP-tagged LeishIF4E-3 over streptavidin beads . Finally , all eIF4E orthologs are known to be phospho-proteins , whereby phosphorylation affects their function [62 , 63] . Trypanosomatid eIF4Es are also phospho-proteins , but their phosphorylation sites follow a variable profiles . For example , LeishIF4E-4 is phosphorylated at several positions in its N-terminal extended region [64] . TbIF4E-3 is also phosphorylated in several positions in its extended N-terminus [45 , 46] . Here we show that LeishIF4E-3 from L . amazonensis is phosphorylated in its N-terminal extension as well , but unlike the multiple sites in TbIF4E-3 , it is phosphorylated only at position S75 . This phosphorylation increased during nutritional stress . L . amazonensis phosphorylation of LeishIF4E-3 varies from that in other Leishmania species , since different sites were identified in L . infantum , ( S84 and S105 ) , as compared to S75 in L . amazonensis . S75 is not conserved in L . infantum , L . major or L . donovani [3] ( S4D Fig ) . S84 in L . infantum is constitutively phosphorylated and S105 is phosphorylated mainly in promastigotes , and not in axenic amastigotes [3] . These findings further highlight differences between Leishmania species , in addition to the variability between leishmanias and trypanosomes . In addition to the observed phosphorylation of LeishIF4E-3 , additional modifications were noted , such as carbamidomethylation and oxidation , possibly explaining the migration pattern of the mutant protein . The S75A substitution of the phosphorylation site of LeishIF4E-3 from L . amazonensis reduced its ability to concentrate in granules , the mutant protein also reduced its capacity to bind LeishIF4G-4 , though not completely . The N-terminal extension of LeishIF4E-4 was recently shown to be responsible for binding LeishPABP1 and a similar prediction was made for LeishIF4E-3 in that paper [50] . Our analysis highlights that LeishIF4E-3 and LeishPABP2 co-migrate to the same granules . In that paper , the interaction of LeishIF4E-4 with LeishIF4G-3 was shown to be mediated by the conserved core region of LeishIF4E-4 [50] . Similar to the assembly mode of this complex , we previously reported that residue W187 of the LeishIF4E-3 core is required for the interaction with LeishIF4G-4 [34] . It now appears that in addition to the core region of LeishIF4E-3 , its phosphorylated N-terminus also influences this interaction . It is conceivable that substitution of the S75 phosphorylation site at the N-terminal region could have an allosteric effect on binding to LeishIF4G-4 , possibly through alteration in the net charge of LeishIF4E-3 [65 , 66] . Additionally , the interaction between the mutated LeishIF4E-3 and LeishIF4G-4 was reduced , but not completely diminished . The formation of LeishIF4E-3-containing granules is reversible , since recovery of the nutrient supply caused the LeishIF4E-3 containing granules to disappear . It is not yet clear whether components comprising the nutritional granules were recycled back into the cytoplasm , or whether the granules were fully removed from the cells , possibly by secretion . Removal of metabolites from trypanosomatid cells via exosomes has been reported [67–69] . RNA secretion was also reported in trypanosomatids , whereby a heat shock stress that led to an inhibition of splicing caused the excess Spliced Leader RNA to concentrate in cytoplasmic vesicles , which are secreted as exosomes in a mechanism that resembles that of microRNA secretion by mammalian cells [70] . Whether a mechanism of similar nature also occurs for the starvation-induced LeishIF4E-3 containing granules and their associated mRNA molecules remains to be seen . | Cells respond to cellular stress by decreasing protein translation , to prevent the formation of partially folded or misfolded new polypeptides whose accumulation can be detrimental to living cells . Under such conditions , the cells benefit from storing inactive mRNAs and stalled ribosomal particles , to maintain their availability once conditions improve; dedicated granules offer a solution for such storage . Leishmania parasites are exposed to a variety of stress conditions as a natural part of their life cycle , including the nutritional stress that the parasites experience within the gut of the sandfly . Thus , Leishmania and related trypanosomatids serve as a good model system to investigate RNA fate during different stress conditions . Various granules appear in Leishmania and related organisms in response to different stress conditions . Here , we investigated how nutritional stress , in particular elimination of purines , induced the formation of granules that harbor a specific cap-binding protein , LeishIF4E-3 . The starvation-induced LeishIF4E-3 containing granules consist of a variety of ribosomal proteins , along with RNA-binding proteins and mature mRNAs . We thus propose that Leishmania modulates the assembly of LeishIF4E-3-containing granules for transient storage of stalled ribosomal particles and inactive mRNAs . Following renewal of nutrient availability , as occurs during the parasite’s life cycle , the granules disappear . Although their fate is yet unclear , they could be recycled in the cell . Unlike other granules described in trypanosomes , the LeishIF4E-3-containing granules did not contain RNA degradation enzymes , suggesting that their function is mainly for storage until conditions improve . | [
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] | 2019 | Nutritional stress targets LeishIF4E-3 to storage granules that contain RNA and ribosome components in Leishmania |
The lateral mobility of individual , incoming human papillomavirus type 16 pseudoviruses ( PsV ) bound to live HeLa cells was studied by single particle tracking using fluorescence video microscopy . The trajectories were computationally analyzed in terms of diffusion rate and mode of motion as described by the moment scaling spectrum . Four distinct modes of mobility were seen: confined movement in small zones ( 30–60 nm in diameter ) , confined movement with a slow drift , fast random motion with transient confinement , and linear , directed movement for long distances . The directed movement was most prominent on actin-rich cell protrusions such as filopodia or retraction fibres , where the rate was similar to that measured for actin retrograde flow . It was , moreover , sensitive to perturbants of actin retrograde flow such as cytochalasin D , jasplakinolide , and blebbistatin . We found that transport along actin protrusions significantly enhanced HPV-16 infection in sparse tissue culture , cells suggesting a role for in vivo infection of basal keratinocytes during wound healing .
Virus entry into target cells is a multistep process that starts with the initial binding of incoming particles to cellular receptors and attachment factors . After binding , animal viruses typically display a period of lateral movement before viruses are internalized by endocytosis or penetrate the plasma membrane [1] . The dynamics of virus movement on the cell surface can be random as described for murine polyomavirus [2] , or highly directional as described for murine leukemia virus and other enveloped RNA viruses [3] . In the latter case , movement was observed along actin-rich protrusions such as filopodia and retraction fibres , and the movement was dependent on actin retrograde flow that directed the virus toward the cell body . Here , we have analyzed the surface dynamics of Human Papillomavirus Type 16 ( HPV-16 ) on tissue culture cells . HPV-16 is a small non-enveloped DNA virus with an icosahedral ( T = 7 ) capsid of 55 nm in diameter . HPV capsids contain two structural proteins , the major protein L1 that comprise the 72 pentamers , and the minor protein L2 that is principally located internally within the virion [4] . HPV-16 infection is linked to the development of cervical cancer . Infectious entry appears to occur specifically in the basal keratinocytes of mucosal epithelium subsequent to binding of virions to the basement membrane of a disrupted epithelium [5] . Since HPV replication and assembly requires infected basal keratinocytes to undergo the stepwise differentiation program of the epithelium [6] , HPV propagation in cell culture is a major challenge . Surrogate production systems that generate infectious L1/L2 capsids containing marker plasmids , termed HPV pseudovirions ( PsV ) , have been developed and successfully used to study aspects of HPV attachment and entry [7]–[13] . In this study , the HPV-16 PsV contained plasmids that upon successful entry expressed GFP or RFP . Attachment and infectious uptake of several different HPV types requires heparan sulfate proteoglycans ( HSPGs ) [14]–[16] . However , a specific HSPG protein core does not seem to be required for HPV infection [17] . Recently , it was shown that HPVs also interact with extracellular matrix components such as laminin-5 or HSPGs [10] , [11] , [18] , [19] . Productive entry involves internalization by endocytosis [20] , a process that for HPV occurs slowly and asynchronously over a period of several hours [9] , [21] . Prior to internalization , certain neutralizing antibodies no longer recognize the surface or lead to a release of bound viruses suggesting conformational changes in the capsid upon binding [10] , [11] , [13] , [15] , [21] . In addition , treatment of cell bound virus with DSTP27 , a heparan sulfate binding drug , results in non-infectious internalization [11] , [15] , [21] . Hence , transfer to a secondary receptor has been proposed . However , the dynamics of HPV interaction with the cell surface during the initial stages of infection are not understood . Using fluorescently labeled HPV-16 PsV that retained their infectivity , we therefore investigated the lateral mobility of capsids on the cell membrane by live cell imaging . Interestingly , several distinct modes of motion were observed including an active tranport towards the cell center .
To visualize the behaviour of cell-bound viruses , purified HPV-16 PsV were covalently labeled with the fluorophores AF488 or FITC . About 200 fluorophores per particle were covalently attached mainly to the major capsid protein L1 ( Fig . 1A , see Materials and Methods ) . A homogeneously labeled particle suspension was obtained as indicated by confocal microscopy of labeled particles attached to glass coverslips ( Fig . 1B , left ) . The fluorescent signal intensity profile of spots followed a single Gaussian distribution , which indicated that light is emitted from single particles ( Fig . 1B , right ) . The signal had a diameter of 0 . 2–0 . 4 µm , similar to other viruses of a comparable size [22] , [23] . The virion structure remained unchanged as judged by negative staining and electron microscopy ( Fig . 1C ) . To test whether labeling would affect the entry properties , we compared labeled and unlabeled HPV-16 PsV in their ability to express RFP ( infection ) from the incorporated plasmid by flow cytometry . We found that the number of infected cells remained virtually unchanged indicating that the particles labeled with fluorophores were fully entry competent ( Fig . 1D ) . When labeled PsVs were added to cells at 37°C , binding was readily observed by fluorescence microscopy ( Fig . 1E ) . The majority of virus particles that bound to cells did so within five minutes ( not shown ) . While most of the viruses bound to the top surface of cells , some drifted into the narrow space between the cell and the cover glass , where they bound to cells . These viruses could be visualized by total internal reflection fluorescence microscopy ( TIRF-M ) , and their movement could be followed by video microscopy ( Fig . 2A , B ) . When HPV-16 particles were bound to the bottom surface of cells , and followed on the cell body by video TIRF-M at 20 frames per s for a total of 100 s ( Videos S1 , S2 , S3 , S4 , S5; Fig . 2A–C ) , it was apparent that the movement of individual particles was heterogeneous . Trajectories extracted from digital image series using a single particle tracking ( SPT ) algorithm [24] showed that the majority of particles were essentially immobile , or displayed a slow drift ( Fig . 2C , D; 3 , 4 ) . Some were , however , highly mobile displaying either random or relatively linear trajectories ( Fig . 2C , D; 1 , 2 ) . To describe the dynamic events and lateral mobility of bound HPV-16 PsV more quantitatively , we used a recently described algorithm that allowed definition of rate and mode of motion for each particle [2] . For convenient access of data in one graph we plotted the linear diffusion coefficients ( D ) , a measure of the particle speed , and the slope of the moment scaling spectrum ( SMSS ) , a measure for the mode of movement , for each viral particle . An SMSS value of 0 . 5 defines random , Brownian movement , whereas values below and above 0 . 5 are characteristic of confined and directed movement , respectively , with an SMSS value of 0 for immobility [2] . When we plotted the D vs . the SMSS values for the recorded particles ( n = 100 ) , four modes of motion could be distinguished: ( i ) confinement ( SMSS<0 . 1; Fig . 2E , 4 ) , ( ii ) confinement with a slow drift ( D<0 . 001 µm2/s , SMSS = 0 . 15–0 . 35; Fig . 2E , 3 ) , ( iii ) fast random motion with transient confinement ( D>0 . 002 µm2/s , SMSS<0 . 5; Fig . 2E , 2 ) , and ( iv ) ballistic , directed movement of PsV ( SMSS>0 . 5; Fig . 2E , 1 ) . Overall , the modes and speeds of motion of viral particles on the cell were comparable to those previously observed for murine polyomavirus [2] , a small nonenveloped DNA virus that binds to the glycolipids GD1a and GT1b , [25] , [26] . However , some HPV-16 particles exhibited , in addition , directed motion such as those shown in Fig . 2D , 1 . When analyzed in more detail , we found that directed motion of viruses observed on the cell body occured much more frequently on finger-like cell protrusions such as filopodia or retraction fibres . Since these protrusions were not always close enough to the cover glass to be easily visualized by TIRF-M , we used wide field or spinning disc confocal microscopy , which allowed us to image the protrusions over their full lengths with only a small decrease in acquisition speed . Protrusions of HeLa cells with HPV-16 PsV bound for 5–120 min were imaged at 2–5 frames per s , virus trajecories were extracted , and their mode of motion was analyzed quantitatively as described above . When we plotted the D vs . the SMSS values for the recorded particles , predominantly two modes of motion along the cell protrusions could be observed regardless of the time after binding of the virus: ( i ) directed particle motion ( SMSS>0 . 5 , n = 29 ) ( Fig . 2 F , G , 1 ) , and ( ii ) random motion restrained by the width of protrusions to an almost 1-dimensional diffusion ( D>0 . 002 µm2/s , SMSS<0 . 5 , n = 27 ) ( Fig . 2 F , G , 2 ) , with the number of confined particles limited to a fraction below 10% ( not shown ) . We surmised that the finger-like cell protrusions constituted filopodia or retraction fibres , because they contained actin in cells transiently transfected with GFP-actin ( Fig . 2 H , Video S6 ) . The binding to and movement along actin-rich protrusions of HPV-16 PsV was reminiscent of the retrograde movement described for several enveloped RNA viruses [3] . These RNA viruses bind to cell receptors and are transported along actin rich protrusions on the outside of the plasma membrane . To determine whether HPV-16 was transported extracellularly along these protrusions , we tested whether the fluorescence of FITC labeled HPV-16 migration was diminished by acidification of the extracellular medium . FITC is a pH sensitive fluorophore that loses its fluorescent properties , when exposed to a pH below 6 . 0 due to protonation [27] . Since the fluorescence of particles was quenched upon acidification , we concluded that the HPV-16 PsV were moving along the outside of the actin protrusions ( Fig . 3A , B , Video S7 ) . Thin section electron microscopy confirmed that the particles were located exclusively on the outside of the plasma membrane when associated with these narrow cell protrusions ( Fig . 3 , C–G ) . Unlike Simian Virus 40 , a structurally similar polyomavirus that is observed juxtaposed to the plasma membrane [28] , a gap of 12 nm±4 nm was seen between the surface of the particle and the plasma membrane arguing that they had bound to a membrane receptor with a large or extended ectodomain ( Fig . 3 , E–G ) . Occasionally , strands of electron dense material were observed inbetween virus and the plasma membrane most likely representing receptor molecules ( Fig . 3G , arrowheads ) . HPV-16 transport along actin protrusions on the outside of the plasma membrane is reminiscent of the transport of polyethylenimine-coated beads [29] , of murine leukemia virus [3] , and the epidermal growth factor ( EGF ) receptor [30] , [31] . These transport phenomena have been attributed to actin retrograde flow , which causes the net transport of actin molecules within filaments from the plus end at the tip of an actin protrusion towards the minus end at the cell body . To find out whether HPV-16 transport may equally be powered by actin retrograde flow , we analyzed the speed of HPV-16 transport and compared it to the speed of actin retrograde flow . To determine the speed of virus movement , we analyzed time lapse movies of individual viruses using kymographs . Individual actin protrusions were oriented such that they reflected a linear track with the tip at the top and the cell body at the bottom . Images were assembled consecutively . The mode of motion could easily be distinguished in the kymographs: directed transport towards the cell body is marked by virus particles on a straight line with a negative slope , particles diffusing randomly are visible as ragged , horizontal line , and confined particles appear aligned on a straight horizontal line ( Figure 4A ) . Interestingly , when fluorescently labeled vesicular stomatitis virus ( VSV , not shown ) , Semliki Forest Virus ( SFV , not shown ) , or Simian Virus 40 ( SV40 , Fig . 4B , Video S8 ) were added to cells , only diffusive motion was observed suggesting that the directed motion of HPV-16 was a specific receptor-mediated process . The slope of HPV-16 particles in kymographs represented the speed of particle movement . Figure 4A shows the movement of several virus particles along a single actin protrusion . The speed of virus particles was slow and averaged 2 . 2±0 . 8 µm/min ( n = 242 ) . The speeds for actin retrograde flow and RNA viruses moving along actin protrusions are 1–5 and 2 µm/min , respectively [3] , [29] , [32] , in line with our assumption that HPV-16 PsV were transported by actin retrograde flow . Short stationary periods were also observed for single particles while other particles continued to move ( Figure 4A , arrows ) . These stationary periods may have resulted from obstacles or interactions of virus with extracellular matrix components . However , when virus particles continued to move , they exhibited exactly the same speed as before . Since the speed of virus particles in directed motion was always identical on the same actin protrusion ( Figure 4A ) , we plotted the speed versus particle number , and found a bipolar speed distribution with two maxima at 1 . 6 and 2 . 9 µm/min . The result suggested that two kinds of actin protrusions in HeLa cells existed , and that viruses moved on one kind at about half of the speed than on the other . Occasionally , we observed HPV-16 particles moving in a diffusive mode of motion , which subsequently switched to directed motion ( Fig . 4A , asterisk ) . The opposite was never observed under unperturbed conditions . However , the switch from diffusive to directed motion was observed at different times post addition of virus , and several particles exhibited diffusive motion as long as 2 h post addition of virus . This indicated that HPV-16 particles required a trigger for directed motion , but possibly not all particles were able to switch from diffusive to directed motion . To compare virus transport with the speed of actin retrograde flow we made use of a photoactivatable GFP-actin ( PAGFP-actin ) fusion construct . Activation of PAGFP was achieved by a brief pulse of short wavelength light , after which the illuminated PAGFP exhibited the fluorescent properties of normal GFP [33] . Expression of PAGFP-actin resulted in incorporation of the molecules into actin filaments ( Fig . 4C ) . When we now activated a spot of PAGFP-actin in actin protrusions and followed the GFP spot over time , we found that this spot moved towards the cell body ( Fig . 4D ) . The fluorescent intensity of the spot decreased over time , which was probably due to photobleaching and diffusion of G-actin . However , we consistently observed a retrograde movement of the spot , that represented activated PAGFP-actin molecules present in the actin filaments , and that could be used to analyze the speed of actin retrograde flow ( Fig . 4D , Video S9 ) . When we analyzed the speed of actin retrograde flow as before , we found a bipolar speed distribution of actin retrograde flow with maxima at 1 . 7 and 3 . 2 µm/min ( average 2 . 5±0 . 9 µm/min , n = 63 ) that matched the speed of virus particle movement ( Fig . 4A , D ) . That virus transport and actin retrograde flow occured at the same rate was also suggested by the movement of EGFP-actin speckles infrequently observed in our virus transport kymographs . These speckles resulted from a patchy incorporation of EGFP-actin molecules into actin filaments [34] , which , in turn , gave rise to an increased EGFP-actin fluorescence in certain regions of cell protrusions ( Fig . 4A , arrowhead ) . Taken together , these results support the concept that virus transport was connected to the net transport of F-actin . Actin retrograde flow is the result of basically three processes . First , actin polymerisation occurs at the tip of filaments and this pushes the filaments towards the cell body . Second , anchored myosin II pulls actin filaments towards the cell body . And third , depolymerisation and fragmentation of actin filaments reduces the barrier tension of the actin cortex-filament interface at the cell body and thus facilitates filament transport towards the cell body . When the three processes are in balance , the length of the actin filaments remains constant with a net transport of individual actin molecules present in the filaments towards the cell body [29] , [35] . To functionally adress the role of actin retrograde flow in virus transport along actin protrusions , we analyzed the contribution of ATP production , actin polymerisation , depolymerisation , myosin II function , and , as a control , microtubule stability using pharmacological inhibitors . We found that microtubule dissociation by nocodazole had no effect on virus transport ( Fig . 5A , Video S10 ) . However , cytochalasin D , which inhibits actin polymerisation by binding to the barbed ends of F-actin , and leads to actin depolymerisation by fragmentation of F-actin upon longer exposure , decelerated virus transport over a time period of 5–40 sec . Afterwards the virus particles frequently exhibited stationary behaviour or diffusive movement along the actin protrusions ( Fig . 5B , Videos S11 , S12 ) . The same was observed when jasplakinolide ( Fig . 5C , Video S13 ) , a F-actin stabilizer and inducer of actin polymerisation , or sodium azide ( ATP depletion , not shown ) were added . The strongest effect was observed when myosin II function was inhibited by either blebbistatin ( Fig . 5D , Videos S14 , S15 ) or the myosin light chain kinase inhibitor ML-7 ( not shown ) ; virus transport stopped almost instantaneously and the frequency of particles that switched to a random diffusive mobility was the highest . Seldomly , particles were observed to move in an outwards direction , which was probably due to filopodial outgrowth induced by inhibition of myosin II [36] , [37] . When we analyzed how many of the directionally mobile particles switched to a confined or diffusive mode of motion , we found that 50% or more virus particles lost their directed motion pattern within 80 s after treatment with inhibitors of actin retrograde flow , as compared to 7% in the nocodazole treated control samples ( Fig . 5E ) . This indicated that virus transport was , indeed , functionally linked to actin retrograde flow . It was interesting to note , that on a single filopodium virus transport was either abrogated for all particles or all particles continued to move with decelerated speeds . With respect to inhibitor sensitivity , there was no significant difference for particles that moved at 1 . 6 µm/min or 2 . 9 µm/min suggesting that both forms of actin retrograde flow , the slower and the faster , are mechanistically similar . To determine whether the movement of virus particles along actin protrusions was involved in productive infection , we tested the effect of the myosin II inhibitor blebbistatin on infection . When actin retrograde flow was inhibited , virus particles could not be actively transported along actin rich protrusions . However , in confluent tissue culture and in epidermal tissues in vivo cells do not usually form long actin protrusions . Accordingly , HPV-16 infection of confluent HeLa cells was insensitive to inhibition of the actin retrograde flow-mediated transport by blebbistatin ( Fig . 6B , white bars ) . This result suggested , in addition , that neither actin transport nor myosin II were required once viruses had bound directly to the cell body in contrast to other viruses such as vaccinia virus [38] . However , when subconfluent cells , that formed actin protrusions , were infected with HPV-16 , and virus transport was blocked , infectivity was reduced by 36% ( Fig . 6B , black bars ) . HeLa cells were used throughout this study for ease of live cell microscopy . To address whether the observed phenomena also occurred in keratinocytes , we analyzed transport of viral particles along actin rich protrusions in HaCaT cells using the methods described previously . Directed transport and diffusion of HPV-16 PsV was observed on stable actin-rich protrusions . PsV exhibiting directed transport moved with a speed of 2±1 µm/min ( Fig . 6A , n = 32; Video S16 ) . Infection of confluent HaCaT cells was insensitive to blebbistatin inhibition , but infection of sparse HaCaT cells was reduced by 50% similar to what we observed in HeLa cells ( Fig . 6B ) . That infection was not entirely blocked by abrogation of transport was most likely due to a significant portion of virus particles binding directly to the cell body as opposed to actin rich protrusions . Particles binding directly to the cell body would be able to access the endocytic machinery for entry whereas particles binding far from the cell body would not . Hence , active transport was not required for infection but had the ability to facilitate infection , and increased the number of infected cells as compared to cells that had no transport ( cells in the presence of blebbistatin ) . This may be an important factor for HPV infection in vivo , where the initial infection occurs only in basal keratinocytes most likely after wounding of the epidermal tissue , and where only few viruses may have access to the target cells .
The cell surface dynamics of HPV-16 indicated that the virus has co-opted a transport mechanism along actin rich cell protrusions to access the endocytic machinery present at the cell body , and thus to enhance infectious entry . The transport was facilitated by binding to receptors that , in turn , were likely to interact with actin filaments to mediate the transport towards the cell body powered by retrograde flow . This mechanism is not without precedent; several enveloped RNA viruses have been found to use a similar mechanism [3] . However , HPV-16 is the first nonenveloped virus found to use such a mechanism . It is to be expected that many other viruses are capable of using this cellular mechanism . We found that certain dynamic properties of HPV-16 on the cell body are similar to murine Polyomavirus ( mPy ) particles: while some particles displayed random , diffusive motion most particles displayed rapid confinement [2] . It is interesting to note , that disruption of actin filaments eliminates the confinement of mPy , as if confinement would depend on a link with actin similar to what we observed for the transport of HPV-16 along actin protrusions . Alternatively , HPV-16 confinement on the cell body may be the result of binding simultaneously to both , extracelluar matrix components and cell surface receptors , or it may be due to limited virus receptor diffusion caused by actin dependent confinement zones [39] . While HPV-16 binds to diverse HSPGs [15]–[17] , mPy binds the glycosphingolipids GD1a and GT1b [25] , [26] , which implies that these structurally distinct and differentially localized receptors exhibit similar diffusion properties . Hence , the diffusive properties of HPV-16 alone cannot be responsible for the unusually slow internalisation kinetics . However , we cannot exclude an interaction of HPV-16 with a putative secondary receptor that may be responsible for cell surface motion and/or subsequent internalization . Our findings showed , however , that HPV-16 binding provided a cellular system where viruses were actively transported along actin protrusions towards the cell . This transport was specific to HPV-16 , since SV40 , a structurally similar nonenveloped DNA virus , displayed only random motion on the protrusions . HPV-16 transport was sensitive to inhibitors of actin polymerisation and depolymerisation , of myosin II and myosin light chain kinase , and of ATP synthesis . These properties are consistent with a transport mechanism based on actin retrograde flow [35] , [36] . The study of Lehmann et al . [3] showed that murine leukemia virus shares the same properties . We could demonstrate , moreover , that HPV-16 transport coincided with the net transport of actin molecules in the respective cell protrusions , and that depending on the protrusion the movement of actin and the virus could each occur at two different rates . All these findings supported a mechanism where viruses bind to a cellular receptor , which in turn must somehow bind to F-actin in order to be pulled by actin retrograde flow towards the cell body . Lehmann et al [3] showed that for transport of murine leukemia virus its receptor ( mCAT-1 ) is clustered by the virus . Clustering of the receptor most likely combined with signal transduction elicited by this event would then provide the cue for a link to actin as has been proposed for the movement of anti-apCAM-antibody-coated beads . These beads cluster the cell adhesion molecule apCAM and are transported along filopodial growth cones by actin retrograde flow [40] . However , extensive clustering itself may not be a prerequisit for the signal transduction event . Using single molecule tracking of epidermal growth factor ( EGF ) , Lidke et al . [31] showed that dimerization of receptors by an EGF molecule is sufficient to trigger transport along filopodia . That such ‚cues' existed for the transport of HPV-16 , and that events leading to a link between the HPV-16-receptor complex and actin occurred , was suggested by the observed switch from diffusion to directed motion . However , switches were observed infrequently and asynchronously after binding of viruses to protrusions . Some particles displayed random motion for as long as 2 h after addition of HPV-16 PsV to cells , indicating that not all particles located on actin protrusions may encounter the ‚cue' for active transport . The interaction of a receptor with actin may occur either directly by the receptor through its cytosolic tail or indirectly through binding to an actin binding protein . The receptor and its ligand–in this case HPV-16–would then be linked to actin retrograde flow . A variety of HSPGs can serve as binding receptors for infectious HPV-16 entry [17] . Of these , the syndecan family member are likely candidates as receptors for transport , as they in contrast to glypicans have a large cytosolic domain known to interact with actin binding proteins [41]–[43] . However , it cannot be excluded that other molecules in the plasma membrane serve as co-receptor for HPV-16 , and that engagement of co-receptors provides the ‘cue’ for active transport . Although our results favour a transport mechanism based on actin retrograde flow rather than transport by an unconventional myosin , some unanswered questions remain . Actin retrograde flow is the result of balanced actin polymerisation , depolymerisation , and myosin II function [29] , [35] . Why did pertubation of one of the three processes abrogate directed virus transport altogether ? When actin polymerisation is blocked , several studies show that cell extensions shrink due to continued myosin II function , actin depolymerisation , and actin retrograde flow . Actin depolymerisation agents and myosin II inhibitors , as expected , induce increased growth of actin protrusions . However , in all cases , retrograde flow is reduced , but not blocked [29] , [35] . Thus , one would expect that viruses would be transported to the cell body , but at reduced speeds . We hypothesize that a reduction of force generation due to reduced actin retrograde flow weakens the connection between actin and the receptor , until the connection is lost and viruses start to diffuse again . Our hypothesis is supported by the finding that when weak forces as low as 10–50 pN are applied to receptor-actin linkages such as the fibronectin-integrin-actin link or the cell adhesion molecule apCAM-actin link the interaction is strengthened [40] , [44] . A variety of cell surface molecules such as members of the immunoglobulin family ( apCAM ) , the EGF receptor , mCAT-1 , the receptor that facilitates murine leukemia virus entry , or potentially HSPGs support transport of ligands along filopodia [3] , [30] , [31] , [40] . The mobility of filopodia and the retrograde transport of receptors allow the cell to sense its environment . In sparse tissue culture , viruses can use receptor transport along filopodia , gain access to cellular entry sites , and thus enhance the probability of infection . In dense cultures , our results show that the effect is not important . During in vivo transmission of HPV-16 , infectious particles are shed after abrasion of terminally differentiated , infected keratinocytes . Viruses will be able to access the target cells ( basal keratinocytes ) most likely in wounded tissue [5] . Since wounding of mucosal or skin epidermis results in upregulation of syndecan-1 , a HPV-16 receptor candidate [17] , [45] , [46] , and in filopodia formation of the basal keratinocytes that it is essential for reepitheliasation [47] , HPV-16 could use actin retrograde flow for efficient transport towards entry sites on the cell body and thus facilitate infection . Clearly , many interesting questions remain: How does actin retrograde transport of HPV-16 translate into kinetics of infection ? Are the viruses internalized immediately after reaching the cell body ? How important is actin retrograde transport in vivo ? How many other viruses use this translocation system ? Is there a common linker protein between viral receptors and actin ?
HeLa and HaCaT cells were cultured in DMEM ( Invitrogen ) containing 10% fetal calf serum . ( - ) -Blebbistatin , cytochalasin D , ML-7 , nocodazole , and sodium azide were from Sigma . Jasplakinolide was from Molecular Probes . HPV-16 PsV containing the pCIneo-mRFP plasmid was produced with the p16L1L2 plasmid by the propagation method described by Buck and Thompson [48] . The PsV was matured for 24 hours in the presence of RNase A to maximize the purification of pseudovirions containing the reporter plasmid , resulting in an improved particle to infectivity ratio [48] . All plasmids and production methods are fully described on the Schiller laboratory's website ( http://ccr . cancer . gov/staff/staff . asp ? profileid=5637 ) . SV40 was produced as described [49] . The plasmid pEGFP-actin was from Clontech . The plasmid pmPAGFP-actin was constructed as follows: the EGFP sequence was excised from pEGFP-actin by NheI/XhoI digestion and replaced by ligation of the mPAGFP sequence from pmPAGFP ( kind gift of George H . Patterson , NIH , Bethesda , USA ) . SV40 was covalently labeled with fluorophores as described [49] . HPV-16 PsV labeling followed essentially the same protocol . Briefly , purified HPV-16 PsV were incubated for 1 h at room temperature in PBS with a ten-fold molar excess of Fluorescein or Alexa Fluor ( AF ) succinimidylesters ( Molecular Probes ) over the major capsid protein L1 . PsV were separated from the labeling reagent by size exclusion chromatography using NAP5 columns ( GE Healthcare ) and stored at 4°C . The degree of labeling ( DOL ) was determined by spectophotometry using DOL = ( Amax×MW ) / ( [protein]×edye ) , with Amax = absorbance of dye at absorbance maximum , MW = molecular weight of a virus particle , [protein] = protein concentration , and edye = extinction coefficient of the dye at its absorbance maximum . Please refer to the manufacturer's instructions for further details . For transfection , cells were trypsinized , pelleted , washed with PBS and transfected with expression plasmids in Nucleofector solution R ( Amaxa ) utilizing program I13 of the Amaxa Nucleofector according to the manufacturer's instructions . Cells were seeded on 18 mm coverslips and used for live cell imaging experiments at 6–14 h post transfection . Microscopy was performed on a custom modified Olympus IX71 inverted microscope . Modifications included a heated incubation chamber that surrounded the microscope stage set to 37°C , an objective-type total internal reflection fluorescence microscopy setup from TILL Photonics ( Grafeling , Germany ) , and a monochromator for epifluorescence excitation with a controller allowing hardware-controlled fast switching between total internal reflection fluorescence and epi-fluorescence excitation and acquisition ( TILL Photonics ) . Images were acquired using a TILL Image QE chargecoupled device camera and TILLVISION software ( both from TILL Photonics ) . The total internal reflection angle was manually adjusted for every experiment . Live HeLa cells on 18-mm coverslips were mounted in custom-made chambers . To avoid changes in membrane or cytoskeleton , the medium was not exchanged when mounting cells . HPV-16 PsV were added at 0 . 1 mg/ml into the 0 . 5 ml of medium on the stage . Movies were recorded at a rate of 20 frames per s for 1 , 000 or 2 , 000 frames in TIRF mode . After each experiment , the cells that had been recorded were imaged by differential interference contrast microscopy to check for viability . Trajectories were harvested and analyzed using a tracking program [24] . The position accuracy for the particles allowed by this program was on average 26 nm ( see Figure S1 ) . Epifluorescence microscopy was performed on a Zeiss 200 M inverted microscope with a heated objective and a heated stage holding the cell chamber . Cells and objective were kept at 37°C , and cells were incubated in CO2-independent medium ( Invitrogen ) . Images were acquired with Openlab Software ( Improvision ) . Spinning disc confocal microscopy was performed on a Zeiss 200 M microscope with a Visitech spinning disc setup . The spinning disc confocal microscope was equipped with heated incubation chamber that surrounded the microscope stage set at 37°C . Images were acquired with MetaMorph Software ( Visitron ) . Live cells that had been transfected with EGFP-actin were mounted on 18-mm coverslips in custom-made chambers , and cells were incubated with normal growth medium . HPV-16 PsV were added at 0 . 1 mg/ml into the 0 . 5 ml of medium on the stage . After 5 min–2 h of binding virus particles to cells image acquisition was performed . Inhibitors were always added directly into the medium during acuisition of images to the final concentrations given . Images were imported into ImageJ ( NIH ) and kymographs of single actin protrusions were assembled showing every second or fourth image as indicated in the figure legends . Microscopy was performed on a Leica SP2 AOBS scanning confocal microscope equipped with a solid state laser ( 405 nm excitation ) and a heated incubation chamber that surrounded the microscope stage set at 37°C . Live HeLa cells that been transfected with pmPAGFP-actin on 18-mm coverslips were mounted in custom-made chambers , and cells were incubated with normal growth medium . After a 500 ms pulse of 405 nm light ( either point or frame ) , images were acquired at 0 . 5 Hz . HPV-16 PsV ( 300 ng ) were added to 1×105 cells for 10 min or 1 h at 37°C , and unbound virus was removed prior to fixation with 2% glutaraldehyde / 2% osmium tetroxide . Sample preparation and thin section electron microscopy was performed according to standard electron microscopy procedures . For negative staining , 0 . 4 mm mesh copper grids were coated with a 4 nm carbon film . Sample containing AF488 labeled or unlabeled HPV-16 PsV was added for 30 s , drained of excess liquid , and stained for an additional 30 s with 2% uranyl acetate in distilled water . After transmission electron microscopy , images were exported as 8-bit TIFF files and processed in PHOTOSHOP 8 . 0 ( Adobe Systems ) . HeLa cells were seeded on 24 h prior to experimentation to result in 20 or 100 % confluency . HPV-16 PsV were added to cells with or without pretreatment of blebbistatin ( 30 min . ) at 0 . 1 transducing particles/cell ( 20 ng or 100 ng ) to result in 30%–40% XFP expressing cells in the unperturbed control . 24 h after addition of virus , cells were trypsinized , fixed in 4% formaldehyde , and analyzed for XFP expression by flow cytometric analysis . | To replicate , viruses have to enter into host cells . Since they have no means of locomotion , they rely entirely on cellular transport systems to access the cellular compartments where replication occurs . Following individual virus particles by video microscopy , we found that human papillomavirus type 16 , the main causative agent of cervical cancer , bound to long finger-like protrusions of cells . There , they were transported from the periphery to the cell body . The transport was mediated by a process termed actin retrograde flow , where viruses bound to cell surface molecules hooked up to filamentuos actin and were dragged along with the actin-like items on a transport belt . Entry into the cell occured at the cell body . The results raised the interesting possibility that viruses use retrograde flow when they infect wounded epidermal keratinocytes , where finger-like protrusions of cells are abundant . | [
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] | 2008 | Human Papillomavirus Type 16 Entry: Retrograde Cell Surface Transport along Actin-Rich Protrusions |
The whipworm Trichuris trichiura is a soil-transmitted helminth that dwells in the epithelium of the caecum and proximal colon of their hosts causing the human disease , trichuriasis . Trichuriasis is characterized by colitis attributed to the inflammatory response elicited by the parasite while tunnelling through intestinal epithelial cells ( IECs ) . The IL-10 family of receptors , comprising combinations of subunits IL-10Rα , IL-10Rβ , IL-22Rα and IL-28Rα , modulates intestinal inflammatory responses . Here we carefully dissected the role of these subunits in the resistance of mice to infection with T . muris , a mouse model of the human whipworm T . trichiura . Our findings demonstrate that whilst IL-22Rα and IL-28Rα are dispensable in the host response to whipworms , IL-10 signalling through IL-10Rα and IL-10Rβ is essential to control caecal pathology , worm expulsion and survival during T . muris infections . We show that deficiency of IL-10 , IL-10Rα and IL-10Rβ results in dysbiosis of the caecal microbiota characterised by expanded populations of opportunistic bacteria of the families Enterococcaceae and Enterobacteriaceae . Moreover , breakdown of the epithelial barrier after whipworm infection in IL-10 , IL-10Rα and IL-10Rβ-deficient mice , allows the translocation of these opportunistic pathogens or their excretory products to the liver causing organ failure and lethal disease . Importantly , bone marrow chimera experiments indicate that signalling through IL-10Rα and IL-10Rβ in haematopoietic cells , but not IECs , is crucial to control worm expulsion and immunopathology . These findings are supported by worm expulsion upon infection of conditional mutant mice for the IL-10Rα on IECs . Our findings emphasize the pivotal and complex role of systemic IL-10Rα signalling on immune cells in promoting microbiota homeostasis and maintaining the intestinal epithelial barrier , thus preventing immunopathology during whipworm infections .
A single layer of intestinal epithelial cells ( IECs ) in conjunction with the overlaying mucus acts as a primary barrier to viruses , bacteria and parasites entering the body via the gastrointestinal tract [1] . Paradoxically , the intestinal epithelium is also the host tissue for diverse pathogens including intestinal parasitic worms [2 , 3] . Amongst the intestinal worms , whipworms ( Trichuris trichiura ) infect hundreds of millions of people and cause trichuriasis , a major Neglected Tropical Disease [4 , 5] . Whipworms live preferentially in the caecum of their host , where they tunnel through IECs and cause inflammation that potentially results in colitis [6 , 7] . It has been proposed that IEC activation , resulting from the initial recognition or physical contact with whipworms , influences the immunological response that ultimately determines whether parasites are expelled from the intestine or persist embedded in the intestinal epithelium causing a chronic disease [2 , 4] . Most of our understanding of the host response to whipworms comes from studies of the natural whipworm infection of mice with T . muris , which closely mirrors that of humans [3 , 7] . Resistance to infection is recapitulated by infecting mice with a high dose ( 200–400 ) of T . muris eggs and is mediated by a type-2 immune response that includes increased production of interleukin 4 ( IL-4 ) , IL-13 , IL-25 , IL-33 , IL-9 and antibody isotypes IgG1 and IgE and results in worm expulsion [3 , 7] . Conversely , chronic disease is modelled by infecting mice with a low dose ( 20–25 ) of T . muris eggs that results in the development of a type-1 immune response characterised by production of inflammatory cytokines , mainly IFN-γ , and the antibody isotype IgG2a/c [3 , 7] . Type-1 immunity promotes intestinal inflammation that when severe can cause colitis [3 , 7] . However , in chronic infections such responses are modulated by the parasite to optimize their residence and reproduction and ensure host survival , thus achieving a balanced parasitism [4 , 7] . This immunomodulation is partly mediated by transforming growth factor ( TGF ) -β , IL-35 and IL-10 production by macrophages and T cells in response to excretory-secretory ( ES ) parasite antigens [3 , 4 , 7] . Besides this immunomodulatory role of IL-10 in chronic infections , IL-10 is important in the induction of host resistance ( through type-2 response ) during acute ( high dose ) T . muris infections [3 , 8] . Intestinal mucosal homeostasis is regulated principally through IL-10 receptor signalling [9] . The IL-10 receptor is a heterotetramer complex composed of two alpha and two beta subunits , IL-10Rα and IL-10Rβ , respectively [9 , 10] . While the IL-10Rα subunit is unique to IL-10 , the IL-10Rβ chain is shared by receptors for other members of the IL-10 superfamily of cytokines [9–12] . Specifically , a single IL-10Rβ subunit pairs with IL-22Rα , IL-20Rα , or IL-28Rα subunits to form the receptors for IL-22 , IL-26 and the interferon λ species ( IL-28α , IL-28β and IL-29 ) , respectively [10–12] ( S1 Fig ) . IL-10 is a key anti-inflammatory cytokine that limits innate and adaptive immune responses [9 , 10] . The development of spontaneous enterocolitis in mice deficient for IL-10 and IL-10Rβ has demonstrated the crucial role of IL-10 in maintaining the integrity of the intestinal epithelium [13 , 14] . Similarly , IL-22 contributes to the homeostasis of mucosal barriers by directly mediating epithelial defence mechanisms that include inducing the production of antimicrobial peptides , selected chemokines and mucus . IL-22 is also involved in tissue protection and regeneration [10 , 12 , 15] . The IL-22 receptor is exclusively expressed on non-haematopoietic cells , such as IECs [10 , 12 , 15] . Likewise , IL-28 receptor expression is largely restricted to cells of epithelial origin , although also expressed in B cells , macrophages and plasmacytoid DCs , where it mediates the antiviral , antitumor and potentially antibacterial functions of the interferon λ species [10 , 16–18] . IL-26 is also reported to promote defence mechanisms against viruses and bacteria at mucosal surfaces in humans , however , the IL-26 receptor in the mouse is an orphan receptor because the Il-26 gene locus is not present in mice [10 , 11 , 19] . Previous studies indicate the importance of the IL-10 receptor signalling in responses to whipworms . Specifically , IL-10 promotes host resistance and survival to whipworm infection , with IL-10 deficiency leading to morbidity and mortality that may be due to a breakdown of the epithelial barrier and the outgrowth of opportunistic bacteria [8 , 20] . Mice lacking the IL-10Rα chain develop a chronic T . muris infection accompanied by intestinal inflammation [21] . Furthermore , in IL-22-deficient mice whipworm expulsion is delayed , correlating with reduced goblet cell hyperplasia [22] . However , the specific role that each subunit ( IL-10Rα , IL-10Rβ , IL-22Rα and IL-28Rα ) plays on the intestinal epithelia barrier maintenance , mucosal homeostasis and broader host response to this parasite remains unclear . There is also little understanding on how these receptors can promote resistance to colonisation by opportunistic members of the microbiota that potentially drive the pathology observed in the absence of IL-10 during whipworm infection . In the present study , we use mutant mice to dissect the role of IL-10Rα , IL-10Rβ , IL-22Rα and IL-28Rα in host resistance to T . muris infections . We demonstrate that IL-10 signalling , exclusively through IL-10Rα and IL-10Rβ , promotes resistance to colonization by intestinal opportunistic bacterial pathogens and maintenance of the intestinal epithelial barrier , thus preventing the development of systemic immunopathology during whipworm infection .
The care and use of mice were in accordance with the UK Home Office regulations ( UK Animals Scientific Procedures Act 1986 ) under the Project licenses 80/2596 and P77E8A062 and were approved by the institutional Animal Welfare and Ethical Review Body . All efforts were made to minimize suffering by considerate housing and husbandry . Animal welfare was assessed routinely for all mice involved . Mice were naïve prior the studies here described . Il10-/- and Il10ra-/- mice in a C57BL/6J background were obtained by treatment of Il10 fl/fl and Il10ra fl/fl [21] embryos with cre recombinase . Il10rafl/fl Vilcre/+ mice were obtained by crossing of Il10rafl/fl with Vilcre/+ mice . Il22-/- mice , as previously described [23] , were received from Prof . Fiona Powrie ( University of Oxford ) . Il10rbtm1b/tm1b , Il22ra1tm1a/tm1a , Ifnlr1tm1a/tm1a , Rag1tm1Mom and wild-type ( WT ) C57BL/6N mice were maintained and phenotyped by the Sanger Mouse Genetics Programme [24] . For experiments with Il10-/- , Il10ra-/- , Il10rbtm1b/tm1b and Il10rafl/fl Vilcre/+ colonies , both WT and mutant mice littermates were derived from heterozygous breeding pairs . All animals were kept under specific pathogen-free conditions , and colony sentinels tested negative for Helicobacter spp . Mice were fed a regular autoclaved chow diet ( LabDiet ) and had ad libitum access to food and water . Recipient mice were irradiated with two 5-Gy doses , 4 h apart , and injected intravenously with bone marrow harvested from donor mice at 2 million cells per 200 μl sterile phosphate-buffered saline . The mice were transiently maintained on neomycin sulfate ( 100mg/L , Cayman Chemical ) in their drinking water for 2 weeks ( wk ) . Bone marrow was allowed to reconstitute for 4 wk before mice were infected with T . muris . Infection and maintenance of T . muris was conducted as described [25] . Age and sex matched female and male WT and mutant mice ( 6–10 wk old ) were orally infected under anaesthesia with isoflurane with a high ( 400 ) or low ( 20–25 ) dose of embryonated eggs from T . muris E-isolate . Mice were randomised into uninfected and infected groups using the Graph Pad Prism randomization tool . Uninfected and infected mice were co-housed . Mice were monitored daily for general condition and weight loss . Mice were culled including concomitant controls ( uninfected and WT mice ) at different time points or when their condition deteriorated ( observation of hunching , piloerection , reduced activity or weight loss from body weight at the beginning of infection reaching 20% ) . Mice were killed by terminal anesthesia followed by exsanguination and cervical dislocation . The worm burden was blindly assessed by counting larvae that were present in the caecum . Blinding at the point of measurement was achieved by the use of barcodes . During sample collection , group membership could be seen , however this stage was completed by technician staff with no knowledge of the experiment objectives . Every other day , from day 35 to day 45 p . i . , whipworm-infected Rag1tm1Mom mice were intraperitoneally injected with an antibody blocking the IL-10Rα ( BioXcell , clone 1B1 . 3A ) or an isotype control ( BioXcell , clone HRPN ) for a total delivery of 2mg per mouse . Adult worms were cultured in RPMI 1640 ( Sigma-Aldrich ) and ES products were collected after 4 h and following overnight culture . The ES were prepared as described [26] . To evaluate disease pathology , caecal and liver segments were fixed in 4% paraformaldehyde and 2–5 μm paraffin sections were stained in haematoxylin and eosin ( H&E ) or Periodic Acid-Schiff ( PAS ) according to standard protocol . Slides were scanned using a Hamamatsu NanoZoomer 2 . 0HT digital slide scanner ( Meyer Instruments , Inc ) and images were analysed using the NDP View2 software . From blinded and randomised histological slides , intestinal inflammation was scored by two research assistants as follows: submucosal and mucosal oedema ( 0 , absent; 1 , mild; 2 , moderate; or 3 , severe ) ; submucosal and mucosal inflammation ( 0 , absent; 1 , mild; 2 , moderate; or 3 , severe ) ; percentage of area involved ( 0 , 0–5%; 1 , mild , 10–25%; 2 , moderate , 30–60%; or 3 , severe , >70% ) . Crypt lengths were measured and goblet cells counted . Liver pathology was documented , including presence of immune infiltrate , granulomas and necrosis . For immunofluorescence , 5 μm sections of frozen caecal and liver tissues were stained with α-Enterococcus spp . antibody ( 1/1000 , LSBio ) , α-Escherichia coli spp . antibody ( 1/1000 , LSBio ) or α-ZO-1 ( 1/200 ThermoScientific ) . Sections were mounted using ProLong Gold anti-fade reagent ( Molecular Probes ) containing 4’ , 6’-diamidino-2-phenylindole ( DAPI ) for nuclear staining . Confocal microscopy images were taken with a Leica SP8 confocal microscope . From each mouse , a slide was examined to determine the presence of bacterial infection in the liver . Although only low sensitivity is possible from a single slide per mouse , bacteria should not be present in the livers of healthy animals , and any detected therefore indicate bacterial translocation . For transmission electron microscopy , tissues were fixed in 2 . 5% glutaraldehyde/2% paraformaldehyde , post-fixed with 1% osmium tetroxide in 0 . 1M sodium cacodylate buffer and mordanted with 1% tannic acid followed by dehydration through an ethanol series ( contrasting with uranyl acetate at the 30% stage ) and embedding with an Epoxy Resin Kit ( Sigma-Aldrich ) . Ultrathin sections cut on a Leica UC6 ultramicrotome were contrasted with uranyl acetate and lead nitrate , and images recorded on a FEI 120 kV Spirit Biotwin microscope on a F415 Tietz CCD camera . Levels of parasite-specific immunoglobulins IgG1 and IgG2a/c were determined by ELISA in serum as described [27] . Briefly , ELISA plates ( Nunc Maxisorp , Thermo Scientific ) were coated with 5 μg/ml T . muris overnight-ES . Serum was diluted from 1/20 to 1/2560 , and parasite-specific IgG1 and IgG2a/c were detected with biotinylated anti-mouse IgG1 ( Biorad ) and biotinylated anti-mouse IgG2a/c ( BD PharMingen ) , respectively . Serum IL-6 and TNF-α were determined with the Mouse IL-6 and TNF-α ReadySet-Go ! ELISA kits ( eBioscience ) . The presence of lipopolysaccharide ( LPS ) in serum was determined with the LAL assay kit ( Hycult Biotech ) . Blood was collected under terminal anaesthesia into heparinized tubes for plasma preparation . Within 1 hour of collection , blood samples were centrifuged and plasma recovered and stored at -20°C until analysis . Clinical chemistry analysis of plasma was performed using the Olympus AU400 analyzer ( Beckman Coulter Ltd ) and was blinded to the operator via barcodes . The majority of parameters were measured using kits and controls supplied by Beckman Coulter . Samples were also tested for haemolysis . Four parameters were measured by kits not supplied by Beckman Coulter: transferrin , ferritin ( Randox Laboratories Ltd ) , fructosamine ( Roche Diagnostic ) and thyroxine ( Thermo Fisher ) . To identify microbial species from the livers of mice , mouse tissues were homogenized aseptically under laminar flow . Organ lysates were immediately cultured in nonselective Luria-Bertani ( LB ) and Brain Heart Infusion ( BHI ) media under aerobic and anaerobic conditions for 36–48 h . All colonies from each plate , or within a defined section , were picked in an unbiased manner for DNA extraction and 16S rRNA gene sequencing using the universal primers: 7F , 50-AGAGTTTGATYMTGGCTCAG-30; 926R , 50-ACTCCTACGGGAGGCAGCAG-30 . Bacterial identifications were performed using the 16S rRNA NCBI Database for Bacteria and Archaea . To study the caecal and liver microbiota composition of uninfected and T . muris-infected mice , luminal contents of the caecum were collected by manual extrusion and a piece of liver was taken upon culling of mice . Bacterial DNA was obtained using the FastDNA Spin Kit for Soil ( MBio ) and FastPrep Instrument ( MP Biomedicals ) . V5-V3 regions of bacterial 16S rRNA genes were PCR amplified with high-fidelity AccuPrime Taq Polymerase ( Invitrogen ) and primers: 338F , 50-CCGTCAATTCMTTTRAGT-30; 926R , 50-ACTCCTACGGGAGGCAGCAG-30 . Libraries were sequenced on an Illumina MiSeq platform according to the standard protocols . Analyses were performed with the Quantitative Insights Into Microbial Ecology 2 ( QIIME2-2018 . 4; https://qiime2 . org ) software suite [28] , using quality filtering and analysis parameters as described in the Supplemental Experimental Procedures . For all analyses , the individual mouse was considered the experimental unit within the studies . Experimental design was planned using the Experimental Design Assistant [29] . A multi-replica design was used , where each replica was run completely independently . Within each replica there were concurrent controls of infected and non-infected animals . The number of animals for each genotype within a replica varies as it was constrained by the outcome of breeding . The effect of genotype on worm burden within infected mice was assessed across multiple replicas using an Integrative Data Analysis ( IDA ) [30] treating each replica as a fixed effect utilising the generalised least square regression function within the nlme version 3 . 1 package of R ( version 3 . 3 . 1 ) . A likelihood ratio test was used to test for the role of genotype by comparing a test model ( Eq 1 ) against a null model ( Eq 2 ) . As genotype was found to be highly significant in explaining variation , a F ratio test for Eq 1 was used to explore the role of genotype as a main effect and whether it interacted with sex . The effect of genotype was not found to interact with sex ( p>0 . 05 ) . The effect of gene knockout on worm burden was assessed for each sex separately using a Mann Whitney U test from the Prism 7 . 0 software ( GraphPad ) . This analysis pools data across replicas as the IDA analysis found that this was not a significant source of variation . A non-parametric test was used as the data is bound and has some non-normal distribution characteristics . Similarly , cytokine levels between infected WT and mutant mice and plasma chemistry parameters between infected isotype and IL-10Rα -treated Rag1tm1Mom mice were evaluated using a Mann Whitney U test from the Prism 7 . 0 software ( GraphPad ) . The survival data , pooled across replicas , was tested for a significant effect of gene knockout for each sex independently using Log-rank Mantel-Cox tests from the Prism 7 . 0 software ( GraphPad ) . To evaluate the effect of the degree of colonization by pathobionts on the survival of infected IL-10 signalling-deficient mice , the mice were classified based on the histological assessment of the liver and survival time ( time after infection that mice succumbed ) , into two groups: severe ( presence of granulomas necrosis and foamy macrophages in the liver , weight loss and poor survival ) and mild ( minor liver infiltration , no weight loss and extended survival ) . Next , the degree of colonization by Enterococcus , Enterobacteriaceae and Escherichia-Shigella ( combined percentage of abundance of these three groups from total microbiota ) among both groups was compared using a Mann Whitney U test from the Prism 7 . 0 software ( GraphPad ) . In addition , a correlation analysis of the degree of colonization of the pathobionts and the plasma levels of the liver enzyme aspartate aminotransferase was performed using a two-tailed Spearman correlation test . A similar IDA analysis was used to study the effect of genotype on infection , for each plasma chemistry variable across multiple replicas . In this IDA , a likelihood ratio test was used to test for an interaction between genotype and infection by comparing a test model ( Eq 3 ) against a null model ( Eq 4 ) . This regression model was fitted to separate the various sources of variation allowing the impact of genotype in the presence of infection to be estimated . P-values were adjusted for multiple testing using the Benjamini and Hochberg method [31] with a false discovery rate of 5% . Percentage change was calculated to allow comparison of the effect across variables by taking the estimated coefficient from the regression analysis and dividing it by the average signal seen for that variable . The model estimates without normalisation are presented in S4 Table . The effect of genotype and infection on caecum score and goblet cells per crypt was assessed across the multiple replicas using an IDA as described for the plasma chemistry variables . For all IDAs , the model fit was assessed by visual exploration of the residuals with quantile-quantile and residual-predicted plots for each genotype group .
To dissect the role of the members of the IL-10 family of receptors during whipworm infection , mouse lines with knockout mutations for the following loci were challenged with T . muris: Il10 , Il10ra , Il10rb , Il22 , Il22ra and Il28ra ( S1 Fig ) . The influence of these mutations on anti-parasite immunity and worm expulsion was evaluated . Like WT mice , a high dose infection with T . muris did not result in chronic infection of IL-22 , IL-22Rα and IL-28Rα mutant mice; after 32 days of infection , the mice had expelled all worms and had high levels of parasite specific IgG1 in their sera that indicated the development of a type-2 response ( S2A , S2B and S2C Fig ) . Moreover , worm expulsion occurred before day 21 post infection ( p . i . ) , accompanied by production of T . muris specific IgG1 ( S3A , S3B and S3C Fig ) . These results are contrary to previous reports describing delayed worm expulsion in IL-22 mutant mice at day 21p . i . [22] . Using low dose infections , at day 35 p . i . , there were also no differences between WT and IL-22 , IL-22Rα and IL-28Rα mutant mice in the establishment of a chronic infection that is characterized by high levels of parasite specific IgG2a/c in serum ( S4A , S4B and S4C Fig ) . These findings indicated that IL-22 and IL-28 signalling are dispensable for the host to mount a response to T . muris infection . When taken together with previous data , these results suggest that the IL-10 receptor is the only member from this family of receptors responsible for the control of host resistance and survival to whipworm infection . We then examined the contribution of IL-10 signalling to the responses to T . muris infection . IL-10 , IL-10Rα and IL-10Rβ mutant mice were infected with a high dose of eggs and survival , tissue histopathology and worm burdens throughout infection up to day 28 p . i . were evaluated . We used WT littermate controls that were co-housed with the mutant mice throughout the experiments . Moreover , we included uninfected WT and mutant mice as additional controls in the cages . IL-10 , IL-10Rα and IL-10Rβ mutant mice did not develop spontaneous colitis in our mouse facility . As previously reported [8] , female and male IL-10 mutant mice succumbed to whipworm infection between day 19 and 24 p . i . , showing a dramatic weight loss and high numbers of worms in the caecum when compared with WT mice ( Fig 1A ) . Similarly , female and male IL-10Rα mutant mice displayed weight loss and all required euthanasia by day 28p . i . concomitant with high worm burdens in the caecum ( Fig 1B ) . Although the defects in the expulsion of worms in IL-10Rα mutant mice have been described [21] , this is the first report of reduced survival of these mice upon whipworm infection . Likewise , high numbers of worms were present in the caecum of IL-10Rβ mutant mice and survival was reduced by 60% and 75% in females and males , respectively ( Fig 1C ) . Defective worm expulsion and survival in T . muris-infected IL-10 , IL-10Rα and IL-10Rβ mutant mice correlated with increased inflammation in the caecum ( Fig 2 ) . Specifically , while infected WT mice presented mild inflammation ( Fig 2A and 2B ) and goblet cell hyperplasia ( Figs 2C and S5 ) , a characteristic response to T . muris , infected IL-10 signalling-deficient mice displayed submucosal oedema , large inflammatory infiltrates in the mucosa with villous hyperplasia , distortion of the epithelial architecture ( Fig 2A and 2B ) and loss of goblet cells ( Figs 2C and S5 ) . Together , these results indicate that during T . muris infections , IL-10 signalling is crucial for controlling worm expulsion and caecal mucosal and submucosal inflammation leading to unsustainable pathology . Reduced survival of whipworm-infected IL-10 signalling-deficient mice correlated with liver pathology , while we did not observe pathology in other systemic organs such as the spleen . Specifically , upon culling and dissection of T . muris-infected IL-10 , IL-10Rα and IL-10Rβ mutant mice , we observed granulomatous lesions in their livers including necrotic areas and lymphocytic and phagocytic infiltrates ( Fig 3 ) . Moreover , 50 , 25 and 12 . 5% of T . muris-infected IL-10 , IL-10Rα and IL-10Rβ mutant mice , respectively , showed extensive numbers of foamy ( lipid-loaded ) macrophages in their livers ( S6 Fig ) . Because survival upon whipworm infection is similarly reduced among IL-10 , IL-10Rα and IL-10Rβ mutant mice but IL-10Rα is the only subunit that is exclusively used for IL-10 signalling , we focused subsequent experiments on IL-10Rα-deficient mice . Liver disease was reflected in changes to plasma chemistry markers of liver damage . Compared to WT mice , T . muris-infected mice with defects in IL-10 signalling presented significantly dysregulated plasma levels of liver enzymes ( decreased concentrations of alkaline phosphatase and increased concentrations of aspartate and alanine aminotransferase ) , accompanied by reduced concentrations of glucose , fructosamine , albumin and thyroxine ( Figs 4A and S7 ) . Upon whipworm infection , we also observed augmented levels of ferritin and transferrin , which are indicators of systemic infection , in IL-10 signalling-deficient , but not in WT mice ( Figs 4A and S7 ) . We found no or minimal differences in plasma chemistry between uninfected WT and mutant mice ( S1 , S2 and S3 Tables ) . The changes in plasma chemistry parameters between infected WT and mutant mice were accompanied by increased circulating concentrations of the inflammatory cytokines IL-6 and TNF-α ( Figs 4B and 4C and S8 ) . Liver pathology appears to be caused by dissemination of gut bacteria or their products to the liver , upon breakdown of the caecal epithelial barrier due to whipworm infection and IL-10 signalling defects . Outgrowth of opportunistic bacteria contributes to the mortality of IL-10 mutant mice during whipworm infection [8 , 20] . Furthermore , intestinal inflammation can promote microbial dysbiosis and impair resistance to colonization by opportunistic pathogens [32 , 33] . We hypothesised that infection of IL-10 signalling-deficient mice with whipworms caused caecal dysbiosis and the overgrowth of opportunistic bacteria from the microbiota . Thus , we analysed the microbiota composition of uninfected and T . muris-infected WT and IL-10 , IL-10Rα and IL-10Rβ mutant mice using high-throughput 16S rRNA sequencing . No significant differences in overall gut microbial profiles and alpha/beta diversity were detected between uninfected IL-10 signalling-deficient and WT mice ( S9 , S10 and S11 Figs ) , thus indicating that IL-10 signalling did not impact caecal microbial community structure , an observation that is consistent with the lack of spontaneous inflammation in these mice in our mouse facility . Similarly , whipworm infection of WT mice did not lead to changes in overall microbial community structure and alpha/beta diversity , as shown by the lack of significant differences between the gut microbial profiles of infected and uninfected WT mice ( S9 , S10 and S11 Figs ) . Conversely , whipworm infection of IL-10Rα mutant mice resulted in a caecal microbial profile distinct from that of infected WT mice ( p = 0 . 001 , CCA , Fig 5A ) , but also of uninfected WT and mutant mice , as shown by both PCoA and CCA ( S10A Fig ) . The observed changes in the caecal microbial community structure were associated with a significant increase in microbial beta diversity ( i . e . differences in species composition between groups; p = 0 . 001 , ANOSIM; Fig 5B ) and a decrease in alpha diversity ( i . e . species diversity within a group ) ( measured through Shannon diversity , p = 0 . 01 , ANOVA; Fig 5C ) in T . muris-infected IL-10Rα mutant mice when compared to WT and uninfected mice ( S10B and S10C Fig ) . In particular , the observed decrease in alpha diversity of the caecal microbiota in T . muris-infected IL-10Rα mutant mice was associated with significant reductions of both microbial richness ( i . e . the number of species composing the microbial community; p < 0 . 001 , ANOVA; Figs 5C and S10C ) and evenness ( i . e . the relative abundance of each microbial species in the community; p < 0 . 001 , ANOVA; Figs 5C and S10C ) . Network analysis identified a positive correlation between the presence and relative abundance of several opportunistic pathogens ( i . e . Enterobacteriaceae , Escherichia/Shigella , Enterococcus , and Clostridium difficile ) , as well as lactic acid-producing bacteria ( i . e . Lactobacillus ) , and the microbial profiles of T . muris-infected IL-10Rα mutant mice ( Fig 5D ) . Moreover , analysis of differential abundance of individual bacterial taxa via Linear Discriminant Analysis Effect Size ( LEfSe ) revealed that Enterobacteriaceae , Enterococcaceae and Lactobacillaceae were significantly more abundant in infected mutant mice , than in any of the other mouse groups ( LDA Score ( log10 ) of 4 . 78 , 4 . 77 , and 4 . 44 respectively; Figs 5E and S10E ) . The increase in abundance of these groups in the T . muris-infected IL-10Rα mutant mice was also observed when comparing the relative OTU abundances ( Fig 5F ) . Similarly , T . muris-infected IL-10 and IL-10Rβ mutant mice presented a clear and consistent overgrowth of Enterobacteriaceae , Escherichia/Shigella and Enterococcus ( S9 and S11 Figs ) . The degree of colonization by these pathobionts correlated with the reduced survival ( time after infection that mice succumbed ) and extent of liver disease observed ( S12 Fig ) . Co-housing of the uninfected and T . muris-infected WT and mutant mice did not result in microbiota transfer by coprophagia as the dysbiosis and presence of pathobionts in the infected mutant mice was not observed in any other groups ( S9 , S10 and S11 Figs ) . Altogether these results indicate that absence of IL-10 signalling during whipworm infection causes intestinal dysbiosis due to the overgrowth of facultative anaerobes , members of the microbiota that have been previously described as opportunistic pathogens [34–36] . Moreover , the presence of the parasite is critical to the development of the observed dysbiotic state . We hypothesized that the opportunistic pathogens ( or their products ) present in the dysbiotic microbiota of the whipworm-infected IL-10 signalling-deficient mice were disseminating to the liver , thus causing lethal disease . To test this hypothesis , we examined whether a breakage of the epithelial barrier allowed bacteria from the Escherichia and the Enterococcus genera to translocate intracellularly through the caecal epithelia . Immunofluorescence labelling for the tight junction protein ZO-1 , showed that in contrast to WT mice that have expelled the worms , and in which tight junctions are intact ( Fig 6A ) , whipworm-infected IL-10Rα-deficient mice lacked tight junctions in regions of the caecal epithelia replaced with high inflammatory infiltrate ( Fig 6B and 6D ) . Transmission electron microscopy of those regions clearly showed a neutrophilic infiltrate and the denuded epithelium ( Fig 6E and 6F ) . Tight junctions of cells in close proximity to the worm are also lost ( Fig 6C ) . Moreover , using transmission electron microscopy , we observed the presence of intracellular cocci and bacilli in the caecal epithelia of whipworm-infected IL-10 signalling-deficient , but not WT mice ( Fig 7A ) . Immunofluorescence staining with antibodies against Escherichia spp . and Enterococcus spp . further indicated the intracellular translocation of these opportunistic pathogens through the enterocytes of the caecum of whipworm-infected IL-10 signalling-deficient mice ( Fig 7B and 7C ) . These observations indicate that both Escherichia spp . and Enterococcus spp . invade the caecal epithelium of IL-10 signalling-deficient mice upon whipworm infection . Furthermore , they suggest that translocation of these opportunistic pathogens or their products to the liver are the potential cause of lethal liver disease observed in these mice . Livers of T . muris-infected WT and IL-10 signalling-deficient mice were cultured under aerobic and anaerobic conditions to identify bacterial isolates using 16S rRNA sequencing . We found E . coli , E . faecalis and E . gallinarum in the livers of some whipworm-infected IL-10 signalling-deficient but not in WT mice ( S5 , S6 and S7 Tables ) . Moreover , using 16S rRNA PCR and sequencing , we found Escherichia/Shigella and Enterococcus were more abundant in whipworm infected IL-10Rα–deficient mice than in uninfected WT and IL-10Rα–deficient mice and for Enterococcus this was also significant when comparing with infected WT mice ( Fig 8A–8C ) . Although systematically examining entire livers was not feasible , we did observe bacteria staining with the Escherichia spp . and Enterococcus spp . antibodies in the sections from livers of some whipworm-infected IL-10 signalling-deficient mice but not in whipworm-infected WT mice ( Fig 8D and 8E ) . In summary , our findings indicate that IL10 signalling , via the IL-10Rα and IL-10Rβ , promotes resistance to colonization by opportunistic pathogens and controls immunopathology preventing microbial translocation and lethal disease upon whipworm infection . Conditional knockout mice lacking IL-10Rα on T cells and monocytes/macrophages/neutrophils did not recapitulate the phenotype of the complete mutant , thus suggesting that these cell types alone are not the main responders to IL-10 during whipworm infection [21] . This indicates that expression of IL-10Rα on other immune cells or IECs or in a combination of effector cells may be responsible for the IL-10 effects on worm expulsion and inflammatory control . To identify whether the main target cells of IL-10 were of haematopoietic or non-haematopoietic ( epithelial ) origin , we generated bone marrow chimeric mice by transferring either WT or IL-10Rα and IL-10Rβ–deficient bone marrow into lethally irradiated WT or IL-10Rα and IL-10Rβ–deficient mice and infected them with a high dose of T . muris . We observed decreased survival around day 20p . i . of 100% of irradiated WT mice reconstituted with bone marrow of IL-10Rα and IL-10Rβ mutant donors ( Figs 9A and S13A ) , which was accompanied by caecal and liver pathology ( Figs 9B and S13B ) . By contrast , WT mice receiving bone marrow cells from WT donors did not show any morbidity signs or caecal inflammation , even when worm expulsion was not always observed ( Figs 9A and S13A ) . Conversely , reconstitution of irradiated IL-10Rα and IL-10Rβ mutant mice with WT donor bone marrow protected them from the unsustainable pathology caused by whipworm infection ( Figs 9C and 9D and S13C and S13D ) . These results suggest that the main target cells responding to IL-10 are of haematopoietic origin . To support these findings and overcome the limitations of bone marrow chimera mice that include incomplete immune system reconstitution and microbiota dysregulation , we generated conditional mutant mice for the IL-10Rα on IECs ( Il10rafl/fl Vilcre/+ ) and infected them with a high dose of T . muris . Similar to WT controls ( Il10ra+l+ Vilcre/+ and Il10rafl/fl Vil+/+ ) , Il10rafl/fl Vilcre/+ mice expelled the worms as early as day 20 p . i . and developed a type 2 response indicated by the presence of specific parasite IgG1 antibodies in the serum ( S14 Fig ) . To further investigate which cells of haematopoietic origin are mediating the regulatory functions of IL-10 during whipworm infections , we infected RAG1-deficient mice , which lack the adaptive immune compartment ( T and B cells ) , with a high dose of T . muris . These mice developed a chronic infection with no symptoms of immunopathology ( Fig 10A and 10C ) . From day 35 to day 45 p . i . , we treated the mice with an antibody blocking the IL-10Rα or an isotype as control . We observed no differences in survival , worm burdens , plasma chemistry parameters , caecal and liver pathology and bacterial translocation between both groups ( Fig 10 ) indicating that cells of the innate immune compartment alone are not the drivers of immunopathology and in this setting IL-10 signalling plays no role . Together , these findings suggest that expression of the IL-10 receptor on several immune cells types , is crucial in controlling the development of lethal liver disease due to dysbiosis and microbial translocation upon whipworm infection .
We have shown that upon infection with whipworms , signalling by IL-10 , but not IL-22 or IL-28 , is crucial for the resistance to colonization by opportunistic pathogens , control of host inflammation , intestinal barrier maintenance and worm expulsion . We dissected the contribution of the IL-10 cytokine and the subunits of its cognate receptor and observed that lack of any of the components resulted in the development of a chronic whipworm infection that led to unsustainable pathology , confirming previous reports [8 , 20 , 21] and extending the observations to deficiency of the IL-10Rβ chain . During whipworm infection IL-10 signalling on cells of haematopoietic origin is critical for both the development of a type-2 response resulting in worm expulsion , and the control of type-1 immunity-driven inflammation and pathology . Specifically , IL-10 promotes type-2 responses [8 , 37 , 38] that are indispensable for IEC turnover to maintain epithelial integrity and goblet cell hyperplasia to increase the mucus barrier . Several important roles are played by this barrier: maintaining bacterial communities that compete against and prevent colonisation by inflammatory pathobionts [39 , 40]; separating IECs from luminal bacteria; and expelling the worm through the direct action of mucins [3 , 41] . In contrast , the absence of IL-10 signalling results in a type-1 inflammatory response [8 , 37 , 38] that fails to induce the mechanisms for worm expulsion and causes intestinal epithelium damage . Inflammation and worm persistence disrupts the intestinal microbiota , affecting colonization resistance and promoting the overgrowth of opportunistic pathogens . The disruption of the epithelial barrier allows these pathobionts or their products to translocate and reach the liver , where they cause inflammation and necrosis resulting in liver failure and leading to lethal disease ( Fig 11 ) . The actions of IL-10 signalling on the control of type-2 and -1 responses during whipworm infections may depend on the timing , cell type and organ where IL-10 is produced and the receptor is expressed . Early in an infection ( before day 15 p . i . ) , IL-10 signalling-deficient mice , infected with T . muris , lacked the type-2 response and goblet cell hyperplasia observed in WT mice [37 , 38] . IL-10 signalling therefore contributes to worm resistance via development of type-2 responses in the caecum and mesenteric lymph nodes , ultimately resulting in worm expulsion in WT mice . At later stages of infection ( day 21–28 p . i . ) , IL-10 signalling controls the type-1 driven pathology both in the caecum and the liver leading to reduced survival . At this time point , infected IL-10 signalling-deficient mice displayed higher levels of IFN-γ , IL-12 , TNF-α and IL-17 and severe caecal and liver inflammation when compared with WT mice [8 , 37] . Also treatment of chronically infected ( low dose ) WT mice after day 30 p . i . with a monoclonal antibody against IL-10R resulted in increased pathology and weight loss accompanied with increased production of type-1 cytokines [6] . Our results clearly demonstrate the haematopoietic origin of the cells that respond to IL-10 upon T . muris infection . In previous studies , IL-10Rα conditionally knocked out in mouse T cells , monocytes , macrophages and neutrophils did not result in inflammation or defects in worm expulsion [21] . These immune cell types alone are clearly not the main responders to IL-10 . Our findings in RAG1-deficient mice indicate that cells of the innate immune compartment alone are not the drivers of immunopathology and there is no immunoregulation in these mice by IL-10 . A remarkable observation is that extensive mechanical damage caused by the worm in the absence of adaptive immunity is insufficient to cause liver disease . This suggest that the immunopathology only develops in the presence of an adaptive immune system . Our findings highlight the complexity of the IL-10 immunoregulatory response and suggest that more than one cell type may be required to respond to IL-10 at different stages during infection . Future studies including rescue experiments transferring defined populations of IL-10R competent cells in IL-10 signalling-deficient mice or using inducible conditional knockout mice at different times during infection will help to identify the critical IL-10 responsive cell ( s ) . The IL-10-responding cells may be stimulated directly by the microbiota or whipworms , through pattern recognition receptors such as MyD88 [42] , Nod2 [39] and Nlrp6 [43] or indirectly , by limiting the inflammatory responses of other cells . Our findings are in agreement with the multi-hit model of inflammatory gut disease [44]: infection with whipworms is a colitogenic trigger that initiates the inflammatory process; lack of IL-10 signalling causes an inflammatory type-1 response that determines the dysregulation of the mucosal immune response; and the microbiota impacts the susceptibility and responses to infection . The dysbiosis that we observed during T . muris infections of mice lacking IL-10 or its receptor was characterized by an increase in the abundance of opportunistic pathogens from the Enterobacteriaceae family ( Escherichia/Shigella ) and Enterococcus genus . These facultative anaerobes occur in much lower levels in the microbiota than obligate anaerobes [45] . However , host-mediated inflammation resulting from an infection or genetic predisposition , such as mutations in IL-10 [32 , 36 , 46 , 47] , increases available oxygen . The higher oxygen tension benefits the growth of aerotolerant bacteria [35 , 36] , disrupting the intestinal microbiota and colonization resistance [32 , 36 , 46 , 47] . Mice deficient in IL-10 signalling do not develop spontaneous inflammation and dysbiosis in our facility . Therefore , changes to the microbiota are directly attributable to the colonization of the intestine by whipworms . We did not observe transfer of microbiota by coprophagy ( in particularly , members of the Enterobacteriaceae family and the Enterococcus genus ) and subsequent colitis susceptibility in co-housed uninfected and infected mice of both WT and mutant strains . Similarly , no transfer of microbiota was observed in IL-10 mutant mice co-housed with Il10-/-Nlrp6-/- mice harbouring an expanded population of the pathobiont Akkermansia muciniphila [43] . Together , these results suggest that deficiency in IL-10 signalling alone is insufficient to trigger dysbiosis; whipworm infection is required to reach this disbalanced state . We did not observe major changes to the microbiota in WT mice that cleared whipworm infections before d15 p . i . [48] . Nevertheless , the microbial alterations detected in IL-10 signalling-deficient mice , which develop chronic infections from a high-dose inoculum , were similar to those of chronically infected ( low-dose ) WT mice . These changes included decreased alpha diversity of the microbiota concomitantly with an increase in the abundances of Lactobacillus and Enterobacteriaceae ( Escherichia/Shigella ) [49 , 50] and Enterococcus [49] . The changes in the microbiota seen during whipworm chronic infection are therefore conserved and occur more rapidly and drastically when type-1 immune responses are not regulated . Increased abundance of Lactobacillus and Enterobacteriaceae has been also observed in the intestinal microbiota of Heligmosomoides polygyrus-infected susceptible mice [51 , 52] , and may indicate that helminth infections favour the establishment of certain bacterial groups and vice versa [49 , 51 , 53] . The significant reduction of bacteria of the genus Mucispirillum ( family Deferribacteraceae ) in the microbiota of whipworm-infected IL-10 signalling-deficient mice , is likely a consequence of the goblet cell loss , as these bacteria colonise the mucin layer of the gut [54]; indeed , Mucispirillum abundance increases during Trichuris infection of both pigs and mice [49 , 50 , 55] , where goblet cell hyperplasia occurs . Both Enterobacteriaceae ( Escherichia/Shigella ) and members of the Enterococcus genus such as E . faecalis are pathobionts that can cause sepsis-like disease when intestinal homeostasis is disrupted [32 , 34 , 35] . In whipworm-infected IL-10 signalling-deficient mice , we observed infiltration of neutrophils and macrophages in the intestinal epithelia and neutrophilic exudates in the lumen , potentially as a mechanism of clearance of these bacteria . Nevertheless , this inflammatory response results in tissue damage and bacteriolysis that induce immunopathology [56] . Tissue damage caused by the worm further increases inflammation and opens a door for opportunistic pathogens and their products to translocate through the intestinal epithelia . When immune cells ( neutrophils and macrophages ) fail to control the bacteria or their products in the intestine , these are drained by the portal vein into the liver [57–59] . Liver Kupffer cells located in the periportal area phagocytise antigens and microorganisms within the portal venous circulation [57–59] and promote anti-inflammatory responses mediated in part by IL-10 [57] . Lack of IL-10 signalling and translocation of opportunistic pathogens and their products to the liver may contribute to granulomatous inflammation and production of proinflammatory cytokines by Kupffer cells and infiltrating bone-marrow-derived-monocytes/macrophages resulting in failure of microbial clearance , tissue damage with consequent liver failure [58 , 59] and lethal disease . We were able to isolate E . coli , E . faecalis and E . gallinarum from the livers of some mutant mice and also observed increased abundances of Enterococcus and Escherichia/Shigella 16S rRNA sequences in the livers of infected IL-10Rα mutant mice . Besides bacterial growth , liver pathology and disease could also be caused by bacterial metabolites and products such as LPS of Gram-negative bacteria and lipoteichoic acid ( LTA ) of Gram-positive bacteria , which are known triggers of sepsis [60] . Similarly , microbial translocation has been described during hookworm [61] and HIV [62] infections that result in intestinal epithelial damage and permeability . Moreover , microbial translocation also occurs during inflammatory bowel disease ( IBD ) [63–65] , where intestinal inflammation and damaged barrier function results from a combination of factors , including dysbiosis and mutations in genes encoding proteins involved in the immune response , such as IL-10 [57] . We did not detect LPS in serum of whipworm-infected IL-10 signalling-deficient mice ( with values below the sensitivity threshold of the assay ) , suggesting that either the pathobionts mediating the disease are Gram-positive and therefore , other microbial products , such as LTA and peptidoglycan , may be the cause of systemic immunopathology or that opportunistic pathogens and their products were confined to the liver where they cause liver failure and disease . Previous publications showing prolonged survival of whipworm infected IL-10-deficient mice conferred by antibiotic treatment clearly support the role of microbial translocation on liver pathology and lethal disease observed in these mice [8 , 20] . Nevertheless , our findings on RAG1-deficient mice showing bacterial translocation in the majority of the mice in the absence of pathology suggest that microbial translocation alone is not the cause of immunopathology , but requires the presence of the adaptive immune system to trigger tissue damage . Liver damage was reflected in changes in plasma chemistry parameters in whipworm-infected IL-10 signalling-deficient mice . Specifically , decreased hepatic synthetic function ( lower plasma albumin , hypoglycaemia ) and release of liver aminotransferases into the circulation are the result of hepatocyte damage and liver necrosis [66 , 67] . Low albumin and enhanced cellular uptake of thyroxine by phagocytic cells results in hypothyroidism [68–70] . Low circulating levels thyroxine are related to decreased alkaline phosphatase [71] and augmented low density lipoprotein ( LDL ) [69] . In phagocytic cells , thyroxine increases phagocytosis , bacterial killing and TNF-α and IL-6 production [72] . Furthermore , TNF-α and IL-6 impact redistribution of iron from plasma into the liver and mononuclear phagocyte system , resulting in low concentration of plasma iron ( hypoferremia ) [73] . During infection , hypoferremia limits iron availability to pathogenic microorganisms and reduces the potential pro-oxidant properties of iron , which may exacerbate tissue damage [73 , 74] . These changes were reflected by increased levels of the iron binding and transport proteins , ferritin and transferrin , which are indicators of liver disease , inflammation and infection [74] . While IL-10 signalling is critical in controlling microbiota homeostasis and gut and liver immunopathology during whipworm infections , our data indicated that IL-22 is dispensable in the responses to T . muris . Interestingly , in our facility IL-22Rα-deficient mice infected with C . rodentium presented similar dysbiosis and sepsis-like pathology ( caused by E . faecalis ) to the one observed in whipworm-infected IL-10 signalling-deficient mice [32] . This may indicate that the intestinal inflammation elicited by C . rodentium infection of the epithelium is enough to trigger dysbiosis upon genetic predisposition by the lack of the IL22rα , while the colonization of the intestinal epithelium of these mice by whipworms is not sufficient to trigger the inflammatory responses that cause breakage of the microbiota homeostasis . In addition , the damage of the epithelium upon whipworm infection is restricted to specific areas where the worm is invading unlike C . rodentium infection which tends to occur more extensively across the epithelium . Moreover , the effect of IL-22 on anti-microbial production may be more relevant in responses to prokaryotic infections , such as those by C . rodentium . Our results on the role of IL-22 signalling during T . muris infection are contrary to a previous report describing a delay in worm expulsion in IL-22 mutant mice due to reduced goblet cell hyperplasia [22] . We hypothesize this difference is due to differences in the kinetics of infection and the microbiota between mouse facilities that clearly affect the epithelial and immune intestinal responses responsible for the expulsion of the worms . Moreover , the microbiota composition of IL-22 mutant mice of each facility is directly influenced by the lack of IL-22 through its effects on antimicrobial production and mucus barrier function and this in turn affects the development of the intestinal immune system [75] . Although a role of IL-22 in inducing goblet cell hyperplasia and promoting microbiota homeostasis during whipworm infections cannot be excluded [76 , 77] , the induction of this mechanism of worm expulsion in the T . muris model is strongly dependent on the actions of IL-13 [3 , 7] and regulated by IL-10 [38] . Similar observations have been made for other helminth infections in rodents including , Nippostrongylus brasiliensis [78] and Hymenolepis diminuta infections [79] . Taken together these observations suggest that in helminth infections IL-22 signalling plays a relatively minor role in worm expulsion . Recent work has suggested that IL-28 plays a protective role in both dextran sulphate sodium and oxazalone-induced colitis in mice [80] . Our data , however , indicates that this cytokine is dispensable in responses to whipworm and consolidates the view that regulation of damage to intestinal tissue is context dependent reflecting extent of epithelial disruption . For whipworm , the data suggests that the focal damage generated by infection only becomes a significant problem in the absence of IL-10 signalling and/or following very heavy infections . Indeed , opportunistic bacteria-driven disease can occur upon heavy T . suis infection of weaning pigs . The resulting necrotic proliferative colitis involves crypt destruction , with inflammatory cells in the lamina propria and loss of goblet cells , and was reduced by antibiotic treatment , implicating enteric bacteria in the disease etiology [81] . Similar to our findings , accumulation of bacteria invading the mucosa was observed at the site of worm attachment and opportunistic members of the Enterobacteriaceae family that included Campylobacter jejuni and E . coli were isolated from these pigs and potentially contributed to the development of severe intestinal pathology [81] . Moreover , heavy T . trichiura infections in children cause Trichuris dysentery syndrome that is accompanied by a chronic inflammatory response , evidenced by high circulating levels of TNF-α [82 , 83] , which can potentially be driven by the overgrowth of opportunistic pathogens of the microbiota . Dysfunction of IL-10 signalling may trigger the development of dysbiosis and pathology during whipworm infection of weaning pigs and children as polymorphisms in the IL-10 gene in humans have been associated with T . trichiura infection [84] . Here , the IL-10 signalling deficient mice serve as a model to understand how polymorphisms in either the cytokine or the receptor impact the responses to whipworm infections . In summary , our data provide critical insights into how IL-10 signalling , but not IL-22 or IL-28 , orchestrates protective immune responses that result in whipworm expulsion while maintaining intestinal microbial homeostasis and barrier integrity . These findings contribute to the understanding on how IL-10 signalling controls colitis during trichuriasis and on the actions of Trichuris ova-based therapies for diseases such as IBD . Further studies will shed light into specific immune populations driving this process through IL-10 production and exerting effector functions in response to its signalling . | The human gut is home to millions of bacteria , collectively called the microbiota , and also to parasites that include whipworms . The interactions between gut cells , the microbiota and whipworms define conditions for balanced parasitism . Cells lining the gut host whipworms but also interact with gut immune cells to deploy measures that control or expel whipworms whilst maintaining a barrier to prevent microbial translocation . Whipworms affect the composition of the microbiota , which in turn impacts the condition of the gut lining and the way in which immune cells are activated . In order to avoid tissue damage and disease , these interactions are tightly regulated . Here we show that signalling through a member of the IL-10 receptor family , IL-10Rα , in gut immune cells is critical for regulating of these interactions . Lack of this receptor on gut immune cells results in persistence of whipworms in the gut accompanied by an uncontrolled inflammation that destroys the gut lining . This tissue damage is accompanied by the overgrowth of members of the microbiota that act as opportunistic pathogens . Furthermore , the destruction of the gut barrier allows these bacteria to reach the liver where they cause organ failure and fatal disease . | [
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] | 2019 | Exclusive dependence of IL-10Rα signalling on intestinal microbiota homeostasis and control of whipworm infection |
Early embryo miscarriage is linked to inadequate endometrial decidualization , a cellular transformation process that enables deep blastocyst invasion into the maternal compartment . Although much of the cellular events that underpin endometrial stromal cell ( ESC ) decidualization are well recognized , the individual gene ( s ) and molecular pathways that drive the initiation and progression of this process remain elusive . Using a genetic mouse model and a primary human ESC culture model , we demonstrate that steroid receptor coactivator-2 ( SRC-2 ) is indispensable for rapid steroid hormone-dependent proliferation of ESCs , a critical cell-division step which precedes ESC terminal differentiation into decidual cells . We reveal that SRC-2 is required for increasing the glycolytic flux in human ESCs , which enables rapid proliferation to occur during the early stages of the decidualization program . Specifically , SRC-2 increases the glycolytic flux through induction of 6-phosphofructo-2-kinase/fructose-2 , 6-bisphosphatase 3 ( PFKFB3 ) , a major rate-limiting glycolytic enzyme . Similarly , acute treatment of mice with a small molecule inhibitor of PFKFB3 significantly suppressed the ability of these animals to exhibit an endometrial decidual response . Together , these data strongly support a conserved mechanism of action by which SRC-2 accelerates the glycolytic flux through PFKFB3 induction to provide the necessary bioenergy and biomass to meet the demands of a high proliferation rate observed in ESCs prior to their differentiation into decidual cells . Because deregulation of endometrial SRC-2 expression has been associated with common gynecological disorders of reproductive-age women , this signaling pathway , involving SRC-2 and PFKFB3 , promises to offer new clinical approaches in the diagnosis and/or treatment of a non-receptive uterus in patients presenting idiopathic infertility , recurrent early pregnancy loss , or increased time to pregnancy .
Progression of embryo implantation into a receptive endometrium relies on endometrial stromal cells ( ESCs ) undergoing decidualization , a critical cellular transformation process which determines the depth of embryo invasion and placentation [1]–[3] . Therefore , inadequate decidualization of ESCs can lead to embryo miscarriage and early pregnancy loss irrespective of whether the development of the blastocyst is normal . Prior to ESC decidualization , the endometrium must first transition from a “pre-receptive” to a “receptive” state within a restricted time period ( the window of receptivity ) during which the endometrial epithelium is transiently responsive to embryo attachment and invasion [4] . Accordingly , successful embryo implantation can only occur following precise synchronization between the emergence of an “activated” blastocyst and the differentiation of the endometrium to a “receptive” state . From a clinical perspective , the inability to diagnose or therapeutically treat a uterus that is incapable of developing a receptive state is considered one of the remaining obstacles to substantially improving the efficacy of assisted reproductive technologies ( ARTs ) which rely on embryo-transfer into a receptive uterus [5] , [6] . In the human and mouse endometrium , the onset of the receptive state is controlled by the coordinated actions of ovarian derived estradiol-17β ( E2 ) and progesterone ( P4 ) [4] . Although the initial cellular events of embryo implantation in the human are interstitial as opposed to eccentric in the mouse [1] , the fundamental developmental steps that lead to the establishment of the receptive uterus are common to both species , suggesting that many of the biochemical and molecular mechanisms underlying E2 and P4 control of these developmental steps also are conserved . During the proliferative ( or follicular ) phase of the human menstrual cycle or estrous cycle in the mouse , rising preovulatory E2 levels induce proliferation of the luminal and glandular epithelia of the endometrium . Following ovulation , however , E2-induced epithelial proliferation is suppressed by P4 derived from newly formed corpora lutea , resulting in a shift from epithelial proliferation to differentiation . Within this early secretory ( or luteal phase ) of the cycle , the anti-proliferative action of P4 in the epithelium is paralleled by rapid P4 induced proliferation of subepithelial stromal fibroblasts and their subsequent differentiation into decidual cells [7] . Unlike their fibroblast progenitors , decidual cells are large polygonal epithelioid cells which in many cases are polyploid . In the case of the day 4 pregnant mouse ( day 1 = detection of vaginal plug ) , ESC proliferation is further enhanced by nidatory E2 to elicit an endometrial receptive state [4] . As a consequence of embryo attachment ( or an artificial deciduogenic stimulus ) , proliferating ESCs differentiate into polyploid multinucleated decidual cells [8] . The resulting decidua expands mesometrially to ultimately contribute to the formation of the chorioallantoic placenta ( hemochorial placentation also occurs in the human [1] ) . Importantly , many of the above endometrial cellular events that lead to the establishment of the materno-fetal interface in the human and mouse can be recapitulated in ovariectomized mice treated with E2 and P4 [9] . Although the key cellular events that underpin the development of endometrial receptivity by E2 and P4 are known , the biochemical and molecular mechanisms by which these steroid hormones choreograph these intracellular changes are unclear . To address this issue , we previously demonstrated that of the three members of the p160/steroid receptor coactivator ( p160/SRC ) family of coregulators [10] , endometrial SRC-2 is indispensable for successful embryo implantation in the mouse [11] , [12] . Significantly , these initial mouse findings provided much needed experimental support for conclusions drawn from clinical studies that deregulation of endometrial SRC-2 expression levels is linked to the infertility diagnosis often made for a subset of patients presenting gynecological disorders such as polycystic ovary syndrome ( PCOS ) [13] . Despite growing support for an important role for SRC-2 in endometrial function and disease processes [12]–[16] , the signaling mechanisms by which this endometrial coregulator exerts its effects have remained elusive . Therefore , using primary human ESCs ( hESCs ) in culture and a conditional knockout mouse model , we report here that SRC-2 is required for early P4-driven ESC proliferation which is essential for the rapid numerical increase of both human and mouse decidual cells during the decidualization progression program . In keeping with SRC-2's pleiotropic role in glucose metabolism [17]–[19] , we reveal that SRC-2 enables P4-driven ESC proliferation through accelerating the glycolytic flux in order to provide the necessary bioenergy and biomolecules ( i . e . amino acids , fatty acids , and nucleotides ) to meet the demands of rapid cellular proliferation during this early stage of the decidualization program . Therefore , our new findings support the conclusion that SRC-2 represents a critical “coregulator accelerant” of the glycolytic flux which is required to rapidly expand the ESC population prior to terminal differentiation to decidual cells . Accordingly , endometrial SRC-2 and its glycolytic targets may represent novel clinical avenues through which to precisely time and/or therapeutically extend the window of receptivity in those women at high risk for early pregnancy loss .
Our previous murine endometrial studies demonstrated that SRC-2 is critical for the process of ESC decidualization [12]; however , the cellular and molecular mechanism ( s ) which rely on SRC-2 to advance this decidual progression program by E2 and P4 is unclear . Using an established hormone-induced uterine receptivity model in which the sequential and co-stimulatory actions of E2 and P4 elicit a receptive uterus in an ovariectomized mouse ( Figure 1A and [20] ) , we asked whether abrogation of SRC-2 expression in the endometrium of the SRC-2d/d mouse would compromise the responsiveness of this tissue to E2 and/or P4 exposure . Immunostaining for BrdU incorporation clearly revealed that abrogation of SRC-2 in the SRC-2d/d mouse uterus is not required for endometrial epithelial proliferation in response to E2 priming or nidatory E2 exposure ( Figure 1B and C ) . However , a significant decrease in the number of subepithelial BrdU positive ESCs was observed in uterus of the SRC-2d/d mouse as compared to its SRC-2f/f sibling control when treated with E2P4 ( Figure 1B and D ) . In an estrogenized endometrium , these data provide support for a critical role for SRC-2 in P4-dependent ESC proliferation , an essential cellular event for developing a receptive uterus responsive to a deciduogenic stimulus . Using an artificial decidual assay ( Figure S1A and [9] ) , we confirmed that the marked decrease in the number of proliferating ESCs resulting from the absence of SRC-2 is sufficient to block the responsiveness of the uterus to a deciduogenic stimulus ( Figure 1E and Figure S1 ) . As expected , P4 responsive ESC genes induced during early decidualization ( i . e . Wnt-4 , Bmp-2 , and Hand-2 [4] , [21]–[24] ) are strikingly attenuated in the absence of SRC-2 function ( Figure S1B ) . Additionally , expression of cell cycle regulatory genes Ccnd3 , Cdc25c and Cdkn1a were significantly altered with the absence of SRC-2 function ( Figure S6 ) . Together , these results provide strong support for a pivotal role for uterine SRC-2 in P4-dependent ESC proliferation that initiates with the development of the receptive uterus and is required to advance a cellular transformation program which leads to terminal differentiation and complete decidualization of the ESC compartment within the murine endometrium . An established in-vitro model for decidualization of hESCs [25] , [26] was used to determine whether the early decidual defect observed in the SRC-2d/d mouse translates to human ( Figure 2A ) . While primary hESCs decidualize over a six-day culture period in response to a cocktail of E2 , the progestin: MPA , and cAMP ( referred hereon as EPC ) , small interfering ( si ) RNA mediated knockdown of SRC-2 levels results in an early block in this decidual response ( Figure 2B , C ) . Unlike control cells , hESCs with reduced SRC-2 levels fail to transform from a fibroblastic to an epithelioid morphology that typifies full hESC decidualization ( Figure 2B ) . This difference in morphological response is reflected at the molecular level by a significant attenuation in the induction of the decidual differentiation markers: prolactin ( PRL ) and insulin-like growth factor binding protein-1 ( IGFBP-1 ) when SRC-2 levels are low ( Figure 2C ) . Induction of a subset of P4 responsive decidual genes ( i . e . WNT-4 and HAND2 ) also is reduced with SRC-2 knockdown ( Figure S2B ) . Because the hESC decidual defect occurs as early as day-3 following administration of the hormone decidual stimulus ( Figure 2C ) , we reasoned that , like in the mouse ( Figure 1 ) , SRC-2 is required for early hESC proliferation that precedes decidualization . Indeed , MTT assays reveal that early proliferation of hESCs ( as early as day 1 and 2 following exposure to the deciduogenic hormone stimulus ) is markedly curtailed with SRC-2 knockdown ( Figure 2D ) . A pro-proliferative role for SRC-2 in early decidualization is further supported by a clonogenic assay which demonstrates that reduced levels of SRC-2 limits hESC colony expansion ( Figure 2E ) . Noteworthy , levels of SRC-1 and SRC-3 are unaffected by SRC-2 knockdown ( Figure S2A ) and reduction in the levels of either one of these coregulators fails to block hESC decidualization ( Figure S3 ) . Collectively , these findings highlight an evolutionary conserved and selective role for SRC-2 in early E2/P4-dependent ESC proliferation which is indispensable for the initiation of the decidual progression program . The early requirement for SRC-2 in the decidual progression program suggests that this coregulator controls a fundamental cellular process which is critical for a quiescent ESC to rapidly proliferate in response to E2/P4 . To meet the bioenergetic and biosynthetic demands of rapid cell division , the rate of gycolysis ( or glycolytic flux ) of a cell must be increased to generate sufficient amounts of energy ( ATP ) and biomass ( intermediates of glucose metabolism , such as isocitrate and succinate ) to enable the formation of two daughter cells [27]–[31] . Because SRC-2 belongs to a coregulator family with wide-ranging metabolic functions [19] , [32] , particularly in regulating the metabolic fate of glucose [18] , [33] , we assessed whether SRC-2 controls the glycolytic flux that underpins E2/P4-dependent ESC decidualization . To first confirm that glycolysis is critical for hESC decidualization , hESCs were treated with EPC in the presence or absence of 2-DG , a potent inhibitor of glucose hexokinase . As the first rate-limiting step in glycolysis , glucose hexokinase converts glucose to glucose-6-phosphate . As shown in Figure 3A , administration of 2-DG blocks hESC decidualization at the cellular and molecular level . With the inhibition of glycolysis , hESCs fail to undergo the typical fibroblastic to epithelioid cellular transformation that normally accompanies decidualization . This cellular phenotype is reflected at the molecular level by a block in the induction of the decidual biomarkers: IGFBP-1 and PRL ( Figure 3A ) . Considering the importance of glucose uptake and utilization to decidualization along with the reported role of SRC-2 in energy homeostasis [17] , [18] , [34]–[37] , we next asked whether SRC-2 is required for hESC glycolysis . To address this question , we employed a glycolysis stress test which measures the rate of conversion of glucose to lactate ( glycolysis ) non-invasively and in real time . The principle of the assay is based on the fact that cells produce and expel protons into the extracellular medium as a result of the conversion of glucose to lactate . In the first step of the assay , the rate of glycolysis ( glycolytic flux ) is measured by evaluating the rate of acidification of the surrounding medium ( or Extracellular Acidification Rate ( ECAR ) ) . In the second step of the assay , the rate of glycolysis is further increased by blocking oxidative phosphorylation using oligomycin ( an ATP synthase inhibitor ) . With oxidative phosphorylation blocked , cells resort to “ramping up” the rate of glycolysis to maintain ATP levels and energy homeostasis ( termed Glycolytic Capacity ) . In the assay's final step , cells are treated with 2-DG which results in a block in glycolysis as measured by a drop in ECAR; this step confirms that ECAR is solely generated by glycolysis . Applying this assay to hESCs treated with EPC in the presence or absence of siRNAs to SRC-2 ( Figure 3B ) , we demonstrate that decreased levels of SRC-2 markedly attenuate both the glycolytic flux and glycolytic capacity of the hESC . These results strongly support an important role for SRC-2 in maintaining hESC energy homeostasis through an increase in the glycolytic flux . Liquid chromatography-tandem mass spectrometry ( LC-MS ) of cell lysates from hESCs treated with EPC in the presence or absence of siRNAs to SRC-2 revealed significant differences in the output of intermediate metabolites which includes essential sugars , amino acids and fatty acids directly or indirectly derived from glycolysis ( Figure 3C ) . Noteworthy , fructose 2 , 6-bisphosphate ( Fru-2 , 6-BP ) is significantly decreased when cellular levels of SRC-2 are reduced ( Figure 3C ( inset ) ) . Generated by the bifunctional 6-phosphofructo-2-kinase/fructose-2 , 6-bisphosphatase 3 ( PFKFB3 ) [38] , Fru-2 , 6-BP acts as a key allosteric activator of 6-phosphofructo-1-kinase ( PFK-1 ) , a critical rate-limiting enzyme of glycolysis which converts fructose-6-phosphate to fructose-1 , 6-bisphosphate [39] ( Figure 3D ) . Based on these findings , we posit that SRC-2 may control the glycolytic flux of the hESC through regulation of intracellular levels of Fru-2 , 6-BP . To determine whether SRC-2 controls Fru-2 , 6-BP levels through regulating the expression of PFKFB3 , transcript levels of PFKFB3 were measured in EPC treated hESCs in the presence or absence of siRNAs to SRC-2 . Transcript levels of PFKFB3 were induced in response to EPC during the early stages of hESC decidualization ( Figure 4A ) . However , induction of PFKFB3 during this period is reduced when SRC-2 levels are attenuated ( Figure 4B ) . Along with the progesterone receptor , ChIP assay revealed that SRC-2 can occupy a PRE containing region within the PFKFB3 promoter ( Figure 4C ) . Together these data suggest that SRC-2 is required for the induction of PFKFB3 by EPC through a direct transcriptional regulatory mechanism . Interestingly , however , our unpublished microarray studies on the murine uterus suggest that SRC-2 regulates not only the transcription of P4 responsive genes ( such as Pfkfb3 ) important for stromal decidualization but also uterine genes not previously reported to be controlled by P4 , indicating a broader coregulator role for SRC-2 in this tissue . To establish the predicted importance of PFKFB3 in hESC decidualization , siRNA mediated PFKFB3 knockdown was applied to EPC treated hESCs in culture for six days . At the cellular level , knockdown of PFKFB3 levels blocked the transformation of hESCs from a fibroblastic to an epithelioid phenotype , the cellular change indicative of decidualization ( Figure 4D ) . The dependency of hESC decidualization on PFKFB3 levels was confirmed at the molecular level by a marked attenuation in the induction of the decidual markers , PRL and IGFBP-1 ( Figure 4E ) . Importantly , clonogenic and MTT assays demonstrated that reduction in PFKFB3 levels attenuated hESC decidualization by reducing the ability of these cells to rapidly proliferate in response to EPC ( Figure 4F and G ) . The above siRNA studies were further supported by using a small molecule inhibitor ( 3- ( 3-pyridinyl ) -1- ( 4-pyridinyl ) -2-propen-1-one ( 3PO ) [40] ) to the kinase activity of PFKFB3 . In the presence of 3PO , hESCs failed to decidualize as evidenced by a marked reduction in the induction of IGFB-1 and PRL in EPC treated hESCs ( Figure 5A ) . These results provided strong support for the proposal that the kinase activity of PFKFB3 , which converts fructose 6-phosphate to fructose 2 , 6-bisphosphate , is required for ESC decidualization . Because previous studies have shown that PFKFB3 is required for rapid proliferation of normal and neoplastic cells [41] , [42] , we reasoned that PFKFB3 may mediate SRC-2's role in early hESC proliferation . In support of this proposal , both clonogenic and MTT assays confirmed that PFKFB3 is essential for early hESC proliferation ( Figure 5B and Figure S4 ) . Together , these studies provided strong support for the proposal that PFKFB3 promotes early hESC proliferation through its kinase catalytic domain . Importantly , Pfkfb3 also is induced in the mouse uterus by P4 which is dependent on SRC-2 and PR expression ( Figure S5A ) . Therefore , to provide in vivo support for conclusions drawn from the above hESC culture studies , systemic administration of 3PO was included into the standard protocol to elicit a decidual response in ovariectomized mice ( Figure S5B ) . Examination of uterine gross morphology two days following the administration of the deciduogenic stimulus clearly revealed that 3PO significantly attenuates the decidual response in 3PO treated mice as compared to vehicle treated controls ( Figure 5C and D ) . Significantly , a marked decrease in ESC proliferation is observed with 3PO administration ( Figure 5C and D ) , suggesting Pfkfb3 kinase activity also is essential for the early proliferative stages of the murine decidual progression program , which is crucial for a full decidual response ( Figure S5C–F ) .
Previous pulse-labeling experiments with rodents and more recent clinical studies provide compelling support that decidual cells of the endometrium derive from highly proliferating ESCs [7] , [43] , [44] . From a metabolic perspective , a high rate of cellular proliferation requires a substantial increase in both glucose uptake and utilization not only to provide bioenergy but to furnish metabolic intermediates ( amino acids , fatty acids , and nucleotides ) to double biomass so that two daughter cells are generated following mitosis [27]–[31] . To fulfill the bioenergetic and biosynthetic demands of increased cell proliferation , the rate of glycolysis from glucose to lactate ( the glycolytic flux ) must be increased to rapidly furnish ATP and the necessary glycolytic intermediates to support anabolic reactions which lead to cell growth to enable mitosis . Therefore , as long as glucose is abundant , acceleration of the rate of glycolysis can provide levels of ATP and metabolic intermediates that exceed those generated by oxidative phosphorylation . Despite growing support for a critical role for enhanced glucose use in endometrial decidualization [34]–[37] , [45]–[47] , the key regulatory signals that direct the metabolic fate of this carbon source during the early proliferative stages of decidualization are unclear . From a clinical standpoint , identification of these regulatory signals may provide molecular insight into the etiopathogenesis of common gynecological disorders that are causally linked with impaired metabolic homeostasis as well as furnish novel targets for the clinical diagnosis and/or treatment of these co-morbidities . We demonstrate here that SRC-2 is indispensable for the E2/P4-induced proliferation of ESCs that precedes their differentiation to decidual cells . Because early expansion of the decidual cell population is considered a key cellular event that enables deep invasion into a hypoxic environment and further development of the conceptus within the endometrium [1] , our findings highlight a critical role for endometrial SRC-2 during the early steps that lead to the establishment of the maternofetal interface . For quiescent ESCs to proliferate , we reveal that SRC-2 plays a pivotal role in accelerating the glycolytic flux within predecidual cells by sustaining the induction level of PFKFB3 expression in response to E2/P4 exposure . As an inducible homodimeric enzyme , PFKFB3 converts fructose-6-phosphate to fructose-2 , 6-bisphosphate [38] , [39] , a signaling molecule which relieves the tonic inhibitory effects of ATP on phosphofructokinase-1 ( PFK-1 ) , a major rate-limiting glycolytic enzyme . With unrestricted acceleration of the glycolytic flux through the PFK-1 checkpoint , anabolic pathways such as the pentose phosphate pathway can be utilized to support rapid ESC proliferation and decidualization [35] . Interestingly , P4 has been reported to induce PFKFB3 in human breast cancer cells [48] , [49] , suggesting that PFKFB3 may mediate P4 mitogenic effects both in normal and abnormal physiologic contexts . Indeed , PFKFB3 is induced by a myriad of mitogenic , inflammatory , and hypoxic stimuli and is constitutively expressed in a number of leukemias and solid tumors [49]–[55] . Although PFKFB3 has been shown to be expressed in human placenta [56] , whether deregulation of this regulatory kinase can lead to proliferative disorders of the endometrium such as endometriosis , hyperplasia , or cancer constitutes an important question for future investigation . Modulation of the ESC glycolytic flux by SRC-2 is in keeping with an expanding role for this coregulator in glucose metabolism [17]–[19] . A member of the p160/SRC family of pleiotropic coregulators of glucose , fatty acid , and protein metabolism [19] , SRC-2 has been shown to be critical for hepatic glucose release during periods of caloric restriction [18] . During persistent periods of energy insufficiency , a critical role for SRC-2 is to release glucose from residual hepatic glycogen stores to maintain survival of the individual . During periods of glucose abundance , however , we show here that SRC-2 is crucial for rapid utilization of this energy source for endometrial decidualization , an essential reproductive process for the perpetuation of the species . In conclusion , we demonstrate that SRC-2 is essential for the metabolic reprogramming of the predecidual ESC into a proliferative phenotype , an essential early step toward endometrial decidualization . Our findings not only offer an important conceptual advance in our understanding of endometrial SRC-2 in peri-implantation biology but may well provide mechanistic underpinnings to explain the role of this coregulator in other areas of endometrial physiology ( i . e . preterm labor [14] ) as well as in endometrial pathophysiologies in women diagnosed with leiomyoma or polycystic ovary syndrome [13] , [16] . Finally , our new findings furnish the pretext for considering SRC-2 and its metabolic targets in the future design of new clinical approaches to more effectively diagnose and/or treat a non-receptive uterus in women with recurrent implantation failure or early pregnancy loss .
The PRCre/+SRC-2f/f ( SRC-2d/d ) bigenic mouse was generated by crossing our PRCre/+ knock-in [57] with the SRC-2f/f mouse ( TIF2 floxed [L2 version] ) [58] . In the SRC-2d/d bigenic , SRC-2 is abrogated specifically in progesterone receptor ( PR ) positive cells [12] . In a 12-hour light: 12-hour dark recurrent cycle , mice were housed within a vivarium maintained at Baylor College of Medicine . Mice received standard rodent chow and water ad libitum and were humanely treated in accordance with institutional guidelines for animal care and use ( IACUC ) . For the hormone-induced uterine receptivity model ( Figure 1A and [20] ) , eight week-old mice were ovariectomized before priming two weeks later with two daily injections of E2 ( 100 ng/day/mouse; Sigma-Aldrich , St . Louis , MO ) . Following two days of rest , mice were administered one of the following hormone treatments: 1 ) four daily injections of sesame oil ( E2 primed ) ; 2 ) three daily injections of sesame oil followed by an injection of E2 ( 50 ng ) on day 4 ( nidatory E2 ) ; or 3 ) three daily injections of 1 mg of P4 ( Sigma-Aldrich ) followed on day 4 by an injection of 1 mg P4 plus 50 ng E2 ( E2P4 ) . Fifteen hours following the final hormone injection , mice were intraperitoneally ( I . P . ) injected with 5′-bromo-2′-deoxyuridine ( BrdU ) ( Amersham Biosciences Corporation , Piscataway , NJ ( 0 . 1 ml/10 g of body weight ) ) two hours before sacrifice . To elicit an artificial decidual response [9] , mice were ovariectomized at six weeks-of-age and received three daily injections of E2 ( 100 ng ) two weeks following ovariectomy ( see: Figure S1A ) . After 2 days of rest , mice received three daily injections of E2P4 ( E2 ( 6 . 7 ng ) plus P4 ( 1 mg ) ) . Six hours after the third E2P4 injection , intraluminal instillation of 50 µl of sesame oil was performed on the left uterine horn ( stimulated ) ; the right horn was not stimulated ( control ) . After oil instillation , mice received daily injections of E2 ( 6 . 7 ng ) plus P4 ( 1 mg ) for either 2 days or 5 days before sacrifice ( Figure 1E and Figure S1 respectively ) . Uterine tissue was collected for wet-weight measurement as well as for histological and molecular analysis . Uterine tissue was fixed in 4% paraformaldehyde ( PFA ) overnight at 4°C , dehydrated through graded ethanol washes , embedded in paraffin wax before being sectioned into 5 µm thick tissue sections onto Superfrost Plus glass slides ( Fisher Scientific , Pittsburgh , PA ) . For immunohistochemical analysis , tissue sections were deparaffinized , rehydrated and boiled in a citric acid based antigen unmasking solution ( Vector laboratories Inc . , Burlingame , CA ) . After blocking , sections were incubated with a rabbit polyclonal anti-phospho-histone H3 antibody ( EMD Millipore , Billerica , MA ) overnight at 4°C . After the primary antibody incubation , sections were incubated with goat anti-rabbit IgG secondary antibody ( Vector laboratories Inc . ) for 1 hour at room temperature followed by an incubation with ZyMax streptavidin-horse radish peroxidase conjugate ( Invitrogen Corporation , Carlsbad , CA ) for 30 minutes at room temperature . Immunoreactivity was visualized with the 3 , 3′-diaminobenzidine ( DAB ) peroxidase substrate kit ( Vector laboratories Inc . ) and counterstained with hematoxylin . Finally , sections were dehydrated and mounted using permount histological mounting medium ( Fisher Scientific Inc . ) . For BrdU immunohistochemical analysis , uterine sections were fixed , processed , embedded , and sectioned as above . Following blocking , sections were incubated with a biotinylated anti-BrdU antibody ( BrdU In-Situ Detection Kit ( BD Pharmingen Inc . , San Jose , CA ) ) overnight at room temperature . Tissue sections were then incubated with vectastain ABC reagent ( Vector laboratories Inc . ) at room temperature for 1 hour and developed with the DAB peroxidase substrate kit . Finally , sections were counterstained with hematoxylin and cover slipped . For quantitative real-time PCR , total RNA from mouse uteri or hESCs ( see below ) was isolated using the RNeasy total RNA isolation kit ( Qiagen Inc . , Valencia , CA ) . Total RNA was reverse transcribed to cDNA using the TaqMan reverse transcription kit ( Applied Biosystems , Foster City , CA ) . The PCR conditions used for cDNA template synthesis were 10 minutes at 25°C , 30 minutes at 48°C , and 5 minutes at 95°C . Quantitative real-time PCR analysis was performed using TaqMan 2× master mix along with validated primers ( Applied Biosystems ) . Resultant amplicons were detected and quantitated using an ABI Prism 7700 sequence detection system; 18S ribosomal RNA was used as an internal control; gene specific primers used in these studies are listed in Table S1 . Prior to endometrial tissue biopsy , written-informed consent was obtained from all participating subjects . Clinical procedures undertaken to biopsy endometrial tissue followed Institutional Review Board ( IRB ) protocols from Baylor College of Medicine and in accordance with the guidelines of the Declaration of Helsinki [59] . Endometrial biopsy samples were obtained during the proliferative phase of the menstrual cycle ( day 7–10 ) of healthy women with regular menstrual cycles . Endometrial tissue was biopsied from the uterine fundus using a Pipelle catheter ( Unimar , Bridgeport , CT ) before being cut into small pieces using sterile scissors and subsequently digested in DMEM/F12 medium containing collagenase ( 2 . 5 mg/ml ( Sigma-Aldrich ) ) and DNase I ( 0 . 5 mg/ml ( Sigma-Aldrich ) ) for 1 . 5 hours at 37°C [26] . After digestion , dispersed cells were collected by centrifugation and layered over a Ficoll-Paque reagent layer ( GE Healthcare Biosciences , Pittsburgh , PA ) to remove lymphocytes . The top layer containing the hESC fraction was collected and filtered through a 40 µm nylon cell strainer ( BD Biosciences , Franklin Lakes , NJ ) . Fractionated hESCs were then resuspended in DMEM/F-12 media containing 10% FBS , 100 units/ml penicillin and 0 . 1 mg/ml streptomycin ( hESC media ) and cultured in tissue culture flasks ( 75 cm2 ) . Experiments were carried out using primary hESCs derived from at least three individual subjects; only early passage hESCs were used in these studies ( ≤ passage 4 ) . Six well culture plates were used to culture hESCs ( 1×105 cells per well ( 9 . 5 cm2 ) in triplicate ) . Smart pools of siRNAs targeting PFKFB-3 ( 1027416 ( Qiagen Inc . ) ) , SRC-1 ( NCOA-1 ( L-005196-00-0005 ) ) , SRC-2 ( NCOA-2 ( L-020159-00-0005 ) ) , SRC-3 ( NCOA-3 ( L-003759-00-0005 ) ) , and or non-targeting siRNA ( D-001810-10-05 ) ( Thermo Scientific , Dharmacon RNAi Technologies , Chicago , IL ) were used to transfect hESCs at 60–70% confluency . Per well , siRNAs ( 60 pmoles ) were transfected into hESCs using Lipofectamine 2000 reagent in 1× Opti-MEM I reduced-serum media ( Invitrogen Corporation ) . Five hours post-transfection , media was replaced with hESC media . Forty-eight hours later , hESCs were treated with 1× Opti-MEM I reduced-serum media containing 2% fetal bovine serum ( FBS ) , E2 ( 100 nM ) , medroxyprogesterone acetate ( MPA: 10 µM ( Sigma-Aldrich ) ) and cAMP ( 50 µM ( Sigma-Aldrich ) ) which constitutes the decidualization media . The first day that hESCs were cultured in decidualization media was assigned day 0 . For these studies , decidualization media was renewed every two days . Cells were harvested at appropriate time points as per experimental conditions . Total RNA was isolated to assess transcript levels of the decidualization markers: prolactin ( PRL ) and insulin-like growth factor binding protein-1 ( IGFBP-1 ) [25] . Forty-eight hours following siRNA transfection , hESCs were trypsinized and counted . For colony formation assays , 5×103 cells were plated per well of six-well plates in triplicate . Cells were grown in either decidualization or hESC medium; culture media was changed every two days for 10 days [60] . After 10 days , hESCs were washed with 1XPBS and stained with Accustain crystal violet solution ( Sigma-Aldrich ) . After staining for 15 minutes , cells were washed three times with water to remove unbound stain before plates were air-dried overnight . The following day , stained colonies were photographed and counted before the retained crystal violet stain was removed with 10% acetic acid; the stain eluate was quantitated by spectrophotometry at 490 nm . For cell viability assays , 103 hESCs were plated per well of a 96-well plate ( in triplicate ) 48 hours following transfection with siRNA . Following adherence to the well floor , cells were treated with decidualization or hESC complete media . Cell viability was determined using the CellTiter 96 Non-Radioactive Cell Proliferation Assay ( Promega Corporation , Madison , WI ) per the manufacturer's instructions . For cell death assays , forty-eight hours following siRNA transfections , hESCs were trypsinized and 103 hESCs were plated per well of a 96-well plate ( in triplicate ) . Following adherence , cells were treated with decidualization or hESC complete media for seventy two hours . Cell death assay was determined using the Apo-ONE Homogeneous Caspase-3/7 Assay ( Promega Corporation , Madison , WI ) as per the manufacturer's instructions . Forty-eight hours following siRNA transfections , protein extracts were prepared from hESCs as explained previously [61] . Protein extracts were resolved on sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred on to polyvinylidene difluoride membranes ( Bio-Rad Laboratories , Hercules , CA ) . Polyclonal anti-rabbit SRC-2 ( Bethyl laboratories , Montgomery , TX ) , Polyclonal anti-rabbit PFKFB3 ( Cell Signaling , Danvers , MA ) and a monoclonal anti-mouse β-actin ( Sigma-Aldrich ) antibodies were used for detecting SRC-2 , PFKFB3 and β-actin proteins respectively . Appropriate horseradish peroxidase-conjugated immunoglobulin G secondary antibodies ( Santa Cruz Biotechnology , Santa Cruz , CA ) were used to amplify the primary antibodies signal and immunoreactive bands were visualized by using enhanced chemiluminescence substrate detection kit ( Pierce Biotechnology , Rockford , IL ) . Chromatin immunoprecipitation ( ChIP ) assays were performed using the ChIP-IT Express kit ( ActiveMotif Inc . , Carlsbad , CA ) . Briefly , hESCs were cultured to 70–80% confluency before fixation with 10% formaldehyde for 10 minutes . After fixation , cells were lysed before being homogenized using a dounce homogenizer to obtain the nuclear fraction . Nuclei were resuspended in chromatin shearing buffer and subjected to sonication to fragment the chromatin . Fragmented chromatin was immunoprecipitated overnight either with a rabbit polyclonal antibody specific to the human progesterone receptor ( ( H-190 ) Santa Cruz Biotechnology Inc . , Santa Cruz , CA ) or human SRC-2 ( Bethyl Laboratories , Montgomery , TX ) . Incubation with a rabbit polyclonal IgG antibody ( Santa Cruz Biotechnology Inc . ) served as a control for non-specific immunoprecipitation . After immunoprecipitation , fragmented chromatin was reverse cross-linked before elution and purification of immunoprecipitated DNA fragments . Quantitative real-time PCR was performed using these DNA fragments as templates to determine the occupancy of SRC-2 and PR at two distinct regions ( termed region 1 and 2 ) within the human PFKFB-3 promoter [49] . Glycolytic flux analysis was performed using the XF Glycolysis Stress Kit with the Seahorse Biosciences XF analyzer as per the manufacturer's protocol ( Seahorse Bioscience , North Billerica , Massachusetts [39] ) . Forty-eight hours following siRNA transfection , hESCs were trypsinized and 1×104 cells were plated per well of 24-well plates as five replicates . After 24 hours of EPC treatment , media was replaced with assay media and incubated at 37°C for 1 hr to equilibrate the media pH prior to assay . After equilibration , three well-defined small molecule modulators of glycolysis: glucose and oligomycin ( both promoters of glycolysis ) and 2-Deoxy-D-glucose ( 2DG ) ( a glycolysis inhibitor ) were sequentially administered . Glycolysis leads to proton extrusion from the cell which results in the acidification of surrounding media . This Extra Cellular Acidification Rate ( ECAR ) was measured at specific time points to assay and profile the glycolytic flux in real time . Determined ECAR rates were plotted versus time after the administration of glucose , oligomycin , and 2DG into the assay media ( error bars represent the standard mean error of biological replicates ) . Control or SRC-2 siRNA were transfected into hESCs derived from three different subjects in triplicate as explained above . Forty-eight hours following siRNA transfection , hESCs were treated with EPC cocktail . Cell pellets were prepared from hESCs following twenty-four hours of culture and subjected to liquid chromatography tandem mass spectrometry ( LC-MS ) analysis to determine the levels of intracellular metabolites [62] . As a positive control , a pool of mouse liver tissue was included in the LC-MS analysis . For each subject , the data from each group of metabolites ( amino acids , sugars , and fatty acids ) were normalized separately using the internal standard . Each data-set was log2 transformed and used to calculate the median based fold change relative to control samples . Heat maps were created using log transformed data to highlight the differences between the control siRNA and SRC-2 siRNA transfected cells . For the hierarchical classification , the average linkage based hierarchical classification was performed using the scaled log transformed data . Statistical analyses of data were performed using one-way ANOVA followed by Tukey's post hoc multiple range test and two-tailed Student's t test with the Instat Tool package version 3 . 0 ( GraphPad software Inc . , La Jolla , CA ) ; results were considered statistically significant with a P value<0 . 05 . | Failure of an embryo to correctly implant into the endometrium is a common cause of pregnancy failure or early embryo miscarriage . Although advances in our understanding of oocyte and embryo development have significantly increased pregnancy success rates , these rates remain unacceptably low due in part to an endometrium that is unreceptive to embryo implantation . Using experimental mouse genetics and a primary human cell culture model , we show here that the development of a receptive endometrium requires steroid receptor coactivator-2 , a factor which modulates the response of an endometrial cell to the pregnancy hormone , progesterone . Specifically , we show that SRC-2 increases progesterone-dependent glycolysis in the endometrial cell to provide energy and biomolecules for the next round of cell division . For an endometrium to be receptive to embryo implantation , specific endometrial cells ( termed stromal cells ) need to divide and numerically increase just prior to development of the receptive state . Therefore , SRC-2 is critical for the metabolic reprogramming of the endometrium to a receptive state , which provides the pretext for considering this factor and its metabolic targets in the design of future clinical approaches to diagnose and therapeutically treat those women at a high risk for early pregnancy loss . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2013 | Acceleration of the Glycolytic Flux by Steroid Receptor Coactivator-2 Is Essential for Endometrial Decidualization |
The translation efficiency of most Saccharomyces cerevisiae genes remains fairly constant across poor and rich growth media . This observation has led us to revisit the available data and to examine the potential utility of a protein abundance predictor in reinterpreting existing mRNA expression data . Our predictor is based on large-scale data of mRNA levels , the tRNA adaptation index , and the evolutionary rate . It attains a correlation of 0 . 76 with experimentally determined protein abundance levels on unseen data and successfully cross-predicts protein abundance levels in another yeast species ( Schizosaccharomyces pombe ) . The predicted abundance levels of proteins in known S . cerevisiae complexes , and of interacting proteins , are significantly more coherent than their corresponding mRNA expression levels . Analysis of gene expression measurement experiments using the predicted protein abundance levels yields new insights that are not readily discernable when clustering the corresponding mRNA expression levels . Comparing protein abundance levels across poor and rich media , we find a general trend for homeostatic regulation where transcription and translation change in a reciprocal manner . This phenomenon is more prominent near origins of replications . Our analysis shows that in parallel to the adaptation occurring at the tRNA level via the codon bias , proteins do undergo a complementary adaptation at the amino acid level to further increase their abundance .
DNA microarrays are now commonly used to measure the expression levels of large numbers of genes simultaneously [1] . Since proteins are the direct mediators of cellular processes , the abundance level of each protein is likely to be a better indicator of the cellular state than its corresponding mRNA expression level . However , genome-wide technologies to detect protein abundance are still lagging behind those that measure mRNA , and only few studies that measure protein abundance on a large scale are currently available [2–6] . The relationship between mRNA and protein abundance levels has been studied by several groups . Genes with similar mRNA levels may have very different protein abundance levels [7] . Yet , the correlation between protein and mRNA abundance after a log-transform was shown to be quite high [8] . A more recent study , combining three technologies for measuring mRNA expression , has yielded correlation levels of about 0 . 7 with protein abundance [9] . Several studies have aimed at correlating protein abundance to various other features of proteins , such as their codon bias , molecular weight , stop codon identity , and more [3 , 4 , 10 , 11] These investigations and other previous proteomic studies [12–14] were usually based on small- to medium-scale measurements . The current study revisits these issues and presents a comprehensive investigation of the relationship between factors that influence protein abundance and the associated protein levels . We begin by constructing a predictor for protein abundance levels , which , in contrast to previous studies , is tested and validated on unseen data ( see Methods ) . To this end , we rely on two large-scale protein abundance datasets [2 , 5] . Overall , to our knowledge this is the first time that the whole body of data currently available is collated and analyzed to this aim , and we obtain a predictor with a correlation of 0 . 76 with experimentally determined abundance levels . Applying the resulting predictor to pertaining mRNA expression data testifies to its utility . Our analysis provides new key insights concerning the regulation of translation efficiency and its evolution .
Genome-wide studies have measured mRNA and protein levels in the yeast Saccharomyces cerevisiae growing either in rich medium ( yeast extract , peptone , and dextrose [YEPD] ) or on poor , defined medium ( synthetic dextrose [SD] ) [2 , 3 , 5] . When protein abundance is compared to the corresponding mRNA levels in a given medium , the translation efficiency ( TE ) , i . e . , the ratio between protein abundance and mRNA levels , exhibits a large variability among genes ( spanning across six orders of magnitude; Figure 1A and 1B ) . However , when the TEs of a given protein are compared across the two different growth conditions , notably very little variation is observed ( Figure 1C ) : the ratios between the TEs of most proteins in the two conditions are close to 1 , with >90% of the proteins showing a ratio between 0 . 5 and 2 . This observation , albeit currently limited to the two types of media for which genome-wide data are available , suggests that the efficiency of translation per mRNA molecule of many genes may be largely invariable under different conditions . This fairly constant TE of yeast genes has motivated us to create a large-scale predictor of protein abundance , with the aim of studying its utility for inferring protein abundance levels across different conditions . The simplest predictor we studied is a linear one based on mRNA expression levels . Training this predictor on a randomly selected subset of the full complement of yeast mRNA and protein levels yields a Spearman rank correlation coefficient of rs = 0 . 55 on held-out test data ( the protein abundance was from [2] and mRNA levels were from [15]; see Methods ) . To improve the prediction accuracy , we examined the potential utility of combining 32 additional protein attributes into a multivariable linear predictor , some of which have been previously shown to have predictive value ( Table S1 ) . A greedy feature selection algorithm identified two useful protein attributes , while the inclusion of all other features resulted in a marginal and insignificant improvement in the performance of the linear , mRNA-based predictor . Performing the prediction by a support vector machine ( SVM ) using a variety of nonlinear kernels did not improve the results ( Methods ) . The two protein features yielding a significant improvement in prediction accuracy were the tRNA adaptation index ( tAI ) [16 , 17] , and the evolutionary rate ( ER ) [18 , 19] . tAI is based on the synonymous codon usage bias and gene copy number of different tRNAs and is related to the codon adaptation index ( CAI ) [16 , 17] . ER measures the rate of evolution of a protein by comparing its orthologs across related species [18 , 19] . These two features have been shown previously to be correlated with protein abundance levels [18 , 20] . Combining tAI with mRNA levels increases the prediction accuracy from the levels of rs = 0 . 55 obtained using mRNA levels alone to a Spearman rank correlation coefficient of rs = 0 . 61 on the same dataset as above . Adding evolutionary rate values increases the correlation to 0 . 63 . The incremental improvement of consecutively adding these two features to the basic linear regression protein abundance predictor is statistically significant ( Figure 2 and Methods ) . Large-scale measurements of mRNA and protein levels tend to be noisy . Thus , in the ( yet rare ) cases where several independent measurements of mRNA and protein levels at the same conditions are available , they can be used to reduce potential individual measurement biases by pooling them together [9] ( the correlation between two proteomic datasets generated by two different techniques and in different labs are between rs = 0 . 6 and rs = 0 . 8; see Text S1 ) . We thus averaged mRNA and protein abundance results obtained with different technologies ( see Methods for the description of the pertaining datasets used to this end ) . This results in a further notable improvement of prediction accuracy ( rs = 0 . 76; Figure 2 ) , suggesting that a considerable fraction of the variability in the datasets is due to experimental measurement errors ( the improvement of the correlations observed upon averaging can also be due to the blurring of the effects of different posttranscriptional regulation processes taking place in the different conditions in which the measurements were done [temperature , strains , media , technique] , but since we averaged over relatively similar conditions , we expect this effect to be relatively minor ) . In the following investigations reported in this paper , multiple independent measurements at the same conditions were not available , and the results reported are hence without pooling and averaging the data . Examining the performance of our YEPD-trained predictor on a new unseen dataset of 238 genes whose protein abundance levels were measured under very different conditions ( exposure to pheromone [13] ) resulted in a high correlation of rs = 0 . 69 . The correlation between mRNA levels solely and protein abundance levels was 0 . 62 , in comparison . The standard deviation of 1 , 000 cross-validation runs of the predictor was 0 . 016 , and the improvement compared to mRNA-based prediction was significant , with p < 10−16 . Further information on the predictors' performance on specific Gene Ontology ( GO ) annotation gene sets is provided in Table S2 . This table also shows that the predictor improves the prediction of protein abundance levels ( compared to mRNA levels ) in 92% of the GO annotation categories . Our predictor obtains higher correlations with protein abundance levels than using mRNA alone across numerous ranges of protein abundance; however , this correlation is not statistically significant in the lowest protein abundance range ( Figure 2C ) . Using our multivariate linear predictor , expression of genes whose products are members of the same complex ( according to SGD [21] ) exhibits significantly higher coherency than when calculated from their corresponding mRNA levels . Table 1 displays the pertaining Spearman rank correlation coefficients for pairs of genes that are part of the same complex . For the cases of experimentally determined and predicted protein abundance levels , we also computed the partial correlations after controlling for the effect of mRNA expression levels ( Methods ) . A similar , but weaker trend is also observed when examining the abundance coherency of protein pairs that exhibit a protein–protein interaction ( Text S2 ) . These results indicate that our prediction approach is likely to be more appropriate for proteins in large macromolecular complexes than for proteins involved in signaling and transcriptional control , since the latter are heavily posttranslationally modified . This notion is further supported by noting that in the highest protein abundance bin ( Figure 2C ) , there are 26 genes that are related to the “Ribosome” GO category , providing a hyper-geometric enrichment of p < 4 . 2 × 10−4 . Given the observation that the TE of most proteins is fairly similar across the two different conditions analyzed , we examined the utility of the protein abundance predictor in interpreting the results of two yeast mRNA gene expression datasets , obtained under a variety of environmental conditions ( see Text S3 ) . The first dataset investigated the yeast response to low-shear modeled microgravity . It included 12 different conditions ( six under low-shear and six controls ) [22] . To analyze this dataset , we clustered and bi-clustered the genes in the microarray data in accordance with the mRNA expression patterns , in a conventional manner . In parallel , we used our predictor to generate predicted protein abundance levels from the expression levels , and repeated the clustering and bi-clustering process on the resulting protein abundance data . We then compared the resulting cluster sets with respect to their functional enrichment in GO annotations ( Methods ) . We performed a similar analysis on a gene expression dataset consisting of 36 timepoints taken from yeast cells growing in continuous , nutrient-limited conditions [23] ( the first dataset includes gene expression measurements of a system that is close to equilibrium , while the second includes gene expression measurements of a system in a transient state; see Text S4 ) . Table 2 shows that the use of the predicted protein abundance values in these datasets results in a significant increase in the percentage of clusters that exhibit enrichment for specific GO terms ( for comparison , random predictors significantly deteriorate the clustering enrichment scores; see Text S5 ) . In the case of Sheehan's data [22] , the protein abundance predictor improved both the separation and the homogeneity . In the case of Tu's data [23] , the homogeneity improved while the separation score deteriorated ( Table 2 ) . A closer analysis provides evidence for the advantage of using the predictor: in the first dataset , a new bi-cluster is detected ( cluster 4 ) in the protein abundance analysis that does not appear in the mRNA level analysis . This bi-cluster spans over 11 of the 12 conditions and is enriched with many GO annotations ( mainly related to metabolism; Table S4 ) . Similarly , in the second dataset , cluster 7 in the predicted protein abundance analysis is a novel group that does not appear when analyzing mRNA levels . This cluster shows a striking periodic expression that coincides with the respiratory bursts observed under continuous nutrient-limited conditions [23] . Thus , using predicted protein abundance levels , a simple conventional clustering method suffices to reveal novel central clusters that were not apparent in the original study at the mRNA expression level . Tables S3 , S4 , S5 , and S6 provide a detailed analysis ( list of clusters , bi-clusters , and GO enrichments ) for the two datasets . We used our protein abundance predictor to reanalyze the intriguing results reported by [24] , showing that only a very small fraction of the genes whose expression is significantly elevated under a specific condition actually cause a significant decrease in fitness when deleted . Overall , we find that the fraction of expressed genes that lead to a significant reduction in fitness when deleted is 2-fold to 3-fold higher than the corresponding fraction reported using mRNA levels ( e . g . , 2 . 9% versus 0 . 76% in the case of yeast cells responding to 1 . 5 M sorbitol , and 13 . 2% versus 6 . 4% in the case of 1 M NaCl ) . Although the absolute fraction of genes accounted for still remains small , the relative increase observed by using the predictor is substantial . Finally , we tested our predictor's ability to correctly estimate protein abundance levels from mRNA expression data in a different organism , Schizosaccharomyces pombe . To this end , we used mRNA and protein data from a recent genome-wide study that reported a Spearman rank correlation coefficient of 0 . 61 between the two measurements [25] . Focusing on a subset of S . pombe genes that have an ortholog in S . cerevisiae , the Spearman rank correlation of the predicted protein levels with actual protein abundance measurements was 0 . 675 . Notably , for the same subset of genes , the Spearman rank correlation between the protein abundance and mRNA levels of S . pombe was only 0 . 629 ( and the rank correlation between the mRNA levels of the two organisms was 0 . 48 ) . These results are quite remarkable , since the predictor used to predict protein abundance in S . pombe was based on the ER and tAI values of the corresponding orthologs in S . cerevisiae . Like previous studies [4 , 26] , we have also found a significant correlation between the abundance of a particular protein and the frequency of certain amino acids composing it , the most prominent being alanine and valine ( positive correlation ) , and serine and aspargine ( negative correlation; Figure S1 ) . This observation has been previously attributed to the different values of the tAI ( or the CAI ) of these amino acids , which can modulate translation efficiency [16 , 17] . However , we find that even after controlling for the effect of their different tAIs , the frequency of these amino acids remains significantly correlated with protein abundance , and their frequency at abundant proteins remains highly significant ( see partial correlations reported in Figure 3 , and similar results after controlling for CAI in Figure S2 ) . The Spearman rank correlation of amino acid frequencies and protein abundance remains significant even after additionally controlling for the effect of mRNA expression levels ( Table S7 ) . This finding suggests that in parallel to the adaptation occurring at the tRNA level via the codon bias [27 , 28] , proteins do undergo a complementary adaptation at the amino acid level via amino acid substitution to further increase their protein abundance . The small , neutral , and nonpolar amino acid alanine is probably ideally suited for this putative substitute role , given its known neutral effect on protein stability [29] . Both alanine and valine are present at relatively high concentrations within the yeast cell , and their corresponding acyl-tRNA synthases are also expressed at high levels ( Table S8 ) , aiding in their efficient incorporation during transcription ( however , adding frequencies of amino acids to our predictor did not improve its performance significantly; see Text S6 ) . The recent direct measurement of absolute protein levels under two distinct growth conditions [5] enabled us to compare the ratio between the translation efficiency observed in cells grown on poor medium versus the one observed in rich medium , i . e . , the relative TE ( RTE; ( p/m ) SD/ ( p/m ) YEPD ) . There is a significant negative correlation ( −0 . 213; p < 10−50 ) between the RTE and the change in transcription levels between the two growth conditions . Even when focusing only on genes that change their protein abundance between the two conditions in a considerable manner ( protein abundance ratio > 1 . 4 or < 1/1 . 4 ) , the resulting negative correlation remains significant ( r = −0 . 08; p = 0 . 018 ) . This may suggest that there is a global homeostasis between transcription and translation , with a tendency to increase translation when transcription decreases , and vice versa . The average RTE is 1 . 091 ( about half the genes , 1 , 072 out of 2 , 204 , have RTE > 1 ) . Since the relative decrease of the ribosomal protein abundance ( pSD/pYEPD = 0 . 88 ) is higher than the total relative decrease of mRNA levels ( mSD/mYEPD = 0 . 98 ) , the number of ribosomes per mRNA is lower in SD . Thus , the findings of average RTE > 1 are probably due to lower protein degradation rates or other causes of higher translation rates in SD , rather than increased ribosomes per mRNA levels ( Figure S3 depicts the mean RTE levels of different GO annotation groups; Text S7 displays the variance in protein abundance levels in the two growth conditions ) . While the large majority of the genes have RTE levels ranging between 0 . 5 and 2 ( Figure 1B ) , two sets have extreme RTE values , one with RTE > 2 . 5 ( 48 genes ) , and the other with RTE < 0 . 45 ( 65 genes; Tables S9 and S10 ) . The distribution of mRNA and protein abundance levels of genes within each of these groups is similar to that of the rest of the genes ( see Figure S4A and S4B ) , and extreme ratios of protein abundance or mRNA levels do not necessarily imply extreme RTE values ( see Figure S4C ) . Interestingly , our predictor obtains more significant improvement in the correlations with actual protein abundance levels on genes with extreme RTEs ( see Figure S4D ) . In contrast to the inverse ( homeostatic ) relation observed in general , the set with extremely high RTE also exhibits extremely high mSD/mYEPD ratios ( an average mRNA ratio of 5 . 35 , 14 times the general average ) . This indicates that the extreme RTE values reflect the fact that the cell is making a concerted effort to maintain their protein abundance levels at the extreme levels needed . By the same token , the mean mRNA ratio for the set with extremely low RTE is 0 . 36 , somewhat below the total average . The group of genes exhibiting extremely high RTE levels is enriched for mitochondrial genes ( 21/48 are mitochondrial genes; chi-square p = 10−16 ) , with many of these genes being related to mitochondrial biosynthesis and metabolism . Thus , the increase in the level of mitochondrial proteins , reflecting the need for higher-yield energy production in poor growth conditions , is achieved mainly by boosting translation efficiency . Interestingly , the high RTE group is also enriched with genes that map very close to origins of replication ( autonomously replicating sequence [ARS] ) , including four genes abutting at the origin of replication ( out of a total of 24 genes with a similar location in the yeast genome , providing a chi-square p = 1 . 1 × 10−6 ) , and twice the expected number of genes located within 1 kbp from an ARS ( p < 0 . 05; see Figure 4 ) . A possible explanation for this intriguing connection is that the replication machinery , when binding to origins of replication , attenuates transcription , either by steric hindrance or by competition for DNA binding [30] . This interference is then compensated in turn by higher translation efficiency and a more flexible regulation of translation , as reflected by its high RTE levels . Indeed , the average mSD /mYEPD ratios of genes that have extremely high RTE and that are less than 1 kb from an ARS is only 0 . 8 . One putative mechanism that may underlie this intriguing phenomenon is that certain proteins that participate in replication and transcription ( e . g . , Rap1 and Abs1 ) could be incorporated into the mRNA , exported from the nucleus , and differentially affect the rate of translation at the ribosome . Similar mechanisms have been suggested for the activity of proteins such as Yra1 , Sub2 , and the THO complex , which affect transcription , splicing efficiency , and nuclear export [31] .
The availability of whole-genome measurements of protein abundance provides a unique opportunity to analyze the forces that affect protein translation and abundance . Combining several protein features yields a predictor of protein abundance that can serve as a useful tool for analyzing gene expression measurements . Our results indicate that highly expressed proteins undergo adaptation at the amino acid level , and that proximity to an origin of replication enhances the efficiency of translation . Translation efficiency is determined by invariant , condition-independent factors such as the amino acid and codon composition of the protein and the availability of the different tRNAs . It is also modulated by dynamic factors such as ribosome occupancy and ribosome density ( determining the total number of ribosomes per mRNA ) , which are dependent on environmental clues [10] . Assuming that TE is constant to a first approximation for most genes ( as its levels across poor and rich media testifies ) , this study has focused on the first group of factors , and has shown the utility of such a predictor in interpreting biological data . We anticipate that as information gradually accumulates concerning the second group of factors , more accurate protein abundance predictors will emerge that can incorporate information on posttranscriptional regulation [32–34] . Recent work has suggested that transcription factors and signaling genes tend to be posttranscriptionally regulated [32] . Indeed , a large proportion of the genes with extreme RTE levels belong to these two categories ( see Tables S9 and S13 ) . However , not all genes regulated at the posttranscriptional level exhibit extreme RTE values: a recent genome-wide study in yeast has identified 16 genes with extreme TE levels , presumably regulated posttranscriptionally [9] . Examination of the RTE levels of these genes reveals that only one has extreme RTE levels ( MET6 , with RTE = 0 . 47 ) ; the rest have RTE levels between 0 . 93 and 1 . 38 ( see Table S13 ) . Finally , protein degradation and turnover are obviously important modulators of protein abundance , and should be considered in future predictors as pertaining reliable data accumulates . That said , it is interesting and encouraging to see how far one can go in predicting protein abundance levels even without this information . An important corollary of our work is that gene expression results obtained with DNA microarray technology may in some cases be misleading . For example , Tables S11 and S12 include a subset of genes that exhibit inversely correlated regulatory trends at the transcription versus the translation level . An increase in mRNA expression levels of a particular gene does not necessarily mean a higher level of its protein . The corresponding protein abundance could not be differentially expressed or could even be differentially expressed but in the opposite direction . As Tables S11 and S12 include about 5% of the yeast genes , this type of error may be nonnegligible at times . Our predictor cannot solve this problem; its solution will probably require much larger biological datasets than those currently available . We demonstrated that our predictor ( which is based on S . cerevisiae ) can be used to successfully predict protein abundance levels in a different organism ( S . pombe ) , which has an evolutionary distance of 350–1 , 000 million y from S . cerevisiae [35] . It will be interesting to examine the effect that evolutionary distance may have on determining the “transferability” of protein predictors across species . However , answers to this question will need to wait until protein abundance data of additional organisms becomes available . Building on the existing large-scale protein abundance data , this study has shown that a predictor of protein abundance levels can improve the interpretation of gene expression measurements and provide new insights into the regulation and evolution of protein translation . The utility of such a tool should be further enhanced as our understanding of the determinants affecting protein abundance and translation improves and the pertaining data continues to accumulate .
For training the predictors , we used all the genes whose required features ( mRNA measurements , protein abundance , ER , tAI ) were available . The series of linear predictors studied were generated using a linear regressor and using the following cross-validation procedure: ( 1 ) randomly choose 80% of the genes ( training set ) and use them for generating a linear predictor; ( 2 ) use the resulting predictor for predicting the protein abundance of the remaining 20% of the genes ( test set ) ; and ( 3 ) for the genes in the test set , calculate the Spearman rank correlation coefficient between the predicted and experimentally measured protein abundance values . This cross-validation procedure is repeated 105 times , and the mean of the Spearman rank correlation coefficient ( computed in step 3 ) is the predictor accuracy reported in the main text . As reported in the main text , we generated a sequence of linear predictors of protein abundance , each time adding the most informative feature in a greedy manner . During this process , we checked if the resulting incremental improvement in prediction performance is statistically significant by performing a t-test , comparing the distribution of Spearman rank correlation coefficients obtained by each predictor over the 105 cross-validation runs . Note that in the case of a multivariate linear predictor , this cross-validation procedure may lead to similar prediction accuracy values as those obtained by training a multivariate regressor on the whole dataset . However , in the general scope of nonlinear predictors investigated in this study , the cross-validation prediction scenario used is conceptually different from a multivariate regression , and the results obtained significantly differ . Going beyond a linear predictor , we used two implementations of SVMs , SVM-light [36] and Partek ( Partek Software , http://www . partek . com ) , and examined radial , polynomial , and sigmoid kernels . The initial set of features included all the 32 features described in Table S1 , and we also examined various forward and backward algorithms for feature selection . Quite surprisingly , none of these SVM predictors gave a significant increase in prediction performance compared to the best linear predictor reported upon in the main text . In constructing the predictors we used the following data sources . Protein abundance and mRNA expression data . We analyzed four protein abundance datasets: ( 1 ) a dataset generated by merging ( with the appropriate normalization ) protein abundance data from numerous small-scale datasets [3]; ( 2 ) a large-scale measurement of protein abundance in yeast ( normal log phase ) [2]; and ( 3 ) protein abundance large-scale measurements by [5] in two different growth media conditions ( YEPD and SD ) . We analyzed two major mRNA expression datasets: ( 1 ) one generated by combining 36 microarray datasets ( wild-type yeast grown in YEPD without any stress ) [10]; and ( 2 ) an mRNA measurement of wild-type yeast grown in YEPD [21] . The dataset of [5] also includes the ratio ( but not the absolute values ) between the mRNA levels in the two conditions ( SD and YEPD ) , mSD /mYEPD . This information , combined with the protein abundance measurements in these two conditions , enabled us to compute the RTEs across growth conditions . Combined with the absolute mRNA measurements from [2] , it was used to calculate the absolute mRNA levels in SD . For computing mean protein abundance levels in constructing the pooled-data predictor , we averaged at least two of three measurements reported in [2 , 5 , 8] . For computing mean mRNA abundance levels to this construction , we averaged at least two of three measurements reported in [21 , 37 , 38] . The averaging was done following the procedure described in [9] . Sources of additional data . Protein half-life measurements were obtained from Belle et al . [39] . The protein properties examined in the construction of the protein abundance predictor ( properties 1–28 in Table S1 ) were obtained from the Saccharomyces genome database [21] . The tAI data were downloaded from [20] . Evolutionary rates of proteins were taken from Wall et al . [19] . The mRNA gene expression data , protein abundance data , and list of 447 relevant orthologous genes needed for testing the predictor performance on S . pombe were from [25] . Relative protein abundance and mRNA levels after exposure to pheromone were downloaded from [13] . We used two mRNA gene expression datasets that were generated by the same technology as that used for training the predictor . The two datasets are measurements by affymetrix GeneChip , and were downloaded from National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus ( GEO; http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=gds ) . The first dataset includes the 12 samples from [22] . The second dataset includes the 36 samples from [23] . Clustering and bi-clustering was performed by using the Expander program [40] . We used CLICK for clustering and SAMBA for bi-clustering . Gene enrichment was computed using the GO categories of [21] ( by computing the hyper-geometric probability of seeing at least x number of genes out of the total n genes in the cluster/bi-cluster annotated to a particular GO term , given the proportion of genes in the whole genome that are annotated to that GO term ) , examining the three ontologies of molecular function , biological process , and cellular components . The resulting enrichments were filtered by false discovery rate ( FDR ) to correct for multiple testing [41] . Protein complex data were downloaded from [21] . We measured coherency of mRNA levels , protein abundance , and predicted protein abundance of genes that are part of the same complex ( in SD and YEPD ) by the following steps: ( 1 ) we listed all pairs of genes in the dataset which are both comembers in one of the complexes; ( 2 ) for each case ( mRNA levels , protein abundance , and predicted protein abundance ) , we generated two vectors , u and v , such that u ( i ) and v ( i ) denote a pair of proteins that are part of the same complex; we calculated the Spearman rank correlation coefficient between the two vectors ( u and v ) ; and we compared the resulting correlation to the correlations between pairs of vectors with the same length that include measurements of randomly selected pairs of genes . For predicting protein abundance , we used a predictor that was trained on a different dataset ( i . e . , the predictor used for YEPD was trained on the SD measurements and vice versa; training the predictor on the same dataset gives an even better result , so we wanted to demonstrate that the results are significantly good even if the trained set and the test set are different . ) . The computation of the pertaining partial correlations and their associated p-values are described in Text S8 . For computing the coherency of expression/abundance of neighboring proteins in the protein interaction network , we used the yeast protein interaction network from the work of [42] . We used a similar procedure to that used to compute the complexes' coherency , but this time u and v are composed of protein pairs that are adjacent in the protein interaction network . For comparing the number of genes that exhibits both an increase in expression levels ( mRNA levels and predicted protein abundance ) and a significant decrease in fitness when adding NaCl or sorbitol , we used the mRNA levels from [43] and fitness profiling from [24] . For each of the two cases ( mRNA levels and predicted protein abundance ) , we used five measurements of expression levels and four measurements of fitness . We focused on the set of genes for which we had all the predictor's features . In the case of fitness profiling , a gene was considered “significant” if it had significant value ( as defined in [24] ) in at least one of the four fitness measurements . In both cases of protein abundance or mRNA expression levels , a gene was considered significant if it exhibited a log ratio of at least 0 . 25 in one of the five measurements . | DNA microarrays measuring gene expression levels have been a mainstay of systems biology research , but since proteins are more direct mediators of cellular processes , protein abundance levels are likely to be a better indicator of the cellular state . However , as proteomic measurements are still lagging behind gene expression measurements , there has been considerable effort in recent years to study the correlations between gene expression ( and a plethora of protein characteristics ) and protein abundance . Addressing this challenge , the current study is one of the first to introduce a predictor for protein abundance levels that is tested and validated on unseen data using all currently available large-scale proteomic data . The utility of this predictor is shown via a comprehensive set of tests and applications , including improved functional coherency of complexes and interacting proteins , better fit with gene phenotypic data , cross-species prediction of protein abundance , and most importantly , the reinterpretation of existing gene expression microarray data . Finally , our revisit and analysis of the existing large-scale proteomic data reveals new key insights concerning the regulation of translation efficiency and its evolution . Overall , a solid protein abundance prediction tool is invaluable for advancing our understanding of cellular processes; this study presents a further step in this direction . | [
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"computational",
"biology"
] | 2007 | Determinants of Protein Abundance and Translation Efficiency in S. cerevisiae |
Replication and pathogenesis of the human immunodeficiency virus ( HIV ) is tightly linked to the structure of its RNA genome , but genome structure in infectious virions is poorly understood . We invent high-throughput SHAPE ( selective 2′-hydroxyl acylation analyzed by primer extension ) technology , which uses many of the same tools as DNA sequencing , to quantify RNA backbone flexibility at single-nucleotide resolution and from which robust structural information can be immediately derived . We analyze the structure of HIV-1 genomic RNA in four biologically instructive states , including the authentic viral genome inside native particles . Remarkably , given the large number of plausible local structures , the first 10% of the HIV-1 genome exists in a single , predominant conformation in all four states . We also discover that noncoding regions functioning in a regulatory role have significantly lower ( p-value < 0 . 0001 ) SHAPE reactivities , and hence more structure , than do viral coding regions that function as the template for protein synthesis . By directly monitoring protein binding inside virions , we identify the RNA recognition motif for the viral nucleocapsid protein . Seven structurally homologous binding sites occur in a well-defined domain in the genome , consistent with a role in directing specific packaging of genomic RNA into nascent virions . In addition , we identify two distinct motifs that are targets for the duplex destabilizing activity of this same protein . The nucleocapsid protein destabilizes local HIV-1 RNA structure in ways likely to facilitate initial movement both of the retroviral reverse transcriptase from its tRNA primer and of the ribosome in coding regions . Each of the three nucleocapsid interaction motifs falls in a specific genome domain , indicating that local protein interactions can be organized by the long-range architecture of an RNA . High-throughput SHAPE reveals a comprehensive view of HIV-1 RNA genome structure , and further application of this technology will make possible newly informative analysis of any RNA in a cellular transcriptome .
As is the case with many natural RNAs , the function of the HIV RNA genome is strongly linked to its ability to form higher-order structure and to interact with protein effectors in each stage of its replication cycle . The 5′ end of the HIV genome alone contains a noncoding , highly structured region that is involved in viral packaging , dimerization , pairing with the cellular tRNA primer for cDNA synthesis , and binding numerous viral proteins [1 , 2] . Immediately downstream of this regulatory region , the HIV genome contains nine open reading frames . The first protein encoded by the HIV-1 genome is Gag , which is ultimately cleaved into a complex set of proteins , including matrix , capsid , and nucleocapsid [2] . The nucleocapsid protein has two dissimilar RNA binding activities . First , as part of the Gag precursor protein , the nucleocapsid domain specifically recognizes and directs the HIV genome to be packaged into new virions [3–5] in the context of a vast excess of other cellular RNAs . In contrast , the nucleocapsid protein also has a significant duplex destabilizing activity , which plays a key role in facilitating the RNA annealing and rearrangement events essential for viral reverse transcription [6 , 7] . Because of its importance in regulating replication and for governing interactions with protein cofactors , extensive efforts have been directed towards developing structural models for the HIV-1 genome and for identifying candidate interaction sites for the nucleocapsid protein . Multiple models based on phylogenetic , chemical mapping , and mutagenesis approaches have been proposed [8–17] . In general , agreement between proposed structures is limited to highly structured hairpins; large zones of disagreement between models are common . The available experimental and sequence information is not sufficient to support one model over another . This lack of consensus in understanding HIV genome structure reflects challenges common to the analysis of any large RNA in its native biological context . One of the most successful methods for determining an RNA secondary structure has been phylogenetic covariation analysis [18 , 19] . However , this method has a narrow range of usefulness in that homologous RNAs must be similar enough to form the same structure , but simultaneously exhibit sufficient polymorphism to be informative . Few RNAs meet this standard . In the case of HIV and most viral RNAs , sequences are typically too similar to each other to provide sufficient constraints over large regions . Alternatively , RNA structural information can be inferred by treatment with chemicals or enzymes that discriminate between paired and unpaired nucleotides [20 , 21] . This reactivity information is then used to choose among various models , usually predicted with the assistance of thermodynamic folding algorithms . Conventional chemical and enzymatic mapping information has a narrow dynamic range and is usually available only for 25% to 50% of nucleotides in an RNA . As a result , conventional mapping experiments tend to focus on short pieces of RNA in artificial contexts and rely heavily on thermodynamic prediction and extrapolation to relate structures and protein binding sites identified in vitro to the biology of large RNAs in their native contexts . When used against intact viral particles , conventional probes have thus far also been unable to detect protein–RNA interactions inside HIV virions [16] . Therefore , comprehensive and accurate analysis of any cellular or viral RNA in a relevant biological context requires a new technology that may be used for any RNA sequence under a wide variety of biologically relevant conditions . Our approach is based on the observation that electrophiles , such as N-methylisatoic anhydride ( NMIA ) , react selectively with flexible RNA nucleotides at the ribose 2′-hydroxyl group ( Figure 1 ) [22] . The RNA is exposed to NMIA at a concentration that yields approximately one 2′-O-adduct per 300 nucleotides ( nts ) . Adducts are detected by their ability to inhibit primer extension by reverse transcriptase . A control extension reaction omitting NMIA to assess background , along with dideoxy sequencing extensions to assign nucleotide positions , are performed in parallel . These combined steps are called selective 2′-hydroxyl acylation analyzed by primer extension , or SHAPE [22–24] . Because the four canonical RNA nucleotides each contain a 2′-hydroxyl group , local nucleotide flexibility at all sites in an RNA is quantitatively interrogated in a single experiment . In this work , we couple SHAPE chemistry with automated data readout and analysis systems so that hundreds of nucleotides of RNA structure can be analyzed in a single experiment . The overarching impact of this technology begins to realize the goal of making RNA structure analysis roughly as simple as contemporary DNA sequencing . High-throughput SHAPE ( hSHAPE ) makes it possible to rapidly measure RNA backbone flexibility at greater than 90% of the nucleotides in an RNA under a variety of biologically relevant conditions . To demonstrate the power of hSHAPE while examining one of the most functionally important regions of an HIV genome , we applied hSHAPE to the first 900 nts of the 5′ end of the HIV-1 genome , under four biologically informative states . The data from these experiments allow us to address a comprehensive set of challenges that all require accurate knowledge of the structure of the HIV RNA genome: Does the viral genome have a global , long-range architecture , or is it largely organized as a series of isolated structural elements ? Is it possible to identify RNA regulatory motifs based on their underlying RNA structure ? Can consensus RNA binding sites be identified for the viral nucleocapsid protein , and if so , are these organized in domains ? Can nucleocapsid interaction sites that function in specific RNA binding and genome packaging be distinguished from those that are targets for duplex destabilization by this same protein ? If two classes of interaction sites for the nucleocapsid protein can be identified , do the specific binding and duplex destabilization activities operate in the same or in neighboring regions of the HIV genome ?
To create the technology necessary to analyze long regions of an RNA in biologically relevant environments in a single experiment , we performed each extension using a primer labeled with a color-coded fluorophore . The resulting cDNA products ( from the ( + ) and ( − ) reagent reactions plus two sequencing traces ) are combined and resolved in one multi-fluor run by automated capillary electrophoresis . In a single multiplex hSHAPE read , we typically obtain 250–400 nts of quantitative RNA structural information at single-nucleotide resolution ( Figure 2A ) . These elution time versus dye amount data resemble DNA sequencing information analyzed by capillary electrophoresis , and require similar preprocessing steps , such as baseline correction and corrections for fluorescent multiplexing [25] . However , whereas DNA sequencing requires detection of only the most intense peak at each position , the amplitudes in the ( + ) and ( − ) NMIA reagent channels in an hSHAPE experiment report quantitative RNA structural information . Peaks with little or no reactivity in the ( + ) NMIA channel correspond to RNA nucleotides constrained by base pairing or other interactions , whereas tall peaks indicate conformationally flexible positions ( red and blue traces , Figure 2A ) . The dynamic range that distinguishes flexible from paired nucleotides is approximately 30-fold . We calculated quantitative SHAPE reactivity information at every nucleotide position by developing hSHAPE-specific processing algorithms that ( 1 ) align the ( + ) and ( − ) NMIA channels to the RNA sequence ( Figure 2A ) , ( 2 ) integrate the ( + ) and ( − ) NMIA peaks ( Figure 2B ) , ( 3 ) correct signal decay ( black line , Figure 2C ) , and ( 4 ) normalize SHAPE reactivities to a universal scale ( Figure 2D ) . In this case , these steps produce a single-nucleotide resolution view of RNA flexibility for HIV-1 nucleotides 8 through 286 ( Figure 2D ) . To verify the accuracy of hSHAPE , we superimposed SHAPE reactivities on the well-characterized TAR and poly ( A ) stem-loops ( nucleotides 1–104 ) , which is the only region in the first 900 nts of the HIV RNA genome longer than approximately 50 nts for which prior analyses have converged on a single structural consensus [9 , 15 , 16] . The SHAPE reactivity information is exactly consistent with the consensus secondary structure model for this region ( Figure 2E ) . Nucleotides with normalized SHAPE reactivities greater than 0 . 5 are almost always single stranded ( orange and red columns , Figure 2E ) , whereas positions with reactivities less than approximately 0 . 2 ( purple bars , Figure 2E ) are almost always paired . Reactivities between these values ( green ) may be paired or participate in other partially constraining interactions . SHAPE reactivities also accurately report fine-scale structural differences . For example , nucleotides in the UCU bulge in the TAR stem show intermediate reactivities ( Figure 2E ) , consistent with nuclear magnetic resonance ( NMR ) studies [26] that indicate that these nucleotides are partially stacked . Our goal was to determine the structure of and derive biological inferences for the 5′ region of the HIV-1 RNA genome , as it exists inside wild-type viral particles . As will be shown below , hSHAPE provides an extraordinarily detailed and high-resolution view of the HIV-1 genome inside authentic virions that reflects multiple biologically important RNA–RNA and RNA–protein interactions . To identify and characterize virion-specific RNA conformational changes and RNA–protein interactions , we used hSHAPE to analyze the structures of four states in total . In addition to ( 1 ) genomic RNA inside virions ( the in virio state ) , we compared the structure of the in virio state with three simpler states involving ( 2 ) authentic HIV-1 genomic RNA that had been gently , but completely , deproteinized and extracted from virions ( ex virio ) , ( 3 ) genomic RNA inside virions , but in which nucleocapsid-RNA interactions were selectively disrupted in situ by treatment with Aldrithiol-2 ( AT-2 treated , described in detail below ) , and ( 4 ) a 976-nt HIV-1 transcript generated in vitro in the absence of any viral component . We used the protein-free ex virio RNA as the reference state for comparison . This RNA state is strongly influenced by the authentic virion environment but simultaneously lacks the complex influence of bound proteins . We constructed quantitative , single-nucleotide resolution profiles for the first 900 nts , or 10% of the HIV-1 genome , for each of these four states by combining the information from overlapping , and highly reproducible ( Figure 3A ) hSHAPE reads . Two to three independent repetitions were obtained for each primer; standard deviations are small , on the order of 0 . 1 normalized SHAPE unit or less . By combining the results of 32 individual hSHAPE experiments of approximately 300 nts each , we obtained structural information for 94% of all nucleotides in these four states plus analysis of three structural mutants , for a total analysis of 9 , 100 nts ( Figures 3B and S1 ) . The nucleotide-resolution SHAPE reactivities for the large analyzed region of the HIV-1 genome represent an unprecedented amount of structural information with which to characterize RNA structure , protein binding sites , and conformational changes that differentiate our four genome states . However , comprehensive single-nucleotide resolution data do not , by themselves , yield a secondary structure model for an RNA . We incorporate SHAPE reactivity information as an additional quasi-energetic constraint into an existing thermodynamic model [27 , 28] for RNA secondary structure prediction . Algorithms used to predict RNA structures from sequence show large increases in accuracy when experimental constraints are included in the prediction [28–30] . Ongoing work from our laboratories shows that using SHAPE information to constrain RNA secondary structure prediction has a dramatic impact on the accuracy of predicted structures . For example , prediction accuracy improves from 52% to 90% for the RNase P specificity domain [30] and from 38% to approximately 90% for the 1 , 542-nt Escherichia coli 16S rRNA ( unpublished data ) . These predictions feature overall topologies that closely resemble the correct structure , with errors generally limited to local rearrangements at multi-helix junctions . SHAPE reactivities provide model-free information about the extent of structure at each nucleotide . Therefore , in addition to proposing a complete secondary structure for our ex virio reference state ( Figure 4A ) , we directly assessed the well determinedness of each helix in the secondary structure by increasing the relative weight of the SHAPE information in calculating low free-energy structures . We term this analysis the “pairing persistence” of each helix . Highly persistent helices ( black and purple bars indicating base pairing in Figure 4A ) form even when SHAPE reactivities were used to impose large pairing penalties for even slightly reactive nucleotides . Less persistent helices ( blue and green bars , Figure 4A ) form only when the SHAPE contributions to the constraints are decreased . The 5′ end of the HIV genome contains two functional regions whose boundary is the AUG start codon for the Gag coding sequence ( nucleotides 336–338; in bold , Figure 4A ) . Positions 5′ of the AUG start codon form a 340-nt–long noncoding regulatory domain that plays multiple important roles in the viral replication cycle . In contrast , nucleotides 3′ of the start codon contain the Gag protein coding region , of which we have analyzed approximately 560 nts . An important unresolved issue has been whether regulatory motifs might be identified from single RNA sequences . This is a challenging problem , and it is currently not possible to distinguish coding versus noncoding regions by thermodynamic secondary structure prediction alone [31 , 32] . hSHAPE analysis directly addresses this issue in two ways . First , the median SHAPE reactivity , a metric for the typical amount of structure in the two regions , is 0 . 14 for the 5′ regulatory domain and 0 . 40 for the 3′ mRNA-like region ( dashed lines , Figure 4B ) . Differences in SHAPE reactivities between the two regions are statistically significant ( Wilcoxon rank sum test p-value < 0 . 0001 ) with both the median and overall distribution being lower in the 5′ regulatory domain ( Figure 4C ) . The inflection point in SHAPE reactivities is nearly coincident with the AUG start codon ( gray bar , Figure 4B ) . Second , the predicted secondary structure model can be used to infer the density of stable base pairing interactions in the 5′ regulatory versus 3′ coding regions . Nucleotides in the 5′ regulatory domain are 1 . 7 times more likely to be paired than those in the 3′ coding region ( Figure 4A ) . Although there are some flexible regions in the 5′ regulatory domain and a few stable duplexes in the 3′ coding region , overall there is a strong and statistically significant difference in the amount of structure in these two regions . Thus , by both criteria , hSHAPE clearly distinguishes between regulatory and coding regions within the HIV-1 genome because the noncoding regulatory domain is more highly structured than are coding sequences . Comparison of the complete SHAPE reactivity profiles for the ex virio reference state with three other very different states—in virio , in virio with compromised nucleocapsid–RNA interactions , and an in vitro transcript—unexpectedly reveals that these four distinct states contain extensive regions with essentially identical structures ( Figure 3B ) . This is a remarkable result , considering the different biochemical histories of the four states . At the two extremes , the in virio RNA was maintained in its native conformation inside virions throughout the chemical interrogation step , whereas the in vitro transcript RNA did not interact with any authentic viral component , but instead , was synthesized and refolded entirely in vitro . We performed several control experiments to test how well hSHAPE detects significant , but local , differences in RNA conformation . These experiments encompassed analyzing an in vitro transcript containing a deletion of the U22CU24 bulge in TAR , comparing the 976-nt in vitro transcript with a 3′ deletion that disrupts the pseudoknot at positions 79–85/443–449 , and dissociating tRNA ( lys3 ) from the ex virio RNA by a heating step . In each case , we detected strong changes in SHAPE reactivity exactly in the region whose structure was disrupted ( see Figure S1 ) . These experiments are consistent with prior work that demonstrates that SHAPE is exquisitely sensitive to small changes in local RNA structure [23 , 29 , 30 , 33–36] and indicates that if structural differences exist between any two of our HIV-1 states , they are detectable by hSHAPE . In all normal retroviral particles , the genomic RNA exists as a dimer , with similar or identical RNA strands linked together by a limited number of base pairs and tertiary interactions . The dimeric structure appears to be a critical element in the selective packaging of the genome [37 , 38] and in template switching between two RNA monomers during reverse transcription that leads to recombination [39–41] , a major source of genetic variation for retroviruses . Dimerization is a complex process that may involve multiple stem-loops in the genome , but no single motif has been shown to be absolutely necessary for dimerization . In order to assess the dimeric nature of our in vitro transcript , we resolved monomeric and dimeric conformations of this RNA in nondenaturing gels ( Figure 5A ) . When the gel does not contain MgCl2 , the HIV transcript RNA has a mobility identical to a monomer marker . In contrast , when Mg2+ is added to the gel and running buffer , the transcript runs as a well-defined species consistent with formation of a dimer . This is exactly the behavior expected if the in vitro transcript forms one or more Mg2+-dependent loop-loop interactions . A similar loop-loop dimer state has been identified for the Moloney murine sarcoma retrovirus [33 , 42] . Dimerization is thought to involve an initial loop-loop interaction [43] at the self-complementary sequence G257CGCGC262 . We find that these nucleotides are unreactive in both the in vitro transcript and ex virio states ( boxed nucleotides , Figure 4A ) . Thus , both SHAPE reactivities and the Mg2+-dependent behavior of the in vitro transcript strongly support formation of intermolecular base pairs at this loop . In contrast , the genomic RNA obtained from HIV viral particles forms a dimer that is stable even when subjected to gel electrophoresis in the absence of Mg2+ [37] . We therefore sought to identify the structural differences that distinguish the loop-loop dimer formed by the in vitro transcript and the stable dimer in the ex virio state . However , we were unable to detect any statistically significant reactivity differences between the in vitro transcript and ex virio RNAs , including in sequences flanking the 257–262 loop ( compare red and black traces in Figures 3B and 5B ) or in base-paired regions of the TAR stem . Nucleotide resolution SHAPE data are therefore inconsistent with models derived from in vitro experiments that postulate [40 , 43] that dimerization in mature viruses is mediated by the formation of stable intermolecular duplexes involving the stem sequences adjacent to the DIS and TAR loops . However , our analysis based on HIV-1 genomic RNA purified from virions is consistent with recent in vivo experiments that show neither the TAR stem [44] nor the ability to form intermolecular base pairs in the DIS stem [45] are required for viral replication . Instead , we suggest that HIV-1 genome dimerization is mediated by loop-loop interactions alone , potentially augmented by interactions that have not yet been identified . Another possibility is that large-scale structural changes occur , but yield almost identical local nucleotide flexibilities in the pre- and post-dimer RNAs . We think this is a remote possibility , given the ability of hSHAPE to detect small changes in local RNA structure ( Figure S1 ) . Finally , within the predominant state for this large region of the HIV-1 genome , there are numerous regions that are persistently accessible to SHAPE chemistry . These regions are expected to hybridize readily with complementary sequences , including antisense and RNA interference ( RNAi ) -based oligomers , and represent multiple new and attractive targets for anti-HIV therapeutics ( red and orange positions , Figure 4A ) . Reactivity profiles for the four states analyzed in this work do show limited structural differences , which are consistent with important , but local , RNA conformation and protein binding effects . Because hSHAPE gives quantitative information at each nucleotide position , structural differences are readily detected both by comparing two reactivity profiles or by subtracting one profile from another to yield a difference plot ( for example , see lower panel in Figure 5B ) . Over the 900 nts of analyzed sequence , there is a single region that shows significant differences between the ex virio reference state and the transcript RNA , refolded in vitro . This region lies between nucleotides 160 and 200 ( Figure 5B ) . The most dramatic difference is that the ex virio state is much less reactive at positions 182 to 199 ( Figure 5B ) . This region maps exactly to the main tRNA ( lys3 ) primer binding site [1] and indicates that the primer is tightly paired to the HIV-1 RNA genome in viral particles . The in vitro transcript , which is not bound by tRNA , folds into a different local structure in these regions ( compare the tRNA ( lys3 ) binding sites in Figures 4A and 5C ) . We confirmed that these structural differences reflect tRNA binding by dissociating the tRNA from the genomic RNA via a heating step ( Figure S1 ) . The tRNA primer also has the ability to form additional interactions with the genomic RNA , although current proposals for these interactions diverge widely [16 , 46 , 47] . The SHAPE data show a clear pattern in which the ex virio state is more reactive at positions 161–166 and less reactive at positions 168–170 , as compared with the in vitro transcript . These reactivity changes are consistent with tRNA ( lys3 ) -induced structural rearrangement in RNA sequences outside the primary 18-bp tRNA primer binding site . The SHAPE reactivities thus strongly support a model in which the cellular tRNA ( lys3 ) molecule interacts with the HIV-1 genome via three distinct base-pairing interactions ( Figure 4A , see tRNA ( lys3 ) label ) . We analyzed the structure of HIV-1 genomic RNA inside native virions by treating intact viral particles with NMIA and then extracting and processing the modified RNA ( the in virio state; see scheme in Figure 6A ) . Over large regions of the HIV-1 genome , SHAPE reactivity patterns for the in virio and protein-free ex virio states are very similar . These data emphasize that NMIA readily penetrates the viral membrane and yields quantitative information about HIV RNA structure in the context of the complex virion environment . We also observe reproducible local differences between the ex virio and in virio states that typically span 4–6 nts ( compare red and green profiles , Figure 7A ) . These differences could reflect either different local RNA conformations , like those identified in the tRNA binding region ( Figures 4A and 5C ) , or the effects of binding by viral proteins . The most prominent protein ligand for genomic RNA in mature HIV virions is the nucleocapsid protein [3 , 6] . The nucleocapsid protein functions in two , almost diametrically opposed , ways . As part of the Gag precursor , the nucleocapsid domain binds specifically to the HIV genome to direct packaging of this RNA into nascent virions . As the mature nucleocapsid , this protein is thought to interact nonspecifically with RNA and thereby destabilize RNA duplexes and facilitate strand rearrangement and annealing events . These activities are mediated , in part , by two copies of a CX2CX4HX4C motif that coordinate a zinc ion to form a compact “zinc knuckle” domain ( Figure 6C ) . This domain has been shown to bind guanosine in model systems [7] . Thus far , it had not been possible to convincingly identify specific sites in the genome that bind or are destabilized by nucleocapsid or Gag proteins in virions . In this work , we took advantage of the prior discovery that “zinc ejecting” agents like 2 , 2′-dithioldipyridine ( or Aldrithiol-2 [AT-2]; Figure 6B ) covalently modify cysteine residues in the zinc knuckle motifs and disrupt interactions between the zinc ion and its cysteine ligands . Treatment of viral particles with AT-2 promotes formation of reagent-nucleocapsid and nucleocapsid–nucleocapsid crosslinking and efficiently compromises nucleocapsid–RNA interactions [48–50] . AT-2–treated virions retain the ability to bind to target cells and to undergo membrane fusion as well as native viral particles; however , these virions are not infectious and cannot undergo the first steps of reverse transcription [49 , 50] . Thus , treatment with AT-2 severely compromises the RNA-binding activity of the nucleocapsid protein , but leaves the surface of the virus particle intact [49 , 50] . To detect nucleocapsid–RNA interactions inside intact viral particles , we therefore treated virions with AT-2 and then analyzed the structure of the resulting genomic RNA using hSHAPE ( see scheme , Figure 6B ) . This approach constitutes a “reverse footprinting” experiment in which the effects of nucleocapsid–RNA interactions are detected by disrupting the zinc knuckle-RNA interactions inside virions . Three initial conclusions are apparent from comparison of the in virio with the ex virio and AT-2-treated states . First , regions showing strong changes in SHAPE reactivity in the AT-2-treated state almost always resemble the protein-free ex virio state ( compare blue and red traces , Figures 7A and 8A ) . These experiments confirm that AT-2 treatment does disrupt nucleocapsid–RNA interactions inside virions , but importantly , that compromising the zinc knuckle structures in the nucleocapsid protein does not cause formation of nonspecific aggregating interactions with the genomic RNA . Second , disrupting nucleocapsid–RNA interactions by AT-2 treatment both increases ( Figure 7 ) and decreases ( Figure 8 ) local nucleotide flexibility in distinct genome regions . The strongest and most densely arrayed effects of AT-2 treatment lie in the 5′ regulatory domain ( upward and downward pointing arrowheads , respectively; Figure 4A ) . Thus , even in the context of an intact HIV-1 genome , nucleocapsid proteins recognize smaller motifs within this large RNA . Third , we observe three different interaction motifs between nucleocapsid protein and the HIV-1 genome . In order to systematically locate and characterize these motifs , we subtract the SHAPE reactivities of the in virio state from the AT-2–treated state , and plot the smoothed differences ( Figures 7B and 8B ) . These difference plots reveal locations in the RNA that are reproducibly affected by nucleocapsid protein binding . Eleven sites show statistically significant ( p < 0 . 001 ) increases in SHAPE reactivity in the AT-2–treated state . Seven of these lie in a single compact domain involving positions 224–334 in the genome ( cyan , Figure 4A ) . The strongest single effect of compromising nucleocapsid–RNA interactions occurs at positions 271–274 , followed closely by positions 239–244 ( emphasized with double asterisks and upwards pointing arrowheads in Figures 4A and 7B ) . These sites , which have not been previously implicated in nucleocapsid or Gag recognition , are likely to be the primary interaction motifs for the viral nucleocapsid protein domain at the 5′ end of the HIV-1 genome . We also identify other sites with smaller AT-2–induced reactivity changes , which we characterize as secondary ( single asterisks; positions 224–227 and 308–312 , Figure 4A ) and tertiary sites ( positions 289–292 , 318–320 , and 326–329 ) . All seven sites feature a general structural consensus characterized by a G-rich single-stranded sequence flanked by stable helices . The remaining four sites that are not clustered in the nucleocapsid interaction domain also occur at G-rich single-stranded regions adjacent to a stable helix ( positions 201–205 , 381–398 , 553–558 , and 606–609 ) . In sum , these experiments indicate that hSHAPE directly identifies RNA interaction motifs inside native virions , that nucleocapsid protein binds at specific sites , and that many nucleocapsid-binding motifs are clustered in a domain within the 5′ regulatory region of the HIV-1 genome . In addition to recognizing specific motifs in the HIV-1 genome , the nucleocapsid protein also functions to destabilize short RNA helices [6 , 7] . If the nucleocapsid protein were functioning as a duplex destabilizer inside virions , compromising the activity of nucleocapsid ( by AT-2 treatment ) would reduce local nucleotide flexibility , and thus decrease SHAPE reactivities , at defined sites . Nucleocapsid-mediated destabilization of HIV RNA genome structure is thus reported as negative peaks in a difference analysis ( Figure 8B ) . Comparison of SHAPE reactivities for the in virio state with those for the AT-2 state in a difference analysis indicates that there are two statistically significant ( p < 0 . 001 ) classes of sites in which local RNA structure is destabilized by intact nucleocapsid protein . A series of very strong effects is seen over the first 185 nts of the genomic RNA ( emphasized with solid gray arrows , Figures 4A and 8B ) . No other region in the first 900 nts of the HIV-1 genome shows this concentrated and large-magnitude structural destabilizing activity by nucleocapsid . We also observe additional sites that lie at relatively widely spaced intervals in the coding region that experience enhanced SHAPE reactivity upon compromising the zinc knuckle motif ( dashed gray arrows , Figure 4A ) . Thus , these experiments emphasize the unanticipated result that the helix-destabilizing activity of the nucleocapsid protein does not fall randomly throughout the genomic RNA , but instead , has its greatest effect in a compact domain immediately 5′ to the tRNA binding site .
The first 10% of the HIV-1 RNA genome represents a microcosm of many possible classes of interactions that occur in any large cellular RNA . This RNA contains an extensive noncoding regulatory domain and also serves as a template for protein synthesis . The RNA also contains large-scale structures that are important for forming a dimeric state that facilitates genetic recombination , for base pairing with a specific cellular tRNA , and for binding of the viral Gag protein or nucleocapsid domain . A complete understanding of HIV RNA genome structure holds promise for identifying new targets for anti-HIV interventions . A critical attribute of hSHAPE analysis is that it is both more complete and quantitative than conventional approaches . This difference is readily seen by comparing the single-nucleotide resolution SHAPE information with prior analyses of HIV genome structure ( Figure 9 ) . It is clear that all of these studies are mapping nearly identical RNA conformations . Nucleotides reported to be reactive towards single-strand selective enzymatic [15] or chemical [8 , 16] reagents almost always have high SHAPE reactivities; whereas , nucleotides that are cleaved by V1 nuclease , which shows a preference for double-stranded RNA , exhibit low SHAPE reactivity . Our secondary structure model is most similar to the proposal of Damgaard et al . [15] , but still contains many substantive differences with respect to this and other models that reflect three innovations unique to the hSHAPE approach . First , hSHAPE yielded structural information for 94% of all nucleotides in the analyzed region , which is far more than any prior effort . Near completeness is essential because RNA secondary structure prediction in the absence of constraining reactivity data yields structures with significant errors , especially as RNA length increases [51–53] . In the case of HIV-1 genomic RNA , relatively little data had been obtained for positions 110–145 , 220–243 , 276–282 , 370–450 , and 465–530 , and no data were available 3′ of position 720 . Second , our model reduces the influence of end-effect artifacts that occur with analysis of small RNA fragments because it is generated via hSHAPE using full-length genomic RNA . For example , structures that involve or lie inside of long-range interactions , such as the 108–114/335–341 stem ( called the U5-AUG interaction [15] ) are mispredicted if the RNA sequence does not include the complete domain . Third , incorporation of SHAPE reactivity information as a pseudo-free energy change term makes the structure prediction calculation relatively insensitive to errors in any single reactivity measurement . hSHAPE analysis of four biologically relevant states of the HIV-1 genome indicates that this RNA has a single , strongly conserved structure . This result challenges previous proposals for multiple conformations in this region of the genome [13 , 40 , 43] and likely reflects that we maintained the native conformation of authentic , long HIV-1 RNAs . Additional conformations may exist at other stages of the viral infectivity cycle . hSHAPE analysis also indicates that regulatory and protein coding regions in HIV-1 are structurally distinct as judged by their quantitative SHAPE reactivities . Regulatory domains are more highly structured than coding sequences ( Figure 4B and 4C ) . hSHAPE may be a broadly useful tool for identifying highly structured regulatory motifs in other viral and cellular RNAs . Understanding the RNA binding specificity and functions of the HIV nucleocapsid protein has proven to be challenging both because nucleocapsid has opposing specific and nonspecific binding activities and also because preferred RNA binding sites had not been clearly defined . In addition , previous in vitro mapping experiments using dimethyl sulfate failed to identify any HIV RNA–protein interactions [16] , likely because the limited structural sensitivity of this reagent . To overcome these challenges , we invented experiments that focused on the native RNA binding activity of the nucleocapsid protein inside wild-type virions . We used the zinc ejecting agent AT-2 [50] ( Figure 6 ) to compromise nucleocapsid–RNA interactions inside virions . The effects of disrupting nucleocapsid–RNA interactions by AT-2 treatment are highly specific because changes in SHAPE reactivity are always localized to a small set of continuous nucleotides at each site . Notably , we observe structural changes consistent with both the specific RNA binding and with the duplex destabilizing activities of nucleocapsid . As judged by ( 1 ) conserved sequences and structures and ( 2 ) their location in the genome , we identify three classes of nucleocapsid protein interactions with the HIV-1 genome . For all three classes , measured differences between the in virio and AT-2–treated states were highly statistically significant ( p < 0 . 001 ) , whereas , reactivity differences between nucleotides outside of these sites were statistically equivalent . The first class is a specific nucleocapsid binding site motif , and the other two classes are sites at which nucleocapsid destabilizes RNA duplex structure . Using hSHAPE technology , we measured structure at 94% of the first 900 nts of the HIV-1 genome under seven sets of instructive conditions for a total analysis of 9 , 100 nts . Comparing structural information from these states indicates that large regions of the HIV-1 genome form a single structure , that regulatory motifs in the genome are highly base paired , that specific and nonspecific RNA binding modes of the nucleocapsid protein are manifest via interactions with domain-like elements of the RNA , and that the duplex destabilizing activity of nucleocapsid is mediated by two distinct classes of RNA sites . Because of its completeness , hSHAPE analysis provides information sufficient to discriminate between otherwise contradictory models for the HIV-1 genome . Just as DNA sequencing has revolutionized our understanding of DNA genome function , high-throughput RNA structure analysis makes possible investigation of intact RNAs from any viral or cellular transcriptome , as a function of multiple biological states . We anticipate that hSHAPE will contribute significantly to establishing the connections between RNA structure and translational regulation , alternative splicing , binding by small interfering and related small RNAs , higher-order folding and packaging in viruses , and many other RNA-based processes .
VSV-G pseudotyped HIV-1 NL4–3 viral particles were produced by cotransfecting the pNL4–3 and pHCMV-G ( VSV-G protein expression construct ) [60] plasmids at a ratio of 3:1 into 293T cells as described [61] , except that TransIT293 ( Mirus Bio ) was used as the transfection agent . In sum , 40 × 150-cm2 culture flasks , seeded at a density of 3 × 106 293T cells , were transfected with plasmids 72 h later . Cultures were then incubated for 48 h and supernatants harvested , clarified by centrifugation at 5 , 000 g for 10 min , filtered through a 0 . 2-μm membrane , and stored at 4 °C overnight . Cultures were incubated for an additional 24 h with fresh culture medium , and virus-containing supernatant was again collected using the same procedure . Supernatants from both harvests were pooled and incubated at 4 °C in preparation for treatment with the AT-2 and NMIA reagents . Viral genomes were quantified by real-time reverse-transcription PCR ( RT-PCR ) [61]; the yield is typically 20 pmol HIV-1 RNA genomes/l cell culture . Aldrithiol-2 ( AT-2 , systematic name 2 , 2′-dithiodipyridine; 0 . 5 M in DMSO , 2 . 0 ml ) or DMSO ( 2 . 0 ml ) was added to 1 . 0 l of virus-containing supernatant and incubated overnight at 4 °C . Virus particles from the ( + ) and ( − ) AT-2 experiments were pelleted separately by centrifugation ( 110 , 000 g , 4 °C , 1 . 5 h ) through a 20% ( w/v ) sucrose cushion in phosphate buffered saline . Pellets from 0 . 5 l of culture fluid were resuspended in 1 . 0 ml of NMIA reaction buffer ( 50 mM Hepes [pH 8] , 200 mM NaCl , 0 . 1 mM EDTA , and 10% fetal bovine serum ) . Concentrated samples of either purified viral particles or particles treated with AT-2 ( 500 μl ) in NMIA reaction buffer were treated with NMIA ( 50 μl , 100 mM in DMSO ) or neat DMSO ( 50 μl ) for 50 min at 37 °C . The virus particle production , AT-2 treatment , and NMIA modification steps were always performed as a single continuous process and without intermediate storage steps . RNA genomes subjected to reaction with NMIA in virio were gently extracted from viral particles as described [37] . In sum , concentrated samples of virus particles ( in 550 μl of NMIA reaction buffer ) were incubated at 22 °C with 5 μl of proteinase K ( 20 mg/ml ) , 33 . 5 μl of 1 M Tris-HCl ( pH 7 . 5 ) , 13 . 4 μl of 5 M NaCl , 1 . 34 μl of 0 . 5 M EDTA , 6 . 7 μl of 1 M DTT , and 4 μl of glycogen ( 20 mg/ml ) for 30 min . RNA was purified by three consecutive extractions with phenol:chloroform:isoamyl alcohol ( 25:24:1 ) , followed by precipitation with ethanol . Samples were resuspended in 1/2× TE to a concentration of 0 . 5 μM , based on quantitative RT-PCR analysis . For the ex virio state , pelleted viral particles were dissolved in 1 ml of 50 mM Hepes ( pH 8 . 0 ) , 0 . 5 mM EDTA , 200 mM NaCl , 1% ( w/v ) SDS , and 100 μg/ml proteinase K and digested for 30 min at 22 °C . The RNA was then extracted against phenol-chloroform , and the resulting deproteinized genomes were then aliquoted ( 2 pmol ) and flash frozen at −80 °C . For SHAPE analysis , the ex virio RNA was treated with NMIA using the same procedure as for modification of the in vitro RNA ( described below ) , except that the initial 90 °C heat step was omitted , and the time for incubation in folding buffer was reduced to 10 min . A DNA template encoding the 5′ 976 nts of the HIV-1 genome and containing a promoter for T7 RNA polymerase was generated by PCR ( 2 ml; 20 mM Tris [pH 8 . 4] , 50 mM KCl , 2 . 5 mM MgCl2 , 0 . 5 μM forward [5′-TAATA CGACT CACTA TAGGT CTCTC TGGTT AGACC] and reverse [5′-CTATC CCATT CTGCA GCTTC C] primers , approximately 1 μg of plasmid template containing a partial sequence of the HIV-1 pNL4–3 molecular clone [obtained from the National Institutes of Health AIDS Research and Reference Reagent Program] , 200 μM each dNTP , and 25 units Taq polymerase; 34 cycles ) . The PCR product was recovered by ethanol precipitation and resuspended in 300 μl of TE ( 10 mM Tris [pH 8] , 1 mM EDTA ) . Transcription reactions ( 3 ml; 37 °C; 5 h; 40 mM Tris [pH 8 . 0] , 5 mM MgCl2 , 10 mM DTT , 4 mM spermidine , 0 . 01% Triton X-100 , 4% [w/v] PEG 8000 , 300 μl of PCR product , and 2 mM each NTP ) were initiated by adding 300 μl of 1 mg/ml T7 RNA polymerase [62] . The RNA product was precipitated and purified by denaturing polyacrylamide gel electrophoresis ( 5% acrylamide , 1× TBE 7 M urea ) , excised from the gel , and recovered by electroelution . The purified RNA ( 0 . 6 nmol ) was resuspended in 100 μl of TE . RNA transcript ( 0 . 54 pmol , 3 μl , 1/2× TE ) was combined with internally [32P]-labeled transcript RNA ( 7 fmol , 1 μl ) and denatured by heating at 95 °C for 5 min and placing on ice . Folding buffer ( 1 μl of 5×; 250 mM Hepes [pH 8] , 1 M potassium acetate [pH 8] , 25 mM MgCl2 ) was added , and the sample was incubated at 37 °C for 1 h . Dimer markers were generated by the same procedure , except that 5 pmol of RNA were used and incubation was at 60 °C; monomer markers were generating by heating labeled transcript RNA ( 7 fmol , 5 μl ) to 95 °C for 10 min in 1/2× TE . Gel loading dye ( 30% glycerol , 1× TBE , 7 mM MgCl2 , 0 . 01% xylene cyanol; 1 . 67 μl ) was added , and all samples were incubated on ice until resolution by electrophoresis ( 2 μl of sample per well , 4% polyacrylamide , 1× TBE , 7 W , 2 . 5 h ) either in the absence or presence of 7 mM MgCl2 . If MgCl2 was present during the separation , it was added to both the running buffer and the gel; the running buffer was recycled every 10 min to maintain a constant [Mg2+] in the gel . RNA ( 2 pmol ) in 14 . 4 μl of 1/2× TE was refolded by heating at 95 °C , placing on ice , adding 3 . 6 μl of folding buffer , and incubating at 37 °C for 60 min . The folded RNA was divided equally between two tubes and treated with either NMIA [22 , 23] ( 1 μl , 32 mM in DMSO ) or neat DMSO ( 1 μl ) and allowed to react for 60 min at 37 °C . RNA from the ( + ) and ( − ) NMIA reagent experiments was recovered by ethanol precipitation and resuspended in 10 μl of TE . In vitro transcript or authentic genomic RNA ( 1 pmol , 10 μl , in 1× TE ) corresponding to either the ( + ) or ( − ) NMIA reactions was heated to 95 °C for 3 min and cooled on ice for 1 min . Fluorescently labeled primer ( 3 μl ) was added to the ( + ) ( 0 . 2 μM Cy5 ) and ( − ) ( 0 . 4 μM WellRED D3 ) NMIA reactions , respectively , and primer-template solutions were incubated at 65 °C for 5 min and 35 °C for 10 min . Primer extension was initiated by addition of enzyme mix ( 6 μl; 250 mM KCl; 167 mM Tris-HCl [pH 8 . 3]; 1 . 67 mM each dATP , dCTP , dITP , and dTTP; 10 mM MgCl2; 52 °C , 1 min ) and Superscript III ( 1 μl , 200 units; Invitrogen ) . Extension continued at 52 °C for 15 min . Sequencing reactions used to identify peaks in the ( + ) and ( − ) reagent experiments contained transcript RNA ( 1 pmol , in 9 μl of TE ) , 3 μl of primer ( 2 μM WellRED D2 or 1 . 2 μM LICOR IR 800 ) , enzyme mix ( 6 μl ) , ddNTP solution ( 1 μl; 0 . 25 mM ddGTP and 10 mM other nucleotides ) , and Superscript III ( 1 μl ) . Four sets of primers were used that were complementary to positions 342–363 ( 5′-CGCTT AATAC CGACG CTCTC GC ) , 535–555 ( 5′-CTTCT GATCC TGTCT GAAGG G ) , 743–762 ( 5′-CCATT TGCCC CTGGA GGTT C ) , or 956–976 ( CTATC CCATT CTGCA GCTTC C ) . Depending on the quality of synthesis , primers were purified by denaturing gel electrophoresis ( 20% polyacrylamide , 1× TBE , 7 M urea; dimensions 0 . 75 cm × 28 . 5 cm [w] × 23 cm [h]; 32W; 90 min ) and passively eluted into 1/2× TE overnight . The four reactions corresponding to a complete hSHAPE analysis ( ( + ) NMIA , ( − ) NMIA , and two sequencing reactions ) were combined and precipitated with ethanol in the presence of acetate , EDTA , and glycogen . Pellets were washed twice with 70% ethanol , dried under vacuum , and resuspended in deionized formamide . cDNA samples were separated on a 33-cm × 75-μm capillary using a Beckman CEQ 2000XL DNA sequencer . Raw fluorescence intensity versus elution time profiles were analyzed using a draft software suite called ShapeFinder . ShapeFinder is derived from BaseFinder [25] , which is a modular , extensible software package originally designed for DNA base calling and sequence analysis , and is currently being refined for analysis of quantitative hSHAPE reactivity information ( S . M . Vasa , N . Guex , K . A . Wilkinson , K . M . Weeks and M . C . Giddings , unpublished data ) . For readers interested in immediate access to this software , a beta version is available for download at http://rd . plos . org/pbio . 0060096 . 1 . Processing steps included ( 1 ) baseline correction , ( 2 ) color separation to correct for spectral overlap of the fluorescent dyes , and ( 3 ) mobility shift correction to align corresponding peaks in the four channels . Areas under each peak in the ( + ) and ( − ) NMIA traces were obtained by ( 1 ) peak detection and interpolation to align peaks in each channel with the RNA sequence and ( 2 ) performing a whole-trace Gaussian-fit integration . Integrated peak intensities were corrected for signal decay [63] as a function of read length by assuming a constant probability for extension at each nucleotide position , after excluding the 2% of most-highly reactive peaks: Where D is the signal decay adjustment factor , A and C are scaling factors that reflect the arbitrary initial and final intensities of the trace , and p is the probability of extension at each nucleotide . Typical values for p spanned 0 . 995–0 . 999 for elution times in units of 2 Hz . Each peak intensity calculated at the same elution time was divided by D . SHAPE intensities at each individual nucleotide were examined manually to identify positions where high background was present in the ( − ) reagent control experiment . A very small number of positions fell into this category ( <6% ) and were marked as containing no data . Quantitative SHAPE reactivities for individual datasets were normalized to a scale in which 0 indicates an unreactive site and the average intensity at highly reactive sites is set to 1 . 0 . The normalization factor for each dataset was determined by first excluding the most-reactive 2% of peak intensities and then calculating the average for the next 8% of peak intensities . All reactivities were then divided by this average . This simple normalization procedure places all absolute reactivities on a scale of 0 to approximately 1 . 5 ( ordinate , Figure 2D ) . Normalized hSHAPE reactivities from each primer extension reaction were processed independently . Processed traces were then found to fall on the same absolute scale , without further adjustment ( for example , compare overlap of closed and open columns , Figure 3A ) . For each state , SHAPE information was obtained by combining information from four overlapping reads of approximately 300 nts each; two to three independent repetitions were obtained for each read; and standard deviations were small , on average 0 . 1 normalized SHAPE unit or less . These experiments are outlined in Figure S1 . PCR templates for the 362-mer transcript and U22CU24-deletion mutants were produced in the context of a 3′ structure cassette [22] using the same reverse cassette primer ( 5′-GAACC GGACC GAAGC CCGAT TGGTA ACCCG AAGGT CACTT CCGCT TAATA CTGAC GCTC ) and different forward primers ( 5′-TAATA CGACT CACTA TAGGC CTTCG GGCCA AGGTC TCTCT GGTTA GACC ) and ( 5′-TAATA CGACT CACTA TAGGC CTTCG GGCCA AGGTC TCTCT GGTTA GACCA GAGAG CCTGG G ) , respectively . tRNA ( lys3 ) was dissociated from the ex virio RNA by heating to 90 °C for 3 min and refolding 10 min in reaction buffer at 37 °C . SHAPE intensities were converted into a pseudo-free energy change term in the RNAstructure program [28] ( K . E . Deigan , T . W . Li , D . H . Mathews , and K . M . Weeks , unpublished data ) using: which is applied to each nucleotide in each stack of two base pairs . Therefore , the pseudo-free energy is added twice per nucleotide paired in the interior of a helix and once per nucleotide paired at the end of a helix . The intercept , b , is the free-energy bonus for formation of a base pair with zero or low SHAPE reactivity , whereas m , the slope , drives an increasing penalty for base pairing as the SHAPE reactivity increases . The b and m parameters were −0 . 6 and 1 . 7 kcal/mol , respectively ( per nucleotide ) . The maximum allowed distance between paired bases was restrained to be 300 nts or less . Increasing the maximum pairing distance to 600 nts yielded a series of short , poorly predicted , and transient pairings . The reactivity of the nucleotides participating in these interactions could be explained by shorter distance pairings . To determine the pairing persistence , structures were also computed for larger values of the b and m parameters , which has the effect of increasing the contribution of the SHAPE reactivity information on the secondary structure calculation . Helices considered to be highly predicted persisted even when these parameters were set to values as high as b = 0 and m > 4 . RNAstructure is available for download at http://rd . plos . org/pbio . 0060096 . 2 . To analyze differences in SHAPE reactivities between groups of nucleotides , such as comparing the 5′ regulatory versus 3′ protein coding regions , several tests were applied to determine the statistical characteristics of the subgroups and to determine the appropriate statistical measures of differences . We applied Levene's test to analyze homoscedasticity , which confirmed that the SHAPE reactivity variances between analyzed groups of nucleotides are equivalent . However , quantile-quantile ( Q-Q ) plots of these data indicated departures from normality for the SHAPE reactivity data , so the standard Student t-test could not be applied . Instead , we applied the Wilcoxon rank sum test to analyze the statistical SHAPE reactivity differences between groups , which eliminates dependence on normality by rank-ordering the reactivity data and deriving statistics from the rankings . In the small number of cases in which reactivity data were missing for a nucleotide in one group , then that nucleotide was removed from the other groups in a given comparison . All analyses were performed using the open source R package ( version 2 . 6 . 1 ) [64] . Differences in structure between regulatory and coding domains . SHAPE reactivity data for the ex virio state were divided into two groups corresponding to the 5′ regulatory ( nucleotides 1–335 ) and Gag coding sequences ( nucleotides 336–906 ) and analyzed by the Wilcoxon rank sum test . Differences between the 5′ regulatory and 3′ coding regions were highly significant ( p < 0 . 0001 ) . Absence of differences between TAR and DIS SHAPE reactivities for in vitro transcript , ex virio , and AT-2–treated states . Reactivity data were analyzed for the TAR ( nucleotides 1–57 ) and DIS ( nucleotides 236–282 ) regions . Three groups of reactivities were collected , reflecting each RNA state ( in vitro , ex virio , and AT-2 treated ) with 141 and 129 nts for the TAR and DIS regions , respectively . We applied a one-way analysis of variance ( ANOVA ) to the three groups for each of the regions , to test whether they displayed statistically significant differences among groups for either region . The F ratio was calculated to determine whether the means of individual groups , corresponding to the three states , represent significant variation from the overall mean . The initial F ratio values calculated showed no significant difference between groups , with p-values of 0 . 63 for TAR and 0 . 98 for DIS , respectively . However , because SHAPE reactivities are not normally distributed , the F ratio can be inaccurate . To assess whether the initial F ratio was representative of the underlying distribution , we performed bootstrap resampling of the SHAPE data from the groups , with replacement [65] . We recalculated the F ratio for each of 15 , 000 repetitions , then calculated p-values by determining the number of times F ratios with larger ( more extreme ) values were obtained . The bootstrap–re-estimated p-values were 0 . 625 for TAR and 0 . 974 for DIS , in close agreement with the initial calculation . This indicates that the original F ratio , which showed no statistical difference between these groups , is representative of the lack of difference between the TAR and DIS structures in these three HIV-1 RNA genome states . Statistical significance of nucleocapsid binding sites . SHAPE reactivity data were divided into two groups corresponding to the in virio and AT-2–treated states . For each state , the nucleotides were divided into the three nucleocapsid protein binding classes ( Figures 7 and 8 ) . This resulted in two groups of 34 , 42 , and 28 nts for the Class 1 , 2 , and 3 sites , respectively . Each of these were compared using the Wilcoxon rank sum test . For all three classes of nucleocapsid protein interaction sites , p-values < 0 . 001 , indicating statistically significant differences in the SHAPE reactivity between in virio and AT-2–treated states . We also performed the inverse analysis , using all nucleotides that were not associated with any of the three classes of nucleocapsid sites . The Wilcoxon test yielded a p-value of 0 . 859 , indicating that nucleotides outside of these sites were statistically equivalent . Information content of nucleocapsid protein binding sites was calculated as described [54 , 55] . Because the number of sequences was relatively small , information content shows some variation depending on the specific alignment . For example , information content was 9 . 6 bits for the alignment shown in Figure 10; alternative alignments yielded information contents of 8 to 12 bits .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession number for the pNL4–3 molecular clone is AF324493 . | The function of the RNA genome of the human immunodeficiency virus ( HIV ) is determined both by its sequence and by its ability to fold back on itself to form specific higher-order structures . In order to describe physical structures in a region of the HIV RNA genome known to play multiple , critical roles in viral replication and pathogenesis , we invent a high-throughput , quantitative , and comprehensive structure-mapping approach that locates flexible ( unpaired ) nucleotides within a folded RNA , assaying hundreds of nucleotides at a time . We find that the first 10% of the HIV-1 genome has a single predominant structure and that regulatory motifs have significantly greater structure than do protein-coding segments . The HIV genome interacts with numerous proteins , including multiple copies of the nucleocapsid protein . We directly map RNA–protein interactions inside virions and discover that the nucleocapsid prottein interacts with viral RNA in at least three distinct ways , depending on the context within the overall genome structure . Further application of the high-throughput RNA-structure analysis tools described here will make it possible to address diverse structure–function relationships in intact cellular and viral RNAs . | [
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"biochemistry",
"infectious",
"diseases",
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] | 2008 | High-Throughput SHAPE Analysis Reveals Structures in HIV-1 Genomic RNA Strongly Conserved across Distinct Biological States |
Hematophagous vectors strictly require ingesting blood from their hosts to complete their life cycles . Exposure of the alimentary canal of these vectors to the host immune effectors necessitates efficient counteractive measures by hematophagous vectors . The Anopheles mosquito transmitting the malaria parasite is an example of hematophagous vectors that within seconds can ingest human blood double its weight . The innate immune defense mechanisms , like the complement system , in the human blood should thereby immediately react against foreign cells in the mosquito midgut . A prerequisite for complement activation is that the target cells lack complement regulators on their surfaces . In this work , we analyzed whether human complement is active in the mosquito midgut , and how the mosquito midgut cells protect themselves against complement attack . We found that complement remained active for a considerable time and was able to kill microbes within the mosquito midgut . However , the Anopheles mosquito midgut cells were not injured . These cells were found to protect themselves by capturing factor H , the main soluble inhibitor of the alternative complement pathway . Factor H inhibited complement on the midgut cells by promoting inactivation of C3b to iC3b and preventing the activity of the alternative pathway amplification C3 convertase enzyme . An interference of the FH regulatory activity by monoclonal antibodies , carried to the midgut via blood , resulted in increased mosquito mortality and reduced fecundity . By using a ligand blotting assay , a putative mosquito midgut FH receptor could be detected . Thereby , we have identified a novel mechanism whereby mosquitoes can tolerate human blood .
Mosquitoes can transmit important parasitic diseases such as malaria and filariasis and viral diseases such as yellow fever , dengue , Rift Valley fever and the West Nile virus . Anopheles , Aedes , Culex , Coquillettidia , Mansonia and Ochlerotatus species are the best known disease transmitting mosquitoes[1] . They all require a blood meal to obtain proteins from their hosts . Blood proteins are needed for the development and laying of eggs to complete the life cycles of the mosquitoes . Parasites and viruses carried in the host blood can therefore be transmitted to other individuals of the same host species and sometimes also to other species if the organisms can multiply inside mosquitoes and survive in the new hosts . Ingestion of host blood has been suggested to pose a danger to mosquitoes as a result of exposing the alimentary canal ( AC ) to bioactive molecules that normally exist in host blood as part of the host defense mechanisms against microbes . Likewise , other ingested blood-derived factors such as antibodies , hemoglobin-derived peptides , enzymes and signaling molecules could alter the physiology of hematophagous vectors ( reviewed in[2] ) . The most immediate system that has been shown to be overcome by mosquitoes and other hematophagous vectors is the coagulation system[3] . Mosquitoes’ and ticks’ salivary molecules were found to inhibit blood clotting at the biting site . The injected saliva contained anti-coagulants that permitted smooth flow of blood from the skin of the host to the vector and prevented blockage of the blood sucking capillary[3] . The complement system is a host defense mechanism that could impose danger to disease vectors upon blood feeding . It is a cascade that attacks the surfaces of foreign cells[4] . Complement plays a central role in the innate immune response to combat microbial infections . There are three pathways to activate complement , the classical , the alternative and the lectin pathway . The classical pathway is triggered when C1 interacts with antibodies bound to their antigens . This results in the cleavage of C4 and C2 and the formation of the classical pathway C3-convertase , C4b2a , which cleaves C3 into C3b . The lectin pathway is activated when the mannan-binding lectin ( MBL ) or one of the three ficolins binds to sugar residues ( Man , GalNAc or acetylated sugars ) on target surfaces . The alternative pathway is initiated through a spontaneous cleavage of an internal thioester bond in C3 . All three pathways converge in the cleavage of the C3 protein . This will direct phagocytosis of targets and leads ultimately to the formation of membrane attack complexes ( MAC ) . MAC is a pore that can cause damage or lysis of the target cells . Under normal circumstances , activation of complement is kept under tight control by the coordinated action of soluble ( e . g . C1-INH , C4bp , factor H , factor I , S-protein and clusterin ) and membrane-associated ( DAF , CR1 , MCP , CRIg and CD59 ) complement regulatory proteins[5] . The control of complement activity involves inhibition of assembly or dissociation of C3 convertases and the inhibition of the membrane attack complex formation[5] . Among the complement regulators , factor H ( FH ) plays an integral role in controlling complement activation . It regulates the positive feedback loop of the alternative pathway , which could otherwise lead to excessive activation of C3 and damage to nearby cells [6 , 7] . FH regulates complement activation on self-cells surfaces by possessing both cofactor activity for the factor I-mediated C3b cleavage , and decay accelerating activity to dissociate the C3-convertase , C3bBb . FH protects self surfaces because it binds to glycosaminoglycans ( GAGs ) and other polyanions that are generally present on host cell surfaces . Microbes usually lack these structures [6 , 7] . Given the importance of FH in controlling complement activation , several pathogens that are known to resist complement-mediated killing have been shown to express FH-binding proteins on their surfaces [5] . We hypothesized that this strategy could be used by the hematophagous vectors , as well , to avoid complement-mediated damage when exposed to blood . We therefore analyzed how complement is regulated in the midgut after a blood meal . We observed that complement remained active and could become activated in the mosquito midgut . This suggested that an evasion mechanism to avoid the detrimental consequences of complement activation must exist in the mosquito midgut cells . Accordingly , FH was found to bind to the luminal surface of the mosquito proventriculus ( located between the esophagus and anterior midgut ) , the anterior midgut and the anterior-posterior region of the midgut . Complement activation on these surfaces was shown to be prevented . Taken together , complement activation was shown to occur in the mosquito midgut but the Anopheles mosquitoes were able to protect themselves at this critical site via a novel evasion mechanism .
Anopheles stephensi ( Nijmegen strain ) and Anopheles gambiae 4ARR ( MR4 strain ) adult mosquitoes were maintained in 20x20x20 cm gauze cages at 28°C , 80 ± 5% relative humidity , and a photo-scotophase of 12:12 light:dark . The mosquitoes had access to a 5% sucrose solution on a cotton pad . The larvae were reared in tap water on plastic trays and fed daily with Tetramin fish food . Pupae were collected daily and placed in adult cages for emergence . Adult mosquitoes were regularly fed on a blood meal that contained 1:1 human erythrocytes and normal human serum ( NHS ) using the glass membrane feeder for maintaining the mosquito rearing cycle . Cold-anesthetized mosquitoes were dissected over a sterile glass slide containing a drop of PBS under a stereomicroscope . ACs ( proventriculus , anterior and posterior midgut ) or midguts ( anterior and posterior midgut ) were isolated and placed in PBS , LB-medium , PBS-EDTA or in a commercial ELISA dilution buffer . To measure the extent of complement activation in the mosquito midguts post blood feeding ( PBF ) , A . stephensi mosquitoes were allowed to feed on a human volunteer arm and midguts were dissected from a pair of mosquitoes at 10 , 30 and 90 min PBF . Each isolated pair of mosquitoes was placed in 200 μl specimen diluent from the MicroVue C3a Plus EIA kit , macerated for 30 sec with the tip of 21G sterile needle and centrifuged at 16 , 000g for 5 min at 4°C . Supernatants were immediately frozen at -70°C until processing . The average serum content in the midgut of a BF mosquito was estimated to be about 1 μl , this value was used when estimating specimen dilution for C3a and C5b-9 ELISAs . C3a concentration ( ng/ml ) was measured using the MicroVue C3a Plus EIA kit according the manufacturer’s instructions . C5b-9 concentration ( AU/ml ) was measured as previously described[8] . To study the effect of complement in the mosquito midgut on bacterial counts ( viability ) PBF , A . stephensi mosquitoes were allowed to feed on a blood meal that contained 1:1 human erythrocytes and either NHS or HIS . At 30 , 90 and 24 h PBF , 5 mosquitoes representing each condition and time point were dissected and the isolated midguts were placed in 1 ml of LB-medium . The midguts were then macerated with 21G sterile needles and the sample tubes were vortexed for 30 sec . The standard plate counting method that involves serial dilutions , plating and counting of bacterial colonies was used under aerobic conditions to determine the number of live bacteria per midgut . Midguts from 5 sugar-fed mosquitoes were treated similarly to estimate the initial microbial load before blood feeding . A bacterial colony morphologically representing the majority of the colonies growing from either feeding on NHS or HIS based blood meal was isolated and analyzed for serum sensitivity , when incubated in 20% NHS or HIS for 1 h as previously described[9] . The identities of both bacterial strains were assessed by 16S ribosomal DNA PCR as previously described[10] . A . stephensi and A . gambiae mosquitoes were allowed to feed on a human volunteer arm . A . stephensi female mosquitoes were also fed on a blood meal that contained normal mouse serum ( containing active complement ) and mouse erythrocytes in a membrane glass feeder and blood-fed mosquitoes were left to rest for 2 h PBF . Blood- and sugar-fed ( BF and SF ) mosquitoes were then cold-anesthetized and placed in PBS , pH 7 . 4 , containing 4% paraformaldehyde for 2 h followed by the dehydration and the clearing steps prior to embedding in paraffin wax . Each block was cut into 4-μm thick sagittal sections and representative slides were stained with HE to check the quality of the mosquito sections . Prior to immunofluorescence staining , the sections were dewaxed in xylene 3x5 min , rehydrated in a series of ethanol baths ( 100 , 95 and 70% ) for 3 min each and rinsed twice in distilled H2O for 5 min each . Dewaxed slides made from BF and SF mosquitoes were then treated with 1% BSA-PBS for 30 min to prevent nonspecific protein binding . BSA-PBS was then replaced by 1:500 dilution of goat polyclonal anti-human FH ( A312 , Quidel , San Diego ) , rabbit polyclonal anti-human C3c ( A0062 , Dako ) , goat polyclonal anti-human C5 ( A306 , Quidel , San Diego ) or murine monoclonal anti-human SC5b-9 ( A239 , Quidel , San Diego ) antibodies . After a 1-hour incubation at room temperature ( RT ) in a humidified chamber , the slides were washed 3x10 min in PBS-0 . 05% Tween 20 ( PBS-T ) . Binding of primary antibodies was then detected by overlying the slides with 1:1000 dilution of Alexa Fluor 488 Donkey anti-goat IgG , goat anti-rabbit IgG or goat anti-mouse IgG ( Invitrogen ) , respectively . Cell nuclei were stained with 30 nM DAPI ( 4′ , 6-diamidino-2-phenylindole ) included in the diluted secondary antibody . Following 1 h of incubation in a dark humidified chamber at RT , slides were washed 3x10 min in PBS-T . Cover slips were mounted on slides using Mowiol-based antifading medium and kept at 4°C for at least 30 min before examining with the Olympus BX51 fluorescence microscope . Images were captured using the Olympus DP70 camera with the help of DP controller software . Slides of sugar-fed mosquitoes were treated similarly and served as negative controls for binding of the primary antibodies to mosquito proteins in the immunofluorescence assays . For colocalizing bound FH with carbohydrates present on the surface of the midgut epithelium , Alexa Fluor 594 conjugated concanavalin-A ( 25 ug/ml ) was combined with Alexa Fluor 488 Donkey anti-goat IgG that detects binding of the primary antibody to FH . Following 1 h of incubation in a dark humidified chamber at RT , slides were washed 3x5 min in PBS-T . Cover slips were mounted on slides using Mowiol-based antifading medium and kept at 4°C for at least 30 min before examining with Leica TCS CARS SP8 confocal microscope . DNA fragmentation , possibly indicative of apoptosis or cell death , was studied using fluorometric TUNEL kit ( TACS 2 TdT-Fluor In Situ Apoptosis Detection Kit , TREVIGEN , Gaithersberg ) . Briefly , A . stephensi mosquito sections on dewaxed slides were treated with proteinase K for 15 min at RT , washed 2x in deionized water for 2 min each and immersed in labeling buffer for 5 min . Sections were then covered with the labeling reaction containing TdT dNTP , Mn2+ , TdT enzyme and labeling buffer and incubated at 37 C for 1h . Labeling reaction was then stopped in stop buffer for 5 min and the slides were washed 2x in deionized water for 5 min each at RT . Sections were then covered with Strep-Alexa 488 conjugate solution and incubated for 20 min at RT followed by washing 2x in PBS for 2 min each and mounted afterwards with Mowiol mounting medium . TUNEL-positive mosquito section slides were prepared by treating dewaxed slides with TACS-Nuclease to induce DNA fragmentation . After 30 min incubation at RT slides were washed 2x in PBS for 2 min each . Induced DNA fragmentation was then detected as previously described . Blood meals that contained 1 mg/ml monoclonal anti-human FH antibody ( 131X ) and 1:1 . 1 human erythrocytes and either NHS or HIS were pre-incubated at RT for 20 min prior to A . stephensi mosquito feeding . Mosquitoes were also fed on NHS and erythrocytes in the same ratio in a third experimental condition . Cold-anesthetized blood-fed mosquitoes were separated and placed in new cages . Dead mosquitoes were counted daily for 7 days PBF . On day 8 , egg collectors were placed inside mosquito cages and the laid eggs were counted the following day . The average number of blood-fed mosquitoes from 3 biological repetitions of NHS+131X , HIS+131X and NHS test conditions was 31 . 8±11 . 3 , 34 . 5±7 . 6 and 31 . 7±7 . 6 , respectively . In a fourth experiment 10 blood-fed mosquitoes from each test condition were collected 2 h PBF and placed in 4% paraformaldehyde for 2 h followed by the dehydration and the clearing steps prior to embedding in paraffin wax . Sagittal sections were then prepared as described above and analyzed for the presence of apoptotic/dead cells using the TUNEL assay described above and fluorescence microscopy . The kinetics of complement C3 activation/degradation in the mosquito midgut following blood feeding was assessed by Western blot analysis . A . stephensi mosquitoes were allowed to feed on a human volunteer arm and midguts were dissected at 10 , 20 , 30 and 90 min from a single mosquito for each time point . Individual midguts were collected in 200 μl of PBS+10 mM EDTA and macerated for 30 sec with 21G needle and centrifuged at 16 , 000g for 5 min at 4°C . Supernatants diluted 1:1 with a reducing SDS-PAGE sample buffer were run on 10% SDS-PAGE gels , and separated proteins were transferred to a nitrocellulose membrane and probed with 1:5000 dilution of rabbit polyclonal anti-human C3c . Binding of the primary antibody was visualized by incubating the membrane with 1:20 , 000 dilution of goat anti-rabbit IgG coupled to HRP ( NEF812 , Perkin Elmer life Sciences , Inc . ) . The blot was then developed by the enhanced chemiluminescent ( ECL ) Western blot analysis system-based detection according to the manufacturer’s instructions ( GE Healthcare , Life Sciences , Bucks , UK ) followed by exposure to Super RX film ( Fujifilm ) . Possible deposition of C3b or the presence of its inactivation products , iC3b or C3d , on the luminal surface of the proventriculus and the midgut epithelium was similarly investigated using ACs isolated from 6 mosquitoes that were fed on NHS+human erythrocytes using the glass membrane feeder . ACs were macerated in PBS+protease inhibitor cocktail ( Roche ) using a 21G needle and washed 4x with PBS-T that contained the protease inhibitor cocktail . After a wash with PBS the AC tissues were pelleted by centrifugation at 16 , 000g for 5 min at 4°C . Proteins were then extracted with 90 μl 1x reducing SDS-PAGE sample buffer and a volume equivalent to 1 AC was run on a gel for C3b , iC3b , C3c or C3d detection . An equivalent number of mosquitoes that were normally fed on 5% sucrose was treated in parallel and served as a control . Binding of FH to the AC epithelium was assessed similarly using the former sample set with 1:5000 dilution of goat polyclonal anti-human FH as the primary WB antibody and a 1:20 , 000 dilution of rabbit anti-goat coupled to HRP ( Dako , P0160 ) as the secondary antibody . Loading control lanes were included in WB analysis , when appropriate . They were probed with the MRA-258 monoclonal antibody ( MR4 , the BEI Resources Repository , NIAID , NIH ) that recognizes ≈150 kDa protein in the mosquito midgut extract . Possible FH binding protein ( s ) in the midgut membrane extract were detected by using the ligand blot analysis[11] . Briefly , membrane proteins were extracted by incubating the A . stephensi mosquito midguts in the membrane extraction buffer ( containing the protein inhibitor cocktail ) from the ProteoJET membrane protein extraction kit ( Fermentas , K0321 ) for 2 h at 4°C with constant shaking . Extracted membrane proteins were recovered by centrifugation at 16 , 000g for 15 min at 4°C . A volume equivalent to 1 midgut per lane was then loaded onto 10% SDS-PAGE gel under non-reducing conditions . Resolved membrane proteins were transferred to nitrocellulose membranes and overlaid after a blocking step with 3% non-fat milk for 1h with either 20% HIS ( a source of FH ) or PBS ( a negative control for FH binding ) and incubated overnight at 4°C . After 5 washes in PBS-T , the membranes overlaid earlier with HIS were further incubated in either PBS ( a negative control for binding of the secondary antibody ) or with 2 μg/ml of the monoclonal anti-human FH antibody ( 131X ) . After a 1-hour incubation at RT and 5 washes in PBS-T , the membranes were incubated in a 1:10 , 000 dilution of goat anti-mouse IgG coupled to HRP ( NFH822 , Perkin Elmer life Sciences , Inc . ) . After a 1-hour incubation at RT and 5 washes in PBS-T , the blots were developed as described above . Data were analyzed with JMP 11 ( SAS Institute Inc . ) . Paired Student’s t-test was used to calculate the statistical significance of complement activation on the bacterial load in the mosquitoes midgut and the in-vitro serum sensitivity of midgut bacteria and of blocking of FH activity with antibodies on egg count . To calculate the statistical significance of blocking FH activity with antibodies on mosquito survival , life tables were constructed for each experimental condition , and survival curves were analyzed by using the Kaplan-Meier log-rank analysis . Asterisks in figures indicate the different p values: * , p < 0 . 05; ** , p < 0 . 01; *** , p < 0 . 001 . All experiments were repeated at least three times . Results from different repetitions of experiments were pooled together and are presented as the mean and , when appropriate , representative images are shown . In all figures , error bars depict standard errors of the mean .
In the current study we tested whether components of the complement system in the blood ingested by Anopheles mosquitoes could injure midgut cells , and if not so , how do the midgut cells manage to survive the potent cytotoxic activity of complement ? We first tested whether the human complement system in the ingested blood becomes activated in the mosquito midgut . Therefore , A . stephensi mosquitoes were allowed to feed on a human volunteer arm . This was followed by measuring the concentrations of the anaphylatoxin C3a and the soluble terminal complement complex ( TCC , also known as SC5b-9 ) as indicators of complement activation . For this , the blood bolus serum samples were isolated from BF mosquito midguts . As shown in Fig . 1A the complement system became strongly activated in the mosquito midgut . The peak level of activation was found 10 min PBF as shown by a 100-fold increase in the concentration of C3a , when compared to the basal level ( zero time ) in the human volunteer serum ( Fig . 1A ) . Moreover , the C3a level dropped to only 7-fold at 30 min PBF and reached the basal level at 90 min PBF ( Fig . 1A ) . A more drastic indication of complement activation was observed , when the SC5b-9 level was measured in the bolus serum samples . The SC5b-9 concentration increased to 150-fold at 30 min PBF , when compared to the basal level in the volunteer serum sample ( Fig . 1A ) . SC5b-9 level went down to 100-fold at 90 min PBF ( Fig . 1A ) . Both C3a and SC5b-9 measurements in blood bolus serum samples indicated that complement was strongly activated in the mosquito midgut . The different kinetics of the two complement activation products are expected because they are raised at different steps of complement activation and , as a multimolecular complex SC5b-9 takes a longer time to form . The clearance rates of C3a and SC5b-9 are also different , C3a being more rapidly cleared . To support the assumption that C3a was produced as a result of complement activation we analyzed whether C3 was converted into C3b and further to iC3b in the same sample set by Western blotting ( WB ) analysis . The WB data showed that more than 50% of the initial C3 had become converted to iC3b in the mosquito midgut 60 min PBF ( Fig . 1B ) . This indicated that C3 had become activated to C3a and C3b and C3b was further inactivated to iC3b . Consequently , we also measured the residual complement activity that remained in the mosquito midgut at 10 , 30 and 90 min PBF . The results showed a complete loss of the alternative and lectin pathway activities in just 10 min PBF ( Fig . 1C ) . At the same time point about 74% of the classical pathway activity was lost . At 90 min approximately 20% of classical pathway activity could be still detected ( Fig . 1C ) . Since the residual complement activity measurements for the classical pathway were based on quantifying the levels of SC5b-9 resulting from activating complement in the test samples , some active C3 must have been remained in the samples . Taken together , these results indicated that the complement system in human blood became activated in the fluid phase in the mosquito midgut . As the initial experiments showed that complement became strongly activated in the midguts of mosquitoes that were fed naturally with human blood , we next asked whether this activation would impose any threat to cells in the mosquito midgut . As a surrogate marker for the ability of complement to kill cells in the mosquito midgut we chose first to study effects on the midgut microbiota . Here , A . stephensi mosquitoes were allowed to feed artificially either on normal human serum ( containing functional complement ) or heat-inactivated serum ( containing non-functional complement ) in addition to human erythrocytes under both conditions . Mosquitoes were dissected at 30 min , 90 min and 24 h PBF and the viable bacterial contents of the midguts were estimated by a plate colony counting procedure . As expected , feeding mosquitoes on NHS decreased the bacterial count in midguts to 34% and 17% of what was recorded for mosquitoes fed on sugar or HIS , respectively , when dissection took place at 30 min PBF ( Fig . 2A ) . At the same time point bacterial count showed two-fold increase in midguts of HIS-fed mosquitoes compared to sugar-fed mosquitoes . This simply shows that feeding on blood containing active complement reduces bacterial count , while feeding on non-active complement containing blood meal increased midgut bacterial load . At 90 min PBF the bacterial counts in midguts of mosquitoes fed on NHS dropped to 5% relative to feeding on HIS ( Fig . 2A ) . At 24 h PBF the difference between bacterial counts in NHS and HIS feeding conditions was not drastic , but remained significant as the decrease in bacterial count in NHS was only 37% of what was recorded in HIS ( Fig . 2A ) . The increasing number of bacterial count at the 24 h time point in midguts of mosquitoes fed on NHS relative to the former time points suggested that serum resistant bacterial strains or just the remaining surviving bacteria might have overgrown . Therefore , morphologically dominant colonies from both NHS and HIS conditions were selected and tested for their ability to resist complement-mediated killing . The bacteria were identified to belong to the genus Enterobacter ( E . cloacae ) in both cases . Enterobacter isolates from both conditions were , found to be sensitive to complement and behave similarly in their response to complement-mediating killing ( Fig . 2B ) . Therefore , the likeliest explanation for bacterial overgrowth in midguts of mosquitoes fed on NHS after 24 h was the lack of sufficient complement activity to kill all midgut bacteria . Altogether , this data clearly shows that complement activation in the mosquito midgut has a detrimental effect on living cells in the midgut lumen . In our previous experiments complement in the mosquito midgut appeared to drastically reduce the bacterial load in the midgut PBF . Mosquitoes’ mortality as a likely indicator of midgut damage does not vary upon feeding on a blood meal containing functional or non-functional complement ( common laboratory observation ) Thus , we hypothesized that mosquito midgut epithelium has developed evasion mechanisms to escape the deleterious effect of complement activation . Acquisition of natural soluble complement regulators ( Cregs ) from blood by microbial surfaces is one of the most common mechanisms of complement evasion[12] . A similar mechanism that involves acquisition of Cregs by midgut epithelial surface could also occur when midgut is full of host blood . We tested this hypothesis by feeding A . gambiae and A . stephensi mosquitoes naturally on a human volunteer arm and looking for binding of Cregs and other complement components to mosquito gut epithelium . Sagittal sections of BF A . gambiae ( Fig . 3A and 3F ) and A . stephensi ( Fig . 4E , 4G and 4F ) mosquitoes were made 2 h post feeding . Coronal sections of A . stephensi ( Fig . 4A , 4B , 4C and 4D ) were also prepared . Immunofluorescence staining with antibodies against human FH , the soluble regulator of the alternative pathway of complement activation , revealed binding of FH to the epithelium surface of the proventriculus ( Fig . 3B and 3C ) , the anterior midgut ( Fig . 3G and 3H ) and the anterior-posterior midgut ( Fig . 3G and 3H ) . Sections made from another blood-fed Anopheles mosquito species , A . stephensi , showed identical results ( Fig . 4 ) indicating that this phenomenon is not species-specific . The specificity of the anti-FH binding to its target was verified by immunofluorescence assays using sections of sugar-fed A . stephensi mosquitoes that showed no binding of anti-FH to mosquito proteins ( Fig . 5 ) . Additionally , binding specificity of the secondary antibody , Alexa Fluor 488 Donkey anti-goat IgG , was also verified in using sections of blood-fed A . stephensi mosquitoes in the absence of goat anti-FH from the IFA assays ( Fig . 6B ) . Parallel immunofluorescence assays did not show any sign of deposition of C3 , C5 or MAC on the epithelial surface of the mosquito’s midgut suggesting protection from complement attack ( S1 Fig ) . To further confirm binding of FH to the mosquito midgut epithelium A . stephensi mosquitoes were allowed to feed on a volunteer arm and their ACs were dissected 2 h PBF . After washing the ACs SDS-soluble material was run on SDS-PAGE gel for detecting bound complement proteins by Western blot ( WB ) analysis . In agreement with the immunofluorescence microscopy observations FH was detected ( Fig . 7A ) among the proteins that were eluted from the AC tissues of BF mosquitoes . In contrast , anti-FH antibody did not detect any proteins in the eluted material from the midguts of SF mosquitoes . Furthermore , no signs of deposition of C3b or its inactivation products on the AC epithelium ( Fig . 7B ) were observed , when the same preparations were used in WB analysis and probed with an antibody that detects human C3 , C3b , iC3b and C3c . The functional activity of FH in the mosquito midgut was also assessed by analyzing the soluble content of the midguts of BF mosquitoes . WB analysis revealed FH-dependent degradation of C3b by FI into iC3b ( Fig . 7C ) in the midgut content that was collected 2 h PBF . iC3b was apparent by the presence of 46- and 43-kDa fragment bands of the processed α’ chain of C3b . The WB analysis also showed that the 68-kDa fragment of iC3b ( Fig . 7C , lane 2 ) was completely cleaved into smaller fragments ( Fig . 7C , lane 1 ) suggesting either further cleavage of iC3b to C3c+C3d , g by erythrocyte CR1 and factor I or the involvement of midgut proteolytic activity in the complement inactivation process in the midgut . Altogether , our data indicates that the surfaces of the proventriculus and the midgut capture FH to inhibit C3 deposition that otherwise could lead to lysis of the target cells . Moreover , complement activation in the soluble phase in the blood bolus seemed to be efficiently controlled by factor I-mediated C3b degradation and midgut proteolytic activity . Mouse serum containing active complement was also used to blood-feed A . stephensi mosquitoes to test for the ability of midgut epithelium to capture mouse FH from serum . Interestingly , immunofluorescence assays using goat anti-human FH that cross-reacts with mouse FH detected mouse FH on the surface of the midgut epithelium ( Fig . 6A ) . FH signal was also more profound on the epithelium of the proventriculus and the anterior midgut as in the case of human FH binding to A . gambiae and A . stephensi midgut anterior midgut epithelium . Altogether , these data suggest that capturing FH by mosquito midgut epithelium from a blood meal could be a common mechanism utilized by blood-feeding mosquito species regardless of the host species . To further characterize FH binding to the midgut epithelium , 2 h PBF A . stephensi mosquito sections were double stained for FH and Concanavalin A ( ConA ) binding to the midgut surface . Confocal microscopy images showed a thick glycocalyx layer covering the outer surface of luminal plasma membrane represented by the red fluorescence of ConA-Alexa 594 ( Fig . 8E and H ) . FH was found to colocalize with approximately one quarter of the luminal side of the glycocalyx layer ( Fig . 8I ) . FH binding also formed a thin layer lining the luminal surface of the glycocalyx ( Fig . 8C , F and I ) . Most of bound FH also appeared to colocalize with the glyococalyx . This data shows the association of FH binding to the mosquito midgut glycocalyx layer confirming binding of FH to a protein or a glycoprotein integrated into the plasma membranes of midgut epithelial cells . Binding of FH to the epithelial surface of the various compartments of the mosquito AC and the absence of C3b deposition prompted us to ask whether interfering with this binding would result in detrimental consequences . Therefore , A . stephensi mosquitoes were fed on blood meals that contained NHS alone or the monoclonal antibody , 131X , which functionally inactivates FH and either NHS or HIS . Mosquito mortality was first recorded at 48 h PBF and increased to 7% in the NHS/anti-FH-fed mosquitoes as compared with 1% and 2% in the NHS- and HIS/anti-FH-fed mosquitoes , respectively ( Fig . 9 ) . Mortality increased to 18% and 3% within 72 h in NHS/anti-FH- and HIS/anti-FH-fed mosquitoes , respectively . Mortality remained unchanged in the case of NHS-fed mosquitoes at the same time point . Highest mortality rates were observed at day 5 PBF in NHS/anti-FH , NHS- and HIS/anti-FH-fed mosquitoes being 22% , 5% and 6% , respectively ( Fig . 9 ) . No increase in mortality was reported until the last observation at day 7 . The overall mortality was significantly different ( Kaplan-Meier log-rank test , p<0 . 001 ) between NHS/anti-FH-and either NHS- or HIS/anti-FH-fed mosquitoes . To determine the effect of blocking FH activity in the midgut on mosquito fecundity , mosquito eggs were collected at day 8 PBF from NHS- , NHS/anti-FH- and HIS/anti-FH-fed surviving mosquitoes . The average number of eggs per mosquito was reduced to 28±17 eggs in the NHS/anti-FH-fed mosquitoes as compared with 44±15 and 48±27 eggs in the NHS- and HIS/anti-FH-fed mosquitoes , respectively ( Fig . 9 ) . From a subsequent identical experiment sagittal sections of mosquitoes were prepared 2 h PBF . Sections from six mosquitoes were then analyzed for the presence of apoptotic cells as a result of neutralizing FH with the 131X monoclonal antibody . TUNEL assays showed signs of cell death presented as green fluorescent nuclei ( Fig . 10E and F ) in midgut epithelium in one mosquito out of six in the 131X-neutralized FH group ( 131X+NHS group ) . No signs of cell death were detected in the absence of the 131X anti-FH antibody in sections of seven mosquitoes ( Fig . 10B and C ) comprising the control group ( NHS group ) . Altogether , these results suggest that blocking FH-mediated complement evasion in the mosquitos has deleterious effect on the mosquito’s survival and the fecundity of those mosquitoes that managed to survive complement-mediated damage . To shed light on the interaction of FH with mosquito midgut proteins , we initiated a search for the potential FH receptor ( s ) . The ACs of A . stephensi mosquitoes that had never fed on blood were dissected and membrane proteins were extracted . The extracted proteins were run onto SDS-PAGE gel under non-reducing conditions and transferred to nitrocellulose sheets for ligand blot analysis to detect binding of FH to mosquito proteins in vitro . As shown in Fig . 11 two mosquito proteins of approximately 40 and 100 kDa were found to be the candidate receptors for binding FH .
In this study we observed binding of the major complement inhibitor factor H to the alimentary canal epithelium of the Anopheles mosquitoes . Apparently , this interaction protects the mosquito’s epithelium from complement-mediated damage and could provide a target for a transmission blocking vaccine-induced immunity . On the other hand , we found that complement becomes activated in the fluid phase in the mosquito midgut . As a consequence , complement activation was able to significantly reduce the number of the bystander bacteria in the midgut , a phenomenon that could help Plasmodium parasites to survive in the mosquito midgut . Further experiments showed that blocking of FH activity by a monoclonal antibody that interferes with its function and boosts complement activation had a detrimental effect on mosquito survival and fecundity . Earlier studies , with a particular interest in the resistance of Plasmodium gametes to complement activity , reported long-lasting persistence of complement components or activity in the mosquito midgut post blood feeding . Margos et al [13] observed that rat complement components necessary to initiate the alternative pathway ( factor B , factor D , and C3 ) as well as C5 were present for several hours following the ingestion of P . berghei-infected rat blood . In a more recent study , Simon et al [14] have shown that the concentration ( OD values ) of C3a , a marker for complement activation , was 2 . 3-fold higher in mosquito midgut at 1 h PBF ( artificial feeding ) compared to control . In the current study , mosquito’s natural feeding on a human volunteer arm showed that complement became strongly activated in the mosquito midgut . C3a concentration ( ng/ml ) was 100-fold higher within 10 min PBF and dropped to 7-fold at 30 min PBF compared to the basal level in the human volunteer serum . The sharp decrease in C3a level was probably due to its rapid binding to receptors and/or to anionic surfaces because of its cationic nature . Leukocytes present in the blood meal carry receptors for C3a ( C3aR ) . SC5b-9 is a more stable complement activation product than C3a and the result of the full activation sequence . Therefore , the SC5b-9 level peaked after the C3a peak , as expected from the kinetics of complement activation . This was followed by a slight decrease in the SC5b-9 level indicating that no more activation was taking place at 90 min PBF . The presence of a considerable amount of C3 in the mosquito midgut at 90 minutes PBF suggested that some C3 activity was still left . The residual activity assays , however , showed complete loss of the alternative pathway activity at 10 min PBF , whereas , about 20% of the classical pathway activity still remained at 90 min PBF in an assay format that detects the ability to generate SC5b-9 complexes . This finding suggested that the classical and the terminal pathway components were partially active and functional C3 was involved in the measured residual activity . In contrast , in the alternative pathway a rate limiting factor was consumed or , alternatively , the pathway had become specifically inhibited . An earlier study showed that soluble molecules from the midgut of the mosquito Aedes aegypti inhibited complement activation and C3b deposition in vitro via the alternative pathway by 52% and via the classical pathway by only 24%[15] . Altogether , the current data suggests that some mechanisms of complement inhibition operate in the mosquito midgut , and that while complement becomes activated for some time in the fluid phase the mosquitoes do not seem to suffer from complement attack . The potential ability of complement in the mosquito midgut to cause damage to cells was recognized from the significant decrease in the number of viable bacteria post mosquito feeding on NHS-supplemented blood as compared with feeding on HIS-supplemented blood . Complement-mediated killing of the midgut bacteria was taken as an indirect indication that this could have been the fate of the midgut epithelium unless there was an evasion mechanism that allowed the epithelial cells to escape complement-mediated damage . Complement-mediated killing of midgut bacteria could also lead to consumption of complement components that could otherwise target the midgut epithelium . The lack of damage to mosquito cells was supported by the absence of nuclear chromatin fragmentation , a sign of cell death , in the midgut epithelial cells . In agreement with our postulation , a previous study in which the bug Triatoma brasiliensis was fed by the forced feeding procedure to bypass mixing blood with the saliva that contains a complement inhibitor showed significant signs of midgut cell death as early as 1 h PBF [15] . This finding supports a possible role for complement inhibitors of hematophagous vectors in protecting midguts from host complement-mediated injury . Additionally , mosquitoes fed on mice immunized with mosquito ACs homogenate have shown a significant reduction in mosquito survival compared to control groups [16] . A similar study has also shown impairment of the development of Plasmodium spp . inside the Anopheles spp . midgut and , thereby , a block in parasite transmission [17] . It is likely that mosquito killing in these studies was mediated by antibody-mediated activation of the classical complement pathway , for which less inhibitory activity was found to operate in the mosquito midgut in our study . On the other hand , antibodies neutralizing an inhibitor of the alternative pathway would enhance complement killing of the midgut cells . In addition to complement-mediated damage , mosquito killing could also be due to interference of anti-AC antibodies with vital cell functions on the mosquito midgut surface . The observed drastic complement-mediated reduction of bacterial count in the mosquito midgut also argues for a special attention of studies focusing on understanding the association of midgut microbiota with the malaria parasite survival in the mosquito midgut [18 , 19] . Natural blood feeding could thus greatly reduce the number of midgut bacteria by complement-mediated killing , as opposed to artificial feeding that is usually based on heat-inactivated serum . Shaping the diversity of gut microflora of hematophagous species was also reported for the Hirudo medicinalis , a medicinal leech , as active complement in the ingested blood limited the gut microflora to only the complement-resistant bacterial strains[20] . Using an indirect immunofluorescence assay FH was detected to bind to the mosquito proventriculus and midgut . C3 , C5 and MAC were absent from the ACs epithelial surface indicating the protective activity of the cell-bound FH . Factor H binding is thus a potential complement evasion mechanism developed by the mosquitoes to protect their ACs from complement-mediated damage . Furthermore , a monoclonal antibody directed against FH that is known to inhibit its function significantly increased the mortality rate in blood-fed mosquitoes within 72 h from about 1% in NHS-supplemented blood meal to 18% when NHS was supplemented with anti-FH . The relatively low level of mortality , albeit significant , in the presence of the anti-FH monoclonal antibody could have resulted from insufficient quantities of the monoclonal antibody , rapid consumption of active complement or mosquito inhibitors of complement activation . Complement can become spontaneously activated , and this process is accelerated in the presence of an anti-factor H antibody . In our attempts to identify mosquito’s FH-binding molecules we confirmed this interaction by the ligand blotting assays and detected two molecules with molecular masses of about 100 and 40 kDa as potential FH binding molecules . The identities of these molecules are the subject of ongoing work . It is also possible that , in addition to specific proteins , mosquito cell surface glycosaminoglycans or other polyanions could play a role in FH binding to mosquito AC cell surfaces . Exploiting soluble complement regulators , particularly FH , from the host by many pathogenic bacteria has been well described [5] . Factor H binding has been reported also to fungi , viruses and parasites [5 , 14] . The current study is to our knowledge the first one to describe the utilization of a similar complement evasion strategy by a hematophagous vector . Previous reports on complement evasion or inhibition by hematophagous vectors were almost exclusively from studies on ticks . Those studies identified other unique strategies to block complement activation . For example , a salivary protein , OmCI , from the Ornithodoros moubata tick , the vector of human relapsing fever caused by Borrelia duttoni , was shown to specifically bind and inhibit C5 , thereby preventing activation of the terminal complement pathway[21] . Another salivary protein , TSLPI from Ixodes scapularis tick , vector of the Lyme disease caused by Borrelia burgdorferi , was shown to interfere with the lectin pathway of complement activation by preventing MBL binding to its ligand[22] . TSLPI was also shown to be beneficial to B . burgdorferi as it resulted in impaired neutrophil phagocytosis and chemotaxis and reduced killing of Borrelia [22] . A third strategy that was shown to be utilized by ticks to block complement activation was binding of tick salivary proteins such as Isac , Irac-1 and-2 , and Salp20 to properdin and displacing it from the alternative pathway C3 convertase resulting in its inhibition[23–25] . So far , no analogous anticomplement strategies have been identified in mosquitoes , except the one in the current study . However , mosquito antihemostatic molecules such as anophelin from Anopheles spp . [26 , 27] , anti-factor Xa from Aedes aegypti[28] and alboserpin from Aedes albopictus[29] that target human coagulation system were already identified in mosquito’s saliva . Molecules involved in complement inhibition from hematophagous vectors are of potential interest to generate an anti-vector vaccine that could interfere with the lifespan of the disease vectors or with infectivity of the pathogen . Studies to envisage the possible use of arthropod vector proteins as anti-vector vaccine already exist[30] , but none of them have attempted to use a complement regulator-neutralizing vaccine so far . Thus , mosquito midgut antigens involved in essential biological processes such as in complement inhibition would serve as more specific candidates for similar studies . Moreover , identifying complement inhibitors from hematophagous vectors could also provide pharmacological agents to treat diseases , where complement activation is known to play a role[31] . In conclusion , we have shown in this study that the complement system becomes immediately activated in the mosquito after ingestion of human blood while , at the same time , the mosquito AC surface molecules captured FH from the blood meal and inhibited the deposition of C3b on the midgut epithelium . The initial complement activation that occurred in the blood bolus in the midgut was able to kill midgut bacteria that were not resistant to complement . On the other hand , acquisition of FH by the midgut epithelial cells contributed to mosquito’s survival against the innate immune system in the ingested blood meal . Interfering with the complement regulatory activity of FH in the mosquito midgut increased mosquito mortality and reduced fecundity . The putative Anopheles mosquito FH binding proteins could be transmission blocking vaccine candidates targeting the malaria parasite carrying vectors . | Mosquitoes are important vectors in the transmission of many human diseases . Their life cycle requires a blood meal to be completed . Ingested blood contains bioactive molecules belonging to the innate immune defense mechanisms against microbes , like the complement system , that can damage foreign cells . We have identified in this study a mechanism whereby mosquitoes can escape the damaging activity of the complement system in the ingested human blood . The mosquito midgut epithelial cell surface captured factor H , a natural regulator of the alternative pathway of complement activation , from the ingested blood . Consequently , the deposition of C3b , a key complement component , on the epithelial cell surface was impaired and cell death was avoided . Interfering with the complement regulatory activity of factor H by monoclonal antibodies , carried to the midgut via blood feeding , increased mosquito mortality and reduced fecundity . The putative Anopheles mosquito factor H binding proteins could be transmission blocking vaccine candidates targeting the malaria parasite carrying vectors . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
] | [] | 2015 | Anopheles Midgut Epithelium Evades Human Complement Activity by Capturing Factor H from the Blood Meal |
Progressive hearing loss is common in the human population , but we have few clues to the molecular basis . Mouse mutants with progressive hearing loss offer valuable insights , and ENU ( N-ethyl-N-nitrosourea ) mutagenesis is a useful way of generating models . We have characterised a new ENU-induced mouse mutant , Oblivion ( allele symbol Obl ) , showing semi-dominant inheritance of hearing impairment . Obl/+ mutants showed increasing hearing impairment from post-natal day ( P ) 20 to P90 , and loss of auditory function was followed by a corresponding base to apex progression of hair cell degeneration . Obl/Obl mutants were small , showed severe vestibular dysfunction by 2 weeks of age , and were completely deaf from birth; sensory hair cells were completely degenerate in the basal turn of the cochlea , although hair cells appeared normal in the apex . We mapped the mutation to Chromosome 6 . Mutation analysis of Atp2b2 showed a missense mutation ( 2630C→T ) in exon 15 , causing a serine to phenylalanine substitution ( S877F ) in transmembrane domain 6 of the PMCA2 pump , the resident Ca2+ pump of hair cell stereocilia . Transmembrane domain mutations in these pumps generally are believed to be incompatible with normal targeting of the protein to the plasma membrane . However , analyses of hair cells in cultured utricular maculae of Obl/Obl mice and of the mutant Obl pump in model cells showed that the protein was correctly targeted to the plasma membrane . Biochemical and biophysical characterisation showed that the pump had lost a significant portion of its non-stimulated Ca2+ exporting ability . These findings can explain the progressive loss of auditory function , and indicate the limits in our ability to predict mechanism from sequence alone .
PMCA2 is one of four isoforms of the plasma membrane Ca2+ pumps of mammalian cells [1] , [2] . The expression of PMCA2 and PMCA3 is largely restricted to brain and muscle , whereas PMCA1 and 4 are ubiquitously expressed . PMCA2 and PMCA3 are more active in exporting Ca2+ than the ubiquitous isoforms [3] , probably due to their higher affinity for the activator calmodulin . PMCA2 , however , is peculiar in its very high activity even in the absence of calmodulin [4] , [5] . In the ear , PMCA2 is expressed at high levels in outer hair cell stereocilia and apical membranes and at moderate levels in inner hair cell stereocilia and in the spiral ganglion [6]–[10] . It actively extrudes Ca2+ that has entered the hair cell during mechanoelectrical transduction [11] . This maintains the low intracellular levels of Ca2+ and may create a relatively higher concentration of Ca2+ in the endolymph surrounding the stereocilia , contributing to the maintenance of the electrochemical gradient needed for transduction to occur [11] . Bulk concentration of Ca2+ in mammalian cochlear endolymph is estimated at ∼20 µM [12] . PMCA2 is also important in maintaining sufficient extracellular Ca2+ in the vestibular system for the formation of the otoconia , the calcium carbonate crystals needed for sensing gravity and acceleration [13] . Transcripts for PMCA2 undergo alternative splicing at two sites . Site A is closer to the N-terminus and site C closer to the C-terminus . In the PMCA2 variant expressed in stereocilia [14] , [15] , the splicing introduces three exons at site A , generating variant w , and two alternative exons at site C , generating variant a . The C-site insert leads to a truncated pump that contains only about half of the original calmodulin binding domain [4] , [5] . The doubly inserted w/a variant , seen in stereocilia , has an unusually limited ability to increase activity rapidly when challenged with a Ca2+ pulse , but has about the same high non-stimulated activity as the full-length z/b variant [16] . The Oblivion ( Obl ) mutant was identified as a new mouse mutant with progressive hearing loss from a large scale ENU mutagenesis screen [17] . The aim of this screen is to provide new models for deafness , especially progressive deafness which is common in the human population , and to identify the genes and underlying pathology in these new mutants . Here we report that the progressive hearing loss in Obl is due to a missense mutation in the gene Atp2b2 , encoding PMCA2 . We describe the hearing impairment and hair cell pathology in the mutants , the dysfunction of Ca2+ export by the mutated PMCA2 pump cloned and overexpressed in model cells and in cultures of utricles from the mutant mice .
Obl/+ heterozygotes have a normal Preyer reflex at one month old , but by two months only 58% offspring from Obl/+×+/+ matings showed a Preyer reflex ( Table S1 ) , suggesting progressive hearing loss in Obl/+ mice . No vestibular defect , indicated by head-tossing or circling behaviour , was seen in these heterozygotes , although no detailed analysis of vestibular function was performed . To measure auditory thresholds , auditory brainstem responses ( ABR ) , a reflection of cochlear and brainstem neural activity , were recorded in P20 , P59–62 and P89–91 mice on their original C3HeB/FeJ genetic background ( Figure 1 ) . ABRs of wild-type mice showed an improvement in thresholds below 12 kHz from P20 to P59–62 , perhaps indicative of maturation of the auditory system . From P20 to P89–91 , wild type mice showed mild and progressive elevations of thresholds above 12 kHz . Obl/+ mice demonstrated a severe and age-related progressive hearing loss . Obl/+ mice showed significantly raised thresholds at all frequencies , compared to age-matched wild-type controls ( t-test , p<0 . 05 ) , of up to 60–70 dB or more . In contrast to the Preyer reflex tests , even at P20 Obl/+ showed large threshold elevations . At P59–62 , the heterozygotes showed further threshold elevations which were most severe at higher frequencies , above 18 kHz . By P89–91 , high frequency losses were compounded by severe losses across the entire range measured . This indicated a progressive hearing loss in Obl/+ mice . The improvement of low frequency ( 3–6 kHz ) ABR thresholds between P20 and P59–62 may indicate maturation of the developing auditory system between these ages . Obl/Obl homozygous mutants show a very severe hearing and vestibular phenotype and are significantly smaller ( 10 . 5 g , SD 2 . 02 ) than age and sex matched Obl/+ littermates ( 17 . 3 g , SD 2 . 11; t-test , p<0 . 05 , 31–34 days old ) . They ( a ) fail to develop a Preyer reflex; ( b ) fall from side to side whilst walking; ( c ) are unable to right themselves; and ( d ) curl towards their belly when lifted by their tail and do not display a reaching response when lowered towards a surface . Homozygotes also show hind limb stiffness and appear ataxic , which are not general features of vestibular dysfunction . The gross morphology of the middle ear ossicles and inner ear appeared normal in Oblivion mutants , both heterozygotes and homozygotes . Scanning electron microscopy in Obl/+ mutants at 3–4 months of age showed degeneration of hair cells , with the basal turn more severely affected than the apex , and outer hair cells ( OHCs ) more affected than inner hair cells ( IHCs ) , a pattern that is commonly reported in damaged cochleas ( Figures 2 and 3 ) . Obl/Obl homozygotes were more severely affected than heterozygotes . However , there were many remaining hair cells with relatively normal appearance in the mutants , including a W-shaped arrangement of stereocilia , especially in the apical turn . Stereocilia fusion was seen in some , an early indicator of hair cell degeneration . At P20 , no significant hair cell loss was detected in Obl/+ mutants compared to their littermate controls ( Figure 3A and 3B ) , despite the fact that we saw significantly raised ABR thresholds in another cohort of P20 heterozygotes ( Figure 1 ) . Hair cell counts from the basal and middle turns at P75 showed no significant OHC degeneration in the middle turn and no significant IHC loss throughout the cochlea in Obl/+ heterozygotes ( Figure 3C and 3D ) . By P121 , there was significant OHC and IHC loss in basal and middle turns in Obl/+ ( Figure 3E and 3F ) . This suggests that the hair cell loss seen in these mutants is a secondary consequence of the hair cell not functioning correctly , rather than being the primary cause of raised thresholds in Obl/+ mutants . In Obl/Obl mutants at P30 there was highly variable hair cell degeneration , both within and between animals . In some regions there was scattered hair cell loss with a pattern similar to that seen in heterozygotes ( Figure 2G and 2H ) , while in some regions towards the base there was complete degeneration of the organ of Corti with a complete absence of specialised cells , including supporting cells such as pillar cells ( Figure 2I ) . Obl/+ mutants on a C3HeB/FeJ background were outcrossed to C57BL/6J and mutant F1 progeny were backcrossed to the original C3HeB/FeJ strain . Backcross litters were assessed for absence of a Preyer reflex and a genome-wide scan was performed on their DNA using 60 polymorphic microsatellite markers . We identified a region of linkage on chromosome 6 ( Figure 4A ) between markers D6Mit104 and D6Mit218 , corresponding to a physical distance of 16 Mb . This region contained a good candidate gene: Atp2b2 . Genomic DNA was used to sequence the 19 coding exons of the gene , including the splice sites . We identified a C/T heterozygous peak in Obl/+ mutants , suggesting a C→T transition ( 2630C→T ) in exon 15 of Atp2b2 , predicted to change a serine to a phenylalanine ( S877F; Figure 4 ) in the mutant allele . This change was also confirmed by a restriction test assay that was used to genotype the colony . This assay was used to screen 19 inbred strains for the Obl mutation and none were found to have it , suggesting that it is not a common polymorphism . We found non-complementation of Obl with the deafwaddler mutant allele , Atp2b2dfw , confirming that the missense mutation we found was the pathogenic mutation ( see Text S1 ) . To investigate the effects of the serine to phenylalanine change on the functionality of the pump , mammalian expression plasmids for the S877F and the wild type variant of the PMCA2 pump were prepared and expressed in CHO cells . Appropriate controls ( Western blotting and quantitative immunocytochemistry ) established that the two pump variants were expressed at about the same levels , and were correctly delivered to the plasma membrane ( Figure 5 ) . CHO cells were transfected with the Ca2+ sensitive photoprotein aequorin ( cytAEQ , [3] ) and stimulated with ATP , an agonist of purinergic P2Y receptors that produces InsP3 generating a cytosolic Ca2+ transient . Under the experimental conditions , the height of the Ca2+ peak , and the kinetics of the return of the Ca2+ transient to baseline were controlled primarily by the PMCA pump: the much larger amounts of the overexpressed PMCA2 pump overshadowed the endoplasmic reticulum Ca2+ pump ( SERCA ) ( see [16] ) and the contribution of plasma membrane Ca2+ influx channels opened by emptying of intracellular stores to the shaping of the Ca2+ trace was disregarded , as their effect would be the same in the wild type and Obl measurements . The overexpressed Obl pump did not further depress the limited ability of the wild type w/a pump to control the height of the Ca2+ peak ( Figure 6 ) . The mutation , however , severely affected the resting activity of the pump that drove the return of the Ca2+ trace to baseline after the peak . The half time of the declining phase was 64 . 15±3 . 02 sec ( n = 6 ) in control , 6 . 55±0 . 72 sec ( n = 9 ) in the wild type w/a variant and 45 . 50±5 . 97 sec ( n = 4 ) , p<0 . 001 , in Obl . To characterize Ca2+ dynamics in the stereocilia of hair cells , cultures of immature utricular maculae were obtained from wild type and mutant mice ( see Materials and Methods ) . Ca2+-dependent changes in fluorescence evoked by the photorelease of intracellular caged Ca2+ ( 4 ns single UV pulse ) were monitored with a temporal resolution of 6 ms using confocal laser scanning microscopy . Immunofluorescence labelling with isoform specific antibodies showed that PMCA2 was correctly located in the stereocilia of homozygous mutant organotypic cultures of utricular maculae ( Figure 7A ) and organ of Corti ( Figure 8 ) . Figure 7B shows a macular hair cell loaded with Fluo-4 . Fluorescence changes in the stereocilia were monitored repeatedly by a line-scan positioned along the hair bundle and extending into the cell soma ( dashed line ) . Time-dependent post UV pulse changes in fluorescence at different parts of the line scan are illustrated in Figure 7C . The time course of the stimulus-evoked changes in fluorescence ( ΔF ) , normalized to basal ( pre-stimulus ) fluorescence ( F0 ) , are compared for wild type ( wt , blue trace ) heterozygous Oblivion mice ( Obl/+ , green trace ) and homozygous Oblivion mice ( Obl/Obl , black trace ) in Figure 7D . The time courses matched well at the peak , although the [Ca2+]i transient decayed more slowly for the Obl/Obl mice . To highlight the differences , the traces are re-plotted in Figure 7E on an expanded time scale ( solid lines ) , together with their respective confidence intervals ( dash-dotted lines ) . A single exponential fit to the first 10 s of these transients yielded significantly longer decay time constants for the Obl/+ and Obl/Obl cultures: τwt = 2 . 8±0 . 4 s for wt , τObl/+ = 4 . 2±1 s for Obl/+ mice ( p = 0 . 02 , if compared to wt using the ANOVA test ) and τObl/Obl = 6 . 9±1 . 3 s for Obl/Obl mice ( p<0 . 01 ) . No significant divergence in the early phase ( first 2 s ) of the decay was found for Obl/+ mice relative to wt , consistent with the lack of evident phenotypic vestibular defects in these mice . At later times , the decay tended to diverge possibly due to the contribution of complex processes such as Ca2+-induced Ca2+ release . These contributions were not investigated further .
Progressive , age-related hearing loss affects 60% of humans over the age of 70 [18] . The condition is a multifactorial disorder to which genetic variation , disease and environmental influences such as acoustic trauma are all contributing factors , making identification of the genes involved in humans difficult . The mouse is an ideal resource to study the genetics of progressive hearing loss due to the possibility of controlling both genetic background and the environment . The present study has identified a new ENU-induced allele of Atp2b2 , Atp2b2Obl in the Obl mouse mutant . This allele contains a C to T missense mutation in exon 15 of Atp2b2 , causing a non-conservative amino acid substitution of serine by phenylalanine in transmembrane domain 6 of the PMCA2 pump [1] , [19] . The serine at position 877 is highly conserved in both human and mouse and also between other members of the PMCA family ( Figures 4C and S2 ) . The S877F mutation is of special interest on at least two accounts . One is the finding that substitutions in the transmembrane residues of the PMCA pump frequently impair its correct plasma membrane targeting [20] . However , the mutant Obl pump was correctly delivered to the plasma membrane in both the model cells and in native stereocilia . In another Atp2b2 mutant allele , Wriggle mouse Sagami ( wri ) , a missense mutation in transmembrane domain 4 completely abolished the expression of PMCA2 protein in stereocilia of cochlear hair cells [21] . The second reason for interest stems from studies of the SERCA pump of endoplasmic reticulum , considered a model for all P-type pumps . Conserved residues within transmembrane domain 6 of SERCA have been shown to be components of the channel through which Ca2+ is translocated [22] and are also present in the PMCA pump . It is pertinent to quote at this point an earlier study on some point mutations in transmembrane domain 6 of another isoform of the pump , PMCA4 [23] . In that study , one of the mutants ( S877A ) appears to have higher activity than the wild type pump . The reason for the different effect of the S877A mutation of PMCA4 and the S877F mutation of PMCA2 that we report here is not clear . However , the PMCA4 study is difficult to compare with the present one , since it analyzed the pump in crude microsomes of overexpressing COS cells , and offered no information on the localization of the expressed pump in the plasma membrane . The Obl mutation is the seventh mutation in mouse Atp2b2 to be reported . In the deafwaddler ( dfw ) mouse a missense mutation in Atp2b2 leads to a partial loss of function [8] and PMCA2 retains 30% of its Ca2+ pumping activity [24] . A spontaneous T692K mutation in Atp2b2 led to clear ataxic behaviour , with normal mRNA levels , in a second unnamed mutant [25] . A further four mutations of mouse Atp2b2 have been described: deafwaddler 2J ( dfw2J ) , deafwaddler 3J ( dfw3J ) , wriggle mouse sagami ( wri ) and a targeted null mutation . Analysis of mRNA transcripts and protein levels suggests that these latter four mutations are all null alleles [13] , [21] , [26] . Homozygotes for these alleles show severe ataxia by 10 days of age as well as profound deafness . The profound deafness and ataxia detected in Obl homozygotes is similar to the phenotype of these other known Atp2b2 null mutants [13] , [26] , [27] . Non-complementation between Obl and dfw confirmed that these two mouse mutants are likely to be allelic . The mutations identified in the Atp2b2 gene are shown in Figure S1 . Both the Obl and the dfw mutations significantly reduce the non-activated Ca2+ exporting ability of the PMCA2 protein . Observations on organotypic cultures showed that the defect of the pump observed in model cells also reduced its ability to remove the Ca2+ produced by UV photolysis in stereocilia . Thus , the Obl mutant has lost a significant portion of the non stimulated , longer-term Ca2+ exporting ability in respect to the w/a PMCA2 pump . In the present work we show that hearing loss in Obl/+ mice is detected at P20 and progresses in its severity with increasing age . Threshold shifts of up to 60-70 dB were found at frequencies corresponding to the basal and middle turns of the cochlea , where the majority of outer hair cell loss was detected . Analysis of Obl/+ mice at P20 showed no OHC or IHC loss despite the fact that they had a significant hearing impairment , confirming the previous suggestion that although hair cells are present they are not functioning normally [28] . IHC loss was detected in the base and middle of the cochlea in Obl/+ mutants at 4 months of age . The early degeneration of the OHCs seen in heterozygous Obl ( and dfw ) mice , leading to complete degeneration of the organ of Corti in the base of the cochlea , is similar to that seen in some human patients with age-related hearing loss [29] , [30] . It is not clear why hair cell dysfunction leads to hair cell degeneration in these mouse mutants , but prolonged abnormal calcium homeostasis may contribute to hair cell death . In dfw mice at approximately P60 , distortion product otoacoustic emission studies show that PMCA2 is important for the correct functioning of outer hair cells , especially at high frequencies [31] . Mice expressing the Atp2b2dfw2J allele , [9] demonstrated a lowered endocochlear potential and reduced endolymphatic calcium concentration , and thus have a reduced capacity for mechanoelectrical transduction . Taken together , these features may explain why ABR thresholds in Obl/+ mice are elevated above controls , but are still recordable . An interesting observation from the ABRs recorded in P59–62 and P89–91 Obl/+ mice is that click thresholds are more sensitive than the best tone threshold , by a factor of 15–22 dB . Clicks produce a more synchronised activation of a wider region of the basilar membrane compared to tone pips , and this may produce a summation of activity , reflected in lower click-evoked ABR thresholds . When Obl was placed on a mixed C3HeJ/FeB and C57BL/6J background , the progressive hearing loss in heterozygotes seemed more severe ( data not shown ) . Onset and severity of progressive hearing loss seen in heterozygotes of other Atp2b2 mutant alleles have been shown to vary considerably depending on the genetic background on which the mutation arose [13] , [21] , [26] , [28] , [32] . This is due to the presence of modifier alleles , one of which has been identified as the G753A variant of Cdh23 at the ahl locus ( also known as modifier of deafwaddler , mdfw ) [32] , [33] . Interactions between heterozygous Atp2b2 mutations and Cdh23 mutations have been shown to worsen the progressive hearing loss seen in some human patients . Heterozygous mutations ( ATP2B2V586M ) increase the severity of the progressive hearing loss seen in human patients with mutations in CDH23 [34] . The ATP2B2V586M mutation reduced the level of PMCA2 protein produced by 50% , although on its own it was not sufficient to cause hearing impairment in humans [34] . More recently , hearing loss has been reported in a human patient with an ATP2B2G293S mutation and a CDH23T1999S mutation . The parents of the patient carried either the ATP2B2 or the CDH23 mutation , but had no hearing impairment [16] . The ATP2B2 gene has recently been implicated in the deafness in 3p- syndrome . The syndrome is characterised by developmental delay , growth retardation and craniofacial abnormalities ( see [35] ) , which is sometimes , but not always , associated with a severe sensorineural hearing loss . In 3p- syndrome patients with a hearing loss , a deletion in the 3p25 . 3 locus was mapped to a region containing 18 genes including ATP2B2 . It is likely that haploinsufficiency of ATP2B2 is responsible for the deafness associated with this syndrome [36] . As progressive hearing loss is so common in the human population and we know so little about its molecular basis , identifying other mutations in and modifiers of the Atp2b2 locus in mouse inbred strains may be of importance in identifying new loci involved in progressive and age-related hearing loss in humans .
The founder mouse carrying the Oblivion mutation ( gene symbol Obl , original identifier DEA14 ) arose from the ENU mutagenesis program at Munich [17] . Mutations were generated by injecting 3 doses of 80–90 mg/kg bodyweight of N-ethyl-N-nitrosourea ( ENU ) into C3HeB/FeJ males . F1 progeny were screened for a range of phenotypes , including deafness and balance disorders . The founder Obl/+ mutant was identified by the absence of a Preyer reflex at 3 months . The mutant colony was maintained on the C3HeB/FeJ background , and all ABR and structural analysis was carried out on this genetic background . The care and use of animals was carried out in accordance with UK Home Office regulations and the Animal Care and Use Committee of the University of Padua . Half heads were fixed in Bodian's fixative and cleared with glycerol using a standard protocol . The inner ear was examined for signs of malformation . Middle ear ossicles were dissected out and studied . Six Obl/+ mutants and six littermate controls between 3–4 months age were analysed . Inner ears were fixed in 2 . 5% glutaraldehyde in 0 . 1 M sodium cacodylate buffer , the organ of Corti was exposed and samples were processed using the osmium tetroxide-thiocarbohydrazide ( OTOTO ) method [37] . After critical point drying and sputter coating with gold , samples were examined using a Phillips XL30 scanning electron microscope at 10 kV or a Hitachi S4800 FE Scanning Electron Microscope at 5 kV . Initial characterisation was performed on 3 Obl/+ and 3 littermate controls at 3–4 months of age . Hair cell degeneration was assessed in the basal turn ( 20–30% of the total distance from the base of the cochlear duct ) and middle turn ( 55–65% from the base ) at 20 ( P20 ) , 72–75 and 121 days old . Between 3 and 7 Obl/+ and +/+ mice were examined at each age . Hair cells with intact stereocilia bundles were counted over a stretch of at least 200–300 µm of the cochlear duct . Hair bundles that were damaged or showed fusion were still counted as being present . A two tailed T-Test was performed on hair cell counts for each hair cell row in each region at 95% confidence intervals , with the number of cases and standard deviations included in the analysis . A two tailed T-test was carried out on the weights of Obl/Obl mutants compared to littermate ( Obl/+ ) controls in the same way . The Preyer reflex ( pinna flick ) was detected using a custom-built click box to deliver a calibrated 20 kHz sound burst at 90 dB SPL . Up to 88 mice from Obl/+×+/+ matings were tested weekly from 3 to 8 weeks of age , although the numbers of mice at each time-point varied . For ABR recordings , a separate cohort of mice were anaesthetised ( urethane 2 mg/g ) and subcutaneous needle electrodes inserted on the vertex ( active ) , and over the left ( reference ) and right ( ground ) bullae . A calibrated sound system was used to deliver free-field click ( 0 . 01 ms duration ) and tone pip ( various frequencies from 3–42 kHz of 5 ms duration , 1 ms rise/fall time ) stimuli at a range of intensity levels in 3 dB ( or multiple ) steps . Averaged responses to 512 stimuli , presented at 21 . 1 s−1 , were analysed and thresholds established as the lowest sound intensity giving a visually-detectable ABR response . ABR recordings were obtained from a total of 61 mice , 28 at P20 ( +/+ , n = 9; Obl/+ , n = 19 ) , 14 at P59–62 ( +/+ , n = 5; Obl/+ , n = 9 ) and 19 at P89–91 ( +/+ , n = 5; Obl/+ , n = 14 ) . Obl/+ mutants on a C3HeB/FeJ background were outcrossed to C57BL/6J wild type females . Obl/+ F1 progeny were then backcrossed to +/+ animals from the original C3HeB/FeJ strain . Offspring from these backcross matings were examined at 2 months of age or older ( as this is the age at which Obl/+ mice on the original genetic background show profound hearing loss ) using the Preyer reflex . Tail and pinna tissue were collected for DNA preparation . A total of 255 backcross mice were analysed ( 129 Obl/+ , 126 +/+ ) . A genome-wide scan was conducted with 60 microsatellite markers approximately 25 cM apart , that had been shown to be polymorphic between C3HeB/FeJ and C57BL/6J inbred strains ( Table S2 ) . Additional microsatellite markers used for fine mapping of the Obl mutation were: D6Mit36; D6Mit104; D6Mit150; D6Mit115; D6Mit218; D6Mit254 . PCR was performed using standard techniques . Sequence analysis was performed on genomic DNA in Obl/+ mutants and littermate controls using primers designed to amplify the coding exons and splice sites of the Atp2b2 gene . The primer sequences are listed in Table S3 . PCR was performed using standard techniques and products were cleaned using magnetic bead separation ( Ampure ) and sequenced using BigDye Terminator Cycle sequencing kit ( Applied Biosystems ) . Sequence traces were analysed using Gap4 software [38] . To confirm the mutation identified in exon 15 and for genotyping of the colony , a PCR-based genotype test was designed . The 2630C→T missense mutation did not change a restriction enzyme recognition site , so primers were designed flanking the mutation site that would incorporate a StyI recognition site in the wildtype ( C ) allele , but not in the mutant ( T ) allele . Primers OblRTF ( 5′-CTT CTT CTC CCT GCC ACT GTC GTA G ) and OblRTR ( 5′-CCA CCG AGA CAC CGG TCC CGG TTC ) were used for PCR . The 111 bp PCR product was digested with StyI ( New England Biolabs ) which cuts the wildtype allele giving an 89 bp fragment while the mutant allele remains uncut . This genotyping tool was used to establish whether the sequence change in Obl DNA was a polymorphism in a total of 17 inbred strains: BALB/C , CBA/Ca , C3HeB/FeJ , DDY/Jc1 , 129X1/SvJ , A/J , Bxd-1/Ty , C58/J , CE/J , DA/HuSn , DBA/2J , FL/1Re , LP/J , NON/LtJ , RBG/Dn , St/bJ and SWR/J . mRFP was amplified from pCDNA3 . 1/zeo-mRFP ( kindly donated by Dr . M . Zaccolo , Padua , Italy ) using the following primers , forward: 5′-GCGCTAGCATGGCCTCCTCCGAGGACGTCA-3′ and reverse: 5′-GCAGATCTGAGGCGCCGGTGGAGTGGCGG-3′ , bearing restriction sites for NheI and BglII , respectively ( in bold ) . The PCR product was then digested with NheI and BglII and inserted in pEGFP-c1 ( Clontech , Palo Alto , CA ) digested with NheI and BglII to create pmRFP-c1 . PMCA2 w/a in pMM2 ( kindly provided by Dr . Strehler , Rochester , MN ) was excised by independent digestion with SalI-EcoRI and EcoRI-KpnI and inserted into XhoI-KpnI sites of pmRFP-c1 in a three-part ligation reaction resulting in pmRFP-PMCA2wa . The construct was controlled by sequencing . Site-directed mutagenesis was carried out to obtain the mutant cloned in the appropriate vector . pmRFP-PMCA2w/a was used as target and experiments were performed according to the manufacturer's standard protocol ( Stratagene , Cedar Creek , TX ) The following primers were used: Obl 5′ CATCATGGACACATTTGCTTTCCTGGCCCTGGCAACAGAGC 3′ ( forward ) and 5′ GCTCTGTTGCCAGGGCCAGGAAAGCAAATGTGTCCATGATG 3′ ( reverse ) CHO cells were grown in Ham's F12 medium , supplemented with 10% fetal calf serum ( FCS ) . Before transfection , they were seeded onto 13 mm glass coverslips and allowed to grow to 50% confluence . Transfection with 3 µg of plasmid DNA ( or 1 . 5 :1 . 5 µg in the case of co-transfection ) was carried out with a Ca-phosphate procedure [39] . Immunocytochemistry quantified the expressed pump proteins in the plasma membrane of transfected cells . CHO cells expressing the PMCA2 variants , were stained with polyclonal isoform-specific PMCA antibody 2N ( Affinity Bioreagent , Inc . , Golden , CO ) or a monoclonal antibody recognizing all pump isoforms ( 5F10 , Affinity Bioreagent , Inc . , Golden , CO ) , at a 1∶100 dilution in PBS . Staining was carried out with Alexa 488 labelled anti-rabbit or anti-mouse secondary antibodies ( Molecular Probes , Invitrogen Corp . , Carlsbad , CA ) at a 1∶50 dilution in PBS . Cells were imaged on a spinning disk confocal microscope ( Ultraview; Perkin-Elmer ) by using a X60 oil-immersion objective at a N . A . ( PlanAPo; Nikon , Tokyo , Japan ) . Regions of interest were selected by applying an edge-finding ( Sobel ) digital filter , thus limiting the analysis to plasma membrane areas . The total fluorescence intensity in membrane-delimiting regions of interest was quantified with software developed in our laboratory . For each construct fluorescence was averaged over a total of 50 cells in 3 different slides . Transfected cytAEQ were reconstituted by incubating CHO cells for 1–3 h with 5 µM coelenterazine in Dulbecco's modified Eagle's medium ( D-MEM ) supplemented with 1% FCS , at 37°C in a 5% CO2 atmosphere . Additions to the KRB medium ( 1 mM CaCl2 , 100 µM ATP ) were made as specified in the figure legends . The experiments and luminescence calibration into [Ca2+] values were carried out according to [40] . The experiments were terminated by lysing the cells with 100 µM digitonin in a hypotonic Ca2+-rich solution ( 10 mM CaCl2 in H2O ) to discharge the remaining aequorin pool . Briefly , a 13-mm round coverslip with the transfected cells was placed in a perfused thermostated chamber in close proximity to a low-noise photomultiplier , with a built-in amplifier discriminator . The output of the discriminator was captured by a Thorn-EMI photon-counting board and stored in an IBM-compatible computer for further analyses . Luminescence was calibrated off-line into [Ca2+] values by using a computer algorithm based on the Ca2+ response curve of wt aequorin . Data are reported as mean±SD . Statistical differences were evaluated by Student's 2-tailed t-test for unpaired samples . A p value<0 . 01 was considered statistically significant . To access utricular maculae of wild type or mutant mice between postnatal day 3 ( P3 ) and P4 , the otic capsule was opened medially and the endolymphatic compartment of the macula cut open . The otolithic membrane was removed after 15 min incubation in dissection saline to which 0 . 1 g/l bacterial subtilisin ( type XXIV; Sigma-Aldrich , St . Louis , MO ) had been added . Dissection saline was composed of Hank's Balanced Salt Solution ( HBSS; part number H6648 , Sigma-Aldrich ) with 10 mM HEPES , 10 . 000 U/l penicillin and 25 µg/l fungizone . HBSS contained ( in g/l ) : 0 . 4 KCl , 0 . 06 KH2PO4 ( anhydrous ) , 0 . 35 NaHCO3 , 8 . 0 NaCl , 0 . 048 Na2HPO4 ( anhydrous ) , 1 D-glucose . The epithelium was fixed by Cell-Tak ( BD Biosciences , Bedford , MA ) , mixed with 90% NaHCO3 , to the lateral side of a glass capillary ( 1 . 5 mm diameter , 5 mm length ) , which had been previously glued to a microscope slide by a small drop of Sylgard Silicon Elastomer ( Dow Corning , Wiesbaden , Germany ) . Cultures were preserved for one day at 37°C in a complete medium of 95% D-MEM/Ham's F-12 ( 1∶1 ) ( concentration 1X , liquid form , containing L-glutamine but no HEPES; Gibco , Invitrogen Corp . , Carlsbad , CA ) and 5% fetal bovine serum . Organotypic cultures dissected from P3 wild type and mutant mice pups were maintained over night at 37°C in D-MEM/Ham's F-12 ( 1∶1 ) medium with 5% fetal bovine serum . Tissue preparations were fixed in paraformaldehyde 4% for 20 min at room temperature , rinsed in washing solution ( PBS containing BSA 2% ) and permeabilised with washing solution containing Triton 0 . 1% for 1 h at room temperature . Incubation with primary PMCA antibody 2N was carried out overnight at 4°C using a 1∶100 dilution in washing solution . FITC-conjugated rabbit anti-IgG antibody ( Invitrogen ) was used as secondary antibody for pump detection ( 1∶200 dilution in washing solution , 2 h at room temperature ) . The preparation was mounted on a coverslip and imaged up side down on an inverted spinning disk confocal microscope ( Ultraview; Perkin-Elmner ) using a 60× oil-immersion objective at a 1 . 4 N . A . ( PlanApo; Nikon , Tokyo , Japan ) . Cochleae dissected from P5 mice were fixed in 4% paraformaldehyde for 20 min at room temperature , rinsed in PBS containing 2% BSA ( rinse solution ) and permeabilized for 1 hour at room temperature with 0 . 1% Triton , dissolved in rinse solution . Tissues were stained with polyclonal isoform-specific PMCA antibody 2N ( Affinity Bioreagent , Inc . , Golden , CO ) by incubation overnight at 4°C with specific polyclonal antibodies ( 2 . 5 µg/ml ) ( Invitrogen ) diluted in rinse solution . A FITC conjugated rabbit anti–IgG antibody ( 5 µg/ml , Invitrogen ) was used as secondary antibody , following incubation in rinse solution for 2 hours at room temperature . F–Actin was stained by incubation for 1 hour at room temperature with rhodamine phalloidin ( 7 µM , Invitrogen , R415 ) diluted in rinse solution . Stained samples were sandwiched between two coverslips and imaged with a 60× oil–immersion objective ( NA 1 . 4 , Plan Apo , Nikon , Tokyo , Japan ) attached to an inverted microscope ( Eclipse 200 , Nikon ) equipped with a Nipkow disk confocal scanning head ( Ultraview , Perkin Elmner , USA ) . Confocal fluorescence images were captured with a scientific grade cooled CCD camera ( Orca , Hamamatsu Photonics , Hamamatzu City , Shizuoka , Japan ) . Cultures were loaded with 10 µM cell permeant Fluo-4 AM ( Invitrogen ) for 50 min at 37°C in D-MEM supplemented with 10 µM cell permeant NP-EGTA AM ( Invitrogen ) , 25 µM sulfinpyrazone and Pluronic F-127 ( 0 . 1% w/v ) . For de-esterification , cultures were transferred to an experimental chamber mounted on the stage of a confocal imaging setup ( Biorad Radiance 2100 ) incorporating an upright microscope ( Eclipse E600FN , Nikon , Tokyo , Japan ) and superfused for 20 min with a medium composed of HBSS supplemented with 4 . 4 g/l glucose and 2 mM anhydrous CaCl2 ( pH 7 . 4 , Osm 330 ) . Experiments were performed with a 100× water-immersion objective ( N . A . 1 . 00 , LUMPlanFl , Olympus , Tokyo , Japan ) using the same perfusion medium . Fluo-4 fluorescence was excited by the 488 nm line of an argon laser coupled by fiber optics to the confocal microscope . Fluorescence emission was selected around 528 nm using a narrow-band ( 50 nm ) interference filter . Fluorescence images of utricle hair cells in the organotypic cultures were acquired with a resolution of 512×512 pixel by scanning at 512 lines per second under control of the Biorad Laser Sharp software . To be retained for subsequent recording , imaged cells had hair bundles extending for their entire length in a single confocal plane ( bundle planarity condition ) , a possibility afforded by having the culture attached to a curved surface . Dynamic fluorescence data were acquired in the ‘linescan’ mode to produce a scan series of fluorescence intensity values , F , measured in the photomultiplier tube PMT units from a value of 0 to a saturating value of 255 . Laser intensity and PMT gain were adjusted to accommodate the dynamic range of changes in F . Typical background fluorescence values , measured from regions devoid of obvious cellular structures , were ∼1 PMT units , while pre-stimulus ( basal ) levels , F0 , averaged over the entire length of the hair bundle , were ∼8 PMT units . In all experiments pinhole aperture was adjusted to the same value , yielding confocal section with 3 µm thickness . To assay the Ca2+ extrusion activity of the PMCAs , an area of ∼3000 µm2 , comprising a few hair cells in the cultured utricular macula , was exposed to UV radiation generated by an air-cooled 337 nm pulsed nitrogen laser ( Model VSL-337ND-S , Spectra Physics , Mountain View , CA , USA ) connected to the microscope through a 600 µm ∅ optical fiber . UV light was directed onto the sample by reflection off a 400 DCLP dichromatic beam splitter ( Chroma ) positioned at 45° just above the microscope objective lens . A single laser pulse ( 4 ns ) delivering a maximum of 326 µJ of energy ( at the laser output ) was used to photorelease Ca2 from the caged state ( NP-EGTA bound to Ca2+ ) in the brief time interval between the 2500th and the 2501st scan line . In a typical record lasting about 120 s , 20 000 consecutive lines were acquired . All data were analyzed offline on a personal computer using the Matlab 7 . 0 ( The MathWorks , Inc . , Natick , MA ) software environment . Data are presented as ΔF/F0 where ΔF = F−F0 . In these expressions , raw pixel values are spatial averages along the hair bundle . Maximal percent fluorescence changes , ( Fmax−F0 ) /F0 , were about 280% . To estimate the slow time constant , τ , of recovery to baseline , transients peaking at ΔFmax , were fitted by a single exponential function during the first 10 s from the UV pulse . Data are given as mean±standard error of the mean ( S . E . M . ) . | Progressive hearing loss is very common in the human population , but we know little about the causes . Environmental and genetic factors each may contribute . Knowledge of the genetic variants involved in hearing loss and understanding of the molecular and cellular mechanism of their action will aid the development of better treatments . One of the few genes known to be involved , in both mouse and humans , is Atp2b2 , which encodes a calcium pump . We have discovered a new mutation in this gene leading to hearing loss in the mouse mutant oblivion . The mutation leads to a serine to phenylalanine substitution in a transmembrane domain . Mutations affecting such transmembrane domains are usually expected to interfere with the normal process of inserting the protein in the membrane and transporting it to its final destination on the plasma membrane of the cell . Surprisingly , the pump is produced and is targeted to the plasma membrane , in both cultured cells expressing the mutant gene and sensory hair cells from the oblivion mutant inner ear . However , we show it has impaired calcium pumping ability , which can account for the progressive hearing loss as well as the progressive degeneration of the sensory hair cells that we observe in the mutants . | [
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] | 2008 | The Novel Mouse Mutation Oblivion Inactivates the PMCA2 Pump and Causes Progressive Hearing Loss |
Viruses utilize host factors for their efficient proliferation . By evaluating the inhibitory effects of compounds in our library , we identified inhibitors of cyclophilin A ( CypA ) , a known immunosuppressor with peptidyl-prolyl cis-trans isomerase activity , can significantly attenuate EV71 proliferation . We demonstrated that CypA played an essential role in EV71 entry and that the RNA interference-mediated reduction of endogenous CypA expression led to decreased EV71 multiplication . We further revealed that CypA directly interacted with and modified the conformation of H-I loop of the VP1 protein in EV71 capsid , and thus regulated the uncoating process of EV71 entry step in a pH-dependent manner . Our results aid in the understanding of how host factors influence EV71 life cycle and provide new potential targets for developing antiviral agents against EV71 infection .
Cyclophilins ( Cyps ) are key cellular factors that function in numerous cellular processes , including transcriptional regulation , immune response , protein secretion , and mitochondrial function [1] . Cyps possess peptidyl-prolyl cis-trans isomerase activity and have high affinity for the immunosuppressant cyclosporine A ( CsA ) . Cyclophilin A ( CypA ) is a key member of the Cyp family and was first shown to mediate the immunosuppressive function of CsA through the formation of a CsA-CypA complex . This complex binds to and inhibits the function of the phosphatase calcineurin , which normally functions to dephosphorylate NF-AT , a transcription factor important for T cell activation [1] . CypA is also known to play critical roles in the proliferation of a number of viruses , including human immunodeficiency virus type 1 ( HIV-1 ) , influenza virus , hepatitis C virus ( HCV ) , vesicular stomatitis virus ( VSV ) , vaccinia virus , severe acute respiratory syndrome coronavirus ( SARS-CoV ) , rotavirus ( RV ) and human papillomavirus ( HPV ) , by interacting with viral proteins or facilitating IFN-β production [2] , [3] . CypA was first shown to be incorporated into HIV-1 virions through its interaction with the capsid protein ( CA ) , and the interaction between newly synthesized HIV-1 CA and CypA is required for HIV-1 to induce dendritic cell maturation [4] , [5] . CypA also interacts with other HIV-1 proteins , such as Vpr and p6 , to regulate HIV infection [6] , [7] . CypA was further revealed to interact with extracellular CD147 , which is the main receptor for CypA on the cell membrane of human leukocytes , and this interaction can induce the phosphorylation of HIV-1 matrix protein to regulate the liberation of the reverse transcriptase complex into cytoplasm during an early stage of HIV-1 infection or can function in HIV-1 attachment to host cells [8] . But a recent research showed that CypA stabilized the HIV-1 capsid and antagonizes HIV-1 uncoating in vitro , indicating the versatile roles of CypA in HIV-1 infection [9] . Moreover , several lines of evidences revealed that Cyps play crucial roles in HCV life cycle . CypB was first reported to be important for HCV replication [10] , but later studies showed that CypA , but not CypB , was required for HCV infection in vitro [11]–[14] . CypA was reported to function in the replication of HCV by increasing the affinity of the HCV polymerase NS5B for viral RNA to enhance HCV replication [13] , or by binding to the HCV NS5A protein to aid in viral replication [15] , [16] . Furthermore , Cyps were demonstrated to play an essential role in HPV infection by facilitating conformational changes in capsid proteins of HPV , resulting in exposure of the N-terminus of L2 protein , and the dissociation of L1 pentamers from recombinant HPV11 L1/L2 complexes in a pH-dependent manner [3] , [17] . Enterovirus-71 ( EV71 ) , a member of the Picornaviridae family , is one of the major causative agents of hand-foot-and-mouth disease ( HFMD ) in pan Asia-Pacific region and results over eight millions of infections and three thousands of dead cases since 2008 [18] , [19] . The genome of EV71 contains a single-stranded , positive-sense RNA ( +ssRNA ) and encodes a polypeptide with a molecular weight of approximate 250 kDa [20] . This polyprotein is initially processed into one structural ( P1 ) and two non-structural ( P2 and P3 ) regions and then undergoes proteolytic cleavage into various precursors , ultimately resulting in 11 mature proteins . Among them , P1 is further proteolyzed into VP1 to VP4 to form the viral capsid , while P2 and P3 are processed into replicase proteins . For a productive infection , virions must uncoat and release viral genome into host cytoplasm , following the successful bindings with functional receptors . Enteroviral uncoating process involves sequential capsid alterations by conformational changes [21] . During uncoating , mature particles with sediment coefficient of 160S are converted to the uncoating intermediate A particles with sediment coefficient of 135S , and subsequent empty 80S particles representing the final production of the entry process [22] . The 80S particles are empty particles that have shed genomic RNA , whereas the 135S particles retain the full complement of genomic RNA but lack some or all of their content of VP4 and have externalized most of the N-terminal extension of VP1 that is normally inside the virions [22] . The involvement of host cellular factors plays essential roles in virus proliferation . However , the knowledge of how EV71 utilizes host factors in its life cycle is limited . Only two extracellular membrane proteins , human P-selectin glycoprotein ligand-1 ( PSGL-1 ) [23] and scavenger receptor B2 ( SCARB2 ) [24] , [25] , as well as heparan sulfate ( HS ) [26] , were recently identified as functional receptors for EV71 infection . Another result suggests that the binding of EV71 to human annexin II on the cell surface enhanced viral entry and infectivity , especially at a low infective dose [27] . Interestingly , SCARB2 was reported to be the exclusive uncoating receptor to trigger conversion of 160S particles to other forms during uncoating process at acidic condition , resulting in the releasing of viral genome [21] . Here we used CypA inhibitors as bioprobes to show that CypA played an essential role in EV71 proliferation . We also elucidated the mechanism by which CypA interacted with and modified the conformation of EV71 VP1 H-I loop , and thus regulated the uncoating process of EV71 entry . This CypA-EV71 capsid functional association not only provides information to understand the cellular factors used in EV71 infection , but also presents a new promising potential for the development of antiviral therapeutics .
Our compound collection , which includes 950 chemically synthesized compounds , was screened by using rhabdomyosarcoma ( RD ) cells infected with the EV71 virus strain AnHui1 . This screen identified compound HL051001P2 ( Figs . 1A ) as a potent inhibitor of viral proliferation , with an EC50 value of 780 nM , by measuring EV71 virus RNA through quantitative RT-PCR ( qRT-PCR ) ( Fig . 1C ) . No significant cytotoxicity was observed from compound HL051001P2 at concentrations below 20 µM , as demonstrated by the WST-1-based assay ( Fig . 1D ) , indicating that the inhibition of EV71 proliferation was specific . Because compound HL051001P2 was previously reported to function as a CypA inhibitor [28] , we next selected CsA ( Fig . 1B ) , a well-known Cyp inhibitor and clinical immunosuppressant drug with antiviral effects , to suppress HIV-1 and HCV replication and to check whether other CypA inhibitor can also inhibit EV71 replication . The results revealed that CsA clearly impaired EV71 proliferation with an EC50 value of 3 . 5 µM ( Fig . 1C ) ; however , CsA had a slightly higher cytotoxicity than HL051001P2 ( Fig . 1D ) . Because Cyps are known to be involved in the viral life cycle , our interest in identifying host factors in the EV71 life cycle and antiviral agents prompted us to initiate further investigations to study the working mechanism of CypA in the EV71 life cycle and the inhibitory mechanism by which Cyp inhibitors block EV71 replication . Over ten subfamilies have been identified in the Cyp family to date , among which CypA and CypB are the most abundant subtypes [10] . To clarify which type of Cyp is most essential for EV71 proliferation , we next used the RNA interference ( RNAi ) method to investigate the impact of CypA or CypB on EV71 virus proliferation . We first introduced short hairpin RNAs ( shRNAs ) that were designed to recognize the 3′ non-coding region of CypA ( sh-CypA ) or CypB ( sh-CypB ) by lentiviral vectors into RD cells to downregulate endogenous CypA and CypB expression [12] . The expression of glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) , a housekeeping gene used as an internal control , was not downregulated ( Fig . 2A ) . We obtained stable knockdown cell lines through resistance gene screening and then infected these shRNA-RD cells by applying an EV71-GFP virus with a multiplicity of infection ( MOI ) of 0 . 5 . The results of the flow cytometric studies revealed that 10 . 98% of the RD cells with negative control shRNA ( RD-sh-control ) were infected with the EV71-GFP virus , and the infection ratio was decreased to 3 . 31% in the RD-sh-CypA cells . However , the infection rate remained at 7 . 41% in the RD-sh-CypB cells , suggesting that CypA dominantly impacted EV71 infection ( Fig . 2B ) . Moreover , when infected with EV71 at a multiplicity of infection ( MOI ) of 1 , the EV71 RNA level in the CypA knockdown cells was diminished to approximately 20% of the levels observed in the RD-sh-control cells , whereas the CypB knockdown did not result in this reduction ( Fig . 2C ) . We also infected the RD-sh-CypA and RD-sh-CypB cells with EV71 at MOIs of 0 . 1 and 50 , respectively , and the results revealed a similar finding as the one caused by EV71 infection at an MOI of 1 ( Fig . 2D ) . Furthermore , the expression level of EV71 VP1 protein , which is the major component of the EV71 capsid [18] , [29] , was significantly reduced by the knockdown of CypA , but not CypB , which is consistent with the impact of CypA reduction on the EV71 RNA level ( Fig . 2E , left half ) . An interesting observation is that the CypB knockdown resulted in a small increase in the EV71 RNA replication and VP1 expression ( Figs . 2C and 2E ) . To verify the impact of CypA in the different cell lines used for EV71 infection , we generated an additional stable CypA knockdown Huh7 . 5 . 1 cell line ( Huh7 . 5 . 1-sh-CypA ) and observed the EV71 infections ( at MOI of 10 ) in Huh7 . 5 . 1-sh-CypA and Huh7 . 5 . 1 cells with the negative control shRNA ( Huh7 . 5 . 1-sh-control ) . The results demonstrated that the decreased EV71 RNA replication ( Fig . 2F ) and VP1 expression ( Fig . 2G ) were similar to those in the RD cell lines . Taken together , all these data suggested that the loss of CypA function is specifically associated with the inhibition of EV71 proliferation . Previous studies suggested that CypA was upregulated during virus infection and is correlated with the final results of the infection [2] , [30] , [31] . Similarly , the expression of host cell CypA was upregulated in the RD cells following EV71 infection ( Fig . 2H ) . We also found that EV71 infection led a very minor alteration of secreted CypA in the supernatant of RD-sh-control cells , and very little CypA could be detected in the RD-sh-CypA culture with or without EV71 infection ( Fig . 2I ) . This finding indicated that CypA was present in host cells , but not those that were secreted in the supernatant , and CypA is upregulated following EV71 infection . CypA was found to interact with different viral proteins and affect different stages of the viral life cycle through distinct mechanisms [2] . To define the working target of CypA in the EV71 virus , we generated an EV71 virus that was resistant to a CypA inhibitor through multiple cell culture passages in the presence of compound HL051001P2 . Sequence analyses of the entire genome of multiple resistant viruses identified only a single T-to-C mutation at nucleotide position 3 , 164 of the EV71 genome ( Table S1 ) . This mutation translated into a single amino acid substitution of a serine to a proline at residue VP1-243 ( all residue numbers correspond to the residue in the sequence of the VP1 protein , not in the polypeptide ) , which is located in the H-I loop of VP1 [32] . To confirm that the VP1-S243P mutant was the mutation that contributed to the resistant phenotype of the selected mutant virus , we engineered a recombinant mutant EV71 virus ( S243P-EV71 ) from the wild type EV71 ( wt-EV71 ) strain AnHui1 through the introduction of a single serine-to-proline substitution at the VP1-243 position and investigated the sensitivity of the S243P-EV71 virus to compound HL051001P2 or CsA in comparison with wt-EV71 . The EC50 value of compound HL051001P2 on S243P-EV71 proliferation ( 3 . 56 µM ) was approximately 5-fold higher than the EC50 value for wt-EV71 , and the EC50 value of CsA was 2-fold higher for the S243P mutated virus ( Table S2 ) . Moreover , following an infection with the drug-resistant S243P-EV71 recombinant virus , the EV71 RNA level in the RD-sh-CypA cells was approximately 70% of the levels in the RD-sh-control cells . This value was much higher than the values observed in RD-sh-CypA cells that were infected with wt-EV71 virus ( approximately 25% ) , suggesting that the VP1-S243P mutant rescued EV71 replication . The expression level of EV71 VP1 protein also consistently recovered to normal levels following infection with the S243P-EV71 virus ( Fig . 2E , right half ) . These data again revealed that the VP1-S243P mutant is specifically associated with resistance to the CypA inhibitor . CypA has peptidyl-prolyl cis-trans isomerase activity to facilitate the conformational modification of proline residue . By examining the protein sequence of EV71 VP1 , we found that there is only one proline residue located close to the VP1-S243 position , i . e . , VP1-P246 . We hypothesized that the resistant mutation from a serine residue to a proline residue at the VP1-243 position may increase the binding affinity of VP1 to CypA and help the virus escape from drug treatment and the depletion of the endogenous CypA . To clarify the mechanisms underlying the CypA regulation of the EV71 life cycle , we analyzed the molecular interaction of CypA with EV71 virions or the VP1 H-I loop by using a GST pull-down assay . We first checked whether recombinant CypA protein could associate with the EV71 virions ( Fig . 3 ) . By using a GST-tagged CypA as a probe ( Fig . 3A ) , we demonstrated that recombinant CypA protein was clearly bound to wt-EV71 , and the interaction of CypA with the EV71 virion was increased by substituting with a proline residue at VP1-S243 ( Fig . 3B , upper panel ) . However , when we replaced both S243 and P246 with alanine residues , the mutated virus , i . e . , S243A/P246A-EV71 , cannot bind with CypA ( Fig . 3B , bottom panel ) . This interaction between CypA and the EV71 virion was also reduced in a dose-dependent manner after treating with CsA and can almost be abolished at a concentration of 4 µM CsA ( Fig . 3C , upper panel ) . Additionally , the S243P mutant rescued the interaction between CypA and EV71 under CsA treatment ( Fig . 3C , bottom panel ) . We further demonstrated that recombinant CypA protein not only bound to wt-EV71 strain AnHui1 but also to other strains of the wt-EV71 virus ( Fig . 3D ) . We next fused the peptide of the H-I loop ( 239-GSSKSKYPL-247 ) ( GST-1S ) and the H-I loop with the S243P substitution ( 239-GSSKPKYPL-247 ) ( GST-1P ) to the GST tag as probes ( Fig . 3A ) to test whether CypA can directly bind to the VP1 H-I loop in vitro . The result demonstrated that CypA directly interacted with the H-I loop of EV71 VP1 , and the mutation of the serine at the VP1-243 position to proline can clearly increase the binding affinity of the H-I loop for CypA ( Fig . 3E ) . We also used NMR spectra to demonstrate that recombinant CypA binding to chemically synthesized EV71 VP1 H-I loop peptides caused chemical shift changes , suggesting that CypA catalyzed the correct cis-trans reaction of the VP1 H-I loop ( Fig . S1 ) . Moreover , a reported catalytic-defective mutant of CypA called H126Q [33] eliminated the interaction between CypA and EV71 virions ( Fig . 3F ) . A similar attenuation of the interaction between the CypA H126Q mutant and virions was also reported in HIV-1 and HCV [12] , [34] . Taken together , all these results revealed that CypA functioned directly at the H-I loop of EV71 VP1 , and the replacement of serine with proline at the VP1-S243 position could increase the binding affinity of CypA with EV71 virions . S243 is located in the H-I loop of the VP1 protein of the EV71 virus , and several loop regions of the VP1 protein are known to play critical roles in the entry step during picornavirus infection [35] . Therefore , we hypothesized that CypA may also act in the entry step of the EV71 life cycle . To verify this hypothesis , we first infected RD cells with EV71 and treated them with 5 µM compound HL051001P2 at −6 , −4 , −2 , 0 , 2 , 4 , 6 and 8 h post-infection ( hpi ) , in which 0 hpi indicates the supply of a virus infection inhibitor . The results showed that the inhibition of EV71 by HL051001P2 represented a clear dependence on the treatment time . The HL051001P2 treatments at −6 to 0 hpi showed the inhibition of EV71 replication , whereas the anti-EV71 effect of the treatments after 2 hpi was significantly attenuated ( Fig . 4A ) . We further transfected an EV71 subgenomic replicon RNA lacking the P1 region in the RD-sh-control and RD-sh-CypA cells and found that the EV71 RNA replication inside the host cells was not affected by the downregulation of CypA ( Fig . 4B ) . These results indicated that CypA affected the early step , but not genome replication , during EV71 infection . By detecting viral RNA at different time points in RD cells that had been infected with the EV71 virus , we found that the amount of EV71 RNA in the RD-sh-CypA cells decreased to less than 50% of that in the RD-sh-control cells at 1 hpi ( Fig . 4C , start point ) , and this reduction was reversed when the RD-sh-CypA cells were infected with S243P-EV71 virus ( Fig . 4D ) . When we detected EV71 VP1 expression , we found that VP1 expression can be clearly and consistently attenuated in EV71-infected RD-sh-CypA cells in comparison with EV71-infected RD-sh-control cells from 10 hpi ( Fig . 4E ) . Moreover , the augmentation of EV71 virus RNA in RD-sh-control cells indicated that the replication of EV71 RNA began at 4 hpi ( Fig . 4C , black line ) ; by contrast , this stage was obviously delayed to 8 hpi in the RD-sh-CypA cells ( Fig . 4C , red line ) . However , when we infected RD or RD-sh-CypA cells with S243P-EV71 , the growth curves revealed a similar curve , indicating that the VP1-S243P mutant confers resistance to CypA depletion ( Fig . 4D ) . When we checked the viral titers in the culture , we found that the viral titers in the supernatant were not clearly altered in EV71-infected RD-sh-CypA and RD-sh-control cells ( Fig . 4F ) . We also infected RD cells by using wt-EV71 with a 5 µM HL051001P2 treatment and measured the viral titers in the supernatant at different hpis ( Fig . 4A ) . The results showed that infectious viral production was affected by the inhibitor at -6 , -4 and -2 hpi , but not as significantly as the impact on the intracellular viral genome at the same hpi . We thus speculated that CypA depletion blocked viral entry during re-infection and left more viruses in the culture . We further examined the internalization of EV71 by using immunofluorescence ( Fig . 4G ) . The RD-sh-control and RD-sh-CypA cells were infected with wt- and S243P-EV71 , and endogenous CypA and EV71 VP1 proteins were analyzed by immunofluorescence . In the RD-sh-control cells infected with wt-EV71 , EV71 VP1 was distributed throughout the cytoplasm at 2 hpi , which was indicative that the virus particle internalization and localization with CypA was random ( Fig . 4G , panel a-c ) . The downregulation of CypA in the RD-sh-CypA cells was first confirmed ( Fig . 2A ) . In RD-sh-CypA cells infected with wt-EV71 , the localization of wt-EV71 was restricted to the cytoplasm of the perimembrane region at 2 hpi ( Fig . 4G , panel d-f ) . We can also observe the colocalization of EV71 VP1 with CypA , suggesting that the knockdown of CypA inhibited the internalization of EV71 and CypA was accumulated around EV71 virions . By contrast , the internalization of EV71 was rescued by the S243P-EV71 mutant ( Fig . 4G , panel g-i ) . A similar observation was also reported in the HPV16 pseudovirus in the presence of the CypA inhibitor [12] . These data indicated that CypA depletion inhibited the internalization of EV71 into the host cells . The entry of EV71 can be further divided into the following two processes: 1 ) receptor binding and 2 ) uncoating to release the viral genome [35] . To demonstrate the exact function of CypA in EV71 entry , we first checked the effect of CypA downregulation in the binding of EV71 virions to host cells . The results showed that EV71 binding to host cells was attenuated by CypA knockdown ( Fig . 5A ) and could be rescued by the substitution of S243 with a proline residue ( from 50% attenuation to 90% attenuation ) ( Fig . 5B ) . We then checked the binding affinity of three reported EV71 functional receptors , i . e . , SCARB2 , PSGL-1 and HS , for the wt-EV71 virions without or with CypA treatment . The results revealed that CypA treatment did not lead to obvious upregulation in the binding with EV71 functional receptors ( Figs . 5C-5E ) . Together with the result showing that CypA directly interacts with the EV71 virion , the CypA treatment is not likely to enhance the binding to all reported receptors , and the attenuation of EV71 virions that are binding to RD-sh-CypA and compensation by S243P mutation are likely to resulted in an interaction change between the virions and CypA located at the cell membrane . The conversion from EV71 160S particles to 135S particles can be induced by uncoating SCARB2 under an acidic condition [21] . However , recent biochemical and structural studies have suggested that simply heating the 160S particles near 60°C induces virus expansion and RNA genome release , and heating over 65°C leads to the subsequent protein melting of virions during uncoating [36] . We next used a previously reported virion flotation assay [21] , which is used to detect the conversion of 160S particles into other forms during viral uncoating , to check whether the uncoating process of EV71 entry could be affected by CypA . To be consistent , EV71 virions that were treated at 61°C exhibited a smaller shift ( Fig . 5F , blue line ) from the native peak ( 160S ) ( Fig . 5F , black line ) after ultracentrifugation in a 1 . 1–1 . 5 g/ml discontinuous CsCl gradient , whereas the virions that were treated at 68°C exhibited a much larger shift ( Fig . 5F , red line ) . We next incubated 160S virions with 20 µg of recombinant CypA followed by incubation at 37°C for 4 h at pH 5 . 5 and pH 6 . 5 , respectively , before subjecting them to ultracentrifugation at 41 , 000 rpm for 10 h at 4°C . The results revealed that CypA cannot trigger the conversion of 160S particles at pH 5 . 5 and pH 6 . 5 ( Fig . 5G ) . On the contrary , when a mixture of 160S particles and CypA was incubated at 37°C at pH 6 . 0 for 4 h , the shift from 160S particles was distinct ( Fig . 5H , black and red lines ) . Moreover , the catalytic-defective mutant of CypA , namely H126Q , was incubated with 160S particles at 37°C under pH 6 . 0 for 4 h , the shift in virions was completely eliminated ( Fig . 5H , blue line ) . All these results support the idea that CypA can regulate the uncoating process of EV71 entry in a pH-dependent manner , which plays a similar role as the only EV71 uncoating receptor , or SCARB2 [21] . To study the fitness of the mutation in the VP1 H-I loop , we transfected RD cells with RNA transcripts of EV71 recombinants containing the 5 coding mutations and generated recombinant viruses , which were designated as S243P-EV71 , S243A-EV71 , P246A-EV71 , S243P/P246A-EV71 , and S243A/P246A-EV71 ( Fig . 6 ) . In comparison with the wt-EV71 , S240A-EV71 and S240P/P246A-EV71 had almost equal supernatant EV71 infectivity titers ( P = 0 . 698 or P = 0 . 106 , respectively ) . P246A-EV71 and S243A/P243A-EV71 had slightly lower EV71 infectivity titers ( P = 0 . 030 or P = 0 . 038 , respectively ) ( Fig . 6A ) . This finding indicated that a proline residue located in the H-I loop acts in the interaction of CypA and EV71 , leading to the correct conformation of viral capsid and further virus uncoating . It is notable that the supernatant infectivity titers of S243P-EV71 were 1 . 75 log10 lower than those of wt-EV71 ( P = 0 . 01 , Fig . 6A ) . The intracellular growth curve showed that S243P-EV71 growth is also slower than that of wt-EV71 ( Fig . 6C ) . However , when we infected RD cells with S243P-EV71 and wt-EV71 viruses under a 5 µM HL051001P2 treatment , we found that the growth of S243P-EV71 was much better than that of wt-EV71 ( Fig . 6D ) . This finding is consistent with the results of the infection by wt-EV71 or S243P-EV71 in RD-sh-CypA cells ( Figs . 2C and 2D ) . Together , these results suggested that replacing S243-VP1 with a proline residue decreased viral fitness but conferred resistance to CypA inhibitors or caused a CypA loss of function .
The results we report here demonstrate that the CypA host factor played a crucial role in the uncoating process during the entry step of EV71 infection , and the action site of CypA was mapped to the H-I loop of capsid protein VP1 . An analysis of all EV71 sequences in GenBank showed that the action position of CypA in the EV71 VP1 H-I loop was strictly conserved in all EV71 genotypes and stains ( Fig . 7A ) , either in the protein sequence or the gene codon . However , a comparison of several representative strains of Coxsackie virus ( CV ) , poliovirus ( PV ) and EV suggest that this position is not conserved among EV71 , CVA16 , CVB3 and PV ( Fig . 7A ) . The dependence of the proliferation of other enteroviruses or picornaviruses on the host factors must be further defined . The crystal structure of mature EV71 particles [32] revealed that the VP1 H-I loop is a mostly solvent-exposed region at the surface of the virus particle ( Fig . 7B ) and usually functions in receptor binding or uncoating . Among all reported EV71 functional receptors , SCARB2 is the only one that can mediate both attachment to the host cell and uncoating [21] . PSGL-1 cannot induce the conversion from mature 160S particles to other forms during the viral uncoating process [21] . In a recent result , Nishimur et al . reported that the H-I loop of VP1 plays an essential role in EV71 recognition through one of its functional receptors , namely PSGL-1 , and they demonstrated that the substitution of VP1-K242 and K244 , which are located in the VP1 H-I loop , significantly attenuated virus binding to PSGL-1 [37] . They also indicated that the VP1 E145 residue modulates the orientation of VP1 K244 and thus regulates the exposure of the positively charged lysine side chain , which in turn regulates receptor binding [37] . Moreover , Tan et al . showed that EV71 binds to heparan sulfate on the cell surface , and they suggested that heparan sulfate may bind to the positively charged amino acids ( including VP1-K242 , K244 , and R161 ) that form a cluster around the five-fold symmetry axis [26] . These findings suggested that the lysine residues at the VP1-242 and 244 positions play essential roles in the binding of the EV71 virus to variable receptors . Interestingly , these two lysine residues are surprisingly very close to the CypA action site in the VP1 H-I loop , which is VP1-S243 ( Fig . 7B ) . In a very recent result , Lee et al . reported an anti-EV71 neutralizing antibody called MA28-7 , which has epitopes at the fivefold vertex that cover the VP1 H-I loop [38]; this study supports the critical role of the correct H-I loop conformation in the EV71 entry step . By contrast , the binding site of the EV71 virion to SCARB2 was mapped at a canyon of VP1 around residue Q172 [21] , which is far away from S243-VP1 . We observed that EV71 virion binding to SCARB2 was not enhanced , but slightly decreased by CypA treatment , suggesting that the CypA function in SCARB2-mediated EV71 entry could be more complicated . Taken together , we propose that CypA plays a role as an uncoating regulator by altering the conformation of the H-I loop in VP1 during the EV71 entry through the PSGL-1 or HS-mediated pathway , and CypA has different impacts on the entry of EV71 through various functional receptors . Another interesting observation is that CypA mediated EV71 uncoating most distinctly at pH 6 . 0 , but not at pH 5 . 5 and 6 . 0 . During virus internalization , endosome acidification increases during maturation , at values ranging from pH 6 . 8 to 6 . 1 in early endosomes to pH 6 . 0 to 4 . 8 in late endosomes [39] , [40] . Because SCARB2 was previously shown to mediate EV71 uncoating most efficiently at pH 5 . 6 [21] , we propose that CypA acts to mediate EV71 uncoating before SCARB2 during the maturation of late endosomes . With the increasing maturation of late endosomes and acidification , the EV71 uncoating regulator transfers from CypA to the next one , namely SCARB2 . A similar observation was also found for the CypA study in HPV entry . CypA treatment induced the release of capsid protein L1 from L2 in a pH-dependent manner , in which L1 dissociation from L2 was most efficient at pH 6 . 0 , less efficient at pH 7 . 4 , and undetectable at pH 5 . 5 and 8 . 0 [17] , suggesting a complicated process during virus uncoating in endosomes . Furthermore , CypA showed complicated impacts on the HIV-1 life cycle; CypA is necessary for HIV-1 infection [1] , [41] , [42] but also blocks HIV-1 uncoating [43] , as revealed in previous reports . We cannot simply exclude the possibility that CypA may not only be associated with EV71 entry but might also affect other intracellular steps of EV71 protein translation , assembly or secretion in addition to its effects on the entry step . In our results , we actually found that the S243P-EV71 virus proliferated more slowly than the wt-EV71 without CypA inhibitor treatment , although the S243P mutant in VP1 can enhance the interaction between virions and CypA . Moreover , when we infected RD-sh-CypA cells with wt-EV71 , although the intracellular genome RNA and VP1 protein level were much lower than that of wt-EV71-infected RD-sh-control cells , the supernatant virus titer exhibited no significant difference . Interestingly , the downregulation of CypB did not attenuate but actually increased EV71 RNA replication and VP1 expression ( Figs . 2C and 2D ) , and the expression of endogenous CypB was also upregulated by EV71 infection ( Fig . 2H ) . Moreover , the S243P mutant recovered both the RNA replication and protein expression of EV71 in RD-sh-CypA cells , but presented discrepancies in RD-sh-CypB cells , i . e . , slightly increased RNA replication but decreased protein expression ( Figs . 2C and 2E ) . Interestingly , two recent works revealed that Cyps inhibited the proliferation of HIV-1 virus , which is in opposition to the previously identified positive function of Cyps in the HIV-1 life cycle [9] , [44] , [45] . All these findings indicated that Cyps may have multiple functions in the EV71 life cycle and may have additional ( or opposing ) effects on viral assembly and secretion . This finding requires further validation . The work we describe here highlights the new function of CypA as an uncoating regulator for EV71 proliferation by facilitating the conformational shift of the VP1 H-I loop . Our results significantly increase our understanding of virus-host interactions and provide an additional target of action for CsA-derived antivirals without immunosuppressive activity that are currently in clinical trials for treating EV71-infection .
RD cells ( a human embryonal rhabdomyosarcoma cell line ) were purchased from ATCC and the Huh7 . 5 . 1 cells were kindly given by Jin Zhong ( Institute Pasteur of Shanghai , Chinese Academy of Science ) . The cells were grown in Dulbecco's modified Eagle's medium ( DMEM ) ( GIBCO ) supplemented with 10% fetal bovine serum ( FBS ) ( GIBCO ) at 37°C in a humidified incubator with 5% CO2 . The plasmids containing human EV71 strain AnHui1 ( GQ994988 . 1 ) and BrCr ( U22521 ) were kindly provided by Prof . Bo Zhang from the Wuhan Institute of Virology . Plasmids containing human EV71 strain SK-EV006 ( AB469182 . 1 ) and EV71-GFP , which contains a GFP reporter gene that is inserted into the SK-EV006 genome , were donated by Prof . Satoshi Koike ( Tokyo Metropolitan Institute of Medical Science ) . The plasmid with EV71 subgenomic replicon RNA was given by Prof . Wenhui Li ( National Institute of Biological Sciences [21] ) . The pNL4-3 plasmid was given by Prof . Linqi Zhang ( School of Medicine , Tsinghua University ) . The EV71 viruses were amplified in RD cells , quantified by making a determination of the 50% tissue culture infective dose ( TCID50 ) per 1 ml in RD cells as previously described [46] , and used for all experiments . A mouse anti-EV71 monoclonal antibody against VP1 ( Abcam , 10F0 , cat #ab36367 ) was used to detect the virus in all experiments . Rabbit anti-CypA monoclonal antibody ( cat #ab41684-100 ) and rabbit anti-CypB monoclonal antibody ( cat #ab16045 ) were purchased from Abcam . The anti-GAPDH monoclonal antibody and anti-GST monoclonal antibody were purchased from JiaMei , China . The secondary antibodies used for western blot analysis and immunofluorescence were purchased from Southern Biotech ( HRP-conjugated goat anti-mouse IgG ( H+L ) ) , CoWin Bioscience ( HRP-conjugated goat anti-rabbit IgG ) , Santa Cruz ( PE-conjugated goat anti-mouse IgG ) , and Life Technologies ( donkey anti-rat IgG ( H+L ) ) . The cyclophilin A inhibitor known as CsA was purchased from Sigma , and HL051001P2 compound was generously provided by Prof . Jian Li [28] . The inhibitors were initially dissolved in DMSO , and stock solutions were stored at −20°C . Immediately before addition , these compounds were diluted to the desired concentrations in DMEM with 10% FBS . TRIzol reagent and a Super Script III First-strand Synthesis System for RT-PCR kit were purchased from Invitrogen . A MEGA script T7 High Yield Transcription kit was purchased from Ambion . A QuantiTect SYBR Green RT-PCR kit was purchased from Qiagen . A cell viability and proliferation assay ( WST-1 ) was purchased from Roche . RNA transcripts and the EV71 subgenomic replicon were obtained by using the MEGA script T7 High Yield Transcription kit ( Ambion ) , and the DNA that was linearized by SalI or XbalI ( NEB ) digestion was used as a template according to the manufacturer's protocol . In vitro transcribed RNA was transfected into RD cell monolayers in 100 mm × 20 mm dishes with Lipofectamine 2000 ( Invitrogen ) , and the cells were then incubated at 37°C in 10 ml DMEM containing 10% FBS per dish . The cytopathic effects ( CPE ) of RD cells were observed at 24 h post transfection . When 90% of the cells exhibited CPE , the cell supernatants were then collected by centrifugation at 4 , 000 rpm for 5 min , and the target viruses were stored at -80°C . The virus titers were determined by using endpoint dilution assays ( EPDA ) , with focus-forming units ( ffu ) as the read-out [47] . In brief , the measurement was performed by seeding 1×104 RD cells per well in 96-well microtiter plates . After overnight culture , the EV71 viruses were serially diluted 10-fold with DMEM containing 10% FBS ( 10−1- to 10−8-fold dilutions ) and added to RD cell . The plates were then incubated at 37°C in 5% CO2 . CPE was observed under the microscope after 3 to 4 days post infection or the GFP expression level was monitored under a fluorescence microscope after 3 days post infection . The virus titer , which was expressed as the TCID50 , was determined by EPDA . Total cellular RNA was isolated with TRIzol reagent according to standard protocols . The following primer sequences were used for qRT-PCR: GAPDH , forward primer 5′-CCCACTCCTCCACCTTTGACG-3′ , reverse primer 5′-CACCACCCTGTTGCTGTAGCCA-3′ , EV71 5′-UTR forward primer 5′-TGAATGCGGCTAATCCCAACT-3′ , and reverse primer 5′-AAGAAACACGGACACCCAAA G- 3′ . qRT-PCR was performed with a QuantiTect SYBR Green RT-PCR kit ( Qiagen ) , and the EV71 and GAPDH transcript levels were determined by ΔΔCT methods . To determine the amount of purified EV71 virions , viral RNA was extracted from 50 µl of PBS buffer containing EV71 virions by using TRIzol LS reagent ( Invitrogen ) . To determine the amount of EV71 virions in the CsCl fractions , viral RNA was extracted from 125 µl of the CsCl fraction containing EV71 virions with an additional 125 µl of nuclease-free water by TRIzol LS reagent ( Invitrogen ) . A pUC18-EV71AH1 plasmid was used as a standard sample to generate a standard curve ranging from 1011–103 copies/ml . EV71 RNA copies were quantified by using the QuantiTect SYBR Green RT-PCR kit ( Qiagen ) . The antiviral activities of the compounds were determined by using a qRT-PCR-based assay with the EV71 virus and RD cells . In brief , 100 , 000 RD cells were seeded in each well of the 24-well tissue culture plates and allowed to attach in complete culture medium overnight . The culture medium was replaced with medium containing serially diluted compounds in the presence of 10% FBS and 0 . 5% DMSO . After 6 h , the RD cells were infected with EV71 at the multiplicities of infection ( MOIs ) indicated in the figure legends , and the compounds were added at the indicated concentrations . Total cellular RNA was isolated by using TRIZOL reagent according to standard protocols at 24 hpi . The qRT-PCR assay was performed as described above . The EV71 and GAPDH transcript levels were determined by ΔΔCT method . The IC50 value represents the concentration of the compound at which the EV71 RNA level in the RD cells was reduced by 50% . To monitor the cytotoxic effects of the compounds , the viability of the RD cells was determined after 24 h of compound treatment; the viability was determined in 96-well tissue culture plates by using cell proliferation reagent WST-1 ( Roche ) . Each data point represents the average of three replicates . The EC50 and cytotoxicity values were plotted by using GraphPad Prism software . RD cells were seeded at 5×105 cells/well in 6-well plates . On the following day , the medium was removed and replaced with DMEM containing 10% FBS and 11 . 4 µM HL051001P2; 0 . 5% DMSO was used as a control . After 6 h , EV71 strain AnHui1 was used to infect RD cells at an MOI of 0 . 1 in complete medium containing the inhibitors . Over the course of selection , the RD cells were split when they reached 70–90% confluence . Fresh complete medium containing inhibitors was added when the cell cultures were split . Viral replication in the presence of compound HL051001P2 was monitored by determining the cytopathic effects ( CPE ) at each passage . The viruses demonstrated apparent CPEs after approximately 5 to 7 days after EV71 infection in the medium containing inhibitors or after 2 days in the medium containing 0 . 5% DMSO . The cell supernatants were then collected following centrifugation at 4 , 000 g for 5 min and were stored at −80°C as EV71-P2 ( passage 2 ) virus . The RD cells were then treated with cyclophilin A inhibitors for 6 h and were infected with the EV71-P2 virus under the same conditions described above . The experiment was repeated for 6 cycles , and the cell supernatants were collected as EV71-P6 ( passage 6 ) virus . The RD cells were lysed with TRIzol reagent . For the EV71 RNA resistance mutation analysis , cellular RNA extraction was performed by using TRIzol reagent ( Invitrogen ) according to the manufacturer's instructions . For reverse transcription PCR , first strand cDNA was synthesized with a gene-specific primer ( 5′- ACCCCCACCAGTCACATTCACG- 3′ ) , and the Super Script III First-strand Synthesis System for RT-PCR kit ( Invitrogen ) was used according to the manufacturer's instructions . The EV71 protein coding region of the genome was amplified by PCR in 5 short fragments as follows: fragment 1 ( EV71-718-sense 5′-ATCTTGACCCTTAACACAGC-3′ , EV71-2046-anti 5′-GACCATTGGGTGTAGTACCC-3′ ) , fragment 2 ( EV71-1975-sense 5′-CGATCCTGGGCGAAGTGGAC-3′ , EV71-3345-anti 5′-TGTTGTCCAAATTTCCCAAG-3′ ) , fragment 3 ( EV71-3248-sense 5′-TACCTATTCAAAGCCAACCC-3′ , EV71-4643-anti 5′-ATAAAGACATATCCTTGCCG-3′ ) , fragment 4 ( EV71-4569-sense 5′-ACGGCTACAAGCAACAGGTG-3′ , EV71-6044-anti 5′-TTCCCTCGAAGATATCATGG-3′ ) , and fragment 5 ( EV71-5972-sense 5′-GGAAGGCTCAACATCAATGG-3′ , EV71-7345-anti 5′-GGGTTGAGGTGTGTATAGCC-3′ ) . The short RT-PCR products of the resistant EV71 virus or the control EV71 virus were ligated into the TA cloning vector PMD18-T ( Takara ) . Multiple individual bacterial colonies were isolated for each time point , and the purified plasmid DNA was sequenced . The sequences were aligned with Sequencher 5 . 0 and BioEdit software . The mutant EV71 recombinant virus clone was constructed on the basis of the pUC18-EV71AH1 plasmid , which contained the original full-length EV71 AnHui1 strain . Site-directed mutagenesis was performed with a QuikChange Lighting Site-Directed Mutagenesis kit ( Stratagene ) . The mutagenic primers were designed as follows: S243P-EV71 ( 5′-GTGGGGACCTCCAAGCCCAAGTACCCTTTAG- 3′ ) , S243A-EV71 ( 5′-GTGGGGACCTCCAAGGCCAAGTACCCTTTAG-3′ ) , P246A-EV71 ( 5′-GGACCTCCAAGTCCAAGTACGCTTTAGTGGTTAGAATTTACATG-3′ ) , S243P/P246A-EV71 ( 5′-GTGGGGACCTCCAAGCCCAAGTACGCTTTAGTGGTTAGA ATTTAC-3′ ) , and S243A/P246A-EV71 ( 5′-GTGGGGACCTCCAAGGCCAAGTACGCTTTA GTGGTTAGAATTTAC-3′ ) . The constructs were confirmed by sequencing . The CypA and CypB stable knockdown RD or Huh7 . 5 . 1 cell lines were produced as previously described , with some modifications [12] . The following shRNA sequences were used in this study: NC , 5′-TTCTCCGAACGTGTGTCACGTTTC-3′; CypA , 5′-CTGGATTGCAGAGTTAAGTTTA-3′; and CypB , 5′-GCCGGGTGATCTTTGGTCTCTT -3′ . shRNA recombinant lentiviruses ( LV2-NC , LV-CypA , LV-CypB ) were produced by Shanghai GenePharma , and the virus titers were determined to be 1×108 TU/ml . RD or Huh7 . 5 . 1 cells were infected at an MOI of 10 . The shRNA recombinant lentivirus was incubated with 5 µg/ml polybrene to enhance the lentivirus infection . All knockdown cell lines were confirmed at 72 h post infection by western blot analysis . For the stable knockdown cell lines , the RD or Huh7 . 5 . 1 cells were incubated in selection medium containing 5 µg/ml puromycin ( Invitrogen ) beginning 48 h after transduction , and the CypA and CypB knockdowns were stable after approximately two weeks of cell culture . RD cells or CypA knockdown cells were grown on cover slips until the cells reached 50% confluence; the cells were then infected with the EV71 virus . The cells were washed with PBS at the indicated times post infection and fixed with 4% paraformaldehyde for 15 min at room temperature , washed , and permeabilized with 0 . 5% Triton X-100 in PBS for 10 min . The cells were then washed and blocked with 1% normal goat serum in PBS for 30 min , followed by a 1 h incubation with primary antibodies ( 1∶400 dilution ) at room temperature . After three washes with PBS , the cells were incubated with FITC- or PE-conjugated secondary antibodies ( a 1∶200 dilution ) for 1 h . After extensive washing with PBS , the cell nuclei were stained with DAPI . Images were captured by using a confocal microscope ( Olympus FluoView FV1000 Confocal Microscope operated by FluoView software ) . The same microscope settings and exposure times were used within the individual experiments . The genes encoding the H-I loop of EV71 VP1 ( residues 239-GSSKSKYPL-247 ) , the H-I loop with S243P substitution ( residues 239-GSSKPKYPL-247 ) , human CypA and the catalytic-defective mutant CypA H126Q were cloned into the pGEX-6p-1 expression vector with a GST tag fused at the N-terminus according to a general protocol . The accuracy of the insert was verified by sequencing . The plasmids were transformed into E . coli BL21 ( DE3 ) cells , and the transformed cells were cultured at 37°C in LB media containing 100 mg/L ampicillin . After the OD600 reached 0 . 5 , the culture was cooled to 16°C , and recombinant protein expression was induced . After overnight induction , the cells were harvested by centrifugation . The pellets were then resuspended in lysis buffer containing 20 mM Tris-HCl ( pH 7 . 5 ) and 150 mM NaCl , followed by homogenization using an ultra-high-pressure cell disrupter ( JNBIO , Guangzhou , China ) at 4°C . The insoluble material was removed by centrifugation at 20 , 000 g . The supernatant was then loaded twice onto a GST column pre-equilibrated with lysis buffer . After loading , the GST column was washed with at least 5 column volumes of lysis buffer to remove the unbound protein . The beads containing recombinant GST-1S , GST-1P , GST-CypA or GST protein were added to Eppendorf tubes and stored at −80°C until use . To obtain recombinant human CypA without a GST tag , the GST tag on CypA was removed by overnight incubation with PreScission Protease , and the target proteins were eluted with lysis buffer . The eluted target proteins were further purified by Superdex-75 gel filtration chromatography ( GE Healthcare ) to remove any contamination . The fractions were analyzed with SDS-PAGE , and the final purity was over 95% . For the immunoblot analysis , the cells were lysed in a lysis buffer containing 50 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 1% Nonidet P-40 , 0 . 1% SDS , 2 mM EDTA , and protease inhibitors; the protein concentrations of the lysates were determined with a spectrophotometer . The proteins were resolved by sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) and transferred to nitrocellulose membranes ( Millipore ) . The membranes were blocked for 4 h with 5% nonfat dry milk solution in Tris-buffered saline . The membranes were then blotted with specific primary antibodies , followed by incubation with secondary antibodies conjugated to horseradish peroxidase . The proteins were visualized by chemiluminescence by using a Clarity Western ECL Substrate ( BIO-RAD ) . To allow the pull-down assays to detect the interaction between CypA and the EV71 virion , we incubated 200 µl of the EV71 strain ( AnHui1 , SK-EV006 , S243A/P246A-EV71 and BrCr ) ( 2×107 TICD50 ) with 50 µl of glutathione-sepharose beads containing GST ( 200 µg ) or GST-CypA ( 200 µg ) in 300 µl of immunoprecipitation buffer ( 50 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 1% Nonidet P-40 , 2 mM EDTA , and protease inhibitors ) overnight at 4°C . The beads were then washed three times with PBS , and the complexes were eluted from the glutathione-sepharose beads with reduced glutathione ( GSH ) solution . We then moved the supernatant to new Eppendorf tubes and added SDS loading buffer . We then incubated the supernatants in boiling water for 5 min and subjected the samples to 12% SDS-PAGE followed by western blot analysis with mouse antibodies against EV71-VP1 or GST . To detect the interaction between CypA and GST-1S or GST-1P , recombinant human CypA was expressed , purified , and concentrated to 20 mg/ml . Recombinant human CypA ( 200 µg ) was incubated with 50 µl of glutathione-sepharose beads containing GST ( 200 µg ) , GST-1S ( 200 µg ) , or GST-1P ( 200 µg ) in 300 µl of immunoprecipitation buffer as described above , and the reactions were incubated overnight at 4°C . We then washed the beads three times with PBS and eluted the complexes from the glutathione-sepharose beads with reduced GSH solution . The samples were then subjected to western blot analysis , as described above , by using mouse antibodies against GST or CypA . The bands were quantified by ImageJ software . The time of addition effect was examined for HL051001P2 . RD cells ( 1 . 5×105 per well in 500 µl of 10% FBS-DMEM medium ) were cultured at 37°C under 5% CO2 in 24-well plates overnight . The cells were subsequently treated with 5 µM HL051001P2 either concurrent with the wt-EV71 ( 0 h ) at an MOI of 50 or at intervals of −6 , −4 , −2 , 0 , 4 , 6 , and 8 hpi . After incubating at 37°C for 12 h , the antiviral activity was determined by measuring the percentage of EV71 RNA from the untreated control cells , and the mRNA level of GAPDH was used as an internal control . The supernatant virus titer , which was expressed as the TCID50 , was determined by EPDA . The virus binding assay was performed by using a previously reported protocol with some modifications [48] . In brief , RD cells were seeded at 1×105 RD cells/well in 24-well plates . The following day , the culture medium was removed and the cells were washed once with cold phosphate-buffered saline ( PBS ) . After that , 500 µl of binding buffer ( PBS containing 1% BSA and 0 . 1% sodium azide ) was added to the cells on ice and incubated for 10 min; the supernatant was subsequently removed from the cells . The EV71 stocks ( Strain AnHui1 ) ( 108 TCID50/ml ) were prepared as previously mentioned . The EV71 virus was diluted in 500 µl of DMEM complete medium ( dilution fold = 1:10 [5×106 TCID50] , or 1:20 [2 . 5×106 TCID50] ) per well and added to the cells . After 1 h of incubation on ice , the unbound virus was removed by three wash steps with 500 µl of PBS , and the cells were lysed in the wells with 500 µl of TRIzol . Viral RNA was extracted and detected by qRT-PCR . The virus binding assays were systematically performed in duplicate , and two individual experiments were performed for each condition . The virus binding assay was performed according to a previously reported protocol [23] , [26] . To detect the influence of CypA on the HS and EV71 virus interaction , 4 ml of EV71 strain AnHui1 ( 4 ×108 TICD50 ) was incubated with or without CypA at 4°C for 2 h . The supernatant was added to a 1 ml HiTrap Heparin HP column ( GE Healthcare , Sweden ) that was previously equilibrated with binding buffer ( 0 . 02 M Tris-HCl and 0 . 14 M NaCl [pH 7 . 4] ) at a flow rate of approximately 0 . 5 ml/min . After loading , the two columns were washed with at least 5 column volumes of binding buffer to remove the unbound virus . The bound viral particles were eluted by using elution buffer ( 0 . 02 M Tris-HCl and 2 M NaCl [pH 7 . 4] ) . Fractions of 1 ml were collected , and the EV71 RNA was isolated and quantified by using the qRT-PCR method described above . Purified CypA was also uploaded to the same HiTrap Heparin HP and did not show any detectable interaction between CypA and the column ( data not shown ) . To detect the CypA influence on the PSGL-1 and EV71 virus interaction , EV71 strain AnHui1 ( 2 ×107 TICD50 ) was incubated with or without recombinant human CypA ( 4 µg ) in 200 µl of DMEM for 2 h at 4°C . Human PSGL-1-Fc ( 3 µg , R&D Systems ) or human IgG VRC01 Fc ( 3 µg ) ( a control ) , were then added to the assays , and the reactions were incubated for 1 h at 4°C . Protein G-agarose ( 50 µl , Roche ) was then added to the mixture , and the tubes were shaken overnight at 4°C . To detect the CypA treatment effect of the SCARB2 and EV71 virus interaction , EV71 strain AnHui1 ( 2×107 TICD50 ) was incubated with or without recombinant human CypA ( 6 µg ) in 200 µl of DMEM for 2 h at 4°C . SCARB2 at a 5 µg quantity was added to the assays , and the reactions were incubated for 1 h at 4°C . Ni-NTA beads ( 50 µl ) were then added to the mixture , and the tubes were shaken overnight at 4°C . All the beads were washed three times with PBS and then eluted with 100 µl of elution buffer containing 20 mM Tris-HCl , pH 8 . 0 , 500 mM NaCl and 1 M imidazole . The EV71 RNA in the supernatant was isolated by TRIzol LS reagent and quantified by using the qRT-PCR method described above . The purification of wt-EV71 virions was performed by using a previously reported protocol with modifications [21] . In brief , RD cells in five T175 cell culture flasks were infected with EV71 ( Anhui1 strain ) at an MOI of 0 . 1 and cultured in 10% FBS . When 90% of the cells exhibited CPE , the supernatant was collected and concentrated by filtration through a 100 kDa-cutoff centrifugal filter ( Millipore ) . The concentrated virus was mixed with 1 . 4 g/ml CsCl at a volume ration of 1∶4 and loaded on the middle of a CsCl gradient ( 1 . 1 g/ml , 1 . 2 g/ml , 1 . 3 g/ml , 1 . 4 g/ml , and 1 . 5 g/ml , discontinuously ) followed by ultracentrifugation at 41 , 000 rpm for 10 h at 4°C in a Beckman SW41Ti rotor . After being dialyzed with PBS , the purified EV71 virus was quantified and stored at -80°C . The RNA copies of purified EV71 virus was quantified as previously described . Fifty µl of purified EV71 virus ( 1×1010 genome copies ) was incubated with 20 µg of purified wt CypA or catalytic-defective mutant CypA H126Q in PBS containing 0 . 5% BSA in a total volume of 200 µl . For the low pH treatments , HCl was added to the mixture to bring the pH values to 5 . 5 , 6 . 0 and 6 . 5 . The mixture was then incubated at 37°C for 4 h and subsequently applied to a 1 . 1-1 . 5 g/ml discontinuous CsCl gradient , which was then ultracentrifuged at 41 , 000 rpm for 10 h at 4°C in a Beckman SW41Ti rotor . The samples were then analyzed by qRT-PCR . The transition states of viral particles during uncoating was performed in vitro with native 160S virions by heating for 10 min in a low salt buffer containing 4 mM CaCl2 , 20 mM HEPES , pH 7 . 4 at 61°C and 68°C . | Enterovirus 71 ( EV71 ) is the major causative agent of hand-foot-and-mouth disease ( HFMD ) in Asia-Pacific region and caused over one million infection cases and nine hundred deaths in the year of 2010 in China mainland . EV71 is known to infect the young children for the sake of their undeveloped immune system . Unlike other Enterovirus ( e . g . coxsackievirus ) , EV71 could cause severe aseptic meningitis , encephalitis , myocarditis , and acute flaccid paralysis , thus leading to high fatality rates . There is no clinically applied therapeutics . In this work , we used CypA inhibitors as bioprobes to show that CypA played an essential role in EV71 proliferation . We also elucidated the mechanism by which CypA interacted with the EV71 VP1 H-I loop and functioned as an uncoating regulator in EV71 entry step . Since there are several non-immunosuppressive CypA inhibitors , e . g . NIM-811 and Debio-025 , have been reported to show antiviral potency , our results provide a potential way to discover clinical therapeutics against EV71 infection . | [
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"antivirals"
] | 2014 | Cyclophilin A Associates with Enterovirus-71 Virus Capsid and Plays an Essential Role in Viral Infection as an Uncoating Regulator |
Nontyphoidal Salmonellae ( NTS ) cause a large burden of invasive and gastrointestinal disease among young children in sub-Saharan Africa . No vaccine is currently available . Previous reports indicate the importance of the O-antigen of Salmonella lipopolysaccharide for virulence and resistance to antibody-mediated killing . We hypothesised that isolates with more O-antigen have increased resistance to antibody-mediated killing and are more likely to be invasive than gastrointestinal . We studied 192 NTS isolates ( 114 Typhimurium , 78 Enteritidis ) from blood and stools , mostly from paediatric admissions in Kenya 2000–2011 . Isolates were tested for susceptibility to antibody-mediated killing , using whole adult serum . O-antigen structural characteristics , including O-acetylation and glucosylation , were investigated . Overall , isolates were susceptible to antibody-mediated killing , but S . Enteritidis were less susceptible and expressed more O-antigen than Typhimurium ( p<0 . 0001 for both comparisons ) . For S . Typhimurium , but not Enteritidis , O-antigen expression correlated with reduced sensitivity to killing ( r = 0 . 29 , 95% CI = 0 . 10-0 . 45 , p = 0 . 002 ) . Both serovars expressed O-antigen populations ranging 21–33 kDa average molecular weight . O-antigen from most Typhimurium were O-acetylated on rhamnose and abequose residues , while Enteritidis O-antigen had low or no O-acetylation . Both Typhimurium and Enteritidis O-antigen were approximately 20%–50% glucosylated . Amount of S . Typhimurium O-antigen and O-antigen glucosylation level were inversely related . There was no clear association between clinical presentation and antibody susceptibility , O-antigen level or other O-antigen features . Kenyan S . Typhimurium and Enteritidis clinical isolates are susceptible to antibody-mediated killing , with degree of susceptibility varying with level of O-antigen for S . Typhimurium . This supports the development of an antibody-inducing vaccine against NTS for Africa . No clear differences were found in the phenotype of isolates from blood and stool , suggesting that the same isolates can cause invasive disease and gastroenteritis . Genome studies are required to understand whether invasive and gastrointestinal isolates differ at the genotypic level .
NTS are a major but neglected cause of invasive disease ( hence iNTS disease ) in Africa [1–3] . Salmonella enterica serovars Typhimurium and Enteritidis account for nearly 80% of all human isolates reported globally [4] . While in developed countries , these predominantly cause a mild self-limiting gastroenteritis [5–7] , in Africa they are responsible for bacteraemia , often associated with meningitis in young children , with incidence rates comparable to invasive S . pneumoniae disease [3] . The true burden of iNTS disease is uncertain due to the absence of a characteristic clinical presentation . Patients often present with nonspecific fever [8–10] and blood culture is necessary for diagnosis . Even where blood culture facilities are available , rapid clinical progression of NTS bacteraemia results in many patients dying before a microbiological diagnosis can be made [10] . No vaccine is available , and clinical management is made difficult by widespread multi-drug resistance and the need for late-generation expensive antibiotics [11–13] . In Kenya , iNTS disease is particularly frequent in rural areas [14] , with incidence rates as high as 568/100 , 000 person-years [15] . A recent study from Western Kenya found an association between NTS diarrhoea and mortality in hospitalized children [16] , indicating that NTS isolates in the region can cause fatal invasive and gastrointestinal disease , but it is currently unknown whether specific microbial phenotypic or genotypic characteristics are associated with each clinical presentations . Whole genome sequencing studies demonstrate that invasive African S . Typhimurium are genetically distinct from those in the rest of the world , and are characterized by a new sequence type , ST313 , that has spread throughout the continent [17] . The ST313 pathovar is associated with genome degradation and pseudogene accumulation [13] . Equivalent studies are ongoing for S . Enteritidis which is more prevalent than S . Typhimurium in some parts of Africa [18 , 19] , and globally [20] . Investigation of the relationship between genotypic and phenotypic features of Typhimurium isolates is ongoing and a systematic characterization of endemic Typhimurium and Enteritidis isolates from one endemic region is awaited . Lipopolysaccharide ( LPS ) forms the outer layer of Salmonella and other Gram-negative bacteria , and is key to the interaction between Salmonella and its environment . The O-antigen chain ( including core sugars , hereafter referred to as ‘OAg’ ) constitutes the outermost part of LPS [21] . In pathogenic bacteria such as Salmonella , LPS plays an important role in the interaction between the bacterium and its host and is a virulence factor required for colonization and resistance to antibody-mediated killing [22] . While the lipid A moiety is the predominant cause of the endotoxic effects of LPS , OAg is the most immunodominant portion of the molecule , and is responsible for serovar-specificity [23] . OAg protects bacteria from the environment and from serum complement [24–26] , which can lead to bacterial killing by membrane attack complex formation . Rough strains of Salmonella that lack OAg are avirulent and succumb readily to complement-mediated killing [26] . The OAg structure plays a role in bacterial virulence , with longer OAg chains associated with increased complement and antibody resistance [27–29] and protection against other host antimicrobial factors [30] . In this study we analysed a bacterial collection of 114 S . Typhimurium and 78 S . Enteritidis isolates derived from patients , mostly children , presenting to three main Kenyan hospitals in Nairobi and from 31 healthy carriers related to these patients . Our primary objective was to characterise NTS isolates circulating in endemic areas , with the goal of identifying bacterial features associated with either invasive or gastrointestinal pathology . We hypothesised that invasive isolates would , by nature , be more resistant to survival in the blood than gastrointestinal isolates , and focused our analysis on the susceptibility of isolates to antibody-mediated killing and characterization of the OAg expressed by the various isolates . We investigated the total amount of OAg expressed by the various isolates and OAg specific characteristics such as molecular weight ( MW ) , O-acetylation and glucosylation levels , parameters which can influence OAg immunogenicity . Additionally , we tested the sensitivity of the isolates to antibody killing , using whole human serum from HIV-uninfected adults [31] . The results obtained were compared with clinical presentation in order to identify possible associations .
The study culture collection contained 192 archived NTS isolates ( 114 S . Typhimurium and 78 S . Enteritidis ) collected between 2000 and 2011 at the Centre for Microbiology Research ( CMR ) , Kenya Medical Research Institute ( KEMRI ) in Nairobi , Kenya . Study isolates were selected from the archived bacterial culture collection based on availability of unequivocal serovar identification and availability of clinical records and metadata . The isolates were stored in Tryptic Soy Broth with 15% glycerol at-80°C . The isolates were from blood and stools with a few additional samples from urine ( 3 samples ) , cerebrospinal fluid ( CSF , 2 samples ) and environmental sources ( 2 samples ) . The isolates were mostly from children admitted to three main hospitals in Nairobi county , namely: Kenyatta National Referral hospital , Gertrude’s Children’s Hospital and Aga Khan University Hospital . This collection also included isolates from healthy carriers ( 31 samples ) . Before use in the present study , the archived isolates were re-cultured and the serotype was confirmed by antibody-based agglutination ( Bio-Rad Laboratories , Inc . , Hercules , CA , USA ) . Based on the clinical sources and sample type , the NTS isolates were divided into four main groups . Those isolates from body fluids that are normally sterile such as blood and CSF were grouped as ‘invasive’ , those from stool samples of patients were grouped as ‘gastroenteric’ , those from stool samples of healthy individuals were grouped as ‘healthy carriers’ while those from soil and sewer water sources were grouped as ‘environmental’ . The three isolates from urine samples were considered clinically significant invasive sterile site isolates since Salmonellae are not part of the normal perineal skin flora and the isolates were from patients with symptoms of urinary tract infection . Undiluted sera from ten healthy HIV-uninfected Malawian adults were used to generate a pooled serum to assess sensitivity to antibody-mediated killing of the Salmonella isolates . Each serum was tested prior to pooling to ensure that killing of the index S . Typhimurium ST313 strain , D23580 , was within the 0 . 9 to 3 . 0 Log10 range previously described for such sera [32] . Sera were handled at 4°C during the pooling process to ensure preservation of endogenous complement activity and frozen in aliquots at-80°C . Ethical approval for preparation of this serum was granted by the College of Medical Research and Ethics Committee , College of Medicine , University of Malawi [31] . The study isolates were tested for their susceptibility/resistance to killing against the serum pool described above , using a protocol involving whole serum and endogenous complement activity as previously described [26] . Briefly , 5 μl washed bacteria at 2 h log-growth phase at an OD of approximately 0 . 2 ( with shaking at 180 rpm ) was added to 45 μl undiluted serum at a final bacterial concentration of 1×106 colony forming units , CFU/ml and incubated at 37°C with the number of viable bacteria count determined by serial dilution on Luria Bertani ( LB ) agar after 0 and 180 min . As a negative control , reference sera were heat-inactivated at 56°C for 45 min to inactivate endogenous complement and included in each SBA experiment . Additional internal controls included testing the representative endemic African S . Typhimurium isolate D23580 , previously shown to be susceptible to serum killing [26] and hyperimmune mouse serum containing high levels of anti-OAg antibodies [33] . Susceptibility to serum killing was determined as any reduction in viable bacterial count compared with the initial Salmonella concentration . OAg extraction was performed by acid hydrolysis [34] . Bacterial isolates were grown overnight in LB medium . As OAg expression can be influenced by growth conditions , identical conditions were used for the growth of all strains [29] . The bacterial OD was measured and the bacterial cultures were concentrated in PBS to OD: 35 . Acetic acid ( 2% v/v ) was then added to the concentrated growth bacterial culture ( pH 3 ) , which were incubated for 3h at 100°C . The reaction was stopped by the addition of 14% ammonium hydroxide , increasing the pH to around 6 . Bacterial debris was removed by centrifugation and OAg in the supernatant was 0 . 22 μm microfiltered and desalted by HiTrap Desalting 5 ml columns ( GE Healthcare , UK ) . Phenol sulphuric acid assay , using glucose as standard , was used for quantification of OAg content [34 , 35] . 1H NMR analysis was performed as a confirmation of the identity of the OAg samples and to verify the presence of O-acetyl groups along the OAg chain as previously described [34] . Glucosylation level of OAg samples was estimated by HPAEC—PAD after acid hydrolysis of the OAg to release the monosaccharides constituting the sugar chain as described previously . Commercial glucose was used for building the calibration curve ( 0 . 5–10 μg/ml ) . Glucose ( Glc ) level is calculated as molar ratio of Glc to rhamnose ( Rha ) , sugar present in each OAg repeating unit . The analysis was also used as confirmation of the OAg samples identity verifying the correct monosaccharides ratios in the repeating unit [34 , 36] . HPLC—SEC analysis was used to estimate the molecular size distribution of OAg populations [34 , 36] . Samples were run , without pre-treatment , on a TSK gel G3000 PWXL column ( 30 cm x_7 . 8 mm; particle size 7 um; cod . 808021 ) with a TSK gel PWXL guard column ( 4 . 0 cm _x 6 . 0 mm; particle size 12 um; cod . 808033 ) ( Tosoh Bioscience , Tokyo , Japan ) . The mobile phase was 0 . 1 M NaCl , 0 . 1 M NaH2PO4 , 5% CH3CN , pH 7 . 2 , at the flow rate of 0 . 5 ml/min ( isocratic method for 30 min ) . OAg peaks were detected by differential refractive index ( dRI ) . Void and bed volume calibration was performed with λ-DNA ( λ-DNA molecular weight ( MW ) Marker III 0 . 12–21 . 2 kb; Roche ) and sodium azide ( Merck , New Jersey , USA ) , respectively . OAg average MW was estimated on standard dextrans ( Sigma ) calibration curve . Serum susceptibility and OAg amounts of the two Salmonella serovars examined ( S . Typhimurium and S . Enteritidis ) was compared using the Mann-Whitney U test . Possible correlations ( i . e . OAg amount and serum susceptibility , glucosylation levels and OAg production ) were analysed by Spearman rank .
Of the 192 NTS isolates in the study , 114 ( 59% ) were S . Typhimurium and 78 ( 41% ) were S . Enteritidis . This study collection included isolates from blood , stools ( patients and healthy contacts ) , CSF , urine and environmental/animal sources as described in Table 1 ( see S1a-b Table for supporting information ) . The isolates were obtained from hospitals based in Nairobi , an urban setting . Clinical information on patients’ age was available for 67% of cases ( 73 with S . Typhimurium , 57 with S . Enteritidis ) . The age ranged from 1 month to 59 years for S . Typhimurium isolates and 0 months to 64 years for S . Enteritidis isolates , the majority ( 65% , 85 isolates ) of these patients being below 5 years ( 53 S . Typhimurium , 32 S . Enteritidis ) . Overall , the median age of subjects from whom S . Typhimurium and S . Enteritidis strains were isolated was 2 and 3 . 5 years , respectively . Ages were not available for the healthy carriers . SBA results using the serum pool demonstrated a marked difference in susceptibility to antibody-mediated killing among individual isolates and a clear difference in susceptibility between S . Typhimurium and S . Enteritidis isolates ( Fig . 1 , and see S2a and S2b Table for supporting information ) . ) . We have previously demonstrated that this killing is through the antibody-dependent complement-mediated mechanism and requires the presence of specific antibodies and intact complement function [26] . We have also demonstrated that the control sera used in this assay from healthy Malawian adults contains abundant antibody levels to NTS [32] . All except one S . Typhimurium isolate ( 0 . 9% , 1/114 ) underwent a reduction in viable bacterial counts over the 180 minute time course of the assay , and so were designated ‘sensitive’ to antibody-mediated killing . Overall median Log10 reduction in viable bacteria after 180 minutes was 2 . 9 for S . Typhimurium . Indeed 50% ( 57/114 ) S . Typhimurium were highly susceptible to killing , undergoing a 3 . 0 Log10 reduction . In contrast S . Enteritidis as a group were less susceptible to antibody-mediated killing than S . Typhimurium , being killed by a median of 1 . 2 Log10 ( p<0 . 0001 , Mann Whitney test ) . However , all S . Enteritidis were sensitive to killing . A small proportion ( 10 . 3% , 6/78 ) was killed by a full 3 . 0 Log10 . OAg was extracted from all 192 NTS study isolates normalized to the same final OD and total amount was quantified by phenol sulphuric acid assay [34] . NTS isolates produced a range of OAg amounts , with low ( <10 μg/ml/OD ) and high producers ( >25 μg/ml/OD ) . On average , S . Enteritidis isolates expressed more OAg than S . Typhimurium , with 70 . 9% of the isolates producing more than 15 μg/ml/OD OAg compared to only 42 . 1% S . Typhimurium . Median OAg production for S . Enteritidis isolates was 16 . 8 μg/ml/OD compared to 14 . 4 μg/ml/OD of S . Typhimurium ( p<0 . 0001 by Mann Whitney test ) ( Fig . 2 ) . All study isolates contained one single main OAg population with average MW of 21–33 kDa ( Fig . 3a ) ; <5% isolates contained OAg population with average MW < 6 kDa . NMR analysis on a set of 118 samples ( 73 S . Typhimurium and 45 S . Enteritidis ) showed that only 6 . 8% of S . Typhimurium OAg were not O-acetylated , compared with 35 . 6% and 42 . 2% of S . Enteritidis OAg with no or traces of O-acetyl groups , respectively . For S . Typhimurium , OAg , O-acetylation was detected on both rhamnose ( Rha ) and abequose ( Abe ) sugar residues in the majority of isolates ( 63% ) , but also on Rha only ( 9 . 6% ) and Abe only ( 17 . 8% ) ( Table 2; Fig . 3b and c; see S3a Table for supporting information ) . HPAEC-PAD was done on a subset of 91 isolates ( 55 S . Typhimurium and 36 SEn ) and showed that for both serovars , the majority of strains ( 72 . 2% ) expressed OAg with 20–50% glucosylation levels ( Fig . 3d , see S3b Table for supporting information ) . SBA results , OAg features and clinical presentation were compared . As mentioned above , there was a clear inter-serovar difference , with S . Enteritidis isolates having more OAg than S . Typhimurium and being more resistant to antibody-mediated killing than S . Typhimurium . Among S . Typhimurium isolates , there was a correlation between amount of OAg and resistance to antibody-mediated killing ( Spearman r = 0 . 29 , 95% CI 0 . 10 to 0 . 45 , p = 0 . 002 ) in accordance with our initial hypothesis , but this was not present for S . Enteritidis ( Fig . 4 ) . No correlation was found between NTS clinical presentation ( invasive , gastrointestinal and carrier ) and either antibody susceptibility or polysaccharide production , for either S . Typhimurium or S . Enteritidis isolates ( Fig . 5 ) . There was no clear correlation between O-acetylation levels/position and glucosylation levels with clinical presentation , antibody susceptibility and OAg production . For S . Typhimurium , we found an association between lower glucosylation levels and higher OAg production ( Spearman r = 0 . 51 , 95% CI-0 . 69 to-0 . 27 , p value<0 . 001 ) ( Fig . 6 ) .
NTS infections are a major problem in sub-Saharan Africa and can either present as bacteraemia , with symptoms of gastroenteritis in under half of patients , or as diarrheal disease without bloodstream infection [1 , 11 , 37] . Both presentations can be life-threatening , with case fatality rates of 20–25% for iNTS bacteremia in children [1 , 2 , 8 , 10] and up to 50% in HIV-infected adults [9] , and a recognized association between NTS diarrhoea with mortality [16] . No vaccine is currently available . The main finding of the study is that all isolates studied , apart from one S . Typhimurium , were susceptible to antibody-dependent complement-mediated killing , using a serum pool from an endemic region . Previously it has been shown that the antibody response specific to NTS is acquired with age , corresponding with a decline in cases of NTS bacteraemia [26 , 38] . The serum pool we used contained high levels of anti-OAg ( both S . Typhimurium and S . Enteritidis ) antibodies as expected among healthy adults in a Salmonella endemic region of sub-Saharan Africa such as Kenya , Malawi or Tanzania [32] . The study therefore confirms the bactericidal activity of anti-Salmonella antibodies against the vast majority of endemic NTS isolates in the current study . These findings support an important role for antibodies in protective vaccines against iNTS disease [31 , 33 , 39] . Another key finding is the difference in relationship between susceptibility to antibody-mediated killing and levels of OAg expression for the two Salmonella serovars studied . According to our study hypothesis , decreased susceptibility of S . Enteritidis compared with S . Typhimurium could be the result of higher levels of OAg , not excluding the contributions of other pathogen features , not investigated here . A previous report from Malaysia found that S . Enteritidis isolates were more often isolated from the blood than stool of patients compared with other Salmonella serovars [40] , but the reasons for this were not explored . Considering that OAg length and density play a role in pathogen virulence [27] , increased levels of OAg expression may enhance the ability of S . Enteritidis isolates to evade host first line defence mechanisms and gain access to the bloodstream . It has also been reported that , when exposed to serum , S . Typhimurium upregulates genes for increased production of very long chain OAg to escape complement-dependent killing [28] . Our finding of a correlation between OAg expression and resistance to antibody-mediated killing among S . Typhimurium isolates is consistent with the hypothesis that high OAg expression protects against such killing . It is also well know that strains lacking OAg are rapidly eliminated from the host [41 , 42] , and there were no such isolates in our collection . However , the lack of correlation between OAg expressed by S . Enteritidis isolates and susceptibility to antibody-mediated killing does not support our hypothesis for this serovar concerning OAg levels and resistance to antibody killing . This finding suggests that other inherent factors of Salmonella , particularly S . Enteritidis , have a role in determining loss of sensitivity . In other words , quantity of OAg is more important for resistance to antibody-killing of S . Typhimurium than S . Enteritidis . Further studies are required to better understand the factors contributing to the inherent resistance of S . Enteritidis . In relation to OAg fine specificity ( O-acetylation ) of the isolates in our collection , S . Typhimurium were more heterogeneous than S . Enteritidis . OAg O-acetylation occurred in almost all S . Typhimurium isolates , and most often at two different sites: C-2 Abe ( known to provide factor O:5 specificity ) and C-2/C-3 Rha . Rha O-acetylation was first reported by Wollin et al [43] and we have previously reported this as part of the characterisation of an invasive Malawian S . Typhimurium isolate [44] . The extent to which this additional O-acetylation was widespread in our collection suggests a possible role in pathogenesis that should be investigated further . Moreover , the fact that we could also identify S . Typhimurium isolates with either Abe- or Rha-O-acetylation only , shows that O-acetylation at both sites can occur independently . A S . Typhimurium OAg-based vaccine for Africa would need to induce antibodies capable of binding to both OAg specificities . In contrast , O-acetylation of S . Enteritidis OAg was not as common , with more than two-thirds of the isolates lacking or having very low levels of O-acetylation . Glucosylation is another OAg modification for which variation has been described for S . Typhimurium and can occur on the O:12 antigen galactose ( C4 position ) generating the 12–2 variant , and on the O:1 antigen galactose ( C6 position ) ( phage-determined ) [45] . It has been reported that O:12 glucosylation in Typhimurium is not constitutive , occurs during intracellular macrophage growth and is associated with enhanced intestinal colonization [46] . All S . Typhimurium isolates in our collection were glucosylated , with less than 30% of them at levels < 20% , showing that glucosylation is a very common feature among endemic strains . Similarly , 86% S . Enteritidis isolates investigated showed a glucosylation level >20% . Glucosylation in S . Enteritidis is not associated with specific OAg factors and it has not been extensively studied . A report on S . Enteritidis food-borne salmonellosis strains showed glucosylated LPS in some mouse isolates , closely related to strains obtained from eggs [47] , but not in archived S . Enteritidis strains . In Shigella , LPS glucosylation promotes bacterial invasion by enhancing type III secretion system ( T3SS ) function and is associated with reduced LPS chain length [48] . In Salmonella , T3SS is necessary for invasion of intestinal epithelium and survival within macrophages and further investigation into different glucosylation levels and T3SS-mediated invasion is still to be performed . The inverse correlation between S . Typhimurium OAg glucosylation and OAg expression observed in the current study may be related to T3SS function , as previously reported for Shigella [48] and S . Typhimurium [30] . Salmonella isolates defective for the synthesis of long and very long OAg species have been shown to have increased translocation of a Salmonella pathogenicity island 1 ( SPI1 ) -T3SS effector protein promoting invasion . Therefore , also for Salmonella , there may be the need to balance T3SS activity , possibly enhanced by shorter and glucosylated LPS , and protection against innate immune effectors , which is favoured by a thicker LPS layer [27 , 28] . The reasons for NTS disease presenting as invasive or enteric disease are not well understood and could relate to host immunity and/or pathogen virulence . Epidemiological data indicate a frequent presentation of the invasive syndrome , without diarrhoea symptoms , in younger age groups [49] , often associated with other infections such as malaria and HIV [50–52] . However , specific investigations on the relationship between invasive and diarrheal disease are extremely limited , partially due to the limited microbiological resources in endemic African countries and few data are available on contemporaneous presence of NTS in stool and blood samples [53] . Another problem is that the concentration of Salmonella in the blood of bacteraemic patients can be as low as 1 CFU/ml [54] and blood culture sensitivity is estimated to be about 50% [54] . The lack of obvious difference between phenotype of NTS isolates from invasive and gastrointestinal disease episodes is consistent with the concept that invasive NTS isolates are able to cause diarrhoea and vice versa . If true , this would implicate host immunity as the major determinant of NTS disease presentation in endemic sub-Saharan African countries . Other previous studies [49] have not managed to identify specific pathogen determinants for either invasive or gastrointestinal syndromes , therefore the interplay between host and pathogen may be key . There are several limitations to the current study: categorization of isolates as “invasive” or “gastroenteric” was based on clinical presentation , as confirmed by microbiological analysis . Blood and stool samples belonging to the same patient were rarely collected , making it impossible to exclude that bacteria from bacteremic patients were not also present in stool and vice versa [53] . Another limitation was the lack of patients’ outcome data , which could give further insights into possible differences in serovar- and isolate pathogenicity , In summary , Kenyan NTS isolates from patients with bacteraemia and gastroenteritis were susceptible to antibody-mediated killing , supporting the development of an antibody-inducing vaccine against NTS for Africa . S . Enteritidis were generally less susceptible to killing than S . Typhimurium isolates and expressed higher levels of OAg , but while OAg expression correlated with antibody resistance for S . Typhimurium isolates , no correlation could be found for S . Enteritidis , supporting a role for other inherent bacterial factors in conferring resistance to antibody killing for this Salmonella serogroup . Serovar- and strain-specific differences in OAg expression and fine specificity were found . Those features could not be correlated to clinical presentation , so the same strains may be able to cause both invasive disease and diarrhoea . Whole genome analysis of invasive and gastrointestinal NTS isolates from Africa will provide additional insights regarding whether these strains are the same or different [53] . | Nontyphoidal Salmonellae ( NTS ) are an emerging major cause of invasive bacterial disease in African children aged less than 5 years and immunocompromised adults , with an estimated case fatality rate of 20–25% . NTS also cause diarrhoea , a killer of about 1 . 5 million young children annually , mainly in low- and middle-income countries . No vaccine against NTS is available , but improved understanding of the Salmonella bacteria that cause disease in Africa would help the development of new vaccines . The authors characterized a collection of 192 Kenyan NTS strains ( 114 S . Typhimurium and 78 S . Enteritidis ) from blood and stool specimens . All strains could be killed to differing extents by antibodies present in the blood of healthy HIV-uninfected African adults , supporting the development of a vaccine that will induce protective antibodies when given to African children . Differences in killing by antibody were partly related to the amount of O-antigen on the bacterial surface . There were no clear distinction between stains causing invasive disease and diarrhoea , suggesting that the same strains may be capable of causing both forms of disease . Clarification of this will require genomic analysis . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Relationship between Antibody Susceptibility and Lipopolysaccharide O-Antigen Characteristics of Invasive and Gastrointestinal Nontyphoidal Salmonellae Isolates from Kenya |
Human populations show rich cultural diversity . Underpinning this diversity of tools , rituals , and cultural norms are complex interactions between cultural evolutionary and demographic processes . Most models of cultural change assume that individuals use the same learning modes and methods throughout their lives . However , empirical data on ‘learning life histories’—the balance of dominant modes of learning ( for example , learning from parents , peers , or unrelated elders ) throughout an individual’s lifetime—suggest that age structure may play a crucial role in determining learning modes and cultural evolutionary trajectories . Thus , studied in isolation , demographic and cultural evolutionary models show only part of the picture . This paper describes a mathematical and computational framework that combines demographic and cultural evolutionary methods . Using this general framework , we examine interactions between the ways in which culture is spread throughout an individual’s lifetime and cultural change across generations . We show that including demographic structure alongside cultural dynamics can help to explain domain-specific patterns of cultural evolution that are a persistent feature of cultural data , and can shed new light on rare but significant demographic events .
Cultural transmission can occur via multiple modes of learning; for example , an individual can learn from parents ( termed vertical transmission ) , from non-parental adults ( oblique transmission ) , or from peers ( horizontal transmission ) [1] . Studies of enculturation and socialization of children suggest that the primary modes of learning change over a lifetime and that children from many different societies learn from parents when young and from peers or other adults as they grow older [2] . Further , the extent to which other modes of learning supersede vertical transmission can vary among populations [3] . Differences in how and when people learn and teach [4] cultural traits , such as the use of a specific tool or the moves of a particular dance , can reflect these culturally preferred modes of learning . Coarse-scale differences in social learning may result from variation in subsistence strategies; for example , Hewlett et al . [5] investigated the different learning trajectories of the Aka hunter-gatherers in central Africa and their small-scale agriculturalist neighbors . They showed that the individuals from whom children learn differed by children’s age and by their group’s subsistence strategy . In the hunter-gatherer groups , children’s learning was predominantly vertical , especially before the age of 12 , whereas children of small-scale agriculturalists learned primarily horizontally and obliquely , beginning at a much younger age . These learning life histories are illustrated in Fig 1 . Such population-specific learning patterns might be influenced by , and in turn influence , the human ecological niche . Through the hunting of game , the construction of large-scale settlements , the domestication of animals , and the spread of agriculture , humans have profoundly modified their ecological niche . This process is known as niche construction and encompasses alterations to the environment that can influence the selection pressures on future generations of humans and other species [6–9] . Similarly , some culturally transmitted behaviors may alter the evolutionary pressures on other cultural and/or genetic traits in a process termed cultural niche construction [9–12] . Social structure and social organization have a significant but understudied effect on the transmission of cultural information [13 , 14] . Similarly , by significantly altering associations between different population members , the cultural niche defined by a subsistence strategy appears to be associated with differences in the predominant mode of transmission ( vertical , oblique , or horizontal ) of cultural traits to the young [5] . In addition , the style of learning could , itself , be considered a cultural niche that determines the types of traits that are learned and the rate at which they spread . In this paper , we develop age-structured models of cultural transmission to investigate the effects of different ‘learning life histories’ ( sensu [3] ) on cultural evolution , defining these learning life histories as ‘population-level life-stage differences in modes of learning’ . We use observed learning life history differences between hunter-gatherers and small-scale agriculturalists to inform the parameters of the model . We then speculate about the potential importance of these different trajectories in transitions from foraging to farming and from predominantly vertical to predominantly horizontal learning . Our analysis explores how different modes of learning can affect the cultural evolution of a number of traits , including a fertility-enhancing trait ( for example a farming practice that improves food stability ) , a fertility-decreasing trait ( for example a small-family norm ) , and a trait controlling the mode of learning itself . We extend an earlier age-structured model of cultural transmission [15] and then simplify that model for application to data on the early learning practices of the Aka hunter-gatherers and their small scale agriculturalist neighbors , the Bofi and agriculturalist Aka populations [5] . To that end , we characterize learning in terms of learning opportunity , replacing the absolute probability of choosing a role model from a particular age class , which is difficult to assess in reality , with the probability of spending time with individuals from that age class , which can be readily measured . Thus , we make the assumption that the amount of time spent with an individual is correlated with the amount learned from that individual . It is important to note , however , that the proportion of learning reported from parents in Aka society ( ~80% [16] ) is considerably higher than the average proportion of time ( ~48% ) that young children spend within arm’s reach of their parents and other adults , although it is more similar to the time they spend with adults and in mixed groups of adults and children ( ~85% ) [5] , implying that vertical learning might happen both when children are alone with their parents and when they are with mixed groups of parents and peers . We use an age-structured model of cultural evolution to investigate whether the distinct learning niches exemplified by these hunter-gatherer and agriculturalist groups lead to qualitatively different evolutionary dynamics .
Our model is based on the age-structured framework developed by Fogarty et al . [15] , which included three age classes , with fertility and survival depending on the transmission of a cultural trait T . Here we consider five age classes . These five age classes loosely represent infancy , early childhood , late childhood , adulthood , and post-reproductive life . The post-reproductive age class represents the elderly or grandparents , who may act as reservoirs of cultural information that can be transmitted to younger age classes . We define the modes of learning in the population as vertical ( learning from parents only ) , oblique ( learning from any individual in an older age class ) , and horizontal ( learning from members of one’s own age class only ) . Individuals may learn throughout their lives until they reach the post-reproductive age class ( age class 5 ) . Suppose that the five discrete age classes , Ai , are of size ni ( with total population size , n=∑i=15ni ) with proportions ai=nin in each age class . The population age structure changes from generation τ to generation τ+1 in accordance with the adapted Leslie matrix , L , which describes life stages rather than age , specified in Eq ( 1 ) below . ( n1n2n3n4n5 ) τ+1= ( f1f2f3f4f5s100000s200000s300000s4s5 ) ︸L ( n1n2n3n4n5 ) τ ( 1 ) In Eq ( 1 ) , the number of individuals in a given age class in generation τ+1 is given by multiplying L by the vector containing the age class numbers in generation τ . Parameters fi represent fertilities , and the number of individuals in age class 1 at generation τ+1 , for example , is given by n1 , τ+1 = f1n1 , τ+f2n2 , τ+f3n3 , τ+f4n4 , τ+f5n5 , τ . In all of the following analyses , however , we assume that only age class 4 can reproduce; that is , f1 = f2 = f3 = f5 = 0 , and thus n1 , τ+1 = f4n4 , τ . Parameters si represents survival probabilities; for example , at time τ+1 the number of individuals in age class 2 is given by n2 , τ+1 = s1n1 , τ , i . e . the proportion of age class 1 individuals who survive to age class 2 multiplied by the number of age class 1 individuals at time τ . The recursions for other age classes follow similarly from Eq ( 1 ) . For this age-structured population , we consider a cultural trait T that has two variants , denoted by T and t . The frequency of T in age class i at time τ is xi , τ . Individuals learn throughout their lifetimes; initially they learn vertically from their parents and subsequently from parents , unrelated adults , or peers with likelihoods that may differ , for example , depending on subsistence strategy ( see below ) . The probability that an individual has the cultural trait T after vertical learning is , therefore , the probability that its parent had T multiplied by a probability , pv , that the individual learns vertically from its parent . The parameter pv represents the effectiveness or fidelity of vertical learning from parent to child . Although many cultural traits may be neutral ( for example , certain decorative elements; see , e . g . [17] ) , some ( such as norms about reproduction , marriage or childcare ) may have a profound effect on demography . For example , a cultural trait that increases fertility may also increase access to high quality food or increase reproductive output in some other way . To model this , we assume that the reproductive age class ( age class 4 ) has a baseline fertility b ( associated with the cultural variant t ) , which increases to b+wf in T individuals , where wf is a fertility increase; that is , an increase in the number of offspring associated with T . The fertility f4 in Eq ( 1 ) is then given by f4= ( b+wf ) x4 , τ+b ( 1−x4 , τ ) ( 2 ) Since age class 4 is the only one that reproduces ( i . e . f1 = f2 = f3 = f5 = 0 ) , age class 4 represents the parents of the new individuals in age class 1 . Therefore , the frequency of T in age class 1 at time τ is given by x1 , τ= ( b+wf ) x4 , τ−1 ( b+wf ) x4 , τ−1+b ( 1−x4 , τ−1 ) pv ( 3 ) From Eq ( 1 ) , at time τ , individuals in age class i survive to age class i+1 at time τ+1 with probability si . Individuals may learn from their parents at the first learning opportunity , but may also learn from individuals of their own generation or an older generation at subsequent learning opportunities throughout their lives . Therefore , the proportion of age class 1 that has cultural variant T after the first learning event is given by Eq ( 3 ) , and the frequencies of T in age classes i = 2 , 3 , 4 at time τ+1 after horizontal and oblique learning are given by xi , τ+1=xi−1 , τ+ ( 1−xi−1 , τ ) ( Vxparentpv+ ( 1−V ) ph∑y=iωny , τxy , τ∑z=iωnz , τ ) , ( 4 ) where V is the proportion of learning at age or stage i that is vertical , xparent is the proportion of the surviving parental population that has T , ph is the probability that a t individual acquires T horizontally or obliquely from contact with a T individual , pv is the probability that a t individual acquires T vertically , and ω represents the oldest age class from whom one can learn non-vertically . For the analyses presented here ω = 5 , but in principle this need not equal the number of age classes in the model . For example , if a certain age class , i , prefers to learn from those slightly older and more experienced , ω might be i+1 , restricting learning interactions to be between those in age class i and i+1 only . If the trait in question has an effect on survival as well as fertility , xparent must take account of different rates of survival in parents with T and parents without ( see below ) . In the following analyses , the vertical and horizontal transmission rates pv and ph are set to be equal in the hunter-gatherer and agriculturalist models , except where otherwise stated . We define life history learning strategies according to the amount of learning in each age class that is vertical , horizontal , or oblique . The age of the learner defines from whom the learning takes place: for example , Fig 1A shows a case where only vertical learning is used in the first three age classes and only horizontal or oblique learning is used in the final two age classes . As reference points , we include two extreme cases: an all-vertical learning strategy where individuals learn only from their parents throughout their lives , and an all-horizontal strategy where , after an initial round of vertical learning in infancy , individuals learn exclusively from same-age peers . We can then situate other learning strategies ( where the time a child spends learning from parents or same-age peers , for example , has been observed and recorded for specific populations; Fig 1 ) between these extremes and examine the consequences of mixed strategies for a population’s cultural evolution and demography . We assume that after age class 4 , adults move to age class 5 where they do not learn . T individuals in the oldest age class , 5 , at time τ+1 include both those maturing into group 5 from group 4 and survivors in group 5 from time τ; specifically , x5 , τ+1=x4 , τs4n4 , τs4n4 , τ+s5n5 , τ+x5 , τs5n5 , τs4n4 , τ+s5n5 , τ ( 5 ) The model described above can be extended to allow the proportion of time the young spend learning from other age classes to depend on the frequency of T in the reproductive population ( i . e . age class 4 ) , for example , through a cultural norm . In this way , T could affect reproductive output for T individuals , as well as the time that both T and t adults spend interacting with younger age classes through changes in diet or food stability for the former , and altered time budgets for the latter [18] . For example , in the Bofi , agricultural practices increase food stability but reduce the time that children spend with their parents while those parents are farming [5] , which could increase the likelihood of oblique or horizontal learning . We use the model in Eq ( 6 ) below to investigate this case and to assess the implications for the evolution of traits such as farming . To these ends , let the cultural trait T affect fertility ( as in Eq ( 2 ) ) and simultaneously affect some population-wide norms related to child rearing . As the frequency of T in the adult population increases , individuals change their views or practices and spend less time teaching their offspring . Thus , an increase in frequency of T in the population leads to a decrease in the time spent with offspring regardless of the actual form of the trait carried by an individual’s parents , which is , however , taken into account when considering the probability of learning ( Eq ( 7 ) ) . To do this , we introduce vj , τ , which , at time τ , represents the proportion of time individuals from age class j spend learning T from their parents , who were in age class 4 when j individuals were born . vj , τ is given by vj , τ=vb ( 1−x4 , τ−jϵ ) , ( 6 ) where vb is the baseline amount of time that age class j individuals spend interacting with their parents who were in age class 4 at time τ−j , ϵ is a scaling factor that determines the strength of the effect of the cultural trait on time spent with offspring , and x4 , τ−j is the frequency of T in the reproductive age class 4 . Here vj , τ represents the time a child spends in the presence of its parent , not the rate of learning from them , although the former may be used as a proxy measure for the latter . In Eq ( 6 ) , as the frequency of T in age class j’s parental generation ( x4 , τ−j ) increases , the amount of vertical contact decreases . We can then modify Eq ( 4 ) , combining it with the formula for vertical learning ( Eq ( 3 ) ) to incorporate vj , τ . The equation for xj , τ+1 becomes: xj , τ+1=xj−1 , τ︸ ( A ) + ( 1−xj−1 , τ ) ︸ ( B ) ( vj , τ+1 ( b+wf ) x4 , τ−j ( b+wf ) x4 , τ−j+b ( 1−x4 , τ−j ) ︸ ( C ) pv+ ( 1−vj , τ+1 ) ∑z=jωnz , τxz , τ∑z=iNnz , τ︸ ( D ) ph ) , ( 7A ) where the trait T increases fertility , or xj , τ+1=xj−1 , τ︸ ( A ) + ( 1−xj−1 , τ ) ︸ ( B ) ( vj , τ+1 ( s4+ws ) jx4 , τ−j ( s4+ws ) jx4 , τ−j+s4j ( 1−x4 , τ−j ) ︸ ( C ) pv+ ( 1−vj , τ+1 ) ∑z=jωnz , τxz , τ∑z=iNnz , τ︸ ( D ) ph ) , ( 7B ) where the trait T increases survival and ws is a survival increase associated with T . Here , j is the focal age class ( j = 2 , 3 , 4 ) , and x4 , τ−j is the frequency of T at the time of reproduction by the age class containing the parents of age class j . In Eq ( 7A and 7B ) , term ( A ) represents the individuals who survived to generation τ+1 , from age class j–1 , who are now in age class j and have already learned T ( note that here T does not affect survival rates ) . ( B ) represents the members of age class j–1 who did not learn T in the previous time step , and so have another chance to learn . These individuals can learn vertically , with probability vj , τ+1 , which is multiplied by ( C ) , an expression adapted from Eq ( 4 ) , which describes the frequency of T in the parental generation j generations ago ( x4 , τ−j ) and ensures that only individuals with T parents can learn T vertically . Finally , individuals can learn horizontally or obliquely with a probability 1−vj , τ+1 , and ( D ) represents the frequency of T , either in the individual’s own age class ( horizontal learning ) or in older ( oblique learning ) weighted by the population size in that age class . If the cultural trait affects survival and not fertility , the survival probabilities in the matrix in ( 1 ) will become si ( 1−xi ) +xi ( si+ws ) where the survival probability of T individuals is increased relative to t individuals by an amount ws . Here we must also consider the differential survival of parents with T and with t when assessing vertical learning as in the case of fertility increases ( Eq 7B ) . In this formulation , children may learn from any individual they contact and the frequency of T in the population determines the percentage of time offspring spend with their parents , with others of the same age , or with other adults . This is likely to be a simplification of human learning processes; for example , Henrich and Broesch [19] reported that small-scale agriculturalist communities in Fiji show adaptive learning biases ( e . g . prestige-biased social learning ) . We suggest that the evolution of a trait that allows children to learn from any individual , not just from their parents , may allow for and promote the evolution of such biases by widening the pool of potential role models .
Here , we assess the effects of different life histories of learning on cultural transmission and demography . We estimate these life histories of learning using the learning parameters suggested by Hewlett et al . [5] for hunter-gatherers and agriculturalists along with two reference strategies: one in which horizontal and oblique learning are used late in life and one in which they are used early ( Fig 1 ) . Using these different proportions of vertical and horizontal/oblique transmission at different life stages , we compare the effects of life histories of learning on cultural evolutionary processes . The model described in eqns ( 1–7 ) ( see code ) was iterated over a number of generations with the frequency of T in each age class ( xi , τ ) , the number of individuals ( n ) in the population at time τ , and the proportions of the population in each age class ( ai , τ ) evolving simultaneously . The number of generations and other parameter values for each simulation are given in the corresponding figure caption . At the beginning of each simulation , the t form of trait T was close to fixation in the population , and T appeared at very low frequency ( 0 . 005 for analyses shown here ) . Fig 2 shows the final frequency ( after 5 , 000 model iterations ) of the T trait for each of these learning life histories in turn . The axes show the rates of horizontal/oblique and vertical learning of the cultural trait . As the rate of vertical learning increases , the final frequency of the trait depends on the rate of the dominant mode of learning to a greater degree . For example , if the rate of horizontal learning is greater than that of vertical learning , the final frequency of the trait depends to a large degree on the rate of horizontal learning . In the model described by Eq ( 7 ) , it is important to note that the trait being transmitted has a baseline advantage when it is transmitted vertically ( or via ‘late horizontal’ life histories ) . We assume that after age class 4 , adults move to age class 5 where they do not learn . The frequency of T is , therefore , highest in age classes 4 and 5 , as individuals continue to learn throughout their lives up to that point . Relying only on parents as role models means that the frequency of T in the subpopulation from which an individual learns will be higher than the average in the population as a whole . Horizontal learners use the average rate of learning across all age classes and so adopt T at a slower rate . Fig 3 accounts for this phenomenon by showing the increase in the spread of the trait due to the trait’s fitness benefit compared to a neutral trait . In this way , we can show that interaction between the exact form of the fitness of a trait and the dominant mode of learning can be crucial . For example , if the fitness benefit of a particular trait to an individual is an increase in fertility , a learning life history that relies heavily on vertical learning allows the cultural trait to gain a stronger advantage ( Fig 3A ) . This increase occurs because a fertility benefit increases the number of offspring born to T individuals ( knowledgeable individuals ) . By learning only from parents , these offspring increase the chance that they learn from a knowledgeable individual compared to sampling from the population as a whole . On the other hand , Fig 3B shows that when the trait increases survival in all age classes , the trait becomes overrepresented in the population as a whole , not just in parents . Therefore , sampling from the population increases the chance that an individual will choose a knowledgeable role model . Note that these results hold for initial age structures that are either even ( as shown in Fig 3 ) or pyramidal ( e . g . S1C and S1D Fig ) but break down for extremely skewed initial structures ( e . g . S1A , S1B , S1E and S1F Fig ) . This result underscores the importance of the life history of learning to both cultural and biological evolution: different types of traits are favored depending on the modes of their learning at different life stages , and these learning life histories can influence both cultural dynamics and population demography . We can also investigate the evolution of these learning life histories in the case that the focal cultural trait ( T ) produces both a fertility increase and a change in the proportion of time children spend in the presence of their parents and peers . Thus T constructs a learning niche , altering the conditions of its own spread as it invades a population [5] . Such conditions might be characteristic of child-rearing practices in small-scale agriculturalist populations , and similar changes to time allocation may accompany the transition from subsistence , that is predominantly hunting and foraging , to predominantly farming . Eq ( 7 ) describes a niche-constructing trait that alters the proportion of learning that is vertical relative to horizontal or oblique . Here we do not distinguish a priori between hunter-gatherer populations and agriculturalist populations . The trait is allowed to spread in the population by affecting both fertility and learning norms . We then examine the effect of the strength of changes in learning norms ( namely , the strength of cultural niche construction ) associated with the trait , which is determined by the parameter ϵ in Eq ( 6 ) . First , we consider a case where the cultural trait increases fertility ( i . e . is fitness enhancing ) and also increases the amount of horizontal or oblique learning that occurs . This is roughly analogous to the suggested consequences of the successful invasion of agriculture [5] . Fig 4A shows that in the absence of niche construction ( i . e . when ϵ = 0 ) , the cultural trait can increase in frequency and persist in the population if the vertical learning rate is high or if both vertical and horizontal learning rates are high . Fig 4B shows that when cultural niche construction is strong ( ϵ = 1 ) , the cultural trait T supports its own transmission under some circumstances . Taking a closer look at the quadrants in Fig 4B ( delineated by black dashed lines ) , it is clear that this phenomenon rests on the balance between vertical and horizontal transmission and on the efficacy of both modes of transmission . In the upper right quadrant of both panels , both vertical and horizontal learning are effective and the trait rises to very high frequencies at some points and fixes in the population regardless of the strength of cultural niche construction . However , if the rate of one mode of learning is higher than that of the other ( ph>pv , upper left quadrant or pv>ph , lower right quadrant ) , increasing reliance on the higher-rate mode has the effect of increasing the parameter space over which the trait can spread . Fig 5 shows the same for a trait that decreases fertility .
We modeled the spread of a cultural trait that can affect demography ( in particular , fertility and survival ) as well as the spread of cultural norms . In turn , this trait affects age structure and population size , and it also influences the life history of learning; that is , when and from whom individuals learn . Agriculture is an important example of such a trait . Farming can increase rates of fertility through increased access to a stable supply of nutrition and as they spread , agricultural practices , such as tending crops , may affect the importance of vertical , horizontal , and oblique learning modes by altering the amount of time children spend in the company of their parents or in groups of same-age peers . As a case study of these differences , we refer to data from Hewlett et al . [5] who recorded the time children spent with members of different age classes in neighboring populations of foragers and agriculturalists . Such cultural traits are likely to have played a central role in human history . Each new mode of production ( for example , foraging , small-scale agriculture , intensive agriculture ) might have led to important changes in lifestyle and , as a result , in the time children spend with parents , same-age peers , or unrelated adults . Our analysis addresses three important questions: 1 . how do primary modes of learning ( i . e . , vertical , horizontal or oblique learning ) affect the rate of spread and accumulation of cultural traits in populations with different learning life histories ? 2 . Do these changes mean that certain types of traits are more likely to spread in some societies than in others ? Could hunter-gatherers , for example , accumulate or maintain some cultural traits that farmers would not , as a result of their dominant modes of learning ? 3 . What are the implications of these phenomena for demographic changes over time ? There is some evidence that modes of learning affect the diversity and composition of cultures . Guglielmino et al . [2] describe vertical learning and one-to-many transmission as being conservative , while horizontal and oblique learning are more likely to support the spread of innovations [1 , 20] However , age-dependent cultural transmission is arguably more common than the dominance of just one mode of learning throughout an individual’s life . For example , age-structured patterns of social learning have been observed for the Aka and Bofi in the Central African Republic [5] and for a number of horticultural and foraging societies in the Democratic Republic of Congo ( DRC ) [3] . Such age-structured learning strategies suggest that a model accounting for life stage is appropriate for human cultural transmission . In our age-structured model , we first show that different age-dependent learning strategies result in strikingly different patterns of cultural evolution . For example , Fig 2 shows that strong reliance on vertical transmission , as seen in hunter-gatherer populations , entails that the spread of a cultural trait relies on the rate of vertical transmission to a greater extent than on the rate of horizontal or oblique learning . It is noteworthy that in many previous verbal and mathematical models [1 , 21 , 22] , vertical learning is regarded as conservative , with new traits failing to spread as widely or as rapidly as they would if they were transmitted horizontally or obliquely . However , our model highlights another aspect of reliance on vertical transmission that is difficult to resolve without knowledge of a population’s age structure , namely , those who learn primarily from parents are learning from the most knowledgeable subset of the population . In our model , as in most real-world populations , individuals learn throughout their lives , and as a result the proportion of the population that is well informed about useful cultural traits increases with age . The most knowledgeable age classes in our model are , therefore , age classes four and five . Relying on the knowledge possessed by younger age classes , as seen with horizontal transmission , reduces the overall probability of learning from a knowledgeable individual and curtails spread of culture relative to individuals who learn only from parents . In this context , optimal learning of important cultural traits would rely heavily on parental knowledge and thus on vertical transmission . These effects would be weakened by extremely fast environmental change that renders cultural information obsolete at a fast rate . This is not modeled here but see [18] . With the advent of farming , parents may spend less time with young children and more time engaged in agricultural activities , thus reducing reliance on vertical learning and increasing the importance of horizontal and oblique modes of learning . In this way , farming can be viewed as a trait that constructs a ‘learning niche’: that is , farming is a cultural trait that can change the way cultural traits are transmitted . We might also expect to see the evolution and spread of further traits or norms or even modes of transmission [23] that compensate for reduced time with offspring–for example many-to-one transmission . In the example shown in Figs 2 and 3 , the proportion of time spent with each age group was estimated from the anthropological literature; in this niche-constructing example , however , we allow this proportion of time to vary with the frequency of the trait T . We can thus utilize this scenario to reflect the early evolution of a trait such as farming . The analysis showed that if the niche constructing effect is strong—the trait has a strong effect on learning norms and practices—it can facilitate its own spread and expand the parameter range over which it can be expected to increase in frequency , as well as substantially increasing the frequency of the trait at equilibrium under certain conditions ( Fig 4 ) . If the transmission becomes one-to-many , these effects would be more pronounced [1 , 22] . Although we expect the evolution of farming to increase fertility , the effects of cultural niche construction on learning norms in a population might also reduce fertility in some cases , which would thus act in opposition to natural selection . In other words , a niche-constructing trait can promote its own spread even if its effect on fertility is negative ( Fig 5 ) . For example , the spread of an education system , such as classroom-based learning , would likely change the learning niche by increasing oblique and horizontal learning , but has also been shown to decrease the desired number of children , and hence fertility [10 , 24] . In real-world populations , there are likely to be domain-specific differences in the transmission of information . For example , among undergraduate students at Stanford University , traits like religious beliefs and political inclinations were over 80% vertically transmitted but preferred forms of entertainment were over 60% horizontally or obliquely transmitted [25] . Further , in the Lese , Mamvu , Budu and Bila cultural groups in the DRC , sexual health practices were predominantly horizontally transmitted between adults [26] . Our model begins to address this interaction between learning strategies and knowledge domain by making explicit the effect of life history of learning on the effectiveness of cultural spread for traits with different types of fitness effects . Finally , the model reveals rare but potentially important large-scale demographic differences between populations with different learning modes , especially when learning cultural traits that alter fertility or survival . Fig ( 6 ) shows an example of the spread of a fertility enhancing cultural trait in a small and precarious population . As discussed above , a population using predominantly vertical transmission can spread fertility-enhancing cultural traits more rapidly and more effectively than populations in which horizontal transmission is more important . Under certain circumstances , this advantage in terms of cultural transmission translates into a cultural demographic rescue for a population in which a particular mode of learning is important ( Fig 6A ) and the slower rate of spread results in demographic collapse in the other ( Fig 6B ) . This shows not only that cultural traits can affect demography , but also the learning norms within a population and the way in which individuals choose to learn could have major effects on a population’s evolutionary and demographic trajectory . The transition from foraging to farming , as described by Hewlett et al . [5] , is accompanied by a change in learning mode; parents spend more time farming and their children spend more time with other children . By contrast , in foraging populations children accompany their parents as they gather food . This shift in the focus of learners from their parents to others in the population may allow individuals to actively choose a cultural role model and pave the way for emergence of cultural learning biases such as prestige bias or conformity bias , which may not be a prominent feature of societies that rely primarily on vertical learning , but are widespread in other societies [19] . Thus , cultural niche-constructing traits that affect the mode and rate of their own transmission may underpin the evolution of more varied and less obvious social learning biases . These , in turn , may have facilitated effective rapid cumulative cultural evolution and driven further changes in subsistence strategy , population size , or population age structure , profoundly affecting the cultural and biological evolutionary trajectories of human populations . | Human populations show great cultural variety and complexity , which cultural evolutionary theory seeks to explain by applying ideas about evolution to the ways in which cultural traits change over time . We combined cultural evolutionary theory with information about how people learn over their lifetimes—changing their role models and teachers as they grow up . The result is a new theory of the interaction between life histories and learning that gives a more complete description of human cultural change . The results of our model show why different cultural traits might spread in one population compared to another and how cultural change might spark large-scale demographic changes . | [
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] | 2019 | The life history of learning: Demographic structure changes cultural outcomes |
Systemic lupus erythematosus ( SLE ) is a genetically complex disease with heterogeneous clinical manifestations . A polymorphism in the STAT4 gene has recently been established as a risk factor for SLE , but the relationship with specific SLE subphenotypes has not been studied . We studied 137 SNPs in the STAT4 region genotyped in 4 independent SLE case series ( total n = 1398 ) and 2560 healthy controls , along with clinical data for the cases . Using conditional testing , we confirmed the most significant STAT4 haplotype for SLE risk . We then studied a SNP marking this haplotype for association with specific SLE subphenotypes , including autoantibody production , nephritis , arthritis , mucocutaneous manifestations , and age at diagnosis . To prevent possible type-I errors from population stratification , we reanalyzed the data using a subset of subjects determined to be most homogeneous based on principal components analysis of genome-wide data . We confirmed that four SNPs in very high LD ( r2 = 0 . 94 to 0 . 99 ) were most strongly associated with SLE , and there was no compelling evidence for additional SLE risk loci in the STAT4 region . SNP rs7574865 marking this haplotype had a minor allele frequency ( MAF ) = 31 . 1% in SLE cases compared with 22 . 5% in controls ( OR = 1 . 56 , p = 10−16 ) . This SNP was more strongly associated with SLE characterized by double-stranded DNA autoantibodies ( MAF = 35 . 1% , OR = 1 . 86 , p<10−19 ) , nephritis ( MAF = 34 . 3% , OR = 1 . 80 , p<10−11 ) , and age at diagnosis<30 years ( MAF = 33 . 8% , OR = 1 . 77 , p<10−13 ) . An association with severe nephritis was even more striking ( MAF = 39 . 2% , OR = 2 . 35 , p<10−4 in the homogeneous subset of subjects ) . In contrast , STAT4 was less strongly associated with oral ulcers , a manifestation associated with milder disease . We conclude that this common polymorphism of STAT4 contributes to the phenotypic heterogeneity of SLE , predisposing specifically to more severe disease .
Systemic lupus erythematosus ( SLE ) ( OMIM 152700 ) is a disabling and chronic autoimmune disease with remarkable heterogeneity . The eleven classification criteria for SLE established by the American College of Rheumatology [1] – any four of which can confirm classification as SLE – include arthritis , renal disease , mucocutaneous manifestations , photosensitivity , neurological disorders , production of a variety of autoantibodies , and hematological disorders . Many of these characteristics are correlated , and may indicate different underlying disease mechanisms . SLE also has an established but complex genetic component [2] . Understanding the relationships between SLE risk genes and subtypes of the disease may help to elucidate disease mechanisms and pathways . Recently , a polymorphism of the STAT4 gene on chromosome 2q has been strongly implicated in the risk for both SLE and rheumatoid arthritis [3] . We investigated whether variation in STAT4 contributes to the heterogeneity of SLE . Using 4 independent SLE case series , a large set of healthy controls , and two independent sets of genotypes for the STAT4 region on these subjects , we have found strong evidence that this is the case . In particular , we have found that the STAT4 susceptibility polymorphism is associated with more severe disease manifestations , including nephritis and early disease onset . It is also strongly associated with SLE characterized by double-stranded DNA autoantibody production .
SLE cases were obtained from four sources . Patients from the University of California , San Francisco ( UCSF ) were participants in the UCSF Lupus Genetics Project and were recruited from UCSF Arthritis Clinics and private rheumatology practices in northern California , as well as by nationwide outreach [4] . SLE patients contributed by the Autoimmune Biomarkers Collaborative Network ( ABCoN ) [5] were recruited from the Hopkins Lupus cohort [6] . A third case series was part of the Multiple Autoimmune Disease Genetics Consortium ( MADGC ) collection [7] . Finally , a fourth set of cases recruited from the Pittsburgh Lupus Registry were obtained from the University of Pittsburgh [8] . Only subjects of self-described European descent were retained . Unrelated controls of European ancestry were from the New York Health Project ( NYHP ) [9] ( http://www . amdec . org/amdec_initiatives/nycp . html ) . The study populations are a superset of those recently used to establish a link between SLE and STAT4 [3] , with the addition of the University of Pittsburgh cases and more than doubling the number of NYHP controls ( see Table 1 ) . The Institutional Review Boards of all investigative institutions approved these studies , and all cases and controls gave written informed consent . Clinical data for the cases was obtained from medical records which were reviewed and tabulated at each institution . We chose to examine the ACR criteria [1] ( http://www . rheumatology . org/publications/classification/SLE/sle . asp ) and age at diagnosis , categorizing the age at diagnosis for association analyses . The mean and median for age at diagnosis were 34 and 32 , respectively; we chose a cutoff for early diagnosis of under 30 years of age versus greater than or equal to 30 years of age . We also chose to examine production of autoantibodies to double-stranded DNA ( anti-dsDNA ) , as this is typically associated with severe disease and this was available in the clinical data from all sites . Finally , we used more detailed nephritis information available for the UCSF and ABCoN cohorts , namely a characterization of those patients with severe nephritis as defined by end-stage renal disease or histopathologic evidence of severe , progressive renal disease on renal biopsy . Genotype data were obtained from two parent studies ( see Table 1 ) . The four SLE case series and 1762 NYHP controls were genotyped using the Illumina HumanHap550 array as part of a genome-wide association study of SLE [10] . In addition , three of the case series ( UCSF , ABCoN , and MADGC ) and 1243 NYHP controls were genotyped for 67 SNPS in the STAT1/STAT4 region of chromosome 2q as part of a case-control study of STAT4 and two systemic autoimmune diseases , rheumatoid arthritis and SLE [3] . Selection and genotyping of these 67 fine-mapping SNPs was done by the National Institute for Arthritis and Musculoskeletal and Skin Diseases ( NIAMS ) , using Sequenom MassARRAY Technology as previously described [3] . From the Illumina 550K panel , 91 contiguous SNPs from the STAT1/STAT4 region , extended with flanking regions 200kb on either side , were selected; of these , 45 were contained in the same region as the 67 SNPs , with 21 of those being identical . Coverage of these SNPs was analyzed using Tagger [11] in Haploview [12] with an r2 threshold of 0 . 8 and pairwise tagging , based on the HapMap Phase II data from CEU ( CEPH residents of Utah with ancestry from northern and western Europe ) with minor allele frequency>0 . 05 . Duplicate genotyping enabled an analysis of SNP concordance between the two genotyping methods , and inclusion of genotypes that were called by either method . Conflicting genotype calls were dropped from analyses when using combined data . Subjects were first removed for whom there was evidence of duplication or relatedness in the Illumina 550K data , using IBS estimation in PLINK [13] ( http://pngu . mgh . harvard . edu/purcell/plink ) . For the choice of which sample to remove , preference was first based on availability of phenotype data , and then on overall genotyping call rate . SNPs were removed from analysis that had a minor allele frequency less than 5% , greater than 10% missing genotypes , or Hardy-Weinberg equilibrium p<0 . 001 in controls . In order to choose loci to examine for phenotype analyses , we performed allelic and conditional tests . These analyses were performed separately for the 91 SNPs from the Illumina 550K panel and the 67 Sequenom SNPs , since they contained overlapping but different sets of SNPs ( Table S1 ) and subjects ( Table 1 ) , and large amounts of missing data could bias haplotype estimation . We also analyzed the full set of 137 SNPs together using only the subset of subjects who were genotyped on both platforms . For each analysis , subjects were removed that had less than 90% genotyping . We first conducted allelic tests of cases and controls using PLINK [13] and selected those that had p<0 . 005; at this first screening stage we used a liberal p-value , considering that there are well over 10 independent haplotype blocks in the complete region . To eliminate redundant SNPs having effects only due to linkage disequilibrium , we then performed conditional analysis using WHAP [14] ( http://pngu . mgh . harvard . edu/purcell/whap ) . SNP rs7574865 was chosen for phenotype analysis based on the allelic and conditional analyses ( see Results ) . SNP rs7574865 was genotyped on both platforms with a very high rate of concordance ( see Results ) , so genotypes from both platforms were combined for the phenotype analysis of rs7574865; the single subject for whom the calls conflicted was dropped . We first performed case-only analyses ( e . g . presence of renal disorder versus no renal disorder ) to establish which subphenotypes are associated with rs7574865 variation . We then performed case-control analyses ( e . g . SLE with renal disorder versus controls ) to examine the risk that is conferred by rs7574865 on subtypes of SLE characterized by each of those subphenotypes . In both sets of analyses , first bivariate odds ratios ( ORs ) and 95% confidence limits were determined for each subphenotype . To correct for variability among strata when combining data from different cohorts , we used Mantel-Haenszel tests and combined ORs . In order to investigate the possibility of associations with unknown but common underlying disease mechanisms , principal components analysis ( PCA ) was performed using all subphenotypes except severe nephritis ( a subclass of nephritis , and available only for the UCSF and ABCoN case series ) . Values for the first two principal components ( PCs ) were evaluated as above as additional subphenotypes , categorized by positive or negative . To address the concern that case-control studies may give spurious associations due to undetected population admixture or population substructure differences between cases and the controls , we utilized ancestry data for the Illumina 550K genotyped subjects . Ancestry was derived from ancestry-informative markers ( AIMs ) contained in the Illumina 550K panel . First a set of 235 AIMs was used to estimate percent European ancestry , using STRUCTURE [15] . For those subjects with >90% European ancestry , another set of 1409 EUROSTRUCTURE [16] AIMs was used to estimate percent Northern European versus Southern ancestry . Finally , a subset of 1253 subjects ( 751 cases and 502 controls ) was identified that was homogeneous based on the first four PCs determined by PCA using the 550K panel and EIGENSTRAT[17] software . Minimum covariance determinant ( MCD ) estimators of PC location and scatter were calculated using R [18]; outliers were then determined using robust Mahalanobis distance . The procedure was applied in two steps , first using both cases and controls ( significance level α = 0 . 05 ) , and then using the case-only robust estimators of location and scatter ( α = 0 . 10 ) , which led to a more homogeneous case-control sample set . The λgc was decreased from 1 . 256 to 1 . 045 for the homogeneous set when assessed using the 550K panel ( see Figure S1 ) . We analyzed the associations between ≥90% European versus <90% European and ≥90% Northern European versus <90% Northern European ancestry and rs7574865 in controls , using an allele-based exact test . We also reanalyzed all tests using the homogeneous subset of subjects . Finally , in multivariate analysis we adjusted for ancestry , sex , and disease duration . For this multivariate analysis , ancestry was a 3-category variable as follows: 1 ) <90% European , 2 ) ≥90% European and ≥90% Northern European , and 3 ) ≥90% European and <90% Northern European . We chose this coding due to the highly skewed distribution of continuous ancestry , and the collinearity between the European and Northern European variables . Since we are examining associations for 13 phenotypes , the issue of multiple testing must be considered . However , since these are not independent phenotypes , a simple Bonferroni correction of α = 0 . 05/13 = 0 . 004 is clearly overly conservative , while an unadjusted α = 0 . 05 is clearly liberal . For this reason we have chosen to present unadjusted p-values so that these may be directly interpreted by the reader . Stata 9 . 2 ( http://www . stata . com/ ) was used for correlations , odds ratios and p-values , Mantel-Haenszel tests and combined ORs , phenotype principal components analysis , and multivariate logistic regressions .
The numbers of independent cases and controls in each cohort and a summary of available genotype and phenotype data are listed in Table 1 . For overlapping SNPs , including rs7574865 , there were 1396 genotyped cases with phenotype data , and 2560 genotyped healthy controls . A summary of subphenotypes by cohort is presented in Table 2 . There were significant differences among the cohorts for all phenotypes except neurologic disorder and age at diagnosis less than 30 years old . Some of these phenotypes are highly correlated; in particular anti-dsDNA is a subcriterion for the ACR immunologic criterion , and is associated with renal disease . Pairwise correlation coefficients , for those pairs having ρ>0 . 1 , are shown in Table 3 . All p-values for these pairs were ≤0 . 0001 . In principal components ( PC ) analysis of the phenotype data , the top 3 components of the first PC are anti-dsDNA , the immunologic criterion , and renal disease . The top 3 components of the second PC are malar rash , photosensitivity , and discoid rash . Variables corresponding to the first and second PCs were included in phenotype analyses ( see Methods ) . Of the 67 Sequenom SNPs ( shown with the study genotypes in Figure 1A ) , 62 passed quality control filters , including MAF≥5% . These 62 SNPs had 86% coverage of the common variation ( MAF≥5% ) in the STAT1/STAT4 region . In the Illumina 550K panel ( shown with the study genotypes in Figure 1B ) , 77 out of 91 SNPs passed quality control and had 83% coverage of the extended region obtained by adding flanking markers 200kb on either side of the Sequenom STAT1-STAT4 region . A complete list of SNPs on both platforms passing our quality control criteria , along with their MAF and percentage genotyped , is provided in Table S1 . We examined the concordance between calls for the 21 overlapping SNPs and 1458 subjects who were genotyped using both methods . Results for this are shown in Supplementary Table S2 . The average and minimum agreement were 99 . 90% and 99 . 65% , respectively . In particular for SNP rs7574865 , the agreement was 99 . 93% . Given this high rate of concordance , we chose to merge genotype data for rs7574865 for the phenotype analyses ( see below ) . Table 4 contains allelic p-values before and after conditioning on the most significant SNP , for those with initial allelic p-values of 0 . 005 or less . We did four separate conditional analyses: ( A ) subjects and SNPs genotyped on the Illumina 550K; ( B ) the genetically homogeneous subset of subjects ( see Methods ) typed on the Illumina 550K; ( C ) subjects and SNPs genotyped on the Sequenom platform; and ( D ) all SNPs for those subjects that were genotyped on both platforms . In the Illumina 550K panel , rs7574865 ( circled in Figure 1B ) was the most significant SNP in both the full set of subjects and the homogeneous subset ( see Methods ) . In the Sequenom 67-SNP set and in the combined set , the 4 top SNPs were in high LD ( D’ = 0 . 97 to 0 . 99 , r2 = 0 . 94 to 0 . 99 ) and made up a 4-marker haplotype for which the components could not be independently analyzed ( circled in Figure 1A ) . Of the estimated individual haplotypes of these 4 markers , over 99% were either CGTC or TTCG , so that any one SNP fully determined the other 3 in the vast majority of subjects . The conditional p-values of Table 4 test the significance of each SNP conditional on the values of the top SNP ( s ) which are given in bold . While there were some results of borderline significance , they were neither strong nor consistent across the different analyses . The only compelling evidence after conditioning was for the 4-locus haplotype above . Since any of the 4 SNPs serves as a marker of this haplotype and rs7574865 is contained in both genotyping sets , we chose to carry out phenotype analysis using this marker for maximum power . We examined the minor allele frequencies for rs7574865 ( Table S3 ) in controls , for subsets as determined by STRUCTURE analyses ( see Methods ) . There were 130 controls with <90% European ancestry , for whom the minor allele frequency was 26 . 9% , versus 22 . 4% in the complementary 1601 controls with ≥90% European ancestry ( p = 0 . 11 ) . ( The minor allele frequencies of the HapMap populations are 33% , 28% , 16% , and 21% , for the HCB , JPT , YRI , and CEU populations , respectively . ) The minor allele frequencies were very similar , 22 . 1% and 22 . 6% respectively , for subjects of either <90% or ≥90% Northern European ancestry; thus we did not observe a Northern-Southern European gradient for rs7574865 . Analyses were repeated with the homogeneous subset of cases and controls ( n = 1253 ) as described in Methods . We first examined each subphenotype for association with rs7574865 within the SLE cases . In unadjusted results ( Table S4 ) , only one phenotype showed borderline evidence for heterogeneity of association among the four SLE case series ( p = 0 . 04 for immunologic disorder ) ; thus we retained combined Mantel-Haenszel odds ratios and p-values . The most significant associations were with anti-dsDNA autoantibodies and the first principal component , OR = 1 . 44 ( 95% CI 1 . 23–1 . 70 ) , p = 10−5 , and OR = 1 . 43 ( 95% CI 1 . 21–1 . 70 ) , p = 3×10−5 , respectively . Severe nephritis , available on a smaller subset of cases , had the highest OR = 1 . 50 ( 95% CI 1 . 11–2 . 01 ) , p = 0 . 0075 . There was also support for associations with immunologic criteria ( OR = 1 . 24 , p = 0 . 017 ) , renal disorder ( OR = 1 . 23 , p = 0 . 024 ) , age at diagnosis under 30 ( OR = 1 . 22 , p = 0 . 018 ) , and an inverse association with oral ulcers ( OR = 0 . 80 , p = 0 . 0087 ) . Table 5 contains case-only analyses , for phenotypes having unadjusted p<0 . 05 , repeated first on the homogeneous subset of subjects ( see Methods ) , and also using multivariate adjustment for ancestry , sex , and disease duration . There is consistency in odds ratios throughout and these analyses continue to support the aforementioned phenotypic associations with rs7574865 . Some associations are even stronger in the homogeneous subset analysis , for example severe nephritis ( OR = 1 . 79 [95% CI = 1 . 20–2 . 67] , p = 0 . 0039 ) , renal disease ( OR = 1 . 48 [95% CI = 1 . 16–1 . 88] , p = 0 . 0016 ) , and oral ulcers ( OR = 0 . 62 [95% CI = 0 . 49–0 . 79] , p = 0 . 0001 ) . Table 6 shows our primary results , the risk of SLE characterized by each subphenotype versus healthy controls . This illustrates a spectrum of minor allele frequencies for certain subphenotypes of SLE , with the most extreme being severe nephritis , MAF = 38 . 1% ( OR = 2 . 12 [95% CI = 1 . 58–2 . 83] , p = 4×10−7 ) , anti-dsDNA autoantibodies , MAF = 35 . 1% ( OR = 1 . 86 [95% CI = 1 . 63–2 . 13] , p = 6×10−20 ) , and the first principal component , MAF = 35 . 0% ( OR = 1 . 85 [95% CI = 1 . 62–2 . 12] , p = 10−19 ) . In contrast , controls had a MAF of only 22 . 5% and cases as a whole had a MAF of 31 . 1% . SNP rs7574865 is also associated with higher risk for SLE with renal disorder ( OR = 1 . 80 , MAF = 34 . 3% ) , diagnosis under 30 years old ( OR = 1 . 77 , MAF = 33 . 8% ) , and immunologic disorder ( OR = 1 . 67 , MAF = 32 . 6% ) . There is also strong evidence that the STAT4 risk allele is less frequent in SLE with oral ulcers , MAF = 28 . 8% , which is generally associated with milder disease . This analysis was repeated with the genetically-homogeneous subset , again showing even stronger results for severe nephritis , MAF = 39 . 2% ( OR = 2 . 35 [95% CI 1 . 54–3 . 56] ) , and stronger inverse results for oral ulcers , MAF = 25 . 0% , versus MAF = 30 . 0% for all homogeneous cases and MAF = 21 . 5% for homogeneous controls .
Genotype-phenotype associations between risk alleles and disease subtypes may give insight into disease etiology and mechanisms . Recent results show that rs7574865 , a variant allele of STAT4 , confers an increased risk for both SLE and rheumatoid arthritis ( RA ) [3] , [19] , suggesting the involvement of common pathways of pathogenesis among these two autoimmune diseases . STAT4-deficiency is associated with accelerated renal disease and increased mortality [20] in a murine lupus model , but with protective effects for arthritis in knockout mice [21] . Since SLE is an extremely heterogeneous disease , with multiple correlated subphenotypes , we sought to investigate whether or not STAT4 appears to contribute to this phenotypic heterogeneity in human SLE . We believe that our data provide compelling evidence that STAT4 is associated with more severe SLE manifestations , particularly with nephritis and with the production of autoantibodies to double-stranded DNA . In contrast , other recently-discovered SLE risk polymorphisms do not appear to be strongly associated with severe disease manifestations [10] . There have been recent successes in the study of genotype-phenotype associations in SLE and other autoimmune diseases . For example , PDCD1 has been shown to be associated with lupus nephritis and anti-phospholipid antibodies in ethnic subgroups [4] , and PTPN22 is primarily associated with anti-cyclic citrullinated peptide ( anti-CCP ) [22] and rheumatoid factor ( RF ) [23] autoantibody positive RA . The STAT4 gene has been shown to be associated with both anti-CCP positive and negative RA [3]; it has not yet been investigated in the context of SLE subphenotypes . Replication of genotype-phenotype associations can be challenging [24]; a strength of our study is the inclusion of four independent case series . Other strengths include the availability of two overlapping genotype sets in the STAT4 region for most of the subjects , including genome-wide data to facilitate ancestry analysis , and of course the availability of detailed phenotype data on all four of the case series . A limitation of our study is that the subjects are of self-reported European ancestry and primarily female . It could be insightful to look at these associations in other populations , particularly since SLE has higher prevalence among African-Americans and other non-European populations [2] . The STAT4 gene has recently been shown to be associated with RA in a Korean population [19]; however significant associations with subphenotypes – namely age at onset , radiographic progression , and serologic status – were not found . Another limitation is the inherent difficulty in obtaining accurate phenotype data . Differences between our 4 SLE cohorts may be true differences in patient characteristics , perhaps as a result of differences in selection , but could also be influenced by different methods of assessment and accuracy of individual records . However , although some of the phenotypes we examined are related to disease activity , and may fluctuate naturally or as a result of treatment , we classified SLE patients according to a history of these specific phenotypes . We are encouraged by the fact that our results were quite homogeneous across the different cohorts . Also , any misclassification would presumably be non-differential with respect to genotypes , thus diluting our results rather than causing type I error . Finally , it is important in genetic studies to protect against false associations due to undetected population substructure . Indeed there were some subjects in our cohort with sizeable non-European ancestry , in spite of being self-reported European , and those had a higher minor allele frequency for rs7574865 . However , reanalysis of a more homogeneous subset of subjects of primarily northern European ancestry was very consistent with our overall results . There is even stronger evidence in this subset for relationships between the STAT4 rs7574865 SNP and nephritis subphenotypes , and for an inverse relationship with oral ulcers . Since the subphenotypes having the strongest risk conferred by rs7574865 were highly correlated , we included clinical variables based on principal components ( PC ) analysis to investigate the possibility of common underlying effects . The first PC , associated with the severe manifestations of anti-dsDNA antibodies , nephritis and immunologic abnormalities , had similar associations as those of its components . Severe nephritis was consistently the most strongly associated subphenotype . The second PC , associated with the milder skin disease manifestations of malar rash , photosensitivity , and discoid rash , was not significantly associated with rs7574865 in any analysis . In summary , our study has identified multiple correlated subphenotypes that are strongly associated with the STAT4 gene , including nephritis , autoantibodies to double-stranded DNA , and early age at diagnosis . The next challenge is identifying how these correlated features fit into causal pathways , and therefore to help elucidate the complex etiology of SLE . | Systemic lupus erythematosus is a chronic disabling autoimmune disease , most commonly striking women in their thirties or forties . It can cause a wide variety of clinical manifestations , including kidney disease , arthritis , and skin disorders . Prognosis varies greatly depending on these clinical features , with kidney disease and related characteristics leading to greater morbidity and mortality . It is also complex genetically; while lupus runs in families , genes increase one’s risk for lupus but do not fully determine the outcome . It is thought that the interactions of multiple genes and/or interactions between genes and environmental factors may cause lupus , but the causes and disease pathways of this very heterogeneous disease are not well understood . By examining relationships between subtypes of lupus and specific genes , we hope to better understand how lupus is triggered and by what biological pathways it progresses . We show in this work that the STAT4 gene , very recently identified as a lupus risk gene , predisposes specifically to severe manifestations of lupus , including kidney disease . | [
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] | 2008 | Specificity of the STAT4 Genetic Association for Severe Disease Manifestations of Systemic Lupus Erythematosus |
During transcription , most eukaryotic genes generate multiple alternative cleavage and polyadenylation ( APA ) sites , leading to the production of transcript isoforms with variable lengths in the 3’ untranslated region ( 3’UTR ) . In contrast to somatic cells , male germ cells , especially pachytene spermatocytes and round spermatids , express a distinct reservoir of mRNAs with shorter 3’UTRs that are essential for spermatogenesis and male fertility . However , the mechanisms underlying the enrichment of shorter 3’UTR transcripts in the developing male germ cells remain unknown . Here , we report that UPF2-mediated nonsense-mediated mRNA decay ( NMD ) plays an essential role in male germ cells by eliminating ubiquitous genes-derived , longer 3’UTR transcripts , and that this role is independent of its canonical role in degrading “premature termination codon” ( PTC ) -containing transcripts in somatic cell lineages . This report provides physiological evidence supporting a noncanonical role of the NMD pathway in achieving global 3’UTR shortening in the male germ cells during spermatogenesis .
Spermatogenesis is a complex cellular differentiation process through which male germline stem cells develop sequentially into spermatogonia , spermatocytes , spermatids , and eventually spermatozoa [1] . Both meiosis ( i . e . spermatocyte development ) and spermiogenesis ( i . e . spermatid differentiation into spermatozoa ) are unique to male germ cell development . In both processes , a large number of protein-coding genes are transcribed without immediate translation , a phenomenon that has been termed “uncoupling of transcription and translation” [2 , 3] . The delayed translation results from the cessation of transcription in step 9 spermatids due to the onset of chromatin condensation and elongation . For example , mRNAs for protamines ( Prm1 and Prm2 ) and transition proteins ( Tnp1 and Tnp2 ) are transcribed in late pachytene spermatocytes , but are not translated until ~one week later when spermatocytes have developed into elongating spermatids in mice [4 , 5] . These mRNA transcripts are sequestered in ribonucleoprotein particles ( RNPs ) , in which the mRNA transcripts are stabilized by RNA-binding proteins ( RBPs ) and small noncoding RNAs ( e . g . , miRNAs ) , and physically separated from the translational machinery . In elongating and elongated spermatids , these transcripts can translocate and get loaded onto the polyribosomes for translation when specific proteins are needed for sperm assembly [3 , 6] . In addition to delayed translation , the transcriptome of meiotic and haploid male germ cells ( i . e . , spermatocytes and spermatids ) is characterized by the enrichment of mRNA transcripts bearing shorter 3’UTRs , which is not shared by most of somatic cell types [6–9] . Given that transcription and translation are uncoupled , enhanced stability and translational efficiency are critical for accurate spatiotemporal expression of a large number of proteins required for sperm assembly during late spermiogenesis [3] . Transcripts with shorter 3’UTRs have been shown to be more stable and more efficient in translation due to the reduced binding sites for RBPs and miRNAs [10 , 11] , which may explain why a repertoire of shorter 3’UTR mRNAs is exclusively expressed during meiosis and spermiogenesis and they are essential for both processes [6–9] . Processing of the 3’ ends of mRNA transcripts is necessary for mRNA maturation and involves the cleavage at the polyadenylation site ( PAS ) by a nuclear endonuclease followed by the addition of a stretch of adenosines ( PolyA tail ) . Notably , the usage of alternative PAS sites and polyadenylation , termed as alternative cleavage and polyadenylation ( APA ) , is a common event in eukaryotic gene transcription , which leads to the generation of mRNA transcripts with variable 3’UTR lengths . In general , the upstream and downstream sequences flanking the PAS cleavage site in a pre-mRNA serve as the cis-elements , which are specifically recognized and bound by the core APA factors . The APA machinery consists of cleavage and polyadenylation specificity factor ( CPSF ) proteins , the cleavage stimulation factor ( CstF ) proteins , and cleavage factor I . Together with auxiliary and tissue-specific protein factors ( e . g . , Nova1 in neuron ) [12] , the APA complex generates temporal or tissue-specific mRNA transcriptomes enriched for mRNAs with different 3’UTR lengths . For example , recent high-throughput sequencing studies have identified that mRNAs with the longest 3’UTRs are predominately present in brain , whereas the testis tends to be enriched in mRNA isoforms with shorter 3’UTRs [13 , 14] . Interestingly , the differential usage of alternative PAS sites is widely observed under stress conditions [15] , in proliferating/cancer cells [16 , 17] , through early embryonic development [18] , and during induced somatic cell reprogramming [19] . Although the enrichment of shorter 3’UTR transcripts in the testis has been known for decades [20] , the underlying mechanism remains elusive [8] . The current dogma emphasizes the biased production of testis-specific transcripts with shorter 3’UTRs through testis-specific APA factors , which prefer the proximal to distal polyadenylation sites , thus achieving global 3’UTR shortening in the testis [6 , 8] . However , such factors remain yet-to-be-identified . Alternative splicing ( AS ) is a common form of post-transcriptional regulation observed in ~75%-90% of human protein-coding genes whereby one gene generates multiple isoforms of mRNA transcripts with variable stability and translational efficiency as well as distinct protein-coding potential [21] . Concomitantly , it has been estimated that one third of the AS events also create aberrant transcript isoforms that would trigger nonsense-mediated mRNA decay ( NMD ) [22] . The NMD pathway is highly conserved across all eukaryotes , and serves as a critical cellular surveillance mechanism by eliminating aberrant mRNA transcripts harboring the so-called “premature termination codon” ( PTC ) , which generally resides >50nt upstream of the last exon-exon junction ( i . e . , “the 50nt rule” ) [23–25] . In mammalian somatic cells , the core NMD machinery includes three trans-acting factors: UPF1 , UPF2 and UPF3 , in addition to SMG1-7 [23 , 24] . UPF2 is considered as a molecular linker that bridges the interaction between UPF3 , which is bound to the exon-exon junction complex ( EJC ) , and UPF1-containing complex ( SURF ) recruited to the stalled ribosome , constituting the core NMD complex that subsequently stimulates phosphorylation of UPF1 to induce decay activity [26] . Supporting its well-established role in eliminating PTC-containing mRNA transcripts during translation [23 , 24] , earlier in vitro studies using cell lines deficient in NMD activity have reported a conspicuous upregulation of a substantial proportion ( up to 60% ) of PTC-positive mRNA transcripts [27–30] . Our in vivo study using conditional Upf2 knockout mice also demonstrates a global upregulation of ~one third of PTC-positive transcripts in liver and bone marrow [31] . Classical NMD substrates include those transcripts bearing PTC that resides >50 nucleotide upstream of the final exon-exon junction complex ( EJC ) [25] . During translation , the ribosomes stall in the PTC , resulting in the failure to remove the downstream EJC complex , which , in turn , promotes NMD-mediated degradation of these PTC-positive transcripts [24 , 26] . In addition to the classical EJC-dependent NMD , more recent genome-wide studies identified that NMD not only degrades mRNA substrates harboring PTCs , but also regulates a selection of normal mRNA transcripts encoding full-length proteins devoid of PTCs through an EJC-independent NMD mechanism [27 , 31–33] . These studies significantly expand the scope of NMD target repertoire , and strongly suggest a critical , physiological role of the NMD pathway in regulating the transcriptomic homeostasis in addition to its canonical roles in eliminating the PTC-positive transcripts [27 , 32 , 33] . One such EJC-independent , NMD-triggering feature identified is the 3’UTR length . In vitro cell lines-based studies have demonstrated that transcripts with aberrant 3’UTR architecture are more susceptible to NMD [32 , 34 , 35] . However , physiological evidence from loss- or gain-of-function studies in vivo to support this notion still remains missing . We were intrigued to explore whether the NMD pathway plays an essential role in male germ cells by inactivating UPF2 , a core NMD factor , specifically in the male germline . Surprisingly , we observed a weak , canonical role of the NMD pathway in degrading the PTC-positive transcripts , but a significant , noncanonical role in selective degradation of mRNA isoforms bearing longer 3’UTRs that are often derived from ubiquitously expressed genes . Our data provide physiological evidence supporting that the 3’UTR-shortened , testis-specific transcriptome is established through , at least in part , eliminating longer 3’UTR transcripts derived from ubiquitously expressed genes by the UPF2-mediated NMD .
To study the testicular function of UPF2 , we first examined its expression and localization in developing and adult testes . Among multiple adult organs examined , UPF2 protein was preferentially expressed in testes ( S1 Fig ) . Upf2 mRNAs became detectable in fetal testes and the levels increased gradually with the postnatal testicular development ( S1B Fig ) . In adult testes , mRNAs for Upf2 and other seven well-known nonsense-mediated decay ( NMD ) factors , including Upf1 , Upf3a , Upf3b , Smg1 , Smg5 , Smg6 and Smg7 , were all predominantly detected in spermatocytes and spermatids ( Figs 1A and S1 ) . Immunofluorescent staining with a well-characterized UPF2 antibody [36] revealed that UPF2 protein was mainly localized to the cytoplasm of spermatocytes and spermatids ( Fig 1B ) . Interestingly , UPF2 became highly concentrated to a perinuclear structure resembling the “chromatoid body” ( CB ) in round spermatids ( Fig 1B ) . The CB is a highly conserved , cloud-like perinuclear structure that moves around the nuclear pores in the cytoplasm of round spermatids , and has been suggested to serve as a RNA processing center essential for spermatogenesis [37 , 38] . To further explore if UPF2 is a CB component , we performed co-immunostaining for both UPF2 and MAEL , a CB marker [39] on adult testicular cryosections . The majority ( >90% ) of the UPF2-positive “dots” co-localized with the MAEL-positive foci in round spermatids ( Fig 1C ) . Consistently , most ( >90% ) of the UPF2-positive “dots” also overlapped with the signals of DDX25 , another well-characterized CB marker , in round spermatids ( Fig 1D ) . Together , these data suggest that UPF2 , as a novel integral component of the CB , may play an important role in male germ cells , especially in spermatocytes and spermatids , by regulating RNA processing . To define the physiological role of UPF2 in male germline development , we first generated prospermatogonia-specific Upf2 conditional knockout mice ( Ddx4-Cre; Upf2fl/Δ , hereafter called Ddx4-KO ) by crossing Ddx4-Cre [40] with Upf2 floxed ( Upf2fl/fl ) mice [41] ( Fig 2A ) . The Cre activity first becomes detectable exclusively in primordial germ cells on embryonic day 15 . 5 ( E15 . 5 ) in Ddx4-Cre mice [40] and thus , the floxed Upf2 allele is expected to be deleted in prospermatogonia and all subsequent male germ cell types ( S2 Fig ) . All adult Ddx4-KO males were infertile and exhibited a drastic reduction in testis size compared to age-matched WT controls ( Fig 2B ) . Marked testicular atrophy was observed during postnatal development in Ddx4-KO males ( Fig 2C ) . Consistently , histological examination revealed that adult Ddx4-KO seminiferous tubules contained no or few spermatogenic cells , but numerous vacuoles , indicative of massive germ cell depletion ( Fig 2D ) . Discernable histological differences between Ddx4-KO and WT testes were observed at as early as postnatal day 10 ( P10 ) ( Fig 3A ) . However , the Ddx4-KO males already displayed a reduced total number of germ cells at P3 ( Fig 3B and 3C ) , as revealed by immunostaining using a germ cell-specific protein marker SOHLH1 [42 , 43] . By P10 , co-immunostaining for both WT1 ( a Sertoli cell-specific marker ) and GCNA ( a germ cell-specific marker ) [44] revealed that very few germ cells remained in the Ddx4-KO seminiferous tubules ( S3 Fig ) , indicating that Upf2-null prospermatogonia/spermatogonia were rapidly depleted during neonatal testicular development in Ddx4-KO testes . Seminiferous tubules in adult Ddx4-KO testes contained mostly Sertoli cells , resembling the “Sertoli-only syndrome” in men [45] . Taken together , these data demonstrate that Upf2 is required for prospermatogonial development . Predominant expression of UPF2 in spermatocytes and round spermatids in adult testes implicates a critical role of UPF2 in the meiotic and haploid phases of spermatogenesis . To define this role , we generated the Stra8-Cre; Upf2fl/Δ ( hereafter called Stra8-KO ) mice line , in which Upf2 was specifically deleted in meiotic and haploid male germ cells [46 , 47] ( Figs 2A and S2 ) . All adult Stra8-KO males were infertile and exhibited a significant reduction ( ~60% ) in testis weight compared to WT controls ( Fig 4A and 4B ) . Histological analyses revealed that zygotene spermatocytes were present in both WT and Stra8-KO seminiferous tubules at P12 . Starting from P14 , multiple defects , including delayed meiotic entry and massive depletion of spermatocytes and spermatids , were observed in Stra8-KO developing testes ( S4 Fig ) . In adult Stra8-KO testes , numerous vacuoles and multinucleated giant cells were present in the seminiferous epithelia ( Figs 4C–4F and S4 ) , indicative of massive depletion of spermatocytes and round spermatids . Consequently , no sperm were present in the cauda epididymis in Stra8-KO males ( Fig 4C ) . These data suggest that Upf2 is essential for not only the first wave of spermatogenesis during testicular development , but also the subsequent spermatogenic cycles in adult testes . The well-known canonical function of the NMD machinery is to eliminate PTC-containing transcripts , which are often derived from aberrant alternative splicing of pre-mRNAs [7 , 48] . Indeed , we have previously demonstrated that selective inactivation of Upf2 leads to the upregulation of ~one third of the PTC-positive transcripts in liver and bone marrow [31] . Given the pronounced alternative splicing activities in developing male germ cells , especially in spermatocytes and round spermatids , we hypothesized that Upf2 inactivation would lead to an accumulation of , alternatively spliced PTC-bearing transcripts , which would be deleterious to spermatogenesis . To test this hypothesis , we performed RNA-Seq analyses using WT and Stra8-KO total testes in biological triplicates at the age of 6 weeks , a time point when the first spermatogenic cycle was fully completed . Full-length transcripts were re-constructed based on the paired-end RNA-Seq data using Cufflinks [49] . The full-length transcripts were then analyzed for PTC using the R package spliceR [50] , which annotates transcripts as PTC-positive , if a stop codon is found >50nt upstream of the last exon-exon junction . Surprisingly , we found that of the 1 , 971 up-regulated transcripts identified in Stra8-KO testes ( FDR <0 . 05 ) , only 137 ( ~7% ) contains a PTC ( Fig 5A and 5B ) . This is far less than the >30% previously found in somatic Upf2-null cells [27 , 31] . As mentioned earlier , active depletion of Upf2-deficient spermatocytes and spermatids were observed during the first wave of spermatogenesis ( Figs 3A and S4 ) . To exclude the possibility that the disproportional cell constituents due to germ cell depletion in Stra8-KO total testes may have masked the upregulated PTC-positive transcripts , we further conducted RNA-Seq analyses using spermatocytes and round spermatids purified and pooled from WT and Stra8-KO total testes ( see methods ) . qPCR analyses further confirmed the absence of Upf2 mRNAs in Upf2-null spermatocytes and spermatids compared to WT controls ( S1 Fig ) . Using spliceR , we analyzed the RNA-Seq data as described above and found no global upregulation of PTC-positive transcripts in either purified Upf2-null spermatocytes or in round spermatids ( Fig 5C ) . Given that the canonical function of NMD is to degrade PTC-positive transcripts , these data do not support a role for UPF2-dependent NMD in scavenging a PTC-positive transcripts in germ cells . Instead , UPF2 appears to function to maintain the transcriptomic fidelity based on the large number of de-regulated transcripts upon Upf2 ablation ( Fig 5A ) . Together , these data , although unexpected , strongly suggest that the UPF2-mediated NMD pathway does not function to eliminate PTC-positive transcripts in germ cells , but is indeed required for maintaining transcriptomic fidelity during male germ cell development . The lack of global PTC upregulation in Upf2-null germ cells contradicts the established canonical function of the NMD pathway in degrading aberrant PTC-containing transcripts [23 , 24] . However , the severe spermatogenic disruptions in the absence of UPF2 clearly suggest that UPF2 plays an essential role independent of elimination of PTC-positive transcripts in male germ cells . Many defects , e . g . , aberrant transcription , failure in exportation from the nucleus , incorrect splicing and/or alternatively polyadenylated , etc . , all can cause the transcriptomic changes observed in Upf2-null testes and male germ cells . To gain mechanistic insight into spermatogenic disruption upon Upf2 ablation , we performed further in-depth bioinformatics analyses by comparing the features of the full-length transcripts reconstructed from the RNA-Seq data . In the total testis , we found that the differentially upregulated transcripts ( FDR < 0 . 05 ) , derived from multi-isoform-expressing genes , displayed a median 3’UTR length of 1 , 446nt , which was significantly longer than both the non- ( 562 nt ) , or down-regulated ( 317 nt ) transcripts ( Fig 5D–5F ) . The differences in 3’UTR length were much greater than those in 5’UTR or ORF lengths ( Fig 5D–5F ) , suggesting that the transcripts with longer 3’UTRs are selectively accumulated in the Upf2-deficient testes . As described earlier , the Stra8-KO testes contain much fewer spermatocytes and spermatids due to active depletion ( Figs 3A and S4 ) . Therefore , to further verify this finding , we performed similar analyses using RNA-Seq data from purified spermatocytes and round spermatids . Specifically , we discovered that >2 , 500 transcripts from multi-isoform-expressing genes with predicted ORFs were primarily expressed in Stra8-KO spermatocytes and round spermatids , suggesting a profound effect on gene expression upon Upf2 inactivation ( Fig 6A , S1 Table ) . Here , “primarily expressed” transcripts are defined as those expressed above 1 normFPKM in one genotype and below 1 normFPKM in the other genotype ( see Methods and Materials ) . Consistent with our total testis analyses , the transcripts primarily expressed in purified Upf2-null spermatocytes and round spermatids also displayed significantly longer 3’UTRs compared to those expressed in control cells ( mean difference >220 nt , p-value < 5 . 11E-37 , Wilcoxon rank test ) ( Fig 6B and 6C ) . To exclude the possibility that upregulation of transcripts with longer 3´UTRs merely reflects a general increase in expression of their parent genes , we further analyzed the fraction by which these transcripts contributed to the expression of their corresponding parent genes . Strikingly , in both total testis and purified spermatocytes and spermatids samples , the percentages by which the upregulated isoforms contributed to the expression of their parent genes were upregulated in the Upf2-deficient conditions ( mean percentage increase >9 . 6% , p-value < 5 . 46e-10 ) , thereby confirming the selective upregulation of these transcripts ( Figs 6D , 6E and S5 ) . Moreover , analyses of changes in average weighted 3’UTR length ( weighted by the relative contribution of each isoform to the expression of its parent gene ) further support this conclusion . Specifically , we find that genes containing isoforms with increased ( > 20% ) contribution to the expression of their parent genes have significantly longer average weighted 3’UTRs in both total and purified testis ( median increase > 120nt , P < 4 . 86e-79 , Mann-Whitney-U test ) ( S5 Fig ) . Similarly , but less pronounced , genes containing isoforms with decreased ( > 20% ) contribution to the expression of their parent have significantly shorter average weighted 3’UTRs in both total and purified testis ( median decrease > 73nt , P < 8 . 18e-48 , Mann-Whitney-U test ) ( S5 Fig ) . Finally , these findings could also be validated by semi-quantitative PCR analyses for selected genes ( Fig 6F and 6G ) . The combined bioinformatics analyses of the RNA-Seq datasets from both the total testis ( Fig 5D–5F ) and the purified spermatogenic cell types ( Fig 6B–6G ) , clearly demonstrate that a group of mRNAs with significantly longer 3’UTRs accumulates in the absence of UPF2 . Furthermore we note that this effect is most pronounced for midrange-expressed genes ( 5–50 FPKM/normFPKM ) ( S5 Fig ) , indicating that the effect is not caused by induction of transcription of genes/transcripts with relatively long 3'UTRs , but rather regulation of the relative stability of transcripts with longer 3’UTR’s . This finding is consistent with the data in several recent reports , in which in vitro reporter and cross-link immunoprecipitation ( CLIP ) assays demonstrated that UPF1 , another core NMD factor , can bind the 3’UTRs , and selectively cause degradation of the mRNA transcripts with longer 3’UTRs via the NMD pathway [32–35] . Taken together , these data suggest that UPF2 can selectively eliminate alternative transcripts with longer 3’UTRs , which might contribute to a transcriptome enriched in transcripts with shorter 3’UTRs in late pachytene spermatocytes and round spermatids during spermatogenesis . It has been well documented that the testis is enriched in transcripts with shorter 3’UTRs , and this transcriptomic feature is essential for spermatogenesis and male fertility [6–9 , 14] . At the transcriptional level , the germ cell-specific APA machinery , including testis-specific CstF64 , is believed to specifically drives the production of shorter 3’UTR transcripts for numerous , well-known testis-specific genes ( e . g . , Tnp1 , Tnp2 , Prm1 and Prm2 ) [5 , 8 , 9] . Although ubiquitously expressed , somatic genes can generate multiple transcripts with variable 3’UTR lengths in the testes , only the alternatively spliced transcripts with shorter 3’UTRs tend to be more stably expressed in the testis [5 , 20 , 51 , 52] , suggesting that the transcripts possessing longer 3’UTRs may have been eliminated through an as-yet-unknown mechanism . To test whether those accumulated transcripts with longer 3’UTRs in the absence of UPF2 are derived from ubiquitously expressed genes , we further conducted gene ontology ( GO ) analyses on de-regulated transcripts in both total testes and purified germ cell populations of Stra8-KO and control males . We discovered a significant enrichment in spermatogenesis-related genes among the downregulated transcripts ( Fig 7A ) , which most likely resulted from decreased proportions of more advanced male germ cells ( i . e . spermatocytes and spermatids ) due to active depletion and/or disrupted testis-specific gene expression ( Figs 4C and S4 ) . In contrast , the upregulated transcripts in Stra8-KO testes ( Fig 7B ) , or isoforms primarily expressed in Upf2-null germ cells ( Fig 7C and 7D ) were involved in a variety of biological processes that were not directly related to germ cell development . This suggests that longer 3’UTR transcripts derived from ubiquitously expressed genes selectively accumulated in Upf2-deficient germ cells . Detailed examination revealed that in Stra8-KO testes or purified Upf2-null germ cells , testis-specific genes , e . g . , Tnp1 , Tnp2 , Prm1 , and Prm2 , expressed the same number of isoforms as those in WT controls , which is usually one or a few ( S2 Table , highlighted in yellow ) , whereas ubiquitous genes produced many more isoforms , among which the ones with longer 3’UTRs were significantly up-regulated ( Fig 6 and S2 Table ) . Taken together , these data support the production of testis-specific transcripts by germ cell-specific APA factors , and are also consistent with the notion that UPF2 selectively degrades longer 3’UTR transcripts derived from ubiquitously expressed genes in male germ cells during spermatogenesis . These two events may both contribute to the establishment of a repertoire of shorter 3’UTR transcripts in the developing male germ cells in the testis .
3’UTRs contain conserved binding sites for both miRNAs and RNA-binding proteins [10 , 11 , 16] . Transcripts with longer 3’UTRs tend to have more such binding sites , and thus , are subject to more comprehensive post-transcriptional regulation . In contrast , transcripts with shorter 3’UTRs could be more stable and more efficient in translation [10 , 17] . Interestingly , it has been documented that more than half of the mammalian mRNA genes utilize the APA machinery to generate multiple transcripts with variable 3’UTR lengths , thereby altering their post-transcriptional fates , including mRNA stability , transportation and translational efficiency [7] . Increasing lines of evidence also suggest that the 3’UTR length control serves as a critical mechanism through which the cells and organs establish and maintain their transcriptome identity and functional status . For example , highly proliferative or cancerous cells tend to have a transcriptome enriched in transcript isoforms bearing shorter 3’UTRs , which is believed to enhance transcript stability and translational efficiency [16 , 17] . In contrast , neuronal cells express abundant long 3’UTR transcripts , which allow for higher-order regulation by small RNAs and RNA-binding proteins [18 , 19 , 21 , 22 , 25] . Unlike the neuronal cells , developing male germ cells , especially spermatocytes and round spermatids , exhibit a transcriptome enriched in short 3’UTR transcripts , which is essential for normal male germ cell development and male fertility [6–9] . The necessity of 3’UTR length control for spermiogenesis ( the haploid phase of spermatogenesis ) is likely due to the fact that proteins required for late stages of sperm assembly ( e . g . chromatin condensation/elongation , and flagellogenesis ) mostly need to be translated using preexisting transcripts that are synthesized and stored in late pachytene spermatocytes and round spermatids , and these proteins need to be translated in a highly efficient manner to meet the tightly regulated timeline for sperm assembly [51 , 53] . Our transcriptome-wide analyses reveal that while germ cell-specific genes constantly produce shorter 3’UTR transcripts in either WT or Upf2-null germ cells , a large number of longer 3’UTR isoform transcripts derived mainly from ubiquitously expressed genes are selectively accumulated in Upf2-null germ cells . This finding strongly suggests that UPF2-mediated degradation of longer 3’UTR transcripts derived from ubiquitously expressed genes , together with testis-specific gene-derived shorter 3’UTR transcripts , both contribute to the characteristic , shorter 3’UTR transcriptomic repertoire in murine testes . In somatic cells , ablation of UPF2 causes an accumulation of PTC-containing transcripts [27 , 31 , 54] . However , in male germ cells , UPF2 ablation does not lead to an apparent accumulation of PTC-containing transcripts . Previous reports [55 , 56] have suggested that the testicular PTC-containing transcripts , as byproducts of the highly active alternative splicing events in the developing male germ cells , must be eliminated efficiently . However , based on our data , this function must be mediated through a UPF2-independent NMD degradation pathway , which remains elusive and needs to be elucidated in the future . The novel role of UPF2 in eliminating longer 3’UTR transcripts derived from ubiquitously expressed genes in the male germ cells is different from its canonical NMD role in degrading PTC-containing transcripts . Consistent with our discovery , a recent study utilizing 3’UTR mRNA reporter coupled with high-throughput sequencing assays has demonstrated that decay of transcripts with longer 3´UTRs requires UPF2 in Hela cells [57] . Nevertheless , a key question remains: how does the UPF2-dependent NMD eliminate transcripts with longer 3’UTRs in the male germ cells ? Several recent studies have demonstrated that UPF1 accumulates at 3’UTRs of full-length mRNA transcripts during the pioneer round of translation because UPF1 bound to other positions is gradually displaced by the termination ribosomes during translation [33–35 , 58] . Consequently , transcripts with longer 3’UTRs tend to accommodate more UPF1-containing NMD complexes compared to the shorter 3’UTR transcripts , which can antagonize the stabilizing effects of poly ( A ) -binding proteins ( e . g . PABPC1 ) , leading to enhanced degradation of mRNA transcripts with longer 3’UTRs via the NMD pathway [32–35] . Because of the unavailability of a cell culture system for either meiotic or haploid male germ cells , one cannot directly recapitulate the above-discussed findings in vitro . However , numerous RNA-binding proteins , including PABPC1 , PABPC2 and ELAVL1/HuR are known to be highly expressed in developing male germ cells , especially in spermatocytes and spermatids [59 , 60] , and they regulate mRNA stability and translational efficiency by binding the 3’UTRs [61–63] . Moreover , major NMD factors ( e . g . UPF1 , UPF2 and UPF3 ) all exhibit abundant expression in both meiotic and haploid germ cells ( S1 Fig ) . Thus , it is conceivable that the UPF2-dependent NMD machinery can operate similarly to cause degradation of longer 3’UTR transcripts in developing male germ cells during spermatogenesis . Intriguingly , we observed that UPF2 is also required for spermatogonial development despite its relatively low expression levels . However , it is likely that UPF2 function through the canonical NMD pathway given that the characteristic shorter 3’UTR transcriptome has not yet been formed in spermatogonial populations . Overall , our major findings include the following: i ) UPF2 is specifically restricted to the RNA-processing center , the chromatoid body; ii ) unlike in somatic cells , conditional ablation of Upf2 does not upregulate PTC-positive transcripts in germ cells; iii ) thousands of longer 3’UTR transcripts , were aberrantly accumulated in the Upf2-null spermatocytes and round spermatids . Based on these findings , we propose a working model for the UPF2-mediated NMD machinery in the 3’UTR length control in male germ cells . In this model , a yet-to-be-identified testis-specific APA machinery ( as suggested in refs . [8 , 9] ) produces shorter 3’UTR transcripts from testis-specific genes , while the UPF2-mediated NMD machinery selectively eliminate transcript isoforms bearing longer 3’UTRs . These transcripts are mostly alternative isoforms of ubiquitously expressed genes and are decayed in the cytoplasmic RNA-processing center , the chromatoid body . The combined actions of these processes thereby shape the male germ cell-specific , shorter 3’UTR transcripts-enriched transcriptome in the testis ( Fig 7E ) . These activities also support that CB is a critical RNA-processing center in haploid male germ cells , which is essential for spermatogenesis [37] . In summary , we have discovered that UPF2 is a new component of the chromatoid body , in which UPF2-mediated scavenging of longer 3’UTR transcripts derived from ubiquitously expressed genes is essential for spermatogenesis and male fertility . This mechanism may be utilized by other cell lineages as well in shaping cell/tissue-specific transcriptomic identity during development , adult physiology and pathophysiology .
Animal protocol for using mice ( Protocol number 00494 ) was approved by Institutional Animal Care and Use Committee ( IACUC ) of the University of Nevada , Reno and are in accordance with the “Guide for the Care and Use of Experimental Animals” established by National Institutes of Health ( NIH ) ( 1996 , revised 2011 ) . The Upf2 loxp mouse line was generated as described [31 , 41] . The Stra8-Cre deletor line was purchased from the Jackson laboratory and backcrossed for 5 generations to the C57BL/6J background . Prospermatogonia-specific ( Ddx4-KO ) and spermatocytes/spermatids-specific ( Stra8-KO ) Upf2 conditional knockout mice were generated by crossing Upf2fl/fl mice with Ddx4-Cre and Stra8-Cre mice , respectively ( S2 Fig ) . Genotyping was performed using tail PCR analyses as described [31 , 41] . Sertoli cells were purified using fluorescence-activated cell sorting ( FACS ) from transgenic mice ( Amh-Cre; mTmG+/tg ) in which membrane-tagged eGFP ( mG ) is specifically expressed in Sertoli cells . Amh-Cre; mTmG+/tg mice were generated by crossing a Sertoli cell-specific Cre ( Amh-Cre ) line [64] with a dual fluorescence reporter line ( Rosa26-mTmGtg/tg ) [65] . Leydig cells were purified using FACS from Cyp17-iCre; mTmG+/tg mice generated by crossing a Leydig cell-specific Cre ( Cyp17-iCre ) deletor line [66] with a dual fluorescence reporter line ( Rosa26-mTmGtg/tg ) . The purities of both Sertoli and Leydig cells were >95% based on microscopic evaluation of the numbers of mG-positive vs . total cell . Spermatogonia were purified from P7 WT mouse testes , and spermatocytes and round spermatids were purified from adult WT and Stra8-KO mice testes using the STA-PUT method as described [67 , 68] . The purities of spermatogonia , spermatocytes and spermatids were all >90% on the basis of microscopic examination and qPCR analyses of marker genes [67 , 68] . Hematoxylin-Eosin ( HE ) staining of paraffin sections of the testes was performed as described [69] . RNA was isolated using a RNA MiniPrep kit ( Direct-zol , Zymo , Cat#R2050 ) following the manufacturer’s protocol . All RNA samples were treated by DNase I ( Ambion , DNA-free Kit , Cat#AM1906 ) before reverse transcription and semi-quantitative or real-time quantitative PCR ( qPCR ) as described [69] . Sequences of PCR primers used are listed in S3 Table . The following antibodies were used in this study: anti-hUPF2 ( Rabbit , a kind gift from Dr . Jens Lykke-Andersen . IF: 1:500 dilution; WB: 1:1000 dilution ) [70] , anti-MAEL ( Guinea pig , a kind gift from Dr . Sadaki Yokota . IF: 1:200 dilution ) [39] , anti-DDX25 ( Rat , a kind gift from Dr . Sadaki Yokota . IF: 1:500 dilution ) [39] , anti-SOHLH1 ( Rabbit , a kind gift from Dr . Aleksandar Rajkovic . IF: 1:200 dilution ) [71] , anti-GCNA ( Rat , a kind gift from Dr . George Enders . IF: 1:10 dilution ) [44] , anti-active CASPASE 3 antibody ( Rabbit , Abcam , ab13847 . IF: 1:500 dilution ) , anti-hUPF2/RENT2 antibody ( Rabbit , Abcam , ab153830 . IF: 1:500 dilution ) , and anti-WT1 ( Rabbit , Santa Cruz , sc-192 . IF: 1:50 dilution ) . Western blot analyses were conducted as described previously [72] . Immunofluorescent staining of testicular cryosections was performed as described [73] . Total RNA was isolated using the Trizol reagent ( Invitrogen; Cat#15596–018 ) from whole WT and Stra8-KO ( Stra8-Cre;Upf2fl/Δ ) testes at the age of 6 weeks in biological triplicates , followed by DNase I treatment and an additional purification using the RNeasy Mini Kit ( Qiagen , Cat#74104 ) . RNA integrity and quantity were determined using the Agilent 2100 Bioanalyzer . Total RNA ( 2μg ) was used to generate sequencing libraries using the TruSeq RNA sample prep kit-v2 ( Illumina , Cat#15027387 ) according to the manufacturer’s instructions , with a size selection between 350bp and 500bp and a PCR cycle number at 10 . Barcoded libraries were pooled and sequenced using an Illumina HiSeq2000 sequencer ( 100bp paired-end reads ) . A summary of sequence reads from the RNA-Seq analyses was listed in S4 Table . Total RNA was isolated from spermatocytes and round spermatids purified from a pool of 8 WT and 12 Stra8-KO ( Stra8-Cre;Upf2fl/Δ ) testes in duplicates at the age of 6 weeks using a Direct-zol RNA MiniPrep kit ( Zymo , # R2050 ) with on-column DNase I treatment . RNA quality and quantity were assessed using the Agilent 2100 Bioanalyzer . Total RNA ( 1 . 5μg ) was used to prepare the RNA-Seq libraries , which were then sequenced on an Illumina HiSeq2000 sequencer , as described above . A summary of sequence reads from the RNA-Seq analyses was listed in S5 Table . Raw sequences were checked for quality using the FASTQC tool ( http://www . bioinformatics . bbsrc . ac . uk/projects/fastqc/ ) . Ends were trimmed with fastx_trimmer ( purified cell populations: f = 10 , l = 78; Total testis: wt: f = 11 , Stra8-KO: f = 12 ) and then the fastq_quality_trimmer was used with parameter t = 30 . The resulting trimmed sequences were mapped with Tophat v . 2 . 0 . 9 [74] ( Default settings plus —b2-very-sensitive , -r 200 and—mate-std-dev to 100 . ) [74] , using Ensembl NCBIM37 ( Hg19 ) as reference transcriptome ( provided through Illumnia’s iGenome ) . Mapped RNA-Seq data were assembled with Cufflinks v . 2 . 1 . 1 [49] ( default settings plus—frag-bias-correct , —max-bundle-length 1e7 , and—multi-read-correct . ) [49] using Ensembl NCBIM37 , as well as a mask GTF-file containing noncoding and other auxiliary RNA species ( Ensembl NCBI37 rRNA , misc_RNA , scRNA_pseudogene , snoRNA , snRNA , miRNA , TR_C_gene , tRNA , and mitochondrial RNA ) . For the total testis data a FDR< 0 . 05 was required for calling differential expression between WT and KO for genes and transcripts . No differential expression analysis was made on the purified spermatocytes and spermatids RNA-seq data since replicates were not available . The resulting full length transcripts were annotated with coding potential and classes of alternative splicing using the Bioconductor package spliceR with default settings as described elsewhere [50] . Briefly , spliceR annotated transcripts with the most upstream compatible Ensemble coding sequence ( CDS ) , translate the downstream open reading frame ( ORF ) and output transcript features , including positions and lengths of ORF , 5’ untranslated region ( UTR ) , and 3’UTR lengths . To account for normalization problems in the RNA-Seq libraries of purified spermatocytes and spermatids , the isoform data was quantile normalized using the normalize . quantiles ( ) function available in preprocessCore package ( v . 1 . 26 . 1 ) of R ( v . 3 . 1 . 0 ) . Here we refer to the units of the resulting values as normFPKM . All transcripts belonging to the same genes were then summed to get the gene expression levels . The fraction of gene expression originating from a transcript was calculated as ( transcript expression ) / ( gene expression ) . Genes having a FPKM/normFPKM below 1 in either WT or KO samples were filtered out to ensure reliability of the fractions calculated . Analyses of isoform fractions and length distributions were conducted using the subset of genes with 2 or more expressed isoforms ( cutoff 1 FPKM/normFPKM ) . The average weighted 3’UTR length for a gene G , with expression eG , which have n isoforms , expressed at levels e1…ei…en and with corresponding 3’UTR lengths l1…li…ln , was calculated as follows: average weighted 3’UTR length=∑i=1nli*eieGn Where ei/eG corresponds to the fraction of gene expression originating from transcript i . Statistical analyses were performed using statistical software R v . 3 . 0 . 1 . , as indicated in figures/legends . Gene Ontology ( GO ) enrichment analysis was performed using DAVID ( v6 . 7 ) online programs [75] with the default settings . Data sets have been submitted to gene expression omnibus ( GEO ) under the accession number GSE55180 . | 3’UTR length control has been identified as a critical mechanism through which the cell establishes and maintains its functional identity . Developing male germ cells , especially spermatocytes and spermatids , display a transcriptome enriched in short 3’UTR transcripts , which has been demonstrated to be essential for spermatogenesis . However , it remains unknown how global 3’UTR shortening is achieved . Here , we report that most of the genes , especially those ubiquitously expressed , are transcribed into multiple isoforms in spermatocytes , and when spermatocytes develop into round spermatids , those long 3’UTR transcripts are selectively degraded by UPF2-dependent nonsense-mediated mRNA decay ( NMD ) , leading to enrichment of the shorter 3’UTR transcripts . We provide physiological evidence supporting a non-canonical role of NMD in the control of 3’UTR length in male germ cells . | [
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] | 2016 | UPF2-Dependent Nonsense-Mediated mRNA Decay Pathway Is Essential for Spermatogenesis by Selectively Eliminating Longer 3'UTR Transcripts |
The evolution of metazoans from their choanoflagellate-like unicellular ancestor coincided with the acquisition of novel biological functions to support a multicellular lifestyle , and eventually , the unique cellular and physiological demands of differentiated cell types such as those forming the nervous , muscle and immune systems . In an effort to understand the molecular underpinnings of such metazoan innovations , we carried out a comparative genomics analysis for genes found exclusively in , and widely conserved across , metazoans . Using this approach , we identified a set of 526 core metazoan-specific genes ( the ‘metazoanome’ ) , approximately 10% of which are largely uncharacterized , 16% of which are associated with known human disease , and 66% of which are conserved in Trichoplax adhaerens , a basal metazoan lacking neurons and other specialized cell types . Global analyses of previously-characterized core metazoan genes suggest a prevalent property , namely that they act as partially redundant modifiers of ancient eukaryotic pathways . Our data also highlights the importance of exaptation of pre-existing genetic tools during metazoan evolution . Expression studies in C . elegans revealed that many metazoan-specific genes , including tubulin folding cofactor E-like ( TBCEL/coel-1 ) , are expressed in neurons . We used C . elegans COEL-1 as a representative to experimentally validate the metazoan-specific character of our dataset . We show that coel-1 disruption results in developmental hypersensitivity to the microtubule drug paclitaxel/taxol , and that overexpression of coel-1 has broad effects during embryonic development and perturbs specialized microtubules in the touch receptor neurons ( TRNs ) . In addition , coel-1 influences the migration , neurite outgrowth and mechanosensory function of the TRNs , and functionally interacts with components of the tubulin acetylation/deacetylation pathway . Together , our findings unveil a conserved molecular toolbox fundamental to metazoan biology that contains a number of neuronally expressed and disease-related genes , and reveal a key role for TBCEL/coel-1 in regulating microtubule function during metazoan development and neuronal differentiation .
Metazoans , or multicellular animals , represent the epitome of biological complexity . Prerequisite for generating this complexity was the development of a multicellular lifestyle , and the ability to coordinate cell division , migration and differentiation to optimize the overall fitness of the organism [1] . Multicellularity emerged several times during evolution ( in algae , plants , fungi and metazoans ) ; however , the one that emerged in metazoans is notable in terms of the extreme diversity of body plans and differentiated cell types that subsequently evolved [2] . Metazoans form a monophyletic group within the opisthokont lineage , a large taxonomic unit containing fungi and several groups of single-celled organisms , including choanoflagellates , ichthyosporea , filasterea and nucleariids [3] ( Figure 1A ) . The properties of the last common ancestor of metazoans and identity of its closest relatives has been the subject of much debate , due to a lack of fossils and sequence data from a broad range of relevant species . This is currently being addressed by genome sequencing efforts such as the UNICORN project [4] . The release of the complete genome sequence of Monosiga brevicollis , now widely recognized as the closest known unicellular ancestor of metazoans , as well as the genomes of several early branching metazoan species—including Amphimedon queenslandica , Trichoplax adhaerens and Nematostella vectensis—have provided new insights into the genetic developments underlying metazoan evolution ( Figure 1A ) [5] . A comparison between the genomes of the sea sponge A . queenslandica and the choanoflagellate M . brevicollis has highlighted some of the most important genetic innovations that coincided with the metazoan multicellular transition—including those associated with cell growth , proliferation , adhesion , differentiation and immunity [6] . On the other hand , a consistent trend seems to be that exaptation of pre-existing genetic tools played an important role during metazoan evolution . For example , the genome of M . brevicollis contains genes associated with a multicellular lifestyle [7] , while basal metazoans such as A . queenslandica or T . adhaerens , which lack differentiated neuronal cells , possess some proteins specifically required for nervous system function in modern eumetazoans ( i . e . ‘true’ metazoans , excluding Porifera , Ctenophora and Placozoa ) [6] , [8] . Bearing in mind that limited phylogenetic sampling poses a challenge to classification of early-branching metazoan species , it appears as if basal metazoans lack some of the genetic innovations conserved throughout the eumetazoan lineage . For example , the body plan diversity and number of differentiated cell types within known poriferan species is limited , suggesting that the genetic toolkit possessed by sponges , such as A . queenslandica , does not code for the biological complexity displayed by their eumetazoan cousins [6] . Therefore , crucial genetic innovations likely occurred on the eumetazoan stem , after the divergence from a more basal metazoan ancestor , which were essential for the development of differentiated cell types such as neurons and muscle cells , and for overall body plan complexity . In this study , we sought to uncover genes of central importance to multicellular metazoan biology , and to initiate the analysis of poorly studied or uncharacterized candidates using C . elegans as a model system . Employing a comparative genomics approach , we identified 526 metazoan-specific genetic innovations conserved across 24 metazoan species but absent from 112 non-metazoan , mostly single-celled eukaryotic and prokaryotic organisms . These 526 ortholog groups could be considered a set of core metazoan genes which define metazoan biology , since they are both specific to metazoans and highly conserved . As expected , many previously characterized metazoan-specific genes have functions associated with multicellularity . Numerous genes are also characterized by neuronal expression in C . elegans and a significant proportion of the human orthologs are linked to human diseases . We highlight 54 core metazoan genes whose biological functions are largely unknown and may represent high-priority targets for understanding fundamental animal biology , and for biomedical research . From our dataset , we chose the poorly studied tubulin folding cofactor E-like ( encoded by TBCEL in H . sapiens , and coel-1 in C . elegans ) as a case study for understanding its metazoan-specific character . Our findings reveal a novel role for C . elegans COEL-1 during development and in neuronal differentiation and maturation , functions that are unique to metazoans .
To uncover the conserved genetic innovations that specifically arose in the metazoan lineage , we identified genes that were highly conserved in metazoans but absent from non-metazoan genomes , including Monosiga brevicollis—the closest known unicellular outgroup to metazoans [7] ( Figure 1A , B ) . Our approach is aimed at uncovering the most inclusive set of metazoan ortholog groups while retaining high stringency , and taking into account different levels of genome completeness [9] ( Table S1; see also Materials and Methods ) . Briefly , ortholog predictions for 138 species , including 25 metazoans , were obtained from OrthoMCL-DB [10] , [11] . We divided the metazoan species into their phylogenetic clades , and for an ortholog to be classified as metazoan-associated , we required it to be found in nearly all of the well-sequenced species in each metazoan clade , although potentially missing from a few species in separate clades . Also , we ensured that the genes were found exclusively in metazoans , while accommodating a limited number of falsely-predicted non-metazoan orthologs . Our comparative genomic analysis identified 526 metazoan-specific ortholog groups ( Table S2 ) conserved in a wide array of metazoan species , including Homo sapiens , Drosophila melanogaster and Caenorhabditis elegans . While we refer here to these genes as metazoan-specific , we acknowledge that some may turn out not to be unique to metazoans as more genomes are sequenced . Some species have undergone gene duplication , resulting in multiple proteins per ortholog group . In the 526 ortholog groups , there were 887 human proteins ( 1 . 6 proteins/group ) compared to only 577 C . elegans proteins ( 1 . 1 proteins/group ) ( Table S2 ) . It should be emphasized that while the genes in this dataset are highly conserved in eumetazoans , we did not require them to be conserved in the genomes of basal metazoans such as A . queenslandica . Our dataset may therefore contain genetic innovations that evolved in the last common ancestor of eumetazoans , after the divergence of basal metazoans . The recently sequenced placozoan T . adhaerens likely represents a phylogenetic intermediate between sponges and cnidaria , and as such , would be the closest known outgroup to the eumetazoans [12] . T . adhaerens is a morphologically simple organism with only four described cell types , lacking specialized neurosensory or muscle cells found in eumetazoans [8] . Given the simple morphology and putative position of T . adhaerens in the metazoan tree ( Figure 1A ) , we identified ortholog groups that are either present ( 346 ) or absent ( 180 ) from this genome ( Figure 1B , Table S2 ) . The observation that most of our dataset ( 66% ) was robustly conserved in T . adhaerens confirms that it is strongly enriched for core metazoan genetic innovations . We further reasoned that differences between these two groups of genes might provide insights into the evolution of biological processes that are unique to eumetazoans . To provide global insights into the nature of the metazoan-specific genes , we performed several analyses . Namely , we ( a ) examined whether the genes are characterized to a significant degree or essentially unstudied; ( b ) positioned annotated human orthologs into functional categories and pathways; ( c ) examined RNAi phenotypes from published genome-wide C . elegans studies; ( d ) evaluated their involvement within interaction networks; and ( e ) assessed them for a causative role in human disease . By searching for existing functional annotations of human genes in the UniprotKB database or C . elegans genes in the well-curated WormBase database , we found that 54 of the 526 ortholog groups ( ∼10% ) appear to be completely or largely uncharacterized . This is a smaller proportion than is true for the whole human genome; however , it remains a significant number , considering the highly conserved nature and presumed fundamental biological roles of these genes . We provide this list in Table S3 , which includes proteins with a wide range of predicted sequence motifs and domains , and an additional 13 groups that have only been very recently characterized . As anticipated , human metazoan-specific orthologs with functional annotations were over-represented in functional categories ( Figure 1C ) and pathways ( Table S4 ) deemed to be important for multicellular animals; these include development , cell-cell communication , and signal transduction . Several organ systems , including nervous , endocrine and circulatory , were also enriched for metazoan-specific genes . In contrast , processes such as DNA replication and repair , transcription , amino acid and carbohydrate metabolism were expectedly under-represented , as most genes implicated in such functions evolved prior to the emergence of the metazoan lineage . In other under-represented categories , such as sensory systems ( corresponding in the KEGG database to olfactory , taste and phototransduction ) , most genes were excluded from our set of core metazoan-specific genes because they are only present in a subset of metazoan species or involve sensory-signaling pathways ( e . g . , cilium-based ) widely conserved across unicellular and multicellular eukaryotes . When we compared the representation in various categories of metazoan-specific genes with or without a T . adhaerens ortholog , glycan biosynthesis and metabolism was the only functional category that showed a significant difference , containing a greater proportion of genes that were absent in T . adhaerens ( Table S4 ) . This is consistent with respect to previous findings suggesting that proteins associated with the extracellular matrix are in some cases eumetazoan ( i . e . , Cnidaria and Bilatera ) innovations [6] . All other categories , including cell-cell communication , signal transduction , development and nervous system , contained similar proportions of metazoan-specific genes with or without a T . adhaerens ortholog ( Table S4 ) . Since T . adhaerens lacks a recognizable nervous system , we looked in more detail at the distribution of core metazoan-specific genes in functional pathways associated with the nervous system , namely neuroactive ligand-receptor interactions and axon guidance . A number of metazoan-specific G-protein coupled receptors ( GPCRs ) involved in human neuroendocrine pathways have orthologs in T . adhaerens ( Figure S1 ) . These include receptors for glycoproteins ( FSHR , LHCGR , TSH ) , neurotransmitters ( GRM1-7 , GABBR2 ) and the neuropeptide galanin ( GALR ) . Similarly , we found that many of the known axon guidance molecules are core metazoan-specific proteins , and several are also found in T . adhaerens , including slit , netrin , selected semaphorins , and the receptors robo and eph ( Figure S2 ) . However , the axonal guidance machinery is by no means complete in T . adhaerens , since several receptors are missing their canonical ligands and vice-versa . These results show that some genes that became associated with modern eumetazoan functions , such as the nervous system , already existed in the last common ancestor of T . adhaerens and eumetazoans ( see also reference [8] ) . We obtained additional insights into metazoan-specific gene function by querying genome-wide C . elegans RNAi data . RNAi phenotypes associated with metazoan-specific genes were compared to a control dataset , namely genes with widely conserved eukaryotic orthologs ( Figure 1D ) ( see Materials and Methods for description of the control dataset ) . Core metazoan genes were collectively less likely to be associated with any particular RNAi phenotype compared with core eukaryotic genes ( 55% versus 74% ) . In addition , we classified RNAi phenotypes into three groups: ( i ) “essential” ( embryonic/larval/adult lethality or sterility ) ; ( ii ) “development” ( growth and body shape defects ) ; and ( iii ) “movement/behavior” ( motility and egg-laying defects ) . Of the genes displaying an RNAi phenotype , the proportion of essential functions was significantly lower for metazoan than for widely conserved eukaryotic genes ( 56% versus 80% ) . In contrast , the proportion of genes causing developmental phenotypes and movement/behavior phenotypes was higher for the metazoan-specific group than the conserved-eukaryote group ( 17% versus 9% and 7% versus 3% , respectively ) . Therefore , when compared to core eukaryotic genes , core metazoan genes are more likely to cause post-embryonic defects in C . elegans . No differences were apparent when comparing metazoan genes with or without a T . adhaerens ortholog . Taken together , these results suggest that metazoan-specific genes might have emerged not to perform essential functions , but rather , were used to modify or enhance existing cellular pathways . We note that a potential caveat is that RNAi in C . elegans is less effective for genes expressed in neurons , potentially masking important functions of some genes important for this cell type . To shed light on the functional relationships among core metazoan genes and more ancient eukaryotic genes ( using the same control dataset described above ) , we performed interaction network analyses using the InnateDB database ( See Materials and Methods ) . Our analysis indicates that although metazoan-specific genes are highly connected with each other ( 60% with one or more connections ) , they also make extensive connections with ancient eukaryotic genes ( 40% ) ( Figure 1E and Figure S3 ) . This supports the idea that the metazoan biological innovations resulted in part from integrating novel components with existing , or evolutionarily more ancient , pathways . The same evolutionary processes of gene duplication and mutation that drive functional innovation are also to blame for the accumulation of heritable diseases [13] . To estimate what proportion of the human orthologs of metazoan-specific genes are linked to human disease , we queried the curated OMIM ( Online Mendelian Inheritance in Man ) database . We found that ∼16% ( 142/887 ) have a clearly defined link to a documented pathology in humans ( Table S5 ) . Diseases linked to the human orthologs of metazoan-specific genes include nervous system disorders ( e . g . , Parkinson's disease ( PARK2/pdr-1 , PINK1/pink-1 ) , Alzheimer's disease ( APP/apl-1; TAU/ptl-1 ) , and torsion dystonia ( TOR1A/tor-1/tor-2/ooc-5 ) ) , as well as neoplasms , loss of sensory perception , and several other diseases ( Table S5 ) . On the whole , these diseases are associated with the proper regulation of cell proliferation and adhesion within certain tissues , and with the development and function of differentiated tissues such as the nervous system . In light of these findings , it is possible that as many as 8 ( 16% of 54 ) of the uncharacterized metazoan-specific genes ( Table S3 ) may represent novel biomedical targets . Reasoning that the expression patterns of metazoan-specific genes might reveal clues regarding their functions , in particular if restricted to particular tissues , we took advantage of the well-developed tools for examining transgenic expression in C . elegans . We generated promoter-GFP-bearing transgenic lines to analyze the expression patterns of 43 core metazoan genes lacking expression data ( Table S6 ) ; these were prioritized by their relative lack of functional characterization and whether or not the human ortholog was implicated in disease . We also compared the previously determined expression patterns of core metazoan genes to those of core ( widely conserved ) eukaryotic genes ( Figure 2A ) . For simplicity , expression patterns were categorized into neuronal , muscle , intestinal , secretory/excretory , hypodermal and reproductive tissues , and quantified using GExplore [14] ( See Materials and Methods ) . There was no significant difference in the overall proportion of genes with tissue-specific expression between core metazoan and core eukaryotic genes ( Figure 2A , left panel ) . Thus , single tissue-specific expression per se does not appear to be a distinguishing feature of metazoan genetic innovations . However , when we compared the tissue types individually , the proportion of neuronal-specific expression was slightly higher for metazoan genes than eukaryotic genes ( 9% versus 6% ) . Fully 16% of our novel metazoan expression patterns were neuron-specific , suggesting a possible bias in our data set or a lack of detailed analysis in genome-wide studies . Examples of such metazoan-specific genes include D2092 . 5/macoilin , a transmembrane and coiled-coil domain-containing protein , which displays pan-neuronal expression ( Figure 2B ) and C15C8 . 4/LRPAP1 , a low-density lipoprotein receptor-related protein that is expressed in a subset of specific neurons ( Figure 2C ) . When assessing expression by tissue type regardless of specificity , we observed that neurons expressed a greater proportion of core metazoan genes than core eukaryotic genes ( 72% versus 54% ) ( Figure 2A , right panel ) . Neurons also expressed 79% of the 43 additional metazoan genes examined in this study . For example , W09G3 . 7/WBSCR16 , a predicted RCC1-like nucleotide exchange factor , is expressed in a pair of sensory neurons , and in the intestine and hypodermis ( Figure 2D ) . C34C12 . 4/C4orf34 , a completely uncharacterized gene with a predicted transmembrane domain , is nearly ubiquitously expressed ( Figure 2E ) , as is F09G2 . 2/C2orf24 , an uncharacterized gene with a cyclin domain ( Figure 2F ) . In contrast , intestinal cells expressed a lower proportion of metazoan-specific genes than core eukaryotic genes , whereas secretory/excretory , hypodermal and reproductive tissues all had similar proportions of both types of genes expressed ( Figure 2A ) . There was no significant difference when comparing genes present or absent from Trichoplax ( data not shown ) . On the whole , our expression data are consistent with the idea that the unique demands of neuronal cell biology were an important raison-d'être for some metazoan-specific genes , and may help reveal other cell-specific functions . Our expression analysis of metazoan-specific genes uncovered the tubulin folding cofactor E-like gene TBCEL as a potential case study for further analysis in C . elegans . TBCEL ( COEL-1 in C . elegans ) was first identified based on sequence similarity to tubulin folding cofactor E ( TBCE ) [15] . The two proteins share UBiquitin-Like ( UBL ) and Leucine-Rich Repeat ( LRR ) domains , but TBCEL lacks a cytoskeleton-associated protein-glycine-rich ( CAP-Gly ) domain present in its counterpart ( Figure S4 ) . TBCEL was shown to depolymerize microtubules when overexpressed in cultured cells by committing α-tubulin to proteasomal degradation , while suppression of its activity increased stable microtubule levels [15] . TBCEL is found in all metazoans , including N . vectensis , but interestingly , does not appear to be present in Trichoplax adhaerens; in contrast , its evolutionary precursor , TBCE , is conserved across all eukaryotes ( Figure S4 ) . Given its potential ‘housekeeping’ role in tubulin turnover , we expected the C . elegans coel-1 gene to be widely expressed across all cell types . Although expressed broadly during embryogenesis ( Figure 3B ) , its expression became restricted to a subset of neurons during larval development ( Figure 3C , D ) and adulthood ( Figure 3E ) . Co-expression of coel-1::GFP with odr-2::CFP [16] was observed in the AIZ interneuron ( Figure 3D , white arrows ) , and based on the position of cell bodies relative to those expressing odr-2::CFP , other coel-1-expressing cells in the head of the animal are likely to be the AVK , AIY , AIM and RIB interneurons , the AWC amphid wing cells , SIBV neurons , OLL neurons and URB neurons . During later stages of larval development , transgenic coel-1 expression became even more restricted , so that by the adult stage expression was typically observed in ∼10 neurons in the head , the ALM touch receptor neurons along the body wall , and the PLM touch receptor neurons in the tail ( Figure 3E ) . The neuronal-specific expression pattern of coel-1 was surprising given its presumed general role in microtubule regulation , yet in keeping with a metazoan-exclusive function in C . elegans . To probe the function of C . elegans coel-1 , we obtained several mutant strains predicted to interfere with coel-1 function , including coel-1 ( tm2136 ) and coel-1 ( gk1291 ) , and we also generated an additional allele , coel-1 ( nx110 ) , using Mos insertion mutagenesis ( Figure 3A ) ( see Materials and Methods ) . The coel-1 ( tm2136 ) allele is predicted to encode a protein with a 77 amino acid deletion in the highly conserved LRR domain ( Figure S5 ) . coel-1 ( tm2136 ) animals do not show any obvious phenotypes; they are viable , fertile , move normally and have a normal life span ( data not shown ) . Both coel-1 ( gk1291 ) and coel-1 ( nx110 ) alleles are predicted to remove the C-terminal UBL domain ( Figure S5 ) , and also exhibit superficially wild-type development and behavior . Since coel-1 is expressed in touch receptor neurons ( TRNs ) , we carried out a gentle body touch assay . No significant difference was observed between wild-type and either coel-1 ( tm2136 ) or coel-1 ( nx110 ) animals ( Figure 3F ) . Given previous reports that cofactor E-like could regulate α-tubulin turnover in cultured mammalian cells [15] , we examined α-tubulin levels throughout C . elegans development . Total , steady-state levels of α-tubulin do not appear significantly different in coel-1 ( tm2136 ) ( Figure 3G ) or coel-1 ( nx110 ) animals ( data not shown ) relative to wild-type . We then tested our available coel-1 mutant strains for changes in microtubule function using the microtubule-stabilizing drug paclitaxel ( taxol ) . Eggs hatched on plates containing low doses of paclitaxel arrest their development at larval stages , and the number of animals that escape this arrest to reach adulthood decreases in a dose-dependent manner [17] . All three alleles of coel-1 ( i . e . tm2136 , gk1291 and nx110 ) cause a similar degree of hypersensitivity to paclitaxel compared to wild-type animals ( Figure 3H ) . Together , these findings suggest that disruption of coel-1 function does not affect touch sensitivity or modulate global levels of α-tubulin , but that it has an impact on microtubule stability during development . In an attempt to rescue the paclitaxel hypersensitivity of the coel-1 mutants , and assess the consequence of increased coel-1 levels , we created an integrated strain carrying additional copies of coel-1 ( nxIs445 ) , hereafter referred to as coel-1XS ( coel-1 ‘excess’ ) . We confirmed a significant overexpression of coel-1 from the coel-1XS allele at the RNA level by qPCR ( Figure S6A ) . Interestingly , coel-1XS animals show a highly variable rate of late-stage embryonic lethality ( among individual animals ) , despite extensive outcrossing . This phenotype has also been observed in animals carrying an extrachromosomal array ( nxEx445 ) of the same coel-1 transgene ( Figure S6B ) . As with coel-1 mutants , no significant change in the total α-tubulin level was observed in the coel-1XS progeny that escape lethality ( Figure 3G ) . However , these animals do have a decreased egg-laying rate ( Figure S6C ) , causing older adults to become full of eggs . We then attempted to test the effect of paclitaxel on coel-1XS animals; however , the variable embryonic lethality hindered our ability to carry out the assay . Contrary to coel-1 mutant animals , coel-1XS worms showed a significant reduction in their response to a mechanical stimulus ( Figure 3F ) . Importantly , injection of coel-1XS animals with coel-1 dsRNA ( to reduce coel-1 transcript levels by RNAi ) rescued the egg-laying and touch sensitivity defects ( Figure S6C , D ) . Together , these data suggest that the phenotypes observed are due to the overexpression of coel-1 rather than rearrangement of the transgene upon integration or gene interruption at the integration site . The phenotypic analyses show that overexpression of coel-1 causes defects in late embryonic development , egg-laying and touch sensation , and that these defects are more severe than coel-1 disruption . This could be due to the presence of partial redundancy which is able to compensate for lost function , but not able to compensate for vast overexpression . A similar pattern , whereby overexpression is less tolerated than disruption , can be seen with other microtubule regulatory proteins , such as stathmin [18]–[21] . Alternatively , or in addition , the relatively subtle defects caused by the coel-1 alleles studied here may be due to a partial loss of coel-1 function . The response to gentle body touch in C . elegans is mediated by the touch receptor neurons ( TRNs ) , and represents a well-studied model system for microtubule-dependent neuronal function [22] . The TRNs consist of two anterior lateral ALM neurons , an anterior ventral AVM neuron , two posterior lateral PLM neurons , and a posterior ventral PVM neuron; each has a single anteriorly-directed process extending from the cell body ( Figure 4A ) . The ALMs are born posterior to the pharynx and migrate to the middle of the animal during embryogenesis , while the AVM and PVM are born post-embryonically and migrate to their final positions during the first larval stage . To examine the role of coel-1 in the TRNs , where the gene is expressed , we used a mec-4::GFP reporter which allowed us to visualize the morphology of these cells in living animals . Measurements of the total length and cell body positioning of the TRNs revealed several subtle changes in coel-1 ( tm2136 ) mutant animals . We observed a significant posterior misplacement of the ALM and the AVM cell bodies in coel-1 animals compared to wild- type ( Figure 4B ) . We also observed an increased length of the PLM neurons in the coel-1 animals ( Figure 4C ) , while cell body positioning was normal ( data not shown ) , indicating an overgrowth of their neuronal processes . coel-1XS animals also exhibit a significant defect in AVM cell body positioning ( Figure 4B , F , G ) . However , in contrast to the overgrowth of PLM processes in coel-1 mutant animals , coel-1XS animals show a reduced outgrowth of AVM processes , which terminate prematurely ( Figure 4D , E ) . In addition , we observed missing or duplicated neurons for the ventral TRNs in coel-1XS animals ( Figure 4H , I ) and a variety of heterogeneous TRN morphology defects , including disorganized nerve ring branches ( data not shown ) . Similar to other phenotypes detected with the integrated coel-1XS transgene , TRN development defects are also observed in worms carrying the extrachromosomal array ( Figure S6C ) . These defects are probably severe enough to result in the partial touch insensitivity that we measured in animals overexpressing coel-1 ( Figure 3F ) . AVM and PVM are both descendants of a pair of bilateral Q neuroblasts that each gives rise through asymmetric cell divisions to three different neurons and two apoptotic cells ( Figure S6D ) [23] . To investigate the possibility that the abnormal ventral TRN cell number observed in coel-1 overexpressing animals is due to a lineage defect , we looked at AQR and PQR , another pair of neurons arising from the Q neuroblast lineage during post-embryonic development [23] . We found a similar AQR/PQR cell number defect as for AVM/PVM , albeit with an opposite frequency with respect to missing versus extra cells ( Figure S6E ) . The inverse correlation between missing and extra AVM/PVM and AQR/PQR cells suggest a Q neuroblast lineage defect , such that when AQR/PQR are not generated , 2 AVM/PVM are made , and conversely . Taken together , our results demonstrate that the alteration of the wild-type function of coel-1 interferes with normal neurodevelopmental processes that control cell fate , cell migration and neurite outgrowth of the TRNs . We did not attempt to address whether the TRN developmental effects of coel-1 overexpression or disruption were cell autonomous , and it therefore remains strictly possible that the TRN phenotypes we observed are due to coel-1 function in other cells or tissues . However , this possibility is less likely given the observed TRN expression of transgenic constructs under control of the coel-1 promoter and because of genetic interactions with genes that are also expressed in the TRNs ( i . e . , mec-17 and atat-2 , see below ) . To address the potential mechanisms behind the morphological and functional TRN defects we observed , we used serial-section TEM to ask whether coel-1 function might affect the structure and organization of the microtubule cytoskeleton in these cells . A unique morphological feature of the TRNs is that they contain 15-protofilament microtubules ( MTs ) arranged in closely packed bundles along their neurites , while most other MTs in C . elegans have 11 protofilaments and are not specifically organized [22] . To visualize individual protofilaments , we prepared wild-type , coel-1XS , and coel-1 ( tm2136 ) mutants using high-pressure freezing and a staining procedure previously developed for this purpose [24] . Using this approach , we found that on average , MTs had the same number of protofilaments ( 15 ) in coel-1 , coel-1XS and wild-type PLM neurites ( Figure 5A ) . However , serial reconstructions of neurite segments revealed that coel-1XS mutants had significantly fewer microtubules ( 26±3 , mean±s . e . m . , n = 4 reconstructions , total L = 8 µm ) than wild-type animals ( 47±4 , mean±s . e . m . , n = 3 , L = 7 . 3 µm ) ( Figure 5A , B ) . In contrast , the number of MTs per section in coel-1 ( tm2136 ) mutants was not statistically different from wild-type ( 35±6 , n = 3 , L = 7 . 85 µm ) . In wild-type animals , MTs were 11 . 4–20 . 0 µm in length , consistent with previous estimates [24] , [25] . In both the coel-1XS background and coel-1 loss of function mutants MTs appeared to be shorter: 3–13 . 7 µm and 2 . 3–14 . 2 µm , respectively . Thus , coel-1 disruption or coel-1 overexpression appear to decrease MT length compared to wild-type , but the variance in these datasets was too high to infer a statistically significant effect of genotype on MT length ( Figure 5C ) . These results indicate that coel-1 overexpression may reduce the MT content in the TRNs , whereas coel-1 disruption appears to have a more subtle effect on tubulin/MT function . Overall , our TEM data provides a link between cofactor E-like function and neuronal MT homeostasis . Tubulins are subject to post-translational modifications that participate in fine-tuning the properties of MTs to suit their cellular functions [26] . α-tubulin acetylation at residue K40 is linked to MT stability and function [27] . In C . elegans , the only α-tubulin bearing K40 is MEC-12 , and acetylated α-tubulin immunoreactivity is found in TRNs , the nerve ring , the VNC and in some ciliated neurons [28] . Given that the phenotypes associated with altered coel-1 activity are related to MT stability , mechanosensation , neuronal development and MT structure in TRNs , we sought a possible functional link between coel-1 and tubulin acetylation . α-tubulin acetylation is regulated by the balance between acetyltransferases and deacetylases . HDAC6 , which is well conserved in C . elegans , is a histone deacetylase that can deacetylate α-tubulin K40 [29] . We obtained the hdac-6 ( tm3436 ) strain , which carries a 476 base-pair deletion spanning exon 4 and intron 4 of hdac-6 and is superficially wild-type . We found that hdac-6 animals are very similar to coel-1 mutant animals . They display subtle TRN morphology defects ( posteriorly displaced ALM cell body position and PLM termination sites ) ( Figure S7A , D ) and respond normally to body touch ( Figure 6F ) . The hdac-6 mutation did not alter the subtle TRN morphology defects associated with the coel-1 ( tm2136 ) allele ( Figure S7A–D ) . In contrast , the hdac-6 allele partially rescued most of the phenotypes associated with the coel-1XS allele , including TRN morphology defects ( Figures 6E , G ) and touch insensitivity ( Figure 6F ) . Similarly , the PLM defect associated with the hdac-6 mutation was reduced by the overexpression of coel-1 ( Figure S7D ) . Two C . elegans paralogs ( mec-17 and atat-2 ) were recently shown to be redundantly responsible for acetylating the α-tubulin MEC-12 at K40 [30]–[32] . Both single and mec-17 ( ok2109 ) ;atat-2 ( ok2415 ) double mutant worms have body touch sensitivity defects [30] . As reported by Topalidou and colleagues [33] , we found that older mec-17;atat-2 mutant animals display TRN morphology defects . These subtle AVM and PLM defects , caused by coel-1 deficiency , were reduced by the mec-17;atat-2 mutations ( Figure 6A , D ) . In contrast , the acetyltransferase mutations enhanced most of the phenotypes associated with the coel-1XS allele , including TRN morphology defects ( Figure 6A , B , E ) and touch insensitivity ( Figure 6F ) . Notably , we also observed the extension of the normally small or non-existent posterior ALM process that was suppressed by the coel-1XS allele , but not the coel-1 ( tm2136 ) allele ( Figure 6C ) . Altogether , these genetic interactions imply that the activities of coel-1 and tubulin acetylation regulators overlap in the development and proper function of the touch receptor neurons . Given the genetic interactions between coel-1 and regulators of tubulin acetylation , we assessed the relative amounts of acetylated tubulin in different strains by western blot analysis . As previously reported [30] , [31] , we found that K40-acetylated α-tubulin is detected in protein extracts from wild-type , but not mec-17;atat-2 mutant worms ( Figure 6H ) . However , no significant change in the levels of acetylated α-tubulin was observed in lysates from hdac-6 , coel-1 or coel-1XS animals ( Figure 6H ) . Contrary to the complete loss of tubulin acetylation in the mec-17;atat-2 acetyltransferase-deficient animals , disrupting HDAC-6 appears to have little or no effect on the steady-state level of acetylated tubulin . This could be due , for example , to a partially redundant function with other tubulin deacetylases ( e . g . , SIRT2; [34] ) . In summary , we conclude from our results that C . elegans COEL-1 influences microtubule homeostasis in the TRNs , and that its function in these cells relates to at least a subset of acetylated tubulin heterodimers . The requirements for tubulin acetylation/deacetylation and microtubule homeostasis likely vary during different stages of neuronal development and function , helping to account for the diverse effects on cell fate , cell migration , neurite outgrowth and mechanosensory behavior observed upon altering COEL-1 activity .
We have used comparative genomics to identify a core of 526 ortholog groups widely conserved among and unique to metazoans—the ‘metazoanome’ . In contrast to previous studies , which have uncovered between 1147 to 1584 animal-specific gene families [5] , [6] , [35] , we did not try to capture all the novelties in the gene repertoire of the metazoan ancestor . Our approach uncovered a much more restricted number of genes that are presumably critical for metazoan biology , by selecting for genes that were innovations in metazoans but also conserved in nearly all well-sequenced metazoan species within the clade . Since we did not require genes to be absolutely conserved in basal metazoans , our dataset includes some genes that may only have emerged in eumetazoans but were then maintained during the evolution of more complex species . As such , our analysis clearly excluded genes that are metazoan-specific but not highly conserved . This is the case , for example , with genes like mdm2 , a negative regulator of p53 . Mdm2 is conserved from T . adhaerens to human but is not found in C . elegans or D . melanogaster [36] , suggesting that elements of the p53 pathway have been lost because they are dispensable in those organisms . It should be emphasized that , as shown in our results and incorporated into our methodology , incomplete genomes represent a major challenge for the identification of conserved metazoan genes across diverse species . As a greater number of metazoan genome sequences and gene annotation are more robustly completed , additional genes may be identified as meeting the highly conserved metazoan-specific criteria . This is particularly true for the earlier-branching metazoan clades , where very few species have been sequenced . Therefore , our dataset undoubtedly omits some genes that could be of significant interest and significance to metazoan biology . Conversely , as additional genome sequences are obtained of closely related non-metazoans , some genes may need to be removed from this list . However , with the number and breadth of species that can now be investigated , we feel that we have identified a landmark set of genes that can be described as metazoan-specific or metazoan-associated , even though , for the reasons described above , the absolute specificity of the dataset cannot be conclusively determined . Our global analysis revealed that the core metazoan genes are proportionately less essential than conserved eukaryotic genes ( Figure 1D ) . We also found that , on the whole , core metazoan genes are deployed in multiple differentiated cell types . Our interaction network analysis suggests that metazoan-specific genes interact with each other and with ancient conserved eukaryotic genes ( Figure 1E ) . Taken together , these results are consistent with the notion that a common property of emergent metazoan-specific proteins is that they evolved as partially redundant modifiers of existing cellular processes—in effect modifying these processes in specific cells to create novel functions in a multicellular context . Our work on C . elegans cofactor E-like , discussed below , illustrates a protein that emerged at the dawn of eumetazoans to influence pre-existing biological processes , including the microtubule cytoskeleton during embryogenesis , as well as differentiation , migration and neurite outgrowth of a subset of neurons required for behavior ( mechanosensation ) . Of the core metazoan-specific genes we identified , approximately one-third ( 34% ) were absent from the genome of the basal metazoan T . adhaerens , and some of these may therefore have been eumetazoan innovations . However , when we compared those genes with the 346 genes conserved in T . adhaerens , we did not find a significant enrichment in any specific functional categories , with the exception of glycan biosynthesis and metabolism , which was underrepresented in T . adhaerens ( Table S4 ) . Glycans are a diverse group of molecules that are important components of the extra-cellular matrix ( ECM ) , which is essential for many aspects of metazoan biology including cell adhesion , differentiation , morphogenesis and immunity [37] . Heparan sulfate proteoglycan ( glypican ) GPC6/gpn-1 is an example of a core metazoan-specific gene that is not found in T . adhaerens ( Table S2 ) . Although it is possible that the absence of GCP6/gpn-1 in T . adhaerens is due to genome incompleteness or a species-specific gene loss , it is equally plausible that its absence , along with other glycans ( T . adhaerens does not produce a distinct ECM [38] ) , reflects an expansion and diversification of genes involved in production and maintenance of the ECM in eumetazoans . Interestingly , the precise roles of glypicans , including GPC6 , remain poorly understood . GPC6 has a broad expression pattern which includes the developing brain , and defects in human GPC6 result in omodysplasia ( severe limb shortening and facial dysmorphism ) [39] . In C . elegans , glypican gpn-1 has been implicated in mediating the proper migration of neuronal precursors [40] . Even in the functional categories associated with higher eumetazoan-specific functions such as the nervous system , the representation of metazoan-specific genes with or without a T . adhaerens ortholog did not differ . For example , orthologs of some neuroendocrine G-protein coupled receptors and axon guidance molecules that have evolved specific neuronal functions in eumetazoans can be found in T . adhaerens , which otherwise lacks a nervous system ( Figure S1 , S2 ) . This is consistent with an important contribution of exaptation to the evolution of new metazoan traits [6] . T . adhaerens has been shown to exhibit behavioral responses to stimuli [8] , and the conserved neuroendocrine pathway components may be part of a primitive stimulus response signaling system that existed in the last common ancestor of T . adhaerens and eumetazoans . Furthermore , genetic analyses in C . elegans have shown that many of the axon guidance genes have other critical functions ( e . g . , ephrins and semaphorins in epithelial formation ) [41] . This suggests that they could have evolved their axonal guidance activities from a more general cell-cell communication system present in basal metazoans prior to the emergence of the nervous system . Of the 526 core metazoan ortholog groups we identified , approximately 10% are uncharacterized or very poorly characterized . For instance , C4orf34/C34C12 . 4 encodes a small transmembrane domain-containing protein of 99 residues with no functional annotations in any organism , including human , mouse , fly , fish or worm . Its predicted transmembrane domain architecture and our finding that the C . elegans ortholog has a broad expression pattern , suggest that it may be involved in a metazoan-specific signal transduction pathway or other conserved cellular process . Importantly , a number of core metazoan genes have been recently characterized ( Table S3 ) ; these genes are associated with the nervous system , developmental function and human disease . For example , consistent with our expression analysis showing pan-neuronal distribution , macoilin/maco-1 has been shown to be involved in regulating neuronal functions in C . elegans [42] , [43] . Another recently characterized metazoan-specific gene is TTC19/ddl-3 . Its disruption causes mitochondrial complex III deficiency and neurological impairment in humans and flies [44] . These discoveries demonstrate the potential of our dataset as a source of candidate genes for novel functions that may be associated with the nervous system and/or human disease . In addition , many metazoan-specific proteins , despite having some functional annotation , are poorly characterized and would benefit from analysis from a whole-organism perspective . For example , the protein attractin/mahogany is found in all sequenced metazoans , but its known interaction partner , MC4R , implicated in weight control , has a more restricted distribution [45] . This suggests that attractin/mahogany likely participates in other , potentially broader cellular roles that are yet-to-be-discovered; these could for example be explored in the genetically-amenable metazoan , C . elegans , where RNAi of the gene ( F33C8 . 1 ) suggest roles in fertility and locomotion that may be relevant to synaptic transmission [46] . In this study , we used C . elegans to investigate the in vivo function of one such poorly characterized metazoan-specific protein , namely tubulin folding cofactor E-like ( TBCEL/coel-1 ) . TBCEL/coel-1 is evolutionarily related to tubulin folding cofactor E ( TBCE ) , which is conserved across all eukaryotes ( Figure S4 ) . In cell culture , TBCEL has been proposed to be a MT-destabilizing factor which disassembles the tubulin heterodimer and promotes the targeting of α-tubulin subunits to the proteasome for degradation [15] . As such , it appears to function together with tubulin folding cofactor E ( TBCE ) as part of a cellular tubulin quality control machinery that includes other tubulin-specific cofactors , and upstream molecular chaperones ( CCT and prefoldin ) required for protein folding/assembly [47] . Contrary to the previous cell culture studies on TBCEL , we did not observe a significant change in the overall , or global tubulin levels as an effect of cofactor E-like overexpression in C . elegans [15] , [48] . This could be due to a more robust function of autoregulatory mechanisms controlling tubulin levels in vivo [49]–[51] or the possibility that COEL-1 acts only on a subset of tubulin isotypes . Regardless , we did find that , consistent with previous observations , C . elegans COEL-1 does regulate microtubule stability in vivo , as indicated by hypersensitivity of coel-1 mutant animals to the MT-stabilizing drug paclitaxel/taxol ( Figure 3H ) and the reduced MT number per section in PLM neurites overexpressing coel-1 ( coel-1XS strain ) . These data underscore a net destabilizing role for COEL-1 . The drug sensitivity phenotype could arise from a role for COEL-1 in embryonic or early larval development , when coel-1 is broadly expressed ( Figure 3B ) . A function for COEL-1 in mitotic cells can be also inferred from aberrant AVM/PVM and AQR/PQR cell numbers observed in coel-1XS animals ( Figure 4J–N ) , which could potentially be explained by a defect in cell polarity and/or asymmetric cell division [23] , [52] . It is possible that additional cell types are missing or duplicated in coel-1XS animals . We also found that coel-1 deficiency altered the final position of ALM and AVM neurons , suggesting a defect in the migration of the ALM neuron during embryogenesis , and the AVM neuron during post-embryonic development . Cell polarity , asymmetric cell division and cell migration are crucial for the development of multicellular animals [53] , and the function of coel-1 in embryos could impact these processes on a broader scale , which could explain the embryonic lethality observed when it is overexpressed ( Figure S6B ) . During larval and adult development , coel-1 becomes restricted to neuronal cells , including the TRNs for which we have shown that coel-1 activity influences cell migration and neurite outgrowth . Neurite outgrowth is the result of growth cone migration and guidance , in a process that is largely analogous to cell migration . These biological processes require the correct balance between microtubule-stabilizing and microtubule-destabilizing forces , as well as efficient microtubule-based transport [54] , [55] . In fact , defects in neuronal migration and axon elongation have been associated with disruption of MAP1B and TAU , both microtubule stabilizing proteins [56] . Moreover , microtubule destabilizing factors like members of the stathmin family are also involved in neurite outgrowth and cell migration [20] , [57] . Our results indicate that coel-1 deficiency and coel-1 overexpression have opposite effects on neurite outgrowth in the TRNs , whereby a subtle overgrowth of the PLM processes in coel-1 ( tm2136 ) animals was observed; conversely , animals carrying the coel-1XS allele displayed premature termination of AVM processes ( Figure 4C , E ) . An opposite effect is also observed for cell migration , whereby the ALM cell body seems to migrate further than their wild-type position when coel-1 function is deficient , while the AVM cell body migrates less when coel-1 is overexpressed compared to wild-type . The results are consistent with a microtubule-destabilizing role for coel-1 necessary for proper cell migration and neurite outgrowth . The overexpression of coel-1 decreases microtubule number , supporting a role of COEL-1 as a microtubule destabilizing factor . A plausible mechanism for this would be a tubulin heterodimer binding and disassembly activity for COEL-1 , as demonstrated for the human ortholog [15] , which , when overexpressed , would push tubulin partitioning from the polymerized microtubule to the free tubulin heterodimer . The functional connections between TRN morphology , their atypical 15-protofilament microtubules and mechanosensory behavior are not yet clear . However , these questions are being addressed [58] . Topalidou et al . [33] have shown for instance that touch response does not depend on the presence of intact 15-protofilament microtubules . In addition , Bounoutas et al . [59] revealed that the polymerization-state of microtubules can regulate protein expression in the TRNs via the p38 MAPK pathway . Based on the observation of reduced microtubule content in coel-1XS TRN neurites , it is possible that the TRN defects observed upon alteration of this gene are in part , or entirely , a consequence of misregulated expression of factors required for TRN development and function . There is compelling evidence that the 15-protofilament microtubules of the TRNs are heavily modified by acetylation of α-tubulin at K40 . Our data show that both the touch insensitivity and the TRN developmental phenotypes associated with the coel-1XS overexpression allele are partially suppressed by mutation of the tubulin deacetylase hdac-6 , and enhanced by mutations in the acetyltransferases mec-17 and atat-2 ( Figure 6 ) . Conversely , hdac-6 has no effect on the TRN developmental phenotypes associated with coel-1 , while the mec-17;atat-2 mutations suppress them . In addition , the coel-1XS allele , but not the coel-1 ( tm2136 ) allele , suppressed the hdac-6 deacetylase PLM phenotype . Collectively , these results suggest that , in the context of the TRN developmental phenotypes , COEL-1 function is antagonistic with respect to tubulin acetylation . However , in light of the recent data showing distinct enzymatic and structural functions for mec-17 [33] as well as the implications of HDAC-6 in multiple processes in the cell [60] , not all the genetic interactions we identified may depend on tubulin acetylation . For example , we found that the coel-1XS allele suppressed the posterior ALM process phenotype of mec-17;atat-2 mutants , a phenotype shown to be independent of mec-17 enzymatic function . We propose a general model for COEL-1 function whereby it binds and disassembles tubulin heterodimers , as demonstrated for the human ortholog [15] , to recycle and/or degrade specific α-tubulin species ( e . g . , tubulin isotypes , modified tubulin , damaged tubulin ) in cooperation with other factors . Our data suggests that the effects associated with COEL-1 deficiency are relatively minor compared to COEL-1 overabundance . In coel-1 ( tm2136 ) mutants , a reduction in the turnover or recycling rate of specific tubulin species , for example MEC-12 α-tubulin , could have subtle and diverse effects on microtubule function depending on the cellular and developmental context . In contrast , COEL-1 overabundance in coel-1XS animals may exert its more dramatic effect on microtubule stability/polymerization by reducing tubulin heterodimer availability below a critical threshold . The special relationship between COEL-1 function and MEC-12 in the TRNs is supported by our observations that eliminating MEC-12 acetylation ( by mec-17;atat-2 mutations ) suppressed effects of coel-1 disruption and enhanced effects of coel-1 overexpression in these cells . However , given the broad expression of coel-1 in developing embryos and in a variety of neurons in developing larvae , COEL-1 function may not be specific to MEC-12 . Tubulin , and the cellular machinery that regulates its acetylation , were ancient eukaryotic innovations [32] , yet the evolution of microtubule regulatory mechanisms is an ongoing process . The microtubule-associated protein 6 ( MAP6/STOP ) family , for instance , has emerged relatively recently and is only found in vertebrates [61] . In this study , we identified several microtubule-related proteins as highly conserved metazoan innovations , including microtubule-associated protein TAU/ptl-1 , the echinoderm microtubule-associated proteins ( EML1-4/elp-1 ) , MIT domain-containing protein 1 ( MITD1/Y66D12A . 10 ) , microtubule-actin cross-linking factor 1 ( MACF1/vab-10 ) , and tubulin folding cofactor E-like ( TBCEL/coel-1 ) . We have shown that coel-1 acts broadly ( i . e . , in most cells of the animal ) for a brief period during embryonic development and in a subset of differentiated neurons throughout the lifespan of the animal . Notably , both elp-1 and ptl-1 are expressed in TRNs of mature C . elegans and needed for full touch sensation [62] , [63] . ptl-1 is required for both embryogenesis and touch sensation [62] , [63] . The apparently dual roles of these microtubule regulatory factors may simply be indicative of an increased demand for remodeling of the microtubule cytoskeleton in these two situations . Furthermore , the elaboration of microtubule regulatory mechanisms may have been an important part of the evolution of metazoan embryonic development and the emergence of a differentiated neuronal cell type . In conclusion , this study provides a current best estimate of a core metazoan-specific genetic toolkit ( the ‘metazoanome’ ) , as well as an overall assessment of some of its features . The detailed analysis of cofactor E-like in C . elegans , revealing a novel role in regulating microtubule homeostasis during development and neuronal differentiation and function , provides an experimentally-validated example of the metazoan-specific character of this dataset . Further study of the uncharacterized or poorly studied core metazoan-specific genes , as well as the interactions between them and with other evolutionarily conserved proteins , should provide important insights into the fundamental biology of multicellular animals and possible targets for neuropathies and other human disorders .
C . elegans strains were maintained and cultured at 20°C under standard condition [64] . Constructs were made using standard molecular biology techniques and fusion PCR as previously described [65] . Alleles used in the study include coel-1 ( tm2136 ) , coel-1 ( nx110 ) , coel-1 ( gk1291 ) , hdac-6 ( tm3436 ) , mec-17 ( ok2109 ) and atat-2 ( ok2415 ) . RT-PCR analysis of the coel-1 ( tm2136 ) allele revealed that in contrast to wild-type animals , in which a major transcript of 1300 bp was detected , there were several transcripts of different sizes which were amplified in the coel-1 ( tm2136 ) mutant worms . Among those transcripts , only one could potentially encode a protein with a deletion of 77 amino acids which would remove the N-terminal half of the LRR domain . We generated the nx110 allele of coel-1 using the Mos-mediated mutagenesis [66] , we used PCR screen for imprecise excision events from ttTi16961 worms , which contains a Mos1 transposon inserted on chromosome X . This allele corresponds to a deletion of 1763 bp spanning exons 5 to 8 . Before use , this strain was outcrossed at least 4 times . RT-PCR analysis of coel-1 ( nx110 ) showed that an mRNA is transcribed and is predicted to be translated in a protein lacking the UBL domain in the C-terminal region of COEL-1 . The allele gk1291 corresponds to an 873 bp deletion including exons 5 to 8 and it is predicted to delete the UBL domain in the C-terminal region of COEL-1 . The following integrated reporter transgenes were used: zdIs5[Pmec-4::GFP] expressing GFP in the touch receptor neurons , iaIs19[Pgcy-32::GFP] expressing in AQR , PQR and URX neurons and hdIs26[odr-2::CFP; sra-6::dsRED2] expressing fluorescent proteins in neurons ( used for cell identification in the coel-1 expression analysis ) [16] . The following transgenes were constructed by PCR and microinjected into worm gonads to generate transgenic lines: ( 1 ) nxEx401[Pcoel-1::GFP+dpy-5 ( + ) ] , expressing an extrachromosomal transgenic array containing 1621 bp of 5′ sequence containing the putative coel-1 promoter fused to GFP; several lines were obtained from this injection and showed the same pattern of expression; ( 2 ) nxEx445[coel-1 ( + ) +dpy-30::dsRED] expressing an extrachromosomal transgenic array containing the entire genomic sequence of coel-1 , including 1621 bp of 5′ sequence and 778 bp of 3′ sequence; three lines were established and one was used for integration by X-ray integration; ( 3 ) nxIs445 , the integrated version of [coel-1 ( + ) +dpy-30::dsRED] , is presumed to be on the X chromosome as nxIs445 males did not generate any nxIs445 male progeny during 6X backcrossing . To confirm coel-1 overexpression and for molecular characterization of the coel-1 mutants , N2 , coel-1XS , coel-1 ( tm2136 ) and coel-1 ( nx110 ) cDNAs were isolated by RT-PCR . Briefly , following suspension of mixed-staged worms in Trizol reagent ( Invitrogen ) and purification with RNeasy ( Qiagen ) , first-strand cDNAs were generated with 1 µg of RNA using the Superscript First-Strand Synthesis System ( Invitrogen ) with a random hexamer . For molecular characterization , PCR amplification was performed using primers annealing to the 5′ and 3′ ends of the coel-1 coding sequence . PCR fragments were incorporated into the PGEM-T Easy vector ( Promega ) and sequenced . To confirm the overexpression of coel-1 , real-time qPCR reactions were set-up using the KAPA SYBR FAST master mix ( KAPA Biosystem ) following the manufacture's protocol . ΔΔCT values were calculated using cdc-42 and ama-1 as housekeeping genes [67] . For RNAi , single stranded RNA was synthesized from PCR product containing flanking T7 and SP6 sites using the RiboMax kit ( Promega ) , annealed and injected at ∼1 mg/ml into both gonad arms of young adult animals . Standard genetic crosses were used to introduce transgenes into different genetic backgrounds and to make double or triple mutant strains . Single-worm PCR reactions were used to genotype the different mutants . A list of all strains generated and used in this study is available in Table S7 . For generation of GFP-reporter transgenic strains , promoter-containing sequences were fused upstream of the GFP-coding region in the pPD95 . 67 GFP-coding cassette . The PCR constructs were injected into the syncytial region of the gonad . The final concentrations of the injection mix were 10 ng/µl of the target construct along with 100 ng/µl of the marker construct , pCeh361 ( dpy-5 ( + ) ) [68] , injected into the target strain dpy-5 ( e907 ) ( CB907 ) . Transgenic F1s ( Dpy-5 rescued ) were individually plated . Wild-type F2 lines were selected to establish the transgenic lines . When available , we analyzed a second , independent transgenic line . To examine touch responsiveness , each worm was tested 10 times by alternately touching the anterior and posterior with an eyebrow hair [69] . Wild-type animals respond to anterior touch by moving backwards and to posterior touches by accelerating forward . Any worm failing to move a significant distance was counted as a non-response . 30–60 young adults were tested blind to genotype . To measure the egg-laying rate , worms were reared at room temperature and staged by alkaline/hypochlorite treatment . Equal numbers of young adult worms of each genotype , including coel-1XS worms injected with coel-1 dsRNA , were placed on separate plates and after 36 hours , transferred to fresh plates and incubated at room temperature . Once the progeny on these plates grew to gravid adults , 60 young adult worms were picked onto 3 separate plates ( i . e . , 20 worms/plate ) for each genotype and the number of eggs laid on each plate was counted after 2 hours at room temperature . To measure embryonic lethality , one-day adult hermaphrodites were allowed to lay eggs at 20°C and removed from plates after 3 hours . The number of eggs laid were counted and monitored every day until hatching . Worms were tested for their ability to develop into gravid adults in the presence of the microtubule drug paclitaxel ( Sigma , T7191; 10 mM in methanol ) , essentially as previously described [17] except that worms were grown on solid medium instead of liquid . Mixed-stage transgenic animals were examined for GFP expression using a Quorum WaveFX spinning disc system . Stacks of confocal images with 0 . 5–1 µm distance between focal planes were recorded , and image acquisition and analyses were done with the Volocity software package ( Improvision ) . Cells were identified by location and morphology in comparison with reference images from Wormatlas ( http://www . wormatlas . org/ ) . Maximum intensity projections of all focal planes were used to generate images for the figures . Touch receptor neurons of young adult worms were visualized by fluorescence microscopy using the zdIs5 transgene . Both fluorescence and DIC pictures were taken using a Nikon A1R laser scanning confocal system or Zeiss Axioskop 2 microscope . ALM , AVM and PLM total lengths were measured from the cell body to the end of the process . The distances from the vulva to the end of the PLM and from the back of the pharynx to the end of ALM and AVM were also evaluated to examine the site of termination of the processes . Cell body positioning was investigated by measuring the distance from ALM and AVM cell bodies to the back of the pharynx and from PLM and PVM cell bodies to the vulva . To control for individual variation in animal length , the different distances were expressed as ratios with respect to the length of either the anterior body part ( tip of the nose to vulva ) for ALM and AVM data or the posterior body part ( vulva to tail ) for PLM and PVM . We also characterized AVM cell position defect scoring as defective any AVM found at the level of or further than the ALM cell bodies . A premature termination was counted when the anterior process terminates before or at the level of the anterior bulb of the pharynx . Synchronized worms grown to the appropriate stage on plates at 20°C were collected , suspended in lysis buffer ( 100 mM Tris , pH 6 . 8 , 4% SDS , 20% glycerol ) and treated by 3 cycles of freezing ( liquid nitrogen ) and boiling . After centrifugation to pellet the insoluble debris , the protein concentration of the supernatants was determined using the BCA protein assay kit ( Pierce ) . Alpha-tubulin and acetylated α-tubulin level were quantitated by fluorescent Western blot . 15 µg of total protein were suspended in Laemmli buffer , separated in 10% SDS-PAGE and electrotransferred to nitrocellulose membranes . Primary antibodies were used at 1∶1000 for the monoclonal mouse anti α-tubulin antibody ( Sigma , #T6199 ) clone DM1A which recognizes amino acids 426–450 of chicken α-tubulins , residues which are highly conserved in C . elegans [70]; 1∶500 monoclonal mouse anti acetylated α-tubulin antibody ( Sigma , #T7451 ) ; 1∶500 polyclonal rabbit anti actin antibody ( Sigma , #A2066 ) . Cy5-conjugated goat anti-mouse ( GE Healthcare , #PA45009 ) and anti-rabbit ( GE Healthcare , #PA45011 ) secondary antibodies were used in 1∶2500 . Processed blots following manufacturer's protocols were scanned in a Typhoon Phosphorimager system ( Molecular dynamics ) and quantitated using ImageQuant software ( Molecular Dynamics ) or ImageJ software ( Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://rsb . info . nih . gov/ij/ , 1997-2009 ) . Adult nematodes were prepared for EM as previously described [71] . Briefly , animals were frozen using an EMPACT2 high-pressure freezer system ( Leica , Vienna , Austria ) . A Leica AFS freeze substitution apparatus was used to preserve and embed nematodes in 2% glutaraldehyde plus 1% osmium tetroxide and in Eponate 12/Araldite 502 . Serial , ultrathin ( 50 nm ) sections were cut with a diamond knife on a Leica Ultracut S microtome and collected on Formvar-coated copper-slot grids . To enhance contrast , sections were post-stained in 3 . 5% uranyl acetate ( 30 sec ) and Reynold's lead citrate preparation ( 3 min ) . The grids were imaged on a transmission electron microscope ( JEOL TEM 1230 , Tokyo , Japan ) and images acquired with an 11 megapixel bottom-mounted cooled CCD camera ( Orius SC1000 , Gatan , Pleasonton , CA ) . Images of consecutive sections were aligned manually and analyzed with Reconstruct [72] . Our approach utilized the ortholog prediction tool OrthoMCL [10] . OrthoMCL initially predicts orthologous gene pairs by reciprocal best BLAST hit ( RBBH ) analysis and then clusters the RBBH pairs into highly connected multi-species ortholog groups . Ortholog predictions for 138 species , including 25 metazoan species were obtained from OrthoMCL-DB version 4 [11] . The metazoan species included 11 Craniate species , 1 Urochordate species , 8 Arthropod species , 3 Nematode species , 1 Cnidarian species and 1 Placozoan species ( Figure 1 , Table S1 ) . To obtain a list of metazoan-specific ortholog groups from the OrthoMCL groups , groups were selected using two main criteria . First , groups were required to have sufficient coverage across metazoan species . One of the challenges in identifying conserved orthologs across species is accommodating the varying degrees of genome completeness . To avoid missing valid metazoan-specific orthologs in cases where a potentially conserved gene was omitted due to incomplete genome sequence , we performed a cursory assessment of genome completeness in the metazoan species using a list of widely-conserved , low-copy number eukaryotic genes ( Table S1 ) . The presence or absence of these conserved eukaryotic genes provides an approximation of the gene coverage [9] . Coverage was on average 94% in the metazoan genomes used in this study , but was observed as low as 79% in the case of Ciona intestinalis . To accommodate this source of error , we adopted flexible criteria for selecting metazoan-specific orthologs . We divided the metazoan species into their phylogenetic clades and for an ortholog to be classified as metazoan-specific , we required for it to be found in the majority of species in every metazoan clade , but could be missing from a few species in separate clades . Groups had to have at minimum , orthologs in: 9 of 11 Craniate species , 6 of 8 Arthropod species , 2 of 3 Nematode species . In addition , since Nematostella vectensis and Ciona intestinalis were sole representatives of their particular phylogenetic clades , we required genes to be conserved in at least one of these genomes . Finally , ortholog groups had to be conserved in a combined total of at least 20 of 24 metazoan species . These criteria ensured a high degree of conservation throughout the metazoan lineage while permitting a limited number of false negatives ( i . e . orthologs missing due to errors by OrthoMCL or incomplete genome information ) . Because these modified criteria ensures that the gene is strongly represented in all metazoan clades , it is likely that we are more often selecting for groups where an ortholog is absent due to incomplete genome sequence rather than gene loss . The second criterion ensures that the group's orthologs are found exclusively in metazoan species , while accommodating a limited number of falsely predicted non-metazoan orthologs . OrthoMCL is an effective tool for identifying metazoan-specific orthologs . It does , however , generate small numbers of falsely predicted orthologs ( false positives ) . In the OrthoMCL data , singular non-metazoan genes would occasionally be clustered with a group of metazoan-conserved orthologs . These genes would often only be predicted to be orthologs to one or two other metazoan species genes in the group based on existence of an RBBH relationship . From a phylogenetic perspective , it is more likely that these weakly-related , singleton genes are false positives rather than true orthologs . To prevent these groups from being excluded in our list of metazoan-specific orthologs , a second criterion was added: an ortholog group could have at maximum , orthologs in 2 of 112 non-metazoan species , provided that those orthologs had 3 or less RBBH connections to the metazoan orthologs in the group . These weakly connected , singular non-metazoan orthologs likely represent false predictions by OrthoMCL . Pathways were obtained from InnateDB [73] and functional categories from KEGG Brite database [74] . To identify pathways and functional categories that were over- or under-represented with metazoan-specific human genes , a hypergeometric test was used . Multiple hypothesis correction was performed using a Benjamini-Hochburg procedure and results were considered significant if the corrected p-value was less than 0 . 05 . The set of genes used for comparison in this analysis comprised all human genes with pathway or category annotations in these databases . Differences in the proportions of human metazoan-specific genes with or without a Trichoplax ortholog in each category were tested using the Fisher's exact test . To determine the genes that were completely uncharacterized we combined several approaches . First , using Genealacart , the batch querying application based on Genecards database ( www . genecards . org ) [75] , we searched for human genes that had no functional annotation from the UniprotKB database . Second , using wormart , the wormbase implementation of Biomart ( http://caprica . caltech . edu:9002/biomart/martview/ ) , we identified the C . elegans genes that had no description . Next , we compared the results between human and C . elegans to obtain a list of uncharacterized genes in those two species . Finally , we searched manually for genes that had no associated papers in Pubmed . In order to assess metazoan-specific genes characteristics , we compared them with a set of highly conserved eukaryotic genes that were identified on OrthoMCL-DB by selecting genes found in H . sapiens , C . elegans , D . melanogaster , S . cerevisiae , A . thaliana and absent in prokaryotes . The total number of eukaryote-specific ortholog groups is 1004 , consisting of 1237 C . elegans and 1630 human genes . To analyze and quantify the functional interactions of metazoan-specific genes and compare them to conserved eukaryotic genes , we used the InnateDB database ( http://www . innatedb . com ) [73] . Specifically we used the “data analysis” page to retrieve experimentally-verified molecular interactions for each gene . This database allowed us to obtain interactions only between genes of the same set . We identified interactions of metazoan-specific genes with each other , interactions of conserved eukaryotic genes with each other and compared them with the list of interactions between metazoan-specific and conserved eukaryotic genes . Differences in the proportion of interactions were tested using the Fisher's exact test . Cytoscape [76] was used for the visualization of the interaction network . Wormart ( http://caprica . caltech . edu:9002/biomart/martview/ ) , the wormbase implementation of Biomart , was used to retrieve the RNAi phenotypes associated with the metazoan-specific and conserved eukaryotic genes . Specifically , the WS220 gene dataset was used and the genes filtered by their “gene ID . ” For the second dataset , we used the RNAi dataset and the “phenotype” filter . To identify genes that have RNAi data we then selected the filter “limit to RNAi that have one or more scored phenotype” . To identify genes that are associated with any RNAi phenotype we chose the filter “limit to RNAi that have one or more observed phenotype” . To identify the genes associated to particular phenotypes , the filters “phenotype annotation includes observed phenotype” have been selected and the wanted phenotypes have been entered in the “limit to phenotype ID” filter case . The phenotypes analyzed were Emb = embryonic lethal , Ste = sterile , Stp = sterile progeny , Lvl = larval lethal and Adl = adult lethal corresponding to the “Essential” category; Gro = slow growth , Lva = larval arrest , Dpy = dumpy , Bmd = body morphology defect , Bli = blistered , Slm = slim , Lon = long , Sma = small , Pvl = protruding vulva , Muv = multivulval for the “Development” category and Unc = uncoordinated , Prl = paralysed , Rol = roller and Egl = egg-laying defective associated with the “Movement/behaviour” category . To identify the numbers of disease-associated genes among the metazoan-specific ones , we queried the OMIM database through BioMart and filtered the output to include only diseases whose molecular basis was known ( i . e . containing #3 in the Phenotype map key column of the MIM Gene Map output ) , as described [13] . The GExplore tool ( http://genome . sfu . ca/gexplore/ ) [14] was used to compare the annotated expression patterns of metazoan-specific and conserved eukaryotic genes in Wormbase . The total number of genes with annotated expression patterns were identified by an ‘expr’ full-text search . For simplicity , anatomical expression patterns were classified as neuronal ( nerv OR neuron ) , muscle ( muscle OR myo ) , intestinal ( gut OR intestin ) , secretory/excretory ( gland OR secretory OR excretory ) , hypodermal ( hypoderm OR epiderm ) and reproductive ( uter OR gonad OR germ OR sperm OR oocyt OR reproduct ) . This classification scheme necessarily omits certain cell types ( e . g . , coelemocytes , male-specific sexual organs , etc . ) and some overlap exists between categories ( e . g . , uterine muscle ) . Nevertheless , due to the limitations of a text search and the variable annotation of expression data , this scheme was deemed sufficient for a crude , global quantitative comparison between sets of genes . Specific patterns of expression ( e . g . , tissue-specific , combinations of tissues ) was determined by simple Boolean searches . Results were plotted as proportions of the number of genes with annotated expression data for each group ( i . e . , 376 ( 333 in Wormbase , 43 novel expression patterns in this study ) for metazoan-specific genes and 479 for conserved eukaryotic genes ) . Differences between metazoan-specific and conserved eukaryotic gene sets were tested using Fisher's exact test . | The evolution of multicellular animals ( metazoans ) from their single-celled ancestor required new molecular tools to create and coordinate the various biological functions involved in a communal , or multicellular , lifestyle . This would eventually include the unique cellular and physiological demands of specialized tissues like the nervous system . To identify and understand the genetic bases of such unique metazoan traits , we used a comparative genomics approach to identify 526 metazoan-specific genes which have been evolutionarily conserved throughout the diversification of the animal kingdom . Interestingly , we found that some of those genes are still completely uncharacterized or poorly studied . We used the metazoan model organism C . elegans to examine the expression of some poorly characterized metazoan-specific genes and found that many , including one encoding tubulin folding cofactor E-like ( TBCEL; C . elegans COEL-1 ) , are expressed in cells of the nervous system . Using COEL-1 as an example to understand the metazoan-specific character of our dataset , our studies reveal a new role for this protein in regulating the stability of the microtubule cytoskeleton during development , and function of the touch receptor neurons . In summary , our findings help define a conserved molecular toolbox important for metazoan biology , and uncover an important role for COEL-1/TBCEL during development and in the nervous system of the metazoan C . elegans . | [
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] | [] | 2013 | Identification of 526 Conserved Metazoan Genetic Innovations Exposes a New Role for Cofactor E-like in Neuronal Microtubule Homeostasis |
One of the goals of cancer research is to identify a set of genes that cause or control disease progression . However , although multiple such gene sets were published , these are usually in very poor agreement with each other , and very few of the genes proved to be functional therapeutic targets . Furthermore , recent findings from a breast cancer gene-expression cohort showed that sets of genes selected randomly can be used to predict survival with a much higher probability than expected . These results imply that many of the genes identified in breast cancer gene expression analysis may not be causal of cancer progression , even though they can still be highly predictive of prognosis . We performed a similar analysis on all the cancer types available in the cancer genome atlas ( TCGA ) , namely , estimating the predictive power of random gene sets for survival . Our work shows that most cancer types exhibit the property that random selections of genes are more predictive of survival than expected . In contrast to previous work , this property is not removed by using a proliferation signature , which implies that proliferation may not always be the confounder that drives this property . We suggest one possible solution in the form of data-driven sub-classification to reduce this property significantly . Our results suggest that the predictive power of random gene sets may be used to identify the existence of sub-classes in the data , and thus may allow better understanding of patient stratification . Furthermore , by reducing the observed bias this may allow more direct identification of biologically relevant , and potentially causal , genes .
The last two decades have seen a proliferation of papers , each proposing a set of genes that are important for cancer progression , metastasis , or patient stratification [1–7] , where most of the findings stem from the computational analysis of gene-expression data from large patient cohorts . While such gene sets have been shown to have good predictive power [8] , gene sets obtained by similar analyses of the same cancer type , but using different cohorts , provided poor overlap in the identity of the genes , and in some cases these sets proved to be non-robust [9] . Furthermore , even within a single dataset it has been shown that multiple gene sets can be obtained with equal predictive power [10 , 11] . This reduces the significance of the identity of individual genes in the gene set , as they can be replaced by many others with little to no loss in predictive power . From a clinical point of view , most genes obtained from such sets did not prove to be useful therapeutic targets . This may not be surprising given the results presented above , but highlights the importance of identifying genes that are not only predictive but are also causal of disease progression . Many methods have been developed to address the problems mentioned above , including generating meta-analyses that combine multiple data sets [12]; adding information about interactions between genes [9 , 13–15]; checking the robustness of the signature when choosing small sub-cohorts [9]; and adding biologically-relevant expert knowledge [11] . Results obtained from such analyses are more likely to identify causal genes , and some of the predicted genes were even validated in in vitro settings [15] , but the clinical implications of such analyses remain to be seen . Recently it was shown by Venet et al . [16] that in breast cancer random gene sets can predict survival much better than can be expected by chance , meaning that when using the expression of random genes to split the cohort into two groups the groups have significantly different survival curves . Let us consider the property that random sets of genes can be used to predict survival . Intuitively , we might think that by using random gene sets to split the cohort we should end up with random assignments into groups . However , if that was the case then such a procedure would provide p-values that are uniformly distributed between 0 and 1 , and thus , 5% of the p-values should fall below 0 . 05 . In contrast , Venet et al . observed that most of the assignments into groups induced by random genes provided a statistically significant separation in survival curves ( or a p-value smaller than 0 . 05 ) . This means that the separation into two groups that is induced by random gene sets is in fact not random , and moreover—that this separation into two groups is related to survival . From here on , when a larger ( or smaller ) than expected proportion of random gene sets predict some clinical property ( e . g . , survival ) in a statistically significant way , we say that the data exhibits random bias . The discovery of random bias in breast cancer gene expression cohorts further questions the causal relevance of individual genes that are identified in signatures based solely on these gene expression cohorts . Venet et al . attributed the phenomenon of random bias to a large proliferation signature that affects a substantial proportion of the genes in the genome . They suggest that most random gene sets are statistically likely to include some genes from this proliferation signature and are thus predictive of proliferations , and by proxy of survival , as well . Therefore , when choosing a random gene set and using it to split the data into two groups these groups are not random , but are separated by the activity of that proliferation signature . They showed that they could remove the random bias by defining a proliferation score ( see the Methods section ) and then removing the effect of that score from all the genes in the expression data . The hypothesis that random bias stems from a large proliferation signature is an attractive one . It stands to reason , however , that if it holds true for breast cancer it should also hold true for many , if not all , cancer types . We used data from the cancer genome atlas ( TCGA ) project to determine the prevalence of random bias in all available cancer types , and whether the removal of a proliferation signature can remove this bias . By removing such random bias , it may be possible to restore some of the causal interpretation for gene signatures discovered by computational analysis . We present the results of this analysis for 34 datasets downloaded from TCGA and show that 24 out the datasets exhibit significant random bias . We further show that for most of these cases the random bias cannot be removed by using the proliferation score as described in Venet et al . We demonstrate how sub-classification by unsupervised clustering ( but not sub-classification by grade ) can help reduce random bias for cases in which the proliferation score was insufficient to remove random bias . We conclude by discussing the implications of our results on further research into genes that are causal for disease progression .
We downloaded TCGA data from the TCGA data matrix on November 30 , 2015 using the TCGA2STAT R package [17]; This gave us a total of 34 RNAseq expression datasets with their adjoining survival and clinical information . We used the level 3 data normalized using RSEM . We used the dataset abbreviations as defined by the TCGA consortium ( as defined in https://tcga-data . nci . nih . gov/docs/publications/tcga/ ) . Two of the datasets comprised an agglomeration of two cancer types , specifically , GBMLGG ( glioblastoma multiforme and brain low grade glioma ) and COADREAD ( colon adenocarcinoma and rectum adenocarcinoma ) , where each of the individual datasets also appears in the data . To define random bias , we follow the methods presented by Venet et al . [16] as follows . Given a normalized dataset with N samples holding gene expression data for M genes , we chose gene sets of an arbitrary size s with uniform probability and without substitution . Next , we performed singular value decomposition ( also known as principal component analysis ) on the new gene-expression sub-matrix to obtain the sample weights for the first principle component . The samples were then split into two groups , depending on whether the weight assigned to each sample was above or below the median value . Finally , we evaluated the separation in survival curves between the two groups using a log-rank test to obtain a p-value . The process was repeated b = 5000 times to obtain a set P of p-values . Given sufficient data , a random assignment of samples into two groups should provide a null distribution of p-values that is uniform between zero and one . This is different from the assignment described above since it does not use the data to choose the random assignment , and is therefore truly random and independent from any clinical outcome . However , since some of the survival data was sparse or had insufficient follow-up time ( such as for PRAD , prostate adenocarcinoma ) , even such a random assignment would have some arbitrary non-uniform distribution of p-values , which served as our null distribution . By performing random assignments of samples into two groups to obtain the p-value for the induced separation in survival curves , and repeating the procedure b times , we obtained a set R of p-values that are drawn from this null distribution . From the null distribution , we obtained the 5th percentile , r , and calculated the proportion of p-values that are smaller than r in P . We call this value the proportion of significant random sets of size s . If no bias exists , this value should be close to 0 . 05 . If the value is significantly larger , we determined the dataset to have a positive random bias . If the value is lower than 0 . 05 , we said that the dataset has negative random bias . To check that the predictive power of random gene sets is consistent we divided the data randomly into two groups , where each sample has a 50% chance of being chosen to each group . We then randomly chose 100 random sets and calculated the p-values in both halves of the data for each of the random sets . Since individual divisions into halves may provide biased results we repeat this procedure 50 times . The resulting vectors are then compared by Fisher exact test to estimate the statistical significance of obtaining random sets that are significant in both halves of the data . Additionally , the vectors are used to evaluate the proportion of random sets that are significant in both halves of the data . We can determine the p-value of the proportion of significant sets by applying the central limit theorem to the difference in proportions obtained in R and in P , providing the statistic Z = r - 0 . 05 r ( 1 - r ) N + 0 . 05 ( 1 - 0 . 05 ) N . ( 1 ) Under the null hypothesis , Z should be drawn from the normal distribution N ( 0 , 1 ) . Thus , we obtain the two-sided significance of the proportion r . If the significance was less than 0 . 05 , then the dataset was deemed to exhibit significant random bias . Our procedure for obtaining and adjusting for the proliferation score follows the method of Venet et al . [16] . They determined a proliferation signature by identifying the 1% of genes whose expression is most correlated with the gene PCNA across many datasets downloaded from the Gene Expression Omnibus . For each sample , we determined the expression of these proliferation genes and the median value across these genes to provide a proliferation score for that sample . To remove the proliferation effect , we performed a linear fit between each gene in the dataset and the proliferation score across all samples . We replaced the expression of each gene by the residual from the linear fit . To perform clustering of the high dimensional gene-expression data using the least number of parameters , we chose to use phenoGraph [18] . The algorithm follows several steps . First , using the similarity between all samples it identifies the k-nearest neighbors of each sample . We used Spearman’s correlation as the distance metric . The algorithm then computes the statistical significance of the overlap in neighbors between each pair of samples . This is done using a Fisher exact test to define a weight for the interaction between each pair of samples , thus creating a weighted network . Finally , phenoGraph applies the Louvain algorithm on the network to find a partitioning into clusters that maximize the modularity of the network [19] . The Louvain algorithm has been successfully used in the context of social networks , and is considered by many to be the state-of-the art [20] . The algorithm is remarkably insensitive to the value of k , has no additional parameters , and the number of resulting clusters is data-driven and not given as a parameter . This makes it suitable for our purpose because we do not focus on the specific clusters that were obtained , rather on their general properties . An implementation of phenoGraph is available in Python by the authors [18] . To perform the analysis , we created an R package implementing phenoGraph , based on the code description . The package is available at https://github . com/yishaishimoni/phenoClust . git .
We analyzed 34 datasets downloaded from TCGA for the existence of random bias , as described in the Methods section . For each dataset we chose b = 5000 random gene sets of sizes N = 20‥10 . Then , using the first principal component of each random set we split the data into two equal-sized groups and obtained the p-value of the separation in survival curves between the two groups . The results of this analysis are summarized in Table 1 , where for each set of 5000 p-values we obtained the following: a ) The proportion of significant sets in percentage values ( Signif % ) b ) The significance of the proportion using a proportion test compared to randomly splitting the dataset into two groups ( P-value ) and c ) The proportion of significant sets after adjusting for the PCNA signature in percentage values ( PCNA % ) Table 1 exhibits the results of this analysis for each of the datasets using random sets of N = 64 genes; 17 out of the 34 datasets exhibit significant positive random bias , while 10 datasets do not exhibit significant random bias . This is somewhat surprising considering the interpretation presented in [16] that random bias stems from a proliferation signature , since such an effect should hold true for most cancer types . Importantly , seven of the datasets exhibited significant negative random bias , or a significant lack of small p-values . Such a property can only occur when the group assignment induced by random genes consistently provides a separation into groups that do not differ in their survival curves . Note that this cannot be explained by separation into random groups , since the p-values in the null distribution are drawn from just such a random assignment into groups , and so the proportion of a random assignment should be similar to that of the null distribution and not less . It also cannot be explained as separation into groups that are statistically unrelated to survival , since this would provide a uniform distribution of p-values , which is not what is observed in the data ( as seen by the cumulative distribution plots in S1 File ) . One conceivable way to obtain such a consistent separation into groups would be by the existence of sub-classes in the data , where those sub-classes share a similar survival profile . We will explore this option in a later part of the manuscript . To investigate whether the random genes that are associated with survival are indeed predictive we repeated this analysis where for each random set we checked the p-value obtained on random halves of the dataset , as explained in the Methods section . For example , when we ran this analysis of the BLCA dataset , for which there are sufficient samples and a large proportion of significant random sets , the proportion of significant sets of size 50 in each half was approximately 0 . 25 , while the proportions of sets that were significant in both sets was approximately 0 . 1 , signifying an odds ratio of about 2 . We then checked the significance of the proportion of random sets that are predictive in both halves of the data using a Fisher exact test , which provided a p-value of 4 . 6e − 25 . The results for other datasets with sufficient sample size and significant random bias are similar , and are summarized in Table 2 . This result indicates that these random gene sets are not only predictive of survival in a significant way , but also in a consistent way . The connection between random bias and the number of genes in the gene set is displayed in Fig 1A , where the x axis represents the number of genes in each random gene set and the y axis represents the proportion of significant sets of that size . For small set sizes , the results span the range of possible significance ratios ( i . e . , between zero and one ) , while large sets tend towards either one , 0 . 05 , or zero . The fact that many of the cohorts exhibit a random bias for even a handful of genes , as seen on the left-most side of the plot , strongly supports the assumption that there is an important biological signature affecting a large proportion of the genes , and that this is the cause for the random bias . The question remains , however , whether or not this biological signature can be associated with proliferation . To check whether proliferation can account for random bias , we repeated the above analysis after adjusting for the PCNA proliferation signature as described in the Methods section and as suggested by Venet et al . [16] . The results of this analysis are presented in the two rightmost columns in Table 1 ( PCNA % , and PCNA p-val ) , as well as in Fig 1B . This analysis shows that the adjustment does decrease the proportion of significant sets for most datasets . However , only two datasets that exhibited significant positive bias lost the statistical significance . For all the other datasets exhibiting random bias , the adjustment to the PCNA signature was insufficient to remove the random bias . Considering these findings , an explanation other than proliferation is required to account for random bias . We propose that in some cases , random bias may be due to other large-scale biological programs within the dataset . In general , such biological programs can span any biological process that is important for cancer progression or initiation , such as any of the hallmarks of cancer [21] , grade , stage , etc . However , these hypotheses cannot explain the phenomena presented earlier , most notably the fact that some datasets exhibit negative random bias , or a lack of small p-values . As explained above , for this to happen the random gene sets must be able to capture a strong and consistent biological difference between subsets , but one that is not associated with survival . Such a difference is unlikely , therefore , to be one of the cancer hallmarks , as those are likely to be highly associated with survival . Another possibility that may be more general is the biological signature of sub-classification ( e . g . , indicating susceptibility to some therapy [11] ) . Such a signature would satisfy both the requirements for consistency and large biological differences , but would not necessarily be associated with survival . To explain this , consider a case where two cancer types arise in the same organ but from two tissues of origin . In general , these two cancer types will differ in their survival profiles . In such a case , a significant difference in gene expression would be observed between the two groups , and a large proportion of the genome will be involved in this difference . When gene-sets are chosen at random , the proportion of differentially expressed genes will be the same in the random set as in the whole genome , and these genes will cause the random gene-set to be predictive of the subclasses , and only by proxy of survival as well . Therefore , having subclasses in the same data-set may easily lead to random bias . The hypothesis that random bias stems from sub-classification is also supported by the fact that the largest random bias was observed in the agglomerated dataset holding samples from both glioblastoma multiforme ( GBM ) and from brain low grade glioma ( LGG ) , which are known to have vastly different prognoses . Individually , LGG and GBM exhibit significant ratios of random gene sets of size N = 64 , with values of 0 . 84 and 0 . 14 , respectively . In the combined dataset , the significant ratio is almost 1 . It should be noted , however , that the dataset containing samples from both colon adenocarcinoma and rectum adenocarcinoma does not show the same effect; neither the individual data sets nor the combined data set exhibit random bias . This may be caused by the fact that both cancer types share very similar survival dynamics and in fact are often referred to together as colorectal adenocarcinoma [22] , and therefore differences in survival dynamics cannot be detected by this method of identifying random bias . This result suggests that , in some cases , merging data sets may not cause random bias . To test the hypothesis that subclasses in the data can explain random bias we chose to use a data-driven unsupervised clustering algorithm called phenoClust [18] , as explained in the Methods section . This algorithm is based on network modularity , which is widely used in social networks , and identifies “neighborhoods” of samples that share nearest neighbors , as determined by similarity . It should be noted that the choice of clustering algorithm is not the focus of this analysis , and that we chose phenoClust as a convenient example . However , to ascertain that the clustering provides biologically relevant results , we compared the clusters provided by phenoClust for the breast cancer dataset ( BRCA ) to the sub-classification obtained by the PAM50 classification method [23] , which has become the state-of-the-art method for computational classification of breast cancer samples using gene-expression data [24] . The clusters that were obtained by phenoClust are in high agreement with the PAM50 classification , as shown in S1 Fig . Specifically , each phenoClust cluster consists mostly of a single PAM50 subclass . We next checked the effect of clustering on random bias , by repeating the random bias analysis ( as described above ) for each cluster separately , and then comparing the proportion of significant sets to the proportion in its original dataset . The results show that in almost all cases , the proportion of significant sets was closer to the expected value of 0 . 05 , as shown in Fig 2A . This is true both for datasets that exhibited positive random bias and for datasets that exhibited negative random bias . Specifically , out of 106 clusters that originated from datasets exhibiting significant random bias , 65 lost their bias . It should be noted that the proportions of significant random sets in the BRCA sub-classes by phenoClust ( 0 . 021 , 0 . 036 , 0 . 04 , 0 . 055 , 0 . 091 , and 0 . 099 ) , were very similar to the proportions in the PAM50 classes ( LumA: 0 . 036 , LumB: 0 . 074 , Basal: 0 . 038 , Her2: 0 . 05 , and Normal: 0 . 046 ) . This indicates that , as expected , biologically driven sub-classification achieves results that are similar to or better than the data-driven approach . We performed similar analyses on random sub-samples of the datasets of sizes identical to those of the cluster obtained by phenoClust . We observed some reduction in random bias ( as expected due to the reduction in statistical power ) , but the effect was less pronounced than when using the phenoClust clusters , as shown in Fig 2B . As a comparison , out of the 106 clusters that originated from datasets exhibiting significant random bias , only 32 lost their bias . We performed the above analysis without removing the PCNA proliferation signature , since the effect of the adjustment had a minor impact on the results , as shown in S2 Fig . This result shows that random bias may be used to detect the existence of sub-classes in a dataset . To examine the possibility that clustering by clinical features may provide a similar explanation to random bias we evaluated the effect of grouping samples according to cancer stage for all data-sets for which this clinical information exists . The results of this analysis are shown in S3 Fig , and show that in most cancer type exhibiting large proportions of significant sets the grade does not allow removal of random bias .
We have shown here that gene expression data from random gene sets has a predictive power for survival in cancer , and is a property that spans many cancer types . The phenomenon was first reported by Venet et al . [16] in breast cancer , in a dataset where expression was measured using microarrays . We demonstrated that the same phenomenon is exhibited by most of the 34 gene-expression datasets appearing in the TCGA data matrix , which were obtained using RNAseq . This implies that this is not an artifact of the platform we used , nor is it specific to the data-set that we used . This result raises concerns regarding the causal significance of genes obtained from analyses using such datasets , and explains the lack of reproducibility in identifying predictive and causal genes . In general , as shown previously , computational analyses can provide sets of genes that are highly predictive of survival , and can provide valuable insights into the biological mechanisms driving cancer survival , progression , and metastasis . We observe that only a handful of genes are truly causal , in the sense that altering their expression or activity will influence survival; however , the fact that many random subsets may arise with similar predictive power hides their identity . Subsequently , our analysis and the proposed changes in methodology may have far-reaching implications for follow-up research on potential therapeutic targets . The cautionary conclusion that can be drawn from these results is that each data set must be examined for random bias . Moreover , this bias must be removed prior to performing any analysis aimed at obtaining genes that are causal for cancer progression or survival , and perhaps for disease progression in general . Clearly , like any analysis of real-world evidence done using static data , even after removing the random bias it is still impossible to guarantee that the genes resulting from such an analysis are causal and not just correlated with survival . However , since a truly causal gene will remain correlated with survival in their relevant context , by removing confounding factors and focusing on specific biological contexts we increase the probability that the remaining genes that are survival related may be biologically relevant and causal . Another result that emerged from our analysis is that in most cases adjusting for a proliferation signature does not remove the predictive power of random gene sets . In fact , even in breast cancer—the very cancer type in which this bias was first observed—the adjustment for proliferation that removed the bias in the NKI dataset [2] no longer removes the bias in the breast cancer dataset provided by TCGA . Rather than giving up or suggesting a dataset-specific gene signature , we suggest that in the cases in which adjusting by the proliferation score ( or any other relevant score ) is insufficient , sub-classification may be required . We have shown that by performing unsupervised and data-driven clustering , we were able to remove most of the random bias . Since individual subclasses do not exhibit random bias , analysis performed on each of those subclasses is much more likely ( but not guaranteed ) to produce genes that are not only predictive but also causal . Since sub-classification of cancer is an active field of research ( e . g . [28–30] ) , this allows for a more positive and pro-active conclusion that the predictive power of random gene sets ( or significant lack thereof ) indicates the existence of confounding factors in the data , such as sub-classification or some biological signature . In conclusion , the analysis presented here can lead to a more refined approach for computational methods aimed at identifying causal genes . It can also guide further efforts to define and identify sub-classes in cancer cohorts that have important biological and clinical implications . | Multiple gene sets have been published as predictive of cancer progression and metastasis in several cancer types . Although many of these sets proved to be highly predictive of survival , even gene sets for the same cancer ( but from different data-sets or different analyses ) exhibit very little overlap and to date did not provide functional therapeutic targets . Recent studies found that in breast cancer , even random gene sets can predict survival much better than would be expected , and on average are better than many published gene sets . Together , these results undermine the causal role of the published gene sets and their potential clinical implications . We show that random gene sets predict survival in many cancer types , and that this property no longer exists after splitting the data into subclasses based on data-driven clusters . This suggests that such sub-classification could increase the likelihood to identify causal genes that are potential therapeutic targets , and that this property can be used as an indication that there may be subclasses within the dataset . | [
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] | 2018 | Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification |
Symbiotic bacteria inhabiting the human gut have evolved under intense pressure to utilize complex carbohydrates , primarily plant cell wall glycans in our diets . These polysaccharides are not digested by human enzymes , but are processed to absorbable short chain fatty acids by gut bacteria . The Bacteroidetes , one of two dominant bacterial phyla in the adult gut , possess broad glycan-degrading abilities . These species use a series of membrane protein complexes , termed Sus-like systems , for catabolism of many complex carbohydrates . However , the role of these systems in degrading the chemically diverse repertoire of plant cell wall glycans remains unknown . Here we show that two closely related human gut Bacteroides , B . thetaiotaomicron and B . ovatus , are capable of utilizing nearly all of the major plant and host glycans , including rhamnogalacturonan II , a highly complex polymer thought to be recalcitrant to microbial degradation . Transcriptional profiling and gene inactivation experiments revealed the identity and specificity of the polysaccharide utilization loci ( PULs ) that encode individual Sus-like systems that target various plant polysaccharides . Comparative genomic analysis indicated that B . ovatus possesses several unique PULs that enable degradation of hemicellulosic polysaccharides , a phenotype absent from B . thetaiotaomicron . In contrast , the B . thetaiotaomicron genome has been shaped by increased numbers of PULs involved in metabolism of host mucin O-glycans , a phenotype that is undetectable in B . ovatus . Binding studies of the purified sensor domains of PUL-associated hybrid two-component systems in conjunction with transcriptional analyses demonstrate that complex oligosaccharides provide the regulatory cues that induce PUL activation and that each PUL is highly specific for a defined cell wall polymer . These results provide a view of how these species have diverged into different carbohydrate niches by evolving genes that target unique suites of available polysaccharides , a theme that likely applies to disparate bacteria from the gut and other habitats .
The human distal gut is home to a densely populated microbial community ( microbiota ) that plays key roles in health and nutrition . The microbial symbionts that occupy this habitat produce an arsenal of enzymes that degrade dietary complex carbohydrates ( glycans ) that cannot be hydrolyzed by host enzymes [1] . The simple sugars generated are fermented into host-absorbable end products , including short-chain fatty acids , that can contribute as much as ∼10% of the calories extracted from the human diet and are thought to play a role in preventing colorectal cancer [2] . Viewed at the broad taxonomic level of bacterial phylum , two groups of bacteria dominate the distal gut microbiota of adult humans and other mammals: the Bacteroidetes and the Firmicutes [3]–[6] . Studies of cultivated human microbiota species indicate that the Bacteroidetes , composed largely of members of the genus Bacteroides , exhibit broad capacities to metabolize a variety of plant- and animal-derived glycans [7] , [8] . Bacteroidetes from a variety of environments including the human gut employ a similar strategy for binding and degrading various glycans [9] . These Gram-negative bacteria have amplified and permuted a series of gene clusters termed polysaccharide utilization loci ( PULs ) . Each PUL that has been characterized to date encodes a suite of cell envelope-associated proteins ( Sus-like system ) that confer the ability to metabolize a single glycan or group of related glycans [8] , [10]–[12] . Each Sus-like system contains at least one pair of outer membrane proteins homologous to SusC and SusD , which are essential for the import and degradation of starch by the prototypic starch-utilization system ( Sus ) for which related systems are named [10] . SusC-like proteins are predicted TonB-dependent receptors that span the outer membrane and transport oligosaccharides in an energy-dependent manner . SusD-like proteins are outer membrane lipoproteins that are oriented towards the external environment; they bind directly to specific glycans and contribute to the capture and delivery of oligosaccharides to the SusC transporter [12] , [13] . SusC- and SusD-like proteins function in concert with other outer membrane glycan binding proteins and polysaccharide degrading enzymes ( glycoside hydrolases , polysaccharide lyases , and carbohydrate esterases ) , which are grouped into sequence-based families in the Carbohydrate Active Enzymes ( CAZy ) database [14] . PULs frequently contain genes encoding inner membrane sensor-regulator systems that control the expression of genes in their associated and usually adjacent locus [12] , [15] , [16] . These sensor-regulators are most commonly extra-cytoplasmic function sigma ( ECF-σ ) /anti-σ factor pairs or hybrid two-component systems ( HTCS ) that contain all of the domains of a classical two-component system phosphorelay in a single polypeptide [17] . While glycans are known to activate PUL-associated HTCS , there is a paucity of information about the actual molecular cues recognized , and the mechanism by which these inducers mediate their effect , although a previous study has indicated that large oligosaccharides are involved in activation of an HTCS controlled xylanase locus from a ruminant Bacteroidetes [18] . In the simplest model , the activating glycan will bind directly to the periplasmic sensor domain , as occurs when fructose activates the B . thetaiotaomicron fructan PUL by binding directly to its HTCS [12] . However , the periplasmic sensor domain of the fructan HTCS is atypical compared to those found in other HTCS ( i . e . , it is around one-third the size and adopts a periplasmic binding protein fold ) . It is therefore possible that the sensor domains most commonly associated with other PULs recognize more complex ligands , that the glycans are presented to the cognate HTCS bound to other periplasmic proteins , or that the HTCS interact with part of their cognate SusC porin in the periplasm in a manner analogous to the trans-envelope signaling that occurs in ECF-σ/anti-σ systems [16] , [19] . Furthermore , the extent to which there is cross-talk between the regulatory systems of different PULs is unclear . Thus , while previous animal feeding experiments have shown which PULs in B . thetaiotaomicron are activated in vivo by diets containing plant polysaccharides [20] , [21] , we do not know the specific components of the diet that activate individual PULs . A major factor shaping the balance between different human gut bacterial phylogenetic types ( phylotypes ) is the ability of each group to compete efficiently for the complex glycans that are delivered to the distal intestine . To understand how human gut bacteria have evolved to occupy distinct niches , we have measured the ability of two closely related Bacteroides phylotypes to metabolize complex dietary glycans . B . thetaiotaomicron and B . ovatus share 96 . 5% nucleotide sequence identity in their 16S rDNA genes . With the exception of cellulose , these species are together capable of using all major glycan classes found in the gut mucosa and in plant cells as sole carbon sources . However , each species alone has evolved to target only a partial set of all possible glycans . We use transcriptional profiling , in conjunction with the characterization of mutants lacking functional PULs , to establish that their ability to metabolize different plant cell wall glycans is contingent on selective expression of PUL-encoded Sus-like systems . Most of the identified plant glycan PULs are linked to an HTCS that is activated by the presence of the PUL's target glycan . Specific activation of each PUL is achieved by direct recognition of signature oligosaccharide cues by the cognate HTCS . B . thetaiotaomicron contains many PULs that are not present in B . ovatus and confer its increased ability to target host mucin O-glycans and expanded capacity to target pectic structures . Conversely , B . ovatus harbors several unique PULs that enable it to use all of the common hemicelluloses , while B . thetaiotaomicron is unable to metabolize this group of plant structural polysaccharides . In both cases , unique species-specific PULs are scattered throughout the bacterial genome rather than being present in one or more large blocks . These data support the concept that adaptation to different glycan niches is driving selective evolution of PULs in these two species . This theme may apply to other bacteria in the human gut and has implications for both the basic ecology of the gut microbiota as well as efforts to intentionally manipulate this community to restore health or alter nutrition .
To investigate the relationship between glycan metabolic phenotypes and underlying genetic architecture in commonly isolated human gut bacteria , we focused on B . thetaiotaomicron ATCC 29148 ( also known as VPI-5482 ) and B . ovatus ATCC 8483 . The rationale for our selection is based on a previous survey showing that while members of these two species can access a wide range of glycans [7] , they display substantial differences in complex carbohydrate utilization . To define in detail the range of glycans that B . thetaiotaomicron and B . ovatus are capable of utilizing , we constructed a custom panel of plant , animal , and microbial carbohydrates arrayed in 96-well format ( Table 1 ) . These polysaccharides are more extensive than those used in previous studies , and include plant polymers such as rhamnogalacturonan I and II and purified fragments from a number of other highly decorated pectins and hemicelluloses . In addition , we monitored anaerobic growth over a defined time interval , enabling quantitative measurements of both growth rate and final culture density for each glycan . B . thetaiotaomicron and B . ovatus each grew on a subset of the glycans tested ( Table 1 ) . Both species grew on plant cell wall pectins except arabinogalactan and arabinan , which were only efficiently utilized by B . thetaiotaomicron . Interestingly , as we had noted during a previous comparison of B . thetaiotaomicron growth on starch and dextran [13] , each species grew more rapidly on several different polysaccharides compared to their corresponding monosaccharide components . Although this result is counter-intuitive because each polysaccharide needs to first be de-polymerized prior to metabolism , it suggests that monosaccharide transport and metabolism pathways are optimally triggered by the presence of polymerized sugar molecules . For example , growth of B . thetaiotaomicron on potato pectic galactan was 2 . 5±0 . 5 times more rapid than on its monosaccharide constituent galactose ( p<0 . 001 , Student's t test ) . More efficient utilization of polymeric glycans may be attributable to the fact that very little free monosaccharide reaches Bacteroides species in the distal intestine , with selective pressures acting on these bacteria to evolve ways to directly couple downstream catabolic pathways to glycan recognition . B . ovatus was the only one of the two species capable of growing on the hemicelluloses tested ( Table 1 ) ; it also grew on the disaccharide cellobiose but not on cellohexaose , suggesting that it is not capable of targeting higher molecular forms of cellulose . We conclude that the ability of B . ovatus to grow on cellobiose is a product of its ability to degrade other molecules that contain β1 , 4-glucosidic linkages , such as barley β-glucan or xyloglucan , a view supported by HTCS specificity data presented below . Additional insight into the sensing and catabolic specificities of B . ovatus for closely related β-glucan substrates was provided by testing a variety of structures that vary with respect to the linkages they contain and their relative ratios in the polysaccharide ( see Text S1 ) . Beyond their utilization of plant cell wall glycans , both species grew well on plant cell storage carbohydrates such as fructans and starch ( amylopectin ) , and on the starch-like molecules pullulan and glycogen . B . thetaiotaomicron and B . ovatus have reciprocal specificities for fructans: B . thetaiotaomicron grows best on the β2 , 6-linkages that occur in levan , whereas B . ovatus does not grow at all on levan but prefers β2 , 1-linked inulin [12] . Only B . thetaiotaomicron grew on mucin O-glycans , a trait that we previously demonstrated to be dependent on expression of over a dozen different PULs [8] . B . thetaiotaomicron also grew much more efficiently than B . ovatus on α-mannan , a fungal cell wall glycan that contains similar α-mannosidic linkages to those found in the core regions of N-linked glycans present on secreted mucus and epithelial surfaces . Lastly , B . thetaiotaomicron exhibited better growth ( more rapid rate and higher cell density ) on the third class of host-derived glycans , glycosaminoglycans ( GAGs ) , which include chondroitin sulfate , heparin , and hyaluronan . Together , the results indicate that these two human gut-associated Bacteroides have evolved distinct and only partially overlapping glycan niches . B . thetaiotaomicron is more adept at foraging the more soluble , and possibly more accessible , pectic components of plant cell walls . It also exhibits a well-developed capacity to metabolize host mucin O-glycans , a trait that could allow it to preferentially colonize the protective mucus layer that overlies the gut epithelium . Conversely , B . ovatus is metabolically specialized to utilize less soluble plant cell wall components like hemicelluloses , in addition to pectins , and thus is more likely to occupy physical microhabitats located in the gut lumen . To examine the molecular basis underlying plant cell wall glycan utilization by these two species , we used custom GeneChips representing 99 . 5% and all of the predicted or known ORFs in the B . thetaiotaomicron and B . ovatus genomes , respectively . Our rationale was that identifying the genes responsible for plant cell wall degradation would allow us to compare the genomic location and organization of these genes between species . Whole-genome transcriptional profiles were generated for each species during exponential growth on individual glycans ( substrates upon which each species was profiled are noted in Table 1 ) . The specific transcriptional responses of each species to growth on a particular glycan were then determined by comparison to a reference dataset of that same species grown in minimal medium with glucose as the sole carbon source ( MM-G ) . We previously demonstrated that genes associated with individual PULs typically exhibit large increases in their transcription when exposed to the substrates they process [8] , [12] . Therefore , we applied a cutoff of ≥10-fold change in expression in minimal medium containing a given glycan compared to MM-G . A total of 280 B . thetaiotaomicron genes exhibited altered expression in response to growth on one or more of the six pectins tested , or pullulan ( a control for expression of the starch utilization system ) . Expression of 268 genes ( 96% ) was increased , while only 12 exhibited decreased expression ( see Table S1 for a list of genes and fold-change values ) . Of the genes with altered expression during growth on one or more glycans , 155 ( 56% ) were associated with 16 different PULs and all but five of these genes were upregulated . These observations indicate that PULs are a primary component of this symbiont's response to different pectins . To better visualize responses of entire PULs to the various glycans tested , genes were grouped into putative operons [22] and the average fold-change of each operon re-calculated from normalized GeneChip values [8] . Eleven PULs had one or more operons that still exhibited ≥10-fold induction when cells were exposed to pectin or pullulan ( Figure 1 ) . Based on the predicted activities of the enzymes encoded by these PULs ( enzymes in CAZy families where pectin degradation is a common feature ) , it is likely that 10 of them ( all except the pullulan-induced starch PUL , BT3698-3704 ) make a significant contribution to pectin degradation ( Table S2; for schematic diagrams of these 10 PULs , see Figure S1 ) . Support for this view is provided by a recent study that showed that the three GH43 enzymes from the arabinan-activated PUL spanning BT0348-69 display arabinan-specific activity [23] . This emerging portrait of the predicted enzyme specificity in PULs that have been empirically matched with specific polysaccharide substrates will provide a valuable template for future functional annotation of the more than 100 Bacteroidetes genomes that are known to harbor similar gene clusters , as well as a starting point for more focused biochemical and enzymatic studies of how these systems each attack their specific substrates . Comparison of our data to previous in vivo studies using gnotobiotic mice colonized with B . thetaiotaomicron alone ( “mono-associated” ) showed that a subset of the pectin-responsive loci were expressed in the ceca of animals consuming a diet composed of wheat- and soy-based plant material ( “plant-rich diet” column in Figure 1 ) , suggesting that these natural dietary substrates contain pectins that are accessible to B . thetaiotaomicron in the absence of other bacteria . When these dietary substrates are withheld , as in mice fed a simple sugar diet [20] or in neonatal mice suckling on mother's milk [21] , expression of the “pectin PULs” is reduced . In contrast , PULs targeting host-derived glycans , especially the O-glycans that are abundant in secreted mucus , are highly expressed regardless of diet , suggesting that B . thetaiotoamicron continuously forages on these substrates in vivo . To better understand the specificity of PUL expression in response to different glycans , we also evaluated the plant glycan-inducible responses of 12 previously identified PULs , known to orchestrate the degradation of host-derived glycans [8] . Despite the fact that some sugars like galactose and fucose are common to plant and host glycans , PULs specific for host glycans were not expressed in the presence of plant pectins or vice versa ( compare columns A and B in the top and bottom sections of Figure 1 ) , suggesting that individual PULs are activated in response to more complicated and unique oligosaccharide signals . Experiments that probe the specificity of HTCS regulators for oligosaccharide signals described later in this report support this view . A notable feature of the pectin-specific PULs summarized in Figure 1 is activation of some PULs ( e . g . , BT4145-4183 ) by multiple substrates . A likely explanation for this phenomenon is that the preparations used for the growth assays were contaminated with trace amounts of other pectic glycans due to the inherent complexity and covalent connections originally present in these molecules prior to purification . This conclusion is supported by compositional analysis data available from the supplier: each preparation contained 5%–18% “contaminating” sugars that are not expected to be part of the purified pectic glycan but are present in other chains attached to a common backbone polysaccharide . Also , consistent with this notion , each PUL typically showed a much stronger transcriptional response on one substrate compared to the others . For example , BT4108-23 showed highest induction on homogalacturonan and lower overall responses to three other pectins—pectic galactan and rhamnogalacturonans I and II . Compositional analysis of the former two substrates indicated the presence of homogalacturonan contamination , whereas homogalacturonan is a backbone component of the rhamnogalacturonan II structure [24] . Thus , it is likely that BT4108-23 primarily targets homogalacturonan and that this substrate is responsible for its activation . This notion is compatible with the observed carbohydrate active enzyme ( CAZyme ) content of this PUL , which encodes seven enzymes that are members of families known to target homogalacturonan . Biochemical analysis of one of the CAZymes from the PUL ( BT4116; a predicted family 1 polysaccharide lyase ) supports this view as the enzyme was able to cleave homogalacturonan in an endo-like fashion but displayed no activity against rhamnogalacturonan I ( Figure S2 and Table S2 ) . A feature shared by 8 of the 10 B . thetaiotaomicron PULs activated by pectin is their association with a HTCS regulator . To further dissect the involvement of specific PULs in pectin degradation , we disrupted the HTCS genes associated with pectin-activated PULs as well as several others associated with additional PULs ( Table 2; see Figure S1 for a schematic of HTCS mutant locations and Table S3 for a list of all HTCS mutant strains tested ) . Six of the mutants with disruptions in HTCS genes linked to pectin-induced PULs resulted in a growth defect on different pectins ( Table 2 and Figure S4 ) . In contrast to the previously observed complete loss of growth after disruption of three B . thetaiotaomicron HTCS regulators associated with GAG- or fructan-utilizing PULs [8] , [12] , none of the disrupted HTCS genes linked to pectin-induced PULs resulted in such a drastic phenotype on any of the pectins tested . Thus , although no individual PUL is absolutely essential for full degradation of a particular pectin preparation tested , each of the six individual PULs identified appear to be optimized for degradation of a specific substrate . This observation is supported by data presented below that show that individual PULs are specifically activated by defined oligosaccharides derived from the different pectins . Our phenotypic analysis indicated that B . ovatus has evolved a capacity to target a series of hemicellulosic polysaccharides that B . thetaiotaomicron cannot access . To determine which B . ovatus genes are involved in hemicellulose metabolism , we performed transcriptional profiling experiments on B . ovatus cells grown on each of the different hemicelluloses ( Table 1 ) . This species was also profiled on the pectin homogalacturonan so that we could compare its responses to that of B . thetaiotaomicron on the same substrate . Growth of B . ovatus on individual hemicellulose preparations or homogalacturonan resulted in altered expression of 259 total genes using the same ≥10-fold cutoff threshold used for B . thetaiotaomicron: 229 of these genes were upregulated , while 30 were downregulated . As with B . thetaiotaomicron , most of the B . ovatus genes were uniquely expressed in response to just one or a few of the carbohydrates ( Table S4 ) . We next identified putative PULs in the B . ovatus genome using the same criteria as those used previously for B . thetaiotaomicron [8] . Minimally , a PUL had to contain at least a pair of genes encoding homologs of the B . thetaiotaomicron SusC/D proteins . This effort yielded 112 candidate B . ovatus PULs encompassing 1 , 129 ORFs ( see Table S5 for a list of genes and annotations by PUL ) . As observed in B . thetaiotaomicron , most annotated PULs also contained one or more genes encoding predicted CAZymes and/or an environmental sensor/transcriptional regulator . In total , 140 ( 61 . 1% ) of the genes that were upregulated in response to growth on one or more glycans were located in PULs . Six different PULs were activated by hemicellulosic polysaccharides: two by xylans , one by galacto-/glucomannan , one by xyloglucan , and two by barley β-glucan ( Figure 2 ) . The predicted CAZyme content of the six PULs activated by hemicelluloses ( Table S2 and Figure S6 ) is consistent with their capacity to orchestrate degradation of the inducing polysaccharides . For example , the xylan- , xyloglucan- , and galacto-/glucomannan-regulated PULs encode GH10 , GH9 , and GH26 enzymes , respectively , families dominated by xylanases ( GH10 ) , endoglucanases ( GH9 ) , and β-mannanases ( GH26 ) . Indeed , a previous study has shown that two enzymes encoded within the smaller of the two xylan activated PULs ( BACOVA_04387 and BACOVA_04386 ) display endo-xylanase and β-xylosidase activities , respectively [25] . In addition , a PUL that is homologous to the smaller B . ovatus xylan PUL was recently identified in the rumen Bacteroidete Prevotella bryantii using a transcriptomic approach and wheat arabinoxylan as a test substrate [11] . Finally , several of the enzymes from the xylan-activated PULs contain carbohydrate binding modules from families known to display xylan binding functionality ( CBM6 , 22 , and 35 ) , adding further support for a role of products of these loci in xylan utilization . Interestingly , the large xylan PUL also contains a number of genes encoding enzymes from CAZyme families not previously implicated in xylan deconstruction ( e . g . , GH31 , GH95 , GH97 , GH98 ) , suggesting that these sequences may exhibit novel specificities or target linkages so far not identified in various xylans ( Table S2 ) . Together , these findings support our conclusion that both B . ovatus and B . thetaiotaomicron rely on similar PUL-based strategies to degrade plant cell wall glycans . They also highlight how broadly Bacteroidetes Sus-like systems have evolved and further define the experimentally demonstrated range of substrates they target to include all major classes of hemicelluloses . We subsequently measured B . ovatus PUL gene expression in the distal gut ( cecum ) of mono-associated gnotobiotic mice fed the same plant glycan-rich diet used to examine in vivo expression of B . thetaiotaomicron . A total of 353 B . ovatus genes exhibited altered expression in vivo relative to in vitro growth in MM-G: 126 genes also exhibited altered expression in the presence of one or more of the plant cell wall glycans tested and 50% of all in vivo responsive B . ovatus genes were located within putative PULs ( Figure S5 and Table S5 ) . Three B . ovatus PULs that were activated by the hemicelluloses xylan and β-glucan were expressed in vivo , suggesting that these substrates were present in the plant-rich diet fed to mice and could be sensed by this species in the absence of other members of the human gut microbiota ( Figure 2 ) . One of two B . ovatus PULs that responded to homogalacturonan in vitro was also expressed in vivo . Given the partially overlapping sets of carbohydrate degradation traits exhibited by B . thetaiotaomicron and B . ovatus , we wanted to examine the degree to which individual orthologous PULs were maintained between these two Bacteroides . We reasoned that PULs that are unique to either genome would provide evidence of independent acquisition or retention of traits that are not shared between the species . Because the genome sequence of B . thetaiotaomicron VPI-5482 has been assembled into a single circular chromosome and contains fewer PULs than B . ovatus , we performed individual searches , using each of the 88 individual B . thetaiotaomicron PULs as queries , to probe for similar loci in the deep draft assembly of the B . ovatus genome . Our method ( described in detail in Materials and Methods ) was based on first comparing the core SusC/D amino acid sequences from each B . thetaiotaomicron PUL to the closest set of homologs in B . ovatus and then to score potentially homologous PULs for both gene homology and synteny within the PUL and in flanking genomic regions . Using this approach , each of the 198 PULs in the two Bacteroides species was scored as “homologous , ” “probably homologous , ” or “unique” to a respective species ( Table S6 ) . Only 28 PULs met our criteria for being homologous between B . thetaiotaomicron and B . ovatus ( i . e . , included in the homologous or probably homologous groups ) , suggesting that differential acquisition or retention of novel PULs is a mechanism underlying the phenotypic differences between these species ( Figure 3 ) . Among the group of PULs shared by both species were loci corresponding to each of the glycan metabolic traits that were strongly exhibited by both species: these include PULs for targeting starch , fructans , glycosaminoglycans , and all pectins except arabinan and arabinogalactan ( PULs with dark or light green labels in Figure 3 ) . Evidence of the divergent evolution of these two species becomes apparent through visualization of the non-homologous PULs in their respective genomes . B . thetaiotaomicron contains at least eight unique PULs associated with targeting host mucin O-glycans ( see PULs with gold labels in Figure 3 that are noted as “O-glycans”; this designation only includes PULs that have been confirmed to respond to purified neutral mucin O-glycans in vitro [8] ) . In addition , three previously validated PULs for degrading α-mannan are also unique to B . thetaiotaomicron . Conversely , B . ovatus contains five unique PULs that underlie its ability to target plant cell wall hemicelluloses . An additional PUL ( BACOVA_0942-46 ) that responded to β-glucan weaker than a second PUL ( BACOVA_02741-47 ) was scored as homologous . The PULs that encode each species' unique phenotypes are scattered throughout each genome , suggesting that they arose through individual genetic events . This latter observation provides evidence for the idea that these two species are adapting to different carbohydrate niches and that these adaptations could serve to exclude access to others . For example , if B . ovatus had obtained its hemicellulose utilization PULs by lateral gene transfer or from a common ancestor , it might be expected that B . thetaiotaomicron would have been exposed to this same pool of traits during its own evolution . However , the sequenced strain of B . thetaiotaomicron analyzed here has not acquired any of these individual loci , nor do any of several dozen different B . thetaiotaomicron strains tested exhibit growth on hemicelluloses [7] ( N . Pudlo and E . C . Martens , unpublished ) . Thus , B . ovatus appears to have evolved a predilection for hemicellulose degradation that has resulted in its retention of PULs that target a family of glycans occupying similar positions in the plant cell wall . A similar picture emerges for B . thetaiotaomicron , which has evolved a predilection for host mucin O-glycans and possibly N-glycans as evidenced by its robust ability to degrade the linkages in α-mannan . The data presented above indicate that each Bacteroides PUL recognizes a specific molecular cue that is a component of its target polysaccharide . The most common class of regulator associated with PULs that target plant cell wall glycans are HTCS , inner membrane spanning proteins with predicted periplasmic sensory domains [12] , [17] . A potential mechanism of signal perception by these regulators is direct binding of an oligosaccharide degradation product to the periplasmic domain of the HTCS . As noted above , this mechanism of signal perception has already been validated for one HTCS ( BT1754 ) from B . thetaiotaomicron's fructan utilization PUL [12] . However , unlike BT1754 , which recognizes monomeric fructose and contains a ∼300 aa sensory domain that adopts the periplasmic binding protein fold , the majority of Bacteroidetes HTCS contain a much larger putative periplasmic sensor domain of ∼700–900 aa [26] . Sequence analysis revealed that these large sensor domains contain multiple short motifs ( Reg_prop , Pfam 07494 ) indicative of an overall β-propeller fold , followed by a domain of ∼120 aa termed YYY ( Pfam 07495 ) [27] , [28] . In addition to the predicted N-terminal periplasmic domain , most HTCS polypeptides possess all of the cytoplasmic domains present in a classical two component system phosphorelay [26] , but in a single polypeptide , including a phosphoacceptor and dimerization domain , histidine kinase , receiver domain , and a DNA binding domain of the HTH_AraC family ( Figure S7 ) . To explore the mechanism of signal perception and identity in the HTCS proteins and further dissect their specificity , we expressed and purified the predicted periplasmic domains of several B . thetaiotaomicron and B . ovatus HTCS identified above as being involved in plant glycan utilization and assessed their ability to bind carbohydrates using isothermal titration calorimetry ( ITC ) . The ITC data reveal that the periplasmic domains of four HTCS bind specifically to oligosaccharides that uniquely define the parent polysaccharide that the cognate PUL is optimized to degrade . Binding data are summarized in Table 3 and Figure S8 , molecular illustrations of oligosaccharide signaling molecules that optimally bind to each HTCS sensor domain are shown in Figure 4 , and detailed findings for each individual glycan are provided in Text S1 . Notably , the binding of oligosaccharides to the HTCS is highly specific with each sensor domain only displaying affinity for glycan fragments derived from a single type of polysaccharide ( e . g . , the arabinan sensor BT0366 binds only arabino-oligosaccharides and not other oligosaccharides tested ) ( see footnote to Table 3 ) . Unlike the previously described fructose-sensing HTCS that binds a simpler monosaccharide signal [12] , all of the HTCS sensors described here interacted directly with oligosaccharides , consistent with the idea that the specific recognition of most polysaccharides requires information contained in both the sugar content and glycosidic linkages . By sensing oligosaccharide cues that uniquely define the parent polysaccharide , B . thetaiotaomicron and B . ovatus ( and by extension other Bacteroidetes that contain PUL-associated HTCS ) are able to differentiate between multiple complex glycans that contain the same sugars , and to respond efficiently by activating only the appropriate PUL for their degradation . Interestingly , the preferred ligands for the HTCS were relatively large glycan fragments ranging from tetra- to octa-saccharides and therefore represent early products of the depolymerization process that are likely transported into the periplasm via their specific PUL-encoded SusC-like outer membrane transporter , as has been suggested for maltooligosaccharides for the starch-utilization system [29] . Together , these data suggest that Bacteroides respond rapidly and specifically to the presence of polysaccharides in their environment . This requirement for a rapid and highly defined response may explain the localization of the signal input and output domains of HTCS in a single polypeptide , as this physical constraint will both maximize the speed of activation and minimize cross-talk among these systems . It is also notable that all four HTCS sensors interacted directly with linear oligosaccharides , suggesting that the presence of branches , which can vary between related glycans from different sources , is not a required signaling component for this subset of sensors . In one case ( mannopentaose versus di-galactosyl-mannopentaose ) , the presence of galactose branching interfered completely with detectable binding , suggesting that removal of these branches is a prerequisite for sensing of galactomannans . To confirm that the oligosaccharides we identified by ITC were able to specifically activate PUL gene expression in bacterial cells , we measured the relative amount of the susC-like gene transcript ( a proxy for expression of the whole operon ) of each PUL when the bacteria were grown on the cognate HTCS ligand . The data reveal that the oligosaccharide that binds preferentially to the HTCS specifically upregulates the susC-like gene , and by inference the whole PUL , associated with that HTCS ( Figure 5 ) . For example , exposure of B . thetaiotaomicron to arabino-octaose ( the preferred ligand for BT0366 HTCS ) results in a 10–100-fold greater induction of the susC homolog present in the BT0366-associated PUL ( BT0348-69 ) compared to its effects on the other PULs that are activated by arabinan , but whose associated HTCS display no binding to arabino-oligosaccharides ( 250–400-fold for susC-like genes BT0362 and BT0364 compared to 15- and 2-fold for the susC-like genes , BT3046 and BT4164 ) . Similarly , growth of B . ovatus on xylotetraose , the preferred ligand for BACOVA_04394 , upregulates the susC-like gene ( BACOVA_04393 ) associated with this PUL , but not two susC-like genes from the larger xylan PUL ( BACOVA_03426 and _03428 ) . These findings support the HTCS binding data and demonstrate that activation of each PUL is by a defined oligosaccharide cue that is specifically recognized by the associated HTCS ( i . e . , there is little or no cross-reactivity between the PULs ) . Additional details of the transcriptional response of B . thetaiotaomicron to pectic oligosaccharides are provided in Text S1 . The distal gut of humans is constantly inundated with a dynamic array of carbohydrates . These substrates feed the dense consortium of microbes that compete in this habitat . The gut also presents biochemical gradients that result from the differential rates of digestion of dietary resources and from the presence of a mucus layer overlying the epithelium . Given the gut's intrinsic biochemical heterogeneity , it is perhaps not surprising that different microbial lineages would evolve to fill distinct glycan niches . The data presented here support the notion that two closely related human gut symbionts have taken divergent paths that have left each species with a partially unique repertoire of metabolic traits . Although B . thetaiotaomicron and B . ovatus are both common in the normal adult human gut microbiota , at least in Western societies [5] , they represent only two of at least 45 different species of Bacteroidetes that have been cultured to date from human specimens . Complete or deep-draft genomic sequences will soon be available for many more members of these species as culturing efforts [30] and sequencing technology progress rapidly . The availability of additional cultured Bacteroidetes , together with the ability to dissect their carbohydrate active phenotypes using the approaches described here , present an opportunity to reveal basic biological parameters that have catalyzed the niche-specific adaptation of gut bacterial lineages throughout human history . At the same time , identifying the molecular machinery for acquisition , import , and catabolism of specific polysaccharides will help inform efforts to engineer carbohydrate active phenotypes in microbes ( via PUL “transplants” ) in order to fulfill important industrial needs , as well as to manipulate human gut microbiome function in ways that restore health or enhance nutrition .
All experiments involving mice used protocols approved by the Washington University Animal Studies Committee in accordance with guidelines set forth by the American Veterinary Medical Association . Trained veterinarians from the Washington University Division of Comparative Medicine supervised all experiments . The laboratory animal program at Washington University is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . B . thetaiotaomicron ATCC 29148 ( VPI-5482 ) and B . ovatus ATCC 8483 were routinely grown in tryptone-yeast extract-glucose ( TYG ) medium [31] or on brain-heart infusion ( BHI; Beckton Dickinson ) agar plus 10% horse blood ( Colorado Serum Co . ) . Antibiotics were added as appropriate: erythromycin ( 25 µg/ml ) and gentamicin ( 200 µg/ml ) . Minimal medium ( MM ) was formulated as previously described [8] . B . thetaiotaomicron HTCS mutant strains were constructed by suicide plasmid insertion ( denoted as “Ω” mutants ) [32] . The ability of B . thetaiotaomicron and B . ovatus to grow on pure carbohydrates was measured using a custom carbohydrate array constructed in a 96-well format . Each well of a flat bottom 96-well plate ( Costar ) was loaded with 100 µl of each sterilized carbohydrate stock ( Table S7 ) at 2× concentration . Each substrate was represented twice on each assay plate in two non-adjacent wells . Two carbohydrate-free water wells were included as negative controls . Cultures for assay inoculations were grown for ∼24 h at 37°C under an atmosphere of 10% H2 , 5% CO2 , and 85% N2 in MM-G , and a 1 ml aliquot centrifuged to pellet bacteria , which were then gently resuspended in 2× MM-no carbohydrate ( MM-NC ) and used to inoculate 50 ml of 2× MM-NC at a ratio of 1∶50 . Each carbohydrate array was loaded with 100 µl of the inoculated 2× medium to produce 96 individual 200 µl cultures . Assay plates were sealed in an anaerobic chamber ( Coy manufacturing , Grass Lake , MI ) under the atmosphere noted above with an optically clear gas-permeable polyurethane membrane ( Diversified Biotech , Boston , MA ) . Plates were then loaded into a Biostack automated plate handling device coupled to a Powerwave HT absorbance reader ( both devices from Biotek Instruments , Winooski , VT ) . Absorbance at 600 nm ( A600 ) was measured for each well at 10–15 min intervals . B . thetaiotaomiocron and B . ovatus were each tested in three separate carbohydrate arrays ( n = 6 replicate cultures ) . Data were processed using Gen5 software ( Biotek ) and Microsoft Excel . Several glycans yielded complicated polyphasic growth profiles rather than a single exponential growth phase . Thus , we quantified growth in each assay by first identifying a minimum time point ( Amin ) at which A600 had increased by 10% over a baseline reading taken during the first 500 min of incubation . Next , we identified the time point at which A600 reached its maximum ( Amax ) immediately after exponential growth . Two growth parameters were generated for each well: “rate” [ ( Amax−Amin ) / ( Tmax−Tmin ) ] , and “density” ( Amax−Amin ) , where Tmax and Tmin are the corresponding time values for each absorbance . Cultures that failed to increase density by at least 0 . 1 ( A600 ) were scored as no growth . Cultures for transcriptional profiling were grown in borosilicate test tubes containing 5 ml of the same MM formulations described above , except that rhamnogalacturonan II was used at 15 mg/ml for transcriptional profiling experiments . All cultures were harvested during mid- to late-exponential phase; absorbance values ( at 600 nm ) of each harvested culture are summarized in Table S8 . Water soluble oat spelt xylan ( OSX ) was prepared by solubilizing oat spelt xylan ( Fluka ) in 1 M NaOH followed by centrifugation ( 8 , 750× g for 30 min ) to remove insoluble material . The soluble supernatant was then adjusted to pH 7 . 0 with HCl , centrifuged again to remove insoluble glycans that precipitated at neutral pH , dialyzed exhaustively against ddH2O and finally dried by lyophilization . Rhamnogalacturonan II ( a kind gift from Malcolm O'Neil at the University of Georgia Complex Carbohydrate Research Center ) was purified from red wine as described previously [33] . Transcriptional profiling was performed using custom Affymetrix GeneChips containing probesets representing >98% of 4 , 779 predicted B . thetaiotaomicron genes [20] , and all of the 5 , 536 predicted B . ovatus genes . GeneChip targets were prepared from whole bacterial RNA and hybridized to the microarrays according to standard Affymetrix protocols ( www . affymetrix . com ) . Data were normalized using Microarray Suite 5 or Expression Console software ( Affymetrix ) and processed using GeneSpringGX 7 . 3 . 1 software ( Agilent ) according to a previously described workflow [8] . Further details concerning bacterial growth conditions and experimental parameters are provided in Table S9 , along with individual GEO accession numbers and file names for each dataset . All mice were from the NMRI-KI inbred line and were reared in gnotobiotic isolators as previously described [21] . Six-week-old male germfree animals were used for B . ovatus colonization . Each mouse was gavaged with 100 µl of a fresh overnight culture containing ∼1×108 cfu/ml . B . ovatus colonization levels in the cecum were between 5×1010 and 5×1011 cfu/ml for all animals . Animals were sacrificed 14 d after colonization and their cecal contents harvested for RNA extraction . To locate putative PULs , the publicly available B . ovatus ORF annotation was searched using an iterative BLAST strategy described for other Bacteroidetes species [34] . This process yielded 112 B . ovatus PULs that minimally contained homologs of susC/susD . To compare PUL gene content between species we used a list of reciprocal best BLASTP hits between the B . thetaiotaomicron and B . ovatus genomes ( E-value cutoff ≤−10 ) and the “show ortholog neighborhood regions” in the Department of Energy Integrated Microbial Genome website ( img . jgi . doe . gov ) to guide analysis of PULs that were shared between these two species . Beginning with the first susC sequence in B . thetaiotaomicron , we searched for an orthologous neighborhood in B . ovatus . If this produced a hit , then we repeated the search with the adjacent B . thetaiotaomicron susD sequence to verify that the same locus was found in B . ovatus . We next compared the genomic regions surrounding each potentially orthologous PUL for conservation of gene content both within the PUL and in neighboring genomic regions . PULs were differentially scored for orthology between species based on the following criteria: ( i ) loci that had identical numbers of homologous PUL genes in the same orientation between species , and also contained at least three syntenic homologous genes in the region flanking the PUL , were scored as “orthologous PULs”; ( ii ) loci that exhibited similar numbers of homologous PUL genes but in different orientation between species and still contained at least three syntenic homologous genes in the region flanking the PUL were scored as “probably orthologous PULs”; ( iii ) PULs that exhibited different numbers of genes with little or no apparent homology , poor conservation of functional predictions ( e . g . , carbohydrate active enzymes ) , and were not located at syntenic genomic regions were scored as “non-orthologous PULs . ” DNA encoding the HTCS periplasmic domains were amplified from the appropriate species' genomic DNA using the primers stated in Table S9 and cloned into pET21d or pET28b ( Novagen ) . The location of signal peptides and internal transmembrane domains in the HTCS proteins were predicted using the web-based programs SignalP 3 . 0 ( http://www . cbs . dtu . dk/services/SignalP/ ) and TMPred ( http://www . ch . embnet . org/software/TMPRED_form . html ) , respectively . E . coli BL21 or Tuner ( Novagen ) cells were used to express recombinant proteins , which were purified in a single step using metal affinity chromatography , as described previously [35] . ITC was performed essentially as described previously [34] , using a Microcal VP-ITC . Proteins ( 50–200 µM , in cell ) were dialyzed into 20 mM HEPES , pH 8 . 0 , and ligands ( 0 . 5–20 mM oligosaccharides , 5–20 mg/ml polysaccharides , in syringe ) were dissolved in the dialysis buffer to minimize heats of dilution . Integrated binding heat effects minus heats of dilution were analyzed by non-linear regression using either a single or two-site binding model ( Microcal Origin 7 . 0 software ) . Additional quantification of transcript expression was performed by qPCR using a Roche Lightcycler 480 and primers listed in Table S9 . Bacteria were cultured in 5 ml of MM containing 0 . 5% carbon source , as described above . Triplicate bacterial cultures were harvested at mid-log phase and placed in RNAprotect ( Qiagen ) prior to purification with RNeasy kit ( Qiagen ) . cDNA was produced with QuantiTect Reverse Transcription kit ( Qiagen ) . qPCR was performed in a 96-well plate with SYBRgreen 480 I Master ( Roche ) . Data were normalized to 16S rRNA transcript levels . All oligosaccharides and polysaccharides ( low viscosity ) used for ITC and qPCR studies were from Megazyme ( Wicklow , Ireland ) , except for oat spelt xylan and cellobiose , which were from Fluka . The water-soluble fraction of oat spelt xylan was used and prepared as described above . Each protein model encoded by the genomes of the two Bacteroides studied here was subjected to a combination of BLAST [36] and HMMer [37] searches against , respectively , sequence libraries built with the individual modules of the proteins found in the CAZy database ( www . cazy . org ) , and HMM models built with each of the families present in CAZy [14] . To avoid missing distant relatives , permissive thresholds were used ( E-value<0 . 1 ) , and all resulting candidate proteins were manually screened by comparison to the CAZy families ( multiple alignments; presence of catalytic residues where known; presence of appended catalytic and non-catalytic modules , etc . ) . | Bacteria inhabiting the human gut are critical for digestion of the plant-derived glycans that compose dietary fiber . Enzymes produced by the human body cannot degrade these abundant dietary components , and without bacterial assistance they would go unused . We investigated the molecular strategies employed by two species belonging to one of the most abundant bacterial groups in the human colon ( the Bacteroidetes ) . Our results show that each species has evolved to degrade a unique subset of glycans; this specialization is reflected in their respective genomes , each of which contains numerous separate gene clusters involved in metabolizing plant fiber polysaccharides or glycans present in secreted mucus . Each glycan-specific gene cluster produces a related series of membrane-associated proteins which together serve to bind and degrade a specific glycan . Expression of each glycan-specific gene cluster is controlled by an environmental sensor that responds to the presence of a unique molecular signature contained in the substrate that it targets . These results provide a view of how related bacterial species have diverged into different carbohydrate niches by evolving genes that sense and degrade unique suites of available polysaccharides , a process that likely applies to disparate bacteria from the gut and other habitats . | [
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] | 2011 | Recognition and Degradation of Plant Cell Wall Polysaccharides by Two Human Gut Symbionts |
Antibodies that mediate killing of HIV-infected cells through antibody-dependent cellular cytotoxicity ( ADCC ) have been implicated in protection from HIV infection and disease progression . Despite these observations , these types of HIV antibodies are understudied compared to neutralizing antibodies . Here we describe four monoclonal antibodies ( mAbs ) obtained from one individual that target the HIV transmembrane protein , gp41 , and mediate ADCC activity . These four mAbs arose from independent B cell lineages suggesting that in this individual , multiple B cell responses were induced by the gp41 antigen . Competition and phage peptide display mapping experiments suggested that two of the mAbs target epitopes in the cysteine loop that are highly conserved and a common target of HIV gp41-specific antibodies . The amino acid sequences that bind these mAbs are overlapping but distinct . The two other mAbs were competed by mAbs that target the C-terminal heptad repeat ( CHR ) and the fusion peptide proximal region ( FPPR ) and appear to both target a similar unique conformational epitope . These gp41-specific mAbs mediated killing of infected cells that express high levels of Env due to either pre-treatment with interferon or deletion of vpu to increase levels of BST-2/Tetherin . They also mediate killing of target cells coated with various forms of the gp41 protein , including full-length gp41 , gp41 ectodomain or a mimetic of the gp41 stump . Unlike many ADCC mAbs that target HIV gp120 , these gp41-mAbs are not dependent on Env structural changes associated with membrane-bound CD4 interaction . Overall , the characterization of these four new mAbs that target gp41 and mediate ADCC provides evidence for diverse gp41 B cell lineages with overlapping but distinct epitopes within an individual . Such antibodies that can target various forms of envelope protein could represent a common response to a relatively conserved HIV epitope for a vaccine .
Eliciting an antibody response to the HIV Envelope protein is thought to be the most likely path to an effective vaccine , and there is evidence that both neutralizing and non-neutralizing HIV-specific antibodies can contribute to protection . Indeed , the only HIV vaccine trial to demonstrate measurable protection from HIV infection implicated non-neutralizing antibodies capable of mediating antibody-dependent cellular cytotoxicity ( ADCC ) [1] . Studies of mother-infant HIV transmission , a setting where both maternal antibodies and antibodies passively acquired by infants in utero are present during the period of transmission risk , have similarly implicated ADCC antibodies in protection . Specifically , ADCC-mediating antibodies isolated from breastmilk were correlated with infant infection outcome in women with high viral load [2] , and passively acquired ADCC-mediating antibodies correlated with clinical outcome in infants who acquired HIV after birth [3] . Evidence from studies in non-human primate models have similarly supported a role for non-neutralizing ADCC-mediating antibodies in limiting disease pathogenesis [4–17] , and antibodies defective in Fc-receptor binding demonstrated reduced protective efficacy [18 , 19] . Further investigation into the epitope targets of ADCC-mediating mAbs and their contribution to protection may help inform future vaccine strategies . Most studies have focused on antibodies directed to gp120 , the extracellular Env glycoprotein . The envelope transmembrane protein , gp41 , which is required for viral entry , is also a target of both neutralizing and non-neutralizing HIV antibodies [20–24] . During the entry process , gp41 undergoes a series of conformational changes that drive viral and host cell membrane fusion , resulting in opportunities for antibodies to recognize different gp41 epitopes at various stages in the process . Gp41 encodes several key functional domains in its extracellular portion ( ectodomain ) where antibodies target . These include the fusion peptide , which becomes exposed as a result of structural changes that promote fusion . There are also two heptad repeat ( HR ) regions ( N terminal HR/NHR and C terminal HR/CHR ) that are separated by a disulfide-bonded loop ( C-C’ loop ) , which presents an immunodominant epitope . The interaction of the NHR and CHR during the entry process leads to a six-helix bundle structure that joins the viral and cell membranes together . The region at the C-terminus of the extracellular domain of gp41 , the membrane proximal region ( MPER ) , is a target of several broadly neutralizing antibodies [22 , 24] . Because the extracellular regions of gp41 are conserved , gp41 is an excellent target for cross-reactive antibodies recognizing diverse viral strains [25] . Further , as virus buds from infected cells , some gp120 proteins are shed . As a result , gp41 stumps are exposed on the cell surface [26] and can be targeted by gp41-specific , ADCC-mediating antibodies [13 , 23 , 27–31] . Env gp41–directed antibodies arise early in infection [32] and several common targets have been described , including antibodies that recognize the C-C’ loop , which encodes an immunodominant epitope of gp41 ( referred to as cluster I antibodies ) and others that recognize the CHR ( cluster II antibodies ) , with cluster I being common in chronic infection [21 , 33–40] and associated with a broad response [39 , 41–43] . Anti-cluster I antibodies inhibit HIV via a variety of mechanisms [8 , 13 , 39 , 44–49] , including neutralization and ADCC [13 , 22 , 24 , 28 , 31 , 33 , 38 , 39] , though gp41-specific ADCC-mediating antibodies have been less well studied than neutralizing antibodies . However , there is evidence that ADCC antibodies could provide protection in both model systems and humans . IgA gp41-targeting antibodies have been isolated from highly exposed , HIV-negative individuals [50–53] and associated with protection [54–57] . Moreover , a gp41-based antigen elicited protection in a macaque model of mucosal infection [58] . Studies investigating the anti-viral effects of passively administered ADCC-mediating antibodies , while few relative to the plethora of passive neutralizing antibody studies , also provide some evidence for a non-sterilizing protective effect of gp41 antibodies [8 , 12 , 39 , 45 , 59–64] , and in particular , an effect of cluster I ADCC-mediating antibodies on viral load [13] . We recently isolated monoclonal antibodies ( mAbs ) from a clade A-infected individual by selecting B cells that bound to HIV virus-like particles ( VLPs ) [65] . While some of the reconstructed mAbs recognized gp120 , others did not , even though they showed detectable binding to the VLPs used as bait . One such antibody showed evidence of antibody-dependent cellular viral inhibition ( ADCVI ) activity [65] , prompting us to further evaluate the HIV-specific mAbs from this individual that did not recognize gp120 . Here we show that several of the VLP-specific antibodies target gp41 and mediate ADCC , including the antibody that demonstrated ADCVI activity . The four mAbs identified in this one individual all arose from independent B cell lineages and target either the immunodominant epitope that defines cluster I or a discontinuous epitope . We used a unique phage display approach to more finely map the epitopes of the two gp41 cluster I antibodies and showed that they have overlapping but distinct epitopes . The two other mAbs both target a similar discontinuous conformational epitope that includes both the CHR and the FPPR portions of gp41 . These mAbs also recognize a structure that mimics gp41 stumps and drive ADCC activity against cells coated with this gp41 mimetic .
We previously described twelve antibodies from a clade A HIV-infected individual , QA255 , that bound HIV clade A VLPs . One mAb ( QA255 . 187 ) demonstrated modest neutralization activity . Three mAbs , QA255 . 105 , QA255 . 157 and QA255 . 253 , mediated ADCC and ADCVI activity; QA255 . 105 also neutralized HIV [65] . The remaining eight mAbs bound the VLP but did not mediate activity in neutralization or in ADCC assays using gp120 as a target . Unexpectedly , QA255 . 006 showed ADCVI activity when included as a negative control mAb in that assay despite the fact that it did not mediate ADCC against gp120-coated cells . To explore the epitope specificity and function of these eight antibodies , a Binding Antibody Multiplex Assay ( BAMA ) that included a panel of 15 antigens was used , with two gp120-specific mAbs from QA255 serving as controls . Each antigen was individually coupled to fluorescent Luminex beads , including two gp41 proteins , five gp120 proteins representing four HIV clades and SIV , a CD4-binding site protein and negative scaffold protein , two clade C V1-V2 peptides , two V3 peptides , and BG505 SOSIP trimer ( Fig 1A ) . Consistent with previous findings that QA255 . 105 targets V3 [65] , this mAb bound to all five HIV gp120 proteins , both V3 peptides and the BG505 trimer . QA255 . 157 , which targets a CD4-induced ( CD4i ) epitope , bound to two of the five HIV gp120 and the BG505 SOSIP trimer . Of the eight mAbs with unknown epitopes , three did not show detectable binding to any of the proteins tested and one ( QA255 . 221 ) bound to only one antigen , the gp41 ectodomain at levels just above background . Four antibodies , QA255 . 006 , QA255 . 016 , QA255 . 067 and QA255 . 072 bound with a range of 628- to 656-fold above background and 272- to 292-fold above background to the C . ZA . 1197 gp41 ectodomain and MN gp41 proteins , respectively , suggesting that these antibodies target the gp41 portion of the HIV trimer ( Fig 1A ) . The very weak binding of these mAbs to the BG505 SOSIP is consistent with prior studies suggesting gp41 epitopes are largely occluded on this soluble form of the trimer [66] . Specificity for gp41 was confirmed by ELISA . QA255 . 006 , QA255 . 067 and QA255 . 072 all bound to MN gp41 protein at similar levels ( endpoint titer of 4 . 9 ng/mL ) , while QA255 . 016 displayed a less potent endpoint titer of 312 . 5 ng/mL . MPER-specific mAb 4E10 demonstrated intermediate binding with an endpoint titer of 78 . 1 ng/mL ( Fig 1B ) . All four antibodies demonstrated comparable binding against C . ZA . 1197 ectodomain protein ( endpoint titer of 4 . 9 ng/mL ) while the MPER-specific mAb 4E10 was unable to bind the ectodomain protein at any concentration tested , consistent with the absence of MPER in this peptide ( Fig 1C ) . The V3-specific antibody QA255 . 105 did not demonstrate binding against either of the proteins at any concentration tested ( Fig 1B and 1C ) . We next tested whether any of the four gp41-specific antibodies could mediate ADCC activity in the RF-ADCC assay [67] , which has shown an association with improved HIV outcomes [2 , 3] . Historically , this assay has used target cells coated with gp120 protein . Given that the four QA255 mAbs targeted gp41 , we chose to instead coat target cells with the gp41 proteins used in the initial ELISA assays as well as a clade A gp140 protein , which included both gp120 and the extracellular portion of gp41 . All four gp41-specific mAbs mediated robust activity against cells coated with gp140 and gp41 . The four QA255 gp41-specific mAbs demonstrated between 14% - 24% activity against MN gp41 and 32% - 37% activity against C . ZA . 1197 gp41 ( Fig 2A and 2B ) . The percent ADCC activity for these four mAbs ranged from 32% - 45% for cells coated with gp140 , levels which were slightly higher than gp120-specific control mAb QA255 . 157 . When tested against either of the gp41 proteins , neither QA255 . 157 nor an influenza-specific mAb , Fi6_v3 mediated measurable activity ( Fig 2A–2C ) , as expected . Similar results were observed with PBMCs from a second donor , although the magnitude of the activity was lower ( S1 Fig ) . To begin mapping the epitope within the gp41 protein , we tested biotinylated variants of each of the four antibodies in competition with a panel of well-characterized gp41-specific antibodies that target distinct nucleotide residues spanning the ectodomain of the gp41 protein ( Fig 3A ) . Because all four QA255 mAbs bound with comparable efficiency to both the full gp41 protein and the C . ZA . 1197 ectodomain variant of gp41 ( Fig 1B and 1C ) , this suggested MPER was not the epitope target and we did not include MPER-targeting antibodies in the competition ELISA . Endpoint ELISAs were performed to confirm binding for the selected six competitor mAbs against the MN gp41 protein . Five of the six mAbs bound with comparable endpoint titers between 4 . 9–19 . 5 ng/mL , while mAb 240-D demonstrated a higher endpoint titer of 78 . 1 ng/mL ( S2 Fig ) . In order to more precisely map the epitopes of these mAbs , we designed a phage immunoprecipitation sequencing approach [69] and determined which peptides in the phage library bound to the QA255 gp41-specific mAbs . A previously defined gp41-specific mAb , 240-D , was tested for comparison [40] . The library contains multiple HIV Env sequences , including consensus sequences for clades A , B , C and D and specific sequences circulating in Kenya . MAb 240-D as well as QA255 . 067 and QA255 . 072 all showed enrichment of gp41 peptides from the phage library that encoded sequences from the C-C’ loop and surrounding region , consistent with the predictions from the competition experiments . Sequences that were enriched by binding to mAb QA255 . 067 shared a common core sequence from 592 to 606 ( based on HXB2 numbering ) , suggesting these amino acids are key parts of the epitope for this mAb ( Fig 4A ) . QA255 . 072 binding enriched for an overlapping but distinct peptide region that had a common core sequence of amino acids 596 to 609 ( Fig 4A ) . The peptides that were enriched by mAb 240-D were also similar but distinct from the QA255 mAbs and encompassed amino acids 596 to 605 ( Fig 4B ) , which is consistent with the known epitope originally defined by linear peptide ELISA as including 579 to 604 [40 , 70] . All HIV strains present in the phage library were represented amongst the significant hits for 240-D , QA255 . 067 , and QA255 . 072 . No non-Env peptides were present in the top 99th percentile of enriched peptides from 240-D , QA255 . 067 , or QA255 . 072 when ranked by–log 10 p-value . MAbs QA255 . 006 and QA255 . 016 did not enrich for any peptides present in the phage library . Fig 4C shows a logo plot of circulating HIV sequences in the region of gp41 targeted by these mAbs indicating that the epitope target is highly conserved . In the case of QA255 . 067 , the epitope appears to exclude the most variable amino acid in this region at position 607 , although it includes the variable position 595 . By contrast , QA255 . 072 excludes the variable position at 595 but includes the variable 607 amino acid . Interestingly , the results from phage display suggest that the QA255 . 072 mAb tolerates variability at position 607 as peptides with a variety of amino acids at that position are enriched . Overall , these data suggest that the QA255 gp41-specific mAbs should recognize diverse strains of HIV from different clades . Interestingly , longitudinal sequences from QA255 , starting at 21 days post-infection , show no variation within the C-C’ loop epitope of QA255 . 067 and QA255 . 072 over time ( S4 Fig ) , perhaps reflecting the highly conserved nature of this domain . The epitopes for the mAbs QA255 . 006 and QA255 . 016 were defined only based on competition experiments with other mAbs . When we examined the epitope of these competing mAbs ( 5F3 and 167-D ) , which are focused on the CHR and potentially the fusion peptide [34 , 40 , 68] we see some evidence of variation in those regions ( S4 Fig ) . However , because we have not finely mapped the QA255 . 006 and QA255 . 016 epitopes , we cannot say with certainty these residues are included within the actual epitope and represent escape variants . Following interaction of gp120 with CD4 and CCR5 on the surface of target cells , the gp120-gp41 complex undergoes a series of conformational rearrangements , including initial formation of a pre-hairpin fusion intermediate for virus-cell fusion followed by rearrangement into a post-fusion stable six-helix bundle . To determine whether any of the four gp41-specific antibodies could bind to , or mediate functional activity against either the pre- or post-fusion gp41 intermediates , we tested the antibodies against two gp41 mimetic structures [71] including: 1 ) a 6-Helix structure that is a six alpha-helix bundle forming a hairpin trimer , and likely forms a post-fusion conformation displayed on the surface of infected cells and 2 ) a 5-Helix structure that is similar to the 6-Helix , but does not contain one C-peptide and presumably acts by inhibiting membrane fusion . When tested for binding activity , QA255 . 006 and QA255 . 016 bound similarly to the 6-Helix and 5-Helix trimers , with EC50 values calculated between 97–135 pM . In contrast , control mAb D5 , which binds to the NHR and was isolated from a HIV-naïve human scFv phage-display library [72] , bound measurably , but poorly , to the 6-Helix protein ( Fig 5A ) , with an EC50 of 1 . 27 nM , and with increased binding to the 5-Helix protein ( 74 pM ) ( Fig 5B ) . Neither QA255 . 067 nor QA255 . 072 demonstrated detectable binding to either the 5-Helix or 6-Helix proteins , which is consistent with the mapping data showing they target the C-C’ loop region ( Fig 5A and 5B ) . We next tested whether any of the QA255 mAbs could mediate functional activity against cells coated with the 6-Helix bundle stump mimetic . Both mAbs QA255 . 006 and QA255 . 016 mediated ADCC against the 6-Helix target at levels significantly higher than control mAbs including QA255 . 157 that targets a CD4-induced epitope on gp120 , and Fi6_v3 , an influenza-specific antibody . Activity was ~ 2-fold lower than the positive control mouse mAb , NC-1 , known to bind the stump mimetic [73] . Neither QA255 . 067 nor QA255 . 072 mediated ADCC in this assay , consistent with their lack of binding to this protein ( Fig 5C ) . All four Cluster I and Cluster II mAbs were tested for ADCC activity against cells infected with HIV-1 , including viruses defective in nef and/or vpu , which leads to increased CD4 on the cell surface [74–76] , enhanced exposure of CD4i epitopes [76–78] and increased Env density due to increased BST-2/Tetherin expression [76 , 79] . A third virus with defective nef and vpu genes containing a mutation in the CD4-binding site ( D368R ) was tested to determine whether the mAbs were dependent on conformational changes induced by CD4 interaction [76 , 78 , 80] . The mAbs were tested with this virus panel for binding to the infected cells and ADCC activity , including with gp120-specific mAbs as controls . The gp120-specific mAbs , QA255 . 157 and QA255 . 253 [65] , showed the highest level of binding to infected cells and corresponding high ADCC activity against cells infected with virus containing both defective nef and vpu genes ( Fig 6A and 6B ) . As expected , this activity was impaired in cells infected with the D368R construct that eliminated CD4-Env binding and therefore exposure of CD4i epitopes [78 , 80] . When the infected cell panel was tested against the four gp41 mAbs , all showed very little binding to cells infected with the wild type virus and this translated into no ADCC activity against these cells . QA255 . 006 , QA255 . 067 and QA255 . 072 showed increased binding and ADCC activity against cells infected with the Vpu-deficient virus and the Vpu- and Nef-deficient viruses while QA255 . 016 showed barely detectable binding or ADCC activity against cells infected with all the viruses tested . This is consistent with Env accumulation at the surface of cells infected with Vpu-deficient viruses , as BST-2/Tetherin can mediate retention of viral particles [74 , 75] on the cell surface . Accordingly , Interferon alpha ( IFNα ) treatment , which also enhances BST-2 retention of viral particles at the cell surface [75 , 81] , increased both recognition ( Fig 6C ) and ADCC susceptibility ( Fig 6D ) of cells infected with wild-type viruses . Finally , we observed no increase above wild type levels for cells infected with the Nef-deficient virus . For all of these mAbs , the presence of a mutation in the CD4 binding site ( D368R ) did not impact binding or ADCC activity , suggesting the epitopes recognized by these Abs are not dependent on structural changes that occur upon Env-membrane-bound CD4 interaction . Because the conditions that allowed detection of ADCC activity in the infected cell assay were when BST-2 levels promoted capture of viral particles , we could not determine if the gp41 mAbs are capable of binding to gp41 on the cell surface or if their binding reflects interaction with trapped viral particles , which would be consistent with the fact that they were isolated using viral particles as a bait [65] . To address this , we tested them in a cell-based ELISA assay where only Env is expressed at the cell surface [82] . QA255 . 006 , QA255 . 067 and QA255 . 072 were able to bind Env at the cell surface , with higher binding detected at higher Env levels ( as detected by 2G12 , Fig 7A ) . Consistent with poor recognition of infected cells by QA255 . 016 ( Fig 6 ) , no binding for this Ab was observed in this system ( Fig 7A ) . Thus , these data indicate that these gp41 mAbs do not require viral particles to interact with Env . Consistent with their ability to recognize a gp41 stump mimetic ( Fig 5 ) , we observed that sCD4-induced shedding as indicated by decreased 2G12 levels upon sCD4 addition , dramatically increased the ability of these mAbs to recognize Env ( Fig 7B ) further supporting the possibility that these mAbs recognize gp41 stumps . In addition , the same pattern is also seen for the anti-gp41 F240 mAb , which has also been suggested to recognize gp41 stumps [83] . How can this be reconciled with the observation that these mAbs do not more efficiently recognize cells infected with a virus deleted in Nef , which have higher levels of CD4 compared to cells infected with Nef containing virus [76 , 77] ? A potential explanation is that in cells infected with Nef- virus , CD4 interacts with Env in cis , thus occluding the access to the epitope , which is not the case when the Env is opened using sCD4 . Supporting this , 8ANC195 does not efficiently recognize cells infected with Nef- virus [80] despite the fact that the structure of this mAb was obtained using a gp120 core stabilized with sCD4 [84] .
There has been renewed interest in antibodies that mediate ADCC based on findings that ADCC antibody activity was associated with protection in the RV144 vaccine clinical trial [1] and in the setting of mother-to-child transmission [2 , 3] . In addition , non-neutralizing ADCC antibodies have been associated with protection and delayed disease in NHP vaccine models and reduced viremia when passively infused prior to infection of NHP [12] . Here we describe four new gp41-specific ADCC mAbs that arose from four independent B cell lineages in one clade A infected individual . Two of these mAbs also recognize gp41 stumps and mediate ADCC against cells coated with stump mimetics . Importantly , these mAbs can mediate cell killing in multiple assays , including killing of productively infected T cells , the major source of virus in HIV infection . Notably , they mediate killing in infected cells exposed to IFN , a condition that is likely to be relevant to HIV infection in vivo . The epitopes of these gp41 mAbs were mapped using both competition experiments and phage peptide display . Immunoprecipitation of a library of phage has the advantage of being able to interrogate a large number of peptides in a single well using deep sequencing to identify the specific peptides within the library that bind the mAbs . Our studies showed that QA255 . 067 and QA255 . 072 target the immunodominant C-C’ loop , which suggests they target cluster I . The phage display method allowed us to define a minimal epitope based on overlap in the sequences that bound these mAbs . These results suggest that there are subtle differences in the epitopes of these mAbs and also in these epitopes compared to a previously defined cluster I mAb , 240-D . Interestingly , the minimal epitope of QA255 . 067 excludes a variable residue at position 607 , whereas this residue is included in the minimal epitopes of QA255 . 072 and variation at this residue appears to be tolerated by mAb QA255 . 072 . Interestingly , longitudinal viruses cloned from QA255 over a more than four-year time period after infection demonstrated no variation in these residues , thus suggesting that ADCC antibody pressure is not sufficient to drive escape in this highly conserved region of gp41 . We also mapped the epitope of a previously described mAb , 240-D to amino acids 596–605 , which refined the epitope compared to the original 240-D epitope mapping study , which indicated the epitope was between 579–604 based on peptide binding studies [40] . Our results are also consistent with later studies examining binding to mutant forms of Env-gp160 protein , which suggested mutations at positions 596 , 599 and 605 impact 240-D binding [70] . Overall , this analysis suggests that phage display could provide a high throughput tool for epitope mapping . While the application of phage immunoprecipitation with deep sequencing was successful for mapping the epitopes of the cluster I mAbs , it was not for the mAbs with more complex , discontinuous epitopes . QA255 . 006 and QA255 . 016 share some properties of cluster II mAbs in that competition studies suggest their epitope includes the CHR . But competition experiments suggest that the target of these mAbs may also be discontinuous and include the fusion peptide proximal region and/or the NHR . These mAbs appear to enhance binding of the C-C’-loop , cluster I mAbs , as do other mAbs that target the CHR , such as 5F3 . Interestingly , a mAb that bound a complex epitope on HIV gp41 was isolated from a clade B infected individual using VLPs to enrich for HIV-specific B cells suggesting these types of mAbs may be readily detected using VLPs as bait [85] . The QA255 derived gp41 mAbs all demonstrated measurable ADCC activity against cells coated with gp41 , including the ectodomain expressed alone as well as within the context of the gp140 protein . Importantly , they also mediated killing of infected target cells , although the activity driven by mAb QA255 . 016 was very low . Poor ADCC activity by QA255 . 016 is consistent with the competition assay observations where QA255 . 006 was able to displace Bt-QA255 . 016 but QA255 . 016 was unable to displace BT-QA255 . 006 . Activity was more readily detected with viruses lacking Vpu , presumably because the cells infected with vpu-deleted viruses have higher cell surface Env expression due to trapped viral particles [74–76 , 81 , 86 , 87] . Accordingly , stimulation with IFNα , known to induce retention of viral particles at the surface of infected cells [75 , 81] , increased recognition and ADCC activity of these Abs . The activity was not dependent on Env-CD4 interaction at the cell surface because it was not increased compared to wild type virus when a nef-deleted virus was used . CD4i epitopes are a common target of non-neutralizing gp120-specific mAbs that mediate ADCC and can result in killing of bystander cells that have shed gp120 on their surface [79 , 88] . As such , the gp41 mAbs would be predicted to have fewer off-target effects that result in this undesirable killing of HIV negative cells . In the pre-hairpin conformation , both the NHR and CHR of gp41 are exposed . As such , this pre-hairpin fusion intermediate has been identified as a target for fusion inhibitors and as a vaccination target . In the post-fusion conformation , the fusion peptide and the transmembrane anchor are irreversibly brought together into a stable 6-helical bundle [89] . Thus , this structural form of HIV gp41 represents a potential target for HIV-specific antibodies . The two mAbs described here that include the CHR within their epitope target are capable of binding to the 6-helix structure and mediating killing of target cells coated with this form of the protein; they also mediate killing of infected cells . Importantly , these two mAbs , along with the more potent of the two mAbs targeting the C-C’ loop bound to Env expressed on the cell surface , with much higher levels of binding under conditions that induced gp120 shedding . Collectively , these findings suggest that these mAbs could participate in the elimination of infected cells that have shed gp120 , which can expose the gp41 stump [26] . The presence of gp41 antibodies that mediate ADCC in plasma has long been appreciated [27 , 90] . Many previous studies showed that gp41-directed antibody responses are generally common in HIV-infection , including responses to epitopes that are similar to those of the mAbs studied here [36 , 40 , 91–93] . Despite the common nature of gp41 plasma antibody responses , relatively few gp41-specific mAbs that mediate ADCC have been described . Many of the previously characterized mAbs are IgG2 [28 , 31] , an isotype which primarily mediates killing via macrophages and neutrophils through the FcγRIIa . IgG2 also has very low affinity for the Fc receptor most important for NK-cell mediated ADCC activity , FcRγIIIa [94] . The gp41 ADCC mAbs described here were encoded as IgG1 , which can interact with a range of FcγRs . IgG1 is also the most abundant antibody and thus a major driver of the ADCC response . In addition to ADCC , gp41-specific mAbs have been shown to block transcytosis of virus [47 , 53] and to inhibit virus infection in dendritic cells and macrophages by mechanisms that likely involve effector functions [44 , 46] . Moreover , gp41-specific IgA activity has been linked to resistance from infection in highly exposed seronegative individuals [57] . Thus overall , gp41-specific antibodies may make unique contributions to decreasing HIV transmission and pathogenesis . In this regard , the effect of IFN on ADCC activity observed here may be particularly relevant given that IFN is an early antiviral response . Four of the twelve HIV-specific mAbs isolated from a clade A infected individual targeted gp41 and they were all derived from independent lineages , even though there were two pairs of mAbs , with each pair targeting similar epitopes . This suggests that gp41-specific mAbs that mediate ADCC may be a common response during chronic HIV infection and the antibodies isolated here will be useful as reagents for testing this hypothesis . These ADCC mAbs from 914 days post infection showed relatively low SHM ( VH: 6 . 5–12 . 9%; VL/VK: 3 . 7–8 . 8% NT ) ( Table 1 ) compared to broadly neutralizing mAbs . Two of the four gp41-specific mAbs described here , QA255 . 067 and QA255 . 072 , utilize gene IGVH1-69 ( Table 1 ) , which is common for cluster I-directed mAbs [95] . One of the challenges in eliciting a protective response against HIV , particularly for eliciting protective neutralizing antibodies , is the diversity of the Env antigen . To date , the gp41-specific mAbs identified after HIV vaccination have tended to be polyreactive and not able to mediate HIV-specific ADCC activity [96] . ADCC Abs tend to target conserved epitopes and show breadth [12 , 65 , 97–100] . In terms of breadth of the gp41 protein , in particular the ectodomain , gp41 is a particularly attractive target because it is more conserved than most gp120 regions targeted by bnAbs [25] . Thus , the new ADCC Abs described here , that target conserved regions in gp41 and mediate killing of HIV infected cells may provide insight into the features of antibodies that can mediate broad protection against HIV infection .
Antibodies from QA255 were originally isolated and cloned as described previously [65] . In brief , paired heavy and light chain DNA clones were co-transfected in equal ratios into 293F cells ( 293 Freestyle cells; Thermo Fisher; 1x106 cells/1 μg of total DNA ) with a 16:1:1 ( OptiPRO Serum-Free Medium:293Max:DNA , Thermo Fisher ) ratio . Antibodies were harvested after 72 h and purified using Protein G resin in hand-packed , gravity flow columns ( Pierce ) . Antibody concentration was determined using protein absorbance at 280 nM ( Nanodrop ) . The BAMA was conducted as described [1 , 32 , 98 , 99] to measure IgG binding to a panel of HIV antigens . Prior to performing the BAMA , antigens were covalently conjugated to carboxylated fluorescent beads ( Luminex ) as described previously [32 , 100] . Antigen-conjugated beads were stored in PBS ( Gibco ) containing 0 . 1% bovine serum albumin ( BSA; Sigma-Aldrich ) , 0 . 02% Tween ( Sigma-Aldrich ) , and 0 . 05% sodium azide ( Sigma-Aldrich ) at the optimal temperature for the unconjugated antigen for up to 1 year . Antigens included in the assay were monomeric gp120 proteins BG505 . W6M . C2 . T332N ( clade A ) , BL035 . W6M . ENV . C1 ( clade A/D recombinant ) , SF162 ( clade B ) , ZM109F . PB4 ( clade C ) , C2-94UG114 ( clade D ) , and SIV/mac239; clade A BG505 SOSIP Env trimer ( courtesy of Marit van Gils , Rogier Sanders and John Moore ) [66]; resurfaced Env core protein ( RSC3 ) and CD4-binding site defective mutant ( RSC3 Δ371I ) ( construct obtained from NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH from Drs . Zhi-Yong Yang , Peter Kwong , Gary Nabel ) and produced as described in [101]; clade C 2J9C-ZM53_V1V2 and 1FD6-Fc-ZM109_V1V2 scaffolded peptides [102]; V3 consensus peptides ConA1 ( CTRPNNNTRKSIRIGPGQAFYATGDIIGDIRQAHC ) and ConB ( CTRPNNNTRKSIHIGPGRAFYTTGEIIGDIRQAHC ) ( Genscript ) ; and two gp41 antigens: clade B MN gp41 monomer ( NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH from ImmunoDX , LLC ) and clade C ectodomain ZA . 1197/MB ( Immune Technology Corp ) . BG505 gp120 was produced by transient transfection of 293F cells ( Thermo Fisher ) followed by Galanthus nivalis lectin purification ( Vector Laboratories ) as described previously [103] . All other gp120 proteins were purchased from Immune Tech . Positive controls included VRC01 , PG9 , PGT121 , 4E10 , 50–69 , and 246-D . VRC01 , PG9 and PGT121 were all produced as described above and 4E10 , 50–69 , and 246-D obtained from the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH ( 4E10 from Polymun Scientific , and 50–69 and 246-D from Dr . Susan Zolla-Pazner ) . Negative controls included both HIV-negative plasma and mock conjugated beads . Binding is measured as the mean fluorescence intensity ( MFI ) and averaged across duplicate wells . Results are reported as fold change over binding by HIV-negative plasma . The gp41 binding ELISA was adapted from [65] . In brief , Immunolon 2-HB plates were coated with 100 μL of MN gp41 ( NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH from ImmunoDX , LLC . ) or ZA . 1197 ( Immune Technology Corp ) at 0 . 5 μg/mL in 0 . 1 M sodium bicarbonate coating buffer ( pH 7 . 4 ) overnight at 4°C . Plates were rinsed 4–5 times using PBS-0 . 05% Tween wash buffer . Plates were blocked with 10% non-fat dry milk ( NFDM ) diluted into wash buffer for at least 1 h . After removing the blocking buffer , 100 μL of primary mAb diluted in blocking buffer was added and incubated at 37°C for 1 hr . Plates were washed a second time and 100 μL of anti-IgG-HRP ( Sigma-Aldrich ) diluted 1:2500 in blocking buffer was added and incubated at room temperature for 1 hr . Plates were washed and 50 μL Ultra-TMB ( Thermo Scientific ) substrate added to each well and incubated at room temperature for 10 min . This reaction was stopped by adding 50 μL of 0 . 1 M H2SO4 ( Sigma-Aldrich ) and the absorbance was read at 450 nM optical density within 30 min . The endpoint titers for all antibodies were defined as the average Ab concentration with binding greater than 2-fold of the negative control , Influenza-specific mAb Fi6_v3 ( courtesy of Jesse Bloom and Kelly Lee ) . Gp41 mimetics 6-helix and 5-helix were expressed in E . coli and purified as described previously [71] . The 6-helix and 5-helix constructs were modified ( K68C ) to allow biotinylation using maleimide chemistry . Briefly , the proteins were reduced in degassed conditions with 5mM TCEP , then incubated with 5 molar excess of EZ-link Maleimide-PEG11-biotin ( Thermo Fisher ) reagent for 1 hr at room temperature . Unreacted biotinylation reagent was removed using a PD-10 desalting column according to manufacturer’s instructions ( GE Healthcare ) . Biotinylation efficiency was determined using HABA reagent ( Pierce ) . For ELISA assays , 96-well plates ( Nunc MaxisorpTM flat-bottom , Thermo Fisher Scientific ) were coated with 5 μg/mL streptavidin ( in 50 mM sodium bicarbonate pH 8 . 75 ) for at least 1 hr , before the addition of 5 μg/mL biotinylated 6-helix or 5-helix . Following coating with antigens , the plates were washed three times with 300 μL 1xPBST and blocked with 300 μL of 1xPBST with 0 . 5% BSA for at least one hour . Following blocking , antibodies were added in serial 10-fold dilutions starting at 75 μg/mL for at least 1hr . The plates were then washed 3x with 300 μL of 1xPBST and an anti-human IgG HRP secondary antibody ( Thermo Fisher ) was added for 1 hr at room temperature . The plates were then washed 6x with 300 μL of 1xPBST and developed using 1-StepTM Turbo TMB ELISA substrate solution ( Thermo Fisher Scientific ) for 6 mins and quenched using 2M H2SO4 . The readout of this colormetric assay was determined using a 96 well plate reader ( Biotek ) , and the intensity of the absorbance at 450 nm was normalized for the path length . Finally , these resulting values were baseline subtracted ( subtracting the average of the background signal from secondary antibody only control wells ) . EC50s were obtained from fitting values to a sigmoidal curve in GraphPad Prism v7 . 0c . MAb D5 , which binds to the highly conserved hydrophobic pocket on the NHR [104] , was obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH from Dr . Danilo Casimiro . The RF-ADCC assay was performed as described [2 , 3 , 65 , 67] . In short , CEM-NKr cells ( AIDS Research and Reference Reagent Program , NIAID , NIH from Dr . Alexandra Trkola ) were double labeled with PKH26-cell membrane dye ( Sigma-Aldrich ) and a cytoplasmic-staining CSFE dye ( Vybrant CFDA SE Cell Tracer Kit , Life Technologies ) . The double-labeled cells were coated with either clade A gp140 ( Q461 . e2 ) [105] , MN gp41 ( NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH from ImmunoDX , LLC . ) , or the 6-helix gp41 mimetic for 1 hr at room temperature at a ratio of 0 . 15–1 . 5 μg protein ( 1 μg/μL ) :1 x 105 double-stained target cells . Coated targets were washed once with complete RMPI media ( Gibco ) supplemented with 10% FBS ( Gibco ) , 4 . 0mM Glutamax ( Gibco ) and 1% antibiotic-antimycotic ( Life Technologies ) . Monoclonal antibodies were diluted in complete RPMI media to a concentration of 50–500 ng/mL , depending on the antigen and mixed with 5 x 103 coated target cells for 10 min at room temperature . PBMCs ( peripheral blood mononuclear cells; Bloodworks Northwest ) from an HIV-negative donor were then added at a ratio of 50 effector cells per target cell . The coated target cells , antibodies , and effector cells were co-cultured for 4 hr at 37°C then fixed in 150 μL 1% paraformaldehyde ( Affymetrix ) . Cells were acquired by flow cytometry ( LSR II , BD ) and ADCC activity defined as the percent of PE+ , FITC- cells with background subtracted where background ( antibody-mediated killing of uncoated cells ) was between 3–5% as analyzed using FlowJo software ( Tree Star ) . The data were plotted with percent ADCC activity on the y-axis and respective mAb on the x-axis ( Graphpad Prism v7 . 0c ) . Antibodies selected for the competition ELISA experiments were all obtained from the NIH AIDS Reagent Program , Division of AIDS , NIAID and included: 5F3 and 2F5 ( provided by H . Katinger ) ; 167-D , 240-D , 50–69 , and 246-D ( provided by S . Zolla-Pazner ) ; F240 ( provided by M . Posner and L . Cavacini ) ; D5 ( provided by D . Casimiro ) . Immunolon 2-HB plates were coated with MN gp41 as described above . Competitor antibodies were added first at a concentration of 10 μg /mL to gp41-coated plates and incubated for 15 min at 37°C . Biotinylated ( BT; Thermo Fisher Scientific ) QA255 antibodies were added next without washing and the competitor/ BT-antibody mixture were incubated together for 45 min at 37°C . Limiting concentrations for each BT mAb were pre-determined as follows: BT-QA255 . 006 at 1 . 25 μg/mL , BT-QA255 . 016 at 10 μg/mL , BT-QA255 . 067 and BT-QA255 . 072 both at 0 . 625 μg/mL . Plates were then washed thoroughly and HRP-conjugated Streptavidin diluted in wash buffer ( 1:1000 ) added and incubated for at least 45 min . After washing , Ultra TMB-substrate and 0 . 1 M H2SO4 were added as previously described . Relative BT-mAb binding was calculated by dividing each BT-mAb binding in the presence of each competitor antibody by the average of the same BT-mAb binding in the presence of blocking buffer . To identify precise epitopes of antibodies in this study , we utilized an approach that couples phage immunoprecipitation and highly-multiplexed sequencing [69] . We used a phage-display library that contains several full-length HIV sequences from each clade , including consensus sequences from Clades A , B , C , and D ( LANL ) , Q23 ( AF004855 . 1 ) , BF520 . W14M . C2 ( KX168094 ) , BG505 . W6 . C2 ( DQ208458 ) , and Env sequences from QA013 . 70I . Env . H1 ( FJ866134 ) , QA013 . 385M . Env . R3 ( FJ396015 ) , QB850 . 73P . C14 ( MK412338 ) , QB850 . 632P . B10 ( MK412339 ) , Q461 . D1 ( AF407155 ) , and QC406 . F3 ( FJ866133 ) . To generate the library , 39-amino acid sequences were generated that tiled over the coding sequences of viral genomes of interest with 20-amino acid overlap . These protein sequences were reverse translated to DNA sequences and codon-optimized for expression in E . coli . Synonymous mutations were introduced to avoid EcoRI and HindIII restriction sites that were used in subsequent cloning steps . Adapter sequences ( 5’: AGGAATTCTACGCTGAGT and 3’: TGATAGCAAGCTTGCC ) were added and the library was ordered on a releasable DNA microarray ( Twist Biosciences ) . The library was then PCR amplified using our T7F ( AATGATACGGCAGGAATTCTACGCTGAGT ) and T7R ( CGATCAGCAGAGGCAAGCTTGCTATCA ) primers , digested with EcoRI and HindIII , cloned into the T7Select® 10-3b Vector , and packaged into T7 phage and amplified according to the manufacturer’s protocol ( EMD Millipore ) . Phage immunoprecipitation was performed as previously described [69] . 96-deep-well plates ( CoStar ) were blocked with 3% BSA in TBST ( Tris-buffered saline-Tween ) by placing on a rotator overnight at 4°C . 1 mL of amplified phage at 2x105-fold representation ( 1 . 2x109 pfu/mL for a library of 5 . 8x103 phage ) was added to each well , followed by either 2ng or 10ng of purified anti-gp41 monoclonal antibody . Each concentration of monoclonal antibody was tested in technical replicate . Phage-antibody complexes were formed by rotating the plate at 4°C for 20 hours . To immunoprecipitate phage-antibody complexes , 40μL of a 1:1 mix of protein A and protein G Dynabeads ( Invitrogen ) was added to each well and rotated at 4°C for 4 hours . After this incubation , a magnetic plate was used to isolate the beads and perform 3 washes with 400μL of wash buffer ( 50mM Tris-HCl , pH 7 . 5 , 150mM NaCl , 0 . 1% NP-40 ) . The beads were resuspended in 40μL of water and isolated phage were lysed by incubating at 95°C for 10 mins . Phage that did not undergo immunoprecipitation ( ‘input’ ) were also lysed to determine the starting frequencies of each phage clone in the library . Isolated phage DNA was then prepared for highly-multiplex sequencing by performing two rounds of PCR with Q5 High-Fidelity DNA polymerase ( New England Biolabs ) to add Illumina adapters and barcodes according to the manufacturer’s suggested protocol ( NEB ) . The first-round PCR was performed with primers R1_F ( TCGTCGGCAGCGTCTCCAGTCAGGTGTGATGCTC ) and R1_R ( GTGGGCTCGGAGATGTGTATAAGAGACAGCAAGACCCGTTTAGAGGCCC ) . 1μL of purified first-round product was added to the second-round PCR with unique dual-indexed primers R2_F ( AATGATACGGCGACCACCGAGATCTACACxxxxxxxxTCGTCGGCAGCGTCTCCAGTC ) and R2_R ( CAAGCAGAAGACGGCATACGAGATxxxxxxxxGTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG ) . In these primer sequences , “xxxxxxxx” corresponds to a unique 8-nt indexing sequence . Second-round PCR products were quantified in each sample using Quant-iT PicoGreen according to the manufacturer’s suggested protocol ( Thermo Fisher ) . Equimolar quantities of each sample were then pooled , gel isolated , and submitted for Illumina sequencing on a MiSeq , where 60 , 000–1 , 100 , 000 reads were obtained for each sample . Bioinformatics analyses of the sequencing data was performed as previously described [69] . In brief , a zero-inflated generalized Poisson significant-enrichment assignment algorithm was used to generate a–log10 ( p-value ) for enrichment of each clone across all samples . A reproducibility threshold was established to call ‘hits’ in technical replicate pairs by first calculating the log10 ( -log10 ( p-value ) ) for each clone in Replicate 1 . We then surveyed these values in Replicate 2 by using a sliding window of width 0 . 01 from -2 to the maximum log10 ( -log10 ( p-value ) ) value in Replicate 1 . For all clones that fell within each window , the median and median absolute deviation of log10 ( -log10 ( p-values ) ) in Replicate 2 were calculated and plotted against the window location . The reproducibility threshold was set as the window location where the median was greater than the median absolute deviation . The distribution of the threshold–log10 ( p-values ) was centered around a median of 2 . 2 . In sum , we called a phage clone a ‘hit’ if the–log10 ( p value ) was at least 2 . 2 in both replicates . Beads-only samples , which serve as a negative control for non-specific binding of phage , were used to identify and eliminate background hits . Peptides called as hits were aligned using Clustal Omega . The shortest amino acid sequence present in all of the hits was what we defined as the “minimal epitope” of an antibody . Of note , peptides were tiled as described above . Methods describing amplification and characterization of envelope clones from PMBC DNA from 189 , 560 , 662 and 1729 days post-infection were previously described [106] . Envelope clones from 21 days post-infection were generated from plasma RNA using similar methods . In both cases , a limiting dilution PCR strategy was used to amplify single genome envelope sequences . For cell surface staining , infected or mock-infected CEM . NKr ( CEM cells resistant to Natural Killer cells killing , from Dr . David T Evans [4] ) were incubated for 30 min at room temperature 48 h post-infection with 5 μg/ml of each tested antibody in PBS . Cells were then washed twice with PBS and stained with 1 μg/ml of goat anti-human antibody ( Alexa Fluor-647 , Invitrogen ) for 15 min in PBS . After two more PBS washing , cells were fixed in a 2% PBS-formaldehyde solution . ADCC was performed with a previously described assay [76] . Briefly , CEM . NKr infected cells were stained with viability ( AquaVivid; Invitrogen ) and cellular ( cell proliferation dye eFluor670; eBiosciences ) markers and used as target cells . Effector PBMCs , stained with another cellular marker ( cell proliferation dye eFluor450; eBiosciences ) , were then mixed at an effector/target ( E/T ) ratio of 10:1 in 96-well V-bottom plates ( Corning ) ; 5 μg/ml of the desired Ab was added to appropriate wells . Co-cultures were centrifuged for 1 min at 300 g and incubated at 37°C for 5–6 hr before being fixed in a 2% PBS-formaldehyde solution containing 5x104/ml flow cytometry particles ( AccuCount Blank Particles , 5 . 3 μm; Spherotech ) . IFN-α ( PBL Assay Science ) was reconstituted in RPMI-1640 complete medium at 1x107 U/mL , aliquoted , and stored at −80°C . IFN-α was then added to the cells at 1000 U/mL 24h post-infection , 24h before cell-surface staining or ADCC assays . Samples were analyzed on an LSRII cytometer ( BD Biosciences ) and acquisition was set to acquire 1000 particles , which allows the calculation of relative cell counts . Data analysis was performed using FlowJo vX . 0 . 7 ( Tree Star ) . The percentage of cytotoxicity was calculated with the following formula: ( ( relative count of GFP+ cells in Targets plus Effectors ) — ( relative count of GFP+ cells in Targets plus Effectors plus antibodies ) ) / relative count of GFP+ cells in Targets . Detection of trimeric Env at the surface of HOS ( human osteosarcoma , ATCC ) cells was performed by cell-based ELISA , as previously described [82 , 107] . Briefly , HOS cells were seeded in T-75 flasks ( 3 × 106 cells per flask ) and transfected the next day with either 3 . 0 ( 1x ) , 7 . 5 , 15 . 0 , 22 . 5 or 45 . 0 μg per flask with the empty pcDNA3 . 1 vector or expressing the codon-optimized HIV-1JRFL envelope glycoproteins with a truncation at position Gly 711 in the cytoplasmic tail ( ΔCT ) , enhancing cell-surface expression . Cells were transfected with the standard polyethylenimine ( PEI , Polyscience Inc , PA , USA ) transfection method . Twenty-four hours after transfection , cells were plated in 384-wells plates ( 2 × 104 cells per well ) and one day later , cells were incubated in Blocking Buffer ( Washing Buffer [25 mM Tris , ph 7 . 5 , 1 . 8 mM CaCl2 , 1 . 0 mM MgCl2 , pH 7 . 5 and 140 mM NaCl] supplemented with 10 mg/ml non-fat dry milk and 5 mM Tris pH 8 . 0 ) for 30 minutes and then pre-incubated or not for 1 h with soluble CD4 ( sCD4 ) ( 10 μg/ml ) diluted in Blocking Buffer at room temperature . Cells were incubated with the anti-HIV-1 Env monoclonal antibodies ( 2G12 , QA255 . 006 , QA255 . 016 , QA255 . 067 , QA255 . 072 , QA255 . 105 , QA255 . 157 , QA255 . 253 , F240 ) in absence or presence of sCD4 ( 10 μg/ml ) in blocking buffer . Cells were washed five times with Blocking Buffer and five times with Washing Buffer . A horseradish peroxidase ( HRP ) conjugated antibody specific for the Fc region of human IgG ( Pierce ) was then incubated with the samples for 45 minutes . Cells were washed again five times with Blocking Buffer and five times with Washing Buffer . All incubations were done at room temperature . 20 μl of a 1:1 mix of Western Lightning oxidizing and enhanced luminol reagents ( Perkin Elmer Life Sciences ) was added to each well . Chemiluminescence signal was acquired for 1 sec/well with the LB 941 TriStar luminometer ( Berthold Technologies ) . | Anti-HIV antibodies can mediate activity by neutralizing cell-free virus , or binding to infected cells and driving antibody-dependent cellular cytotoxicity ( ADCC ) . While numerous discovery efforts have identified and characterized neutralizing antibodies , much less is known about antibodies that mediate ADCC . Here we describe four new antibodies that target the gp41 transmembrane protein of the HIV envelope . Competition experiments and peptide mapping studies together helped narrow down the binding sites for the four antibodies to two conserved regions of the protein . One pair of antibodies targets a common epitope of gp41 while the other pair binds to a more complex discontinuous epitope . In vitro activity assays indicated that this second pair of antibodies could drive killing against cells coated with various forms of gp41 , and both pairs of antibodies could drive killing of HIV-infected cells . Inducing these types of antibodies following vaccination may represent a more straightforward path to generating a consistent , functional response to a more conserved portion of the HIV envelope protein . | [
"Abstract",
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] | 2019 | Identification of HIV gp41-specific antibodies that mediate killing of infected cells |
Immune responses mounted by the malaria vector Anopheles gambiae are largely regulated by the Toll and Imd ( immune deficiency ) pathways via the NF-kappaB transcription factors Rel1 and Rel2 , which are controlled by the negative regulators Cactus and Caspar , respectively . Rel1- and Rel2-dependent transcription in A . gambiae has been shown to be particularly critical to the mosquito's ability to manage infection with the rodent malaria parasite Plasmodium berghei . Using RNA interference to deplete the negative regulators of these pathways , we found that Rel2 controls resistance of A . gambiae to the human malaria parasite Plasmodium falciparum , whereas Rel 1 activation reduced infection levels . The universal relevance of this defense system across Anopheles species was established by showing that caspar silencing also prevents the development of P . falciparum in the major malaria vectors of Asia and South America , A . stephensi and A . albimanus , respectively . Parallel studies suggest that while Imd pathway activation is most effective against P . falciparum , the Toll pathway is most efficient against P . berghei , highlighting a significant discrepancy between the human pathogen and its rodent model . High throughput gene expression analyses identified a plethora of genes regulated by the activation of the two Rel factors and revealed that the Toll pathway played a more diverse role in mosquito biology than the Imd pathway , which was more immunity-specific . Further analyses of key anti-Plasmodium factors suggest they may be responsible for the Imd pathway–mediated resistance phenotype . Additionally , we found that the fitness cost caused by Rel2 activation through caspar gene silencing was undetectable in sugar-fed , blood-fed , and P . falciparum-infected female A . gambiae , while activation of the Toll pathway's Rel1 had a major impact . This study describes for the first time a single gene that influences an immune mechanism that is able to abort development of P . falciparum in Anopheline species . Further , this study addresses aspects of the molecular , evolutionary , and physiological consequences of the observed phenotype . These findings have implications for malaria control since broad-spectrum immune activation in diverse anopheline species offers a viable and strategic approach to develop novel malaria control methods worldwide .
The innate immune system of the African malaria vector , Anopheles gambiae , is the mosquito's main line of defense against Plasmodium parasites and is engaged at multiple stages of parasite infection [1] , [2] . As part of this defense , two major signaling pathways , Toll and Imd , transduce the pathogen recognition signal to activate nuclear translocation of the NF-kappaB Rel family transcription factors , which transcriptionally induce effector genes such as antimicrobial peptides [3] . Previous studies have implicated both pathways in the modulation of the mosquito's susceptibility to the rodent malaria parasite P . berghei , while a variety of immune effector genes have been shown to mediate the killing of the human pathogen P . falciparum [4]–[6] . Interestingly , defense responses to these two Plasmodium species have been shown to be quite different , as exemplified by the identification of several parasite species-specific anti-Plasmodium genes [6] , [7] . In A . gambiae , Rel1 ( previously Gambif1 ) is controlled by the Toll pathway and is an analog of the D . melanogaster Dif , while Rel2 is controlled by the Imd pathway and is the ortholog of the D . melanogaster Relish [4] , [8]–[11] . In both species , the negative regulator of the Toll pathway , Cactus , is bound to Dif/Rel1 under naïve conditions , sequestering the transcription factor in the cytoplasm [3] , [12] . When Toll is activated , the signal is transmitted through adaptor proteins triggering phosphorylation of Cactus which tags it for degradation . Destruction of Cactus frees Dif/Rel1 , which can then enter the nucleus and initiate transcription [13] . The activity of the Imd Rel factor ( Relish/Rel2 ) is held in check by its own inhibitory domains ( ankyrin repeats ) which can be cleaved by Dredd , a caspase-8 homolog , to cause activation [14] , [15] . Regulation of Relish has been shown by Kim and colleagues to be negatively controlled by Caspar , a homolog of the mammalian Fas-associating factor [16] . After observing an increased resistance to Gram negative bacterial exposure and constitutive production of the antimicrobial peptide Diptericin , they determined that Caspar is a negative regulator specific to the Imd pathway that inhibits nuclear localization of Relish by blocking Dredd from cleaving Relish under naïve conditions [16] . Since this is the first and , so far , only study of Caspar , our knowledge of the exact nature and extent of Caspar's inhibitory role is limited . A variety of A . gambiae immune-responsive genes have been identified via gene expression profiling in which mosquitoes infected with bacteria , fungi or malaria parasites have been compared to uninfected controls [6] , [17]–[20] . Assays comparing bacteria-challenged and naïve Rel1- or Rel2-depleted mosquitoes have identified both Toll and Imd pathway-regulated immune genes [4] , [5] . Many of these pathway-regulated genes , such as TEP1 ( thioester-containing protein 1 ) and LRIM1 , have been shown to play key roles in parasite elimination [6] , [21] , [22] . What is unknown about some of these immune pathways and effectors is the role they play during P . falciparum infection and whether they work together or independently while exerting their anti-parasitic action . The complex network of pathogen detection , signal transduction and effector action that ultimately leads to parasite destruction is of utmost interest for the development of vector-based malaria control strategies . Over 40 Anopheles mosquito species act as malaria vectors throughout the world , encountering different environments , food sources , microbial exposures and parasite strains , all of which could impose evolutionary pressures on the insect immune system [23] , [24] . A pressing question is whether the anti-Plasmodium mechanisms are common or unique among the different Anopheles species . This is particularly interesting for anti-Plasmodium mechanisms based on immune responses because certain elements of an integrated immune system are conserved while others are highly divergent [25] , [26] . Here we have for the first time addressed the role of the Toll and Imd immune signaling pathways in mosquito defense against the human malaria parasite P . falciparum not only in A . gambiae but also in A . stephensi and A . albimanus to assess the potential universality of these defense mechanisms . Towards this we have also directly compared the implication of these pathways in the defense against different malaria parasite species . A finely tuned balance between the capacity of the mosquito's immune system to kill malaria parasites and the impact of Plasmodium infection on mosquito fitness exist . A negative correlation has been established between fitness and immune induction but only under chronic conditions which is not the case during transient immune pathway activation [27] . To gain a better insight of the fitness cost of a transient activation of a highly potent immune pathway-mediated anti-Plasmodium defense on the mosquito's fitness we have looked at how a cactus- or caspar-silencing-based immune activation affect the ability of mosquitoes to survive and reproduce . These novel findings have important implications for understanding molecular and evolutionary components of mosquito immunity to the Plasmodium parasite and for the development of vector-based immune-mediated malaria control strategies .
The Anopheles cactus gene was previously identified through a comparative immunogenomics study of the Drosophila and Anopheles immune systems and has been functionally confirmed as a negative regulator of Rel1 [3] , [5] . Sequence comparison between the putative A . gambiae caspar and D . melanogaster caspar showed that the mosquito protein sequence shares 43% identity and 59% similarity . Four conserved sequence regions occur similarly in Anopheles Caspar as they do in both Drosophila Caspar and human FAF1: the Fas-associating region , the DED-interacting region , the ubiquitin-associated domain ( UAS ) and the ubiquitin-like domain . Fas-associating regions between the two insects' Caspar amino acid sequences were 46% similar , the DED-interacting region , UAS and Ubx domains share 55% , 78% and 81% similarity , respectively ( Figure 1A ) . To investigate potential functional conservation we combined an RNAi ( RNA interference ) -mediated gene-silencing approach with expression analyses of putative Imd pathway immune markers . A dsRNA targeting caspar or cactus was used to silence the respective mRNA transcript in adult female mosquitoes . The average silencing efficiency was 79% for caspar and 75% for cactus , while rel1 and rel2 displayed significantly lower gene-silencing efficiencies of 62% and 55% , respectively , as previously reported ( Figure 1B ) [5] . The resulting effects on Imd pathway-dependent gene expression were then quantified using quantitative real-time PCR ( qRT-PCR ) amplification of a panel of antimicrobial peptides ( AMPs ) previously predicted as Toll or Imd pathway marker genes as well as two genes identified via microarray transcription analyses as regulated by one pathway but not the other [4] , [28] . All AMPs showed moderate induction upon Caspar depletion within a 2- to 5-fold range , suggesting that the Anopheles caspar is functionally conserved to the Drosophila caspar ( Figure 1C and Table S1B ) . To further confirm this relationship and specifically link the Caspar-mediated induction of immune markers to the Rel2 transcription factor , a double gene-silencing assay was performed in which the effect of Caspar depletion was reversed through simultaneous Rel2 depletion . A similar strategy was used for Cactus and Rel1 to ensure consistency . For almost all markers tested , double knockdown reversed the transcriptional increase observed for single knockdown of either caspar or cactus ( Figure 1C ) , suggesting that Caspar has an influence on Rel2 that affects transcription initiation of key immune genes . A complete reversal of the effects of caspar or cactus silencing through rel1 and rel2 silencing would not be expected , given the transient and partial efficiency of RNAi-mediated protein depletion . To investigate the role of these negative regulators in controlling mosquito permissiveness to either the human malaria parasite P . falciparum or the rodent malaria parasite P . berghei , we depleted female A . gambiae of Cactus and Caspar and then fed them on either P . falciparum-infected blood through a membrane or on a P . berghei infected mouse . Infection levels were compared between the gene-silenced mosquitoes and the GFP dsRNA-treated controls by counting oocysts in the midgut tissue . Strikingly , of the 94 mosquitoes tested in three independent biological assays , only 15 Caspar-depleted mosquitoes ( 15 . 5% ) allowed a small number of P . falciparum to develop to the oocyst stage , as compared to 61 . 3% of the Cactus-depleted and 65 . 4% of the control GFP dsRNA-treated mosquitoes ( Figure 2A , left panel ) . While the median number of oocysts per gut was 0 in Caspar-depleted mosquitoes , there were still several individuals with low levels of infection . This is likely to have resulted from incomplete gene silencing in these few individuals , which still displayed an unusually low infection level with a median of 1 oocyst ( mean = 1 . 25 oocysts ) ( Figure 2A , right panel ) . This is in stark contrast to that of GFP dsRNA-treated mosquitoes , which had a median infection intensity of 9 P . falciparum oocysts per midgut ( mean = 21 oocysts ) ( Figure 2A , right panel ) . The Cactus-depleted mosquitoes had a median intensity of infection of 2 oocysts ( mean = 8 oocysts ) which was just less than one-third of that detected in the control mosquitoes , suggesting that the Toll pathway-controlled Rel1 also exerts an anti-P . falciparum effect but not nearly as potent as that of Rel2 ( Figure 2A , right panel ) . Remarkably , experiments with the rodent P . berghei parasite yielded the reverse phenotype: caspar silencing had a degree of impact on oocyst development ( over 50% reduction ) but it was cactus silencing that caused a drastic reduction in infection intensity although prevalence was comparable among all groups ( Figure 2B ) . This is in agreement with the phenotype observed by Frolet and colleagues and contributes to an emerging hypothesis that some anti-Plasmodium mechanisms , including those utilizing the immune system , show different rates of success when tested comparatively against both P . berghei and P . falciparum [6] , [7] . Bearing in mind that species specificities exist not just within Plasmodium but also within Anopheles , we were concerned about the possibility that different mosquito malaria vector species use different molecules and/or mechanisms to contain Plasmodium infections . We investigated the conservation of the anti-P . falciparum phenotype inflicted by caspar silencing by replicating silencing and infection assays in two additional anopheline species . Specifically , we looked at caspar and cactus silencing in Anopheles albimanus , a South American vector , and Anopheles stephensi , an Asian vector . These species were chosen for their status as major malaria vectors in separate geographical areas and their relatedness to A . gambiae . A . stephensi belongs to the same sub-genus as A . gambiae ( Cellia ) yet distinctly branches from the A . gambiae species complex while A . albimanus belongs to a different sub-genus ( Nyssorhynchus ) and is distinctly separated from A . gambiae in phylogenic trees generated using single copy , ribosomal and mitochondrial sequences as well as those based on morphological characteristics [29]–[31] . Comparisons of these three species provided insight on the conservation of anti-Plasmodium defenses between mosquitoes that are separated both spatially and evolutionarily . Degenerate primers were used to identify species-specific caspar and cactus sequences for generation of dsRNA . Using these specific dsRNAs and the same gene-silencing and P . falciparum infection protocol used for A . gambiae , we again observed that caspar silencing causes near-refractoriness . Since the phenotype of these mosquito species mirrored that of A . gambiae , we hypothesize that caspar silencing activates a conserved mechanism for P . falciparum inhibition , consistent with its proposed position in the Imd pathway [16] . dsGFP-treated controls for A . stephensi and A . albimanus exhibited median infection intensities of 6 and 4 oocysts per gut , respectively ( means for both = 6 oocysts ) , while the caspar-silenced mosquitoes from each group had a median of 1 oocyst per gut ( means ≤1 ) . The effect of cactus silencing seems more pronounced in A . albimanus , showing a 75% reduction compared to controls , while cactus-silencing in A . stephensi results in a 50% reduction in median number of oocysts per gut . The complexity of an immune response that is more active against one Plasmodium species than another and is conserved among Anopheles species spurred deeper investigation into the molecular outcome of caspar silencing . Identification of molecules affected by caspar silencing would provide insight into how the Caspar-driven phenotype is produced . Towards this we investigated the transcriptional influences of caspar and cactus silencing via a genome-wide comparative transcriptome analysis between Cactus- and Caspar-depleted adult female mosquitoes and control mosquitoes . The robustness of the resulting microarray gene expression data was validated by real-time quantitative PCR ( Tables S1A and S1B; Figure S1A and S1B ) . In total , 588 ( 472 induced and 116 repressed ) genes were significantly regulated upon Cactus depletion and only 116 ( 61 induced and 55 repressed ) were regulated in response to Caspar depletion ( Figure 3A and 3B and Tables S3 and S4 ) . In both cases , the majority of the regulated genes belonged to a diverse or unknown functional group; however , there were several remarkable patterns that emerged with regard to other groups of genes ( Figure 3A ) . The activation of Rel1 via Cactus depletion induced the transcription of 84 genes involved in replication , transcription and translation . The next most-represented functional groups were immunity , redox/stress responses and metabolism . Caspar-depleted mosquitoes also displayed an elevated expression of genes involved in immunity , redox/stress and replication , transcription and translation . Immune genes regulated by either silencing treatment are outlined in Table S2; description of individual genes of interest regulated by these treatments can be found in Text S1 . Of note is that several key anti-Plasmodium genes such as TEP1 , LRRD7 ( leucine rich repeat domain protein 7 ) ( also known as APL2 ) , and those encoding several FBNs , CLIP domain serine proteases and anti-microbial peptides were regulated by silencing either regulator [6] , [21] , [32] . Since the microarray depicted only a single time point ( 3 days post-silencing ) and tight temporal regulation of immune genes has been reported [5] , we focused more specifically on the transcriptional regulation of genes encoding three key anti-Plasmodium factors , FBN9 ( fibrinogen immunolectin 9 ) , TEP1 and LRRD7 , at 6 , 12 , 24 and 48 hours post caspar dsRNA injection ( h . p . i . ) to gain a better insight on the temporal nature of this response . Results of these experiments show clearly that these genes are regulated at the transcriptional level by caspar silencing and that this regulation is complex and variable among genes . FBN9 exhibited drastic up-regulation at 6 h . p . i . and its transcripts began to wane as time progresses to reach almost baseline levels ( as determined by dsGFP-injected controls ) at 24 and 48 hours ( Figure 3C , left panel ) . TEP1 had the opposite expression phenotype where mRNA abundance was still at a baseline level at 6 h . p . i . but climbed incrementally through 24 hours and remained above the baseline level at 48 h . p . i . ( Figure 3C , middle panel ) . LRRD7 followed a similar pattern as FBN9 but with a time lag: the spike in LRRD7 transcript abundance occurred at 12 h . p . i . and began to wane , although expression was still enhanced compared to dsGFP controls even at 48 h . p . i . ( Figure 3C , right panel ) . To ensure that the transcriptional immune response we observed at the mRNA level was tied to the lack of oocyst development in caspar-silenced mosquitoes , we designed assays to test whether TEP1 , FBN9 and LRRD7 are indeed part of the caspar silencing–mediated anti-Plasmodium mechanism . Each of these three genes was silenced independently as well as together with caspar in A . gambiae females which were then infected with P . falciparum . Oocyst counts from the midguts of these mosquitoes are shown in Figure 4 . When TEP1 was silenced alone a median of 42 oocysts would develop while concurrent silencing with caspar resulted in 31 oocysts . A median of 27 oocysts developed following FBN9 silencing alone , 23 oocysts when co-silenced with caspar while LRRD7 silencing permitted a median of 69 oocysts which was muted to 42 with caspar silencing . Compared to the dsGFP-treated median infection of 18 oocysts and the caspar-silenced median of 1 , each effector significantly reverses the refractoriness conferred by caspar silencing; however , the infections were not as heavy as those that resulted from silencing of any factor alone . We also observe that co-silencing two factors , FBN9 and TEP1 , together with caspar did slightly increase infection intensity compared to either the TEP1+caspar or FBN9+caspar group ( median = 39 oocysts ) . Given the number of genes and variety of functional gene groups that displayed transcriptional induction or repression after Caspar or Cactus depletion ( Figure 3 ) , we hypothesized that these P . falciparum-resistant mosquitoes might display a reduced level of fitness . Studies of the trade-offs between lifespan and immune defense have shown that mounting an immune response accelerates aging in insects; more specifically , chronic and sustained but not acute and transient Relish ( Rel2 ) -dependent immune signaling reduces the lifespan of D . melanogaster [27] , [33] . The kinetics of RNAi-mediated gene silencing suggest that pathway induction achieved through silencing of negative regulators would persist for a certain period of time and then diminish over time as the dsRNAs are degraded . Furthermore , our study and that of Frolet et al . suggest that a molecular boosting of basal immunity , prior to infection via pathway activation , causes a different profile of immune gene transcription than persistent infection does [5] . Since the impact of this type of pathway activation on parameters that determine fitness is currently unknown , we asked whether our strategy for immune induction could influence mosquito survival and fecundity under laboratory conditions . Both fitness measures were assessed in A . gambiae females that had been treated with either dsRNA against cactus or caspar or control GFP dsRNA . Non-injected mosquitoes were also monitored to account for injury-related effects . All groups were tested under three different conditions with regard to nutrition and infection status . Mosquitoes were maintained according to one of three conditions: 1 . provision of 10% sucrose throughout the duration of life; 2 . given a single naïve blood meal followed by sucrose provision; or 3 . given a single P . falciparum-laden blood meal followed by sucrose provision . Cactus-depleted mosquitoes given only sucrose showed a slightly impaired survival rate; this was ameliorated by provision of a blood meal but was exacerbated by provision of an infectious blood meal . In stark contrast , Caspar-depleted mosquitoes , regardless of nutritional or infectious status , displayed a similar survival rate to the GFP dsRNA-treated controls and non-injected controls . As expected , non-injected mosquitoes tended to fare better than all injected mosquitoes though not significantly ( Figure 5A–5C ) . The number of eggs laid per female and hatch rates of those eggs were also similar between non-injected controls , GFP dsRNA-treated controls and mosquitoes treated with caspar dsRNA while a quite significant decrease in both measures was observable in the cactus-silenced group ( Figure 5D ) .
How the Toll and Imd immune signaling pathways mediate anti-bacterial and anti-Plasmodium responses through the Rel1 and Rel2 transcription factors is of key interest , but only recently have some details about pathway regulation become known . Several recent discoveries have shed light on the mechanisms by which pathogens trigger immune gene transcription via Relish/Rel2 . Kim and colleagues have shown in Drosophila that Caspar is likely to inhibit Dredd-dependent cleavage of Relish [16] . However , in both mosquitoes and mosquito cell lines , Rel2 has been shown to have two active isoforms , one long and one short , with differences in their gene targets [4] , [34] . Considering that only the long form , Rel2-F , has inhibitory domains that need to be cleaved for activation , Caspar may only be regulating one branch of Rel2-dependent transcription . Rel2-F has also been shown to be involved in regulating transcription of key Imd pathway marker genes and melanization , and in reducing the number of P . berghei parasites in the midgut [4] , [28]; however , because our dsRNAs target common domains of both Rel2 forms , we cannot rule out the possibility that Caspar may also be somehow regulating Rel2-S in the mosquito . The mosquito's immune response against Plasmodium is currently under scrutiny as a target for malaria control strategies yet there is much to learn about how immune responses are mounted against Plasmodium and what the physiological implications are or how the responses are conserved . Our data first indicate that manipulation of the mosquito's immune system can produce a near-complete loss of the vector's ability to transmit an unusually virulent strain of the human malaria parasite . Note that we used a P . falciparum strain that produces exceptionally high infection levels when compared to naturally acquired Plasmodium in the field [35] . This exceptionally virulent strain failed to develop in 85% of the Caspar-depleted mosquitoes , and even those mosquitoes that became infected showed a 94% loss of oocysts when compared to the controls ( Figure 2A ) . Caspar-depleted A . gambiae are therefore likely to be completely refractory to natural parasites in the field . Second , we establish that the reduction in oocysts resulting from depletion of either Cactus or Caspar indicates that both the Toll and Imd immune pathways are involved in the defense against both P . falciparum and P . berghei , but to different degrees depending on the parasite species ( Figure 2A and 2B ) . This and other studies have shown that activation of the Toll pathway through cactus silencing renders mosquitoes almost non-permissive to infection with the rodent malaria-causing parasite P . berghei [5] , while our data indicate that the same treatment results in an increased but only partial resistance to infection with the human P . falciparum . Previous studies have also shown profound differences in the immune responses mounted by A . gambiae to P . berghei and P . falciparum [6] , [7] . Our data further support the existence of a distinction between immunity-based resistance to rodent and human malaria parasites and this distinction appears to depend , at least in part , on which Rel factor is activated; i . e . Rel2-based immunity seems most efficient against P . falciparum while Rel1-based immunity is most efficient against P . berghei . It will be interesting to identify the underlying cause of this distinction; experiments that identify mosquito proteins specifically regulated by either parasite coupled with experiments identifying unique parasite surface molecules may help clarify the distinction . Third , we observe that the infection-resistant phenotype is not constrained by Anopheles species; silencing A . albimanus and A . stephensi caspar orthologs reproduces the reduction in P . falciparum oocysts ( Figure 2C and 2D ) . This , in light of the relative divergence of these mosquito species , brings about important insight into both the evolution of the Anopheles-Plasmodium interaction as well as the implications for the development of malaria control strategies based on mosquito immunity . Co-evolution is at the crux of the complex interactions between vector , parasite and host . How is it that some interactions can be consistent from one species to another while others are unique ? What pressures are exerted by the environment or microbial exposures that precipitate changes or , alternatively , what needs are only met through conservation ? Caspar's proposed role in the Imd pathway obviously marks a position in the immune system that is needed by mosquitoes to combat P . falciparum and , as a negative regulator of a powerful pathway , may be involved in maintaining the evolutionary balance between controlling the parasite and avoiding chronic immune up-regulation . But how , then , does Caspar depletion circumvent species-specificity ? At the cellular and molecular level , the possible differences between species in the Anopheles genera are indefinite , meaning slight alterations in any number of molecules , cells or mechanisms could be responsible for variable success of an anti-Plasmodium response . We think it is likely that Caspar depletion transcends this limitation because we have targeted a conserved protein in a conserved pathway that leads to the activation of a battery of anti-Plasmodium mechanisms . It is certainly possible that some constituents of these downstream mechanisms differ from mosquito species to species but the sum of all parts is a robust overall anti-Plasmodium falciparum immune response . This same principle would mean that it is unlikely that P . falciparum would acquire resistance to this strategy since there are multiple effectors working in concert . From a malaria control aspect , in this way we can gain refractoriness in a variety of vectors . Fourth , we have begun to delve deeper into the molecular outcome of caspar silencing . Dissection of the downstream battery of immune effectors and how/if they interact is a formidable task and would require extensive experimentation and collaborative efforts . However , using transcriptional and functional genomics , we do establish here that such a mechanism exists and that several key anti-Plasmodium genes are involved . The microarray-derived gene expression profiles illustrate that silencing negative regulators activates an arsenal of genes , immune-relevant and otherwise , that may or may not have a direct effect on malaria parasites . Depletion of Cactus resulted in the transcriptional regulation of a more than five-fold higher number of genes than did Caspar depletion , suggesting that Rel1 is a more ubiquitous transcription factor than Rel2 ( Figure 3A and 3B ) . In fact , the Toll pathway has been linked with a variety of processes in development and hemocyte proliferation , and this range of effects was reflected by the broad range of functional gene groups that we found to be influenced by cactus silencing ( Figure 3A ) [12] , [36] , [37] . Similar observations have also been made in Dif/Dorsal-deficient D . melanogaster larvae and other Toll pathway factor mutants that have shown differences in the expression of a large number of non-immune genes , whereas mutations in Imd factors have affected immune gene expression more specifically [37] . Hence , the Toll pathway appears to be a more general and versatile signaling pathway , while Imd is more immunity-specific . Many genes that responded to Cactus depletion are involved in replication , transcription and translation—a likely consequence of the cellular response needed to accommodate the increased activation of a potent transcription factor such as Rel1 and the hemocyte proliferation known to follow Toll pathway activation . Immunity , redox/stress responses and metabolism were also well-represented; immunity and redox reflect Rel1's major role in the response to pathogens and the stress caused by infection while the concurrent enrichment of metabolism- and apoptosis-related genes may provide a transcriptional link between the immune response and recovery from infection . Although caspar silencing influenced transcription of significantly fewer genes , a considerable proportion of regulated genes belonged to the immunity class ( see further discussion on individual genes in Text S1 ) . Within this class were genes encoding quite a few known or putative anti-Plasmodium effectors that are good candidates for mediating the molecular outcome of caspar silencing that results in parasite killing . Some of these candidates , such as FBN9 , TEP1 and LRRD7 ( APL2 ) , have already been described as performing anti-Plasmodium roles and we have shown here that they display a temporally regulated transcription profile when caspar is silenced ( Figure 3C ) [6] , [21] , [32] . We focused on these 3 genes because of their relevance to the infection phenotype addressed in this study . Using co-silencing techniques , we found that depletion of Caspar cannot confer resistance to P . falciparum when FBN9 , TEP1 or LRRD7 was co-depleted ( Figure 4 ) . These double-silenced mosquitoes harbored similar numbers of oocysts as mosquitoes depleted of the effector alone . Intriguingly , double silenced mosquitoes tended to carry fewer oocysts than the singly silenced which further our theory that a battery of anti-Plasmodium genes , and not just one or two effectors , is regulated by Caspar . This was further illustrated by cocktail silencing experiments in which Caspar , along with two or more effectors were depleted . These triple gene-silenced mosquitoes showed a slightly greater ( though not additive ) infection intensities compared to those with just caspar and a single effector silenced , suggesting a concerted effort occurring either downstream or in parallel to Caspar . That the Imd pathway could control regulation of a fibrinogen-domain-containing molecule and a complement-like molecule that together interact to combat invading parasites is not unlikely . In vertebrates , ficolins are the fibrinogen-like domain containing pattern recognition receptors that activate the lectin complement pathway [38] , [39] . Evidence of lectin pathway components in chordates and jawless vertebrates suggests that it is the most ancient complement activation pathway [40] , [41] . The precedence set in the vertebrate system for these two molecules to cooperate to destroy pathogens as well as the observation that both TEP1 and FBN9 co-localize to the parasite surface justify speculation that a lectin complement pathway–like mechanism may also exist in mosquitoes ( [21] and Dimopoulos lab , submitted ) . Finally , to investigate the potential fitness impact of this highly potent anti-Plasmodium defense , we performed several proof-of-principle assays to determine if longevity or fecundity could be influenced by the RNAi-mediated transient immune response activation . We concede that this is by no means a thorough study of how Imd and Toll activation pathway activation impact fitness , but it provides baseline indications on any obvious physiological consequences of this immune boosting . The fact that caspar-silenced , but not cactus-silenced , mosquitoes lived as long as controls indicates that the products of the Imd , but not the Toll , pathway have a negligible effect on longevity under these conditions . Since our gene expression data indicated that cactus silencing influences five times more genes than does caspar silencing , it may be that the Toll pathway's transcriptional output is more costly than that of the more immune-specific Imd pathway . This difference could also reflect a greater toxicity of Toll products in the host . Reduced fecundity could also impart a reduction in fitness yet we observed that the same number of viable offspring were produced per caspar-silenced female as dsGFP-treated or non-injected controls; taken with the longevity data , we conclude that fitness by these measures and in this environment is unimpaired by the transient Rel2 –mediated immune response . The contrasting fitness cost of cactus-silencing suggests that manipulation of the Toll pathway may be too detrimental to mosquito fitness for its use in a malaria control strategy , though it would be quite interesting to continue investigating how Toll pathway activation impacts fitness and how insects negotiate the cost/benefit of Toll pathway activity . On the other hand , that Caspar manipulation does not confer a noticeable fitness cost warrants future evaluation in more realistic contexts and further analysis of its molecular mechanism . We do suspect that fitness costs will be less obvious in this context than in others , since the immune induction here was not sustained for a lifetime but was relatively temporary because of the kinetics of the RNAi process . This situation is in accordance with reports showing that sustained NF-kappaB-dependent chronic immune responses are correlated with a shorter life while temporary acute responses are not [27] . We also questioned whether this analysis could have become compromised by the experimental method that required injection of dsRNA which also could facilitate introduction of bacteria that could alter survivorship; the caspar dsRNA treated mosquitoes may in this case experience an advantage due to immune activation We must use this technique as gene silencing and over expression by other methods are prohibitively difficult in A . gambiae . As a control , hemolymph from mosquitoes injected with dsRNA against caspar and GFP as well as unwounded mosquitoes was perfused with saline and plated on LB agar at sterile conditions . Resulting colony counts indicated that individual mosquitoes from all groups contained a large range of bacterial quantities and no significant differences were found among groups suggesting that if bacteria are introduced into the hemocoel during injection , they are in negligible amounts or are unable to survive and/or proliferate ( data not shown ) . Upon caspar silencing , we did not observe any spontaneous melanization that was the compelling phenotype for the discovery of Caspar deficiency in Drosophila [16] . It is likely that the absence of melanization in mosquitoes simply reflects differences in how deficiencies are generated; a P-element insertion in Drosophila can cause Caspar loss of function throughout the body for a lifetime while the RNAi-mediated silencing strategy we employed is transient and causes differing levels of transcript degradation in different tissues . Because of this , we can not rule out that a constitutive and thorough Caspar loss of function would cause a similar hyper-melanization phenotype in mosquitoes . By manipulating Caspar and Cactus in the present study , we were able to produce a transcriptional immune response that potentially involved a variety of anti-Plasmodium factors . Thus , caspar silencing was probably so effective against P . falciparum because it simultaneously enhanced an entire battery of relevant effectors and mechanisms . Global induction is advantageous for development of control strategies , since it avoids reliance on a single gene and the associated limitations in effectiveness: that it may only be effective against a certain parasite stage or strain and that resistance may more easily develop . This may also be why , as a physiological and biochemical mechanism , immune system activation produces a more extensive reduction in parasite burden than synthetic methods . If our strategy of depleting Caspar proves to be an effective way of limiting vector competence , short-term immune induction could be achieved through infection-inducible promoters that drive recombinant Rel2 expression . A further understanding of the impact of immune pathway activation on the mosquito's fitness will necessitate not only the development of an injury-independent mechanism but also the assessment of fitness under field conditions .
Peptide sequences for Drosophila Caspar ( CG8400 ) and its predicted A . gambiae ortholog were retrieved from Ensembl and subject to alignment using BLAST and Clustal W and the BLOSUM62 scoring matrix . Sequence alignment achieved an expected value of 5e−150 . Domain similarity was determined by BLAST alignment of amino acid sequences of Drosophila Caspar regions as defined by Kim et al . [16] against the entire Anopheles Caspar sequence . Domain regions were then verified using the SMART database version 5 . 1 [42] . A . gambiae Keele strain , A . albimanus Santa Tecla and A . stephensi mosquitoes were maintained on a 10% sugar solution at 27°C and 80% humidity with a 12-h light/dark cycle according to standard procedures [43] . For all mosquitoes , ∼250 larvae per 30×34 cm tray were reared with daily addition of cat food pellets and a ground fish food supplement upon water change . Adults were reared in a 20×20×20 cm3 cage and provided 10% sucrose . Assays were performed according to standard protocol [6] . Sense and antisense RNAs were synthesized from ∼300–600 bp PCR-amplified gene fragments using the T7 Megascript kit ( Ambion ) and primers indicated in Text S1 . About 69 nl dsRNA ( 2–3 µg/µl ) in water was introduced into the thorax of cold-anesthetized 2–4 day old female mosquitoes by a nano-injector ( Nanoject , Drummond ) with glass capillary needles according to an established methodology [21] . Assays were performed according to standard protocol [6] . Total RNA from adult females was extracted using the RNeasy kit ( QIAGEN ) , quantified using a Beckman DU640 spectrophotometer and subjected to reverse transcription using Superscript III ( Invitrogen ) with random hexamers . Real-time quantification was performed using the QuantiTect SYBR Green PCR Kit ( Qiagen ) and ABI Detection System ABI Prism 7000 . Primer sequences are given in Text S1 . All qPCR reactions were performed in triplicate; to check for the specificity of the PCR reactions , melting curves were analyzed for each data point . The levels of expression in gene-silenced samples were determined by normalizing cDNAs using the ribosomal protein S7 gene and comparing to controls treated with dsRNA against GFP . P-values were determined using a student's T-test . P . falciparum and P . berghei infections were administered according to standard protocol [6] . For P . falciparum infections , mosquitoes were fed on NK54 gametocytes in human blood through a membrane feeder at 37°C 4 days after dsRNA treatment . Unfed mosquitoes were removed within 24 h after feeding , and the rest were maintained at 24°C for 7 days . For P . berghei infections , mosquitoes were fed on Swiss Webster mice infected with the wt Anka 2 . 34 strain of P . berghei at 21°C 4 days after dsRNA treatment . Unfed mosquitoes were removed from the group within 24 h after feeding and the rest were maintained at 21°C for 14 days . For both infections , mosquito midguts were dissected and stained with mercurochrome , and oocyst numbers were recorded using a light-contrast microscope ( Olympus ) . Each assay was done with at least 25 mosquitoes , and data represent the results of three independent assays . P-values were determined using a Mann-Whitney test . Using known sequences from A . gambiae , D . melanogaster and A . aegypti , degenerate primer pairs ( with sequence bias toward A . gambiae ) were designed to amplify regions spanning the entire transcript of either gene . Regions were amplified from total cDNA from each mosquito species using standard PCR protocol and cycling program . PCR products were excised from a 1% agarose gel , extracted ( Qiagen Qiaquick gel extraction kit ) , sequenced ( Applied Biosystems 3730×l DNA Analyzer ) , and aligned to A . gambiae sequenced using BLAST and Clustal W and the BLOSUM62 scoring matrix . Compared to A . gambiae , sequences of regions of A . albimanus cactus and caspar had identities of 77–85% and 77–100% , respectively while regions of A . stephensi cactus and caspar had identities of 70–92% and 80–93% , respectively . Regions used for generating double-stranded RNA achieved the following nucleotide identities: A . albimanus cactus , 77%; A . albimanus caspar , 79%; A . albimanus cactus 75% and A . stephensi caspar , 92% . Assays and analysis were performed according to standard protocol [6] . All arrays performed using female A . gambiae at 3 days post silencing . Gene expression values were compared between Cactus- and Caspar-depleted adult female mosquitoes and dsGFP-treated controls . Total RNA was extracted from 10–15 whole mosquitoes 3 days after dsRNA treatment using the RNeasy kit ( QIAGEN ) . Quantification of RNA was performed using a Beckman DU640 spectrophotometer , and quality was assessed by RNA Nano LabChip analysis on an Agilent Bioanalyzer 2100 . Probes were synthesized with 2–3 µg RNA using the Agilent Technologies low-input RNA labeling kit according to the manufacturer's instructions . Hybridizations were done with the Agilent technologies in situ hybridization kit according to manufacturer's instructions . Arrays were washed then dried using pressurized air . Microarray scanning was done with an AXON 4200AL scanner , with the laser power set to 60% and the PMT gain adjusted manually to maximize effective dynamic range and avoid spot saturation . Images were analyzed with Genepix 6 . 0 software to determine spot size , location and quality , and potentially confounding spots were manually removed from the analysis . The data were then processed with TIGR MIDAS software [44] . The intensity threshold was set to 100 for both Cy5 and Cy3 channels , and the signal-to-background cutoff was set to 2 . 0 in both channels . Data for each experimental condition were derived from three biological replicates . The median spot intensities for each spot meeting the above criteria were normalized according to a LOWESS normalization method . The TIGR MeV software [44] was then used to Log2 transform the Cy5/Cy3 ratios and perform t-tests with the following parameters: mean value to be tested against = 0 , the Welsh approximation method for degree of freedom calculation , P-values based on t-distribution , the overall alpha ( critical p-value ) set to 0 . 05 , no p-value correction . According to established methods , a cut-off value for the significance of the gene regulation was set to 0 . 8 in a log2 scale ( 1 . 74-fold ratio ) [45] . Values for genes that had a significant p-value for one experimental set were included in other experimental sets , providing that the direction of regulation was consistent and the value was within a range of <0 . 5 fold . Two-day-old adult female A . gambiae were treated with dsRNA against cactus , caspar or GFP as described , then incubated at 27°C with 70% humidity while being maintained on sterilized 10% sucrose solution . Injected mosquitoes and non-injected mosquitoes from the same generation were monitored simultaneously to control for injection-induced mortality . All cohorts were monitored daily for survival , and dead mosquitoes were removed each day . Monitoring continued until all mosquitoes had perished . Percentages represent the mean survival between 3 biological replicates of 50 mosquitoes each . Statistical significance was determined using Kaplan-Meier and log-rank analyses . For each of three biological replicates , at least 30 two-day-old female mosquitoes were treated with dsRNA then at 3 d . p . i . ( days post injection ) allowed to feed on human blood through an artificial membrane system for 30 minutes . Fed mosquitoes were transferred to individual wax-lined cardboard cups outfitted with cotton soaked in 10% sucrose solution and an oviposition cup filled with water and lined with filter paper . Individual chambers were incubated under normal rearing conditions . Eggs oviposited on filter paper were counted after 2 days using light microscopy . Those females with no eggs on day 2 were maintained and filter papers were examined on day 3 . After each count , eggs were submerged en masse in a standard larval pan for rearing according to standard methods ( see rearing methods ) . Second or third instar larvae were counted and removed from larval pan daily . Statistical significance of oviposition was determined using Mann-Whitney and statistical significance of hatch rates was determined using one way ANOVA ( analysis of variance ) . For each of three biological replicates , at least 10 two-day old female mosquitoes were microinjected with dsRNA or reserved as non-injected controls . Three days post silencing , mosquitoes were surface sterilized with 70% ethnol and PBS and sterile PBS was perfused through the thorax and collected with a sterile needle according to previous established protocol ( ref ) . Collected hemolymph was serially diluted in PBS and dilutions were each plated on LB agar . Plates were sealed and kept in insectary conditions for 2 days then colonies were counted . Number of bacteria per mosquito was determined using an average of all serial dilutions for that individual . All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies , and all animal work was approved by the appropriate committee . | The relationship between malaria parasites and the mosquitoes that transmit them to humans comprises complex molecular interactions including mosquito immune responses . Anopheles can mount potent anti-Plasmodium immune responses; we show here that the gene caspar , which encodes a negative regulator of the immune signaling pathway Imd , controls mosquito resistance to the human malaria parasite . Silencing of this Imd pathway regulator results in complete resistance to human Plasmodium in three divergent Anopheline malaria vector species , yet does not cause complete resistance to a rodent Plasmodium species , indicating conservation of defense function among mosquito but not diverse parasite species . We also identify a panel of genes that are transcriptionally regulated by silencing of the caspar gene . Some of these genes contribute directly to parasite resistance . Finally , we show that the transient immune activation that renders mosquitoes resistant to the human malaria parasite has little to no effect on mosquito fitness as a measure of survival or fecundity under laboratory conditions . In sum , this study shows that the mosquito's immune pathway , Imd , can regulate resistance to Plasmodium through immune responses that entail several known anti-Plasmodium genes . | [
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] | 2009 | Caspar Controls Resistance to Plasmodium falciparum in Diverse Anopheline Species |
Prion diseases are fatal infectious neurodegenerative disorders in man and animals associated with the accumulation of the pathogenic isoform PrPSc of the host-encoded prion protein ( PrPc ) . A profound conformational change of PrPc underlies formation of PrPSc and prion propagation involves conversion of PrPc substrate by direct interaction with PrPSc template . Identifying the interfaces and modalities of inter-molecular interactions of PrPs will highly advance our understanding of prion propagation in particular and of prion-like mechanisms in general . To identify the region critical for inter-molecular interactions of PrP , we exploited here dominant-negative inhibition ( DNI ) effects of conversion-incompetent , internally-deleted PrP ( ΔPrP ) on co-expressed conversion-competent PrP . We created a series of ΔPrPs with different lengths of deletions in the region between first and second α-helix ( H1∼H2 ) which was recently postulated to be of importance in prion species barrier and PrP fibril formation . As previously reported , ΔPrPs uniformly exhibited aberrant properties including detergent insolubility , limited protease digestion resistance , high-mannose type N-linked glycans , and intracellular localization . Although formerly controversial , we demonstrate here that ΔPrPs have a GPI anchor attached . Surprisingly , despite very similar biochemical and cell-biological properties , DNI efficiencies of ΔPrPs varied significantly , dependant on location and inversely correlated with the size of deletion . This data demonstrates that H1∼H2 and the region C-terminal to it are critically important for efficient DNI . It also suggests that this region is involved in PrP-PrP interaction and conversion of PrPC into PrPSc . To reconcile the paradox of how an intracellular PrP can exert DNI , we demonstrate that ΔPrPs are subject to both proteasomal and lysosomal/autophagic degradation pathways . Using autophagy pathways ΔPrPs obtain access to the locale of prion conversion and PrPSc recycling and can exert DNI there . This shows that the intracellular trafficking of PrPs is more complex than previously anticipated .
Prion diseases or transmissible spongiform encephalopathies ( TSEs ) are fatal infectious neurodegenerative disorders causing Creutzfeldt-Jakob disease ( CJD ) in humans , bovine spongiform encephalopathy ( BSE ) in cattle , scrapie in sheep and goat , and chronic wasting disease ( CWD ) in cervids [1]–[5] . The major component of the infectious agent in the pathogenesis of these diseases is the β-sheet rich and partially protease-resistant protein denoted PrPSc , derived from post-translational conversion of the α-helical , protease-sensitive cellular prion protein ( PrPc ) [6] , [7] . Prions replicate by template-directed refolding of PrPc into pathological PrPSc , a process which is believed to involve a direct physical interaction of these two isoforms [8] , [9] . Although there are a number of proteins whose β-sheet rich conformers are associated with diseases [10] , prion diseases are unique among them because prions are clearly infectious at the inter-individual level and can exist in many strains with a stable heritage of the strain-specific properties [11] . The molecular and cellular mechanisms underlying these strain-specific features are still enigmatic . Since PrP isoforms have to physically interact , investigating the PrP-PrP interactions , either PrPSc-PrPSc or PrPc-PrPSc , will provide important information on the molecular mechanisms of prion propagation and delineate new molecular targets for intervention in prion diseases . PrPSc-PrPSc interaction is important because prion infectivity is enciphered in the structure of PrPSc [12] and these structures are stably maintained only in the context of PrPSc oligomers [13] , [14] . PrPc-PrPSc interaction is the initial step in the prion conversion process; it therefore affects efficiencies of prion propagation in a host , species barrier phenomena , and high-fidelity inheritance of strain-specific traits [1] , [11] , [15] , [16] . Prion conversion is highly sensitive to mismatches in the primary structures between substrate PrPC and template PrPSc , which can occur in interspecies transmissions or in hosts with polymorphic sites in their Prnp genes . Occasionally , even a one-residue mismatch hampers prion propagation and subsequent development of disease [17] , [18] . Apparently , such mismatches render the PrPc substrate conversion-incompetent , presumably by impairing its binding to PrPSc and/or compromising the thermodynamic stability in the conformation as preferred by the PrPSc template . Of note , besides conversion of itself , a conversion-incompetent PrP occasionally inhibits conversion of a co-existing conversion-competent PrP . This phenomenon is known as dominant-negative inhibition ( DNI ) or trans-dominant inhibition [17] , [19] . Notably , this phenomenon seems also to be of importance in pathologic in vivo situations , best exemplified by the naturally occurring protective polymorphisms against scrapie in sheep , CWD in deer , or CJD in humans [20]–[24] . Interestingly , a conversion-incompetent PrP is not synonymous with a DNI-causing PrP; only some conversion-incompetent PrPs exhibit an efficient DNI [17] , [25] , [26] . Kaneko and colleagues have systematically investigated which substitutions in PrP cause DNI in scrapie-infected mouse cells and postulated a region as an interaction interface with the postulated factor X [17] . DNI was also observed with mutants with internal deletions lacking the secondary-structure components , specifically the first β-strand ( B1 ) , the first α-helix ( H1 ) and the second β-strand ( B2 ) [27] . Within a series of consecutive seven-residue insertions in PrP , some of the insertions also caused DNI in various cell-culture systems [26] . Notably , DNI has been recently reported in cell-free systems , suggesting that this mechanism can occur at the level of PrPc-PrPSc interaction , independent of cellular co-factors [25] , [28] . A physiological N-terminally truncated degradation product of host-encoded PrP , often referred to as C1 fragment , also exerted DNI in vivo [29] . However , the molecular determinants of the potency of mutant PrP DNI have not yet been defined in detail to our knowledge . To further investigate PrPC-PrPSc interaction , we utilized the DNI effect for identifying regions of PrP critical for PrP-PrP interaction . We hypothesized that mutant PrPs with efficient DNI must have a high affinity for the template PrPSc or the substrate PrPC , whereas those with inefficient DNI should have lower affinities , and that the difference in affinities originate from the structural integrity and homology in the interaction interface , an analogous logic as postulated for the factor X hypothesis [17] . Unlike observing conversion efficiencies of substrate PrPC which are also affected by thermodynamic stability of the nascent PrPSc , DNI efficiency would mostly depend on the binding affinity of the mutant PrP for PrPSc or PrPC . This allows for a simpler interpretation of results: If mutations in a region affect DNI , the region might compose or be part of the interaction interface . Based on that hypothesis , we focused on the region between H1 and H2 ( H1∼H2 ) which includes the loop between B2 and H2 ( B2-H2 loop ) . Recently , the importance of the B2-H2 loop in PrPC-PrPSc conversion has been highlighted and it was postulated that properties of the B2-H2 loop differ between species and might correlate with species barrier effects [30] , [31] . Another recent , although controversial , study provided experimental evidence for a ‘domain-swapping’ event in prion conversion; where N-terminal located subdomains are exchanged between two PrP molecules and thereby B2-H2 loops stretched into β-sheet structures in PrP fibrils which were used as PrPSc surrogates [32] . To test the importance of the H1∼H2 region in interactions between PrP molecules , we created a series of mutant PrPs with deletions increasing in size in the H1∼H2 portion . Irrespective of deletion size , these mutant PrPs were similar in their biochemical properties ( e . g . solubility and PK resistance ) , subcellular localization , glycosylation profile , and GPI anchoring . However , they were significantly different in DNI efficiencies with an inverse relation between deletion size and DNI efficacy: The larger the deletion the lower was the dominant-negative effect . We also found that the position of the deletions highly affect DNI efficiency . Surprisingly , even mutant PrP with a deletion of the entire region N-terminal to H1∼H2 still showed efficient DNI . These data imply that the structural integrity and the positioning of H1∼H2 are important for PrP-PrP interactions and corroborate the postulated significance of the B2-H2 region for PrPC-PrPSc conversion . Furthermore , we also found that these PrP mutants with internal deletions are trafficked directly from the endoplasmic reticulum to endosomal/lysosomal compartments for degradation through class-III PI3 kinase-dependent pathways , presumably involving autophagic processes . This is highly suggestive of the site where prion conversion occurs and sheds light on the complexity of intracellular trafficking of prion proteins .
All buffers and media for cell culture , Hank's balanced salt solution ( HBSS ) and 10× PBS ( pH 7 . 4 ) were purchased from Invitrogen Corporation ( Carlsbad , CA , USA ) . Plasmid purification and DNA gel extraction kits were from Omega Bio-Tek ( Norcross , GA , USA ) . Triton X-100 ( TX100 ) , deoxycholic acid ( DOC ) , Triton X-114 ( TX114 ) , sodium hydroxide ( NaOH ) , N-lauroylsarcosin ( sarcosyl ) , chymotrypsin , and proteinase K ( PK ) were purchased from Sigma-Aldrich Co . , LLC ( St . Louis , MO , USA ) . Guanidine hydrochloride ( GdnHCl ) was from Promega Corporation ( Madison , WI , USA ) . Site-directed mutagenesis kit was purchased from Agilent Technologies , Inc . ( Santa Clara , CA , USA ) . Tween 20 , Bafilomycin A1 ( Baf ) and acrylamide ( 40% , 37 . 5∶1 ) solution were from EMD Chemicals Inc . ( Gibbstown , NJ , USA ) . Pentosan polysulfate was from Bene-Arzneimittel GmbH ( Munich , Germany ) . 3-methyladenine ( 3MA ) was from Thermo Fisher Scientific Inc . ( Waltham , MA , USA ) . MG-132 was purchased from Cayman Chemical Co . ( Ann Arbor , MI , USA ) . The anti-PrP monoclonal antibody ( mAb ) 4H11 was described before [33] . The anti-PrP monoclonal antibody ( mAb ) 3F4 which recognizes residues108–111 was purchased from Covance ( Princeton , NJ , USA ) . Anti-PrP C16-S rabbit mAb , which was raised against the C-terminal part of human H3 , was purchased from Novus Biologicals ( Littleton , CO , USA ) . Anti-LAMP1 rat mAb was from BD Biosciences ( San Jose , CA , USA ) . Anti-calnexin ( CNX ) rabbit polyclonal antibody was from Assay Design's Inc . ( Ann Arbor , MI , USA ) . All the secondary antibodies , DyLight488-conjugated anti-rat IgG antibody , DyLlight594-conjugated anti-mouse IgG antibody and HRP-conjugated or DyLight488-conjugated anti-rabbit IgG ( with minimal cross-reactivity with serum proteins from other species ) were purchased from Jackson Immunoresearch ( West Grove , PA , U . S . A . ) . The plasmids pEGFP-LC3 , -Rab7 and -Rab9 have previously been described [34] , [35] . The mouse neuroblastoma cell line N2a was purchased from American Type Culture Collection ( Manassas , VA , U . S . A . ) and persistently infected with the mouse prions strain 22L ( 22L-ScN2a ) [36] . A cell population with a high and stable level of PrPSc was selected by single-cell cloning and used throughout for the experiments described here . The non-infected counterpart ( N2a ) was prepared by disinfecting 22L-ScN2a cells by pentosan polysulfate ( 2 µg/ml ) treatment over seven passages . All primers used in these experiments were ordered from Integrated DNA Technologies , Inc . ( Coralville , Iowa , U . S . A . ) and are listed in Table S1 . Mutations were made by site-directed mutagenesis according to the manufacturer's instruction , using a 3F4-epitope ( methionine at position 108 and 111 ) tagged mouse Prnp gene cloned into pcDNA3 . 1 ( + ) as template; therefore all ΔPrPs were 3F4-epitope tagged . Created mutations were verified by sequencing from both strands at Clemson University Genomics Institute ( Clemson , SC , U . S . A . ) . For evaluating the expression levels of ΔPrPs , N2a cells on 24-well culture plates were transiently transfected with 0 . 32 µg/well of each ΔPrP construct . For evaluation of DNI effect , 22L-ScN2a cells on 24-well culture plates were transiently co-transfected with 0 . 25 µg/well each of ( 3F4 ) MoPrP and ΔPrP ( total DNA amount 0 . 5 µg/well; 1∶1 molar ratio ) . For transfection of cells on 6-well culture plates , 4-fold larger amounts of plasmid and transfection reagent per well than for 24-well plates were used . 22L-ScN2a or N2a cells were plated on 24-well or 6-well culture plates and transfected with plasmid DNA with Lipofectamine LTX Plus kit ( Invitrogen ) according to the manufacturer's instruction . 2-mercaptoethanol was added to the culture medium ( final concentration 50 µM ) during transfection . Cells were kept in medium containing plasmid and transfection reagent for 24 hours and then harvested or the old medium was replaced with fresh full medium without plasmid until harvesting . For evaluating effects of various chemical compounds on ΔPrP levels , the old medium was replaced with fresh full medium with either DMSO ( 0 . 15% ) , bafilomycin A1 ( 120 nM ) , 3MA ( 10 mM ) or MG132 ( 10 µM ) at this point and the transfected cells were incubated further for 7 hours before harvest . Cells were harvested with phosphate-buffered ( pH 7 . 4 ) 0 . 5% TX100 , 0 . 5% DOC ( TX100/DOC ) lysis buffer , 40 µl/well for 24-well and 300 µl/well for 6-well plate . After removal of nuclear debris by centrifugation ( microcentrifuge Eppendorf AG , Hamburg , Germany ) at 21 , 130 g for 1 . 5 minutes , the supernatant was transferred to another tube as TX100/DOC postnuclear lysate . For samples tested for protease-resistant PrP cores , lysates were digested with chymotrypsin ( at indicated concentrations ) or proteinase K ( PK ) ( 25 µg/ml ) at 37°C for 30 minutes . Digestion was stopped by addition of Pefabloc ( Roche Applied Science , Indianapolis , IN , U . S . A . ) at 2 mM , and 1/4-volume of 5× sample buffer ( 5×SB; 12% SDS , 250 mM Tris-HCl , pH 7 . 1 , 40% glycerol and bromophenol blue ) . Finally , lysates were boiled for 10 minutes at 95°C . Samples were resolved on 10–12 . 5% SDS-PAGE gels and electrotransferred to PVDF membranes ( Milipore , Billerica , MA , U . S . A . ) by semi-dry blotting method . PVDF membranes were blocked with 5% Blotto ( non-fat dry milk from Bio-Rad . , Hercules , CA , U . S . A . ) in tris-buffered saline supplemented with 0 . 1% Tween 20 ( TBST ) for 30 minutes and then incubated with anti-PrP antibodies 3F4 ( mAb; 1∶10 , 000 dilution ) , C16-S ( mAb; 1∶5 , 000 ) or 4H11 ( mAb; 1∶1 , 000 ) in 5% Blotto in TBST . Then membranes were washed in TBST , incubated with HRP-conjugated anti-mouse IgG for mAbs 3F4 and 4H11 or anti-rabbit IgG for mAb C16-S , 1∶10 , 000 in 5% Blotto in TBST for 1 hour , washed again in TBST four times , then the labeled proteins were visualized with Pierce ECL Plus Western Blotting Substrate ( Thermo Scientific , Rockford , IL , U . S . A . ) . For detection , membranes were exposed to X-ray films ( Thermo Scientific , Rockford , IL , U . S . A . ) and developed . X-ray films were scanned and quantified by densitometry with image processing software Image J ( http://rsb . info . nih . gov/ij/ ) . Acquired data were analyzed with statistics software “R” ( www . r-project . org ) for two-tail paired t-tests with p<0 . 05 as significance level . When re-probing with mAb 4H11 was required , the PVDF membrane was incubated in 100% MeOH for 20 minutes to remove bound antibodies . Then , the membrane was washed in TBST to remove residual MeOH , followed by incubation with mAb 4H11 in 5% Blotto in TBST . The following steps including secondary antibody incubation and washes in TBST were as described above . N-lauryl sarcosyl was added to TX100/DOC lysates from N2a or 22L-ScN2a cells expressing ΔPrPs to a final concentration of 4% . Lysates were ultracentrifuged using an OptimaTL ultracentrifuge ( Beckman Instruments GmbH , Munich , Germany ) at 100 , 000 g , 4°C , for 1 hour . The supernatant was carefully removed , transferred to fresh tubes and subjected to methanol/chloroform ( MeOH/CHCl3 ) precipitation ( described below ) . The pellet fraction was washed once with 100 µl of TX100/DOC lysis buffer and ultracentrifuged again at 100 , 000 g for 15 minutes . After removal of supernatant , the pellet was sonicated in 1× SB ( 2 . 4% SDS; 50 mM Tris-HCl , pH 7 . 1; 8% glycerol; bromophenol blue ) and boiled for 10 minutes . First , 1/5-volume of methanol and 4/5-volume of chloroform were added to lysates , followed by rigorous vortexing for 30 seconds . The mixture was incubated on ice for 20 minutes and then centrifuged at 16 , 100× g , 4°C , for 30 minutes . The denatured proteins make a sheet between the upper phase with MeOH and the lower phase with CHCl3 . After removal of the upper phase , MeOH of 9-fold volume of the lower phase was added and mixed again . The mixture was centrifuged at 16 , 100× g , 4°C , for 30 minutes . Subsequent to centrifugation , the supernatant was thoroughly removed and the protein pellet was dried , dissolved in 1× SB and finally boiled . ΔPrPs were digested with endoglycosidase H ( EndoH; New England Biolab Inc . , Ipswich , MA , U . S . A . ) according to manufacturer's instructions . Briefly , first 100 µl of TX100/DOC lysates were subjected to MeOH/CHCl3 precipitation . Protein pellets were re-dissolved in 25 µl of 1× denaturation buffer provided with the enzyme and boiled for 10 minutes . Then , 20 µl of deionized water , 5 µl of G5 reaction buffer and 2 µl of EndoH were added and incubated at 37°C for 1 . 5 hours . After digestion , 1/4-volume of 5× SB was added and boiled for 10 minutes . For PNGaseF digestion , following MeOH/CHCl3 precipitation , the pelleted proteins were reconstituted in dilute sample buffer ( 5× SB diluted with deionized water , 1∶15 ) and boiled with shaking at 1 , 400 r . p . m . for 10 minutes . The denatured proteins were supplemented with 1/10-volume of G7 buffer and 10% NP40 attached to PNGaseF ( New England Biolab ) and then PNGaseF was added and incubated at 37°C for 2 hours . After incubation , 1/4-volume of 5× SB was added and the samples were boiled for 10 minutes . N2a cells expressing ΔPrPs on 6-well culture plates were incubated in PBS with 3 mM EDTA for 3 minutes and then mechanically detached by pipetting . The cell suspension was collected in a 1 . 5-ml tube and centrifuged at 1 , 000× g for 5 minutes at 4°C . After the centrifugation , the supernatant was thoroughly discarded and the pelleted intact cells were resuspended in 400 µl of phosphate-buffered ( pH 7 . 4 ) 2% TX114 lysis buffer ( 2% TX114; 137 mM NaCl; 2 . 7 mM KCl; 8 mM Na2HPO4; 2 mM KH2PO4 ) and incubated on ice for 30 minutes , with vortexing from time to time . Subsequently , the cell suspension was centrifuged at 16 , 100× g , for 2 minutes at 4°C and the supernatant was transferred to another tube as TX114 lysate . Phase separation of TX114 lysates was done by incubating the lysates at 37°C for 10 minutes and centrifugation at 21 , 130× g for 10 minutes . The aqueous phase was transferred to another tube and subjected to MeOH/CHCl3 precipitation . The remaining detergent phase was diluted with 0 . 1% TX114 wash buffer ( 0 . 1% TX114; 137 mM NaCl; 2 . 7 mM KCl; 8 mM Na2HPO4; 2 mM KH2PO4 ) and subjected to MeOH/CHCl3 precipitation . The precipitated proteins were then processed as described above for ‘MeOH/CHCl3 precipitation’ . For phase-separation after denaturation with GdnHCl , 120 µl of TX114 lysates were mixed with 120 µl of 6 M GdnHCl and incubated at room temperature for 45 minutes . Then , 150 µl of 2% TX114 lysis buffer along with 1 , 000 µl of 0 . 1% TX114 wash buffer were added to dilute out GdnHCl , and lysates were subjected to phase separation . After separation , the aqueous phase was transferred to a fresh tube for MeOH/CHCl3 precipitation , the detergent phase ( ∼50 µl ) was diluted with 0 . 1% TX114 wash buffer up to 200 µl and then subjected to MeOH/CHCl3 precipitation . The following steps for sample preparation were as above . For the in vitro PIPLC digestion of GdnHCl-treated lysates , after the first phase separation after denaturation with GdnHCl , the aqueous phase containing GdnHCl was discarded and the detergent phase ( ∼50 µl ) was diluted with 550 µl of 0 . 1% TX114 wash buffer to further reduce GdnHCl concentration to a sufficiently low level for not inhibiting PIPLC activity . After lysates were cleared on ice , 4 µl of PIPLC ( 2 U/µl ) was added to ‘PIPLC+’ samples and both the lysates with or without PIPLC were incubated at room temperature for 2 . 5 hours . Then , the lysates were subjected to a second round of phase separation and the aqueous and the detergent phases were processed as described above , except that 3 µl of 10 mg/ml bovine serum albumin ( BSA ) was added to each phase as a carrier before MeOH/CHCl3 precipitation . Cells were plated on glass coverslips placed at the bottom of 24-well culture plates and transient transfection was performed as described above . Next day , the transiently transfected cells on the coverslips were rinsed twice with HBSS and then fixed with 4% paraformaldehyde ( USB Corporation , Cleveland , OH , U . S . A . ) at room temperature for 30 minutes . After fixation , cells to be permeabilized were treated with 0 . 2% Triton X-100 in PBS for 10 minutes at room temperature and rinsed with PBS three times . When antigen retrieval by GdnHCl treatment was required , the permeabilized cells were incubated with 6 M GdnHCl for 45 minutes and then the cells were rinsed with PBS four times before incubation with the primary antibody . Permeabilized and non-permeabilized cells were incubated with mAb 3F4 ( 1∶2 , 000 ) in 3% BSA in PBS for 60 minutes on a rocking platform , followed by washing with PBS four times , then incubated with DyLight488-conjugated sheep anti-mouse IgG ( 1∶1 , 000; Jackson Immunoresearch , West Grove , PA , U . S . A . ) in 3% BSA in PBS for 45 minutes and finally washed in PBS four times . After immunolabeling , the coverslips with cells on them were taken out from the 24-well plates and mounted on slide glass with a drop of Permafluor mountant ( Thermo Scientific , Rockford , IL , U . S . A . ) . When the mountant was dried , samples were analyzed on a laser scanning confocal microscope , Zeiss710 ( Carl Zeiss Inc . , Thornwood , NY , U . S . A . ) , in the Robert A . Jenkins Microscopy Facility of the University of Wyoming . Samples were studied with an objective lens , EC Plan-Neofluar 100×/1 . 3 Oil Pol M27 , and the wave length of the excitation lasers were 488 nm for DyLight488 and EGFP and 594 nm for DyLight594 . The acquired image data were processed with Image J ( NIH , USA ) . For observation of colocalization of ΔPrPs with LAMP1 , to minimize cross-reactivity of anti-mouse secondary antibody to anti-LAMP1 rat mAb cells were first labeled in the following order: mAb 3F4 , DyLight594-anti-mouse , anti-LAMP1 and then DyLight488-anti-rat . If needed , transfected cells were treated with bafilomycin A1 for ∼6 hours before fixation to inhibit the degradation of ΔPrPs by lysosomal proteases . Only a single slice at the level of nucleus where the punctate fluorescence was most abundant was used for analysis of co-localization . N2a cells on a 6-well plate were transiently transfected as described above . Twenty-four hours later , the old medium was replaced with fresh medium , with or without bafilomycin A1 , and cells were cultured for 6 more hours . Cells were rinsed once with PBS without calcium or magnesium , then incubated for a few minutes in 3 mM EDTA in PBS until cells could be easily detached from the plate by pipetting . Detached cells in PBS were collected in 1 . 5 ml tubes and centrifuged at 200× g at 4°C for 5 minutes . After removal of supernatant pelleted cells were resuspended in 200 µl of homogenization buffer [8 . 5% ( w/w ) sucrose; 4 mM Tris-HCl , pH 7 . 1; 2 mM EDTA; 30 µg/ml cycloheximide ( Sigma Aldrich ) and complete protease-inhibitor cocktail ( Roche Applied Science , Indianapolis , IN , USA ) ] . Before homogenization , 10 µl of cell suspension was taken for whole-cell lysate measurement . The rest of the cells were homogenized by passing through a 25G ultra-thin-wall needle ( Terumo Medical Corporation , Somerset , N . J . ) until >90% of cells were disrupted . The homogenate was centrifuged at 2 , 000× g for 5 minutes and the supernatant , ∼175 µl , was transferred to another 1 . 5 ml tube as post-nuclear fraction and mixed well with 240 µl of a 62% ( w/w ) sucrose solution to a final concentration of ∼42% . The post-nuclear fraction was placed at the bottom of a centrifuge tube and three layers of sucrose solutions , 36% , 33% or 30% , and homogenization buffer were overlaid from the bottom to the top . The gradient was ultra-centrifuged at 40 , 000 rpm at 4°C for 1 . 5 hours with a Beckman SW50 . 1 swing rotor in a Beckman L8-80M ultracentrifuge . After ultra-centrifugation , fractionated organelles were visible as milky-white bands in interphases between the layers and 350 µl was carefully pipetted from each interphase to collect organelles as completely as possible . Collected interphases were first diluted with 300 µl of PBS with 0 . 01 µg/µl of BSA and then subjected to MtOH/CHCl3 precipitation , as described above . Pelleted proteins were dissolved in 1× SB and boiled to make “interphase” samples . For “whole-cell lysate” samples , 10 µl of cell suspensions were mixed with 30 µl of TX100/DOC lysis buffer . After centrifugation to precipitate the cell debris , supernatants were collected in another tube as whole-cell lysates . After addition of 10 µl of 5× SB , they were boiled to prepare “whole-cell lysate” samples .
We decided to engineer a series of mutant PrPs ( ΔPrPs ) with different deletion sizes , starting with a deletion of a single residue ( i . e . glutamine 159 ) immediately after the H1 region , and extending C-terminally towards the H2 region ( Fig . 1A ) . We reasoned that such deletion mutants would be more straight-forward for analysis of structure-function relationships than other types of mutations . We had chosen this region in particular because of its postulated importance for prion conversion . In addition , dominant negative inhibition of ΔPrPs with deletion of the B2 region has been previously reported [27] and we could expect that some of our mutant PrPs with deletions near B2 would show efficient DNI . If the DNI is then attenuated at a certain point when the deletion is C-terminally extended , this transition would be suggestive of the region involved in PrP-PrP interaction . For convenience of detection , all ΔPrPs contain a 3F4-epitope tag . First , we studied the expression of ΔPrPs upon transient transfection into murine neuroblastoma ( N2a ) cells . All constructs were expressed at comparable levels ( Fig . 1B , lanes 2–10 ) , although substantially lower than a 3F4-tagged wild-type PrP [ ( 3F4 ) MoPrP] used as control ( Fig . 1B , lane 1 ) . Immunoblot appearance of ΔPrPs were also uniform with more demarcated and narrower bands than that of ( 3F4 ) MoPrP and with the diglycoform ( Fig . 1B , closed arrowhead ) predominant over the mono- and non-glycoforms ( Fig . 1B , open arrowheads ) . Interestingly , Δ159–167 provided another band over the presumed diglycoform band ( Fig . 1B , lane 7 and arrow ) . This was proven to be an extra N-glycan attached due to creation of a third glycosylation sequon ( Asn-Tyr-Ser169 ) by this deletion . Digestion with endoglycosidase H ( EndoH ) resulted in the expected non-glycoform band ( Fig . 2B , lane 3 , arrowhead ) . Further proof for this was obtained when serine 169 was deleted [Δ159–167 ( 169 ) ] , resulting in the previous glycosylation pattern ( Fig . 2B , lane 2 and 4 ) . Taken together , all mutant PrPs were expressed in neuronal cells at comparable levels and with similar immunoblot patterns . For evaluating the DNI of ΔPrPs , we co-transfected ΔPrPs with ( 3F4 ) MoPrP in a 1∶1 ratio and evaluated DNI on the co-transfected ( 3F4 ) MoPrP , rather than on endogenous PrPSc . As shown previously [17] , this method is very sensitive in detecting differences in newly formed PrPSc levels , requires shorter transfection times and thereby minimizes possible side effects caused by aberrant properties of ΔPrPs . We initially engineered the two constructs with the shortest and longest deletions , Δ159 and Δ159–175 , and went on to confirm their DNI . We co-transfected them with ( 3F4 ) MoPrP into N2a cells persistently infected with 22L prions ( 22L-ScN2a ) or transfected them alone ( Fig . 1C ) . Cells were lysed , lysates were subjected to proteinase K ( PK ) digestion , separated by SDS-PAGE and evaluated for PrPSc levels in immunoblot analysis using the 3F4 monoclonal antibody ( mAb ) . Under conditions as used for PK digestion ( 25 µg/ml for 30 min ) , ΔPrPs expressed in 22L-ScN2a cells were completely digested ( Fig . 1C , lanes 5 and 6; Fig . S1 ) and 3F4-immunopositive PrPSc in co-transfection represents only those derived from ( 3F4 ) MoPrP ( Fig . 1D ) . When performing co-transfections there were significant differences between the two constructs . Δ159 reduced PrPSc levels of ( 3F4 ) MoPrP to 20% of the empty-vector control ( Fig . 1D , lane 1 and 2 ) , whereas Δ159–175 reduced only to ∼70% ( Fig . 1D , representative immunoblot and densitometric analysis ) . Taken together , we show that Δ159 and Δ159–175 are not converted into PrPSc but are able to exert DNI on co-transfected wild-type PrP . Interestingly , Δ159 containing only a deletion of a single residue showed a much more pronounced DNI . Prion proteins with internal deletions in the H1∼H2 portion had been reported before to be aberrant in that they have EndoH-sensitive high-mannose-type N-glycans , a mainly intracellular localization , detergent insolubility , and protease resistance , all independent of the presence of PrPSc [27] , [37] , [38] . As such aberrant properties might affect DNI efficiency and since there was an obvious difference in DNI efficiencies between Δ159 and Δ159–175 , we next tested whether there is a detectable difference in biochemical and cellular properties , explaining the difference in DNI between them . Next , we investigated by which degradation systems ΔPrPs are degraded . Since DNI involves a direct physical interaction between PrPc and PrPSc , the subcellular trafficking of ΔPrPs should lead into compartments where PrPSc molecules reside . First , we studied the effects of inhibitors of lysosomal or proteasomal degradation on levels of ΔPrPs , specifically the V-ATPase inhibitor bafilomycin A1 ( Baf ) , the autophagy inhibitor 3-methyladenine ( 3MA ) , and the proteasome inhibitor MG132 . Cells were incubated only up to 7 hours to prevent interference with additional degradation systems [45] . There was no difference between Δ159 and Δ159–175 , suggesting that these mutant PrPs share the same metabolic pathway . Baf and 3MA most efficiently inhibited degradation of the diglycoform of ΔPrP ( Fig . 5A , square bracket ) . Therefore , at least part of ΔPrPs seemed to be transferred to a 3MA-sensitive , presumably class-III PI3K-dependent pathway and eventually degraded in acidic compartments . The banding pattern of ΔPrPs from MG132-treated cells was clearly different from those of Baf or 3MA-treated cells , with a main increase in the non-glycoform of ΔPrP ( Fig . 5A , bracket ) , as expected for proteins subjected to endoplasmic reticulum-associated degradation ( ERAD ) [46] . This data suggests that ΔPrPs undergo a mixed degradation and are subjected both to lysosomal/autophagic pathways and the proteasome . A lysosomal degradation of ΔPrPs was unexpected , as the EndoH-sensitive N-glycans of ΔPrPs suggested that they would not reach the medial Golgi from which a trafficking pathway to lysosomes has been suggested [47] . To confirm lysosomal degradation , we next studied whether ΔPrPs co-localize with LAMP1 , calnexin , Rab7 , Rab9 and microtuble-associated protein 1 light chain 3 ( LC3 ) in immunofluorescence analysis/confocal microscopy . Non-transfected cells ( for LAMP1 and calnexin ) or cells transiently transfected with GFP-Rab7 , -Rab9 and -LC3 , respectively , were pre-treated with Baf for 6 hours to prevent lysosomal degradation . Without GdnHCl pretreatment of cells we did not observe a significant co-localization of ΔPrPs and LAMP1 ( Fig . 5B , upper panels , arrowheads ) , GFP-Rab7 or GFP-Rab9 ( data not shown ) . A very weak to moderate co-localization was found for ΔPrPs and LC3 or calnexin , respectively ( Fig . S2 and S3 ) . Since ΔPrPs forms aggregates , we hypothesized that the epitope for anti-PrP antibodies might not be accessible , just as is the case for PrPSc [48] . Therefore , we applied the antigen retrieval procedure for PrPSc by treating the cells with GdnHCl . After treatment with 6 M GdnHCl , punctuate-vesicular fluorescent structures were observed and some of them clearly co-localized with LAMP1 ( Fig . 5B , lower panels , arrows ) . Unfortunately , these harsh conditions were not appropriate for detection of GFP-Rab7 , -Rab9 and -LC3 ( Fig . S4 ) , presumably due to denaturation of EGFP . To obtain further evidence for lysosomal degradation of ΔPrPs we utilized subcellular fractionation of Δ159 transfected N2a cell lysates on sucrose gradients ( Fig . S5 ) . Part of the studies involved pre-treatment of cells with bafilomycin A1 to study effects of this drug on distribution of Δ159 in the gradients ( Fig . S5B ) . We found that a substantial proportion of Δ159 , importantly only the glycosylated form , was present in low-density fractions , between 8 . 5 and 30% ( w/w ) , where ER components are scarce , whereas late endosomes , lysosomes and autophagosomes are enriched there . This fraction was also most enriched with LAMP1 and LC3-II , and the amounts of the Δ159 diglycoform were strongly increased by bafilomycin A1 treatment ( Fig . S5B ) . Calnexin was most abundant in the high-density fraction , specifically in the interphase between 36 and 42% ( w/w ) . Interestingly , the mono- and non-glycoforms of Δ159 were also mainly present in the interphase where calnexin was most enriched . In conclusion , these findings strongly corroborate the microscopic findings that substantial amounts of glycosylated Δ159 are present in amphisomes and lysosomes and are consistent with a lysosomal degradation pathway as suggested by increase of the diglycoforms of ΔPrPs by bafilomycin A1 or 3MA treatment . Taken together , we demonstrate that a portion of ΔPrPs is subjected to lysosomal degradation by a 3MA-sensitive process . As there was no detectable difference in biochemical or cell-biological properties between Δ159 and Δ159–175 , we speculated that their difference in DNI is most likely attributable to the size and positioning of the internal deletions . Therefore , we next studied DNI efficiencies of all ΔPrPs by co-transfection with ( 3F4 ) MoPrP ( Fig . 6A ) . DNI efficiencies of Δ159 and Δ159–175 were reproduced with PrPSc levels ∼20% and 70–80% , respectively , and ΔPrPs with deletions between these two constructs showed a gradual reduction of DNI efficiency as deletions were extended , i . e . an inverse relation between DNI efficiency and deletion size ( Fig . 6A ) . The relatively low DNI efficiency of Δ159–163 might be due to the unique primary structure with two prolines and only one intervening residue , making the regional structure less flexible and disadvantageous for inter-PrP interaction ( Fig . 1A ) . On the other hand , Δ159–167 showed a comparable DNI effect to adjacent ΔPrPs , despite the extra N-glycan ( Fig . 1B ) . Next , we assessed whether small deletions in the C-terminal part of H1-H2 exert DNI . PrPΔ171–175 ( Fig . 7A ) was expressed at similar levels as Δ159–175 ( Fig . 6B , left panel ) and , interestingly , its DNI efficiency was also comparable to that of Δ159–175 ( Fig . 6B , right panel ) , suggesting that the length of H1∼H2 is not the sole determinant of DNI and that the positioning of the mutation is also important for efficient DNI . Taken together , all PrPs with deletions in the H1–H2 region exerted DNI , although more pronounced when the C-terminal part was preserved . The observed correlation between DNI efficiencies and the size and/or positioning of the internal deletion suggested that the H1∼H2 region is critically important for PrP-PrP interaction , presumably forming part of the interaction interface . However , there was still the possibility that ΔPrPs with smaller deletions had higher affinities , hence more efficient DNI , because their entire molecular structure was more similar to intact PrPc , whereas those with larger deletions had lower affinities . Alternatively , the interaction interface might be a discontinuous epitope over N- and C-terminal domains with H1∼H2 being the hinge connecting the domains . In such scenarios H1∼H2 itself would not contribute to the interaction interface . To address this experimentally , we created ΔPrPs lacking the entire sequence from residue 31 to 160 ( Δ31–160 ) and tested their DNI . We preserved the polybasic motif ( residues 23–28 ) and the following glycine residues ( Fig . 7A ) , because this sequence was reported to affect subcellular trafficking and DNI [47] , [49] . In addition , we created variants of Δ31–160 with deletions in the C-terminal end of H1∼H2 , namely Δ31–160 ( 175 ) , Δ31–160 ( 171–175 ) , and Δ31–175 ( Fig . 7A ) . All constructs were expressed at similar levels upon transient transfection and banding patterns were similar before and when subjected to EndoH digestion , suggesting a similar subcellular trafficking and metabolism . Of note , their N-glycans were not completely EndoH-sensitive with an additional band ( Fig . 7B , left panel , square bracket ) not converged to the non-glycoform band ( Fig . 7B , left panel , bracket ) . This feature is different from that of ΔPrPs presented above . These additional bands were even unchanged after treatment with 100mM of DTT ( Fig . 7B , right panel ) , although they were absent in PNGaseF treated samples ( Fig . 7D , lane 6 ) . In summary , it was evident that a substantial part of N-glycans of Δ31–160 and variants of it were EndoH-resistant . This fraction of PrP might exit ER , pass medial Golgi and reach endosomal/lysosomal compartments [47] or the cell surface , being consistent with the cell-surface distribution of a mutant PrP with the similar structure [50] . Again , all constructs were expressed at similar levels and their efficiencies varied substantially ( Fig . 7C , right panel ) . Δ31–160 showed efficient DNI , whereas DNI of Δ31–160 ( 175 ) was significantly less , although only one additional residue was deleted ( Fig . 7C ) . As before , there was an inverse correlation between size of deletion and DNI . DNI of Δ31–175 which almost completely lacks H1∼H2 was rather low as expected ( Fig . 7C , lane 6 ) . Compared to ΔPrPs with preserved N-terminal portions , PrPs lacking the N-terminal region were higher expressed ( see Fig . 7B , lane 11 , arrowhead ) , which might bias their DNI efficacy . We finally analyzed protease resistance of these constructs and found that they were mildly resistant to chymotrypsin digestion ( Fig . 7D ) . Taken together , ΔPrPs lacking the N-terminal part of PrP were higher expressed and showed a different EndoH resistance pattern . The N-terminal part of PrP was not necessary for exerting DNI and the inverse relationship between size of deletion within the H1∼H2 region and DNI was preserved .
A question of superior importance in the interpretation of our results is what the interaction target of ΔPrPs is to exert their DNI . Initially , protein or factor X has been postulated as target of mutant PrPs for exerting DNI [17] . Recent studies using in vitro conversion systems , e . g . protein misfolding chain amplification ( PMCA ) , have denied the necessity for other factors and DNI in vitro can occur only between the substrate PrP , template PrPSc , and the inhibitory mutant PrPs [25] , [28] . From a stoichiometric point of view , an interaction between PrPSc and inhibitory mutant PrPs was the most likely cause of DNI [25] . These in vitro findings in concert with missing experimental evidence for a factor X make it reasonable to consider that ΔPrPs exert DNI in prion-infected cultured cells by direct interaction with template PrPSc or substrate PrPC . Of importance is also the vast difference in expression levels when substrate ( 3F4 ) MoPrP and ΔPrPs are co-transfected . Although ΔPrPs are much less expressed , they are nevertheless able to exert substantial DNI effects , as was also reported before [27] . This suggested that the interaction target of ΔPrPs is PrPSc and not PrPC . However , the possibility that substrate PrPC is a target still cannot be fully excluded , given that only a small moiety of the total PrPC population might find its way into the cellular compartment of prion conversion , where ΔPrPs also have access as we report here . The variation in DNI efficiencies between Δ159 , Δ171–175 and Δ159–175 suggests a critical importance of the integrity of the H1∼H2 region and its positioning relative to helix 2 and 3 . We did not expect to see a similar tendency in DNI variation among the group of ΔPrPs which contain deletions of almost the entire stretch N-terminal to H1∼H2 , like Δ31–160 , Δ31–160 ( 175 ) , Δ31–160 ( 171–175 ) and Δ31–175 . Unfortunately , we cannot directly compare DNI efficiencies of Δ31–160 and Δ159 to deduce the direct contribution of the N-terminal deleted region in DNI , e . g . by calculating a ‘DNI per PrP mutant’ of each . This is because their properties seem to be rather distinct from each other , especially with regard to EndoH sensitivity which reflects subcellular localization and trafficking . In addition , Δ31–160 lacks almost the entire N-terminal region , including pre-octarepeat and octarepeat regions , deficiencies of which were shown to impair interaction efficiencies of mutant PrPs with PrPSc and/or PrPC [54]–[56] . However , the obviously very efficient DNI of Δ31–160 demonstrates that the DNI contribution of the region encompassing residue 32 to the end of H1 is very small to negligible in our experimental set-up . In any case , the finding that Δ31–160 variants with additional deletions in H1∼H2 behaved very similar in DNI as the initial ΔPrPs strongly corroborates our findings and its interpretation . Among the currently postulated models for PrPSc , our results are best compatible with the “domain-swapping model” or the “parallel in-register extended β-sheet model” [32] , [57]–[59] . Here , H1∼H2 forms intermolecular anti-parallel or parallel β-sheets and H2 and H3 contribute to the stabilization of the superstructure by interaction with H1∼H2 , H2 and/or H3 of the other PrP molecule [32] , [60] . With such models , the inverse correlation between deletion size and DNI efficiency of ΔPrPs can be explained by changes in the surface area of the interaction interface . In addition , the requirement for appropriate positioning of H1∼H2 relative to H2 and H3 is also explained . Interestingly , these models with H1∼H2 forming intermolecular β-sheet structures were mainly based on in vitro synthesized PrP fibrils [32] , [57] , [60] . So it is conceivable that the modalities of PrP-PrP interaction as postulated in fibril formation in vitro are relevant also in living cells and form the molecular basis for DNI as observed here . In case full-length PrP and ΔPrPs share similar DNI mechanisms , the implications from the present study might be more widely applicable to scenarios which involve full-length PrP molecules . There is a caveat to this point of view . It is the transgenic mice expressing PrP106 , which completely lack the H1∼H2 region and nevertheless develop prion disease following inoculation with scrapie prions , albeit with extended incubation periods [37] . This suggests that PrP-PrP interactions still occur in this case . On the other hand , PrP106 did not show any DNI in transgenic mice co-expressing endogenous PrP and PrP106 [37] , which is again consistent with our hypothesis . One explanation is that PrP106 still maintains some affinity for PrPSc through other regions than H1∼H2 . This point of view is supported by our findings that even Δ159–175 and Δ31–175 still maintain a certain degree of DNI , although much less pronounced . In addition , H2 and H3 have been reported to have a substantial aggregation tendency to form fibrils in vitro [61] . Such weak affinity might be sufficient for PrP106 to facilitate prion propagation in vivo . Once PrP106 has acquired a PrPSc conformation , such nascent PrPSc composed of PrP106 would convert normal-isoform PrP106 much more efficiently than would do wild-type PrPSc . The B2-H2 loop is thought to be important for prion propagation especially in inter-species transmission situations [31] . Interestingly , our data suggest that H1∼H2 including the B2-H2 loop is a critical determinant of DNI efficiencies . If full-length PrP interacts with PrPC or PrPSc in the same modalities as does ΔPrP , H1∼H2 , along with the B2-H2 loop , might be important for prion propagation because it affects PrP-PrP-interaction efficiencies . Investigations to test whether ΔPrPs and full-length PrPs similarly interact with PrPSc are under way . Further investigation of ΔPrP effects would also have practical implications . Identification of the structure which is required for efficient PrP-PrP binding can provide novel therapeutic targets and might lead to the development of small-molecule compounds which recognize PrP structural elements and hamper PrP-PrP interaction . For exerting DNI which involves a direct physical interaction between ΔPrP and PrPSc , these two proteins have to meet , ideally in the cellular locale of prion conversion . On the other hand , ΔPrPs were not complex glycosylated and were obviously subject to the known cellular quality control mechanisms in the secretory pathway . They did not reach the plasma membrane and were retained in ER and early-Golgi compartments . To resolve this obvious paradox , another focus of our analysis was on the subcellular trafficking and degradation pathways of these prion proteins . This lead to the finding that ΔPrPs undergo a mixed degradation and have access to subcellular trafficking routes previously not assumed for prion proteins . A first unexpected finding was that the ΔPrPs studied here have GPI anchors attached , which is of importance as it affects subcellular trafficking and metabolism . An even more surprising finding was that ΔPrPs are subject to both the lysosomal and proteasomal degradation system . The effects of proteasome inhibitors on ΔPrP levels were also unexpected as GPI-anchored PrPs have previously been reported to be unsuitable for ERAD and proteasomal degradation [46] . The lysosomal degradation of ΔPrPs is even more interesting because it explains how ΔPrPs can eventually encounter template PrPSc . We and others have previously reported a post-ER cellular quality control pathway which re-routes aggregated PrP or EndoH-resistant mutant PrPs from Golgi apparatus or TGN to acidic digestive compartments [47] , [62] . Unlike such EndoH-resistant mutant PrPs which reach at least the medial or late Golgi compartment , ΔPrPs are obviously retained earlier and their trafficking pathway to lysosomes must be a different one . The pronounced sensitivity to 3MA inhibition suggests that this process is class III PI3K-dependent and part of the macro-autophagy pathway . A similar degradation mechanism for ER-retained proteins , e . g . glycoproteins with high-mannose-type N-glycans [63] , misfolded dysferlin [64] and procollagen aggregates [65] , was already reported . It utilizes autophagic sequestration and eventually directs these proteins in autophagic vesicles to lysosomes . The same system might be operative in the lysosomal degradation of ΔPrPs . Importantly , before fusion with lysosomes and degradation of its contents , autophagosomes can fuse with late endosomes to form amphisomes which are immunopositive both for LC3 and LAMP1 [66] . Possibly , such amphisomes are the site where ΔPrPs encounter PrPSc template first . Of note , this observation might provide novel insights into the cellular biology of prion conversion and involved trafficking and re-cycling pathways . It is clear that substrate PrPC is converted to PrPSc either at the plasma membrane [67] or after endocytosis on the way to lysosomes where it undergoes N-terminal truncation [9] , [68] , [69] . There is now good experimental evidence that a main intracellular locale of prion conversion is the ERC compartment [70] , [71] which strongly implies that there is a re-cycling of PrPSc back towards the plasma membrane , in order to sustain continuous presence of PrPSc template in conversion-competent compartments . Of note , there is no classical trafficking pathway described from late endosomes back to ERC or early endosomes . On the other hand , a re-cycling of PrPSc molecules from late endosomes to TGN has been reported by us and others [35] , [72] . From there , such re-routed PrPSc has access to either ERC or plasma membrane , closing the cycle . In the context of our experimental findings , ΔPrPs re-routed to late endosomes via autophagy pathways have to reach template PrPSc or substrate PrPc in a stage before conversion into bona fide PrPSc is accomplished . Whether this is the case in late endosomes fused with amphisomes is questionable , although not impossible . It is conceivable that the process of making bona fide and fully PK resistant PrPSc is a multi-step pathway which involves more than one cellular compartment . In addition , also ΔPrPs might be subjected to the above described re-cycling from late endosomes/amphisomes back to TGN and plasma membrane . Interestingly , the stoichiometry is not in favor of ΔPrPs compared to wild-type PrP , nevertheless they exert a very efficient DNI . Either only a certain minor subpopulation of PrPc is prone to be converted into PrPSc or ΔPrPs have access to a locale which is extremely powerful in the process of cellular prion conversion . Another implication of our work is that other proteins or factors residing in ER and early Golgi might also have access to the locale of prion conversion . Without using the known exocytic and endocytic pathways such factors could be involved in the course of prion conversion or PrPSc degradation . Given such a scenario , even ER-resident proteins , e . g . ER chaperons , might have the possibility to interact with substrate PrPC or template PrPSc . Alternatively , the non-protein co-factors as mainly described in in vitro systems [73]–[76] might get access . The confinement of such co-factors in small vesicles might change their stoichiometry and their ability to negatively or positively interfere with prion conversion and propagation . Overall , our data reinforce the notion that autophagy pathways can influence prion propagation [77] . We also show that the intracellular trafficking of PrP isoforms is much more complex than previously anticipated . | Prion diseases are deadly infectious diseases of the brain characterized by accumulation of a pathologic protein ( PrPSc ) which is derived from the normal prion protein ( PrPc ) . Prions replicate by direct contact in a template-directed refolding process which involves conversion of PrPC into PrPSc . Identifying the modalities of this interaction can advance our molecular understanding of prion diseases . Like substrates and competitive inhibitors of enzymes , a conversion-incompetent PrP can inhibit conversion of normal PrPC , a phenomenon known as dominant-negative inhibition ( DNI ) . Interestingly , some conversion-incompetent PrPs efficiently cause DNI but others do not , presumably depending on affinity for PrPSc and integrity of interaction interface . We utilized DNI to characterize the PrP-PrP interaction interface in cultured cells . We created a series of PrPs with internal deletions in the region between helix 1 and 2 and evaluated their DNI . We found an inverse correlation between deletion size and DNI which suggests that this region plays an important role in PrP-PrP interaction . We also found that such PrPs are subject to various cellular degradation pathways and that a fraction of them reaches the intracellular locale of prion conversion . Further investigation of such prion proteins might help elucidating the cellular mechanisms of the PrPC-PrPSc interaction . | [
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] | 2013 | Critical Significance of the Region between Helix 1 and 2 for Efficient Dominant-Negative Inhibition by Conversion-Incompetent Prion Protein |
To characterize the histological appearance and expression of pro-inflammatory mediators , growth factors , matrix metalloproteinases and biomarkers of epithelial-mesenchymal transition ( EMT ) in healthy control and trachomatous trichiasis ( TT ) conjunctival tissue . Conjunctival biopsies were taken from 20 individuals with TT and from 16 individuals with healthy conjunctiva , which served as controls . Study participants were of varying ethnicity and were living in a trachoma-endemic region of northern Tanzania . Formalin-fixed paraffin-embedded tissue sections were stained using hematoxylin and eosin or by immunohistochemistry using antibodies against: IL-1β , IL-6 , IL-17A , IL-22 , CXCL5 , S100A7 , cleaved caspase 1 ( CC1 ) , PDGF , CTGF , TGFβ2 , MMP7 , MMP9 , E-cadherin , vimentin , and αSMA . Tissue from TT cases had a greater inflammatory cell infiltrate relative to controls and greater disruption of collagen structure . CTGF and S100A7 were more highly expressed in the epithelium and IL-1β was more highly expressed in the substantia propria of TT cases relative to controls . Latent TGFβ2 was slightly more abundant in the substantia propria of control tissue . No differences were detected between TT cases and controls in the degree of epithelial atrophy , the number of myofibroblasts or expression of EMT biomarkers . These data indicate that the innate immune system is active in the immunopathology of trachoma , even in the absence of clinical inflammation . CTGF might provide a direct link between inflammation and fibrosis and could be a suitable target for therapeutic treatment to halt the progression of trachomatous scarring .
Trachoma is a blinding disease initiated by infection of the conjunctival epithelium with the intracellular bacterium Chlamydia trachomatis ( Ct ) . Individuals living in trachoma-endemic communities are repeatedly infected with Ct , which causes a follicular conjunctivitis . Chronic , recurrent inflammation , even in the absence of detectable Ct infection , is associated with progressive scarring [1] . The fibrotic response results in the inward turning of the lid margin ( entropion ) and abrasion of the cornea by the eyelashes ( trichiasis ) . Mechanical damage to the cornea and subsequent opportunistic infections eventually lead to corneal opacity and blindness . Trachoma is endemic in 51 countries and impairs the eyesight of 2 . 2 million people worldwide , 1 . 2 million of whom are irreversibly blind [2] . Although trachoma control programs have made good progress in reducing active disease , there is now some evidence that established scarring disease continues to progress even when chlamydial infection appears well controlled [1] . Therefore , a large number of people remain at risk of developing incident trichiasis , especially in areas where mass drug administration has had a partial effect [3 , 4] . In order to develop a vaccine or therapeutic treatments to prevent the progression to trichiasis , a better understanding of the immunopathology of scarring trachoma is required . A number of clinical studies have shown that transcriptional signatures in trachomatous scarring ( TS ) and trichiasis ( TT ) are consistent with a pro-inflammatory epithelial response and tissue remodeling , supporting the cellular paradigm of chlamydial disease pathogenesis [5] . The gene expression of a number of pro-inflammatory mediators ( IL17A , IL1B , CXCL5 , S100A7 ( psoriasin ) , growth factors ( CTGF ( connective tissue growth factor ) ) and matrix metalloproteinases ( MMP7 , MMP9 ) were up-regulated in TS and TT [1 , 6–9] . Expression was increased further when clinical inflammation was present [1 , 6 , 7] . Immunohistochemistry ( IHC ) studies using tissue from a small number of individuals with active trachoma have shown that MMP9 , CTGF , platelet derived growth factor ( PDGF ) and IL-1β were up-regulated in infiltrating monocytes/macrophages and that IL-1β was increased in the conjunctival epithelium [10–12] . Inflammatory mediators , growth factors and MMPs can stimulate epithelial cells to differentiate into pro-fibrotic mesenchymal cells , a process known as epithelial-mesenchymal transition ( EMT ) [13–15] . Epithelial cells undergoing EMT lose expression of E-cadherin and gain mesenchymal ‘expression’ markers vimentin and α-smooth muscle actin ( αSMA ) as they migrate through the basement membrane into the stroma , where they contribute to fibrosis [16] . Inflammation-induced EMT normally ceases when inflammation resolves; therefore EMT only becomes pathological in an environment of chronic inflammation . The evidence of chronic pro-inflammatory cytokine and growth factor expression in various stages of trachoma combined with a fibrotic tissue response suggests that EMT may contribute to the pathology of trachoma . The aim of this IHC study of trachomatous conjunctival tissue was to investigate the relative protein level and tissue localization of pro-inflammatory mediators , growth factors , EMT biomarkers and MMPs and to characterize the changes in tissue architecture that occur in TT . Molecular markers studied include factors that were previously shown to be up-regulated in TS/TT ( S100A7 , IL-1β , IL-17A , CXCL5 , CTGF , MMP7/9 ) , EMT biomarkers ( αSMA , vimentin , E-cadherin ) and other factors that may play a role in immunopathology ( IL-6 ( pleiotropic pro-inflammatory cytokine ) , IL-22 ( mucosal defense and epithelial integrity ) , PDGF , transforming growth factor beta 2 ( TGFβ2 ) ( both growth factors associated with fibrosis ) , and cleaved Caspase 1 ( CC1 ) , a marker of inflammasome activation ) .
This study adhered to the tenets of the Declaration of Helsinki and was approved by the London School of Hygiene and Tropical Medicine Ethics Committee , the Tanzanian National Institute of Medical Research Ethics Committee and the Kilimanjaro Christian Medical Centre Ethics Committee . Written , informed consent was obtained from individuals before enrollment in the study . Study participants were examined using a bright torch and x2 . 5 loupes . The clinical phenotype of individuals for follicles , papillary inflammation and trichiasis was graded using the World Health Organization 1981 FPC trachoma grading system [17] . Conjunctival scarring was graded in finer detail using the system described by Hu et al [18] . Biopsy samples were collected from individuals undergoing bilamellar tarsal rotation surgery for TT ( cases ) and from individuals without clinical evidence of trachoma undergoing cataract surgery ( controls ) , matched by age and sex where possible . The eyelid was anaesthetized with an injection of 2% lignocaine ( Vital Healthcare , Mumbai , India ) and the eye was cleaned with 5% povidone iodine . Biopsy samples were taken from the upper tarsal conjunctiva using a 3mm trephine: 2mm from the lid margin at the junction of the medial ⅔ and lateral ⅓ of the everted lid . Samples were fixed in 10% neutral buffered formalin and subsequently embedded in paraffin wax . Formalin-fixed paraffin-embedded ( FFPE ) samples were cut perpendicular to the conjunctival surface in 4μm thick sections . Sections were stained with hematoxylin and eosin ( H&E ) for examination of tissue health and composition . Sections for IHC staining were dewaxed and stained with antibodies for pro-inflammatory cytokines and chemokines ( IL-6 , IL-1β , IL-17A , IL-22 , CXCL5 ) , antimicrobial peptide psoriasin ( S100A7 ) , cleaved caspase 1 ( CC1 ) , growth factors ( PDGF , CTGF , TGFβ2 ) , matrix metalloproteinases ( MMP7 , MMP9 ) and biomarkers of EMT ( E-cadherin , vimentin , αSMA ) . Antibodies and retrieval methods used are listed in S1 Table . IHC staining was automated and performed using Novocastra Bond Polymer Refine Red Detection reagents on a Leica BOND instrument ( Leica Biosystems , Milton Keynes , UK ) . Sections were covered with a cover-slip for microscopic examination . Tissue sections were graded by an ophthalmic pathologist masked to the clinical status of the samples . Where more than one H&E section was available for a sample the slide with the most tissue was analyzed . H&E slides were graded on a scale of 0 to 3 for the degree of epithelial atrophy ( where 0 is none and 3 is severe atrophy ) , the number of inflammatory cells present and the number of myofibroblasts present ( 0 = no visible staining , 1 = few cells , 2 = moderate number of cells and 3 = abundant cells ) . H&E sections were viewed under cross-polarized light in order to view collagen fiber deposition and grade fibrosis . Fibrotic scarring was graded for 3 patterns , ‘block’ , ‘wavy’ and ‘fine’ , each on a scale of 0 to 3: 0 = none seen , 1 = focal patches , 2 = abundant areas and 3 = extensive . Antibodies were graded according to strength and location of staining . For each antibody the section was graded separately for the epithelial and the subepithelial compartments . The subepithelial compartment ( substantia propria ) contained the stroma and inflammatory cell infiltrate if present . Antibody staining was recorded on a scale of: 0 = no visible staining , 1 = few cells , 2 = moderate number of cells and 3 = abundant cells . For the antibodies targeting E-Cadherin , vimentin and αSMA staining was recorded in the epithelial compartment only . For E-cadherin , the total area of the epithelium that stained positive was recorded in quartiles: 0–25% = 1 , 26–50% = 2 , 51–75% = 3 and 76–100% = 4 . Data were analysed in R ( https://www . r-project . org ) . Fishers Exact Tests were used to test for differences between case-control status and: age ( categorized by decade ) , sex , ethnic group , H&E and IHC scores . IHC targets were excluded from the analysis where ≤2/36 sections had a grade >0 . An unadjusted P value of <0 . 05 was considered statistically significant for hypothesis-generating purposes . Radial plots were generated by calculating the average score per person for TT cases and controls for each antibody or H&E feature .
Thirty-six conjunctival biopsy specimens were collected from 20 individuals with TT ( cases ) and 16 individuals with no clinical signs of trachoma ( controls ) . The demographic and clinical phenotypes of individuals whose samples were used in this study are described in Table 1 . There was no significant difference in sex ( P = 0 . 31 ) or age ( P = 0 . 074 ) between cases and controls . There was a significant difference in ethnic groups between cases and controls ( p<0 . 0001 ) ; 18/20 cases were of the Massai ethnic group whereas only one control subject was Massai . No follicles were detected in cases or controls . One TT case had a papillary inflammation grade of 3 , equivalent to trachomatous inflammation–intense using the simplified grading system [19] . All cases had varied degrees of conjunctival scarring . None of the controls had papillary inflammation , scarring or trichiasis ( Table 1 ) . H & E staining was used to visualize tissue structure and prevalence of inflammatory cells in sections . There was no difference in the degree of epithelial atrophy or in the number of myofibroblasts between cases and controls ( Table 2 ) . There were significantly more inflammatory cells evident in cases ( P = 0 . 001 ) . Three patterns of subepithelial tissue deposition became apparent when sections were viewed under cross-polarized light: “block” , “wavy” and “fine” . Representative photographs of these phenotypes are shown in S1 Fig . Tissue from cases had significantly more wavy ( P = 0 . 0075 ) and fine patterns ( P = 0 . 0005 ) of subepithelial tissue deposition , whereas individuals with healthy conjunctiva had more block type patterns ( P = 0 . 0005 ) , Table 2 and Fig 1 . The relative expression of the different molecular markers detected by IHC in the epithelial and subepithelial compartments were analysed by case-control status and the results are shown in Table 3 . The average IHC score per person for TT cases and controls for each molecular marker is also represented in Fig 1 . Staining was generally highest in the epithelium of TT cases . CTGF , IL-1β and CC1 had greater average expression in TT cases in both epithelial and subepithelial compartments . CTGF expression was greater in the epithelium of TT cases relative to controls ( P = 0 . 0085 ) . Of the samples that scored >0 for CTGF , 10/23 had a clinical papillary inflammation grade >0 . Epithelial expression of CTGF was localized in 14/24 of the samples that stained positive . In 4/24 CTGF positive samples ( two cases , two controls ) expression was more concentrated in the deep epithelium ( Fig 2A ) . CTGF expression was slightly greater in the subepithelial tissue of cases but the difference was not significant . Significantly more expression of IL-1β was detected in the subepithelial tissue of TT cases relative to controls ( P = 0 . 012 ) . IL-1β expression was localized around the inflammatory cell infiltrate ( Fig 2C ) , however , only 5/11 of samples that stained >0 for IL-1β had a clinical papillary grade >0 . Expression of IL-1β tended to be higher in the epithelium of TT cases but the difference was not statistically significant . S100A7 expression was significantly higher in the epithelium of TT cases ( P = 0 . 0095 ) . Expression of S100A7 within the epithelium was patchy and in 2 samples expression was localized to pseudoglands of Henle ( Fig 2E ) . All controls stained negative for S100A7 . Six of the nine samples that scored >0 for S100A7 had a clinical papillary grade >0 . S100A7 was not detected in the subepithelial tissue in any sample . TGFβ2 expression was slightly greater in the subepithelial tissue of controls ( P = 0 . 037 ) . Four controls were weakly positive ( Fig 2H ) and one case ( papillary grade 3 ) had stronger expression of TGFβ2 in the subepithelial tissue . For the remaining 31 samples TGFβ2 was not detected in the subepithelial tissue . Epithelial expression of TGFβ2 was not different between cases and controls . There were no statistical differences in the expression of EMT biomarkers E-cadherin , vimentin and αSMA in the epithelium between cases and controls . There were also no statistical differences between cases and controls in epithelial or subepithelial tissue expression of CC1 , MMP9 , PDGF , IL-17A , IL-22 and CXCL5 ( Table 3 ) . IL-17A and CXCL5 were not detected in the subepithelial tissue in any samples . IL-6 and MMP7 were detected in ≤2 of the 36 samples tested .
In this study we found that conjunctival tissue of TT cases had significantly greater S100A7 and CTGF expression in the epithelium and significantly greater IL-1β expression in the subepithelial tissue . The average expression of CTGF , IL-1β and CC1 was greater in TT cases in both epithelial and subepithelial compartments . Controls tended to have more expression of TGFβ2 in the subepithelial tissue . We did not detect an increase in the expression of EMT biomarkers in the epithelium of samples from individuals with TT . We found that individuals with TT had different patterns of collagen deposition and an increased inflammatory cell infiltrate in the subepithelial compartment relative to individuals without clinical evidence of trachoma . In this study the age distribution of cases and controls was comparable . There were more females among the TT cases , however , this was not a statistically significant . There were substantially more Maasai people among the cases . This probably reflects greater environmental and behavioral risk factors [20 , 21] . Maasai people live in close contact with their livestock ( flies are often abundant ) and in areas with fairly limited access to water . Furthermore the uptake of antibiotics for trachoma control may be lower in these communities [22] . Although genetic risk factors in Maasai people cannot be excluded , the behavioral and environmental risk factors leading to increased exposure to C . trachomatis infection probably account for the higher proportion of Maasai among TT cases . Changes in tissue morphology were clearly evident with a transition from a “block” type pattern of collagen deposition in controls to “wavy” and “fine” type patterns in cases . This probably reflects the progressive disruption of normal connective tissue . Degradation of organized bundles of collagen fibers running parallel with the epithelium ( “block” type ) by MMPs or oedema could create the fragmented “wavy” and “fine” patterns observed . A similar observation has previously been shown in the subepithelial tissue of individuals with scarring trachoma and was found to correlate with tissue scarring determined by in vivo confocal microscopy ( IVCM ) [23] . There is an apparent mismatch in the features of TS when tissue is observed by the 2 different methods; using IVCM , defined bands of scarring were observed , whereas by histology collagen bundles appear fragmented and amorphous . It is possible that the bands of scarring ( observed by IVCM in 3 dimensions ) disrupt the parallel collagen bundles ( seen on a section in 2 dimensions ) to produce wavy and fine patterns of collagen in cases . It was not possible to grade fibrosis in subepithelial tissue with the same grading system described in Hu et al [23] as tissue sections were not sufficient in size . The baseline “T” collagen structure indicative of healthy tissue in controls was visible only in one control section . More inflammatory cells were identified in tissue from TT cases . This result is in keeping with the range of clinical inflammatory grades observed in cases , whereas all controls had a clinical inflammation grade of 0 . It is perhaps surprising that epithelial atrophy , thought to be a common feature of scarring trachoma , and the number of myofibroblasts were not different between cases and controls . Epithelial atrophy has been reported in two studies that used samples from 11 and 29 individuals with TT and entropion , however neither study included controls [24 , 25] . The same two studies also reported epithelial hyperplasia and psuedogland formation [24 , 25] . Persistent and recurrent conjunctival inflammation and associated hyperplasia are thought to promote the formation of pseudoglands of Henle , which are crypts formed by invagination of the epithelium [26] . Bacteria and debris can become trapped by mucus within these crypts and entrapped secretions within pseudoglands were observed in individuals with TT [24 , 26] . Myofibroblasts have contractile properties therefore one might speculate that they have an increased role during TS and TT . In line with our observations we did not detect any significant differences in the epithelial expression of the EMT biomarkers vimentin , αSMA or E-cadherin between cases and controls , although vimentin expression was slightly increased in TT cases . The number of samples in this study was relatively small and there was only one sample from an individual with trachomatous inflammation–intense , therefore it is possible we did not have sufficient power within the study to detect subtle , transient or rare events . We only graded loss of E-cadherin and gain of vimentin and αSMA expression in the epithelium , as it would not be possible to distinguish cells expressing vimentin and αSMA in the subepithelial tissue from normal fibroblasts/myofibroblasts . Future work such as multiplex staining or application of new techniques such as laser ablation mass cytometry are required to distinguish complex cell phenotypes and rare events such as cells undergoing EMT [27] . IHC staining was generally greater in the epithelial compartment relative to the subepithelial compartment . CC1 , CTGF and IL-1β were increased in both epithelial and subepithelial compartments in TT cases ( Fig 1 ) and S100A7 was increased in the epithelium . CC1 cleaves IL-1β into its active form and the concomitant upregulation of CC1 and IL-1β reflects activation of the inflammasome [28] . In the subepithelial tissue of TT cases IL-1β was localized around the inflammatory cell infiltrate . Just over half of the samples that stained positive for IL-1β in the subepithelial tissue had no evidence of clinical inflammation; therefore considerable levels of IL-1β were expressed in the absence of clinical signs . Recent evidence showed that IL-1β expression was weakly associated with progressive scarring trachoma and strongly associated with inflammatory episodes [1] . It is possible that IL-1β remains up-regulated in the subepithelial tissue in individuals without evidence of clinical inflammation , as this study might suggest , but that cytokines expressed in the subepithelial tissue are less readily detected when samples are collected using a superficial conjunctival swab . Chronic IL-1 induced inflammation is known to result in tissue remodeling [29 , 30] . CTGF modulates the interaction of cells with the extracellular matrix; promoting collagen deposition , mesenchymal cell activation and differentiation ( including EMT ) and tissue remodeling [14 , 31] . CTGF was previously shown by IHC to be upregulated in infiltrating monocytes/macrophages of children with active trachoma [12] , however we demonstrate an upregulation of CTGF in both the subepithelium and epithelium of TT cases . This difference could reflect the different clinical stages of trachoma in the samples studied . TGFβ induces CTGF expression in fibroblasts and epithelial cells therefore it is surprising that we did not see a concomitant up-regulation of TGFβ in TT cases alongside CTGF [32–34] . A number of bacteria have been shown to stimulate CTGF expression in epithelial cells via the lysophosphatidic acid receptor [35] , therefore it is possible that CTGF is directly induced in the epithelium by the altered ocular microbiota observed in individuals with trachoma [18 , 36] . Over-expression of CTGF drives fibrosis in a number of diseases [32 , 37 , 38] and it has become apparent that epithelial-derived CTGF can drive fibrosis in the underlying subepithelial tissue [34 , 39] . CTGF was detected in the basal epithelium in four samples ( Fig 2A ) . CTGF staining in the basal epithelium has previously been reported in the context of gingival fibrosis , where it was thought to have a role in cell proliferation and epithelial hyperplasia [40] . This could drive the formation of pseudoglands in addition to driving fibrosis in the underlying tissue . CTGF was strongly associated with clinical inflammation in adults with progressive scarring trachoma [1] , however in the present study CTGF did not appear to be preferentially detected in adults with evidence of clinical inflammation . S100A7 is a pro-inflammatory antimicrobial peptide secreted by epithelial cells . S100A7 was only detected in the epithelium of TT cases in this study and expression was generally patchy , possibly suggesting a localized antimicrobial response . In addition to direct antibacterial action , S100A7 recruits CD4+ T cells and neutrophils and amplifies pro-inflammatory cytokine responses [41–43] . In two samples staining was detected around pseudoglands of Henle , possibly reflecting a local inflammatory response to bacteria that had accumulated within the pseudogland . Positive staining of the epithelium around these pseudoglands was also noted for IL-1β in 4 TT cases . No positive staining was detected around pseudoglands in control tissue for any of the antibodies tested . Due to the small size of the tissue sections it was not possible to compare the number of pseudoglands between cases and controls . Further study is required to identify whether trachomatous inflammation promotes pseudogland formation and whether bacteria trapped within pseudoglands have a role in exacerbating inflammation . Latent TGFβ2 , PDGF and MMP9 expression tended to be slightly higher in controls . TGFβ2 expression in controls was relatively weak and non-specific therefore could be attributed to a high background . The antibody used detected the latent form of TGFβ2 , therefore it is possible that more latent TGFβ2 was present in controls whereas cases had activated and released TGFβ2 . Despite having a well-defined role in tissue fibrosis , no previous associations have been found between TGFβ2 and trachoma at both expression and protein levels ( Holland , Mabey and Bailey; personal communication ) [44] . Full characterization of the role of TGFβ2 in trachoma has been limited due to its complex post-translational modifications . IL-17A , CXCL5 , PDGF , MMP7 and MMP9 have previously been associated with various stages of trachoma ( trachomatous inflammation–follicular [10 , 11 , 44 , 45] , TS [1 , 6 , 7] and TT [8 , 9] ) at mRNA and protein expression levels , however they were not demonstrably up-regulated in TT cases in the present study . MMP7 , CXCL5 and IL-17A were strongly associated with inflammatory episodes but not with progressive scarring in two large cohorts of individuals with trachoma [1] . The failure to detect differences in staining in this study could be due to the lack of clinical inflammation in the individuals from whom samples were obtained , or due to a lack of study power to detect more subtle differences . It could also be biological and might suggest that differences at the expression level are not maintained at the protein level . The failure to detect MMP7 and IL-6 could likewise be due to a lack of clinical inflammation , a lack of expression or due to the sensitivity of the antibodies used . IL-22 is released alongside IL-17 by Th17 cells and contributes to mucosal defense and maintenance of epithelial integrity but also to the pathogenesis of psoriasis [46 , 47] . Although IL-22 has not previously been associated with trachoma we hypothesized it might have a role in conjunctival epithelial inflammation or health [48] , however no differences in expression were detected . We did not to collect swabs for C . trachomatis PCR because the swabbing process would have probably altered the surface tissue appearance . However , from contemporary studies it is known that the prevalence of C . trachomatis infection in this region among individuals with trachomatous scarring is very low , and therefore it is likely that few if any of these case would have been infected [1] . Similarly , it was not possible to collect conjunctival swab samples for mRNA gene expression analysis from these individuals as this would have affected the histological analysis . Our previous gene expression work used mRNA collected from surface swabs . Therefore , we would not necessarily expect these to exactly correspond to this immunohistochemistry study , which is assessing protein mostly in deeper levels . We have demonstrated that individuals with TT had significantly increased levels of CTGF and S100A7 in the epithelium and IL-1β in the subepithelial tissue , even in the absence of marked clinical inflammation . CTGF , IL-1β and CC1 were increased in TT cases in both epithelial and subepithelial compartments . We suggest that microbial stimulation of the epithelium , ongoing sub-clinical inflammation and inflammasome activation in the connective tissue and CTGF-driven fibrosis contribute to the pathology of trachoma . We also described a potential role for pseudoglands of Henle in trachoma that warrants further investigation . These results and hypothesized mechanisms are summarized in a model figure ( Fig 3 ) . CTGF could be responsible for driving inflammation-induced fibrosis in trachoma , making it a potential therapeutic target [49] . Future research should focus on the stimuli that lead to up-regulation of CTGF , S100A7 and IL-1β and potential inhibitors that could halt the progression of scarring . | Progressive scarring of the conjunctiva in individuals with trachoma causes the eyelids to contract , drawing the eyelashes inwards ( trichiasis ) so that they scratch the cornea , causing pain and eventually blindness . Disease is initiated in childhood by repeated conjunctival infection with Chlamydia trachomatis ( Ct ) , however , infection is not commonly found in adults , yet chronic inflammation and fibrosis progress throughout the lives of many individuals . A better understanding of the mechanisms driving inflammation and fibrosis are required in order to develop treatments to halt disease progression . The tissue expression and localization of a number of pro-inflammatory cytokines , growth and matrix factors were investigated in eyelid tissue from 20 individuals with trichiasis and from 16 control individuals . By staining tissue sections with dyes and specific antibodies , pro-inflammatory signaling molecules IL-1β and S100A7 and pro-fibrotic growth factor CTGF were found to be more highly expressed in individuals with trichiasis . CTGF and S100A7 were highly expressed in the epithelium; the outermost layer of the conjunctiva , whereas IL-1β was more highly expressed deeper in the tissue , where scarring occurs . Numerous inflammatory cells were found in the tissue of trichiasis patients even in the absence of clinically apparent inflammation . Future research should seek to describe a causative mechanism linking these factors . | [
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] | 2016 | Increased Epithelial Expression of CTGF and S100A7 with Elevated Subepithelial Expression of IL-1β in Trachomatous Trichiasis |
Understanding the evolution of biological systems requires untangling the molecular mechanisms that connect genetic and environmental variations to their physiological consequences . Metal limitation across many environments , ranging from pathogens in the human body to phytoplankton in the oceans , imposes strong selection for improved metal acquisition systems . In this study , we uncovered the genetic and physiological basis of adaptation to metal limitation using experimental populations of Methylobacterium extorquens AM1 evolved in metal-deficient growth media . We identified a transposition mutation arising recurrently in 30 of 32 independent populations that utilized methanol as a carbon source , but not in any of the 8 that utilized only succinate . These parallel insertion events increased expression of a novel transporter system that enhanced cobalt uptake . Such ability ensured the production of vitamin B12 , a cobalt-containing cofactor , to sustain two vitamin B12–dependent enzymatic reactions essential to methanol , but not succinate , metabolism . Interestingly , this mutation provided higher selective advantages under genetic backgrounds or incubation temperatures that permit faster growth , indicating growth-rate–dependent epistatic and genotype-by-environment interactions . Our results link beneficial mutations emerging in a metal-limiting environment to their physiological basis in carbon metabolism , suggest that certain molecular features may promote the emergence of parallel mutations , and indicate that the selective advantages of some mutations depend generically upon changes in growth rate that can stem from either genetic or environmental influences .
Adaptation is a product of genetic modification and natural selection imposed by environmental challenges . A complete understanding of adaptation of biological systems thus requires identification of how selection acts upon organismal traits and mapping adaptive phenotypes to underlying genotypic changes . Experimentally testing the genotype-phenotype association and phenotypic effects of mutations is an ongoing research direction in many fields of biology [1]–[3] . Studies on mutations have shown that genetic interactions ( epistasis ) are common in biological systems [4]–[7] and fitness effects of beneficial mutations can vary greatly depending on environmental conditions ( genotype-by-environment interactions , G×E ) [8]–[10] . Many studies of beneficial mutations , however , stop short of elucidating the exact molecular mechanisms connecting genotypic changes to phenotypic adaptation [11]–[13] . The lack of this level of information has rendered prediction of fitness effects , epistasis , and G×E interactions elusive . On the other hand , much of our current knowledge of biological systems has come from studying phenotypes of deleterious gene knockouts . Such approaches have uncovered many gene functions and genetic interactions but provided little information about the quantitative response of biological networks to environmental or genetic perturbations as well as the functional significance of a gene in the context of adaptation . A complementary approach to studying the function and evolution of biological systems , therefore , is to characterize molecular mechanisms through which beneficial mutations alter physiology , and reciprocally , how physiological differences due to genetic backgrounds or environments influence the effects of beneficial mutations . In recent years , evolution experiments using microorganisms have offered a powerful means to investigate the genetic basis of adaptation [14] . Evolution of experimental populations is often conducted using resource-limiting conditions , a challenge many organisms encounter in nature . One competitive strategy to survive under such a scenario is to enhance resource uptake through transport systems . If physiological acclimation is insufficient to alleviate resource limitation , natural selection can favor mutations that further increase uptake capacity . Phenotypes competent to import resources at low concentrations emerge frequently in microbial populations subjected to evolution under resource limitation [10] , [15] , [16] . Interestingly , beneficial mutations emerging from evolution experiments often occur repeatedly at particular loci [17] . Phylogenetic and quantitative genetic studies of natural populations have also identified many cases of parallel genetic evolution in both micro- and macroorganisms . Frequent observation of genetic parallelism underlying adaptation suggests that , in addition to environmental factors that confine the direction of phenotypic evolution , certain features of the genetic architecture , such as DNA sequence space , genome structure , and the organization of physiological networks may further constrain the breadth of evolutionary trajectories [18] . Metals are essential but often growth-limiting in nature . They are involved in a wide range of physiological processes , such as stabilizing protein structure , relaying cellular signals , and facilitating catalysis in nearly one-third of enzymes [19] , [20] . Their most biologically active forms , free cations , however , are limiting in many ecosystems due to oxidation or complexation with organic or inorganic matter [21] , [22] . Metal deficiency has been shown to limit the bioproductivity in marine ecosystems and tropical agricultural systems worldwide [23] , [24] . For host-pathogen arms races , both animals and plants secrete ligands to sequester metal cations in body fluids to suppress pathogen proliferation , while pathogens have evolved counter-strategies to snatch metals from these ligand-metal complexes [25]–[27] . Clearly , sophisticated metal transport systems and metal-dependent gene regulation mechanisms represent biological adaptation to maintaining metal homeostasis [28] , [29] and emphasize the importance of metal acquisition as a prominent fitness component under metal limitation . In this study , we examined the genetic and physiological basis of adaptation to metal limitation in experimental populations of Methylobacterium extorquens AM1 ( hereafter Methylobacterium ) grown in media that we present here as being metal-deficient . In addition to multi-carbon ( multi-C ) substrates like succinate , Methylobacterium can grow on single-carbon ( C1 ) compounds like methanol and serves as a model to dissect and engineer metabolic systems of C1-utilizing bacteria [30] . Growth of Methylobacterium on methanol and on succinate , however , involves several distinct biochemical pathways , and dramatic differences in global gene expression and metabolic profiles have been observed between these two growth modes [31]–[34] . Experimental evolution of populations of Methylobacterium grown for 1500 generations on methanol or succinate revealed tradeoffs during adaptation to these two substrates [35] . These tradeoffs were found to be both asymmetric and variable: methanol-evolved populations consistently showed improvements on both substrates , whereas approximately half of the succinate-evolved populations completely lost the ability to grow on all C1 compounds . Unexpectedly , in these experiments examining tradeoffs , as well as those applying experimental evolution to select for improved growth of Methylobacterium bearing an engineered metabolic pathway , we later discovered that the growth media used for evolution was metal deficient due to over-chelation by ethylenediaminetetraacetic acid ( EDTA ) present in the media . Long-term evolution under such conditions led to the emergence of mutants with enhanced metal uptake in experimental populations founded by either genotype . Here we show that adaptation of Methylobacterium to metal limitation entailed remarkably parallel transpositions of an insertion sequence ( IS ) element that increased expression of a novel cobalt transporter system . The selective advantage of improving cobalt uptake was specific to methanol growth under metal limitation and seemed to result from sustaining biosynthesis of vitamin B12 , a cobalt-containing cofactor , to support two vitamin B12-dependent reactions in C1 metabolism . Intriguingly , this mutation provided a higher selective advantage in genetic backgrounds or growth conditions that conferred faster growth rates , indicating growth-rate dependent epistatic and G×E interactions . This generic growth-rate dependence suggests that as growth results from the performance of the entire physiological system , genes or environmental factors that affect distinct physiological functions may thus interact through their convergent effects on growth phenotypes .
The IS transposition that occurred across multiple experimental populations was first identified in an evolved isolate , CM1145 , from one of the eight methanol-evolving populations ( termed F1 to F8 ) founded by an engineered Methylobacterium strain ( hereafter termed the EM strain ) ( Table S1 ) . In the EM strain , the endogenous formaldehyde oxidation pathway required for growth on C1 compounds was replaced with a phylogenetically-unrelated formaldehyde oxidation pathway from Paracoccus denitrificans [36] ( see Plasmid and Strain Construction in the Materials and Methods section ) . To identify physiological changes that occurred during adaptation of F populations founded with the EM strain , we performed a preliminary microarray analysis to compare genome-wide mRNA pools between the EM strain and the evolved isolate CM1145 ( GEO accession no . GSE14875 ) . Further analysis of changes in the transcriptional profile during adaptation in this , and other replicate populations is underway ( Chou and Marx , unpublished ) . Among the observed transcriptional changes from our initial experiment , a putative metal transport cassette increased expression by 50-fold in strain CM1145 , relative to the EM strain . Real-time PCR analysis of the two uncharacterized genes in this cassette , icuA and icuB ( improved cobalt uptake phenotype , GenBank accession no . EU679505 ) , revealed 70 . 8±13 . 0-fold and 20 . 0±4 . 7-fold increased transcription , respectively ( throughout we report the mean and 95% confidence intervals based on three replicates ) . Open reading frames ( ORFs ) of icuA and icuB overlap by 4 bp . The icuA gene encodes a 704-amino acid protein homologous to TonB-dependent outer membrane receptors . The icuB gene encodes a protein of 243 amino acids exhibiting no significant sequence similarity to any characterized gene in public databases . The CD-Search program [37] clustered IcuB with a group of uncharacterized ORFs ( CDD accession no . COG5266 ) predicted to encode periplasmic components of the ABC-type cobalt transport system . PCR amplification of the icuAB locus of strain CM1145 detected a 1 . 6 kb size increase within its 5′ upstream region . Sequencing of the PCR product revealed transposition of an insertion sequence , ISMex4 ( GenBank accession no . EU679504 ) , into a site 113 bp upstream of the icuA start codon ( icuAB1145 allele with a ‘Type I’ insertion , thus here icuABT1 ) ( Figure 1A ) . Previous studies have shown that transpositions of IS elements may activate transcription of downstream genes by introducing IS-associated outward-directed promoters or by creating hybrid promoters at the junction of insertion [38] . To investigate how ISMex4 insertions enhance transcription of the downstream icuAB genes , we measured the promoter activity of the 5′ upstream region of the WT icuAB allele ( icuABWT ) and fragments covering various parts of the icuABT1 5′ upstream regions with a promoter-probe plasmid using transcriptional fusions to GFPuv ( Figure 2 ) . The promoter activity of either a 113-bp or a 968-bp 5′ upstream region of the icuABWT allele were below the detection limit during growth on methanol . By contrast , ISMex4 alone exhibited significant promoter activity , and the highest activity was observed in the full-length icuABT1 5′ upstream region ( ISMex4 plus the adjacent 113-bp 5′ upstream region ) . Interestingly , a 282-bp fragment spanning the icuABT1 insertion junction did not exhibit detectable promoter activity . These results suggested that insertion of ISMex4 raised transcription of icuAB genes through its outward promoter activity and a synergistic effect between ISMex4 and the adjoining 5′ upstream region , rather than through formation of a hybrid promoter at the insertion junction . We used the aforementioned PCR-based screen to survey the icuAB locus of evolved isolates across all 8 F populations grown in methanol , as well as the 8 replicate populations each from 4 different evolution experiments founded by the WT strain . These populations , ( Table 1 , termed A , B , C & D ) were grown for 1500 generations on methanol , succinate , both , or alternating between them , respectively [35] . Insertions of ISMex4 into the icuAB 5′ upstream region occurred in evolved isolates from 30 out of the 32 A , C , D , and F populations , all of which were evolved solely or partially on methanol . On the contrary , none of isolates from the 8 B populations evolved solely in succinate acquired such mutation . PCR amplification using the 8 B population samples did not detect ISMex4 insertion into the icuAB locus among these populations . The pattern of ISMex4 insertions present among A , C , D , F populations versus that of B populations is significantly different ( Fisher's exact test , P<10−6 ) . Sequencing the icuAB 5′ region revealed that isolates from 26 populations had an identical ISMex4 insertion as icuABT1 . In addition , a second type of ISMex4 insertion was found 12 bp upstream of the icuA start codon in strain CM1059 from population C3 ( icuAB1059 allele with a ‘Type II’ insertion , or icuABT2 ) and subsequently in four other populations ( Figure 1A ) . This extreme parallelism cannot be accounted for by the presence of these mutations at low frequencies in the ancestral stocks because two types of ancestral genotypes were used in these experiments . In addition , each population was inoculated from a single colony of its respective ancestor . The icuABT2 allele increased transcription of icuA and icuB by 5 . 9±0 . 3 and 6 . 1±1 . 4 fold , respectively . For both icuABT1 and icuABT2 , the transposase gene of ISMex4 was in inverse orientation to the icuAB genes . Sequencing of the icuAB 5′ upstream and coding regions of evolved isolates from B populations and the two F populations free of ISMex4 insertion did not identify mutations of any type . ISMex4 has 8 identical copies in the Methylobacterium genome [39] . Analysis of these 8 insertion sites along with new insertions identified in this study deduced a 4-bp consensus target sequence ( 5′-BTAR-3′ ) that duplicates upon transposition of ISMex4 ( Figure 1B ) [40] . Analysis by the Mfold program suggested that ISMex4 insertion sites tend to locate in regions prone to form single-strand DNA ( ssDNA ) secondary structure ( Figure 1C and S1 ) [41] . To investigate the phenotypes of ISMex4 insertions and the corresponding selection pressure , the icuABT1 or icuABT2 alleles were introduced into WT Methylobacterium to replace icuABWT . Since transposition of ISMex4 dramatically elevated transcription of two putative metal-transport genes , we tested whether metal uptake was enhanced by measuring growth rate and fitness of the WT strain and icuABT1 mutant on methanol in media prepared with various doses of trace metal solution ( TMS ) . Growth rate and fitness of the icuABT1 mutant were significantly higher than the WT strain in media prepared with 0 . 5- , 1- ( regular dose ) , 2- , 3- , and 4-fold TMS , but differences between these two strains diminished with increasing dose , becoming indistinguishable with a 5-fold dose ( Figure 3A ) . The selective advantage of the icuABT1 mutant and its growth rates relative to those of the WT strain were tightly correlated across tested conditions ( Pearson's r = 0 . 990 , P<0 . 001 ) . These results indicated: ( 1 ) Growth media made with the regular dose of TMS were metal deficient and insufficient to sustain optimal growth of Methylobacterium on methanol; ( 2 ) Faster growth of the icuABT1 mutant under metal limitation offered a significant competitive advantage . The observation of poor growth of the WT strain in media with the regular dose of TMS ( k = 0 . 098±0 . 002 ) was surprising , given that the growth rate of the same strain at this dose was much higher ( k = 0 . 186±0 . 003 ) during the early evolution of these populations . Two observations suggested that the chemical properties of TMS may change upon light exposure: ( 1 ) The color of TMS shifted from purple to orange after light exposure ( Figure 3B ) ; ( 2 ) Growth media made with light-exposed TMS tended to confer faster growth . One potential light-sensitive component in TMS is EDTA , a metal chelator widely applied in growth media to prevent metal precipitation . Previous studies have shown that over-chelation by EDTA can inhibit growth by depleting free metal cations [42] , [43] . However , such growth inhibition can be alleviated by exposing media to light , which causes photo-dissociation and photo-degradation of metal-EDTA complexes [44] . We tested if suboptimal growth of Methylobacterium in our media resulted from a similar issue . Indeed , the growth rate difference seen above between the WT and icuABT1 mutant vanished in growth media made with light-exposed TMS , consistent with the EDTA over-chelation model ( Figure 3C ) . To ensure the consistency throughout the experiments , TMS and growth media were stored in the dark . Growth media made with the regular dose of TMS were thus termed metal-poor ( MP ) media . In addition , a different TMS enriched for unchelated metal cations was developed for making metal-rich ( MR ) media to facilitate the characterization of the Icu phenotype ( see Growth Media in the Materials and Methods section ) . MR media served as a negative control treatment as growth phenotypes of the WT strain and icuABT1 mutant in MR media were indistinguishable from each other ( Figure 3C ) . As faster growth of the icuABT1 mutant in MP media supported our hypothesis that increased icuAB expression enhanced uptake of certain metal species , we tested each of the 7 metals in TMS ( Ca , Co , Cu , Fe , Mn , Mo , Zn ) to see which one accounted for the beneficial effect . We first measured growth rates of the WT strain and icuABT1 mutant in MP media supplemented with a 3-fold extra dose of EDTA or each of the 7 metal species . While 3-fold extra EDTA completely inhibited growth of both strains , addition of any of the metal species improved growth rates of the icuABT1 mutant ( data not shown ) . Growth rates of the WT strain increased to a smaller extent , and only in response to Co , Fe , Mn , or Zn . These results suggest two possibilities: ( 1 ) Growth of Methylobacterium in MP media is deficient in all 7 metal species , and overexpression of icuAB confers a fitness advantage by enhancing uptake of all of these metals; ( 2 ) Addition of any of these metals saturated the metal-chelation capacity of EDTA , resulting in an increase of free metal cations , one ( or more ) of which was responsible for poor growth and specifically transported by IcuAB . To circumvent the potentially confounding factor of EDTA chelation , we tested growth of the WT strain and icuABT1 mutant on methanol in EDTA-free growth media ( see Growth Media in the Materials and Methods section ) titrated for the availability of Co , Fe , Mn , or Zn . In the absence of EDTA , only cobalt limitation dramatically slowed growth of both strains . Critically , growth rates of the icuABT1 mutant were higher than the WT strain at 1 . 05 ( 0 . 062 ppm ) and 2 . 1 ( 0 . 124 ppm ) nM Co2+ ( P<0 . 05 ) ( Figure 3D ) . By contrast , growth responses of both strains were indistinguishable under Fe , Mn , or Zn titration ( Figure S2 ) , suggesting that the beneficial effect of IcuAB overexpression likely resulted from improving cobalt uptake . As ISMex4 transpositions ahead of icuAB were nearly universal in populations grown solely or partially on methanol yet were never observed in populations grown solely on succinate , this dichotomy suggested that the advantage of enhancing cobalt uptake came from biochemical reactions specific to methanol ( or C1 ) but not succinate ( or multi-C ) metabolism . Indeed , in MP media the icuABT1 and icuABT2 mutants received higher fitness gains ( 15 . 4±0 . 7% and 7 . 3±0 . 2% , respectively ) during growth on methanol than on succinate ( 0 . 5±0 . 3% and 2 . 2±0 . 8% , respectively ) ( Figure 4 ) . To identify the responsible biochemical pathway in C1 , metabolism , we characterized growth phenotypes of the WT strain and icuABT1 mutant on C1 ( methanol , formate ) , C2 ( ethanol ) , 3C1+C2 ( betaine ) , C3 ( pyruvate ) , and C4 ( succinate ) compounds in MP and MR media . In MR media , growth rates of the WT stain and icuABT1 mutant were indistinguishable on all tested substrates ( Figure 5A ) . In MP media , growth of the icuABT1 mutant was significantly faster than the WT strain only on methanol , formate , ethanol , and betaine ( P<0 . 05 ) . As consumption of these four substrates involves metabolism of C1 or C2 units , this pattern suggests that the demand for cobalt may reside in the overlap of C1 and C2 metabolism . One such candidate is the ethylmalonyl-CoA ( EMC ) pathway . This pathway is required to regenerate glyoxylate from acetyl coenzyme A ( acetyl-CoA ) during growth on C1 and C2 , but not C3 and C4 compounds of Methylobacterium [45]–[47] . Notably , two enzymes in the EMC pathway , methylmalonyl-CoA mutase ( MCM ) and ethylmalonyl-CoA mutase ( ECM ) , require adenosylcobalamin ( AdoCbl , a type of vitamin B12 ) for catalytic function . We thus hypothesized that cobalt limitation may lower production of AdoCbl , which impedes growth of Methylobacterium on C1 and C2 compounds by slowing down regeneration of glyoxylate through the EMC pathway . This hypothesis predicted: ( 1 ) Supplementation with glyoxylate , which has been shown to complement mutants defective in the EMC pathway [48] , [49] , should enhance growth in MP media; ( 2 ) Addition of cobalamin ( Cbl ) should produce similar effects . Indeed , adding glyoxylate to MP media significantly increased the growth rate of the WT strain but to a lesser extent for the icuABT1 mutant ( P<0 . 05 ) ( Figure 5B ) , while adding glyoxylate to MR media did not elevate growth rates of either strain . Furthermore , adding Cbl to MP media increased the growth rate of the WT strain slightly ( P<0 . 05 ) but had no effect on the icuABT1 mutant . Adding Cbl to MR media had no effect on growth of either strain . These results support our hypothesis that shortage of AdoCbl reduces production of glyoxylate via the EMC pathway and thus decelerates C1 metabolism of the WT strain under cobalt limitation . Insertion of ISMex4 increases expression of the downstream icuA and icuB genes . To investigate the individual contribution of these two genes to fitness gain for methanol growth under metal limitation , we overexpressed icuA , icuB , or icuAB , at two expression levels using expression plasmids carrying the Plac and Ptac promoters , respectively . The promoter activity of the Ptac promoter is approximately 9-fold higher than the Plac promoter [50] , [51] . In MP media , overexpression of icuA , icuB , and icuAB by the Plac promoter conferred 16% , 5% , and 16% fitness increases ( Figure 6A ) . Overexpression of the icuA and icuB by the Ptac promoter provided 1% and 2% fitness increases , respectively . Notably , overexpression of icuAB by the Ptac promoter incurred a 13% fitness cost under the same growth condition . As overexpression of membrane proteins is often toxic to the organism [52] , the negative impact of expressing icuAB genes at a higher level may result when its cost exceeds the benefit . In MR media , overexpression of icuA , icuB , and icuAB by the Plac promoter conferred no benefit and became deleterious when being expressed by the Ptac promoter . Collectively , these results suggest: ( 1 ) Overexpression of icuA is sufficient to produce a fitness gain similar to the icuABT1 allele; ( 2 ) An intermediate optimal expression level exists for the icuA , icuB , and icuAB genes; ( 3 ) Expression of icuA or icuB alone by the Ptac promoter provided positive selective advantages; however , when these two genes were co-expressed by the same promoter , the sum of fitness effect became negative , indicating a negative sign epistasis [53] . To investigate the functional essentiality of the icuAB gene cassette , we characterized the phenotypes of ΔicuA , ΔicuB , and ΔicuAB strains grown on methanol or succinate in MP or MR media . Deletion of the icuAB cassette was close to selectively neutral ( 1 . 006±0 . 005 ) during growth on methanol in MR media but resulted in 1 . 6±0 . 4% , 1 . 8±0 . 7% , and 1±0 . 1% fitness loss during growth on methanol in MP media , on succinate in MP media , and on succinate in MR media , respectively ( Figure S3 ) . Results suggest that Methylobacterium possesses alternative systems to uptake cobalt . In the WT genetic background , acquiring the icuABT1 allele increased growth rate on methanol by 30% in MP media , but introducing this allele to replace icuABWT of the EM strain did not increase its growth rate under the same growth condition ( k = 0 . 061±0 . 002 and 0 . 062±0 . 004 , respectively ) . In addition , growth rates of the EM strain on methanol in MP and MR media were indistinguishable ( k = 0 . 063±0 . 001 ) . As the icuABT1 allele emerged in 6 of 8 F populations , these findings raised two questions: ( 1 ) Why did the icuABT1 allele exert no detectable effect on growth rate in MP media in the EM genetic background ? ( 2 ) Why were growth rates of the EM strain in MP and MR media indistinguishable ? Growth is a process of biomass assimilation whose rate depends on the rates of multiple resource inputs . A decrease in growth rate may thus weaken advantages conferred by beneficial mutations , like icuABT1 , that enhance uptake rates under resource limitation . Since growth of the EM strain was ∼3-fold slower than that of the WT strain , this remarkable difference led us to hypothesize that the selective advantage of the icuABT1 allele may scale generically with the baseline growth rate of the strain . This hypothesis predicts: ( 1 ) the selective advantage of the icuABT1 allele should increase when introduced into genetic backgrounds with higher baseline growth rates and ( 2 ) the selective advantage should correlate with growth rates modulated by environmental factors independent of cobalt concentrations . First , we measured the fitness effect of the icuABT1 relative to icuABWT alleles in a panel of genetic backgrounds exhibiting different growth rates: the WT strain , the EM strain , strain CM1145 , and three EM-derived strains each bearing individual beneficial mutations found in strain CM1145 ( see Plasmid and Strain Construction in the Materials and Methods section ) . Second , we measured the fitness effect of the icuABT1 allele in the WT genetic background across a range of growth rates resulting from incubation at different temperatures . A potential limitation of this approach is that the genetic and environmental treatments applied undoubtedly modify various phenotypes besides just growth rate , such that each perturbation might display unique interactions with the icuABT1 allele . Intriguingly , the selective advantage of the icuABT1 allele in MP media showed a simple and generic trend: significant positive correlations with the with baseline growth rates across all genetic backgrounds ( Pearson's r = 0 . 940 , P<0 . 01 ) and incubation temperatures ( Pearson's r = 0 . 989 , P<0 . 02 ) ( Figure 6B ) . On the contrary , fitness and growth rates with or without icuABT1 were indistinguishable across all genetic backgrounds and incubation temperatures in MR media where cobalt is not limiting ( data not shown ) . The above results suggest that: ( 1 ) the physiological demand on cobalt uptake is higher under faster growth and ( 2 ) the selective advantage of the Icu phenotype in MP media may increase as populations adapt toward faster growth .
Despite having been discovered fortuitously , our work represents the first study to investigate the genetic basis of adaptation to metal limitation in an experimental evolution system . As a component of Cbl ( or vitamin B12 ) , cobalt is critical to biosynthesis of this important coenzyme [54]–[56] . Low concentrations of cobalt in the agricultural and marine ecosystems has been shown to impact human and animal health and reduce vitamin B12 production in the ocean , respectively [57] , [58] . In this study , evolution under cobalt limitation resulted in emergence of mutants with enhanced cobalt uptake from independent microbial populations . The genetic basis of these independent adaptive events were unusually parallel: resulting from transpositions of ISMex4 into two sites in the icuAB 5′ upstream region in 30 of 32 populations grown partial or solely on methanol . On the contrary , such mutation events were never detected in the 8 populations grown solely on succinate . The highly parallel but distinct evolutionary consequences prompted us to investigate the physiological basis of adaptation and molecular features that might promote parallel genetic evolution . We showed that ISMex4 transposition resulted in overexpression of icuAB genes , which enhanced cobalt uptake and conferred a substantial fitness increase during growth on methanol in MP media but to a minimal extent on succinate . Our physiological assays further pinpointed the major selective advantage to the need for Cbl in the EMC pathway specifically required for methanol metabolism of Methylobacterium , likely resulting from its two AdoCbl-dependent reactions catalyzed by ECM and MCM , respectively . Though the genome sequence suggests two additional Cbl-dependent enzymatic reactions ( methione synthase and two ribonucleotide reductases ) in Methylobaterium [39] , the specific growth defect of the WT strain on methanol in MP media and its complementation by glyoxylate support this notion . Like other bacteria , the cytosolic concentration of Cbl in Methylobacterium is quite low ( ∼590 nM ) [59] . Cobalt deficiency may thus reduce biosynthesis of Cbl , further lowering its concentration in the cytosol , consequently preventing adenosylation of Cbl and its assembly into ECM and MCM . Our findings from a laboratory system might have profound implications on how cobalt limitation impacts microbial ecology and evolution in nature . Methylobacterium spp . are plant-associated bacteria commonly found on leaves where they compete for nutrients secreted by plants [60] . The ability to utilize methanol , a by-product of plant cell wall synthesis , provides a substantial selective advantage to Methylobacterium during epi- and endophytic growth [61] . Nevertheless , the scarce concentration of cobalt ( <8 ppb ) in plant tissue may pose a difficulty to cobalt transport of Methylobacterium as well as other plant-associated bacteria [62] . The importance of cobalt to C1 metabolism of Methylobacterium makes it compelling to investigate the functional significance of icuAB during plant colonization . In fact , cobalt limitation in plants has been demonstrated to inhibit growth and root nodulation of nitrogen-fixing rhizobia [63] , [64] . Cobalt may also play a role in the crown gall disease caused by Agrobacterium tumefaciens . This pathogen requires indole-3-acetic acid synthesized by a cobalt-containing enzyme to induce abnormal proliferation of plant cells [65] . It would be interesting to apply experimental evolution to study adaptation of plant-associated bacteria in plants grown in cobalt depleted soils . On the other hand , as cobalt is nonessential to plants but essential to many plant microflora [62] , it is tempting to ask if the cobalt requirement from plant microflora causes indirect selection on regulation of plant cobalt concentration to welcome mutualistic symbionts or repel harmful pathogens . On a broader scale , low cobalt concentrations in the environment can greatly impact the supply of vitamin B12 to ecosystems as vitamin B12 is essential to many organisms but only synthesized by prokaryotes [66] . Across the North Atlantic Ocean , the abundance of phytoplankton and dissolved vitamin B12 were found to correlate with cobalt concentrations ( 0 . 88−4 . 77 ppb ) [58] . The same study also demonstrated the ability of cobalt to stimulate growth of phytoplankton and vitamin B12 production in seawater . Prochlorococcus and Synechococcus , two dominant photosynthetic bacteria in the open ocean , have an absolute cobalt requirement and appear to secret high-affinity ligands to facilitate cobalt uptake [67] , [68] . Combined with genetic and genomic tools , experimental evolution with marine microorganisms represents a promising approach to unravel the genetic and physiological bases of adaptation to metal limitation in the ocean . In addition , the presence of an icuB homologue ( 72% amino acid similarity , Genbank accession no . ZP_00051365 ) in the genome of the marine magnetotactic bacterium Magnetospirillum magnetotacticum MS-1 makes it appealing to address its evolutionary origin and ecological significance . While environmental and physiological constraints set the stage for the emergence of the Icu phenotype , parallel evolution at the genetic level appeared to be promoted by transposition preference of ISMex4 , the chromosomal location of the icuAB locus , and clonal interference . In the present study , transposition of ISMex4 conferred a selective advantage by increasing icuAB expression , whereas in another study we found an ISMex4 transposition that increased fitness by reducing the transcript level of an overexpressed gene ( Chou and Marx , unpublished ) , stressing the versatile role of IS elements in the evolution of gene expression . Transposition of most IS elements exhibit some degree of target site selectivity [69] . Analysis of ISMex4 insertion sites revealed a 4-bp conserved target sequence that tends to locate in regions prone to form a stem-loop structure . The presence of two ISMex4 copies 15 kb and 38 kb downstream of the icuAB genes , respectively , may have increased its chance of transposition into this nearby locus as several IS elements exhibit a high frequency of local-hopping . In addition to aforementioned features that may promote recurrent ISMex4 transpositions , the predominance of the high-fitness icuABT1 allele over the icuABT2 allele across populations suggests a potential role for clonal competition among asexual lineages ( Table 1 ) . Identification of two methanol-evolved populations ( F5 , F6 ) free of icuABT1 and icuABT2 alleles pointed to the possibility of alternative mutational targets . Compared to growth in MR media , growth rates of evolved isolates from these two populations were just 20% lower in MP media ( data not shown ) , similar to the phenotype of the icuABT1 mutant . Collectively , these results suggest a model shaping genetic parallelism in our system: local-hopping and target selectivity of ISMex4 may lead to high frequency but limited types of transposition while the large fitness advantages gained by icuABT1 and icuABT2 alleles allow them to outcompete other weaker beneficial mutations conferring similar phenotypes in these asexual populations . As the proposed genetic features favoring ISMex4 transposition and its resulting selective advantage can be manipulated easily through mutagenesis and trace metal supplementation , respectively , our system offers the power to experimentally address how mutation rates and the strength of natural selection affect parallel evolution and the dynamics of adaptation . The physiological effects of an allele depend on expression levels , genetic backgrounds , and environmental conditions . Predicting the behavior and evolution of biological systems requires a comprehensive understanding of how these parameters influence physiology and thus shape the fitness landscape . Experimental evolution offers a valuable alternative besides conventional genetic approaches to uncover biochemical functions and physiological links of genes as well as their contribution to fitness in the evolutionary context . In this study , growth phenotypes of icuAB knockout mutants are minute and unspecific to either carbon substrate or growth media , providing no clue to the functional significance of this gene cassette . Nevertheless , by characterizing the phenotypes of beneficial mutations and reconstructing their fitness effect through overexpression experiments , our results revealed the biochemical function of this gene cassette and demonstrated an intermediate optimal expression level that constrains the breadth of phenotypic evolution . Moreover , identification of the physiological processes icuABT1 and icuABT2 contribute to sets the stage to address whether they interact with other mutations or environments in a manner similar to those tested for deleterious mutations . Previous work has shown that growth defects of deleterious mutations tend to be reduced by either environmental stress or the presence of other deleterious mutations [7] , [70] , [71] . These results have supported a simple model where growth rate is mainly limited by the slowest physiological process [7] . It has remained unclear whether the same principle would apply to certain beneficial mutations , such that they become more advantageous when limitations imposed by other physiological processes are relieved . By modulating growth rate through either incubation temperatures or genetic backgrounds , we found a consistent increase in the selective advantage of a beneficial mutation with increasing growth rate . This growth-rate dependence is in accord with the model described above: By alleviating genetic or environmental constraints , increases in growth rate raised the fitness effect of increased cobalt uptake . The synthesis of previous work with deleterious mutations and current findings from a beneficial mutation suggest a physiology-mediated mechanism through which mutations and environments interact . This mechanism has two profound implications for the evolution and function of biological systems: ( 1 ) Some mutations will only be beneficial ( or deleterious ) when favorable mutations or environmental changes alleviate other physiological limitations , suggesting a general mechanism for historical contingency and environment-dependent evolutionary potential . ( 2 ) As higher-order phenotypes ( e . g . growth , differentiation , development , locomotion ) integrate across multiple physiological inputs , genes and environmental factors that affect seemingly distant physiological processes may thus interact through their convergent effects upon higher-order phenotypic outputs . We anticipate similar observations will continue to emerge from further exploration of the commonality of epistatic , G×E , and even environment-by-environment interactions as flavors of the same phenomenon: systems-wide physiology-mediated interactions .
Unmarked allelic exchange plasmids for introducing adaptive mutations or deleting genes were constructed based on pCM433 , a sacB-based suicide plasmid [72] . Two 3 , 380-bp PCR fragments containing icuABT1 and icuABT2 alleles were cloned into pCM433 to generate pHC40 and pHC82 , respectively ( Table S1 ) . The 606-bp and 579-bp PCR products containing pntA1145 ( encoding membrane-bound transhydrogenase ) and gshA1145 ( encoding γ-glutamylcysteine synthetase ) alleles from strain CM1145 were cloned into pCM433 to generate pHC36 and pHC38 , respectively . Plasmids pHC65 , pHC67 , and pHC68 designed to delete icuA , icuB , and icuAB , respectively , were generated by consecutively cloning the 5′ upstream and 3′ downstream regions encompassing the designed deletions into pCM433 . A constitutive expression plasmid and a fluorescent promoter-probe plasmid were constructed based on pCM132 , a broad-host-range plasmid conferring kanamycin resistance [51] . The lacZ gene of pCM132 was deleted and replaced by a 33-bp multiple cloning site to generate pHC41 . The promoter-probe plasmid , pHC42 , was generated by cloning a 734-bp PCR fragment containing the ribosome binding site ( RBS ) of the fae gene ( encoding formaldehyde-activating enzyme ) of Methylobacterium and the reporter GFPuv gene from pKF133 into pHC41 [73] , [74] . A 51-bp synthetic fragment containg the constitutive promoter Ptac was inserted upstream of this reporter to make pHC62 . Fragments for testing promoter activity were PCR amplified and inserted into the same cloning site of pHC42 to make pHC44 , pHC46 , pHC47 , pHC51 , pHC55 . Expression plasmids , pHC60 and pHC91 , were generated by cloning the aforementioned 51-bp fragment containing Ptac and a 44-bp synthetic fragment containing the Plac promoter into pHC41 , respectively . PCR fragments containing RBSfae-icuA , RBSfae-icuB , or RBSfae-icuAB were subsequently cloned into pHC60 and pHC91 to generate pHC69 , pHC70 , pHC71 , and pHC92 , pHC93 , pHC94 , respectively . The EM strain is a variant ( Chou and Marx , unpublished ) of a previous strain ( CM253K . 1 with pCM106 ) shown to be capable of slow growth on methanol [36] . This strain lacks a functional tetrahydromethanopterin-dependent formaldehyde oxidation pathway due to deletion of mptG ( encoding β-ribofuranosylaminobenzene 5′-phosphate synthase [75] ) that eliminated tetrahydromethanopterin biosynthesis . Instead , two genes belonging to the foreign glutathione-dependent formaldehyde oxidation pathway of Paracoccus denitrificans ( flhA , encoding hydroxymethyl-glutathione dehydrogenase and fghA , encoding formyl-glutathione hydrolase ) were expressed from the strong PmxaF promoter in plasmid pCM160 [51] . This replacement resulted in restoration of growth on methanol at a one-third the rate of WT [36] . EM-derived strains carrying one of the three other adaptive mutations from strain CM1145 were generated as above . These beneficial mutations affected the expression of aforementioned fghA , pntAB , and gshA genes . Further analysis of the physiological effects of these beneficial mutations will be described subsequently ( Chou and Marx , unpublished ) . The icuABWT allele was introduced into strain CM1145 by the same allelic exchange method using pHC39 . The icuABT1 allele was introduced into the WT strain , the EM strain , and EM-derived strains bearing individual adaptive mutations using pHC40 . The icuABT2 allele was introduced into the WT strain using pHC82 . Gene knockouts of icuA , icuB , and icuAB were generated by deleting the whole open reading frames from the WT strain . The genotypes of resultant mutants were confirmed by PCR . Strains carrying promoter-probe plasmids or expression plasmids were made by introducing these plasmids from E . coli 10-beta ( New England Biolabs ) into WT Methylobacterium , or its isogenic strain CM1180 [35] expressing the yellow fluorescent protein Venus , through tri-parental mating [76] . The general formula for one liter of all growth media consists of 1 ml of TMS , 100 ml of phosphate buffer ( 25 . 3 g of K2HPO4 and 22 . 5 g of NaH2PO4 in 1 liter of deionized water ) , 100 ml of sulfate solution ( 5 g of ( NH4 ) 2SO4 and 0 . 98 g of MgSO4 in 1 liter of deionized water ) , 799 ml of deionized water , and the desired carbon sources . One liter of the TMS ( pH 5 ) used in MP media and growth media for evolution experiments consists of 12 . 738 g of EDTA disodium salt dihydrate , 4 . 4 g of ZnSO4·7H2O , 1 . 466 g of CaCl2·2H2O , 1 . 012 g of MnCl2·4H2O , 0 . 22 g of ( NH4 ) 6Mo7O24·4H2O , 0 . 314 g of CuSO4·5H2O , 0 . 322 g of CoCl2·6H2O , and 0 . 998 g of FeSO4·7H2O in 1 liter of deionized water [35] . The growth media used for evolution experiments were prepared with this photoactive TMS stored under variable light exposure [35] . MP media were prepared with the same TMS but stored in dark to prevent photochemical reactions . For light exposure experiments , the same TMS was aliquotted into 15 ml plastic tubes ( Falcon ) covered or uncovered with aluminum foil , then subject to constant light source ( broad spectrum , 81 µmol photons m−2 s−1 ) for 1 month at 25°C . TMS used in MR media was modified by adding a 4-fold extra dose of iron to displace chelated metals from EDTA-metal complexes [77] . This modified TMS consisted of 10 ml of 179 . 5 mM FeSO4 , 80 ml of premixed metal mix ( 12 . 738 g of EDTA disodium salt dihydrate , 4 . 4 g of ZnSO4·7H2O , 1 . 466 g of CaCl2·2H2O , 1 . 012 g of MnCl2·4H2O , 0 . 22 g of ( NH4 ) 6Mo7O24·4H2O , 0 . 314 g of CuSO4·5H2O , and 0 . 322 g of CoCl2·6H2O in 1 liter of deionized water , pH 5 ) , and 10 ml of deionized water . EDTA-free media were prepared without adding premixed TMS . Instead , each of the 7 trace metal species was supplemented as 0 . 1 ml of separate solutions ( 153 . 02 mM ZnSO4 , 99 . 71 mM CaCl2 , 51 . 13 mM MnCl2 , 1 . 78 mM ( NH4 ) 6Mo7O24 , 12 . 58 mM CuSO4 , 13 . 53 mM CoCl2 , and 35 . 9 mM FeSO4 ) . Glassware used with EDTA-free media was pre-washed with 0 . 05 N HCl to eliminate trace metal remnants . The A , B , C and D populations were founded by the WT strain [35] while the F populations were founded by the EM strain . All populations were grown in 9 . 6 ml of growth media contained in 50 ml Erlenmeyer flasks and incubated in a 30°C shaking incubator at 225 rpm . Growth media for evolution experiments consisted of identical minimal growth media supplemented with different carbon sources: A and F populations with 15 mM methanol , B populations with 3 . 5 mM succinate , C populations with 7 . 5 mM methanol and 1 . 75 mM succinate , and D populations alternating between 15 mM methanol and 3 . 5 mM succinate . The A , B , C and D populations were transferred into fresh growth media at 1/64 dilution rate every two days . Due to the slow growth of the EM strain , the F populations were transferred at the same dilution rate every four days in the first 300 generations of evolution . Transfers of the F populations after generation 300 were switched to two-day cycles . Populations were sampled periodically and preserved at −80°C for later analysis . For each strain , three independent mid-exponential phase cultures ( defined as half-maximal OD600 ) were harvested and processed by a method described previously [31] . Total RNA was extracted using the RNeasy Mini Kit ( QIAGEN ) , followed by removing residual genomic DNA with the Turbo DNA-free Kit ( Ambion ) . The absence of DNA contamination was verified by PCR amplification of an untranscribed region using primers CM-mxaEdf ( 5′CTAAGGAAGCCCTGCGATG-3′ ) and CM-mxaEdr ( 5′-CCCTCCCGTCTGTTTTTCC-3′ ) . RNA was quantified by a Nanodrop ND-1000 Spectrophotometer ( Thermo Scientific ) and checked for degradation by an Agilent Bioanalyser 2100 or agarose gel electrophoresis . The preliminary microarray experiment used three independent RNA isolations from each strain that were pooled together with equal quantity . cDNA synthesis , labeling , hybridization to Agilent 60-mer oligo microarrays , and scanning of microarrays were performed by MOgene by following a previously described procedure [31] . cDNA synthesis for real-time PCR was performed using 1 µg total RNA with the qScript cDNA Synthesis Kit ( Quanta Biosciences ) according to the manufacturer's instructions . The primers used to amplify and detect transcripts of the icuA , icuB , and rpsB genes are HCAM105 ( 5′-GCTTGCCACCTTCAGCCAGATC-3′ ) and HCAM106 ( 5′-ATGGTGACCTTGTTGAAGGCGTTGTA-3′ ) , HCAM107 ( 5′-TCATCCTCACCGCGCTGC-3′ ) and HCAM108 ( 5′-GCTTTGAGCGCGGGCATTG-3′ ) , and HCAM111 ( 5′-TGACCAACTGGAAGACCATCTCC-3′ ) and HCAM113 ( 5′-TTGGTGTCGATCACGAACAGCAG-3′ ) , respectively . Two-step real-time PCR experiments were performed in three replicates with the PerfeCTa SYBR Green SuperMix ( Quanta Biosciences ) according to the manufacturer's instructions on a DNA Engine Opticon2 ( MJ Research ) . The rpsB gene ( encoding 30S ribosomal protein S2 ) was chosen as the reference gene for data normalization . Data analysis was performed with the Opticon Monitor v . 2 . 02 ( MJ Research ) . The average threshold cycle ( Ct ) value for each gene was calculated from triplicate reactions for RNA samples by following a previously described method [78] . The ΔCt value described the difference between Ct of the target gene and Ct of the reference rpsB gene . The ΔΔCt value described the difference between the ΔCt of the WT strain and that of the evolved or mutant strains . The difference in expression was calculated as 2ΔΔCt . Genomic DNA of 3–6 isolates from each evolved populations was prepared using an alkaline lysis DNA extraction method [79] . The 5′ upstream region of the icuABWT allele was amplified by primer HCAMp7 ( 5′-CCGATGGTGAGATCTGGGTCTTCAG-3′ ) and HCAMp8 ( 5′-CGTCACCTCCTGACATCTCGATTTAC-3′ ) . Insertions of ISMex4 upstream of the icuAB cassette were detected by PCR amplification using primer HCAMp7 and HCAMp38 ( 5′-ACCAGCACCCGTCCGAGC-3′ ) . The sizes of PCR products were determined by electrophoresis in 1% ( w/v ) TAE agarose gel . In cases where no PCR product was obtained from sampled isolates , the genomic DNA of the corresponding populations was extracted and PCR amplified through the same means . PCR products of interest were purified with the QIAquick PCR Purification Kit ( QIAGEN ) and sequenced by MWG Biotech . Prior to growth rate assays and competition experiments , all strains were acclimated in growth medium supplemented with carbon sources used in the ensuing assays . Three replicate cultures of each strain were sampled periodically and the change in OD600 was measured using a Bio-Rad microplate reader model 680 . Competition experiments were performed by following a previously described procedure [35] . Briefly , after one round of acclimation , test strains and a reference strain expressing the yellow fluorescent protein Venus were mixed by a 1∶1 volume ratio , diluted 1∶64 into 9 . 6 ml of fresh media which were incubated under the conditions described above . The ratios of non-fluorescent cells in mixed populations were measured by passing population samples before ( R0 ) and after ( R1 ) competition growth through a BD LSR II flow cytometer ( BD Biosciences ) for at least 50000 cell counts per sample . Fitness values ( W ) relative to the reference strain were calculated by a previously described equation assuming an average of 64-fold size expansion of mixed populations during competitive growth [35]: Prior to fluorescence measurements , strains harboring plasmids derived from pHC42 were acclimated in MP media plus 15 mM methanol and 25 µg/ml kanamycin sulfate . Cultures were then grown to exponential phase in the same media without antibiotic . Optical density values at 600 nm ( OD600 ) and fluorescence intensities were measured by a Safire2 microplate reader ( Tecan ) . The excitation and emission wavelengths for GFPuv were set as 397 nm and 506 nm , respectively [80] . The WT strain was used as control to determine cellular autofluorescence of Methylobacterium . To normalize the fluorescence intensity , the fluorescence value of a sample was first divided by its OD600 . This ratio for the negative control was then subtracted from those of samples to obtain the fluorescence above background . Finally , these values were normalized by dividing the negative control ratio to give the GFPuv fluorescence relative to the background autofluorescence . | Effects of mutations can change under different genetic backgrounds or environmental factors , also known as epistasis and genotype-by-environment interactions ( G×E ) , respectively . Though epistasis and G×E are traditionally treated as distinct phenomena , our study of a beneficial mutation highlights their commonality . This mutation resulted from insertion of the same transposable element upstream of a novel cobalt transport system in 30 of 32 independent populations during evolution in metal-limited media . The resulting increased cobalt uptake provided a selective benefit that depended upon two environmental factors: cobalt limitation and growth substrates whose metabolism requires a particular vitamin B12 ( which contains cobalt ) -dependent biochemical pathway . Furthermore , this mutation exhibited epistatic and G×E interactions with other cellular processes in a generic way , such that its selective advantage increased as cells were able to grow faster . This growth-rate dependence accords with a simple model: the slowest of multiple physiological processes needed for growth exerts the greatest control over an organism's growth rate . It suggests that as growth results from the performance of the entire physiological system , genes or environmental factors that affect distinct physiological processes may thus interact through their convergent effects on growth phenotypes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"microbiology/environmental",
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] | 2009 | Fast Growth Increases the Selective Advantage of a Mutation Arising Recurrently during Evolution under Metal Limitation |
Calcium imaging has been used as a promising technique to monitor the dynamic activity of neuronal populations . However , the calcium trace is temporally smeared which restricts the extraction of quantities of interest such as spike trains of individual neurons . To address this issue , spike reconstruction algorithms have been introduced . One limitation of such reconstructions is that the underlying models are not informed about the biophysics of spike and burst generations . Such existing prior knowledge might be useful for constraining the possible solutions of spikes . Here we describe , in a novel Bayesian approach , how principled knowledge about neuronal dynamics can be employed to infer biophysical variables and parameters from fluorescence traces . By using both synthetic and in vitro recorded fluorescence traces , we demonstrate that the new approach is able to reconstruct different repetitive spiking and/or bursting patterns with accurate single spike resolution . Furthermore , we show that the high inference precision of the new approach is preserved even if the fluorescence trace is rather noisy or if the fluorescence transients show slow rise kinetics lasting several hundred milliseconds , and inhomogeneous rise and decay times . In addition , we discuss the use of the new approach for inferring parameter changes , e . g . due to a pharmacological intervention , as well as for inferring complex characteristics of immature neuronal circuits .
Calcium imaging has been used to record neuronal activity indirectly [1] . Individual neurons or neuronal populations are labeled by applying fluorescent calcium indicators so that the intracellular calcium transients become measurable by optical means . The technique has a temporal resolution up to the millisecond scale and allows measurements at multiple structural levels , from cellular sub-compartments [2 , 3] and individual neurons [4 , 5] to rather large neuronal ensembles [6–8] . Moreover , calcium imaging can be applied to both in vivo [6 , 9 , 10] and in vitro brain preparations [11 , 12] . While calcium imaging techniques have a wide range of applications , the reconstruction of the quantities of interest such as spike ( or action potential ) trains and intracellular free calcium concentration , [Ca2+] , kinetics from fluorescence , Ft , traces is restricted mostly due to the intrinsic low temporal resolution of calcium traces , relative to the fast time scale of spikes . Several algorithms for the reconstruction of spikes from calcium imaging data have been proposed . These include template matching [7 , 9 , 13 , 14] , first-derivative methods including deconvolution [15–17] , reverse correlation [18] and a sequential Monte Carlo method [19] . One common feature of these previously described reconstruction methods is that they are not based on biophysical models for how spikes are generated . Such biophysical models have been developed to model different biophysical characteristics and firing patterns observed in electrophysiological data [20 , 21] . Here , we specify a generative forward model by explicitly linking the membrane potential ( V ) , rather than only spikes , to fluorescence traces through [Ca2+] kinetics . We pursue the idea that reconstruction of spikes and , potentially , inferring the underlying neuronal dynamics , can be performed by inverting this generative forward model . The inversion of such biophysically plausible models has been found useful for magneto- and electroencephalography and can be performed using Bayesian inference techniques [22 , 23] . The expected advantages for the analysis of calcium imaging data , relative to existing approaches , are an increased robustness to noise and artefacts , incorporation of biophysically sensible prior information , and the biophysical interpretation of inferred variables and identified parameters . In addition , the Bayesian inference approach captures the uncertainty about the inferred quantities of interest , thereby allowing one to assess which dynamics and parameters can be inferred from the fluorescence traces , and which ones cannot . As an illustration and proof-of-concept of the proposed modelling approach , we use both synthetic and in vitro calcium imaging data . We show for both data sets that spikes can be reconstructed accurately , even under low signal to noise ( SNR ) conditions , through inferring the neuronal dynamics such as membrane potential , [Ca2+] kinetics , membrane refractoriness and voltage-gated ionic currents . One important , potential application of the approach may be its use to reconstruct spikes from imaging data at high sampling rates but with rather low SNRs . We will show that the biophysical model reliably infers the within-burst or single spikes and also quantitatively captures and infers several experimental phenomena , e . g . fluorescence transients with both typical ( i . e . fast ) [13 , 24–26] or slow rise kinetics [27] , and the somatic bursts of hippocampal pyramidal neurons [28–30] . We will further demonstrate the usefulness of the approach for experimental setups like a pharmacological intervention where predictions about the change in specific biophysical parameters can be tested directly . To illustrate the method on real data , we use our recorded data from immature neurons of neonatal mice which impose a challenging spike reconstruction task , due to the slow rise kinetics of their spike-evoked fluorescence transients . In addition , we apply the method to a publicly available in vitro data set with fast rising fluorescence transients . To reduce the computational complexity of the method we also introduce and use two new , differentiable integrate-and-fire models for repetitive spiking and bursting firing patterns .
Our approach for model-based analysis of calcium imaging data is based on linking the neuronal membrane potential , to the fluorescence traces by using the kinetics of [Ca2+] as an intermediate variable . This requires three components: ( i ) a model for the membrane dynamics and how spikes are generated , ( ii ) a model for the [Ca2+] kinetics , where the entry via Ca2+ influx is regulated by a nonlinear function representing the activation-state of high-voltage-activated ( HVA ) calcium channels , and ( iii ) an observation function which provides a noisy nonlinear mapping from the generated [Ca2+] time series to the fluorescence trace . These three model components form a so-called generative model which explains how data are generated starting from the membrane potential . Given a fluorescence trace , we ‘invert’ this model by making inference on the neuron’s underlying hidden states ( i . e . dynamics ) and/or parameters , see Fig 1A . For inference , we use a recently developed approximate ( variational ) Bayesian inference approach [31] for stochastic , nonlinear state-space models , i . e . discretized stochastic nonlinear dynamic systems . In the following , we refer to this variational Bayesian approach as VB-Laplace . This approach performs efficient and robust parameter estimation even if both the evolution and observation equations ( as with the proposed calcium imaging model ) are nonlinear . As the framework enables analytical update equations , the method is generic and efficient in terms of computation time . Critically , the approach also enables the estimation of parameters ( e . g . , calcium decay time-constant ) and precision-parameters , e . g . noise level on fluorescence trace . As we will show below , this is important for the application to calcium imaging data . As with any Bayesian approach , the method relies on the specification of the prior distributions of the parameters . This is useful because the generative model is biophysical and plausible prior knowledge can be derived directly from previous modelling and experimental studies . The generative model is defined in terms of its state ( i . e . evolution ) and observation ( i . e . likelihood ) equations: Generative model:{xt+1=f ( xt , θ , ut ) +ηtyt=g ( xt , φ , ut ) +εt ( 1 ) where x denote the states of neuronal dynamics such as V and [Ca2+] , yt refers to the fluorescence responses , θ and φ are quantities that parameterize the state evolution function f and observation function g , respectively , and ut is the synaptic ( or applied ) input time series to the membrane . In this generic formalism , ηt denotes state-noise with precision ( i . e . , inverse variance ) α and εt denotes measurement-noise with precision σ . The both random variables are drawn from zero-mean Gaussians: ηt ∼ N ( 0 , ( αϒx ) −1 ) and εt ∼ N ( 0 , ( σϒy ) −1 ) , where ϒx and ϒy denote their corresponding inverse covariance matrices . The state equations ( Eq 1 ) are derived from a set of first-order nonlinear differential equations which formulate the interaction between the underlying neuronal mechanisms . By integrating these equations and passing the [Ca2+] states through the observer equation , Ft traces are generated . This operator is assumed to be a saturating ( nonlinear ) function . In what follows , we use Eq 1 as the basis for all following models , where the evolution and observation functions are specified in detail to derive several models that we used to analyze calcium imaging data . One guiding principle for the following selection of biophysical models is that we prefer models with low complexity and dimensionality , i . e . a low number of parameters and variables . Theoretical considerations and preliminary analyses showed that it is unlikely that all parameters of highly detailed , complex models can be inferred from fluorescence traces , which are temporally smooth in comparison to spike data . Evolution functions are the neuronal models which formulate the neuron biophysics; they link the dynamic change in the membrane potential ( in response to input ) to the [Ca2+] kinetics . These models are comprised of three units: ( i ) a spiking model which ( mainly ) governs the membrane potential , ( ii ) a model for the mechanism of HVA calcium channels where the membrane potential controls the amount of calcium current entering the neuron , and ( iii ) an equation to model the modulation of [Ca2+] due to both the removal mechanisms of free cytoplasmic Ca2+ and the change in calcium current . The resulting [Ca2+] kinetics will be used later in the observation equation in order to compute the fluorescence trace ( see Eq 12 ) . Spiking models can be categorized broadly into two groups: single- and multi-compartment models . The spiking models reported in the literature , for example [29 , 32–37] , can reproduce a wide-range of repetitive spiking and/or bursting firing patterns of neurons; for reviews see also [20 , 21 , 38 , 39] . Since the fluorescence traces we considered were extracted exclusively from the somata , it is a sufficient approximation here to adopt single-compartment models of the cell body . In principle , any model based on differential equations can be used as a generative model of spikes . To show this anecdotally but also to investigate whether there is any particular model that may be best suited for modelling calcium transients , we selected three different models . We used two repetitive spiking models ( i . e . , the Fitzhugh-Nagumo model and Quadratic-Gaussian integrate-and-fire ( I&F ) model ) with different model complexities ( i . e . , number of parameters and variables ) , as well as a model for compound spiking and/or bursting patterns ( i . e . , bursting-Quadratic-Gaussian I&F ) . We motivate the choice of each of these three models , in the three next sections below . In all spiking models considered next , the total input current to neuron is I = Iapp + Isyn , where Iapp and Isyn are applied and synaptic currents , respectively . The transformation between [Ca2+] kinetics and fluorescence responses can be described by a saturating Hill-type function [19 , 69 , 70]: g ( [Ca2+] ) =Ft=κF[Ca2+][Ca2+]+Kd+dF ( 12 ) where κF and dF are scale and offset parameters for the fluorescence trace and parameter Kd is the so-called dissociation constant [26] , a quantitative measure of the affinity of the fluorescent indicator to calcium . In practice , for each fluorescence trace , we estimate the measurement SNR by dividing the minimum amplitude of the fluorescence transient by the standard deviation ( std ) of the baseline fluorescence , similarly to [19 , 69 , 71] . In brief , each generative model ( Eq 1 ) is a combination of the equations for a spiking model , the HVA calcium channel ( Eqs 9 and 10 ) , [Ca2+] kinetics ( Eq 11 ) , and the observation ( Eq 12 ) . As spiking models we use the FHN model , ( Eq 2 ) , and the family of differentiable integrate-and-fire models , for single spikes ( QGIF , Eqs 3–6 ) , and spike bursts ( bursting-QGIF , Eqs 4–7 ) , see also Table 3 for an overview . The parameter values for all models have been summarized in Table 1 , or are indicated in the figure captions of the Results section below . More details are available in the S1 Appendix . This section describes the main concepts of the VB-Laplace method , a Bayesian inference method for stochastic nonlinear state-space models [31] . The inference is based on a probabilistic generative model which quantitatively describes how observed data are generated . For a given generative model , say model m and data time series yt , the task is to infer the moments of the posterior ( the so-called conditional ) distributions p ( υ|yt , m ) for the parameters/variables of interest υ = {xt , φ , θ , α , σ} ( see Eq 1 ) , by using variational Bayes [72] . In this method the moments of the posterior distribution ( conditional mean μ and covariance ∑ ) are updated iteratively by optimizing a free-energy lower bound , F ( q , yt ) , on the log-evidence ( i . e . the logarithm of the model evidence ) with respect to an approximate posterior density , q ( υ ) . The free energy is the difference between the Kullback-Leibler divergence ( denoted by DKL ( ⋅||⋅ ) ) of true and approximate posterior densities , and the log-evidence [72]: F ( q , yt ) =lnp ( yt|m ) −DKL ( q ( υ ) ‖p ( υ|yt , m ) ) ( 13 ) Variational Bayes aims at minimizing the Kullback-Leibler divergence so that the approximations to both posterior p ( υ|yt , m ) ≈q ( υ ) and log-evidence ln p ( yt|m ) ≈ F ( q , υ ) become analytical ( i . e . can be computed iteratively in a computationally efficient fashion ) . This minimization is equal to maximizing the free-energy , with respect to q ( υ ) . Note that the divergence is a non-negative value ( thus , the free-energy is a lower bound on the log-evidence ) , and qexact ( υ ) = p ( υ|yt , m ) . The VB-Laplace method [31] inverts the generative model under two simplifying assumptions: ( i ) a mean-field separability assumption [73] which factorizes the q ( υ ) into the product of approximate marginal posterior densities , over the model unknown quantities ( i . e . υ ) , and ( ii ) a Laplace approximation whereby each of these marginal densities ( except those for precision-parameters ) is approximated by a Gaussian density , namely q ( υi ) ≈N ( μυi , ∑υi ) . The first assumption facilitates the iterative maximization of free energy , and the latter finesses the analytical intractability problem of the inference; this problem arises from the nonlinearities in the likelihood ( i . e . observation ) functions . To update the marginal densities , the VB-Laplace method uses an iterative regularized Gauss-Newton scheme [74] . The precision-parameters are updated without requiring the Laplace approximation . Furthermore , the update rules of the hidden states exploit a variational Bayesian Laplace treatment of the extended Kalman-Rauch filter/smoother . Conceptually , given the full data time series , these probabilistic filters evaluate the approximate marginal posterior density on the hidden-states time point by time point , i . e . qi ( xt|y1:T ) , instead of capturing the full joint density over the whole time series , i . e . qi ( x1:T|y1:T ) . Therefore , the VB-Laplace method can control the lagged Kalman forward pass [31 , 75] by specifying to what extent this lag , k , is going to be applied . That is , for time t , this scheme approximates the lagged posterior density , pi ( xt|y1:t+k , m ) , by making inference on hidden-state at the current time , i . e . xt , after observing all data up to time t + k , i . e . y1:t+k . This step should ( in general ) improve the precision and the temporal smoothness of the inference on the hidden states . For full details of the VB-Laplace method we point the interested reader to [31 , 60] . All Bayesian inference procedures described in this study have been implemented as Matlab ( MathWorks ) code in the VBA toolbox ( http://mbb-team . github . io/VBA-toolbox/ ) developed by Jean Daunizeau and colleagues . The experimental data recorded in our lab were six in vitro fluorescence traces for which simultaneous electrophysiological recordings ( thus , veridical spike times ) were also acquired . All experimental procedures were carried out with approval by the local government and complied with international and European Union norms . Experiments were performed on acute brain slices prepared from C57BL/6J mice at postnatal day ( P ) 3–4 ( P0 –day of birth ) . Animals were decapitated under deep isoflurane anesthesia . The brain was removed quickly and transferred into ice-cold saline containing ( in mM ) : 125 NaCl , 4 KCl , 10 glucose , 1 . 25 NaH2PO4 , 25 NaHCO3 , 0 . 5 CaCl2 , and 2 . 5 MgCl2 , bubbled with 5% CO2/95% O2 ( pH = 7 . 4 ) . Horizontal slices ( 350 μm ) comprising the CA3 region of the hippocampus were cut on a vibratome ( VT1200 S , Leica ) and stored for at least 1h before use at room temperature in artificial cerebrospinal fluid ( ACSF ) containing ( in mM ) : 125 NaCl , 4 KCl , 10 glucose , 1 . 25 NaH2PO4 , 25 NaHCO3 , 2 CaCl2 , and 1 MgCl2 , bubbled with 5% CO2/95% O2 ( pH = 7 . 4 ) . For recordings , slices were placed into a submerged-type recording chamber on the stage of an Eclipse FN1 microscope ( Nikon ) . Cells were loaded with the AM-ester of the Ca2+ indicator Oregon Green 488 BAPTA-1 ( OGB1 , 340 μM ) by pressure-ejection ( 30 s ) from a glass pipette ( 3–6 MΩ ) [76] . Fluorescence traces were acquired using a 16×/0 . 8 NA water-immersion objective ( Nikon ) at a frame rate of 22 . 6 Hz using a CSU10 Nipkow-disc scanning unit ( Yokogawa ) and a Rolera-XR camera ( QImaging ) controlled by the software Streampix 5 ( Norpix ) . Excitation light at 488 nm was provided by a single wavelength solid-state laser ( Sapphire CDRH-LP , Coherent ) . Electrophysiological signals were acquired using a Multiclamp 700B amplifier , a 16-bit AD/DA board ( Digidata 1440A ) and the software pClamp 10 . 4 ( Molecular Devices ) . Signals were low-pass filtered at 3 kHz and sampled at 20 kHz . Loose-patch ( seal resistance < 1 GΩ ) or tight-seal cell-attached recordings from cells in stratum pyramidale of the CA3 region were performed in voltage-clamp mode using borosilicate glass pipettes ( 8–12 MΩ ) filled with 154 mM NaCl . Alexa Fluor 488 ( 25–75 μM ) was frequently added for pipette visualization . Holding current was manually zeroed prior to each experiment . Brief LED light pulses were used to synchronize optical and electrophysiological signals . All experiments were performed at 32–34°C at an ACSF flow rate of ~3 ml min-1 . We extracted the “somatic” fluorescence traces from a set of fluorescence image sequences recorded from the hippocampal CA3 neurons ( see previous section ) ; Fig 3A depicts the mean frame of one of the recorded image sequences for the whole field of view . We then converted the traces to the relative fluorescence changes ( Ft = ΔF / F0 ) after subtraction of its resting ( pre-stimulus ) intensity level , F0 [77] . A preliminary analysis of the recorded fluorescence traces ( see Experimental methods ) showed strong evidence of temporal low frequency drifts , which might be attributed to , for example , mechanical movements or photobleaching . In particular , we found ( downward ) drifts in the data lasting several hundred seconds ( see Fig 3B , blue trace ) . This is far beyond the plausible ranges of calcium decay time-constants . Such low-frequency drifts have often been reported in time series of ECG and fMRI data , for example [78–80] . These drifts have commonly been treated as confounds , for example [81] , because they can induce pronounced autocorrelation in the residual noise structures [82] . This autocorrelation may in turn decrease inference accuracy . Therefore , several methods have been suggested to remove low frequency drifts prior to analysis , for example [78 , 83] . Here , we decided to apply a fourth degree polynomial detrending method [78 , 84] to the fluorescence traces . Details for this method can be found in the S2 Appendix . To extract the onset times of reconstructed firing events by the QGIF and FHN models ( see Results ) , we threshold the inferred membrane potentials . For simplicity , we use as voltage threshold ( which we used as spike detection threshold ) the value zero . After a threshold event , we also discount any other threshold passing from negative to positive for the next 6 ms to prevent false spike detection from potential high frequency fluctuations in the inferred membrane potentials . The Bayesian approach allows us to specify prior distributions to quantities of the generative model . Following [31] , Gaussian prior distributions are assumed on both the evolution and observation parameters , and the initial conditions of hidden states . Each Gaussian prior is defined by its mean , μ , and covariance , ∑; the mean determines the prior expectation , and the covariance embodies the prior beliefs or information about the quantities of interest: p ( θ|m ) =N ( μθ0 , ∑θ0 ) p ( φ|m ) =N ( μφ0 , ∑φ0 ) ( 14 ) p ( x0|m ) =N ( μx00 , ∑x00 ) where the upper index 0 denotes these moments belong to prior distributions . Moreover , the form of the evolution and observation equations ( note the Gaussian state- and measurement- noises in Eq 1 ) yields the Gaussian transition and likelihood densities , respectively: p ( xt+1|xt , θ , α , m ) =N ( f ( xt , θ , ut ) , ( αϒx ) −1 ) p ( yt|xt , φ , σ , m ) =N ( g ( xt , φ , ut ) , ( σϒy ) −1 ) ( 15 ) Hence , the mean and variance of the prior distribution on hidden states and observations can be assigned through the definition of the evolution and observation functions , and precision quantities . In addition , Gamma priors are used on precision-parameters , where each distribution is parameterized by its shape , a , and rate , b , parameters: p ( α|m ) =Ga ( aα0 , bα0 ) p ( σ|m ) =Ga ( aσ0 , bσ0 ) ( 16 ) In the present models , there are five subsets of parameters: ( i ) calcium-dynamics-modulating parameters , ( ii ) membrane-potential-modulating parameters , ( iii ) initial conditions , ( iv ) observation parameters , and ( v ) precision-parameters . In the following , we motivate our choices for the prior distributions of these parameters . Not unexpectedly and due to the temporal smoothness of the fluorescence transients , our preliminary analyses revealed that the biophysical models are rather too complex and potentially over-fitting the data . To avoid over-parameterization , we therefore fixed several parameters to physiologically plausible values . Operationally , for a fixed parameter we set its prior variance equal to zero . This effectively prevents updating of the parameter in the VB-Laplace method . To fit the data , we kept those parameters free which influenced the kinetics of the fluorescence transients as these parameters may be inferred from the data . In the following , we will specify which parameters are fixed for the five different model components ( see Table 1 for the parameter values ) : In summary , we specify prior distributions on initial conditions , six evolution parameters θ = {κCa , τCa , [Ca2+]base , Δth , gM , gNaP} , two observation parameters φ = {κF , dF} and precision-parameters {α , σ} . The prior distributions are listed in Table 2 . To ensure the positivity of the evolution parameters ( except [Ca2+]base; see the S1 Appendix ) and the observation scaling parameter ( i . e . κF ) we re-parameterized them as θi=θi0exp ( χi ) and κF=κF0exp ( χκF ) , see also [60] . That is , we estimate the posterior distribution over , e . g . , parameter θi under Gaussian prior assumption on its modal parameter χi; thus p ( χi ) =N ( μχi0 , vχi0 ) . μχi0 and vχi0 are the prior mean and variance of χi . Accordingly , we set μχi0=0 , while prior knowledge about the prior expectation of the parameter will be effectively embodied in the value of θi0 . To assign a proper prior to calcium decay time-constant , we consider the following relationship: τCaframeexp ( χτCa ) ×dtframe=τCareal×dt ( 17 ) where τCaframe=τCa0 is an a-priori specified value for τCa , τCareal is the actual value of calcium decay time-constant in real time , dtframe is the inverse sampling frequency ( in [ms] ) , and dt is the integration time-step size of neuron dynamics ( in [ms] ) . Eq 17 informs the generative model about the temporal precision of fluorescence data: the inversion operates on two different time scales simultaneously , a slow and fast relating to fluorescence traces ( observation equation ) and neural dynamics ( evolution equations ) . The slow time scale evolves in an image frame resolution and the fast in a sub-millisecond resolution . Note that most of the fixed parameters in both simulating and inverting tasks ( Table 1 ) , as well as the prior means for the free parameters ( Table 2 ) were set to values according to previously reported modelling or experimental studies ( e . g . , see the “References” column in Table 1 ) . See also the S1 Appendix for a more detailed explanation about our choices for a number of fixed parameters , like V1/2 and [Ca2+]base . To illustrate the method , we simulated the synthetic fluorescence traces using each of the FHN , QGIF and bursting-QGIF models , followed by inversion for each data set . For each model , the membrane was stimulated by a set of square pulses of depolarizing currents with various widths and strengths so that the spiking and/or bursting firing patterns were triggered . These traces were down-sampled at the desired frame rates ( see Results ) . We added background noise , i . e . the trace recorded in the absence of fluorescence emission ( when the laser was switched OFF ) , after scaling to the fluorescence traces in order to achieve the desired SNRs . In this study , the fluorescence traces which we used as data had a range around 0 . 2 ( for high SNR ) < max ( Ft ) < 1 ( for low SNR ) , with the baseline set to zero . For data within different ranges , one can use the priors used in this study , following by an automated normalization , see also [71] , where first the baseline of the drift-corrected trace is set to zero , followed by a scaling: Ft ← ( scale × Ft / max ( Ft ) ) , where scale = 1 . We compare the spike reconstruction efficiency of our method to two different types of established spike reconstruction methods: 1 ) template matching [85] , and 2 ) a deconvolution-based method [71] . For the first type , we used a widely-used template matching ( TM ) method using an optimally scaled template [85] implemented in the pCLAMP 10 . 2 software package ( Molecular Devices , Sunnyvale , CA , 2009 ) . To perform the comparisons , we use two data sets with different types of , in particular , rise kinetics ( i . e . fast or slow , see Results ) . In an initial , interactive phase we defined a distinct template for each data set , as follows . For data with slowly rising transients , we first extracted around twenty veridical fluorescence transients from the six in vitro low SNR fluorescence traces , recorded in our lab . After averaging these transients , which were evoked during synchronized network activities , we found that a two-term Boltzmann equation can be well fitted to this averaged ( empirical ) template ( see Results ) . In a similar way , we defined a template for the data set with fast rising transients; namely , by averaging fifty-six veridical single-spike-evoked transients extracted from twelve adopted in vitro traces ( see below ) , followed by fitting a two-term Boltzmann equation to the averaged transient . Accordingly , we adopted the fitted templates by the TM method in order to detect the spiking activities in fluorescence traces . For this method a threshold ( ThrTM ) parameter for the detection-criterion needs to be set manually . In brief , this threshold embodies both the optimum scaling factor and the goodness of fit; for more detail see [85] . For each trace , we used four different thresholds as ThrTM = 1 , 1 . 5 , 2 and 2 . 5 . We then used the available joint electrophysiological and optical recordings in order to select the optimal threshold for each data set , separately ( see Results ) . All these steps were carried out using pCLAMP 10 . 2 . As a deconvolution-based method , we used the fast filter technique [71] which is one the most well-established , probabilistic spike reconstruction methods . This method performs a fast nonnegative deconvolution of fluorescence traces in order to infer the most likely spike trains . It uses a first-order generative model: The calcium transients are generated through convolving the spikes , sampled from a Poisson distribution , with a decaying exponential . This model assumes an instantaneous rise time for each evoked transient , whose amplitude is linearly scaled by the number of spikes in a time frame . The generated [Ca2+] trace is converted to fluorescence intensities by using a linear ( or saturating ) mapping and adding Gaussian noise . Given the model and a fluorescence trace , the method aims to find the maximum a posteriori ( MAP ) spike train ( the filter’s output ) . Note that the computed MAP is an approximation to the actual MAP . This is because for the inversion the method replaces the Poisson distribution with an exponential distribution , due to analytical intractability issues . This approximation removes the integer constraint from the number of spikes , which had been primarily determined by the Poisson distribution . Consequently , an optimal detection threshold for the filter’s output must be determined , in order to extract the best possible solution to the most likely spike train ( see [71] ) . In other words , this thresholding is required for reporting the spike or event ( single spike or burst ) detection results . In our analyses , we were interested in quantifying the event , rather than individual spike detection errors ( see Results ) . Accordingly , to make the comparison to the proposed method appropriate , when searching for an optimal threshold , we only counted the first inferred spike per event . For implementing this method we used available Matlab code ( https://github . com/jovo/fast-oopsi/ ) , with the parameter initializations performed as described in the main paper [71] , including the sampling rate of the fluorescence data . More details about the method can be found in [71] .
In this section , we show the inversion results of the new approach for fluorescence traces simulated by the three different generative models ( see Methods ) . More specifically , we will show that through making a reliable inference about the neuronal dynamics ( like , membrane potential ) , our approach has the following central features: To show that our approach can accurately reconstruct spikes from fluorescence traces , we first applied it to simulated traces resembling the single-spike-evoked transients in adult neurons with fast rise kinetics . The generated traces are shown in Fig 4 ( first row ) for the two non-bursting models , QGIF and FHN . We inverted these models for fluorescence traces containing the transients with fast rise times , i . e . less than 5 ms ( thus , imaged within one frame in the given sampling frequency of 33 . 3 Hz ) . The fast rise kinetics are shown in zoomed-up , representative fluorescence transients in the last row of Fig 4 . This figure shows that the neuronal dynamics can be inferred reasonably accurately , including the non-saturating [Ca2+] kinetics ( third row ) and membrane potentials ( fourth row ) . Consequently , the spikes were detected accurately , as compared to simulated spikes ( grey stars in fourth row ) . For data with high SNR this can be seen in Fig 4A and 4B; QGIF: SNR ≈ 30 , number of veridical spikes ( n ) = 12 , missed spikes ( M ) = 0 and falsely detected spikes ( FD ) = 0 , FHN: SNR ≈ 20 , n = 13 , M = 0 and FD = 0 . The uncertainty ( shaded areas ) was mainly elevated for near-rest membrane potentials . This is because we modeled only HVA calcium channels , which open mainly during spikes ( see Methods ) . We also evaluated the inference under low SNRs , see Fig 4C . These low SNRs present a lower limit of the typical SNR expected in regular experimental setups . Fig 4C ( fourth panel ) shows that even for this low SNR ( Fig 4C , first panel ) , the estimated spikes match accurately the true spikes ( grey stars ) and only one spike was missed ( red star ) ( Fig 4C , fourth panel; SNR ≈ 2 . 5 , n = 13 , M = 1 and FD = 0 ) . For the low SNR case the inferred non-saturating [Ca2+] kinetics ( Fig 4C , third panel ) display a higher level of rest [Ca2+] as compared to the true kinetics ( Fig 4C , second panel ) . This indicates that CaBBI has optimized the fit by estimating larger values for basal calcium concentration . For this low SNR case we used only the FHN model; qualitatively similar results can be obtained by inverting the QGIF model ( not shown ) . In sum , the proposed approach can reconstruct spikes veridically from single-spike-evoked transients with fast rise times , even at a low SNR level . For slowly rising transients there is a delay ( on the order of 100 ms ) between the onset of the spike and the peak of the fluorescence transient . Although this may not be a crucial issue for reconstruction methods , this delay may cause difficulty for reconstruction methods that rely on a rather instantaneous relationship between spike and fluorescence transient peak , e . g . the sequential Monte Carlo [19] or finite rate of innovation [92] methods . Here , we show that the proposed method can precisely reconstruct single spikes even if the transients have slow rise kinetics . The interpolated rise kinetics of the fluorescence transients lasted around 200–450 ms , see the fourth row of Fig 5 . We inverted the non-bursting models ( i . e . QGIF and FHN ) for the traces containing single-spike-evoked fluorescence transients ( first row of Fig 5 ) . The non-saturating [Ca2+] kinetics and veridical spikes can be accurately estimated for data with high SNR ( Fig 5A and 5B ) , second and third rows; QGIF: SNR ≈ 25 , n = 12 , M = 0 and FD = 0 , FHN: SNR ≈ 25 , n = 13 , M = 0 and FD = 0 ) , and for the trace with low SNR ( Fig 5C; FHN: SNR ≈ 2 , n = 13 , M = 0 and FD = 0 ) ; compare grey stars to inferred spikes in the third row of Fig 5 . We performed these reconstructions with the same parameterization as in the previous section ( synthetic adult neurons with faster rise times ) , since our generative models do not incorporate any parameter which can explicitly capture the slow rise times in the data ( τCa corresponds only to the ‘decay’ but not the rise kinetics of the calcium transient; see Eq 11 ) . Still , the results displayed in Fig 5 show that the models can be inverted reasonably well given data with slow rise kinetics . This is because the inference procedure takes into account the stochastic dependencies among the neuronal dynamics ( see Methods ) , whose evolutions over time are constrained by their prior precisions . Accordingly , CaBBI adapts to different fluorescence transient kinetics ( as for example here with slow rise kinetics ) by using a suitable amount of state-noise on the neuronal dynamics . Many neurons fire bursts , possibly intermixed with single spikes . Here we show that if the temporal resolution of the fluorescence measurements is high enough , we can still accurately reconstruct spike timing ( see Fig 6 ) . This holds true not only for single spikes but also for the spikes within a burst . For bursts the reconstruction of spike activity from fluorescence traces becomes challenging: For high frequency and/or a high number of spikes within each burst , the impact of calcium accumulation [77] and fluorescence saturation [93 , 94] becomes relevant ( especially when high-affinity indicators are used ) ; in principle , these two mechanisms result in smaller spike-evoked fluorescence transients ( thus , smaller effective SNR ) with ( probably ) slower decays [19 , 26] . These strong nonlinearities will make the spike reconstruction challenging mainly due to the less differentiable transients during bursts . In general , fluorescence traces with low temporal resolution , e . g . acquired at 4 . 2 Hz [95] , cannot resolve the single transients evoked by each distinct spike within a burst [17 , 95 , 96] . However , recent developments in calcium imaging have made it possible to obtain fluorescence measurements at very high sampling rates such as 1 kHz , for example [13 , 25] . Assuming a sampling rate of 700 Hz , we generated a synthetic fluorescence trace ( Fig 6A ) using the bursting-QGIF model . To generate complex burst patterns ( Fig 6D ) , we used doublet , triplet and multiplet ( 5 and 10 spikes ) bursts , and interspersed single spikes ( indicated by grey stars in Fig 6C ) . Although we imposed relatively high noise on the trace , all of single spikes and the within-burst spikes were inferred accurately ( Fig 6C; SNR ≈ 3 , bursting-QGIF: n = 25 , M = 0 and FD = 0 ) . In addition , the durations of the bursts’ active phase and the interburst intervals , as well as the periods of quiescence were reasonably well estimated ( compare the simulated and inferred membrane potentials in Fig 6C and 6D ) . In addition and as shown in Fig 6C , for each burst the reconstructed spikes have correctly inferred incomplete repolarizations and are located on top of a plateau; this is a common bursting characteristic of many neurons , including hippocampal cells [28 , 29 , 58] . The non-saturating [Ca2+] kinetics can also be accurately inferred ( Fig 6B ) from these saturated traces with highly accumulated transients ( Fig 6A ) . In summary , we found that the method can read out spikes within bursts observed in synthetic fluorescence traces ( with fast rise kinetics ) when the temporal measurement resolution is high enough , e . g . 700 Hz . Here , we compare the detection accuracy of our method , CaBBI , to two well-established , widely-used spike reconstruction methods: a template-matching method [85] , and a deconvolution-based fast filter method [71] . Above , we already showed the reasonably high accuracy of our method in reconstructing GDP onset times ( see Fig 8 ) . We now show the usefulness of this accuracy in inferring the propagation patterns of GDPs , as the complex characteristics of immature neuronal circuits . We illustrate the reconstruction of GDP characteristics using one representative fluorescent image sequence ( out of six ) recorded in our experiment . For this image sequence , the fluorescence traces were extracted from 40 well identifiable neurons . The raster plot of color-coded high SNR fluorescence traces in Fig 10A ( upper panel ) shows clearly visible , spontaneous synchronous network events ( i . e . GDPs ) . The same data under low SNR condition ( around 2 . 5; after contaminating the traces by scaled background noise; see above ) is shown in the lower panel of Fig 10A . Using the new method and as a proof of principle , we reconstructed the onset times of all 40 neurons during GDPs , under the low SNR condition . For inversions , we used the QGIF model because of its lower timing error in extraction of GDPs’ onset times , as compared to the FHN model and the TM method ( see above ) . The reconstructed events are shown in Fig 10B , where it can easily be seen that their timings show a close match to the large fluorescence changes in Fig 10A ( upper panel ) . The histogram of onset latencies of neurons over all GDPs is plotted in Fig 10C . For each GDP , the latencies were computed with respect to the median onset time of neurons during that GDP . In this histogram , the medians are centered on 0 ms so that earlier and later activations of neurons have negative and positive values , respectively . The observed latency distribution can be approximated by a Gaussian distribution with a mean of -14 . 5 ms and standard deviation of 90 ms , which is consistent with previous reports for the developing hippocampus [27] . For visualization of the difference between onset times , we plotted the reconstructed onset times of the second and third GDPs in Fig 10D . In addition , in Fig 10E we show the inferred onset latencies for these two representative GDPs color-coded at their actual spatial positions in the field of view . In the first of these images ( second GDP ) , the GDP pattern starts roughly in the center of the image and spreads to both left-upward and bottom-right , i . e . from the stratum pyramidale ( SP ) in CA3 towards both CA1 and dentate gyrus ( DG ) . The second image ( third GDP ) shows , instead , a rather clear unidirectional orientation towards DG . Such wave-like patterns of propagation of these two illustrative GDPs are consistent with previous reports [27 , 105] . In this section , we show how our approach can be used for quantifying biophysically interpretable parameter changes based on calcium imaging data , e . g . for inferring changes of parameters due to pharmacological interventions . To provide a proof-of-concept , we used two synthetic data sets where we changed: 1 ) the calcium decay time-constant and 2 ) the conductance of M-type K+ channels .
The biophysically informed model has the key advantage that the possible fluorescence trace variations are highly constrained by the equations of the generative model ( see Table 3 ) . This guards the model inference effectively against noise sources which are unlikely to be caused by noise in the modelled neuronal and calcium dynamics . In addition , CaBBI allows the incorporation of prior knowledge about the biophysically interpretable model quantities . The specification of prior distributions is a convenient compromise between fixing and freeing parameters when inverting the model . By varying the width of the prior distribution , one can effectively control how much each parameter or combinations of parameters are determined by the data or by prior knowledge . Although not shown here , formal model comparison can be used to select the best model among different prior specifications [107 , 108] . The biophysical modelling aspect enables CaBBI to directly infer and quantify biophysically interpretable changes caused by selective manipulations of physiological parameters using a pharmacological intervention ( see Fig 11 ) . Critically , one can test specific hypotheses by using suitable priors which are susceptible for the expected changes caused by an intervention . Using model comparison , one can proceed to test this change-sensitive model against an alternative model that does not expect this change . In this paper , we showed such an application for quantifying a particular pharmacologically-induced change: i ) in the calcium decay time-constant ( Fig 11A and 11B , and ii ) in the conductance of M-type K+ channels ( Fig 11C and 11D ) . We found that CaBBI could reasonably reliably infer about the changes in these protocols , since we appropriately fixed and/or constrained its model quantities thereby informing it about the purpose of each intervention ( see the caption of Fig 11 ) . In general , such informative constraints can augment the accuracy of corresponding inference schemes , by guarding them against over-parameterization , non-identifiability , and non-interpretability . These issues may arise from the relative complexity of the generative models ( see below ) , and the nonlinear relationship between the observed fluorescence kinetics and the neuronal dynamics . In principle , we expect that this approach ( exemplified in Fig 11 ) can be applied to other physiological parameters under different experimental conditions , as long as suitable model constraints are used . To our knowledge , CaBBI is the first method which enables analyzing calcium imaging data based on biophysical models of spike and burst generations . Accordingly , in addition to proposing a pure spike reconstruction method , our aim was to establish a calcium imaging modelling framework for incorporating and inferring neuronal quantities of conductance-based neuronal networks . The main feature of CaBBI is that , using the same parameterization , it can model fluorescence transients with rather different kinetics . Such variability can , in principle , render the spike reconstruction difficult for methods which are based on fixed or prototypical templates , e . g . [7 , 9 , 12–14 , 17 , 109] . In contrast , CaBBI , similar to some previously proposed methods [19 , 25 , 71] , does not rely on a fixed template but can adapt itself to the transient kinetics of each individual neuron in order to precisely reconstruct spikes . We showed this for fluorescence traces containing transients with significantly variable kinetics , i . e . with rather inhomogeneous rise and decay kinetics ( e . g . see Fig 10 ) . The reason for this adaptation ability is that CaBBI is informed by the generative biophysical model about the possible kinetics and infers the exact kinetics from the data . Therefore , CaBBI does not require an initial training phase neither for constraining the prior distributions [25] nor for setting the optimal method parameters manually . One potential limitation is that CaBBI , as compared to alternative methods , requires rather long computer run times due to the Bayesian inference and the implementation in Matlab . For example , the reconstruction of the spikes of a single fluorescence trace with 6 , 000 frames using default parameters and the QGIF model requires less than an hour on a standard desktop computer . To compute all inversions reported in this paper in an acceptable time , we made use of compute servers . A second potential limitation is that due to the relative complexity of the generative models we had to fix many of the parameters at some suitable values reported by previous experimental and modelling studies ( see Table 1 ) . Without these constraints or using proper prior distributions ( see Table 2 ) , CaBBI may be too unconstrained . In this paper , we demonstrated that such suitable constraints exist for the generative models and data we have used . Finally , the current generative models of CaBBI are not able to reconstruct the spike counts from the burst-evoked transients which have similar shape as single-spike-evoked transients , e . g . due to a low sampling rate . For adult neurons , usually , such transients differ mainly in their amplitudes depending on the spike counts of their underlying events . Although only a few methods ( like the fast filter [71] ) are , at least in principle , able to decode the bursts’ spike counts from such variability ( e . g . in the amplitudes ) , our current generative models treat all such transients as having been evoked by single spikes ( e . g . see Fig 9 ) . This is because of the biophysical essence of our neuron models , which relate each resolved transient to a spike , and compensate for such variability in the rise kinetics through a proper regulation of the neuronal dynamics . For CaBBI to be applied , one requires a biophysically informed model based on continuous over time , differentiable dynamics . In the literature , a wide range of both spiking and bursting models was reported [21] , including Hodgkin-Huxley-type models [38] , Morris-Lecar model [110] , and Hindmarsh-Rose model [36] . Another criterion for CaBBI to work properly is that the generative model should have a rather low number of variables and parameters as the fluorescence trace is not too informative about the underlying neuronal variables due to the temporal smearing of the calcium responses . As a representative of such continuous models we have selected the widely-used FHN model ( 2D ) , which is able to produce single spikes [40–42] . Alternatives would have been analogous 2D models such as the Morris-Lecar model [110] or reduced versions of the Hodgkin-Huxley model [111]; see also [49] . We also used integrate-and-fire ( I&F ) models . To make them continuous and avoid the discontinuous reset conditions of I&F models , e . g . [33 , 112 , 113] , we described two new models called QGIF ( 1D ) and bursting-QGIF ( 2D ) , see Table 3 . These two models adopt a minimum number of required variables for producing the single ( QGIF ) and burst ( bursting-QGIF ) spiking patterns . In addition , the QGIF model can be readily extended to describe different types of neurons with their specific active ionic currents [114] , e . g . we showed this by creating the bursting-QGIF model for hippocampal pyramidal neurons . With respect to the reconstruction efficiency we found that the FHN model is better suited for more accurate single spike and event ( like GDP occurrence ) detection than the QGIF model . Under a low SNR , the inferred membrane potentials of the QGIF model were usually relatively noisy and the inferred spikes sometimes did not cross the detection threshold ( see Methods ) of zero , and were thus counted as missed events ( e . g . see Table 4 ) ; this indicates that to increase the detection accuracy of the QGIF model one may need to use a lower detection threshold , like -10 mV . The greater robustness of the FHN model is possibly due to its recovery variable ( Eq 2 ) whose negative feedback on membrane potential constrains the membrane potential kinetics . For fluorescence transients with slow rise kinetics observed in our experimental data , we found that the QGIF model can reconstruct the onset times of GDP events more accurately than the FHN model ( see Fig 8 , and text ) . The precise GDP onset time reconstruction of the QGIF model was preserved even for low SNR traces . For the QGIF model , the optimization process converges usually quicker than the FHN model . Therefore , in terms of time consumption the QGIF model can be more reasonable to use for data of , e . g . , a population of neurons imaged by regular experimental setups which usually acquire data at rather high SNR levels . An advantage of CaBBI is that one can readily modify the generative models or replace them with other models . For instance , using CaBBI for the reconstruction of spikes from slowly rising fluorescence transients acquired at near-millisecond temporal resolution may further require a modification of the generative models . To do this , one can incorporate , e . g . , an extended model of calcium dynamics ( Eq 11 ) which prolongs the Ca2+ influx so that the mechanism of delay between the spike occurrence and the fluorescence transient peak is explicitly captured . For data providing such temporal resolution , one can also model the experimentally observed double-exponential decay kinetics of calcium transients including the typically observed rise time [13 , 115] . In general , this observation reflects the contribution of two [Ca2+] decay mechanisms with different time-constants , which can be modeled in Eq 11 , similarly to [115] . This generalization should provide a better fit to data showing such kinetics , and thus enhance the reconstruction precision . Large-scale calcium imaging from populations of individual neurons aims to provide a better understanding of neuronal circuit dynamics [1 , 7 , 11] . CaBBI provides a new model-based calcium imaging framework , and as an outlook , may be extended to analyze the data of imaged populations . More specifically , the presented approach is a first step towards incorporating the networks of biophysical spiking neuron models ( e . g . see [116 , 117] ) , together with their ubiquitous mechanisms such as synaptic plasticity ( e . g . see [117–119] ) . We expect that such a network extension enables studying the neuronal dynamics and biophysical parameters of complex neuronal circuits measured indirectly by calcium imaging . | Calcium imaging of single neurons enables the indirect observation of neuronal dynamics , for example action potential firing . In contrast to the precise timing of spike trains , the calcium trace is temporally rather smeared and measured as a fluorescence trace . Consequently , several methods have been proposed to reconstruct spikes from calcium imaging data . However , a common feature of these methods is that they are not based on the biophysics of how neurons fire spikes and bursts . We propose to introduce well-established biophysical models to create a direct link between neuronal dynamics , e . g . the membrane potential , and fluorescence traces . Using both synthetic and experimental data , we show that this approach not only provides a robust and accurate spike reconstruction but also a reliable inference about the biophysically relevant parameters and variables . This enables novel ways of analyzing calcium imaging experiments in terms of the underlying biophysical quantities . | [
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] | 2016 | Inferring Neuronal Dynamics from Calcium Imaging Data Using Biophysical Models and Bayesian Inference |
Human Papillomavirus ( HPV ) 16 E7 protein promotes the transformation of HPV infected epithelium to malignancy . Here , we use a murine model in which the E7 protein of HPV16 is expressed as a transgene in epithelium to show that mast cells are recruited to the basal layer of E7-expressing epithelium , and that this recruitment is dependent on the epithelial hyperproliferation induced by E7 by inactivating Rb dependent cell cycle regulation . E7 induced epithelial hyperplasia is associated with increased epidermal secretion of CCL2 and CCL5 chemokines , which attract mast cells to the skin . Mast cells in E7 transgenic skin , in contrast to those in non-transgenic skin , exhibit degranulation . Notably , we found that resident mast cells in E7 transgenic skin cause local immune suppression as evidenced by tolerance of E7 transgenic skin grafts when mast cells are present compared to the rejection of mast cell-deficient E7 grafts in otherwise competent hosts . Thus , our findings suggest that mast cells , recruited towards CCL2 and CCL5 expressed by epithelium induced to proliferate by E7 , may contribute to an immunosuppressive environment that enables the persistence of HPV E7 protein induced pre-cancerous lesions .
Cervical and other anogenital cancers represent around 5% of all cancers , and are mostly due to infection of anogenital epithelium by one of more than 15 recognized “high risk” human papillomaviruses ( HPV ) [1] . Worldwide , ∼50% of sexually active women are believed to become infected by HPV16 , the HPV genotype of highest risk , during their lifetime . Although most high risk HPV infections spontaneously regress , 1 to 2% of infected subjects develop persistent infection , which can progress to pre-cancerous lesions , and to cervical cancer if untreated [2] . Mast cells ( MCs ) are an important subset of immune cells , often considered as the first responders to opportunistic pathogens and allergens [3] , [4] . Defined by the expression of two surface markers , the receptor for IgE ( FcεRI ) and the receptor for stem cell factor ( SCF ) c-Kit , MCs are distributed at various densities below epithelial surfaces that interface with the external environment such as in the skin , airways and gastrointestinal tract , where they are strategically placed to rapidly alert the inflammatory infantry when required . In HPV associated cancers , MCs occur in HPV induced premalignant CIN 2/3 lesions at twice the frequency observed in normal cervix [5] . Most CIN2/3 lesions , as well as cervical cancers , are known to be associated with infection of high-risk HPVs [6] , [7] . In this study , we used HPV16-K14 . E7 transgenic mice , in which sustained expression of HPV16 E7 protein in keratinocytes mimics pre-cancerous lesions caused by HPV infection , to address three important questions . Firstly , does HPV16 E7 expression recruit MCs toward the epithelium ? Secondly , what mechanistic pathway is involved in this process ? And thirdly , do MCs recruited juxtaposed to the epithelium by HPV16 E7 expression mediate local immune suppression ? We show that MCs are recruited preferentially to the basal layer of HPV16 E7 expressing epithelium , that this recruitment is dependent on HPV16 E7 induced epithelial hyperproliferation , and that it is likely MCs are recruited by a CCL2/CCL5 dependent mechanism . Using skin graft experiments in which HPV16 E7 expressing skin containing or lacking MCs is transferred onto wild-type C57BL/6 mice , in which we have previously demonstrated that graft rejection is CD8 T cell mediated , enhanced by IL-1 and γδ T cells , and hindered by passenger NKT cells in E7 transgenic skin , but not by regulatory T cells , [8]–[11] , we now show that MCs make a key contribution to the immunosuppressive environment imposed by HPV16 E7-expression .
K14 . E7 transgenic mice ( E7 mice ) express the HPV16 E7 oncoprotein in epidermal keratinocytes under the control of the keratin 14 promoter . These mice are characterized by epidermal and dermal thickening , with an extensive dermal lymphoid infiltrate ( Fig . 1A , B ) consisting of an increased number of T cells [8] , NKT cells [12] compared to C57BL/6 ( C57 ) mice . Based on our previous findings that FoxP3+ T regulatory cells are not required to inhibit CD8 T cell function in HPV16 E7 skin graft tolerance [10] , we hypothesized that other cells , along with NKTs , could interfere with HPV infection resolution . MCs represent one such population as there is important new evidence that they have anti-inflammatory functions in certain settings [13]–[16] . Mice transgenic for the entire genome of HPV16 demonstrate increased numbers of MCs in skin relative to non-transgenic animals [17] . To determine whether this was a consequence of E7 expression and associated epidermal hyperplasia , we assessed MC numbers in the dermis of E7 and wild-type skin by metachromatic toluidine blue staining ( Fig . 1C , D ) . Wild-type skin showed detectable MCs ( mean = 27 . 96 cells per mm cartilage length ) distributed predominantly at the dermal/hypodermal interface and sparsely in the upper dermis ( Fig . 1C; bottom panel ) , whereas E7 skin exhibited significantly higher numbers of MCs tightly juxtaposed to the basal layer of the epidermis ( mean = 106 . 74 cells per mm cartilage length , p = 0 . 0007 ) ( Fig . 1C; upper panel and Fig . 1D ) . To determine whether E7 expression or the epithelial hyperplasia resulting from E7 expression is the primary cause of the increased numbers of MCs in E7 skin , we tested the impact of E7-induced hyperplasia on MC numbers . This was achieved using mice double transgenic for E7 and mutant Rb ( E7 . Rbmut ) in which E7 cannot disrupt normal cell cycle regulation by segregating Rb [18] . We confirmed the lack of hyperplasia in E7 . Rbmut skin when compared to E7 mouse skin , which has unmodified Rb ( Fig . 2A bottom panel ) . The density of MCs in E7 . Rbmut skin was decreased to the level of C57 mouse skin and Rbmut skin not transgenic for E7 , as confirmed by analysis of MC numbers ( Fig . 2B ) ( mean = 97 . 88 , 33 . 58 , 33 . 93 and 31 . 10 MCs/mm cartilage length for E7 . Rbwt , E7 . Rbmut , Rbwt and Rbmut , respectively , p = 0 . 0002 ) . These findings strongly suggest that E7-mediated pRb inactivation is important for the recruitment of MCs to the epidermis . The accumulation of MCs in hyperplastic skin associated with E7 expression might be due to prolonged survival , to local proliferation , or to increased recruitment [19] . To determine whether E7 induced epithelial hyperplasia induces MC proliferation , we stained serial ear pinna sections with toluidine blue to detect MCs and with PCNA to detect cell proliferation . Purple MCs and red PCNA stain did not co-localize ( Fig . S1A ) in wild-type or in E7 skin . To confirm this result , we cultured CFSE-labeled bone marrow-derived cultured MCs ( BMCMCs ) in medium alone , or with E7 and wild-type ear skin explant culture supernatants ( all supplemented with IL-3 ) for 24 , 48 and 72 h . Although BMCMCs proliferated in culture ( Fig . S1B ) , their numbers were not different between cultures exposed to E7 or wild-type supernatants ( Fig . S1C ) . Thus , there was no evidence that supernatants derived from hyperplastic E7 transgenic skin contained factors at levels high enough to induce MC proliferation . To test whether E7 skin specifically recruited MCs , we examined the production of three relevant chemokines SCF , CCL2 ( MCP-1 ) and CCL5 ( Rantes ) . Although SCF mRNA expression was comparable between full thickness ( dermis and epidermis ) normal C57 versus E7 mouse skin , levels of CCL2 mRNA and CCL5 mRNA were significantly increased or decreased , respectively , in E7 skin ( Fig . 3A ) . By contrast , E7 epidermis alone expressed lower levels of SCF mRNA ( p<0 . 0001 ) , and higher levels of both CCL2 and CCL5 than non-transgenic skin ( p<0 . 0001 ) ( Fig . 3B ) . To determine whether MCs exposed to E7 skin might be induced to express the receptors for the chemokines upregulated in E7 skin , we co-cultured BMCMCs with ear skin explant supernatants for 72 h . BMCMCs exposed to E7 supernatants upregulated CCR2 mRNA ( CCL2 receptor ) , and some but not all BMCMCs expressed CCR1 or CCR5 ( CCL5 receptors ) ( Fig . 3C ) . Further , CCR2 mRNA was also expressed at higher levels by MCs sorted from E7 ear skin than by BMCMCs ( Fig . 3D ) . To assess the migration capacity of MCs , we used BMCMCs in a migration assay . We first confirmed that BMCMCs migrate towards the direct ligand of cKit , SCF , as previously reported [20] . Then , we observed that BMCMCs migrate towards E7 ear skin explant supernatant , but not towards medium only or 98°C heated supernatant , suggesting that thermosensitive compounds attract BMCMCs ( Fig . 4A ) . Moreover , BMCMCs do not migrate when the supernatant is added to the upper well chamber; demonstrating migration is due to chemotaxis rather than enhanced chemokinesis . Finally , we showed that this migration towards ear skin supernatant can be abrogated by blocking CCL2 and CCL5 , together or alone ( Fig . 4A ) . C57BL/6-KitW-sh/W-sh mice have an inversion mutation that affects transcriptional regulatory elements upstream of the c-kit transcription start site on mouse chromosome 5 which impairs Kit function and results in a profound mast cell-deficiency in adult mice [21] , [22] . To further establish in vivo that MCs are recruited preferentially to E7-expressing skin , we performed adoptive transfer i . v . of 1 . 4×107 BMCMCs to KitW-sh/W-sh and E7 . KitW-sh/W-sh mice . The recipient KitW-sh/W-sh and E7 . KitW-sh/W-sh mice exhibited successful transfer of MC populations as evidenced by a substantial MC infiltration in the spleen of each mouse ( Fig . S2 ) [21] . Notably , we observed preferential recruitment of the injected MCs to E7-expressing , as opposed to non-transgenic , ear skin tissues ( Fig . 4B , C ) . Altogether , these data suggest that BMCMCs are preferentially recruited to E7-expressing epidermis by a mechanism that is likely to involve the local production of CCL5 and CCL2 . Following the observation that BMCMCs were recruited to E7 . KitW-sh/W-sh ear skin , we also noticed their close contact with the basal layer of the epidermis in both the engrafted E7 . KitW-sh/W-sh mice and the E7 mice . Fully granulated MCs can be identified as dark purple stained cells using toluidine blue , while degranulated MCs are light purple stained with some exterior granules [23] , [24] . In E7 skin , we observed that the closer MCs were to the keratinocytes , the more degranulated they appeared ( Fig . 1C and Fig . S3A ) . Using a heparin stain , we assessed the degranulation status of MCs in E7 and C57 skin and we showed that E7 skin contained more degranulated MCs ( Fig . S3B ) . Although the mechanism ( s ) underlying MC activation in this setting are yet to be fully elucidated , a potential candidate is endothelin-1 ( ET-1 ) which can induce MC degranulation [25] and its expression is significantly elevated in E7 ear skin ( Fig . S4 ) . Thus , these data suggest that interaction of MCs with the basal epidermal layer in E7 skin , or with a factor secreted by the epidermis such as ET-1 , can cause activation of MCs , with release of granule contents . MCs can regulate local immune responses to tumors [17] , [26] and allografts [27]–[29] . E7 skin grafted onto syngeneic , non-transgenic animals is not rejected [12] . To determine whether MCs contribute to the local immunoregulatory environment in this model , we grafted E7 skin with ( E7 ) or without ( E7 . KitW-sh/W-sh ) MCs onto syngeneic immunocompetent C57BL/6 recipients . E7 skin grafts without MCs were rejected within 3 weeks in 8 out of 9 mice , whilst E7 skin grafts with MCs , and non-transgenic skin with or without MCs , were not rejected ( Fig . 5A ) . 28 days after grafting , host-derived MCs had repopulated the MC-deficient grafts , with the highest infiltration occurring within any remaining E7 . KitW-sh/W-sh skin ( Fig . 5B and 5C ) , confirming the results of Fig . 4 . These dataset demonstrate that MCs have a locally immunosuppressive effect on graft rejection , a regulatory function that might reduce CD8 T cell activity that we have previously shown to be necessary for graft rejection [8]–[11] .
Infection of the cervix with high risk HPVs is necessary though not sufficient for the development of cervical cancer , and continued expression of HPV E6 and E7 non-structural proteins is the hallmark of HPV transformed epithelium [2] . Here we show , using a mouse with persisting epithelial expression of the E7 protein of HPV16 , the high risk HPV most often associated with cervical cancer , that MCs are recruited to E7-expressing hyperplasic epithelium in high numbers , and that such recruitment is dependent on the ability of E7 to sequester Rb protein . We show further that degranulated MCs are found juxtaposed to the basal keratinocyte layer and are attracted to the skin by chemokines released by hyperproliferative epithelium , and that this accumulation might hinder , directly or indirectly , CD8 cytotoxic T cell mediated rejection of E7 expressing epithelium [8]–[11] . In mice transgenic for the whole HPV16 early gene region expressed from a K14 promoter , progression of epithelial hyperplasia and papillomatosis to dysplasia correlates with MC accumulation and activation [30] , [31] , but the mechanisms promoting the accumulation of MCs are poorly defined . Many chemoattractants are involved in the recruitment of MCs into tissues [32] . MCs are recruited to tumors by tumor-derived SCF [33] , engaging a signaling pathway for MC differentiation , migration , maturation and survival [34] . In virus associated tumors , additional chemokines , and their respective receptors expressed on the cell surface of circulating bone marrow-derived progenitors [35] or resident mature MCs , are likely to provide migratory or proliferative signals . Coussens et al . [17] , [36] , [37] observed , in a HPV16 transgenic mouse in which the entire early gene region of HPV16 was expressed in the skin under the K14 promoter , that there was infiltration of MCs and increased angiogenesis in association with keratinocyte proliferation and increased skin thickness . To study the contribution of MCs , Coussens et al . attempted to use c-kit mutant KitW/W-v mice that exhibit a profound loss of c-kit activity , resulting in a systemic MC and basophil deficiency , as well as other phenotypic abnormalities including anemia , neutropenia and sterility [14] . A single KitW/W-v MC-deficient mouse expressing HPV16 early genes was the sole survivor of 89 KitW/W-v MC-deficient pups among 700 littermates , precluding analysis of the role of MCs in HPV associated pathology . To establish a role for MCs in conferring local immunosuppression or promoting local pathology in HPV transformed epithelium , we therefore studied a mouse which expresses HPV16 E7 , the single HPV early gene most relevant to cervical epithelial progression to cancer , from a keratin 14 promoter . We used a C57BL/6-KitW-sh/W-sh mouse , in which an inversion mutation of the transcriptional regulatory elements disrupts Kit transcription leading , as in the KitW/W-v mouse , to a profound systemic MC deficiency . This KitW-sh/W-sh MC-deficient strain exhibits a neutrophilia [22] , [38] rather than the neutropenia of KitW/W-v mice , and otherwise exhibits a much reduced range of phenotypic abnormalities [21] , [22] , [38] . When crossed with our HPV16 E7 mice , the C57BL/6-KitW-sh/W-sh mice produce viable E7 transgenic and MC-deficient offspring . In our MC competent HPV16 E7 mice , we confirmed Coussens' observation of the accumulation of MCs in the dermis [17] , juxtaposed to the basal layer of the epidermis where E7 is expressed . We therefore proceeded to establish the mechanism for accumulation of MCs at that site . E7 expressed under the K14 promoter interacts with many proteins and also binds its main target Rb inhibiting the sequestration of E2F family proteins , and thus driving keratinocyte proliferation [8] , [18] , [21] , [39] . A mutation of Rb is recognized that hinders binding of E7 but not of E2F proteins [8] . E7 . Rbmut mice , expressing E7 and this mutated Rb , do not show the epithelial hyperplasia associated with E7 transgenic mice . We observed no increase in MCs in E7 . Rbmut mice , confirming that MC infiltration of the basal epidermis is associated with HPV16 E7 induced epithelial hyperproliferation . We have previously observed that IFNγ is expressed in E7 transgenic mouse skin [12] , [40] , and therefore chemokines expressing an IFN regulatory factor-1 response element in their non-coding gene regions , such as CCL2 and CCL5 [41] , [42] , represent potential MC chemoattractants to E7 skin . We show here that MCs migrate towards HPV16 E7-expressing ear skin explant cultures , and that migration towards the supernatant of HPV16 E7-expressing ear skin cultures can be blocked by neutralizing CCL2 and CCL5 , which confirmed a role for these chemokines in recruiting MCs to HPV16 infected epithelium . Adoptive transfer of BMCMCs repopulated the MC population in the ear skin of MC-deficient HPV16 E7 . KitW-sh/W-sh mice but not MC-deficient KitW-sh/W-sh mice , demonstrating that expression of HPV16 E7 induces MC migration . High levels of CCL2 and CCL5 transcription associated with HPV16 E7 expression in skin are consistent with the hypothesis that expression of these chemokines , induced by E7 , accounts for the MC infiltrate in E7 transgenic mouse skin . To further decipher the role of Rb and epithelial hyperproliferation in MC chemotaxis , future experiments will determine whether CCL2 and CCL5 levels are concomitantly decreased in Rbmut/PV16-K14 . E7 Tg mouse epidermis . As shown in Figure 1 and Supplemental Figure 3 , those MCs recruited to E7 skin appear more degranulated . Although the mechanisms underlying MC degranulation at the interface with HPV16 E7 epithelium are yet to be fully understood , production of ET-1 by the virus affected epithelium [43] or by the surrounding microenvironment in E7 transgenic mouse skin ( Fig . S4 ) , and the ability of this peptide to promote tumor invasion [44] as well as induce MC degranulation [25] represents a potential means of MC activation in our current study . MC degranulation typically involves release of pre-formed and stored granule associated mediators [14] , including histamine which can contribute to systemic immunosuppression in response to UVB-irradiation of the skin [45] , and the tryptase mMCP6 which can actively deplete the local environment of IL-6 to maintain skin allograft tolerance [46] . The activated state of MCs in HPV16 E7 skin also indicates that MCs might be releasing de novo synthesized mediators . We have shown that MC-derived IL-10 can curtail inflammation associated with certain settings of allergic contact dermatitis and low-dose UVB irradiation of the skin [15] . More recently , other immunoregulatory roles for MC-IL-10 have emerged , including an ability to reduce graft-versus-host-disease independently of Tregs during hematopoietic cell transplantation [16] , [47] and a capacity to drive tolerance in chronic bacterial infection by suppressing humoral and cell-mediated immunity [13] . However , while evidence from such studies supports the notion that MC-IL-10 , histamine or mMCP6 might be involved in regulating immune responses in the HPV-infected microenvironment , further work is required to determine which MC-derived mediators specifically orchestrate local immune suppression in this setting . MCs are involved in many pathologies and a role in cancer has been indicated ( reviewed in [48] , and [26] , [49] , [50] ) . The accumulation of MCs in the vicinity of tumor tissue strongly correlates with poor prognosis in many aggressive cancers , including gastrointestinal [51] , [52] and pancreatic cancers [53] in humans , and in mice . MCs can promote angiogenesis , tumor invasion , immune suppression , and the recruitment of other immune cells including regulatory T cells [29] . However , the role of MCs in cervical cancer associated with HPV infection is largely unknown , but in such a setting it is possible that MCs promote persistence of infection by contributing to an immunosuppressive microenvironment . MC prevalence at different anatomical sites is under genetic control and can be influenced by extrinsic factors ( e . g . extent of sun exposure of the skin ) [54] . Thus MC heterogeneity in prevalence , as well as inter-individual differences in the microenvironments in which the MCs reside might be factors that contribute to allowing persistence of HPV infection , and hence increased risk of cancer , in only 2% of those infected . Persisting HPV infection is not resolved by the current preventive vaccines [55] , [56] , and new therapeutic strategies are needed to treat the many women at risk of cervical cancer through persisting HPV infection [57] . A specific immunotherapy against E6 and/or E7 remains elusive [2] , [58] , [59] , though whether HPV infection directly suppresses aspects of the host immune response is largely unknown [60] . Vaccine immunotherapy against an HPV16 E7 expressing non-small cell lung cancer ( NSCLC ) line has been shown to be more effective with an anti-CCL2 blocking antibody [61] . Taken together , our data suggest that HPV16 E7-expression in the epithelium recruits MCs , which like tumor associated macrophages [62] and myeloid suppressor cells [63] in other malignant settings , appear to exhibit an immunosuppressive function in the E7-influenced microenvironment . Thus , it is plausible that CCL2 and/or CCL5 blockade might reduce such immunosuppression and facilitate immunotherapy of HPV associated cancers .
C57BL/6 mice ( C57 ) were obtained from the Animal Resources Centre ( ARC , Perth , Australia ) . HPV16 K14 . E7 transgenic C57BL/6 mice ( E7 mice ) , in which E7 oncoprotein is expressed under the K14 promoter were maintained locally at the Princess Alexandra Hospital Biological Research Facility ( BRF , Brisbane , QLD , Australia ) under specific pathogen-free conditions . RbDLXCXE ( Rbmut ) mice and HPV16 K14 . E7x-RbDLXCXE ( E7 . Rbmut ) mice on a mixed 129/FVB/C57 background have been previously described [8] , [18] and were bred at the McArdle Laboratory Cancer Center Animal Care Facility , USA , and generously provided by PF Lambert lab , Madison , Wisconsin , USA . Genetically c-kit mutant mast cell-deficient C57BL/6-KitW-sh/+ mice backcrossed with C57BL/6J mice for 14 generations were used as breeding pairs to produce mast cell-deficient B6 . KitW-sh/W-sh mice and were maintained at the IMVS Animal Facility ( Centre for Cancer Biology , Adelaide , SA , Australia [21] , [64] ) . B6-KitW-sh/W-sh mice were crossed with E7 mice to obtain mast cell-deficient mice expressing the HPV16-E7 oncoprotein ( E7 . KitW-sh/W-sh mice ) . All mice were sex matched for all experiments and were used at 10 to 16 weeks of age . Experiments were performed in compliance with the ethical guidelines of the National Health and Medical Research Council of Australia , with approval from the IMVS Animal Ethics Committee and the University of Queensland Animal Ethics Committee . Ear thickness was measured with a micrometer gauge ( Ozaki MFG ) on anesthetized mice . Ears were harvested and separated into dorsal and ventral halves using forceps . For epidermal removal , the skin was incubated epidermis-down in 1 . 2 mg/ml Dispase II ( Roche ) at 37°C . After an hour , the epidermal layer was peeled off the dermis . To release cells , skin were torn into small fragments and digested for 1 h in 1 mg/ml collagenase D , 0 . 5 mg/ml type 2 hyaluronidase and 20 ug/ml Dnase 1 ( all from Roche ) at 37°C . Tissues were passed through a cell strainer and washed in PBS containing 3% FBS . Isolated cells were then stained for flow cytometry or cell sorting using anti-CD3 ( clone 2C11 , 1 . 0 µg/ml ) , anti-CD45R/B220 ( clone RA3-6B2 , 1 . 0 µg/ml ) , and anti-CD117 ( cKit clone 2B8 , 1 . 25 µg/ml ) antibodies from BD Pharmingen , and anti-CD45 . 2 ( clone 104 , 0 . 5 µg/ml ) , anti-FcεRIα ( clone MAR-1 , 0 . 5 µg/ml ) antibodies and streptavidin PE ( 0 . 4 µg/ml ) from eBioscience , and anti-CD11c ( clone N418 , 2 . 5 µg/ml ) from BioLegend . MCs were gated as CD45 . 2+ , CD3− , B220− , CD11c− , cKit+ and FcεR1α+ . For mRNA isolation , sorted MCs were directly collected into lysis buffer ( Bioline ISOLATE II RNA Micro Kit ) . As previously described [15] , [64] , bone marrow cells were collected from femurs and tibiae and cultured in DMEM supplemented with 10% Fetal Calf Serum and a source of mouse IL-3 which is necessary for MC differentiation and proliferation ( i . e . 20% WEHI-3 conditioned medium supplemented with recombinant mouse IL-3 ( R&D Systems ) to consistently achieve a total of 3–4 ng/mL IL-3 ) . After 5 to 6 weeks >95% of the cells were identified as MCs by May-Grunwald-Giemsa staining histologically or by flow cytometry using anti-CD45 . 2+ , cKit+ and FcεRIα+ staining . 5 to 6 week old BMCMCs were washed twice in PBS and 1 . 4×107 cells injected i . v . into KitW-sh/W-sh and E7 . KitW-sh/W-sh mice . 12 weeks after BMCMC transfer into the mice , ear skin and spleen were collected to confirm the presence of MCs in these tissues by toluidine blue staining and histological analysis , as previously described [21] . Ears were collected from C57 or E7 mice on ice , split into halves , and placed dermis side down in complete WEHI-conditioned medium at 37°C . Medium was replaced after 1 h and again after 3 h with 600 µl of fresh conditioned medium , to reduce cell-death related release of cytokines and danger signals . Ear explants supernatants were collected 20 h later and stored at −80 degrees until use . For BMCMC culture with ear skin explant supernatants , 5×105 BMCMCs were first labelled with 2 . 5 µM CFSE for 15 min at 37°C , and then washed twice with PBS . BMCMCs were then seeded in 24 well plates in WEHI-conditioned medium and ear skin explant supernatant ( 1∶1 ) for 4 , 24 , 48 or 72 h , following which cells were collected for mRNA extraction . CFSE dilution was analyzed by flow cytometry within cKit+ FcεRIα+ double-positive cells . Samples with analyses below the assay detection level were assigned a value of not detected ( ND ) for display and statistical analysis . Transwell migration assays were performed using 5 µm pore size Transwell inserts ( Corning , NY ) . 2×105 3–6 week old BMCMCs were placed in the top chamber in 100 ul of medium without IL-3 . Recombinant mouse SCF ( RnD Systems ) at 0–100 ng/ml or E7/C57 ear skin explant culture supernatant without IL-3 were placed in the bottom chamber . When indicated , anti-CCL2/Rantes ( clone 53405 ) or anti-CCL5/JE/MCP-1 ( clone 123616 ) blocking antibodies from RnD Systems were added in the bottom chamber at 10 µg/ml and the plate was then incubated at 37°C . Four hours later , cells were collected from the bottom chamber and counted by trypan blue exclusion on a hemocytometer , and phenotyped by flow cytometry for anti-CD45 . 2 , anti-FcεRIα and anti-cKit expression . Donor ear skin was grafted onto recipient flanks as previously described [12] . Briefly , dorsal and ventral surfaces of ear skin from transgenic mice were placed onto the thoracic flank region of an anesthetized C57BL/6 recipient . Grafts were held in place with antibiotic-permeated gauze ( Bactigras; Smith and Nephew , London , U . K . ) and bandaged with micropore tape and Flex-wrap ( Lyppard , Queensland , Australia ) . After 7 days , bandages were removed and grafts were monitored three times a week for 4 weeks or longer . Graft rejection was assessed by a loss of distinct border and signs of ulceration and/or necrosis to >80% of the graft area . Mice were culled by CO2 inhalation and samples of ear pinnae were fixed in 4% formalin . Samples were coded using a serial number , so the evaluator was not aware of their identity and sent to the histology facility to be embedded in paraffin ( ensuring a cross-sectional orientation ) and cut as 4–6 µm sections . Sections were then stained with toluidine blue , pH 1 , for the detection of mast cells ( purple ) , with hematoxylin/eosin , alcian blue-safranin-O or with Proliferating Cell Nuclear Antigen ( PCNA , Sigma ) . Images of coded samples were taken with a 20× microscope objective ( Nikon Brightfield , final magnification , ×200 ) . Field lengths ( µm ) were determined using NIS-Element software ( Nikon ) . Mast cells were counted manually by image analysis using NIS-Element on 4 to 10 consecutive fixed fields of view along the entire length of ear skin and calculated per mm cartilage length . At collection , samples were snap-frozen in dry ice and stored at −80°C until mRNA extraction . Ear skin samples were then lysed in RNase-free microtubes using Trizol ( Sigma ) and an IKA T10 Ultra-Turrax homogenizer , and incubated for 5 min at RT . Total RNA extraction was performed as per manufacturer's recommendations . Briefly , 0 . 2 ml of mRNA-grade chloroform was mixed with each sample and incubated for 2–3 min . Samples were centrifuged 12000 rpm for 15 min at 4°C . The aqueous , colorless phase containing RNA was then collected without disturbing the white interphase and transferred into a fresh tube . RNA was precipitated using cold 100% isopropanol ( vol/vol ) , incubation for 10 min at RT and centrifugation 12000 rpm for 10 min at 4°C . The RNA pellet was washed twice in 75% ethanol and air-dried for 10–15 min before being dissolved in 10 µl of RNase/Dnase free water at 55°C . Genomic DNA was digested using the Qiagen RNase-free DNase kit ( DNase kit; #79254 ) . RNAs were then quantified at 260/280 ratio by nanodrop spectrophotometry . RNAs were stored at −80°C until used for retrotranscription . For cell-sorted MCs , the Isolate II RNA Micro kit ( Bioline ) was used following manufacturer's instructions . For reverse transcription , 500 ng of RNA was combined with 25 mM MgCl2 , 25 mM dNTPs , oligoDT , RNase inhibitor , and MuLV Taq polymerase in buffer ( all from Applied Biosystems ) for 25 min at 25°C , 60 min at 42°C and 5 min at 95°C . cDNAs were stored at −20°C until used for PCR . For semi-quantitative Real-Time PCR , samples were amplified using a Sybr premix Taq II ( TAKARA ) following the manufacturer's instructions . The amplification program was run on a ABI7900 ( Applied Biosystems ) - 1×30 sec at 95°C , 45× ( 5 sec 95°C and 30 sec at 60°C ) , followed by a dissociation stage ( 15 sec at 95°C , 60 sec at 60°C , 15 sec at 95°C ) . For the detection of ET-1 , the following cycling conditions were performed: 1×15 min at 95°C , 45× ( 15 sec 95°C , 15 sec at 55°C , 20 sec at 72°C ) , followed by a hold of 30 sec at 72°C . Primers were designed using IDT ( Integrated DNA Technologies , www . idtdna . com ) ( Table S1 ) . A non-parametric Mann-Whitney t-test or unpaired t-test were used as indicated for assessment of differences between groups . A Log-rank ( Mantel-Cox ) test was used to compare survival curves . Differences were considered to be significant when the p value was less than 0 . 05 . Prism ( GraphPad Software , La Jolla , CA ) software was used to prepare graphs and for statistical analysis . | Worldwide , around 50% of sexually active women are believed to become infected by Human papillomavirus type 16 , the major cause of cervical cancer , and 2% will remain infected and therefore at lifetime risk of developing cancer . Why some women remain infected is unknown . Here we used a mouse engineered to express the HPV16 protein ( E7 ) in skin , which drives development of pre-cancer lesions . This protein induces skin thickening , and the thickened skin releases molecules that attract mast cells . We show further that these cells locally suppress the function of the immune effector cells that can reject E7 expressing skin . We believe that targeting mast cells or impairing their attraction to the HPV infected tissue might therefore reduce the risk of cervical cancer for women infected with HPV16 by enabling them to clear their chronic infection . | [
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"immunomodulation"
] | 2014 | HPV16-E7 Expression in Squamous Epithelium Creates a Local Immune Suppressive Environment via CCL2- and CCL5- Mediated Recruitment of Mast Cells |
Mucorales are a group of basal fungi that includes the casual agents of the human emerging disease mucormycosis . Recent studies revealed that these pathogens activate an RNAi-based pathway to rapidly generate drug-resistant epimutant strains when exposed to stressful compounds such as the antifungal drug FK506 . To elucidate the molecular mechanism of this epimutation pathway , we performed a genetic analysis in Mucor circinelloides that revealed an inhibitory role for the non-canonical RdRP-dependent Dicer-independent silencing pathway , which is an RNAi-based mechanism involved in mRNA degradation that was recently identified . Thus , mutations that specifically block the mRNA degradation pathway , such as those in the genes r3b2 and rdrp3 , enhance the production of drug resistant epimutants , similar to the phenotype previously described for mutation of the gene rdrp1 . Our genetic analysis also revealed two new specific components of the epimutation pathway related to the quelling induced protein ( qip ) and a Sad-3-like helicase ( rnhA ) , as mutations in these genes prevented formation of drug-resistant epimutants . Remarkably , drug-resistant epimutant production was notably increased in M . circinelloides f . circinelloides isolates from humans or other animal hosts . The host-pathogen interaction could be a stressful environment in which the phenotypic plasticity provided by the epimutant pathway might provide an advantage for these strains . These results evoke a model whereby balanced regulation of two different RNAi pathways is determined by the activation of the RNAi-dependent epimutant pathway under stress conditions , or its repression when the regular maintenance of the mRNA degradation pathway operates under non-stress conditions .
RNA interference ( RNAi ) is a complex gene regulatory system that blocks the expression of target genes and is highly conserved in most eukaryotic organisms . The discovery of RNAi mechanisms , along with the development of next generation sequencing technologies , has revealed a myriad of regulatory small RNAs that is revolutionizing our knowledge of RNA functions . In this system , the common core of the different RNAi pathways represses gene expression through a homology-dependent mechanism mediated by small non-conding RNAs ( sRNAs ) . These sRNAs are produced by RNaseIII Dicer enzymes from double-stranded RNA ( dsRNA ) precursors , and then loaded into an RNA-induced silencing complex ( RISC ) to function as probes to recognize the target RNA . Once captured the target RNA is either degraded or its translation is blocked by Argonaute proteins , which are the main components of the RISC complex . Besides this common core , in both fungi and plants the RNAi mechanism also requires RNA-dependent RNA polymerases ( RdRPs ) to synthesize dsRNA from single-stranded RNA ( ssRNA ) [1 , 2] . RNAi was first described as a defense mechanism against invasive nucleic acids . However , diverse regulatory roles of the RNAi machinery have been documented since its discovery , including post-transcriptional ( mRNA accumulation and translation ) and transcriptional ( chromatin silencing ) gene regulation , as well as programmed DNA rearrangements and genome surveillance [3] . Quelling , meiotic silencing by unpaired DNA ( MSUD ) , and sex induced silencing ( SIS ) are distinct RNAi pathways that have been described in Neurospora crassa and Cryptococcus neoformans , respectively , and new RNAi pathways are still being discovered in other fungi [4 , 5] . In fact , two novel and unusual mechanisms have been recently reported in the human fungal pathogen Mucor circinelloides that differ from the canonical exogenously or endogenously activated RNAi pathways described previously [6 , 7] . These are an RNAi-dependent epimutation pathway [4] and an RdRP-dependent Dicer-independent degradation cascade that acts on endogenous mRNA [5] . These discoveries position M . circinelloides as an innovative model to elucidate the intrinsic and complex RNAi pathways and their biological functions . The RNAi-dependent epimutation mechanism was discovered due to the ability of M . circinelloides to develop transient resistance to the antifungal drug FK506 . Mucor is a dimorphic fungus that typically grows as a filamentous hyphae yet only grows as a yeast under low oxygen and high CO2 conditions [8] or when the calcineurin pathway is inhibited or mutated [9 , 10] . The antifungal drugs FK506 and rapamycin interact with the prolyl isomerase FKBP12 [11] , conserved throughout eukaryotes , and the resulting protein-drug complexes inhibit the protein phosphatase calcineurin and the TOR kinase , respectively [12 , 13] . Exposure to FK506 blocks hyphal growth of M . circinelloides and enforces yeast growth . However , M . circinelloides is able to acquire resistance to FK506 and return to hyphal growth via two distinct routes: the first involves Mendelian mutations of the fkbA gene encoding FKBP12 or the calcineurin A or B subunit genes cnaA and cnbR [10]; the second route involves an epigenetic RNAi-mediated pathway based on reversible silencing of the fkbA gene [4] . As mentioned above , a recently published study described a novel RdRP-dependent Dicer-independent degradation mechanism for endogenous mRNA [5] . This unusual Dicer-independent regulatory pathway controls the expression of conserved and highly expressed genes via specific degradation of the corresponding target mRNAs . A new RNase III-like protein named R3B2 has been found to function as the primary RNase protein in this pathway instead of Dicer . R3B2 is also involved in the canonical RNAi pathway , likely by functioning in combination with the Dicer proteins [5] , although its specific role in this pathway is still unknown . How R3B2 discriminates between both pathways is also unknown . One possibility is that interaction with Dicer or the RdRP proteins targets the RNA for degradation via either the canonical RNAi mechanism or the Dicer-independent pathway , respectively . Here , we identify a link between the RNAi-dependent epimutation pathway and the RdRP-dependent Dicer-independent RNA degradation pathway . Our study dissecting the role in the epimutation mechanism of different genetic elements involved in canonical and non-canonical RNAi reveals a competitive relationship between these pathways as mutants defective in the RNA degradation mechanism produced epimutants at a much higher frequency relative to the wild type . These results reveal that the epimutation pathway is either negatively controlled by the non-canonical RdRP-dependent degradation pathway , or the two compete for target mRNAs , highlighting the complexity of RNAi-related pathway interactions in fungi . Furthermore , the enhanced ability to activate the epimutation pathway exhibited by several M . circinelloides f . circinelloides isolates from human and animal sources that are virulent in a murine model suggests that the epimutant pathway may be relevant to the host-pathogen interaction .
In light of the results implicating R3B2 in RNA metabolic pathways , and to better understand the relationship between the three different known RNAi mechanisms that operate in M . circinelloides , we investigated whether R3B2 functions in the RNAi-dependent epimutation mechanism . To this end , two independent r3b2 null mutant strains ( MU412 and MU429 , [5] ) were incubated in the presence of 1 μg/ml FK506 on solid medium [4] . M . circinelloides grows as a yeast on YPD media supplemented with 1 μg/ml of FK506 [9] , but after 6 to 15 days of incubation at 25°C , the fungus can develop resistance to the drug either via spontaneous Mendelian mutations in the target genes , fkbA or the calcineurin genes , or via an epigenetic RNAi-mediated pathway [4] . These FK506 resistance mechanisms restore Mucor growth as a filamentous fungus in a stable or unstable fashion , depending on the mechanism involved ( mutation or epimutation ) [4] . The two independent r3b2 null mutants were grown on YPD media supplemented with FK506 and white patches of filamentous growth , indicative of the development of FK506 resistance , were observed emanating from the edge of all of the cultures . Resistant sectors developed more rapidly in the r3b2 null mutants compared to the wild type strain ( S1 Fig ) , which in general required longer incubation ( 6 to 15 days ) before filamentous growth could be observed . Spores from patches with hyphal growth were transferred to YPD solid medium containing FK506 and incubated again for 3 days . This process was repeated at least one more time before analyzing the isolates to ensure a high proportion of the nuclei were carrying the mutation or the fkbA siRNAs in the mycelial syncytium , as described previously [4] . These resistant isolates were cross-screened on YPD medium supplemented with 100 μg/ml of rapamycin to identify those that were FK506R/rapaR versus FK506R/rapaS ( see S1 Table ) , and then grown on MMC media pH 4 . 5 supplemented with 1 μg/ml of FK506 and genomic DNA was extracted to sequence the FK506 target genes ( fkbA and calcineurin genes ) . Only 11 out of 61 FK506-resistant isolates from the R3B2 null mutant background ( 3/33 and 8/28 respectively from the two independent mutants ) were found to have Mendelian mutations in the fkbA or cnaA/cnbR genes ( Table 1 and S1 Table ) . The other 50 isolates were examined for the presence of fkbA siRNAs to analyze if FK506 resistance resulted from RNAi-dependent epimutations . Small RNAs ( sRNAs ) were extracted and tested for fkbA antisense sRNAs ( [4]; see Methods ) . From the 50 isolates , at least 48 showed accumulation of antisense sRNA complementary to the fkbA mRNA indicating activation of the epimutation pathway ( Fig 1A , quantified by densitometry in Fig 1B ) . These epimutant isolates represent almost 80% of the total number of resistant isolates ( Table 1 ) . As the wild type frequency of epimutations is usually 20 to 30% ( [4]; Table 1 ) this result indicates an elevated epimutation frequency , similar to that caused by deletion of rdrp1 , which encodes an RNA dependent RNA polymerase I previously demonstrated to play a role in repressing epimutant production [4] . Therefore , these findings provide evidence that , in contrast to the positive role R3B2 plays in the progression of the non-canonical Dicer-independent and canonical Dicer-dependent pathways , R3B2 negatively regulates the epimutation pathway . To better understand the interactions between the distinct RNA silencing pathways operating in M . circinelloides , we searched for other RNAi components that could exhibit a differential role in the distinct RNA degradation pathways characterized thus far . QIP is an exonuclease that interacts with Argonaute to facilitate the activation of the RISC complex in N . crassa , and thereby promotes the production of single stranded siRNAs from duplex siRNAs [14 , 15] . Interestingly , an in silico analysis of the M . circinelloides genome ( http://genome . jgi-psf . org/Mucci2/Mucci2 . home . html ) revealed the existence of a putative protein with a conserved Ribonuclease H domain ( ID 110517 ) that had 25 . 5% similarity and 15 . 2% global identity with its ortholog the QIP protein of N . crassa ( both proteins were reciprocal best hits in a BlastP analysis ) . This putative ID 110517 ORF contains the three exonuclease 3’-5’ characteristic motifs from the DEDDh superfamily with all of the critical acidic amino acids conserved ( D and E in the I motif; H and D in the II motif; H and D in the III motif , being the last histidine essential for the exonuclease activity ) ( S2 Fig ) . We first tested if this putative QIP orthologous gene is expressed . Liquid cultures of the wild type strain were grown for 48 hours in the dark in YNB medium pH = 4 . 5 and total RNA was extracted from the mycelia after being exposed to light for different periods of time . The RNA was analyzed by northern blot with hybridization under stringent conditions with a qip specific probe . A single mRNA was detected with the expected size of the qip mRNA , 1 . 1 kb ( S3 Fig ) , indicating that qip is a bona fide gene that is expressed during vegetative growth in M . circinelloides . Furthermore , QIP expression is independent of light in contrast to other RNAi genes ( S3 Fig ) [16] . A qipΔ null mutant ( MU430 ) was generated by transformation , homologous recombination , and gene replacement utilizing the pyrG gene as the selectable marker ( S4 Fig and description of plasmids and generation of deletion mutants is presented in S1 Supporting Information ) . PCR and Southern blot analysis confirmed that precise gene replacement and no ectopic integrations had occurred ( S4 Fig and Methods ) . The response of the qipΔ mutant to different RNAi silencing triggers was examined and compared to the wild type strain to determine the role of QIP in the exogenously activated silencing response . Expression of carB , which encodes the enzyme phytoene dehydrogenase , is required for the production of carotenoids that endow Mucor with its characteristic orange pigmentation [17]; thus , in the absence of carB expression Mucor forms white colonies . The mutant and wild type strains were transformed with two different carB containing plasmids that are capable of triggering RNAi , plasmid pMAT1279 expresses a sense carB transgene [18] , whereas plasmid pMAT1253 produces a carB RNA hairpin known to result in increased RNA silencing [19] . Both of these plasmids are self-replicating and express the carB alleles under the control of the strong M . circinelloides gpd1 ( glycerol-3-phosphate dehydrogenase ) promoter . Transformation of the wild type strain with pMAT1279 or pMAT1253 resulted in a high proportion ( 95% and 99% with the sense and hairpin constructs , respectively ) of transformed colonies that were white under illuminating conditions , indicating that carB expression had been silenced . However , transformation of the qipΔ mutant strain yielded a vast majority of orange transformants and only a few transformed colonies with mixed orange and white sectors were obtained ( ~0 . 32% and 6% with the sense and hairpin constructs , respectively ) , and no homogeneous white colonies were observed , indicating that the qipΔ mutation dramatically blocks carB silencing ( Fig 2 ) . These results provide evidence that , similarly to N . crassa [15] , QIP has an essential role in transgene-induced silencing in M . circinelloides , further demonstrating an analogous function for both proteins . Moreover , the role of QIP in silencing is not dependent on the nature of the silencing trigger because both sense and inverted repeat transgenes showed a similar reduction in the efficiency of silencing in the qipΔ mutant . Furthermore , the rare qipΔ carB transformants showing white sectors rapidly reverted to a wild type phenotype ( orange ) following one cycle of vegetative growth on YNB under illumination and only 13% remained white . In contrast , in accord with previous observations [18] , the white carB silenced transformants obtained in the wild type background were more stable and 76% remained white after one cycle of vegetative growth ( Fig 3A ) . The ability of the qipΔ mutant to accumulate sRNAs was also analyzed . sRNA were extracted from the non-silenced ( orange ) isolates obtained following transformation of the qipΔ mutant with plasmid pMAT1253 . Antisense sRNAs corresponding to carB sequences were detected by northern blot , with the silenced R7B strain serving as the positive control ( see Methods ) . The non-silenced isolates from the qipΔ mutant background were unable to accumulate antisense sRNA complementary to the carB gene ( Fig 3B ) , even those containing the silencing trigger and with the remainder of the RNAi pathway components intact ( RdRP1 , Dcl2 , Ago1 , RdRP2 ) . These results indicate that QIP is required for sRNA production in the transgene-induced silencing pathway . Next , we tested if the QIP exonuclease plays a role in the RNAi–dependent epimutation pathway . The qipΔ mutant strain was exposed to FK506 for several days until filamentous growth was observed emerging from the typical yeast colonies formed by M . circinelloides in the presence of the drug , as described above [4] . Of twenty-three independent FK506-resistant isolates that were analyzed , 21 harbored Mendelian mutations in the target genes ( fkbA or calcineurin genes ) ( Table 1 , S1 Table ) . The remaining two isolates were FK506-resistant but rapamycin sensitive , which indicates that these are not fkbA epimutants as FKBP12 is the target protein of both FK506 and rapamycin . In the absence of FKBP12 , as occurs when fkbA is silenced , rapamycin lacks its target and has no effect on M . circinelloides [11] . This suggests that these two isolates either harbor a Mendelian mutation that has not yet been detected in the calcineurin genes or in the region of fkbA that affects its interaction with FK506 but not with rapamycin , or may result from a different , specific FK506-resistance mechanism . The absence of epimutants among the 23 FK506-resistant isolates and the inability of the qipΔ mutant to respond to RNAi silencing triggers indicate that QIP is essential for both the epimutation and the exogenously induced RNAi pathways in M . circinelloides . In other model fungal systems , RNA helicases are known to play important roles in RNA silencing; for example , the putative RNA helicase Sad-3 mediates meiotic silencing by unpaired DNA ( MSUD ) in N . crassa [20] and the Schizosaccharomyces pombe Hrr1 is an RNA helicase that is required for RNAi-mediated heterochromatin assembly [21] . A homology search performed with the amino acid sequences of SAD-3 and Hrr1 identified a putative RNA helicase in M . circinelloides . The coding region of this gene ( named rnhA ) is 4134 bp and its deduced protein product ( Protein ID 143979 ) of 1231 amino acids contains the conserved UvrD-like helicase C-terminal domain with high similarity to that present in SAD-3 , as well as a DEAD-like helicase superfamily domain and a putative zinc binding domain ( Fig 4A ) . rnhA gene and its location are conserved throughout all of the closely related fungal species . In all of these fungi the gene is located in close proximity to the sex determining genes sexM and sexP and is the first gene in the 3’ flanking region of the sex locus . In some Mucor sub-species the promoter of the rnhA gene lies within the sex specific region of the genome ( Fig 4B , [22] ) . rnhA gene expression is elevated during mating conditions , for example , during co-culture of ( + ) and ( - ) mating type cells or during vegetative growth of either mating type cell when supplemented with the sex pheromone trisporic acid ( S5 Fig ) . However , the expression of the rnhA gene during vegetative growth is not as high as in mating conditions . We found that M . circinelloides RnhA is an ortholog of NcSad-3 and SpHrr1 ( S6 Fig ) . McRnhA and SpHrr1 are reciprocal best hits in a blast analysis . In addition , SpHrr1 and NcSad-3 are also reciprocal best hits ( S6A Fig ) . NcSad-3 hit McRnhA first; on the other hand , McRnhA hit NcSad-3 third in the blast analysis , with two uncharacterized proteins , NCU09357 and NCU05861 , as the first and second hits , respectively . Interestingly , when the three putative N . crassa RNA helicases were blasted against the M . circinelloides genome , they hit McRnhA as the most homologous protein . Subsequent phylogeny analysis indicated that the RnhA proteins in three Mucorales fungi are closely related to NcSad-3 and SpHrr1 ( S6B Fig ) , and NCU09357 and NCU05861 are more distantly related to the RnhA/Sad-3/Hrr1 cluster . It is possible that N . crassa Sad-3 , NCU09357 , and NCU05861 are paralogs . The M . circinelloides genome encodes another putative RNA helicase , e . gw1 . 14 . 1 . 1 , which is also distantly related to the RnhA/Sad-3/Hrr1 cluster . Our further analysis revealed that the three putative RNA helicases share key helicase domains ( S6C Fig ) . These results demonstrate that M . circinelloides RnhA is an ortholog of NcSad-3 and SpHrr1 . Aspergillus fumigatus AFUA5G09090 lies within the RhnA/Sad-3/Hrr1 cluster , suggesting a potential role of this protein in RNA silencing in this fungus . To identify a role for the putative RnhA helicase in RNAi in Mucor , an rnhA null mutant was generated by gene replacement ( S7 Fig and description of plasmids and generation of deletion mutants in S1 Supporting Information ) . Following 8 transformations and analysis of 357 transformants , one null mutant was obtained , which was named MU437 . That correct gene replacement by homologous recombination and no ectopic integration events had occurred were confirmed by PCR and Southern blot analyses ( S7 Fig and Methods ) . The response of this rnhAΔ mutant to the activation of the different RNAi pathways was studied and compared to the wild type strain . First , the rnhAΔ mutant and the wild type strain were transformed with the two different silencing triggers described above , pMAT1279 and pMAT1253 . Only a minor defect was observed for the exogenously activated RNA silencing pathway , as the number of white colonies in which the carB gene had been silenced , leading to a loss of carotene production in light , was only modestly reduced compared to the wild type: from 92 to 74% with pMAT1253 and from 79 to 45% with pMAT1279 ( Fig 2 ) . These silenced isolates did not exhibit any difference compared to the wild type silenced isolate with respect to their stability as they remained completely white after several vegetative cycles . These results suggest a minor role , if any , for this helicase in the canonical RNAi pathway operating during vegetative growth . Based on its expression pattern ( S5 Fig ) , it is possible that RnhA may play roles in the canonical RNAi pathway during sexual development . Next the rnhAΔ mutant was grown in the presence of FK506 to test for a role in epimutation-mediated development of drug resistance . After 3 weeks of growth in YPD supplemented with FK506 , 30 drug resistant isolates were recovered and analyzed . None of them resulted from the activation of the epimutation pathway , as evidenced by the fact that 28 of 30 isolates contained Mendelian mutations in the FK506 target genes ( fkbA and calcineurin genes ) . For only two of the isolates , were we unable to find Mendelian mutations in the FK506 target genes; one was FK506 resistant but rapamycin sensitive which , as noted above , rules out silencing of the fkbA gene; the second one has a probable insertion due to the lack of product from PCR amplifications of the gene region ( Table 1 and S1 Table ) . These results indicate that despite its minor role in exogenously activated RNAi silencing , the RnhA helicase therefore plays an essential role in the epimutation mechanism of M . circinelloides . It is intriguing that the rnhA gene is expressed at a low level ( S5 Fig ) during vegetative growth , yet plays an important role in epimutation . It is therefore possible that epimutations may be evoked at a higher frequency during mating when rnhA is more highly expressed . Because of the proximity of the rnhA gene to the sex locus ( Fig 4B ) , we analyzed the possible contribution of the sexM and sexP genes to the RNAi silencing pathways in M . circinelloides . First we tested the effects of the sexMΔ null mutation ( strain MU423 , [23] ) on silencing of the carB hairpin and the carB sense transgenes as described above . No differences were found between the wild type strain and the sexMΔ mutant in the exogenously activated silencing frequencies ( Fig 2 ) . Next , the sexMΔ null mutants MU423 and MU424 were analyzed for the development of FK506 resistance . Following incubation in the presence of FK506 , 15 ( from MU423 ) and 16 ( from MU424 ) isolates were sequenced from which 12 and 13 isolates respectively , were found to have Mendelian mutations in the fkbA gene . The remaining resistant isolates showed accumulation of antisense fkbA sRNAs ( S8 Fig ) . The frequency of epimutations arising in the sexMΔ null mutant background is therefore 19 to 20% , similar to that observed in the R7B wild type strain ( [4]; Table 1 ) . These results rule out any roles for sexM in silencing of exogenous transgenes or silencing mediating epimutational drug resistance during vegetative mitotic growth . There are two known RdRP proteins in M . circinelloides . First , RdRP1 is: 1 ) responsible for dsRNA synthesis from sense transgenes in the canonical RNAi pathway ( [18]; 2 ) required for the production of different classes of endogenous siRNAs ( [6]; and 3 ) the most important element in the RdRP-dependent Dicer-independent degradation mechanism of endogenous mRNA [5] . Second , RdRP2 is essential for the transgene-induced silencing mechanism because of its prominent role in the amplification of the silencing signal [18] . However , it has only a minor role in the endogenously induced silencing and the RdRP-dependent Dicer-independent degradation mechanism [5 , 6] . RdRP2 is also essential for the epimutation pathway [4] , probably because amplification of the signal is necessary to activate and maintain silencing of the target gene . RdRP1 was found to have an unexpected negative role in the epimutation pathway [4] . Regardless , both mutant strains , rdrp1Δ and rdrp2Δ , were able to produce an antisense mRNA complementary to the fkbA mature mRNA , which is hypothesized to have a role in the activation of the epimutation pathway by binding to the fkbA mRNA and generating a dsRNA molecule as a trigger for RNAi [4] . Given the opposing roles of the two RdRPs in the epimutation mechanism , it seemed unlikely that they collaborate to generate the antisense mRNA and thus , the existence of a third RdRP protein was postulated . The RdRP2 amino acid sequence was employed to blast version 2 . 0 of the translated genomic sequence of M . circinelloides ( see above ) , which was not available when RdRP2 was identified [18] . A third rdrp gene was identified , which was named rdrp3 ( ID 80729 , previously named as r2d2 ) . There are five introns in the 3947 bp rdrp3 coding region that were confirmed by cDNA sequencing and comparison with the genomic sequence . The deduced 1210 aa protein product contains the conserved RdRP domain that extends from position 407 to 944 ( PFAM 4 . 84e-101 ) . This putative protein shares 30% identity and 48% similarity with RdRP2 and 17% and 29% , respectively with the RdRP1 protein . To analyze the role of this third RdRP protein in the different RNAi pathways in M . circinelloides , an rdrp3Δ null mutant was generated by gene replacement ( S9 Fig and description of plasmids and generation of deletion mutants is presented in S1 Supporting Information ) . Four independent rdrp3 deletion mutant strains were obtained ( named MU438 , MU439 , MU440 , and MU500 ) . One of the rdrp3Δ mutant strains ( MU500 ) was transformed with the same two self-replicative silencing vectors described above , and its exogenously activated silencing response to different silencing triggers was analyzed . After subculture of the original transformants on YNB pH = 4 . 5 solid medium and incubation for 4 to 5 days under the light as described previously [18] , the number of white/orange colonies was compared between the rdrp3Δ and the wild type strain . No difference was found between these two strains ( Fig 2 ) in the activation of the transgene-induced silencing with any of the trigger constructs . These results suggest that , differently to RdRP1 and RdRP2 , RdRP3 does not have a role in either the activation or the amplification leading to transgene-induced silencing . Next , three rdrp3Δ mutant strains ( MU439 , MU440 , and MU500 ) were exposed to FK506 to examine the role of the RdRP3 protein in the epimutation pathway . The development of FK506 resistance was more rapid in these mutants than in the wild type strain , and comparable to the result observed in the r3b2Δ mutant strain . Following sequencing of the target genes in the different FK506 resistant isolates , only a few ( 7/64 ) were found to harbor Mendelian mutations , while most ( 57 out of 64 , >80% , Table 1 and S1 Table ) exhibited distinctive epimutation phenotypes including production of antisense fkbA sRNAs ( Fig 5A , quantified by densitometry in Fig 5B ) to result in silencing of fkbA gene expression . These results illustrate that , similar to RdRP1 and R3B2 , the RdRP3 protein plays a repressing role in the epimutation pathway . Given that RdRP1 and R3B2 are the principal components of the non-canonical RdRP-dependent Dicer-independent RNA degradation pathway , the participation of RdRP3 in this mechanism was investigated . Towards this end , we analyzed mRNA accumulation from representative loci regulated by this pathway in the wild type , dicer1Δ/2Δ , r3b2Δ , rdrp1Δ , rdrp2Δ and rdrp3Δ mutants by Northern blot analysis of RNA samples , isolated from cultures grown for 24 hours in liquid MMC medium . Two different target genes , P1 and P2 , whose transcript accumulation is regulated by the rdrp-dependent dicer-independent degradation pathway , were selected for analysis [5] . As demonstrated before [5] , lack of function of the non-canonical RNA degradation pathway in the rdrp1 , rdrp2 , and r3b2 null mutants resulted in an increased mRNA accumulation of the target genes , compared to the wild type and dicer null mutants ( Fig 6A and 6B ) . Similarly , both tested mRNAs were also up-regulated in the rdrp3Δ mutant , in which accumulation of both mRNAs increased more than two-fold compared to the wild type and dicer null mutant . This suggests that RdRP3 plays a crucial role in the degradation of specific mRNAs via the rdrp-dependent dicer-independent non-canonical RNA silencing pathway . In order to complete the genetic dissection of the rdrp-dependent dicer-independent non-canonical RNA silencing pathway , the role of the qip and rnhA genes that were found to be essential in the epimutant pathway was also studied in the first pathway ( Fig 6C and 6D ) . These studies showed a significant increase in the expression of the two target genes P1 and P2 in the rnhA mutant , which reveals a new role for this gene in positively controlling the non-canonical pathway . It has been shown that neither RdRP1 nor RdRP2 are individually necessary to generate the antisense strand that is thought to allow M . circinelloides to activate fkbA epimutation [4] . The putative role of RdRP3 in the production of antisense fkbA RNA was therefore tested . The rdrp3Δ mutant and wild type strains , as well as all of the other strains used in this study , were grown 48 hours on MMC pH = 4 . 5 and total RNA was extracted from the mycelia . The RNA was analyzed by northern blot , hybridizing under stringent conditions with an fkbA antisense specific probe ( see Methods ) . A single mRNA with the expected size of the fkbA mRNA was detected in all of the strains tested ( including the rdrp3Δ mutants ) except the fkbAΔ mutant ( S10 Fig ) . Because the antisense fkbA RNA is generated from the sense fkbA RNA as a template [4] it should therefore be produced by an RdRP activity . Because none of the three known RdRPs present in Mucor are essential for the synthesis of the antisense fkbA strand , the generation of this molecule is either attributable to redundant action of some combinations of RdRP1 , 2 , and 3 or to an as yet unknown RdRP . In a previous study [4] , we demonstrated that the epimutation pathway was active in not only the M . circinelloides f . lusitanicus lab strains ( NRRL3631 and R7B ) but also in a M . circinelloides f . circinelloides wild type strain ( 1006PhL ) , which is considered a distinct species because of mating barriers and phylogenetic separation [4] . Interestingly , this strain ( 1006PhL ) showed an increased rate of fkbA epimutant silencing ( 90% ) , comparable to that obtained in the rdrp1Δ , rdrp3Δ , and r3b2Δ null mutants . To generalize this phenomena , we analyzed fkbA silencing induced by FK506 in other wild type M . circinelloides f . circinelloides strains: IP1873 . 89 [24] , CNRMA03 . 154 [24] , NRRL3615 [25] , and NRRL3614 [25] . Three ( IP1873 . 89 , NRRL3615 , and NRRL3614 ) out of the four strains showed a surprisingly high phenotypic plasticity similar to that detected in 1006PhL . The FK506 resistant isolates appeared readily and filamentous growth quickly appeared from the edge of the yeast colony in each subculture , usually from more than one place in each colony ( only those isolated from different petri dishes were considered independent epimutants ) . Except for the CNRMA03 . 154 strain , which did not show any epimutant isolates , a high number of the isolates from the other three different backgrounds ( 84% in IP873 . 89 , 76% in NRRL3615 , and 96% in NRRL3614 ) showed the presence of fkbA sRNAs , indicating activation of the epimutation pathway ( Table 2 , S11 Fig , and S1 Table ) .
RNAi mechanisms are both complex and conserved . Despite extensive investigations into the roles of RNAi in gene regulation , novel pathways and functions continue to be discovered . In fungi , two of these novel regulatory RNAi pathways are the non-canonical RdRP-dependent Dicer-independent silencing pathway and the endogenous silencing of mRNA via epimutations , and both of these pathways operate in M . circinelloides . The first mechanism has been recently described as a non-canonical RNAi pathway involved in an mRNA degradation mechanism and its main unique feature is that the rdrp genes are required but not the Dicer ribonucleases . Instead , the ribonuclease activity is provided by the new protein R3B2 , an RNase III-like protein that features a unique domain architecture specific to basal fungi and plants [5] . The profiling of sRNAs in the wild type and silencing mutants showed that this silencing mechanism controls the expression of target genes through the specific degradation of mRNAs by R3B2 . This new role of RdRPs in the degradation of RNA could represent an evolutionary link between mRNA degradation and RNA silencing [5] . The second mechanism is an epigenetic RNAi-mediated pathway that was revealed as a new adaptive mechanism . This pathway controls phenotypic plasticity by silencing key genes to produce an epigenetically modified offspring that is better adapted to new environmental conditions . It is a remarkable mechanism that can readily respond to changes in the environment and rapidly produce a temporally adapted offspring . The main advantage of this adaptive mechanism is its reversibility [4 , 7] . Thus , if changes in the environment are punctate anomalies and continued growth enables escape from a stressful condition , the new phenotype can reverse to the wild type . When the change is permanent , the temporally adapted population presents an outstanding resistance to the new condition , and thereby , a higher probability to establish new members in the population through classical mutational evolution . Genetic analysis revealed that RdRP2 , Ago1 , and both Dicer proteins are required for the epigenetic RNAi mechanism , whereas RdRP1 had an unexpected role constraining epimutation silencing [4] . This novel role of RdRP1 suggested that the epimutation pathway might be controlled by other genetic elements that repress the generation of epimutations under unknown conditions . To elucidate the molecular mechanisms that regulate the epimutation pathway , we conducted a genetic dissection of newly identified genes that could be involved in the different RNAi pathways of M . circinelloides . Among these genes , we have analyzed a quelling induced protein ( qip ) , a Sad-3 helicase ( rnhA ) , a new RdRP protein ( rdrp3 ) , and a novel ribonuclease involved in the non-canonical RNAi pathway ( r3b2 ) . To establish the relationship between the non-canonical RNAi and the epimutation pathways , we first analyzed the ribonuclease protein R3B2 . Our results showed a central role of R3B2 in all of the RNAi pathways described in M . circinelloides . Thus , in addition to the essential role of R3B2 in the regular vegetative RNAi and non-canonical RNAi pathways [5] , we showed that r3b2 mutation results in an increase in the epimutation frequency similar to that detected in rdrp1 mutants [4] . These results indicate that both R3B2 and RdRP1 are required to activate the non-canonical RNAi pathway and suggest a constraining role of this pathway over the epimutation pathway . In support of this hypothesis , similar to r3b2 and rdrp1 mutants , mutant strains lacking rdrp3 , a newly identified RDRP encoding gene , showed a blocked non-canonical pathway and an overactive epimutation pathway , indicating the same constraining role observed above . There are therefore three RdRP proteins operating in M . circinelloides . RdRP2 amplifies the different silencing signals [4 , 18] . RdRP1 activates the exogenously induced silencing mechanism by generating the strand complementary to transgenes introduced in Mucor , and in addition represses the epimutation pathway [4 , 18] . RdRP3 plays only a minor role in the canonical silencing pathway; however , it also represses the epimutation pathway . Surprisingly , despite their different contributions to these silencing pathways , the three rdrp genes have an essential role in the non-canonical dicer-independent RNA silencing mechanism , as all of the rdrp-defective mutants show a marked reduction of mRNA degradation from the two highly expressed genes analyzed ( P1 and P2 , Figs 6 and 7 ) . The functional analysis of rdrp1 , rdrp3 , and r3b2 indicates an essential role of these genes in the non-canonical RNAi pathway . Interestingly , this analysis revealed that rdrp1 , rdrp3 , and r3b2 mutants exhibiting a blocked non-canonical RNAi pathway also showed an enhanced epimutation RNAi pathway . The opposing actions of the non-canonical RNAi and the epimutation pathways could be explained by a competition between these two pathways for aberrant RNA ( aRNA ) . In this competition , success might be determined by the nature and severity of the triggering signal/stress and the ability of the activated RNAi pathway to cope with it . Similarly , a competition for aRNA between mRNA decay pathways and RNAi has been proposed in other organisms such as plants , yeasts , and flies [26–29] . In these systems , mutations in key elements of the mRNA decay machinery leads to an increased activity of the RNAi pathways , suggesting that regular clearing of aRNAs limits their entry into the silencing pathways . An alternative RNA degrading pathway was found in Caernorhabditis elegans , which also regulates the efficiency of the RNAi pathway , although the targets of this pathway are the mature siRNAs instead of the initial aRNA [30 , 31] . In M . circinelloides , the non-canonical pathway acts as a clearing machinery degrading aRNAs and reducing the expression of the target genes whereas the epimutation pathway processes the aRNAs into signaling siRNAs that completely block the expression of the target genes . As previously shown , the epimutation pathway provides phenotypic plasticity to allow rapid adaptation to challenging environments , which suggests that this pathway might control aRNAs under stress conditions . In this regard , we found an increased production of drug-resistant epimutants in three virulent isolates ( including a human isolate ) ( IP1873 . 89 , NRRL3615 , and NRRL3614 ) , which exhibit virulence in a murine model [25] ( Table 2 ) . M . circinelloides f . circinelloides is the Mucor species more frequently found associated with human infection [4] . The enhanced ability to develop FK506 resistance exhibited by three out of the four analyzed strains is consistent with our model that the epimutation pathway may enable adaptation to environmental niche conditions . More studies are needed to determine why these strains have an elevated frequency ( 76–96% ) of epimutation when compared with M . circinelloides f . lusitanicus , which usually shows a frequency of 20–30% in the wild type strains . To complete the genetic analysis of RNAi genes that could be involved in the generation of epimutants , two other genes , qip and rnhA , were functionally tested in the canonical RNAi , the epimutation pathway and the non-canonical RNAi pathway . The QIP exonuclease is an essential component of both the vegetative RNAi and MSUD pathways in N . crassa [14 , 15] . Its fundamental role in the activation of the RISC complex with the production of single stranded siRNAs suggested that it also plays an important role in the different RNAi pathways of M . circinelloides . Our results demonstrate that the M . circinelloides QIP ortholog plays an essential role in both the vegetative RNAi and the epimutation pathways . A qip mutant is incapable of producing either regular siRNAs in response to dsRNA-trigger or drug-resistant epimutants following exposure to FK506 . These results indicate that QIP could also play a role in the production of mature siRNA and activation of the RISC complex in collaboration with Ago proteins , which were shown to be essential in both the vegetative RNAi and the epimutation pathways in M . circinelloides [4 , 16] . Our genetic analysis demonstrated that , similarly to QIP , the RNA helicase RnhA plays an essential role in the epimutation mechanism of M . circinelloides , as no FK506 resistant epimutants were detected in the rnhAΔ background . In contrast , RnhA plays a minor role in exogenously activated RNAi silencing , suggesting the existence of other RNA helicases specific to this pathway . Surprisingly , the RnhA helicase showed an important role in positively controlling the rdrp-dependent dicer-independent RNAi pathway , which adds a new component to the already complex non-canonical pathway . RNA helicases are ATP-dependent enzymes that unwind dsRNA , suggesting RnhA involvement in the separation of the two strands of the siRNAs and therefore a cooperative role with QIP and AGO proteins during the production of mature siRNAs . However , recent studies showed that the RNA helicase activity of these enzymes might not be required , and have suggested a more extensive function at every level of the RNAi pathway [32] . Our findings implicate RnhA in the production of epimutants in M . circinelloides and the rdrp-dependent dicer-independent RNAi pathway , expanding the role of RNA helicases in the RNAi mechanism . Interestingly , the rnhA gene is more highly expressed during mating , indicating that RnhA might have a sexual development specific role in the canonical RNA silencing that is not observed during vegetative growth . In addition , overexpression of RnhA during mating might result in a higher frequency of epimutations . Future investigations will address the roles of RnhA in the RNAi pathway during mating . In summary , our genetic and functional analysis identified four new RNAi pathway components including R3B2 , RdRP3 , Qip , and RnhA , which expands the complexity of RNAi mechanisms and pathways operating in M . circinelloides ( Fig 7 ) . Remarkably , our study revealed opposing roles for the non-canonical and epimutation RNAi pathways in M . circinelloides and delineates their functions during optimal growth conditions or in generating phenotypic plasticity under stress conditions , respectively . Accordingly , we found that clinical or human associated isolates show a favored epimutation RNAi pathway , suggesting that selective pressure of the host-pathogen interaction might promote the phenotypic plasticity provided by this pathway . These results provide a comprehensive study of the mechanisms developed by emerging pathogens to acquire antifungal drug resistance , which may also impact the host-pathogen interaction .
The leucine auxotroph R7B , derived from the ( - ) mating type M . circinelloides f . lusitanicus CBS 277 . 49 ( syn . Mucor racemosus ATCC 1216b ) , was used as the WT strain . Strain MU402 that was used to generate the deletion mutants is a uracil and leucine auxotroph derived from strain R7B [33] . All of the mutants used and generated in this study were derived from strain MU402 . Strains MU412 and MU429 are independent deletion mutants for the r3b2 gene [5] . Strains MU423 and MU424 are independent deletion mutants for the sexM gene [25] . Strains MU419 and MU420 correspond to rdrp1Δ and rdrp2Δ mutants , respectively [18] . Cultures were grown at ~25°C on yeast extract peptone dextrose agar ( YPD , 10 g/l yeast extract , 20 g/l peptone , 20 g/l dextrose , 2% agar ) , MMC medium pH = 4 . 5 ( 1% casamino acids , 0 . 05% yeast nitrogen base without amino acids and ammonium sulfate , 2% glucose ) or minimal YNB media as described previously [4 , 33] . Media was supplemented with uridine ( 200 μg/ml ) , leucine ( 20 μg/ml ) , FK506 ( 1 μg/ml ) or rapamycin ( 100 ng/ml ) when required . The cultures were routinely incubated for 48 hours except when noted otherwise or for the isolation of FK506 resistant patches , when the cultures were incubated for as long as 3 weeks in some cases . Transformation was carried out as described previously [34] . Because primary transformants are heterokaryons due to the presence of several nuclei in the protoplasts , to increase the proportion of transformed nuclei transformants were grown in selective medium for several vegetative cycles . M . circinelloides f . circinelloides strains: IP1873 . 89 was collected from human feces and CNRMA03 . 154 from human skin in France . Contributed by Francois Dromer at Institut Pasteur [24] . NRRL3615 was obtained from beef in Germany and NRRL3614 from pig feces in the Netherlands . Provided by Wiley Schell from Duke University [25] . 1006PhL was collected from human skin in the USA and provided by Julie Segre at NIH [22] . Competent cells of E . coli DH5α strain were used for cloning experiments . FK506 resistant isolates were obtained as described previously [4] . The different strains were grown on YPD containing 1 μg/ml of FK506 for 3 days to 3 weeks at room temperature ( ~25°C ) , until patches with hyphal growth were observed . Each isolate was derived from an independent subculture grown on a different Petri dish . The FK506 resistant isolates were grown in the presence of FK506 for at least 2 vegetative cycles to ensure that a high proportion of the nuclei in the mycelial syncytium were mutant or silenced before being analyzed . A PCR-based strategy was used for gene cloning and generation of deletion alleles to disrupt the qip , rnhA , and rdrp3 genes . A precise description of the constructs and the procedures used can be found in the S1 Supporting Information . Genomic DNA from M . circinelloides mycelia grown for 48 hours on MMC media pH = 4 . 5 ( supplemented with 1 μg/ml of FK506 when needed ) was extracted with phenol-chloroform or CTAB-chloroform as previously described [4 , 33] . The FK506-resistant isolates obtained from the different mutant backgrounds were verified by junction PCR for the deletion of the corresponding gene . To identify correctly integrated deletion alleles in the candidate transformants , a rapid protocol for PCR assay was used [33] . Total RNA was isolated from liquid nitrogen frozen mycelia ( 100 mg ) using Trizol reagent following the supplier’s recommendation ( Invitrogen ) , or the RNeasy Plant Mini Kit ( QIAGEN ) . Small RNA was isolated as described previously [4] , or using the miRVana kit ( Ambion ) following the supplier’s instructions . Southern and Northern blot hybridizations were carried out under stringent conditions [33] . DNA probes were labeled with [α-32P] dCTP using Ready-To-Go Labeling Beads ( GE Healthcare Life Science ) . For Northern blot hybridization of the qip gene , a 1 . 05 kb fragment isolated from plasmid pMAT1501 ( S1 Supporting Information ) by ScaI/HindIII double digestion was used . Probe ‘a’ from the qip gene used for Southern blot hybridization corresponds to a 1 . 1 kb fragment PCR amplified with primers Qip1 and Qip3 from plasmid pMAT1502 that contains the deletion allele for the qip gene ( S1 Supporting Information ) . Probes 1 and 2 for the rnhA gene used for Southern blot hybridization correspond to 1 kb fragments that were PCR amplified using the primer pairs rnh-767/sexM1750 and rnh5672/rnh-6713 , respectively . P1 and P2 probes for Northern blot hybridizations shown in Fig 5 were directly amplified from genomic DNA using specific primers ( S2 Table ) . Sequencing of the fkbA and calcineurin genes was carried out as described before [4] . rdrp3 cDNA was generated from RNA extracted with the RNeasy Plant Mini Kit following the protocol for filamentous fungi supplied by the distributor . The FirstChoice RLM-RACE kit ( Ambion ) was used for cDNA amplification of the 5’ or 3’ half region of the gene . Gene-specific intron-adjacent primers used for confirmation are listed in S2 Table . Sequences were aligned with Serial-Cloner software . Signal intensities were estimated from autoradiograms using a Shimadzu CS-9000 densitometer and the ImageJ application , an open source image analysis program ( rsbweb . nih . gov/ij/ ) . Detection of sRNAs in Northern blot experiments was carried out as described [5 , 35] . sRNAs ( 25–35 μg ) were separated by electrophoresis on 15% TBE-Urea gels ( Invitrogen ) , electrotransferred to Hybond N1 filters at 400 mA for 1 h in 0 . 5X TBE , and cross-linked by ultraviolet irradiation ( 2 pulses at 1 . 2x105 μJ per cm2 ) . Ultrasensitive hybridization buffer UltraHyb ( Ambion ) was used for prehybridization and hybridization . Antisense-specific riboprobes were prepared by in vitro transcription ( MAXIscript transcription kit; Ambion ) and labeled with [α-32P] dUTP following supplier-recommended protocols . Riboprobes were treated as described previously [7] to result in an average size of 50 nucleotides ( nt ) . An antisense-specific probe for the fkbA gene was amplified from genomic DNA with primers JOHE23654 and JOHE23559 and in vitro transcribed from the T7 promotor contained within the JOHE23654 primer sequence ( S2 Table ) . This in vitro transcribed probe was also used to detect antisense fkbA mRNA in the different strains by Northern blot ( S10 Fig ) . The carB antisense-specific riboprobe was obtained from the in vitro transcription of linearized plasmid pMAT652 [24] . JOHE37682 and JOHE37683 were used to amplify the 5S rRNA from genomic DNA and in vitro transcribed from the T7 promotor contained in both primer sequences ( S2 Table ) . Computational sequence analysis was carried out using European Bioinformatics Institute Server software ( EMBL Outstation , Hinxton , U . K . ) , and the National Center for Biotechnology Information Server ( NCBI , Bethesda , MD , USA ) . | Mucormycosis is a fungal infection that is attracting the attention of both clinical and research communities because of the lack of effective antifungal treatments and its often fatal prognosis . Our previous studies revealed an RNAi-mediated epimutation mechanism that operates in the casual human fungal pathogens ( Mucorales species ) and which might underlie the lack of efficacy of some antifungal treatments . This epimutation mechanism represses the expression of antifungal drug target genes and thereby generates antifungal drug resistant strains . Here , we studied the regulation and identified new components of the epimutation pathway . We found that a newly identified mRNA degradation pathway , named the non-canonical RdRP-dependent Dicer-independent silencing pathway , exerts an inhibitory effect on the RNAi-mediated epimutation mechanism and operates during growth under non-stressful conditions . Interestingly , the RNAi-based epimutation mechanism is more active in M . circinelloides f . circinelloides isolates from human and other animal sources , suggesting that this mechanism may influence host-pathogen interactions . These results further our understanding of the mechanisms deployed by fungal pathogens to survive and adapt under stressful environmental conditions that may include the host niche . | [
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] | 2017 | A non-canonical RNA degradation pathway suppresses RNAi-dependent epimutations in the human fungal pathogen Mucor circinelloides |
The use of autozygosity as a mapping tool in the search for autosomal recessive disease genes is well established . We hypothesized that autozygosity not only unmasks the recessiveness of disease causing variants , but can also reveal natural knockouts of genes with less obvious phenotypic consequences . To test this hypothesis , we exome sequenced 77 well phenotyped individuals born to first cousin parents in search of genes that are biallelically inactivated . Using a very conservative estimate , we show that each of these individuals carries biallelic inactivation of 22 . 8 genes on average . For many of the 169 genes that appear to be biallelically inactivated , available data support involvement in modulating metabolism , immunity , perception , external appearance and other phenotypic aspects , and appear therefore to contribute to human phenotypic variation . Other genes with biallelic inactivation may contribute in yet unknown mechanisms or may be on their way to conversion into pseudogenes due to true recent dispensability . We conclude that sequencing the autozygome is an efficient way to map the contribution of genes to human phenotypic variation that goes beyond the classical definition of disease .
Autozygosity , or the biparental inheritance of the identical founder haplotypes , is a genomic signature of a limited reproductive pool as in the case of populations with a high rate of consanguinity or a significant founder effect; although it is also observed but at a much smaller scale in outbred populations [1] . Historically , autozygosity has been used as a mapping tool in the search for autosomal recessive disease genes variants because it unmasks the recessiveness of mutations in these genes . It facilitates variant identification because the entire set of autozygous intervals ( autozygome ) can be mapped as homozygous blocks upon genome wide genotyping using polymorphic markers [2] , [3] . More recently , autozygome analysis became a useful clinical diagnostic tool . Others have even shown that the occurrence of heterozygous markers within the autozygome can point to de novo events thus facilitating the calculation of the frequency of such events in the human genome [4] , [5] . Although the role of autozygosity in unmasking disease-causing recessive alleles is well established , little attention has been paid to its potential in unmasking recessive alleles that do not result in human disease . In a proof of concept paper , we have shown that studying copy number alterations within the autozygome can uncover entire segments of DNA that are nullizygous i . e . completely absent in apparently healthy individuals [6] . Many of these segments span genes , which raises interesting questions about the extent to which humans tolerate the complete loss of function of some protein-coding genes [6] . While systematic mapping of “non-essential” genes has been possible in lower organisms [7] , a different approach is needed in humans by searching for naturally occurring inactivating mutations . This has only been possible recently , thanks to the advent of next generation sequencing that allows the unbiased examination of all genes in an individual . Indeed , a recent study by the 1000 Genomes Consortium has provided valuable insight into the occurrence of loss of function ( LoF ) variants in “healthy” humans [8] . However , most of the LoF variants reported in that study were in the heterozygous state so those individuals who harbor them do not represent a true “knockout” of the genes involved . On the other hand , LoF variants within the autozygome may fully inactivate the involved genes due to their biallelic presence in a homozygous state . Thus , next generation sequencing of individuals born to first cousin parents ( in order to maximize the size of sequenced autozygome ) is expected to enrich for the occurrence of homozygous LoF variants , which can then be studied in the context of phenotypic consequences . The systematic identification of genes with biallelic LoF variants will provide an important resource that is likely to inform various lines of investigation into the human genome . First , the fundamental question of “what constitutes the bare minimum genetic material to sustain life” which is now being asked at the level of single cells in the context of synthetic biology is only going to grow more relevant to more complex forms of life with time [9] . Cataloging genes with a presumed complete knockout and yet are compatible with live birth and at least early survival of human subjects will help address this question . Second , with the race to crack the code of Mendelian genes at its highest pace , it is extremely helpful for researchers to have a catalog of genes that can accumulate biallelic loss of function without causing a discernible Mendelian phenotype . Many labs are doing this individually by putting less emphasis on genes they frequently observe to accumulate inactivating mutations . However , the ability to identify such low priority genes relies heavily on the frequency of such inactivating mutations and genes that only rarely accumulate inactivating mutations will require a prohibitively large number of individuals to be screened . Even then , finding the rare individual carrier of one such allele is not very helpful because his/her presumed benign haploinsufficiency does not rule out an adverse outcome caused by a biallelic loss of function of that same gene . On the other hand , even very rare LoF variants can effectively become homozygous through autozygosity , thus making autozygome sequencing an appealing shortcut to identify such rare homozygous occurrences without resorting to sequencing a very large number of individuals . Third , with so much debate about the role of rare variants in shaping the risk for common diseases , we believe that the identification of biallelic loss of function mutations in some genes can accelerate their identification as risk loci , as we have shown recently for systemic lupus erythematosus and inflammatory bowel disease [10] , [11] . In an attempt to test the effectiveness of autozygome sequencing in addressing the above questions , we conducted exome sequencing of 77 well phenotyped individuals all born to first cousin parents . This approach provided the highest yield of LoF variants per individuals tested , and revealed a list of biallelically inactivated genes comparable to the list published by the 1000 Genomes Consortium with less than half the number of individuals studied by the consortium ( 77 vs . 185 ) and with a very strict definition for loss of function . Further , we show that many such genes may participate in modulating important phenotypic aspects and are therefore important candidates in human phenotypic variation .
In total , 77 individuals were enrolled in this study . As expected from the consanguineous nature of the parents , there was preponderance of autozygosity in these individuals ( on average 7 . 7% per genome , ranging from <1% to 21 . 6% , based on ROHs 2MB or longer ) . Since this is the largest collection of exome data on Arabs , and given the intense interest in learning about Arab-specific SNPs , particularly in the homozygous state , to inform future disease mapping projects involving Arab patients , we list all autosomal SNPs and indels detected in our cohort in Supp . Table S1 . The focus of this study , however , is on genes that appear to be fully inactivated as a result of harboring biallelic LoF variants . The NGS reported over 6 . 2 million SNPs and indels across the 77 exomes . Of these we identified 214 , 774 alleles that were homozygous variants in autosomal chromosomes . However , only 678 candidate alleles fit our criteria for a complete LoF allele: either a frameshifting insertion or deletion or a stop-gain SNP . We specifically chose not to consider other highly disruptive genetic mutations so we could focus solely on what can be considered a full knockout of the gene . Upon closer manual inspection of the candidate alleles ( see methods ) , many were found to potentially be artifacts and were excluded: some arose from the NGS pipeline but many also appeared to be sequence errors or rare variants in the reference genome itself ( listed in Supp . Table S7 ) . We also excluded alleles where the candidate LoF variant interacted with other mutations such that the overall effect was more local than expected ( for example: two complementary frameshifting indels in nearby positions , or a stop-gain SNP together with another SNP in the same codon ) . We then performed over a thousand Sanger sequencing reactions and manually examined sequencing trace files to confirm the variants reported by NGS ( see methods ) . Combining the output of several computational and experimental highly conservative filters , we report 175 highly reliable LoF variants found in 169 distinct genes in 77 individuals , at an average of 22 . 8 knocked out genes per genome . Most genes had a single LoF variant but some had two ( Supp . Table S2 ) . In order to rank the potential for the reported allele to actually fully ablate the expected gene product , we manually assigned each LoF variant a simple integer score from 1 ( least likely ) to 3 ( most likely ) . The score is essentially based on the location of the allele within the gene's open reading frame ( ORF ) and whether or not the allele was in an alternatively spliced exon ( see methods ) . More than 53% of our reported variants have a score of 3 and only about 7% fall in the group least likely to fully knockout the gene function ( due to the presence of unaffected alternative transcripts and that the mutation only affects the last 10% of the ORF for the affected transcript ) . Clearly , the biological impact of an LoF variant is also strongly affected by the cellular state and environment . However , those are elements which are well beyond the scope of this work . As expected , most of the homozygous LoF variants were low in frequency ( median: 3 . 9% ) and were significantly lower in frequency than nonsynonymous ( median: 6 . 5% , p<0 . 0001; Mann-Whitney non-parametric test ) and synonymous ( median: 9 . 1% , p<0 . 0001; MW test ) variants observed in the same gene set ( Figure 1 ) . Because homozygous LoF variants are expected to be found within a block of homozygous sequence ( a run of homozygosity; ROH ) we examined the relationship between the enrichment for homozygous LoF alleles and ROH length; especially longer ROH since these are more likely to be autozygous ( identical-by-descent; IBD ) compared to the shorter ROH ( possibly arising from homozygosity for long , but common haplotypes , less likely to contain LoF mutations , or through identity-by-state; IBS ) . Starting from ROHs 0 . 7 megabases ( Mb ) or longer we found homozygous LoF allele enrichment to be 100% higher than random expectation ( that is , if LoF alleles were distributed evenly randomly across the individuals' genomes without regard to ROHs ) , reaching a peak of >110% for ROHs 1 Mb or longer and leveling at about 60% above random expectation for ROH lengths of 7 Mb or longer ( Figure 2 ) . Specifically , when restricting ROHs to a minimum length cutoff of 2 Mb we find that 14 . 4% of all our confirmed homozygous LoF alleles reside within an ROH block that is likely autozygous . Considering that these ROHs span only about 7 . 4% of the entire genome size ( averaged over all individuals ) , the enrichment above random expectation is thus 95% ( Figure 2; see supp . methods for details ) . Because autozygosity can render homozygous variants that arose as recent as two generations ago , we hypothesized that LoF variants within the autozygome are more likely to be private or at least of lower frequency compared to those outside the autozygome and that was indeed confirmed ( median freq . : 11 . 7% vs . 60 . 4% respectively , p<0 . 0005; MW test . Figure 3 ) . Given the detailed phenotyping of the individuals in our cohort , we expected to see no additional homozygous LoF variants in genes known to cause Mendelian disorders , other than those that we had identified as causative for each individual . However , seven genes were found to harbor an LoF variant and were also listed in OMIM as causative of autosomal recessive diseases . Yet , the diseases were absent from the individuals carrying the defective genes ( Supp . Table S3 ) . Upon closer examination we found that for the two most frequent alleles ( in VPS13B and SETBP1 ) , the LoF was in fact on an alternatively spliced exon and near the end of the protein , only affecting its C-terminus and so the effect might not be severe or equal in all cells . Interestingly , these alleles were also reported on the Exome Variant Server ( EVS ) at relatively high frequencies and the LoF allele actually recovers the ancestral allele . For the two rarest alleles ( occurring only in one patient each; one in NLRP12 and one in TRPM1 ) we found the LoF to either occur on an alternatively spliced exon or near the end of the amino acid chain . Both alleles were also found on the EVS at very low frequencies . Interestingly , the CLDN16 LoF variant , affecting 6 patients did not seem phenotypic even though it was a single base deletion in the ORF that appeared to affect all transcripts of the gene ( assigned a score of 3 ) . We found this LoF to be also reported in the 1000 Genomes project ( rs56086318; no frequency information ) and on the EVS ( relatively low frequencies ) . It is somewhat close to the start of the protein ( codon 55 of 305 ) at a position that is not highly conserved . In the rat and the wild boar , the orthologous open reading frame starts at a nearby alternative Methionine downstream of the variant , and so this mutation would be in the 5′UTR in these organisms . Additionally , all pathogenic mutations reported in OMIM for this gene were much further downstream in the sequence . We therefore suspect that in this case the protein remains functional even with the presence of what strongly appears to be an inactivating biallelic LoF variant . There are , however , other possibilities , such as a compensating pathway or environmental interaction , which require further focused experiments to resolve . Lastly , in two more genes ( ACY1 and UPB1 ) an LoF highly expected to be effective ( assigned a score 3 ) was detected and confirmed in only one individual each and was never reported in a homozygous state in the EVS . It turns out however that even though the genes were reported in OMIM as causative for certain phenotypes , their pathogenicity was in fact contested in the literature , with reports of many cases where the genes where fully knocked out in patients that lack the described neurological phenotypes [12] , [13] . These two cases provide a good illustration of the complexity of the genotype-phenotype link and the imperfect nature of some causality reports . We examined each of the 169 genes in our list for potential phenotypic consequences as a result of complete inactivation . Interestingly , many of these genes ( >85% ) are indeed predicted to have consequence when inactivated and the resulting phenotypes comprised hematological , immunological , metabolic and external phenotypic variation , among other categories ( Table 1 ) . In addition to the set of fully inactivating biallelic LoF variants , we also examined heterozygous LoF alleles in autosomal recessive disease-associated genes ( ARDGs ) . By combining our NGS data to OMIM annotation , we identified 327 ARDGs with at least one heterozygous LoF variant in at least one of our patients . However , after attempting to validate >70% of the NGS reported alleles by Sanger sequencing , we could confirm LoF variants in only 43 genes ( Supp . Table S6 ) . Most of the unconfirmed alleles were of low frequency and likely to be NGS or genome artifacts . Based on the confirmed variants only , we find that on average , each genome is a carrier for 1 . 9±0 . 2 ( s . e . ) ARDGs ( median is 1 . 0 ) . In comparison , extracting comparable data from the published 1000 Genomes Consortium [8] data yields an average of 18 . 0 ARDGs per genome , but only 160 ARDGs were identified in total . Notably , if the 1000 Genomes dataset is restricted to the high confidence LoF variants only , the figures drop sharply to 34 genes averaging 1 . 3 ARDGs per genome , with only 3 genes in common with our list .
Historically , the study of the relationship between genes and phenotypes has been largely driven by the quest to identify genes that cause diseases ( which can be considered as extreme phenotypes ) while the role genes play in modulating more subtle phenotypes was unknown , mostly due to technical limitations . The advent of high density SNP arrays in the first decade following the completion of the Human Genome Project made it possible for the first time to interrogate human phenotypes like height , ear wax type , etc . in a hypothesis-free genome wide fashion ( Genome Wide Association Studies or GWAS ) . However , GWAS essentially miss the contribution of rare variants even though some of these are likely to have a large effect size [14] . Indeed , we show in this study that LoF variants tend to be low in frequency and many can be considered private . Therefore , sequencing-based strategies are now widely viewed as the way forward to more comprehensively study the relationship between genes and phenotypes [14] . A recent study by the 1000 Genomes Consortium showed that LoF variants are more common than previously thought , although in the majority of cases their frequency is still too low for inclusion in the commonly used SNP platforms [8] . They reported that individuals of Northern and Western European ancestry had ( counting only stop-gain and frameshift variants ) 14 . 4 homozygous LoF variants per individual , Chinese and Japanese individuals had 15 . 9 , and Yoruban individuals from Nigeria had 14 . 3 . Their list of 221 genes that harbor 233 biallelic LoF variants ( all LoF mutations included ) was based on sequencing 185 individuals . On the other hand , we show that by sequencing just 77 individuals enriched for autozygosity we are able to generate a comparable list of 169 genes , without including splice-site mutations which we opted to exclude since their validation would require RT-PCR assays in a variety of cell/tissue types . Another important advantage of our study is that all variants reported were verified by direct Sanger sequencing in at least three individuals ( or all reported individuals if below 4 ) . Therefore , we believe our list represents a conservative lower estimate as we have clearly missed many additional LoF variants by design and by conservative filtering . Consistent with the low frequency of many of these LoF , we find that less than one third of our list overlaps with that published by the 1000 Genomes Consortium . This suggests that many more individuals will have to be sequenced to generate a more comprehensive list of genes that are “non-essential” and yet likely to contribute to other phenotypic aspects . We plan to expand the number of sequenced individuals in the future . Further , we suggest that sequencing of the autozygome is an efficient way to achieve this goal , as it allows a major reduction in the size of the sequencing target without a significant reduction in the expected yield of homozygous LoF . More importantly , the rarer LoF variants are more likely to be found within the autozygome than outside of it . Closely examining the list of genes reported , we find that several interesting phenotypes can be linked to the genes that we showed to harbor homozygous LoF variants and some examples are highlighted below . FUT2 encodes a fucosyltransferase that participates in the H antigen synthesis but with different tissue specificity . LoF alleles in FUT2 have been observed ( at the same locus as in our report and at another ) , with a non-secretor phenotype that was found to be linked to lower plasma vitamin B ( 12 ) level [15] . Interestingly , the subtlety of the phenotype associated with inactivation of this gene ( confirmed hematologically in these cases ) is a likely explanation for how these LoF alleles have reached a relatively high frequency ( 23 . 4% for FUT2 ) . Twenty six of the genes in our list with an established function code for proteins involved in metabolic pathways ( the above examples on H antigen synthesis and modification can also be viewed as such ) . FUK codes for the enzyme fucokinase ( FUK ) , which mediates the salvage of L-fucose molecules produced from the degradation of cellular glycoproteins and glycolipids as well as dietary L-fucose , and this pathway is considered an alternative pathway for the production of GDP-Fucose [16] . Lack of an apparent phenotype in individuals with LoF variants in this gene , therefore , is expected due to a likely redundancy between the two pathways and may be limited to an intermittent presence of reducing substance in urine . Similarly , ACY1 codes for the enzyme aminoacylase 1 , which is involved in the salvage pathway of acylated L-amino acids , producing L-amino acids and an acyl group . Individuals with biallelic LoF mutations in ACY1 have been reported with high levels of acetylated amino acids in urine , and while some of those patients demonstrated variable neurological symptoms , others had normal development [13] . This enzyme appears to be less important in states of good supply of amino acids and may serve as an example of context-specific dispensability , in this case nutritional state . A closely related examples is the enzyme dihydrodiol dehydrogenase DHDH , encoded by DHDH , which is involved in the intoxication of naphthalene and benzene and oxidation of certain pentose and hexose monosaccharides [17] . In humans , DHDH is expressed in the intestine suggesting a role in dietary metabolism of dihydrodiol of aromatic hydrocarbons and free radical formation from such molecules [18] . We identified four individuals with a homozygous LoF variant in DHDH with apparently benign consequences , and several other individuals were also reported by the 1000 Genomes Consortium . It is possible that this deficiency is only consequential upon the ingestion of dietary substrates of DHDH . An interesting category of genes with homozygous LoF are those involved in perception . The best example is genes involved in olfaction since we show that 35 olfactory receptor genes harbor homozygous LoF variants . Another example is LoF alleles in genes regulating taste perception , namely PKD1L3 and PKD2L1 , also reported by the 1000 Genomes Consortium , that code for a transient receptor potential ion channel shown to function as sour taste receptor [19] . Interestingly , very recent data suggest that these genes may be under selection pressure in certain areas with special dietary preferences [20] . Additional homozygous LoF alleles were found in P2RX5 ( reported by the 1000 Genomes paper ) that codes for the purinergic receptor P2x5 that was observed to have a potentiating effect on membrane current produced by the acid-activated sodium channel Asic3 , which in turn mediates sensation of ischemic pain in exercising muscle [21] . It will be interesting to test individuals with biallelic inactivating mutation in P2RX5 for variation in pain threshold in response to intense exercise . Another interesting phenotypic aspect is that of external appearance and this category involves homozygous LoF variants in genes coding for keratins and keratin associated proteins KRT24 , KRTAP1-1 , KRTAP13-2 ( this variant was reported by the 1000 Genomes paper ) and KRTAP19-6 . These proteins are critical structural proteins of hair and nail , and variations in these genes are of great economic importance in the sheep wool industry [22]–[24] . More recently , variations in these genes in humans have also been studied in the context of hair texture [25] , [26] . Thus , it is possible that the inactivation of these genes will have an effect on hair texture but the number of individuals we have with such mutations is too small to allow for statistical analysis . Our study also provides an opportunity to study the effect of complete inactivation in genes that have been proposed to modulate the risk of multifactorial disorders . For instance , we have 28 individuals with homozygous LoF in CLECL1 , which encodes a novel C-type lectin–like molecule expressed by antigen presenting cells and has been hypothesized to modulate risk of autoimmune diseases . Interestingly , follow up assessment revealed that three of these 28 individuals have elevated antinuclear antibody ( ANA ) titers including two who meet the clinical definition of systemic lupus erythematosus . Similarly , NLRC3 codes for a member of a family of proteins that modulate the inflammatory response by negatively regulating vital inflammatory proteins , it has been shown that Nlrc3 attenuates the activation of macrophages brought upon by toll-like receptor stimulation . Surprisingly , Nlrc3-null mice did not show any marked difference from wild type mice across a wide range of parameters , but when challenged with lipopolysaccharide ( LPS ) they exhibited an exaggerated response characterized by higher body temperature and delayed recovery of lost weight compared to wild type controls [27] . By extending this observation to the individual reported in this study with bi-allelic LoF in NLRC3 , it is possible that the phenotype is context-dependent e . g . exposure to LPS . Furthermore , the gene CILP codes for a protein involved in cartilage structure , that is expressed abundantly in intervertebral discs and a SNP in this gene was shown to be a modulator of susceptibility to lumbar disc disease [28] . The individuals with homozygous LoF variants in this gene may be considered at high risk for this very common multifactorial phenotype and longitudinal follow up data on them will be informative . We also identified homozygous LoF variants in Resistin like protein beta ( RETNLB ) which is known to promote liver insulin resistance and increase glucose production in spite of the presence of physiologic insulin levels [29] . Although in our study the young individuals with presumed deficiency in this protein do not have evidence of impaired glucose homeostasis , longitudinal follow up data will be needed to better understand the role such genes may have , if any , in diabetes risk . In addition to the above examples , we provide a comprehensive list of functions and proposed phenotypic consequence of the remaining genes in Supp . Table S4 . Those genes for which we could not provide a hypotheses about the effect of inactivation may indeed be involved in modulating important yet unrecognized aspects of the phenotype and we hope that future discovery of additional LoF variants in these genes will make such proposed effects more tractable . This work is strongly connected to several important yet unanswered questions . Knowledge about the “minimally required” genes to support early human development is only likely to be more important as the question of the minimum requirement for life in a single cell that has been pursued recently in a synthetic biology approach becomes more relevant to more complex organisms . There is also need to identify genes whose biallelic LoF does not appear to cause a discernible phenotype in order to inform projects that focus on the mapping of Mendelian genes . Obviously , these genes will not be a priority when investigators filter through sequencing data to identify their most likely candidate disease-causing mutation . Finally , we believe the mapping of genes that accumulate biallelic LoF variants addresses an important aspect about human evolution by highlighting genes that are likely to be on their way to true dispensability and converting into pseudogenes . It is unclear if the allele frequency is the best indicator of the latter possibility or if it is the “burden” of LoF for a given gene; because if a gene is truly dispensable it is predicted to freely accumulate various LoF alleles . Since most of the genes in our list are homozygous for just one LoF allele , it will be very interesting to follow up on our list in different populations to investigate whether some may indeed be accumulating various LoF alleles that would suggest true dispensability , particularly if the frequency of such alleles is high . In summary , we show that focusing sequencing efforts on the autozygome is an efficient way to catalog human LoF variants in the homozygous states in contexts that go beyond the traditional definition of health and disease . The extent to which complete inactivation of genes contributes to human phenotypic variability has just begun to be appreciated and we hope this study will stimulate continued interest in this very important research field .
This study was conducted on a subset of our patients with autosomal recessive disorders whose underlying causal mutation we resolved via exome sequencing . These patients typically have had a thorough medical and family history , physical examination and a number of tests that include basic hematological and metabolic parameters . All patients were recruited in Riyadh , Saudi Arabia ( but came from nearly all regions of the country ) , were of Arabic heritage and were born to first cousin parents . A written informed consent was obtained from all participants in a protocol approved by the IRB at King Faisal Specialist Hospital and Research Center . Exome capture was performed using TruSeq Exome Enrichment kit ( Illumina ) following the manufacturer's protocol . Blood samples were prepared as an Illumina sequencing library , and in the second step , the sequencing libraries were enriched for the desired target using the Illumina Exome Enrichment protocol . The captured libraries were sequenced using the Illumina HiSeq 2000 Sequencer . Sequence quality , depth and target alignment results are summarized and listed in Supp . Table S5 . In order to confirm that none of the candidate homozygous LoF variants that are used for subsequent analysis was an NGS artifact , PCR amplification followed by Sanger sequencing was attempted for each homozygous LoF allele in a sample of the individuals where the allele was detected as follows . For alleles detected in up to three individuals , all instances were Sanger sequenced . For alleles detected at a higher frequency , a random sample of three or more individuals was selected . In some cases , we sequenced the allele amplicon in all 77 individuals . Amplicons targeting a region of about 400 bases around each candidate allele were manually selected followed by primer design using Primer3 . For some targets it was not possible to design suitable primers and so the alleles were disqualified and excluded from any further analysis . Genomic DNA was extracted from blood samples and PCR amplified and the resulting amplicons were then Sanger sequenced; all following standard protocols . Each sequence trace file was examined manually to confirm concordance with the NGS exome results . In about 45% of the tested alleles , the trace files in at least two individuals ( or one if the variant was reported only once by NGS ) showed either a heterozygous locus or signal that could not reliably support the NGS report . These alleles were thus deemed unreliable and also excluded from any further analysis . For the remaining 55% , trace files strongly supported the NGS reports in >98% of all tested individuals . In the few cases where the allele could not be reliably confirmed in only one individual but confirmed in two or more other individuals the NGS report was considered reliable and reported . The overall flow of the analysis is depicted in Figure S1 and briefly described here . Full details are provided in Text S1 . For efficient management , all data were loaded onto an MS Access ( http://www . microsoft . com ) relational database and manipulated via SQL queries . Some plots and supplementary tables were produced using MS Excel ( http://www . microsoft . com ) spreadsheet software and statistical analysis was performed using Prism5 ( http://www . graphpad . com ) statistics software . After exome sequencing the 77 samples with NGS , the sequences were aligned to the NCBI reference human genome ( GRCh37/hg19 ) using BWA ( http://bio-bwa . sourceforge . net/ ) . The variants ( small insertions/deletions and SNPs ) were then called using SAMTOOLS ( http://samtools . sourceforge . net/ ) by comparison against the reference genome . Functional prediction of the impact of each variant was produced using ANNOVAR ( http://www . openbioinformatics . org/annovar/ ) by comparison against RefSeq mRNA sequences . The tool also linked variants to dbSNP reports if known . We then filtered the resulting variants for autosomal , homozygous LoF alleles , where an LoF was specifically defined as a stop-gain SNP or a frameshifting ( non-mod 3 ) indel in a protein coding gene . The full set of resulting variants was aggregated by position and mutation type . This set of candidate LoF alleles was then manually examined on the UCSC genome browser ( http://genome . ucsc . edu ) to verify the accuracy of each allele in a broader genomic and transcriptomic context . The examination mainly utilized the UCSC genes , conservation and dbSNP tracks , as well as a various other tracks as needed . The examination aimed to verify the SAMTOOLS annotation , detect artifacts , put the allele within the alternative transcription perspective and identify potential rescue mechanisms that might prevent the candidate variant from becoming a LoF allele . A separate contextual variations report was generated to allow examination of each variant against all other variants on the same gene in the same individual . This was used to detect variant interactions that might also prevent the candidate allele from being an effective LoF variant . After filtering out artifacts , counteracting interactions and suspected highly polymorphic loci , the resulting set of alleles were examined on the genome browser to select a suitable target region . PCR primers were designed using Primer3 ( http://primer3 . wi . mit . edu/ ) web tool . The results of variant validation by Sanger sequencing were entered into the database to produce a subset of now validated and verified variants; our confirmed variants list ( CVL ) . The CVL was cross linked to EVS ( http://evs . gs . washington . edu/EVS/; ESP6500SI data ) , OMIM ( http://www . omim . org/ ) and HGNC ( http://www . genenames . org/ ) to provide additional information on known genotype frequencies , disease association and gene families , respectively . HGNC was also utilized to resolve naming ambiguities resulting from the use of multiple resources for annotation . Prior to NGS , each sample was also genotyped on the Axiom Genome-Wide Population-Optimized Human SNP chips ( CEU version , about 600K total SNPs ) . That data was collected and formatted for input into PLINK ( v1 . 07; http://pngu . mgh . harvard . edu/purcell/plink/ ) to detect ROH blocks . After filtering the probes for quality and stability , we called as ROH all blocks of 100 , 000 bases or longer and entered the results into the database for crosslinking against the CVL . Heterozygous variants were filtered and validated following essentially the same methods but were not subjected to a thorough manual contextual examination . The initial candidates list was additionally filtered based on a minimum NGS call quality score to reduce false positives . The final validated heterozygous variants list is restricted to genes annotated in OMIM with an autosomal recessive disease connection only , and does not represent the full list of all heterozygous LoF variants detected . Further details for these methods and other subsections of this work are available in the Text S1 . | Identification of disease-causing gene variants by taking advantage of autozygosity mapping in consanguineous pedigrees is well established . However , autozygous intervals can also result in making homozygous those loss of function variants in genes that may not result in a discernible phenotype even under a complete knockout . The advent of next-generation sequencing makes it possible to systematically sequence all autozygous intervals per individual ( the autozygome ) and uncover all apparent homozygous loss of function variants therein . By applying this approach on well phenotyped offspring of first cousin marriages , we were able to uncover >160 genes that appear to be completely inactivated , and we show that the apparent lack of phenotype may be context-dependent . This work expands the spectrum of phenotypic consequence of human knockout to include apparent lack of discernible phenotypes . | [
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] | [] | 2013 | Autozygome Sequencing Expands the Horizon of Human Knockout Research and Provides Novel Insights into Human Phenotypic Variation |
Both podoconiosis and soil-transmitted helminth ( STH ) infections occur among barefoot people in areas of extreme poverty; however , their co-morbidity has not previously been investigated . We explored the overlap of STH infection and podoconiosis in Southern Ethiopia and quantified their separate and combined effects on prevalent anemia and hemoglobin levels in podoconiosis patients and health controls from the same area . A two-part comparative cross-sectional study was conducted in Wolaita zone , southern Ethiopia . Data were collected from adult patients presenting with clinically confirmed podoconiosis , and unmatched adult neighborhood controls living in the same administrative area . Information on demographic and selected lifestyle factors was collected using interviewer-administered questionnaires . Stool samples were collected and examined qualitatively using the modified formalin-ether sedimentation method . Hemoglobin level was determined using two different methods: hemoglobinometer and automated hematology analyzer . A total of 913 study subjects ( 677 podoconiosis patients and 236 controls ) participated . The prevalence of any STH infection was 47 . 6% among patients and 33 . 1% among controls ( p<0 . 001 ) . The prevalence of both hookworm and Trichuris trichiura infections was significantly higher in podoconiosis patients than in controls ( AOR 1 . 74 , 95% CI 1 . 25 to2 . 42 , AOR 6 . 53 , 95% CI 2 . 34 to 18 . 22 , respectively ) . Not wearing shoes and being a farmer remained significant independent predictors of infection with any STH . There was a significant interaction between STH infection and podoconiosis on reduction of hemoglobin level ( interaction p value = 0 . 002 ) . Prevalence of any STH and hookworm infection was higher among podoconiosis patients than among controls . A significant reduction in hemoglobin level was observed among podoconiosis patients co-infected with hookworm and ‘non-hookworm STH’ . Promotion of consistent shoe-wearing practices may have double advantages in controlling both podoconiosis and hookworm infection in the study area .
People living in rural areas of low-income countries are commonly affected by more than one of the neglected tropical diseases ( NTDs ) . These conditions share common risk factors including lack of clean water or poor sanitation , and commonly promote poverty through their impact on child health and development and economic productivity [1] , [2] . There is increasing global interest in geographical overlap in distribution of NTDs and in interactions in their effects on human health [3] . We describe a study aimed to explore the overlaps and interactions between two NTDs common in highland Ethiopia: podoconiosis and soil transmitted helminth ( STH ) infection . Podoconiosis is a geochemical ( non-filarial ) elephantiasis thought to be caused by the absorption of ultrafine mineral particles from the soil through the skin of the feet [4] , [5] . In Ethiopia , 11 million people are at risk through exposure to irritant soil , and an estimated 500 , 000 to 1 million people are affected nationwide [6] . Despite its public health significance in Ethiopia , there is little current information on podoconiosis distribution , though maps from the 1970s [Price , 1976] suggest that areas in which podoconiosis has been documented may overlap with areas of high STH prevalence [7] . Like STH infection , podoconiosis occurs in barefoot populations in areas of great poverty where subsistence farming is the main occupation [8] , [9] . In Ethiopia , the distribution and prevalence of STHs vary from region to region because of variations in environmental , social and geographic factors [10] , [11] , [12] . ‘This Wormy World’ ( http://www . thiswormyworld . org/maps/ethiopia ) used data from 269 surveys of STHs in Ethiopia from 1981–2009 to predict countrywide distribution [13] . These predictions indicate that most of highland Ethiopia is ‘likely’ or ‘very likely’ to have STH prevalence exceeding 20% . Intestinal helminths may cause anemia through reduced food intake , malabsorption and endogenous nutrient loss . The main anemia-causing intestinal helminths are hookworms ( Ancylostoma duodenale , Necator americanus ) , Trichuris trichiura and Schistosoma , with hookworms being most common . Hookworms cause chronic intestinal blood loss by attaching to the mucosa of the upper small intestine and ingesting tissue and blood [14] , [15] . Podoconiosis is thought to be the result of abnormal inflammatory responses to one or more mineral triggers . Little is known of the mediators involved in this inflammatory process , though transforming growth factor β may play a role [16] . We hypothesized that patients with podoconiosis might manifest an anemia of chronic disease , and that anemia might be more pronounced in the presence of anemia-causing intestinal helminths . In this study , we aimed to explore the overlap of STH infection and podoconiosis in southern Ethiopia by comparing the prevalence of STH infections in podoconiosis patients and healthy neighborhood controls . We also quantified the separate impact of respective infections on hemoglobin levels , and determined the combined impact of podoconiosis and STH on hemoglobin levels .
A two-part comparative cross-sectional study was conducted in Wolaita zone , southern Ethiopia in January/February 2010 and June 2010 representing dry season and rainy season , respectively , in this part of Ethiopia . These two seasons were chosen to minimize any seasonal effect of malaria on the hematological outcomes measured . Wolaita zone was selected because it is known to be highly endemic for podoconiosis [6] , and because a local NGO , the Mossy Foot Treatment and Prevention Association ( MFTPA ) has its base there . The MFTPA provides treatment to approximately 30 , 000 podoconiosis patients per year , through a carefully structured system of 15 ‘outreach clinics’ . This infrastructure has been utilized successfully for previous research studies , and the investigators have a long history of research collaboration with the MFTPA [4] , [6] , [8] , [9] . The ‘outreach clinics’ at which cases were identified are situated between 1300 and 2050 meters above sea level , altitudes at which lymphatic filariasis would be uncommon . The MFTPA has excellent links with the communities in which the outreach clinics are situated , through ‘Network Groups’ that advocate for and support people with podoconiosis . Approval for the study was given by the Institutional Review Board ( IRB ) of Addis Ababa University Medical Faculty . The IRB approved use of oral informed consent documented by a witness after the objectives of the study had been explained . All subjects provided informed consent . Patients 18 years of age and older , presenting with clinically confirmed podoconiosis , and unmatched adult neighborhood controls living in the same administrative area were included . No children were included in the study , because onset of podoconiosis is uncommon under the age of 10 . The sample size was originally calculated to investigate differences in T-cell subsets , and was based on the projected standard deviation , which was expected to be considerably larger in cases than controls , based on national reference standards . New adult podoconiosis patients were selected from those attending outreach clinics for the first time , using outreach clinic registration books as the sampling frame for selection of patients . Neighborhoods controls were identified through the MFTPA Network Groups and outreach clinic staff . They were examined carefully to exclude sub-clinical disease before being recruited . Our group has documented that clinical diagnosis of podoconiosis in this endemic area has high validity [17] . After informed consent was obtained from study participants , information on demographic and selected lifestyle factors was collected by interviewer-administered questionnaires . The questionnaires addressed socio-demographic information such as age , sex , educational status , occupational status , disease stage and shoe wearing habits . Each participant was given a leak-proof plastic container with clear instructions on how to provide a faecal sample . The faecal samples were placed in a plastic container containing 10% formalin and transported for analysis to the School of Clinical Laboratory Sciences , Addis Ababa University . Hemoglobin level was determined using two different methods; hemoglobinometer ( HemoCue TM , Angelholm , Sweden ) and automated hematology analyzer ( Abbott Diagnostics , Abbott Park , IL , USA ) . Hemoglobinometer measurements were made using a fingerprick sample , whereas automated hematology analysis was performed on 5 ml samples , as described below . The 5 ml sample was taken for more detailed hematology and immunology measurements , which will be reported separately . Both HemoCue and automated hematology analyzer are acceptable methods for measuring hemoglobin level and produce comparable results [18] , [19] . The function of the HemoCue photometer was checked on a daily basis by measuring the control cuvette and a standard of known concentration . Three set controls ( Low , Normal and High ) were run daily to ensure the function of the Cell Dyn 1800 . Hemoglobin values were used to assess the status of anemia based on the following WHO cut-off levels: below 11 g/dL for pregnant women; below 12 g/dL for non-pregnant women; and below 13 g/dL for men . Severe anaemia is defined as hemoglobin below 7 g/dL [21] . Data were coded and entered using EPI Info 2002 ( Centre for Disease Control and Prevention Atlanta , GA ) and analyzed using SPSS version 15 software ( SPSS INC , Chicago , IL , USA ) . Age was grouped into five categories: 15–24 years , 25–34 years , 35–44 years , 45–54 years and 55 years and older . Stage of disease ( for podoconiosis patients ) was defined according to the staging system developed and tested in the same study setting in 2007 [22] . Four categories of parasite infection were defined: ‘any STH’ if any geohelminth infection was present; ‘hookworm’ if either A . duodenale , or N . americanus was present; ‘non-hookworm STH’ if any of A . lumbricoides , T . trichuria or S . stercoralis was identified , but not hookworm; and ‘no STH’ if no STH infection of any kind was present . Binary and multiple logistic regressions were subsequently conducted to determine the correlates of prevalent soil transmitted helminth infections . The independent t test was used to compare the mean difference in hemoglobin level across groups . Interactions between hemoglobin levels and categories of parasite infection were computed using multiple linear regression . P-values of less than 0 . 05 were taken to be statistically significant .
A total of 913 study subjects ( 677 podoconiosis patients and 236 controls ) were involved . Just under half were male ( 46 . 5% of patients and 49 . 2% of controls , p = 0 . 49 ) , and patients were older than controls ( mean age 39 . 9 years vs 35 . 3 years , p<0 . 001 ) . The great majority of patients and controls ( 94 . 3% and 81 . 4% , respectively ) were either illiterate or had not attended school beyond primary level , and most were farmers or housewives . Participants were asked about their shoe wearing history , and 159 ( 23 . 5% ) podoconiosis patients and 51 ( 21 . 0% ) controls said they never wore shoes ( p<0 . 001 ) , whereas 25% of patients and 53 . 4% of controls said they always wore shoes ( Table 1 ) . The results of the faecal examinations are summarized in Table 2 . Infection with any STH was detected in 400 ( 43 . 8% ) study subjects: 322 ( 47 . 6% ) of the 677 patients and 78 ( 33 . 1% ) of the 236 controls . The prevalence of ‘any STH’ infection was significantly higher among podoconiosis patients than among controls ( AOR = 1 . 80 , 95% CI 1 . 31 to 2 . 47 , p<0 . 001 ) . Hookworm was the predominant intestinal helminth infection , detected in 40 . 9% of patients and in 27 . 5% of controls , and Ascaris lumbricoides was the second most frequently detected intestinal parasite with prevalence of 14 . 5% in patients and 9 . 3% in controls . When considering parasites separately , the prevalences of hookworm and Trichuris trichiura infections were significantly higher in podoconiosis patients than in controls ( AOR = 1 . 74 , 95% CI 1 . 25–2 . 42 , AOR = 6 . 53 , 95% CI 2 . 34–18 . 22 , respectively ) . However there was no significant difference between patients and controls for any other soil-transmitted helminth ( Table 2 ) . The relationship between socio-demographic variables and infection with STH was analyzed using univariate and multivariate logistic regression . Being a patient and being a farmer were both significantly positively associated with infection with ‘any STH’ . Reported shoe-wearing was also associated with STH infection . Comparing with ‘always’ wearing shoes , the adjusted OR for ‘sometimes’ wearing shoes was 3 . 40 ( 95% CI 1 . 99–5 . 80 ) , while that for ‘never’ using shoes was 2 . 45 ( 95% CI 1 . 37–4 . 38 , Table 3 ) . Mean ( SD ) hemoglobin level was 13 . 7 ( 2 . 29 ) g/dl among patients and 14 . 7 ( 2 . 09 ) g/dl among controls ( p<0 . 001 ) . Hemoglobin level was 13 . 5 g/dl and 13 . 9 g/dl ( p<0 . 001 ) among patients with and without STH infection , respectively , and 14 . 6 g/dl and 14 . 9 g/dl among controls with and without STH infection , respectively . Anemia was present in 123 ( 39 . 0% ) and 100 ( 27 . 7% ) male and female patients and in 16 ( 13 . 8% ) and 20 ( 16 . 9% ) male and female controls , respectively . The prevalence of anemia was significantly higher among male and female podoconiosis patients than among controls ( Table 4 , Figure 1 ) . A multivariate linear regression model was made to assess the relationship between STH infections and hemoglobin after adjusting for confounding covariates . Among podoconiosis patients , hemoglobin level was inversely related to presence of any STH infection and with hookworm infection ( β = −0 . 59 , 95% CI −0 . 94 to −0 . 25 , and β = −0 . 56 , 95% CI −0 . 92 to −0 . 21 , respectively ) . However , no statistically significant association between hemoglobin value and infection with ‘any STH’ was observed among controls ( Table 5 ) . In the final analysis , the effects of helminth infection in three categories ( ‘any STH’ , ‘hookworm’ , and ‘non-hookworm STH’ ) on hemoglobin level were explored only in patients with podoconiosis . Multiple linear regression analysis with adjustment for age , sex , occupation , educational status showed that ‘any STH’ and ‘hookworm’ infections were both associated with lower hemoglobin levels ( −1 . 07 g/dl , 95% CI: −1 . 62 to −0 . 52 , and −1 . 02 g/dl , 95% CI −1 . 62 to −0 . 41 , respectively , with p-values of 0 . 001 for each of these interactions ) . When combined effects were assessed , even greater differences in hemoglobin were observed than with single infections ( −1 . 54 g/dl , 95% CI −2 . 89 to −0 . 19 for podoconiosis*non-hookworm STH*hookworm , with interaction p value of 0 . 002 , Table 6 ) .
This study is the first , to our knowledge , to compare the prevalence of STH infection among people with podoconiosis with that in healthy controls . We found a significantly higher prevalence of STH infections among podoconiosis patients than controls . The overall prevalence of STH infection exceeded that of several other studies in otherwise healthy adult populations in Ethiopia [11] , [12] , [23] . However , our study showed similar prevalence of STH infection as other studies investigating STH co-infection in patients with HIV and TB in Hawassa ( southern Ethiopia ) , Jimma ( south-western Ethiopia ) and north Gonder ( northern Ethiopia ) [24] , [25] , [26] . Higher prevalence of STH in patients than healthy controls in the latter two studies was related to disease progression and immune responses , suggesting that investigation of the effects of the STH infections on disease progression and immune response in podoconiosis would be useful . Our study , however , does have limitations . We did not do quantitative egg estimation to measure the intensity of infections because of the lack of adequate laboratory facilities in this very remote rural setting . We cannot therefore provide information on infection intensity . The results presented here demonstrate a significantly higher prevalence of hookworm among podoconiosis patients than controls , and adjustment suggested that this difference was due to the higher proportion of study subjects not consistently using protective footwear . Hookworm infection was the most common STH infection among controls , consistent with other adult population studies [12] , [27] . Although associations between reported shoe use and hookworm infection are inconsistent , Smillie & Augustine [28] report extent of footwear use to be a major factor influencing hookworm transmission , and a recent study in Thailand indicated use of footwear to be the dominant factor in protection against hookworm infection [29] . We assessed the use of footwear by asking participants directly and grading their responses on a simple subjective scale as ‘never’ , ‘sometimes’ or ‘always’ . We found significantly higher risk of hookworm infection among individuals who ‘never’ or ‘sometimes’ wore shoes than among those that ‘always’ did . Risk of hookworm infection was also non-significantly higher among those who ‘sometimes’ wore shoes than those who ‘never’ did . There are several possible explanations for this – first , that because shoes are considered a valuable asset in rural Ethiopia , they may be removed when working in the fields , a prime time for STH exposure . Secondly , occasional wearing of shoes may have the effect of softening the skin of the feet and permitting easier penetration by third stage larvae . Quantification of shoe wearing in relation to village activities by direct observation ( as has been done successfully in relation to water-contact and schistosomiasis [30] ) , would enable more thorough exploration of the relationship between shoe wearing , STH infection and podoconiosis occurrence . Prevalence of infection with A . lumbricoides was not significantly different between podoconiosis patients and controls , presumably because A . lumbricoides is transmitted through eating food without washing hands [31] , [32] . Though we did not measure this behavior , we assume the probability of exposure by this route is the same for both groups . Multiple STH infections were more likely among podoconiosis patients than controls . A study conducted in Iran showed that multiple parasitic infections were more frequent in immune-compromised patients than controls , indicating that reduced immunity facilitated establishment of STH infection [33] . Again , future studies of immune status in podoconiosis would be useful in exploring this relationship further . In the present study we have documented reduced hemoglobin level among podoconiosis patients compared to controls , the difference being significant in each STH infection category . Anemia in podoconiosis patients in the absence of STH infection may reflect the chronic inflammatory process thought to be associated with disease progression in podoconiosis patients [4] , or may reflect the marginalisation and undernutrition that are consequences of disease for many patients . Hemoglobin levels were lower in podoconiosis patients infected with any STH than patients without parasitic infection , probably reflecting direct blood loss ( through ingestion and mechanical damage of the mucosa ) and indirect blood loss by affecting the supply of nutrients necessary for erythropoiesis [34] , [35] , [36] . Analyses of the separate and combined effects of each STH and podoconiosis on hemoglobin levels suggests that among STH infections , hookworm plays the most important role in influencing hemoglobin levels . However , in terms of magnitude , the effect of podoconiosis on hemoglobin level appears more pronounced than that of hookworm . STH infections appeared to exert an interactive effect on anemia in podoconiosis patients , which is consistent with other studies reporting lower mean hemoglobin among people co-infected with hookworm and trichuris relative to people without infection or with single infection [37] . We conclude that STH infections occur more commonly among podoconiosis patients than healthy controls , suggesting that targeted anthelminthic distribution to control STH among adult podoconiosis patients might be considered in addition to school-based distribution . Further research into the relationship between hookworm infection and shoe use , using more objective measures of shoe use , is urgently needed . This would generate clear evidence for or against shoe wearing in prevention of hookworm infection . If shoe use is found to be protective , integrated prevention of STH and podoconiosis through consistent shoe use must be considered . | Podoconiosis and soil-transmitted helminth infections are neglected tropical diseases occurring among barefoot people in areas of extreme poverty , and both promote poverty through effects on education , economic productivity and disability . In Ethiopia , little research on podoconiosis has been conducted and though social , economic and psychological burdens have been described , no previous study has investigated co-morbidity with other neglected tropical diseases . We therefore aimed to explore the overlap of soil-transmitted helminth infection and podoconiosis in southern Ethiopia by comparing the prevalence of STH infections among podoconiosis patients and healthy controls . We also demonstrate the separate and combined impact of STH infection and podoconiosis on hemoglobin level . We found that the prevalence of any STH and hookworm infection was higher among podoconiosis patients than among controls . A significant reduction in hemoglobin level was observed among podoconiosis patients co-infected with hookworm and ‘non-hookworm STH’ . Based on the current findings , integrated control programs that include targeted anthelminthic distribution to control STH among podoconiosis patients , and promotion of consistent shoe-wearing practices are recommended in the study area . | [
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] | 2013 | Podoconiosis and Soil-Transmitted Helminths (STHs): Double Burden of Neglected Tropical Diseases in Wolaita Zone, Rural Southern Ethiopia |
Dengue is an important vector-borne pathogen found across much of the world . Many factors complicate our understanding of the relationship between infection with one of the four dengue virus serotypes , and the observed incidence of disease . One of the factors is a large proportion of infections appear to result in no or few symptoms , while others result in severe infections . Estimates of the proportion of infections that result in no symptoms ( inapparent ) vary widely from 8% to 100% , depending on study and setting . To investigate the sources of variation of these estimates , we used a flexible framework to combine data from multiple cohort studies and cluster studies ( follow-up around index cases ) . Building on previous observations that the immune status of individuals affects their probability of apparent disease , we estimated the probability of apparent disease among individuals with different exposure histories . In cohort studies mostly assessing infection in children , we estimated the proportion of infections that are apparent as 0 . 18 ( 95% Credible Interval , CI: 0 . 16 , 0 . 20 ) for primary infections , 0 . 13 ( 95% CI: 0 . 05 , 0 . 17 ) for individuals infected in the year following a first infection ( cross-immune period ) , and 0 . 41 ( 95% CI: 0 . 36 , 0 . 45 ) for those experiencing secondary infections after this first year . Estimates of the proportion of infections that are apparent from cluster studies were slightly higher than those from cohort studies for both primary and secondary infections , 0 . 22 ( 95% CI: 0 . 15 , 0 . 29 ) and 0 . 57 ( 95% CI: 0 . 49 , 0 . 68 ) respectively . We attempted to estimate the apparent proportion by serotype , but current published data were too limited to distinguish the presence or absence of serotype-specific differences . These estimates are critical for understanding dengue epidemiology . Most dengue data come from passive surveillance systems which not only miss most infections because they are asymptomatic and often underreported , but will also vary in sensitivity over time due to the interaction between previous incidence and the symptomatic proportion , as shown here . Nonetheless the underlying incidence of infection is critical to understanding susceptibility of the population and estimating the true burden of disease , key factors for effectively targeting interventions . The estimates shown here help clarify the link between past infection , observed disease , and current transmission intensity .
Dengue is an important vector-borne disease found across much of the world [1] . The four dengue virus serotypes have complex immunological interactions whereby infection with one serotype is thought to lead to a period of short-term protective immunity against all serotypes , followed by a period in which infection with a different serotype is more likely to result in severe disease [2] . At each of these immune stages a large proportion of infections result in few or no symptoms [3] , while others result in severe illness . It is well documented that a second infection with a heterologous serotype after the period of protection is more likely to result in severe disease [4] , but less clear whether this second infection is also more likely to be apparent than a primary infection . The first evidence of cross-protection came from Sabin’s studies of experimental dengue infections showing protection against virus and/or symptoms upon experimental infections after a previous dengue infection [5 , 6] . Sabin’s studies showed some protection against symptomatic infection up to nine months after infection ( the longest time after first infection that was tested ) . Previous estimates of the proportion of dengue infections that are apparent have come from cohort studies and cluster studies . Cohort studies follow the same individuals over time , usually recording antibody titres at consistent intervals ( months to years ) , as well as recording whether individuals experienced a symptomatic dengue infection in these intervals . Cluster studies focus on testing individuals living in close proximity to known dengue cases and recording whether those individuals have experienced disease . An asymptomatic or inapparent infection is usually defined as a substantial rise in antibody titres between two measurements in a participant not experiencing symptoms . Symptomatic or apparent infections are infections concurrent with compatible symptoms , with the infection usually virologically confirmed . A recent review found that inapparent proportion estimates varied from 8–100% across relevant studies [3] . The review also highlighted a positive correlation between the proportion of cases which are inapparent , and the incidence in the previous year . This relationship , which has also been shown in some of the individual papers [7] , was thought to be linked to the period of short-term cross-protective immunity . Various investigators also suggest that the proportion of inapparent infections varies between locations due to virus serotype , genotype or age of host [3 , 8] . Because these different factors are sources of variation , estimating the overall proportion of infections resulting in symptomatic disease , or considering the impact of each of these factors in turn is challenging . We therefore conducted an analysis to estimate the apparent proportion in each of these studies separately and together , in a pooled analysis . We estimated the proportion of infections that were apparent for individuals in different immunological phases and upon infections with the different serotypes .
We made several key assumptions about the infection risk and the risk of symptomatic disease in order to formulate our model . First , we assumed that for a given study ( j ) and time period ( i ) , the infection risk ( ρi , j ) was equal for immunologically naïve individuals and for individuals with a previous infection . We then assumed that the proportion of infections resulting in symptomatic disease ( γ ) for each infection group ( primary: γ’ , or secondary: γ” ) was equivalent across all time periods and studies ( though we also made study-specific estimates ) . We then used study data to identify for each study and year: ( 1 ) the number of subjects with no previous dengue exposure ( Nnaïve , i , j ) who were susceptible to primary infection ( indicated as IgG negative ) , ( 2 ) the number of subjects with previous dengue exposure ( Nprev , i , j ) ( IgG positive ) , ( 3 ) the number of inapparent infections ( seroconversion ) , O’inapp , i , j and O”inapp , i , j , for primary and secondary infections , respectively , and ( 4 ) the number of symptomatic infections ( acute seroconversion or detection of viral RNA ) , O’app , i , j and O”app , i , j for primary and secondary infections , respectively . For each class of observation ( O ) , we assumed that number of observed infections came from a binomial distribution with the respective population of each group from that the study , N , and a year and location-specific probability of infection ( ρi , j ) and the group-specific probability of having apparent disease ( γ’ or γ” ) : O ( appi' ) ∼Binomial ( Nnaive , ρiγ' ) O ( appi' ) ∼Binomial ( Nnaive , ρi ( 1−γ' ) ) O ( inappi'' ) ∼Binomial ( Nprev , ρiγ'' ) O ( inappi'' ) ∼Binomial ( Nprev , ρi ( 1−γ'' ) ) We extended the model to include a period of possible altered immunity ( cross-immunity ) in the year following infection . This was modelled by including a third susceptibility group Nrec , i , j , which was individuals who had experienced an infection in the preceding cohort year , and allowing the model to fit a different probability of apparent infection during this period ( γrec , i , j ) . Each model was fit to the data in a Bayesian framework using rStan [9] . We assumed that the γ and ρ parameters were unknown and assigned each a naive beta prior ( α = 1 and β = 1 ) . The model code is available in the supplementary materials ( S1 File ) .
For each individual cohort study , the estimated apparent proportion for secondary infection was close to or slightly higher than the estimate for primary infection ( Fig 1 ) . There were two exceptions; in Peru the secondary estimate was lower than the primary and in Thailand-KPP , the secondary estimate was substantially higher than the primary . In the analysis including all 12 cohort studies with shared parameters for the proportion of infections experiencing disease and different local infection risks , we estimated an overall apparent proportion that was significantly higher for secondary infections ( 0 . 24 , 95% Credible Interval , CI: 0 . 22 , 0 . 26 ) than for primary infections ( 0 . 18 , 95% CI: 0 . 16 , 0 . 19 ) ( Fig 2i ) . The modelling framework incorporated local data on primary and secondary cases with apparent and inapparent infection as well as global parameters for the probability of apparent disease in primary and secondary infection to estimate transmission intensity ( Fig 3 ) . Those estimates showed high variability in incidence of infection between locations and between years in the same cohorts . Three studies had data collected across multiple years and therefore sufficient information to estimate the impact of short-term cross-protective immunity . The estimated apparent proportion for primary infections among these studies was similar to Analysis A , 0 . 18 ( 95% CI: 0 . 16 , 0 . 20 ) ( Fig 2ii ) . Among secondary infections , however , we estimated that infections within one year of the primary infection had an apparent proportion less than or equal to that of primary infections , 0 . 13 ( 95% CI: 0 . 05 , 0 . 17 ) , indicating short-term protection from apparent disease . On the other hand , secondary infections after the cross-immune period had a substantially higher symptomatic proportion , 0 . 41 ( 95% CI: 0 . 36 , 0 . 45 ) , ( Fig 2ii ) . We then estimated the apparent proportion using data exclusively from the four cluster studies that included sufficient data . The primary infection estimate of 0 . 22 ( 95% CI: 0 . 15 , 0 . 29 ) from these data was similar to that from the cohort studies ( Analyses A and B ) . However , the secondary infection estimate of 0 . 57 ( 95% CI: 0 . 49 , 0 . 68 ) was significantly higher than the general estimate for all cohorts ( Analysis A ) and closer to the estimates for secondary infections more than one year after primary infection ( Analysis B ) ( Fig 2 ) . As for the cohort studies , we simultaneously estimated the probability of individuals in the cluster being infected in the follow up period ( Fig 4 ) . The mean estimates were generally higher that estimates for cohort studies , yet they generally had large credible intervals due to small sample sizes . Finally , we estimated serotype-specific apparent proportions for the five cohort studies that had sufficient data available ( Fig 5i and 5ii ) . For primary infections , each serotype overlapped substantially . For secondary infections , there appeared to be some differences , most notably with DENV1 . However the data for DENV1 came from a single study , the Thailand-KPP study , and thus only indicate that a high proportion of infections resulted in apparent disease in that cohort with that genotype at that time and place . The result is therefore not generalizable for other locations and times where circulating genotypes and infection histories may be very different . Likewise , the DENV3 and DENV4 estimates were based on data from a single study in Peru . Only DENV2 data came from multiple locations . So although we were able to fit this model , the data were too limited to generate generalizable results .
We developed a statistical framework to assess the proportion of dengue virus infections that result in apparent disease and how that proportion depends on immune status . This framework allowed us to assess this proportion across different geographical areas , study types , and transmission intensities . The most comprehensive data , from multi-year cohort studies predominantly of children , showed that approximately 18% of primary infections experienced apparent disease . This proportion remained low , approximately 13% , for infections in the year following , but then increased substantially for secondary infections beyond the first year to approximately 45% . The other data analysed substantiated these differences: in individual cohort studies , secondary infections tended to have higher apparent proportions; across 12 cohort studies , the average estimated apparent proportion was significantly higher for secondary infections; and in cluster studies , the difference was even more pronounced . This finding substantiates evidence from individual cohort studies that have shown that the apparent proportion varies by year and that this variation is related to the incidence of infection in the previous year [3 , 24 , 25] . In contrast to previous work , we have for the first time used data from multiple cohorts to explicitly quantify those probabilities and how they change over time related to the local history of dengue virus transmission . This finding has important implications for estimating the force of infection in locations where only data on apparent infections are available , as the relationship between that data and the incidence of infection changes based on immunological history . For example , after a major outbreak , clinical masking of transmission may occur as secondary infections are less likely to be apparent when many people have temporary cross-protective immunity . On the other hand , extensive transmission several years later may appear as a much larger epidemic due to the increased risk of apparent disease in secondary infections several years after primary infection . The finding of the difference in the probability of apparent disease during the cross-protective immune period compared to other periods is striking . This finding is , however , in agreement with other findings showing a relationship between increased cases in one year with an increased inapparent proportion in the next year [24] . While our findings suggest that primary infection confers some short-term protection against apparent disease upon secondary infection , it is unclear how this protection alters the immune response to a secondary infection within that time period and how that may affect immunity to future infections . Grange et al . [3] previously reviewed most of the studies analysed here . Without considering specific infection histories and the strength of data across locations , estimates of the proportion of infections that were apparent across these studies ranged from 0% to 92% for both primary and secondary infections ( presented in the paper as the inapparent proportion ) . By considering the role of immunity , the incidence of infection , and a shared probability of apparent disease , we were able to further resolve this data and show a significant different between primary and secondary infections . Our estimates are also similar to the infection to symptomatic ratio of 4 . 3 used by Bhatt et al . in generating global estimates of disease burden . Their ratio is roughly equal to 18% of infections being apparent , very close to what we estimated for primary infections , however lower than our estimates for secondary infections after the cross immune period . This difference could importantly change the estimates of the number of clinically apparent dengue cases . Using data from cluster studies , we estimated higher apparent proportions for both primary and secondary infections . It is possible that cluster studies are better at capturing milder infections than cohort studies as there may be increased effort to identify illness and follow up tends to happen over a shorter time period when recall may be better . Indeed , Grange et al . [3] noted that cases in cluster studies compared to the cohorts were milder . It is also possible that previous exposure plays a role here . Given similar infection risk , apparent cases ( i . e . index cases for clusters ) are less likely to appear in areas with recent transmission and more cross-immunity . Therefore , cluster studies may be biased towards areas with higher risk of apparent disease specifically due to transmission history . This is supported by our finding that the cluster-based apparent proportion for secondary infections was close to our cohort-based estimate only after accounting for cross-protective immunity . The clusters are also different from the cohorts because they routinely include adult cases , which may have a different probability of being apparent compared to children , even given similar infection history . Although further work is needed to help consider these biases , the general results are largely in agreement , with an apparent proportion on the order of 20% for primary infections and 2–3 times higher for secondary infections . The analytical approach used here enabled simultaneous estimation of multiple unknowns ( infection risk and multiple apparent proportions ) in a context of limited outcome information . We drew on data from different locations where infection history and risk may drive differences in apparent incidence . As suggested here , the apparent proportion may actually be quite similar across locations , despite possibly appearing different due to different histories of recent exposure . Combining data from multiple locations has great benefit in increasing overall sample size and generalizability and allowed us to disentangle the effects of each covariate . An additional benefit of this method is that we were able to estimate the probability of infection over the study period , as has been estimated before [26] , but now incorporating shared information about the probability of apparent disease . Furthermore , this approach allowed us to directly compare the probability of infection in cluster studies , which had more uncertainty but indicated that incidence was likely higher . Indeed transmission is likely to be more intense on hyper-local scales with known transmission , as in the clusters . This analysis represents an important first step towards aggregating knowledge of dengue transmission and disease dynamics globally . This work could be further extended to estimate the contribution of other important factors such as age; available cohort data largely focuses on children . Additional data would also allow assessment of possible differences between serotypes . The current serotype-specific estimates were mainly derived from information from one study per serotype , limiting our ability assess the possibility of serotype-specific effects . Also , variations in the way infections were detected and confirmed ( e . g . case definitions , follow-up methods , assay or change in titre ) across studies could be better controlled for with the individual level data . These differences may contribute to the variability in the apparent proportion estimates for each study . We have also not explicitly addressed third and fourth infections , which likely also exhibit different patterns . Indeed , some of the infections considered as secondary infections here may have been third or fourth infections , as noted particularly in Peru [17] . Their inclusion may influence the relatively unique results shown for Peru . Though the current assumption is that the probability of any infection is the same in both primary and secondary infections , with the individual level data it may be possible to estimate probabilities of infection separately for primary and secondary cases in cohorts . Differences in the probabilities of infection for primary and secondary cases will give us information on whether exposure risk is different between those groups due to heterogeneous transmission risk and if susceptibility to infection is also changed in the short- or long-term after primary infection . The methods we developed here could also be extended to include additional types of data allowing us to estimate the proportion in different disease severity categories ( e . g . severe or hospitalised ) , which is important to both the health impact and the ability of surveillance systems to detect dengue [27] . Only by drawing on detailed data across multiple years and populations experiencing different infection histories were we able to make general inferences about the relationship between the proportion of infections that are apparent and immune status . These estimates show a clear difference in the apparent proportion between primary and secondary infections , and are helpful for understanding the relationship between the incidence of dengue virus infection and the incidence of disease . For example , even in the presence of an effective intervention , the incidence of disease may increase as there may be an increased delay between primary and secondary infection [28] . Conversely , an ineffective intervention may appear effective if implemented after a major epidemic when cross-immunity may be high . Because the apparent proportion is the key link between infection risk and the observation of disease , a better understanding of this relationship is important for designing , implementing , and evaluating interventions and understanding the dynamics of dengue globally . | Dengue disease severity is known to vary widely from the very severe to asymptomatic . There is a wide range of estimates of how many infections result in each of these outcomes . It is known that after a first infection the outcome of a second infection with a different serotype varies over time , but this has not been taken into account in these previous estimates . In this paper , we use modelling methods , combined with information from published dengue research in which individuals are followed over time , to estimate the proportion of infections that result in symptoms at different times after infection . We estimated the proportion of infections that are symptomatic for first infections as 0 . 18 ( 95% Credible Interval , CI: 0 . 16 , 0 . 20 ) , 0 . 13 ( 95% CI: 0 . 05 , 0 . 17 ) for individuals infected in the year following a first infection and 0 . 41 ( 95% CI: 0 . 36 , 0 . 45 ) for those experiencing secondary infections after this first year . The estimates here will help understand how cases relate to underlying transmission , which is vital for understanding how much of the population are susceptible to infection and for effectively targeting interventions . | [
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] | 2017 | Immune status alters the probability of apparent illness due to dengue virus infection: Evidence from a pooled analysis across multiple cohort and cluster studies |
During meiosis , homologous chromosomes pair at close proximity to form the synaptonemal complex ( SC ) . This association is mediated by transverse filament proteins that hold the axes of homologous chromosomes together along their entire length . Transverse filament proteins are highly aggregative and can form an aberrant aggregate called the polycomplex that is unassociated with chromosomes . Here , we show that the Ecm11-Gmc2 complex is a novel SC component , functioning to facilitate assembly of the yeast transverse filament protein , Zip1 . Ecm11 and Gmc2 initially localize to the synapsis initiation sites , then throughout the synapsed regions of paired homologous chromosomes . The absence of either Ecm11 or Gmc2 substantially compromises the chromosomal assembly of Zip1 as well as polycomplex formation , indicating that the complex is required for extensive Zip1 polymerization . We also show that Ecm11 is SUMOylated in a Gmc2-dependent manner . Remarkably , in the unSUMOylatable ecm11 mutant , assembly of chromosomal Zip1 remained compromised while polycomplex formation became frequent . We propose that the Ecm11-Gmc2 complex facilitates the assembly of Zip1 and that SUMOylation of Ecm11 is critical for ensuring chromosomal assembly of Zip1 , thus suppressing polycomplex formation .
Meiosis is a special type of cell cycle necessary for sexual reproduction [1] . During meiosis , a diploid cell undergoes one round of DNA replication followed by two rounds of successive nuclear segregation , meiosis I and meiosis II respectively . At meiosis I , homologous chromosomes are segregated to opposite poles whereas at meiosis II , sister chromatids separate . As a result , four haploid gametes form from one diploid progenitor cell . In many organisms , homologous recombination plays two critical roles in ensuring the faithful segregation of homologous chromosomes at meiosis I [2] . First , in early prophase I , homologous recombination provides a means for chromosomes to find their homologous partners , thus facilitating pairing of homologous chromosomes . Second , crossover recombination events provide a physical connection that maintains homologous associations until chromosomes are properly aligned on the metaphase I spindle . Homologous recombination is concurrent with the dynamic morphological changes of chromosomes . Sister chromatids condense to form chromosome axes , and sister chromatid axes of homologs are juxtaposed at close proximity along their entire lengths , with a proteinaceous transverse filament structure situated in between . This meiosis-specific chromosomal structure is called the synaptonemal complex ( SC ) . In budding yeast , the Zip1 protein serves as the transverse filament [3] . The deposition of Zip1 occurs progressively , starting at either centromeres or future crossover sites on chromosome arms [4] , [5] . The initiation of homologous recombination is a prerequisite for Zip1 polymerization along chromosomes . In the absence of meiotic recombination ( e . g . , in the spo11 background where no meiotic double-strand breaks ( DSBs ) occur ) , SC components form an aggregate , called the polycomplex , which is not associated with chromosomes [6] . The initiation of Zip1 polymerization also requires a group of proteins belonging to the synapsis initiation complex ( SIC ) , namely Zip2 , 3 , 4 and Spo16 [6]–[10] . The absence of these proteins leads to a great reduction in Zip1 loading between homologous chromosomes , usually with a high incidence of polycomplex formation . The SC is important for the control of meiotic recombination . Typically , mutations in genes encoding SIC components or Zip1 disrupt the close association of homologous chromosomes , reduce interhomolog crossing over and alter the pattern of crossover deposition along paired chromosomes [11] , [12] . On the other hand , defects in homologous recombination lead to abnormal morphogenesis of the SC [13]–[16] . These observations provide evidence for the close relationship between homologous recombination and SC formation . Zip1 also plays a distinct role at centromeres . Zip1 loading at a centromere is independent of the initiation of homologous recombination and SIC components . Zip1 functions at the centromere to associate two centromeres of either homologous or non-homologous chromosomes , possibly facilitating the recognition of homologous chromosomes [4] . Furthermore , centromeres serve as potential synapsis initiation sites [17] , the timing of which is coordinated by Fpr3 and Zip3 , so that meiotic recombination precedes SC formation [18] . The centromere association formed between non-homologous chromosomes is corrected to establish associations between homologous chromosomes as prophase I progresses , and this regulation employs the phosphorylation of Zip1 . This phosphorylation is controlled by the DNA damage checkpoint kinase Mec1 and protein phosphatase 4 [19] . Centromeres play an important role at meiosis I , especially when homologs fail to form a crossover [20] . Zip1 stays at centromeres throughout meiosis I , promoting proper chromosome segregation by directly mediating centromere associations [21] , [22] . The small ubiquitin-related modifier ( SUMO ) protein plays an important role in controlling SC formation [23] . First , Zip3 , a component of the SIC , has SUMO E3 ligase activity [24] . Second , Zip1 colocalizes with SUMO , both on chromosomes and at the polycomplex , and interacts with SUMO-conjugated proteins [24] , [25] . Third , Red1 , a major component of meiotic chromosome axes , interacts with SUMO , and is SUMOylated in a Zip3-dependent manner [26] , [27] . However , SUMO-decorated SC assembles in the absence of Zip3 ( although its extent is diminished relative to wild type ) , and little SUMO is detectable on chromosome axes in the absence of Zip1 despite the presence of Red1 . Thus the mechanism of SUMOylation of SC central region and the precise role of SUMO in mediating homologous synapsis have remained somewhat mysterious . In this work , we have identified Ecm11 and Gmc2 as novel SC components . They localize initially to synapsis initiation sites , and then to synapsed regions of meiotic chromosomes with extensive overlap with the Zip1 protein . The absence of these proteins compromises the assembly of Zip1 into SC central region and into polycomplex . Furthermore , Ecm11 is SUMOylated in a Gmc2-dependent manner . This SUMOylation is also partly dependent on Zip1 and the SIC components Zip2 , Zip4 and Spo16 . The SUMOylation of Ecm11 is essential for the proper assembly of Zip1 , which is crucial for proper chromosome synapsis in meiosis . Unexpectedly , polycomplex formation became frequent in the SUMOylation-negative ecm11 mutant . We propose that the Ecm11-Gmc2 complex promotes SC formation by facilitating the assembly of Zip1 and that SUMOylation of Ecm11 is critical for promoting assembly of Zip1 on chromosomes , thus suppressing polycomplex formation .
Genes important for meiotic recombination tend to be upregulated during the early stages of prophase I , and proteins directly involved in meiotic recombination tend to associate strongly with meiotic chromosomes , showing distinct localization patterns , typically observed as foci or lines on meiotic chromosomes ( e . g . , [3] , [6] , [8] ) . In order to identify proteins potentially involved in meiotic recombination , the localization patterns of proteins encoded by poorly characterized genes whose transcripts are upregulated during early prophase I were systematically examined on spread chromosomes ( Materials and Methods ) . The screening identified two genes , ECM11 and GMC2 . The encoded proteins contain domains highly likely to form coiled-coil structures; the C-terminal region , from amino acids 250 to 300 in Ecm11 , and two regions from the middle toward the C-terminus , from 100 to 140 and from 160 to 188 respectively , in Gmc2 . No obvious orthologs of these proteins have been found in other organisms . Both Ecm11 and Gmc2 show a line-shaped staining pattern throughout the length of paired pachytene chromosomes , reminiscent of the staining pattern of Zip1 . ECM11 was originally proposed to be involved in cell surface biosynthesis based on sensitivity of the null mutant to calcofluor white [28] . In our study , however , we found no evidence that the ecm11 mutation enhances sensitivity to calcofluor white ( Figure S1 ) . Previous reports characterised the ECM11 gene as a positive effector of meiosis [29] , [30] . A recent report identified GMC2 as a gene important for meiotic recombination and/or SC formation [30] . The molecular functions of these genes , however , remain unclear . To understand the role of ECM11 and GMC2 , the entire ORF of these genes were deleted and the phenotypes were examined . Consistent with previous reports , we found that meiotic cell cycle progression was substantially delayed to a similar level in ecm11 , gmc2 and ecm11 gmc2 double mutants ( Figure 1A , Figure S2 ) [29] , [30] . Although sporulation was delayed , the resultant tetrads showed relatively high spore viability; 88% , 76% and 87% of spores were viable in ecm11 , gmc2 , and ecm11 gmc2 double mutant respectively , compared to 98% in wild type ( Table 1 ) . Spore viability of the ecm11 mutant reported previously is much lower ( 51% ) than that of our strain [29] . The reason has been unclear: the difference could be due to the different strain backgrounds used ( SK1 in [29] versus BR1919-8B in this work ) . The cell cycle delay was bypassed by introducing the spo11 mutation , suggesting that the cause of the cell cycle delay is associated with a defect in meiotic recombination ( Figure 1A ) . A similar bypass effect was reported for the gmc2 mutant [30] . These observations prompted us to examine the effect of these mutations on meiotic recombination directly . Crossing over was assayed physically in diploid strains carrying one linear and one circular copy of chromosome III . A single crossover between one linear and one circular chromatid results in the production of a linear dimer . A double crossover involving one linear chromatid and both chromatids of the circular chromosome generates a linear trimer . The linear monomers , dimers , and trimers can be separated by pulsed-field gel electrophoresis . In wild type , the level of crossovers plateaued at ∼45% by 19 hours , while only ∼30% was observed at 36 hours in ecm11 and gmc2 mutants , showing that crossing over is both delayed and reduced in these mutants ( Figure 1B , 1C ) . Therefore , both Ecm11 and Gcm2 are important for meiotic crossing over . The apparent similarity of the localization patterns between Ecm11 , Gmc2 and Zip1 prompted us to examine a potential role for Ecm11 or Gmc2 in the assembly of Zip1 . Meiotic cells were surface spread and the localization of Zip1 was detected by immunostaining . In wild type , Zip1 shows a linear confluent staining pattern throughout the length of paired chromosomes from mid to late prophase I . However , in the absence of either/both Ecm11 and Gmc2 , the localization of Zip1 becomes rather discontinuous ( Figure 2A ) . This effect was analyzed quantitatively . The area showing continuous Zip1 staining , defined as Zip1 stretch area , became much smaller in the absence of either/both Ecm11 and Gmc2 ( Figure 2B ) . Correspondingly , the number of Zip1 stretches became higher ( Figure 2C ) . These results strongly suggest that both Ecm11 and Gmc2 are important for the efficient assembly of Zip1 onto chromosomes . The similarity of the phenotypes between each single mutant and the double mutant suggests that Ecm11 and Gmc2 function in the same pathway . In the absence of Spo11 , initiation of meiotic recombination does not occur and SC components form an aggregate called the polycomplex , which is not associated with chromosomes ( Figure 2D ) . A major component of the polycomplex is Zip1 . Even in the polycomplex , Zip1 is thought to maintain a highly ordered structure similar to that found in the context of the SC [31] . In the spo11 mutant , a polycomplex was found in almost 80% of spread chromosomes ( Figure 2D , 2E ) . Strikingly , polycomplex formation was almost completely abolished in the absence of Ecm11 or Gmc2 . These results further support the role of Ecm11 and Gmc2 in facilitating the assembly of Zip1 . Zip1 is known to have a function independent of homologous recombination , which is to associate two chromosomes together via their centromeres , or “centromere coupling” [4] . The involvement of Ecm11 and Gmc2 in this aspect of Zip1 function was examined . Centromere coupling can be assessed by visualizing centromeres using a spo11 diploid . Ctf19 , a component of the yeast kinetochore , was tagged with the myc epitope to identify the location of centromeres . A diploid budding yeast cell contains 32 chromosomes representing 16 pairs of homologs . In the spo11 mutant , ∼18 centromere foci were observed on average whereas the number went up to ∼30 in the spo11 zip1 mutant , consistent with the involvement of Zip1 in centromere coupling as reported previously ( Figure S3 ) [4] . The absence of Ecm11 or Gmc2 has little effect , if any , on the number of centromere foci detected , ∼20 and ∼19 respectively , suggesting that they are dispensable for centromere coupling . To further investigate the protein behavior of Ecm11 and Gmc2 , these proteins were tagged with the myc epitope ( see Materials and Methods ) . Using a diploid homozygous for either ECM11-myc or myc-GMC2 , these proteins were detected by Western blotting . We employed the ndt80 mutant background to arrest the meiotic cell cycle at prophase I , since the strain background we used ( BR1919-8b background ) does not support efficient synchronous entry into meiosis . Consistent with the behavior of the transcripts of the ECM11 and GMC2 genes , both proteins were produced specifically during meiosis ( Figure 3A ) . Furthermore , the Ecm11 protein was detected as multiple bands including three major bands , suggesting that Ecm11 is post-translationally modified . To rule out the possibility that this modification is specific to the ndt80 background , we employed the SK1 strain background in which cells can be synchronously introduced into meiosis . Essentially , the same migration pattern of Ecm11 was obtained in the SK1 background ( Figure 3B ) . The amount of the protein peaked at around 5 hours , which is right before the nuclear division of meiosis I , consistent with the idea that Ecm11 functions during meiotic prophase I . Zavec et al . ( 2008 ) presented some evidence that Ecm11 is SUMOylated during meiosis . We confirmed this idea by attaching three copies of the FLAG epitope at the N-terminus of the Smt3 protein , the budding yeast SUMO protein , in strains containing ECM11-myc . The addition of FLAG makes the molecular weight of Smt3 higher , thus a protein covalently attached with FLAG-Smt3 should migrate more slowly in SDS-PAGE than one with untagged Smt3 . Indeed , the top two bands were observed with reduced mobility specifically when Smt3 was tagged with FLAG while the bottom band did not change position ( Figure 3C ) , arguing that the upper two bands are SUMOylated while the bottom is not . To further verify the entity of these slow migrating bands , the whole cell extract from cells carrying Ecm11-myc and FLAG-Smt3 were used to immunoprecipitate Ecm11 and the immunoprecipitates were examined for the presence of Ecm11 and SUMO by using anti-FLAG and anti-myc antibodies . Only the two bands that slowed down the migration in Figure 3C were detected with anti-FLAG antibodies ( Figure S4E ) , providing further evidence that Ecm11 is SUMOylated . There are two canonical SUMOylation target sites within Ecm11 , at Lysine 5 ( K5 ) and Lysine101 ( K101 ) [32] . To examine the importance of these sites , they were mutated to Arginine ( R ) ( hereafter referred to as K5R and K101R respectively ) and their effect on Ecm11 modification was examined ( Figure 3D ) . Neither K5R nor K101R completely abolished SUMOylation; residual SUMOylation was detected in both K5R and K101R mutants . However , SUMOylation was completely abolished when these two sites were simultaneously mutated , suggesting that both of these sites are modified by SUMOylation . We also mutated the Lysine 5 and 101 to Asparagine [32] . These mutants behaved essentially the same as K5R and K101R ( Figure S4D ) , further supporting the idea that both K5 and K101 are SUMOylated . Next we examined the role of other synapsis proteins in the SUMOylation process of Ecm11 ( Figure 3E ) . The bands associated with SUMOylation were barely detectable in the absence of Gmc2 . A level of SUMOylation comparable to that of wild type was seen in the spo11 mutant , indicating that the initiation of meiotic recombination is not necessary for Ecm11 SUMOylation . Interestingly , the SUMOylation became less efficient in the absence of Zip1 . Next we examined components of the SIC: Zip2 , Zip3 , Zip4 and Spo16 . A substantial reduction in SUMOylation was found in the zip2 , zip4 and spo16 mutants . In zip3 , no prominent reduction was observed; instead , slower-migrating species than the slowest of the three major bands appeared darker than in wild type , suggesting that Ecm11 might be more extensively modified in zip3 . To obtain further insight into the function of the Ecm11 and Gmc2 proteins , their meiotic localization was examined . They showed extensive colocalization throughout meiotic prophase I; both proteins initially appeared as foci at early prophase I , before forming a line-like staining pattern at late prophase I ( Figure 4A , Figure S5A ) . At pachytene , they showed extensive colocalization with Zip1 , positioned continuously between paired homologs along their length ( Figure 4B , Figure S5A ) . Next , to examine the relationship between Ecm11-Gmc2 and synapsis initiation sites , Ecm11-Gmc2 were co-immunostained with Zip3 . At early prophase I , extensive colocalization was found between Ecm11 and Zip3 ( Figure 4C ) . Of all the foci carrying Zip3 and/or Ecm11 , 17 . 3% of foci contained Zip3 but not Ecm11 whereas only 5 . 4% of foci contained Ecm11 but not Zip3 ( 220 foci were counted ) . This observation supports the idea that Ecm11 is recruited after the chromosomal localization of Zip3 . At later stages , Ecm11 became linear while Zip3 remained as punctate foci ( Figure 4C ) . Similar localization behavior of Ecm11 and Gmc2 on meiotic chromosomes , along with phenotypic similarities in meiotic recombination and the effect on Zip1 assembly , strongly suggests that Ecm11 and Gmc2 function in the same pathway , possibly as part of the same complex . To directly address this point , a potential interaction between Ecm11 and Gmc2 was examined using yeast two hybrid analysis ( Y2H ) . Full-length Ecm11 was fused to the Gal4-DNA-binding domain ( DBD ) and full length Ecm11 and Gmc2 to the Gal4 transcription activation domain ( AD ) . These fusion plasmids were used to test for interactions between proteins . Ecm11 and Gmc2 showed a strong interaction ( Figure 4D ) . Gmc2-DBD fusion constitutively activated the reporter by itself , thus was not included for the interaction assay . To further obtain in vivo evidence for the interaction between Ecm11 and Gmc2 , these proteins were immunoprecipitated from meiotic cell extracts and the precipitates were analyzed by Western blotting . When Ecm11-FLAG was immunoprecipitated with anti-FLAG antibodies , myc-Gmc2 was also present in the precipitate . The reverse is also true; when myc-Gmc2 was immunoprecipitated with anti-myc antibodies , Ecm11-FLAG was precipitated as well . The coimmunoprecipitation of Gmc2 and Ecm11 was resistant to nuclease treatment ( Materials and Methods ) , arguing against the possibility that these proteins are only associated indirectly through DNA . Overall , the Y2H and coimmunoprecipitation assays strongly suggest that Ecm11 and Gmc2 are part of the same protein complex . The strong similarity in the localization pattern of Ecm11 and Zip1 prompted us to examine a possible physical association between them . We attempted to address the possibility by immunoprecipitaion . However , Zip1 was found to be highly unstable under native conditions in the meiotic whole cell extract , and the immunoprecipitation efficiency was extremely low , which kept us from testing the physical association of these proteins ( data not shown ) . Next , the genetic requirement for the chromosomal localization of Ecm11 and Gmc2 was examined . Gmc2 localization to chromosomes was completely abolished in the absence of Ecm11 , while an observable degree of Ecm11 remained localized to chromosomes in the absence of Gmc2 ( Figure 5A ) . This chromosomal Ecm11 remains as foci , not exhibiting extensive colocalization with Zip3 , with a 0 . 29 Pearson's correlation coefficient ( median , n = 23 ) compared to 0 . 76 in the presence of Gmc2 ( in the zip1 mutant background where Ecm11 remains as foci , Figure S5B ) . These data highlight the importance of Gmc2 in the efficient targeting of Ecm11 to the synapsis initiation site . In the absence of Zip1 , both Ecm11 and Gmc2 still showed extensive colocalization , but remained as distinct foci without showing a line-like localization pattern ( Figure 5B top , Figure S5B ) . Ecm11 colocalized with Zip3 between aligned chromosomes ( Figure 5B middle , Figure S5B ) and recruitment of Ecm11 to these sites depended on SIC components , Zip3 and Zip4 ( Figure S6A ) . These sites tend to be in close proximity to the axial association sites visualized by localizing the Red1 protein , a component of meiotic chromosome axes ( Figure 5B bottom ) . These results suggest the following three properties of the Ecm11 and Gmc2 proteins . First , they can recognize synapsis initiation sites independently of Zip1 . Second , they are not components of the meiotic chromosome axis , since they are not localized to chromosome axes in the absence of Zip1 . Third , the deposition of Ecm11 and Gmc2 along the central region between paired homologs requires Zip1 . In the absence of Zip3 , initiation of synapsis becomes less efficient , leading to a decreased number of fully synapsed chromosomes with an elevated frequency of polycomplex formation . Ecm11 and Gmc2 were localized to both synapsed regions and the polycomplex in the zip3 mutant , showing extensive colocalization with Zip1 ( Figure 5C and Figure S6B ) . In the absence of Zip4 , they appeared exclusively localized to the polycomplex along with Zip1 . The chromosomal localization of Zip1 is almost completely abolished in the absence of Zip4 , except at centromeres [8] . These observations are consistent with the idea that the confluent loading of Ecm11 and Gmc2 between paired homologs relies on Zip1 . The absence of Ecm11 foci in either zip3 or zip4 mutant suggests that the recruitment of Ecm11 and Gmc2 to the synapsis initiation sites requires both Zip3 and Zip4 , consistent with the observation that the recruitment of Ecm11 to axial association sites in the zip1 mutant requires SIC components ( Figure S6A ) . The localization of Ecm11 in the absence of the initiation of meiotic recombination was examined next . In the spo11 mutant , Ecm11 was primarily localized to the polycomplex along with Zip1 ( Figure S6C ) . However , unlike Zip1 , Ecm11 was not localized to centromeres ( Figure S6C ) , consistent with the observation that the ecm11 mutation has little effect on centromere coupling ( Figure S3 ) . To understand the physiological role of SUMOylation of Ecm11 , mutations that partially or completely abolish the SUMOylation of Ecm11 were examined to observe their effect on Zip1 assembly by measuring the area of regions of continuous Zip1 staining ( Zip1 stretches ) and the number of Zip1 stretches per nucleus . We employed the K5R and K101R mutants , as well as the K5R K101R double mutant . The K5R mutant and K5R K101R double mutant exhibited a marked reduction in the average Zip1 stretch area and the number of stretches increased to levels comparable to that of the ecm11 null mutant whereas K101R showed little effect ( Figure 6A–6C and Figure S5C ) , suggesting a more critical role for the SUMOylation at K5 in facilitating the assembly of the interchromosomal Zip1 filament . While the localization of Zip1 was being examined , we noticed that polycomplex formation was present ∼30% more frequently in the K5R mutant and the K5R K101R double mutants ( Figure 6A and 6D ) . This is strikingly different from the ecm11 or gmc2 null mutant where polycomplex formation is rare . Taken together , these results suggest that chromosomal Zip1 assembly , as opposed to the polycomplex , is selectively compromised when the SUMOylation at K5 is prohibited , thus highlighting the role of SUMOylation in specifically facilitating the assembly of Zip1 at the proper location , between paired homologs .
Here we have identified two meiosis-specific proteins , Ecm11 and Gmc2 , as novel components of the SC . They are likely to function in the central region of the SC rather than the lateral elements , since they show extensive colocalization with Zip1 , a major component of the central region of the SC , and their localization is highly dependent on Zip1 . In the absence of Zip1 , Ecm11 and Gmc2 are not found on chromosome axes but are localized to the synapsis initiation sites . Our observations indicate that Ecm11 and Gmc2 function as a complex . First , the ecm11 and gmc2 single mutants exhibit identical phenotypes . Zip1 assembly and crossing over are compromised to a similar level in both mutants . Second , they show extensive colocalization on meiotic chromosomes throughout meiotic prophase I . Third , the localization of Gmc2 is completely abolished without Ecm11 . Fourth , the Ecm11 and Gmc2 proteins interact , as shown by Y2H and immunoprecipitation assays . Thus , the complex consisting of Ecm11 and Gmc2 is hereafter referred to as the E-G complex . Our observations strongly argue for a function of the E-G complex in facilitating the assembly of Zip1 in the context of both chromosomes and the polycomplex . First , the E-G complex shows extensive colocalization with Zip3 , a component of the SIC whose role is to initiate assembly of Zip1 filaments . Second , the E-G complex shows extensive colocalization with Zip1 . Third , the absence of the E-G complex compromises the assembly of chromosomal Zip1 filaments . Fourth , in the spo11 mutant background , polycomplex formation becomes dramatically reduced in the absence of the E-G complex . We further provide evidence that the SUMOylation of the Ecm11 protein , especially at K5 , is important for promoting the interchromosomal assembly of Zip1 filaments . In the SUMOylation-negative ecm11 mutant , assembly of the chromosomal Zip1 was compromised substantially while polycomplex formation was not . Importantly , this phenotype is different from that of the ecm11 null mutant in which the assembly of Zip1 is compromised in the context of both chromosomes and the polycomplex . These results suggest that the E-G complex is capable of facilitating the assembly of Zip1 without being SUMOylated , and that the SUMOylation helps specify the location of where Zip1 assembly is promoted . We cannot rule out the possibility that the pronounced polycomplex formation found in the K5R mutant is associated with a possible protein misfolding caused by the introduced mutation instead of lack of SUMO conjugation , although we do know the mutant proteins retain stability comparable to the wild type counterpart ( Figure 3D ) . Based on our results , we propose a mechanism of the E-G complex in facilitating the chromosomal assembly of Zip1 as the following steps ( Figure 7 ) . First , Ecm11 and Gmc2 form a complex ( the E-G complex ) , which facilitates the recruitment of the E-G complex to a SIC . At the same time , the SIC , possibly along with the E-G complex , facilitates the initial assembly of Zip1 . Second , the presence of Gmc2 , the SIC and Zip1 strongly promotes SUMOylation of Ecm11 , thus facilitating chromosomal assembly of Zip1 while discouraging polycomplex formation . The SUMOylation of Ecm11 almost completely requires Gmc2 , and is also dependent , to a lesser extent , on Zip1 , Zip2 , Zip4 and Spo16 . Third , the E-G complex stays with the assembled Zip1 , possibly contributing to the stabilization of the assembled Zip1 . Based on these results , the E-G complex is likely to function through at least two different protein-protein interactions . First , the E-G complex can be recruited to the SIC in the absence of Zip1 , suggesting that it interacts with at least one component of the SIC . Later , it acts together with Zip1 at locations where the SIC is absent . It is thus likely that the E-G complex interacts with Zip1 or other component ( s ) of the central region of the SC . Intriguingly , we show that Zip1 behavior in the ecm11 null mutant is different to Zip1 behavior in the SUMO-negative mutant , highlighting the importance of SUMOylation in facilitating Zip1 assembly in a chromosomal context . One possibility is that the SUMOylation might promote/enhance the association between the lateral elements and transverse filament , making the structure of parallel lateral elements mediated by transverse filaments more rigid . In that sense , it is interesting that Red1 , a component of the lateral element , has a SUMO-interacting motif ( SIM ) [27] . Given that the E-G complex is a component of the central region , the interaction between the E-G complex and Red1 through SUMO should contribute to the further stability of the interaction between the central region and the lateral elements of the SC . Red1 is also known to be SUMOylated , and Zip1 also has SIMs [24] , [26] , [27] . Previous work proposed that this potential interaction between Red1 and Zip1 contributes to the association between the central region and the lateral element [26] , [27] . However , the SC was still established , although with slower kinetics , in the SUMO-negative red1 mutant , suggesting there might be other interaction ( s ) stabilizing the SC . One such interaction could be between the E-G complex and Red1 . It is also possible that the interaction between Zip1 SIM and SUMOylated E-G complex promotes SC formation by , for example , stabilizing the assembled Zip1 . Further characterization will be able to address the role of SUMOylation of the E-G complex in SC formation at the molecular level . In this work , we have characterized the E-G complex as a facilitator for the assembly of the transverse filament ( Zip1 ) . Although we have not been successful in identifying orthologs of the Ecm11 or Gmc2 protein in other model organisms based on amino acid sequence similarity , proteins proposed to retain a similar function to the E-G complex have been reported in other model organisms . Proteins cytologically localized to the central element of the SC are proposed to facilitate assembly of the transverse filament by serving as stabilizing pillars [33] . These are SYCE1 , SYCE2 , SYCE3 and TEX12 in mice , and Corona in Drosophila [34]–[38] . In the absence of these proteins , SC formation is severely compromised or abolished . In the absence of Corona , not only is SC formation abrogated , but polycomplex formation is also abolished [38] . These apparent phenotypic similarities caused by the absence of the E-G complex and the central element proteins raise the possibility that the E-G complex might be functionally related to the central element proteins , possibly serving as a pillar to stabilize the transverse filament .
Strains used are listed in Table S1 [39] , [40]–[42] . Strains used in each Figure are summarized in Text S1 . Gene deletions and C-terminus epitope tagging was performed using PCR-mediated gene replacement and tagging techniques as described previously [43] . N-terminus epitope tagging was performed as described previously [44] . Based on the kinetics of sporulation and spore viability , the functionality of the tagged proteins is comparable to that of untagged counterparts . ecm11-K5R , -K101R , -K5N and -K101N were created using the delitto perfetto method [45] . For yeast two-hybrid protein analyses , PCR-amplified ECM11 and GMC2 excluding the intron were cloned between the PvuII and NcoI sites of the pOAD and pOBD2 plasmids [46] . GAL sequences were fused to the 5′ end of each ORF . In a haploid strain carrying MATa and MATα , and also carrying a hop2 mutation ( TBR569 ) , a PCR-mediated method [43] was used to integrate the sequence encoding 13 copies of the myc antigen at the 3′-end of candidate genes whose transcripts are upregulated during early prophase I based on microarray analysis [47] , [48] . The genes examined were: CIK1 , ECM11 , ECM38 , GMC2 , HDR1 , HSN1 , IME4 , MSN5 , PIG1 , RTG1 , YBR231C , YDL012C , YDR018C , YDR374C , YGL081W , YHR202W , YLR387C , YMR147W , YSP3 . The tagged strains were introduced into meiosis and meiotic chromosomes were surface spread . Mouse anti-myc antibodies ( Covance ) were used to visualize candidate proteins . Patches were made from a single colony of strains on YPD plates and incubated at 30°C overnight . The plates were replica plated onto sporulation medium and sporulation was examined at the indicated time points . For each strain , spore formation was measured in three independent experiments , with at least 300 cells scored in each experiment . Meiotic chromosomes were surface spread , and immunostaining was carried out as described previously [42] . Rabbit and mouse anti-Red1 antibodies were used at 1∶500 and 1∶1000 dilutions respectively [49] . Rabbit anti-Zip1 antibodies were used at 1∶300 dilution [3] . Mouse and rabbit anti-myc antibody were used at 1∶300 dilution ( Covance ) . Mouse anti-FLAG antibody was used at 1∶200 dilution ( Sigma ) . Images were captured using the Deltavision IX70 system ( Applied Precision ) , and softWoRx imaging software . Images were processed by deconvolution using the constrained iterative deconvolution algorithm within softWoRx , and appropriate consecutive deconvolved z-slices were projected together to form the final processed image . Zip1 distribution was quantified using various tools within softWoRx . Projected spread images of pachytene nuclei ( determined by presence of condensed chromatin by DAPI staining ) were used to obtain signal intensity values for the spread-containing region of the image in the green channel . The 90th percentile value for signal intensity was calculated and utilised as a threshold value for the ‘Polygon Finder’ tool within softWoRx , which identified continuous regions of Zip1 localization or ‘stretches’ . The region incorporating the whole spread area , and a threshold perimeter of 10 µm for polygons , was specified and polygons were calculated . Polygons identified outside the DAPI-stained area were manually de-selected and not used in the data . Polygon number and area were recorded and used to evaluate Zip1 distribution . At least 20 spread projections were analysed per strain . Projected images were selected and analysed for colocalization between channels using the colocalization tool within softWoRx , which calculated Pearson's correlation coefficient to represent the degree of colocalization . The region of the image used for analysis was determined by DAPI staining . At least 10 spreads were analysed for each pair of proteins . Native whole cell extracts ( WCE ) were prepared using 50 ml sporulating culture . Pelleted cells were resuspended in 400 µl of lysis buffer ( 1 mM DTT , 0 . 05% Igepal CA-630 , 200 mM NaCl , 10 mM EDTA , 10% Glycerol , 50 mM Tris-HCl , pH 8 . 0 ) containing protease inhibitors ( 1 mM PMSF and 1× protease inhibitor , EDTA-free ( Roche ) ) . Cells were lysed by beating six times for 20 seconds each in the presence of zirconia/silica beads at 4°C . Anti-FLAG or anti-myc antibody was incubated with WCE at 1∶125 dilution . Bound proteins were retrieved using protein G-coated Dynabeads ( Invitrogen ) . Beads were washed , then bound proteins were eluted by SDS and used for Western blotting . In parallel , after the wash stage , the immunoprecipitates were treated with 0 . 125 unit/µl Benzonase ( Merck ) in lysis buffer without EDTA , supplemented with 2 . 5 mM MgCl2 and incubated for 30 min at 4°C . This treatment was enough to completely digest 3 . 75 µg of plasmid DNA ( no trace of DNA was found , examined by agarose gel electrophoresis ) . The immunoprecipitates were subjected to Western blotting . Statistical analysis was undertaken using InStat3 and Prism software ( www . graphpad . com ) . Significance testing was done using the Kruskal-Wallis ( nonparametric ANOVA ) test with Dunn's multiple comparisions test . Graphs for Zip1 stretch number and area were drawn using the beeswarm and boxplot overlay packages in the R Project for statistical computing ( Bioconductor ) . Denatured protein extracts and Western blotting were done as described before [25] . Mouse and rabbit anti-myc antibodies were used at 1∶2000 and 1∶1000 dilutions respectively ( Covance ) . Mouse anti-FLAG antibody was used at 1∶1000 dilution ( Sigma ) . The physical recombination assay was done as described before [42] , [50] . For quantitation shown in Figure 1C , the signals from bands corresponding to linear dimer and trimer were measured and expressed as the percentage of the total signal ( linear monomer , dimer and trimer ) . | Meiosis is central to the life cycle of sexually reproducing organisms . The first round of division ( meiosis I ) is unique to meiosis in that homologous chromosomes are segregated to opposite poles . The tight association between homologous chromosomes is essential for their faithful segregation . To establish such association , meiosis employs a unique , homologous recombination-dependent mechanism that facilitates the recognition , association , and reciprocal exchange of DNA strands of homologous chromosomes , thus providing physical connections between homologous chromosomes . All these events take place in the context of an intricate structure called the synaptonemal complex ( SC ) . Within this complex , the axis of one chromosome is aligned at close proximity with the axis of its homologue . This alignment stretches along the entire length of the chromosome pair , with zipper-like structures , called transverse filaments , holding axes together . In this work , we identified the Ecm11-Gmc2 complex as a novel component of the SC , promoting the assembly of transverse filaments . Importantly , we demonstrate that post-translational modification of Ecm11 with SUMO ( small ubiquitin-like modifier ) is critical for ensuring the chromosomal loading of transverse filaments . Thus , our work provides a molecular basis for how homologous chromosomes become tightly associated during meiotic prophase . | [
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] | 2013 | The Ecm11-Gmc2 Complex Promotes Synaptonemal Complex Formation through Assembly of Transverse Filaments in Budding Yeast |
This genome-scale study analysed the various parameters influencing protein levels in cells . To achieve this goal , the model bacterium Lactococcus lactis was grown at steady state in continuous cultures at different growth rates , and proteomic and transcriptomic data were thoroughly compared . Ratios of mRNA to protein were highly variable among proteins but also , for a given gene , between the different growth conditions . The modeling of cellular processes combined with a data fitting modeling approach allowed both translation efficiencies and degradation rates to be estimated for each protein in each growth condition . Estimated translational efficiencies and degradation rates strongly differed between proteins and were tested for their biological significance through statistical correlations with relevant parameters such as codon or amino acid bias . These efficiencies and degradation rates were not constant in all growth conditions and were inversely proportional to the growth rate , indicating a more efficient translation at low growth rate but an antagonistic higher rate of protein degradation . Estimated protein median half-lives ranged from 23 to 224 min , underlying the importance of protein degradation notably at low growth rates . The regulation of intracellular protein level was analysed through regulatory coefficient calculations , revealing a complex control depending on protein and growth conditions . The modeling approach enabled translational efficiencies and protein degradation rates to be estimated , two biological parameters extremely difficult to determine experimentally and generally lacking in bacteria . This method is generic and can now be extended to other environments and/or other micro-organisms .
In the era of “omics” , systems biology has emerged with the availability of genome-wide data from different levels , i . e . genome , transcriptome , proteome , metabolome [1] , [2] . This approach aims at integrating omics data , mainly through computational and mathematical models [3] , [4] so as to decipher biological systems as a whole [5] . The integration of transcriptomic and proteomic results is a huge challenge by itself . The literature usually exploits these two approaches as complementary tools and does not often provide a correct confrontation of the two datasets . Until now , only a few researchers , mainly interested in yeast physiology [6] , [7] , have been working on this aspect and the results typically revealed modest correlations between those two datasets [8]–[10] . These weak correlations between transcript and protein levels can be the consequence of the involvement of post-transcriptional regulations [11] , such as translation control and protein degradation as evidenced by Brockmann et al . [12] . Translation regulations are believed to be involved in protein level control but are generally studied at the level of controlling specific molecular mechanisms and not at the genome scale [13]–[15] . Although polysome profile analysis allows translation efficiencies to be experimentally determined for the various transcripts simultaneously , this technique has been only rarely used and almost exclusively for S . cerevisiae [16] . Protein stability can also influence intracellular protein level and the correlation between transcript and protein [10] , [17] , [18] . However protein stability is rarely studied at the genome scale and data are only available for S . cerevisiae [19] , [20] . Finally , the rate of protein disappearance due to protein dilution by cellular growth is also potentially involved in protein level modifications but this physical phenomenon is generally neglected . More generally , even if translation efficiency , protein degradation and dilution rate can all influence protein levels , these parameters are not usually studied simultaneously . The role of each parameter in a whole cellular adaptation process has not been elucidated and it is not clearly known today which parameter is preponderant and if the control is constant or not when environmental conditions are modified . The aim of this study was to analyse the control of intracellular protein level taking into account all the parameters of this control , in a prokaryotic organism , the model of lactic acid bacteria , Lactococcus lactis . To achieve this purpose , transcriptomic and proteomic analyses were performed with cells from the same culture . Transcriptomic data were already available [21] and the corresponding proteome measurement was performed . The whole protein related processes including translation , dilution rate and protein degradation were modelled , and , since biological data were obtained at steady state , equations describing the protein levels equilibrium were solved . This modeling approach allowed translation efficiency and protein degradation to be estimated and the relative involvement of all the various parameters of protein control to be analysed .
L . lactis was grown in continuous culture at different growth rates in the conditions previously described [21] and samples were taken for both transcriptome and proteome analysis at three dilution rates , i . e . 0 . 09 , 0 . 24 and 0 . 47 h−1: the lowest growth rate ( μ = 0 . 09 h−1 ) was chosen as reference . Despite the small size of the L . lactis genome ( 2310 genes [22] ) , a total of 346 different proteins were quantified corresponding to 308 different proteins measured in each repetition for each of the 3 steady states . Among these proteins , 193 showed differential profiles in response to a growth rate increase: 88 with reduced level and 105 with higher level . All the proteins displaying a significant level of modification for at least one of the dilution rates are listed in Table 1 . In accordance with what has previously been found with transcriptome analysis [21] , increased levels of proteins related to biogenesis were observed when the growth rate was increased , i . e . proteins related to transcription ( GreA , NusA , QueA , RpoA ) , translation and more specifically ribosomal proteins ( GatA , GatB , RplE , RplI , RplJ , RplK , RplM , RplN , RpmE , RpsA , RpsF , RpsT ) , enzymes related to fatty acid and phospholipid metabolism ( AccA , AccD , FabD , FabF , FabG1 , FabH , FabZ1 , HmcM , ThiL , YdiD , YscE ) , two proteins involved in cell division ( FtsY , FtsZ ) , and some proteins associated with purine , pyrimidine , nucleoside and nucleotide metabolism ( Add , Adk , Apt , DeoB , GuaA , GuaC , Hpt , NrdE , PydA , PyrC , PyrE , RmlA , RmlB , Upp ) . Proteome profiles differed between the various stress-related proteins . On one hand , the two chaperones DnaK and GroEL , the superoxide dismutase associated to oxygen stress SodA , and DpsA , were found in higher quantity , while on the other hand , the cold shock associated protein CspE , ClpC and the adaptation related peroxidase Tpx , had decreased levels in response to growth rate increase . Besides those opposite punctual regulations , other proteins encoding important functions involved in stress protection such as ATPases or peptidases ( excepting PepP ) , were present at constant levels , independently of the growth rate . This lack of general tendency observed here at proteomic level was also observed at transcriptomic level [21] . In contrast , a wide down-regulation of genes involved in stress protection was observed in yeast when growth rate was increased [23] , [24] . Finally , one can notice that the two single phage-related proteins measured in those proteomics experiments showed significantly reduced levels at high growth rate . This last observation can be connected with the previously described massive down regulation of the expression of phage-related genes [21] . Transcriptomic and proteomic analyses were performed with cells collected simultaneously from the same fermentor; thus data can be strictly compared . Proteins and their corresponding transcript levels were compared individually . Transcriptomic data were already available [21] but were nevertheless re-processed so as to obtain concentrations rather than abundances ( see Materials and methods ) . For proteomic data , concentration and abundance values are expected to be similar ( see Materials and methods ) . For each growth rate , transcriptomic and proteomic mean values with their standard deviations are given in supplementary data ( Table S1 ) . The mRNA/protein ratios were not constant for the different genes since , at a given growth rate , data were spanned among five orders of magnitude ( Figure 1C ) . These variations were linked both to protein and mRNA changes though protein concentrations were globally more spanned than mRNA concentrations ( 4 and 2 log of magnitude respectively; see figure 1A and 1B ) . mRNA/protein ratios were compared between two conditions using the lowest growth rate ( 0 . 09 h−1 ) as reference ( Figure 1D ) and they globally increased with the growth rate . This tendency was confirmed when we analyzed similarly data corresponding to the maximum growth rate of 0 . 88 h−1 . These last data , also available in our group , were obtained in batch culture during the exponential growth phase , since this high growth rate could not be reached in continuous culture without any wash out of the cells from the chemostat [25] . What normally occurs in bacterial cells is the transcription of genes into mRNA , which are then translated into proteins that can be either diluted by growth or degraded ( Figure 2 ) . Hence , protein concentration is determined not only by translation rates but also by dilution and degradation , therefore the following balance equation can be written: ( 1 ) Rates of mRNA translation or protein degradation/dilution are assumed to be not constant and related to mRNA or protein concentration respectively . Biological rates were expressed as first order kinetics of their substrate concentration as previously postulated in L . lactis [26] , [27] but also in yeast strains [19] , [28] . Such modeling approach at the genomic scale is rare in the literature and dynamic experimental data allowing more elaborated kinetics to be hypothesized are not available . Making more complex those rate expressions would thus not make sense today . Dilution constant corresponds to the growth rate ( μ ) , degradation constant ( k″ ) is proportional to protein half-life ( t1/2 = ln ( 2 ) /k″ ) and k′ represents the translation efficiency . At steady state , the various concentrations are expected to remain constant , the time derivative of the protein concentration is equal to zero and the previous equation can be simplified and reorganized as follows: ( 2 ) In the chemostat cultures at the various growth rates , cells are at steady state; similarly , during the exponential growth phase , cells are physiologically stable and are also considered to be at steady-state [29] . The previous observation , establishing a relationship between mRNA/protein ratios and the growth rate for these four steady states ( see above ) , is in accordance with this last equation ( 2 ) . 171 different mRNA and protein couples were available in each repetition of the various steady states ( intersection of 308 couples in the 3 chemostat steady states and 191 in the batch ) . For only a few proteins were probes missing on the microarray; hence it was not possible to rebuild these couples . In order to estimate translation efficiency and protein degradation rate , the best mathematical solution to the equation ( 2 ) was sought , using numerical estimations performed on Matlab . The k′ and k″ values were postulated to be positive , in accordance with biological reality . Different solutions with k′ and/or k″ constant , directly or inversely proportional to μ were investigated . Estimation of the best fitting solution was based on the least square criterion [30] . For the 171 couples , the mean sum of the squared residuals ( difference between a ratio and its estimation ) associated to every combination are given in Table 2 . Considering the lower mean sum of the squared residuals , the best solution was obtained when both k′ and k″ were proportional to 1/μ ( k′ = α/μ and k″ = β/μ ) . Hence the equation ( 2 ) could be written as follows: ( 3 ) The mRNA/protein ratios were thus linked to the growth rate ( μ ) through a polynomial function of order two ( μ2 ) , which is consistent with the visual observation of the various curves ( not shown ) . For each mRNA–protein couple the reliability of the two estimated constants α and β was evaluated by their associated R2 . All regression coefficients are listed in Table 3 . The mean linear coefficient ( R2 ) associated to this model was 0 . 83±0 . 04 . Finally , the consistency of our modeling approach was checked when removing the data of the batch exponential growth phase from the analysis . A high mean R2 of 0 . 77±0 . 12 was still obtained using chemostat data exclusively . On the contrary , data not at steady state coming from other growth phases in batch cultivation could not be included . Indeed when taking into account mRNA/protein values during growth deceleration or during stationary phase , R2 was strongly affected and dropped to 0 . 21±0 . 03 and 0 . 24±0 . 04 respectively . The model ( 1 ) states that translation rate is proportional to the concentration of mRNA species which assumes that translation is mRNA-limited . An alternative hypothesis , would be the saturation of the ribosome with mRNA , as previously postulated in E coli [31] , which implicitly suggests competition of any specific mRNA with all others to be the determining factor in synthesis of the corresponding protein . We have thus tested the model ( 2 ) with mRNA abundances rather than concentration values . The modeling approach was robust since similar k′ and k″ dependencies to the growth rate ( k′ = α/μ and k″ = β/μ ) were obtained ( Table 2; data in italic ) . However the expression of individual protein/mRNA as a function of μ2 ( 3 ) had generally lower R2 ( only 48 couples with R2≥0 . 90 compared to 130 with concentrations ) , indicating the modeling approach with abundances was less satisfactory than with concentrations . In order to carry on our modeling approach , the data corresponding to mRNA concentrations were filtered and only couples with R2≥0 . 90 ( 130 ) were retained for further analyses ( Table 3 ) . It could be noticed that among the 41 eliminated couples , 15 displayed non monotonous evolutions of their mRNA/protein ratios against μ2 and 20 had very low mRNA/protein ratios , which were thus more sensitive to errors . The k′ and k″ coefficients were numerically calculated for each protein and in each growth condition from the values of α and β ( Table 2 ) by the relation k′ = α/μ and k″ = β/μ . Value distributions ( Figure 3 ) demonstrate the wide variability of k′ and k″ among proteins but also between growth conditions . The k″ decreases when growth rate increases which is consistent with the general idea that protein degradation is high in stationary phases [32] . Protein half-lives were calculated and median values of t1/2 were respectively 23 , 61 , 119 and 224 min for 0 . 09 , 0 . 24 , 0 . 47 and 0 . 88 h−1 . These values are in the same order of magnitude as those obtained recently for S . cerevisiae ( mean 43 min at a growth rate of 0 . 1 h−1 [19] ) . Like k″ , the translation efficiency k′ is also expressed as 1/μ function , indicating that , when the degradation process increases at low growth rate , the translation efficiency is also increasing in order to attenuate this negative biological effect . Due to the restricted size of the dataset but also to the non-uniform distribution of detected proteins in the various functional categories , it was not possible to use statistical tests to rigorously determine functional enrichments in extreme values of k′ or k″ . However , among the 15 genes that are translated the most efficiently ( highest α values in Table 3 ) , one can notice the over-representation of genes involved in major cellular processes: the Tig chaperone [33] and proteins involved in replication ( HslA , which can unwind DNA and plays a role in its supercoiling , [34] ) , and translation ( ribosomal proteins: RplA , RplF , RplK , RplN , RpsT ) . Carbon metabolism is also represented by 7 proteins ( GapB , EnoA , FbaA , Pmg , Pyk , TpiA , Ldh ) , all belonging to glycolysis which is the major metabolic pathway for energy production in L . lactis . The extremely stable proteins correspond to null values of β , and consequently k″ , were represented by a group of 26 proteins . Remarkably , half of them were related to stress responses: ClpE protease , PepC and PepP peptidases , three reductases that are usually linked to oxidative stress ( AhpC , TrxB1 , YpjH ) , but also MurF , involved in parietal structure , YtgH , which is homolog to Staphylococcus aureus alkaline stress protein [35] , YtaA and YahB two hypothetical protein sharing homologies with E . coli universal stress protein Usp [36] , YuhE , whose E . coli homologue is involved in copper resistance [37] , and two cysteine desulfurases ( YeiG and YseF ) whose corresponding genes in E . coli are involved in oxygen and copper stress responses [38] . Moreover , those extremely stable proteins are rather in the last third for translation efficiency . Thus L . lactis may limit degradation of stress-related proteins so as to maintain a minimal pool ready to use in case of emergency , which is biologically relevant . Biological determinants of translation efficiency and protein stability were investigated through correlation studies . Correlations providing a Spearman coefficient ( RSpearman ) with associated p-values lower than 0 . 05 were considered as significant . The codon adaptation index ( CAI ) positively correlates with k′ ( RSpearman = 0 . 57 ) . Since CAI directly reflects translation efficiency during the elongation step [39] , this result validates our translation efficiency estimations . Translation efficiency is also tightly related to the amino acid composition of proteins . A negative correlation of k′ was obtained with tyrosine , cysteine , histidine , aspartic acid and isoleucine frequencies while lysine and alanine richness had a positive influence ( Table 4 ) . The amino acids the most used have a positive influence on k′ whereas those with a negative effect are the less frequent ones ( Table 4 ) . The single exception is for isoleucine , but since it is the limiting nutrient it is not surprising to find it negatively correlated with translation efficiency , despite its high frequency in L . lactis proteins . This amino acid bias , together with the codon bias ( CAI ) , shows that translation efficiency is strongly dependent of the gene sequence . This optimized functioning state is probably the result of a long evolutionary process . Finally it was found that translation efficiency is affected by protein length: the longer the protein , the more k′ decreases ( RPearson of −0 . 18 ) . This negative correlation with length has already been reported for yeast [19] and can possibly be explained by a decrease of the ribosome density on long mRNA as previously shown for S . cerevisiae [40] . The only apparent correlation emerging for protein degradation constants k″ is a negative influence of cysteine richness ( Table 4 ) . Degradation and dilution by growth are both involved in protein disappearance and are competitive reactions . The degradation and dilution constants , k″ and μ , can be directly compared . The k″ is higher than μ at low growth rate but becomes lower after a critical value of 0 . 39 h−1 ( Figure 4 ) . The role of the degradation may thus be major at low growth rate while dilution may become the main phenomenon at fast growth . More generally , variations in protein concentration between two conditions can be related to changes in the three rates: protein synthesis , degradation and dilution . In order to better understand this regulatory node and identify which are the major controls , the quantitative involvement of the different actors was analyzed . Regulation coefficients corresponding to the protein level control were estimated with a method based on the one developed on S . cerevisiae [41] , [42] . Derivation of equation ( 2 ) leads to the following relationship: ( 4 ) The term of the equation ( 4 ) represents translation control on protein concentration and is called ρt while the term − , named ρd , includes both the dilution and the degradation and represents protein control by disappearance . ρt and ρd were estimated for each growth rate interval ( between 0 . 09 and 0 . 24 h−1; between 0 . 24 and 0 . 47 h−1; between 0 . 47 and 0 . 88 h−1 ) . The values of ρt were used to elucidate the nature of the control and are given in supplementary data ( Table S2 ) . If ρt≤0 , protein disappearance is the major controlling mechanism; if ρt≥·1 , it is translation; and if 0·< ρt·< ·1 , the control of protein is shared . The nature of the control for a given protein and its strength differed in the various the growth rate intervals . However a constant control by disappearance was observed for 6 proteins distributed all over the metabolism ( Als , GreA , LplL , PyrC RplQ , ThrC , see Table 3 for associated functions ) . Inversely the unknown protein YpdC was the single one constantly controlled by translation process . Independently of the growth rate , protein levels are mostly controlled by disappearance ( Table 5 ) . Translation control strongly decreases , and protein level control becomes less specific and more and more shared with increasing growth rates . Similar conclusions were valid when ρd was used for the control analysis instead of ρt ( data not shown ) .
The comparison of mRNA and protein ratios revealed a strong heterogeneity among genes but also for a given gene , at different growth conditions . Variability among genes has recently been reported for the model yeast S . cerevisiae but these ratios remained constant between the two studied conditions , i . e . a rich and a poor media [43] . Though lacking in this publication , the maximum growth rates of S . cerevisiae were estimated to be 0 . 46 and 0 . 35 h−1 respectively in a rich and a poor media ( Parrou J . L . , personal communication ) . Thus it is postulated that the growth rate difference between these two conditions was too small to induce changes in mRNA/protein ratios . The combination of two modeling approaches , one based on biological knowledge and the other on experimental data fitting , has enabled translation efficiency and protein degradation rate to be determined for each protein , phenomena which have been shown to be protein specific and growth rate dependant . The positive correlation of translation efficiency with codon bias in L . lactis is consistent with the results obtained for the yeast , though translation efficiencies have been calculated differently [12] . The presence of genes related to major cellular processes essential for growth were marked among the best translated . This finding corroborates what was found in archaebacteria for ribosomal proteins [44] . In L . lactis , the growth-rate dependant variations in translation efficiency are probably not related to changes in the amount of intracellular ribosomes if the constant ratio between mRNA and ribosomal RNA ( see Material and methods ) is taken into account . However one has to bear in mind that rRNA does not necessarily means assembled and/or active ribosomes . It is known for example that E . coli ribosome activity can be modulated by the inter-conversion between a functional 70S and a dimerized 100S inactive form [45] . To resolve this question , it will be necessary to investigate genome-wide ribosomal activity via polysome distribution which would provide key information to decipher the regulatory processes controlling translation . Polysome profile technology is already available for yeast but may be difficult to adapt to bacteria due to the co-localisation of transcription and translation in the cytoplasm . Protein half-lives for the whole genome have never been determined nor estimated in any bacteria and data are only available in the literature for S . cerevisiae . However studies disagreed in terms of average half-life values: 31 h for Pratt et al . against 43 min for Belle et al . [19] , [20] . Those differences could be explained by methodological reasons since one study used pulse chase experiments [20] whereas the other one consisted in a direct measurement of each epitope-tagged proteins [19] . In our study , for L . lactis , protein median half-lives ranged from 23 to 224 min . These low values are in good agreement with most recent values obtained for S . cerevisiae [19] and indicate that protein degradation is considerably more rapid than was once believed . Degradation rates in L . lactis were negatively correlated to cysteine content in proteins . In yeast , stable proteins were previously found to have a higher valine density whereas unstable ones are enriched in serine [19] . It is difficult to strictly compare those results since amino acid bias may be species specific and reflect the particularities of proteases involved in protein degradation . The negative correlation with cysteine could nevertheless be related to the potential formation of disulfide bridges known for stabilizing proteins [46] . The current work also revealed the presence of stress related proteins among the most stable . This last observation differs from results obtained in yeast indicating that ribosomal proteins and enzymes from amino-acids metabolism have the higher half-life [19] . This high stability of stress protein together with the lack of global transcriptional stress response observed in L . lactis when the growth rate is changed clearly underlines differences of stress adaptation mechanisms between the two micro-organisms . Protein degradation exerts a major role in the cellular adaptation process since protein half-live data depend on the growth rate ( 1/μ function ) . Moreover , the degradation rate is even higher than dilution rate at low growth rate ( Figure 4 ) . Considering that protein degradation is an ATP consuming process [47] , high protein degradation at slow growth rate may contribute to the increase of maintenance energy that is generally observed in such conditions [48] . Like protein degradation , translation efficiency is also increased at slower growth rates . Effects of translation efficiency and protein degradation are thus antagonist and this mode of regulation is probably dedicated to attenuate biological changes . Inversely , proteins with the lowest degradation rates also corresponded to low translation efficiencies . The analysis of the regulation involved in the control of protein concentrations demonstrated that it is not constant in the different ranges of growth rate . At low growth rates , disappearance seems to be the main controlling mechanism , which could be attributed to high degradation rate . At high growth rate , the control becomes more complicated with some proteins regulated at the level of synthesis , disappearance or both ( shared control ) . This increased complexity is consistent with cells approaching their maximum growth performance . With this modeling approach , we have estimated translational efficiencies and protein degradation rates . These two biological parameters are extremely difficult to measure experimentally and have even never been previously determined in bacteria . The method was based on an in depth comparison of proteome and transcriptome data and was developed with the small genome bacterium L . lactis on a limited number of mRNA - protein couples ( 171 ) . It will be possible in the future to broaden these couples since other proteomic methodologies , such as the APEX technology [8] , allow more proteins to be detected . The approach remains generic and can be applied to all microorganisms . Modelling equations were solved because steady-states cultures were used: chemostat fermentation technology enabling steady states to be studied has thus proved to be a powerful tool to understand microbial physiology . We have demonstrated that bacteria exert a sharp control on intracellular protein levels , through a multi-level regulation involving three growth rate dependant actors: translation , dilution and degradation . Here , the growth rate was changed via chemostat cultures , but such growth rate modifications are also encountered in nature when cells have to face new environments . In this case , the adaptation process involves growth rate adaptation as well as other specific metabolic adaptations . It remains to be determined how the protein control is exerted in such natural environment .
Lactococcus lactis ssp . lactis IL1403 , whose genome has been entirely sequenced [22] , was grown as previously described [21] . Briefly , three different growth rates have been studied , namely 0 . 09 , 0 . 24 and 0 . 47 h−1 during anaerobic chemostat cultures ( under nitrogen atmosphere and regulated pH ) on a chemically defined medium limited by isoleucine concentration . For each steady-state , samples have been harvested in at least quadruplicate with a minimum delay of five doubling time between each sampling . Transcriptomic data ( geo platforms GSE10256 [21] for chemostat culture and GSE12962 for batch exponential phase ) were already available . Briefly , these transcriptomic analyses had been obtained with a constant amount ( 10 µg ) of total RNA ( mRNA , ribosomal RNA and transfer RNA ) labeled by retro-transcription ( 33P ) and hybridized on nylon membrane as previously described [49] . Three independent biological repetitions were used . These transcriptomic data had been normalized by all spots' mean intensity and thus corresponded to mRNA abundances . They were reprocessed here in order to calculate mRNA concentrations with the method previously described [49] . Raw data were first standardized by the all spots' mean intensities of the reference membrane ( and not with its proper membrane ) in order to eliminate the bias of the radioactivity level between the various repetitions and then corrected by total RNA concentration in order to take into account changes in intracellular RNA yield in the cells . Consistent with previous results [50] , this yield increased significantly with the growth rate in L . lactis ( 3 . 58±0 . 39 , 4 . 92±0 . 52 , 7 . 34±0 . 28 and 11 . 06±0 . 23 g for 100 g cell dry weight at μ = 0 . 09 , 0 . 24 , 0 . 47 and 0 . 88 h−1 respectively ) . Since the amount of RNA to perform transcriptomic analysis is maintained constant in order to avoid retro-transcription labelling bias , these RNA yield changes are completely hidden by the technology . The total raw intensity of the membrane without any normalisation represents the amount of mRNA in the RNA sample used for transcriptomic analysis ( 10 µg ) . This total intensity was constant at each growth rate and lower than the saturation threshold ( mean value of 1660±584 , 1462±383 , 1389±366 , 1474±367 , 1496±425 at μ = 0 . 09 , 0 . 24 , 0 . 47 and 0 . 88 h−1 respectively ) . Thus , it can be deduced that the ratio mRNA/total RNA was constant and assuming that ribosomal RNA is the major component of total RNA we can postulate that the fraction mRNA/ribosomal RNA is independent of the growth rate . For each condition , three repetitions were performed with independent cultures , extractions and electrophoresis . Bacteria were harvested from the cultures and cell pellets were washed twice with ice-cold 200 mM Na-phosphate , pH 6 . 4 and re-suspended in 4 ml of 20 mM Na-phosphate buffer , pH 6 . 4 , 1 mM EDTA , 10 mM tributylphosphine , a cocktail of protease inhibitors ( P8465; Sigma Aldrich , St Louis , MO ) 20-fold diluted and catalase 40 U/ml ( C3155; Sigma Aldrich , St Louis , MO ) to limit isoform formation . The cell suspension ( approximately 35 units of optical density at 600 nm [OD600]/mL , ) was transferred to the pre-cooled chamber of a BASIC Z cell disrupter ( Celld , Warwickshire , United Kingdom ) and was subjected to a pressure of 2 , 500 bars . The suspension was centrifuged at 5 , 000×g for 20 min at 4°C to remove unbroken cells and large cellular debris . The supernatant was collected and centrifuged at 220 , 000×g for 30 min at 4°C . The total protein concentration in the resulting supernatant ( cytosolic fraction ) was determined with the Coomassie protein assay reagent ( Pierce , Rockford , IL ) using bovine serum albumin as standard and was included between 1 and 2 mg/mL . The cytosolic fraction was aliquoted and stored frozen at −20°C . R2 calculations and equations resolution were perform with MATLAB software . Correlations were estimated using R free statistical software to calculate Spearman rank correlation coefficient and the associated p-value . | This work is in the field of systems biology . Via an in-depth comparison of proteomic and transcriptomic data in various culture conditions , our objective was to better understand the regulation of protein levels . We have demonstrated that bacteria exert a tight control on intracellular protein levels , through a multi-level regulation involving translation but also dilution due to growth and protein degradation . We have estimated translational efficiencies and protein degradation rates by modeling . These two biological parameters are extremely difficult to measure experimentally and have not been previously determined in bacteria . We have found that they are growth rate dependent , indicating a fine control of translation and degradation processes . We have worked with the small genome bacterium Lactococcus lactis on a limited number of mRNA-protein couples but keeping in mind that this approach could be extended to other micro-organisms and biological phenomena . We have exhibited that mathematical modeling associated to experimental steady-states cultures is a powerful tool to understand microbial physiology . | [
"Abstract",
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] | [
"computational",
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] | 2009 | Transcriptome and Proteome Exploration to Model Translation Efficiency and Protein Stability in Lactococcus lactis |
Visceral leishmaniasis ( VL ) is a chronic and fatal disease in humans and dogs caused by the intracellular protozoan parasites , Leishmania donovani and L . infantum ( L . chagasi ) . Relapse of disease is frequent in immunocompromised patients , in which the number of VL cases has been increasing recently . The present study is aimed to improve the understanding of mechanisms of L . donovani persistence in immunocompromised conditions using alymphoplastic aly/aly mice . Hepatic parasite burden , granuloma formation and induction of regulatory T cells were determined for up to 7 months after the intravenous inoculation with L . donovani promastigotes . While control aly/+ mice showed a peak of hepatic parasite growth at 4 weeks post infection ( WPI ) and resolved the infection by 8 WPI , aly/aly mice showed a similar peak in hepatic parasite burden but maintained persistent in the chronic phase of infection , which was associated with delayed and impaired granuloma maturation . Although hepatic CD4+Foxp3+ but not CD8+Foxp3+ T cells were first detected at 4 WPI in both strains of mice , the number of CD4+Foxp3+ T cells was significantly increased in aly/aly mice from 8 WPI . Immunohistochemical analysis demonstrated the presence of Foxp3+ T cells in L . donovani–induced hepatic granulomas and perivascular neo-lymphoid aggregates . Quantitative real-time PCR analysis of mature granulomas collected by laser microdissection revealed the correlation of Foxp3 and IL-10 mRNA level . Furthermore , treatment of infected aly/aly mice with anti-CD25 or anti-FR4 mAb resulted in significant reductions in both hepatic Foxp3+ cells and parasite burden . Thus , we provide the first evidence that CD4+Foxp3+ Tregs mediate L . donovani persistence in the liver during VL in immunodeficient murine model , a result that will help to establish new strategies of immunotherapy against this intracellular protozoan pathogen .
Visceral leishmaniasis ( VL ) is a chronic and fatal disease caused by the intracellular protozoan parasites Leishmania donovani and L . infantum ( chagasi ) , which infect a range of mammalian hosts , including humans , dogs and rodents [1] . Liver , spleen , bone marrow ( BM ) and lymph nodes are the major sites for parasite growth and disease pathology . Transplantation of infected kidney , liver , heart , lung , pancreas or BM has been shown to cause VL in transplant recipients , indicating lifelong parasite persistence in the viscera [2] . Moreover , malnutrition is a risk factor for the development of VL [3] . Recent experiments in protein energy- , zinc- and iron-deficient mice suggest that this effect is mediated primarily through functional failure of the lymph node barrier and increased early visceralization of the parasites [4]–[6] . Loss of the control of parasite persistence in VL causes the reactivation of parasites and relapse of the disease is frequent in the immunocompromised patients , in which the number of visceral leishmaniasis cases has been increasing recently [7] . However , the mechanisms underlying the parasite persistence in the immunocompromised condition have not been clearly clarified . To develop effective prophylactic or therapeutic strategies against VL , understanding of the precise immune mechanisms including T-cell functions in the chronic stage of infection is required [8] . The role of secondary lymphoid organs for immune responses to Leishmania infection has not been investigated . The aly/aly mouse is an autosomal recessive natural mutant C57BL/6 strain that carries a point mutation within the gene encoding NF-κB inducing kinase ( NIK ) [9] , which prevents the induction of the non-canonical NF-κB pathway [10] . The aly/aly mice lack all lymph nodes and Peyer's patches with the abnormal architecture of spleen and thymus and exhibit severely impaired humoral response [9] . This mutant mouse strain has been used to examine the role of secondary lymphoid organs for immune responses to intracellular pathogens , including Mycobacterium leprae , Listeria monocytogenes , vesicular stomatitis virus , vaccinia virus , lymphocytic choriomeningitis virus and human T-cell leukemia virus [11]–[14] , and different susceptibilities to these pathogens have been reported . Organ-specific immunity has been described in various experimental VL studies in mouse models [15] , [16] . The liver is the site of an acute but resolving infection . In contrast , the spleen becomes a site of parasite persistence with associated immunopathological changes [17] . In BALB/c and C57BL/6 mice , the inflammatory granuloma reaction around infected Kupffer cells is developed and the infection is resolved by 4–8 weeks after infection [18] . However , low levels of hepatic parasite persistence for 6–12 additional months occur and administration of anti-CD4 antibodies result in the relapse of hepatic quiescent L donovani infection [19] , suggesting that CD4+ T cells are required for the maintenance of acquired immunity and prevention of relapse . However , no additional data explaining the underlying mechanisms of CD4+ T cell-mediated control of persistent parasites have been presented . Cellular and molecular interactions mediated by Kupffer cells , monocytes , CD4+ and CD8+ T cells and a number of cytokines and chemokines are required for effective hepatic granuloma formation [16]–[18] , [20] , [21] . Defects in these cellular and molecular factors cause ineffective parasite clearance from the liver , but most murine studies have focused on the first few weeks of infection and not the persistent stage of infection [18] . The present study is aimed to improve the understanding of mechanisms of L . donovani persistence in an immunocompromised condition . Our data presented herein offered a novel insight into the involvement of CD4+Foxp3+ regulatory T cells ( Tregs ) in L . donovani persistence in the liver of immunodeficient aly/aly mice . Moreover , treatment of infected aly/aly mice with anti-CD25 or anti-FR4 mAb revealed the significant reductions in both hepatic Tregs and parasite burden . These results suggest that manipulation of Tregs may provide a promising immumotherapeutic strategy for VL .
Female ALY® NscJcl aly/aly and aly/+ mice of 6–8 weeks of age were purchased from CLEA Japan , Inc . ( Tokyo , Japan ) . Mice were maintained , inoculated and sacrificed within a safety facility of Hokkaido University . A virulent line of L . donovani ( MHOM/SU/62/2S-25M-C2 ) [22] was maintained by passage of the frozen stabilized parasites in NNN medium containing 5% defibrinated hemolyzed rabbit blood . Then , parasites were consecutively sub-passaged in liquid M199 medium supplemented with 15% heat-inactivated fetal calf serum ( HIFCS ) , 25 mM HEPES and 50 µg/ml gentamycin . The stationary growth phase of subcultures with less than five passages was used for mouse inoculation . Mice were infected by injecting stationary phase promastigotes ( 5×107 ) intravenously via the lateral tail vein and were sacrificed at 1 , 2 , 4 , 8 , 12 , 16 and 28 weeks post infection ( WPI ) . One group of non-infected animals was used as naïve control . This study was carried out under the guidance of the Institute for Laboratory Animal Research ( ILAR ) . All animals were housed in a facility in strict accordance with the recommendations in the Guidelines for the Care and Use of Laboratory Animals of Graduate School of Veterinary Medicine , Hokkaido University , which was based on Fundamental Guidelines for Proper Conduct of Animal Experiment and Related Activities in Academic Research Institutions under the jurisdiction of the Ministry of Education , Culture , Sports , Science and Technology , Japan and approved by the American Association for Accreditation of Laboratory Animal Care ( AAALAC ) international . The protocol was approved by the Committee on the Ethics of Animal Experiments of Hokkaido University ( Permit Number: 10-0009 ) . Giemsa-stained impression smears of the liver were prepared and parasite burden was determined as Leishman-Donovan Units ( LDU ) , in which LDU is the number of amastigotes per 1 , 000 host nuclei , multiplied by the liver weight in gram [23] . Genomic DNA ( gDNA ) was isolated from different tissues , including liver , spleen , BM , blood , heart , lung , kidney , brain and skin , using the QIAamp® DNA Mini Kit ( Qiagen , MA , USA ) . Real-time quantitative ( qPCR ) assays were performed on the StepOne™ and the StepOnePlus™ Real-Time PCR Systems ( Applied Biosystems , CA , USA ) , following the manufacturer's instructions . A typical 20-µl reaction mixture contained approximately 100 ng gDNA , 1× SYBR® Premix Ex Taq™ II ( Takara , Tokyo , Japan ) , 0 . 4 µM each primer ( Table S1 ) and 1× Rox™ Reference Dye . All samples were run in triplicate and underwent an initial 30 sec incubation step at 95°C , followed by 40 cycles of 5 sec at 95°C and 30 sec at 65°C for the Leishmania surface protease gp63 gene or 60°C for the mouse brain-derived neurotrophic factor ( mBDNF ) gene [24] , [25] . The average threshold cycle of amplification ( Ct ) values was determined , and standard deviation ( SD ) of all the reaction was analyzed by the software provided with the instrument . The relative amounts of the gp63 gene were then calculated using standard curve method normalized to the amounts of the mBDNF gene . The livers were fixed in 10% neutral phosphate-buffered formalin . Paraffin-embedded organs were cut into 4 µm-thick sections , followed by staining with hematoxylin and eosin for light microscopy . For the detection of parasites , liver sections were subjected to indirect immunohistochemical staining using L . infantum-infected dog serum ( 1∶1000 dilution ) [26] and horseradish peroxidase ( HRP ) -conjugated goat anti-dog IgG heavy and light chain antibody ( 1∶300; Bethyl Laboratories , TX , USA ) . Peroxidase was visualized using 3 , 3′-diaminobenzidine ( DAB ) -H2O2 ( Wako , Tokyo , Japan ) and the sections were counterstained with Mayer's hematoxylin before dehydration and mounting . Hepatic immune responses were categorized into ( 1 ) “No granuloma”: no inflammation with no mononuclear cell ( MNC ) around the parasitized Kupffer cells; ( 2 ) “Immature granuloma”: less than 10 MNCs around the parasitized Kupffer cells; ( 3 ) “Mature granuloma”: epithelioid cells and more than 10 MNCs around the parasitized Kupffer cells; and ( 4 ) “Involuting granuloma”: devoid of amastigotes and tissue inflammatory nearly resolved [18] , [23] . The number of infected foci with each tissue response including “No granuloma” , “Immature granuloma” , “Mature granuloma” and “Involuting granuloma” was counted for 25 consecutive microscopic fields per mouse liver at ×400 magnification . Hepatic mononuclear cells were isolated using a 33% ( vol/vol ) Percoll solution , as described elsewhere [27] . Briefly , livers were minced , pressed through a stainless steel mesh and suspended in RPMI1640 medium ( Sigma , MO , USA ) supplemented with 3% HIFCS ( wash buffer ) . After washing , the cells were resuspended in 33% Percoll solution containing heparin ( 100 U/ml ) and centrifuged at 800× g for 30 min to remove liver parenchymal cells . The pellet was treated with an RBC lysis solution ( 155 mM NH4Cl , 10 mM KHCO3 , 0 . 1 mM EDTA ) , washed and re-suspended in 2 . 4G2 mAb solution to block the Fc receptor before staining with antibody . Antibodies used for FACS included PE-labeled rat anti-mouse CD4 ( L3T4 ) ( BD Pharmingen , CA , USA ) , FITC-labeled rat anti-mouse CD8a ( Lyt-2 ) ( BD Pharmingen ) , APC-labeled rat anti-mouse Foxp3 ( FJK-16s ) ( eBioscience , CA , USA ) and the proper isotype staining control , according to the manufacturer's instructions . Flow cytometry analysis of the labeled cells was performed on a FACS Calibur ( BD Pharmingen ) , running the Cell Quest program provided with the instrument . Lymphocytes were identified by forward scatter ( FSC ) and side scatter ( SSC ) characteristics , gated and further analyzed with Cell Quest software ( BD Pharmingen ) or FlowJo software V . 5 . 7 . 2 ( Tree Star Inc . , OR , USA ) . Immunohistochemical analysis of the 4 µm-thick paraffin-embedded sections of the liver was performed to determine the localization of Foxp3+ Tregs . After deparaffinization and rehydration , heat-induced epitope retrieval ( HIER ) was conducted by autoclaving at 100°C for 17 min using Target Retrieval Solution ( pH 9 . 0 ) ( Dako , Uppsala , Sweden ) . Endogenous peroxidase was blocked by incubating sections in 0 . 3% H2O2 in absolute methanol for 30 min at 4°C , followed by flushing with water and incubation with 10% goat serum for 1 h at room temperature ( RT ) to block crystallized receptor fragments . The sections were incubated overnight with rat anti-mouse/rat Foxp3 mAb , clone FJK-16s ( eBioscience ) , in 1∶100 diluted with 0 . 1% Triton X in PBS ( pH 7 . 4 ) . For negative control sections , PBS was used instead of the primary antibody . After washing three times in PBS ( 5 min each ) , sections were incubated in 1∶100 biotin-conjugated goat anti-rat IgG ( H+L ) antibody ( Invitrogen , MD , USA ) for 30 min at RT . Sections were then washed , which was followed by incubation with streptavidin-peroxidase conjugate ( Histofine SAB-PO® Kit ) for 30 min at RT . The streptavidin-biotin complex was visualized with DAB-H2O2 solution , pH 7 . 0 , for 4 min . Sections were washed in distilled water , and finally counterstained with Mayer's hematoxylin . The mean counts of Foxp3-expressing cells were assessed microscopically at 400× magnification by counting a total of 25 consecutive fields . The number of immunoreactive cells was estimated in each hepatic granuloma assembly . Values are expressed as the means of immunoreactive cells present in 25 fields . Double immunofluorescence staining was also conducted to locate Tregs in the liver . Formalin-fixed and paraffin-embedded liver sections were subjected to the deparaffinization , rehydration and HIER as described above . After blocking of crystallized receptor fragments with 10% goat serum , sections were incubated overnight with rat anti-mouse/rat Foxp3 mAb ( clone FJK-16s; 1∶100; eBioscience ) at 4°C . Then , the sections were incubated with FITC-goat anti rat IgG ( 1∶200; Zymed , CA , USA ) for 30 min at RT and successively incubated in 10% donkey or rabbit serum to block the crystallized receptor fragments . For double staining of Foxp3-expressing cells and T cells , the sections were incubated with rabbit anti-mouse CD3 mAb ( 1∶200; Nichirei ) overnight at 4°C and then with TRITC-donkey anti rabbit IgG ( 1∶200; Abcam , MA , USA ) for 30 min at RT . On the other hand , double staining of Foxp3-expressing cells and L . donovani amastigotes was conducted using L . infantum-infected dog serum ( 1∶1 , 000 ) and TRITC-goat anti dog IgG ( 1∶200; Rockland , PA , USA ) . Finally , the sections were mounted using a Fluoromount™ ( DBS , CA , USA ) and examined under an IX70 confocal microscope ( Olympus , Tokyo , Japan ) . Laser microdissection ( LMD ) was performed in RNase-free conditions as described previously [28] . Cryosections of 7 µm thickness were prepared from the frozen livers of naïve and infected mice and embedded in Tissue-Tek OTC compound ( Sakura , Tokyo . Japan ) . The sections were mounted on glass slides pre-coated with LMD films ( Meiwafosis , Osaka , Japan ) and fixed with absolute methanol for 3 min at 4°C . After staining with 0 . 5% toluidine blue for 10 sec , approximately 20 “Mature granulomas” were microdissected from each frozen liver sample by using Ls-Pro300 ( Meiwafosis ) . Total RNA was purified from the frozen whole liver tissue and microdissected “Mature granulomas” , using the RNAqueous®-Micro Kit ( Ambion , Texas , USA ) . Expression levels of Foxp3 , TGF-β and IL-10 mRNA were determined by quantitative RT-PCR ( qRT-PCR ) using the PrimeScript™ RT Reagent Kit ( Takara ) and the relative number of these molecules to 1000 housekeeping glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) was calculated using a standard curve method . The PCR reaction was performed as described above using primers shown in Table S1 [29] , [30] . At 26 WPI , three L . donovani-infected aly/aly mice were intraperitoneally injected three times every other day with 0 . 5 mg of rat anti-mouse CD25 mAb ( clone PC61; Biolegend , CA , USA ) , 0 . 05 mg of rat anti-mouse FR4 mAb ( clone TH6; Biolegend ) or 0 . 5 mg of rat IgG ( Jackson ImmunoResearch , PA , USA ) as a control . The mice were euthanized at 10 days post-antibody injection for examination of host responses as described above . Statistical differences between aly/aly mice and aly/+ mice at the indicated time points were tested using Student's t-test ( Microsoft Excel software ) and two-way ANOVA as well as post hoc Bonferroni test ( Prism software version 5 , GraphPad , CA , USA ) . All data are presented as the mean values ± SE unless otherwise stated . p<0 . 05 was considered as statistically significant .
Long-term persistence after clinical cure of the primary infection is a characteristic feature of many intracellular pathogens , including protozoan parasites of the genus Leishmania , but the underlying mechanisms are not fully understood [31] . We measured parasite burdens in the livers of aly/+ and aly/aly mice for up to 28 WPI by two different methods . The number of amastigotes in hepatic impression smears was expressed as LDU ( Figure 1A ) , and relative amounts of Leishmania gp63 gene to mBDNF gene were determined by qPCR ( Figure 1B ) . In aly/+ mice , parasite burden peaked at 4 WPI and reduced to near-baseline levels by 8 WPI . In aly/aly mice , parasite burden also peaked at 4 WPI but the maximum parasite burden was lower than that of aly/+ mice . Although the parasite load decreased by 8 WPI as observed in aly/+ mice , the parasite persisted in the liver of aly/aly mice during the observation period of 28 WPI ( Figure 1 ) . Persistent L . donovani infection was also demonstrated in the spleen and BM of both mice strains but the parasite burden was much higher in aly/aly mice during the chronic phase of infection . Nevertheless , parasite was not detected in the skin and internal organs , such as lung , kidney , heart and brain during infection by qPCR ( data not shown ) . Efficient granuloma development around infected Kupffer cells is a key event in the control of hepatic L . donovani infection [8] , [16] , [18] . The infected foci in the liver were examined and made a quantitative analysis of granuloma formation around the parasitized Kupffer cells . The progression of granuloma formation from “No granuloma” to “Immature granuloma” , “Mature granuloma” and finally “Involuting granuloma” was observed in aly/aly mice as well as aly/+ mice ( Figure S1 ) , indicating that aly/aly mice have ability to generate hepatic cell-mediated immunity to some extent as shown in the previous study [32] . The number of infected foci was well correlated with the hepatic parasite loads ( Figure 1 ) ; the number of foci in aly/+ mice reached a peak at 4 WPI and was drastically reduced at 8 WPI ( Figure 2A ) and the involuting granuloma was well formed ( Figure 2B ) . However , the number of involuting granuloma in the liver of aly/aly mice were much less than those in aly/+ mice at 4 and 8 WPI ( Figure 2C ) while the 30–40% of the infection foci with no granulomas was found in the liver of aly/aly mice at 4–16 WPI . These may reflect that effective but insufficient clearance of the parasites in granuloma of aly/aly livers renders persistent release of the parasites , which results in increased proportion of infected Kupffer cells in the later stages . Foxp3+ Tregs influence immunity to viral , bacterial or parasitic infections [33] . To begin to characterize the mechanism by which parasites persist in the liver , we examined whether Tregs expand in the livers of aly/aly mice and where they localize during L . donovani infection . Flow cytometry analysis of hepatic lymphocytes revealed no expansion of CD8+Foxp3+ T cells in the liver during L . donovani infection in either strain of mice ( Figure 3A ) . In contrast , CD4+Foxp3+ T cells were first detected at 4 WPI in both strains of mice . In aly/aly mice , the proportions of CD4+Foxp3+ T cells to CD4+ T cells ( Figure . 3B ) as well as the absolute number of CD4+Foxp3+ T cells ( Figure 3C ) were higher than those of aly/+mice especially at 8–16 WPI although the total number of hepatic CD4+ T cells was not significantly different between aly/+ and aly/aly mice ( Figure S2 ) . There have been no reports describing the localization of Foxp3-expressing cells in the liver during VL . To address this , we stained Foxp3 in liver sections of naïve and L . donovani-infected aly/+ and aly/aly mice . Foxp3-expressing cells were localized in the “Immature granuloma” and “Mature granulomas” as well as the perivascular areas of infected aly/aly mice . Furthermore , the density of Tregs increased , especially in the perivascular areas , during the course of infection ( Figure 4A and B ) . Development of such abnormal lymphocyte infiltration or neo-lymphoid aggregates at perivascular areas is a feature found in aly/aly and other alymphoplastic mice [32] . In addition , the frequency of “Mature granulomas” containing more than 5 Tregs increased during infection in aly/aly mice ( 5% at 4 WPI , 18% at 12 WPI and 39% at 28 WPI ) , suggesting the accumulation of Tregs at sites of inflammatory foci . On the other hand , Foxp3-positive Tregs were limited to the parenchyma , granulomas and perivascular areas at 4 WPI and hardly detectable in the liver of infected aly/+ mice at 12 WPI ( Figure 4A and B ) . Double immunofluorescence analysis of hepatic granuloma revealed that Foxp3-expressing cells ( green in Figure 5A ) and CD3+ cells ( red in Figure 5A ) were present in the granuloma , and Foxp3+ cells expressed CD3 molecules ( Figure 5A-merged image ) . Some CD3+Foxp3+ cells ( yellow arrows in Figure 5A-merged image ) were adjacent to the CD3+Foxp3− cells ( pink arrows in Figure 5A-merged image ) . In addition , L . donovani amastigotes ( red in Figure 5B ) were surrounded by Foxp3+ cells ( green in Figure 5B ) in the hepatic granuloma . These results suggested that the interaction among parasitized cells ( Kupffer cells ) , CD3+Foxp3+ cell ( Tregs ) and CD3+Foxp3− cells ( non Tregs , probably CD4+ and/or CD8+ effector T cells ) . Evidence has accumulated regarding the essential roles of Tregs in the control of a variety of physiological and pathological immune responses , but it is still obscure how Tregs control other lymphocytes at the molecular level [34] . Quantitative RT-PCR was performed for Foxp3 , IL-10 and TGF-β mRNA levels in the whole liver and micro-dissected “Mature granulomas” liver tissue samples of L . donovani-infected aly/aly mice . The Foxp3 mRNA expression was increased after infection in the whole liver ( Figure 6A ) and mature granuloma samples ( Figure 6B ) . Although the TGF-β mRNA transcripts showed similar levels at 4 and 12 WPI in both tissue samples , the levels of IL-10 mRNA markedly increased in mature granuloma but not whole liver samples at 12 WPI ( Figure 6A and B ) , suggesting that IL-10 may be involved in function of Tregs . Manipulation of Tregs by treatment with antibodies has been used to examine the roles of Tregs in many infectious diseases [33] . Effects of anti-CD25 and anti-FR4 mAb on hepatic immune responses in L . donovani-infected aly/aly mice at 26 WPI were examined . Ten days after injection with anti-CD25 or anti-FR4 , reduction in Foxp3 mRNA expression was observed ( Figure 7A ) . This reduced Foxp3 mRNA expression was associated with decreases in parasite burden ( Figure 7B ) and infected foci ( Figure 7C ) . Instead , the frequency of “Mature granulomas” was increased after treatment with especially anti-FR4 mAb ( Figure 7D ) , suggesting that depletion of Tregs can activate hepatic cellular immune responses and accelerate parasite killing . Furthermore , immunohistochemical analysis confirmed a reduction in Foxp3-immunoreactive cells in the liver parenchyma , granulomas ( Figure 7E ) and perivascular neo-lymphoid areas ( data not shown ) .
In the present study , aly/aly mice were used as an immunodeficient VL murine model and immunohistopathologically investigated during L . donovani infection for up to 28 WPI . CD4+Foxp3+ T cells were increased in the granulomas and perivascular areas of the liver in the chronic phase and the impairment of granuloma maturation was observed . The depletion of Tregs by the administration of either anti-CD25 or anti-FR4 mAb resulted in significant reductions in hepatic Tregs , infected foci and parasite burden . To our knowledge , this is the first definitive evidence that CD4+Foxp3+ Tregs are involved in hepatic L . donovani persistence in a murine model of VL . The aly/aly mice have been used to examine the role of secondary lymphoid organs on immune responses in various infection models . Disruptive architecture of the thymus and spleen could affect the development and expansion of T cells . Several studies using bone marrow chimeras between aly/aly and wild type mice showed that antiviral CTL responses were clearly improved in the wild type environment [11] . However , expansion of CD25+CD4+ Treg is impaired in the spleen of aly/aly mice [35] . This suggests that expansion of functional CD4+Foxp3+ Treg in the liver of aly/aly mice during L . donovani infection is likely related to the parasite persistence but not to the structural defects of secondary lymphoid organs although this possibility will be confirmed by BM chimera experiments in future . The NIK gene mutation may contribute to other immune defects due to the partial blocked NF-κB activation [10] , [36] . NF-κBp52 knockout mice showed less parasite burden in the liver , perhaps due to less number of B cells ( unpublished data; Ato M . , Kaye PM ) . NF-κBp50 ( NF-κB1 ) would be important for TNF/TLR signaling which is involved in canonical TLR/TNFR signaling for activation of dendritic cells and macrophages . NIK is associated in CD40/LT-αβR but not in TNF . CD40 signaling is one of DC activation factors , but the function of DC of aly/aly are controversial . Yamada et al [36] has reported that DC from aly/aly mice exhibit grossly normal development and function . However , Tamura et al [35] had reported that DCs from aly/aly mice showed impaired antigen presentation ability . Lower hepatic parasite loads was unexpectedly observed in aly/aly mice than aly/+ mice in the first 4 WPI . This may be not due to lower number of the sessile Kupffer cells ( unpublished data ) , but associated with the strong innate immunity as reported during Listeria monocytogenes infection in aly/aly mice [12] . Partial hepatic granuloma progression and neo-lymphoid aggregates in aly/aly mice imply that mice lacking secondary lymphoid tissues can still generate T cell-mediated immune responses to some extent [32] . Although anti-CD25 mAbs have been used for depletion of Tregs in various experimental cases , administration of anti-FR4 mAb also reduced Treg numbers and provoked effective tumor immunity [37]–[39] . In the present study , 10 days after the third injection of infected aly/aly mice with anti-CD25 and anti-FR4 mAb , the hepatic parasite burdens were reduced by 88% and 89% of that of control mice , respectively ( Figure 7B ) . Likewise , treatment with either anti-CD25 or anti-FR4 mAb also reduced parasite burdens in the spleen and BM ( Figure S3 ) . The reason why anti-FR4 mAb was more effective than anti-CD25 mAb in reducing parasite burden is unknown , but the present study is the first to report effectiveness of anti-FR4 mAb to control systemic infection of L . donovani in mice . Thus , anti-FR4 antibodies may be an alternative measure to manipulate Tregs in chronic VL . However , since anti-CD25 mAb can also affect effector T cells and effective immunity [39] , and anti-FR4 mAb can also deplete a small population of CD4+ Foxp3− T cells in the lymph node [38] , probably including IL-10-producing conventional CD4+ T cells , further studies of the role of Tregs in VL are required . Studies of Tregs in cutaneous leishmaniasis demonstrated the involvement of CD4+CD25+ Tregs in cutaneous leishmaniasis caused by L . major [40] , [41] and L . amazonensis in mice [42] and by L . braziliensis in humans [43] . Regarding VL , the role of Tregs is uncertain and the primary source of IL-10 is controversial . In the spleen of VL patients in India , CD4+CD25−Foxp3− cells were identified as the major producers of IL-10 [44] . In L . infantum-infected BALB/c mice , CD4+CD25+ Foxp3+ cells expanded in a pooled fraction of draining lymph nodes and spleen cells at 7 and 28 days of infection [45] . In L . donovani-infected BALB/c mice , the number of splenic CD4+ CD127dimCD25+GITR+ T cells expressing higher Foxp3 and IL-10 increased at 21 days of infection [46] . IL-10 production by splenic CD4+CD25−Foxp3− IL10+ T cells , representing type 1 regulatory T ( Tr1 ) cells , was a strong correlate of disease progression in L . donovani-infected C57BL/6 mice [47] . Further analyses using quantitative RT-PCR of IL-10 and Foxp3 transcripts in selected populations of CD25+ and CD25− enriched hepatic CD4+ T cells , and/or by intracellular cytokine staining , will elucidate the issue . Nevertheless , in the present study , Treg and IL-10 augment immunosuppressive effects in hepatic granuloma of L . donovani-infected aly/aly mice . Maintenance of relatively higher expression levels of TGF-β in the chronic phase of the infection in aly/aly mice may be related to the generation and maintenance of CD4+Foxp3+ Tregs [48] rather than the inhibition of granuloma maturation [49] . In conclusion , we focused on immune responses to the chronic phase of murine VL caused by L . donovani infection in an immunodeficient host . In the last decade when the number of visceral leishmaniasis in immunocompromised patients has been increasing , our data presented herein offered a novel insight into the possibly involvement of CD4+Foxp3+ Tregs in persistent L . donovani infection in the liver of immunodeficient hosts . The manipulation of Tregs may provide a promising immumotherapeutic strategy for VL . | The protozoan parasite Leishmania donovani is the causative agent of visceral leishmaniasis ( VL ) with a variety of outcomes ranging from asymptomatic to fatal infection . In the last decade , an increasing number of VL cases in immunocompromised conditions have been reported . Loss of the control of parasite persistence causes relapse of the disease in these patients . To clarify why parasite persistence and disease are caused in an immunocompromised condition , we examined L . donovani infection in alymphoplastic aly/aly mice that completely lack lymph nodes and have disturbed spleen architecture . Although parasites grew in the liver of aly/+ mice for the first 4 weeks post infection ( WPI ) and parasites were eliminated by 8 WPI , we found that parasites persisted in the liver of aly/aly mice with the ineffective of granuloma formation to kill the parasites . These aly/aly mice showed significant increases in CD4+Foxp3+ regulatory T cells in the liver . Consequently , we treated infected mice with anti-CD25 or anti-FR4 mAb to inhibit the function of Tregs , and found significant reductions in both hepatic Foxp3+ cells and parasite burden . These results clearly demonstrated for the first time that the expansion of CD4+Foxp3+ Tregs is involved in hepatic L . donovani persistence in immunodeficient murine model . | [
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"immunopathology",
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"leishmaniasis",
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] | 2012 | Involvement of CD4+ Foxp3+ Regulatory T Cells in Persistence of Leishmania donovani in the Liver of Alymphoplastic aly/aly Mice |
Hepatitis C virus ( HCV ) is an important cause of chronic liver disease . Several highly diverse HCV genotypes exist with potential key functional differences . The HCV NS5A protein was associated with response to interferon ( IFN ) -α based therapy , and is a primary target of currently developed directly-acting antiviral compounds . NS5A is important for replication and virus production , but has not been studied for most HCV genotypes . We studied the function of NS5A using infectious NS5A genotype 1–7 cell culture systems , and through reverse genetics demonstrated a universal importance of the amphipathic alpha-helix , domain I and II and the low-complexity sequence ( LCS ) I for HCV replication; the replicon-enhancing LCSI mutation S225P attenuated all genotypes . Mutation of conserved prolines in LCSII led to minor reductions in virus production for the JFH1 ( genotype 2a ) NS5A recombinant , but had greater effects on other isolates; replication was highly attenuated for ED43 ( 4a ) and QC69 ( 7a ) recombinants . Deletion of the conserved residues 414-428 in domain III reduced virus production for most recombinants but not JFH1 ( 2a ) . Reduced virus production was linked to attenuated replication in all cases , but ED43 ( 4a ) and SA13 ( 5a ) also displayed impaired particle assembly . Compared to the original H77C ( 1a ) NS5A recombinant , the changes in LCSII and domain III reduced the amounts of NS5A present . For H77C ( 1a ) and TN ( 1a ) NS5A recombinants , we observed a genetic linkage between NS5A and p7 , since introduced changes in NS5A led to changes in p7 and vice versa . Finally , NS5A function depended on genotype-specific residues in domain I , as changing genotype 2a-specific residues to genotype 1a sequence and vice versa led to highly attenuated mutants . In conclusion , this study identified NS5A genetic elements essential for all major HCV genotypes in infectious cell culture systems . Genotype- or isolate- specific NS5A functional differences were identified , which will be important for understanding of HCV NS5A function and therapeutic targeting .
Hepatitis C virus ( HCV ) chronically infects 130–170 million people and leads to increased risk of severe liver disease . HCV belongs to the Flaviviridae family and has a positive-strand RNA genome containing one long open reading frame ( ORF ) . The ORF encodes a polyprotein that is co- and post-translationally cleaved into structural proteins ( Core , E1 , E2 ) , p7 and nonstructural ( NS ) proteins NS2 , NS3 , NS4A , NS4B , NS5A and NS5B . Significant diversity is found among HCV isolates , which in phylogenetic analysis cluster into seven major genotypes and numerous subtypes [1] , [2] . Genotypes , subtypes and isolates/strains differ at around 30% , 20% and 2–10% , respectively , at the nucleotide and amino acid level . A higher variability is found in certain genome regions . Among different genotypes , the NS5A protein sequence varies up to 50% in composition and by more than 20 residues in length . Important differences between HCV genotypes were identified in biology [3] and in sensitivity to neutralizing antibodies [4]–[7] . The HCV genotype is an important determinant for response to the current interferon ( IFN ) -α based treatment regimens; sustained virological response is achieved for 80–90% of genotype 2 and 3 and for around 50% of genotype 1 and 4 infected patients [8] . Several HCV genes , including E2 , NS3 and NS5A , were suggested to influence the response to IFN [9] . Directly-acting antiviral compounds are currently being developed with the NS3 protease , the NS5B polymerase and NS5A as primary targets [10] . Genotype- and isolate-specific responses to treatment with directly-acting antivirals have been reported in vitro [11] , [12] and in clinical trials [13] , [14] . The NS5A phosphoprotein is a component of the viral replication complex [15] and consists of three domains separated by low-complexity sequences ( LCS ) [16] . It is anchored to intracellular membranes through the N-terminal amphipathic alpha-helix [17] , [18] . A crystal structure was solved for domain I [19] , [20] , which contains a zinc-binding motif [16] and a highly basic channel with RNA binding capacity [21] . The amphipathic alpha-helix , domain I and II are essential for the genotype 1b replicon system [16] , [22] , [23] . In LCSI and domain II , covering parts of the binding site for the IFN-induced dsRNA-dependent protein kinase R ( PKR ) [24] , an IFN-sensitivity determining region ( ISDR ) was described for genotype 1b [25] , although , its existence is controversial [26] . Several studies on genotype 2a suggest a primary role for domain III in production of infectious particles [27]–[29] . For most genotypes , however , the role of NS5A in the viral life cycle has not been studied . Until development of the genotype 2a JFH1 cell culture system [30] , and the more efficient J6/JFH1 system with the Core-NS2 region from another 2a isolate [31] , studies on HCV NS5A relied on the genotype 1b and 2a replicon systems , recapitulating only parts of the viral life cycle [9] . J6/JFH1-based infectious cell culture systems for NS5A genotypes 1–7 [12] allowed us to study the function of the highly variable NS5A protein for all major HCV genotypes in context of the complete viral life cycle . We analyzed individual NS5A domains for their influence on steps of the viral life cycle and found genotype- and isolate-specific effects of introduced mutations and modifications .
To determine the importance of the individual NS5A domains for the major HCV genotypes in context of the complete viral life-cycle , we introduced selected mutations ( Figure 1A ) into J6/JFH1-based NS5A genotype 1–7 recombinants , which we previously demonstrated were infectious and efficient in vitro ( Materials & Methods ) [12] . In previous studies , it was found that the Con-1 ( genotype 1b ) replicon system was inhibited by the I12E mutation shown to disrupt the hydrophobic face of the amphipathic alpha-helix [22] , by C57G and C59G mutations interfering with zinc-ion binding of domain I [16] , and by mutating the conserved W329 in domain II [23] ( Numbering throughout is according to individual H77 reference proteins , GenBank accession number AF009606 ) . The S225P mutation in LCSI was shown not to be permissible in vivo when introduced into the Con-1 ( 1b ) full-length infectious clone [32] , even though it had an enhancing effect on the Con-1 ( 1b ) replicon system [33] , [34] . These residues were all highly conserved among HCV patient isolates ( Figure 1B ) . To analyze the effect of these mutations in cell culture , RNA transcripts of more than 30 mutants of the H77C ( 1a ) , J4 ( 1b ) , JFH1 ( 2a ) , S52 ( 3a ) , ED43 ( 4a ) , SA13 ( 5a ) , HK6a ( 6a ) and QC69 ( 7a ) NS5A recombinants were transfected into Huh7 . 5 cells in parallel with the positive control J6/JFH1 ( for consistency denoted JFH1 ( 2a ) according to the NS5A isolate ) [31] . While around 30% of cells were HCV positive one day after JFH1 ( 2a ) transfection , the I/V12E , C57G/C59G , S225P and W329A genotype 1–7 mutants were all highly attenuated , as no or very few HCV positive cells were observed in immunostainings one and three days post-transfection . Since reversion was observed in vivo for the Con-1 ( 1b ) S225P mutant coinciding with detection of high viral titers [32] , we followed the J4 ( 1b ) S225P mutant until it , after two weeks , infected most cells . In virus recovered after passage to naïve cells , S225P had reverted . Thus , for this mutant the findings in the infectious cell culture system were in accordance with findings in vivo but not with findings in the replicon system [33] , [34] . To determine whether another mutant with a replicon-enhancing alteration was also attenuated we changed the J4 ( 1b ) NS5A recombinant to encode the Δ47 deletion , which replaces residues 235–282 in NS5A LCSI/domain II by a single tyrosine ( Figure 1A ) [33]; this mutant was followed for three weeks without detection of HCV positive cells . Thus , the positive effect of NS5A replicon-enhancing mutations apparently led to the opposite effect in the infectious cell culture system . We further investigated whether the phenotype observed for the highly attenuated NS5A mutants corresponded to that of mutants of other HCV nonstructural proteins previously shown to abrogate HCV RNA replication . No HCV positive cells were observed one day after transfection of JFH1 ( 2a ) mutants of the NS3 protease active site ( NS3pro− , S139A [35] ) , the NS3 helicase active site ( NS3hel , D290A [35] ) , the NS4A transmembrane segment ( G21V [36] ) , the NS4B C-terminal end ( W252S [37] ) , the NS5B polymerase active site ( GND-mutant , D318N [38] ) , for a JFH1 ( 2a ) mutant with a stop codon in the NS3 N-terminus ( Y6[stop] ) , or for the JFH1 ( 2a ) NS5A domain I mutants C57G , C59G or C57G/C59G included for comparison . However , infection emerged subsequently for the NS3hel , NS4BW252S , NS5AC59G , NS5BGND and NS3Stop mutants when following cultures for more than two weeks . Reversion of the knockout mutations and the presence of silent marker mutations engineered to exclude contamination were confirmed by sequencing . We found that in a total of 13 experiments done with NS5BGND , 10 led to emerging infection . Viruses recovered from five of these cultures were subsequently sequenced , in all of which the attenuating mutation had reverted; the marker mutations were maintained . Thus , the phenotype of attenuated NS5A mutants corresponded to that of single mutations of other non-structural genes expected to be detrimental for viral replication . For these mutants , reversion was detected in most cases . We speculate that this was due to transfected RNA pools containing genomes that were reverted to wild-type as a result of the high error-rate of T7 polymerase driven in vitro RNA transcription . The W329 in the C-terminal end of NS5A domain II was found to be of critical importance for replication of NS5A genotype 1–7 recombinants , while it was previously reported that a JFH1 NS5A mutant with a deletion of residues 246–308 covering a large N-terminal region of domain II ( Δ2222–2280 mutant ) apparently was fully viable [27] . We had similar findings for a J6/JFH1 virus without NS5A residues 250–293 [39] , and therefore wanted to investigate whether deletion of the corresponding residues was generally permitted for NS5A isolates . Thus we constructed H77C ( 1a ) and TN ( 1a ) Δ250–293 mutants ( Figure 1A and S1 ) and transfected RNA transcripts into Huh7 . 5 cells . Infectivity titers after transfection were reduced for these mutants , most significantly for H77C ( 1a ) ; as previously observed , titers for the JFH1 ( 2a ) mutant were not decreased ( Figure 2A ) . This corresponded to immunostainings with 20–30% HCV positive cells one day post-transfection , except for the H77C ( 1a ) Δ250–293 mutant , for which only 5% were positive . All recombinants spread to the majority of cells within 8 days . Analysis of NS5A of recovered passaged viruses of JFH1 ( 2a ) , H77C ( 1a ) and TN ( 1a ) Δ250–293 mutants confirmed the deletion . Amino acid changes D444G ( D2416G ) and C447R ( C2419R ) were identified in H77C ( 1a ) and TN ( 1a ) Δ250–293 , respectively . No NS5A changes were observed for the JFH1 ( 2a ) mutant . To investigate whether viral replication , assembly or release was affected for H77C ( 1a ) and TN ( 1a ) Δ250–293 mutants , we transfected CD81-deficient Huh7-derived S29 cells that are not susceptible to HCV infection [40] . HCV Core levels were used as a measure for RNA replication [41]; validation experiments confirmed the correlation between intracellular HCV RNA and Core levels ( Figure S2 ) . From separate cultures , assembly and release was evaluated by titration of intra- and extra-cellular infectivity . While replication and virus production was not affected for the JFH1 ( 2a ) mutant , minor decreases in replication levels and intracellular virus production was observed for TN ( 1a ) ( Figure 2B and C ) . The H77C ( 1a ) mutant exhibited greater reductions , in particular for intra- and extra-cellular virus production that was reduced more than 10-fold . Thus , while the C-terminal region of domain II was critical for replication of all HCV genotypes , deletion of the N-terminal residues 250–293 led to a highly attenuated phenotype for H77C ( 1a ) , an intermediate phenotype for TN ( 1a ) and no attenuation of JFH1 ( 2a ) . The NS5A LCSII region was shown to influence HCV replication and virus production [23] , [42] . The Con-1 ( 1b ) replicon system was reported to be highly attenuated by the P343A LCSII mutation [23] . However , changes at this genotype-specific position ( P343A for genotype 1 and 5 , A343P for other genotypes , Figure S1 ) did not influence infectivity titers after transfection of Huh7 . 5 cells for any of the NS5A genotype 1–7 recombinants ( Figure 3A and data not shown ) . No changes were identified in the complete ORF of the recovered JFH1 ( 2a ) A343P mutant , and residue 343 had not reverted for mutants of other NS5A isolates . This was in agreement with findings in a more recent Con-1 ( 1b ) replicon study [42] . In addition , we generated the JFH1 ( 2a ) P346A/P351A/P354A and P346A/P351A/P353A/P354A/P355A LCSII mutants , in which highly conserved prolines ( Figure 1B and S1 ) in two putative SH3 interaction domains [42]–[44] were mutated . Infectivity titers after transfection of Huh7 . 5 cells were decreased <10-fold ( Figure 3A ) , and the mutations did not revert after passage to naïve cells . Next , the P346A/P351A/P354A mutations were introduced into the NS5A genotype 1–7 recombinants , and tested for viability in Huh7 . 5 cells . While supernatant HCV infectivity titers were only slightly decreased for the J4 ( 1b ) mutant , reductions by 10-fold or more were observed for H77C ( 1a ) , S52 ( 3a ) , ED43 ( 4a ) , SA13 ( 5a ) , HK6a ( 6a ) and QC69 ( 7a ) mutants ( data not shown ) . To analyze whether attenuation was caused by reduced replication capacities , we measured intracellular HCV Core after transfection of S29 cells . RNA replication of ED43 ( 4a ) and QC69 ( 7a ) P346A/P351A/P354A mutants was highly attenuated , with intracellular HCV Core levels similar to those observed for the NS5BGND mutant ( Figure 3B ) . These findings were in agreement with immunostainings , which on day 1 after transfection of Huh7 . 5 cells were HCV negative for the ED43 ( 4a ) and QC69 ( 7a ) mutants . For all other LCSII mutants , replication was decreased with up to 10-fold reductions in intracellular HCV Core levels 48 and 72 hours post-transfection . While intra- and extra-cellular infectivity titers from transfected S29 cells were only slightly decreased for the J4 ( 1b ) and JFH1 ( 2a ) mutants ( Figure 3C ) , greater than 10-fold reductions were observed for the H77C ( 1a ) , S52 ( 3a ) , SA13 ( 5a ) and HK6a ( 6a ) mutants . Thus the P346A/P351A/P354A mutations had a profound impact on intracellular virus production for some NS5A isolates but not for others . No infectivity was observed for the ED43 ( 4a ) and QC69 ( 7a ) mutants , as expected from RNA replication assays . Thus , various NS5A isolates tolerated changes in LCSII to different extents . The ED43 ( 4a ) and QC69 ( 7a ) mutants were highly attenuated in viral replication compared to other isolates , while assembly of intracellular infectious viral particles was affected by LCSII mutations at an isolate-specific level . In genotype 2a studies , NS5A domain III was reported to have a primary role in virus production , e . g . deletion of JFH1 domain III significantly attenuated virus production while replication was less affected [23] , [27] , [28] . The high degree of sequence conservation of residues 414-428 ( Figure S1 ) suggested an important function of this region; thus we generated deletion mutants for the NS5A genotype 1–7 recombinants ( Figure 1A ) . While the JFH1 ( 2a ) Δ414-428 mutant was not attenuated , infectivity titers after transfection of Huh7 . 5 cells were decreased up to 10-fold for deletion mutants of other NS5A isolates ( Figure 4A ) . This correlated with previous reports on a 382–428 JFH1 deletion mutant [27] . After spread of infection in culture and passage to naïve cells , sequencing of the ORF from recovered viruses confirmed the deletion for all recombinants and identified changes for the H77C ( 1a ) , TN ( 1a ) , S52 ( 3a ) , SA13 ( 5a ) and QC69 ( 7a ) deletion mutants , primarily in the NS5A region downstream of the deletion and in p7 , NS2 and NS3 ( Table 1 ) . To analyze whether attenuation of Δ414-428 mutants was caused by reduced replication capacities , we measured intracellular HCV Core after transfection of S29 cells . For most mutants , replication was decreased less than 2-fold , but greater than 3-fold reductions in intracellular Core were observed at multiple time points after transfection with H77C ( 1a ) , TN ( 1a ) and S52 ( 3a ) mutants ( Figure 4B ) . The effect of deletion mutants on intra- and extra-cellular infectivity after transfection of S29 cells corresponded to observations from Huh7 . 5 culture supernatant , with greater than 3-fold reductions in titers for the H77C ( 1a ) , J6 ( 2a ) , S52 ( 3a ) , ED43 ( 4a ) and SA13 ( 5a ) recombinants; in this assay JFH1 ( 2a ) and QC69 ( 7a ) were not affected by the deletion ( Figure 4C ) . For most recombinants , reductions in replication capacities could explain the decreases in intra- and extracellular titers observed . However , the less than 2-fold decrease in replication capacity for the ED43 ( 4a ) and SA13 ( 5a ) mutants is not likely to explain the greater than 3-fold reductions in intra- and extracellular titers observed . This indicated an effect on virus assembly in addition to the effect on replication for certain NS5A isolates . To determine whether the genotype 2-specific 20 residue insertion immediately downstream of residue 428 ( Figure 1A and S1 ) could be responsible for the efficient replication and virus production of the JFH1 ( 2a ) Δ414-428 mutant ( Figure 4 ) , we replaced the 414-428 region of the H77C ( 1a ) recombinant by the JFH1-specific insertion . For this mutant , “Δ414-428+20aa” , replication capacity was slightly decreased , but unlike for the H77C ( 1a ) Δ414-428 mutant , no attenuation of intra- or extracellular infectivity was observed and no additional mutations were identified after passage to naïve cells . Thus , the genotype 2-specific insertion could potentially compensate for the deletion of residues 414-428 . Serines in domain III play an important role for genotype 2a NS5A function [28] , [29] , [45]; in particular the S437A mutation ( Figure 1 ) was reported to attenuate J6/JFH1 virus production [28] . After transfection of Huh7 . 5 cells with RNA transcripts of S437A mutants of NS5A genotype 1–7 we did , however , not identify significant attenuation of virus production ( Figure 5 and data not shown ) . No changes were identified in the ORF of the recovered JFH1 ( 2a ) S437A mutant and residue 437 had not reverted for mutants of other NS5A isolates . We further analyzed the conserved arginine/lysine motif at residue 356–359 and the partially conserved 363–380 region that covers the completely conserved residues 363 , 372 and 376 . Huh7 . 5 cells were transfected with JFH1 ( 2a ) mutants containing point mutations or a deletion of the 363–380 region ( Figure 1 and S1 ) . A minor decrease in infectivity titers was observed for the R356A/R357A/R358A/R359A mutant , but not for L363A/L372A , L363H/L372H or Δ363–380 mutants ( Figure 5 ) . We confirmed the presence of point mutations and the 363–380 deletion in recovered virus after passage to naïve Huh7 . 5 cells . Thus , no major effect of the introduced mutations was observed , even after deletion of a larger conserved region of NS5A domain III . NS5A domain III was previously reported to be important for efficient virus production of genotype 2a [27] , [29] . The present findings indicate that domain III is also of importance for replication , with isolate-specific dependence on the highly conserved 414-428 region for replication and virus assembly . To further investigate the functional impact of mutations in LCSII and domain III , we analyzed the potential effect on NS5A stability . Due to limitations in available NS5A antibodies recognizing domain III deletion mutants of the different isolates , we focused this analysis on H77C ( 1a ) and the mutants P346A/P351A/P354A in LCSII as well as Δ414-428 and “Δ414-428+20aa” in domain III . Huh7 . 5 cells were transfected with the various recombinants , and cultures with >80% infected cells where lysed for analysis in western blots . To control for impact on replication , NS5A amounts were normalized to amounts of Core . All analyzed mutants had relative NS5A amounts decreased to 30–50% of that of the original H77C ( 1a ) ( Figure 6 ) . Thus , assuming that changes in NS5A did not influence stability of Core , we concluded that the P346A/P351A/P354A mutations in LCSII and the Δ414-428 deletion in domain III decreased stability of H77C ( 1a ) NS5A . As seen in Table 1 , two recombinants with modifications in NS5A domain III acquired the F26S ( F772S ) mutation in p7 . We previously reported that this mutation compensated for the exchange of NS5A in the J6 ( 2a ) and ED43 ( 4a ) recombinants , and in the DH6 ( 1a ) NS5A recombinant this residue changed to leucine [12] . To further investigate a putative genetic linkage between NS5A and p7 sequences , we introduced p7 F26S into H77C ( 1a ) , TN ( 1a ) and JFH1 ( 2a ) NS5A recombinants , which did not rely on adaptation [12] . After transfection and passage to naïve Huh7 . 5 cells we sequenced p7 and NS5A of viral genomes recovered from supernatants . The H77C ( 1a ) and TN ( 1a ) mutants acquired C447R ( C2419R ) or V446L ( V2418L ) in NS5A , respectively , while the JFH1 ( 2a ) mutant did not acquire additional mutations ( Table 2 ) . This was seen in four separate experiments for each mutant , while four replicate experiments with the original H77C ( 1a ) , TN ( 1a ) and JFH1 ( 2a ) recombinants did not lead to accumulation of any mutations ( Table 2 ) . No variation is observed among genotype 1a and 2a isolates in the HCV database at these two residues . To cross-check whether the observed changes in NS5A of the genotype 1a recombinants compensated for the p7 mutation , we transfected H77C ( 1a ) NS5AC447R and TN ( 1a ) NS5AV446L mutants in triplicates . After passage to naïve cells , all H77C ( 1a ) NS5AC447R cultures and two of three TN ( 1a ) NS5AV446L cultures acquired changes in p7 , including F26S ( Table 2 ) . Most cultures in addition acquired changes in NS5A . We hypothesized that if the F26S ( p7 ) and C447R or V446L ( NS5A ) mutations were compensatory , combining these mutations should abolish the need for further mutations . Indeed , after transfection and subsequent passage of H77C ( 1a ) p7F26SNS5AC447R and TN ( 1a ) p7F26SNS5AV446L mutants in triplicates , no additional mutations were observed except for one additional coding change in NS5A for one TN ( 1a ) p7F26SNS5AV446L culture ( Table 2 ) . Thus , modifications of NS5A from several isolates induced changes in p7 , and p7 mutations induced changes in NS5A for genotype 1a . JFH1 ( 2a ) apparently better tolerated the change in p7 and did not rely on compensatory NS5A mutations . In phylogenetic analysis of NS5A , genotype 2 clusters separately from other genotypes . To investigate the functional significance of genotype-specific residues in the highly variable NS5A protein , we changed positions in JFH1 ( 2a ) , where genotype 2a residues were different from all or almost all isolates of genotypes 1 , 3 , 4 , 5 and 6 ( Figure 7 ) . Most of these positions were in NS5A domain I , and in most cases genotype 2b isolates had residues identical to genotype 2a . Mutations were introduced into JFH1 ( 2a ) singly or combined: E95T/Q97P , I110L , H124V , S126D , I140C , S151T/W152E , Q157R , P165C/F168L/F169L and C436V . After transfection , <10% of Huh7 . 5 cells were HCV positive for the I140C and S151T/W152E mutants , while around 30% were positive for JFH1 ( 2a ) and other mutants . Virus production was attenuated for these two mutants and for the E95T/Q97P , Q157R and P165C/F168L/F169L mutants ( Figure 8A ) . After immediate or delayed spread of infection in the transfection culture , supernatants were passaged to naïve cells and the NS5A gene of recovered mutants was sequenced; the I140C and S151T/W152E mutants both acquired V130I . In addition the I140C mutant acquired L188F , and the S151T/W152E mutant acquired T122M . In two independent experiments , the H124V mutant acquired I289T or S300P in domain II . No mutations in NS5A were observed for the other mutants . In functional analyses we investigated the five mutants attenuated after transfection of Huh7 . 5 cells . As demonstrated by measurement of intracellular HCV Core after transfection of S29 cells , replication was highly attenuated for the I140C and S151T/W152E mutants while Core accumulation was delayed for the E95T/Q97P , Q157 and P165/F168/F169 mutants ( Figure 8B ) . Intra- and extracellular infectivity titers after transfection of S29 cells were reduced up to 10-fold for the E95/Q97 , Q157R , S151T/W152E and P165C/F168L/F169L mutants , while reductions of 100-fold or more were observed for the I140C mutant ( Figure 8C ) . Thus the genotype 2 or 2a-specific residues E95T/Q97P , Q157R and P165C/F168L/F169L seemed to be important both for viral replication and intracellular virus assembly . To address whether the observed attenuation of JFH1 ( 2a ) mutants was indeed due to genotype-specific requirements at the analyzed positions , we introduced the reverse mutations into the H77C ( 1a ) NS5A recombinant . After transfection of Huh7 . 5 cultures , around 10% HCV positive cells were observed for the T95E/P97Q , C140I , T151S/E152W and R157Q mutants , while 30% were positive for the original H77C ( 1a ) NS5A recombinant; no positive cells were observed for the C165P/L168F/L169F mutant . This was reflected by decreased infectivity titers for all mutants ( Figure 8D ) . Thus , mutation of the H77 ( 1a ) NS5A recombinant to genotype 2- or 2a-specific residues also led to attenuation . Since NS5A function depended on several genotype-specific residues in domain I , we wanted to determine whether this domain could function as a genotype-specific entity . We thus replaced NS5A domain I for JFH ( 2a ) by H77C sequence and for H77C ( 1a ) by JFH1 sequence , thereby generating two J6/JFH1 recombinants with either domain I or domain II–III from H77C . After transfection into Huh7 . 5 cells , the H77C domain II–III recombinant had slightly delayed viral spread and infectivity titers decreased by more than 10-fold compared to JFH1 ( 2a ) , while the domain I recombinant was highly attenuated ( Figure 8E ) . Data from our previous study showed attenuation even for a J6/JFH1-based recombinant with domain I from the J6 isolate also of genotype 2a [12] . Thus , NS5A function depended on genotype-specific residues in domain I and genotype-specific interactions existed between NS5A domain I and domain II–III .
Due to the clinical and biological importance , there has been great interest in the study of HCV genotype-specific functional differences [9] . However , most functional studies of HCV in infectious culture systems have depended on a single HCV isolate ( JFH1 ) . The NS5A protein was so far primarily studied in genotype 1 and 2 replicon systems or in the JFH1 genotype 2a cell culture system . Interestingly , replicon-enhancing mutations were not permissible in vivo [32] , emphasizing that conclusions from the replicon systems should be drawn with caution . In this study , we used infectious NS5A genotype 1–7 recombinants [12] , and demonstrated a universal dependence of viral replication on the NS5A amphipathic alpha-helix , domain I , LCSI and domain II . Thus , it was demonstrated that all HCV genotypes require these domains for replication . Interestingly , isolate-specific effects of mutations in LCSII and of deletions in domain II and III revealed novel functional differences between NS5A isolates . Furthermore , NS5A function was shown to depend on genotype-specific residues in domain I , a finding that could influence the effect of directly acting antiviral compounds directed against this NS5A domain . Thus , functional isolate-specific differences are emerging for HCV [3] , [42] , which will be of critical importance for our understanding of HCV biology and for development of antiviral strategies that target NS5A and other regions with isolate variability . We addressed the importance of the various regions of NS5A for replication and virus production , by analyzing more than 80 mutants in culture . We showed that viral replication in context of the complete life-cycle was critically dependent on the NS5A amphipathic-alpha helix , domain I , LCSI and domain II for isolates of genotypes 1a , 1b , 2a , 3a , 4a , 5a , 6a and 7a . The absence of HCV positive cells by immunostaining early after transfection corresponded to observations for knockout mutants of non-structural proteins NS3 , NS4A , NS4B , and NS5B , and a mutant with an introduced stop-codon , all expected to abolish replication . We demonstrated that reversion could occur even for such single-residue mutants , potentially due to the high error-rate of the T7 polymerase used for RNA in vitro transcription . Thus , abrogation of replication due to mutations shown to disrupt the hydrophobic face of the amphipathic alpha-helix , mutation of two zinc-binding cysteines in domain I , or due to exchange of a conserved tryptophan in domain II confirmed and extended previous findings in the Con-1 ( 1b ) replicon system [16] , [22] , [23] . Contrarily , dependence on LCSI in the infectious cell culture system was demonstrated by the highly attenuated phenotype caused by the replicon-enhancing mutation S225P [33] , which was also not permissible in vivo [32] . Thus , at least for this mutant the infectious NS5A cell culture systems reflected findings in vivo better than replicon systems . Recently , S225P was shown to enhance replication but inhibit HCV Core release of the full-length Con-1 ( 1b ) isolate in vitro [46] , which was not in agreement with our data from infectious culture systems . In theory , inhibition of virus production but not replication could be specific for the Con-1 ( 1b ) isolate , however , the Con-1 ( 1b ) and J4 ( 1b ) NS5A protein sequences deviate by less than 5% . Replication seemed to depend also on highly conserved residues downstream in LCSI ( Figure S1 ) , as deletion of J4 ( 1b ) NS5A residues 235–282 ( Δ47 ) in this study abolished replication , while residues 246–308 previously were deleted in J6/JFH1 with no apparent effect on viability [27] . Interestingly , we observed decreased infectivity titers and mutations in the C-terminus of NS5A for domain II Δ250–293 mutants of genotype 1a but not 2a ( Figure 2 and [39] ) . Thus , HCV apparently shows an isolate-specific dependence on the highly variable N-terminal region of NS5A domain II . The genotype 2 protein sequence deviates in this region from most other genotypes ( Figure S1 ) , potentially reflected by differences in structure or function and thereby also in the effect of the deletion . With the critical dependence on selected residues or regions of the NS5A amphipathic alpha-helix , domain I , LCSI and domain II for replication , these regions would be obvious targets for antiviral therapy [47] . Great differences in the effect on viral infectivity titers were observed among the NS5A genotype 1–7 mutants when residues 346 , 351 and 354 in LCSII were mutated , with J4 ( 1b ) and JFH ( 2a ) being least affected . Measurements of intracellular HCV Core showed that most NS5A LCSII mutants had up to 10-fold reduced replication capacities , while ED43 ( 4a ) and QC69 ( 7a ) mutants were severely attenuated . Greater than 10-fold reductions in intra- and extracellular infectivity for the H77C ( 1a ) , S52 ( 3a ) , SA13 ( 5a ) and HK6a ( 6a ) mutants indicated an additional effect on virus production for these isolates . Findings on the JFH1 ( 2a ) LCSII mutant were in agreement with previous findings that a Con-1 ( 1b ) but not a JFH1 ( 2a ) replicon depended on P346 for replication , while mutation of the infectious full-length JFH1 recombinant led to lower levels of infectivity [42] . In line with this previous study , we found that mutation of P346/P351/P354 inhibited replication at an isolate-specific level . Interestingly , replication of the J4 ( 1b ) mutant was only slightly decreased , while the Con-1 ( 1b ) P346A replicon mutant was severely attenuated [42] . This might be due to differences between the isolates or between the two in vitro systems studied . Our QC69 ( 7a ) LCSII mutant lacked 14 nucleotides in the poly-pyrimidine tract ( Materials & Methods ) , however , it is unlikely that this caused the observed attenuation of replication since previous studies demonstrated efficient replication of J6/JFH1 with much shorter poly-pyrimidine tracts [48] . In agreement with previous findings for residue 343 mutants [42] , we could not confirm dependence on this position [23] for replication of any of the NS5A recombinants . Deletion of the highly conserved residues 414-428 led to isolate-specific decrease in infectivity titers and replication ( Figure 4 ) . The deletion had very limited effect on the JFH1 ( 2a ) mutant , while most other NS5A isolate mutants had reduced replication capacities; most pronounced decreases were observed for H77C ( 1a ) , TN ( 1a ) and S52 ( 3a ) mutants . Reductions in replication capacities might explain the decrease in intra- and extracellular titers observed for most mutants , however , the less than 2-fold reduction in replication capacity for ED43 ( 4a ) and SA13 ( 5a ) mutants compared to the greater than 3-fold reduction in produced infectious particles indicated an effect of the 414-428 deletion on assembly of intracellular infectious particles for these recombinants . The finding that a genotype 1a mutant with residues 414-428 replaced by the downstream genotype 2-specific insertion of 20 residues produced infectivity titers comparable to the original NS5A recombinant , indicated that this insertion could compensate for the 414-428 deletion . Others previously studied JFH1 mutants without the 382–428 region [27] or the genotype 2 insertion sequence [49] and found only minor effects on replication , production of infectious particles and co-localization of NS5A and Core on lipid droplets [27] . This correlated with our results with the JFH1 ( 2a ) 414-428 deletion mutant . However , deletion of the 408–437 region [29] , which covered the 414-428 region and the genotype 2 -specific sequence , or the almost entire domain III ( residues 356–439 ) [27] of genotype 2a , was highly attenuating for viral infectivity but not for replication . Thus , domain III residues outside the 414-428 region appear to be the most important determinants of efficient virus production . Experiments with H77C ( 1a ) demonstrated that selected LCSII and domain III mutants led to decreased amounts of NS5A normalized to Core protein levels . Assuming that Core stability was not affected by introduced mutations , this suggested a decreased stability of the NS5A protein . Similar observations were recently reported when two or four of the serines S432 , S434 , S437 and S438 were changed to alanine in a JFH1 recombinant carrying NS5A domain III from H77 [50] . Such effects could possibly be associated with disruption of NS5A co-localization with Core on lipid droplets [27] or disruption of interactions with annexin A2 [51] . The J6/JFH1 S437A mutation was previously shown to significantly decrease infectivity titers 48 hours after transfection [28] , however , another study indicated that mutation of at least two of the S432 , S434 and S437 residues was required for a significant reduction of particle production and impaired NS5A-Core interaction , and that viral kinetics were attenuated only early after transfection [29] . Similar findings were reported in the JFH1 background with H77 NS5A domain III [50] . This might explain why we observed only a less than 10-fold reduction in infectivity titers on day 3 but not thereafter ( Figure 5 ) . Surprisingly , deletion or point mutation of the relatively conserved 363–380 region in domain III ( Figure S1 ) did not significantly affect JFH1 ( 2a ) virus production . Similarly , mutation of arginines 356–359 in a putative nuclear localization signal [52] led to only slight reduction of infectivity titers for JFH1 ( 2a ) . Most interestingly , changing highly conserved residues in NS5A LCSII and domain III of various HCV isolates led to very different effects on replication and virus production . This emphasizes the evolutionary development of functional differences among the HCV genotypes in particular in variable regions of the genome [3] . Investigation of the role of these regions in vivo would be of interest for future studies . When replacing the entire NS5A gene of J6/JFH1 with that of other isolates [12] , or conducting NS5A reverse genetic experiments as done here , putative compensatory mutations were observed outside of NS5A; particularly in p7 , NS2 and NS3 ( Table 1 , [12] ) . The F26S mutation in p7 provided adaptation to several NS5A recombinants [12] , and in this study F26S was acquired by NS5A Δ414-428 deletion mutants . In addition , the p7 mutation also adapted J6/JFH1 recombinants with genotype-specific NS3/4A protease [53] . Interestingly , introduction of F26S into the genetically stable H77C ( 1a ) and TN ( 1a ) NS5A recombinants led to compensatory mutations in NS5A domain III . Moreover , introduction of these NS5A mutations led to mutations in p7 , while combining the p7 and NS5A mutations led to stable recombinants . These findings indicated a genetic linkage between NS5A and p7 . NS5A interactions might also involve NS3/NS4A , where several Δ414-428 mutants acquired changes , possibly in concert with p7 or other proteins in the Core-NS2 region , since I399T in NS3 ( I1425T ) acquired by the TN ( 1a ) mutant also adapted a Core-NS2 genotype 1a recombinant [54] . Additionally , a number of potentially compensatory mutations for NS5A deletion mutants in the third transmembrane domain of NS2 ( Table 1 ) , for the J4 ( 1b ) NS5A recombinant [12] , and for Core-NS2 recombinants [7] , indicated an interaction between NS5A and NS2 . Pull-down , co-localization and reverse genetic experiments demonstrated NS2 interactions with E1 , E2 , p7 , NS3 and NS5A , and studies of compensatory mutations identified the importance of such interactions during production of virus particles [55]–[58] . Furthermore , the p7 F26L mutation was shown to compensate for changes in Core [59] , possibly reflecting involvement of these proteins in viral assembly . Unfortunately , useful antibodies targeting p7 are scarce and the protein can not be directly detected in immunostainings . Tagging of p7 was not compatible with efficient virus production ( data not shown and [56] ) , and was therefore not a useful approach for studying protein interactions likely to take place during viral assembly and release . Thus , despite elaborate efforts we failed to establish co-immunoprecipitation or co-localization based evidence for an interaction between p7 and NS5A in the infectious culture system; better reagents are needed for conclusive studies on p7 interactions . Mutation from genotype 2a to 1a or vice versa of the NS5A genotype 2 or 2a-specific residues 95/97 , 140 , 151/152 , 157 or 165/168/169 led to attenuation in cell culture . This was of particular interest , since the introduced amino acids led to attenuation irrespective of the presence of these amino acid residues at the given position in numerous infectious HCV isolates . This indicated that genotype 2 evolved specific sequence requirements for function of NS5A domain I . Putative genotype-specific compensatory mutations were identified for the JFH1 ( 2a ) H124V mutant that changed serine at position 300 ( domain II ) , present in all genotype 2 and only few other isolates , and the JFH1 ( 2a ) S151T/W152E mutant that changed threonine at position 122 , present only for genotype 2 and 1b isolates . Residues 130 and 188 that changed in the I140C culture are located in close proximity to residue 140 in the NS5A domain I structure [19] , suggesting a functional interaction between these residues . Compensatory mutations in domain II and the reduced viability of domain I exchange recombinants suggested important genotype-specific interactions between domain I and other regions of NS5A . Such interactions might in particular be between domain I and II , since a recent study demonstrated that domain III alone could be exchanged between H77 and JFH1 recombinants [50] . To our knowledge this is the first time the function of an HCV protein was found to depend on genotype-specific residues . Since NS5A inhibitors under current development target domain I [14] , [47] , this may pose challenges for future antiviral therapy . Although this study significantly increases the number of studied NS5A isolates , only a single isolate was analyzed for most genotypes . Since differences were observed between isolates of the same genotype , e . g . between domain II deletion mutants of H77C ( 1a ) and TN ( 1a ) , studies of more isolates will be important to discriminate between isolate- and genotype-specific findings . Since the original NS5A recombinants used in this study all were efficient without requirement for further adaptation , any effects observed for NS5A mutants are likely to be attributed to the particular NS5A isolate . However , it is possible that particular mutations would render NS5A non-functional in the J6/JFH1 genetic background but not in a full-length background of that particular isolate . Thus , it will eventually be of importance to develop full-length cell culture systems for all HCV genotypes . In conclusion , we demonstrated that all major genotypes depended on the NS5A amphipathic alpha-helix , domain I , LCSI and domain II for viral replication . Interestingly , dependence on LCSII and domain III for HCV RNA replication and virus production varied with the NS5A isolate . Additionally , functional genotype-specific differences of NS5A domain I residues were identified . Our study highlights the emerging evidence of significant functional differences between diverse HCV isolates . Observed differences in NS5A will be important to consider in functional understanding and therapeutic targeting of this protein . Further studies in vitro and in vivo will be important for understanding and targeting this pleiotropic viral protein .
Reverse genetic studies were done with the J6/JFH1 recombinant [31] , and the J6/JFH1-based NS5A genotype 1–7 recombinants H77C ( 1a ) , TN ( 1a ) , J4 ( 1b ) R867H , C1185S , J6 ( 2a ) F772S , S52 ( 3a ) D1975G , ED43 ( 4a ) F772S , Y1644H , E2267G , SA13 ( 5a ) R1978G , S2416G , HK6a ( 6a ) I2268N and QC69 ( 7a ) , expressing the entire NS5A protein ( Numbering of mutations according to the H77 reference polyprotein ) [12] . Culture adaptive mutations in NS5A are indicated in Figure S1 . All mutations analyzed in this study were introduced using site-directed mutagenesis . Marker mutations ( according to the H77 reference ORF sequence ) introduced to exclude contamination in studies of reversion were T3431C ( NS3stop ) , G3830A ( NS3pro ) , C4280T ( NS3hel ) , G5369A ( NS4AG21V ) , C6224T ( NS4BW252S ) , C6419T ( NS5AC57G ) , C6440T ( NS5AC59G ) , T2719C or C8558T ( NS5BGND ) . The complete HCV sequence of final plasmid preparations was confirmed , except for NS5A domain mutants that did not acquire mutations in the ORF after passage in cell culture and did not produce decreased infectivity titers after transfection . Sequencing identified the following exceptions; J6/JFH1P165C/F168L/F169L carried the additional non-coding C5485T mutation . The J4 ( 1b ) Δ414-428 and QC69 ( 7a ) P346A/P351A/P354A mutants lacked 2 and 14 nucleotides in the poly-pyrimidine tract , respectively . Culturing of Huh7 . 5 hepatoma cells [31] was done as described [60] . One day before transfection or infection , 4×105 cells were plated per well in six-well plates . In vitro transcription of RNA was described previously [5] . For transfection , 2 . 5 µg RNA were incubated with 5 µL Lipofectamine2000 ( Invitrogen ) in 500 µL Opti-MEM ( Invitrogen ) for 20 min at room temperature . Cells were incubated with transfection complexes for 16–24 hours in growth medium . The individual transfection efficiencies of 20 independent experiments , as measured by HCV Core ELISA ( see below ) after 4 hours , varied less than 2-fold from the positive control . Intra- and extracellular infectivity titers after transfection of S29 cells [40] were determined as described [61] . For infection experiments , cells were inoculated with virus-containing supernatant for 16–24 hours . Supernatants collected during experiments were sterile filtered and stored at −80°C . Infected cultures were monitored by immunostaining using mouse anti-HCV Core protein monoclonal antibody ( B2 , Anogen ) as described [5] , [60] . Infectivity titers were determined by adding 100 µL of triplicate sample dilutions ( diluted 1∶2 or more ) to 6×103 Huh7 . 5 cells/well plated out the day before on poly-D-lysine-coated 96-well plates ( Nunc ) . Cells were fixed and immunostained for HCV 48 hours after infection using a previously established protocol [60] . Primary antibody was Hepatitis C Virus NS3 antibody ( H23 , Abcam ) . The previously used anti-NS5A 9E10 antibody [31] gave no signal for the J6 ( 2a ) NS5A recombinant and for Δ414-428 mutants and suboptimal signals for several other NS5A recombinants . The number of focus-forming units ( FFU ) was determined by manual counting or on an ImmunoSpot Series 5 UV Analyzer ( CTL Europe GmbH ) with customized software as previously described [61] . HCV RNA quantification was done using an in-house assay as described [60] . For measurement of intracellular HCV Core , 105 S29 cells [40] per well plated the day before in 24-well plates were transfected with HCV RNA transcripts for the indicated time period . After 4 , 24 , 48 and 72 hours , cells were trypsinized , centrifuged at 1000× g for 5 minutes at 4°C , washed in cold PBS and lysed in cold RIPA-buffer supplemented with protease inhibitor cocktail set III ( Calbiochem ) . Cell lysates were clarified at 20 , 000× g for 15 minutes at 4°C before measuring HCV Core levels using ORTHO HCV antigen ELISA test kit ( Ortho Clinical Diagnostics ) . Huh7 . 5 cells were trypsinized , washed in cold PBS and lyzed in 200 µl RIPA buffer ( Thermo Scientific ) with protease inhibitor cocktail set III ( Calbiochem ) on ice for 10 min . Lysates were treated with RQ1 DNase ( Promega ) to reduce viscosity , and clarified by centrifugation at 20 , 000×g for 15 min at 4°C . Protein lysates were loaded on 10% Bis-Tris gels ( Invitrogen ) and subsequently transferred to 0 . 45 µm Hybond-P PVDF membranes ( GE Healthcare Amersham ) . Following overnight incubation with specific antibodies ( anti-core C7-50 , Enzo Life Science ) or anti-NS5A ( H26 , Abcam ) at 4°C , unsaturated chemiluminescense images were acquired and protein amounts were quantified based on band intensity using ImageJ . RNA extraction , RT-PCR and direct sequence analysis ( Macrogen Inc ) [60] as well as primers specific for the NS5A region [12] were previously described . Sequence analysis was performed with Sequencher ( Gene Codes Corporation ) . HCV sequences were retrieved from the European HCV database and the Los Alamos HCV sequence database . Sequence logos were done using WebLogo [62] . | Hepatitis C virus ( HCV ) is a major public health burden and leads to chronic liver disease , including liver cirrhosis and liver cancer . Understanding the biological functions of the virus is crucial to the development of a vaccine and to improve current therapy through development of directly-acting antiviral compounds . The NS5A protein is a promising antiviral target , but much remains to be understood about its role in the viral life cycle . Great diversity among the seven major HCV genotypes poses challenges for broadly active inhibitors . Here we used infectious cell culture systems for NS5A of the seven major HCV genotypes , and demonstrated that all genotypes depended on the NS5A amphipathic alpha-helix , domain I , low-complexity sequence ( LCS ) I and domain II for viral replication . Interestingly , effects on replication and virus production by changes in LCSII and domain III varied greatly among NS5A isolates . Furthermore , we found that genotype 2 had evolved genotype-specific residues in domain I of importance for viral function . Thus , the highly diverse sequence of the NS5A protein reflected functional differences between HCV genotypes and isolates . Such differences will be important to consider in understanding HCV biology and for future development of antiviral compounds . | [
"Abstract",
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] | [
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] | 2012 | Analysis of Functional Differences between Hepatitis C Virus NS5A of Genotypes 1–7 in Infectious Cell Culture Systems |
During clathrin-mediated endocytosis in yeast cells , short actin filaments ( < 200nm ) and crosslinking protein fimbrin assemble to drive the internalization of the plasma membrane . However , the organization of the actin meshwork during endocytosis remains largely unknown . In addition , only a small fraction of the force necessary to elongate and pinch off vesicles can be accounted for by actin polymerization alone . In this paper , we used mathematical modeling to study the self-organization of rigid actin filaments in the presence of elastic crosslinkers in conditions relevant to endocytosis . We found that actin filaments condense into either a disordered meshwork or an ordered bundle depending on filament length and the mechanical and kinetic properties of the crosslinkers . Our simulations also demonstrated that these nanometer-scale actin structures can store a large amount of elastic energy within the crosslinkers ( up to 10kBT per crosslinker ) . This conversion of binding energy into elastic energy is the consequence of geometric constraints created by the helical pitch of the actin filaments , which results in frustrated configurations of crosslinkers attached to filaments . We propose that this stored elastic energy can be used at a later time in the endocytic process . As a proof of principle , we presented a simple mechanism for sustained torque production by ordered detachment of crosslinkers from a pair of parallel filaments .
The cytoskeleton protein actin assembles into three major structures in yeast cells , including endocytic actin patches , actin cables , and the contractile ring [1 , 2] . In actin cables and the contractile ring , formin-nucleated actin filaments are crosslinked into long bundles with a length on the order of microns [3–5] . Computational models of these actin structures typically treat actin filaments as semi-flexible polymers that are connected by rigid segments [6–9] . In contrast , the organization of the actin network in actin patches formed during clathrin-mediated endocytosis is drastically different from that in actin cables or the contractile ring . The length of filaments in actin patches is strongly limited by capping and severing proteins [10] , and mathematical modeling predicted that the average length of filaments is less than 200 nm [11] . Filaments of this length scale can be considered as straight rods , because the persistence length of actin filaments is on the order of 10μm [12–14] , which allows them to sustain forces larger than 10pN without buckling [15] . At the endocytic actin patch , a small area of the flat plasma membrane invaginates towards the cytoplasm upon assembly of actin . In budding yeast , the invagination elongates up to 140nm in depth , and then is pinched off , releasing a tear-shaped vesicle [16] . Actin is essential for many of these steps , from the initiation of invagination to vesicle scission [17–19] . Despite extensive experimental work that characterized the overall dynamics of assembly , disassembly and ensemble movements of proteins of the actin meshwork [19–26] , the precise structural organization of actin filaments within the endocytic patch remains unknown . Indeed , individual filaments are not resolvable even in electron micrographs , in which the actin network appears as a ribosome-exclusion zone , which is about 200nm in depth and 100nm in width [16] . Actin crosslinking proteins play a crucial role in determining the mechanical responses of the actin network to force perturbation [27–29] . Fimbrin ( Fim1p ) is the second most abundant protein recruited to the endocytic patch during clathrin-mediated endocytosis in fission yeast , after actin [20] . It has two actin binding domains that enable it to crosslink adjacent filaments . Deletion of fimbrin results in significant defects in endocytic internalization [24 , 30 , 31] . In vitro experiments have shown that fimbrin efficiently bundles long actin filaments , but bundling efficiency is reduced in the presence of capping protein as a result of decreased filament length [31] . It remains unclear how this length-dependent bundling activity arises and how this activity is related to the role of fimbrin during clathrin-mediated endocytosis . Internalization of the endocytic membrane is hindered by the high turgor pressure ( P ∼ 0 . 8 × 106 Pa [32 , 33] ) inside yeast cells [34] . Under such high pressure , theoretical studies suggest that the force needed to initiate membrane invagination is on the order of 3000pN [35 , 36] and actin polymerization is thought to provide the driving force . However , assuming no more than 150 filaments are simultaneously generating the force [11 , 20 , 21] , each of these filaments has to generate a force of at least 20pN , an order of magnitude larger than the maximum polymerization force of ∼1pN of actin filaments measured in vitro [37] . This number of 20pN is likely an underestimate since the calculation here uses a very generous estimate for the number of growing filaments , up to 20-fold of what mathematical modeling predicts [11] . Therefore , actin polymerization alone is not enough to provide the force necessary to elongate a clathrin-coated pit . Even though type-I myosins participate in endocytosis , their low power output over a narrow range of forces suggest that they are more likely force-sensing tethers rather than force generators [38–40] . In this paper , we present a computational model for dynamic crosslinking of rigid actin filaments in conditions relevant to clathrin-mediated endocytosis . We show that kinetic and mechanical properties of the crosslinkers finely tune the structural transition of actin network between bundles and meshworks . In addition , we show that the chemical binding energy is converted into elastic energy upon binding of crosslinkers . The elastic energy stored in individual crosslinkers is significantly higher than their thermal energy . This surprising property is a consequence of the helical pitch of actin filaments , which leads to torsional strains between crosslinkers attached to a common pair of filaments . We discuss the mechanical implications of these torsionally stressed crosslinkers and propose a possible mechanism to generate directed rotation of filaments by orderly detaching the crosslinkers .
We model actin filaments as rigid cylindrical rods with subunits that carry a helical pitch ( Fig 1A ) . The length of filaments is restricted to the range of actin filament size during endocytosis ( 81nm to 216nm ) , around 2 orders of magnitude shorter than their persistence length ∼10μm . Filaments of this length scale remain virtually straight under ∼10pN of force and untwisted under ∼100pN ⋅ nm of torque ( see the Methods section ) . Therefore we can neglect bending and twisting , and describe the motion of a filament by its translational velocity Vc of center of mass , and angular velocity Ω relative to the center of mass . Details of the model can be found in the Methods section . We model actin crosslinkers as elastic springs that connect two actin subunits in different filaments . Each spring has three elastic components: one in extension , which represents how much the spring is stretched , and two in torsion , each one representing how much the orientation of the axis of the crosslinker linking both actin subunits deviates from the vector normal to the binding interface of each actin subunit ( Fig 1C ) . This elasticity is the simplest model to account for: ( i ) the intra-molecular flexibility between both actin binding domains , and ( ii ) the flexibility in the binding interface between each actin binding domains and the actin subunits they are bound to . Specifically , the elastic energy E of a crosslinker is composed of an extensional part Eext and a torsional part Etor , E = Eext + Etor . The extensional energy accounts for ( i ) , E ext = 1 2 κ ext ( l c - l 0 ) 2 , ( 1 ) where κext denotes the extensional stiffness , lc denotes the length , and l0 denotes the rest length of the crosslinker ( Fig 1C ) . The torsional energy accounts for ( ii ) , E tor = 1 2 κ tor ( θ i 2 + θ j 2 ) , ( 2 ) where κtor denotes the torsional stiffness , θi and θj denote the angles between the actin subunits and the axis of the crosslinker ( Fig 1C ) . Note that our model does not take into account the stiffness of the rotation of filaments around the axis of the crosslinker . Doing so would force us to consider a preferred relative orientation of the filaments with each other , and strongly favor either bundles or meshworks . No experimental values for the extensional and torsional stiffnesses of fimbrin are available in the literature . For our simulations , we used values within a few orders of magnitude of stiffnesses measured for fascin and antibodies [41 , 42] . Crosslinker turnover is modeled as Poisson processes with a crosslinker formation rate constant kf and a breakage rate constant kb , which increases exponentially with the total elastic energy ( Fig 1B ) . A more detailed description is presented in the Methods section . The length of filaments is kept constant in a given simulation and the total number of actin subunits in the filaments is fixed to Nactin = 7000 for all simulations [20] . The maximum occupancy of crosslinkers on filaments is constrained to remain under 25% ( or 1 crosslinker for 4 subunits ) , which is equivalent to a maximum of 875 attached crosslinkers , close to the peak value ∼900 measured experimentally [20] . We initiate each simulation with uncrosslinked filaments that are randomly positioned and oriented . Reflecting boundary conditions are imposed to ensure filaments stay in a cubic box of 500nm in size . Simulations are performed using the reference values listed in Table 1 unless otherwise mentioned . To quantify the organisation of actin filaments , we introduced the global and local nematic order parameters Sglobal and Slocal . These quantities characterize the degree of alignment between filaments in the entire simulation space , or in a local neighborhood , respectively . Their values range from 0 to 1 , and a larger value indicates that filaments are more aligned with each other globally , for Sglobal , or locally , for Slocal . Note that , in practice , the minimum reachable value for Slocal is usually close to 0 . 4 ( S6 Fig ) . The detailed mathematical definition of these parameters can be found in the Methods section . Actin assembly and disassembly takes tens of seconds during endocytosis . We set the total simulation time to be 50s . Within this period , in most of our simulations , the metrics reach steady state . However , there are cases where the system is in a transient state , mostly due to slow convergence of the global nematic order parameter Sglobal ( S8 Fig ) . Therefore , we chose the local nematic order parameter Slocal averaged from 40s to 50s as the major metric to characterize the organisation of actin networks . Conclusions based on Slocal are more robust than based on Sglobal . We first studied how filament length influences the structure of actin clusters . For crosslinking rates kf below 0 . 1s−1 , the number of attached crosslinkers remained small , and actin filaments did not organize into higher order assemblies ( S1 Fig ) . When the crosslinking rate kf was high enough , initially disconnected short filaments ( 81nm ) quickly formed small clusters that eventually coalesced into three to four larger clusters ( Fig 2A ) . The number of attached crosslinkers rapidly saturated to a dynamic steady-state ( Fig 2C ) , as the crosslinkers underwent constant turnover . Filaments within the cluster are organized into a disordered meshwork , which is characterized by a small local nematic order parameter ( Slocal ∼ 0 . 4 , Fig 2B ) . Long filaments ( 216nm ) rapidly aligned with their neighbors into small bundles ( Fig 2D ) , as indicated by the fast convergence of the local nematic order parameter Slocal to its steady state value around 1 ( Fig 2E ) . Throughout the simulation , these locally aligned filaments remained connected with each other , and the number of clusters remained small ( Fig 2F ) . These connected bundles slowly adjusted their orientations to eventually coalesce into a few large bundles . When filament length was increased from 135nm to 162nm , the structure of the actin network transitioned from meshwork to bundle , indicated by the sharp increase of the local nematic order parameter Slocal from ∼0 . 5 to above 0 . 9 ( Fig 2G ) . The global nematic order parameter Sglobal showed similar trend as Slocal , but with a smaller magnitude , since filaments formed 2 to 4 independent clusters ( Fig 2H ) . Altogether these results show that crosslinked actin filaments with a size and crosslinker density comparable to what is measured during endocytosis can self-organize into either meshworks ( for short filaments ) or bundles ( for long filaments ) , and the phase transition is tightly controlled by filament length . Next , we explored the influence of the mechanical properties of actin crosslinking proteins on the organization of actin filaments . The organization of medium length filaments ( 135nm ) varied dramatically for different combinations of κext and κtor ( Fig 3A–3C ) . For low extensional stiffness κext , actin filaments organized into a meshwork ( Fig 3A ) , while they formed bundles for higher κext values ( Fig 3B ) . The transition between meshwork and bundle was tightly controlled , since the local nematic order parameter had a sharp increase around κext = 0 . 1pN/nm ( Fig 3E , red ) at given κtor = 10pN ⋅ nm ⋅ rad−1 . This sharp transition was even more pronounced for longer filaments ( 189nm ) , as reported in both local and global nematic order parameters ( Fig 3E and S2A Fig , orange ) . In contrast , when filaments were short ( 81nm ) , the transition was relatively smooth ( Fig 3E , blue ) . From the above results , we conclude that crosslinkers with large extensional stiffness favor bundle formation . This result can be intuitively explained by the following simplified but heuristic example involving only two filaments . If two filaments are initially aligned with each other , a slight change in orientation between both filaments results in the stretching of crosslinkers bound at different positions , which leads to a restoring torque to realign the filaments . The torque is proportional to the extensional stiffness and the distance between the positions of attached crosslinkers , thus stiffer crosslinkers create larger realignment torque than softer crosslinkers . Longer filaments not only have more crosslinkers , but also crosslinkers that are more distantly positioned , therefore more extended and more inclined to create a larger torque that will restore the parallel alignment of filaments . We next investigated the impact of torsional stiffness κtor on the organization of actin networks , keeping the extensional stiffness at a relatively small value ( κext = 0 . 1pN/nm ) . Torsional stiffness had virtually no influence on the organization of short filaments ( 81nm ) ( Fig 3F , blue ) . However , medium length filaments ( 135nm ) had a sharp transition from bundle to meshwork at κtor = 10pN ⋅ nm ⋅ rad−1 ( Fig 3F , red ) . A similar trend was also observed for long filaments ( 189nm ) ( Fig 3F , orange ) . At highest torsional stiffness tested ( κtor = 100pN ⋅ nm ⋅ rad−1 ) , crosslinker attachment lifetime was extremely short because their torsional energy often became much larger than the critical energy Ec that modulates the detachment rate ( see Eq 16 ) , therefore the number of attached linkers was significantly reduced ( S2H Fig ) . The uncrosslinked actin network formed at this regime was different from the connected meshwork formed at κtor = 10pN ⋅ nm ⋅ rad−1 , though their nematic order parameters Slocal were similarly low . The above results show that crosslinkers with high torsional stiffness disfavor bundle formation . This result can be explained by the fact that , when torsional stiffness is high , formation of several crosslinks between two aligned filaments results in very high torsional energies , due to the frustrated interactions between crosslinkers . This will become clear later in the paper . Therefore , it is energetically more favorable to form a few but over-stretched crosslinkers with many distant filaments than to form many but under-stretched crosslinkers with a few proximal filaments . In the former situation , filaments form a highly entangled actin meshwork . This explanation is supported by the decreasing number of clusters ( S2E Fig ) , as well as the increasing extensional strains with κtor in the neighbourhood of κtor = 10pN ⋅ nm ⋅ rad−1 ( S4C Fig ) . We have shown that high extensional stiffness of crosslinkers favors bundle formation . When the breakage rate k b 0 was reduced to 0 . 01s−1 , even for large extensional stiffness ( 1pN/nm ) , filaments formed a structure where small bundles were interconnected but did not align with each other ( Fig 3D ) , as indicated by the relatively low local nematic order parameter at k b 0 = 0 . 01 s - 1 compared with Slocal at k b 0 = 10 s - 1 ( Fig 3G ) . The difference in the global nematic order parameters Sglobal was even more pronounced for long filaments ( 189nm ) ( S2C Fig , orange ) . At very high breakage rate , the lifetime of bonds between filaments was so short that filaments formed an essentially random , uncrosslinked network . Altogether , we conclude that crosslinker turnover is essential for bundle formation , as alignment of bundles requires the breaking of the bonds that disfavor filament alignment . Parameter dependence of the actin network structure is summarized in Fig 4 where we plotted the local nematic order parameter Slocal and the number of attached crosslinkers Nattach as a function of crosslinking rate constant kf and filament length L . For a combination of low extensional and high torsional stiffnesses ( κext = 0 . 1pN/nm , κtor = 10pN ⋅ nm ⋅ rad−1 ) , values of Slocal are concentrated either close to 1 or close to 0 . 5 ( Fig 4A , yellow and blue regions , respectively ) . These two regions are separated by a narrow transition band around Slocal = 0 . 75 ( Fig 4A , green ) . Values of Nattach are clustered either close to the saturation number 875 , or less than 100 , divided by a transition band around Nattach = 300 ( Fig 4B , yellow , blue and green regions , respectively ) . Therefore we chose the lines Slocal = 0 . 75 and Nattach = 300 as the boundaries to define the phase diagram ( Fig 4C ) . These two lines divide the parameter space into three regions: ( 1 ) Above the line Slocal = 0 . 75 , actin filaments are locally aligned into a bundle; ( 2 ) Between the lines Slocal = 0 . 75 and Nattach = 300 , filaments form a crosslinked , disordered meshwork; ( 3 ) Below the line Nattach = 300 , filaments are essentially uncrosslinked over the entire simulation time . For larger extensional stiffness ( κext = 1pN/nm ) , the relative positions of the regions are similar , but most of the phase diagram corresponds to bundles ( Fig 4F ) , and only the shortest filaments ( 81nm ) form meshworks . In all cases , the existence of three regions in these phase diagrams requires moderate to high breakage rates . When the breakage rate is low ( k b 0 = 0 . 01 s - 1 ) , meshworks occupy the entire parameter space ( S3 Fig ) . Though crosslinkers in our model were not active elements , we found that crosslinkers rapidly became stretched in length , and twisted in angle . To quantify the crosslinkers’ deformations , we introduced the extensional strain ϵ = ( lc − l0 ) /l0 , which measures the relative change of a crosslinker’s length lc from its rest length l0 , and the torsional strain θ , which measures the angle between the crosslinker and its bound actin subunits . For all stiffness values , the distributions of the extensional strain ϵ and the torsional strain θ significantly deviated from the corresponding Boltzmann distribution for a single independent free spring ( Fig 5A–5C ) . Strikingly , the extensional strain ϵ peaked at ∼0 . 5 but not zero ( Fig 5A–5C , top ) , indicating that the crosslinkers were stretched on average . The distribution had a narrower width for higher extensional stiffnesses κext . The peak of torsional strain θ decreased with increasing torsional stiffness , while the widths of the distributions were essentially the same as in the Boltzmann distribution ( Fig 5A–5C , bottom ) . The deviation from the Boltzmann distribution can be primarily accounted by the coupling between crosslinkers that are attached to the same pair of filaments . Indeed , a simpler 1D example of the Brownian motion of two particles each subject to a spring follows similar properties ( S5 Fig ) . In this example , each spring generates a force of −κx if the particle is displaced from the equilibrium position 0 to x . If the movement of the two particles were independent ( S5A Fig ) , the joint distribution of their positions x1 and x2 would simply be the product of identical individual distribution p ( x1 , x2 ) = g ( x1 ) g ( x2 ) , where g ( x ) = κ 2 π k B T e - κ x 2 / 2 k B T denotes the Boltzmann distribution of an individual particle . However , if the movements of the two particles are coupled , for instance , subject to the constraint x1 − x2 = 2x0 ( S5B Fig ) , the position distribution of particle 1 becomes p ( x1 ) = Cg ( x1 ) g ( x1 − 2x0 ) , where C is the normalization constant . Therefore particle 1 is displaced by x0 due to the coupling with particle 2 . Similarly , if two rigid filaments are bound by several crosslinkers , the extensional and torsional strains of these crosslinkers are coupled , and this coupling gives rise to significant strains in the crosslinkers . We then determined the dependence of the elastic energy stored in the crosslinkers on the crosslinker stiffness . At fixed torsional stiffness κtor = 10pN ⋅ nm ⋅ rad−1 , the average magnitude of the extensional strain |ϵ| decreased with increasing extensional stiffness κext ( Fig 5D ) . However , the average extensional energy per crosslinker E ext = 1 2 κ ext l 0 2 ϵ 2 increased ( Fig 5E ) , from ∼0 . 3kBT for soft extensional springs up to ∼5kBT for stiff ones , with only a weak dependence on filament length ( Fig 5E ) . The average torsional energy per crosslinker reads E tor = 1 2 κ tor ( θ i 2 + θ j 2 ) , with θi and θj being the average torsional strains at the two actin subunits . They have the same magnitude θ . At fixed extensional stiffness κext = 0 . 1pN/nm , Etor increased with torsional stiffness κtor from ∼0 . 2kBT to up to ∼10kBT , with again a weak dependence on filament length ( Fig 5G ) . Both extensional and torsional energies vary relatively smoothly with the corresponding stiffnesses over three orders of magnitude , which is in stark contrast with the sharp structural transition between meshwork and bundle when stiffnesses are varied over the same range ( Fig 3E and 3F ) . Compared with the increase of the extensional energy Eext with κext ( ∼8 − 17 fold ) , the increase of torsional energy Etor with κtor ( ∼40 − 50 fold ) was more pronounced . This was likely due to stronger coupling between torsional strains of crosslinkers than between extensional strains . Elastic energy plateaued and then slightly decreased for very high stiffnesses ( κext = 10pN/nm or κtor = 100pN ⋅ nm ⋅ rad−1 ) , which was the consequence of higher detachment rate leading to a smaller number of attached linkers , thus reducing the frustrated interactions ( S2 Fig ) . We have shown that crosslinking of filaments leads to elastic energy stored in the crosslinkers . How can this energy be transformed into mechanical work ? Here , we propose a mechanism for torque generation through orchestrated detachment of crosslinkers by studying a simple model with only two filaments . Let us consider a pair of short filaments , where every other subunit of each filament is crosslinked ( Fig 6 ) . For simplicity , we assume the two filaments are parallel and consider only the rotation of filaments around their axes . We show that consecutive detachment of crosslinkers from the pointed end to the barbed end lets the filament rotate in the same direction by π/13 for each detachment ( Methods ) . Building on this simple proof of principle , we show that sustained directional rotation can be achieved with any filament length and crosslinker spacing and configurations such that ( i ) the angles between two consecutive crosslinkers along the pair of filaments have the same sign , and ( ii ) the sum of all these angles is smaller than 2π . Under these conditions , breakage of crosslinkers from one end to the other produces directional torque ( see Methods ) .
In this paper , we showed that highly crosslinked actin networks made of rigid filaments ( < 200nm ) can form either disordered meshworks or ordered bundles , depending on the filament length and the mechanical and kinetic properties of the crosslinkers . A recent in vitro study of short actin filaments ( 200nm ) showed that with increasing density of filamin , the initially sparsely distributed actin filaments condensed into a spindle-shaped aggregate , in which the organization of actin filaments displayed nematic order [50] . This observation is consistent with the phase diagram in Fig 4C of our study for filaments of 200nm with increasing crosslinking rate . We investigated the possible structures formed by actin filaments in the presence of elastic crosslinkers . However , our study does not take into account other actin regulating proteins involved in endocytosis , such as the Arp2/3 complex and capping protein , and thus does not completely resolve the question of the organization of filaments crosslinked with fimbrin at the site of endocytosis in yeast . Even with this limitation , these results provide valuable insights about possible actin filament architectures for endocytosis and other cellular processes that involve short actin filaments . Using the rate constants for fimbrin that have been measured in vitro [31 , 45] , and the fimbrin concentration in fission yeast cytoplasm ( 3 . 7 μM ) [20] , these values correspond to rates in our model kf = 0 . 2s−1 and k b 0 = 0 . 04 s - 1 . Our simulations suggest that the slow off-rate of fimbrin should favor an assembly of actin filaments into a meshwork ( S3 Fig ) . Further simulations with branched filaments , and with geometries and dynamics more representative of endocytosis will tell us which type of structure is present at endocytic sites . In addition , further experimental characterization of the mechanical properties of fimbrin and other crosslinkers will be key to understanding the self-organization of actin filaments in diffraction limited structures , and to test the predictions of our simulations . Our simulations demonstrate that individual actin crosslinkers are able to store up to 10kBT of elastic energy , which is one order of magnitude higher than the elastic energy stored in an uncoupled spring in a thermal bath ( 1 . 5kBT ) , and about half of the energy released by ATP hydrolysis ( ∼25kBT ) . To get a better sense of the amount of energy stored in the crosslinkers , one can make a comparison with the energy necessary to deform the plasma membrane into an endocytic vesicle . A back of the envelope calculation estimates the work needed to create a cylindrical invagination of Rt = 25nm in radius and Dt = 140nm in depth [16] against the turgor pressure P ∼ 0 . 8 × 106 Pa is P π R t 2 D t = 5 . 4 × 10 4 k B T . The results of our model suggest that crosslinking filaments once with ∼900 crosslinkers , around 104kBT energy could be stored , or about 1/6 of the total energy needed . One may wonder where this large elastic energy comes from . We can show in a simplified model that the chemical binding energy of crosslinkers is indeed the source of the elastic energy . Let us consider two filaments with fixed positions and orientations , each having N subunits . In the following , we will consider the chemical balance between configurations where there is either n = 0 or 1 crosslinker between the two filaments . The rate at which a crosslinker is formed is kfΓ ( 1 ) , where Γ ( n ) denotes the number of possible pairings to form n crosslinkers between subunits in the two filaments . The value of Γ ( 1 ) depends on the orientations and positions of the two filaments , and the mechanical properties of the crosslinkers , and could vary from 1 for very stiff crosslinkers and orthogonal filaments , to N2 for infinitely soft crosslinkers and parallel filaments . Here we assume Γ ( 1 ) = N , which implies that for each subunit in one filament , there is a unique subunit in the other filament that is within the reaction distance to allow a crosslinker to be formed . When one crosslink is formed , its detachment rate is k b 0 e E / E c , assuming the crosslinker bears an elastic energy E . If the attachment rate is greater than the detachment rate , the system is more likely to be crosslinked , even though the elastic energy E stored in the crosslinker tends to drive down the crosslinker occupancy . Noting μ eff ≡ E c ln ( N k f / k b 0 ) the effective chemical binding energy that tends to drive up the occupancy of crosslinkers , this condition can be expressed as comparison between the effective chemical binding energy and the elastic energy μeff > E . Using the parameters k b 0 = 10 s - 1 , kf = 1s−1 , Ec = 10kBT and N = 50 , we estimate μeff = 16kBT for the first crosslink formation , which is larger than the 10kBT of elastic energy per crosslinker computed in our simulations . If we now consider the case where there are n crosslinkers formed between the filaments , the effective energy for binding an extra crosslinkers becomes μ eff ( n ) = E c [ ln ( k f / k b 0 ) + ln ( Γ ( n + 1 ) / Γ ( n ) ) ] . The second term in the bracket represents an entropic contribution that comes from the different ways of building n or n + 1 crosslinks between subunits of both filaments . This simplified two filament system illustrates how in our simulations with multiple filaments crosslinker occupancy is driven up by a similar entropic contribution in the chemical binding energy . Our model considers “slip-bond” crosslinker detachements , i . e . crosslinkers are more likely to detach if force and torque are exerted on them ( Eq 16 ) . However , we could consider the case of “catch-bond” detachments , where crosslinkers are less likely to detach under force and torque , as it has been shown for some cytoskeleton proteins [40 , 51 , 52] . In this case , we expect that the elastic energy stored in crosslinkers would be larger than what we have observed in our simulations , and the conformational change required for the catch-bond behavior would increase the available energy limit . The main reason energy storage is possible is that short actin filaments are rigid , which creates geometrical constraints on bound crosslinkers , forcing virtually all of them to fluctuate around average lengths and angles that are different from their rest lengths and angles . This implies that the crosslinkers are rigid enough to store elastic energy , but not as rigid as the filaments , so that filaments cannot be twisted or bent when crosslinked , or the distance over which filaments are twisted and bent in order to reduce the frustration , noted Ltb , is much longer than the filament length L . In the opposite case , when filament length L ≫ Ltb , filaments could form bundles in which individual filaments are twisted . Experiments conducted at this regime suggest that the frustrated interaction serves as a mechanism to control the size of the bundle [53] and cooperative binding of actin crosslinkers [54] . The two regimes have been theoretically studied by C . Heussinger and G . Grason [55] . Only a small fraction of the force necessary to deform the plasma membrane during clathrin-mediated endocytosis in yeast can be accounted for by actin polymerization alone . We predict that at least some of the missing force can come from the conversion of the elastic energy stored in the crosslinkers into force and/or torque . In this paper , we proposed a specific mechanism for torque production by orchestrated detachment of crosslinkers . This mechanism is different from the Brownian ratchet mechanism of force production that is directly coupled to ATP hydrolysis [56 , 57] . However , the ordered detachment has to be coupled to a non-equilibrium process to provide the information necessary for the ordered detachment . Treadmilling of filaments coupled to ATP hydrolysis could play such a role . To estimate the order of magnitude of free energy necessary to provide this information , let us consider a pair of short actin filaments ( e . g . 50-subunit long ) that are crosslinked by 10 crosslinkers . The free energy cost of detaching the crosslinkers in a specific order among all the 10 ! possibilities is ∼kBT ln 10 ! = 15kBT , which is only a small fraction of the energy provided by the ATP hydrolysis of two actin filaments undergoing treadmilling ( 50 × 25 = 1250kBT ) . We stress that this ordered detachment is only one possible mechanism to use the energy and more mechanisms need to be discovered . Future work with more realistic models for endocytosis or other actin-based processes will likely uncover new orchestrated mechanisms for force production . In our system , crosslinkers are driven up to a mechanically pre-stressed state by chemical binding energy . In principle , in order to release the elastic energy stored in crosslinkers , change in energetics of crosslinker binding/unbinding is necessary to induce collective detachment of crosslinkers . When actin polymerization is considered , ATP-bound actin is incorporated at the barbed end and undergoes hydrolysis after incorporation . The nucleotide content change could alter the crosslinkers’ binding affinity , causing rapid detachment of crosslinkers , which is accelerated by the pre-stressed structure . Elastic energy released during this process could be converted into work by the reorganization of actin filaments . We developed a computational model to study the dynamic assembly of actin filaments mediated by elastic crosslinkers . The organization of actin filaments were classified into either a meshwork or a bundle , characterized by their nematic order parameter and the number of attached crosslinkers . We showed that the elastic energy stored in crosslinkers increased with their stiffness due to coupling between crosslinkers bound to rigid filaments . As a proof of principle , we showed that the elastic energy could be converted into mechanical work by orchestrated detachment of crosslinkers between two parallel filaments . Our work provides a new perspective to study the mechanisms of force and torque production by actin filaments , in addition to the traditional end polymerization . It also provides an alternative energy source to account for the insufficient force production by actin polymerization during clathrin-mediated endocytosis .
Actin filaments are modeled as rigid cylindrical rods with diameter b and length L . The position of a filament is represented by its center of mass C . A unit vector N pointing from the filament’s pointed end to the barbed end indicates the orientation of the filament . The i-th subunit ( counting from the pointed end ) carries a unit vector Oi , which is normal to the binding surface with a crosslinker ( Fig 1A ) . We assume all Oi-s are perpendicular to the filament’s orientation N . Based on the atomic structure of actin filaments [43] , two consecutive subunits Oi and Oi+1 span an angle of 14π/13 calculated counter-clockwise from Oi to Oi+1 . This means two consecutive subunits on different strands have their binding interface in almost opposite directions , and two consecutive subunits on the same strand have their binding interface at an angle of 2π/13 ( 28° ) . We arbitrarily choose the filament’s rotational vector M as the normal vector of the first subunit M = O1 . Thus the orientational degree of freedom of the filament is fully captured by three orthonormal vectors N , M and N × M . The motion of a filament is described by its translational velocity Vc and angular velocity Ω , which are defined by the following equations: d C d t = V c , ( 3 ) d N d t = Ω × N , ( 4 ) d M d t = Ω × M . ( 5 ) The velocities Vc and Ω are governed by the force-balance and torque-balance equations: Ξ t V c = F e + F s , ( 6 ) Ξ r Ω = T e + T s . ( 7 ) Here the 3 × 3 matrices Ξt and Ξr denote the frictional matrix associated with translational and rotational motion of the filament , respectively . The vectors Fe and Te denote the total deterministic force and torque generated by crosslinkers or induced by steric interactions between filaments . The vectors Fs and Ts denote the stochastic force and torque , which obey the fluctuation-dissipation relations: ⟨ F i s ( t ) F j s ( t ′ ) ⟩ = 2 k B T δ ( t - t ′ ) Ξ i j t , ( 8 ) ⟨ T i s ( t ) T j s ( t ′ ) ⟩ = 2 k B T δ ( t - t ′ ) Ξ i j r . ( 9 ) Here the subscript indicates the element of the vectors or matrices . The frictional matrices are anisotropic , and given by [58]: Ξ t = ξ ‖ t N ⊗ N + ξ ⊥ t ( I - N ⊗ N ) , ( 10 ) Ξ r = ξ ‖ r N ⊗ N + ξ ⊥ r ( I - N ⊗ N ) , ( 11 ) where ξ ‖ t and ξ ⊥ t are the frictional coefficients for translational movement parallel with and perpendicular to the filament’s central axis , and ξ ‖ r and ξ ⊥ r are the corresponding frictional coefficients for rotation . The 3 × 3 identity matrix is denoted by I , and ⊗ denotes the outer product of two vectors . The anisotropic frictional coefficients depend on filament length L and diameter b via the relations [43]: ξ ‖ t = 2 π η L ln ( L / b ) - 0 . 2 , ξ ⊥ t = 4 π η L ln ( L / b ) + 0 . 84 , ( 12 ) ξ ‖ r = π η b 2 L , ξ ⊥ r = π η L 3 3 ( ln ( L / b ) - 0 . 66 ) . ( 13 ) Here η denotes the viscosity of the medium . To account for the steric interaction between filaments , if the shortest distance rmin between two filaments is less than the diameter b of a filament , a constant repulsive force fst is applied along the lines connecting the two nearest points . In each time step , we calculate all the forces and torques acting on a filament and determine the translational velocity Vc and angular velocity Ω of the filament according to Eqs ( 6 ) and ( 7 ) . The center of mass of a filament is then updated as: C ( t + Δ t ) = C ( t ) + V c Δ t . ( 14 ) The updated orientations are: N ( t + Δ t ) = Rot ( Ω Δ t ) N ( t ) , ( 15 ) where Rot ( ΩΔt ) denotes the rotation matrix defined by the vector ΩΔt . The rotation vector M ( t ) is updated in the same way . Each actin crosslinking protein is modeled as an elastic spring that bridges two actin subunits in two separate filaments . The crosslinking of two unoccupied subunits proceeds with a rate constant of kf , as long as the subunits are less than rc apart . The breakage of an established crosslink is assumed to follow a “slip-bond” mechanism and occurs with an energy-dependent rate constant: k b = k b 0 e E / E c ( 16 ) where k b 0 denotes the strain-free breakage rate constant , E denotes the total elastic energy , and Ec denotes the critical energy that determines the sensitivity of the bond breakage on the forces and torques . The elastic energy E of a crosslinker that bridges actin subunits in filaments α and β is a function of the positions , orientations and rotations of both filaments , as well as its positions in the filament , E = E ( Cα/β , Nα/β , Mα/β ) . The force generated by the crosslinker on filament α reads: F α = - ∂ E ∂ C α . ( 17 ) To determine the torque generated by the crosslinker on filament α , we choose three orthnormal vectors e1 , e2 , e3 and virtually rotate filament α by an infinitesimal angle ϕi around the axis ei . These operations are equivalent to applying the following infinitesimal changes to the orientational vectors of filament: N α → N α + ϕ i e i × N α , ( 18 ) M α → M α + ϕ i e i × M α . ( 19 ) The elastic energy correspondingly has an infinitesimal change E → E + ΔE . The torque then reads: T α = - ∑ i = 1 3 ∂ Δ E ∂ ϕ i e i ( 20 ) Forces and torques acting on filament β are derived in a similar way . The total elastic force and torque are obtained by summing ( 17 ) and ( 20 ) over all the crosslinkers bound to the filament . At each time step Δt of the simulation , we perform the following operations: In our simulation , we always set the time step Δt at least 100 times smaller than the relaxation time of the spring τ = min ( ξ ‖ / ⊥ t / κ ext , ξ ‖ / ⊥ r / κ tor ) to ensure that we correctly capture the dynamics of the springs . For computational reasons , we used a high viscosity value η = 10Pa ⋅ s , such that the relaxation time τ ∼ 0 . 01s for κext = 0 . 1pN/nm and κtor = 10pN ⋅ nm ⋅ rad−1 . We tested values of lower viscosity down to η = 0 . 1Pa ⋅ s . There is no significant difference in the local nematic order parameter Slocal and the elastic energies Eext and Etor between η = 0 . 1Pa ⋅ s and η = 10Pa ⋅ s . However , the global nematic order parameter Sglobal for long filaments is increased to 1 at lower viscosity ( S7 Fig ) . This is because the enhanced diffusion increases the probability of filaments moving close to each other . As a result , the separated bundles observed at high viscosity merge into a single bundle when the viscosity is low , increasing the global nematic order parameter . We characterize the structure of actin clusters by introducing local and global nematic order parameter Slocal and Sglobal . We map the connections between filaments into an undirected graph , with filaments being the nodes , and the number of crosslinkers being the value of the edges connecting two nodes . Filaments in a connected component of the graph are said to form a cluster if the number of filaments in the component is more than 10 . The nematic order parameter S for a group of filaments is the maximum eigenvalue of the following matrix [59]: Q = 1 G ∑ α ( N α ⊗ N α - 1 3 I ) , ( 21 ) where G denotes the number of filaments in the group , Nα denotes the orientational vector of filament α . For global nematic order parameter Sglobal , the group in Eq ( 21 ) includes all the filaments . For a particular filament α* , S local α * is defined by grouping the filament α* and its connected nodes in Eq ( 21 ) . The local nematic order parameter Slocal is the average of S local α over all the filaments that have at least 2 connected nodes . Both Slocal and Sglobal are in the range of [0 , 1] . Values of Slocal close to 1 indicate that filaments are locally aligned with their connected neighbors . Values of Sglobal close to 1 indicate that all the filaments are aligned . In general Slocal is greater than Sglobal , and reaches steady state more rapidly , because filaments that are in close proximity can rapidly align , but it takes time for distant clusters of filaments to collide and reorient . Slocal is also more consistent over different simulations than Sglobal , as reported by smaller error bars for Slocal than for Sglobal ( e . g . Fig 2G ) . If the viscosity of the medium η is reduced to 1Pa ⋅ s , filaments form a single cluster and the error bars of Sglobal become comparable with Slocal ( S7B and S7C Fig ) . Note that in a sparsely connected network with filaments in random orientation , Slocal ∼ 0 . 5 ( S1B Fig ) is higher than one should expect ( ∼0 ) . This artifact is due to the fact that the sum in Eq ( 21 ) is done over a very small number of filaments ( ∼3 ) . We confirmed this property by numerically calculating the nematic order parameter for three unit vectors with random orientations . The resulting distribution of Slocal has a peak at 0 . 45 ( S6 Fig ) . This almost uncrosslinked network should be distinguished from the densely connected actin meshworks , which have local nematic order parameters Slocal in the same range ( ∼ 0 . 4 Fig 2B ) but possess a large number of attached crosslinkers . Therefore , the number of crosslinkers in the meshwork is required to distinguish these two structures . In our model , we assumed that filaments are rigid . This rigidity assumption implies that ( i ) thermal fluctuations , and ( ii ) forces and torques exerted by crosslinkers do not significantly bend or twist the filaments . We can verify a posteriori that these conditions are actually fullfiled in our simulations . Indeed , the maximum force produced by a crosslinker in our simulations is ∼10pN when the extensional stiffness κext reaches 10pN/nm , and the maximum torque is ∼100pN ⋅nm when the torsional stiffness κtor reaches 100pN ⋅ nm ⋅ rad−1 . Given the persistence length of actin filament for both bending and twisting is Lp ∼ 10μm [12 , 14 , 60 , 61] , for a filament of length L = 200nm which consists of N = L/δ = 74 subunits , the angular change between two consecutive subunits due to thermal fluctuation is arccos ( e - L / L p ) / N = 0 . 15 ° . The angular change due to bending caused by a force of f = 10pN in the middle of the filament when the two ends are fixed is arctan ( f L 2 48 L p k B T ) / N = 0 . 15 ° [62] . The twisting angle by a torque of T = 100pN ⋅ nm is ( T L L p k B T ) / N = 0 . 37 ° . Therefore , it is safe to consider filaments as stiff , and the energy stored in crosslinkers would not be dramatically different even if the finite stiffness of filaments was taken into account . We consider the rotation of two parallel filaments around their axes by consecutive detachment of crosslinkers from the pointed end to the barbed end . We assume that every other subunit of each filament is crosslinked , such that the i-th crosslinker has an angle of θ i = θ 1 + ( i - 1 ) 2 π / 13 ( 22 ) with its attached actin subunit . The torque generated by the i-th crosslinker on the filament thus is −κtorθi . Here the crosslinker label i is ordered according to their distance to the pointed end of filaments . At torque balanced state , i . e . , ∑ i = 1 n θ i = 0 , we have θ1 = − ( n − 1 ) π/13 . Upon detachment of crosslinker 1 , the total torque becomes imbalanced and the filament makes a rotation of angle Δϕ to reach a new torque balanced state , i . e . , ∑ i = 2 n ( θ i + Δ ϕ ) = 0 . This leads to Δϕ = −π/13 . Similarly we can show that attachment of a new crosslinker at the ( n + 1 ) -th position θn+1 = θ1 + n2π/13 will cause the filament to rotate the same angle in the same direction as caused by detachment of the first crosslinker . Note that even though the rotation angle Δϕ is independent of the number of attached crosslinkers , it is required that crosslinkers are present in large enough number or are stiff enough to ensure that rotation will be significantly larger than thermal fluctuations , i . e . 1 2 κ tor θ 1 2 ≫ k B T . For instance , if the number of crosslinkers n = 10 , this requires κtor ≫ 2pN ⋅ nm ⋅ rad−1 . The above calculation can be easily extended to situations with more relaxed conditions than Eq ( 22 ) . The angular rotation Δϕ ( i ) of the filament upon detachment of the i-th crosslinker satisfies the recursive relation: Δ ϕ ( i ) = θ i + ∑ j = 1 i - 1 Δ ϕ ( j ) n - i . ( 23 ) Directed rotation requires that Δϕ ( i ) have the same sign for all i . This condition is equivalent to considering that the angles between consecutive crosslinkers ( θi − θi−1 ) have the same sign for all i . We consider a simplified model in which , when there are n crosslinkers formed , each crosslinker stores an elastic energy of En . In fact , En is varied among different crosslinkers and dependent on their positions and orientations in the filaments . Here we assume En only depends on n , and the probability distribution P ( n , t ) for the number of crosslinkers is governed by d P ( n , t ) d t = k f Φ ( n | n - 1 ) P ( n - 1 ) + ( n + 1 ) k b 0 e E n + 1 / E c P ( n + 1 ) - [ k f Φ ( n + 1 | n ) + n k b 0 e E n / E c ] P ( n ) , ( 24 ) where Φ ( n|n − 1 ) denotes the number of ways to build the n-th crosslinker , given there are already n − 1 crosslinkers formed . The steady state distribution reads P ss ( n ) = P ( 0 ) ( k f k b 0 ) n Γ ( n ) e - 1 E c ∑ i = 1 n E i , ( 25 ) where Γ ( n ) = 1 n ! ∏ i = 1 n Φ ( i | i - 1 ) is the number of possible ways to build n crosslinkers . By comparing the distribution of Pss ( n + 1 ) with Pss ( n ) , we have P ss ( n + 1 ) P ss ( n ) = e - E n + 1 - μ eff ( n ) E c , ( 26 ) where μeff ( n ) is the effective binding energy defined in the text . If μeff ( n ) > En+1 , the system is driven up to the ( n + 1 ) -state , storing an elastic energy of En+1 . | In many cellular processes that involve the deformation of membranes or the movement of vesicles and organelles , the energy from biochemical reactions is converted into forces . The biological filaments called actin are one of the major force producing machineries of the cell . It is commonly believed that the elongation of these filaments at their tip is the only way actin filaments can exert force . However , the amount of force produced by this mechanism can only account for a small fraction of the force in key cellular processes , such as clathrin-mediated endocytosis . In this paper , we demonstrate that connecting actin filaments with each other with flexible proteins called crosslinkers is a new way to transform biochemical energy into mechanical energy , and that this stored mechanical energy can be used to rotate filaments in a sustained direction . This mechanism of chemical energy conversion into mechanical work is a new paradigm for understanding how the actin filaments can produce forces without considering polymerization or molecular motors . | [
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"motion"
] | 2018 | Structural organization and energy storage in crosslinked actin assemblies |
HIV-1 mucosal transmission begins with virus or virus-infected cells moving through mucus across mucosal epithelium to infect CD4+ T cells . Although broadly neutralizing antibodies ( bnAbs ) are the type of HIV-1 antibodies that are most likely protective , they are not induced with current vaccine candidates . In contrast , antibodies that do not neutralize primary HIV-1 strains in the TZM-bl infection assay are readily induced by current vaccine candidates and have also been implicated as secondary correlates of decreased HIV-1 risk in the RV144 vaccine efficacy trial . Here , we have studied the capacity of anti-Env monoclonal antibodies ( mAbs ) against either the immunodominant region of gp41 ( 7B2 IgG1 ) , the first constant region of gp120 ( A32 IgG1 ) , or the third variable loop ( V3 ) of gp120 ( CH22 IgG1 ) to modulate in vivo rectal mucosal transmission of a high-dose simian-human immunodeficiency virus ( SHIV-BaL ) in rhesus macaques . 7B2 IgG1 or A32 IgG1 , each containing mutations to enhance Fc function , was administered passively to rhesus macaques but afforded no protection against productive clinical infection while the positive control antibody CH22 IgG1 prevented infection in 4 of 6 animals . Enumeration of transmitted/founder ( T/F ) viruses revealed that passive infusion of each of the three antibodies significantly reduced the number of T/F genomes . Thus , some antibodies that bind HIV-1 Env but fail to neutralize virus in traditional neutralization assays may limit the number of T/F viruses involved in transmission without leading to enhancement of viral infection . For one of these mAbs , gp41 mAb 7B2 , we provide the first co-crystal structure in complex with a common cyclical loop motif demonstrated to be critical for infection by other retroviruses .
The induction of HIV-1 broadly reactive neutralizing antibodies ( bnAbs ) by experimental vaccines is a critical goal of HIV-1 vaccine development efforts . However , bnAbs cannot be induced by existing HIV-1 vaccine candidates [1] . The RV144 ALVAC/AIDSVAX B/E HIV-1 vaccine efficacy trial demonstrated 31 . 2% estimated vaccine efficacy 42 months after the vaccination regimen was initiated [2] . Antibodies that mediated antibody dependent cell-mediated cytotoxicity ( ADCC ) , or Tier 1 neutralizing antibodies in the presence of low envelope IgA antibodies , were identified as correlates of decreased transmission risk [3–6] . Thus , there is considerable interest in determining if commonly elicited ADCC-mediating , but non-broadly neutralizing antibodies against HIV-1 envelope have potential for protection against transmission [7 , 8] . Holl et al . have described non-neutralizing antibodies that bind to the immunodominant region ( aa 579–613 ) of gp41 that can prevent HIV-1 infection of macrophages in vitro [9] . Others have demonstrated that these types of gp41 immunodominant antibodies bind to virions [10–12] and mediate ADCC [13 , 14] . Recently the HIV-1 gp41 immunodominant loop structure was determined for the first time in the context of the pre-fusion viral spike [15] . In that structure , the loop was disulfide bonded and buried under the trimer gp120 head groups and other elements of the observed pre-fusion gp41 fold [15] . However , to date , the only antibody to the immunodominant loop with its structure determined is that of unliganded mAb 3D6 [16 , 17] . Ferrari et al . [18] , Guan et al . [19] , Bonsignori et al . [20] and Veillette et al . [21 , 22] have described non-neutralizing gp120 antibodies that bind to a conformational epitope on Env in the first constant ( C1 ) region of primary virus-infected CD4+ T cells were potent mediators of ADCC . The crystal structures of the C1 conformational A32-like antibodies N5-i5 and 2 . 2c were found to recognize overlapping epitopes formed by mobile layers 1 and 2 of the gp120 inner domain , including the C1 and C2 regions , but bind gp120 at different angles via juxtaposed VH and VL contact surfaces [23] . Mucosal transmission involves a series of events wherein HIV-1 or HIV-1-infected cells traverse genital tract mucus and epithelia , and subsequently infect epithelial or sub-mucosal CD4+ T cells [24 , 25] . Theoretically , antibodies that can bind virions at the initial stages of the transmission event [26] might be able to prevent virus movement across mucus and epithelium , thus preventing infection . Moreover , antibodies that mediate ADCC may be able to sensitize infected CD4+ T cells in the submucosa for natural killer ( NK ) cell killing [18 , 19] , and abort transmission events . Alternatively , some studies have raised the issue that non-neutralizing antibodies might enhance infection [27–30] . BnAbs passively administered to rhesus macaques provide protection from SHIV challenges at mucosal surfaces [31] . Fc receptor interactions with natural killer ( NK ) cells are important for CD4 binding site bnAb neutralization in the macaque model [32] . However , non-neutralizing Env antibodies , when administered systemically or locally , have been reported to have only limited or no protective effect against vaginal SHIV challenge in rhesus macaques [30] . Moog et al . have demonstrated that local administration of gp41 immunodominant region non-neutralizing mAbs did not prevent infection with SHIV but did slow the onset of viremia in one of six animals and blunted the peak viremia in two others [13] . Enumeration of transmitted/founder ( T/F ) genomes has proved to be an important method of discerning the number of infecting viruses in various clinical settings [33 , 34] , and has been used to monitor infection in macaques following SHIV challenge [30] . In this latter study , the mean number of T/F viruses was approximately 1 in most groups , thus , the contribution of non-neutralizing antibodies in limiting founder viruses compared to the control antibody was difficult to analyze [30] . Instead , this study showed a statistically significant enhancement in the number of founder viruses resulting in productive clinical infection in animals treated with the non-neutralizing CD4bs mAb , b6 [30] . Thus , the critical question remains as to whether antibodies that have an effector profile including recognition of virus particles and/or engaging FcR on effector cells can prevent HIV-1 transmission to any degree , or conversely , do such antibodies enhance infection ? In this study , we used T/F virus enumeration as a measure of relative protection from infection along with VL set point and CD4 preservation . We studied the activity of two human antibodies with characteristics of antibodies commonly induced by HIV-1 vaccine candidates , mAb 7B2 IgG1_AAA directed against the envelope gp41 immunodominant region [11 , 35] and the gp120 C1 mAb A32 IgG1_AAA , in the setting of high dose SHIV-BaL mucosal challenge . We show that while these non-neutralizing mAbs were unable to reduce the rate of productive infection in the high dose SHIV-BaL rhesus macaque challenge model , they reduced the number of T/F viruses involved in transmission events . Importantly , the challenge studies did not show evidence of antibody-mediated enhancement of virus infection .
The epitope specificities of two of the three HIV-1 Env mAbs examined in the rhesus macaque challenge model ( i . e . A32 IgG [18] and CH22 IgG [36] ) have already been previously described . Thus , here we characterized the epitope specificity of 7B2 IgG1 . 7B2 IgG1 mAb recognizes a linear epitope in the gp41 immunodominant region with cross-clade reactivity ( clades A , B , C , D , CRF1 and CRF2 ) as measured by peptide microarray [37] ( Fig 1A ) . Specific interacting residues within this gp41596-606 11mer were examined with peptide alanine substituted mutants and surface plasmon resonance assays ( Fig 1B ) . The footprint from epitope mapping demonstrated that residues in the 7B2 epitope were Cys598 , Gly600 , Leu602 , Ile603 , and Cys604 . Ala substitution of Cys604 resulted in a peptide that bound with a higher peak response , yet had an off-rate approximately 2 . 8 times that of the Cys604 peptide . From these data we postulated that Cys604 mediated cyclization of the peptide that would be critical for the generation of a stable 7B2-peptide complex . Therefore we repeated the binding experiments of 7B2 using both longer and minimal epitope-containing wild-type gp41 peptides in standard ( oxidizing ) and reducing conditions ( Fig 1C ) . In reducing conditions , the steady state binding of 7B2 mAb to both the longer and shorter peptides was at a relatively lower level with 100 to 150 fold increased off rates . These data suggested two primary factors for antibody binding , first , a disulfide-bonded , cyclical structure , and second , an induced fit is a likely factor in binding . The co-crystal structure of 7B2 Fab bound to a gp41596-606 peptide was solved by molecular replacement and refined to a resolution of 2 . 7 Å ( Table 1 ) . The most prominent finding in the structure occurred in the central segment of the epitope-bearing peptide where a closed loop was displayed by virtue of the disulfide bond between Cys598 and Cys604 . The cyclical conformation of the gp41 peptide was fostered by Gly600 , where any side chain would diminish the backbone's ability to adopt the closed conformation and would clash with the side chain of Tyr32 in CDR-H1 . Overall , the majority of the contacts between 7B2 antibody and the gp41 nominal peptide occurred through the base of CDR-H3 and the cyclical portion of the gp41 peptide ( Figs 1D and S1 ) . We concluded that the cysteine-cysteine bond and resulting loop conformation of the gp41 immunodominant region was indispensable for 7B2 binding . The disulfide-linkage of the gp41 immunodominant loop in the 7B2 complex structure was similar to those seen in the chain reversal regions of other viruses [40–45] . However , the only component of the chain reversal motif that HIV-1 has in common with filoviruses and most retroviruses is the core , disulfide-linked motif , where it likely served the same function as in other retroviruses , specifically stabilization of the chain reversal region and a role in the transition state and formation of the 6HB [40 , 42 , 46] . The structure of the unliganded , wild-type gp41 immunodominant loop has been determined in solution by NMR [38 , 39] , however a superposition of the solution structure upon that seen in our complex did not suggest a compatible conformation for strict docking ( Fig 1E ) . Similar comparisons were seen between the coordinates of the gp41 peptide in our crystal structure and the coordinates of the SIV gp41 and HIV-1 gp41 solution structures [47 , 48] . The structure of the gp41 immunodominant loop has also been determined in the context of the Env pre-fusion trimer [15] . The immunodominant loop as seen in the BG505 SOSIP . 664 structure resembled neither the conformation in the 7B2 complex structure nor the NMR solution peptide structures ( Fig 1E ) . A radically different conformation of the polypeptide backbone atoms within the loop resulted in different positions for several key residues . The conformational variability of the immunodominant loop is evident in comparing these structures . Thus , we concluded that the wild-type 7B2 epitope-containing peptide was likely subject to induced fit though we could not rule out conformational selection of the disulfide-bonded structure as observed in other published cases of antibody-antigen recognition [49–53] . Moreover , the immunodominant loop appeared in an ordered and disulfide-bonded state in the SOSIP structure , but it was buried beneath glycoprotein . Thus , the immunodominant loop is inaccessible on pre-fusion spikes though it is present on Env stumps [54–59] , so 7B2 and antibodies like it may capture virus and bind to virus-infected cells but fail to neutralize because they bind stumps and post-fusion structures . We constructed 7B2 IgG1 optimized for binding to human FcRIII ( CD16 ) by introducing alanine substitutions at positions 298 , 333 and 334 ( S298A , E333A and K334A ) [60] . The ability of 7B2 IgG1_AAA to mediate antiviral function depends on both antigen recognition as well as engagement of FcγR on effector cells . We determined the binding of mAb 7B2 IgG_AAA to human FcγR1 ( CD64 ) , FcγRII ( CD32 ) and FcγRIIIa ( CD16 ) by SPR measurements ( Fig 2A ) . 7B2 IgG1_AAA mAb bound with high affinity to FcγRI/CD64 ( Kd = 49 nM ) and with lower affinities to FcγRII/CD32a ( Kd = 0 . 17 μM ) and FcγRIIIa/CD16 ( Kd = 1 . 1 μM ) proteins . Among the three FcγR proteins , binding to FcγRIIIa ( CD16 ) displayed fast kinetics , with an off rate that was almost two orders of magnitude faster when compared to FcγRI binding . The range of the observed differences in binding Kd and kinetics of 7B2 IgG_AAA mAb was consistent with previous report of IgG binding to the three classes of human Fc receptors [61–63] . As expected , the 7B2 IgG1_AAA Fab fragment control did not bind any FcRs . ( Fig 2A ) . We next tested the binding of 7B2 IgG1 mAb to rhesus FcR ( FcγRIIIa-1/-2 and FcγRIIIa-3 ) . 7B2 IgG1mAb bound most avidly to rhesus macaque FcγR3A ( Fig 2B ) . 7B2_AAA bound to rhesus FcγR3A ( both allelic variants ) with higher affinities ( due to slower off-rates ) than 7B2_SEK , which contains Fc region aa S298 , E333 and K334 ( Table 2 ) . CH22 and A32 mAbs bound to both human FcγR3A-1 and FcγR3A-3 with similar affinities ( Fig 2C and 2D , Table 2 ) . To confirm that 7B2 IgG1_AAA Fc bound to rhesus FcRs on monocytes , we coated the SP400 gp41 immunodominant region peptide on sheep red blood cells ( SRBC ) , then coated the SP400- SRBC with 7B2 IgG1_AAA or a negative control antibody . Next we compared the abilities of human and rhesus monocytes to phagocytose the 7B2 IgG1_AAA-opsonized SRBC . Rhesus monocytes were able to phagocytose 7B2 IgG1_AAA mAb-coated SRBC equally as well as human monocytes ( 55 +/- 10% of rhesus monocytes with ≥2 internalized SRBC; 57 +/- 7% of human monocytes with ≥2 internalized SRBC ) . It was important to determine if the 7B2 IgG1_AAA mAb could capture infectious HIV-1 virions using an assay that could differentiate between infectious and noninfectious virus using both viral RNA and an infectious virus readout [11 , 64] . We previously reported that whereas mAb 2G12 captured the majority of infectious virus , mAb 7B2 IgG1_AAA captured both non-infectious as well as infectious virions [11 , 12 , 64] . However , mAb 7B2 IgG1_AAA could not , in any experiment , capture all infectious virions . Thus , mAb 7B2 IgG1_AAA captured a subset of both infectious and non-infectious virus particles . Here we tested whether mAb 7B2 IgG1_AAA could capture the infectious CCR5- tropic SHIV virions ( SHIV-BaL and SHIV-SF162P3 ) . As with HIV-1 , 7B2 IgG1_AAA was able to capture a portion of infectious virions of both SHIVs ( Fig 3A and 3B ) . In contrast , A32 mAb did not significantly capture any virions ( Fig 3C and 3D ) . We next asked whether recognition of HIV-1 virions by 7B2 IgG1_AAA mAb was dependent on CD4 binding . Using a virion capture assay that measures capture of virus particles without an infectious readout [65] we determined whether sCD4 binding to virus makes it more susceptible to capture by 7B2 IgG1_AAA mAb . We found that mAb 7B2 IgG1_AAA capture of HIV-1 SF162 . B , BG1168 . B , 6535 . B , 6846 . B and CAP 45 . C virions , was augmented in the presence of sCD4 ( S1 Table ) . Similarly , mAb 7B2 IgG1_AAA effectively captured parental HIV-1 SF162 . B and BaL . B as well as SHIV-SF162P3 and SHIV-BaL ( Fig 3C and 3D ) in the presence of sCD4 . The ability of 7B2 IgG1_AAA mAb to prevent infection of human colorectal tissue was assessed using an established ex-vivo rectal explant model [66] . 7B2 IgG1_AAA mAb had no direct impact on infection of colorectal explants ( Fig 3E ) . 7B2 IgG_AAA IgG1 mAb did reduce dissemination of infection from dendritic cells that emigrate from the tissue during the first 24 hours of culture on incubation with CD4+ indicator T cells ( Fig 3E ) . However , 7B2 and A32 IgG1 mAbs did not inhibit infection of monocyte derived DC or DC mediated trans-infection of co-culture with T cells ( S2 Fig ) . MAb 7B2 IgG1_AAA when expressed in both the SEK and AAA IgG1 backbones effectively neutralized HIV-1 BaL with ID50s of 0 . 05 μg/ml in peripheral blood monocytes differentiated into macrophages ( Fig 4A ) . The negative control , anti-respiratory syncytial virus mAb , palivizumab , had no inhibitory effect at 100 μg/mL . 7B2 IgG1 mAb also neutralized SF162 ( 0 . 2 IC90 for both the SEK and _AAA mAbs ) and TV-1 ( 1 IC90 for SEK and 0 . 5 for _AAA ) ( Fig 4B ) . In another assay , 7B2 IgG1 mAb was confirmed to mediate virus inhibition of infection in macrophages [9] ( Fig 4C ) . The negative control IgG and A32 mAb at 50 μg/ml did not block infection ( 107% , 109% of control infection ) , respectively . Moreover , 7B2 IgG1 mAb could mediate virus inhibition in tissue derived peritoneal macrophages ( Fig 4D ) and in antibody-dependent cell mediated virus inhibition assays ( ADCVI ) ( S2 Fig ) . To determine if 7B2 IgG1_AAA mAbs could coat virus infected targets and arm natural killer ( NK ) cell effectors for antibody dependent cellular cytotoxicity ( ADCC ) , we first confirmed the ability of 7B2 IgG1 SEK , 7B2 IgG1_AAA and A32 IgG1 mAb to bind to the surface of HIV-1 B . BaL infected CD4+ T cells ( Fig 5A and 5B ) . Palivizumab and A32_AAA were included as negative and positive controls , respectively . Both the SEK and _AAA versions of mAb 7B2 IgG1 had similar ability to bind the surface of HIV-1 B . BaL infected cells . Since our challenge stock was SHIV-BaL , we next assayed for the ability of mAb 7B2 IgG1_SEK ( and 7B2 IgG1_AAA to mediate ADCC against HIV-1 B . BaL-infected CD4 T cells . We found that 7B2 IgG1_AAA as well as A32_AAA IgG1 mAb , and the neutralizing V3-region specific mAb CH22 IgG1_AAA could indeed mediate killing of HIV-1 B . BaL-infected CD4+ T cells . In contrast , when mutation of the S298A as well as E333A and K334A in the AAA form was reverted back to SEK in the Fc domain of the mAb , it reduced the ability of the 7B2 IgG1 mAb to mediate ADCC ( Fig 5C ) . Therefore we used the 7B2 IgG1_ AAA mutant for passive infusion into macaques to determine the ability to protect against SHIV-BaL intrarectal challenge . To determine if the human HIV-1 specific mAbs can engage the rhesus FcR on NK cells , we examined binding of the mAbs to CD16 on rhesus NK cells by flow cytometry ( Fig 6 ) . Peripheral blood mononuclear cells ( PBMCs ) were isolated at a pre-infusion time point from each of the rhesus macaques enrolled in the passive infusion study ( both palivizumab and HIV-1 mAb ) . Binding of 7B2 mAb , A32 mAb and CH22 mAb to these cells was measured to ensure that there was intact FcR-Ab engagement in these animals . In all rhesus macaques tested , there was substantial binding of the infused mAb to rhesus NK cells . In vivo PK studies were performed prior to passive protection studies for all antibodies to determine the concentrations and the half-lives of the antibodies in circulation and at the mucosal sites ( Table 3; Fig 7 ) . Two rhesus monkeys that were infused once with 7B2 IgG1_SEK IgG1 at 30 mg/kg had ~10 μg/ml of 7B2 IgG1_SEK in the rectal secretions . For the PK study using 7B2 IgG1_AAA IgG1 , the Ab was administered to three rhesus monkeys twice at 50 mg/kg at 0 and 48 hours [67] . This resulted in a peak concentration of 30 μg/ml of 7B2 IgG1_AAA in the rectal secretions after the first infusion and ~90 μg/ml after the second infusion . We also tested the non-neutralizing , A32 IgG1_AAA mAb that is one of the more potent of the ADCC-mediating antibodies , and is known to bind the surface of virus infected CD4+ T cells [18 , 20 , 65] , but does not bind to Env on virions , and thus is unable to capture infectious virions . To address the question of whether the commercially prepared anti-RSV control antibody , palivizumab , may , in some way have affected transmission , we produced another control antibody , the influenza neutralizing anti-hemagglutinin IgG1 mAb CH65 [68] , using the exact same protocol and methods as used in the production of 7B2 IgG1_ AAA and A32 IgG1_ AAA . Finally as a positive control , we produced CH22 , an anti-V3 mAb derived from the RV144 HIV-1 vaccine efficacy trial [36] that neutralized SHIV-BaL in vitro in the TZM-bl neutralization assay at an IC50 of 1 . 9 μg/ml , in contrast to 7B2 IgG1_AAA and A32 IgG1_AAA that did not neutralize SHIV-BaL ( >50 μg/ml ) . With these new reagents , we completed PK studies to determine the time of peak mAb concentration at the mucosal sites to perform the challenge studies . In the passive protection trial 7B2 IgG1_ AAA was administered at 50 mg/kg at 0 and 48 hours in six Indian-origin rhesus monkeys and the monkeys were challenged with 1 ml of SHIV-BaL ( 2×105 TCID50/ml ) via the intrarectal route at time 56 hours , the time of the peak antibody concentration post-second infusion . Another six rhesus monkeys received the control antibody palivizumab at the same dose and times as 7B2 IgG1_ AAA and were challenged at the same time based on the antibody concentration post-infusion in PK studies . We found that infusion of mAb 7B2 IgG1_AAA had no effect on peak viral load or on viral load at day 42 post-challenge ( Fig 8A ) . Moreover , there was no significant impact on CD4 counts ( Fig 8B ) . Thus , infusion of 7B2 IgG1_AAA resulting in peak mucosal antibody levels of 90 μg/ml at the time of challenge ( Table 3 , Fig 7 ) , yet had no protective effect on SHIV-1 acquisition or control of viremia following high dose challenge with virus . We then carried out studies using four new groups of Indian-origin rhesus macaques with 6 animals in each group , and each group infused with 50 mg/kg antibody at times 0 and 48 hours . The first group was infused with A32 IgG1_AAA ADCC antibody , the second group with the positive control CH22-IgG1_AAA V3 neutralizing antibody , the third group with negative control antibody , palivizumab , and the fourth group with new negative control anti-influenza neutralizing antibody , CH65 . In order to insure the SHIV-BaL challenge coincided with peak plasma levels of mAb , monkeys infused with A32 IgG1_ AAA mAb and palivizumab control antibody were challenged intra-rectally with SHIV-BaL immediately following the first infusion whereas , monkeys infused with CH22 mAb and CH65 control mAbs were challenged at 60 hours using the same virus and same route . SHIV-BaL challenges were performed at the time of peak concentration of mAbs at the mucosal sites for each experimental mAb based on the findings from the PK studies . Infusion of antibody A32 IgG1_ AAA into rhesus macaques had no effect on clinical acquisition of SHIV-BaL infection with 6 of 6 animals infected or on viral load or CD4 counts ( Fig 8C and 8D ) . In contrast , infusion of the positive control V3-loop neutralizing antibody , CH22 IgG1 mAb , resulted in prevention of infection in 4 of 6 monkeys ( Fig 8E ) . Similarly infusion of the negative control antibodies palivizumab and CH65 IgG1 ( influenza neutralizing antibody ) did not prevent infection in any of the monkeys ( Fig 8C–8E ) . Keele and others have shown that in ~80% of cases of primary HIV-1 infection , one T/F viral genome ( range 1–6 ) established productive clinical infection [33 , 69 , 70] . In men who have sex with men ( MSM ) , one T/F genome ( median 1 , range 1–12 ) accounted for approximately 60% of cases , and in injection drug users this proportion fell to about 40% of cases ( median number of T/F viruses 3 , range 1–16 ) [34 , 71 , 72] . The env diversity present in the SHIV-BaL challenge stock ( mean 0 . 3% , range 0–0 . 7% ) is substantially less than the HIV-1 env diversity that is typically found in chronically infected humans ( >>1% ) [73] , but it is nonetheless sufficient for distinguishing discrete T/F genomes ( S3 Fig ) . We estimated the minimum number of T/F genomes responsible for productive SHIV-BaL infection ( see Materials and Methods ) in the 18 control animals to range from 1–27 with a median of 6 . 5 ( Table 4 ) . In contrast , after infusion of mAb 7B2 IgG1_AAA , the estimated numbers of T/F viruses was reduced 58% from the control Ab median of 6 . 0 to a median of 2 . 5 with a range of 1–5 . Given that 60 sequences per animal were used to estimate numbers of T/F variants in the 7B2 IgG1_AAA treated and matched control animals , we could be 95% confident of sampling every variant that was present at >5% prevalence in each animal . Thus , the results revealed a significant reduction in the numbers of T/F viruses in the 7B2 treated animals compared with the six matched control animals ( Mann-Whitney Rank Sum Test , p = 0 . 01 ) and when compared with the larger control group of 18 matched and unmatched control animals ( p = 0 . 001 ) ( Table 4 ) . We next evaluated the number of T/F viruses in control mAb and A32 IgG1_AAA and CH22 infused monkeys ( Table 4 ) . The median number of T/F viruses in the first palivizumab control group performed with the 7B2 IgG1_AAA trial was 6 , and the median number of T/F viruses in the second palivizumab control group was also 6 . The median number of T/F viruses in the third control group infused with the new control antibody CH65 IgG1 was 10 with a range of 1 to 27 ( Fig 9 ) . Thus , among the 18 control animals , the median number of T/F variants was 6 . 5 , and the difference in median T/F numbers between palivizumab and CH65 control antibody treated animals was not significant ( p = 0 . 12 , Mann-Whitney rank-sum test ) . The median number of T/F viruses with the non-neutralizing ADCC-mediating A32 IgG1_AAA mAb was 3 , a 50% reduction compared with the palivizumab control group where the median number of T/F viruses was 6 ( p = 0 . 03 , Mann-Whitney rank sum test ) ( Table 4 ) . In the positive control CH22 antibody group , there were a median of 2 T/F viruses in each of the two infected animals , compared to a median of 10 founder viruses in the control group ( p = 0 . 01 Mann-Whitney rank sum test ) ( Fig 9 ) . Given a mean of 37 sequences per animal in the A32 IgG1_AAA study and a mean of 40 sequences per animal in the CH22 IgG1_AAA study , we are 95% confident that variants >8% prevalence in each animal are represented in the T/F enumeration . There was no evidence of selection pressure on the breakthrough viruses nor impact on the neutralization sensitivity of these viruses in animals treated with CH22 ( S4 and S5 Figs ) . Additionally , for A32 IgG1_AAA and 7B2 IgG1_AAA passive infusion , there were no phylogenetically corrected signatures that were significant with a q value < = 0 . 3 that might suggest a selective pressure or sieve effect on breakthrough viruses . The lack of selection pressure on the antibody contact residues are shown in S6 Fig . The lack of evidence of virus escape from these mAbs is not surprising , since mutation in the highly conserved residues recognized by the A32 and 7B2 mAbs is likely destabilizing to the HIV-1 envelope glycoprotein and would alter virus fitness , and in the case of the A32 epitope , might decrease CD4 and co-receptor binding [74] . Thus , the high dose SHIV-BaL intra-rectal challenge model of rhesus macaque with an infection dose of ~105 TCID50 was able to show protection by a V3 loop neutralizing antibody that neutralized the SHIV-BaL challenge stock . Using the same challenge model , both of the ADCC mediating antibodies 7B2 IgG1_ AAA and A32 IgG1_ AAA were not able to prevent productive clinical infection nor reduce viral load , but both mAbs were able to reduce the number of founder variants by ~50% . Importantly , none of the anti-HIV Env mAbs tested showed any evidence of enhancement of viral transmission .
In this study we have shown that two non-neutralizing antibodies , the gp41 targeted , ADCC-mediating , virus-capturing antibody 7B2 IgG1_AAA and the gp120 ADCC-mediating antibody A32 IgG1_AAA , each with mutations that enhance FcR binding [60] , were able to limit the number of T/F viruses of SHIV-BaL in a high dose intra-rectal challenge model in rhesus macaques . While both antibodies did not reduce the frequency of clinical infection by SHIV-BaL , they did reduce the number of founder viruses by ~50% . In 7B2 IgG1_AAA treated animals , the median number of T/F viruses was reduced from 6 to 2 . 5 ( p = 0 . 01 ) . In A32 IgG1_AAA treated animals , the median number of T/F viruses was reduced from 6 to 3 ( p = 0 . 03 ) . In animals treated with the positive control V3 loop antibody CH22 , virus transmission was eliminated altogether in 4 of 6 animals and the numbers of T/F viruses in the remaining two animals reduced to two compared with a median of 10 in the control group ( p = 0 . 01 ) . There are caveats to the interpretation of these findings . First , the numbers of animals in treated and control groups were small ( n = 6 for each ) . Second , the diversity in the SHIV-BaL challenge stock is limited ( median env diversity 0 . 3%; range 0–0 . 7% ) , so distinguishing between discrete T/F genomes and genomes that acquire shared mutations post-transmission can be problematic . Third , the in vivo error rate of the SIV reverse transcriptase and the number of virus generations from infection to sampling must be estimated in distinguishing distinct T/F viruses from evolved viruses; we used previous empirical data and mathematical modeling of early viral replication dynamics [33 , 73] to estimate the frequency of mutations that might be expected in the first 10–21 days of infection to guide the identification of T/F genomes ( see Methods ) . Fourth , APOBEC-mediated G-to-A hypermutation can confound T/F lineage analysis [33]; in the analysis presented we deleted G-to-A hypermutated sequences from our analyses [30 , 33 , 73] . Of note , the results regarding reduction in the number of transmitted strains in the presence of the non-neutralizing antibodies were supported when all G-to-A APOBEC motifs were removed from the analysis [33 , 75] . Fifth , our measurements of T/F genomes are point measurements that depend on depth of sampling for their sensitivity . They are not corrected for variants that might be present but not observed , and they are not expressed as point estimates with confidence limits . Alternative approaches to estimating numbers of T/F genomes that account for these limitations are in development ( L . Blair , B . Korber , T . Bhattacharya ) . SHIV-BaL is a CCR5-tropic tier 1 virus that is relatively easy to neutralize and is not a pathogenic SHIV . This SHIV was used to establish a sensitive model for non-neutralizing antibodies and to determine if any protection was present compared to previous studies using SHIV-BaL that have shown full protection with bnAb infusions [67] . While the challenge dose was high , it was the least amount of virus that caused infection of 100% of challenged animals ( 2 . 0 x105 TCID50 ) . Previous work has demonstrated that non-neutralizing antibodies administered locally or systemically were not as potent in protecting against SHIVs in rhesus macaques as antibodies that neutralize the challenge SHIVs . The present study sought to define the protective capacity of antibodies that are not able to neutralize HIV-1 in the TZM-bl assay but can mediate ADCC and other types of anti-HIV-1 immune effector functions . One caveat of this study is that the amount of virus present in the challenge stock , 2 . 9 x 109 copies/ml ( 2 . 0 x 105 TCID50 ) far exceeds the average amount of virus in semen of untreated HIV-1 infected individuals which is 0 . 426 x 104 copies/ml ( range 0 . 01–6 . 9 x 104 ) [76] . If this seminal viral load reflects the amount of virus responsible for natural infection in the RV144 clinical trial , in which ADCC responses correlated with lower risk of infection in vaccinees with low anti-Env IgA responses , then we used a SHIV challenge dose that was 5 orders of magnitude greater than what may be responsible for natural HIV-1 infection . Therefore , it is possible that , in order to achieve infection in 100% of the control animals , we were considerably above the challenge threshold for the amount of virus that can be controlled by these non-neutralizing Abs . Thus , transmitted/founder virus enumeration was a more sensitive approach to study the impact of these non-neutralizing antibodies on transmission in this high dose challenge model . A vaccine induced antibody response is unlikely to induce a single antibody specificity at the plasma concentrations present during the time of challenge ( 1 , 324–2 , 034 μg/ml ) ; however , the mucosal antibody concentrations present in this study at the lower end of the range ( 0 . 96–27 . 4 μg/ml ) are likely concentrations to be induced by vaccination . Whether or not the level of mucosal antibody concentrations present in individual animals in this study played a role in protection or reduction of founder viruses is unknown , but is worth further study to better understand the antibody concentrations needed for protection . Antibodies that neutralize HIV-1 in conventional neutralization assays protect in passive protection trials . However , these neutralizing antibodies must have breadth and potency to be effective when passively administered . Administration of bnAbs clearly demonstrates the relevance of mAb breadth and ability to neutralize the few R5 SHIVs that are available for testing as challenge isolates . Hessell et al . have shown that the combination of conventional neutralization activity and IgG FcR-mediated activity such as ADCVI provides optimal protection in the setting of passive protection trials [32] . However , they have also demonstrated that nonfucosylated antibodies with better ADCC function were not better at protection[77] . Thus , additional studies are needed to determine whether the most likely attributes of protective IgG antibodies are to have conventional neutralizing activity with sufficient breadth to be clinically relevant and to potentially be able to mediate FcR dependent anti-HIV-1 activities . Moreover , additional passive infusion studies utilizing antibodies engineered to have optimal FcRn binding [78] to improve the antibody half-life can provide a way to examine the role of different non-broadly neutralizing antibody specificities in protection in a low dose mucosal challenge study . MAb 7B2 IgG1_AAA and similar gp41 antibodies capture a subset of infectious virions in a CD4–dependent manner , mediate ADCC , and neutralize BaL in macrophage cultures . In contrast , mAb A32 IgG1_AAA does not capture virons , does not mediate macrophage neutralization , but is a very potent mediator of ADCC [18] . There is some controversy on the role of macrophages either as a virus reservoir or as part of the initial foci of infection after rectal or vaginal challenge . However , we broadly tested Fc-mediated activity of these mAbs , including macrophage neutralization and phagocytosis to characterize their potential effector function in vivo . That both of these antibodies ( but not control antibodies ) limited the number of founder SHIV-BaL viruses suggests that if viruses with traits like the tier 1 SHIV-BaL are indeed involved in HIV-1 transmission , then these common types of dominant gp41 and gp120 antibodies may play a role in protecting against HIV-1 transmission . Burton et al . found a suggestion of protection in 2 of 5 challenged animals using the gp41 non-neutralizing mAb F240 , but the result was not statistically significant [30] . The authors counted T/F viruses in this study but there was no reduction in T/F viruses by F240 IgG mAb in infected animals [30] . Recently , in a live SIV vaccine ( SIVmac239delta Nef ) model , gp41 reactive antibodies , with properties similar to F240 mAb were shown to correlate with protection [79] . Also in the Burton study , the CD4 binding site non-neutralizing Ab b6 IgG mAb appeared to result in enhancement in the numbers of founder viruses compared with control animals [30] . This was not seen in the present study for 7b2 or A32 mAbs . Others have demonstrated that the ability of an antibody to block virion transcytosis through epithelia is a predictor of protection both in the setting of subjects who are exposed and uninfected , and in the setting of vaccination [80] . The 7B2 IgG1_AAA antibody has recently been reported to block transcytosis in vitro [81] , although in our mucosal explant models , neither A32 IgG1_AAA nor 7B2 IgG1_AAA blocked HIV-1 infection in the explant model in vitro . It has previously been recognized that the 7B2 IgG1_AAA mAb can capture both infectious and non-infectious virions [11] and that the ability to capture virions does not correlate with ability of an antibody to neutralize HIV-1 [64] . MAb 7B2 IgG1_AAA most likely recognizes gp41 stumps from which gp120 has been shed [54] or some other non-native form of envelope on the virion . Since virions can contain a mixture of native and non-native Env forms [54–57] , it was conceivable that an antibody like 7B2 IgG1_AAA could provide some measure of protection by binding to non-native Env forms on infectious virions . We do not know whether virus capture , ADCC , both or neither was involved in limiting founder viruses by 7B2 IgG1_AAA . However , A32 IgG1_AAA also limited founder viruses . Thus , we hypothesize that the effector mechanism was ADCC or a mechanism other than virus capture and retardation of virus transport across epithelia . One reason for this limited effect on prevention of founder virus number by 7B2 IgG1_AAA may be that although 7B2 IgG1_AAA does bind to infectious virions , it does not bind to all infectious virions ( Fig 3 ) . We recently demonstrated that although mAb 7B2 IgG1 cannot capture all infectious virus , the combination of two mAbs , 7B2 IgG1 and the V2 antibody CH58 IgG1 mAb , increased the capture of infectious virions [12] . It was of interest that our positive control , CH22 mAb , a V3 mAb isolated from an RV144 vaccinee [36] did neutralize SHIV-BaL ( 1 . 9 μg/mL IC50/10 . 4 μg/mL IC80 ) , mediated ADCC ( peak activity at 50 μg/mL ) , and protected 4 of 6 macaques from SHIV-BaL infection . V3 specific antibodies , in the context low specific Env IgA responses , were associated with a reduced risk of infection in the RV144 trial [82] . However , the infecting viruses in RV144 were Tier-2 , while CH22 mAb only neutralized tier 1 viruses . Thus the protective capacity of CH22 mAb for the RV144 setting remains unclear . Moreover , in contrast to the protection of 4/6 animals by the linear V3 specific CH22 antibody , PGT121 , a glycan V3 bnAb protected all of the animals [83] . The difference in the protective capacities of PG121 and CH22 are likely due to the differences in their epitope recognition and breadth/ potency for primary isolates ( i . e . PGT121 recognizes glycans in the V3 region and is a broadly neutralizing antibody , unlike CH22 that is against a linear V3 and neutralizes only Tier 1 viruses ) . We also show the co-crystal structure of gp41 immunodominant region mAb 7B2 Fab with its gp41 nominal epitope . The most prominent finding in the structure was the presence of a cysteine-cysteine loop with intact disulfide bond in the center of the gp41 Env contact region . Most recently , the BG505 SOSIP . 664 trimer structure has showed partial structural detail of the pre-fusion gp41 , in particular an ordered , disulfide-bonded immunodominant loop structure that was buried inside the glycoprotein complex [15] . This disulfide-bonded immunodominant loop structure of HIV-1 gp41 is involved in gp41-gp120 interactions [47 , 84–86] and is homologous to similar regions in other enveloped viruses and key structural elements in those virus infection mechanisms [48 , 86] . The extended conformation and hydrophobic nature of the residues at the tip of 7B2 CDR-H3 are reminiscent of the properties of gp41 membrane proximal broadly neutralizing antibodies , specifically that they tend to bear long , highly mutated CDR-H3s [1 , 87 , 88] . However , 7B2 was not polyreactive [88] , does not have its epitope in the membrane proximal external region [89] , and the CDR-H3 is not exceptionally long [1 , 87 , 90] . The data in this report demonstrate an exposed gp41 structure at amino acids 596–606 of Env that is exposed both on gp41 on HIV-1 virions and on virus-infected cells . This is the first structure of an antibody complex that shows binding at the functionally important disulfide bond in the chain reversal region of a retroviral entry protein . The cyclical loop motif has been demonstrated to be critical for function in other retroviruses , in particular to help constrain the local structure of the chain reversal region and to participate in formation of the hairpin structures during the fusion process . Vaccines that induce immunodominant gp41 antibody responses , targeting the same epitope as 7B2 IgG1_AAA mAb , are in HIV-1 clinical trials [91] . It is important to note that these vaccines induce this antibody specificity in the context of a polyclonal antibody response . A recent non-efficacious HIV-1 vaccine trial ( HVTN 505 ) [92] used a gp140 that had the C-C immunodominant loop region bound by 7B2 IgG1_AAA deleted , and thus could not induce 7B2 IgG1_AAA-like mAbs . For those vaccine regimens that do induce antibody specificities similar to 7B2 mAb [91] ( Seaton et al . , in preparation ) , our study can shed light on potential antiviral functions induced by HIV-1 vaccination . In our current study , non-neutralizing antibodies with defined specificities and functional characteristics were tested individually and some restriction in the number of founder viruses was observed . Future proof of concept studies in NHP can combine different Abs such as A32 IgG1_AAA ( conformational C1 ) , 7B2 IgG1_AAA ( linear gp41 ) and CH58 IgG1_AAA ( V2 ) [65] to determine whether a well-defined polyclonal mixture of antibodies can improve protection against mucosal infection .
Rhesus macaques ( Macaca mulatta ) were housed at BIOQUAL , Inc . ( Rockville , MD ) , in accordance with the standards of the American Association for Accreditation of Laboratory Animal Care . The protocol was approved by the BIOQUAL's Institutional Animal care and Use Committee under OLAW Assurance Number A-3086-01 . Bioqual is IAAALAC accredited . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health ( NIH ) and with the recommendations of the Weatherall report; “The use of non-human primates in research” . All procedures were performed under anesthesia using ketamine hydrochloride , and all efforts were made to minimize stress , improve housing conditions , and to provide enrichment opportunities ( e . g . , social housing when possible , objects to manipulate in cage , varied food supplements , foraging and task-oriented feeding methods , interaction with caregivers and research staff ) . Animals were euthanized by sodium pentobarbital injection in accordance with the recommendations of the panel on Euthanasia of the American Veterinary Medical Association . Human peripheral blood mononuclear cells and peritoneal macrophages from HIV-1 negative individuals were collected with IRB approval by the Duke Medicine Institutional Review Board for Clinical Investigations ( Protocol Pro00006526 , Pro00000873 , Pro00009459 ) and from healthy human subjects enrolled in the UC Irvine Normal Blood Donors Program ( HS #2002–2430 ) . All subjects were consented following 45 CFR 46 and written informed consent was obtained by all participants . No minors were recruited into this study . Additionally , monocytes were purified from blood packs purchased from the blood bank , with written informed consent from the donors . The approval to collect and store tissue was issued by the Imperial Tissue Bank with reference number Med_RS_11_014 . Approval to use the tissue was given under Project number R11021 . Approval for this project was granted by the Tissue management Committee at Imperial College Healthcare Trust in July 2011 , and ethics thus conveyed through this process by MREC Wales , reference number 07/MRE09/54 . The 7B2 IgG monoclonal antibody was isolated from a HIV-1 chronically infected subject using Epstein-Barr ( EB ) virus B cell transformation and heterohybridoma production [35] . To produce recombinant 7B2 IgG1 antibody , the variable regions of immunoglubulin heavy and light chain ( VHDJH and VLJL ) genes of 7B2 IgG1 were isolated from the 7B2 IgG1 cell line by RT/PCR using the primers and methods as described [93] , where the VH and VL gene rearrangements expressed by 7B2 IgG1 were determined by sequence analysis and annotated using the IMGT database . For structural studies , after affinity capture the 7B2 Fab was further purified via gel filtration chromatography using a HiLoad 26/60 Superdex 200pg 26/60 column at 1 ml/min with a buffer of 10 mM Hepes pH 7 . 2 , 50 mM NaCl , 0 . 02% NaN3 . Peak fractions of Fab were pooled and exchanged into 50 mM Hepes pH 7 . 5 via five dilute/concentrate cycles in an UltraFree 4 ml 10K MWCO , then run over a cation exchange column ( Mono S 5/50 GL ) at 1 ml/min . At pH 7 . 5 , the Fab passes through the column and excess light chain binds . The excess light chain is later eluted with 50 mM Hepes pH 7 . 5 , 1 M NaCl . At lower pH values , the Fab binds the column and can be eluted with a gentle salt gradient , e . g . 0–15% . Peak fractions of Fab were pooled and exchanged into 10 mM Na Hepes pH 7 . 5 , 50 mM NaCl , 0 . 02% NaN3 via four cycles of dilution-concentration and brought to a concentration >20 mg/ml for subsequent dilution . Two forms of recombinant IgG1 heavy chain Fc were produced , one with the wild type IgG1 sequence termed as 7B2 IgG1_ SEK , and the other termed 7B2 IgG1_AAA . 7B2 IgG1_SEK contains Fc region aa of S298 , E333 and K334 and 7B2 IgG1_AAA contains the Fc region aa of S298A as well as E333A and K334A , amino acid mutations previously reported to augment antibody ability to bind to FcRIIIa and to augment antibody ADCC activity [60] . The 7B2 IgG1_AAA mAb was expressed in CHO cells ( Catalent , Somerset , NJ ) . The A32 IgG1 monoclonal antibody was isolated from a chronically infected HIV-1 infected patient using Epstein-Barr virus B cell transformation and heterohybridoma production [94] . To produce recombinant A32 IgG1 antibody , the A32 IgG1 VHDJH and VLJLgenes were isolated from the cloned A32 IgG1 cell line by RT/PCR as described [93] . The VHDJH and VLJL gene rearrangements of A32 IgG1 were determined by sequence analysis . The recombinant A32 antibody was expressed in CHO cells ( Catalent , Somerset , NJ ) as IgG1_AAA to be optimized for binding to FcRIIIa [60] . Palivizumab , a commercial anti-respiratory syncytial virus antibody ( Medimmune , Inc , Quakertown , PA ) , as well as recombinant CH65 , an anti-influenza hemagglutinin mAb [68] , were used as negative controls in the passive protection studies . Finally , CH22 neutralizing mAb reactive with the V3 region of HIV-1[36] was produced as a recombinant CH22 IgG1_AAA mAb in CHO cells ( Catalent , Somerset , NJ ) . As a control for CH22 , the influenza mAb CH65 [68] was produced as recombinant IgG1_AAA in CHO cells using the same technologies for 7B2_AAA , A32_AAA and CH22_AAA mAbs . MAb binding 7B2 IgG_AAA to peptides and gp140 proteins was performed by ELISA , by binding antibody multiplex assays [26 , 95] and by surface plasmon resonance ( SPR ) assays [96 , 97] . Epitope mapping of mAbs was performed by peptide microarray microarray [37 , 98] . Gp41596-606 peptides with N-terminal biotin tags were commercially synthesized for SPR experiments . They were the wild type peptide and each residue substituted with Ala in turn . An additional gp41596-606 peptide was synthesized with an acetylated N-terminus and amidated C-terminus for crystallography . SP400 peptide ( gp41579-622 ) was synthesized solely for SPR [26] . Surface plasmon resonance ( SPR ) . SPR binding assays were performed on a BIAcore 3000 ( BIAcore Inc , Piscataway , NJ ) instrument at 25°C and subsequent epitope mapping was carried out using a BIAcore 4000 instrument . Data analyses were performed using the BIAevaluation 4 . 1 software ( BIAcore ) as previously described [88] . Each residue of the 11mer WGCSGKLICTT ( gp41596-606 ) was mutated to Ala and the peptides were made with N-terminal Biotin tag to facilitate their coupling to a BIAcore streptavidin chip . The biotinylated 11mer and its alanine-substituted variants were screened for binding by 7B2 ( at 20 μg/ml ) . Binding responses of an irrelevant antibody Synagis was used to subtract out responses due to non-specific interactions . For the gp41-binding experiments , both 7B2 and the negative control Synagis at a 50 μg/ml concentration were flowed at a 50 μl/min rate over SP400 tetramer ( ~4000 RU ) and recombinant gp41 MN ( ~5700 RU ) immobilized on a CM5 chip . The Gp41MN is a commercially available protein ( Immunodiagnostics , Inc . product number 1091 ) and SP400 is a gp41-derived peptide with sequence RVLAVERYLRDQQLLGIWGCSGKLICTTAVPWNASWSNKSLNKI which was commercially synthesized ( CPC Scientific ) and tetramerized with streptavidin tags . For the experiments testing reducing conditions , biotinylated peptides were coupled to a BIAcore streptavidin chip . The peptides were initially screened for binding by 7B2 ( at 10 μg/ml ) at a flow rate of 20 μl/min with a PBS running buffer . Binding response to biotinylated SP62WT ( a peptide containing gp41 MPER sequence ) was used to subtract out non-specific interactions . The BIAcore 3000 was then primed with PBS , 20 mM DTT running buffer . The peptides were screened for 7B2 ( at 10 μg/ml ) at 20 μl/min with the PBS , 20mM DTT running buffer . Non-specific interactions were accounted for using the previously stated method . Peptides of sequence WGCSGKLICTT ( gp41596-606 ) were commercially synthesized with acetylated N-termini and amidated C-termini in both reduced and disulfide-bonded forms ( CPC Scientific ) . Lyophilized peptides were solubilized to 100 mg/ml in DMSO with no further dilution in aqueous solution prior to mixing with 7B2 Fab in a 1:3 Fab:peptide molar ratio . The complex was then diluted with 10 mM Na Hepes pH 7 . 5 , 50 mM NaCl buffer to a total protein concentration of 12 . 5 mg/ml . Fab-peptide complexes were screened against various sparse matrix screens . Plates were incubated at 20°C . Crystals of the Fab with cyclical peptide were observed within two weeks in the Hampton Research PegRX screen condition H9 , a solution of 5% 2-propanol , 0 . 1 M citric acid pH 3 . 5 , 6% PEG 20K . Crystals were replicated in a fine matrix screen about the hit condition , the optimal of which was 4–6% 2-propanol , 0 . 1 M citric acid pH 3 . 5 , 8% PEG 20K . Crystals were cryoprotected with reservoir solution supplemented with 30% ethylene glycol immediately prior to cryocooling . Data were collected at SER-CAT BM at 1 Å wavelength . Data were reduced in HKL-2000 [99] in space group P21 ( P1211 ) . Matthews analysis suggested two Fab-peptide complexes of approximate molecular weight 49 kD each in the asymmetric unit corresponding to a solvent content of 66% and a Matthews coefficient of 3 . 6 [100] . The structure was phased by molecular replacement in PHENIX [101] . Source models were the light chain of humanized antibody CC49 [102] ( 86% identity to the light chain of 7B2 ) and the heavy chain of an antibody to neuropilin [103] ( 77% identity to the heavy chain Fab fragment of 7B2 ) with its CDR-H3 deleted . Conformations of the CDRs were rebuilt and the peptide antigen was constructed de novo as features of the electron density map improved with refinement . Rebuilding and real-space refinements were done in Coot [104] with reciprocal space refinements in PHENIX [105] and validations in MolProbity [106] . Non-crystallographic symmetry was employed throughout refinement . Coordinates and structure factors have been deposited into the Protein Data Bank ( www . rcsb . org ) with accession code 4YDV . Antibodies tested for binding to FcR were the same lots as used for the passive infusion studies . Detection reagents were made as described [20 , 107] . All experiments included aliquots of the same human PBMC control to verify experimental consistency . Aliquots of rhesus PBMC were incubated with the appropriate mAb ( 7B2 IgG , A32 IgG , CH22 IgG , CH65 IgG [negative control] , or mock ) at 4°C for 30 min . For experiments using gp120 Env proteins for detection , cells were then blocked with an anti-CD4 mAb ( clone SK3; Biolegend , San Diego , CA ) at 4°C for 15 min . Cells were washed with 1x PBS + 1% BSA and stained at 4°C for 30 min with an NK cell panel consisting of CD3 PerCP-Cy5 . 5 , CD4 PE-Cy7 , CD14 PE , CD20 FITC , CD32 APC ( BD Biosciences , San Jose , CA ) ; CD16 BV570 , CD64 APC-Cy7 ( Biolegend , San Diego , CA ) ; and CD8 PE-TexasRed ( Invitrogen , Carlsbad , CA ) . The NK cell cocktail also contained the detection reagent matched to the FcR-bound mAb: 7b2 detected with gp41 immunodominant region peptide tetramer ( sequence biotin-GGGKQLQARVLAVERYLKDQQLLGIWGCSGKLICTTAV ) ; CH22 detected with gp120 clade B consensus V3 peptide tetramer ( sequence biotin-GGGTRPNNNTRKSIHIGPGRAFYTTGEIIGDIRQAH ) ; A32 detected with gp120 A244 Env tetramer . Cells were washed again and resuspended in 2% formaldehyde in PBS and stored at 4°C prior to acquisition on a BD LSRII flow cytometer ( BD Biosciences , San Jose , CA ) . Data were analyzed in FlowJo ( TreeStar , Ashland , OR ) . Rhesus NK cells were gated as negative for CD3 and CD14 , CD20 dim/negative , CD8 bright [108] Mean fluorescence intensity of detection reagent binding was assessed for CD16+ NK cells only . Briefly , a 1ml packed suspension of pooled sheep red blood cells ( SRBCs ) were washed 3 times in 0 . 85% NaCl and spun at 3 , 000 rpm for 5 min . The SRBCs were then added to 1ml of a 0 . 5mg/ml gp41 immunodominant peptide ( SP400: sequence biotin-GGGKQLQARVLAVERYLKDQQLLGIWGCSGKLICTTAV ) antigen solution and 10 ml of chromium chloride solution ( 0 . 1 mg/ml ) . The coupling suspension was incubated at 30°C for 40 minutes in a shaking incubator ( 100 rpm ) and then centrifuged for 10 minutes at 1000xg , 4°C . Coupled cells were then washed with 0 . 85% NaCl and centrifuged again for 10 minutes at 1000xg , 4°C . Coupled cells were resuspended as a 25% solution ( v/v ) in HBSS . The antigen coupling of sp400 to SRBCs was analyzed via flow cytometry . Antibodies 17b IgG mAb and 7B2 IgG_AAA ( 10 μg/ml ) were incubated with 100 μl of a 2% solution ( v/v in 0 . 85% NaCl ) of both uncoupled and sp400 coupled SRBCs at room temperature for 30 minutes . Cells were washed twice with 3ml FACS Buffer ( 1x PBS + 1% BSA , pH 7 . 2 ) and centrifuged at 3000 rpm , 5 min to pellet cells . Supernatant was aspirated , and cells were gently re-suspended via agitation . Goat anti-human FITC secondary antibody ( 1:50 dilution in 1xPBS ) was then added to the SRBC suspensions and incubated for 30 min at room temperature . Cells were again washed twice with 3ml FACS Buffer and then resuspended in 1ml FACS buffer . Antibodies 7B2 IgG_AAA and palivizumab ( both at 10 μg/ml ) were incubated with gp41 immunodominant peptide coupled SRBCs as well as uncoupled ( control ) SRBCs for 45 minutes on ice . SRBCs were then washed 3 times with PBS and centrifuged at 3000rpm , 5 min . In 4ml tubes , 5x106 coupled or sp400 coupled SRBCs were mixed with 1x106 human or rhesus PBMCs in 1ml RPMI 1640 . Cells were pelleted by centrifugation at 3000 rpm for 5 minutes and incubated as a pellet at 37°C for 30 min . After incubation , the pellet was resuspended in the 1ml media supernatant . Cytopreps were prepared and stained with Wrights stain . MAb binding to recombinant purified Fc receptors ( FcRs ) was performed using Surface Plasmon Resonance . Full sequences of the Fc receptors ( recombinant FcRγI ( CD64 ) , FcRγIIa ( CD32 ) and FCRγIIIb ( CD16 ) ( R&D Systems ) ) . and the purification protocol of the rhesus FcRs is described elsewhere ( Cocklin et al , 2014 in preparation ) . Briefly , rhesus FcR allelic variants were synthesized by Genscript with a hexa-histidine tag and cloned into the pcDNA3 . 3 mammalian expression vector ( Invitrogen , Carlsabad , CA ) . Stable 293 HEK cell lines expressing rhesus FcR variants were prepared by nucleofection ( AMAXA , Lonza ) and antibiotic selection . Supernatants containing the soluble FcRs were harvested , centrifuged at 3500rpm for 1h and the supernatant was filtered through 0 . 45μm membranes prior to purification by Immobilized metal affinity chromatography ( IMAC ) using the Profinia protein purification system ( Biorad , Hercules , CA ) . IMAC eluates were incubated with human pooled IgG sepharose for to remove serum protein contamination and for functional confirmation . IgG eluates were concentrated and dialysed into PBS , pH7 . 4 . Purity was confirmed at >95% by SDS PAGE and coomassie staining and bicinchoninic acid ( BCA ) assay was used to determine the protein concentration . PBMC and HIV-1 envelope pseudotyped virus neutralization assays were performed as described [109 , 110] . Ability of the mAb to inhibit HIV-1 infection of monocyte/macrophages was performed as described [9] . Briefly , human peripheral blood ( PB ) monocytes were differentiated into macrophages and seeded on 48 well plates . Cells were infected with primary R5 HIV-1 BaL at a concentration of 200 ng/mL viral p24 antigen in the presence or absence of different concentrations of 7B2 IgG_AAA Ab and cultured in AIM plus Glutamax 1 and GM-CSF ( 10 ng/mL , R&D System ) . Productive infection was quantified by flow cytometry by detection of intracellular viral p24 antigen in MDM after 48 hours of culture . The percentage of infected cells in presence of Abs compared to control infected cells was determined . The TZM-bl neutralization assay was performed with the SHIV BaL challenge stock and the antibody concentration ( μg/ml ) at which relative luminescence units ( RLUs ) were reduced 50% compared to virus control wells were reported . Surgically resected specimens of intestinal tissue were collected at St Mary’s Hospital , London , United Kingdom , after receipt of signed informed consent . All patients were HIV negative . All tissues were collected under protocols approved by the Local Research Ethics Committee . Colorectal tissue was obtained from patients undergoing rectocele repair and colectomy for colorectal cancer . Only healthy tissue obtained 10 to 15 cm away from any tumor was employed . Following transport to the laboratory , muscle was stripped from the resected tissue , which was then cut into 2- to 3-mm3 explants comprising both epithelial and muscularis mucosae . Colorectal explants were maintained with Dulbecco’s minimal essential medium containing 10% fetal calf serum , 2 mM L-glutamine , 2 . 5 μg/ml Fungizone ( Life Technologies ) and antibiotics ( 100 U of penicillin/ml , 100 μg of streptomycin/ml , and 80 μg of gentamicin/ml ) at 37°C under an atmosphere containing 5% CO2 as previously described [66] . Antibodies at 50 μg/ml were pre-incubated with cell free HIV-1BaL ( 5 x 104 TCID50 ) for 1 hour at 37°C . The 2-3mm3 dissected colorectal tissue explants were then exposed to virus and antibody for 2 hours at 37°C . Following viral incubation explants were washed three times with PBS and placed into 96-well tissue culture plates and cultured with fresh media containing antibody at the same concentration . The next day the tissue explants were transferred into a new 96-well tissue culture plate and washed twice with PBS . Tissue explants were subsequently cultured for 14 days in 200 μl of medium supplemented . On days 4 , 7 , 11 and 14 post-infection , 100 μl of supernatant was harvested and replaced with 100 μl of fresh media without further antibody exposure . To assess migratory cells present in the overnight culture of explanted tissue , cells were washed twice with PBS and co-cultured with 4 x 104 PM-1 indicator T cells in 200 μl of medium containing antibodies at 50 μg . The supernatant was collected on days 4 , 7 , 11 and 14 post-infection and replaced with fresh media without further antibody exposure . All assays were performed in triplicate unless otherwise stated and controls included: virus only; medium only; and antibody isotype controls . Levels of p24 in tissue explant supernatants at day 11 post-infection were quantified using HIV-1 p24 enzyme-linked immunosorbent assays ( ELISA; SAIC-Frederick , Inc . , Frederick , MD ) according to the manufacturer’s instructions . Each experiment was performed in triplicate , using tissues from different donors . Replication competent HIV -1 BAL was utilized to infect tissue macrophages and blood-derived macrophages . Peritoneal tissue macrophages were obtained under informed consent by IRB approval at Duke University . Virus input was normalized to RNA copies/mL . HIV replication was quantified by measuring the amount of luciferase in macrophage lysates at either day 4 or day 14 post-infection . HIV production was quantified by measuring luciferase in TZM-bl reporter cells infected with macrophage supernatants collected at regular intervals ( 4 , 7 , 11 , and 14 days post infection ) . HIV-1 reporter viruses ( provided by Dr . John Kappes and Christina Ochsenbauer , University of Alabama ) used were replication-competent infectious molecular clones ( IMC ) designed to encode the BaL ( subtype B ) env genes in cis within an isogenic backbone that also expresses the Renilla luciferase reporter gene and preserves all viral ORFs . The Env-IMC-LucR virus used was NL-LucR . T2A-BaL . ecto ( IMCBaL ) [111 , 112] . IMCs were titrated in order to achieve maximum expression within 36 hours post-infection by detection of Luciferase activity and intra-cellular p24 expression . We infected 2x106 cells with IMCBAL by incubation with the appropriate TCID50/cell dose of IMC for 0 . 5 hour at 37°C and 5% CO2 in presence of DEAE-Dextran ( 7 . 5 μg/ml ) . The cells were subsequently resuspended at 0 . 5 x 106/ml and cultured for 36 hours in complete medium containing 7 . 5 μg/ml DEAE-Dextran . Infection was monitored by measuring the frequency of cells expressing intracellular p24 . The assays performed using the IMC-infected target cells were considered reliable if the percentage of viable p24+ target cells on assay day was ≥ 20% . Binding of mAbs to the surface of infected cells was performed as previously described [18] . Polyclonal activated CD4+-enriched T cells were obtained and infected by spinoculation ( 1200 x g for 2 hours; [113] ) with NL-LucR . T2A-BaL . ecto ( IMCBaL ) . Cells spinoculated in the absence of virus ( mock-infected ) were used as a negative infection control . Following 72 hours of infection in RPMI 1640 medium ( Invitrogen , Carlsbad , CA ) , supplemented with 20% Fetal Bovine Serum ( FBS ) ( Gemini Bio-Products , West Sacramento , CA ) ( R20 ) in presence of 20 U/ml rhIL-2 ( Peprotech , Rocky Hill , NJ ) ( R20-IL2 ) , CD4+-enriched T cells were washed in PBS , dispensed in 96-well V-bottom plates at 1x105 viable cells/well , and stained with a vital dye ( LIVE/DEAD Fixable Aqua Dead Cell Stain , Invitrogen ) to exclude non-viable cells from subsequent analyses . The cells were then incubated at 4°C for 25 minutes with the primary Ab . After two washes , cells were stained with Phycoerythrin ( PE ) -conjugated goat anti-human IgG secondary ( 2ary ) Ab ( Southern Biotech , Birmingham , AL ) for 2 hours at 37°C . Cells were subsequently washed 3 times and fixed with 1% formaldehyde PBS . The samples were acquired on a LSRII ( BD Biosciences ) within 24 hours . A minimum of 10 , 000 total singlet events was acquired for each test to identify live events . Data analysis was performed using FlowJo 9 . 6 . 4 software ( TreeStar Inc . , Ashland , OR ) . Luciferase-based ADCC assays were performed as previously described luciferase-based assay [65 , 114] . The HIV-1 IMCBAL infected CEM . NKRCCR5 cell line ( NIH AIDS Research and Reference Reagent Repository ) was used as target cells . The target cells were incubated in the presence of 4-fold serial concentrations of mAbs starting at 40 μg/ml . Purified CD3-CD16+ NK cells obtained from a HIV seronegative donor with the F/F Fc-gamma Receptor ( FcRγ ) IIIa phenotype were used as effector cells . The cells were isolated from the cryopreserved PBMCs by negative selection with magnetic beads ( Miltenyi Biotec GmbH , Germany ) after overnight resting . The NK cells were used as effector cells at an effector to target ratio of 5:1 . The cells and mAbs were incubated in duplicate wells for 6 hours at 37°C in 5% CO2 . The final read-out was the luminescence intensity generated by the presence of residual intact target cells that have not been lysed by the effector population in presence of ADCC-mediating mAb . The % of killing was calculated using the formula: % killing = ( RLU of Target + Effector well ) — ( RLU of test well ) / ( RLU of Target + Effector well ) * 100 . In this analysis , the RLU of the target plus effector wells represents spontaneous lysis in absence of any source of Ab . The humanized monoclonal antibody ( IgG1k ) directed to an epitope in the A antigenic site of the F protein of respiratory syncytial virus , ( Palivizumab ( MedImmune , LLC; Gaithersburg , MD ) was purchased from the manufacturer and used as a control . To measure the captured infectious IC , we adopted the Ig-virus capture assay described previously [26 , 64] . Briefly , Microplates ( NUNC ) were coated overnight at 4°C with mouse monoclonal anti-human IgG ( Southern Biotech ) at a concentration of 1 μg/ml diluted in PBS . After coating and washing , coated plates were blocked for 2 h with PBS supplemented with 5% Goat serum , 5% milk , 0 . 05% Tween . The indicated concentration of antibodies was mixed with the viral stock containing 5X 106 viral RNA and then centrifuged 90 min at 2 , 000 rpm . Then the mixture was centrifuged at 21 , 000 x g for 45 min at 4°C to remove the virus free antibody [116] the pellet was resuspended in the same volume of PBS . 50 μl of the IC mixture was added to each coated well in triple wells for a 90 min incubation . Then , the wells were washed 4 times and the indicator cell line ( M7-luc or TZM-bl ) was added . HIV-1 replication was assessed on day 5 after infection for M7-luc and on day 3 for TZM-bl . The infection was measured by the firefly luciferase assay and was expressed as RLU . T/F viral sequences were obtained by single genome amplification ( SGA ) followed by direct amplicon sequencing by methods modified from Keele et al . [73] and published in Klein et al . [117] . Viral RNA was purified from the first or second virus positive plasma sample from each animal by the Qiagen QiaAmp viral RNA mini kit and subjected to cDNA synthesis using 1X reaction buffer , 0 . 5 mM of each deoxynucleoside triphosphate ( dNTP ) , 5 mM DTT , 2 U/mL RNaseOUT , 10 U/mL of SuperScript III reverse transcription mix ( Invitrogen ) , and 0 . 25 mM antisense primer SHIVBalEnvR1 5’- CTG TAA TAA ATC CCT TCC AGT CC -3’ located in the nef open reading frame ( nt 9458–9480 in SIVsmm239 ) . The resulting cDNA was end-point diluted in 96 well plates ( Applied Biosystems , Inc . ) and PCR amplified using Platinum Taq DNA polymerase High Fidelity ( Invitrogen ) so that ≤30% of reactions were positive in order to maximize the likelihood of amplification from a single genome . A second round of PCR amplification was conducted using 1 μl of the first round products as template . SHIVBalEnvR1 and SIVsm/macEnvF1 5’-CCT CCC CCT CCA GGA CTA GC-3’ ( nt 6130–6146 in SIVsmm239 vpx ) were used in the first round PCR amplification step , followed by a second round with primers envB5-in 5’- TTA GGC ATC TCC TAT GGC AGG AAG AAG -3’ ( nt 5960–5983 in the HXB2 tat coding region ) and BKSIVsm/macEnvR261 5’- ATG AGA CAT RTC TAT TGC CAA TTT GTA -3’ ( nt 9413–9436 in SIVsmm239 nef ) . PCR was carried out using 1X buffer , 2 mM MgSO4 , 0 . 2 mM of each dNTP , 0 . 2 μM of each primer , and 0 . 025 U/μL Platinum Taq High Fidelity polymerase ( Invitrogen ) in a 20-μL reaction . Round 1 amplification conditions were 1 cycle of 94°C for 2 minutes , 35 cycles of 94°C for 15 seconds , 58°C for 30 seconds , and 68°C for 4 minutes , followed by 1 cycle of 68°C for 10 minutes . Round 2 conditions were one cycle of 94°C for 2 minutes , 45 cycles of 94°C for 15 seconds , 58°C for 30 seconds , and 68°C for 4 minutes , followed by 1 cycle of 68°C for 10 minutes . Round 2 PCR amplicons were visualized by agarose gel electrophoresis and directly sequenced using an ABI3730xl genetic analyzer ( Applied Biosystems ) . The final amplification product was ~3160 nucleotides in length exclusive of primer sequences and included all of rev and env gp160 , and 336 nucleotides of nef . Partially overlapping sequences from each amplicon were assembled and edited using Sequencher ( Gene Codes , Inc ) . Sequences with ≥2 double peaks indicating amplification from multiple templates were discarded . Sequences with one double peak were retained as this most likely represents a Taq polymerase error in an early round of PCR rather than multiple template amplification; such sequence ambiguities were read as the consensus nucleotide . Sequence alignments and phylogenetic trees were constructed using ClustalW and Highlighter plots were created using the tool at http://www . lanl . gov . Detailed descriptions of the mathematical models of early sequence evolution , star phylogeny determination , estimates of viral fitness , and power calculations for estimating the likelihood of detecting transmitted envelope variants with minor representation are provided elsewhere [118] . In order to compare T/F numbers accurately across different control and antibody-infused groups , we ensured that the number of sequences per group was consistent . In the 6 animals infused with 7b2 and the 6 matched controls , T/F numbers were determined by using 60 sequences per animal , for a total of 720 env sequences . We can be 95% confident of detecting all variants that are at greater than 5% prevalence in each animal . For the A32-treated and matched controls , a mean of 37 sequences were used from each group ( range 33–42 over all 12 animals ) . For the CH22 study , 6 control animals had a mean of 40 sequences each , with a range of 35–45 , while the 2 infected animals with CH22 infusion had 40 and 42 sequences , respectively . The A32 and CH22 studies were powered to detect any variants greater than 8% prevalence with 95% confidence . Therefore , all T/F numbers shown are a minimum estimate . The following rules were followed in order to identify and enumerate T/F variants: In clusters of related sequences determined through visual analysis of phylogenetic trees [FigTree version 1 . 4 ( http://tree . bio . ed . ac . uk/software/figtree/ ) ] , we allowed sequences with up to 3 mutations to be part of the same cluster from days 7–14 post-infection . At day 21 or 28 we allowed up to 4 changes in any one sequence . Variants exceeding these limits were identified as the progeny of distinct T/F genomes . Changes at positions predicted by the Hypermut algorithm 2 . 0 to be potential G-A hypermutation caused by APOBEC 3G/3F were reverted for analysis if there were ≤ 2 . Sequences that had ≥ 3 potential APOBEC 3G/3F mutated positions were not considered for the T/F analyses ( Hypermut , http://www . hiv . lanl . gov [119] ) . Sequence clusters of ≥ 2 sequences with ≥ 2 shared mutations were classified as distinct T/F variants . Sequences representing recombinants between two distinct T/F lineages were identified as previously described [33] and were excluded from the analyses . Sequences were deposited in Genbank under accession numbers KR608795—KR610312 ( http://www . ncbi . nlm . nih . gov/Genbank/ ) . Mamu-A01-negative Indian-origin rhesus monkeys were housed and maintained in an Association for Assessment and Accreditation of Laboratory Animal Care accredited institution in accordance with the principles of the National Institute of Health . All antibodies were produced in CHO cells ( Catalent , Inc . , Somerset , NJ ) and were administered by the intravenous route in rhesus monkeys . Monkeys were challenged with 1 ml of SHIV-BaL ( 2 × 105 TCID50 ) by the intra-rectal route at times specified in the “Results” section for each of the antibodies tested . The SHIV-BaL challenge stock used in the present study was uncloned . The original SHIV-BaL molecular clone was developed by Pal et al . [120] , which was transfected into cells to derive a virus isolate , which in turn was serially passaged in 4 pig-tail macaques and then expanded in the PM-1 cell line . Our laboratory expanded this SHIV-BaL isolate further in human PBMCs and generated a large volume stock for rhesus challenge experiments . Mean genetic diversity of Env determined by single genome sequencing in this stock is 0 . 3% , with maximum diversity of 0 . 7% . SIV plasma viral RNA measurements were performed at the Immunology Virology Quality Assessment ( IVQA ) Center Laboratory Shared Resource , Duke Human Vaccine Institute , Durham , NC . Plasma viral loads were assessed using a Qiagen QIAsymphony DSP Virus/Pathogen Midi Kit using the QIAsymphony SP platform and real-time PCR reaction carried out on the StepOnePlus ( Applied Biosystems ) instrument . Data from the real-time PCR reaction was analyzed using the StepOnePlus software . The sensitivity of this SIV viral load assay is 250 copies per ml . CD4+ T lymphocyte subsets were determined by multi-channel flow cytometry for CD3 , CD4 , CD8 , CD28 , CD95 , CCR5 and CCR7 . CD4+ T lymphocyte counts were calculated by multiplying the total lymphocyte count by the percentage of CD3+ CD4+ T cells . Briefly , 100 μl of EDTA-anticoagulated whole blood was stained with anti-CD3-A700 ( clone SP34 . 2 ) , anti-CD4-PerCP Cy5 . 5 ( clone L200 ) , anti-CD8-APC H7 ( clone SK1 ) , anti-CCR5-PE ( clone 3A9 ) , anti-CD95 APC ( clone DX2 ) all from BD Biosciences , anti-CD28-PE CY7 ( clone CD28 . 2; eBiosciences ) , and anti-CCR7-FITC ( clone 150503; R&D Systems ) . Fixed cells were collected ( 30 , 000 events ) on a LSRII instrument using FACSDiva software version 6 . 1 . 1 ( BD Biosciences ) and data were analyzed using FlowJo Software ( TreeStar , Ashland , OR ) . | Antibodies specifically recognize antigenic sites on pathogens and can mediate multiple antiviral functions through engagement of effector cells via their Fc region . Current HIV-1 vaccine candidates induce polyclonal antibody responses with multiple antiviral functions , but do not induce broadly neutralizing antibodies . An improved understanding of whether certain types of non-neutralizing HIV-1 specific antibodies can individually protect against HIV-1 infection may facilitate vaccine development . Here , we test whether non-neutralizing antibodies with multiple antiviral functions mediated through FcR engagement and recognition of virus particles or virus-infected cells can limit infection , despite lacking classical virus neutralization activity . In a passive antibody infusion-rhesus macaque challenge model , we tested the ability of non-neutralizing monoclonal antibodies to limit virus acquisition . We demonstrate that two different types of non-neutralizing antibodies , one that recognizes both virus particles and infected cells ( 7B2 ) and another that recognizes only infected cells ( A32 ) were capable of decreasing the number of transmitted founder viruses . Further , we provide the structure of 7B2 in complex with the gp41 cyclical loop motif , a motif critical for entry . These findings provide insights into the role that antibodies with antiviral properties , including virion capture and FcR mediated effector function , may play in protecting against HIV-1 acquisition . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Human Non-neutralizing HIV-1 Envelope Monoclonal Antibodies Limit the Number of Founder Viruses during SHIV Mucosal Infection in Rhesus Macaques |
Cancer treatments including ionizing radiation ( IR ) can induce cancer stem cell-like properties in non-stem cancer cells , an outcome that can interfere with therapeutic success . Yet , we understand little about what consequences of IR induces stem cell like properties and why some cancer cells show this response but not others . In previous studies , we identified a pool of epithelial cells in Drosophila larval wing discs that display IR-induced stem cell-like properties . These cells are resistant to killing by IR and , after radiation damage , change fate and translocate to regenerate parts of the disc that suffered more cell death . Here , we report the identification of two new pools of cells with IR-induced regenerative capability . We addressed how IR exposure results in the induction of stem cell-like behavior , and found a requirement for IR-induced caspase activity and for Zfh2 , a transcription factor and an effector in the JAK/STAT pathway . Unexpectedly , the requirement for caspase activity was cell-autonomous within cell populations that display regenerative behavior . We propose a model in which the requirement for caspase activity and Zfh2 can be explained by apoptotic and non-apoptotic functions of caspases in the induction of stem cell-like behavior .
Regeneration is essential to tissue homeostasis and health . Conversely , regeneration of tumors after treatment leads to tumor recurrence and treatment failure . Understanding mechanisms that underlie regeneration is therefore important not only for understanding basic biology but also for optimizing treatment of diseases like cancer . Our understanding of regeneration has benefited immensely from experimental systems with dedicated stem cells that form the cellular basis for regeneration . Examples include regeneration of vertebrate gut and Drosophila intestine [1–3] . Tissues also regenerate despite the lack of a dedicated stem cell pool . A prime example is the vertebrate liver , which regenerates by proliferation of the surviving cells of each cell type [4–6] . If proliferation of hepatocytes is blocked during liver regeneration , however , biliary epithelial cells can dedifferentiate , proliferate and re-differentiate into hepatocytes [4–6] . Such plasticity has been documented in other mammalian organs [7–9] , and in some models of amphibian limb and fish fin regeneration [10] . This report addresses the molecular basis for cell fate plasticity during regeneration using Drosophila larval cells as a model . Drosophila larval imaginal discs are precursors of adult organs . Imaginal discs lack a dedicated stem cell pool yet can regenerate fully even after surgical ablation of 25% of the disc , after genetic ablation of a disc compartment ( e . g . by expressing a pro-apoptotic gene in the anterior compartment ) , or after exposure to doses of ionizing radiation ( IR ) that kills about half of the cells [11 , 12] . We recently identified a previously unknown mode of regeneration in Drosophila larval wing discs , whereby epithelial cells acquire stem cell-like properties during regeneration after damage by IR [13] . These properties include resistance to killing by IR , the ability to change cell fate , and the ability to translocate to areas of the wing disc with greater need for cell replenishment . The ability to behave like stem cells in response to IR is limited to certain cells within the continuous epithelium of the wing disc . Specifically , a subset of future hinge cells ( see Fig 1P for the fate map in the wing disc ) is protected from IR-induced apoptosis by the action of STAT92E ( Drosophila STAT3/5 , to be called ‘STAT’ hereafter ) and by Wg ( Drosophila Wnt1 ) -mediated repression of pro-apoptotic gene reaper [13] . These hinge cells lose the hinge fate and translocate to the pouch region that suffers more apoptosis and participate in regenerating the pouch . Without IR , these cells differentiate into the adult wing hinge , indicating that cell fate plasticity is IR-induced . In above-described studies , regeneration of the pouch by the hinge was observed in nearly all irradiated discs [13 , 14] . In about 20% of irradiated discs , we observed , in addition , abnormal regeneration that produced an ectopic wing disc [14] . Ectopic discs were wing discs based on staining for the protein markers Ubx and Wg , and were composed of an ectopic pouch and an ectopic hinge [14] . Ectopic discs were neither duplications ( e . g . not pouch-to-pouch ) nor transdeterminations ( e . g . not leg-to-wing ) described in classical studies of regeneration after surgical ablation [15] . Our efforts to dissect the cellular origin for the ectopic discs showed that cells of the hinge that regenerate the pouch are unlikely to be responsible for ectopic discs [14] . Therefore , we hypothesized that there are additional pools of cell in the wing disc that show stem cell like properties after IR damage by participating in abnormal regeneration to produce an ectopic wing disc . Here , we report the mapping of cell lineages during regeneration of Drosophila larval wing discs following damage by X-rays , a type of IR . To express lineage tracers , we used FlyLight GAL4 drivers that display simple expression patterns because they use small ( ~3kb ) enhancers from various genes [16] . In addition to the subset of future hinge cells we previously identified as capable of behaving like stem cells [13] , two more cell populations , in the notum and in the dorsal-posterior hinge/pleura region , were found to show this potential . While the previously identified hinge cells are responsible for normal regeneration to restore the wing disc , the newly identified regenerative cells undergo abnormal regeneration to produce ectopic discs . Cells of the pouch , we find , lack the capacity for plasticity and do not change fate or translocate . Of possible consequences of X-ray exposure , we identified caspase activity as an essential determinant for inducing stem cell-like properties , and further localize this requirement to the regenerative cells . Zfh2 , a transcription factor and a STAT effector , we found , was needed to regulate IR-induced caspase activity and for fate change . Cancer treatments including IR induce stem cell-like properties in non-stem cancer cells [17–20] , but we understand little about what consequences of IR induces stem cell-like properties . This report describes similar phenomena in Drosophila cells and offers molecular insights into IR-induced cell plasticity .
30A-GAL4 was used in our recent studies and is active in a subset of hinge cells and a few ( fewer than 10 ) of notum cells ( Fig 1A–1C and 1J–1L , arrows point to RFP+ notum cells; [13 , 14] ) . The dynamics of 30A-GAL4 activity is such that cells expressing it show stable lineage; very few were GFP+RFP- . In contrast , another hinge driver , FlyLight R73G07-GAL4 produced GFP+ cell in most of the disc including the pouch , the hinge , and most of the notum , even though RFP is restricted to the hinge in 3rd instar wing discs ( Fig 1D–1F ) . R73G07 is a 3028 bp enhancer from the zfh2 locus and is apparently active in most cells of the wing disc before becoming restricted to the hinge . Zfh2 is a transcription factor important for wing development [22] . Temporal restriction of R73G07-GAL4 activity with repressor GAL80ts according to the temperature shift protocol shown in Fig 1M confined GFP to the hinge and the pouch ( Fig 1G–1I ) . Increasing larval age from 4–5 days to 5–6 days after egg deposition ( AED ) before temperature shift to induce GAL4 did not restrict the GFP+ domain further . Aging the larvae beyond 5–6 days AED before inducing GAL4 may help eliminate GFP in the pouch and restrict it to the hinge , but this schedule is incompatible with our goal because we need to monitor regeneration for 72 h after IR before losing the larvae to pupariation . These results illustrate that while some GAL4 drivers show stable lineage expression and could be used to monitor fate changes after irradiation , others show lineage changes without IR . This was confirmed using fifteen additional FlyLight GAL4 drivers ( S1 Fig ) . Therefore , we used GAL80ts and the protocol shown in Fig 1M in all subsequent experiments to identify regenerative cell populations , even if their lineages were stable as in the 30A-GAL4 example . Although we selected FlyLight drivers with apparently exclusive expression in the disc region of interest ( https://flweb . janelia . org/ ) , many , we found , show additional expression elsewhere in the wing disc and are unsuitable for lineage tracing ( S1 Fig ) . Some GAL4 drivers were active in the cells of the peripodial membrane that covers the wing disc epithelium on the apical side , and in wing-disc associated tracheal cells on the basal side . In such cases , peripodial and tracheal cells could be identified based on their larger nuclear size compared to columnar epithelial cells and on their location in optical sections that book-end the columnar epithelium ( S2 Fig ) . Our analyses focused on the columnar epithelium by excluding other optical sections . Antibody staining shows that Zfh2 protein expression resembles R73G07-GAL4>RFP expression ( compare Fig 1H and 1K ) . In contrast , 30A>RFP is expressed in only a subset of these cells ( compare Fig 1K and 1L ) . Of relevance to subsequent sections is the expression of R73G07-GAL4 but not 30A-GAL4 in the dorsal-posterior hinge and the pleura ( arrowheads in Fig 1J–1L and 1O–1P ) . We used 4000R of X-rays for experiments and analyzed regenerated discs 72 h after irradiation . This level of IR kills more than half of the cells but the discs could still regenerate to produce viable adults [11 , 12] . In our published studies of lineage tracing after irradiation , 30A-GAL4 expressing hinge cells translocated to the pouch but showed little movement dorsally towards the notum [13 , 14] . In contrast , R73G07-GAL4>G-trace experiments show GFP+ cell populations that extend from the hinge dorsally along both the anterior and posterior margins of the wing disc ( Fig 2 ) . The extending GFP+ cell population is contiguous with both the hinge ( arrowheads ) and the pleura ( white arrows , Fig 2F–2H and 2J–2L ) . Some GFP+ cells in the notum lack RFP ( for example , Fig 2H arrow ) while others express RFP ( for example , Fig 2L arrow ) . In discs that show an ectopic disc ( Fig 2M–2P , yellow arrows ) , many cells of the ectopic disc are GFP+RFP+ . To better understand the source of GFP+ cells in the notum , we repeated the experiment but analyzed the discs at different times after IR . We analyzed R73G07>G-trace larvae from the same cohort at 24 , 48 and 72 h after IR , in two independent time course experiments ( Fig 3 ) . Wing discs , we found , fell into four categories depending on the abundance and location of GFP/RFP cells in the notum . Un-irradiated discs showed very few GFP/RFP cells in the notum ( Fig 2A–2D , ‘category 1’ ) . Category 2 discs showed GFP+RFP+ cells spreading dorsally from the hinge ( arrowhead ) and the pleura ( arrow , Fig 3A and 3B , magnified in C ) . Category 2 predominates at 24 h after IR ( Fig 3O ) . The presence of RFP in the hinge cells that had spread into the notum could be due to the persistence of GAL4 , RFP , or both . The movement of hinge/pleura cells was seen along both the anterior and the posterior disc margins , but not in the central portion of the disc ( Fig 3B ) . In category 3 discs , GFP+ cells in the notum increased in number compared to category 2 , were found deeper ( more dorsal ) in the notum , and most lacked RFP ( Fig 3E–3H ) . Category 3 predominated at 48 h after IR ( Fig 3O ) . We interpret these data to mean that cells continued to translocate into the notum from the R73G07-GAL4 domain between 24 and 48 h after IR , with many terminating R73G07>RFP expression , indicative of fate change . As in category 2 discs , GFP+ cell population in the notum of category 3 discs appeared contiguous with both the hinge ( arrowhead ) and the pleura ( arrow , Fig 3E , magnified in F ) . Moreover , GFP+ cells in the notum were more numerous in the posterior half ( post ) than in the anterior half ( ant ) in some discs ( Fig 3G , magnified in H ) , which may explain the finding that ectopic discs seen at 72 h after IR always appear along the posterior wing margin ( e . g . Fig 2M–2P; [14] ) . In category 4 discs , GFP+ cells in the notum were more numerous and more dorsal than in category 3 , and most expressed RFP . In some category 4 discs , GFP+RFP+ cells were contained within the notum ( similar to Fig 2I–2L ) while in others GFP+RFP+ cells were in an ectopic disc ( Fig 3I–3N , similar to Fig 2M–2P ) . Category 3 still predominated at the 72 h time point but the fraction of category 4 increased between 48 and 72 h after IR in both time courses ( Fig 3O ) . Likewise , ectopic discs appeared between 48 and 72 h after IR , which agrees with our published results using a different GAL4 driver [14] . RFP+GFP+ cells of the ectopic discs appear contiguous with cells of the hinge and the pleura ( arrowhead and arrow , respectively , in Fig 3I–3K ) . How were GFP+RFP+ cells in the notum of category 4 discs produced ? There are three possibilities . First , they are translocated hinge/pleura cells that never lost their original fate . Second , they are GFP+RFP- cells in class 3 discs that re-gained their hinge/pleura fate to re-express RFP . Third , they formed de novo and bear no relation to the hinge/pleura of the primary disc . The finding that GFP+RFP+ cells in the notum appear contiguous with the primary hinge and the pleura makes us favor the first two possibilities , which are not mutually exclusive . In our published time courses , we never saw cells of the 30A domain spreading into the notum [13 , 14] . We interpret these data to mean that hinge cells that translocate into the notum originate from part the hinge outside the 30A domain ( arrowheads in Fig 1J–1L and 1O ) . Confocal imaging and close inspection of each optical section showed that ectopic discs included cells that lacked both RFP and GFP ( Fig 3L–3N , arrows ) . In these experiments , the only cells that lacked RFP and GFP were notum cells , suggesting that cells of the notum also contribute to ectopic discs . We addressed this possibility directly by lineage-tracing with a notum-specific GAL4 driver ( Fig 4 ) . R76A10-GAL4 , bearing an enhancer fragment from the tailup locus , is active exclusively in a subset of notum cells ( Fig 4A ) . Without IR , cell fate in this domain was stable as seen by GFP/RFP overlap ( Fig 4A ) . At 72 h after IR , GFP/RFP overlap looked similar to–IR in most discs ( Fig 4B ) . The exceptions were irradiated discs with ectopic growths , where we observed an expansion of the GFP+ cell population beyond the RFP+ area ( Fig 4C , 4D and 4G ) . The lack of RFP in these cells suggests that they had lost their original fate as detected by R76A10-GAL4>RFP expression . Such GFP+RFP- cells were part of the ectopic disc , although the extent of their contribution to the ectopic disc and their location within the ectopic disc varied from disc to disc ( arrows in Fig 4C , 4D and 4G ) . Nubbin is a pouch marker that is not normally expressed in the notum ( Fig 4F ) . Some GFP+RFP- former notum cells in ectopic discs expressed Nub ( Fig 4G–4G”‘ ) , indicating that notum-to-pouch fate change occurred as these cells participated in the formation of an ectopic disc . In our previous studies , cells of the pouch , marked with rn-GAL4>G-trace , did not change fate or translocate after irradiation [13] . Even in experiments when we directed cell death to the hinge and left the pouch cells alive , the hinge was repaired with the hinge cells and not the pouch cells [13] . We confirmed these findings in new experiments with rn-GAL4 as well as two additional pouch drivers , R42A07-GAL4 ( from the dve locus ) and R85E08-GAL4 ( from the salm locus ) ( S3 Fig ) . Collectively , the data in Figs 2–4 show that within a wing disc , cells in the hinge , the pleura and the notum can change fate and translocate after irradiation . Of these , hinge cells in the 30A-GAL4 domain translocate and change fate in nearly all irradiated discs to regenerate the pouch ( [13]; see also Fig 5 ) . In contrast , hinge cells outside the 30A domain , pleura and notum cells produce an ectopic disc in a fraction of irradiated discs ( summarized in Fig 4E ) . Drosophila wing disc is sub-divided into compartments , Anterior/Posterior and Dorsal/Ventral , for example , with cell lineages restricted to each compartment during development . During regeneration after ablation of a specific compartment , compartment boundaries collapse and are rebuilt , with some cells switching compartment identities [23] . In these models , one compartment suffered massive damage while the others were untouched . In contrast , damage by IR is scattered throughout the disc . Using the same compartment-specific GAL4 drivers as in the published study , ci- , ap- , and en-GAL4 , we found that compartment boundaries remained intact in the primary disc during regeneration after IR damage ( S4 Fig ) . In the same experiments , ectopic discs showed fluid compartment boundaries ( further discussed in DISCUSSION ) . Regenerative cells proliferate , change fate , and change location , in order to rebuild damaged tissue . Two aspects of regenerative behavior studied here , cell fate change and translocation , do not occur without IR ( for example , Fig 1A and Fig 4A ) . Therefore , we next addressed how IR exposure is linked to these two aspects of regenerative behavior . IR has many effects on cells including DNA breaks , cell cycle arrest by checkpoints , and apoptosis . Of these , apoptosis has been demonstrated to induce proliferation of the surviving cells in a phenomenon known as Apoptosis-induced-Proliferation or AiP ( reviewed in [24] ) . Therefore , we investigated whether apoptosis is also required for the induction of cell fate change and translocation after IR in the primary disc and for the formation of ectopic discs . In these experiments , we used 30A-GAL4>G-trace ( expression pattern in Fig 1A–1C and 1J–1L , Fig 5A ) , which we used previously to show that former hinge cells lose GAL4 expression ( become GFP+RFP- ) and translocate towards the pouch ( [13]; arrow in Fig 5B , which is from new experiments that reproduced the published findings ) . We confirmed that such cells gain the pouch fate as detected by Vestigial Quadrant Enhancer-lacZ or VgQ-Z ( Fig 5C and 5D , arrows point to GFP+ former hinge cells that express VgQ-Z; [25] ) . We showed previously that regenerative behavior of the hinge cells could be quantified by measuring the GFP+RFP- area inside the 30A circle ( Fig 5A , quantified in M; see figure legend and [13] for quantification method ) . We showed previously that ectopic disc formation could be quantified by classifying the discs according to 30A-GAL4>RFP pattern ( Fig 5N; see figure legend and [14] for quantification method ) . As reported previously , unirradiated w1118 controls show classes 0 and I only; ectopic disc classes II-IV are IR-induced ( [14]; Fig 5O ) . Chromosome deficiency H99 deletes several genes including three pro-apoptotic genes , hid , rpr and skl; H99 heterozygous wing discs show delayed and reduced IR-induced caspase activation and apoptosis [26] . We expressed 30A-GAL4>G-trace in this background , and saw a reduction in fate change and translocation by the hinge cells ( Fig 5 , compare F to B , quantified in Fig 5M ) . We conclude that caspase activity and/or cell death is required for IR-induced regenerative behavior of the hinge cells . H99 heterozygosity also prevented ectopic disc formation in irradiated discs ( Fig 5O ) , indicating that IR-induced apoptosis/caspase activity is also needed for the formation of ectopic discs . H99 deficiency reduces apoptosis and caspase activity throughout the disc . Prior studies of regeneration in larval wing discs showed that apical caspase activity is needed in the dying cells to stimulate the neighbors to proliferate [24] . But no study we are aware of has addressed the need for caspase activity in the regenerative cells . Yet , there is mounting evidence for the role of caspases in cell fate changes [27 , 28] . Our identification of regenerative cells in the hinge and the ability to target UAS-transgenes using 30A-GAL4 allowed us to address this possibility . We found that 30A-GAL4-driven expression of UAS-p35 , an inhibitor of effector caspases , inhibited the translocation and fate change of the hinge cells ( Fig 5 , compare H to B , quantified in Fig 5M ) . 30A>p35 also inhibited the formation of ectopic discs ( Fig 5O ) . We conclude that effector caspase activity is needed cell-autonomously ( within the red cells in Fig 5N ) for IR-induced regenerative behavior . In reciprocal experiments , we expressed p35 in the pouch , to ask if effector caspase activity is needed in the pouch for the hinge to change fate and translocate . This is of interest because we and others have shown that dying cells secrete signals that change the behavior of surviving cells , including mitogenic signals [24] and ‘do not die’ signals [29] . We followed an experimental set-up used previously to show that when pouch cells were killed by rn-GAL4>UAS-hid , a pro-apoptotic gene , regeneration occurred with cells that immigrated from the hinge ( [30]; the role of caspases were not addressed in this study ) . In these experiments , because GAL4>UAS was used in the pouch , the hinge cells were marked using the orthologous QF>QUAS system . We used the same GH146-QF driver , which is active in two pockets of hinge cells ( [30]; see also Fig 6A and 6B ) , to mark the hinge , rn-GAL4 to express p35 in the pouch , and induced cell killing by IR ( Fig 6 ) . We found that GFP+ former hinge cells translocated into the pouch but only in irradiated discs ( compare Fig 6B to 6F and 6J ) . p35 inhibits effector caspases but not initiator caspases in Drosophila [31 , 32]; expression of p35 in the context of apoptosis induction results in ‘undead’ cells that initiate but cannot complete the apoptosis program and instead produces tissue overgrowth and disorganization [33] . We saw such overgrowth in the pouch of p35 expressing cells in the context of IR-induced apoptosis ( arrowheads in Fig 6G and 6K ) . It was possible to discern the pouch using DNA staining at 48 h after IR but not at 72 h after IR because of overgrowth . Regardless , we observed GFP+ cells in the pouch ( Fig 6H ) or the pouch area ( Fig 6K and 6L ) , and conclude that effector caspase activity is not needed in the pouch for the hinge cells to lose RFP and translocate . Apical caspase Dronc is needed to activate effector caspases [34] and is required for X-ray induced apoptosis in the wing [35] and the eye [36] discs . We co-expressed a catalytically inactive C->A mutant ( UAS-proDroncDN ) [37] , and found that both fate change and translocation of hinge cells and ectopic disc formation were inhibited to similar extent as did H99/+ and p35 ( Fig 5I and 5J , quantified in M and O ) . Dronc , together with stress-responsive JNK kinase , also functions within dying cells for mitogenic signaling [24] . This function of Dronc is considered separable from its function in activating effector caspases . In the rn-GAL4>hid model described above , inhibition of JNK in the pouch by overexpression of phosphatase Puc inhibited the immigration of hinge cells into the pouch [30] . We performed a reciprocal experiment to ask if JNK activity was needed in the hinge in our IR-based model . Co-expression of UAS-Puc with 30A-GAL4>G-trace had a statistically significant effect on the translocation of the hinge cells ( Fig 5K and 5L , quantified in Fig 5M ) . But the effect was minor; normalized GFP+RFP- area decreased from 0 . 56±0 . 14 in ‘w1118+IR’ to 0 . 36±22 in ‘puc+IR’ , p = 0 . 02 ) . In contrast , puc had a stronger inhibitory effect on the formation of ectopic discs ( Fig 5O ) . This is similar to our previous finding that depletion of nucleosome remodeling factor Nurf-38 with 30A-GAL4>RNAi had a greater effect on ectopic disc formation than on hinge translocation [14] . We had reported that while ectopic disc formation requires a temperature shift ( irradiated larvae kept at 25°C throughout the experiment do not form ectopic discs; [14] ) , a temperature shift is not necessary for the hinge-to-pouch regeneration [13] , further distinguishing the two modes of regeneration . Zfh2 is a transcription factor and a downstream effector of JAK/STAT signaling during wing development [38] . In addition , Zfh2 has been shown to prevent apoptosis in tissues under stress [39] . During normal development , Zfh2 expression is confined to the hinge in the 3rd instar wing disc ( Fig 1K ) . We published before that in irradiated discs , the hinge is protected from apoptosis ( [13]; Fig 7A , brackets indicate the dorsal hinge ) . Therefore , we asked if Zfh2 has a role in preventing IR-induced apoptosis in the hinge . Zfh2 is required for wing hinge development [22 , 38] . To deplete it we followed a protocol , described in Fig 7 legend , which is similar to what we used in published studies to deplete STAT and Wg that are also required for hinge development [14] . Zfh2 was conditionally depleted by en-GAL4-driven RNAi in the posterior half of the disc while the anterior half served as an internal control . We found that this treatment increased IR-induced apoptosis specifically in the posterior hinge ( Fig 7B and 7C , compare arrows to arrowheads; brackets indicate the dorsal hinge ) . This increase was detected using Acridine Orange , which is excluded from live cells but penetrates dying cells [40 , 41] , and by antibody staining for cleaved caspase Dcp-1 . Without Zfh2 RNAi , cell death was similar in anterior and posterior hinge ( Fig 7A ) and Zfh2 RNAi did not induce apoptosis without IR ( Fig 7E ) . We conclude that Zfh2 is needed to protect the hinge cells from IR-induced apoptosis . We next investigated the consequences of conditional Zfh2 depletion on the regenerative behavior of hinge cells , using the 30A-GAL4>G-trace ( Fig 7F and 7G ) . Similarly treated w1118 controls show the translocation of hinge cells towards the pouch ( Fig 7F , quantified in H ) . Depletion of Zfh2 inhibited the appearance of GFP+RFP- cells in the pouch area ( Fig 7G , quantified in H ) . Collectively , these data suggest a cell-autonomous requirement for Zfh2 in the hinge to temper IR-induced apoptosis and to promote IR-induced fate change and translocation . As in the case of H99/+ and p35 , 30A-GAL4>Zfh2 RNAi also prevented ectopic disc formation ( Fig 7I ) . Our published data offer an explanation for how the hinge is protected from IR-induced caspase activation and apoptosis [13] . After irradiation , hid mRNA increases throughout the disc . rpr is transcriptionally induced only in the notum and the pouch and not in the hinge where it is repressed by Wg . Thus , while the cells in the notum and the pouch experience increased hid and rpr and undergo apoptosis , only hid is induced in the hinge and appears insufficient for apoptosis ( [13] , summarized in Fig 7J ) . These published data allow us to interpret the results reported here ( modeled in Fig 7K ) . During normal development ( -IR ) , cells of the hinge lack effector caspase activity and do not die , change fate , or translocate . The same situation applies in irradiated discs expressing 30A-GAL4>p35 ( +IR+p35 ) . After irradiation ( +IR ) , hid is induced in the hinge but not rpr , which we propose leads to caspase activity that is insufficient for apoptosis but sufficient for fate change and translocation; 30A-GAL4>p35 blocks this intermediate caspase activity to inhibit regenerative behavior in irradiated discs . Upon depletion of Zfh2 in the hinge ( this report ) or inhibition of STAT/Wg activity [13] , IR induces sufficient caspase activity , which is compatible with apoptosis but not with fate change and translocation . Thus , Zfh2 serves to keep alive cells with radiation stress and a low level of effector caspase activity , thereby allowing them to adopt new fate and location . This is in agreement with the report that Zfh2 is needed to keep alive JNK-active regenerative cells in a genetic ablation model in the wing disc [39] . Similar regulatory mechanisms may apply in the irradiated notum but with a lot fewer cells displaying intermediate effector caspase activity , which can explain why ectopic discs form only in a small fraction of irradiated discs . One prediction of the above model is that an irradiated wing disc contains cells that activated caspases but did not die and instead contributed to the regenerated disc . To test this prediction , we used ‘CaspaseTracker biosensor’ , which is a membrane-tethered GAL4 that is ubiquitously expressed in all cells in Drosophila [42 , 43] . In the presence of active effector caspases , the tether is cleaved at DQVD to release GAL4 , which enters the nucleus to activate G-trace ( Fig 8D ) . CaspaseTracker>RFP is not a real time reporter of caspase activity because it takes several hours to express [42] , but CaspaseTracker>GFP lineage expression effectively marks cells that activated caspases but did not die . The sensitivity of CaspaseTracker is such that there is a substantial number of GFP+ cells without IR or without GAL80ts [43] . Therefore , we used GAL80ts and optimized the conditions to express CaspaseTracker within a narrow ( 6 h ) window immediately following irradiation ( Fig 8E ) . This protocol produced very few GFP+ cells in unirradiated discs ( Fig 8A ) , and allowed us to detect changes in irradiated discs ( Fig 8B ) . Caspase-resistant controls in which DQVD has been mutated to DQVA [42] do not show GFP+ cells even with irradiation ( Fig 8C ) , indicating that we are capturing caspase activity . Clusters of CaspaseTracker>GFP+ cells were found throughout the irradiated disc ( Fig 8B ) . We do not know how their origin is spatially distributed within the disc; addressing this issue will require a non-FLP/FRT-based lineage tracing system for use in conjunction with CaspaseTracker , which we plan to develop in the future . Most GFP+ clusters were composed of at least 8 cells each ( Fig 8B’ ) , supporting the idea that irradiated discs include cells that activated effector caspases but did not die and instead went through at least three cell cycles .
This study identified multiple specific pools of cells that show IR-induced changes in cell fate and location . A subset of hinge cells can do so , to become part of the regenerated pouch in nearly all irradiated discs . A different subset of cells in the hinge , together with cells of the pleura and the notum , produce an ectopic disc in about a tenth of irradiated discs . There are , however , limits to fate changes . For example , we observed very little disruption of A/P and D/V boundaries in the primary hinge , which means hinge cells that changed fate and translocated remained in their original quadrant . The A/P boundary is already established and the ventral marker Ap is already active in early 2nd instar disc when the distinction between the hinge and the pouch is yet to be made [44] . Thus , the hinge-to-pouch conversion we detect reflects a developmentally more recent fate choice than A/P or D/V choices , identifying a limitation in IR-induced fate change . The fluidity of the A/P boundary in ectopic discs ( S4 Fig ) , together with notum-to-pouch conversion in their generation suggest that fate changes during ectopic disc formation reflects events further back in development . But ectopic disc formation is rare and requires a temperature shift protocol , again illustrating that IR-induced fate change is not limitless . In another example , cells of the pouch display little indication that they change fate or translocate ( S3 Fig ) and not even when we directed cell death to the hinge and left the pouch cells alive [13] . This parallels how IR induces Cancer Stem Cell-like properties in some cancer cells but not others . Understanding how IR-induced fate changes are limited would be an important future goal . There are several insightful reports of regeneration after genetic ablation where cell death is directed to a specific compartment of the wing disc ( for example , [45–48] ) . Different models of regeneration including ours rely on common factors such as STAT and Wg , but also show different molecular requirements . For example , heterozygotes of transcriptional factor CtBP increased the incidence of ectopic discs in a genetic ablation model [48] . But the same alleles of CtBP , we found , did not increase IR-induced ectopic discs or hinge-to-pouch fate change and translocation ( S5 Fig ) . Even within genetic ablation models , the choice of apoptotic gene used to kill cells can have different outcomes on the regenerative behavior of the surviving cells . For example , ectopic discs formed in the notum when the pouch was ablated with Eiger ( Drosophila TNF ) but not with Rpr [48] . A recent study used genetic ablation to probe the regenerative potential of the notum . After ablation of the pouch and the hinge , the notum showed no increase in proliferation , did not regenerate the hinge or the pouch , and instead duplicated itself . The authors concluded that the notum cells have little regenerative potential [46] . This is in sharp contrast to IR-induced regenerative behavior we see in the notum . After IR , we detect a 3-fold increase in mitotic activity in the notum [14] , and lineage tracing shows the notum contributes to the ectopic discs ( this report ) . Our results parallel more closely what happens after genetic ablation of the pouch , which resulted in the production of ectopic wing discs in some mutant backgrounds [48] . Lineage tracing suggested that notum cells changed fate to contribute to the ectopic disc in that model , which agrees with our results . We add to this picture by identifying additional pools of cells in the dorsal-posterior hinge/pleura that contribute to the ectopic discs ( Fig 4E ) . Collectively , these data illustrate how different regenerative models rely on different molecules and cellular behaviors , hence the need to study each to learn the range of possibilities . This , we believe , is particularly true in the case of IR where damage ( i . e . cell death ) is not confined to any particular compartment but scattered throughout the wing disc in a reproducible pattern [13] . One key gap concerns the question ‘what are the consequences of IR that induce stem cell-like behavior ? ’ The answer , we report here , is caspase activity . Surprisingly , we detect this requirement in regenerative cells that translocate and change fate . We propose that the outcome depends on the extent of effector caspase activity as modeled in Fig 7K . Regeneration typically relies on surviving cells proliferating and re-programming to replace cells lost to cell death or surgical removal . Prior work on Drosophila wing discs found that cell death itself induces Apoptosis-induced Proliferation in the surviving cells . In AiP , signaling through apical caspase Dronc and JNK in dying cells act through Dpp ( TGF–β ) and Wg to promote cell division in the surviving cells [33 , 49–51] . The role of Dronc in AiP is in addition to its role in activating effector caspases and apoptosis . This and other similar mechanisms that operate in other larval discs explain the proliferative aspect of regeneration , but the re-programming aspect remained to be better understood . Our study fills the gap in the knowledge by identifying the role of caspases . We note a key difference between AiP and fate change/translocation . The former requires apical caspase Dronc but not effector caspases [24] , while the latter requires both apical and effector caspases ( this report ) . The requirement for caspases we identified lies within the regenerative cell population , e . g . the hinge . This could be because a few apoptotic cells within this population stimulate others to display regenerative behavior . If such instigators exist , they must be very small in number because 30A-GAL4>p35 experiments showed no sign of overgrowth indicative of undead cells , even when they were readily visible in the pouch ( Fig 6 ) . Alternatively , caspases could play a non-apoptotic role within the regenerative cell population . There is precedent for non-apoptotic roles of effector caspases ( [50 , 52–54]; reviewed in [27] ) . In a particularly relevant study , effector caspase CED-3 in C . elegans was found to cleave cell fate determinant Lin-28 , which is unrelated to apoptosis , in order to ensure that cell fate changes and developmental transitions occur normally [28] . The emerging view is that for a cell to change fate , it is insufficient to change only the transcriptional activity . Transcripts and proteins associated with the old fate must also be eliminated , and miRNAs and caspases work together to complete this task [28] . An intriguing hypothesis in Drosophila is that IR activates caspases that similarly down-regulate key fate determinants ( to terminate the hinge identity , for example ) and allow cell fate changes to reach completion . Validating this hypothesis will require the identification and functional analysis of caspase targets in this process .
These stocks are described in Flybase: w1118 , 30A- GAL4 ( on Ch II , Bloomington stock# or BL37534 ) , Ptub-GAL80ts ( on Ch III ) , rn-GAL4 ( on Ch III ) , en-GAL4 ( on Ch II ) , ci-GAL4 ( on Ch II ) , ap-GAL4 ( on Ch II ) , UAS-p35 ( on Ch III ) . These stocks are described in publications: vgQ-lacZ [25] , UAS-Zfh2 RNAi [22] , UAS-DroncDN [37] , UAS-puc [55] , and CaspaseTracker and caspase resistant stocks [43] . The stock used for lineage tracing is also described in Flybase; w*; P{UAS-RedStinger}4 , P{UAS-FLP . D}JD1 , P{Ubi-p63E ( FRT . STOP ) Stinger}9F6 /CyO ( BL28280 ) . Genotypes for some BL stocks are in S1 Table and include FlyLight stocks [16] . Stocks to express QF and rn-GAL4 were generated by standard Drosophila recombinant protocols , using starting stocks listed in S1 Table . Larvae were raised on Nutri-Fly Bloomington Formula food ( Genesee Scientific ) . The cultures were monitored daily for signs of crowding , typically seen as ‘dimples’ in the food surface as larvae try to increase the surface area for access to air . Cultures were split at the first sign of crowding . Larvae in food were placed in petri dishes and irradiated in a Faxitron Cabinet X-ray System Model RX-650 ( Lincolnshire , IL ) at 115 kv and 5 . 33 rad/sec . Antibodies to Zfh2 ( 1:400 , rat polyclonal , [56] ) , Ci ( 1:500 , rat monoclonal 2A1 , deposited into Developmental Biology Hybridoma Bank by R . Holmgren [57] ) , Nubbin ( 1:50 , mouse monoclonal 2D4 , deposited into Developmental Biology Hybridoma Bank by Michalis Averof ) , cleaved caspase Dcp1 ( 1:100 , rabbit polyclonal , Cell Signaling #9578S ) and fluorescently labelled secondary antibodies ( 1:200 , Jackson ) were used ( see also S1 Table ) . In all experiments , wing discs were dissected in PBS , fixed in 4% para-formaldehyde in PBS for 30 min , and washed three times PBTx ( 0 . 1% Triton X-100 ) . For antibody staining , the discs were washed in PBS instead of PBTx after the fixing step , permeabilized in PBTx with 0 . 5% Triton X-100 for 10 min and rinsed in PBTx . The discs were blocked in 5% Normal Goal Serum in PBTx for at least 30 min and incubated overnight at 4°C in primary antibody in block . The discs were rinsed thrice in PBTx and incubated in secondary antibody in block for 2 h at room temperature . Stained discs were washed in PBT . The discs were counter-stained with 10 μg/ml Hoechst33342 in PBTx for 2 min , washed 3 times , and mounted on glass slides in Fluoromount G ( SouthernBiotech ) . With the exceptions noted below , the discs were imaged on a Perkin Elmers spinning disc confocal attached to a Nikon inverted microscope , using an SDC Andor iXon Ultra ( DU-897 ) EM CCD camera . The NIS- Elements acquisition software’s large image stitching tool was used for the image capture . 15–20 z-sections 1 um apart were collected per disc . Sections that exclude the peripodial cells were collapsed using ‘maximum projection’ in Image J . The exceptions are images in Fig 7A–7E , Fig 8C and S1 Fig , which were acquired on a Leica DMR compound microscope using a Q-Imaging R6 CCD camera and Ocular software . For sample size justifications , we used a simplified resource equation from [58]; E = Total number of animals − Total number of groups , where E value of 10–20 is considered adequate . When we compare two groups ( w1118 vs H99/+ , for example ) , 6 per group or E = 11 would be adequate . All samples subjected to statistical analysis exceed this criterion . Two tailed student t-tests were used to analyze the fate change and translocation of the hinge ( Fig 5M and Fig 7H , S5 Fig ) and Fisher Exact Test was used to analyze ectopic disc formation ( Fig 5O , Fig 7I and S5 Fig ) . In the latter application , IR-induced classes ( II-IV ) were binned together to compare the number of class 0-I discs and class II-IV discs for one condition against the same for another condition . | Ionizing Radiation ( IR ) , alone or in combination with other therapies , is used to treat an estimated half of all cancer patients . Yet , we understand little about why some tumors cells respond to treatment while others grow back ( regenerate ) . We identified specific pools of cells within a Drosophila organ that are capable of regeneration after damage by IR . We also identified what it is about IR damage that allows these cells to regenerate . These results help us understand how tissues regenerate after IR damage and will aid in designing better therapies that involve radiation . | [
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] | 2018 | Ionizing radiation induces stem cell-like properties in a caspase-dependent manner in Drosophila |
Plasmodium parasites express a potent inhibitor of cysteine proteases ( ICP ) throughout their life cycle . To analyze the role of ICP in different life cycle stages , we generated a stage-specific knockout of the Plasmodium berghei ICP ( PbICP ) . Excision of the pbicb gene occurred in infective sporozoites and resulted in impaired sporozoite invasion of hepatocytes , despite residual PbICP protein being detectable in sporozoites . The vast majority of these parasites invading a cultured hepatocyte cell line did not develop to mature liver stages , but the few that successfully developed hepatic merozoites were able to initiate a blood stage infection in mice . These blood stage parasites , now completely lacking PbICP , exhibited an attenuated phenotype but were able to infect mosquitoes and develop to the oocyst stage . However , PbICP-negative sporozoites liberated from oocysts exhibited defective motility and invaded mosquito salivary glands in low numbers . They were also unable to invade hepatocytes , confirming that control of cysteine protease activity is of critical importance for sporozoites . Importantly , transfection of PbICP-knockout parasites with a pbicp-gfp construct fully reversed these defects . Taken together , in P . berghei this inhibitor of the ICP family is essential for sporozoite motility but also appears to play a role during parasite development in hepatocytes and erythrocytes .
Every year over 200 million people suffer from malaria infection worldwide , with an estimated 655 , 000 deaths annually ( WHO , 2011 ) . The causative agent of malaria is the unicellular parasite Plasmodium that belongs to the phylum Apicomplexa . Much effort has been made to develop drugs against this parasite , however multi-drug-resistant Plasmodium strains are frequently identified in field isolates [1]–[3] and new strategies to combat malaria are therefore urgently needed . Cysteine proteases play a pivotal role in the life cycle of Plasmodium parasites and , thus , might be good targets for anti-malarial strategies . Plasmodium cysteine proteases are involved in a variety of biological processes , such as hemoglobin degradation , protein trafficking , rupture of membranes , host cell invasion , and egress from host erythrocytes and host hepatocytes [4]–[13] . Cysteine proteases are also believed to mediate the unusual form of programmed host cell death that occurs at the end of liver stage development and which clearly differs from classical apoptosis [13] , [14] . Furthermore , cysteine proteases are essential for parasite development in the mosquito vector [15] . Processing of the major surface protein CSP ( circumsporozoite protein ) , which is critical for hepatocyte invasion , is mediated by a still unidentified parasite cysteine protease [16] . In higher eukaryotes , cysteine proteases are controlled by endogenous inhibitors such as cystatins and α2-macroglobulin . In protozoa , no cystatin homologs have been identified , but a family of cysteine protease inhibitors ( ICPs ) has recently been described . The first identified ICP was chagasin from Trypanosoma cruzi [17] . Subsequently , ICPs have been found in Trypanosoma brucei [18] , Leishmania [19] , Entamoeba histolytica [20] , and all Plasmodium species analyzed thus far including human , rodent and avian Plasmodium species [21] , [22] . Related proteins have been described in Pseudomonas aeruginosa but are absent from multicellular eukaryotes [23] . Recently , the structure of the ICPs from Leishmania mexicana , T . cruzi , and Plasmodium berghei were described as immunoglobulin-like [24] , [25] . ICPs inhibit parasite proteases , in the case of T . cruzi [26] and T . brucei [27] , and both parasite proteases and host cell proteases in the case of Leishmania [19] . Plasmodium ICPs belong to the MEROPS I42 family of inhibitors . They are tight-binding , reversible inhibitors of cathepsin-L-type cysteine proteases but do not block the activity of cathepsin-B- and C-like proteases [28] . Whereas the Plasmodium falciparum ICP ( PfICP or falstatin ) is a weak caspase inhibitor , the P . berghei ICP ( PbICP ) is not capable to inhibit caspases at all [25] , [29] . All known Plasmodium ICPs consist of a C-terminal chagasin-like domain and a long N-terminal domain of unknown function . PfICP has been analyzed extensively during blood stage development . It is expressed by mature schizonts , merozoites , and young ring stages but not by trophozoites [22] . During merozoite egress , PfICP is released upon rupture of the infected erythrocyte . Pre-incubation with anti-PfICP antiserum leads to decreased infectivity of blood stage merozoites , suggesting that PfICP has a role in limiting unwanted proteolysis during erythrocyte invasion [22] . In pre-erythrocytic stages , ICPs have been investigated in rodent and avian Plasmodium species . The ICP of Plasmodium gallinaceum is expressed and secreted by salivary gland sporozoites [21] . By contrast , PbICP is constitutively expressed and proteolytically processed throughout the life cycle of the parasite . While the N-terminal part of the protein is rapidly degraded after processing , the chagasin-like C-terminal part is sufficient for inhibition of cysteine proteases [25] , [29] . In sporozoites , PbICP co-localizes with the thrombospondin related anonymous protein ( TRAP ) in micronemes and is secreted by salivary gland sporozoites and young liver stage trophozoites [29] . Our previous results suggested that PbICP promotes hepatocyte invasion by sporozoites since pre-incubation with anti-PbICP serum resulted in a significant impairment of sporozoite invasion [29] . During liver stage development , PbICP is predominantly detected in the parasite cytosol and the parasitophorous vacuole ( PV ) [29] . Upon parasitophorous vacuole membrane ( PVM ) breakdown , PbICP is released into the host cell cytosol . PbICP has the capacity to interfere with the host cell apoptosis machinery by blocking host cell cysteine proteases involved in cell death execution [29] . In contrast to PbICP expression in sporozoites , the ICP of Plasmodium yoelii ( PyICP ) was mainly detected in rhoptries and only partially co-localized with TRAP in micronemes [30] . PyICP was not detected in protein trails left behind by gliding parasites or following induction of microneme secretion , nor did pre-treatment of salivary gland sporozoites with an antiserum to PyICP inhibit hepatocyte invasion . However , these contradictory results could not be clarified since all attempts to delete PyICP were unsuccessful [30] . Interestingly , in the same study , down-regulation of PyICP did not affect blood stage development , but PyICP knockdown parasites could not be recovered from infected mosquitoes [30] . This result led to the hypothesis that Plasmodium ICPs might only be essential for development of the parasite in the mosquito . In a recent study describing the successful knockout of the icp gene in P . berghei this result was confirmed [31] . Here , PbICP knockout sporozoites were not able to migrate to salivary glands and to infect hepatocytes . In contrast to this study , we have employed a stage-specific knock out approach [32] . This technique allowed us to analyze the function of PbICP during the entire life cycle of Plasmodium . Although the main phenotype associated with PbICP loss was detected in pre-erythrocytic stages , PbICP-negative parasites also exhibited a clear attenuated phenotype in the blood stage , confirming an important role of cysteine protease regulation by PbICP in all major life cycle stages . Complementation of PbICP-deficient parasite clones with a pbicp-gfp construct reversed all knockout defects , confirming that the observed effects are indeed due to the lack of the inhibitor .
To analyze the function of PbICP during the entire life cycle of the parasite we generated three different parasites clones: a conditional knockout clone ( PbICPcond ) to analyze the role of PbICP in liver stage development , a complete knockout clone ( PbICPKO ) to investigate whether PbICP is essential for any particular parasite stage and a pbicp complementation on the PbICPKO background ( PbICPcomp ) to confirm that the observed effects are indeed PbICP-derived . To obtain PbICPcond parasites we employed the UIS4/Flp conditional gene deletion approach that has been described previously [33] , [34] . In this approach , the Saccharomyces cerevisiae site-specific recombinase Flp is expressed under the sporozoite-specific uis4 promoter . We first introduced FRT sites into the 3′ regulatory sequence ( UTR ) of the pbicp locus by double crossover homologous recombination [33] , [35] . However , even though excision of the FRTed 3′UTR occurred with great efficiency in salivary gland sporozoites , it was not sufficient to completely suppress expression of the pbicp gene . Therefore , using an alternative strategy , one FRT site was introduced between the sequence coding for the C-terminal inhibitor domain and the N-terminal domain of the protein ( Figure S1A , pPbICP/FRT ) . Following integration by double crossover homologous recombination , Flp-mediated excision of the sequence between both FRT sites ( pbicp 3′coding region and 3′UTR ) was expected to block expression of the functional inhibitor . The linearized pPbICP/FRT was transfected into the non-fluorescent NK65 P . berghei receiver clone , UIS4/Flp ( − ) [33] . The expected double crossover recombination event in the parasite population was confirmed by PCR using different sets of primers ( Figure S1B ) . Subcloning by limiting dilution was performed and a clone ( PbICPcond ) that had successfully integrated the pPbICP/FRT construct into the correct locus was isolated ( Figure S1C ) . For the complete knockout , the PbICPcond clone was passaged through mosquitoes and hepatocytes . Single detached cells were injected into mice for direct cloning [36] and the clone obtained was named PbICPKO . Similar to previous work [31] , these PbICP-deficient parasites could establish a blood stage infection . Finally , to complement the deletion of the pbicp-c locus , we transfected PbICPKO parasites with a plasmid that allows expression of pbicp-gfp under the control of the constitutive promoter pbeef1aa [29] ( Figure S2A ) . Selection of transgenic add-back parasites ( PbICPcomp ) was facilitated by the fact that , in PbICPKO parasites , the hDHFR resistance cassette was removed during the excision event ( Figure S1A ) . Integration of pbicp-gfp ( and of gfp as a control ) by single crossover into the ssurna locus of PbICPKO parasites was confirmed by PCR analysis using gDNA extracted from transfected PbICPKO and control UIS4/Flp ( − ) blood stage parasites ( Figure S2B ) . To confirm that the GFP fusion does not block the inhibitor function of PbICP , protease assays were performed with recombinant fusion proteins expressed in E . coli bacteria: 200 nM of either MBP-PbICP or MBP-PbICP-GFP were equally able to inhibit the cysteine protease papain ( Figure S2C ) . Genotyping of blood stage parasites from different clonal parasite populations confirmed the presence of the pbicp locus in PbICPcond parasites and its excision in PbICPKO and add-back parasites ( PbICPcomp ) ( Figure 1A , upper panel ) . As expected , pbicp DNA was detected in PbICPcond and PbICPcomp parasites . In PbICPcond parasites , the pbicp gene was only slightly modified by the addition of FRT sites and thus allowed amplification of a pbicp fragment by PCR ( Figure 1A , lower panel ) . In PbICPcomp parasites part of the pbicp locus was deleted but the integration of the pbicp-gfp cassette into the ssurna locus also allowed amplification of a diagnostic pbicp fragment by PCR . To confirm PbICP expression in PbICPcond and PbICPcomp parasites , protein extracts were prepared from the different blood stage parasite strains and Western blot analysis using antiserum directed against PbICP-C was performed ( Figure 1B ) . The PbICP-C antiserum detected bands of 55 kDa and 23 kDa in control parasites ( P . berghei strain expressing the Flp recombinase , UIS4/Flp ( − ) ) and PbICPcond parasites , corresponding to the full-length and processed PbICP , respectively , as described earlier [29] . Although the modified PbICP in PbICPcond parasites , harboring the FRT site in the open reading frame , is expected to be slightly larger ( 2 kDa ) , no size differences could be detected by Western blot analysis , most likely because the gel was unable to resolve this small difference ( Figure 1B , lane 1 and 2 ) . No PbICP protein was detected in PbICPKO parasites ( Figure 1B , lane 3 ) , but expression of full-length PbICP-GFP ( 81 kDa ) as well as the processed form , PbICP-GFP ( 49 kDa ) , was detected in cell extracts of PbICPcomp parasites ( Figure 1B , lane 4 ) . Although PbICP is not essential for blood stage development , we wanted to determine whether knockout of the inhibitor has an effect on parasite development at this stage . Blood from mice infected with PbICPKO , PbICPcomp , or control parasites was transferred to naïve mice by i . p . injection . In comparison to control parasites , the increase in parasitemia of PbICP-deficient parasites was clearly delayed ( Figure 1C , left graph ) , indicating a significant level of attenuation of these parasites . By contrast , PbICPcomp parasites and control parasites did not differ in the establishment of parasitemia ( Figure 1C , right graph ) confirming that exogenous expression of the PbICP-GFP fusion protein fully restored virulence to blood stage parasites . The differences in parasitemia with the different parasite strains was statistically evaluated at day 3 when all mice were still alive ( Figure S2D ) . As expected , parasitemia in mice infected with PbICPKO parasites was significantly lower than in mice infected with control or add-back parasites ( PbICPcomp ) . The PbICPcond clone was used to infect mosquitoes and excision of pbicp-c was followed by PCR analysis ( Figure S1C ) . It is important to note that in parasites with freshly excised genes , translation of the already produced mRNA is still ongoing and thus protein is expected to be expressed for several hours . This was very important for us as it has been shown that the traditional knockout of the pbicp gene completely blocked sporozoite invasion of hepatocytes [31] and one of the main objectives of the present study was to analyze the role of PbICP during liver stage development . Although excision is very efficient it is never complete [33] . We therefore expected that in a very small fraction of parasites the gene would not be excised and that these parasites would show a normal phenotype . Before we analyzed hepatocyte invasion and liver stage development , we first investigated the development of PbICPKO and PbICPcomp parasites in mosquitoes and compared them to control parasites . Oocysts were counted and sporozoites were collected from midguts , hemocoel , and salivary glands at different times and counted . At day 10 post-infection , the number of oocysts ( Figure S3A ) and the development of sporozoites in the oocysts ( Figure S3B ) appeared to be similar in all three parasite clones . At day 18 post-infection , the number of midgut sporozoites as well as the release of sporozoites into the hemocoel was still not significantly different ( Figure 2A ) . However , by this stage only very few PbICPKO sporozoites reached the salivary glands , similar to a previous report for PbICPKO parasites obtained by a traditional knockout strategy [31] . It should be noted that at later stages ( 20–26 days post-infection ) , more parasites had accumulated in the salivary glands and allowed a restricted set of experiments ( see below ) . Importantly , exogenous constitutive expression of PbICP-GFP in PbICPcomp parasites completely restored salivary gland infectivity confirming that the observed phenotype was indeed due to the loss of PbICP ( Figure 2A ) . Since we confirmed excision in PbICPcond parasites occurred during development in the salivary glands ( Figure S1C ) , we next investigated PbICP expression in salivary gland sporozoites by IFA using a specific anti-PbICP-C antiserum . PbICPcond sporozoites expressed levels of PbICP comparable to control sporozoites ( Figure 2B ) although excision of the gene was very efficient ( Figure S1C ) . Since translation of already produced mRNA is still ongoing , PbICP protein was expected to be expressed for several hours . In addition , we reasoned that already expressed PbICP is stored in micronemes during sporozoite development and is available for successful invasion of hepatocytes . In this experiment PbICPKO sporozoites served as a negative control ( Figure 2B ) and confirmed the specificity of the anti-PbICP-C antiserum . In PbICPcomp sporozoites , PbICP-GFP expression was detected by the anti-PbICP-C antiserum as well as an anti-GFP antibody ( Figure 2B ) . The predominant phenotype of PbICPKO sporozoites is impaired gliding motility ( Figure S3C ) . Importantly , PbICP-GFP expression restored gliding motility of sporozoites as evidenced by CSP positive circles around the parasite ( Figure 2B , bottom panel ) . Interestingly , these circles were also positive for PbICP-GFP , confirming secretion of the inhibitor as suggested earlier [21] , [29] . To understand the molecular basis of impaired gliding motility in PbICPKO sporozoites , we investigated proteins known to be involved in this process . CSP processing is important for switching from the sporozoite migratory state to the invasion state [37] . Processing of CSP is mediated by a cysteine protease and given that both ICP and CSP proteins are secreted by sporozoites [29] , [37] , we hypothesized that PbICP could be one of the regulatory factors in CSP processing . We reasoned that premature CSP processing would prevent parasite migration . Indeed when CSP was analyzed in midgut and salivary gland lysates of PbICPKO sporozoites by Western blot , it was extensively processed compared to control or PbICPcomp parasites ( Figure 2C , upper panel ) . However , since PbICPKO parasites are strongly impaired in gliding , the phenotype is clearly different from mutant parasites that only express the processed form of CSP on their surface . These CSP mutant sporozoites are able to glide and invade cells but cannot complete their migration from the inoculation site of the skin [37] . We therefore concluded that loss of PbICP must also affect key proteins important for gliding motility , such as TRAP [38] . TRAP is a transmembrane protein that connects the parasite's motor complex to the substrate . Normal gliding requires proteolytic cleavage of TRAP to remove its bound extracellular adhesion domains [39] . In a previous study , we localized TRAP and PbICP to parasite micronemes [29] , therefore we investigated TRAP processing in the absence of the inhibitor ( in PbICPKO parasites ) . Protein extracts of PbICPKO salivary gland sporozoites demonstrated a major processed form of TRAP ( approximately 75 kDa ) , instead of the 95 kDa form found in supernatant of control parasites ( Figure 2C , lower panel ) . This result argues for alternative TRAP processing in PbICPKO parasites . To exclude the possibility that the observed processing is a non-specific event due to death of the parasites , we performed metabolic labeling . Salivary gland PbICPKO parasites incorporated equivalent amounts of radio-labeled amino acids as control parasites , confirming that PbICPKO parasites are metabolically active and viable ( Figure S3D ) . In a previous study , incubation of salivary gland sporozoites with anti-PbICP antiserum neutralized PbICP and led to significantly reduced levels of HepG2 infection but had no effect on the ability of sporozoites to traverse cells [29] . Now we found that PbICP-deficient sporozoites exhibited severely impaired gliding motility ( Figure S3C ) and , thus , it was not surprising that PbICPKO parasites were not able to transmigrate through host cells ( Figure S3E ) . We therefore reasoned that loss of PbICP-C has more severe effects on sporozoite biology than those elicited by neutralization of secreted protein ( s ) by the anti-PbICP antiserum . Following transmigration , sporozoites invade hepatocytes and reside within a PV . In invasion assays , we counted infected HepG2 cells at 5 and 30 hours after incubation ( hpi ) with control , PbICPcond , PbICPKO , or PbICPcomp sporozoites ( Figure 3A and S4A ) . Overall , significantly fewer PbICPcond parasites than control parasites successfully infected cells , although all PbICPcond sporozoites and early-invaded parasites ( 5 hpi ) still expressed PbICP . The amount of PbICP protein was likely reduced owing to the gene deletion . PbICP might be stored to a certain extent in some compartments involved in invasion ( i . e . , micronemes ) and allow invasion of at least some parasites . However , analysis of protein concentration is beyond the resolution of IFA . IFA is an extremely powerful qualitative method , but it is not suitable for determining small differences in protein concentration because photo-bleaching cannot be precisely controlled . Still , as we observed PbICP-negative parasites already at 30 hpi , excision obviously results in a marked drop of PbICP levels . An essential role of PbICP in invasion was revealed when PbICPKO parasites were investigated . Remarkably , not a single PbICPKO sporozoite was able to invade a HepG2 cell , whereas PbICPcomp parasites exhibited an even improved invasion rate compared to the control strain ( Figure 3A ) . This result is consistent with a previous study showing that overexpression of a PbICP-GFP fusion protein had a positive effect on invasion of HepG2 cells compared to wild-type parasites [29] . Since recombinantly expressed PbICP maintains the capacity to inhibit cysteine proteases [29] we reasoned that externally added recombinant PbICP might restore the invasion phenotype in PbICPKO parasites by blocking unregulated processing of TRAP . However , we did not observe any effect of added inhibitor on invasion of HepG2 cells by PbICPKO parasites ( Figure S4C ) and concluded that unregulated processing of TRAP might occur within the parasite rather than following secretion . Obviously , externally added recombinant PbICP did not have access to intracellular compartments and , thus , could not prevent processing . This assumption is supported by the fact that PbICP and TRAP co-localized in micronemes [29] . It will now be highly interesting , although very challenging , due to the small number of salivary gland sporozoites , to investigate TRAP and CSP processing in PbICPKO parasites in detail . Together , all phenotypes observed in PbICPKO sporozoites can be explained by the lack of gliding motility . Salivary gland invasion , as well as transmigration and finally invasion of hepatocytes , all depend , at least in part , on gliding motility . Successful metabolic labeling of PbICPKO parasites confirmed that the parasites are still viable . Importantly , the add-back parasite strain expressing PbICP-GFP reversed the strong knockout phenotypes completely , confirming that the observed defects are indeed due to the lack of the inhibitor . Liver stage development is characterized by an extensive growth phase . To investigate intrahepatic growth of PbICP-C-deficient parasites , the size of fixed PbICPcond parasites in infected HepG2 cells was analyzed at 30 hpi and 60 hpi and compared to control exo-erythrocytic forms ( EEFs ) . PbICP-negative PbICPcond parasites were significantly smaller compared to controls at 30 hpi and the difference became even more pronounced 60 hpi ( Figure 3B ) . To determine whether PbICP-negative parasites were developmentally delayed , we examined the expression and localization of different parasite marker proteins that have distinct subcellular localizations and expression profiles throughout EEF development . HepG2 cells were infected with salivary gland sporozoites of control , PbICPcond or PbICPcomp parasites and fixed at different times after infection ( Figure 4 and 5A ) . At the trophozoite/early schizont stage , infected cells were examined by IFA using an antibody that stains the PVM-marker protein Exp1 ( Figure 4 ) . For later stages , an antibody detecting the GPI-anchored parasite membrane ( PM ) protein MSP1 was used ( Figure 5A , Figure S5A ) , which is expressed from the late schizont stage onwards and is essential for the formation of hepatic merozoites [33] . Anti-PbICP-C antiserum was used to distinguish PbICP-negative and -positive parasites in the PbICPcond population . PbICPcomp parasites were not stained with anti-MSP1 antibodies , because four-color staining was limited by the detection of expression of PbICP-GFP fusion protein . However , PbICPcomp parasites develop to infectious merozoites , indicating that they express MSP1 normally . IFAs of parasites fixed at 30 hpi ( trophozoite/schizont stage ) showed that PbICP-negative PbICPcond parasites appear to be retarded in development compared to control parasites ( Figure 4 ) . At this stage , control and PbICPcomp schizonts had already started schizogony characterized by multiple nuclei , whereas most PbICP-negative parasites were still at the trophozoite stage with a single nucleus ( Figure 4 ) . A PbICP deficiency at this stage did not affect maintenance of the PVM . At 48 hpi ( late schizont stage ) , we frequently observed that PbICP-negative parasites were smaller in size , but the PVM showed normal dimensions and thus the lumen of the PV was much larger than that of control parasites , where the PV is hardly seen ( Figure 4 ) . From 55 hpi onwards , PbICP-negative parasites exhibited an abnormal nuclear distribution and did not develop into the characteristic cytomere stage ( Figure S5A ) . At 60 hpi parasites were counted and distinguished by IFA in infected cells with PbICP positive and PbICP negative parasites ( Figure S5B ) and their size was measured ( Figure 3B ) . In contrast to 30 hpi ( Figure 3A ) , at 60 hpi most parasites were found to be PbICP negative . The number of PbICPcond parasites was significantly reduced in comparison with control parasites but was similar to the numbers of invaded PbICPcond parasites at 30 hpi ( Figure 3A ) suggesting that PbICPcond parasites that are able to invade , persist but are obviously smaller ( Figure 3B ) and developmentally retarded ( Figure S5A ) . Our next aim was to analyze the final steps of liver stage development of PbICP-negative parasites . Merozoites egress from infected hepatocytes in packets called merosomes , which is a tightly regulated process involving proteases [13] , [40] . We hypothesized that PbICP is an important regulatory element in this process . To address the consequences of PbICP depletion on merosome formation , HepG2 cells were infected with control , PbICPcond , PbICPKO , or PbICPcomp parasites . At 48 hpi , the number of infected cells was counted and this value was normalized to 100% of the successfully developing parasite population . Since PbICPKO parasites did not infect HepG2 cells at all , they were not included in further analysis . Detached cells and merosomes were harvested at 65 hpi but only detached cells ( containing a Hoechst 33342-positive host cell nucleus ) were counted to determine the number of infected cells completing development ( Figure 5B ) . Including budded merosomes ( without a host cell nucleus ) in the counting would have artificially increased the developmental success rate . As shown in Figure 5B , at 65 hpi , only 6 . 9% of PbICPcond parasites had completed development , in contrast to 36 . 2% of control and 31 . 7% of PbICPcomp parasites . To exclude the possibility that PbICPcond parasites are only delayed in development , we monitored detachment between 65 and 70 hpi and no additional detached cells were detected for PbICPcond parasites ( Figure S5C ) . The significant reduction in the ability of the PbICPcond population to form detached cells confirmed our original hypothesis that PbICP is an important regulator of parasite cysteine proteases involved in egress . To analyze merozoite formation and detached cell morphology in more detail , IFA of control , PbICPcond and PbICPcomp parasites was performed . In most control parasites , in vitro merozoite formation is completed at approximately 60 hpi ( Figure S5A ) . The resultant merozoites are homogenous in size and shape and are surrounded by a MSP1-positive membrane . The PVM starts to disintegrate as shown by fragmented Exp1 staining and merozoites are released into the host cell cytoplasm . PbICP-negative PbICPcond parasites , by contrast , exhibited abnormal MSP1 and Exp1 staining around clusters of parasites . In contrast to control parasites , only a few merozoite-like structures could be detected in PbICP-negative parasites and these were surrounded by MSP1-positive membranes ( Figure S5A ) . The majority of these parasites formed large vacuole-like structures surrounded by MSP1-positive membranes . These data clearly show that PbICP expression is crucial for parasite development and merozoite formation . Nonetheless , a small proportion of PbICP-negative parasites were able to complete development and induce host cell detachment ( Figure 5A , middle panel ) . These few parasites may have maintained PbICP levels longer than others and were thus able to continue development . PbICP-negative parasites also contained merozoites with an abnormal morphology and some were not even surrounded by MSP1-positive membranes ( Figure 5B , middle panel; S5A ) . Interestingly , the few normally developed MSP1-positive merozoites in detached cells , which derived from PbICPcond parasite-infected cells , were infective by the single detached cell injection method ( which was , in fact , the method used to generate PbICPKO parasites , as described earlier ) . In contrast to PbICPcond parasites , PbICPcomp parasites grew normally in HepG2 cells and were able to complete liver stage development ( Figure 5A , lower panel and 5B ) , confirming that i ) the observed phenotype in PbICPcond parasites is indeed due to the knockout of pbicp and ii ) PbICP-GFP is capable of complementing the knock out phenotype in exo-erythrocytic stages . To investigate the in vivo infectivity of the different parasite strains generated in this study , we infected mice with equal numbers of salivary gland sporozoites and monitored parasitemia . In vivo infectivity of PbICPcond sporozoites was dramatically reduced . Compared to control mice where 9 of 10 sporozoite injections resulted in a blood stage infection , only 1 mouse out of 10 developed a blood infection when PbICPcond sporozoites were injected ( Table 1 ) . Strikingly , no blood infection could be detected in mice after injection of PbICPKO sporozoites , while the PbICPcomp parasites elicited infection rates similar to control parasites ( Table 1 ) . Taken together , parasites lacking PbICP are greatly impaired in invasion of salivary glands and are incapable of infecting hepatocytes , confirming the predicted role of PbICP in sporozoite invasion processes .
The data obtained in this study indicate that impaired invasion of salivary glands by PbICPKO sporozoites is due to a lack of gliding motility . Gliding motility is a substrate-dependent form of active motion that is powered by a subpellicular actomyosin system . The actomyosin complex is linked to the sporozoite surface by members of the TRAP family , which are secreted from specialized secretory organelles called micronemes [38] , [41] . TRAP is a type I transmembrane protein exhibiting an extracellular adhesive domain and a cytoplasmic domain that binds to F-actin and hence connects to myosin A [42] , [43] . The forward motion of sporozoites results from the posterior translocation of the substrate-TRAP-motor complex assembly . Drug treatments , mutations of the motility machinery , or deletion of TRAP all result in motility defects [38] , [42] . Furthermore , non-motile sporozoites are incapable of invading host cells , which directly links gliding motility to host cell invasion . For fast and effective gliding motility , a delicate balance between adhesion and detachment must be achieved [44] , [45] . Adhesins may be proteolytically processed either intracellularly ( en route to or within the micronemes ) or extracellularly after their release onto the surface [46] . Extracellular processing is required to break interactions between the parasite surface and host cell and also to control exposure of adhesive domains in parasite-host cell interactions . In a recent study TRAP was shown to be processed and , thus , removed from the sporozoite surface by a rhomboid serine protease . If canonical rhomboid activity is prevented , TRAP accumulates on the surface and motility is significantly reduced , linking serine proteases to the process of host cell invasion [39] . However , in parasites with a mutated rhomboid cleavage site , cleavage of TRAP , albeit inefficient , is mediated by another unknown protease . This processing occurs at an alternative juxtamembrane cleavage site and the processed TRAP is smaller in size [39] . Interestingly , the size of the cleaved form of TRAP in PbICPKO sporozoites was much smaller than in control parasite lysates and comparable in size to TRAP processed at the alternative site . Pre-incubation of PbICPKO sporozoites with recombinant PbICP did not reverse the immotile phenotype of sporozoites; therefore , we hypothesize that the unregulated TRAP site is processed intracellularly . In T . gondii , the endogenous cathepsin-L-like protease TgCatL is localized in a vacuolar compartment and functions as a protein maturase within the endo/exocytic system . TgCatL mediates proteolytic maturation of propeptides targeted to micronemes [47] . If such a compartment also exists in P . berghei , PbICP could control the activity of cathepsin-L-like proteases during maturation of micronemal proteins . In fact , PbICP and TRAP localize to the same compartments , namely , micronemes [29] and , when PbICP is absent , a putative cysteine protease might become active prematurely . Taken together , our results imply that not only serine proteases but also cysteine proteases are important for motility and invasion and their precise regulation is essential for gliding motility . The decrease in both mosquito salivary gland and vertebrate liver infectivity of PbICPKO sporozoites can be explained entirely by ICP's role in gliding motility . However , it cannot be excluded that part of these phenotypes is due to the premature processing of CSP observed in PbICPKO sporozoites . Previous studies have shown that CSP is processed by a parasite cysteine protease , resulting in removal of the N-terminal part of the protein which leads to exposure of its C-terminal cell-adhesion domain [37] . As sporozoites migrate from the mosquito midgut to the mammalian liver , the N-terminus masks the cell adhesion domain that appears to maintain the sporozoite in a migratory mode . Upon contact with hepatocytes , CSP is cleaved , the adhesive domain is exposed and there is an associated switch from a migratory to an invasive state . Mutant sporozoites , which constitutively express only the cleaved form of CSP on their surface , do not efficiently invade mosquito salivary glands because they non-specifically bind to other organs in the mosquito hemocoel and when injected intradermally into the mammalian host , they cannot exit the inoculation site in the skin . However , when placed directly onto hepatocytes , they invade normally . So although dysregulation of CSP cleavage could interfere with sporozoite infectivity of mosquito salivary glands and infectivity in the mammalian host after inoculation by mosquito bite , our finding of severe attenuation of hepatocyte infectivity of PbICPKO sporozoites , i . e . in vitro and in vivo , points to another primary cause , namely TRAP processing and its impact on gliding motility . PbICPcond as well as PbICPKO sporozoites showed a severe defect in hepatocyte invasion . While PbICPcond sporozoites had a significantly reduced ability to infect hepatocytes , PbICPKO sporozoites were completely blocked in this regard . These results strongly support the hypothesis that PbICP is critical for hepatocyte invasion . All PbICPcond parasites that were able to infect hepatocytes were initially positive for PbICP . The reasons why their invasion ability and intracellular development differs quite substantially from PbICPKO parasites are manifold . First , excision is not coordinated among salivary gland sporozoites as it depends on uis4 promoter activity that is activated in late sporozoite stages and is still active in the liver stage . Excision might therefore occur at any of these stages . Secondly , even upon excision of the gene , the mRNA is still present and can be transcribed . The amount of pbicp mRNA may vary within a parasite population . Another fact to consider is that PbICP is a rather stable protein and protein turnover may vary within a parasite population . Lastly , a few parasites stayed PbICP-positive until the end of liver stage development and we assume that in these parasites the pbicp gene was not excised . Given these data , PbICPcond salivary gland sporozoites are likely to exhibit variations in their loss of PbICP over time . We hypothesize that the amount of PbICP in sporozoite is the determining factor for a successful invasion event . If PbICP levels are too low , or the protein is absent , cysteine protease activity is enhanced and premature processing of key invasion molecules occurs , resulting in defective invasion . The essential role of PbICP in sporozoite invasion of both mosquito salivary glands and hepatocytes provides an eminently suitable target for pre-erythrocytic intervention strategies . In comparison to control parasites , PbICPcond liver stage parasites were clearly delayed in development and growth ( Figure 4 , 5 , and S5 ) . Morphological analysis revealed defects in nuclear replication , PV maintenance , PM invagination , and merozoite formation . Cysteine proteases are important for parasite development throughout the liver stage [13] , [16] and the data presented here confirm this . Although the exact function of PbICP during liver stage development remains to be determined , some features of PbICP-deficient parasites are very interesting . Very few PbICP-deficient parasites developed to the cytomere stage and beyond . The fact that some parasites still succeeded suggests that they either had sufficient PbICP protein that was beyond the limit of detection by IFA , or that PbICP was dispensable for their development . If the latter is true , how can it be that the inhibitor is non-essential for some parasites ? A possible explanation would be that PbICP is an emergency protease inhibitor during liver stage development , which inhibits accidentally-released and activated cysteine proteases . Because accidental release does not occur in every case , some parasites can complete development even without PbICP . This study also confirmed our earlier observation of PbICP being necessary for merozoite egress from hepatocytes ( Figure 5 ) . Cysteine proteases are involved in breakdown of the PVM in late liver stage development . When infected cells were treated with the cysteine protease inhibitor E64 in vitro [13] , PVM rupture did not occur and only very few infected cells were able to detach . Upon PVM disintegration , merozoites are released into the host cell cytosol , a process that is closely associated with induction of an unusual type of host cell death . Host cysteine proteases such as caspases , calpain , and cathepsins , which are often key enzymes in programmed cell death , are not activated and other classical signs of programmed cell death , such as DNA fragmentation and disintegration of the host cell plasma membrane , are absent [13] . Since breakdown of the PVM also correlates with release of PbICP into the host cell cytosol , the inhibitor may modulate activation of apoptosis by inhibiting cysteine proteases [29] . A recent study hypothesized that PbICP , in addition to its inability to inhibit calpain-1 , and cathepsin-B and -C type cysteine proteases , cannot inhibit SERA proteases [25] , which play a role in PVM breakdown . Although protease activity of SERAs has not been confirmed , it is widely accepted that they are involved in parasite egress [5] . Of the nine SERA proteases identified in P . falciparum , PfSERA5 and PfSERA6 have been refractory to gene deletion . Processing of PfSERA5 is essential for egress of blood stage merozoites from the erythrocyte . The first known step in this cascade is DPAP3-mediated subtilisin 1 ( SUB1 ) processing , which is then released into the PV lumen , where it processes PfSERA5 [5] , [7] , [48]–[50] . PfSERA5 then triggers downstream processing of cellular substrates and has been associated with 28 interaction partners in a protein-association network study [51] . In P . berghei , the homolog of PfSERA6 , PbSERA3 , is highly expressed and processed in late liver stages ( presumably by PbSUB1 ) , where it localizes to the PVM/PV . Upon breakdown of the PVM , it is released into the host cell cytoplasm [52] . Presumably cysteine proteases of the parasite that are not inhibited by PbICP are involved in PVM breakdown . Parasite cysteine proteases of the cathepsin-B or -C type are therefore likely candidates to mediate PVM breakdown . SERA proteases might be involved as well , but the molecular mechanism remains to be elucidated . Upon PVM breakdown , Plasmodium ICPs prevent an immediate and , thus , premature host cell death by inhibiting host cell cathepsin-L-like proteases . Parasite proteases initiate an ordered and slow host cell death that cannot be inhibited by ICPs . Irrespective of the underlying mechanism , late liver stage development and egress processes were greatly impaired in PbICP-negative PbICPcond parasites and only very few infected host cells were able to detach , confirming a role for PbICP at this stage . The precise role of PbICP during host cell death processes at the end of liver stage development is subject of current research . The few PbICP-negative merosomes successfully isolated in vitro were injected into naïve mice and were capable of establishing a blood stage infection confirming a recent study in which the entire pbicp gene was deleted [31] . In contrast to our data , no effect on blood stage development was observed in the previous study . This was surprising because attempts to knockout the icp gene in the closely related parasite P . yoelii [30] and the human parasite P . falciparum [22] have failed , suggesting an important role for this inhibitor during the blood stage . Indeed , we did not succeed to delete this gene in P . berghei using straight knockout approaches suggesting an important role for this inhibitor during the blood stage as well . Since gene deletion in the previous study occurred in the blood stage [31] , selective pressure to compensate for the loss of the inhibitor was high and the obtained knockout clones may have carried modifications in addition to the pbicp knockout . In our study , we obtained blood stage parasites by injecting mice with hepatocyte-derived merozoites and , thus , the parasites did not have the chance to adapt to the loss of PbICP by compensatory mutations or epigenetic modifications . Furthermore , we showed a complete reversion of the pbicp knockout phenotype by add-back transfection of PbICPKO parasites , strongly suggesting that the observed effects are solely due to the loss of PbICP function . In the previous study [31] , this complementation experiment was not performed , which leaves the possibility that compensatory modifications occurred in the obtained clones . However , all other observed effects match very well in both studies , confirming the important role of PbICP in gliding motility , ordered CSP processing , and invasion . Analysis of the PbICP deficiency in liver stage development was not possible in the previous study since hepatocyte invasion was completely blocked in pbicp knockout sporozoites [31] . The question of why the constitutively expressed ICPs are not equally important for different life cycle stages remains . During blood stage development , cysteine proteases are mainly active in the food vacuole , where premature activation of cysteine proteases might have less deleterious effects . In the food vacuole , the major role of falcipains and other proteases is to degrade hemoglobin to provide nutrients for the parasite . By contrast , liver schizonts do not form food vacuoles and falcipains might localize to other compartments of the parasite , where deleterious effects might be more likely . Another reason why ICPs are more crucial for some stages than others might be due to differences in gliding distances and the length of time required to reach the relevant host cell . Most apicomplexan invasive stages are released in close proximity to their future target host cell and do not need to move large distances . For example , when erythrocytic-stage merozoites are released within a blood vessel , parasites do not need to glide to infect a new RBC . By contrast , sporozoites developing in oocysts in the mosquito midgut wall are far from their ultimate target , the mammalian liver . After passive circulation in the hemocoel of the insect , they actively enter the mosquito salivary glands , from where they are inoculated into the mammalian dermis , actively exit the dermis to enter the blood circulation and , upon being carried by the blood stream to the liver , actively penetrate the sinusoidal barrier of the liver to reach the hepatocytes [53] . Thus , sporozoites need to remain viable and infective for an extended period of time , which , in turn , requires a prolonged and precise regulation of protease activity . Since merozoite invasion of RBCs is a fast process , tightly regulated protease activity might be less important . Differences in invasion strategies among sporozoites of different Plasmodium species may also be possible [37] , [53] . If these strategies involve different proteases , the severity of phenotypes observed in pbicp deficient parasites at different life cycle stages might be affected . Interestingly , ICPs from other protists have also been shown to function during invasion processes . Crypotastatin , an ICP of Cryptosporidium parvum , may play a role in invasion of host cells [54] , the overexpression of chagasin in T . cruzi reduces the infection rate in vitro and in vivo , and a chagasin null mutant had a lower differentiation rate due to changes in activity of parasite cell surface cysteine proteases [26] , [27] , [55] . However , T . brucei chagasin null mutants reached higher parasitemia levels in mice [27] . In T . cruzi , chagasin promotes modulation of cysteine protease zymogens in the Golgi apparatus and its deletion causes higher zymogen conversion , disruption of intracellular traffic , and abnormalities in the secretory pathway , which , surprisingly , leads to increased virulence in vivo [18] , [56] . The ICP of L . mexicana is secreted by the parasite and even though a lack of ICP had no impact on infectivity in vitro , ICP overexpressors , as well as knockout parasites , showed a reduced virulence and infectivity in vivo [19] . These observations suggested that LmICP regulates proteases of the host rather than those of the parasite . The in vivo roles of the E . histolytica ICP are neutralization of endogenous parasite proteases to regulate self-proteolysis , as well as inactivation of host proteases during parasite invasion [20] . In T . cruzi , cruzipain is released by trypomastigotes and was shown to promote host cell invasion by processing a trypomastigote molecule associated with parasite-shed membranes [26] . Overall , these studies confirm the general importance of tight regulation of cysteine proteases involved in invasion , growth and egress by endogenously expressed protease inhibitors . Our work provides evidence that ICPs are the molecular regulators of cysteine protease activity during several life cycle stages of Plasmodium parasites . These results may allow new and innovative approaches to control Plasmodium invasion and intracellular development .
This study was carried out in strict accordance with the guidelines of the German Tierschutzgesetz ( TierSchG; Animal Rights Laws ) . Mice were obtained from Charles River Laboratories . The protocol was approved by the Department of Veterinary Affairs of the Hamburg state authorities ( Permit Number: FI 28/06 ) . Blood feeding was performed under ketavet/rompun anesthesia , and all efforts were made to minimize suffering . All parasites are derivatives of Plasmodium berghei strain NK65 . Anopheles stephensi mosquitoes were reared using standard procedures [57] . Mosquitoes were fed on infected mice 3–5 days after parasite injection and kept at 21°C with 70% humidity . For in vitro experiments , sporozoites were isolated from infected salivary glands 18–25 days after the infectious blood meal . The backbone of the targeting plasmid ( Phdhfr/FRT-Flp ) has been described previously [32] . The plasmid pPbICP/FRT contains the following elements , listed with the numbers of the relevant primer pairs in parentheses . In pPbICP/FRT , the first 0 . 85 kb ( 1 ) ( PbICP-N ) of the PbICP coding sequence ( PlasmoDB: PBANKA_081300 ) is immediately followed by 16 nucleotides ( tcgttttcgtttaact ) , a first FRT site , the last 0 . 7 kb ( 2 ) ( PbICP-C ) of the PbICP coding sequence , the hdhfr 3′regulatory sequence ( 3 ) ( 0 . 45 kb ) , the hdhfr cassette , the second FRT site , the plasmid backbone and 0 . 76 kb ( 4 ) of the PbICP 3′regulatory sequence . The coding and regulatory sequences of pbicp and hdhfr were cloned from gDNA of P . berghei NK65 wild-type blood stages or Phdhfr/FRT-Flp plasmid using Phusion Taq High-Fidelity DNA polymerase ( Finnzyme ) and the following primer pairs: ( 1 ) fw , 5′ -ATGCATGCCGTGTTTAATATATGfCTCCATCCTAGCC- 3′ , and ( 1 ) rv , 5′ -ATATGCGGCCGCAGTTAAACGAAAACGATATATCTTCGCTATTATCAGAAAAATTACTTGCTG- 3′; ( 2 ) fw , 5′-ATGATATCGAAGATAATCAAAAATACCCAACTAC- 3′ , and ( 2 ) rv , 5′-ATGATATCTTATTGGACAGTCACGTATATAATTTTAGTG- 3′; ( 3 ) fw , 5′-ATATGGGCCCCGTTTTTCTTACTTATATATTTATACCAAT- 3′ , and ( 3 ) rv , 5′-ATATGGGCCCATTGAAGGAAAAAACATCATTTG- 3′; ( 4 ) fw , 5′-ATATAAGCTTGTATATATGCGTATATATAATATATGCAATAATAATTTTTTTTTATGCC- 3′ , and ( 4 ) rv , 5′-ATGCATGCGAAATTGTGGAAAGAATGAAAAAGGGGTG- 3′ . A transgenic conditional pbicp-c knock out ( KO ) parasite line that was generated , later subcloned and named PbICPcond . The Plasmodium berghei expression plasmid pL0017 ( pL0017-GFP ) , obtained from Chris Janse through the Malaria Research and Reference Reagent Resource Center ( www . mr4 . org ) , was used to generate the PbICPcontrol-GFP parasite line . The pL0017-PbICP-GFP plasmid was generated previously [29] and was used as an add-back construct in this study . The transgenic PbICPKO-add-back parasite line that was generated is referred to as PbICPcomp . GFP and the fusion protein PbICP-GFP are expressed via the pbeef1aa promoter . The pL0017 plasmid contains target sequences for integration into the c- or d-ssu-rRNA locus of the genome and the tgdhfr/ts selectable marker cassette , which allows selection of transfected parasites using pyrimethamine . P . berghei erythrocytic stages of the receiver strain PbNK65 UIS4/Flp ( − ) [33] ( PbICPcontrol ) and the PbICPKO strain ( described below ) were transfected by electroporation using Amaxa technology , as described previously [58] . When the parasitemia in parental and transfer mice was higher than 5% , infected blood was collected by cardiac puncture and P . berghei genomic DNA was extracted and analyzed . The uncloned transgenic PbICPcond parent parasite line was cloned by limiting dilution of blood stage parasites . Integration of the pPbICP/FRT constructs ( Figure S1B ) into the genome of the UIS4/Flp ( − ) strain was analyzed by diagnostic PCR using the following primers: P1 ( fw ) : 5′- ATGATATCGAAGATAATCAAAAATACCCAACTAC . P2 ( rv ) : 5′- TTAAACGAAAACGAAAGAATGAAAAAGGGGTGTACTTGTTATATC -3′ . P3 ( rv ) : 5′- CATCGACCCTTTCTCTGTATGAACATCTTCTAC -3′ . P4 ( fw ) : 5′- CCCAGCTTAATTCTTTTCGAG . pbicp: ( fw ) 5′- GAAGATAACGACATATACTCTTTTGATATC -3′; ( rv ) 5′- TTATTGGACAGTCACGTATATAAT -3′ . PbICPcond always refers to subcloned parasites if not mentioned specifically that it still is uncloned . Genomic integration of the pL0017-PbICP-GFP construct into the PbICPKO strain and of pL0017-GFP into the UIS4/Flp ( − ) strain ( Figure S2B ) was analyzed by diagnostic PCR using the following primers: P1 ( fw ) : 5′- ATACTGTATAACAGGTAAGCTGTTATTGTG -3′ . P2 ( fw ) : 5′- GTGTAGTAACATCAGTTATTGTGTG -3′ . P3 ( rv ) : 5′- TTTCCCAGTCACGACGTTG -3′ . P4 ( rv ) : 5′- CTTAGTGTTTTGTATTAATGTCGATTTG -3′ . The transgenic PbICPcond parasite line was cloned by injection of a single infected detached hepatocyte or merosome into mice , as previously described [36] . Briefly , HepG2 cells were infected with PbICPcond sporozoites and the resulting detached infected hepatocytes were collected 65 hours later . Individual detached cells were isolated and injected intravenously into 6- to 8-week-old female NMRI mice . The cloned parasites from the resulting blood stage infections were collected and used for further analysis . Correct excision of pbicp-c was analyzed by PCR ( Figure 1A ) and expression of PbICP-C was analyzed by Western blot ( Figure 1B ) . The subcloned transgenic PbICPcond parasite line generated is referred to as PbICPKO . HepG2 cells were cultured in Minimum Essential Medium ( MEM ) with Earle's salts ( Gibco ) supplemented with 10% heat-inactivated FCS ( Sigma ) , 1% penicillin/streptomycin , and 1% L-glutamine ( all from PAA Laboratories , Austria ) . Cells were cultured at 37°C and 5% CO2 and maintained by twice-weekly passage using Accutase ( PAA Laboratories , Austria ) . For infection , HepG2 cells were seeded in 24-well plates ( Nunc ) . Salivary glands of female A . stephensi mosquitoes infected with UIS4/Flp ( − ) ( control or PbICPcontrol ) , PbICPcontrol-GFP , PbICPcond , PbICPKO or PbICPcomp parasites , were isolated , collected in PBS and stored on ice . After disruption of the salivary glands using a pestle , sporozoites were quantified using a hemocytometer . HepG2 cells were incubated with the desired number of sporozoites in 200 µl MEM per well for 1 h at 37°C in 5% CO2 . The medium was then changed to standard HepG2 growth medium ( see above ) supplemented with 2 . 5 mg/ml Amphotericin B ( PAA ) . HepG2 cells were seeded at a density of 80 , 000 cells per well on glass coverslips in 24-well plates . The next day , 10 , 000 sporozoites of each parasite line were mixed with 1 mg/ml dextran-fluorescein ( 10 , 000 MW , Molecular Probes ) in PBS prior to co-incubation with HepG2 cells . After a 1 h incubation ( 37°C , 5% CO2 ) , cells were washed three times in PBS , fixed with 4% PFA/PBS ( 20 min , room temperature ) and permeabilized with ice-cold methanol ( 10 min ) . To visualize sporozoites , CSP-staining was performed ( rabbit anti-CSP , Alexa594-labeled anti-rabbit antibody , Molecular Probes ) . DNA was stained with 10 mg/ml DAPI ( Sigma ) . The number of transmigrated cells was determined by calculating the percentage of dextran-fluorescein-positive cells compared to mock-treated HepG2 cells . HepG2 cells were seeded at a density of 80 , 000 cells per well on glass coverslips in 24-well plates . The day after seeding , cells were infected with 10 , 000 sporozoites per well after or without pre-incubation with 100 nM recombinant PbICP on ice for 30 min . After culturing the infected cells for the times indicated , cells were used for IFA and the number of infected HepG2 cells per coverslip was determined . Antiserum directed against PbICP-C was used to distinguish between ICPpos ( SSR− PbICPcond parasites , in which site-specific recombination had not taken place ) and pbicp-c knock out PbICPcond parasites . To monitor parasite growth over the course of development , parasite size was measured using the density slice module of the OpenLab software version 5 . 0 . 4 . [59] . Briefly , at different time points after infection ( 30 hpi and 60 hpi ) , infected HepG2 cells were fixed and stained using anti-P . berghei antisera ( see above ) to visualize the parasite cytosol . Parasites were photographed using an Axiovert 200 microscope ( Zeiss ) at ×20 magnification . Images from each time point were merged and OpenLab software version 5 . 0 . 4 was used to calculate parasite area . To quantify the formation of infected , detached cells , HepG2 cells were seeded at a density of 40 , 000 cells per well on glass coverslips in 24-well plates . The next day , wells with seeded cells were infected with equal numbers of sporozoites , as described above . To count non-fluorescent parasites , cells were infected in duplicate; one set was fixed at 48 hpi to determine the number of infected cells and the other set was used for assessment of detached cell formation at 65 and 70 hpi . Infected cells per coverslip at 48 hpi were normalized as 100% of the parasite population for each of three independent experiments . Corresponding cover slips with infected cells were transferred to new wells in 24-well plates . At 65 hpi , the culture supernatant containing the infected , detached cells and merosomes of the infected cells was transferred to a fresh well and pre-warmed media was added to the residual infected cells . At 70 hpi , the culture supernatant containing the residual infected , detached cells and merosomes was transferred to a fresh well . To visualize host cell nuclei by live imaging , parasites were stained with Hoechst 33342 ( 1 µg/µl , Molecular Probes ) . In this assay , only infected , detached cells that contain a HepG2 cell nucleus were counted using an Axiovert 200 microscope ( Zeiss ) . The ratio of mature detached cells was calculated in relation to the number of infected cells 48 hpi . Female NMRI mice were each infected by intravenous ( i . v . ) injection of 5 , 000 sporozoites of either the control UIS4/Flp ( − ) , PbICPcond , PbICPKO , or PbICPcomp strain or by transfer of 100 µl blood by intraperitoneal ( i . p . ) injection from an infected mouse with a parasitemia of 5% . Sporozoites were harvested from infected mosquitoes at day 24–26 because before sporozoite numbers in salivary gland of mosquitoes infected with the PbICPKO strain were too small to be analysed . The onset of blood stage infection was determined by blood smears beginning one day post-infection . For analyzing expression of PbICP-C and MSP in blood stage parasites by Western blot , saponin pellets of schizont stage parasites were resuspended in 2× Laemmli buffer . For detection of CSP and TRAP release in midgut and salivary gland sporozoites , 2–5×104 sporozoites of each parasite line were directly placed in 1× Laemmli buffer or incubated in Dulbecco's Modified Eagle Medium ( DMEM ) , 2 . 5% fetal bovine serum , 50 µg/ml hypoxanthine and 25 mM HEPES for 35 min at 28°C . The incubated samples were then centrifuged at 20 , 817 g for 4 min at 4°C and supernatant and pellet were harvested separately . The supernatant was analyzed to quantify the release of CSP and TRAP while the pellet was used as a loading control . Proteins were separated on 8–12% SDS-PAGE gels under reducing conditions and transferred to nitrocellulose membranes . The membranes were incubated over night with antisera directed against PbICP-C ( mouse antisera , 1∶1000 dilution ) [29] , MSP1 ( rat antisera , 1∶1000 dilution ) , or CSP ( rabbit antisera , 1∶1000 dilution ) . Alternatively , the membranes were incubated overnight with mAb 3D11 ( 1 µg/ml ) [60] , anti-TRAP ( 1∶300 dilution ) [61] , or mAb 2E6 ( 1 µg/ml ) [62] . Horseradish peroxidase-conjugated secondary antibodies ( Pierce ) were subsequently applied for 1 h . The signal was detected using the SuperSignal West Pico Chemiluminescent Substrate ( Pierce ) . Control and PbICPKO sporozoites were metabolically labeled in DMEM lacking Cys/Met , containing 0 . 5% BSA and 400 µCi/ml with L-[35S]Cys/Met , for 45 min at 28°C . After labeling , sporozoites were washed and resuspended in incubation medium and kept on ice or chased in DMEM with Cys/Met and 0 . 5% BSA for 30 min at 28°C . Labeled sporozoites were pelleted after incubation by centrifugation at 20 , 800 g for 4 minutes at 4°C . The sporozoite pellets were lysed in 1% SDS , 4 M urea , 150 mM NaCl , 50 mM Tris-HCl ( pH 8 . 0 ) , with protease inhibitor cocktail for 1 hour at 4°C . Protein extracts were separated on an 8% SDS-PAGE gel , as previously described [37] . The gel was fixed , enhanced with Amplify ( Amersham Pharmacia ) , dried , and placed on film . A . stephensi mosquitoes were fed on mice infected with control or transgenic parasites . On day 10 after the infective blood meal , 15–20 mosquito midguts were mounted on microscope slides and oocysts were quantified using phase microscopy . On day 18 after the infective blood meal , mosquito midguts , salivary glands , and hemolymph were harvested for determination of sporozoite numbers . For midgut and salivary gland sporozoites , 10 mosquitoes were dissected , organs pooled and homogenized , and released sporozoites were collected and counted using a hemocytometer . Hemolymph from 10 mosquitoes was collected by perfusion of the thorax and abdomen with 5 µl of DMEM , and sporozoites were counted using a hemocytometer . Glass eight-chambered Lab-Tek wells ( Nunc ) were coated with 10 µg/ml mAb 3D11 in PBS overnight at 25°C and then washed three times with PBS . Sporozoites were dissected in DMEM containing 3% BSA . 5×104 sporozoites per well were added to the coated wells , incubated for 1 h at 37°C and fixed with 4% PFA . To visualize CSP-containing trails , the wells were then incubated with biotinylated mAb 3D11 followed by Streptavidin-FITC ( 1∶100 dilution , Amersham Pharmacia ) . Gliding motility was quantified by counting the number of sporozoites associated with trails and the number of circles . MBP , MBP-PBICP and MBP-PbICP-GFP were recombinantly expressed in E . coli bacteria and purified by affinity chromatography on amylose resins . Papain ( Sigma ) was incubated with 30 , 8 µM Z-Phe-Arg-AMC substrate ( Bachem ) in the presence of 200 nM of the recombinant purified fusion proteins . Assay buffers were 100 mM acetate buffer , 10 mM DTT , pH 5 . 5 . Photometric product formation ( E ) was measured every 10 seconds and activity was calculated from the slope of the linear part of the graph ( ΔE/Δt ) . Protease activity in the presence of the control protein MBP was set to 100% and residual activity in the presence of recombinant fusion proteins was calculated . | Coordinated protease activity is essential to parasite survival . Throughout its life cycle , the Plasmodium parasite expresses a potent cysteine protease inhibitor that has the potential to inhibit parasite as well as host cell cysteine proteases . We have generated a stage-specific knockout of this inhibitor and were able to analyze its function in all life cycle stages . Interestingly , although constitutively expressed , the inhibitor primarily appears to play an important role in sporozoite gliding , liver stage development and egress from hepatocytes whereas blood stage parasites lacking the inhibitor exhibited only mild attenuation . Parasite sexual stage development was not affected and development continued normally within the mosquito . However , sporozoites lacking the inhibitor show a strong phenotype; they are completely blocked in motility and thus cannot transmigrate or invade cells . Complementation of knockout parasites by exogenous expression of the inhibitor completely restored parasite virulence . | [
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] | 2014 | A Cysteine Protease Inhibitor of Plasmodium berghei Is Essential for Exo-erythrocytic Development |
High-throughput RNA-seq technology has provided an unprecedented opportunity to reveal the very complex structures of transcriptomes . However , it is an important and highly challenging task to assemble vast amounts of short RNA-seq reads into transcriptomes with alternative splicing isoforms . In this study , we present a novel de novo assembler , BinPacker , by modeling the transcriptome assembly problem as tracking a set of trajectories of items with their sizes representing coverage of their corresponding isoforms by solving a series of bin-packing problems . This approach , which subtly integrates coverage information into the procedure , has two exclusive features: 1 ) only splicing junctions are involved in the assembling procedure; 2 ) massive pell-mell reads are assembled seemingly by moving a comb along junction edges on a splicing graph . Being tested on both real and simulated RNA-seq datasets , it outperforms almost all the existing de novo assemblers on all the tested datasets , and even outperforms those ab initio assemblers on the real dog dataset . In addition , it runs substantially faster and requires less memory space than most of the assemblers . BinPacker is published under GNU GENERAL PUBLIC LICENSE and the source is available from: http://sourceforge . net/projects/transcriptomeassembly/files/BinPacker_1 . 0 . tar . gz/download . Quick installation version is available from: http://sourceforge . net/projects/transcriptomeassembly/files/BinPacker_binary . tar . gz/download .
The advent of RNA-seq techniques are changing how transcription , splicing variations and associated mechanisms can be studied since they provide unprecedented accuracy about the mRNA expression level [1] . They allow accurate elucidation of all splicing variants , including the rare and lowly expressed splicing isoforms . This clearly opens many new doors for studying the mechanisms of various human diseases that are related to abnormal splicing [1] , including cancers . With the RNA-seq techniques , there come new challenges associated with the interpretation of the generated datasets . Although sequencing reads from PacBio RS II sequencer are long enough to cover multiple exons , they have not been commonly used to improve the state of the art transcripts reconstruction because they are suffering from higher error rates [2] . Therefore the RNA-seq techniques for short sequencing reads [3] remain necessary . One major challenge is how to accurately assemble the short sequencing reads into full-length transcripts possibly involving multiple splicing variants , the so-called RNA-seq based transcriptome assembly problem . According to the literatures [4–6] , there are various alternative splicing events capable of producing multiple isoforms in eukaryotic genes . Event types include skipped exons , retained introns and mutually exclusive exons . Even more complicated , some exons may be partially involved in transcripts during the alternative splicing process . At first glance , the transcriptome assembly is similar to genome assembly , but they are actually fundamentally different . In contrast , the following facts make the transcriptome assembly more challenging: ( i ) some transcripts have a very low expression level , while others may be expressed in a dramatically high level [7]; ( ii ) each locus usually produces multiple transcripts due to various alternative splicing events [8]; ( iii ) some transcripts with low expression level may be submerged due to the sequencing errors [8 , 9] . Therefore , a successful transcriptome assembler should overcome all these difficulties , and be capable of recovering all full-length transcripts of variable lengths , expression levels and noises . Computational strategies for transcriptome assembly can be generally divided into two categories , ab initio and de novo [1 , 8] . If a reference genome is available , ab initio approaches , such as Cufflinks [10] and Scripture [11] , usually start by mapping RNA-Seq reads to the reference genome , and then sequences with overlapping alignment are merged into a connectivity graph on which the well studied min-cost minimum path cover model is subtly employed to extract a minimum set of paths which explain the RNA-seq dataset . A very recently published ab initio assembler , StringTie [12] , also first maps RNA-Seq reads to the reference genome , then constructs alternative splicing graphs and then assembles transcripts by using a maximum-flow network model . De novo approaches , such as ABySS [13] , SOAPdenovo-Trans [14] , Oases [15] and IDBA-Tran [16] , directly use the reads to assemble transcripts , without mapping them to a reference genome , which is important when the reference genome is unavailable , incomplete , highly fragmented or substantially altered as in cancer tissues . These de novo approaches which were developed based on the techniques used in genome assembly are not solving all the transcriptome assembly problems in general [7] . Trinity [8] which was designed specifically for de novo transcriptome assembly has substantially improved the state of the art de novo transcriptome assemblers . It starts by extending short reads through overlaps into contigs , connecting contigs into a graph , and then extracts paths from this graph to construct splicing variants based on a brute-force enumeration strategy . Trinity does improve previous de novo assemblers which have their roots in genome assembly techniques , but it does not introduce an appropriate model to optimize its solution , and even not incorporate sequencing coverage depth information into the assembly procedure either , although the authors in Trinity have noticed that similarity of the coverage depth across different coding regions in a transcript could be useful . To this end , we have recently presented a new de novo transcriptome assembler , Bridger [17] , which “bridges” between Cufflinks and Trinity so that the techniques used in Cufflinks can be employed to overcome the limitations of Trinity . Bridger does incorporate the coverage information into the assembly procedure via an appropriate model , but it could not guarantee a genuine solution due to ( 1 ) in-weight and out-weight are defined somewhat arbitrarily in Bridger; ( 2 ) a node with both in-edges and out-edges has no chance to be an end of any transcripts . Therefore , there still remains room for improvement . In this paper we develop a novel de novo algorithm , BinPacker , to assemble full-length transcripts by remodeling the problem as tracking a set of trajectories of items over a splicing graph , which is constructed by employing the techniques used in Bridger [17] with several updates described in Methods . The set of trajectories of items over the splicing graph can be achieved by solving a series of variants of the bin-packing problem , which are different from the traditional bin-packing problem , which is defined to pack a given number of items of different sizes into as few bins of a given size as possible , and each bin can only hold items with the sum of their sizes no more than the size of the bin . We have tested and compared BinPacker with seven competitive de novo assemblers , Trinity [8] , ABySS [13] , Trans-ABySS [18] , SOAPdenovo-Trans [14] , Oases [15] , IDBA-Tran [16] and Bridger [17] on real and simulated datasets . The simulation dataset is generated as described in Results section . For the real datasets , three datasets are used , including two standard RNA-seq datasets , one dog and one human , and one strand-specific mouse RNA-seq dataset . The comparison results show that BinPacker outperforms almost all the compared assemblers on all datasets , including real and simulated , in terms of commonly used standards for evaluation of transcriptome assemblers . Even more surprisingly , it outperforms StringTie , a most recently published ab initio assembler [12] , on dog dataset .
We ran and tested all the 9 assemblers on three real RNA-seq datasets which include two standard ( non-strand specific ) Illumina datasets from dog and human , and one strand-specific dataset from mouse . It is necessary to test the assemblers using simulated RNA-seq dataset since we may know all the genuine transcripts hidden in it in advance . An in silico RNA-Seq data generator , Flux Simulator [22] , is applied to UCSC hg19 gene annotation to generate an error-free dataset of approximately 50 million paired-end strand-specific RNA-seq reads . To demonstrate the advantage of BinPacker over other assemblers on the simulated dataset , we ran all the assemblers and did comparison among them in terms of their sensitivities , accuracies and their distributions against recovered sequence length rates . Our comparison results show that BinPacker not only reaches the highest sensitivity , but also the highest accuracy levels of both types . Furthermore , BinPacker keeps the highest sensitivity and accuracy of both types in the whole interval [80% , 100%] . Therefore it can be concluded that BinPacker has the highest reliability among all the de novo assemblers we are comparing with in terms of their distributions of both sensitivity and accuracy against recovered sequence length rates on the simulated dataset . See Fig 4 and S1 Text for details . We examined the computing resources required by these de novo assemblers: the running time and the memory usage on the same server . The results are shown in Figs 5 and 6 . ABySS uses the least memory ( Fig 5 ) , while SOAPdenovo-Trans takes the shortest time ( Fig 6 ) . Oases performs well on dog dataset but it consumes the most memory and has almost the longest running time on both human and mouse dataset . We noted that the computing resource requirement by Oases is sensitive to the k-mer value , which had also been found in another research paper [23] . As an enumeration algorithm , Trinity consumes the most memory on dog dataset and takes the longest time on both dog and mouse datasets . For the memory usage ( Fig 5 ) BinPacker and Bridger almost require the same amount of memory , more than most of the compared assemblers except Trinity and Oases , which consume much more memory than BinPacker on human and mouse datasets . For the time usage ( Fig 6 ) , BinPacker is among the fastest assemblers and it has also made a great improvement compared to Bridger , which takes much more time than BinPacker on both human and mouse datasets .
In this study , we presented a novel de novo method BinPacker for transcriptome assembly using short RNA-seq reads . Compared with Trinity , one of the most popular de novo assemblers , BinPacker has the following advantages: ( i ) Trinity uses a fixed k-mer length 25 , which is not necessarily optimal for all datasets , while BinPacker allows different user-specified k-mer values for different problems for optimal performance . One crucial parameter of BinPacker is the k-mer length . Generally speaking , with larger k values it performs better on high expression datasets or longer reads and with smaller k values it performs better on low expression datasets or shorter reads [17] . In light of our testing results , k = 25 is chosen to be the default value , however , larger k values are recommended for reads with length longer than 75bp . ( ii ) Compared to the exhaustive enumeration method used in Trinity , BinPacker uses a rigorous mathematical model to search for an optimal set of paths from the splicing graph , which makes BinPacker achieve a lower false positive rate at the same level of sensitivity . ( iii ) BinPacker makes full use of the sequencing depth information , which is applied to define the junction weights of the splicing graphs , constraining the deconvolution of splicing graphs into individual transcripts , and hence making its assembly results more accurate . ( iv ) BinPacker makes a different use of the paired-end information compared with Trinity . While Trinity uses the paired-end information to search for paths in the de Bruijn graphs , this information is mainly used in our process of constructing splicing graphs . Firstly , the paired-end information is used to help reconstruct more complete splicing graphs , making contigs even not covered by overlapping k-mers be recovered during assembly . Secondly , paired-end information is also used to trim error branches of the constructed splicing graphs , removing error junctions from splicing graphs . In practice , BinPacker uses less memory space and shorter running time . As showed in Results section , the assemblers have a high variance in sensitivity , accuracy and time and memory usage across the different RNA-seq datasets . Several facts may cause such a variance . 1 ) Different RNA-Seq datasets may contain different transcripts expression levels and different sequencing depths , both of which lead to the same transcripts in different RNA-Seq datasets covered by quite a different number of reads . And so they could have a large effect on sensitivity , accuracy and time and memory usage . 2 ) The reads in different RNA-Seq datasets may have different lengths , maybe shorter than 50 , and maybe longer than 100 , which may also cause differences in sensitivity , accuracy and time and memory usage . 3 ) The qualities of reference transcripts for different species are also quite different . For example , human and mouse genomes have been studied more extensively than dog genome , so the rate of known reference transcripts will certainly be larger than that of dog . We have seen in our comparison the sensitivity and accuracy of dog is lower than that of human and mouse . 4 ) Other reasons , such as different sequencing error rates , the usage of paired-end reads or single-end reads , may also contribute to the variance in sensitivity , accuracy and time and memory usage . The E . coli dataset is also adopted to evaluate the performance of the de novo assemblers on low complexity genome species without alternative splicing isoforms . Since the dataset is much smaller than that of dog , human and mouse , all the compared assemblers use much less running time and memory usage . For the sensitivity and accuracy , because most compared assemblers are designed to assemble transcripts from genes with alternative splicing events , they all do not perform very well on low complexity genome species such as E . coli without alternative splicing isoforms . Details are described in the first section of the S1 Text . As far as we know , BinPacker is the first algorithm using the bin-packing strategy for de novo assembly , without the utilization of any other reference information . Tested on both real and simulated RNA-seq datasets , BinPacker shows the best sensitivity and accuracy compared to all the other de novo assemblers , and even outperforms the most popular ab initio assembler StringTie on real dog dataset , only slightly worse than Trinity in some aspects on real human dataset . In addition , it requires fewer computational resources and less running time compared to most of the other assemblers . With these demonstrated advantages , we anticipate that BinPacker will play an important role for new discoveries in transcriptome study using RNA-seq datasets , especially for cancer transcriptomic data analyses .
BinPacker constructs splicing graphs based on the method of Bridger [17] with several updates as follows . First of all , while Bridger is not able to process RNA-Seq reads with different lengths , BinPacker can handle reads with variable lengths . Secondly , Bridger trims the branches of the splicing graphs after all splicing graphs have been constructed . However , BinPacker trims splicing graphs during the construction of splicing graphs . Two directed edges in a splicing graph are said to be compatible if they may come from one directed path , and incompatible otherwise ( see Fig I in S1 Text ) . We may imagine that the splicing graphs one-to-one correspond to the expressed genes , with nodes corresponding to exons and edges corresponding to splicing junctions . Since exons are linearly arranged in a gene , we may suppose that the nodes in the splicing graph of the gene are also linearly arranged , but not necessarily to be identical to that of the gene . We did this linearly arrangement by topological ordering of the splicing graph , which can be solved in linear time [25] . After topological ordering , all nodes with only out-edges are moved to the leftmost of the graph and all nodes with only in-edges to the rightmost . From now on , we refer to the splicing graph with all nodes being linearly arranged as a canonical splicing graph . Note that each directed edge in the canonical splicing graph can only go in the direction of the gene ( Fig I in S1 Text ) . Each edge in a splicing graph is assigned a weight using its sequencing depth ( number of reads spanning the junction edge in the splicing graph ) . It is obvious that the edges crossing two consecutive nodes in the splicing graph are pairwise incompatible ( Fig J in S1 Text ) . In fact , the maximum set of edges crossing two consecutive nodes in a canonical splicing graph must be a maximal set of pairwise incompatible edges in the splicing graph ( see Theorem 1 in S1 Text ) . BinPacker will iteratively execute a series of bin packing programs based on such a maximal set of pairwise incompatible edges . BinPacker iteratively calls a variant of bin packing model to comb all the transcripts encoded in a splicing graph . To do so , we add a source node s and a sink node t into the splicing graph ( Fig I in S1 Text ) , and connect s to the nodes with only out-going edges , and connect all the nodes with only in-coming edges to t . The weight of the new edge connecting s and u is assigned to be the sum of the weights of the edges going out from u . Similarly , the new edges going to t can be weighted . All the 0–1 ILPs are optimally solved by GLPK-4 . 40 . The GLPK ( GNU Linear Programming Kit ) package is intended for solving large-scale linear programming ( LP ) , mixed integer programming ( MIP ) , and other related problems . It is a set of routines written in ANSIC and organized in the form of a callable library . Since each programming is modeled from one node of a splicing graph , the number of variables of the 0–1 ILP is |Iin|⋅|Iout|⋅ ( |Iout|+3 ) /2 or |Iout|⋅|Iin|⋅ ( |Iin|+3 ) /2 . In most cases , |Iout|<3 and |Iin|<3 , so the number of variables of the 0–1 ILP is almost always less than 27 and it can be optimally solved by GLPK extremely fast . And even in many cases , |Iout| = 1 or |Iin| = 1 , in which cases , items can be directly packed into corresponding bins without using GLPK . The solution {xij} tells us that item j is in bin i if and only if xij = 1 . All the bins ( edges ) containing the same item induce an s-t path in the splicing graph of a gene which may correspond to a transcript of the gene . Finally , BinPacker outputs all the transcripts induced by individual items in the splicing graph of the gene . | The availability of RNA-seq technology drives the development of algorithms for transcriptome assembly from very short RNA sequences . However , the problem of how to ( de novo ) assemble transcriptome using RNA-seq datasets has not been modeled well; e . g . sequence coverage information has even not been accurately and effectively integrated into the appropriate assembling procedure , leading to a bottleneck that all the existing ( de novo ) strategies have encountered . We present a novel approach to remodel the problem as tracking a set of trajectories of items with their sizes representing the coverage of their corresponding isoforms by solving a series of bin-packing problems . This approach , which subtly integrates the coverage information into the procedure , has two exclusive features: 1 ) only splicing junctions are involved in the assembling procedure; 2 ) massive pell-mell reads are assembled seemingly by moving a comb along junction edges on a splicing graph . Being tested on both real and simulated RNA-seq datasets , it outperforms almost all existing de novo assemblers on all the tested datasets , even outperforms those ab initio assemblers on the dog dataset , in terms of commonly used comparison standards . | [
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] | 2016 | BinPacker: Packing-Based De Novo Transcriptome Assembly from RNA-seq Data |
Using a genome-wide screening approach , we have established the genetic requirements for proper telomere structure in Saccharomyces cerevisiae . We uncovered 112 genes , many of which have not previously been implicated in telomere function , that are required to form a fold-back structure at chromosome ends . Among other biological processes , lysine deacetylation , through the Rpd3L , Rpd3S , and Hda1 complexes , emerged as being a critical regulator of telomere structure . The telomeric-bound protein , Rif2 , was also found to promote a telomere fold-back through the recruitment of Rpd3L to telomeres . In the absence of Rpd3 function , telomeres have an increased susceptibility to nucleolytic degradation , telomere loss , and the initiation of premature senescence , suggesting that an Rpd3-mediated structure may have protective functions . Together these data reveal that multiple genetic pathways may directly or indirectly impinge on telomere structure , thus broadening the potential targets available to manipulate telomere function .
The physical ends of linear chromosomes resemble double-strand breaks ( DSBs ) in many respects with the exception that DSBs result in the activation of the DNA damage response and are eventually subject to repair; activities to which telomeres are refractory [1] . This essential quality of telomeres exists as a result of their repetitive sequence that is bound by specific proteins ( shelterin and CST complexes ) , which in turn inhibit DNA damage checkpoints , DNA repair activities and exonuclease-mediated degradation [2]–[3] . In yeast , the CST ( Cdc13-Stn1-Ten1 ) complex is essential for viability and prevents the accessibility of 5′ exonucleases ( primarily Exo1 ) to the telomere [4]–[6] . Upon inactivation of CST with temperature-sensitive alleles of CDC13 and STN1 , cells undergo a DNA damage-mediated checkpoint arrest due to the accumulation of single-stranded ( ss ) telomeric DNA [6]–[8] . In parallel , the Rap1 , Rif1 and Rif2 complex , also contribute to telomere end protection by limiting telomeric ssDNA accumulation and subsequent checkpoint activation [9]–[10] . In most human somatic cells , telomeres shorten during each cell division due , in part , to the end-replication problem [11]–[12] . Eventually , the loss of telomeric DNA leads to telomere dysfunction , checkpoint activation and cellular senescence . Some cell types as well as most cancer cells avoid telomere attrition-induced senescence by expressing the specialized reverse transcriptase , telomerase . Telomerase elongates telomeres through the iterative addition of short sequence repeats to the 3′ ends of telomeres , compensating for the end-replication problem [12] . Wild type S . cerevisiae constitutively express telomerase , however the cellular senescence phenotype can be induced following its inactivation/deletion [13] . In yeast , reporter genes become silenced when placed in the vicinity of telomeres [14] . This telomere-induced silencing is dependent on the Sir2/3/4 lysine deacetylation ( KDAC ) complex , which is recruited to chromosome ends via the telomere binding protein , Rap1 [15] . Apart from the Sir2/3/4 complex , other KDACs also contribute to the heterochromatic constitution of telomeres and sub-telomeres . The class I KDAC , Rpd3 ( the yeast ortholog of human KDAC1 ) , consisting of two sub-complexes , Rpd3L and Rpd3S [16] , also localizes to telomeres and is important to establish the euchromatin/heterochromatin boundary in the sub-telomeric regions [17] , as well as to prevent hyper-silencing [18] . The class II KDAC , Hda1 , also contributes to chromatin regulation at yeast telomeres [19] . The relationships between heterochromatin and telomere structure/function remain unclear . It has been postulated that telomere protection may stem , in part , from a higher-order chromatin structure . Analysis of telomeric DNA from human and mouse cells has revealed that the telomere terminus can be hidden in a lariat-like structure termed a t-loop [2] , [20]–[21] . T-loops , thought to form through the strand invasion of the 3′ telomeric overhang into the double-stranded region of the telomere , have also been found in chickens , worms , plants , and protozoa [22]–[25] . Via electron microscopy , telomeric loops have also been observed in yeast ( K . lactis ) with over-elongated telomeres [26] , and the telomere associated S . pombe protein , Taz1 , has been shown to re-model model DNA substrates into t-loops [27] . However , due to the small size of yeast telomeres it has been difficult to both prepare and analyze wild type length yeast telomeres via electron microscopy [26] . In the budding yeast , S . cerevisiae , both genetic and chromatin immunoprecipitation-based experiments have revealed that wild type telomeres do fold-back onto themselves and into the subtelomeric region [26] , [28]–[30] , suggesting that loops or fold-back structures are indeed important for telomere function in yeast . Apart from the Sir2/3/4 deacetylase complex in budding yeast being important for this fold-back [29] , and the shelterin component , TRF2 in human cells being required for t-loop formation [20] , [25] , the regulation of such telomeric structures remains poorly understood . In this study we have taken an unbiased genome-wide screening approach in yeast to better understand how telomere structure/fold-back is regulated in vivo . We demonstrate that multiple biological processes influence telomere structure , including the state of the subtelomeric heterochromatin as dictated by multiple lysine deacetylases . Furthermore , we find that there are direct correlations between the inability of a telomere to fold-back and telomere dysfunction , implying that the loop structure may make important contributions to telomere protection .
By placing a TATA-less galactose-inducible UAS ( upstream activating sequence ) downstream of the URA3 gene ( from hereon referred to as construct 2 ) , URA3 transcription is only achieved when the UAS loops back and comes into proximity with the URA3 promoter [28]–[29] ( Figure 1A ) . Fold-back-induced transcription only takes place when this construct is integrated at the telomere and does not occur when it is integrated at an internal chromosomal locus [29] . Transcription of URA3 results in lethality on media containing the drug 5-fluoroortic acid ( 5-FOA ) , providing a robust readout ( cell death on 5-FOA ) for successful telomere looping . To better understand how the telomere fold-back structure in yeast is regulated , we introduced construct 2 into the yeast haploid deletion collection using the synthetic genetic array ( SGA ) procedure [31] , resulting in the construction of ∼4800 haploid deletion mutants harboring construct 2 ( Figure 1B ) . Robotic pinning of these strains in quadruplicate onto galactose media in the presence and absence of 5-FOA revealed potential looping defective mutants that grew on 5-FOA ( Figure 1B , bottom panel example of looping defective mutant ) . All positively scoring mutants were independently re-constructed and spotted as serial dilutions onto galactose +/− 5-FOA media in duplicate . We confirmed 112 yeast mutants that were defective for telomere looping and subsequently ranked them qualitatively for growth on 5-FOA ( Table 1 , Figure S1A ) . Using the Cytoscape BinGO plugin [32] , the statistically over-represented GO ( gene ontology ) categories were determined for our positive scoring candidates ( Figure 1C ) . The confirmed mutants formed the “positive hit set” whereas the “reference set” consisted of all 4800 genes screened . The analysis used a hypergeometric test and significance was tested at 5% ( p<0 . 05 ) after applying Benjamini & Hochberg False Discovery Rate ( FDR ) correction for multiple testing . This protocol revealed histone deacetylation as a significantly enriched GO term in the GO-Cellular Component and the GO-Biological Process ontologies . Moreover , the Rpd3L , Rpd3S and Hda1 KDAC complexes were specifically over-represented ( Figure 1C , Table 1 ) . We introduced the looping construct into deletion mutants of all members of the Rpd3L/S and Hda1 complexes ( including those that did not score positive in the screen ) and determined that all complex members tested were important for wild type-like telomere structure ( Figure 1D ) . Importantly , we replicated the 5-FOA plates onto media lacking uracil to ensure that the FOA resistance observed was not due to inactivation of the URA3 gene ( Figure 1D ) . FOA resistant strains maintain the ability to grow on media lacking uracil due to low basal levels of the URA3 transcript ( see Figure S1D ) . To confirm that there was not an inherent problem of inducing transcription within the subtelomere of these mutants , we generated and introduced construct 4 ( Figure 1E ) into Rpd3L , Rpd3S and Hda1 mutants where the TATA-less UAS was placed upstream of URA3 and found that ( unlike with construct 2 ) upon galactose induction all mutants were dead on 5-FOA containing media ( Figure 1E ) . Figure 1E demonstrates that in the mutants of the Rpd3L/S and Hda1 complexes , if the UAS in construct 2 were able to loop back to the URA3 promoter it would be able to induce transcription to an extent that would result in cell death on FOA , as is the case with wild type cells . We excluded that telomere length variation may affect the looping read-out in the rpd3Δ and hda1Δ mutants , as no significant changes in telomere length were detectable when comparing the KDAC mutants to isogenic wild type cells ( Figure S1B , S1C ) . Finally , we demonstrated that the plate read-out effects that we have observed with construct 2 on 5-FOA are due to changes in levels of the URA3 transcript ( Figure S1D ) as has previously been reported [29] and not an unrelated artifact of 5-FOA . In conclusion , an unbiased genome-wide screen has implicated lysine deacetylation through the Rpd3L , Rpd3S and Hda1 complexes in promoting a structural change ( likely a fold-back ) at budding yeast telomeres . To demonstrate that the looping defect we observe is not specific to modified telomere 7L ( construct 2 ) , we employed a previously established chromatin immunoprecipitation ( ChIP ) technique where it has been shown at a natural telomere ( telomere 6R ) that α-Rap1 antibodies are able to precipitate subtelomeric DNA greater than 2 kb away from the start of the telomeric tract , despite the fact that chromatin is sheared into fragments of 0 . 5 kb [30] . From this study , it was concluded that the subtelomeric ChIP signal from cross-linked Rap1 extracts was a result of the telomere looping back into the subtelomeric region ( Figure 2A , top ) . We predicted that the subtelomeric signal would be lost in mutants identified in our above-described screen ( Figure 2A , bottom ) . In agreement with previous reports , we could detect cross-linked Rap1 at a position 0 . 5 kb and to a lesser extent 1 kb away from the subtelomere/telomere transition point in wild type cells , indicative of a telomeric loop-back structure ( Figure 2B ) . Unlike the previous report [30] , we did not detect reproducible differences at positions farther than 1 . 5 kb from the telomere ( not shown ) , which is likely due to the smaller chromatin fragment size used in our ChIP protocol . Strikingly , the Rap1 signal was diminished in the subtelomere in hda1Δ mutants and lost to a greater extent in sin3Δ cells ( Rpd3L/S common subunit ) , consistent with the 5-FOA assay using construct 2 ( Figure 2B , Figure 1D ) . sir4Δ cells were used as a looping defective positive control for the Rap1 ChIP assay ( Figure 2B ) [29] and indeed displayed the greatest loss of Rap1 signal in the subtelomeric region . Importantly , the bulk of our chromatin was sheared to 0 . 3 kbp fragments or less , excluding the possibility that our subtelomeric signals come from inefficient sonication ( Figure S2A ) . Furthermore , Rap1 protein levels were not affected in any of the above-mentioned mutant backgrounds ( Figure S2B ) . In order to rule out the unlikely possibility that Rap1 spreading into the subtelomere may account for a portion of the ChIP signal in the assay described above ( Figure 2A ) , we repeated the ChIP experiments using an epitope-tagged Cdc13-TAP ( Tandem Affinity Purification ) allele . Cdc13 associates with the 3′ ssDNA telomeric overhang , and therefore is not prone to spread into the subtelomere . Furthermore we reasoned that by using Cdc13-TAP we would be able to more easily reconcile differences at the -1 kb position due to its distal positioning at the 3′end . Consistently we were also able to detect Cdc13-TAP associated with subtelomeric DNA 1000 bp upstream of the telomeric tract ( indicative of a fold-back ) , and the signal was reduced to background levels in the sin3Δ mutant ( Figure 2C ) . The sin3Δ mutation did not result in decreased Cdc13-TAP protein levels , which could have potentially accounted for the reduced ChIP signal ( Figure S2C ) . It is important to note that we did not enrich significant amounts of subtelomeric DNA at the -6 and -500 bp positions following the Cdc13-TAP ChIP ( Figure 2C ) although Cdc13 has been previously shown by similar methods to localize to subtelomeres [33] . We interpret this to indicate that our sonication was extremely efficient and fragments above 400 bp ( the approximate length of the wild type telomere ) were extremely rare and did not give a signal significantly above background ( untagged control ) . In order verify this notion we deleted telomerase ( EST2 ) in Cdc13-TAP cells and let telomeres shorten over 25 and 50 generations ( Figure S2D and S2E ) . We predicted that upon telomere shortening we would be able to increasingly detect a signal at the -6 position as telomeres would be shorter than our sheared chromatin fragments ( see Figure 2C for visualization ) . Indeed , we found that as telomere length decreased to under 300 bp ( our average chromatin fragment size ) we were able to detect a robust Cdc13-TAP ChIP signal at the -6 position at natural telomere 6R ( Figure S2F ) . In summary we have confirmed that the looping defect observed in hda1Δ and sin3Δ ( Rpd3L/S subunit ) mutants using construct 2 and 5-FOA as a read-out ( Figure 1D ) can be recapitulated using an independent method ( ChIP ) at natural telomere 6R ( Figure 2B , 2C ) . Among the list of looping defective mutants we were intrigued that RIF1 , a regulator of telomere length , was also implicated in promoting a fold-back structure ( Table 1 ) . Since Rif2 works in parallel with Rif1 to regulate telomere length we introduced construct 2 into rif2Δ cells and found that like rif1Δ cells , rif2Δ mutants also displayed a looping defect ( Figure 3A ) . Using the Rap1 ChIP assay ( Figure 2A ) it was also evident that both rif1Δ and rif2Δ mutants had structural defects in terms of folding back into the subtelomere ( Figure 3B ) . As with the above described Rap1 ChIP , we confirmed that Rap1 protein levels were not altered in rif1Δ and rif2Δ cells which may have accounted for observed differences ( Figure S3A ) . From here on we have performed further analysis only with the rif2Δ mutant rather that rif1Δ cells due to the fact that apart from telomere length regulation , Rif1 also plays an important role in telomere capping [10] , checkpoint regulation [34]–[35] as well as telomere localization [36] , which greatly complicated the interpretation of rif1Δ cells and their genetic interactions . Ongoing studies are directed at better understand the contributions of Rif1 in promoting the telomere fold-back structure . We constructed double mutants between rif2Δ and mutants of the Rpd3L , Rpd3S and Hda1 complexes harboring construct 2 in order to assess potential genetic interactions between different pathways involved in telomere looping . Whereas the Rpd3S specific mutants ( eaf3Δ and rco1Δ ) displayed a slight additive growth advantage on 5-FOA when combined with rif2Δ mutants compared to the respective single mutants ( Figure 3C , bottom panels ) there were no additive effects with rif2Δ mutants and the Rpd3L complex ( sap30Δ and rxt2Δ ) ( Figure 3C , top panel ) . sin3Δ rif2Δ double mutants were not additive in comparison to the respective single mutants ( Figure 3C , middle panel ) , as Sin3 belongs to both L and S complexes . As expected , rxt2Δ ( Rpd3L ) and eaf3Δ ( Rpd3S ) double mutants had a slight additive looping defect in comparison to the single mutants and further deletion of RIF2 did not exacerbate this defect ( Figure 3D ) . The looping defect of hda1Δ and hda2Δ mutants was also additive with rif2Δ mutants ( Figure 3E ) . The results of this genetic epistasis analysis suggest that Rif2 and Rpd3L may function together in a common pathway to promote a telomere fold-back . To mechanistically understand the genetic relationships between the Rpd3L complex and Rif2 , we performed ChIP experiments to determine if the rif2Δ mutation had an effect on Rpd3L ( Rxt2-TAP ) localization at telomeres . Subtelomeric DNA was enriched above non-tagged wild type control cells ( background ) with an epitope-tagged Rxt2 allele both close to ( -6 bp ) ( Figure 3F ) and up to 2000 base pairs away from the telomeric tracts ( Figure 3F ) as previously described [18] . This enrichment was decreased to near background levels in a rif2Δ mutant ( Figure 3F ) . The loss of ChIP signal is not due to altered expression levels of Rxt2-TAP in rif2Δ mutants as confirmed by western blot analysis ( Figure S3B ) . Unlike Rpd3L , the ability to cross-link the Rpd3S complex ( Rco1-TAP ) to subtelomeric regions was not altered in rif2Δ cells ( Figure 3G and Figure S3C ) . Together these data suggest that the Rif2 promotes a structural alteration at telomeres through the recruitment of the Rpd3L KDAC complex . Moreover , the Hda1 KDAC as well as the Rpd3S complex promote the same fold-back structure , but independent of the Rif2/Rpd3L pathway . To better understand the function of the loop structure and whether or not it may have a protective role at the telomere we impaired looping ( deletion of SIN3 ) in various genetic backgrounds where telomere function was compromised . In cdc13-1 sin3Δ double mutants we observed a temperature-dependent synthetic lethality in the double mutant , compared to the respective single mutants ( Figure 4A ) , indicating that partially uncapped telomeres ( cdc13-1 ) may require a Rpd3-mediated structure for viability . The negative genetic interaction was suppressed by the further deletion of EXO1 , the nuclease responsible for the majority of telomere resection at dysfunctional telomeres ( Figure 4A ) . To ensure that this interaction was a direct consequence of telomere dysfunction , we assayed the accumulation of telomeric ssDNA following the shift of nocodazole-arrested cdc13-1 and cdc13-1 sin3Δ cells from 23°C to the semi-permissive temperature of 26°C ( Figure 4B ) . In agreement with the negative genetic interaction ( Figure 4A ) , we observed an Exo1 dependent increase in telomeric ssDNA in the double mutant above that seen in the cdc13-1 single mutant ( Figure 4B ) . Importantly , we did not detect an increase in ssDNA at telomeres in sin3Δ single mutants when compared to isogenic wild type control cells ( Figure S4A ) . To determine if Rpd3 dependent telomere structure may have an influence on the rate of cellular senescence , we compared senescence onset in both est2Δ rad52Δ ( where HR and telomerase-mediated telomere elongation are impaired ) and est2Δ rad52Δ sin3Δ mutants . The deletion of SIN3 resulted in a dramatic increase in the rate of cellular senescence in the absence of telomerase and homologous recombination ( Figure 4C ) . The accelerated loss of viability associated with the sin3Δ mutation is specifically related to the absence of telomerase as rad52Δ cells maintain viability to a similar extent as rad52Δ sin3Δ cells ( Figure 4C ) . To better understand the cause of premature senescence in sin3Δ mutants , genomic DNA was prepared from the senescence curves ( Figure 4C ) and both single stranded telomeric DNA accumulation and telomere length were analyzed . As is the case when combined with the cdc13-1 mutant , we found that sin3Δ est2Δ rad52Δ mutants had increased levels of ssDNA at telomeres compared to isogenic est2Δ rad52Δ cells ( Figure 4D ) . The telomere shortening rate is unaffected between sin3Δ est2Δ rad52Δ and est2Δ rad52Δ strains ( Figure S4B , S4C ) , however there is evidence of early rapid telomere loss events at some telomeres in the triple mutant compared to the isogenic double ( Figure S4B ) . Taken together these results imply that the Rpd3 lysine deacetylase is essential to prevent excessive nuclease-mediated resection specifically at uncapped telomeres . In the absence of a telomere lengthening mechanism this resection may lead to excessive telomere shortening . We propose that this protective function may involve the formation of a fold-back structure at the chromosome ends .
We have demonstrated that multiple biological processes influence the ability of telomeres to form a higher-order fold-back structure . High-throughput screening coupled to stringent bioinformatic analysis , has revealed that class I ( Rpd3 ) and class II ( Hda1 ) KDAC activities were among the most significantly enriched biological processes required to promote the formation/maintenance of a telomere loop . In addition , the Rap1 binding proteins , Rif1 and Rif2 , which localize directly to telomeres , were implicated in telomere fold-back establishment . Through genetic epistasis analysis , we found that rif2Δ and mutants of the Rpd3L complex did not have additive structural defects at telomeres , suggesting that they may function together in a single pathway . This genetic interaction was confirmed by demonstrating that Rpd3L was no longer able to localize to telomeres in rif2Δ cells . Furthermore , we have shown that the telomeres in mutants with looping defects are more susceptible to uncapping , nucleolytic degradation , telomere loss and promote accelerated rates of cellular senescence in the absence of telomere maintenance . A recent study has reported that the Rpd3 complex is required to prevent chromosome end fusions in Drosophila melanogaster [37] . Together , these data suggest that the regulation of such telomere fold-back structures may be conserved , and furthermore indicate that multiple cellular processes , apart from those that directly impinge on DNA metabolism , may have effects on telomere structure/function . Although we have focused on the telomere dysfunction phenotypes associated rpd3 mutants , it is of interest that many of the other mutants recovered in our telomere structure screen have been previously identified to have negative synthetic interactions with cdc13-1 ( rrd1Δ , swr1Δ , arp6Δ , spe1Δ , spe3Δ , oca1Δ , oca2Δ , oca5Δ , elp4Δ , pep8Δ , rif1Δ , yme1Δ , htd2Δ , ski3Δ , rim101Δ , ede1Δ ) [10] , [38]–[39] as well as an increased rate of replicative senescence ( hda1Δ , sin3Δ , sec28Δ , med1Δ , asf1Δ , rif1Δ , rif2Δ , arp6Δ , elp4Δ ) [40]–[41] ( see Table 1 for a complete overview of overlaps between our screen and other selected telomere function screens ) . Moreover , the Sir2/3/4 complex , which is required to form a telomere fold-back structure [29] also prevents premature senescence [42] . This overlap between our screen and previously published data suggests that telomere structure may make significant contributions towards preserving telomere integrity when telomere function is compromised ( Figure 5 ) . Consistently , sin3Δ single mutants do not exhibit increased ssDNA accumulation at telomeres nor do they have any changes in telomere length in comparison to isogenic wild type strains . This would suggest that in the absence of a telomere fold-back , the CST complex and other capping factors are sufficient to maintain a protected state ( Figure 5 ) . However , when CST function is compromised ( e . g . cdc13-1 ) in combination with an inability to fold-back , resection becomes accelerated ( Figure 4B , 5 ) . In terms of telomere-induced cellular senescence in the absence of telomere maintenance , the increased resection in non-looped mutants would also lead to increased telomere shortening ( Figure 5 ) . It will be of interest to perform an extensive genetic epistasis of all mutants isolated in the loop screen in order to determine if the negative interactions with cdc13-1 are epistatic ( i . e . due to a fold-back defect ) . There was also a large overlap between mutants found in our screen and mutants that have been implicated in both the positive and negative regulation of telomere length ( vps28Δ , trk1Δ , ctk1Δ , mrt4Δ , rif1Δ , sur4Δ , nut1Δ , siw14Δ , leo1Δ , hit1Δ , pcp1Δ , pdx3Δ ) [43]–[44] . This would suggest that either telomere looping has a direct effect on telomere length regulation , or conversely , may indicate that telomere length changes impinge on the ability to form a fold-back . Our results suggest that telomere looping does not affect telomere length homeostasis directly , as many of the mutants recovered in our screen , even those with “strong” looping defects , have wild type telomere length . On the other hand , telomere length changes could indeed have a drastic effect on telomere structure in terms of the chromatin alterations that occur with respect to length changes . Telomere shortening , for example , results in de-silencing in the subtelomeric region [45] due to the decreased capacity of shortened telomeres to recruit the Sir2/3/4 histone deacetylase complex , which is required for loop formation in yeast [29] . Long telomeres , in contrast , promote a hyper-silenced state in the subtelomere [45] , much like what occurs in rif1Δ and rif2Δ mutants , where , in the case of the latter mutant , Rpd3L fails to localize to telomeres . Indeed , Rpd3 mutants ( L and S ) are hypersilenced in the subtelomeric zone . One possibility would be that long telomeres ( as seen in rif2Δ ) mutants fail to properly localize Rpd3L , which leads to a subsequent fold-back defect . Although our screen implicates proper length regulation as a key regulator of telomere looping , they remain correlative and require further investigation in order to draw concrete conclusions . Whereas both the Rpd3S and Hda1 complexes were additive with rif2Δ mutants in terms of a looping defect , the Rpd3L complex was epistatic . This relationship was confirmed mechanistically as we noticed that Rpd3L is not able to properly localize to telomeres in rif2Δ cells . These epistasis analyses revealed that multiple KDACs contribute to telomere looping ( Figure 5 ) . Rpd3L , Rpd3S and Hda1 all promote telomere looping in parallel pathways whereas the relationship between the Sir2/3/4 complex and the other KDACs in terms of telomere structure remains enigmatic . The KDACs are best known for their deacetylation of histones and they are known to contribute significantly to silencing at subtelomeric loci [18] . Consistent with a connection between chromatin modification and the telomere fold-back structure , we also found that many members of the Swr1 chromatin remodeling complex as well as the histone chaperone Asf1 ( Table 1 ) were important for telomere folding . A future challenge will be to determine the targets of the KDACs . Although subtelomeric histones are prime candidates , telomeric proteins themselves may be targets . Furthermore , it will be important to characterize how the other mutants revealed in the screen contribute to telomere looping and to understand if these mutants are epistatic with the KDACs . Indeed , it has been difficult to understand why mutations that affect such diverse biological pathways may have synthetic growth defects with cdc13-1 or in some cases , senesce rapidly [38]–[39] , [41] . Since many signaling pathways activate effectors via activation/repression of target genes through chromatin remodeling and/or histone acetylation/deacetylation , we propose that activation or repression of these pathways may influence the ability of the KDACS ( Rpd3/Hda1 and Sir2 ) to act at telomeres and in turn directly or indirectly influence telomere structure . This work has uncovered multiple regulators of the telomere fold-back structure , including lysine deactylation and chromatin remodeling . The results of our screen correlate well with screens that have been performed to elucidate genes implicated in telomere function and cellular senescence suggesting that the fold-back structure may be important for chromosome end protection . Previous models have speculated that the fold-back in yeast may be important to establish silent chromatin within the telomeric/subtelomeric loci [46]–[47] . As an alternative , it was also suggested that silent telomeric chromatin may be required to establish a particular architecture that contributes to chromosome end protection [47] . Our results indicate that silent chromatin can be established in the absence of a telomere fold-back since many of the mutants recovered in our screen are not compromised for silencing ( Table 1 ) or even have slightly enhanced silencing ( e . g . rpd3 mutants ) . Sir2/3/4-mediated silencing as well as other chromatin modifications are however important to establish a telomere loop , which likely promotes end protection . Interestingly , Rpd3 dependent histone deacetylation has been shown to prevent Sir2/3/4 protein spreading towards the centromere [17] , which may potentially deplete SIR protein levels immediately adjacent to the telomere , raising the possibility that SIR2/3/4 disruption and RPD3L/S disruption may be one in the same in terms of a fold-back defect . Further characterization of the yeast fold-back structure and the relationship to silent chromatin will be essential in order to clarify these issues . In summary , by understanding how different biological processes impinge on chromosome end structure , we increase the possibilities to manipulate telomere function , both positively and negatively , which may have important implications for diseases that stem from telomere dysfunction .
Standard yeast media and growth conditions were used [48] . Yeast strains used in this study are listed in the Table S1 . For spotting assays , yeast cells were incubated overnight at appropriate temperature in YPD . Cells were diluted to OD600 0 . 5 and spotted in ten-fold dilutions onto 2% raffinose , 1% galactose plates either with ( +FOA ) or without ( −FOA ) 5-FOA . Cells were incubated for 3 days at proper temperature , imaged and then replica plated on SD-URA plates for 2 days at the same temperature before imaging . Telomere PCR was performed using 100 ng genomic DNA diluted in 1× NEB4 buffer and water . Samples were denatured for 10 min at 96°C and cooled to 4°C . Tailing mix ( 4 U/µl terminal transferase ( NEB ) , 1× NEB4 buffer , 1 mM CTPs ) was added to a final concentration of 10% . Tailing reaction was performed as the follows: 37°C 30 min , 65°C 10 min , 96°C 5 min , arrest at 65°C . 3× volume of preheated PCR-MIX ( 1 µM oligo dG reverse primer , 1 µM telomere specific forward primer either 1L , 6R , 7L or Y′ , 0 . 267 mM dNTPs , 0 . 083 U/µl Phusion polymerase ( NEB ) , PCR buffer ( 89 . 11 mM Tris-HCl pH 8 . 8 , 21 . 28 mM ( NH4 ) 2SO4 , 6 . 65% glycerol , 0 . 0133% Tween-20 ) was added and PCR reaction was performed using: 95°C 3 min , 45 cycles: ( 95°C 30 s , 68°C 15 s , 72°C 20 s ) , 68°C 5 min , hold on 12°C . Samples were mixed with DNA loading buffer and separated on a 1 . 8% agarose gel for 30 min at 100 V . Bands were detected using LAS-4000 ( Fujifilm ) and quantified using Multi Gauge Software ( Fujifilm ) . A complete lis of oligonucleotides used in this study can be found in Table S2 . Spore-colonies of dissected heterozygous diploids were suspended in water and diluted in 5 ml YPD medium to a final concentration of OD600 0 . 01 . Cells were incubated for 24 h at 30°C and absorption at 600 nm was measured . Cultures were re-diluted to OD600 0 . 01 in 5 ml YPD and inoculated for further 24 h at 30°C . Each day cell samples were harvested and genomic DNA was prepared for telomere length analysis ( Quiagen genomic DNA prep . Kit ) . Population doublings ( PD ) were calculated as log2 ( OD60024 h/0 . 01 ) . All PD values refer to PD after the spore colony had been harvested from the dissection plate ( about 25 generations ) . Graphs were made in Prism5 ( GraphPad ) . Synthetic Genetic Array ( SGA ) methodology was used ( strains R1459 and R1460 ) to obtain haploid gene deletion mutants containing either construct 1A or construct 2 ( Tong and Boone , 2006 ) . Cells were then replica-pinned onto media containing galactose , with and without 5-FOA . Growth on 5-FOA was compared between construct 2-containing deletion mutants and construct 1-containing mutants ( comparison 1 ) . Growth of construct 2-containing mutants was also compared with and without the presence of 5-FOA ( comparison 2 ) . Construct 2-containing mutants that grew better by either comparison 1 or 2 were selected for validation . Validation was carried out by manually crossing and dissecting tetrads from independent starter strains followed by duplicate spot assays onto media with and without 5-FOA . Yeast cells were grown over night at 30°C and diluted to OD600 0 . 2 . They were grown until exp . Phase ( OD600 0 . 6–1 . 0 ) , crosslinked for 8 min ( 20 min for Rxt2-TAP-ChIP , 10 min for Cdc13-TAP-Chip ) with formaldehyde ( final conc . 1 . 2% ) and quenched with glycine ( 360 mM final ) . After adjusting the volume to the same OD all samples were washed two times with 1× PBS , resuspended in FA Lysis Buffer ( 50 mM HEPES-KOH pH 7 . 5 , 140 mM NaCl , 1 mM EDTA pH 8 , 1% Triton X-100 , 0 . 1% Sodium deoxycholate ) and lysed with Matrix C tubes via FastPrep ( 6 . 5 M/s , 2°—30 sec with 1 min break ) . Cell extracts were recovered , centrifuged and the soluble potion of the lysate was discarded . Pellets were resuspended in FA buffer +SDS ( 2% final ) and split up for sonication . Chromatin was sheared 30 sec on/off for 15 min . Supernantant ( ChIP extract ) was diluted to 1 mg/ml protein concentration in FA buffer and used for immunoprecipitation ( IP ) . Pre incubated protein G sepharose beads ( washed with 1×PBS , FA Buffer and pre-incubated with 5% BSA for 1 h at 4°C ) were added to the 1 mg/ml solution to perform an addition precleaning step before the IP ( 1 h at 4°C ) . After precleaning anti-Rap1 antibody ( Santa Cruz ) was added to the solution ( 1∶100 ) and incubated with fresh beads over night at 4°C , rotating . For Tap-ChIPs ( Rxt2 and Cdc13 ) IgG-Sepharose Beads ( washed with 1× PBS and FA-buffer ) were added to the 1 mg/ml solution and IP was incubated over night at 4°C . Sonication efficiency was tested via cleaning 100 µl of the ChIP extract and performing agarose gelelectrophoresis . IP was washed with FA-Lysis buffer , FALysis buffer 500 ( FA buffer with 500 mM NaCl ) , Buffer3 ( 10 mM Tris-HCl pH 8 1 mM EDTA pH 8 , 250 mM LiCl , 1%NP-40 , 1% Sodium deoxycholate ) , and TE ( pH 8 ) . For elution buffer B ( 50 mM Tris-HCl ph 7 . 5 , 1% SDS , 10 mM EDTA pH 8 ) was added and IP was incubated at 65°C for 8 mins . For reverse-crosslinking proteinase K was added to the IP and INPUT control ( ChIP extract , 1 mg/ml solution without IP ) and incubated at 65°C , rotating overnight . Samples were cleaned with Quiagen “QIAquick PCR Purification Kit” and qPCR analysis was performed using Roche standard PCR protocol for Sybr-Green detection with 55°C annealing temperature , all oligonucleotides used are listen in Table S2 . Measured ct values were corrected to INPUT and normalized to the actin signal using the following formulas . Rap1 ChIP:Rxt2-TAP ChIP:Cdc13-TAP ChIP: Cells were grown overnight at 23°C in 10 ml YPD . Saturated cultures were diluted to OD600 0 . 2 in 150 ml YPD and incubated at 23°C until they reach log phase ( 0 . 6–0 . 8 ) . Nocodazol ( 20 µg/ml final ) was added and cells were incubated for a further 3 h at 23°C , shaking . Cells were checked under the microscope until >90% were largebudded . “Pre” samples were harvested and cells were subsequently shifted to 26°C . Additional samples were collected for all time points ( 30 min , 60 min , 90 min and 120 min ) after the shift . For ssDNA analysis , dot blotting was performed . DNA was extracted using genomic DNA Kit ( Quiagen ) . Isolated DNA was either denatured using 0 . 2 M NaOH and 65°C for 15 min or kept on ice for native conditions . For blotting , 4 µg DNA ( native ) or 0 . 5 µg ( denatured ) were suspended in 200 µl 2×SSC and loaded to the dot blot apparatus using nylon membrane ( GE Healthcare Amersham H-bond ) . After crosslinking ( UV Stratalinker 2400 , Stratagene ) DIG labeling ( DIG labeled probe oBL207 ) and detection was performed as described by the product guidelines ( Roche DIG oligonucleotide 3′labeling KIT ) . Cells were grown overnight at 30°C in 5 ml SD medium containing 2% Raffinose ( S-Raf ) . Saturated cultures were diluted to OD600 0 . 2 in 8 ml S-Raf and incubated at 30°C until they reach log phase ( 0 . 6–0 . 8 ) . Cells were split and 2% galactose or 2% glucose ( final ) were added . Cells were incubated for 2 1/2 h at 30°C , shaking . Cells were centrifuged down and RNA was extracted and Northern Blotting was performed as described previously [42] . URA3 and actin were detected using DiG labeled PCR products ( Roche , “DiG High Prime” labeling ) gained from a PCR reaction with oBL17 , oBL18 for URA3 and oBL292 , oBL293 for actin ( 1 . 98°C 30 sec , 2 . 98°C 10 sec , 3 . 60°C 30 sec , 4 . 72°C 1 min , 5 . 72°C 5 min , 12°C forever , repeating steps 2–4 for 33 cycles ) . Quantification was performed using Multi Gauge software ( Fujiifilm ) and signal was displayed as URA3 over actin . 3 ml of culture ( log phase ) was centrifuged at 13000 rpm for 2 min . Pellets were resuspended with 150 µl solution 1 ( 0 , 97 M β-mercaptoethanol , 1 , 8 M NaOH ) and incubated on ice for 10 min . 150 µl , 50% TCA was added and cells were incubated 10 min on ice , centrifuged at 13000 rpm for 2 min at 4°C and the pellet was resuspended with 1 ml acetone . Solution was centrifuged at 13000 rpm for 2 min at 4°C and the pellet was resuspended in 140 µl UREA buffer ( 120 mM Tris-HCL pH 6 , 8 , 5% Glycerol final , 8 M Urea final , 143 M 2-mercaptoethanol final , 8% SDS final , a little bit of bromphenol blue indicator ) . Protein extract was incubated 5 min at 95°C , centrifuged and loaded on a pre-cast gradient Gel ( BioRad ) . | Impaired telomere elongation eventually results in telomere dysfunction and can lead to diseases such as dyskeratosis congenita , which is associated with bone-marrow failure and pulmonary fibrosis . Cancer cells require continuous telomere maintenance to ensure continued cellular proliferation . Therefore the regulation of telomere function , both positively ( in the case of dyskeratosis congenita ) and negatively ( for cancer ) , may be of therapeutic benefit . In this study we have used yeast to determine which genetic factors are important for a certain telomeric structure ( the loop structure ) , which may help to maintain chromosome ends in a protected state . We found that multiple genetic factors and pathways affect telomere structure , ranging from metabolic signaling to specific telomere-binding proteins . We found that proper chromatin structure at the telomere is essential to maintain a telomere fold-back structure . Importantly , there was a strong correlation between telomere structure and function , as the mutants found in our screen ( looping defective ) were often associated with rapid senescence and telomere dysfunction phenotypes . We believe that , through the regulation of the various genetic pathways uncovered in our screen , one may be able to both positively and negatively influence telomere function . | [
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] | 2012 | Rif2 Promotes a Telomere Fold-Back Structure through Rpd3L Recruitment in Budding Yeast |
Nontyphoidal strains of Salmonella are a leading cause of death among HIV-infected Africans . Antibody-induced complement-mediated killing protects healthy Africans against Salmonella , but increased levels of anti-lipopolysaccharide ( LPS ) antibodies in some HIV-infected African adults block this killing . The objective was to understand how these high levels of anti-LPS antibodies interfere with the killing of Salmonella . Sera and affinity-purified antibodies from African HIV-infected adults that failed to kill invasive S . Typhimurium D23580 were compared to sera from HIV-uninfected and HIV-infected subjects with bactericidal activity . The failure of sera from certain HIV-infected subjects to kill Salmonella was found to be due to an inherent inhibitory effect of anti-LPS antibodies . This inhibition was concentration-dependent and strongly associated with IgA and IgG2 anti-LPS antibodies ( p<0 . 0001 for both ) . IgG anti-LPS antibodies , from sera of HIV-infected individuals that inhibit killing at high concentration , induced killing when diluted . Conversely , IgG , from sera of HIV-uninfected adults that induce killing , inhibited killing when concentrated . IgM anti-LPS antibodies from all subjects also induced Salmonella killing . Finally , the inhibitory effect of high concentrations of anti-LPS antibodies is seen with IgM as well as IgG and IgA . No correlation was found between affinity or avidity , or complement deposition or consumption , and inhibition of killing . IgG and IgM classes of anti-S . Typhimurium LPS antibodies from HIV-infected and HIV-uninfected individuals are bactericidal , while at very high concentrations , anti-LPS antibodies of all classes inhibit in vitro killing of Salmonella . This could be due to a variety of mechanisms relating to the poor ability of IgA and IgG2 to activate complement , and deposition of complement at sites where it cannot insert in the bacterial membrane . Vaccine trials are required to understand the significance of lack of in vitro killing by anti-LPS antibodies from a minority of HIV-infected individuals with impaired immune homeostasis .
African clades of nontyphoidal Salmonella ( NTS ) , particularly S . enterica serovars Typhimurium and Enteritidis , are a major cause of bacteremia in sub-Saharan Africa [1 , 2] . Case fatality rates are around 20–25% [1] , and up to 47% in HIV-infected adults [3] prior to the availability of antiretroviral therapy . Diagnosing NTS bacteremia is difficult due to a lack of specific clinical presentation . The emergence of multi-drug resistant isolates [4] has added to the problem of management and no vaccine is currently available . NTS bacteremia in Africa occurs most frequently among infants and HIV-infected patients [1 , 2] . The underlying mechanisms of susceptibility are not fully understood . We have previously shown that sera from African children under two years of age lack Salmonella-specific antibodies , resulting in an impaired ability to kill Salmonella . Sera from adults with Salmonella-specific antibodies can induce complement-mediated killing of Salmonella and placental transfer of IgG offers protection to infants [5] , suggesting a role for antibody in protection against invasive NTS ( iNTS ) disease . Mice can be protected against an intraperitoneal challenge with S . Typhimurium either by immunization with experimental LPS O-antigen-based conjugate vaccines [6 , 7] or passive transfer of monoclonal antibodies against O-antigens [8 , 9] . The role of anti-LPS antibodies in protection against iNTS disease in man is not fully appreciated , although antibodies to S . Typhimurium LPS O-antigen correlate with serum killing of S . Typhimurium D23580 in Malawian children [10] . Immunity against iNTS in HIV-infected adults is complex . Our work in Malawi demonstrates an association between impaired serum killing of NTS and dysregulated production of high levels of antibodies to S . Typhimurium LPS in some HIV-infected African adults [11] . Removal of LPS-specific antibodies restores bactericidal activity . In this study , we investigate the mechanism of interference with killing of NTS by antibodies to LPS O-antigen in HIV-infected African adults . This is important for understanding the potential effectiveness of an NTS O-antigen-based vaccine in Africa , particularly in the context of HIV infection .
Sera were from HIV-infected and HIV-uninfected Malawian adults ( S1 Table ) and were the same as previously studied [11] . No individuals had a known clinical history of iNTS disease . The study was approved by the College of Medical Research and Ethics Committee , College of Medicine , University of Malawi . Written informed consent was obtained from participants prior to inclusion in the study . Invasive African S . Typhimurium D23580 belonging to the ST313 pathovar [5 , 12] , D23580 galE- , S . Typhimurium LT2 [13] , S . Enteritidis D24854 , S . Enteritidis SL7488 [14] , S . Senftenberg 20050439 and S . Agona 20071186 [15] were used . This was as described previously [5] . Briefly , bacteria were mixed with 10% serum ( final Salmonella concentration 2 × 108 CFU/ml ) . After washing , bound antibodies were detected with FITC-conjugated anti-human IgG , IgA , and IgM antibodies ( Sigma-Aldrich , Milan , Italy ) . FL1 channel fluorescence indicates anti-Salmonella antibody binding . S . Typhimurium LPS ( Alexis Biochemicals , Vinci , Italy ) was coated onto ELISA plates at 5 μg/ml and dilutions of serum sample [10] added . Anti-LPS antibodies were detected using alkaline-phosphatase-conjugated anti-human isotype-specific antibodies ( Southern Biotech , Milan , Italy ) . These were as previously described [11 , 16] . For SBA involving endogenous complement , bacteria in log-growth phase were added to undiluted serum ( final Salmonella concentration 106 CFU/ml ) and incubated at 37°C . Viable Salmonellae were determined after 180 min . For SBA involving exogenous complement , bacteria were added to a mixture of heat-inactivated test serum ( 56°C for 30 minutes ) and 75% baby rabbit serum ( BRS , AbD Serotec , Kidlington , UK ) . For SBA testing inhibition of serum bactericidal activity , bacteria were added to a mixture of the purified antibodies and 50% normal human adult serum . Non-Salmonella-specific isotype-matched control antibodies were purified paraproteins . To obtain total antibody of each isotype ( IgG , IgA , IgM ) , serum was incubated sequentially with combinations of human IgA and IgM affinity matrices ( CaptureSelect , Leiden , Netherlands ) and protein G affinity matrix ( GE Healthcare ) to remove IgA , IgM and IgG respectively . Resulting isotype-specific antibodies preparations were dialyzed against PBS . Anti-Salmonella LPS antibodies were extracted from affinity-purified total IgG , IgA and IgM using a S . Typhimurium D23580 LPS O-antigen column , as described previously [17] . Anti-LPS antibodies were eluted with 0 . 1 M glycine pH 3 and neutralized with 1 M Tris-HCl pH 8 . 0 . Extracted antibodies were dialyzed against PBS . Binding of serum antibodies to S . Typhimurium LPS were assessed using a Biacore 3000 system ( GE Healthcare ) . A hydrophobic HPA sensor chip was coated by passing 1 mg/ml LPS across the chip surface for 30 min at 2 μl/min , washed with 0 . 1 M hydrochloride acid and blocked with 0 . 1 mg/ml bovine serum albumin . Sera were diluted 1:2 and passed across the chip surface for 10 min at 5 μl/min . kd values were calculated by fitting the binding curves to a best-fit Langmuir 1:1 model using BiaEvaluation . ELISA plates were coated with S . Typhimurium LPS at either a non-limiting concentration of 5 μg/ml or limiting concentration of 0 . 5 μg/ml . Diluted human sera were added to both plate types and incubated with alkaline-phosphatase-conjugated anti-human IgG , IgA or IgM ( Southern Biotech ) , then SigmaFast . Affinity was calculated as the ratio of the antibody titer with limiting plates to titer with non-limiting plates [18] . Plates were coated with S . Typhimurium LPS at 5 μg/ml and diluted human serum added . Half the wells were washed with 6 M urea and half with PBS-0 . 05% Tween 20 , then incubated with secondary antibodies followed by SigmaFast , as above . The avidity index is the antibody titer in the presence of urea as a percentage of titer in the absence of urea [19] . Anti-S . Typhimurium LPS antibody concentrations were determined by ELISA using control antibodies of known concentration . Plates were coated with goat anti-human IgA , IgG or IgM antibodies ( Sigma-Aldrich ) at 5 μg/ml . Purified anti-LPS antibody eluates , together with the control antibodies , were added in step-wise dilutions . Bound antibodies were detected using secondary antibodies , then SigmaFast , as above . These were by flow cytometry as previously described [5] . Bacteria in log-growth phase were mixed with undiluted serum ( final concentration 2 × 108 CFU/ml ) , then FITC-conjugated mouse anti-C3 antibody or mouse anti-C5b-9 antibody followed by FITC-conjugated anti-mouse immunoglobulin . The bacteria were first gated on FSC and SSC to exclude bacterial cell debris . GMFI in the FL1 channel was used to indicate C3 and C5b-9 deposition . Total complement activity and alternative hemolytic complement activity were measured by radial immunodiffusion assays according to manufacturer’s instructions ( Binding Site , Grassobio , Italy ) . Spearman rank was used for estimation of correlation . Comparisons of data from different groups of sera were performed by Mann-Whitney U-test .
We previously reported that ability of serum from HIV-infected Africans to induce complement-mediated killing of S . Typhimurium correlates inversely with concentration of anti-LPS antibody [11] . When S . Typhimurium D23580 is cultured with serum from HIV-uninfected healthy adults , the number of viable bacteria falls to between 10% and 1% of the starting value after 180 minutes . By contrast , sera from HIV-infected adults with high anti-LPS titers fail to kill D23580 . Our first objective was to test whether anti-LPS antibodies of a particular class or subclass fail to induce killing of S . Typhimurium D23580 and act as a competitive inhibitor of antibodies that induce killing . As a preliminary , we assessed which serum immunoglobulin classes and subclasses are represented among anti-LPS antibodies found in HIV-uninfected and HIV-infected African adults . The ability of each individual serum to kill S . Typhimurium D23580 compared to IgG , IgM , IgA , IgG1 and IgG2 anti-LPS concentration of that serum is shown in Fig 1 . There is a trend towards negative correlation between serum killing capacity and anti-LPS levels of each antibody isotype that only fails to reach statistical significance for IgM ( Fig 1 ) . The strongest negative correlation is shown for IgA and IgG2 , the antibody classes that are least able to fix complement and consequently the strongest candidates as competitive inhibitors of Salmonella killing . For more direct evidence about the capacity of different anti-LPS antibody classes to kill S . Typhimurium , total IgA , IgG and IgM were prepared from serum of three HIV-uninfected subjects ( ‘HIV-ve bactericidal’ serum ) , four HIV-infected subjects that effect normal killing of S . Typhimurium ( ‘HIV+ve bactericidal’ serum ) , and five HIV-infected subjects whose serum does not kill ( ‘HIV+ve inhibitory’ serum ) . Purified total IgG , IgM and IgA from each of these 12 sera were tested in a modified SBA with S . Typhimurium D23580 and dilutions of affinity purified immunoglobulin and 75% baby rabbit serum ( BRS ) as the source of complement [16] ( Fig 2 ) . Purified total IgG ( top panel ) from HIV-ve bactericidal sera and HIV+ve inhibitory sera were bactericidal against S . Typhimurium D23580 in the presence of BRS . Importantly , there is no loss of killing at 500μg/ml with all eight sera in these two groups , indicating that even high concentrations of these IgG preparations are not anti-complementary . Strikingly , none of the total IgG purified from HIV+ve bactericidal sera induced killing at any concentration tested . The highest IgG concentration tested ( 500 μg/ml ) was below normal physiological levels in blood ( 6–16 mg/ml ) . The killing observed in whole serum from many HIV-infected subjects may be primarily be due to IgM anti-LPS , since purified IgM HIV+ve bactericidal serum killed S . Typhimurium in the presence of BRS ( Fig 2 , central panel ) . Total IgM from HIV+ve inhibitory serum was also bactericidal , as was total IgM from HIV-ve bactericidal serum . All of these total IgM preparations induced somewhat less killing at the highest concentration tested ( 500 μg/ml ) than at a five-fold lower concentration . None of the total IgA preparations from any of the three groups induced killing of S . Typhimurium D23580 at any concentration tested ( Fig 2 , bottom row ) . This is consistent with IgA not activating antibody-dependent classical complement pathway [18] . It also suggests that anti-LPS IgA in high concentration can act as a competitive inhibitor of antibody-induced complement-mediated killing of S . Typhimurium . To relate these findings to S . Typhimurium LPS-specific antibodies , we affinity purified anti-LPS antibodies from each total immunoglobulin preparation and repeated the modified SBA . Remarkably , the results with anti-LPS antibodies of each class almost completely mirrored those with total immunoglobulins of each class , consistent with anti-LPS antibodies in each serum effecting the killing observed ( Fig 3 ) . The relative lack of bactericidal activity of anti-LPS IgG from HIV+ve bactericidal sera total IgG was again surprising . We speculated whether differences in the fine specificities of the S . Typhimurium O-antigen epitopes recognized by these IgG antibodies could be responsible . Since anti-LPS antibodies were affinity-purified using O-antigen from S . Typhimurium D23580 ( consisting of O:1 , O:4 , O:5 and O:12 epitopes ) , anti-LPS IgG from HIV+ve bactericidal sera potentially could target a different balance of O-antigen epitopes compared with anti-LPS IgG from the other sera groups . To test this , we examined the ability of purified anti-LPS IgG from HIV+ve bactericidal sera to kill Salmonella with different O-antigen profiles ( S2 Table , Fig 4A–4F ) . As well as being unable to kill S . Typhimurium D23580 , anti-LPS IgG from HIV+ve bactericidal sera exhibited weak bactericidal activity against S . Typhimurium LT2 ( both O:1 , O:4 , O:5 , O:12 ) , possibly due to the higher serum sensitivity of LT2 compared with D23580 [16] . These IgG antibodies could not kill S . Enteritidis D24954 ( O:1 , O:9 , O:12 ) , but could kill S . Senftenberg ( O:1 , O:3 , O:19 ) . As O:1 antigen is the only O-antigen common between S . Senftenberg and S . Typhimurium D23580 ( used to purify the anti-LPS antibodies ) , killing of S . Senftenberg was most likely mediated by antibodies to O:1 antigen , an α ( 1→6 ) glucosylated galactose found on the LPS backbone [9 , 19] . Absence of bactericidal activity with S . Typhimurium D23580 and S . Enteritidis D24954 , which also express O:1 , could be due to differences in O-antigen chain length , and glucosylation and O-acetylation levels which impact on the tridimensional structure of these side chains , reducing accessibility of IgG antibodies to the O:1 antigen . Anti-LPS IgG from HIV+ve bactericidal sera killed S . Enteritidis SL7488 ( O:1 , O:4 , O:12 ) and S . Agona ( O:4 , O:12 ) , likely due to antibodies to O:4 antigen which is present on both strains , but is less accessible to antibody in the presence of the O:5 antigen of S . Typhimurium D23580 and LT2 . Analysis of the anti-LPS IgG from the HIV+ve bactericidal sera by flow cytometry ( Fig 4G ) showed higher binding to S . Enteritidis SL7488 and S . Agona compared to the other bacterial strains . This is consistent with antibody binding to the exposed O:4 epitope on these two strains . To test the possibility that specific IgA acts as a competitive inhibitor of antibody-induced killing of S . Typhimurium D23580 , we added purified anti-LPS antibodies from each of the total IgG , IgM and IgA preparations to SBA of S . Typhimurium D23580 with 50% HIV-ve bactericidal adult African serum as a source of both anti-Salmonella antibodies , that induce killing , and complement ( Fig 5 ) . Perhaps surprisingly , 500 μg/ml anti-LPS of each of the three immunoglobulin classes from the sera of all three groups inhibited antibody-induced complement-mediated killing of S . Typhimurium D23580 . This inhibition was lost after a four-fold dilution of the anti-LPS IgA and IgG , and after one further dilution with anti-LPS IgM . As a control , purified non-Salmonella specific human antibody preparations were added to the SBA and failed to inhibit serum killing at 500 μg/ml . The finding that high concentrations of anti-LPS antibody of all isotypes from each clinical group inhibits complement-induced killing , prompted us to explore three possible mechanisms for this effect . First , we tested whether HIV+ve inhibitory sera consume complement . Second , we investigated whether concentrations of anti-LPS antibodies that saturate O-antigen epitopes on S . Typhimurium prevent formation of membrane attack complex ( MAC ) of complement . Third , we checked whether MAC is formed , but prevented from inserting in the bacterial cell wall in a damaging way by saturating concentrations of anti-LPS antibody . To test for complement depletion in sera , SBA against S . Typhimurium D23580 were first set up with the different sera . After 180 mins , the sera were sterile-filtered and complement function assessed . All post-SBA filtrates could deposit C3 complement on S . Typhimurium as assessed by flow cytometry . Most of the filtrates , including 5 of 6 from the HIV+ve inhibitory group , also deposited MAC . There was no relative impairment of complement deposition with HIV+ve inhibitory filtrates compared with those of the other two groups ( Fig 6A and 6B ) . All post-SBA filtrates retained total and alternative pathway hemolytic complement activity ( Fig 6C and 6D ) , although there was a trend for lower activity in the HIV+ve inhibitory filtrates compared with the HIV-ve bactericidal filtrates . We previously demonstrated that 20% human complement can effect bactericidal activity against S . Typhimurium D23580 in the presence of specific antibodies [5] . Finally , post-SBA filtrates were tested for killing capacity in a second SBA with S . Typhimurium D23580 galE- . This strain is sensitive to complement-mediated killing in the absence of antibodies [11] . All filtrates in each group , including the HIV+ve inhibitory group , effected maximal killing of S . Typhimurium D23580 galE- after 45 minutes . Heat-inactivation at 56°C for 30 minutes destroyed the lytic capacity of the filtrates ( Fig 6E and 6F ) . We speculated that differences in affinity and avidity of antibodies targeting S . Typhimurium LPS might contribute to the lack of serum bactericidal activity . Using Biacore , we determined the dissociation constant ( kd ) of anti-S . Typhimurium D23580 LPS antibodies ( Fig 7A ) in each group of sera ( antibody titers shown in Fig 8 and S3 Table ) . The kd of anti-LPS antibodies in HIV+ve inhibitory ( P = 0 . 004 ) and HIV+ve bactericidal sera ( P = 0 . 016 ) were lower than for HIV-ve bactericidal sera . However there were no differences , as measured by ELISA , in affinity ( Fig 7B ) and avidity ( Fig 7C ) of anti-LPS IgA , IgG and IgM between the three groups .
S . Typhimurium LPS O-antigen has been considered as a vaccine candidate for many years . We previously reported an association between impaired serum killing of S . Typhimurium and high levels of S . Typhimurium LPS-specific IgG in some African HIV-infected adults [11] . This study extends the association to high titers of anti-LPS IgA , but not IgM . Factors we examined that do not appear to cause impaired killing are antibody affinity , avidity and complement consumption . The latter was previously postulated as an explanation [11] . The first key finding is that antibody concentration is an important determinant of the presence or absence of serum killing of Salmonella . HIV+ve inhibitory sera have elevated LPS-specific IgA and IgG , compared with HIV-ve and HIV+ve bactericidal sera . While whole undiluted HIV+ve inhibitory sera cannot kill S . Typhimurium D23580 , these sera kill Salmonella in the presence of exogenous complement when diluted . The concentration dependency of killing is also observed with purified antibodies from all groups of sera . Killing is also isotype-dependent , being mediated by IgG and IgM in HIV-ve bactericidal sera , but predominantly by IgM in the HIV+ve bactericidal sera tested . The counter-intuitive lack of killing observed with purified total and LPS-specific IgG from HIV+ve bactericidal sera could be due to the epitopes recognized by these antibodies , suggesting that the specific O-antigen epitopes recognized are important for efficient killing of Salmonella . The SBA and flow cytometry experiments with Salmonella strains of different O-antigen profiles supports this hypothesis and offers a possible explanation whereby preferential targeting of anti-LPS IgG from the HIV+ve bactericidal group to O:4 could underlie this finding . The O:5 antigen , present in S . Typhimurium D23580 , is an acetylated form of the O:4 antigen [6 , 20] , and can hinder accessibility of antibodies targeting O:4 . The underlying reasons for this difference in epitope specificity of IgG anti-LPS antigens in HIV+ve bactericidal and HIV+ve inhibitory sera are not clear . The difference may be partly explained by the small number of sera available for antibody extraction . With larger numbers of sera , it is conceivable that both bactericidal and inhibitory sera would be found with antibodies preferentially targeting the O:4 or O:5 epitopes . The presence of a skewing in antibody response to particularly epitopes is consistent with the well-recognized B cell dysregulation and dysfunction observed in HIV infection [21 , 22] , characterized by various abnormalities including oligoclonal antibody responses [23] . The second key finding is that anti-LPS antibodies from HIV-infected and HIV-uninfected African adults can effect complement-dependent killing , indicating their potential for mediating protective immunity against NTS . Purified anti-LPS IgG and IgM kill Salmonella , while IgA does not . Hence , bactericidal activity is dependent on antibody isotype , which is consistent with the known ability of IgM , IgG1 and IgG3 , but not IgA , and only to a limited extent IgG2 , to activate complement [24–26] . While purified IgA from African adults could not effect complement-mediated killing of Salmonella , we recently found that a vaccine-induced mouse monoclonal IgA against O:4 of S . Typhimurium LPS has bactericidal activity [8] . This could simply be due to species differences between human and mouse IgA . While serum IgA did not kill Salmonella , secretory IgA , which was not examined in this study , is likely to be important for reducing Salmonella colonization via the oral route . Concentration of anti-LPS IgA and IgG2 in total serum from HIV-infected participants correlated most strongly with inhibition of Salmonella killing ( P<0 . 0001 for both ) and it is possible that the high levels of this immunoglobulin class and subclass in HIV+ve inhibitory serum contributes to impaired killing . Very few HIV-uninfected participants had raised IgG2 levels in comparison with HIV-infected participants . Although IgG1 is the most abundant IgG subclass in human serum , IgG2 appears to be induced preferentially to bacterial polysaccharides [27] . As well as its poor ability to activate complement , high levels of IgG2 to Pseudomonas aeruginosa LPS in patients with bronchiectasis have been found recently to inhibit serum killing of these bacteria [28] . The relevance of different antibody isotypes for protection has implications for vaccine development against invasive Salmonella disease . Vaccine design , formulation and regimes that induce antibody responses consisting predominantly of IgG1 and IgM are more likely to elicit bactericidal antibodies which can counter bacteremia , while IgA induction is likely to be important for preventing the initial invasion from the gastrointestinal tract . The third key finding is that while anti-LPS IgG and IgM are bactericidal at low concentrations , high concentrations inhibit normal serum killing . Together , these findings suggest that impaired killing with HIV+ve inhibitory sera is primarily the outcome of high anti-LPS antibodies concentrations . The exact mechanism by which this inhibition occurs is still unclear , but could partly be due to the very high levels of anti-LPS IgA and IgG2 antibodies in HIV+ve inhibitory sera which are non/poorly-complement activating . This does not , however , appear to explain the similar profiles of killing and lack of killing by anti-LPS antibodies extracted from HIV-infected and HIV-uninfected sera over the range of concentrations tested , with the possible exception of lack of killing of IgG with HIV+ve bactericidal sera if this was predominantly IgG2 subclass . As previously postulated [11] , binding of an excess of antibodies to the Salmonella surface could sterically hinder insertion of the MAC into the bacterial membrane , so that although MAC is formed , as we have detected , it is unable to kill Salmonella . Lack of killing could also result from deposition of MAC at a distance away from the bacterial surface , since anti-LPS antibodies will bind theoretically along the length of the LPS molecule , with some locations distal to the bacterial surface . Location of MAC deposition has previously been highlighted as an important factor in killing of Salmonella Typhimurium [29] . However , this alone cannot be the full explanation , since anti-LPS antibodies are bactericidal [7 , 8 , 30] , unless distal MAC deposition occurs preferentially at higher anti-LPS antibody concentrations . Such distally-positioned MAC may be prone to shedding from the bacteria as previously demonstrated for S . Minnesota [31 , 32] . Finally , it is conceivable that high concentrations of antibodies bound to individual bacteria may result in defective formation of the MAC , leading to a lack of killing . Trebicka et al . have recently described the absence of killing of S . Typhimurium by sera from HIV-infected Americans associated with a lack of IgG antibodies to LPS [33] , rather than the high levels of these antibodies that we have found in HIV-infected Africans . This is consistent with the concept that some , but not excessive , antibodies to LPS are required to kill Salmonella . The reason for the absence of antibodies to LPS may relate to differences in exposure to Salmonella in the USA compared with Africa , leading to reduced antigenic stimulation of LPS-specific B cells , or to differences in the nature of B cell dysfunction and dysregulation between the two study populations . Interestingly , despite the lack of anti-LPS antibodies , Trebicka et al found that these HIV-infected sera inhibited killing of Salmonella by control sera , suggesting multiple mechanisms for impairment of killing of Salmonella in HIV infection . Although data on levels of IgA and IgM antibodies to LPS were not shown , the authors speculated that high levels anti-LPS IgM present in some study participants could be the reason for inhibition of killing . This is consistent with the findings in our current study . The LPS-specific hypergammaglobulinemia found in some HIV-infected African adults is not present in HIV-uninfected subjects , and appears to occur secondary to natural exposure to S . Typhimurium in the context of the dysregulated humoral immunity that accompanies HIV infection [11] . Inhibition of normal serum killing of Salmonella by anti-LPS IgG and IgM required 500 μg/ml and 125 μg/ml purified antibodies respectively . The combined concentrations of all anti-LPS antibody isotypes exceeded 500 μg/ml in HIV+ve inhibitory sera , but were below 500 μg/ml in HIV+ve and HIV-ve bactericidal sera . Immunization has been reported to induce ∼15 μg/ml specific IgG in healthy adults [34 , 35] . Therefore , a S . Typhimurium O-antigen-based vaccine should induce antibodies at bactericidal rather than inhibitory concentrations in HIV-uninfected individuals . Several factors could determine the immunological response to such a vaccine in HIV-infected individuals . HIV infection has a global effect on the immune system characterized by impaired immune homeostasis , and is associated with higher risk and increased severity of infections including pneumonia and tuberculosis [36 , 37] . The difference in immunity resulting from vaccination instead of natural exposure to NTS is uncertain , as is the effect antiretroviral therapy will have on anti-Salmonella LPS antibody levels . Therefore , it is not known whether vaccination will induce a protective response or a dysregulated excess of anti-LPS antibodies that impairs serum Salmonella killing . In conclusion , antibodies against S . Typhimurium LPS O-antigen are present in the blood of HIV-infected and HIV-uninfected African adults , most likely following natural exposure to S . Typhimurium . The IgG and IgM isotypes of these antibodies have in vitro bactericidal activity against invasive African S . Typhimurium , but at high concentrations , all three isotypes ( IgG , IgA and IgM ) can inhibit killing of Salmonella . | Bacteremia caused by nontyphoidal Salmonellae are a major health burden in Africa . While antibody-induced complement-mediated killing protects healthy Africans against Salmonella , increased levels of anti-LPS antibodies in some HIV-infected Africans block this killing . Little is known about the mechanism of the interference of killing by these antibodies . Here , we compared sera and affinity-purified antibodies from African HIV-infected adults that are unable to kill invasive S . Typhimurium D23580 , with sera from HIV-uninfected and HIV-infected subjects with bactericidal activity . We found that the blocking effect of anti-LPS antibodies is a factor of antibody concentration , rather than antibody structure or specificity . While all three isotypes ( IgG , IgA and IgM ) can inhibit killing of Salmonella at grossly high concentrations , the IgG and IgM isotypes of the anti-LPS antibodies have in vitro bactericidal activity against invasive African S . Typhimurium . Inhibition of killing did not associate with antibody affinity or avidity , or complement deposition or consumption . It is possible that a LPS-based vaccine would induce antibodies at bactericidal rather than inhibitory concentrations in HIV-uninfected individuals . In HIV-infected individuals , it is uncertain whether vaccination will induce a protective response or a dysregulated excess of anti-LPS antibodies that impairs serum killing of Salmonella . | [
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] | 2016 | Bactericidal Immunity to Salmonella in Africans and Mechanisms Causing Its Failure in HIV Infection |
The infectious pathogen responsible for prion diseases is the misfolded , aggregated form of the prion protein , PrPSc . In contrast to recent progress in studies of laboratory rodent-adapted prions , current understanding of the molecular basis of human prion diseases and , especially , their vast phenotypic diversity is very limited . Here , we have purified proteinase resistant PrPSc aggregates from two major phenotypes of sporadic Creutzfeldt-Jakob disease ( sCJD ) , determined their conformational stability and replication tempo in vitro , as well as characterized structural organization using recently emerged approaches based on hydrogen/deuterium ( H/D ) exchange coupled with mass spectrometry . Our data clearly demonstrate that these phenotypically distant prions differ in a major way with regard to their structural organization , both at the level of the polypeptide backbone ( as indicated by backbone amide H/D exchange data ) as well as the quaternary packing arrangements ( as indicated by H/D exchange kinetics for histidine side chains ) . Furthermore , these data indicate that , in contrast to previous observations on yeast and some murine prion strains , the replication rate of sCJD prions is primarily determined not by conformational stability but by specific structural features that control the growth rate of prion protein aggregates .
Prions are a novel class of infectious agents that are composed solely of self-replicating misfolded protein aggregates [1] . In mammals , prions cause a group of invariably fatal and rapidly progressive neurodegenerative diseases , originally described as transmissible spongiform encephalopathies ( TSEs ) [1 , 2] . The most common of the human prion diseases is sporadic Creutzfeldt-Jakob disease ( sCJD ) [3] , accounting for ~90% of all CJD cases worldwide [4] . One of the most intriguing features of these diseases is their vast phenotypic heterogeneity [1 , 4] . In patients homozygous for methionine in the PRNP gene , there are two major subtypes of sCJD: MM1 and MM2 . These types differ with regard to the progression rate of the disease , pattern of proteinase K ( PK ) -resistant fragments of infectious prion protein aggregates PrPSc , ( Fig 1a ) , neuropathological characteristics of brain lesions , and transmissibility properties in transgenic mice [4–10] . A substantial progress has been made in recent years in prion research using laboratory rodent-adapted , cloned prion strains . These studies revealed , among others , that phenotypic variability of these model prions is directly linked to ( and likely encoded in ) structural differences of PrPSc , and suggested that prion replication rates are inversely proportional to conformational stability of rodent PrPSc ( as defined by the concentration of denaturant needed to dissociate/unfold PrPSc aggregates ) [11 , 12] . By contrast , our understanding of the molecular basis of human prions such as those causing sCJD is far less advanced . These prions are present in human brain at a very low concentration ( approximately 100-fold lower compared to that in a prion-infected rodent brain ) and , thus , are much more difficult to purify and characterize . In fact , no direct structural data are available for PrPSc present in sCJD brains beyond the evidence that the N-terminus is variably resistant to denaturation and proteolytic digestion ( Fig 1a ) [5 , 7 , 13–17] . Even though earlier studies suggest that phenotypic diversity in human prion disease is somehow related to distinct PrPSc isoforms , conformational spectrum of these isoforms and the issue of strains of human prions are poorly understood , hindering efforts to develop generally accepted international classification of human prion disease . Moreover , the classical approach—isolation and definition of a full repertoire of sCJD prion strains in transgenic mice models with uniform genetic background—had not been successful due to the constrains imposed by the extensive phenotypic and genetic diversity of sCJD [4] and very long incubation time and/or limited transmissibility to transgenic mice [6 , 8–10] . The characterization of human prions is further complicated by the frequent co-existence of diverse prion particles [18 , 19] and prion adaptation and evolution in a new host [9 , 19] . To bridge some of these gaps , here we purified sCJD prions from two cases of phenotypically very distant sCJD types , determined their replication tempo as well as characterized structural organization using recently emerged approaches based on mass spectrometry-detected hydrogen/deuterium exchange . Our data provide direct experimental evidence that different phenotypes of sCJD are associated with structurally distinct PrPSc aggregates . Furthermore , these data suggest that , in contrast to the observations for murine prion strains [11 , 12] , the replication rate of sCJD prions is not a pure function of conformational stability but is rather dictated by specific structural features of PrPSc .
From the collection of samples obtained from 340 patients with an unequivocal diagnosis of Type 1 ( MM1 ) and Type 2 ( MM2 ) sCJD , we selected one case that is representative of each neuropathology group ( S1 Fig ) and displayed ≥99% pure Type 1 or Type 2 proteinase K-resistant PrPSc ( rPrPSc ) , as detected by both conformation dependent immunoassay ( CDI ) and Western blots [13 , 14 , 20] . The disease duration in these representative cases , as well as biochemical characteristics of brain PrPSc associated with them ( levels of total PrPSc and rPrPSc , size of PrPSc particles , conformational stability of PrPSc ) correspond to the respective median values reported previously for each group [13 , 14] ( Table 1 ) . The native prion particles containing rPrPSc from these two cases were purified for structural studies with a scaled up protocol we developed previously for purification of infectious and structurally intact Sc237 prions from Syrian hamster brains [21] . The Western blot patterns of purified MM1 and MM2 rPrPSc in the final fraction 8 ( F8 ) and in the original brain homogenates ( BH ) were superimposable , documenting complete qualitative recovery of rPrPSc from brain homogenates ( Fig 1b ) . As expected [22] , the mass of unglycosylated fragments was ~21 kDa in Type 1 and ~19 kDa in Type 2 rPrPSc , and Type 2 rPrPSc was not detectable with mAb 12B2 due to the missing N-terminal epitope ( Fig 1b and S2b Fig ) . The 12–13 kDa C-terminal fragments were more abundant in Type 1 rPrPSc and detectable in Type 2 after longer exposure ( Fig 1b ) . The silver-stained gels demonstrated the pattern of rPrPSc corresponding to the major bands on Western blots , and the isolated rPrPSc was ~90% pure ( Fig 1c ) . These patterns were highly reproducible upon purification of rPrPSc from different cortical areas of the same brain ( S2 Fig ) . To investigate the prion size , we separated sCJD prion particles according to sedimentation velocity in sucrose gradient [13] . Consistent with previous data , the peak sedimentation velocity of MM1 rPrPSc was found to be substantially slower than that of MM2 rPrPSc [13] ( Fig 2a ) . Based on calibration with standard proteins [13] , we estimate that the majority of MM1 rPrPSc particles have a molecular mass of 9-11x106 Da ( ~380–460 monomers ) , whereas the respective value for MM2 rPrPSc particles is ≥14x106 Da ( ≥600 monomers ) ( Fig 2a and Table 1 ) . Using CDI , we compared the conformational stability of rPrPSc obtained from different brain cortex areas in four independent purification rounds from each sCJD case ( Fig 2b ) . The average stability of MM1 rPrPSc against denaturation by GdnHCl was significantly higher than that of MM2 rPrPSc , with GdnHCl concentration corresponding to midpoint denaturation of 3 . 0 and 2 . 3 M , respectively ( Fig 2b and Table 1 ) . Next , we assessed the seeding efficacy ( amplification index ) of MM1 rPrPSc and MM2 rPrPSc in vitro using two different methods , QuIC and sPMCA . The amplification index ( potency ) of different seeds is expressed as a ratio between the concentration of the PrPSc conformers produced with PMCA or QuIC divided by the concentration of PrPSc in the seed after subtracting the background obtained in control unseeded samples . Detailed protocols of these methods and control experiments showing lack of spontaneous prion protein conversion in the unseeded reactions have been described previously [13] . In both assays , the seeding efficacy of MM1 rPrPSc was markedly higher compared to that of MM2 rPrPSc ( Fig 2c and Table 1 ) . This higher replication efficacy of MM1 prions from the case selected for the present structural studies is consistent with ~4-fold higher median replication potency of MM1 prions compared to MM2 prions we previously observed ( using both QuIC and sPMCA techniques ) for prions from ten MM1 and ten MM2 sCJD cases ( Supplemental S2 Fig in [13] ) . These data in vitro are also in accord with available bioassay data that demonstrate higher transmission rates and significantly shorter incubation times of MM1 sCJD prions in transgenic mice expressing human PrPC ( 129M ) or human/mouse PrPC chimeras [8 , 9] . The higher replication efficiency of the conformationally more stable MM1 rPrPSc is both intriguing and unexpected , as some previous experiments with mouse prion strains suggest that there is an inverse correlation between prion replication rate and conformational stability of total PrPSc ( i . e . , the less stable conformers should replicate faster ) [11 , 12] . Our present data indicate that this previously suggested relationship does not apply to sCJD rPrPSc . Clearly , understanding the molecular basis of phenotypic variability in sCJD requires structural characterization of PrPSc that goes beyond relatively crude assays such as proteolytic fragmentation followed by Western blotting or conformational stability measurements . This is a challenging task because most of the methods developed for structural studies of protein aggregates are not applicable to brain-derived PrPSc , as they require isotopic labeling or introduction of other spectroscopic probes . However , new opportunities in this regard are offered by two mass spectrometry based methods: backbone amide hydrogen/deuterium exchange coupled with mass spectrometry ( HXMS ) [23] and histidine hydrogen/deuterium exchange mass spectrometry ( His-HXMS ) [24] . Here , we used these two methods for structural comparison of MM1 rPrPSc and MM2 rPrPSc . The HXMS method measures the rate of H/D exchange of protein backbone amide hydrogen atoms . Since the exchange rates are much faster for protein segments that are unstructured as compared to those that are involved in H-bonded structures such as α-helices or β-sheets , these measurements provide a sensitive tool for conformational analysis . This approach , which we recently successfully used for structural analysis of strain-specific differences in murine prions [23] , is especially useful for studying amyloids and related protein aggregates , as the exchange rates within the β-sheet cores of these aggregates are exceptionally slow [25–30] . The first step in HXMS analysis is the generation of peptic fragments that can be separated by ultrahigh performance liquid chromatography ( UHPLC ) and identified by MS . Both for MM1 and MM2 rPrPSc , we were able to identify 27 peptic fragments that give rise to MS spectra with a signal-to-noise ratio sufficient for reliable calculation of deuterium incorporation . These fragments ( some of them partially overlapping ) cover ~85% of the C-terminal region 117–224 , with the only significant gap for the segment 169–181 that contains one of the glycosylation sites ( likely due to a very low concentration of peptic fragment ( s ) derived from the nonglycosylated component of rPrPSc ) . No peptic fragments could be analyzed from the N-terminal region up to residue 116 , presumably due to the ragged N-terminus of human rPrPSc . The extent of deuterium incorporation for MM1 rPrPSc and MM2 rPrPSc after 5 min and 240 h incubation in D2O is shown in Fig 3 . For both rPrPSc types , the region of relatively little protection against deuterium incorporation maps to residues ~145–160 . This is in striking contrast to murine prion strains studied to date , in which case this central region is characterized by high degree of protection against H/D exchange [23] . Thus , it appears that the ~145–160 region of sCJD prions is structurally less ordered than the same region in cloned murine prion strains . In contrast to similar protection against deuterium incorporation in the ~145–160 regions of MM1 and MM2 rPrPSc , there are substantial differences in other parts of rPrPSc corresponding to distinct sCJD phenotypes . This is especially evident for the 117–144 region , as peptic fragments derived from this part of MM1 rPrPSc consistently show higher degree of H/D exchange as compared to those corresponding to the same region in MM2 rPrPSc . The difference is particularly striking for the 117–133 region , in which case the degree of deuterium incorporation after 240 h exchange is 2 . 5–3 fold lower for MM1 rPrPSc , indicating markedly higher level of structural order in this part of MM1 rPrPSc as compared to MM2 rPrPSc . An opposite trend is observed for the C-terminal region ~161–224 , where higher protection against H/D exchange is observed for MM2 rPrPSc ( Fig 3 ) . Altogether , these data clearly demonstrate substantial structural differences between rPrPSc corresponding to two different phenotypes of sCJD . The resolution of HXMS alone is not sufficient to propose any specific structural model that could account for these differences . However , within the context of the frequently considered model based on the parallel in-register β-structure motif [31 , 32] , region-specific differences in resistance to H/D exchange observed between MM1 PrPSc and MM2 PrPSc could likely reflect factors such as different proportions in these regions of residues involved in β-strands and loops between them and/or packing differences between individual β-strands . As in the case of murine prions [23] , high level of protection against H/D exchange in the C-terminal region of sCJD PrPSc is not compatible with the structural model proposing that residues ~89–175 form left-handed β-helices , with the C-terminal region retaining the native-like α-helical conformation of PrPC [33] . However , the present data alone do not exclude the possibility that the entire PK-resistant region of PrPSc could form a β-helix-like structure . Structural properties of sCJD prions were further probed using the recently developed approach of His-HXMS which measures the rate of H/D exchange of C2 protons in histidine side chains [34–36] . Information provided by this method is complementary to that obtained from amide HXMS measurements: while amide HXMS probes protein structural organization and dynamics at the level of the polypeptide backbone , His-HXMS probes the microenvironment ( water accessibility ) of specific His side chains [34–36] . As shown in a recent study with recombinant prion protein amyloid fibrils , the latter approach can be particularly useful in probing quaternary structure of ordered protein aggregates , providing information about the packing arrangement and interfaces between β-sheets [31] . There are six His residues in the PK-resistant region of human PrPSc ( His99 , His111 , His140 , His150 , His177 and His187 ) . MS signal for the peptide fragment containing His99 was too weak to allow reliable measurements . However , high quality H/D exchange data could be obtained for five other His residues . In the native structure of the PrPC monomer , all these His side chains are fully exposed to water . Thus , as expected for unprotected histidines [34 , 35] , the half-times of exchange are about 2–3 days . In the rPrPSc structures , these half-times are substantially longer , indicating that all His side chains are located in at least partially water-protected environment ( Fig 4 ) . However , the degree of this protection for individual His side chains varies greatly between MM1 rPrPSc and MM2 rPrPSc . For example , in MM1 rPrPSc , His177 is still in a relatively water accessible environment ( exchange half-time of 9 days ) , whereas in MM2 PrPSc , this side chain is much more protected from water ( exchange half-time of 56 days ) . An opposite situation is observed for His111 , in which the environment of the side chain is much more water-protected in the structure of MM1 rPrPSc than that of the MM2 counterpart ( exchange half-times of 67 and 16 days , respectively ) . Recent crystallographic studies with amyloidogenic peptides identified two types of interfaces between β-sheets in amyloid structures: one that is highly water protected with strong interdigitation of side chains ( “dry” steric zipper ) and one that is more accessible to water [37 , 38] . Within this context , the differences in water exposure of individual His side chains observed between MM1 rPrPSc and MM2 rPrPSc could be explained by distinct packing arrangements of β-sheets in these two structures ( i . e . , the same His side chain being in dry or wet interface depending on the rPrPSc type ) . It should be noted , however , that even for the most protected His side chains in rPrPSc , the exchange half-times are substantially shorter than those recently observed in synthetic amyloid fibrils prepared from the recombinant PrP ( hundreds of hours ) , suggesting that steric zippers in brain-derived rPrPSc might be less perfect than those in synthetic amyloid fibrils . This is not entirely surprising given that rPrPSc particles contain glycosylated isoforms , and glycans may interfere with packing between β-sheets . Experiments with two strains of yeast prion [PSI+] demonstrated that , in this case , the critical determinant of the strength of prion phenotype is the susceptibility of prion aggregates to fragmentation ( that creates new ends for monomer recruitment ) , with the less stable structure corresponding to the stronger phenotype [39] . This fragmentation and maintenance of the yeast prion state in vivo is believed to be mediated by the molecular chaperone Hsp104 [39 , 40] . Even though there are no known mammalian homologs of the disaggregating chaperone HsP104 , the general hypothesis that less stable prions are more virulent has been adopted in the field of mammalian prions , and this model appeared to be supported by studies in vivo with some rodent prion strains [11 , 12 , 41] , even though the results of these studies could also be explained by strain-dependent differences in prion clearance rates . Furthermore , an inverse correlation was found between the replication tempo in vitro and the conformational stability of the protease-sensitive sCJD PrPSc ( but not the protease-resistant component , rPrPSc ) [13] . However , data for some other rodent prion strains appear to be inconsistent with this model [42 , 43] . The picture is further complicated by the fact that there are no reliable direct assays to probe fragmentation susceptibility of PrPSc aggregates , and their stability is typically assessed by measuring resistance to denaturation with SDS or chaotropes; the relationship between the latter property and fragility is not necessarily straightforward . Our present data clearly demonstrate that , in contrast to the observations for some murine prions , lower conformational stability of sCJD rPrPSc does not result in higher replication rate of these prions . Thus , at least in the case of human prions , conformational stability of rPrPSc ( as defined by resistance to denaturation with SDS or chaotropes ) is definitely not a reliable predictor of the incubation period of the disease . Importantly , our structural studies allowed us to identify substantial differences between the molecular organization of MM1 and MM2 rPrPSc , both at the level of the polypeptide backbone as well as the quaternary packing arrangements . As shown in our recent study with recombinant PrP amyloid fibrils[24] , the differences in packing between β-sheets may result in distinct conformational stabilities . However , it appears that it is not the conformational stability per se that controls the replication rate of rPrPSc , as the observed faster replication of MM1 sCJD prions when compared to MM2 counterparts would imply a paradoxical scenario in which higher stability of rPrPSc results in a faster replication tempo . Instead , our data strongly suggest that distinct replication rates of MM1 and MM2 sCJD prions are dictated by specific structural features of corresponding rPrPSc aggregates , features that control the intrinsic growth rate of these aggregates ( i . e . , the rate of templated conformational conversion of the PrPC substrate ) . Thus , the balance of factors controlling strain-specific replication tempo of sCJD prions appears to be diametrically different from that described for yeast prions [PSI+] that are associated with aggregation of Sup35 protein . In the latter case , the intrinsic elongation rate of Sup35 amyloid fibrils Sc4 corresponding to the stronger ( faster replicating ) prion phenotype is slower than that of fibrils Sc37 corresponding to the weaker phenotype , but this is more than compensated by lower stability ( and thus higher effective concentration of ends ) for Sc4 fibrils [39] . By contrast , the faster replicating strain of sCJD prion is characterized by higher conformational stability , implying that , in this case , the dominant factor in controlling the replication tempo is not prion stability but the intrinsic growth rate ( i . e . , the rate of the conversion of PrPC monomers ) . Considerable structural differences between type 1 and type 2 PrPSc in sCJD are especially intriguing given frequent coexistence of these two prion strains in affected individuals [20] . Whether this strain coexistence is the result of a primordial spontaneous misfolding or conformational evolution due to the template flipping during passage through cells expressing different post-translationally modified PrPC remains to be determined [19] . It should also be noted that type 1 and type 2 sCJD prions represent only a small fraction of the spectrum of human prions . It is likely that the structural variability among PrPSc corresponding to different familial forms of human prion diseases might be even larger than the extent of structural differences described herein for type 1 and type 2 sCJD prions .
All procedures were performed under protocols approved by the Institutional Review Board at Case Western Reserve University . In all cases , written informed consent for research was obtained from patients or legal guardians and the material used had appropriate ethical approval for use in this project . All patients’ data and samples were coded and handled according to NIH guidelines to protect patients’ identities . We selected two representative subjects from a group of 340 patients with definitive diagnosis of sCJD . The criteria for inclusion were: ( 1 ) availability of clinical diagnosis of CJD according to WHO criteria [44 , 45] and clearly determined and dated initial symptoms upon neurologic examination to ascertain the disease duration; ( 2 ) methionine homozygous at codon 129 of the human prion protein ( PrP ) gene ( PRNP ) ; ( 3 ) unequivocal classification as pure Type 1 or Type 2 sCJD according to WB pattern; ( 4 ) unequivocal classification of pathology as definite Type 1 or 2 at the National Prion Disease Pathology Surveillance Center ( NPDPSC ) in Cleveland , Ohio; ( 5 ) demographic data distribution within 95% confidence interval of the whole group , resulting in no difference between selected cases and the whole group in any of the statistically followed parameters . The purification of rPrPSc from human brains was performed as described previously for 263K prions from Syrian hamster brains [21] with the following additional steps . The partially purified samples containing ~10 μg of human PrPSc were resuspended in PBS , pH7 . 4 containing 2 mM CaCl2 and 2% Sarkosyl , sonicated in a sonication bath ( 3 x 5 s ) , and incubated with 70 μg/ml of Collagenase ( Worthington Biochemical Corporation ) with shaking at 600 rpm in Eppendorf Thermomixer for 4 h at 37°C . After adding MgCl2 to a final concentration of 5 mM , the samples were incubated with 50 IU/ml of Benzonase ( Novagen/EMP ) for additional 1 h at 37°C , followed by 1 h incubation with 100 μg/ml of proteinase K ( Amresco , Solon , OH/ Invitrogen ) at 37°C . The PK was blocked with protease inhibitor ( PI ) cocktail containing 0 . 5mM PMSF , and 5 μg/ml of aprotinin and leupeptin , respectively . The pellet obtained after centrifugation ( 30 min , 18 , 000 x g , 4°C ) in Allegra centrifuge equipped with F2402H rotor was resuspended in 400 μl of 10% NaCl containing 1% Sarkosyl and PI cocktail , and spun again . The final pellet was resuspended in PBS containing 2% Sarkosyl and PI cocktail ( 1:1000 , v/v ) , and delipidated overnight with four volumes of Methanol/Chloroform ( 2:1 , v/v ) at -20°C . Finally , the sample was collected by centrifugation , resuspended in water containing 0 . 1% Sarkosyl and stored at -80°C . The purified rPrPSc was analyzed by SDS PAGE followed by silver staining and/or western blots , and by conformation-dependent immunoassay ( CDI ) . The latter assay was performed as described previously [6 , 14] with the following minor modifications . First , we used white Lumitrac 600 High Binding Plates ( E&K Scientific , Santa Clara , California ) coated with mAb 8H4 ( epitope 175–185 ) [46] in 200 mM NaH2PO4 containing 0 . 03% ( w/v ) NaN3 , pH 7 . 5 . Second , aliquots of 20 μl from each fraction containing 0 . 007% ( v/v ) of Patent Blue V ( Sigma ) were directly loaded into wells of white strip plates prefilled with 200μl of Assay Buffer ( Perkin Elmer , Waltham , Massachusetts ) . Finally , the captured PrP was detected by a europium-conjugated [47] anti-PrP mAb 3F4 ( epitope 107–112 ) and the time-resolved fluorescence ( TRF ) signals were measured by the multi-mode microplate reader PHERAstar Plus ( BMG LabTech , Durham , North Carolina ) . The recHuPrP ( 90–231 , 129M ) and PrP ( 23–231 , 129M ) used as a calibrant in the CDI was prepared and purified as described previously [48] . The conformational stability of rPrPSc was determined with CDI as described previously [14 , 47] and the raw CDI signal was converted into the apparent fractional change and fitted by least square method with a sigmoidal transition model to determine GdnHCl concentration where 50% of PrPSc is unfolded ( [Gdn HCl]1/2 ) [14] . The sedimentation velocity and mass of sCJD prions was determined with calibrated sucrose gradient ultracentrifugation as described [13] . The Quaking-induced Conversion ( QuIC ) [49] and sonication-driven serial Protein Misfolding Cyclic Amplification ( sPMCA ) [50] procedures were performed essentially as described previously [13 , 14] . Briefly , rhuPrP ( 23–231 , 129M ) used as a substrate in QuIC was expressed , purified , and refolded to α-helical conformation as described previously [48] , and its initial concentration was calculated from absorbance at 280 nm using the molar extinction coefficient 56650 M-1cm-1 . The stock of rhuPrP ( 23–231 ) in 10 mM sodium acetate buffer , pH 4 . 0 , was pretreated with 12 mM HCl [rhuPrP:HCl ratio ( v/v ) of 1:3 . 9] for 7 . 5 min and immediately diluted to a final concentration of 0 . 1 mg/ml into the reaction buffer composed of 20 mM NaH2PO4 , 130 mM NaCl , pH 6 . 9 , and containing 0 . 1% SDS , 0 . 1% Triton X-100 , and 1:5000 ( v/v ) N2 ( Invitrogen , Carlsbad , California ) . The QuIC was performed with final volume of 100 μl per well in a sterile V-bottom , low-binding polypropylene 96-well plate ( VWR , Arlington Heights , Illinois ) equipped with a 3 mm diameter PTFE bead ( Fisher Scientific , Pittsburgh , Pennsylvania ) in each well . The aliquots of sCJD brain homogenates were diluted into the complete QuIC reaction buffer to obtain final 10-4 dilution of sCJD prions , and the plates were sealed with sterile AxyMat Silicone Sealing Mat ( VWR , Arlington Heights , Illinois ) . The QuIC was performed in samples seeded with sCJD PrPSc at 55°C for 20 hrs in an Eppendorf Thermomixer ( Eppendorf , Hauppauge , New York ) set for 1 min shaking at 1400 rpm , followed by 1 min incubation . The reaction was stopped by adding to each well 50 μl of PBS ( pH 6 . 9 ) containing 3% ( w/v ) Sarkosyl and Proteinase K ( PK; Amresco , Solon , Ohio ) to obtain the final Sarkosyl concentration of 1% ( w/v ) and PrP/PK ratio of 10:1 ( w/w ) . The plates were incubated for 1 h at 37°C with shaking at 1200 rpm on the Eppendorf Thermomixer with 1 min intervals . The PK was blocked in each well with protease inhibitors ( 0 . 5 mM PMSF , 5 ug/ml of aprotinin and leupeptin ) and the PK-resistant PrP was measured with CDI [13 , 14] . Sonication-driven serial Protein Misfolding Cyclic Amplification ( sPMCA ) of sCJD samples was performed as described [50] with the following modifications . Human PrPSc was replicated using brains of transgenic mice overexpressing human PrP with methionine at position 129 [51] . The 10% brain homogenates from sCJD patients were diluted 1000-fold into 10% normal brain homogenate and 100 μl was transferred into 0 . 2 ml PCR tubes equipped with 2 . 38 mm diameter PTFE ball ( K-mac Plastics , Wyoming , Michigan ) . Tubes were positioned on an adaptor placed on the plate holder of a microsonicator ( Misonix Model 3000 , Farmingdale , New York ) programmed to perform cycles of 60 min incubation at 32°C followed by a 30 s pulse of sonication set at 80% power . Samples were incubated , without shaking , and immersed in the water of the sonicator bath . After a round of 24 cycles , a 10 μl aliquot of the amplified material was diluted into 90 μl of normal transgenic mouse brain homogenate and a new round of 24 PMCA cycles was performed . This procedure was repeated four times to reach a final 106-fold dilution of the initial sCJD brain homogenate , and the replication rate was calculated from PrPSc content measured before and after sPMCA with CDI [13 , 14 , 19] . To initiate deuterium labeling , 10 μl aliquots of purified sCJD rPrPSc ( ~1 . 8 μg ) were collected by centrifugation ( 21000×g , 30 min , 4°C ) and added to 100 μl of 10 mM phosphate buffer ( pH 7 . 3 ) in D2O . After incubation at room temperature for different time periods , samples were collected by centrifugation and dissociated into monomers by adding 20 μl of ice cold 100 mM phosphate buffer ( pH 2 . 5 ) containing 7 M GdnHCl and 0 . 1 M Tris ( 2-carboxyethyl ) phosphine hydrochloride . After 5 minutes incubation ( ~0°C ) , the solution was diluted 10 times with ice cold 0 . 05% trifluoracetic acid and digested for 5 min with pepsin as described previously [23] . The peptic fragments were collected in a C18 trap column ( Symmetry C18 NanoEase , Waters , USA ) , washed to remove salts , and eluted on a UPLC BEH-C18 HPLC column ( Waters , USA ) using a gradient of 2–45% acetonitrile at a flow rate of 23 μl/min . Peptides separated on the column were analyzed by an LTQ Orbitrap XL mass spectrometer ( ThermoElectron , San Jose , CA ) . To minimize back-exchange , both the trap and the analytical column were placed in a cooled chamber ( ~2°C ) integrated with a LEAP TriValve system ( LEAP Technologies , USA ) . The extent of deuterium incorporation in each peptic fragment was determined from mass spectra ( with a correction for back-exchange ) as described previously [23] . For these measurements , samples of purified sCJD rPrPSc from human brain ( ~3 μg ) were suspended in D2O buffer ( 10 mM sodium phosphate , 10 μM EDTA , 50 μM Pefabloc , 1 ug/ml Aprotinin , pH 9 . 0 ) . After incubation for 5 days at 37°C , samples were collected by centrifugation and deglycosylated with PNGase F . To obtain fragments containing single His residues , samples were then digested with immobilized pepsin , followed by digestion with immobilized trypsin . Finally , the peptic fragments were separated on an UPLC column and analyzed by mass spectrometry as described above for HXMS experiments . The pseudo-first-order rate constant ( k ) of His hydrogen exchange reaction was determined by the equation: k = -ln{1-[ ( ( R ( t ) —R ( 0 ) ) / ( ( 1 + R ( t ) —R ( 0 ) ) ] x 1/P}/t , where P is the fractional D2O content in the solvent , R is the ratio of M+1/M isotopic peak of a given peptide before ( time = 0 ) and after the H/X reaction ( time = t ) . The half-life ( t1/2 , days ) of His exchange reaction was calculated using the equation: t1/2 ( day ) = ln2/k/24 , where k ( hour-1 ) is the rate constant at the alkaline conditions ( pH = 9 ) [34 , 35] . | Sporadic Creutzfeldt-Jakob disease ( sCJD ) represents ~90% of all human prion diseases worldwide . This neurodegenerative disease , which is transmissible and invariably fatal , is characterized by variable progression rates and remarkable diversity of clinical and pathological traits . The infectious sCJD prions propagating the pathology mainly in the brain are assemblies of abnormally folded isoform ( PrPSc ) of a host-encoded prion protein ( PrPC ) . The structure and replication mechanisms of human prions are unknown , and whether the PrPSc subtypes identified by proteolytic fragmentation represent distinct strains of sCJD prions has been debated . Here , we isolated sCJD prions from patients with two very distant phenotypes of the disease , compared their structural organization using recently developed biophysical techniques , and investigated their replication in vitro . Our data indicate that these sCJD prions are characterized by different secondary structure organization and quaternary packing arrangements , and that these structural differences are responsible for distinct prion replication rates and unique phenotypic characteristics of the disease . Furthermore , our analysis reveals that , contrary to previous observations for yeast prions , the replication tempo of sCJD prions is determined not so much by their conformational stability but by specific structural features that control the growth speed of prion particles . | [
"Abstract",
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"Results",
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] | [] | 2015 | Structural Determinants of Phenotypic Diversity and Replication Rate of Human Prions |
Gibbon species have accumulated an unusually high number of chromosomal changes since diverging from the common hominoid ancestor 15–18 million years ago . The cause of this increased rate of chromosomal rearrangements is not known , nor is it known if genome architecture has a role . To address this question , we analyzed sequences spanning 57 breaks of synteny between northern white-cheeked gibbons ( Nomascus l . leucogenys ) and humans . We find that the breakpoint regions are enriched in segmental duplications and repeats , with Alu elements being the most abundant . Alus located near the gibbon breakpoints ( <150 bp ) have a higher CpG content than other Alus . Bisulphite allelic sequencing reveals that these gibbon Alus have a lower average density of methylated cytosine that their human orthologues . The finding of higher CpG content and lower average CpG methylation suggests that the gibbon Alu elements are epigenetically distinct from their human orthologues . The association between undermethylation and chromosomal rearrangement in gibbons suggests a correlation between epigenetic state and structural genome variation in evolution .
Gibbons ( Hylobatidae ) are small arboreal apes that inhabit the tropical and semi-deciduous forests of Southeast Asia and a portion of South- and East-Asia; their closest relatives are the great apes ( human , chimpanzee , gorilla and orangutan ) . They are an excellent model in which to study mechanisms of chromosomal rearrangement during evolution , because their chromosomes have been accumulating changes at an accelerated rate in comparison to other apes [1]–[3] . As a result of this instability , the four genera of the gibbon family possess four different karyotypes ( 2n from 38 to 52 ) . The genome shuffling observed in gibbons is in striking contrast to the high degree of karyotype conservation found in the other hominoids: there is only a single inter-chromosomal rearrangement separating humans from the great apes [4] , but more than 40 such rearrangements have taken place on the gibbon lineage . Recent estimates based on the inferred karyotype of the common gibbon ancestor suggest that the rate of chromosomal rearrangements in these species is 20 times higher than in other primates [3] . Given the great taxonomic diversity found within the family ( four genera and fifteen species ) , it is tempting to speculate that segregating chromosomal changes mediated the speciation events in a relatively short time . The cause of this abundance of chromosomal changes is still undefined [5] . Primate genomes harbor millions of interspersed repetitive elements [6] , creating numerous opportunities for Non-Allelic Homologous Recombination ( NAHR ) events to produce deletions , duplications and chromosomal rearrangements . Chromosomal rearrangements caused by NAHR are nevertheless quite rare , and even on an evolutionary time scale mammalian chromosomes have proven to be very stable . Comparison of multiple mammalian karyotypes indicates that the average rate of gross chromosomal rearrangements is only approximately two events over 10 million years [7] . Many repetitive DNA elements are rich in CpGs , which in mammalian cells are typically methylated . CpG methylation is an essential component of epigenetic mechanisms that maintain repetitive elements in a transcriptionally repressed state , thereby suppressing their proliferation [8] , [9] . Cancer cells frequently exhibit a global decrease in genomic 5-methylcytosine , and it has been speculated that hypomethylation of repeat elements is an underlying factor in the high frequency of chromosomal rearrangements in cancer cells [10] . In search of an explanation for the abundance of evolutionary chromosomal changes in gibbons , we have now characterized the sequence and molecular structure of 57 breakpoint sites in the northern white-cheeked gibbon ( Nomascus leucogenys leucogenys , NLE ) . We had previously created a high-resolution physical map of the break of synteny regions for this species [11] , using the human genome as a reference . This map allowed us to localize the breakpoints within an 80 Kbp range . We have identified an association between the breakpoints and Alu retroelements , and we find that Alu elements in the gibbon are undermethylated in comparison to their human orthologues . Our findings suggest that epigenetic activity of Alu sequences may have facilitated karyotypic evolution and disruption of the uniform rate of chromosomal changes in gibbon species .
To identify the breakpoints at the sequence level we selected 80 Bacterial Artificial Chromosome ( BAC ) from the Nomascus leucogenys leucogenys ( NLE ) genomic BAC library ( CHORI-271 ) spanning the breakpoints of translocations and inversions . These BACs were selected from a high-resolution map that we constructed [11] and from a complementary list of gibbon BACs identified as spanning breakpoints by BAC End Sequencing ( BES ) . Out of these 80 BACs , 23 were sequenced using a shotgun approach and assembled to high quality sequence ( Table S1 ) . The final assembled sequences were individually aligned by BLAT [12] to the most recent human genome assembly ( hg18 ) , and we identified the breakpoints between human and gibbon at the base pair level ( Table S2 ) . As we sequenced the BACs , we discovered multiple breakpoints inside the same clone in 8 of the 23 BACs including two cases previously reported by us . The complex structure of three of these BACs may be explained by their centromeric location in the gibbon ( Table S2 ) . In a few instances the presence of human segmental duplications did not allow for unambiguous mapping . To enrich our breakpoint dataset in a cost effective way , we pooled and shotgun sequenced at lower coverage the remaining 57 gibbon BACs ( Protocol S1 ) . This approach added 33 breakpoints to our dataset: 25 at the base pair level and 7 at the resolution of a small insert clone ( about 6 Kbp ) ( Table S2 ) . The remaining breakpoints could not be identified , due either to densely repeated regions or to lack of coverage . This brought the final number of breakpoints to 68 ( 57 at the base pair level ) . These results indicate that the frequency of breakpoints is higher than BES mapping alone can estimate . Hence assembly of the gibbon genome will be necessary to pinpoint all the breakpoints . In a previous study we uncovered a significant association between gibbon break of synteny regions , identified at a resolution of 80 Kbp , and human segmental duplications ( hSD ) [11]: 42% of the breakpoints were found to overlap with at least one hSD . In the current study we were able to identify the breakpoints at a higher resolution . This allowed us to further examine their relationship with SDs by measuring the correlation between a 1 Kbp window ( −/+500 bp ) including the breakpoint mapped on human and hSDs . We found that 15% of the breakpoints overlap with at least one hSD , which is significant ( p = 0 . 0002 ) based on a random sampling simulation ( performed as described in Materials and Methods ) ( Figure S1A ) . Recent studies have shown that a burst in duplication occurred in humans and chimpanzees after their divergence from other hominoids [13]–[15] . Thus we assume that the hSDs do not always correspond to gibbon SDs ( gSD ) . As an assembled gibbon genome is not yet available , we used two methods to identify gSDs . First , we performed array-comparative genomic hybridization ( array-CGH ) of gibbon genomic DNA against human genomic DNA . This experiment allowed identification of large ( >300 Kb ) duplications/deletions that distinguish the two species . Second , following the method described by Bailey et al . [16] , we mapped gibbon reads from the Trace Archives onto the human genome and identified putative gSD regions by detecting a higher depth of coverage by the reads ( supporting online material ) . Of the gSDs identified by array-CGH , 37% were also identified as putative gSDs based on read coverage . Using the random sampling simulation approach mentioned above , we noticed that the overlap between the gibbon breakpoints and the gSDs is extremely large and statistically significant ( Figure S1B ) , more than the overlap observed for the hSD . Examples of gibbon segmental duplications in breakpoints that could be detected by FISH are shown in Figure 1A . Even though the array-CGH and read-coverage-based gSD datasets do not show exact correspondence , we observed a significant correlation ( p = 7 . 63e-9 by Fisher's Exact Test ) . We also validated , by Fluorescence in situ Hybridization ( FISH ) , 11 duplications and 11 deletions identified by both methods ( Figure 1B ) . Of note , the array-CGH results showed an excess of deletions in the gibbon relative to human ( data not shown ) . We verified that 30% of the deletions are regions that are present in human at a higher copy number than in gibbon , confirming the occurrence of abundant human-specific duplication events . We looked at the relationship between breakpoints and genes . When mapped onto the human genome , 53% ( 36 out of 68 ) of the breakpoints occur within a gene and 19% occur within non-coding transcripts ( Table S2 ) . We hypothesize that when a breakpoint disrupts a gene , the selective pressure on the sequence should be reduced as a consequence of loss of function , unless the truncated protein is rescued and still functional . As a measure of relaxed selective constraint on these disrupted genes , we calculated the dN/dS ratio between non-synonymous ( dN ) and synonymous ( dS ) substitutions between human and gibbon ( using macaque as the outgroup ) . This analysis was carried out only on the 23 fully sequenced BACs ( Protocol S2 ) . The same method was applied to an equal number of randomly selected gibbon BACs sequenced by the NIH intramural sequencing center ( NISC ) comparative vertebrate sequencing project [9] ( Table S3 ) . This analysis showed a significant increase ( p = 0 . 01 , Mann-Whitney's U test ) in the dN/dS ratio of gibbon genes when the breakpoint BACs are compared to the NISC BACs ( Figure S2 ) . It is worth noting that the p value becomes even smaller when the genes at <50 Kbp distance are considered , indicating a possible position effect . To confirm this trend , we sampled additional gibbon genes located at 500 Kbp and 1 M bp from the breakpoints , and found no differences when gibbon was compared to macaque ( Table S4 ) . Frequently , genes affected by the breakpoints are part of clusters: the ABCC family on HSA 16 , the ABCA family on HSA 17 , the growth hormone cluster on HSA 17 , RFPL on HSA 22 , MUC4 and MUC20 on HSA3 , PLSCR ( phospholipid scramblase ) on HSA 3 . The association between breakpoints and gene-clusters has at least two biological implications . First , gene clusters result from duplication events that may cause genome instability through NAHR . Second , the presence of other genes with redundant functions could mitigate natural selection against chromosomal rearrangements that disrupt genes . The role of repeats in evolutionary or disease-causing chromosomal rearrangements is well documented [17]–[20] . We identified repeats within 150 bp of the 57 sequenced breakpoints with Repeat Masker . 81% of the breakpoints co-localized with at least one interspersed repeat . Alus and L1 LINEs are the most frequently represented , followed by simple repeats , as illustrated in Table 1 . In 11 instances , one or more repeats span the breakpoint site in the gibbon . This could result either from an insertion after the breakage , or from a recombination event ( Figure 2A ) . In the remaining cases , the repeats flank the breakpoint , and they are frequently truncated by the rearrangement event . Moreover , three breakpoints are next to blocks of repeats that were inserted sequentially in the gibbon genome , creating complex arrangements ( Table S2 ) . Out of the 57 breakpoints , 11 co-localize with simple repeats of various types . Most of these breakpoints ( 6 out of 11 ) overlap with ( AT ) n-rich repeats which are either gibbon specific ( CH271-254H12 , CH271-171B20 and CH271-122E24 ) or shared by human ( CH271-228C1 , CH271-86M19 , CH271-40A18 ) . A different case is the breakpoint of a translocation HSA 3–5 that falls in the intra-genic tandemly repeated region ( TR ) of the mucin gene MUC4 ( 3q29 ) . We were intrigued by the predominance of Alus at the breakpoint sites , as Alu-Alu recombination events have been reported as examples of Non Allelic Homologous Recombination ( NAHR ) [20] , [21] . We verified that the proportion of Alus associated with breakpoints was significant when compared to other repeats by using a random sampling simulation ( Figure S3A ) ( p = 0 . 001 ) . At the same time this method showed that the association with LINE L1 in human was lower than expected by chance ( Figure S3B ) . We then looked for features of Alus that may be distinctive in gibbon compared to other hominoids . To carry out this analysis we used the 23 assembled BACs to represent portions of the gibbon genome surrounding the breakpoints . First , we observed a decline in Alu density within the BACs with increasing distance from the breakpoints ( Figure 2B ) . Furthermore , Alu fragments at or near ( <150 bp ) the breakpoints were almost twice as CpG-rich as the remaining Alu sequences in the same BAC ( 4 . 5 CpGs/100 bp compared to 2 . 4 CpGs/100 bp; t-test p<0 . 001 ) . As shown in Figure 2C , the number of CpG doublets per 100 bp of Alu sequence declines rapidly as the distance from the breakpoint increases . Active Alus contain a relatively high number of CpG dinucleotides , which are linked to active retrotransposition [9] . Normally , the epigenetic apparatus of the cell suppresses the activity of retrotransposons by adding methyl groups to cytosines in CpGs [22] , [23] . Methyl-C tends to decay to T or A ( therefore CpG become TpG/CpA ) through a process known as CpG decay [22] . Our data thus suggest a higher concentration of “active” Alus associated with breakpoints . We hypothesized that the higher rate of chromosomal breakage observed in gibbons is due to an active epigenetic state of these elements in the gibbon as compared to the common ancestor of the hominoids; the higher CpG content of these Alus suggests that they have been less methylated and consequently that they may have a different epigenetic state . The hypothesis predicts reduced CpG methylation of the gibbon breakpoint Alus in comparison to their human orthologues . We tested this prediction by performing bisulfite allelic sequencing of 14 orthologous Alus in human and gibbon , 8 of which were located near the breakpoint sites ( <150 bp from the breakpoint ) and 6 Alus outside of breakpoint regions but with similar CpG content to the breakpoint Alus ( Materials and Methods and Table S5 ) . As orthologous Alus are inserted in the genome of the common ancestor , we can safely assume that the CpG groups had the same amount of time to be methylated . Our results ( Figure 3 ) demonstrate a significant reduction of CpG methylation in gibbon compared to human ( p<0 . 001 , Mann-Whitney U test ) .
Gibbon species carry an extraordinary number of chromosomal rearrangements , accumulated in a relatively short evolutionary time ( 15–18 mya ) . In order to uncover a possible genetic source for the genomic reshuffling observed in these species , we carried on a detailed analysis of 57 sequenced synteny breakpoints between the northern white cheeked gibbon ( NLE ) and human . Our molecular analysis revealed a scenario which , at a first glance , is similar to that described in other primates [19] , where segmental duplications and repeats play a major role in chromosomal rearrangements ( Figure 4 ) . But a broader analysis , which took into account epigenetic modifications , uncovered a possible explanation for the high frequency of evolutionary chromosomal changes . The gibbon breakpoints are associated with Alu elements that have an unusually high CpG content , and in the gibbon these Alu elements are less methylated than their human orthologues . This may indicate that the epigenetic state of these Alus has predisposed them to recombination . In this study we were able to confirm the correlation between breakpoints and human SD which we had reported previously [11] . The higher resolution achieved in the present study , and the availability of gibbon sequences , allowed us to confirm association of the breakpoints with gibbon-specific SDs . As many breakpoints could not be mapped , due to the presence of these duplications ( Table S2 ) , the overlap is very likely to be more frequent than we have been able to demonstrate . It is noteworthy that we found only two breakpoints where SDs were present in both gibbon and human . As the intersection between gSD and hSD over the whole genome is much higher ( 32% ) , this observation suggests that the chromosomal rearrangements are mainly associated with “species-specific” duplications . The two cases of breakpoints in shared duplications may be explained by independent reuse of a breakpoint in regions susceptible to rearrangements [24] . Nevertheless , we do not believe that SDs can be considered an underlying cause of the breakpoints , as we have only few examples of erroneous recombination events in these regions ( Table 2 ) . Very similar observations have very recently been reported [5] . When studying evolutionary chromosomal rearrangement , it is tempting to search for sign of selection on genes that have been disrupted by the breakages . Recent work by Girirajan et al . [5] found evidence that 3 of their 11 genes disrupted by breakpoints exhibited signatures of relaxed evolutionary constraint ( average dN/dS = 1 . 09 ) . Our approach was different , as we looked at all the genes within the sequenced BACs , and compared them with randomly selected regions of the gibbon genome . We did , however , identify 5 genes in our sample that are disrupted by breakpoints and for which we had adequate coverage . Although we found that their values of dN/dS ( average dN/dS = 0 . 56 ) were not as high as those reported by Girirajan et al . [5] , subsequent analysis on the remaining dataset of all non-disrupted genes located within 50 Kbp of a break point , revealed a significantly reduced difference of dN/dS between gibbons and macaques ( from p = 0 . 001 to p = 0 . 06 ) ( Table S4 ) . We hypothesize that some of these genes may still be functional , perhaps producing a smaller transcript , and that some may have become non-functional recently enough that non-synonymous substitutions have not had a chance to accumulate . Nevertheless , it appears that there are position effects on genes near to but not interrupted by breakpoints , perhaps due cis effects of chromatin in the breakpoint region , leading to changes in expression . A genome-wide expression assay would be needed to define the major trend for the genes that have been disrupted but this approach may be complicated by the scarcity of tissues available from this endangered species . Breakage regions were found to co-localize with repeats . Whereas the known link between simple repeats and fragile genomic regions makes this observation intriguing , it is difficult to predict a cause-effect relationship between these repeats and the gibbon breakpoints . For many breakpoints we could readily observe that simple repeats were the result of gibbon-specific insertions by the repair mechanism after the break occurred . We therefore defined them as “filling” ( Table 2 ) and we can assume that they followed the double-strand breaks . Our data point to a role for both Non-Allelic Homologous Recombination ( NAHR ) and non-homologous end joining ( NHEJ ) in double-strand break repair , with a prevalence of the latter . In 9 cases NAHR was driven by either Alu-Alu or SD mediated recombination ( Table S2 ) . In additional 15 cases , where long stretches of homology were not detected , we observed micro-homology or “filling” sequences which are both signs of NHEJ [25] . In NHEJ the double-strand breaks are fused together without a requirement for extensive homology . For the remaining breakpoints it was not possible to pinpoint a mechanism , even though the absence of homology would lead us to speculate that NHEJ or some other complex mechanism occurred in most of them [25] . While seeking a mechanism associated to the chromosomal reshuffling of gibbon species , our approach was to investigate Alu elements in more detail , given their higher concentration at the breakpoints . Independent evidence shows that this family of retrotransposons is particularly active in gibbons [26] , strengthening our hypothesis . Our in silico and experimental data suggested that CpG cytosines in Alus are less methylated in gibbon than in human . CpG methylation has a major role in epigenetic suppression of endogenous retroelements in mammals . If this mechanism is attenuated , the repeated DNA sequences may threaten genome integrity: demethylation leading to an open chromatin structure at repeated sequences may cause structural and numerical variations [10] . Multiple examples of correlations between methylation state and genome structural variation have recently come to light in cancer cells , where disrupted methylation patterns are common [10] , [27] . Furthermore , it was recently observed that hypomethylated blocks in tumor cell lines correspond to fragile regions of the genome and synteny breakpoints in the mouse [28] . This correlation suggests a common source of instability independent from genomic sequence and related to the epigenetic state of the DNA . O'Neill and colleagues showed that the genome of a hybrid between two species of Australian wallaby ( marsupials ) was hypomethylated when compared to the parental species [29] . In these hybrids a hypomethylated retroviral element was abnormally replicated causing an evident centromeric expansion . The same group also reported double-minute chromosome formation in mouse interspecific hybrids ( M . musculus×M . caroli ) [30] . Together with our findings this observation indicates that changes in methylation levels may explain perturbations of the uniform rate of genome evolution . Other mammal species ( dog , mouse and rat ) , display very rearranged karyotypes and it will be important to investigate if the scenario we described in the gibbon is common to these species as well . Nevertheless , at the moment , the resolution of the synteny breakpoints for these species is still very far from the one needed to carry out an analysis comparable to the one we performed on the gibbon genome . We have presented here a scenario that may explain the genome reshuffling observed in gibbon species: hypomethylation of certain Alu elements may predispose them to recombination . We are currently investigating the magnitude of the genome hypomethylation in gibbon repeat elements , and whether repeats other than Alu are involved . At the moment we can only speculate about the possible causes of the difference in levels of methylation of Alus that we observed in the gibbon . One hypothesis is linked to the observation that CpG methylation is disrupted in hybrids [29] , [30] . Population genetics theories propose that speciation may occur after hybrid recombination , followed by inbreeding and reproductive isolation due to the new genetic make-up . This idea is well accepted for plants , and it has recently been proposed for gibbon species [31] . Hybridization may have gradually disturbed the apparatus responsible for the methylation of repeats in the hybrids , leading to higher numbers of chromosomal rearrangements [30] . Very recently the implications of a specific class of small RNAs ( piRNAs ) in methylation of repeats have been discovered . A rapid divergence of these sequences during speciation could therefore explain the reduction in the cytosine methylation efficiency in cross-species hybrids [32] .
The statistical analysis of the breakpoints repeat and duplication content was performed with the help of a C# application written in-house [11] . Tracks of genome-wide repeat content for different subcategories of repeats and for segmental duplications content were prepared for input to the simulation software using data from http://genome . ucsc . edu human genome ( hg18 release ) . The measure we used counted up the existence of at least one element of the corresponding track in each region of the set and returned a detailed report for the set . To attain the simulation , the program reallocates randomly al the regions maintaining the chromosome of origin and size as the initial counterpart . The same measurements were taken for each random set after a reiteration of 5 , 000 times . The resulting sampling distribution was then plotted to compare the original set of regions with the global genomic landscape . The track relative to gibbon specific segmental duplication was built as result of our in silico analysis of the trace archives . Subsequently the latter tracks were used in order to perform different overlap measurements with the set of 57 breakpoints . When mapped on the human genome and the regions with ambiguous mapping are removed , the dataset corresponds to 120 regions of about 500 bp size ( on average ) . Another set which we called “stringent set” was also used to determine the overlap with hSD . In this set all the breakpoints form two BACs ( CH271-298N13 and CH271-372B11 ) known to be centromeric in the gibbon and containing multiple breakpoints , were excluded . Gibbon reads were downloaded from the NCBI Trace Archives and screened for quality . A total of 24 , 350 , 447 reads that passed quality screening were mapped to the human genome ( build NCBI 36 . 1 , UCSC hg18 ) using Pash [33] , [34] . In order to remove highly ambiguous mappings , reads mapping to >500 locations with a score within 6% of its top mapping score were removed from consideration . Furthermore , reads that overlapped by >75% with repeats , as identified by RepeatMasker [35] , were removed from consideration . A total of 15 , 518 , 707 mapped reads remained after filtering . Putative gibbon segmental duplications were identified following the method outlined in Bailey , et al . , 2002 [16] . The number of gibbon mapped reads was determined in 5000 bp windows across the human genome . The mean ( 31 . 11 ) and standard deviation ( 18 . 75 ) of mapped read counts was calculated across windows not overlapping with human segmental duplications . A read count cutoff of 3 standard deviations from the mean was applied meaning any 5000 bp region with >87 mapped reads was identified as a putative gibbon segmental duplication . This resulted in 1630 identified gibbon segmental duplications . 32 , 855 BACs , spanning 95% of the human euchromatic genome , have been assembled and re-arrayed into 384-well microtiter dishes [36] , [37] . DNA was purified , amplified using the DOP-PCR method , and spotted on CMT-GAPS coated glass slides ( Corning , UltraGaps ) . Genomic DNA from NLE was obtained from blood and anonymous human reference DNA was obtained from Children's Hospital Oakland Research Institute . Labeling and hybridization were performed essentially as described by [38] . Hybridization images were generated by scanning the slides on a 4000B scanner ( Axon ) . The images were first processed using GenePix Pro 5 . 1 ( Axon Instruments ) . The primary experimental data ( GenePix Results files ) were subjected to fully standardized data-analysis ( flagged spots removal , background subtraction and loess normalization ) by uploading them to the BASE micro-array analysis software installation [39] which performs standard normalization . The final output was human chromosome specific plots of Log2ratio values vs chromosome location as well as a whole genome view . Chromosome preparations were obtained from peripheral blood following standard procedures . Briefly , blood was incubated with cell culture media and phytohemagglutinin ( GIBCO ) for 72 hours ( 37°C , 5% CO2 ) . Colcemid was then added ( final concentration 0 . 05 ug/ml ) and cells were harvested after a 1 hour incubation . Cells were spun down by centrifugation , the media was discarded and the pellet was resuspended in 8 ml of hypotonic solution . After incubating for 20 minutes , the standard fixative solution ( 1 part Acetic Acid , 3 parts Methanol ) was added and cells were centrifuged at 2500 rpm for 5 minutes . The pellet was washed with fixative solution and cells were kept at 4°C overnight . DNA from BACs was extracted using PureLink Miniprep kit ( Invitrogen , Cat#K2100-10 ) . FISH experiments were performed essentially as described by Lichter et al . [41] . BACs were labeled either with Cy3-dUTP or FITC-dUTP by standard nick-translation assay . Images were acquired using Nikon 80i microscope , equipped with CCD camera Cool Snap HQ2 ( Photometrics ) and software Nis Elements Br ( NIKON ) . Elaboration of the images was done using Photoshop . Primers for 14 Alus ( Table S5 ) were designed using “MethPrimer” ( http://www . urogene . org/methprimer/ ) [42] making sure to target unique sequences flanking the Alu . Out of the 14 Alus , 8 were near the breakpoints ( <150 bp ) ; as our goal was to amplify Alus orthologous in human and gibbon , we had to take into account the synteny between human and gibbon and had to eliminate all the cases where the Alus were located across the breakpoint . The remaining Alus were located randomly in the gibbon genome but had to have a CpG content high enough to allow us to make a statistic . The genomic DNA from whole-blood from gibbon and human was bisulfite converted using EpiTect Bisulfite kit ( Qiagen , Cat . # 59104PCR ) and the amplification was performed using the FastStart Taq DNA Polymerase ( Roche , Cat#12032929001 ) . PCR products were purified and cloned using TA-cloning procedures ( Qiagen PCR cloning Kit , Cat . # 231124 ) . We sequenced 12 clones for each Alu in order to have fair representation of all the alleles . | Mammalian genomes are remarkably stable ( with few exceptions ) . In humans , wrong recombination events occur quite rarely , manifesting themselves in genomic disorders or cancer . On exceptional occasions , the rate of genome evolution has been accelerated by genome-wide reshuffling events giving rise to some highly derivative karyotypes . The genomes of gibbon species ( Hylobatidae ) are an example of accelerated genome structural evolution; gibbons display a rate of chromosome evolution 10–20 fold higher than the default rate found in mammals ( one chromosome change every 4 million years ) . As we are interested in investigating the possible genetic causes of this phenomenon , we sequenced a considerable number of chromosomal breakpoints in the northern white-cheeked gibbon genome and analyzed the genomic features of these sites . We observe that the gibbon breakpoints are mostly associated with endogenous retrotransposons called Alus , which are normally abundant in the genomes of primates . Furthermore , our analysis revealed that gibbon Alus have a lower content of methylated CpG when compared to the orthologous human Alus . In mammals , CpG methylation is known to be responsible for keeping retrotransposons in a repressed state and protect genome integrity . We therefore suggest that a glitch in the methylation apparatus might have driven the higher genome recombination in gibbons . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
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"Methods"
] | [
"molecular",
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"evolutionary",
"biology/evolutionary",
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"genetics"
] | 2009 | Evolutionary Breakpoints in the Gibbon Suggest Association between Cytosine Methylation and Karyotype Evolution |
The filoviruses , which include the marburg- and ebolaviruses , have caused multiple outbreaks among humans this decade . Antibodies against the filovirus surface glycoprotein ( GP ) have been shown to provide life-saving therapy in nonhuman primates , but such antibodies are generally virus-specific . Many monoclonal antibodies ( mAbs ) have been described against Ebola virus . In contrast , relatively few have been described against Marburg virus . Here we present ten mAbs elicited by immunization of mice using recombinant mucin-deleted GPs from different Marburg virus ( MARV ) strains . Surprisingly , two of the mAbs raised against MARV GP also cross-react with the mucin-deleted GP cores of all tested ebolaviruses ( Ebola , Sudan , Bundibugyo , Reston ) , but these epitopes are masked differently by the mucin-like domains themselves . The most efficacious mAbs in this panel were found to recognize a novel “wing” feature on the GP2 subunit that is unique to Marburg and does not exist in Ebola . Two of these anti-wing antibodies confer 90 and 100% protection , respectively , one hour post-exposure in mice challenged with MARV .
Filoviruses are filamentous , enveloped viruses that can cause highly lethal hemorrhagic fever in both humans and non-human primates . The filovirus family includes the major genera ebolavirus and marburgvirus and the newly discovered cuevavirus . In the ebolavirus genus are five known species: Ebola virus ( EBOV ) , Sudan virus ( SUDV ) , Bundibugyo virus ( BDBV ) , Reston virus ( RESTV ) , and Taï Forest virus ( TAFV ) . In the marburgvirus genus , there is one species , the eponymously named Marburg virus ( MARV ) [1] . MARV is further subdivided into different strains , including Ci67 , Musoke , Ravn and Angola . Ravn is the most divergent strain of MARV , differing by 21% in genomic sequence from other Marburg strains [2] , and is sometimes referenced as a separate filovirus species . Marburg virus was the first filovirus to be identified when it sickened laboratory workers handling infected animals originating from Uganda in 1967 [3–5] . Marburg virus has since re-emerged at least 8 times , and has been imported to the United States and Europe by travelers who became infected in Africa [6–9] . Angola , the most lethal strain of Marburg virus [10] , emerged in 2004 and caused the largest MARV outbreak known to date with an extremely high case fatality rate of 88% [11] . The emergence of Ebola virus in West Africa in 2014 has caused an outbreak unprecedented in magnitude , and is a grim reminder of the devastation that can be caused by filoviruses . The filoviruses present a single viral protein on their envelope surface , the glycoprotein ( GP ) , which is responsible for attachment and entry of viruses into target cells . GP is expressed as a precursor that is cleaved by furin in the producer cell to yield two subunits: GP1 and GP2 , which remain linked by a disulfide bond [12 , 13] . GP1 contains the putative receptor-binding region [14] , as well as two heavily glycosylated domains: a glycan cap which sits immediately atop the putative receptor-binding site and a larger , largely unstructured mucin-like domain [15 , 16] . The mucin-like domains contain a dense clustering of N- and O-linked glycans and likely mask the GP from immune surveillance [17 , 18] . The second subunit of GP , termed GP2 , possesses the transmembrane domain that anchors GP into the viral surface and the hydrophobic fusion peptide required for fusion . In ebolaviruses , the furin cleavage site lies at residue 501 and the entire mucin-like domain is attached to the GP1 subunit . In Marburg virus , however , the furin cleavage site lies at residue 435 , splitting the mucin-like domain so that a portion of it remains attached to the GP2 subunit [19] . We have termed this 66 amino-acid N-terminal GP2 extension the “GP2 wing” . After cell entry by macropinocytosis [20 , 21] filovirus GP undergoes additional cleavage by host cathepsin proteases in the endosome [22 , 23] . This cleavage event removes the glycan cap and the mucin-like domain , resulting in a loss of over 70% of the molecular mass of GP [23–25] . Endosomal cleavage renders GP competent for receptor binding [22 , 26 , 27] , allowing the exposed GP1 head to bind a shared filovirus receptor , Niemann-Pick C1 ( NPC1 ) [28 , 29] . Although antibodies that broadly cross-react among ebola- and marburgvirus GPs would be highly desirable , only one such antibody , MR72 , has been described [30] . Recent work in non-human primates has demonstrated that passive administration of monoclonal antibody ( mAb ) cocktails against GP can provide highly effective post-exposure therapy for EBOV infection [31–35] . Polyclonal sera against Marburg virus has shown similar efficacy , suggesting that antibodies could also be a viable treatment option for MARV infection [36] . However , fewer monoclonal antibodies , from which such cocktails could be developed , are currently available for MARV . One human survivor panel has recently been described; most of these mAbs compete for the same site on the GP1 core [16 , 30] . Antibodies targeting other epitopes on Marburg GP would be desirable in order to form a treatment cocktail . In general , monoclonal antibody cocktails are most effective when the component antibodies display synergistic effects . Combining mAbs with non-overlapping epitopes can significantly increase the overall potency of the cocktail over the individual mAbs alone [37] , and can mitigate antigenic escape by the virus [38 , 39] . Anti-viral antibodies are often selected based on neutralization , or the ability of the mAbs to prevent viral entry into target cells in vitro . However , for filoviruses as well as other viruses , neutralization in vitro does not necessarily correlate with protection in vivo [40 , 41] . Non-neutralizing antibodies are known to confer protection by antibody-dependent cellular cytotoxicity ( ADCC ) , phagocytosis , prevention of virus budding , and other mechanisms [42 , 43] . Indeed , one successful anti-EBOV oligoclonal cocktail is composed entirely of antibodies that are not potent neutralizers [32 , 44] . In this study we produced a diverse panel of antibodies against Marburg virus by immunization of mice with different strains of the surface GP antigen . Immunogens included GP1-mucin-deleted ectodomains ( GPΔmuc ) from Marburg strains Ci67 , Musoke , Angola , and Ravn . Mucin-deleted immunogens were used to direct the immune response away from the highly variable mucin-like domains . Ten antibodies were chosen and analyzed for in vitro neutralization , in vivo efficacy , and biochemical recognition of MARV and EBOV GPs . Antibodies against multiple epitopes were found . Four antibodies target a novel MARV-specific “wing” epitope on GP2 ( 30G3 , 30G4 , 30G5 and 54G2 ) , and confer 60–100% protection in mice challenged with MARV . A separate MARV-specific antibody , 9A11 , directed against an epitope in GP1 , confers 65% protection . Another mAb directed against GP1 , 40G1 , confers 40% protection and was found to be broadly cross-reactive among the core of filovirus GPs , including both marburg- and ebolaviruses .
To generate MARV GP-specific mAbs , BALB/c mice were immunized with GPΔmuc antigens from either MARV strain Ci67 , Musoke , Angola , or Ravn ( Fig 1A ) . Mice for each subset were immunized and boosted with the same antigen with the exception of the 54 series ( 54G1 , 54G2 , 54G3 ) . Eight of the ten mAbs in the panel are mouse IgG1 . The remaining two mAbs , 9A11 and 2D8 , are IgG2a ( Fig 1A ) . To characterize the binding of mAbs , we performed enzyme-linked immunosorbent assays ( ELISAs ) with recombinant GPs from four MARV strains , and determined EC50 values for binding with different forms of MARV Ravn GP: GP , GPΔmuc , GPcl ( Fig 2A ) . All ten mAbs exhibit medium binding ( EC50 between 20ng/ml and 200ng/ml , colored dark yellow ) to high binding ( EC50 <20ng/ml , colored orange ) against Ravn GP and GPΔmuc ( Fig 1B ) , but only seven of the mAbs cross-react with GP from other MARV strains ( Fig 1C ) . All mAbs bind the protease-cleaved Ravn GP core , termed GPcl , as well as GPΔmuc , with the exception of 9A11 . Antibody 9A11 exhibits an 8-fold decrease in binding to GPcl as compared to GPΔmuc ( Fig 1B ) . Additionally , to evaluate whether the mAbs have the capacity to bind cell-surface GP , ELISAs were performed with virus-like particles ( VLPs ) bearing full-length wild-type MARV Ravn GP . Eight mAbs bind as well ( or nearly as well ) to VLPs as purified recombinant Ravn GP . In contrast , 2A12 exhibits nearly 10-fold weaker binding to VLPs than to GP ectodomain , and 54G3 binding to VLPs is lost at the highest concentration tested ( Fig 1B ) . To determine antibody epitopes , we performed western blotting with Ravn GP and pepscan analysis with overlapping 15-mer pins of peptides from Ravn or Musoke GP . Five of the mAbs bind GP1 , and five bind GP2 by western blot ( S1 Fig panel A ) . Pepscan identified linear epitopes for only four mAbs , 30G3 , 30G4 , 30G5 and 54G2 , all of which overlap within residues 451–471 ( Fig 2B ) . This shared region lies in an extension of GP2 that is specific to MARV ( as a result of the furin cleavage site shift from 501 in EBOV to 435 in MARV ) , which we have termed the GP2 wing ( Fig 2A ) . In order to confirm the pepscan results , we engineered a GPΔmuc with an additional deletion of residues 436–483 , termed GPΔmucΔw ( Fig 2A ) . Indeed , binding to GPΔmucΔw is lost for only the four anti-wing mAbs , whilst the remaining six mAbs against different epitopes do bind GPΔmucΔw ( S2 Fig ) . No definitive epitope information could be identified by pepscan for the remaining 6 antibodies , suggesting that these mAbs bind conformational epitopes . Sequence alignment of MARV GP residues 449–471 reveals that while Ci67 , Musoke and Angola are completely conserved in this region , Ravn has 4 unique residues . The most notable change is residue 465 , which is a Glu ( E ) in Ravn but a Lys ( K ) in the other strains ( Fig 2B ) . Wing mAbs 54G2 and 30G3 are specific for MARV Ravn . Correspondingly , ELISA data comparing binding of wild-type Ravn GPΔmuc to E465K Ravn GPΔmuc confirm that the presence of Lys at position 465 ( as exists in other strains of MARV ) likely hinders binding of 54G2 and 30G3 . 30G5 , however , still retains some binding to E465K , while 30G4 is unaffected by this mutation , retaining binding at 2 and 0 . 2ug/ml ( Fig 2C ) . These results agree with pepscan results ( based on 15-mer peptides overlapping by 5 amino acids ) which define the epitope for 30G4 as slightly shifted away from position 465 , towards the N-terminus of GP2 ( Fig 2B ) . This shift may explain why 30G4 is the most cross-reactive of the 4 anti-wing mabs ( Fig 1C ) . Antibodies were screened for in vitro neutralization using a VSV-pseudovirus containing MARV Ravn GP on the surface . Six of the ten mAbs exhibit partial neutralization at the highest concentration tested ( 50ug/ml ) , reducing entry by 35–55% . The remaining four mAbs do not neutralize ( Fig 3 ) . Notably , all five GP2-directed mAbs produced in this study exhibit some neutralization , while only one GP1-directed mAb , 9A11 , inhibits entry of Ravn GP pseudovirions . Polyclonal sera from mice that yielded the 30 series mAbs ( 30G3 , 30G4 and 30G5 ) reduces entry by only about 60% , suggesting that mAbs 30G3 , 30G4 , and 30G5 represent the maximum potency of the polyclonal population ( Fig 3 ) . Human survivor mAb MR78 was used as a positive control and reduces pseudovirion entry by almost 95% . All mAbs were evaluated for in vivo protection using BALB/c mice challenged with a lethal dose of MARV virus [45] . One hour after challenge with 1 , 000 pfu mouse-adapted MARV Ravn , mice were treated IP with 500 μg purified mAb . Two separate studies were performed , with half of the mAbs repeated in both studies . Control animals in study #1 , treated with PBS , exhibited 1/10 survival ( Fig 4A ) . Both control groups in study #2 , treated with PBS or anti-HA mAb , exhibited 0/10 survival ( Fig 4B ) . MARV mAb treatment groups varied widely in efficacy , ranging from 0–100% protection . All four mAbs against the GP2 wing were found to be moderately or highly protective: mAb 30G3 conferred 70% survival ( 14/20 ) , mAb 30G4 60% survival ( 6/10 ) , mAb 30G5 100% survival ( 20/20 ) , and mAb 54G2 90% survival ( 18/20 ) . Monoclonal antibody 9A11 , against GP1 , conferred 65% survival ( 13/20 ) ( Fig 4A and 4B ) . Other mAbs against the GP1 core exhibited 0–40% survival; of these , only 40G1 offered strongly significant protection ( P value 0 . 0029 ) . The only mAb against a GP2 epitope other than the wing , mAb 54G1 , exhibited zero protection ( 0/10 ) ( Fig 4A ) . In both studies , mice in all treatment groups displayed an elevation of disease score by Day 4 ( Fig 4A and 4B ) , and there were no significant differences in weight loss between treatment groups and control groups . In study #1 , 30G5-treated mice faired only modestly better than the other groups , reaching a disease score maximum of 2 and fully recovering by Day 9 ( Fig 4A ) . Two of the highly cross-reactive MARV antibodies , mAbs 40G1 and 2D8 , also exhibit binding to Ebola , Sudan , Bundibugyo and Reston virus mucin-deleted GPs by ELISA ( Fig 5A ) . Binding curves show that the affinity of 40G1 and 2D8 for mucin-containing EBOV GP is weak , affinity for GPΔmuc is stronger , and binding to EBOV GPcl ( the receptor-binding competent core ) is strongest and equal to that of MARV GPcl ( Fig 5B ) . Hence , the 40G1 and 2D8 epitopes are conserved across the filovirus family , exposed on all versions of Marburg virus GP , but masked on ebolavirus GP by the mucin-like domain and the glycan cap . Single particle electron microscopy of the most protective anti-GP1 ( 9A11 ) and anti-GP2 ( 30G5 ) antibodies was performed in complex with purified antigen . Negative stain 2D class averages of 9A11 Fabs in complex with MARV Ravn GPΔmuc show one , two , or three Fabs bound to the dense trimeric GP core ( Fig 6A ) . In contrast , 2D class averages of the anti-GP2 wing mAb 30G5 in complex with MARV Ravn GPΔmuc show a single Fab bound to GP , at a distance further away from the high density GP trimer ( Fig 6B ) . Deuterium exchange mass spectrometry ( DXMS ) studies suggest this GP wing region is unstructured and likely flexible ( S2 Fig ) . To ensure that the wing epitope is not artificially positioned in GPΔmuc as compared to the biologically relevant mucin-containing GP , we also performed EM with 30G5 Fab in complex with the complete ectodomain of MARV Ravn GP . Images obtained were similar to those with GPΔmuc , with only one Fab binding per trimer ( Fig 6C ) . Likely footprints of Fabs 9A11 and 30G5 are drawn onto the MARV Ravn GPcl crystal structure ( Fig 6D ) .
In this study , a small panel of mAbs targeting MARV GP were isolated from immunized mice . Those that conferred the greatest in vivo protection are directed against a novel “wing” domain on MARV GP2 . This wing region is a MARV-specific portion of the mucin-like domain attached to GP2 . Such an epitope does not exist in ebolaviruses because the entire mucin-like domain is attached to GP1 . Although this study size was small , we note that GP2 wing-directed mAbs were only obtained when mice were immunized with mucin-deleted Ravn GP . It may be tempting to assume that this epitope is masked by the mucin-like domain; however , anti-wing mAbs are able to access their epitope on mucin-containing GP , neutralize pseuodviruses bearing mucin-containing GP and provide in vivo efficacy when challenged with Marburg virus . We believe that the elicitation of anti-wing antibodies when using Ravn GPΔmuc may instead result from the greater homogeneity and stability of Ravn GPΔmuc over other MARV antigens . A seven-year protein engineering effort in our laboratory to identify crystallizable versions of MARV GP indeed found that GPs produced from strain Ravn are the most homogenous , and have a lesser tendency to aggregate than those from other strains of MARV [16] . The homogeneity may have lead to improved presentation of this protective epitope within this study . It is interesting to note that among this panel of murine mAbs and the recently published panel of human survivor mAbs [30] , no antibodies that bind the GP1- and GP2-containing base of MARV GP were identified . The “base” of GP is a common site of neutralization for the ebolaviruses and is the epitope target of anti-EBOV neutralizing antibodies KZ52 [15] , 2G4 and 4G7 [46] , as well as the anti-SUDV mAb 16F6 [47] . Perhaps the presence of the flexible GP2-wing in MARV blocks access to this site on the GP core . Nonetheless , antibodies directed against the GP wing itself do have the potential to be fully protective , and represent a novel epitope in MARV for therapeutic cocktail design . The most protective of these mAbs , 30G5 , is promising but only binds with high affinity to the GP from Ravn , and hence , protection by 30G5 against other MARV strains may be limited . In contrast , monoclonal antibody 30G4 only confers 60% efficacy , yet cross-reacts with mucin-containing GPs from four strains of MARV . However , 30G4 is a murine IgG1 , an isotype that typically exhibits weaker immune effector activity than murine IgG2a [48] . Replacement of the constant domain framework may improve its in vivo efficacy . In this panel , two mAbs against GP1 were identified which also bind the GP cores of ebolaviruses . These antibodies , 40G1 and 2D8 , bind all MARV GPs , but only bind Ebola , Sudan , Bundibugyo and Reston GP from which the mucin-like domain is deleted ( Fig 5 ) . Hence , these highly conserved epitopes are exposed on marburgvirus GPs , but masked on ebolavirus GPs . These observations parallel those obtained from a panel of anti-MARV GP antibodies isolated from a human survivor [30] , and support structural observations that the orientation of the mucin-like domains differs between EBOV and MARV [16] . Indeed , no cross-filovirus anti-GP antibody ( reactive to both ebola and marburg ) has yet been elicited by an ebolavirus GP immunogen , nor has any such antibody yet been isolated from an ebolavirus survivor . Although the filovirus cross-reactive mAb 40G1 confers only 40% survival , 40G1 or another antibody like it [30] may be useful in an immunotherapeutic cocktail because a highly conserved epitope would likely be less subject to antigenic escape . Antibody 9A11 is also directed against GP1 but its pattern of binding is distinct from 40G1 and 2D8 . 9A11 is the only mAb in this panel that has a lower affinity to GPcl than GP or GPΔmuc . This suggests that the epitope of 9A11 is partially lost upon cleavage and that 9A11 could be similar to a glycan cap binder like 13C6 or 1H3 for EBOV GP [46] . Unfortunately , due to the single preferred orientation of GPΔmuc + 9A11 Fab particles on negative stain EM grids , a high-resolution reconstruction could not be determined , and better understanding of the 9A11 epitope awaits further study . 9A11 affords 65% protection in vivo and is highly cross-reactive among MARV ectodomain GPs . For Ebola virus , in vitro neutralization is not necessarily an effective predictor of in vivo protection . One anti-EBOV cocktail is composed entirely of non-neutralizing or weakly neutralizing mAbs , yet still confers in vivo protection , presumably by recruiting immune effector function [44 , 49] . More recent cocktail formulations have included a mix of neutralizing and non-neutralizing antibodies [50] . In this study of mAbs against MARV , none of the mAbs offered significant in vitro neutralization , yet several did confer partial to complete in vivo protection against MARV one hour after challenge . Although this study is limited in scope , we note that among this set of antibodies , those that exhibited in vitro neutralization also conferred the best in vivo protection . ( There was only one mAb that weakly neutralized but offered no protection , mAb 54G1 ) . Future studies , performed at longer time periods after challenge and with lower treatment doses , will test the limits of efficacy of the individual mAbs . Promising mAbs could then be evaluated in non-human primates ( NHPs ) to predict therapeutic potential in humans . In this work , we provide biochemical and structural mapping of antibody epitopes on MARV GP , and analyze the conservation of these epitopes among different strains of MARV . We find antibodies against a novel GP2 “wing” epitope that confer 90–100% protection in vivo , and two mAbs against different sites in GP1 that confer 40% and 65% protection . mAb cocktails are thought to be most effective when the component antibodies display synergistic effects . Combining mAbs with non-overlapping epitopes can significantly increase the overall potency of the cocktail over the individual mAbs alone [37 , 39] , and can mitigate antigenic escape by the virus [51] . The panel of antibodies described here , although limited in number , provides three possible components of an anti-MARV immunotherapeutic cocktail: an anti-GP1 core mAb such as 40G1 ( or a neutralizing MR mAb ) , the anti-GP1 mAb 9A11 , and an anti-GP2 wing mAb such as 30G4 or 30G5 . Future studies will determine the limits of protection and therapeutic potential of these antibodies when delivered in combination .
This study was approved and carried out in accordance with protocols provided by the Institutional Animal Care and Use Committee ( IACUC ) at TSRI , Emergent Biosolutions , NIAID , and USAMRIID . Research at USAMRIID was conducted in compliance with the Animal Welfare Act and other federal statutes and regulations relating to animals , and adhered to principles stated in the Guide for the Care and Use of Laboratory Animals , National Research Council , 1996 . Purified Ravn GPcl was evaluated by Deuterium Exchange Mass Spectrometry ( DXMS ) as previously described [24] . For negative stain EM analysis , MARV Ravn ectodomains were produced in Drosophila S2 cells as described above . Fab 30G5 and 9A11 fragments were generated by standard papain digestion ( Sigma ) of IgG and purified by Mono Q ( GE Healthcare ) ion-exchange chromatography . Five molar excess Fab was added to trimeric GPΔmuc or GP and allowed to bind overnight at 4°C . Complexes were diluted to 0 . 03mg/ml in TBS buffer and deposited onto to carbon-coated 400 copper mesh grids which had been plasma cleaned for 20 sec ( Gatan ) and stained for 30 sec with 4 μL of 2% uranyl formate . The stain was blotted off the edge and the grid was allowed to dry . Data were automatically collected with Leginon [54] using a FEI Tecnai F20 electron microscope operating at 120 keV with an electron dose of 30 e-/Å2 and a magnification of 52 , 000X that resulted in a pixel size of 2 . 65 Å at the specimen plane when collected with a Spirit 4k x 4k CCD camera ( for 30G5 ) and 2 . 05 Å at the specimen plane when collected with a Tietz 4k x 4k CCD camera ( for 9A11 ) . Images were acquired at a constant defocus value of -1 . 3 μm at various tilt angles from 0 to 50° . Particles were picked automatically using DoG Picker [55] and placed into a particle stack using the Appion software [56] . Reference-free 2D class averages were calculated by using particles binned by 2 with the Xmipp Clustering 2D Alignment software [57] and sorted into ~50–100 particles per class . | The filoviruses have caused multiple outbreaks among humans this decade , including a 90% lethal outbreak of Marburg virus in Angola and a significant , sustained outbreak of Ebola virus in West Africa . The viral surface glycoprotein ( GP ) , which enables filoviruses to infect host cells , is the primary target of the immune system . Antibodies that target filovirus GP have been shown to provide life-saving therapy in nonhuman primates . However , the majority of known antibodies are only reactive against Ebola virus and not other emerging filoviruses . In this study , we present ten antibodies against Marburg virus , elicited by immunization of mice using engineered forms of its GP . Surprisingly , two antibodies exhibit some cross-reactivity to ebolaviruses ( including species Ebola , Sudan , Bundibugyo , Reston ) . Other antibodies in this panel recognize a novel “wing” feature on a portion of GP that is unique to Marburg and does not exist in ebolaviruses , and protect 90%-100% of mice from lethal exposure . These antibodies , and their structural and functional analysis presented here , illuminate directions forward for therapeutics against Marburg virus . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | Protective mAbs and Cross-Reactive mAbs Raised by Immunization with Engineered Marburg Virus GPs |
Many intracellular pathogens rely on host cell membrane compartments for their survival . The strategies they have developed to subvert intracellular trafficking are often unknown , and SNARE proteins , which are essential for membrane fusion , are possible targets . The obligate intracellular bacteria Chlamydia replicate within an intracellular vacuole , termed an inclusion . A large family of bacterial proteins is inserted in the inclusion membrane , and the role of these inclusion proteins is mostly unknown . Here we identify SNARE-like motifs in the inclusion protein IncA , which are conserved among most Chlamydia species . We show that IncA can bind directly to several host SNARE proteins . A subset of SNAREs is specifically recruited to the immediate vicinity of the inclusion membrane , and their accumulation is reduced around inclusions that lack IncA , demonstrating that IncA plays a predominant role in SNARE recruitment . However , interaction with the SNARE machinery is probably not restricted to IncA as at least another inclusion protein shows similarities with SNARE motifs and can interact with SNAREs . We modelled IncA's association with host SNAREs . The analysis of intermolecular contacts showed that the IncA SNARE-like motif can make specific interactions with host SNARE motifs similar to those found in a bona fide SNARE complex . Moreover , point mutations in the central layer of IncA SNARE-like motifs resulted in the loss of binding to host SNAREs . Altogether , our data demonstrate for the first time mimicry of the SNARE motif by a bacterium .
Chlamydia are obligate intracellular bacterial pathogens of eukaryotic cells . They infect a variety of animals , including humans , and cause acute and chronic diseases [1]–[3] . Chlamydia replicate primarily within epithelial cells , in a membrane-bound compartment called the inclusion . The membrane of the inclusion is of dual origin , reflecting its position at the interface between host and pathogen . The bacteria use a type III secretion process to translocate into it the Inc proteins , a large family of Chlamydia specific proteins of mostly unknown function [4]–[6] . Host cell proteins might be less abundant in the inclusion membrane , which lacks conventional markers of early and recycling endosomes and avoids fusion with acidic degradative compartments [6] , [7] . However , several lines of evidence indicate a contribution of host cell compartments to the inclusion growth [8]–[10] . They suggest that Chlamydiae control their interactions with the host intracellular traffic , allowing some fusion events while avoiding others . In eukaryotic cells , SNARE ( soluble NSF ( N-ethylmaleimide-sensitive factor ) attachment protein receptors ) proteins play an essential role in compartment fusion [11] . They share a conserved motif , the SNARE motif , and have been classified as Q-SNAREs ( glutamine containing SNAREs ) and R-SNAREs ( arginine containing SNAREs ) based on a highly conserved residue at the centre of this motif [12] . SNARE proteins anchored in two lipid bilayers associate in complexes involving three Q-SNARE and one R-SNARE motifs . Complex formation is needed for the fusion of the two lipid bilayers . More recently , it appeared that SNAREs can also have inhibitory role in membrane fusion , by substituting for or binding to a subunit of a fusogenic SNARE bundle to form a nonfusogenic complex [13] . As central regulators of membrane fusion , SNARE proteins appear as possible targets for intracellular organisms , which often rely on subverting the host intracellular traffic . However , although there have been suggestions for the presence of SNARE-like motifs in Legionnella effector proteins [14] , there is no definite example of mimicry of the SNARE motif by an intracellular bacterium . We have previously shown that one Chlamydia inclusion protein might interact with itself to form a complex similar to that of the SNARE complex , and thus facilitate the homotypic fusion of inclusions [15] . This finding led us to hypothesize that one of the functions of some inclusion proteins is to control intracellular trafficking by mimicking SNAREs .
To identify SNARE-like motifs in Chlamydia proteins , we used a bioinformatic approach . SNARE motifs are made of heptad repeat sequences that form coil-coiled structures . Position a and d of the heptads are occupied by hydrophobic residues , except at the centre of the motif , where a very conserved glutamine or arginine residue occupies position d , defining the zero layer . Many coiled-coil regions meet these criteria , hampering the identification of SNARE-like motifs from genomic data . However , in this case , we reasoned that the access to several different Chlamydia genome sequences should allow us to identify SNARE-like motifs in Chlamydia proteins with a high level of confidence . We restricted the search to proteins containing a large bilobed hydrophobic motif , which is the hallmark of Inc proteins [4] . These proteins are good candidates to interact with SNAREs , since they are most probably exposed on the cytosolic face of the inclusion membrane . Each Chlamydia genome encodes more than 40 putative Inc proteins . Among them , we found that the C . trachomatis genome encodes 11 proteins predicted to engage in coiled-coil interactions ( see Materials and Methods ) . We aligned the 11 identified sequences against a SNARE motif profile compiled from 261 referenced SNAREs . The best score was obtained with a carboxy-terminal region of the inclusion protein IncA , which is anchored in the membrane by its N-terminal extremity ( Figure 1A ) . We extended the search for SNARE motifs to all IncA sequences known to date , from a variety of Chlamydia strains . The level of similarity between IncA from different species is low , a characteristic of all Inc proteins [4] . Strikingly , in spite of this low conservation , SNARE-like motifs were identified in all IncA homologues , with the single exception of that of C . pneumoniae ( Figure 1B ) . The conserved polar residue at the position corresponding to the zero layer of the SNARE motif was a glutamine or an arginine residue , which are the canonical residues in SNARE motifs . The presence of SNARE motif characteristics in IncA from six different Chlamydia strains strongly supports the hypothesis that the similarity to eukaryotic SNARE motifs is not fortuitous , and illustrates the usefulness of sequencing many strains of this intracellular bacterium which we can not genetically manipulate . In addition to this motif , bioinformatics also revealed the presence of a second , membrane proximal , motif , which shared characteristics of SNARE motifs ( Figure 1C ) . We designate it as motif Nter , and the carboxy-terminal motif as motif Cter . For each species , motif Cter always gave a higher score in the alignments with eukaryotic SNAREs than motif Nter . In particular , the polar residue in the zero layer of motif Nter was a threonine rather than a glutamine or an arginine for two Chlamydia species . Using a less systematic approach , we had previously observed that motif Nter showed similarities with SNARE motifs , and shown that it might be involved in the formation of homotetramers of IncA [15] . Finally , among the other C . trachomatis Inc proteins predicted to form coiled-coils , the Inc protein CT813 [16] and the putative Inc protein CT223 also showed similarities with SNARE motifs ( amino acids 191 to 264 of CT813 , 169 to 236 of CT223 ) , although the alignment with host SNARE motifs was poorer than IncA's , and the zero layer was more difficult to define . Orthologs of CT813 and CT223 are only found in the closely related C . muridiarium species , so in this case sequence comparison could not be used to validate the identification of SNARE-like motifs . We did not identify SNARE-like motifs in the the eight remaining Inc proteins predicted to form coiled-coils . To determine whether host SNAREs interact with inclusion proteins , we investigated the distribution of several of them in cells infected with C . trachomatis serovar D . Since C . trachomatis IncA SNARE-like motif Cter had a glutamine in its central layer , we hypothesized thas it might interact with R-SNAREs and investigated the localization of several host R-SNAREs , whose distribution and role in intracellular traffic is well documented ( see Table 1 for details ) . In addition to their expected punctate distribution throughout the cell , endogenous Vamp3 and Vamp7 formed a patchy circle around the inclusion . Sec 22 , another R-SNARE , did not encircle the inclusion , indicating that the redistribution observed in infected cells did not apply to all SNAREs ( Figure 2A and C ) . Endogenous Vamp4 and Vamp8 expression was too low for detection . However , when cells were transfected with GFP-Vamp8 prior to infection , this protein showed a ring pattern around the inclusion , while GFP and GFP-Vamp4 did not ( Figure 2B and C ) . Transfected GFP-Vamp7 relocalized to the inclusion while GFP-Vamp7 deleted of its SNARE motif ( GFP-Longin ) did not , indicating that the SNARE motif is necessary for the recruitment to the inclusion membrane . The circular pattern of specific SNAREs can be due to their accumulation around the inclusion , and/or to their presence in the inclusion membrane itself . To discriminate between these possibilities , we used electron microscopy . In agreement with our observation that SNAREs of different compartments are recruited to the inclusion membrane , we observed an abundance of varied intracellular compartments in the immediate vicinity of the inclusion membrane ( Figure 3A ) . Using immunogold labeling , we confirmed the enrichment of GFP-Vamp8 relative to GFP-Vamp4 around the inclusion , as 35% of the gold particles that labeled GFP-Vamp8 were less than 50 nm distant from its membrane , against 14% for those associated with GFP-Vamp4 ( p-value<0 . 001 ) . Among the gold particles that labeled GFP-Vamp8 within a 50 nm range of the inclusion membrane , 22% were on its membrane , while the rest were associated with compartments around the inclusion ( Figure 3B ) . This result shows that the accumulation of GFP-Vamp8 around the inclusion observed in Figure 2B is mainly due to the recruitment of GFP-Vamp8 positive compartments around the inclusion , rather than to the presence of this host protein in the inclusion membrane . The same probably applies to GFP-Vamp3 and GFP-Vamp7 positive compartments , since endocytic markers of these compartments accumulate around the inclusion and are excluded from it [7] . Our observation that GFP-Vamp8 can reach the inclusion membrane , as well as the recent finding of CD63 on it , support the idea that multivesicular bodies , which contains both markers , might contribute to its growth [10] . The recruitment of Vamp8 positive compartments in the immediate proximity of the inclusion membrane suggests a possible direct interaction between this SNARE and SNARE-like proteins on the inclusion membrane . We next asked whether IncA could mediate the recruitment of Vamp8-positive compartments , and of other intracellular compartments . HeLa cells were simultaneously transfected with plasmids coding for C . trachomatis IncA and with different GFP-tagged SNARE proteins . One day later , SNARE proteins were immunoprecipitated using anti-GFP antibody . IncA was present in the GFP-immunoprecipitate when co-expressed with GFP-Vamp3 , GFP-Vamp7 or GFP-Vamp8 , but not GFP alone ( Figure 4A ) . Importantly , IncA was not found in the GFP-immunoprecipitate when co-expressed with GFP-Vamp7 deleted of its SNARE motif ( GFP-Longin ) , indicating that the SNARE motif of the SNARE protein is important for the interaction with IncA . It co-immunoprecipitates very poorly with GFP-Vamp4 , indicating that IncA interacts preferentially with only a subset of SNAREs . These biochemical observations correlate well with the selective recruitment of SNAREs around the inclusion observed in Figure 2 . In vitro pull-down assays were performed to determine whether the interaction between IncA and host SNAREs was direct . Two populations of liposomes , one containing purified Vamp3 , Vamp4 or Vamp8 with an amino-terminal glutathione-S-transferase tag , and one containing purified IncA-His , were mixed and incubated together for 16 hrs at 4°C . Proteins were then solubilized , and Vamps were pulled-down using glutathione-agarose beads ( Figure 4B ) . IncA was pulled-down together with Vamp3 or Vamp8 , showing that the interaction between IncA and these SNAREs is direct . IncA was not pulled-down with Vamp4 , which correlates with the very weak co-immunoprecipitation of this inclusion protein with GFP-Vamp4 observed in Figure 4A , and confirms that IncA interacts preferentially with only a subset of SNAREs . IncA is not expressed by C . trachomatis until 10 hrs post-infection [17] . To see whether the timing of the recruitment of SNAREs was consistent with the temporal expression of IncA , we observed the localization of SNAREs early in infection . Eight hours after infection , inclusions were very small , and we could not quantify the recruitment of SNAREs with confidence at this stage ( data not shown ) . Eleven hours after infection , the recruitment of GFP-SNAREs was much less pronounced than 18h after infection ( Figure S1 ) , showing that the level of recruitment of SNAREs correlates with the timing of expression of IncA . To test directly IncA's contribution to the recruitment of SNAREs to the inclusion membrane , we used a strain of C . trachomatis that does not express IncA , Ds5058 [18] . This strain grows significantly slower than the wild type strain [19] , indicating that IncA plays an important role in infection . Cells were transfected with GFP-Vamp8 , and then infected with the IncA negative strain . The cells were incubated for 48 hrs , in contrast to 24 hrs for the wild type strain , in order to observe inclusions of similar size ( Figure 5A ) . Recruitment of GFP-Vamp8 to the inclusion was observed in about 40% of cells infected with the IncA negative strain , in contrast to 70% of cells infected with the wild type strain . Similarly , the circular pattern of GFP-Vamp3 or GFP-Vamp7 around the inclusion was observed in fewer cells infected with the IncA negative strain than with wild-type bacteria . At this level of analysis , in cases where host SNAREs were recruited to the IncA negative inclusions , no difference could be noted with the pattern of their distribution in cells infected with the wild type strain ( Figure 5B and Figure S2 ) . We used an independent approach to confirm the predominant role of IncA in SNARE recruitment . As IncA associates with itself [15] , we reasoned that heterologous overexpression of IncA might titrate the protein on the inclusion membrane and prevent association with SNAREs . Unfortunately , overexpression of C . trachomatis IncA inhibits the development of the bacteria [15] , and could not be used for this purpose . However , we had previously shown that IncA from C . trachomatis ( CtrIncA ) and C . caviae ( CcaIncA ) shared biochemical properties [15] and we have identified here two SNARE-like motifs in C . caviae IncA ( Figure 1B and C ) . We hypothesized that CcaIncA , if expressed by the infected cell , might associate with endogenous CtrIncA at the inclusion membrane . Indeed , when overexpressed by HeLa cells , CcaIncA was present in the endoplasmic reticulum , as previously observed [15] , and around the inclusion membrane ( Figure S3 ) . The number of cells in which Vamp3 and Vamp7 were scored as recruited to the inclusion was reduced by about 50% in the population of cells expressing CcaIncA compared to non transfected cells ( Figure 5C and Figure S3 ) . Interestingly , overexpression of CtrIncA deleted from its hydrophobic domain ( Δ75IncA ) was not enriched around the inclusion and had no effect on the recruitment of Vamp3 and Vamp7 to the inclusion ( Figure 5C ) . This result confirms our previous observation that overexpressed IncA needs to be inserted into a membrane compartment to be able to interact with endogenous IncA [15] . These results indicate that IncA plays a predominant , although not exclusive , role in the recruitment of SNAREs around the inclusion . Other inclusion proteins such as CT223 and CT813 , which show similarities with SNARE motifs , may contribute to SNARE recruitment around the inclusion . To test this hypothesis , we cloned these two genes in mammalian expression vectors . After transfection , CT223 was not expressed by HeLa cells , as assessed by immunofluorescence , and was not studied further . CT813 was expressed in the endoplasmic reticulum . Co-expression and immunoprecipitation experiments showed that CT813 interacts with GFP-Vamp7 and GFP-Vamp8 , and not with GFP alone GFP-Vamp4 or GFP-Vamp7 deleted of its SNARE motif ( GFP-Longin ) . Interaction of CT813 with GFP-Vamp3 could not be assessed because the expression levels of both constructs were low ( Figure 6 ) . This result indicates that CT813 can interact with host SNAREs and that , in addition to IncA , several Inc proteins have evolved the ability to interact with SNAREs . Interestingly , more CT813 was pulled-down with Vamp7 than with Vamp8 , while more IncA was pulled down with Vamp8 than with Vamp7 , suggesting that affinities between inclusion proteins and different SNAREs vary . Other Inc proteins might partly compensate for the absence of IncA and explain why the clinical strain defective in IncA expression was still able to recruit SNAREs around its inclusion , although less efficiently that the wild type strain . Interestingly , CT813 and CT223 are specific to C . trachomatis and C . muridarium , suggesting that different Chlamydia species may have evolved different Inc proteins , targeting the SNARE machinery of their host in subtly different manners . In support of this hypothesis , we observed that , while C . caviae and C . pneumoniae were also able to recruit SNAREs to the inclusion membrane , the level of recruitment varied with strains ( Figure S4 ) . C . caviae , whose IncA has SNARE-like motifs and behaves similarily to C . trachomatis IncA [15] , recruited the same set of SNAREs as C . trachomatis . In contrast , C . pneumoniae , whose IncA does not possess a clear SNARE-like motif , recruited good level of Vamp8 in the vicinity of its inclusion , but only little Vamp7 . This observation suggests that , in C . pneumoniae , other Inc proteins than IncA might specifically interact with a subset of SNAREs . Importantly , targeting the host SNARE machinery is not the sole method for Inc proteins to interfere with host trafficking: members of the family of rab proteins , which also participate in recognition and fusion of cell compartments [20] , were also shown to interact with Inc proteins [21] , [22] . Using molecular modelling , we previously showed that motif Nter was fully compatible with the formation of stable homotetramers , associated in a structure similar to that of the SNARE complex [15] . Using the same approach , we now asked whether IncA SNARE-like motifs could fit in the structure of a SNARE complex involving host SNAREs . We chose to use IncA SNARE-like motif Cter because it aligned better with eukaryotic SNARE motifs than motif Nter ( Figure 1 ) . We modelled the association of three identical motifs Cter ( in place of Q-SNAREs ) in association with one SNARE motif from a R-SNARE , for three reasons: ( i ) IncA has a high propensity to form dimers or other multimeric structures [15] , ( ii ) motif Cter of C . trachomatis IncA classifies as a Q-SNARE , and aligned better with Q-SNAREs than R-SNAREs ( Figure 1B ) ( iii ) IncA can interact with R-SNAREs ( Figure 4 ) . We modelled the heterotetramers between the SNARE motif of host Vamp8 and a trimer of IncA motif Cter ( Figure S5 ) . The model of the complex between three molecules of IncA and Vamp8 is very similar to the structure of the endosomal SNARE complex ( Figure 7 ) . In particular , several side-chains of Vamp8 are involved in salt bridges with side-chains in IncA . There is a cluster of salt bridges close to the central layer of the complex , involving residues at positions +5 and +8 from the central Argnine in Vamp8 , with resdidues at positions +3 and +6 from the central Glutamine in IncA , and two additional salt bridges N-terminal and C-terminal of the central layer . The patterns of predicted interaction energies , evaluated as the difference in total energy between the complex and separated helices , are similar . The predicted arrangement of glutamine residues around the central arginine of Vamp8 is very similar to that found in the X-ray crystal structure of the endosomal SNARE complex [23] ( Figure 7 , inset ) . These data support a model in which IncA makes SNARE complexes with host SNAREs via its SNARE-like motif Cter . Our models , and numerous reports on eukaryotic SNARE complexes , suggest that , if the IncA SNARE-like motif functions as a bona fide SNARE motif , forming complexes with R-SNAREs , introduction of a large charged amino acid , such as an arginine , in the zero layer of the IncA SNARE-like motif might destabilize the SNARE complex sufficiently to lose the interaction between IncA and host SNAREs . To test this hypothesis , we introduced point mutations in the zero layer of IncA SNARE-like motif Nter ( IncAT126R ) , motif Cter ( IncAQ244R ) or both ( IncAT126RQ244R ) . All constructs were expressed at a level comparable to wild type IncA when transfected in HeLa cells . However , coexpression of the Q244R mutant together with GFP-Vamps constructs resulted in very weak expression of both transfected genes . The reasons for this phenomenon are unclear , and we could not assay the ability of this mutant to co-immunoprecipitate with GFP-SNAREs . We performed co-immunoprecipitation experiments in cells co-expressing different GFP-SNAREs and either IncA wild type , or mutated in motif Nter , or in both SNARE-like motifs . The single mutant ( IncAT126R ) co-immunoprecipitated with GFP-Vamp3 , GFP-Vamp7 and GFP-Vamp8 , to the same extent as IncA wild-type . However , IncA mutated in both SNARE-like motifs ( IncAT126RQ244R ) did not co-immunoprecipitate with any of the Vamps ( Figure 8A ) . This experiment demonstrates that the interaction between IncA and host SNAREs is mediated by IncA SNARE-like motifs . Further analysis would be needed to determine whether only motif Cter is able to engage in SNARE complexes with host SNAREs , or whether any of the two SNARE-like motifs can do so and mutation of both motifs is needed to lose the interaction . In any case , these experiments demonstrate that IncA SNARE-like motifs function as bona fide SNARE motifs since point mutations in residues known to be critical for the formation of SNARE complexes result in the loss of the interaction between IncA and host SNAREs . To confirm this important result by a different approach , we investigated the effect of the point mutations in IncA SNARE-like motif on the ability of IncA to engage in homotypic interactions . We have previously shown that heterologous expression of IncA , which localizes at the endoplasmic reticulum , inhibits inclusion development . This effect requires a direct interaction between IncA molecules at the inclusion and on the endoplasmic reticulum , and we hypothesized that it might be due to the formation of homotypic SNARE complexes between IncA molecules present on the two compartments [15] . Cells , transfected with IncA wild- type or with the different mutants , were infected for 20 hrs before fixation and labeling of the bacteria and IncA-His by immunofluorescence . Development of inclusions was largely impaired in cells expressing IncA wild type or IncAT126R ( Figure 8B ) . The Q244R mutation in the IncA SNARE-like motif Cter partially restored the growth of the inclusion in the transfected population , and the double mutation in both SNARE-like motifs restored the growth further , although not totally . Expression of IncA deleted of its hydrophobic domain ( Δ75IncA ) had no effect on the development of the bacteria , confirming that IncA needs to be inserted in a membrane to disrupt the development of the inclusion [15] . Altogether , this experiment shows that homotypic interaction between IncA molecules is mediated by its SNARE-like motifs . It suggests that both SNARE-like motifs contribute to the interaction , although motif Cter is able to compensate for the mutation in motif Nter , while motif Nter is not fully able to compensate for the mutation in motif Cter . The observation that the double mutant only partially ( about 50% ) restores the ability of the bacteria to grow in the transfected cells indicates that the point mutations are not sufficient to fully disrupt IncA homotypic interactions . Altogether , point mutations in IncA SNARE-like motifs impaired its ability to associate with host SNAREs and with itself . These results show that IncA SNARE-like motifs behave as bona fide SNARE motifs , and strongly support our hypothesis that IncA interaction with host SNAREs is mediated by the formation of SNARE complexes . In agreement with previous reports from several laboratories , we have observed that a variety of cellular compartments accumulate around the inclusion . Here we bring evidence that SNARE mimicry is one mechanism by which the Chlamydia recruit a specific subset of host SNAREs . We have identified SNARE-like motifs in the inclusion protein IncA , and showed that a mutant strain that does not express IncA presents reduced ability to recruit SNAREs around its inclusions . Our data also suggest that other inclusion proteins may use SNARE motif mimicry to interact with SNAREs . These conclusions open two important questions: in which SNARE complexes is IncA engaged , and what are the consequences in term of membrane fusion ? To start answering the first question , we have tested a variety of IncA/SNARE interactions . By co-imunoprecipitation experiments , we found that IncA was able to interact with several different SNAREs , but not all . We observed a good correlation between this result and the specific recruitment of a subset of SNAREs around the inclusion membrane . The basis of the specificity of interaction between IncA and a subset of host SNAREs is not yet known . A model between the SNARE motif of Sec22 and a trimer of IncA motif Cter resulted in an unstable complex ( data not shown ) , a finding that correlates with the absence of recruitment of Sec22 around the inclusion membrane . However , modelling predicted a stable association of Vamp4 with IncA motif Cter , while we observed no or very weak interaction between IncA and Vamp4 ( Figure 4 ) . This result suggests that , in addition to motif Cter , other domains of IncA contribute to define its specificity . It may also be that , in vivo , interactions between the inclusion membrane and the host SNAREs involve a combination of SNARE-like motifs from several different inclusion proteins and/or of SNARE motifs from several different host SNAREs present in the same compartment . Dissecting the complexes in which IncA is involved therefore remains a challenging task for the future . It is also difficult to bring a definite answer to the second question , regarding the mechanistic roles of SNARE-like motifs in inclusion proteins , especially in the absence of tools to manipulate the Chlamydia genome . The three SNAREs recruited to C . trachomatis inclusion membrane are SNAREs involved in endosomal trafficking , while Vamp4 and Sec22 , which are more involved in the secretory pathway , were not , suggesting that SNARE mimicry may have preferentially evolved in C . trachomatis to target the endosomal pathway . Depletion of host SNAREs Vamp3 , Vamp4 , Vamp7 or Vamp8 using siRNA had no impact on the growth of the bacteria ( Figure S6 ) . This might be due to an insufficient depletion of the pools , especially in the case of Vamp3 , or because other host SNAREs present in the same compartment as the siRNA target can compensate for the depletion . It might also reveal some degree of redundancy in the interactions between the inclusion and different intracellular pathways , as was observed for another intracellular bacterium , Legionella pneumophila [24] . In the absence of functional indications regarding the role of the interaction between inclusion proteins and host SNAREs in infection , we can propose several models . SNAREs are mostly known for their role in membrane fusion . By engaging in fusion competent SNARE complexes , inclusion proteins might permit the fusion of some cellular compartments with the inclusion membrane . If this is the case , the cellular SNARE should be present in the inclusion membrane , at least transiently . By electron microscopy , we observed that Vamp8 is found in the inclusion membrane , suggesting that indeed , IncA-SNARE interaction might promote fusion of specific intracellular compartments , in this case probably multivesicular bodies , with the inclusion . Conversely , SNARE motifs in inclusion proteins might play an inhibitory role on fusion , by engaging in fusion incompetent SNARE complexes [13] , or by titrating individual SNAREs . One argument to support this opposite model is the long distance between motif Cter and the transmembrane domain of IncA . SNARE mediated fusion requires proximity between the SNARE complex and the lipid bilayers , which might be difficult to achieve if motif Cter is engaged in the SNARE complex . Another observation that supports this model is the absence of effect of Vamp7 depletion on infection . Although siRNA against Vamp7 reduced its level by more than 90% , it did not affect the growth of the bacteria ( Figure S6 ) . Similarily , expression of GFP-Longin , a dominant negative form of Vamp7 , did not affect Chlamydia infection . These results support a model in which IncA/Vamp7 interaction prevents Vamp7 mediated fusion of late endocytic compartments with the inclusion . Finally , it is also possible that SNARE-like domains in inclusion proteins serve as mere anchors . By associating with various SNAREs , they contribute to the accumulation of vesicles in the immediate vicinity of the inclusion . This might be beneficial to the development of the bacteria , without requiring fusion to occur . Chlamydiaceae have very probably been intracellular for several hundred million years [25] . Therefore , a high degree of complexity in the interaction between inclusion proteins and host organelles should be expected . Bacterial proteins involved in these interactions are just beginning to be identified . In light of our results , we propose that co-evolution shaped some of the bacterial inclusion proteins into bacterial SNARE proteins .
C . trachomatis serotype D ( 27F0734 ) was from ATCC . C . trachomatis serotype D ( s ) 5058 is a clinical isolate , which does not express IncA , and was kindly given by Drs . D . Rockey and W . Stamm [18] . The GPIC strain of C . caviae and the CWL029 strain from C . pneumoniae were obtained from Drs . R . Rank ( University of Arkansas ) and G . Christiansen ( University of Aarhus , Denmark ) respectively . HeLa cells were used in all experiments except for infection with C . pneumoniae , which was performed in Hep2 cells . Infections were performed as described [15] . In some experiments , HeLa cells were transfected 8 to 18 hrs prior to infection and processed for immunofluorescence as described [15] . The inclusion membrane was observed using either anti-IncA ( generous gift from Dr Ted Hackstadt , Rockey Mountain Laboratories , NIH , NIAID ) or anti-Cap1 polyclonal antibodies which we obtained from New Zealand White rabbits immunized with His-tagged recombinant protein CT529 purified from E . coli . Antibodies against Vamp4 were kindly provided by Dr . Andrew A . Peden ( Cambridge Institute for Medical Research , Cambridge ) , rabbit anti-Vamp3 , rabbit anti-Vamp8 and mouse anti-Vamp7 and vectors coding for GFP-Vamp3 , GFP-Vamp7 , GFP-Longin and GFP-Vamp8 were kindly provided by Dr Thierry Galli ( Institut Jacques Monod , Paris ) , rabbit anti-Sec22 was described previously [26] , plasmid coding for GFP-Vamp4 was a gift of Dr Ludger Johannes ( Institut Curie , Paris ) . Endogenous SNAREs were first labeled using the corresponding antibodies , followed with Alexa Fluor-488-conjugated anti-rabbit antibodies ( Molecular Probes ) . In that case , the inclusion membrane could not be visualized with rabbit antibodies , and bacterial DNA was labeled using 0 . 5 µg/ml Hoechst in the mounting medium . Coverslips were examined under an epifluorescence microscope ( Axiophot , Zeiss , Germany ) equipped with a 63× Apochromat objective and a cooled CCD-camera ( Photometrics , Tucson , AZ ) , driven by Metaview software ( Universal Imaging , Downingtown , PA ) . For quantification of the recruitment of SNAREs around the inclusion membrane , more than 100 infected cells ( and in some case transfected with GFP-SNARE ) were counted in each case . Cells were scored as positive when the entire circumference of the inclusion was surrounded by the SNARE protein . HeLa cells were transfected by electroporation with the indicated constructs , and lysed 24 h later on ice in 50 mM Tris , 150 mM NaCl , 1% Triton X-100 , 10 mM EDTA , pH 7 . 5 and cocktails of inhibitors . Immunoprecipitation of GFP-tagged proteins was performed using anti-GFP monoclonal antibodies ( clones 7 . 1 and 13 . 1 , Roche Applied Science ) . Immunoprecipitated proteins were incubated in sample buffer , boiled and loaded on acrylamide gel for western blot detection of IncA or of the histidine tag ( #14-6757 , eBioscience , San Diego CA ) . Expression level and immunoprecipitation of SNAREs proteins were checked by stripping the membrane and incubating it with anti-GFP antibodies ( #sc-8334 Santa Cruz Biotechnology ) followed by horseradish peroxidase-linked ( HRP ) anti-rabbit antibodies ( Amersham Biosciences ) . GST-Vamp3 , GST-Vamp4 , and GST-Vamp8 were expressed , purified and reconstituted into a first population of clear liposomes as described [27] , [28] . Full-length IncA-His was subcloned in the pet28 vector , expressed in BL21 E . coli for 20 hrs at 16°C , and subsequently purified in buffer A ( 100 mM KCL , 25 mM HEPES , 10% glycerol , 1% octyl-β-D-glucopyranoside ) using the protocol described in [27] . IncA-His was reconstituted into a second population of clear liposome as described [28] . After incubating each GST-SNAREs containing liposome with IncAHis-containing liposomes together for 16 hrs at 4°C , the mixture was dissolved in 2 . 5% wt/vol n-dodecyl-maltoside ( Boehringer ) . The proteins/lipids mixture was further diluted into buffer A , and GST complexes were pulled-down using glutathione agarose equilibrated in buffer A . The glutathion agarose beads were then pelleted and washed three times with the buffer A . Pull-down complexes were resolved on SDS-PAGE gels and proteins stained with Coomassie blue . Transfected and infected HeLa cells were fixed with a mixture of 2% ( wt/vol ) paraformaldehyde and 0 . 5% ( wt/vol ) glutaraldehyde in a 0 . 2 M phosphate buffer ( PB ) pH 7 . 4 , 4 h at room temperature . After many washing with 0 . 1 M glycin in PBS , cells were processed for ultracryomicrotomy as described [29] . Incubations were performed in blocking buffer ( 20 mM glycin , 0 . 1% of cold water fish skin gelatin , Biovalley , in PBS ) and sections were labeled with anti-GFP ( #A11122 , Invitrogen Life Technologies ) and visualized with protein A coupled to 15-nm gold . Sections were observed and acquired under a Philips CM120 electron microscope ( FEI , Eindoven , Netherlands ) . Digital acquisitions were made with a numeric camera Keen View ( Soft Imaging System , Munster , Germany ) . Construction of CtrIncA and CcaIncA with a carboxyterminal His tag was described elsewhere [15] , Δ75IncA-His was constructed in the same way with a deletion of the first 75 amino acids . The same cloning strategy was used to clone CT813 using the primers atgctaccatggctactcttcccaataattgcactt and agtcggtacctatcgaaccacgtcttcctg , but for unknown reasons , the gene corresponding to the full-length protein could not be cloned over several attempts . A clone , designated CT813His* , was obtained , with a deletion of one nucleotide which did not permit expression of the full-length protein , but of a protein missing its 23 first amino acids ( initiation of translation from methionine 24 ) . This deletion does not affect the transmembrane domain of CT813 , or its SNARE-like motif . The selection of specific oligonucleotides and the procedures for RNAi experiments to silence the expression of endosomal v-SNAREs ( Vamp3 , Vamp7 , Vamp8 ) will be published elsewhere ( Danglot et al . , 2008 in preparation ) , Vamp4 siRNA ( AAGAUUUGGACCUAGAAAUGA ) was designed by Dr . Andrew A . Peden ( Cambridge Institute for Medical Research , Cambridge ) . 5×106 cells were electroporated in the presence of 2 pmoles of siRNA at day 1 and again at day 4 , plated in 24-well plates and one 10-cm dish at day 5 and infected at day 6 in the 24-well plates . On the day of infection , cell lysates were prepared from the 10 cm dish and normalized to equal protein concentration . The level of expression of each SNARE was assessed by western blot using specific antibodies . Nitrocellulose membrane were incubated with ECL western blotting reagents ( Amersham ) and processed for detection using a Luminescent Image Analyzer LAS-3000 ( Fujifilm ) . Digital images were acquired using the software Image Reader LAS-3000 v2 . 2 ( Fujifilm ) and analyzed by MultiGauge v3 . 0 ( Fujifilm ) . All the quantification were performed using the expression of β-tubulin to normalize the samples , and for each SNARE , the level of expression is expressed as the percentage of expression in control cells transfected with the si-β-globin . One day after infection , the cells were detached from 24-well plates in 0 . 5 mM EDTA in PBS , fixed for one hour in 70% ethanol , centrifuged and washed in PBS . Bacteria were then stained using anti-EfTu antibody from Dr Y-X Zhang ( Boston ) followed by Cy5-coupled anti-mouse antibody ( Amersham ) and the samples were analyzed by flow cytometry . Transmembrane domains were identified in Chlamydia proteome using Phobius predictor , which detects in a single search signal peptides and transmembrane helixes [30] . Proteins with two transmembrane domains separated by 2 to 40 amino acids , and with no obvious functional attribution , were considered as putative Inc proteins . Putative Inc proteins predicted to engage in coiled coil using a hidden markov model ( Marcoil [31] ) were CT119 ( IncA ) , CT222 , CT223 , CT224 , CT225 , CT226 , CT228 , CT229 , CT233 ( IncC ) , CT616 and CT813 . A database made of SNARE motifs from 261 referenced SNARE proteins was used to perform optimal alignments searches [32] . Models of the complexes were generated essentially as described before [15] , see Figure S5 . Electrostatic energies were calculated with the “ACE” Generalized Born model [33] , implemented in X-plor [34] , with an dielectric constant of 1 for the interior of the protein , and a cut-off for electrostatic interactions of 15 Å . To estimate the relative stabilities of the complexes , we calculated the difference between the energies for the tetramer and the four helices separated by 200 Å . Solvatation energies were estimated by the difference in solvent accessible surfaces multiplied by 0 . 005 kcal/ ( mol Å2 ) . The profiles shown in Figure 6 represent the sum of electrostatic energies , van der Waals energies , and solvatation energies , averaged over the equivalent residues in the four helices . | Chlamydiae are obligate intracellular bacteria that have co-evolved with eukaryotic cells and adapted to a wide range of hosts , causing several diseases in humans and animals . For example , one species pathogenic to humans , Chlamydia trachomatis , is the leading cause of preventable blindness and of bacterial sexually transmitted diseases worldwide . Chlamydiae multiply inside a membrane-bound compartment , the inclusion . The exchanges between the membrane of the inclusion and other intracellular membranes are tightly controlled by the bacteria , for example avoiding fusion with some degradation compartments , while acquiring lipids . Inclusion proteins , made by the bacteria and secreted into the inclusion membrane , are thought to play a central role in controlling these interactions , although their exact function is mostly unknown . We have identified , in three inclusion proteins , a motif common to proteins that are essential for the fusion of two compartments in eukaryotic cells , the SNARE proteins . Via this motif , inclusion proteins interact specifically with a subset of SNAREs of the host , which leads to the selective recruitment of intracellular compartments around the inclusion . This study thus provides a striking example of mimicry of the host by an intracellular pathogen . | [
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Strongyloides stercoralis , the chief causative agent of human strongyloidiasis , is a nematode globally distributed but mainly endemic in tropical and subtropical regions . Chronic infection is often clinically asymptomatic but it can result in severe hyperinfection syndrome or disseminated strongyloidiasis in immunocompromised patients . There is a great diversity of techniques used in diagnosing the disease , but definitive diagnosis is accomplished by parasitological examination of stool samples for morphological identification of parasite . Until now , no molecular method has been tested in urine samples as an alternative to stool samples for diagnosing strongyloidiasis . This study aimed to evaluate the use of a new molecular LAMP assay in a well-established Wistar rat experimental infection model using both stool and , for the first time , urine samples . The LAMP assay was also clinically evaluated in patients´ stool samples . Stool and urine samples were obtained daily during a 28-day period from rats infected subcutaneously with different infective third-stage larvae doses of S . venezuelensis . The dynamics of parasite infection was determined by daily counting the number of eggs per gram of feces from day 1 to 28 post-infection . A set of primers for LAMP assay based on a DNA partial sequence in the 18S rRNA gene from S . venezuelensis was designed . The set up LAMP assay ( namely , Strong-LAMP ) allowed the sensitive detection of S . venezuelensis DNA in both stool and urine samples obtained from each infection group of rats and was also effective in S . stercoralis DNA amplification in patients´ stool samples with previously confirmed strongyloidiasis by parasitological and real-time PCR tests . Our Strong-LAMP assay is an useful molecular tool in research of a strongyloidiasis experimental infection model in both stool and urine samples . After further validation , the Strong-LAMP could also be potentially applied for effective diagnosis of strongyloidiasis in a clinical setting .
Strongyloidiasis , a soil-transmitted helminth human infection , is considered by World Health Organization ( WHO ) as a neglected condition affecting an estimated 30–100 million people worldwide [1] . The accuracy of these estimates remains actually uncertain due to lack of efficient guidelines for screening the population in epidemiological surveys [2 , 3] . At least , two species of nematodes of the genus Strongyloides , namely Strongyloides stercoralis ( the most common human pathogen species ) and S . fuelleborni , are known to infect humans causing strongyloidiasis [4 , 5 , 6] . Human infection is primarily acquired by the filariform larvae ( the infective third-stage larvae , iL3 ) penetrating the skin or mucous membranes through unprotected contact with contaminated soil [7] . S . stercoralis biology is complex involving two separate life cycles , the free-living heterogonic cycle and a parasitic cycle [8 , 9] . The exceptional ability of this parasite to replicate in the human host permits ongoing cycles of autoinfection thus resulting in a chronic strongyloidiasis that can therefore persist for several decades without further exposure to a new exogenous infection [6] . Inmunocompetent patients with uncomplicated strongyloidiasis usually develop an asymptomatic , mildly symptomatic or chronic infection , which are typically associated with low intestinal worm burdens and intermittent larval excretion [10 , 2] . However , a deregulation of the host´s immune response during the latent infection may lead in an uncontrolled multiplication of the parasites ( hyperinfection syndrome ) which can be life-threatening [6 , 2 , 10 , 11 , 7] with mortality rates of up 87% [12 , 13] . Thus , detecting latent cases of S . stercoralis is crucial to decrease morbidity and mortality of the infection . The diagnosis of strongyloidiasis is suspected when clinical signs and symptoms , eosinophilia or serologic findings are observed [14 , 6] , but definitive diagnosis is accomplished by parasitological examination of stool samples allowing the morphological identification of S . stercoralis , including direct smear in saline , the spontaneous sedimentation method [15] , centrifugation [16] , the Baermann´s technique [17] , the agar plate culture [18] and the Harada-Mori´s filter paper culture method [19] . These methods have classically low sensitivity because of the low and irregular load of larvae in the feces [6]; the collection of a larger number of stool samples on alternate days instead of a single one and the combination of several diagnostic methods may increase the sensitivity [20] . On the other hand , several immunological methods have been described for diagnosing strongyloidiasis showing a high sensitivity when compared with parasitological methods but a limitation in the standardization of more specific serological tests in order to avoid the possibility of cross-reaction with other helminths still remains [21 , 22] . Several DNA-based techniques ( i . e . single-PCR , nested-PCR , PCR-RFLP , real-time PCR ) have provided useful alternatives not only for identification of Strongyloides species [23 , 24] but also for S . stercoralis DNA detection in feces with high accuracy in the diagnosis of strongyloidiasis [22 , 25 , 26 , 27] . Nevertheless , such molecular methods have a very limited use in routine diagnostic , particularly under field conditions in endemic areas requiring special equipment manipulated by trained personnel . Thus , the development of new , simple , applicable and cost-effective alternative molecular assays is necessary to diagnose human strongyloidiasis , mainly in those immunocompromised individuals in which the infection can be fatal . At present , there is a nucleic acid amplification method named loop-mediated isothermal amplification ( LAMP ) [28] . Compared to PCR , the simplicity of the LAMP method makes it suitable for field testing in developing countries [29 , 30] , and many LAMP reactions have already been developed for molecular detection and diagnostics of infectious diseases , including parasitic diseases [31] . In this sense , a first LAMP assay for the detection of S . stercoralis in feces has been recently developed and preliminary evaluated with human stool samples [32] . To date , all new successfully approaches for molecular methods to be used for Strongyloides spp . DNA detection have been focused in analyzing mainly stool samples and no other type of sample , such as urine , has been considered for the detection of parasite DNA . Urine is a biological sample that would have a number of advantages in diagnosis of strongyloidiasis over the stool samples since it has less inconvenience to obtain from patients as well as it is easier in handling and storing . It has been demonstrated that small amounts of cell-free circulating DNA are able to pass the kidney barrier and end up in urine [33 , 34 , 35]; this circulating DNA from the bloodstream that passes into the urine can be isolated and used in diagnostic applications . This study aimed to assess the diagnostic utility of a new designed LAMP assay in an active experimental rodent strongyloidiasis in parallel with parasitological method by direct fecal examination . We used as a source for Strongyloides spp . DNA detection both stool and , for the first time , urine samples from rats experimentally infected with different doses of S . venezuelensis iL3 . The LAMP developed in this work ( namely , Strong-LAMP ) was shown to be sensitive and specific in detecting Strongyloides spp . DNA . The potential diagnostic applicability of the Strong-LAMP could be also demonstrated on a number of human clinical stool samples with previously parasitological and real-time PCR confirmed strongiloidiasis .
The study protocol was approved by the institutional research commission of the University of Salamanca . Ethical approval was obtained from the Ethics Committee of the University of Salamanca ( protocol approval number 48531 ) , which approved the animal protocol . Animal procedures in this study complied with the Spanish ( Real Decreto RD53/2013 ) and the European Union ( European Directive 2010/63/EU ) guidelines on animal experimentation for the protection and humane use of laboratory animals and were conducted at the accredited Animal Experimentation Facility of the University of Salamanca ( Register number: PAE/SA/001 ) . The human stool samples used in this study were obtained as part of public health diagnostic activities at Severo Ochoa and Gregorio Marañón Hospitals , Madrid , Spain . A standardized epidemiological questionnaire and clinical information were obtained from each participant included in the study . Participants were given detailed explanations about the aims , procedures and possible benefit of the study . Written informed consent was obtained from all subjects and samples were coded and treated anonymously . The study received the approval of the Committee of Research Ethics and Animal Welfare from the Instituto de Salud Carlos III ( PI number: CEI PI06_2012-v2 ) . Twelve six-week-old male Wistar rats weighing 150–175 g ( Charles River Laboratories , Barcelona , Spain ) were used in our study as the source for stool and urine samples . Animals were housed at the accredited Animal Experimentation Facility of the University of Salamanca in individual metabolic polycarbonate cages and placed in humidity and temperature controlled environment with a 12 hour photoperiod and received sterilized food and water ad libitum . Animals were monitored regularly by qualified members in animal welfare at the Animal Experimentation Facility of the University of Salamanca . Strongyloides venezuelensis used in this study was obtained from a strain originally used in the Department of Parasitology , University of Minas Gerais , Belo Horizonte , Brazil . This strain has been maintained by serial passages in laboratory rats routinely infected in the Laboratory of Parasitic and Molecular Immunology , CIETUS , University of Salamanca . Feces from infected rats were cultured using vermiculite mixed with distilled water at 28°C for 3–7 days and infective third-stage larvae ( iL3 ) which came out of the feces were then collected and concentrated by using the Baermann extraction method as described elsewhere [36] . Recovered larvae were washed in phosphate-buffered saline ( PBS ) and their viability was checked using a light microscope prior to infection . The number of viable iL3 was determined and the animals were afterwards infected subcutaneously with different iL3 doses of S . venezuelensis to ensure a potential range of low , middle and high fecal egg production during the development of infection [37] , as follows: group one ( n = 3; each rat infected with 40 iL3 ) , group 2 ( n = 3; each rat infected with 400 iL3 ) , group 3 ( n = 3; each rat infected with 4 , 000 iL3 ) and group 4 ( n = 3; non infected , as control group ) . During the 28-day infection , the animals were housed individually in metabolic cages , thus allowing separate collection of urine and feces from rodents and also to eliminate the possibility of rats re-infecting themselves from fecal sources during the experimental period . Infected rats were euthanatized in a CO2 gas chamber 29 days after the infection . Human stool samples ( n = 12 ) were obtained from outpatients ( including Spanish nationals , immigrants , tourists and aid workers ) attending Severo Ochoa and Gregorio Marañón Hospitals in Madrid , Spain , during June 2010 to June 2012 as a part of a collaborative research study on human strongyloidiasis . Those patients showed significant levels of IgE , eosinophilia or other symptoms suggestive of disease . Stool samples were examined after arrival by qualified laboratory technicians . Eleven of these 12 stool samples ( nos . 030 , 140 , 231 , 232 , 338 , 339 , 069 , 259 , 331 , 468 and 126 ) were subjected to different parasitological methods as screening tests for strongyloidiasis , including microscopic examination ( MOE ) for the presence of rabditiform larvae in direct fecal smears , agar plate culture ( APC ) or Harada-Mori´s filter paper culture method ( HMM ) . Unfortunately , for one sample ( no . 496 ) was not possible to perform any of the parasitological methods . Strongyloidiasis was confirmed in 7/11 stool samples by one or more parasitological tests applied . Four samples ( nos . 259 , 331 , 468 and 126 ) were found to be negative; however , in two of these negative samples ( nos . 468 and 126 ) eggs from Taenia saginata and "hookworm" , respectively , could be observed upon microscopic inspection . All patients´ samples were obtained before treatment with ivermectin . Thereafter , patients´ stool samples were sent to the Instituto de Salud Carlos III ( ISCIII ) , Madrid , Spain , for further DNA extraction and molecular analyses by real-time PCR ( RT-PCR ) as described below . Table 1 shows the patients´ stool sample numbers , the parasitological tests applied at Hospitals as well as results obtained in parasitological and molecular tests performed . Patients´ stool samples included in this study were firstly tested by a RT-PCR optimized at ISCIII , Madrid , Spain , as described by Saugar et al . [27] . Briefly , the RT-PCR was standardized in laboratory settings using the specific primers Stro18S-1530F ( 5′-GAATTCCAAGTAAACGTAAGTCATTAGC-3′ ) and Stro18S-1630R ( 5′-TGCCTCTGGATATTGCTCAGTTC-3′ ) to amplified a 101 base pair ( bp ) region of S . stercoralis 18S rRNA ( Gene Bank accession no . AF279916 . 2 ) as previously described by Verweij et al . [25] . The amplification was performed with a 25 μL reaction mix containing 5 μL of DNA extracted from stool samples , 1X Quantimix Easy Master Mix ( Biotools B&M Laboratories ) , 0 . 2 μM of each Stro18S-1530F and Stro18S-1630R primer and 0 . 5 μL of 50X SYBR Green I ( Invitrogen ) . The program consisted of an initial step of 15 min at 95°C followed by 50 cycles of 10 s at 95°C , 10 s at 60°C and 30 s at 72°C . The reaction and fluorescence detection were performed on the Corbett Rotor-Gene 6000 real-time PCR System ( QIAGEN , Hilden , Germany ) and The Rotor Gene 6000 Series software v . 1 . 7 was used for data analysis . In each RT-PCR run both negative ( DNA from uninfected stool sample ) and positive ( DNA from stool samples artificially infected with different amounts of S . venezuelensis iL3 DNA ) controls were routinely included . After searching on literature reports to identify potential sequences of DNA to be used in detection of Strongyloides spp . , a 329 nucleotide bp corresponding to a linear genomic DNA partial sequence in the 18S rRNA gene from S . venezuelensis was selected and retrieved from GenBank ( Accession No . AJ417026 . 1 ) for the design of specific primers [39] . A BLASTN search and alignment analysis [40] indicated that the sequence had 94–99% similarity with other sequences reported for Strongyloides spp . and no regions of similarity between this sequence and other sequences reported for possible human pathogens were detected . The 329 bp sequence selected was also tested in silico for similarity in the currently available genome databases for S . stercoralis at NemBase4 ( www . nematodes . org ) and a 94% identity with a partial sequence in contig SSC06134_1 annotated for the parasite was obtained . Forward and backward outer primers ( F3 and B3 ) and forward and backward inner primers ( FIP: F1c-F2 and BIP: B1c-B2 , respectively ) were designed using the online primer design utility , Primer Explorer v . 4 ( Eiken Chemical Co . , Ltd . , Japan; http://primerexplorer . jp/e/ ) . Several LAMP primer sets were suggested by the software and further refinement in primer design was developed manually based on the criteria described in “A Guide to LAMP primer designing” ( http://primerexplorer . jp/e/v4_manual/index . html ) . LAMP primers sequences finally selected are indicated in Table 2 and their positions relative to the 329 bp target sequence of S . venezuelensis compared with the 94% similarly partial sequence of S . stercoralis in contig SSC06134_1 are shown in Fig 1 . All the primers were of HPLC grade ( Thermo Fisher Scientific Inc . , Madrid , Spain ) ; the lyophilized primers were resuspended in ultrapure water to a final concentration of 100 pmol/μL and stored at -20°C until use . The outer LAMP primer pair , designated F3 and B3 ( Table 2 ) , was firstly tested for S . venezuelensis specificity by a touchdown-PCR to verify whether the correct target was amplified . The PCR assay was conducted in 25 μL reaction mixture containing 2 . 5 μL of 10x buffer , 1 . 5 μL of 25 mmol/L MgCl2 , 2 . 5 μL of 2 . 5 mmol/L dNTPs , 0 . 5 μL of 100 pmol/L F3 and B3 , 2 U Taq-polymerase and 2 μL ( 10 ng ) of DNA template . Initial denaturation was conducted at 94°C for 1 min , followed by a touchdown program for 15 cycles with successive annealing temperature decrements of 1 . 0°C ( from 57°C to 52°C ) every 2 cycles . The specificity of PCR was also tested with a panel of 22 heterogeneous DNA samples from other parasites included in the study . Besides , the sensitivity of the PCR was also assayed to establish the detection limit of S . venezuelensis DNA with 10-fold serial dilutions prepared as mentioned above . We tried to evaluate the LAMP primer set designed by using different in house reaction mixtures each containing a different Bst polymerase ( namely , Bst DNA polymerase Large Fragment , Bst DNA polymerase 2 . 0 and Bst DNA polymerase 2 . 0 WarmStart; New England Biolabs , UK ) as well as varying concentration of betaine ( Sigma , USA ) and supplementary MgSO4 ( New England Biolabs , UK ) to compare results in S . venezuelensis DNA amplification . Thus , LAMP reactions mixtures ( 25 μL ) contained 40 pmol of each FIP and BIP primers , 5 pmol of each F3 and B3 primers , 1 . 4 mM of each dNTP ( Bioron ) , 1x ThermoPol Reaction Buffer -20 mM Tris-HCl ( pH 8 . 8 ) , 10 mM KCl , 10 mM ( NH4 ) 2SO4 , 2 mM MgSO4 , 0 . 1% Triton X-100; New England Biolabs , UK- ( when using Bst Polymerase Large Fragment ) or 1x Isothermal Amplification Buffer -20 mM Tris-HCl ( pH 8 . 8 ) , 50 mM KCl , 10 mM ( NH4 ) 2SO4 , 2 mM MgSO4 , 0 . 1% Tween20; New England Biolabs , UK- ( when using either Bst DNA polymerase 2 . 0 or Bst DNA polymerase 2 . 0 WarmStart ) , betaine ( ranging 0 . 8 , 1 , 1 . 2 , 1 . 4 or 1 . 6 M ) , supplementary MgSO4 ( ranging 2 , 4 , 6 or 8 mM ) and 8 U of the tested Bst polymerase in each case with 2 μL of template DNA . All LAMP reactions mixtures were performed in 0 . 5-mL micro centrifuge tubes that were incubated in a heating block ( K Dry-Bath ) at a range of temperatures ( 61 , 63 and 65°C ) for 60 min to optimize the reaction conditions and then heated at 80°C for 5–10 min to inactivate the enzyme and thus to terminate the reaction . In each case , the optimal temperature was determined and used in the subsequent tests . As the LAMP reaction is highly sensitive , possible DNA contamination and carry-over of amplified products were prevented by using sterile tools at all times , performing each step of the analysis in separate work areas , minimizing manipulation of the reaction tubes and even closing them with a plastic paraffin film . Template DNA was replaced by ultrapure water as negative control in each LAMP reaction . Amplified DNA in the LAMP reaction causes turbidity due to the accumulation of magnesium pyrophosphate , a by-product of the reaction . Once the reaction was finished and following a brief spin of the reaction tubes , the turbidity of reaction mixture was visually inspected by naked eyes . The LAMP amplification results could also be visually inspected by adding 2 μL of 1:10 diluted 10 , 000X concentration SYBR Green I ( Invitrogen ) to the reaction tubes . To avoid as much as possible the potential risk of cross-contamination with amplified products , all tubes were briefly centrifuged and carefully opened before adding the fluorescent dye . Green fluorescence was clearly observed in successful LAMP reaction , whereas it remained original orange in the negative reaction . The LAMP products ( 3–5 μL ) were also monitored using 2% agarose gel electrophoresis stained with ethidium bromide , visualized under UV light and then photographed using an ultraviolet image system ( Gel documentation system , UVItec , UK ) . The specificity of the LAMP assay to amplify only S . venezuelensis DNA was tested against 22 DNA samples obtained from other parasites used as controls as mentioned above . To determine the lower detection limit of the LAMP assay , genomic DNA from S . venezuelensis 10-fold serially diluted as mentioned above was subjected to amplification in comparison with the PCR using outer primers F3 and B3 . To evaluate the ability of the LAMP assay designed to amplify S . venezuelensis DNA in real samples , we used DNA extracted from the pooled feces and urine samples taken daily from each experimentally infected group of rats with different iL3 doses . To check whether LAMP assay designed was also able to amplify DNA from S . stercoralis in clinical samples , we used the patients´ stool samples included in the study . In all amplification assays , positive ( S . venezuelensis DNA ) and negative ( DNA mix from non-infected rats stool or urine samples or ultrapure water ) controls were always included .
In the three infected groups , parasite eggs were detected for the first time in feces on the 6th day p . i . regardless of the initial infecting doses ( Fig 2 ) . The maximal fecal egg count was 3 , 921 EPG on day 10 p . i in group 1 ( Fig 2A ) , 12 , 092 EPG on day 9 p . i . in group 2 ( Fig 2B ) and 116 , 016 EPG on day 8 p . i . in group 3 ( Fig 2C ) . When a PCR verification reaction was performed using primers F3 and B3 to amplify S . venezuelensis DNA a 215 bp PCR product was successful amplified; the minimum amount of DNA detectable by PCR was 0 . 01 ng . When a panel of 22 DNA samples from other parasites were subjected to this PCR assay , amplicons were never obtained ( S1 Fig ) . Considering the most consistent color change by adding SYBR Green I into the tubes , the intensity of the ladder-like pattern on agarose gel electrophoresis as well as reproducibility of tests , the best amplification results were always obtained when the reaction mixtures contained Bst DNA polymerase 2 . 0 or Bst DNA polymerase 2 . 0 WarmStart combined with 1 M of betaine and supplementary 6 mM of MgSO4 and the reaction tubes were incubated at 63°C for 60 min . We also obtained amplification results when using Bst polymerase LF in such conditions , but the color change in reaction tubes as well as the intensity of the ladder-like pattern on agarose were always less evident compared to that obtained when using the other two enzymes; additionally , we did not get a good reproducibility of amplification trials so we discarded to use Bst polymerase LF in the following applications . When we evaluated the sensitivity of both LAMP reaction mixtures containing Bst polymerase 2 . 0 and Bst polymerase 2 . 0 WarmStart , the limit of detection in S . venezuelensis DNA amplification was 0 . 1 ng and 0 . 01 ng , respectively . As sensitivity was tenfold higher when using Bst polymerase 2 . 0 WarmStart , the reaction mixture containing this enzyme was used in assessing the specificity of the LAMP assay . Then , the LAMP assay was positive only for S . venezuelensis and no positive DNA products were observed when other parasites species were used as templates ( S2 Fig ) . Thereby , the LAMP reaction mixture containing Bst polymerase 2 . 0 WarmStart was set up as the most suitable to analyze all the samples included in the study and thenceforth was namely Strong-LAMP . In addition , all non-template controls were negative for each batch of LAMP reactions , thus indicating that there was no cross contamination and that with the primers set used there was no template free amplification [41 , 42] . We tested by LAMP each daily pool of stool samples obtained from each infection group of animals during a 28-day period ( Fig 3 ) . To avoid possible cross-contamination LAMP assays were performed into two batches of 14 samples each . When testing stool samples from infected rats with 40 iL3 ( group 1 ) we obtained LAMP positive results continuously from day 6 p . i . -when parasite eggs were detected in feces for the first time- until the end of infection at day 28 ( Fig 3A ) . When testing stool samples from infected rats with 400 iL3 ( group 2 ) and 4 , 000 iL3 ( group 3 ) we obtained in both groups LAMP positive results continuously from day 5 p . i . -one day before the onset of parasite eggs in feces- until the end of infection at day 28 ( Fig 3B and 3C ) . Negative controls ( pooled DNA samples from feces from non-infected rats; group 4 ) were never amplified and in all LAMP positive reactions a green fluorescence was clearly visualized under natural light . LAMP assay was also performed in each daily pool of urine samples obtained from each infection group of animals during a 28-day period ( Fig 4 ) . The 28 urine samples obtained from each infection group were tested in two batches of 14 samples each . Analyses of urine samples from rats infected with 40 iL3 ( group 1 ) showed LAMP positive results on days 6 , 11–14 , 16–23 and 26 p . i . ( Fig 4A ) . Analyses of urine samples from rats infected with 400 iL3 ( group 2 ) showed LAMP positive results on days 3 , 7–8 , 10–23 and 25 p . i . ( Fig 4B ) . Finally , analyses of urine samples from rats infected with 4 , 000 iL3 ( group 3 ) showed LAMP positive results on days 3 , 6–7 and continuously from day 9 until the end of infection on day 28 ( Fig 4C ) . Four urine samples considered as positive results ( including those obtained on day 19 from group 1 , on day 20 from group 2 and on days 12 and 21 from group 3 ) did not show a color change to green fluorescent as appreciable as other LAMP positive results , but a faint ladder-like pattern could be observed on agarose gel electrophoresis . We obtained DNA amplification in pooled urine sample from group 1 on the 6th day ( the same day that parasite eggs were detected in feces for the first time ) , but regarding group 2 and group 3 , we detected DNA amplification in pooled urine sample on the 3rd day ( two days before than onset of parasite eggs in feces ) . Curiously , LAMP positive results were not obtained on those days that the maximal fecal egg count was observed in each infection group ( i . e . , day 10 for group 1 , day 9 for group 2 and day 8 for group 3 , respectively ) . All patients´ stool samples were tested by RT-PCR and LAMP to compare results . The RT-PCR resulted positive in 6/7 patients´ stool samples with confirmed strongyloidiasis by parasitological tests previously applied . In addition , a positive result was obtained in a sample to which none parasitological test could be performed; negative results were obtained in negative parasitological samples for S . stercoralis ( Table 1 ) . All patients´ stool samples with confirmed strongyloidiasis by parasitological tests could be detected by LAMP , including the sample which resulted negative in previous RT-PCR analyses . In addition , as for RT-PCR , we also obtained a positive result in the sample to which parasitological tests were not available . Negative parasitological patients´ stool samples for strongyloidiasis resulted in a negative LAMP amplification , including those positive samples for Taenia saginata and "hookworn" , respectively ( Table 1; S3 Fig ) .
There are many difficulties in correctly diagnosing strongyloidiasis because most patients are asymptomatic and the lack of sensitivity and specificity of the commonly used parasitological and serological diagnostic methods , respectively [43] . Several PCR-based molecular methods offering high sensitivity and specificity have been recently proposed in diagnosing strongyloidiasis [25 , 22 , 26 , 27] . A LAMP method could be an economic , simple and applicable alternative to PCR-based methods in field conditions for diagnostic assays [44] . On the other hand , all new PCR-based approaches for Strongyloides spp . DNA detection have been always mainly assayed for stool samples from both experimentally infected animals and clinical stool samples [45 , 46 , 22 , 25 , 26 , 27] but no other biological samples , such as urine , have been investigated for molecular diagnostic purposes . In our work , we used a S . venezuelensis rodent model in order to test a new LAMP assay for diagnosing strongyloidiasis both in stool and , for the first time , urine samples . We used an experimental infection with S . venezuelensis since this parasitic nematode has been widely used as a tool and laboratory model for human and animal strongyloidiasis research [47 , 48] . The use of a S . venezuelensis rodent model allowed us to collect well-defined stool and urine samples that would otherwise have been very difficult to obtain from human patients , including samples from recently acquired infections and samples with low parasite load resembling to those likely obtained in chronic human infections . Additionally , a classical parasitological diagnostic method , such as direct faecal examination by counting EPG was used for monitoring infection as well as to compare results in parallel with molecular assays . Results obtained by counting EPG showed a similar dynamics of S . venezuelensis infection to that previously reported not only by this parasite or by S . ratti in Wistar rats [49] but also in Lewis rats [46] and in male Sprague-Dawley rats [50] . To design specific primers for our LAMP assay , a 329 nucleotide bp from the 5´ end of a linear DNA partial sequence in the 18S ribosomal RNA gene from S . venezuelensis was selected [39] . For Strongyloides species , 18S ribosomal RNA gene ( rDNA ) has been analyzed [51 , 52 , 39 , 53] . It is considered that small subunit ribosomal RNA ( SSU rDNA ) sequences within Strongyloides species are all very similar making the resolution of their phylogeny problematic as many branch lengths are inferred to be very short when distance and likelihood methods are applied [39 , 53] . Closer analysis of the SSU rDNA sequences from a number of Strongyloides species have been shown to identify a putative molecular synapomorphy ( comprising 8 to 10 nucleotides ) within the E9-2 stem-loop of the V2 variable region , thus allowing to distinguish two clades within Strongyloides genus: one containing Strongyloides spp . ex snake , S . stercoralis and S . fuelleborni ( namely "stercoralis" clade , with a 10 nucleotides sequence: ATTTTATATT ) , and another containing S . ratti , S . suis , S . venezuelensis , S . cebus , S . fuelleborni kelleyi and S . papillosus ( namely "cebus" clade , with a 8 nucleotides sequence: ATT—TTTTC ) [39] . Among the set of primers automatically generated when designing LAMP for specific amplification of S . venezuelensis , the F2 primer was finally manually selected to be used since its sequence at 3´ end -which location serve as the replication starting point after annealing- would allow not only a specific annealing in the 8 nuleotides sequence of "cebus" clade but also , theoretically , in the 10 nucleotides sequence of "stercoralis" clade if present in samples . Thus , the LAMP assay may be employed for simultaneous detection of several Strongyloides species . At present , only S . stercoralis and S . fuelleborni are known to cause infection in humans but infection with other species might be possible . Besides , the designed LAMP can also be use in the S . venezuelensis experimental infection rodent model . After verifying the operation and specificity of PCR F3-B3 , we attempted to establish the most suitable reaction mixture for the set of primers operation in the LAMP assay . The limit of detection of the LAMP assay resulted tenfold higher when Bst polymerase 2 . 0 WarmStart was used in comparison with Bst polymerase 2 . 0 ( corresponding to 0 . 01 ng vs . 0 . 1 ng , respectively ) . It has been previously reported a number of advantages of Bst polymerase WarmStart version compared to other commercially available versions , such as faster amplification [54] , increased stability at room temperature [55] and also greater sensitivity [56 , 57] . We emphasize the importance of setting up the best conditions and molecular components for primers set operation in a LAMP assay . When analyzing the stool samples , the Strong-LAMP resulted more sensitive than microscopy , at least in moderate and high levels of infection . A similar result has been also reported for RT-PCR in comparison to microscopy in detecting first-stage larvae of S . ratti in a rodent model infected subcutaneously with 2 , 500 iL3 [58] . When analyzing the urine samples daily collected , we obtained Strong-LAMP positive results during the course of infection depending on infection dose . In a work carried out by Marra et al . [46] , in which the migration route of S . venezuelensis was evaluated by PCR and histological analysis in Lewis rats infected subcutaneously with 4 , 000 iL3 , it was noted that the appearance of larvae in alveoli was already clear at 48 h p . i . . It was also observed that at 72 h p . i . all infected animals had larvae in the lungs and no larvae were found in any other organs that were examined . It was at this time , at 72 h p . i . , when we obtained Strong-LAMP positive results in urine samples from groups 2 and 3 , thus indicating the possibility of detecting S . venezuelensis free circulating DNA as a consequence of destroying larvae passing through the lungs and ending up in urine . Also according to that study , at 120 h p . i . larvae begin to disappear from the lungs and were found inserted in the small intestine villosities at 48–72 h later . Interestingly , it is also at this time in our study ( approximately on the 9th day p . i . ) when urine samples from group 3 ( infected with 4 , 000 iL3 ) resulted Strong-LAMP positive every day until the end of infection . In group 2 ( 400 iL3 ) a first positive result was also obtained by Strong-LAMP in the pool of urine samples at 72 h p . i . ; however , a time lag in the appearance of positive results until the end of infection in comparison to group 3 ( 4 , 000 iL3 ) was detected , possibly related to the lower initial infective dose . Such time lag in the appearance of positive results was much more apparent when testing urine samples from group 1; since group 1 was infected with the lowest infective dose of larvae ( 40 iL3 ) , a first positive result obtained on day 6 p . i . would suggest that parasites reached the lungs later and consequently it would take longer to settle them in the small intestine villosities . Unexpectedly , we did not obtained Strong-LAMP positive results in urine samples on days in which the maximal fecal egg count was observed in each infection group . The absence of information on this event or similar in already published data does not allow us to compare our results . We can only speculate on the possibility of some features related to the dynamics of the biological cycle of the parasite . The potential clinical applicability of the Strong-LAMP could be demonstrated on a number of human clinical stool samples . We obtained positive results in those stool samples with both parasitological demonstration and confirmed detection by RT-PCR of S . stercoralis . In addition , the analysis of one sample ( no . 496 ) with no parasitological test applied but positive by RT-PCR , resulted positive by Strong-LAMP . Moreover , another sample ( no . 069 ) with parasitological demonstration of S . stercoralis but negative by RT-PCR resulted also positive by Strong-LAMP; however , considering the limited number of human samples tested it is difficult at this time to suggest a potential greater sensitivity of LAMP assay than RT-PCR in detecting S . stercoralis DNA in stool samples . Furthermore , confirmed negative stool samples for S . stercoralis both by parasitological and RT-PCR methods resulted negative by Strong-LAMP as well , even those samples infected with T . saginata and hookworm , thus corroborating once again the specificity of our designed LAMP assay for exclusively detection of Strongyloides spp . DNA . Although specificity was also previously determined in silico by using a thoroughly BLASTN search and alignment analysis in online databases and no cross-reaction of other sequences reported for possible human pathogens were detected , it is important to note that , considering the absence of a single gold standard for strongyloides diagnosis , and because LAMP products cannot be routinely sequenced to confirm identity , other micro-organisms that may be commonly found in stool samples ( e . g . bacteria and fungi , such as Candida spp . ) should be investigated in order to further validate the LAMP assay for human diagnosis . In this work , we report for the first time , on the development of a new LAMP assay ( Strong-LAMP ) for sensitive detection of S . venezuelensis DNA in both stool and urine samples in a well-established Wistar rats experimental infection model . In addition , this Strong-LAMP assay can be also applied effectively for the detection of S . stercoralis DNA in patients´ stool samples . Clearly , in terms of potential human diagnostic , this assay requires additional validation using a greater number of clinical stool samples . The successfully amplification of Strongyloides spp . DNA in infected urine samples by LAMP assay as well as the advantages that urine would have in collection , storage and processing in comparison to patients´ stool samples , should make us consider the possibility of starting to use urine specimens in diagnosing human strongyloidiasis . However , it will be convenient to further consider the difference between the samples from a S . venezuelensis rodent model in acute disease and chronic human S . stercoralis infections . Since urine is actually an unusual requested biological sample from patients to detect S . stercoralis , further studies using clinical urine samples for human diagnostics of strongyloidiasis are strongly- ( LAMP ) recommended . | Human strongyloidiasis , a soil-transmitted infection mainly caused by Strongyloides stercoralis , is one of the most neglected among the so-called neglected tropical diseases ( NTDs ) . The difficult diagnosis lead to an underreporting of infection rates . Strongyloidiasis can easily be misdiagnosed because many infections remain asymptomatic and the lack of sensitivity of the conventional fecal-based techniques for morphologically identification of infective larvae in feces . Although serologic tests are useful , a limitation in standardization to avoid cross-reactions still remains . There is an urgent need to improve more sensitive and specific diagnostic tests , particularly in immunocompromised patients or candidates to immunosuppressive treatments . Several molecular approaches for Strongyloides spp . DNA detection have already been assayed , but they have a very limited use in routine diagnostic , particularly in endemic areas . In addition , all molecular approaches for Strongyloides spp . DNA detection have always been mainly assayed for stool samples and no other more advantageous biological samples , such as urine , have been investigated for molecular purposes . In this study we have developed , for the first time , a molecular assay using LAMP methodology as a simple , sensible and robust method for the detection of S . venezuelensis DNA in a well-established Wistar rats experimental infection in both stool and urine samples . The LAMP assay was also successfully evaluated in patients´ stool samples . Our LAMP assay ( Strong-LAMP ) is an useful molecular tool in a strongyloidiasis experimental infection model and could be a potential field-friendly diagnostic test in a clinical setting , following further validation . | [
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] | 2016 | Strong-LAMP: A LAMP Assay for Strongyloides spp. Detection in Stool and Urine Samples. Towards the Diagnosis of Human Strongyloidiasis Starting from a Rodent Model |
In vitro disease modeling based on induced pluripotent stem cells ( iPSCs ) provides a powerful system to study cellular pathophysiology , especially in combination with targeted genome editing and protocols to differentiate iPSCs into affected cell types . In this study , we established zinc-finger nuclease-mediated genome editing in primary fibroblasts and iPSCs generated from a mouse model for radiosensitive severe combined immunodeficiency ( RS-SCID ) , a rare disorder characterized by cellular sensitivity to radiation and the absence of lymphocytes due to impaired DNA-dependent protein kinase ( DNA-PK ) activity . Our results demonstrate that gene editing in RS-SCID fibroblasts rescued DNA-PK dependent signaling to overcome radiosensitivity . Furthermore , in vitro T-cell differentiation from iPSCs was employed to model the stage-specific T-cell maturation block induced by the disease causing mutation . Genetic correction of the RS-SCID iPSCs restored T-lymphocyte maturation , polyclonal V ( D ) J recombination of the T-cell receptor followed by successful beta-selection . In conclusion , we provide proof that iPSC-based in vitro T-cell differentiation is a valuable paradigm for SCID disease modeling , which can be utilized to investigate disorders of T-cell development and to validate gene therapy strategies for T-cell deficiencies . Moreover , this study emphasizes the significance of designer nucleases as a tool for generating isogenic disease models and their future role in producing autologous , genetically corrected transplants for various clinical applications .
Studying the molecular pathology of human disease is often difficult due to the limited availability of particular primary cells , their limited lifespan , or because complex developmental differentiation procedures cannot be easily followed in vivo . In vitro disease modeling with induced pluripotent stem cells ( iPSCs ) provides a practical alternative , and the study of several disorders has benefitted enormously from the convergence of three key technologies: modern genomics that links genetic variants to disease phenotypes , the ability to generate patient-specific iPSCs that can be differentiated into cell types affected by disease , and powerful tools for editing complex genomes [1 , 2] . T lymphocytes play an important role in adaptive immunity against invading pathogens or in fighting tumor cells . A natural microenvironment for T-cell lymphopoiesis is provided by the thymus . Inherited defects in T-cell function or in T-cell development can lead to severe combined immunodeficiency ( SCID ) , a group of life threatening disorders of the immune system [3] . Radiosensitive SCID ( RS-SCID; OMIM #602450 ) is characterized on the molecular level by dysfunctional non-homologous end-joining ( NHEJ ) , the most important pathway to repair DNA double strand breaks ( DSBs ) . In human patients , defective DNA repair can lead to a cellular hypersensitivity to ionizing radiation . Moreover NHEJ is essential for physiological B- and T-lymphocyte development as it plays an important role in the B-cell receptor ( BCR ) and T-cell receptor ( TCR ) recombination process [4] . The diversity of BCRs and TCRs results from the multitude of variable ( V ) , divers ( D ) and joining ( J ) gene segments that are almost randomly reassembled in a process called V ( D ) J recombination . During V ( D ) J recombination , specific enzymes cleave at specific recombination signal sequences flanking these gene segments and NHEJ factors play a crucial role in reassembly and final ligation of these gene segments [5 , 6] . The NHEJ process involves a number of different enzymes , including DNA-dependent protein kinase ( DNA-PK ) . DNA-PK is a polyprotein complex , formed by the Ku70/Ku80 heterodimer and the DNA-PK catalytic subunit ( DNA-PKcs ) [7] , that binds to DNA end structures and serves as a docking site for additional NHEJ factors that mediate DNA repair [8] . Hypomorphic mutations in PRKDC , the locus encoding DNA-PKcs , have recently been described for radiosensitive T and B deficient SCID patients [9] . Hence , DNA-PK dependent signaling is a paradigmatic example of how a single molecule can be simultaneously involved in both , DNA repair and T- and B-cell development , and of how such a process can be disturbed by a single point mutation . These particularities make PRKDC an optimal target for novel site-specific gene therapy approaches , such as designer nuclease mediated genome editing . For disease modeling , iPSCs can be generated from affected somatic cells by expression of four transcription factors Oct4 , Sox2 , Klf4 and c-Myc [10 , 11] . Similar to pluripotent embryonic stem cells , iPSCs have the capacity for unlimited self-renewal , are permissive for transfection with foreign DNA , and importantly , can be expanded in a clonal fashion for characterization . Thus far , iPSCs have been derived from several patients suffering from different hematopoietic and immunological disorders and have been used for disease modeling and gene targeting approaches [12] . Several protocols for in vitro [13–21] and in vivo [22 , 23] differentiation of iPSCs to hematopoietic cells have been reported . The availability of Notch ligand based cell culture systems , such as the murine stromal cell line OP9-DL1 , allows for further differentiation of hematopoietic stem cells into T-cells in vitro [24 , 25] Targeted genome modification in iPSCs is an essential tool in disease modeling [12] , and gene editing with designer nucleases has developed into a powerful instrument , which has been successfully applied to generate various genetically modified model organisms or human cells to study gene function or the pathophysiology of disease causing mutations . Designer nucleases , like meganucleases [26] , zinc-finger nucleases ( ZFNs ) [27] , transcription activator-like effector nucleases ( TALEN ) [28] , or the clustered regularly interspaced short palindromic repeats ( CRISPR ) /Cas9 system [29] , induce site-specific DNA double strand breaks ( DSBs ) at chosen sites . These DSBs activate one of two major DNA repair mechanisms , NHEJ or homology directed repair ( HDR ) , which can be employed to disrupt genes or to target the integration of exogenous donor DNA sequences to a specific site in the genome , respectively [30] . The goal of this study was to establish an in vitro disease model for T-cell deficiencies and to employ this model to evaluate a designer nuclease-based genome editing strategy . To this end , we generated iPSCs from adult ear fibroblasts of NOD . SCID mice , a model for RS-SCID [31] , and established a protocol to recapitulate T-lymphopoiesis from iPSCs in vitro . We used ZFNs to edit DNA-PK deficient fibroblasts and iPSCs and demonstrated that designer nuclease mediated gene correction led to rescue of DNA-PK dependent signaling , normal radiosensitivity , restoration of T-cell maturation , and polyclonal TCR recombination . We hence provide proof that the combination of two promising technology platforms , iPSCs and designer nucleases , with a protocol to generate T-cells in vitro represents a powerful paradigm for SCID disease modeling and the evaluation of therapeutic gene editing strategies .
In the murine disease model RS-SCID is caused by a T-to-A transversion mutation in exon 85 of the prkdc locus . The introduced premature stop codon ( Y4046* ) leads to an 83 aa long C-terminal truncation of the encoded DNA-PKcs protein , leading to decreased protein stability and low kinase activity [31] . ZFNs targeted to intron 84 of prkdc were generated using the OPEN platform [32] and their activity verified by in vitro cleavage assays and plasmid-based recombination assays ( S1 Fig ) . To restore function of DNA-PK , we generated a donor DNA encompassing the wild-type cDNA sequence of prkdc exons 85 and 86 , preceded by a splice acceptor site and followed by a poly ( A ) signal ( Fig 1A ) . Targeting an intron allowed us to co-introduce a neomycin selection marker cassette to enrich for cells that underwent correct gene targeting . To validate our targeting strategy , fibroblasts from a 12-week old male NOD . SCID mouse , in which the SCID mutation in prkdc was confirmed by sequencing , were isolated . Upon culturing in vitro these cells transformed spontaneously , probably due to their intrinsic DNA repair deficiency . The fibroblasts were transfected with various ratios of donor DNA to ZFN expression plasmids before G418 selection was applied . An inside-out PCR strategy was used to verify correct gene targeting in polyclonal cell populations ( Fig 1B ) . All samples transfected with ZFN expression plasmids and donor revealed successful gene targeting . Splicing of exon 84 to the integrated cDNA was verified by inside-out reverse transcription ( RT ) -PCR ( Fig 1C ) . To determine the efficiency of the gene targeting approach , cell clones were generated by single cell dilution . Six out of 20 analyzed clones showed correct targeting . To confirm that re-routed splicing of exon 84 to the artificial exon 85/86 restored DNA-PK activity , cells were treated with camptothecin ( CPT ) , a compound known to induce DSBs during DNA replication by blocking topoisomerase I . Under these experimental conditions , RPA2 is exclusively phosphorylated by DNA-PK at the stalled replication forks [33] . Upon CPT treatment of SCID fibroblasts ( Fib . S ) , a gene edited fibroblast clone ( Fib . T ) and a donor-containing clone ( Fib . D ) , phosphorylation of RPA2 was detected in Fib . T cells , but not in Fib . S and Fib . D cells ( Fig 2A ) . The fibroblast cell line NIH-3T3 served as a positive control . Fibroblasts of RS-SCID mice are sensitive to gamma-irradiation or the radiomimetic drug bleomycin [34] . To verify that successful gene targeting could abrogate radiosensitivity , colony survival assays with bleomycin were conducted . We found that the corrected cell line Fib . T displayed similar resistance to the drug as NIH-3T3 cells , while both Fib . D and Fib . S cells were highly sensitive to bleomycin ( Fig 2B ) . In conclusion , successful ZFN-mediated genome editing restored activity of DNA-PK , which was able to phosphorylate downstream target proteins and to rescue the radiosensitive phenotype of RS-SCID cells . While fibroblasts served as an important model to evaluate DNA-PK dependent signaling , the full therapeutic potential of genome editing at the prkdc locus can only be assessed in lymphoid cells . iPSCs have the capacity for unlimited self-renewal , allowing long-term in vitro culture and generation of single-cell derived subclones . As iPSCs can be differentiated into hematopoietic cells , including T lymphocytes , they are an ideal platform for disease modeling and the evaluation of gene therapeutic approaches . We generated iPSCs from fibroblasts isolated from a 6-week-old NOD . SCID mouse by transduction with a polycistronic lentiviral vector expressing the reprogramming factors Oct4 , Klf4 , Sox2 and c-Myc [35] . Since the DNA repair-deficient phenotype interferes with efficient reprogramming [36] , we conducted the experiment under hypoxic conditions and added ascorbic acid to reduce damage by reactive oxygen species ( ROS ) [37] . In addition , small molecule inhibitors for MAP kinase ( MEK ) , glycogen synthase kinase 3 ( GSK3 ) and TGF-beta were used , which have been reported to permit derivation of iPSCs of NOD-derived mouse strains and enhance the reprogramming progress [38 , 39] . All analyzed iPSC clones expressed pluripotent stem cell markers ( S2 Fig ) , and RT-PCR demonstrated expression of the embryonic stem cell-specific genes in a NOD . SCID iPSC clone ( iPS . S6; Fig 3A ) . In addition , cells from ectodermal ( neural rosette-like structures ) , endodermal ( gut-like structures ) and mesodermal ( smooth muscle patches ) origin were detected in teratoma derived from clone iPS . S6 ( Fig 3B ) . Genome integrity was assessed before and after ZFN mediated genome engineering by spectral karyotyping ( Figs 3C and S2 ) . The NOD . SCID iPSC clone iPS . S6 displayed no gross genetic abnormalities and was used for subsequent gene targeting experiments . In summary , we showed that DNA-repair deficient NOD . SCID fibroblasts could be reprogrammed into iPSCs that display pluripotent behavior and characteristics similar to murine embryonic stem cells . For targeted genome editing , cells of iPSC clone iPS . S6 were nucleofected with donor and ZFN expression plasmids . Following selection and clonal expansion , inside-out PCR amplification was applied on genomic DNA to detect correct targeting . Of note , 41 out of 46 analyzed clones ( 89% ) showed correct integration of the artificial exon 85/86 . Extended PCR analysis of five targeted iPSC clones verified correct 5´- and 3´-junctions between genomic and donor DNA , respectively . An allelic discrimination PCR confirmed mono-allelic targeting in all cases ( Fig 4A ) . Furthermore , expression of the DNA-PKcs encoding mRNA and re-routed splicing to artificial exon 85/86 was validated by inside-out RT-PCR ( Fig 4B ) . All targeted iPSC clones were positive for expression of pluripotency markers ( Fig 3A ) , formed all three germ layers in teratoma assays ( Figs 3B and S2 ) , had an intact karyotype ( Figs 3C and S2 ) , and did not show any signs of NHEJ-mediated mutagenesis at the top 15 predicted off-target sites in the mouse genome ( S1 Table , S1 Text ) . The polycistronic lentiviral vector used for generation of iPSCs contained Flp recognition target ( FRT ) sites in the U3 region of the long-terminal repeats , which allowed us to excise the reprogramming cassette using retroviral-mediated transfer of Flp recombinase [40] . Southern blot analysis confirmed successful removal of the lentiviral vector genome ( S3 Fig ) and targeted integration of the artificial exon 85/86 into intron 84 of the prkdc locus ( S3 Fig ) . Since DNA-PK is essential for V ( D ) J recombination , the RS-SCID immunophenotype is characterized by a lack of T and B-lymphocytes [41] . The stromal cell line OP9-DL1 leads to activation of the DL1-mediated Notch signaling in co-cultured cells , which in turn is a prerequisite to induce the T-lymphoid program in multipotent hematopoietic progenitors [24] . Initial experiments showed that OP9-DL1 co-cultivation of C57BL/6-derived lineage negative bone marrow cells enabled the differentiation of these multipotent stem cells through all ( CD4-/CD8- ) double-negative ( DN ) thymocyte stages , as determined by CD25 and CD44 surface expression . Further culturing of these T-cell precursors on OP9-DL1 led to the generation of CD4+/CD8+ double-positive ( DP ) T lymphocytes , which expressed the beta chain of the T-cell receptor ( TCRß ) , indicating that these cells have successfully undergone V ( D ) J recombination and beta-selection in vitro ( Fig 5 ) . Since V ( D ) J recombination is initiated at the DN2 ( CD44+/CD25+ ) stage and beta-selection occurs at the DN3 ( CD44-/CD25+ ) stage , we hypothesized that corrected RS-SCID iPSC-derived hematopoietic progenitor cells ( HPCs ) should be able to differentiate to CD4+/CD8+ double-positive T lymphocytes , while T-cells derived from uncorrected SCID-derived iPSCs would stop at the DN2 thymocyte stage due to their defect in V ( D ) J recombination ( Fig 5A ) . To this end , we established an embryoid body ( EB ) -based differentiation protocol for the generation of HPCs from iPSCs . Differentiated and dissociated EBs from all iPSC clones contained cells carrying the early hematopoietic surface markers CD41 and cKit ( S4 Fig ) . Co-cultivation of these cells on OP9-DL1 stroma cells induced differentiation towards T-lymphocytes . After two weeks , thymocyte maturation of iPSC-derived HPCs was measured by flow cytometry , revealing the presence of CD44+/CD25- ( DN1 ) , CD44+/CD25+ ( DN2 ) , CD44-/CD25+ ( DN3 ) , CD44-/CD25- ( DN4 ) , and CD4+/CD8+ ( DP ) cells from wild-type iPSCs ( iPS . WTX; Fig 5 ) . As hypothesized , T-cell differentiation of NOD . SCID iPSCs ( iPS . S6X ) was blocked in early DN1 and DN2 thymocyte stages and these T-cell precursors showed neither expression of CD4/CD8 nor TCRß . In contrast , differentiation from genetically corrected iPSC clones ( iPS . T25X ) reached DN3 and DN4 stages as well as the CD4+/CD8+ DP T-cell stage , with a fraction of cells expressing TCRß ( Fig 5B ) . Although the same experimental conditions were applied , the absolute numbers of generated T-cells varied in between different experiments . To confirm T-cell receptor recombination on the genome level , V ( D ) J recombination was verified by spectratyping . Control T-cells isolated from the thymus and in vitro generated T-cells from bone marrow lineage negative cells showed a polyclonal T-cell repertoire at Vß chains 1 , 6 , 8 . 1 , 8 . 3 , 10 , 12 , 14 and 20 ( Figs 6 and S5 ) . While V ( D ) J recombination was undetectable in T-cell precursors derived from SCID iPSCs , T lymphocytes derived from WT or gene targeted iPSCs underwent V ( D ) J recombination and revealed a polyclonal T- cell repertoire . In summary , we developed a protocol , which allowed us to model T-cell differentiation in vitro . We showed that iPSCs can be differentiated into hematopoietic progenitors and further to various stages of thymocyte development . While wild-type and corrected NOD . SCID iPSCs could be maturated into CD45+ CD4+/CD8+ DP T-cells that express TCRß , differentiation of DNA-PK-deficient cells stopped at the DN2 thymocyte stage . These results provide a proof of concept that iPSC-based in vitro disease modeling is able to reflect in vivo thymocyte maturation and that such modeling can be used for both to investigate T-cell maturation defects and to validate gene therapy strategies .
SCID is a group of monogenetic disorders of the immune system characterized by the absence of T-cells , sometimes in combination with a lack of functional B-lymphocytes and/or natural killer cells . RS-SCID is a special form of SCID disorders and serves as a paradigm for radiosensitivity and immunodeficiency . On top of the absence of T- and B-lymphocytes , the pathophysiology of RS-SCID is characterized by a strong sensitivity of all somatic cells to radiation and DNA damaging agents due to a defective DNA repair pathway . The underlying mutations are found in genes coding for NHEJ factors , including LIG4 [42] , Artemis [43] , XLF [44] and DNA-PKcs [9] . Disease modeling based on patient-derived iPSCs is particularly valuable when studying rare disorders , like RS-SCID , for which patient cells are not easily accessible , have a limited lifespan , or do not develop due to a differentiation block . Designer nuclease-based gene editing in iPSCs makes this instrument even more attractive because it enables scientists to correlate genotype to phenotype in an isogenic background , either by creating disease models through the insertion of disease specific mutations in normal cells [45] or by correcting the underlying genetic mutation back to wild-type in patient-derived iPSCs [16 , 46] . Particularly in combination with genetic engineering , iPSCs are preferred over fibroblasts because of their unlimited proliferative potential and their ability of clonal expansion . Hematopoietic differentiation protocols offer the possibility to investigate maturation of various blood lineages in vitro , e . g . to study the impact of genomic mutations on protein function in mature blood cells or where specific mutations lead to a block in lymphopoiesis , myelopoiesis , or erythropoiesis [17 , 18 , 46] . While designer nuclease-based gene editing in iPSCs has been established in several labs , differentiation of genetically modified iPSCs to mature immune cells has remained challenging . Differentiation of iPSCs derived from a patient suffering from X-linked chronic granulomatous disease ( X-CGD ) to granulocytes was the first example to show functional correction of a genetic defect by targeted integration of a gp91phox expression cassette into the putative safe harbor site AAVS1 [15] . Myeloid differentiation from patient-derived iPSCs for disease modeling and/or drug development has also been established e . g . for severe congenital neutropenia [47] and pulmonary alveolar proteinosis [20] . Differentiation of iPSCs to lymphocytes , on the other hand , has been reported only from a few labs [18 , 19] . In the present study , we describe an improved in vitro differentiation procedure for iPSCs to T-cells that is based on previously published protocols [18 , 48 , 49] , and , to our knowledge , use this protocol for the first time to model the functional defects of an immunodeficiency in vitro and to investigate the effect of genetic engineering of disease iPSCs on T-cell maturation . Because the generated hematopoietic progenitor cells supported the maturation through all early stages of thymocyte differentiation , including V ( D ) J recombination and beta-selection , we were able to reproduce the stage-specific block induced by the point mutation in the prkdc locus in vitro . This setup can also be used to screen for genotype-phenotype correlations or to characterize the consequence of newly identified genetic mutations on T-lymphopoiesis and/or T-lymphocyte function in more detail . For instance , as compared to in vivo models , individual effects of the microenvironment , cytokines and/or small molecules affecting T-cell maturation and expansion , like IL-7 or IL-2 , can be analyzed by simple addition to the culture medium . Moreover , existing stroma-free models can be further developed [50] to identify factors downstream of Delta-like Notch ligands that promote T-cell development . Finally , the efficiency of T-cell related gene therapy approaches can be assessed in vitro , without the need of hematopoietic stem cells of the patients . In our study we applied ZFNs for genetic modification of RS-SCID iPSCs . The generation of highly specific ZFNs can be rather challenging and several studies have described off-target cleavage activity of ZFNs [51 , 52] . While the specificity of ZFNs can be improved , e . g . by optimizing the DNA binding properties of the zinc-finger arrays [32] , selecting appropriate linker domains [53 , 54] and employing obligate heterodimeric FokI nuclease domains [55 , 56] , alternative designer nucleases , such as TALENs [28] and CRISPR/Cas9 based nucleases [29] , are easier to engineer . Our system provides a basis for further development of iPSC-derived cell products with the potential for various clinical applications . However , although we have tried to transplant iPSC-derived hematopoietic stem/precursor cells into NOD . SCID mice , we did not observe any engraftment of these cells . This is in line with published data showing that transplantation worked only with iPSC-derived hematopoietic stem/precursor cells that were produced in vivo [22 , 23] . Further studies will be needed to establish optimal culture conditions to generate transplantable stem cells in vitro . Hence , combining in vitro protocols with physiologic in vivo differentiation seems more promising . For example , transplantation of iPSC-derived early thymocyte progenitor populations could allow for thymic reconstitution and maturation to create polyclonal T-cell effector populations [50] . Infusions of in vitro derived autologous T-cells could be used to stabilize patients suffering from primary immunodeficiencies , like SCID or hemophagocytic lymphohistiocytosis , or after conventional hematopoietic stem cell transplantation to close the gap until graft-derived lymphocytes arise . Moreover , given the clinical success of autologous T-cells expressing tumor specific chimeric antigen receptors ( CARs ) [57] , iPSC-derived autologous CAR-T-cells represent an interesting alternative to current protocols , as recently shown [19] . Finally , autologous , CCR5 knockout iPSC lines could present a source to provide HIV patients with HIV-resistant T-cells to reconstitute the adaptive immune system [58] . However , before iPSC-based cell therapies can enter clinical practice , safety concerns , especially with regard to the generation of iPSC-derived teratoma , have to be addressed and full functionality of iPSC-derived cells proven . In conclusion , our study describes an iPSC-based disease model for RS-SCID . Our in vitro protocol allowed us to differentiate iPSCs to T-cells and to analyze the influence of NHEJ deficiency on V ( D ) J recombination . Moreover , it emphasizes the significance of designer nucleases as a tool in generating isogenic disease models and their future role in producing iPSC-based , patient-specific , genetically corrected autologous transplants for various applications in the clinic .
NIH . 3T3 and HEK293T cells were cultured in DMEM ( Biochrom ) supplemented with 10% FCS ( PAA ) , penicillin/streptomycin ( P/S; PAA ) , L-glutamine ( Biochrom ) and sodium pyruvate ( PAA ) . OP9 and OP9-DL1 cells ( obtained from Juan Carlos Zúñiga-Pflücker ) were expanded in OP9 medium [alpha-MEM ( Gibco ) , 20% OP9-tested FCS ( PAA ) , P/S and L-glutamine] . Primary mouse ear fibroblasts were cultured in MEF medium [DMEM low glucose ( PAA ) with 15% FCS , L-glutamine , nonessential amino acids ( NEAA; Gibco ) , P/S , 100 μM of ß-mercaptoethanol ( Sigma-Aldrich ) , sodium pyruvate and 50 μg/μl phospho-ascorbic acid ( P-VitC , Sigma-Aldrich ) ] . ES . CCE cells were cultivated in ES medium [Knockout-DMEM ( Gibco ) with 15% ES-tested FCS ( PAA ) , P/S , L-glutamine , NEAA , 150 mM monothioglycerol ( MTG , Sigma-Aldrich ) and ESGRO mouse LiF ( Millipore ) ] . iPSCs were cultivated in iPS medium [Knockout-DMEM supplemented with 15% ES-tested FCS , NEAA , P/S , L-Glutamine , 100 μM of ß-mercaptoethanol and ESGRO mouse LiF , 50 μg/μl of P-VitC , 4 μM of SB431542 , 1 μM of PD0325901 and 3 μM of CHIR99021 ( all Axon Medchem , together termed 3i ) and passaged with Accutase ( Gibco ) . ES . CCE cells and iPSCs were cultivated either on irradiated C3H or CF-1 MEF feeders on gelatin-coated plates or feeder-free in vented flasks ( Sarstedt ) . Lineage negative cells ( HSC ) were isolated by flushing the tibiae and femurs of C57BL/6N mice ( Charles River ) and purified by magnetic cell sorting ( MACS ) with the Lineage Cell Depletion Kit ( MACS Miltenyi ) according to the manufacturer’s protocol . Cells were stained with Trypan Blue ( Sigma-Aldrich ) and counted at 100x microscope magnification prior to in vitro T-cell differentiation . Cell clones were generated either by limiting dilution ( fibroblasts ) or colony picking ( iPSCs ) . All but HEK293T cells were cultivated under hypoxic conditions ( 7% CO2 / 5% O2 ) . Prkdc-specific zinc-finger arrays ( S1 Fig ) were generated with the OPEN protocol [32] . To generate ZFNs , the zinc-finger arrays were codon-optimized ( GeneArt ) and cloned into pRK5 vectors , with and without NLS [59] , containing the cleavage domains of wild-type FokI or the obligate heterodimeric FokI variant KV/EA [55] and the LRGS linker [54] . The target plasmid pCMV . LacZsPK∂GFP was generated by replacing the “31” target site of pCMV . LacZs31∂GFP [59] by the ZFN target site aGTTTGCGCCtaactGAAGGTGACa ( capital letters indicate target site for ZFN ) . The donor plasmid pJet . SAE8586Neo ( Fig 1A ) consists of ( i ) a splice acceptor ( SA ) [60]; ( ii ) a cDNA consisting of prkdc exons 85 and 86 , which was PCR amplified from pMEPK7 ( kindly provided by Masumi Abe ) with primers PRK-F/PRK-R ( S2 Table ) ; ( iii ) an SV40 polyadenylation signal ( pA ) ; ( iv ) a NeoR cassette comprise the aminoglycoside phosphotransferase coding sequence flanked by the HSV thymidine kinase promoter and an SV40 pA ( kindly provided by Stefan Weger ) ; ( v ) left and right homology arms , which were PCR amplified from Fib . S gDNA . For expression analysis , ZFNs were expressed in HEK293T for immunoblotting as previously described [59] . The in vitro cleavage assay was basically performed as defined before [61] . Briefly , a target DNA was amplified by PCR from Fib . S gDNA using primers IV-F/IV-R ( S2 Table ) . ZFNs were in vitro transcribed/translated with the TNT SP6 Coupled Reticulocytes Lysate System ( Promega ) , 150 ng of target DNA was mixed with the reticulocyte lysates , incubated for 1 . 5 h at 37°C , and analyzed on a 1 . 5% agarose gel . The plasmid-based gene targeting assay was conducted as described before [59] . Flow cytometry to determine the percentage of EGFP and REX positive cells was performed on FACSCalibur with CellQuestPro software ( BD Biosciences ) . For targeted integration into Fib . S fibroblasts , 1x105 cells were transfected 24 h after seeding with Lipofectamine 2000 ( Life Technologies ) . 1 . 6 μg of endotoxin-free DNA was mixed with 4 . 8 μl of transfection reagent in 200 μl OptiMEM ( Gibco ) . The ZFN expression plasmids were co-transfected with the donor pJet . SAE8586Neo at different ratios and filled up with pUC118 to 1 . 6 μg . Selection with 500 μg/ml of G418 ( Sigma-Aldrich ) was applied 5 days after transfection for 7 days . iPSCs were grown feeder-free before and after transfection . 3x106 cells were nucleofected with 10 μg of pJET . SAE8586Neo and 5 μg of each ZFN expression plasmid using the Mouse ES Cell Nucleofector Kit ( LONZA ) and Nucleofector II with program A-030 . After 5 days of recovery , G418 selection was applied for 7 days at a concentration of 400 μg/ml . After 1 week , iPSC clones were isolated and cultivated on feeders . Genomic DNA was extracted with the QIAamp DNA Blood Mini Kit ( QIAGEN ) . G418 selected fibroblast and iPSC clones were analyzed for legitimate targeted integration by inside-out PCR using Phire Hot Start II DNA polymerase kit ( Thermo Scientific ) . RNA was isolated with TRIzol ( Life Technologies ) , and all RT-PCR reactions performed with the QuantiTect Reverse Transcription Kit ( QIAGEN ) . All used primers are listed in S2 Table . For Southern blot analysis [62] , genomic DNA was digested with EcoRV or BamHI , separated on a 0 . 8% agarose gel and transferred to Biodyne B nylon membrane ( PALL Life Sciences ) . DNA was hybridized with a 32P-labeled fragment of PRE ( for detection of the reprogramming vector ) or NeoR ( for detection of donor copies ) using the DecaLabel DNA Labeling Kit ( Fermentas ) . Labeled HindIII digested Lambda DNA was used as a marker . To measure DNA-PK dependent RPA2 phosphorylation , 8x105 fibroblasts were treated with 1 μM of camptothecin ( Sigma-Aldrich ) for 1 h . Cells were harvested in RIPA buffer supplemented with Complete Protease Inhibitor and PhosSTOP phosphatase inhibitor cocktails ( both Roche ) . Western blot was basically performed as described before [63 , 64] . RPA2 and ß-actin were detected with rat anti-RPA32 ( 1:1000 , 4E4 , Cell Signaling ) and rabbit anti-ß-actin ( 1:1000 , Cell Signaling ) , respectively , and visualized with HRP-conjugated anti-rat and anti-rabbit antibodies ( 1:20 , 000 , Dianova ) and West Pico Chemiluminescence substrate ( Thermo Scientific ) . For the colony survival assay , 1x105 fibroblasts were treated 1 day after seeding with the indicated amounts of bleomycin ( Sigma-Aldrich ) for 2 h . Cells were washed with PBS , trypsinized and 5 , 000 cells seeded into a 10-cm plate ( N = 3 ) . After 4 days the plates were stained with 0 . 5% ( w/MeOH ) crystal violet ( Sigma-Aldrich ) and colonies counted . Murine adult fibroblasts were extracted from ears of 6-week old NOD/ShiLtJ and NOD . CB17-Prkdc scid/J male mice as described before [62] . Fibroblast from 12-week old NOD . CB17-Prkdc scid/J mouse gave rise to spontaneously transformed Fib . S . The “4-in-1” reprogramming vector pRRL . PPT . SF . mOKSMco . idTom . PRE , co-expressing the transcription factors Oct4 , Klf4 , Sox2 and c-Myc with the fluorescent marker tdTomato , has been previously described [35] . To generate versions that allow for Flp recombinas-mediated excision ( pRRL . PPT . SF . mOKSMco . idTom . PRE . FRT ) , FRT sites were introduced into the promoter-deprived U3 region . Virus production has been described elsewhere [65] . The reprogramming was conducted as described before [35] . Briefly , NOD . CB17-Prkdc scid/J or NOD/ShiLtJ-derived fibroblasts were seeded in MEF medium on gelatin-coated 6-well-plates at 8x104/well for transduction . After 2 days , cells were transduced with an MOI of 5 and incubated for 8 h , following 2 times washing with PBS . MEF medium with 2 mM VPA ( Sigma Aldrich ) was added . After 4 days medium was changed to iPS medium with VPA , and after 7 days 3i was added . After 14 days , emerging iPSC colonies were isolated and expanded for characterization . A total of 12 iPSC clones derived from NOD . CB17-Prkdc scid/J ( iPS . S ) were initially characterized by assessing expression of SSEA-1 by flow cytometry and staining of alkaline phosphatase ( Millipore ) followed by documentation with the Olympus IX71 system . Determination of the vector copy number ( VCN ) , teratoma formation , Flp recombinase-mediated excision , fluorescence in situ hybridization ( FISH ) and pluripotency factors RT-PCR analysis have been described previously [35 , 62 , 66] . Clone iPS . S6 was used for gene targeting and three out of 41 corrected clones ( iPS . T8 , iPS . T25 , iPS . T44 ) were characterized in detail . The parental uncorrected clone iPS . S6 was included as a negative control , a wild-type NOD/ShiLtJ derived clone ( iPS . WT ) as a positive control . The protocol was adapted from previously published work [48 , 49] . For embryoid body ( EB ) formation , iPSCs were split with Collagenase IV ( Gibco ) and 5x104 cells were cultured in suspension plates in 2 ml of EB medium [IMDM ( Biochrom AG ) with 15% ES cult FCS ( Stem Cell Technologies ) , 5% PFHM II ( Gibco ) , P/S , L-Glutamine , 50 μg/ml P-VitC , 150 mM MTG , 200 μg/ml human transferrin ( Sigma-Aldrich ) ] in a normoxic incubator on a shaker at 60 rpm . At day 2 . 5 , 0 . 5 ml of EB medium plus cytokines rhBMP-4 , activinA , rhVEGF165 and rhFGF-2 at 5 ng/ml final concentration each ( all R&D Systems ) was added . At day 8 , EBs were harvested , washed with PBS and collected in Trypsin-EDTA , diluted 1:15 in Collagenase IV . After 30 min , 2 . 5 ml of cell dissociation buffer ( Gibco ) was added and cells transferred through a 70-μm mesh . Hematopoietic progenitor cells ( HPCs ) were washed with PBS and analyzed for CD41/cKit expression by flow cytometry prior to hematopoietic expansion . To this end , 106 EB-derived HPCs were cultivated for 3 days under hypoxic conditions in STFV medium [IMDM , 10% OP9-tested FCS , P/S , L-glutamine , 10 ng/ml mSCF , 20 ng/ml mTPO , 100 ng/ml rhFlt3-L ( all Peprotech ) , and 40 ng/ml rhVEGF165 ( R&D Systems ) ( final concentration each ) ] . At day 3 , cells were harvested through a 100-μm mesh and washed with PBS prior to in vitro T-cell differentiation . To this end , up to 3x105 expanded HPCs or 0 . 5-1x105 HSCs were added in T-cell differentiation medium [OP9 medium , supplemented with 1 ng/ml mIL-7 ( Peprotech ) and 5 ng/ml rhFlt3-L] . After 3 days , 2 ml medium was added and cultivation continued for up to 4 weeks . Every 7 days cells were harvested through a 100-μm mesh , washed with PBS , transferred to a new OP9-DL1 cell layer , and analyzed for T-cell differentiation by flow cytometry . For flow cytometric analysis , cells were resuspended in FACS buffer [PBS supplemented with 2% FCS , 1 mM EDTA and 0 . 1% sodium azide ( both Sigma-Aldrich ) ] . To stain for pluripotency marker SSEA-1 , iPSCs were rinsed with PBS and stained with biotinylated anti-SSEA-1 antibody ( eBioscience ) diluted in FACS buffer for 20 min at 4°C . After rinsing the secondary staining was performed with a streptavidin-APC antibody ( eBioscience ) . Hematopoietic cells were pretreated with Mouse BD Fc block ( BD Biosciences ) before antibody staining . Antibody staining was performed for 20 min at 4°C . EB-derived HPCs were stained with CD41-PE , cKit-APC , or respective isotype controls ( all eBioscience ) . iPSC-derived T-cells were stained with CD44-PE and CD25-APC , or CD4-PE and CD8-APC . Viability staining with 7-AAD was performed for 2 min during the last rinsing , before samples were measured on a FACSCalibur . Alternatively , iPSC-derived T-cells were stained with CD45-APC-Cy7 , CD4-PerCPR-Cy5 . 5 , CD8-PE-Cy7 ( all BD Biosciences ) , CD44-PE , CD25-APC , TCRß-FITC ( eBioscience ) and DAPI , before analysis on a FACSCanto II with FACSDiva ( BD Biosciences ) . All samples were analyzed with FlowJo software ( Tree Star ) . T-cell receptor diversity was analyzed by CDR3 spectratyping as previously described [67] . All experiments were performed at least three times . Error bars represent standard deviation ( SD ) . Statistical significance was determined with a two-sided Student's t-test with unequal variance . The National Center for Biotechnology Information ( NCBI ) Nucleotide database ( http://www . ncbi . nlm . nih . gov/nuccore ) accession number for the ZFN target site in intron 84 of the prkdc gene on mouse chromosome 16 is AB030754: 189732 . | Due to the limited availability and lifespan of some primary cells , in vitro disease modeling with induced pluripotent stem cells ( iPSCs ) offers a valuable complementation to in vivo studies . The goal of our study was to establish an in vitro disease model for severe combined immunodeficiency ( SCID ) , a group of inherited disorders of the immune system characterized by the lack of T-lymphocytes . To this end , we generated iPSCs from fibroblasts of a radiosensitive SCID ( RS-SCID ) mouse model and established a protocol to recapitulate T-lymphopoiesis from iPSCs in vitro . We used designer nucleases to edit the underlying mutation in prkdc , the gene encoding DNA-PKcs , and demonstrated that genetic correction of the disease locus rescued DNA-PK dependent signaling , restored normal radiosensitivity , and enabled T-cell maturation and polyclonal T-cell receptor recombination . We hence provide proof that the combination of two promising technology platforms , iPSCs and designer nucleases , with a protocol to generate T-cells in vitro , represents a powerful paradigm for SCID disease modeling and the evaluation of therapeutic gene editing strategies . Furthermore , our system provides a basis for further development of iPSC-derived cell products with the potential for various clinical applications , including infusions of in vitro derived autologous T-cells to stabilize patients after hematopoietic stem cell transplantation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Rescue of DNA-PK Signaling and T-Cell Differentiation by Targeted Genome Editing in a prkdc Deficient iPSC Disease Model |
The differentiation of post-meiotic spermatids in animals is characterized by a unique reorganization of their nuclear architecture and chromatin composition . In many species , the formation of sperm nuclei involves the massive replacement of nucleosomes with protamines , followed by a phase of extreme nuclear compaction . At fertilization , the reconstitution of a nucleosome-based paternal chromatin after the removal of protamines requires the deposition of maternally provided histones before the first round of DNA replication . This process exclusively uses the histone H3 variant H3 . 3 and constitutes a unique case of genome-wide replication-independent ( RI ) de novo chromatin assembly . We had previously shown that the histone H3 . 3 chaperone HIRA plays a central role for paternal chromatin assembly in Drosophila . Although several conserved HIRA-interacting proteins have been identified from yeast to human , their conservation in Drosophila , as well as their actual implication in this highly peculiar RI nucleosome assembly process , is an open question . Here , we show that Yemanuclein ( YEM ) , the Drosophila member of the Hpc2/Ubinuclein family , is essential for histone deposition in the male pronucleus . yem loss of function alleles affect male pronucleus formation in a way remarkably similar to Hira mutants and abolish RI paternal chromatin assembly . In addition , we demonstrate that HIRA and YEM proteins interact and are mutually dependent for their targeting to the decondensing male pronucleus . Finally , we show that the alternative ATRX/XNP-dependent H3 . 3 deposition pathway is not involved in paternal chromatin assembly , thus underlining the specific implication of the HIRA/YEM complex for this essential step of zygote formation .
Assembly of octameric nucleosomes in eukaryotic chromatin is a stepwise process where deposition of a histone H3-H4 heterotetramer precedes incorporation of two H2A-H2B dimers [1] . While the bulk of de novo chromatin assembly occurs during genome replication and mainly involves canonical histone H3 , alternative , replication-independent ( RI ) chromatin assembly pathways use the conserved histone H3 variant H3 . 3 [2] , [3] . Canonical ( or replicative ) H3s ( H3 . 1 and H3 . 2 in mammals , H3 . 2 in Drosophila ) are synthesized in early S phase and deposited at DNA replication forks by the trimeric CAF-1 ( Chromatin Assembly Factor-1 ) complex [4] . In contrast , H3 . 3 is expressed throughout the cell cycle and is deposited at various genomic regions in a DNA-synthesis independent manner [5]–[8] . During the past decade , research on H3 . 3 has largely focused on the ability of this histone to be deposited at transcribed genes , opening the possibility that H3 . 3 could constitute an epigenetic mark of active chromatin [9]–[13] . Recent advances in the field have let emerge a more complex view of H3 . 3 biology . Although H3 . 3 is indeed enriched at transcribed gene bodies , it is now established that this histone is also deposited at various chromatin regions , such as regulatory elements , mammalian telomere repeats or satellite DNA blocks [5]–[7] , [14]–[18] . This surprising versatility of H3 . 3 could simply reflect its ability to be deposited in regions that are subjected to nucleosome depletion or rapid histone turnover [5] , [7] , [19] . In metazoa , H3 . 3 is also implicated in a variety of nuclear processes that specifically occur in germ cells and in early embryos [7] , [20]–[22] . In mouse spermatocytes , for instance , H3 . 3-containing nucleosomes are assembled on sex chromosomes during their inactivation and accumulate over the whole sex body [23] . Moreover , an insertion mutation in the mouse H3 . 3A gene induces male subfertility , among other phenotypes [24] . Certain lysine residues of H3 . 3 are also important for the establishment of heterochromatin during reprogramming in mouse zygotes [25] . Recently , knock-down experiments in Xenopus laevis demonstrated a specific and critical requirement of H3 . 3 during embryo gastrulation [26] . In Drosophila , H3 . 3 deficient animals are viable but are both male and female sterile [27] , [28] . H3 . 3 is notably required for the proper segregation of meiotic chromosomes in spermatocytes [28] and for the global organization of early spermatid chromatin [28] , [29] . A remarkable H3 . 3 deposition process also occurs during the decondensation of the male pronucleus at fertilization [21] . This unique , genome-wide assembly of H3 . 3 nucleosomes follows the rapid removal of sperm-specific nuclear basic proteins ( SNBPs ) from the fertilizing sperm nucleus , after its delivery in the egg cytoplasm . In many animal species , during spermiogenesis , histones are progressively replaced with SNBPs , such as the well-characterized protamines [30]–[32] . The nature and extent of this replacement is highly variable in metazoans [32] . In Drosophila , protamine-like proteins are encoded by two paralogous genes named Mst35Ba and Mst35Bb [33] , [34] . In this species , the vast majority of sperm DNA is packaged with protamines and with other non-histone SNBPs [21] , [35] , implying that de novo assembly of paternal nucleosomes at fertilization after SNBP removal must occur over the entire male genome . We had previously shown that this unique RI assembly requires the conserved H3 . 3 histone chaperone HIRA [36] , [37] . Indeed , loss of function mutations in Hira are viable in Drosophila , but nucleosome assembly in the male pronucleus is completely abolished in eggs laid by mutant females , resulting in the loss of the paternal set of chromosomes and the development of gynogenetic haploid embryos [36] , [37] . In mice , HIRA is present in the decondensing male nucleus [38] and is most likely responsible for the strong paternal H3 . 3 enrichment observed in the zygote [38] , [39] . Recently , HIRA has been implicated in the formation of the male pronucleus in the crucian carp [40] , confirming the widespread role of this histone chaperone in paternal nucleosome assembly at fertilization . The Hir/HIRA complex is composed of a small number of proteins that are conserved between yeast and human . In S . cerevisiae , the Hir chromatin assembly complex includes the HIRA-related proteins Hir1 and Hir2 , Asf1 ( Anti Silencing Factor 1 ) , Hir3 and Hpc2 [41]–[43] . Hir3 is a poorly conserved protein related to Hip3 ( S . pombe ) and human CABIN1 , but which does not seem to have an ortholog in Drosophila [43]–[45] . Hpc2 is functionally related to Hip4 in fission yeast and to the HIRA-associated proteins Ubinuclein 1 and Ubinuclein 2 ( UBN1/UBN2 ) [8] , [44] , [46]–[48] . Interestingly , the strongest conservation between Hpc2 orthologs resides in a ∼50 amino-acid domain called HRD ( Hpc2-Related Domain ) or HUN ( Hpc2-Ubinuclein-1 ) domain [44] , [48] and to a smaller domain called NHRD [49] . In Drosophila , Yemanuclein ( YEM; also named Yemanuclein-α [50] , [51] ) is the only protein with a HRD domain [44] . The yem gene has a strong ovarian expression and encodes a nuclear protein that accumulates in the germinal vesicle of growing oocytes [51] . Recently , a mutant allele of yem ( yem1 ) has been characterized as a V478E replacement , which results in female sterility [52] . In this first report on YEM function , YEM was implicated in the segregation of chromosomes during the first female meiotic division but the sterility of mutant females suggested the existence of yet unknown roles for YEM [52] . In this paper , we have explored the implication of YEM in HIRA-dependent RI nucleosome assembly in the zygote . We show that the cooperation of YEM and HIRA in vivo is critical for the assembly of H3 . 3-containing nucleosomes in the male nucleus at fertilization .
The original yem1 point mutation causes a single amino-acid replacement ( V478E ) in YEM protein ( Figure 1A ) [52] . This mutation induces female sterility but has no detectable effect on the level of yem transcripts in ovaries nor on the accumulation of YEM protein in the oocyte nucleus ( or germinal vesicle , GV ) ( Figure 1B , 1C ) . To obtain a more severe mutant allele of yem , we mobilized a P-element inserted near the transcriptional start site of the yem gene ( Figure 1A ) . One of the imperfect excisions of this P-element generated a 3180 bp deletion ( named yem2 ) that spans the 5′ UTR and most of the coding region of yem . Accordingly , the yem2 allele induced female sterility in association with yem1 or with the large non-complementing deficiency Df ( 3R ) 3450 ( Table 1 ) . In yem2/Df ( 3R ) 3450 females , yem transcripts ( corresponding to a region of the gene not covered by the yem2 deletion ) were greatly reduced compared to yem1/Df ( 3R ) 3450 females , and the YEM protein was not detected in the oocyte nucleus ( Figure 1B , 1C ) . Finally , the female sterility of both yem mutant alleles was rescued by expressing a transgenic YEM protein tagged in its C-terminus with the Flag peptide ( YEM-Flag ) ( Table 1 ) . Taken together , these data suggest that yem2 is a null or at least a strong loss of function allele of yem . The YEM protein has been previously detected in a HIRA complex purified from embryonic nuclear extracts [53] , suggesting that it could represent the Drosophila ortholog of UBN1/Hpc2 . To more directly test the interaction of HIRA and YEM , we performed co-immunoprecipitation experiments using functional Flag-tagged and GFP-Flag-tagged transgenic versions of YEM and HIRA proteins , respectively . We confirmed that , in ovarian protein extracts , HIRA was able to co-immunoprecipitate with YEM , and vice versa ( Figure 2A ) . In the same experiments , however , the ATP-dependent chromatin remodeling factor CHD1 was not detected in the HIRA immune complex , in contrast to what was previously reported [54] . Although the reason for this apparent discrepancy with the study by Konev et al . is not clear , it reinforces the fact that , in our experimental conditions , HIRA and YEM show reproducible and specific interaction , confirming that these proteins are subunits of a common complex . HIRA and YEM were previously shown to display a remarkable and specific accumulation in the nucleoplasm of the GV throughout oogenesis [36] , [51] . Similarly , immunodetection of the Flag-tagged versions of HIRA and YEM recapitulates their endogenous accumulation in the GV , where both proteins co-localize ( Figure 2B ) . The oocyte nucleus is a large nucleus that essentially contains nucleoplasm , as the oocyte chromosomes remain confined within a small , compact structure called the karyosome [55] . Surprisingly , we observed that HIRA-Flag accumulation in the GV was completely abolished in yem2/Df ( 3R ) 3450 mutant oocytes . Conversely , we found that YEM-Flag was undetectable in the GV of about half of null HiraHR1 mutant oocytes ( Figure 2C ) . These effects could not be explained by reduced protein levels in mutant flies , as HIRA-Flag and YEM-Flag expression were apparently not affected in yem2/Df ( 3R ) 3450 an HiraHR1 mutants , respectively ( Figure 2D ) . These results indicate that YEM and HIRA are mutually required for their localization or for their stabilization in the oocyte and suggest that these proteins interact prior to their release in the egg cytoplasm , after GV breakdown . Taken together , these results confirm that YEM and HIRA belong to the same complex in vivo . The female sterility associated with yem1 or yem2 mutations actually results from a maternal effect embryonic lethality phenotype . Indeed , eggs from yem1/Df ( 3R ) 3450 or yem2/Df ( 3R ) 3450 females ( referred to as yem mutant eggs for simplicity ) are normally fertilized and they initiate development , but the embryos systematically die before hatching ( Table 1 and not shown ) . These features are reminiscent of the maternal effect embryonic lethality phenotype of Hira mutants , where embryos develop as non-viable gynogenetic haploids after the loss of paternal chromosomes during the first zygotic division [36] , [37] , [56] . We thus examined male pronucleus formation in yem mutant eggs . In wild-type eggs , shortly after fertilization , while maternal chromosomes complete meiotic divisions , the decondensing male nucleus is strongly and specifically stained with an antibody recognizing acetylated histone H4 , a mark of newly assembled chromatin [36] , [37] . Strikingly , we observed that in yem mutant eggs , acetylated H4 was practically not incorporated in the male pronucleus ( Figure 3A ) . At pronuclear apposition , male pronuclei in yem mutant eggs always appeared round and condensed ( Figure 3B ) , in a way identical to the male nucleus in Hira mutants [36] , [37] , [57] . Paternal chromosomes subsequently failed to integrate the first zygotic division in yem eggs ( Figure 3C ) , resulting in gynogenetic haploid development and embryonic lethality ( Figure 3D ) . It should be mentioned however that exceptional gynogenetic development of adults can occur if the female pronucleus is diploid as the result of defective meiosis [52] . While the yem-flagHPF16 transgene efficiently rescued yem female sterility , another insertion of the same construct ( yem-flagHPF1 ) only restored fertility to very low levels , likely because of its weak expression ( Table 1 ) . Interestingly , in eggs laid by yem1/Df ( 3R ) 3450; yem-flagHPF1 females , the male pronucleus still appeared round and condensed but consistently incorporated significant levels of acetylated histone H4 ( Figure 3B ) . This suggests that the level of maternal YEM protein is limiting for both nucleosome assembly and male pronucleus decondensation . We have previously shown that HIRA-dependent nucleosome assembly in the male pronucleus exclusively uses the histone H3 variant H3 . 3 [36] , [37] . To observe H3 . 3 deposition in the male pronucleus , we used a previously described , maternally expressed Flag-tagged transgenic version of H3 . 3 ( H3 . 3-Flag ) [37] . In contrast to control eggs , H3 . 3-Flag was not incorporated in paternal chromatin of yem1 eggs , similarly to Hira mutants ( Figure 4A ) . However , the female pronucleus in yem eggs still incorporated low levels of H3 . 3-Flag during the first round of DNA replication , arguing that , like HIRA , YEM does not participate to the limited S phase deposition of H3 . 3 which occurs in replicating nuclei of early embryos [36] ( Figure 4A ) . As a complementary approach , we analyzed the yem mutant phenotype using a commercially available monoclonal anti-H3 . 3 antibody . In wild-type fertilized eggs , the antibody specifically stained the decondensing male pronucleus , but not the maternal chromosomes , thus confirming its specificity for H3 . 3 ( Figure 4B ) . In agreement with the results obtained with H3 . 3-Flag , no staining was detected above background when Hira and yem mutant eggs were stained with the anti-H3 . 3 antibody ( Figure 4C ) . Altogether , these results demonstrate the critical requirement of YEM for the assembly of H3 . 3-containing nucleosomes on paternal DNA . Although mutant yem1/Df ( 3R ) 3450 and yem2/Df ( 3R ) 3450 adults were viable , survival rates were reduced for yem2/Df ( 3R ) 3450 individuals ( Table S1 ) indicating that YEM also functions in somatic cells . Interestingly , the partial lethality of yem2 mutant individuals was not aggravated when combined with the HiraHR1 null allele . Thus , HIRA and YEM do not have redundant functions but , instead , are obligate partners not only for male pronucleus chromatin assembly but presumably also for other somatic RI nucleosome assembly processes . Consistent with its critical role in paternal chromatin assembly , maternally expressed HIRA is recruited to the male nucleus shortly after fertilization in both Drosophila and mouse [37] , [38] . Strikingly , while robust HIRA-Flag staining is observed in the decondensing male nucleus in control eggs , HIRA-Flag was not detected in eggs from yem1 and yem2 females ( n>20; Figure 5D ) . Thus , YEM is required for the recruitment or for the stabilization of HIRA in the male nucleus . As expected , maternal YEM-Flag was also detected in the decondensing male nucleus before pronuclear apposition ( Figure 5B ) . However , in contrast to the homogeneous distribution of HIRA-Flag in the male nucleus , YEM-Flag appeared also enriched in a small number of discrete foci of unknown nature ( Figure 5A ) . We verified that these foci localized neither to the centromeres nor to the telomeres of the male pronucleus ( Figure 5C ) . Interestingly , the formation of these YEM-Flag foci appeared largely independent of HIRA , whereas the rest of YEM-Flag was not detected in a large majority of Hira mutant eggs ( Figure 5B ) . Thus , with the exception of these discrete regions , our experiments demonstrate that HIRA and YEM are interdependent for their localization within the male pronucleus and for paternal chromatin assembly . Several groups have recently established that in mammalian cells , RI H3 . 3 deposition is mediated by at least two distinct protein complexes . HIRA and its partners are involved in the enrichment of H3 . 3 at active genes and at upstream regulatory elements of both active and repressed genes [6] . In contrast , ATRX , a member of the SNF2 family of ATP-dependent chromatin remodeling factors and the histone chaperone DAXX ( Death-Associated protein ) are essentially responsible for the enrichment of H3 . 3 nucleosomes at heterochromatin loci [6] , [58]–[60] . In Drosophila , the ATRX homolog XNP ( or dATRX ) colocalizes with H3 . 3 throughout the chromatin of somatic cells [16] . To investigate the potential involvement of this chromatin remodeler in the assembly of paternal nucleosomes in the newly fertilized egg , we first determined its distribution in oocytes and eggs using a specific antibody recognizing both XNP isoforms [16] . Interestingly , XNP was found to accumulate in the oocyte nucleus , in a way remarkably similar to HIRA and YEM ( Figure 6A ) . However , XNP was not observed in the decondensing male nucleus at fertilization ( n>20 ) and the protein remained absent from early cleavage nuclei until their migration to the embryo periphery , at the syncytial blastoderm stage ( Figure 6B and not shown ) . In addition , we observed that chromatin assembly in the male nucleus occurred normally in eggs from xnp2/xnp3 mutant females ( n>20; Figure 6C ) . Finally , females homozygous for the semi-lethal allele xnp3 , which abolishes the expression of the long XNP isoform [61] , produced a limited amount of eggs that nevertheless hatched ( not shown ) . We conclude that dATRX/XNP is most likely not involved the assembly of paternal nucleosomes at fertilization .
In contrast to the knock-out of the Hira gene in mouse , which is zygotic lethal in early embryos [62] , null mutants of Drosophila Hira are viable but homozygous females are completely sterile [36] . This indicates that only the maternal contribution of Hira is essential , at least to form the male pronucleus . Our characterization of a null yem2 allele allowed us to reach the same conclusion for YEM . Remarkably , the phenotype of the male pronucleus in eggs laid by yem mutant females appeared indistinguishable to what we previously reported for Hira mutants . In both cases , RI deposition of H3 . 3-containing nucleosomes is practically abolished , typically preventing the full decondensation of the male nucleus and its integration into the zygotic nucleus . Thus , YEM and HIRA are equally required to assemble paternal nucleosomes at fertilization . This unique and major function of the HIRA complex is most likely conserved in animal groups where histones , and notably H3 and H4 , are replaced with SNBPs in sperm . This is for instance the case of mammals , where protamines package about 95% and 85% of mouse and human sperm DNA , respectively [30] , [32] . In fact , HIRA has been previously detected in the decondensing male nucleus at fertilization in mouse , which incorporates H3 . 3 before the first round of DNA replication [38] , [39] . We thus expect Ubinuclein1/2 to be also involved in paternal chromatin assembly in mammals . In apparent contradiction with this prediction , a transgene expressing human UBN1 in the female germline could not rescue the sterility of yem mutant females ( Figure S1 and not shown ) . However , this absence of complementation of YEM and UBN1 can be explained by the strong divergence of these orthologous proteins at the primary sequence level and it suggests that UBN1 can only function within its native , human HIRA complex . The apparent lack of a CABIN1 homolog in Drosophila also underlines the central role played by the HIRA-UBN1/YEM pair in the complex . Interestingly , while the implication of HIRA and UBN1 for RI deposition of H3 . 3 in vivo was recently demonstrated in human cells , CABIN1 seemed to play only an auxiliary role in this context [63] . Possibly , CABIN1 could be important for human-specific functions of the HIRA complex , such as the formation of senescence-associated heterochromatin foci [45] , [64] . We had previously shown that HIRA specifically accumulates in the sperm nucleus shortly after its delivery in the egg cytoplasm [37] . Here , we have established that maternally expressed YEM similarly accumulates in the male nucleus at fertilization and until pronuclear apposition . Strikingly , we have also shown that HIRA and YEM are mutually dependent for their targeting to the male nucleus , strongly suggesting that these proteins physically interact during the assembly of paternal nucleosomes . However , nothing is known about the mechanism responsible for their rapid and specific localization in the fertilizing sperm nucleus , which is delivered in the cytoplasm of the gigantic egg cell . We had previously established that the HIRA-dependent assembly of paternal nucleosomes occurs after the removal of sperm protamines [36] . This opens the simple possibility that the HIRA complex could recognize exposed sperm DNA immediately after the removal of SNBPs . Interestingly , pioneer work on YEM by Aït-Ahmed et al . had established that this maternal protein was able to bind DNA in vitro [51] . This property could be important to efficiently target the HIRA complex to sites of de novo nucleosome assembly in the decondensing male nucleus . This hypothesis has recently received indirect experimental support in human cultured cells [63] . In their study , Ray-Gallet et al . established that HIRA , UBN1 and CABIN1 were all individually able to bind DNA in vitro and they proposed that this remarkable property could allow the HIRA complex to target naked DNA for H3 . 3 deposition . Accordingly , this HIRA-dependent nucleosome gap-filling mechanism has been shown to participate in the maintenance of genome integrity [63] , but could also be employed , at the genome-wide scale , for de novo assembly of paternal chromatin at fertilization . Finally , the observation that YEM accumulates in discrete nuclear regions in both the male nucleus ( this study ) and the oocyte karyosome [52] opens the possibility that YEM could perform additional roles not related to nucleosome assembly . Despite its expression in the female germline , we found that Drosophila ATRX/XNP is not targeted to the male nucleus and does not seem to play any role in male pronucleus formation . Among the 17 SNF2 type chromatin remodelers present in Drosophila [16] , the Chromodomain-helicase-DNA-binding protein 1 ( CHD1 ) is the only one that has been implicated in the remodeling of paternal chromatin at fertilization [21] , [54] . In contrast to Hira and yem , mutations in chd1 do not drastically affect H3 . 3 incorporation in paternal chromatin but still severely compromise the decondensation of the male nucleus , which appears aberrant in shape [21] , [54] . In contrast to the HIRA/CHD1 interaction reported by Konev et al . [54] , we could not detect any interaction between these proteins in ovaries , using experimental conditions that permitted co-immunoprecipitation of HIRA and YEM . Our results thus suggest that the role of CHD1 in the male nucleus is distinct from the nucleosome assembly process mediated by the HIRA complex . Although the implication of the HIRA histone chaperone in paternal chromatin assembly was firmly established a few years ago , it has remained unclear until now if this highly specialized RI assembly process also involved other subunits of the HIRA complex or other histone deposition pathways . In fact , we have previously reported that the histone chaperone ASF1 [65] , which is known to interact with both the CAF1 and HIRA complexes , was actually absent from the decondensing male nucleus [36] . Although the role , if any , of ASF1 in paternal chromatin assembly awaits a proper functional characterization , we do not expect this histone chaperone to be directly involved in the assembly of nucleosomes on paternal DNA . Accordingly , ASF1 has been previously shown to be dispensable for direct de novo RC or RI histone deposition in Xenopus egg extracts [66] . The complete failure of the male nucleus to assemble its chromatin in Hira or yem mutant eggs demonstrates that no other nucleosome assembly machinery can substitute for the HIRA-YEM complex in this peculiar context . However , the functional requirement of H3 . 3 itself in this process is not known . In Drosophila , H3 . 3 is not absolutely required for survival but it is essential for both male and female fertility [27] , [28] . Viability of His3 . 3A; His3 . 3B double null mutants could be explained by the fact that , in the absence of H3 . 3 , canonical H3 can be assembled in a RI manner [28] . Although the mode of RI deposition of replicative H3 in these mutants is not known , it opens the possibility that HIRA could use canonical H3 in certain critical circumstances , such as a limiting availability of H3 . 3 . This compensatory mechanism , however , is apparently not possible in Drosophila spermatocytes , where H3 . 3 is required for the correct segregation of chromosomes during meiotic divisions , underlining the importance of this variant for sexual reproduction [28] . Similarly , future work should aim at determining whether H3 . 3 is specifically required for the assembly of paternal nucleosomes at fertilization . Both HIRA and YEM proteins , which are presumably expressed from germinal nurse cells , display a remarkable accumulation in the oocyte nucleus during oogenesis [36] , [51] . Most of the volume of the large germinal vesicle is devoid of DNA as the maternal genome is tightly packaged within the karyosome . The presence of HIRA and YEM in the nucleoplasm of the GV is thus not related to nucleosome assembly . However , the fact that HIRA and YEM are mutually dependent for their accumulation in the GV suggests that they are stored in this compartment as a complex . In contrast to the null alleles , point mutations do not affect HIRA/YEM localization in the GV , suggesting that the mechanisms controlling their recruitment to the GV or to the male pronucleus are distinct . This could reflect the fact that the HIRA complex is active in the male pronucleus where these proteins are in a chromatin environment in contrast to their nucleoplasm distribution in the GV . Whether or not this transient accumulation of HIRA/YEM in the GV plays any role in the maturation of the complex before paternal chromatin assembly at fertilization remains to be tested . Interestingly , it has been proposed that in human cells , formation of senescence-associated heterochromatin foci by HIRA requires its prior localization to promyelocytic leukemia nuclear bodies , suggesting that these structures could participate in the formation of the HIRA complex before its translocation to chromatin [48] , [67] . It should be mentioned , however , that dATRX/XNP also accumulates in the GV despite its dispensability for paternal chromatin assembly . A recent study [68] reported the presence of several nuclear proteins in the GV with no known function in the oocyte , suggesting that this structure could serve as a storage compartment for a large number of nuclear proteins . In conclusion , our characterization of Drosophila Yemanuclein demonstrates that this protein is a functional partner of HIRA in vivo . It also establishes that HIRA and YEM directly cooperate in the male nucleus for the genome-wide replacement of sperm protamines with H3 . 3-containing nucleosomes . The specific requirement of the HIRA complex in this unique developmental chromatin assembly process implies the existence of specific properties not shared with other H3 . 3-deposition pathways . In this regard , future work should explore the potentially conserved DNA binding property of the HIRA complex [51] , [63] and its potential role in targeting the fertilizing sperm nucleus in animals .
Flies were grown in standard conditions at 25°C . The w1118 stock was used as a wild-type control in all experiments . The Hirassm and HiraHR1 alleles and the Hira-flag transgenic constructs have been described earlier [36] , [37] . For the construction of the Hira-GFP-FLAG fusion gene , the eGFP coding sequence was inserted between the Hira and Flag tag sequences of PW8-Hira-3xflag [37] . The yem1 mutation is a T>A substitution falling in the fifth exon of yem which results in a V478E mutation [51] , [52] . The GFP-K81 transgene is described in [69] . To mark paternal telomeres we used w1118/Y; 5′K81-GFP::K81; K812 males [69] . The w; P[w+ , g-EGFP-cid]III . 2 [70] stock has been kindly provided by Stefan Heidmann . The Df ( 3R ) 3450 deficiency , the P{EPgy2}EY23024 insertion and the xnp2 and xnp3 mutant alleles [61] were obtained from the Bloomington Drosophila Stock Center . The yem2 mutation was isolated after standard remobilization of the P{EPgy2}EY23024 element and selected for its non-complementation of the yem1 chromosome . yem2 is a 3180 bp deletion from position +2 in the 5′UTR ( positions 24945416 to 24948596 in the genome ) , uncovering the first 5 exons and part of exon 6 of the yem gene . Note that we only refer in this study to the original gene model [51] identified as RA in Flybase ( Flybase ID# FBtr0085415 ) and not to the recently predicted longer RB transcript ( see Flybase . org ) . Total RNAs were extracted with the Trizol method ( Invitrogen ) from at least 50 whole adults , ovaries or carcasses . Reverse transcription was performed using oligo ( dT ) primers and the SuperScript First-Strand Synthesis system for RT-PCR ( Invitrogen ) . For the yem and RP49 PCR reactions , the following primers were used YEMAPRIMER15/YEMAPRIMER16 and RP49FWD/RP49REV ( see primers section ) . YEMAPRIMER2: TGCGAAAACCGCGACCAGTG YEMAPRIMER9: GGGCAGTTGTTGCGTGGATG YEMAPRIMER15: GGATCCCATTCCTCCGCTTG YEMAPRIMER16: CTCAGGCAGCAGCACTCAAT RP49FWD: AAGATCGTGAAGAAGCGCAC RP49REV: ACTCGTTCTCTTGAGAACGC OA37: ACGTCCAAGCAGCTAGCTGCCA OA38: GAATCTAGACTTGTCATCGTCGTCCTTGTAGTCTTGGCGCGTGGGCGTACT Eggs were collected , dechorionated , devitellinized and fixed in methanol as described [56] . Eggs were then rehydrated in TBS-Triton 0 , 15% and incubated with primary and secondary antibodies at the indicated dilution . Finally , eggs were incubated in a 2 mg/ml RNAse A solution for 1 h at 37°C and were mounted in a mounting medium ( DAKO S3023 ) containing 5 µg/ml propidium iodide . For anti-YEM AS2 antibody staining , ovaries were dissected in PBS-Triton 0 , 1% and were immediately incubated with the antiserum without fixation , stained with DAPI and mounted , as described [74] . For other experiments , ovaries were dissected in PBS-Triton 0 , 1% and fixed at room temperature in 4% PFA in PBS for 25 minutes . Ovaries were then stained with propidium iodide and mounted as described above . Slides were observed under an LSM 510 META confocal microscope ( Zeiss ) . Images were treated with LSM image browser , Image J or Photoshop CS2 ( Adobe ) . We used the following antibodies: AS2 anti-YEM antibody ( 1/100; [51] , [74] ) , M2 monoclonal anti-Flag antibody ( 1∶500 in ovaries , 1∶1000 in embryos; Sigma ) , anti-polyacetylated histone H4 ( 1∶200; Millipore 06-589 ) , monoclonal anti-H3 . 3 ( H3F3B ) ( 1∶800 , Abnova ) , anti-XNP [16] ( 1∶5000 ) and anti-UBN1 ( 1∶200 ) [75] . Secondary antibodies were Alexa488 goat anti-mouse or goat anti-rabbit ( 1∶1000 , Invitrogen ) and Cy3 donkey anti-rabbit ( 1∶800 , Millipore ) . 50 µl of ovaries were homogenized in lysis buffer ( 15 mM Hepes ( pH 7 . 6 ) ; 10 mM KCl; 5 mM MgCl2; 0 . 5 mM EDTA; 0 . 5 mM EGTA; 350 mM Sucrose; 1 mM DTT ) with protease inhibitors ( Halt Protease Inhibitor Single Use Cocktail , Thermo Scientific; 1 mM PMSF ) . The protein extract was centrifuged , isolated from debris and stocked in half volume of glycerol at −80°C . SDS-Page electrophoresis was carried out on 8% acrylamide gels and western blot was performed using standard procedures using Pierce ECL Western Blotting Substrate ( Thermo Scientific ) . The following antibodies were used: M2 anti-Flag ( 1∶1000; Sigma ) , anti-Tubulin ( 1/1000; Sigma ) , Peroxydase-coupled goat anti-mouse ( 1∶10000; Beckman ) . For co-immunoprecipitation experiments , we essentially used the protocol described in Jäger et al . , 2001 [76] with some modifications as indicated . A hundred ovaries were dissected manually in 250 µl lysis buffer on ice . Lysis buffer was as described [76] to the exception of the protease inhibitors . In our conditions , Roche tablets of EDTA-free protease inhibitor cocktail were used as recommended by the supplier . PMSF was also added to a 1 mM final concentration . Before homogenization 250 µl ice-cold lysis buffer were added . The homogenates were cleared by centrifugation and the supernatant was adjusted to 1 ml in lysis buffer . The protein extracts were then submitted to the immunoprecipitation procedure after 2×30 µl were set aside to be used as input in western blot experiments . G-Sepharose beads ( Sigma ) were used as recommended by the supplier with the following antibodies at a 1/250 dilution: mouse monoclonal Flag M2 ( Sigma ) for HIRA and the AS2 rabbit polyclonal for YEM . Rabbit preimmune serum was used as negative control . Gel separation and western blots analysis were performed as indicated above . The rabbit CHD1 antibody ( a gift from A . Lusser ) was used at a 1/250 dilution . Secondary antibodies were goat peroxydase-coupled anti-mouse and anti-rabbit antibodies ( 1∶10000; Beckman ) . Revelation was performed with the Millipore Immobilon Western Chemiluminescent substrate as recommended by the supplier . | Chromosome organization relies on a basic functional unit called the nucleosome , in which DNA is wrapped around a core of histone proteins . However , during male gamete formation , the majority of histones are replaced by sperm-specific proteins that are adapted to sexual reproduction but incompatible with the formation of the first zygotic nucleus . These proteins must therefore be replaced by histones upon fertilization , in a replication-independent chromatin assembly process that requires the histone deposition factor HIRA . In this study , we identified the protein Yemanuclein ( YEM ) as a new partner of HIRA at fertilization . We show that , in eggs laid by yem mutant females , the male pronucleus fails to assemble its nucleosomes , resulting in the loss of paternal chromosomes at the first zygotic division . In addition , we found that YEM and HIRA are mutually dependent to perform chromatin assembly at fertilization , demonstrating that they tightly cooperate in vivo . Finally , we demonstrate that the replication-independent chromatin assembly factor ATRX/XNP is not involved in the assembly of paternal nucleosomes . In conclusion , our results shed new light into critical mechanisms controlling paternal chromosome formation at fertilization . | [
"Abstract",
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"Results",
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"Materials",
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"Methods"
] | [
"genetics",
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] | 2013 | Drosophila Yemanuclein and HIRA Cooperate for De Novo Assembly of H3.3-Containing Nucleosomes in the Male Pronucleus |
The response of a neuron to a time-dependent stimulus , as measured in a Peri-Stimulus-Time-Histogram ( PSTH ) , exhibits an intricate temporal structure that reflects potential temporal coding principles . Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation . As a modeling framework , we use the spike response model , also known as the generalized linear neuron model . Because of refractoriness , the effect of the most recent spike on the spiking probability a few milliseconds later is very strong . The influence of the last spike needs therefore to be described with high precision , while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings . Based on these insights , we derive a ‘quasi-renewal equation’ which is shown to yield an excellent description of the firing rate of adapting neurons . We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons . The problem of decoding the stimulus from the population response ( or PSTH ) is addressed analogously . We find that for small levels of activity and weak adaptation , a simple accumulator of the past activity is sufficient to decode the original input , but when refractory effects become large decoding becomes a non-linear function of the past activity . The results presented here can be applied to the mean-field analysis of coupled neuron networks , but also to arbitrary point processes with negative self-interaction .
Encoding and decoding of information with populations of neurons is a fundamental question of computational neuroscience [1]–[3] . A time-varying stimulus can be encoded in the active fraction of a population of neurons , a coding procedure that we will refer to as population coding ( Fig . 1 ) . Given the need for fast processing of information by the brain [4] , population coding is an efficient way to encode information by averaging across a pool of noisy neurons [5] , [6] and is likely to be used at least to some degree by the nervous system [7] . For a population of identical neurons , the instantaneous population firing rate is proportional to the Peri-Stimulus Time Histogram ( PSTH ) of a single neuron driven repeatedly by the same stimulus over many trials . When driven by a step change in the input , the population of neurons coding for this stimulus responds first strongly but then adapts to the stimulus . To cite a few examples , the activity of auditory nerve fibers adapt to pure tones [8] , cells in the retina and the visual cortex adapt to contrast [9] , [10] and neurons in the inferior temporal cortex adapt to higher order structures of images [11] . Adaptation is energy-efficient [12] but leads to a potentially ambiguous code because adapting responses generate a population activity which does not directly reflect the momentary strength of the stimuli [13] . Putting the case of sensory illusions aside , the fact that our perception of constant stimuli does not fade away indicates that the adapting responses can be efficiently decoded by the brain areas further down the processing stream . In fact , illusions such as the motion after-effect are believed to reflect errors in decoding the activity of neuronal populations [14] . But what is the correct rule to decode population activity ? What elements of the population history are relevant ? What are the basic principles ? Synapse- and network-specific mechanisms merge with intrinsic neuronal properties to produce an adapting population response . Here we focus on the intrinsic mechanisms , commonly called spike-frequency adaptation . Spike-frequency adaptation appears in practically all neuron types of the nervous system [15] . Biophysical processes that can mediate spike-frequency adaptation include spike-triggered activation/inactivation of ion-channels [16]–[18] and a spike-triggered increase in the firing threshold [19]–[22] . Neurons adapt a little more each time they emit a spike , and it is the cumulative effect of all previous spikes that sets the level of adaptation . The effect of a single spike on future spiking probability cannot be summarized by a single time constant . Rather , the spike-triggered adaptation unfolds on multiple time scales and varies strongly across cell-types [22] , [23] . Mean-field methods were used to describe: attractors [24]–[28] , rapid-responses [6] , [29] and signal propagation [30] . While adaptation is important to correctly predict the activity of single neurons [22] , [31]–[33] , it is difficult to include it in mean-field methods . A theory relating spike-frequency adaptation to population dynamics should be general enough to encompass a variety of different spike-triggered adaptation profiles , as observed in experiments . In the literature we find six main approaches to the population coding problem . The first and most simple one formulates the rate of a neuronal population ( or the time-dependent rate in a PSTH ) as a linear function of the stimulus . This phenomenological model relates to the concept of receptive fields [34] and can be made quantitative using a Wiener expansion [35] . Yet , early experimental tests showed that linear filtering must be complemented with a non-linear function [35] , [36] . The linear-non-linear model can thus be considered as the second approach to population coding . In combination with a Poisson spike generator it is called the LNP model for Linear-Nonlinear-Poisson . It makes accurate predictions of experimental measurements for stationary stimulus ensembles , but fails when the stimulus switches either its first or second order statistics . Neural refractoriness is in part responsible for effects not taken into account in this linear-nonlinear model [37]–[40] . In a third approach proposed by Wilson and Cowan [41] the population activity is the solution to a non-linear differential equation . Unfortunately this equation has only a heuristic link to the underlying neuronal dynamics and cannot account for rapid transients in the population response . The fourth approach formulates the population activity in terms of an integral equation [6] , [41] , [42] which can be interpreted as a ( time-dependent ) renewal theory . While this renewal theory takes into account refractoriness ( i . e . the effect of the most recent spike ) and captures the rapid transients of the population response and PSTH , neither this one nor any of the other encoding frameworks mentioned above consider adaptive effects . To include adaptation into previously non-adaptive models , a common approach is to modify the effective input by rescaling the external input with a function that depends on the mean neuronal firing rate in the past [15] , [43] , [44] . This forms the fifth method . For example , Benda and Herz [15] suggested a phenomenological framework in which the linear-non-linear approach is modified as a function of the past activity while Rauch et al . [43] calculated the effective rate in integrate-and-fire neurons endowed with a frequency-dependent modification of the input current . Finally , there is also a sixth method to determine the population activity of adapting populations . Inspired by the Fokker-Planck approach for integrate-and-fire neurons [27] , this last approach finds the population activity by evolving probability distributions of one or several state variables [45]–[49] . Isolating the population activity then involves solving a non-linear system of partial differential equations . The results described in the present article are based on two principal insights . The first one is that adaptation reduces the effect of the stimulus primarily as a function of the expected number of spikes in the recent history and only secondarily as a function of the higher moments of the spiking history such as spike-spike correlations . We derive such an expansion of the history moments from the single neuron parameters . The second insight is that the effects of the refractory period are well captured by renewal theory and can be superimposed on the effects of adaptation . The article is organized as follows: after a description of the population dynamics , we derive a mathematical expression that predicts the momentary value of the population activity from current and past values of the input . Then , we verify that the resulting encoding framework accurately describes the response to input steps . We also study the accuracy of the encoding framework in response to fluctuating stimuli and analyze the problem of decoding . Finally , we compare with simpler theories such as renewal theory and a truncated expansion of the past history moments .
How does a population of adapting neurons encode a given stimulating current ? Each neuron in the population will produce a spike train , denoted by , such that the population can be said to respond with a set of spike trains . Using the population approach , we want to know how the stimulus is reflected in the fraction of neurons that are active at time , that is , the population activity ( Fig . 1 ) . The population activity ( or instantaneous rate of the population ) is a biologically relevant quantity in the sense that a post-synaptic neuron further down the processing stream receives inputs from a whole population of presynaptic neurons and is therefore at each moment in time driven by the spike arrivals summed over the presynaptic population , i . e . the presynaptic population activity . Mathematically , we consider a set of spike trains in which spikes are represented by Dirac-pulses centered on the spike time : [3] . The population activity is defined as the expected proportion of active neurons within an infinitesimal time interval . It corresponds , in the limit of a large population and small time interval , to the number of active neurons in the time interval divided by the total number of neurons and the time interval [3]: ( 1 ) The angular brackets denote the expected value over an ensemble of identical neurons . Experimentally , the population activity is estimated on a finite time interval and for a finite population . Equivalently the population activity can be considered as an average over independent presentations of a stimulus in only one neuron . In this sense , the population activity is equivalent to both the time-dependent firing intensity and the Peri-Stimulus Time Histogram ( PSTH ) . Since the population activity represents the instantaneous firing probability , it is different from the conditional firing intensity , , which further depends on the precise spiking history , or past spike train . Suppose we have observed a single neuron for a long time ( e . g . 10 seconds ) . During that time we have recorded its time dependent input current and observed its firing times . Knowing the firing history for and the time-dependent driving current for , the variable describes the instantaneous rate of the neuron to fire again at time . Intuitively , reflects a likelihood to spike at time for a neuron having a specific history while is the firing rate at time averaged on all possible histories ( see Methods ) : ( 2 ) Ideally , one could hope to estimate directly from the data . However , given the dimensionality of and , model-free estimation is not feasible . Instead we use the Spike Response Model ( SRM; [6] , [50]–[52] ) , which is an example of a Generalized Linear Model [53] , in order to parametrize , but other parametrizations outside the exponential family are also possible . In particular , can also be defined for nonlinear neuron models with diffusive noise in the input , even though explicit expressions are not available . The validity of the SRM as a model of neuronal spike generation has been verified for various neuron types and various experimental protocols [22] , [31] , [32] . In the SRM , the conditional firing intensity increases with the effective input : ( 3 ) where is the total driving force of the neuron: ( 4 ) where ‘’ denotes the convolution , is the input current convolved with the membrane filter , encodes the effect of each spike on the probability of spiking , is a scaling constant related to the instantaneous rate at the threshold with units of inverse time ( see Methods for model parameters ) . The link-function can take different shapes depending on the noise process [3] . Here we will use an exponential link-function since it was shown to fit the noisy adaptive-exponential-integrate-and-fire model [54] as well as experimental data [22] , [32] , [55] . The exponential link-function: corresponds to after absorbing the scaling parameter in the constant and and in the functions and to make these unit-free . To see that the function can implement both adaptation and refractoriness , let us first distinguish these processes conceptually . The characteristic signature of refractoriness is that the interspike interval distribution for constant input is zero or close to zero for very short intervals ( e . g . one millisecond ) - and in the following we use this characteristic signature as a definition of refractoriness . With this definition , a Hodgkin-Huxley model ( with or without noise ) or a leaky integrate-and-fire model ( with or without diffusive noise ) are refractory , whereas a Linear-Nonlinear-Poisson Model is not . In fact , every neuron model with intrinsic dynamics exhibits refractoriness , but Poissonian models do not . While refractoriness refers to the interspike-interval distribution and therefore to the dependence upon the most recent spike , adaptation refers to the effect of multiple spikes . Adaptation is most clearly observed as a successive increase of interspike intervals in response to a step current . In contrast , a renewal model [56] , where interspike intervals are independent of each other , does not exhibit adaptation ( but does exhibit refractoriness ) . Similarly , a leaky or exponential integrate-and-fire model with diffusive noise does not show adaptation . A Hodgkin-Huxley model with the original set of parameters exhibits very little adaptation , but addition of a slow ion current induces adaptation . Conceptually , contributions of multiple spikes must accumulate to generate spike frequency adaptation . In the Spike Response Model , this accumulation is written as a convolution: . If for and vanishes elsewhere , the model exhibits absolute refractoriness of duration but no adaptation . If for and with ms , then the model exhibits adaptation in addition to refractoriness . In all the simulations , we use with and , With this choice of we are in agreement with experimental results on cortical neurons [22] , but the effects of adaptation and refractoriness cannot be separated as clearly as in the case of a model with absolute refractoriness . Loosely speaking , the long time constant causes adaptation , whereas the short time constant mainly contributes to refractoriness . In fact , for and equal to the membrane time constant , the model becomes equivalent to a leaky integrate-and-fire neuron [3] , so that the neuron is refractory and non-adapting . In the simulations , is longer than the membrane time constant so that , for very strong stimuli , it may also contribute to adaptation . We note that the formalism developed in this paper does not rely on our specific choice of . We only require ( i ) causality by imposing for and ( ii ) so that the effect of a past spike decreases over time . The effects described by can be mediated by a dynamic threshold as well as spike-triggered currents [22] . Throughout the remainder of the text we will refer to as the effective spike after-potential ( SAP ) . It is , however , important to note that has no units , i . e . it relates to an appropriately scaled version of the experimentally measured spike after-potential . A depolarizing ( facilitating ) SAP is associated with , while a hyperpolarizing ( adapting ) SAP is associated with . In a population of neurons , every neuron has a different spiking history defined by its past spike train where is the most recent spike , the previous one and so on . To find the population activity at any given time , we hypothesize that the strong effect of the most recent spike needs to be considered explicitly while the rest of the spiking history merely introduces a self-inhibition that is similar for all neurons and that depends only on the average firing profile in the past . Thus for each neuron we write the past spike train as where is the time of the last spike . Our hypothesis corresponds to the approximation , i . e . the last spike needs to be treated explicitly , but we may average across earlier spike times . This approximation is not appropriate for intrinsically bursting neurons , but it should apply well to other cell types ( fast-spiking , non-fast-spiking , delayed , low-threshold ) . According to this hypothesis , and in analogy to the time-dependent renewal theory [3] , [42] we find ( derivation in Methods ) : ( 5 ) Unfortunately Eq . 5 remains insolvable , because we do not know . Using Eqs . 3 and 4 we find: ( 6 ) As mentioned above , we hypothesize that the spiking history before the previous spike merely inhibits subsequent firing as a function of the average spiking profile in the past . In order to formally implement such an approximation , we introduce a series expansion [57] in terms of the spiking history moments ( derivation in Methods ) where we exploit the fact that is a moment generating function: ( 7 ) The first history moment relates to the expected number of spikes at a given time . The second history moment considers the spike-spike correlations and so on for the higher moments . We truncate the series expansion resulting from Eq . 7 at the first order ( ) . We can then write Eq . 6 as: ( 8 ) We can insert Eq . 8 in Eq . 5 so as to solve for as a function of the filtered input . The solutions can be found using numerical methods . We note that by removing the integral of from Eq . 8 we return exactly to the renewal equation for population activity ( ) . Adaptation reduces the driving force by an amount proportional to the average spike density before , that is , the average spiking density before the most recent spike . In other words , instead of using the specific spike history of a given neuron , we work with the average history except for the most recent spike which we treat explicitly . We call Eqs . 5 and 8 the Quasi-Renewal equation ( QR ) to acknowledge its theoretical foundations . It is renewal-like , yet , we do not assume the renewal condition since a new spike does not erase the effect of the previous history ( see Methods ) . Let us now assess the domain of validity of the QR theory by comparing it with direct simulations of a population of SRM neurons . To describe the single neurons dynamics , we use a set of parameters characteristic of L2–3 pyramidal cells [22] . The SAP is made of two exponentials: one with a short time constant ( 30 ms ) but large amplitude and another with a long time constant ( 400 ms ) but a small amplitude . The results presented here are representative of results that can be obtained for any other physiological set of parameters . For details on the simulation , see Methods . The response to a step increase in stimulating current is a standard paradigm to assess adaptation in neurons and used here as a qualitative test of our theory . We use three different step amplitudes: weak , medium and strong . The response of a population of , say , 25 , 000 model neurons to a strong step increase in current starts with a very rapid peak of activity . Indeed , almost immediately after the strong stimulus onset , most of the neurons are triggered to emit a spike . Immediately after firing at , the membrane potential of theses neurons is reset to a lower value by the contribution of the SAP; . The lower membrane potential leads to a strong reduction of the population activity . Neurons which have fired at time are ready to fire again only after the SAP has decreased sufficiently so that the membrane potential can approach again the threshold . We can therefore expect that a noiseless population of neurons will keep on oscillating with the intrinsic firing frequency of the neurons [6]; however , due to stochastic spike emission of a noisy population the neurons in the population gradually de-synchronize . The damped-oscillation that we see in response to a strong step stimulus ( Fig . 2C ) is the result of this gradual de-synchronization . Similar damped oscillations at the intrinsic firing frequency of the neurons have also been observed for a Spike Response Model with renewal properties [6] , i . e . , a model that only remembers the effect of the last spike . In contrast to renewal models ( i . e . , models with refractoriness but no adaptation ) , we observe in Fig . 2C that the population activity decays on a slow time scale , taking around one second to reach a steady state . This long decay is due to adaptation in the single-neuron dynamics , here controlled by the slow time constant ms . The amount of adaptation can be quantified if we compare , for a given neuron its first interspike interval after stimulus onset with the last interspike interval . The mean first interspike interval ( averaged over all neurons ) for the strong step stimulus is 93 ms while the last interval is nearly twice as long ( 163 ms ) , indicating strong adaptation . For smaller steps , the effect of refractoriness is less important so that adaptation becomes the most prominent feature of the step response ( Fig . 2A ) . An appropriate encoding framework should reproduce both the refractoriness-based oscillations and the adaptation-based decay . The QR equation describes well both the damped oscillation and the adapting tail of the population activity response to steps ( Fig . 2 ) . Also , the steady state activity is predicted over a large range ( Fig . 2D ) . We note that an adaptation mechanism that is essentially subtractive on the membrane potential ( Eq . 4 ) leads here to a divisive effect on the frequency-current curve . Altogether , we conclude the QR theory accurately encode the response to step stimulus . Step changes in otherwise constant input are useful for qualitative assessment of the theory but quite far from natural stimuli . Keeping the same SAP as in Fig . 2 , we replace the piecewise-constant input by a fluctuating current ( here Ornstein-Uhlenbeck process ) and study the validity of QR over a range of input mean and standard deviation ( STD ) , see Fig . 3 . As the STD of the input increases , the response of the population reaches higher activities ( maximum activity at STD = 80 pA was 89 Hz ) . The prediction by the QR theory is almost perfect with correlation coefficients consistently higher than 0 . 98 . Note that the correlation coefficient is bounded above by the finite-size effects in estimating the average of the 25 , 000-neuron simulation . Over the range of input studied , there was no tendency of either overestimating or underestimating the population activity ( probability of positive error was 0 . 5 ) . There was only a weak tendency of increased discrepancies between theory and simulation at higher activity ( correlation coefficient between simulated activity and mean square error was 0 . 25 ) . Decoding the population activity requires solving the QR equation ( Eq . 5 and 8 ) for the original input ( see Methods ) . Input steps can be correctly decoded ( Fig . 4A–C ) but also fluctuating stimuli ( Fig . 4D–E ) . Again , the input mean does not influence the precision of the decoding ( Fig . 4E ) . The numerical method does not decode regions associated with population activities that are either zero or very small . Accordingly , the correlation coefficient in Fig . 4E is calculated only at times where decoding could be carried out . Note that unless one is to estimate the statistics of the input current and assume stationarity , it is impossible for any decoder to decode at times when . If the size of the population is decreased , the performance of the QR decoder decreases ( Fig . S1 ) . Finite size effects limit decoding performance by increasing the error on the mean activity ( as can be seen by comparing the effect of filtering the average population activity ( Fig . S1A and B ) ) . Another finite-size effect is that at small population sizes there is a greater fraction of time where an estimate of the activity is zero and the decoding cannot be performed ( Fig . S1D–F ) . Also , decoding errors are larger when is close to zero ( Fig . S1C ) . Nevertheless , for an input with STD = 40 pA and a population of 250 neurons , QR decoding can be performed 55% of the times with a correlation coefficient of 0 . 92 . If the filtering of the population activity is on a longer time scale ( 20 ms instead of 2 ms ) then decoding is possible 82% of the times and the accuracy is roughly the same ( Fig . S1 ) . We will consider two recent theories of population activity from the literature . Both can be seen as extensions of rate models such as the Linear-Nonlinear Poisson model where the activity of a homogeneous population is where is a linear filter and some nonlinear function . First , we focus on adaptive formulations of such rate models . For example Benda and Herz [15] have suggested that the firing rate of adapting neurons is a non-linear function of an input that is reduced by the past activity , such that the activity is where is a self interaction filter that summarizes the effect of adaptation . Second , we compare our approach with renewal theory [3] , [42] which includes refractoriness , but not adaptation . How does our QR theory relate to these existing theories ? And how would these competing theories perform on the same set of step stimuli ? To discuss the relation to existing theories , we recall that the instantaneous rate of our model depends on both the input and the previous spike trains . In QR theory , we single out the most recent spike at and averaged over the remaining spike trains : . There are two alternative approaches . One can keep the most recent spike at and disregard the effect of all the others: . This gives rise to the time-dependent renewal theory , which will serve as a first reference for the performance comparison discussed below . On the other hand , one can average over all previous spikes , that is , no special treatment for the most recent one . In this case ( 9 ) The right-hand side of Eq . 9 can be treated with a moment expansion similar to the one in Eq . 7 . To zero order , this gives a population rate , that is , an instantiation of the LNP model . To first order in an event-based moment expansion ( EME1 ) we find: ( 10 ) Therefore , the moment expansion ( Eq . 7 ) offers a way to link the phenomenological framework of Benda and Herz ( 2003 ) to parameters of the SRM . In particular , the nonlinearity is the exponential function , the input term is and the self-inhibition filter is . We note that Eq . 10 is a self-consistent equation for the population activity valid in the limit of small coupling between the spikes which can be solved using standard numerical methods ( see Methods ) . A second-order equation ( EME2 ) can similarly be constructed using an approximation to the correlation function ( see Methods ) . We compare the prediction of EME1 , EME2 and renewal theory with the simulated responses to step inputs ( Fig . 5 ) . All the encoding frameworks work well for small input amplitudes ( Fig . 5A ) . It is for larger input steps that the different theories can be distinguished qualitatively ( Fig . 5C ) . Renewal theory predicts accurately the initial damped oscillation as can be expected by its explicit treatment of the relative refractory period . The adapting tail , however , is missing . The steady state is reached too soon and at a level which is systematically too high . EME1 is more accurate in its description of the adapting tail but fails to capture the damped oscillations . The strong refractory period induces a strong coupling between the spikes which means that truncating to only the first moment is insufficient . The solution based on EME2 improves the accuracy upon that of EME1 so as to make the initial peak shorter , but oscillates only weakly . We checked that the failure of the moment-expansion approach is due to the strong refractory period by systematically modifying the strength of the SAP ( Fig . S2 ) . Similarly , when the SAP is weak , the effect of will often accumulate over several spikes and renewal theory does not capture the resulting adaptation ( Fig . S2 ) . Fluctuating input makes the population respond in peaks of activity separated by periods of quiescence . This effectively reduces the coupling between the spikes and therefore improves the accuracy of EME1 . The validity of EME1 for encoding time-dependent stimulus ( Fig . S3 ) decreases with the STD of the fluctuating input with no clear dependence on the input mean . Decoding with EME1 is done according to a simple relation: ( 11 ) where the logarithm of the momentary population activity is added to an accumulation of the past activity . The presence of the logarithm reflects the non-linearity for encoding ( the link-function in Eq . 3 ) and leads to the fact that when the instantaneous population activity is zero , the stimulus is undefined but bounded from above: . Fig . S4 shows the ability of Eq . 11 to recover the input from the population activity of 25 , 000 model neurons . We conclude that Eq . 11 is a valid decoder in the domain of applicability of EME1 . In summary , the EMEs yield theoretical expressions for the time-dependent as well as steady-state population activity . These expressions are valid in the limit of small coupling between the spikes which corresponds to either large interspike intervals or small SAP . Renewal theory on the other hand is valid when the single-neuron dynamics does not adapt and whenever the refractory effects dominate .
The input-output function of a neuron population is sometimes described as a linear filter of the input [41] , as a linear filter of the input reduced as a function of past activity [58] , [59] , as a non-linear function of the filtered input [60] , or by any of the more recent population encoding frameworks [47] , [48] , [61]–[65] . These theories differ in their underlying assumptions . To the best of our knowledge , a closed-form expression that does not assume weak refractoriness or weak adaptation has not been published before . We have derived self-consistent formulas for the population activity of independent adapting neurons . There are two levels of approximation , EME1 ( Eq . 10 ) is valid at low coupling between spikes which can be observed in real neurons whenever ( i ) the interspike intervals are large , ( ii ) the SAPs have small amplitudes or ( iii ) both the firing rate is low and the SAPs have small amplitudes . The second level of approximation merges renewal theory with the moment-expansion to give an accurate description on all time-scales . We called this approach the QR theory . The QR equation captures almost perfectly the population code for time-dependent input even at the high firing rates observed in retinal ganglion cells [55] . But for the large interspike intervals and lower population activity levels of in vivo neurons of the cortex [66] , [67] , it is possible that the simpler encoding scheme of Eq . 10 is sufficient . Most likely , the appropriate level of approximation will depend on the neural system; cortical sparse coding may be well represented by EME , while neuron populations in the early stages of perception may require QR . We have focused here on the Spike Response Model with escape noise which is an instantiation of a Generalized Linear Model . The escape noise model , defined as the instantaneous firing rate given the momentary distance between the ( deterministic ) membrane potential and threshold should be contrasted with the diffusive noise model where the membrane potential fluctuates because of noisy input . Nevertheless , the two noise models have been linked in the past [51] , [54] , [68] . For example , the interval-distribution of a leaky integrate-and-fire model with diffusive noise and arbitrary input can be well captured by escape noise with instantaneous firing rate which depends both on the membrane potential and its temporal derivative [51] . The dependence upon accounts for the rapid and replicable response that one observes when an integrate-and-fire model with diffusive noise is driven in the supra-threshold regime [68] and can , in principle , be included in the framework of the QR theory . The decoding schemes presented in this paper ( Eq . 11 and 45 ) reveal a fundamental aspect of population coding with adapting neurons . Namely , the ambiguity introduced by the adaptation can be resolved by considering a well-tuned accumulator of past activity . The neural code of adapting populations is ambiguous because the momentary level of activity could be the result of different stimulus histories . We have shown that resolving the ambiguity requires the knowledge of the activity in the past but to a good approximation does not require the knowledge of which neuron was active . At high population activity for neurons with large SAPs , however , the individual timing of the last spike in the spike trains is required to resolve the ambiguity ( compare also Fairhall et al . [13] ) . Unlike bayesian spike-train decoding [55] , [69] , [70] , we note that in our decoding frameworks the operation requires only knowledge of the population activity history and the single neuron characteristics . The properties of the QR or EME1 decoder can be used to find biophysical correlates of neural decoding such as previously proposed for short term plasticity [71] , [72] , non-linear dendrites [73] or lateral inhibition [74] . Note that , a constant percept in spite of spike frequency adaptation does not necessarily mean that neurons use a QR decoder . It depends on the synaptic structure . In an over-representing cortex , a constant percept can be achieved even when the neurons exhibit strong adaptation transients [75] . Using the results presented here , existing mean-field methods for populations of spiking neurons can readily be adapted to include spike-frequency adaptation . In Methods we show the QR theory for the interspike interval distribution and the steady-state autocorrelation function ( Fig . 6 ) as well as linear filter characterizing the impulse response function ( or frequency-dependent gain function ) of the population . From the linear filter and the autocorrelation function , we can calculate the signal-to-noise ratio [3] and thus the transmitted information [1] . The autocorrelation function also gives an estimate of the coefficient of variation [76] and clarifies the role of the SAP in quenching the spike count variability [49] , [77] , [78] . The finite-size effects [27] , [79]–[81] is another , more challenging , extension that should be possible . The scope of the present investigation was restricted to unconnected neurons . In the mean-field approximation , it is straight-forward to extend the results to several populations of connected neurons [6] . For instance , similar to EME1 , a network made of inter-connected neurons of cell-types would correspond to the self-consistent system of equation: ( 12 ) where is the scaled post-synaptic potential kernel from cell-type to cell-type ( following the formalism of Gerstner and Kislter [3] ) , is an external driving force , each subpopulation is characterized by its population activity and its specific spike after potential . The analogous equation for QR theory is: ( 13 ) where is: ( 14 ) Since the SAP is one of the most important parameter for distinguishing between cell classes [22] , the approach presented in this paper opens the door to network models that take into account the neuronal cell-types beyond the sign of the synaptic connection . Even within the same class of cells , real neurons have slightly different parameters from one cell to the next [22] and it remains to be tested whether we can describe a moderately inhomogeneous population with our theory . Also , further work will be required to see if the decoding methods presented here can be applied to brain-machine interfacing [82]–[84] .
All simulations were performed on a desktop computer with 4 cores ( Intel Core i7 , 2 . 6 GHz , 24 GB RAM ) using Matlab ( The Mathworks , Natwick , MA ) . The Matlab codes to numerically solve the self-consistent equations are made available on the author's websites . The algorithmic aspects of the numerical methods are discussed now . When assessing the accuracy of the encoding or the decoding , we used the correlation coefficient . The correlation coefficient is the variance-normalized covariance between two random variables and : ( 52 ) where the expectation is taken over the discretized time . | How can information be encoded and decoded in populations of adapting neurons ? A quantitative answer to this question requires a mathematical expression relating neuronal activity to the external stimulus , and , conversely , stimulus to neuronal activity . Although widely used equations and models exist for the special problem of relating external stimulus to the action potentials of a single neuron , the analogous problem of relating the external stimulus to the activity of a population has proven more difficult . There is a bothersome gap between the dynamics of single adapting neurons and the dynamics of populations . Moreover , if we ignore the single neurons and describe directly the population dynamics , we are faced with the ambiguity of the adapting neural code . The neural code of adapting populations is ambiguous because it is possible to observe a range of population activities in response to a given instantaneous input . Somehow the ambiguity is resolved by the knowledge of the population history , but how precisely ? In this article we use approximation methods to provide mathematical expressions that describe the encoding and decoding of external stimuli in adapting populations . The theory presented here helps to bridge the gap between the dynamics of single neurons and that of populations . | [
"Abstract",
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] | [
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] | 2012 | Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram |
A major force contributing to the emergence of novelty in nature is the presence of cooperative interactions , where two or more components of a system act in synergy , sometimes leading to higher-order , emergent phenomena . Within molecular evolution , the so called hypercycle defines the simplest model of an autocatalytic cycle , providing major theoretical insights on the evolution of cooperation in the early biosphere . These closed cooperative loops have also inspired our understanding of how catalytic loops appear in ecological systems . In both cases , hypercycle and ecological cooperative loops , the role played by space seems to be crucial for their stability and resilience against parasites . However , it is difficult to test these ideas in natural ecosystems , where time and spatial scales introduce considerable limitations . Here , we use engineered bacteria as a model system to a variety of environmental scenarios identifying trends that transcend the specific model system , such an enhanced genetic diversity in environments requiring mutualistic interactions . Interestingly , we show that improved environments can slow down mutualistic range expansions as a result of genetic drift effects preceding local resource depletion . Moreover , we show that a parasitic strain is excluded from the population during range expansions ( which acknowledges a classical prediction ) . Nevertheless , environmental deterioration can reshape population interactions , this same strain becoming part of a three-species mutualistic web in scenarios in which the two-strain mutualism becomes non functional . The evolutionary and ecological implications for the design of synthetic ecosystems are outlined .
The evolution of complexity is largely grounded in the emergence of new forms of cooperation capable of holding together higher-order entities from simpler ones . Cooperative interactions have played a great role in the so-called major transitions in evolution [1] . Cooperation pervades the rise of molecular systems capable of overcoming mutation thresholds , multicellular assemblies incorporating division of labour or the appearance of insect societies . Each of these structures incorporates new properties that cannot be observed at the level of its component parts . Despite the burden involved in sustaining the new , larger entity , the advantage of staying together can overcome , under some circumstances , the cost of the association . Cooperation can be achieved in particular by means of closed catalytic loops . Mutualistic interactions pervade ecological communities at many different scales , from bacterial communities to microbiomes and large-scale ecosystems [2] . The presence of these reciprocal relations was already outlined by Charles Darwin in one of his memorable studies on the ecology of earthworms [3 , 4] and summarised by the diagram of Fig 1a . Earthworms improve soil porosity and organic content that helps plants to grow , which results in more organic matter and mechanisms of soil preservation ( which favours the earthworm population ) . This is a simple , two-component ( n = 2 ) diagram , but ecosystems are characterised by the presence of multiple feedback loops and thus interactions might be more complex , like the three-member ( n = 3 ) loop shown in ( Fig 1b ) . Here vegetation is grazed by animals , whose activity enhances the survival of invertebrates , which in turn improve soil quality thus favouring plant growth . Because of their ecological and evolutionary relevance , cooperative interactions have also been a major topic in synthetic biology [5–9] . The possibility of engineering de novo cooperative loops is of relevance for several reasons . On one hand , engineered mutualisms could be used to build desirable ( even optimal ) functionalities that require the presence of a tight metabolic dependence [10 , 11] . Moreover , the possibility of designing mutualistic interactions and even symbiotic pairs [11–15] provides a unique opportunity for exploring the emergence of cooperation in evolution under a ‘synthetic” perspective [16] . Mutualistic interactions are also required to sustain stable communities , particularly when harsh conditions are present . An example ( Fig 1c ) is provided by drylands [17] and in particular the interactions between the so-called biological soil crust ( BSC ) and vascular plants [18] . The BSC defines in itself a complex ecosystem enclosed within a few centimetres of the topsoil , largely controlling the energy and matter flow through the soil surface , helping vegetation thrive under semiarid conditions . The soil microbiome plays a major role in sustaining plant diversity and its dynamics , with the latter often completely dependent on their microbial symbionts [19] . Since these ecosystems might experience sudden declines due to climate change [20 , 21] understanding their dynamics is crucial to predicting their future . In this context , it has been suggested that engineering new synthetic mutualistic loops in endangered ecosystems could help prevent catastrophic shifts [22 , 23] . Understanding cooperation , its rise and fall and how can it overcome competitive interactions is an important problem . A great insight has been obtained from both field and theoretical studies [2] . An elegant description of this class of cooperative loops is the hypercycle , first suggested within the context of prebiotic evolution [24–28] . Here a simple catalytic system is defined ( as in Fig 1a and 1b ) forming a closed graph where the replication of each component is catalysed by a previous one in the loop , while it also catalyses the replication of the next . The simplest case is the one shown in Fig 1d for a two-member system [24 , 29] . If we indicate by Φ1 and Φ2 their population sizes , a pair of coupled equations allows us to represent the hypercycle model as follows: d Φ 1 d t = α 12 Φ 1 Φ 2 ( 1 - Φ 1 + Φ 2 K ) - δ 1 Φ 1 d Φ 2 d t = α 21 Φ 1 Φ 2 ( 1 - Φ 1 + Φ 2 K ) - δ 2 Φ 2 ( 1 ) where αij ( i ∈ [1 , 2] , j ∈ [1 , 2] ) stand for the replication rates of the cross-catalytic loop , δi is the degradation ( death ) rate of species i , and the carrying capacity K takes into account saturation effects that confine the hyperbolic reaction kinetics to relatively low ( or moderate ) population densities [30] . As defined , we can see that no proliferation of any of the two partners will occur in the absence of the other , as a consequence of the second-order kinetics that requires the product of the two concentrations . The hypercycle can outcompete other non-cooperative species [24 , 26] but a major drawback is that it can also be easily threatened by a parasite ( Fig 1e ) capable of destabilising the whole system [31] . Interestingly , mathematical and computer models indicate that this problem can be limited by the presence of diffusion in a spatial domain [32–35] . Hypercycles displaying spatial structures ( Fig 1f ) are obtained from n > 4 loops capable of exhibiting oscillations . In a nutshell , the spatial structure imposes a limitation to the spread of the parasite , and it can even go extinct if the inaccessibility of its target species , combined with its death rate , makes it non-viable [36] . Since mutualistic interactions are widespread in ecological networks , and the role of both space and parasites is known to be essential to sustain diversity and enhance ecosystem function , we can ask whether the concepts above can be used to study ecological interactions . The answer is yes , but needs some important clarification . As discussed in [37] we should be careful in using the label “hypercycle” to describe all types of mutualistic interactions sharing the presence of second-order terms as those described by the previous equations . We made this distinction since we will apply this class of model framework to synthetic ecosystems , which formally share this class of kinetic description but are not based on cross-catalytic replication . However , since all these model systems do share a common mathematical structure , we should expect to observe similar dynamical behaviours when space or parasites are introduced . In fact , living organisms may impose particular constraints that are classically not acknowledged in hypercycle theories . For example , physical features such as cell shape can critically influence the spatial structure of microbial populations [38] , and even determine which species will survive in a given community [39] . Here , we propose engineered microbial ecosystems as an experimental system where some predictions from hypercycle-related models can be tested . In this context , recent studies involving engineered microbial mutualists have described that mutualism enhances species intermixing [40] , while genetic drift [41 , 42] acts against this effect during range expansions [43] . Moreover , microbial mutualists can exhibit spatial self-organization that disfavours parasites when growing into open space [44 , 45] . Nevertheless , the number of studies focusing on the spatial dynamics of microbial mutualists is very limited , and determining to what extent these results are universal and which features are associated to the specific experimental system remains as an open problem . In this paper , we address this problem by studying how engineered bacterial mutualists expand in different environments . A minimal two-member cooperative loop model provides qualitative understanding on how the mutualists transit from an obligate mutualism ( dominated by hyperbolic growth ) to a competition scenario ( governed by Malthusian growth ) as the environment becomes richer in growth-limiting resources . Surprisingly , we find that the range expansion process can be slowed down in richer environments , a feature that is associated to enhanced genetic drift effects preceding local resource depletion . Moreover , we show that a parasite strain can threaten the synthetic mutualistic community in well-mixed populations , and that environmental conditions can determine the fate of the parasite during range expansions . While the parasite is excluded from the expanding population in environments where the two-strain mutualistic loop can succeed , environmental deterioration ( e . g . associated to a toxic molecule ) can reshape the species interactions leading to an advancing population that necessarily includes the three strains .
Our model system for studying mutualistic interactions is composed of a pair of bacterial strains engineered to exchange essential amino acids ( Fig 2a ) . The I - strain ( depicted in yellow ) cannot produce the isoleucine ( iso ) amino acid but overproduces and leaks leucine ( leu ) , while L- ( in blue ) cannot produce leu but overproduces and leaks iso [6] . Therefore , the strains are able to engage in a cross-feeding mutualism that permits growth in coculture , in a minimal medium lacking both amino acids where neither I - nor L- can grow in monoculture ( obligate mutualism scenario in Fig 2b ) . However , both I - and L- are able to grow in monoculture when this same medium is supplemented with 10−4M of both iso and leu . Under these conditions , the dominant interaction between I - and L- cells in coculture is competition for additional resources ( competition scenario in Fig 2b , see also S1 and S2 Figs ) . In order to further characterize the dynamics of our synthetic mutualistic system , we seeded the cross-feeding strains on agar plates with different concentrations of iso and leu . Fig 2c shows the spatial structure close to the edge of the population front after 4 days of incubation ( see S3 Fig ) . When no amino acids are supplemented into the medium , cells are only able to grow if mutualistic partners remain close enough . The population engages in an obligate mutualism , which leads to a self-organized distribution with a characteristic high intermixing of the two strains . This high genetic intermixing of the obligate mutualists leads to relatively thin single-strain patches , whose avarage size remains approximately constant as the range expansion takes place , as shown in Fig 2d . In contrast , the competition scenario reveals a remarkably different spatial structure . When amino acids are supplemented at 10−4 μM , the driving interaction is competition for space and resources , since cells no longer need their mutualistic partners in order to obtain the amino acids required to grow . The range expansion dynamics is thus governed by genetic drift [41] , which leads to demixing of the population into progressively wider ( single-strain ) patches . In between of the above two modes of invasion , we found the environmental conditions that allow a facultative mutualistic behaviour . Single-strain patches are wider than those observed in the absence of supplemented iso and leu , although genetic diversity is still preserved ( patch width remains approximately constant ) as the front propagates , Fig 2c and 2d . In other words , in the facultative scenario , the concentration of amino acids added to the media permit the strains to grow into wider patches ( compared to those of obligate mutualists ) , but both strains still benefit from the cross-feeding . It is worth noting that , while ( both obligate and facultative ) mutualism scenarios lead to stable coexistence at the front , the competition scenario would lead to the exclusion of one of the strains at larger timescales . Results in Fig 2 show that environmental conditions can modulate the interactions between the mutualistic species , which can lead to different dynamics during range expansions . The scenarios in Fig 2c ( see also S3 Fig ) reveal a qualitatively identical interplay between mutualism and genetic drift in range expansions of yeast populations Ref . [40 , 43] . Even though different systems exhibit specific traits that depend on their model organisms ( such as the fractal dimension of the boundary domains [38] , see S5 Fig ) , the qualitative agreement between the results in Fig 2 and those in Refs . [40 , 43] suggests an inherent dynamics of mutualism to some extent independent of the mutualistic agents . How the speed of mutualistic range expansions is affected by the environment ? To approach this problem , let us first modify the minimal model [Eq . set ( 1 ) ] , in order to be able to describe the population expansion as a propagating front ( a phenomenon that is widely used to model biological range expansions such as those of genes [46] , microbial populations [47] , cooperators [48] and even cultural invasions [49] ) . Moreover , given that single-strain cultures grow in amino acid rich environments ( Fig 2 ) , we consider amino acid supplementation as a way to introduce Malthusian growth rates in the system ( as done in Ref . [9] for mutualistic yeast strains ) . Thus , our minimal Reaction-Diffusion ( RD ) model describing the spatiotemporal dynamics of the synthetic mutualistic replicators reads ( see Methods ) : ∂ I ∂ t = D ∂ 2 I ∂ r 2 + ( μ I I + α I L I L ) ( 1 - I + L k ) , ∂ L ∂ t = D ∂ 2 L ∂ r 2 + ( μ L L + α L I I L ) ( 1 - I + L k ) ( 2 ) where I and L stand for the population density of the I - and L- strain respectively , t and r are the time and spatial coordinates , D is the diffusion coefficient , μi is the Malthusian growth rate of species i ∈ [I , L] , and αij ( ≥ 0 ) is the growth rate of species i assisted by its mutualistic partner j ∈ [I , L] . Note that , as in the case of the hypercycle model [30] , an effective hyperbolic growth is confined to relatively low population densities by the carrying capacity k . The above set of equations generalised the two-member model by including , on the one hand , the spatial context ( through the diffusion terms D∂2/∂r2 ) and , on the other , by considering both mutualistic ( αij ≥ 0 ) and Malthusian ( 0 ≤ μ i ≤ μ i C ) growth terms . The above minimal model [Eq . set ( 2 ) ] is able to provide some analytical estimations for the front speed of the bacterial mutualistic loop . On the one hand , if we consider the absence of either species in the set Eq ( 2 ) , we recover the one-species Fisher RD model [46 , 50] that leads to the well-known expression for the invasion speed: c I F = 2 μ I D for L = 0 , c L F = 2 μ L D for I = 0 ( 3 ) Moreover , the Fisher speed establishes the asymptotic invasion speed for our two-species system in Eq . set ( 2 ) as μi >> αij ( for i = I , L and i ≠ j = I , L ) . In the case of two purely competing species ( μi > 0 , and αij = 0 ) we should expect the front to propagate at the speed of the faster competitor because this species will be more efficient at conquering the available space at the edge of the population front . In contrast , for the case of two purely mutualistic species ( i . e . , a pure loop with μi = 0 , and αij > 0 ) , we derived the analytical solution for the invasion speed ( see Methods ) : c = D k α I L α L I 2 ( α I L + α L I ) ( 4 ) Our minimal model ( 2 ) thereby predicts two different invasion modes for our pair of mutualistic strains I - and L- . Indeed , in the competition scenario , the invasion speed Eq ( 3 ) is governed by the growth rate at low population densities ( which gives rise to a pulled front [47 , 51 , 52] ) . In contrast , the carrying capacity k appearing in Eq ( 4 ) is a hallmark of an invasion front governed by the growth dynamics at high population densities . This gives rise to a pushed front [47 , 51 , 52]: individuals at the edge of the front are pushed from the inside bulk where individuals reproduce at higher rates . Moreover , note that the invasion speed Eq ( 4 ) is the same for the two mutualists I - and L- , consistent with their need for a mutualistic partner in order to grow and spread . Fig 3a shows how the transition between the two invasion modes takes place , according to the RD model . In the absence of Malthusian replication ( μi = 0 ) , both strains spread at the same speed . As both μI and μL are increased towards their observed value ( see S1 Fig ) in the competition scenario , the front speed increases due to the corresponding enhancement in growth rates . However , once μi induces stronger effects on the front than αij , competition becomes important and the coupled advance of the two strains is replaced by two differentiated front speeds . At this point , further increasing the Malthusian growth rates μi benefits the faster species ( in this case , the L- strain ) , while the second one is slowed down in a relatively abrupt way ( changes in the corresponding population density profiles are shown in S6 Fig ) . This eventually leads the I - strain to be excluded from the front ( which propagates at the Fisher’s speed c L F as Malthusian growth rates approach the observed values in competition ) . It is worth noting that , according to the RD model , the minimal invasion speed of the population corresponds to that in the obligate mutualism scenario ( i . e . any increase in Malthusian growth rates would lead to a faster population front , for at least one of the species ) . Fig 3b reveals a slowdown in the invasion speed for facultative mutualists that the RD minimal model was unable to predict . We measured the front speed for cocultures spreading on agar surfaces ( see Methods ) , observing particularly low values of the front speed at the transition between the obligate mutualism and the competition scenario . According to the RD model , even if one of the strains is slowed down because of competition , the edge of the front will keep travelling at the speed of the fastest strain ( which should exceed the speed of the obligate mutualistic loop in order to overcome its partner species at the edge of the front ) . Thus , the decrease of the observed front speed as supplemented amino acids are increased indicates that other , more complex phenomena are driving the dynamics of the synthetic mutualistic feedback . In particular , the physical embodiment of bacterial cells ( not taken into account by the RD model ) may affect their access to the extracellular amino acids , thus influencing the invasion speed . Local nutrient depletion leads to the range expansion slowdown of facultative mutualists . Simulations in Fig 3c and 3d capture a slowdown in the invasion speed similarly as observed in experimental conditions . As the snapshots in Fig 3d illustrate , nutrients and amino acids are mainly consumed by cells at the edge of the front , their depletion leaves a population of stagnant cells that effectively constitutes a fossil record of the invasion process [41] . In the obligate mutualism case , single-strain patches keep a characteristic width determined by the distance at which cells can sustain the cross-feeding mutualism ( cells near the front can temporarily become stagnant when their location prevents an effective cross-feeding ) . This process shapes the spatial distribution of the population , leading to a relatively high fraction of active cells at the edge of the front ( Fig 3c and 3d ) . However , in the case of facultative mutualism , the dynamics can be marked by episodes of opportunistic growth that exploits the available amino acids in the environment . During these periods , the dynamics are locally governed by genetic drift ( single-strain sectors become wider ) . However , once the supplemented amino acids are locally depleted , a significant number of cells ( remote to the boundary domains where cross-feeding is still effective ) can become stagnant ( arrow in Fig 3d ) . Fig 3c shows how the ratio of active cells is correlated with the invasion speed , suggesting that the dynamics in facultative mutualism scenarios can slow-down the invasion speed of the synthetic mutualists . Several processes ( such as mutations or the arrival of foreign , invader species ) may give rise to new organisms exploiting cooperative feedbacks in a given ecosystem . The introduction of a new replicator organism that makes use of the limited resources in the medium will restrict the growth of the coupled system , specially if this new organism is a parasite ( hereafter P cells ) that takes advantage of the cross-feeding ( Fig 4e ) . In order to experimentally study the ecological implications of such parasites , we used the synthetic parasitic strain P ( see Methods ) that exploits one of the cross-feeding amino acids ( namely , iso ) . The coculture of those three organisms in well-mixed conditions , for both the obligate mutualism and the competition scenarios , give as a result a restricted growth of I - or L- strains ( Fig 4a shows lower fluorescence values for both strains than those in Fig 2b ) . Moreover , for the competition scenario in Fig 4a , the P strain exhibits a relatively high Malthusian growth rate ( see S1 Fig ) that leads it to overcome the growth of the mutualistic pair . To test whether spatial structure can limit the parasitic exploitation , we coculture combinations of the three strains ( I - , L- and P ) on M63-agar plates . In the absence of supplemented amino acids , when I - or L- cells are lacking , no growth was observed . This means that P cells can be considered a hypercycle parasite , because they are unable to close an effective cross-feeding loop ( see Fig 2a ) with either I - or L- cells . When the three strains are present ( Fig 4b ) , despite an initial success of the parasite at colonising available space ( see Fig 4c , red line ) , the parasitic strain is progressively left behind as the range expansion takes place . This is because , in the spatial scenario , cell location determines a preferential access to the cross-feeding metabolites [44 , 45] . Therefore , the presence of a P patch increases the distance between I - and L- and leads to restricted growth . This gives a significant advantage to mutualistic I - and L- neighbouring patches that engage in an efficient cross-feeding . Hence , spatial structure benefits the hypercycle species , eventually leading the hypercycle ensemble to overcome the parasite at the edge of the front ( Fig 4b and S7 Fig ) . The ecological role of a species in a given community can be strongly dependent on its environment and transitions can occur between mutualism and parasitism as external conditions change [2 , 53–56] . In our three-member microbial consortium , composed by I - L- and P , we studied whether environmental deterioration can make this community to develop a more complex mutualistic network . In order to do this , the three-member microbial consortium was seeded on m63-agar plates containing a lethal concentration of ampicillin , for which P cells are resistant . The P cells are able to degrade extracellular Ampicillin ( by secreting beta-lactamase ) . Now , two different mutualistic motives are present in this scheme ( Fig 4e ) : ( amino acids ) cross-feeding and ( antibiotic ) cross-protection . Remarkably , the hypercycle trio was able to solve the complex environmental problem and develop the range expansion process on the corresponding agar layers . Fig 4d shows the observed spatial structure displayed by this new mutualistic ensemble while invading the available space . In contrast to the previous parasitic case , the fraction of the P strain is approximately constant as the population front advances ( see Fig 4c ) . The definition of the three-member consortium as an agent-based model allows us to make some predictions on how the system would spread within heterogeneous environments and captures the main spatial dynamics features of the system ( see Supp Info ) . Simulation in a heterogeneous environment , that presents an asymmetric spatial antibiotic distribution , allows us to see how the P strain remains present at the edge of the front in the top region of the colony , which is precisely where the population is exposed to higher doses of antibiotic . In contrast , in the lower region where the antibiotic dose is much lower , the P strain is excluded from the edge of the front ( consistently with our previous results ) , ( Fig 4f ) . This is an interesting result particularly within the context of bioengineering soils [22 , 23] by the rewiring of the ecological interactions within the biological soil crust ( BSC ) . Here the vertical structure defines a heterogeneous set of conditions where different species and physicochemical spatial gradients are present . Both in the BSC and around the plant root system a complex microbiome exists . Soil engineering under a systems perspective is a promising domain to harness and restore different functionalities [57] . This approach could be complemented by designed microbiomes that exploit mutualistic ties following some of the basic findings reported here . Since different soil conditions might sustain different qualitative functional traits , the previous synthetic three-species ecosystem can inspire novel forms of improving soil communities and plant efficiency .
Most experimental and theoretical studies concerning the dynamics of microbial populations are grounded in competition . However , cooperation is a crucial component of ecological dynamics on all scales , and is much needed to truly understand the behaviour of a wide range of systems from populations growing on biofilms to the gut microbiome or even solid tumor ecosystems [58 , 59] ( in which multiple cancer strains can cooperate to succeed ) . Moreover , it has been suggested that synthetic cooperation can help to design ecological circuits capable of preventing endangered ecosystems from collapsing [22 , 23] . Previous studies have analysed a family of models involving closed mutualistic loops . These systems are known as hypercycles , and because of their second-order kinetics , they are capable of hyperbolic growth , allowing the hypercycle to overcome the simple Malthusian replicators . Theoretical works show that hypercycles can prevent their own decay due to the presence of parasites by exploiting the constraints imposed by a spatially extended system . However , these models require some special properties concerning the nonlinear dynamics of hypercyclic sets , which are not feasible in realistic conditions . Instead , we have analysed persistence and response to parasites associated to this kind of systems by means of experimental setups where populations of engineered mutualists spread on a two-dimensional medium . Our study reveals that , as predicted by theoretical models involving both linear ( Malthusian ) growth and hypercyclic cooperation , spatial dynamics ( e . g . in the context of propagating fronts ) can introduce critically important effects for the survival or extinction of hypercycle species . This is shown by both the microscopic impact of bacterial shapes ( which can lead to characteristic boundary domains [38] ) and by the local correlations required to sustain cooperation , which favour an enhancement of contact domains between the two cell populations . Hypercyclic growth has been characterised using diverse sets of metrics and the front speed mathematically derived from a diffusion model . The experiments and models confirm the picture of spatial mutualists as dynamical systems where the mutualistic tie forces the formations of complex structures that guarantee the propagation of the cooperative consortium . We have also studied the tradeoffs associated with Malthusian growth and the conditions pervading the breakdown of hypercyclic cooperation thus showing the presence of two phases: one associated with competitive interactions and another phase associated with scarce resources promoting the mutualistic feedback . Interestingly , we have shown that , as the interactions transit from obligate mutualism to competition , population range expansions can be slowed-down despite the richer resource availability in the environment . In such richer environments , genetic drift especially influences the spatial structure ( creating wide single-strain sectors ) while the population exploits local resources [60] . This decreases the cross-feeding efficiency between mutualists , which can lead to slow down the front speed once resources are locally depleted . The second set of experiments and models are related to the impact of parasitic strains on the stability of the hypercycle . We designed synthetic parasitic strains capable of exploiting a given amino acid while not completing the mutualistic cycle . Such parasite ( which has a small component of Malthusian growth ) has been shown to overcome and kill the hypercycle under liquid conditions but becomes a much less harmful component under spatial constraints . These results suggest that spatial constraints can favour mutualistic populations over parasitic mutants that are likely to arise [1 , 44 , 61] over evolutionary timescales . For cross-feeding mutualisms , parasitic mutants could avoid the cost of the mutualism by reducing ( or cutting ) the overproduction of mutualistic metabolites ( here , aminoacids ) . Moreover , selfish mutants could follow alternative ( perhaps additional ) evolutionary routes leading to avoid the need for the mutualistic partner ( e . g . , by developing the ability to metabolize both essential amino acids ) . Despite the relatively short timescales involved in our experiments , we occasionally observed mutant sectors exhibiting a different spatial structure ( S8 Fig ) than the rest of the sectors in the colony ( suggesting that the corresponding mutant strain modified its mutualistic interactions ) [62] . It is worth noting that , as long as alternative ways to optimize growth rates are available , fitter mutants could also arise without changing their population interactions . For example , S8 Fig shows a case in which a mutant sector exhibited a cut in its fluorescent reporter , whose expression is metabolically costly . Finally , we have shown that environmental deterioration ( e . g . , due to a toxic molecule ) can reshape population interactions , leading this ( otherwise parasitic ) strain to become a member of a three-strain hypercycle . It was recently shown that resource availability can modulate the interactions between microbial cross-feeding mutualists [9 , 43] . Our work is , as far as we know , the first experimental design of a synthetic ecological network showing how different contexts allow mutualism , competition or parasitism to succeed or even transition from one to the other in a spatially extended context . Further work should explore how these results translate into more realistic contexts , from the gut microbiome to soil ecosystems .
Our theoretical RD model for the two-species hypercycle considers that the dynamics of the species I ( r , t ) and L ( r , t ) is governed by diffusion and population growth as: ∂ I ∂ t = D ∇ 2 I + ( μ I I + α I L I L ) ( 1 - I + L k ) , ∂ L ∂ t = D ∇ 2 L + ( μ L L + α L I I L ) ( 1 - I + L k ) . ( 5 ) For simplicity , we have neglected the death rates in Eq . set ( 1 ) , considering that the logistic term sufficiently captures growth inhibition effects ( as it is a standard approach when studying biological range expansions [47] ) . Moreover , we are interested in the asymptotic front speed ( r → ∞ and t → ∞ ) for the case of short-range , isotropic migration . Thus , the Laplacian in polar coordinates simplifies into: ∇ 2 U = 1 r ∂ ∂ r ( r ∂ U ∂ r ) + 1 r 2 ∂ U ∂ θ 2 ≃ ∂ 2 I ∂ r 2 ( 6 ) which leads us to Eq ( 2 ) , i . e . : ∂ I ∂ t = D ∂ 2 I ∂ r 2 + ( μ I I + α I L I L ) ( 1 - I + L k ) , ∂ L ∂ t = D ∂ 2 L ∂ r 2 + ( μ L L + α L I I L ) ( 1 - I + L k ) . ( 7 ) For convenience , we rewrite this set of equations in terms of dimensionless variables I* = I/k , L* = L/k , t* = αILkt and r* = ( αILk/D ) 1/2r , and dimensionless parameters α* = αLIk/αIL . Thus , the new set reads: dI*dt*=∂2I*∂r*2+I*L* ( 1−I*−L* ) ( 8 ) d L* d t* = ∂ 2 I* ∂ r * 2 + α* I* L* ( 1 - I* - L* ) , ( 9 ) Let us assume that there exist travelling wave-shaped solutions of the previous equations of the form: I* ( r * , t* ) = U I ( z ) = ξ I 1 ( 1 + a e b z ) s , ( 10 ) L* ( r * , t* ) = U L ( z ) = ξ L 1 ( 1 + a e b z ) s , ( 11 ) with s > 0 , b > 0 , a > 0 , and z = r − ct ( where c is the speed of the travelling wave , i . e . the front speed of the hypercyclic population ) . Using d U i d x = d U i d z = U i ′ d U i d t = - c d U i d z = c U i ′ with i ∈ [I , L] , the set Eq ( 9 ) can be rewritten as: U I ′ ′ + c U I ′ + U I U L ( 1 - U I - U L ) = 0 ( 12 ) U L ′ ′ + c U L ′ + α* U I U L ( 1 - U I - U L ) = 0 , ( 13 ) Developing the derivatives U I ′ ′ and U I ′ , Eq ( 12 ) reads: ε I [ s ( s + 1 ) η - s - 2 a 2 b 2 e 2 b z - s η - s - 1 a b 2 e b z - s c η - s - 1 a b e b z + ε L η - 2 s - ε I ε L η - 3 s - ε L 2 η - 3 s ] = 0 , ( 14 ) where η = ( 1 + aebz ) . Neglecting the trivial solution ( εI = 0 ) for Eq ( 14 ) , and reorganising terms according to powers of ebz , we obtain the characteristic equation for the front speed c: e 2 b z [ s ( s + 1 ) a 2 b 2 ] + e b z [ - s a η ( b 2 + b c ) ] + ε L η - s + 2 + ε I ε L η - 2 s + 2 + ε L 2 η - 2 s + 2 = 0 ( 15 ) Solutions for the travelling wave have to be valid ∀z , and thus each line in Eq ( 15 ) gives an independent expression that must necessarily vanish . Analysing the terms in the last line in Eq ( 15 ) leads to the necessary condition s < 2 . This leads to s = 1 because we only consider solutions with s > 0 . Then , considering s = 1 , we develop the conditions given by the different powers of ebz in Eq ( 15 ) , which leads to: ε I = 1 - ε L , ( 16 ) c = ε L - b 2 b , ( 17 ) and b = c . ( 18 ) Combining Eqs ( 16 ) – ( 18 ) leads to: c = ε L / 2 = ( 1 - ε I ) / 2 . ( 19 ) With an analogous procedure to the one performed above for Eq ( 12 ) , analysis of Eq ( 13 ) leads to: c = α* ε I / 2 = α* ( 1 - ε L ) / 2 . ( 20 ) Combining Eqs ( 19 ) and ( 20 ) we obtain the expressions for the species abundances in the travelling front: ε I = 1 / ( 1 + α* ) , ε L = α* / ( 1 + α* ) ( 21 ) Replacing terms from Eqs ( 21 ) into ( 20 ) , we obtain the analytical solution for the front speed in dimensionless variables: c = α* 2 ( 1 + α* ) . ( 22 ) Finally , recovering dimension variables , the speed of the front reads: v = c D k α I L = D k α I L α L I 2 ( α I L + α L I ) ( 23 ) Our approach to the study of hypercycles reveals the importance of considering cells as embodied entities , both as interacting elements on a microscopic scale and as spatially extended populations . Moreover , cells need to incorporate the molecular circuits associated to the specific regulatory mechanisms along with chemical reactions , spatial diffusion and molecular signalling . To this goal , we used the specification language gro [63] as the platform for individual-based simulation of growing populations . Our model integrates the main physical features of bacterial shape and growth [63] , as well as the cross-feeding and cross-protection interaction between I - L- and P strains . We used a very simple approach that considers a few step ( Heavyside ) functions to emulate cell behaviour . A list of the considered cell behaviour features follows: The corresponding logical loop experienced by a given L- cell at each time step is illustrated in Fig 5 . I - and P cell dynamics follow analogous logical schemes . Furthermore , in order to consider a fitter parasitic strain that evades the cost of the mutualism in antibiotic-free scenarios , we consider the growth rate of P cells to be higher ( by a 10% difference ) than that of I - and L- cells . As shown above , the hypercycle was able to escape the parasite despite such faster growth rate . Admittedly , actual cell dynamics is far more complex than this Heavyside representation . However , our goal for the agent-based model was to use a minimal set of assumptions , in order to provide an easy understanding of the key features governing the system dynamics . Remarkably , the Heavyside-based cell behaviour is enough to capture the essential dynamics , as discussed in the Results section . The source code and additional details on specific values for metabolic rates and concentration threshold values can be found in the Supp . Info . Both the I - and the L- strains are from E . coli strain DH1 ( National BioResource Project , National Institute of Genetics , Shizuoka , Japan ) and were genetically modified to cross-feed as described in [6] . The I - ( L- ) strain carries the dsred . T3 ( gfpuv5 ) gene that provides the corresponding fluorescence labelling . Cloning for the P strain was carried out using the Biobrick assembly method and the parts: B0014 , J23100 , B0032 and E0020 , from the Spring 2010 iGEM distribution assembled into a low copy number plasmid pSB4A5 . A complete description of the construction protocols can be found at [64 , 65] . All regular cultures and amplifications were done at 37°C in well-mixed media Lysogeny Broth ( LB ) . Bacterial strains were cryopreserved in LB-glycerol 20% ( v/v ) at -80°C . Along experiments , cells were grown at 37°C in well-mixed Modified-M63 ( mM63 ) media ( pH 7 . 0 , 62 mM K2HPO4 , 39 mM KH2PO4 , 15 mM ammonium sulfate , 1 . 8 μM FeSO4 − 7H2O , 15 μM thiamine hydrochloride , 0 . 2 mM MgSO4 − 7H2O and 22 mM glucose [66] ) . For individual cloning selection , I- and L- cells from frozen stocks were grown overnight 16h in LB at 37°C , diluted and plated on Petri dishes with LB agar ( 1 . 2% agar ) and the appropriate selective antibiotic ( chloramphenicol 30 μg/ml , kanamycin 20 μg/ml , and 25 μg/ml for the for the I - , the L- , and the P strain , respectively ) . Before each experiment , colonies of each strain were selected and grown separately in LB supplemented with both 10−4M of auxotrophic amino acid and the corresponding selective antibiotic . After 16h overnight culture at 37°C , we performed a 100-fold dilution ( 500-fold in the case of P strain cultures ) into fresh LB ( supplemented with auxotrophic amino acid and selective antibiotic ) , and let cultures grow to OD660∼0 . 4 . Fresh cultures at OD660∼0 . 4 were washed twice using mM63 medium . In order to set the initial cell density for experiments , optical density was adjusted to OD660nm = 0 . 1 per strain ( which means that cocultures involving 2 or 3 strains exhibited OD660nm = 0 . 2 or OD660nm = 0 . 3 , respectively ) after culture washing . Well-mixed culture experiments were performed in flat bottom 96-well microplates ( Sarstedt AG & Co . Germany ) . Growth was monitored over time , by quantification of fluorescence identifying each strain ( mRFP , GFP , and CFP for I- , L- , and P cells , respectively ) . M63 without cells was included in the incubation as a background control for both fluorescence and absorbance . Fluorescence time courses for well-mixed cultures were performed on a Synergy MX-microplate reader ( BioTek Instruments , USA ) , using the reading settings for RFP ( ex: 560±9 nm , em: 588±9 nm ) , GFP ( ex: 478±9 nm , em: 517±9 nm ) and CFP ( ex: 450±9 nm , em: 476±9 nm ) at gain 90 , as well as optical density ( OD at 660 nm ) . Incubation was performed at 37°C with continuous orbital shaking ( medium speed ) . Fresh cultures at OD660∼0 . 4 were washed twice using mM63 medium , and then resuspended in mM63 medium while adjusting the OD660nm = 0 . 15 per bacterial strain , in order to adjust the initial cell density for experiments . For range expansions in environments including ampiciline , we used an initial OD660nm = 0 . 3 per bacterial strain . 0 . 4 μL of the corresponding cultures where then inoculated in mM63 1 . 2% agar plates ( supplemented with amino acid and antibiotic as required by the experimental scenario ) . Colonies were incubated for 4 days ( 7 days for the case of front speed measurements ) at 37°C and humidity 90% . Colonies were observed using a Leica TCS SP5 AOBS ( inverted ) confocal microscope . | In order to achieve greater levels of complexity , complex systems often display cooperative interactions that enable the formation and stabilisation of mutualisms . Theoretical models have shown that closed chains of cooperative species or hypercycles might have been crucial in the evolution towards complexity in early molecular replicators . However , parasites can easily destroy the cooperative loop , unless the system is embedded in a spatial context where interactions are limited to nearest neighbours . A dynamically similar phenomenon occurs in ecological webs , where closed positive feedback loops contribute to global stability and ecophysiology . Here we explore this problem by engineering synthetic cooperative strains of microbes that grow and interact in a cell culture under the absence and presence of a synthetic parasitic strains . By analysing the impact of cooperation under different conditions , we find that cooperative replication is successful and overcomes competitive interactions in nutrient-poor environments . However , the same closed loop fails to establish in nutrient-rich media . Moreover , parasitic entities that jeopardise cooperation under well-mixed conditions can be overcome by hypercycles when growing in a two-dimensional space . | [
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] | 2017 | Spatial dynamics of synthetic microbial mutualists and their parasites |
Plague was introduced to Madagascar in 1898 and continues to be a significant human health problem . It exists mainly in the central highlands , but in the 1990s was reintroduced to the port city of Mahajanga , where it caused extensive human outbreaks . Despite its prevalence , the phylogeography and molecular epidemiology of Y . pestis in Madagascar has been difficult to study due to the great genetic similarity among isolates . We examine island-wide geographic-genetic patterns based upon whole-genome discovery of SNPs , SNP genotyping and hypervariable variable-number tandem repeat ( VNTR ) loci to gain insight into the maintenance and spread of Y . pestis in Madagascar . We analyzed a set of 262 Malagasy isolates using a set of 56 SNPs and a 43-locus multi-locus VNTR analysis ( MLVA ) system . We then analyzed the geographic distribution of the subclades and identified patterns related to the maintenance and spread of plague in Madagascar . We find relatively high levels of VNTR diversity in addition to several SNP differences . We identify two major groups , Groups I and II , which are subsequently divided into 11 and 4 subclades , respectively . Y . pestis appears to be maintained in several geographically separate subpopulations . There is also evidence for multiple long distance transfers of Y . pestis , likely human mediated . Such transfers have resulted in the reintroduction and establishment of plague in the port city of Mahajanga , where there is evidence for multiple transfers both from and to the central highlands . The maintenance and spread of Y . pestis in Madagascar is a dynamic and highly active process that relies on the natural cycle between the primary host , the black rat , and its flea vectors as well as human activity .
Throughout recorded history , Yersinia pestis , etiologic agent of plague , has spread multiple times from foci in Central Asia in greatly widening swaths as human-mediated transport became more efficient [1] . Plague attained its current global distribution during the current “third” pandemic , which began in 1855 in the Chinese province of Yünnan , when it was introduced to many previously unaffected countries via infected rats on steam ships [2] . Plague caused widespread outbreaks during this introduction period ( ∼1900 A . D . ) , and though disease incidence has since largely decreased , plague remains a significant human health threat due to the severe and often fatal nature of the disease , the many natural plague foci [2] and its potential as a bioterror agent ( it is currently classified as a Class A Select Agent [3] ) . Plague is of particular significance in Madagascar , which has reported some of the highest human plague case numbers ( 18%–60% of the world total each year between 1995 and 2009 ) [4] and was the origin of a natural multi-drug resistant strain of Y . pestis [5] , [6] . Plague has been a problem in Madagascar since its introduction during the current pandemic . It was first introduced to Toamasina in 1898 [7] , likely via India [1] , with outbreaks in other coastal cities soon after . In 1921 , plague reached the capital , Antananarivo , likely via infected rats transported on the railroad linking Toamasina and Antananarivo . Subsequent rat epizootics signaled the establishment of plague in the central highlands [7] . Plague then disappeared from the coast and now exists within two large areas in the central and northern highlands above 800 m in elevation [8] . This elevational distribution of plague is linked to the presence of the flea vectors Xenopsylla cheopis and Synopsyllus fonquerniei , which are less abundant and absent , respectively , below 800 m [9] , [10] . Plague has never disappeared from this region and although it was relatively controlled in the 1950s due to public hygiene improvements and the introduction of antibiotics and insecticides , disease incidence began increasing in 1989 [8] , [11] , [12] . Human plague cases peaked in 1997 but continue to occur at high frequencies , making Madagascar among the top three countries for human plague cases during the past 15 years [4] . A third , newly emerged plague focus outside the central and northern highlands is the port city of Mahajanga , located ∼400 km by air from Antananarivo [8] . Plague first appeared in Mahajanga during an outbreak in 1902 . Subsequent outbreaks occurred in 1907 and between 1924 and 1928 [7] . Plague then disappeared from Mahajanga for a period of 62 years before reappearing during a large outbreak in 1991 [13] . Subsequent outbreaks occurred from 1995–1999 [14]–[16] . During this time , the Mahajanga focus was responsible for ∼30% of the reported human plague cases in Madagascar [14] . Interestingly , this focus likely represents one of the only examples of plague being reintroduced to an area where it had gone extinct , rather than emergence from a silently cycling rodent reservoir without telltale human cases [17] . Molecular subtyping of Y . pestis for epidemiological tracking has been difficult due to a lack of genetic diversity [18] . SNP genotyping [1] , [19] , [20] , ribotyping [21] , IS100 insertion element restriction fragment length polymorphism ( RFLP ) analysis [18] , PCR-based IS100 genotyping [19] , [22] and pulsed-field gel electrophoresis ( PFGE ) [23] have been used to differentiate global isolate collections , however , SNP genotyping provides the most robust phylogenetic reconstructions . SNP genotyping [1] , ribotyping [24] , IS100 insertion element RFLP analysis [25] , different region ( DFR ) analysis [26] , clustered regularly interspaced short palindromic repeats ( CRISPR ) analysis [27] , ERIC-PCR [28] , ERIC-BOX-PCR [28] and PFGE [25] , [29] have shown limited to moderate ability in differentiating isolates on a regional scale . Of these , ribotyping has been applied to a set of 187 Malagasy isolates , but only revealed four ribotypes , three of which were unique to Madagascar [24] . SNP genotyping of 82 Malagasy isolates provided greater and more phylogenetically informative resolution , revealing two major groups and an additional 10 subgroups derived from these two major groups that were mostly isolate-specific [1] . In contrast to these other molecular subtyping methods , multi-locus variable-number tandem repeat ( VNTR ) analysis ( MLVA ) has shown high discriminatory power at global [19] , [30] , [31] , regional [30] , [32]–[35] and local scales [32] , indicating its likely usefulness for further differentiation among Y . pestis isolates from Madagascar . The use of SNPs and MLVA together , in a hierarchical approach , has been successfully applied to clonal , recently emerged pathogens [36]–[38] . Point mutations that result in SNPs occur at very low rates , making SNPs relatively rare in the genome , but discoverable through intensive sampling ( i . e . , whole genome sequencing ) . In addition , since each SNP likely occurred only once in the evolutionary history of an organism , SNPs represent highly stable phylogenetic markers that can be used for identifying key phylogenetic positions [36] . However , SNPs discovered from a limited number of whole genome sequences will have limited resolving power [36] since they will only be able to identify phylogenetic groups along the evolutionary path ( s ) linking the sequenced genomes [39] . In contrast , VNTRs possess very high mutation rates and multiple allele states , allowing them to provide a high level of resolution among isolates . Unfortunately , these high mutation rates can lead to mutational saturation and homoplasy which can obscure deeper phylogenetic relationships , leading to inaccurate phylogenies . Using these two marker types together , in a nested hierarchical approach , with SNPs used to identify major genetic groups followed by VNTRs to provide resolution within those groups , allows for both a deeply rooted phylogenetic hypothesis and high resolution discrimination among closely related isolates [36] . We investigated the phylogeography and molecular epidemiology of Y . pestis in Madagascar through extensive genotyping and mapping of genetic groups . We genotyped 262 Malagasy isolates from 25 districts from 1939–2005 using 56 SNPs and a 43-marker MLVA system to identify island specific subclades . We then spatially mapped the subclades to examine island-wide geographic-genetic patterns and potential transmission routes .
The DNAs analyzed in this study ( Table S1 ) were extracted from Y . pestis cultures that were previously isolated by the Malagasy Central Laboratory for plague and Institut Pasteur de Madagascar as part of Madagascar's national plague surveillance plan . The Malagasy Ministry of Health , as part of this national plague surveillance plan , requires declaration of all suspected human plague cases and collection of biological samples from those cases . These biological samples are analyzed by the Malagasy Central Laboratory for plague and Institut Pasteur de Madagascar which also maintains any cultures derived from these samples . These cultures are all de-linked from the patients from whom they originated and analyzed anonymously if used in any research study . Thus , for purposes of this study , all of the DNAs derived from Y . pestis cultures from human patients were analyzed anonymously . No Malagasy review board existed during the collection period of the cultures ( 1939–2001 ) from which the DNAs used in this study were derived . In addition , the Institutional Review Board of Northern Arizona University , where the DNA genotyping was done , did not require review of the research due to the anonymous nature of the samples . DNA was obtained from 262 isolates from 25 different districts from 1939–2005 ( Figure S1 , Table S1 ) . DNAs consisted of simple heat lysis preparations or whole genome amplification ( WGA ) ( QIAGEN , Valencia , CA ) products generated from the heat lysis preps . Most of the isolates were collected by the Malagasy Central Laboratory for plague supervised by the Institut Pasteur de Madagascar and were primarily isolated from human cases with a few isolated from other mammals or fleas . A handful of other isolates were from other institutions ( still originally collected by the Malagasy Central Laboratory for plague ) or represent publically available whole genome sequences ( Table S1 ) . A total of 56 SNPs were chosen to genotype the Malagasy isolates because they either marked the branches leading to or from the Madagascar clades in a worldwide analysis [1] or were polymorphic among Malagasy isolates ( Table S2 ) . These SNPs were either previously identified in a worldwide SNP study on Y . pestis using a combination of denaturing High Performance Liquid Chromatography ( dHPLC ) and whole genome sequence comparisons [1] or identified here through whole genome sequence comparisons among 2 Malagasy whole genome sequences ( MG05-1020 [GenBank:AAYS00000000] and IP275 [GenBank:AAOS00000000] [1] ) and 14 other Y . pestis strain sequences ( CO92 [GenBank:AL590842] [40] , FV-1 [GenBank:AAUB00000000] [41] , CA88-4125 [GenBank:ABCD00000000] [42] , Antiqua [GenBank:CP000308] , Nepal 516 [GenBank:CP000305] [43] , UG05-0454 [GenBank:AAYR00000000] [1] , KIM 10 [GenBank:AE009952] [44] , F1991016 [GenBank:ABAT00000000] , E1979001 [GenBank:AAYV00000000] , K1973002 [GenBank:AAYT00000000] , B42003004 [GenBank:AAYU00000000] [45] , Pestoides F [GenBank:CP000668] [46] , Angola [GenBank:CP000901] [20] and 91001 [GenBank:AE017042] [47] ) . These whole genome sequence comparisons involved comparing the predicted gene sequences of the closed genome of Y . pestis strain CO92 [40] to the completed and draft genomes of all other strains using MUMmer and in-house Perl scripts [48] . For genomes with deposited underlying Sanger sequencing read information , a polymorphic site was considered of high quality when its underlying sequence in the query comprised at least three sequencing reads with an average Phred quality score >30 [20] , [49] . A TaqMan-minor groove binding ( MGB ) assay or a melt mismatch amplification mutation assay ( Melt-MAMA ) was developed for each SNP for use in genotyping the Malagasy DNAs . A TaqMan-MGB assay was designed around one SNP known to divide Malagasy isolates into two major groups ( Mad-43 , Table S2 ) . Melt-MAMA assays were designed around the other 55 SNPs as previously described [38] . SNP locations , primer sequences , primer concentrations and other information for these assays are presented in Table S2 . Primers and probes were designed using Primer Express 3 . 0 software ( Applied Biosystems , Foster City , CA ) . Each 5 µl TaqMan-MGB reaction contained primers and probes ( for concentrations see Table S2 ) , 1× Platinum Quantitative PCR SuperMix-UDG with ROX ( Invitrogen , Carlsbad , CA ) , water and 1 µl of template . Each 5 µl Melt-MAMA reaction contained 1× SYBR Green PCR Master Mix ( Applied Biosystems ) or 1× EXPRESS SYBR GreenER qPCR Supermix with Premixed ROX ( Invitrogen ) ( for assay-specific master mix see Table S2 ) , derived and ancestral allele-specific MAMA primers , a common reverse primer ( for primer concentrations see Table S2 ) , water and 1 µl of diluted DNA template . DNA templates were diluted 1/10 for heat lysis preparations or 1/50 for WGA products . All assays were performed on an Applied Biosystems 7900HT Fast Real-Time PCR System with SDS software v2 . 3 . Thermal cycling conditions for the TaqMan-MGB assay were as follows: 50°C for 2 min , 95°C for 2 min and 50 cycles of 95°C for 15 s and 66°C for 1 min . Thermal cycling conditions for the Melt-MAMA assays were as follows: 50°C for 2 min , 95°C for 10 min and 40 cycles of 95°C for 15 s and 55–65°C for 1 min ( see Table S2 for assay-specific annealing temperatures ) . Melt-MAMA results were interpreted as previously described [38] . All 262 Malagasy isolates were also genotyped using a 43-marker MLVA system as previously described [32] . In general , missing SNP data ( <0 . 5% of dataset ) were not a factor in node assignment ( see SNP phylogenetic analysis below ) since data were usually available for an equivalent SNP , thus leading to unambiguous node assignments for most isolates . However , there were four cases where the node assignment was potentially ambiguous . For three isolates missing data for SNP Mad-21 ( branch 1 . ORI3 . k-1 . ORI3 . o , Table S2 ) , the ancestral allele state was assumed for that SNP for those isolates , since in this and in a previous worldwide analysis [1] , only a single isolate , not included among these three , belonged to node “o . ” For a single isolate missing data for SNP Mad-46 ( branch 1 . ORI3 . d-1 . ORI3 . h1 , Table S2 ) the derived state was assumed , due to the placement of that isolate in MLVA subclade II . B in a neighbor-joining analysis and the observed congruence between the “h” nodes and MLVA subclade II . B ( see phylogenetic analyses below , Table S1 ) . A hierarchical approach was applied to the phylogenetic analysis of the Malagasy isolates . First , a SNP phylogeny was generated using data from all 56 SNPs ( Figure 1 ) . Second , neighbor-joining dendrograms based upon MLVA data were constructed using MEGA 3 . 1 [50] for the two main groups in the SNP phylogeny , Groups I and II ( Figure 2A–B ) . These groups corresponded to the two major Malagasy groups in a previous worldwide analysis [1] and so were separated prior to analyzing with MLVA . The remaining SNPs showing variation among the Malagasy isolates mostly defined subclades observed in the MLVA phylogenies or were specific to single isolates , and so were not used to further separate the isolates prior to applying MLVA . The locations of these additional SNPs are marked on the two MLVA phylogenies where applicable ( Figure 2A–B ) . A small set of SNPs provided very fine-scale resolution of the lineage leading to the whole genome sequenced MG05-1020 strain and are not marked on the MLVA phylogeny due to disagreement between the SNP and MLVA phylogenies on this small scale . Distance matrices for the two MLVA phylogenies were based upon mean character differences . Bootstrap values were based upon 1 , 000 simulations and were generated using PAUP 4 . 0b10 ( D . Swofford , Sinauer Associates , Inc . , Sunderland , MA ) . Branches with ≥50% bootstrap support and/or supported by one or more SNPs were identified as subclades . One other cluster ( II . A ) was also considered a subclade despite a lack of bootstrap support because of the proximity of a SNP-defined subclade ( Figure 2B ) . We mapped the geographic distributions of the Group I and II subclades we identified to determine their phylogeographic patterns ( Figure 3 ) . Analysis of similarity ( ANOSIM ) [51] tests were performed using PRIMER software version 5 to test the hypotheses that 1 ) Groups I and II form distinct geographic groups and 2 ) the identified subclades form distinct geographic groups . These tests were performed on all subclades with ≥5 members ( N = 221 isolates ) , thus excluding the unaffiliated isolates and subclades I . C , I . H , I . I and I . G ( Table S1 ) . The results of all 55 pairwise comparisons among the subgroups were evaluated at α = 0 . 000909 ( global α of 0 . 05 divided by 55 ) . To determine if there was a rank relationship between genetic distance and geographic distance , a Spearman correlation coefficient was generated using the RELATE function in PRIMER software with significance of the resulting statistics determined using 10 , 000 random permutations of the data . This analysis utilized all isolates with any geographic data ( N = 256 ) , with district centroids used as the geographic location for isolates for which only district level geographic information was available ( N = 33 ) ; city/commune point geographic data were used for the remaining 223 isolates . Six isolates lacking any geographic information were excluded from both statistical analyses ( Table S1 ) .
Our hypervariable-locus and genome-based approaches identified a relatively high level of genetic diversity among the 262 Malagasy isolates from 25 districts from 1939–2005 . We confirmed the presence of two major genetic groups , Groups I and II , differentiated by a single SNP , Mad-43 ( Figure 1 , Table S2 ) , and many VNTR mutational steps . Groups I and II were further differentiated into eleven ( I . A–I . K , Figure 2A , Table S1 ) and four ( II . A–II . D , Figure 2B , Table S1 ) subclades , respectively , based upon MLVA and/or SNPs . All but one of these subclades was at least weakly supported by bootstrap values ≥50 and/or one or more SNPs ( Figure 2A–B ) . The high mutation rates at VNTR loci can lead to homoplasy and , consequently , to low bootstrap support for deeper phylogenetic relationships when analyzing isolates from regional or worldwide collections [19] , [34] , [36] , [52] . Nevertheless , subsequent analyses using more phylogenetically stable molecular markers ( i . e . , SNPs ) have confirmed MLVA-determined clades with weak or even no bootstrap support [19] , [38] , leading us to use even weak bootstrap support to validate subclades in this analysis . Of the two MLVA identified subclades without bootstrap support , II . A and II . B , subclade II . B was supported by SNP Mad-46 ( Table S2 ) and subclade II . A was designated due to its proximity to and clear separation from the SNP-identified subclade II . B ( Figure 2B ) . Subclades I . B , I . F and I . H were supported by SNPs Mad-26 to 31 , Mad-42 and Mad-09 to 17 ( Table S2 ) , respectively , and bootstrap analysis ( Figure 2A ) . MLVA also identified 23 and 5 isolates in Groups I and II , respectively , that did not belong to any of the identified subclades within those groups ( hereafter referred to as unaffiliated isolates ) ( Figure 2A–B , I . NONE and II . NONE isolates in Table S1 ) . Four of these unaffiliated isolates and isolates in subclades I . B , I . H and II . B were also identified by apparently isolate-specific SNPs ( Figure 2A–B ) . Overall , MLVA identified 226 genotypes among the 262 isolates , constituting far better resolution than that achieved using ribotyping [24] . The SNP and MLVA analyses showed remarkable congruence . Nearly all of the nodes in the SNP phylogeny either corresponded to MLVA subclades or were specific to individual isolates , allowing the combined analysis of SNP and MLVA data discussed above . Three nodes ( f , m and n , Figure 1 ) did not have representatives in this study , but appeared to be specific for individual isolates in a previous analysis [1] . The only exception to this congruence was within the lineage leading to the whole genome sequenced strain , MG05-1020 ( q nodes in Figure 1 and subclade I . B in Figure 2A ) . In this case , the SNP phylogeny ( q nodes , Figure 1 ) was more accurate than and provided nearly as much resolution as the corresponding MLVA phylogeny ( I . B , Figure 2A ) . This fine-scale phylogenetic resolution was due to the use of a high resolution SNP discovery method , whole genome sequence comparisons , to discover SNPs along this lineage as opposed to the lower resolution dHPLC method used to discover most of the other Malagasy SNPs [1] . Interestingly , comparable resolution was not seen in the lineage leading to the other whole genome sequenced strain , IP275 ( l nodes in Figure 1 and subclade I . H in Figure 2A ) , likely due to the very low number of isolates ( N = 2 ) within that lineage in this analysis . Missing data for two SNP assays suggested a potential genomic rearrangement ( e . g . , deletion ) in some of the Malagasy strains . Twenty-five of the 262 isolates were missing data for two SNP assays despite repeated attempts at amplification ( Table S1 ) . The two SNPs , Mad-28 and Mad-41 , were located <850 bp apart at CO92 positions 2 , 208 , 345 and 2 , 207 , 531 , respectively ( Table S2 ) , suggesting that there may have been a genomic rearrangement affecting this region in these strains . Intriguingly , IS100 elements were located flanking these SNPs at CO92 positions 2 , 135 , 459-2 , 137 , 412 and 2 , 236 , 265-2 , 238 , 215 . IS elements are important facilitators of genomic rearrangements in Y . pestis [42] , [43] and may have played a role in this result . If so , the same or a similar genomic rearrangement must have occurred multiple times since the 25 isolates were members of six different nodes in the SNP phylogeny ( Table S1 ) . This hypothesis is supported by the fact that IS100 elements are known potential hotspots for genomic rearrangements and excisions in Y . pestis [19] , [42] . Significant geographic separation was observed among the identified subclades . Overall , there was a small , but highly significant relationship between genetic and geographic distance ( Spearman correlation coefficient ρ = 0 . 226 , p<0 . 0001 ) . In addition , the two main genetic groups , Groups I and II , formed distinct geographic groups based upon an ANOSIM ( R = 0 . 091 , p = 0 . 0007 ) . Group II isolates , which possessed the derived state for SNP Mad-43 ( Table S2 ) , were essentially restricted to three of the most active plague districts in the central highlands , Betafo , Manandriana and Ambositra [11] , and an adjacent district , Ambatofinandrahana ( Figure 3 , S1 ) . The only exceptions to this were the five unaffiliated Group II isolates , which were scattered in districts to the east and north ( + symbols , Figure 3 ) . In contrast , Group I isolates were found in all three foci , both the central and northern highlands and Mahajanga . Geographic separation among the individual Group I and II subclades was also apparent ( Figure 3 ) and statistically supported in an ANOSIM ( R = 0 . 232 , p<0 . 0001 ) . Post-hoc analyses of the pairwise comparisons among subclades indicated that most of the eleven tested subclades formed distinct geographic groups ( data not shown ) . Indeed , several interesting geographic patterns were apparent for the different subclades , only some of which are described below . Separate Group I subclades were found in the northern ( I . C , I . G and I . I , Figure 3 , Table S1 ) versus the central ( I . A , I . B , I . D , I . E , I . F , I . H , I . J and I . K , Figure 3 , Table S1 ) highlands . Subclade I . A , the largest single subclade , was the dominant subclade found in the capital , Antananarivo , and the surrounding area ( Figure 3 , S1 ) . With the exception of two isolates , it was also the only subclade found in Mahajanga ( Figure 3 , S1 , Table S1 ) , indicating a central highlands origin for the Y . pestis responsible for the series of Mahajanga plague outbreaks from 1991–1999 [13]–[16] . Subclade I . B was the only subclade found in the northeastern portion of the central highlands ( Figure 3 ) . Geographic analysis of the corresponding SNP phylogeny ( q nodes , Figure 1 ) for this subclade revealed some additional geographic-genetic patterns . Isolates with the same SNP genotype tended to be clustered geographically , although no distinct spreading pattern could be discerned , possibly due to the limited number of isolates ( Figure 4 ) . Subclade I . E was predominantly found in the southern central highlands , in district Fianarantsoa , and also appears to be the subclade responsible for the reemergence of plague in the Ikongo district [53] , adjacent to Fianarantsoa on the southeast ( Figure 3 , S1 ) . Three subclades , I . F , I . H and I . K , did not show distinct geographic patterns ( Figure 3 ) . In the cases of subclades I . F and I . H , this may be due to the limited numbers of isolates within those subclades ( Figure 2A , Table S1 ) . The geographically widespread nature of subclade I . K isolates , however , may be related to their older dates of isolation . All of the subclade I . K isolates were isolated between 1940 and 1955 ( Figure 2A , Table S1 ) , just 19–34 years after plague was introduced to the central highlands . Therefore , these isolates may represent a subclade that was formerly spread throughout much of the central highlands but that currently does not exist in nature in Madagascar . Similarly , subclade I . I , although it was not geographically widespread ( Figure 3 ) , only contained isolates isolated from 1971–1976 ( Figure 2A , Table S1 ) and may represent a former , now extinct subclade from the northern highlands . However , the limited number of isolates makes this difficult to determine . Alternatively , these subclades may still exist , but may have decreased in frequency and/or be very rare in nature . Interestingly , the other older isolates tended to be the unaffiliated isolates . Eighteen of the 28 unaffiliated isolates were isolated between 1939 and 1978 . Another 3 had unknown dates of isolation ( Table S1 ) . Given their older dates of isolation , these unaffiliated isolates may also be representatives of older , now extinct subclades from Madagascar . The lack of comparable isolates to these unaffiliated isolates among the rest of the isolate collection could be due to the limited sampling from earlier years ( Table S1 ) . Alternatively , the unaffiliated isolates may simply be representatives of very rare subclades . A final possibility could involve the accumulation of VNTR mutations due to repeated passages associated with prolonged storage in the laboratory , which could lead to the older isolates being inaccurate representatives of the original isolates . This is unlikely , however , as the rate of VNTR evolution in the laboratory , even with passaging , should be much slower than in nature . Thus , while these isolates may not be exactly the same as when they were first isolated , they should be close . Also , multiple copies of a subset of the Malagasy isolates in this study that were stored at different temperatures showed identical MLVA genotypes ( data not shown ) , indicating that these VNTR loci are relatively stable in these isolates under the storage conditions used . Regardless , the unaffiliated nature of many of the older isolates is consistent with and most likely related to their older dates of isolation . Several cities and communes yielded isolates of subclades predominantly found elsewhere , suggesting importation from other locations . Antananarivo , in particular , contained isolates from five subclades in addition to the dominant subclade ( Figure 3 , S1 ) . Commune Andina Firaisana in the Ambositra district is another example , containing representatives of four different subclades ( Figure 3 , S1 ) . One of these , subclade I . A , was also found in the nearby surrounding area . However , this area is considerably south of the area where the majority of subclade I . A isolates were found , suggesting that this subclade may have been imported to this area from further north or vice versa ( Figure 3 ) . Of the other three subclades found in Andina Firaisana , subclades II . A and II . B are also found in nearby areas and so may be naturally occurring in Andina Firaisana rather than due to transfer events . Subclade II . C , in contrast , appears to have been transferred to Andina Firaisana from the Betafo district in the northwest or vice versa ( Figure 3 , S1 ) . Another nearby commune , Ivato , contained a single subclade I . E isolate , suggesting a transfer event from district Fianarantsoa in the south ( Figure 3 , S1 ) . Our data suggest that Y . pestis was reintroduced to Mahajanga from the central highlands . The majority of the Mahajanga isolates ( 39 of 44 ) belonged to a single subcluster within subclade I . A ( hereafter referred to as the Mahajanga I . A subcluster ) ( Figure 2A ) , suggesting that there was an introduction to Mahajanga from the central highlands that became established in Mahajanga and then underwent local cycling . Though this Mahajanga I . A subcluster did not have either SNP or MLVA support ( Figure 2A ) , close examination of the isolates within this subcluster revealed very close genetic relationships , with most differences involving only a single repeat change at a single VNTR locus ( data not shown ) . This is consistent with an outbreak scenario originating from a single introduction and strengthens the identification of this subcluster as a genetic group . In contrast , subclade I . A isolates outside of the Mahajanga I . A subcluster exhibited much greater variation both in the number of VNTR loci displaying polymorphisms and the number of alleles observed at those loci ( data not shown ) , consistent with an older , more geographically dispersed and more differentiated set of isolates . Our data also suggest that there have been multiple transfers of Y . pestis between Mahajanga and the central highlands . Specifically , seven isolates within the Mahajanga I . A subcluster were isolated from central highland locations rather than from Mahajanga ( Figure 2A ) , suggesting that Y . pestis was also transferred back from Mahajanga to the central highlands . Two other Mahajanga isolates belonged to subclade I . F and were unaffiliated , respectively ( Figure 2A ) , suggesting that there has been more than one introduction of Y . pestis to Mahajanga as well . The final three Mahajanga isolates , although they belonged to subclade I . A , were not part of the Mahajanga I . A subcluster and were instead more closely related to subclade I . A isolates from the central highlands ( Figure 2A ) , again suggesting multiple introductions . However , it is unclear as to whether any of these other introductions became established in Mahajanga due to the lack of other Mahajanga isolates similar to these five outliers . Finally , although our data suggest that there have been multiple transfers of Y . pestis between Mahajanga and the central highlands , there is no evidence in these data for an introduction to Mahajanga from the northern highlands , as was previously suggested by PFGE analyses [14] , [17] .
Madagascar is one of the most active plague regions in the world . However , few studies have investigated the molecular epidemiology of Y . pestis from Madagascar and none have done so using very high resolution genomic methodologies . Here , we investigated the phylogeography and molecular epidemiology of Y . pestis in Madagascar by using a combination of SNPs and MLVA to analyze 262 Malagasy isolates from 25 districts from 1939–2005 . In contrast with previous analyses that utilized ribotyping or SNPs alone [1] , [24] , we identified a very high level of genetic diversity with 226 MLVA genotypes among the 262 isolates . These genotypes were distributed amongst 15 subclades that displayed significant geographic separation ( Figure 3 ) , leading to insights into the maintenance and spread of plague in Madagascar . The use of MLVA was particularly effective at identifying genetic groups in Madagascar . SNPs , though useful , mostly provided confidence in genetic groups that were already apparent via MLVA . This is somewhat counter to the conventional hierarchical approach wherein SNPs are used first to identify major genetic groups followed by MLVA to provide resolution within those groups , thus minimizing the problems of mutational saturation/homoplasy that can occur with highly variable markers such as VNTRs [36] . In this study , only SNP Mad-43 ( Table S2 ) , which differentiated Groups I and II , was useful in this conventional sense to identify “major genetic groups” that were obscured in the MLVA phylogeny ( data not shown ) . All of the other subclades identified by SNPs were also identified by MLVA , suggesting that at this regional scale , MLVA alone may be effective at identifying robust genetic groups . Importantly , though MLVA was excellent at identifying these genetic groups , the relationships among those groups , such as the division between Groups I and II , remained unclear using MLVA alone ( data not shown ) whereas they were very clearly depicted as a star phylogeny in the SNP phylogeny ( Figure 1 ) . Where knowledge of deeper genetic relationships or fine-scale phylogenetic analysis of specific lineages ( e . g . , the strain MG05-1020 lineage here ) is desired , SNPs will remain the preferred methodology for clonal pathogens such as Y . pestis . However , until whole genome sequencing for entire isolate collections becomes feasible , MLVA will continue to be a useful tool for examining genetic diversity whether used in conjunction with SNPs or alone . Our analyses suggest that plague is being maintained in Madagascar in multiple geographically separated subpopulations . We revealed significant geographic separation among the identified subclades ( Figure 3 ) , suggesting that these subclades are undergoing local cycling with limited gene flow from other subclades . This is consistent with the population genetics and ecology of the black rat ( Rattus rattus ) , the primary plague host in rural Madagascar [7] , [9] . The black rat in Madagascar exhibits limited gene flow between subpopulations [54] as well as limited geographic ranges [55] . This limited mobility , a high reproduction rate [10] and the development of some resistance to plague [56] are all likely important factors that allow the black rat to maintain plague in these genetically distinct , geographically separated subpopulations . The two flea vectors , X . cheopis and S . fonquerniei [9] , [10] , may also play a role in maintaining genetically distinct subpopulations ( i . e . , Groups I and II ) , though more data would be needed to confirm this hypothesis . In contrast , transport of Y . pestis across longer distances in Madagascar is likely human-mediated . Historically , there is ample evidence for the influence of human traffic on the spread of plague , including transport along trade routes such as the Silk Road in the early pandemics and transport via steam ship to numerous new locations during the “third” pandemic [1] , [2] . The SNP phylogeny determined by Morelli et al . [1] suggests the progression of plague from Israel to Madagascar to Turkey ( Figure 1 ) , a series of transfer events that were almost certainly human-mediated , though the details remain unknown . In Madagascar , plague was most likely transported from its introduction point on the coast to the central highlands , where it became permanently established , via the railroad linking Toamasina and Antananarivo [7] . More recently , plague was most likely reintroduced to Mahajanga via the transport of infected rats and fleas together with foodstuffs from the central highlands . Indeed , our data suggest multiple transfers between Mahajanga and the central highlands , all likely human-mediated . Additional long distance transfers of Y . pestis in Madagascar are suggested by the multiple subclades identified in cities/communes such as Antananarivo and Andina Firaisana ( Figure 3 , S1 , Table S1 ) . Though long distance transfers of Y . pestis undoubtedly occur , it is unclear how often such transfers result in the successful establishment of the transferred genotypes in new locations . At least one transfer to Mahajanga became successfully established and underwent local cycling as evidenced by the Mahajanga I . A subcluster described here ( Figure 2A ) . However , many of the other examples of long distance transfers where multiple subclades were found in a single location are not as clear regarding the establishment of the transferred subclade ( s ) . Antananarivo , for example , is clearly dominated by subclade I . A with only 1–2 representatives of each of the other five subclades identified there ( Figure 3 , S1 , Table S1 ) , suggesting that the presence of these alternative subclades may have been only transitory . Successful establishment of subclades in new locations following a long distance transfer may be related to adaptive advantages possessed by some genotypes [57] . For instance , subclade I . A appears to be particularly successful in our analysis . The earliest subclade I . A isolate in our dataset was collected in 1974 from the Ambositra district ( Table S1 ) , one of the most active plague districts in Madagascar [11] . Subsequent isolates indicate that this subclade continued to exist in a small area of the Ambositra district but also became well established over a large geographic area including and surrounding the capital , Antananarivo . This subclade was also successfully introduced to and established in Mahajanga and appears to have been transferred to the Fianarantsoa district , though it is unclear whether or not it became established there ( Figure 3 , S1 , Table S1 ) . This widespread geographical success may indicate that this subclade possesses an adaptive advantage that enhances its ability to be transferred long distances and become established in new locations [57] . Alternatively , the particular success of this subclade may simply be due to chance . The central highlands focus remains the most active plague focus in Madagascar [11] and is , consequently , a likely place for new genotypes to emerge . This is particularly true for those central highlands districts with the highest plague activity . For instance , the three unique ribotypes identified in a previous study belonged to isolates from two highly active districts , Ambositra and Ambohimahasoa [24] . Here , isolates belonging to Group II and its subclades were found in three highly active districts , Betafo , Manandriana and Ambositra ( Figure 3 , S1 ) . As discussed above , Ambositra may also have been the district of origin for the highly successful subclade I . A . Overall , the Ambositra district was one of the two most diverse districts in our analysis , containing representatives from six different subclades ( Figure 3 , Table S1 ) . This diversity is consistent with the Ambositra district's status as one of the three most important plague districts in Madagascar [8] , [11] . The maintenance and spread of Y . pestis in Madagascar is a dynamic and highly active process , depending on the natural cycle between the black rat and its flea vectors as well as human activity . Y . pestis in Madagascar is maintained in multiple , genetically distinct , geographically separated subpopulations , likely via the black rat . The exact geographic landscape of these subpopulations is probably ever changing , with some subclades going extinct or decreasing in frequency ( e . g . , subclade I . K ) , new subclades emerging and becoming established and some subclades being transferred to new locations , where they may become established either temporarily or more long-term . Much of the long distance spread of Y . pestis in Madagascar is likely due to human activities that allow for the transport of plague infected rats and fleas from one location to another . | Plague , caused by the bacterium Yersinia pestis , has been a problem in Madagascar since it was introduced in 1898 . It mainly affects the central highlands , but also has caused several large outbreaks in the port city of Mahajanga , after it was reintroduced there in the 1990s . Despite its prevalence , the genetic diversity and related geographic distribution of different genetic groups of Y . pestis in Madagascar has been difficult to study due to the great genetic similarity among isolates . We subtyped a set of Malagasy isolates and identified two major genetic groups that were subsequently divided into 11 and 4 subgroups , respectively . Y . pestis appears to be maintained in several geographically separate subpopulations . There is also evidence for multiple long distance transfers of Y . pestis , likely human mediated . Such transfers have resulted in the reintroduction and establishment of plague in the port city of Mahajanga where there is evidence for multiple transfers both from and to the central highlands . The maintenance and spread of Y . pestis in Madagascar is a dynamic and highly active process that relies on the natural cycle between the primary host , the black rat , and its flea vectors as well as human activity . | [
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] | 2011 | Phylogeography and Molecular Epidemiology of Yersinia pestis in Madagascar |
For cells the passage from life to death can involve a regulated , programmed transition . In contrast to cell death , the mechanisms of systemic collapse underlying organismal death remain poorly understood . Here we present evidence of a cascade of cell death involving the calpain-cathepsin necrosis pathway that can drive organismal death in Caenorhabditis elegans . We report that organismal death is accompanied by a burst of intense blue fluorescence , generated within intestinal cells by the necrotic cell death pathway . Such death fluorescence marks an anterior to posterior wave of intestinal cell death that is accompanied by cytosolic acidosis . This wave is propagated via the innexin INX-16 , likely by calcium influx . Notably , inhibition of systemic necrosis can delay stress-induced death . We also identify the source of the blue fluorescence , initially present in intestinal lysosome-related organelles ( gut granules ) , as anthranilic acid glucosyl esters—not , as previously surmised , the damage product lipofuscin . Anthranilic acid is derived from tryptophan by action of the kynurenine pathway . These findings reveal a central mechanism of organismal death in C . elegans that is related to necrotic propagation in mammals—e . g . , in excitotoxicity and ischemia-induced neurodegeneration . Endogenous anthranilate fluorescence renders visible the spatio-temporal dynamics of C . elegans organismal death .
While mechanisms of cell death such as apoptosis are well characterized [1] , less is known about the mechanisms of organismal death , particularly in invertebrate model organisms . Here we investigate organismal death in the nematode C . elegans , using a newly discovered , endogenous fluorescent marker of death . One possibility is that organismal death results from a cascade of cell death . As first defined by Kerr et al . in 1972 [1] , cell death has been viewed as taking two forms: controlled ( apoptotic ) or uncontrolled ( necrotic ) . However , more recent elucidation of the mechanisms underlying necrotic cell death reveals that it too can be a regulated process [2]–[5] . Biochemical hallmarks of necrosis include calcium-mediated initiation , lysosomal membrane permeabilization ( LMP ) , and activation of noncaspase proteases ( calpains and cathepsins ) [5]–[7] . Necrosis as a regulated process has been characterized mainly in mammalian neuronal models . Excitotoxic neuronal cell death occurs in response to overstimulation with the excitatory neurotransmitter glutamate ( e . g . , under conditions of ischemia or stroke ) [7] . Sustained activation of glutamate receptors causes a cytosolic influx of extracellular Ca2+ [8] . Increased Ca2+ levels lead to cell death , largely through activation of associated proteases [9] . Moreover , Ca2+ may spread between cells via connecting gap junctions , and gap junction inhibition reduces ischemia-induced neurodegeneration [10] , [11] . Through the study of ischemia-induced death in mammalian CA1 hippocampal neurons , Yamashima and co-workers identified the calpain-cathepsin cascade as an effector of necrotic cell death . Ischemia increases intracellular Ca2+ levels , which activate Ca2+-dependent cysteine proteases ( calpains ) [12] . These calpains cause lysosomal lysis , leading to cytosolic acidosis and the destructive release of lysosomal cathepsin proteases [13] . Many components of the calpain-cathepsin cascade are present in C . elegans , where necrotic cell death can be induced in neurons by mutations such as mec-4 ( u231 ) [14] . For example , mec-4-induced neurodegeneration requires the calcium-dependent calpains TRA-3 and CLP-1 and the cathepsins ASP-3 and ASP-4 [15] . LMP is a central event in the necrotic cascade , and the degree of LMP can influence the cellular decision to live or to die via necrosis or apoptosis [3] , [5] , [16] . In C . elegans , lysosomes are required for osmotic stress-induced necrotic death [17] and interventions that increase lysosomal pH can ameliorate mec-4 ( d ) -induced neurodegeneration [18] . C . elegans intestinal cells contain both lysosomes and gut granules , which are large , melanosome-like lysosome-related organelles [19] . Under ultraviolet light , gut granules emit blue fluorescence , with maximal intensity at λex/λem 340/430 nm ( Figure 1A–B ) [20] . This fluorescence has been attributed to lipofuscin [21] , [22] , a heterogeneous , cross-linked aggregate of oxidatively damaged lipids and proteins . Lipofuscin accumulates with age in postmitotic mammalian cells and so has frequently been used as a biomarker of aging [23]–[25] . Lipofuscin composition is highly variable but can be identified by virtue of its autofluorescence [24] . If excited by UV light in vitro it emits blue fluorescence , which may reflect formation of fluorescent Schiff bases between carbonyl and amino groups [26] , [27] . However , UV excitation of lipofuscin in vivo results in peak fluorescence in the 540–640 nm ( orange-yellow ) range [28] . Several observations have led to the suggestion that the fluorescent material in the C . elegans intestine is lipofuscin . Its fluorescence peak at λex/λem 340/430 nm is similar to that of lipofuscin in vitro , it is localized to the lysosome-like gut granules , and its levels increase in aging populations [20]–[22] , [29] . It is often used as a biomarker of aging—for example , to verify that treatments that shorten worm lifespan do so by accelerating aging . The presence of lipofuscin in C . elegans would support the view that aging is caused by accumulation of molecular damage . Yet it remains possible that the fluorescent substance in gut granules is not lipofuscin . For example , studies of flu mutations causing altered gut granule fluorescence suggest that it corresponds to fluorescent tryptophan metabolites [30] . In this study , we describe how a reassessment of blue fluorescence in C . elegans led to the discovery of the phenomenon of death fluorescence ( DF ) , a burst of blue fluorescence that accompanies death in C . elegans . We establish that both DF and gut granule fluorescence originate not from lipofuscin , but from tryptophan-derived anthranilic acid glucosyl esters . We then show that DF is generated by the calpain-cathepsin necrotic cell death pathway , and requires calcium signaling for organismal propagation . Finally , we show that inhibition of this pathway can protect animals against stress-induced death , supporting a role of systemic necrotic cell death in organismal death .
Lipofuscin is formed through accumulation of oxidatively damaged proteins and lipids [24] . For example , raised oxygen level ( 40% O2 ) increases lipofuscin levels in human fibroblasts [25] . To probe whether the blue fluorescent material in C . elegans gut granules ( Figure 1A–B ) is lipofuscin , we exposed them to normobaric hyperoxia ( 90% O2 ) , and elevated iron levels . Both treatments significantly increased protein oxidative damage but neither increased blue fluorescence levels ( Figure 1C–F ) . Elevated expression of hsp-4::gfp is indicative of the unfolded protein response [31] , symptomatic of protein damage . Heat shock increased hsp-4::gfp expression but not blue fluorescence ( Figure S1 ) . These results imply that C . elegans blue fluorescence is not generated by oxidative damage , suggesting that it is not lipofuscin . Like lipofuscin in mammals , mean fluorescence levels rise gradually with age in C . elegans population cohorts [20] , [29] . However , population mean data do not address heterogeneity in the fluorescence of individual worms . This concern was raised by a previous study [20] , as follows . Aging worms can be classed according to their degree of motility: class A animals move normally , class B animals move more slowly , and class C animals do not move away when touched , and are near to death [32] . Notably , blue fluorescence levels did not differ significantly between class A and B , and only increased in class C worms [20] . This suggests that blue fluorescence levels in worms increase only as they approach death . To test this directly , fluorescence levels of individually cultured , wild-type C . elegans in situ on nematode growth medium ( NGM ) agar plates were examined at intervals throughout life ( DAPI filter; λex/λem 350/460 nm ) . As animals approached death ( as indicated by reduced movement ) , time-lapse imaging was used to capture fluorescence changes during death . This revealed that fluorescence levels in individual animals change little until immediately prior to death . A striking and sudden ∼400% increase in fluorescence level then occurs , coinciding with cessation of movement ( i . e . , death ) ( Figure 2; Video S1 ) . This rise begins at ∼2 h prior to death , and then fades by ∼6 h after death ( Figure 2B–C ) . Blue fluorescent bursts are not only associated with death from old age , but were also induced by killing—for example , by placing a heated worm pick on the agar adjacent to the worm ( Figure 3A–B ) or by freeze-thaw or low pH ( Figure S2A , B ) . Hot pick-induced killing also caused fluorescent bursts in young adults of both sexes , and in larvae ( Figures 3A–B , S2C; Video S2 ) , and in the nematodes C . briggsae and Pristionchus pacificus ( Figures 3A , S2D–E ) . In both aged and killed worms , fluorescence distribution also changed during death , from punctate to diffuse , and eventually spread from the intestine to other tissues ( Figures 2B , S3 ) . We named this phenomenon death fluorescence ( DF ) . Next we characterized the spatiotemporal dynamics of DF in C . elegans , as a potential marker of cellular and organismal death . DF typically originates in the anterior-most cells of the intestine ( the int1 cells ) . It then spreads rapidly along the intestine in an anterior to posterior wave ( Figures 3C–D , S3 ) . In adults a second focus of fluorescence sometimes appears in the mid-body ( Figure S3 ) . When a hot pick was applied to the animals' tails ( rather than near the head ) , DF initially only arose locally and did not spread from posterior to anterior ( Figure 3D ) but only , eventually , from anterior to posterior ( unpublished data ) . This suggests that the anterior intestine represents an organismal weak point in C . elegans , where a local crisis in homeostasis can trigger a DF wave . Several types of autofluorescence with different spectral properties have been described in C . elegans [29] , [33] , [34] ( see Figure S4A for an overview of worm fluorescence ) . Using a more sensitive detection system [33] , we examined the dynamics of blue , green , and red fluorescence over life and aging-induced death . Again , no significant age-increase in blue fluorescence was seen ( Table S1 ) . The much weaker green and red fluorescence did increase significantly with age ( Figure S5; Table S1 ) , and all three forms of fluorescence increased during death ( Figure S5; Table S1 ) . These results imply the presence of multiple fluorophores in C . elegans . We then investigated the chemical identity of the blue fluorophore , using glo-1 ( zu437 ) ( gut granule loss 1 ) mutants that lack gut granules [19] . glo-1 animals showed little blue fluorescence either during life or death due to aging or thermal injury ( Figures S4B , S6A–B ) , implying that gut granule fluorescence and DF have a common origin . Blue fluorescence was present in aqueous extracts of N2 ( wild type ) worm homogenates . HPLC analysis revealed one major peak with fluorescence at λex/λem 340/430 nm in N2 but not glo-1 extracts ( Figure S6C–D ) . We therefore used glo-1 mutants as a negative control for chemical identification of the blue fluorophore , using 2D NMR-based comparative metabolomics [35] . This approach allows identification of compounds whose production depends on a specific genetic background without extensive chromatographic fractionation . Comparison of 2D NMR spectra acquired for the N2 and glo-1 extracts revealed several groups of signals that were much reduced or absent in the glo-1 spectra ( Figure S7 ) . The most differentially expressed compounds were the anthranilic acid glucosyl ester ( angl#1 in Figure 4A ) and N-glucosyl indole ( iglu#1 ) and their corresponding 3′-phosphorylated compounds ( angl#2 and iglu#2 ) . N2 but not glo-1 extracts also contained smaller quantities of free anthranilic acid . These structural assignments were confirmed via high-resolution mass spectrometry and synthesis of authentic samples of anthranilic acid glucosyl ester and N-glucosylindole ( Tables S2 , S3 , S4 ) . Moreover , fluorescence spectra for angl#1 and worm blue fluorescence were alike ( Figure S8 ) . Anthranilic acid ( AA ) is synthesized from L-tryptophan ( Trp ) by action of the kynurenine pathway ( Figure 4B ) , and has previously been observed in C . elegans [36] , [37] , but neither angl#1 nor angl#2 have previously been identified in animals . AA derivatives show fluorescence at λex/λem 340/430 nm [38] . The HPLC retention times of these AA derivatives matches those detected in the initial HPLC analysis of the N2 extract ( Table S4 ) . The indole glucosides iglu#1 and iglu#2 , also not previously reported in animals , did not emit blue fluorescence ( unpublished data ) . To verify the identity of the blue fluorophore , we next used a genetic approach . The first step in the conversion of Trp to AA is catalyzed by tryptophan 2 , 3-dioxygenase ( TDO ) . The C . elegans gene tdo-2 ( C28H8 . 11 ) encodes a putative TDO ( Figure 4B ) [36] . tdo-2 ( RNAi ) suppressed both gut granule blue fluorescence and DF ( freeze-thaw ) ( Figure 4C–D ) . RNAi or mutation of flu-2 ( kynureninase ) also reduced DF , while inactivating kynurenine 3-monooxygenase by kmo-1 ( RNAi ) or flu-1 mutation increased DF , all as predicted ( Figure S9 ) . Both tdo-2 ( RNAi ) and mutation of flu-2 greatly reduce AA levels [37] . We also tested whether exogenous AA is sufficient to cause blue gut granule fluorescence . tdo-2 ( RNAi ) worms were incubated in a range of solutions of synthetic anthranilic acid ( Sigma ) . Incubation with 5 mM AA restored gut granule fluorescence to a wild-type level ( Figure S10A–B ) . We conclude that gut granule fluorescence and DF emanate from AA . Why do fluorescence levels increase at death ? One possibility is that AA levels increase at the point of death . To test this we compared DF in tdo-2 ( RNAi ) worms with restored gut granule fluorescence and in L4440-treated control worms . Interestingly , the magnitude of DF was not reduced in the tdo-2 ( RNAi ) worms ( Figure S10C–D ) . This strongly implies that the DF burst is not the result of increased AA levels . An alternative possibility is that concentration of AA within gut granules results in quenching of fluorescence . To probe this idea we employed the dye uranin , whose green fluorescence is partially quenched at low pH [39] . Treatment of wild-type worms with uranin led to punctate green fluorescence in the intestine that co-localized with blue gut granule fluorescence ( Figure S11A ) . Thus , uranin accumulates within gut granules . Killing after uranin treatment caused a burst of green fluorescence in wild-type worms , but not glo-1 ( zu437 ) mutants without gut granules ( Figure S11B–C ) . This supports the view that the burst of fluorescence at death is caused by dequenching of AA and uranin fluorescence due to increased pH upon release from the acidic milieu of the gut granules . It also shows that uranin is an excellent marker for loss of integrity of membranes bounding acidic compartments ( e . g . , lysosomes and lysosome-like organelles ) . The presence of blue fluorescence within lysosome-related organelles ( i . e . , gut granules ) and the central role of LMP in multiple instances of necrotic cell death suggested that DF might be generated by necrosis . If correct , then inhibition of necrosis might be expected to suppress DF . To test this we used freeze-thaw induced death , which is convenient for rapid and accurate quantitation of DF . The calpain-cathepsin necrotic cascade ( Figure 5A ) is involved in C . elegans neurodegeneration [15] . Neuronal necrosis requires Ca2+ release from ER stores . Mutations in the ryanodine and inositol-1 , 4 , 5-triphosophate receptors , and the ER Ca2+ binding protein calreticulin all suppress neuronal necrotic cell death [17] , [40] . Each of these mutations , unc-68 ( e540 ) , itr-1 ( sa73 ) and crt-1 ( bz29 ) , respectively , also significantly reduced DF ( Figure 5B ) . During cellular necrosis , increased intracellular Ca2+ can activate calpains ( Ca2+-dependent cysteine proteases ) . The calpain TRA-3 is required for neuronal necrotic cell death in C . elegans [41] . tra-3 ( e1107 ) reduced DF ( Figure 5B ) . The necrosis cascade requires lysosomal lysis for cytosolic acidification and cathepsin release . In worms , the vacuolar proton-translocating ATPase ( V-ATPase ) , which mediates lysosomal acidification , is required for necrosis [18] . We therefore tested two hypomorphic V-ATPase mutants , vha-12 ( ok821 ) and unc-32 ( e189 ) , but these did not significantly reduce DF ( Figure 5B ) . Finally , we asked whether cathepsins promote DF . cad-1 ( j1 ) mutants have 10%–20% of wild-type cathepsin D activity [42] and asp-4 encodes an aspartyl protease: both genes are required for necrosis [15] , [18] . Again , both cad-1 ( j1 ) and asp-4 ( ok2693 ) reduced DF ( Figure 5B ) . ced-3 , ced-4 , and ced-9 are required for apoptosis in worms [43] . To test whether apoptotic cell death machinery contributes to DF , we examined DF in killed ced-3 ( n717 ) , ced-4 ( n1162 ) , and ced-9 ( n1950 ) mutants . ced-9 mutants showed no decrease in DF , ced-4 mutants only a slight decrease , and ced-3 actually showed an increase in DF ( Figure S12A ) . In other negative controls ( ftn-1 , mdl-1 , and rol-6 ) no effects on DF levels were seen either ( Figure S12B ) . Thus , mutations that inhibit ER Ca2+ release , and calpain and cathepsin activity both inhibit necrosis and lower DF . We conclude that attenuation of elements of necrosis reduces DF . This suggests not only that cellular necrosis generates DF , but also that cellular necrosis occurs during organismal death . This in turn suggests the possibility that necrotic cell death contributes to organismal death . The spread of DF through the intestine is reminiscent of Ca2+ wave transmission in the intestine during the defecation cycle [44] . Innexins ( invertebrate gap junction proteins ) are required for the defecation cycle as they create Ca2+ channels between adjacent intestinal cells , and the innexin INX-16 is required for Ca2+ transmission during defecation [44] . We asked whether Ca2+ signaling might play a role in DF wave propagation . Upon being killed ( by freeze-thaw ) , inx-16 ( ox144 ) mutants showed reduced DF levels , and ( by a hot pick ) a failure in DF wave propagation ( Figures 6A–B , S13A; Videos S3 , S4 ) . Note that DF dynamics appear largely independent of the mode of killing ( Figures 3 , S2 ) . Moreover , an intestinally expressed calcium reporter revealed increased Ca2+ levels during death ( by oxidative stress; t-BOOH ) ( Figure 6C ) . This increase occurred first in the anterior and then in the posterior intestine ( Figures 6C , S13B; Video S5 ) , consistent with a wave of Ca2+ influx during death . The death-induced Ca2+ wave was blocked by inx-16 ( ox144 ) , as observed for DF ( Figure 6D ) . Thus , Ca2+ signaling is required for , and precedes , the spread of DF . This suggests that during death an anterior to posterior wave of Ca2+ influx drives a wave of necrosis that leads to DF . Cytosolic acidosis and LMP also typically occur during necrotic cell death , and we therefore asked whether they accompany DF . To test for cytosolic acidosis , we used an intestinally expressed pH-sensitive reporter , pnhx-2::pHluorin [45] , [46] . Upon killing ( with t-BOOH ) , pH in the intestinal cytosol dropped from ∼pH 7 . 4 to 6 . 6 ( Figure 6E , Video S6 ) and , again , cytosolic acidosis occurred first in the anterior intestine before spreading to the posterior ( Figures 6E , S13C–D ) . Next we used uranin ( green ) and the lysosomotropic dye lysotracker ( red ) to examine gut granule membrane integrity during organismal death . Killing ( with t-BOOH ) resulted in a burst of uranin fluorescence , and a loss of punctate green and red fluorescence that coincided with DF ( Figure 7A ) . These changes were inhibited by inx-16 ( ox144 ) ( Figure S14 ) . The punctate red staining took slightly longer to decay than the green , likely reflecting residual staining of lysosomal membranes with lysotracker but not uranin . Thus , both cytosolic acidosis and LMP occur in intestinal cells at death . This provides further evidence that DF is generated by necrotic cell death . If systemic necrosis contributes to organismal death , then its inhibition should prevent death . To test this we examined the effect of inhibiting necrosis on death induced by aging or stress . In most cases , inhibiting necrosis did not prevent death due to aging: necrosis mutants were either normal lived or short lived ( Figure S15 ) . The exception was inx-16 , which was long lived; however , the slow growth and starved appearance of this strain suggests that its longevity may be caused by dietary restriction . By contrast , inhibition of each point in the necrosis pathway ( calcium release , calpains , lysosomal acidification , cathepsins , and innexins ) was able to delay death induced by lethal osmotic stress ( Figure 5C ) , although not all mutants showed resistance . Most necrosis mutants also showed resistance to lethal thermal stress ( Figure S16 ) . Moreover , it was previously reported that inhibition of necrosis can delay infection-induced death [47] . However , necrosis mutants showed little protection against death induced by oxidative stress ( t-BOOH ) ( unpublished data ) . These findings suggest that some stressors cause death in C . elegans by triggering systemic necrosis . One possibility is that the release of AA from gut granules stimulates intestinal necrosis . To test this we first examined the effect of removing AA by tdo-2 ( RNAi ) on resistance to heat stress , and found that resistance was increased ( Figure S17A–C ) . However , it was previously shown that tdo-2 ( RNAi ) enhances proteostasis and lifespan by increasing Trp levels [37] . To test whether tdo-2 ( RNAi ) protects against heat stress by reducing AA , we asked if replenishment in tdo-2 ( RNAi ) worms would suppress their stress resistance , but it did not ( Figure S17C ) . Moreover , AA supplementation of N2 worms did not reduce heat stress resistance . Also in a range of mutants with altered AA levels there was no correlation with resistance to either thermal or osmotic stress ( Figure S17D–E ) . Thus , heat stress resistance resulting from tdo-2 ( RNAi ) is not caused by reduced AA but may instead reflect increased Trp levels . These results imply that DF does not promote intestinal cell necrosis but , rather , is a bystander phenomenon ( or epiphenomenon ) . We also tested the effect of insulin/IGF-1 signaling on DF . Mutation of the daf-2 insulin/IGF-1 receptor increases lifespan , and this effect requires the daf-16 FoxO transcription factor [48] . In both freeze-thaw- and aging-induced death , the mutation daf-2 ( e1370 ) markedly reduced DF ( Figure S18 ) . This could imply that systemic necrosis is attenuated in daf-2 mutants . daf-16 ( mgDf50 ) modestly increased DF during death from old age only ( Figure S18 ) .
Evidence presented here implies that during death in C . elegans , the intestine , the largest somatic organ , undergoes a stereotyped process of self-destruction involving an intra- and intercellular cascade of cellular necrosis . The mechanisms involved are similar to those active in the propagation of cellular necrosis in mammals . In worms , necrotic propagation requires the innexin INX-16 , while in mammals connexin ( mammalian gap junction proteins ) inactivation reduces ischemia-induced neurodegeneration [10] . Thus , the C . elegans intestine is a potential new model for understanding the propagation of necrotic cell death , and its prevention . Previous studies of the cellular necrosis pathway have largely focused on neurodegeneration , in mammals and C . elegans . Our findings imply similar action of this pathway in the worm intestine . However , generation of DF appears to be restricted to the intestine , and is not detectable in necrotic mec-4 ( d ) neurons ( unpublished data ) . Our results imply that intestinal self-destruction by systemic necrosis occurs during both stress- and aging-induced death . However , only in stress-induced death did inhibition of systemic necrosis prevent death . This suggests that while lethal stress causes death by inducing systemic necrosis , aging causes death by a number of processes acting in parallel , likely including systemic necrosis ( given that it destroys a major organ ) . Here there are potential parallels in human aging: estimations of the likely upper limits of human longevity have calculated that removal of a major age-related disease ( e . g . , cardiovascular disease , cancer ) would cause only small increases in lifespan [49] . This is because multiple pathologies act in parallel to increase age-related mortality . A feature of intestinal necrosis is its origin in the anterior int1 cells . This suggests that the unusual vulnerability of these cells to necrotic death might represent a breaking point within organismal homeostasis; analogously , in humans localized failure ( e . g . , in the heart or kidneys ) can cause rapid organismal death . The existence of an anterior to posterior ( A-P ) Ca2+ wave is unexpected , given that the defecation-associated Ca2+ wave previously characterized in the intestine flows in the opposite direction , from posterior to anterior [44] . How the A-P Ca2+ wave is specified is unknown . One possibility is that extracellular Ca2+ levels are elevated near the anterior intestine , creating vulnerability to necrosis [50] . In this study we have defined a new phenomenon , death fluorescence , which may be useful in future as a marker of death in lifespan assays . While DF means that blue fluorescence cannot be used as a biomarker of aging we confirm that red fluorescence does increase with age . Moreover , tdo-2 ( RNAi ) can be used to abrogate blue intestinal fluorescence to aid the viewing of expression of intestinally expressed fluorescent reporters . The anthranilic acid glucosyl ester angl#1 and its corresponding 3′-phosphorylated derivative angl#2 account for both death and gut granule-associated fluorescence . glo-1 mutants lack both forms of fluorescence and AA derivatives , and inhibition of the kynurenine pathway blocks both forms of fluorescence , establishing that this blue fluorescence is not lipofuscin . Whether lipofuscin accumulation occurs during aging in C . elegans remains an open question . However , our finding that blue fluorescence is not lipofuscin removes one reason for believing that aging in C . elegans is caused by accumulation of stochastic molecular damage . The kynurenine pathway that generates gut granule and DF is also involved in mammalian neurodegeneration , and has recently been shown to regulate protein folding homeostasis in C . elegans [37] , [51] . During organismal death , AA fluorescence increases as a consequence of necrosis . That DF can occur in the absence of AA synthesis ( Figure S10C–D ) implies that the burst is not a consequence of synthesis of additional AA . One possibility is that AA fluorescence within gut granules is partially quenched , perhaps due to low pH and/or increased concentration . In this scenario , loss of gut granule membrane integrity causes rapid dequenching of AA fluorescence , leading to the burst . In a similar fashion , dequenching of uranin fluorescence upon gut granule permeabilization leads to a burst of green fluorescence ( Figure S11 ) . It remains to be investigated whether cellular necrosis in other organisms leads to increased AA fluorescence , but increases in blue fluorescence accompanying cell death have been reported—e . g . , in budding yeast [52] and hepatocytes [53] . Despite their prominence , C . elegans gut granules are organelles whose function has yet to be established . The finding that they contain large quantities of AA further adds to the mystery . What is all this anthranilic acid for ? Possibilities include protection against UV irradiation , or against pathogen invasion into the intestine . Notably , AA can be cytotoxic; for example , 3-hydroxyanthranilic acid can induce cell death in lymphocytes [54] and neurons [55] , and AA can inhibit growth of bacterial pathogens ( e . g . , Legionella pneumophila ) [56] . This might explain its presence in multiple species of soil nematodes . The presence of a mechanism , systemic necrosis , that brings about organismal death in C . elegans raises questions about its evolutionary origin . Could such an organismal self-destruct mechanism serve as an adaptation ? When food is limiting , gravid hermaphrodites typically die with multiple embryos in their uterus , which hatch internally and consume their mother's corpse ( “bagging” ) . Potentially , this improves the mother's fitness by increasing survival of her genetically identical offspring [57] . One possibility , then , is that systemic necrosis enhances fitness by aiding efficient transfer of nutrients from mother to offspring during bagging . Alternatively , systemic necrosis may be the nonadaptive product of antagonist pleiotropy , or a quasi-program [58] , [59] . By this view , elements of the necrosis cascade contribute to early life fitness , while systemic necrosis is an unselected , deleterious consequence of their action under lethal stress or as a result of aging .
Standard C . elegans strain maintenance and genetic manipulations were used [60] . All strains were grown at 20°C on NGM plates seeded with E . coli OP50 as food source unless otherwise specified . N2 ( Bristol ) was the wild-type . The necrosis mutants are as follows: ZB1028 crt-1 ( bz29 ) V , CB540 unc-68 ( e540 ) V , JT73 itr-1 ( sa73 ) IV , CB4027 tra-3 ( e1107 ) eIs2137 IV , CB189 unc-32 ( e189 ) III , RB938 vha-12 ( ok821 ) X , RB2035 asp-4 ( ok2693 ) X , and PJ1 cad-1 ( j1 ) II . The apoptosis mutants are as follows: MT1522 ced-3 ( n717 ) IV , MT4770 ced-9 ( n1950 ) III , and MT2547 ced-4 ( n1162 ) III . The strains used for calcium measurements are as follows: KWN190 pha-1 ( e2123ts ) III , him-5 ( e1490 ) V; rnyEx109 [pKT67 ( Pnhx-2::D3cpv ) ; pCL1 ( pha-1 ( + ) ] , KWN26 pha-1 ( e2123ts ) III; him-5 ( e1490 ) V; rnyEx006 [pIA5nhx-2 ( Pnhx-2::pHluorin ) ; pCL1 ( pha-1 ( + ) ] . The strains used for pH measurements are as follows: KWN385 inx-16 ( ox144 ) I; pha-1 ( e2123ts ) III; him-5 ( e1490 ) V rnyEx006 [pIA5nhx-2 ( Pnhx-2::pHluorin ) ; pCL1 ( pha-1 ( + ) ] . Other: BA671 spe-9 ( hc88 ) I , CB1002 flu-1 ( e1002 ) V , CB1003 flu-2 ( e1003 ) X , GH10 glo-1 ( zu437 ) X , EG144 inx-16 ( ox144 ) , GA91 ftn-1 ( ok3625 ) V; GA1200 mdl-1 ( tm311 ) X , GA200 wuEx41 [rol-6 ( su1006 ) ] , RB784 nkat-1 ( ok566 ) X , SJ4005 zcIs4 [hsp-4::GFP] . Worms were either imaged in situ on NGM plates or anaesthetized on agar pads on glass slides . Images were acquired using an Orca digital camera ( Hamamatsu ) and a Leica DMRXA2 microscope . Blue fluorescence was observed through a DAPI filter cube ( λex/λem 300–400 nm/410–510 nm ) ( ET DAPI , set 49000 , Chroma ) . Green fluorescent protein ( GFP ) fluorescence was observed through a GFP filter , ( λex/λem 450–490 nm/500–550 nm ) ( Endow GFP Bandpass , 41017 Chroma ) . Images were acquired using the application Volocity Acquisition ( Improvision , Perkin-Elmer ) . Fluorescence was quantified by manually tracing around worm peripheries using an Intuos graphics tablet ( Wacom ) , and measuring mean pixel density using Volocity Quantitation . Worm fluorescence was estimated as the mean pixel density of the worm image area minus the pixel density of the image background . Worms were killed in three ways , detailed below , all of which result in bursts of blue fluorescence of similar magnitude . Heat killing was used to observe DF dynamics in individual animals in situ on NGM plates . This approach allows lethal stress to be applied near the head or tail , and allows observation of spatial changes in fluorescence , but is relatively difficult to quantitate . Killing by oxidative stress was used for higher resolution microscopy for which it was necessary to view worms under cover slips . Freeze-thaw assays of worms in microtitre plates were used for accurate quantitation of DF to compare genotypes cohorts of worms , and for acquisition of whole spectrum excitation/emission scans . Synchronous populations of L4 animals were transferred to NGM plates seeded with E . coli OP50 and containing 50 µM FUdR ( 24°C ) . One-day-old adults were then rinsed off the plates and washed using S buffer . For each strain , three replicate aliquots of 100 µl worm suspensions were loaded in black microtiter plates ( Greiner ) . Aliquots resulted in ∼1 mg alkali-extracted protein , determined by standard BCA assay ( ThermoScientific ) . The 10-nm step fluorescence emission spectra of living worm suspensions were measured upon excitation at 250–450 nm ( 10 nm intervals ) using a microplate reader ( Spectramax Gemini XS , Molecular Devices ) . Worms were then killed by freeze-thaw and fluorescence measurements repeated . All data shown are averages of three technical replicates , corrected by a blank measurement , and normalized by protein content of the worm suspensions . A 55 mM stock of anthranilic acid ( Sigma ) was prepared by dissolving AA solid in PBS at 55°C and then diluted further in PBS . Worms were incubated in AA solution for 3 h at 20°C in a 96-well microtitre plate with constant shaking . L4 or 1-d-old adult worms were incubated for 2 h in 475 µL M9 plus 10 µL Lysotracker Red DND-99 ( Life Technologies , USA ) plus 15 µL 20 mg/mL uranin . Worms were then washed 5× in 1 mL M9 and left to feed on OP50-seeded NGM plates for 30–60 min . They were then placed on a 2 . 5% agarose pad prepared on a glass slide between two cover slip spacers ( to avoid squashing of adult worms between the pad and the cover slip ) , and mounted in 0 . 2% levamisole under a cover slip . Worms were then imaged under at 100× magnification on a DM RXA2 upright microscope ( Leica , Germany ) every 10 s for up to 1 h using Volocity software ( PerkinElmer Inc . , USA ) . We added 15 µL of Luperox TBH70X tert-butylhydroperoxide solution ( Sigma Aldrich , Germany ) using a pipette , assuring even dispersal of the liquid between the slide and the cover slip within the first minute of imaging . DF appeared within the first 15–30 min and the time-lapse acquisition was stopped once the blue fluorescence wave had propagated from head to tail . The flu-2 , kmo-1 , nkat-1 , and tdo-2 clones were acquired from the Ahringer library , and the inserts confirmed by DNA sequencing . E . coli HT115 were transformed with the clone and fed to animals as described [68] . Lifespans of synchronized population cohorts were measured as previously described [72] at 20°C with 15 µM FUdR topically applied . Lifelong fluorescence measurements were acquired from individual animals . Data were normalized to time of each animal's death and average fluorescence level acquired at hours prior to death . Data measuring groups of animals used mean data , with Student's t tests performed to check for statistical significance . All mean data were repeated at least in triplicate . Lifespan and thermotolerance data were analyzed by log-rank for significance; osmotic stress by one-way ANOVA of means . All fluorescent images represent averages seen . | In the nematode Caenorhabditis elegans , intestinal lysosome-related organelles ( or “gut granules” ) contain a bright blue fluorescent substance of unknown identity . This has similar spectral properties to lipofuscin , a product of oxidative damage known to accumulate with age in postmitotic mammalian cells . Blue fluorescence seems to increase in aging worm populations , and lipofuscin has been proposed to be the source . To analyze this further , we measure fluorescence levels after exposure to oxidative stress and during aging in individually tracked worms . Surprisingly , neither of these conditions increases fluorescence levels; instead blue fluorescence increases in a striking and rapid burst at death . Such death fluorescence ( DF ) also appears in young worms when killed , irrespective of age or cause of death . We chemically identify DF as anthranilic acid glucosyl esters derived from tryptophan , and not lipofuscin . In addition , we show that DF generation in the intestine is dependent upon the necrotic cell death cascade , previously characterized as a driver of neurodegeneration . We find that necrosis spreads in a rapid wave along the intestine by calcium influx via innexin ion channels , accompanied by cytosolic acidosis . Inhibition of necrosis pathway components can delay stress-induced death , supporting its role as a driver of organismal death . This necrotic cascade provides a model system to study neurodegeneration and organismal death . | [
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] | 2013 | Anthranilate Fluorescence Marks a Calcium-Propagated Necrotic Wave That Promotes Organismal Death in C. elegans |
Cytoplasmic actins are abundant , ubiquitous proteins in nucleated cells . However , actin expression is regulated in a tissue- and development-specific manner . We identified a novel cytoplasmic-γ-actin ( Actg1 ) transcript that includes a previously unidentified exon ( 3a ) . Inclusion of this exon introduces an in-frame termination codon . We hypothesized this alternatively-spliced transcript down-regulates γ-actin production by targeting these transcripts for nonsense-mediated decay ( NMD ) . To address this , we investigated conservation between mammals , tissue-specificity in mice , and developmental regulation using C2C12 cell culture . Exon 3a is 80% similar among mammals and varies in length from 41 nucleotides in humans to 45 in mice . Though the predicted amino acid sequences are not similar between all species , inclusion of exon 3a consistently results in the in the introduction of a premature termination codon within the alternative Actg1 transcript . Of twelve tissues examined , exon 3a is predominantly expressed in skeletal muscle , cardiac muscle , and diaphragm . Splicing to include exon 3a is concomitant with previously described down-regulation of Actg1 in differentiating C2C12 cells . Treatment of differentiated C2C12 cells with an inhibitor of NMD results in a 7-fold increase in exon 3a-containing transcripts . Therefore , splicing to generate exon 3a-containing transcripts may be one component of Actg1 regulation . We propose that this post-transcriptional regulation occurs via NMD , in a process previously described as “regulated unproductive splicing and translation” ( RUST ) .
All mammals express six isoforms of actin: α-cardiac ( Actc1 , NM_009608 ) , α-skeletal ( Acta1 , NM_009606 ) , α-aortic ( Acta2 , NM_007392 ) , γ-enteric ( Actg2 , NM_009610 ) , β-cytoplasmic ( Actb , NM_007393 ) , and γ-cytoplasmic ( Actg1 , NM_009609 ) . Each actin is encoded on a separate chromosome but the coding sequence of the actins are 71% identical and there is 92% amino acid sequence identity between actin proteins . This degree of conservation is indicative of intolerance of these proteins to changes in amino acid composition , presumably because of the large number of proteins that interact directly with actin . Although the coding sequences are similar between actin isoforms , the genomic architecture of actin isoforms differs between the cytoplasmic ( six exons ) , smooth muscle ( nine exons ) , and cardiac and skeletal isoforms ( seven exons ) . The genomic sequence of Actg1 was first described in 1986 by Erba and colleagues [1] and no splice variants of this gene have been reported . In most dividing cells the two cytoplasmic actins are expressed at high levels . For example , mature skeletal and cardiac muscle derive from myoblasts , which express high levels of β- and γ-actin in their undifferentiated form . However , during differentiation , and in mature skeletal and cardiac muscle , the cytoplasmic actins are down-regulated to comprise only a small fraction of the total actin content , and α-skeletal and α-cardiac actins , respectively , become the predominant isoforms [2]–[4] . Nevertheless Actg1-null mice demonstrate that γ-actin is crucial for the normal function of mature skeletal muscle , as its complete absence results in a progressive myopathy in adult mice [5] , [6] . C2C12 mouse myoblast cell culture is widely used to study the expression and regulation of genes during skeletal muscle development . In this system , myoblasts proliferate until induced to differentiate either via serum-starvation or substitution of horse serum into the growth medium . Differentiation of myoblasts involves exit from the cell cycle , fusion with neighboring cells , elongation into myotubes , and movement of the nuclei to the periphery of the myotubes . Subsequent maturation is characterized by bundling of α-actin thin filaments to form myofibrils . Down-regulation of Actb in differentiated C2C12 cells was previously attributed to a 40 nucleotide long conserved element in the 3′ UTR of the Actb transcript [7] . In contrast , Actg1 down-regulation was proposed to involve inhibition of splicing of intron 3 from the primary Actg1 transcript , thus preventing the production of a mature RNA [2] . A potentially relevant mechanism for post-transcriptional down-regulation is Regulated Unproductive Splicing and Translation [8] . RUST occurs by alternative splicing to include a regulatory exon which either contains , or creates via frameshift , a premature termination codon ( PTC ) . Introduction of a PTC results in subsequent degradation of the mRNA by nonsense-mediated decay ( NMD ) . Recent evidence suggests that as many as 4% of transcripts include alternatively spliced exons with PTCs , though there is some debate as to whether this splicing is functional or an artifact of highly active splicing factors in cells and therefore should be considered transcriptional noise [9] , [10] . To help resolve this , Zhang and colleagues proposed criteria for deciding whether a splicing event is functional or noise . Briefly , they have suggested that alternative splicing is likely to be functional if the exon is: 1 ) conserved among species , 2 ) developmentally regulated , and 3 ) tissue specific [11] . We identified an alternatively spliced exon in Actg1 transcripts from mouse skeletal muscle cDNA . This alternative transcript includes a novel 45 bp exon ( exon 3a ) located in the middle of Actg1 intron 3 that introduces a PTC . A similar alternative Actg1 transcript is found in skeletal muscle from multiple other mammals . In mouse , splicing to include this exon is regulated in a tissue and development specific manner . Here we demonstrate that inclusion of exon 3a results in post-transcriptional down-regulation of Actg1 by RUST .
While investigating γ-actin expression in a knock-in mouse model harboring a targeted mutation in exon 4 of Actg1 , we identified a novel , alternatively spliced Actg1 transcript in wild-type animals . PCR amplification of skeletal muscle cDNA designed to amplify mouse Actg1 exon 3 to 4 ( Figure 1A , Table 1 ) , but to not allow amplification of Actb and Acta1 , yielded an unexpected product . In addition to the predicted 102 bp product , an amplicon of 147 bp was observed in skeletal muscle , heart and diaphragm ( Figure 1B ) . Sequencing of the 147 bp product from skeletal muscle revealed an alternatively spliced transcript that includes a 45 bp exon located in intron 3 , which we designate exon 3a ( Figure 2A , B ) . It should be noted that in addition to the PCR products corresponding to the two alternatively spliced transcripts , larger amplicons that were not present in the no-RT controls were observed . Gel extraction and Sanger sequencing of these products revealed these were partially spliced products which included regions of intron 3 upstream and downstream of exon 3a ( Figure 2C ) . In mouse , exon 3a is flanked by canonical splice acceptor and donor sites , and inclusion predicts introduction of an in-frame termination codon ( Figure 2B , D ) . A BLAST search of the NCBI mouse EST database revealed transcripts that included exon 3a ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . To determine if this transcript was present in other tissues of the adult mouse , cDNA was prepared from diaphragm , skeletal muscle , heart , intestine , spleen , kidney , testis , eye , lung , and brain from adult mice . Only skeletal and cardiac muscle were positive for the presence of alternatively spliced transcripts in addition to the normal Actg1 transcript , which was the major product amplified ( Figure 1B ) . To quantify expression of Actg1 isoforms , we designed primers compatible with qPCR to specifically amplify transcripts resulting from either an exon 3 - exon 4 ( normal transcript ) or an exon 3a - exon 4 splice ( alternative transcript ) ( Figure 1A ) . Unlike competitive end-point PCR , the qPCR assay permits detection of low levels of the alternatively spliced Actg1 3a transcript . Using this method , we were able to obtain the relative abundance of normal Actg1 and alternative Actg1 transcript levels across tissues ( Figure 1C , D ) . We found that brain exhibited the highest level of normal Actg1 , whereas skeletal muscle and liver had the least . As expected from the results of the end-point PCR , skeletal muscle had the highest level of alternative Actg1 . The relative level of the alternative transcript was compared to that of the normal transcript using combined data from three qPCR experiments . The efficiencies of the PCR reactions for the normal and alternative transcript were within <1% of each other and the threshold was set at 0 . 2 for both reactions . This analysis revealed average ΔCt values of 20 . 8 for the normal and 22 . 7 for the alternative transcripts in skeletal muscle corresponding to a 3 . 5∶1 ratio of normal to alternatively spliced Actg1 ( Table 2 ) . Evolutionary conservation of nucleotide sequence is typically indicative of functional significance . While no conservation of intron 3 is detected in fish or chicken cytoplasmic actin , the nucleotide sequence of Actg1 intron 3 is highly conserved among mammals . Specifically , the region containing the 45 bp alternatively spliced exon and flanking splice sites are 80% identical between humans and mice ( Figure 2A , B ) . To determine if splicing of the Actg1 transcript is an evolutionarily conserved event in vivo , we prepared cDNA from human , dog , and cat skeletal muscle total RNA . Species- and isoform-specific primers were designed similar to the competitive end-point PCR assay for mouse described above and outlined in Figure 1A . Splicing to include exon 3a was observed in skeletal muscle cDNA from the species assayed ( Figure 2C ) . All PCR products were sequenced to confirm the imputed exon 3a sequence ( Figure 2B , human , cat , dog ) . Alignment of sequences obtained from the UCSC genome browser ( http://genome . ucsc . edu ) indicates that inclusion of exon 3a is predicted to introduce an in-frame PTC in non-primate mammals . In humans and rhesus , exon 3a is 41 nt in length and results in a frameshift of the ACTG1 coding sequence , thus creating a PTC in exon 4 . While the amino acid sequence of the predicted polypeptide generated by inclusion of exon 3a is highly conserved in non-primate mammals , the frameshift generated by the 4 nt deletion in primates results in a complete loss of this conservation ( Figure 2D ) . To investigate the potential function of the alternative Actg1 transcript in a relevant tissue , we utilized the well-characterized C2C12 mouse myoblast cell line as a proxy for skeletal muscle development . C2C12 cells are frequently used to study transcriptional and proteome changes during the differentiation of myoblasts into myotubes ( shown in Figure 3A ) [12] , [13] . Using these cells , we first asked if Actg1 alternative splicing is developmentally regulated . Total RNA was isolated from myoblasts prior to addition of differentiation medium and at 2 day intervals after addition of differentiation media . qPCR was used to determine expression levels of normal and alternatively spliced Actg1 . In agreement with previous studies by Lloyd and Gunning [2] , our results demonstrate that normal Actg1 is down-regulated during myoblast differentiation ( Figure 3B ) . These data also reveal that concurrent with the decrease in normal Actg1 expression , alternatively spliced Actg1 transcripts increase during differentiation ( Figure 3B ) . Given the lack of conservation in the amino acid sequence generated by inclusion of exon 3a ( Figure 2D ) , we hypothesized that a protein product is not produced from the alternative Actg1 transcript . To address this , we first sought to determine if the alternative transcript is exported to the cytoplasm and therefore available for translation . Total RNA was isolated from both cytoplasmic and nuclear fractions of mature myotube cultures . Using competitive end-point PCR , we found that the nuclear fraction contained all splice products including putative splicing intermediates , whereas only the normal and alternatively spliced Actg1 transcripts were present in the cytoplasmic fraction ( Figure 4A , B ) . Knowing that the alternative transcript is exported to the cytoplasm , we used western blotting to detect a protein product corresponding to either the use of the premature stop codon or a read-through of the stop codon . Because the levels of alternative transcript in the skeletal muscle samples exceed that of the myotubes in culture , we used skeletal muscle lysate for this experiment . Using an anti-γ-actin specific antibody directed against the N-terminus of the polypeptide , we probed for the presence of a protein product from alternatively spliced Actg1 transcripts . Based on the qPCR data , there should be a 3∶1 ratio of normal ACTG1 to alternative ACTG1 ( Table 2 ) . We loaded 1∶2 serial dilutions to determine our lower-limit of detection by western blot , beginning with 10 µg of total protein . No band corresponding either to usage of the termination codon ( 15 kDa ) or a read-through of the termination codon in exon 3a ( 45 kDa ) was detected ( Figure 4C ) . We did observe a larger , 52 kDa protein in these samples which is consistent in size with previously described modifications of cytoplasmic and skeletal muscle actins , specifically mono-sumoylation [14] or mono-ubiquitination [15] . We reasoned that exon 3a is alternatively spliced to post-transcriptionally down-regulate expression of Actg1 . To address the hypothesis that exon 3a represses translation of Actg1 by targeting the transcript for NMD , we treated cells with cycloheximide to block translation . Cycloheximide targets the small ribosomal subunit and can be used to inhibit translation-dependent NMD of PTC-containing transcripts [16] . Cultures of proliferating myoblasts and mature myotubes were treated with either 40 µg/mL cycloheximide in ethanol or an ethanol-only control for three hours in otherwise standard growth conditions . A three-hour treatment with cycloheximide resulted in an approximately 7-fold increase of exon 3a transcripts as measured by qPCR ( Figure 5 ) . These data strongly suggest that exon 3a targets the transcript for translation-dependent NMD . Furthermore , they indicate that splicing to include exon 3a is a frequent event in mature myotubes , given the rapid increase in the relative abundance of the alternatively spliced product .
In this study , we identified a novel Actg1 splice variant enriched in cardiac and skeletal muscle . We propose that production of this alternative transcript is regulated and functional . Despite the fact that cytoplasmic actins are well-studied and widely used as reference genes , this is the first report of alternative splicing for any actin transcript . Why might exon 3a containing transcripts have been overlooked ? The most divergent sequence for distinguishing between actin transcripts reside in the 5′ and 3′ UTRs . PCR primers designed to specifically amplify one isoform are typically in the UTRs and inclusion of a small exon , such as the 41–45 nt exon 3a , is likely to be overlooked in a large , >1 kb PCR product . Furthermore , short PCR products in which a 41–45 nt difference could be resolved are unlikely to involve exons 3–5 of the actin transcript because the high degree of nucleotide similarity between actin isoforms in these exons . Given that the alternative exon appears to be specific to γ-actin , a product corresponding to the alternative transcript would be lost among the abundance of the other actin cDNAs amplified . Previous studies suggested that the phenomenon of retention of intron 3 in the primary Actg1 RNA would be responsible for the down-regulation of γ-actin during differentiation of myoblasts [2] , [17] . Here we add support for this hypothesis and provide evidence for regulated splicing to incorporate an additional exon situated in the middle of intron 3 which is sufficient for down-regulation of Actg1 via a mechanism not previously shown for an actin transcript . Cycloheximide inhibition of translation results in an elevation in the level of exon 3a-containing transcripts , a finding that is consistent with translation-dependent NMD . Cycloheximide is commonly used as an inhibitor of NMD [16] , [18] , [19] , however there exists the possibility that the increase in exon 3a-containing transcripts is the result of another effect of the cycloheximide treatment rather than a direct correlation with inhibition of the NMD pathway . Therefore , assuming no additional effects of treatment with cycloheximide , we hypothesize that γ-actin is down-regulated via alternative splicing to introduce a PTC , leading to the degradation of Actg1 transcripts via NMD [8] , [20] . We considered the possibility that the presence of the alternatively spliced Actg1 transcript is the result of ‘noisy splicing’ . Large-scale analyses indicate that the majority of alternatively spliced transcripts are likely generated in error because of their low abundance across multiple tissues and lack of correlation with expression differences in the genes examined [9] , [21] , [22] . However , several examples of RUST as a mechanism of post-translational down-regulation exist [23]–[26] . Using the guidelines established by Zhang and colleagues to distinguish between noisy and functional splicing , we evaluated whether inclusion of exon 3a in Actg1 transcripts is likely to be spurious [11] , [22] . A primary feature of ‘noisy splicing’ is lack of conservation of the alternative splice form in other species . However , we find that , similar to the normal coding exons of Actg1 , exon 3a is highly conserved in mammals . Furthermore , in agreement with criteria for functional splice variants , we demonstrated that the alternative splice form is abundant only in skeletal and cardiac muscle , and that this splicing event is differentially regulated in developing myotubes . Splicing to include exon 3a in Actg1 transcripts maintains the proper reading frame in most species , however in primates exon 3a is only 41 nucleotides , thereby creating a frameshift . This subtle difference in the sequence of exon 3a between primates and lower mammals further supports the regulatory hypothesis in that it is the generation of a PTC and not a translated product that is evolutionarily conserved . Previous studies of RUST indicate that this type of regulation is not only conserved across species , but is typically found as a regulatory mechanism for members of the same gene family . We believe RUST may post-transcriptionally regulate both cytoplasmic actins , γ and β . Both cytoplasmic actins have a similar genomic structure and similar to γ -actin , β-actin has high degree of conservation in a portion of intron 3 [27] . In addition , it was recently demonstrated that an expression construct utilizing the β-actin promoter in combination with the 3′ UTR resulted in high level expression in skeletal muscle [28] , [29] . It is possible the conserved region of intron 3 of β-actin may confer an additional level of regulation in addition to the previously reported conserved 40 nt element in the 3′UTR [7] . The genomic structure of the non-cytoplasmic actins is markedly different from the cytoplasmic actins , and lack the high degree conservation observed in Actg1 and Actb intron 3 . Therefore , RUST is an unlikely mechanism of regulation for the non-cytoplasmic actin isoforms . The premise of RUST seems counter-intuitive as a regulatory mechanism , since the most efficient means of down-regulation would be at the transcriptional level . However , Soergel et al note that the production of large transcripts in any instance can be an inherently wasteful endeavor , as introns can constitute up to 95% of a primary RNA transcript [30] , [31] . It is possible that RUST can serve as a mechanism to quickly modulate expression of genes that are typically highly expressed , form very stable transcripts , or are essential in the cell . When a particular environmental or physiologic change dictates that only moderate levels of protein are required , production can be down-regulated without entirely switching-off transcription . In such an instance , post-transcriptional degradation of a portion of excess transcripts produced via NMD may be more energy efficient for the cell than either switching on or off transcription or post-translational degradation of unnecessary proteins . This model is suitable for γ-actin in muscle , as it is expressed at high levels in proliferating myoblasts , but is also necessary at lower levels in differentiated myotubes and developed skeletal muscle . RUST due to inclusion of exon 3a is unlikely to be the only mechanism of regulation for cytoplasmic actin transcripts . Though oftentimes used as a constitutive promoter in in vitro systems , the Actb and Actg1 promoters may also supply further control to the down-regulation of actin at a transcriptional level . Furthermore , retention of the entire intron 3 of the immature Actg1 message may contribute to delayed processing of mature transcripts [2] . Indeed , the increased abundance of incompletely spliced heterogeneous RNA observed when exon 3a-containing transcripts were present ( figures 1B , 2C , 4a ) may suggest a role for intron retention in down-regulation . While the alternative transcript is enriched in skeletal and cardiac muscle , we were also able to detect very low levels in brain , eye , and intestine; tissues which also expressed normal Actg1 at high levels . Regulation of β-actin synthesis was previously shown to be influenced by the levels of actin monomers in cells treated with an actin depolymerizing agent , latrunculin A [32] . We speculate that γ-actin expression may also involve an auto-regulatory mechanism by which an excess of free actin monomers in the cell induces splicing to include exon 3a of the Actg1 transcript , thus temporarily halting production in cell types which normally require high levels of actin . In such a situation , very low levels of exon 3a-containing transcipts would be expected in tissues with a high cytoplasmic actin content . In skeletal and cardiac muscle , exon 3a splicing may be regulated by splicing enhancer or splicing repressor elements . The intronic region immediately adjacent to the 5′ donor site of exon 3a is well conserved among mammals ( Figure 6 ) . During differentiation of myoblasts , multiple transcription and splicing factors are required to coordinate changes in gene expression crucial for differentiation . As such , the intronic conservation immediately 3′ of exon 3a may contain recognition motifs for splicing enhancers or repressors . Indeed , an exon splicing enhancer prediction software , ESE Finder v3 . 0 , identified several splicing enhancer recognition motifs ( Figure 6 ) . Of particular interest are the recognition motifs for SF2/ASF and SC35 , which were shown previously to effect alternative splicing of β-tropomyosin in a tissue-specific manner [33] . In closing , this report documents the first identification and characterization of an alternatively spliced actin transcript . These data provide evidence for the dynamic regulation of Actg1 and further functional evidence for RUST .
Sequence ascertainment and analysis was performed using UCSC Genome Browser/BLAT ( http://www . genome . ucsc . edu/ ) , Ensembl ! ( http://www . ensembl . org ) , Sequencher 6 . 0 ( Gene Codes Corp . ) , and Clustal X2 ( http://www . clustal . org ) . Primers were designed using Primer3 software ( http://frodo . wi . mit . edu/ ) . Potential exon splicing enhancers were identified using ESEFinder v3 . 0 ( http://rulai . cshl . edu/cgi-bin/tools/ESE3/esefinder . cgi ) All animals were maintained according to Michigan State University IACUC and NIH guidelines . Tissue samples were harvested from three 1 year old C57Bl/6J mice , snap frozen on dry ice , and stored at −80°C . Prior to use for RNA or protein isolation , samples were chopped into 100–200 mg pieces . Cat and dog skeletal muscle samples were provided courtesy of Dr . John Fyfe ( Michigan State University ) . Human skeletal muscle total RNA was purchased from Ambion ( catalog AM7982; Austin , TX ) . All cell culture media and supplements were purchased from Invitrogen ( Carlsbad , CA ) , unless noted otherwise . C2C12 myoblast cells were purchased from ATCC ( Manassas , VA; CRL-1772 ) and propagated in DMEM containing 10% heat-inactivated fetal bovine serum and 2 mM L-glutamine . Differentiation of myoblasts into myotubes was achieved by culturing cells >70% confluent in DMEM supplemented with 10% horse serum in place of fetal bovine serum . Forty-eight hours post differentiation , DMEM with 2% horse serum and 10 µM Ara-C ( Sigma , St . Louis , MO ) was used to maintain differentiated myotubes and inhibit the proliferation of myoblasts . Cells were maintained at 37°C in 5% CO2 . Total RNA isolation from tissue and cell culture samples was achieved using TRIzol ( Invitrogen , Carlsbad , CA ) purification followed by a DNaseI treatment using RNeasy mini-columns ( Qiagen , Hilden , Germany ) . Total RNA was quantified using a NanoDrop ( Thermo , Wilmington , DE ) . cDNA was synthesized using SuperScriptIII reverse transcriptase ( Invitrogen , Carlsbad , CA ) according to manufacturer's instructions . Reaction volume was 20 µl; for qPCR , 300 ng of RNA was used per reaction , and for endpoint PCR , 1 µg of RNA was used . Following incubation at 55°C for 1 hour , samples were heat inactivated at 75°C for 20 minutes then stored at −20°C until used . Myotube cultures were lysed rapidly with 1% Triton X-100 in PBS , cell debris and nuclei were gently scraped from the culture dishes and evaluated by brightfield microscopy for the presence of intact nuclei . Samples were centrifuged at 1 , 000×g for 5 minutes to pellet nuclei . The cytosol-containing supernatant was examined under the microscope to assure the absence of nuclei in the cytosolic fraction prior to the addition of TRIzol LS to the supernatant and TRIzol to the pellet ( Invitrogen , Carlsbad , CA ) . RNA purification of both the cytosolic and nuclear fractions was done according to manufacturer's instructions . Actg1 cDNA was amplified using Promega GoTaq polymerase per manufacturer's instructions ( Madison , WI ) with 5 µM each of species- and isoform-specific primers located in exons 3 and 4 of Actg1 ( Table 1 ) . For most reactions , 28 cycles were sufficient to amplify within the perceived linear range . Products were evaluated on 3% agarose gels containing 0 . 3 µg/mL ethidium bromide and visualized using BioRad GelDoc System ( Hercules , CA ) . Pixel intensity of the PCR products was quantitated using GelDoc software ( BioRad , Hercules , CA ) for semi-quantitative analysis . Quantitative PCR ( qPCR ) was performed on an ABI7000 , using Power SYBR Green ( Invitrogen , Carlsbad , CA ) as the reporter dye , and data were collected using StepOne Plus software ( Applied BioSystems , Carlsbad , CA ) . Data were analyzed using qbasePLUS software ( Biogazelle , Zwijnaarde , Belgium ) . Actg1 transcripts were normalized to Ppia for experiments using C2C12 cells and to Rplp0 and Rrn18s for experiments using mouse tissues . See Table 1 for primer sequences . Averaged data represent two technical replicates for each of three biological replicates . A two-tailed type 2 Student's T-test was used to compare expression differences between samples . Sequence data for exon 3a were obtained by Sanger sequencing on an ABI Prism 3700 DNA Analyzer at the Research Technology Support Facility ( Michigan State University ) using competitive , end-point PCR ( Table 1 ) . Cycloheximide ( Sigma , St . Louis , MO ) was dissolved in 100% ethanol at a stock concentration of 40 mg/mL and added to growth medium at a final concentration of 40 µg/mL . Cells were incubated in cycloheximide containing medium for 3 hours and immediately harvested in TRIzol for RNA isolation and cDNA synthesis as described above . Each experiment was repeated three times . Approximately 100 mg of skeletal muscle from a 1 year old mouse was lysed using a Polytron rotor homogenizer in lysis buffer containing 100 mM KCl , 10 mM PIPES , 5 mM EGTA , 1% Triton X-100 and Complete Protease Inhibitors ( Roche , Basel , Switzerland ) , and incubated on ice for 1 hour to allow further lysis . Protein content in the total lysate was determined using a Bradford assay ( BioRad , Hercules , CA ) . Proteins were separated via SDS-PAGE on discontinuous 12% bis-acrylamide gels [10] . Proteins were transferred in 10 mM Tris pH 7 . 4 , 100 mM glycine , 15% methanol ( transfer buffer ) at 4°C overnight at a constant current of 5 mAmp onto polyvinylidene difluoride ( PVDF ) membranes ( BioRad , Hercules , CA ) . Membranes were incubated in PBS ( pH 7 . 4 ) containing 5% non-fat milk and 0 . 025% Tween-20 ( blocking buffer ) for one hour at room temperature . A previously validated rabbit polyclonal anti-γ-actin specific antiserum raised against the first 15 amino acids of the polypeptide [5] was diluted 1∶10 , 000 in blocking buffer . Membranes were incubated with primary antiserum for 2 hours at room temperature . Goat polyclonal anti-rabbit IgG-HRP conjugated secondary antibody ( Sigma , St . Louis , MO ) was used at 1∶3 , 000 in blocking buffer for one hour at room temperature . Proteins were detected using an ECL Detection Kit ( GE Healthcare , Waukesha , WI ) with Amersham Hyperfilm MP autoradiography film ( GE healthcare , Waukesha , WI ) . | Actin is a well-studied protein that plays an essential role in nearly all cell types . Cytoplasmic actins are considered to be ubiquitously expressed in most tissues of the body with the exception of developing skeletal muscle , where muscle specific actins are up-regulated and γ-actin is repressed . Interest in the regulation of this transcript led to the hypothesis that intron retention is responsible for down-regulation of cytoplasmic γ-actin in skeletal muscle during development . Since the publication of the sequence of γ-actin cDNA over two and a half decades ago , no additional splice variants or cDNAs of this gene have been described . In this paper , we identify an alternatively spliced transcript in muscle that allowed us to elucidate how the γ-actin is downregulated during the important transition from myoblast to differentiated muscle cells . This is the first description of regulation of an actin transcript by regulated unproductive splicing and translation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | A Novel Actin mRNA Splice Variant Regulates ACTG1 Expression |
CD4+ T cells support host defence against herpesviruses and other viral pathogens . We identified that CD4+ T cells from systemic and mucosal tissues of hosts infected with the β-herpesviridae human cytomegalovirus ( HCMV ) or murine cytomegalovirus ( MCMV ) express the regulatory cytokine interleukin ( IL ) -10 . IL-10+CD4+ T cells co-expressed TH1-associated transcription factors and chemokine receptors . Mice lacking T cell-derived IL-10 elicited enhanced antiviral T cell responses and restricted MCMV persistence in salivary glands and secretion in saliva . Thus , IL-10+CD4+ T cells suppress antiviral immune responses against CMV . Expansion of this T-cell population in the periphery was promoted by IL-27 whereas mucosal IL-10+ T cell responses were ICOS-dependent . Infected Il27rα-deficient mice with reduced peripheral IL-10+CD4+ T cell accumulation displayed robust T cell responses and restricted MCMV persistence and shedding . Temporal inhibition experiments revealed that IL-27R signaling during initial infection was required for the suppression of T cell immunity and control of virus shedding during MCMV persistence . IL-27 production was promoted by type-I IFN , suggesting that β-herpesviridae exploit the immune-regulatory properties of this antiviral pathway to establish chronicity . Further , our data reveal that cytokine signaling events during initial infection profoundly influence virus chronicity .
Human cytomegalovirus ( HCMV ) is a ubiquitous β-herpesvirus that establishes lifelong infection . Infectious virus is usually acquired by horizontal transmission via mucosal secretions and urine . HCMV infection is typically asymptomatic in healthy individuals . However in the immunocompromised such as HIV-infected individuals and patients receiving immune-suppressive drugs , the virus can reactivate with debilitating consequences [1 , 2] . Further , HCMV is the leading congenital infection in the World , infecting up to 2 . 5% of live births and causing life-long neurological defects [3] . HCMV employs a range of immune evasion strategies to facilitate persistence . These include down-regulation of MHC and co-stimulatory ligands and modulation of host cytokine production [4 , 5] . Notably , HCMV encodes its own ortholog of the immune-regulatory cytokine interleukin ( IL ) -10 [6] . Two isoforms of viral IL-10 exist , both of which suppress immune cell activation in vitro [7 , 8] and induce expression of cellular IL-10 [9 , 10] , suggesting the importance of the immune suppressive functions of IL-10 in HCMV infection in vivo . The murine CMV ( MCMV ) model is well established as a model for HCMV infection in vivo due to similar cellular and tissue tropism , and comparable anti-viral immune responses [11] . While MCMV does not encode a vIL-10 , cellular IL-10 is induced upon infection of macrophages in vitro [12] . Data obtained from the MCMV model has demonstrated an important protective role for cellular IL-10 during acute CMV infection . Myeloid cells and B cells are the predominant sources of IL-10 during initial MCMV infection [13 , 14] . IL-10 limits virus induced weight loss , pro-inflammatory cytokine production and activation-induced NK cell death [13–15] . Sustained acute MCMV replication in situations of high virus load also induces IL-10 production by NK cells that restricts CD8+ T cell-mediated immune pathology [16] . In contrast , production of IL-10 during chronic MCMV infection suppresses viral clearance . Indeed , IL-10-deficient mice exhibit dramatic expansions of virus-specific T cell responses , reduced virus persistence in the salivary glands [14] and fewer viral genome copies in peripheral tissues during chronic/latent infection [17] . Furthermore , persistent MCMV replication in the salivary glands is dramatically restricted by the blockade of IL-10R signaling [18] . These data suggest that although blocking the action of IL-10 during acute CMV infection may be harmful to the host , targeting IL-10-mediated regulation of antiviral T cell responses may impinge on virus chronicity and restrict horizontal virus transmission via mucosal surfaces . Previous studies have shown that TH1 cells can produce IL-10 under certain conditions [19–24] . IL-10 producing TH1 cells have been shown to be protective in parasitic infections by limiting infection-related pathology [24–26] . However , in the context of lymphocytic choriomeningitis ( LCMV ) infection , virus replication is accompanied by the production of IL-10+ T cells [27 , 28] , and genetic deletion of IL-10 within T cells ( or LysM+ cells ) reduces virus chronicity [29] . HCMV-encoded latency associated antigens induce CD4+/IL-10+ responses in healthy donors [30] . MCMV-specific IL-10 production by CD4+ T cells has also been described [17 , 31 , 32] , and CD4+ cell-derived IL-10 suppresses control of virus replication and leukocyte accumulation during acute MCMV infection [33] . A substantial proportion of CD4+ T cells express IL-10 upon polyclonal stimulation in salivary glands during MCMV persistence [18] . However it is currently unknown how the expansion of these cells is controlled during infection , and whether IL-10 production by T cells impacts on MCMV chronicity . Type-I IFNs are prototypic antiviral cytokines that exert important control of viral replication . However inappropriate type-I IFN responses promote pathogenesis associated with acute viral infections , and prolonged expression of type-I IFNs in chronic viral infections including human immunodeficiency virus and hepatitis B virus is implicated in driving immune pathology and antagonizing antiviral T cell responses ( reviewed in [34] ) . Further , in the LCMV model of infection , blockade of type-I IFN receptor signaling in certain situations may enhance CD4+ T cell immunity and control of virus chronicity , an outcome associated with reduced development of immune-suppressive DCs [35 , 36] . Although type-I IFN is critical for early control of CMV replication [37] , the role that type-I has in shaping adaptive immunity during CMV infection in incompletely understood . The IL-12 family member IL-27 exhibits a broad spectrum of functions , including the regulation of infection-induced pathologies ( reviewered in [38] ) . Indeed , experiments in murine influenza infection demonstrated that IL-27 restricts virus-induced pathology associated with exuberant neutrophil , TH1 and TH17 responses [39] . IL-27 induces IL-10 expression by CD8+ T cells [40–42] , and IL-27-dependent control of influenza-induced inflammation is partially dependent upon IL-10 [39] . However , IL-27 impairs control of acute mouse hepatitis replication and associated pathology following infection of the CNS and this phenotype is associated with reduced accumulation of IL-10+ CD4+ T cells [43] . Paradoxically , IL-27 exerts cell intrinsic positive regulation of antiviral CD4+ T cell responses during chronic LCMV infection , promoting control of virus replication [44] . Moreover , IL-27 inhibits replication of both HIV and HCV via the induction of interferon-stimulated genes [45 , 46] , suggesting that IL-27 is beneficial to the host during chronic viral infections . Herein we investigated the role that IL-10+CD4+ T cells play in regulation of MCMV specific T cell immunity . We identified that T cell-derived IL-10 suppressed T cell response during persistent infection and subsequently promoted chronic virus replication . We revealed that an axis involving type-I IFN and IL-27 promotes the generation of peripheral IL-10-producing T cells that suppress antiviral immunity , suggesting that CMV exploits this immune-regulatory pathway induced early in infection to ensure viral persistence within mucosal tissue .
We previously demonstrated that CD4+ but not CD8+ T cells produce IL-10 during MCMV infection of the salivary glands , and that IL-10 promotes MCMV persistence [18] . Therefore we sought to assess virus-specific CD4+ T cell production of IL-10 over time . Using a panel of MHC class II-restricted peptides identified to induce CD4 T cell responses during acute and persistent MCMV infection [32] , we first defined the antigen hierarchy of CD4+/IL-10+ production in both the mucosa and periphery during MCMV infection using CD4-Cre-IL-10flox/flox ( Cre- ) mice , and mice lacking T cell derived IL-10 ( CD4-Cre+IL-10flox/flox , Cre+ ) as controls for IL-10 staining . In accordance with data derived from polyclonally-stimulated CD4+ T cells [18] , high frequencies of virus-specific IL-10+CD4+ T cells were observed in salivary glands after MCMV infection ( Fig 1A and 1B ) , peaking at d14 post-infection ( pi ) . Frequencies of salivary gland IL-10+CD4+ T cells were substantially higher than CD4+ T cells producing IFNγ ( Fig 1C ) suggesting that , at this time-point , IL-10+ T cells were the dominant MCMV-specific T cell response . We also detected virus-specific CD4+/IL-10+ T cells in the periphery ( Fig 1A and 1D ) , highlighting that IL-10 production was not restricted to mucosal CD4+ T cells [14 , 32] . The peripheral IL-10+CD4+ T cell response reactive to M25 , m142 and , to a lesser extent , m139 , peaked during acute infection d7 pi ( Fig 1D ) although , unlike salivary gland responses , were not present at higher frequencies than IFNγ+ cells ( Fig 1E ) . Splenic IL-10+ CD4+ T cell frequencies also contracted rapidly , remaining at low but significant levels during the remaining course of infection . MCMV-induced IL-10+CD4+ T cells do not express TH2-associated cytokines or the regulatory T cell-associated transcription factor FoxP3 [18] . IL-10 is expressed by numerous T helper subsets , including TH1 cells [24 , 25] . Interestingly , although a small CD4+IFNγ+IL-10+ response was detected in the spleen after stimulation with peptide for 6 hours , few co-producing cells were detected in the salivary glands upon stimulation with cognate peptide ( Fig 1A and 1F ) , whereas MCMV-induced IL-10+CD4+ T cells in the spleen and salivary glands can co-express IFNγ when poly-clonally stimulated with PMA and ionomycin . To further investigate the phenotype of IL-10+CD4+ T cells , we performed transcription factor profiling of these cells using 10-BiT reporter mice that express the surface marker CD90 . 1 ( Thy1 . 1 ) under control of the IL-10 promoter without impacting on endogenous IL-10 production [47] . We studied CD4+IL-10+ cells in the spleen and salivary glands at peak responsiveness ( d7 and d14 , respectively ) . In accordance with the ability to express IFNγ in response to polyclonal stimulation , significant expression of the TH1-associated transcription factor T-Bet was observed in salivary gland and , to a lesser extent , splenic Thy1 . 1+ CD4+ T cells ( Fig 2A–2C ) . In contrast , Thy1 . 1+ CD4+ T cells did not express the TH17-associated transcription factor , aryl hydrocarbon receptor , nor did they co-express Tr1 cell markers CD49b and LAG-3 ( S1 Fig ) . Instead , splenic Thy1 . 1+ CD4+ T cells co-expressed the TH1-associated chemokine receptors CXCR3 and CCR5 ( Fig 2D ) . Although Thy1 . 1+ and Thy1 . 1- salivary gland CD4+ T cells expressed low levels of CXCR3 and CCR5 14 days pi ( Fig 2D ) , high expression of their cognate chemokines during salivary gland MCMV infection [48] is consistent with the hypothesis that these receptors are internalized upon binding to their chemokine ligands within infected tissue . To further explore the transcription control of IL-10 production by CD4+IL-10+ cells , we examined the expression of Blimp-1 and c-Maf [49] . Significant expression of both transcription factors was detected in Thy . 1 . 1+ cells from the spleen and salivary glands of infected mice ( Fig 2A–2C ) . Furthermore , with the exception of Blimp-1 expression by salivary gland CD4+ T cells , increased expression of T-Bet , Blimp-1 and c-Maf by Thy1 . 1+ was measured as compared to Thy1 . 1- cells ( Fig 2A–2C ) . Overall , the combination of transcription factor and chemokine receptor expression of IL-10+CD4+ T cells induced in response to MCMV indicated that these cells originated from a TH1 lineage . We next asked whether HCMV-specific T cells reactive to common HCMV antigens would also produce IL-10 . Healthy colon and peripheral blood was obtained from colorectal cancer ( CRC ) patients undergoing colonic resection and we assessed glycoprotein B ( gB , UL55 ) and tegument ( pp65 , UL83 ) specific IL-10/IFNγ production using fluorospot . As compared to the low baseline IL-10/IFNγ production by medium-stimulated T cells ( Fig 3A ) , notable albeit variable frequencies of pp65 and gB specific cytokine production were measured in blood ( Fig 3B ) . Interestingly , IL-10 production dominated pp65- and gB-specific responses in 3 of 4 and 2 of 4 patients , respectively ( Fig 3B ) . Importantly , we also detected HCMV-specific cytokine production in leukocytes isolated from the colon ( Fig 3A and 3B ) , demonstrating the presence of HCMV-specific IL-10-producing T cells in human mucosal tissues . To confirm HCMV-specific IL-10 production was derived from CD4+ T cells , larger volumes ( 50mls ) of blood were drawn from healthy donors , enabling the purification of CD4+ T cells prior to peptide stimulation . Again , both CD4+/IFNγ+ and CD4+/IL-10+ cells were observed following stimulation with pp65 and , to a far lesser extent , gB peptide pools ( Fig 3A and 3B ) . As observed in MCMV infection ( Fig 1A and 1F ) , very few virus-specific cells co-produced IL-10 and IFNγ ( Fig 3A and 3B ) . Thus , overall , these data demonstrate the presence of HCMV-specific IL-10+ T cells in mucosal tissue and peripheral blood . We sought to investigate the impact of IL-10 production by CD4+ T cells on CMV-specific immunity in vivo . We utilized CD4-Cre+IL-10flox/flox ( Cre+ ) mice in which CD4+ cells do not produce IL-10 . We detected no compensatory IL-10 production by other leukocytes in response to MCMV in these mice ( S2 Fig ) . As compared to CD4-Cre-IL-10flox/flox ( Cre- ) control mice , Cre+ mice mounted elevated CD4+/IFNγ+ responses in the salivary glands to multiple MCMV antigens throughout the course of infection ( Fig 4A ) , most notably M25- and m142-specific cells at d14 pi when peak IL-10+ responses were observed in Cre- mice ( Fig 1B ) . The absence of CD4+ cell-derived IL-10 also consistently increased the accumulation of peripheral virus-specific IFNγ+ CD4+ T cells during the persistent phase of infection from d14 pi ( Fig 4B ) , supporting the hypothesis that initial production of IL-10 by T cells in the periphery impinged on later TH1 cell accumulation . It has been shown that IL-10 deficiency leads to an increased splenic DC accumulation and expression of co-stimulatory ligands during acute infection [14] . Cre+ mice exhibited no notable increased accumulation of splenic DCs or salivary gland myeloid cells either d7 or d14 pi ( S3 Fig ) . However , we observed an increased frequency of myeloid cells expressing co-stimulatory ligands previously demonstrated to enhance MCMV-specific T cell responses during virus persistence [50–52] , in the spleens ( d7 and d14 pi ) and salivary glands ( d7 and d14 pi ) of Cre+ mice ( S3 Fig ) . Although we report some inter-experiment variability regarding the impact of T cell-derived IL-10 on myeloid cell CD86 expression , collectively these data are consistent with a role for T cell-derived IL-10 in modulating myeloid cell function . Furthermore , in accordance with elevated CD4+ T cell responses in Cre+ mice ( Fig 4A and 4B ) , we observed an increased accumulation of MCMV-specific CD8+ T-cells in peripheral tissues ( Fig 4C ) . As observed in studies of IL-10-/- mice [17] , T cell-derived IL-10 preferentially suppressed CD8+ T cell responses reactive to IE3 ( Fig 4C ) , consistent with observation that IE3-specific CD8+ T cell inflation is particularly dependent upon CD4+ T cell help [53] . Unlike experiments performed in BALB/c mice [33] , we detect no significant virus replication in the salivary glands during acute infection of C57BL/6 mice ( Fig 4D ) . Reduced persistent virus replication is observed in salivary glands of Il-10-/- mice [14] and in WT C57BL/6 mice in which IL-10R signaling is antagonized [18] . Associated with elevated CD4+ T cell responses d14 pi in Cre+ mice , fewer replicating virions were detectable in salivary glands as compared to controls . Furthermore , 5 out of 10 Cre+ mice had cleared replicating MCMV by d30 pi whereas MCMV replication was detectable in all Cre- mice ( Fig 4D ) . Importantly , reduced virus load in Cre+ mice was accompanied by decreased shedding of virus in the saliva throughout the course of infection ( Fig 4E ) . Furthermore , improved control of virus replication in Cre+ mice was not accompanied by virus-induced Sjögrens Syndrome-like disease , as demonstrated by comparable salivary gland accumulation of TRAILR-expressing CD4+ T cells and serum Sjögren Syndrome Antigen ( SSA ) -specific IgG ( S4 Fig ) , both of which are implicated in this disease [54] . One possible caveat of studies using CD4-cre mice is that CD8+ T cells ( that express CD4 during thymic development ) and CD4+ dendritic cells also lack IL-10 expression . To prove that IL-10 derived from CD4+ T-cells facilitated MCMV persistence , we negatively selected CD4+ T-cell from WT or Il-10-/- mice and adoptively transferred cells into rag1-/- mice . Whereas transfer of WT CD4+ T cells reduced MCMV titers by 1 log 14d pi , Il-10-/- T cells reduced virus load by 2 logs ( Fig 4F ) , comparable to virus load in Cre+ mice at this time-point ( Fig 4D ) . Thus , two independent approaches supported the conclusion that CD4+ T cell derived IL-10 facilitate MCMV persistence . We sought to define the factors promoting the generation of CD4+IL-10+ T cells . IL-27 is a potent inducer of IL-10 production [38] . We detected a substantial IL-27 p28 production during acute MCMV infection in the spleen that peaked d2 pi ( Fig 5A ) . Elevated IL-27 production in the salivary glands during viral persistence was also detected , albeit much lower than concentrations measured in the spleen ( Fig 5A ) . IL-27 is produced by multiple cells types with predominant expression by myeloid cells [55] . In accordance , we detected a high frequency of IL-27+ splenic DCs ( Fig 5B and 5C ) and , to a lesser extent , macrophages , neutrophils and also B cells ( Fig 5B and 5C ) . To establish the link between CMV-induced IL-27 production , and the generation of IL10+CD4+ T cells , the following experiments were conducted . Il-27rα-/- ( wsx1-/- ) mice were employed which do not respond to IL-27 as a consequence of the loss of the IL-27 receptor complex . Development of splenic IL-10+CD4+ T cells during acute infection was almost completely abrogated in Il-27rα-/- ( wsx1-/- ) mice , as were the smaller frequencies of IL-10+ cells present d14 pi ( Fig 6A and 6B ) . In contrast , development of IL-10+CD4+ T cells in the salivary glands was comparable in WT and Il-27rα-/- mice ( Fig 6A ) . The common IL-27 cytokine receptor glycoprotein 130 ( gp130 ) is down-regulated by virus-specific memory T cells [41] and IL-10+ ( Thy1 . 1+ ) and IL-10- ( Thy1 . 1- ) CD4+ T cells in the salivary glands d14 pi expressed less gp130 than splenic T cells isolated d7 pi ( Fig 6C and 6D ) . Importantly , salivary gland IL-10+ T cells expressed high levels of the co-stimulatory molecule ICOS ( Fig 6E ) that has previously been implicated in the development of IL-10-secreting CD4+ T cells during viral infection [56] . Monoclonal antibody blockade of ICOS from 6 days pi significantly inhibited the development of salivary gland MCMV-specific IL-10+CD4+ T cells 14 days pi ( Fig 6F ) . In contrast , splenic IL-10+CD4+ T cells were unaffected by ICOS blockade ( Fig 6G ) . These data suggest that differential signals are responsible for induction of MCMV-specific IL-10+ T cells in different tissues during MCMV infection . Given the reduced splenic IL-10+CD4+ T cell accumulation in Il-27rα-/- mice , we asked whether antiviral TH1 responses were enhanced . IL-27R deficiency did not influence virus-specific TH1 responses during acute infection ( Fig 7A ) . However , in accordance with data derived from Cre+ mice , Il-27rα-/- mice exhibited a significantly increased accumulation of virus-specific TH1 cells within the periphery and salivary glands during the persistence phase of infection ( Fig 7A ) . As observed in Cre+ mice ( Fig 4C ) , elevated TH1 cell were also accompanied by increased virus-specific CD8+ T-cell response d30 pi ( S5 Fig ) . In contrast , blockade of ICOS from 6 days pi did not enhance virus-specific IFNγ+CD4+ T cell responses either in the spleen or salivary glands 14 days pi ( S6 Fig ) , suggesting a dominant inhibitory effect of IL-27-dependent IL-10+ T cell responses during the first 14 days of MCMV infection . Given that IL-27 induction of IL-10 significantly impaired anti-MCMV T cell immunity , our attention focused on whether IL-27 impacted on viral persistence . In the absence of IL-27 signaling , viral load was decreased d14 pi and MCMV replication was absent in salivary glands of most mice by d30 pi ( Fig 7B ) . Furthermore , we detected a significant decrease in virus shedding by Il-27rα-/- mice in the saliva , with few viral genomes detected in the saliva by d30 pi ( Fig 7C ) . These data are consistent with the hypothesis that IL-27 induction of IL-10+CD4+ T cells in the periphery during acute infection contributes to the suppression of TH1 immunity and subsequent viral persistence and transmission . We examined the factors regulating IL-27 production in response to MCMV . Type-I interferon exerts critical control of MCMV infection in vivo [57 , 58] . However , type-I IFN induction of IL-27 during bacterial infection has been described [59] . Hence a paradoxical situation may arise where type-I IFN controls CMV on one hand , but induces IL-27 leading to inferior control of persistent virus replication . We examined whether type-I IFN orchestrated IL-27 production in response to MCMV , using an established in vitro MCMV infection system of bone marrow-derived macrophages [60] . Productive infection of WT macrophages induced substantial IL-27 mRNA expression , peaking 6hrs pi ( Fig 8A ) . Similarly , IL-27 expression was also maximally induced 6hrs pi with by a non-productive viral replication , as demonstrated using replication-deficient ( ΔIE3 ) MCMV . As the primary macrophage response to viral infection involves type-I IFN signaling we next directly tested the dependency of IL-27 induced expression in macrophages that are genetically ablated for either IFNβ production or type-I IFN signaling . MCMV infection of either Ifnβ-/- or Ifnαr-/- macrophages failed to induce IL-27 , a result further mirrored by IFN-dependent IL-27 expression upon stimulation with the TLR3 agonist , Poly: ( IC ) ( Fig 8A ) . Moreover , administration of a blocking anti-IFNαR-1 monoclonal antibody during MCMV infection in vivo inhibited IL-27 expression by all cellular subsets examined ( Fig 8B ) . In the case of macrophages , B-cells and neutrophils , IL-27 production was almost completely abrogated by IFNαR-1 blockade ( Fig 8B ) . To examine whether early IFN-induced IL-27 production shaped antiviral responses , we treated MCMV-infected mice with neutralizing anti-IL-27 antibody at the time of infection ( Fig 8C ) . IL-27 neutralization significantly enhanced total virus-specific TH1 responses and reduced virus shedding 14 days pi ( Fig 8D and 8E ) whilst concurrently reducing accumulation of virus-specific IL-10+CD4+ T cells in the spleen at this time ( Pooled m09 , M25 , m139 and m142-specific CD4+IL-10+ T cells: Isotype control = 0 . 00162 × 107 cells versus αIL-27 treated group = 0 . 000908 × 107 cells ) . Importantly , IL-10R blockade from the time of IL-27-induced IL-10+ T cell responses ( day 6 pi ) had no additional inhibitory influence on IFNγ+ T cell responses and virus shedding ( Fig 8D and 8E ) . Thus , IL-27 production in response to MCMV is dependent upon type-1 IFNR signaling , and this axis acts during the initial days of infection to promote the accumulation of MCMV-specific IL-10+CD4+ T cells , subsequently promoting virus persistence and shedding from the mucosa .
Herein we reveal that cytokine responses induced upon acute MCMV infection influence antiviral T cell responses and virus replication during pathogen persistence . We identified that IL-27 , which was produced by myeloid cells during the initial days of infection , promoted the generation of IL-10+CD4+ T cells in the spleen . Our data suggest that this IL-27-dependent induction of peripheral IL-10+CD4+ T cells was sufficient to impinge on T cell mediated control of virus chronicity in the mucosa . IL-27 production was dependent upon type-I IFN , implying that CMV exploits the immune-regulatory actions of this prototypic antiviral cytokine pathway to enable persistence and dissemination . The dynamics of virus-specific IFNγ versus IL-10 production by CD4+ T cells suggested that contraction of the IL-10 response preempts expansion of IFNγ+ T cell numbers and subsequent control of virus replication . Preventing IL-10 production by T cells both enhanced and accelerated the generation of virus-specific TH1 responses . These data suggest that IL-10+ T cells provide a window of opportunity for MCMV to replicate within and shed from the salivary glands . Conversely , these results also imply that inhibiting virus-specific IL-10+ T cell development may be therapeutically beneficial . Our data suggest that peripheral IL-10+ T cells inhibit the control of virus replication within mucosal tissue independently of mucosal IL-10+ T cell populations , highlighting that appropriate modulation of peripheral T cell responses via systemic vaccination approaches may be capable of overcoming inhibitory pathways that act in the mucosa , thus enabling effective control of virus replication within mucosal tissues . Using a combination of conditional knockout mice and a cell transfer model , we demonstrated an important role for CD4+ T cell-derived IL-10 in facilitating MCMV persistence . This result is in apparent contradiction to bone marrow chimera experiments suggesting no role for CD4+ cells in IL-10-mediated chronic MCMV replication [14] . There are several explanations for these apparent discrepancies . Firstly , whereas we used salivary gland-propagated Smith strain MCMV in our experiments , Mandaric et al examined tissue culture-propagated virus deficient in m157 , a viral ligand for the NK cell activating receptor Ly49H [61] . These viruses likely replicate with different kinetics and , possibly , tropisms that may influence the induction of IL-10 expression by T cells and other cells . Furthermore , the mixed chimera experiments performed by Mandaric et al used IL-10-/- mice as recipients whereas our experiments were performed in mice from IL-10-sufficient backgrounds . Given that the time-point assessed by Mandaric et al ( d14 pi ) was prior to the maximal impact of CD4-derived IL-10 in our model , such variations in experimental design may influence the impact of CD4+ T cell-derived IL-10 on virus persistence . Importantly however , significant expression of IL-10 by HCMV-specific T cells in peripheral blood and mucosal tissue highlights the potential importance of T cell-derived IL-10 in regulating anti-CMV immunity and viral persistence , as suggested by findings obtained from our model of chronic MCMV infection , and further supported by experiments studying acute MCMV infection [33] . HCMV-specific IL-10 production was detected in response to common lytic antigens pp65 and gB , although T cell responsiveness to gB was low in peripheral blood of all healthy volunteers examined . IL-10 production by CD4+ T cells reactive to latency-associated HCMV antigens have also been described [30] . Persistent shedding of HCMV in children is associated with poor TH1 responses [62] . Our data derived from murine experiments demonstrates that preventing the generation of HCMV-specific IL-10+ CD4+ T cells may be beneficial for the improvement of protective T cell immunity following vaccination , particularly in the context of restricting horizontal transmission via mucosal surfaces . IL-27 promoted the development of splenic but not salivary gland IL-10+CD4+ T cells . Why IL-27 had no impact on salivary gland CD4+IL-10+ accumulation is incompletely understood . Salivary glands APCs , which are phenotypically indicative of tissue-resident macrophages [63] , are tightly regulated via inhibitory signals such as CD200 expressed by endothelial cells [60] . Thus , restricted activation of this cell population may contribute to the relatively low IL-27 production measured in this organ . Furthermore , the infection phase may also influence the impact of IL-27 on IL-10+ T cell development . Memory CD8+ T cells generated in influenza infection down-regulate gp130 [41] and CD4+ T cells in persistently-infected salivary glands expressed less gp130 expression than splenic CD4+ T cells during acute infection . This down-regulation of gp130 correlated with a reduced impact of IL-27R signaling on salivary gland IL-10+ CD4+ T cell accumulation during virus persistence . Instead , ICOS co-stimulation was required for the accumulation of IL-10+CD4+ T cells within the salivary glands , thus demonstrating that differential signals promote IL-10+ T cell responses in different anatomical locations of MCMV infection and suggesting that ICOS acts independently of IL-27 in the induction of salivary gland CD4+IL-10+ T cells . This contrasts in vitro findings demonstrating that ICOS acts downstream of IL-27 in promoting IL-10 production by Tr1 cells in vitro [64] . However , salivary gland IL-10+CD4+ T cells in our model were phenotypically distinct from Tr1 cells . Moreover , here we examined the influence of ICOS co-stimulation of T cells during virus persistence rather than during initial T cell activation as studied by Pot et al . Therefore , although the impact of ICOS on CD4+IL-10+ T cell generation during acute infection was not examined in our study , these data collectively imply that the type of IL-10-producing T cell and the timing of IL-10-inducing signals may influence the relationship between IL-27 and ICOS in the induction of CD4+IL-10+ T cells . Notably , blockade of ICOS was not accompanied by an elevated virus-specific TH1 responses d14 pi despite dramatically reducing salivary gland CD4+IL-10+ accumulation . This may reflect a possible dual function for ICOS in positive co-stimulation [65] in addition to IL-10 induction . However , our data does not preclude a possible role for ICOS and , possibly , other IL-10-inducing receptors in the suppression of antiviral control during the latter stages of MCMV persistence . We demonstrate that IL-27 is critical for the induction of peripheral IL-10+CD4+ T cell responses and subsequent suppression of TH1 responses during the persistent phase of infection . Experiments using anti-IL-27 and anti-IL-10R suggest that early IL-27-mediated induction of IL-10+ T cells has a long-term impact on antiviral immunity and virus persistence . However , our data does not preclude an IL-10-independent inhibitory function for IL-27 during the later stages of infection . Indeed , antagonizing both IL-10R and IL-27 14 days pi led to an additive , albeit modest , increase in virus-specific IFNγ+CD4+ T cells by day 30 ( S7 Fig ) . Intriguingly , IL-27 can induce dendritic cell expression of the ATPase CD39 [66] . However , in contrast to IL-27rα-/- mice , mice deficient in the ecto-5’-nucleotidase CD73 that acts downstream of CD39 in the purinergic system have no alteration in MCMV-specific memory T cell accumulation and only a moderate and transient reduction in MCMV replication in the salivary glands [67] . Taken together with data derived from CD4-CreIL-10flox and IL-27rα-/- mice , these results collectively suggest that early induction of IL-10+CD4+ T cells is the dominant mechanism through which IL-27 impinges on anti-MCMV T cell immunity . In vivo and in vitro experiments revealed that the induction of IL-27 during MCMV infection was promoted by type-I IFN . Although the uncontrolled virus replication and associated disease and inflammation observed in mice treated with anti-IFNαR-1 antibody precluded the assessment of the direct impact of IFNαR-1 signaling on both IL-27-driven IL-10+ T cell development and virus persistence , our data implies that type-I IFN may impinge on antiviral immunity via the induction of IL-27 . Comparable IFN-dependent induction of IL-27 was observed upon in vitro stimulation with either MCMV or the TLR3 ligand , poly ( I:C ) . Endosomal TLRs including TLR3 are activated upon acute MCMV infection and induce type-I IFN expression [68 , 69] . Thus , our data suggest that MCMV may exploit an immune-regulatory aspect of this “antiviral” response to promote persistence within mucosal tissue and subsequently increase the window of opportunity for horizontal transmission via mucosal secretions .
CD4-Cre-IL-10flox/flox ( Cre- ) /CD4-Cre+IL-10flox/flox ( Cre+ ) were generated by Werner Muller ( University of Manchester ) and were kindly provided by Jean Langhorne ( Francis Crick Institute ) , 10-BiT mice were given to us by Padraic Fallon ( Trinity College Dublin ) and Il-27rα-/- ( wsx1-/- ) , Rag1-/- and Il-10-/- mice were obtained from The Jackson Laboratory . All mice were bred in-house . C57BL/6 WT mice were purchased from Charles River or Envigo . Mice were infected with MCMV that was prepared by sorbital gradient purification as described previously [70] . Mice were infected with 3 × 104 pfu MCMV intraperitoneally ( i . p . ) . In some experiments , CD4-Cre-IL-10flox/flox mice were administered 2 mg αIFNαR-1 ( clone MAR1-5A3 , BioXcell ) or IgG1 Isotype control ( clone MOPC-21 BioXcell ) at the time of infection , or 200 μg anti-ICOS ( clone 7E . 17G9 or JmAb51-TM ) or IgG control ( clone LTF2 or R347-TM ) , respectively ) on days 6 and 10 pi . In other experiments mice were treated with 500 μg anti-IL-27 ( clone MM27-7B1 , Biolegend ) , 250 μg anti-IL-10R ( clone 1B1 . 3A BioXcell ) and also in combination or Isotype control ( clone MOPC-21 BioXcell ) on days stated in the legend . In some experiments , CD4+ T cells were isolated from spleens of C57BL/6 and IL-10-/- mice by negative separation ( Miltenyi Biotec ) . 5 x 106 cells were then transferred intravenously ( i . v . ) into rag1-/- mice one week prior to MCMV infection . Leukocytes were extracted from murine spleen , salivary glands and lungs as described previously [13 , 60] . For CD4+ functional responses , isolated leukocytes were stimulated for 2 hours with 3 μg/ml m09 , ( GYLYIYPSAGNSFDL ) , M25 ( NHLYETPISATAMVI ) , m139 ( TRPYRYPRVCDASLS ) , and m142 ( RSRYLTAAAVTAVLQ ) MCMV MHCII peptides ( Genscript ) . Brefeldin A ( Sigma ) was then added and cells incubated for a further 4 hours . For CD8+ functional responses , leukocytes were stimulated with 2 μg/ml m139 , IE3 , M38 , and M45 MCMV MHCI peptides in the presence of anti-mouse CD107a-FITC ( Biolegend ) with monensin ( BD Biosciences ) and brefeldin A for 6 hours . Cells were subsequently stained with Zombie Aqua fixable viability dye ( Biolegend ) or LIVE/DEAD-Aqua ( Life-Technologies ) , stained with anti-CD16/CD32 Fc-block ( Biolegend ) and then with either anti-CD4 Pacific-Blue or PercP , ( clone RM4-5 , Biolegend ) or with anti-CD8 PercP ( clone 53–6 . 7 , Biolegend ) and anti-CD262 ( TRAILR DR5 , clone MD5 . 1 , eBioscience ) . Cells were fixed , saponin permeabilised and stained for anti-IFNγ FITC or Pacific-Blue ( clone XMG1 . 2 , Biolegend ) and anti-IL-10 APC ( clone JES5-16E3 , eBioscience ) . Data was acquired using a BD FACSCantoII or a BD LSR II fortessa flow cytometer ( BD Biosciences ) and analysed with FlowJo software ( TreeStar ) . Direct ex vivo intracellular IL-27 production was detected as previously described [71] . Cells were stained with a combination of anti-I-A/I-E ( clone M5/114 . 15 . 2 , Biolegend ) , anti-CD11c Pe-Cy7 ( clone HL3 BD , Pharmingen ) , anti-Ly6C FITC ( clone AL-21 BD Pharmingen ) , anti-CD11b APC-Cy7 ( clone M1/70 , Biolegend ) , anti-F480 Brilliant-Violet 711 ( BV711 ) ( clone BM8 , Biolegend ) , anti-CD45R/B220 BV785 ( clone RA3-6B2 , Biolegend ) , SiglecH APC ( clone 551 , Biolegend ) , anti-Ly6G PerCP-Cy5 . 5 ( clone 1A8 , Biolegend ) , and anti-CD3ε BV605 ( clone 145-2C11 , Biolegend ) . Cells were fixed , saponin permeabilised and stained with anti-IL-27 p28 PE ( clone MM27-7B1 , Biolegend ) . To determine T cell transcription factor profiles cell were surface-stained with anti-CD4 BV785 ( clone RM4-5 , Biolegend ) , anti-CD90/CD90 . 1 ( Thy1 . 1 ) BV650 ( clone OX-7 , Biolegend ) anti-CD130 PE ( gp130 ) ( clone KGP13 , eBioscience ) , fixed and permeabilised using BD Cytofix/Cytoperm solution ( BD Biosciences ) and stained with anti-T-Bet BV421 ( clone 4B10 , Biolegend ) , anti-BLIMP-1 Alexa-Fluor 647 ( AF647 ) ( clone 5E7 , Biolegend ) , and anti-cMaf eFluor 660 ( clone sym0F1 , eBioscience ) . All data was acquired using a BD LSRForetssa flow cytometer ( BD Biosciences ) and analyzed with FlowJo software ( TreeStar ) . For analysis of co-stimulatory ligand expression , cells were surface stained with a combination of anti-CD11c APC-Cy7 ( clone N418 , Biolegend ) , anti-I-A/I-E PerCP-Cy5 . 5 ( clone M5/114 . 15 . 2 , Biolegend ) anti-F480 Brilliant-Violet 421 ( BV421 ) ( clone BM8 , Biolegend ) , anti-CD80 APC ( clone 16-0A1 , Biolegend ) , anti-CD86 Pe-Cy7 ( clone GL-1 , Biolegend ) , anti-OX40L APC ( clone RM134L , Biolegend ) , anti-4-1BBL PE ( clone TKS-1 , Biolegend ) , anti-CD40 FITC ( clone 3/23 , Biolegend ) and anti-Ly6G PE ( clone 1A8 , Biolegend ) , anti-ICOS APC-Cy7 ( clone C398 . 4A , Biolegend ) , anti-AhR Alexa-Flour 488 ( AF488 ) ( clone 4MEJJ eBioscience ) , anti-CD49b Pe-Cy7 ( clone DX5 , Biolegend ) , anti-LAG3 PE ( clone C9B7W , Biolegend ) anti-NK-1 . 1 Pe-Cy7 ( clone PK136 , Biolegend ) , anti-CD19 FITC ( clone 6D5 , Biolegend ) , anti-CXCR3 PerCP-Cy5 . 5 ( clone CXCR3-173 Biolegend ) , and anti-CCR5 AF488 ( clone HM-CCR5 , Biolegend ) . Data was acquired using a BD FACSCantoII flow cytometer ( BD Biosciences ) and analysed with FlowJo software ( TreeStar ) . The following MCMV MHCI biotinylated were refolded and kindly provided by the NIH tetramer Core Facility- H-2D ( b ) M45 985–993 HGIRNASFI , H-2k ( b ) m139 TVYGFCLL , H-2k ( b ) M38 316–323 SSPPMFRV , H-2k ( b ) IE3 RALEYKNL . Biotinylated monomers were tetramerised as described previously [72] . Isolated leukocytes were stained with viability dye and then with 25 μg/ml tetramer conjugated to PE or APC for 15 minutes at 37°C . Cells were subsequently stained with Fc-block and anti-CD8 APC-Cy7 ( clone 53–6 . 7 , Biolegend ) . Data was acquired using a BD FACSCantoII flow cytometer ( BD Biosciences ) and analysed with FlowJo software ( TreeStar ) . Peripheral blood and healthy colon specimens were obtained from patients undergoing primary tumor resection for colorectal adenocarcinoma at the University Hospital of Wales , Cardiff . Blood samples were collected on the day of but prior to surgery . The tissue used in this study was autologous colon samples that were cut from a macroscopically normal section of the excised tissue , at least 10 cm from the tumor . Colon tissue was washed in extraction medium consisting of DMEM supplemented with 100 U/ml Penicillin , 100 mg Streptomycin and 2 mM L-Glutamine , 20 μg/ml Gentamincin and 2 μg/ml Fungizone ( all Life-Technologies ) as described previously [73] . Samples were finely cut with scalpel blades in a petri dish and forced through a 70 μM and subsequently a 40 μM cell strainer . Cells were washed and pelleted by multiple centrifugations and lymphocytes were isolated using Lymphoprep ( Axis-Shield , Scotland ) . Peripheral blood mononuclear cells ( PBMCs ) from healthy HCMV sero-positive healthy volunteers were isolated using Lymphoprep . For target cells , some PBMCs were γ-irradiated with 3000 Rad , plated out @ 1 × 105 cells/well and pulsed with 2 μg/ml of Peptivator CMV pp65 overlapping peptide pool ( Miltenyi Biotec ) , 2 μg/ml PepMix HCMVA UL55 overlapping peptide pool ( JPT ) , or with DMSO vehicle control for 2 hours @ 37°C . CD4+ T cells were then isolated from the remaining PBMCs by negative selection ( Miltenyi Biotec ) . Pulsed autologous targets were then transferred to a human IFN+/IL-10+ Flurospot plate thus removing ( IL-10-producing ) monocytes that adhered to the plate . 3 × 105 purified CD4+ T-cells and 1/100 human anti-CD28/CD49d co-stimulation antibodies ( BD Pharmingen ) were then added for 18 hours @ 37°C . E:T ratios ranging from 1:3 to 3:1 were tested . After incubation , plates were assayed according to the manufacturer’s instructions ( Mabtech ) and quantified using a CTL Immunospot Fluorospot Line plate reader ( CTL ) . Cells were maintained in RPMI , 100 U/ml Penicillin , 100 mg Streptomycin , 2 mM L-Glutamine ( all Life-Technologies ) and 5% human AB serum ( Welsh Blood Transfusion Service ) . Autofluoresence is adjusted using the analysis software and removed from the final SFC count . For analysis of colon leukocyte cytokine production , PBMCs from the colon donor was used as autologous targets and γ-irradiated in an identical manner as that described previously . 1 . 5 × 105 cells per well and pulsed with peptide pools or DMSO vehicle control for 2 hours @ 37°C . Cells were transferred to a human IFN+/IL-10+ Fluorospot plate with the addition of 5 × 104 cells from peripheral blood and Colon processed tissue with the inclusion of 1/100 human anti-CD28/CD49d co-stimulation antibodies for 18 hours @ 37°C . Plates were assayed and measured as described above . Cardiac punctures were performed on CD4-Cre-IL-10flox/flox , CD4-Cre+IL-10flox/flox mice on either naïve mice or after 60 days of MCMV infection and plasma isolated . Plasma was then assayed for anti-Sjögrens Syndrome Antigen IgG by ELISA ( Alpha Diagnostics ) . Samples were analysed according to the manufacturer’s instructions and Total IgG antibody levels present in the plasma was calculated by interpolation from the calibrator curve provided with the kit . Excised tissue from both Spleen and Salivary Gland ( approx . 50–100 mg ) were weighed , washed and re-suspended in DMEM . Supernatant was assayed in triplicate for IL-27 p28 production by ELISA and performed according to the manufacturer’s instructions ( eBioscience ) . Infectious virus quantification was determined using plaque assays as previously described [60] . Viral DNA copy number in saliva was determined using qPCR for relative expression of IE1 , as described previously [74] . Oral lavage was performed on the sublingual cavity of anaesthetised mice using 20 μl of sterile PBS . 1 μl of sample was used for qPCR and measured using SYBR green ( Bio-Rad ) using MCMV IE1 forward ( 5′-AGCCACCAACATTGACCACGCAC-3′ ) and MCMV IE1 reverse ( 5′-GCCCCAACCAGGACACACAACTC3′ ) primers . PCR was performed using an MJ Mini Personal Thermal Cycler ( Bio-Rad ) using the conditions as described previously [74] . To establish a standard curve , DNA copy/μl was determined from the known concentration and molecular weight of the MCMV pARK25 BAC ( a kind gift from Alec Redwood , Murdoch University ) and assayed for relative IE1 expression as above . WT , IfnαR-/- and Ifnβ1-/- BM-DM were generated as previously described [60] . Cells were infected with WT-MCMV , MCMVΔIE3 ( MOI = 1 ) or were mock infected [75] , and RNA isolated using RNeasy Mini kit at different time-points ( Qiagen ) according to manufacturer’s instructions . Some cells were stimulated with Poly ( I:C ) ( Invivogen ) . Quality control ( QC ) was performed using an Agilent Bioanalyser and , total RNA labeled . RNA was hybridised to Mouse Gene 1 . 0ST microarrays ( Affymetrix ) using a WT Expression kit ( Ambion , UK ) . GC metrics of captured data were assessed using Affymetrix Expression Console software and arrays were imported into Partek Genomics Suite ( Partek ) for downstream analysis . Arrays were normalised using the gcRMA algorithm [76] and data was filtered to include genes with at least 1 signal value of > = 150 across the time course . All mice experiments were performed under the UK Home Office project Licence ( PPL 30/2969 ) . For human colon and PBMC samples , informed consent was obtained from participants in writing . The Wales Research Ethics Committee granted ethical approval for sample use in this study . For use of HCMV sero-positive donor PBMC samples used in this study informed consent was obtained in writing . Power calculations were performed in R using data from a pilot study using CD4-Cre/IL-10 mice . It was determined that a minimum of 5 participants per group would be needed to detect a difference in means with 90% power and an alpha value set at 0 . 05 . Statistical analysis was performed using the Mann-Whitney U test for paired analysis of viral-load analysis and flow cytometry data . Where more than 2 groups were assessed concurrently ( viral-load analysis , ELISA analysis ) , 1-way ANOVA analysis of data was performed . To assess biological replication: all in vivo experiments were performed multiple times at different times , as stated in figure legends . All outliers were included in datasets , as shown . For all tests performed , p values are reported as *≤0 . 05 , **≤0 . 01 , and ***≤0 . 001 . | Viruses including the pathogenic β-herpesvirus human cytomegalovirus ( HCMV ) can replicate within and disseminate from mucosal tissues . Understanding how to improve antiviral immune responses to restrict virus replication in the mucosa could help counter virus transmission . Studies in the murine cytomegalovirus ( MCMV ) model have demonstrated the importance of the CD4+ T cells in control of mucosal MCMV replication . However , this process is inefficient , allowing virus persistence . Herein , we reveal that production by CD4+ T cells of the immune-suppressive soluble protein , or cytokine , interleukin ( IL ) -10 facilitates virus persistence in mucosal tissue . Mice deficient in T cell-derived IL-10 mounted heightened T cell responses and reduced virus replication in the salivary glands and shedding in the saliva . The cytokine IL-27 induced IL-10-producing CD4+ T cells in the periphery whereas a cell surface-expressed protein , ICOS , promoted mucosal IL-10+ T cell responses . IL-27 acted in the initial stages of infection to impinge on T cell responses and antiviral control . In turn , IL-27 production in response to viral infection was triggered by type-I interferon , a prototypic antiviral cytokine . Thus , our data suggest that herpesviruses may exploit immune-suppressive properties of this early antiviral cytokine response to facilitate persistence within and shedding from mucosal tissue . | [
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] | 2016 | Cytomegalovirus-Specific IL-10-Producing CD4+ T Cells Are Governed by Type-I IFN-Induced IL-27 and Promote Virus Persistence |
Despite treatment with agents that enhance β-cell function and insulin action , reduction in β-cell mass is relentless in patients with insulin resistance and type 2 diabetes mellitus . Insulin resistance is characterized by impaired signaling through the insulin/insulin receptor/insulin receptor substrate/PI-3K/Akt pathway , leading to elevation of negatively regulated substrates such as glycogen synthase kinase-3β ( Gsk-3β ) . When elevated , this enzyme has antiproliferative and proapoptotic properties . In these studies , we designed experiments to determine the contribution of Gsk-3β to regulation of β-cell mass in two mouse models of insulin resistance . Mice lacking one allele of the insulin receptor ( Ir+/− ) exhibit insulin resistance and a doubling of β-cell mass . Crossing these mice with those having haploinsufficiency for Gsk-3β ( Gsk-3β+/− ) reduced insulin resistance by augmenting whole-body glucose disposal , and significantly reduced β-cell mass . In the second model , mice missing two alleles of the insulin receptor substrate 2 ( Irs2−/− ) , like the Ir+/− mice , are insulin resistant , but develop profound β-cell loss , resulting in early diabetes . We found that islets from these mice had a 4-fold elevation of Gsk-3β activity associated with a marked reduction of β-cell proliferation and increased apoptosis . Irs2−/− mice crossed with Gsk-3β+/− mice preserved β-cell mass by reversing the negative effects on proliferation and apoptosis , preventing onset of diabetes . Previous studies had shown that islets of Irs2−/− mice had increased cyclin-dependent kinase inhibitor p27kip1 that was limiting for β-cell replication , and reduced Pdx1 levels associated with increased cell death . Preservation of β-cell mass in Gsk-3β+/−Irs2−/− mice was accompanied by suppressed p27kip1 levels and increased Pdx1 levels . To separate peripheral versus β-cell–specific effects of reduction of Gsk3β activity on preservation of β-cell mass , mice homozygous for a floxed Gsk-3β allele ( Gsk-3F/F ) were then crossed with rat insulin promoter-Cre ( RIP-Cre ) mice to produce β-cell–specific knockout of Gsk-3β ( βGsk-3β−/− ) . Like Gsk-3β+/− mice , βGsk-3β−/− mice also prevented the diabetes of the Irs2−/− mice . The results of these studies now define a new , negatively regulated substrate of the insulin signaling pathway specifically within β-cells that when elevated , can impair replication and increase apoptosis , resulting in loss of β-cells and diabetes . These results thus form the rationale for developing agents to inhibit this enzyme in obese insulin-resistant individuals to preserve β-cells and prevent diabetes onset .
Despite treatment with agents that enhance β-cell function and insulin action , reduction in β-cell mass is relentless in type 2 diabetes ( T2DM ) [1–4] . Why β-cells fail in some individuals is a central issue in diabetes research today . The molecular mechanisms enabling β-cell adaptation to insulin resistance are being discovered primarily in animal models [5–7] . Important genetic models have focused on the requirement for insulin signaling through β-cell insulin/insulin-like growth factor 1 ( IGF1 ) receptors ( reviewed in [8 , 9] ) . Whereas mice with total-body deficiency for insulin receptor substrate 1 ( Irs1−/− ) have insulin resistance and significant expansion of β-cell mass , insulin receptor substrate 2–deficient mice ( Irs2−/− ) have insulin resistance yet develop postnatal β-cell loss and severe diabetes ( reviewed in [10] ) . In this model and in others , the primacy of PI-3K/Akt activity in expansion and postnatal maintenance of β-cell mass was apparent ( reviewed in [11 , 12] ) . The remarkable ability of β-cell mass to expand via enhanced proliferation and reduced apoptosis was demonstrated in transgenic mice expressing constitutively active Akt in β-cells [13 , 14] , illustrating the potential importance of this pathway for expanding β-cells in patients and perhaps resisting the apoptosis that accompanies long-standing diabetes . Knowing that increased expression of Akt in β-cells leads to marked expansion , these results have focused interest on the role of two negatively regulated Akt substrates , FoxO1 and Gsk-3β , each known to regulate carbohydrate and lipid metabolism in insulin target tissues while also exhibiting antiproliferative and proapoptotic properties when expressed at high levels [15 , 16] . There is substantial evidence , mostly from overexpression of a constitutively nuclear FoxO1 , that FoxO1 has detrimental effects on β-cell proliferation and survival [17] . On the other hand , there is little known about the effects of expression of Gsk-3β on β-cell proliferation and/or survival . Glycogen synthase kinase-3 ( Gsk-3 ) was originally identified as a serine/threonine kinase that inactivates glycogen synthase [18] . Early studies showed that insulin inhibits Gsk-3 activity through PI-3K/Akt-induced phosphorylation promoting glycogen synthesis and glucose disposal [19–22] . Later , the enzyme was shown to affect many cellular processes , including transcription , translation , cell cycle regulation , and apoptosis [16 , 23–27] . Mammals express two isoforms , Gsk-3α and Gsk-3β , which share similar kinase domains but differ considerably in their termini . Inactivation of Gsk-3β appears to be the major route by which insulin activates glycogen synthesis [22 , 28] , and recent studies have demonstrated that elimination of Gsk-3β is more effective at promoting neuronal survival than is elimination of Gsk-3α [29] . Gsk-3 activity has been shown to be increased in peripheral tissues in diabetic animals and patients [30–32] , and diabetes was reversed in obese diabetic mice treated with Gsk-3 inhibitors [33–35] . Because inhibitors have differing degrees of kinase specificity , Gsk-3–deficient genetic models were created . Disruption of the Gsk-3β gene in mice results in embryonic lethality [23] , yet mice with loss of one allele ( Gsk-3β+/− ) are viable and express reduced levels of protein and enzymatic activity [23] . Although Gsk-3β+/− mice have been little studied , they have been shown to have behavioral effects similar to lithium-treated mice , suggesting that Gsk-3β is the main determinant of Gsk-3 activity in the nervous system [36] . The availability of these Gsk-3β+/− mice provided the opportunity to assess the role of Gsk-3β on insulin sensitivity and pancreatic β-cell function . Mice haploinsufficient for the insulin receptor ( Ir+/− ) have insulin resistance with expanded islet β-cell mass and hyperinsulinemia [37] . Crossing Ir+/− mice with mice haploinsufficient for FoxO1 ( Foxo1+/− ) improved insulin sensitivity and reduced islet mass [17] . In the current study , we hypothesized that in Ir+/− mice , increased Gsk-3β as well as FoxO1 activity could be contributing to the insulin-resistant phenotype . We crossed Ir+/− mice with mice lacking one allele of Gsk-3β ( Gsk-3β+/− ) and found that Gsk-3β+/−Ir+/− mice , like Foxo1+/−Ir+/− mice , also had improved insulin sensitivity and reduced β-cell mass . Next , we investigated a mouse that is missing two alleles of the insulin receptor substrate 2 ( Irs2−/− ) , that is also insulin resistant , but develops profound β-cell destruction resulting in marked diabetes [38] . Although crossing Irs2−/− mice with Foxo1+/− mice increased β-cell mass and proliferation [39] , suggesting that increased β-cell FoxO1 activity was contributing to β-cell loss in Irs2−/− mice , we found that Gsk-3β activity in islets of Irs2−/− mice was also markedly elevated . We determined that Gsk-3β+/−Irs2−/− mice had reduced , but persistent , insulin resistance , yet do not develop diabetes , as a result of maintaining islet β-cell mass . Preservation of β-cell mass in Gsk-3β+/−Irs2−/− mice appeared to be due to accelerated proliferation and decreased apoptosis of β-cells . Reduction of Gsk-3β , like reduction of FoxO1 , results in preservation of β-cell mass and rescues the diabetes in this model . The results of these studies now define a new , negatively regulated substrate of the insulin signaling pathway specifically within β-cells that when elevated , can impair replication and increase apoptosis , resulting in loss of β-cells and diabetes .
To determine whether Gsk-3β is a downstream contributor to the insulin resistance of insulin receptor–deficient mice , Gsk-3β+/− mice were crossed with mice missing one allele of the insulin receptor ( Ir+/− ) , previously shown to have insulin resistance and elevation of insulin levels in adult animals [37] . Fasting and fed glucose and insulin levels were assessed in Gsk-3β+/− , Ir+/− , and compound heterozygous ( Gsk-3β+/−Ir+/− ) mice and compared to levels in wild type ( WT ) at 26 wk of age ( Figure 1A ) . Both fasting and fed insulin levels were significantly reduced in mice lacking one allele of Gsk-3β , thus indicating that genetic deficiency of Gsk-3β activity improves insulin sensitivity . In Ir+/− mice , both fasting and fed insulin levels were higher than in WT mice , consistent with previous reports [37] . In compound heterozygous Gsk-3β+/−Ir+/− mice , the serum insulin values were significantly decreased relative to those in Ir+/− mice in both the fasting and the fed state , although the values were significantly elevated relative to that in Gsk-3β+/− mice ( p < 0 . 05 ) , suggesting that Gsk-3β+/−Ir+/− mice are still insulin resistant . Similar differences in glucose and insulin values were observed in mice at 8–10 wk of age ( Figure S1A and S1B ) . Gsk-3β+/−Ir+/− mice were found to exhibit improved glucose tolerance relative to that in Ir+/− mice ( Figure S1C ) . The results of these experiments thus indicate that ( 1 ) endogenous Gsk-3β activity contributes to ambient insulin sensitivity , and ( 2 ) that Gsk-3β activity is a downstream mediator of the insulin resistance of the Ir+/− mice . To further characterize the apparent improvement of insulin sensitivity , hyperinsulinemic-euglycemic clamps were performed . The rates of glucose disposal and glucose infusion were increased in Gsk-3β+/−Ir+/− mice relative to those in Ir+/− mice , confirming enhanced insulin sensitivity ( Figure 1C and 1D ) . Hepatic glucose production did not appear to differ between Ir+/− mice and Gsk-3β+/−Ir+/− mice ( Figure 1E ) , suggesting that the beneficial effects of genetic reduction of Gsk-3β on carbohydrate metabolism were a result of enhanced effects on peripheral insulin-mediated glucose disposal . These results are consistent with those of Patel et al . ( S . Patel , B . W . Doble , K . MacAulay , E . M . Sinclair , D . J . Drucker , and J . R . Woodgett , unpublished data ) in which tissue-specific knockout of Gsk-3β in skeletal muscle improved insulin sensitivity , whereas elimination of the gene in liver had no apparent effect on carbohydrate metabolism . Pancreatic sections with insulin staining of each of the four genotypes are shown in Figures 2A–2D . Akthough there were no differences in pancreatic areas ( unpublished data ) , the β-cell mass was increased in Ir+/− mice as previously noted [37] , and reduced in Gsk-3β+/−Ir+/− mice as assessed by pancreatic morphometry ( Figure 2E ) . In conclusion , mice missing one allele of Gsk-3β when crossed with Ir+/− mice had reduced hyperinsulinemia associated with reduced β-cell mass . Whereas the Ir+/− mice have peripheral insulin resistance , Irs2-deficient mice ( Irs2−/− ) have both peripheral insulin resistance as well as impaired insulin signaling , as measured by reduced Akt activity in islets [40] . Because Akt is a negative regulator of Gsk-3β activity and Gsk-3β is a known regulator of both proliferation and apoptosis [16] , we hypothesized that increased Gsk-3β activity could also contribute to the reduced β-cell mass of Irs2−/− mice . Islets from Irs2−/− mice were examined at 6 wk of age and shown to have decreased phosphorylation at serine 473 of Akt and phosphorylation at serine 9 of Gsk-3β in Irs2−/− compared with WT mice ( Figure 3A ) . Additionally , islets from Irs2−/− mice were found to have a 4-fold elevation of phosphorylated glycogen synthase , a substrate of Gsk-3β and a measure of its increased activity ( Figure 3B ) . Because of the antiproliferative and proapoptotic effects of Gsk-3β activity in other tissues [16] , finding increased Gsk-3β activity in islets from Irs2−/− mice was consistent with the possibility that this may contribute to the decreased β-cell mass and function of these mice . We therefore crossed mice haploinsufficient for Gsk-3β with Irs2−/− mice to determine whether it would have beneficial effects on preserving β-cell mass and prevent diabetes . We generated double knockout mice ( Gsk-3β+/−Irs2−/− ) by interbreeding Gsk-3β+/− and Irs2+/− mice . The Irs2−/− mice gained weight until about 9 wk , when body weight plateaued and began to decline . In contrast , the Gsk-3β+/−Irs2−/− mice continued to increase body weight , indistinguishable from that of the WT or Gsk-3β+/− mice at 12 wk ( Figure 3C ) and at 24 wk ( Figure S2A ) . Fed glucoses were determined at 6 , 8 , 10 , and 12 wk of age ( Figure 3D ) . Irs2−/− mice developed a progressive increase with severe hyperglycemia , mean glucose >500 mg/dl , at 12 wk of age , confirming previous observations [38] . In contrast , blood glucose concentrations in Gsk-3β+/−Irs2−/− mice were significantly reduced relative to that in Irs2−/− mice , although plasma insulin did not differ ( Figure 3E ) . Fasting glucose levels at 6 wk of age did not differ , but did at 8 wk ( Table S1 ) . To interpret the basis for plasma insulin levels , insulin sensitivity was examined in Irs2−/− and Gsk-3β+/−Irs2−/− mice relative to that in WT mice by insulin tolerance testing in 6-wk-old mice ( Figure 3E ) . Interestingly , the Gsk-3β+/−Irs2−/− mice maintained insulin resistance relative to that in WT mice , suggesting that the beneficial effects of genetic deficiency of Gsk-3β on restoration of glucose homeostasis is not solely due to altered insulin sensitivity . Islet morphology in mice of each genotype was assessed at 8 wk of age . Immunostaining for insulin and glucagon on pancreatic sections are shown in Figure 4A–4F . The insulin immunoreactive area in Irs2−/− mice was severely reduced ( Figure 4B and 4E ) relative to that in Gsk-3β+/− mice , consistent with previous reports [38 , 39] . In contrast , there appeared to be preservation of β-cell mass in the Gsk-3β+/−Irs2−/− mice ( Figure 4C and 4F ) . As quantified in Figure 4G , islet mass of Irs2−/− mice was reduced to 30% of WT and Gsk-3β+/− mice , whereas the mass of Gsk-3β+/−Irs2−/− mice did not differ from that of the control mice . Although the α-cell mass was not determined , the reduction in β- to α-cell ratio ( Figure 4H ) was consistent with the reduction in β-cell mass . The progressive loss of β-cell mass in Irs2−/− mice has been shown to be associated with reduced proliferation and increased apoptosis [38–41] . Preservation of β-cell mass in the Gsk-3β+/−Irs2−/− mice relative to that in the Irs2−/− mice could be due to increased proliferation or reduced apoptosis or both . We measured Ki67-positive cells in β-cells to assess proliferation in Irs2−/− and Gsk-3β+/−Irs2−/− mice , as shown in Figure 5A . The percent of Ki67-positive β-cells in Irs2−/− mice was markedly reduced compared to that in WT and Gsk-3β+/− mice at 8 wk of age ( Figure 5B ) . Remarkably , the percentage of Ki67-positive cells in Gsk-3β+/−Irs2−/− mice was over four times greater than in Irs2−/− mice , and even increased approximately 2-fold relative to that in WT and Gsk-3β+/− . We next assessed apoptosis by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick-end labeling ( TUNEL ) staining in pancreatic β-cells at 8 wk of age ( Figure S3 ) . The number of TUNEL-positive β-cells was markedly increased in Irs2−/− mice compared to that in WT and Gsk-3β+/− mice ( Figure 5C ) . Whereas TUNEL positivity was significantly increased in Gsk-3β+/−Irs2−/− mice relative to that in WT and Gsk-3β+/− mice , importantly there was approximately a 60% reduction in TUNEL-positive cells in Gsk-3β+/−Irs2−/− mice compared to Irs2−/− mice . These observations indicate that the preservation of β-cell mass in Gsk-3β+/−Irs2−/− mice was associated with reversal of decreased proliferation and increased apoptosis of Irs2−/− mice . To examine possible molecular mechanisms for the preservation of islet β-cell mass in Gsk-3β+/−Irs2−/− mice , islets from WT , Irs2−/− , and Gsk-3β+/−Irs2−/− mice at 6 wk of age were examined by western blot analysis to assess insulin signaling upstream and downstream of Gsk-3β . Phosphorylation of Akt was equally reduced in Irs2−/− and Gsk-3β+/−Irs2−/− mice ( Figure 6A ) , indicating that haploinsufficiency for Gsk-3β did not alter the insulin signaling pathway through Akt . Interestingly , total Gsk-3 activity remained reduced in Gsk-3β+/−Irs2−/− , indicating that neither the intact Gsk-3β allele nor the Gsk-3α alleles compensated to increase Gsk-3 activity . Gsk-3β+/−Irs2−/− mice had no change in Irs1 expression levels relative to those in Irs2−/− mice ( Figure 6B ) . Loss of β-cells in Irs2−/− mice was shown to be associated with decreased islet Pdx1 protein [39 , 42] , whereas transgenic expression of Pdx1 rescued the diabetic phenotype [43] . Associated with preservation of β-cell mass , Gsk-3β+/−Irs2−/− mice had preservation of Pdx1 levels relative to that in Irs2−/− mice by western blot analysis ( Figure 6B ) and nuclear localization by immunocytochemistry ( Figure 6C ) . The Irs2−/− mice have been shown to have increased levels of the cyclin-dependent kinase inhibitor p27kip1 in β-cells , and the loss of β-cell mass and diabetes was corrected when these mice were placed on a p27kip1 null background [40] . This established p27kip1 as a rate-limiting determinant of proliferation in this insulin-resistant model . Interestingly , it was recently shown that in non–β-cells , Gsk-3β stabilizes p27kip1 [44] . We confirmed that islets of Irs2−/− mice have increased p27kip1 protein , and found that the increased proliferation in β-cells of Gsk-3β+/−Irs2−/− mice was associated with decreased p27kip1 levels ( Figure 6B ) . Immunostaining on islets of WT mice revealed that only some of the islet cells were positive for p27kip1 ( Figure 6D , left ) . In contrast , virtually all of the nuclei of the islet cells in Irs2−/− mice were positive ( Figure 6D , middle ) . In Gsk-3β+/−Irs2−/− mice , p27kip1 could only be detected in some of the nuclei of the islet cells ( Figure 6D , right ) , with a staining that appears to be weaker than observed in Irs2−/− mice , consistent with the increased proliferation observed in these mice . To separate peripheral versus β-cell–specific effects of reduction of Gsk-3β activity on preservation of β-cell mass , mice homozygous for a floxed Gsk-3β allele ( Gsk-3βF/F ) were then crossed with rat insulin 2 promoter-Cre ( RIP-Cre ) mice to produce β-cell–specific knockout of Gsk-3β ( βGsk-3β−/− ) . As shown in Figure 7A , expression of islet Gsk-3β was reduced more than 80% . βGsk-3β−/−Irs2−/− mice also maintained relatively normal plasma glucose compared to the severe hyperglycemia of the Irs2−/− mice for the 20 wk of observation ( Figures 7B ) . Additionally , the hyperinsulinemia of the Irs2−/− mice was maintained in βGsk-3β−/−Irs2−/− mice ( Figure 7C ) . To further examine effects of reduction in Gsk-3 activity on protein stability of p27kip1 , mouse insulinoma cells were pretreated with lithium to inhibit Gsk-3 activity , and protein levels were assayed 4 h after addition of cyclohexamide to inhibit new protein synthesis . Lithium treatment reduced Gsk-3 activity and markedly reduced levels of p27kip1 compared to cells treated with NaCl as an osmotic control ( Figure 8A ) . To confirm the physiological significance of the effects of Gsk-3 activity on p27kip1 levels , primary mouse islets were examined . Islets were isolated from WT mice and incubated for 4 h in serum-free medium , followed by 3 h with no addition , or with the addition of either IGF-1 to activate the insulin signaling pathway , with lithium , or with both , or islet protein lysates blotted for p27kip1 levels ( Figure 8B ) . Addition of IGF-1 and lithium resulted in reduced p27kip1 levels , with maximum reduction with addition of both . These results provide physiological support for the conclusion that Gsk-3 activity stabilizes p27kip1 levels in pancreatic islets .
The current study offers specific genetic approaches to assess the role of Gsk-3β in control of β-cell mass in insulin-resistant diabetic models , and as a consequence , several novel observations were made . Loss of one allele of Gsk-3β in WT mice promotes insulin sensitivity and in Ir+/− mice reduces insulin resistance and improves glucose tolerance by enhancing glucose disposal . Severely insulin-resistant Irs2−/− mice were found to have elevated islet Gsk-3 activity associated with severe reduction of β-cell proliferation and elevated apoptosis . Loss of one allele of Gsk-3β in Irs2−/− mice reversed these findings , preserving β-cell mass and preventing diabetes . Additionally , Pdx1 levels were depressed and p27Kip1 levels were increased in islets of Irs2−/− mice , and they were also reversed by loss of one allele of Gsk-3β . β-cell–specific deficiency of Gsk-3β reversed the diabetes of the Irs2−/− mice , indicating the importance of Gsk-3β in islet β-cells . Finally , in vitro studies demonstrated that Gsk-3 activity stabilizes p27kip1 levels , suggesting a mechanism for impairment of proliferation . The results of these studies thus indicate that in insulin-resistant animals , Gsk-3β impairs replication and enhances cell death , leading to postnatal β-cell loss and diabetes . FoxO1 and Gsk-3β are both negatively regulated targeted proteins of the insulin/PI-3K/Akt signaling pathway . Previous studies showed that Irs2−/− mice crossed with Foxo1+/− mice resulted in partial correction of fed plasma glucose , β-cell mass , and proliferation [39] , along with improved Pdx1 expression . In the current study , we found that islets of Irs2−/− mice had increased Gsk-3 activity ( Figure 3A and 3B ) , now demonstrating that both Gsk-3 and FoxO1 significantly contribute to the impaired proliferation and increased apoptosis in Irs2−/− mice . What are the mechanisms that could account for postnatal loss of β-cell mass in the insulin-resistant models ? Evidence to date suggests that FoxO1 is contributing through impaired proliferation and enhanced apoptosis via transcriptional mechanisms , as it has been shown to repress Pdx1 transcription in insulinoma cells [39] . In non–β-cells FoxO1 has also been shown to increase p27Kip1 expression [45] . The results of the current studies suggest a novel mechanism for regulation of Pdx1 and p27Kip1 levels in insulin-resistant β-cells through Gsk-3β activity . In non–β-cells , the half-life ( t1/2 ) of p27Kip1 protein was 12 h in the absence of growth factors , and 20 min when growth factors were restored , or when cells were treated with Gsk-3β inhibitors [44] . Gsk-3β phosphorylated and stabilized p27Kip1 , whereas Gsk-3β inhibitors targeted p27Kip1 for proteosomal degradation . These results suggest a possible mechanism by which Gsk-3 activity might regulate cell proliferation in β-cells through altered p27Kip1 stability . There is substantial evidence that this mechanism is operational in β-cells . First , p27kip1 levels were increased in islets from Irs2−/− mice ( Figure 6B and 6D , and [40] ) , and levels were reduced with elimination of one allele of Gsk-3β . Second , in vitro evidence is presented showing that Gsk-3 activity regulates p27Kip1 protein stability in insulinoma cells ( Figure 8A ) . Third , physiological support for the conclusion that Gsk-3 activity stabilizes p27kip1 levels in pancreatic islets is presented ( Figure 8B ) . In contrast to p27Kip1 protein stabilization , Gsk-3 activity was shown to destabilize Pdx1 protein levels in insulinoma cells [46] . We have confirmed this effect of Gsk-3 activity on Pdx1 in MIN6 cells ( unpublished data ) , and now provide in vivo evidence that reduction of islet Gsk-3β activity restores Pdx1 levels in islets of Gsk-3β+/−Irs2−/− mice ( Figure 6B and 6C ) . The Irs2−/− mice have severe impairment of the insulin signaling PI-3K/Akt pathway , and rapidly lose β-cell mass . Elimination of one allele of Gsk-3β in Irs2−/− mice preserves β-cell mass and , for the most part , maintains glucose homeostasis , yet Gsk-3β is only one of many substrates regulated by the insulin signaling pathway . For example , Irs2−/− mice have increased FoxO1 [39] , and perhaps decreased S6K levels , along with alterations in other Akt substrates . Although the Gsk-3β+/−Irs2−/− mice maintain apparently normal β-cell mass , they are still insulin resistant , and therefore not fully functional; they would be anticipated to have expanded β-cell mass as shown in the Ir+/− mice ( Figure 2E ) . Thus Gsk-3β is only one protein among many necessary for fully functional β-cells . Elimination of one allele of Gsk-3β in insulin-resistant Ir+/− mice enhanced insulin sensitivity by augmenting peripheral insulin-mediated glucose disposal , independent of effects on hepatic glucose output ( Figure 1C–1E ) . These results are consistent with those in which tissue-specific knockout of Gsk-3β in skeletal muscle enhanced insulin sensitivity ( S . Patel , B . W . Doble , K . MacAulay , E . M . Sinclair , D . J . Drucker , and J . R . Woodgett , unpublished data ) . Could enhanced peripheral insulin sensitivity by loss of one allele of Gsk-3β account for the preservation of β-cell mass in the Irs2−/− mice ? The results with conditional knockout of the Gsk-3β gene in β-cells indicate the importance of this protein in the β-cell with impaired insulin signaling and that under these circumstances , Gsk-3α is not playing a major role . Although the exact contribution of Gsk-3α to β-cell function has yet to be determined , the results of Patel et al . ( S . Patel , B . W . Doble , K . MacAulay , E . M . Sinclair , D . J . Drucker , and J . R . Woodgett , unpublished data ) and MacAulay et al . [47] emphasize the isoform and tissue-selective effects of the two mammalian Gsk-3s in skeletal muscle and liver . The results of these studies now define a new , negatively regulated substrate of the insulin signaling pathway specifically within β-cells that when elevated , can impair replication and increase apoptosis , resulting in postnatal loss of β-cells and diabetes . These results thus form the rationale for developing agents to inhibit this enzyme in obese insulin-resistant individuals to preserve β-cells and prevent diabetes onset .
Generation and genotyping of Gsk-3β+/− , Ir+/− , and Irs2−/− mice have been described [23 , 37 , 38 , 48] . We maintained Ir+/− mice on the C57BL/6J background and the Irs2−/− mice on a mixed C57BL/6J × 129Sv background , and crossed them with Gsk-3β+/− mice on the C57BL/6J × 129Sv background to obtain Gsk-3β +/−Ir+/− and Gsk-3β+/−Irs2+/− mice on a mixed C57BL/6J × 129Sv background . Double-heterozygote F1 offspring were intercrossed ( Gsk-3β+/−Irs2+/− ) to obtain Gsk-3β+/−Irs2−/− mice . The Gsk-3β+/−Irs2−/− progeny was observed at the expected Mendelian frequency in both instances . WT control mice have been obtained from littermates of double-heterozygous breeding . The generation of mice expressing a conditional allele of Gsk-3β will be described in further detail ( S . Patel , B . W . Doble , K . MacAulay , E . M . Sinclair , D . J . Drucker , and J . R . Woodgett , unpublished data ) . In brief , R1 embryonic stem ( ES ) cells were electroporated with a modified Gsk-3β targeting vector whereby LoxP sites were introduced by PCR into the intronic region flanking exon 2 of Gsk-3β , and a neomycin resistance cassette was inserted and flanked by FLP recombinase target ( FRT ) sites ES cell clones that had undergone correct homologous recombination were identified by Southern blot and microinjected into C57/B6J blastocysts . The resultant chimeric mice were crossed to C57/B6J , and germline transmission of the Gsk-3β floxed allele was verified by PCR . Resultant interbreeding of these mice yielded Gsk-3β floxed mice that were viable , healthy , born with the expected Mendelian frequency , and expressed Gsk-3β at levels indistinguishable from WT animals . Pancreatic β-cell–specific Gsk-3β knockout mice ( βGSK-3β–/– ) were generated by breeding Gsk-3β floxed mice with mice that express the Cre recombinase gene under the control of the promoter of the rat insulin 2 gene [49] . To obtain βGsk-3β–/–Irs2+/− mice , F1 offspring ( Gsk-3βF/+Irs2+/− ) were intercrossed . They were then further intercrossed with F2 offspring ( βGsk-3β–/–Irs2+/− ) to obtain βGsk-3β–/–Irs2−/− mice . Blood glucose as well as serum insulin concentrations were determined as previously described [13] . For the glucose tolerance test , mice were subjected to an overnight fast followed by intraperitoneal glucose injection ( 2 . 0 g/kg ) . Blood samples were collected at 0 , 30 , 60 , and 120 min after the injection . For the insulin tolerance tests , mice were subjected to a 4-h fast followed by intraperitoneal human regular insulin injection ( 0 . 5 U/kg ) . Blood samples were collected 0 , 30 , 60 , and 120 min after the injections . After sacrificing the mice , the pancreas was removed and weighed . All experiments were carried out with male mice . This project was approved by the Animal Ethics Committee of Washington University School of Medicine . Clamp experiments were essentially performed as previously described [50 , 51] . Double-lumen catheters were placed and 3-[3H] glucose was infused to steady state . Regular human insulin was infused at 20 mU/kg per min with 25% d-glucose to maintain the blood glucose at 120 mg/dl for at least 90 min . The 3-[3H] glucose infusion was continued during the clamp , with labeled glucose included in the 25% d-glucose infusion to match blood specific activity at steady state . The rate of appearance of glucose ( Ra ) , equal to the rate of glucose utilization ( Rd ) at steady state , was determined by dividing the infusion rate of labeled glucose by the specific activity . Endogenous glucose production was calculated by subtracting the cold glucose infusion rate from the clamp Rd . Pancreas were isolated and fixed from 8-wk-old Irs2−/− , Gsk-3β+/−Irs2−/− , and Gsk-3β+/− mice . Isolated pancreas were fixed overnight in 3 . 7% formaldehyde at room temperature . Tissue was then routinely processed for paraffin embedding , and 5-μm sections were cut and mounted on glass slides . The sections were immunostained with antibodies to insulin ( Dako ) , glucagon ( Sigma Aldrich ) , Ki67 ( Zymed Laboratories/Invitrogen ) , Pdx-1 ( Joel Habener ) , and p27kip1 ( BD Transduction Laboratories ) . Histomouse-SP ( Zymed Laboratories/Invitrogen ) was used for secondary antibodies for brightfield microscopy and β-cell mass , whereas Cy3- or fluorescein isothiocyanate−conjugated ( FITC ) secondary antibodies ( Jackson Immunoresearch ) were used for fluorescence microscopy . All images were acquired on a DM4000 B microscope ( Leica Microsystems ) . The β-cell area was determined after the analysis of a number of random sections stained for insulin and analyzed with NIH Image 1 . 38x software [52] . The total β-cell mass was then calculated using the following calculation: [ ( islet area/total pancreas area ) × pancreas weight] . Five pancreatic sections from each animal , including representative sections of pancreas , and at least 100 islets per mouse were counted . Adjacent nonoverlapping fields were analyzed to obtain a true representation of average islet/β cell distribution throughout the pancreas . TUNEL staining was performed on pancreatic sections using the ApopTag in situ apoptosis detection kit and according to the manufacturer's instructions ( Chemicon/Milipore ) . At least 3 , 000 insulin-positive cells were counted for each mouse to assess the percentage of TUNEL-positive cells among insulin-positive cells . Quantitative data are obtained from at least three mice in each group , unless indicated . Islet isolation were carried out as described previously [53] . For immunoblot analysis , isolated islets were lysed in ice-cold cell lysis buffer consisting of 50 mM HEPES ( pH 7 . 5 ) , 1% ( v/v ) Nonidet P-40 , 2 mM activated sodium orthovanadate , 100 mM sodium fluoride , 10 mM sodium pyrophosphate , 4 mM EDTA , 1 mM phenylmethylsulfonyl fluoride , 1 μg/ml leupeptin , and 1 μg/ml aprotinin , then passed through a syringe ten times; particulate material was removed by centrifugation ( 10 , 000 × g; 10 min; 4 °C ) . The supernatant was collected . The extracts ( 50 μg of total protein ) were subjected to immunoblot analysis with antibodies to Gsk-3β phosphorylated on Ser9 , Akt phosphorylated on Ser473 , Akt phosphorylated on Thr308 , total Akt , total Gsk-3α/β ( Cell Signaling ) , total Gsk-3β ( BD Transduction Laboratories ) , and glycogen synthase phosphorylated on Ser 641 and 645 ( Biosource/Invitrogen ) , Irs2 ( Upstate ) , Pdx1 ( Santa Cruz SC14664 ) , p27kip1 ( BD Transduction Laboratories ) , α-tubulin , and β-actin ( Sigma-Aldrich ) . For immunoblot analysis with mouse insulinoma MIN6 cells , lysates of MIN6 cells treated with either 40 mM lithium or 40 mM NaCl for 1 h , then cotreated with 25 μg/ml cyclohexamide and lithium for 4 h , were prepared . The lysates were probed with the antibodies listed above . Islets isolated from 16-wk-old WT mice were deprived of serum for 4 h and were incubated in the absences or presence of 10% FBS , 100 nM IGF-1 , or 40 mM LiCl for 3 h . Lysates were then prepared from islets and were subjected to western blot analysis with the antibodies listed above . Immune complexes were revealed using ECL Advance Western Blot Detection kit ( Amersham ) , and the images were acquired using a FluoroChemi 8800 digital camera acquisition system ( Alpha Innotech ) . Band intensities in the blots were later quantified using ImageJ 1 . 39j [53] and α-tubulin or β-actin bands were used to adjust for loading differences . Quantitative data are presented as the mean ± the standard error of the mean ( S . E . M . ) from at least three independent experiments and at least 100 islets from more than three mice , unless indicated . We assessed interactions among variables by two-way analysis of variance and used the Student' t-test to compare independent means . A p-value of 0 . 05 was considered statistically significant . | Diabetes is often characterized by a failure of insulin production by pancreatic β-cells to properly regulate glucose homeostasis . Insulin resistance can lead to β-cell failure , and our studies have focused on elucidating the mechanisms involved in this postnatal failure . In this study , we evaluated a new , negatively regulated enzyme of the insulin signaling pathway , glycogen synthase kinase 3 ( Gsk-3 ) , specifically within insulin-producing pancreatic β-cells . When this enzyme is elevated , it can impair replication and increase cell death , resulting in loss of insulin-producing cells and diabetes . Gsk-3 is also known to regulate cell death and proliferation in neurons . We assessed the role of Gsk-3 on glucose homeostasis in two different mouse models of insulin resistance . We demonstrated that genetically reducing the levels of Gsk-3β in the insulin-resistant mouse improved glucose homeostasis . In another model in which severe insulin resistance is associated with destruction of β-cells , reducing Gsk-3β not only preserved β-cells by increasing proliferation and reducing cell death , but it also corrected diabetes . Controlling activity of Gsk-3 could lead to new hopes for maintaining or improving β-cell number and prevention of diabetes . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [
"diabetes",
"and",
"endocrinology"
] | 2008 | Genetic Deficiency of Glycogen Synthase Kinase-3β Corrects Diabetes in Mouse Models of Insulin Resistance |
We report in this work that Leptospira strains , virulent L . interrogans serovar Copenhageni , attenuated L . interrogans serovar Copenhageni and saprophytic L . biflexa serovar Patoc are capable of binding fibrinogen ( Fg ) . The interaction of leptospires with Fg inhibits thrombin- induced fibrin clot formation that may affect the haemostatic equilibrium . Additionally , we show that plasminogen ( PLG ) /plasmin ( PLA ) generation on the surface of Leptospira causes degradation of human Fg . The data suggest that PLA-coated leptospires were capable to employ their proteolytic activity to decrease one substrate of the coagulation cascade . We also present six leptospiral adhesins and PLG- interacting proteins , rLIC12238 , Lsa33 , Lsa30 , OmpL1 , rLIC11360 and rLIC11975 , as novel Fg-binding proteins . The recombinant proteins interact with Fg in a dose-dependent and saturable fashion when increasing protein concentration was set to react to a fix human Fg concentration . The calculated dissociation equilibrium constants ( KD ) of these reactions ranged from 733 . 3±276 . 8 to 128±89 . 9 nM for rLIC12238 and Lsa33 , respectively . The interaction of recombinant proteins with human Fg resulted in inhibition of fibrin clot by thrombin-catalyzed reaction , suggesting that these versatile proteins could mediate Fg interaction in Leptospira . Our data reveal for the first time the inhibition of fibrin clot by Leptospira spp . and presents adhesins that could mediate these interactions . Decreasing fibrin clot would cause an imbalance of the coagulation cascade that may facilitate bleeding and help bacteria dissemination
The spirochete Leptospira interrogans is a highly invasive pathogen and the causal agent of leptospirosis , one of the most widespread zoonosis of human and veterinary concern [1] , [2] . The transmission occurs through contact with environmental water contaminated by leptospires shed in the urine of animal carriers [1] , [3] . Humans are accidental and terminal hosts in the transmission process of leptospirosis . The leptospires enter the body via abrasions on skin or actively through mucosa , spreading to any tissue , and colonizing target organs [4] , [3] . Leptospira can cause damage of the endothelium of small blood vessels , leading to hemorrhage and localized ischemia in multiple organs . As a consequence , renal tubular necrosis , hepatocellular damage and development of leptospirosis-associated pulmonary hemorrhage syndrome ( LPHS ) may occur in the host [1] , [5] , [6] . The mechanisms responsible for bleeding in leptospirosis are poorly understood . Hemolysins could play an important role in this toxic response and several genes coding for predicted hemolysins were identified in the genome sequencing L . interrogans [7] . Yet , when evaluated , these proteins failed to show hemolytic activity in human erythrocytes [8] , [9] . Fg and PLG are key proteins in the coagulation cascade and fibrinolysis , respectively , and critical determinants of bacterial virulence and host defense [10] . Fg is the major clotting protein present in blood plasma with an important role in blood coagulation and thrombosis . PLG under the action of its activators generates PLA , a serine-protease capable of degrading ECM components , fibrin , facilitating the pathogen penetration and invasion [11] . We have previously shown that PLA-associated Leptospira renders the bacteria with proteolytic activity capable of degrading ECM components [12] that in turn , may help bacterial penetration and dissemination . Furthermore , PLA-coated leptospires have also shown to degrade IgG and C3b that could facilitate the bacterial immune evasion [13] . The adhesion of physiological osmotically induced Leptospira with Fg was described but their effect on fibrin formation was not ascertained [14] . The leptospiral proteins LigB and OmpL37 were shown to interact with Fg and LigB was reported to reduce fibrin clot formation [15] , [16] , [17] . We thus decided to evaluate if Fg-associated Leptospira was capable to inhibit the fibrin clot formation and the ability of seven recombinant proteins to act as leptospiral Fg-receptors . We report that Leptospira strains and the recombinant ECM- and PLG-interacting proteins , rLIC12238 [18] , Lsa33 , Lsa25 [19] , Lsa30 [20] , OmpL1 [21] , rLIC11360 and rLIC11975 [22] , are capable to adhere to Fg . We also show that this interaction inhibits fibrin clot formation by thrombin-catalyzed reaction . Moreover , PLA-coated Leptospira was capable to degrade Fg . Altogether , the results suggest possible pathways that Leptospira may interfere with the coagulation/bleeding process .
All animal studies were approved by the Ethical Committee for Animal Research of Instituto Butantan , São Paulo , SP , Brazil under protocol no 798/11 . The Committee in Animal Research in Instituto Butantan adopts the guidelines of the Brazilian College of Animal Experimentation . The non-pathogenic L . biflexa serovar Patoc strain Patoc 1 , the pathogenic attenuated L . interrogans serovar Copenhageni strain M-20 and the virulent strains of L . interrogans serovar Kennewicki strain Pomona Fromm ( LPF ) and serovar Copenhageni strain Fiocruz L1-130 were cultured at 28°C under aerobic conditions in liquid EMJH medium ( Difco ) with 10% rabbit serum , enriched with L-asparagine ( wt/vol: 0 . 015% ) , sodium pyruvate ( wt/vol: 0 . 001% ) , calcium chloride ( wt/vol: 0 . 001% ) , magnesium chloride ( wt/vol: 0 . 001% ) , peptone ( wt/vol:0 . 03% ) and meat extract ( wt/vol: 0 . 02% ) [23] . Leptospira cultures are maintained in Faculdade de Medicina Veterinária e Zootecnia , USP , São Paulo , SP , Brazil . Unless otherwise stated , experiments were performed with leptospires resuspended in low salt – lsPBS ( 50 mM ) , instead of PBS that contains 137 mM NaCl , because it is an osmolarity condition closer to cultivation , which is approximately 35 mM . For the immunofluorescence assay ( IFA ) , live L . interrogans sorovar Copenhageni strain M-20 suspensions ( ∼108 ) were harvested at 5 , 000× g for 15 min , washed three times with lsPBS , resuspended in 200 µL lsPBS , and fixed with 200 µL with 4% paraformaldehyde for 40 min at 30°C . After the fixation , propidium iodide ( Sigma ) diluted 1∶50 in lsPBS was added to stain the nuclei , and the suspensions were incubated for 45 min at 30°C . After this time , the leptospires were gently washed three times with lsPBS and blocked with lsPBS containing 5% BSA for 90 min at 30°C , followed by incubation with 16 µg Fg in 200 µL lsPBS for 60 min at 37°C . The leptospires were washed three times with lsPBS plus 1% BSA and incubated with goat-produced antiserum against human Fg at a 1∶50 dilution for 45 min at 37°C . Leptospires were washed three times again and incubated with rabbit anti-goat IgG antibodies conjugated to fluorescein isothiocyanate ( FITC; Sigma ) at a dilution of 1∶400 for 45 min at 37°C . After this incubation , the leptospires were washed twice with lsPBS containing 1% BSA and once with distilled water . Finally they were resuspended in lsPBS-antifading solution ( ProLong Gold; Molecular Probes ) . The immunofluorescence-labeled leptospires were examined by use of a confocal LSM 510 META immunofluorescence microscope ( Zeiss , Germany ) . As a control for unspecific binding , Fg was absent from the reaction mixtures . For accessing the leptospiral binding to soluble human Fg , ELISA plates were coated with 108 leptospires in lsPBS per well ( L . interrogans sorovar Copenhageni strain Fiocruz L1-130 , L . interrogans serovar Copenhageni strain M-20 , and L . biflexa serovar Patoc strain Patoc 1 ) , allowed to set for 3 h at 37°C and then blocked with 200 µL of phosphate-buffered saline containing 0 . 05% Tween ( PBS-T ) with 10% non-fat dry milk . After 2 h incubation , plates were washed with PBS-T and then 100 µL of a Fg solution ( Fg- Sigma ) ( 10 µg/mL in lsPBS ) were added per well and incubation proceeded for 2 h . After extensive washing , 100 µL of solution containing goat anti-Fg ( 1∶25 , 000 in lsPBS ) were added per well and incubation was carried out for 1 h at 37°C . After three washings , 100 µL of solution containing peroxidase ( HRP ) -conjugated rabbit anti-goat IgG ( 1∶50 , 000 in lsPBS ) were added per well and the reaction continued for 1 h at 37°C . The wells were washed three times , and o-phenylenediamine ( OPD ) ( 1 mg/mL ) in citrate phosphate buffer ( pH 5 . 0 ) plus 1 µL/mL H2O2 was added ( 100 µL per well ) . The reaction was allowed to proceed for 10 min and interrupted by the addition of 50 µL of 8 M H2SO4 . Readings were taken at 492 nm in a microplate reader ( Multiskan EX; Thermo Fisher Scientific , Helsinki , Finland ) . The binding of Fg to each leptospiral strain was performed in triplicate and a negative control in which Fg was omitted was included in the experiment . In the no-cell control , BSA replaced leptospires . For statistical analyses , the binding of Fg to different strains and control BSA was performed using one-way ANOVA , followed by Tukey post-test for pairwise comparisons . Live leptospires ( L . interrogans sorovar Copenhageni strain Fiocruz L1-130 , L . interrogans serovar Copenhageni strain M-20 , and L . biflexa serovar Patoc strain Patoc 1 ) , 109 , 107 , 105 cells/mL were harvested at 5 , 000× g for 20 min at room temperature , washed once with lsPBS , resuspended in 0 . 5 mL of lsPBS plus 1 mg/mL of Fg ( Sigma ) , and incubated for 2 h at 37°C . ELISA plates were placed with 90 µL/well of leptospires plus Fg and 10 µL/well of thrombin ( 10 U/mL - Sigma ) . The fibrin clot formation was measured every 1 min for 10 min and then every 5 min for 35 min by an ELISA plate reader ( Multiskan EX Thermo Fisher Scientific ) at OD595nm . The positive control of the reaction employed Fg ( 1 mg/mL ) plus thrombin ( 10 U/mL ) while in the negative control thrombin was omitted . For statistical analyses , inhibition of fibrin clot formation was performed by one-way ANOVA , followed by Tukey post-test for pairwise comparisons . Three independent experiments were performed . Virulent L . interrogans serovar Kennewicki strain Pomona Fromm ( 108 leptospires/sample ) were treated in 200 µL lsPBS with the addition of: ( a ) 10 µg PLG ( b ) 3U urokinase ( uPA ) ( c ) 10 µg PLG and 3U uPA ( generating PLA-coated leptospires ) or ( d ) only lsPBS with no additions ( untreated ) . The bacteria were incubated for 1 h at 37°C with the PLG , and for one more hour after the addition of uPA . The cells were washed three times with lsPBS to remove the free PLG , uPA and PLA . Then , the bacteria were resuspended in 100 µL lsPBS containing goat anti-human Fg human purified Fg ( Sigma , USA ) as substrate , and incubated for 16 h at 37°C . Three additional controls were employed: ( a ) one sample received 1 µg of the protease inhibitor aprotinin ( Sigma , USA ) to the Fg-leptospires incubation , ( b ) one sample contained only Fg without leptospires , and ( c ) one sample received the PLG and uPA treatment without the addition of leptospires to rule out the free PLA Fg degradation . The leptospires were removed by centrifugation , 20 µL of the supernatants were separated by 8% SDS-PAGE and then transferred to nitrocellulose membranes in semi-dry equipment . The membranes were blocked by incubating overnight at 4°C with 5% non-fat dry milk and 1% BSA . The Fg detection was performed by incubations with goat anti-human Fg antibodies and rabbit anti-goat secondary antibodies conjugated with HRP , followed by ECL ( GE Healthcare ) development . In another assay , after the incubation for Fg degradation , the pelleted leptospires and the remaining cell-free supernatants were also evaluated for the presence of Fg by ELISA . The cells were resuspended in 100 µL lsPBS and coated onto ELISA plates overnight at 4°C , as well as the cell-free supernatants previously diluted 20 times . The plates were washed three times with PBS-T and blocked with solution containing 5% BSA and 5% non-fat dry milk for 2 h at 37°C , following incubations with anti-Fg polyclonal antibodies ( 1∶25 , 000 ) and secondary antibodies conjugated to peroxidase ( 1∶50 , 000 ) . The reactions were developed by addition of 100 µL/well of solution containing 1 mg/mL OPD and 1 µL/mL H2O2 , and stopped by addition of 50 µL/well H2SO4 . The samples in which the leptospires received no treatment ( only PBS ) were considered as having 100% of Fg for comparative purposes . Predicted coding sequences ( CDSs ) were analyzed for their cellular localization by PSORT program , http://psort . nibb . ac . jp [24] , [25] . The web servers SMART , http://smart . embl-heidelberg . de/ [26] , [27] , PFAM , http://www . sanger . ac . uk/Software/Pfam/ [28] , and LipoP , http://www . cbs . dtu . dk/services/LipoP/ [29] were used to search for predicted functional and structural domains within the amino acid sequences of the CDSs . Amplification of the CDSs was performed by PCR from L . interrogans serovar Copenhageni strain M-20 genomic DNA using complementary primer pairs ( Table 1 ) . The gene sequences were amplified without the signal peptide tag and cloned into pAE expression vector [30] . The final constructs were verified by DNA sequencing on an ABI Prism 3730_L sequencer ( Seq- Wright , Houston , TX ) with appropriate vector-specific T7 ( F: TAATACGACTCACTATAGGG ) and pAE ( R:CAGCAGCCAACTCAGTTCCT ) primers . Detailed of cloning , expression and purification of the recombinant proteins Lsa33 , Lsa25 , rLIC12238 , Lsa30 , OmpL1 , rLIC11360 and rLIC11975 has been previously described [18] , [19] , [21] , [20] , [22] . The proteins rLIC11360 and Lsa30 were kept at pH 12 required for their solubility . Five female BALB/c mice ( 4–6 weeks old ) were immunized subcutaneously with 10 µg of each recombinant protein adsorbed in 10% ( vol/vol ) of Alhydrogel ( 2% Al ( OH ) 3 , Brenntag Biosector , Denmark ) , used as adjuvant . Two subsequent booster injections were given at 2-week intervals with the same recombinant proteins preparation . Negative - control mice were injected with PBS plus Alhydrogel . Two weeks after each immunization , the mice were bled from the retro - orbital plexus and the pooled sera were analyzed by ELISA for determination of antibody titers . The binding of the recombinant proteins to Fg was evaluated by a modified ELISA , as follows: 96-well plates ( Costar High Binding , Corning ) were coated overnight in PBS at 4°C with 100 µL of 10 µg/mL of the human Fg ( Sigma ) ; LigB7-12 protein of L . interrogans , known as having Fg-binding domain [31] , [17] , generous donation from Dr . Odir Dellagostin , UFPEL , RS , Brazil , was employed as a positive control . PspA , an outer membrane protein of Streptococcus pneumoniae [32] , kind gift from Dr . Luciana Leite , Instituto Butantan , SP , Brazil , was employed as an unrelated negative control protein . Gelatin ( Difco ) was also employed as negative protein control . Plates were washed three times with PBS-T and blocked for 2 h at 37°C with PBS with 10% ( wt/vol ) non-fat dry milk . The blocking solution was discarded and 100 µL of 10 µg/mL recombinant proteins in PBS was incubated for 2 h at 37°C . Wells were washed three times with PBS-T and incubated for 1 h at 37°C with polyclonal mouse antiserum produced against each recombinant protein . The serum dilution used was determined by titration curve with the corresponding recombinant protein and the value of 1 . 0 at OD492nm was employed . These values are: 1∶1 , 000 for rLIC12238; 1∶750 for Lsa33; 1∶500 for rLIC11975 and rLIC11360; 1∶400 for Lsa30 , 1∶800 for OmpL1 , 1∶500 for Lsa25 and 1∶200 for PspA . After incubation , plates were washed again and incubated with HRP-conjugated anti-mouse immunoglobulin G ( IgG ) , diluted 1∶5 , 000 in PBS . After three washings , 100 µL/well of 1 mg/mL OPD plus 1 µL/mL H2O2 in citrate phosphate buffer ( pH 5 . 0 ) were added . The reactions were carried out for 15 min and stopped by the addition of 50 µL/well of H2SO4 ( 8 M ) . Readings were taken at OD492 nm . In another assay , the assessment of bound proteins was performed by incubation for 1 h at 37°C with monoclonal anti-polyhistidine-HRP ( Sigma ) at appropriate dilutions: 1∶5 , 000 for rLIC12238; 1∶10 , 000 for rLIC11975 and rLIC11360; 1∶1 , 000 for Lsa30; 1∶500 for Lsa33 , 1∶400 for OmpL1 , 1∶200 for Lsa25 and 1∶200 for LigB7-12 . The reaction was developed with 1 mg/mL OPD plus 1 µL/mL H2O2 , as described above . The rate of interaction of recombinant proteins to Fg was determined by measuring the reaction as a function of time . The OD492nm value after 2 h interaction was considered the maximal binding ( 100% ) and was used for statistical analyses , using Student's two-tailed t test . For determination of dose-response curves of the binding of recombinant proteins to human Fg , protein concentrations varying from 0 to 4 , 000 nM in PBS were used . Binding was detected with polyclonal antibodies against each protein at the dilution described above , except for LigB7-12 where monoclonal anti-polyhistidine-HRP was used ( 1∶200 ) . For statistical analyses , the binding of recombinant proteins to human Fg was compared to its binding to gelatin by Student's two-tailed t test . 96-well plates were coated with 1 µg of Fg in 100 µL of PBS and allowed to set overnight at 4°C . The wells were washed three times with PBS-T and then blocked with 200 µL of 10% ( wt/vol ) nonfat dry milk for 2 h at 37°C . Prior to the next step , the proteins were incubated for 1 h at 37°C with the respective antibodies diluted 1∶50 in 100 µL of PBS . After the incubation , each recombinant protein ( 1 µg ) was added per well in 100 µL of PBS , and allowed to attach to Fg for 90 min at 37°C . Polyclonal serum obtained in mice against another leptospiral protein , DnaK ( 1∶50 ) , was employed as a control that is not specific for the studied proteins . After washing six times with PBS-T , bound recombinant proteins were detected by adding monoclonal HRP-conjugated mouse anti-polyhistidine-HRP ( Sigma ) at dilutions described above . Incubation proceeded for 1 h at 37°C . The detection was performed with OPD , as previously described . BSA or gelatin was used as negative control ( data not shown ) . For statistical analyses , the attachment of blocked recombinant proteins to Fg was compared to the binding with the untreated proteins by the two-tailed t test ( * P<0 . 05 ) . ELISA plates were coated overnight at 4°C with 100 µL of 10 µg/mL Fg . Plates were washed three times with PBS-T and blocked with 200 µL of 10% ( wt/vol ) nonfat dry milk for 2 h at 37°C . The recombinant proteins were denatured by incubation at 96°C for 10 min; 1 µg of each was added per well in 100 µL of PBS . The recombinant proteins were allowed to attach to Fg at 37°C for 90 min . After washing six times with PBS-T , bound recombinant proteins were detected by incubation with mouse serum raised against the respective protein ( dilutions described above ) at 37°C for 1 h . After three washings with PBS-T , 100 µL of a 1∶5 , 000 dilution of HRP-conjugated rabbit anti-mouse IgG ( Sigma ) in PBS was added per well for 1 h at 37°C . The detection was performed with OPD , as previously described . BSA or gelatin was used as negative control ( data not shown ) . For statistical analyses , the attachment of denatured recombinant proteins to Fg was compared to untreated recombinants binding by the two-tailed t test ( * P<0 . 05 ) . The ELISA data were used to calculate the dissociation equilibrium constant ( KD ) according to the method previously described [33] based on the equation: KD = ( Amax . [protein] ) /A ) – [protein] , where A is the absorbance at a given protein concentration , Amax is the maximum absorbance for the ELISA plate reader ( when the equilibrium is reached ) , [protein] is the protein concentration and KD is the dissociation equilibrium constant for a given absorbance at a given protein concentration ( ELISA data point ) . The assay of thrombin-catalyzed fibrin clot inhibition was performed in the presence of recombinant proteins . We have employed the concentration of recombinant proteins in which there was a binding saturation to Fg . Proteins that do not bind Fg were employed at the same concentration range . Protein concentration employed: Lsa33 – 3 , 000 nM , OmpL1 – 3 , 000 nM , rLIC12238 – 3 , 500 nM , rLIC11975 – 2 , 000 nM , LigB7-12 – 1 , 500 nM , Lsa25 – 3 , 000 nM , PspA – 3 , 000 nM . Recombinant proteins were resuspended in 0 . 5 mL of PBS plus 1 mg/mL of Fg and incubated for 2 h at 37°C . ELISA plates were coated with 90 µL/well of recombinant proteins plus Fg and 10 µL/well of thrombin ( 10 U/mL ) . The fibrin clot formation was measured as previously described . Reduction of the fibrin clot formation was calculated by comparing the value of the last reading point , at 45 min , with the positive control ( 100% ) . Analysis was performed using one-way ANOVA , followed by Tukey post-test for pairwise comparisons . ELISA plates were coated with Fg ( 1 µg/well ) in PBS and allowed to set overnight at 4°C; the wells were then washed and blocked with 10% non-fat dry milk in PBS-T for 2 h at 37°C . The blocking solution was discarded , and the wells were incubated for 90 min at 37°C with increasing concentrations of recombinant proteins ( 0 to 4 . 5 µM ) . After three washings , 100 µL/well of 4×107 live L . interrogans serovar Copenhageni strain M-20 were added for 90 min at 37°C . The unbound leptospires were washed and the quantification of bound leptospires was performed indirectly by anti-LipL32 antibodies produced in mice ( 1∶4 , 000 ) , based on the fact that LipL32 is a major expressed membrane leptospiral protein [34]; the procedure was followed by the addition of HRP-conjugated anti-mouse IgG antibodies , essentially as described in Atzingen et al . , ( 2008 ) [35] . The detection was performed by OPD , as above described .
The ability of L . interrogans sorovar Copenhageni strain M-20 cells to bind human Fg was performed by immunofluorescence assay ( IFA ) . Leptospires were visualized by propidium iodide staining ( Fig . 1 , panel A ) followed by protein detection with goat anti- human Fg , in the presence of anti-goat IgG antibodies conjugated to FITC . Green fluorescence could be observed for Fg ( Fig . 1 -Fg1B , Fg2B ) . The localization of the protein-green light within the leptospires was achieved by superimposing both fields and the results obtained are shown in Fig . 1 - Fg1C and Fg2C . The FgØ shows the control of the reaction in which Fg was absent . We have evaluated the capability of soluble human Fg ( 10 µg/mL ) to interact with immobilized strains of leptospires by ELISA . We performed the experiments using virulent L . interrogans serovar Copenhageni strain Fiocruz L1-130 , pathogenic attenuated L . interrogans serovar Copenhageni strain M-20 and one saprophytic L . biflexa serovar Patoc strain Patoc 1 . The binding of Fg to each leptospiral strain was performed in triplicate and the data represent the mean ± the standard deviation from one representative experiment is depicted in Fig . 2A . The measurements were performed in the presence ( Fg+ ) and absence ( Fg− ) of human Fg while in the no-cell control , BSA replaced leptospires . For statistical analyses , the attachment of Fg to leptospiral strains was performed by one-way ANOVA followed by Tukey post-test for pairwise comparison , asterisks above the bars refer to comparison to BSA ( no cell control ) ( **P<0 . 01 ) . The comparison among leptospiral strains is also shown ( #P<0 . 05 and ## P<0 . 01 ) . The results show that all strains tested were able of binding human Fg , but the virulent strain was more efficient . We have assessed the effect of Leptospira-bound to Fg on the inhibition of thrombin-catalyzed fibrin clot formation . The reaction was analyzed with the same strains and readings were taken at OD595nm every 1 min for the first 10 min and then every 5 min for 35 min . The complete reaction , Fg plus thrombin , was used as a positive control while in the negative one , thrombin was missing . The determination was performed in two independent experiments and a representative assay is shown in Fig . 2B . The data show that all the strains studied promoted an inhibition of fibrin formation statistically significant compared to the reaction positive control . However , the small difference observed between the strains pathogenic and saprophyte was not statistically relevant . The effect of PLA-coated virulent L . interrogans serovar Kennewicki strain Pomona Fromm on human Fg was evaluated by Western blotting using anti-human Fg antibodies ( Fig . 3A ) . The results show that PLA generation on the surface of Leptospira cause degradation of human Fg ( Fig . 3A , lane 5 ) , which is not observed when at least one of the reaction components is missing ( Fig . 3A , lanes 1 , 2 , 3and 4 ) and completely prevented in the presence of a serine protease inhibitor , aprotinin ( Fig . 3A , lane 6 ) . The data suggest that PLA-coated leptospires were capable to employ their proteolytic activity to interfere with one of the coagulation cascade substrate . The Fg degradation by PLA-coated leptospires was also evaluated by ELISA . When leptospires were treated with PLG+uPA , the detection of Fg remaining in cell-free solution is decreased when compared to untreated , only PLG or only uPA-treated or when aprotinin was added to the Fg incubation ( Fig . 3B ) . An additional control lacking leptospires was added in order to rule out the contribution of free PLA to the Fg degradation . As expected , the data showed that the remaining Fg level in this control was comparable with the non-proteolytic controls . Cell-bound Fg was also evaluated . PLA-coated leptospires retain Fg binding ability , though it seems to be diminished ( Fig . 3B ) , probably due to Fg degradation . The rationale for protein selection was mostly based on cellular localization , since surface proteins are potential receptors for Fg . We have selected seven proteins , all of them previously shown to be leptospiral adhesins and were already published , rLIC12238 [18] , [36] , Lsa33 , Lsa25 [19] , Lsa30 [20] , OmpL1 [21] , rLIC11360 and rLIC11975 [22] . Table 1 summarizes features of the selected proteins , gene locus , given name , gene conservation within the sequenced genomes , the sequences of primers used for cloning techniques and molecular mass . The amplified coding sequences , excluding the signal peptide tags , were cloned and expressed as full-length proteins in E . coli . The recombinant proteins were expressed with 6×His tag at the N-terminus and purified by nickel affinity chromatography , as previously described [35] . Proteins of Leptospira have been reported to bind Fg [15] , [16] , [17] . We thus decided to investigate whether the selected surface-exposed proteins were capable of binding human Fg in vitro . Seven recombinant leptospiral proteins , expressed and purified in our laboratory , LigB7-12 , used as positive control [31] , [16] , [17] , and the negative protein controls gelatin and pneumococcal recombinant protein PspA , were individually placed onto 96-wells and incubated with previously immobilized human Fg . The binding was quantified by ELISA and the results obtained from three independent experiments are shown in Fig . 4 . The protein bound to Fg was probed with the respective homolog polyclonal antiserum raised in mice ( Fig . 4A ) or with the monoclonal antibody anti-histidine tag ( Fig . 4B ) . The percentage of binding for each protein with Fg , measured as a function of time is shown in Fig . 4C . The protein rLIC11360 promptly reacted with Fg with 40% of binding achieved after 5 min reaction , contrasting with rLIC11975 that showed very low binding activity at this time ( Fig . 4C ) . The binding of recombinant proteins to Fg was also assessed after blocking the proteins with the corresponding antibody and after submitting them to denaturing conditions at 96°C for 10 min ( Fig . 4D ) . The binding was totally inhibited by antibody-blocked protein in the case Lsa30 , Lsa33 , rLIC11975 , rLIC12238 and rLIC11360 , while a very low percentage of the binding remained for OmpL1 ( 8 . 4% ) , suggesting the participation of non- immunogenic epitopes on the interaction ( Fig . 4D ) . Anti-DnaK serum , control employed as unrelated antibody , promoted a partial decrease on the binding of rLIC11360 and Lsa30 , no effect with the protein Lsa33 , while a slightly increase was detected with the proteins rLIC11975 , rLIC12238 and OmpL1 . At any rate , these differences were not statistically significant and are probably due to the presence of non-specific antibodies in the polyclonal serum . The adhesion of heat-denatured proteins to Fg was almost totally abolished in the case of rLIC12238 and OmpL1 , 24% remained with rLIC11360 , while 63–77% of the binding continued with Lsa30 , Lsa33 and rLIC11975 ( Fig . 4D ) . The results suggest that for some proteins the binding depends on their conformational structures while with others it probably relies on their linear primary conformation . The interactions between the recombinant proteins and Fg was assessed on a quantitative basis , as indicated in Fig . 5A . Dose-dependent and saturable binding was observed when increasing concentrations ( 0 to 4 , 000 nM ) of recombinant proteins rLIC12238 , Lsa33 , OmpL1 , rLIC11975 and rLIC11360 , or ( 0 to 2 , 000 ) of Lsa30 , were allowed to individually adhere to a fixed human Fg amount ( 10 µg/mL ) for 2 h . Saturation was reached with all except Lsa30 protein , due to the impossibility to achieve this protein at higher concentrations . In the case of LigB7-12 , dose-response curve was measured using monoclonal anti-His tag antibodies , and the results depicted in the insert of Fig . 5A shows that saturation was not reached in the protein concentration range employed . Based on the ELISA data , the calculated dissociation equilibrium constants ( KD ) for the recombinant proteins with Fg are depicted in Fig . 5B; the highest and the lowest KD values were for rLIC12238 ( 733 . 3±276 . 8 nM ) and Lsa33 ( 128±89 . 9 nM ) , respectively . Since Leptospira bound to Fg inhibited thrombin-catalyzed fibrin formation , we decided to investigate whether the recombinant proteins bound to Fg were capable to mediate this interaction . The concentration of recombinant proteins used was the one in which there was a binding saturation to Fg ( Lsa33 – 3 , 000 nM , OmpL1 – 3 , 000 nM , rLIC12238 – 3 , 500 nM and rLIC11975 – 2 , 000 nM ) . LigB7-12 , previously shown to inhibit thrombin-catalyzed fibrin formation [16] , [17] , was employed as positive control at 1 , 500 nM . Recombinant proteins Lsa25 and pneumococcal PspA that do not interact with Fg , were employed as negative controls , at 3000 nM . Each recombinant protein was pre-incubated with 1 mg/mL of Fg at 37°C for 2 h . The reaction mixtures were used to coat ELISA plates and 10 U/mL of thrombin was added per well . The fibrin clot formation was measured every 1 min for 10 min and then every 5 min for 35 min . The percentage of inhibition was calculated as a function of time , taking the complete reaction at the last time-point , in the absence of proteins , as a 100% fibrin formation ( positive control ) . In the negative control , thrombin was omitted from the reaction . The measurements were performed in triplicate and representative curves of two independent experiments are shown in Fig . 6 . The proteins rLIC11360 and Lsa30 were not employed on these assays because they were kept at pH 12 for solubility and thrombin is not active above pH 10 . The data show that the four proteins tested , Lsa33 , rLIC12238 , rLIC11975 and OmpL1 , elicited an inhibition of 40–50% on the fibrin clot formation , similar to the one elicited by LigB7-12 ( Fig . 6 ) . This effect on clot formation by the recombinant proteins was similar with all of them , although a lag time was observed within the rLIC12238 inhibition curve . The inhibition promoted by all proteins was statistically relevant when compared to the positive reaction control . The results are consistent with the KD of the proteins towards Fg that are in the same order of magnitude . In contrast , Lsa25 and PspA , proteins that do not react with Fg , were not capable to inhibit thrombin-catalyzed fibrin production , resulting in curves similar to the positive control of the reaction ( Fig . 6 ) . The inhibitory effect exerted by recombinant proteins on leptospiral adherence to Fg was quantified by ELISA . Fg-coated microtiter wells were incubated with increasing concentration ( 0–4 . 5 µM ) of rLIC12238 ( Fig . 7A ) , rLIC11975 ( Fig . 7B ) , Lsa30 ( Fig . 7C ) , rLIC11360 ( Fig . 7D ) , Lsa33 ( Fig . 7E ) and OmpL1 ( Fig . 7F ) for 90 min prior to the addition of 4×107 L . interrogans serovar Copenhageni strain M-20 . Wells were probed with anti-LipL32 serum , given the fact that LipL32 is a major membrane leptospiral protein [34] . The results are depicted in Figure 7 A to F , and show that the proteins caused a modest , but significant reduction in the number of leptospires adhering to Fg ( *P<0 . 05 ) with 0 . 5 , 1 . 0 , 1 . 0 , 0 . 1 , 0 . 25 and 1 . 0 µM of rLIC12238 , rLIC11975 , rLIC11360 , Lsa30 , Lsa33 and OmpL1 , respectively . We have performed three independent experiments with comparable results .
Fg is the major clotting protein present in blood plasma with an important role in coagulation and thrombosis . Several studies have suggested roles of Fg and PLG in host bacterial interactions [11] . The fibrinolytic system that generates PLA from PLG and its activators , decomposes the fibrin clot by degrading fibrin [37] . This system is negatively regulated by PLG activator inhibitors . A balance between coagulation system and anticoagulation is necessary to avoid pathophysiological conditions , such as , bleeding and thrombosis [38] . Several Fg-binding proteins of important pathogens , such as , Staphylococcus aureus [39] , group B Streptococcus [40] , Lactobacillus salivarius [41] and the spirochetes Treponema pallidum [42] , T . denticola [43] , [44] , [45] have been reported and characterized . These Fg-binding proteins are bacterial surface or secreted adhesins that although acting through different mechanisms will ultimately lead to enhance the bacterial survival in the host [46] . The adhesion of virulent L . interrogans strain Fiocruz L1-130 to Fg has been shown to be induced by physiological osmolarity , but the effect of this binding on fibrin clot formation was not evaluated [14] . The leptospiral proteins , LigA and LigB [16] , [17] and OmpL37 [15] have been described as Fg-binding proteins . In addition , the interaction of LigB with Fg was shown to inhibit thrombin-catalyzed fibrin formation [16] , [17] . In this work , we show that L . interrogans serovar Copenhageni are capable of binding human Fg on their surface , as visualized by indirect IFA . Moreover , we have employed ELISA to assess the interaction of human Fg with virulent L . interrogans serovar Copenhageni Fiocruz L1-130 , attenuated L . interrogans serovar Copenhageni M-20 and non-pathogenic saprophytic L . biflexa serovar Patoc Patoc 1 , and the effect of this binding on the thrombin-catalyzed fibrin formation . Moreover , we studied seven leptospiral adhesin- and PLG-binding proteins , rLIC12238 [18] , Lsa33 , Lsa25 [19] , Lsa30 [20] , OmpL1 [21] , rLIC11360 and rLIC11975 [22] for their capacity to bind Fg and to inhibit fibrin clot formation . We show that all strains tested bind Fg , including the saprophytic one , and that this interaction inhibits fibrin clot formation . Though the virulent strain appears to be more efficient , the values obtained were not statistically significant . Attachment of recombinant proteins to Fg was specific , dose-dependent and saturable with all proteins but Lsa30 . The interaction of the proteins with Fg , similar to the Leptospira , promoted an inhibition on thrombin-induced fibrin clot formation . Thus , Leptospira , similar to other pathogens , express multiple Fg-binding proteins [38] , [46] . As expected , recombinant proteins partially inhibited attachment of intact L . interrogans to immobilized Fg . The inhibitory effect exerted by the recombinant proteins was moderate , ranging from 0 . 1 to 1 . 0 µM of protein concentration to reach significance , and could be explained by the existence of additional L . interrogans binding proteins contributing to the leptospiral adherence to Fg . A correlation between inhibition and affinity was detected with the recombinant protein Lsa33 , while no correspondence was seen with the others . The inhibitory effect on fibrin formation observed with leptospires and with the six recombinant proteins was partial , similar to the LigB , fragment 7–12 , employed in this work as positive control , and previously reported by Choy et al . , 2011 [16] , using LigB fragment 9–11 . Our data differ with the total clotting inhibition promoted by the Fg-binding proteins , SdrG of S . epidermidis [47] , ClfA of S . aureus [48] and LigBCen2R [17] . The data suggest that in Leptospira other mechanisms might be involved in fibrin formation and/or the main function of these Fg- binding proteins is not associated with the clotting . We have described the interaction of Leptospira with fibrinolytic system and shown that it occurs with virulent , attenuated and saprophyte strains of Leptospira . Moreover , we demonstrated that this association renders the bacteria with proteolytic activity capable of degrading ECM components [12] . Several membrane proteins were identified as PLG-binding receptors capable of generating PLA in the presence of activator , suggesting that the interaction with the fibrinolytic system might be important during leptospirosis [49] , [18] , [50] , [36] , [19] , [21] , [20] . Indeed , the increased plasma levels of Fg degradation products detected in leptospirosis has provided evidence for fibrinolysis activity [51] , [52] . We show now that Leptospira surface-associated PLA activity is capable to degrade Fg in vitro , suggesting one possible pathway to generate Fg metabolites during the disease . Fg is considerably upregulated during inflammation or under exposure to stress such systemic infections [46] . The activation of coagulation cascade with increased levels of plasma Fg during leptospirosis has been detected [53] , [51] , [54] . It has been suggested that these findings are possibly associated to severe tissue damage , vascular endothelial injury or a compensating production by the liver in response to the augmented Fg utilization [55] , [53] , [51] . Our data show that Leptospira either through their Fg-binding proteins or coated with PLA activity would increase the consumption of Fg molecules by sequestering or degrading them . Under these circumstances , a reduction on fibrin clot formation is expected . In addition , leptospirosis patients with clinical bleeding were reported to have lower platelet counts when compared to other patients [54] , a condition that would help decrease thrombosis , facilitate bleeding and help bacterial dissemination . In conclusion , we have provided molecular evidence of the mechanisms that Leptospira could employ to interact with components of the coagulation cascade and the fibrinolytic system . In addition , we have shown that six adhesins could mediate the binding of Leptospira to Fg and impair thrombin- induced fibrin clot formation . We believe that our results should contribute to the understanding of the complex coagulopathy observed during leptospirosis . | Leptospirosis is probably the most widespread zoonosis in the world . Caused by spirochaetes of the genus Leptospira , it has greater incidence in tropical and subtropical regions . The disease has become prevalent in cities with sanitation problems and a large population of urban rodent reservoirs , which contaminate the environment through their urine . Understanding the mechanisms involved in pathogenesis of leptospirosis should contribute to new strategies that would help fight the disease . We show in this work that Leptospira strains , virulent , attenuated or saprophytic are capable of binding fibrinogen ( Fg ) . The interaction of leptospires with Fg inhibits the formation of fibrin clot that may result of an imbalance in the haemostatic equilibrium . In addition , we show that plasminogen ( PLG ) /plasmin ( PLA ) generation on the surface of leptospires can lead to Fg degradation , showing evidence of possible route of fibrinolysis in leptospirosis . We also present six leptospiral proteins , as novel Fg-binding proteins , capable of inhibiting fibrin clot formation by thrombin-catalyzed reaction , suggesting that in Leptospira these multifunctional proteins could mediate Fg interaction . Our data suggest possible mechanisms that leptospires could employ to affect the coagulation cascade and fibrinolytic system that might lead to bacteria spreading . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"microbial",
"pathogens",
"host-pathogen",
"interaction",
"biology",
"microbiology",
"pathogenesis"
] | 2013 | Adhesins of Leptospira interrogans Mediate the Interaction to Fibrinogen and Inhibit Fibrin Clot Formation In Vitro |
Endemicity mapping is required to determining whether a district requires mass drug administration ( MDA ) . Current guidelines for mapping LF require that two sites be selected per district and within each site a convenience sample of 100 adults be tested for antigenemia or microfilaremia . One or more confirmed positive tests in either site is interpreted as an indicator of potential transmission , prompting MDA at the district-level . While this mapping strategy has worked well in high-prevalence settings , imperfect diagnostics and the transmission potential of a single positive adult have raised concerns about the strategy’s use in low-prevalence settings . In response to these limitations , a statistically rigorous confirmatory mapping strategy was designed as a complement to the current strategy when LF endemicity is uncertain . Under the new strategy , schools are selected by either systematic or cluster sampling , depending on population size , and within each selected school , children 9–14 years are sampled systematically . All selected children are tested and the number of positive results is compared against a critical value to determine , with known probabilities of error , whether the average prevalence of LF infection is likely below a threshold of 2% . This confirmatory mapping strategy was applied to 45 districts in Ethiopia and 10 in Tanzania , where initial mapping results were considered uncertain . In 42 Ethiopian districts , and all 10 of the Tanzanian districts , the number of antigenemic children was below the critical cutoff , suggesting that these districts do not require MDA . Only three Ethiopian districts exceeded the critical cutoff of positive results . Whereas the current World Health Organization guidelines would have recommended MDA in all 55 districts , the present results suggest that only three of these districts requires MDA . By avoiding unnecessary MDA in 52 districts , the confirmatory mapping strategy is estimated to have saved a total of $9 , 293 , 219 .
Endemicity mapping is the essential first step for countries striving to eliminate neglected tropical diseases . For lymphatic filariasis ( LF ) , a parasitic disease targeted by the World Health Organization ( WHO ) for elimination as a public health problem by 2020 [1] , endemicity mapping ( or ‘mapping’ ) is conducted at the district level following standard guidelines to determine whether the disease is endemic . In districts found to be endemic the entire at risk population is treated with preventive chemotherapy delivered through mass drug administration ( MDA ) . To date , tremendous progress towards global elimination of LF has been made . By 2015 , of the 73 endemic countries , 18 had entered post-MDA surveillance , 25 countries had achieved 100% geographic coverage , 20 were in the process of scaling up MDA to all endemic districts and only 10 countries had yet to start MDA [2] . The current WHO strategy for mapping LF is meant as a practical approach to quickly and easily identify districts where active transmission is occurring . In the African region , two sites ( e . g . , villages ) considered to be at higher risk than other areas are purposefully selected , based on the presence of persons with chronic morbidity , high mosquito densities or other permissive factors , and within each site a convenience sample of 50–100 people is tested for LF . Though there is some regional variation in the age group sampled , in Africa , where the majority of the mapping gap remains [3] , sampling is limited to adults >15 years old [4] . In areas where W . bancrofti is possibly endemic , LF mapping is conducted using either an immunochromatographic test ( ICT ) or filariasis test strip ( FTS ) to measure filarial antigenaemia [5] . Where Brugia spp . are possibly endemic , mapping requires blood films to detect microfilaremia . According to WHO guidelines , if the prevalence of microfilaremia or antigenaemia is ≥1% in either of the two sites , the district is classified as endemic and MDA is required [6] . This WHO mapping strategy has been used successfully to map thousands of districts , helping countries to rapidly scale up their LF elimination programs . With its low threshold–a 1% prevalence essentially equates to finding one or more positive individuals–the current strategy favorsprogrammatic action in high prevalence settings; however , in low prevalence settings this mapping approach has several limitations . Where the prevalence is low , there is often limited information about LF transmission , making it hard to identify sites where the risk of transmission is the greatest . In the absence of detailed clinical information , and given the focality of LF , the reliance on two purposefully selected sites in a district may be insufficient to rule out ongoing transmission where the prevalence is low . Secondly , in low prevalence settings it is hard to know if antigen-positive adults are indicative of active transmission , a reflection of earlier infection , or the introduction of individuals from endemic areas , due to ever-increasing population mobility . Finally , the diagnostics used to detect filarial antigen have imperfect specificity [7] , which calls into question the significance of a single positive result . Furthermore , these tests have been shown to share some cross-reactivity with individuals harboring high intensity Loa loa infections [8] . Given these limitations , when the current mapping strategy results in the detection of a single antigen-positive adult , the implications for ongoing transmission are uncertain . Indeed , some NTD program managers have been hesitant to base the decision to start resource-intensive MDA on such borderline mapping results . Nonetheless , in order to reach the ultimate goal of elimination , countries must be able to determine the LF endemicity status of each district so that appropriate action can be taken . To provide greater confidence in the decision to start or forego MDA when the initial mapping results are uncertain , an LF ‘confirmatory mapping’ tool has been developed . The confirmatory mapping tool utilizes cluster sampling of school-attending children and is meant to provide a statistically rigorous tool for programmatic decision-making . This tool was recently piloted in 45 districts of Ethiopia , where initial mapping surveys found only one antigen-positive result per district , and in 10 districts in Tanzania , where endemicity was also uncertain due to the amount of time that had passed since the initial mapping and independent studies suggesting little or no transmission in the area . In this manuscript , we provide a detailed description of the methodology underlying the confirmatory mapping tool . We present summary findings from the first two pilots of this strategy in Ethiopia and Tanzania , an analysis of the strategy’s cost effectiveness , and the results from two small comparison studies using both the standard mapping strategy and the confirmatory mapping approach .
Ethical clearance from the local institutional review boards was obtained in advance of each study . In Ethiopia , ethical clearance was received from the Ethiopian Public Health Institute . In Tanzania , the National Medical Research Institute provided clearance . All participation in the survey was voluntary . Permission to conduct the survey was obtained from the directors of the selected schools and a letter to parents explaining the study was sent home with students during the days leading up to the study . Only children with written consent forms signed by their parent or guardian were allowed to participate in the study . There were no adult participants in this study . All children were provided with their test results and positive children were referred to health authorities . Both the Ethiopian and Tanzanian national LF programs were committed to providing treatment with ivermectin and albendazole through MDA according to WHO guidelines . If a district did not qualify for LF MDA based on the confirmatory mapping results , any antigenemic children identified through the survey received individual treatment . Primary schools , which includes all public , private or confessional schools , make up the primary sampling units for the confirmatory mapping tool , due to the logistical advantages schools offer over community-based sampling [9] . Children in grades 4–8 of primary school , which typically corresponds to ages 9–14 years , were targeted for inclusion in the survey . The decision to target older children rather than 6–7 year olds , as in the Transmission Assessment Survey , was based on a desire to improve the chances of detecting infected individuals with the survey . In treatment-naïve settings , older children have a longer period of potential exposure to infection and previous studies suggest that infection in older children is representative of infection in the population as a whole [10 , 11] . Due to the wide range foreseen for district sizes , two sampling strategies were proposed . In districts with fewer than 40 primary schools , systematic element sampling was used , whereby all schools in the district were visited and a set fraction of students in the targeted grades were included , after adjusting for the expected non-response rate . The same sampling fraction ( f ) was used in each school , resulting in an equal probability of selection for each student in the district ( Eq 1 ) . In larger districts with at least 40 schools , cluster sampling was recommended , whereby 30 schools were selected from a sampling frame that included all primary schools in the district , using sampling with probability proportionate to estimated size . To maintain an equal probability of selection , an independent sampling fraction was calculated for each selected school , based on the expected school enrollment ( schooli ) , expected non-response rate , and the target sample size per school ( Eq 2 ) . When the selected school was small and had fewer students expected in the targeted grades than the required sample size , the school was merged with the next school on the list during the first stage of sampling . If one of these merged schools was selected , a sampling fraction for the combined size of the two schools was calculated and the survey team would visit both schools and apply the same sampling fraction in order to reach the desired sample size . The sample size and decision rules for this survey are based on the null hypothesis that the average prevalence of antigenemia in older school children is ≥2% . A hypothesis test is constructed using the hypergeometric distribution to calculate the probability of finding no more than d antigen-positive children in a sample of n target-grade children , drawn from a total survey population of N such children . Districts in which the number of children testing positive is less than or equal to the critical cutoff , d , are said to “pass” the survey and are considered not in need of MDA ( i . e . reject the null hypothesis ) . Conversely , districts in which the number of positive children is greater than the critical cutoff fail to reject the null hypothesis and are considered endemic and in need of MDA . The critical cutoff , d , was determined based on <6% risk of Type I error ( e . g . , the risk of falsely concluding that the prevalence is <2% ) and power of approximately 35% of rejecting the null hypothesis when prevalence is 1 . 0% ( half of the threshold ) . The actual ranges of Type 1 error ( α ) and power for each sample size are shown in Table 1 . The designation of such a low power makes it harder for non-endemic districts ( i . e . , those with a true antigenemia prevalence <2% ) to pass the survey; however , this was deemed advantageous because it is a more conservative approach and biases the tool in favor of starting MDA . The low power of this confirmatory mapping tool , compared to a survey such as the LF Transmission Assessment Survey , which has 75% power when the true prevalence is half the threshold , has the added advantage of dramatically reducing the sample size . Finally , in order to account for the potential clustering of cases by school , the sample size of the cluster-based surveys was multiplied by the estimated design effect of 1 . 5 . The resulting sample sizes and cutoffs , referred to jointly as the ‘decision rules’ for the survey , are shown in Table 1 . To operationalize the sampling strategy , an Excel-based Confirmatory Mapping Survey Builder tool ( http://www . ntdsupport . org/resources/confirmatory-mapping-survey-builder ) was created and used by the field teams to select the primary sampling units and generate the sampling lists for each school . Approximately 160ul of whole blood was taken from each participant via finger prick . One hundred microliters were used to assess the presence of Wuchereria bancrofti circulating filarial antigen using the immunochromatographic card test ( ICT; Alere , Scarborough , ME , USA ) . Any child testing positive by ICT was retested to confirm the result . The remaining 60ul of whole blood was placed onto filter paper and frozen for future laboratory-based antibody testing . In Ethiopia , a nationwide mapping exercise , undertaken in 2013 , identified 45 districts ( locally referred to as woredas ) with only one ICT-positive individual [12] . Due to the uncertainty regarding the significance of a single antigen-positive adult , the confirmatory mapping tool was applied in all 45 of these districts . In addition , four districts that were declared endemic by the 2013 mapping exercise , with ICT positivity ranging from 4%–8% , were included to compare the standard WHO protocol with the confirmatory mapping strategy and see if both protocols result in a similar ‘endemic’ classification ( Fig 1 ) . All districts were reportedly treatment naïve for ivermectin at the time of selection . The confirmatory mapping in Ethiopia took place in two phases: phase 1 took place December , 2014 –January , 2015 and phase two took place December , 2015 –March , 2016 . In Tanzania , the national LF program conducted initial mapping for LF from 1999 to 2004 using the standard WHO protocol and found several districts with ≈1% prevalence of mf . However , subsequent investigations in these same districts by two different research groups found little to no LF , which , combined with the mass distribution of long lasting insecticidal nets and indoor residual spraying for malaria , called into question the need for MDA . Of 63 such districts where LF endemicity was considered uncertain , 10 were selected for confirmatory mapping in 2015 ( the remaining 53 districts were mapped at a later date ) , all of which were ivermectin naïve at the time of selection . Children attended schools in their same community of residence . To enable direct comparison between results of the standard WHO mapping protocol and those of the confirmatory mapping protocol , in each of the initial 10 Tanzanian districts , two communities with the highest suspected risk of ongoing LF transmission were selected ( Fig 1 ) . In each purposively selected community , the standard WHO protocol was applied , whereby a convenience sample of 100 individuals ≥ 15 years were tested for LF antigen , for a total sample size of 200 additional individuals per district . To calculate the cost-effectiveness of the confirmatory mapping tool , the actual costs of conducting the confirmatory mapping exercise in all districts were subtracted from the costs averted by avoiding treatment of districts found by confirmatory mapping to be non-endemic for LF . In accordance with current WHO guidelines for LF , the cost of conducting a successful LF elimination program in a district include , at minimum , the costs associated with conducting five rounds of MDA at 65% coverage , two sentinel and spot check assessments , and three transmission assessment surveys [13] . Because treatment costs vary according to district population , we estimated district population using a database maintained by the WHO AFRO Region . A detailed explanation of how the costs for each of these components were estimated is presented as supplementary material ( S1 Appendix ) .
The confirmatory mapping strategy was conducted in 55 districts , 45 in Ethiopia and 10 in Tanzania ( Table 2 ) . A total of 22 , 614 children in grades 4–8 were tested from 1483 schools . Systematic sampling of schools was called for in 18 smaller Ethiopian districts , while cluster sampling was utilized in the remaining 27 Ethiopian districts and all 10 Tanzanian districts . Twenty-eight ICT positive children were detected from 9 of the 45 districts in Ethiopia; in three of these districts the number of ICT-positive children exceeded the critical cutoff , meaning that the district “failed to pass” the confirmatory mapping assessment and should be considered endemic and in need of MDA . The remaining 42 districts “passed” the confirmatory mapping , meaning that the number of positive individuals was equal to or below the critical cutoff , and were declared not in need of MDA . In Tanzania , only one ICT positive individual was confirmed ( two additional children tested positive but a confirmatory test found both to be negative ) . All 10 districts in Tanzania passed the confirmatory mapping assessment and were declared not in need of MDA for LF . The design effect for the surveys where cluster sampling was used , and which had at least one positive result , ranged from 0 . 9–5 . 3 , with the majority of the design effects falling between 1 . 0–2 . 0 . The data used to generate these results is provided in the supplementary information files ( S1–S3 Datas ) . The total cost of conducting the confirmatory mapping surveys across the 55 districts was $451 , 936 , with an average cost of $8 , 217 per district ( Table 3 ) . The average cost per survey was less in Ethiopia compared with Tanzania ( $7 , 910 compared with $9 , 599 ) . The 52 districts that passed the confirmatory mapping assessment represent 8 . 1 million people no longer in need of MDA and $9 . 7 million in adverted costs due to the MDA and accompanying monitoring and evaluation that are no longer necessary . This translates into an estimated savings of $9 . 2 million ( $5 . 7 million in Ethiopia and $3 . 5 million in Tanzania ) , after accounting for the cost of all 55 confirmatory mapping surveys and the costs associated with MDA in the 3 endemic districts . In Ethiopia , the confirmatory mapping protocol was implemented in four additional endemic districts ( according to the WHO mapping surveys conducted in 2013 ) . Systematic sampling was used to visit children from all schools in three of these districts , while cluster sampling was used in the fourth ( Table 4 ) . In two of the districts ( Boneya Bushe and Haro-Limu ) no ICT-positive children were found , while in the other two districts ( Benatsemay and Dugdadewa ) six ICT-positive children were found in each district . In Tanzania , the results from the standard WHO mapping strategy , conducted in parallel with the confirmatory mapping in the same 10 districts , are shown in Table 5 . In each district , approximately 200 individuals were sampled , typically from two purposefully selected hamlets; however , in two of the districts the hamlets were so small that additional hamlets were added to reach the sample size , while in one district the first selected hamlet was so large that the sample size was met without adding a second hamlet . Two of the ten districts had at least one site with an ICT positive result ( three ICT positives were found in Same District Council and one ICT positive in Siha District ) , both of which would qualify the districts for MDA according to WHO guidelines .
While mapping is an essential first step for lymphatic filariasis elimination programs , the current approach can produce uncertain results in some districts , leading to challenges for program managers regarding the decision to implement MDA . Given how resource-intensive it is for country programs to conduct MDA , it is important to get this decision right . This paper describes a confirmatory mapping tool that overcomes many of the pitfalls of the current approach , allowing program implementers to feel more confident in their decision to start or forego MDA . The main advantages of this new tool include a sampling strategy that results in a geographically-representative and unbiased sample , while still maintaining the efficiency of school-based sampling; a critical cutoff that protects against the potential for a single false positive test to drive endemicity status; and the targeting of upper-level primary school students as opposed to adults , for whom the presence of filarial antigens are likely indicative of recent , and not historic , transmission . It is important to emphasize that this confirmatory mapping tool is not meant as a replacement for the standard WHO approach , but rather as a complementary tool that can be used to confirm whether active transmission is likely to be present when the results from the standard approach are inconclusive ( e . g . , only one positive adult ) . The standard WHO mapping approach is quick , simple , inexpensive , and effective at identifying high-transmission areas in need of mass treatment and thus should remain the primary approach for ruling-in areas with active transmission . The confirmatory mapping tool is based on a null hypothesis that transmission is active and therefore works well as a second-stage tool to rule-out uncertain areas . As of the end of 2015 , there were 971 endemic districts requiring MDA that have yet to start [2] . For some of these districts , the initial mapping exercise took place years ago and there may be a need for a more rigorous survey to determine whether MDA is still required . Another potential use for the confirmatory mapping tool is in post-endemic countries that have never undergone MDA but are ready to validate elimination . This may happen in areas where there is historical evidence of transmission but better infrastructure , the use of insecticide-treated nets ( a secondary benefit of the malaria control programs ) , decreased contact with vectors through a reduction in breeding sites , or historical misdiagnosis of filarial morbidity mean that the country is no longer endemic . For these countries , as well as regions within endemic countries that have never received MDA and for which endemicity status is considered uncertain , the confirmatory mapping tool can provide greater confidence that no detectable foci of on-going transmission remain . While the confirmatory mapping tool is more expensive and resource-intensive than the standard WHO approach , a cost analysis from Ethiopia and Tanzania suggests that when the long-term implications are considered , it is highly cost-effective when compared to unnecessary MDA . Specifically , remapping 55 districts in Ethiopia and Tanzania cost $450 , 000 , but it avoided the unnecessary treatment of 8 . 1 million people across 52 districts , saving over $10 , 000 , 000 . For NTD programs , the ability to avoid unnecessary MDA not only saves precious financial resources , it also means more time , energy and human capital can be dedicated to the areas where it is needed the most . But does the confirmatory mapping tool always lead to the correct MDA decision ? Unfortunately , the global LF elimination program does not have the luxury of time required for a long-term , empirical validation of the tool , which would require waiting at least five years to observe the persistence of low-level transmission or an emergence . Simulations suggest that geographically representative samples with many clusters are better able to detect transmission where it is low or focal [14]; however , short of sampling everywhere , no sampling strategy will be 100% accurate . The two comparison studies in Ethiopia and Tanzania suggest the confirmatory mapping tool performs well but it is not possible to conclude that the results are ‘validated . ’ In Ethiopia , where four districts had been declared endemic by the WHO mapping approach when remapped using the confirmatory mapping tool , the tool classified two as not needing MDA . Interestingly , further investigation revealed that at least one of these districts had received treatment with ivermectin since the initial mapping was performed . In Tanzania , where the standard WHO approach was implemented at the same time as the confirmatory mapping tool , two of the ten districts were considered in need of MDA by the WHO mapping approach , compared with none of the ten districts requiring MDA by the confirmatory mapping tool . In one of these districts where the conclusions differed , only one positive adult was found by the WHO approach , calling into question the true need for MDA . The 2015 results of the WHO approach differ from the original WHO mapping results implemented in 1999–2004 , as eight of the 10 districts were found to be non-endemic upon reexamination . Potential reasons for this difference include the increasingly widespread use of insecticide-treated nets for malaria , which target the same vector species as LF; a natural decline in infection intensity over time , perhaps aided by infrastructure improvements , in an area that was low to begin with; and chance , due to the imprecise nature of sampling from only two sentinel sites . It is important to point out that the thresholds used for the WHO and confirmatory mapping approaches are different . According to the WHO mapping approach , a district fails if the point estimate of prevalence in a small convenience sample of adults is ≥1% and passes if it is <1% , while the threshold of confirmatory mapping is 2% . This latter figure may be somewhat misleading , however , because the critical cutoff for the confirmatory mapping approach requires there be a <6% chance that the true prevalence is ≥2% , which means the point estimate for the critical cutoff is actually <1% . The bigger question is whether either of these thresholds is correct . The WHO mapping threshold of 1% is based on the Chinese experience with microfilaremia in post-MDA settings , whereby communities with 1% MF at the time MDA was stopped tended to see infection continue to zero in the absence of MDA [15] . While this is based on empiric evidence , significant extrapolation is required to assume that the same threshold applies to pre-MDA settings . The threshold of <2% antigenemia , used in this confirmatory mapping tool , is taken directly from the LF transmission assessment survey guidelines and was chosen to serve as a conservative proxy for a prevalence of <1% MF [13] . Here again , the same concern with extrapolating this threshold to a pre-MDA setting applies . In short , there is no empiric evidence , of which we are aware , that directly addresses the optimal threshold for basing mapping decisions . While the confirmatory mapping approach utilizes the efficiency of school-based sampling , the limitation of using the school platform is that it systematically excludes children who do not attend school . A study examining the difference in antigenemia among school-attending and non-attending children failed to find any significant difference , but this was in the context of an LF transmission assessment survey in Burkina Faso [16] . Because school attendance often declines with age , it may be important to investigate whether antigenemia is associated with school attendance among older school-age children in treatment naïve settings . Nonetheless , it is important to recognize that , for making MDA stopping decisions , WHO considers school-attending children to be sufficiently representative of all similarly aged children in settings where school attendance is at least 75% [13] . Finally , the utility of the confirmatory mapping tool has the potential to extend beyond LF to other diseases . In fact , this survey tool can be modified to test virtually any threshold , extending its value to other validation exercises . An immediate opportunity for extension of this tool is for onchocerciasis . With the onchocerciasis program target moving from control to elimination , areas that were categorized as hypo-endemic or of unknown endemicity , and consequentially left untreated , now require mapping to determine if MDA is necessary . A similar two-stage approach , whereby purposeful sampling is used to quickly and efficiently rule-in those areas in need of treatment and a confirmatory mapping tool is used in uncertain areas to rule-out those areas where MDA is not necessary , may provide a more robust framework for decision-making in onchocerciasis , as well as greater cohesiveness and clarity across PC NTD programs . The confirmatory mapping tool represents an important addition to the monitoring and evaluation toolkit for program managers . In low-prevalence settings , this tool may enable program managers to make treatment decisions in districts previously blocked by inconclusive results or poor data . It has the potential to save time , money , resources and avoid unnecessary treatments , and it may provide sufficient evidence for programs in some areas to proceed from mapping directly to validation of elimination as a public health problem . With the 2020 elimination targets on the horizon , the confirmatory mapping tool may prove to be particularly useful for ‘shrinking the map’ and conserving resources for use in areas where they are needed most . | Mapping is used by lymphatic filariasis ( LF ) elimination programs to determine if mass drug administration ( MDA ) is required . The current mapping approach , designed to be simple and practical , has worked well in high-prevalence settings but concerns about its reliability in low-prevalence settings have been raised . To address these concerns , a confirmatory mapping strategy was developed that utilizes probability-based sampling of school attending children to determine if the prevalence of LF antigenemia is below a 2% threshold . The confirmatory mapping strategy was implemented in 45 districts in Ethiopia and 10 in Tanzania where the need for MDA was uncertain . In 52 of the 55 districts , the number of LF antigen-positive children identified by the confirmatory mapping strategy was below the predetermined threshold and MDA was deemed unnecessary , while in three districts the number of positive children exceeded the threshold , suggesting that MDA is required . The use of this mapping strategy , to confirm whether MDA is required , is estimated to have saved the Ethiopian and Tanzanian programs $9 , 293 , 219 by avoiding unnecessary MDA in 52 districts . | [
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] | 2017 | The rationale and cost-effectiveness of a confirmatory mapping tool for lymphatic filariasis: Examples from Ethiopia and Tanzania |
Many viral pathogens are persistently transmitted by insect vectors and cause agricultural or health problems . Generally , an insect vector can use autophagy as an intrinsic antiviral defense mechanism against viral infection . Whether viruses can evolve to exploit autophagy to promote their transmission by insect vectors is still unknown . Here , we show that the autophagic process is triggered by the persistent replication of a plant reovirus , rice gall dwarf virus ( RGDV ) in cultured leafhopper vector cells and in intact insects , as demonstrated by the appearance of obvious virus-containing double-membrane autophagosomes , conversion of ATG8-I to ATG8-II and increased level of autophagic flux . Such virus-containing autophagosomes seem able to mediate nonlytic viral release from cultured cells or facilitate viral spread in the leafhopper intestine . Applying the autophagy inhibitor 3-methyladenine or silencing the expression of Atg5 significantly decrease viral spread in vitro and in vivo , whereas applying the autophagy inducer rapamycin or silencing the expression of Torc1 facilitate such viral spread . Furthermore , we find that activation of autophagy facilitates efficient viral transmission , whereas inhibiting autophagy blocks viral transmission by its insect vector . Together , these results indicate a plant virus can induce the formation of autophagosomes for carrying virions , thus facilitating viral spread and transmission by its insect vector . We believe that such a role for virus-induced autophagy is common for vector-borne persistent viruses during their transmission by insect vectors .
Many viral pathogens that cause significant global health and agricultural problems are transmitted via insect vectors . To maximize transmission efficiency , viruses generally can modulate the biology and behavior of their vectors [1 , 2] . Many arthropod-borne animal viruses ( arboviruses ) and plant viruses have evolved to be well adapted for persistent infection and maintenance in their insect vectors and may have some characteristics of insect pathogens [1 , 2] . Such viruses circulate in the insect body and induce a variety of cellular responses that modulate the efficiency of viral transmission [2 , 3] . However , the detailed mechanisms underlying the cellular responses induced by viral infection in insect vectors are poorly understood . In mammals , viral infection can induce or activate autophagy , an important cellular response , which generally plays an important role against viruses [4 , 5] . Autophagy is a highly conserved catabolic process that mediates the clearance of long-lived proteins and damaged organelles via a lysosomal degradative pathway [6 , 7] . The mammalian target of rapamycin ( mTOR ) signaling pathways has been shown to control autophagy [9 , 10] . These factors work in coordination to regulate autophagy , including the formation of autophagosomes and their fusion with lysosomes [4] . Under normal conditions , autophagy proceeds at a basal level , but it is significantly activated in response to a variety of stimuli , such as viral infection , nutrient starvation , and energy depletion [4 , 11] . Although autophagy commonly serves as a defense mechanism against viral infection , some viruses appear to have evolved to exploit this mechanism to promote their survival and replication in different ways [12–23] . Thus , the role of autophagy in host-virus interactions is diverse for different viruses . How autophagy induced by viral infection affects viral transmission by insect vectors is not well known . Previously , by using the model organism Drosophila , autophagy has been proved to play a direct antiviral role against the arbovirus vesicular stomatitis virus [24] . However , little is known about the role of autophagy in the interaction of insect vectors with plant viruses that they transmit . Recently , Wang et al . report that the begomovirus tomato yellow leaf curl virus ( TYLCV ) can activate autophagy in whitefly vectors to induce resistance to viral infection [25] . Thus , autophagy could serve as a defense mechanism against viral infection in insect vectors . Whether viruses can evolve to activate and exploit autophagy to promote their transmission by insect vectors is not well known . In this study , we choose rice gall dwarf virus ( RGDV ) , a plant reovirus , and its rice leafhopper vector Recilia dorsalis ( Hemiptera: Cicadellidae ) to determine how autophagy is activated to play a positive role in viral propagation and transmission by insect vectors . RGDV , which causes substantial yield loss in southern China and Southeast Asia , was first described in 1979 in Thailand [26] . RGDV propagates well and circulates in the body of its insect vector R . dorsali [27 , 28] . Previously , we find that RGDV infection can directly remodel and utilize a variety of cellular structures and pathways for efficient propagation in its insect vector [29–31] . For example , RGDV particles are arrayed in an orderly manner close to the mitochondrial outer membrane during viral replication in vector cells , suggesting that mitochondria might support the energy demands of viral propagation in the insect vector [29] . Furthermore , RGDV particles are associated with microtubules either directly or via intermediate filaments to facilitate intracellular viral spread [30 , 31] . By contrast , a conserved small interfering RNA ( siRNA ) antiviral response is triggered by RGDV infection to control viral propagation , avoiding excessive viral accumulation in insect vectors [32] . During viral infection in insect vector cells , abundant RGDV particles can be sequestered in vesicular compartments [2] . Similar vesicular compartments are involved in a nonlytic release of rice dwarf virus ( RDV ) , also a plant reovirus , after fusion with the plasma membrane in virus-infected cultured leafhopper Nephotettix cincticeps cells [33–36] . In RGDV-infected leafhopper cells , we show that such compartments contain autophagy protein ATG8 , indicating a potential role for a cellular autophagy pathway in nonlytic viral release . The subversion of the autophagic pathway by RGDV for in vivo viral spread also is investigated . Thus , we show that a plant virus can evolve to activate and exploit the autophagy to promote its transmission in insect vectors .
To determine whether the autophagy pathway can be induced upon RGDV infection , we first monitored the expression of 3 autophagy-related genes ( Ulk1/Atg1 , Atg5 and Atg8 ) in continuous cultured vector cells in a monolayer ( VCM ) derived from R . dorsalis using an RT-qPCR assay . Our results showed that after inoculation with RGDV at a multiplicity of infection ( MOI ) of 0 . 4 , the expression of 3 autophagy-related genes increased rapidly and significantly ( P < 0 . 05 ) after 48 hours post inoculation ( hpi ) ( S1 Fig ) . The autophagy-specific protein marker ATG8 is selectively enclosed within autophagosomes , and its breakdown allows measurement of the autophagic rate [8] . Thus , the conversion of ATG8-I to ATG8-II is generally considered to be a reliable indicator of autophagy [8 , 25] . Here , we used ATG8-specific IgG to detect the autophagy pathway induced by RGDV infection in virus-infected R . dorsalis cells . At 48 hpi , the accumulation level of ATG8-II was increased notably in virus-infected VCMs ( Fig 1A ) . We then used two inhibitors of autophagy , 3-methyladenine ( 3-MA ) and Brefeldin A ( BFA ) [37 , 38] , to confirm RGDV infection activated the autophagy pathway . 3-MA inhibits autophagy due to the suppression of class III PtdIns 3-kinase [37] , and BFA exerts its disruptive effect at the cis-Golgi , further demarcating a contribution of the Golgi with regard to autophagosome biogenesis [38] . At 48 hpi , we found that ATG8-II was almost lost after the treatment with 3-MA or BFA during viral infection of VCMs ( Fig 1A and 1B ) , indicating that viral infection can induce the conversion of ATG8-I to ATG8-II . It seems that RGDV infection can induce the autophagy pathway in its insect vector cells . We then used immunofluorescence microscopy to observe ATG8-specific autophagosomes in virus-infected VCMs . While nearly no autophagic signal was detected in uninfected VCMs , at 48 hpi , more than 20-fold ATG8-specific autophagosomes were found in RGDV-infected VCMs , and about 80% of them were colocalized with viral major outer capsid protein P8 ( Fig 1C–1E ) , suggesting that viral particles may be enclosed by virus-induced autophagosomes . Rapamycin , an autophagy inducer that directly inhibits the action of TORC1 ( target of rapamycin complex 1 ) , was used as a positive autophagy indicator [4] . Confocal microscopy also showed that after rapamycin or dsTorc1 treatment the level of autophagic signal was obviously detected in uninfected VCMs ( S2 Fig ) . We further observed that ATG8-specific autophagosomes did not colocalize with the viroplasms of viral nonstructural protein Pns9 ( Fig 1E ) , the site of viral replication and assembly of progeny virions [29 , 30] , suggesting that virus-induced autophagosomes were not the sites for viral replication . At 48 hpi , electron microscopy showed that virus-containing single- or double-membrane vesicular compartments appeared in the cytoplasm of virus-infected VCMs , in which cytosolic components or organelles were sequestered ( Fig 1G and 1H ) . We observed that the number of double- or single- membrane vesicles increased more than 7-fold in the cytoplasm of virus-infected VCMs but rarely in virus-free VCMs ( Fig 1M ) . Immunoelectron microscopy confirmed that ATG8-specifc IgG can specifically label such compartments , namely , autophagosomes in virus-infected VCMs , but did not recognize specific compartments in virus-free VCMs ( Fig 1I ) . Thus , RGDV particles , assembled at the periphery of the viroplasm [29 , 30] , can be engulfed by virus-induced autophagosomes . Usually , these virus-containing autophagosomes were distributed within the cytoplasm ( Fig 1J ) , at the periphery of cell membrane ( Fig 1K ) or outside the cells ( Fig 1L ) . It seemed that virus-containing autophagosomes at the periphery of infected cells , can directly fuse with the plasma membrane to release viral particles ( Fig 1K ) . Thus , virus-induced autophagy pathway may be involved in a nonlytic release of RGDV from insect vector cells . Autophagic flux is a continuous and complete process of autophagy by which lysosomes fuse with autophagosomes [4] . Autophagic adapter SQSTM1/p62 ( sequestosome 1 ) is an indicator to assess autophagic flux because SQSTM1 can target specific cargo for autophagy and is specifically degraded by the autophagic-lysosomal pathway [4] . RT-qPCR assay showed that the expression of Sqstm1 gene increased rapidly and significantly ( P < 0 . 05 ) after inoculation with RGDV at a MOI of 0 . 4 ( S1 Fig ) . However , at 48 hpi , western blot assay showed that the accumulation of SQSTM1 was significantly decreased during viral infection of VCMs ( Fig 2A ) . We further revealed a clear correlation between the increase for the accumulation of ATG8-II and the decrease for the accumulation of SQSTM1 from 12 hpi to 96 hpi ( S1 Fig ) . Thus , a clear autophagic flux occurred after viral infection of VCMs . Bafilomycin A1 ( BAF ) is a widely used inhibitor of the vacuolar type H+-ATPase ( V-ATPase ) that disturbs the fusion of autophagosomes with the lysosomes [39] . Next , the VCMs were treated with BAF , then inoculated with RGDV at a MOI of 0 . 4 . At 48 hpi , we detected that the accumulation of SQSTM1 was significantly increased after BAF treatment during viral infection of VCMs ( Fig 2A ) . Thus , viral infection may further cause the degradation of SQSTM1 by the autophagic-lysosomal pathway . However , BAF treatment would inhibit such virus-induced degradation process . Furthermore , western blot assay showed that the accumulation of viral major outer capsid protein P8 was substantially decreased after BAF treatment in virus-infected VCMs ( Fig 2A ) . We thus determined that virus-induced autophagic flux was beneficial to viral infection , rather than serving as a defense mechanism for the degradation of viral particles . Immunofluorescence microscopy further showed that LysoTracker-stained lysosomes were clearly found in RGDV-infected VCMs at 48 hpi , while uninfected VCMs exhibited almost no positive signs ( Fig 2B and 2C ) . Furthermore , about 80% of LysoTracker-stained lysosomes were colocalized with viral major outer capsid protein P8 or ATG8-tagged autophagosomes ( Fig 2C ) . Together , these results indicated that virus-containing autophagosomes were able to fuse with lysosomes , and insect vector cells underwent an autophagic process following RGDV infection . To confirm the existence of such a viral release pathway by virus-induced autophagosomes , we studied the effects of inhibitors or inducers of autophagy applied during viral infection in VCMs . We inhibited or induced autophagy by the drugs 3-MA or rapamycin , respectively . We also silenced the expression of Atg5 or Torc1 genes by RNA interference ( RNAi ) to inhibit or induce autophagy , respectively [4 , 9 , 40] . VCMs were treated with drugs ( 3-MA or rapamycin ) or dsRNAs targeting to Atg5 , Torc1 or GFP genes ( dsAtg5 , dsTorc1 or dsGFP ) . At 8 h after treatment , the treated VCMs were inoculated with RGDV at a MOI of 0 . 1 . At this low MOI , the early viral infection rate was low ( about 15–30% ) , and the spread of viruses among VCMs could be easily monitored . By 48 hpi , immunofluorescence microscopy indicated that the treatment with dsTorc1 or rapamycin increased the percentage of infected cells from an average of 65% or 70% to 95% or 90% when compared with dsGFP- or PBS-treated cells , respectively ( Fig 3A ) . In contrast , treatment with dsAtg5 or 3-MA decreased the percentage of infected cells from an average of 65% or 70% to 15% or 30% when compared with the dsGFP-or PBS-treated cells , respectively ( Fig 3A ) . As expected , ATG8-specific autophagosomes were clearly observed in virus-infected regions , but not in virus-free regions ( Fig 3A ) . RT-qPCR assay showed that the treatment with dsTorc1 or rapamycin increased viral titers by about 2–17-fold ( Fig 3B ) . By contrast , the treatment with dsAtg5 or 3-MA significantly reduced viral titers ( Fig 3B ) . Thus , viral infection was positively correlated to autophagosome formation . We confirmed that the transcript levels of Atg8 gene were significantly reduced by treatment with 3-MA or dsAtg5 , but was increased after treatment with rapamycin or dsTorc1 ( Fig 3C ) . Accordingly , western blot revealed that the treatment with 3-MA or dsAtg5 reduced the accumulation of ATG8-II , but the treatment with rapamycin or dsTorc1 increased the accumulation of ATG8-II in virus-infected VCMs ( Fig 3D ) . In addition , there was a clear correlation between the increase for ATG8-II accumulation and the decrease for SQSTM1 accumulation ( Fig 3D ) . These observations indicated that the inducing of autophagy would facilitate viral infection , while the blocking of autophagy will inhibit viral infection . Thus , autophagy is beneficial to viral infection in insect vector cells . We further determined whether the autophagy pathway triggered by viral infection can facilitate viral release from insect vectors . VCMs were treated with dsTorc1 or dsAtg5 . At 8 h after treatment , the treated VCMs were inoculated with RGDV at a MOI of 10 to guarantee that the early viral infection rate was high ( 100% ) . The extracellular medium was collected , and the viral titer was quantified by RT-qPCR assay . As expected , the treatment with dsTorc1 significantly increased viral titers in the medium , whereas dsAtg5 decreased titers ( Fig 3E ) . All these results suggested that the autophagy pathway induced by viral infection was necessary for the release of RGDV into the cell culture medium . To determine whether autophagy is triggered upon RGDV infection in the body of insect vectors in vivo , ATG8-specific IgG was used to detect virus-induced autophagosomes in viruliferous R . dorsalis . RT-qPCR assay indicated that the expression of 3 autophagy-related genes ( Ulk1 , Atg5 and Atg8 ) and Sqstm1 gene increased significantly ( P < 0 . 05 ) in viruliferous leafhoppers ( S1 Fig ) . Western blot assay showed that ATG8-II can be specifically detected in the viruliferous , but not in the nonviruliferous R . dorsalis ( Fig 4A ) . Furthermore , the accumulation of SQSTM1 was decreased notably in viruliferous R . dorsalis ( Fig 4A ) . There was a clear correlation between the increase for ATG8-II accumulation and the decrease for SQSTM1 accumulation as well during viral infection of insect vectors ( S1 Fig ) . Together , these results indicated that RGDV induced the autophagy activity in R . dorsalis . To further verify that the autophagy pathway was activated by RGDV infection , the formation of autophagosomes in the intestine of R . dorsalis was examined using immunofluorescence microscopy . No specific labeling of ATG8 was detected in the intestines of nonviruliferous R . dorsalis ( Fig 4B ) . At 4 days post-first access to diseased plants ( padp ) , we observed that ATG8-specific autophagosomes colocalized with P8 of RGDV in the epithelium and visceral muscles of the intestine in viruliferous R . dorsalis ( Fig 4C and 4D ) . Immunoelectron microscopy confirmed that ATG8-specifc IgG can specifically label virus-containing autophagosomes in the intestinal epithelium ( Fig 4F ) . Electron microscopy further revealed that virus-containing single- or double-membrane autophagosomes distributed in the epithelium cytoplasm ( Fig 4G ) , the microvilli ( Fig 4H ) and the gut lumen ( Fig 4I ) . Thus , RGDV may exploit these autophagosomes to move from the intestinal epithelium to the gut lumen through the microvilli . Furthermore , RGDV may also use such autophagosomes to move along actin filaments to spread through the visceral muscles ( Fig 4J ) . Together , our results suggested that RGDV activated the autophagy pathway in R . dorsalis to mediate viral release from the intestinal epithelium . To confirm whether the autophagy pathway promoted viral infection in the insect body , from 3 to 18 days padp , we daily sampled 30 live viruliferous leafhoppers that microinjected with dsAtg5 , dsAtg8 , dsTorc1 or dsGFP , then calculated viral genome copies for the major outer capsid protein P8 gene of RGDV . RT-qPCR assay showed that the mean viral genome copies in dsRNAs-treated viruliferous leafhoppers increased rapidly before 8–10 days padp , and then remained nearly stable ( Fig 5A ) . Previously , we have shown that RGDV infection in insect vectors triggered a strong siRNA antiviral response , which can efficiently control viral accumulation below the pathogenic threshold to maintain the persistent infection [32] . Thus , our results were consistent with the persistent infection pattern of RGDV in insect vectors . It was clear that the mean viral genome copies in dsTorc1-treated viruliferous leafhoppers were significantly ( P < 0 . 05 ) higher than those in dsGFP-treated controls ( Fig 5A ) . By contrast , the mean viral genome copies in dsAtg5- or dsAtg8-treated viruliferous leafhoppers were significantly ( P < 0 . 05 ) lower than those in dsGFP-treated controls ( Fig 5A ) . We confirmed that the transcript levels of Atg8 gene were significantly reduced by treatment with dsAtg8 or dsAtg5 , but was increased after treatment with dsTorc1 in viruliferous leafhoppers ( Fig 5B ) . Furthermore , western blot assay confirmed that dsAtg5 or dsAtg8 treatment reduced ATG8-II accumulation , but increased SQSTM1 accumulation in viruliferous leafhoppers ( Fig 5C ) . However , dsTorc1 treatment increased ATG8-II accumulation , but reduced SQSTM1 accumulation in viruliferous leafhoppers ( Fig 5C ) . Taken together , our results suggested that the activation of autophagy by dsTorc1 treatment facilitated viral accumulation , whereas inhibiting of autophagy by dsAtg5 or dsAtg8 treatment blocked viral accumulation in insect vectors . Using immunofluorescence microscopy at 4 days padp , we also examined individual intestines of R . dorsalis that had been treated with dsRNAs . Our results showed that RGDV accumulated in a particular corner of the filter chamber after the dsAtg5 or dsAtg8 treatment ( Fig 5D ) . However , in the dsTorc1 treatment , RGDV spread from the filter chamber into the adjacent midgut regions ( Fig 5D ) . In dsGFP-treated leafhoppers , RGDV had spread from the filter chamber into the visceral muscles ( Fig 5D ) , confirming that the autophagy promoted viral infection . We next determined whether virus-induced autophagy pathway facilitated the transmission of RGDV via insect vectors to rice plants . Individual leafhoppers microinjected with dsRNAs were fed on individual rice seedlings in individual tubes to test the transmission rates , which were calculated based on the number of virus-infected rice plants/total number of rice plants tested . We found that the transmission rates for dsRNA-treated viruliferous leafhopper increased steadily from 5 to 8 or 10 days padp , and then remained stable from 8 or 10 to 17 days padp ( Fig 5E ) . Compared with the dsGFP treatment , the dsTorc1 treatment considerably increased the transmission rates from 5 to 17 days padp , whereas dsAtg5 or dsAtg8 treatment significantly compromised leafhopper ability to transmit the virus to rice seedlings from 6 to 17 days padp ( Fig 5E ) . Together , these data demonstrated that the autophagy pathway facilitated viral transmission efficiency by R . dorsalis . To determine whether RGDV also induce autophagy pathway in rice hosts . We inhibited autophagy in rice plant by the drug 3-MA . We found that the transcript levels of Atg8 gene were significantly reduced by 3-MA treatment . However , viral genome copies in 3-MA -treated rice plants were no statistical differences ( P > 0 . 05 ) compared with the PBS-treated controls ( S3 Fig ) . Furthermore , no virus-containing autophagosomes were observed in virus-infected rice plants by electron microscopy ( S3 Fig ) . Thus , it seemed that RGDV did not induce autophagy pathway in rice hosts . RDV , a plant reovirus closely related to RGDV , is also transmitted by rice leafhopper N . cincticeps . Immunofluorescence microscopy showed that virus-containing vesicular compartments were positive for the autophagy marker ATG8 in RDV-infected VCMs ( Fig 6A and 6B ) . In contrast to the virus-free cells , it appeared that more than 15-fold ATG8-specific puncta in RDV-infected VCMs , and about 80% of ATG8-specific autophagosomes can colocalize with viral major outer capsid protein P8 , but they never were overlapped with viroplasms of viral nonstructural protein Pns12 ( Fig 6A–6C ) . Previously , we had shown that progeny virions that assembled at the periphery of viroplasm can be sequestered into vesicular compartments , which would mediate nonlytic viral release from insect vector cells [34–36] . We observed that the number of double- or single- membrane vesicles increased more than 9-fold in the cytoplasm of virus-infected VCMs but rarely in virus-free VCMs ( Fig 6D , 6E and 6G ) . Furthermore , immunoelectron microscopy further indicated that ATG8-specific IgG can specifically recognize such virus-containing vesicular compartments , namely , autophagosomes ( Fig 6F ) . Similar to RGDV , western blot assay also showed that RDV infection activated the appearance of ATG8-II , but reduced SQSTM1 accumulation ( Fig 6H ) . An autophagy inhibitor , 3-MA , strongly inhibited the conversion of ATG8-I to ATG8-II and the degradation of SQSTM1 during viral infection ( Fig 6H ) . Thus , we determined that RDV infection also triggered the autophagy pathway in its insect vector cells . We further determine whether the autophagy pathway triggered by RDV infection also facilitate viral spread among insect vector cells . After treated with 3-MA or rapamycin , VCMs were inoculated with RDV at a MOI of 10 . At 48 hpi , RT-qPCR assay showed that the treatment with rapamycin significantly increased viral titers , whereas the treatment with 3-MA decreased viral titers in the medium ( Fig 6I ) . Western blot assay further confirmed that rapamycin promoted the conversion of ATG8-I to ATG8-II and the degradation of SQSTM1 ( Fig 6J ) . Thus , the inhibiting of virus-induced autophagy blocked vial release , whereas the activating of virus-induced autophagy facilitated viral release form insect vector cells . Our results revealed a common mechanism for plant reoviruses to induce autophagy for viral efficient spread in insect vector cells .
Viruses can induce or activate cellular responses such as apoptosis or autophagy to facilitate viral infection cycle in hosts or vectors [41 , 42] . Here , we demonstrated that infection by the plant reovirus RGDV significantly triggered an increase in virus-containing single- or double-membrane autophagosomes , the colocalization of ATG8-II with viral particles , and the conversion of ATG8-I to ATG8-II in virus-infected R . dorsalis cells ( Fig 1 ) , indicating that autophagy pathway was activated by RGDV infection in insect vector cells . We further showed that RGDV infection promoted the degradation of autophagic adapter SQSTM1 and caused the fusion of virus-containing autophagosomes with lysosomes ( Figs 2 and S1 ) . The treatment of lysosome inhibitor BAF suppressed such degradation of SQSTM1 during viral infection ( Fig 2 ) . Thus , the autophagic flux was triggered by RGDV infection . By inhibiting or activating autophagy with chemical reagents 3-MA and rapamycin or by RNAi induced by dsRNAs targeting Atg5 or Torc1 genes , we demonstrated a proviral role for virus-induced autophagy pathway in RGDV release from insect vector cells ( Fig 3 ) . In our electron micrographs , progeny RGDV virions assembled at the periphery of viroplasms [29 , 30] , were engulfed by virus-induced autophagosomes , which then evidently mediated nonlytic viral release by fusion with the plasma membrane in insect vector cells ( Fig 1 ) . Thus , RGDV infection activated the autophagy pathway , which facilitated viral spread rather than controlling viral infection in insect vector cells . We further showed that the plant reovirus RDV also induced the autophagy pathway and subsequently mediated nonlytic viral release from its N . cincticeps vector cells ( Fig 6 ) . Generally , plant reoviruses in insect vector cells are sequestered in spherical vesicular compartments [2] . We thus deduced that the exploitation of virus-induced autophagy pathway for viral spread among insect vector cells may be a conserved mechanism for plant reoviruses . Previously , we have shown that plant reoviruses enter insect vector cells through a receptor-mediated , clathrin-dependent endocytosis , and then are sequestered in the early endosomes [34] . The low pH in the early endosomes is necessary for the proteolytic processing of reovirus outer capsid proteins , which is the essential step for the early stage of viral infection [43–45] . Our current results showed that , after viral replication and assembly of progeny virions , plant reoviruses can be sequestered in the autophagosomes or lysosomes . This later infection event is quite different from the early entry stage of plant reoviruses . Thus , the proteolytic processing of reovirus outer capsid proteins may not occur in the virus-induced autophagosomes or lysosomes at the later infection stage of plant reoviruses in insect vectors cells . Generally , autophagy is an important antiviral cellular response for degradation of viral proteins or interference with viral replication [46] . On the other hand , as a result of continuous coevolution , many arboviruses have developed sophisticated mechanisms to subvert autophagy pathway and thus promote different stages of viral life cycle in their mammalian hosts [47–52] . However , until the present study , the role of autophagy in natural interactions of arboviruses with their insect vectors has been less studied . In Drosophila , autophagy-induced by arboviruses was considered as an antiviral immunity response [24] . So far , only one study has considered a potential antiviral role for autophagy in plant virus-insect vector system . Wang et al . reported that a single-stranded DNA plant virus TYLCV can activate the whitefly autophagy pathway , which leads to subsequent degradation of the virus in vivo [25] . However , TYLCV-induced autophagosomes do not contain viral particles or capsids [25] . Here , we showed that the persistent infection of RGDV can trigger the accumulation of abundant virus-containing autophagosomes in the intestine epithelium of leafhopper vector ( Fig 4 ) . However , it is clear that RGDV can escape lysosomal degradation and exploit such autophagosomes to release from intestinal epithelium into the lumen by passing through actin-based microvilli ( Fig 4 ) . Furthermore , such autophagosomes may move along actin-based visceral muscles surrounding the intestinal epithelium ( Fig 4 ) . Thus , we demonstrated that virus-induced autophagy pathway played a critical role in viral spread in vector insects , enabling to accomplish a latent period for the virus , and subsequent ability to transmit the virus to plant hosts . Similarly , in cultured mammalian cells , a potential role for autophagy pathway in nonlytic release of human poliovirus or hepatitis A virus in vitro has been demonstrated [52–54] . However , little is known about the mechanisms of these human viruses spread via the internal mammalian host tissues such as the intestine , muscle tissue , and peripheral neurons during a natural infection . Our novel model for virus-induced autophagy pathway exploited by a virus to spread in insect intestine may be a common mechanism of spread for other viruses in vivo . We believe that such a role for virus-induced autophagy pathway is common for vector-borne persistent viruses during their transmission by insect vectors . Plant reoviruses , once ingested by the insects , establish their primary infection in a limited number of intestinal epithelial cells , then the invading viruses can initiate the formation of nascent viroplasms for viral multiplication [1–2] . Later , the progeny virions directly crossed the basal lamina into the visceral muscles , and spread into the salivary glands to be horizontally transmitted to healthy plants or into the female ovary to be vertically transmitted to offspring [1–2] . Here , we demonstrated that autophagosomes induced by plant reoviruses assist viral particles pass through the membrane barriers , facilitating viral transmission . However , one key question remains unanswered to understand this model . Why do plant reoviruses such as RGDV and RDV induce the autophagy pathway for viral spread in insect vectors ? Our recent study shows that a conserved siRNA antiviral immunity response is triggered by persistent replication of plant reoviruses in their insect vector [32 , 55] . We thus deduced that the autophagosomes may be exploited by plant reoviruses to escape the direct defense from virus-induced siRNA antiviral immunity responses . Furthermore , the membrane structure of virus-induced autophagosome is a useful vehicle to carry virions to overcome any membrane or tissue barriers in insect vectors . Previously , we show that plant reoviruses can exploit the tubules constituted by viral nonstructural proteins to pass through the actin-based microvilli of the intestine epithelium either into the lumen or across the basal lamina into the circular visceral muscle of insect vectors [31 , 33 , 56 , 57] . Based on these considerations , we deduce that , in an apparent trade-off between plant reoviruses and insects , the autophagy pathway and other modes of cellular remodeling are induced to facilitate viral accumulation , whereas the insect’s innate immune responses , such as the siRNA antiviral pathway , are induced to maintain viral accumulation below the pathogenic threshold [32 , 55] . Thus , the two mechanisms for the spread of plant reoviruses in insect vectors not only facilitate rapid viral dissemination but may also promote evasion of immune defenses , guaranteeing that the virus can be transmitted with high efficiency while maintaining a persistent infection that is not lethal .
Leafhoppers ( R . dorsalis and N . cincticeps ) were collected from Guangdong Province in southern China . VCMs derived from R . dorsalis and N . cincticeps were maintained on the growth medium as described previously [58] . The RGDV and RDV isolates were maintained on rice plants via transmission by R . dorsalis and N . cincticeps , respectively , as reported previously [59] . The major outer capsid protein P8 and the nonstructural protein Pns9 of RGDV , as well as the major outer capsid protein P8 and the nonstructural protein Pns12 of RDV were prepared as described previously [33 , 36] . IgGs , isolated from the polyclonal antibodies were conjugated to fluorescein isothiocyanate ( FITC ) , rhodamine or Alexa Fluor 647 carboxylic acid ( Invitrogen ) according to the manufacturer’s instructions . VCMs derived from R . dorsalis growing on a coverslip were inoculated with RGDV at a MOI of 0 . 4 or 1 . 0 for 2 h as described previously [32] . At 48 hpi , VCMs were immunolabeled for autophagosomes with ATG8-specific IgG ( prepared by our laboratory ) conjugated to FITC ( ATG8-FITC ) , for viral particles with P8-specific IgG ( prepared by our laboratory ) conjugated to rhodamine ( P8-rhodamine ) , and for viroplasms with Pns9-specific IgG ( prepared by our laboratory ) conjugated to Alexa Fluor 647 carboxylic acid ( Pns9-Alexa Fluor 647 ) , and then processed for immunofluorescence microscopy as already described [57] . For lysosome staining , the mock- or virus-infected VCMs were treated with 1 μM LysoTracker ( Green DND-26 , Invitrogen ) at 37°C for 30 min [60] . At 48 hpi , VCMs were fixed , immunolabeled with ATG8-FITC and P8-specific IgG conjugated to Alexa Fluor 647 carboxylic acid ( P8-Alexa Fluor 647 ) , and then processed for immunofluorescence microscopy , as already described [32 , 60] . To detect whether RDV infection induced autophagy pathway in insect vector cells , VCMs derived from N . cincticeps growing on a coverslip were inoculated with RDV at a MOI of 0 . 4 for 2 h . At 48 hpi , VCMs were immunolabeled with ATG8-FITC , P8-rhodamine , Pns12-specific IgG conjugated to Alexa Fluor 647 carboxylic acid ( Pns12-Alexa Fluor 647 ) , and then processed for immunofluorescence microscopy ( Leica TCS SP5 II ) as already described [57] . Immunofluorescence labeling of the intestine of R . dorsalis after acquisition of RGDV from diseased rice plants was described as previously [57] . Second R . dorsalis instars were fed on diseased rice plants for 1 day and then transferred to healthy rice seedlings . At 4 days padp , the intestines were dissected , fixed , immunolabeled with ATG8-FITC , P8-rhodamine and actin dye phalloidin-Alexa Fluor 647 carboxylic acid ( Invitrogen ) , and then processed for immunofluorescence microscopy , as described previously [28 , 32] . Cells containing more than two ATG8-speific puncta were defined as autophagy-positive cells . The number or the percentage of the cells showing the ATG8-positive signs were counted under a fluorescence microscope [50] . Virus-infected rice plants , VCMs on coverslips or insect intestines were fixed , dehydrated and embedded and thin sections cut as described previously [57 , 58] . Sections were then incubated with ATG8-specific IgG and immunogold labelled with goat antibodies against rabbit IgG which had been conjugated with 10- or 15-nm-diameter gold particles ( Sigma ) [57 , 58] . The average number of autophagic vesicle ( AV ) per cell was evaluated . A minimum of 30 cells were observed . Cell counting was done by three independent experiments and data are presented as mean ± standard deviation [SD] . Total proteins from VCMs or intact insects were extracted using the sample buffer and separated by 10 or 12% SDS-PAGE , then transferred to polyvinylidine difluoride membranes ( Bio-Rad ) . The membranes were blocked with 5% nonfat milk in PBS with 0 . 1% Tween 20 and then incubated with RGDV P8-specific IgG , ATG8-specific IgG , ACTB-specific IgG ( Purchased from Sigma ) , or SQSTM1-specific IgG ( Purchased from Cell Signal Technology ) . After incubation with secondary antibody ( MultiSciences Biotech ) , proteins were visualized with the Luminata Classico Western HRP Substrate ( Millipore ) and imaged with the Molecular Imager ChemiDoc XRS+ System ( Bio-Rad ) . To confirm RGDV or RDV infection activated the autophagy pathway , autophagy inhibitors ( 100 nM 3-MA , Sigma; 1 μM BFA , Selleckchem; 20 nM BAF , Enzo ) were used to treat the VCMs to inhibit autophagosome formation . VCMs were transfected with ( + ) and without ( - ) 3-MA , BFA or BAF for 8 h , and then inoculated with RGDV or RDV at a MOI of 1 . 0 for 2 h . At 48 hpi , the cells were collected for western blot assay . Total RNAs were extracted from VCMs or intact insects using TRIzol reagent ( Invitrogen ) according to the manufacturer’s protocol . RT-qPCR assay were performed as previously described [32 , 59] . The number of RGDV genome copies in the individual viruliferous R . dorsalis was calculated as the log of the copy number/μg insect RNA based on a standard curve for the RGDV P8 gene . The relative transcript expression of autophagy-related genes , Sqstm1 gene and RGDV P8 gene in VCMs or R . dorsalis was analyzed by relative RT-qPCR assay according to the 2−ΔΔCt method [59] . VCMs derived from R . dorsalis growing on a coverslip were inoculated with RGDV at a MOI of 1 . 0 for 2 h . At 12 , 24 , 36 , 48 , 72 and 96 hpi , the VCMs were collected at various time points to determine if viral infection induced autophagy pathway . Alternatively , about 500 second-instar R . dorsalis were fed on RGDV-infected rice plants for 1 day , and then transferred to healthy rice seedling . At different days padp , 100 nonviruliferous or viruliferous leafhoppers were collected at various time points to determine if viral infection can induce autophagy pathway . The activation of autophagy pathway at various time points after viral infection was analyzed by RT-qPCR and western blot assays . The Ulk1 , Atg5 , Atg8 , Sqstm1 and Torc1 sequence obtained from the high-throughput transcriptome sequencing of R . dorsalis in our laboratory , and the obtained gene sequences for Ulk1 , Atg5 , Atg8 , Sqstm1 and Torc1 of R . dorsalis were deposited in GenBank with accession numbers MF038047 , MF038044 , MF038045 , MF038048 and MF038046 , respectively . Autophagy-related Atg5 , Atg8 and Torc1 genes of R . dorsalis and the GFP gene were amplified by RT-PCR assay . T7 RiboMAX Express RNAi System kit ( Promega ) was used to synthesize in vitro dsRNAs for these four genes according to the manufacturer’s instructions . To examine the effects of synthesized dsRNAs or the drugs ( autophagy inhibitor , 3-MA; autophagy inducer , rapamycin , Woburn ) on viral infection , VCMs were transfected with dsRNAs in the presence of Cellfectin ( Invitrogen ) or drugs for 8 h , and then inoculated with RGDV at a low MOI of 0 . 1 . At 48 hpi , VCMs were fixed , immunolabeled with ATG8-FITC and P8-rhodamine , and then processed for immunofluorescence microscopy as described previously [57] . To further determine whether the autophagy pathway triggered by RGDV infection can facilitate viral release , VCMs were treated with dsRNAs ( dsTorc1 , dsAtg5 or dsGFP ) in the presence of Cellfectin for 8 h , and then inoculated with RGDV at a MOI of 10 for 2h . Then the VCMs were washed by the fresh culture medium for 3 times to remove the viruses that were not absorbed . Alternatively , to detect whether RDV infection induced autophagy pathway can facilitate viral release , VCMs were treated with 100 nM 3-MA or 20 μM rapamycin ( dissolved in PBS ) in the presence of Cellfectin for 8 h , and then inoculated with RDV at a MOI of 10 for 2h . Then the VCMs were washed by the fresh culture medium for 3 times to remove the viruses that were not absorbed . The supernatant of the infected cells was collected at 48 or 72 hpi and sedimented to remove cell debris [60] . Total RNAs were extracted from the supernatant using TRIzol reagent . The number of viral genome copies in the extracellular of VCMs in supernatant was calculated as described above . Three thousands second-instar R . dorsalis were fed on RGDV-infected rice plants for 1 day , microinjected with 200 nl ( 0 . 5 μg/μl ) dsRNAs ( dsAtg5 , dsAtg8 , dsTorc1 or dsGFP ) using a Nanoject II Auto-Nanoliter Injector ( Spring ) , and then kept on healthy rice seedling . At different days padp , 30 live leafhoppers were sampling daily , and continued for 18 days . Total RNA was extracted and viral genome copy was calculated as described above . In addition , at 4 days padp , the intestines of viruliferous R . dorsalis treated with dsRNAs were immunolabelled with P8-rhodamine and the ACTB dye phalloidin-FITC ( Invitrogen ) , then examined for immunofluorescence microscopy as described previously [57] . For examining the effects of autophagy pathway on viral transmission , 400 second-instar leafhoppers were microinjected with dsRNAs ( dsAtg5 , dsAtg8 , dsTorc1 or dsGFP ) after they had fed on RGDV-infected rice plants for 1 day , and then kept on healthy rice seedlings . From 3 to 17 days padp , an individual insect was fed on a healthy rice seedling in one glass tube , and the rice seedlings were replaced daily . The replaced rice seedlings were grown in the greenhouse ( at 25 ± 1°C , under conditions of 75 ± 5% relative humidity and a photoperiod of 16 h of light and 8 h of darkness ) about 15 days to observe the appearance of disease symptoms , and then the total RNA was extracted from inoculated rice seedlings to determine the presence of transcripts for the RGDV P8 gene to calculate transmission rates . Transmission rate was calculated according to the number of positive rice plants/total number of survival rice plants fed by dsRNAs-treated leafhoppers . To detect whether autophagy pathway can facilitate viral infection in rice plants , 100 healthy rice seedlings ( 15 days old ) were treated with 100 nM 3-MA for 1 day . We then inoculated the treated seedlings with RGDV for 2 days using viruliferous leafhoppers . At different days inoculation , 5 rice plants positive for transcript of RGDV P8 gene were used for assay of viral genome copies , which were calculated as the log of the copy number of P8 gene/μg rice RNA . At 4 days inoculation , relative expression levels of Atg8 gene were detected by RT-qPCR assay as well . All data were analysed with SPSS , version 17 . 0 . Percentage data were arcsine square-root transformed before analysis . Multiple comparisons of the means were conducted based on Tukey’s honest significant difference ( HSD ) test using a one-way analysis of variance ( ANOVA ) . The data were back-transformed after analysis for presentation in the text and figures . All relevant data are within the paper and its Supporting Information files . | Of the approximately 700 plant viruses , more than 75% are transmitted by insect vectors . However , the detailed mechanisms underlying the cellular responses induced by viral infection in insect vectors are poorly understood . We found that a plant reovirus could activate the autophagic process during persistent infection of leafhopper vectors . Furthermore , virus-induced autophagosomes can facilitate a nonlytic viral release and subsequent transmission by insect vectors . This work brings to a novel facet that a virus has evolved to activate and exploit autophagy to promote its transmission by insect vector , which may be a general mechanistic for vector-borne persistent viruses during their transmission by insect vectors . | [
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] | 2017 | Autophagy pathway induced by a plant virus facilitates viral spread and transmission by its insect vector |
Recurrently coupled networks of inhibitory neurons robustly generate oscillations in the gamma band . Nonetheless , the corresponding Wilson-Cowan type firing rate equation for such an inhibitory population does not generate such oscillations without an explicit time delay . We show that this discrepancy is due to a voltage-dependent spike-synchronization mechanism inherent in networks of spiking neurons which is not captured by standard firing rate equations . Here we investigate an exact low-dimensional description for a network of heterogeneous canonical Class 1 inhibitory neurons which includes the sub-threshold dynamics crucial for generating synchronous states . In the limit of slow synaptic kinetics the spike-synchrony mechanism is suppressed and the standard Wilson-Cowan equations are formally recovered as long as external inputs are also slow . However , even in this limit synchronous spiking can be elicited by inputs which fluctuate on a time-scale of the membrane time-constant of the neurons . Our meanfield equations therefore represent an extension of the standard Wilson-Cowan equations in which spike synchrony is also correctly described .
Since the seminal work of Wilson and Cowan [1] , population models of neuronal activity have become a standard tool of analysis in computational neuroscience . Rather than focus on the microscopic dynamics of neurons , these models describe the collective properties of large numbers of neurons , typically in terms of the mean firing rate of a neuronal ensemble . In general , such population models , often called firing rate equations , cannot be exactly derived from the equations of a network of spiking neurons , but are obtained using heuristic mean-field arguments , see e . g . [2–6] . Despite their heuristic nature , heuristic firing rate equations ( which we call H-FRE ) often show remarkable qualitative agreement with the dynamics in equivalent networks of spiking neurons [7–10] , and constitute an extremely useful modeling tool , see e . g . [11–28] . Nonetheless , this agreement can break down once a significant fraction of the neurons in the population fires spikes synchronously , see e . g . [29] . Such synchronous firing may come about due to external drive , but also occurs to some degree during spontaneously generated network states . As a case in point , here we focus on partially synchronized states in networks of heterogeneous inhibitory neurons . Inhibitory networks are able to generate robust macroscopic oscillations due to the interplay of external excitatory inputs with the inhibitory mean field produced by the population itself . Fast synaptic processing coupled with subthreshold integration of inputs introduces an effective delay in the negative feedback facilitating the emergence of what is often called Inter-Neuronal Gamma ( ING ) oscillations [30–38] . Modeling studies with networks of spiking neurons demonstrate that , in heterogeneous inhibitory networks , large fractions of neurons become frequency-entrained during these oscillatory episodes , and that the oscillations persist for weak levels of heterogeneity [30 , 32 , 34] . Traditional H-FRE ( also referred to as Wilson-Cowan equations ) fail to describe such fast oscillations . To overcome this limitation , explicit fixed time delays have been considered in H-FRE as a heuristic proxy for the combined effects of synaptic and subthreshold integration [9 , 10 , 36 , 39] . Here we show that fast oscillations in inhibitory networks are correctly described by a recently derived set of exact macroscopic equations for quadratic integrate-and-fire neurons ( that we call QIF-FRE ) which explicitly take into account subthreshold integration [40] . Specifically , the QIF-FRE reveal how oscillations arise via a voltage-dependent spike synchronization mechanism , missing in H-FRE , as long as the recurrent synaptic kinetics are sufficiently fast . In the limit of slow recurrent synaptic kinetics intrinsically generated oscillations are suppressed , and the QIF-FRE reduce to an equation formally identical to the Wilson-Cowan equation for an inhibitory population . However , even in this limit , fast fluctuations in external inputs can drive transient spike synchrony in the network , and the slow synaptic approximation of the QIF-FRE breaks down . This suggests that , in general , a correct macroscopic description of spiking networks requires keeping track of the mean subthreshold voltage along with the mean firing rate . Additionally , the QIF-FRE describe the disappearance of oscillations for sufficiently strong heterogeneity which is robustly observed in simulations of spiking networks . Finally , we also show that the phase diagrams of oscillatory states found in the QIF-FRE qualitatively match those observed in simulations of populations of more biophysically inspired Wang-Buzsáki neurons [30] . This shows that not only are the QIF-FRE an exact mean-field description of networks of heterogeneous QIF neurons , but that they also provide a qualitatively accurate description of dynamical states in networks of spiking neurons more generally , including states with significant spike synchrony .
A heuristic firing rate description of the spiking network simulated in Fig 1 takes the form [1 , 5] τ m R ˙ = - R + Φ ( - J τ m S + Θ ) , ( 1a ) τ d S ˙ = - S + R . ( 1b ) where R represents the mean firing rate in the population , S is the synaptic activation , and the time constants τm and τd are the neuronal and synaptic time constants respectively [39 , 43] . The input-output function Φ , also known as the f-I curve , is a nonlinear function , the form of which depends on the details of the neuronal model and on network parameters . Finally , J ≥ 0 is the synaptic strength and Θ is the mean external input current compared to threshold . In contrast with the network model , the H-FRE Eq ( 1 ) cannot generate sustained oscillations . In fact , a linear stability analysis of steady state solutions in Eq ( 1 ) shows that the resulting eigenvalues are λ = - α ( 1 ± 1 - β ) , ( 2 ) where the parameter α = ( τm + τd ) / ( 2τm τd ) is always positive . Additionally , the parameter β = [4τm τd ( 1 + Jτm Φ′ ) ]/ ( τm + τd ) 2 is also positive , since the f-I curve Φ ( x ) is an increasing function , and its derivative evaluated at the steady state is then Φ′ > 0 . Therefore the real part of the eigenvalue λ is always negative and hence steady states are always stable , although damped oscillations are possible , e . g . for strong enough coupling J . Introducing an explicit fixed time delay in Eq ( 1 ) can lead to the generation of oscillations with a period on the order of about twice the delay [9 , 10 , 36] . Nonetheless , inhibitory networks of spiking neurons robustly show oscillations even in the absence of explicit delays , as seen in Fig 1 . This suggests that there is an additional mechanism in the network dynamics , key for driving oscillatory behavior , which H-FRE do not capture . Here we show that the mechanism responsible for the generation of the oscillations shown in Fig 1 is the interplay between the mean firing rate and membrane potential , the dynamics of which reflect the degree of spike synchrony in the network . To do this , we use a set of exact macroscopic equations which have been recently derived from a population of heterogeneous quadratic integrate-and-fire ( QIF ) neurons [40] . We refer to these equations as the QIF-FRE . The QIF-FRE with exponential synapses have the form τ m R ˙ = Δ π τ m + 2 R V , ( 3a ) τ m V ˙ = V 2 - ( π τ m R ) 2 - J τ m S + Θ , ( 3b ) τ d S ˙ = - S + R . ( 3c ) where Δ is a parameter measuring the degree of heterogeneity in the network and the other parameters are as in the H-FRE Eq ( 1 ) . Eq ( 3 ) are an exact macroscopic description of the dynamics in a large network of heterogeneous QIF neurons with inhibitory coupling . In contrast with the traditional firing rate equations Eq ( 1 ) , they contain an explicit dependence on the subthreshold state of the network , and hence capture not only macroscopic variations in firing rate , but also in spike synchrony . Specifically , a transient depolarizing input which drives the voltage to positive values ( the voltage has been normalized such that positive values are suprathrehsold , see Materials and methods ) will lead to a sharp growth in the firing rate through the bilinear term in Eq ( 3a ) . Simulations in the corresponding network model reveal that this increase is due to the synchronous spiking of a subset of neurons [40] . This increase in firing rate leads to a hyperpolarization of the mean voltage through the quadratic term in R in Eq ( 3b ) . This term describes the effect of the neuronal reset . This decrease in voltage in turn drives down the mean firing rate , and the process can repeat . Therefore the interplay between mean firing rate and mean voltage in Eq ( 3 ) can generate oscillatory behavior; this behavior corresponds to transient bouts of spike synchrony in the spiking network model . It is precisely the latency inherent in this interplay which provides the effective delay , which when coupled with synaptic kinetics , generates self-sustained fast oscillations . In fact , in the limit of instantaneous synapses ( τd → 0 ) , Eq ( 3 ) robustly display damped oscillations due to the spike generation and reset mechanism described in the preceding paragraph [40] . Contrast this with the dynamics in Eq ( 1 ) in the same limit: the resulting H-FRE is one dimensional and hence cannot oscillate . On the face of things the Eq ( 3 ) appear to have an utterly distinct functional form from the traditional Wilson-Cowan Eq ( 1 ) . In particular , the f-I curve in H-FRE traditionally exhibits an expansive nonlinearity at low rates and a linearization or saturation at high rates , e . g . a sigmoid . There is no such function visible in the QIF-FRE which have only quadratic nonlinearities . However , this seeming inconsistency is simply due to the explicit dependence of the steady-state f-I curve on the subthreshold voltage in Eq ( 3 ) . In fact , the steady-state f-I curve in the QIF-FRE is “typical” in the qualitative sense described above . Specifically , solving for the steady state value of the firing rate in Eq ( 3 ) yields R * = Φ ( - J τ m R * + Θ ) , ( 4 ) where Φ ( I ) = 1 2 π τ m I + I 2 + Δ 2 . ( 5 ) The f-I curve Eq ( 5 ) is shown in Fig 2 for several values of the parameter Δ , which measures the degree of heterogeneity in the network . Therefore , the steady-state firing rate in Eq ( 3 ) , which corresponds exactly to that in a network of heterogeneous QIF neurons , could easily be captured in a heuristic model such as Eq ( 1 ) by taking the function Φ to have the form as in Eq ( 5 ) . On the other hand , the non-steady behavior in Eq ( 3 ) , and hence in spiking networks as well , can be quite different from that in the heuristic Eq ( 1 ) . We can investigate the emergence of sustained oscillations in Eq ( 3 ) by considering small amplitude perturbations of the steady-state solution . If we take R = R* + δReλt , V = V* + δVeλt and S = S* + δSeλt , where δR , δV , δS ≪ 1 , then the sign of the real part of the eigenvalue λ determines whether the perturbation grows or not . Plugging this ansatz into Eq ( 3 ) yields three coupled linear equations which are solvable if the following characteristic equation also has a solution − 2 J τ m R * = ( 1 + τ d λ ) [ ( 2 π τ m R * ) 2 + ( τ m λ + Δ π τ m R * ) 2 ] . ( 6 ) The left hand side of Eq ( 6 ) is always negative and , for τd = 0 , this implies that the solutions λ are necessarily complex . Hence , for instantaneous synapses , the fixed point of the QIF-FRE is always of focus type , reflecting transient episodes of spike synchrony in the neuronal ensemble [40] . In contrast , setting τd = 0 in the H-FRE , the system becomes first order and oscillations are not possible . This is the critical difference between the two firing rate models . In fact , and in contrast with the eigenvalues Eq ( 2 ) corresponding to the growth rate of small perturbations in the H-FRE , here oscillatory instabilities may occur for nonvanishing values of τd . Fig 4 shows the Hopf boundaries obtained from Eq ( 6 ) , as a function of the normalized synaptic strength j = J / Θ and the ratio of the synaptic and neuronal time constants τ = Θ τ d / τ m , and for different values of the ratio δ = Δ/Θ —see Materials and methods , Eqs ( 19 ) – ( 21 ) . The shaded regions correspond to parameter values where the QIF-FRE display oscillatory solutions . We have seen that the oscillations which emerge in inhibitory networks for sufficiently fast synaptic kinetics are correctly described by the firing rate equations Eq ( 3 ) , but not by the heuristic Eq ( 1 ) . The reason for this is that the oscillations crucially depend on the interaction between the population firing rate and the subthreshold membrane potential during spike initiation and reset; this interaction manifests itself at the network level through spike synchrony . Therefore , if one could suppress the spike synchrony mechanism , and hence the dependence on the subthreshold membrane potential , in Eq ( 3 ) , the resulting equations ought to bear resemblance to Eq ( 1 ) . In fact , as we saw in Fig 3 , the two firing rate models become more similar when the synaptic kinetics become slower . Next we show that the two models become identical , formally , in the limit of slow synaptic kinetics . To show this , we assume the synaptic time constant is slow , namely τ d = τ ¯ d / ϵ where 0 < ϵ ≪ 1 , and rescale time as t ¯ = ϵ t . In this limit we are tracking the slow synaptic dynamics in while the neuronal dynamics are stationary to leading order , i . e . R * = Φ ( - J τ m S + Θ ) . ( 9 ) Therefore , the dynamics reduce to the first order equation τ d S ˙ = - S + Φ ( - J τ m S + Θ ) . ( 10 ) Notably , this shows that the QIF-FRE Eq ( 3 ) , and the H-FRE ( 1 ) , do actually have the same dynamics in the limit of slow synapses . In other words , Eq ( 10 ) is formally equivalent to the Wilson-Cowan equations for a single inhibitory population , and this establishes a mathematical link between the QIF-FRE and Heuristic firing rate descriptions . Additionally , considering slow second order synaptic kinetics ( not shown ) , allows one to reduce the QIF-FRE with either alpha or double exponential synapses to a second-order system that formally corresponds to the so-called neural mass models largely used for modeling EEG data , see e . g . [6 , 55–58] .
Firing rate models , describing the average activity of large neuronal ensembles are broadly used in computational neuroscience . However , these models fail to describe inhibition-based rhythms , typically observed in networks of inhibitory neurons with synaptic kinetics [30–38] . To overcome this limitation , some authors heuristically included explicit delays in traditional FRE , and found qualitative agreement with the oscillatory dynamics observed in simulations of spiking neurons with both synaptic kinetics and fixed time delays [9 , 10 , 36 , 39] . Nonetheless it remains unclear why traditional H-FRE with first order synaptic kinetics do not generate inhibition-based oscillations . Here we have investigated a novel class of FRE which can be rigorously derived from populations of spiking ( QIF ) neurons [40] . Networks of globally coupled QIF neurons with fast inhibitory synapses readily generate fast self-sustained oscillations . The corresponding exact FRE , which we call the QIF-FRE , therefore also generates oscillations . The benefit of having a simple macroscopic description for the network dynamics is its amenability to analysis . In particular , the nonlinearities in Eq ( 3 ) , which arise due to the spike initiation and reset mechanism in the QIF model , conspire to generate damped oscillations which reflect transient spike synchrony in the network . This oscillatory mode can be driven by sufficiently fast recurrent inhibitory synaptic activation , leading to sustained oscillations . This suggests that any mean-field description of network activity which neglects subthreshold integration will not properly capture spike-synchrony-dependent dynamical behaviors , including fast inhibitory oscillations . The QIF-FRE have also allowed us to generate a phase diagram for oscillatory behavior in a network of QIF neurons with ease via a standard linear stability analysis , see Fig 4 . This phase diagram agrees qualitatively with that of an equivalent network of Wang-Buzsáki neurons , suggesting that the QIF-FRE not only provide an exact description of QIF networks , but also a qualitatively accurate description of macroscopic behaviors in networks of Class I neurons in general . In particular , the QIF-FRE capture the fragility of oscillations to quenched variability in the network , a feature that seems to be particularly pronounced for Class 1 neuronal models compared to neural models with so-called Class 2 excitability [59] . Finally we have shown that the QIF-FRE and the heuristic H-FRE are formally equivalent in the limit of slow synapses . In this limit the neuronal dynamics is slaved to the synaptic activation and well-described by Eq ( 10 ) , as long as external inputs are stationary . In fact , in the absence of quenched heterogeneity ( Δ = 0 ) , the approximation for slow synapses Eq ( 10 ) is also fully equivalent to the reduction for slow synapses in networks of Class 1 neurons derived by Ermentrout in [60] . This further indicates that the QIF-FRE are likely valid for networks of Class 1 neurons in general . However , we also show that in the more biologically plausible scenario of time-varying external drive some degree of neuronal synchronization is generically observed , as in ( Fig 6 ) , and the slow-synapse reduction Eq ( 10 ) is not valid . The results presented here are obtained under two important assumptions that need to be taken into account when comparing our work to the existing literature on fast oscillations in inhibitory networks . ( i ) A derivation of an exact firing rate model for a spiking neuron network is only possible for ensembles of QIF neurons , which are the canonical model for Class 1 excitability [45 , 61] . Although many relevant computational studies on fast inhibitory oscillations also consider Class 1 neurons [30 , 32 , 34 , 39 , 62–64] , experimental evidence indicates that fast spiking interneurons are highly heterogeneous in their minimal firing rates in response to steady currents , and that a significant fraction of them are Class 2 [65–68] —but see also [69] . ( ii ) Our derivation of the QIF-FRE is valid for networks of globally coupled QIF neurons , with Lorentzian-distributed currents . In this system inhibition-based oscillations are only possible when the majority of the neurons are self-sustained oscillators , i . e . for Θ > 0 in Eq ( 14 ) , and are due to the frequency locking of a fraction of the oscillators in the population [41 , 42] —as it can be seen in the raster plot of Fig 1 ( c ) . In this state , the frequency of the cluster of synchronized oscillators coincides with the frequency of the mean field . The value of the frequency itself is determined through an interplay between single-cell resonance and network effects . Specifically , the synchronized neurons have intrinsic spiking frequencies near that of the mean-field oscillation and hence are driven resonantly . This collective synchronization differs from the so-called sparse synchronization observed in inhibitory networks of identical Class 1 neurons under the influence of noise [34 , 36 , 62 , 63] . In the sparsely synchronized state neurons fire stochastically at very low rates , while the population firing rate displays the fast oscillations as the ones reported here . Oscillatory phenomena arising from single-cell resonances , and which reflect spike synchrony at the population level , are ubiquitous in networks of spiking neurons . Mean-field theory for noise-driven networks leading to a Fokker-Planck formalism , allows for a linear analysis of the response of the network to weak stimuli when the network is in an asynchronous state [43 , 70] . Resonances can appear in the linear response when firing rates are sufficiently high or noise strength sufficiently low . Recent work has sought to extend H-FRE in this regime by extracting the complex eigenvalue corresponding to the resonance and using it to construct the linear operator of a complex-valued differential equation , the real part of which is the firing rate [29] . Other work has developed an expression for the response of spiking networks to external drive , which often generates resonance-related damped oscillations , through an eigenfunction expansion of the Fokker-Planck equation [71] . Our approach is similar in spirit to such studies in that we also work with a low dimensional description of the network response . In contrast to previous work our equations are an exact description of the macroscopic behavior , although they are only valid for networks of heterogeneous QIF neurons . Nonetheless , the QIF-FRE are simple enough to allow for an intuitive understanding of the origin of fast oscillations in inhibitory networks , and in particular , of why these oscillations are not properly captured by H-FRE .
We model fast-spiking interneurons , the dynamics of which are well-described by the Hodgkin-Huxley equations with only standard spiking currents . Many models of inhibitory neurons are Class 1 excitable [72] , including for example the Wang-Buszáki ( WB ) [30] , and the Morris-Lecar models [73] . Class 1 models are characterized by the presence of a saddle-node bifurcation on an invariant circle at the transition from quiescence to spiking . One consequence of this bifurcation structure is the fact the spiking frequency can be arbitrarily low near threshold . Additionally , near threshold the spiking dynamics are dominated by the time spent in the vicinity of the saddle-node itself , allowing for a formal reduction in dimensionality from the full neuron model to a reduced normal form equation for a saddle-node bifurcation [2 , 45 , 61] . This normal form , which is valid for any Class 1 model near threshold , is known as the quadratic integrate-and-fire model ( QIF ) . Using a change of variables , the QIF model can be transformed to a phase model , called Theta-Neuron model [74] , which has an strictly positive Phase Resetting Curve ( PRC ) . Neuron models with strictly positive PRC are called Type 1 neurons , indicating that perturbations always produce an advance ( and not a delay ) of their phase . In general , Class 1 neurons have a Type 1 PRC [45] , but see [75 , 76] . In a network of QIF neurons , the neuronal membrane potentials are { V ˜ i } i = 1 , … , N , which obey the following ordinary differential equations [7 , 64 , 74]: C d V ˜ i d t = g L ( V ˜ i - V t ) ( V ˜ i - V r ) ( V t - V r ) + I 0 , i ( 11 ) where C is the cell capacitance , gL is the leak conductance and I0 , i are external currents . Additionally , Vr and Vt represent the resting potential and threshold of the neuron , respectively . Using the change of variables V ˜ i ′ = V ˜ i - ( V t + V r ) / 2 , and then rescaling the shifted voltages as V i = V ˜ i ′ / ( V t - V r ) , the QIF model ( 11 ) reduces to τ m V ˙ i = V i 2 + I i ( 12 ) where τm = C/gL is the membrane time constant , Ii = I0 , i/ ( gL ( Vt−Vr ) ) −1/4 and the overdot represents derivation with respect to time t . Note that in the model ( 12 ) the voltage variables Vi and the inputs Ii do not have dimensions . Thereafter we work with QIF model its simplest form Eq ( 12 ) . We assume that the inputs are I i = η i - J τ m S , ( 13 ) where J is the inhibitory synaptic strength , and S is the synaptic gating variable . Finally , the currents ηi are constants taken from some prescribed distribution that here we consider it is a Lorentzian of half-width Δ , centered at Θ g ( η ) = 1 π Δ ( η - Θ ) 2 + Δ 2 . ( 14 ) In numerical simulations the currents were selected deterministically to represent the Lorentzian distribution as: ηi = Θ + Δtan ( π/2 ( 2i − N − 1 ) / ( N + 1 ) ) , for i = 1 , … , N . In the absence of synaptic input , the QIF model Eqs ( 12 ) and ( 13 ) exhibits two possible dynamical regimes , depending on the sign of ηi . If ηi < 0 , the neuron is excitable , and an initial condition V i ( 0 ) < - η i , asymptotically approaches the resting potential - - η i . For initial conditions above the excitability threshold , V i ( 0 ) > - η i , the membrane potential grows without bound . In this case , once the neuron reaches a certain threshold value Vθ ≫ 1 , it is reset to a new value −Vθ after a refractory period 2τm/Vθ ( in numerical simulations , we choose Vθ = 100 ) . On the other hand , if ηj > 0 , the neuron behaves as an oscillator and , if Vθ → ∞ , it fires regularly with a period T = π τ m / η i . The instantaneous population mean firing rate is R = lim τ s → 0 1 N 1 τ s ∑ j = 1 N ∑ k ∫ t - τ s t d t ′ δ ( t ′ - t j k ) , ( 15 ) where t j k is the time of the kth spike of jth neuron , and δ ( t ) is the Dirac delta function . Finally , the dynamics of the synaptic variable obeys the first order ordinary differential equation τ d S ˙ = - S + R . ( 16 ) For the numerical implementation of Eqs ( 15 ) and ( 16 ) , we set τs = 10−2 τm . To obtain a smoother time series , the firing rate plotted in Fig 3 was computed according to Eq ( 15 ) with τs = 3 ⋅ 10−2 τm . Recently Luke et al . derived the exact macroscopic equations for a pulse-coupled ensemble of Theta-Neurons [77] , and this has motivated a considerable number of recent papers [78–86 , 88] . This work applies the so-called Ott-Antonsen theory [89–91] to obtain a low-dimensional description of the network in terms of the complex Kuramoto order parameter . Nevertheless , it is is not obvious how these macroscopic descriptions relate to traditional H-FRE . As we already mentioned , the Theta-neuron model exactly transforms to the Quadratic Integrate and Fire ( QIF ) model by a nonlinear change of variables [45 , 61 , 74] . This transformation establishes a map between the phase variable θi ∈ ( −π , π] of a Theta neuron i , and the membrane potential variable Vi ∈ ( −∞ , +∞ ) of the QIF model Eq ( 12 ) . Recently it was shown that , under some circumstances , a change of variables also exists at the population level [40] . In this case , the complex Kuramoto order parameter transforms into a novel order parameter , composed of two macroscopic variables: The population-mean membrane potential V , and the population-mean firing rate R . As a consequence of that , the Ott-Antonsen theory becomes a unique method for deriving exact firing rate equations for ensembles of heterogeneous spiking neurons —see also [92–94] for recent alternative approaches . Thus far , the FRE for QIF neurons ( QIF-FRE ) have been successfully applied to investigate the collective dynamics of populations of QIF neurons with instantaneous [40 , 86 , 87] , time delayed [95] and excitatory synapses with fast synaptic kinetics [96] . However , to date the QIF-FRE have not been used to explore the dynamics of populations of inhibitory neurons with synaptic kinetics —but see [83] for a numerical investigation using the low-dimensional Kuramoto order parameter description . The method for deriving the QIF-FRE corresponding to a population of QIF neurons Eq ( 12 ) is exact in the thermodynamic limit N → ∞ , and , under the assumption that neurons are all-to-all coupled . Additionally , if the parameters ηi in Eq ( 13 ) ( which in the thermodynamic limit become a continuous variable ) are assumed to be distributed according to the Lorentzian distribution Eq ( 14 ) , the resulting QIF-FRE become two dimensional . For instantaneous synapses , the macroscopic dynamics of the population of QIF neurons ( 12 ) is exactly described by the system of QIF-FRE [40] τ m R ˙ = Δ π τ m + 2 R V , ( 17a ) τ m V ˙ = V 2 - ( π τ m R ) 2 - J τ m R + Θ , ( 17b ) where R is the mean firing rate and V the mean membrane potential in the network . With exponentially decaying synaptic kinetics the QIF-FRE Eq ( 17 ) become Eq ( 3 ) . In our study , we consider Θ > 0 , so that the majority of the neurons are oscillatory —see Eq ( 14 ) . To investigate the existence of oscillatory instabilities we use Eq ( 6 ) written in terms of the non-dimensional variables and parameters defined previously , which is − 2 j r * = ( 1 + λ ˜ τ ) [ ( 2 π r * ) 2 + ( λ ˜ + δ π r * ) 2 ] . ( 22 ) Imposing the condition of marginal stability λ ˜ = i ω ˜ in Eq ( 22 ) gives the system of equations 0 = 2 j r * + 4 π 2 r * 2 + 4 v * 2 - ( 1 - 4 v * τ ) ω ˜ 2 ( 23a ) 0 = ω ˜ ( 4 v * - 4 π 2 r * 2 τ - 4 v * 2 τ + τ ω ˜ 2 ) ( 23b ) where the fixed points are obtained from Eq ( 4 ) solving 0 = v * 2 - π 2 r * 2 - j r * + 1 , ( 24 ) with v * = - δ 2 π r * Eq ( 23b ) gives the critical frequency ω ˜ = 2 τ ( π τ r * ) 2 + τ v * ( τ v * - 1 ) . The Hopf boundaries can be plotted in parametric form solving Eq ( 24 ) for j , and substituting j and ω ˜ into Eq ( 23a ) . Then solving Eq ( 23a ) for τ gives the Hopf bifurcation boundaries τ ± ( r * ) = π 2 r * 2 - 1 + 7 v * 2 ± ( π 2 r * 2 - 1 ) 2 - ( 14 + 50 π 2 r * 2 ) v * 2 - 15 v * 4 16 v * ( π 2 r * 2 + v * 2 ) . ( 25 ) Using the parametric formula ( j ( r * ) , τ ± ( r * ) ) ± = ( v * 2 / r * + 1 / r * − π 2 r * , τ ± ( r * ) ) . we can be plot the Hopf boundaries for particular values of the parameter δ , as r* is changed . Fig 4 shows these curves in red , for δ = 0 . 05 and δ = 0 . 075 . They define a closed region in parameter space ( shaded region ) where oscillations are observed . We perform numerical simulations using the the Wang-Buzsáki ( WB ) neuron [30] , and compare them with our results using networks of QIF neurons . The onset of oscillatory behavior in the WB model is via a saddle node on the invariant circle ( SNIC ) bifurcation . Therefore , the populations of WB neurons near this bifurcation are expected to be well described by the theta-neuron/QIF model , the canonical model for Class 1 neural excitability [45 , 74] . We numerically simulated a network of N all-to-all coupled WB neurons , where the dynamics of each neuron is described by the time evolution of its membrane potential [30] C m V i ˙ = - I Na , i - I K , i - I L , i - I syn + I app , i + I 0 . The cell capacitance is Cm = 1 μF/cm2 . The inputs Iapp ( in μA/cm2 ) are distributed according to a Lorentzian distribution with half width σ and center I ¯ . In numerical simulations these currents were selected deterministically to represent the Lorentzian distribution as I app , i = I ¯ + σ tan ( π / 2 ( 2 i - N - 1 ) / ( N + 1 ) ) , for i = 1 , … , N . The constant input I0 = 0 . 1601 μA/cm2 sets the neuron at the SNIC bifurcation when Iapp = 0 . The leak current is I L , i = g L ( V i - E L ) , with gL = 0 . 1 mS/cm2 , so that the passive time constant τm = Cm/gL = 10 ms . The sodium current is I Na , i = g Na m ∞ 3 h ( V i - E Na ) , where gNa = 35 mS/cm2 , ENa = 55 mV , m∞ = αm/ ( αm + βm ) with αm ( Vi ) = −0 . 1 ( Vi + 35 ) / ( exp ( −0 . 1 ( Vi + 35 ) − 1 ) ) , βm ( Vi ) = 4exp ( − ( Vi + 60 ) /18 ) . The inactivation variable h obeys the differential equation h ˙ = ϕ ( α h ( 1 - h ) - β h h ) , with ϕ = 5 , αh ( Vi ) = 0 . 07exp ( − ( Vi + 58 ) /20 ) and βh ( Vi ) = 1/ ( exp ( −0 . 1 ( Vi + 28 ) ) + 1 ) . The potassium current follows I K , i = g K n 4 ( V i - E K ) , with gK = 9 mS/cm2 , EK = −90 mV . The activation variable n obeys n ˙ = ϕ ( α n ( 1 - n ) - β n n ) , where αn ( Vi ) = −0 . 01 ( Vi + 34 ) / ( exp ( −0 . 1 ( Vi + 34 ) ) − 1 ) and βn ( Vi ) = 0 . 125exp ( − ( Vi + 44 ) /80 ) . The synaptic current is Isyn = kCm S , where the synaptic activation variable S obeys the first order kinetics Eq ( 16 ) and k is the coupling strength ( expressed in mV ) . The factor Cm ensures that the effect of an incoming spike to the neuron is independent from its passive time constant . The neuron is defined to emit a spike when its membrane potential crosses 0 mV . The population firing rate is then computed according to Eq ( 15 ) , with τs = 10−2 ms . In numerical simulations we considered N = 1000 all-to-all coupled WB neurons , using the Euler method with time step dt = 0 . 001 ms . In Fig 1 , the membrane potentials were initially randomly distributed according to a Lorentzian function with half width 5 mV and center −62 mV . Close to the bifurcation point , this is equivalent to uniformly distribute the phases of the corresponding Theta-Neurons in [−π , π] [2 , 7 , 61 , 74] . The parameters were chosen as I ¯ = 0 . 5 μ A / cm 2 , σ = 0 . 01 μA/cm2 and k = 6 mV . The population firing rate was smoothed setting τs = 2 ms in Eq ( 15 ) . In Fig 5 , we systematically varied the coupling strength and the synaptic time decay constant to determine the range of parameters displaying oscillatory behavior . For each fixed value of τd we varied the coupling strength k; we performed two series of simulations , for increasing and decreasing coupling strength . In Fig 5 we only show results for increasing k . All quantities were measured after a transient of 1000 ms . To obtain the amplitude of the oscillations of the mean membrane potential , we computed the maximal amplitude V ¯ max - V ¯ min over time windows of 200 ms for 1000 ms , and then averaged over the five windows . | Population models describing the average activity of large neuronal ensembles are a powerful mathematical tool to investigate the principles underlying cooperative function of large neuronal systems . However , these models do not properly describe the phenomenon of spike synchrony in networks of neurons . In particular , they fail to capture the onset of synchronous oscillations in networks of inhibitory neurons . We show that this limitation is due to a voltage-dependent synchronization mechanism which is naturally present in spiking neuron models but not captured by traditional firing rate equations . Here we investigate a novel set of macroscopic equations which incorporate both firing rate and membrane potential dynamics , and that correctly generate fast inhibition-based synchronous oscillations . In the limit of slow-synaptic processing oscillations are suppressed , and the model reduces to an equation formally equivalent to the Wilson-Cowan model . | [
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] | 2017 | Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks |
Cerebellar Purkinje cells mediate accurate eye movement coordination . However , it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns , namely tonic , bursting , and spike pauses . Here , a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex ( VOR ) adaptation . The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites . A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output . Our results suggest that pauses following Purkinje complex spikes ( bursts ) encode transient disinhibition of target medial vestibular nuclei , critically gating the vestibular signals conveyed by mossy fibres . This gating mechanism accounts for early and coarse VOR acquisition , prior to the late reflex consolidation . In addition , properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation ( LTD/LTP ) to be finely shaped at mossy fibre-medial vestibular nuclei synapses , which optimises VOR consolidation . Tonic Purkinje cell firing maintains the consolidated VOR through time . Importantly , pauses are crucial to facilitate VOR phase-reversal learning , by reshaping previously learnt synaptic weight distributions . Altogether , these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation .
The cerebellum controls fine motor coordination including online adjustments of eye movements [1] . Within the cerebellar cortex , the inhibitory projections of Purkinje cells to medial vestibular nuclei ( MVN ) mediate the acquisition of accurate oculomotor control [2 , 3] . Here , we consider the role of cerebellar Purkinje cells in the adaptation of the vestibular ocular reflex ( VOR ) , which generates rapid contralateral eye movements that maintain images in the fovea during head rotations ( Fig 1A ) . The VOR is crucial to preserve clear vision ( e . g . , whilst reading ) and maintain balance by stabilising gaze during head movements . The VOR is mediated by the three-neuron reflex arc comprised of connections from the vestibular organ via the medial vestibular nuclei ( MVN ) to the eye motor neurons[3–5] . VOR control is purely feed-forward [6] and it relies on several cerebellar-dependent adaptive mechanisms driven by sensory errors ( Fig 1C ) . Because of its dependence upon cerebellar adaptation , VOR has become one of the most intensively used paradigms to assess cerebellar learning [6] . However , very few studies have focused on the relation between the characteristics spike response patterns of Purkinje cells and VOR adaptation , which is the main focus of this study . Purkinje cells provide the major output of the cerebellum through MVN . Purkinje cells receive two main excitatory ( glutamatergic ) afferent currents ( Fig 1B ) . The first excitatory input originates from the parallel fibres ( PFs ) , i . e . the axons of the granule cells ( GCs ) . The second comes from the climbing fibres ( CFs ) , i . e . the projections of the inferior olive ( IO ) cells . These excitatory inputs drive Purkinje cell simple or complex spike patterns , respectively [9 , 10] . Simple spikes of Purkinje cells are elicited topically at high frequencies [11 , 12] . Complex spikes consist of a fast initial large-amplitude spike followed by a high-frequency burst [13] . This burst is made of several slower spikelets of smaller amplitude separated from one another by 2–3 ms [13–15] . Complex spikes are caused by the activation of a single IO neuron that produces a large electrical event in the soma of the post-synaptic Purkinje cell . This electrical event generates calcium-mediated action potentials in the Purkinje cell dendrites that , in turn , shape the complex spike . Simple spike activity is , in fact , mostly suppressed during complex spiking [15] . After each CF-evoked burst , a spike pause prevents Purkinje cells from firing for a period that increases in the presence of extra dendritic spikes [16–18] . The CF-evoked spike burst-pause sequences of Purkinje cell responses critically regulate the inhibitory ( GABAergic ) drive of MVN synapses , which determines the cerebellar output during sensorimotor adaptation . Therefore , understanding the dynamics of the characteristic Purkinje cell spike patterns is relevant to linking cerebellar cell properties to cerebellar-dependent behavioural adaptation . Recent studies have paved the road in gaining knowledge on the behavioural implication of Purkinje cell spike modes [3 , 15 , 19] . In particular , Herzfeld and colleagues suggested that the cerebellum predicts real-time motion of the eye through the organisation of Purkinje cells into clusters that share similar CF projections from the IO [3] . The combined activity of bursting and silent Purkinje cell populations can predict both the actual speed and direction of rapid accurate eye movements ( saccades ) . However , these studies have not assessed the interplay between the different Purkinje cell spike patterns and the plasticity mechanisms at stake at MVN synapses in shaping sensorimotor adaptation . MVN neurons , in addition to receiving the inhibitory inputs from Purkinje cells , are also innervated by the excitatory afferents from the mossy fibres ( MFs ) , which convey vestibular signals about head movements ( Fig 1B ) . This vestibular information also converges onto Purkinje cells through the mossy fibre-granule cell-parallel fibre pathway ( MF-GC-PF; Fig 1B ) . Therefore , the characteristic firing patterns of Purkinje cells are likely to play a key role in driving the associative plasticity mechanisms operating at MF-MVN excitatory synapses [20–22] and at Purkinje cells-MVN inhibitory synapses [23–26] . The CF-evoked spike burst-pause sequences of Purkinje cells depend indeed upon the activation of CFs , which are assumed to convey an ‘instructive’ signal encoding sensory error information [6 , 15 , 27] . Therefore , the properties of the CF-evoked spike burst-pause patterns ( e . g . , the relative duration of the bursts versus the pauses ) reflect sensory error related information [15 , 19 , 28] . The activation of CFs is critical for inducing different forms of plasticity at PF-Purkinje cell synapses and , indirectly , at Purkinje cell-MVN synapses [29 , 30] . Importantly , plasticity at MF-MVN synapses also seems to be dependent on Purkinje cell signals [31–33] , generated through the MF-GC-PF pathway and through CF activation . Some computational studies have proposed that plasticity mechanisms at MF-MVN and Purkinje cell-MVN synapses as key factors in determining cerebellar adaptive gain control [31 , 32 , 34] . These models support the hypothesis of a two-state cerebellar adaptation process [35 , 36] , with a fast adaptive phase mediated by the cerebellar cortex ( involving plasticity at Purkinje cell synapses ) and a slow adaptive process occurring in deeper structures , involving plasticity at MVN synapses [33 , 35–39] . However , these computational studies do not account for the interaction between the different spiking modes of Purkinje cells ( in particular CF-evoked spike burst-pause dynamics ) and the distributed plasticity mechanisms underpinning cerebellar adaptive control [34] . The spiking cerebellar model presented here addresses these issues within a VOR adaptation framework ( Fig 1A and 1C ) . We simulate horizontal VOR ( h-VOR ) experiments with mice undertaking sinusoidal ( ~1 Hz ) whole body rotations in the dark [40] . The model incorporates the main anatomo-functional properties of the cerebellar microcircuit , with synaptic plasticity mechanisms at multiple cerebellar sites ( Fig 1B; see Materials & Methods ) .
The detailed Purkinje cell model reproduces the characteristic response patterns observed experimentally: tonic simple spiking ( 20–200 Hz ) , complex spiking ( bursts with high-frequency spikelet components up to 600 Hz ) , and post-complex spike pauses ( Fig 2A ) . In the model , CF discharges trigger transitions between the Purkinje cell Na+ spike output , CF-evoked bursts , and post-complex spike pauses . As evidenced in [41] , in in-vitro slice preparations at normal physiological conditions , 70% of Purkinje cells spontaneously express a trimodal oscillation: a Na+ tonic spike phase , a Ca-Na+ bursting phase , and a hyperpolarised quiescent phase . On the other hand , Purkinje cells also show spontaneous firing consisting of a tonic Na+ spike output without Ca- Na+ bursts [41–43] . McKay et al . [41] report Purkinje cell recordings exhibiting a tonic Na+ phase sequence followed by CF-evoked bursts ( via complex spikes ) and the subsequent pause ( Fig 2A ) . The frequency of Purkinje cell Na+ spike output decreases with no correlation with the intervals between CF discharges [41] . The model mimics this behaviour under similar CF discharge conditions ( Fig 2B ) . It also replicates the relation between spike pause duration and pre-complex spike inter-spike interval ( ISI ) duration observed through electrophysiological recordings [44] ( Fig 2C; R2 = 0 . 9879; p<0 . 0001 ) . Only ISIs immediately following complex spikes were considered for this analysis . This relation was measured by maintaining the CF stimulation constant whilst incrementally increasing the amplitude of the PF input current . The probability distribution of post-complex spike ISIs is also consistent with experimental data [44] ( Fig 2D ) . The kurtosis ( ‘peakedness’ ) of the ISI distribution is 4 . 24 , which is in the range of kurtosis values measured after tetanisation of mouse Purkinje cells [44] . Model post-complex spike ISI values are skewed rightward ( positive skewness value of 0 . 6463 ) , consistently with the asymmetric distribution shape observed experimentally [44] . Finally , the duration of the model post-complex spike pauses is non-linearly related to burst duration ( S1A and S1B Fig ) , assuming that CF stimuli carrying large error-related signals ( as during VOR adaption ) elicit both somatic and extra dendritic Purkinje spikes [16–18] . We assessed h-VOR adaptation by simulating a 1 Hz horizontal head rotation to be compensated by contralateral eye movements ( Fig 1A ) . First , we tested the role of Purkinje spike burst-pause dynamics in the absence of cerebellar learning , i . e . by blocking synaptic plasticity across all model projections ( i . e . , MF-MVN , PF-Purkinje cell , Purkinje cell-MVN ) . Synaptic weights were initialised randomly and equally within each projection set . The CF input driving Purkinje cells was taken as to signal large retina slips , which generated sequences of complex spikes made of 4 to 6 burst spikelets [15] ( Fig 3A , top ) . The elicited Purkinje spike burst-pause sequences shaped the temporal disinhibition of target MVN neurons , allowing the incoming input from MFs to drive MVN responses ( Fig 3A , middle ) . This facilitated a coarse baseline eye motion ( Fig 3A , bottom ) . Blocking complex spiking in the Purkinje cell model ( through the blockade of muscarinic voltage-dependent channels , see Methods ) prevented MF activity from eliciting any baseline MVN compensatory output ( Fig 3B ) . These results suggest that the gating mechanism mediated by Purkinje spike burst-pause sequences , which encode transient disinhibition of MVN neurons , is useful for early and coarse VOR , prior to the adaptive consolidation of the reflex through cerebellar learning . We then activated the LTD/LTP plasticity mechanisms at MF-MVN , PF-Purkinje cell , and Purkinje cell-MVN synapses ( see Materials & Methods ) . During 10000 s , the model faced a 1 Hz horizontal head rotation , and cerebellar h-VOR learning took place to generate compensatory contralateral eye movements . A sensitivity analysis identified the critical LTD/LTP balance at MF-MVN and PF-Purkinje cell synapses in order to achieve VOR adaptation ( in terms of both gain and phase ) . This analysis predicts a very narrow range of values for which LTP slightly exceeding LTD at MF-MVN synapses ensures learning stability through time . By contrast , PF-Purkinje cell synapses admitted a significantly broader range for the LTD/LTP ratio ( S2 and S3 Figs ) . The same parameter sensitivity analysis for the cerebellar model with no bursting and pause dynamics shows a much wider range of values for the LTD/LTP balance at both PF-Purkinje cell and MF-MVN synapses ( S4 Fig ) . A comparison of VOR adaptation accuracy in the presence vs . absence of CF-evoked Purkinje spike burst-pause dynamics shows that VOR gain plateaued three times faster in the presence of Purkinje complex spikes ( Fig 4A , left ) . Also , the VOR gain converged to [0 . 8–0 . 9] , which is consistent with experimental recordings in mice [40] , monkeys [45] , and humans [46] ( S5 Fig ) . Conversely , without Purkinje bursting-pause dynamics the VOR gain saturated to a value >1 ( i . e . over learning ) at the end of the adaptation process . In terms of VOR phase , convergence to 180° ( i . e . , well synchronised counter-phase eye movements ) was reached after approximately 1000 s under both conditions ( Fig 4A , right ) . A more accurate VOR gain adaptation in the presence of Purkinje complex spiking reflected a more selective synaptic modulation across learning ( Fig 4B–4D ) . In particular , Purkinje spike burst-pause dynamics facilitated a sparser weight distribution at MF-MVN synapses ( Fig 4B ) , which ultimately shaped VOR adaptation [21] . Indeed , Purkinje burst sizes , which were assumed to reflect the sensed errors [15 , 19 , 28] , regulated the inhibitory action of Purkinje cells on MVN , and induced error-dependent LTD at MF-MVN synapses ( see Materials & Methods ) . On the other hand , post-complex spike pauses ( disinhibiting MVN ) induced error-dependent LTP at MF-MVN synapses ( the larger the error , the larger the burst size , and the wider the post-complex spike pause in the presence of extradendritic Purkinje cell spikes , S1 Fig . At the beginning of VOR adaptation , the error was larger , and so were the burst and pause durations . Because the durations of pauses remained always larger than bursts ( S1 Fig . LTP dominated over LTD at MF-MVN synapses , increasing the learning rate . Therefore , the spike burst-pause dynamics enhanced the precision of cerebellar adaptation at MVN cells , by ( i ) recruiting the strictly necessary MF-MVN projections ( i . e . , higher kurtosis value of the synaptic weight distribution; Fig 4B ) , ( ii ) making a better use of the synaptic range of selected projections ( larger standard deviations with lower overall gains; Fig 4C ) , and the rate by ( iii ) varying synaptic weights selectively ( lower averaged synaptic weight variations; Fig 4D ) . Phase-reversal VOR is induced when a visual stimulus is given simultaneously in phase to the vestibular stimulation but at greater amplitude ( 10% more ) [29] . This creates a mismatch between visual and vestibular stimulation making retinal slips reverse direction[47] . Cerebellar learning is deeply affected by VOR phase reversal since the synaptic weight distribution at both PF-Purkinje cell and MF-MVN synapses must be reversed . Here , we first simulated an h-VOR adaptation protocol ( 1 Hz ) during 10000 s ( as before ) . Then , h-VOR phase reversal took place during the next 12000 s . Finally , the normal h-VOR had to be restored during the last 12000 s ( Fig 5 ) . Our results suggest that the presence of Purkinje spike burst-pause dynamics is instrumental to phase-reversal VOR gain adaptation ( Figs 5A and S7 ) allowing for fast VOR learning reversibility consistently with experimental recordings [2] ( Fig 5B ) . Conversely , the absence of Purkinje complex spiking led to impaired VOR phase-reversal learning with significant interference ( Fig 5A and 5B ) . The two models ( i . e . , with and without Purkinje complex spiking ) behaved similarly in terms of VOR phase adaptation during the same reversal learning protocol ( S6 Fig ) . VOR phase-reversal learning demanded first the reduction of the VOR gain , which can be regarded as a ‘forgetting phase’ ( Fig 5B , days 1&2 ) . Then , a ‘synchronisation phase’ took place with a reverse adaptive action that gradually increased the VOR gain ( Fig 5B , days 3&4 ) . During the forgetting phase , LTD dominated over LTP at MF-MVN synapses ( Purkinje burst sizes were maximal ) , thus erasing the memorised weight patterns . During the synchronisation phase , Purkinje post-complex spike pauses led to a dominant LTP at MF-MVN synapses , reversing the learnt configuration . The interplay between bursts and post-complex spike pauses allowed synaptic adaptation at MF-MVN projections to be highly selective , which resulted in a sparser weight distribution as compared to the case without Purkinje complex spiking ( Fig 6A ) . Therefore , VOR reverse learning required the adjustment of fewer MF-MVN synapses , thus facilitating the eye counteraction of the head velocity movement ( S8 Fig ) , and the weight distribution was reshaped more efficiently with negligible interferences from the previously learnt patterns ( Fig 6B and 6C ) . VOR phase-reversal learning can take place across several days [2] ( Fig 5 ) . Dark periods in-between training sessions cause reversal VOR gain discontinuities ( Fig 7 ) . This phenomenon has been assumed to result from the decaying of synaptic weights back to their initial values during sleep [2] . However , the mechanisms underlying this decaying process remain unknown . We explored possible cerebellar LTD/LTP balance modulation scenarios occurring during sleep as a consequence of changes in cerebellar activity . During rapid eye movement sleep ( REMs ) , the mean firing activity of Purkinje cells shows increased tonic firing and decreased bursting in both frequency and size [48] . The CF average activity during REMs remains constant at a low frequency regime , showing a tendency in many IO neurons to diminish their overall frequency [49] . The activation of MFs varies during REMs , unrelatedly to any apparent behavioural changes , up to 60 MFs/s on average [49] . We modelled Purkinje cell , CF and MF activities during REMs . CFs were stochastically activated at 1 Hz [48 , 49] following a Poisson distribution ( S9 Fig ) . CF activations were also modulated to generate a large event in the Purkinje soma able to elicit bursts of 3 spikes on average [48] . MFs were stochastically activated by mimicking their activity during REMs ( with an upper bound firing rate of 8–13 Hz ) . We tested three hypotheses , based on different levels of cerebellar activity during 6 REMs stages of 3000 s each ( i . e . , 18000 s of simulation ) between days 1 and 2 . In the first scenario , we considered high levels of MF activity ( average firing rate 10 Hz ) , which led to a dominance of LTP at both PF-Purkinje cell and MF-MVN synapses during REMs . Consequently , the cerebellar model kept ‘forgetting’ the memory traces as during the reversal VOR learning of day 1 ( Fig 7 , blue curve ) . In the second scenario , we considered an average MF activity of 2 . 5 Hz , which made the LTP driven by vestibular activity counterbalance the LTD driven by the CFs . Under this condition , the cerebellar model consolidated reversal VOR adaptation thus maintaining the synaptic weights at PF-Purkinje and MF-MVN synapses ( Fig 7 , green curve ) . Finally , we considered a low level of MF activity ( average 1 Hz ) , which made LTD block the LTP action driven by the vestibular ( MF ) activity . Under this third scenario , the cerebellar model showed a consistent tendency for weights at PF-Purkinje and MF-MVN synapses to decay back towards their initial values ( Fig 7 , red curve ) . Therefore , the model predicts that LTP blockades during REMs stages might underlie the reversal VOR gain discontinuities in-between training sessions , in agreement with experimental data [2] ( Fig 7 , black curve ) . During transient VOR adaptation and phase reversal learning , retina slips were large causing vigorous CF discharges ( up to 10 Hz ) to encode the sensed errors . Consequently , Purkinje cell complex spike-pauses were elicited at high frequency during adaptation ( Fig 8 ) . As the VOR error decreased , the frequency of CF-evoked Purkinje bursts decayed to ~1 Hz upon completion of adaptation ( Fig 8 ) . Therefore , during post ( and pre ) VOR adaptation , model Purkinje tonic Na+ spike output dominated and Purkinje cells tended to fire steadily ( similar to spontaneous activity ) with only rare complex spike-pause firing . Under stationary VOR conditions , ( i . e . , during pre/post VOR adaptation ) model CFs were stochastically activated at ~1 Hz ( S9 Fig shows the Poisson-based generative model for the IO firing ) . Such a CF baseline discharge at ~1 Hz allowed non-supervised LTP to be counterbalanced at PF-Purkinje cell synapses ( see Materials & Methods ) , thus preserving pre/post cerebellar adaptation . Luebke and Robinson [50] found that directly stimulating CFs at 7 Hz during 30 min after 3 days of VOR adaptation would impair the reflex . Model CFs discharged at frequencies larger than 1 Hz only to signal retina slips ( i . e . , during VOR adaptation ) . However , a direct ( and error independent ) high-frequency stochastic stimulation of CFs would lead to VOR impairment . To illustrate this , we simulated a protocol similar to the one used by [50] . As expected , the number of CF-evoked Purkinje burst-pauses increased as the CF frequency was artificially incremented through a 7 Hz direct stimulation ( Fig 8 ) . Therefore , the VOR gain error tended to increase indicating an impairment/blockade of the acquired reflex ( Fig 8 ) and a decrease in VOR gain even with similar CFs discharges observed during VOR adaptation .
This work assumes a gradually modulated CF activity capable of providing an ‘instructive’ signal to Purkinje cells [92] . Evidence exists showing that the presence of the CF signal enables VOR acquisition even in the absence of PF-Purkinje LTD [93] , whereas erasing the CF signal impairs VOR adaptation [90] . Nonetheless , the information conveyed by CFs onto Purkinje cells and its potential role in sensorimotor adaptation is under strong debate . The controversy about the nature of CF activity has been further roused by the fact that IO functional properties have so far not been univocally identified [63 , 91 , 94 , 95] . On the one hand , proponents of the Marr-Albus-Ito motor learning theory hypothesise that CFs carry a binary feedback-error signal computed by the IO [96] . Yet , recent studies have questioned the hypothesis of a binary CF signal by demonstrating that the duration of Purkinje cell complex spikes ( evoked by CF afferents ) can accurately be adjusted based on information that a binary instructive signal could not support [15 , 16 , 77 , 97 , 98] . Our model embraces this second hypothesis . On the other hand , despite the CF instructive-role hypothesis is widely accepted in cerebellar learning [6] , the overall assumption about IO-mediated feedback-error learning is contrasted by a body of research proposing different roles for the IO rather than coding error [99] . These works focus on the periodic nature of CF activity and they put the CF signalling in relation to the timing aspects of motion [99 , 100] , and , in particular , to the onset of motion [101] . These counter hypotheses may be classified under five categories: ( i ) CFs may act as a temporal information encoder which operates independently of awareness [102–104] . Subjects were scanned ( using event-related functional MRI ) whilst observing changes in stimulus timing that were presented near each subject’s detection threshold such that subjects were aware of such changes in only approximately half the trials . The IO and multiple areas within the cerebellar cortex showed a robust response to time changes regardless of whether the subjects were aware of these changes . ( ii ) CFs may play a key role in associative somatosensory learning [105] . In this approach , CFs are thought to provide little or no information about self-produced motion and , therefore , they are not useful for correcting or improving motor performance . Yet , during classical conditioning , the IO may provide the cerebellum with a representation of the unconditioned stimulus for associative learning . ( iii ) CF may act as a periodic low-frequency synchroniser [106] . Under this framework , CFs are believed to convey many different types of information , each of which is supposedly assigned to a different narrow time window . Because movement parameters ( i . e . , end-point error in a cerebellar target-reaching task ) are different from trial to trial , it is further hypothesised that a group of CFs innervating a longitudinal synchronous band shall be recruited to convey one particular form of information in each trial at a particular timing . Rhythmicity , randomness , and synchrony could therefore coexist . ( iv ) CFs may be responsible for motor timing and reset [107 , 108] . Some of the most characteristic morphological features of the olivary neuropil , the glomeruli with their dendrodendritic gap junctions , seem to enable the synchronous activation of clusters of neurons that may act as a temporal clock for motricity . Conversely , subthreshold IO oscillation would allow for a “clock” resetting of a group of neurons . ( v ) CFs may be functioning in both motor timing and motor learning [109] . Although synchronous activation of clusters of IO neurons seem to favor the timing hypothesis , it was hypothesised that the olivary micro circuitry ( with its unique characteristics , such as the combined excitatory and inhibitory input to the olivary spine ) might be able to support both the timing and learning hypotheses , but not the original Marr-Albus-Ito comparator hypothesis . The cerebellar model presented here assumes a perfect transmission of CF bursts to target Purkinje cells , thus neglecting occasional spike transmission failures observed in vivo [16] . Thus , in the model , there exists a linear relationship between the number of CF stimuli and the length of Purkinje complex spikes . Another limitation is that no distinction between somatic and dendritic spikes is drawn because the Purkinje model consists of a single compartment . Therefore , a key assumption of the model is that CF stimuli elicit both somatic spikes and extra dendritic spikes . Under this assumption , the model predicts a non-linear relation between the length of CF-evoked bursts and the duration of post-complex spike pauses . Indeed , since we adopt a graded representation of the CF instructive signal [15 , 19 , 28] , incremental errors are translated into incremental Purkinje dendritic stimulation intensities . Purkinje calcium-dependent potassium channels activated by Ca2+ influx provoke an after-hyperpolarisation that inhibits the spike generation and modulates the lengthening of the pause [110] . Furthermore , larger numbers of CF stimuli were observed to trigger extra Purkinje dendritic spikes , which influenced Purkinje cell pauses [17 , 18] . Also , increasing the number of spikes within the CF burst in the absence of additional dendritic calcium spikes was reported to lead to a decrease in the length of the Purkinje post-complex spike pauses [16] . The model thus assumes a cerebellar operation with a non-linear modulation of the lengths of Purkinje cell post-complex spike pauses due to extra dendritic spikes and large dendritic stimulations during VOR adaptation . Finally , the proposed model considers stationary physiological conditions in the generation of the Purkinje post-complex spike pause ( indeed , temperature increase of the cerebellar tissue [111] or delivery of anaesthesia [112] can induce longer , >500 ms , Purkinje simple spike pauses , which ultimately may compromise the spike burst-pause dynamics ) . A simplification of our model is that it does not account for the putative role of inhibitory interneurons in the supervised learning mechanism . Understanding the role of this inhibitory network has stimulated numerous experiments and fuelled a debate [113 , 114] . It was observed that genetic removal of GABAA receptors of Purkinje cells does not significantly impair mice’s gait and baseline VOR [115] , although it does affect the adaptive cerebellar motor control [116] . It was also proposed that this inhibition might regulate the excitatory drive on Purkinje cells by granule cell activity [117] . In the rat cerebellar cortex , GABAergic molecular layer interneurons ( which converge on Purkinje cells ) are only a small fraction ( about 2 . 3% ) of granule excitatory neurons [118] . Overall , these observations point towards the fact that this inhibitory network might not be at the core of the discriminability of input states , whereas it might sub-serve the processes of facilitating cerebellar learning and the correct operation of the cerebellar network [31 , 110 , 119 , 120] . The model suggests that CF-evoked Purkinje cell spike burst-pause dynamics is critical to shape MF-MVN synapses , as to optimise the accuracy and consolidation rate of VOR adaptation . We show that burst and spike pause sequences facilitate sparser MF-MVN connections , which increases coding specificity during the adaptation process . The results predict that the spike burst-pause dynamics should be central to retune MF-MVN synapses during VOR phase-reversal adaptation . First , it is shown that blocking complex spike responses ( and post-complex spike pauses ) in Purkinje cells impairs reverse VOR adaptation . More strikingly , the results indicate that Purkinje cell bursting and spike pauses ensure the reversibility of the adaptation process at MF-MVN synapses . Bursts selectively facilitate LTD at MF-MVN connections , which rapidly erases previously learnt memory traces at these synapses . Subsequently , post-complex spike pauses induce strong LTP at MF-MVN synapses , which allows the cerebellar output to become rapidly reverse-correlated to the sensed error . In addition , the memory consolidation of VOR adaptation during sleeping [2 , 73 , 121] is also supported by the CF-evoked Purkinje cell spike burst-pause dynamics . CF stochastically activations at a low frequency ( 0 . 9 Hz ) during REMs stages maintain a base Purkinje bursting that ultimately facilitates LTP blockades at PF-Purkinje cell and MF-MVN synapses , and it preserves the on-going learning process .
We simulated horizontal VOR ( h-VOR ) experiments with mice undertaking sinusoidal ( ~1 Hz ) whole body rotations in the dark [40] . The periodic functions representing eye and head velocities ( Fig 1A ) were analysed through a discrete time Fourier transform . The VOR gain was calculated as the ratio between the first harmonic amplitudes of the forward Fourier eye–and head–velocity transforms: VORGAING=A1eye−velocityA1head−velocity ( 1 ) In order to assess the VOR shift phase , the cross-correlation of the eye and head velocity time series was computed: xcorr= ( x*y ) [γ]=def∑n=−∞+∞x* ( n ) y ( n+γ ) ( 2 ) where x* is the complex conjugate of x , and γ the lag ( i . e . , shift phase ) . The ideal eye and head velocity lag is ±0 . 5 after normalisation , with cross-correlation values ranged within [–1 , 1] , which is equivalent to a phase shift interval of [–360° 360°] . We simulated a rotational chair test , in which a subject ( mouse , monkey , or human ) is seated in a rotatory table . In this protocol , the velocity of rotation is controlled and the subject’s head is restrained , assuming that the movement of the table is equal to the subject’s head movement . During normal VOR adaptation , a visual target is provided in anti-phase with vestibular stimulation . The eyes must follow the visual target , thus minimising retinal slips . The vestibular stimulation and the eye output functions in our simulation were taken as: Vestibularstimulation=sin ( 2⋅π⋅t ) Eyeoutputfunction=AE⋅sin ( 2⋅π⋅t+π⋅ϕE ) ( 3 ) where the ideal VOR experiment values correspond to AE=1 , ϕE=0 ( visual field fixed ) . During VOR phase-reversal learning , the visual stimulus is given in-phase with the visual field but it turns twice the distance of the turntable , i . e . AE = -1 . The cerebellar circuit was modelled as a feed–forward loop capable of compensating head movements by producing contralateral eye movements ( Fig 1B ) . The connectivity and the topology of the simulated cerebellar network involved five neural populations: mossy fibres ( MFs ) , granule cells ( GCs ) , medial vestibular nuclei ( MVN ) , Purkinje cells , and inferior olive ( IO ) cells [33 , 122–125] . During simulated 1 Hz head rotations , sensorimotor activity was translated into MF activity patterns that encoded head velocity . MFs transmitted this information to both MVN and GCs . The latter generated a sparse representation of head velocity signals , which was sent to Purkinje cells through the PFs . Purkinje cells were also driven by the CFs , which conveyed the instructive signal encoding sensory error information ( i . e . , retina slips due to the difference between actual and target eye movements , [82] ) . Finally , Purkinje cells’ output inhibited MVN neurons , which closed the loop by shaping cerebellar-dependent VOR control . The CF-Purkinje cell-MVN subcircuit was divided in two symmetric micro-complexes for left and right h-VOR , respectively . The input-output function of the cerebellar network model was made adaptive through spike-timing dependent plasticity ( STDP ) at stake at multiple sites ( Fig 1C ) . These STDP mechanisms led to both long-term potentiation ( LTP ) and long-term depression ( LTD ) of the ~50000 synapses of the cerebellar model see [126] . This spiking neural network model was implemented in EDLUT [86 , 127 , 128] an efficient open source simulator mainly oriented to real time simulations . We used a detailed Purkinje cell model based on the experimental work by Middelton et al . [78] , and on the modelling work by Miyasho et al . [79] . The model consisted of a single compartment with five ionic currents: dVdt=−gK⋅n4⋅ ( V+95 ) −gNa⋅m0[V]3⋅h⋅ ( V−50 ) −−gCa⋅c2⋅ ( V−125 ) −gL⋅ ( V+70 ) −gM⋅M⋅ ( V+95 ) ( 4 ) with gK denoting a delayed rectifier potassium current , gNa a transient inactivating sodium current , gCa a high-threshold non-inactivating calcium current , gL a leak current , and gM a muscarinic receptor suppressed potassium current ( see Table 1 ) . The dynamics of each gating variable evolved as follows: x·=x0[V]−xτx[V] ( 5 ) where x indicates the variables n , h , c , and M . The implemented equilibrium function is determined by the term x0[V] and time constant τx[V] ( Table 2 ) . The sodium activation variable was replaced and approximated by its equilibrium function m0[V] . M-current presents a temporal evolution significantly slower than the rest of the five variables thus provoking a slow-fast system able to reproduce the characteristic Purkinje cell spiking modes ( Fig 2 ) . The final voltage dynamics for the Purkinje [78 , 79]cell model was given by: dVdt=−gK⋅n4⋅ ( V+95 ) −gNa⋅m0[V]3⋅h⋅ ( V−50 ) −gCa⋅c2⋅ ( V−125 ) −gL⋅ ( V+70 ) −gM⋅M⋅ ( V+95 ) +InjectedCurrentMembraneAreaMembraneCapacitance ( 6 ) where the parameters Membrane Area and Membrane Capacitance are provided in Table 3 , and Injected Current is the sum of all contributions received through individual synapses ( see Eqs 7–9 below ) . First , we validated the detailed Purkinje cell model ( Eqs 4–6 ) in the Neuron simulator . Subsequently , we reduced the Purkinje cell model to make it compatible with an event-driven lookup table simulator ( i . e , the EDLUT simulator ) for fast spiking neural network simulations [86 , 127] . In the reduced Purkinje cell model , IK and INa currents were implemented through a simple threshold process that triggers the generation of a triangular voltage function each time the neuron fires [129] . This triangular voltage depolarisation drives the state of ion channels similarly to the original voltage depolarisation during the spike generation . We inserted the differential equation defined in Eq 6 within EDLUT . We used an in-house fixed-step numerical integration method compatible with GPUs ( bi-fixed integrative method ) . Our numerical integration method provided similar accuracy than the variable step-size numerical integration methods provided by NEURON with considerably less computational cost [128] . To make NEURON simulation comparable with EDLUT as in S1 Fig , the stimulation of the Purkinje cell was carried out by spike trains through an AMPA synapse ( single decaying exponential with τ = 1 ms , Eexc = 0 mV ) . We emulated the effect of the PF over the Purkinje cell through a spike train of 55 Hz and a synaptic weight of gexc = 8 μS . We used CF synaptic stimulations through an AMPA synapse with weight of gexc = 80 μS , ( see asterisks in Fig 2C ) . The spike timing traces of the Purkinje spike burst-pause dynamics under equal Purkinje stimulation were consistent in NEURON and EDLUT . Both the EDLUT and NEURON source codes are available at the following URLs: www . ugr . es/~nluque/restringido/Burst-pause_Purkinje_dynamics_regulate_motor_adaptation_NEURON_MODEL_COMPLETE . rar www . ugr . es/~nluque/restringido/CODE_Burst-pause_Purkinje_dynamics_regulate_motor_adaptation_EDLUT . rar User: REVIEWER , password: REVIEWER ( for both ) . The other cerebellar neurons ( granule cells , MVN cells , … ) were simulated as leaky integrate–and–fire ( LIF ) neurons , with excitatory ( AMPA ) and inhibitory ( GABA ) chemical synapses: Cm⋅dVm−cdt=gAMPA ( t ) ⋅ ( EAMPA−Vm−c ) +gGABA ( t ) ⋅ ( EGABA−Vm−c ) +Grest⋅ ( Erest−Vm−c ) ( 7 ) where Cm denotes the membrane capacitance , EAMPA and EGABA are the reversal potential of each synaptic conductance , Erest is the resting potential , and Grest indicates the conductance responsible for the passive decay term towards the resting potential . Conductances gAMPA and gGABA integrate all the contributions received by each receptor type ( AMPA and GABA ) through individual synapses and they are defined as decaying exponential functions [86 , 130]: gAMPA ( t ) ={0 , t≤t0gAMPA ( t0 ) ⋅e− ( t−t0 ) τAMPA , t>t0 ( 8 ) gGABA ( t ) ={0 , t≤t0gGABA ( t0 ) ⋅e− ( t−t0 ) τGABA , t>t0 ( 9 ) with t representing the simulation time , t0 being the time arrival of an input spike , and τAMPA and τGABA denoting the decaying time constant for AMPA and GABA receptors , respectively . Note that we also used the LIF neuronal model ( Eqs 7–9 ) to simulate Purkinje cells that could only express tonic spike firing ( Fig 3B ) . These Purkinje cells with compromised CF-evoked spike burst-pause dynamics provided a coarse phenomenological model of Kv3 . 3-deficient Purkinje neurons ( as in Kcnc3 mutants , in which the absence of voltage-gated potassium channel Kv3 . 3 drastically reduces spikelet generation within complex spikes of cerebellar Purkinje cells ) [131] . Note , however , that completely suppressing CF-evoked spike burst-pause dynamics would require more severe actions . A plausible way for obtaining no-bursting-after-CF stimulus may consist in modulating GABAB Purkinje cell receptors via Baclofen , the specific GABAB agonist ( as shown in [132] ) . Table 4 summarises the parameters used for each cell and synaptic receptor type . | Cerebellar Purkinje cells regulate accurate eye movement coordination . However , it remains unclear how cerebellar-dependent oculomotor adaptation depends on the interplay between Purkinje cell characteristic response patterns: tonic , high frequency bursting , and post-complex spike pauses . We explore the role of Purkinje spike burst-pause dynamics in VOR adaptation . A biophysical model of Purkinje cell is at the core of a spiking network model , which captures the cerebellar microcircuit properties and incorporates spike-based synaptic plasticity mechanisms at different cerebellar sites . We show that Purkinje spike burst-pause dynamics are critical for ( 1 ) gating the vestibular-motor response association during VOR acquisition; ( 2 ) mediating the LTD/LTP balance for VOR consolidation; ( 3 ) reshaping synaptic efficacy distributions for VOR phase-reversal adaptation; ( 4 ) explaining the reversal VOR gain discontinuities during sleeping . | [
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] | 2019 | Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation |
Gene expression is subject to stochastic noise , but to what extent and by which means such stochastic variations are coordinated among different genes are unclear . We hypothesize that neighboring genes on the same chromosome co-fluctuate in expression because of their common chromatin dynamics , and verify it at the genomic scale using allele-specific single-cell RNA-sequencing data of mouse cells . Unexpectedly , the co-fluctuation extends to genes that are over 60 million bases apart . We provide evidence that this long-range effect arises in part from chromatin co-accessibilities of linked loci attributable to three-dimensional proximity , which is much closer intra-chromosomally than inter-chromosomally . We further show that genes encoding components of the same protein complex tend to be chromosomally linked , likely resulting from natural selection for intracellular among-component dosage balance . These findings have implications for both the evolution of genome organization and optimal design of synthetic genomes in the face of gene expression noise .
Gene expression is subject to considerable stochasticity that is known as expression noise , formally defined as the expression variation of a given gene among isogenic cells in the same environment [1–3] . Gene expression noise is a double-edged sword . On the one hand , it can be deleterious because it leads to imprecise controls of cellular behavior , including , for example , destroying the stoichiometric relationship among functionally related proteins and disrupting homeostasis [4–8] . On the other hand , gene expression noise can be beneficial . For instance , unicellular organisms may exploit gene expression noise to employ bet-hedging strategies in fluctuating environments [9 , 10] , whereas multicellular organisms can make use of expression noise to initiate developmental processes [11–13] . By quantifying protein concentrations in individual isogenic cells cultured in a common environment , researchers have measured the expression noise for thousands of genes in the bacterium Escherichia coli [14] and unicellular eukaryote Saccharomyces cerevisiae [15] . Nevertheless , because genes are not in isolation , one wonders whether and to what extent expression levels co-vary among genes at a steady state , which unfortunately cannot be studied by the above data . By simultaneously tagging two genes with different florescent markers , Stewart-Ornstein et al . discovered strong co-fluctuation of the concentrations of some functionally related proteins in yeast such as those involved in the Msn2/4 stress response pathway , amino acid synthesis , and mitochondrial maintenance , respectively [16] , and the expression co-fluctuation of these genes is facilitated by their sharing of transcriptional regulators [17] . Here we explore yet another mechanism for expression co-fluctuation . We hypothesize that , due to the sharing of chromatin dynamics [18] , a key contributor to gene expression noise [18–20] , genes that are closely linked on the same chromosome should exhibit a stronger expression co-fluctuation when compared with genes that are not closely linked or unlinked ( Fig 1 ) . We refer to this potential influence of chromosomal linkage of two genes on their expression co-fluctuation as the linkage effect . The linkage-effect hypothesis is supported by two pioneering studies demonstrating that the correlation in expression level between two reporter genes across isogeneic cells in the same environment is much higher when they are placed next to each other on the same chromosome than when they are placed on separate chromosomes [21 , 22] . However , neither the generality of the linkage effect nor the chromosomal proximity required for this effect are known . Furthermore , the biological significance of the linkage effect and its potential impact on genome organization and evolution have not been investigated . In this study , we address these questions by analyzing allele-specific single-cell RNA-sequencing ( RNA-seq ) data from mouse cells [23] . We demonstrate that the linkage effect is not only general but also long-range , extending to genes that are tens of millions of bases apart . We provide evidence that three-dimensional ( 3D ) chromatin proximities are responsible for the long-range expression co-fluctuation through mediating chromatin accessibility covariations . Finally , we show theoretically and empirically that the linkage effect has likely impacted the evolution of the chromosomal locations of genes encoding members of the same protein complex .
Let us consider two genes A and B each with two alleles respectively named 1 and 2 in a diploid cell . When A and B are chromosomally linked , without loss of generality , we assume that A1 and B1 are on the same chromosome whereas A2 and B2 are on its homologous chromosome ( Fig 2A ) . Expression co-fluctuation between one allele of A and one allele of B ( e . g . , A1 and B2 ) is measured by Pearson's correlation ( re , where the subscript "e" stands for expression ) between the expression levels of the two alleles across isogenic cells under the same environment . Among the four possible pairs of an A and a B allele , A1-B1 , A2-B2 , A1-B2 , and A2-B1 , the former two pairs are physically linked whereas the latter two pairs are unlinked . The linkage-effect hypothesis asserts that , at a steady state , expression correlations between linked alleles ( cis-correlations ) are greater than those between unlinked alleles ( trans-correlations ) . That is , δe = [re ( A1 , B1 ) +re ( A2 , B2 ) −re ( A1 , B2 ) −re ( A2 , B1 ) ]/2>0 . Note that this formulation is valid regardless of whether the two alleles of the same gene have equal mean expression levels . While each of the four correlations could be positive or negative , in the large data analyzed below , they are mostly positive and show approximately normal distributions across gene pairs examined ( S1 Fig ) . To verify the above prediction about δe , we analyzed a single-cell RNA-seq dataset of fibroblast cells derived from a hybrid between two mouse strains ( CAST/EiJ × C57BL/6J ) [23] . Single-cell RNA-seq profiles the transcriptomes of individual cells , allowing quantifying stochastic gene expression variations among isogenic cells in the same environment [24–26] . DNA polymorphisms in the hybrid allow estimation of the expression level of each allele for thousands of genes per cell . The dataset includes data from seven fibroblast clones and some non-clonal fibroblast cells of the same genotype . We focused our analysis on clone 7 ( derived from the hybrid of CAST/EiJ male × C57BL/6J female ) in the dataset , because the number of cells sequenced in this clone is the largest ( n = 60 ) among all clones . We excluded from our analysis all genes on Chromosomes 3 and 4 due to aneuploidy in this clone and X-linked genes due to X inactivation . To increase the sensitivity of our analysis and remove imprinted genes , we focused on the 3405 genes that have at least 10 RNA-seq reads averaged across cells mapped to each of the two alleles . Note that this gene set constitute >30% of all expressed genes in the cells concerned . While most of the 3405 genes tend to be highly expressed , 16% of them have lower expressions than the median expression of all expressed genes . The 3405 genes form 3404×3405/2 = 5 , 795 , 310 gene pairs , among which 377 , 584 pairs are chromosomally linked . For each pair of chromosomally linked genes , we computed their δe by treating the allele from CAST/EiJ as allele 1 and that from C57BL/6J as allele 2 at each locus . The fraction of gene pairs with δe > 0 is 0 . 61 ( Fig 2B ) . This fraction has a rather narrow 95% confidence interval ( Fig 2B ) , demonstrating that the fraction is significantly higher than the null expectation of 0 . 5 . Because a gene can appear in multiple gene pairs , which are not mutually independent , we applied a binomial test in a subset of gene pairs where each gene appears only once . Specifically , we randomly shuffled the relative positions of all genes on each chromosome and considered from one end of the chromosome to the other end non-overlapping consecutive windows of two genes . The observed fraction of gene pairs with positive δe still significantly exceeds the null expectation of 0 . 5 ( P < 2 . 4×10−16 , binomial test ) . That most gene pairs exhibit δe > 0 holds in each of the 17 chromosomes examined , with the trend being statistically significant in six chromosomes even by the above conservative test ( nominal P < 0 . 05; Fig 2C ) . As a negative control , we analyzed gene pairs located on different chromosomes , treating alleles the same way as described above . As expected , this time the fraction of gene pairs with δe > 0 is not significantly different from 0 . 5 ( P = 0 . 25; Fig 2B ) . The fraction of gene pairs with δe > 0 appears to vary among chromosomes ( Fig 2C ) . To assess the significance of this variation , we compared the fraction of independent gene pairs with δe > 0 between every two chromosomes by Fisher's exact test . After correcting for multiple testing , we found no significant difference between any two chromosomes . To examine the generality of the findings from clone 7 , we also analyzed clone 6 ( derived from the hybrid of CAST/EiJ male × C57BL/6J female ) , which has 38 cells with RNA-seq data . Because 10 of these cells are aneuploidy for different chromosomes ( see the supplementary materials in [23] ) , we analyzed the remaining 28 cells . Similar results were obtained ( S2A and S2B Fig ) . Because clone 6 was from a male whereas clone 7 was from a female , our results apparently apply to both sexes . We also analyzed 47 non-clonal fibroblast cells with the same genetic background ( cell IDs from 124 to 170 , derived from the hybrid of CAST/EiJ male × C57BL/6J female ) , and obtained similar results ( S2C and S2D Fig ) . These findings establish that the linkage effect on expression co-fluctuation is neither limited to a few genes in a specific clone nor an epigenetic artifact of clonal cells , but is general . The linkage effect on co-fluctuation ( and the decrease of the effect with genomic distance shown below ) is robust to the definition of δe , because similar results are obtained when correlation coefficients are replaced with squares of correlation coefficients in the definition of δe . We next investigated how close two genes need to be on the same chromosome for them to co-fluctuate in expression . We divided all pairs of chromosomally linked genes into 100 equal-interval bins based on the genomic distance between genes , defined by the number of nucleotides between their transcription start sites ( TSSs ) . The median δe in a bin is found to decrease with the genomic distance represented by the bin ( Fig 2D ) . Furthermore , even for the unbinned data , δe for a pair of linked genes correlates negatively with their genomic distance ( Spearman's ρ = -0 . 029 ) . To assess the statistical significance of this negative correlation , we randomly shuffled the genomic coordinates of genes within chromosomes and recomputed the correlation . This was repeated 1000 times and none of the 1000 ρ values were equal to or more negative than the observed ρ . Hence , the linkage effect on expression co-fluctuation of two linked genes weakens significantly with their genomic distance ( P < 0 . 001 ) . Surprisingly , however , median δe exceeds 0 for every bin except when the genomic distance exceeds 150 Mb ( Fig 2D ) . Hence , the linkage effect is long-range . To statistically verify the potentially chromosome-wide linkage effect , we focused on linked gene pairs that are at least 63 Mb apart , which is one half the median size of mouse chromosomes . The median δe for these gene pairs is 0 . 017 , or 68% of the median δe for the left-most bin in Fig 2D . We randomly shuffled the genomic positions of all genes and repeated the above analysis 1000 times . In none of the 1000 shuffled genomes did we observe the median δe greater than 0 . 017 for linked genes of distances >63 Mb , validating the long-range expression co-fluctuation in the actual genome . The same can be said even for linked genes of distances >90 Mb ( P < 0 . 001 , shuffling test ) . The above observations are not clone-specific , because the same trend is observed for cells of clone 6 ( S2B Fig ) . Notably , a previous experiment in mammalian cells [21] detected a linkage effect for chromosomally adjacent reporter genes ( δe = 0 . 834 ) orders of magnitude stronger than what is observed here . This is primarily because expression levels estimated using single-cell RNA fluorescence in situ hybridization in the early study [21] are much more precise than those estimated using allele-specific single-cell RNA-seq [27] here . We thus predict that the linkage effect detected will be more pronounced as the expression level estimates become more precise . As a proof of principle , we gradually raised the required minimal number of reads per allele in our analysis , which should increase the precision of expression level estimation but decrease the number of genes that can be analyzed . Indeed , as the minimal read number rises , the fraction of chromosomally linked gene pairs with a positive δe ( Fig 2E ) , median δe for all chromosomally linked gene pairs ( Fig 2F ) , and median δe for the left-most bin ( Fig 2F ) all increase significantly . Furthermore , the low capturing efficiency of single-cell RNA-seq substantially reduces the observed size of the linkage effect , and our lower-bound estimate of the median true δe is 0 . 15 ( see Materials and Methods ) . Because what matters to a cell is the total number of transcripts produced from the two alleles of a gene instead of the number produced from each allele , we also calculated the pairwise correlation in expression level between genes using either the total number of reads mapped to both alleles of a gene or normalized expression level of the gene . We similarly found a long-range linkage effect ( S3 Fig ) , with trends and effect sizes close to the observations based on allele-specific expressions . Previous studies reported that the relative transcriptional orientations of neighboring genes influence their expression co-fluctuation [28] . This impact , however , is unobserved in our study ( S4 Fig ) , which may be due to the limited precision of the expression estimates and the fact that only 422 pairs of neighboring genes satisfy the minimal read number requirement . What has caused the chromosome-wide expression co-fluctuation of linked genes ? One simple explanation is the asynchronous DNA replication in dividing cells , where closely linked genes tend to be replicated at the same time so show positively correlated gene copy numbers and hence expression levels . But a simple calculation demonstrates that this explanation is not tenable . There are 104 to 105 replication origins per mammalian cell [29] . Given the size of the mammalian genome ( ~3×109 bases ) , DNA segments within 0 . 03–0 . 3 Mb share a replication origin . The asynchronous DNA replication could result in the expression co-fluctuation of genes in the range of 0 . 03 to 0 . 3 Mb , which cannot explain our observation of expression co-fluctuation at the scale of >60 Mb . Individual chromosomes in mammalian cells are organized into territories with a diameter of 1~2 μm [30] , whereas the diameter of the nucleus is ~8 μm [30] . Thus , the physical distance between chromosomally linked genes is below 1~2 μm , whereas that between unlinked genes is usually > 1~2 μm and can be as large as ~8 μm . Because it takes time for macromolecules to diffuse in the nucleus , linked genes tend to have similar chemical environments and hence similar transcriptional dynamics ( i . e . , promoter co-accessibility and/or co-transcription ) when compared with unlinked genes . We thus hypothesize that the linkage effect is fundamentally explained by the 3D proximity of linked genes compared with unlinked genes ( Fig 3A ) . Below we provide evidence for this model . Note , while our computational analyses cannot prove causation , they examine correlations among various quantities that are predicted by our hypothesis and hence can provide strong evidence for or against the hypothesis when performed rigorously and interpreted appropriately . We started by comparing the 3D distances between linked alleles with those between unlinked alleles . The 3D distance between two genomic regions can be approximately measured by Hi-C , a high-throughput chromosome conformation capture method for quantifying the number of interactions between genomic loci that are nearby in 3D space [31] . The smaller the 3D distance between two genomic regions , the higher the interaction frequency between them [32] . It is predicted that the interaction frequency between the physically linked alleles of two genes ( cis-interaction ) is greater than that between the unlinked alleles of the same gene pair ( trans-interaction ) . To verify this prediction , we analyzed the recently published allele-specific 500kb-resolution Hi-C interaction matrix [33] of mouse neural progenitor cells ( NPC ) . For any two linked loci A and B as depicted in the left diagram of Fig 2A , we computed δi = [F ( A1 , B1 ) +F ( A2 , B2 ) −F ( A1 , B2 ) −F ( A2 , B1 ) ]/2 , where F is the interaction frequency between the two alleles in the parentheses and the subscript "i" refers to interaction . We found that 99% of pairs of linked loci have a positive δi ( P < 2 . 2×10−16 , binomial test on independent locus pairs; Fig 3B ) . By contrast , among unlinked gene pairs , the fraction with a positive δi is not significantly different from that with a negative δi ( P = 0 . 90 , binomial test on independent locus pairs; Fig 3B ) . In the analysis of unlinked loci , we treated all alleles from one parental strain of the hybrid as alleles 1 and all alleles from the other parental strain of the hybrid as alleles 2 in the above formula of δi . These results clearly demonstrate the 3D proximity of genes on the same chromosome when compared with those on two homologous chromosomes . To examine if the above phenomenon is long-range , we plotted δi as a function of the distance ( in Mb ) between two linked loci considered . Indeed , even when the distance exceeds 63 Mb , one half the median size of mouse chromosomes , almost all locus pairs still show positive δi ( Fig 3C ) . Similar to the phenomenon of the linkage effect on gene expression co-fluctuation , we observed a negative correlation between the genomic distance between two linked loci and δi ( ρ = -0 . 81 for unbinned data ) . This correlation is statistically significant ( P < 0 . 001 ) , because it is stronger than the corresponding correlation in each of the 1000 negative controls where the genomic positions of all genes are randomly shuffled within chromosomes . As mentioned , 3D proximity should synchronize the transcriptional dynamics of linked alleles . Based on the bursty model of gene expression [34] , transcription involves two primary steps . In the first step , the promoter region switches from the inactive state to the active state such that it becomes accessible to the transcriptional machinery . In the second step , RNA polymerase binds to the activated promoter to initiate transcription . In principle , the synchronization of either step can result in co-fluctuation of mRNA concentrations . Because the accessibility of promoters can be detected using transposase-accessible chromatin using sequencing ( ATAC-seq ) [35] in a high-throughput manner , we focused our empirical analysis on promoter co-accessibility . Note that although the bursty model does not consider certain details of transcription such as polymerase pausing and productive elongation , there is mounting evidence that the bursty model provides a good approximation of stochastic gene expression [21 , 36–38] , which is what matters to our study . To verify the potential long-range linkage effect on chromatin co-accessibility , we should ideally use single-cell allele-specific measures of chromatin accessibility . However , such data are unavailable . We reason that , the accessibility covariation of genomic regions among cells may be quantified by the corresponding covariation among populations of cells of the same type cultured under the same environment . In fact , it can be shown mathematically that , under certain conditions , chromatin co-accessibility of two genomic regions among cells equals the corresponding chromatin co-accessibility across cell populations ( see Materials and Methods ) . Based on this result , we analyzed a dataset collected from allele-specific ATAC-seq in 16 NPC cell populations [39] . We first removed sex chromosomes and then required the number of reads mapped to each allele of a peak to exceed 50 for the peak to be considered . This latter step removed imprinted loci and ensured that the considered peaks are relatively reliable . About 3500 peaks remained after the filtering . This sample size is comparable to the number of genes used in the analysis of expression co-fluctuation . For each pair of ATAC peaks , we computed δa = [ra ( A1 , B1 ) +ra ( A2 , B2 ) −ra ( A1 , B2 ) −ra ( A2 , B1 ) ]/2 , where ra is the correlation in ATAC-seq read number between the alleles specified in the parentheses ( following the left diagram in Fig 2A ) across the 16 cell populations and the subscript "a" refers to chromatin accessibility . The fraction of peak pairs with a positive δa is significantly greater than 0 . 5 for linked peak pairs but not significantly different from 0 . 5 for unlinked peak pairs ( binomial test on independent peak pairs; Fig 3D ) . Furthermore , after grouping ATAC peak pairs into 100 equal-interval bins according to the genomic distance between peaks , we observed a clear trend that δa decreases with the genomic distance between peaks ( ρ = -0 . 05 for unbinned data , P < 0 . 001 , within-chromosome shuffling test; Fig 3E ) . In addition , even for linked peak pairs with a distance greater than 63 Mb , their median δa is significantly greater than that of unlinked peak pairs ( P < 0 . 001 , among-chromosome shuffling test ) . Together , these results demonstrate a long-range linkage effect on chromatin co-accessibility . Similar to δe , the observed δa is small . This is again at least in part a result of low capturing efficiencies in high-throughput sequencing . We estimated that the median true δa is at least 0 . 03 ( see Materials and Methods ) , an order of magnitude larger than the observed value . Because we hypothesize that the linkage effect on expression co-fluctuation is via 3D chromatin proximity that leads to chromatin co-accessibility ( Fig 3A ) , we should verify the relationship between 3D proximity and chromatin co-accessibility for unlinked genomic regions to avoid the confounding factor of linkage . To this end , we converted ATAC-seq read counts to a 500kb resolution by summing up read counts for all allele-specific chromatin accessibility peaks that fall within the corresponding Hi-C bin , because the resolution of the Hi-C data is 500kb . Because alleles from different parents are unlinked in the hybrid used for ATAC-seq , for each pair of bins , we computed the mean correlation in chromatin accessibility between the alleles derived from different parents among the 16 cell populations , or trans-ra = ra ( A1 , B2 ) /2 + ra ( A2 , B1 ) /2 . For the same reason , we computed the sum of Hi-C contact frequency between the alleles derived from different parents , trans-F = F ( A1 , B2 ) /2+F ( A2 , B1 ) /2 . Because interaction frequencies in Hi-C data are generally low for unlinked regions , we separated all pairs of bins into two categories , contacted ( i . e . , trans-F > 0 ) and uncontacted ( i . e . , trans-F = 0 ) . We found that trans-ra values for contacted bin pairs are significantly higher than those for uncontacted bin pairs ( P < 0 . 0001; Fig 3F ) , consistent with our hypothesis that 3D chromatin proximity induces chromatin co-accessibility . The above statistical significance was determined by performing a Mantel test using the original trans-ra matrix of the aforementioned allele pairs and the corresponding trans-F matrix . Corroborating our finding , a recent study of single-cell ( but not allele-specific ) chromatin accessibility data also found that the co-accessibility of two loci rises with their 3D proximity [40] . To test the hypothesis that chromatin co-accessibility leads to expression co-fluctuation ( even for unlinked alleles ) ( Fig 3A ) , we analyzed the allele-specific ATAC-seq data and single-cell allele-specific RNA-seq data together . Although these data were generated from different cell types in mouse , we reason that , because the 3D chromosome conformation is highly similar among tissues [41] , chromatin co-accessibility , which is affected by 3D chromatin proximity ( Fig 3F ) , may also be similar among tissues . Hence , it may be possible to detect a correlation between chromatin co-accessibility and expression co-fluctuation . To this end , we used unbinned ATAC-peak data to compute trans-ra but limited the analysis to those peaks with at least 10 reads per allele . We used the allele-specific RNA-seq data to compute trans-re = re ( A1 , B2 ) /2+re ( A2 , B1 ) /2 for pairs of linked genes . We then assigned each gene to its nearest ATAC peak and averaged trans-re among gene pairs assigned to the same pair of ATAC peaks . We subsequently grouped ATAC peak pairs into 100 equal-interval bins according to their co-accessibilities , and observed a clear positive correlation between median trans-ra and median trans-re across the 100 bins ( Fig 3G ) . For unbinned data , trans-ra and trans-re also show a significant , positive correlation ( ρ = 0 . 021 , P = 0 . 027 , Mantel test ) . Although the assignment of a gene to its nearest ATAC peak may not be biologically meaningful in some cases , such potential errors only add noise to our analysis , meaning that the true signal should be stronger than what is observed . As predicted , there is also a positive correlation between δi and δe ( ρ = 0 . 014 , P = 1 . 6×10−17 ) . In this analysis , for each linked pair of Hi-C bins , we computed δi . We assigned each gene in our dataset to its nearest Hi-C bin , estimated δe for each pair of genes that are respectively located in the two Hi-C bins considered , and computed the average δe between the two bins . The correlation between δi and δe can be visualized in Fig 3H , where Hi-C bin pairs are separated into 100 equal-size groups based on δi . Note that although the impact of 3D proximity ( Fig 3H ) appears weaker than the impact of genomic distance ( Fig 2D ) on δe , these two plots are not directly comparable because of the following reasons . First , the Hi-C contact frequency is not an accurate measure of 3D proximity , especially for region pairs that rarely contact , which apply to the vast majority of region pairs ( Fig 3C ) . By contrast , genomic distance is measured accurately . Second , the resolution of the Hi-C data used is much lower ( 500 kb ) than that of the genomic distance ( 1 bp ) . Third , the Hi-C data and gene expression co-fluctuations data are not from the same cell type , which reduces the observed impact of 3D proximity . Together , the above results support our hypothesis that , compared with unlinked genes , linked genes have a shared chemical environment due to their 3D proximity and hence chromatin co-accessibility , which leads to their expression co-fluctuation ( Fig 3A ) . However , 3D proximity can lead to promoter co-accessibility by several means , which have been broadly summarized into three categories of mechanisms [30]: 1D scanning , 3D looping , and 3D diffusion . 1D scanning refers to the spread of chromatin states along an entire chromosome , but 1D scanning is rare , with only a few known examples such as X-chromosome inactivation [30] . Hence , 1D scanning is unlikely to be the mechanism responsible for the broad linkage effect discovered here . 3D looping refers to the phenomenon that a chromosome often forms loops to bring far-separated loci into contact , whereas 3D diffusion refers to chromosome communication by local diffusion of transcription-related proteins . For tightly linked loci , our data do not allow a clear distinction between 3D looping and 3D diffusion in causing the linkage effect discovered here . But 3D diffusion seems more likely for the long-range effect , because the range of 3D looping seems limited to loci separated by no more than 200 kb simply due to the rapid decrease of the contact frequency with the physical distance between two loci [42] , evident in Fig 3C ( note the log scale of the Y-axis ) . It has been estimated that loci separated by 10 Mb behave essentially the same as two loci that are on different chromosomes in terms of the contact frequency [30] , and any contact-based mechanism is unlikely to be long-range ( e . g . , topologically associating domains ) [41] . Therefore , the most likely cause of our observed long-range linkage effect is 3D diffusion . In the 3D diffusion mechanism , which molecule is most likely responsible for the observed long-range linkage effect on expression co-fluctuation ? If the chemical influencing transcription has a diffusion time in the nucleus much shorter than the interval between transcriptional bursts , two genes have essentially the same environment with respect to that chemical regardless of their 3D distance [43] and hence no linkage effect is expected ( top cell in Fig 3I ) . On the contrary , if the chemical diffuses too slowly , the linkage effect will be local [43] and hence cannot be chromosome-wide ( bottom cell in Fig 3I ) . Therefore , the diffusion rate of the chemical responsible for the long-range linkage effect cannot be too low or too high such that they become evenly distributed in a chromosome territory but not the whole nucleus in a time comparable to the interval between transcriptional bursts ( middle cell in Fig 3I ) . The typical transcriptional burst interval is 18–50 minutes in mammalian cells [37 , 38] . The time for a chemical to distribute evenly in a given volume with radius R is on the order of R2/D , where D is the diffusion coefficient of the chemical [34] . Most molecules in the nucleus are rapidly diffused . For example , transcription factors typically have a diffusion coefficient of 0 . 5–5 μm2/s in the nucleus [34 , 44] , meaning that they can diffuse across the whole nucleus in about 3 to 30 seconds . By contrast , core histone proteins such as H2B proteins diffuse extremely slowly due to their tight binding to DNA . They are usually considered immobilized because diffusion is rarely observed during the course of an experiment [44 , 45] . Therefore , none of these molecules are responsible for the long-range linkage effect observed . Interestingly , linker histones , which include five subtypes of H1 histones in mouse that play important roles in chromatin structure and transcription regulation [46] , have a diffusion coefficient of about 0 . 01 μm2/s [47] . Thus , it takes H1 proteins 25–100 seconds to diffuse through a chromosome territory , but about 30 minutes to diffuse across the whole nucleus . The former time but not the latter is much smaller than the typical transcriptional burst interval . Hence , it is possible that H1 diffusion in the nucleus is the ultimate cause of the linkage effect . We provide empirical evidence for this hypothesis in a later section . Our finding that chromosomal linkage leads to gene expression co-fluctuation implies that linkage between genes could be selected for when expression co-fluctuation is advantageous . Due to the complexity of biology , it is generally difficult to predict whether the expression co-fluctuation of a pair of genes is beneficial , neutral , or deleterious . However , the expression co-fluctuation of genes encoding components of the same protein complex is likely advantageous . To see why this is the case , let us consider a dimer composed of one molecule of protein A and one molecule of protein B; the heterodimer is functional but monomers are not . We denote the concentration of dissociated protein A as [A] , the concentration of dissociated protein B as [B] , and the concentration of protein complex AB as [AB] . At the steady state , [AB] = K[A][B] , where K is the association constant [48] . Furthermore , the total concentration of protein A , [A]t , equals [A] + [AB] , while the total concentration of protein B , [B]t , equals [B] + [AB] . Based on these relationships , we simulated 10 , 000 cells , where the mean and coefficient of variation ( CV ) are respectively 1 and 0 . 2 for both [A]t and [B]t ( see Materials and Methods ) . We assumed K = 105 based on empirical K values of stable protein complexes [49] . We found that , as the correlation between [A]t and [B]t increases , mean [AB] of the 10 , 000 cells rises ( Fig 4A ) . We also considered a wide range of other K values ( 10−1 , 100 , 101 , 102 , 103 , and 104 ) and found the result largely unchanged . The lower-bound mean [AB] is about 3% higher under co-fluctuation than under no co-fluctuation . Furthermore , the effect size rises substantially with CV . For example , when CV = 0 . 5 , which is not unusual in eukaryotes [49] , the effect increases to 20% . We also considered protein complexes with other stoichiometries and the scenario when the mean [A]t to mean [B]t ratio deviates from the stoichiometry ( see Materials and Methods ) . In all parameter combinations examined , the mean [AB] increases with the correlation between [A]t and [B]t , albeit with a wide range of effect size ( 0 . 001% to 27% higher mean [AB] under co-fluctuation than under no co-fluctuation ) . If we assume that fitness rises with [AB] , the co-fluctuation of [A]t and [B]t is beneficial , compared with independent fluctuations of [A]t and [B]t . In addition , because mean [A] and mean [B] must decrease with the rise of mean [AB] , the co-fluctuation of [A]t and [B]t could also be advantageous because it lowers the concentrations of the unbound monomers that may be toxic . Indeed , past studies found better expression co-fluctuations of genes encoding members of the same protein complex than random gene pairs [50 , 51] , suggesting that expression co-fluctuation of members of the same protein complex is selectively favored . Because our simulation considers protein concentrations instead of gene expressions , it directly applies to both haploid and diploid cells . The only difference is that protein concentrations such as [A]t and [B]t have a lower CV in diploid than haploid cells [6] . Further , as shown in S3 Fig , mouse cells analyzed here do show a higher correlation in the total expression level of two alleles between linked than unlinked genes . To test if genes encoding components of the same protein complex tend to be linked , we used the mouse protein complex data from CORUM and downloaded the chromosomal positions of all mouse protein-coding genes from Ensembl [52] . Because genes may be linked due to their origins from tandem duplication [53] , the data were pre-processed to produce a set of duplicate-free mouse protein-coding genes ( see Materials and Methods ) . We then randomly shuffled the genomic positions of the retained genes encoding protein complex components among all possible positions of the duplicate-free mouse protein-coding genes . The observed number of linked pairs of genes encoding components of the same protein complex is significantly greater than the random expectation ( Fig 4B ) . For comparison , we also computed the number of linked pairs of genes encoding components of different protein complexes . This number is not significantly greater than the random expectation ( Fig 4C ) . Thus , the enrichment in gene linkage is specifically related to coding for subunits of the same protein complex . Interestingly , the observed median distance between the TSSs of two linked genes encoding protein complex subunits is not significantly different from the random expectation , regardless of whether subunits of the same ( Fig 4D ) or different ( Fig 4E ) protein complexes are considered . The phenomenon that members of the same protein complex tend to be encoded by linked genes could have arisen for one or both of the following reasons . First , selection for co-fluctuation among proteins of the same complex has driven the evolution of gene linkage . Second , due to their co-fluctuation , products of linked genes may have been preferentially recruited to the same protein complex in evolution . Under the first hypothesis , originally unlinked genes encoding members of the same protein complex are more likely to become linked in evolution than originally unlinked genes that do not encode members of the same complex . To verify this prediction , we examined mouse genes using rat and human as outgroups ( Fig 4F ) , because our δe estimates are from the mouse . We obtained pairs of genes encoding components of the same protein complex in both human and mouse . Hence , these pairs likely encode members of the same protein complex in the common ancestor of the three species . Among them , 875 pairs are unlinked in human and rat , suggesting that they were unlinked in the common ancestor of the three species . Of the 875 pairs , 25 pairs become linked in the mouse genome , significantly more than the random expectation under no requirement for gene pairs to encode members of the same complex ( P = 0 . 005; Fig 4F; see Materials and Methods ) . Therefore , the first hypothesis is supported . Under this hypothesis , the result in Fig 4D may be explained by the long-range linkage effect on expression co-fluctuation , such that once two genes encoding components of the same protein complex move to the same chromosome , selection is not strong enough to drive them closer to each other . To test the second hypothesis , we need gene pairs encoding proteins that belong to the same protein complex in mouse but not in human nor rat , which require such low false negative errors in protein complex identification that no current method can meet . Hence , we leave the test of the second hypothesis to future studies . Note that the above tests have two caveats . First , it is possible that some tandem duplicates remain in our data , which will compromise the analysis in Fig 4B . However , the result in Fig 4F is robust to such potential errors because the gene pairs concerned are originally unlinked so could not have arisen by tandem duplication . Thus , our evidence for positive selection for gene linkage holds even when the data are contaminated by tandem duplicates . Second , we inferred ancestral gene linkage by the parsimony principle , which may occasionally be incorrect . But such errors add only random noise to our analysis , suggesting that the actual strength of evidence for our hypothesis is likely stronger than what is shown here . As mentioned , our theoretical consideration suggests that , due to their intermediate diffusion coefficients , H1 histones may be responsible for the observed chromosome-wide expression co-fluctuation . Because the local H1 concentration fluctuates more when its cellular concentration is lower , we predict that the benefit and the selection coefficient for linkage of genes encoding members of the same protein complex are greater in tissues with lower H1 concentrations . Given that gene expression is costly , for a given gene , it is reasonable to assume that the relative importance of its function in a tissue increases with its expression level in the tissue [54 , 55] . Hence , we predict that , the more negative the across-tissue expression correlation is between a protein complex member gene and H1 histones , the higher the likelihood that the gene is driven to be linked with other genes encoding members of the same protein complex . To verify the above prediction , we used a recently published RNA-seq dataset [56] to measure Pearson's correlation between the mRNA concentration of a gene that encodes a protein complex subunit and the mean mRNA concentration of all H1 histone genes across 13 mouse tissues . Indeed , the linked protein complex genes show more negative correlations than the unlinked protein complex genes ( P = 0 . 012 , one-tailed Mann-Whitney U test; Fig 4G ) . The disparity is even more pronounced when we compare linked protein complex genes that become linked in the mouse lineage with unlinked protein complex genes ( P = 0 . 00068 , one-tailed Mann-Whitney U test; Fig 4G ) . This is likely owing to the enrichment of genes that are linked due to the linkage effect in the group of evolved linked protein complex genes ( Observed−nullexpectationnullexpectation=25−1313=92% ) when compared with the group of linked protein complex genes ( Observed−nullexpectationnullexpectation=200−161161=24% ) . The above three groups of genes ( evolved linked protein complex genes , linked protein complex genes , and unlinked protein complex genes ) were constructed using stratified sampling to ensure that their mean expression levels across tissues are not significantly different ( see Materials and Methods ) . For comparison , we performed the same analysis but replaced H1 histones with TFIIB , a general transcription factor that is involved in the formation of the RNA polymerase II preinitiation complex and has a high diffusion rate [57] . The trends shown in Fig 4G no longer holds ( unlinked vs . linked: P = 0 . 11 , one-tailed Mann-Whitney U test; unlinked vs . evolved linked: P = 0 . 63 , one-tailed Mann-Whitney U test ) . We also performed the same analysis but replaced H1 histones with core histone proteins , which are immobilized [45] . Again , the trends in Fig 4G disappeared ( unlinked vs . linked: P = 0 . 48 , one-tailed Mann-Whitney U test; unlinked vs evolved linked: P = 0 . 89 , one-tailed Mann-Whitney U test ) . These results support our hypothesis about the role of H1 histones in the linkage effect of expression co-fluctuation .
Using allele-specific single-cell RNA-seq data , we discovered chromosome-wide expression co-fluctuation of linked genes in mammalian cells . We hypothesize and provide evidence that genes on the same chromosome tend to have close 3D proximity , which results in a shared chemical environment for transcription and leads to expression co-fluctuation . While the linkage effect on expression co-fluctuation is likely an intrinsic cellular property , when the expression co-fluctuation of certain genes improves fitness , natural selection may drive the relocation of these genes to the same chromosome . Indeed , we provide evidence suggesting that the chromosomal linkage of genes encoding protein complex subunits is beneficial owing to the resultant expression co-fluctuation that minimizes the dosage imbalance among these subunits and has been selected for in genome evolution . Although many statistical results in this study are highly significant , the effect sizes appear small in several analyses , most notably the δe and δa values for linked genes . The small effect sizes are generally due to the large noise in the data , less ideal types of data used , and mismatches between the data sets co-analyzed . For instance , δe between linked genes estimated here ( Fig 2D ) is much smaller than what was previously estimated for a pair of linked florescent protein genes [21] , due in a large part to the inherently large error in quantifying mRNA concentrations by single-cell RNA-seq [58] . The small size of δa ( Fig 3E ) is likely caused at least in part by the low efficiency of ATAC-seq in detecting open chromatin ( see Materials and Methods ) . The positive correlation between trans-ra and trans-re ( Fig 3G ) is likely an underestimate due to the use of different cell types in RNA-seq and ATAC-seq . As shown in Fig 2E and 2F , the actual effect sizes would be much larger should better experimental methods and/or data become available . Hence , it is likely that many effects are underestimated in this study . Indeed , we estimated that the true effect sizes of δe and δa are at least an order of magnitude larger than observed ( see Materials and Methods ) . In addition , the co-fluctuation effect detected by Raj et al . may be unusually large because in that study the chromosomal distance between the two genes was extremely small and the two genes used identical regulatory elements [21] . Regardless , we stress that whether an effect is large/important depends on whether it is detectable by natural selection , and our results in Fig 4 suggest that the effects appear visible to natural selection , as reflected in the preferential chromosomal linkage of genes encoding protein complex subunits . Note that natural selection can detect a selective differential as small as the inverse of the effective population size , which is about 70 , 000 in mouse [59] . Because the expression co-fluctuation of two genes can be achieved by the sharing of regulatory elements and/or linkage , it is important to understand the relative contributions of the two mechanisms . But this question is generally difficult to answer because the extent to which two genes share regulatory elements is usually unknown . However , a lower bound contribution of the linkage effect can be estimated by examining two ( equally regulated ) alleles of the same gene . In this extreme case , the contribution of the linkage effect on expression co-fluctuation is about one thirteenth the contribution of sharing regulatory elements ( see Materials and Methods ) . Although linkage likely makes a smaller contribution than regulatory element sharing to the expression co-fluctuation of genes , linkage can increase the expression co-fluctuation to a level that regulatory element sharing alone cannot reach . This additional improvement can be important under certain circumstances , as shown recently for genes encoding enzymes of the yeast galactose use pathway [60] . Because we used RNA-seq to measure expression co-fluctuation , our results apply to the co-fluctuation of mRNA concentrations . In the case of protein complex components , it is presumably the co-fluctuation of protein concentrations rather than mRNA concentrations that is directly beneficial . Although the degree of covariation between mRNA and protein concentrations is under debate [61 , 62] , the two concentrations correlate well at the steady state [21] . One key factor in this correlation is the protein half-life , because , when the protein half-life is long , mRNA and protein concentrations may not correlate well due to the delay in the effect of a change in mRNA concentration on protein concentration [21] . It is interesting to note that in Raj et al . 's study [21] , mRNA and protein concentrations still correlate reasonably well ( r = 0 . 43 ) when the protein half-life is 25 hours , which is much longer than the reported mean protein half-life of 9 hours in mammalian cells [63] . Corroborating this finding is the recent report [64] that mRNA and protein concentrations correlate well across single cells in the steady state ( mean r = 0 . 732 ) . Note that , although the correlation between mRNA and protein concentrations measured at the same moment may not be high when the protein half-life is long , the current protein level can still correlate well with a past mRNA level [65] . Because our study focuses on cells at the steady state , co-fluctuation of mRNA concentrations is expected to lead to co-fluctuation of protein concentrations . We attributed the preferential linkage of genes encoding protein complex subunits to the benefit of expression co-fluctuation , while a similar phenomenon of linkage was previously reported in yeast and attributed to the potential benefit of co-expression of protein complex subunits across environments [66] , where co-expression refers to the correlation in mean expression level . In mammalian cells , our hypothesis is more plausible than the co-expression hypothesis for five reasons . First , across-environment ( or among-tissue ) variation in mean mRNA concentration does not translate well to the corresponding variation in mean protein concentration [62 , 67] , but mRNA concentration fluctuation explains protein concentration fluctuation quite well [21 , 64] . Hence , gene linkage , which enhances mRNA concentration co-fluctuation and by extension protein concentration co-fluctuation , may not improve protein co-expression across environments . Second , co-expression of linked genes appears to occur at a much smaller genomic distance than the linkage effect on co-fluctuation detected here [68] . Thus , if selection on co-expression were the cause for the non-random distribution of protein complex genes , these genes should be closely linked . This , however , is not observed ( Fig 4D ) . Hence , the previous finding that genes encoding members of ( usually not the same ) protein complexes tend to be clustered is best explained by the fact that certain chromosomal regions have inherently low expression noise and that these regions attract genes encoding protein complex members because stochastic expressions of these genes are especially harmful ( i . e . , the noise reduction hypothesis ) [4 , 69] . Third , the protein complex stoichiometry often differs among environments , which makes co-expression of complex components disfavored in the face of environmental changes [70 , 71] . Nonetheless , under a given environment , protein concentration co-fluctuation remains beneficial because of the presence of an optimal stoichiometry at each steady state . Fourth , gene linkage is not necessary for the purpose of co-expression , because the genes involved can use similar cis-regulatory sequences to ensure co-expression even when they are unlinked . In fact , a large fraction of co-expression of linked genes is due to tandem duplicates [68] , which have similar regulatory sequences by descent . However , even for genes with the same regulatory sequences , linkage improves expression co-fluctuation at the steady state . Finally , the co-expression hypothesis or noise reduction hypothesis cannot explain our observation of the relationship between the expression levels of H1 histones and those of linked genes encoding protein complex members across tissues ( Fig 4G ) . Taken together , these considerations suggest that it is most likely the selection for expression co-fluctuation rather than co-expression across environments that has driven the evolution of linkage of genes encoding members of the same protein complex . Several previous studies reported long-range coordination of gene expression [62 , 72–79] , but most of them was about co-expression above explained . One study used fluorescent in situ hybridization of intronic RNA to detect nascent transcripts in individual cells [72] . The authors reported independent transcriptions of most linked genes with the exception of two genes about 14 Mb apart that exhibit a negative correlation in transcription . Their observations are not contradictory to ours , because they measured the nearly instantaneous rate of transcription , whereas we measured the mRNA concentration that is the accumulated result of many transcriptional bursts . As explained , having a similar biochemical environment makes the activation/inactivation cycles of linked genes coordinated to some extent , even though the stochastic transcriptional bursts in the activation period may still look independent . Our work suggests several future directions of research regarding expression co-fluctuation and its functional implications . First , it would be interesting to know if the linkage effect on expression co-fluctuation varies across chromosomes . Although we analyzed individual chromosomes ( S5 Fig ) , addressing this question fully requires better single-cell expression data , because the current single-cell RNA-seq data are noisy . This also makes it difficult to detect any unusual chromosomal segment in its δe distribution . Second , our results suggest that 3D proximity is a major cause for the linkage effect on expression co-fluctuation . In particular , diffusion of proteins with intermediate diffusion coefficients such as H1 histones is likely one mechanistic basis of the effect . However , the diffusion behaviors of most proteins involved in transcription are largely unknown . A thorough research on the diffusion behaviors of proteins inside the nucleus will help us identify other proteins that are important in the linkage effect . As mentioned , our data do not allow a clear distinction between 3D looping and 3D diffusion in causing the linkage effect on tightly linked genes . To distinguish between these two mechanisms definitively , we would need allele-specific models of mouse chromosome conformation [80] , which require more advanced algorithms and more sensitive allele-specific Hi-C methods . Third , our study highlights the importance of the impact of sub-nucleus spatial heterogeneity in gene expression . This can be studied more thoroughly via real-time imaging and spatial modeling of chemical reactions [43 , 81] . The lack of knowledge about the details of transcription reactions prevents us from constructing an accurate quantitative model of gene expression , which can be achieved only by more accurate measurement and more advanced computational modeling . Fourth , we used protein complexes as an example to demonstrate how the linkage effect on expression co-fluctuation influences the evolution of gene order . Protein complex genes are by no means the only group of genes for which expression co-fluctuation can be advantageous . Previous work suggested that expression co-fluctuation of genes on the same signaling or metabolic pathway can be beneficial [82 , 83] , which was recently experimentally confirmed for the yeast galactose catabolism pathway [60] . But , to understand the broader evolutionary impact of the linkage effect , a general prediction of the fitness consequence of expression co-fluctuation is necessary . To achieve this goal , whole-cell modeling may be required [84] . Note that some other mechanisms such as cell cycle [85] can also lead to gene expression co-fluctuation , so should be considered in the study of the relationship between gene expression and fitness . Fifth , physical proximity might impact aspects of gene expression regulation other than co-fluctuation . For instance , previous research found that selective expression of genes that are clustered on the same chromosome ( i . e . , stochastic gene choice ) is strongly dependent on intrachromosomal looping , which alters the pairwise physical distance between genes in the same gene array [86–88] . It will be interesting to explore whether the principle that governs the linkage effect studied here applies to stochastic gene choice . Sixth , because expression co-fluctuation could be beneficial or harmful , an alteration of expression co-fluctuation should be considered as a potential mechanism of disease caused by mutations that relocate genes in the genome . Seventh , our analysis focused on highly expressed genes more than lowly expressed ones due to the limited sensitivity of single-cell RNA-seq . Because lowly expressed genes are affected more than highly expressed genes by expression noise [89] , expression co-fluctuation may be more important to lowly expressed genes than highly expressed ones . More sensitive and accurate single-cell expression profiling methods are needed to study the expression co-fluctuation of lowly expressed genes . Eighth , we focused on mouse fibroblast cells because of the limited availability of allele-specific single-cell RNA-seq data . To study how expression co-fluctuation impacts the evolution of gene order , it will be important to have data from multiple cell types and species . Last but not least , as we start designing and synthesizing genomes [90] , it will be important to consider how gene order affects expression co-fluctuation and potentially fitness . It is possible that the fitness effect associated with expression co-fluctuation is quite large when one compares an ideal gene order with a random one . It is our hope that our discovery will stimulate future researches in above areas .
The processed allele-specific single-cell RNA-seq data were downloaded from https://github . com/RickardSandberg/Reinius_et_al_Nature_Genetics_2016 ? files=1 ( mouse . c57 . counts . rds and mouse . cast . counts . rds ) . The Hi-C data [33] were downloaded from https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE72697 , and we analyzed the 500kb-resolution Hi-C interaction matrix with high SNP density ( iced-snpFiltered ) . The processed ATAC-seq data were provided by authors [39] , and the data from 16 NPC populations were analyzed . All analyses were performed using custom programs in R or python . The mouse protein complex data were downloaded from the CORUM database ( http://mips . helmholtz-muenchen . de/corum/ ) [91] . The coordinates for all mouse protein-coding genes were downloaded from Ensembl BioMart ( GRC38m . p5 ) [52] . To produce duplicate-free gene pairs , we also downloaded all paralogous gene pairs from Ensembl BioMart . Note that these gene pairs can be redundant , meaning that a gene may be paralogous with multiple other genes and appear in multiple gene pairs . We then iteratively removed duplicate genes based on the following rules . First , if one gene in a pair of duplicate genes has been removed , the other gene is retained . Second , if neither gene in a duplicate pair has been removed and neither encodes a protein complex component , one of them is randomly removed . Third , if neither gene in a duplicate pair has been removed and only one of them encodes a protein complex member , we remove the other gene . Fourth , if neither gene in a duplicate pair has been removed and both genes encode protein complex components , one of them is randomly removed . Applying the above rules resulted in a set of duplicate-free genes with as many of them encoding protein complex members as possible . We obtained all mouse genes that have one-to-one orthologs in both human and rat , and acquired from Ensembl their chromosomal locations in human , mouse , and rat . Gene pairs are formed if their products belong to the same protein complex in human as well as mouse , based on protein complex information in the CORUM database mentioned above . Among them , 875 gene pairs from 342 genes are unlinked in both human and rat , of which 25 pairs become linked in mouse . To test whether the number 25 is more than expected by chance , we compared these 342 genes with a random set of 342 genes that also form 875 unlinked gene pairs in human and rat . These unlinked pairs are highly unlikely to encode members of the same complex , so serve as a negative control . Because of the difficulty in randomly sampling 342 genes that form 875 unlinked gene pairs , we adopted Gibbs sampling [92] , one kind of Markov-Chain Monte-Carlo sampling [93] . The procedure was as follows . Starting from the observed 342 genes , represented by the vector of ( gene 1 , gene 2 , … , gene 342 ) , we swapped gene 1 with a randomly picked gene from the mouse genome such that the 342 genes still satisfied all conditions of the original 342 genes described above . We then similarly swapped gene 2 , gene 3 , … , and finally gene 342 , at which point a new gene set was produced . To allow the Markov chain to reach the stationary phase , we discarded the first 1000 gene sets generated . Starting the 1001st gene set , we retained a set every 50 sets produced until 1000 sets were retained; this ensured relative independence among the 1000 retained sets . In each of these 1000 sets , we counted the number of gene pairs that are linked in mouse . The fraction of sets having the number equal to or greater than 25 was the probability reported in Fig 4F . Let us consider the chromatin accessibilities of two genomic regions , A and B , in a population of N cells ( N = 50 , 000 in the data analyzed ) [39] . Let us denote the chromatin accessibilities for the two regions in cell i by random variables Ai and Bi , respectively , where i = 1 , 2 , 3 , … , and N . We further denote the corresponding total accessibilities in the population as random variables AT and BT , respectively . We assume that Ai follows the distribution X , while Bi follows the distribution Y . We then have the following equations . AT=∑i=1NAiandBT=∑i=1NBi . ( 1 ) Pearson's correlation between AT and BT across cell populations all of size N is Corr ( AT , BT ) =E ( AT⋅BT ) −E ( AT ) E ( BT ) Var ( AT ) Var ( BT ) =E ( ∑i=1N∑j=1NAiBj ) −N2E ( X ) E ( Y ) N2Var ( X ) Var ( Y ) =∑i=1N∑j=1NE ( AiBj ) −N2E ( X ) E ( Y ) NVar ( X ) Var ( Y ) . ( 2 ) Because cells are independent from one another , when i ≠ j , E ( AiBj ) =E ( Ai ) E ( Bj ) . ( 3 ) Thus , ∑i=1N∑j=1NE ( AiBj ) =∑i=1NE ( AiBi ) +∑i=1N∑j=1j≠iNE ( Ai ) E ( Bi ) =NE ( XY ) + ( N2−N ) E ( X ) E ( Y ) . ( 4 ) Combining Eq ( 2 ) with Eq ( 4 ) , we have Corr ( AT , BT ) =NE ( XY ) −NE ( X ) E ( Y ) NVar ( A ) ⋅Var ( B ) =E ( XY ) −E ( X ) E ( Y ) Var ( X ) ⋅Var ( Y ) =Corr ( X , Y ) . ( 5 ) Hence , if the number of cells per population is a constant and there is no measurement error , correlation of chromatin accessibilities of two loci among cells is expected to equal the correlation of total chromatin accessibilities per population of cells among cell populations . To examine how violations of some of the above conditions affect the accuracy of Eq ( 5 ) , we conducted computer simulations . We assume that the accessibility of a genomic region in a single cell is either 1 ( accessible ) or 0 ( inaccessible ) . This assumption is supported by previous single-cell ATAC-seq data [40] , where the number of reads mapped to each peak in a cell is nearly binary . Now let us consider two genomic regions whose chromatin states are denoted by A and B , respectively . The probabilities of the four possible states of this system are as follows . Pr ( A=0 , B=0 ) =p , Pr ( A=0 , B=1 ) =q , Pr ( A=1 , B=0 ) =r , andPr ( A=1 , B=1 ) =s , ( 6 ) where p + q + r + s = 1 . Hence , we have E ( A ) =r+s , E ( B ) =q+s , E ( AB ) =s , Var ( A ) = ( r+s ) ( p+q ) , Var ( B ) = ( q+s ) ( p+r ) . ( 7 ) With Eq ( 7 ) , we can compute Corr ( A , B ) . In other words , for any given set of p , q , r , and s , we can compute the among-cell correlation in chromatin accessibility between the two regions . We then generated 10 , 000 random sets of p , q , r , s from a Dirichlet distribution . For each set of p , q , r , and s , we simulated the state of a cell by a random sampling from the four possible states . We did this for 16 cells as well as 16 cell populations each composed of 50 , 000 cells . We computed the total accessibility of each region in each cell population by summing up the corresponding accessibility of each cell . As expected , the among-cell correlation between the two regions in accessibility matches the true correlation ( S6A Fig ) . The deviation from the true correlation is due to sampling error . Based on Eq ( 5 ) , the among-cell-population correlation between the two regions in total accessibility approximates the true correlation , which is indeed observed in our simulation ( S6B Fig ) . Nevertheless , accessibility of a region may be undetected due to low detection efficiencies of high-throughput methods , which makes the observed correlation between the accessibilities of two regions lower than the true correlation . To assess the impact of such low detection efficiencies on the correlation , we simulated a scenario with a 10% detection efficiency , which is common in high-throughput methods [58] . That is , for every accessible region , it is detected as accessible with a 10% chance and inaccessible with a 90% chance; every inaccessible region is detected as inaccessible with a 100% chance . Our simulation showed that the observed correlation between the accessibilities of two regions is weaker than the true correlation regardless of whether the data are from individual cells ( S6C Fig ) or cell populations ( S6D Fig ) . The framework developed in the above section allows using computer simulation to acquire a lower-bound estimate of the true δa . We simulated δa by considering two pairs of regions simultaneously . For each pair of regions , we first randomly sampled p , q , r , and s , followed by the computation of the true correlation using Eq ( 7 ) . The difference between the true correlations of the two pairs of regions is equivalent to the true δa . Then , for each pair of regions , we can obtain the estimated δa from estimated correlations using simulation . In our allele-specific ATAC-seq data , only 55% of all reads are allele-specific . Given that in high-throughput sequencing data , the detection efficiency is 10 to 20% when all reads are considered [94] , we choose 8 . 25% ( = 0 . 15 × 0 . 55 ) as the detection efficiency in our simulation . We repeated this procedure 10 , 000 times and plotted the result in S7A Fig . We inferred the corresponding true δa from the observed median δa in the actual data using the regression in S7A Fig . To obtain a lower-bound estimate of the true δe , we performed a simulation incorporating the known parameters of single-cell RNA-seq in our dataset . The simulation was performed as follows . First , we determined the mean expression levels for a pair of genes , A and B , by sampling from the distribution of mean expression levels of genes analyzed , which was obtained based on the estimation that 1 RPKM corresponds to 1 transcript per cell in the original dataset [23] . Note that the mean expression level of each allele ( A1 , A2 , B1 , and B2 ) is one half the above sampled value . Second , we generated the expression levels across 60 cells for a pair of alleles ( A1 and B1 ) from the joint multivariate normal distribution . The multivariate normal distribution can be uniquely determined once the correlation coefficient between the two alleles and their CV are chosen . We fixed the CV of the two alleles at 0 . 5 , based on sm-FISH experiments in mammalian cells for genes whose expression levels are similar to the genes we analyzed [95] . Note that the CV used here is the mRNA CV , not the protein CV . The correlation between the two alleles was randomly sampled from the range ( -1 , 1 ) . We name this correlation r1 . Third , for each allele in each cell , we used binomial sampling to determine the detected transcript level . In our data set , only 17% of the reads are allele-specific . Because the capturing efficiency is around 10–20% for full-length single cell RNA-seq data [94] , we used 2 . 55% ( = 0 . 15 × 0 . 17 ) as the sampling probability . Fourth , we computed the observed correlation between A1 and B1 across cells after binomial sampling . Fifth , we repeated the above steps 2 to 4 . We named the newly sampled correlation r2 . The true δe would be r1−r2 , and the observed δe is the difference between the observed correlations . Sixth , we repeated 10 , 000 times steps 1 to 5 , with all true δe and observed δe recorded ( S7B Fig ) . We inferred the corresponding true δe from the observed median δe in the actual data using the regression in S7B Fig . Let the concentration of protein complex AB be [AB] . To study the average [AB] across cells in a population , we first simulated the concentrations of subunit A and subunit B in each cell . We assumed that the total concentrations of A and B , denoted by [A]t and [B]t respectively , are both normally distributed with mean = 1 and CV = 0 . 2 . We used CV = 0 . 2 because this is the median expression noise measured by CV for enzymes in yeast [6] , the only eukaryote with genome-wide protein expression noise data [15] . Thus , the joint distribution of [A]t and [B]t is multivariate normal , which can be specified if the correlation ( r ) between [A]t and [B]t is known . With a given r , we simulated [A]t and [B]t for 10 , 000 cells by sampling from the joint distribution . We set the concentration to 0 if the simulated value is negative . We computed [AB] in each cell by solving the following set of equations . [A]t=[A]+[AB] , [B]t=[B]+[AB] , and[AB]=K[A][B] , ( 8 ) where we used K = 105 based on the empirical values of association constants of stable protein complexes [49] . The mean complex concentration is the average [AB] among all cells . We also performed the simulation with other K values ( 10−2 , 10−1 , 100 , 101 , 102 , 103 , and 104 ) . The above simulation can be extended for studying protein complexes with various stoichiometries . In general , for protein complex AMBN , we have [A]t=[A]+M[AMBN] , [B]t=[B]+N[AMBN] , and[AMBN]=K[A]M[B]N . ( 9 ) We considered ( M , N ) = ( 1 , 1 ) , ( 1 , 2 ) , ( 1 , 3 ) , ( 2 , 2 ) , ( 2 , 3 ) , and ( 3 , 3 ) , respectively . In addition , we studied the consequence of having suboptimal mean [A]t or mean [B]t . That is , we set the ratio of mean [A]t to mean [B]t at M/N , 2M/N , or 0 . 5M/N . We considered CV = 0 . 2 or 0 . 5 .
The maximum effect of sharing regulatory elements on expression co-fluctuation , referred to as δre , can be estimated by the median correlation coefficient in expression level between two alleles of the same gene minus the corresponding value for two alleles of different genes . We found that median δe is approximately one thirteenth of δre . Thus , the linkage effect improves expression co-fluctuation brought by regulatory element sharing by at least one thirteenth . Software used and the underlying numerical data for all figures can be downloaded from Github ( https://github . com/mengysun/Linked_noise ) . | Gene expression is subject to substantial stochastic noise or fluctuation . We hypothesize that expressions of neighboring genes on the same chromosome co-fluctuate because of their common chromatin dynamics . To test this hypothesis , we make use of the fact that each diploid cell contains a maternal and a paternal copy of the same chromosome that are differentiable by their DNA sequences . Hence , allele-specific single-cell RNA-sequencing can quantify the expression level of each allele in individual diploid cells , allowing measuring the expression co-fluctuation of linked alleles as well as that of unlinked alleles of the same genes . Using such data from mouse cells , we discover chromosome-wide gene expression co-fluctuation and provide evidence that this long-range effect arises in part from chromatin co-accessibilities of linked loci attributable to three-dimensional proximity . We show that genes encoding protein complex subunits tend to be chromosomally linked , likely resulting from natural selection for intracellular among-component dosage balance . Thus , minimization of the deleterious effect of gene expression noise has probably produced a nonrandom distribution of genes in the genome . These findings have implications for the evolution of genome organization and optimal design of synthetic genomes . | [
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] | 2019 | Chromosome-wide co-fluctuation of stochastic gene expression in mammalian cells |
AT-rich interactive domain 1A gene ( ARID1A ) loss is a frequent event in endometriosis-associated ovarian carcinomas . Endometriosis is a disease in which tissue that normally grows inside the uterus grows outside the uterus , and 50% of women with endometriosis are infertile . ARID1A protein levels were significantly lower in the eutopic endometrium of women with endometriosis compared to women without endometriosis . However , an understanding of the physiological effects of ARID1A loss remains quite poor , and the function of Arid1a in the female reproductive tract has remained elusive . In order to understand the role of Arid1a in the uterus , we have generated mice with conditional ablation of Arid1a in the PGR positive cells ( Pgrcre/+Arid1af/f; Arid1ad/d ) . Ovarian function and uterine development of Arid1ad/d mice were normal . However , Arid1ad/d mice were sterile due to defective embryo implantation and decidualization . The epithelial proliferation was significantly increased in Arid1ad/d mice compared to control mice . Enhanced epithelial estrogen activity and reduced epithelial PGR expression , which impedes maturation of the receptive uterus , was observed in Arid1ad/d mice at the peri-implantation period . The microarray analysis revealed that ARID1A represses the genes related to cell cycle and DNA replication . We showed that ARID1A positively regulates Klf15 expression with PGR to inhibit epithelial proliferation at peri-implantation . Our results suggest that Arid1a has a critical role in modulating epithelial proliferation which is a critical requisite for fertility . This finding provides a new signaling pathway for steroid hormone regulation in female reproductive biology and furthers our understanding of the molecular mechanisms that underlie dysregulation of hormonal signaling in human reproductive disorders such as endometriosis .
Endometriosis is one of the most significant diseases affecting females of reproductive-age and affects an estimated 5 million women in the United States . Endometriosis is defined as the presence of endometrium-like tissue outside of the uterine cavity . The incidence increases up to 50% in patients with infertility and up to 45% in patients with chronic pelvic pain [1 , 2] . Infertility and pregnancy loss are major public health concerns for reproductive-age women . Establishment of uterine receptivity by the sequential actions of estrogen ( E2 ) and progesterone ( P4 ) on uterine cells is critical for successful embryo apposition , attachment , implantation , and pregnancy maintenance . Lack of sufficient E2 and P4 action can result in infertility and pregnancy loss in humans [3 , 4] and mice [5] . One of the primary effects of E2 on the endometrium is stimulation of epithelial proliferation , while the primary effects of P4 are to inhibit epithelial proliferation and induce differentiation to an embryo receptive state [6 , 7] . Cellular E2 and P4 actions can occur directly on a specific cell type and indirectly via paracrine activity mediated by another cell type . P4 through its cognate receptor , the progesterone receptor ( PGR ) , have important roles in the establishment and maintenance of pregnancy [7–10] . P4 attenuates E2 stimulated epithelial cell proliferation by epithelial PGR [11] . At the time of embryo implantation , the expression of PGR is promptly downregulated in the luminal epithelium in both humans and mice , and its expression is increased in stromal cells , anticipating the role of PGR in induction of decidualization [12] . Epithelial PGR acts to inhibit E2-induced epithelial proliferation . Epithelial PGR female mice are infertile due to embryo implantation defects indicating that epithelial PGR is essential for uterine function [11] . Endometriosis regression was found in some patients with endometriosis during pregnancy or who were exposed to progestin-based therapeutics [13 , 14] . However there are endometriosis patients who do not respond to treatment due to progesterone resistance . The molecular changes by P4 in the eutopic endometrium from women with endometriosis are either blunted or undetectable . P4 cannot inhibit E2-dependent growth of endometriosis[15] . The previous microarray studies of comparing women with and without endometriosis reported that many of the P4 target genes were altered at the time of implantation when P4 levels are highest [16 , 17] . P4 therapy also prevents the development of endometrial cancer associated with unopposed E2 by blocking E2 actions [18] . Expression of PGR was known as positively correlated with a good prognosis and responsiveness to progestin treatment [19] . However , more than 30% of patients with progestin treatment did not respond to progestin due to de novo or acquired progestin resistance [20–24] . The mechanism of progestin resistance is still unknown . Understanding the molecular mechanisms regulating E2 and P4 actions in the endometrium is critical in developing therapeutic approaches to alleviate this women’s health crisis . Ovarian clear-cell and endometrioid carcinomas are associated with endometriosis through distinct but currently unknown mechanisms [25–27] . One of the possible mechanisms is linked to mutation of the AT-rich interactive domain 1A gene ( ARID1A ) [28] . ARID1A encodes BAF250a ( ARID1A ) protein which is one of the subunits in the switch/sucrose non-fermentable ( SWI/SNF ) chromatin remodeling complex [29] . ARID1A mutations leading to loss of the protein expression [30] have been found in 46% of ovarian clear-cell carcinomas and 30% of endometrioid ovarian carcinomas [28 , 31] . ARID1A is also critical for embryogenesis in mice and the maintenance of ES cell self-renewal , as well as lineage-specific differentiation of ES cells in vitro [32] . Embryos lacking one allele resulted in late embryonic lethality , complete loss of ARID1A led to developmental arrest around E6 . 5 without formation of a primitive streak and mesoderm . Ablation of ARID1A in mice ES cells led to altered cell morphology and proliferation [33] . However , little is known about the physiological or pathological effects of ARID1A expression in the endometrium . We found that ARID1A levels are remarkably lower in endometrium from women with endometriosis compared to women without endometriosis . In an effort to overcome embryonic lethality of Arid1a knock-out mice , we have used conditional Arid1a knock-out mice in the uterus . In this study , we observed that the mutant mice are sterile due to increased epithelial cell proliferation which resulted in implantation defects . Our results suggest that Arid1a suppresses E2 signaling with PGR by modulating KLF15 expression indicating the critical role of Arid1a in the peri-implantation period .
We examined the levels of ARID1A in endometrium from spontaneously cycling women using immunohistochemical analysis . We observed the most abundant levels of ARID1A protein throughout the menstrual cycle in women without endometriosis ( S1 Fig ) . ARID1A proteins were strongly detected in the stromal and epithelial cells of endometrium from the proliferative phase and early , mid , and late secretory phases in women without endometriosis ( n = 7 per stage ) . However , the levels of ARID1A were significantly lower in both the stromal and epithelial cells of endometrium from proliferative and secretory phase endometriosis patients ( n = 28 ) compared to women without endometriosis ( n = 28 ) ( Fig 1 ) . To determine whether ARID1A is expressed during pregnancy , we next examined the mRNA and protein levels of ARID1A in the uteri of wild-type mice during early pregnancy by real-time RT-PCR and immunohistochemical analysis ( S2 Fig ) . The initiation of pregnancy was marked by the presence of the postcoital vaginal plug ( 0 . 5 dpc ) . The expression of Arid1a mRNA was strongly detected on 0 . 5 dpc , which consistently expressed until 6 . 5 dpc in the uterus . To further investigate the spatiotemporal expression profiles of ARID1A protein in the uterus during early pregnancy , we performed immunohistochemistry analysis during sequential time points . Consistent with the real-time PCR results , ARID1A proteins were also consistently strong in the nucleus of epithelial and stromal cells during early pregnancy . These data suggest that ARID1A may play an important role during early pregnancy . Arid1a knock-out mice resulted in embryo lethality [33] . Therefore , in order to investigate the role of Arid1a in the uterus , we generated a mouse model in which Arid1a gene expression is ablated specifically in the PGR-expressing cells ( Pgrcre/+Arid1af/f; Arid1ad/d ) . ARID1A proteins were remarkably reduced in Arid1ad/d mice by western blot ( S3A Fig ) . The uteri of Arid1af/f control mice showed abundant ARID1A proteins at the luminal epithelium , glandular epithelium and stroma , whereas this staining was absent in the Arid1ad/d mice ( S3B Fig ) . These results confirm our successful ablation of Arid1a within the uterus of Arid1ad/d mice . To investigate the impact of ablation of Arid1a on female fertility , female control ( Arid1af/f ) and Arid1ad/d mice were mated with wild-type male mice for 6 months . Arid1af/f mice ( n = 9 ) had an average of 7 . 21± 0 . 29 pups/litter , whereas Arid1ad/d mice ( n = 9 ) had no pups ( S1 Table ) . These results revealed that Arid1ad/d mice were sterile . To test for an ovarian phenotype , female Arid1ad/d mice were examined for their ability to ovulate normally in response to a superovulatory regimen of gonadotropins [34] . Arid1ad/d mice yielded 19 . 86 ± 0 . 99 oocytes which did not differ significantly from Arid1af/f mice ( 19 . 50 ± 1 . 85 ) ( S2 Table ) . Also , histological analysis of the Arid1ad/d ovary did not show any alterations in ovarian morphology Arid1ad/d mice showed normal development of corpora lutea ( n = 5 ) ( S4A Fig ) . The serum level of E2 and P4 were 4 . 40± 0 . 71 pg/ml and 11 . 57± 1 . 88 ng/ml , respectively in Arid1af/f mice , meanwhile 5 . 43± 0 . 50 pg/ml and 15 . 69± 1 . 96 ng/ml , respectively in Arid1ad/d mice . The serum level of E2 and P4 showed no significant statistical difference between the mice at 3 . 5 dpc ( n = 3 per genotype ) ( S4B Fig ) . This result shows that ovarian morphology and functioning were not affected in the Arid1ad/d females suggesting that the fertility defect is primarily due to a uterine defect . To determine the cause of infertility in Arid1ad/d mice , 8-week-old female Arid1af/f and Arid1ad/d mice were mated with intact wild-type male mice . Females were euthanized at 5 . 5 dpc of pregnancy , and the numbers of implantation sites were counted . Implantation sites were detected in the uterine horn of Arid1af/f mice , whereas there were no implantation sites in Arid1ad/d mice ( Fig 2A ) . Histological analysis revealed that embryos could not attach to the uterine horn of Arid1ad/d mice while embryos were attached well in Arid1af/f mice and surrounded by decidualized cells ( n = 5 ) ( Fig 2B ) . To address a defect of embryo implantation in Arid1ad/d mice , mice were dissected at 4 . 5 dpc . Free-floating embryos ( 4 . 67 ± 1 . 33 per mouse ) were found in the uterine horn of Arid1ad/d , whereas well attached embryos ( 5 . 50 ± 0 . 65 per mouse ) were found in the uterine horn of Arid1af/f mice ( n = 5 ) ( Fig 2C ) . These results suggest that a failure of embryo attachment is one of the causes of the infertility observed in Arid1ad/d mice . Embryo invasion transforms endometrial stromal cells into a decidual phenotype [35–37] . Patients with gynecological pathologies contributing to infertility , such as endometriosis , display markedly reduced decidualization and impaired uterine receptivity [38] . To access ARID1A function in stroma cells , we examined the levels of ARID1A in human primary endometrial stromal cells ( hESCs ) from patients with or without endometriosis by Western blot . All 6 hESCs from women without endometriosis showed strong expression of ARID1A in hESCs from women without endometriosis . Interestingly , 5 of 6 hESCs from women with endometriosis did not detect ARID1A protein ( Fig 3A ) . This result suggests that ARID1A loss may cause an impaired decidualization in patients with endometriosis . Therefore , we next examined the role of Arid1a in decidualization . We next examined the ability of Arid1ad/d mice to undergo decidualization after artificial hormonal induction . Ovariectomized Arid1af/f and Arid1ad/d mice were treated with E2+P4 , and the uteri were mechanically stimulated to mimic the presence of an implanting embryo and to induce decidualization [34] . Control mice showed a decidual uterine horn that responded well to this artificial induction . However , Arid1ad/d mice exhibited a significant defect of decidual response . The weight ratio of stimulated to control horn was highly decreased in Arid1ad/d mice compared to Arid1af/f mice ( Fig 3B ) . Histological analysis confirmed that well-developed decidual cells were detected in the decidual uterine horn of Arid1af/f mice , while differentiation of uterine stromal cells to decidual cells was not observed in the decidual uterine horn of Arid1ad/d mice ( Fig 3C ) . In addition , the expression of known markers of decidualization , Bmp2 , Fst , and Fkbp5 , were significantly decreased in the decidual uterine horn of Arid1ad/d mice compared to the decidual uterine horn of Arid1af/f mice ( Fig 3D ) . These data show that Arid1ad/d mice have a decidualization defect . In normal pregnant uteri , abundant proliferation was detected in epithelial cells and stromal cells at 2 . 5 dpc . Proliferation is markedly reduced in epithelial cells at 3 . 5 dpc for embryo attachment [39] . To determine whether a defect of embryo attachment is caused by an alteration in cell proliferation , we examined the expression of Ki67 , a proliferative marker , at 3 . 5 dpc by immunohistochemistry . Ki67 immunohistochemistry showed that proliferation was highly increased in uterine epithelial cells of Arid1ad/d mice compared to Arid1af/f mice ( Fig 4 ) . These results suggest that abnormal epithelial proliferation in Arid1ad/d mice is one of the causes of the embryo attachment defect . E2 promotes epithelial cell proliferation in the uterus [6] . Since an increase of epithelial proliferation is observed in Arid1ad/d mice , we further investigated whether excess E2 signaling is caused by Arid1a ablation . To address excess E2 signaling , the expression of E2 responsive genes , C3 , Clca3 , Muc-1 , and Ltf , were examined by real-time RT-PCR analysis . The expression of C3 , Clca3 , Muc-1 , and Ltf were highly increased in Arid1ad/d mice compared to Arid1af/f mice ( Fig 5A ) . An increase of phospho-ESR1 , MUC1 and LTF protein expression was detected in the epithelium of the Arid1ad/d mice compared to the Arid1af/f mice , but ESR1 was not changed between the mice ( Fig 5B ) . Arid1af/f mice had an average of 71 . 39± 2 . 58% , meanwhile Arid1ad/d mice had an average 69 . 02± 2 . 90% of positive stromal pESR1 cells . There are no significant differences . These results demonstrate that estrogen receptor activity is enhanced in the uterine epithelial cells of the Arid1ad/d mice . Since excess E2 signaling is detected in the Arid1ad/d mice , we next investigated whether Arid1a ablation altered the expression of PGR . We performed PGR immunohistochemistry and real-time RT-PCR to assess the expression of PGR and its target genes in Arid1ad/d mice . Interestingly , epithelial PGR expression was highly reduced in Arid1ad/d mice compared to control mice ( Fig 6A and 6B ) . The mRNA expression level of epithelial P4 target genes , Fst , Gata2 , Areg , and Lrp2 were highly downregulated in Arid1ad/d mice . However the expression of Il13ra2 and Hand2 which are known as stromal P4-target genes were not changed ( Fig 6C ) . These results suggest that Arid1a mediates estrogen activity by regulating epithelial PGR expression . In order to identify the pathways that Arid1a regulates at implantation , we performed high density DNA microarray analysis on the uteri from Arid1af/f and Arid1ad/d mice at 3 . 5 dpc ( GEO accession number: GSE72200 ) . The microarray analysis showed that 1 , 358 were more highly expressed in Arid1ad/d mice and 1 , 198 genes were decreased by more than 1 . 5-fold . From the pathway analysis using Ingenuity Pathway Analysis ( QIAGEN , Redwood City , CA ) , the altered pathways including cell-cycle control , DNA replication , and modification processes were identified ( Table 1 and S3 Table ) . The results have been validated by qPCR analysis ( Fig 7A ) . The immunohistochemistry results showed that the levels of MCM2 and MCM6 were increased in Arid1ad/d mice at 3 . 5 dpc ( Fig 7B ) . Two Kruppel-like factors ( KLFs ) have been implicated in E2 and P4 modulation of uterine proliferation [40 , 41] . Klf4 is increased by E2 and promotes DNA replication , whereas Klf15 is increased by P4 and inhibits growth via regulation of Mcm2 [41] . The down-regulation of PGR by ARID1A loss coincides with the down-regulation of Klf15 transcript abundance , which led to the hypothesis that ARID1A positively regulates Klf15 expression with PGR . To determine whether ARID1A and PGR bind to the putative Klf15 promoter , ChIP was performed on uterine chromatin from Arid1af/f and Arid1ad/d mice at 3 . 5 dpc . ChIP analysis exhibited that recruitment of PGR on HRE is significantly decreased by the absence of Arid1a indicating that klf15 is directly regulated by ARID1A and PGR ( Fig 8A ) . We examined whether ARID1A physically interacts with PR-A or PR-B protein using immunoprecipitation analysis . We transfected with PGR constructs expressing either human PR-A or PR-B into Ishikawa cells . The lysates were then immunoprecipitated with anti-ARID1A antibodies , and then performed western blot analysis using anti-PGR antibodies . The immunoprecipitation results showed that ARID1A physically interacts with PR-A , not PR-B ( Fig 8B ) . Next , we examined the protein levels of KLF4 and KLF15 to determine whether their dys-regulation might contribute to aberrant epithelial proliferation in Arid1ad/d mice . The expression of KLF4 was remarkably increased in Arid1ad/d mice compared to Arid1af/f mice while the expression of KLF15 was decreased in Arid1ad/d mice ( Fig 8C ) . These results suggest that ARID1A regulates transcriptional activation of KLF15 through physical interaction with PR-A . To better understand the integration of ARID1A in endometriosis , immunohistochemistry analysis for KLF4 and KLF15 was performed with eutopic endometrium from secretory phase women with and without endometriosis ( Fig 8D and 8E ) . As shown in the Arid1ad/d mice , eutopic endometrium from women with endometriosis showed increased KLF4 levels compared to control endometrium . The expression of KLF15 was very weak in eutopic endometrium from women with endometriosis , while its expression was strong in endometrial cells in control endometrium . These data suggest that KLF15 is a downstream mediator of the anti-proliferative action of P4 on E2-induced epithelial cell proliferation and ARID1A regulates KLF15 expression with PGR .
Somatic ARID1A mutations are uniquely associated with endometriosis-related ovarian neoplasms [42–45] . ARID1A is located within chromosomal region 1p36 , a region frequently deleted in a variety of human cancers [46 , 47] . Indeed , many studies have analyzed ARID1A expression in a variety of human cancers and demonstrated loss of ARID1A expression [43 , 48–50] . ARID1A was mutated in 46% of ovarian clear-cell carcinomas and 30% of endometrioid ovarian carcinomas [28 , 31] . Loss of ARID1A is also frequent in endometrial carcinoma [51–53] . Interestingly , Arid1ad/d mice showed aberrant active epithelial proliferation , but did not develop endometrial hyperplasia or cancer . Our results suggest that Arid1a loss alone is not enough to lead to the development of endometrial cancer . Endometriosis is a common cause of infertility [54] . However , the roles of ARID1A in infertility and endometrial function have not been studied . In the present study , we report that ARID1A protein levels are significantly lower in the eutopic endometrium of women with endometriosis compared to women without endometriosis , and mice with conditional ablation of Arid1a in PGR positive cells ( Arid1ad/d ) were sterile . These results suggest a relationship between ARID1A loss and infertility . Since PGRCre mice show Cre recombinase activity in the pituitary , ovary , uterus and mammary glands , these mice may have infertility due to a defect of Arid1a in any of these tissues [55] . Arid1ad/d mice had normal ovarian function indicating that the conditional loss of Arid1a in the granulosa cells of the ovary did not influence ovarian function . Although our study does not rule out a pituitary defect , a failure of embryo attachment and decidualization in Arid1ad/d mice suggest that the fertility defect is primarily due to a uterine defect . Receptivity in the mouse endometrium is dependent on ovarian steroid hormones . On 0 . 5 and 1 . 5 dpc , E2 promotes uterine epithelial cell proliferation and growth . On 2 . 5 dpc , P4 inhibits this epithelial proliferation , promoting receptivity , and inducing stromal cell proliferation [56] . We observed increased proliferation in the epithelium of Arid1ad/d mice at 3 . 5 dpc indicating enhanced epithelial E2 signaling . It is reported that enhanced epithelial E2 activity leads to implantation failure [57 , 58] . We also showed that conditional ablation of Arid1a results in elevated levels of phospho-ESR1 , the active form of ESR1 , and ESR1 target genes , C3 , Clca3 , Muc-1 , and Ltf which plays an essential role in uterine receptivity and embryo attachment [59–61] . These results indicate that the attachment defect observed in Arid1ad/d mice is due to a failure of P4 to squelch E2 signaling in luminal epithelium as indicated by altered expression of MUC-1 and LTF which is tightly regulated at pre-implantation [62 , 63] . COUP-TF II mediates Bmp2 expression by controlling ESR1 activity in the murine uterus [58] , and its expression is promoted by SWI/SNF in vascular endothelium [64] . Thus , we examined the expression of COUP-TF II in Arid1ad/d mice at 3 . 5 dpc by immunohistochemistry . COUP-TF II immunostaining were not different in uterine stroma cells of Arid1ad/d mice compared to Arid1af/f mice . Previous studies have shown that PGR has an important role in inhibiting E2 induced epithelial proliferation [65 , 66] . A decrease of epithelial PGR is observed in Arid1ad/d mice resulting in down-regulated PGR target genes , Fst , Gata2 , Areg , and Lrp2 [67] . Gata2 , Areg , and Lrp2 localization is limited to the epithelium [68–70] . We examined the expression of Il13ra2 and Hand2 [71] which are known as stromal P4-target genes by RT-qPCR . These mRNA levels were not different between Arid1af/f and Arid1ad/d mice . These data suggest that Arid1a is mainly functional in the epithelial cells at the peri-implantation stage . However , ARID1A is expressed both in epithelium and stroma . It will be useful to ascertain its cell type specific role using epithelium cell specific knockout mouse models [72–74] . Stromal functions including proliferation and the expression of Hand2 and Il13ra2 are not altered at the peri-implantation stage of Arid1ad/d mice . However , its function in stroma cells may play an important role because the phenotypes of COUP-TFII [58] and Hand2 [71] knockout mice have a similar phenotype . SWI/SNF complexes interact with several nuclear receptors , including glucocorticoid receptors , estrogen receptors and vitamin D3 receptors , to activate transcription of specific target genes [47 , 75] . Several studies have linked SWI/SNF and ARID1A to transcriptional regulation , particularly nuclear hormone-induced transcription and expression of cell-cycle regulators [76–78] . Our results suggest that ARID1A is pivotal to regulating transcription of PGR target genes to prepare receptivity in the uterus . Loss of ARID1A may have many effects on SWI/SNF complexes that lead to transcriptional dysfunction , including disruption of nucleosome sliding activity , assembly of variant SWI/SNF complexes , targeting to specific genomic loci , and/or recruitment of coactivator/corepressor activities . An impaired P4 response is seen in the endometrium of women with infertility and endometriosis [79–81] . However , molecular mechanisms of aberrant PGR function in uterine diseases remain uncertain . Although this study has not clearly addressed why the epithelium PGR was decreased in the Arid1a knockout uterus , our results show that ARID1A regulates PGR signaling to prepare receptivity in the uterus . ARID1A may regulate stability of PGR proteins . However , it is also possible that PGR is a target gene of ARID1A . Further investigation is required to elucidate the exact mechanism underlying a possible regulatory role of ARID1A in the regulation of steroid hormone signaling . Interestingly , this Arid1ad/d phenotype is similar to Wnt7a-Cre PGRf/- mice , with epithelial-specific ablation of PGR . Wnt7a-Cre PGRf/- mice were infertile due to defects in embryo attachment , stromal cell decidualization , the inability to cease estrogen-induced epithelial cell proliferation , and the lack of P4 regulated expression of its epithelial target genes [11] . Stromal-epithelial cross talk is critical in pregnancy [11 , 82] and P4 achieves inhibition of E2-induced epithelial cell proliferation by coordinating stromal-epithelial cross-talk [7 , 9 , 10] . The Stromal functions including proliferation and the expression of Hand2 and Il13ra2 in Arid1ad/d mice are not altered at 3 . 5 dpc . These results suggest that Arid1a is mainly functional in the epithelial cells of the peri-implantation stage . An epithelium cell specific Arid1a knockout mouse model using Wnt7a-cre or Lactoferrin-iCre mouse [72–74] will be an invaluable approach to ascertain its cell type specific role . However , ARID1A is expressed both in epithelium and stroma . Its function in stroma cells may play an important role because the phenotypes of Hand2 [71] and COUP-TFII [58] knockout mice have similar phenotypes . Determining the role of Arid1a in stromal-epithelial cross talk will be critical in understanding the role of steroid hormone signaling and dysfunction associated with infertility and endometriosis . To investigate the global impact on gene expression caused by the loss of Arid1a , we conducted microarrays at the peri-implantation stage and identified over 2 , 500 misregulated genes in the absence of Arid1a . Dr . Pollard’s group demonstrated that P4 blocks E2-induced DNA synthesis through the inhibition of replication licensing including MCM proteins [83 , 84] . There is a significant overlap in the list of genes associated with cell cycle and DNA replication between Dr . Pollard’s and our microarray results . In the uterine epithelium , E2 stimulates the expression of the MCMs while P4 inhibits the transcript abundance of MCM 2 to 6 [83 , 85] . The immunohistochemistry results showed aberrant overexpression of MCM2 and MCM6 in the epithelial cells of Arid1ad/d mice at the peri-implantation stage . A similar action can be ascribed to P4 and E2 in human endometrial epithelium as a loss of MCM proteins occurs in the secretory phase , and therefore P4 dominated this phase of the menstrual cycle [81 , 86] . However , aberrant overexpression of MCM2 and MCM6 may cause abnormal epithelial proliferation and early pregnancy loss . Despite the importance of this regulation in mice and humans , the molecular basis for the P4 and E2 regulation of DNA replication licensing is not understood . Our results demonstrate that ARID1A loss results in increased E2 sensitivity of the uterus in the presence of P4 . Kruppel-like factors ( KLF ) family play important roles in cellular proliferation , survival , differentiation , pluripotency , and epithelial-to-mesenchymal interactions [87] . The members of the KLF family are ubiquitously expressed in the uterus and have been increasingly implicated as critical co-regulators and integrators of steroid hormone actions [88] . The expression of KLF9 is lower in eutopic endometrium of women with endometriosis and endometrial KLF9 deficiency promotes endometriotic lesion establishment by the coincident deregulation of Notch- , Hedgehog- , and steroid receptor-regulated pathways [89–91] . However , the expression of Klf9 is not altered in Arid1ad/d mice . Klf4 and Klf15 play a critical role in uterine proliferation by modulating E2 and P4 [40 , 41] . E2 induces Klf4 expression and promotes DNA replication , whereas Klf15 is induced by P4 and inhibits growth via regulation of Mcm2 [41] . Therefore , we focused on transcriptional regulation of Klf15 as an E2 regulated transcription factor . KLF15 binds to the MCM2 promoter in a P4 and E2 dependent fashion , which negatively regulates RNA Pol II association [41] . Klf15 expression suppresses E2 mediated MCM2 transcription . In vivo , Klf15 expression in the E2 exposed uterus mimics P4 action by inhibiting Mcm2 expression and epithelial cell DNA synthesis . ChIP analysis demonstrated that ARID1A and PGR directly bind to the PRE region of Klf15 promoter . Immunohistochemistry analysis showed an increase of KLF4 and a decrease of KLF15 expression in Arid1ad/d mice at the peri-implantation stage . These data establish Klf15 as a downstream mediator of the anti-proliferative action of P4 on E2-induced epithelial cell proliferation and ARID1A regulates E2-induced epithelial proliferation by modulating klf15 expression with PGR . Following embryo attachment , the uterus again changes during a process known as decidualization whereby the epithelium undergoes apoptosis and the stroma proliferates and differentiates into a more epitheliod cell type [66] . We demonstrated that Arid1ad/d mice exhibited a defect of the decidual response . Bmp2 and Fkbp4 null females exhibited a defect of implantation and decidualization suggesting a critical role in decidualization [92–94] . Fst is a known Bmp2 target [95] . The decidualization markers , Bmp2 , Fst , and Fkbp5 , were significantly decreased in Arid1ad/d mice indicating that uterine specific ablation of Arid1a caused a significant decidualization defect . In conclusion , ARID1A has a key role in implantation and decidualization , and that ARID1A expression is lost in endometriosis . Ablation of Arid1a affects epithelial proliferation in part via dysregulating KLF15 expression with PGR . Aberrant proliferative conditions of the human endometrium are common . Inappropriate proliferation of the uterus is one cause leading to endometriosis [96] . Determining the mechanism of Arid1a in uterine dysfunction associated with infertility and endometriosis will be critical to understanding both of these common uterine diseases for future therapy .
The study has been approved by Institutional Review Committee of Michigan State University ( IRB number: 07–712; r047700 ) , Greenville Health System ( IRB number: Pro00013885 and Pro00000993 ) and University of North Carolina ( IRB number: 05–1757 ) , and written informed consent was obtained from all participants . All protocols related to animals were overseen and approved by the Institutional Animal Care and Use Committee at Michigan State University ( AUF number: 11/13-248-00 ) . Animals were maintained in a designated animal care facility in accordance with Michigan State University’s institutional guidelines . The human endometrial samples were collected from Michigan State University’s Center for Women’s Health Research Female Reproductive Tract Biorepository , the Greenville Hospital System , and the University of North Carolina . Samples were collected as previously reported [97 , 98] . Briefly , to compare gene expression patterns of eutopic endometrium between those with and without endometriosis , 28 samples were collected from proliferative , early , mid , and late secretory phases ( n = 7 per phase ) . For endometriosis eutopic endometrium , 28 samples were collected from proliferative , early , mid , and late secretory phases ( n = 7 per phase ) . Endometrial biopsies were obtained at the time of surgery from regularly cycling women between the age of 18 and 45 . The presence or absence of disease was confirmed during surgery . Women laparoscopically negative for this disease were placed into the control group , whereas women laparoscopically positive for this disease were placed in the endometriosis group . Use of an intrauterine device ( IUD ) or hormonal therapies in the 3 months preceding surgery was exclusionary for this study . Histologic dating of endometrial samples was done based on the criteria of Noyes [99] and confirmed by subsequent histo-pathological examination by an experienced Fertility specialist ( B . A . L . ) . Isolation of human primary endometrial stromal cells ( hESCs ) has been previously described [39] . hESCs were isolated from proliferative phase patients with or without endometriosis . Proteins were extracted using lysis buffer ( 150 mM NaCl , 0 . 125% Nonidet P-40 ( vol/vol ) , 2 . 5 mM EDTA , and 10 mM Tris-HCl ( pH 7 . 4 ) included with both a phosphatase inhibitor cocktail ( Sigma Aldrich , St . Louis , MO ) and a protease inhibitor cocktail ( Roche , Indianapolis , IN ) . Twenty μg of protein lysates were electrophoresed via SDS-PAGE and were then transferred onto polyvinylidene difluoride membrane ( Millipore Corp . , Bedford , MA ) . Western blot analysis was performed using anti-ARID1A ( Abnova , Neihu District , Taipei City , Taiwan ) and anti-Actin ( Santa Cruz ) antibodies . Arid1a conditional knockout mice were generated by crossing Pgrcre/+ [55] with Arid1af/f [8] mice ( Pgrcre/+Arid1af/f; Arid1ad/d ) . Pregnant uterine samples were obtained by mating Arid1af/f and Arid1ad/d female mice with C57BL/6 male mice with the morning of a vaginal plug being designated as 0 . 5 dpc . Mice were sacrificed at 3 . 5 , 4 . 5 and 5 . 5 dpc and the number of implantation sites identified on 5 . 5 dpc . The level of progesterone and estrogen in serum were analyzed by the University of Virginia Center for Research in Reproduction Ligand Core . Uterine tissues were snap-frozen at the time of dissection and either stored at -80°C for RNA/protein extraction or fixed with 4% ( vol/vol ) paraformaldehyde for histology . For the fertility studies , adult female Arid1af/f and Arid1ad/d female mice were placed with wild-type male mice ( n = 9 ) . The mating cages were maintained for 6 months and the number of litters and pups born during that period was recorded . For ovulation and fertilization test , female mice ( n = 3 per genotype ) were superovulated by i . p . injection of 5 IU of PMSG ( Fisher Sci . ) followed 48 h later by 5 IU of hCG ( Sigma-Aldrich ) and mated with wild-type male mice . The following morning ( 0 . 5dpc ) , ovulated eggs were flushed from the oviducts on 1 . 5 dpc . The hormonally induced decidual response has been previously described [100] . Briefly , Arid1af/f and Arid1ad/d female mice at 6-weeks of age were ovariectomized ( n = 3 per genotype ) . Two weeks post ovariectomy , Arid1af/f and Arid1ad/d were subjected to the following hormonal regimen: 100 ng of E2 per day for three days; two days rest; then , three daily injections of 1 mg of P4 + 6 . 7 ng of E2 . Six hours following the third P4 and E2 injection , the left uterine horn was mechanically stimulated by scratching the full length of the anti-mesometrial side with a burred needle . The other horn was left unstimulated as a control . Daily injections of P4 ( 1 mg/mouse ) + E2 ( 6 . 7 ng/mouse ) were continued for five days to maximize the decidual response . Then , mice were sacrificed on day 5 . The uteri were then excised , weighed and fixed in 4% paraformaldehyde for histological analysis . RNA was extracted from the uterine tissues using the RNeasy total RNA isolation kit ( Qiagen , Valencia , CA , USA ) . mRNA expression levels of decidual marker genes ( Bmp2 , Fst , and Fkbp5 ) , Esr1 target genes ( C3 , Clca3 , Muc-1 , and Ltf ) and Pgr target genes ( Fst , Gata2 , Areg , Lrp2 , Il13ra2 , and Hand2 ) were measured by real-time PCR TaqMan analysis using an Applied Biosystems StepOnePlus system according to the manufacturer's instructions ( Applied Biosystems , Foster City , CA , USA ) and using pre-validated probes , primers , 18S RNA and Universal Master mix reagent purchased from Applied Biosystems ( Applied Biosystems ) . Template cDNA was produced from 1 μg of total RNA using random hexamers and MMLV Reverse Transcriptase ( Invitrogen Corp . ) . All real-time PCR was done by using three independent RNA sets . The mRNA quantities were normalized against 18S RNA using ABI rRNA control reagents . Uterine sections from paraffin-embedded tissues were cut at 5 μm and mounted on silane-coated slides , deparaffinized , and rehydrated in a graded alcohol series before blocking with 10% normal goat serum in PBS ( pH 7 . 5 ) and incubating with primary antibody diluted in 10% normal goat serum in PBS ( pH 7 . 5 ) overnight at 4°C at the following dilutions: 1:500 for anti-ARID1A ( Sc-98441 , SantaCruz ) , 1:100 for anti-Ki67 ( ab15580 , Abcam ) , anti-ESR1 ( DAKO Corp . ) , 1:100 for anti-phospho-ESR1 ( Ab31477 , Abcam ) , 1:1000 for MUC-1 ( ab15481 , Abcam ) , 1:2000 for LTF ( 07–682 , Millipore ) , MA ) , 1:20000 for MCM2 ( Sc-9839 , SantaCruz ) , 1:20000 for MCM6 ( Sc-9843 , SantaCruz ) , 1:5000 for KLF4 ( Sc-20691 , SantaCruz ) , 1:5000 for FLK15 ( ab2647 , Abcam ) , and 1:1000 for anti-total PGR antibody ( A0098 , DAKO Corp . ) . On the following day , sections were washed in PBS and incubated with the appropriate species-specific HRP-conjugated secondary antibody ( 2 μg/ml; Vector Laboratories ) for 1 hr at room temperature . Immunoreactivity was detected using the Vectastain Elite DAB kit ( Vector Laboratories ) . A semiquantitative grading system ( H-score ) was used to compare the immunohistochemical staining intensities as previously described [101] . The number of PGR and Ki67-positive cells was counted in 200 epithelial cells and eight random fields of stromal cells . Biotinylated cRNA were prepared according to the standard Affymetrix protocol from 500ng total RNA ( Expression Analysis Technical Manual , 2001 , Affymetrix ) . Following fragmentation , 15 ug of aRNA were hybridized for 16 hr at 45C on GeneChip Mouse Genome Array . GeneChips were washed and stained in the Affymetrix Fluidics Station 450 . GeneChips were scanned using the Affymetrix GeneChip Scanner 3000 7G . The data were analyzed with RMA using Affymetrix default analysis settings and global scaling as the normalization method . The trimmed mean target intensity of each array was arbitrarily set to 100 . The normalized , and log transformed intensity values were then analyzed using GeneSpring GX 12 . 6 ( Agilent technologies , CA ) . Fold change filters included the requirement that the genes be present in at least 150% of controls for up-regulated genes and lower than 66% of controls for down-regulated genes . Hierarchical clustering data were clustered groups that behave similarly across experiments using GeneSpring GX 12 . 6 ( Agilent technologies , CA ) . Clustering algorithm was Euclidean distance , average linkage . ChIP analysis was conducted by Active Motif ( Carlsbad , CA , USA ) using frozen mouse uteri of Arid1af/f and Arid1ad/d at 3 . 5 dpc . Uterine tissue samples ( approximately 180 mg ) were submersed in PBS containing protease inhibitors , cut into small pieces , and treated with fixation solution for 15 min at room temperature . Fixation was stopped by the addition of stop solution for 5 min . The tissue pieces were washed twice with PBS washing buffer , incubated with Chromatin Prep Buffer containing protease inhibitors and PMSF for 10 min on ice , homogenized by glass homogenizer for 30 strock , and finally spun down . Chromatin was isolated from disrupting the cells with a ChIP buffer containing protease inhibitors and PMSF . Lysates were sonicated using a Sonic Dismembrator FB120 ( Fisher Scientific , Pittsburgh , PA , USA ) to break chromatin into fragments with an average length of 0 . 5–1 kb . For each ChIP reaction , 100 μg of chromatin was immunoprecipitated by 4 μg of antibodies against PGR ( sc7208; Santa Cruz Biotechnology , Santa Cruz , CA , USA ) and ARID1A ( H00008289-M02; Abnova , Zhongli District , Taoyuan City 320 , Taiwan ) . Following overnight incubation at 4°C , protein G agarose beads were added , and incubation at 4°C continued for another 3 hours . Immune complexes were washed five times with Wash Buffer AM1 , eluted from the beads with Elution Buffer AM4 and subjected to RNase treatment and proteinase K treatment . Crosslinks were reversed by incubation for 30 min at 55°C and for 2 hours at 80°C . ChIP DNA was purified by DNA purification column . Purified DNA was used for real-time qPCR . Real-time qPCR was carried out in triplicate using SYBR Green Supermix ( Bio-Rad Laboratories , Inc . , Hercules , CA ) . The sequences of the primers used for HRE binding region [40 , 102] in Klf15 gene were 5’- TAACCATCTGGGAAGTGGCT-3’ and 5’-GCCACTCTGGAACAGGATG-3’ , and for negative control region in Klf15 gene were 5’-TCTCACTCGGGTGTGAAGCC-3’ and 5’-GTGGGAAGCGATGCACTTTG-3’ ( S5 Fig ) . Immunoprecipitation with normal rabbit IgG was performed as a negative control . The resulting signals were normalized to input DNA . Ishikawa cells were cultured in DMEM/F12 medium ( Gibco , Grand Island , NY ) containing 10% fetal bovine serum ( FBS; Gibco ) , and 1% penicillin streptomycin ( Gibco ) at 37°C under 5% CO2 . The cells were transfected with the human PR-A and PR-B expression vectors using Lipofectamine 2000 ( Invitrogen Corp . ) . The transfected cells were lysed by lysis buffer ( 150 mM NaCl , 0 . 125% Nonidet P-40 ( vol/vol ) , 2 . 5 mM EDTA , and 10 mM Tris-HCl ( pH 7 . 4 ) ) included with both a phosphatase inhibitor cocktail ( Sigma Aldrich , St . Louis , MO ) and a protease inhibitor cocktail ( Roche , Indianapolis , IN ) . Protein lysates were then immunoprecipitated with ARID1A antibodies ( Abnova ) with protein A-agarose ( Pierce Biotechnology , Rockford , IL ) and incubated overnight at 4°C . Immunocomplexes were washed 5 times with 1 ml of lysis buffer and were then subjected to western blot analysis using anti-PGR antibody ( SantaCruz ) . The western blot analysis was performed as described previously [103] . For data with only two groups , the Student’s t test was used . For data containing more than two groups , one way ANOVA was used , followed by Tukey’s post hoc multiple range . All data are presented as means ± SEM . p < 0 . 05 was considered statistically significant . All statistical analyses were performed using the Instat package from GraphPad ( San Diego , CA , USA ) . | Endometriosis afflicts about 10% of women of reproductive age and is a major cause of pain and infertility . We showed attenuation of endometrial ARID1A in women with endometriosis as compared to women without endometriosis , and thus hypothesized that ARID1A plays an important role in ensuring normal fertility in the uterus . To test this hypothesis , we generated uterine-specific Arid1a knock-out mice , which were infertile due to defective implantation and decidualization . The mutant mice demonstrated increased endometrial epithelial proliferation with enhanced estrogen signaling and attenuation of epithelial PGR . Microarray and ChIP analysis revealed that Arid1a suppresses epithelial proliferation with PGR by regulating Klf15 expression . These data suggest that Arid1a plays an important role in steroid hormone signaling in endometrial function and dysfunction . Further investigation of ARID1A will be important for understanding altered endometrial function in infertility and endometriosis and in developing therapies for these disorders . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | ARID1A Is Essential for Endometrial Function during Early Pregnancy |
Notch is a conserved signaling pathway that specifies cell fates in metazoans . Receptor-ligand interactions induce changes in gene expression , which is regulated by the transcription factor CBF1/Su ( H ) /Lag-1 ( CSL ) . CSL interacts with coregulators to repress and activate transcription from Notch target genes . While the molecular details of the activator complex are relatively well understood , the structure-function of CSL-mediated repressor complexes is poorly defined . In Drosophila , the antagonist Hairless directly binds Su ( H ) ( the fly CSL ortholog ) to repress transcription from Notch targets . Here , we determine the X-ray structure of the Su ( H ) -Hairless complex bound to DNA . Hairless binding produces a large conformational change in Su ( H ) by interacting with residues in the hydrophobic core of Su ( H ) , illustrating the structural plasticity of CSL molecules to interact with different binding partners . Based on the structure , we designed mutants in Hairless and Su ( H ) that affect binding , but do not affect formation of the activator complex . These mutants were validated in vitro by isothermal titration calorimetry and yeast two- and three-hybrid assays . Moreover , these mutants allowed us to solely characterize the repressor function of Su ( H ) in vivo .
The Notch pathway is a highly conserved cell-to-cell signaling mechanism that is essential for cell fate decisions during embryogenesis and postnatal tissue homeostasis [1] . In humans , aberrant Notch signaling underlies the pathogenesis of many diseases , including certain types of cancer [2] , congenital syndromes [3] , and cardiovascular defects [3] . The involvement of Notch in human disease has led to considerable efforts to identify pharmaceuticals that modulate Notch signaling for therapeutic purposes [2] . The central components consist of the receptor Notch , the ligand DSL ( Delta , Serrate , Lag-2 ) , and the nuclear effector CSL ( CBF1/RBP-J , Su ( H ) , Lag-1 ) ( Fig 1A ) [4] . DSL and CSL are initialisms for the mammalian , fly , and worm orthologous proteins , respectively . Notch and DSL are multidomain transmembrane proteins with a single transmembrane spanning region , and CSL is a DNA binding transcription factor [4] . As shown in Fig 1A , signaling occurs when Notch and DSL molecules on neighboring cells interact , which results in cleavage of Notch and release of the Notch intracellular domain ( NICD ) from the cell membrane [1] . Subsequently , NICD localizes to the nucleus and directly binds CSL and the coactivator Mastermind ( MAM ) , forming the transcriptionally active CSL-NICD-MAM ternary complex [4] . CSL-NICD-MAM binds at the promoter and enhancer regions of Notch target genes , recruits coactivators , such as p300/CBP and Mediator [5 , 6] , and up-regulates transcription at these sites [7] . Signaling is terminated when NICD is phosphorylated within its PEST domain , resulting in its ubiquitin-mediated degradation [5] . At the structural level , much is known about CSL and the activator complex it forms with NICD and MAM [8–12] . As shown in Fig 1B , the structure of CSL consists of three domains: NTD ( N-terminal domain ) , BTD ( β-trefoil domain ) , and CTD ( C-terminal domain ) [11] . The NTD and CTD are immunoglobulin ( Ig ) domains structurally related to the Rel family of transcription factors ( e . g . , NF-κB and NFAT ) . CSL binds DNA as a monomer ( e . g . , CGTGGGAA ) , in which its NTD and BTD specifically bind bases in the major and minor grooves of DNA , respectively . In addition , CSL-NICD-MAM complexes can also bind as cooperative dimers on DNA at SPS ( Su ( H ) paired site ) elements [13 , 14] . The RAM ( RBP-J associated molecule ) and ANK ( ankyrin repeats ) domains of NICD bind the BTD and CTD of CSL , respectively . MAM adopts an extended α-helix that binds the CTD-ANK interface and the NTD of CSL [8 , 10 , 12] . CSL can also function as a repressor by interacting with transcriptional corepressor proteins [7] , such as Hairless in flies [15] , and SMRT/HDAC1 associated repressor protein ( SHARP ) [16 , 17] ( also known as MSX-2 interacting nuclear target [MINT] ) and KyoT2 [18] in mammals ( Fig 1 ) . Corepressors are components of large multiprotein complexes that contain histone modification activity , which convert the local chromatin into a repressive environment . While biochemical/cellular studies in mammals demonstrate that CSL interacts with corepressors and functions as a repressor [17 , 19–21] , there is limited genetic data on CSL repressor function . On the other hand , there is extensive biochemical , cellular , and genetic data from the model organism Drosophila melanogaster demonstrating that Su ( H ) ( Suppressor of Hairless—the fly ortholog of CSL ) functions as a transcriptional repressor at Notch target genes [22] . In this case , Su ( H ) binds the antagonist Hairless ( Fig 1B ) [15] , which in turn interacts with the corepressors Groucho and CtBP ( C-terminal binding protein ) to repress transcription [23–25] . Previously , we defined the region of Hairless that interacts with Su ( H ) and showed that it binds with low nanomolar affinity to Su ( H ) [26] . Nonetheless , the structural details of Su ( H ) -Hairless interactions are unknown . Here , we determine the 2 . 14 Å X-ray structure of the Su ( H ) -Hairless repressor complex bound to DNA . As predicted from our previous studies [26] , Hairless binds exclusively to the CTD of Su ( H ) , but does so in a strikingly unusual manner . Hairless wedges itself between the two β-sheets that compose the Ig fold of the CTD , significantly distorting the overall fold of this domain . This results in Hairless largely interacting with residues that form the hydrophobic core of the CTD . We designed site-directed mutations to validate our structure and identify the residues critical for Su ( H ) -Hairless complex formation . Moreover , we were able to design Su ( H ) mutants that largely affect Hairless binding , but not NICD or MAM , which allowed us to solely characterize its repressor function in cellular assays and in flies . Taken together , our studies provide significant molecular insights into how the antagonist Hairless interacts with the transcription factor Su ( H ) , reveal the remarkable structural plasticity of CSL molecules , and identify a novel binding cleft on the CTD of CSL that could potentially be exploited for modulating Notch signaling .
To determine the Su ( H ) -Hairless-DNA crystal structure , we purified recombinant Su ( H ) ( 98–523 ) and Hairless ( 232–269 ) proteins from bacteria and stoichiometrically formed a complex with a 15-mer duplex DNA , containing a single Su ( H ) binding site . Su ( H ) ( 98–523 ) corresponds to the structural core of CSL proteins ( NTD , BTD , CTD ) [11] and Hairless ( 232–269 ) comprises the conserved CSL-ID previously shown to be sufficient for Su ( H ) binding [26] . While crystals were obtained of the Su ( H ) -H-DNA complex , the crystals diffracted weakly , precluding structure determination . We took two approaches to improve the diffraction properties of our complex crystals: ( 1 ) we introduced surface entropy reduction ( SER ) mutations [27] into our Su ( H ) construct ( R155T and N281G ) ; and ( 2 ) , we employed a fixed-arm carrier approach [28] , in which Hairless ( 232–269 ) was purified as a maltose binding protein ( MBP ) fusion protein ( MBP-H ) . Subsequently , we were able to isolate Su ( H ) /MBP-H/DNA crystals that diffract to 2 . 14 Å at a synchrotron source and belong to the space group C2 . The Su ( H ) /MBP-H/DNA complex structure ( PDB ID: 5E24 ) was solved by molecular replacement and refined to a final R factor and free R factor of 17 . 5% and 19 . 6% , respectively ( Table 1 ) . The contents of the asymmetric unit and representative electron density from the complex structure are shown in S1 Fig . In subsequent figures , which illustrate the details of the Su ( H ) -Hairless complex , the MBP moiety is not shown for clarity . As shown in Figs 2 and 3 , Hairless binds the CTD of Su ( H ) , severely perturbing the overall fold of CTD when compared to an apo structure of mouse CSL ( also known as RBP-J ) [9] . It should be mentioned that there are no apo structures of Su ( H ) solved , necessitating the comparison with the apo RBP-J structure . However , given the high degree of sequence similarity between fly and mouse CSL proteins ( S2 Fig ) , we reason that the apo RBP-J structure is a good approximation of the apo Su ( H ) structure . For example , within the structural core of CSL , the primary sequence of Su ( H ) and RBP-J are 82% identical ( 90% similar ) ; and within the CTD , fly and mouse orthologs are 75% identical ( 88% similar ) . Moreover , of the 33 residues in the CTD that are different between Su ( H ) and RBP-J , 27 of these are surface exposed , likely having minimal effects on folding; and of the remaining 6 residues that are either partially or entirely buried in the CTD , there are only conservative differences between fly and mouse ( e . g . , Leu→Met , Thr→Val , Ile→Val , and Ser→Thr ) ( S2 Fig ) . Nonetheless , in the absence of an apo Su ( H ) structure we cannot rule out minor differences in structure between mouse and fly CSL orthologs . The Ig-fold of CTD is a seven-stranded β-sandwich , in which four β-strands ( a , b , e , d ) form half of the sandwich , and the remaining three strands ( c , f , g ) form the other half ( Fig 3A and 3B ) . Strikingly , Hairless wedges itself between the first and last strands of the β-sandwich ( Fig 2A and 2B ) , making extensive interactions with the hydrophobic core of the CTD and burying ~900 Å2 in surface area . For example , Hairless interacts with 10 residues in the CTD that have less than 20 Å2 in solvent accessible surface area in a corresponding apo structure of CSL ( Fig 2C ) . The site of Hairless binding is remarkably well conserved in mammalian CSL orthologs ( S2 Fig ) . Hairless binding produces relatively modest changes in the first five β-strands of the CTD , with the exception of the first β-hairpin formed by residues L436-M444 , which is translated outward by as much as 6 Å ( Fig 3C ) . However , the region between β-strands e and f undergoes the largest structural change upon Hairless binding ( Fig 3B ) , resulting in a completely new conformation of this loop , as well as displacement of the terminal β-strand by as much as 4 . 5 Å ( Fig 3C ) . We queried the Dali server [29] to identify similar protein complexes . Given the multitude of Ig structures in the database , it was surprising that the search did not uncover any other structurally related complexes . These data suggest that the Su ( H ) -Hairless complex represent a heretofore novel interaction mode between an Ig domain and its cognate binding partner . Previously , we showed that the region of Hairless that binds Su ( H ) is a random coil in solution [26]; however , Hairless assumes an extended β-hairpin conformation when bound to the CTD of Su ( H ) ( Fig 2A and 2B ) . Hairless residues G232-R249 form a classic β-hairpin , which is followed by an extended loop structure and a third β-strand that makes an anti-parallel interaction with the β-hairpin ( Fig 2B ) . The Hairless β-hairpin is amphipathic , in which its hydrophobic surface , created by residues L235 , F237 , L245 , L247 , and W258 , buries itself within the core of the CTD ( Fig 2B and 2C ) . In particular , F237 is buried the deepest , anchoring the interaction between Hairless and Su ( H ) . To analyze the structural changes that occur within CTD when bound to Hairless , we performed molecular dynamics ( MD ) simulations of the Su ( H ) -Hairless-DNA complex structure and compared these results with MD simulations of the apo RBPJ-DNA structure ( PDB ID: 3BRG ) ( Fig 3D and S3 Fig ) [30] . Two simulations were performed , in which Hairless was removed from the model and the resulting Su ( H ) -DNA structure was allowed to sample different conformations over the time course of the experiment . Both simulations converged to a root mean square deviation ( RMSD ) value of ~0 . 35 nm for the cα atoms ( S3 Fig ) , which was similar to the apo CSL-DNA structure simulation . Root mean square fluctuation ( RMSF ) analysis revealed that the largest changes occurred within the CTD of Su ( H ) ( residues 473–498 , the region between β-strands e and f ) ( S3 Fig ) , which was expected , given that this is the region in Su ( H ) that incurs the largest structural change upon Hairless binding . This structural rearrangement results in the CTD assuming a more compact conformation that is similar to the apo CSL-DNA structure ( S3 Fig ) . Closer inspection revealed that Su ( H ) residue F516 undergoes a dramatic change in its side chain dihedral angle ( ~100° ) when comparing bound and unbound structures ( S3 Fig ) . Interestingly , the shift in residue F516 of Su ( H ) is the result of Hairless residue F237 occupying a similar position deep within the core of the CTD . Moreover , when Hairless is removed from the structure , within the first 2–3 ns of the simulation F516 flips its dihedral angle to a conformation similar to the unbound CSL-DNA conformational state and remains in this position for the rest of the simulation ( S3 Fig ) . We also performed principal component analysis of the simulations ( Fig 3D ) , which revealed Su ( H ) samples two distinct conformational regions ( Fig 3D ) : ( 1 ) when bound to Hairless ( colored cyan ) or ( 2 ) in an unbound state ( colored yellow ) . Interestingly , when Hairless is removed from the complex ( colored magenta ) , very early in the simulations Su ( H ) moves from the bound to unbound conformational region and doesn’t sample the bound conformational region for the remainder of the simulation . Taken together , these data suggest that Hairless binds and stabilizes a strained conformation of Su ( H ) , which is likely a rare high-energy conformer within the ensemble of Su ( H ) molecules in solution . Su ( H ) interacts with the antagonist Hairless and the coactivators NICD and MAM to repress and activate , respectively , transcription from Notch target genes [15 , 22] . Previous studies suggest that Hairless and NICD/MAM binding to Su ( H ) are mutually exclusive [26] . Fig 2D shows a side-by-side comparison of the Hairless binding site mapped onto the surface of CSL for the Su ( H ) -Hairless repression complex and the CSL-NICD-MAM transcriptional activation complex . Clearly , the binding sites for Hairless and NICD partially overlap , and the conformational changes induced in CTD by Hairless binding would sterically block interactions with ANK and MAM . Thus , the Su ( H ) -Hairless structure supports a model in which Hairless and NICD/MAM binding to Su ( H ) are mutually exclusive . However , in spite of their partially overlapping binding sites , we speculated that due to their different modes of binding we could design mutations in Su ( H ) that primarily affect Hairless binding , but largely leave interactions with NICD unaffected . Characterization of these and other mutants by ITC , cellular assays , and in vivo assays in the fly are described below . To analyze the contributions individual residues make to the Su ( H ) -Hairless complex , we designed mutations in Su ( H ) and Hairless , based on the structure , and tested the effect these mutations had on binding using ITC . The thermodynamic binding parameters ( ΔG° , ΔH° , TΔS° ) of these interactions are contained within Tables 2 and 3 . As shown Fig 4A ( Table 2 ) , Hairless strongly binds Su ( H ) with 2 nM affinity . Consistent with Hairless being a random coil in solution prior to interacting with Su ( H ) , complex formation is enthalpically driven at 25°C ( Table 2 ) . We made alanine substitutions at Hairless residues L235 , F237 , L245 , L247 , and W258 , which are buried at the interface with Su ( H ) ( Fig 2B and 2C ) , and determined their affinity for Su ( H ) . HL235A and HF237A affect binding ~30-fold and 140-fold , respectively ( Fig 4B and 4C and Table 2 ) , consistent with these residues burying the most amount of surface area at the Su ( H ) -Hairless interface . HL245A , HL247A , and HW258A , whose side chains are more surface exposed , reduce binding to a lesser extent , 5–12-fold ( Fig 4D–4F and Table 2 ) . Next , we performed alanine-scanning mutagenesis of the residues in Su ( H ) that contact Hairless ( Fig 4G–4I , Table 3 and S1 Table ) . Some of these residues lie within the hydrophobic core of the CTD , e . g . , F460 and I500 , and when these were mutated to alanine resulted in insoluble protein , precluding analysis . Alanine substitutions at other sites within the CTD resulted in binding comparable to wild type ( S1 Table ) . However , we were able to identify two mutants , Su ( H ) L445A and Su ( H ) L514A , which individually only affected binding 8-fold and 3-fold , respectively , but in combination [Su ( H ) L445A/L514A] reduced binding 65-fold ( Fig 4G and 4H and Table 3 ) , suggesting that these residues are coupled . We reasoned that side chains near L445 and L514 , when substituted for alanine , might also display nonadditive effects on binding . This led us to identify the double mutant Su ( H ) L445A/F516A , which reduced binding to Hairless 31-fold ( Fig 4I and Table 3 ) . We also screened numerous combinations of mutants by yeast two-hybrid ( Y2H ) assays that led to the triple mutants Su ( H ) L434A/L445A/L514A and Su ( H ) L445A/L514A/F516A , which nearly completely abrogated binding to Hairless ( Fig 5A ) . The Y2H assay also confirmed that the double mutant Su ( H ) L445A/L514A has reduced binding to Hairless . To support that the Hairless binding site is conserved in mammals , we performed Y2H assays with wild-type and triple-leucine mutant RBP-J molecules . Similar to Su ( H ) , wild-type RBP-J , but not the triple-leucine mutant , binds Hairless ( Fig 5A ) . We performed several stability and functional assays on the single , double , and triple mutants described above to ensure that these mutant constructs were folded correctly and functionally active for binding to NICD . For the single and double Su ( H ) mutants , far UV circular dichroism ( CD ) experiments showed some differences in the spectra between wild-type and mutant proteins; however , analysis of the CD data with Contin-LL [31] revealed similar amounts of secondary structure between native and mutant Su ( H ) proteins ( S4 Fig ) . Similarly , thermal shift assays showed some destabilization of the mutants compared to wild type , but not substantial misfolding , and EMSA confirmed that the mutants bind DNA comparable to wild-type Su ( H ) ( S4 Fig ) . As NICD also binds the CTD of Su ( H ) [26 , 32] , we used ITC to test whether the mutants affect NICD binding . As shown in Fig 5B–5F ( Table 3 ) , NICD binds Su ( H ) with 60 nM affinity , and importantly , the single and double mutants of Su ( H ) have little to no effect on NICD binding , suggesting that the mutations do not significantly affect the fold or function of the CTD . Unfortunately , we were unable to purify enough protein of the Su ( H ) triple mutants to analyze these constructs in vitro . However , we do demonstrate that the triple mutants can interact with NICD and MAM in a yeast three-hybrid assay ( Fig 5A ) , again suggesting generally correct folding and function . Taken together , despite their partially overlapping binding sites on Su ( H ) , we have identified mutations in Su ( H ) that affect complexes with Hairless , but leave interactions with NICD and MAM largely intact; albeit , with the caveat that the mutations do effect the stability of Su ( H ) to some degree . Henceforth , we refer to these mutants as Su ( H ) LL/AA , Su ( H ) LLL/AAA , and Su ( H ) LLF/AAA , respectively . To test our Su ( H ) mutants within cells , we transfected Drosophila Schneider S2 cells with the NRE ( Notch Response Element ) reporter , which contains Su ( H ) binding sites coupled to the luciferase gene [33] . S2 cells express Su ( H ) and Hairless , but require the cotransfection of NICD to activate the reporter ( Fig 6A ) [23 , 33] . Providing additional Su ( H ) in the cells raises the activity about 3-fold , as observed previously [23 , 26 , 34] . Cotransfection of the Su ( H ) mutant constructs ( Su ( H ) LL/AA , Su ( H ) LLF/AAA , Su ( H ) LLL/AAA ) result in a very similar increase in reporter activity , in accordance with activator complex ( Su ( H ) -NICD-MAM ) assembly on DNA ( Fig 6A ) . To address whether the repressor activity of Su ( H ) is compromised by the mutations , S2 cells were cotransfected with Hairless . Cotransfection of NICD and Hairless results in a 2-fold loss in reporter activity , which is restored by transfection of Su ( H ) ( Fig 6A ) [23 , 26 , 34] . Apparently , the competition of NICD and Hairless for Su ( H ) determines the overall reporter activity level . However , cotransfection of the Su ( H ) mutants , which are deficient for Hairless binding , result in activation of the reporter similar to cotransfection of Su ( H ) ( native or mutant ) and NICD ( Fig 6A ) . We attribute the slight differences in reporter activity to repression mediated by endogenous Su ( H ) with the addition of exogenous Hairless and/or the mutations having a minor effect on NICD binding and/or residual binding of the mutants to Hairless . Nonetheless , these data suggest that in S2 cells our Su ( H ) mutants are competent to form an activator complex with NICD and MAM , but are defective for interacting with Hairless . To address the in vivo activity of our Su ( H ) mutants , we established transgenic fly lines that allow for tissue-specific overexpression [26] . Insertion at the identical site ( 96E ) avoided unwanted position effects [26 , 35] . In addition , combined overexpression of Hairless and the Su ( H ) constructs was made possible by recombination [26] . The capacity of our Su ( H ) mutants to activate transcription was analyzed with the vgBE-lacZ reporter gene , a readout for the Notch target gene vestigial [36] . Ectopic expression of the constructs ( wild type and mutants ) was induced in the central domain of the wing anlagen . In response to Notch activation , the vgBE-lacZ reporter is expressed along the presumptive wing margin [36] ( red stripe , Fig 6B panel 1 ) , which can be easily scored for differences in Notch signaling . Similar to ectopic expression of wild-type Su ( H ) , the three mutant Su ( H ) constructs ( Su ( H ) LL/AA , Su ( H ) LLF/AAA , Su ( H ) LLL/AAA ) result in a weak expansion of vgBE-lacZ expression ( Fig 6B panels 2–5 ) . In addition , activation of Notch , due to ectopic expression of Su ( H ) , causes an overgrowth of the wing disc [23 , 26 , 34] . Similar overgrowth is observed for the three Su ( H ) mutants when compared to wild-type Su ( H ) ( asterisks in Fig 6B panels 2–5 ) , suggesting that the mutations have little to no effect on assembly of the activator complex and activation of a Notch target gene in vivo . To address whether the Su ( H ) mutants affect repressor complex formation in vivo , they were coexpressed with Hairless and vgBE-lacZ reporter expression was monitored ( Fig 6B ) . As a control , we ectopically expressed Hairless alone and in combination with wild-type Su ( H ) , obtaining the expected results from a down-regulation of Notch signaling . Ectopic Hairless expression strongly repressed the vgBE-lacZ reporter , which was accompanied by a loss of tissue in the overexpression domain ( Fig 6B panel 6 ) . Combined overexpression of Su ( H ) and Hairless resulted in widespread silencing of the vgBE-lacZ reporter and a nearly complete loss of tissue within the overexpression domain ( Fig 6B panel 7 ) , consistent with a hyper-repression of Notch signaling in this tissue [26 , 34] . This phenotype has been interpreted as a result of ectopic repressor complex formation , reflecting Su ( H ) and Hairless protein binding in a highly sensitized manner [24 , 37] . In the case of a complete loss of binding between the Su ( H ) mutants and Hairless , i . e . , independent activity of the two components , the combined overexpression should result in an additive phenotype: ( 1 ) activation of Notch signaling by the Su ( H ) mutant defective in Hairless binding and ( 2 ) repression by Hairless . The two should level out each other , and hence , wing discs resembling wild type would be expected . Any residual binding between Su ( H ) mutants and Hairless , however , would be uncovered by the strong super-repression effect resulting from their combined overexpression . As shown in Fig 6B ( panel 10 ) , ectopic expression of the triple mutant Su ( H ) LLL/AAA with Hairless resulted in normal expression of the vgBE-lacZ reporter and the wing disc was similar to the control , suggesting an almost complete loss of Hairless binding by the mutant . Ectopic expression of Su ( H ) LLF/AAA with Hairless was less effective ( Fig 6B panel 9 ) , perhaps reflecting residual binding to Hairless and/or potentially subtle defects in activator complex formation , which our other assays were not sensitive enough to detect . The appreciable binding of the double mutant Su ( H ) LL/AA to Hairless observed in vitro was uncovered in our in vivo experiment with a repression of Notch activity similar to wild type Su ( H ) ( Fig 6B panel 8 ) . Finally , these results were confirmed by a phenotypic analysis of adult flies , in which the Su ( H ) mutant constructs and Hairless were coexpressed during eye development . As shown in S5 Fig , and consistent with our previous results , ectopic induction of all three Su ( H ) mutants in the eye imaginal discs effects eye morphology indistinguishable from wild-type Su ( H ) , but the triple mutants Su ( H ) LLL/AAA and Su ( H ) LLF/AAA retain little to no binding of Hairless in vivo . However , in this in vivo context Su ( H ) LL/AA retained considerably less Hairless-mediated repressor activity , reflected by a milder phenotype compared to the Su ( H ) /H co-overexpression .
In the Notch pathway , extracellular interactions are transduced into changes in gene expression via the transcription factor CSL [1] . To activate transcription , CSL forms a ternary complex with the intracellular domain of the Notch receptor ( NICD ) and the coactivator Mastermind ( MAM ) , which binds at the promoter and enhancer regions of Notch target genes . A number of crystal structures that correspond to the CSL-NICD-MAM complex have been determined , and accompanying functional studies have scrutinized its role as an activator [4] . To repress transcription , CSL binds corepressor proteins , such as Hairless from Drosophila , and SHARP or KyoT2 from mammals , which recruit other factors involved in transcriptional repression , such as Groucho , CtBP , NCoR , and Polycomb-group proteins , thus localizing the transcriptional repression machinery to Notch target genes [7] . While CSL is absolutely required to activate transcription at all target genes , its role as a repressor in different organisms is not as clear-cut . In mammals , RBP-J ( CSL ortholog in mammals ) has been shown to directly bind SHARP [16 , 17 , 20] and KyoT2 [18 , 19] , and cellular studies have shown that corepressors and their associated complexes are recruited to Notch target genes [21 , 38]; for certain cases , loss of RBP-J or its associated corepressor results in the de-repression of some , but not all target genes [21 , 38 , 39] . These in vitro data , however , have not yet been fully replicated in vivo , as there a only a few examples in mouse knockout studies that suggest RBP-J functions as a transcriptional repressor [40–42] . On the contrary , in Drosophila there is substantial evidence both in vitro and in vivo that Su ( H ) ( CSL ortholog in flies ) binds the antagonist Hairless and functions as a transcriptional repressor at Notch target genes [15 , 22] . To provide a detailed structural basis for this interaction and gain additional insights into its function , here we determined the high-resolution structure of the Su ( H ) -Hairless repressor complex bound to DNA ( Fig 2 ) , and presented convincing in vitro and in vivo mechanistic studies that complement and support our structure ( Figs 4–6 ) . The most striking feature of the Su ( H ) -Hairless complex is the substantial conformational change that occurs in the CTD of Su ( H ) upon Hairless binding ( Fig 3 ) . This results in the two β-sheets that compose the Ig-fold of the CTD to be splayed apart , exposing residues in the hydrophobic core of the CTD that form the basis for the Su ( H ) -Hairless interaction . To our knowledge , this conformational change and binding mode observed in our Su ( H ) -Hairless complex structure is without precedence in other Ig domain-containing structures . On the other hand , this illustrates the remarkable structural plasticity of this Ig domain in CSL . It will be interesting to see whether this occurs in other Ig-fold proteins , i . e . , whether this binding mode is a basic principle of Ig domains or is particular to CSL proteins . The structural change in Su ( H ) upon Hairless binding represents the largest and most striking conformational change observed in all CSL-mediated complex structures determined to date . This raises the question as to what role the structural change plays in Su ( H ) function . One possibility is that the conformation Su ( H ) assumes when bound to Hairless serves to recruit specific binding partners or target the Su ( H ) -Hairless complex to specific sites in the genome . While the identities of these potentially new binding partners are unknown , it would be interesting in future studies to use screening approaches in order to identify factors that only bind the Su ( H ) -Hairless complex . A similar approach was used previously to identify Mastermind in Caenorhabditis elegans [43] , a factor that only binds CSL when NICD is present . Moreover , our Su ( H ) mutants that are defective in Hairless binding may be useful in genome-wide studies to identify DNA sites bound by the Su ( H ) -Hairless complex . Our MD simulations of the Su ( H ) -Hairless complex structure demonstrate that the conformation Su ( H ) must assume to bind Hairless is energetically unfavorable , and therefore , likely to be a rare conformer in the ensemble of Su ( H ) molecules in solution . The fact that the region of Hairless that interacts with Su ( H ) is unstructured prior to complex formation [26] suggests that the kinetics of Su ( H ) -Hairless association ( kon ) is a relatively slow process . If this is indeed the case , then this would require the off rate ( koff ) to also be relatively slow in order to achieve the 2 nM affinity ( Kd = koff/kon ) determined for Su ( H ) -Hairless complexes , which is the strongest CSL binding partner measured to date . While the functional consequences of these slow on/off rates for Su ( H ) -Hairless complexes are not immediately evident , intriguingly these maybe important for its role as a transcriptional repressor , its subcellular localization , and/or replacement of activation complexes at Notch target genes . In the latter case , given that dimeric Su ( H ) -NICD-MAM complexes are stabilized at SPS sites by cooperative interactions between the ANK domain of NICD [13 , 14] , it is conceivable that the displacement dynamics between monomeric versus dimeric Notch activation complexes and Su ( H ) -Hairless repressor complexes at Notch target genes are different . However , to our knowledge , there are no data available to support this hypothesis , but it would be interesting to investigate this in the future . Regarding the subcellular localization of Su ( H ) , Hairless shuttles Su ( H ) into the nucleus [44 , 45] , similar to NICD [46 , 47] . Moreover , there are two examples that demonstrate the amount of nuclear Su ( H ) is dependent on Hairless: ( 1 ) when Hairless is overexpressed , then Su ( H ) accumulates in the nucleus [44 , 45]; and ( 2 ) when cell clones are lacking Hairless , then Su ( H ) protein displays a conspicuously lower abundance [45] . Interestingly , an inter-dependence of Notch and Su ( H ) protein levels has been reported in the Drosophila embryo [48] . Perhaps , Hairless and/or NICD binding to Su ( H ) , and the nuclear import of these complexes may protect Su ( H ) from degradation in the cytosol . In mammals , it has been shown that RBP-J requires interactions with either corepressors or NICD for nuclear localization [49] , and RBP-J is subjected to degradation in response to phosphorylation by p38 MAP kinase [50] . Interestingly , these data raise the possibility that a similar mechanism in flies and mammals exists to regulate CSL subcellular localization and turnover . While several corepressors that interact with RBP-J have been identified in mammals [4 , 7] , there are no Hairless orthologs found outside of insects [15] . Moreover , the mammalian corepressors all interact with the BTD of RBP-J similar to the RAM domain of NICD , which provides a potential mechanism whereby corepressors and NICD could compete for binding to RBP-J . For example , both the corepressor KyoT2 and RAM interact with the BTD of CSL in very similar conformations [19] , whereas Hairless exclusively binds the CTD to a newly identified binding cleft on Su ( H ) . Moreover , there are no known corepressors in Drosophila that interact with the BTD of Su ( H ) . Interestingly , the corepressor SHARP , which has been suggested to be a Hairless analogue in mammals , interacts with both the BTD and CTD of RBP-J [17 , 20] . Given the high degree of sequence similarity between fly and mammalian CSL proteins ( S2 Fig ) , perhaps SHARP binds the CTD of RBP-J in a manner similar to Hairless . Nonetheless , future studies that focus on the structure and function of the RBPJ-SHARP corepressor complex will be important for elucidating the enigmatic repressor role RBP-J plays in mammals . Finally , there have been extensive efforts in the pharmaceutical industry and academic labs to identify reagents that modulate Notch signaling for therapeutic endpoints [2] . For example , γ-secretase inhibitors and monoclonal antibodies that target the extracellular domains of Notch receptors and ligands have been developed to treat certain types of cancer . However , in comparison , there has been very little progress in identifying and developing small molecules that directly target RBP-J and the transcription complexes it forms with coregulators . Our Su ( H ) -Hairless structure provides a new binding cleft that could be targeted . Consistent with this notion , previous studies from our groups demonstrated that Hairless binds RBP-J and when only the CSL-ID of Hairless is expressed in mammalian cells , it acts as a potent antagonist of Notch signaling in transcriptional reporter assays [26] .
The codons that correspond to Su ( H ) residues 98–523 , which represents the structural core of CSL ( NTD , BTD , CTD ) , were cloned into pGEX-6P1 . Site directed mutagenesis was used to introduce surface entropy reduction ( SER ) mutations ( R155T and N281G ) into pGEX-6P-1-Su ( H ) for purification and crystallization purposes . Wild-type and R155T/N281G Su ( H ) proteins were demonstrated to have nearly identical binding to Hairless [26] . Similarly , site directed mutagenesis was used to make the Su ( H ) mutant constructs described herein . As described previously [26] , GST-Su ( H ) was overexpressed in BL21 Tuner cells ( Novagen ) and purified to homogeneity using a combination of affinity ( glutathione resin ) , ion exchange ( SP ) , and size exclusion chromatography . The purified protein was concentrated , flash frozen in liquid nitrogen , and stored at -80°C in a buffer containing 20 mM MES pH 6 . 0 , 0 . 5 M NaCl , 1% ethylene glycol , and 0 . 1 mM TCEP . The codons that correspond to Hairless ( H ) residues 232–269 , which comprise the conserved CSL-ID previously shown to be sufficient for Su ( H ) binding [26] , were cloned into pMAL-E , which produces an MBP-H fusion protein . The MBP moiety also contains SER mutations to facilitate crystallization [28] . MBP-H was overexpressed in BL21 Tuner cells and purified to homogeneity using affinity ( amylose resin ) and size exclusion chromatography . The purified protein was concentrated , flash frozen in liquid nitrogen , and stored at -80°C in a buffer containing 20 mM Tris pH 7 . 4 , 0 . 5 M NaCl , 1 mM EDTA , 1% ethylene glycol , and 5 mM maltose . The codons that correspond to Hairless residues 232–358 were cloned into pSMT3 , producing a SMT3-H fusion protein with an N-terminal His tag , which was used for ITC binding studies with Su ( H ) , as described previously [26] . Site-directed mutagenesis was used to introduce Hairless mutants into the pSMT3-H construct . The SMT3-H fusion protein was overexpressed in BL21 ( DE3 ) Tuner cells and purified using affinity ( Ni-NTA resin ) and ion exchange ( SP ) chromatography . Wild-type and mutant SMT3-H proteins were dialyzed into 50 mM sodium phosphate pH 6 . 5 and 150 mM NaCl for ITC binding studies . In order to crystallize and determine the X-ray structure of the Su ( H ) -Hairless-DNA complex , recombinant Su ( H ) ( 98–523 ) and MBP-Hairless ( 232–269 ) proteins were purified to homogeneity from bacteria and stoichiometrically bound to an oligomeric 15-mer duplex DNA , containing a single Su ( H ) binding site ( TTACTGTGGGAAAGA , AATCTTTCCCACAGT ) . Crystals were grown out of a mother liquor containing 0 . 1 M Tris pH 8 . 0 , 19% PEG3350 , 0 . 2M ( NH4 ) 2SO4 , were cryoprotected with 20% ethylene glycol , and were flash frozen in liquid nitrogen for data collection . X-ray diffraction data were collected on frozen crystals ( 200 K ) at the Advanced Photon Source on the LS-CAT beamline 21-ID-F ( λ = 0 . 97872 Å ) and processed with XDS [51] . Su ( H ) /MBP-H/DNA complex crystals belong to space group C2 with unit cell dimensions ( a = 177 . 7 , b = 93 . 9 , c = 154 . 4 , β = 109 . 8° ) ( Table 1 ) . The Su ( H ) /MBP-H/DNA structure was solved using molecular replacement ( Phaser ) [52] with the search models 3BRG [9] and 3H4Z [28] , which contain RBP-J bound to DNA and MBP , respectively . The asymmetric unit of the crystals contains two Su ( H ) , two MBP-H , and one DNA duplex ( S1 Fig ) . The Hairless structure was built manually with COOT [53] , and the structure was refined with Phenix [54] and Buster [55] to a final R factor and free R factor of 17 . 0% and 19 . 4% , respectively ( Table 1 ) . The model was evaluated with Molprobity [56] and the Ramachandran statistics for the final structure are 97 . 2% of the residues in the favored region with 0 . 24% as outliers . The structure indicates that Hairless residues G232-R263 form the core structural motif that interacts with Su ( H ) , whereas residues K264-P269 neither contact Su ( H ) nor the β-hairpin motif of Hairless , and their extended structure is stabilized by interactions with the crystal lattice . Representative electron density of the CTD-Hairless interaction is shown in S1 Fig . The asymmetric unit ( AU ) of the crystals contains two Su ( H ) /MBP-H complexes , but surprisingly , only one of the complexes is bound to DNA ( S1 Fig ) . While the explanation for the crystallization of these asymmetric complexes is unclear , the overall conformation of Su ( H ) -Hairless in the two complexes is similar ( RMSD 0 . 92 for 442 cαatoms ) . The primary difference lies in the temperature factors ( B factors ) between the two complexes in the AU , in which the regions in the NTD and BTD that bind DNA have much higher B factors for the complex that is missing DNA ( S1 Fig ) . All structure figures were created with PyMOL [57] . ITC experiments were carried out using a MicroCal VP-ITC microcalorimeter . All experiments were performed at 25°C in a buffer composed of 50 mM sodium phosphate pH 6 . 5 and 150 mM NaCl . Su ( H ) and SMT3-H ( Hairless ) proteins were degassed and buffer-matched using dialysis and size exclusion chromatography . A typical experiment contained 5 μM Su ( H ) in the cell and 50 μM SMT3-H in the syringe . The data were analyzed using ORIGIN software and fit to a one-site binding model . Molecular dynamics simulations of an apo CSL structure ( PDB ID: 3BRG ) and the Su ( H ) -Hairless complex structure were performed with the AMBER11 package , using the AMBER FF99SB force field [58] . A dodecahedral box of water molecules , treated as in the TIP3P model [59] , was built around the complex and a physiological concentration of 0 . 15 M NaCl was used . For each experiment , the following protocol was used: ( 1 ) in vacuo minimization ( 1 , 000 steps ) ; ( 2 ) minimization , keeping the complexes fixed , allowing water molecules and ions to equilibrate ( 1 , 000 steps of steepest descent plus 1 , 000 steps of conjugate gradient ) ; ( 3 ) minimization of the entire system , without restrictions ( 1 , 000 steps of steepest descent plus 1 , 000 steps of conjugate gradient ) ; ( 4 ) NVT equilibration , 1 ns; and ( 5 ) 2 x 100 ns production phase . All calculations were performed with the CUDA-enabled version of PMEMD [60] , using TESLA GPUs at the High Performance Computing ( HPC ) cluster of the University of Cambridge . Analysis of the trajectories was performed with the AMBERTOOLS 1 . 5 , GROMACS [61] and RStudio packages . Cavity analysis of the most representative structures of the principal clusters of the Su ( H ) trajectory was performed using trj_cavity [62] . The yeast two- and three-hybrid experiments were performed in triplicate as described previously [26 , 34] . pEG-MAM corresponds to D . melanogaster Mastermind , comprising codons 118 to 194 . pESC-NICD corresponds to the RAM and ANK domains of Drosophila Notch , comprising codons 1762 to 2176 . pEG-Hairless corresponds to Drosophila Hairless , comprising codons 171 to 357 . Amino acid substitutions in full-length Su ( H ) and RBP-J were introduced using QuickChange II XL Site directed Mutagenesis Kit ( Agilent ) and ultimately cloned into pJG . The three promising mutants ( L445A/L514A , L445A/L514A/F516A , L434A/L445A/L514A ) were shuttled into pUAST-attB and pMT vectors . All constructs were sequence verified . Reporter assays were performed in triplicate as described in Maier et al . [26] . Schneider S2 cells ( obtained from the DGRC ) were transiently transfected with 1 μg of the NRE-luciferase reporter [33] and 0 . 2 μg of control Renilla plasmid ( Promega ) to normalize transfection . pMT-NICD was cotransfected with 0 . 5 μg of the relevant pMT-Su ( H ) construct and/or 0 . 5 μg pMT-Hairless . The total amount of transfected DNA was kept constant at 3 μg by using the pMT-A vector ( Promega ) . Constructs were induced 6 h after transfection by adding 0 . 5 mM CuSO4; 18 h later , cells were harvested and luciferase activity was measured in duplicate with a luminometer ( Lumat LB9507 ) , using the dual-luciferase reporter assay system according to the manufacturer’s protocol ( Promega ) . The effects of the addition of respective Su ( H ) variants relative to the controls were assessed statistically using ANOVA and Dunnett’s test ( ***p ≤ 0 . 001 ) . Integration of the pUAST-attB Su ( H ) constructs at chromosomal position 96E was done with the help of the PhiC31 integration system , as described previously [26 , 35] . Several lines were obtained that behaved similarly . Tissue specific overexpression was induced with the driver lines omb-Gal4 and gmr-Gal4 ( FlyBase , http://flybase . org ) . Recombination of the UAS-Su ( H ) variants with UAS-Hairless HFL at 68E [26] was done with standard genetic protocols and verified by PCR . Crosses were set up multiple times and representative images of stage-matched larvae are shown . Expression of the vgBE-lacZ reporter [36] was monitored by antibody staining against β-galactosidase ( clone 40-1a; developed by J . R . Sanes; obtained from Development Studies Hybridoma Bank , University of Iowa , IA ) ; expression of the constructs was controlled by appropriate antibody staining using anti-H and anti-Su ( H ) [44] . Secondary antibodies coupled to FITC , Cy3 , and Cy5 were purchased from Jackson ImmunoResearch Laboratory ( West Grove , PA ) . Pictures were taken with a Zeiss Axiophot linked to a confocal microscope ( Bio-Rad MRC 1024 ) using LaserSharp Version 2 . 0 software . Fly heads were captured with an ES120 camera ( Optronics ) linked to a Wild stereo microscope and Pixera Viewfinder Version 2 . 0 software . Phenotypes were consistent over multiple trials; n = 20 heads of each genotype were evaluated . CD measurements were taken in triplicate using an Aviv Circular Dichroism Spectrometer model 215 at 25°C in a 0 . 02 cm cuvette . Wavelength scans were performed between 190 and 240 nm using 1 . 0 nm increments . Su ( H ) proteins were in a buffer containing 10 mM Tris-phosphoric acid pH 7 . 4 and 50 mM NaF . Protein concentrations were between 2–4 mg/ml . CD data were analyzed using Contin-LL ( Provencher and Glockner Method ) [31] with the SMP180 reference set . An Applied Biosystems StepOne Real Time PCR system was used to collect the thermal shift data and the data were processed with their proprietary Protein Thermal Shift Software v1 . 2 . Su ( H ) proteins were used at a concentration of 7 μM in a buffer containing 25 mM MES pH 6 . 0 , 0 . 5 M NaCl , 1 mM EDTA , and 1 mM TCEP . EMSAs were performed as described previously [9 , 26] . Wild-type or mutant Su ( H ) constructs were bound to an oligomeric 19-mer duplex that contains a single CSL-binding site and separated on a 7% polyacrylamide gel containing 0 . 5x Tris-borate buffer , pH 7 , for several hours at 4°C . Complexes were visualized on the gel using SYBR-GOLD stain ( Invitrogen ) . | Notch signaling is a form of cell-to-cell communication , in which extracellular receptor-ligand interactions ultimately result in changes in gene expression . The Notch pathway is highly conserved from the model organism Drosophila melanogaster to humans . When mutations occur within Notch pathway components , this often leads to human disease , such as certain types of cancers and birth defects . Transcription of Notch target genes is regulated by the transcription factor CSL ( for CBF1/RBP-J in mammals , Su ( H ) in Drosophila , and Lag-1 in Caenorhabditis elegans ) . CSL functions as both a transcriptional activator and repressor by forming complexes with coactivator and corepressor proteins , respectively . Here we determine the high-resolution X-ray structure of Su ( H ) ( the fly CSL ortholog ) in complex with the corepressor Hairless , which is the major antagonist of Notch signaling in Drosophila . The structure unexpectedly reveals that Hairless binding results in a dramatic conformational change in Su ( H ) . In parallel , we designed mutations in Su ( H ) and Hairless based on our structure and showed that these mutants are defective in complex formation in vitro and display functional deficiencies in in vivo assays . Taken together , our work provides significant molecular insights into how CSL functions as a transcriptional repressor in the Notch pathway . | [
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] | 2016 | Structure and Function of the Su(H)-Hairless Repressor Complex, the Major Antagonist of Notch Signaling in Drosophila melanogaster |
Schistosomiasis is a serious disease currently estimated to affect more that 207 million people worldwide . Due to the intensive use of praziquantel , there is increasing concern about the development of drug-resistant strains . Therefore , it is necessary to search for and investigate new potential schistosomicidal compounds . This work reports the in vivo effect of the alkaloid epiisopiloturine ( EPI ) against adults and juvenile worms of Schistosoma mansoni . EPI was first purified its thermal behavior and theoretical solubility parameters charaterised . In the experiment , mice were treated with EPI over the 21 days post-infection with the doses of 40 and 200 mg/kg , and 45 days post-infection with single doses of 40 , 100 and 300 mg/kg . The treatment with EPI at 40 mg/kg was more effective in adult worms when compared with doses of 100 and 300 mg/kg . The treatment with 40 mg/kg in adult worms reduced parasite burden significantly , lead to reduction in hepatosplenomegaly , reduced the egg burden in faeces , and decreased granuloma diameter . Scanning electron microscopy revealed morphological changes to the parasite tegument after treatment , including the loss of important features . Additionally , the in vivo treatment against juvenile with 40 mg/kg showed a reduction of the total worm burden of 50 . 2% . Histopathological studies were performed on liver , spleen , lung , kidney and brain and EPI was shown to have a DL50 of 8000 mg/kg . Therefore EPI shows potential to be used in schistosomiasis treatment . This is the first time that schistosomicidal in vivo activity of EPI has been reported .
Schistosomiasis is a disease mainly found in tropical regions , whose infectious agents are Schistosoma spp . , including Schistosoma mansoni [1] . This disease is currently one of the most widely occurring neglected tropical diseases with high levels of incidence in Asia , Africa and Latin America . Studies have shown that more than 207 million people have been infected worldwide and about 779 million people are liable to infection [2] . Currently praziquantel ( PZQ ) is effective against all specie of Schistosoma . Presenting good efficacy and low toxicity , it is currently the reference drug for schistosomiasis treatment [3–6] . However , as PZQ is the only drug used in therapy against schistosomiasis , concern about the development of drug resistance has been reported [7 , 8] . The search for new antischistosomal drugs has led to the study of natural substances such as artemisinin and its derivatives ( e . g . artemether , artenusate ) , curcumin , phytol and epiisopiloturine ( EPI ) has been intensified [9–14] . We have been specifically interested in EPI which is an imidazole alkaloid extracted from Pilocarpus microphyllus , whose in vitro activity against S . mansoni at the concentration of 150 μg/mL led to the observation of dead parasites , and at the sub-lethal dose , prevented egg laying [14] . Moreover , this alkaloid showed potent anti-inflammatory activity [15] , which might help combat the granuloma and inflammatory reaction caused by S . mansoni eggs . Despite this promising evidence of in vitro activity , nothing has been reported until now about in vivo antischistosomal activity of EPI . In this paper , we describe the in vivo schistosomicidal activity of EPI against adult and juvenile S . mansoni worms for the first time . We also show scanning electron microscopy data revealing action of the alkaloid on the parasite tegument . Furthermore , the effects of the alkaloid in egg laying , reduction of hepatosplenomegaly , histopathology analysis and analysis of granulomas , in treated mice , are also characterised .
This step was based on Véras et al . , 2013 [16] by preparative high performance liquid chromatography—HPLC ( SHIMADZU Prominence , SIL-10AF , CTO-20A , DGU-20A5 , LC-6AD , CBM-20A , SPD-20A , Tokyo , Japan ) , and the molecular mass confirmation was performed by spectrometry ( AmaZon SL , Bruker Daltonics , Bremen , Germany ) . The acidity constant of EPI was potentiometrically determined in MeOH:H2O ( 1:1 , v:v ) at t = 25±1°C and at constant ionic strength ( I = 0 . 1 M ( NaCl ) ) . Solutions of NaOH ( 0 . 1 M ) and HCl ( 0 . 1 M ) were prepared in MeOH:H2O ( 1:1 , v:v ) and potentiometrically standardized . EPI was dissolved in methanol and diluted with the equivalent volume of aqueous 0 . 2 M NaCl ( cEPI = 9 . 7789×10-4 M ) . Titrations were performed using CRISON pH-Burette 24 2S equipped with CRISON 50 29 micro-combined pH electrode ( CRISON INSTRUMENTS , S . A . Spain ) . The electrode was calibrated by means of a strong acid—strong base titration , using GLEE—glass electrode evaluation software [17]; standard potential E0 = 395 . 7 mV , slope factor of the electrode ( actual slope divided by ideal Nernstian slope ) sf = 1 . 010 , and pKW 13 . 84±0 . 01 obtained as mean values of four titrations . Prior to titration , 100 . 0 μL of the standard 0 . 1 M HCl solution was added to 4 . 00 mL of the EPI solution . All probes were titrated with 2 . 0 μL increments of the standard 0 . 1 M NaOH solution in 2 . 9–11 . 2 pH range . HyperQuad 2008 software [18] was used to calculate the value of acidity constant ( pKa ) from four repeated titrations . A theoretical pKa study was performed . The ChemAxon method [19 , 20] , implemented in the MarvinSketch package ( Marvin 5 . 4 . 1 . 1 , 2011 , ChemAxon ( http://www . chemaxon . com ) , was applied . This method is based on empirically calculated physico-chemical parameters ( mainly partial charges ) that are obtained from ionization site specific regression equations , it uses three types of calculated parameters ( intramolecular interactions , partial charges and polarizabilities ) to determine the micro ionization constants pKa of monoprotic molecules . In addition , we calculated the intrinsic solubility for the compound , which is a crucial solubility parameter . The prediction uses a fragment-based method that identifies different structural fragments in the molecule and calculates their solubility contribution . The implementation is based on the article of Hou et al . , 2004 and it can found in ChemAxon package [21] . In addition , the thermal properties of EPI were evaluated . Differential scanning calorimetry ( DSC ) and thermal gravimetric analysis ( TGA ) were used to determine the thermal mass loss , as well as to study the EPI thermal decomposition to determine if it’s possible that polymorphic forms were present . Simultaneous DSC/TGA and DSC analysis were carried out with an initial sample mass of 5 . 0 mg in alumina pans ( 90 lL ) . A SDT-Q600 calorimeter , allowing simultaneous measurement of weight change and differential heat flow ( TA Instruments ) was applied in these studies . Experimental parameters for TGA curve included the mass used of 5 mg , heating rate of 10°C min-1 under N2 flow ( 50 mL min-1 ) and final temperature of 1000°C . In this study the BH strain of Schistosoma mansoni , originated from Belo Horizonte , Minas Gerais , Brazil was used . The life cycle of S . mansoni was maintained in Biomphalaria glabrata snails at the Department of Animal Biology , IB , Unicamp . As the definitive host , Balb/C mice female , weighing ~ 20 g and 4 weeks of age , were previously infected exposure to a suspension containing approximately 70 cercaria using the tail immersion technique as described by Oliver and Stirewalt , 1952 [22] . The experiments were approved by the Ethics Commission for the Use of Animals ( CEUA/UNICAMP , protocol no 2170–1 ) , as they were in accordance with the ethical principles of animal experimentation adopted by CEUA . The animals of all groups were sacrificed 15 days post-treatment , the mice were euthanized using cervical dislocation and S . mansoni worms were retrieved by perfusion of the hepatic portal system and mesenteric veins according to Pellegrino and Siqueira , 1956 [24] . The percentage of worm reduction ( WR ) was calculated according to Delgado et al . , 1992 [25] . The counting of the eggs eliminated at the faeces was performed on the day of the analysis of the treatments . The faeces were collected before the euthanasia and were examined , utilizing the Kato-Katz quantitative method [26] . The slides were examined for S . mansoni eggs under a light microscope . After the perfusion portal system , a fragment of the intestine ( 2 cm ) was cut off and processed for oogram . Eggs were then counted and classified according to different stages of development [27] . Parasites were initially fixed in AFA solution , a 2:9:30:59 mixture of acetic acid , formaldehyde , ethanol ( 95% ) and distilled water [28] . AFA was removed and replaced with a secondary fixative , osmium tetroxide ( OsO4 ) in cacodylate buffer 100mM , pH 7 . 3 . Secondary fixing took place for 2 h with gentle shaking . OsO4 was removed and replaced with cacodylate as above—twice . The cacodylate buffer was then replaced with ultrapure water—twice . Samples were then air-dried from water onto adhesive carbon tape , coated with gold-palladium for conductivity , and imaged in the SEM . The SEM was a FEI quanta 400 SEM , operating in secondary electron mode . Samples were imaged with a working distance of 10–15 mm and an accelerating voltage of 15kV . The back-scattered electron detector was removed from the SEM to increase the field of view . Regarding the histological analysis , liver samples ( left lobe ) were taken from of three mice: treated and control group ( infection untreated ) . The samples were processed for histopathology following standard techniques [29] , stained with hematoxylin and eosin and analysed with the Leica® DM500-ICC50 HD photomicroscope . Measurements were made only for granulomas containing a single egg in their centers . The mean diameter ( μm ) of each granuloma was obtained by measuring two diameters of the lesions at right angles to each other with the help of an ocular micrometer [30] . The percentage of degenerated eggs was calculated from the number of degenerated miracidia ( acellular or partially or completely degenerated ) using the formulation: mean number of degenerated ova/mean number of granulomas X 100 [31] . For the determination of lethal dose capable of killing 50% of animals ( DL50 ) , Swiss female mice ( n = 4 ) , aged 3 . 5 ± 0 . 5 months and weighing 29 . 8 ± 1 . 3 g were used , based on Valadares ( 2006 ) [32] . Concentrations higher than the therapeutic dose of 40 mg/kg , which was previously identified in treating animals infected with S . mansoni were used in the assay . Initially , EPI was diluted in 10% DMSO and subsequently in 0 . 9% saline solution; the concentrations ( 0 , 70 , 130 , 270 , 530 , 1070 , 2130 , 4270 , 8000 mg/kg ) were calculated based on the average weight of the animals and administered to animals intraperitoneally . During the assay , the animals were monitored periodically to identify visible clinical signs in the heart ( abnormal heartbeat ) , lung ( abnormal respiratory rate ) and motor system ( ataxia and/or prostration ) . Histopathological studies were performed on liver , spleen , lung , kidney and brain . Some animals were killed due to the administration of higher concentration , while the rest of them were killed 7 days after the intraperitoneal injection of the drug . Hematoxilin & Eosin staining was used with 100X magnification . [33] . Statistical tests were performed with Graphpad Prism ( version 5 . 0 ) software . Dunnet’s test was used to analyze the statistical significance of differences between mean experimental and control values and by applying Tukey’s test for multiple comparisons with the level of significance set at P < 0 . 05 .
The HPLC retention time and characteristic fragments generated through mass spectrometry is in accordance with previously described data [16 , 34] . In the physiological pH range , EPI acts as a base with an sp2 nitrogen in the imidazole ring that can be protonated ( Fig . 1A ) . According to potentiometrically determined pKa value ( pKa 6 . 25 ± 0 . 05 ) and the distribution diagram shown on Fig . 1B , at pH 7 . 4 , 93 . 5% of EPI is present in its molecular form and 6 . 5% in the protonated form . The pKa calculation for EPI presents 3 different ionic forms shown in Fig . 1B . By plotting the theoretical titration curves , i . e . the ratio of the ionized and neutral forms versus the pH ( Fig . 1C ) , one can identify the ionic species which are present in the mixture at each pH value . From this plot we identify the predicted pKa values as pKa1 = 6 . 4 and pKa2 = 13 . 9 . The microspecies distribution at pH = 7 . 40 was 1 = 90 . 55% , 2 = 9 . 45%and 3 = 0 . 0% . The solubility was also calculated and the results show that EPI has a high apparent solubility in water at pH 7 . 4 ( higher than 0 . 06 mg/mL ) , with intrinsic solubility equal to -1 . 76 logS units . An analysis of the thermal data indicates that EPI degradation occurs in one step at a high temperature ( Fig . 1D ) . The single stage involves a mass decay of 91 . 22% ( calculated: 4 . 051 mg; onset: 222 . 81°C ) associated with an endothermic DTA signal ( Fig . 1E ) . The analysis of this thermal data indicates that EPI can be used safely , with no risk of thermally-activated degradation or alteration in the organism . The mice treated with the dose of 40 and 300 mg/kg of EPI , after 21 days of infection showed significant differences ( P<0 . 01 and P< 0 . 01 ) regarding the reduction of total worm burden . The treatments performed against juvenile worms showed a moderate reduction of the total worm burden of 50 . 2% ( SD: 14 . 9 ) and 46 . 3% ( SD: 16 . 2 ) when the mice received EPI at 40 and 300 mg/kg , respectively , compared to the untreated infected control group . In Table 1 , all results are summarized . The efficacy of EPI was tested against the adult parasite life stage in an experimental mammalian host . After 45 days of infection , male and female worms had matured and paired , and eggs were found in the liver , intestine , and faeces . In mice infected by S . mansoni , there was a reduction in worm burden upon oral treatment by EPI , as shown in Table 2 . After 45 days of infection , the treatments performed with EPI in doses of 40 , 100 and 300 mg/kg reduced the worm burden of the mice to 70 . 0% ( P<0 . 001 ) ( SD: 15 . 7 ) , 39 . 0% ( P<0 . 01 ) ( SD: 14 . 1 ) and 46 . 7% ( P<0 . 001 ) ( SD: 9 . 2 ) respectively . The effect of EPI on egg development stages ( oogram pattern ) and fecal egg burden at 45 days post infection is shown in Table 3 . In relation to the number of eggs excreted in the faeces , the results showed reductions of 80% ( SD: 9 . 1 ) , 45 . 7% ( SD: 22 ) and 59 . 6% ( SD: 35 . 2 ) in response to treatments with 40 , 100 and 300 mg/kg of EPI , respectively . It can be seen that in the groups treated by EPI at 40 , 100 and 300 mg/kg , the percentage of immature eggs was significantly reduced to 24% ( SD: 13 . 6 ) , 16 . 6% ( SD: 11 . 1 ) and 56 . 8% ( SD: 13 . 7 ) of the total number of eggs , compared with infected untreated controls , in which 79% ( SD: 6 . 1 ) of the eggs were immature . The oogram also showed significant increase in percentage of dead eggs in the mice treated with EPI ( 36% ( SD: 9 . 9 ) , 24% ( SD: 5 . 6 ) and 12% ( SD: 6 . 1 ) respectively ) compared with the control group ( 2% ) ( SD: 1 . 2 ) . In addition , treatment with EPI 40 mg/kg decreases liver and spleen pathology . A significant reduction in the weight of liver ( P<0 . 01 ) and spleen ( P<0 . 01 ) of the group treated with EPI compared to the infected untreated control was demonstrated ( Table 3 ) . Treatment with EPI 100 mg/kg was significantly reduced only in the liver ( P<0 . 05 ) . On the other hand , mice treated with EPI 300 mg/kg did not see any reduction in the weight of these organs ( Table 4 ) . In short , EPI significantly decreases S . mansoni egg and worm burdens , as well as hepatosplenomegaly . The alterations of the tegument on the S . mansoni surface treated with EPI were analyzed by scanning electron microscopy ( SEM ) . In control ( untreated ) parasites the tubercles and spines appeared intact , and at high magnification , the surface showed spiny tubercles and ridges between the tubercles which have a pitted appearance and bear sensory papillae ( Fig . 2A and B ) . The tegument was dramatically damaged following the treatment with EPI , spines completely absent and tubercules badly damaged ( Fig . 2C and D ) . The changes included the swelling of tubercles , and the loss of spines on tubercles , and sloughing and erosion . All worms examined showed shrinking with furrowing , extensive sloughing around the tubercles on the tegument exposing subtegumental tissues ( Fig . 2C ) . In comparison with untreated worms , the overall structure of the worms treated by EPI showed far greater changes , with tegumental ridges no longer visible at low resolution ( Fig . 2D ) , and far fewer intact tubercles visible , and no spines seen at all . It is also worth noting that control worms and EPI—treated worms were obtained whole . Whereas the dose of 40 mg / kg led to a greater reduction in parasite burden , we evaluated the presence of granulomas in the liver of mice treated with this dose . A detailed study of histological sections of liver from the same area from BALB/c mice infected with S . mansoni and treated with dose of 40 mg/kg of EPI ( Fig . 3A and B ) and the untreated control ( Fig . 3C and D ) allowed the identification of granulomas at various stages of evolution containing infiltrate inflammatory compound of eosinophils , macrophages , neutrophils around the egg and more externally lymphocytes and fibroblasts . Based on the classification of Lenzi et al . , 1998 [35] , we identified in the liver histological sections granulomas containing some disorganized inflammatory infiltrate around the egg ( pre-granulomatous phase initial reaction ) , excess diffuse inflammatory infiltrate ( pre-granulomatous phase exudate ) , inflammatory infiltrating loose and necrotic cells around the egg ( necrotic-exudative phase ) , initial deposition of collagen fibers by fibroblasts lining the inflammatory cells ( productive phase ) and thick bands of collagen fibers surrounding the eggs and their remains ( phase healing by fibrosis ) . Decreases in the granuloma sizes were observed in the livers of the group treated with EPI . Upon analysis of the images , it could be seen that the average diameter of the hepatic granulomas was significantly smaller ( P<0 . 01 ) in the group treated with EPI , in comparison to the untreated infected control group ( Table 5 ) . Furthermore , most of the S . mansoni eggs from the animals treated with EPI showed of severe degeneration ( 71% versus 50% for the controls ) . Toxicity and histopathology studies were carried out to assess the toxicological profile of EPI in vivo . The concentrations tested were considerably higher than the therapeutic doses used to measure effects on S . mansoni , since the toxicity of PZQ is low , with a DL50 around 2 , 400 mg/kg [36] . Intraperitoneal injection of EPI at a concentration equal to or higher than 530 mg/kg caused visible clinical changes such as tachycardia , tachypnea , ataxia and prostration . After a period ranging from 15 minutes to 6 hours , these changes were spontaneously reversed , eventually becoming imperceptible to observation , in a dose dependent way . The maximum concentration of 8 , 000 mg/kg killed 50% of animals with 18 hours of intraperitoneal injection of the drug . Fig . 4 shows images illustrating the histology of the liver ( Fig . 4A , B and C ) , spleen ( Fig . 4D , E , F ) , lung ( Fig . 4G , H , I ) , kidney ( Fig . 4J , K , L ) , and brain ( Fig . 4M , N , O ) treated with 0 . 0 ( first column ) , 530 ( second column ) or 8000 ( third column ) mg/kg of EPI . The histopathological analysis showed that the animals treated with the highest concentration ( 8 , 000 mg/kg ) showed generalized congestion and edema in the histological sections of lung , spleen , kidney , and brain; depression in the red pulp and uncountable cells undergoing apoptosis in the spleen , as well damage in the endothelium of the vessels in the liver were also observed ( Fig . 4F ) . Other effects observed following 8 , 000 mg/kg injection included intra-alveolar hemorrhage and fibrin deposition in the lung ( Fig . 4I ) and focal hydropic degeneration of the kidney ( Fig . 4L ) ; no morphological changes were observed in the parenchyma of the liver ( Fig . 4C ) . Animals treated with 530 mg/kg EPI showed focal depression of the red pulp ( Fig . 4E ) after seven days of intraperitoneal injection of the drug , while the other organs presented no morphological changes ( Fig . 4B , H , K , N ) . The control group showed normal morphology in all organs analyzed ( Fig . 4A , D , G , J , M ) .
In conclusion EPI significantly reduced parasite worm burden and the alkaloid showed no measurable toxicity in the model tested , at concentrations superior to effective treatment concentrations . At this time , EPI is a promising candidate drug . More studies are needed to evaluate the therapeutical effectiveness of EPI , and to evaluate the efficacy of this drug against different stages of the life cycle as well as other species of Schistosoma . In the future , pharmacokinetic studies to explore the fate of the drug in the body are required . Additionally , the detailed mechanism of action of EPI in schistosomes remains to be investigated with other preclinical tests . | Schistosomiasis , a disease caused by worms from the genus Schistosoma , is one of the most widespread neglected tropical diseases in the world . Due to the intensive use of praziquantel , some reports of drug resistance have emerged . Thus it is important to search for new chemotherapeutic agents for the treatment disease . This research showed that epiisopiloturine , an imidazole alkaloid extracted from the leaves of Pilocarpus microphylus , has activity against S . mansoni in vivo . This material is produced as a by-product of an existing commercial process , and thus can be produced in large quantities at relatively low cost . The low toxicity and high drug tolerance in mammals shows the potential of epiisopiloturine as a new candidate schistosomicidal compound . | [
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"Discussion"
] | [] | 2015 | Anthelmintic Activity In Vivo of Epiisopiloturine against Juvenile and Adult Worms of Schistosoma mansoni |
The Flavivirus genus comprises several human pathogens such as dengue virus ( DENV ) , Japanese encephalitis virus ( JEV ) , and Zika virus ( ZIKV ) . Although ZIKV usually causes mild symptoms , growing evidence is linking it to congenital birth defects and to increased risk of Guillain-Barré syndrome . ZIKV encodes a polyprotein that is processed to produce three structural and seven nonstructural ( NS ) proteins . We investigated the evolution of the viral polyprotein in ZIKV and in related flaviviruses ( DENV , Spondweni virus , and Kedougou virus ) . After accounting for saturation issues , alignment uncertainties , and recombination , we found evidence of episodic positive selection on the branch that separates DENV from the other flaviviruses . NS1 emerged as the major selection target , and selected sites were located in immune epitopes or in functionally important protein regions . Three of these sites are located in an NS1 region that interacts with structural proteins and is essential for virion biogenesis . Analysis of the more recent evolutionary history of ZIKV lineages indicated that positive selection acted on NS5 and NS4B , this latter representing the preferential target . All selected sites were located in the N-terminal portion of NS4B , which inhibits interferon response . One of the positively selected sites ( 26M/I/T/V ) in ZIKV also represents a selection target in sylvatic DENV2 isolates , and a nearby residue evolves adaptively in JEV . Two additional positively selected sites are within a protein region that interacts with host ( e . g . STING ) and viral ( i . e . NS1 , NS4A ) proteins . Notably , mutations in the NS4B region of other flaviviruses modulate neurovirulence and/or neuroinvasiveness . These results suggest that the positively selected sites we identified modulate viral replication and contribute to immune evasion . These sites should be prioritized in future experimental studies . However , analyses herein detected no selective events associated to the spread of the Asian/American ZIKV lineage .
The Flavivirus genus ( family Flaviviridae ) comprises a large number of viral species , many of which are important human pathogens; these include dengue virus ( DENV ) , yellow fever virus ( YFV ) , Japanese encephalitis virus ( JEV ) , West Nile virus ( WNV ) , and the latest emerged pathogen , Zika virus ( ZIKV ) . ZIKV was first discovered in 1947 in Uganda , in a sentinel rhesus monkey , and subsequently in mosquitoes of the Aedes genus . Between 1947 and 2006 , only sporadic human cases were reported in Africa and in Southeast Asia , until multiple outbreaks in the Pacific islands occurred . The first sizable outbreak was reported in the Federated States of Micronesia ( Yap Island ) in 2007 , followed by an outbreak in French Polynesia in 2013 . In 2014 , the epidemic spread to Cook Islands , New Caledonia and Easter Island , and reached South America in late 2014 –early 2015 [1–3] . As of May 18 , 2016 , sixty countries/territories have reported ZIKV cases ( http://www . who . int/emergencies/zika-virus/situation-report/en/ ) . Although ZIKV infection is often asymptomatic or causes only mild symptoms , the WHO declared that the spread of ZIKV should be regarded as a public health emergency of international concern . In fact , growing evidence suggests that ZIKV infection during pregnancy increases the risk of microcephaly , brain damage , and congenital abnormalities [4–6] . Also , retrospective studies indicated that ZIKV can trigger Guillain-Barré syndrome ( GBS ) , a severe neurological disorder characterized by progressive muscle weakness [7] . Moreover , even if Aedes mosquitoes species such as Aedes aegypti and Aedes albopticus represent the primary vectors for natural transmission , perinatal and congenital infections , as well as sexual transmission and infection through blood transfusion have been recently documented [1] . ZIKV is a member of the Spondweni ( SPOV ) serocomplex and , like other members of the Flavivirus genus , it is a single-stranded positive-sense RNA virus . Its genome consists of about 11 , 000 nucleotides with two flanking non-coding regions and a single long open reading frame . The encoded polyprotein is co- and post-translationally processed by viral and host proteases to produce three structural ( capsid , C; pre-membrane , prM; envelope , E ) and seven nonstructural ( NS ) proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , NS5 ) [8] . Genetic and phylogenetic studies indicated that ZIKV has evolved into 2 major lineages: African and Asian/American , this latter responsible of the recent outbreaks and associated with reports of GBS and fetal malformations [9 , 10] . Analysis of ZIKV genomes from microcephaly cases revealed no shared amino acid changes , suggesting that viral genetic features alone are not responsible for fetal abnormalities [2] . Likewise , inspection of amino acid differences between the Asian and African lineages provided no clear indication of viral genetic features that may result in altered virulence or increased pathogenicity , although no functional study of these variants has been performed to date [2] . It was thus proposed that , if the link with GBS and fetal abnormalities is confirmed , factors other than viral genetics , including infection with other viruses or the host genetic background [2] , are responsible for these adverse effects . An alternative possibility is that all ZIKV lineages increase the risk of microcephaly and/or GBS , but the association has been previously missed due to the limited size of African outbreaks and to the lack of surveillance programs . Whereas addressing these questions will require extensive epidemiological and clinical surveys , analysis of all ZIKV strains and of their evolution within the wider perspective of closely related flaviviruses can identify positively selected amino acid changes . These latter are expected to entail a functional effect and can therefore be prioritized in further studies of viral pathogenesis . Indeed , evolutionary analyses in WNV have detected positively selected changes that modulate viral phenotypes such as virulence [11] and superinfection exclusion [12] . Herein we investigated the evolution of the viral polyprotein in ZIKV and in related flaviviruses . Results indicate that NS1 was a major selection target during flavivirus speciation and revealed ongoing selection in ZIKV strains in NS4B and NS5 .
Coding sequences were retrieved from the NCBI database ( http://www . ncbi . nlm . nih . gov/ ) , all flaviviruses analyzed were selected to have full coding sequence information . A list of accession number is reported in S1 Table . Alignment errors are common when divergent sequences are analyzed and can affect evolutionary inference . Thus , we used PRANK [13] to generate multiple sequence alignments and GUIDANCE2 [14] for filtering unreliably aligned codons ( we masked codons with a score <0 . 90 ) , as suggested [15] . Substitution saturation was checked using the Xia's index implemented in DAMBE [16 , 17]; this test compares an entropy-based index of saturation ( Iss ) with a critical value ( Iss . c ) . If Iss is significantly lower than Iss . c , sequences have not experienced substitution saturation . The presence of recombination was assessed using two methods , GARD [18] and Recco [19] . Whereas GARD uses phylogenetic incongruence among segments in the alignment to detect the best-fit number and location of recombination breakpoints , Recco is based on cost minimization and dynamic programming . In GARD , the statistical significance of putative breakpoints is evaluated through Kishino-Hasegawa ( HK ) tests; breakpoints were considered significant if their p value was < 0 . 05 . The Recco's output includes a p value for the whole dataset that , controlling for false positives , provides an indication as to whether a significant amount of recombination is detectable in the whole alignment . We concluded that no substantial recombination was present when the dataset p value was >0 . 05 . For alignments showing evidence of recombination in Recco ( dataset p value <0 . 05 ) , we considered sequences as recombinants if the number of savings was >20 , and the sequence p value was <0 . 001 , as suggested [20] . Recombination breakpoints were defined accordingly . Phylogenetic trees were reconstructed using the program phyML with a maximum-likelihood approach , a General Time Reversible ( GTR ) model plus gamma-distributed rates and 4 substitution rate categories . Branch support was evaluated using a non parametric bootstrap analysis ( 100 replicates ) [21] . The nonsynonymous/synonymous rate ratio ( dN/dS or ω ) is a widely used method to detect positive selection . Positive selection is inferred when the rate of nonsynonymous ( dN ) substitutions is higher than that of synonymous ( dS ) substitutions ( dN/dS >1 ) . To test for the action of episodic positive selection in flaviviruses , we applied the branch-site test [22] from the codeml software [23] . The test estimates selective pressure changes among branches and sites in the phylogenetic tree . Two nested models ( MA and MA1 ) are compared: MA allows positive selection on one or more lineages ( called foreground lineages ) , and the MA1 does not allow such positive selection . Twice the difference of likelihood for the two models ( ΔlnL ) is compared to a χ2 distribution with one degree of freedom [22] . A false discovery rate correction was applied to take into account a multiple hypothesis issue generated by analyzing different branches on the same phylogeny [24] . A Bayes Empirical Bayes ( BEB ) analysis was used to evaluate the posterior probability that each codon belongs to the site class of positive selection on the foreground branch , only when 2ΔlnL was statistically significant . BUSTED ( branch-site unrestricted statistical test for episodic diversification ) [25] is an alternative approach implemented in the HyPhy package [26] designed to describe episodic positive selection acting on specific branches in the phylogenetic tree at a proportion of sites . A model that allows the action on positive selection on foreground branches is compared with a null model that doesn't allow ω >1 . Twice the ΔlnL of the two models is then compared to a χ2 distribution ( with two degrees of freedom ) ; if the null model is rejected , at least one site is under positive selection on the foreground branches . To detect selection at individual sites , twice the difference of the likelihood for the alternative and the null model at each site is compared to a χ2 distribution ( one degree of freedom ) . To be conservative , we considered a site under episodic positive selection if it showed both a p value ≤ 0 . 05 in BUSTED and a BEB posterior probability ≥ 0 . 90 . To better understand the evolution of ZIKV genomes , we also applied two random site ( NSsite ) models implemented in codeml: a null model ( M7 ) that assumes that 0<ω<1 and is beta distributed among sites in all branches of the phylogeny , and a positive selection model ( M8 ) ; this model is the same as M7 but also includes an extra category of sites in the alignment with ω>1 . A χ2 distribution is used to assess statistical significance of 2ΔlnL of the two models . Positively selected sites were identified using the posterior probability ( ≥ 0 . 90 ) from M8 BEB . Individual sites under diversifying positive selection were also identified using Random effects likelihood ( REL ) [27] and Fast Unconstrained Bayesian AppRoximation ( FUBAR ) [28] methods from the HyPhy package . REL estimates ω at each site by inferring a gene distribution for both synonymous and non-synonymous rate variations and assuming independent draw at each site from this distribution . We considered a site under positive selection if it showed a Bayes Factor > 50 . FUBAR is an approximate hierarchical Bayesian method that uses an unconstrained distribution of selection parameters by averaging over a large number of predefined site classes . Given this distribution , FUBAR estimates the posterior probability of positive diversifying selection at each site in the alignment ( with a cutoff ≥ 0 . 90 ) . In order to be conservative , we finally considered a site as under diversifying positive selection if it was detected by at least two different methods . Data on DENV experimentally verified immune epitopes were obtained from the NIAID Virus Pathogen Database and Analysis Resource ( ViPR ) online ( http://www . viprbrc . org ) [29] . Human epitopes were searched for by using the gene product name as a query . Linear epitopes with positive results in any assay type category ( B cell , T cell and MHC binding ) were included . We used ClustalOmega [30] to align epitopes onto the DENV protein sequence and from this onto the ZIKV sequence . The structure of NS1 of ZIKV was obtained by homology modeling using the West Nile virus NS1 ( PDB ID: 4O6C ) structure as a template; analysis was performed through the SWISS-MODEL server [31] . The accuracy of the model was assessed with VADAR ( Volume , Area , Dihedral Angle Reporter ) , which uses several algorithms to calculate different parameters for individual residues and for the entire protein [32] . Images were created using PyMOL ( The PyMOL Molecular Graphics System , Version 1 . 5 . 0 . 2 Schrödinger , LLC ) . The membrane protein topology for the ZIKV NS4B protein was predicted by using TMHMM ( http://www . cbs . dtu . dk/services/TMHMM/ ) [33] .
As mentioned above , ZIKV belongs to the Spondweni group of mosquito-borne flaviviruses . In addition to Spondweni virus ( SPOV ) , the viral species more closely related to ZIKV include the Kedougou ( KEDV ) and dengue ( DENV ) viruses [10] . To investigate selective events that took place during the speciation of ZIKV and closely related flaviviruses , we obtained complete coding sequence information for 21 ZIKV , 1 SPOV and 1 KEDV , as well as 11 DENV . ZIKV sequences were selected to represent viruses sampled in both African and in non-African countries , in distinct years , and from different hosts ( see S1 Table ) . In the case of KEDV and SPOV , only one complete genome is available for each virus . For DENV , sequences were selected to belong to the four major serotypes ( DENV1 to DENV4 ) ; for each serotype , sequences representative of the most common genotypes ( based on complete E nucleotide sequences in [34] ) were included . DENV sequences were also selected to cover different geographic locations and isolation dates . The structural and nonstructural coding regions were analyzed separately , and the alignments were pruned of unreliably aligned codons using the GUIDANCE utility ( see Methods ) . This procedure resulted in the masking of 8 . 6% and 9 . 5% of codons in the structural and nonstructural regions , respectively . A test for substitution saturation was performed using Xia's method [16] and indicated no substantial saturation in either alignment ( S2 Table ) . We next analyzed the alignments for the presence of recombination using two different methods , GARD ( Genetic Algorithm Recombination Detection ) [18] and Recco [19] . No evidence of recombination in the nonstructural region was detected using either program , whereas Recco ( but not GARD ) suggested the presence of a recombination breakpoint around position 1350–1360 ( relative to AY632535 coding sequence ) in the E region ( Fig 1A ) . The structural region was thus split into two sub-regions for the following analyses , so as to avoid false positive inferences of positive selection as a result of unaccounted recombination [35] . Phylogenetic trees of the three regions were obtained with phyML . Trees were very similar and fully consistent with previously reported phylogenies for flaviviruses [36] , with African and non-African ZIKV isolates forming distinct branches [3] ( Fig 1B ) . We next searched for evidence of episodic positive selection along the internal branches of flavivirus phylogenies using branch-site tests ( Fig 1B ) . Two different methods were applied to ensure consistency: the branch-site unrestricted statistical test for episodic diversification ( BUSTED ) [25] and the maximum-likelihood models ( MA/MA1 ) implemented in the PAML suite [23] . These two approaches rely on different assumptions of ω ( nonsynonymous/synonymous rate ratio ) variation among branches . Episodic positive selection at each tested branch was declared when statistically significant support was obtained with both methods . Using this criterion , we found no evidence of positive selection in the structural region . Conversely , both tests detected evidence of positive selection on one branch in the phylogeny of the nonstructural region ( Fig 1 and Table 1 ) . Selected sites along this branch were identified using the Bayes empirical Bayes ( BEB ) procedure from model MA and with BUSTED; again , only sites detected by both methods were considered . A total of 16 positively selected sites were detected ( Fig 1A ) ; notably , seven of such sites are located in the NS1 protein . To test whether this number is higher than expected , we performed random sampling across codons in the nonstructural region ( i . e . we assumed that all codons that were not masked by GUIDANCE in any sequence have the same probability of being called as positively selected ) . Results indicated that the likelihood of having 7 selected sites in NS1 amounts to 0 . 0039; thus , this protein represented a preferential selection target during flavivirus speciation . We note that the percentage of codons masked by GUIDANCE ranged widely among protein regions , from 3 . 3% in NS3 to more than 30% in the 2k and NS2A region ( S3 Table ) . Whereas this does not affect the significant enrichment we obtained for NS1 ( as we accounted for masked codons ) , the power to detect selection in extensively masked regions is clearly reduced; in fact , the ultimate result of masking is fewer codons available for analysis or a shallow phylogeny at available codons . In NS1 , the 7 positively selected sites are distributed along the entire protein region ( Fig 1C ) . To gain insight into the role and the spatial localization of these sites , we performed homology modeling of ZIKV NS1 using the West Nile virus protein structure ( PDB ID: 4O6C ) as a template ( Fig 2 ) . We also retrieved the location of experimentally validated immune epitopes in NS1 . Selected sites 112 and 114 map on a disordered loop of the wing domain; this loop is exposed , and several DENV immune epitopes were described in this region , most of them covering both positions ( Fig 1C ) . In DENV , an alanine mutation at the 114 site affects virus particle production [37] . This residue was also suggested to have a role in the interaction between NS1 and the envelope glycoprotein [37] . Interestingly , three sites ( residues 77 , 112 , 114 ) in the “wing” domain localize in close proximity on the protein structure ( Fig 2 ) , suggesting that they are involved in formation/stabilization of the same interactions . NS1 position 164 is located in a hydrophobic protruding loop , flanking a smaller loop that is essential for DENV viral replication [38] . Mutations in flanking positions ( residues 160 and 162 ) affect both RNA synthesis and virus viability [38] . Indeed , residues 159–162 of the connector domain together with the β-roll ( where the Y22 selected site maps ) form a hydrophobic protrusion that faces the membrane ( Fig 2 ) . This hydrophobic structure is thought to be involved in the interaction between the NS1 homodimer and the replication complex through the NS4A and NS4B proteins [38] . The β-roll domain is also involved in NS1 dimerization [38] . Site 22 localizes in spatial proximity to the first β-strand of the β-ladder domain , where the G185 positively selected site also maps . Both sites are located at the dimerization interface [38 , 39] . As for selected sites in proteins other than NS1 , site V15 in NS2A maps to a hydrophobic protein region within the lumen of the endoplasmic reticulum ( ER ) ; mutations at nearby residues impair DENV virion assembly [40] . Positively selected sites were also detected in NS3 ( M41 , P82 , T570 , P577 ) . Interestingly , site 570 flanks a conserved asparagine that is essential for NS3-NS5 binding in DENV [41] . Finally , the positively selected site in NS4B ( I162 ) is located in a cytoplasmic loop involved in the interaction with NS3 and with host proteins [42] . We next investigated whether positive selection also occurred during the recent evolution of ZIKV . To this purpose , we retrieved coding sequence information for all complete ZIKV genomes ( n = 39 , as of March 26th , 2016 ) ( S1 Table ) . Again , the structural and nonstructural regions were analyzed separately . In the structural region , GARD detected no recombination , whereas Recco inferred possible breakpoints at nucleotides 802–838 ( relative to AY632535 coding sequence ) in the M region . Both Recco and GARD detected a recombination breakpoint in the nonstructural region ( position 8994 , GARD; position 9040–9054 , Recco ) within the NS5 region . The portions encompassing the breakpoint positions were thus removed and the alignments were split into two sub-regions . Inspection of the Recco output indicated that in all cases recombination involved sequences from the African isolates . In fact , the phylogenetic trees for all sub-regions showed a clear separation of the African and non-African sequences ( S1 Fig ) . To obtain an estimate of the degree of constraint along ZIKV genomes , we used FUBAR to identify sites showing significant evidence of negative selection . This analysis indicated an uneven distribution of negatively selected sites , with the weakest selective pressure acting on the structural portion; conversely , more than 80% of sites are negatively selected in the NS1-NS4B region ( Fig 1A ) . We next tested for positive selection using both the codeml site models ( M7 vs M8 ) and the branch-site models ( MA1 vs MA ) . These latter models were used to test for selection on the branch of the phylogeny that separates the African and non-African sequences . No evidence of positive selection was obtained for the two sub-regions from the structural portion . Conversely , for the nonstructural region covering nucleotides 2371–8994 , a model of evolution that allows a class of codons to evolve with ω >1 ( NSsite model M8 ) better fitted the data than the neutral model ( NSsite models M7 ) , supporting the action of positive selection ( -2ΔlnL = 18 . 89 , degrees of freedom = 2 , Bonferroni- corrected p value for two tests = 1 . 58 X 10−4 ) . Positively selected sites were identified using the BEB procedure from M8 and with two additional methods from the HyPhy suite , REL and FUBAR . Sites were defined as being positively selected if they were detected by at least two different methods . Using this conservative criterion , 5 positively selected sites were detected ( Table 2 and Figs 1A and 3 ) . Three of them are located in the relatively short NS4B region ( M26 , M87 , and H88 ) ; using the same approach as above , we determined that this number is unlikely to occur by chance ( random sampling , p value = 0 . 007 ) , indicating that NS4B is the preferential positive selection target in ZIKV . To gain insight into the location of positively selected sites in NS4B , we performed an in silico prediction of transmembrane helices . The resulting topology model was very similar to those previously proposed or determined for other flaviviruses [42] ( Fig 3B ) . Residue M26 maps to the N-terminal region located in the ER lumen . Interestingly , the corresponding position was previously found to be positively selected in sylvatic DENV2 isolates; in JEV , a nearby residue is positively selected , as well [43 , 44] ( Fig 3B ) . Residues 87 and 88 are also located in the ER lumen and reside in the second loop ( Fig 3B ) , a region involved in NS4B-NS1 interaction in WNV [45] . Two other positively selected sites are located in NS5 ( N287 and V374 ) . Position 374 is part of the nuclear localization signal ( NLS ) region of NS5 . Dengue virus serotypes have different nuclear localization , and these differences are due to changes in their NLS [46] . Analysis of DENV immune epitopes indicate that some of them comprise positions 374 and 287 ( Fig 3C ) .
Herein we provide an analysis of the selective forces acting on ZIKV and related flaviviruses . We show that positive selection contributed to the genetic diversity of these human pathogens and we report ongoing adaptive evolution in ZIKV strains . The evolutionary analysis of viral genomes poses challenges related to the possible presence of recombination , as well as to the high sequence divergence , with consequent saturation issues and alignment uncertainties . We accounted for all these possible confounding effects , which would otherwise affect inference of positive selection . Indeed , we adopted recommended alignment and filtering criteria to minimize erroneous codon alignments [15] , and we tested for substitution saturation . As for recombination , we applied two methods , based on different features of the data , to screen the alignments and to infer the most likely position of breakpoints . These latter were used to split alignments into sub-regions that were separately analyzed . In this respect , it is worth noting that we did not detect recombination breakpoints in the nonstructural region for the extended flavivirus phylogeny , whereas we found evidence of recombination when all ZIKV strains alone were analyzed . The explanation for this apparently contradictory finding is that one single ZIKV African sequence contributed to the recombination events and it was not represented in the flavivirus phylogeny . Moreover , the flavivirus alignment was partially masked to remove unreliably aligned codons . This procedure clearly determines the removal of the most divergent regions , which may derive from recombination events . This most likely accounts for the discrepancy between our results and those from a previous report that indicated recombination between Asian ZIKV strains and SPOV within NS2B [47] . Another previous study analyzed African and non-African ZIKV isolates and reported the presence of four recombination breakpoints in ZIKV genomes [48] . In our analysis we only detected two breakpoints . We believe that the main reason for the discrepancy with this previous analysis derives from the fact that the ArD142623 strain , which contributed most recombination events in Faye's dataset [48] , was not included in our study because its genome sequence is not complete and because its polyprotein sequence is annotated as “nonfunctional due to mutation” in GenBank . In this respect , it is worth mentioning that despite our findings and those previously reported by others for ZIKV [47 , 48] and DENV [34] , experimental data have indicated that flaviviruses have very low propensity for recombination [49 , 50] . Moreover , under laboratory conditions specifically devised to detect recombination , extremely rare events were observed that generated aberrant JEV genomes with reduced growth properties [50] . These observations raise the possibility that recombination events identified through analysis of existing sequences in public databases are artifacts of laboratory contamination or incorrectly assembled sequence files . This was previously suggested to be the case for some “recombinant” DENV sequences [34 , 51] . All the recombination events we detected involved one or few sequences from African ZIKV isolates . Whereas we cannot control for the accuracy of the deposited sequences , we have to take the possible recombination events into account; failure to do so would affect positive selection inference , irrespective of whether recombination actually occurred . Clarification of these potential caveats , though , is extremely relevant for epidemiological and preventive purposes . Because ZIKV , DENV , and other arboviruses can co-circulate during outbreaks [10] , it will be extremely important to assess if and with what efficiency these viruses can recombine . The branch-site tests we applied to analyze the flavivirus phylogeny were aimed at detecting episodic positive selection—i . e . selective events on one branch of the phylogeny and thus likely to have occurred during or after speciation . Using this approach we were able to show that positive selection acted on the branch that separates DENV from the other analyzed flaviviruses and mainly targeted NS1 . It should be noted that the branch-site tests have low false positive rates and are largely insensitive to violations of the assumption of neutral evolution on the background branches [22 , 24] , but lack power [52] . Thus , selection may act on additional branches than the one we detected and more selected sites may exists . When analysis was performed on ZIKV genomes , which are characterized by much lower divergence compared to the flavivirus sequences in the inter-species analysis , tests to detect episodic and pervasive positive selection were applied . The branch-site test showed no evidence of episodic selection and we consequently identified no selective events leading to the spread of the Asian ZIKV lineage . We mention , however , that the branch-site test may have failed to detect weak selection or selection at a very limited number of codons . We also stress that the lack of selection signatures does not imply that amino acid differences between African and non-African ZIKV lineages are irrelevant or nonfunctional . Conversely , we identified pervasive selection—i . e . selective events that involve all ZIKV lineages- and , again , selected sites were found to occur in nonstructural portions of the ZIKV genome ( NS4B and NS5 ) . These portions also display the strongest level of selective constraint . Structural proteins ( the E protein in particular ) might be a priori considered to be preferential selection targets during flavivirus evolution for least two reasons: these proteins [1] mediate the initial and essential steps of host infection via host cell binding and entry and [2] represent major targets for immune responses influencing antigenic selection [53] . Nevertheless , we found no evidence of positive selection in structural regions , either in flaviviruses or in ZIKV isolates . To our knowledge , no study has investigated the occurrence of positive selection in ZIKV or during flavivirus speciation , but efforts at detecting positive selection in DENV strains or isolates were performed . Depending on the serotype analyzed , on the geographic and temporal origin of the viruses , as well as on their transmission cycle ( sylvatic or endemic ) , different genomic regions were found to represent targets of positive selection in DENV [54–59] . These regions were not limited to the structural portion , but also included nonstructural proteins [54–58] . Likewise , analysis of JEV sequences revealed selection in both structural and nonstructural regions [43 , 44] . Also , ample evidence indicated that although most neutralizing antibodies are directed against flavivirus E proteins [53] , non-neutralizing anti-NS1 antibodies are protective against severe disease during DENV , YFV , and JEV infection [60–64] . Finally , cell-mediated immunity was shown to target both structural and nonstructural DENV proteins , with the vast majority of T-cell epitopes located in nonstructural proteins [65] . In this respect , it is worth noting that several of the positively selected sites we detected in NS1 and in NS5 are located within immune epitopes . This observation suggests that the underlying selective pressure responsible for selection at these sites is exerted by the host adaptive immune system . These data are likely to be relevant for the current efforts to develop a ZIKV vaccine and to assess the possible cross-protection afforded by natural or vaccine-induced immunity against other related viruses . Nonstructural proteins play different roles in flavivirus life cycles and several of them interact with innate immunity molecules . NS1 , the major selection target in the flaviviruses we analyzed herein , is essential for viral RNA replication and is involved in immune system evasion . In particular , secreted hexameric NS1 represents a major antigenic marker of viral infection for all DENV serotypes . Soluble NS1 in the serum of patients has been found to correlate with severe clinical disease [66] , suggesting that the NS1 protein also plays an important role in the pathogenesis of dengue . Importantly , the NS1 protein from WNV and DENV2 interacts with multiple components of the complement system ( C1S , C4 , C4-binding protein , CFH ) , as well as with toll-like receptors ( TLR3 , TLR2 , and TLR6 ) [67 , 68] . The molecular details of these interactions are presently unknown , but the presence of several positively selected sites in NS1 suggests a possible arms race with the host innate immune system . It will be extremely important to assess whether amino acid differences in flavivirus NS1 proteins affect the interaction with innate immunity components and consequently modulate the host response to ZIKV or DENV . NS1 is also required for efficient viral genome replication . Recently , it has been proposed that dimeric NS1 plays an organizational role in the formation of the replication complex on the cytoplasmic side of the ER membrane [69] , and that this function is mediated by interactions with NS4A and NS4B [45 , 70] . Interestingly , the positively selected site in the connector domain ( residue 164 ) is located in the hydrophobic protrusion that may contact NS4A and NS4B [38] . Although mutation of residue T164 to alanine has no effect on RNA replication or on the assembly of DENV particles [37] , we cannot exclude its involvement in ( de ) stabilizing the interaction with the ER membrane; in fact , the introduction of a histidine ( Fig 2 ) at this site might affect the protein function more importantly than the conservative alanine substitution . Scaturro and colleagues [37] also reported that NS1 plays a critical role in the biogenesis of DENV virions , a function that is mediated by interaction with structural proteins . In this context , a key role is played by two residues ( 114 and 115 ) in the flexible loop of the NS1 wing domain . Indeed , alanine mutation of residue S114 abrogates DENV2 NS1 binding to E , prM , and C [37] . Notably , we identified residue 114 as positively selected in the flavivirus phylogeny and two additional selected sites were located in close spatial proximity . Overall , these observations suggest that positive selection at NS1 is acting to optimize viral fitness by modulating viral replication efficiency and/or favoring evasion from the host immune system . Similar considerations may apply to NS4B , which displays 3 positively selected sites in ZIKV isolates . This membrane protein has a role in the formation of the replication complex and in virus pathogenesis [71] . Several mutations in the NS4B region of JEV , YFV , and WNV were shown to modulate neurovirulence and/or neuroinvasiveness [42] . Notably , one of the positively selected sites we identified in NS4B ( 26M/I/T/V ) was previously reported to represent a selection target in sylvatic DENV2 isolates but not in the endemic strains [57] . A nearby residue was also found to evolve adaptively in JEV , both in genotype I and genotype III isolates [43 , 44] . However , variation at this site seems not to be associated with host preferences in JEV [44] . Although the functional significance of changes at position 26 in NS4B remains to be clarified , the fact that this residue or a flanking one is targeted by positive selection in three closely related flaviviruses suggests an important role in viral adaptation . Indeed , the N-terminal region of flavivirus NS4B ( amino acids 1–125 ) inhibits interferon ( IFN ) response by blocking IFN-α/β signaling [72] . This region includes two additional positively selected sites ( M87 and H88 ) and is involved in host protein ( e . g . STING ) binding . It is interesting to note that YFV NS4B , but not DENV NS4B , can bind STING [73] , suggesting that positive selection in this region results from adaptation to the host innate immune system to modulate binding of viral sensors . The region surrounding positions 87 and 88 is also responsible for NS1-NS4B binding and the same study demonstrated the importance of the F86C mutation in WNV NS4B to rescue viral replication in presence of NS1 nonfunctional mutations [45] . Finally , the DENV NS4B region spanning residues 84 to 146 is required for interaction with NS4A , another molecule involved in flavivirus replication [74] . Thus , based on data from other flaviviruses , the three positively selected sites we identified in NS4B of ZIKV are located in a protein region important for interaction with other viral proteins and with host molecules . The relatively sparse sampling of ZIKV genomes and the paucity of ZIKV sequences isolated from humans in Africa and from mosquitoes in Asia/America , prevents drawing any definite conclusion about the role of selected sites on host preference , pathogenicity , or infectivity . Moreover , as anticipated above , a potential issue associated with viral sequence analysis concerns laboratory contaminations , especially during serial passages in culture . Contaminations were previously suggested to account for discrepancies in DENV phylogenies [34] , and a few of the African ZIKV strains we included in the study were passaged several times in suckling mice or cell culture [9] . This process may also introduce variants that are not present in nature , potentially affecting evolutionary inference . These issues are unlikely to affect the analyses we performed on the extended flavivirus phylogeny , as variation on terminal branches has minor effects . However , these caveats should be kept in mind in the analysis of ZIKV strains , especially for positively selected sites showing variability in a minority of sequences . Further evolutionary analysis of ZIKV will greatly benefit from the sequencing and inclusion of additional isolates , not only from the ongoing American epidemic , but also from African countries . Despite these limitations , we suggest that the positively selected sites we identified should be prioritized in future experimental studies . These amino acids changes are expected to modulate aspects of viral fitness , either in mosquitoes or vertebrate hosts . In this respect , reverse genetic approaches will be instrumental to assess the role of specific changes on different viral phenotypes including transmission by distinct Aedes mosquito vectors or alternative ( e . g . human-to-human ) transmission modes , increased viremia in humans , and altered tissue tropism . Finally , we note that NS1 and NS4B are regarded as attractive candidates as direct or indirect targets for antiviral drugs in flavivirus infections [42 , 75] . Nonetheless , these proteins are fast evolving in ZIKV and related flaviviruses , and the numerous selected sites are expected to entail functional differences among closely related viruses or even among viruses belonging to the same species . Thus , our data suggest that compounds developed against DENV NS4B [42] or drugs that result in DENV NS1 misfolding [75 , 76] may not be active against ZIKV . | Zika virus is mainly transmitted by mosquitoes and is phylogenetically related to other human pathogens ( e . g . dengue virus ) . After the outbreak in South America , the WHO declared that the spread of ZIKV should be regarded as a public health emergency . In fact , growing evidence suggests that ZIKV infection during pregnancy increases the risk of congenital birth defects . Moreover , ZIKV can trigger Guillain- Barré syndrome , a severe neurological disorder characterized by progressive muscle weakness . Evolutionary studies can help identify sites that allow viral adaptation—i . e . that increase viral fitness at least in some conditions . We analyzed the evolution of the polyproteins encoded by ZIKV and by related viruses and identified several sites in nonstructural proteins that were subject to natural selection . Most of these are located in protein regions that mediate interaction with the host immune system or that may regulate viral RNA synthesis . In ZIKV isolates , the NS4B protein was the preferential selection target with three selected residues . Changes at these sites are expected to modulate some aspect of viral fitness , either in mosquitoes or vertebrate hosts . Future studies to clarify the mechanisms of ZIKV pathogenicity should address the role of these sites in the modulation of viral phenotypes . | [
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] | 2016 | Nonstructural Proteins Are Preferential Positive Selection Targets in Zika Virus and Related Flaviviruses |
The objectives of this systematic review , commissioned by WHO , were to assess the frequency and severity of clinical manifestations of human brucellosis , in view of specifying a disability weight for a DALY calculation . Thirty three databases were searched , with 2 , 385 articles published between January 1990–June 2010 identified as relating to human brucellosis . Fifty-seven studies were of sufficient quality for data extraction . Pooled proportions of cases with specific clinical manifestations were stratified by age category and sex and analysed using generalized linear mixed models . Data relating to duration of illness and risk factors were also extracted . Severe complications of brucellosis infection were not rare , with 1 case of endocarditis and 4 neurological cases per 100 patients . One in 10 men suffered from epididymo-orchitis . Debilitating conditions such as arthralgia , myalgia and back pain affected around half of the patients ( 65% , 47% and 45% , respectively ) . Given that 78% patients had fever , brucellosis poses a diagnostic challenge in malaria-endemic areas . Significant delays in appropriate diagnosis and treatment were the result of health service inadequacies and socioeconomic factors . Based on disability weights from the 2004 Global Burden of Disease Study , a disability weight of 0 . 150 is proposed as the first informed estimate for chronic , localised brucellosis and 0 . 190 for acute brucellosis . This systematic review adds to the understanding of the global burden of brucellosis , one of the most common zoonoses worldwide . The severe , debilitating , and chronic impact of brucellosis is highlighted . Well designed epidemiological studies from regions lacking in data would allow a more complete understanding of the clinical manifestations of disease and exposure risks , and provide further evidence for policy-makers . As this is the first informed estimate of a disability weight for brucellosis , there is a need for further debate amongst brucellosis experts and a consensus to be reached .
Brucellosis is one of the most common zoonotic infections globally [1] . This bacterial disease causes not only a severely debilitating and disabling illness , but it also has major economic ramifications due to time lost by patients from normal daily activities [2] and losses in animal production [3] . In a review of 76 diseases and syndromes of animals , brucellosis lies within the top ten in terms of impact on impoverished people [4] . A brucellosis disability weighting of 0 . 2 has been previously proposed for Disability-Adjusted Life Years ( DALY ) calculation , based on the pain and impaired productivity known to result from infection [3] . However , a more informed estimate is needed for an accurate assessment of disease burden . In 1992 , the World Bank commissioned the original Global Burden of Disease ( GBD ) study , providing a comprehensive assessment of 107 diseases and injuries and 10 risk factors in eight major regions [5] . This review did not include any neglected tropical zoonoses . Such diseases often do not attract the interest of health researchers or sufficient resources for adequate control , yet they continue to impact significantly on human health and wellbeing , livestock productivity , and local and national economies [6] . There is a need for more accurate data relating to the burden of neglected zoonoses to facilitate more effective implementation of disease control interventions . In 2009 , the Foodborne Disease Burden Epidemiology Reference Group ( FERG ) of the World Health Organization ( WHO ) commissioned a series of systematic reviews on the burden of neglected zoonotic diseases , with the aim of incorporating the findings into the overall global burden of disease assessments . This report presents a systematic review of scientific literature published between 1990–June 2010 relating to morbidity from human brucellosis infection . The objectives of this review were to assess the frequency and severity of the clinical manifestations of brucellosis , the duration of disease , the associated disabilities and important risk factors , with a view to estimating an appropriate disability weight for calculation of the brucellosis DALY . A systematic review of scientific literature investigating the incidence and prevalence of brucellosis globally is the subject of a companion paper [7] .
Thirty three databases were searched for relevant articles using the search terms of ( brucellosis OR malta fever OR brucella melitensis OR brucella abortus ) AND ( symptom* OR sequelae* OR morbidity OR mortality OR transmission mode OR foodborne ) , with a publication limitation of 1990–30 June , 2010 . The search term was adapted to the predominate language of the database . If a database did not allow the combining of Boolean operators , ( 18 of 33 databases ) , ‘brucellosis’ was used as the sole term . Reference Manager bibliographic software was used to manage citations . Duplicate entries were identified by considering the author , the year of publication , the title of the article , and the volume , issue and page numbers of the source . In questionable cases , the abstract texts were compared . The articles were sorted by a team of four reviewers with a combined fluency in English , German , French , and Spanish . Articles in other languages were noted for future translation , pending resources . All reports were classified into one of two categories , based on their abstracts: Category 1: Relevant – articles related to human brucellosis related to brucellosis infection in populations ( i . e . disease frequency ) or cases of human brucellosis ( i . e . disease morbidity ) ; Category 2: Irrelevant - articles related to non-human brucellosis; articles addressing topics not related to the current review , such as genetics , laboratory diagnostic tests , experimental laboratory animal studies . The abstracts of studies belonging to Category 1 and meeting the following criteria for disease morbidity studies were retained: published between 1990 and 30 June 2010 , at least 10 study subjects , clinical symptoms/syndromes described , and some information relating to diagnostic tests provided . Articles relating to disease frequency and meeting the following criteria were also retained: published between 1990 and 30 June 2010 , at least 100 study subjects drawn from the general population , prevalence or incidence data included , and some information relating to diagnostic tests provided . The assessment and classification of frequency articles will be the subject of a companion paper and will not be considered further here . Articles for which the necessary data for classification could not be obtained were identified for possible future assessment , according to availability of resources . In general , non peer-reviewed or review articles , conference proceedings and book chapters were excluded . After applying the aforementioned screening steps , the full text of each selected article was retrieved for detailed analysis . Each article was reviewed by two or three reviewers , and classification discrepancies were resolved by discussion . Using a pre-designed Access database , articles were coded according to the following parameters: 1 ) Study type Studies were classified as a prospective case series , a retrospective case series , a case-control study , or of another type . 2 ) Study population The populations studied were grouped according to age category – children only ( <15 years ) , adults only ( ≥15 years ) , or including both children and adults . Additionally , they were coded according to whether the study population represented the general population of brucellosis cases in the age category , or only a specific sub-group . 3 ) Diagnostic methods Studies were classified according to their use of microbial culture to diagnose brucellosis patients . In order for studies to be included in the review , they had to not only mention culture in their methods but to also present laboratory results . 4 ) Overall study quality Studies were given an overall quality grade of 1 , 2 , or 3 . Quality 1 studies provided data drawn from general brucellosis cases , of which 75% or more were diagnosed by culture , and had well described study design and methods . Quality 2 studies also presented data from general brucellosis cases , utilised culture as a method and presented relevant laboratory results . However , unlike for Quality 1 studies , the majority of cases did not have to be diagnosed by positive culture in order to be included as Quality 2 . Quality 3 studies were either drawn from only a specific sub-group of brucellosis cases such that general conclusions could not be drawn , did not use culture as a diagnostic method or failed to present culture results , or had poorly described study design and methods such that the quality of the data could not be assured . Based on brucellosis literature [8] a comprehensive list of clinical manifestations associated with brucellosis cases was developed: Numbers of subjects with each symptom/syndrome were recorded for each study , as well as the number of male and female patients . For the sex-related outcomes of epididymo-orchitis and abortion , the study population was considered to be only the male and pregnant female sub-groups of the study population respectively . Information relating to duration of disease prior to treatment and exposure to potential risk factors were also recorded wherever provided . To calculate the proportion of patients by sex , numbers of male and female patients were aggregated across all studies as well as within each age category . 95% confidence intervals were calculated using the normal approximation to the binomial . Where appropriate data were available from two or more studies , pooled proportions of patients with each clinical manifestation were estimated using generalized linear mixed models . Pooled estimates with 95% confidence intervals were calculated both within age categories and overall across all studies , using a Freeman-Tukey double arscine transformation . Homogeneity across studies was assessed using a Cochrane's Q test and total variability due to between-study variation was reflected in the I2 index . The meta-analysis was performed with R statistical software [9] using the meta package [10] . Additionally , in order to assess the impact of study design , the same analysis was conducted according to study type category . The pooled estimates for proportions of patients with each clinical manifestation were compared with the disability weights used in the GBD 2004 study [11] . A disability weight for brucellosis was then proposed . Median proportions of patients with exposure to particular risk factors were calculated . Data relating to duration of illness and diagnostic delay were recorded . In order to assess the duration of untreated illness , an additional , non-systematic search for data prior to the availability of appropriate antibiotics was undertaken by manually searching library records .
Table 1 lists the databases searched and the number of hits obtained for each . A total of 28 , 824 studies were identified , of which 59% were duplicates , leaving 11 , 000 original reports . Figure 1 shows a flow diagram of the process for the selection of articles included in the review . In total , 289 frequency and morbidity studies were selected , for which full text was available for 153 . However , 14 of these were in languages in which the team was not competent ( Croatian ( 6 ) , Turkish ( 4 ) , Korean ( 2 ) , Persian ( 1 ) , Mandarin ( 1 ) ) , leaving 96 morbidity studies for quality assessment . Some articles contained both frequency and morbidity data and were thus counted in both categories . Of the 96 morbidity studies for quality assessment , five were classified as Quality 1 and 52 as Quality 2 . Thirty-nine were excluded from further analysis as Quality 3 , one of which was due to duplication of data from another larger study . Two pairs of Quality 2 studies were based on the same data [12]–[15] . These studies were included because each provided some unique information; however , the duplicated data were only included once in the meta-analysis . Except for two articles in Spanish and one in French , all Quality 1 and 2 studies were in English . The median number of study subjects was 143 ( IQR: 85-283 ) , ranging from 20-1028 . Studies from high income countries such as Germany , France , and USA were generally situated at the lower end of the range ( less than 60 subjects ) , although larger studies were reported from Spain , including one study of over 900 subjects . Of the 57 studies selected , 24 were from Turkey . The next most represented country was Saudi Arabia , with 8 studies , followed by Spain with 4 and Greece with 4 . One or two studies each came from Cuba , France , Germany , Israel , India , Iran , Jordan , Kuwait , Tunisia , USA , Uzbekistan and Yemen . The geographic distribution of the selected studies is shown in Figure 2 . In terms of study type , 37 were classified as retrospective case series with data retrieved from medical records , and 19 as prospective case series . One study was a case-control . Seventeen studies provided detailed information about cases with specific syndromes , e . g . neurological brucellosis [16]–[19] , epididymoorchitis [20]–[23] , osteoarticular complications [13] , [14] , [24] , [25] , spondylitis [26] , [27] , pulmonary brucellosis [28] , pancytopaenia [29] , and pregnant women [30] . As these studies also provided some information about proportions of general brucellosis cases with specific symptoms/syndromes , they were included in the review . Twenty-three studies included both children and adult participants [12]–[15] , [18] , [20] , [24] , [30]–[44] . Twelve studies investigated only children [29] , [45]–[55] , with an upper age limit ranging from 13 years to 18 years . Of the 19 studies with an adult population of 15 years or older [16] , [17] , [21]–[23] , [25]–[27] , [56]–[67] , five consisted of only male participants [21]–[23] , [64] , [65] . Three studies did not clearly state the age category [19] , [28] , [68] and were analysed as if containing data for both adults and children . In studies consisting of only children , 64% patients ( 95% CI: 60–68% ) were male . The proportion of male patients in adult studies was significantly lower , at 56% ( 95% CI: 55–58% ) . In studies including both children and adult patients , 48% were male ( 95% CI: 46–51% ) . Overall , 55% patients ( 95% CI: 54–56% ) across all studies were male . Table 2 shows the pooled proportions of patients estimated by the random-effects model , according to clinical manifestations by age category . Forest plots are provided as Supplementary Information . An analysis by study type did not show any significant changes or trends . Documented fever was common , with an estimated 78% of patients affected across the three age categories . Estimates of the proportions of patients with self-reported symptoms of sweats , chills , fatigue , headache , and malaise , were significantly lower in children , ranging from 9–24% depending on symptom , compared to 33–81% for adults . Weight loss in children , at 13% , was also lower than the 31% reported in adults . Abdominal-related manifestations of pain , splenomegaly and hepatomegaly were fairly uniformly distributed across age categories , with overall estimated proportions of 19% , 26% and 23% , respectively . The number of studies reporting the presence of hepatitis was small , totalling only seven , with an estimated 4% patients affected overall . Arthralgia was common , affecting 65% patients overall , whereas arthritis affected only 26% patients . In adult patients , 56% and 49% suffered from myalgia and back pain , respectively . Only two studies reported myalgia and back pain in children . Overall , spondylitis and sacroiliitis were detected in 12–36% adults . In relation to reproductive problems , only one study reported abortion rates as a proportion of pregnant female participants , which was 46% [30] . Overall , 10% male patients had epididymo-orchitis . For more severe outcomes , endocarditis was reported in an overall 1% patients , and neurological manifestations in 4% . Neurological outcomes reported included motor deficits , cranial nerve deficits , sciatica , confusion and/or psychological disturbances , meningitis and seizures . 6% of patients suffered from respiratory manifestations , including cough , bronchopneumonia , pleural adhesion and pleural adhesion . Cutaneous changes were reported in 6% patients . As most studies were case series without a control group , an evaluation of the importance of risk factors was not possible . However , median proportions were calculated from 27 studies which provided some exposure history . Median proportions of brucellosis cases with exposure to a potential risk factor were 64% ( IQR: 34–78% ) for consumption of unpasteurised dairy products , 42% ( IQR: 23–59% ) for contact with livestock , and 6% ( IQR: 3–19% ) for occupational exposure , including veterinarians , butchers , and abattoir workers . From fifteen studies , the median proportion of cases with a history of brucellosis in a family member was 20% ( IQR: 17–46% ) . Only six studies included in the systematic review provided data regarding duration of illness prior to diagnosis and treatment [32] , [41] , [52] , [55] , [57] , [62] . The age of the patient and the nature of the illness were influential factors . One study reported a longer duration of illness in adults compared to children under 15 years , averaging 8 weeks versus 4 weeks , respectively [41] . In another study , the average duration of illness prior to diagnosis and treatment was 40 days , but cases with osteoarticular disease generally experienced longer periods of illness , extending to 6 months [62] . The GBD 2004 study estimated the disability weights for low back pain due to chronic intervertebral disc disease and osteoarthritis of the knee to be 0 . 121 ( range 0 . 103–0 . 125 ) and 0 . 129 ( range 0 . 118–0 . 147 ) , respectively [11] . Given the high proportion of patients in our systematic review with joint , back , or muscular pain , a disability weight of at least 0 . 150 is proposed as a minimum estimate for localised , chronic brucellosis . Generalised , non-specific clinical manifestations were also common . Acute , non-localised brucellosis could be approximated by an episode of malaria , estimated to be 0 . 191 ( range 0 . 172–0 . 211 ) by the GBD 2004 study [11] .
Morbidity could vary geographically according to epidemiological setting . Well designed epidemiological studies from regions under-represented in this review would greatly contribute to an overall assessment of the global disease burden . A surveillance system amongst fever patients in malaria-endemic countries could be particularly informative . Additionally , risk factors for disease should be investigated through case-control studies . This would provide invaluable information to guide disease control interventions and policy . Studies for which a title or abstract was not published in a language using the Latin alphabet , such as those published only in Chinese characters or Arabic script , may not have been identified during the original database search . Of the foreign language studies that were identified , those published in languages in which the team was not competent were excluded from the analysis . It is possible that some of these studies contained data that could have contributed to this global assessment of brucellosis morbidity . Additionally , although studies in English were independently reviewed by three team members , this was not always possible for studies reviewed in other languages ( German , French , Spanish ) . There were likely some differences between the case definitions and diagnostic capacity of different studies . For neurological and respiratory syndromes , many studies provided only an overall aggregated estimate without details of the different disease forms . A respiratory case could potentially vary from a patient with only a cough to severe bronchopneumonia , or a neurological case from altered behaviour and confusion to nerve deficits , meningitis or seizures . All patients were positive by culture in only 3 studies . Given the complexity of brucellosis serology interpretation , it is possible that some patients in other studies were misdiagnosed as cases of active brucellosis . The studies provide data from brucellosis patients presenting to health centres . It is possible that cases that do not present to health centres are less severe . The results of this review may , therefore , be biased towards more severe cases . As with the estimation of other disability weights , the proposed brucellosis disability weight estimate assumes that a given clinical manifestation will result in the same disability in all settings , which is unlikely [75] . This systematic review adds to the understanding of the global burden of brucellosis , one of the most common and important zoonotic diseases worldwide . Brucellosis is shown to have a severe , debilitating , and often chronic impact on its sufferers . Significant delays in appropriate diagnosis and treatment are the result of both health system inadequacies and socioeconomic factors . Well designed epidemiological studies from those regions identified to be lacking in data would allow a better understanding of the clinical manifestations of disease and exposure risks and provide further evidence for policy-makers . Based on the findings of this systematic review and the disability weights from the 2004 Global Burden of Disease Study , a disability weight of 0 . 150 is proposed as the first informed estimate for chronic , localised brucellosis and 0 . 190 for acute brucellosis . As this is the first informed estimate of a disability weight for brucellosis , there is a need for further debate amongst brucellosis experts and a consensus to be reached . | Brucellosis is a bacterial disease transmitted to humans by consumption of infected , unpasteurised animal milk or through direct contact with infected animals , particularly aborted foetuses . The livestock production losses resulting from these abortions have a major economic impact on individuals and communities . Infected people often suffer from a chronic , debilitating illness . This systematic review on the symptoms of human brucellosis is the first ever conducted . Using strict exclusion criteria , 57 scientific articles published between January 1990–June 2010 which included high quality data were identified . Severe complications of brucellosis infection were not rare , with 1 case of endocarditis and 4 neurological cases per 100 patients . One in 10 men suffered from testicular infection , which can case sterility . Debilitating conditions such as joint , muscle , and back pain affected around half of the patients . Given that most patients had fever , brucellosis poses a diagnostic challenge in malaria-endemic areas where fever is often assumed to be malaria . More high quality data is needed for a more complete understanding of the clinical manifestations of disease and exposure risks , and to provide further evidence for policy-makers . | [
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] | 2012 | Clinical Manifestations of Human Brucellosis: A Systematic Review and Meta-Analysis |
Nuclear mRNA export is a crucial step in eukaryotic gene expression , which is in yeast coupled to cotranscriptional messenger ribonucleoprotein particle ( mRNP ) assembly and surveillance . Several surveillance systems that monitor nuclear mRNP biogenesis and export have been described , but the mechanism by which the improper mRNPs are recognized and eliminated remains poorly understood . Here we report that the conserved PIN domain protein Swt1 is an RNA endonuclease that participates in quality control of nuclear mRNPs and can associate with the nuclear pore complex ( NPC ) . Swt1 showed endoribonuclease activity in vitro that was inhibited by a point mutation in the predicted catalytic site . Swt1 lacked clear sequence specificity but showed a strong preference for single-stranded regions . Genetic interactions were found between Swt1 and the THO/TREX and TREX-2 complexes , and with components of the perinuclear mRNP surveillance system , Mlp1 , Nup60 , and Esc1 . Inhibition of the nuclease activity of Swt1 increased the levels and cytoplasmic leakage of unspliced aberrant pre-mRNA , and induced robust nuclear poly ( A ) + RNA accumulation in mlp1Δ and esc1Δ strains . Overexpression of Swt1 also caused strong nuclear poly ( A ) + RNA accumulation . Swt1 is normally distributed throughout the nucleus and cytoplasm but becomes concentrated at nuclear pore complexes ( NPCs ) in the nup133Δ mutant , which causes NPC clustering and defects in mRNP export . The data suggest that Swt1 endoribonuclease might be transiently recruited to NPCs to initiate the degradation of defective pre-mRNPs or mRNPs trapped at nuclear periphery in order to avoid their cytoplasmic export and translation .
Nuclear export of messenger RNA ( mRNA ) is a fundamental process of eukaryotic gene expression . In yeast , newly synthesized pre-mRNAs are cotranscriptionally assembled into messenger ribonucleoprotein particles ( mRNPs ) with the assistance of transcription-export ( TREX ) complexes . The THO/TREX ( Tho2-Hpr1-Mft1-Thp2-Tex1-Sub2-Yra1 ) and TREX-2 ( Sac3-Thp1-Sus1-Cdc31 ) complexes couple mRNA transcription with the recruitment of RNA binding proteins ( e . g . , Yra1 , Npl3 , and Nab2 ) , and the conserved mRNA export receptor Mex67-Mtr2 ( TAP-p15 in humans ) [1 , 2] . In the absence of TREX or TREX-2 components , nascent mRNAs are not correctly assembled into mRNPs , which can lead to hybridization with the DNA template ( R-loops formation ) , defective polyadenylation , and accumulation at the 3′ end of the gene [3–5] . As a result , export-incompetent mRNPs accumulate in the nucleus [6 , 7] . During maturation and transport , nuclear mRNPs are monitored by surveillance systems to prevent the export and subsequent translation of aberrant mRNAs ( reviewed in [8 , 9] ) . In TREX mutants , imperfect mRNPs can be targeted and degraded by the nuclear exosome together with the TRAMP polyadenylation complex [10] . An important task of nuclear mRNA quality control is to avoid an export of intron-containing pre-mRNAs to the cytoplasm . The export of unspliced pre-mRNAs is rare in wild-type cells , whereas substantial leakage of pre-mRNA is seen in strains with defects in early splicing factors or for transcripts mutated in the 5′ splice site or the branch-point sequence [11–13] . Leakage of pre-mRNAs that lack any clear splicing defects was observed in cells lacking Mlp1 ( a homolog of human TPR ) or Nup60 , two structural constituents of the nucleoplasmic basket of the nuclear pore complex ( NPC ) [14] . This suggested that Nup60-Mlp1 selectively traps intron-containing pre-mRNAs , and perhaps other aberrantly processed mRNAs , to prevent their exit from the nucleus [14 , 15] . More recently , Pml39 , the binding partner of Mlp1 , and Esc1 , a protein of the nuclear periphery involved in the correct localization of the Nup60-Mlp1 complex , were shown to contribute to the function of Mlp1-Nup60 [16 , 17] . However , the mechanism by which the aberrant transcripts are recognized and eliminated remains unclear . The yeast PIN domain protein Swt1/Yor166c ( Synthetic lethal with TREX 1 ) was identified in a screen for synthetic lethality with the TREX subunit Hpr1 , interacts functionally with the TREX complex and is required for optimal transcription rates [18] . The PIN ( PilT N terminus ) module is approximately 130 residues in length and adopts a fold similar to 5' exonucleases and FLAP endonucleases [19 , 20] . Typical features are four invariant acidic residues located in a negatively charged surface cleft , forming the predicted catalytic center [21] . These residues probably chelate a divalent ion and , together with two adjacent hydroxyl residues , are involved in the nucleophilic attack on the substrate phosphodiester bond . The PIN domain of human SMG6 exhibited endonuclease activity in vitro and is required for its function in nonsense-mediated mRNA decay ( NMD ) [22] . The yeast genome encodes eight PIN domain–containing proteins , including Nob1 and Utp24/Fcf1 , that assist in endonucleolytic cleavages of rRNA precursors during ribosome biogenesis [23 , 24] and the Rrp44/Dis3 subunit of the exosome ( C . Schneider and D . Tollervey , unpublished data ) . However , none of these yeast proteins has been demonstrated to function as an endonuclease . Here , we report that the evolutionarily conserved protein Swt1 shows RNA endonuclease activity that is dependent on the PIN domain . Swt1 is essential in cells compromised for cotranscriptional mRNP formation or perinuclear mRNP quality control and can associate with NPCs . These findings reveal an important role for endonucleolytic cleavage in the process of nuclear mRNA surveillance .
To identify factors that interact with TREX-2 , a transcription-export complex , which functions at the inner side of the NPC , we performed a synthetic lethality ( sl ) screen with the sac3Δ strain . Among the candidates , we identified a mutant allele of the SWT1/YOR166 gene . The genetic interaction was verified by absence of growth of a haploid sac3Δ swt1Δ strain , generated by plasmid loss ( Figure S1 ) . Other TREX-2 components Thp1 and Sus1 were either sl ( thp1Δ ) or showed strong synergistic interaction ( sus1Δ ) with swt1Δ , demonstrating an intimate functional interaction between Swt1 and TREX-2 complex ( Table 1 and Figure S1 ) . To extend these studies , we tested whether SWT1 functionally overlaps with other factors involved in mRNP biogenesis and turnover . Strong genetic interactions were observed between swt1Δ and THO/TREX components ( tho2Δ , hpr1Δ , mft1Δ , thp2Δ , and sub2–85 ) consistent with previous findings [18] . In contrast , no genetic interactions were detected between swt1Δ and the mRNA export receptor Mex67 or adaptor Yra1 [18] . In addition , we found an sl interaction between swt1Δ and NPL3 , which encodes an mRNA binding protein with multiple roles in mRNP biogenesis [25 , 26] ( Table 1 and Figure S1 ) . These studies also uncovered genetic interactions between SWT1 and MLP1 , NUP60 , and ESC1 ( Table 1 and Figure S1 ) . Mlp1 and Nup60 are NPC-associated factors that are thought to constitute the nuclear basket of NPC and participate in mRNP quality control before nuclear export [14] . Esc1 is a protein of the inner side of the nuclear membrane recently shown to affects the association of the Mlp1-Nup60 complex with the NPC [17] . Several factors implicated in various steps of mRNP production , export and surveillance did not interact genetically with swt1Δ , including subunits of the nuclear exosome ( Rrp6 and Rrp44/Dis3 ) , components of the TRAMP complex ( Trf4 and Mtr4 ) , the Rat1 5′-exonuclease , and factors involved in cytoplasmic mRNA turnover ( see Table 1 for factors tested ) . These genetic analyses indicate that Swt1 interacts specifically with factors required for cotranscriptional assembly and perinuclear quality control of nuclear mRNPs . The PIN domain of Swt1 , which lies in the central region ( residues 130–262 ) , is evolutionarily conserved and contains all four conserved acidic residues implicated in catalysis ( Figure S2 ) . We therefore tested whether Swt1 purified from yeast has in vitro nuclease activity . We generated FLAG-tagged wild-type Swt1 and an Swt1 mutant expected to be catalytically inactive ( Swt1-D135N; Asp 135 is predicted to be a residue of the catalytic center ) . These were expressed in yeast and purified by affinity chromatography ( Figure 1A ) . The ability of these proteins to degrade single-stranded RNA in vitro was assessed using 5′- or 3′-radiolabeled oligo ( A ) 30 substrates . Wild-type Swt1 efficiently degraded both 5′- and 3′-labeled oligo ( A ) 30 RNA . In contrast , the Swt1-D135N mutant protein exhibited no activity on the 3′-labeled RNA substrate and only very weak activity on the 5′-labeled probe ( Figure 1B ) . The residual activity on the 5′-labeled substrate could be due to a contaminating 3′-5′ exonuclease that is frequently observed in exonuclease assays ( C . Schneider , unpublished data ) . The lack of degradation of the 3′-labeled substrate by this activity probably reflects protection by the presence of a 3′-phosphate on the [32P]pCp-labeled RNA . Time course experiments further showed that wild-type Swt1 degrades both 5′- and 3′-labeled substrates with very similar kinetics ( Figure 1C ) , consistent with endoribonuclease activity . To assess whether Swt1 functions as an endonuclease , a structured 52-nucleotide ( nt ) RNA substrate was used , which contains a stable stem ( from the mouse 5 . 8S rRNA ) that closes the 5'- and 3'-ends of the RNA ( Figure 1D ) . The remaining part of the RNA ( nt 11–43 ) is predicted to be at least partially single stranded . Wild-type Swt1 , but not the Swt1-D135N mutant , efficiently cleaved the structured 52-nt RNA endonucleolytically , with a clear preference for single-stranded regions of the RNA substrate ( Figure 1D ) . Taken together , the data show that Swt1 exhibits in vitro endonuclease activity without clear sequence specificity but with a preference for single-stranded RNA . To further investigate the in vivo role of Swt1 in the context of transcription-coupled mRNA export and nuclear mRNP surveillance , we performed mRNA export and pre-mRNA leakage assays with the viable double mutants ( Figure S1 ) carrying swt1Δ together with thp2Δ , mft1Δ ( THO/TREX ) , sus1Δ ( TREX-2 ) , nup60Δ , mlp1Δ , and esc1Δ ( perinuclear mRNP quality control ) . Deletion of SWT1 significantly enhanced nuclear poly ( A ) + RNA accumulation relative to the thp2Δ , mft1Δ , sus1Δ , and nup60Δ single mutants ( Figures 2A , S3A and unpublished data ) . Moreover , swt1Δ induced nuclear accumulation of poly ( A ) + RNA in the mlp1Δ and esc1Δ mutants , which was not detected in the single mutants ( Figure 2A; see also [27 , 28] ) . To assess whether the poly ( A ) + RNA accumulation reflects the accumulation of nuclear mRNA , we tested a specific transcript , the heat-shock SSA1 mRNA ( Figure 2A ) . This mRNA was clearly accumulated in the nucleus of the mpl1Δ swt1Δ double mutant , but not in the corresponding single mutants . In contrast , no nuclear accumulation was observed when the nuclear export of tRNA was assayed ( unpublished data ) . To quantify whether the levels of SSA1 transcript were increased in the mlp1Δ swt1Δ double mutant , we measured the amount of SSA1 mRNA before and after heat shock by real-time reverse-transcription polymerase chain reaction ( RT-PCR ) . The SSA1 levels were mildly increased in the single mutants , but a synergistic increase was observed in the mlp1Δ swt1Δ double mutant in comparison to wild-type strain ( Figure S4 ) . To test whether the double mutants of swt1Δ and mlp1Δ or nup60Δ result in synergistic defects in pre-mRNP surveillance , we monitored cytoplasmic leakage of unspliced pre-mRNA reporters [11 , 12 , 14] . No leakage of the reporter with intact splicing signals ( pJCR1 ) was observed in the swt1Δ strain , whereas the nup60Δ strain showed increased leakage of the unspliced pre-mRNA , which was synergistically enhanced in the swt1Δ nup60Δ double mutant ( Figure 2B ) . Next , we tested whether pre-mRNA reporters with a mutation in the 5′ splicing site ( mut5′SS ) or in the branch-point site ( mutBP ) exhibit an increased cytoplasmic leakage in swt1Δ cells . Notably , leakage of the mutBP reporter was increased approximately 4-fold in the swt1Δ or mlp1Δ single mutants relative the wild type ( Figure 2B; see also [14 , 16] ) . Cytoplasmic leakage of both mutant pre-mRNA reporters was again strongly synergistically enhanced in the swt1Δ mlp1Δ double mutant ( Figure 2B ) . We conclude that loss of Swt1 leads to increased cytoplasmic leakage of splicing-defective pre-mRNAs , which can be synergistically enhanced by mutations in the Mlp1-Nup60 complex . This suggests that Swt1 and Mlp1-Nup60 have distinct but overlapping roles in nuclear mRNP surveillance . At first sight , it appears contradictory that an increased pre-mRNA leakage goes along with nuclear accumulation of poly ( A ) + RNA . However , these two effects are necessarily not mutually exclusive . When nuclear quality control is severely compromised ( e . g . , in mlp1Δ swt1Δ or nup60Δ swt1Δ cells ) , larger amounts of aberrant mRNPs could accumulate in the nucleus and hence have a higher chance to leak out . In a previous study [18] , Swt1 lacking the PIN ( endonuclease ) domain was shown to complement the lethality of swt1Δ in THO mutant strains . However , Swt1 is a low-abundance protein ( see below ) , and the constructs were overexpressed from the relatively strong NOP1 promoter . To assess whether the endonuclease activity of Swt1 is essential for complementation of swt1Δ phenotypes under physiological expression levels , we analyzed the catalytically inactive Swt1-D135N mutant expressed from the endogenous promoter ( Figure 3A ) . Swt1-D135N and wild-type Swt1 were expressed at similar levels ( Figure 3B ) , but Swt1-D135N failed to complement the growth defects of swt1Δ in any double-mutant combination ( Figure 3A and unpublished data ) . Moreover , Swt1-D135N did not rescue the nuclear mRNA accumulation induced by swt1Δ in the background of the mlp1Δ , nup60Δ , sus1Δ , or thp2Δ mutations ( Figure 3C and unpublished data ) . Finally , the nuclear leakage of the unspliced pre-mRNA reporter ( mutBP ) in Swt1-D135N–expressing cells ( Figure 3D ) was indistinguishable from the leakage observed in the swt1Δ strain ( Figure 2B ) . Similar loss-of-function phenotypes were observed for the swt1 truncation mutant lacking the entire PIN domain ( Swt1ΔPIN ) ( Figure S5 and unpublished data ) . However , Swt1-D135N , Swt1ΔPIN , and other partial deletion constructs could complement swt1Δ for growth in the tho2Δ background , when overexpressed under the control of the strong ADH1 promoter ( Figure S5 ) . In contrast , other swt1Δ phenotypes , including nuclear pre-mRNA leakage and nuclear poly ( A ) + RNA accumulation in mlp1Δ swt1Δ strains , could not be suppressed by overexpression of Swt1-D135N or Swt1ΔPIN mutants from the ADH1 promoter ( Figure 3D and unpublished data ) . Thus , under physiological expression levels , the endonuclease activity of Swt1 is essential for its function , whereas the requirement for catalytic activity can be partially relieved by overexpression of the inactive Swt1 protein . To assess possible roles for the endonuclease activity of Swt1 in the degradation of defective mRNPs , we determined whether an “aberrant” transcript is stabilized in swt1-D135N cells relative to the otherwise isogenic wild type . As an abnormal transcript , we used the intron-containing lacZ construct ( expressed from mutBP plasmid; see above ) that was used in the pre-mRNA leakage assays . This harbors a mutation in the splicing branch-point site that efficiently blocks pre-mRNA splicing [12] . The wild-type intron-containing lacZ transcript encoded by plasmid pJCR51 was used as a control pre-mRNA . Accumulation of the aberrant transcript was substantially elevated in swt1-D135N cells relative to wild-type strain ( mut . bars in Figure 3E ) , whereas the levels of normally processed lacZ mRNA was unchanged between the swt1-D135N and wild-type strains ( norm . bars in Figure 3E ) . These results suggest that the endoribonuclease activity of Swt1 participates in the selective elimination of defective mRNA transcripts . As Swt1 participates in nuclear mRNP surveillance , its expression and/or targeting may be subject to regulation . Quantitative western blot analysis using anti-Swt1 antibodies indicated that in vivo levels of Swt1 endonuclease are very low ( ∼200–300 molecules per cell ) ( unpublished data; see also [18] ) , which might be important to avoid inappropriate RNA degradation . In a large-scale analysis , Yor166/Swt1 was reported to be toxic following overexpression by the Tet-off system [29] . Consistent with this finding , a dominant-lethal phenotype was induced by overexpression of wild-type Swt1 but not of catalytically inactive mutant Swt1-D135N from the GAL1 promoter ( Figure 3F ) . The majority of Swt1 overexpressing cells showed strong nuclear accumulation of both poly ( A ) + RNA ( Figure 3E ) and SSA1 mRNA , whereas tRNA was normally exported ( Figure S3B and unpublished data ) . Notably , when the localization of intron-containing/intronless lacZ reporter constructs was analyzed , the nuclear accumulation of an intron-containing pre-mRNA reporter was stronger than for the intronless mRNA ( Figure S3C ) , consistent with a defect in pre-mRNA surveillance . These analyses demonstrate that overexpression of catalytically active Swt1 is toxic and interferes with mRNA export and/or pre-mRNA surveillance . Subcellular fractionation previously indicated that TAP-tagged Swt1 is located in the nucleus [18] . The exact location of Swt1 could not be determined , however , because the abundance of green fluorescent protein ( GFP ) - or immuno-labeled Swt1 expressed from the endogenous promoter was too low to be visualized ( unpublished data and [18] ) . The attachment of multiple GFP tags can allow the detection of low-abundance proteins ( N . Daigle , A . Bancaud , and J . Ellenberg , unpublished data ) . We therefore generated a functional 4xGFP-Swt1 construct expressed from SWT1 endogenous promoter , which complemented the double-deletion strains swt1Δ mlp1Δ , swt1Δ nup60Δ , or swt1Δ tho2Δ ( Figure S6 and unpublished data ) . When analyzed in the swt1Δ strain , 4xGFP-Swt1 was localized in both the nucleus and cytoplasm , with a slight nuclear enrichment in some cells ( Figure 4A ) . When expressed in the nup133Δ nuclear pore-clustering mutant [30] , 4xGFP-Swt1 exhibited significant coclustering with NPCs at the nuclear periphery , which was lost upon complementation of the nup133Δ strain by plasmid-derived NUP133 ( Figure 4A ) . When a 4xGFP-Swt1 construct lacking the catalytic PIN domain ( 4xGFP-Swt1ΔPIN ) was expressed in swt1Δ cells , it was concentrated in a few spots . These were always close to the NPCs , as revealed by covisualization of Nup120-mCherry ( Figure 4B ) , and 4xGFP-Swt1ΔPIN also associated with NPCs in nup133Δ cells . Targeting of Swt1ΔPIN to the NPCs did not depend on Mlp1 , Nup60 , or Esc1 . Instead , 4xGFP-Swt1ΔPIN showed more distinct and punctuate nuclear envelope staining in the mlp1Δ , nup60Δ , and esc1Δ strains than in the corresponding isogenic wild-type strains ( Figure 4B and unpublished data ) . The nucleocytoplasmic localization of full-length 4xGFP-Swt1 in the mlp1Δ , nup60Δ , and esc1Δ cells was not altered ( unpublished data ) . Together with the genetic data , these observations indicate that Swt1 interacts with the NPCs .
We show in this study that Swt1 is an RNA endonuclease that can associate with NPCs and participates in the systems that control the quality of nuclear mRNPs prior to export to the cytoplasm . The endonuclease activity of the PIN domain of Swt1 is crucial for its in vivo function at physiological ( i . e . , low ) expression levels . Heavily overexpressed Swt1 constructs lacking the PIN domain could fulfill some of the in vivo functions of Swt1 , whereas overexpression of active Swt1 was lethal . This indicates that Swt1 functions both as a nuclease and as a protein component of mRNP surveillance complexes . To avoid the cytoplasmic translation of defective mRNPs into a potentially deleterious proteins , mRNP biogenesis and surveillance systems cooperate in the nucleus . The endoribonuclease activity of Swt1 is essential when the function of complexes that cotranscriptionally form and check the nuclear mRNPs is impaired , suggesting that Swt1 could be involved in degrading the mRNA component of aberrant nuclear mRNPs . An endonuclease activity could be important because transcriptionally trapped mRNPs , export-incompetent mRNPs , or pre-mRNPs incapable of splicing all lack accessible RNA ends that would allow degradation by the nuclear 5′-exonuclease Rat1 or the 3′-exonuclease activity of the exosome . Endonuclease cleavage of defective mRNA and mRNPs by Swt1 could generate free mRNA ends for further exonucleolytic degradation . Consistent with this model , an aberrant pre-mRNA reporter construct that cannot be spliced was selectively accumulated in the catalytically inactive Swt1-D135N mutant , and loss of Swt1 from the mlp1Δ strain stabilized the nuclear-restricted SSA1 mRNA . High levels of Swt1 were toxic and induced strong nuclear poly ( A ) + RNA accumulation . Among the several possibilities , which could explain this phenotype , overexpressed Swt1 could induce erroneous RNA cleavage in properly formed RNP complexes . We estimate the number of Swt1 molecules in the cell to be only about 200–300 copies , and this low level may be important to suppress off-target RNA cleavage . The nuclear accumulation of an intron-containing reporter pre-mRNA induced by Swt1 overexpression was stronger than for the corresponding intronless pre-mRNA , consistent with a defect in pre-mRNA surveillance . Notably , overexpression of Mlp1 , but not deletion of its gene , also caused nuclear accumulation of intron-containing pre-mRNAs [14 , 28] , consistent with the model that Swt1 and Mlp1 function together in this pathway . Our data are consistent with the previously suggested role of Swt1 in TREX-assisted mRNA transcription [18] . TREX/TREX-2 complexes are required for a cotranscriptional mRNP assembly , and failure in their function leads to formation of aberrant mRNP structures , which impede RNAPII transcription [3–5 , 9] . We predict that these defective mRNP particles represent important substrates for endonuclease degradation by Swt1 . Consistent with this model , defective and export-incompetent mRNPs formed in TREX mutants accumulate in association with the NPCs [9 , 31] , where Swt1 can also be detected . In wild-type cells , Swt1 was distributed between the nucleus and cytoplasm , and hence could potentially detect the formation of defective pre-mRNPs all along the mRNP biogenesis pathway . However , Swt1 was accumulated at the NPCs when these were clustered in the nup133Δ mutant [30] . This NPC localization of Swt1 cannot be correlated to an impaired mRNA export ( nup133Δ cells also exhibit an mRNA export defect ) , as 4xGFP-Swt1 did not accumulate at the nuclear envelope in the mex67–5 mRNA export mutant ( unpublished data ) . Moreover , Swt1 lacking its endonuclease domain became concentrated in perinuclear spots even in cells with a normal complement of nucleoporins . We speculate that Swt1 is transiently recruited to the NPCs via interactions with nucleoporins and/or with export-incompetent pre-mRNPs blocked at the NPCs , and function there to prevent inappropriate pre-mRNA export . Analyses of nuclear degradation of defective RNAs in yeast have largely concentrated on the activities of the 5′- and 3′-exonucleases . This work demonstrates that endonuclease cleavage is also likely to play an important role in nuclear RNA surveillance .
Yeast strains used in this study are listed in Table S1; plasmid constructs are described in Table S2 . Site-directed mutagenesis of the SWT1 gene to create the swt1-D135N mutant was performed by fusion PCR [32] . The synthetic lethality screen with the sac3Δ strain was performed as previously described [33] . For in vitro measurements of Swt1 nuclease activity , the FLAG-Swt1 wild-type and D135N mutant protein were expressed from plasmids pADH111-FLAG-SWT1 or pADH111-FLAG-swt1-D135N ( ADH1 promoter ) and purified by anti-FLAG affinity chromatography . Briefly , 4 l of yeast culture were harvested at an optical density at 600 nm ( OD600 ) of approximately 4 . 0 , resuspended in FLAG buffer ( 50 mM Tris-HCl [pH 7 . 5] , 200 mM NaCl , 0 . 5 mM DTT , 0 . 05% NP-40 ) to a final volume of 50 ml , lysed by bead-beating ( Pulverissete 6; Fritsch ) and incubated with 120 μl of anti-FLAG M2 agarose ( Sigma ) for 120 min at 4 °C . Anti-FLAG slurry was washed by 750 volumes of FLAG buffer , and the bound material was eluted with FLAG peptide according to the manufacturer's instructions ( Sigma ) followed by concentration and buffer exchange . The analyses of nuclease activity of the purified proteins on 5′-end– or 3′-end–radiolabeled RNAs were performed at 30 °C in 15 mM Tris-HCl ( pH 7 . 6 ) , 75 mM NaCl , 2 mM DTT , 100 μg/ml BSA , 0 . 8 U/μl RNasin , 4 . 5% glycerol , 0 . 05% NP-40 , 0 . 3 μM Escherichia coli tRNA , and 7 . 5 mM MnCl2 . Prior to addition of 20–30 fmoles of the labeled RNA substrate , the 20 or 30 μl reactions containing 10 pmoles of protein were pre-incubated for 15 min at 30 °C . Subsequently , 4-μl aliquots were taken at the time points indicated in Figure 1 , and the reaction was stopped by the addition of formamide-containing RNA loading buffer . Reaction products were resolved on a denaturing 12% polyacrylamide/8 M urea gel and visualized by autoradiography . 5′-[32P]– and 3′-[32P]–labeled RNA substrates ( Dharmacon ) were labeled and gel-purified as described [34] . At least four independent experiments were performed for each strain containing an indicated lacZ pre-mRNA leakage reporter ( pJCR1 , pmut5′SS , and pmutBP ) or intronless lacZ control ( pLGSD5 ) . Transformants were pooled and grown overnight in selective raffinose medium . Cells were diluted , grown to mid-log phase and the expression of reporter was induced by 2% galactose for 150 min . β-Galactosidase assays were performed in triplicate for each sample according to the published protocol [11] with the modifications described in [16] . To induce SSA1 expression , heat shock ( 42 °C for 15 min ) was applied to exponentially grown cultures by the addition of an equal volume of prewarmed medium . The expression of lacZ pre-mRNA reporters ( pmutBP or pJCR51 ) was induced by an addition of galactose ( 2% final concentration ) to early-log phase cultures and their subsequent incubation in minimal galactose-raffinose medium for 6 h . Cells were lysed by glass beads and vortexing , and total RNA was isolated with the RNeasy Mini Kit ( QIAGEN ) . RNA was reverse transcribed using the QuatiTect Reverse Transcription Kit ( QIAGEN ) , and levels of specific RNA transcript were measured by quantitative real-time PCR using the ABI Prism 7000 system ( Applied Biosystems ) and ABsolute QPCR ROX Mix ( ABgene ) . Each cDNA sample , as well as controls without reverse transcriptase , was amplified in triplicate with the primer and probe sets described in Table S3 . The levels of SSA1 and lacZ RNA were normalized to RPB4 and GAL1 mRNA levels , respectively , which were found to be unchanged under the tested conditions . Fluorescence in situ hybridization ( FISH ) using oligo ( dT ) , SSA1 transcript- , tRNA- , and lacZ-specific Cy3-labeled probes was performed as described [33 , 35 , 14] . The localization of the 4xGFP-Swt1 , 4xGFP-Swt1ΔPIN , and Nup120-mCherry fusions was analyzed in a Zeiss Axio Imager . Z1 microscope equipped with AxioVision 4 . 6 . 3 software . The expression of Swt1 was analyzed by immunoblotting using rabbit polyclonal anti-Swt1 serum generated against the recombinant Swt1 ( PickCell Laboratories ) and anti-rabbit HRP-conjugate as a secondary antibody ( BIO-RAD ) . The expression of FLAG-tagged Swt1 protein variants was analyzed by anti-FLAG-HRP conjugate ( Sigma ) . | Nuclear export of messenger RNA ( mRNA ) is a crucial step during eukaryotic gene expression . Newly synthesized precursor mRNAs are processed during synthesis , packaged into messenger ribonucleoprotein particles ( mRNPs ) , and transported through the nuclear pore complex to the cytoplasm . To avoid nuclear export of aberrant transcripts and their translation in the cytoplasm , the quality of nuclear mRNPs is monitored by several surveillance systems . Here we show that the conserved protein Swt1 is an RNA endoribonuclease , an RNA-degrading enzyme , that becomes indispensable when factors involved in co-transcriptional mRNP assembly and mRNP quality control are mutated . We found that inactive Swt1 increases the levels and cytoplasmic leakage of aberrant , unprocessed precursor mRNA . Moreover , Swt1 accumulates at the nuclear pore complexes in the pore-clustering nup133Δ mutant . Thus , we speculate that the Swt1 endoribonuclease can be transiently recruited to the nuclear periphery to initiate the degradation of defective , pore-trapped pre-mRNPs in order to prevent their inappropriate cytoplasmic export . | [
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] | 2009 | An Endoribonuclease Functionally Linked to Perinuclear mRNP Quality Control Associates with the Nuclear Pore Complexes |
Treatment of Buruli ulcer , or Mycobacterium ulcerans disease , has shifted from surgical excision and skin grafting to antibiotic therapy usually with 8 weeks of daily rifampin ( RIF ) and streptomycin ( STR ) . Although the results have been highly favorable , administration of STR requires intramuscular injection and carries the risk of side effects , such as hearing loss . Therefore , an all-oral , potentially less toxic , treatment regimen has been sought and encouraged by the World Health Organization . A combination of RIF plus clarithromycin ( CLR ) has been successful in patients first administered RIF+STR for 2 or 4 weeks . Based on evidence of efficacy of clofazimine ( CFZ ) in humans and mice with tuberculosis , we hypothesized that the combination of RIF+CFZ would be effective against M . ulcerans in the mouse footpad model of M . ulcerans disease because CFZ has similar MIC against M . tuberculosis and M . ulcerans . For comparison , mice were also treated with the gold standard of RIF+STR , the proposed RIF+CLR alternative regimen , or CFZ alone . Treatment was initiated after development of footpad swelling , when the bacterial burden was 4 . 64±0 . 14log10 CFU . At week 2 of treatment , the CFU counts had increased in untreated mice , remained essentially unchanged in mice treated with CFZ alone , decreased modestly with either RIF+CLR or RIF+CFZ , and decreased substantially with RIF+STR . At week 4 , on the basis of footpad CFU counts , the combination regimens were ranked as follows: RIF+STR>RIF+CLR>RIF+CFZ . At weeks 6 and 8 , none of the mice treated with these regimens had detectable CFU . Footpad swelling declined comparably with all of the combination regimens , as did the levels of detectable mycolactone A/B . In mice treated for only 6 weeks and followed up for 24 weeks , there were no relapses in RIF+STR treated mice , one ( 5% ) relapse in RIF+CFZ-treated mice , but >50% in RIF+CLR treated mice . On the basis of these results , RIF+CFZ has potential as a continuation phase regimen for treatment of M . ulcerans disease .
Buruli ulcer ( BU ) , or Mycobacterium ulcerans disease , has become a more readily treatable disease since 2004 with the transition from only surgery and skin grafting to antibiotic therapy with the combination of rifampin ( RIF ) and streptomycin ( STR ) , the WHO preferred regimen or with RIF plus a fluoroquinolone or clarithromycin if STR is contraindicated , complemented by surgery in the case of extensive lesions to prevent or correct functional disabilities [1] . Although initial trials with the RIF+STR regimen included few reports of ototoxicity , objective measurements of hearing loss indicate that it may occur rather frequently [2] . For this reason and also operational issues with STR , an all-oral regimen is sought . Currently , there is hope that RIF plus clarithromycin ( CLR ) can be an acceptable oral regimen . However , this regimen would still require eight weeks of drug administration and may be complicated by gastrointestinal intolerance and the induction of CLR metabolism by RIF [3–7] . In addition , CLR is administered twice daily in some settings [5 , 6 , 8] . To date , a pilot study in Benin [9] has shown that patients with limited lesions can be successfully treated without relapse if treated with RIF+CLR for 8 weeks . Trials in Ghana have shown that treatment with RIF+STR for 4 weeks , followed by RIF+CLR for 4 weeks [10] or RIF+STR for 2 weeks , followed by RIF+CLR for 6 weeks [11] are as efficacious as RIF+STR for 8 weeks . In mice , RIF+CLR was inferior to RIF+STR and also inferior to the combination of the long-acting rifamycin , rifapentine plus CLR whether assessed by bactericidal activity , regression of footpad swelling , or recurrence ( relapse ) of swelling after treatment completion [12] . Unlike tuberculosis and leprosy , which have been treated with antibiotics for nearly 70 years , experience with successful antimicrobial treatment of BU has been limited to a decade . Alternative regimens and rhythms of treatment have had little opportunity for testing . Earlier studies of single drug treatment were uniformly disappointing , including a trial [13] of clofazimine ( CFZ ) , a drug used in the combined drug regimen for multibacillary leprosy [14 , 15] . Based on recent favorable results in clinical [16] and mouse model studies [17 , 18] , CFZ is receiving renewed attention for treatment of TB . Its mechanism of action is complex [19–22] and studies are ongoing to better define it . From murine model TB studies , it is apparent that its activity is delayed but features strong bactericidal and sterilizing attributes [17] . Because CFZ has never been studied in combination with other drugs in BU treatment , we hypothesized that together with RIF , CFZ could be a potent alternative to CLR and that a regimen of RIF+CFZ could be as effective as the standard RIF+STR regimen . Here , we report that all three combinations , RIF+STR , RIF+CLR and RIF+CFZ achieved culture conversion by six weeks even against a high burden M . ulcerans infection in a mouse footpad model of BU . These regimens also stopped production of mycolactone A/B , the principal virulence factor of M . ulcerans [23] . CFZ had indeed poor initial activity when used as monotherapy but in combination with RIF , it was able to provide protection against relapse that was as effective as STR and more effective than CLR .
M . ulcerans 1059 ( Mu1059 ) , originally obtained from a patient in Ghana , and Mu1615 , originally obtained from a patient in Malaysia , were generously provided by Dr . Pamela Small , University of Tennessee . Autoluminescent Mu1059 ( Mu1059AL ) was generated in our laboratory [24 , 25] . These strains all produce mycolactone A/B and this toxin kills macrophages and fibroblasts in vitro [26 , 27] . The Mu1617 type strain , originally isolated in Australia , has apparently lost the ability to produce mycolactone [26 , 28] . The Mu1059 strain was passaged in mouse footpads before use in these studies . The bacilli were harvested from swollen footpads at the grade 2 level , i . e . , swelling with inflammation of the footpad [29] . All animal procedures were conducted according to relevant national and international guidelines . The study was conducted adhering to the Johns Hopkins University guidelines for animal husbandry and was approved by the Johns Hopkins Animal Care and Use Committee , protocol permit number MO11M103 . The Johns Hopkins program is in compliance with the Animal Welfare Act regulations and Public Health Service Policy and also maintains accreditation of its program by the private Association for the Assessment and Accreditation of Laboratory Animal Care International . RIF , CFZ , and STR were purchased from Sigma ( St . Louis , MO ) . CLR was kindly provided by Abbott ( Abbott Park , IL ) . STR and RIF were dissolved in distilled water , and CLR and CFZ were suspended in distilled water with 0 . 05% agarose . All drugs were given 5 days per week in 0 . 2 ml . RIF ( 10 mg/kg ) , CFZ ( 25 mg/kg ) , and CLR ( 100 mg/kg ) were administered by gavage . STR ( 150 mg/kg ) was administered by subcutaneous injection . of CFZ for M . ulcerans Mu1059 , as well as for Mu1615 , Mu1059AL , and Mu1617 was determined using a range of drug concentrations ( 0 . 06 , 0 . 12 , 0 . 25 , 0 . 5 , 1 . 0 and 2 . 0 μg/ml ) by the proportion method on Middlebrook 7H11 medium . The MIC was defined as the lowest drug concentration preventing at least 99% of the growth observed on drug-free plates . The MIC for all strains tested was between 0 . 25 and 0 . 5 μg/ml . BALB/c mice ( N = 290 ) , age 4–6 weeks ( Charles River , Wilmington , MA ) , were inoculated in both hind footpads with approximately 3 . 3 log10 ( 2 . 0 x103 ) CFU of Mu1059 in 0 . 03 ml PBS . Treatment was begun 7 weeks after infection when footpad swelling increased to approximately the grade 2 level . Treatment with RIF+STR , RIF+CLR , RIF+CFZ or CFZ alone was to be administered for 8 weeks ( until week 15 after infection ) . Footpads were harvested before treatment initiation and then every two weeks from mice ( 5 footpads for CFU count , 3 for CFZ concentrations , 2 for ML detection ) ( Table 1 ) . Mice were euthanized if they reached grade 3 swelling . Footpad tissue was harvested , minced with fine scissors , suspended in 1 . 0 ml PBS , serially diluted , and plated on Middlebrook selective 7H11 plates ( Becton-Dickinson , Sparks , MD ) . Footpad homogenates from mice treated with CFZ were also plated in parallel on Middlebrook 7H11 containing 0 . 4% activated charcoal to reduce drug carryover effects . Plates were incubated at 32°C and colonies were counted after 12 weeks of incubation . Footpads were harvested for detection of mycolactone by removal of soft tissue from the dorsal and ventral footpads and then immediately immersing it into a polypropylene Micrewtube tube with O-ring and screw cap ( Simport Scientific , Beloeil , QC , Canada ) containing 1500 μl absolute ethanol . Tubes were wrapped in foil and kept in the dark at room temperature . Samples were usually shipped overnight to the Kishi lab within 24 hours . The extraction procedure has been described in detail previously [31] . The extracted material was spotted onto glass TLC plates along with synthetic mycolactone A/B standards and developed in 90:10:1 chloroform:methanol:water , air-dried , and dipped in boronic acid [17] , heated for 5–10 seconds at 100°C . After wiping the glass back with acetone on a paper towel , the plate was placed on a UV lamp with a 365 nm filter . Fluorescent spot intensity was compared to that of the standards to estimate the amount of mycolactone A/B in the sample , as previously described [31] . For mice receiving CFZ alone or RIF+CFZ , footpad tissue from three of the five sacrificed mice at weeks 2 , 4 , 6 and 8 of treatment was submitted for quantification of CFZ . The footpad tissue was minced in 2 drops of PBS , placed in 900 μl absolute EtOH , and kept at 4°C until shipment to the Adamson lab . One hundred microliters of each prepared sample was added to 100 μl of water and 400 μl of acetonitrile , vortexed for 15 seconds and then centrifuged for 7 minutes at 16 , 000 × g at 4°C . After centrifugation 500 μL of the supernatant was combined with an equal volume of water in a capped LC/MS sample vial . LC/MS analysis was performed on an AB Sciex 5500 Q-Trap triple quadrupole MS system coupled with an Agilent 1200 system with refrigerated autosampler . A Waters Exterra 2 . 1 mm × 50 mm C18 column was used for chromatographic separation , along with an isocratic mobile phase consisting of 50% water and 50% acetonitrile , both solvents containing 0 . 1% ( v/v ) formic acid . The lower limit of quantitation was 0 . 048 μg per ml of tissue homogenate . The following parameters were used for concentration determination: for CFZ , transition 474/431 . 9; declustering potential 231 volts; collision energy CE 51 volts; entrance potential of 10 volts . LC/MS reagents , including LC/MS-grade acetonitrile , molecular grade formic acid and the clofazimine standard , were purchased from Sigma . Milli-Q water was used throughout the LC/MS procedure . GraphPad Prism 6 was used to compare group means by student’s T test and analysis of variance and linear regression analysis for comparison of slopes and intercepts . Survival comparisons were assessed by the Gehan-Breslow-Wilcoxon test .
Treatment was initiated seven weeks after the inoculation of 1 . 89±0 . 23 log10 CFU of M . ulcerans strain Mu1059 , when the mean footpad swelling index was 1 . 78±0 . 32 [29] , the mean CFU count was 4 . 64 ± 0 . 14 log10 , mycolactone A/B concentrations averaged 10 ng per footpad ( Fig 1 ) . Mice were evaluated daily . Failure to respond to treatment , as manifested by progressive footpad swelling and ulceration , necessitated euthanasia of some mice . Untreated mice and mice treated with CFZ alone were euthanized at day 12 , after only 9 doses of CFZ due to deteriorating footpad lesions . At this time the swelling grade had advanced to 3 . 31 ± 0 . 26 and 3 . 25 ± 0 . 31 in the control and CFZ alone mice , respectively ( Fig 1A ) . Mycolactone A/B concentrations were higher among untreated controls compared to CFZ-treated mice ( 18 . 75 ± 1 . 77 ng/100 mg footpad versus 7 . 50 ± 2 . 39 ng/footpad , respectively ) ( Fig 1B ) . However , due to co-migration of a pink spot with mycolactone in the TLC , the concentrations may not have been accurately discerned in CFZ-treated mice . Mean CFU counts in footpads were 5 . 37 ± 0 . 07 and 4 . 72 ± 0 . 59 in control and CFZ alone mice , respectively ( Fig 1C ) . From these data , we conclude that treatment with CFZ alone had little or no impact on lesion size and bacterial load . At the initiation of treatment , swelling grade , an indicator of disease severity , was similar in all the groups except that the RIF+STR group had a lower swelling grade than other groups ( S1A Fig ) . By day 11 after treatment initiation , 9 RIF+STR , 16 RIF+CLR , and 37 RIF+CFZ mice had been euthanized from among the 60 mice allocated to each group . In terms of survival , treatment with RIF+CFZ was superior to both no treatment ( p<0 . 0001 ) and treatment with CFZ alone ( p = 0 . 0023 ) but inferior to RIF+STR and RIF+CLR ( p <0 . 0001 and p = 0 . 002 , respectively ) ( S1B Fig ) . Among surviving mice , the combination regimens resulted in improvement in all parameters . By week 2 , footpad swelling indices decreased to 1 . 33 ± 0 . 29 , 1 . 33 ± 0 . 24 , and 1 . 28 ± 0 . 25 in mice treated with RIF+STR , RIF+CLR , and RIF+CFZ , respectively ( Fig 1A ) . Mycolactone A/B concentrations declined to 8 . 75 ± 5 . 30 , 6 . 25 ± 1 . 77 , and 7 . 50 ± 3 . 54 in these same groups , with the same caveat about estimating mycolactone concentrations in CFZ-treated mice ( Fig 1B ) . CFU counts were lowest among mice treated with RIF+STR ( 3 . 39 ± 0 . 29 ) , but were also lower among mice treated with RIF+CLR ( 4 . 24 ± 0 . 50 ) and RIF+CFZ ( 4 . 23 ± 0 . 49 ) ( Fig 1C ) . By week 4 , footpad swelling grades averaged less than 1 in RIF+STR ( 0 . 8 ± 0 . 23 ) and RIF+CLR ( 0 . 88 ± 0 . 18 ) treated mice but were not significantly reduced in RIF+CFZ ( 1 . 58 ± 0 . 50 ) treated mice . Mycolactone A/B was detectable at 7 . 50 ± 3 . 54 , 5 . 00 ± 0 . 00 , and 11 . 25 ± 1 . 77 ng/footpad in the three groups , respectively . CFU counts declined to 1 . 59 ± 0 . 21 , 1 . 37 ± 0 . 73 , and 2 . 22 ± 1 . 11 in the RIF+STR , RIF+CLR , and RIF+CFZ groups , respectively . At week 6 , mean footpad swelling was limited , with mean grades of 0 . 30 ± 0 . 16 , 0 . 43 ± 0 . 17 , and 0 . 70 ± 0 . 20 . Mycolactone A/B was undetectable in the RIF+STR and RIF+CFZ mice while small amounts ( 3 . 75 ± 1 . 77 ng/footpad ) remained in the RIF+CLR mouse footpads . CFU counts were uniformly negative at this time point . By week 8 , swelling had disappeared in most footpads of RIF+STR treated mice ( 0 . 05 ± 0 . 11 ) , was minimal in RIF+CLR treated mice ( 0 . 16 ± 0 . 26 ) and was limited in RIF+CFZ treated mice ( 0 . 42 ± 0 . 13 ) . Traces of mycolactone A/B were detectable in RIF+STR mice ( 0 . 50±0 . 71 ) and RIF+CLR mice ( 1 . 00±0 . 00 ) ; RIF+CFZ mice were not tested at this time point in order to reserve mice for week 8 CFU and week 6-relapse analysis . No CFUs were isolated from the footpads of mice treated with all combination regimens . We conclude that RIF+STR has the most rapid bactericidal effects while RIF+CLR and RIF+CFZ , with time , were ultimately as effective as RIF+STR in arresting footpad swelling and eliminating cultivable bacteria . CFZ is well known to cause discoloration of tissues , particularly ears and tails of mice and subcutaneous fat , and organs such as lung and spleen [17 , 18] . Compared to control mice ( Fig 2A ) , we noted both increased inflammation and reddening of footpads in mice treated with CFZ ( Fig 2B , S2 Fig ) whereas other mice treated with , e . g . , RIF+CLR ( S2 Fig ) had inflammation of footpads but no discoloration of ears or tails . To confirm that CFZ reached infected footpads , we carried out a pharmacokinetic analysis . Mice treated with CFZ alone were found to have 6 . 97 ± 4 . 12 μg/100 mg footpad tissue 11 days after treatment initiation at which time all mice in this group were sacrificed . Mice treated for 2 weeks with the combination of RIF+CFZ had 14 . 88 ± 5 . 30 μg/100 mg footpad and the concentration increased to 93 . 85 ± 23 . 14 at week 4 before plateauing at 55 . 20 ± 14 . 26 and 56 . 10 ± 11 . 32 μg/100 mg footpad at weeks 6 and 8 . The discoloration waned and was no longer detectable three weeks after treatment cessation . Untreated and RIF+STR controls had no detectable CFZ ( Fig 2C ) . Mice were monitored weekly for recurrence of footpad swelling after treatment cessation . Footpads in which a recurrence of swelling was observed were taken for histologic and microbiological confirmation of the presence of M . ulcerans . Among mice treated for 6 weeks , all ( 10 mice , i . e . , 20 footpads for RIF+STR and RIF+CLR groups; 5 mice , i . e . , 10 footpads for RIF+CFZ group ) treated with one of the combination regimens remained relapse free for 10 weeks after treatment . After 10 weeks , mice treated with RIF+CLR started to display swollen footpads that progressed from grade 1 to grade 2 . By week 28 following treatment cessation , 50% of RIF+CLR-treated mice had recurrent footpad swelling . Additional mice treated with RIF+CLR for 6 weeks developed lesions after treatment cessation , resulting from dissemination beyond the footpads—on the ear ( Fig 3A and 3B ) or tail ( Fig 3C ) without showing relapse in the footpad . The median time to relapse as assessed by development of a lesion anywhere in the body was 21 weeks in this group ( Fig 4A ) . In contrast , mice treated with RIF+STR or RIF+CFZ for 6 weeks remained relapse free with the exception of a single RIF+CFZ mouse in which dissemination to the tail was observed at week 33 just before the end of the experiment at week 34 . The presence of M . ulcerans was confirmed in all footpads by the presence of numerous acid-fast bacilli at histology and/or culture . Among mice treated for 8 weeks , all ( 21 mice , i . e . , 42 footpads for RIF+STR and 13 mice , i . e . , 26 footpads for RIF+CLR groups ) treated with one of the combination regimens remained relapse free for 15 weeks after treatment at which time , mice treated with RIF+CLR began to show relapse . However , the percentage of recurrent swelling of footpads after 8 weeks of treatment did not exceed 25% ( Fig 4B ) . Treatment with RIF+STR for 8 weeks prevented relapse in all mice .
Although the initial bactericidal activity of RIF+CFZ was worse than that of RIF+STR and RIF+CLR ( as measured by CFU and the number of mice requiring euthanasia ) , the sterilizing ( relapse protection ) was comparable to RIF+STR and superior to the currently proposed all-oral regimen of RIF+CLR . The delayed bactericidal activity of CFZ-containing regimens against M . ulcerans , associated with increased sterilizing activity ( absence of relapse ) , parallel what has been observed against M . tuberculosis [17 , 18] . The consequences of relapse are potentially much more serious in tuberculosis , a fatal disease , than in Buruli ulcer , a disabling disease . However , adequate treatment durations are required to prevent further morbidity and the need for retreatment in either disease . Limited data indicate that relapse is rare in patients treated with RIF+STR whereas paradoxical reactions that may be confused with relapse are more frequent [32 , 33] . Given the anti-inflammatory properties of CFZ , its incorporation into BU therapy could potentially reduce the frequency of paradoxical reactions in addition to reducing the treatment duration necessary for cure . The superior sterilizing activity of RIF+CFZ compared to RIF+CLR suggests that the former may be a more promising all-oral regimen to eliminate or minimize the need for STR , an injectable drug that also has potential toxic side effects . However , despite the drawbacks of STR , its potent antibacterial activity should not be overlooked . These tradeoffs have shaped the design of recent clinical trials that have adopted the concept of intensive and continuation phase regimens that are standard in the treatment of TB . In these studies treatment with RIF+STR was reduced from the WHO-recommended 8 weeks to 4 and even 2 weeks with a continuation of treatment with RIF+CLR for the balance of the 8 weeks [10 , 11] . The results were favorable with all of these alternatives and limited exposure to STR but did not totally eliminate it . At this time , the frequency of relapse after RIF+CLR treatment is not known . Interestingly , there are reports [5 , 9] of cases of nodular , non-ulcerating Buruli ulcer not responding to this regimen . The principal drawback of CFZ is the skin discoloration that results from its administration . However the negative cosmetic impact of such skin discoloration is likely to be minimal , and to resolve rapidly , with treatment durations of 8 weeks or less . Although the initial response to CFZ treatment was delayed , as is also seen in TB [17 , 18 , 34] , mean footpad swelling and detectable mycolactone A/B in any combination regimen was reduced comparably after two weeks of treatment in the remaining mice while the CFU counts ( bacillary burden ) were significantly ( p = 0 . 01 ) lower in the RIF+STR mice than in the RIF+CLR or RIF+CFZ treated mice . As in other mycobacterial diseases , it is important to have a balance of early bactericidal activity , sterilizing activity , safety , and administrative simplicity in a drug regimen . M . ulcerans disease requires antibiotic treatment for only 8 weeks—much shorter than the times for tuberculosis ( 6 months for drug-susceptible TB ) or leprosy ( 6 months for paucibacillary leprosy and at least one year for multibacillary leprosy ) —even though the bacterial burden per gram of tissue can be extremely high in M . ulcerans lesions . Achieving a better balance in the treatment of M . ulcerans disease therefore might include treatment for one or two weeks with three drugs: RIF , STR , and CFZ . Beyond the initial phase , the duration of which remains to be determined , STR could be discontinued having achieved the early bactericidal activity . RIF and CFZ could be continued to avoid STR-induced toxicities , provide continued bactericidal activity , allow for outpatient drug administration , and provide strong sterilizing activity . CFZ is administered for two years normally for lepromatous leprosy with transient skin discoloration being the only side effect . This proposed regimen of three drugs for the first one or two weeks followed by RIF+CFZ alone could potentially also be shortened to 6 weeks or less with no loss of therapeutic benefit . Future experiments will test these regimens in the mouse model of M . ulcerans disease . Our study has several potential limitations . The first is that the greater early mortality observed in the RIF+CFZ group may have introduced a survival bias that exaggerated the sterilizing activity of this combination relative to the other regimens . Nevertheless , the fact that only 2 ( of 20 ) footpads of mice treated with RIF+CLR survived without relapse more than 28 weeks after treatment compared with all 10 footpads of mice treated with RIF+CFZ for the same duration indicates superior sterilizing activity of the RIF+CFZ regimen even if approximately twice as many RIF+CFZ-treated mice could not be assessed due to early mortality . The second potential limitation is that relapse in mice cannot be equated with relapse in human BU patients . Evidence that BU may heal spontaneously and that some lesions harboring viable bacteria at the end of treatment resolve without further treatment suggest that the human immune response participates in the sterilization process , perhaps more than the murine immune response does [10 , 11 , 35] . However , even if one assumes that sterilization of BU lesions by chemotherapy is not necessary for cure in some patients , the duration of therapy needed for efficacy in a population is likely to be determined by those patients with an inadequate host response , where chemotherapy may have a more important role in sterilization and ultimately determine the duration of treatment needed for the population as a whole . We consider relapse to be the best outcome measure for comparing the relative ability of regimens to eradicate persisting bacteria in mice and use it under the assumption that the duration of treatment required for efficacy in the clinical setting is determined by the rate at which such persisting bacilli are eliminated . In this context , RIF+CFZ had superior sterilizing activity over RIF+CLR . Only future clinical trials comparing RIF+CLR and RIF+CFZ head-to-head as part of a treatment regime will enable further evaluation of the predictive accuracy of this experimental endpoint . | Buruli ulcer ( BU ) is caused by Mycobacterium ulcerans and its toxin , mycolactone . Since 2004 , BU has been treated primarily with antibiotics rather than surgery and skin grafting . The current first-line regimen is an oral drug , rifampin ( RIF ) , and an injectable drug , streptomycin ( STR ) , daily for 8 weeks . Because STR injections are painful and have potential side effects , such as hearing loss , a replacement drug is sought . Emerging evidence of the efficacy of the anti-leprosy drug clofazimine ( CFZ ) against tuberculosis prompted an evaluation of CFZ + RIF as well as another all-oral regimen , RIF + clarithromycin ( CLR ) in a mouse model of BU . The results showed that RIF+CFZ initially acts more slowly against M . ulcerans than RIF+STR or RIF+CLR but it stops mycolactone production and is as good as RIF+STR and better than RIF+CLR at preventing relapse of infection . A drug regimen with a combination of three drugs , RIF+STR+CFZ , for one or two weeks followed by RIF+CFZ has the potential to limit the duration of STR treatment and achieve comparable cure . | [
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] | [] | 2015 | Efficacy of Rifampin Plus Clofazimine in a Murine Model of Mycobacterium ulcerans Disease |
Once biological systems are modeled by regulatory networks , the next step is to include external stimuli , which model the experimental possibilities to affect the activity level of certain network’s nodes , in a mathematical framework . Then , this framework can be interpreted as a mathematical optimal control framework such that optimization algorithms can be used to determine external stimuli which cause a desired switch from an initial state of the network to another final state . These external stimuli are the intervention points for the corresponding biological experiment to obtain the desired outcome of the considered experiment . In this work , the model of regulatory networks is extended to controlled regulatory networks . For this purpose , external stimuli are considered which can affect the activity of the network’s nodes by activation or inhibition . A method is presented how to calculate a selection of external stimuli which causes a switch between two different steady states of a regulatory network . A software solution based on Jimena and Mathworks Matlab is provided . Furthermore , numerical examples are presented to demonstrate application and scope of the software on networks of 4 nodes , 11 nodes and 36 nodes . Moreover , we analyze the aggregation of platelets and the behavior of a basic T-helper cell protein-protein interaction network and its maturation towards Th0 , Th1 , Th2 , Th17 and Treg cells in accordance with experimental data .
Biological networks are often formed by interacting proteins and molecules . Their change in time is often biologically regulated to adapt to different conditions . Detailed mathematical models describe changes of proteins , for instance their phosphorylation state or activity in general by differential equations . We analyze here in particular the question , how we can calculate the result of a pharmacological intervention and identify the best target points ( receptors or downstream in the cascade or any other point in the network ) to shift the network state into a new activity pattern as desired ( either for medical or for research purpose ) . Our approach thus allows the user to define the intervention points of choice and we then systematically calculate the optimal steering options the user has available to drive the network then into the desired state . We include the information how well the objective can be met with this choice . Mathematically speaking , regulatory networks are commonly modeled by a system of coupled ordinary differential equations ( ODEs ) [1] . Then , a network is analyzed with respect to its steady states of the corresponding system of coupled ODEs [2 , 3] and associated with stable states observed in the corresponding real network like an expression pattern of different genes influencing each other in a cell . If once the system is in a steady state , it remains there for all times and it is not able to switch its state . Such an approach considers the network as a closed system where there are no interactions with the environment , in contrast to an open system which interacts with its environment . Usually , one models just a section of reality . This section has to interact with its environment such that we can notice it . Out of this thought , we realize that a model is supposed to consider interactions with the environment in which it is embedded . Following this concept , we extend the model presented in [3] and [1] by external stimuli . These external stimuli act on certain nodes of the network by increasing or decreasing the level of activation of a node , called activation or inhibition , respectively . A catchy example for including external stimuli into regulatory networks is signalling between different cells . For instance the secretion of interferons or interleukins secreted by T-cells can serve as an external stimulus for another cell type of the immune system . Further , based on the concept of regulatory networks with external stimuli , one can couple different model networks to see if the steady state of one network where some certain nodes are active can induce a switch in another model network where these active nodes of the first network affect the second network as external stimuli . In fact , we show in the present work that these external stimuli can cause a switch between an initial steady state of a network and a desired final steady state of the network . That means that we address the observation that a real system can change its quite stable state if an external perturbation is strong enough to cause this switch . A plausible example is the differentiation of stem cells . Different tissues are associated with different steady states of the corresponding regulatory network . Now , by applying agents which are the mentioned external stimuli , the stem cells differentiate to different tissues , corresponding to the external stimuli , see for example [4 , 5] . Another issue is that the cells can even be reprogrammed that means that the cell being a certain type of tissue changes its type of tissue , see for example [6] . A further special case is the switch from a cancer cell to apoptosis [7 , 8] where each genetic expression program associated with a cancer cell or apoptosis , respectively , can be associated with a certain steady state of an appropriate network of ODEs . Our approach provides a rational method to analyze networks with respect to the influence of external stimuli on the considered network , especially its steady states . For this purpose , for example Jimena [3] , SQUAD [2] or [1] aim at analyzing and finding steady states of a network where no external stimulus is applied to . This method and software tool presented in the present work is a systematical approach to steady state analysis and the transfer of one steady system state to a different one . This can be achieved by interpreting the external stimuli framework as an optimal control framework among others and thus different algorithms can be used in order to determine appropriate external stimuli for the desired switch . With the virtue of this framework it is possible to analyze huge networks ( more than 100 nodes ) where simultaneously possible intervention points , which are the external stimuli and can be drugs for instance , are registered , that can even affect several nodes at once , that means the expression of several genes at once . Then one can calculate the best drug combination that influences the network in the desired direction from all the information of interaction and intervention points that is encoded in the interaction graph of the nodes with the corresponding external stimuli . In Subsection “An extension of regulatory networks with external stimuli” ( Methods ) , we give a mathematical description of the model presented in [1] and explain in detail how to model these external stimuli by mathematical equations and how to extend the mathematical model by external stimuli , followed by Subsection “A mathematical calculus to determine external stimuli” , the mathematical calculus to determine external stimuli causing the desired switch between two different steady states of the network . For this purpose , the problem is formulated as an optimal control problem where a target functional is minimized subject to constraints . The corresponding optimum of the target functional is obtained by the optimal controls that correspond to optimal external stimuli . The optimal controls are calculated essentially by a Lagrange ansatz or alternatively by the Pontryagin maximum principle . In order to solve the resulting equations , a projected gradient method and a sequential quadratic Hamiltonian method are implemented . In Subsection “Analysis of the aggregation of platelets” , we analyze a regulatory network for platelet aggregation proposed in [9] that is fitted to experimental data . We validate our framework by finding these external stimuli that trigger aggregation in the corresponding experiment . In Subsection “A switch between two different types of T-helper cells” , we investigate the model for T-cell differentiation into Th1 , Th2 , Th17 or Treg from Th0 proposed in [10] , apply the framework of external stimuli and predict strategies how to switch from the Th17 cell type to the Treg cell type . Furthermore , we provide a discussion of these results to validate our method in a biological context . For this purpose , we compare our calculated interventions with experimental evidence from three experimental groups confirming our interventions for the calculated switch from Th17 cell type to Treg cell type . In the Discussion we compare our software package to alternative software dealing with the analysis of regulatory networks and explain how to combine different software packages to bunch it together to a great tool for network analysis and modeling with regulatory networks . Furthermore we discuss the sensitivity of the proposed method and how the method can be used for modeling taking systematically the external stimuli into account . We conclude the Discussion by illustrating the realization of our predictions . A conclusion completes the Discussion . Furthermore , besides the Matlab codes , supplementary material is provided with the present work where mathematical and algorithmic details of the used methods are explained .
An extension of regulatory networks with external stimuli hence means that we can mathematically describe the result of network interventions , either pharmacological or by molecular manipulations such as RNAi , knock downs or any protein inactivation by other methods including chemical or genetic activity inhibition in the following only mentioned as “knock down” as well as any other method , e . g . changing environmental conditions such as pH . In this subsection , we extend the model for continuous regulatory networks described in [1] such that our model includes external stimuli . Their model is a set of equations that can be used to translate the graph of any regulatory network into a continuous dynamical system . Hence these are all differential equations as shown in detail in the following , see Eq ( 1 ) . Following ( 1 ) , we insert the activating and inhibiting nodes of the considered networks . By our provided software only the graphs depicted in the corresponding figure in this work are needed to create the corresponding equations . If there is a need to see the equations used for the calculations here or in results , please see the provided Matlab files where the equations are implemented in the main-file that are available for downloading . Our model extends this now by ordinary differential equations for external stimuli , see ( 2 ) . Eq ( 2 ) can also be obtained with the provided software by inputting the graph depicted in Fig 1 . By these external stimuli , we can influence the network , which may result in a switch between different steady states of the regulatory network . We start with a network of nodes where the change in time of the activation level x k : R 0 + → [ 0 , 1 ] of each node k ∈ {1 , … , n} is described by the following ordinary differential equation d x k d t = - e 1 2 h + e - h ( ω k - 1 2 ) ( 1 - e 1 2 h ) ( 1 + e - h ( ω k - 1 2 ) ) - γ k x k ( 1 ) with ω k = { A I if x k has activators and inhibitors A if x k has only activators I if x k has only inhibitors and A = ( 1 + ∑ j ∈ A k α j k ∑ j ∈ A k α j k ) ( ∑ j ∈ A k α j k x j 1 + ∑ j ∈ A k α j k x j ) , I = 1 - ( 1 + ∑ j ∈ I k β j k ∑ j ∈ I k β j k ) ( ∑ j ∈ I k β j k x j 1 + ∑ j ∈ I k β j k x j ) where the activators of node k are elements of the subset {xj| j ∈ Ak ⊆ {1 , … , n}} ⊆ {xk| k ∈ {1 , … , n}} where Ak contains all the indices of {1 , … , n} of the nodes which activate node k and the corresponding α j k > 0 weights the contribution of the activation level xj of node j to the total activation of node k . Analogously , the inhibitors of node k are elements of the subset {xj| j ∈ Ik ⊆ {1 , … , n}} ⊆ {xk| k ∈ {1 , … , n}} where Ik contains all the indices of {1 , … , n} of the nodes which inhibit node k and the corresponding β j k > 0 weights the contribution of the activation level xj of node j to the total inhibition of node k . Furthermore , h > 0 where h models the cooperativity . If h is big , then the behavior of the equation is close to a switcher while small h are closer to a linear behavior of the activation level with respect to the input activation level . According to [1] , the first term of ( 1 ) is called the activation function or activation term and the second term is called the decay where γk > 0 models the speed of decay of the activation level xk of node k . In the next step , we show how to extend models consisting of ordinary differential equations with external stimuli on the example of the model ( 1 ) . External stimuli are all the intervention possibilities that an experimenter has to influence the considered system or the modeled experiment , respectively . For this purpose , we define the set of all external stimuli S ≔ {uj| j ∈ {1 , … , m}} where u j : R 0 + → [ 0 , 1 ] . In our model uj = 0 means that the external stimulus is not active while the activity level of the stimulus is supposed to be linearly interpolated to its maximum activity level that is modeled by uj = 1 . An activation of node k by an external stimulus j is modeled by adding σkj uj ( 1 − xk ) to the right hand-side of ( 1 ) . That means if the external stimulus uj ≡ 0 , then there is no activation of node k by the external stimulus j and if node k has full activity , then the external stimulus has also no effect on the activation level xk of node k . Analogously , an inhibition of node k by an external stimulus j is modeled by subtracting ηkjujxk from the right hand-side of ( 1 ) . That means , if the external stimulus uj ≡ 0 , then there is no inhibition of node k by the external stimulus j and if node k has no activity , then the external stimulus j has no effect on the activation level xk of node k . The parameters σkj ≥ 0 and ηkj ≥ 0 are used to fit the value uj to experimental activation or inhibition of nodes caused by the modeled external stimulus or can be used to weight the contribution of external stimulus uj to the activation or inhibition , respectively , of node k where σkj = 0 or ηkj = 0 means that external stimulus j does not directly effect node k . Our model for the change in time of the activation level xk of node k is given as in the discussion above but exchanging ( 1 ) by d x k d t = - e 1 2 h + e - h ( ω k - 1 2 ) ( 1 - e 1 2 h ) ( 1 + e - h ( ω k - 1 2 ) ) - γ k x k + ∑ j = 1 m σ k j u j ( 1 - x k ) - ∑ j = 1 m η k j u j x k ( 2 ) for k ∈ {1 , … , n} . We remark that in the framework of modeling , without any external stimuli , we have an unperturbed network . That especially means that the values of the steady states of a regulatory network are independent of the external stimuli . Thus these steady states are a priori given or computable , respectively , without knowing what external stimuli might effect the regulatory network . This is reasonable because if the steady states of regulatory network model all the genetic programs which a cell can perform for instance , then these programs are an intrinsic property of this cell independent of possible external stimuli which ever can exist . However , an external stimulus might cause a change from one genetic program to another if applied . Next , we illustrate how these external stimuli can be realized in a real experiment . In general , we say that the activation level of a node is high if the product which corresponds to the node has a high concentration and is low if the corresponding product’s concentration is low . An example is the expression of a protein by a gene . If the concentration of the protein is high , then we say that the corresponding gene is at a high activation level and analogously reverse . That means , that activation of a node is every operation on the node which increases the concentration of the product associated with a certain node . Therefore , increasing the activation level can be done by adding the product of the corresponding node to the system with which it influences other nodes , like RNA or protein . This simulates a higher activation level of the corresponding node . Another effect which increases the activity of the node is adding a substrate to the system which improves transcription or translation . Imaginable is the activation of an enhancer region close to a promoter associated to a node . We emphasize that these effects are not like a knock in of a gene as this operation changes the topology of the network which means that nodes or edges are added to the network . The inhibition of a node in model ( 2 ) means that the concentration of the product associated with that node is decreased , like an additional decay . This can be done , for example , by antibodies which bind to the product , degradation of the product by enzymes or any other modification , like post transcriptional or post translational modifications , at the product which inhibits its intended function in the system and thus converts the product to a biologically inactive form . Consequently the concentration of biologically active product decreases and this is considered as a decay of the activation level of the corresponding node . This is associated with a so called knock down . Analogously to a knock in , a knock out changes the topology of the network as well and corresponds to the deletion of a node or an edge from the network . This operation is not modeled by the action of the external stimuli within the framework proposed in this work . Model ( 2 ) can be extended to external stimuli which act on the transcriptional and translational level in the following way . Maybe a substrate cannot decrease the concentration of the product xk of a corresponding node k but its activation term . For example , if one blocks areas in the promoter region by oligopeptides such that transcription factors can bind worse to the DNA , then the expression of the corresponding product slows down as the activation term is smaller . Such a model can be formulated as follows d x k d t = - e 1 2 h + e - h ( ω k - 1 2 ) ( 1 - e 1 2 h ) ( 1 + e - h ( ω k - 1 2 ) ) ( ∏ j = 1 m ( 1 - ζ k j u j ) ) - γ k x k + ∑ j = 1 m σ k j u j ( 1 - x k ) - ∑ j = 1 m η k j u j x k ( 3 ) where 0 ≤ ζkj ≤ 1 . If ζkj = 0 for all k and j , then model ( 3 ) transforms into ( 2 ) . By the coefficients ζkj , one can adjust how much the external stimulus j affects the activation term of the node k even at full activity of uj . That means that for ζkj = 0 , the external stimulus j has no effect on node k and for ζkj = 1 , a fully activated external stimulus j , that means uj = 1 , totally prevents the expression of the product of node k . If 0 < ζkj < 1 , then even a fully activated external stimulus j cannot totally prevent the expression . This captures the nature of an equilibrium reaction as it appears when , for example , transcription factors compete with other substrates in binding to the DNA . Therefore , ζkj can also be used to fit the influence of uj to experimental data . Besides drugs or some other chemicals acting as external stimuli , there are further external stimuli like physical signals . For example light or mechanical stress , which is detected by receptors and is converted into activation or inhibition of a node . For instance , DNA damage caused for example by X-rays activates p53 [11] or temperature sensed by RNA thermometers can chance expression patterns [12] . This can be modeled within our framework by covering the effects of these external stimuli by the functions uj , j ∈ {1 , … , m} that activate or inactivate the corresponding nodes . We remark that this is a very effective modeling as we just consider the essential effects of the interactions within a real system that we model . In order to compare the results from these models with results from an experiment in detail , one has to check if the behavior of the real network is the same like the one of the model network when applying an external stimulus as far as the nodes’ activity is concerned . Furthermore , one can check if an external stimulus has the same effect on a node’s activity like used in the model in order to adjust the coefficients σkj , ηkj and ζkj . In this way , the coupling strength of an external stimulus can be taken into account . For example , if an inhibiting external stimulus j supposed to cause a knock down of node k cannot force a node’s activation level below a certain level although fully applied , then one can adjust the corresponding coefficient ηkj or ζkj until the model has the same behavior . Analogously , if an external stimulus cannot steer a node’s activation level to its full amplitude although fully applied , then one can adjust the corresponding coefficient σkj until the model shows the same behavior like the real system . One should also check if the external stimulus like a certain chemical agent used to put the external stimulus from the model into effect has an exclusive effect on the corresponding node in the real system or if the utilized agent has an multi target effect on several nodes in the real system . If the utilized external stimulus has multi target effects on several nodes , then such an stimulus j can be considered with the model above in that way that the coefficients ζkj , σkj or ηkj are greater then zero for more than just one k . Then the external stimulus j appears in more then one equation meaning that it has an effect on the corresponding nodes . In this mathematical part we discuss how to formulate the task of calculating external stimuli that shift the regulatory network into a new state such that a given set of nodes are modulated and a given network state is achieved as a mathematical optimal control problem . Specifically , we present a way how to calculate external stimuli which are able to switch the regulatory network from one steady state to another . Let x 0 ≔ ( x 1 0 ⋮ x n 0 ) ∈ R n denote the steady state in which our regulatory network starts and let x d ≔ ( x 1 d ⋮ x n d ) ∈ R n denote the steady state in which we desire our regulatory network to be . We consider xd as a constant function over time . The activation levels xk follow the dynamics given by ( 2 ) for t ∈ ( 0 , T ) where we have T > 0 and x k ( 0 ) = x k 0 for all k ∈ {1 , … , n} . Next , we formulate the distance of two steady states with a mathematical function as follows . The smaller the sum of integrals 1 2 ∑ k = 1 n ∫ 0 T | x k ( t ) - x k d | 2 d t ( 4 ) is , the faster each activation level xk reaches its desired steady state x k d through the action of our external stimuli . Analogously , the smaller the sum of integrals ∑ j = 1 m ∫ 0 T u j ( t ) d t ( 5 ) is , the less external stimuli are needed for switching the steady states , both the number of different external stimuli and the time they are applied to the network . By construction , without any external stimuli , the regulatory network rests in the steady state x0 . If we multiply ( 5 ) by α > 0 and add it to ( 4 ) , we obtain J ( x , u ) ≔ 1 2 ∑ k = 1 n ∫ 0 T | x k ( t ) - x k d | 2 d t + α ∑ j = 1 m ∫ 0 T u j ( t ) d t ( 6 ) where x ≔ ( x 1 ⋮ x n ) and u ≔ ( u 1 ⋮ u m ) . Minimizing ( 6 ) means to bring the regulatory network from the initial point of rest x0 as close to the desired state xd as possible , in the best case to the desired steady state , while using as few external stimuli as possible . The constant α weights which term of ( 6 ) is more important to be little . If α is big , then it is more important to use few external stimuli than to steer the regulatory network to xd . However , it can happen , when α is too big , that the external stimuli are calculated as constant zero functions for all times and the regulatory network remains in its initial steady state because its too costly to have non-zero external stimuli which have the desired perturbation on the network . On the other hand , little α makes it more important that the regulatory network is steered from x0 to xd and the number of non-zero external stimuli might increase as well . Therefore with the constant α we can reduce the number of active external stimuli which is important for pharmacological applications . More specific , the constant α can be utilized to determine only a small number of efficient external stimuli whose input gives the biggest gain . This is demonstrated in the Supplemental material S1 File , experiment with the Eq . ( 14 ) . Roughly spoken , the larger T is and the smaller α is , the more it is important , that we obtain external stimuli which steer the regulatory network from the initial steady state to the desired one , according to ( 6 ) . Finally , the mathematical formulation of the problem above is as follows . Minimize ( 6 ) such that d d t x k = f k ( x , u ) , x k ( 0 ) = x k 0 is fulfilled for all k ∈ {1 , … , n} and t ∈ ( 0 , T ) where fk ( x , u ) is the right hand-side of ( 2 ) , that means f k ( x , u ) ≔ - e 1 2 h + e - h ( ω k - 1 2 ) ( 1 - e 1 2 h ) ( 1 + e - h ( ω k - 1 2 ) ) - γ k x k + ∑ j = 1 m σ k j u j ( 1 - x k ) - ∑ j = 1 m η k j u j x k . ( 7 ) This can be equivalently formulated as follows min x , u J ( x , u ) s . t . d d t x k = f k ( x , u ) , x k ( 0 ) = x k 0 for all k ∈ { 1 , . . . , n } and t ∈ ( 0 , T ) ( 8 ) which is called optimal control problem . Solution strategies for solving ( 8 ) with respect to finding effective intervention points in regulatory networks can be found in the Supplementary material S File . There we also find the following three algorithms that are used in the present work . Further in the Supplementary material S1 File we demonstrate the basic application of the optimal control framework ( 8 ) to external stimuli analysis . For this purpose , we use a small network of 4 nodes and the dynamics ( 2 ) to get familiar with the approach for network analysis and its scope in general before we focus on its application in Subsection “Analysis of the aggregation of platelets” and Subsection “A switch between two different types of T-helper cells” to models with real data in order to validate the framework . We remark that the proposed optimal control framework holds for any dynamics consisting of ordinary differential equations . For example the framework is applicable to dynamics where the term - e 1 2 h + e - h ( ω k - 1 2 ) ( 1 - e 1 2 h ) ( 1 + e - h ( ω k - 1 2 ) ) - γ k x k in ( 7 ) is replaced by terms that are derived from the chemical reaction network theory [13–15] . In general any fk can be given by any function that defines a well posed right hand-side of an ordinary differential equation . We conclude this subsection with the following remark about the desired state xd . Remark 1 In this framework , the desired state xd does not need to be a constant function a steady state . It can be a time-dependent function as well . With a time-dependent function one can give a desired trajectory to which the state x is supposed to be as close as possible subject to the constraints ( 2 ) and to the given set of controls or external stimuli , respectively . For example xd can model a time-dependent expression pattern where the values for each node vary with respect to time . Moreover , one can think of pharmacological applications where the external stimulus may be a step function or a peak ( bolus administration of a drug , typical for oral administered drugs and intravenous injections ) or a continuous stimulus ( pharmacological administration of an infusion ) . More complex tasks may be calculated , for instance , the expression pattern of a network is supposed to be different between day and night time ( e . g . according to the circadian rhythm ) . Also such a desired dynamic network state change can be achieved with external stimuli .
For pharmacological illustration , what to gain by Jimena2 in a concrete application , we look at the aggregation of platelets . We provide here also experimental data validating our work . For this purpose , we use the so called SQUAD model that can be found in the supplementary information of [9] . The model is fitted to experimental data with the software Potterswheel that is mentioned in the Discussion . A schematic of the network can be seen in Fig 1 . For our Matlab implementation , we have the following numbers of the nodes . We have P2Y12 is node 1 , P2Y1 is node 2 , Ca is node 3 , Rap1 is node 4 , Akt is node 5 , Int is node 6 , Src is node 7 , PI3K is node 8 , PTP is node 9 , Throm is node 10 and ThromR is node 11 . According to [9] a high integrin ( Int ) activity is associated with irreversible platelet aggregation . We have identified two steady states . First one is ( 0 0 0 0 0 0 . 91 0 0 0 0 . 1 0 ) which has a high integrin activity and thus it is associated with irreversible platelet aggregation . The second one is ( 0 0 0 0 0 0 0 0 0 0 . 1 0 ) which has a low integrin activity and thus it is associated with reversible platelet aggregation . Note that the values of the steady state close to zero are rounded to zero . In [9] , we find that adenosine diphosphate ( ADP ) activates the irreversible aggregation by stimulating the G-protein-coupled receptors ( P2Y1 and P2Y12 ) . For all our calculations we use a time horizon T = 100 time units and a discretization dt = 0 . 01 to obtain a stable numerical solution of the system of ordinary differential equations that has the correct asymptotic behavior . In the following experiment , we would like to induce a switch from the reversible aggregation to the steady state which is associated with irreversible aggregation to demonstrate that the framework is able to find a biological meaningful solution . We have the same system of equation as in [9 , SQUAD model] . However the activation of P2Y1 and P2Y12 is modeled as follows . We have d d t x 1 = - 1 . 3 x 1 + 0 . 6 u 1 d d t x 2 = - 0 . 47 x 2 + 0 . 6 u 2 where u1 and u2 are an activation stimulus like ADP for example for P2Y12 and P2Y1 , respectively . As by the fitting of the data , the nodes’ activity levels are not restricted to the interval [0 , 1] and thus we do not have to multiply the activity stimulus by an additional term considering the maximum activity level of the node forcing the activating stimulus term to zero if the node’s activity level reaches its maximum . Furthermore , we equip PTP with an activating stimulus , ThromR with an inhibiting stimulus and Akt with an inhibiting stimulus . We use the output from Algorithm 3 for maxNum = 2 , tol = 0 . 1 , η = 0 . 5 and τ = 0 . 9 as an input for Algorithm 2 for α = 0 . 1 that converges here faster than the projected gradient method . From Algorithm 3 , we obtain that applying stimulus u1 for 22 time units induces already a switch . From Algorithm 1 , we also obtain that additionally external stimulus u2 steers the system towards irreversible platelet aggregation . This is in accordance with previous results [9] on the activation of the two G-protein coupled receptors , i . e . that they together trigger irreversible platelet aggregation . Numerous publications detail results of different pharmacological modulations of platelets and their activation networks [16 , 17] and the effects of individual receptor stimulations in our model correlate well with these data . However , our method allows now to exactly calculate external manipulation of the two receptors with or without any other pharmacological intervention point of choice , for instance also inhibition of the cascade , such that either the aggregation threshold is exactly reached , never reached or goes well beyond it . To know and calculate accurately these different pharmacological strengths are critical for fine-tuning antithrombotic therapy and are provided here for the first time in a calculation package . In practice , various antithrombotic drugs exist which often stimulate , besides their cognate receptor , also other targets . Again , our method for the exact calculation of the network effects of combination treatment provides thus here a rational basis for optimal therapeutic intervention . The results from Algorithm 2 can be seen in Figs 2 and 3 . Pharmacologically most often a bolus or continuous infusion is given ( orally or intravenous administered drug ) . This bolus function or continuous drug level can also easily be considered as external stimulus or even a combination therapy involving both: Notice that once the activity levels of the external stimuli are related to a concentration or intensity ( model fitting of the coupling constants σkj , ηkj of the external stimuli with the nodes ) in the real experiment and the most effective external stimuli are identified ( external stimuli with a non-zero time curve ) , the exact time curves of the external stimuli are not that important with respect to inducing a switch , see the experiment corresponding to Figs F and G in S1 File and the corresponding text below Fig G in S1 File . We can concentrate on the most effective external stimuli and can try different time curves , like constant ones , that are easier to implement in a real experiment and still perform the desired switch . There are other manipulations of the network that also cover an important application area , in particular regarding differentiation , cell maturation ( e . g . in immunity ) , effects of gene knockdowns etc . To study this in a suitable example we chose T-cell maturation . In this subsection , we analyze a network modeling the differentiation of different types of T-helper cells as well as CD4+ Foxp3+ regulatory T cells ( Treg ) . The model whose schematic is given in Fig 4 is investigated in [10] with respect to its steady states and it is shown there that this network has five steady states . There is one steady state for each special type of CD4+ T-helper cell , namely Th0 , Th1 , Th2 , Th17 and , in addition , Treg . We use ( 2 ) where all the parameters are set to 1 except h = 50 , see [10 , Table 1] . We introduce the following external stimuli . External stimulus u1 activates IFN-β , external stimulus u2 activates IL-12 , external stimulus u3 activates IL-18 , external stimulus u4 activates IL-2 , external stimulus u5 activates IL-23 , external stimulus u6 activates the TCR , external stimulus u7 activates TGF-β and external stimulus u8 inhibits RORγt , u9 inhibits IL-6 and u10 inhibits IL-6R which is the receptor for IL-6 . With this set of external stimuli , we intend to induce a switch from Th17 with the corresponding steady state of the network [0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 1 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 1 , 1 , 0 , 0 , 1 , 0 , 1 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0] which serves as initial state for the network to Treg with the corresponding steady state [0 . 9998 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0] , see Table 2 in [10] for all the steady states . We remark that the model is optimized to get either a number close to 1 if the corresponding node is active or 0 if the corresponding node is inactive in order to obtain a clear expression pattern for each type of T-cell . In Fig 5 , we see the result from Algorithm 3 for maxNum = 4 , tol = 0 . 1 , η = 0 . 5 and τ = 0 . 9 . We see that according to our model , inactivation of RORγt and activation of IL-2 and TGF-β for about 4 . 5 time units performs the desired switch . We take the result from Algorithm 3 as initial guess 0u for Algorithm 1 for the parameters α = 0 . 5 , T = 20 and dt = 0 . 1 . In Fig 6 we see the results , i . e . that the inactivation of IL-6 , u9 and IL-6R , u10 supports the switch , that means that the network switches faster to the Treg cell . Furthermore , we can deduce more structure from the model that first the inhibiting stimuli , u8 , u9 , u10 are active before the activating external stimuli u4 and u7 come into play . This can be interpreted in the way that activation of the T-cell by IL-2 and TGF-β is only efficient to induce the desired switch from Th17 to Treg if the specific Th17 expression pattern is knocked down to a certain degree . For this purpose it is sufficient to knock down RORγt that can be supported by the knock down of IL-6 and IL-6R . We stress that by virtue of the self activation of RORγt , a direct knock down of the node RORγt is necessary to induce the switch according to our model . A switch from Th17 to Treg has , indeed , been described e . g . in the tumor setting [18] . Here , soluble factors contained in ovarian cancer ascites were capable of mediating the transdifferentiation from Th17 to Treg . While the Foxp3-inducing effect of the cancer ascites was mimicked by the addition of recombinant TGF-β , the ascites and TGF-β differed in that TGF-β alone induced IL-17A together with Foxp3 expression . Therefore , the cancer ascites must contain additional factors which suppress RORγt expression—as we show here a prerequisite to complete the switch from Th17 to Treg . Furthermore , our predictions , for example those of Figs 5 or 6 , regarding which external stimuli induce a switch can be used for a data bank search as follows: The soluble factors of the ascites can be analyzed and the information can be stored in a data bank . Then , by our calculations , we have generated candidates for which we can look out for in our data bank to explain the observed switch: More specific , we can search for proteins that have a DNA-binding site for an IL-2 enhancer or the IL-2 promotor . In addition , we can look for proteins that have IL-2-like domains . On the other hand , we can search in the data bank for proteins that bind to a silencer of RORγt or have interacting domains that are able to bind to sites of RORγt and thus biologically inactivate RORγt . In addition , we remark that the exact time curve in Fig 6 is not important for the switch , compare with Fig 5 . Hence also typical drug action curves ( step function or exponential decay ) work also here . For details see the corresponding remark about this issue in S1 File , which is the corresponding experiment to Figs F and G in S1 File and the remark before Fig 2 in the present manuscript . In our experiments which we perform with the network proposed in [10] , we notice that once the network has taken a steady state corresponding to a T-cell type Th1 , Th2 , Th17 , Treg , the corresponding attractor is quite robust under perturbations of our external stimuli and is stable with respect to switches between different cell types . However , according to our model presented here in the external stimuli framework for desired switches , roughly spoken , mostly one has to knock down the lineage-specifying transcription factor of the cell type in which one starts and activate the related cytokines and lymphokines of the desired cell type in order to induce the desired switch of cell types into any desired one , i . e . Th1 , Th2 , Th17 , Treg . The switch from Th17 to Treg differentiation is explained in detail and specific stimuli ( effects of IL-6 , IL-2 , receptor stimulation e . g . IL-6R ) correlate well with the available experimental data . However , in experiments the best combination of external stimuli to switch the immune cell phenotype is far less clear and generally tested empirically in numerous experiments as the network effects are not intuitively clear . Here our method provides unprecedented clarity and saves time and experiments . Moreover , a further gain from our in silico model is that also the transdifferentiation to the other cell types ( e . g . Th1 to Th2 ) are perfectly charted by our approach .
A detailed mathematical extension of a regulatory network to controlled regulatory networks with external stimuli was presented . Different methods for a systematic calculation of the external stimuli inducing the desired switch were illustrated . A software tool was developed . The considerations presented in the presented work hold for any model consisting of well-posed ordinary differential equations , like chemical reaction networks [13–15] . Since the model equations are an input of the software tool , the provided implementations can be applied to a wider class of models than the ones used in the present work . The application of the proposed framework how to analyze a controlled regulatory network with respect to finding an optimal selection of external stimuli which cause a desired switch between two different steady states of a regulatory network was demonstrated with examples . Biological validation was made by considering a regulatory network modeling platelet aggregation and identifying and fine-tuning the receptors that were associated with triggering the irreversible aggregation . Furthermore , a switch from a T-helper cell type Th 17 to Treg is predicted by our theoretical investigations and discussed to validate this statement . Furthermore , the proposed algorithm strategy directly calculates the best intervention points and strength of intervention . We show how it extends already available software such as Potterswheel , ODEFY or COPASI by an optimal targeting method allowing to calculate best combinations of external interactions . It has many applications for improved pharmacological treatment , recognition of the optimal target or drug combination as well as for molecular interventions . | Organisms can be seen as molecular networks being able to react on external stimuli . Experiments are performed to understand the underlying regulating mechanisms within the molecular network . A common purpose for these efforts is to reveal mechanisms with which the molecular networks can be affected to achieve a desired behavior . To cover the complexity of life these models of molecular networks often need to be quite huge and need to have many cross connections between the different agents of the network that regulate the behavior of the network . A useful tool to structure this complexity are mathematical methods . Once the model based on experiments is set up the experimental data can be further processed by mathematical methods . As experiments are cumbersome , the present work provides a framework that can be used to systematically figure out intervention points in molecular networks to cause a desired effect . In this way promising intervention strategies can be obtained . For instance the process of obtaining new drugs for pharmacological modulation can be shortened as in the best case the calculated intervention strategy just has to be validated with one experiment and the time consuming procedure of searching an intervention strategy with several experiments can be saved . | [
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] | 2019 | Analyzing pharmacological intervention points: A method to calculate external stimuli to switch between steady states in regulatory networks |
South Sudan is one of the most endemic countries for visceral leishmaniasis ( VL ) , and is frequently affected by large epidemics . In resource-limited settings , clinicians require a simple clinical tool to identify VL patients who are at increased risk of dying , and who need specialised treatment with liposomal amphotericin B and other supportive care . The aim of this study was to develop and validate a clinical severity scoring system based on risk factors for death in VL patients in South Sudan . A retrospective analysis was conducted of data from a cohort of 6 , 633 VL patients who were treated in the Médecins Sans Frontières ( MSF ) hospital in Lankien between July 2013 and June 2015 . Risk factors for death during treatment were identified using multivariable logistic regression models , and the regression coefficients were used to develop a severity scoring system . Sensitivity and specificity of score cut-offs were assessed by receiver operating characteristic ( ROC ) analysis . In multivariable models , risk factors for death in adult VL patients were: anaemia ( odds ratio ( OR ) 4 . 46 ( 95% CI 1 . 58–12 . 6 ) for Hb <6g/dL compared with ≥9g/dL ) , nutritional status ( OR 4 . 84 ( 2 . 09–11 . 2 ) for BMI <13 kg/m2 compared with ≥16 kg/m2 ) , weakness ( OR 4 . 20 ( 1 . 82–9 . 73 ) for collapsed compared with normal weakness ) , jaundice ( OR 3 . 41 ( 1 . 17–9 . 95 ) ) , and oedema/ascites ( OR 4 . 86 ( 1 . 67–14 . 1 ) ) . For children and adolescents the risk factors were: age ( OR 10 . 7 ( 6 . 3–18 . 3 ) for age <2 years compared with 6–18 years ) , anaemia ( OR 7 . 76 ( 4 . 15–14 . 5 ) for Hb <6g/dL compared with ≥9g/dL ) , weakness ( OR 3 . 13 ( 22 . 8–105 . 2 ) for collapsed compared with normal weakness ) , and jaundice ( OR 12 . 8 ( 4 . 06–40 . 2 ) ) . Severity scoring predictive ability was 74 . 4% in adults and 83 . 4% in children and adolescents . Our evidenced-based severity scoring system demonstrated sufficient predictive ability to be operationalised as a clinical tool for rational allocation of treatment to VL patients at MSF centres in South Sudan .
Visceral leishmaniasis ( VL , or kala-azar ) is a neglected tropical disease which is caused by the obligate intracellular protozoa of the Leishmania donovani complex ( L . donovani and L . infantum ) ( 1 ) . In East Africa VL is caused by L . donovani , and is transmitted anthroponotically by phlebotomine sandfly vectors [1] . VL targets the lymphatic and reticuloendothelial system , affecting spleen , liver , mucosa of the small intestine and respiratory tract , bone marrow , lymph nodes and the other lymphoid tissues , causing persistent fever , organomegaly , pancytopenia and wasting . Patients with VL are severely immunocompromised , and death occurs from opportunistic or concomitant infections , or from complications such as malnutrition , anaemia or bleeding . Without treatment VL is typically fatal [1 , 2] . South Sudan is one of the most endemic countries for VL , with an annual incidence of 7 , 400–14 , 200 cases [3] . The country has been affected by frequent large epidemics , often associated with mass displacement due to armed conflict , and causing high mortality [4 , 5] . Current first line treatment for VL in South Sudan is the pentavalent antimonial , sodium stibogluconate ( SSG ) , in combination with an aminoglycoside , paromomycin ( PM ) . SSG/PM is given on an ambulatory basis over 17 days with daily intramuscular injections [6 , 7] . However , SSG is often poorly tolerated , and toxicity results in a significant incidence of serious adverse events such as pancreatic , hepato- and nephrotoxicity , cardiotoxicity , gastro-intestinal disorders and , in pregnant women , spontaneous abortion [8–11] . For this reason , SSG is contraindicated for specific patient groups ( e . g . pregnant women or HIV co-infected ) or in patients with severe VL . These patients should be treated with liposomal amphotericin B ( AmBisome ) [12] . AmBisome is much better tolerated but is much more expensive and requires cold chain transportation , cool storage , intravenous administration , and hospitalisation for at least 12 days . These are major challenges in the resource limited context of South Sudan , meaning that rational use of AmBisome is currently a key operational requirement . AmBisome needs to be reserved for severely ill VL patients who are at high risk of dying or at risk of SSG intolerability [12–14] , whilst standard SSG/PM treatment can continue to be administered to patients with uncomplicated VL [7] . In resource-limited settings , clinicians require a simple clinical tool to identify VL patients who are at increased risk of dying , and who need specialised treatment with liposomal amphotericin B and other supportive care . The aim of this study was to develop an evidenced-based risk scoring system which could be used as a clinical decision making tool in the field , to help clinicians decide whether a VL patient requires intensive VL care and treatment with AmBisome or standard VL management and less intensive monitoring . The risk scoring system would be based on risk factors for death during treatment in a retrospective cohort of VL patients , and would be validated internally and against older cohorts of VL patients treated by MSF in South Sudan .
This research fulfilled the exemption criteria set by the Médecins Sans Frontières Ethical Review Board for a posteriori analyses of routinely collected clinical data , and thus did not require MSF ERB review . It was conducted with permission from the Medical Director of the MSF Operational Centre Amsterdam . All data were anonymised before analysis . A retrospective analysis was conducted of routinely collected data from a cohort of 6 , 821 VL patients who attended the hospital of Médecins Sans Frontières ( MSF ) in Lankien , Jonglei state , South Sudan during the VL outbreak between July 2013 and June 2015 . The data were stored in an electronic database , and included key dates , demographic , anthropometric , diagnostic and clinical characteristics of patients , treatment regime and outcome . Patients with incomplete data and who defaulted from treatment were excluded from analyses . VL-HIV coinfected patients were excluded because treatments and outcomes are different in this small immunocompromised subgroup ( during the study period the HIV/VL rate in Lankien was only 0 . 15% ) . Our analysis was based on a binary outcome of died ( during or immediately after treatment ) or survived treatment . “Survived” means that the patient was discharged after successful clinical response to treatment: absence of fever , reduction of spleen and liver size , increased haemoglobin , restored appetite , and feeling well . Patients with an increased risk of treatment failure or relapse ( i . e . patients with a prior episode of VL , or patients with inadequate or doubtful clinical response ) require a negative parasitological test-of-cure by aspirate microscopy to confirm cure of VL . There is no systematic follow up of patients after discharge , because they return to areas remote from the treatment centre . A defaulter was defined as a patient who did not complete treatment , and had an unknown outcome . The dataset included the following potential risk factors for death in VL: age ( years ) , sex , presence/absence of jaundice , lymphadenopathy , oedema/ascites; prior episode/relapse of VL , Hb level ( g/dL ) , spleen size ( cm below the left costal margin ) , self-reported duration of illness ( months ) , and nutritional status ( body mass index ( BMI ) in patients ≥19years ( kg/m2 ) and weight for height Z scores ( WHZ ) in patients <19 years old ) . State of weakness , was defined as ‘normal weakness’ ( non-severe ) ; ‘severe weakness’ ( if patient needs assistance in walking or , in babies , if unable to sit up ) ; or ‘collapse’ ( if patient is unable to sit or drink or , in babies , if hypotonic and unable drink unaided ) . All of these variables have been identified as risk factors for death in earlier VL patient cohorts from South Sudan [2 , 5 , 15 , 16] . All patients presenting with a history of fever more than 2 weeks and splenomegaly and/or lymphadenopathy and/or wasting ( BMI <16 mg/m2 or <-2 Zscore ) were considered VL suspects and underwent further diagnostic evaluations . Patients without prior VL treatment history ( suspect primary VL ) were first screened using the rK39 rapid diagnostic test ( IT-Leish , Bio-Rad laboratories , USA ) and a positive result confirmed VL . Those testing negative were screened with the leishmania direct agglutination test ( DAT , Royal Tropical Institute , Amsterdam , The Netherlands ) and a high titer ( ≥1:6400 ) confirmed VL . Those with an intermediate DAT titer ( 1:800–1:3200 ) underwent tissue aspiration ( spleen or lymph node ) and positive result confirmed VL . Patients with prior VL treatment history ( suspect relapse VL ) underwent tissue aspiration and a positive result confirmed VL . A clinical diagnosis was made in patients contra-indicated for spleen aspirate ( i . e . severely anaemic , bleeding , pregnant or collapsed ) and didn’t have palpable lymph nodes or those with negative lymph node aspirate results but with persistent strong VL clinical suspicion in the absence of differential diagnoses [17] . The first line treatment was with SSG ( 20 mg/kg ) in combination with PM ( 15 mg/kg ) given on an ambulatory basis over 17 days with daily intramuscular injections . Specific patients groups with contra-indication for SSG ( pregnant women , HIV co-infected ) or patients with known poor tolerability of SSG ( age >45 years , severe VL ) were treated with AmBisome by 6 IV infusions of 5 mg/kg on alternate days . Treatment had started without delay on the same day as confirmation of diagnosis . If clinical and laboratory investigations confirm severe VL disease , specialised treatment was started immediately . ‘Specialised treatment’ means besides AmBisome also rehydration , aggressive antibiotic treatment ( ceftriaxone IV ) , other supportive treatment ( e . g . for malnutrition ) , intensified monitoring and treatment of any suspected abnormality . As the aim is to predict a patient’s prognosis at the time of diagnosis , the treatment was not included in the analysis .
The initial dataset included 6 , 821 VL patients . After excluding patients with incomplete data ( n = 8 ) , HIV co-infection ( n = 11 ) , who defaulted from treatment ( n = 159 ) or referred to another non MSF facility ( n = 10 ) the total sample size was 6 , 633 . Of these , 3 , 631 ( 54 . 7% ) were male and 3 , 002 ( 45 . 3% ) were female . Out of the 6 , 614 patients of whom the duration of illness was known 6 , 087 ( 92% ) presented within 1 month after onset of symptoms , and no patient presented later than 6 months . Of the 6 , 615 patients whose treatment regime was known , 5 , 149 patients were treated with SSG/PM and 1 , 466 patients with AmBisome . The data comprised 4 , 931 ( 74 . 3% ) children and adolescents ( aged < 19 years ) and 1 , 702 ( 25 . 7% ) adults ( ≥19 years ) . Mortality data was captured during the complete time of admission in the hospital , until discharge or death; the longest admission duration was 134 days . In total 6 , 447 patients ( 97 . 7% ) survived and 186 ( 2 . 8% ) died during treatment; 33% ( 49/186 ) of the deaths occurred within 48 hours of admission . The characteristics of patients in each age group who died compared with those who survived are shown in Table 1 . Mortality in children and adolescents was 2 . 4% compared with 4 . 1% among adults ( OR 1 . 78 ( 95% CI 1 . 32–2 . 41 ) ) . Univariate analysis showed that age , Hb , state of weakness , nutritional status , jaundice , and oedema/ascites were strongly associated with VL mortality ( Table 2 ) : patients >45 years old had 3 . 4-fold higher odds of death ( OR 3 . 42 ( 95% CI 1 . 64–7 . 16 ) ) compared with patients aged 18–25 years; patients in a state of collapse were 8 times more likely to die ( OR 7 . 80 ( 95% CI 3 . 66–16 . 6 ) ) compared with patients who arrived in a ‘normal’ state of weakness; Hb levels <6g/dL increased the odds of dying almost 9-fold ( OR 8 . 67 ( 95% CI 3 . 32–22 . 6 ) ) compared with levels ≥9g/dL; and BMI <13 kg/m2 was associated with 7 . 6-fold higher odds of death ( OR 7 . 56 ( 95% CI 3 . 45–16 . 6 ) ) compared with BMI ≥ 16 kg/m2 . Presence of jaundice and oedema/ascites increased the odds of dying 8-fold ( OR 7 . 95 ( 95% CI 3 . 07–20 . 6 ) ) and almost 7-fold ( OR 6 . 87 ( 95% CI 2 . 69–17 . 5 ) ) , respectively . Sex , lymphadenopathy , prior episode VL , duration of illness , and spleen size were not associated with risk of death . The effects of Hb , state of weakness , nutritional status , jaundice , and oedema/ascites were reduced by mutual adjustment , and age was eliminated from the final prediction model ( Table 2 ) . In this model , patients in a state of collapse were 4 times more likely to die ( OR 4 . 20 ( 95% CI 1 . 82–9 . 73 ) ) compared with patients who arrived in a ‘normal’ state of weakness; Hb levels <6g/dL increased the odds of dying 4 . 5-fold ( OR 4 . 46 ( 95% CI 1 . 58–12 . 6 ) ) compared with levels ≥9g/dL; and BMI <13 kg/m2 was associated with almost 5-fold higher odds of death ( OR 4 . 84 ( 95% CI 2 . 09–11 . 2 ) ) compared with BMI ≥ 16 kg/m2 . Presence of jaundice and oedema/ascites increased the odds of dying >3-fold ( OR 3 . 41 ( 95% CI 1 . 17–9 . 95 ) ) and almost 5-fold ( OR 4 . 86 ( 95% CI 1 . 67–14 . 1 ) ) , respectively . The univariate analyses of children and adolescents showed that age , Hb , state of weakness , jaundice , oedema/ascites and WHZ were strongly associated with VL mortality ( Table 3 ) . Patients < 2 years old had >17-fold higher odds of death ( OR 17 . 3 ( 95% CI 10 . 5–28 . 6 ) ) compared with patients aged 6–18 years; patients in a state of collapse were 39 times more likely to die ( OR 39 . 3 ( 95% CI 12 . 2–126 . 3 ) ) compared with patients who arrived in a ‘normal’ state of weakness; Hb levels <6g/dL increased the odds of dying 19-fold ( OR 19 . 0 ( 95% CI 10 . 8–33 . 4 ) ) compared with levels ≥9g/dL; and WHZ <-4 was associated with almost 3-fold higher odds of death ( OR 2 . 74 ( 95% CI 1 . 58–4 . 90 ) ) compared with WHZ > -2 . Presence of jaundice and oedema/ascites increased the odds of dying >9-fold ( OR 9 . 38 ( 95% CI 3 . 50–25 . 2 ) ) and >5-fold ( OR 5 . 31 ( 95% CI 2 . 25–12 . 7 ) ) , respectively . For patients with lymphadenopathy , the odds of dying reduced by half . ( OR 0 . 49 ( 95% CI 0 . 33–0 . 73 ) Sex , prior episode VL , and spleen size were not associated with risk of death . Duration of illness did not have sufficient cases for analyses . The effects of age , Hb , state of weakness and jaundice were reduced by mutual adjustment , and lymphadenopathy , nutritional status and oedema/ascites were eliminated from the final prediction model ( Table 3 ) . In this model , patients < 2 years old had almost 11-fold higher odds of death ( OR 10 . 7 ( 95% CI 6 . 31–18 . 26 ) ) compared with patients aged 6–18 years; patients in a state of collapse were almost 23 times more likely to die ( OR 22 . 8 ( 95% CI 4 . 95–105 . 2 ) ) compared with patients who arrived in a ‘normal’ state of weakness; Hb levels <6g/dL increased the odds of dying nearly 8-fold ( OR 7 . 76 ( 95% CI 4 . 15–14 . 5 ) ) compared with levels ≥9g/dL; and presence of jaundice increased the odds of dying 3 . 4-fold ( OR 3 . 41 ( 95% CI 1 . 17–9 . 95 ) ) . The scores for each variable in the final prediction model are shown in Table 4 , and the range of probabilities for each score ( due to rounding up or down of regression coefficients ) are presented in Table 5 . For adults , probability of death exceeded 10% for risk scores ≥3 ( 6 . 1% ( 104/1695 ) of adults ) ; in children and adolescents , the threshold for exceeding a 10% probability of death was a score ≥6 ( 6 . 8% ( 339/4921 ) of children ) . For the severity scoring of the adults the classification matrix resulted in 80 . 8% correctly predicted deaths . The AUC of the severity scores of the adults gave an overall predictive performance of 74 . 4% ( 95 CI 68 . 0%-81 . 0% ) , indicating ‘fair’ predictive ability ( Fig 1 ) . For children and adolescents , the classification matrix showed 80 . 6% correctly predicted . The AUC was 83 . 4% ( 95% CI 78 . 0%-86 . 8 ) , interpreted as ‘good’ predictive ability ( Fig 2 ) . Sensitivity >55% required a score ≥2 in adults ( sensitivity 57% , specificity 82% ) and ≥5 in children and adolescents ( sensitivity 64% , specificity 91% ) . Sensitivity >75% required a score ≥1 in adults ( sensitivity 86% , specificity 44% ) and ≥4 in children and adolescents ( sensitivity 75% , specificity 81% ) ( Table 6 ) . External validation of the scoring for adults with the Lankien datasets of 1999–2002 , 2002–2005 and the Malakal dataset of 2002–2005 yielded AUC of 72 . 2% , 79 . 5% and 71 . 2% , respectively . Discriminative ability did not differ significantly across the four datasets ( p = 0 . 48 ) . For children and adolescents , corresponding AUC were 72 . 2% , 82 . 8% and 76 . 6% , with only very weak evidence of a difference in discriminative ability across the three datasets ( p = 0 . 13 ) .
In this study , the risk factors for death in VL patients in South Sudan during the VL epidemic from 2013 to 2015 were analysed . Significant risk factors for adult patients were nutritional status ( BMI ) , Hb , weakness status , jaundice and oedema/ascites . In children and adolescents , the risk factors were age , Hb , weakness status and jaundice . Using these risk factors , an evidence-based clinical severity scoring system was developed that may be able to determine reliably and easily a patient’s risk of dying , thereby enhancing the rational use of more costly and complex VL treatments . The overall accurate predictive ability of this severity scoring system was confirmed by external validation in other data from the same setting . VL treatment effect was not analysed , because in observational studies indications for treatment are usually not standardised , and confounding by indication could lead to bias . Moreover , as the aim is to predict a patient’s prognosis at the time of diagnosis , the treatment should not be included . In operationalising a risk scoring system , erring towards a higher sensitivity over a lower specificity would mean that >28% of child and > 56% of adult patients should receive specialised VL care . This is unfortunately not possible in a severely resource limited setting such as South Sudan . The optimal threshold will be a compromise between sensitivity and specificity , i . e . a threshold needs to be chosen that includes as many patients with increased risk of dying as possible , whilst maintaining a rational use of scarce resources . Whilst our focus has been to reduce VL mortality by identifying those patients most at risk of dying , the severity scoring system could be a useful tool in the management of patients at lower risk . VL patients with a low risk of dying ( <3 . 0% ) , could be treated on an ambulatory basis in outpatient treatment centres . Patients with a moderate risk of dying ( 3–10% ) could be treated in an outpatient department , but under close monitoring by experienced clinicians . Patients with a higher score and thus a higher risk of dying ( >10% ) should be admitted as inpatients for specialised VL care and intensive monitoring . In this study there was no association of acute malnutrition ( WHZ <-2 ) with death in children and adolescents , although severe acute malnutrition ( WHZ <-4 ) was associated with increased risk of death . This finding is in contrast with earlier studies in South Sudan in 2004 and 1991 [15 , 16] . The lack of association at less severe levels of malnutrition in our study could be explained by improved treatment of malnourished children in an intensive therapeutic feeding centre ( ITFC ) , where malnourished children receive specialised medical and nutritional care . This specialized treatment may have mediated the impact of malnutrition on VL mortality in children , except for the very severely malnourished . In our final prediction model , severely malnourished children would score highly for being in a state of collapse and/or presenting with very low Hb levels , and would exceed the threshold for specialised care . A finding which is in line with previous studies in South Sudan is that young age was a strong predictor for VL mortality [15 , 16] . The eleven-fold higher odds of death in children below 2 years ( compared with older children ) demonstrate the vulnerability of this youngest age group . Accordingly , age <2 years contributed 4 points , and even without additional risk factors already is at ‘moderate risk’ . On the other hand , older age ( >45 years ) , which was strongly associated with mortality in the univariate analysis , was not retained as an independent risk factor in the multivariable regression model , contrary to earlier studies [15 , 16 , 22] . In these earlier studies , most elderly patients were treated with SSG , whereas in the present study 87% of the patients older than 45 years were treated with AmBisome [15 , 16 , 28] . Several studies had demonstrated a high mortality in older patients due to SSG toxicity , and during those earlier studies there was no or only limited AmBisome available [12] . Therefore in our dataset , AmBisome may have mediated the effect of older age on VL mortality . This seems to confirm the recommendation that treatment with AmBisome may be lifesaving for the elderly VL patients [9] . In contrast with two previous studies we did not find that splenomegaly was associated with increased risk of death [15 , 29] . Conversely , we found a crude inverse association of lymphadenopathy with risk of death ( although not evident when adjusted for other risk factors ) . We can think of no plausible explanation for these apparently anomalous findings . The mortality rate in this study cohort was much lower than in earlier studies ( 2 . 8% vs . 10 . 9% and 10 . 0% ) [7 , 16] . Despite the fact that the difficult context of civil war , violence and displacement , the weak and unstable health care system was similar between this study and the earlier studies in South Sudan [30 , 31] . This may be partially explained by earlier presentation at the Lankien hospital: more than 92% of the patients were able to present early , i . e . within one month after onset of clinical disease , whereas only 65% of the patient were able to during those earlier studies . As there were only 13 deaths in 527 patients presenting more than 1 month after onset of symptoms , duration of illness could not be identified as a risk factor . This seems to support the interpretation that specific joint efforts led since 2009 by the World Health Organisation in collaboration with Ministry of Health and nongovernmental organisations ( NGOs ) , have been successful in achieving improved access to VL care in South Sudan ( by decentralising VL treatment services and ensuring supply of tests and drugs ) [3 , 30] . The main strength of our study is that it was based on a large dataset with very few patients excluded because of missing data . Also , a robust external validation was possible , because of the availability of MSF datasets from previous VL outbreaks in South Sudan . The study was based on a cohort of South Sudanese patients , and therefore the predictors of death and the severity scoring system should not be generalised to patient populations in other countries . For example , where VL is caused by other Leishmania strains , where HIV co-infection is more prevalent , or where resistance to SSG is more common or AmBisome is more affordable . Another limitation is that this retrospective study was conducted with routinely collected programme data . Therefore it may be missing out on other important risk factors that were not included in this database , such as laboratory parameters ( e . g . related to electrolyte disturbances , blood cell counts , or immune status ) . Although one third of deaths occurred within the first 48 hours after admission , which may limit the impact of our severity scoring system on mortality , the predictive ability and simplicity of the system means that it can be easily operationalised and implemented in the field . The scoring system presented in this study only includes clinical parameters and a simple Hb lab test , and it therefore presents a practical tool that can be used in all field hospitals and health centres in the VL endemic areas in South Sudan . Clinicians will use their clinical judgement and experience to make treatment decisions , aided by the risk scoring tool . Given the strong associations between known risk factors and mortality , it would not be ethical to attempt a randomised trial of the severity scoring system , but we would hope to see a ( continued ) overall improvement in treatment outcomes in VL programmes in South Sudan until new safe , effective , and affordable treatment becomes available for all patients . | Visceral leishmaniasis ( VL ) , also known as kala-azar , is a neglected tropical disease caused by a parasite ( Leishmania ) and transmitted to humans through the bite of a sandfly . South Sudan is one of the highest endemic countries for VL , frequently affected by epidemics . In South Sudan are different treatment options for VL available: the standard therapy given on ambulatory bases by intramuscular injections for 17 days , and specialized treatment for patients with severe VL , administered intravenously in a hospital over 12 days . In the extremely resource limited context of South Sudan , the most optimal treatment to patients with severe VL should be provided , but a rational use of drugs maintained . In this study , we identified risk factors for death in VL patients of South Sudan , and based on these risk factors we developed a severity scoring system . This severity scoring system will be a clinical decision making tool for allocation of VL patients to the appropriate treatment and to minimise the mortality of the VL patients in South Sudan . | [
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] | 2017 | A clinical severity scoring system for visceral leishmaniasis in immunocompetent patients in South Sudan |
The contribution of innate immunity to immunosurveillance of the oncogenic Human Herpes Virus 8 ( HHV8 ) has not been studied in depth . We investigated NK cell phenotype and function in 70 HHV8-infected subjects , either asymptomatic carriers or having developed Kaposi's sarcoma ( KS ) . Our results revealed substantial alterations of the NK cell receptor repertoire in healthy HHV8 carriers , with reduced expression of NKp30 , NKp46 and CD161 receptors . In addition , down-modulation of the activating NKG2D receptor , associated with impaired NK-cell lytic capacity , was observed in patients with active KS . Resolution of KS after treatment was accompanied with restoration of NKG2D levels and NK cell activity . HHV8-latently infected endothelial cells overexpressed ligands of several NK cell receptors , including NKG2D ligands . The strong expression of NKG2D ligands by tumor cells was confirmed in situ by immunohistochemical staining of KS biopsies . However , no tumor-infiltrating NK cells were detected , suggesting a defect in NK cell homing or survival in the KS microenvironment . Among the known KS-derived immunoregulatory factors , we identified prostaglandin E2 ( PGE2 ) as a critical element responsible for the down-modulation of NKG2D expression on resting NK cells . Moreover , PGE2 prevented up-regulation of the NKG2D and NKp30 receptors on IL-15-activated NK cells , and inhibited the IL-15-induced proliferation and survival of NK cells . Altogether , our observations are consistent with distinct immunoevasion mechanisms that allow HHV8 to escape NK cell responses stepwise , first at early stages of infection to facilitate the maintenance of viral latency , and later to promote tumor cell growth through suppression of NKG2D-mediated functions . Importantly , our results provide additional support to the use of PGE2 inhibitors as an attractive approach to treat aggressive KS , as they could restore activation and survival of tumoricidal NK cells .
Human herpesvirus 8 ( HHV8 ) , although known as Kaposi's sarcoma-associated herpesvirus ( KSHV ) , is a γ herpes virus able to establish a predominantly latent , life-long infection in host's monocytes , dendritic cells ( DCs ) , B lymphocytes , and endothelial cells . HHV8 is the etiological agent of Kaposi's sarcoma ( KS ) , a multifocal angiogenic tumor consisting of spindle-shaped cells of endothelial origin and infiltrating leukocytes [1] , [2] . HHV8 lytic cycle generally occurs following primary infection , and rapidly the virus enters the latent state . Reactivation leads to the initiation of the lytic cycle , which is necessary for virus propagation and survival . Within KS lesions , HHV8 infection is predominantly latent . KS is the most common neoplasm in untreated AIDS patients . It also occurs in immunosuppressed organ transplant recipients , and in some African or Mediterranean populations in the absence of overt immunosuppression ( classical KS ) . The marked decline in the incidence of AIDS-KS since the advent of antiretroviral therapy ( ART ) , and the frequent resolution of transplant-related KS after reduction of immunosuppression , highlight the key role of cellular immune responses in the control of HHV8 infection . We and others recently demonstrated the crucial role of HHV8-specific cytotoxic T lymphocytes ( CTL ) in controlling HHV8 replication , preventing malignancies in latently infected subjects , and conferring genuine resistance to persistent infection [3] , [4] . The multiple mechanisms elaborated by herpesviruses to escape immune responses prompted us to explore further other immune cells involved in the control of HHV8 infection . NK cells play a key role in early control of viral infections , through direct lysis of infected cells and secretion of cytokines controlling viral replication . NK cells also influence specific T-cell priming through direct cross talk with DCs , and thus participate to the establishment of antiviral adaptive responses [5]–[7] . NK cells are able to prevent and control the development and dissemination of tumors [8] . NK cells are therefore likely to represent critical obstacles HHV8 needs to effectively overcome , not only very early during infection prior to de novo viral gene transcription , but also later for the maintenance of persistent infection and establishment of tumors . NK cell activity is tightly regulated by a fine balance between activating and inhibitory signals [9] . The latter are mostly generated by the binding of HLA-1 molecules to the Killer cell Immunoglobulin-like Receptors ( KIRs ) and CD94/NKG2A , which guarantees that healthy self cells will be spared from NK cell-mediated lysis . Activating receptors , in particular NKG2D and the natural cytotoxicity receptors ( NCR ) NKp30 , NKp44 and NKp46 , detect the presence of stress-induced or infectious non-self ligands on abnormal cells . Other receptors , such as CD94/NKG2C , DNAM-1 , NKp80 , 2B4 and CD161 , can modulate NK cell effector functions . HHV8 has exploited several evasion mechanisms to avoid immune recognition [10] . In particular , the K3 and K5 ubiquitin ligases , expressed during the early lytic cycle , downregulate HLA-1 molecules on infected cells to avoid virus-specific CTL recognition [11] . This will at the same time sensitize the virus to missing-self recognition by NK cells , unless other evasion mechanisms operate simultaneously . Interestingly , K5 also downmodulates NKG2D ligands and the ICAM-1 adhesion molecule [12] , thus helping HHV8 to evade NK cell surveillance in the early phase of HHV8 infection before establishing latency , and later during reactivation and viral replication . NK cells from HIV-viremic AIDS patients with active KS have a decreased cytolytic capacity against HHV8-infected body cavity B-cell lymphoma ( BCBL-1 ) cells [13] . However , little is known about how HHV8 by itself , in the absence of HIV infection , prevents NK cells from killing latently-infected endothelial cells and KS tumor cells . To address these questions , we investigated NK cell phenotype and functions in a large series of HHV8-infected subjects . Our data revealed substantial alterations in the expression of several NK cell receptors , even in asymptomatic HHV8 carriers . In addition , NK cells from patients with active KS showed a significant decrease of NKG2D expression , which was associated with impaired cytotoxic capacity . We identified PGE2 , a known tumor-derived inflammatory molecule , as a factor responsible for NKG2D down-modulation , and for inhibition of IL-15-mediated activation and survival of NK cells . These observations are consistent with sequential immunoevasion mechanisms that may allow HHV8 to escape NK cell recognition at early stages of infection in order to establish latency , and later to promote tumor cell growth . Importantly , they give further support to the idea that PGE2 inhibition based therapy might provide an effective way to treat the active KS lesions .
We addressed the putative influence of HHV8 infection on NK cell phenotype in a cohort of 70 HHV8-infected individuals , including 25 asymptomatic carriers ( HHV8+ KS− ) and 45 KS patients ( HHV8+ KS+ ) in comparison with 45 HHV8-negative controls , all sub-grouped according to the presence or absence of HIV co-infection . To avoid any confounding effect of HIV replication on the NK cell repertoire , all HIV+ subjects from the different subgroups were HIV-aviremic , and were matched for age , CD4 T cell count , CD4 nadir , and duration of disease and ART . No gross abnormality in NK cell distribution was observed in the different patient groups , with levels of total NK cells , and relative proportions of CD56bright and CD56dim NK cell subpopulations being comparable to those in controls ( Figure 1a ) . Accumulation of dysfunctional CD56-negative NK cells was recently reported in HIV-viremic patients , with suppression of HIV-viremia upon ART restoring normal proportions of CD56+ NK cells [14]–[16] . In line with the fact that all study subjects were HIV-aviremic , we did not observed any abnormal expansion of CD56-negative NK cells in HHV8-infected patients ( Figure S1 ) . We next analyzed HLA class 1-specific NK cell receptors , including those belonging to the KIR family ( KIR2DL1/S1 , KIR2DL2/L3/S2 , KIR3DL1/S1 , KIR2DS4 ) , and the HLA-E-specific CD94/NKG2A and CD94/NKG2C receptors ( Figure 1b ) . Although large inter-individual variations were observed , the mean percentage of cells expressing individual KIRs or NKG2A was overall similar in the different study groups . The fraction of NKG2C+ NK cells varied within a wide range in infected patients ( <0 . 1% to 54% ) , but the difference with uninfected controls was not statistically significant . CMV seropositivity has been associated with high frequencies of NKG2C+ NK cells [17] , [18] . We thus wondered if the enrichment of NKG2C+ NK cells in some HHV8-infected patients was related to CMV co-infection . Notably , 68 out of the 70 HHV8- and/or HIV-infected patients were CMV IgG positive , and the two remaining CMV IgG-negative patients had very low NKG2C+ NK cell frequency ( 1 . 56% and 0 . 25% , respectively ) . Therefore , it is likely that previous CMV infection has driven expansion of NKG2C+ NK cells in HIV- or HHV8-infected patients . We next studied expression of NK cell receptors that recognize virus-associated or stressed-induced molecules ( Figure 2a ) . Levels of NKp30 , NKp46 and CD161 were significantly decreased in HHV8-infected patients compared to controls , whatever the presence or absence of KS , indicating that HHV8 is able to skew the NK cell receptor repertoire in otherwise healthy individuals , before overt tumor transformation . DNAM-1 expression was not significantly different between the groups . NKp44 was never detected ( not shown ) . Notably , the profile of NKG2D expression was clearly distinct from that of other receptors , as NKG2D levels were decreased in patients with classical KS ( HIV− HHV8+ KS+ ) , but neither in HIV+ KS+ patients nor in asymptomatic HHV8-infected subjects ( HHV8+ KS− ) . Because all HIV+ KS patients were HIV-aviremic upon ART for more than 1 year and showed complete clinical remission of KS at time of study , we wondered if NKG2D expression was correlated with the KS activity . Indeed , NKG2D levels were twofold lower in patients with active KS ( all classical KS ) than in patients with resolved lesions or in healthy controls ( Figure 2b , c ) . By contrast , expression of other receptors was similarly decreased in KS patients , whatever disease activity ( data not shown ) . The variable degrees of NK cell receptor modifications among patients prompted us to examine whether there was any correlation between phenotypic changes . We observed highly significant correlations between expression levels of NKp30 , NKp46 and CD161 , the three receptors downmodulated in asymptomatic HHV8-infected subjects ( Figure 3 ) , suggesting that a common mechanism sustained these alterations . At contrast , there was no correlation between expression of any of these receptors and expression of NKG2D . Taken together , these results show that HHV8 infection selectively imprints the NK cell receptor repertoire even at an asymptomatic stage , with a coordinate decrease in NKp30 , NKp46 and CD161 expression . At a more advanced stage , a specific down-modulation of NKG2D occurs in patients progressing to KS , which is likely mediated by a distinct , KS-specific mechanism . As a first step to study the molecular interactions underlying NK cell recognition of infected cells , we analyzed the phenotype of HHV8-infected endothelial cells , which are considered as one of the precursors of KS tumor cells . Primary infection of the microvascular endothelial cell line HMEC with a recombinant virus , rKSHV . 152 , expressing the green fluorescent protein ( GFP ) and neo ( conferring resistance to G418 ) [19] results in the establishment of latent HHV8 infection , with a very few percentage of cells undergoing lytic replication , a situation thought to mimic in vivo replication . HHV8-infected HMECs exhibited a two to threefold decreased expression of HLA-1 and ICAM-1 compared to uninfected HMECs ( Figure 4a left panel ) , although the remaining expression was still very consistent . We thought that the partial dowmodulation of HLA-1 and ICAM-1 might be explained by the presence of low levels of early lytic immunoregulatory proteins such as K3 or K5 in these latently-infected cells , as previously reported [20] , [21] . Indeed , qPCR analysis demonstrated some expression of K3 and K5 mRNAs in infected cells ( Figure 4b ) . In line with the relatively conserved expression of HLA-1 molecules , HHV8-infected HMECs showed a consistent expression of the non-classical HLA class 1b molecule HLA-E , which is dependent on signal sequences from classical HLA-1 molecules for its stabilization [22] . Because the CMV-encoded UL40 protein can also contribute a peptide cargo for HLA-E [23] , we searched for potential HLA-E binding peptide motifs in the HHV8 proteins . We did not find any relevant peptide motif , indicating that HHV8 by itself is unlikely to stabilize HLA-E expression ( data not shown ) . We next analyzed whether HHV8-infected cells expressed ligands for NK cell receptors . We did not observe any expression of NKp30 or NKp46 ligands , or of LLT1 , the ligand of CD161 , on infected nor uninfected cells . When looking at NKG2D ligands , it appeared that MICA and MICB were up-regulated on HHV8-HMECs compared to uninfected cells , while ULBP-1 was not detected , and ULBP-2 and ULBP-3 were similarly expressed . The ligands of DNAM-1 ( CD155/PVR and CD112/Nectin-2 ) were strongly expressed on both uninfected and infected cells , with a stronger expression of CD155 on HHV8- HMECs . In attempt to study a model of HHV8-infected cells more clinically relevant to KS than microvascular endothelial cells , we generated an immortalized HIV-negative KS-derived cell line . These SV2G cells exhibited phenotypic characteristics of endothelial cells , as demonstrated by expression of CD146 , CD131 and CD141/thrombomodulin ( data not shown ) . However , HHV8 genome was lost early after the first 2 passages , as in most KS-derived cell lines [24] . We thus infected SV2G cells in vitro with rKSHV . 152 , used above for infecting HMECs . The resulting HHV8-SV2G cell line showed predominantly latent infection , with some expression of K3 and K5 mRNAs , like HHV8-HMECs ( Figure 4b ) . Comparing the phenotype of SV2G and HHV8-SV2G cells , we observed that , for most markers analyzed , modifications paralleled those observed in HHV8-HMEC cells , except for an increased expression of ULBP-2 and ICAM-1 and a weak but significant detection of NKp30 ligand in HHV8-SV2G cells compared to uninfected cells ( Figure 4a , right panel ) . Collectively , these data show that persistently HHV8-infected cells , which show the same latency program as KS spindle cells , express a variety of ligands that should allow engagement of activating NK cell receptors such as NKG2D , DNAM-1 and NKp30 . At the same time however , they show a decreased , but yet strong expression of HLA-1 molecules that likely protects them from NK cell lysis . To determine whether NK cell receptor/ligand interactions occur at the tumor site , we performed immunohistochemical staining of KS biopsies . In line with the flow cytometric data on infected cells , we readily detected expression of HLA-1 and MICA/B molecules in tumor cells ( Figure 4c ) . We did not observe any staining using the NKp30-Fc or NKp46-Fc reagents . Ligands of DNAM-1 and CD161 could not be analyzed due to the lack of commercially available markers working in paraffin-embedded tissues . To our surprise , although large inflammatory infiltrates were observed in most KS samples , we did not detect any CD56-positive cell , suggesting that NK cells did not reach tumor lesions , or could not survive in the tumor microenvironment . We next evaluated the putative consequences of receptor/ligand modifications on NK cell functions . NK cells from patients with active or resolved classical KS and healthy controls were used as effector cells in CD107a degranulation and intracellular IFNγ production assays in the presence of uninfected or HHV8-infected KS-derived target cells . As anticipated from the relatively strong HLA-1 expression on these target cells , NK cell degranulation was weak and not different between controls and KS patients , whatever the KS activity ( Figure 5a ) . However , degranulation was slightly but significantly higher in the presence of HHV8-infected compared to uninfected targets . Whether this was related to the observed up-regulation of NKG2D , DNAM-1 and NKp30 ligands on infected cells could not be determined . Responses to PMA/ionomycin , used as positive control , were not impaired in patient NK cells ( mean CD107a+ cells 40 . 5% compared to 45 . 1% in controls ) , indicating that the granule-exocytosis pathway was intact . Intracellular IFNγ accumulation was almost undetectable in all conditions , both in controls and patients ( data not shown ) . We then evaluated the ability of NK cells to recognize K562 target cells , which not only lack HLA-1 molecules , but also express ligands for NKG2D , DNAM-1 , and NKp30 ( personal data and [25] ) . Compared to healthy controls , patients with resolved classical KS showed intact NK cell degranulation in spite of decreased expression of NKp30 , NKp46 and CD161 . At contrast , patients with active KS showed significantly impaired NK cell degranulation ( Figure 5b ) , supporting our hypothesis that the NKG2D down-modulation observed in these patients might alter NK cell lytic capacity . Indeed , monoclonal antibody-mediated masking of NKG2D sharply reduced K562-induced degranulation of NK cell from healthy controls and resolved KS patients , but had no effect on NK cells from patients with active KS ( Figure 5c ) . Moreover , we observed a positive correlation between CD107a degranulation and expression of NKG2D ( Figure 5d ) , but not of NKp30 or NKp46 . Lastly , comparative analysis of NK cells obtained before and after successful treatment of active classical KS in 3 patients showed that regression of KS was associated with significant restoration of NK cell degranulation , and a parallel increase in expression of NKG2D , but not of NKp30 and NKp46 ( Figure 5e ) . Taken together , these results suggest that NKG2D may play an important role in the control of KS progression in HHV8-infected individuals . Shedding of NKG2D ligands constitutes a major countermechanism of tumors to subvert NKG2D-mediated immunosurveillance . Thus , soluble MICA released from tumor cells by proteolytic cleavage drives down-regulation of NKG2D and is associated with compromised immune response and progression of disease in cancer patients [26]–[28] . We quantified soluble MICA in the serum of KS patients and controls , but found similar low levels in both groups . In addition , we did not detect soluble MICA in culture supernatants of HHV8-infected or uninfected cells ( data not shown ) . HHV8-infected endothelial cells secrete a variety of pro- and anti-inflammatory cytokines , growth factors and angiogenic factors [29]–[31] . In addition , Cyclooxygenase-2 ( COX-2 ) and its metabolite prostaglandin E2 ( PGE2 ) , two pivotal proinflammatory molecules , have been shown to play crucial roles in the establishment and maintenance of HHV8 latency , and in inflammatory , angiogenic and invasive events during HHV8 infection [32]–[34] . In attempt to identify the factors responsible for phenotypic changes of NK cells in HHV8-infected patients , we focused on IL-10 , TGFβ IL-8 , VEGF and PGE2 , because they have been involved in the modulation of NK cell functions [35]–[40] . Serum levels of IL-10 and TGFβ were low and not significantly different between patients and controls ( data not shown ) . By contrast , levels of VEGF , IL-8 and PGE2 were significantly increased in KS patients , particularly in those with active classical KS ( Figure 6a ) . To determine if these factors could modify the NK cell phenotype , control PBMCs were exposed in vitro for 48 h to VEGF , IL-8 , or PGE2 , after which NK cell receptor expression was evaluated ( Figure 6b ) . TGFβ used as positive control , decreased expression of NKG2D and NKp30 as described [35] , but had no significant effect on NKp46 , DNAM-1 or CD161 . IL-8 and VEGF did not modify the NK cell phenotype . Notably , PGE2 induced a significant decrease of NKG2D expression , but no reproducible modification of the other NK cell receptors . The PGE2-induced down-modulation of NKG2D was dose-dependent ( Figure 6c ) , and was already observed at concentration comparable with those found in the serum of some KS patients . In line with this observation , NKG2D levels on patient NK cells negatively correlated with PGE2 serum levels ( r = −0 . 70 , p 0 . 01 ) . Taken together , our results suggest that PGE2 may specifically alter NKG2D expression on NK cells , thus preventing NKG2D-mediated elimination of KS cell precursors and favoring the development and/or progression of KS in persistently infected patients . IL-15 plays a pivotal role in the activation , function and survival of NK cells . IL-15 is a surface-bound cytokine , presented by dendritic cells via its high-affinity receptor , IL-15Rα to the neighboring NK cells that express IL-2/IL-15Rβ and γ chains . PGE2 has been reported to suppress cytotoxicity of NK cells through down-regulation of IL-15Rγ [39] . Notably , IL-15 is also a potent inducer of NKG2D expression [41] , and IL-15 signaling potentiates NKG2D-mediated cytotoxicity of NK cells [42] . To determine if PGE2 could inhibit IL-15-induced up-regulation of NKG2D , control PBMCs exposed for 48 h to 5 ng/ml of IL-15 in the presence or absence of PGE2 ( 10–1 , 000 ng/ml ) , after which NK cell phenotype was analyzed ( Figure 7a ) . IL-15 alone strongly up-regulated expression of NKG2D , NKp30 and CD161 , but had no or minor effect on NKp46 and DNAM-1 . PGE2 , even at low concentration ( 10 ng/ml ) , fully abrogated the IL-15-induced up-regulation of NKG2D and NKp30 , partially inhibited up-regulation of CD161 , and had no effect on expression of NKp46 and DNAM-1 . Because immunochemistry did not detect any CD56-positive cells within KS lesions , we next wondered if PGE2 could mediate a defect in survival of NK cells . We analyzed IL-15-induced NK cell proliferation ( expression of Ki67 ) and survival ( expression of the anti-apoptotic protein Bcl-2 ) , together with surface expression of IL-15Rβ and IL-15Rγ ( Figure 7b ) . IL-15Rβ was expressed on almost all NK cells , and was not significantly modified by PGE2 . Conversely , PGE2 fully prevented the IL-15-induced up-regulation of IL-15Rγ , as already described . Furthermore , PGE2 was able to abolish the proliferative and pro-survival responsiveness of NK cells to IL-15 . Staining for annexin V indicated that PGE2 , at the concentrations used ( up to 10 µg/ml ) , did not affect NK cell viability ( not shown ) . Taken together , our results indicate that PGE2 inhibits IL-15 signaling in NK cells through down-regulation of IL-15Rγ , thus preventing IL-15 from promoting NKG2D signaling .
Infection by the oncogenic virus HHV8 raises issues of control of latent infection and control of tumor growth . HHV8 must overcome host's immune responses not only very early during infection prior to de novo viral gene transcription , but also after latent viral gene expression and later on during tumor transformation . Furthermore , the virus must simultaneously avoid innate and adaptive responses , using strategies that are sometimes mutually exclusive . Multiple evasion mechanisms have been dedicated by herpesviruses to thwart NK cell responses [43]–[45] . They can downmodulate ligands for NK cell activating receptors , provide competitors for their ligands , interfere with their translation , or directly target the activating receptors . It is likely that several viral inhibitor mechanisms play in concert to simultaneously or sequentially prevent NK cell activation . Our study shows for the first time to our knowledge , that distinct NK cell modifications are observed at the different stages of HHV8 infection , suggesting that selective pressure allows the virus to evade the successive waves of host immune responses . First , asymptomatic HHV8 carriers , as well as KS patients , exhibited significant down-regulation of the NKp30 and NKp46 activating receptors . Whether ligands of these receptors were expressed on HHV8-infected cells could not be ascertained in the absence of specific antibodies , although our data suggest that at least NKp30 ligand was present at the surface of infected cells . Thus , elimination of NKp30 ligand-expressing cells might be compromised in case of NKp30 down-modulation on effector cells . Expression of CD161 was also reduced in asymptomatic HHV8 carriers . This observation was rather surprising , given CD161 is described as an inhibitory receptor in NK cells [46] , [47] . We did not evidence expression of its ligand LLT1 on HHV8-infected cells , implying that CD161 may not directly participate in the elimination of these cells . Interestingly , LLT1 is expressed on TLR-activated DCs and B cells [48] , suggesting that it may contribute to their resistance to NK cell-mediated lysis . The loss of CD161 might thus result in the accumulation of a population of NK cells with a lower activation threshold that may be more easily triggered , which could lead to the elimination of activated DCs and participate in the defective establishment of antiviral adaptive responses . Because HHV8 infection of monocytes/macrophages and B lymphocytes has been demonstrated in KS patients [49] , [50] , further studies are required to explore LLT1 expression on these cells , and address whether reduced expression of CD161 modifies NK-DC interactions during HHV8 infection . Notably , expression of DNAM-1 , another key receptor in NK-cell mediated recognition of several tumors [51]–[54] , was not significantly altered in HHV8-infected individuals . Since DNAM-1 ligands were strongly expressed on HHV8-infected cells , it is possible that at least the DNAM-1 recognition pathway may operate in NK cells during HHV8 infection . Our observation of a co-modulation of NKp30 , NKp46 and CD161 in HHV8 infected subjects suggested us that common microenvironmental factors , acting early during asymptomatic stage of infection , might be involved . Multiple inflammatory and angiogenic factors are produced by HHV8-infected cells . We could not identify a unique factor capable to induce concomitant modifications of these receptors . Although IL-8 , VEGF and PGE2 were present in high amounts in patient sera , as reported in AIDS-related KS patients [2] , [55] , [56] , they had no significant effect on expression of these NK cell receptors . TGFβ down-regulated the surface expression of NKp30 as reported [35] , [57] , but not that of NKp46 , or CD161 . We cannot exclude that intercellular contact indirectly contribute to alter NK cell phenotype , for instance by inhibiting the functional maturation of DCs , and therefore compromising the DC-NK crosstalk . An effect of the microenvironment on NK cell precursors could also determine their skewed maturation during HHV8 infection , leading to the prevalent expansion of mature NK cells with an altered phenotype . However , we did not observe any skewing in the distribution of CD56bright , CD56dim and CD56-negative NK cell subpopulations , as reported in other chronic infections ( HIV , HCV ) . Finally , the possibility that HHV8 infection of NK cells themselves may modify their phenotype cannot be excluded so far [58] . Various leukocyte subsets support HHV8 latency , including B cells , monocytes , macrophages , DCs and even CD34+ hematopoietic progenitors [49] , [50] , [59]–[62] . Studies in HHV8-infected NOD/SCID mice demonstrated the presence of LANA+ NK cells in the spleens , suggesting that HHV8 can also target NK cells [63] , but this has not been confirmed in the human . Secondly , in addition to the decreased expression of NKp30 , NKp46 and CD161 , patients with active classical KS exhibited a specific down-modulation of NKG2D and a parallel defect in NK cell lytic capacity . Resolution of KS after treatment correlated with restoration of NKG2D levels and NK cell activity . A decreased NK cell activity was previously reported in AIDS patients with progressive KS . NK cell function was restored upon ART treatment , but whether this was only related to HIV clearance was not determined [13] . Our results indicate that HHV8 by itself is responsible for the down-modulation of NKG2D in HIV-negative patients with classical KS . Consequently , NKG2D-mediated NK cell cytotoxicity is hampered . A small fraction of KS cells express the cascade of lytic cycle genes , in particular K5 , which down-modulates HLA-1 , ICAM-1 and certain MICA/B molecules [12] . Loss of surface MICA/B may help HHV8 to evade NK cell surveillance in the early phase of lytic HHV8 infection before establishing latency . This mechanism is clearly not operational in persistently infected KS cells , which express high levels of MICA/B molecules . Instead , the decreased expression of NKG2D appears as an efficient mean for HHV8-infected cells to evade anti-viral immunity and develop their tumoral program . A similar strategy of decreasing NKG2D expression is also adopted by another persistent virus , hepatitis C virus , to evade NK-cell mediated responses in chronically-infected patients [64] . Our results thus reinforce the notion that NKG2D plays an important role not only in control of viral infections , but also in surveillance of tumor development , by protecting the host from tumor initiation and growth [65]–[67] . Soluble NKG2D ligands and TGFβ are known mechanisms for down-regulating NKG2D expression [26] , [27] in some cancer patients , but were not involved in KS patients . Chronic expression of NKG2D ligands on tumor tissues also induces the down-regulation of NKG2D [68] . That MICA was strongly expressed in situ within KS lesions may sustain the hypothesis that it is an efficient mechanism to repress antitumor activity by inducing NKG2D down-modulation on intra-tumoral NK cells . However , it does not easily explain why NKG2D was reduced on circulating NK cells . Notably , we observed that PGE2 was able to decrease NKG2D expression on NK cells in vitro , and PGE2 levels in patient sera negatively correlated with NKG2D expression on NK cells . PGE2 is a major inhibitory factor produced by tumor cells or their surrounding microenvironment [69] . The rate-limiting enzyme in PGE2 synthesis is COX-2 , which is over-expressed in many cancers , leading to an over-production of PGE2 often linked to an adverse clinical outcome [70] , [71] . COX-2 and PGE2 play crucial roles in the establishment and maintenance of HHV8 latency program [32] . In addition , HHV8-induced PGE2 regulates VEGF , which controls cell growth , adhesion , angiogenesis , proliferation and differentiation . COX-2/PGE2 expression is induced during the early stages of infection of primary HMEC cells [32] and in latently-infected human umbilical vein endothelial cells [34] , and abundant COX-2/PGE2 expression is detected in KS tissues [33] , [34] , [72] . We did not detect the presence of PGE2 in the supernatant of rKSHV . 152-infected cells ( data not shown ) , preventing us from reproducing the effect of synthetic PGE2 on NK cells with infected cell supernatants . It must be noted that , although these cells expressed low levels of the early lytic proteins K3 or K5 , our attempts to switch latent infection into lytic cycle were always unsuccessful . In KS lesions , KSHV-infected cells show predominantly latent infection , and occasionally undergo lytic reactivation . Interestingly , PGE2 was shown to downregulate IL-15Rγ chain on NK cells , thus suppressing IL-15-activated NK cell functions [39] . IL-15 is critical for NK cell-dependent clearance of several viral infections , in particular infections by human herpesviruses [73] . Since IL-15 up-regulates expression of NKG2D , and potentiates NKG2D signaling through Jak3-mediated phosphorylation of the NKG2D adaptor DAP10 [41] , [42] , it is conceivable that PGE2 may profoundly affect NKG2D-dependent NK cell activities . We found that PGE2 not only decreased NKG2D expression on resting NK cells in vitro , but also fully prevented IL-15-induced up-regulation of NKG2D , NKp30 and CD161 . Thus , PGE2 overproduction by KS cells may preclude activation of NK cells during HHV8 infection , and promote a progressive drift towards hyporesponsive NK cells . We were surprised by the absence of any CD56+ NK cell within KS lesions , suggesting a defect in homing or survival of NK cells in the vicinity of tumor cells . Indeed , we found that PGE2 inhibited IL-15-induced proliferation and expression of the pro-survival protein Bcl-2 in NK cells , as previously shown in CD4 T cells [74] . Altogether , our results strongly suggest that , by inhibiting IL-15-induced proliferation , activation and NKG2D-mediated function in NK cells , PGE2 appears as a critical factor in preventing immune surveillance of KS development in HHV8-infected individuals . Our results also corroborate recent studies showing the deleterious effect of PGE2 released from mesenchymal stem cells or melanoma-derived fibroblasts on IL-2-induced NK cell activation [37] , [75] . In conclusion , our study provides additional clues of the multifactorial complexity of HHV8-host interactions governing KS progression . In addition to previously reported alterations of HHV8-specific CD8 T cell responses in KS patients , we now report how HHV8 stepwise modifies NK cell-mediated activities . These changes may not only affect the early control of HHV8 infection at an asymptomatic stage , but also preclude efficient prevention and immunosurveillance of KS . We also provide new evidence that HHV8 utilizes the inflammatory PGE2 to its advantage in the KS microenvironment , not only for maintaining latency as previously reported , but also for inhibiting NK cell activation , function and survival in response to proinflammatory cytokines . These results strongly support the potential for COX-2/PGE2 inhibitors in treating KS , as they could simultaneously control latency gene expression and chronic inflammation , reduce angiogenesis and cell adhesion , promote NK cell survival and restore IL-15-induced priming of NKG2D-mediated cytotoxicity .
The study was performed in accordance with the Declaration of Helsinki and French legislation , and received approval of the Saint-Louis Hospital Ethical Committee ( P040105 ) . All participants provided written informed consent . The HHV8-infected group consisted of 70 individuals ( mean age 56 years ) , including 25 asymptomatic HHV8 carriers ( HHV8+ KS− ) and 45 patients with a history of KS ( HHV8+ KS+ ) . Because HHV8 infection frequently occurs in the context of HIV infection , patients were sub-classified as follows: HIV− HHV8+ KS− asymptomatic carriers ( n = 10 , recruited from a cohort of ketosis-prone type 2 diabetes patients [76] ) ; HIV+ HHV8+ KS− asymptomatic carriers ( n = 15 ) ; HIV− HHV8+ KS+ patients ( n = 31 classical KS , including 10 active KS and 21 resolved KS ) ; HIV+ HHV8+ KS+ patients ( n = 14 AIDS-related KS , all with resolved KS following antiretroviral therapy ) . The HHV8-negative control group consisted of 45 age-matched subjects , including 38 healthy blood donor volunteers ( HIV− HHV8− ) , and 7 HIV+ individuals ( HIV+ HHV8− ) . All HIV+ subjects were on stable antiretroviral therapy and had undetectable HIV load for at least 1 year before study . In addition , they were matched for age , CD4 T cell count at time of study , CD4 nadir , duration of disease and duration of ART in the different subgroups . Determination of IgG antibodies against latent and lytic HHV8 antigens was performed by indirect immunofluorescence . Cytomegalovirus ( CMV ) -specific IgG were detected by ELISA . Blood samples were processed within 2 h of collection and PBMCs were separated by Lymphoprep gradient centrifugation ( Abcys ) . When indicated , NK cells were freshly purified from PBMCs by negative selection using magnetic microbead separation ( StemCell Technologies ) with purity higher than 95% . Cells were incubated for 20 min at 4°C with combinations of the following antibodies: FITC-conjugated anti-CD3; PE-conjugated anti-CD56 , anti-NKp30 , anti-CD158e1e2 ( KIR3DL1/S1 ) , anti-CD158i ( KIR2DS4 ) ; APC-conjugated anti-CD56 , anti-CD158ah ( KIR2DL1/S1 ) , anti-CD158bbj ( KIR2DL2/L3/S2 ) , PE-Cy7-conjugated anti-CD56 , Pacific Blue-conjugated anti-CD3 ( all from Beckman Coulter ) ; PE-conjugated anti-CD3; PercP-conjugated anti-CD3 , APC-conjugated anti-NKp46 , anti-CD161; FITC-conjugated anti-CD94 , anti-DNAM-1 ( BD Pharmingen ) ; PE-conjugated anti-NKG2D ( eBioscience ) ; FITC-conjugated anti-CD122 ( IL-15Rβ ) APC-conjugated anti-NKG2A , anti-NKG2C , anti-CD132 ( IL-15RγR&D Systems ) . For intracellular detection of Ki67 and Bcl-2 , cells were fixed in 1% formaldehyde , permeabilized with 0 . 2% saponin and stained with FITC-conjugated anti-Bcl-2 and PE-conjugated anti-Ki67 ( BD Pharmingen ) . Cells were analyzed on FACSCalibur or LSRFortessa ( BD Biosciences ) , collecting a total of 100 , 000 events in a live gate . Data were analyzed using FlowJo software . SV40-immortalized human microvascular endothelial cells ( HMEC ) were infected with rKSHV . 152 , a recombinant virus expressing the green fluorescent protein ( GFP ) and neo ( conferring resistance to G418 ) , and able to establish cells containing only latent HHV8 [19] . HMEC and HHV8-latently infected HMEC cells ( thereafter called HHV8-HMEC ) were cultured in MCDB131 medium supplemented with 10 ng/ml epidermal growth factor ( EGF ) and 1 µg/ml hydrocortisone ( Sigma ) , 10% fetal calf serum ( FCS ) , 2% glutamine , penicillin ( 100 U/ml ) and streptomycin ( 100 U/ml ) . In addition , HHV8-HMEC medium contained G418 at 700 µg/ml . SV2G is an SV40-immortalized HIV-negative KS-derived cell line . Because HHV8 genome was lost early after the first 2 passages , SV2G was also infected in vitro with rKSHV . 152 . The resulting HHV8-SV2G cells line also showed predominantly latent infection . SV2G cells were cultured in RPMI 1640-10% FCS , while HHV8-SV2G cells were cultured in 50% HMEC medium/50% SV2G medium . Cells were seeded at subconfluent density , and were recovered by trypsine/EDTA treatment . Culture supernatants were collected and kept frozen . Cell viability ( ViaProbe , Pharmingen ) and phenotype were analyzed by flow cytometry after cell surface staining with antibodies specific for HLA class I ( W6/32 ) , MICA and MICB [77]; ULBP-1 , ULBP-2 , ULBP-3 , PVR , LLT1 ( all from R&D System ) , Nectin-2 ( BD Pharmingen ) , HLA-E ( clone 3D12 , eBioscience ) and ICAM-1 ( AbD Serotec ) . Expression of NCR ligands was investigated using NKp30-Fc and NKp46-Fc fusion proteins ( R&D ) and FITC-anti-human Fc antibody ( Jackson Immunoresearch ) . These reagents were validated for staining NKp30 and NKp46 ligands on K562 and Hela cell lines , previously reported to express these ligands [25] , [78] , [79] . Purified NK cells ( 105 per U-bottom well ) were incubated with target cells at 1∶1 effector:target ratio for 6 hr . FITC-conjugated anti-CD107a ( 20 µg/mL , BD Biosciences ) was added directly . After 1 hour at 37°C in 5% CO2 , brefeldin A ( 1 µg/ml ) and monensin ( 6 µg/ml , Sigma ) were added for additional 5 hr , and cells were stained with CD56-APC and CD3-PE antibodies , and Viaprobe . Where indicated , NK cells were preincubated with NKG2D blocking antibody ( 20 µg/ml , Coulter Immunotech ) or isotype control . For intracellular IFNγ analysis , cells were fixed following staining with anti-CD3 and anti-CD56 , permeabilized with 0 . 2% saponin and stained with IFNγ FITC antibody ( BD ) for an additional 30 min . Recombinant TGFβ , VEGF , IL-8 , and IL-15 were purchased from R&D Systems . PGE2 ( Cayman Chemicals ) was dissolved at 100 mg/ml in 95% ethanol and further diluted with RPMI 1640 . The final concentration of ethanol had no effect on NK cell viability and function . ELISA was used to quantify serum IL-8 , IL-10 , VEGF and TGFβ ( R&D Systems ) , PGE2 ( Cayman Chemicals and soluble MICA ( [80] ) . Total RNA was extracted from HHV8-infected or uninfected HMEC and KS-derived cells ( RNeasy system; Qiagen ) and retrotranscribed to cDNA with the use of Superscript III reverse transcriptase and random primers ( Invitrogen ) . For real-time quantitative polymerase chain reaction , Light Cycler 480 SyBR Green I Master and Light Cycler 480 detection system ( Roche ) were used . The level of K3 and K5 amplified transcripts was determined using a 25-fold dilution of each cDNA , and normalized to glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) mRNA levels . Primers used for quantifying the expression of K3 and K5 mRNAs were as follows: 5′-gCAAACCCTgTggAAggATA-3′ ( forward ) and 5′-AAgCTgCAgggTACAAggAA-3′ ( reverse ) for K3; 5′-ACCACCACAgACATCAgCAA3′ ( forward ) and 5′-gTAgggAAgAggTggggAAC-3′ ( reverse ) for K5 . Paraffin-embedded KS biopsy samples from 5 patients were obtained from the Pathology Department . After antigen retrieval in 10 mM citrate buffer pH 6 . 0 at 100°C , sections were blocked with hydrogen peroxide and PBS containing 10% pooled human AB serum , 1∶30 goat serum and incubated at 20°C for 1 hour with antibodies directed to HLA-1 ( W6/32 ) , MICA/B ( SR99 [77] ) and CD56 ( clone 1B6 , MBL ) , or control isotypes , and staining was visualized by using the Envision+ AEC System ( Dako ) . For detection of NCR ligands , sections were incubated with NKp30-Fc or NKp46-Fc fusion proteins ( R&D , 8 µg/ml in PBS containing 0 . 3% normal goat serum ) , followed by biotin-goat anti-human Fcγ ( 1∶2 , 000; Jackson Immunoresearch ) . All statistical tests were performed with Instat 3 ( GraphPad software ) . Comparisons between two groups were performed using the Wilcoxon and the Mann-Whitney t tests for paired and unpaired groups , respectively . Multiple comparison analyses were performed using Kruskall-Wallis test ( non parametric ANOVA ) with Dunn's multiple comparison test . Two-sided p values less than 0 . 05 were considered significant . Correlation analysis was performed using non parametric Spearman rank correlation . Name: NKG2D , Accession number ( Swissprot ) : P26718 , Entry name: NKG2D_HUMAN . | Natural Killer ( NK ) cells are part of the innate immune response against virus infections and tumors . Their activation is the net result of signals emanating from a panel of inhibitory and activating receptors recognizing specific ligands on target cells . Human Herpes Virus 8 ( HHV8 ) is an oncogenic virus responsible of Kaposi Sarcoma ( KS ) , a multifocal angiogenic tumor . How NK cells contribute to the control of infection by HHV8 infection and development of KS , is unclear . In this paper , we show different strategies used by HHV8 to escape NK cell response . Patients with asymptomatic infection or KS have down-modulated expression of NKp30 , NKp46 and CD161 receptors . In addition , patients with active KS show additional down-modulation of the NKG2D activating receptor , associated with impaired NK-cell cytotoxicity against target cells . Resolution of KS correlates with regained NKG2D expression and cytotoxic function . We present evidence that down-modulation of NKG2D is mediated by inflammatory prostaglandin E2 ( PGE2 ) , known to be released by KS cells , and show that PGE2 acts by preventing IL-15-mediated activation of NK cells . These results strongly support the use of PGE2 inhibitors as an attractive approach to treat active KS . | [
"Abstract",
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] | 2012 | Human Herpesvirus 8 (HHV8) Sequentially Shapes the NK Cell Repertoire during the Course of Asymptomatic Infection and Kaposi Sarcoma |
In multicellular organisms , tight regulation of gene expression ensures appropriate tissue and organismal growth throughout development . Reversible phosphorylation of the RNA Polymerase II ( RNAPII ) C-terminal domain ( CTD ) is critical for the regulation of gene expression states , but how phosphorylation is actively modified in a developmental context remains poorly understood . Protein phosphatase 1 ( PP1 ) is one of several enzymes that has been reported to dephosphorylate the RNAPII CTD . However , PP1's contribution to transcriptional regulation during animal development and the mechanisms by which its activity is targeted to RNAPII have not been fully elucidated . Here we show that the Drosophila orthologue of the PP1 Nuclear Targeting Subunit ( dPNUTS ) is essential for organismal development and is cell autonomously required for growth of developing tissues . The function of dPNUTS in tissue development depends on its binding to PP1 , which we show is targeted by dPNUTS to RNAPII at many active sites of transcription on chromosomes . Loss of dPNUTS function or specific disruption of its ability to bind PP1 results in hyperphosphorylation of the RNAPII CTD in whole animal extracts and on chromosomes . Consistent with dPNUTS being a global transcriptional regulator , we find that loss of dPNUTS function affects the expression of the majority of genes in developing 1st instar larvae , including those that promote proliferative growth . Together , these findings shed light on the in vivo role of the PNUTS-PP1 holoenzyme and its contribution to the control of gene expression during early Drosophila development .
Development must be tightly coupled with cellular metabolism to ensure that necessary nutritional and energetic requirements are met and the available resources are utilised effectively to sustain appropriate levels of tissue growth . A particularly dramatic example of how development is coupled to metabolism is during the transition through the larval stages of Drosophila development , during which animals accumulate a 200-fold increase in body mass . The metabolic needs to sustain this rapid expansion are underpinned by transcriptional programmes initiated in the embryo; as maternal products become exhausted , large numbers of zygotically expressed genes , responsible for converting raw materials into cell mass , are induced to sustain developmental growth [1] . Elucidating what factors are necessary to drive these transcriptional programmes is not only critical for understanding tissue and organism size regulation during normal development , but is also important for understanding numerous disease processes characterized by inappropriate gene expression . Reversible phosphorylation plays important roles in the regulation of transcriptional networks and in coordinating spatial and temporal patterns of gene expression . Phosphorylation of RNA polymerase II ( RNAPII ) at multiple sites on its C-terminal domain ( CTD ) is critical for gene expression and its regulation [2] . Different phospho-forms of the CTD appear at different stages of the transcription cycle , and these are thought to facilitate initiation , elongation and termination by recruiting specific histone and RNA modifiers [3] , [4] . The consensus view from studies of RNAPII occupancy in budding yeast is that there is a stereotypical pattern of phosphorylation at most gene loci during the transcription cycle [5] , [6] . However , numerous lines of evidence suggest that there is active control of CTD phosphorylation in response to environmental cues [7]–[9] and during developmental transitions , e . g . in which restriction of CTD phosphorylation to particular lineages [10] is used to control cell fate [11] . Furthermore , studies of the enzymes responsible for regulating CTD phosphorylation indicate that phosphorylation may be modified at specific loci to determine gene-specific patterns of expression [12] , [13] . Serine/threonine protein phosphatase type 1 ( PP1 ) is one of four protein phosphatases known to contribute to the regulation of CTD phosphorylation [14] , the others being FCP1 [15] , SCP1 [11] and Ssu72 [16] . In Drosophila , PP1 is found at multiple sites on chromosomes where it has been postulated to play important roles in regulating developmentally controlled gene expression [17] , [18] . However , analysing the role of PP1 in transcriptional regulation has been complicated by its pleiotropic roles [19] and broad in vitro substrate specificity . In vivo , PP1 has been shown to associate with different targeting subunits that restrict its activity towards particular substrates [20] . Therefore , a full understanding of PP1 function requires the identification and characterisation of these regulatory proteins . In mammalian cells , the PP1 Nuclear Targeting Subunit ( PNUTS ) is one of the two most abundant PP1-interacting proteins in the nucleus [21] and is known to be chromatin-associated during interphase and not during mitosis [22] , [23] . Its reassociation with chromatin during telophase and its ability to augment chromosome decondensation in vitro [24] and in vivo [25] have indicated a possible role in cell cycle progression . Several lines of evidence also indicate that PNUTS is required for cell survival [26]–[30] and contributes to cellular responses to environmental stress , including hypoxia [31] and DNA damage [32] . These roles may be especially important during ageing since loss of PNUTS expression is associated with an age-dependent increase in cardiomyocyte apoptosis and decline in cardiac function [33] . Targeting of PNUTS to chromatin is likely to be in part through association with the DNA-binding factor Tox4/Lcp1 [25] , [34] , which is capable of recognising DNA adducts generated by platinum anticancer drugs [35] . PNUTS and Tox4 have also been reported to form a stable multimeric complex with Wdr82 [25] , which was previously identified as an integral component of a distinct complex containing Set histone H3-Lys4 methyltransferases . Although the role of Wdr82 bound to PNUTS is not known , Wdr82 may mediate interactions with initiating and early elongating RNAPII by recognising Ser5-phosphorylated CTD , as it does when it is associated with the Set1 complex [36] . A role for PNUTS in transcription has been further suggested by recent reports that it associates with RNAPII complexes [37] . Despite these insights , an understanding of the physiological roles of PNUTS remains incomplete . Here we show that null mutants in the D . melanogaster orthologue of PNUTS ( dPNUTS ) , display a larval growth defect and are larval lethal . Mutant clones show a cell autonomous growth defect and are eliminated from wild type epithelia due to cell competition . RNA-Sequencing ( RNA-Seq ) analysis indicates that dPNUTS affects the expression of the majority of genes in 1st instar larvae , including those that are highly expressed and are involved in cellular metabolism and larval development . The function of dPNUTS in tissue development is dependent on binding to the catalytic subunit of Protein phosphatase 1 ( PP1 ) , which is targeted by dPNUTS to RNA polymerase II in cell extracts and at many active sites of transcription on polytene chromosomes . Loss of dPNUTS function , or displacement of dPNUTS-PP1 using a non-PP1 binding mutant of dPNUTS , results in hyperphosphorylation of the C-terminal domain of RNA Polymerase II in whole animal extracts and on chromosomes . Taken together , these data suggest that dPNUTS-PP1 is a global regulator of gene expression via effects on RNAPII phosphorylation and is required in larvae to promote normal developmental growth .
Sequence homology searches have suggested that PNUTS is a metazoan PP1-binding protein [38] . However , its absence from species such as C . elegans indicates that it has not been retained in all metazoa . D . melanogaster contains one gene encoding PNUTS: CG33526/dPNUTS . Comparison of full-length PNUTS cDNA and genomic sequences shows that all four of dPNUTS intron/exon boundaries are shared with human PNUTS ( hPNUTS; Figure S1 ) , indicating that hPNUTS and dPNUTS are derived from a single ancestral gene . dPNUTS encodes two protein isoforms: dPNUTS and dPNUTS-S , a truncated version containing only the N-terminal region of dPNUTS . There is extensive homology between dPNUTS and mammalian PNUTS in a number of protein domains ( Figure S1 ) . PNUTS has been identified in all mammalian tissues so far examined [22] , [23] , but the highest level of expression is reported to be in testis , brain , and intestine . In situ hybridisation revealed that dPNUTS transcripts are maternally provided and are uniformly distributed in most tissues during Drosophila embryogenesis . However , strikingly , there was stronger staining in the developing gut and in the nervous system during phases of rapid development ( Figure 1A ) . To determine the subcellular distribution of dPNUTS , we generated transgenic fly lines capable of expressing epitope-tagged dPNUTS under UAS-GAL4 control . Ectopic dPNUTS shows a similar subcellular localisation to mammalian PNUTS: dPNUTS is nuclear and associates with chromatin during interphase when ectopically expressed in the wing disc , but is excluded from condensed chromosomes at metaphase ( Figure 1B ) . In polytene nuclei , ectopic dPNUTS was visible in both the nucleoplasm and on polytene chromosomes as revealed by co-staining the DNA with Hoechst ( Figure 1C , D ) . Strong Hoechst staining is associated with condensed chromosomal bands , which contain a high concentration of DNA , whilst weak , or no Hoechst signal is detected in interband regions of less tightly packed chromatin , which are thought to contain actively transcribed genes . dPNUTS is predominantly associated with regions of less condensed DNA corresponding to interbands that stain weakly with Hoechst ( Figure 1D ) . To examine the chromosomal association of dPNUTS further , we generated antibodies specific to dPNUTS and used them to stain polytene chromosomes from 3rd instar larval salivary glands . Although the dPNUTS antibodies worked well on polytene squash preparations we were unable to obtain a reliable signal from whole tissue mount preparations . We found that dPNUTS is localised at a large number of discrete sites of varying strength along all the chromosomes . To confirm the specificity of the dPNUTS antiserum on polytene squashes , we knocked down dPNUTS levels in salivary glands using heritable double-stranded RNA interference ( RNAi ) . Flies carrying an inverted repeat ( IR ) construct under UAS control were crossed to a salivary gland GAL4 source to induce expression of intron-spliced hairpin dsRNA for dPNUTS in the progeny . In squash preparations from relatively normal looking glands expressing UAS-dPNUTSIR we found greatly reduced dPNUTS staining ( Figure S2 ) . To explore the possibility that dPNUTS may be associated with transcriptionally active sites , we performed double labelling experiments with antibodies against the active form of RNA polymerase II ( RNAPII ) . We found that the relative levels of dPNUTS and RNAPII vary at many sites , but , on close inspection , it is clear that the dPNUTS antibody marks a large number of transcriptionally active sites containing active RNAPII ( H5 antibody , detecting RNAPII Ser2-P ) ( Figure 1E , F ) , suggesting that dPNUTS might have a role in transcriptional regulation . To determine the in vivo role of Drosophila PNUTS , we generated two deletion alleles , dPNUTS9B and dPNUTS13B , by imprecise excision of a P element transposon ( P[SUPor-P] dPNUTSKG00572 , referred to as dPNUTSKG572 hereafter ) . Molecular analysis revealed that virtually all of the fourth coding exon of dPNUTS is deleted in dPNUTS9B , and the entire coding region , including the translation start site , is deleted in dPNUTS13B ( Figure 2A ) . Consistent with these findings , quantitative RT-PCR analyses revealed the absence or almost complete loss of dPNUTS transcripts in dPNUTS9B and dPNUTS13B homozygotes . dPNUTS levels were also greatly reduced in dPNUTSKG572 homozygotes compared to revertant controls ( dPNUTSexKG ) in which the P element had been precisely excised ( Figure 2B ) . dPNUTS9B and dPNUTS13B are recessive lethal in combination with each other and over Df ( 2L ) ast4 , a deficiency that removes the dPNUTS gene . The phenotype of dPNUTS9B and dPNUTS13B homozygotes was indistinguishable from that of dPNUTS9B or dPNUTS13B/Df ( 2L ) ast4 hemizygotes , so we conclude that the excision alleles have little or no residual dPNUTS function . To confirm that disruption of the dPNUTS transcription unit is responsible for the larval lethality , we generated transgenic flies carrying a genomic fragment containing the entire PNUTS locus . A single copy of the transgenic construct was capable of fully rescuing the homozygous lethality of dPNUTS9B and dPNUTS13B mutants ( Table S1 ) . To examine the lethal phase of dPNUTS mutants we combined the mutant alleles with a GFP-balancer chromosome and examined the development of mutant ( non-GFP ) larvae alongside their heterozygous ( GFP marked ) siblings . Homozygous dPNUTS9B and dPNUTS13B animals developed to 1st instar larvae but died in the ensuing 8 days without further growth and development ( Figure 2C , D ) . To further assess the requirement for dPNUTS in tissue development we made use of the ey-FLP system to produce genetically mosaic flies that are otherwise heterozygous but in which the eye is composed exclusively of cells homozygous mutant for dPNUTS . Cells that are not derived from the homozygous mutant cells are eliminated by eye-specific expression of the pro-apoptotic gene hid [39] . Eyes of heterozygous dPNUTSKG572 , dPNUTS9B and dPNUTS13B flies resembled wild type . Flies with eyes homozygous for dPNUTSKG572 were modestly reduced in size , with fewer and poorly organised ommatidia . Eyes homozygous for either dPNUTS9B or dPNUTS13B showed a more severe effect , indicating that cells lacking dPNUTS are incapable of developing into adult eyes ( Figure 2E ) . To understand more about the cellular role of dPNUTS , we generated clones of homozygous null dPNUTS mutant cells in otherwise dPNUTS heterozygous wing imaginal discs during early or mid-larval development using Flp/FRT-mediated recombination [40] and analysed them at the wandering 3rd instar larval stage . To do this we used a heat shock inducible Flipase ( Flp ) enzyme to induce mitotic recombination between two FRT chromatids , one of which carried a mutant dPNUTS allele and the other which expressed a GFP marker . Mitotic recombination events produce a GFP-negative cell clone that are homozygous for the mutant allele , together with a “twin-spot” marked by the presence of two copies of GFP . Surrounding heterozygous tissue is labelled with one copy of GFP . We failed to recover homozygous mutant cells when clones were induced in early 1st instar larvae , whereas wild type cells induced at the same stage proliferated to generate large clonal patches ( Figure 3A , B ) . When we shortened the time between clone induction and analysis by inducing clones later on in 2nd instar larvae , we were able to observe very small patches of dPNUTS mutant cells ( Figure 3C , D ) . However , in optical cross sections through the tissue it was apparent that mutant cells accumulated at the basal face of the epithelium and stained positive for cleaved caspase antibody ( Figure 3E–J ) , indicating that dPNUTS mutant cells were undergoing cell death . This prompted us to examine whether clones were dying due to cell competition , a process in which slow-growing cells are eliminated by their faster-growing neighbours . To test this , we gave the dPNUTS mutant cells a growth advantage by generating them in tissues that were heterozygous for a dominant Minute ( M ) allele of RpL27A . Notably , under conditions in which dPNUTS mutant clones in a wild-type background are normally eliminated , we recovered dPNUTS clones in M/+ discs ( Figure 3K , L and Figure S3 ) and mutant clones spanned the entire wing disc epithelium indicating they were not being eliminated ( Figure 3Q , R and Figure S3 ) . However , mutant clones colonised a significantly smaller area of M/+ discs compared with wild-type clones , indicating that they were still growth impaired ( compare Figure 3K and 3L ) . To obtain an insight into the molecular basis for the growth defects in dPNUTS mutants and assess the impact of dPNUTS loss of function on gene expression , we analysed the transcriptomic signature of dPNUTS9B and dPNUTS13B mutant larvae by RNA-Seq . The control for these experiments was an isogenic strain that carried the same background mutation ( w1118 ) as the dPNUTS mutant strains . Homozygous dPNUTS9B and dPNUTS13B mutant 1st instar larvae had widespread changes in gene expression compared to control animals of the same stage ( Figure 4A ) , with a comparable pattern of genes being affected in both mutants ( Figure S4 ) . In total , approximately 30% of genes ( 2819/9483 ) previously reported to be expressed in 1st instar larvae [40] were underexpressed , and a similar proportion ( 2850/9483 ) were overexpressed >1 . 5-fold in both dPNUTS9B and dPNUTS13B mutant animals relative to control larvae . Therefore , we conclude that disruption of dPNUTS function affects the expression of the majority of genes in developing 1st instar larvae . To assess whether there was any enrichment of genes belonging to functionally-related biological processes , we analysed the distribution of Gene Ontology ( GO ) terms amongst differentially expressed genes . When compared to the frequency of GO terms amongst all genes encoded by the genome , we observed significant ( P≤10−4 ) enrichment of terms for cell death and stress responses amongst genes overexpressed in dPNUTS mutants ( Figure S5 , Table S2 ) . Overexpression of these groups of genes might indicate that the animals are under stress and is consistent with their poor survival . The most significantly enriched GO terms amongst the underexpressed genes in dPNUTS mutants , were terms for cellular metabolic processes that drive proliferative growth , including ribosome biogenesis , rRNA processing , translation and metabolism of energy sources ( Figure S5 , Table S2 ) . We observed a similar pattern of GO enrichment when comparing differentially expressed genes in the dPNUTS mutants to genes expressed in our developmentally matched control ( Table S3 ) . These patterns of transcriptional change are consistent with the larval growth defect exhibited by the dPNUTS mutants . In addition , Ingenuity analysis identified a number of different transcriptional networks involved in organismal growth that are likely to be affected by loss of dPNUTS ( Table S4 ) . While these analyses provide biological insight into the likely processes underpinning the dPNUTS mutant phenotype , it is important to note that the enrichment of biologically-relevant GO categories is correlated with the expression level of the representative genes in 1st instar larvae ( Figure 4A ) . Indeed , GO categories pertaining to cellular metabolism are also enriched amongst highly expressed genes in the control ( median expression level > ( log2 ) 2 . 9 FPKM; data not shown ) . Taken together with the widespread effects on transcript abundance , these data indicate that dPNUTS globally affects gene expression and in 1st instar larvae is required to promote expression of highly expressed genes that support developmental growth . To confirm the RNA-Seq results , we selected genes representative of enriched GO categories for quantitative real-time qRT-PCR analysis . Measurements of relative mRNA expression level determined by qRT-PCR were consistent with our RNA-Seq data ( Figure 4B , Table S5 ) . dPNUTS was originally isolated from a two-hybrid screen for putative PP1-binding proteins and contains a canonical PP1-binding motif - K/R , ( x ) , V/I/L , x , F/W that in PNUTS/p99 is necessary for binding to , and inhibition of , PP1 [23] , [41] . This motif ( residues 722–726 ) is also contained within all the dPNUTS two-hybrid clones , including the shortest interacting fragment encoding residues 608 to 1135 [42] , ( Figure 5A ) . When we retested binding in the two-hybrid system with full-length proteins , dPNUTS , but not dPNUTS-S , interacted strongly with all four D . melanogaster PP1 isoforms ( Figure 5B ) , consistent with a role for this motif in binding PP1 . To determine the importance of the putative PP1-binding motif for interaction with PP1 , we compared binding of endogenous PP1 , to ectopically expressed wild type dPNUTS ( dPNUTSWT ) and a mutant form in which Trp726 was replaced with Ala ( dPNUTSW726A ) . Immunoprecipitation with antibodies against Myc-tagged dPNUTS , followed by immunoblotting with antibodies against PP1 , showed that PP1 co-precipitated very efficiently with dPNUTSWT but not dPNUTSW726A ( Figure 5C ) , indicating that Trp726 is crucial for interaction with PP1 . To further explore the association between PP1 and dPNUTS in vivo , we examined the distribution of dPNUTS and PP1 on polytene chromosomes from 3rd instar larvae . We previously reported that ectopic HA-tagged PP187B , the major PP1 isoform in Drosophila [43] , localised to many discrete chromosomal loci [17] , [18] . Like the ectopic protein , we found a large number of discrete sites widely dispersed along the chromosomes that were stained with an anti-peptide antibody to Drosophila PP1 ( Figure 5D ) . When we co-stained for dPNUTS , we found that most sites staining for dPNUTS also stained strongly for PP1 although the relative staining varied greatly ( Figure 5D ) . Since salivary glands from 1st instar dPNUTS mutant larvae were too small to analyse in squash preparations , we were unable to test whether loss of dPNUTS function displaces PP1 from chromosomes . Therefore , to examine whether PP1 is dependent on dPNUTS for its localisation or vice versa , we utilised our transgenic overexpression construct dPNUTSW726A , which exhibits reduced binding to PP1 . We reasoned that if PP1 is necessary for dPNUTS localisation we would expect to observe loss of dPNUTSW726A from chromosomes; conversely , if dPNUTS is responsible for recruiting PP1 then overexpressed dPNUTSW726A should stoichiometrically compete with endogenous PNUTS-PP1 complexes for binding to chromosomes resulting in the displacement of PP1 . Chromosomal PP1 staining , but not total PP1 levels , was reduced in glands overexpressing dPNUTSW726A compared to those expressing dPNUTSWT ( Figure 6A ) . To quantify the effect on PP1 localisation , we performed line scans to measure fluorescence intensity at a readily identifiable site on the X chromosome , where endogenous PP1 and dPNUTS co-localise ( Figure 5D ) . Intensity of PP1 staining at this site on chromosomes from animals overexpressing dPNUTSW726A was on average reduced 0 . 6 fold ( Figure 6B ) . Taken together , these data suggest that dPNUTS is responsible for targeting PP1 to many distinct chromosomal loci . Anti-Myc staining of ectopically expressed Myc-tagged dPNUTS was of relatively poor quality but , in general there was a comparable distribution of dPNUTSW726A and dPNUTSWT in squash preparations ( Figure 6C ) . Levels of dPNUTSW726A sometimes appeared weaker than dPNUTSWT but this is accounted for by differences in the quality of squash preparations and a lower expression level of dPNUTSW726A relative to dPNUTSWT , as revealed by immunoblotting ( Figure 6D ) . Taken together these data suggest that PP1 binding is not necessary for dPNUTS localization to polytene chromosomes . To elucidate the functional significance of the interaction between PP1 and dPNUTS in vivo , we examined whether dPNUTSW726A was capable of rescuing the reduced eye phenotype exhibited by our dPNUTS mutants . dPNUTSWT rescued the effect of both dPNUTS9B and dPNUTS13B . However , ectopic overexpression of dPNUTSW726A failed to rescue either mutant ( Figure 6E ) , indicating that binding to PP1 is critical for dPNUTS function in tissue development . We also took another approach to examine the effect of reducing PP1 activity in dPNUTS mutant eyes . For this , we generated flies that were homozygous for dPNUTSKG572 , which resulted in a modest reduction in eye size , and also heterozygous for mutations in PP187B that reduce the total PP1 activity by approximately 40% [44] . Reduced eye phenotypes caused by dPNUTSKG572 mutants were dominantly enhanced by PP187B , consistent with dPNUTS acting as a positive regulator of PP1 function during imaginal disc development ( Figure 6F ) . RNAPII has recently been reported to co-precipitate PNUTS from mammalian cell extracts [37] . Given the widespread effects of dPNUTS mutations on transcription and its colocalisation with active RNAPII at many transcriptionally active sites on chromosomes , we wondered whether dPNUTS also physically associates with RNAPII complexes . To test this , we immunoprecipitated endogenous dPNUTS from wild type embryo extracts and examined precipitates for the presence of RNAPII . Two RNAPII species , representing unphosphorylated ( RNAPIIa ) and phosphorylated RNAPII ( RNAPIIo ) , can be detected using an antibody ( ARNA-3 ) that recognises a peptide mapping to central region of RNAPII . Both these forms precipitated with dPNUTS-S , but only RNAPIIA co-precipitated efficiently with dPNUTS ( Figure 7A ) . Since PP1 was previously shown to be capable of dephosphorylating RNAPIIo in vitro [14] , we wondered whether the pattern of binding we observed was because dPNUTS is capable of binding PP1 and dPNUTS-S is not . To test the role of PP1 in endogenous dPNUTS complexes , we repeated our immunoprecipitations in the presence of Inhibitor-2 ( I-2 ) , a specific inhibitor of PP1 [45] . There was no apparent difference in the abundance of RNAPIIa or RNAPIIo in dPNUTS-S precipitates . However , when we precipitated dPNUTS in the presence of I-2 , we found reduced levels of RNAPIIa and elevated levels of RNAPIIo ( Figure 7A ) . I-2 selectively targets PP1 over PP2A , which is the next most closely related member of the PPP family of phosphatases [46] . Therefore , we conclude that PP1 is likely to be the major RNAPII phosphatase in these complexes . Mammalian PNUTS has been reported to bind to Wdr82 , which targets RNAPII phosphorylated on Ser5 of its CTD repeats ( RNAPII CTD Ser5-P ) . Although the degree of functional conservation between mammalian and Drosophila Wdr82 ( dWdr82 ) has not yet been fully determined , we found that dWdr82 co-precipitated with dPNUTS from Drosophila cell extracts indicating that the ability of Wdr82 to bind PNUTS is shared between fly and human orthologues ( Figure S6A ) . This prompted us to assess the effect of dPNUTS loss of function on the levels of RNAPII CTD Ser5-P . Using an antibody ( 4H8 ) that recognizes the Ser5-phosphorylated C-terminal domain [47] , we observed elevated levels of RNAPII CTD Ser5-P in total extracts from dPNUTS mutant larval extracts by Western Blotting compared to wild type ( w1118 ) or revertant ( dPNUTSexKG/dPNUTSexKG ) controls ( Figure 7B ) . Using a panel of independent anti-phospho CTD antibodies [48] , [49] we further confirmed the effect of dPNUTS loss of function on RNAPII CTD Ser5-P levels . We also observed a modest increase in levels of RNAPII CTD Ser2-P but little or no change in levels of RNAPII CTD Thr4-P or Ser7-P , in mutant extracts ( Figure S6B ) . To test whether dPNUTS regulates RNAPII phosphorylation on chromosomes , we generated mutant clones in the salivary gland and examined RNAPII phosphorylation on polytene chromosomes in whole mount preparations . Levels of RNAPII CTD Ser5-P , as detected with an antibody ( H14 ) , which recognizes RNAPII Ser5-P in the context of Ser2 phosphorylation [48] , were also elevated in this context ( data not shown ) . Interestingly , on wild type polytene chromosome squashes , we observed relatively little co-localisation between dPNUTS and RNAPII Ser5-P ( H14 ) ( Figure S7 ) , suggesting that the presence of dPNUTS at chromosomal loci is associated with a reduction of Ser5 phosphorylation at these sites . To confirm the role of dPNUTS-bound PP1 , we expressed Myc-tagged dPNUTSWT and dPNUTSW726A in embryos , and tested their ability to bind to RNAPII CTD Ser5-P . Immunoprecipitation with anti-Myc antibodies , followed by immunoblotting with anti-RNAPII CTD Ser5-P ( 4H8 ) antibody , revealed that RNAPII CTD Ser5-P was only recovered in Myc-dPNUTSW726A and not Myc-dPNUTSWT precipitates ( Figure 7C ) , further indicating that dPNUTS-bound PP1 dephosphorylates RNAPII Ser5-P . To further test the role of PP1-bound dPNUTS , we examined the effect of ectopic dPNUTSW726A on RNAPII phosphorylation on polytene chromosome spreads . Since dPNUTSW726A shows reduced binding to PP1 , we predicted that ectopic expression of this mutant form would compete with endogenous PNUTS-PP1 complexes and thereby reduce RNAPII dephosphorylation by PP1 . Correspondingly , we found that levels of RNAPII CTD Ser5-P appeared modestly elevated on chromosomes from glands overexpressing dPNUTSW726A ( Figure 7D ) . To quantitate this effect , we compared the levels of RNAPII CTD Ser5-P staining on chromosomes from larvae with or without ectopic dPNUTSW726A . Since RNAPII CTD Ser5-P staining was variable from slide to slide , chromosomes from animals over-expressing dPNUTSW726A were prepared alongside control samples labelled with histone-H2B YFP and stained on the same slides to ensure identical staining conditions between the two samples . Line scans and measurements of average signal intensity at a site at which dPNUTSW726A displaces endogenous PP1 ( Figure 6A , B ) , indicated an average increase of 1 . 59 fold in RNAPII Ser5-P on chromosomes from larvae ectopically expressing dPNUTSW726A compared to wild type animals ( Figure 7E ) . Ectopic dPNUTSWT on average had no effect on RNAPII Ser5-P staining relative to histone-H2B YFP labeled chromosomes ( data not shown ) . Together , these results indicate that the dPNUTS-PP1 holoenzyme associates with RNAPII and regulates the dephosphorylation of its C-Terminal Domain . Relatively little is known about the effect of RNAPII Ser5 hyperphosphorylation on gene expression , but it has been associated with decreased elongation rate or pausing of RNAPII when it occurs on the body of genes [50] , [51] . To assess whether effects on RNAPII occupancy might result from disrupting dPNUTS binding to PP1 , we examined the effect of ectopic dPNUTSW726A on gene expression and the distribution of RNAPII at specific gene loci . Overexpression of dPNUTSW726A in 3rd instar larvae using da-GAL4 ( da>dPNUTSW726A ) had a similar , but weaker , effect on gene expression to that of dPNUTS loss-of-function in 1st instar larvae ( Figure S8A ) . This might be because the transgenic line of dPNUTSW726A that we used had only a weak dominant-negative effect ( animals expressing this construct were viable with da-GAL4 ) and/or because the regulation of some target loci is different at this later developmental stage . Amongst the genes we examined , ImpL3 , nop56 and ACC were underexpressed , whereas the stress response gene Thor was overexpressed in response to ectopic dPNUTSW726A . Next , we examined the distribution of RNAPII at selected loci by Chromatin Immunoprecipitation ( ChIP ) . For these experiments , chromatin was extracted from da>dPNUTSW726A or control larvae and precipitated with either mouse IgG or anti-total RNAPII antibody ( 8WG16 ) . We determined the abundance of precipitated chromatin by qPCR with gene specific primers . Control precipitations with mouse IgG showed a low level of non-specific background in all of these experiments ( Figure S8B–E ) . When we analysed the distribution of RNAPII at selected loci , we did not observe a significant change in the occupancy of total RNAPII at the 5′ ends or coding regions of genes in da>dPNUTSW726A samples . Together , these results provide evidence of the link between the disruption of PP1 binding to dPNUTS and the misregulation of RNAPII-mediated gene expression , but suggest that changes in gene expression that we have observed may be linked to effects on co-transcriptional processes , such as mRNA capping , rather than transcription per se . Indeed , Ser5-P has been shown to bind and stimulate the activity of mammalian capping enzyme ( Mce1 ) [52] , [53] . Furthermore , in yeast , lethality resulting from substitution of all CTD Ser5 residues with Ala can be rescued by the tethering of Mce1 to the CTD , suggesting that the essential function of CTD Ser5 is in capping enzyme recruitment [54] .
Here we report the functional analysis of Drosophila PNUTS , a regulatory subunit of PP1 that is highly conserved between flies and humans . We find that dPNUTS is essential for organismal growth , with mutant animals arresting early in larval development . Survival of the null zygotic mutants until the early larval stage is most likely due to perdurance of maternal dPNUTS gene products , raising the possibility of additional roles for dPNUTS during embryological development that we have not uncovered here . Clonal analysis indicates that dPNUTS has a cell autonomous effect on growth , with mutant clones failing to survive unless given a growth advantage . Transcriptomics characterisation of dPNUTS mutant animals indicates that the larval arrest phenotype is associated with the underexpression of many RNAPII-dependent genes , including those that normally support developmental growth . Of particular interest in this regard is the significant enrichment of genes involved in cellular metabolism . The underexpression of these genes suggests that an important role of dPNUTS during larval growth might be to ensure transcription of highly expressed metabolic pathways responsible for fuelling energy production and generating the macromolecular precursors for RNA and protein synthesis . Metabolic state is monitored in developing epithelia , ensuring that the fittest cells are selected as organ precursors [55] . The failure to compete with wild type neighbours is consistent with an altered metabolic state that is recognised by cell competition , triggering cells to be outcompeted by their neighbours and lost by caspase-dependent apoptosis . Is the effect on RNAPII-dependent transcription the cause of growth defects ? It is conceivable that roles that have been assigned to hPNUTS , e . g . in the DNA damage response and chromatin condensation , are conserved in dPNUTS and these might contribute to the larval lethality exhibited by dPNUTS mutants . Indeed the non-identical distribution of dPNUTS and RNAPII on chromosomes suggests that dPNUTS is present in chromatin-associated complexes lacking RNAPII . Notably we do not see any detectable condensation defects in dPNUTS mutant clones but we cannot exclude the possibility that dPNUTS may also contribute to other processes that underlie tissue growth , such as transcription-independent cell cycle control , as has been reported for other enzymes that regulate CTD phosphorylation , such as FCP1 [56] . Nevertheless , loss of expression of any one of the cell metabolism pathways affected by dPNUTS ( Table S4 ) is sufficient to cause larval growth arrest and is likely to explain the failure of dPNUTS larvae to grow in size prior to their eventual demise . Like its mammalian counterpart , we have shown that dPNUTS is a nuclear protein that localises to chromatin during interphase . By utilising larval polytene chromosomes , which are readily visible by light microscopy , we have been able to extend this analysis by determining the distribution of dPNUTS on interphase chromosomes in situ . These analyses show co-localisation of dPNUTS with many transcriptionally active sites marked with RNAPII , suggesting that the widespread changes in gene expression that we observe upon loss of dPNUTS function are likely to be due to the direct involvement of dPNUTS in RNAPII-mediated transcriptional regulation . Correspondingly , we find that dPNUTS is complexed to the large subunit of RNAPII in cell extracts . However , it is important to note that not all RNAPII sites stain for dPNUTS ( and vice versa ) and the relative amounts of the two proteins vary widely amongst these sites . This suggests that the association of dPNUTS with RNAPII , or with associated factors , which may affect the availability of the dPNUTS epitope for detection by our antibody , may be differentially regulated . PNUTS contains a number of conserved macromolecular-interaction domains , which have led to the suggestion it might serve as a multivalent adapter protein . However , it has not yet been established to what extent the known interactors , Tox4 and Wdr82 aid in the recruitment of PNUTS to chromosomal loci . These issues will require investigation of the genome-wide sites of dPNUTS binding , as well as identification and comprehensive characterisation of dPNUTS-interacting proteins and their role in dPNUTS recruitment . Since we found that PP1-binding is necessary for dPNUTS function , we reasoned that dPNUTS affects transcription by targeting PP1 to specific substrates on chromosomes . Several lines of evidence indicate that one important target of dPNUTS-PP1 in this context is the CTD of RNAPII: i ) dPNUTS is complexed with RNAPII in nuclear extracts and regulates RNAPII CTD phosphorylation in a PP1-dependent manner; ii ) RNAPII CTD Ser5-P levels are elevated in dPNUTS mutant larval extracts and tissues; iii ) dPNUTS colocalises with PP1 and RNAPII on chromosomes; iv ) ectopic expression of a mutant version of dPNUTS that displaces PP1 from polytene chromosomes results in elevated RNAPII CTD Ser5-P levels on chromosomes . dPNUTS-PP1 appears to preferentially target Ser5-P of the CTD as we observed only a modest effect on Ser2-P levels and no effect on phosphorylation of other RNAPII-CTD residues in dPNUTS mutant larval extracts by Western blotting ( Figure S6B ) . However , PNUTS/PP1 is not the only PP1 holoenzyme that has been implicated in regulation of RNAPII phosphorylation [37] , raising the possibility that different PP1 holoenzymes possess different RNAPII CTD specificities . Changes in the pattern of gene expression that we have observed in dPNUTS mutant animals are correlated with the normal expression level of the affected transcripts; these changes may also reflect the spatial distribution of dPNUTS expression during development . During embryogenesis we observed that the levels of dPNUTS expression in the gut and the ventral nerve cord correlates with stages in which these tissues are undergoing periods of rapid expansion and development . In an analogous fashion to SCP1 , which restricts RNAPII dephosphorylation of neuronal genes to non-neuronal cells by virtue of its expression pattern [11] , the enrichment of dPNUTS in proliferating tissues may function to promote expression of highly expressed transcripts , such as those involved in cellular metabolism , in these tissues , to support their development . In mammals , the gradual decrease from a high level of PNUTS during embryogenesis to a relatively low level in adults has been taken to imply that PNUTS could play a role in cortical development [22] , but could equally reflect a requirement during growth of developing tissues . Notably , PNUTS is not found in some metazoans such as C . elegans , where strictly controlled cell lineage determines tissue architecture . An evolved function of PNUTS might therefore be to support proliferative states in organisms where compensatory mechanisms such as cell competition are at play . How do dPNUTS and RNAPII hyperphosphorylation regulate gene expression ? Studies of other enzymes that control CTD phosphorylation state indicate that maintaining correct levels of CTD phosphorylation is critical for normal levels of transcription and that hyperphosphorylation of RNAPII can increase or reduce gene expression depending on what stage of the transcriptional cycle phosphorylation is affected . For instance , FCP1 targets Ser2-P in vivo [57] and is thought to recycle RNAPII after the complex has dissociated from the transcribed region [58] . Correspondingly , conditional knockout of FCP1 in yeast results in a global defect in transcription affecting 77% of genes [59] . SCP1 and Ssu72 both target Ser5-P [16] , [60] , but have contrasting roles in transcriptional regulation: knockdown of SCP1 unmasks neuronal gene expression , indicating it normally acts as a transcriptional repressor [11] , whilst Ssu72 facilitates transcription by promoting the elongation stage of the transcription cycle [61] . ChIP experiments from larvae expressing dPNUTSW726A suggest that displacement of PP1 binding to dPNUTS does not result in accumulation of RNAPII on the coding region of affected loci . The precise mechanisms of how loss of dPNUTS function and RNAPII hyperphosphorylation disrupt gene expression require further investigation . However , we might expect processes dependent on normal CTD phosphorylation , including RNA processing , transcription-coupled chromatin modification and transcription-associated homologous recombination [4] , to be affected . In this regard , it is notable that inhibition of TFIIH kinase activity , which phosphorylates promoter-bound RNAPII at Ser5 , predominantly affects mRNA capping and stability rather than transcription per se [62]–[64] . In summary , the analysis of dPNUTS described here reveals an important function for this evolutionarily conserved chromatin-associated protein , via association with PP1 , in the regulation of RNAPII phosphorylation and the appropriate expression of genes during larval development , which support organismal growth . These findings provide insight into the role of PNUTS and RNAPII phosphorylation during normal development , and may also be of relevance to the understanding of aberrant gene expression patterns observed in disease processes and ageing .
Drosophila melanogaster stocks were kept at 18°C or 25°C on standard agar-cornmeal-yeast medium . Genotypes are provided in Text S1 . Isolation of a null allele of dPNUTS by P element excision from dPNUTSKG was carried out by crossing w; dPNUTSKG/CyO , P ( Delta2-3 ) males to y , w; Tft/CyO females . From each cross , a single w revertant male in which the P element was excised , was individually crossed back to w; Tft/CyO females . To determine the molecular lesion in excisions , genomic DNA surrounding the original dPNUTSKG insertion site was amplified from heterozygous mutants by PCR using flanking primers ( see Text S1 ) and sequenced . For genetic complementation tests , a 9 . 1 kb BamHI restriction fragment from P1 clone DS02684 , which contains all of the transcribed dPNUTS sequence , was subcloned into the BamHI site in pW8 and injected into flies . Details of the growth arrest experiment can be found in Text S1 . Full-length cDNAs for dPNUTS-S and PNUTS-L cloned into pNB40 were isolated from a 3rd instar larval library ( see Text S1 ) . dPNUTSW726A was generated by PCR-based site-directed mutagenesis . For ectopic expression in flies , full-length dPNUTSWT and dPNUTSW726A were subcloned into pUAS-HM , a modified of pUAST that contains an N-terminal 3× His 6× Myc ( HM ) tag . UAS-HM-PNUTS flies were made by P element-mediated germline transformation into a w1118 stain by Genetic Services Inc . ( Cambridge , MA ) . Tagged dPNUTSWT and dPNUTSW726A were ectopically expressed ubiquitously using da-GAL4 or in salivary glands using AB1-GAL4 . pNB40-dPNUTS clones were used to generate Digoxigenin ( DIG ) -labelled RNA probes . RNA in situ hybridisation was essentially performed as previously described [45] , [65] . Following hybridization , DIG-labelled probes were detected with an alkaline phosphatase conjugated anti-digoxygenin antibody in the presence of Nitro-blue tetrazolium salt ( NBT ) and X-phosphate/5-Bromo-4-chloro-3-indolyl-phosphate ( BCIP ) . RNA was extracted using the Qiagen RNeasy Mini kit and was reverse transcribed using the High Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) . Quantitative PCR was performed following the incorporation of SYBRGreen ( using the Applied Biosystem StepOnePlus Real Time PCR System ) . Primers are described in Text S1 . All samples were normalized to 18S RNA . The ΔΔCT method was used for the calculation of the relative abundances [66] . RNA from approximately 5000 1st instar larvae/genotype was extracted using the Qiagen RNeasy Mini kit following the manufacturer's instructions . Total RNA quality and quantity was verified on a NanoDrop1000 spectrophotometer ( Thermofisher ) and Bioanalyzer 2100 . mRNA was polyA selected using Dynabeads mRNA Purification Kit for mRNA Purification from Total RNA Preps ( Invitrogen ) . The libraries were prepared according to the SOLiD Total RNA-Seq Kit protocol ( Part Number 4452437 Rev . A , Applied Biosystems ) . RNA samples were sequenced on an AB SOLiD sequencing platform with v4 chemistry , generating single-end 50 bp colour-space reads . More than 93M reads were generated for each sample . Reads were filtered for quality and mapped onto the dm3 D . melanogaster reference genome [67] , [68] using TOPHAT [69] . Only uniquely mapped reads were retained for analysis and reported as a BAM [70] file . Cufflinks [71] software took the BAM files to calculate expressions levels for annotated and predicted transcripts using FPKM ( fragments per kilobase of transcript per million fragments mapped ) values . Differentially expressed genes in the dPNUTS mutants were defined as genes with <0 . 67 or >1 . 5 fold change relative to controls . A significance threshold of 1 FPKM [72] was also applied . To analyse the enrichment of the genes belonging to specific biological processes , genes differentially expressed in both dPNUTS mutants were further analysed by Database for Annotation , Visualization and Integrated Discovery ( DAVID ) ( http://david . abcc . ncifcrf . gov/ ) against the D . melanogaster database . To increase the reproducibility , enrichment of gene function was identified with EASE score ≤0 . 001 , which is a conservative adjustment to Fisher exact probability , and a fold change enrichment ( FE ) ≥1 . 5 in all samples . The GO terms were hierarchically classified using AMIGO . Human orthologues of differentially expressed genes were identified by BioMart ( www . biomart . org ) and used to reconstruct functional networks and predict upstream regulators using Ingenuity IPA ( Ingenuity Systems Inc . ) , see Text S1 for details . Immunoprecipitation from 2–18 hr old Oregon R Drosophila embryonic nuclear extracts was performed as in [73] , with minor modifications ( see Text S1 ) , using the following primary antibodies: rabbit anti-Myc ( A14 , Santa Cruz Biotechnology , 1∶100 ) ; mouse anti-Myc ( 9E10 , 1∶50 ) ; guinea pig anti-dPNUTS and anti-dPNUTS-S ( 1∶10 ) . The following primary antibodies were used for Western Blotting: mouse anti-RNAPII ( ARNA-3 , Research Diagnostics/Millipore , 1∶500 ) , which recognizes both phosphorylated and unphosphorylated forms of RNAPII; mouse anti-RNAPII Ser5-P ( 4H8 , Active Motif , 1∶1000 ) ; purified rabbit anti-PP1 ( 1∶500 ) ; rabbit anti-Myc ( A14 , Santa Cruz Biotechnology , 1∶500 ) ; mouse anti-Actin ( C4 , Millipore , 1∶5000 ) . For quantitation , X-ray film was digitized with an ImageQuant biomolecular imager ( GE Healthcare ) and quantified using ImageJ ( http://rsbweb . nih . gov/ij/ ) . Tissues were fixed and stained using standard approaches ( see Text S1 ) with the following primary antibodies: rabbit anti-Cleaved Caspase-3 ( Cell Signalling Technology , 1∶100 ) ; mouse anti-Discs large ( Developmental Studies Hybridoma Bank , 1∶100 ) ; rabbit anti-Myc ( A14 , Santa Cruz Biotechnology , 1∶100 ) ; mouse anti-phospho-Histone H3 ( Millipore , 1∶500 ) . TO-PRO-3 ( Invitrogen , 1∶1000 ) was used to visualise DNA . Polytene chromosome squashes were prepared as reported previously [74] ( see also Text S1 ) and stained with the following primary antibodies: guinea pig anti-dPNUTS ( 1∶30 ) ; rabbit anti-PP1 ( 1∶50 ) ; mouse anti-RNAPII Ser2-P ( H5 , Covance , 1∶50 ) ; mouse anti-RNAPII Ser5-P ( H14 , Covance , 1∶50 ) ; rabbit anti-Myc ( A14 , Santa Cruz Biotech , 1∶100 ) . For DNA staining , slides were incubated with either DAPI or TO-PRO-3 . Images were captured on Zeiss 510 and 710 Confocal Microscopes equipped with 405 nm , 488 nm , 561 nm and 633 nm lasers using a Plan Apochromat 40x/1 . 3NA oil immersion objective . Images were imported to Adobe Photoshop and adjusted for brightness and contrast uniformly across entire fields . Projected images of wing discs in XY were generated using ImageJ . XZ projections were generated using the Cut function in Zen 2011 ( Zeiss ) . Line scans of polytene chromosomes were generated using ImageJ . For this analysis , we imaged a region at end of the X chromosome that could be reliably identified on chromosomes from multiple squashes . Images were taken with identical microscope and laser settings , with signal intensities below the level of saturation . The mean intensity of RNAPII Ser5-P and PP1 fluorescence was determined for each genotype by calculating the average fluorescence intensity through the center of unprocessed images of the same chromosomal region from 6 samples , parallel to the long axis of the structure . The accession numbers for the dPNUTS and dPNUTS-S nucleotide sequences reported in this paper are AJ580979 and AJ580980 , respectively . | During development , cells rely on appropriate patterns of gene expression to regulate metabolism in order to meet cellular demands and maintain rapid tissue growth . Conversely , dysregulation of gene expression is critical in various disease states , such as cancer , and during ageing . A key mechanism that is ubiquitously employed to control gene expression is reversible phosphorylation , a molecular switch that is used to regulate the activity of the transcriptional machinery . Here we identify an enzyme that binds to and regulates the phosphorylation state of RNA Polymerase II , a central component of the general transcription machinery . We also show that an essential role of this enzyme is to support normal patterns of gene expression that facilitate organismal growth . These findings are not only of relevance to the understanding of normal enzyme function but may also assist in the development of therapeutic strategies for the treatment of aberrant patterns of gene expression that occur during ageing and disease progression . | [
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] | [] | 2013 | PNUTS/PP1 Regulates RNAPII-Mediated Gene Expression and Is Necessary for Developmental Growth |
We show proof of concept for gene targets ( polA , tprL , and TP_0619 ) that can be used in loop-mediated isothermal amplification ( LAMP ) assays to rapidly differentiate infection with any of the three Treponema pallidum subspecies ( pallidum ( TPA ) , pertenue ( TPE ) , and endemicum ( TEN ) ) and which are known to infect humans and nonhuman primates ( NHPs ) . Four TPA , six human , and two NHP TPE strains , as well as two human TEN strains were used to establish and validate the LAMP assays . All three LAMP assays were highly specific for the target DNA . Amplification was rapid ( 5–15 min ) and within a range of 10E+6 to 10E+2 of target DNA molecules . Performance in NHP clinical samples was similar to the one seen in human TPE strains . The newly designed LAMP assays provide proof of concept for a diagnostic tool that enhances yaws clinical diagnosis . It is highly specific for the target DNA and does not require expensive laboratory equipment . Test results can potentially be interpreted with the naked eye , which makes it suitable for the use in remote clinical settings .
Human yaws is a tropical skin disease of children caused by the bacterium Treponema pallidum subsp . pertenue ( TPE ) [1] . Skin ulcers are the most characteristic clinical manifestations associated with infection in all three active disease stages ( primary , secondary , and tertiary yaws ) [2] . The disease is currently subject to global eradication efforts [3] , which face challenges that arise from the biology and distribution of the yaws bacterium as well as its diagnosis and treatment [4] . It is largely believed that the first yaws eradication campaigns conducted in the mid-1950s to late 1960s were successful in terms of reducing the prevalence by 95% but failed to eradicate the disease when local efforts to prevent new cases proved insufficient [5] . The majority of affected populations belong to poor and marginalized societies , with only rudimentary access to health care systems ( ‘Where the road ends , yaws begins’ ) [6] . Until today , standard diagnosis of yaws in clinical settings is based on clinical manifestations in combination with serology [1] . T . pallidum ( TP ) elicits a strong antibody response [7 , 8] . Although it is possible to distinguish current infection ( active or latent ) from past infection when non-treponemal and treponemal tests are used in combination [9] , it remains impossible based on serology and in some instances clinical manifestations , to differentiate yaws infection ( TPE ) from syphilis ( caused by subsp . pallidum ( TPA ) ) or bejel ( caused by the subsp . endemicum ( TEN ) ) . Moreover , it has been shown that other diseases are capable of mimicking yaws infection . In particular , Haemophilus ducreyi has been reported to cause yaws-like skin ulcers [10] . Lastly , a larger number of skin ulcers in rural Africa remains etiologically undiagnosed [11] , which increases the chance of overlooked infection with TPE . Other diseases which are capable of mimicking yaws infection are cutaneous leishmaniasis , scabies , or fungal infections [1] . Eradication of yaws is further challenged by the finding that nonhuman primates ( NHPs ) are infected with TP [12 , 13] . Notably , all whole genome sequenced simian strains must be considered TPE strains [14 , 15] . NHPs therefore must be considered as a possible natural reservoir for human infection [13] . The West African simian TPE strain Fribourg-Blanc , which was isolated from a Guinea baboon ( Papio papio ) in the 1960s [16] , caused sustainable infection when inoculated into humans [17] . Post-eradication surveillance following the currently ongoing mass-azithromycin treatment phase [4] would benefit from rapid and cost-effective molecular tests that are able to distinguish TPE infection [18] from infections with all other TP subspecies ( TPA and TEN ) and bacteria that are involved in tropical skin ulcers and which may fall together with TP seropositivity . Potentially a single overlooked yaws case would result in a failure of global yaws eradication . Loop-mediated isothermal amplification ( LAMP ) was first described by Notomi et al . in 2000 [19] and since then has been extensively used to improve infectious disease diagnostics [20] . The highly specific method recognizes the DNA target using six distinct sequences initially and four distinct sequences subsequently [19] . LAMP uses a DNA polymerase with high strand displacement activity to perform a fast running auto-cycling strand displacement synthesis . Reactions run at constant temperature ( isothermal ) and therefore do not require expensive technical equipment such as PCR cycling machines . Our objective was to identify suitable gene targets that can be used for LAMP assay design to rapidly distinguish between yaws infection , including simian strains , and syphilis or bejel .
DNA samples of human TPA laboratory strain Mexico A , Nichols , Seattle 81–4 , SS14 , TPE strain Gauthier , CDC-1 , CDC-2 , Samoa D , Sei Geringging K403 , Kampung Dalan K363 , as well as the simian TPE strain Fribourg-Blanc were obtained from rabbit-in vivo inoculation experiments ( S . A . Lukehart and DS ) . These experiments were not directly associated with this study . DNA extracts from human TEN strain Bosnia A and Iraq B originate from whole genome amplified clinical samples ( DS ) that were not directly associated with this study . DNA from a TP-infected olive baboon ( Papio anubis; 6RUM2090716 ) originates from a clinical sample collected for a different study at Ruaha National Park ( RNP ) in Tanzania in 2015 ( DFG KN1097/3-1 ( SK ) ) . Details and further reference for each strain included into the study can be found in the Supplementary S1 Table . ‘Good Veterinary Practice’ rules were applied to all procedures where animals were handled . Three different LAMP assays were designed . First , we generated a LAMP assay that is able to detect DNA of all three TP subspecies ( TPA , TPE , and TEN ) . This assay served as an initial control and was designed for the use in NHPs where little is known about the TP subspecies that circulate in wild NHP populations . Second , a LAMP assay was designed to distinguish TPE strain infection from infection with TPA or TEN strains . Third , a LAMP assay that differentiates between infection with TPE or TEN and infection with TPA strains has been established . All LAMP reactions were run with four human TPA , six human TPE and two simian TPE strains , as well as two human TEN strains of known copy number ( S1 Table ) . All tests were run as triplicates and included a DNA-free negative control . Dilution series of target DNA were used to identify the analytic limits of detection for each of the specific LAMP reactions using appropriate strain material . 10-fold serial dilutions of the target DNA were applied to cover a range of at least five decimal powers , from the maximum of TP copy numbers ( strain Nichols 10E+5 , all other strains 10E+6 ) until 10E+0 . Negative controls that contained no DNA and dilution steps that contained ≤10E+2 TP copies were run as at least six replicates . A StepOnePlus Real-Time PCR System ( ThermoFisher Scientific ) was used to run the reactions and to collect the data . Due to software restrictions , it was necessary to introduce a ( neglectable ) thermal cycling step into the protocol . Each LAMP run therefore encompassed continuous 40 cycling steps each consistent of 63°C for five seconds followed by 64°C for one minute and data collection . LAMP reactions were performed in a volume of 25 . 0 μl using the Mast Isoplex DNA Kit ( #REF67dnamp , Mast Diagnostica GmbH ) . According to the manufacture’s guidance , each reaction consisted of 12 . 5 μl of the kit’s 2x reaction mix , 1 . 0 μl Bst polymerase enzyme , 1 . 0 μl fluorochrome dye , and 2 . 0 μl of the primer mix . One microliter target DNA was included and distilled water ( molecular grade ) was used to top up the reaction volume until 25 . 0 μl were reached . All primers were heat pre-treated at 95°C for 5 min and immediately cooled on ice prior to adding them to the master mix . The primer mix contained 1 . 6 μM each FIP and BIP , 0 . 2 μM each F3 and B3 , as well as 0 . 8 μM each LF and LB primer . All reactions were run on a MircoAmp Fast Optical 96-well reaction plate ( #4346907 , ThermoFisher ) . Oligonucleotide primers were designed using the PrimerExplorer v5 Software ( http://primerexplorer . jp/e/ ) . Each LAMP primer set consisted of six oligonucleotide primers ( Table 1 ) . The design followed the description given by Yoshida et al . 2005 [21] . Briefly , a set of four primers ( F3 , B3 , the forward inner primer [FIP] , and backward inner primer [BIP] ) , which bind six loci of the target gene ( F1 , F2 , F3 , B1 , B2 , and B3 ) are necessary . The two inner primers ( FIP and BIP ) are a sequence combination of sense and antisense sequences of the DNA . This is essential for the priming in the first stage and the self-priming in the later stages . Therefore , FIP primers consist of the combination of sequences defined as F1c ( c = complementary ) and F2 . Likewise , BIP primers are composed of primer sequences B1c and B2 . To enhance amplification efficacy , two loop primers LF and LB were added to each of the LAMP primer sets . To confirm the specificity of the newly designed primers , we performed a search for orthologous sequences using BLASTn at the NCBI homepage ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) . The LAMP primer set ‘TP’ targets the polymerase I ( polA ) gene ( TP_0105 ) of TP . The locus is highly specific for all TP subspecies [22] and has only one orthologue in the lagomorph infecting Treponema paraluisleporidarum ecovar Cuniculus . The latter is not capable of infecting humans [23 , 24] . This locus therefore allows the reaction to become positive for DNA of any known TPA , TPE , or TEN strain ( Fig 1A ) . LAMP primer set ‘TPA/TEN’ targets the tprL gene ( TP_1031 ) of TP . At this locus , a 278-bp long deletion exists that distinguishes known human TPA and TEN strains from human and simian TPE strains [25 , S1 Fig] . This primer set was specifically designed to bind within the deletion part , which creates the specificity for TPA and TEN strains ( Fig 1B ) . LAMP primer set ‘TPE/TEN’ targets the T . pallidum TP_0619 gene , which has recently been investigated in a multilocus-typing study on TPE strains [26] . This locus has a 179-bp long sequence part that distinguishes known human and NHP TPE as well as TEN strains from TPA human strains ( Fig 1C , S2 Fig ) . Primer sequence data of all three LAMP primer sets are listed in Table 1 . All TP strains used in this study were quantified using an established [27] but slightly modified TaqMan PCR ( qPCR ) targeting the polA gene . A dilution series of a plasmid containing the target amplicon was used as a standard curve from 10E+7 to 10E+0 copy numbers . Briefly , each reaction volumes contained 10 . 0 μl TaqMan Universal Mastermix II ( no Uracil N-glycosylase , Applied Biosystems ) , 1 . 8 μl each 10 μM primer and probe , 3 . 6 μl molecular grade water ( RNase-free; Qiagen ) , and one microliter of the target DNA . Samples were quantified using a StepOne Plus Real-time system with the following temperature steps: 50°C for two minutes , 95°C for ten minutes , followed by 50 cycles of 95°C for 15 seconds and 60°C for one minute . At the end of each cycle , fluorescence was measured . All samples and standards were run as triplicates . LAMP performance as well as qPCR data were retrieved from the StepOnePlus Real-Time PCR System and extracted as RAW data into Excel sheets utilizing the StepOne Software v2 . 3 ( Life Technologies ) . Statistical analyses were performed with Prism 7 . 0 ( GraphPad Software ) . In LAMP dilution series with low copy numbers ( ≤10E+2 ) and in qPCR data , single replicate outliers were excluded .
The LAMP assay targeting the polA gene was positive for all tested TP strain samples including the four TPA , six human TPE , two simian TPE , and the two human TEN strains ( Fig 1A , S1 Table ) . The tprL targeting LAMP was positive for all tested TPA and TEN strains , while human and NHP TPE strains did not amplify ( Fig 1B ) . The LAMP assay that uses a part of the TP_0619 gene generated positive results for all TPE strains including simian TPE strains as well as the two human TEN strains ( Fig 1C ) . The onset of exponential fluorescence increase ( ΔRn ) started reproducibly between 5 min and 15 min incubation time ( Fig 1A–1C ) . Melting curves for each LAMP assay are shown in S3 Fig . All curves were of appropriate shape and without any additional peaks indicative for unwanted side products of primer dimers . Analytic limits of detection were assessed as demonstrated in several published studies [28–30] . The LAMP assay that targets the polA locus amplifies all TP strains but differed slightly in its detection limit across the different TP subspecies . While the TPA strain Nichols failed to amplify between 10E+3 and 10E+2 copies ( Fig 2A ) , the TPE strain Gauthier showed a non-exponential increase in fluorescence at 10E+2 copies ( Fig 2B ) . TEN strain Bosnia A failed to exponentially amplify at 10E+1 copies ( Fig 2C; Table 2 ) . The LAMP targeting the tprL locus had a detection limit of 10E+2 copies for Nichols ( Fig 2D ) and 10E+3 for TEN strain Bosnia A ( Fig 2E; Table 2 ) . The LAMP assay that utilizes the TP_0619 locus amplified TPE ( strain Gauthier ) and TEN ( strain Bosnia A ) DNA until a total copy number of 10E+2 copies was reached ( Fig 2F and 2G; Table 2 ) .
In many areas where endemic treponematoses occur , syphilis can also be found at meaningful prevalence rates ( e . g . , Ghana 3 . 7% [31] , Papua New Guinea 7 . 9% ( men ) -12 . 9% ( women ) [32] ) . While this is a problem for the serological based diagnosis of yaws in the presence of etiologically unrelated skin ulcers , it is not an issue for LAMP assays , which specifically target the DNA of the pathogen . The TPE/TEN LAMP was able to reliably discriminate yaws and simian TPE infection from infection with syphilis causing strains . It will , however , not discriminate yaws-causing strains from those known to cause bejel ( TEN strains ) . While in theory this could be a problem , bejel is a disease found in the dry areas of Sahelian Africa and Saudia Arabia and thus its spatial distribution does not overlap with yaws reporting countries in Western and Central Africa , Southeast Asia , and the Pacific Islands [33] . In cases where a clear differentiation between yaws and bejel infection is important , the combination of the LAMP targeting tprL1 and TP_0619 will enable the distinction of both subspecies since only TEN strains will amplify in both assays . In the future , either TEN specific assays or a LAMP multiplex assay can be designed [34] . The latter , however , would require a fluorescence measuring device and thus may restricts the use in remote tropical health care facilities . Our study used the StepOnePlus Real-Time PCR System , but all LAMP assays described in this study can be run equally well on a portable system ( e . g . , ESEQuant TS2 , Qiagen ) that allows easy transportation and use under field conditions . In low-income clinical settings , it would even be possible to detect amplification by the naked eye through the detection of turbidity generated by the precipitation of magnesium pyrophosphate or through the addition of calcein , a fluorescent metal indicator [35] . Lyophilization allows for ambient storage of formulated LAMP reagents [36] . As indicated in the methods , all three gene targets that were selected for the LAMP assays are highly specific for the human and NHPs specific pathogenic TP , but also the lagomorph infecting T . paraluisleporidarum ecovar Cuniculus and Lepus , respectively . However , lagomorph infecting treponemes are not capable of infecting humans [23 , 24] and most probably also NHPs . False positive test results due to infection with non-TP bacteria are therefore unlikely . In light of a recently published report on failure of qPCR due to variations in primer binding sites [37] , it should be noted that the number of published genomes , in particular non-draft genomes , in any of the TP subspecies is low . At this stage , a general statement on genome variability at the selected gene target sites is therefore not possible . However , based on our research , which included representatives of the full range of published TP genomes ( Table 1 , S1 and S2 Figs ) , the relevant primer binding sites are conserved across the different subspecies and strains . It has been proposed that yaws eradication in humans is possible through total community treatment in combination with subsequent total target treatment [38] . Rapid and reliable identification of yaws infection is important because successful global eradication requires an infinite zero-case scenario . In the first years after eradication has been declared in humans , it might well happen that few cases reemerge from yet unidentified relapsing latent yaws cases as well as there is a theoretical change that sporadic spillover from infected wild NHPs in Africa occurs . Either way , an available molecular test such as a LAMP assay could effectively and timely identify new cases from etiologically unrelated skin ulcers at the very beginning and could help to prevent yaws from re-emerging in areas where PCR machines and expensive laboratory equipment are not available . The analytic limits of detection for all three LAMP assays were around 10E+2 copies per reaction ( Table 2 ) , which is sufficient for clinical samples from human primary and secondary syphilis infection [39] . The same numbers can be expected for human yaws samples . Furthermore , the amount of TP in chronically infected monkeys also falls within the detection range of the TPE/TEN LAMP [27] . NHP TPE strains have been discussed as a possible source for human yaws infection in Africa [13] . The identification of NHP populations that harbor the pathogen , not only in Africa but also Asia [12] , must be considered an important research priority [4] . Post-treatment surveillance needs to focus in particular on areas where NHPs and humans are in close contact . The TPE/TEN LAMP performance of the NHP samples ( strain Fribourg-Blanc and DNA extracted from a clinical sample of a baboon at Ruaha National Park in Tanzania ( RNP ) ) that were included into this study were similar to the results obtained for the human yaws-causing strains ( Fig 1C ) . This is not surprising , given the fact that NHP TPE strains are genetically and functionally highly similar to human yaws causing strains [14 , 15] . However , the full diversity of NHP infecting TP is unknown and it is possible that monkeys from Sahelian Africa and Saudia Arabia may carry TEN strains . In this case , the TPE/TEN LAMP assay would become positive . Due to the fact that currently all naturally occurring NHP infections with TP should be whole genome sequenced to fuel our understanding on yaws epidemiology and evolution , the TPE/TEN LAMP assay result would be more of academic than practical interest . The whole genome data derived from simian isolates would reveal the subspecies status of the isolate . In humans , infections with all TP subspecies have reported potential to cause atypical clinical manifestations . A striking example is the frequent syphilis-like manifestations associated with TEN strains [40 , 41] . A rapid , highly sensitive and specific LAMP assay would therefore contribute to the identification of atypical clinical manifestations caused by TP . It would further help to identify possible NHP-to-human infection in countries like Tanzania , where human yaws has not been reported since decades . Syphilis screening programs in Tanzania would currently not detect possible NHP-to-human transmission events , since serological tests cannot discriminate between the TPA and TPE infection . Our target selection for LAMP assays that discriminate infection with TP from other causes of skin ulcers , represents a basis for the implementation of a One Health approach in yaws eradication and its post-eradication surveillance . Fig 3 illustrates the proposed new way of diagnosing TPE infection in humans . The new LAMP assays would simplify and accelerate yaws diagnosis . We note here that we have reached proof of concept for the suitability of the described gene targets , but further validation in a statistically adequate number of clinical samples is necessary to achieve confidence of the LAMP assays to be used in a non-research environment . The selected gene targets are suitable for the diagnosis and discrimination of all three TP subspecies , which is currently not possible using clinical signs of infection in combination with serology . The next step must be to conduct tests to evaluate the sensitivity and specificity of the newly created assays in various clinical samples that originate from humans and NHPs . The designed LAMP assays do not require expensive laboratory equipment and can be run in virtually any clinical setting . Results are available within a few minutes and thus outrun the existing methods , which require reasonable laboratory infrastructure [4] . | Sustainable eradication of human yaws benefits from applicable and reliable assays to detect possible reemerging yaws cases . Our study provides proof of concept for LAMP assays that are capable of rapid diagnoses and discrimination of active Treponema pallidum infection . While current clinical diagnosis is based on the clinical manifestations in combination with serology , the selected targets and LAMP assays allow for DNA based differentiation between skin ulcers caused by the subsp . pallidum ( syphilis ) , subsp . pertenue ( yaws ) , and the subsp . endemicum ( bejel ) . The presented LAMP assays require limited expensive technical equipment and can be run in virtually any clinical setting . The method is thus capable of enhancing yaws diagnosis in particular in a low capacity environment . | [
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] | 2018 | Gene target selection for loop-mediated isothermal amplification for rapid discrimination of Treponema pallidum subspecies |
Pseudomonas aeruginosa strain PA14 is a multi-host pathogen that infects plants , nematodes , insects , and vertebrates . Many PA14 factors are required for virulence in more than one of these hosts . Noting that plants have a fundamentally different cellular architecture from animals , we sought to identify PA14 factors that are specifically required for plant pathogenesis . We show that synthesis by PA14 of the disaccharide trehalose is required for pathogenesis in Arabidopsis , but not in nematodes , insects , or mice . In-frame deletion of two closely-linked predicted trehalose biosynthetic operons , treYZ and treS , decreased growth in Arabidopsis leaves about 50 fold . Exogenously co-inoculated trehalose , ammonium , or nitrate , but not glucose , sulfate , or phosphate suppressed the phenotype of the double ΔtreYZΔtreS mutant . Exogenous trehalose or ammonium nitrate does not suppress the growth defect of the double ΔtreYZΔtreS mutant by suppressing the plant defense response . Trehalose also does not function intracellularly in P . aeruginosa to ameliorate a variety of stresses , but most likely functions extracellularly , because wild-type PA14 rescued the in vivo growth defect of the ΔtreYZΔtreS in trans . Surprisingly , the growth defect of the double ΔtreYZΔtreS double mutant was suppressed by various Arabidopsis cell wall mutants that affect xyloglucan synthesis , including an xxt1xxt2 double mutant that completely lacks xyloglucan , even though xyloglucan mutants are not more susceptible to pathogens and respond like wild-type plants to immune elicitors . An explanation of our data is that trehalose functions to promote the acquisition of nitrogen-containing nutrients in a process that involves the xyloglucan component of the plant cell wall , thereby allowing P . aeruginosa to replicate in the intercellular spaces in a leaf . This work shows how P . aeruginosa , a multi-host opportunistic pathogen , has repurposed a highly conserved “house-keeping” anabolic pathway ( trehalose biosynthesis ) as a potent virulence factor that allows it to replicate in the intercellular environment of a leaf .
The ubiquitous bacterium Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen that infects a wide diversity of hosts . For example , P . aeruginosa strain PA14 is infectious in several model genetic hosts including the plant Arabidopsis thaliana [1] , the insect Drosophila melanogaster [2] , and the nematode Caenorhabditis elegans [3] . Using these model hosts , we and others have sought to identify PA14 virulence-related factors that play key roles in pathogenesis with the goal of elucidating conserved mechanisms underlying the pathogenic process and to determine whether the spectrum of virulence-related genes in a multi-host opportunistic pathogen are distinct from the virulence genes in more specialized pathogens [1]–[7] . Our work to date suggests that PA14 virulence depends primarily on genes that are part of a conserved P . aeruginosa genome [8] rather than on an arsenal of host-specific virulence-related factors [8] , [9] . One of the most unusual and unexpected features of P . aeruginosa is its ability to infect both plants and animals . Because plant cells are distinguished from metazoan cells primarily by their rigid and tough cellulosic walls , we reasoned that P . aeruginosa pathogenesis in plants may rely on plant-specific virulence factors related to the plant cell walls . Presumably as a consequence of these tough plant cell walls , most bacterial foliar pathogens replicate extracellularly in intercellular spaces and subvert plant cellular processes such as sugar transporters to obtain nutrients from mesophyll cells rather than attempting to directly breech plant cell walls . For example , Xanthomonas oryzae pv oryzae utilizes the Type III secretion system to inject transcriptional activators into plant mesophyll cells that upregulate the expression of sugar transporters that are not normally expressed in these cells [10] . In this paper , we report that the non-reducing disaccharide trehalose , made of two glucose residues joined by an atypical α , α-1 , 1-glucoside linkage , is a key virulence factor for P . aeruginosa PA14 pathogenesis in Arabidopsis leaves , but is not required for virulence in nematodes , flies , or mice . Trehalose is a common metabolite that has been shown to be involved in conferring tolerance to a variety of environmental stresses in diverse prokaryotic and eukaryotic species [17] , [25] , [26] . In PA14 , trehalose is synthesized by enzymes encoded in two adjacent predicted operons , treYZ and treS , that utilize distinct mechanisms of synthesis . Deletion of these trehalose biosynthetic genes results in a highly attenuated non-pathogenic phenotype that can be rescued by trehalose and by various ammonium and nitrate sources , but not by sucrose or glucose . In addition , Arabidopsis mutants defective in the synthesis of the cell wall polymer xyloglucan also suppress the non-pathogenic phenotype of P . aeruginosa trehalose mutants . These data suggest that trehalose promotes the acquisition of nitrogen-containing nutrients and that the xyloglucan component of the plant cell wall is involved in this process , thereby allowing P . aeruginosa to replicate in the nutrient-poor intercellular spaces in a leaf . Our data show how pathogens can utilize what are normally considered to be “house-keeping” functions , such as the wide-spread ability to biosynthesize trehalose , as a potent virulence factor that allows them to replicate in the particular environment of a host .
Reasoning that the tough cellulosic walls of plant cells may pose a unique challenge to plant pathogens , we surveyed the fully sequenced and annotated P . aeruginosa PA14 genome [4] to determine whether canonical cell wall degrading enzymes including cellulases , xylanases , and pectinases are encoded in the genome . In susceptible ecotypes ( wild accessions ) of Arabidopsis , P . aeruginosa PA14 causes soft-rot symptoms [1] , typically caused by pathogens that secrete pectinases and other hydrolytic cell wall degrading enzymes . Moreover , PA14 infection causes extensive degradation of Arabidopsis mesophyll cell walls including the generation of “holes” approximately the diameter of P . aeruginosa through which the bacteria enter host cells [11] . We thus expected that the PA14 genome would encode a variety of cell wall degrading enzymes ( CWDEs ) . However , our survey of the PA14 genome identified only a single , candidate cellulase , identified ambiguously as “cellulase/peptidase” ( PA14_36500 ) . Although PA14_36500 was upregulated two and three days post-inoculation in planta , correlating with the development of disease symptoms ( Figure S1A ) , a transposon insertion in PA14_36500 ( PA14_36500::MAR2xT7 ) , in-frame deletion of the cellulose/peptidase gene ( ΔPA14_36500 ) , or in-frame deletion of a putative cellulase/peptidase operon ( ΔPA14_36480-36520 ) did not cause a significant attenuation in virulence in Arabidopsis leaves ( Table S1 ) . Because PA14_36500 , which encodes the putative cellulose/peptidase , was induced during plant infection and because genes are often functionally clustered on bacterial genomes , we sought to identify genes adjacent to PA14_36500 that are co-regulated with PA14_36500 . This led to the identification of a set of 38 genes ( 42 . 23 kb region; PA14_36375 to PA14_36830 ) spanning the cellulase/peptidase gene that is coordinately down-regulated in an mvfR ( multiple virulence factor regulator ) mutant grown under various culture conditions [12] , [13] . Importantly , the quorum sensing-associated transcriptional regulator MvfR is required for maximum PA14 virulence in Arabidopsis [7] . Consistent with the in vitro transcriptional profiling data , cellulase/peptidase PA14_36500 expression was significantly reduced in planta in an mvfR mutant ( Figure S1B ) . Besides the putative cellulase/peptidase , the PA14_36375–36830 42 . 23 kb region encodes putative glucanolytic enzymes ( PA14_36590 , PA14_36630 , PA14_36740 ) as well as two closely linked predicted operons ( http://www . pseudomonas . com ) , PA14_36570-36630 consisting of six genes , and PA14_36710-37640 consisting of three genes , referred to hereafter as the “treYZ” and “treS” operons , respectively , that encode enzymes involved in two different trehalose biosynthetic pathways ( Figure 1; Table S2 ) . TreY and TreZ convert maltodextrins into trehalose in a two-step enzymatic reaction [14] , whereas TreS catalyzes conversion of maltose into trehalose in a single reaction [15] ( Figure S2 ) . In addition to treY ( PA14_36605 ) and treZ ( PA14_36580 ) , the predicted treYZ operon contains glgA ( PA14_36570 ) , malQ ( PA14_36590 ) , hypothetical gene ( PA14_36620 ) and glgX ( PA14_36630 ) . glgA , malQ , glgX encode enzymes with a putative role in α-1 , 4-linked glucan synthesis ( glgA ) and degradation ( malQ , glgX ) , that could serve as precursors for trehalose synthesis . In addition to treS , the treS operon contains a predicted α-amylase ( PA14_36740 ) , and glgB ( PA14_36710 ) , a predicted α-1 , 4-branching enzyme ( Figure 1 ) . The 42 . 23 kb PA14_36375–36830 region containing 38 genes is highly conserved among several sequenced P . aeruginosa strains that were examined and the treYZ and treS operons are conserved among pseudomonads in general ( Table S2 ) . We utilized a previously constructed non-redundant PA14 transposon insertion mutant library [16] to determine whether particular PA14 genes in the 38-gene region promote pathogenesis in Arabidopsis . Among 16 transposon insertions in 16 different genes that were available in the library , two were significantly attenuated in virulence . These mutants , with insertions in glgA and treZ , exhibited a decrease in virulence of 20 and 16 fold , respectively , as measured by in planta growth ( Table S1 ) . glgA and treZ are the first two genes in the treYZ operon , pointing to an important role for trehalose in the infectious process . To further investigate whether the trehalose operons and/or other genes in the 38-gene cluster are required for virulence , we constructed an in-frame deletion of the entire 42 . 23 kb region ( referred to hereafter as Δ42 ) by homologous recombination . In contrast to insertions in glgA and treZ , which exhibited at most a 20 fold decrease in growth compared to wild-type , the Δ42 mutant exhibited severe attenuation in virulence , affecting growth of PA14 infiltrated into Arabidopsis leaves about 120 fold and preventing the appearance of pathogenic symptoms ( Figure 2A ) . Similar results were obtained with four independently constructed Δ42 mutants ( data not shown ) , demonstrating that the non-pathogenic phenotype was caused by the deletion of the 42 . 23 kb region . Importantly , the Δ42 mutant does not appear to be slow growing or to be generally deficient in a variety of phenotypes associated with virulence in P . aeruginosa . The Δ42 deletion mutant was not auxotrophic , grew at the same rate as wild-type PA14 in a variety of minimal and rich media , and had no observable phenotypes with respect to the production of pyocyanin ( Figure S3 ) , motility , or biofilm formation ( Table S3 ) , and similar results were obtained with a second independently-constructed Δ42 mutant ( Figure S3; Table S3 ) . Because independently-constructed Δ42 mutants exhibited the same phenotypes , one of the Δ42 mutants was chosen for subsequent experiments . We next constructed several smaller deletions within the 42 kb region to determine which of the 38 encoded genes are primarily responsible for the severe avirulent phenotype of Δ42: ΔPA14_36375-36560 ( sub-region I ) contains a deletion of the cellulase/peptidase operon and several adjacent genes , and ΔPA14_36570-36700 ( sub-region II ) and ΔPA14_36710-36830 ( sub-region III ) contain deletions of the treYZ and treS genes , respectively , including some neighboring genes ( Figure 1 ) . Deletion of sub-region I that includes the putative cellulase/peptidase gene had a modest 3 . 3 fold reduction in virulence . In contrast , deletion of sub-region II that contains the treYZ operon had a much more significant effect on virulence ( 28 . 7 fold decrease in growth; Figure S4 ) , whereas deletion of sub-region III that contains the treS operon caused a 5 . 9 fold decrease in growth ( Figure S4 ) . These experiments suggested that the treYZ and treS operons play a significant role in PA14 pathogenesis in Arabidopsis . To corroborate the involvement of the trehalose genes in plant pathogenesis we constructed ΔPA14_36570-36630 ( ΔtreYZ ) and ΔPA14_36710-36740 ( ΔtreS ) containing deletions of only the two putative operons containing the treYZ and treS genes , respectively , and ΔPA14_36570-36630;PA14_36710-36740 ( ΔtreYZΔtreS ) containing deletions of both of the trehalose biosynthetic operons ( Figure 1 ) . Deleting either the putative treYZ or the treS operons ( Figure 2B ) had approximately the same effects as deleting the more extensive corresponding subregions II or III , respectively ( Figure S4 ) , and deleting both trehalose operons resulted in an approximately 50 fold decrease in virulence compared to the approximate 120 fold decrease observed with the Δ42 mutant ( Figure 2B ) . These data show that the treYZ and treS operons play a key role in pathogenesis in Arabidopsis leaves , but that genes in the 42 kb region in addition to those involved in trehalose biosynthesis also play a role in plant pathogenesis . Further evidence suggesting an important role for trehalose biosynthesis in plant pathogenesis was obtained by measuring the levels of trehalose synthesized in vitro by PA14 wild-type and trehalose biosynthetic mutants . While wild-type PA14 synthesized readily detectable levels of trehalose , there was approximately 50% less trehalose in the ΔtreS mutant , and there were undetectable levels of trehalose in the glgA , treZ , ΔtreYZ , ΔtreYZΔtreS , and Δ42 mutants ( Table 1 ) . These data show that the treYZ and treS operons encode enzymes involved in trehalose biosynthesis . These data also suggest that treS operon may be dependent on treYZ for trehalose production , as reported previously [17] . When we compared the levels of trehalose synthesized in vitro ( Table 1 ) and the extent of growth of the various strains in Arabidopsis leaves ( Figure 2B; Table S1 ) , we found an excellent positive correlation coefficient ( R2 = 0 . 87 ) . Importantly , we found that co-infiltration of the PA14 trehalose mutants and pure trehalose essentially completely suppressed the avirulent phenotypes of the ΔtreYZ , ΔtreS , and ΔtreYZΔtreS mutants and mostly suppressed the phenotype of the Δ42 mutant ( Figures 2B and 2C ) . However , 0 . 25 mg/ml trehalose also rescued the Δ42 mutant almost as well as 2 . 5 mg/ml , and 0 . 025 mg/ml trehalose partially suppressed the growth defect of the Δ42 mutant ( Figure 2C ) . These data indicated a requirement for trehalose for PA14 virulence in planta , potentially at physiologically relevant concentrations . In summary , the data in this section shows that the ΔtreYZ , ΔtreS , and ΔtreYZΔtreS mutants are less virulent in planta , that they either synthesize undetectable ( ΔtreYZ and ΔtreYZΔtreS ) or reduced ( ΔtreS ) levels of trehalose , that their level of virulence positively correlates with the level of trehalose they synthesize , and that their reduced virulence phenotype can be suppressed by exogenous trehalose . These data demonstrate that the virulence deficient phenotypes of the ΔtreYZ , ΔtreS , and ΔtreYZΔtreS mutants are a consequence of the inability of these strains to synthesize trehalose , thereby correlating the genotype of these mutants with their avirulent phenotypes . As described in the Introduction , PA14 infection models have previously been established in C . elegans [3] , D . melanogaster [18] , and mice [19] , [20] , as well as in other metazoans . Interestingly , the ΔtreYZΔtreS double trehalose mutant was not less virulent in a C . elegans killing model or in a murine acute pneumonia model . In fact , the ΔtreYZΔtreS appeared to be slightly more virulent in the metazoan hosts ( Figure 3 ) . Similar results were obtained with the Δ42 mutant in these two models as well as in a D . melanogaster ingestion model and in a chronic oropharyngeal colonization model in transgenic mutant mice lacking the cystic fibrosis transmembrane conductance regulator protein ( see Materials and Methods for the mutant description ) ( Figure S5 ) . These data suggest that trehalose appears to be specifically required for plant but not for metazoan pathogenesis . In the sections that follow , we considered several hypotheses concerning the role of trehalose in promoting the virulence of P . aeruginosa during the infectious process in plants but not in animals . Since a major difference between plant and animals cells is the plant cellulosic cell wall , we reasoned that trehalose may function in a process that involves the plant cell wall . Because PA14 infection in Arabidopsis leaves causes extensive degradation of mesophyll cell walls [11] , we first investigated the possibility that trehalose enhances the activity of cell wall degrading enzymes ( CWDEs ) . We tested whether trehalose enhanced the activity of a variety of commercial CWDEs to hydrolyze partially purified Arabidopsis cell walls in vitro to generate reducing sugars , which were measured using the Somogyi-Nelson assay [21] , [22] . However , we were not able to conclusively demonstrate that trehalose enhanced the activity of the CWDEs tested ( data not shown ) . We next reasoned that if trehalose interacts with the plant cell wall , specific Arabidopsis cell wall mutants might suppress the phenotype of the ΔtreYZΔtreS mutant . We tested the growth of wild-type PA14 and the ΔtreYZΔtreS mutant in several Arabidopsis cell wall mutants involved in xyloglucan ( mur2-1 , mur3-2 , xxt1/xxt2 ) , arabinose ( mur4-1 ) , or cellulose ( mur10-2 ) synthesis . Remarkably , the ΔtreYZΔtreS mutant grew to the same titer as wild-type PA14 in an xxt1/xxt2 double mutant that completely lacks xyloglucan in its cell walls and in a mur4-1 mutant that has decreased levels of arabinose in pectins , xylans , and xyloglucans [23] ( Figure 4 ) . Similar results were obtained with the Δ42 mutant; i . e . , the Arabidopsis xxt1/xxt2 mutant completely suppressed and the mur4-1 mutant mostly suppressed the avirulent phenotype of the Δ42 mutant ( Figure S6 ) . We ruled out the possibility that the Arabidopsis cell wall mutants suppress the avirulent phenotype of the PA14 trehalose mutants simply because they are generally more susceptible to pathogen attack . As shown in Figure 5A , the cell wall mutants did not exhibit enhanced susceptibility to the P . syringae pv . tomato strain DC3000 , a well-studied bona fide Arabidopsis pathogen . The Arabidopsis cell wall mutants were also not more susceptible to a DC3000 hrcC mutant ( Figure 5B ) , which is greatly impaired in virulence , or to the bean pathogen P . syringae pv . phaseolicola strain 3121 ( Figure 5C ) , which is not normally pathogenic in Arabidopsis . Consistent with these data , we also showed that the xxt1/xxt2 mutant , which exhibits the most severe cell wall defect of the Arabidopsis mutants tested , mounts a normal defense response when challenged with the flagellin peptide flg22 ( Figure 5D ) . Flg22 elicits so-called “pattern triggered immunity” in Arabidopsis . When Arabidopsis leaves are pre-infiltrated with flg22 , flg22 exerts a protective effect against subsequent infection with P . syringae DC3000 [24] . As shown in Figure 5D , flg22 elicits the same level of protection against P . syringae DC3000 in xxt1xxt2 plants as in wild-type plants . As described in the Introduction , because bacterial plant pathogens primarily replicate in the intercellular spaces in a leaf , they need to acquire nutrients from plant mesophyll cells . We therefore tested whether trehalose may be involved in the acquisition of a variety of nutrient sources including carbon , nitrogen , sulfur and phosphorous . If this were the case , we reasoned that co-infiltration of particular nutrients with the ΔtreYZΔtreS or the Δ42 mutant would suppress their non-pathogenic phenotypes . Co-infiltration of the ΔtreYZΔtreS double mutant with glucose ( Figure 6 ) or co-infiltration of the Δ42 mutant with glucose or sucrose ( Figure S7A ) did not rescue the attenuated phenotype in the Arabidopsis leaf assay . These experiments showed that the ΔtreYZΔtreS or the Δ42 mutant is not limited by carbon . The fact that trehalose but not glucose or sucrose suppressed the phenotype of the Δ42 mutant also shows that the putative cellulase/peptidase and other hypothetical glucanolytic enzymes encoded in the 38 gene region deleted in the Δ42 mutant do not play a critical role in supplying a carbon source to PA14 . We also entertained the possibility that PA14 could accumulate trehalose as a storage sugar , analogous to glycogen or starch , and then hydrolyze trehalose using the enzyme trehalase ( PA14_33450 , treA ) and utilize the resulting glucose as a carbon source , thereby promoting virulence . We ruled out this possibility , however , by showing that co-infiltration of a double Δ42treA::MAR2xT7 mutant ( which cannot metabolize trehalose ) with trehalose rescued the non-pathogenic phenotype similarly as co-infiltration of the Δ42 mutant with trehalose ( Figure S7B ) . We also confirmed that the Δ42treA::MAR2xT7 cannot metabolize trehalose and utilize it as a carbon source ( see Materials and Methods ) . Finally , we tested various salts to determine whether they would suppress the phenotypes of the ΔtreYZΔtreS ( Figure 6 ) or the Δ42 mutant ( Figure S8 ) . Interestingly , ammonium and nitrate ions almost completely suppressed the lack of growth phenotype of the ΔtreYZΔtreS ( Figure 6 ) or the Δ42 mutant ( Figure S8 ) , whereas sulfates and phosphates did not have a significant effect . The data in this section suggest that trehalose enhances access to nitrogen sources during an Arabidopsis infection . An alternative model is that ammonium nitrate ( as well as trehalose ) suppresses the avirulent phenotype of the PA14 trehalose mutants by suppressing the plant defense response . To test this possibility , we tested whether infiltration of leaves with trehalose or ammonium nitrate resulted in enhanced susceptibility to P . syringae DC3000 ( Figure 7A ) , the DC3000 hrcC mutant ( Figure 7B ) , or P . syringae pv . phaseolicola strain 3121 ( Figure 7C ) ; however , neither trehalose nor ammonium nitrate increased the susceptibility to any of these strains . Moreover , infiltration of trehalose or ammonium nitrate did not block the ability of flg22 to elicit protection against infection by P . syringae DC3000 ( Figure 7A ) . Trehalose is well-studied as a so-called compatible solute , which is defined as a molecule that functions as an osmolyte and helps an organism survive osmotic stress . We therefore tested whether other di- and trisaccharide compatible solutes would suppress the avirulent phenotype of the Δ42 mutant . Indeed , as shown in Figure S9 , both maltose and maltotriose functioned similarly to trehalose in allowing the Δ42 mutant to grow in planta , albeit somewhat less efficiently than did trehalose . Given these results , we next considered the hypothesis that trehalose enhances the virulence of PA14 by ameliorating a variety of environmental stresses [17] , [25] , [26] . However , the Δ42 mutant was not more susceptible than wild-type PA14 to osmotic stress in response to 0 . 5 M NaCl ( Figure 8A ) . As a positive control for the osmotic stress experiment , we constructed an in-frame deletion of a predicted ( http://www . pseudomonas . com ) three-gene operon ( PA14_19350-19370 ) responsible for the synthesis of a major organic osmoprotectant in P . aeruginosa , N-acetylglutaminylglutamine amide ( NAGGN ) [27] . As expected , the ΔPA14_19350-19370 mutant ( ΔNAGGN ) was more susceptible to 0 . 5 M NaCl than wild-type PA14 or the Δ42 mutant ( Figure 8A ) . We further tested whether trehalose functions to protect PA14 from osmotic stress in vitro by comparing its ability to enhance growth in minimal medium supplemented with 0 . 5 M NaCl compared to the well-studied osmoprotectant molecule betaine [27] . In vitro , betaine rescued the growth of PA14 , Δ42 , and the ΔNAGGN mutant in 0 . 5 M NaCl whereas trehalose had no effect ( Figure S10A ) . We also tested whether betaine would rescue the Δ42 mutant for in planta growth , similarly to trehalose . However , as shown in Figure 8B , betaine had no significant effect in rescuing Δ42 growth in planta , showing that the ability of trehalose to rescue Δ42 in planta is not likely due to the fact that it is functioning to protect Δ42 from osmotic stress . In contrast to Δ42 , the ΔNAGGN mutant , which is very susceptible to osmotic stress in vitro , had no significant impairment in growth in planta ( Table S1 ) . These data show that the Δ42 mutant is not highly susceptible to osmotic stress and that trehalose does not play a major role as an osmoprotectant in PA14 . As an alternative to functioning as an osmoprotectant , we investigated whether trehalose protects PA14 from reactive oxygen-mediated stress generated as a consequence of the plant innate immune response . However , we found no significant difference between the Δ42 mutant and wild-type PA14 with respect to tolerance to paraquat or hydrogen peroxide ( Figures 8C and 8D , respectively ) . Because a P . aeruginosa zwf mutant has been reported to be hyper-sensitive to paraquat-mediated killing [28] , we also tested a PA14 zwf::MAR2xT7 mutant [16] as a positive control for determining the sensitivity of PA14 and Δ42 to paraquat . As shown in Figure 8C , the zwf mutant exhibited enhanced susceptibility to paraquat in vitro , but did not exhibit an impaired growth phenotype in planta ( Table S1 ) . These data show that it is unlikely that trehalose functions to protect PA14 from oxidative stress . In addition to oxidative and osmotic stress , we also tested whether the Δ42 mutant is susceptible to pH or temperature stress , displayed a defect in biofilm formation under osmotic stress , or was deficient in the generation of persister cells in the presence of antibiotics . However , wild-type PA14 and the Δ42 mutant were indistinguishable in all of these tests ( Figure S10B–E ) . The data in the previous section suggest that trehalose does not function intracellularly to protect PA14 from a variety of stresses during free-living growth . To provide evidence that trehalose functions extracellularly , we tested whether wild-type PA14 “complements” the growth defect of PA14 trehalose mutants in planta . Specifically , we co-inoculated Arabidopsis leaves with equal mixtures of wild-type PA14 and the ΔtreYZΔtreS double trehalose mutant carrying plasmids that express GFP or DsRed , respectively ( Figure 9A ) . Dramatically , co-inoculation of wild-type PA14 with ΔtreYZΔtreS completely rescued the growth defect of ΔtreYZΔtreS ( Figure 9A ) , strongly suggesting that trehalose is most likely acting extracellularly and not internally within PA14 cells . Similar results were obtained when PA14 expressing GFP was mixed with the Δ42 mutant expressing DsRed ( Figure 9B , left panel ) . In this latter experiment , to make sure that the expression of red or green fluorescent protein does not affect bacterial strain viability , we also carried out an experiment in which the plasmids expressing fluorescent proteins were switched in wild-type PA14 and the Δ42 mutant and obtained the same result ( Figure 9B , right panel ) .
An important result from this work is that in contrast to many other bacteria and fungi , trehalose appears to have very little effect on protecting P . aeruginosa PA14 from a variety of diverse stresses , including osmotic , oxidative , pH , antibiotic , and temperature stress , and yet trehalose mutants are highly impaired in virulence in Arabidopsis . Instead of trehalose , our data show that N-acetylglutaminylglutamine amide ( NAGGN ) and glycine-betaine appear to be the primary stress response molecules in P . aeruginosa , in agreement with published data showing that osmotically stressed P . aeruginosa cultures accumulate NAGGN and glycine-betaine [27] , [29] . Specifically , we found that a ΔNAGGN mutant was highly impaired in growth under osmotic stress and that exogenously added glycine-betaine , but not trehalose , protected Δ42 , ΔNAGGN , and wild-type PA14 from osmotic stress ( Figures 8A and S10A ) . Importantly , however , even though glycine-betaine is a potent stress protection molecule in vitro , it did not rescue the Δ42 mutant in vivo ( Figure 8B ) . Conversely , the ΔNAGGN or a zwf mutant , which are highly susceptible to osmotic or oxidative stress , respectively , were not impaired in plant infection ( Table S1 ) . These data suggest that trehalose does not play a role as a stress protection molecule in P . aeruginosa during plant infection . Does trehalose function as a virulence factor for other bacterial phytopathogens in addition to P . aeruginosa ? As shown in Table S2 , the treYZ and treS trehalose biosynthetic operons are highly conserved among pseudomonads , including P . syringae , but it is not known whether trehalose functions as a virulence factor in these species . A recent study showed that deletion of P . syringae trehalose biosynthetic genes resulted in lowered fitness on the surface of plant leaves , but whether this was due to reduced virulence or increased susceptibility to hyperosmotic stress is not known [17] . What is the explanation for our observation that P . aeruginosa does not require trehalose for pathogenesis in at least three diverse metazoan hosts ( mice , insects , and nematodes ) , and in fact may be a detriment for infection ? In contrast to plants , mammals do not synthesize trehalose [30] , and it is likely that trehalose , which is a stable and non-reactive molecule , has little effect on mammalian cells , at least at relatively modest concentrations . In the case of insects , trehalose is a major component of the hemolymph . Trehalose is also synthesized by C . elegans , where it accumulates during the formation of desiccation-resistant dauer larvae [31] and exogenous trehalose promotes C . elegans longevity [32] . Thus in the case of flies and worms , trehalose is beneficial and it appears unlikely that the additional levels of trehalose that are synthesized by P . aeruginosa would have a significant physiological affect . The apparent hypervirulence of the trehalose mutants in metazoan models of infection may simply be the result of increased fitness of the strain , which conserves energy by not synthesizing trehalose . Because Arabidopsis cell wall mutants suppress the non-pathogenic phenotype of trehalose mutants , it seems likely that the virulence-enhancing role of trehalose is mediated through the plant cell wall . Can we attribute the lack of a particular plant cell wall polymer as playing a key role in the suppression of the non-pathogenic phenotype of the trehalose mutants ? As shown in Figures 4 and S7 , several Arabidopsis mutants that we tested either completely ( xxt1/xxt2 and mur4-1 ) or partially ( mur2-1 , mur3-2 , and mur10-2 ) suppressed the phenotype of the trehalose mutants . A common feature of all of the cell wall mutants that we tested ( Figures 4 and S6 ) is that they exhibit alterations in xyloglucan , the most abundant hemicellulose in the walls of dicotyledonous plants . The xxt1/xxt2 mutant completely lacks xyloglucan [33] , mur2-1 and mur3-2 display altered side chains in xyloglucan [34] , [35] , mur4-1 has decreased levels of arabinose in xyloglucan [23] , and mur10-2 exhibits alterations in xyloglucan remodeling throughout the plant [36] . Interestingly , wild-type PA14 grew significantly less in mur3-2 than in Col-0 plants , showing that mur3-2 is more resistant to PA14 than wild-type plants . Thus , the fact that the trehalose mutants grew to the same extent in mur3-2 as in Col-0 ( Figures 4 and S6 ) suggests that mur3-2 also partially suppresses its growth defect . These data suggest that xyloglucan may be a key component of the cell wall that affects the virulence of P . aeruginosa . At the mechanistic level , it is not necessarily the case that the rigid plant cell wall is functioning , for example , simply as a physical barrier that blocks the ability of P . aeruginosa to extract nutrients from the cytoplasm of mesophyll cells . If the primary role of trehalose is to facilitate nutrient uptake , the source of the nutrients could be the apoplastic fluid or even components of the cell wall itself , such as specific cell-wall associated proteins . Importantly , the enhanced susceptibility of the Arabidopsis cell wall mutants to the P . aeruginosa trehalose mutants is not simply a consequence of enhanced susceptibility to pathogens in general or the inability of the cell wall mutants to elicit an effective defense response . As shown in Figure 5 , the cell wall mutants are not more susceptible to virulent or non-pathogenic P . syringae strains and appear to mount an effective innate immune response when challenged with the flagellin peptide flg22 . Specialized bacterial foliar phytopathogens primarily replicate in the intercellular spaces between mesophyll cells . It is poorly understood which plant-derived nutrients are critical for bacterial growth in this environment as well as the mechanisms utilized by pathogens to obtain nutrients from their hosts . The majority of these pathogens utilize type III effectors not only to suppress the host innate immune response [37] , [38] but also to extract nutrients [10] from mesophyll cells . Interestingly , however , the P . aeruginosa type III secretion system is not necessary for pathogenesis in plants [6] and it seems unlikely that a broad host range pathogen such as P . aeruginosa would encode host-specific effectors that subvert the Arabidopsis sugar export system analogously to the Xanthomonas effectors that activate glucose efflux in mesophyll cells [10] . Indeed , it also seems highly unlikely that any particular P . aeruginosa strain has extensively co-evolved with any particular host [4] , [9] . Instead , our finding that P . aeruginosa utilizes trehalose as a major virulence factor for plant pathogenesis is consistent with our studies with P . aeruginosa as a C . elegans pathogen , which have shown that the majority of virulence related factors required to infect nematodes correspond to genes that encode conserved global transcriptional regulators or “house-keeping” genes that encode enzymes involved in conserved metabolic processes [4] , [9] . Trehalose biosynthesis is highly conserved . All pseudomonads ( Table S2 ) and at least 30% of sequenced prokaryotic genomes encode presumptive trehalose biosynthetic enzymes ( J . Urbach and F . Ausubel , unpublished data ) . It appears that P . aeruginosa has capitalized on what is mostly likely an ancient biosynthetic pathway to promote plant pathogenesis . How does trehalose promote pathogenesis in an Arabidopsis leaf ? Trehalose can serve as a carbon and energy source for growth of many bacteria and fungi including P . aeruginosa [25] , [39] . However , we have shown that sucrose and glucose do not suppress the phenotype of P . aeruginosa trehalose mutants and co-inoculation of a double Δ42treA::MAR2xT7 mutant ( which cannot metabolize trehalose ) with trehalose rescues the non-pathogenic phenotype similarly as co-inoculation of the Δ42 mutant with trehalose ( Figure S6 ) . These data suggest that either the level of carbon is not limiting or that trehalose is not involved in carbon acquisition . In addition , experiments designed to determine whether trehalose promotes activity of CWDEs failed to provide evidence that trehalose plays a significant role in cell wall degradation , with the caveats , however , that the experiments we carried out were done in vitro with commercial CWDEs and that in our particular assay a relatively low level of trehalose – enhanced hydrolysis would have not been detected . Does trehalose function as a general toxin to disrupt host cellular processes ? A number of studies have shown that exogenously applied trehalose can have a major negative impact on seedling growth and development [40]–[42] . On the other hand , the concentrations of trehalose used in these seedling experiments ( from 30 mM to 100 mM ) were significantly higher than the levels that would be expected to be encountered under natural conditions . By way of contrast , in our experiments we used mature four-week old plants and substantially lower concentrations of trehalose ( most often 1 mM ) . In mature plants , trehalose did not have a toxic effect as evidenced by the lack of any visible symptoms following trehalose ( 1 mM ) infiltration ( data not shown ) . Importantly , in our experiments , trehalose concentrations as low as 0 . 74 mM largely suppressed the non-pathogenic phenotype of the Δ42 mutant and 0 . 074 mM had a significant effect ( Figure 2C ) . Taken together , our data indicate that trehalose does not have a toxic effect on mature plants in the P . aeruginosa - plant infection model . Does trehalose upregulate PA14 virulence genes expression ? If so , it would have to specifically upregulate genes required for plant pathogenesis because as shown in Figures 3 and S5 , PA14 trehalose mutants are not less virulent in nematodes , flies , or mice . However , we do not favor this explanation . As shown in Figures 6 , S8 , and S9 , nitrate , ammonium , maltose and maltotriose functioned similarly to trehalose in suppressing the inability of the PA14 trehalose mutants to grow in planta . It seemly highly unlikely that all three sugars as well as nitrate and ammonium would function similarly to each other as signaling molecules . Since the non-pathogenic phenotype of P . aeruginosa trehalose mutants can also be suppressed by ammonium nitrate , we propose that trehalose promotes the acquisition of nitrogenous compounds and that nitrogen is limiting in the intercellular environment . The intercellular spaces in leaves are mostly filled with air [43] and very little is known about the mechanisms that plant pathogens utilize to obtain nutrients in this dry environment . Nitrogen limitation during P . aeruginosa infection in plants has been reported previously [44] , [45] . One way that trehalose could promote nitrogen acquisition is by modulating host nitrogen metabolism , thereby diverting nitrogen-containing compounds to invading P . aeruginosa cells . Several in planta studies have shown that trehalose-6-phosphate ( T6P ) plays a key role in the regulation of carbon and nitrogen metabolism [41] , [46] , [47] and is associated with altered cell wall structure and starch accumulation [48]–[50] . In our study , preliminary transcriptional profiling analysis has shown that infiltration of trehalose into Arabidopsis leaves at a concentration that is effective in rescuing the trehalose mutants ( 1 mM ) has only a very modest effect on Arabidopsis gene expression ( S . Djonovic and F . Ausubel , unpublished data ) . In addition , we showed that trehalose or ammonium nitrate does not modulate plant defense responses , since infiltration of ammonium nitrate or trehalose into Arabidopsis leaves did not make them more susceptible or resistant to virulent or non-pathogenic P . syringae strains or interfere with their ability to mount an effective innate immune response when challenged with the flagellin peptide flg22 ( Figure 7 ) . Finally , another way that P . aeruginosa could use trehalose to promote nitrogen acquisition is by generating a high local concentration of trehalose to create an osmotic gradient that causes an efflux of nitrogen containing nutrients from neighboring plant cells , perhaps in conjunction with P . aeruginosa-encoded pore forming toxins . The data in Figure 9 , which shows that trehalose functions externally to P . aeruginosa , is consistent with these proposed models . We have found that P . aeruginosa-synthesized trehalose plays a key role as a virulence factor during infection of plant leaves . Although the mechanistic details remain to be elucidated , our data suggest that a role of trehalose during the infectious process involves the procurement of nitrogen-containing molecules . In contrast to specialized plant pathogens that utilize highly evolved Type III virulence effectors to promote virulence , the multi-host opportunistic pathogen P . aeruginosa , which is not likely to have co-evolved with particular plant hosts , appears to have repurposed a highly conserved anabolic pathway ( trehalose biosynthesis ) as a potent virulence factor .
Experiments with mice were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The animal protocol was approved by the Harvard Medical Area Institutional Animal Care and Use Committee ( Permit Number: 404 ) . All efforts were made to minimize suffering . P . aeruginosa strain UCBPP-PA14 [1] , P . syringae pv . tomato strain DC3000 [51] , and P . syringae pv . phaseolicola strain 3121 [52] have been described . A nonpolar hrcC mutant of P . syringae strain DC3000 ( CUCPB5112 ) was obtained from A . Collmer and B . Kvitklo , Cornell University . Escherichia coli strain SM10 λpir was used for triparental mating [53] . Strains were routinely maintained at 37°C on Luria-Bertani ( LB ) agar plates or cultured in LB broth supplemented with appropriate antibiotics as needed . The concentrations of antibiotics were: ampicillin or carbenicillin , 50 µg/ml for E . coli or 300 µg/ml for P . aeruginosa; and rifampicin 100 µg/ml . Minimal medium ( M63 ) or modified minimal A medium ( MinA ) that contained glucose ( 0 . 3% ) [39] were also used for the growth of P . aeruginosa . The ΔPA14_36375-36830 deletion mutant ( Δ42 ) was constructed using a 2 . 25 kb sequence containing regions immediately flanking the deleted region that was generated by a standard 3-step PCR protocol using FastStart Taq DNA Polymerase ( Roche , Germany ) and cloned into the KpnI and BamHI sites of pEX18Ap [54] creating plasmid pEX18PA14_36375-36830Δ1 . The resulting plasmid was used to introduce the deleted PA14_36375-36830 region into the wild-type PA14 genome by homologous recombination [53] . Similar strategies were used to construct other deletion mutants . For ΔPA14_36375-36560 , ΔPA14_36570-36630 , ΔPA14_36570-36700 , ΔPA14_36710-36740 , ΔPA14_36710-36830 , and ΔPA14_19350-19370 , 12 . 64- , 10 . 58- , 16 . 28- , 7 . 50- , 12 . 81- , and 4 . 78 kb wild-type sequences were deleted by recombination using 1 . 30 , 1 . 27 , 1 . 06 , 1 . 28 , 1 . 30 , and 1 . 26 kb fragments , respectively , containing the relevant flanking sequences . A double mutant lacking both trehalose operons was constructed by recombining the deleted treS operon in pEX18PA14_36710-36830Δ1 into the ΔPA14_36570-36700 ( treYZ ) mutant background . A double Δ42treA mutant was constructed by recombining the Δ42 deletion in pEX18PA14_36375-830Δ1 into a treA::MR2xT7 transposon insertion mutant [16] . We confirmed that treA::MR2xT7 mutant could not grow when provided trehalose as sole carbon source in diluted LB and that treA::MR2x7 could not hydrolyze trehalose to glucose using the Somogyi-Nelson assay [21] , [22] . All deletion mutants were confirmed by PCR analysis and sequencing . PA14 wild-type and the Δ42 mutant were transformed with pSMC2 carrying green fluorescent protein ( GFP ) [55] . To construct strains expressing red fluorescent protein ( RFP ) , a variant of DsRed2 , DsRed . T3 ( DNT ) , from Vibrio fischeri [56] was transferred ( on a 719 bp SphI – XbaI fragment from pVSV208 ) into the SphI – XbaI sites of pUCP19 [57] generating pUCP19/DsRed . T3 ( DNT ) , which was designated pAA100 . pAA100 was transformed into PA14 wild-type and Δ42 by electroporation . Twitching and swimming motility assays were performed as previously described [58] . To compare growth rates of wild-type and mutants , the cultures were grown at 37°C overnight in LB , centrifuged , washed and resuspended into minimal medium ( M63 ) . Bacterial growth was monitored in vitro by plating and counting CFU/ml at 3- , 6- and 9- hour time points . Growth rate ( h−1 ) was calculated using the equation for exponential growth: μ = ( lnN1−lnN0 ) / ( t1−t0 ) , where N0 and N1 equal bacterial abundance ( CFU/ml ) at the beginning ( t0 ) and end ( t1 ) of the exponential growth phase . Each experiment was repeated at least twice with similar results . Arabidopsis ecotypes Columbia ( Col-0 ) was obtained from the Arabidopsis Biological Resource Center ( Columbus , OH ) . Plants were grown on 30-mm Jiffy-7 peat pellets ( Jiffy Products , Shippagan , New Brunswick , Canada ) in a Conviron E7/2 chamber ( Winnipeg , Manitoba , Canada ) set at a 23°C/20°C day/night regime with a 12-h photoperiod at a light intensity of 100 µE m−2 s−1 and 60% relative humidity . Arabidopsis cell wall mutants were obtained from the Arabidopsis Biological Resource Center: mur2-1 ( AT2G03220; CS8565 ) , mur3-2 ( AT2G20370 , CS8567 ) , mur4-1 ( at1g30620 , CS8568 ) , mur10-2 ( at5g17420 , CS8578 ) , and an xxt1/xxt2 double T-DNA insertion line ( at3g6272; SALK_119658C/at4g02500; SALK_1013080 ) as previously published [33] . Plant infection assays were carried out as previously described [1] with some modifications . P . aeruginosa strains were grown in LB medium overnight , subcultured and grown to an OD600 of 2 . 5 . Cells were centrifuged , washed and resuspended in 10 mM MgSO4 . Leaves of four-week old plants were inoculated with a 1×105 CFU/ml suspension of PA14 wild-type or various PA14 mutants , which corresponds to 1×103 CFU/cm2 leaf area . Infected plants were incubated in a growth chamber at 28°C with a 12-h photoperiod at a light intensity of 60 µE m−2s−1 and 90% relative humidity . Six to eight leaves were harvested from three to four plants for CFU determination . Each experiment was repeated at least two to four times with similar results . Co-inoculation of bacteria with betaine , trehalose or sucrose ( Sigma , St . Louis , MO ) was performed as described above . Before inoculation of leaves , betaine , trehalose , glucose , or sucrose was added to bacterial suspensions at the indicated concentrations , or various phosphate , sulfate , nitrate , or ammonium salts were added at 1 mM . Trehalose was initially added at 2 . 5 mg/ml , but after we carried out dose response curves and found that 0 . 25 mg/ml ( 0 . 74 mM ) was an effective concentration , subsequent experiments were carried out using 1 mM trehalose . Infection assays with P . syringae strains were performed the same way as with P . aeruginosa with a few exceptions . The temperature in the growth chamber was 22°C and bacterial strains were cultured in King's B medium ( protease peptone , 10 mg/ml; glycerol , 15 mg/ml; K2HPO4 , 1 . 5 mg/ml; MgSO4 , 5 mM , pH 7 . 0 ) until late logarithmic phase . Elicitation assays were performed by infiltration of leaves with 1 µM flg22 , 1 mM trehalose , 1 mM glucose or 1 mM ammonium nitrate ( or in combinations ) 24 hours prior to bacterial inoculation . C . elegans slow killing assays were performed as previously described [3] . Briefly , PA14 , Δ42 , or ΔtreYZΔtreS mutants were grown overnight in LB and 10 µl of each liquid culture was spread onto 3 SK plates ( modified NGM medium; [3] ) . The plates were incubated at 37°C for 24 hours and then at 25°C for 20–24 hours . 35–45 fer-15;fem-1 sterile L4 nematodes were picked to the SK plates seeded with PA14 , Δ42 , or ΔtreYZΔtreS and the plates were incubated at 25°C . Live and dead animals were counted daily for approximately 8 days . A worm was scored dead when it no longer responded to touch . Infection survival assays in D . melanogaster were conducted with D . melanogaster strains w[118] ( Bloomington stock #6326 ) or Oregon R , which were grown under non-crowded conditions on standard cornmeal-molasses medium . Fly husbandry and infections were carried out at 25°C , 70% humidity , 12 hours light cycle . For infections assays , P . aeruginosa was grown aerated at 37°C in LB medium containing 50 µg/ml rifampicin , and subcultured to an OD600 = 2 . 3–2 . 5 . The bacterial culture was diluted to a final concentration 80% LB , 4% sucrose , 50 µg/ml rifampicin and 3×108 CFU/ml and 7 ml of infection mixture was pipetted onto sterilized cotton balls at the bottom of clean , empty fly vials . 25 male flies , 4 days old , were added and their survival monitored several times a day . Mouse experiments complied with institutional and federal guidelines regarding the use of animals in research . For the acute pneumonia model , a modified version of a previously described method of intranasal inoculation of anesthetized mice was utilized [19] . Briefly , 6- to 8-week-old female C3H/HeN mice ( Harlan ) were sedated with ketamine and xylazine and then 10 µl of a bacterial suspension was applied to each nostril . Bacterial suspensions were prepared in PBS ( OD600 = 0 . 5 ) after overnight growth of frozen stock on TSA . Doses were determined by serial dilution and plating on MacConkey agar ( 1 . 5×107 CFU/20 µl for PA14 , 1 . 4×107 CFU/20 µl for Δ42 ) . After 18 hr , mice were euthanized with carbon dioxide and then lungs and spleens were removed , weighed , and homogenized in 1 ml of 1% proteose peptone in water . Viable counts were determined by serial dilution and plating . For the chronic oropharyngeal colonization model in transgenic CF mice , we utilized mouse strain Cftrtm1Unc-TgN ( FABP-CFTR ) ( denoted FABP-CFTR ) , which has a stop codon in the murine cftr gene ( S489X ) but also expresses human CFTR in the gut epithelium due to transgenic introduction of human Cftr under the control of the fatty acid binding protein ( FABP ) promoter [59] . These FABP-CFTR mice have been bred into the FVB/N genetic background ( breeding pairs were initially provided by Dr . J . Whitsett , University of Cincinnati ) . These FABP-CFTR mice are susceptible to chronic oropharyngeal colonization with P . aeruginosa after exposure in the drinking water [20] . To establish colonization , age- and gender-matched mice were given oral levofloxacin in their drinking water for 5 days , followed by gentamicin for 2 days , followed by bacteria ( either PA14 or the Δ42 mutant ) suspended in water at 107 CFU/ml . Bacterial levels in the drinking water were unchanged at the end of 7-day exposure . Throat cultures were then taken every 1–2 weeks using a swab inserted into the oropharynx of mice anesthetized with isofluorane . The swab was placed in 1 ml tryptic soy broth and incubated at 37°C for 3 hours . Next , 1 ml of nitrofurantoin ( 2 mg/ml ) was added to suppress the growth of any contaminating Enterobacter spp . , which can interfere with detection of P . aeruginosa . The culture was incubated overnight at 37°C and then subcultured overnight on cetrimide agar . All mice in both groups ( n = 9 for the Δ42 mutant , n = 8 for PA14 WT ) had positive throat cultures after colonization . Mice were then followed for survival . To assign putative functions to genes within the block of PA14_36375 through PA14_36830 , each protein in the 42 kb cluster was used as a query in a BLAST or PSI-BLAST homology search . In the process of assigning putative functions , several types of information were taken into account: homologous proteins with experimentally assigned function; homologous proteins with computationally predicted function; matches to HMMs from conserved domain databases; and the genomic/operon context of close homologs . The protein and nucleotide sequences of prokaryotic genomes were obtained from NCBI ( ftp://ftp . ncbi . nih . gov/genomes/Bacteria/ ) . Additionally , two P . aeruginosa genomes ( P . aeruginosa 2192; P . aeruginosa C3719 ) were obtained from the Broad Institute of Harvard and MIT ( http://www . broadinstitute . org/annotation/genome/pseudomonas_group/MultiHome . html ) . To identify orthologs to PA14_36375-36830 genes , two criteria were used . First , putative ortholog pairs were required to be reciprocal best hits , with an e-value less than or equal to 0 . 0001 for best hits of the PA14 proteins against compared protein sets , and an e-value less than or equal to 0 . 001 for reciprocal best hits against the PA14 protein set . Secondly , the putative orthologs were required to align for at least 80 percent of their lengths and have less than a 20% difference in protein sequence lengths , thereby conserving overall domain structure . Of these constraints , the e-value and sequence length constraints are very permissive , whereas the requirement for alignment length is stringent . P . aeruginosa strains were grown at 37°C in MinA medium with 0 . 5 M NaCl to an early stationary phase . Trehalose was extracted from a 19 ml culture by pelleting the cells , resuspending in 0 . 5 ml water , and heating at 95°C for 20 min [60] . The concentration of trehalose in the supernatants was determined using an enzymatic assay by converting trehalose to glucose with trehalase and then measuring the glucose using a trehalose assay kit ( Megazyme International Ireland Limited ) . The pre-existing glucose in each sample was determined in a control reaction without trehalase and subtracted from the total glucose . The experiment was repeated at least twice with similar results . Osmotic stress sensitivity: P . aeruginosa was grown at 37°C in MinA containing 17 mM glucose [39] , washed and subcultured into MinA containing 0 . 5 M NaCl . Bacterial growth was monitored by plating CFU . Persistence assay: This assay was performed as previously described [61] . Briefly , persisters were determined by exposure of stationary cultures to antibiotics at concentrations exceeding the corresponding bacterial minimal inhibitory concentrations ( MICs ) . The antibiotics were used at the following concentrations: 6 µg/ml tobramycin , 2 µg/ml ciprofloxacin and 3 mg/ml carbenicillin . Oxidative stress resistance: Hydrogen peroxide was added directly to an overnight culture grown for 14 h in LB medium and the cells were incubated for 8 h at 37°C . The following concentrations of hydrogen peroxide were used: 1 M ( non-lethal ) , 2 M ( sub-lethal ) , and 3 M ( lethal dose ) . To test sensitivity to paraquat ( Sigma , 856177 ) , overnight MinA cultures were diluted 100 fold in MinA containing different amounts of paraquat ( 0 . 1 , 1 and 10 mM ) and cultured for three days . pH stress: Cultures were grown to stationary phase in LB medium that had been titrated with HCl to pH 4 , 5 , 6 , or 7 . Thermotolerance: Small volumes of stationary phase cells were heated in Eppendorf tubes in a heating block at different temperatures and incubation times , then rapidly diluted and plated . Biofilm formation: Biofilm attachment assays were performed using wild-type PA14 , Δ42 and ΔPA14_19350-19370 ( ΔNAGGN ) cultures grown in 96-well polyvinylchloride ( PVC ) plates as described previously [62] . Overnight cultures were diluted 1/100 in MinA medium or MinA medium supplemented with 0 . 5 M or 0 . 75 M NaCl . Aliquots of 100 µL were dispensed into the wells of PVC microtiter plates and incubated at 37°C . Attachment was detected by staining with 1% crystal violet dissolved in water . Dye not associated with bacteria was removed by rinsing with water . Bacteria-associated dye was solubilized using 95% ethanol and absorbance was determined at 550 nm . Each experiment was repeated at least twice with similar results . Statistical analyses in animal experiments were performed using GraphPad Prism 5 software ( La Jolla , CA ) and a log rank ( Mantel-Cox ) test to assess the significance of differential survival , and a Mann-Whitney U non-parametric test for significance of CFU data , which were not normally distributed . Statistics in all other experiments was performed using analysis of variance ( ANOVA ) and a Fisher's PLSD test ( Statview v . 5 . 0 . 1 , SAS Institute , Cary , NC ) . | Pseudomonas aeruginosa is an opportunistic human bacterial pathogen that infects a wide range of plants and animals , including the model laboratory plant Arabidopsis thaliana . P . aeruginosa utilizes many of the same virulence-related factors to infect both plants and animals . However , because plants have fundamentally different cellular architecture than animals , we hypothesized that P . aeruginosa synthesizes specific factors required for infecting plants but not animals . We found that synthesis of the sugar molecule trehalose , an unusual dimer of glucose , is required for plant but not animal pathogenesis . Although P . aeruginosa mutants defective in trehalose synthesis are non-pathogenic in Arabidopsis , Arabidopsis mutants that lack the polysaccharide xyloglucan in their cell walls can be infected by P . aeruginosa trehalose mutants . Moreover , application of ammonium nitrate overcomes the requirement for trehalose for infecting an Arabidopsis leaf . Our data suggest that trehalose promotes the acquisition of nitrogen-containing nutrients , thereby allowing P . aeruginosa to replicate in the nutrient-poor intercellular spaces in a leaf . This work shows how an opportunistic pathogen has repurposed a highly conserved “house-keeping” function ( trehalose biosynthesis ) as a potent virulence factor . | [
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] | 2013 | Trehalose Biosynthesis Promotes Pseudomonas aeruginosa Pathogenicity in Plants |
Dendritic cells ( DCs ) play a central role in initiating immune responses . Some persistent viruses infect DCs and can disrupt their functions in vitro . However , these viruses remain strongly immunogenic in vivo . Thus what role DC infection plays in the pathogenesis of persistent infections is unclear . Here we show that a persistent , B cell-tropic gamma-herpesvirus , Murid Herpesvirus-4 ( MuHV-4 ) , infects DCs early after host entry , before it establishes a substantial infection of B cells . DC-specific virus marking by cre-lox recombination revealed that a significant fraction of the virus latent in B cells had passed through a DC , and a virus attenuated for replication in DCs was impaired in B cell colonization . In vitro MuHV-4 dramatically altered the DC cytoskeleton , suggesting that it manipulates DC migration and shape in order to spread . MuHV-4 therefore uses DCs to colonize B cells .
Dendritic cells ( DCs ) act as sentinels against infection: they encode pathogen-responsive receptors , abound at pathogen entry sites , and orchestrate both innate and adaptiveimmune responses [1] , [2] . Virus-infected DCs are generally immunogenic [3]–[5] , and DC infection may be important for optimal T cell priming [6] . However several persistent viruses , which might be expected to limit their exposure to host immunity , efficiently infect DCs [7]–[10] . The infected DCs may function abnormally in vitro [11] , [12] , but the corresponding in vivo infections remain potently immunogenic . Therefore how DC infection benefits these persistent viruses , or whether it instead benefits the host , is unclear . Murid Herpesvirus-4 ( MuHV-4 ) is a gamma-herpesvirus that readily allows in vivo analysis of host colonization [13] , [14] . Like Epstein-Barr virus and the Kaposi's Sarcoma-associated Herpesvirus , MuHV-4 persists in B cells [15] , [16] . It also acutely infects macrophages and dendritic cells [17]–[19] . Myeloid infection provides MuHV-4 with a site of persistence in B cell-deficient mice , but the antibody deficiency of these mice leads to a somewhat atypical chronic lytic infection [20]; in immunocompetent mice , infected macrophages and dendritic cells are hard to detect long-term [19] . Therefore myeloid infection seems more likely to be important for establishing MuHV-4 host colonization than for maintaining it . Epstein-Barr virus and Kaposi's Sarcoma-associated Herpesvirus can also infect myeloid cells in vitro [9] , [21] . While they seem rarely to do so in vivo , their clinical presentations post-date infection by at least 1 month [22] , so early host colonization is rarely studied . Thus acute myeloid cell infection may not be unique to MuHV-4 . The difficulty of curing established gamma-herpesvirus infections makes early events in host colonization important to understand . It has been suggested that incoming Epstein-Barr virus infects B cells and so establishes latency directly [23] . This would argue against an important role for myeloid infection . However , good supporting evidence for direct B cell infection is lacking . Indeed vaccination to block Epstein-Barr virions binding to B cells failed to reduce the incidence of infection [24] . And while DNA from lung-inoculated , replication-deficient MuHV-4 has been found associated with B cells [25] , [26] , viral genome-positive B cells did not reach the spleen , so whether the detected DNA was a viable primary infection or merely adsorbed debris was unclear . That fibroblast-propagated MuHV-4 infects mice well [27] and B cells poorly [28] would argue against B cells being a significant primary target . Natural host entry probably occurs via the upper respiratory tract rather than the lung [29] , but MuHV-4 lacking thymidine kinase or ribonucleotide reductase fails to infect by this route and neither enzyme is required for replication in B cells [30] , [31] . Therefore here too B cells would seem an unlikely primary target . How then does MuHV-4 reach B cells ? HIV can infect T cells via DCs [32] , [33] , and DCs also communicate with B cells [34] , so exploiting DC/lymphocyte interactions could be a common theme among lymphotropic viruses . However , in vivo evidence is again sparse . HIV-infected DCs are hard to find in vivo , and most HIV taken up by DCs in vitro is degraded . Thus it has been possible only to hypothesize that DCs contribute to host entry [35] . MuHV-4 allows more comprehensive analysis , and this is what we undertook here . As removing DCs causes immunosuppression [36] , we used DC-specific cre recombinase expression to inhibit MuHV-4 replication in DCs or to mark genetically viruses that had replicated in a DC , while leaving normal DC functions intact .
That MuHV-4 colonizes B cells is indisputable , but where it first infects them is unclear . To identify the likely anatomical site we used viral luciferase expression [29] to track host colonization after upper respiratory tract infection ( Fig . 1 ) . Live imaging signals were evident in noses 3 days after inoculation and in the neck after 10 days ( Fig . 1a ) . Signals were present in both sites after 6 days , indicating that the virus spread from nose to neck at 3–6 days post-infection . Ex vivo imaging of dissected tissues at the peak of neck infection - day 10 - ( Fig . 1b ) established that this signal came entirely from the superficial cervical lymph nodes ( SCLN ) , which receive lymphatic drainage from the nose [37] . No other tissues were luciferase+ at this time . Imaging dissected mice at day 6 ( Fig . 1c ) revealed luciferase expression in the nasal turbinates and in the SCLN , but not in intervening sites . Nor was luciferase expression evident in other lymph nodes , or in the nasal-associated lymphoid tissue that lies on either side of the palate . Therefore MuHV-4 appeared to move directly from the nose to the SCLN . We next tracked infection by quantitative PCR ( Q-PCR ) of viral DNA ( Fig . 2a ) . In agreement with the luciferase imaging , viral genomes were detected in the nose at day 3 post-inoculation and not consistently in the SCLN until day 5 . Recovering replication-competent virus by infectious centre assay ( Fig . 2b ) was less sensitive but showed similar trends , arguing that the DNA signals in the SCLN were due to infection rather than just accumulated viral debris . Both upper and lower respiratory tract infections showed viral genomes being most abundant in the peripheral epithelial site at day 5 post-inoculation , and most abundant in lymphoid tissue at day 14 ( Fig . 2c ) . At day 5 viral genomes were more abundant in draining lymph nodes than in spleens , whereas by day 14 these sites were equivalent . These results were consistent with epithelial infection seeding first to its draining lymph nodes , and then disseminating to the spleen , presumably via B cells [38] . Previous analyses of MuHV-4 genome loads in different cell types have focussed on peak splenic titers , when most infected cells are B cells [18] , [19] . We reasoned that if B cells only became infected once virus reached lymph nodes , then early on viral genome loads might be lower in lymph node B cells than in the cells first responsible for taking the virus there . Antigen transport of lymph nodes is a major function of DCs . We therefore tested whether DC infection might precede B cell infection , by separating these sub-populations from acutely infected lymph nodes on affinity columns and determining their viral genome loads by Q-PCR ( Fig . 2d ) . At days 11 and 14 after virus inoculation into either noses or lungs , viral genome loads were higher in lymph node B220+ cells ( B cells ) than in CD11c+ cells ( DCs ) , but at days 5 and 8 they were higher in DCs . Further analysis ( Fig . 2e ) identified significantly higher viral genome loads also in CD11c-CD11b+ lymph node cells ( macrophages ) than in B cells early after virus inoculation into the lungs , but not after virus inoculation into the nose . At 8 days after virus inoculation into the upper respiratory tract , 105 cells purified from the SCLN of pooled pairs of mice yielded 20 . 0±7 . 6 infectious centres for macrophages , 53 . 3±26 . 2 for DCs and 14 . 7±4 . 3 for B cells ( mean ± SEM , n = 3 ) . Each population yielded <1 p . f . u . per 105 cells by plaque assay . Therefore after upper respiratory tract infection , a predominantly latent infection of DCs appeared to precede that of B cells . SCLN suspensions are typically 50% B cells and <5% DCs , so at all time points B cells accounted for most of the recoverable viral DNA . However virus seeds to the SCLN at a low level , is rapidly passed to B cells , which proliferate , and seeds asynchronously between individual mice . Thus by the time DC infection is readily detected , some B cell infection and amplification has inevitably occurred . The kinetic changes in relative genome load argued strongly for DC infection preceding B cell infection: the genome+ DCs at day 14 may have acquired infection from B cells , but this was unlikely at day 5 because B cell viral loads then were low . We sought next to visualize infected cells directly , using MuHV-4 that expresses eGFP from an intergenic EF1α promoter [39] ( Fig . 3 ) . At day 11 post-infection - that is after the onset of virus-driven lymphoproliferation - flow cytometry identified viral eGFP expression mainly in B cells ( Fig . 3a ) : approximately 1% of CD19+ lymph nodes cells were eGFP+ . The eGFP+ cells were also positive for surface immunoglobulin and MHC class II . Most were CD69+ , consistent with MuHV-4 up-regulating this acute activation marker on B cells [40] . B cells are normally syndecan-4+ [41] , so a surprising finding was that most eGFP+ cells were syndecan-4- . Syndecan-4 is a carrier of heparan sulfate , on which MuHV-4 infection strongly depends [42] , so MuHV-4 may down-regulate syndecan-4 on infected B cells to prevent super-infection . No clear CD19-eGFP+ population was identifiable by flow cytometry . However , immunohistochemistry at day 11 post-infection identified both eGFP+B220+ and eGFP+CD11c+ cells in lymph nodes ( Fig . 3b ) . At day 7 ( Fig . 3c ) eGFP+ cells in the SCLN were 64 . 0±13 . 0% CD11c+ , 14 . 1±5 . 1% B220+ and 21 . 9±12 . 3% neither ( mean ± SEM , n = 6 mice , counting >100 eGFP+ cells per mouse ) . At day 11 , SCLN eGFP+ cells were 12 . 0±2 . 8% CD11c+ , 78 . 0±2 . 4% B220+ , and 10 . 0±3 . 1% neither . Therefore both PCR of viral DNA and immunostaining of virus-expressed eGFP showed DC infection early in lymph node colonization , before B cell infection was well established . Our failure to detect a clear population of infected DCs by flow cytometry possibly reflected that these cells are difficult to isolate intact . Cre-lox recombination allows transient infection events to be recorded by a permanent genetic mark [43] . We used this technology to identify MuHV-4 that had replicated in cre recombinase+ cells , by inserting a loxP-flanked eCFP expression cassette between the 3′ ends of ORFs 57 and 58 ( Fig . 4a ) . The MuHV-4 BAC/eGFP cassette is also flanked by loxP sites [44] , so BAC-derived viruses retain a single loxP site at the genome left end . To avoid recombination between this site and those flanking eCFP , we incorporated a point mutation into the spacer region of the latter . We then transfected BAC DNA into BHK-21 cells , passed the recovered eGFP+eCFP+ virus once through cre+ NIH-3T3 cells ( 2 p . f . u . /cell ) and selected eGFP-eCFP+ virus clones from the mixed progeny ( Fig . 4b ) . LoxP-eCFP MuHV-4 ( eGFP-eCFP+ ) showed no in vivo growth deficit ( Fig . S1 ) . In vitro it rapidly lost eCFP expression when passed through cre+ NIH-3T3 cells ( Fig . 4c ) . We quantitated this loss by counting plaques under phase contrast and typing each plaque as eCFP+ or eCFP- under ultra-violet illumination ( Fig . 4d ) . LoxP-eCFP MuHV-4 also lost eCFP expression in cre+DCs - derived from CD11c-cre transgenic mouse bone marrow by growth in GM-CSF - albeit less dramatically than in cre+ fibroblasts ( Fig . 4c ) . Thus eCFP loss provided a minimum estimate of the proportion of virions passing through a DC . We looked for a possible selective effect of eCFP excision by infecting non-transgenic ( cre- ) mice with different mixtures of eCFP+ and eCFP- derivatives of the loxP-eCFP virus , and 20 days later typing splenic infectious centres for eCFP expression ( Fig . 4e ) . No marked difference in eCFP+ frequency was observed between the input and recovered viruses . ECFP loss therefore provided an unbiased marker of in vivo virus exposure to cre . A limitation of eCFP-based analysis was that even non-transgenic mice yielded 6 . 1±2 . 8% eCFP- plaques ( mean±SD , n = 12 ) . PCR across the ORF57/58 junction identified loxP recombination in 6/6 eCFP- plaques recovered from CD11c-cre mice infected with loxP-eCFP MuHV-4 . 3/3 eCFP- plaques recovered from non-transgenic mice ( which were rare ) did not show loxP recombination , and were presumably due to cassette silencing: that viral expression cassettes are not always active even in lytically infected cells is well-established [45] . Therefore we took 10% eCFP- plaques ( >1 SD above the background ) as indicating significant virus exposure to cre recombinase . We then infected CD11c-cre transgenic mice with loxP-eCFP MuHV-4 in the upper respiratory tract . The infectious virus recovered from noses showed little eCFP loss above the 10% cut-off ( Fig . 4f ) . However eCFP loss from SCLN virus was significantly greater . Therefore DCs contributed relatively little to peripheral viral lytic replication , and much more to SCLN colonization . Splenic virus showed no further eCFP loss , consistent with the spread from the SCLN to here being accomplished by B cells . Substantial eCFP expression loss from the viruses of flow cytometrically sorted B cells ( Fig . 4f ) established that a significant proportion of the virus establishing long-term latency had passed through a DC . The relative inefficiency of cre-mediated eCFP excision in CD11c-cre mice made it difficult to estimate exactly what proportion of the virus in B cells had passed through a DC . To answer this better we exposed DCs grown from CD11c-cre bone marrow in vitro to MuHV-4 ( 5 p . f . u . /cell , 6 h , 37°C ) , then washed them in pH = 3 buffer to inactivate non-endocytosed virions and 24 h later - the earliest new infectivity can be recovered from MuHV-4-exposed DCs - titrated cell supernatants by plaque assay . ECFP typing under ultra-violet illumination identified 14 . 2±3 . 0% of plaques as eCFP- ( mean ± SD of 10 cultures , counting at least 100 plaques for each culture ) . Thus assuming that an overnight infection of bone marrow-derived DCs equates roughly in cre exposure to DC-mediated in vivo virus transfer , essentially all the virus derived from B cells after upper respiratory tract infection had passed through a DC . The virus recovered from lungs after lower respiratory tract infection ( Fig . 4g ) showed only modest eCFP loss . Unlike after upper respiratory tract infection , there was no increased eCFP loss upon reaching the draining lymph nodes ( MLN ) . Therefore lung infection appeared to provide MuHV-4 with an alternative , DC-independent route to lymphoid tissue . Viruses recovered from spleens after lung infection showed more eCFP loss . However , nose infections inevitably accompany lung infections , so the splenic virus would also have come from the SCLN . Thus while DCs took virus to lymph nodes from the upper respiratory tract , they were less important when a more invasive entry point was used . As a second approach to establishing the functional importance of DCs for host colonization , we generated a MuHV-4 mutant in which cre-mediated recombination would cause a lethal genomic deletion . Thus we inserted between ORFs 8 and 9 a loxP site compatible with that remaining at the genome left end after BAC cassette excision ( Fig . 5a ) . Complete recombination between loxP sites would now excise both the BAC/eGFP cassette and the left end of the viral genome , including the essential ORFs 6 , 7 and 8 [46] . It would also excise the M2 latency gene [47] , the viral tRNA/miRNAs [48] and a likely promoter element for ORF73 [49] , and so would probably also compromise the capacity of the viral genome to persist as a latent episome . Thus in contrast to eCFP excision from loxP-eCFP MuHV-4 , which had no effect on viral fitness and so provided an index of virus exposure to cre recombinase , 8/9-loxP MuHV-4 would tell us the functional consequence of impaired DC infection . To excise just the BAC cassette from 8/9-loxP MuHV-4 we passed it once through NIH-3T3-cre cells ( 2 p . f . u . /cell ) and selected viable eGFP- clones . These grew normally in cre- cells , but were severely attenuated for growth in cre+ fibroblasts ( Fig . 5b ) . They were moderately attenuated for growth in cre+ DCs . To confirm the mechanism of attenuation we infected cre- and cre+ fibroblasts with wild-type or 8/9-loxP MuHV-4 , then isolated infected cell DNA , digested it with HinDIII and probed it with a HinDIII-N viral genomic clone that spans the ORF8/ORF9 junction [50] . Cre-mediated deletion of M1-ORF8 was predicted to remove the left end of the HinDIII-N locus and join its remainder to the viral terminal repeats , thereby changing the probed fragment from 3632 bp to >20 kb ( as MuHV-4 has up to 30 copies of its 1 . 2 kb terminal repeat unit ) , with a 1 . 2 kb submolar ladder due to variation in terminal repeat copy number . This is precisely what we observed ( Fig . 5c ) . 8/9-loxP MuHV-4 showed a significant defect in SCLN colonization at day 7 after inoculation into the noses of CD11c-cre mice relative to non-transgenic controls , whereas wild-type MuHV-4 showed no difference ( Fig . 6a ) . An independently derived 8/9-loxP mutant showed the same phenotype , while a revertant virus did not ( Fig . S2 ) . Therefore DC-specific virus attenuation reduced virus spread to lymph nodes . A similar cre-dependent defect was observed in the colonization of B cells , purified from SCLN by flow cytometric sorting ( Fig . 6b ) . 8/9-loxP virus lytic titers , by contrast , were not significantly different between the noses of cre+ and cre- mice ( Fig . 6c ) , consistent with the limited loss of intergenic eCFP expression seen in noses ( Fig . 4f ) . Surprisingly , by day 15 post-infection ( spleens and SCLN ) the substantial early defect in SCLN colonization ( Fig . 6d ) of cre+ mice by 8/9-loxP MuHV-4 was no longer evident . MuHV-4 single gene knockouts [51] , [52] have shown the same phenomenon of early replication defects not translating into differences in long-term latent loads . This possibly reflects that lower virus loads elicit less host response , such that mutant and wild-type viruses differ more in their speed of colonization than in their final set points . A different situation arises when mutant and wild-type viruses compete for a limited latency niche . To test this we co-infected cre+ and cre- mice with a 1∶100 mix of EF1a-eGFP and 8/9-loxP viruses ( Fig . 6e ) , and determined their relative levels of host colonization 30 days later by typing spleen and lymph node infectious centres as eGFP- or eGFP+ . The viruses recovered from non-transgenic mice showed the expected eGFP+:eGFP- ratio of 1∶100 . In contrast , those recovered from CD11c-cre mice showed ratios of 1∶3–1∶4 , a 25-30-fold bias against 8/9-loxP MuHV-4 . Therefore in the face of virus competition , an attenuation of DC infection substantially reduced host colonization . DCs transport peripherally acquired antigens to lymph nodes even without viral infection [1] . Therefore MuHV-4 could reach lymph nodes simply by infecting DCs peripherally and remaining latent . However , viruses often alter cell behaviour to make host colonization more efficient . We looked for such effects using DCs grown from bone marrow stem cells with GM-CSF . After overnight MuHV-4 infection ( 3 p . f . u . /cell ) , most bone marrow-derived DCs are latently infected - typically 20–30% express late lytic genes [45] . However almost all virus-exposed DCs showed cytoskeletal changes ( Fig . 7a ) : actin was rapidly relocated to peripheral cytoplasmic projections , and after overnight infection the DCs had become flattened with many displaying long cytoplasmic processes . Phosphotyrosine and more specifically Y925-phosphorylated ( activated ) focal adhesion kinase ( Fig . 7b ) adopted peripheral , punctate distributions consistent with focal adhesion formation . MuHV-4-exposed DCs do not become non-specifically activated [44] , [53] , [54] , and equivalent cytoskeletal changes were not induced by DC activation with lipopolysaccharide . Therefore these cytoskeletal changes were induced specifically by MuHV-4 . Real-time imaging ( Video S1 ) showed that DCs became flattened and adherent within 6 h of exposure to MuHV-4 , yet continued to show rapid changes in shape . This phenotype persisted at 20 h post-infection ( Video S2 ) , by which time the combination of adherence and dynamic remodelling and had led to the long cytoplasmic extensions seen in Fig . 7 . Thus it appeared that infected DCs were motile but unable to detach from plastic . How these in vitro changes relate to in vivo DC migration is unclear , as tissue culture plastic provides a rather artificial surface , but it was clear that MuHV-4 actively manipulates the DC cytoskeleton .
Lymphotropic viruses arrive at mucosal epithelia , whereas naive lymphocytes circulate through organized lymphoid tissue . Therefore lymphotropic viruses face a problem in reaching their target cells . Lymphatic channels normally provide a route for DCs and cell-free antigens to travel from epithelia to lymph node subcapsular sinuses . Small , soluble antigens can pass directly into B cell follicles via specialized conduits [55]–[57] , but larger antigens - immune complexes [58] , virus-sized particles [59] , and cell-free virions [60] , [61] - are first captured by subcapsular sinus macrophages . Thus viruses can enter lymph nodes via migratory DCs or subcapsular sinus macrophages . Most analysis of DC migration has depended on indirect measures such as T cell priming [62] . In contrast , virion capture by subcapsular sinus macrophages has been observed directly [60] , [61] . However , direct imaging has depended on injecting large virion numbers . Such high doses may reveal mainly high-capacity rather than high-efficiency capture pathways , and the tissue pressures created by injection tend to force material into and along lymphatics . Therefore the relevance to non-invasive infections of antigen injections must still be established . Here we analyzed lymph node colonization after a non-invasive infection . MuHV-4 does not establish a detectable cell-free viraemia [63] , and depends for its in vivo propagation on cell/cell spread [64] more than on cell-free virion binding [65] or release [66] . Thus it might be expected to follow a cell-associated route to lymph nodes . Consistent with this idea , we identified a major role for DCs in passing infection to B cells . Thus MuHV-4 sets a new precedent for host exploitation by a lymphotropic virus . We could not determine whether MuHV-4 reaches lymph nodes only via DCs , but the similar efficiencies of eCFP excision and 8/9-loxP virus attenuation between in vitro DC infection and in vivo host colonization argued that DC infection plays a predominant role . This would also be consistent with MuHV-4 still infecting the lymph nodes of B cell-deficient mice [67] . CD11c is a well-established DC marker , but is not exclusive to DCs [68] . Expression on activated T cells can be discounted here as MuHV-4 does not infect T cells . Lung macrophages express CD11c [68] , as do subcapsular sinus macrophages at a low level [60] . However lysM-cre mice , which express cre in macrophages [69] , showed substantial eCFP loss from loxP-eCFP MuHV-4 only after lower respiratory tract infection ( unpublished data ) . Therefore while macrophages may feature prominently in MuHV-4 lung infection , DCs provided the major route of its transfer from nose to lymph nodes . Thus MuHV-4 exploits olfaction to enter the upper respiratory tract [29]; lymphocytes to persist [70]; and DCs to link them by virus transport . Cells migrate by forming cytoplasmic protrusions , adhering these to the extracellular matrix , then detaching and contracting their trailing edges [71] . Actin , focal adhesion kinase and tyrosine phosphorylation all play central roles , so the redistributions of these markers in infected DCs , independent of viral lytic gene expression , was consistent with MuHV-4 inducing latently infected DCs to migrate . ORF27/ORF58-dependent actin rearrangements [72] could then promote further virion spread upon lytic reactivation . However , in vitro DC migration was prevented by infected DCs not detaching from plastic , and we cannot exclude that the infected SCLN DCs were resident and infected through antigen capture , rather than being migratory . Therefore virus-induced DC migration needs further investigation; current evidence establishes only that DC infection is important for establishing B cell infection in lymph nodes . MuHV-4-infected DCs may also promote the amplification of B cell infection by secreting the M3 chemokine binding protein to protect in trans against CD8+ T cell attack [73] . In both this setting and that of virus transfer , the B cell latency defects of MuHV-4 lacking its MHC class I down-regulation gene K3 [51] or its bcl-2 homolog M11 [52] could reflect that these genes function in DCs [74] , [75] . Exploiting DCs presumably brings gamma-herpesviruses advantages of efficiency and stealth . Whether it also creates possibilities for therapeutic intervention remains to be seen .
All animal experiments were approved by the Cambridge University ethical review board and by the UK Home Office ( PPL 80/1992 ) , and carried out in accordance with the Animals ( Scientific Procedures ) Act 1986 . C57BL/6 and BALB/c mice were obtained from Harlan U . K . C57BL/6 back-crossed CD11c-cre mice , which express cre recombinase in DCs [76] , were obtained from Jackson Laboratories and maintained as heterozygote × C57BL/6 non-transgenic crosses . Mice were typed by PCR of tail DNA , using the primers 5′-ACTTGGCAGCTGTCTCCAAG , 5′-GCGAACATCTTCAGGTTCTG ( transgene-specific ) and 5′ CAAATGTTGCTTGTCTGGTG , 5′- GTCAGTCGAGTGCACAGTTT ( internal control ) . Mice were infected with MuHV-4 when 6–12 weeks old . Intranasal infections with anaesthesia were in 30 µl; those without were in 5 µl . For luciferase imaging , mice were injected intraperitoneally with luciferin ( 2 mg/mouse ) , anaesthetized with isoflurane , then scanned with an IVIS Lumina ( Caliper Life Sciences ) . To image specific tissues mice were killed and dissected after luciferin injection . All experiments conformed to local animal ethics regulations and Home Office Project Licence 80/1992 . Baby hamster kidney ( BHK-21 ) cells , NIH-3T3 cells and NIH-3T3-CRE cells [50] were propagated in Dulbecco's modified Eagle medium ( Invitrogen Corporation ) supplemented with 2 mM glutamine , 100 U/ml penicillin , 100 mg/ml streptomycin and 10% fetal calf serum ( complete medium ) . Dendritic cells were derived from bone marrow progenitors of CD11c-cre mice or non-transgenic litter-mates . Bone marrow cells were depleted of adherent cells ( 30 min , 37°C ) and then cultured in RPMI with 10% fetal calf serum , 50 µM 2-mercaptoethanol , 100 U/ml penicillin , 100 mg/ml streptomycin and 7 . 5 ng/ml GM-CSF ( PeproTech ) . The medium was changed every 2 d , and non-adherent cells harvested after 7 d . These were >90% GR-1-CD11c+ by flow cytometry . Lymph nodes were removed post-mortem , finely minced , digested ( 20 min ) with type II collagenase ( 1 mg/ml , Worthington Biochemicals ) plus DNaseI ( 20 µg/ml , Boehringer Mannheim ) , then incubated ( 5 min ) in 100 mM EDTA to disrupt cell/cell conjugates . Debris was removed by filtration ( 100 µm ) . B cells , DCs and macrophages were then separated with antibody-coated magnetic beads ( Miltenyi Biotec ) . First B cells were selected with anti-CD45R , then DCs were selected with anti-CD11c , then macrophages were selected with anti-CD11b . Each population was >90% pure by flow cytometry . Luciferase+ [29] , EF1a-eGFP+ ( May and Stevenson , 2010 ) , and gM-eGFP+ [77] MuHV-4 reporter viruses have been described . To insert a floxed eCFP expression cassette into the MuHV-4 genome , the eCFP coding sequence was amplified by PCR ( Pfu ploymerase , Promega Corporation ) from pMSCV-IRES-eCFP using EcoRI and KpnI-restricted primers which also incorporated modified loxP sites . Specifically the GCATACAT spacer region was changed to GTATACAT [78] = loxP* . The loxP*-flanked eCFP coding sequence was then cloned as an EcoRI/KpnI-restricted fragment into the corresponding sites of pSP73-M3-pA [79] . This placed it between a 500 bp MuHV-4 M3 promoter and a bovine growth hormone polyadenylation site . The M3-driven eCFP expression cassette was then excised from pSP73-M3-pA with BglII+XhoI , blunted with Klenow fragment DNA polymerase and cloned into the blunted MfeI site ( genomic co-ordinate 77176 ) [80] of a BglII MuHV-4 genomic clone ( co-ordinates 75338–78717 ) , again in pSP73 . This placed it between the 3′ ends of ORFs 57 and 58 . The eCFP expression cassette plus genomic flanks was then subcloned as a BglII fragment into the BamHI site of the pST76K-SR shuttle vector and recombined into a MuHV-4 BAC [44] . To insert a loxP site into the MuHV-4 genome between ORF8 ( genomic coordinates 16526–19054 ) and ORF9 ( genomic coordinates 19217–22300 ) , we amplified by PCR 2 genomic flanks around genomic coordinate 19055 , generating the loxP site from overlapping 3′ extensions of the inner primers . The 2 PCR products were then mixed and re-amplified with the outer primers to generate a single product . A SmaI/BglII-restricted fragment , corresponding to genomic coordinates 18614–19424 with the new loxP site at 19055 , was then excised from this product , cloned into the SmaI/BamHI sites of pST76K-SR , and recombined into the MuHV-4 BAC . Mutant viruses were identified by restriction enzyme mapping and by DNA sequencing across the insertion site . We also derived a revertant virus by shuttle vector-mediated reconstitution of the original genome region without a loxP site . BAC DNA was reconstituted into infectious virus by transfection into BHK-21 cells ( Fugene-6 , Roche Diagnostics ) . The floxed BAC/GFP cassette was removed by virus passage through NIH 3T3-CRE cells , followed by plaque purification . Virus stocks were prepared in BHK-21 cells . Infected cell debris was removed by centrifugation ( 400×g , 3 min ) , and virions then recovered from supernatants by ultracentrifugation ( 38000×g , 90 min ) . Virus stocks were titered by plaque assay on BHK-21 cells [66] . Cell monolayers were incubated with virus dilutions ( 2 h , 37°C ) , overlaid with 0 . 3% carboxymethylcellulose , and 4 days later fixed in 4% formaldehyde and stained with 0 . 1% toluidine blue for plaque counting . Infectious virus in lungs and noses was measured by freeze-thawing the tissues , then homogenizing them in 1 ml complete medium prior to plaque assay . Latent virus was measured by infectious centre assay [66]: spleen cells were co-cultured with BHK-21 cells , then fixed and stained for plaque counting after 4 days . Plaque assay titers of freeze-thawed lymphoid homogenates were always <1% of infectious center assay titers , so the latter essentially measured reactivable latent virus . To distinguish eCFP+ and eCFP- ( or eGFP+ and eGFP- ) viruses , plaque or infectious centre assays were performed in limiting dilution format . Each well was then scanned under normal illumination , and for each positive well a chosen plaque scored as fluorescent or not under ultra-violet illumination . Thus each positive well was counted just once . MuHV-4 genomic co-ordinates 4166–4252 , corresponding to the M2 locus , was amplified from tissue DNA ( 50–80 ng ) by quantitative PCR ( Rotor Gene 3000 , Corbett Research ) . The PCR products were quantitated by hybridization with a Taqman probe ( genomic coordinates 4218–4189 ) and converted to genome copies by comparison with a standard curve of cloned plasmid template amplified in parallel [81] . Cellular DNA was quantitated in parallel by amplifying part of the adenosine phosphoribosyl transferase gene . Viral DNA was extracted by alkaline lysis [66] , digested with HinDIII , electrophoresed and transferred to nylon membranes ( Roche Diagnostics ) . A 32P-dCTP labelled probe ( APBiotech ) was generated by random primer extension ( DECA prime II kit , Ambion ) . Membranes were hybridised with probe ( 65°C , 18 h ) , washed in 30 mM sodium chloride/3 mM sodium citrate/0 . 1% sodium dodecyl sulfate at 65°C and exposed to X-ray film . Cells infected with eCFP+ or GFP+ viruses were trypsinized , washed in PBS and analysed directly for eGFP and eCFP fluorescence on a Fortessa flow cytometer ( BD Biosciences ) . For specific staining of lymph node cells , IgG Fc receptors were blocked by pre-incubation ( 30 min , 4°C ) with unlabelled rat anti-CD16/32 mAb , then stained for CD19 , CD69 , MHC class II or syndecan-4 with phycoerythrin-conjugated rat mAbs ( all from BD Biosciences ) and for surface immunoglobulin with Alexafluor 633-conjugated goat anti-mouse Ig ( H+L ) pAb ( Invitrogen ) . After washing , cells were analysed on a FACS Calibur using Cellquest ( BD Biosciences ) and Weasel ( Walter and Eliza Hall Institute of Medical Research ) . For flow cytometric sorting , spleen cells were pooled from pairs of mice and stained with phycoerythrin-conjugated rat anti-mouse CD19 mAb and Alexafluor 633-conjugated goat anti-mouse Ig ( H+L ) pAb . CD19+Ig+ cells were selected with a FACS Vantage ( BD Biosciences ) . The recovered cells were >98% pure . DCs were plated overnight onto poly-D-lysine-coated coverslips after 7 days of culture , then infected or not with MuHV-4 . After further culture , the cells were washed with PBS , fixed in 2% formaldehyde , permeabilized with 0 . 1% Triton-X-100 , blocked with 3% BSA and stained for actin with Alexafluor568-conjugated phalloidin ( Invitrogen ) , for phospho-tyrosine with mAb PY99 ( Santa Cruz Biotechnology ) plus Alexafluor568-conjugated goat anti-mouse pAb ( Invitrogen ) , and for Y925-phosphorylated focal adhesion kinase with ab38512 ( AbCam ) plus Alexafluor568-conjugated goat anti-rabbit pAb ( Invitrogen ) . EGFP fluorescence was visualized directly . After washing in PBS , the cells were mounted in Prolong Gold with DAPI ( Invitrogen ) and imaged with a Leica Confocal microscope . Lymph nodes were removed post-mortem , fixed ( 24 h , 4°C ) in 1% formaldehyde/10 mM sodium periodate/75 mM L-lysine , then equilibrated in 30% sucrose , and frozen in OCT mounting medium . 5 µm sections were blocked with 2% goat serum then stained for CD45R ( mAb RA3-6B2 , B cells ) or for CD11c ( mAb HL3 , DCs ) ( BD Biosciences ) plus Alexafluor568 or 488-conjugated goat anti-rat or anti-hamster IgG pAb ( Invitrogen ) . Infected cells were detected with rabbit anti-eGFP pAb ( Abcam ) plus Alexafluor488 or 568-conjugated goat anti-rabbit IgG pAb ( Invitrogen ) . The sections were mounted in Prolong Gold ( Invitrogen ) and imaged with a Leica Confocal microscope . | We detect invading viruses with dendritic cells and eliminate them with lymphocytes . A key interaction is lymphocyte activation by dendritic cells presenting viral antigens . Not all viruses can be eliminated , and some that persist deliberately colonize lymphocytes and dendritic cells , such that parasitism and host defence co-exist within the same sites . Once established , these infections are very hard to eliminate . Therefore to vaccinate against them we must determine how infection first occurs . Here we show that a gamma-herpesvirus relation of the Kaposi's Sarcoma-associated Herpesvirus and Epstein-Barr virus - B cell-tropic human pathogens that cause cancers - uses dendritic cells to reach and infect B lymphocytes . Dendritic cells were infected before B cells; viruses marked genetically in dendritic cells were recovered from B cells; and a virus unable to replicate in dendritic cells infected B cells poorly . Thus dendritic cells not only present viral antigens to lymphocytes , but can be exploited by evasive viruses to infect lymphocytes . Therefore targeting dendritic cell infection could be an effective means of vaccine-primed host defence . | [
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] | 2011 | Murid Herpesvirus-4 Exploits Dendritic Cells to Infect B Cells |
Aedes aegypti is the primary mosquito vector of dengue viruses ( DENV; serotypes 1–4 ) . Human-mosquito transmission cycles maintain DENV during epidemics but questions remain regarding how these viruses survive when human infections and vector abundance are minimal . Aedes mosquitoes can transmit DENV within the vector population through two alternate routes: vertical and venereal transmission ( VT and VNT , respectively ) . We tested the efficiency of VT and VNT in a genetically diverse laboratory ( GDLS ) strain of Ae . aegypti orally infected with DENV2 ( Jamaica 1409 ) . We examined F1 larvae from infected females generated during the first and second gonotrophic cycles ( E1 and E2 ) for viral envelope ( E ) antigen by amplifying virus in C6/36 cells and then performing an indirect immunofluorescence assay ( IFA ) . RT-PCR/nested PCR analyses confirmed DENV2 RNA in samples positive by IFA . We observed VT of virus to larvae and adult male progeny and VNT of virus to uninfected virgin females after mating with males that had acquired virus by the VT route . We detected no DENV2 in 30 pools ( 20 larvae/pool ) of F1 larvae following the first gonotrophic cycle , suggesting limited virus dissemination at 7 days post-infection . DENV2 was detected by IFA in 27 of 49 ( 55% ) and 35 of 51 ( 68 . 6% ) F1 larval pools ( 20 larvae/pool ) from infected E2 females that received a second blood meal without virus at 10 or 21 days post-infection ( E2-10d-F1 and E2-21-F1 ) , respectively . The minimum filial infection rates by IFA for E2-10d-F1 and E2-21d-F1 mosquitoes were 1:36 and 1:29 , respectively . The VNT rate from E2-10d-F1 males to virgin ( uninfected ) GDLS females was 31 . 6% ( 118 of 374 ) at 8 days post mating . Twenty one percent of VNT-infected females receiving a blood meal prior to mating had disseminated virus in their heads , suggesting a potential pathway for virus to re-enter the human-mosquito transmission cycle . This is the first report of VNT of DENV by male Ae . aegypti and the first demonstration of sexual transmission in Aedes by naturally infected males . Our results demonstrate the potential for VT and VNT of DENV in nature as mechanisms for virus maintenance during inter-epidemic periods .
Dengue viruses ( DENV ) include four serologically related but genetically distinct viruses , DENV1-4 ( Flavivirus; Flaviviridae ) . DENV are arthropod-borne viruses ( arboviruses ) transmitted between susceptible humans by the bite of infected Aedes spp . DENV and their mosquito vectors are present throughout the tropics causing an estimated 390 million human infections per year [1] . DEN disease outbreaks in Florida and Hawaii have heightened awareness and concern of DENV transmission in the USA [2–4] . Ae . aegypti is the most important mosquito vector during large DENV outbreaks in tropical and subtropical urban regions [3] . DENV epidemics occur when mosquitoes and susceptible humans are sufficiently plentiful to maintain the vector-human transmission cycle . Ae . aegypti take multiple blood-meals during each gonotrophic cycle [5–8] . Ingestion of multiple uninfected blood feedings boost a previous incipient infection , enhancing virus dissemination to secondary tissues and transmission [9] . This effect was also observed for Zika virus ( ZIKV ) in Aedes albopictus [10] . Vertical transmission ( VT ) from one generation to the next and venereal transmission ( VNT ) during mosquito mating are mechanisms of DENV transmission within Aedes populations [11–13] . The potential impact of VT and VNT in sustaining DENV in the vector population remains unclear . Given the large public health consequences of DENV infections globally , a determination of the role VT and VNT plays in DENV maintenance in mosquito populations could lead to a more complete understanding of DENV persistence in nature and new insights into DENV transmission dynamics . VT of arboviruses occurs by either transovarial transmission ( TOT ) , in which the virus infects germinal tissues of the female ( including oocytes ) [14] , and by trans-ovum transmission , which occurs at the time of fertilization or by virus contamination of the egg surface during oviposition [14 , 15] . VT mechanisms are not mutually exclusive . In this report , we define VT rate ( VTR ) as the number of infected females in a population that produce at least one infected offspring . The filial infection rate ( FIR ) is the proportion of infected progeny produced from infected parents , given that VT has occurred . The minimum filial infection rate ( MFIR ) is the total number of positive mosquito pools divided by total mosquitoes and the effective VTR ( eVTR ) is the average number of infected progeny per infected female ( VTR multiplied by the FIR ) [4 , 16 , 17] . Ae . aegypti typically become infected when the female ingests a blood meal from a viremic human . Ae . aegypti females lay their eggs within days of acquiring a blood meal . Ae . aegypti embryos can become dormant at the end of embryogenesis and remain viable inside the egg for up to six months [18] . When conditions become favorable for larval development , dormancy is interrupted and the eggs hatch to larvae [19 , 20] . Flaviviruses that persist in eggs can be transmitted transstadially from larvae to pupae to adults [13] . Infected females potentially generate hundreds of eggs in their lifetime providing opportunities for VT and VNT within the vector population . In early studies , Rosen detected minimal VT of DENV among Ae . aegypti [15 , 21] but more recent reports have provided consistent evidence of VT in field-collected Ae . aegypti [22–25] , although significant variations in the frequency of VT have been noted among strains of Ae . aegypti infected with different DENV serotypes and genotypes [12] . DENV have been detected in field-collected larvae [22] and field caught adult male mosquitoes [26–28] . The detection of DENV infected male mosquitoes suggests that males could play a role in the maintenance of DENV in nature [29] . Few studies have addressed the role of male mosquitoes in DENV transmission dynamics and only one study focused on VNT of DENV in Ae . albopictus [11] by determining VNT by males that were artificially injected with virus . In this context , we tested VT and VNT of DENV2 in a genetically diverse laboratory strain ( GDLS ) of Ae . aegypti derived from mosquito populations collected in Chiapas , Mexico [30] . Initially , we investigated whether VT in GDLS was more efficient in the first or second gonotrophic cycle ( E1 and E2 ) . We clearly observed that orally infected GDLS mosquitoes vertically transmitted DENV2 to their F1 progeny during the second gonotrophic cycle ( E2 ) and VT increased if a second blood meal was given 10 or 21 days post infection . Significantly , the MFIR determined by either RT-semi-nested-PCR ( RT-N-PCR ) or IFA was 8–10 fold higher than previously reported for flaviviruses [17 , 31] . We detected DENV2 in ovarian tissues and oocytes of infected females suggesting VT can occur by TOT . The present study also demonstrated that males acquiring DENV2 by the VT route were capable of transmitting the virus to un-infected virgin GDLS females during mating . Our results suggest that VT and VNT of DENV in mosquito populations are potential mechanisms for virus maintenance during inter-epidemic periods .
LLC-MK2 and C6/36 ( Ae . albopictus ) cells were cultured in modified Eagle’s medium ( MEM ) supplemented with 8% fetal bovine serum , L-glutamine , non-essential amino acids and penicillin/streptomycin and maintained at 37°C , 5% CO2 and 28°C , 5%CO2 , respectively . DENV2-JAM1409 strain [32] was used to infect fresh cultures of C6/36 cells to prepare infectious blood meals . DENV2-JAM1409 belongs to the American-Asian genotype [33 , 34] and was originally obtained from the Centers for Disease Control and Prevention ( CDC- Fort Collins , CO , USA ) . The virus has been routinely passaged ( > 25 times ) in C6/36 cell culture . Briefly , monolayers of C6/36 cells were exposed to DENV2 at a multiplicity of infection ( MOI ) of 0 . 01 and incubated at 28°C; 6 days later medium was replaced and infected cells and medium were harvested at 12–14 days to prepare the blood meal [35] . The Ae . aegypti GDLS strain was derived from a mixture of equal numbers of 10 geographically distinct collections made in 2008 from Chiapas , Mexico [30] and eggs were hatched and reared to adults ( 28°C/75-80% relative humidity ( RH ) ; photocycle of 16:8 L:D ) . To infect mosquitoes , groups of 100–150 one-week-old adult females were placed in 2 . 5 L cartons , deprived of sugar and water overnight and allowed to feed on artificial blood meals consisting of virus-infected C6/36 cell suspension ( 60% vol/vol ) , 40% ( vol/vol ) defibrinated sheep blood ( Colorado Serum Co . , Boulder , CO ) and 1 mM ATP [36] . Mosquitoes fed for 60 min with blood meals contained viral titers of 2 . 7 ± 2 × 106 PFU/ml . We selected engorged females , incubated them for up to 17 days ( 28°C and 75–80% RH ) and then analyzed to determine the infection rate . Virus in the blood meal was quantified by plaque titration on LLC-MK2 cells as previously described [37] . Tissue from whole 4th instar larvae , adult heads , or the male or female reproductive tracts were homogenized , filtered ( Acrodisc Syringe filters with 0 . 22 μm HT Tuffryn membrane ) and filtrates used to infect 12-well plates containing C6/36 cell monolayers on round cover slips . A positive control of homogenate from Ae . aegypti intrathoracically injected with DENV2 and a negative control ( uninfected mosquito ) were included in each assay . The cells were maintained at 28°C , 5% CO2 for 7 days and DENV2 E antigen detected using an IFA with 3H5 mAb as previously described [38–40] . Trizol ( Invitrogen , Carlsbad , California ) was used for RNA extraction from mosquito homogenates following the manufacturer’s recommendations . Detection of viral RNA was performed using a semi-nested PCR described by Gunther et al . [41] with the following modifications: Primers for the NS3 gene of DENV2 [41 , 42] were used to generate a 362 bp amplicon . The cDNA was amplified in a second step ( nested PCR ) , using 3 μl of DNA from the RT-PCR reaction in the presence of forward primer ( 5’AATTGTCGACAGAAAAGGAAA 3’ ) and reverse primer ( 5’ GGCTGGGGTTTGGTATC 3’ ) . The reaction contained 2X PCR master mix ( Promega M750B ) , followed by 30 cycles of 94°C for 30 sec , 55°C for 1 min , 72°C for 1 min , with an additional extension step of 72°C for 10 min and held at 4°C [43] . Female GDLS mosquitoes were fed a blood meal containing >106 pfu/mL of DENV2 and held in a cage containing an oviposition cup . We analyzed E1 eggs ( E1-F1 ) for VTR . After the first oviposition cycle , females were offered a second blood meal with no virus ( BMnV ) at either 7 , 10 or 21 days pi ( E2-7d , E2-10d and E2-21d respectively ) to support E2 egg production ( E2-F1 ) . Blood-engorged females were maintained at 28°C and 80%RH in the insectary as previously described . Non-blood fed females were removed after the second blood meal and stored at -80°C . Virus titrations determined DENV2 infection rates from the initial infectious blood meal . Eggs from each gonotrophic cycle were collected for VT analyses . To evaluate TOT , E1 and E2 eggs from DENV2-infected females were surface-sterilized by soaking in 0 . 05% sodium hypochlorite for 5–10 min , then for 1 min ( 3 times ) in 70% ethyl alcohol , and finally washed with distilled water three times [17] before hatching . Fourth instar larval progeny ( F1 ) were collected in pools of 20 and stored at -80°C . Each pool of larvae was homogenized and filtered prior to infecting C6/36 cell monolayers grown on coverslips in 12- well plates . Cells were analyzed by IFA for DENV2 E antigen as previously described . Total RNA was extracted from the pellet to conduct RT-N-PCR for confirmatory analysis of viral RNA . GDLS females were offered a blood meal containing ~106 pfu/mL of DENV2 JAM1409 and a second BMnV at 10 dpi . GDLS females engorged with a second BMnV were transferred and held individually in 50-ml centrifuge tubes lined with a dry strip of paper towel . Distilled water was added one day after blood feeding to support egg production . E2 eggs ( E2-10d-F1 ) from each female were stored in individual plastic bags . Each female was stored at -80°C in a numbered vial prior to testing for infection status . Ae . aegypti E2 eggs from each DENV2 positive female were hatched to develop to F1 adults . Males were separated at the pupal stage and adult females stored at -80°C . FIR was determined later . Mating . Individual E2-10d-F1 male progeny ( 5–6 days post-eclosion ) transferred into small cardboard cartons were separated into two groups ( Group I and II ) . Two sugar-fed uninfected , virgin females were added to Group I cartons prior to mating . Two uninfected , virgin females that had received a BMnV three days previously were added to Group II cartons prior to mating . Following a 48-h mating period , males were removed from the cartons and their reproductive tracts ( testes , accessory glands and seminal vesicle ) obtained by severing the last two abdominal segments and stored at -80°C prior to determining DENV2 infection status . The mated females were incubated at 28°C in Group I and II cartons until 8 days post mating . Thereafter , female reproductive tracts ( ovaries and spermathecae ) were dissected for analysis . Additionally , female mosquito carcasses ( without reproductive tracts ) were stored at -80°C . Male and female reproductive tissues were homogenized , filtered and filtrates used to infect C6/36 cells to detect DENV2 following the same procedures used for virus detection in larvae described above . We then tested by RT-N-PCR the heads of females ( already shown to be virus positive by IFA in their reproductive tract ) to observe DENV2 dissemination . The average proportion of infected female reproductive tracts , female carcasses , and male reproductive tracts were calculated by averaging the number infected divided by the total tested . The proportion of infected males that transferred virus to females was determined for each pairing . All data were analyzed with GraphPad prism software ( version 5 . 0 , La Jolla , CA , USA ) was used to test for significant differences ( P < 0 . 05 ) in TOT and FIR ( by Fisher’s exact test ) and to analyze the correlation between female parent titer and proportion on infected progeny ( Pearson correlation ) . Analysis of variance ( ANOVA ) was used to determine the statistical significance of acquiring a second blood meal on virus replication .
Since vector competence among Ae . aegypti populations varies according to the infecting DENV serotype and genotype and the genotype of mosquitoes , we initially evaluated the competence of Ae . aegypti ( GDLS ) strain for DENV2-JAM1409 . Vector competence was analyzed by offering mosquitoes a blood meal containing 1×106 to 4 ×106 PFU/mL DENV2-JAM1409 . We selected and assayed engorged mosquitoes for DENV2 at 7 , 10 , and 17 days post-infection ( dpi ) by plaque assay . The prevalence of DENV2 in Ae . aegypti ( GDLS ) was calculated as the proportion of mosquitoes infected among total mosquitoes receiving an infectious blood meal . The prevalence of DENV2 in GDLS females was approximately 65% ( viral titers: 2 +/- 0 . 72 ×103 , 7 +/- 1 . 3 ×103 and 1 . 1 +/- 0 . 25 ×104 PFU/mL ( per mosquito ) at 7 , 10 and 17 dpi , respectively ( Fig 1 ) . Midgut infection rates ( MIR ) and disseminated infection rates ( DIR ) were determined by detection of DENV-2 E antigen in midgut and head tissues ( sampled pairwise ) at 14 and 17 dpi . MIRs were 78 . 3% ( 18/23 ) , 76 . 7% ( 23/30 ) and 75 . 7% ( 25/33 ) at 7 , 14 and 17 dpi , respectively and DIRs were 63 . 3% ( 19/30 ) at 14 dpi and 81 . 8% ( 27/33 ) at 17dpi . DIRs were not calculated for the 7-day time point due to our previous studies that showed minimal dissemination at that time [44] . The MIRs and DIRs indicated that GDLS mosquitoes were highly susceptible and competent for DENV2-JAM1409 . Per os infection of Ae . aegypti with DENV2-JAM1409 has been described spatially and temporally in several Ae . aegypti populations [7 , 45] . However , in those studies mosquitoes used a single infectious blood meal . In nature , Ae . aegypti females feed frequently and may ingest 2 to 3 blood meals during a single gonotrophic cycle [8] . Therefore , we determined DENV2 titers in mosquitoes receiving a second BMnV . The DENV2 titers at 17 dpi were significantly higher in GDLS females receiving a second BMnV at 10 dpi than GDLS females receiving a single , infectious blood meal ( T test , P<0 . 0133 . r = 0 . 0421 ) ( Fig 1 ) however , no difference was observed in either prevalence or DIRs in the two groups . Several studies have shown that offspring produced during the second or later gonotrophic cycles display higher VT [13 , 46 , 47] . These observations may be linked to increased dissemination to the ovaries over time and it has been postulated that expanded parous ovaries , with stretched and squeezed follicular epithelial cells facilitate higher viral infection rates of oocytes , contributing to increased VT in subsequent cycles [48 , 49] . With this in mind , we tested the presence of the virus in the ovaries in DENV2 infected female mosquitoes . After the first oviposition cycle , females were separated into two groups . Group I females did not receive a second BMnV , Group II females were offered a second BMnV at 10 dpi and allowed to oviposit autogenously produced eggs . At 17 dpi , ovaries from both groups were dissected in PBS and fixed in 4% paraformaldehyde ( Electron Microscopy , Hatfield , PA ) for at least 4 h . DENV2 E antigen was detected using IFA with 3H5 mAb as previously described [38–40] . DENV2 antigen was detected in ovaries and oviducts in both groups at 17 dpi ( Fig 2A ) . IFA staining was less intense in ovaries of Group I females than in Group II females . We observed infection of a small number of oocytes in ovaries of Group II females ( Fig 2B ) . Oocyte infections demonstrated the potential for TOT , a VT pathway more efficient than trans-ovum transmission [50] . No infection was detected in ovaries from females of each group at 10 dpi suggesting VT by TOT is dependent on time and the number of blood meals ingested . We tested whether VT in GDLS was more efficient in the first or second gonotrophic cycle and if VT efficiency depended on the interplay between gonotrophic cycle and viral infection dynamics . Following oviposition , we collected eggs from each female . We then determined the infection status of all mothers but used only the eggs of virus positive females . The progeny were designated E1-F1 ( Fig 3A ) . Infected GDLS females were given a second BMnV at either 7 , 10 or 21 dpi to support E2 egg production and generate E2-F1 offspring . Eggs from the second cycle were collected and designated E2-7d-F1 , E2-10d-F1 and E2-21d-F1 depending on the timing of the BMnV ( Fig 3A ) . Since VTR mean values were not significantly different among three replicates for each designated collection , the data were analyzed as a single experiment . DENV2 was not detected by IFA in 30 pools of larvae analyzed from the E1 progeny ( infection rate = 0; 95% CI = 0 . 0–6 . 01 ) ( Fig 3B ) . IFA readily detected VT of DENV2 in GDLS E2 progeny . For E2-7d mosquitoes , 23 of 119 ( 19 . 3% ) pools were positive for DENV2 . VTR increased when infected females received a second BMnV at 10 or 21 dpi with 55% ( 27 of 49 ) of larvae pools positive for E2-10d and 68 . 6% ( 35 of 51 ) positive for E2-21d . ( Fig 3B ) . However , there was no significant difference in DENV2 positivity between pools of E2-10d and E2-21d ( P = 0 . 22 ) larvae . A significant difference was observed between VT of E2-7d and E2-10d and between E2-7d and E2-21d ( P<0 . 0001 ) . The VTR and MFIR from larvae pools detected by RT-N-PCR were similar to results obtained by IFA ( Fig 3B ) . The sequence of the amplicon confirmed that the DENV2 strain detected in E2-F1 larvae was the same as the one used to infect the parental female . Since we observed that GDLS mosquitoes infected with DENV2 vertically transmitted virus to larvae , we determined if F1 adult males were infected and if infected could , in turn , venereally transmit virus to virgin females . We tested virgin F1 male progeny arising from infected mothers to quantify FIR , VTR and VNT . GDLS females were given a blood meal containing >106 pfu/mL of DENV2 JAM1409 and a 2nd BMnV at 10 dpi . Mean DENV2 titers for infected mothers were 2 . 3 ± 6 . 5 ×102 PFU/ml in replicate1 ( R1 ) , 9 ± 4 . 9 × 102 PFU/ml in replicate 2 ( R2 ) and 8 . 3 ± 1 . 3 × 103 PFU/ml in replicate 3 ( R3 ) . E2 eggs from individual females positive for DENV2 were hatched to rear F1 adults . One 6-day-old male progeny ( E2-10d-F1 ) mated with two uninfected , unfed , virgin females or virgin females that received a BMnV 3 days prior to mating . After mating , the male reproductive tracts ( testes , seminal vesicle and accessory glands ) from E2-10d-F1 were screened by IFA for DENV2 E antigen . We detected viral antigen in the testes and peripheral tissue of the accessory glands ( Table 1 , data R1; Fig 4 ) . Dissected male reproductive tract homogenates were used to infect C6/36 cells . DENV2 E antigen was detected by IFA in cultured cells ( Table 1 , R2 and R3 ) . The FIRs in F1 ( E2-10d-F1 ) were 30 , 27 and 30 . 5% for R1 , R2 and R3; VTR was 48% , 57% and 52 . 4% and eVTR were 14 . 4% , 15 . 4% and 15 . 6% for R1 , R2 and R3 , respectively ( Table 1 ) . No statistically significant difference was observed in VTR between replicates ( Fisher’s exact test , P = 0 . 144 ) . No significant correlation was detected between DENV2 titer from the female parent and the proportion of progeny that were positive for virus among the replicates ( Pearson correlation , r = 0 . 049 , P = 0 . 804; r = 0 . 267 , P = 0 . 176; r = 0 . 013 , P = 0 . 933 , for R1 , R2 and R3 respectively ) ( Fig 5 ) . DENV2 was not detected in E2-10d progeny from uninfected females . These results confirm VT to male progeny and suggest the potential for VNT of DENV2 from males to females during mating . We tested whether enhancement of VNT of DENV2 occurred if an uninfected female had a blood meal prior to mating and used the method outlined in Fig 6A . Other studies have observed that male Ae . albopictus experimentally infected with DENV 1 , 2 , 3 , or 4 sexually transmitted virus to females [11] and VNT was enhanced if the females had taken a blood meal 2 to 7 days prior to mating [11 , 51] . It also has been reported that uninfected female mosquitoes that were given blood meals 3–4 days prior to mating were more likely to develop systemic infections after mating with infected males than females that had never received a blood meal [51] . VT infected male mosquitoes ( GDLS ) were mated to either unfed , non-infected , virgin GDLS females or GDLS females that had received a BMnV three days prior to mating . After mating , the males were separated and infection rates of their reproductive tracts determined 8 days later by IFA . The infection rate of the males was 28 . 7% ( 140/488 ) . We mated all F1 males with females but only the females that mated with DENV2 positive males were used to determine VNT rate . Female reproductive tracts were collected and infection rates determined by IFA and RT-N-PCR . The VNT rate was 31 . 6% ( 118 of 374 ) in blood fed females and 31 . 7% ( 102 of 322 ) in non-blood fed females . Data were analyzed as a single experiment since VNT rates among three replicates were not significantly different ( blood fed , P = 0 . 16; non-blood fed P = 0 . 06; Fig 6B ) . VNT to blood fed and non-blood fed GDLS females were not significantly different ( χ2< 0 . 001 , df = 1 , P = 0 . 97 Fisher’s exact test P = 1 . 00 ) . Additional VNT data were obtained using uninfected virgin males that had been intrathoracically inoculated with DENV2-JAM1409 ( 100 pfu ) 14 days prior to mating . The infection rates in the reproductive tract of males were determined ( RT-N-PCR and IFA ) before mating by randomly sampling 20 mosquitoes at 12 dpi . DENV2 E antigen was detected in the testes and peripheral tissue of the accessory glands in 100% of the injected mosquitoes ( Fig 4 ) . These results were similar to those reported in ultrastructural studies of intrathoracically infected male mosquitoes in which DENV2 was detected in the testes , seminal vesicle and accessory glands [52] . However , no virus was found in the germ cells ( spermatogonia , spermatocytes , spermatid , spermatozoa ) . The mechanism of sexual transmission of DENV2 from infected male to female mosquitoes remains unclear . Once DENV2 was confirmed within reproductive tract of males , the remaining mosquitoes were allowed to mate following the same protocol previously described . Female reproductive tracts were collected 8 d after mating and infection rates were determined with RT-N-PCR and IFA . Forty three percent ( 12/28 ) of females were positive for DENV2 by IFA . VNT rates for females receiving a BMnV prior to mating and those receiving no BMnV were not significantly different ( P = 0 . 20 ) . The VNT rate was slightly higher using the inoculated males although not significantly different from VNT rates using males that had acquired their infection by the VT route and regardless of the mates BMnV feeding status ( bloodfed , P = 0 . 216; non-bloodfed P = 0 . 154 ) . The data demonstrate that DENV2 vertically or artificially infected males were capable of transmitting virus efficiently to females during mating . To determine if virus acquired by VNT could disseminate , we tested heads of BMnV and non-BMnV females by RT-N-PCR . We did not test experimental transmission of the virus in saliva , but we did detect the presence of virus in the head of female mosquitoes that had acquired virus by VNT . Twenty one percent ( 8 of 38 ) females receiving a BMnV prior to mating had DENV2 in their heads and 18 . 8% ( 6 of 32 ) of the non-BMnV female heads were positive ( Fisher’s exact test P = 1 . 00 ) . These data suggest that VNT-infections may disseminate to female salivary glands , but further work is required to show transmission .
Arboviruses are maintained by horizontal transmission ( HT ) between arthropod vectors and vertebrate hosts in nature . Arboviruses potentially can be transmitted vertically in the vector population from mother to offspring , which is a possible maintenance mechanism during adverse conditions for HT . Dry seasons in tropical areas , cold seasons in temperate regions , or insecticide spraying campaigns can drastically reduce vector density and thus opportunities for HT [47] . In addition , arbovirus infections in vertebrates usually result in long-lasting protective immunity so that high levels of herd immunity will minimize HT following epidemics . Several hypotheses have been suggested to explain the maintenance of arboviruses during inter-epidemic periods and include virus re-introduction and amplification of the virus in unknown host species ( i . e . , reservoirs ) [53] . VT and VNT are alternate transmission mechanisms DENVs might use to maintain themselves in a vector population independent of feeding on viremic humans [29] . Although male mosquitoes are not hematophagous , they can acquire virus by VT from an infected female parent . In experimental studies , Aedes male mosquitoes infected by virus injection into the hemoceol transmitted virus to non-infected adult females during mating leading to infected F1 progeny [54 , 55] . VNT of arboviruses by male mosquitoes has been demonstrated in mosquito vectors of bunyaviruses [54] , alphaviruses [56 , 57] , flaviviruses [58] and rhabdoviruses [59] . . VNT has been observed in Ae . albopictus by intrathoracically injecting males with DENV1 prior to mating [11] . However , no previous studies have reported VNT by male progeny of Ae . aegypti females that were naturally infected ( per os ) with DENV . A relatively small number of stably infected females could maintain virus prevalence at a constant level if germarium infection occurred , assuming that any detrimental effects of the infection ( e . g . , longevity , fecundity , and development ) are balanced by horizontal transmission [60 , 61] . Stabilized infections with California encephalitis serogroup viruses ( Orthobunyavirus: Bunyaviridae ) have been demonstrated in several Aedes mosquito species [60 , 61] . Our studies show that DENV2 infection of GDLS Ae . aegypti mosquitoes can persist by VT and VNT . The infection rates obtained with orally infected females indicate that GDLS mosquitoes are highly susceptible to DENV2 . GDLS show an increased DENV2 titer when mosquitoes take a second BMnV . This study used two techniques to show DENV2 VT in GDLS Ae . aegypti: 1 ) RT-N-PCR for amplification of viral RNA and 2 ) IFA detection of DENV2 antigen . Infectious DENV demonstrated by IFA in mosquito C6/36 cells exposed to mosquito tissue homogenates . The latter assay is considered the gold standard of evidence for VT . Although PCR-based assays are rapid and have higher sensitivity than virus isolation , they are prone to contamination or amplification of virus sequence incorporated into the mosquito genome . Consequently , resulting data should be interpreted cautiously and outcomes confirmed by virus isolation or IFA . In this report , we used RT-N-PCR assays to confirm IFA results for the presence DENV2 . Most previous studies demonstrated VT after intrathoracic injection of the virus [62 , 63] . The observed VTR in the GDLS strain could be due to a higher efficiency of initially infecting GDLS females with artificial blood meals containing biologically relevant virus titers . In our study , we initially infected mosquitoes via a blood meal and DENV2 VT was analyzed after the second gonotrophic cycle ( E2-7d , E2-10d , and E2-21d ) . E2-F1 mosquitoes were analyzed as a counter-weight to previous studies that showed little to no VT occurring after the first oviposition cycle . Lack of VT from E1 females to F1 progeny could be due to poor virus dissemination to reproductive tissues before the first egg batch is produced and laid , and/or decreased permeability of virus to nulliparous ovaries during oogenesis [48] . These findings were consistent with earlier reports for other flaviviruses ( Zika virus ( ZIKV ) , yellow fever virus ( YFV ) , and West Nile virus ( WNV ) , which showed infected female mosquitoes vertically transmit virus after the second oviposition cycle [31 , 64 , 65] . VTR were higher when infected females received a second BMnV at 10 or 21 dpi . However , a significant difference in VTR was observed even when infected females received a second BMnV at 7 dpi ( E2-7d ) . These observations were similar to reports for La Crosse virus [66] and WNV [64] where VT occurred only during the second or later ovarian cycles . Previous VT studies conducted with YFV and DENV1 reported that the VT actually decreased with successive ovarian cycle of infected mosquitoes [17 , 21] . In contrast , Diallo et al . reported that females vertically transmitted DENVs during second and third ovarian cycles , with the rates being highest in the third cycle [67] . As previously stated , VT is probably rare in the first ovarian cycle probably because eggs are oviposited 3–8 days post infection before the virus disseminates to the reproductive tract . Delayed oviposition by Cx . pipiens females of 11–14 dpi and 25 dpi resulted in VT of St . Louis encephalitis virus during the first ovarian cycle [68] . In support , we observed DENV2 in the ovaries , oviducts and oocytes of E1 females only at later times post-infection ( 17 dpi ) . Rearing temperatures , passage level of virus , viral strain and mosquito strain/species have all been reported to influence the efficiency of VT of flaviviruses [63 , 68–70] . In our study , we used DENV2 JAM1409 , a viral strain that is not a natural match to the geographic origin of our mosquitoes and passaged in cell culture multiple times . Future studies of VT and VNT should explore closer geographic matches of low generation mosquito and viral strains . Overall , Aedes mosquitoes display higher VTR than Culex mosquitoes . Aedes eggs are generally more resistant to desiccation than Culex eggs [71] , which may confer a selective advantage to vertically transmitted viruses . In addition , Aedes mosquitoes had higher VTRs in arid climatic conditions compared to equatorial or warm temperate climatic conditions [50] . This supports the hypothesis that VT could be a maintenance mechanism when conditions are adverse for HT , in agreement with earlier observations [47 , 72 , 73] . It had been reported that eVTR measured in immature developmental stages were higher than in the corresponding adults [13 , 69] . Aedes aegypti larvae vertically infected with YFV [17] , Kunjin virus , and Japanese encephalitis virus [74] appeared to be slower in their development and vertically infected larvae may also suffer lower survival , hence leading to lower infection prevalence in adults [13] . Previous experimental infections under laboratory conditions revealed DENV VTR of 1–4% [21 , 75 , 76] . Detection of VT in nature is rare , due primarily to the low frequency of adult Ae . aegypti females infected with DENV . However , the occurrence of VT in nature has been documented by detection of DENVs in adult males [23 , 26] . Recently others have reported VT of ZIKV in Ae . aegypti and Ae . albopictus [65] . They evaluated VT in larval pools of perorally infected Ae . aegypti and Ae . albopictus adult female mosquitoes or in adults mosquitoes intrathoracically injected with ZIKV [31] . Although VTR in those experimental studies were low , the efficiency may be sufficient to allow ZIKV ( and other flaviviruses ) to persist within the vector population in eggs during hot dry periods or cold weather , when adult vectors are absent or in low numbers . A mathematical model that investigated parameters conditioning natural transmission and persistence of DENVs suggested that observed VTR in field settings are insufficient to maintain the virus in nature [4] . The modeling further suggested that the eVTR must be at least 20–30% to allow virus maintenance in nature in the absence of human-mediated amplification of DENVs . In our studies , MFIR from E2-10d mothers was 1:36 by IFA and RT-N-PCR , values substantially higher than reported for ZIKV ( 1:290;[31] ) , and YFV ( 1:596;[17] ) . The eVTR for E2-10d-F1 adult , infected mosquitos was 14–15% ( Table 1 ) , reflecting VT efficiencies that approach those suggested in the mathematical modeling study for DENV maintenance in nature [4] . Experimental studies of flavivirus VT in mosquitoes have variable results and indicate multiple factors can affect the frequency of VT and FIRs . These factors include mosquito species and geographic strain , mosquito age and mortality rate , virus genotype , persistence of virus during transstadial transitions , virus assay method , larval rearing temperature , interval between initial infection and second blood meal , and the ovarian cycle [31 , 46 , 75 , 77] . These factors could certainly lower the eVTR of Ae . aegypti in nature and their contributions to eVTR in a given vector population should be further studied . Nevertheless Ae . aegypti biology and virus-vector interactions may enhance their eVTR . These positive factors include the mosquito’s acquisition of blood meals every few days and their demonstrated ability to go through multiple ovarian cycles . DENV maintenance in the vector population could be enhanced further by virus persistence in diapaused embryoes , DENV infection of oocytes and ovaries after the females acquire additional bloodmeals , and the relative efficiency of VNT . We hypothesize these attributes may counter the negative factors and allow the virus to persist in the vector population from one season to the next . Overall , VT of DENVs in Ae . aegypti may be underestimated as a potential force driving the epidemiology of DENV infection . It is possible that infected males could maintain the virus at a low threshold without causing outbreaks in human populations , and initiate new infections by VT and VNT within the vector population [56] . We did not observe any significant differences in the efficiency of VNT between mosquitos fed ( BMnV ) and not fed prior to mating as previously reported in Ae . albopictus [11] . We did not observe any difference in DIR in heads of fed versus non-fed mosquitoes . In our study , evidence for VNT in Ae . aegypti was further supported by microscopy studies of DENV2 infected males by detecting viral antigen the testes , seminal vesicle and accessory glands [52] . Our results clearly demonstrate that infected GDLS female mosquitoes can vertically transmit DENV2 to their progeny and support our hypothesis that DENV VT efficiency largely depends on the interplay between gonotrophic cycle and viral infection dynamics . Offspring produced during the second or later gonotrophic cycles display higher VTR than offspring produced during the first gonotrophic cycle . Vertically infected males that have DENV in their reproductive tracts can sexually transmit DENV to non-infected virgin GDLS females during mating . This study demonstrates the potential for VNT of DENV by male Ae . aegypti and the first study to naturally infect adult male mosquitoes via VT and then show VNT of virus to virgin , uninfected females . Future studies are required to determine if the disseminated infection in females infected during mating leads to epidemiologically relevant transmission of DENV to progeny or to humans in nature . Importantly , Ae . aegypti infections with flaviviruses constitute a tractable experimental system to further understand the genetics , biology and epidemiological consequences of VT and VNT . | Aedes aegypti mosquitoes are the primary vectors of dengue viruses ( DENV ) and other medically important arthropod-borne viruses ( yellow fever , Zika , chikungunya ) . DENVs replicate in Ae . aegypti and humans in a transmission cycle that maintains the virus in nature . However , adverse conditions ( i . e . dry seasons and low temperatures , vector populations , and host susceptibility ) can suppress the transmission cycle . How does the virus survive in nature when the mosquito-human epidemic cycle subsides ? Alternative DENV transmission routes can allow the virus to remain in the vector population . DENV can vertically transmit from mother to offspring and venereally transmit from infected male offspring to females during mating . In this report , we show that DENV2 efficiently pass from one generation to the next after the infected female mosquito acquires a second blood meal . Furthermore , infected male progeny arising from vertical transmission can then infect females during mating . Vertical transmission of DENV2 , possibly in combination with venereal transmission , can potentially maintain DENV2 for sufficient time for the vector-human transmission cycle to resume . | [
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] | 2018 | Demonstration of efficient vertical and venereal transmission of dengue virus type-2 in a genetically diverse laboratory strain of Aedes aegypti |
Adaptation to novel environments is often associated with changes in gene regulation . Nevertheless , few studies have been able both to identify the genetic basis of changes in regulation and to demonstrate why these changes are beneficial . To this end , we have focused on understanding both how and why the lactose utilization network has evolved in replicate populations of Escherichia coli . We found that lac operon regulation became strikingly variable , including changes in the mode of environmental response ( bimodal , graded , and constitutive ) , sensitivity to inducer concentration , and maximum expression level . In addition , some classes of regulatory change were enriched in specific selective environments . Sequencing of evolved clones , combined with reconstruction of individual mutations in the ancestral background , identified mutations within the lac operon that recapitulate many of the evolved regulatory changes . These mutations conferred fitness benefits in environments containing lactose , indicating that the regulatory changes are adaptive . The same mutations conferred different fitness effects when present in an evolved clone , indicating that interactions between the lac operon and other evolved mutations also contribute to fitness . Similarly , changes in lac regulation not explained by lac operon mutations also point to important interactions with other evolved mutations . Together these results underline how dynamic regulatory interactions can be , in this case evolving through mutations both within and external to the canonical lactose utilization network .
Changes in gene regulation are an important and common cause of adaptation . Support for this comes from bioinformatic evidence that changes in regulatory elements are associated with presumably adaptive phenotypic changes ( reviewed in [1] , [2] ) , comparative experimental studies [3] and from experimental evolution studies , which often find regulatory changes occurring during adaptation to novel environments [4]–[12] . Indeed , in some of these cases direct links have been established between regulatory changes and adaptation [6]–[8] , [10] . These experiments directly demonstrate that small and local regulatory changes can significantly contribute to adaptation . In most cases , however , the physiological basis for selection of regulatory changes is unknown . Previously , we described the evolution of populations of Escherichia coli in defined environments that differed only in the number and presentation of the limiting resource [13] . Populations were evolved in environments supplemented with a single limiting resource or combinations of two limiting resources either presented together or fluctuating daily . These populations adapted to the environments in which they were evolved and this adaptation was , at least to some extent , environment-specific [13] . Here , we focus on a subset of 24 populations that evolved in environments supplemented with glucose and/or lactose and examine changes in the regulation of the lac operon in these populations . Several attributes make the lac operon a good candidate in which to observe regulatory changes and relate them to their physiological and fitness effects . First , the costs and benefits of lac operon expression are environmentally dependent . Expression of the lac operon is necessary for utilization of lactose for growth , but expression in the absence of lactose can impose a significant cost [14]–[16] . Second , the molecular components of the lactose utilization network are well characterized and their activity can be measured in vivo . The ability to assay changes in the lac regulatory network ‘output’ provides a means to identify and test activity of evolved regulatory changes . Third , the lac operon has been the subject of much theoretical work , leading to the development of mathematical models to explain important features of lac operon physiology [14] , [17]–[22] and evolution [16] , [23] , [24] . Fourth , the utility of the lac operon for examining evolution of regulatory changes has been demonstrated experimentally . For example , lac operon constitutive [25] , [26] , loss of function [14] and duplication [11] mutants have been recovered following growth in different selective environments , demonstrating that lac operon regulation is evolutionarily flexible and can be a target of selection . It has even been possible to predict the evolution of regulatory changes on the basis of their expected fitness effects . Dekel and Alon ( 2005 ) used a cost-benefit analysis to predict the optimum expression level of the lac operon in different inducer concentration environments . They found that populations selected in environments containing a high level of gratuitous inducer , but various concentrations of lactose , generally evolved to regulate expression of the lac operon to the predicted level . In this study , we examine changes in lac operon regulation associated with selection in environments differing in the presentation of its natural substrate and inducer , lactose , and repressor , glucose . In addition to quantitatively characterizing the changes that have occurred , we examine the genetic and demographic basis for selection of different modes of lac regulation . We find that regulatory changes in the lac operon evolved in many replicate populations selected in environments containing lactose . Much , but not all , of these changes were due to mutations in the LacI repressor or its major operator binding site within the lac promoter . By themselves , these mutations conferred significant fitness benefits in all of the evolution environments that contained lactose . We also present evidence for interactions between lac mutations and mutations in genes outside of the canonical lac utilization network and show that these interactions impact lac operon regulation and fitness . Finally , operator and repressor mutations fixed at different frequencies in different selective environments , although the selective basis of this is currently not known .
Previously we reported propagation of 24 replicate populations of E . coli B REL606 for 2000 generations in one of four environments that differed only in the concentration and/or presentation of glucose and lactose [13] . Environments used were glucose ( Glu ) , lactose ( Lac ) , glucose and lactose presented simultaneously ( G+L ) or alternating daily between glucose and lactose ( G/L ) . To determine whether regulation of the lac operon had changed during the evolution of our experimental populations , we screened ≥1000 clones from each population on LacZ indicator plates ( see Materials and Methods ) . All six Glu populations had LacZ phenotypes that were indistinguishable from the ancestor . By contrast , in all other evolution environments at least some replicate populations showed clear changes in LacZ activity ( Lac , 3 of 6; G+L , 6 of 6; G/L , 6 of 6 ) ( Table 1 , Figure 1A ) . Some of these populations also had within-population variation in LacZ activity and colony morphology . To facilitate molecular and physiological studies of these changes we identified and isolated 46 clones ( at least one from each evolved population ) that encompassed the range of LacZ activity and morphology types present across all populations ( Table 1 ) . These clones were used in all subsequent analyses . To characterize evolved changes in lac operon regulation , we used a dual fluorescence reporter system to independently quantify with single cell resolution the activity of the major transcriptional regulators of the lac operon , LacI and CRP . LacI and CRP bind the lac promoter at different locations , repressing and activating transcription of the lac operon , respectively ( Figure 1B ) . The ancestor and each of the 46 evolved clones were transformed with Plac-GFP ( LacI and CRP reporter ) and PlacO ( - ) -RFP ( CRP reporter ) constructs . For each strain we used flow cytometry to measure the population distribution of steady-state GFP and RFP expression over a range of thiomethyl-galactoside ( TMG ) concentrations ( TMG is a non-metabolizable inducer of the lac operon ) . The Plac-GFP reporter captured several key features of lac regulation . The inducer response of the ancestor is ultra-sensitive , showing a sharp transition from low to high expression states as a function of TMG concentration and is bimodal , with populations showing a mix of non-induced and fully induced cells at intermediate levels of TMG [19] , [27] , [28] ( Figure 1C , Figure 2 ) . The CRP-only reporter ( PlacO ( - ) -RFP ) shows a unimodal distribution with a constant mean over the range of TMG concentrations , confirming that CRP activity of the ancestor is independent of LacI activity and lac operon expression state ( Figure 1C ) . To facilitate comparisons between the ancestral and evolved genotypes , we quantified three aspects of lac regulation: the TMG concentration required for half maximal population expression ( TMG½ Max ) , the range of TMG concentrations causing a bimodal population response and the fully induced ( maximum ) steady state level of lac expression . For the ancestor , TMG½ Max is 25 µM , the range of bimodality is between 15–30 µM TMG , and the level of Plac-GFP at full induction ( 100 µM TMG ) is ∼84 RFU ( Figure 2 ) . Evolved changes in the inducer response profiles were common and fell into three broad classes ( Figure 2 ) . 1 ) Constitutive: operationally defined as mean Plac-GFP expression varying by less than 2-fold across the range of tested TMG concentrations . Constitutive clones were observed in at least some populations of each of the treatments containing lactose ( Lac , G+L and G/L ) , but were not observed in any of the Glu evolved populations . 2 ) Lower threshold/graded response: increased sensitivity to inducer with TMG½ Max values ranging from 2 µM to 8 µM . In addition , all but one clone with a lower induction threshold ( G+L2-1 ) also evolved a graded response , with a unimodal distribution of Plac-GFP expression that increased continuously as a function of TMG concentration . 3 ) Bimodal: bimodal induction response differing from the ancestor by generally being less sensitive to the inducer , with TMG½ Max values ranging from 25 µM to 50 µM TMG ( Figure 2 ) . Higher inducer thresholds were observed for all clones evolved in Glu as well as some clones evolved in Lac and G/L environments . Representative inducer response curves of each class are shown in Figure 3A and the distribution of response types across environments is shown in Figure 3B . Interestingly , clones with a lower threshold response were found exclusively in populations evolved in the G+L environment , fixing in four of the six populations . This pattern is unlikely to occur by chance ( Fisher's exact test omitting the polymorphic G+L population , P = 0 . 002 ) . By contrast , populations evolved in the G/L environment were almost exclusively composed of clones with constitutive lac expression and populations evolved in Lac had either bimodal or constitutive regulation . All clones evolved in Glu showed a bimodal response type ( Figure 2 , Figure 3B ) . The level of Plac-GFP expression at maximum induction ( 100 µM TMG ) was higher than the ancestor in 36 of the 46 evolved clones ( Figure 2 ) . To examine this observation in more detail , we repeated our measurements of Plac-GFP expression in each evolved clone , but this time only in the presence of 100 µM TMG , which enabled us to include all clones in duplicate in the same experimental block ( Figure 4A ) . We again saw a strong trend toward an increase in lac expression with 40 of 46 clones having a mean reporter expression level greater than the ancestor . Furthermore , the mean change in expression level of clones evolved in lactose containing environments was significantly higher than the ancestor ( two tailed t-test with unequal variance: Lac , P = 0 . 008; G+L , P = 0 . 004; G/L , P<0 . 001 ) but not significantly different for clones evolved in the Glu environment ( P = 0 . 242 ) . To verify that changes in the level of our GFP reporter accurately reflected changes in the expression level of the native lac operon , we used a direct assay to measure the lac promoter activity of a focal evolved clone G+L3-1 , which shows an approximate 2-fold increase in maximal Plac-GFP expression [29] . We found that expression from the lac promoter was significantly higher in the G+L3-1 clone than the ancestor and this increase agreed with our estimate based on flow cytometry ( Figure 4B ) . Measurements taken at two time-points during exponential growth gave similar promoter activity estimates for all strains tested , indicating that promoter activities are representative of the lac system at steady state . Lastly , we measured the expression level of the CRP activity reporter ( PlacO ( - ) -RFP ) as a function of inducer concentration . All evolved clones showed a unimodal distribution with a mean response that was independent of inducer concentration , suggesting that , similar to the ancestor , evolved clones maintained predominantly non-cooperative interactions between the LacI and CRP regulators and that CRP activity is independent of lac expression level ( Figure S1 ) . To determine the genetic basis of changes to lac regulation we first sequenced the main lac regulatory regions , lacI and Plac , of each evolved clone ( excluding G/L5 clones whose lac regulatory region could not be amplified by PCR ) . We found that all but one clone classified as constitutive had either a deletion or insertion of a 4 bp sequence within the lacI gene , which results in a frame shift ( Figure 5 ) . This region of lacI has three 4 bp direct repeats and is known to be a mutational hotspot , accounting for ∼75% of all spontaneous lacI null mutations [30] , [31] . Constitutive clone G/L4-3 had a nonsynonymous mutation in lacI conferring a leucine to glutamine change at residue 71 . This mutation is predicted to cause a severe defect in the ability of LacI to repress lac expression [32] . In addition , we found that all clones that evolved a lower induction threshold contained a single base pair substitution in the primary LacI repressor binding site of the lac promoter ( lacO1 ) . We identified three unique lacO1 mutations , two of which occurred twice in independent G+L populations ( Figure 5 ) . Previous work has demonstrated that all three lacO1 mutations can reduce the binding efficiency of LacI to the operator , thereby reducing repression of the lac operon in the absence of a specific inducer [33]–[36] . Lastly , populations in the bimodal inducer response class did not have any mutations within lacI or the lac promoter region , even though other aspects of their lac regulation , such as the region of bimodalilty and TMG½Max values , were altered ( Figure 2 ) . The inducer response profiles presented above used a GFP reporter controlled by the ancestral Plac promoter . It is possible that this promoter does not accurately reflect the activity of the mutant promoters that evolved in the G+L isolated strains . To investigate the effect of this difference we constructed a version of the Plac-GFP reporter with the lacO1G11A mutation found in the G+L3 and G+L5 populations ( PlacO1-GFP ) and used it to generate inducer response profiles in the G+L3-1 clone ( Figure S2A ) . Inducer response profiles for G+L3-1 with PlacO1-GFP are qualitatively similar to those obtained with the Plac-GFP reporter , showing a graded inducer response and a lower induction threshold . These characteristics were confirmed with β-Gal enzymatic assays that directly examined mean LacZ activity ( Figure S2B ) . The lacI insertion/deletion and lacO1 mutations are clearly good candidates to explain the evolved changes in lac operon regulation , but additional mutations may also be influential . To test the effect of the identified mutations on lac expression , we added the evolved lacI 4 bp deletion allele and the lacO1G11A mutation individually into the ancestral reporter strain . If these mutations play a major role in determining the evolved regulatory change , we expect these constructed strains to have inducer profiles similar to those of the evolved strains from which the mutations were isolated . Indeed , the lacI deletion recapitulated the inducer response profiles found in all constitutive clones , causing Plac-GFP expression levels to become independent of TMG concentration ( Figure 6A , lacIΔTGGC versus Figure 3A , Constitutive ) . Similarly , adding the lacO1G11A mutation into the ancestral background recreated the lower threshold/graded inducer response associated with evolved clones harboring mutations in lacO1 ( Figure 6A , lacO1G11A versus Figure 3A , Lower threshold ) . The TMG½Max value for the lacO1G11A reconstructed strain was 4 µM , which is similar to the TMG½Max of evolved clones with this mutation ( G+L3-1 , 8 µM; G+L5-1 , 4 µM ) , demonstrating that the lacO1 mutation is the primary cause of evolved changes in inducer sensitivity . The lacI and lacO1 mutations can explain many , but not all , of the lac regulation changes seen in the evolved strains . Specifically , neither mutation confers the increase in maximal expression that was seen in most evolved clones ( Figure 6B , Figure 4B ) . To conclusively establish that additional evolved mutations impacted lac regulation in the G+L3-1 evolved clone , we replaced the lacO1 mutation with the ancestral operator sequence . This strain maintained the ∼2-fold increase in maximum Plac-GFP expression level relative to the ancestor , indicating that mutations outside the canonical lac operon regulatory network contribute to evolved changes in lac regulation ( Figure 6B ) . Whole genome sequencing of G+L3-1 found 6 additional mutations ( in the genes or gene regions rbsDACB , ECB_00822 , fabF , sapF , mreB and malT ) , none of which are in genes previously characterized as affecting lac operon expression . That multiple lacO1 and lacI mutations arose independently in replicate populations and affect a trait of relevance in the evolution environments suggests that they confer a selective advantage . Further , the presence of lacO1 mutations exclusively in the G+L evolved populations suggests that they confer a greater advantage in this environment than do lacI mutations . To test these predictions we introduced the lacI and lacO1 mutants individually into the ancestor and measured the fitness of these constructed strains relative to the ancestor in each of the four evolution environments . We found that lacO1 and lacI mutations conferred a fitness benefit in all environments containing lactose ( mean relative fitness effect and 2-tailed t-test: Lac environment . lacI: 8 . 9% , P<0 . 001; lacO1: 7 . 9% , P<0 . 001 . G+L environment . lacI: 8 . 4% , P<0 . 001; lacO1: 8 . 0% , P = 0 . 001 . G/L environment . lacI: 6 . 6% , P<0 . 001; lacO1: 2 . 2% , P = 0 . 02 ) . By contrast , both mutations imposed a small fitness cost in the glucose environment , consistent with them not being observed in Glu populations ( Glu environment: lacI: −4 . 0% , P<0 . 001; lacO1: −1 . 8% , P = 0 . 16 ) ( Figure 7 ) . Intriguingly , despite the lacO1 mutations being significantly overrepresented among G+L populations , they did not confer a greater fitness advantage in this environment . Similarly , lacI mutations did not confer a greater advantage in Lac or G/L environments , where they were dominant . To address the possibility that some non-transitive interaction could complicate our indirect comparison of the relative fitness benefits of the two mutations , we also performed direct competitions between the two constructed strains . Again , we found that the fitness of the lacI mutant was not significantly different from that of the lacO1 mutant in any environment ( fitness of lacI relative to lacO1 , 2-tailed t-test: Glu environment: 0 . 5% , P = 0 . 69; Lac environment: 0 . 4% , P = 0 . 60; G+L environment: −0 . 5% , P = 0 . 39; G/L environment: 2 . 1% , P = 0 . 05 ) ( Figure 7 ) . To examine the basis of the fitness effects conferred by the lacI and lacO1 mutations , we quantified their effect on population growth dynamics , focusing on the transitions between glucose and lactose utilization that are encountered in the G+L and G/L environments ( environments in which lacO1 and lacI mutants predominated ) . We found that the lacI and lacO1 mutations significantly decreased the lag phase , relative to the corresponding ancestral alleles , following a shift from growth on glucose to growth on lactose ( a part of the G/L environment ) ( Figure 8A , Table 2 ) . Furthermore , both lacI and lacO1 mutations eliminated the diauxic lag phase measured for the ancestor when switching from glucose to lactose utilization in the G+L environment . Neither mutation had a significant effect on lag time following a shift from growth in lactose to glucose , indicating that the change in lag time was specific to lactose utilization . The maximum growth rate constant ( μMax ) for lacI and lacO1 mutants was not significantly different from that of the ancestor , except during growth on lactose in the G+L environment ( Figure 8A , Table 2 ) . In this case , both lacI and lacO1 mutants had significantly higher growth rates than the ancestor . In agreement with fitness measurements , strains containing the lacI and lacO1 mutations show no significant differences in the length of lag phases or maximum growth rates in any of the environments tested . To decrease experimental noise in these experiments , we used higher sugar concentrations than present in the evolution experiment . Analysis of growth dynamics using the exact evolution environments yielded qualitatively similar results ( Figure S3 ) . In contrast to the lacI mutant , the lacO1 mutant can repress lac expression to some degree ( Figure 2 , Figure 3A ) . To examine how this difference in regulation translates to the evolution environment , we measured LacZ activity of the ancestor and the lacI and lacO1 mutants in the G+L environment ( Figure 8B ) . The ancestor shows the anticipated LacZ expression profile , with activity decreasing 97% during growth on glucose and increasing back up to the initial level of activity after switching from growth on glucose to lactose . Interestingly , lacI and lacO1 mutants showed indistinguishable LacZ activity profiles , with LacZ activity much higher than the ancestor at all time points during growth in G+L medium . These results suggest that the relatively low levels of lactose present in the G+L environment induce the lacO1 mutant lac operon , even in the presence of glucose concentrations sufficient to prevent induction of the ancestral lac operon . Analysis of the steady state levels of lac expression during growth in DM+Glu ( 2 mg/mL ) with and without lactose ( 87 . 5 µg/mL ) supports this conclusion , with repression of lac expression only occurring in the absence of lactose ( Figure S4 ) . In summary , in the ancestral background , lacO1 and lacI mutations are indistinguishable with respect to their effect on fitness , growth dynamics and lac expression dynamics in the G+L environment . A possible explanation for the success of lacO1 mutations in the G+L populations despite them not conferring any advantage relative to more frequent lacI mutations is that they interact synergistically with other mutations that fixed during the evolution of these populations [37] . To test this , we compared the fitness advantage conferred by lacO1 and lacI alleles in the ancestral background relative to the advantage they confer in the genetic background of evolved clone G+L3-1 , which substituted a mutation in lacO1 during evolution in the G+L environment ( Figure 5 ) . If epistasis was important in selection of lacO1 alleles in the G+L environment , we predicted that the fitness advantage conferred by the lacO1 mutation would be significantly larger in the background of this evolved clone than in the ancestral background , and that this positive effect will be less pronounced for the lacI mutation . The lacO1 mutation did confer a bigger benefit in the evolved background ( two tailed t-test , fitness in evolved background minus fitness in ancestral background = 4 . 4% , P = 0 . 03 ) ( Figure 9 ) . However , a similar effect was seen for the lacI mutation ( two tailed t-test , fitness in evolved minus fitness in ancestral background = 4 . 8% , P<0 . 001 ) and there was no significant difference in the fitness conferred by the lacI and lacO1 mutations in the evolved background when they were directly competed against one another ( lacI versus lacO1 in evolved background , relative fitness difference = 1 . 3% , P = 0 . 07 ) . Therefore , epistatic interactions increase the fitness effect of both the lacI and lacO1 mutations in the evolved background , but do not explain the enrichment of lacO1 mutations in populations evolved in the G+L environment . In the absence of a measurable difference in the fitness conferred by lacI and lacO1 mutations , what could explain our finding that lacOI mutations only occurred in populations evolved in the G+L environment ? If the lacI and lacO1 mutations occurred with equal probability , the distribution of mutation types over selection environments we observed is unlikely to have occurred by chance ( Fisher's exact test omitting the polymorphic G+L population , P = 0 . 002 ) . In fact , our observations are even more unlikely than this test implies because the lacO1 mutation will almost certainly occur at a much lower rate than the lacI mutation . The frequency of lacI null mutations in the E . coli strain used in this experiment is ∼3×10−6 per generation ( Hana Noh and TFC unpub . obs . ) , which is in good agreement with a previous measurement for E . coli K12 [31] . By contrast , we conservatively estimate the lacO1 mutation frequency to be <3×10−9 per generation ( Materials and Methods ) . Without some unique advantage , it is difficult to see how lacO1 mutations could reach high frequency in any population , let alone predominate as in the G+L populations . The ∼1000 fold difference in mutation frequency does , however , provide an explanation for the absence of lacO1 mutations in Lac or G/L environments . We considered the possibility that cross-contamination could be responsible for the occurrence of identical lacO1 mutations in two of the G+L populations , which would reduce the number of independent lacO1 populations to three . This is unlikely since replicate populations were not propagated in adjacent wells , and no evidence for cross contamination was found during the course of the experiment ( see Materials and Methods ) . Furthermore , the environmental association remains significant even if only unique lacO1 mutations are considered ( Fisher's exact test omitting one polymorphic population and populations with non-unique lacO1 mutations , P = 0 . 006 ) . Finally , it is possible that the statistical association between the lacO1 mutation and the G+L evolution environment , despite its high significance , is nevertheless spurious . In this case , repeat evolutionary experiments of the ancestor in the same G+L and G/L environments ( where selection of lacI mutations was most consistent ) would not be expected to lead to significant mutation-environment association . To test this , we began 12 ‘replay’ populations in each of the G+L and G/L environments , founding each population with the same ancestor as used in the original experiment . Every 100 generations , we examined the frequency of lacI and lacO1 mutations in each population using LacZ indicator plates and by sequencing select clones . Although lacO1 mutants were detected in the majority of G+L replay populations , their frequencies never rose above 4% in any one population ( Table S1 ) . In contrast , lacI mutants rose to high frequency , accounting for >98% of clones in all populations by 400 generations . Replay experiments in the G/L environment followed a similar trend , although lacO1 mutations were detected in only 2 of the 12 populations over the course of the experiment ( Table S2 ) . In summary , despite being highly improbable , the failure of lacO1 mutations to establish in our replay experiments suggests that their enrichment over competing lacI mutations in the original G+L populations may have occurred by chance .
We sought to test whether evolution in environments that differed only in the availability and presentation of lactose selected for changes in the regulation of the lac utilization network . Our analysis of inducer response profiles found three broad classes of lac regulation change among evolved clones . Two of these classes , constitutive expression and a lower threshold/graded inducer response , represent substantial changes from ancestral regulation and were observed only in populations evolved in environments containing lactose . Sequencing of lac regulatory regions in evolved clones uncovered mutations in the lac repressor ( lacI ) and the primary lac operator ( lacO1 ) that correlated with the constitutive and the lower threshold/graded inducer response , respectively . Addition of these mutations to the ancestor demonstrated that they explained many , but not all , of the broad scale changes in regulation we observed and that , by themselves , they can confer fitness benefits in environments containing lactose . These fitness benefits were relatively large , representing 20% , 27% and 28% of the total mean fitness improvement in the Lac ( lacI mutation ) , G+L ( lacO1 mutation ) and G/L ( lacI mutation ) evolved populations , respectively [13] . Together these results indicate that regulatory changes were common , complex — occurring both within and outside of the recognized lac regulatory elements — and adaptive . Extensive previous study of lac operon regulation offers the opportunity to connect the genetic and phenotypic changes we observed . Twenty-one of the 22 lacI mutants we identified mapped to a mutational hotspot within lacI [30] , [31] . These mutations generate a frameshift in the coding sequence that results in expression of a nonfunctional repressor , which provides a good explanation for the complete loss of negative regulation we observed in lacI mutants . Similarly , the three lacO1 mutations we identified in the G+L evolved populations have been reported as reducing the binding affinity of LacI for this binding site [33]–[35] . Consistent with the mutations reducing , but not completely preventing , LacI binding , strains containing lacO1 mutations are able to repress the lac operon , but are induced at much lower TMG concentrations than the ancestor . lacO1 mutations also conferred a graded response to increasing inducer concentration , which contrasted with the canonical bimodal response of the ancestor . The same regulatory outcome was demonstrated by Ozbudak et al . ( 2004 ) , who found that decreasing the effective concentration of LacI by providing extra copies of lacO1 binding sites resulted in a graded unimodal induction of the lac operon [19] . The similarity in regulatory changes suggests that the graded induction we observe is a consequence of decreased LacI-lacO1 affinity , reducing the effective concentration of LacI repressor . More generally , our results support the concept that small changes in the activity of cis-regulators have the potential to transform the output of a regulatory network between binary and graded responses [38] . Both lacI and lacO1 mutations were shown to confer significant fitness benefits in the three lactose containing evolution environments ( Lac , G+L and G/L ) . Analysis of the growth dynamics of strains containing only these mutations indicated that a large part of this benefit is due to a reduction in lag phase when switching from glucose to lactose utilization . Interestingly , when added to the ancestor , both lacO1 and lacI mutations abolished the diauxic lag that separates glucose and lactose growth phases during growth in the G+L environment . This phenomenon is well documented for lacI mutants [39] , but to the best of our knowledge has not been demonstrated for lacO1 mutants . In the case of lacI mutants , constitutive expression of the lac operon primes the cell for utilization of lactose as soon as glucose resources are exhausted . In contrast to lacI mutants , lacO1 mutants are still capable of repressing lac expression in the absence of inducer . However , when grown in media containing both glucose and lactose the lacO1 mutation essentially phenocopies a lacI mutant , causing constitutive lac expression . Evidently glucose-mediated blockage of lactose import through LacY ( inducer exclusion ) is insufficient to prevent lactose accumulating in cells to a concentration sufficient to allow lac operon induction in lacO1 mutants [39] , [40] . The loss of lac repression in lactose ( 3/6 ) , but not glucose ( 0/6 ) , evolved populations is consistent with the ‘use it or lose it’ hypothesis [23] , [24] . This hypothesis proposes that negative regulation will be maintained during evolution in environments in which gene products , in this case the LacI repressor , are used because mutants that lose the repressor will needlessly express the lac operon and be selected against . If a repressor is seldom used , as in the lactose evolution environment , loss of function mutations will not be effectively selected against and can fix through genetic drift . However , in its simplest form , this mutation accumulation mechanism does not capture the dynamics of the regulatory changes we see . First , loss of the lacI repressor actually confers a benefit during growth on lactose , so that underlying mutations will increase in frequency faster than expected if they were neutral . Second , repressor mutations also occurred in environments where glucose was just as common as lactose . Analysis of growth curves suggest a mechanism for this; lac repressor mutants were able to quickly begin growth following a switch from glucose to lactose . Fitness measurements indicated that this advantage outweighed the cost of unnecessary lac expression during growth in glucose . Given the large benefit conferred by lacI mutations in the Lac environment , it is interesting that only three of the six Lac populations were enriched for lacI mutations . We identify two possible explanations for this observation . First , clonal interference may have resulted in lacI mutations being outcompeted by higher effect beneficial mutations . Second , populations that did not enrich lacI mutations may have fixed alternative mutations that genetically interact with lacI mutations to reduce their fitness benefit . To distinguish between these possibilities , we are continuing the evolution experiment and tracking the frequency of lacI mutations in the Lac populations . In addition , we are examining the fitness benefit conferred by lacI mutations when introduced into clones from Lac evolved populations that did not fix lacI . We can explain why lac mutations occurred only in lactose containing selective environments . A second layer of environment specificity is less clear; why do lacO1 mutations only reach high frequency in the G+L environment ? The lacI and lacO1 mutations had no differential effect on fitness in either the ancestor or an evolved background and conferred indistinguishable growth dynamics in all evolution environments . Without a selective advantage over lacI mutations it is difficult to understand how lacO1 mutants were fixed in 4 of 6 G+L populations , especially considering that the rate of lacI mutations is likely on the order of 1000-fold greater than for lacO1 mutations . In the absence of a plausible mechanism to explain enrichment of lacO1 mutations in the G+L environment , we investigated whether environment-specific selection of lacO1 mutants was reproducible . This was not the case , with all 12 of the independent replay populations selected in G+L eventually fixing ( >98% ) lacI mutations . It remains formally possible that subtle differences in media or experimental conditions during competition assays or the replay evolution experiments could affect the fitness advantage experienced by lacO1 mutants in focal G+L evolved populations . However , taken at face value , the different outcome between replay and primary populations suggests that , notwithstanding mutation rate differences and the strong statistical association between environment and mutation type , the enrichment of lacO1 mutations over lacI mutations in the G+L environment might have occurred by chance . Models incorporating interactions between key regulatory elements can successfully predict aspects of lac operon regulation [18] , [19] . Nevertheless , recent studies demonstrate that regulation of the lac operon is evolutionarily plastic , such that interactions can arise or be altered to fine tune regulation and better fit E . coli to its environment [11] , [14] , [41] . By characterizing changes in regulation without a priori assumptions as to the nature of regulatory changes or the mutations causing them , we were able to identify evolved clones with changes in lac regulation that are likely due to novel interactions with mutations in genes outside of the canonical lac operon . Two results support this conclusion . First , we identified numerous clones with maximal steady state expression levels of the lac operon that were higher than the ancestor . Further examination of evolved clone G+L3-1 indicated that the increase in maximal lac expression level could not be explained by the lacO1 mutation present in this clone . Second , the fitness benefit conferred by the lacO1 mutation in this same evolved clone was significantly greater than in the ancestor , indicating that one or more evolved mutations interact with the lacO1 mutation to determine its effect on fitness . Whole genome sequencing of G+L3-1 identified six additional mutations , however , none of these mutations mapped to the lac operon or genes known to directly impact CRP-cAMP activity . It seems likely , therefore , that one or more mutations in the G+L3-1 clone have directly or indirectly led to new regulatory control of the lac operon . Is there an optimal level of lac expression in each of the three lactose environments ? Dekkel and Alon ( 2005 ) found that , in the presence of a gratuitous inducer , lac operon expression evolved to a level predicted on the basis of a cost-benefit analysis , dependent on the concentration of lactose in the selection environment [14] . Our results support the idea that maximal expression level is a plastic feature of the lac operon and can be tuned to best fit the environment . At this time , however , we do not know the genetic or molecular basis for the widespread increase in maximum lac expression observed in many evolved clones . Possible ‘local’ explanations include: increases in the level of cAMP , mutations in the lacZYA genes that affect mRNA stability , or changes in DNA supercoiling that increase lac operon transcription . It is also possible that changes in lac maximum expression reflect an alteration in some global process . For example , changes in the function or concentration of ribosomes could affect expression of all genes . In future work we aim to identify the evolved mutations that are responsible for changes in maximum lac expression and then construct strains that will allow us to test the adaptive value of different expression levels as well as probe the underlying molecular mechanisms . An additional widespread change in lac regulation was that evolved clones displaying bimodal inducer responses tended to also have higher induction thresholds ( TMG½Max ) than the ancestor . This trend was not environment specific , occurring in clones isolated from Glu , Lac and G/L evolved populations . However , the parallel and large-scale increases in induction threshold observed for Glu-evolved clones suggests that this change in lac regulation is a direct or correlated response to adaptation . The mechanistic bases of increases in induction threshold are currently not understood , but could be the result of both direct and indirect mechanisms . For example , reduction in the activity and/or concentration of the permease LacY could increase the concentration of extracellular inducer required to achieve intracellular levels of inducer sufficient to inactivate LacI . In glucose evolved populations , changes in LacY activity may result from mutations in the PTS system that optimize glucose transport but lead to elevated levels of unphosphorylated EIIAglc , which is a known inhibitor of LacY activity [40] . Alternatively , higher growth rates of evolved strains will also tend to decrease the steady state intracellular concentration of inducer thereby increasing the external concentration required for induction of the lac operon . Further study will be required to discern between these and other hypotheses . In summary , we have identified and characterized widespread changes in lac operon regulation that occurred during selection of replicate populations in different lactose containing environments . In our view , the most important aspects of our findings are how common these changes were and that they likely involve mutations both within and outside of the set of genes that are recognized as regulating the lac operon . Identification of these changes will provide a rare insight into how regulatory networks can be rewired in response to an environmental change .
The ancestral strains used for experimental evolution studies were E . coli B REL606 ( ara− ) and an otherwise isogenic ara+ derivative , REL607 [42] . For routine culturing , cells were grown in lysogeny broth ( LB ) medium [43] . Davis minimal ( DM ) medium was used for experimental evolution and subsequent analysis of evolved clones [42] . Sugars were added to base DM medium at the following concentrations to make single and mixed resource environments: glucose ( Glu ) 175 µg/mL , lactose ( Lac ) 210 µg/mL , and Glucose+Lactose ( Glu+Lac ) 87 . 5 µg/mL and 105 µg/mL , respectively . These concentrations were chosen to ensure that each environment supports approximately the same stationary phase density of bacteria ( ∼3 . 5×108 cfu/mL ) [13] . Strains were grown at 37°C unless otherwise stated . T medium contains 1% Bacto tryptone , 0 . 1% yeast extract and 0 . 5% sodium chloride . Antibiotics were used at the following concentrations: chloramphenicol ( Cm ) , 20 µg/mL; kanamycin ( Km ) , 35 µg/mL; streptomycin ( Sm ) , 100 µg/mL . Evolved populations were screened for qualitative changes to lac operon regulation using TGX medium , which consisted of T agar plates supplemented with 0 . 5% glucose and 30 µg/mL of the colorimetric LacZ substrate 5-bromo-4-chloro-3-indolyl-beta-D-galactopyranoside ( X-Gal ) . On this medium , the degree of blue coloration correlates with the degree of LacZ activity and , therefore , lac operon expression . Clones representing the diversity in LacZ activity and colony morphology within each evolved population were recovered , scored and stored for future analysis . TGX plates enabled us to distinguish between the ancestor and clones with lacO1 and lacI mutations ( mutations that we subsequently identified in evolved populations ) , which appear white , faint blue and dark blue , respectively . We developed a dual fluorescent reporter system that enabled us to independently monitor LacI and CRP activity with single cell resolution . In this system the native lac promoter ( containing LacI binding sites O1 and O3 ) , coupled with an optimized ribosomal binding site , drives expression of the fast maturing GFP derivative , GFPmut3 . 1 [44] . This Plac-GFP module was cloned into a mini-Tn7 delivery vector , to make pTn7-Plac-GFP , and integrated into the chromosome in a site-specific manner [45] . Expression of GFP from the Plac-GFP reporter depends on both LacI and CRP activity . To isolate these effects we developed a second reporter to monitor the contribution of CRP to lac operon expression independent of LacI . To do this , we constructed a version of the lac promoter ( PlacO ( - ) ) with defined mutations in the O1 and O3 operators which have been shown to prevent binding of the repressor LacI while maintaining the integrity of the major binding site for CRP [46] . This synthetic promoter was used to drive expression of the red shifted fluorescent protein DsRed express2 [47] . This reporter was cloned into a stable low copy plasmid ( ∼5 copies per cell ) to generate pRM102-3 . Control experiments confirmed that the introduced mutations abolished LacI binding while retaining promoter response to cAMP dependent activation of CRP ( Figure S5 ) . This reporter system differs from a previously published system in three important ways [19] . First , the fluorescent signal is bright enough to allow analysis by flow cytometry . Second , the CRP reporter is a derivative of the lac promoter that has the LacI binding sites deleted , rather than an unrelated reporter subject to regulation by CRP . Third , DsRed express2 shows low cytotoxicity relative to other commonly used RFP's [47] . The Tn7-Plac-GFP reporter was integrated into the chromosome of focal evolved clones by tri-parental mating with the MFDpir ( pTn7-Plac-GFP ) donor strain and the MFDpir ( pTSN2 ) helper strain [48] . Strains were grown overnight in LB and washed once in LB before being resuspended in 1/10 volume LB . Recipient , donor and helper strains were mixed at a 4∶1∶1 ratio and incubated on an LB plate for 3 hrs . Transconjugants were selected on LB plates supplemented with Km and Sm . A PCR assay was used to confirm that the mini-Tn7 had inserted into the single characterized chromosomal integration site [49] . Finally , all reporter strains were screened to ensure that they retained the LacZ phenotype that formed the basis of their initial selection from evolved populations . Strains were inoculated from glycerol stocks into 500 µL LB media in 2 mL deep-well plates ( Phenix Research ) and grown overnight at 37°C on a microplate shaker at 750 rpm ( Heidolph Titramax 1000 ) . After overnight growth , cultures were diluted 1∶1000 into 500 µL DM+0 . 4% glycerol and incubated for a further 24 hrs at 37°C . To determine the inducer response of each strain , overnight cultures were diluted 1∶1000 into separate wells of a 96-well plate containing DM+0 . 4% glycerol supplemented with TMG at concentrations ranging from 0 to 100 µM and incubated at 37°C for a further 15–18 hrs . This time period encompassed approximate steady state reporter expression in ancestral and evolved clones ( Figure S6 ) . TMG induces the lac operon by binding and inactivating the LacI repressor . Unlike the natural inducer , allolactose , TMG is metabolically stable , which is advantageous for quantitative studies because it allows accurate control of TMG concentrations through the course of the experiment . Importantly , import of TMG into the cell is dependent on the lactose permease LacY , which is not true of other commonly used synthetic inducers such as Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) . Flow cytometry was performed with a FACScalibur ( BD Biosciences ) equipped with a high throughput sampler . PMT voltages for the flow cytometer were set as follows: SSC-H - E02 , FSC-H 580 V , FL1-H 800 V and FL2-H 800 V . The threshold was set at 250 on the SSC-H channel . For each expression assay , a total of 25 , 000 events were captured at a rate of 1000–3000 events/s . Data was acquired in log mode with no hardware compensation . We examined day-to-day reproducibility by measuring ancestral inducer response curves on 5 separate days using independent cultures ( Figure S7 ) . The level and distribution of Plac-GFP expression in response to TMG was similar between replicates , indicating that our protocol for measuring inducer response profiles was robust . Routine analysis of the flow cytometry data and plotting of inducer response profiles was performed in R ( version 2 . 12 ) using the Bioconductor packages FlowCore and FlowViz [50]–[52] . To control for cross talk between GFP and RFP reporter detection , a compensation matrix was calculated using the appropriate single reporter control strains and used to correct flow cytometry data post acquisition . To minimize detection noise and enrich for cells of similar size , all data was filtered with FlowCore's norm2Filter on the FSC-H and SSC-H channels using the default settings . This typically resulted in retention of 50–60% of all collected events . For quantitative analysis of inducer responses , flow cytometry data were processed using Matlab ( Mathworks , Inc . ) . An elliptical gate corresponding to a Mahalanobis distance of 0 . 5 , centered at peak cell density in the FSC-SSC coordinates , was used to minimize the effects of varying cell size and granularity on the resulting fluorescence histograms . A custom bimodality detection algorithm ( to be described elsewhere ) was applied to determine the region of bimodality for each histogram . Inducer sensitivity was determined as the point where the population-mean of GFP expression was halfway between baseline and saturation . Assays were carried out as described by Zhang and Bremer ( 1995 ) with modifications [53] . Specifically , 1–2 mL of cell culture was pelleted and resuspended in 250 µL unsupplemented DM medium to remove any remaining lactose . Cell concentration was estimated by measuring absorbance at OD600 with a microplate reader ( Tecan ) . Cells were permeabilized by mixing 20 µL of cell suspension with 80 µL of permeabilization solution ( 100 mM Na2HPO4 , 20 mM KCl , 2 mM MgSO4 , 0 . 8 mg/mL cetyl-trimethylammonium bromide ( CTAB ) , 0 . 4 mg/mL sodium deoxycholate , 5 µL/mL β-mercaptoethonal ) . After incubation for 10 minutes at room temperature , measurement of LacZ activity was initiated by adding 150 µL of o-nitrophenyl-β-D-galactoside ( ONPG , 4 mg/mL ) and mixing . Yellow color development was stopped by addition of 250 µL 1 M sodium carbonate and the reaction time recorded . Samples were centrifuged to remove cell debris and absorbance at OD420 was measured for 200 µL of the supernatant . LacZ activity ( Miller units ) was calculated as ( 1000×OD420 ) / ( volume ( mL ) ×OD600×reaction time ( min ) ) . Strains were inoculated into LB medium from freezer stocks , incubated overnight at 37°C and then diluted 1∶100 into DM medium supplemented with Glu , Lac or G+L at the concentrations used in the original evolution experiment . Strains were grown overnight and diluted 1∶100 into fresh DM media and incubated for a further 24 hrs to allow them to become physiologically adapted to their resource environment . To measure growth dynamics , a 1∶100 dilution of pre-conditioned culture was inoculated into 200 µL of DM supplemented with Glu , Lac or G+L in a clear 96-well plate . Concentration of sugars was either the same as in the evolution environments or to facilitate higher cell densities and correspondingly more precise OD measurements , were as follows: Glu , 0 . 2 mg/mL; Lac , 1 . 8 mg/mL; G+L , 0 . 2 mg/mL & 1 . 5 mg/mL , respectively . Incubation and optical density measurements were performed with a Bioscreen C plate reader ( Oy Growth curves AB Ltd ) at 37°C with continuous shaking and OD600 measured at 5 min intervals . The maximal growth rate constant ( μMax ) of each strain was calculated by linear regression of the plot of ln ( OD600 ) versus time ( hrs ) using a sliding window of 10 data points . The steepest of these slopes was used to calculate μMax with units hrs−1 . Lag time was calculated by extrapolating the μMax regression line to its intersection with OD600 = 0 . 06 . Extrapolating to the initial density of individual growth curves would have been preferable , however , we found that these measurements were quite variable . To account for this we adopted the approach described by Friesen et al . ( 2004 ) where a constant reference density is used , assuming that starting biomass is similar for all strains [54] . For strains showing diauxic growth , we analyzed both growth phases separately to derive μMax-1 and μMax-2 . The diauxic lag phase ( lag-2 ) was calculated by determining the difference between the times when the regression lines for μMax-1 and μMax-2 intersect with the OD600 value coinciding with the end of the first growth phase . Assays were performed as described by Kuhlman et al . ( 2007 ) [29] . Briefly , steady state levels of LacZ activity were measured for strains grown in DM+0 . 2% glucose supplemented with a saturating concentration of the gratuitous inducer IPTG ( 1 mM ) . β-Gal assays were performed as described above except that color development was followed over time by measuring absorbance at OD420 and linear regression used to fit a line of best slope to the plot of OD420 vs time ( min ) . LacZ activity ( Miller units ) was calculated as ( 1000×slope ) / ( assay volume ( mL ) ×OD600 ) . Doubling rate was measured for each strain in the experimental conditions by linear regression of log2 ( OD600 ) plotted against time , with the steepest of these slopes designated as the maximum doubling rate ( doublings/hr ) . Promoter activity was calculated as the product of LacZ activity ( Miller units ) and doubling rate ( hrs−1 ) . To confirm that the inducer concentration we used was sufficient to completely inactivate LacI , we also measured expression from a lacI null mutant . This mutation caused a similar expression increase as induction with 1 mM IPTG , indicating that this level of inducer was sufficient to fully induce the lac operon . Constructs and approaches used for the manipulation of each mutation were as follows . The PlacO1 and araA- mutations were introduced using a suicide plasmid approach that has been described previously [6] . Briefly , PCR products containing the relevant evolved alleles were separately cloned into pDS132 [55] . Resulting plasmids were introduced into recipients by conjugation and CmR cells ( formed by chromosomal integration of the plasmid ) were selected . Resistant clones were streaked onto LB+sucrose agar to select cells that lost the plasmid ( which carries the sacB gene conferring susceptibility to killing by sucrose ) . These cells were then screened for the presence of the evolved alleles by a PCR-RFLP approach using the enzyme HaeIII ( araA- ) or on LacZ indicator medium ( PlacO1 ) . Putative allelic replacements of the evolved ara- and lacO1 alleles were confirmed by sequencing . The lacI ( - ) mutation was obtained by isolating spontaneous mutants of relevant strains that could grow on minimal media supplemented with P-Gal as the only carbon source and confirmed by sequencing [56] . We isolated independent lacI ( - ) mutants that had either insertion or deletion mutations in a previously identified mutational hotspot [57] . Preliminary experiments indicated that these mutant types had identical fitness in each of the environments used here . For this reason , we used only the deletion mutant in the experiments reported in Results . The fitness of constructed strains was measured relative to the ancestor strain used to found the evolution experiment or directly to each other . Competing strains contained opposite Ara marker or lac regulation types , which allowed them to be distinguished on tetrazolium arabinose ( TA ) [42] or LB+X-Gal indicator medium , respectively . Before each fitness assay , competing strains were grown separately for one complete propagation cycle in the environment to be used in the assay so that they reached comparable cell densities and physiological states . Following this step , competitors were each diluted 1∶200 into the assay environment . A sample was taken immediately and plated on indicator plates to estimate the initial densities of the competing strains . At the end of the competition a further sample was plated to obtain the final density of each competitor . The fitness of the test strain relative to the reference strain was calculated as ln ( NT2/NT0 ) /ln ( NR2/NR0 ) , where NT0 and NR0 represent the initial densities of the test and reference strains , respectively , and NT2 and NR2 represent their corresponding densities at the end of the competition with correction for the number of transfer cycles the competition occurred over . Competitions were generally carried out over two transfer cycles . All assays were carried out with at least four-fold replication unless reported otherwise in Results . To estimate the per generation frequency of loss of function lacO1 mutations , we assume that mutation of the lacO1 region is random and equally likely for each of the 21 bp that define lacO1 . Using a mutation rate of 5×10−10 bp/generation [58] the probability of generating a single substitution in lacO1 is ∼1×10−8 per generation . However , only a subset of these mutations will severely compromise LacI binding . A survey of the literature indicates that approximately 16 single base pair substitutions within lacO1 have been reported to reduce the affinity of LacI by >90% and/or reduce the effective repression of LacZ expression >90% [33] , [34] . Based on this , we conservatively estimate that a third of the possible 63 single base pair substitutions will severely compromise LacI binding , giving a mutation frequency of ∼3×10−9 per generation . This is likely an overestimate since only three lacO1 alleles were selected during evolution , two of which were selected twice in independent populations , indicating that relatively few of the possible lacO1 mutations may actually confer an advantage in the evolution environments . In addition , the lacO1 alleles selected during evolution have been reported to reduce the affinity for LacI by >98% , further reinforcing the stringency of our operational criteria for estimating the number of possible lacO1 mutations [33] , [36] . | Differences in gene regulation underlie many important biological processes and are thought to be important for the adaption of organisms to novel environments . Here we focus on the regulation of a group of well-studied genes , the lac operon , that control the utilization of lactose sugar , and we examine how their regulation changes during the adaptation of populations of Escherichia coli bacteria to environments that differ only in the presence of lactose . We find that lac operon regulation is altered in almost all populations that evolve in the presence of lactose and identify two classes of mutations that explain a large part of this change and that confer significant fitness benefits . Interestingly , our study indicates that other mutations , lying outside of the commonly recognized control region , cause new regulation of the lac operon . Together these findings reinforce the importance of changes in gene regulation during evolution and suggest that the biological basis of these changes can be complex and involve novel interactions between genes . | [
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] | 2012 | Adaptive Evolution of the Lactose Utilization Network in Experimentally Evolved Populations of Escherichia coli |
The molecular chaperone Hsp90 is essential in eukaryotes , in which it facilitates the folding of developmental regulators and signal transduction proteins known as Hsp90 clients . In contrast , Hsp90 is not essential in bacteria , and a broad characterization of its molecular and organismal function is lacking . To enable such characterization , we used a genome-scale phylogenetic analysis to identify genes that co-evolve with bacterial Hsp90 . We find that genes whose gain and loss were coordinated with Hsp90 throughout bacterial evolution tended to function in flagellar assembly , chemotaxis , and bacterial secretion , suggesting that Hsp90 may aid assembly of protein complexes . To add to the limited set of known bacterial Hsp90 clients , we further developed a statistical method to predict putative clients . We validated our predictions by demonstrating that the flagellar protein FliN and the chemotaxis kinase CheA behaved as Hsp90 clients in Escherichia coli , confirming the predicted role of Hsp90 in chemotaxis and flagellar assembly . Furthermore , normal Hsp90 function is important for wild-type motility and/or chemotaxis in E . coli . This novel function of bacterial Hsp90 agreed with our subsequent finding that Hsp90 is associated with a preference for multiple habitats and may therefore face a complex selection regime . Taken together , our results reveal previously unknown functions of bacterial Hsp90 and open avenues for future experimental exploration by implicating Hsp90 in the assembly of membrane protein complexes and adaptation to novel environments .
In eukaryotes , the universally conserved and essential chaperone Hsp90 aids the folding of key proteins in development and responses to environmental stimuli [1]–[3] . In yeast , up to 10% of all proteins are estimated to be Hsp90 clients under standard culture conditions [4] . Hsp90 function is even more important under stressful conditions that challenge protein folding , such as increased temperature [5] . The activity of eukaryotic Hsp90 is further modulated by various co-chaperones , which confer substrate specificity and alter protein folding kinetics [2] , [5] . Depletion of eukaryotic Hsp90 in vivo increases phenotypic variation , reveals ‘cryptic’ heritable variation , and increases penetrance of mutations [6]–[9] . Accordingly , eukaryotic Hsp90 enables organisms to maintain a stable phenotype in the face of environmental and genetic perturbation and to correctly interpret environmental stimuli . In stark contrast , in prokarya , Hsp90 is not essential [10] and many bacterial genomes lack Hsp90 altogether [11] . Among Archaea , only very few species contain Hsp90 , and those are thought to have gained Hsp90 horizontally from bacteria [11] , [12] . This fragmented phylogenetic pattern likely results from multiple independent gains and losses , though phylogenetic reconstructions are confused by ancient Hsp90 paralogy [11] , [12] . At the amino acid level , the Escherichia coli Hsp90 ( High-temperature protein G or HtpG ) is 42% identical to its human homolog , suggesting strong stabilizing selection consistent with functional conservation [13] . Indeed , E . coli Hsp90 appears to retain generic protein chaperone activity [14] and homologous Hsp90 mutations cause chaperone defects in both the prokaryotic E . coli and eukaryotic yeast [15] . However , there are no identified obligate Hsp90 co-chaperones in bacteria , adding to the uncertainty regarding the extent of its client spectrum and specificity . To date , only three proteins have been implicated as Hsp90 clients in bacteria , with non-overlapping functions in ribosome assembly , the assembly of light-harvesting complexes , and the CRISPR/Cas immunity system [16]–[18] . Several other proteins have been shown to physically interact with the chaperone [19] , [20] . Together with our knowledge of eukaryotic Hsp90 function , these data have given rise to the speculation that Hsp90 may facilitate the assembly of oligomeric protein complexes in bacteria , much like it does in eukaryotes [21] . Unlike in eukaryotes , however , further exploration of Hsp90's functional role in bacteria has proven challenging because there are no pleiotropic Hsp90-dependent phenotypes . To address this challenge , we used a genome-scale co-evolutionary ‘guilt-by-association’ approach [22] , [23] to explore the spectrum of conserved Hsp90-associated genes , functions , and organismal traits . Hsp90-associated genes tended to function in flagellar assembly , chemotaxis , and secretion . Consistent with these functions , Hsp90-associated organismal traits included the ability to inhabit multiple environments . To add to the sparse list of known bacterial Hsp90 clients , we further developed a statistical method to predict putative Hsp90 clients , which included flagellar , ribosomal , and chaperone proteins . We validated our predictions experimentally , focusing on two candidates functioning in motility and chemotaxis . Indeed , both the flagellar protein FliN and the kinase CheA were found to be Hsp90 clients in vivo . Our findings demonstrate the power of co-evolutionary inference to correctly identify substrates and functions of conserved genes like bacterial Hsp90 .
Our method for inferring the function of bacterial Hsp90 is based on the analysis of its distribution across the bacterial phylogeny . However , this analysis is complicated by the existence of multiple ancient Hsp90 paralogs in bacteria . These paralogs may be older than existing phyla in bacteria [11] , [12] , and may have evolved distinct functions on this enormous time scale . To address this issue and to identify each paralog , we first clustered bacterial Hsp90s by sequence identity . We identified 897 bacterial Hsp90 protein sequences in the KEGG database [24] and built a neighbor-joining gene tree of bacterial Hsp90s ( Figure S1A–B ) . We observed two well-supported long-branching clades as well as several less confident divisions in the tree ( Figure S1B ) . These two long-branching clades contain sequences corresponding to the ‘hsp90B’ and ‘hsp90C’ paralogs that were described previously [11] , [12] . All other branches correspond to ‘hsp90A’ [11] , which is the largest of the Hsp90 families in bacteria ( Figure S1C , Text S1 ) . Notably , hsp90A is the lineage out of which all eukaryotic Hsp90s ( excluding mitochondrial and chloroplast Hsp90s ) are derived . Moreover , the E . coli gene htpG belongs to the hsp90A family , and its gene product is the best-studied bacterial Hsp90 protein . For these reasons , we restricted our analysis to hsp90A . We set out to identify orthologous groups whose presence and absence profiles across bacterial species are associated with the presence and absence profile of hsp90A . To avoid spurious associations , any such comparative analysis must go beyond a naïve comparison of presence/absence patterns across genomes and incorporate phylogenetic information [25] . To this end , we used BayesTraits [26]–[28] , a computational framework for phylogenetic analysis of character evolution . Given the states ( e . g . , presence/absence ) of two characters across some set of species and a phylogenetic tree relating these species , BayesTraits evaluates the likelihood of various evolutionary models throughout the tree . This approach can be utilized , for example , to determine whether these two characters evolve in a mutually dependent vs . an independent fashion . We used BayesTraits to detect associations between hsp90A and 4646 other orthologous groups in bacteria ( which hereafter we shall refer to as ‘genes’ for simplicity ) . We used the tree constructed by Ciccarelli et al . [29] as a model phylogeny ( Figure 1 ) . In this initial analysis , we tested for any kind of dependency between hsp90A and other genes , and did not make specific assumptions about the nature of the relationship between hsp90A and the genes in question [28] . Specifically , we compared a model in which the rate of gain and loss of a given gene is independent of the rate of gain and loss of hsp90A ( independent evolution ) vs . a model in which the rate of gain and loss of this gene is affected by the presence or absence of hsp90A or vice-versa ( co-evolution ) . In total , we found 327 genes that co-evolve with hsp90A ( Dataset S1 ) . We will refer to this set as hsp90A-associated genes . These hsp90A-associated genes were significantly enriched for annotations related to the flagellum and to bacterial secretion systems ( Table 1 ) . Moreover , out of the 16 hsp90A-associated bacterial secretion genes , 10 were part of the non-flagellar Type III secretion system , suggesting that hsp90A is associated specifically with this system rather than with secretion systems in general . Using a different and markedly more extensive phylogeny [30] provided similar results ( see Text S1 , Table S1 ) , as did a pruned Ciccarelli tree without the species containing the hsp90B or hsp90C ( see Text S1 ) . The associations of hsp90A with other genes identified above are agnostic to the specific nature of the dependency between hsp90A and the gene in question . For example , our initial analysis could not distinguish between a positive association ( i . e . genes tend to be gained and lost together ) and a negative association ( i . e . genes tend not to co-occur in genomes ) . Similarly , this analysis did not distinguish between genes whose gains and losses are affected by the presence of hsp90A ( but that do not themselves affect hsp90A evolution ) and genes that exhibit mutually dependent dynamics with hsp90A . Without a quantitative estimate of the effects that hsp90A and its co-evolving partners have upon one another , inference of Hsp90A function and its relationship with other genes is challenging . To characterize the specific nature of the dependency between hsp90A and hsp90A-associated genes , we therefore examined rates of gain and loss inferred by BayesTraits . We focused on the two major non-overlapping hsp90A-associated functional categories , flagellar assembly and bacterial secretion . Considering , for example , fliI , a representative flagellar gene , we found that its gain and loss was strongly affected by the presence of hsp90A . Specifically , in the presence of hsp90A , fliI was often gained and rarely lost , whereas it was rarely gained and often lost when hsp90A is absent ( Figure 2A ) . This pattern was common to all hsp90A-associated flagellar genes ( Figures 2C , S2 ) , suggesting a positive association between hsp90A and flagellar genes throughout evolution . In contrast , the co-evolutionary relationship between hsp90A and yscN , a representative nonflagellar type III secretion system gene , was markedly different , with yscN presence strongly affecting the gain and loss of hsp90A ( Figure 2B ) . Specifically , the presence of yscN was associated with a large increase in the rates of gain and ( even more dramatically ) loss of hsp90A relative to these rates in its absence . Again , this pattern was common to all hsp90A-associated bacterial secretion genes ( Figures 2D , S3 , S4 ) , suggesting a negative association between hsp90A and nonflagellar secretion genes throughout evolution . To further validate the fundamentally distinct co-evolutionary dynamics of these two groups of genes , we considered four different co-evolutionary models: ( 1 ) hsp90A and the gene in question are independent ( null ) ; ( 2 ) hsp90A and the gene in question are mutually dependent; ( 3 ) hsp90A is dependent on the gene in question but not vice versa , and ( 4 ) the gene in question is dependent upon hsp90A but not vice versa ( Methods ) . We used the Akaike Information Criterion ( AIC [31] ) to determine which of these 4 models best fit the co-evolutionary dynamics of each hsp90A-associated gene . As expected , none of the hsp90A-associated genes fit the independent model . Of the 27 hsp90A-associated flagellar genes , 25 were classified as being dependent on hsp90A but not vice-versa ( model 4 ) . Of the 16 hsp90A-associated secretion system genes , 10 genes were classified as mutually dependent with hsp90A ( model 2; 6 of which were Type III secretion system genes ) , whereas 6 were classified as affecting the evolution of hsp90A ( model 3 ) . Furthermore , considering all hsp90A-associated genes , we found that genes that best fit each of the evolutionary dependency models above ( models 2 , 3 , and 4 ) were enriched for different functions ( Table 1 ) . Specifically , among genes dependent on hsp90A , flagellar motility was strongly enriched , whereas among genes mutually dependent on hsp90A , secretion system components were enriched . Taken together , these patterns suggest that flagellar genes and secretion system genes had markedly different regimes of co-evolution with hsp90A . Although many genes exhibited distinct patterns of co-evolution with hsp90A , these patterns could be the result of indirect evolutionary relationships rather than the outcome of a direct interaction with Hsp90A . We therefore aimed to predict specific genes that encode putative hsp90A clients . Our method is based on the assumption that strong , conserved clients should be heavily dependent on Hsp90A , and thus should be found only rarely in the absence of hsp90A throughout evolution . To estimate the expected frequency of each hsp90A-associated gene with and without hsp90A , we used the inferred BayesTraits rates to calculate the steady-state probabilities of each of the 4 possible two-gene presence/absence states ( Methods ) . These probabilities represent the proportion of the time that some arbitrary bacterial lineage will spend in each of the presence/absence states throughout evolution . From these probabilities we calculated a Putative Client Index ( PCI ) for each hsp90A-associated gene to evaluate how often it was present without hsp90A throughout evolution , compared to a null expectation ( see Methods ) . This index is close to zero for genes that were infrequently present without hsp90A and were hence likely to be Hsp90A clients . We defined the genes with the lowest PCI values as putative clients ( Table 2; see also Text S1 ) . Consistent with our prior analysis , several flagellar genes behaved as potential clients ( Table 2 ) . In particular , our set of putative clients included several genes ( fliH , fliI , fliN ) whose products had been previously shown to physically interact with Hsp90A in E . coli [19] . The products of these genes are cytoplasmic components of the flagellar rotor and export apparatuses . In contrast , nonflagellar type III secretion genes were all absent from the list of potential clients . In fact , nonflagellar type III secretion system components were rated as some of the least likely clients by our index ( Figure 3 ) . This disparity in predicted client status mirrors the different evolutionary relationships of these complexes with hsp90A ( Figure 2 ) . Chaperone/proteases ( e . g . ClpA and PpiD ) also ranked high in our list of potential clients . Hsp90A is known to collaborate with other chaperone systems such as DnaK [14] , [32] but to date no obligate co-chaperones have been described . The identified chaperone/proteases may represent such co-chaperones or collaborating chaperone systems , since our index cannot discriminate between Hsp90 clients and Hsp90 co-chaperones ( or other collaborating proteins ) . Alternatively , these observed associations could simply indicate that components of the cytoplasmic stress response are dependent upon Hsp90A . We also found several unexpected putative clients , such as the 3-hydroxybutyryl-CoA dehydrogenase PaaH and the transcription termination factor Rho , which we predict to be the two strongest clients . Further study will be necessary to understand these associations and the underlying cause of the co-evolutionary association between these genes and hsp90A . Our putative clients and the predicted chaperone role of Hsp90A in flagellar assembly are consistent with previous observations . Specifically , the deletion of E . coli hsp90A , also known as htpG , resulted in reduced surface swarming movement [33] . We also previously observed physical interactions between the HtpG protein and certain flagellar proteins [19] . Yet , these observations lacked a clear demonstration of client status or mechanism , and E . coli swarming is a complex behavior that depends on numerous factors in addition to flagellar function [34] . We therefore set out to test our hypothesis that Hsp90A is physiologically important for flagellar assembly and function and that flagellar components are indeed Hsp90A clients . We examined the swimming motility phenotype of ΔhtpG E . coli strains on soft-agar plates ( Methods ) . In contrast to surface swarming , swimming is a less complex behavior , in which bacteria use functional flagella and chemotaxis components to swim from an inoculation point through agar pores , following nutrient gradients that are created by nutrient depletion within the colony . The soft-agar assay is routinely used to assay bacterial swimming motility and chemotaxis . To enhance our ability to detect differences between wild-type and ΔhtpG cells , the assays were performed competitively . Competitive assays emphasize small differences between strains and reduce experimental error , thereby increasing the sensitivity of the assay . After mixing equal amounts of YFP-labeled WT and CFP-labeled ΔhtpG strains , this mixture was inoculated in the center of a soft-agar plate and incubated at 34°C for 8 hrs . We then counted cells of each strain in the plate center vs . the outer edge using fluorescence microscopy ( Figure 4A ) . ΔhtpG mutants migrated less efficiently to the plate's outer edge relative to WT , confirming that they are partially deficient in their motility and/or chemotaxis ( Figure 4B ) . This defect is apparently subtle , since little difference between WT and ΔhtpG cells was observed in a non-competitive assay ( Figure S5 ) , but it could be revealed due to strong selection for cells with optimal motility and chemotaxis at the outer edge of the spreading bacterial population . We also tested the phenotype of the HtpG ( E34A ) mutant , which has reduced rates of ATP hydrolysis and is deficient in substrate refolding [14] , [35] . Since HtpG ATPase activity is necessary for release of clients , HtpG ( E34A ) is less efficient at releasing clients [36]–[38] . Indeed , this mutant showed stronger motility/chemotaxis defects than the ΔhtpG strain ( Figure S5 ) , presumably due to sequestration of its client proteins . We therefore employed the HtpG ( E34A ) mutant in all subsequent assays as a more sensitive test of HtpG involvement . Taken together , our observations suggest that the motility defect may be due to the improper function or sequestration of HtpG clients . To further investigate the in vivo interaction of HtpG with flagellar components , we used htpG-yfp and htpG ( E34A ) -yfp constructs expressed in WT cells to perform acceptor photobleaching FRET between HtpG and FliN-CFP over an E . coli growth curve . Motility of E . coli is known to increase at the transition from the early exponential to post-exponential phase of growth [39] , and this experimental design enabled us to examine the HtpG-FliN interaction in the context of the flagellar assembly process . If HtpG is indeed involved in the assembly process of these structures , the interaction of HtpG with FliN should correspond temporally to the timing of flagellar assembly . Indeed , we found that the interaction with FliN peaked at OD600 = 0 . 2 ( Figure 5A ) and correlated well with the onset of cell motility in wild-type cells ( Figure 5B ) . Moreover , the interaction of HtpG ( E34A ) with FliN was stronger and delayed compared to the binding of wild-type HtpG . Correspondingly , the onset of motility was delayed in cells expressing HtpG ( E34A ) ( Figure 5B ) . This is consistent with the delayed release of clients by HtpG ( E34A ) , suggesting that HtpG's role in motility derives from a direct involvement in flagellar complex assembly . Given that both bacterial and eukaryotic Hsp90s are known to collaborate with Hsp70 in refolding proteins [14] , [40]–[42] , we considered the possibility that this was also the case for bacterial flagellar assembly . We previously showed that some flagellar motor components interact with DnaK , the E . coli Hsp70 homolog [19] . Therefore , we repeated the FRET experiments testing for interactions between HtpG or HtpG ( E34A ) and FliN in a ΔcbpAΔdnaJ background . CbpA and DnaJ are DnaK co-chaperones and are essential for DnaK–dependent refolding activity [14] . DnaK should not be able to pass substrates to HtpG in this mutant background . Indeed , we found that FRET interactions with FliN disappear for both HtpG proteins in this background ( Figure S6A ) , suggesting that DnaK-dependent remodeling precedes HtpG action in flagellar complex assembly . Since a recent high-throughput assay showed kinases to be overrepresented among eukaryotic Hsp90 clients [43] , [44] , we next examined whether the HtpG-dependent defects in chemotaxis may also be due to defective chemoreceptor kinase activity . Although no chemotaxis proteins were found in our list of the strongest putative clients , we did observe a significant enrichment of these components in the hsp90A-associated set ( Table 1 ) . We thus tested interactions between six chemoreceptor cluster components and HtpG ( E34A ) using , as before , acceptor photobleaching FRET ( Table S4 ) . We observed a strong interaction of HtpG ( E34A ) with the chemoreceptor kinase CheA . Our results suggest that the FliN/HtpG and CheA/HtpG interactions are direct and do not depend on other flagellar or chemotaxis proteins , since these interactions are robust to deletion of flhC , which ablates expression of all endogenous flagellar and chemotaxis genes ( Table S4 ) [19] . Moreover , the CheA dimerization domain was required for association with HtpG , supporting the hypothesis that HtpG aids oligomerization of its clients [17] , [45] . Testing HtpG interactions with other chemotaxis proteins of E . coli revealed an additional strong interaction with the dimeric phosphatase CheZ but not with other proteins ( Table S4 ) . We again examined the temporal dynamics of these interactions . Due to the hierarchical order of flagellar and chemotaxis gene expression [39] , [46] , the assembly of chemoreceptor clusters is delayed compared to the assembly of flagellar motors as non-motile cells transition into motile cells . Indeed , the interaction of HtpG with CheA peaked at OD600 = 0 . 3 , after the FliN peak ( Figure 5A ) . Just as for FliN , the interaction of HtpG ( E34A ) with CheA was stronger and delayed compared to wild-type HtpG , and the HtpG-CheA interaction disappeared in a ΔcbpAΔdnaJ background ( Figure S6B ) . Collectively , these findings suggest that HtpG plays an important role in the assembly of both the flagellar motor and chemoreceptor clusters through separate client interactions . Given the role of HtpG in chaperoning proteins that mediate interactions with the environment , and the known role of eukaryotic Hsp90 in phenotypic robustness , we finally examined whether hsp90A directly co-evolved with certain bacterial organismal traits . We considered several organismal traits , including aerobism , thermophilicity , halophilicity , the ability to form endospores , pathogenicity , motility , and habitat preferences ( see Methods ) . We used BayesTraits and the Ciccarelli tree to identify traits that co-evolve with hsp90A . Out of the 11 analyzed traits , 4 exhibited significant associations with hsp90A ( p<0 . 05; Table S5 ) , with the strongest association observed between hsp90A and the capacity to inhabit multiple habitats . Moreover , examining the gain and loss rates obtained , we found that hsp90A is gained and lost at significantly higher rates in organisms that inhabit multiple habitats ( with no gains inferred in single habitat organisms ) , suggesting that a preference for multiple habitats imposes a different selection regime on hsp90A ( Figure 6 ) . We also tested whether the co-evolutionary dependency between hsp90A and multiple-habitat preferences was unidirectional , as we observed for some hsp90A-associated genes . Comparing the four co-evolutionary models described above and applying AIC to identify the best-fitting model , we found that hsp90A gain and loss depended on habitat preference , but not vice versa . This observation suggests that in organisms inhabiting multiple environments hsp90A is subjected to dynamically shifting selective pressures , potentially alternating between selection for and against hsp90A .
We set out to discover Hsp90 functions conserved throughout the bacterial tree of life . We found that hsp90A , the most common paralog of bacterial Hsp90 , bore strong signatures of co-evolution with several hundred genes and with specific life history traits , shedding light on its function and impact on evolutionary history . Most notably , we found that hsp90A co-evolved with membrane protein complexes such as flagella and other Type III secretion ( T3S ) systems . Our results suggest that Hsp90's role in sensing and responding to environmental stimuli is conserved between bacteria and eukaryotes . Similar to verified eukaryotic Hsp90 clients [5] , our predicted putative Hsp90A clients were a diverse group of proteins ( e . g . the flagella protein FliN , the chaperone ClpA , and the ribosomal protein RluB; see Table 2 ) that tended to belong to specific functional categories ( e . g . flagellar proteins , chaperones , and ribosomal components ) . As our methods can only infer associations between genes that are frequently gained and lost , we may substantially underestimate the number of hsp90A-associated genes and clients . However , the non-essentiality and frequent loss of hsp90A throughout bacterial diversity argues that genes not captured in our analysis ( since they are not frequently gained and lost ) are unlikely to be strongly dependent on the chaperone throughout bacteria . The subtlety of the bacterial Hsp90 mutant phenotypes that we ( and others ) report implies that Hsp90's role in cellular physiology has diverged between eukaryotes and prokaryotes [17] , [45] , [47] . In other words , either essential pieces of cellular physiology changed , or Hsp90 function changed . We favor the first hypothesis , because Hsp90 is well-conserved among bacteria , archaea , and humans at the sequence level [13] , and retains a similar quaternary structure [48] and biochemical activity [15] , [37] , [44] . In contrast , bacterial and archaeal cells differ significantly from eukaryotic cells . Eukaryotic cells have higher cell compartmentalization , longer and multifunctional proteins with multiple domains [49] , and increased protein interactome complexity [50] . Together with the existence of many eukaryotic Hsp90 co-chaperones , all these features may contribute to the greater essentiality of Hsp90 in eukaryotes . The dependence of HtpG-client interactions upon the DnaK chaperone system , as observed by us and by others [14] , [15] , argues that Hsp90A is well-integrated with other chaperone systems . Our putative clients included ClpA , the substrate adaptor for the ClpAP/ClpAXP chaperone/protease complexes , and PpiD , a periplasmic chaperone [51] . Like HtpG , PpiD is necessary for optimal swarming motility [33] , suggesting that it may participate in flagellar assembly . We speculate that these proteins act as Hsp90A co-chaperones in some bacteria; alternatively , their dependence on Hsp90A may represent an example of collaborating chaperone systems . The best-characterized Hsp90 client in bacteria is the structural ribosomal protein L2 [15] , [18] , which is near-universally conserved throughout life ( and hence not detectable by our method ) . In addition to L2 , other ribosomal proteins were found to interact with HtpG in large-scale proteomics analyses . In agreement with these observations , we found the ribosomal proteins RlmE and RluB among the predicted hsp90A clients . Although these chaperone and ribosomal proteins were predicted to be stronger clients than flagellar proteins , our experimental validation focused on the latter as their client status was suggested by previous observations [19] , [33] . We present four lines of evidence for HtpG client status for the flagellar protein FliN and the chemoreceptor kinase CheA , including direct interactions with HtpG , physiologically relevant timing of HtpG-FliN/CheA interactions , phenotypic consequences of reduced HtpG function in CheA/FliN-dependent traits , and dependence of CheA/FliN interactions with HtpG upon the Hsp40-Hsp70 pathway . The identification of FliN and CheA as HtpG clients is consistent with the hypothesis that bacterial Hsp90 facilitates the assembly of large membrane-associated protein complexes [17] , [45] . Curiously , whereas the flagellar T3S system contained Hsp90A clients , the nonflagellar T3S system is predicted to have an antagonistic relationship with Hsp90A . Nonflagellar T3S systems and the flagellar T3S systems are closely related ( NF-T3SS and F-T3SS ) [52] , [53] . 9 NF-T3SS components are directly homologous to flagellar components , of which 8 were found to co-evolve with hsp90A in our analysis . Yet , these 8 genes are predicted to co-evolve antagonistically with hsp90A ( Figure 3 ) , whereas their flagellar homologs are mostly predicted to be clients ( for instance , the fliI and yscN genes shown in Figure 2 are homologous ) . This result suggests that some relationship with Hsp90A is conserved between the two T3S systems , but with apparently opposite effects in each system . This result may reflect the fact that each of these systems is an adaptation to different ecological challenges . Specifically , we have shown that Hsp90A is important for flagellum-enabled motility and chemotaxis in E . coli . This mode of motility is strongly adaptive in certain physical environments [34] , [54] , [55] , and thus Hsp90A is likely to be associated with fitness in these environments through flagellar assembly . The presence of NF-T3SS is likewise an adaptation to certain biotic environments [55] , [56] . Our observation that organisms inhabiting multiple habitats experience fluctuating selection for hsp90A is also consistent with competing selection pressures . Representative genes of these homologous T3S families were not significantly associated with habitat preferences , arguing that hsp90A's association with habitat preferences is not a byproduct of associations with T3S systems . Nonetheless , we suggest that these two T3S systems constitute a link between Hsp90A and phenotypic robustness across different environments . Inferring function from evolutionary associations has some caveats . For instance , F-T3S systems can be found in genomes that lack hsp90A . If F-T3S systems include Hsp90A clients , then what may render Hsp90A-dependent stabilization dispensable in some bacteria ? Experimental validation will be necessary to answer such questions , and to distinguish true client relationships from indirect co-evolutionary associations . As discussed before , our method is subject to gene set bias , in that only genes that are gained and/or lost frequently will have enough statistical power to reject the null hypothesis . Similarly , as our method assumes that relationships are maintained throughout the analyzed phylogeny , we cannot reliably detect genes that are associated with hsp90A in some organisms but not in others . Although much work remains to articulate the precise mechanistic relationships between hsp90A and its co-evolving genes , our results highlight the tremendous potential of evolutionary inference for guiding experimental research . More generally , our study provides a successful example of how evolutionary perspectives and phylogenetic analyses can inform and advance the study of complex biological systems and the inference of elusive biological functions .
We downloaded all Hsp90 amino acid sequences ( including all paralogs ) for bacteria with full KEGG genome annotations from the KEGG database [24] , [57] . We aligned these sequences using ClustalO [58] , and used the PHYLIP package [59] to construct neighbor-joining trees and assess their phylogenetic support through bootstrapping . We assigned Hsp90 families to branches according to bootstrap support for the branch and previous classifications [11] , [12] . We acquired presence/absence patterns of genes across organisms from the KEGG database release 60 . 0 ( in the form of KEGG Orthology/KO profiles ) [57] , and functional annotations from KEGG Class . Genes that were either present in fewer than five species or absent in fewer than five species in the tree of interest were dropped from our analysis , as these genes are unlikely to show meaningful signatures of co-evolution by this method . We obtained the tree constructed by Ciccarelli et al . ( Ciccarelli tree ) [29] and pruned it to 148 bacterial species for which KEGG genome data was available . We also obtained the LTP104 version of the 16S/23S rRNA tree from the All-Species Living Tree Project ( Yarza tree ) [30] , [60] . We used ARB [61] to prune this tree to bacterial species for which KEGG genome data was available . We further pruned this tree to omit clades placed paraphyletically at the taxonomic levels of phylum , class , order , and family . This filtered tree included 797 bacterial species . As BayesTraits cannot process trees with zero-length branches , all branch lengths equal to zero were replaced with a negligible branch length ( 0 . 00001 , approximately an order of magnitude smaller than the next smallest branch length in each tree ) . We acquired organismal trait data from the NCBI Entrez genome project , November 2011 [62] . We recoded all traits into presence/absence patterns for the trait in question . For instance , an organism found to be pathogenic towards any other organism was coded as ‘1’ for the trait of pathogenicity , whereas an annotated organism that was never found to be pathogenic was coded as ‘0’ . Similarly , we coded both thermophilic and hyperthermophilic organisms as ‘1’ for the trait of thermophilicity , whereas all other annotated organisms were coded as ‘0’; anaerobic organisms were coded as ‘0’ for the trait of aerobicity , whereas all other annotated organisms were recoded as ‘1’ . We define as inhabiting multiple habitats any organism that inhabits more than one of NCBI's habitat categories . For BayesTraits analysis , the tree was pruned to include only species annotated for the trait in question ( each trait analysis was accordingly performed on a slightly different set of species; see Table S5 for details on species number for each analysis ) . A complete description of the BayesTraits ( v1 . 0 ) framework can be found elsewhere [26] . Briefly , consider a character with 2 states , 0 and 1 . If a species has 2 such distinct characters , it can occupy 4 possible states: 1: ( 0 , 0 ) , 2: ( 0 , 1 ) , 3: ( 1 , 0 ) , and 4: ( 1 , 1 ) . Specifically , if these 2 characters represent the presence or absence of two genes , hsp90A and gene X , these four states correspond to ( hsp90A− , X− ) , ( hsp90A+ , X− ) , ( hsp90A− , X+ ) , and ( hsp90A+ , X+ ) . Evolution is then the process by which these genes are gained and lost over time . Consider accordingly an evolutionary process where only one character can change state at a time . Such a process can then be described by 8 parameters for the rates of transition per unit time between these 4 states: Q = [q12 , q13 , q21 , q31 , q24 , q34 , q42 , q43] , where qxy is the rate of transition from state x to state y . BayesTraits implements this model of evolution as a continuous-time Markov process and estimates each of these rate parameters by maximum-likelihood ( ML ) . We further validated that these ML-based rates are consistent with reversible-jump Markov chain Monte Carlo-derived estimates ( Methods; Text S1 ) . This estimation is based on a phylogeny and on the states of the two characters at the tips of the phylogeny . Having estimated these rates , BayesTraits additionally calculates the likelihood of the model based on the character states at the tips of the phylogeny . We can further compare different models of evolution by forcing certain parameters to be equal . We specifically considered the following 4 models: We used discrete from the BayesTraits package [26]–[28] to infer associations between hsp90A and other bacterial genes and between hsp90A and various organismal traits . We first tested for an evolutionary association with hsp90A by comparing model 1 to model 2 above with a likelihood ratio test ( LRT ) , as previously described [28] . In our likelihood-ratio tests , the 2Log ( LR ) approximates a χ2 test statistic for rejecting the independent model as a null hypothesis , and is calculated as twice the difference of the log-likelihoods of a co-evolutionary model and a model of evolutionary independence . The set of genes for which model 2 is preferred ( i . e . , model 1 is rejected as a null hypothesis ) have an evolutionary association with hsp90A . Since different runs of the BayesTraits maximum likelihood method can potentially produce different parameter values , we repeated this procedure 100 times , each potentially resulting in a different gene set . We validated that these sets are similar and the choice of gene set does not substantially affect downstream analysis ( Text S1 ) . Any gene that was found to be associated with hsp90A in at least 90 runs was defined as hsp90A-associated gene . See Text S1 for more details . We selected 10 genes at random from the hsp90A-associated set and used the BayesTraits implementation of reversible-jump Markov chain Monte Carlo to estimate the rate parameters for their gain and loss in concert with hsp90A [63] . For each of these 10 genes , we used an exponential rate prior with mean and variance equal to 30 , and ran the chain for 150 million iterations while sampling every 100 iterations . We discarded the first 75 million iterations as burn-in and used the remaining iterations as a posterior distribution of rate parameter estimates . We used Tracer v1 . 5 [64] and previously described criteria to evaluate chain convergence in this remaining sample [65] . For each rate , we used the median of its posterior distribution in this sample as a point estimate . To provide an accurate description of the co-evolutionary dynamics of hsp90A-associated genes , we further applied BayesTraits to these genes , estimating the likelihood of each of the four models described above . We identified the best fit model for each gene using the Akaike Information Criterion ( AIC ) [31] , taking into account both the likelihood score and the number of parameters in each model . We again repeated this procedure 100 times and classified a gene into a specific co-evolutionary model only if it fit this same model in at least 90 runs ( see Text S1 for more details ) . This two stage scheme , first identifying associated genes and then selecting a model that best describes their evolutionary relationship with hsp90A , provides a more stringent test of co-evolution and supports a simple approach for multiple testing correction . We used BayesTraits-derived evolutionary transition rates under the fully unrestricted model to estimate residence times in specific states ( for instance , the proportion of time spent by bacteria in a state where both hsp90A and some other gene are present , vs . the time when only the other gene is present ) under steady state dynamics . For a given gene , the probability of being in one of the four states , A: ( hsp90A absent , Gene absent ) , B: ( hsp90A present , Gene absent ) , C: ( hsp90A absent , Gene present ) , D: ( hsp90A present , Gene present ) at a very small increment of time Δt after time t is given by:We can differentiate this to obtain the instantaneous change in each probability:At steady state dA/dt = 0 , dB/dt = 0 , etc . , and therefore:This set of linear equations can be solved for A , B , C , and D , with the requirement that A+B+C+D = 1 . We replaced 0 rates with the smallest nonzero rate in the model multiplied by 0 . 001 to allow transitions between all states . The positive nonzero solution for A , B , C , and D can then be conceived as the expected residence times along some arbitrary bacterial lineage . We used these residence times to estimate a Putative Client Index , PCI , denoting the normalized residence time in state C:Notably , if Hsp90A and the gene's product have no client relationship , the proportion of time spent in state C is expected to be equal to ( C+D ) ( A+C ) , so a PCI close to 1 indicates that the observation does not differ from the expectation . Smaller values of PCI therefore indicate that a gene is observed less frequently than expected without hsp90A , and is thus more likely to be a client . Since no obvious threshold value can be defined , we considered the 20 genes with the lowest PCI values as putative clients ( Figure 3 and Table 2; Methods ) . To account for variation in rates between BayesTraits runs we repeated this procedure 100 times and defined as putative clients those that were identified as clients in at least 90 of these runs ( see Text S1 ) . PCI scores shown in Table 2 and Figure 3 are averages across all runs . We used a hypergeometric test to assess whether each KEGG Class functional annotation is overrepresented in the various Hsp90-associated gene sets . As a background set in each case we used the entire set of genes analyzed . Any annotation present in less than 4 copies in the background set was not considered . We accepted enrichments at a 5% FDR . Escherichia coli K-12 strains and plasmids used in this study are listed in Table S2 . Cells were grown in tryptone broth ( TB; 1% tryptone and 0 . 5% NaCl ) and when necessary supplemented with ampicillin , chloramphenicol and/or kanamycin at final concentrations of 100 , 35 and 50 µg/ml , respectively . Overnight cultures , grown at 30°C , were diluted 1∶100 and grown at 34°C for about 4 h , to an OD600 of 0 . 45–0 . 5 . All expression constructs for YFP and CFP fusions were constructed as described previously [19] , [66] , [67] . Induction levels for protein expression were 1 µM IPTG ( pHL24 , pHL35 , pVS129 and pVS132 ) , 20 µM IPTG ( pVS64 and pVS99 ) , 25 µM IPTG ( pDK36 , pDK90 and pDK91 ) , 50 µM IPTG ( pDK19 and pVS18 ) , 0 . 005% arabinose ( pHL13 , pVS108 and pVS109 ) and 0 . 01% arabinose ( pHL52 , pHL70 , pDK14 , pDK29 , pDK30 and pDK49 ) . Cells were harvested by centrifugation ( 4 , 000 rpm , 5 min ) , washed once with tethering buffer ( 10 mM potassium phosphate , 0 . 1 mM EDTA , 1 mM L-methionine , 67 mM sodium chloride , 10 mM sodium lactate , pH 7 ) and resuspended in 10 mL tethering buffer prior to FRET measurements . TB soft agar plates were prepared by supplementing TB with 0 . 3% agar ( Applichem ) and when necessary with 100 g/mL ampicillin and 1 µM IPTG . Equal amounts of cells from different overnight cultures , adjusted depending on their optical density to the equivalent of 2 . 5 µl of culture with OD600 of 2 . 0 , were inoculated and allowed to spread at indicated temperatures for indicated times . Following incubation , photographs of plates were taken with a Canon EOS 300D ( DS6041 ) camera . Images were analyzed with ImageJ ( Wayne Rasband , NIH , http://rsb . info . nih . gov/ij/ ) to determine the diameter of the rings of spreading colonies . For analysis of motility at different growth stages ( indicated by OD600 value ) , percentages of motile cells were estimated from the microscopy movies of swimming cells . The experiment was performed with the RP437 strain , which is non-motile above 37°C . Cells were grown overnight in TB medium at 37°C to completely inhibit their motility . After dilution in fresh TB medium to OD600 0 . 01 , cells were grown at 34°C for measurements . For microscopy , cells were taken from the soft-agar plates and applied to a thin agarose pad ( 1% agarose in tethering buffer ) . Fluorescence imaging was performed on a Zeiss AxioImager microscope equipped with an ORCA AG CCD camera ( Hamamatsu ) , a 100× NA 1 . 45 objective , and HE YFP ( Excitation BP 500/25; Emission BP 535/30 ) and HE CFP ( Excitation BP 436/25; Emission BP 480/40 ) filter sets . Each imaging experiment was performed in duplicate on independent cultures . All images were acquired under identical conditions . Images were subsequently analysed using ImageJ software . FRET measurements by acceptor photobleaching were performed on a custom-modified Zeiss Axiovert 200 microscope as described before [66] . Briefly , cells expressing YFP and CFP fusions of interest were concentrated about tenfold by centrifugation , resuspended in tethering buffer and applied to a thin agarose pad ( 1% agarose in tethering buffer ) . Excitation light from a 75 XBO lamp , attenuated by a ND60 ( 0 . 2 ) neutral-density filter , passed through a band-pass ( BP ) 436/20 filter and a 495DCSP dichroic mirror and was reflected on the specimen by a Z440/532 dual-band beamsplitter ( transmission 465–500 and 550–640 nm; reflection 425–445 and 532 nm ) . Bleaching of YFP was accomplished by a 20 sec illumination with a 532 nm diode laser ( Rapp OptoElectronic ) , reflected by the 495DCSP dichroic mirror into the light path . Emission from the field of view , which was narrowed with a diaphragm to the area bleached by the laser , passed through a BP 485/40 filter onto a H7421-40 photon counter ( Hamamatsu ) . For each measurement point , photons were counted over 0 . 5 s using a counter function of the PCI-6034E board , controlled by a custom-written LabView 7 . 1 program ( both from National Instruments ) . CFP emission was recorded before and after bleaching of YFP , and FRET was calculated as the CFP signal increase divided by the total signal after bleaching . ΔflhC strains were used to define direct interactions between HtpG and flagellar and chemotaxis components . In this background expression of endogenous flagellar and chemotaxis genes is inhibited , thus eliminating indirect interactions that may result from concomitant binding of HtpG and tested protein to a third flagellar or chemotaxis protein . | Hsp90 is a chaperone protein that aids the folding of many other proteins ( clients ) , which tend to be signal transduction proteins . Hsp90 is particularly important when organisms are under environmental or mutational stress ( e . g . in cancerous cells ) . Although Hsp90 is well-studied in eukaryotic species from yeast to humans , little is known about its counterpart in bacteria . To address this challenge , we analyzed the presence and absence of thousands of genes across numerous bacterial species and identified genes that co-evolved with Hsp90 . These genes provide insights into potential functions of Hsp90 in bacteria . We found that Hsp90 co-evolves with membrane-associated protein complexes such as the flagellum and that Hsp90 is associated with a preference for inhabiting multiple habitats . We extended our analysis to identify genes that exhibit evolutionary dynamics characteristic of Hsp90 clients . Many of the putative clients were involved in flagellar assembly , suggesting a crucial role of Hsp90 in the regulation of bacterial motility . We experimentally confirmed that E . coli Hsp90 interacts with selected candidates and demonstrated Hsp90's role in flagellar motility and chemotaxis . The computational approach described here , identifying novel functions and specific clients of bacterial Hsp90 , further provides exciting starting points for research in bacterial chaperone biology . | [
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] | 2013 | Genome-scale Co-evolutionary Inference Identifies Functions and Clients of Bacterial Hsp90 |
LET-23 Epidermal Growth Factor Receptor ( EGFR ) signaling specifies the vulval cell fates during C . elegans larval development . LET-23 EGFR localization on the basolateral membrane of the vulval precursor cells ( VPCs ) is required to engage the LIN-3 EGF-like inductive signal . The LIN-2 Cask/LIN-7 Veli/LIN-10 Mint ( LIN-2/7/10 ) complex binds LET-23 EGFR , is required for its basolateral membrane localization , and therefore , vulva induction . Besides the LIN-2/7/10 complex , the trafficking pathways that regulate LET-23 EGFR localization have not been defined . Here we identify vh4 , a hypomorphic allele of agef-1 , as a strong suppressor of the lin-2 mutant Vulvaless ( Vul ) phenotype . AGEF-1 is homologous to the mammalian BIG1 and BIG2 Arf GTPase guanine nucleotide exchange factors ( GEFs ) , which regulate secretory traffic between the Trans-Golgi network , endosomes and the plasma membrane via activation of Arf GTPases and recruitment of the AP-1 clathrin adaptor complex . Consistent with a role in trafficking we show that AGEF-1 is required for protein secretion and that AGEF-1 and the AP-1 complex regulate endosome size in coelomocytes . The AP-1 complex has previously been implicated in negative regulation of LET-23 EGFR , however the mechanism was not known . Our genetic data indicate that AGEF-1 is a strong negative regulator of LET-23 EGFR signaling that functions in the VPCs at the level of the receptor . In line with AGEF-1 being an Arf GEF , we identify the ARF-1 . 2 and ARF-3 GTPases as also negatively regulating signaling . We find that the agef-1 ( vh4 ) mutation results in increased LET-23 EGFR on the basolateral membrane in both wild-type and lin-2 mutant animals . Furthermore , unc-101 ( RNAi ) , a component of the AP-1 complex , increased LET-23 EGFR on the basolateral membrane in lin-2 and agef-1 ( vh4 ) ; lin-2 mutant animals . Thus , an AGEF-1/Arf GTPase/AP-1 ensemble functions opposite the LIN-2/7/10 complex to antagonize LET-23 EGFR basolateral membrane localization and signaling .
C . elegans vulval cell induction requires a highly conserved Epidermal Growth Factor Receptor ( EGFR ) /Ras GTPase/Mitogen Activated Protein Kinase ( MAPK ) signaling pathway providing an in vivo model in which to study signaling in a polarized epithelia [1] , [2] . During larval development , an equivalence group of six vulval precursor cells ( VPCs ) , P3 . p-P8 . p , have the potential to be induced to generate the vulva . The anchor cell in the overlying gonad secretes the LIN-3 EGF-like ligand , inducing the closest VPC , P6 . p , to adopt the primary vulval fate , and a combination of graded LIN-3 EGF signal and lateral signaling by LIN-12 Notch specifies the neighboring VPCs , P5 . p and P7 . p , to adopt the secondary vulval fate . Together P5 . p-P7 . p generate the 22 nuclei of the mature vulva , eight cells from the primary cell and seven from each of the secondary cells . The remaining VPCs , P3 . p , P4 . p , and P8 . p , divide once and fuse with the surrounding hypodermal syncytium ( 50% of the time P3 . p fuses prior to dividing ) and thus adopt a tertiary non-vulval fate . Inhibition of LET-23 EGFR signaling causes a Vulvaless ( Vul ) phenotype in which less than the normal three VPCs are induced . Conversely , increased LET-23 EGFR signaling causes a Multivulva ( Muv ) phenotype in which greater than three VPCs are induced . LET-23 EGFR localizes to both the apical and basolateral membranes of the VPCs , though , it is the basolateral localization that is thought to engage LIN-3 EGF and induce vulva induction [3] , [4] , [5] . A tripartite complex of proteins , LIN-2 Cask , LIN-7 Veli , and LIN-10 Mint ( LIN-2/7/10 ) , interacts with the C-terminal tail of LET-23 EGFR and is required for its basolateral localization [3] , [4] . Mutations in any component of the complex , or the let-23 ( sy1 ) mutation , which deletes the last six amino acids of LET-23 EGFR that are required for its interaction with LIN-7 , result in LET-23 EGFR localizing only to the apical membrane and a strong Vul phenotype [3] , [4] , [6] , [7] , [8] . The Vul phenotype of lin-2/7/10 mutants or the let-23 ( sy1 ) mutant are easily suppressed to a wild-type or even a Muv phenotype by loss of negative regulators of LET-23 EGFR signaling such as sli-1 Cbl , gap-1 RasGAP , rab-7 GTPase , and unc-101 AP-1μ [5] , [9] , [10] , [11] , [12] . Thus far , no suppressors of the lin-2/7/10 mutant Vul phenotype have been shown to restore LET-23 EGFR to the basolateral membrane . UNC-101 and APM-1 are two μ1 subunits for the AP-1 adaptor protein complex , which function redundantly to antagonize vulva cell induction [12] , [13] . In mammals , AP-1 localizes to the trans-Golgi network ( TGN ) and endosomes , promotes formation of clathrin-coated vesicles , and is involved in regulated secretion from the TGN . [14] , [15] , [16] . In epithelial cells , AP-1 sorts cargo , including EGFR , to the basolateral membrane , which would be inconsistent with AP-1 antagonizing signaling [17] , [18] . The small GTPase , Arf1 , recruits AP-1 to the TGN and thus facilitates the formation of clathrin-coated vesicles [15] , [19] , [20] . BIG1 and BIG2 are Sec7 domain containing guanine nucleotide exchange factors ( GEFs ) for class I Arf GTPases [21] , [22] , and are required for recruitment of AP-1 to the TGN and endosomes [23] , [24] . To date , neither Arf1 nor the BIG1/2 GEFs have been implicated in EGFR/Ras/MAPK signaling . Here we identify C . elegans AGEF-1 , a homolog of yeast Sec7p and the mammalian BIG1 and BIG2 Arf GEFs , as negatively regulating EGFR/Ras/MAPK-mediated vulva induction . We show that AGEF-1 regulates protein secretion in multiple tissues , regulates polarized localization of the SID-2 transmembrane protein in the intestine , and regulates the size of late endosomes/lysosomes with the AP-1 complex in the macrophage/scavenger cell-like coelomocytes . Genetic epistasis places AGEF-1 upstream or in parallel to LET-23 EGFR . We find that the ARF-1 . 2 and ARF-3 GTPases also negatively regulate LET-23 EGFR signaling . Moreover , our genetics are consistent with AGEF-1 BIG1/2 , ARF-1 . 2 Arf1 and UNC-101 AP-1μ1 functioning together in preventing ectopic vulva induction . It has been 20 years since UNC-101 was identified as a negative regulator of LET-23 EGFR signaling , however its mechanism of action has remained an enigma [12] . Contrary to the role of AP-1 in basolateral sorting in mammalian cells , we demonstrate that AGEF-1 BIG1/2 and UNC-101 AP-1μ1 antagonize the basolateral membrane localization of LET-23 EGFR in the VPCs . Thus , the AGEF-1/Arf GTPase/AP-1 ensemble antagonizes LET-23 EGFR-mediated vulva induction via regulation of LET-23 EGFR membrane localization .
We previously reported that rab-7 ( ok511 ) , a maternal effect embryonic lethal mutant , strongly suppresses the lin-2 ( e1309 ) Vul phenotype [11] . To identify new candidate regulators of LET-23 EGFR trafficking and signaling we conducted a clonal screen for essential suppressors of lin-2 ( e1309 ) ( see Materials and Methods ) . In this screen we identified vh4 as a strong suppressor of the lin-2 ( e1309 ) Vul phenotype ( Figure 1A–D; Table 1 , lines 1–4 ) . The vh4 mutation can suppress the 100% Vul phenotype of lin-2 ( e1309 ) to 20% Vul , and 30% Muv . In a lin-2 ( + ) background , however , vh4 mutant animals have 100% wild-type vulva induction . Consistent with a potential role in vesicular trafficking , the coelomocytes ( macrophage-like scavenger cells ) of vh4 mutants accumulate abnormally large vesicular structures ( Figure 1E , F ) . Additionally , vh4 mutants have a dumpy body morphology , uncoordinated movement and ∼50 percent embryonic lethality ( Figure 1G , H ) . To determine the molecular identity of vh4 , we used a single nucleotide polymorphism ( SNP ) mapping strategy [25] . Genome-wide mapping located vh4 to the right arm of chromosome I , and interval mapping placed vh4 in a 2 . 75 map unit region between SNPs haw14137 and pkP1071 at positions 20 . 65 and 23 . 4 map units , respectively ( Figure 2A ) . We further refined the genomic interval by complementation with chromosomal deficiencies dxDf2 and eDf3 . vh4 failed to complement the large deficiency dxDf2 , but complemented the small eDf3 , indicating that vh4 lies in a 0 . 9 map unit region ( 20 . 65–21 . 51 ) containing 27 genes . We found that one obvious candidate , vps-28 , was an RNAi suppressor of the lin-2 Vul phenotype [11] . However , vh4 complemented the vps-28 ( tm3767 ) deletion allele and no lesion in the vps-28 coding sequence of vh4 animals was detected by DNA sequencing suggesting that vh4 is not an allele of vps-28 . Whole genome sequencing revealed a homozygous G to A transition at position 3082 in exon 11 of the agef-1 gene ( AAA TTT TTG GAA AAG GGA GAA CTT CCG AAT TTC CGA TTT ) that corresponds to a glutamate to lysine substitution in a conserved region of the predicted AGEF-1 protein ( Figure 2B ) . Consistent with vh4 being a mutation in agef-1 , we find that agef-1 ( RNAi ) suppresses the severity of the lin-2 Vul phenotype ( Table 1 , lines 3 and 7 ) and agef-1 ( vh4 ) mutant oocytes have defects in CAV-1 body formation as previously seen with agef-1 ( RNAi ) ( Figure S1J–M ) [26] . Finally , agef-1 ( vh4 ) fails to complement two deletion alleles , agef-1 ( ok1736 ) and agef-1 ( tm1693 ) , resulting in a strong embryonic lethal phenotype . These data indicate that vh4 is a hypomorphic allele of agef-1 . To determine the identity of the large vesicles in agef-1 ( vh4 ) coelomocytes , we used GFP tagged endosomal and Golgi proteins . Since the vesicles are presumably in flux , we measured the diameter of the largest GFP-positive vesicle per coelomocyte . We found a modest , but significant increase in the size of vesicles positive for the early endosomal 2×FYVE::GFP and the pan-endosomal RME-8::GFP markers in agef-1 ( vh4 ) animals as compared to wild-type ( Figure 3A–C and Figure S2A–C ) . However , the large vesicles in agef-1 ( vh4 ) coelomocytes visible by DIC optics correspond to LMP-1::GFP , a marker for late endosomes/lysosomes ( Figure 3D–F ) . This finding corroborates a concurrent study identifying large LMP-1 positive vesicles in the coelomocytes of agef-1 ( RNAi ) animals [27] . LMP-1 is a transmembrane protein , whose mammalian homolog , Lamp1 , can transit from Golgi via the plasma membrane and endosomes to the lysosome [28] . Therefore , we assessed the morphology of the Golgi in agef-1 ( vh4 ) mutants using a mannosidase II::GFP marker . While there might be a slight increase in the size of the Golgi mini-stacks , they were distinct from the large LMP-1::GFP positive vesicles seen by DIC ( Figure S2D–F ) . Of note , the mannosidase II::GFP strands that appear to interconnect the Golgi mini-stacks in wild-type ( 30/37 coelomocytes ) were largely absent in agef-1 ( vh4 ) mutants ( 6/42 coelomocytes are interconnected ) . These data show that agef-1 ( vh4 ) disrupts endosome and Golgi morphology and possibly trafficking . To test if agef-1 ( vh4 ) coelomocytes have an endocytosis defect we analyzed the internalization of a signal secreted GFP ( ssGFP ) that is expressed in body wall muscle cells , secreted into the pseudocoelom , and endocytosed by the coelomocytes [29] . We found less ssGFP in the coelomocytes of agef-1 ( vh4 ) animals as compared to wild-type ( Figure 4A–E ) . However , we did not detect a significant accumulation of ssGFP in the pseudocoelom of agef-1 ( vh4 ) mutants as would be expected for an endocytosis defect . Rather there was a clear accumulation of ssGFP in the body wall muscle cells of agef-1 ( vh4 ) animals as compared to wild-type ( Figure 4F–J ) . While this does not rule out a potential endocytosis defect in agef-1 ( vh4 ) coelomocytes , it does indicate that agef-1 ( vh4 ) mutants have a secretion defect in the body wall muscle cells . We also analyzed a Yolk::GFP fusion ( YP170::GFP ) that is secreted from the intestine , and internalized by maturing oocytes [30] . We did not detect a difference in the uptake of YP170::GFP by oocytes ( Figure S1A–D ) , however YP170::GFP levels in the intestine were higher in agef-1 ( vh4 ) animals than in wild-type ( Figure S1E–I ) . An independent study also found impaired secretion of yolk in agef-1 ( RNAi ) animals [31] . Thus , agef-1 ( vh4 ) mutants have impaired protein secretion from both body wall muscle and intestinal cells . To understand the role of AGEF-1 in the LET-23 EGFR/LET-60 Ras signaling pathway , we made double mutants with agef-1 and several mutations in core components of the pathway . A gain of function mutation in let-60 ras ( n1046 ) causes a Muv phenotype that can be enhanced by loss of a negative regulator of the pathway [11] , [32] , [33] , [34] . agef-1 ( vh4 ) significantly enhances the Muv phenotype of let-60 ( n1046 ) , consistent with AGEF-1 being a negative regulator of signaling ( Table 1 , lines 10–11 ) . We performed epistasis analysis to determine at which step of the pathway AGEF-1 functions . We found that agef-1 ( vh4 ) strongly suppresses the Vul phenotype of the let-23 ( sy1 ) mutant ( Table 1 , lines 12–13 ) . The sy1 allele truncates the last six amino acids of LET-23 EGFR that are required for its interaction with the LIN-2/7/10 complex , and thus behaves identical to mutations in components of this complex [3] , [8] . However , agef-1 ( vh4 ) fails to suppress the Vul phenotype of the let-23 ( sy97 ) allele that results in a more severe truncation of LET-23 EGFR that blocks signaling to the LET-60 Ras ( Table 1 , lines 14–15 ) [7] . We next tested if agef-1 ( vh4 ) can suppress the Vul phenotype of lin-3 ( e1417 ) , a strong hypomorphic allele of lin-3 EGF [35] . We found that agef-1 ( vh4 ) partially suppressed the lin-3 ( e1417 ) Vul phenotype ( Table 1 , lines 16–17 ) . These data are consistent with AGEF-1 antagonizing signaling upstream or in parallel to LET-23 EGFR . SLI-1 Cbl , a putative E3-ubiquitin ligase , and UNC-101 AP-1μ are negative regulators of LET-23 EGFR signaling that also function at the level of LET-23 EGFR [9] , [12] , [36] . Like agef-1 ( vh4 ) , mutations in sli-1 Cbl and unc-101 AP-1μ do not cause a vulval phenotype alone , but double mutants cause a synergistic Muv phenotype . Therefore , we tested if agef-1 ( vh4 ) is Muv in combination with strong loss-of-function alleles of sli-1 Cbl and unc-101 AP-1μ . We found that agef-1 ( vh4 ) ; sli-1 ( sy143 ) animals are strongly Muv , suggesting that AGEF-1 might function in parallel to SLI-1 Cbl ( Table 1 , lines 18–19 ) . We were unable to identify unc-101 ( sy108 ) agef-1 ( vh4 ) double mutants segregating from unc-101 ( sy108 ) agef-1 ( vh4 ) /unc-101 ( sy108 ) mothers suggesting that they are zygotic lethal . Thus , we fed L1 larvae RNAi and found that both unc-101 ( RNAi ) agef-1 ( vh4 ) and unc-101 ( sy108 ) agef-1 ( RNAi ) animals have a strong Muv phenotype ( Table 1 , lines 20–22 ) . Since there are two AP-1µ genes , unc-101 and apm-1 , that are functionally redundant , we cannot conclude whether AGEF-1 functions in parallel to UNC-101 , or whether they function together; we favor the later , see below . However , the strong genetic interactions of agef-1 ( vh4 ) and mutations in sli-1 and unc-101 further support AGEF-1 functioning at the level of LET-23 EGFR to negatively regulate signaling . The identification of a putative Arf GEF as a negative regulator of LET-23 EGFR signaling suggests that one or more of the four C . elegans Arf GTPases might also regulate LET-23 EGFR signaling . The mammalian Arf GTPases have been placed in three classes based on homology [37] . To gain a better understanding of the relationship of the C . elegans and human Arf GTPases we undertook a phylogenetic analysis ( Figure 2C ) . From this we conclude that C . elegans ARF-1 . 2 is homologous to Class I Arfs; ARF-3 is related to both Class I and II Arfs , but clusters with the Class II; ARF-6 is a homolog of the Class III Arf , whereas ARF-1 . 1 appears to be a Caenorhabiditis specific Arf GTPase that is distinct from the Arf-like Arl GTPases ( Figure 2C ) . We used RNAi and deletion mutants to test each arf gene for suppression of the lin-2 ( e1309 ) Vul phenotype . RNAi of either arf-1 . 2 or arf-3 partially suppressed of the lin-2 ( e1309 ) Vul phenotype ( Table 2 , lines 1–3 ) . The arf-1 . 2 ( ok796 ) deletion mutant was a much more potent suppressor of the lin-2 ( e1309 ) Vul phenotype consistent with RNAi being less effective in the VPCs [11; this study] ( Table 2 , lines 4–5 ) . The arf-3 ( tm1877 ) deletion is zygotic lethal and did not permit analysis . However , arf-3 ( RNAi ) into arf-1 . 2 ( ok796 ) ; lin-2 ( e1309 ) animals led to an even stronger suppression of the Vul phenotype comparable to that of agef-1 ( vh4 ) ; lin-2 ( e1309 ) ( Table 2 , line 8 ) . Neither the arf-6 ( tm1447 ) nor the arf-1 . 1 ( ok1840 ) deletions were able to suppress the lin-2 ( e1309 ) Vul phenotype ( Table 2 , lines 9–12 ) . These data suggest that ARF-1 . 2 and ARF-3 function in a partly redundant manner , possibly with AGEF-1 , to antagonize LET-23 EGFR signaling . To test if arf-1 . 2 was required in the VPCs we generated two transgenic extrachromosomal arrays , vhEx7 and vhEx8 , expressing ARF-1 . 2::GFP under the control of the VPC-specific promoter , lin-31 [38] . Both transgenic lines were able to strongly rescue arf-1 . 2 ( ok796 ) suppression of the lin-2 ( e1309 ) Vul phenotype ( Table 2 , lines 6–7 ) . Thus , ARF-1 . 2 functions in the VPCs to negatively regulate LET-23 EGFR signaling . We next tested whether VPC-specific overexpression of ARF-1 . 2::GFP can revert the suppression of Vul phenotype in agef-1 ( vh4 ) ; lin-2 ( e1309 ) . Both lines , vhEx7 and vhEx8 , led to a more severe Vul phenotype when expressed in agef-1 ( vh4 ) ; lin-2 ( e1309 ) animals ( Table 1 , lines 5–6 ) . This suggests that AGEF-1 functions in the VPCs through ARF-1 . 2 to antagonize LET-23 EGFR signaling . Having observed a strong Muv phenotype in unc-101 ( RNAi ) agef-1 ( vh4 ) and unc-101 ( sy108 ) agef-1 ( RNAi ) doubles ( Table 1 , lines 21–22 ) , we hypothesized that arf-1 . 2 ( ok796 ) would have similar interactions . Indeed , both unc-101 ( sy108 ) ; arf-1 . 2 ( ok796 ) and agef-1 ( vh4 ) ; arf-1 . 2 ( ok796 ) animals have a strong Muv phenotype ( Table 1 , lines 24–25 ) . Given that agef-1 ( vh4 ) is a weak hypomorphic allele and ARF-1 . 2 and UNC-101 AP-1μ are each functionally redundant with ARF-3 and APM-1 AP-1μ , respectively; these data are consistent with AGEF-1 , ARF-1 . 2 and UNC-101 AP-1μ functioning together to negatively regulate LET-23 EGFR signaling . If AGEF-1 , the ARF GTPases and the AP-1 complex function together , we expect that they will have shared phenotypes . We tested whether the ARFs and the AP-1 complex regulate the size of vesicles in coelomocytes as does AGEF-1 . While unc-101 ( sy108 ) mutants do not have large vesicles , further depletion of the AP-1 complex by RNAi of apm-1 AP-1μ or apg-1 AP-1γ in the unc-101 ( sy108 ) background resulted in enlarged LMP-1::GFP vesicles in the coelomocytes ( Figure 3G–I ) . Consistent with previous studies , we found no evidence for arf-1 . 2 or arf-3 in regulating the size of vesicles in the coelomocytes [27] , nor do deletions in arf-1 . 1 or arf-6 . The complement of ARF GTPases that function with AGEF-1 and AP-1 in coelomocytes remains to be determined . The AP-1 complex has recently been shown to restrict both apical and basolateral membrane protein localization in the C . elegans intestine [39] , [40] . Similarly , we found that the apically localized SID-2 transmembrane protein [41] , was mislocalized to the cytoplasm and basolateral membranes in agef-1 ( vh4 ) mutants ( Figure S3A–D ) , suggesting that AGEF-1 and AP-1 might function together to regulate polarized localization of membrane proteins in the intestine . The role of AGEF-1 in restricting SID-2::GFP on the apical membrane suggests that AGEF-1 , the ARF GTPases and the AP-1 complex might restrict LET-23 EGFR to the apical membrane in the VPCs . To test this hypothesis we made use of two transgenic strains expressing a LET-23 EGFR GFP fusion ( zhIs035 and zhIs038 ) that mimic the localization of endogenous LET-23 EGFR as seen by antibody staining [5] . In wild-type animals , LET-23::GFP localizes to both the apical and basolateral membranes of P6 . p and in the lin-2 ( e1309 ) animals LET-23::GFP localizes strictly to the apical membrane ( Figure 5A , C and Figure S4A , C ) . At the Pn . px stage , some basolateral , or lateral only localization is seen in lin-2 ( e1309 ) animals . Despite the lack of basolateral localization at the Pn . p stage , we find that the LET-23::GFP transgenes fully rescue the lin-2 ( e1309 ) Vul phenotype ( Table 1 , lines 8 and 9 ) , suggesting that the levels of LET-23 EGFR at the basolateral membrane required for VPC induction are below the level of detection . Similarly , the gaIs27 LET-23::GFP transgene , that is only detectable by immunostaining with anti-GFP antibody , suppressed the lin-2 ( e1309 ) egg-laying defective phenotype [11] . To determine if AGEF-1 regulates LET-23 EGFR localization we compared the ratio of basolateral versus apical localization of LET-23::GFP in the P6 . p cell of wild-type and agef-1 ( vh4 ) animals ( Table 3 ) . In wild-type , the average basal/apical intensity of LET-23::GFP in P6 . p was 0 . 49 for zhIs035 and 0 . 65 for zhIs038 . In agef-1 ( vh4 ) animals , the average basal/apical intensity of LET-23::GFP in the P6 . p cell is 0 . 79 for zhIs035 and 0 . 93 for zhIs038 reflecting a decrease in apical intensity and an increase in basolateral intensity . We also found LET-23::GFP is present on the basolateral membrane of the intestinal cells in agef-1 ( vh4 ) animals whereas we did not see this in wild-type by confocal microscopy ( Figure S3E–H ) . Therefore , AGEF-1 represses basolateral localization of LET-23::GFP in the VPCs and intestinal cells . We next tested if agef-1 ( vh4 ) could restore the basolateral localization of LET-23::GFP in lin-2 ( 1309 ) animals . We found that ∼40% of agef-1 ( vh4 ) ; lin-2 ( e1309 ) animals with zhIs035 have weak basolateral membrane localization of LET-23::GFP in P6 . p compared to 9% in lin-2 ( e1309 ) animals ( Figure 5C–D , G ) . Similarly , at the P6 . px stage , we see an increase in the number of animals with basolateral localization of LET-23::GFP in agef-1 ( vh4 ) ; lin-2 ( e1309 ) as compared to lin-2 ( e1309 ) single mutants ( Figure 5C′–D′ , G′ ) . No basolateral LET-23::GFP was seen with agef-1 ( vh4 ) ; lin-2 ( e1309 ) animals with the lower expressing zhIs038 ( Figure S4C′–D′ , F′ ) . Since agef-1 ( vh4 ) is a weak hypomorphic mutation , we tested if knocking down the AP-1 complex via unc-101 ( RNAi ) can further restore basolateral localization of LET-23 EGFR in agef-1 ( vh4 ) ; lin-2 ( e1309 ) mutants . We found that unc-101 ( RNAi ) agef-1 ( vh4 ) ; lin-2 ( e1309 ) animals with either zhIs035 or zhIs038 had an increase in basolateral membrane localization of LET-23::GFP in both the intensity and the number of animals ( Figure 5F–G′ and Figure S4E , F ) . unc-101 ( RNAi ) ; lin-2 ( e1309 ) animals only showed mild restoration of LET-23::GFP using the zhIs035 transgene ( Figure 5E , E′ , G and G′ ) . The restoration of LET-23 EGFR on the basolateral membrane in agef-1 ( vh4 ) ; lin-2 ( e1309 ) , unc-101 ( RNAi ) ; lin-2 ( e1309 ) and unc-101 ( RNAi ) agef-1 ( vh4 ) ; lin-2 ( e1309 ) animals suggests that AGEF-1 and UNC-101 AP-1μ negatively regulate LET-23 EGFR signaling by limiting basolateral membrane localization .
Regulators of LET-23 EGFR trafficking are likely required for viability , as is the case for the RAB-7 GTPase [11] . In a screen for essential negative regulators of LET-23 EGFR-mediated vulva induction we identified a hypomorphic allele in the agef-1 gene . AGEF-1 is the C . elegans homolog of the yeast Sec7p and human BIG1 and BIG2 Arf GEFs , which function with class I Arf GTPases and the AP-1 complex to regulate cargo sorting and trafficking from the TGN [42] . We demonstrate that AGEF-1 regulates protein secretion , polarized protein localization , and late endosome/lysosome morphology . We show that AGEF-1 antagonizes signaling in the VPCs , upstream or in parallel to LET-23 EGFR , and that the class I/II Arf GTPases , ARF-1 . 2 and ARF-3 , also negatively regulate signaling . Our genetic and phenotypic data are consistent with AGEF-1 , the ARF-1 . 2 and ARF-3 GTPases , and the AP-1 complex together preventing ectopic vulva induction . The AGEF-1/Arf GTPase/AP-1 ensemble antagonizes the basolateral membrane localization of LET-23 EGFR in the VPCs; and hence , LET-23 EGFR-mediated vulva induction . The clonal screen for suppressors of the lin-2 ( e1309 ) Vul phenotype was initially aimed at identifying maternal effect lethal mutants , like rab-7 ( ok511 ) . Instead , we identified two strong suppressors of lin-2 ( e1309 ) with partial embryonic lethal phenotypes , agef-1 ( vh4 ) and vh22 ( J . Meng , O . S . and C . E . R . , unpublished data ) ; which we currently believe function independently of each other and rab-7 . The agef-1 deletion alleles are zygotic lethal and RNAi in the VPCs with agef-1 , arf-1 . 2 and rab-7 has proven less effective than their corresponding genetic mutations [11; this study] . Therefore , the agef-1 ( vh4 ) mutation , being a recessive partial loss-of-function allele , provides a unique tool to study the function of agef-1 , particularly in tissues refractory to RNAi such as the VPCs and neurons . The agef-1 ( vh4 ) lesion changes a conserved negatively charged Glutamic Acid in the HDS2 domain to a positively charged Lysine . Collectively , the HDS2 , HDS3 , and HDS4 domains of yeast Sec7p have been shown to have an autoinhibitory function [43] . However , the specific function of the HDS2 domain is not known . Given the recessive nature of the agef-1 ( vh4 ) allele , it suggests that the HDS2 domain has a positive role in promoting AGEF-1 function . Consistent with yeast Sec7p and human BIG1/BIG2 functioning in the secretory pathway , we found that agef-1 ( vh4 ) animals had defects in secretion of ssGFP from body wall muscles and Yolk::GFP from the intestine . Similar yolk secretion defects were recently reported for agef-1 ( RNAi ) [31] . We found that agef-1 ( vh4 ) coelomocytes accumulated enlarged LMP-1::GFP positive late endosomes/lysosomes . Independently , Tang et al . [27] found that agef-1 ( RNAi ) also caused enlargement of LMP-1::GFP vesicles and proposed a role for AGEF-1 in late endosome to lysosome trafficking , however , they did not find a defect in lysosome acidification or protein degradation . We do not know the reason for the enlarged late endosomes/lysosomes , but it could reflect a defect in retrograde transport from late endosomes to the Golgi as has been shown for knockdown of BIG1 and BIG2 or the AP-1 complex in mammalian cells [23] . Consistent with this idea , we found that knockdown of the AP-1 complex also induced enlarged LMP-1::GFP vesicles . Tang et al . [27] also reported that ssGFP accumulated in the pseudocoelom suggesting an uptake defect in the coelomocytes . We found that agef-1 ( vh4 ) mutants accumulated ssGFP in the body wall muscles rather than the pseudocoelom; thus the reduced ssGFP in coelomocytes could be explained by reduced secretion from the body wall muscles . However , we cannot rule out an uptake defect in the coelomocytes as well . Perhaps these discrepancies reflect a difference in reducing the levels of agef-1 by RNAi versus the vh4 missense mutation . Our genetic analysis with agef-1 ( vh4 ) indicate that AGEF-1 is a potent negative regulator of LET-23 EGFR-mediated vulva induction . Similar to other negative regulators , agef-1 enhanced the Muv phenotype of the gain-of-function Ras mutant , let-60 ( n1046 ) , and was a potent suppressor the Vul phenotypes of lin-2 ( e1309 ) and let-23 ( sy1 ) mutations , restoring vulva induction and even inducing a Muv phenotype . However , agef-1 ( vh4 ) failed to suppress a strong let-23 ( sy97 ) allele similar to sli-1 and unc-101 mutations and consistent with a role for AGEF-1 upstream or in parallel to LET-23 EGFR . In accordance with AGEF-1 being an Arf GEF , we found that ARF-1 . 2 and ARF-3 , Class I/II Arf GTPases , also negatively regulate LET-23 EGFR signaling . The arf-1 . 2 ( ok796 ) deletion allele was a less potent suppressor of the lin-2 ( e1309 ) Vul phenotype as compared to the agef-1 ( vh4 ) mutant . However , arf-3 ( RNAi ) in arf-1 . 2 ( ok796 ) ; lin-2 ( e1309 ) doubles showed suppression comparable to that in agef-1 ( vh4 ) ; lin-2 ( e1309 ) mutants . Therefore , ARF-1 . 2 and ARF-3 appear to function in a partly redundant manner during vulva development . Furthermore , expression of an ARF-1 . 2::GFP fusion in the VPCs rescued the suppressed Vul phenotype of both arf-1 . 2 ( ok796 ) ; lin-2 ( e1309 ) and agef-1 ( vh4 ) ; lin-2 ( e1309 ) animals indicating that ARF-1 . 2 antagonizes signaling in the VPCs likely downstream of AGEF-1 . In mammalian cells , the BIG1/BIG2 proteins and Arf1 recruit the AP-1 adaptor protein complex to the TGN and endosomes [15] , [19] , [20] . Both of the C . elegans AP-1μ subunits , unc-101 and apm-1 , negatively regulate LET-23 EGFR mediated vulva development [12] , [13] . In fact , apm-1 ( RNAi ) unc-101 ( sy108 ) animals had a Muv phenotype , indicating that UNC-101 and APM-1 are functionally redundant during vulva induction , thus revealing a role for the AP-1 complex in inhibiting ectopic vulva induction [13] . Our findings that various double-mutant combinations between agef-1 ( vh4 ) , arf-1 . 2 ( ok796 ) and unc-101 ( sy108 ) AP-1μ result in a synergistic Muv phenotype are consistent with AGEF-1 , the Arfs and AP-1 functioning together to inhibit ectopic vulva induction . However , we cannot conclude whether they function in parallel pathways or in a common pathway due to the fact that agef-1 ( vh4 ) is not a null allele and the unc-101 ( sy108 ) and arf-1 . 2 ( ok796 ) mutations , while severe loss-of-function or null alleles , function in a partly redundant manner with apm-1 and arf-3 , respectively . We favor a model whereby AGEF-1 , the Arfs , and AP-1 function in a common pathway since this is most consistent with data from yeast and mammals , and that loss of AGEF-1 and components of the AP-1 complex have similar phenotypes in coelomocytes and the intestine . While synergistic genetic interactions are typically more indicative of genes in parallel pathways , we interpret that no single mutation in the AGEF-1/Arf/AP-1 pathway is sufficient to increase LET-23 EGFR signaling above a threshold necessary for ectopic induction . It is only when the activity of the AGEF-1/Arf/AP-1 pathway is further compromised by two mutations that LET-23 EGFR signaling increases above a threshold to induce a synergistic Muv phenotype . It is important to note that the AGEF-1/Arf/AP-1 pathway is essential , and only animals that survive to the fourth larval stage can be scored for vulva induction phenotypes . Thus , LET-23 EGFR signaling and localization phenotypes would likely be more severe if we were able to assess true null mutations in the VPCs only . In polarized epithelial cells , the AP-1 complex mediates sorting and polarized distribution of transmembrane proteins , including EGFR , and thus the AGEF-1/Arf GTPase/AP-1 ensemble could regulate signaling via LET-23 EGFR localization . In the P6 . p cell , we showed that the localization of LET-23 EGFR is altered in agef-1 ( vh4 ) animals using two transgenic lines ( zhIs035 and zhIs038 ) expressing LET-23::GFP [5] . In wild-type animals , LET-23::GFP is present on both the apical and basolateral domains , however the average levels of LET-23:GFP on the apical membrane are double ( zhIs035 ) or close to double ( zhIs038 ) that on the basolateral membrane ( Figure 6A ) . In agef-1 ( vh4 ) animals there was a redistribution of LET-23::GFP from apical to basolateral membrane bringing the average intensities closer to equal , suggesting that AGEF-1 either promotes apical localization or antagonizes basolateral localization of the receptor ( Figure 6A , B ) . In the lin-2 ( e1309 ) background , LET-23::GFP is apical only ( Figure 6C ) . In the more highly expressed line , zhIs035 , we see some lateral only or faint basolateral in the P6 . p descendants , P6 . pa and P6 . pp of lin-2 ( e1309 ) larvae . In the zhIs035 line , agef-1 ( vh4 ) partially restores LET-23::GFP on the basolateral membrane in lin-2 ( e1309 ) larvae . RNAi of unc-101 also partially restores basolateral localization and enhances the effect of agef-1 ( vh4 ) such that we see increased levels of LET-23::GFP , with both lines , in lin-2 ( e1309 ) larvae ( Figure 6D ) . Therefore , AGEF-1 and UNC-101 AP-1μ cooperate to antagonize LET-23 EGFR basolateral localization and thus provide a mechanism by which these genes/proteins antagonize LET-23 EGFR signaling . Despite the lack of basolateral localization of LET-23::GFP in lin-2 mutant animals , the two LET-23::GFP transgenes used in this study rescued the lin-2 ( e1309 ) Vul phenotype , suggesting that the levels of receptor required for VPC induction are below detection . Therefore , the modest amount of LET-23::GFP restored to the basolateral membrane in agef-1 ( vh4 ) ; lin-2 ( e1309 ) or unc-101 ( RNAi ) ; lin-2 ( e1309 ) could be more than sufficient to explain the strong restoration of VPC induction in these double mutants . Our findings that an AGEF-1/Arf GTPase/AP-1 ensemble antagonizes the basolateral localization of LET-23 EGFR is contradictory to the established role of the mammalian AP-1A and AP-1B complexes in sorting transmembrane proteins to the basolateral membrane through the specific binding of basolateral sorting motifs in the cytoplasmic tail [18] . In fact , the AP-1B complex promotes the basolateral localization of EGFR in MDCK cells [17] . LET-23 EGFR does have several putative AP-1 sorting motifs , and thus could be a direct target for AP-1 regulation , but this would imply that AP-1 is impeding basolateral localization . A precedent for AP-1 having an antagonistic role in protein sorting or secretion has been found with the yeast Chs3p and Fus1p proteins , which rely on the exomer for secretion [44] , [45] . In the absence of exomer , Chs3p and Fus1p are retained internally in an AP-1 dependent manner [45] , [46] , [47] . An analogous situation whereby the LIN-2/7/10 complex sorts/maintains LET-23 EGFR localization on the basolateral membrane and the AGEF-1/Arf/AP-1 pathway plays an antagonistic role could exist . Recent studies in C . elegans and mice have shown that both basolateral and apical membrane cargos are mislocalized in the absence of the AP-1 complex [39] , [40] , [48] , suggesting that the AP-1 complex is required to maintain the polarity of the epithelial cells [reviewed in 18] . Similarly , we find that agef-1 ( vh4 ) mutants mislocalized the SID-2 protein to the basolateral membranes , which is strictly apical in wild-type animals . Therefore , AGEF-1 might function with AP-1 to maintain polarity in the intestinal epithelia and by extension the AGEF-1/Arf GTPase/AP-1 ensemble could indirectly regulate LET-23 EGFR localization via maintenance of VPC polarity . In summary , an AGEF-1/Arf GTPase/AP-1 ensemble functions opposite the LIN-2/7/10 complex to regulate apical versus basolateral localization of LET-23 EGFR in the VPCs , thus explaining how it negatively regulates LET-23 EGFR-mediated vulva induction . We don't yet know whether the AGEF-1/Arf GTPase/AP-1 ensemble directly regulates LET-23 EGFR sorting and localization or whether it is indirect via maintenance of VPC polarity . Further studies will be required to sort out the mechanisms by which the AGEF-1/ARF GTPase/AP-1 ensemble regulates LET-23 EGFR localization .
General methods for the handling and culturing of C . elegans were as previously described [49] . C . elegans Bristol strain N2 is the wild-type parent for all the strains used in this study; E . coli stain HB101 was used as a food source . The Hawaiian strain CB4856 was used for SNP mapping . All experiments were performed at 20°C . Information on the genes and alleles used in this work can be found on WormBase ( www . wormbase . org ) and are available through Caenorhabditis Genetics Center ( www . cbs . umn . edu/cgc ) unless otherwise noted in the strain list ( Table S1 ) . lin-2 ( e1309 ) L4 hermaphrodites were mutagenized as previously described [49] . F1 progeny ( m/+; lin-2 ) were transferred to individual plates . Due to the strong Vul phenotype of lin-2 ( e1309 ) animals , the self-progeny hatch internally [6] . F2 progeny were screened at the adult stage in order to identify plates that had a large number of eggs and egg-layers , additional preference was given to plates that had Muv , embryonic lethality , or dumpy phenotypes , similar to the rab-7 ( ok511 ) mutant . Progeny of a total of 2430 F1 animals ( 4860 haploid genomes ) were screened and two lin-2 ( e1309 ) suppressor mutants that are dumpy and partly embryonic lethal were identified , vh4 and vh22 . Single nucleotide polymorphism ( SNP ) mapping was used to place vh4 to the right arm of chromosome I [25] . Chromosome mapping showed linkage of vh4 to SNPs at 13 ( F58D5 ) , 14 ( T06G6 ) and 26 ( Y105E8B ) map units ( m . u . ) . Interval mapping using two sets of recombinants , 141 animals in total , was conducted using the following SNPs: pkP1133 at 17 . 4 m . u ( A/T Bristol/CB4856 , RFLP DraI ) ; pkP1134 at 18 . 95 m . u . ( T/C Bristol/CB4856 , RFLP AflIII ) ; haw14061 at 19 . 51 m . u . ( T/C Bristol/CB4856 , sequencing ) ; haw14137 at 20 . 65 m . u . ( T/A Bristol/CB4856 , sequencing ) ; haw14164 at 21 . 04 m . u . ( C/T Bristol/CB4856 , sequencing ) ; CE1-248 at 21 . 97 m . u . ( T/A Bristol/CB4856 , sequencing ) ; CE1-220 at 23 . 16 m . u . ( A/G Bristol/CB4856 , sequencing ) ; pkP1071 at 23 . 4 m . u . ( C/T Bristol/CB4856 , RFLP EcoRI ) . In the course of interval mapping the following predicted sequencing SNPs were confirmed: haw14061 and haw14137 as T/C and T/A Bristol/CB4856 , respectively . Genomic DNA from vh4 and vh22 was isolated and submitted to Genome Quebec for Illumina sequencing . Within the defined map region , the agef-1 gene was the only gene carrying a non-synonymous mutation unique to the vh4 strain . RNAi feeding was performed essentially as previously described [50] using the unc-101 ( I-6G20 ) , agef-1 ( I-6L22 ) , arf-1 . 2 ( III-3A13 ) , and arf-3 ( IV-4E13 ) clones from Ahringer RNAi library ( Geneservice , Cambridge , United Kingdom ) . Clones were verified by DNA sequencing . To avoid embryonic and larval lethal phenotypes , synchronized L1 larvae were placed on RNAi plates and scored for vulva induction when the animals reached L4 stage 36–48 hours later . General methods for live animal imaging using Nomarski differential interference contrast ( DIC ) microscopy were as previously described [51] . Animals were analyzed on an Axio Zeiss A1 Imager compound microscope ( Zeiss , Oberkochen , Germany ) and images were captured using an Axio Cam MRm camera and AxioVision software ( Zeiss , Oberkochen , Germany ) . Muv and Vul phenotypes were scored by counting the numbers of vulval and non-vulval descendants of P3 . p–P8 . p in L4 stage larvae as described previously [11] . Fisher's exact test ( www . graphpad . com/quickcalcs ) was used for statistical analysis of the vulval phenotypes . Comparison of GFP intensities wild-type and agef-1 ( vh4 ) was performed using identical exposure times for conditions being compared . Fiji image processing tool was used to measure intensities in raw images; any adjustments to contrast/brightness were for presentation purposes and were performed after analysis [52] . Tissue/organ of interest was outlined using free hand selection tool followed by measurement of the average pixel intensity . Images selected for figures are representative of the mean value for average pixel intensity for the group . Statistical analysis and graphing was done using Prism 5 ( GraphPad Software , Inc . , La Jolla , CA ) . Confocal analysis was performed using a Zeiss LSM-510 Meta laser scanning microscope with 63× oil immersion lens ( Zeiss , Oberkochen , Germany ) in a single-track mode using a 488 nm excitation for GFP . Images were captured using ZEN 2009 Image software ( Zeiss , Oberkochen , Germany ) . Animals at the L4 larval stage were selected for visualization of endocytic/secretory compartments in the coelomocytes . Images selected for figures are representative of the mean value for the largest vesicle diameter for the group . Statistical analysis and graphing was done using Prism 5 ( GraphPad Software , Inc . , La Jolla , CA ) . Confocal analysis of zhIs038 transgene-carrying animals was performed at early L3 larval stage using the Zeiss LSM-510 Meta laser scanning microscope . Confocal analysis of zhIs035 was performed using the Zeiss Axio Observer Z1 LSM-780 laser scanning microscope with 63× oil immersion lens ( Zeiss , Oberkochen , Germany ) in a single-track mode using an Argon multiline laser with 488 nm excitation for GFP . Images were captured using ZEN 2010 Image software ( Zeiss , Oberkochen , Germany ) . The apical and basal LET-23::GFP intensities were measured using Fiji by drawing a line through the center of the nucleus in the DIC channel and transferring the selection into the GFP channel to prevent bias . arf-1 . 2 was amplified by PCR from wild-type cDNA using the primers 5′-CATAAGAATAGTCGACATGGGAAACGTGTTCGGCAGC-3′ ( forward ) and 5′-GATTCTGATTACCGGTTCAGATCTATTCTTGAGCT-3′ ( reverse ) containing SalI and AgeI cut sites , respectively . The PCR product was cloned into pEGFP-N1 plasmid using SalI 639 and AgeI 666 sites . arf-1 . 2::GFP was digested using SalI and NotI and subcloned into the p255 lin-31 promoter plasmid . Transgenic animals were generated by DNA microinjection [53] of the Plin-31::ARF-1 . 2::GFP plasmid and a marker plasmid Pttx-3::GFP at a concentration of 50 ng/µl of each into N2 animals using maxiprep quality DNA . Two of three lines were used for this study , vhEx7 and vhEx8 . Rescue of agef-1 ( vh4 ) and arf-1 . 2 ( ok796 ) mediated suppression of the lin-2 ( e1309 ) Vul phenotype was scored in animals expressing ARF-1 . 2::GFP in the VPCs . Analysis of the Arf GTPases was performed using MAFFT version 7 multiple alignment program for amino acid or nucleotide sequences online ( http://mafft . cbrc . jp ) [54] . Input sequences were human NP_001649 . 1 ( Arf1 ) , NP_001650 . 1 ( Arf3 ) , NP_001651 . 1 ( Arf4 ) , NP_001653 . 1 ( Arf5 ) , AAV38671 . 1 ( Arf6 ) , NP_001168 . 1 ( Arl1 ) and C . elegans NP_501242 . 1 ( ARF-1 . 1 ) , NP_498235 . 1 ( ARF-1 . 2 ) , NP_501336 . 1 ( ARF-3 ) , NP_503011 . 1 ( ARF-6 ) , NP_495816 . 1 ( ARL-1 ) . Phylogenetic tree was constructed and visualized using Archaeopteryx [55] , [56] . | In the nematode , Caenorhabditis elegans , an evolutionarily conserved Epidermal Growth Factor Receptor ( EGFR ) signaling pathway is required to induce three epithelial cells to initiate a program of vulva development . EGFR on the basolateral membrane is essential to engage and transmit this signal . Here we demonstrate that AGEF-1 and the AP-1 clathrin adaptor complex function with two Arf GTPases to regulate EGFR localization and signaling . In humans , EGFR also localizes to the basolateral membrane of epithelial cells , and excessive EGFR signaling is a major driver of cancer . In C . elegans , we show that loss of AGEF-1 results in an increase in basolateral EGFR localization in the vulva precursor cells , and in sensitized genetic backgrounds , a corresponding increase in vulva induction . While the human AGEF-1 proteins , BIG1 and BIG2 , have not been previously implicated in EGFR signaling and cancer , mutations in BIG2 are causal of periventricular heterotopia , a condition whereby neurons fail to migrate to the cerebral cortex during brain development . As migrating neurons require polarized protein localization , BIG2 and AGEF-1 may have similar functions in these polarized cell types . | [
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] | 2014 | An AGEF-1/Arf GTPase/AP-1 Ensemble Antagonizes LET-23 EGFR Basolateral Localization and Signaling during C. elegans Vulva Induction |
Primary ciliary dyskinesia ( PCD ) is a hereditary defect of motile cilia in humans and several domestic animal species . Typical clinical findings are chronic recurrent infections of the respiratory tract and fertility problems . We analyzed an Alaskan Malamute family , in which two out of six puppies were affected by PCD . The parents were unaffected suggesting autosomal recessive inheritance . Linkage and homozygosity mapping defined critical intervals comprising ~118 Mb . Whole genome sequencing of one case and comparison to 601 control genomes identified a disease associated frameshift variant , c . 43delA , in the NME5 gene encoding a sparsely characterized protein associated with ciliary function . Nme5-/- knockout mice exhibit doming of the skull , hydrocephalus and sperm flagellar defects . The genotypes at NME5:c . 43delA showed the expected co-segregation with the phenotype in the Alaskan Malamute family . An additional unrelated Alaskan Malamute with PCD and hydrocephalus that became available later in the study was also homozygous mutant at the NME5:c . 43delA variant . The mutant allele was not present in more than 1000 control dogs from different breeds . Immunohistochemistry demonstrated absence of the NME5 protein from nasal epithelia of an affected dog . We therefore propose NME5:c . 43delA as the most likely candidate causative variant for PCD in Alaskan Malamutes . These findings enable genetic testing to avoid the unintentional breeding of affected dogs in the future . Furthermore , the results of this study identify NME5 as a novel candidate gene for unsolved human PCD and/or hydrocephalus cases .
Primary ciliary dyskinesia ( PCD ) is a rare genetic disease caused by defects in the structure or function of the motile cilia . Motile cilia are present in the respiratory tract including the paranasal sinuses , in the auditory tube and middle ear , in male and female reproductive tracts , sperm cells , and in the ependyma lining the ventricular system and central canal of the brain and spinal cord . Abnormal ciliary function typically leads to recurrent and chronic infections of the upper and lower respiratory tract beginning in neonates due to reduced mucociliary clearance [1] . Bronchiectasis , recurrent ear infections and infertility are also common findings in patients with PCD . During embryogenesis , cilia are important to establish correct left-right asymmetry . Therefore , situs inversus is present in approximately 50% of PCD patients [2 , 3] . Primary ciliary dyskinesia with situs inversus has been termed Kartagener syndrome [4] . Motile cilia have a characteristic 9 + 2 structure with nine microtubular doublets arranged in a circle around a central pair of microtubules . Additional ultrastructural elements , such as the outer and inner dynein arms or the radial spokes are important for the proper function of motile cilia [3 , 5] . Most forms of PCD are inherited with an autosomal recessive mode of inheritance . However , cases with autosomal dominant or X-linked mode of inheritance have also been described [6 , 7] . In humans , variants in 40 different genes have been reported to cause PCD [3 , 8 , 9] . PCD is also known in dogs and has been described in numerous dog breeds including Alaskan Malamutes and mongrels [10–23] . The genetic analysis of PCD affected Old English Sheepdogs unveiled a missense variant in CCDC39 as causative variant and led to the subsequent discovery of CCDC39 variants in human PCD patients [11 , 12] . To the best of our knowledge , no other canine PCD causative variant has been reported in the literature . In this study we describe the clinical , pathological and genetic analysis of PCD in Alaskan Malamutes .
An Alaskan Malamute litter with six puppies originating in Switzerland initiated the study . A few days after birth , two intact female puppies presented with bilateral mucoid to mucopurulent nasal discharge and subsequent chronic productive cough . Neither the puppies’ siblings nor the parents showed similar clinical signs . The affected puppies were in good body condition , bright , alert and responsive . Increased lung sounds were identified on thoracic auscultation in both dogs . Hematology and biochemistry was normal in one dog and revealed abnormalities consistent with chronic non-specific inflammation in the other dog . Further investigations revealed severe bronchial lung pattern and bronchiectasis on thoracic radiographs in both dogs and abnormalities compatible with bronchopneumonia in one dog ( Fig 1 ) . No evidence of situs inversus was seen in the radiographs of the affected dogs . Direct rhinoscopy and bronchoscopy revealed hyperemic mucosa , medium to large amount of mucopurulent secretions along the upper and lower airway tracts and moderate to severe turbinate lysis in the nasal cavity in both dogs ( Fig 1 ) . Bronchoalveolar lavage fluid was compatible with chronic active purulent bronchopneumonia . Microbial culture yielded β-haemolytic Streptococcus in one dog and Pseudomonas fluorescens and Pasteurella multocida in the other dog . Parasites or fungi were not detected . Biopsies of nasal and bronchial mucosa from the two affected dogs were examined . In the first puppy , the nasal mucosa had a reduced number of cilia and showed moderate purulent rhinitis . The bronchial mucosa also showed signs of mild chronic purulent bronchitis . Ultrastructural examination of the cilia from bronchial mucosa revealed a small proportion of cilia with an abnormal 10 + 2 conformation of the microtubules . Furthermore , about 60% of the outer and 95% of the inner dynein arms were shortened or absent ( Fig 2 ) . Nasal and bronchial samples of the second puppy revealed similar , but more pronounced ciliary alterations . Inner dynein arms were absent in nearly 100% of cilia , outer arms were either extremely shortened or absent in about 80% of the cilia . In addition , compound cilia , absence of one or both central complex tubules , reduction of microtubular singlet or doublets and disarrangement of tubules were observed . The pedigree of the two affected puppies with a documented inbreeding loop suggested an autosomal recessive mode of inheritance ( Fig 3 ) . Linkage analysis in the available family identified 20 linked genome segments totaling 319 Mb . We additionally performed homozygosity/autozygosity mapping in the two affected littermates . They shared 63 homozygous segments >1 Mb with identical alleles . The intersection of the linked and homozygous intervals comprised 20 chromosome segments spanning 117 , 799 , 906 bp ( Fig 4; S1 Table ) . We sequenced the genome of one PCD affected Alaskan Malamute at 36x coverage and called single nucleotide variants ( SNVs ) and small indel variants with respect to the CanFam 3 . 1 reference genome . We then compared these variants to whole genome sequence data from 8 wolves and 593 control dogs from genetically diverse breeds ( Table 1 , S2 Table ) . We identified seven private homozygous protein-changing variants in the critical intervals of the case genome ( Table 2 ) . At this stage of the project , we became aware of another previously reported PCD affected Alaskan Malamute from the United States . This dog had persistent nasal discharge starting before the age of six weeks . Due to chronic respiratory infections , the dog was euthanized at 8 months of age . At necropsy , PCD with bronchopneumonia , bronchiectasis and hydrocephalus were diagnosed . Abnormal cilia arrangement lacking inner and outer dynein arms was found in transmission electron microscopy [10] . We genotyped the seven private protein-changing variants on DNA from an archived FFPE tissue sample in the additional American PCD case . The additional case was homozygous for the alternative allele at only one of the seven variants , a frame-shifting single base deletion in the second exon of the NME5 gene ( S3 Table ) . The full designation of this variant is XM_003639378 . 4:c . 43delA . It is predicted to result in an early premature stop codon , which truncates more than 90% of the wildtype NME5 protein , XP_003639426 . 1:p . ( Thr15LeufsTer56 ) . The variant and the expected co-segregation with the PCD phenotype in the family were confirmed by Sanger sequencing ( Fig 5; S3 Table ) . We further genotyped 404 Alaskan Malamute samples and 539 control dogs from 72 genetically diverse breeds . This experiment confirmed the perfect association between the genotypes at NME5:c . 43delA and the PCD phenotype . The mutant allele was not detected outside of the Alaskan Malamute breed ( Table 3 , S3 Table ) . We investigated the NME5 protein expression in nasal mucosa by immunohistochemistry . While there was a strong positive signal in the nasal epithelium from an unaffected control , NME5 protein expression was not detectable in the nasal mucosa of a PCD affected Alaskan Malamute ( Fig 6A and 6B ) . NME5 immunogold transmission electron microscopy on nasal mucosa specimens demonstrated an overall low positive antibody binding in cilia from the control dog ( 5/20 cilia ) . Outer and inner microtubuli showed positive reactions . The most frequently observed binding sites were in the region of the inner dynein arms in the proximity of the outer microtubuli ( Fig 6C , 6D and 6E ) . In nasal ciliated epithelium from the affected dog , there were less positive signals than in the control ( 3/20 cilia ) . The antibody binding sites appeared more irregular and were different from the binding localizations of the control specimen ( Fig 6F , 6G and 6H ) .
The present investigation identified the NME5:c . 43delA frameshift variant as most likely candidate causative genetic variant for an autosomal recessive form of PCD in Alaskan Malamutes . The perfect genetic association in a large cohort of dogs , correct segregation of the mutant allele in the Swiss family , and demonstrated absence of NME5 protein expression in nasal epithelia from an affected dog all support the causality of NME5:c . 43delA . The NME5 gene encodes the NME/NM23 family member 5 , also known as nonmetastatic protein 23 , homolog 5 ( NM23H5 ) or radial spoke 23 homolog ( Chlamydomonas; RSPH23 ) . The NME gene family contains 10 paralogs in humans ( NME1 –NME10 ) [24] . The encoded proteins share a conserved 152 amino acid nucleoside diphosphate kinase domain . RSP23 , the Chlamydomonas ortholog of NME5 , has been shown to associate with the radial spoke necks of flagellar cilia . RSP23 has a nucleoside kinase activity and was hypothesized to be involved in GTP generation required for flagellar beating in Chlamydomonas [25 , 26] . However , the vertebrate NME5 has lost its nucleoside kinase activity , and its precise function remains elusive [27] . Human NME5 has been detected in sperm flagella [27 , 28] and more recently also been identified as a lowly abundant component of the radial spokes in human airway cilia [29] . Our data confirm that NME5 is expressed in ciliated airway epithelia and suggest that it is a lowly abundant component of cilia , which is tightly associated with the central or peripheral microtubules . This localization is consistent with previously reported findings in human sperm flagella [27] . Our ultrastructural localization must be interpreted with caution as it is based on a routine diagnostic specimen from a single unaffected dog . Nonetheless , a physiological localization at the peripheral microtubule pairs fits well with the observed defects in the outer and particularly inner dynein arms present in NME5 mutant dogs with PCD . To the best of our knowledge , no human patients with genetic variants in NME5 have been reported . Nme5-/- knockout mice have functional defects in their motile cilia . Their phenotype is primarily characterized by doming of the skull together with moderate to marked hydrocephalus . In male Nme5-/- knockout mice , late-stage spermatogenic arrest and flagellar defects were noticed . The cilia on respiratory epithelium and ependymal cells had a histologically normal appearance . However , it was also described that several of the Nme5-/- knockout mice had proteinaceous and suppurative exudates in nasal sinuses and passageways [30] . The two Swiss and the American case showed comparable clinical signs including bronchopneumonia and bronchiectasis . Post mortem necropsy in the American case revealed a hydrocephalus in addition to the changes seen in the airways [10] . It remains unclear whether the two Swiss cases have evidence of hydrocephalus , as they were still alive when the study was completed . The Swiss cases did not show any neurological signs and were thus not subjected to an MRI to evaluate the presence of hydrocephalus . The ultrastructural changes in the cilia , which were most prominent for the inner and outer dynein arms , were comparable in the affected Alaskan Malamutes from Switzerland and America [10] . All the phenotypic parallels between the three reported cases suggest a common underlying etiology . Recurrent respiratory infections represented the primary clinical sign in the affected dogs . This is quite distinct from the Nme5-/- knockout mice , in which hydrocephalus was the predominant feature . However , both entities are the result of motile cilia dysfunction . Hydrocephalus may be caused by deficient motility of ependymal cilia during brain morphogenesis and a subsequent altered flow of cerebrospinal fluid between the ventricles [30] . The additional Alaskan Malamute case with PCD and hydrocephalus from the United States illustrates the phenotypic similarities between NME5 mutant mice and dogs . Mice might be particularly sensitive to develop hydrocephalus as consequence as their cerebral aqueduct is relatively long and narrow [30] . None of the Nme5-/- knockout mice or NME5 mutant dogs had situs inversus totalis indicating that NME5 may be dispensable for the establishment of a correct left/right asymmetry . In conclusion , our data identify NME5:c . 43delA as most likely causative variant for PCD in Alaskan Malamutes . This form of PCD can be associated with hydrocephalus . Our findings enable genetic testing to avoid unintentional breeding of affected dogs in the future . Furthermore , NME5 should be considered as candidate gene for unsolved human PCD and/or hydrocephalus cases .
All animal experiments were performed according to the local regulations . The dogs in this study were examined with the consent of their owners . The study was approved by the “Cantonal Committee For Animal Experiments” ( Canton of Bern; permit 75/16 ) . This study included samples from 407 Alaskan Malamutes ( 3 PCD cases / 404 controls ) . Two cases were female littermates originating from Switzerland . The third , previously reported case originated from the USA and was only included after the completion of the whole genome sequencing experiments [10] . The three PCD cases were diagnosed due to abnormal findings of motile cilia structure in transmission electron microscopy . The remaining 404 Alaskan Malamutes represented population controls for which we had no reports of PCD . These samples came from different collections: 19 from the Vetsuisse Biobank ( Switzerland ) , 153 from Finland , 120 from the United Kingdom and 112 from the United States . As additional controls , we used samples of 539 dogs from 72 different other breeds , which had been donated to the Vetsuisse Biobank ( S4 Table ) . Two affected Swiss female littermates were referred to a diplomate of the American College of Veterinary Internal Medicine ( MIHG ) for diagnostic investigations of chronic nasal discharge at the age of 5 months and 18 months respectively . Physical examination and thoracic radiographs were performed in both affected dogs . Abdominal radiographs were performed in one dog . Due to the neonatal onset of the disease and its chronic recurrent course of the two littermates , primary ciliary dyskinesia was suspected . Blood samples for hematology , biochemistry and DNA extraction were obtained . Rhinoscopy and bronchoscopy were performed under general anesthesia and allowed macroscopic evaluation and biopsy sampling of the upper and lower respiratory airways . Bronchoalveolar lavages were cultured for bacteria and examined cytologically . Nasal and bronchial biopsies were fixed in formalin and 2 . 5% glutaraldehyde . Tissue samples for transmission electron tissue were fixed with 2 . 5% glutaraldehyde ( EMS ) in 0 . 1 M sodium phosphate buffer ( pH 7 . 4 ) overnight and washed three times in 0 . 1 M sodium phosphate buffer . Specimens were post fixed in 1% osmium tetroxide ( Sigma-Aldrich ) and dehydrated in an ascending ethanol series followed by propylen oxide and infiltration in 30% and 50% Epon ( Sigma-Aldrich ) . At least three 0 , 9 μm toluidine blue stained semithin sections per localisation were produced . Representative areas were trimmed . Subsequently , 90 nm lead citrate ( Merck ) and uranyl acetate ( Merck ) contrasted ultrathin sections were produced and viewed under a Phillips CM10 transmission electron microscope , operating with Gatan Orius Sc1000 ( 832 ) digital camera and Gatan Microscopical Suite , Digital Micrograph , Version 230 . 540 . Genomic DNA was extracted from EDTA blood samples according standard methods using the Maxwell RSC Whole Blood DNA kit in combination with the Maxwell RSC machine ( Promega ) . Genomic DNA from formalin-fixed paraffin-embedded ( FFPE ) tissue samples was extracted using the Maxwell RSC DNA FFPE kit according manufacturer’s instructions . Eight dogs were genotyped for 220 , 853 SNVs on the illumina canine_HD chip by GeneSeek/Neogen . The raw SNV genotypes are given in the S1 File . We performed linkage analysis and homozygosity mapping with eight dogs from one family ( Figure Mapping ) . The genotype data of these eight dogs from Illumina canine_HD chip were used for a parametric linkage analysis . For all dogs , the call rate was > 95% . Using PLINK v 1 . 09 [31] , markers that were non-informative , located on the sex chromosomes , or missing in any of the eight dogs , had Mendel errors , or a minor allele frequency < 0 . 05 , were removed . The final pruned dataset contained 81 , 116 markers . For parametric linkage analysis , an autosomal recessive inheritance model with full penetrance , a disease allele frequency of 0 . 4 and the Merlin software [32] were applied . For homozygosity mapping , the genotype data of two affected dogs from this litter were used . Markers that were missing in one of the two cases , markers on the sex chromosomes and markers with Mendel errors in the family were excluded . Regions of homozygosity with shared alleles between the cases were identified by using the—homozyg and—homozyg group options in PLINK . An Illumina PCR-free TruSeq fragment library with 400 bp insert size of a PCD affected Alaskan Malamute ( AM019 ) was prepared . We collected 286 million 2 x 150 bp paired-end reads on a NovaSeq 6000 instrument ( 36x coverage ) . Mapping and alignment were performed as described [33] . The sequence data were deposited under the study accession PRJEB16012 and sample accession SAMEA4848707 at the European Nucleotide Archive . Variant filtering was performed as previous described [33] . SnpEFF software [34] was used to predict the functional effects of the called variants together with the NCBI annotation release 105 on the CanFam 3 . 1 reference assembly . To identify case-specific private variants we used 601 control genomes , which were either publicly available [35] or produced during other projects of our group or contributed by members of the Dog Biomedical Variant Database Consortium . The accession numbers of all used genomes are given in S5 Table . We used the dog CanFam 3 . 1 reference genome assembly for all analyses . Numbering within the canine NME5 gene corresponds to the NCBI RefSeq accessions XM_003639378 . 4 ( mRNA ) and XP_003639426 . 1 ( protein ) . We used Sanger sequencing to confirm the NME5: c . 43delA variant and to genotype the other dogs included in this study . A 312 bp PCR product was amplified from genomic DNA using the AmpliTaqGold360Mastermix ( Life Technologies ) together with primers 5‘-TCG AAA AAG ATT CGG CAG TT -3‘ ( Primer F ) and 5’- TCA TCA TGC CCA GAA GTT ACC -3’ ( Primer R ) . For the FFPE sample , a 171 bp PCR product was amplified from genomic DNA using the AmpliTaqGold360Mastermix ( Life Technologies ) together with primers 5’- TAC CCT GGA AAG GCA GAA TG -3’ ( Primer FFPE F ) and 5’- CAT CAT CAT CAT GCC CAG AA -3’ ( Primer FFPE R ) . After treatment with exonuclease I and alkaline phosphatase , amplicons were sequenced on an ABI 3730 DNA Analyzer ( Life Technologies ) . Sanger sequences were analyzed using the Sequencher 5 . 1 software ( GeneCodes ) . For immunohistochemistry investigations , nasal mucosa samples were fixed in 3 . 5% buffered formaldehyde and routinely processed for paraffin embedding , 3 μm sections were produced and after antigen retrieval with citrate buffer ( 1h , room temperature , pH 6 . 0 ) , sections were incubated with polyclonal anti-rabbit Anti-NME5 antibody ( Abcam ab231631 , dilution 1:800 ) , followed by horseradish peroxidase-labeled goat anti-rabbit antibody ( UltraVision anti-rabbit HRP detection system , Thermo Scientific ) , with subsequent visualization with diaminobenzidintetrahydrochloride ( DAB ) . As non-altered specimen ( control ) , a sample from a normal nasal mucosa of a 1 . 5 year old , male castrated mixed breed dog was used . For immunogold transmission electron microscopy , anti-NME5 polyclonal antibody was applied for 4 h at room temperature in 1:10 dilution after pretreatment of grids in acidic citrate buffer for 16 h at 60°C . The secondary antibody ( 18 nm Gold Goat Anti-Rabbit IgG; Jackson Immuno Research , 111-215-144 ) was applied at room temperature for 2 h , dilution 1:20 , in Dako Antibody Diluent ( Dako REAL ) . Subsequently , 90 nm ultrathin specimens were routinely contrasted with lead citrate ( Merck ) and uranyl acetate ( Merck ) . | Motile cilia are required for clearing mucous , infectious agents and inhaled dust from the airways . Primary ciliary dyskinesia ( PCD ) is a hereditary defect of motile cilia . Clinical findings may include recurrent airway infections , fertility problems , and sometimes hydrocephalus . We analyzed an Alaskan Malamute family , in which two out of six puppies were affected by an autosomal recessive form of PCD . Whole genome sequencing of an affected dog identified a one base pair deletion in the NME5 gene , c . 43delA , leading to an early frame-shift and premature stop codon . Later in the study , we became aware of a previously published Alaskan Malamute with PCD involving respiratory infections and hydrocephalus . We observed perfect concordance of the NME5 genotypes with the PCD phenotype in all three affected Alaskan Malamutes and more than 1000 controls . The fact that the third case , which had no documented close relationship to the initial two cases , was homozygous for the same rare mutant NME5 allele , strongly supports our hypothesis that NME5:c . 43delA causes the PCD phenotype . We confirmed absence of NME5 protein expression in nasal epithelium of an affected dog . Our results enable genetic testing in dogs and identify NME5 as novel candidate gene for unsolved human PCD cases . | [
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] | 2019 | NME5 frameshift variant in Alaskan Malamutes with primary ciliary dyskinesia |
Tungiasis ( sand flea disease ) is a neglected tropical disease , prevalent in resource-poor communities in South America and sub-Saharan Africa . It is caused by an inflammatory response against penetrated female sand fleas ( Tunga penetrans ) embedded in the skin of the host . Although associated with debilitating acute and chronic morbidity , there is no proven effective drug treatment . By consequence patients attempt to remove embedded sand fleas with non-sterile sharp instruments , such as safety pins , a procedure that represents a health threat by itself . In this proof-of-principle study we compared the topical application of a mixture of two dimeticones of low viscosity ( NYDA ) to the topical application of a 0 . 05% solution of KMnO4 in 47 school children in an endemic area in rural Kenya . The efficacy of the treatment was assessed during a follow up period of seven days using viability signs of the embedded parasites , alterations in the natural development of lesion morphology and the degree of local inflammation as outcome measures . Seven days after treatment , in the dimeticone group 78% ( 95% CI 67–86% ) of the parasites had lost all signs of viability as compared to 39% ( 95% CI 28–52% ) in the KMnO4 group ( p<0 . 001 ) . In the dimeticone group 90% ( 95% CI 80–95% ) of the penetrated sand fleas showed an abnormal development already after 5 days , compared to 53% ( 95% CI 40–66%; p<0 . 001 ) in the KMnO4 group . Seven days after treatment , signs of local skin inflammation had significantly decreased in the dimeticone group ( p<0 . 001 ) . This study identified the topical application of dimeticones of low viscosity ( NYDA ) as an effective means to kill embedded sand fleas . In view of the efficacy and safety of the topical treatment with dimeticone , the mechanical extraction of embedded sand fleas using hazardous instruments is no longer warranted .
Tungiasis ( sand flea disease ) is a neglected tropical disease frequent in South America , The Caribbean and in sub-Saharan Africa . [1] , [2] , [3] . It is prevalent in resource-poor rural and urban communities , where animal reservoirs are present and people live in poverty [2] , [4] , [5] , [6] , [7] , [8] . In the last decade , tungiasis has re-emerged in East Africa in epidemic dimensions [9] . In 2010 , Ahadi Kenya Trust , a non-governmental organization , reported several hundred thousand cases of tungiasis in Kenya alone , of which the majority were children [4] , [10] , [11] . Sand flea disease is the result of an intense inflammatory response against penetrated sand fleas embedded in the skin of the host . The mechanisms underlying the inflammation are complex and only partially understood [11] , [12] , [13] . Immediately after a successful penetration the female sand flea starts to hypertrophy reaching the size of a pea after 10 days [14] . Through its abdominal rear cone the parasite remains in contact with the environment [14] . The tiny opening in the skin ( 250 to 500 µm ) is needed for copulation with male sand fleas , breathing , defecation and expelling eggs [14] . After expulsion of all eggs the female sand flea dies in situ and is discarded from the epidermis by tissue repair mechanisms [14] . Although by its nature a self-limiting infection , tungiasis is actually a debilitating disease in endemic areas [15] . Sequels are common and are related to repeated and severe infection . They include acute and chronic inflammation of toes , deformation and loss of toe nails , fissures and lymphoedema [11] . Bacterial super-infection is almost invariably present [13] . It increases the inflammation and leads to intense pain [16] . If embedded sand fleas are removed by using inappropriate sharp instruments , severe mutilation of the feet may develop including deep ulcers , gangrene and loss of toes [15] . Septicaemia has also been described [17] and tetanus is a known deadly sequel in non-vaccinated individuals [18] . Hitherto , the only effective treatment is the surgical extraction of embedded sand fleas under sterile conditions in medical facilities . However , in the endemic areas patients do not have access to appropriately equipped health centers and therefore use any kind of sharp instruments ( safety pins , sewing needles , hair pins , sharpened pieces of wood , etc . ) to remove embedded sand fleas . Attempts to remove the embedded parasites by using a sharp instrument , invariably causes a ( micro ) hemorrhage [9] . As the same instrument is frequently used to remove embedded sand fleas from different persons , this procedure increases the risk of the transmission of blood-borne pathogens , such as hepatitis B and C virus [19] . In an act of desperation , patients may apply toxic substances to the skin with the intention of killing the embedded parasites . In Brazil and Madagascar , for instance , kerosene , used petrol , and insecticides are used [9] , [20] . In rural Uganda , a crop pesticide used in tomato cultivation is applied ( H . Feldmeier , unpublished observation 2013 ) . In the absence of safe and effective treatment options , Ahadi Kenya Trust recommends to bath the feet in a 0 . 05% solution of potassium permanganate ( KMnO4 ) for 10 minutes [10] . However , the efficacy of this approach is not known . In Brazil several antihelminthic compounds , including ivermectin , have been tested , but none proved to be a really effective [21] . Dimeticones are silicone oils of low viscosity with a low surface tension and excellent creeping properties . They are highly effective against head lice [22] . The substance creeps into the tracheae of head lice and leads to lethal asphyxia within one minute [23] . The mode of action is purely physical . Dimeticones are biochemically inert and are not absorbed when applied to the skin or swallowed [24] . They are neither carcinogenic nor teratogenic and are considered wholly non-toxic [24] . Previous observation in rats infested with T . penetrans showed that if a drop of a solution of two dimeticones of low viscosity ( NYDA ) was applied on top of the protruding rear cone of an embedded sand flea , the parasite rapidly lost signs of viability ( H . Feldmeier , unpublished observation 2011 ) . Based on this observation we decided to investigate the efficacy of the dimeticone for the treatment of tungiasis in a proof-of-principle study in rural Kenya . The results show that wetting the skin of the feet with dimeticones with low viscosity effectively kills embedded sand fleas and reduces tungiasis-associated inflammation within seven days .
The study was performed in Gatundu North District , central Kenya , approximately one hour north of Nairobi . Tungiasis is endemic in this region . People live in small hamlets in houses made of wood or bricks . Families earn their living from subsistence farming . Most households possess animals , dogs , chicken and pigs . The animals live on the compound or are brought back to it in the night . Living conditions are generally very poor . The study participants were school children aged five to sixteen years enrolled at the public Kiamwangi Primary School and Ikuma Primary School , which are situated five km to each other . The classrooms consist of simple houses without a solid floor . Both schools have a limited access to water , so that the schoolyards and rooms cannot be cleaned regularly . Most pupils wore worn-out sandals or walked barefoot . The study was carried out between January 10 and February 17 , 2012 . This period coincides with the high transmission period of T . penetrans . To allow comparison between the new approach ( the application of the dimeticone ) and the local reference procedure ( bathing feet in a 0 . 05% solution of KMnO4 ) , one foot was bathed in the KMnO4 solution for 10 minutes and to the other foot the dimeticone was applied three times during this period ( see below ) . Since bathing a foot in a 0 . 05% KMnO4 solution changes the color of the skin into dark purple , neither the patient nor the examiner were blinded with regard to the treatment applied . Individuals , aged ≥5 years , with at least one lesion in stage IIa – IIIa of the Fortaleza classification on each foot were eligible [14] . In IIa the sand flea is already completely embedded in the skin of the host and has started to hypertrophy [14] . Lesions in stage IIIa correspond to a fully developed parasite with a characteristic watchglass-like appearance . In this stage the female sand flea starts to expel eggs [14] . In stage IIIb egg expulsion stops , thereafter the sand flea dies and the lesion changes into stage IV: the lesion becomes crusted , viability signs become rare and eventually completely disappear [14] . Hence , sand fleas in stage IIa – IIIa are most suitable to assess viability and alterations in the normal development of the parasites . The inclusion criterion for an eligible lesion was the presence of at least 2 out of 4 viability signs at the baseline examination: expulsion of eggs , excretion of a faecal thread , excretion of faecal liquid or pulsations/contractions of the parasite . Viability signs were determined using a handheld digital video microscope ( eScope iTEZ , Hongkong , China ) ( see supplementary electronic material 1 ) . When several eligible lesions were present on one foot only those ( at most three ) were selected for evaluation that allowed a clear discernment of the developmental stage of the embedded parasite and a quantification of the inflammatory response around the lesion . Hence , lesions occurring in cluster and lesions which the patient had attempted to manipulate were excluded . Other exclusion criteria were: Presence of gross inflammation , abscess or ascending lymphangitis or lymphedema on either foot . Children with such complications of tungiasis were referred to the nearest health facility for treatment . For practical reasons we decided to treat always the same foot with dimeticone and KMnO4 , respectively . At the beginning of the study a coin was tossed for randomizing the two treatments . This resulted in application of the dimeticone to the left foot and of KMnO4 to the right foot . Children were informed not to manipulate the lesions during the next seven days . Before each examination the feet of the participants were washed properly with water and soap and dried with a clean towel . Then , the left foot was wetted with NYDA up to the ankle three times within 10 minutes . In the interval , the foot was kept in an upright position to allow surplus dimeticone to evaporate . Simultaneously , the right foot was put into a bucket containing a 0 . 05% KMnO4 solution , and remained there for 10 minutes . After sun drying the right foot , vaseline was applied to compensate the desiccation of the skin caused by KMnO4 . The immersion of the foot in 0 . 05% KMnO4 for 10 minutes and the subsequent oiling with vaseline is the standard procedure applied by Ahadi Kenya Trust . After treatment the children were allowed to continue their daily activities . The lesions were monitored daily for viability signs and the abnormal development of the embedded parasite for a total of seven days . One week reflects the period of normal development of a sand flea from stage IIa to stage IIIa [14] . Thereafter , it looses its characteristic watchglass-like appearance , but does not increase in size anymore [14] . Hence , abnormalities in development are difficult to be detected . In order to detect a change of tungiasis-associated inflammation an inflammation score was developed . In addition to the classic signs of local inflammation ( erythema , oedema and warmness ) the score included the presence of suppuration , ulcers and fissures as well as itching and pain . The inflammation score ranged from 0 to 27 points [25] . In total , 48 participants were recruited and 47 were randomized . The flow diagram is shown in Figure 1 . Two major outcome measures were defined . First , the proportion of viable embedded sand fleas which lost viability signs after seven days of follow-up . An embedded sand flea was considered to be dead when none of the four viability signs ( expulsion of egg , excretion of faecal thread , excretion of faecal liquid , pulsations/contractions ) was detected during 15 minutes of observation with the digital handhold video microscope on two consecutive follow-up examinations . Videos were recorded and reviewed in the evening of the examination day ( see supplementary electronic material 1 ) . Second , the proportion of embedded sand fleas in which the normal development was interrupted . We defined a development as abnormal , when the lesion did not change its size within two consecutive follow ups and/or morphological abnormalities developed , e . g . discoloring or desiccation of the abdominal rear cone [14] . A secondary outcome measure was the intensity of local inflammation , as assessed semi-quantitatively by the inflammation score . The observation units for all outcome measures were single sand flea lesions . The sample size calculation was based on the following assumptions: with a level of confidence set at 95% together with a power of 90% assuming equal number of lesions in treatment and control group , 45 lesions in each group were needed to determine a difference of 35% in the major outcome measure between the two treatments assuming a 40% effect of the standard treatment . Fisher's exact test was used to compare proportions . General estimation equations were used to analyze the evolution of the inflammation score during the observation period . The study was approved by the Ethics Committee of the Ministry of Health , Nairobi ( MMS/ADM/3/8/Vol 111 ) , and was registered at Controlled-trials . com ( ISRCTN: 91405042 ) . The study was performed in accordance with the ethical standards of the Ethics Committee of the Ministry of Health , and with the Declaration of Helsinki as amended 2013 by the World Medical Association . Informed written consent was obtained from the guardians of the participants in English before starting the study . For ethical reasons no controls were included . During the study , food was provided free of charge to the participants . At the end of the study , any remaining viable sand fleas were removed under sterile conditions and the wounds were dressed following standard procedures . All patients received a new pair of closed solid shoes .
The baseline characteristics of the feet of the 47 participants are summarized in Table 1 . None of the variables differed significantly between the two feet . In the NYDA group , 88 lesions were included in the study , in the KMnO4 group 82 . Table 2 shows the efficacy of treatment based on the disappearance of viability signs . Already three days after application of dimeticone 50% of the parasites lost all viability signs ( efficacy = 50% ) , whereas the efficacy in the KMnO4 group was 14% ( p<0 . 001 ) . At day 7 the efficacy was 78% ( 95% CI 67–86% ) after treatment with dimeticone and 39% ( 95% CI 28–52% ) after treatment with KMnO4 ( p<0 . 001 ) ; a difference of 39% ( 95% CI 23–54% ) . In the dimeticone group , lesions in an early stage of development lost viability signs more often than lesions in later stages ( efficacy = 88% ( 95% CI 75–95% ) versus 65% ( 95% CI 47–79% ) at day 7 ( p = 0 . 01 ) ) . In the KMnO4 group , there was no difference between lesions in early and later stages of development . The effect of treatment on the morphological development of the lesions is shown in Table 3 . Already after 5 days in the dimeticone group 90% ( 95% CI 80–95% ) of sand flea lesions showed an abnormal development as compared to 53% ( 95% CI 40–66% ) ( p<0 . 001 ) in the KMnO4 group . Figure 2A–C and 3A–C show the macroscopic development of lesions after the treatment with dimeticone or KMnO4 , respectively . Figure 4A–D and 5A–D depict the microscopic development of lesions after treatment as seen through the digital handhold video microscope . In the dimeticone group the inflammation score decreased from a median of 6 . 0 at baseline to a median of 4 . 75 at day 7 . In contrast , in the KMnO4 group , the inflammation score increased ( median 4 . 5 versus 5 . 0 ) . Both differences were significant ( p<0 . 0001 and p = 0 . 009 , respectively ) . During the study period three sand fleas were extracted by the participants or their caregiver in the NYDA group and 11 in the KMnO4 group .
Tungiasis , a wide spread neglected tropical disease , is prevalent in resource-poor rural and urban communities , where animal reservoirs are present and people live in poverty [2] , [4] , [5] , [6] , [7] , [8] . Elimination of sand flea disease is not possible as long as the precarious living conditions , which are characteristic of the endemic areas , prevail and animal reservoirs exist . Taking into consideration the high prevalence of tungiasis , the absence of appropriate infrastructure in the endemic areas and the health hazards associated with the traditional treatment , there is an urgent need for a safe and effective drug treatment . Recently , dimeticones have emerged as highly effective chemicals against ectoparasites such as head lice [26] . Since dimeticones have a purely physical mode of action and are considered to be non-toxic , they have become the standard treatment of pediculosis capitis in Europe [22] . We considered the last abdominal segments of an embedded sand flea , which protrude through the skin by forming a miniature cone and through which the parasite breathes , defecates and excretes eggs , as an Achilles heel , which can be targeted by dimeticone . Since the opening leading to internal organs measures less than 1 mm , we decided to use a combination of two dimeticones of very low viscosity with a low surface tension and excellent creeping properties ( NYDA ) [23] . We defined a set of viability signs of embedded sand fleas detectable through a handhold digital video microscope . We used the presence of viability signs as the major outcome measure and compared the efficacy of a 0 . 05% solution of KMnO4 – the standard treatment used in mass campaigns in Kenya – to wetting the foot with dimeticone three times during a period of 10 minutes . The observation period was limited to seven days , since a certain number of embedded sand fleas will die even without any intervention during this period [14] . After 7 days , 78% of the lesions did not show any sign of viability in the dimeticone group , whereas the proportion was 39% in the KMnO4 group . True efficacy of a 0 . 05% solution of KMnO4 alone may be lower since KMnO4 is a disinfectant and has no insecticidal properties . It is unlikely that KMnO4 diluted in water will creep into vital organs of embedded sand fleas through the parasite's abdominal cone . Presumably , the observed effect in the KMnO4 treated lesions was due to the vaseline which was applied to the skin for cosmetic reasons ( because bathing the feet in KMnO4 makes the skin rough and cracked ) . Applied on the skin , vaseline rapidly turns into oil , particularly in hot climate countries . Liquid fatty acids of the vaseline may thereby creep into the abdominal rear cone and suffocate the parasite . Interestingly , the efficacy of dimeticone to kill embedded sand fleas depended on the stage of development: parasites being in an early stage of development were more susceptible than those who had already fully developed ( efficacy = 88% versus 66% ) . This is plausible , since embedded sand fleas increase their size by a factor of approximately 2000 within 6–7 days during the development from stage IIa to stage IIIa [14] . Such a rapid growth requires an intense metabolism , which in turn needs constant supply of oxygen . During the early stages of development supply of oxygen might be at a critical limit . This makes the parasite vulnerable for suffocating compounds such as low-viscosity . Since it is important to kill sand fleas as soon as they have penetrated in order to prevent the development of clinical pathology [16] , the enhanced effect of dimeticone on early developmental stages is an additional advantage . The early death of the embedded parasite will also prevent the expulsion of eggs – which starts about one week after penetration – and , thereby , may have an impact on transmission . 92% of the embedded fleas treated with dimeticone showed an abnormal development . This could indicate that no ( or fewer ) eggs are produced and released into the environment . Hence , if applied on the population level , treatment with dimeticones could have even an impact on the off-host cycle of the parasite , possibly resulting in lower attack rates over time . In the dimeticone group , the inflammation score started to decrease after 3 days and became significantly lower after 7 days , whereas in the KMnO4 group the inflammation slightly increased . It is conceivable that the resolution of inflammation reflects the rapid death of the parasites . Previous studies have shown that tungiasis-associated inflammation comes to a halt and tissue repair mechanism begins , when the parasites are dead [25] , [27] . Another indicator of the efficacy of the dimeticone was that in the course of the study 11 sand fleas were extracted from the feet treated with KMnO4 by the patients themselves , whereas in the NYDA treated feet only 3 sand fleas were removed . Similarly , when the study participants were asked at the end of the study about their satisfaction , only 10 participants preferred KMnO4 , but 37 preferred the dimeticone . Children also disliked that KMnO4 colored the skin into deep purple for a few days which led to teasing in school ( Figure 6 ) . This study on the treatment of a neglected parasitic disease is particularly in the sense that an Achilles heel of the parasite was identified first and then a compound was identified that is able to target the vulnerable body part . The abdominal cone which protrudes through the skin and through which the parasite breathes , defecates , excretes liquids and expels eggs was considered to be an ideal target for a dimeticone with a low viscosity and excellent creeping properties . Although this was a proof-of-principle study with a small number of units of observations , it can be concluded that the topical application of a mixture of two dimeticones ( NYDA ) comprises a promising approach to treat sand flea disease . The treatment can be performed by the patient himself with minimal input from the health sector . Hence , surgical extraction with all its associated complications is no longer warrantable . After the sand flea has died in situ , the inflammation resolved . Importantly , future resistance of the parasites against dimeticone treatment is highly unlikely to evolve , since the drug acts only physically . | Tungiasis ( sand flea disease ) , a parasitic skin disease , causes important morbidity , and eventually leads to mutilation of the feet . Hitherto , the only effective treatment is the surgical extraction of embedded sand fleas . In the endemic areas this is done using inappropriate sharp instruments and causes more harm than good . We identified the three last abdominal segments of Tunga penetrans which protrude through the skin and through which the parasite breathes , defecates , and expels eggs - as an Achilles heel of embedded sand fleas . In a proof-of-principle study we investigated whether this Achilles heel is vulnerable to dimeticone with a low viscosity and a high creeping property . We randomized the left and the right feet to either receive a topical application of KMnO4 ( the standard treatment in Kenya ) or of dimeticone . The major outcome measure was the absence of viability signs of the treated sand fleas . The study shows that the topical application of a mixture of two dimeticones ( NYDA ) effectively kills embedded sand fleas within seven days . Since dimeticones are considered to be wholly non-toxic and are not expensive the new treatment could become a means to control tungiasis-associated morbidity on the population level . | [
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] | 2014 | Treatment of Tungiasis with Dimeticone: A Proof-of-Principle Study in Rural Kenya |
Human outbreaks of Ebola virus ( EBOV ) are a serious human health concern in Central Africa . Great apes ( gorillas/chimpanzees ) are an important source of EBOV transmission to humans due to increased hunting of wildlife including the ‘bush-meat’ trade . Cytomegalovirus ( CMV ) is an highly immunogenic virus that has shown recent utility as a vaccine platform . CMV-based vaccines also have the unique potential to re-infect and disseminate through target populations regardless of prior CMV immunity , which may be ideal for achieving high vaccine coverage in inaccessible populations such as great apes . We hypothesize that a vaccine strategy using CMV-based vectors expressing EBOV antigens may be ideally suited for use in inaccessible wildlife populations . To establish a ‘proof-of-concept’ for CMV-based vaccines against EBOV , we constructed a mouse CMV ( MCMV ) vector expressing a CD8+ T cell epitope from the nucleoprotein ( NP ) of Zaire ebolavirus ( ZEBOV ) ( MCMV/ZEBOV-NPCTL ) . MCMV/ZEBOV-NPCTL induced high levels of long-lasting ( >8 months ) CD8+ T cells against ZEBOV NP in mice . Importantly , all vaccinated animals were protected against lethal ZEBOV challenge . Low levels of anti-ZEBOV antibodies were only sporadically detected in vaccinated animals prior to ZEBOV challenge suggesting a role , at least in part , for T cells in protection . This study demonstrates the ability of a CMV-based vaccine approach to protect against an highly virulent human pathogen , and supports the potential for ‘disseminating’ CMV-based EBOV vaccines to prevent EBOV transmission in wildlife populations .
Ebola virus ( EBOV ) , a member of the Filoviridae family , causes rapidly progressing viral hemorrhagic fever culminating in multi-organ failure , shock and death [1] . EBOV can be subdivided into four distinct and a fifth putative species [2] , [3] . EBOV species differ in level of virulence , with Zaire ebolavirus ( ZEBOV ) being the most virulent ( 80–90% case fatality ) [4] . The unpredictable nature of EBOV outbreaks in endemic areas of Africa , combined with the potential for accidental and deliberate introduction into non-endemic nations ensures that EBOV will most likely remain a global health concern well into the future . Potential for rapid dissemination to non-endemic countries was demonstrated in 2008 by importation of Marburg virus ( a filovirus closely related to EBOV ) to the US [5] and Netherlands [6] by tourists infected in Uganda . Animal species involved in EBOV transmission to humans are not completely defined [7] . Asymptomatically infected fruit bats have been identified during EBOV outbreaks , suggesting that bats may be a reservoir [8] . EBOV infection is also observed in great apes ( chimpanzees/gorillas ) , where it is highly pathogenic with a similar disease course to humans [9]–[11] . Handling and butchering of EBOV-infected wildlife and carcasses including great apes is an important mode of transmission to humans [7] , [9] , [12] , [13] . In the eighteen outbreaks of EBOV in Africa since its discovery in 1976 , three were associated with exposure to environments inhabited by bats , and seven resulted from contact with great ape carcasses ( the source of the remaining outbreaks was not established ) [3] , [7] . Although EBOV was identified in fruit bats in the EBOV outbreaks of 2001 and 2003 , all known transmissions to humans resulted from handling great ape carcasses [9] . Due to its high pathogenicity in great apes , EBOV infection is also regarded as a major threat to the survival of great ape species in the wild [9]–[11] . Given the threat by EBOV for the extinction of great apes and the role of great apes in EBOV transmission to humans , vaccination of these animals in the wild has been proposed to save these endangered wildlife species and to reduce the incidence of human EBOV outbreaks [7] , [14] . A number of candidate EBOV vaccines have been developed that are protective against infection in animal models [15] , [16] . Replication-defective adenovirus ( Ad ) expressing EBOV glycoprotein ( GP ) alone [17] or in combination with nucleoprotein ( NP ) [18] , virus-like particles comprised of virus matrix protein ( VP40 ) and GP with or without NP [19] , [20] , and replication-competent human parainfluenza virus type 3 ( HPIV3 ) [21] , and vesicular stomatitis virus ( VSV ) expressing GP [22] , [23] are all able to consistently induce protective immunity in small animal and non-human primate ( NHP ) models . Oral immunization with the VSV-based vaccine has been shown to induce protection in mice and NHPs [24] , [25] , leading to the suggestion of its use for food baiting [7] , [14] . However , all of these EBOV vaccine approaches induce immunity only in the vaccinated individual as they are unable to disseminate through the population . A ‘disseminating’ cytomegalovirus ( CMV ) -based vaccine offers an alternative approach whereby high coverage would be achieved by vaccine spread from initial vaccinees through the target population by animal-to-animal contact . CMV is an ubiquitous , but benign β-herpesvirus that establishes life-long , latent/low level persistent infection within the host [26] . During latent infection the virus is believed to be maintained as an extrachromosomal chromatin-complexed episome [27] . After initial infection , CMV is shed from epithelial surfaces into body fluids ( saliva , urine , genital secretions and breast milk ) , and transmission generally involves mucosal exposure to such fluids , most commonly in early childhood or adolescence [28] , [29] . CMV possesses the remarkable ability to reinfect and establish a persistent infection regardless of host CMV immunity [30]–[33] . CMV is also one of the most immunogenic viruses known [34] , inducing a characteristic immune response that is highly enriched for ‘effector’ memory ( TEM ) T cells [33] . TEM cell localization is shifted toward non-lymphoid , mucosal sites , and TEM cells are functionally primed for immediate anti-pathogen effector function [35] . Due to this high immunogenicity , interest in developing CMV as a vaccine vector is increasing [36]–[38] . The recent capacity to manipulate the CMV genome using bacterial artificial chromosome ( BAC ) -based technology has facilitated development of CMV as a vaccine vector [39] . To date , target antigens have been expressed in CMV either as single T cell epitopes fused to a non-essential CMV gene [36] , or as single full-length proteins under the control of heterologous promoters [33] , [40] . CMVs are host-specific , with each mammalian host being infected with its own distinct CMV [41] , [42] . The high efficacy of CMV-based vaccines was recently demonstrated by the ability of a panel of rhesus CMV ( RhCMV ) -based vectors each expressing a distinct simian immunodeficiency virus ( SIV ) antigen to prevent systemic SIV infection of rhesus macaques ( a NHP model for HIV ) , which is the first vaccine to prevent acquisition of fully pathogenic SIV [33] , [43] . Our long-term goal is to develop a ‘disseminating’ vaccine against EBOV based on chimpanzee/gorilla-specific CMV vectors that will prevent EBOV infection in gorillas and chimpanzees . We hypothesize that protection of these animals from EBOV will interfere with the transmission of EBOV from these species to humans . In the present report we have constructed a mouse CMV ( MCMV ) -based EBOV vector expressing a single CTL epitope from NP of ZEBOV ( MCMV/ZEBOV-NPCTL ) as a prototype vector to establish ‘proof-of-concept’ for this approach . MCMV/ZEBOV-NPCTL was shown to be highly immunogenic , inducing durable CD8+ CTL responses ( IFNγ+/TNFα+ ) against ZEBOV NP in multiple strains of mice . Importantly , MCMV/ZEBOV-NPCTL conferred protection against lethal challenge with a mouse-adapted ZEBOV variant . The general absence of antibodies against ZEBOV in protected animals prior to ZEBOV challenge , and lack of protection in controls receiving wild-type ( WT ) ‘empty’ MCMV vector were consistent with protection being , at least in part , T cell-mediated . This is the first study to demonstrate the ability of a CMV-based vaccine to protect against an human pathogen , and supports the concept of ‘disseminating’ CMV-based EBOV vaccines to prevent EBOV transmission in wild animal populations .
All animal use complied with the Guide for the Use and Care of Laboratory Animals , USDA Animal Welfare Regulations , PHS Policy on Humane Care and Use of Laboratory Animals and other relevant regulations . All procedures were approved by the respective IACUC committees at Rocky Mountain Laboratories , Division of Intramural Research , National Institute of Allergy and Infectious Diseases , National Institutes of Health ( RML , DIR , NIAID , NIH ) , and Oregon Health and Science University ( OHSU ) . MCMV-based vectors were constructed by lambda-phage based linear recombination using a strategy identical to that used for construction of other CMV recombinants [33] . MCMV ( Smith strain ) BAC pSMfr3 [44] , [45] in which the natural killer ( NK ) cell activating m157 MCMV gene has been deleted ( pSMfr3Δm157 ) was used as the genetic background for these vectors . Deletion of m157 was necessary to avoid attenuation of CMV replication by inadvertent high NK cell control in the C57BL/6 mouse strain that expresses the corresponding Ly49H NK receptor [46] . For construction of MCMV/ZEBOV-NPCTL an H2b-restricted T cell epitope from NP of ZEBOV ( 43-VYQVNNLEEIC-54 ) [47] , [48] was fused ‘in-frame’ to the carboxyl terminus of MCMV IE2 ( ie2 ) generating the recombinant MCMV BAC , pMCMV/ZEBOV-NPCTL . IE2 is a nonessential MCMV protein to which we and others have fused defined T cell epitopes as a strategy for induction of T cell responses following infection of mice with the corresponding recombinant MCMV [36] . A contiguous frt-flanked kanamycin resistance marker ( KanR ) was inserted into the MCMV BAC genome at the same time as the NP epitope to enable selection of recombinant BACs on the basis of kanamycin resistance . Following selection of recombinant BACs on the basis of KanR , the frt-flanked KanR marker was removed by arabinose induction of Flp-recombinase and screening for kanamycin sensitivity . Virus was reconstituted from BACs by transfection into murine embryo fibroblasts ( MEFs ) . Presence of the BAC cassette within the MCMV genome decreases in vivo replication , and serial in vitro passage of the BAC-derived virus was performed to remove the BAC cassette [45] . Absence of the BAC cassette from reconstituted MCMV vectors was confirmed by PCR using BAC cassette-specific primers . MCMV/ZEBOV-NPCTL viruses were assessed for growth in vitro on MEFs . To avoid effects of inadvertent second site mutations , two independently derived MCMV/ZEBOV-NPCTL clones ( 5A1 and 5D1 ) were selected , and growth was compared to WT MCMV ( MCMVΔm157 ) . For assessment of virus growth kinetics , cells were infected at a multiplicity of infection ( MOI ) of 0 . 1 and media was harvested for quantitation of virus at increasing times post-infection by standard plaque assay . DNA sequencing of BAC and reconstituted viral DNA was used to confirm integrity of the NP epitope within the MCMV genome . Mice were purchased from NCI at Frederick , MD . All experiments were performed with age-matched female 129S1/SvlmJ/Cr and C57BL/6 mice . Mice were provided food and water ad libitum . For analysis of the kinetics of the peripheral blood anti-NP T cell response , mice received a single intraperitoneal ( i . p . ) inoculum of MCMV/ZEBOV-NPCTL ( 1×105 pfu ) , and were then bled over a 33 week period at times indicated . In all other cases , MCMV-vaccinated mice were inoculated i . p . with 5×105 pfu of MCMV recombinants followed by an identical i . p . ‘boost’ after 4 weeks . After 10 weeks , C57BL/6 mice were challenged i . p . with 103 LD50 of mouse-adapted ZEBOV ( ma-ZEBOV ) as previously described [24] . For these studies , VSVΔG/ZEBOVGP ( given as a single i . p . dose of 5×105 pfu ) served as a positive control for vaccine protection . The VSVΔG/ZEBOVGP is a recombinant VSV , in which the native glycoprotein G has been exchanged for GP ZEBOV ( Mayinga strain ) , and has been shown to induce protective immunity against ma-ZEBOV [24] . Following challenge , disease severity was monitored on the basis of clinical signs using an approved scoring index , and mortality rate was recorded over the 28 day post-challenge period . ZEBOV in vivo replication was also directly determined by quantification of ma-ZEBOV viremia levels at time of peak viremia in mice ( day 4 post-challenge ) by virus titration using a focus forming unit immunodetection assay with titre expressed as focus forming units ( FFU/ml ) . All BSL-4 level infectious work was performed at RML . Frequencies of CD8+ T cells directed against the ZEBOV NP CTL epitope , or MCMV-encoded M38 and M45 proteins in pooled peripheral blood or spleen were determined by intracellular cytokine staining ( ICS ) . Cells were stimulated in the presence of brefeldin A ( BFA ) ( 10 µg/ml ) with peptides representing defined H2b-restricted epitopes of ZEBOV NP ( VYQVNNLEEIC ) [47] , MCMV M38 ( SSPPMFRV ) or M45 ( HGIRNASFI ) [49] , or prostate-specific antigen ( PSA ) ( HCIRNKSVI ) [50] . PSA was used as an irrelevant antigen control , and the peptides representing the well-characterized H2b-restricted epitopes in MCMV-encoded M45 and/or M38 [49] served as an indicator of MCMV vector infection . Incubation without antigen served as a background control . After 6 hours of stimulation , cells were stained using the following monoclonal antibodies ( Mabs ) in designated combinations: a ) from BD Biosciences , RM4-5 ( CD4; Pacific Blue ) , 53-6 . 7 ( CD8a; PerCP-Cy5 . 5 ) , MP6-XT22 ( TNFα; PE ) , XMG1 . 2 ( IFNγ; APC ) , and b ) from eBioscience 17A2 ( CD3e , APC-eFluor780 ) . After surface and intracellular staining with conjugated Mabs , polychromatic flow cytometric analysis was performed on a LSR II ( BD Biosciences ) , and data was analyzed by using FlowJo software ( version 9 . 1; Tree Star , Inc . ) . Samples were performed in triplicate . Response frequencies were determined by subtracting background and then averaging background subtracted responses . Sera was collected from vaccinated mice at times indicated , and analyzed for ability to neutralize ZEBOV infection in an in vitro neutralization assay [51] . Briefly , heat inactivated sera ( 56°C for 45 minutes ) was serially diluted in DMEM , and then mixed 1∶1 with ZEBOV expressing the EGFP reporter ( ZEBOV-EGFP ) ( 200 FFU/well ) . After incubation at 37°C for 60 minutes , 20 µl of the mixture was transferred onto subconfluent Vero cells in a 96-well plate format and incubated for 30 minute at 37°C . Following addition of 180 µl of DMEM supplemented with 1 . 5% carboxymethyl cellulose and 5% FBS , cells were cultured for 4 days at 37°C . Cells were washed by PBS and fixed in 10% neutral buffered formalin overnight under BSL-4 conditions . Prior to removal from the BSL-4 conditions , formalin was changed and plates were processed under BSL-2 conditions by conventional methods . Values shown are the sera dilutions resulting in 50% reduction in EGFP-positive cells following infection of Vero cells with ZEBOV-EGFP . Mab#226 is a neutralizing mouse monoclonal antibody made against ZEBOV GP [52] . Total IgG antibody responses to ZEBOV NP , GP and VP40 were quantified by ELISA using ZEBOV VLPs ( VP40/NP/GP ) as an antigen source [25] . Generation of VLPs has been previously described [53] . Briefly , 96 well microtiter plates ( NUNC , Rochester , NY ) were coated with ZEBOV VLPs ( 2 µg/ml ) in PBS at 4°C overnight , and then blocked with 5% skim milk in PBS containing 0 . 05% Tween 20 ( PBST ) for 2 hours at room temperature . After three washes with PBST , 50 µl of diluted heat-inactivated serum sample was added and the plates were incubated for 1 hour at 37°C . After an additional three washes with PBST , secondary antibody conjugated with horseradish peroxidase ( HRP ) was added and plates were incubated for an additional 1 hour at 37°C . Bound antibodies were quantified using the ABTS Peroxidase Substrate System ( KPL , Gaithersburg , MD ) by measuring absorbance at 405 nm on a microplate spectrophotometer . Values shown are the end-point dilution titre ( using a 4-fold dilution series ) . Samples were deemed positive when the value was higher than the mean plus 4 standard deviations of negative ( Mock ) mouse sera [25] . Statistical analyses were performed using GraphPad version 5 . 0 d for Mac OS X , GraphPad Software , San Diego CA , USA , www . graphpad . com . An unpaired Student's two-tailed t-test was used to compare treatment groups . A Kaplan-Meier estimator and a log-rank test were used to compare survival rates between treatment groups in ma-ZEBOV challenge studies .
To assess the potential of CMV for development as a vaccine against EBOV , we designed a prototype murine cytomegalovirus ( MCMV ) -based EBOV vaccine ( MCMV/ZEBOV-NPCTL ) expressing a CD8+ CTL epitope from ZEBOV NP ( 43-VYQVNNLEEIC-53; NP43 ) [47] , [48] , [54] fused to a non-essential MCMV protein , IE2 ( Figure 1a ) . MCMV/ZEBOV-NPCTL was constructed by lambda-based linear recombination using a BAC containing the MCMV genome ( pSM3fr ) [33] , [45] . Independent pMCMV/ZEBOV-NPCTL clones ( 5A1 and 5D1 ) were selected for characterization . Restriction enzyme digestion followed by electrophoresis showed no gross genomic rearrangements compared to WT parental BAC ( Figure S1 ) . Viruses were reconstituted by transfection of BAC DNA into MEFs . In vitro growth analysis of reconstituted viruses showed replication kinetics comparable to WT MCMV ( Figure S2 ) . CMV induces high levels of T cells against both endogenous and heterologously expressed proteins [33] , [34] , [36] . To assess the level of NP-specific CD8+ CTL responses induced by MCMV/ZEBOV-NPCTL , we performed immunogenicity studies in H2b-restricted 129S1/SvlmJ/Cr mice . Mice ( n = 5/group ) were immunized intraperitoneally ( 5×105 pfu; i . p . ) with MCMV/ZEBOV-NPCTL ( clone 5A1 or 5D1 ) , MCMV/PSA ( clone 3-1 ) , WT MCMV or diluent ( Mock ) . MCMV/PSA ( clone 3-1 ) is a control MCMV expressing an irrelevant H2b-restricted epitope from PSA [50] . After 4 weeks , mice were ‘boosted’ using an identical inoculum . After 8 weeks , splenocytes were harvested for analysis of T cell responses ( Figure 1b ) . Antigen-specific T cells were analyzed by ICS following a 6 hour in vitro incubation with EBOV and MCMV peptides representing different H2b-restricted epitopes . All MCMV/ZEBOV-NPCTL vaccinated mice exhibited significant CD8+ CTL responses against ZEBOV NP ( Figure 1b ) . The level of NP responses elicited by 5A1 and 5D1 were not significantly different , and were considered together as a single data set . The ZEBOV NP-specific T cell responses induced were substantial ( mean = 2 . 83% of total CD8+ T cells; range = 0 . 32 to 5 . 99% ) , CD8+ phenotype ( no response in CD4+ cell compartment ) , and specific ( directed against ZEBOV NP , but not PSA control ) . CD8+ CTLs induced against ZEBOV NP primarily expressed both IFNγ and TNFα effector cytokines ( Figure 1c ) . All mice except mock-vaccinated controls had CD8+ CTLs directed against the MCMV-encoded M45 protein . A unique characteristic of CMV-induced immune responses is their ‘inflation’ over time with maturation into stable ‘effector’ T cell ( TEM ) memory that persists for life [55] . Compared to classical ‘central’ memory ( TCM ) cells , TEM are biased toward localization at mucosal epithelial effector sites , and have more immediate effector function [56] , [57] . To determine the durability of ZEBOV NP-specific T cell responses from a single MCMV/ZEBOV-NPCTL inoculation , mice ( n = 14 ) were vaccinated ( 1×105 pfu; i . p . ) with MCMV/ZEBOV-NPCTL , and peripheral T cell responses were followed longitudinally . NP-specific CD8+ T cell responses gradually accumulated to high levels and persisted ( increasing from 0 . 79% after 8 weeks , to 3 . 08% after 33 weeks following the single inoculation ) ( Figure 2 ) . Although delayed , the NP-specific CTL response was comparable in kinetics of induction and magnitude to the TEM-biased ‘inflationary’ response directed against MCMV M38 [49] , [58] . Importantly , these results show that a CMV-based EBOV vaccine can induce high levels of CD8+ T cells against an EBOV antigen that increase with time and are durable . To determine whether MCMV/ZEBOV-NPCTL was able to induce protective immunity against lethal ZEBOV challenge , we performed challenge studies in C57BL/6 mice using ma-ZEBOV [24] , [59] . The ma-ZEBOV is lethal in unvaccinated mice , which succumb 5–7 days post-challenge [24] , [59] . Four groups of mice ( n = 20/group ) were immunized ( 5×105 pfu; i . p . ) with MCMV/ZEBOV-NPCTL 5A1 or 5D1 , MCMV WT or diluent , and boosted after 4 weeks ( Figure 3 ) . After 8 weeks ( 4 weeks following the boost ) , splenocytes from 6 mice/group were analyzed for T cell responses . 5A1 and 5D1 induced comparable responses against NP , enabling mice receiving either clone to be considered as a single data set . MCMV/ZEBOV-NPCTL induced considerable levels of CD8+ T cells against ZEBOV NP ( mean = 1 . 34% of total CD8+ T cells; range = 0 . 05 to 2 . 68% ) . These results also show that the ability of MCMV/ZEBOV-NPCTL to induce NP-specific T cells is independent of the mouse strain ( Figures 1b and 3 ) . All mice except mock-vaccinated controls had CD8+ CTLs directed against MCMV-encoded M38 and M45 . After 10 weeks ( 6 weeks following the boost ) , the remaining mice ( n = 14/group ) were challenged i . p . with 103 LD50 of ma-ZEBOV . An additional group ( n = 14 ) that had received VSVΔG/ZEBOVGP ( 5×105 pfu; i . p . ) , which confers high levels of protection against ma-ZEBOV , was included as a control for vaccine protection [24] . ZEBOV disease was then monitored on the basis of survival , morbidity based on clinical signs ( ruffled fur , hunched posture , paralysis and weight loss ) and viremia . Mock and MCMV WT vaccinated controls exhibited ZEBOV disease and significant morbidity ( Figure 4b ) with 90% of mice succumbing between days 5–7 post-challenge ( Figure 4a ) . In contrast , MCMV/ZEBOV-NPCTL vaccinated mice showed no evidence of ZEBOV disease , with 100% survival and no signs of morbidity ( Figures 4a and 4b ) . As a quantitative analysis of vaccine efficacy , viremia at day 4 post-challenge ( peak of ZEBOV viremia in the mouse model ) was measured in a subset of mice ( n = 3–4/group ) harvested at this time ( Figure 4c ) . MCMV/ZEBOV-NPCTL vaccination resulted in a significant level of control of ZEBOV replication . Specifically , 5 of 8 mice showed no detectable levels of viremia; the remaining 3 mice showed a 2 . 8-log reduction in viremia compared to WT MCMV vaccinated controls . Given the expression of a single CTL epitope from NP it was highly unlikely that anti-ZEBOV antibodies ( either neutralizing or total ) would be induced by vaccination . However , it was possible that a low-level of ZEBOV replication in vaccinated animals would result in induction of anti-ZEBOV antibodies . To investigate the possibility that neutralizing antibodies induced by the challenge virus were positively impacting protection , we waited a sufficient period of time ( 28 days ) for any antibodies induced by challenge to have risen to detectable levels . At 28 days post-challenge ma-ZEBOV neutralizing activity in sera from a randomly selected subset ( n = 6 ) of protected MCMV/ZEBOV-NPCTL mice was measured . VSVΔG/ZEBOVGP control mice had low , but detectable levels of neutralizing activity following challenge as previously observed [24] . In contrast , neutralizing activity was not detected in any convalescent serum from MCMV/ZEBOV-NPCTL vaccinated mice demonstrating that the role of neutralizing antibodies in mediating protection in these mice was minimal ( Table S1 ) . Vaccination of either NHPs [22] or mice [24] with VSVΔG/ZEBOVGP is known to induce a total IgG anti-ZEBOV response ( presumably directed against ZEBOV GP ) . Although VSVΔG/ZEBOVGP confers a level of protection that results in the complete lack of detectable ZEBOV viremia [22] , the anti-ZEBOV antibody response induced by VSVΔG/ZEBOVGP is subsequently boosted by ZEBOV challenge . An antibody-capture ELISA ( using VLPs comprised of VP40 , NP and GP as a source of antigen ) [53] was used to measure levels of total anti-ZEBOV IgG antibodies in MCMV/ZEBOV-NPCTL vaccinated mice , both prior to challenge and at day 28 post-challenge ( Table S2 ) . Consistent with expression of the single NP CTL epitope , anti-ZEBOV antibodies were not detected , or present sporadically at only low levels ( in one of three mice tested ) in MCMV/ZEBOV-NPCTL vaccinated mice prior to ma-ZEBOV challenge . In contrast , convalescent sera from protected mice in this treatment group had high levels of total IgG directed against ZEBOV ( at 28 days post-challenge ) . VSVΔG/ZEBOVGP-vaccinated mice responded as previously described with anti-ZEBOV IgG antibodies being induced by vaccination , which were then boosted by ma-ZEBOV challenge ( Table S2 ) . Whether the anti-ZEBOV antibody response in either of these treatment groups is induced by active ZEBOV replication or represents exposure to the initial antigen bolus received at the time of challenge is unclear . The relatively greater anti-ZEBOV IgG response observed in MCMV/ZEBOV-NPCTL compared to VSVΔG/ZEBOVGP vaccinated mice following challenge may indicate an higher level of ongoing ZEBOV replication in the MCMV/ZEBOV-NPCTL vaccinated mice following challenge . The absence of neutralizing antibodies and barely detectable and sporadic levels of total IgG against ZEBOV in MCMV/ZEBOV-NPCTL vaccinated mice prior to ma-ZEBOV challenge suggests that NP-specific CTL , and not antibodies , are playing a greater role in protection . These results do not exclude other mechanisms being involved in protection , such as neutralizing or non-neutralizing antibodies below the level of detection in our assay , as well as non-specific innate responses induced by the CMV vector itself . Although the inability of WT MCMV to afford any level of protection would presumably suggest minor involvement of non-adaptive , innate responses . De novo ZEBOV-specific CTL responses directed against ZEBOV-encoded antigens other than NP induced by the challenge virus can also not be excluded . Great apes are an important source of EBOV transmission to humans [7] , [8] , [9] , [12] , [13] . Vaccination campaigns for rabies in European and US wildlife [60] have shown the effectiveness of targeting animal species involved in transmission . Vaccination of great apes to interrupt EBOV transmission may therefore be an effective strategy to decrease human EBOV outbreaks . In the current study , we demonstrate that a prototype CMV-based vaccine expressing a single CTL NP epitope can induce a considerable level of protection against ZEBOV . The present study therefore establishes a ‘proof-of-concept’ for a CMV-based EBOV vaccine in the C57BL/6 mouse challenge model prior to moving forward with more complex CMV-based vectors ( species-specific ) expressing full-length EBOV proteins in more robust NHP challenge models . Given the outbred nature of primates with their expression of a diverse repertoire of MHC I alleles , a final CMV based vaccine will assuredly need to encode single or perhaps multiple full-length EBOV proteins . In addition to being highly immunogenic , CMV has evolved a remarkable ability to spread between individuals , and therefore may be suited for development as a ‘disseminating’ vaccine platform to target geographically inaccessible wild animal populations like great apes at relatively low cost . In this strategy , vaccination of ‘founder vaccine recipients’ would be used to initiate spread of the CMV-based EBOV vaccine through animal populations eliminating the need for immunization of each individual . An important characteristic of CMV that makes this vector ideally suited to development as a ‘disseminating’ vaccine is its remarkable ability to reinfect and establish a persistent infection regardless of host CMV immunity [29] , [31] , [32] , [61] . Recent studies in rhesus macaques show that immunogenicity and capacity to re-infect the CMV immune host is relatively independent of CMV dose , as inoculums as low as 100 pfu of rhesus CMV ( RhCMV ) were able to reinfect and induce immunity in RhCMV sero-positive animals [62] . As CMV is transmitted through breast milk from mothers to offspring , a CMV-based vaccine could also afford a more permanent solution to the EBOV problem . Although vertical transmission of superantigens encoded by endogenous mammary tumour viruses ( MMTV ) can cause clonal deletion of reactive T cell subsets [63] , vertical transmission of CMV appears to be distinctly different to MMTV , with mature and functional human CMV specific T cells being consistently observed in congenitally infected neonates ( and one aborted foetus of 28 weeks gestation ) [64] , [65] . There are clearly some potential risks associated with use of such a ‘disseminating’ vaccine approach targeting wildlife populations . However , in addition to the impact of EBOV on human health , EBOV is regarded as a major threat to survival of great apes [11] . With potential for achieving high levels of coverage in inaccessible and environmentally harsh regions , a ‘disseminating’ CMV-based EBOV approach may not only benefit humans , but may also positively impact survival of these great ape species in the wild . Concerns regarding the possible environmental impact of releasing a recombinant vaccine vector that can spread within the target population , especially a population that is endangered , cannot be overstated . However a number of additional characteristics of this vaccine approach help to allay these concerns . First , CMV has been shown to be ubiquitous within its respective host population in all NHP species studied [66]–[69] . Therefore , a CMV-based vaccine for use in great ape species represents infection with a benign virus ( either gorilla or chimpanzee CMV ) that is already present within the population , differing only by expression of EBOV antigens . Second , CMVs are believed to be highly host-specific with each mammalian host studied carrying its own species-specific CMV [70] . This host-specificity is recapitulated to a certain level in vitro , with only CMVs from closely related species being able to replicate in cells from other closely related species . For example , human CMV ( HCMV ) was able to replicate , but at a 10-fold lower level , in primary chimpanzee compared to human fibroblasts [71] . However , HCMV is unable to replicate in mouse fibroblasts [72] , and MCMV is reciprocally unable to replicate or is severely compromised for replication in human fibroblasts [72] , [73] . For rodent CMVs , the block in cross-species infection has been shown to be due to an inability to control apoptosis following infection of the cross-species cell type [73] . The responsible mechanism for species restriction in primate CMVs is not known . CMV is ubiquitous in all NHP species studied ( baboons , drill monkeys and rhesus macaques ) [66]–[68] . A chimpanzee CMV strain ( CCMV , Panine herpesvirus 2 ) has been isolated from a chimpanzee in captivity and the genome fully sequenced . The CCMV genome was largely co-linear with that of HCMV , but with a moderate level of divergence [74] . Leendertz et al [75] have subsequently detected multiple additional CMV species in samples from captive and wild gorillas ( Western Lowland ) , chimpanzees ( West and East African ) , as well as orangutans using a degenerate PCR-based assay targeting conserved genes . Together with HCMV , as well as the earlier isolated CCMV and old world and new world monkey CMVs , Leendertz et al [75] separated primate CMVs into six major clades on the basis of partial sequence of a conserved essential protein , gB . HCMV strains localized within their own clade ( 93–95% amino acid identity ) , whilst gorilla and chimpanzee CMVs were contained within two clades [CG1 clade: CCMV , Pan troglodytes CMV genogroup 1 , and Gorilla gorilla CMV genogroup 1 ( 76–77% amino acid identity with HCMV gB ) ; and CG2 clade: Pan troglodytes CMV genogroup 2 , and Gorilla gorilla CMV genogroup 2 ( 81–82% amino acid identity with HCMV gB ) [75] . Consistent with earlier studies , most of the primate CMVs were found only in their respective host species with which they had co-evolved . However , the presence of chimpanzee and gorilla CMVs together within the same clade raises the possibility of horizontal bi-directional transmission of CMV between chimpanzees and gorillas . CMV detection was based solely on the presence of subgenomic fragments of viral DNA within tissue samples by PCR and not isolation of infectious virus . However , any possibility for transmission between closely related primate species clearly indicates a need for empirical studies to confirm the high species-specificity of CMV between closely related primate species . In summary , using the mouse ma-ZEBOV challenge model , we have established ‘proof-of-concept’ for CMV as a potential ‘disseminating’ vaccine for EBOV . Future and ongoing studies are focused on the design of CMV vectors expressing full-length EBOV proteins , as well as assessment of efficacy in the NHP model using RhCMV-based vectors . The high immunogenicity , combined with the ability of CMV to spread regardless of prior CMV immunity and host-specificity , make ‘disseminating’ CMV-based vaccines a novel vaccine platform that may be ideal for targeting EBOV , as well as other pathogens , in animal populations that are inaccessible due to geography or vaccination cost . | Human outbreaks of hemorrhagic disease caused by Ebola virus ( EBOV ) are a serious health concern in Central Africa . Great apes ( gorillas/chimpanzees ) are an important source of EBOV transmission to humans . Candidate EBOV vaccines do not spread from the initial vaccinee . In addition to being highly immunogenic , vaccines based on the cytomegalovirus ( CMV ) platform have the unique potential to re-infect and disseminate through target populations . To explore the utility of CMV-based vaccines against EBOV , we constructed a mouse CMV ( MCMV ) vector expressing a region of nucleoprotein ( NP ) of Zaire ebolavirus ( ZEBOV ) ( MCMV/ZEBOV-NPCTL ) . MCMV/ZEBOV-NPCTL induced high levels of long-lasting CD8+ T cells against ZEBOV NP in mice . Importantly , all vaccinated animals were protected against lethal ZEBOV challenge . The absence of ZEBOV neutralizing and only low , sporadic levels of total anti-ZEBOV IgG antibodies in protected animals prior to ZEBOV challenge indicate a role , albeit perhaps not exclusive , for CD8+ T cells in mediating protection . This study demonstrates the ability of a CMV-based vaccine approach to protect against ZEBOV , and provides a ‘proof-of-concept’ for the potential for a ‘disseminating’ CMV-based EBOV vaccine to prevent EBOV transmission in wild animal populations . | [
"Abstract",
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] | 2011 | A Replicating Cytomegalovirus-Based Vaccine Encoding a Single Ebola Virus Nucleoprotein CTL Epitope Confers Protection against Ebola Virus |
This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis . In this case , weights correspond to the similarity indexes among protein sequences , which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties . The analyses discussed here are mainly based on the modular character of protein similarity networks , explored through the Newman-Girvan algorithm , with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong . Sound biological information can be retrieved by the computational routines used in the network approach , without using biological assumptions other than those incorporated by BLAST . Usually , all the main bacterial phyla and , in some cases , also some bacterial classes corresponded totally ( 100% ) or to a great extent ( >70% ) to the modules . We checked for internal consistency in the obtained results , and we scored close to 84% of matches for community pertinence when comparisons between the results were performed . To illustrate how to use the network-based method , we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1 , 695 organisms , downloaded from GenBank on May 19 , 2007 . A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian , distance , likelihood , and parsimony criteria suggests that the former is as reliable as these commonly used methods . We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks , which is useful for phylogenetic analysis .
In networks , module or community structure plays a central role when it comes to understand network topology and dynamics . To advance solutions to many problems related to biological networks , we need to identify , thus , the community structure in datasets . Consequently , the introduction of new efficient and robust methods that are able to perform such a task in a variety of situations is of utmost importance . We are interested , here , in giving a contribution to the complex issue of phylogenetic inference by appealing to the complex network approach , which has been successfully applied to uncover organizing principles that govern the constitution and evolution of various complex biological , technological , and social systems [1]–[4] . Recent studies using complex network approaches in the fields of both genomics and proteomics have contributed to a better knowledge of the structure and dynamics of the complex webs of interactions of a living cell [5]–[12] . Several kinds of biologically relevant networks have been studied in the last years , mainly protein interaction , transcriptional , and metabolic networks [1] . In this study , we work with another set of relationships , namely , the evolutionary relationships between proteins throughout phylogeny , and introduce a new method to identify communities in generally weighted complex networks . The reliability and overall applicability of a new proposed method is the subject of a long term research program , which necessarily starts with a clear formulation of the key steps of the method , alongside with the analysis of a non trivial problem that has been analyzed before , such as , for instance , phylogenetic inference . There are four families of methods of phylogenetic analysis that are commonly used , namely: maximum parsimony , distance , maximum likelihood , and Bayesian [13] . Promising prospects of developing new trustful methods to infer phylogenetic relationships are offered by the possibility of using primary information about protein sequences contained in open access databases and the derived protein similarity measures . We introduce here a methodology to identify community structure in such primary data sets , based on the concept of distance between complex networks , and apply it to the specific problem of retrieving useful information that can be used to infer phylogenetic relationships . In this process , we avoid as much as possible the use of any qualitative pre-existing biological information . We show here that a method based on complex network theory can recover information about the evolutionary relationships between organisms , as expressed in the similarities and differences between their protein or DNA sequences . Depending on the way the nodes are connected within a network , it may be possible to identify one or more subsets of nodes such that the average number of connections among nodes within any of these subsets is distinctly larger than the average number of connections with nodes outside this subset . The identification of such subsets ( usually called communities , modules , components , clusters , etc . ) , a key issue that has not been completely solved within complex network theory , is of utmost importance for biological applications . Indeed , modular properties are found to be very common features in any branch or level of biological network investigations . Over the past years , the amount of research in identifying communities in networks is really astonishing . There are several review articles discussing this subject , based on mathematical and computational approaches [14]–[16] . Furthermore , comparative analyses of the available methods are also found in the literature [17] , [18] . Computationally efficient approaches based on similarity matrices and cluster analyses for the exploration of protein databases with little or no prior knowledge are important tools for phylogenetic analysis . A number of approaches are currently being used to infer evolutionary relationships between proteins . For instance , the Markov Cluster ( MCL ) Algorithm [19] , [20] is an unsupervised cluster algorithm that has been applied to the analysis of graphs in several different domains , mostly in bioinformatics . The MCL Algorithm was used , for instance , for the detection of protein families [21] , a major research goal in structural and functional genomics . MCL was also extended to the identification of orthologous groups by OrthoMCL [22] . It was also used to develop phylogenomic analyses of specific taxa , such as the Ascomycota [23] . A hybrid approach to sequence-based clustering of proteins was developed , combining Markov with single-linkage clustering , with the intention of obtaining both specificity ( as allowed by MCL ) and the preservation of topological information as a function of threshold information about protein families ( as in single-linkage clustering ) [24] . Another recently developed method for automatic and unsupervised detection of protein families and genome annotation is the Global Super Paramagnetic Clustering ( SPC ) Algorithm , which showed higher accuracy , specificity and sensitivity of clustering than MCL [25] . Finally , Kóvacs et al . [18] introduced ModuLand , an integrative network module determination method family , which can determine overlapping network modules as hills of an influence function-based , centrality-type community landscape . The new method to identify communities in generally weighted complex networks proposed here is quite powerful and innovative in the use of a distance δ ( to be defined in the next section ) to determine an optimal value of the threshold on similarity . Two main tasks are crucial to derive an objective , mathematically based community identification: First , to define a measure suitable to distinguish non-modular from modular character , and , second , to identify the communities , when this is the case . The distance δ used herein is able to help the identification of the modular character in a very clear way . Therefore , our major contribution , based on complex network theory , is to use this measure together with the protein similarity matrix ( in fact , the weight matrix of any weighted network ) to identify the minimal set of links that are included in the network in order to preserve the relevant biological information necessary to unveil the modular character within the data set at stake . Once such optimally chosen network is found , any proposed community detection method may be used to retrieve the existing communities . We use here the Newman Girvan algorithm ( NGA ) [26] , which , although time consuming , also allows to identify the sequence of branching events , leading to useful and well defined dendrograms . Since several organic biomolecules are required for basic metabolic purposes , they can be found in large number of organisms , making it possible to use techniques derived from complex network theory to explore information that is useful for phylogenetic inferences . Enzymes that are involved in the synthesis of ubiquitous and metabolically important molecules seem particularly promising for such complex network approach . They are likely to be found in many distinct organisms and , if they are involved in ancient metabolic pathways , they can be found in the three life domains , Archaea , Bacteria , and Eukarya . Even though distinct organisms use their own enzyme variants to produce a given molecule , these variants will tend to look more similar in their amino acid sequences the closer the species are in phylogenetic terms . Thus , species can be gathered in phylogenetically meaningful groups by analyzing the degree of similarity of enzymes involved in some basic metabolic pathway . We show here how the similarity of the amino acid sequences of enzymes derived from completely sequenced genomes of extant organisms can be used for network construction and , subsequently , the network structure can be analyzed so as to recover phylogenetically useful information from its properties and statistics . The methods described here can be used for any set of proteins involved in basic metabolic pathways . We will work in this paper with data from enzymes involved in chitin synthesis . Chitin , the β-1 , 4-linked linear homopolymer of N-acetylglucosamine , is a structural endogenous carbohydrate , which is a major component of fungal cell walls [27] , cephalopod beaks [28] , integuments of larvae and young nematodes [29] , and arthropod exoskeletons [30] . Chitin is the second most abundant polysaccharide in nature after cellulose . It occurs only in eukaryotic organisms of the Metazoa-Fungal clade . This suggests that chitin may have evolved before the crown eukaryotic radiation . Chitin is synthesized by a sequence of six successive reactions: ( i ) conversion of Glu-6P into Fru-6-P by phosphoglucoisomerases ( E . C . 5 . 3 . 1 . 9 ) ; ( ii ) conversion of Fru-6-P into GlcN-6-P by glucosaminephosphate isomerases ( E . C . 2 . 6 . 1 . 16 ) ; ( iii ) acetylation of GlcN-6-P generating GlcNAc-6-P by phosphoglucosamine acetylases ( E . C . 2 . 3 . 1 . 4 ) , ( iv ) interconversion of GlcNAc-6-P into GlcNAc-1-P by acetylglucosamine phosphomutases ( E . C . 5 . 4 . 2 . 3 ) or , alternatively , by acetylglucosamine phosphate deacetylases ( E . C . 3 . 5 . 1 . 25 ) ; ( v ) uridilation of GlcNAc-1-P by UDP-acetylglucosamine pyrophosphorylases ( E . C . 2 . 7 . 7 . 23 ) ; and ( vi ) conversion of UDP-GlcNAc into chitin by chitin synthases ( E . C . 2 . 1 . 4 . 16 ) [31] , [32] . Chitin degradation is achieved by chitinases ( E . C . 3 . 2 . 1 . 14 ) , either by exochitinases , which convert chitin into N-acetylglucosamine residues , or by endochitinases , which convert chitin into chitobiose , which , in turn , may be converted into N-acetylglucosamine residues by hexoaminidases ( E . C . 3 . 2 . 1 . 52 ) . N-acetylglucosamine residues may be activated by acetylglucosamine kinases ( E . C . 2 . 7 . 1 . 59 ) to form N-acetylglucosamine-6-P , restoring the precursor of the short feedback cycle of chitin metabolism . Chitin may also be deacetylated by chitin deacetylases ( E . C . 3 . 5 . 1 . 41 ) , converted into chitosan , which is degraded by chitosanases ( E . C . 3 . 2 . 1 . 132 ) into glucosaminide , which , when converted into glucosamine , may be activated by hexokinase type IV glucokinases ( E . C . 2 . 7 . 7 . 1 ) , which restore the precursor of N-acetylglucosamine-6-P , Glucosamine-6-P , configuring a longer feedback cycle [33] . Even though chitin itself is found only in the Metazoa-Fungal clade , we can find proteins which are homologous to enzymes involved in chitin synthesis in other clades , including bacterial and archaeobacterial ones . Therefore , the chitin metabolic pathway can be used to recover phylogenetically relevant information in the three life domains . In this paper , we use the complex network approach as a theoretical and methodological tool to perform a comparative study of the enzymes related to the chitin metabolic pathway in extant organisms of the three life domains , Archaea , Bacteria , and Eukarya . We will show how the information derived from the network structure and statistics can be used to uncover phylogenetically useful modules , retrieving sound biological information by computational routines , without using biological assumptions other than those incorporated by BLAST .
Our primary database consists of protein sequences of completely sequenced genomes of extant organisms that can be freely accessed at the GenBank - NCBI [34] ( http://www . ncbi . nlm . nih . gov/Genbank/ ) . Protein data provide essential information to the identification of any given organism , as well as to comparative studies on evolutionary paths followed by different organisms . Our data set , downloaded from GenBank at May 19th , 2007 , contains information from 1695 organisms . We used completely sequenced genomes to assure that all putative proteins and their isoforms , if any , could be adequately retrieved [35] . We developed automatic procedures to filter the protein related data in the complete downloaded database . In the first step of the process , we extracted from the primary database the relevant information for the current work , namely , the molecular source of protein sequences , their structural and functional information , and the taxonomic classification of the organisms in which the proteins are found . Next , we scrutinized the secondary database obtained in this manner , in order to identify which proteins ( i . e . , the organism-specific protein variants that play the same biological function ) are present in a large number of organisms . One way to optimize this search , in the sense of finding many organisms with the same protein , is to pre-select a basic biomolecule , such as chitin , and look for the enzymes involved in its metabolism . Indeed , our search revealed that some of the proteins with the largest number of entries in the database are enzymes that take part in the metabolic synthesis or degradation of chitin . In Table 1 , we indicate five such enzymes , satisfying the condition of being present in more than 100 organisms from the 1695 original set [33] . The remarkably large number of bacterial records in the database reflects the fact that there are much more completely sequenced organisms of the Bacteria domain than of the Archaea and Eukarya domains . After identifying the sets of organisms that possessed each of the proteins listed in Table 1 , we used BLAST 2 . 2 . 15 [36] , with a pairwise alignment , to perform quantitative comparisons among the protein sequences pertaining to each set . From the BLAST outputs , we used in our study the similarity index . Then , a similarity matrix S was constructed based on the similarity level between protein sequences , where any element of the similarity matrix Sij∈0 , 100] is the similarity index associated with the protein sequences i and j . Since S is not necessarily symmetric ( Sij≠Sji ) , it is important to consider a symmetric version S , where the elements are defined by Sij = min ( Sij , Sji ) . The programs were executed both on LINUX- and WINDOWS-running computers . Databases were managed through MySQL . Scripts and auxiliary programs were written in PERL , BASH , C , C++ and FORTRAN 77 . PAJEK [37] was used to generate network images . In the sub-section Network construction , we describe how we used S to generate complex networks depending on a similarity threshold for each one of the five proteins shown in Table 1 . Networks were analyzed by the methods described in the sub-section Network analysis , while the modular patterns generated by complex network approach were biologically interpreted in the light of the phylogenetic relationships of organisms . Before defining the networks used in this study , let us recall that the most used characterization of network properties is based on a series of measures [38] , including: the number of nodes , N; the shortest path d ( i , j ) between nodes i and j; the average minimal distance 〈d〉 taken over all pairs of nodes; the network diameter D , defined by the largest value of d ( i , j ) ; the node clustering coefficient ci , which measures how strongly connected the neighbors of node i are; the network clustering coefficient C , corresponding to the average value over the ci; the node degree , ki , defined by the number of links of a node i and its average value over all nodes 〈k〉; the functional relationships p ( k ) , the probability distribution of nodes with k links , and C ( k ) , the distribution of node clustering coefficients with respect to the node degree k . In general , the key step in the construction of a system interaction network is to define a meaningful criterion to place an edge between two nodes , which should be able to identify the presence and strength of the interaction between them . In the current study , the concept of interaction corresponds to protein similarity , which is related , in turn , to the evolutionary relationships between the organisms possessing the proteins at stake [35] . Therefore , the similarity matrix S constitutes the starting point to obtain the protein similarity networks ( PSN ) . In a PSN , the nodes correspond to the protein sequences , and the presence of edges between two nodes depends on how similar the related proteins are . Each network can be defined by its adjacency matrix ( AM ) M , for which any matrix element mi , j is set to 1 , if the nodes i and j are connected , or to 0 , if not . Note that it is straightforward to switch from the AM network description to the list description , in which the network is characterized by a list of L pairs of nodes connected by a link . To be more precise , let us define a network family depending on a threshold value σ , where the elements of its adjacency matrix M ( σ ) satisfy: ( 1 ) This strategy makes it possible to replace one single weighted network defined in terms of S by a family of unweighted networks , which can be analyzed by a large number of recently developed methods and measures [38]–[41] . Depending on the value of σ , the interaction network may be completely distinct: for small values of σ it is highly connected , while for large values of σ it is poorly connected . As we will show in the next section , we have performed a detailed investigation of the dependence of the network properties on the value of σ . We are able to establish a well defined criterion for optimal choices of σ , in the sense that the networks generated within a relatively narrow range of values of σ display a modular pattern that can be interpreted in phylogenetic terms , as addressed in the section of results and discussion of the present paper . To fine tune the value of σ that makes it possible to unveil the modular character , we use the concept of higher order neighborhoods of a node [42] . Two nodes i and j are neighbors of order ℓ when the shortest path between them consists of ℓ edges . In this manner , it is possible to define a ℓ-th order neighborhood of a given network represented by M if we connect all pairs of nodes that are ℓ steps apart . Such networks can be defined in terms of M ( ℓ ) , the corresponding AM of order ℓ . The elements of this matrix are defined as: ( 2 ) The knowledge of the set {M ( ℓ ) } , where ℓ∈[1 , D] , allows us to define the following neighborhood matrix ( 3 ) The matrix elements of , denoted as i , j , indicate the shortest path between the nodes i and j . If the network is assembled by two or more disjoint clusters , the distance d ( i , j ) between two nodes , say i and j , belonging to two distinct clusters is ill-defined . In order to sidestep this indeterminacy and continues operating with , we set i , j = 0 whenever this occurs . The importance of for a deeper analysis of the neighborhood structure of a network has been indicated in a series of previous studies [43]–[45] . The utility of ranges from providing an insightful visualization of the neighborhood structure by means of color plots to defining a distance between pairs of networks [45] . This last measure can be used to identify how similar two networks are . For this purpose we define the distance δ ( α , β ) between any two networks with the same number of nodes ( α and β ) by: ( 4 ) where D ( α ) represents the diameter of the network α . In a general comparison process , the obtained value of δ ( α , β ) depends on the adopted node enumeration for both networks , although the network topology does not depend on it . Therefore , for the purpose of providing a useful measure , the definition ( 4 ) can be made more precise by restricting the value of δ ( α , β ) to the minimal value assumed when all possible node enumerations are taken into account ( see [45] ) . In the current study , α and β are two distinct protein networks , generated by one same dataset , but where the edges are inserted according to Eq . ( 1 ) when we consider α = σ1 = σ and β = σ2 = σ+Δσ . In this definition , we suppose that σ1 and σ2 are two nearby values of σ . Since the nodes represent the same proteins , it is not necessary to consider different enumerations , but just to use the same enumeration to generate both networks . If we plot δ ( σ , σ+Δσ ) as function of σ , it turns out that the graph is characterized by the presence of sharp peaks . Such series of consecutive values of δ ( σ , σ+Δσ ) marks the points where the obtained networks are about to suffer important topological changes [43] , i . e . , to be split into separate communities . The value of σ plays a key role in the network definition , which is similar to the probability p to establish an edge in a random Erdös-Rényi network . By varying the value of p , the network changes to an assembly of disconnected edges at p = 0 to a complete graph when p = 1 . The most interesting situation , however , occurs in the neighborhood of one critical value pcr≈1/N , which is related to the emergence of a giant cluster that contains the overwhelming majority of nodes . The investigation reported in this paper is based on the measures defined in the previous subsection , and also in other measures that allow for the identification of modularity properties of the network , if any . Loosely speaking , a module in a network is composed of a sub-set of nodes that are overwhelmingly more connected among themselves than with other network nodes . The link betweeness degree bij between nodes i and j is the basic concept within the NGA to identify network communities . bij counts the fraction of all shortest paths connecting the N ( N−1 ) /2 pairs of nodes that pass through the ( i , j ) link , providing a quantitative measure of the relevance of each link for the optimized network information traffic . NGA proceeds by sequentially eliminating the edges with largest value of bij [26] . As a result , it is possible to obtain a network dendrogram where the number of branches increases with the number r of eliminated links . In this way , the dendrogram has one single branch when r = 0 – in the case of a connected network – and N single-node communities when r = L . Each value of r informs the set of nodes that are still connected in a given cluster . Since this is a time consuming program , faster tracks have been proposed to analyze very large networks [38]–[41] , [46] . In the current case , however , we are able to work with this method , given that our networks are not too large . In our analyses , we used the NGA to identify existing communities for any value of σ . As the detected communities may be quite distinct from one value of σ to another , the NGA results corroborate our claim that the identification of the optimal value of σ using the distance δ is the crucial step of the whole procedure . To reveal the modular structure of the network , NGA requires a node re-enumeration , a step that is also included in our procedure . Therefore , it is possible to use the re-enumerated form of to visualize the modularity of the protein similarity networks with color plots . The modularity structure becomes quite clear when we draw color plots for the elements of using the same node labeling obtained at the final step of the dendrogram evaluation . We want to comment further that the concept of distance δ ( α , β ) can also be used to follow the process of link elimination within NGA . In this particular case , α and β identify two networks characterized by having m and m+1 eliminated links within NGA ( see [26] ) . A graph of δ ( m , m+1 ) as a function of m indicates , by high peaks , those events of link eliminations that correspond to branching points in the dendrogram . As it was shown in [45] , the distance δ ( m , m+1 ) is able to indicate the branching points in a much clearer way when compared to , e . g . , the modularity function Q introduced by Newman and Girvan [26] . As shown in Table 1 , we constructed networks for five enzymes of the chitin metabolic pathway , which provided , in turn , different classifications for the organisms included in the database . In order to quantitatively assess the possible differences between the classification provided by the networks based on different enzymes , say φ and ψ , we evaluated a congruence index G ( φ , ψ ) according to the following prescription: i ) we count the number R ( φ , ψ ) of common organisms that are present simultaneously in both networks; ii ) we look for the correspondence between the different communities from φ and ψ that maximizes the number of matching organisms Q ( φ , ψ ) , i . e . , organisms that are placed in the same communities in the two networks . In doing this , we must observe that , if the number of communities in φ and ψ are different , it is necessary to make a correspondence of two or more communities of network φ to the same community in networkψ . The value G ( φ , ψ ) is defined as the ratio Q ( φ , ψ ) /R ( φ , ψ ) . To conclude , the methodology that is applied to generate the results presented in the next section can be summarized in terms of the following steps:
Let us now illustrate how the behavior of δ ( σ , σ+Δσ ) provides a precise way of characterizing the dependence of the networks on σ ( step ( E ) in the summary of the methodology presented in the previous section ) . This behavior is illustrated in Figure 1a for the Acetyl network . The results were obtained by making the values of σ differ in Δσ = 1% . The graph shows three well defined maxima of δ ( σ , σ+Δσ ) for σ in the interval [30% , 50%] , the largest of which occur at σ = σmax = 42% . These results should be interpreted as follows: if σ = 0 , the network consists of a completely connected single cluster . By increasing the value of σ , we restrict the number of bonds in the network , so that 〈d〉 increases together with the values of the matrix elements . Since the distance δ ( σ , σ+Δσ ) makes a comparison of the influence of changing σ on d ( i , j ) , a sharp increase in its value indicates that the bond removal is leading to large changes in the values of some of d ( i , j ) . This suggests also that important network topological changes are about to occur . The most drastic events , expressed by the first sharp peaks , are usually related to the disassembling of one large set of nodes ( module ) from the original , completely connected cluster . This network , which we will call the critical network , is selected to be analyzed . Later on , smaller peaks indicate the splitting of larger modules into smaller ones . This occurs when the last bonds linking these modules to the network are removed . The very high peak at σ = σmax = 42% indicates that a large topological change occurred at this particular value . The same scenario is observed in Figure 1b for the δ ( σ , σ+Δσ ) results obtained from the UDP network . Note that the peaks occur at higher values of σ , in comparison to the Acetyl network , and a richer structure of peaks of comparable sizes is found . Despite these quantitative changes , the two graphs show similar features , representing the kinds of structural changes in the network due to the variation of the threshold similarity value . The presented interpretation of the influence of σ on δ ( σ , σ+Δσ ) is corroborated by other network measures . Let us consider how Nc , the size of the largest connected component in the network , depends on σ . This is illustrated in Figures 2a and 2b for the Acetyl and UDP networks , respectively ( see also [35] ) . In both figures we notice a rapid decrease of Nc in a relatively narrow interval of values of σ . This effect is related to the detachment of large groups of nodes from the main cluster as the restriction on establishing links between nodes is increased . As anticipated in the previous section , the curves follow the same qualitative features as those for the Erdös-Rényi networks as a function of the attachment probability p close to pc . Figures S1 and S2 illustrates how δ and Nc depend on p for networks with the average size of the analyzed PSN's ( N = 256 ) and also in the limit of large N ( see also Text S1 ) . Hereafter , we will consider the dendrograms , the neighborhood matrices , and the usual representation of the network associated with the proteins listed in Table 1 for the values of σ such that the distance shown in Figures 1a and 1b assumes a maximum value . Concerning UDP , the figures are not shown , since they were already presented in a previous paper [35] , in which the criterion for setting up the range of σ that reveals the modular structure of network was based on the region of transition associated with C and 〈d〉 . It is important to call the attention to the fact that the criterion based on the distance δ ( σ , σ+Δσ ) reveals in a much more precise way , in comparison to C and 〈d〉 , the value of σ in which the modular structure is observed . The influence of σ on the network structure can be better appreciated by comparing two dendrograms in Figure 3 for the Acetyl networks at σ = 30% and σ = σmax = 42% . In the first situation ( Figure 3a ) , the very large number of edges does not allow one to perceive the system modular structure . Accordingly , the NGA based on bij is characterized by a progressive detachment of small groups of nodes from the original giant cluster . In turn , the dendrogram for σ = σmax ( Figure 3b ) reveals a lot of structure . It starts , at r = 0 , with some already isolated clusters , corresponding to the modules that were detached at σ = σmax , σ = 45% , and σ = 48% . Then , we note the separation of a large cluster at a low value of r , which is caused by the elimination of the few bonds with very large betweenness degree connecting nodes of the different modules . Such cluster detachment is exactly the same one produced by increasing the value of σ to 42% , causing the absolute δ ( σ , σ+Δσ ) maximum in Figure 2a . The subsequent elimination of bonds leads to further branching in the dendrogram , some of which can be related to local maxima in the σ>σmax region of the δ ( σ , σ+Δσ ) ×σ plot . Dendrograms evaluated at intermediate σ values , e . g . , σ = 40% , are able to clearly identify network modules corresponding to those clusters detached from the giant cluster by selecting σ close to this peak value at σmax . However , the picture that emerges for those clusters that detach at larger values is still rather blurred . As anticipated in the previous section , let us put together supplementary results in the dendrogram construction to display the network modular structure with the help of the neighborhood matrix . To avoid line crossings in the dendrogram , the order at which the isolated nodes are drawn for the largest value of r does not necessarily follow the original numbering . This ordering defines a new node labeling which leaves untouched the network topology . If we now use a color code to represent with relabeled nodes , the modularity structure becomes quite clear , as shown in Figure 4 . Running from blue ( immediate neighbors ) to red ( farthest apart nodes ) , the colors clearly indicate how the nodes are grouped into modules , as well as the existence of sub-clusters within the modules and the average distance between nodes in distinct modules . Note that we use gray to indicate the value d ( i , j ) = 0 , so that the communities that have been detached from the main cluster at lower values of σ appear isolated from one another in the color diagram . We identify 11 modules ( C1–C11 ) , the biological significance of which will be discussed below . We note also a number of isolated nodes or small sub-graphs that do not constitute a module on its own . Figure 4 shows the color plot for the neighborhood structure for the Acetyl network at σ = σmax . It is relatively easy to infer the structure of the dendrograms from the position of the modules . It is important to stress that both graphs not only show the modular structure of the network , but also clearly depict how the retrieved communities are related to each other . The information obtained from the described procedure can be also used for the usual network representation formed by nodes and links . In Figure 5 , we draw such representation for the Acetyl network at σ = σmax . Here , the colors used to draw the nodes represent the different communities they belong to . The set of isolated nodes and small sub-graphs is characterized by the C12 label . This discussion shows that the proposed method allows us to find the most relevant networks , namely those at critical values of σcr . These values , where the network topology changes abruptly , correspond to optimal choices between inter-community edge elimination ( noise effect ) and intra-modules bond preservation ( valuable information ) . They allow us to identify distinct communities , which can be related , then , to the sets of organisms to which the proteins belong ( see also Figures S3 , S4 , S5 , S6 and S7 ) . We observe that σmax corresponds to the particular σcr , where δ ( σcr , σcr+Δσ ) reaches the largest value . We show in Table 2 the values of σmax , the number of nodes , and the number of communities obtained for each of the five enzyme networks . In the case of UDP , we observe the highest σmax value , indicating that , in the case of this protein , the disassembling of the original , completely connected cluster happen at higher values of similarity . This is a protein with a central role in the chitin synthesis , and , consequently , it is not surprising that it shows the greatest degree of sequence conservation throughout evolution , among the proteins studied in this work . This suggests additional features of the method discussed here , in that there is a relationship between the σmax value , the degree of sequence conservation of proteins ( a structural feature ) , and their centrality in metabolic networks ( a functional feature ) . It is relevant to notice that , up to this point , all discussed results have been obtained without any previous knowledge of phylogenetic classification . We only constructed computer routines to proceed with the data analysis , network construction , and network analysis , leading to community identification . If we now interconnect the results discussed above with taxonomic and phylogenetic data , sound biological information can be promptly retrieved by these computational routines , without using biological assumptions other than those incorporated by BLAST in the production of its outputs . The Acetyl modules that can be identified at σ = σmax ( Figure 4 ) correspond , in a clear and rather precise manner , to bacterial phyla and/or classes ( and even orders , in some communities ) . As already discussed , we restricted our analysis to those phyla due to the fact that most of the protein sequences in the database were derived from this biological domain . All cyanobacterial representatives formed only one and exclusive group retrieved in the module C8 ( a ) . Furthermore , there are six communities [C3 ( a ) , C4 ( a ) , C5 ( a ) , C6 ( a ) , C7 ( a ) , C10 ( a ) , C11 ( a ) ] that are formed exclusively by representatives of one single bacterial phyla or class and , in some cases , order: community C3 ( a ) is exclusively formed by species of the same bacterial order ( Mollicutes ) ; community C4 ( a ) are all composed of representatives of Actinobacteria , high G+C Gram-positive monoderm bacteria , of the same class ( Actinomycetales ) ; community C5 ( a ) exclusively includes alpha-proteobacteria of the class Rhodobacterales; and community C11 ( a ) contains only species of Firmicutes , low G+C Gram-positive monoderm bacteria , belonging to the very closely related orders Bacillales and Lactobacillales . Although not entirely composed of representatives of the same phyla , 18 out of 20 nodes ( 90% ) of community C2 ( a ) are from the same bacterial phyla ( Proteobacteria ) and 16 ( 80% ) are from the most phylogenetically related classes of beta- and gamma-proteobacteria [47] . Four modules are retrieved in the Glucosaminephosphate isomerase ( gluco ) network at σ = σmax = 40% , and , as in the case of UDP and Acetyl , most of them correspond to single bacterial phyla and/or classes ( and even orders ) : community C2 ( g ) is exclusively composed by bacterial representatives of phyla Firmicutes of only two classes: Bacillales and Lactobacillales; community C4 ( g ) is entirely formed by sequences of the order Alteromonadales of the class gamma-proteobacteria; and 21 out of 23 sequences ( 91 . 3% ) of community C3 ( g ) are representatives of the phyla Proteobacteria ( Figures S5a , S6a , and S7a ) . A total of 9 modules occur in the Hexosaminidase ( hexo ) network at σ = σmax = 37% and three of them , which contain the greatest number of nodes , are almost exclusively formed by only one bacterial phyla or class: Community C1 ( h ) is composed of 97 nodes , of which 95 ( 98% ) are representatives of phyla Proteobacteria; community C2 is almost exclusively formed by species of the class alpha-proteobacteria; and community C4 ( h ) contains only members of the most phylogenetically related classes of beta- and gamma-proteobacteria [47] . The other communities are all composed by few nodes corresponding to species of distinct phyla ( Figures S5b , S6b , and S7b ) . Five modules occur in the Phosphoglucoisomerase ( phospho ) network at σ = σmax = 37% and , similarly to the other enzymes of the chitin metabolic pathway , there is a rather strict correspondence between these modules and bacterial phyla . Community C1 ( p ) is mainly composed by cyanobacterial representatives ( 71% ) , community C2 ( p ) is almost exclusively formed by species of Firmicutes ( 96 . 4% ) , and the very large community C5 ( p ) , with 328 nodes , is mainly represented by sequences of Proteobacteria ( 76% ) ( Figures S5c , S6c , and S7c ) . Finally , UDP can be decomposed into 6 clearly identified modules C1 ( u ) –C6 ( u ) , as has been shown previously [35] . C1 ( u ) is composed by 16 nodes , 14 ( 87 . 5% ) of which are protein sequences from representatives of the phylum Cyanobacteria . One of the nodes corresponds to a sequence from a species of Deinococcus-Thermus , a Gram-negative diderm bacterial group of extremophiles that is closely related to Cyanobacteria [48] . C2 ( u ) contains 135 nodes and , among them , 132 ( 97 . 8% ) are sequences from species of both beta- and gamma-proteobacteria , which are considered to be more closely related to each other than to any other proteobacterial class [47] . C3 ( u ) is entirely constituted by 80 sequences from Firmicutes species , of three phylogenetically related orders: Bacillales , Lactobacillales , and Clostridiales . C4 ( u ) contains 33 vertices , of which 31 ( 93 . 4% ) are sequences from the presumed monophyletic group of alpha-proteobacteria [47] . C5 ( u ) is entirely formed by sequences from Actinobacteria , all from the same order: Actinomycetales . Finally , C6 ( u ) comprises only nine nodes from the putative monophyletic group of epsilon-proteobacteria [47] , all from the same order: Campylobacterales . Usually , all the main bacterial phyla ( Actinobacteria , Cyanobacteria , Firmicutes , Proteobacteria ) and , in some cases , also some bacterial classes ( alpha- , beta- and gamma-Proteobacteria ) , corresponded totally ( 100% ) , or with a substantial number of representatives ( >70% ) , to the modules formed as a result of the complex network analysis of the proteins of the chitin metabolic pathway . Even when there were few completely sequenced genomes exhibiting one of the studied proteins , all the representatives of the same phyla were generally grouped together in the same community . In each of the protein networks , the nodes with the highest degree numbers , or hubs , occurred inside the same community . Although these hubs were not the same in the five different protein networks , many of them were from the same bacterial species for distinct proteins , e . g . Yersinia pestis for gluco , hexo , and UDP; Escherichia coli for acetyl , hexo , and UDP . In contrast to all other proteins , the hubs in the gluco network were mainly archeal representatives . The results for a phylogenetic analysis provided by several distinct methods do not necessarily agree with each other , as one can verify by a direct comparison of the outputs produced by each of them . Although we will not make here a detailed comparison between our method and other procedures used to recover phylogenetically useful information , but limit ourselves to take into account the classification obtained for the original dataset , we are in a position to discuss the internal consistency of our method . The modules defined by the five different enzymes do not necessarily agree with each other for two distinct reasons: first , because not all organisms possess all the enzymes involved in the chitin pathway . This is already clear by the different number of nodes in each of the five networks . Second , because during the course of evolution some enzymes may have suffered more changes than the corresponding enzyme in other organisms , so that the similarity index Sij between organisms i and j may take distinct values for two different enzymes . Such quantitative changes may alter the way the organisms are arranged into communities in the corresponding networks . In particular , it may happen that different networks produce distinct number of communities because different enzymes may have changed to a different extent in the organisms , so that one organism may belong to different communities in the networks obtained for different enzymes . Since the same protein may have been independently inserted more than once into the database during the process of uploading the recordings available in Genbank , we have found that the number of distinct organisms in each of the 5 networks is always smaller than the number of nodes ( Table 2 ) . We avoided , then , to advance biological hypotheses before the elimination of the isoforms . The congruence of the classification provided by the distinct networks obtained for the five enzymes of the chitin metabolic pathway was evaluated by means of the congruence index G ( φ , ψ ) , defined in the previous section as the ratio Q ( φ , ψ ) /R ( φ , ψ ) . For instance , if we take into account the classifications provided by acetyl and UDP we notice that they consist , respectively , of 176 and 327 nodes , which actually correspond to 88 and 245 organisms , distributed into 12 and 7 communities ( Table 2 ) . The number of common organisms and correct matches are R ( φ , ψ ) = 44 and Q ( φ , ψ ) = 40 , so that G ( φ , ψ ) = 0 . 91 . The results for the other pairs of networks are shown in Table 3 . In Figure 6 we display the results obtained from the community identification for all 5 networks ( In Figure S8 , one can see the same figure with the horizontal axis expanded for better visualization ) . In this representation we take into account only the number of 382 distinct organisms represented by the original 1695 entries . The used association ( number , organism ) is available in the Supplementary Information . Each of the five networks is represented by a horizontal sequence of spikes , which identify which organisms are present in each network . Within a given network , the color of the spikes identifies to which community the organism belongs . Since different networks have different numbers of communities , there is no color correspondence between distinct network classifications . Congruence can be measured by the same color criterion: if the spikes corresponding to organisms i and j have the same color in network φ and network ψ , the classification provided by φ and ψ is congruent , even if the common color in φ is different from the common color in ψ . The subsequent steps of our research program comprise a detailed comparison between the results obtained with the complex network approach reported in this paper and the outcomes of other methods used to analyze phylogenetic relationships based on molecular data . Although this is a computationally complex task [49] , [50] , the results of which need to be discussed in another work , it is possible to advance that preliminary results for a much smaller data set than that used herein are promising – namely , data about chitin synthase , another protein of the chitin metabolic pathway . Using the PAUP 4 . 0 program [51] to perform distance , likelihood , and parsimony analyses , and Mr . Bayes 3 . 02 [52] to perform Bayesian analysis , we provided a comparison between the proposed phylogenetic classification with those based on the Bayesian , distance , likelihood , and parsimony criteria . The results shown in Table 4 are based on the same congruence criterion we used to compare the data in Table 3 . In particular , the average congruence of our method with the four other methods reaches 69% , while the average taken over the six pair-wise comparisons among the four methods ( B , D , L . P ) reaches only 60% . These results allow us to conclude that the methodology reported in this paper is as reliable as those commonly used methods . This work reports a method based on complex network theory that can recover information about the evolutionary relationships between organisms , as expressed in the similarities and differences between their protein sequences , which is useful for phylogenetic inference . The system interaction network constructed is based on protein similarity as the meaningful criterion to place an edge between two nodes . Each node in the network is a specific protein sequence and the placement of edges depends on a threshold value σ , related to the protein similarity required to such a placement . We performed a comparative study of the enzymes related to the chitin metabolic pathway in completely sequenced genomes of extant organisms of the three life domains , Archaea , Bacteria , and Eukarya , in order to show how the information derived from the network structure and statistics can uncover phylogenetic patterns . The results concerning phylogenetic classification discussed in this paper are mainly based on the modular character of protein similarity networks . Once the critical value of σ ( σcr ) using the distance measure δ ( α , β ) is found , we can choose the optimal network for community detection , namely , that in which the network topology changes abruptly , corresponding to optimal choices between inter-community edge elimination ( noise effect ) and intra-modules bond preservation ( valuable information ) . Although the NGA can be used to identify communities for any value of σ , it is in this optimal network that the best results can be achieved with regard to the identification of distinct communities , which can be related , in turn , to the sets of organisms to which the proteins belong . With this method , sound biological information can be promptly retrieved by computational routines , without using biological assumptions other than those incorporated by BLAST . Usually , all the main bacterial phyla and , in some cases , also some bacterial classes corresponded to a great extent ( 70%–100% ) to the modules obtained by means of the complex network analysis of the proteins of the chitin metabolic pathway . Therefore , the method reported here can be used as a powerful tool to reveal relationship patterns among both organisms we have knowledge about and organisms about which we do not have much information available . We provided results showing the internal consistency of the results obtained through our method for the data corresponding to five different enzymes . Despite the different rates of changes suffered by these enzymes during evolution , we found 84% of matches for community pertinence when comparisons between the results were performed . Moreover , a preliminary comparison between the results obtained with the complex network approach reported here and the outcomes of methods based on Bayesian , distance , likelihood , and parsimony criteria suggests that the methodology reported in this paper is as reliable as these commonly used methods . There are , however , some possible advantages of the complex network method when compared to these other methods . One of them concerns the fact that we can determine the value of σ in which the complex network retrieve most of the phylogenetic information available in the data set . Second , even though all these methods use substitution matrices – including ours – , the complex network method is not dependent upon patterns inferred from the detailed study of any organisms . The next steps in our research program will be the application of the method presented here to new sets of protein sequences , a more thorough comparison of the results obtained through our complex network approach with the outcome of other methods employed to retrieve information from molecular data that is useful for phylogenetic inference , and the application of our method to address relevant research questions within different fields of biology . | Complex weighted networks have been applied to uncover organizing principles of complex biological , technological , and social systems . We propose herein a new method to identify communities in such structures and apply it to phylogenetic analysis . Recent studies using this theory in genomics and proteomics contributed to the understanding of the structure and dynamics of cellular complex interaction webs . Three main distinct molecular networks have been investigated based on transcriptional and metabolic activity , and on protein interaction . Here we consider the evolutionary relationship between proteins throughout phylogeny , employing the complex network approach to perform a comparative study of the enzymes related to the chitin metabolic pathway . We show how the similarity index of protein sequences can be used for network construction , and how the underlying structure is analyzed by the computational routines of our method to recover useful and sound information for phylogenetic studies . By focusing on the modular character of protein similarity networks , we were successful in matching the identified networks modules to main bacterial phyla , and even some bacterial classes . The network-based method reported here can be used as a new powerful tool for identifying communities in complex networks , retrieving useful information for phylogenetic studies . | [
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] | [
"computational",
"biology/evolutionary",
"modeling",
"evolutionary",
"biology/bioinformatics",
"computational",
"biology/genomics"
] | 2011 | Detecting Network Communities: An Application to Phylogenetic Analysis |
Onchocerciasis , also known as river blindness , is a parasitic disease . More than 99 percent of all cases occur in Africa . Bioko Island ( Equatorial Guinea ) is the only island endemic for onchocerciasis in the world . Since 2005 , when vector Simulium yahense was eliminated , there have not been any reported cases of infection . This study aimed to demonstrate that updated WHO criteria for stopping mass drug administration ( MDA ) have been met . A cross-sectional study was conducted from September 2016 to January 2017 . Participants were 5- to 9-year-old school children . Onchocerciasis/lymphatic Filariasis ( LF , only in endemic districts ) rapid diagnostic tests ( RDTs ) were performed . Blood spots were collected from RDT positive children and 10 percent of the RDT negatives to determine Ov16 and Wb123 IgG4 antibodies through enzyme-linked immunosorbent assay ( ELISA ) . Skin snips were collected from RDT positives . Filarial detection was performed by PCR in positives and indeterminate sera . Black fly collection was carried out in traditional breeding sites . A total of 7 , 052 children , ranging from 5 to 9 years of age , were included in the study . Four children ( 0 . 06% ) were Ov16 IgG4 RDT positives , but negative by ELISA Ov16 , while 6 RDT negative children tested positive by ELISA . A total of 1 , 230 children from the Riaba and Baney districts were tested for LF . One child was Wb123 RDT positive ( 0 . 08% ) , but ELISA negative , while 3 RDT negative children were positive by Wb123 ELISA . All positive samples were negative by PCR for onchocerciasis and LF ( in blood spot and skin snip ) . All fly collections and larval prospections in the traditional catching and prospection sites were negative . WHO criteria have been met , therefore MDA in Bioko Island can be stopped . Three years of post-treatment surveillance should be implemented to identify any new occurrences of exposure or infection .
Onchocerciasis is a parasitic disease caused by the filarial worm Onchocerca volvulus . It is transmitted through the bites of infected Simulium blackflies , which breed in fast-flowing streams and rivers . Symptoms include rashes , severe itching and various skin lesions , and blindness . The disease is endemic in 31 countries in sub-Saharan Africa , two countries in Latin America , and in Yemen . An estimated 18 million people are infected with the disease and have dermal microfilariae . 99% of the infected individuals live in Africa [1 , 2] . Human onchocerciasis is one of the two filarial helminth “neglected tropical diseases” targeted for geographically local elimination [3] . In the Americas , onchocerciasis elimination has traditionally been considered feasible as most onchocerciasis foci were confined and usually small . Since 2013 , the World Health Organization ( WHO ) has certified four countries in Latin America as free of human onchocerciasis [4] . In Africa , where onchocerciasis has been endemic over vast areas , with highly efficient vectors and much higher endemicity levels , elimination was not initially considered to be feasible [5] . The Onchocerciasis Control Programme in West Africa ( OCP ) was launched in 1974 by the World Health Organization ( WHO ) , followed by the African Programme for Onchocerciasis Control ( APOC ) , initiated in 1995 . Both programs established mass treatment with ivermectin combined with aerial spraying of breeding sites with selected insecticides in fast-flowing rivers as principal methods for controlling onchocerciasis [3] . Great progress has been made towards elimination . In most OCP/APOC countries , nationwide onchocerciasis elimination now seems to be an obtainable objective [6] . This paradigm shift from control to elimination occurred in 2012 due to success in Latin America , the shift to integrated NTD control , and the successful elimination of onchocerciasis in some parts of Africa [7] . The island of Bioko is part of the Republic of Equatorial Guinea and is the only island in the world where onchocerciasis is endemic [8] . Initially , the island was considered free of loiasis . The presence of intermediate hosts [9] , and the recent reporting of an imported case of loiasis in a US traveler returning from the island [10] call for attention and further research . Two out of four districts have reported cases of Lymphatic Filariasis ( LF ) . In 1990 , several control activities were launched by the OCP , including long-term ivermectin mass treatment in all 52 island communities [11 , 12] . Afterwards , APOC became the sponsoring agency and introduced community-directed treatment with ivermectin ( CDTI ) throughout the island in 2000 [8] . In addition , a vector elimination project started when APOC was established in 1995 . A feasibility study was carried out in 1996 , confirming the high vectorial efficiency of the endemic Bioko form of Simulium yahense [13] and the distribution of the vector breeding sites [14] . From 2001 to 2005 , a large-scale larviciding trial using ground-based applications was undertaken using helicopter and ground-based applications of temephos [8 , 15] . In 2005 , the endemic Bioko form of S . yahense was finally eliminated from the island . Since then , there has not been any reported transmission or any serious epidemiological situation in Malabo City or elsewhere on the island [8] . According to personal communication from the Ministry of Health and Social Welfare of Equatorial Guinea ( MINSABS in Spanish ) , the last MDA with ivermectin was administered in 2012 in urban Malabo and in 2016 elsewhere on the island . A 2014 cross sectional study found no positive MF skin snip assessment in 544 study participants ages 5 years and older [16 , 17] . In the recently updated WHO guidelines for stopping mass drug administration and verifying elimination of human onchocerciasis ( 2016 ) , new tools were proposed to verify the transmission interruption , stop CDTI and begin post-treatment surveillance . Following the WHO updated recommendations , the objectives of this study were: a ) to verify onchocerciasis transmission interruption in Bioko Island , Equatorial Guinea; b ) to validate a methodology to assess Ov16 prevalence in children younger than 10 years of age and; c ) to develop a protocol to verify onchocerciasis elimination that can be applied in other African countries with hypoendemic intensity of transmission or where mass drug administration ( MDA ) has been conducted for a number of years . Data on LF were also collected as part of the study .
The Island of Bioko is part of the Republic of Equatorial Guinea , which also includes Rio Muni on the mainland and the island of Annobon . It is located in the Bay of Guinea in Central Africa , about 40 km southwest of the Cameroon coast . Bioko Island covers an area of approximately 2 , 017 km2 ( 779 square miles ) . It is 72 km ( 44 . 7 miles ) long and is divided into four districts ( Malabo , Luba , Riaba and Baney ) . Bioko Island has 334 , 463 inhabitants . Most of the population is concentrated on the northern part of the island in the Malabo district , where Malabo , the capital of Equatorial Guinea is located [18] . Tropical rainforest covers much of the interior of the island and the topography is characterized by steeply sloping volcanoes and calderas . Bioko Island has a humid tropical climate with an average annual temperature of 25°C and two distinct seasons: a dry season from November to March and a rainy season from April to October . A cross-sectional study was conducted from September 2016 to January 2017 . The eligible participants were 5- to 9-year old school children who had lived in Bioko Island for the past three years . According to the most recent WHO guidelines , a sample size of 1 , 100 to 2 , 000 children younger than 10 years of age per administrative unit is required for Ov-16 serology testing in order to detect a prevalence of less than 0 . 1% at the upper bound of the 95 percent confidence interval . When the eligible population of children is less than 1 , 100 , all eligible children are to be tested [19] . First , we obtained an updated census report on school-aged children from the Ministry of Education . According to this data , sampling was not required in two districts ( Riaba and Luba ) , since the population was below 1 , 100 . All the children from the Baney district and rural Malabo who met the inclusion criteria were included in the study because the numbers were lower than first estimated . In urban Malabo , a random sampling method was implemented . A second visit took place in May 2017 to obtain a second RDT and blood spot for ELISA/PCR in patients whose results were RDT/ELISA positives and/or were in the detection threshold limit . Entomological surveys were carried out in the rivers with potential breeding sites . Two teams , consisting of three local technicians , one supervisor and two coordinators were assembled and trained before the field work began . Prior to starting the study , a comprehensive field training program was provided along with training on the proper use of diagnostic tools . The participation questionnaire was pre-tested one week before the beginning of the project . Questionnaires were provided to each school administrator , who distributed them to the children before the science team came to the school to conduct the testing . Children were instructed to take the forms home and have their parents or guardians complete them before they were returned . At least two reminders were given before the testing date . The form required the participant’s sex , birthdate , age , and asked whether the child had lived in Bioko Island for the past three years and if the child had ever take ivermectin . Test results were later recorded in the same document . The geographic coordinates of each school ( latitude and longitude ) were collected using the offline maps application MAPS . ME . This study combined the use of serological tests based on recombinant antigens with the later confirmation of diagnostic results by molecular diagnosis . The SD BIOLINE Onchocerciasis IgG4 rapid diagnostic test , manufactured and distributed by Standard Diagnostics , Inc . ( SD ) , was performed following the product instructions . Further information on this technique has been described elsewhere [20] . Sterile techniques were used to obtain finger-prick blood samples . Fingers were cleaned using an alcohol swab and sterile cotton balls . Technicians wore disposable gloves . All materials were safely disposed of . In those districts considered co-endemic to lymphatic Filariasis and onchocerciasis ( Riaba and Baney ) , SD BIOLINE LF IgG4 RDT was also performed , together with SD BIOLINE onchocerciasis/LF IgG4 biplex . Blood spots were collected from all the children with either a positive Ov RDT or a positive LF RDT and from a random sample of 10 percent of the negative RDT children for determination of Ov16 and Wb123 IgG4 antibodies through enzyme-linked immunosorbent assay ( ELISA ) . This subsample ( 10% of negative RDT ) was randomly selected by equal probability systematic sampling . Every tenth RDT negative child was selected for further blood spots collection . Sterile techniques were used to place finger-prick blood spots onto circles on 5 x 5 cm Whatman 903 protein saver cards . Skin samples were obtained near the iliac crest of each individual through Walser matrix forceps . Samples , stored and transported at 4°C , were delivered to the National Center of Microbiology ( NCM ) laboratory at the Institute of Health Carlos III in Madrid for further analysis . An ELISA protocol was run to detect anti-Ov16 IgG4 antibodies in the eluted blood from the Whatman filter paper ( S1 ELISA Protocol ) . Plates were sensitized with 0 . 5 μg/ml of Ov16 recombinant protein , obtained and purified as described in Hernández-Gónzalez et al . , 2016 [16] . A poly-His tail was added to the carboxy-terminus of the Ov-16 sequence amplified from a vector ( kindly donated by Professor JE Bradley , School of Life Sciences and University of Nottingham , UK ) . Then , the construction was subcloned into pGEX-6P-1 plasmid ( GE Healthcare , Little Chalfont , UK ) . Further expression and purification steps were undertaken as explained in the indicated paper . Two positive controls were included: ( i ) an anti-Ov16 human recombinant monoclonal antibody ( hIgG4 , Bio-Rad ) and ( ii ) a pool of positive sera from patients with onchocerciasis ( clinical and parasitological confirmation ) . A standard curve from each positive control was used to identify positive samples on each plate allowing comparisons among plates and days . The anti-Ov16 IgG4 mAb positive control was diluted as follows: 12 , 6 , 3 , 2 , 1 . 5 and 1 ng/ml and in the case of the positive sera , pool dilutions ranked from 1/400 to 1/3 . 200 . The cut- off for the recombinant positive control ( anti-OV16 mAb; stock: 2 . 9 mg / ml ) , was set at 2 . 01 ng / ml following the indications of Golden et al . , 2017 [21] . The dilution 1/800 from the pool of positive sera was chosen as the cut-off , with optical densities similar to those obtained with the anti-Ov16 mAb at 2 . 01 ng/ml . A similar ELISA protocol was run to detect anti-Wb123 IgG4 antibodies . The only difference in methodology was that wells were sensitized with Wb123 recombinant protein . The Wb123- pUC57 construction , corresponding to the GenBank HQ438580 sequence , was obtained from Genscript ( Piscataway , NJ , USA ) and further directionally subcloned in pQE-30 expression vector . Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) 0 . 01 mM was used to induce protein expression with ON incubation at 16°C . The Wb123 recombinant protein was purified by Ni2+- Sepharose 4B affinity chromatography ( GE Healthcare ) in native conditions and according to the manufacturer’s recommendations . Finally , the Wb123 recombinant antigen was dialyzed against PBS and quantified using the Pierce BCA Protein Assay Kit ( Thermo Scientific Rockford , IL , USA ) . A standard curve was developed; in this case , with serial dilutions ( 1/400 to 1/3 , 200 ) of a Wuchereria bancrofti positive sera pool ( WHO collection provided by Dr . N . Weiss , Swiss Tropical Institute , Basel ) . The chosen cut-off was the OD corresponding to the 1/1600 control sera dilution . In both ELISAs ( Ov16 and Wb123 ) , the sera were classified as positive , indeterminate or negative , following the criteria described below: Molecular protocols ( PCR ) were undertaken to confirm serological positive samples: blood spots for FL and onchocerciasis positives and skin snip for Onchocerca positives . DNA extraction from skin snips specimens and the blood spots were performed with the QIAamp DNA mini kit ( QIAGEN , IZASA , Madrid ) . Filarial detection was carried out by two independent Polymerase chain reaction ( PCR ) methods . A real-time PCR , modified by Tang et al . ( 2010 ) [22] , targets the internal transcribed spacer in one region of the ribosomal gene ( ITS-1 ) , which enables the identification of positive samples by post reaction analysis by melting temperature ( Tm ) curve of the amplified fragments ( Tm = 77 . 50°C ± 1 . 0°C ) and by the size of the amplified fragment ( 344bp for O . volvulus , 312bp for Mansonella spp . , 301bp for W . bancrofti and 286bp for Loa loa ) and a conventional PCR targeting of the mitochondrial COI gene [23] ( see further details in S1 Table ) . All samples were made in duplicate . PCR products were analyzed by automatic electrophoresis ( QIAxcel , QIAGEN , IZASA , Madrid ) or conventional electrophoresis in 2 percent agarose gels stained with Pronosafe ( Pronadisa , Madrid ) . The confirmation of the filarial species was performed by sequencing the amplified fragment using the Big Dye Terminator v3 . 1 Cycle Sequencing Kit ( Applied Biosystems , Massachusetts , EEUU ) on an ABI PRISM 3700 DNA analyzer ( Applied Biosystems , Massachusetts , USA ) . Previously , PCR products were purified using the Illustra DNA and Gel Band Purification Kit ( General Electric Healthcare , Little Chalfont , UK ) . All amplified products were sequenced twice in both directions . All sites accomplishing APOC experts’ criteria for harboring larvae of the simulids were visited . Black fly collection was carried out in representative catching sites by one team in each community , consisting of two fly collectors and a human attractant ( human bait ) . The human bait method involved having the individual sit in one place barefoot for up to one hour . During this worktime , the fly collector exposed his legs and caught all landing Simulium flies with the help of an exhaustor ( “pooter” ) . Collections began , local time , at 0700 and ended at 1700 . Collectors received ivermectin 1 week before beginning the collection process . Individual data , RDT and laboratory results were analyzed to obtain frequencies of each variable . Prevalence ( with 95 percent confidence intervals ) of onchocerciasis and LF were calculated from RDT results . In three out of the four districts ( and rural Malabo ) , all the eligible children attending school the day of the visit were included . It was not confirmed whether any children were absent on the day of the survey or whether any local children were not attending school at all . Prevalence and confidence limits were computed following the recommendations of Brown et al . for interval estimation for a binomial proportion [24] . The Wilson interval for small n ( when n<100 ) , and the interval suggested in Agresti and Coull for larger n ( when n≥100 ) , were used . A univariate analysis was performed to explore if any variable showed a significant relationship with positive onchocerciasis RDT cases . Analyses were performed using the statistical package Stata 14 . 0 . Schools were mapped using the free software QGIS version 2 . 18 . 7 . The study was approved by the MINSABS on Bioko Island and the research ethics and animal welfare committee at the Health Institute Carlos III ( ISCIII in Spanish ) in Spain , under the number CEI PI 22_2016-v3 . The school headmasters and children´s parents/guardians were informed of the day of the visit and the scope of the study by an official letter from the MINSABS . Written informed consent was obtained from all parents or guardians prior to study inclusion . Data were analyzed anonymously .
Finger-prick blood samples for Ov16 IgG4 RDT were obtained from all 7 , 052 school children . From the overall sample , four children ( 0 . 06 percent ) were found to be positive for Ov16 IgG4 antibodies by RDT ( 95 percent CI upper limit = 0 . 11 ) . One was from the Riaba district and three lived in rural Malabo ( Fig 2 ) . No significant association with onchocerciasis RDT positive cases was found for any variable , except for district . Rural Malabo presented the highest frequency of onchocerciasis RDT positive cases ( n = 3; p<0 . 001 ) . Blood spots were collected from 720 school children . Five of them were obtained from positive individuals and 715 ( 10 percent ) were collected from a randomized selection of RDT negative children . Skin snips were obtained from the RDT positive children ( n = 4 ) . The 4 onchocerciasis RDT positive children were negative by ELISA Ov16 , while 6 RDT negative children from urban Malabo tested positive to onchocerciasis by ELISA ( prevalence = 0 . 83 percent; 95 percent CI = 0 . 34–1 . 85 ) . Three samples were considered as indeterminate sera , because they were close to the ELISA cutoff threshold . PCR analysis was negative for all positive/indeterminate blood and skin samples ( n = 13; Table 2 ) . A total of 1 , 230 children from Riaba and Baney were tested for lymphatic Filariasis . In one Baney district school ( with n = 19 ) , the LF IgG4 RDT was not performed due to logistical problems . One 9-year-old girl ( 0 . 08% ) from Baney was found positive for Wb123 by LF IgG4 RDT ( Table 3 ) , while no child tested positive for Wb123 , according to onchocerciasis/LF IgG4 biplex results . Samples were processed by ELISA with recombinant Wb123 . The LF RDT positive child was ELISA negative , while 3 children from Baney and 2 from Riaba who tested negative by RDT tested positive when sera was processed by Wb123 ELISA ( Fig 2 ) . One onchocerciasis RDT positive child was also positive to LF by Wb123 ELISA . All LF serologically positive children ( n = 6 ) were negative by PCR ( Table 3 ) . In May 2007 , a second serum sample was obtained from 16 children who were either RDT positive ( Onchocerciasis or LF ) or ELISA positive/indeterminate for confirmatory test ( n = 18 ) . Two school children were unavailable during the second visit . Out of the above population , 2 children were onchocerciasis RDT positive ( prevalence = 0 . 03%; 95% CI = 0 . 00–0 . 11 ) , 1 by Ov16 IgG4 RDT and the other by Oncho/LF biplex IgG4 RDT . Both were negative by Ov16 ELISA and blood spot/skin snip PCR . The child , who was previously found positive to LF , was negative by biplex RDT in this second round . The second Ov16-IgG4 ELISA analysis was negative for all the samples ( n = 16 ) except one . Regarding those samples that were positive by ELISA with recombinant Wb123 in the preliminary analysis ( n = 5 ) , 3 remained positive , 1 became negative and the fifth one had been collected from one of the two unavailable children . Skin snips were obtained from the confirmed biplex RDT ( n = 2 ) and Ov16 ELISA ( n = 1 ) positive children . PCR analysis was negative for all of them ( Table 4; S2 Table ) . Updated WHO guidelines established that serologically positive children found negative by PCR testing of skin snips are considered negative for patent infection with O . volvulus and are accepted as not contributory to the 0 . 1% threshold calculation [19] . According to this criterion , no child was positive for onchocerciasis ( Table 2 ) . The 2 RDT and the remaining ELISA positive child ( according to the sera obtained on the second visit ) were considered as O . volvulus “exposed . ” The MINSABS will re-examine them 1 to 1 . 5 years after the first visit to determine if they have developed patent infection . If so , they will be treated accordingly , following WHO recommendations [19] . Entomological assessment was performed from the 29th of August to the 23rd of September , 2016 . Due to fieldwork difficulties and the need for extra personnel , the Ureka area ( breeding sites placed in rivers Mohaba , Ehola and Osha ) was visited in February 2017 ( Fig 3 ) . All fly collection and breeding site prospection were negative .
Our study has several limitations . First , the lower participation rate in urban Malabo may affect the interpretation of the results in this particular district . Moreover , the school attendance rates for 5- to 9-year-old children were not provided by the Ministry of Education ( probably this information is not systematically recorded ) . Nevertheless , the sample size was larger than would have been required in this administrative unit according to the WHO guidelines . Second , the recombinant positive control antibody AbD19432_hIgG4 was complex to standardize , due to the reagent instability when it was diluted to the final concentration to be used in ELISAs . Secondary extractions from indeterminate serological samples ( within the detection limit threshold ) were obtained in May 2017 to verify the results . Only one of the conflictive samples requested yielded a positive absorbance value by both positive controls ( recombinant control and serum control ) . Third , pre-analytical bias due to the complex field logistics might exist . To reduce these potential biases , detailed guideline and standard operational procedures ( SOPs ) were prepared and piloted prior to the field work . All the SOPs were also tested by internal control before and during the field work . In conclusion , WHO criteria have been met in Bioko Island . Consequently , MDA can be stopped and 3 years of post-treatment surveillance should begin to identify any new occurrences of exposure or infection . Successful elimination of onchocerciasis infection throughout Equatorial Guinea may be a feasible goal for the relatively near future . To that end , a survey extending to the continental area is needed before talks about countrywide elimination begin . | Onchocerciasis , commonly called river blindness , is a chronic parasitic disease particularly prevalent in Africa . It is transmitted through the bites of infected Simulium blackflies . Onchocerciasis is endemic in Equatorial Guinea . Huge achievements have been made in human and vector control during the last two decades , especially on Bioko Island . Eliminating onchocerciasis transmission on Bioko is feasible given its isolation from other landmasses , which also reduces the risk of reinvasion by the disease vector . Recently updated WHO guidelines for stopping mass drug administration ( MDA ) and verifying elimination of human onchocerciasis ( 2016 ) established a new critical threshold to verify elimination of onchocerciasis transmission based on novel serological tests . We applied these techniques in a representative sample of 5- to 9-year-old school children . An entomological assessment was also carried out . We found no evidence of current infection or recent transmission . There was no evidence of onchocerciasis vectors , and our results from the sample population meet the current WHO serologic criteria for stopping MDA . Based on these results , we recommended to the Ministry of Health and Social Welfare of Equatorial Guinea that MDA on Bioko Island be stopped and that 3 years of post-treatment surveillance should be undertaken to identify any new occurrences of exposure or infection . | [
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] | 2018 | Interruption of onchocerciasis transmission in Bioko Island: Accelerating the movement from control to elimination in Equatorial Guinea |
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