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10.1371/journal.pmed.1002509 | The potential impact of case-area targeted interventions in response to cholera outbreaks: A modeling study | Cholera prevention and control interventions targeted to neighbors of cholera cases (case-area targeted interventions [CATIs]), including improved water, sanitation, and hygiene, oral cholera vaccine (OCV), and prophylactic antibiotics, may be able to efficiently avert cholera cases and deaths while saving scarce resources during epidemics. Efforts to quickly target interventions to neighbors of cases have been made in recent outbreaks, but little empirical evidence related to the effectiveness, efficiency, or ideal design of this approach exists. Here, we aim to provide practical guidance on how CATIs might be used by exploring key determinants of intervention impact, including the mix of interventions, “ring” size, and timing, in simulated cholera epidemics fit to data from an urban cholera epidemic in Africa.
We developed a micro-simulation model and calibrated it to both the epidemic curve and the small-scale spatiotemporal clustering pattern of case households from a large 2011 cholera outbreak in N’Djamena, Chad (4,352 reported cases over 232 days), and explored the potential impact of CATIs in simulated epidemics. CATIs were implemented with realistic logistical delays after cases presented for care using different combinations of prophylactic antibiotics, OCV, and/or point-of-use water treatment (POUWT) starting at different points during the epidemics and targeting rings of various radii around incident case households. Our findings suggest that CATIs shorten the duration of epidemics and are more resource-efficient than mass campaigns. OCV was predicted to be the most effective single intervention, followed by POUWT and antibiotics. CATIs with OCV started early in an epidemic focusing on a 100-m radius around case households were estimated to shorten epidemics by 68% (IQR 62% to 72%), with an 81% (IQR 69% to 87%) reduction in cases compared to uncontrolled epidemics. These same targeted interventions with OCV led to a 44-fold (IQR 27 to 78) reduction in the number of people needed to target to avert a single case of cholera, compared to mass campaigns in high-cholera-risk neighborhoods. The optimal radius to target around incident case households differed by intervention type, with antibiotics having an optimal radius of 30 m to 45 m compared to 70 m to 100 m for OCV and POUWT. Adding POUWT or antibiotics to OCV provided only marginal impact and efficiency improvements. Starting CATIs early in an epidemic with OCV and POUWT targeting those within 100 m of an incident case household reduced epidemic durations by 70% (IQR 65% to 75%) and the number of cases by 82% (IQR 71% to 88%) compared to uncontrolled epidemics. CATIs used late in epidemics, even after the peak, were estimated to avert relatively few cases but substantially reduced the number of epidemic days (e.g., by 28% [IQR 15% to 45%] for OCV in a 100-m radius). While this study is based on a rigorous, data-driven approach, the relatively high uncertainty about the ways in which POUWT and antibiotic interventions reduce cholera risk, as well as the heterogeneity in outbreak dynamics from place to place, limits the precision and generalizability of our quantitative estimates.
In this study, we found that CATIs using OCV, antibiotics, and water treatment interventions at an appropriate radius around cases could be an effective and efficient way to fight cholera epidemics. They can provide a complementary and efficient approach to mass intervention campaigns and may prove particularly useful during the initial phase of an outbreak, when there are few cases and few available resources, or in order to shorten the often protracted tails of cholera epidemics.
| The risk of cholera around households of cholera cases is higher than in the general population in the days after cholera symptoms start.
Rapid targeting of cholera interventions to neighbors of cholera cases may provide an effective and resource-efficient way to avert cholera cases and deaths and reduce the duration of epidemics.
Interventions targeted to neighbors of cases using combinations of antibiotics, oral cholera vaccine, and/or water, sanitation, and hygiene measures have been used in Africa and the Americas to fight cholera, yet limited evidence exists on the potential impact of this approach, the optimal mix of interventions, and the extent of the target population.
Using computational models, we simulated cholera epidemics similar to a large urban cholera outbreak in Chad and evaluated the potential impact of targeted interventions administered to people living within a fixed radius (e.g., 100 m) around reported cholera cases.
Targeted interventions with oral cholera vaccine were predicted to have the largest impact on reducing cases and shortening epidemics, followed by water treatment interventions and by prophylactic antibiotics, regardless of when interventions started during epidemics.
The combined use of oral cholera vaccine and water treatment within 100 m around cases starting early in epidemics were estimated to lead to 70% (interquartile range [IQR] 65% to 75%) fewer epidemic days and 82% (IQR 71% to 88%) fewer cases than uncontrolled epidemics.
Compared to traditional mass intervention campaigns, targeted interventions can have a similar or larger impact on epidemics and use less resources.
The optimal radius to target around incident case households differed by intervention type, with antibiotics having an optimal radius of 30 m to 45 m compared to 70 m to 100 m for oral cholera vaccine and point-of-use water treatment.
Interventions targeted to neighbors of cholera cases can be an effective and resource-efficient strategy to fight cholera epidemics; they may be particularly useful during the early phase of an outbreak, when the number of cases is still low, and to truncate the tails of outbreaks, after a mass intervention campaign.
While field studies and/or clinical trials are needed to measure the effectiveness of targeted interventions, these results provide a rationale to focus efforts on interventions with oral cholera vaccine and water treatment interventions in a roughly 100-m radius around case households.
| With over 130,000 cases and 2,400 deaths reported globally in 2016, cholera continues to be a major public health threat, particularly in sub-Saharan Africa [1]. These numbers likely represent an underestimate of the true burden due to poor access to health care, insensitive surveillance systems, and political sensitivities around reporting cases and deaths [2,3]. Cities in sub-Saharan Africa are regularly struck by cholera outbreaks, causing disruption and hindering social and economic development [4,5]. These cities may act as local, national, and/or international hubs of disease spread due to regular travel and migration, and quickly controlling cholera outbreaks in these areas may significantly reduce the number of cholera cases both within the cities and elsewhere.
The cornerstone of cholera prevention and control is improved access to safe water, sanitation, and hygiene (WaSH) and appropriate case management. WaSH includes a heterogeneous mix of interventions, ranging from provision of safe water through infrastructure or point-of-use water treatment (POUWT) tools to latrine building and hygiene behavior change measures [6]. Antibiotics have been used to shorten the duration of shedding in cholera cases and, in some instances, to provide short-term prophylaxis for household contacts, although their prophylactic use is not part of current guidelines by WHO, Médecins Sans Frontières (MSF), the US Centers for Disease Control and Prevention, or icddr,b [7,8]. Recently, oral cholera vaccines (OCVs) have been added to this arsenal and are widely available as a result of the global cholera vaccine stockpile and the addition of new, affordable WHO-prequalified vaccines to the stockpile [9,10]. However, supply of these vaccines remains limited, and countries must often contend with fewer doses than needed to cover the population at risk [11]. OCVs have been shown to be safe, immunogenic, and protective, with 2-dose protection (the standard regimen) lasting at least 3 years, and single-dose protection at least 6 months [12,13], a similar time scale to many cholera epidemics.
These tools are used preventively [14] in areas deemed at high risk for cholera transmission, or reactively in response to a cholera outbreak [15–17]. Control measures are typically given to the population at-large through mass campaigns within high-risk areas, although targeted interventions to households or neighborhoods of cases, including delivery of OCV, antibiotics, and POUWT [7,8,18–20], are common. In Haiti and other countries, efforts to establish rapid response teams tasked with implementing highly targeted interventions are currently underway [21]. The benefits of this type of approach remain unclear, and there is little understanding about when in an epidemic these interventions may have a greater impact than more traditional community-wide interventions, how large an area to target around case households, or the best mix of interventions.
Spatiotemporal clustering of cholera cases—at distances ranging from tens to hundreds of meters—has been observed during numerous cholera outbreaks in endemic and epidemic areas [22–28]. A previous analysis showed that suspected cholera cases were significantly clustered up to distances of at least 200 m from incident case households within the first 5 days of a case presenting for care during epidemics in 2 urban African settings in Chad and the Democratic Republic of the Congo [29]. This clustering has been attributed to common risk factors in those living close to one another, in addition to the risk of transmission often being higher the closer one lives to an infected individual. Intervention strategies targeting disease hotspots [30], particularly vulnerable neighborhoods and camps [31] and other communities, have been successfully applied in the past. Limited literature exists, however, on reactive case-area targeted interventions (CATIs), which take advantage of the inherent spatiotemporal clustering of cholera cases by targeting people living within a given distance around reported cholera cases. Such a strategy could not only present efficient alternatives to reactive mass intervention campaigns in outbreak situations, where resources may be limited or their availability delayed, but may also be used as a complementary approach to mass campaigns when cholera incidence is low, such as during the initial phase or declining tail of an epidemic.
Here we aim to understand the potential impact of CATIs on epidemic cholera using computational transmission models fit to data from a 2011 cholera epidemic in Chad. We aim to provide practical guidance on the best mix of interventions (OCV, POUWT, and/or prophylactic antibiotics), ring size, and timing to maximize efficiency and impact.
During the 2011 cholera epidemic in N’Djamena, Chad, field staff from MSF collected the household coordinates of all suspected cholera cases presenting at the main cholera treatment center starting on June 22 by visiting people at their home (S1 Fig). From August—when the case load began to increase rapidly—through the end of the epidemic in December, household coordinates were collected for every third patient. To minimize potential selection biases, every third patient was identified at the cholera treatment center by an epidemiologist from MSF/Epicentre (NN) who then provided the address to a team of data collectors who visited each household. The resulting dataset, combining the overall epidemic curve of suspected cholera cases (citywide) with GPS coordinates of patient’s homes, has been described previously [29] (S2 Fig). The epidemic totaled 4,352 reported cases (within a population of 993,500) and lasted for 232 days. The attack rate varied between 11.6 and 59.6 per 10,000 among the 10 districts (arrondissements) of the city (S2 Fig). As these data were originally collected for operational purposes, this study did not have a prospective protocol, and it was deemed to be exempt research by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board.
To quantify the spatiotemporal clustering of cholera cases, we used the τ statistic, a measure of the relative risk that a person living at a given distance from a known cholera case also becomes a case compared to any person in the entire population becoming a case within the same time frame [32–36] (S1 Text). In the presence of spatiotemporal clustering at a particular distance and time, τ is greater than 1.
We developed an individual-based, spatially explicit stochastic model (S1 Text) and calibrated it to the 2011 cholera outbreak in N’Djamena. All 993,500 inhabitants of the city were assigned a geographical location according to the population density estimated using remotely sensed built-up density as a proxy [37,38] (S2 Fig). Demographic processes, like births and deaths, were assumed to be negligible during the short time course of the outbreak. In the model, each individual’s state (e.g., susceptible, exposed, infectious, or recovered) is tracked during the outbreak (Fig 1A). Susceptible individuals are exposed to a spatially distributed force of infection originating from infectious individuals and decreasing with distance (Fig 1B). The force of infection is modulated by rainfall, which has been shown to be an important environmental driver of cholera epidemics in several settings [39–43]. Exposed individuals can become either symptomatically infected, after an incubation period with a mean duration of 2 days [44], or mildly/asymptomatically infected, in which case it is assumed that they do not significantly contribute to the force of infection [45,46]. Symptomatic infection lasts for an average of 5 days before individuals recover [47].
The 4 free parameters of our model are the ratio of symptomatic to asymptomatic infections, a kernel-independent transmission rate, a shape parameter of the power-law transmission kernel, and a coefficient governing the influence of rainfall. Model calibration was performed using an approximate Bayesian computation population Monte Carlo (ABC-PMC) approach (S1 Text) [48]. Specifically, we calibrated the model to the number of newly reported cases per day (i.e., the epidemic curve; Fig 2A) and the spatiotemporal clustering of the case households, as captured by the τ statistic. We estimated τ at 3 different representative distance windows (15 to 45 m, 45 to 105 m, and 105 to 225 m)—chosen to fit the spatial discretization of the model domain—and focused on cases occurring within 5 days after each case (Fig 2B). For simplicity and interpretability, we used the sum of squared errors as a goodness of fit measure for both criteria. The calibration was run with 512 particles, which were accepted if the sums of squared errors of both criteria were lower than predefined thresholds adapted after every calibration step (S1 Text).
To evaluate the benefits (e.g., averted symptomatic cases) and resource needs (e.g., number of people targeted and number of clusters targeted through CATI) of different types of interventions, we simulated a total of 111 scenarios (and several sensitivity analyses), combining different intervention types, modes of allocation, and intervention starting times (S1 Table). Out of an initial 1,000 epidemics simulated without interventions, the ones with at least 10 new cholera cases during the week preceding the initiation time (833, 836, and 829 for interventions starting on day 50, 130, and 180 of the epidemic, respectively) were resimulated for every intervention scenario. This threshold of 10 was selected through trial and error to ensure that simulated epidemics reflected those where interventions were likely to be put in place (e.g., in epidemics with clear evidence of ongoing transmission) and to reduce the number of comparisons where the differences between uncontrolled and intervention simulations was largely a result of stochastic effects rather than the interventions. Simulations were run up to 1 year from the first cases to stay within the realm of historic outbreak durations. For each intervention scenario we then computed the median and interquartile range for the number of averted cases and the number of epidemic days reduced, over all simulations. These interquartile ranges (or prediction intervals) capture the uncertainty related to the model parameters (intervention- and disease-transmission-related parameters) and stochastic variations. Note that, using this method, some simulations may generate a higher number of cases and/or a longer epidemic with interventions than without, because the course of the simulated epidemic trajectories is subject to stochastic processes.
We evaluated 3 types of interventions both individually and in combination: the administration of a single dose of antibiotics (e.g., azithromycin), the administration of a single dose of OCV, and a POUWT intervention (Fig 1A). We reconstructed probability distributions (S14 Fig) for each of the intervention effects, with mean effect sizes and measures of variability derived from the literature (Table 1), and drew from these distributions in simulations. A detailed description of the effects of the 3 types of interventions and their implementation is given in S1 Text. Antibiotics and OCV were assumed to reduce an individual’s probability of becoming symptomatic if infected [49,50]. In addition, we assumed that antibiotics reduce a symptomatic individual’s infectious period [51]. Given the limited cholera-specific effectiveness data for POUWT (or any WaSH intervention [52]), we based our estimate of effectiveness on a meta-analysis of POUWT interventions in urban/peri-urban areas on all cause diarrhea [6]. We assumed that POUWT leads to a 26% reduction in exposure to Vibrio cholerae and thus reduces the probability of getting infected [6]. While we assumed that antibiotics and POUWT provide immediate protection, we considered OCV to be fully effective after a lag of 7 days (with 0 efficacy before), based on the design of a clinical trial assessing its efficacy [50]. The effects of antibiotics were assumed to last for 2 days [53], whereas protection from OCV and POUWT was assumed to last through the end of each epidemic. In our main analyses, a person can receive prophylactic antibiotics only once during the entire epidemic even if they live within overlapping targeted areas at different times. In secondary analyses, we included a scenario where a person is eligible for antibiotics each time he/she is in a targeted cluster, provided there has been at least a 2-week gap since the last time he/she was targeted with antibiotics (S1 Text).
In our model, CATIs are implemented by targeting people within a given radius (15, 30, 45, 70, or 100 m; S13 Fig) around each reported (i.e., symptomatic) case. In N’Djamena, rings of those radii contain an average (range) of 9 (2 to 21), 42 (5 to 76), 75 (10 to 120), 167 (14 to 263), and 295 (55 to 456) people, respectively (S12 Fig).
To account for the fact that an intervention team visiting a target cluster would not be able to reach all inhabitants because they might be absent, be unwilling to receive the intervention, not comply with interventions, or be under 1 year of age (minimum age for antibiotics and OCV), we assumed that a random sample of 70% of the target population can be effectively reached [18]. We accounted for a variable delay between the onset of symptoms of the initial case and the deployment of an intervention team to the target area based on data from South Sudan and other settings [18]; the delay was drawn from a distribution ranging from 0 to 7 days with a mode of 2 (S16 Fig). Of note, our mechanistic modeling approach implicitly accounts for the fact that with longer intervention delays, the proportion of case neighbors already immune to cholera increases.
To understand the relative value of CATIs, we compared their impact and efficiency with (1) small mass campaigns within the entire city, assuming that the same total number of people gets targeted, (2) mass intervention campaigns reaching 70% of the city population, and (3) mass intervention campaigns targeted to the 3 districts with the highest attack rate at the start of the campaign with 70% coverage in each (S1 Text). To provide an estimate of the logistical implications, we also computed the number of rings that were targeted per day with different CATIs.
As timing is key in controlling outbreaks, we considered 3 different scenarios for the start of interventions (CATIs and mass campaigns). Interventions started during the early (stable) phase in the epidemic (day 50 of the epidemic, May 31), around the peak of the epidemic (day 130, August 19), or late, as the epidemic is declining after the peak (day 180, October 8) (Fig 2). Whereas randomly allocated interventions and mass campaigns were presumed to take 2 weeks to complete (with the same number of interventions administered every day), we assumed that CATIs continue until the end of the simulated epidemic (e.g., no more exposed nor infected individuals in the model) or until the maximum simulation time of 1 year.
The calibrated model reproduced key characteristics of the 2011 epidemic in N’Djamena, including the epidemic curve and the spatiotemporal clustering of cases (τ; Fig 2). In simulated uncontrolled epidemics, a median of 3,381 (IQR 1,535 to 5,811) cases occurred, with epidemics lasting a median of 262 (IQR 218 to 311) days. In the calibrated model, individuals living within 15 m to 45 m of a cholera case had a 12.1-fold (IQR 10.1 to 13.9) greater risk than the general population of becoming a cholera case within 5 days of the primary case developing symptoms (Fig 2B). The posterior distributions of fitted parameters are shown in S3 Fig.
Each of the primary interventions—POUWT, OCV, and antibiotics—rapidly decreased incidence when targeted to individuals living within 100 m of a case (Fig 3) and reduced the duration of epidemics. Antibiotics led to the sharpest short-term reduction in incidence due to the high degree of short-term protection. However, the rate of incidence reduction was not sustained due to the short-lasting protection of antibiotics.
POUWT and OCV led to faster extinction of outbreaks than antibiotics (Fig 4; Table 2). When interventions started early (day 50; Fig 2), OCV reduced epidemic durations by 68% (IQR 62% to 72%), POUWT by 21% (IQR 7% to 35%), and antibiotics by 2% (IQR −11% to 8%). When interventions started around the peak (day 130), epidemics were shortened by 35% (IQR 26% to 44%) using OCV, by 15% (IQR 4% to 24%) using POUWT, and by 2% (IQR −9% to 14%) using antibiotics. Even when intervening late in epidemics (day 180), each of the interventions truncated the epidemic, with a 24% (IQR 14% to 35%) reduction from OCV, 11% (IQR 2% to 21%) from POUWT, and 3% (IQR −7% to 14%) from antibiotics. We found similar qualitative results when interventions were targeted to different radii around incident cholera cases (S1 Table).
The number of cases averted by CATIs focused within 100 m from cases varied and depended on the timing and type of intervention (Table 2; Fig 5A). When interventions started close to the peak of the epidemic, the reduction in cases compared to uncontrolled epidemics for CATIs within 100 m was 43% (IQR 35% to 49%) with OCV alone, 20% (IQR 14% to 27%) with POUWT alone, and 14% (IQR 6% to 21%) with antibiotics alone. Regardless of the intervention type, more cases were averted when interventions were initiated earlier in the epidemic. Interventions that averted more cases in a shorter period of time reduced the duration of epidemics and ultimately required less resources (i.e., number of people and clusters targeted) (Fig 5B and 5C).
Combinations of the different interventions, each with its own mechanism of protection, led to larger, quicker, and more robust impacts on the epidemic (Table 2). CATIs with OCV and antibiotics in a radius of 100 m from case households led to 70% (IQR 64% to 74%) shorter epidemics when started early, 37% (IQR 28% to 46%) shorter epidemics when started around the peak, and 25% (IQR 16% to 36%) shorter epidemics when started late (Fig 6). When combining OCV and POUWT in a 100-m radius, epidemics were shortened by 70% (IQR 65% to 75%), 38% (IQR 29% to 47%), and 25% (IQR 16% to 36%) when starting interventions early, around the peak, and late, respectively. CATIs with antibiotics and POUWT reduced epidemic durations by 26% (IQR 11% to 45%), 15% (IQR 5% to 25%), and 15% (IQR 5% to 23%) starting early, around the peak, and late, respectively.
Interventions in a radius of 100 m combining OCV with antibiotics and/or POUWT led to a similar number of cases averted. CATIs starting early during the epidemics reduced the cases by 83% (IQR 72% to 88%) using OCV and antibiotics, by 82% (IQR 71% to 88%) using OCV and POUWT, by 8% (IQR 5% to 13%) using POUWT and antibiotics, and by 83% (IQR 72% to 89%) when using all 3 types of intervention. When starting at the epidemic peak, OCV and antibiotics led to a 50% (IQR 41% to 55%) reduction in cases, with OCV and POUWT leading to a 47% (IQR 38% to 52%) reduction, antibiotics and POUWT to a 30% (IQR 24% to 36%) reduction, and all 3 types of intervention to a 51% (IQR 42% to 57%) reduction. Starting late during the epidemic, a combination of OCV and antibiotics resulted in 10% (IQR 6% to 16%) fewer cases, OCV and POUWT in 9% (IQR 5% to 14%) fewer cases, POUWT and antibiotics in 8% (IQR 5% to 13%) fewer cases, and all 3 interventions in 11% (IQR 7% to 16%) fewer cases.
For OCV and POUWT, the number of averted cases steadily increased with the CATI radius until 70 to 100 m. For CATIs with antibiotics, the curves of averted cases by ring radius peaked at 30 m, roughly 2 to 3 times as high as at 100 m, and similar to OCV at 100 m (Fig 7A–7C). This effect results from the short-lasting protection from antibiotics in combination with the limitation that every person can only be targeted once. The epidemic wave arrives at distances farther from the primary case after the protective effect from antibiotics has already vanished. For all 3 types of intervention, the ring size that led to the most efficient reduction in cases was similar regardless of the intervention timing. However, the number of clusters targeted decreased with increasing ability to rapidly stop epidemics (Fig 7G–7I). The number of persons targeted over different radii was governed by 2 contrasting effects, a decrease with better performing interventions and an increase with larger cluster radii (Fig 7D–7F).
The most efficient type of CATI in a radius of 100 m was OCV, with 1.6 (IQR 1.0 to 2.6) people targeted per case averted when starting interventions early, 7.4 (IQR 5.8 to 10) when starting around the peak, and 31 (IQR 17 to 53) when starting late (Fig 8B). For POUWT, the number of people targeted per case averted was 7.1 (IQR 4.0 to 12) when starting interventions early, 23 (IQR 15 to 35) when starting around the peak, and 47 (IQR 26 to 90) when starting late. For antibiotics, it was 16 (IQR 7.1 to 33) when starting early, 33 (IQR 20 to 58) when starting around the peak, and 46 (IQR 27 to 79) when starting late.
Mass intervention strategies, where a large proportion (i.e., 70% in our case, corresponding to approximately 700,000 people) of the city population was targeted in a short time period, achieved similar numbers of averted cases (S6 Fig), but typically required hundreds to tens of thousands of people to be targeted in order to avert a single case. For an intervention campaign starting around the epidemic peak, CATIs within a radius of 100 m were 58 times (IQR 36 to 112) more efficient than a mass intervention campaign using OCV and 43 times (IQR 25 to 85) more efficient using POUWT.
To gauge the relative value of spatial targeting of interventions, we simulated small mass campaigns allocating the same number of doses as used by CATIs throughout the entire city. This mode of intervention did not effectively stop epidemics nor reliably avert significant numbers of cases (S7 Fig), with almost 50% of the simulated epidemics with interventions showing no improvement. When taking into account only simulated epidemics with a positive number of averted cases, CATIs starting around the epidemic peak in a 100-m ring around a primary case led to a 6-fold (IQR 3 to 11) higher reduction in cases using OCV and a 3-fold (IQR 2 to 7) higher reduction using POUWT than their non-targeted counterparts.
As mass interventions are not often applied to an entire city (due to limited resources, especially of OCV [11]), we simulated mass campaigns targeting 70% of the population of the 3 (out of 10) districts with the highest attack rate at the time of intervention (district-targeted campaigns). The number of cases averted using this approach was similar to that achieved with CATIs, but the number of people targeted was considerably higher (up to 300,000 people; S6 Fig) and epidemics typically lasted longer. District-targeted campaigns with OCV shortened epidemics by 37% (IQR 12% to 61%) when started early, by 21% (IQR 11% to 32%) when started at the epidemic peak, and by 15% (IQR 4% to 25%) when started late. In campaigns with POUWT alone, epidemics were shortened by 9% (IQR −5% to 24%), 8% (IQR −2% to 18%), and 6% (IQR −3% to 17%) starting early, at peak, and late, respectively. Combining both OCV and POUWT led to 38% (IQR 11% to 62%) shorter epidemics when starting early, 24% (IQR 12% to 33%) shorter epidemics when starting at peak time, and 15% (IQR 5% to 25%) shorter epidemics when starting late. The number of people needed to target per case averted with a district-targeted campaign starting early was 44 (IQR 27 to 78) times higher than for CATIs within a radius of 100 m with OCV, 18 (IQR 8 to 40) times higher than for CATIs with POUWT, and 50 (IQR 33 to 93) times higher than for CATIs with OCV and POUWT combined (Fig 8).
The maximum number of rings needed to target per day varied with intervention type and timing of the start of interventions and was lower for interventions that truncated epidemics faster (Table 3). For CATIs with OCV alone, a maximum of 13 (IQR 9 to 19) rings per day needed to be targeted when starting early in a radius of 100 m. Adding POUWT to this reduced the maximum number of rings to 11 (IQR 8 to 16). The average number of rings needed to target each day was considerably lower, at 4.1 (IQR 2.7 to 6.0) rings per day for OCV and 3.7 (IQR 2.5 to 5.3) rings per day for OCV and POUWT, starting early with a radius of 100 m around reported cases.
We performed several sensitivity analyses to explore the impact of key model assumptions on our results. These analyses related to assumptions about the natural history of cholera, transmission pathways, and mechanisms by which interventions protect, and assumptions related to more practical elements. Detailed descriptions of the implementation and results of the sensitivity analyses are reported in S1 Text.
In our primary analyses, we assumed that asymptomatically infected individuals are not infectious, based on the evidence that they produce far less stool and that the stool they do produce contains V. cholerae for less time and of orders of magnitude lower concentration than symptomatic cases [45,46,55]. To explore if the shedding of V. cholerae by asymptomatic individuals may have an influence on intervention outcomes, we recalibrated the model and ran additional simulations assuming that each asymptomatic person was 10% as infectious as a symptomatic patient for a single day after infection. The results suggest that allowing asymptomatic individuals to be infectious reduced the overall impact of CATIs, both in terms of cases averted and epidemic time reduced. However, the overall rank order of different interventions did not change (S23 Fig). For example, using CATIs with OCV in a 100-m radius around the peak of an epidemic in a model with infectious asymptomatic individuals led to a 21% (IQR 15% to 31%) reduction in cases averted compared to a 43% (IQR 35% to 49%) reduction in our primary analysis.
In our main analysis, all symptomatic cases were assumed to be detected and followed with a CATI deployment. However, this is unlikely to be the case in reality for a variety of reasons related to inadequacies in surveillance, care-seeking behavior, and logistical constraints. To explore how imperfect CATI response could impact our main findings, we reran simulations targeting OCV at a 100-m radius around a fraction (5%–100%) of symptomatic cases. Results suggest that CATIs responding to as few as 40% of symptomatic cases led to similar impact in terms of cases averted and epidemic days truncated (S20 and S21 Figs). Targeting 100-m rings around 40% of all symptomatic cases with OCV around the peak of an epidemic led to 38% (IQR 30% to 44%) fewer cases than simulations without interventions, and the epidemic duration was reduced by 31% (IQR 22% to 41%). As expected, when targeting CATIs within 100 m around very few symptomatic cases (5%), we saw a significant decrease in the intervention effect.
In our primary analyses, we assumed that POUWT interventions reduce the likelihood of infection by reducing exposure to V. cholerae. However, it may be that POUWT reduces the likelihood of becoming symptomatic given the clear dose–response relationship seen in a human challenge model of V. cholerae O1 [46,56,57]. To understand how this mechanistic assumption impacts our primary results, we reran simulations assuming POUWT reduced the risk of symptomatic infection by 74% (95% CI 65% to 85%) instead of reducing exposure. With this alternative POUWT mechanism, CATIs implemented within 100 m around case households led to a larger impact (cases and epidemic days averted) than in our primary analysis (S22 Fig). For example, when intervening around the peak of an epidemic in a radius of 100 m, the number of cases was reduced by 39% (IQR 31% to 44%) and the epidemic duration was reduced by 25% (IQR 16% to 34%), compared with 20% (IQR 14% to 27%) and 15% (IQR 4% to 24%), respectively, in the primary analyses.
Finally, the structure of our model makes long-distance transmission events, which cause the rapid spread of the disease from one neighborhood to another by the means of travelers and commuters [30,42], unlikely to occur. We thus performed sensitivity analyses investigating the influence of adding long-distance transmission events to the model and found that the results were qualitatively similar to those of the primary analyses (S1 Text; S16–S19 Figs).
Using a spatially explicit transmission model fit to data from a large cholera outbreak in Chad, we found that CATIs using OCV, antibiotics, and/or POUWT have the potential to efficiently and effectively mitigate the impact of cholera epidemics in similar urban areas. Of the 3 intervention types explored, OCV most effectively stopped epidemics (e.g., simulated epidemic durations were cut by a third when CATIs started around the epidemic peak and by a fourth when started late in the epidemic), whereas antibiotics had a more pronounced short-term impact. Combinations of the 3 types of interventions can be used to further reduce cases and deaths, although the combinations, as modeled, did not lead to synergistic effects. Our findings suggest that CATIs, which require targeting tens to hundreds of persons per case averted, are far more resource-efficient than mass intervention campaigns, which typically require hundreds to tens of thousands of people to be targeted per case averted. The optimal ring size around a case depends on the type of intervention. For antibiotics, which offer only short-term protection, the optimal CATI radius is around 30 m to 45 m, whereas for OCV and POUWT, which offer longer-lasting protection, the intervention impact increases until CATI radii reach 70 m to 100 m.
Visiting case households is not new to public health [58–60] nor to cholera control [7]. In a number of countries, it is standard practice to visit the households of cholera cases to provide health hygiene education, soap, and sometimes water and or latrine disinfectants and antibiotics [7,18]. While our findings suggest that CATIs work well even when only a fraction of cases are targeted, rapid detection and confirmation of cases is key to maximizing impact. The use of cholera rapid tests may provide one avenue for improving the precision and timeliness of CATIs given that traditional diagnostics (i.e., culture) require days to complete for some patients [61]. CATIs are logistically complex to implement and require well-trained, highly mobile teams that rapidly visit case households, delimit target areas, transport necessary supplies, and deliver interventions.
Knowing that an average of 295 (range 55 to 456) people live in each 100-m ring in N’Djamena (and similar African cities with cholera like Conakry, Monrovia, Lubumbashi, or Nairobi [62]; S12 Fig), and that several rings may overlap, the number of rings that are to be targeted each day during different variants of CATIs can provide understanding of the number of teams required to implement interventions. Other challenges to CATI implementation include finding cases’ households and negotiating with local leaders to efficiently deliver the interventions.
While our study suggests that CATIs can be effective and efficient, it remains unclear when this approach should be used, particularly in contrast to mass campaigns, the current standard for outbreak response. As shown, early CATIs can have profound impacts on the trajectory of an epidemic. If resources such as OCV are limited, as they are at the time of writing this paper, CATIs may be the most appropriate strategy to target those at highest risk. When only a few cases are detected, especially when they are spatially dispersed, ministries of health may want to initiate CATIs to efficiently quell the epidemic with supplies already in the country, while making contingency plans for mass interventions if the epidemic continues to grow. Finally, these strategies may be used late in epidemics or epidemic seasons, possibly after a mass campaign, to quickly stop the often protracted tails of epidemics [18,63], which can ultimately save scarce health system resources used during time periods when an epidemic is officially ongoing. CATIs with POUWT interventions, focused on larger geographical units (villages) than simulated in this paper, are currently being deployed to fight the cholera epidemic in Haiti through rapid response teams, although their effectiveness in reducing cases and deaths remains unknown [21].
Prophylactic antibiotic use is not part of most guidelines on cholera prevention and control; however, a number of countries, like Kenya, use antibiotics for household contacts as part of their standard outbreak response. Our results suggest that antibiotics could play a role in CATIs, although the potential impact appears lower than for other interventions in the main analyses. It is unclear how a short (or single-dose) course of antibiotics used in CATIs would affect short- and long-term antimicrobial resistance profiles in the community. Any decision to use CATIs with antibiotics should be accompanied by increased antimicrobial resistance monitoring in the community [64].
While our study used a rigorous approach to calibrate the models and capture uncertainty in both the epidemic processes and the intervention effects, it comes with a number of limitations. First, cases used in these analyses were suspected cholera cases, only some of which were confirmed [29]. If confirmed cases represent a simple random proportion of suspected cases (in terms of space and time), our characterization of space–time clustering should not be affected [32]. It is possible that cases living around other cases are more likely to present for care, which could lead to an artificial increase in our estimates of the space–time clustering of cases, meaning that our estimated impact of CATIs could be overly optimistic. However, given that estimates of space–time clustering have been relatively similar across settings with different surveillance systems and health-seeking behavior [22,25,29], we do not believe that this is likely to cause major biases in this study.
We made a number of assumptions related to cholera transmission processes. In our main analyses we assumed that asymptomatic individuals were not infectious based on evidence that asymptomatic individuals have an orders-of-magnitude lower concentration of bacteria in their stool, shed virus for less time, and excrete less stool [45]. In sensitivity analyses, we found that the results were qualitatively similar when this assumption was relaxed although the overall impact of CATIs was diminished. We did not account for a potential hyperinfectious state, where freshly shed V. cholerae may be orders of magnitude more infectious [65,66]. However, if the hyperinfectious state plays an important role in transmission, we expect that it would be in part reflected by clustering at short distances, which should be accounted for in our model through the τ fitting of the function.
The quantification of the effects and mechanisms of each intervention were based on limited data. The purpose of this study was to explore the impact of CATIs over the course of a single outbreak, disregarding the long-term effects of interventions, which may influence future epidemics. Given that the effectiveness of OCV and POUWT interventions likely wane differently over time, it is possible that there are different optimal mixes of interventions depending on the timescale of interest. The protective effects assumed for prophylactic antibiotic use were based on meta-analyses, clinical studies, and modeling studies (S1 Text) representative of the current state of evidence, some of which only included data on post-exposure antibiotic use. However, as pointed out by a recent meta-analysis [8], the studies supporting the effectiveness of prophylactic antibiotic use have a high risk of bias. We incorporated modest variability in the protective effects given the limited and sometimes weak data and the diversity of different drugs studied in the literature [51]. We assumed that antibiotic effects lasted only 2 days based on a review of the literature and consultations with experts; however, if the duration was longer, we expect that the impact of antibiotics would be larger than our results suggest. For OCV, we assumed that a single-dose regimen had an efficacy of 67% for the duration of an outbreak although there exists only 1 clinical trial estimating the single-dose efficacy—in Bangladesh over a 6-month period, where cholera is highly endemic [50]—and 1 observational study, in South Sudan, measuring short-term protection in the first 2 months after vaccination [15].
While there are a variety of WaSH interventions used to fight cholera [6], we chose to model POUWT as it is often a key WaSH intervention in outbreak settings. POUWT interventions come in different shapes and sizes and can only be effective if people appropriately comply with the intervention, which poses challenges to incorporating them into a generalizable model. We based the POUWT effect estimate on a meta-analysis of diarrhea reductions from POUWT interventions conducted in a variety of settings, including some that do not regularly report cholera cases (e.g., Bolivia) [6,67,68]. By using these estimates, we implicitly assumed that the reductions in cholera from POUWT interventions would be similar to reductions in diarrhea, which may not be the case if the diarrhea in the studies in the meta-analysis was caused by etiologic agents with different transmission pathways than cholera. For simplicity, in our main analysis we assumed that the entire mechanism of POUWT protection was to reduce the exposure probability to V. cholerae, although POUWT may both reduce exposure to water with V. cholerae and reduce the likelihood of becoming symptomatic by reducing the concentration of bacteria in any contaminated water [56]. With CATIs, it is likely that each household will have a single opportunity to receive behavior change messaging and training on using the POUWT intervention, which could lead to lower compliance, and thus lower effectiveness against cholera, than what we modeled, especially as the time since the household visit increases [68]. However, during outbreaks, mass behavior change campaigns (e.g., through radio) are common and may help sustain any changes in behavior catalyzed in the CATI household visit.
Although the modeled relative risk, τ, matches the observed data well, we did not fit τ at very short distances (i.e., between 0 m and 15 m from a case) as our model does not include household structure and is based on a grid of 30 m by 30 m cells. Small-scale spatial structure has been shown to have significant impact on transmission for other diseases [69], and neglecting it could lead to underestimation of the effect of CATIs in our model. This limitation means that we could not adequately capture the effects of targeting household contacts with interventions, which may be especially important for antibiotics [8,70].
Our results are based on a large number of epidemic trajectories of a model fit to a single outbreak; however, the relative impact of CATIs is largely shaped by the spatiotemporal clustering of cholera cases, which has been shown to be similar in both epidemic and endemic settings around the globe [22–29,61]. This commonality between the clustering of cholera cases provides reassurance that our findings may not only be representative of the potential impacts in this single outbreak but likely reflect (qualitatively) the impacts in other similar settings. Thus, we advocate that CATIs can be a promising approach to control cholera epidemics in urban areas. While the optimal target radius may vary between settings, due to population density and logistical constraints, antibiotic interventions will likely continue to have a smaller optimal radius than OCV and POUWT, because of the duration of protection and likely delays in responding to each ring, which are unlikely to vary significantly across settings.
Our results suggest that CATIs may be an effective and efficient approach to reducing morbidity and mortality and saving public health resources in cholera epidemics. While more work is needed to understand how and when to best use this approach in outbreaks across different settings, taking into account both human resource capacity and supply availability, our study provides data-based support to public health programs currently using CATIs to fight cholera.
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10.1371/journal.ppat.1003579 | Functional Specialization of the Small Interfering RNA Pathway in Response to Virus Infection | In Drosophila, post-transcriptional gene silencing occurs when exogenous or endogenous double stranded RNA (dsRNA) is processed into small interfering RNAs (siRNAs) by Dicer-2 (Dcr-2) in association with a dsRNA-binding protein (dsRBP) cofactor called Loquacious (Loqs-PD). siRNAs are then loaded onto Argonaute-2 (Ago2) by the action of Dcr-2 with another dsRBP cofactor called R2D2. Loaded Ago2 executes the destruction of target RNAs that have sequence complementarity to siRNAs. Although Dcr-2, R2D2, and Ago2 are essential for innate antiviral defense, the mechanism of virus-derived siRNA (vsiRNA) biogenesis and viral target inhibition remains unclear. Here, we characterize the response mechanism mediated by siRNAs against two different RNA viruses that infect Drosophila. In both cases, we show that vsiRNAs are generated by Dcr-2 processing of dsRNA formed during viral genome replication and, to a lesser extent, viral transcription. These vsiRNAs seem to preferentially target viral polyadenylated RNA to inhibit viral replication. Loqs-PD is completely dispensable for silencing of the viruses, in contrast to its role in silencing endogenous targets. Biogenesis of vsiRNAs is independent of both Loqs-PD and R2D2. R2D2, however, is required for sorting and loading of vsiRNAs onto Ago2 and inhibition of viral RNA expression. Direct injection of viral RNA into Drosophila results in replication that is also independent of Loqs-PD. This suggests that triggering of the antiviral pathway is not related to viral mode of entry but recognition of intrinsic features of virus RNA. Our results indicate the existence of a vsiRNA pathway that is separate from the endogenous siRNA pathway and is specifically triggered by virus RNA. We speculate that this unique framework might be necessary for a prompt and efficient antiviral response.
| The RNA interference (RNAi) pathway utilizes small non-coding RNAs to silence gene expression. In insects, RNAi regulates endogenous genes and functions as an RNA-based immune system against viral infection. Here we have uncovered details of how RNAi is triggered by RNA viruses. Double-stranded RNA (dsRNA) generated as a replication intermediate or from transcription of the RNA virus can be used as substrate for the biogenesis of virus-derived small interfering RNAs (vsiRNAs). Unlike other dsRNAs, virus RNA processing involves Dicer but not its canonical partner protein Loqs-PD. Thus, vsiRNA biogenesis is mechanistically different from biogenesis of endogenous siRNAs or siRNAs derived from other exogenous RNA sources. Our results suggest a specialization of the pathway dedicated to silencing of RNA viruses versus other types of RNAi silencing. The understanding of RNAi mechanisms during viral infection could have implications for the control of insect-borne viruses and the use of siRNAs to treat viral infections in humans.
| RNA interference (RNAi) utilizes small non-coding RNAs in association with an Argonaute (Ago) protein to regulate gene expression in virtually all eukaryotes [1], [2], [3]. In animals, there are three major classes of small non-coding RNAs: microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small interfering RNAs (siRNAs) [4]. Each small RNA class requires different enzymes for its biogenesis, and each class tends to associate with distinct Ago proteins [3]. siRNAs are made from long double stranded RNA (dsRNA) precursors derived from transposable elements, extended RNA hairpins, and sense-antisense RNA pairs [5]. Exogenous dsRNA introduced by injection or transfection can also generate siRNAs. In Drosophila, exogenous and endogenous dsRNAs are processed into siRNAs by Dicer-2 (Dcr-2) in association with the PD isoform of Loquacious (Loqs-PD) [6], [7]. There are four Loqs isoforms that participate in the biogenesis of distinct classes of small RNAs but only isoform PD is required for siRNA processing [8], [9], [10]. Endo-siRNAs from endogenous precursors and exo-siRNAs from exogenous precursors are then sorted by a protein complex composed of Dcr-2 and R2D2 to be loaded onto Argonaute-2 (Ago2) [6], [11]. Ago2 then ejects one strand of the siRNA duplex to generate a mature RNA-induced silencing complex (RISC) containing only the guide strand of the siRNA [12], [13]. The mature Ago2-RISC is then capable of cleaving single-stranded RNAs complementary to the guide siRNA [5].
The siRNA pathway is a major arm of the antiviral response in plants and invertebrate animals [14], [15]. In Drosophila, Ago2, R2D2 and Dcr-2 mutant individuals exhibit increased sensitivity to infection by several viruses [16], [17], [18], [19]. Virus-derived siRNAs (vsiRNAs) are generated in adult individuals and cell lines infected with different viruses [19], [20], [21], [22], [23], [24]. For example, Drosophila S2 cells infected with Flock house virus (FHV) generate 21-nucleotide (nt) vsiRNAs that preferentially map to the 5′ region of both RNA segments of the viral genome [20], [21]. Similarly, FHV-infected adults generate vsiRNAs from the positive strand of the viral genome unless a replication deficient FHV is used, in which case the vsiRNAs map to both strands [17]. This has been interpreted to suggest that Dcr-2 targets nascent dsRNA formed as intermediates of FHV genome replication [21]. Adult flies infected with Vesicular Stomatitis virus (VSV) also generate 21-nt vsiRNAs but these show no obvious bias for RNA strand or region of the genome [22]. These studies suggest that different mechanisms exist for activation of the siRNA pathway during infection with different RNA viruses.
Here, we utilize wildtype and mutant Drosophila to characterize the siRNA responses triggered by two RNA viruses, Sindbis virus (SINV) and VSV. SINV belongs to the Togaviridae family and has a positive RNA genome, while VSV belongs to the Rhabdoviridae family and has a negative RNA genome. We chose SINV and VSV because they have distinct strategies of replication, allowing us to uncover common and unique features of each antiviral response. Our results indicate that biogenesis of siRNAs from viral RNA is mechanistically distinct from siRNA biogenesis from endogenous or exogenous sources of dsRNA. We propose a mechanism whereby dsRNAs generated during viral replication and transcription are sources of vsiRNAs, and viral transcripts are major targets of RISC-mediated silencing.
Although Loqs-PD and R2D2 execute different steps in the endo-/exo-siRNA pathway, their roles in the antiviral siRNA pathway are less clear. To explore this issue, we infected Drosophila adults by injecting either SINV or VSV into their hemocoelic cavities. We monitored viral RNA genome levels for three days post-infection (dpi), and observed significantly higher levels of SINV and VSV genomes in Dcr-2 and R2D2 mutants, compared to wildtype (Fig. 1A,B). In contrast, loqs mutants showed viral genome levels indistinguishable from wildtype. We also analyzed host survival after viral infection. When wildtype adults were injected with VSV or SINV, they showed a weak reduction in survival compared to mock-injected animals (Figs. 1C and S1, Table S1). Likewise, loqs mutants showed a comparably weak reduction in lifespan due to VSV or SINV injection when compared to mock-injected. In contrast, R2D2 mutants had a significantly reduced lifespan upon injection of either VSV or SINV (Figs. 1C and S1, Table S1).
The loqs mutants carried a null mutant allele over an allele that still has low but detectable loqs-pd mRNA expression [10]. It was possible that the residual Loqs-PD was sufficient to rescue the antiviral response that we had detected in the mutants. Therefore, we infected loqs null mutants that also carried a loqs transgene only expressing the Loqs-PB isoform. This transgene is able to rescue the miRNA pathway but leaves the siRNA pathway completely disabled [8]. The infected mutants displayed similar VSV RNA levels compared to wildtype (Fig. 1D). We also infected null mutants that carried a transgene expressing both Loqs-PB and Loqs-PD, which rescues both miRNA and siRNA pathways [8]. These mutants behaved similarly to the PB-only mutants (Fig. 1D). Together these results indicate that Loqs-PD is completely dispensable for inhibiting virus replication and promoting host survival after infection.
The surprisingly superfluous character of Loqs-PD suggested that there might be redundancy between R2D2 and Loqs-PD, as can happen under some circumstances [6]. Therefore, we analyzed viral infection of loqs R2D2 double mutants. We injected recombinant viruses expressing green fluorescent protein (GFP) to facilitate the direct comparison between viruses, since GFP expression faithfully reflects replication levels for both VSV and SINV [25], [26]. In VSV or SINV infected animals, GFP expression was similarly elevated in loqs R2D2 double mutants compared to R2D2 single mutants (Fig. 1E). There was slightly less GFP expression in the double mutant compared to R2D2 alone, which could suggest that Loqs-PD enhances viral replication in the absence of R2D2. Nevertheless there was no evidence of an additive effect between loqs and R2D2. We also looked at host lifespan after VSV infection, and observed that R2D2 and loqs R2D2 mutants showed similar lifespan reduction (Fig. 1C and S1). Although SINV infection similarly affected R2D2 and loqs R2D2 lifespans, this result was complicated by the reduction in lifespan already observed in mock-injected animals (Fig. 1C and S1). Since R2D2 and loqs R2D2 mutant animals showed similar effects on virus replication and host survival, it suggests that even in the absence of R2D2, Loqs-PD does not have an impact on viral infection.
Exogenous dsRNA, when injected into Drosophila cells, requires Loqs-PD to generate an RNAi response [6]. It was intriguing that viral RNA, though extrinsic to cells, does not require Loqs-PD to generate an antiviral response. We hypothesized that either intrinsic features of viral RNA, its virion packaging, its route of entry, or the nature of the infected cells could determine this Loqs-PD independence. We had previously found that injection of exogenous dsRNA into Drosophila embryos triggered silencing in a manner highly dependent upon Loqs-PD [6]. Therefore, we injected RNA purified from SINV virions into Drosophila embryos. RNA levels were measured at 24 hours post injection (hpi) and normalized to the levels detected at 2 hpi. Wildtype embryos experienced a 500-fold increase in SINV genome levels between 2 and 24 hpi (Fig. 1F), indicating that the injected RNA was competent for replication. RNA replication was strongly enhanced in Dcr-2 and R2D2 mutant embryos compared to wildtype. In contrast, loqs mutant embryos experienced replication levels that were no greater than wildtype (Fig. 1F). This result indicates that the Loqs-independent antiviral response recognizes intrinsic features of the viral RNA or its replicative forms.
We sequenced small RNAs from infected animals to study vsiRNA production. A time of 48 h post-infection was chosen for analysis because it is the time when viral RNA levels approach a plateau. Dcr-2, R2D2 and loqs mutants were analyzed and compared to wildtype to characterize the roles of these genes (Tables S2 and S3). Small RNAs derived from the Drosophila genome were initially analyzed. As detected by RNA read density along the major autosomes, overall distributions of RNAs along the genomes of R2D2 and loqs mutants were similar to wildtype (Fig. S2). As previously reported [6], [27], [28], loqs, Dcr-2 and R2D2 mutants showed a decreased abundance of specific endogenous small RNAs such as 21-nt endo-siRNAs derived from Drosophila mRNAs (Fig. S3).
SINV and VSV have single-stranded RNA genomes, and they synthesize an antigenome RNA of opposite polarity in order to synthesize more genomes [29], [30], [31]. The antigenome is typically less abundant than the genome since one antigenome template can be copied several times. In wildtype hosts, SINV and VSV produced a 6.3- and 5.5-fold excess of genomes over antigenomes, respectively (Fig. 2A). As expected for canonical siRNAs, the majority of VSV and SINV vsiRNAs were 21 nt in length (Fig. 2B,C). These mapped in roughly equal numbers to both genome and antigenome strands of SINV and VSV. Thus, the ratio of vsiRNAs derived from genome and antigenome strands was clearly different from the relative abundance of genomes and antigenomes (Fig. 2A–C). An equal distribution of vsiRNAs to both strands indicates that the preferred substrate for their biogenesis is sense-antisense viral dsRNA.
Processing of dsRNA by Dcr-2 is dependent on dsRNA substrate concentration in vitro [32], and thus substrate abundance is likely to affect the abundance of siRNAs in vivo. The ratio of siRNA product to dsRNA substrate is therefore an indirect measure of processing activity. Therefore, we normalized the levels of vsiRNAs to the levels of viral genomes (see Methods for details). We found that Dcr-2 mutants had virtually no VSV vsiRNAs when compared to wildtype (Fig. 2B). This result confirmed that the 21 nt RNAs can be considered canonical vsiRNAs. In R2D2 mutants, SINV vsiRNAs levels were similar to wildtype, and VSV vsiRNA abundance was slightly reduced (Fig. 2B,C). loqs mutants had little or no effect on the levels of VSV and SINV vsiRNAs. The distributions of vsiRNAs from R2D2 and loqs mutants were homogeneous along the length of the viral genomes, as was also observed for wildtype (Fig. 3A,B). In contrast, vsiRNAs in Dcr-2 mutants were strongly biased towards the 5′ ends of the VSV genome and antigenome (Fig. 3A). To summarize, R2D2 and Loqs-PD appear largely dispensable for Dcr-2-mediated biogenesis of VSV and SINV vsiRNAs.
R2D2 is essential for sorting and loading of exo- and endo-siRNAs onto Ago2. This can be detected in vivo by a characteristic enrichment of a C base, and sometimes, depletion of a U base at the 5′-end of loaded siRNAs [6], [33], [34], [35]. R2D2 mutants exhibit loss of C enrichment at the 5′ end of endo-siRNAs [6], [36]. We asked whether R2D2 loads vsiRNAs onto Ago2 by looking for the nucleotide bias at the 5′ end of vsiRNAs. vsiRNAs derived from infection of wildtype animals showed significant C enrichment and U depletion at the 5′ end (Fig. 4A,B and Table S4). C enrichment was lost in R2D2 mutants but was unaffected in loqs mutants. These results indicate that R2D2 has a sorting/loading function in the vsiRNA pathway that is similar to its role in the exo- and endo-siRNA pathway.
To determine if specific vsiRNAs are commonly made during infection, we calculated the pairwise correlation between different libraries. There was very low correlation for pairwise comparisons between wildtype, loqs and R2D2 mutants (Table S5). However, low vsiRNA numbers made it difficult to make a definitive conclusion. Therefore, we compared our libraries to other libraries prepared from insects infected with SINV and VSV [22], [37]. The summary of this comparison is shown in Table S6. We found consistent results between all libraries in terms of the size, abundance, and coverage of vsiRNAs. However, there was also low correlation for pairwise comparisons between our libraries and those of Mueller et al [22] (Table S7). It is clear that library construction and sequencing platform can significantly influence the results of small RNA sequencing [38], [39], [40]. However, our inability to identify common individual vsiRNAs might be explained by heterogeneity of the viral dsRNA substrates subjected to Dcr-2 processing. Substrate heterogeneity is not unique to siRNAs. Sequencing of piRNAs in the Drosophila germline by different groups also failed to find common individual piRNAs despite reaching general conclusions about their origins and features [41], [42]. In contrast, identification of common individual miRNAs is possible when comparing different libraries due to the fact that miRNAs arise from well-defined precursors [6], [33].
We sought to determine the source of sense-antisense viral dsRNA from which vsiRNAs were processed. Viral dsRNA can arise from virus transcription or genome-antigenome intermediates generated during replication. Although VSV and SINV generate similar types of replication intermediates, their transcription is very different. For VSV, the viral RNA polymerase transcribes its negative sense genome into a set of mRNA transcripts. Transcription initiates at the 3′ end of the viral genome, yielding an uncapped leader RNA of 47 nt, and then reinitiates at the nearby N gene promoter to produce capped and polyadenylated N mRNA [29], [31]. As it moves along the viral genome, the viral RNA polymerase reinitiates at internal promoters of the downstream genes and produces the corresponding capped and polyadenylated P, M, G, and L transcripts. Since some polymerase complexes fall off the template at intergenic junctions before reinitiating, the genes located near the 3′ end of the genome are expressed at higher levels than those located further downstream [31]. It was possible that VSV vsiRNAs were generated from transcript-genome hybrids or antigenome-genome duplexes. If they were generated from transcript-genome hybrids, then we predicted that intergenic promoter regions would be devoid of vsiRNAs. We examined the occupancy of vsiRNAs along the viral genome and detected several regions that exhibited no vsiRNA coverage (Fig. 3A,B). We then calculated the probability that each gap in coverage did not occur by chance (see Methods). Gaps with highly significant E-values in vsiRNA coverage included the regions between the N, M, G, P and L genes, close to or inside the intergenic promoters (Figs. 3C and S4). A low number of vsiRNA reads in the wildtype sample weakened our ability to detect significant gaps, though the E-values around gene promoters were more significant than the rest. However, highly significant gaps around gene promoters were consistently found in R2D2 and loqs mutants. The gaps were detected in samples from R2D2 mutants, which are competent for processing but not sorting of vsiRNAs. This suggests that the gaps are due to biases in processing. We also analyzed sequenced libraries of vsiRNAs prepared from VSV-infected DL-1 cells [43], S2 cells, and wildtype or Ago2 mutant flies infected by VSV [22]. We observed highly significant vsiRNA gaps at gene promoters in these independent datasets, particularly the L promoter (Fig. S5). The simplest interpretation is that many vsiRNAs derived from central regions of the VSV genome are processed from genome-transcript hybrids. However, the absence of gaps at more distal gene promoters suggests that a significant fraction of VSV vsiRNAs from these regions come from genome-antigenome duplexes.
The sense SINV RNA genome also serves as a mRNA transcript for translation of viral proteins [30]. An additional subgenomic RNA is generated from the 3′ region of the SINV genome and functions as a mRNA transcript for structural proteins. Since there was no greater abundance of SINV vsiRNAs from the subgenomic region (Fig. 3B), it suggests that SINV vsiRNAs primarily derive from genome-antigenome duplexes. This has also been suggested by others [37]. We did not observe a reproducible pattern of significant gaps in vsiRNA coverage of the SINV genome, also consistent with the hypothesis that these vsiRNAs are generated from genome/antigenome duplexes (Fig. 3D).
Our data indicates that vsiRNA production by Dcr-2 is an active mechanism that requires efficient processing of viral dsRNA substrates of diverse origins. Dcr-2 has RNase III domains that cleave dsRNA, and it also has an ATP-dependent helicase domain that is required for efficient processing [32]. To determine if the Dcr-2 helicase is essential, we infected a Dcr-2 mutant that specifically disables the helicase domain (Dcr-2A500V) with SINV [44]. Similar to Dcr-2 null mutants, Dcr-2A500V mutants showed increased levels of SINV replication (Fig. 5A). This result suggests that helicase activity is essential for the antiviral response. The helicase has been shown to enhance two features of dsRNA dicing. It is required for Dcr-2 to recognize dsRNA ends that are blunt or have 5′ overhangs [45]. It also allows multiple siRNAs to be produced along the length of a dsRNA without Dcr-2 dissociation [32]. One of the consequences of this Dcr-2 processivity is the production of siRNAs with defined spacing between the 5′ end of one siRNA and its nearest neighbors on the same strand [32]. This phasing has been detected in siRNAs generated from dsRNA substrates in vitro and in vivo as a discrete end-to-end distance peak [46], [47].
We wondered whether Dcr-2 processivity occurred along viral dsRNA substrates. Therefore, we examined the relationship of vsiRNAs to their neighbors as measured by end-to-end distance along the same strand (Fig. S6). We failed to detect phasing between VSV vsiRNAs (Fig. S6) and did not detect phasing in libraries prepared from other VSV-infected flies [22] (data not shown). A phasing signal was detected in VSV-derived siRNAs generated from cultured Drosophila DL-1 cells [43]. The reason for the differences between animal and cell culture studies remains unclear. For SINV, phasing was not detected in wildtype or loqs mutants (Fig. S6B). However, there was a phasing peak of 21 nts in R2D2 mutants that was primarily due to vsiRNAs located within 1000 nts of the genome's ends (Fig. 5B and Fig. S6B). The stronger phasing signal near the genome ends suggests that processivity is weakened as Dcr-2 moves away from the genome ends. It further suggests that vsiRNA sorting by R2D2 is able to distort or mask the phasing signal. We also analyzed libraries prepared from SINV-infected mosquitoes [37] and cell lines [48], and observed a phasing peak from adult mosquitoes but none from cell lines (Fig. S7).
vsiRNAs originate from both strands along the entire length of the VSV and SINV genomes, and so they could potentially inhibit positive-stranded, negative-stranded, and transcript viral RNAs. During infection, production of each viral RNA species is dependent on the others; genomes make transcripts; transcripts make replication proteins, which make genomes and antigenomes. Thus, vsiRNAs that directly inhibit one class of RNAs would indirectly inhibit production of other viral RNAs. We hypothesized that loss of inhibition would lead to more pronounced changes in the levels of direct vsiRNA targets than downstream RNAs. To measure the abundance of negative- and positive-stranded viral RNAs, we employed strand-specific RT-qPCR. We confirmed that mispriming did not significantly affect our measurements by using no-primer control reactions [49] (data not shown). We measured the abundance of polyadenylated viral RNAs by oligo dT-directed RT-qPCR. We then compared the abundance of viral RNAs extracted from wildtype hosts versus Dcr-2 mutants. Levels of all SINV and VSV RNA species were derepressed in Dcr-2 mutants (Fig. 6A,B). We calculated the level of derepression for each species of viral RNA. Polyadenylated viral RNA was more strongly derepressed than either negative- or positive-stranded viral RNA; 2.3-fold for SINV (p = 0.004) and 3.9-fold for VSV (p = 0.001) (Fig. 6C). This greater sensitivity of polyadenylated RNA to inhibition suggests that it is the primary target of vsiRNAs.
The SINV genome is of positive polarity and can also function as a transcript. However, not all SINV positive-stranded RNA is polyadenylated [50], explaining the difference we observed in repression of total positive-stranded SINV RNA versus polyadenylated SINV RNA. There were no significant differences in derepression of genome versus antigenome RNAs for either SINV (p = 0.49) or VSV (p = 0.48). If there was direct targeting of genomes and antigenomes, we predicted that the antigenome RNA levels would be more strongly affected. This is because vsiRNA levels from both strands are equivalent but the level of genome RNA greatly exceeds the level of antigenome RNA (Fig. 2).
R2D2 helps load siRNAs onto Ago2, and is also required to efficiently inhibit SINV and VSV replication. This would suggest that siRNA-loaded Ago2 (RISC) mediates the bulk of the inhibitory effect by slicing viral target RNAs. However, it is possible that cleavage of viral RNA by Dcr-2 is the major inhibitory mechanism, and Ago2 simply acts as a sink to drive the cleavage reaction. To distinguish between these mechanisms, we assayed an Ago2 mutant with an amino acid substitution at position 966 (V→M) that impairs slicer activity of the protein [13]. We observed that mutant animals had significantly increased VSV RNA levels compared to controls (Fig. 6D). Since the point mutation impairs but does not completely ablate slicing activity, the effect on viral RNA silencing was not as great as observed with Ago2 null mutants (Fig. 6D). These results indicate that the predominant inhibitory mechanism against VSV is mediated by Ago2 slicing activity.
Our results indicate the existence of a siRNA pathway dedicated to antiviral defense that is distinct from the one triggered by endogenous and exogenous dsRNA in Drosophila. The major difference between the two pathways seems to lie in the mechanism of siRNA biogenesis. In the antiviral pathway, virus dsRNA can be processed by Dcr-2 without Loqs-PD. In contrast, the canonical pathway relies upon Dcr-2 and Loqs-PD to process exogenous and endogenous dsRNAs. Downstream of processing, the two pathways appear to merge. vsiRNAs, exo-siRNAs, and endo-siRNAs are all sorted by a Dcr-2/R2D2 complex and loaded onto Ago2. These siRNA-Ago2 complexes inhibit target gene expression by a RNA slicing mechanism. Our results are consistent with other studies. Han et al [24] found that a weak loqs mutant had normal vsiRNA production and antiviral defense against FHV infection. However, it was possible that residual Loq-PD activity in the mutant rescued an antiviral function for the gene. We found that complete loss of Loqs-PD has no effect on antiviral silencing. Obbard et al [51] showed that Ago2, R2D2 and Dcr-2 are among the fastest evolving genes in the Drosophila genome. Since many host defense and pathogen genes co-evolve in a genetic arms race, rapid evolution of Ago2, Dcr-2 and R2D2 is possibly related to their antiviral functions [14], [52]. Strikingly, the loqs gene shows no sign of rapid evolution.
There are at least four possible interpretations of our results. First, Dcr-2 could process virus dsRNA in partnership with a dsRBP cofactor other than Loqs-PD. We have ruled out R2D2 as a potential substitute. Several other dsRBPs are encoded in the Drosophila genome, and two of these were found to interact with Dcr-2, but they are unlikely to mediate a global antiviral response since their expression is restricted to the male testis [53]. Moreover, no dsRBP gene other than R2D2 has been identified as rapidly evolving as Dcr-2 and Ago2 [51]. A second explanation is that Dcr-2 alone processes virus dsRNA. In vitro studies have demonstrated that purified Dcr-2 protein efficiently processes dsRNA substrates and does not require a cofactor for its processing activity [32]. In fact, R2D2 inhibits the in vitro processing activity of Dcr-2 [32]. A third explanation is that the Dcr-2/Loqs-PD heterodimer recognizes and processes virus dsRNA, but unlike other substrates, the presence of Loqs-PD is not essential. Note that the molecular function of Loqs-PD in Dcr-2 processing activity in vivo is still unknown. A fourth explanation is that the Dcr-2/R2D2 heterodimer recognizes and processes virus dsRNA, although processing is not affected by the absence of R2D2. If virus dsRNA is processed by Dcr-2/R2D2, then vsiRNA products could be directly loaded onto Ago2 and avoid loading competition with endogenous siRNAs. This might enhance the antiviral response.
If Dcr-2 acts on virus RNA without the need of a dsRBP cofactor, then how does the enzyme recognize virus RNA as different from other types of dsRNA? Purified SINV RNA injected into cells replicates over time in a manner that is unaffected by Loqs-PD. Thus, it is not virion structure or mode of entry that signals Dcr-2 to differentially recognize virus RNA. Instead, it indicates that Dcr-2 specifically recognizes something intrinsic to the virus RNA or its intermediates. Preliminary experiments injecting in vitro synthesized SINV RNA into cells also show no effect of the loqs mutant on RNA replication (data not shown). Therefore, it is unlikely that Dcr-2 recognizes chemical modifications of SINV RNA as the distinguishing feature. If Dcr-2 does not recognize modified features of virus RNA, what is the nature of the signal? RNA virus transcription and replication are typically sequestered into ribonucleoprotein “factories” that contain concentrated levels of RNA and enzymes [29], [30], [54]. This is distinct from exo- and endo-dsRNAs, which can be found dispersed within a cell. Limited accessibility of viral dsRNA by Dcr-2/Loqs-PD could be one reason that dsRNA processing is indifferent to these complexes. Alternatively, greater substrate heterogeneity might distinguish virus dsRNA from other kinds of dsRNA. In this regard, we have found the Dcr-2 helicase domain is required for antiviral silencing and, at least in vitro, is also necessary for Dcr-2 to recognize non-canonical ends of dsRNA duplexes [45].
Our work also addresses the origin of vsiRNAs. Others have suggested that viral replication intermediates are the exclusive substrates for vsiRNA production [20], [21], [22], [24]. Our analysis of SINV is consistent with a replication intermediate exclusive mechanism. However, we find evidence that both replication intermediates and transcript-genome hybrids can be precursors for VSV vsiRNAs. Our analysis also has explored how Dcr-2 cleaves the virus dsRNAs. When Dcr-2 processively cleaves dsRNA, initiating from ends that are common to different dsRNA molecules, a phasing signal is seen in sequence data. No phasing is seen if Dcr-2 is not processive or if dsRNA ends are highly heterogeneous. For SINV, there is a weak sign of phasing. Indeed, we detect stronger phasing of SINV vsiRNAs near the ends of the genome, where SINV dsRNAs would tend to have common ends.
What is the mechanism by which vsiRNAs inhibit viral replication? Some have proposed that Dcr-2 mediated processing of viral dsRNA is primarily responsible for the reduction seen in viral RNA levels [20]. Alternatively, vsiRNAs loaded onto Ago2 could potentially carry out many rounds of virus RNA destruction because RISC is a multiple turnover enzyme [55]. Two lines of evidence indicate it is the latter mechanism that mediates the bulk of VSV inhibition. First, R2D2 shows an antiviral activity that is comparable to the antiviral activity of Dcr-2 (Fig. 1). Since R2D2 sorts and loads vsiRNAs downstream of Dcr-2 mediated processing, it suggests that loading of Ago2 is required for the mechanism. Second, we show that Ago2 slicer activity is required for silencing of VSV RNA. Thus, Ago2 is not merely acting to sequester free vsiRNAs in order to drive the dsRNA processing reaction. Rather, vsiRNA-loaded Ago2 slices viral RNAs and substantially contributes to the inhibitory mechanism.
All mutant alleles used in this study were previously described. The different Drosophila mutants analyzed were: Dcr-2L811fsX and Dcr-2A500V [44], R2D21/R2D2S165fsX [18], [56], loqsf00791/loqsKO [10], [57] and loqsKO R2D2S165fsX/loqsf00791 R2D21 [6], Ago2414 [58], Ago2V966M [13] and loqsKO PB and PD rescue lines [8]. Wildtype referred to in this study had each mutation in trans to wildtype chromosomes, making a heterozygous state. Chromosome 2 had an FRT42D insertion. All stocks tested negative for the endosymbiont Wolbacchia, which has been shown to influence Drosophila antiviral defense [59]. SINV, SINV-GFP and VSV-GFP were a kind gift from Dennis Brown, Ilya Frolov and Curt Horvath, respectively. Viruses stocks were prepared and titered in BHK-21 cells as described previously [25], [60]. The titers for VSV and SINV used in this study were 5×108 pfu/mL and 4×1010 pfu/mL, respectively.
To avoid possible complications related to differences in background, microbiota or rearing, we crossed heterozygous animals bearing mutant alleles to each other, and we infected their mutant and heterozygous wildtype offspring at the same time. These then served as mutant and wildtype control samples for each experiment. We utilized a microinjector to inject 50 nl of a PBS solution containing the viruses into the thorax of 2–4 day old female adults. Animals were injected with 5,000 pfu of VSV and 20,000 pfu of SINV in all experiments.
For the survival analysis, three groups of 20 adults of each genotype were injected separately and survival was monitored daily. Each experiment was repeated at least three times. Survival graphs and median survival were plotted and calculated using Prism (GraphPad). A two-tailed student t test was used to statistically analyze differences in median survival between groups.
Total RNA from adults was extracted using Trizol reagent according to the manufacturer's protocol (Invitrogen). 1 µg of total RNA was reverse transcribed using 250 ng of random primers, 500 ng of anchored oligo dT primers or 2 pmol of gene and strand specific primers per reaction. The resulting cDNA was used as template for qPCR reaction containing Sybr Green (Invitrogen) and primers specific for the amplification of the genes of interest. The relative amount of the indicated RNAs normalized to an internal control (GAPDH, Rpl32 or Actin 5C) was calculated using the delta Ct method. A two-tailed student t test was used to statistically analyze differences in viral RNA transcript levels between control and mutant animals. For strand specific qPCR, 2 pmols of primers for one strand were used during reverse transcription. Reverse transcription reactions were performed in the absence of primers or enzyme as negative controls for qPCR to ensure the identity of the products. Oligonucleotides designed in this study for RT and qPCR are described in Table S8. Rpl32, GFP and Actin5 were used as normalization standards as described previously [6].
Embryo injections were performed as described [6]. Genomic SINV RNA was extracted from purified virions using Trizol (Invitrogen). The RNA was diluted to a concentration of 200 ng/µl in 0.1 mM NaPO4 pH 7.8, 5 mM KCl solution for the injections. Female and male adults of a given genotype were placed inside an egg collecting cage, and eggs were collected every hour at 25°C, dechorionated, and injected within the next 45 minutes. SINV RNA was injected at the posterior end of eggs with a volume of ∼100 pL. After injection, embryos were incubated at 23°C under halocarbon oil in an oxygenated chamber, and harvested after 2 or 24 hours post injection. They were directly put into 100 µl of Trizol for RNA extraction. An average of 30 embryos were pooled per sample per time point. RNA was reverse transcribed using anchored oligo-dT primers, and qPCR reactions were performed with SINV-specific primers at the 3′ end of the SINV sense genome (Table S3). A two-tailed student t test was used to statistically analyze differences in viral RNA transcript levels. The experiment was repeated four times with similar results. The endogenous gene Rpl32 qPCR was used as normalization standard.
For the construction of the small RNA libraries, total RNA was isolated from adults at 48 h post injection of virus using Trizol (Invitrogen). Low molecular weight (LMW) RNA was prepared from total RNA, and small RNAs between the 18–34 nt size range were PAGE purified from the LMW RNA as described previously [6]. 200 ng of the small RNA preparation was used to prepare a library using the SOLiD total RNA expression kit according to the manufacturer's protocol (Ambion). Sequencing of the libraries was performed using the SOLiD platform according to the manufacturer's protocol (ABI) at the Genomics Core of the Feinberg School of Medicine (Northwestern University). Sequencing reads were aligned to release 5.2 of the reference Drosophila genome, VSV (J02428.1) and SINV (J02363.1) genomes deposited on NCBI using the Small RNA Analysis Pipeline Tool (Rna2Map) available from Applied Biosystems. Briefly, Rna2Map uses the Mapreads program from Applied Biosystems to simultaneously align reads to a reference and to filter out contamination from sequencing adaptors. Mapping was done allowing up to one mismatch in color space for the overall alignment. Reads aligning to the Drosophila and viral genomes were retained, whereas reads aligning to sequencing primers were removed from further analysis.
Reads mapping to the Drosophila reference genome were further analyzed as described previously [6]. Briefly, reads mapping to miRNAs were found by collecting the coordinates of known Drosophila miRNAs from Flybase v5.18 and searching the genomic alignment for reads overlapping these coordinates. The same process was repeated for mRNAs and transposons, where mRNA coordinates were taken from Flybase and transposons coordinates were taken from version dm3 of the UCSC RepeatMasker annotations. Reads mapping to rRNA sequences were determined by filtering transposon, miRNA, and mRNA reads from the Drosophila genome alignment and matching the remaining reads against rRNA sequences from Flybase v5.18. Ad hoc perl scripts were used in all steps, including the calculation of read size/strand distributions. The numbers of reads (>16 nt) mapping to rRNA, mRNA, miRNA and TEs for each sample are detailed in Tables S2 and S3. The total number of reads aligning to the Drosophila genome was used to normalize the libraries to allow for comparison between different libraries. Abundance of specific Drosophila small RNAs was plotted as the number of reads in a thousand reads from the total number of reads each library described [48]. Although minor distortions have to be taken into account, we believe the major conclusions of our analysis are not affected by this normalization.
Reads mapping to the VSV and SINV genomes were first normalized to the total size of the library as described above. Viral genome RNA levels were then used to normalize vsiRNA numbers to allow comparison between the different experimental samples. Importantly, viral genome RNA levels were determined by strand specific PCR in the total RNA extracted from the same animals that were used to make the small RNA libraries.
The sequencing datasets were deposited on the Gene Expression Omnibus website at the NIH. Accession numbers are: GSE36449 GSM893954 GSM893955 GSM893956 GSM893957 GSM893958 GSM893959 GSM893960.
For all the subsequent analyses (weblogo, phasing, occupancy and gaps), we separated only 21-nt reads mapped against the virus reference.
In order to compare our sequencing results to the results of other groups using different platforms [22], [37], we created a different analysis pipeline that could be applied to all strategies. We did this to avoid any potential differences that could be caused by the bioinformatic analysis and not the library construction strategy and sequencing platform used. The sequencing datasets were obtained from the SRR database at the NCBI website under accession numbers SRR059800, SRR059801 and SRR059803 from Mueller et al [22] and SRR400496 from Myles et al [37]. The summary of these results are on Table S6. Briefly, the libraries were analyzed through an automated pipeline containing three main steps. In the first step, reads from SOLiD were converted from color space to base space and filtered using scripts from Solid Software Tools (http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/solid-next-generation-sequencing/ngs-data-analysis-software/software-community.printable.html).
Reads from Illumina were filtered using fastx-toolkit (http://hannonlab.cshl.edu/fastx_toolkit/index.html). In both cases, reads below the minimum quality threshold were discarded. In the second step, adapters were removed using the cutadapt software (http://code.google.com/p/cutadapt/). In the third step, the remaining reads were mapped against the virus genome references using SHRiMP [61] considering only single best mapping and a minimum of 80% similarity. For all the subsequent analyses (weblogo, phasing, occupancy and gaps), we separated only 21-nt reads mapped against the virus reference.
Nucleotide probability cartoons for small RNAs were generated using Weblogo 3 (http://weblogo.threeplusone.com/create.cgi).
For each sample, we tested whether the base C is enriched at the first position of 21-nt reads compared to two different references: the genome-wide base composition and, separately, compared to the base composition from all reads used to make the weblogos. Similarly, we also tested whether the base U is depleted at the first position. For example, the genome-wide base composition for SINV is 0.283, 0.261, 0.249 and 0.208 for A/C/G/U respectively. Thus for testing the enrichment of the base C, we are testing the hypotheses as follows:
For testing the depletion of U, the hypotheses are:
The p-values were calculated based on the Z-test for the one-sample proportion.
Let Xi and Yi be the frequency of observed sequence reads on the sense and antisense strands starting at position i for i = 1,…,n where n is the length of the entire genomic region. We use the standard auto-correlation function (ACF) from R software to investigate whether the read starting positions from the same strand are correlated. The 95% cutoff line for positive correlation or negative correlation (the null hypothesis is that the correlation = 0 at each lag) is shown in the plots. If the auto correlation at a given lag exceeds the cutoff line it can be regarded as significant (either >0 or <0, depending on which direction the correlation goes).
We defined the occupancy of any given position as the total number of reads (sense+antisense) that cover this position. A gap is defined as a region that is not covered by any reads from the sense or antisense strand. Suppose we observe a gap of length k from position j to j+k−1 (sense strand position). This implies that there are no reads starting at position j−20 to j+k−1 on the sense strand, and no reads starting at positions j to j+k−1+20 on the antisense strand. Suppose we observe a total of T1 and T2 reads from the sense and antisense strands respectively and the length of entire region is n. Under the null hypothesis, that is, the reads are evenly distributed across the entire region and the reads distribution on the two strands are independent, then we can use a Poisson distribution with mean T1/n and T2/n respectively to approximate the reads count distribution at each position given n is very large. Let p1(0) and p2(0) be the zero-probabilities under the two Poisson distributions. The probability that we observe a gap of exact length k is given by p(k) = p1k+20(0)p2k+20(0)(1−p1(0) p2(0))2 (i.e. a gap of exact length k requires co-occurrence of no reads starting in the k+20 bp range in either strand, and in addition that in the immediate upstream or downstream base pair of the two stands cannot simultaneously both have gaps). The p-value of a gap of length k, defined as the probability to observed a gap of length k or even longer is given by p_value = ∑m≥kp(m) for integer m. m is the summation index, i.e. sum over integer m≥k+20. The expected value (E-value) thus is approximately n×p_value where n is the length of the entire genomic region.
To test whether two conditions have different distribution patterns of reads count along the entire region for wildtype and mutant samples, we first divided each mRNA transcript into bins. Due to the very small sample size, when we comparing widltype to either R2D2 or loqs mutants, we used a relatively larger bin size of 50 nt. For comparing the two mutants we used a bin size of 20 nt instead. The entire region contains five different transcripts with the start and end positions as follows: start = (51,1386,2209,3049,4723) end = (1376,2199,3039,4713,11095). We tested the difference for each bin on each strand within each transcript sequentially. For a given transcript, suppose we observe a total of T1 and T2 tags on one strand for the two conditions respectively. For a particular bin under testing, let X and Y be the number of reads that start in this bin. We consider a Poisson distribution to approximate the sampling distribution of X and Y, the mean parameters of which are denoted as λ1 and λ2. The null hypothesis is, the reads distribution pattern everywhere is the same between these two conditions. Therefore the mean parameters λ1 and λ2 are proportional to the total number of reads observed in the two conditions. Given the observed total number of reads, the null hypothesis can be stated as:
We can test this hypothesis based on the conditional distribution of X|(X+Y), which is known as a binomial distribution:
A two-sided p-value is calculated based on this conditional distribution for each bin. In the plot, we plotted the log10 of the p-value with a “+” or “−” sign attached as follows. If P(X≥x|X+Y)≤0.5, which indicates the observed x is in the right tail, we attach a “+” sign, and otherwise a “−” sign. A “+” sign essentially means condition 1 has more reads than expected under the null, and “−” sign means the opposite.
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10.1371/journal.pgen.1007088 | Loss of the Caenorhabditis elegans pocket protein LIN-35 reveals MuvB's innate function as the repressor of DREAM target genes | The DREAM (Dp/Retinoblastoma(Rb)-like/E2F/MuvB) transcriptional repressor complex acts as a gatekeeper of the mammalian cell cycle by establishing and maintaining cellular quiescence. How DREAM’s three functional components, the E2F-DP heterodimer, the Rb-like pocket protein, and the MuvB subcomplex, form and function at target gene promoters remains unknown. The current model invokes that the pocket protein links E2F-DP and MuvB and is essential for gene repression. We tested this model by assessing how the conserved yet less redundant DREAM system in Caenorhabditis elegans is affected by absence of the sole C. elegans pocket protein LIN-35. Using a LIN-35 protein null mutant, we analyzed the assembly of E2F-DP and MuvB at promoters that are bound by DREAM and the level of expression of those "DREAM target genes" in embryos. We report that LIN-35 indeed mediates the association of E2F-DP and MuvB, a function that stabilizes DREAM subunit occupancy at target genes. In the absence of LIN-35, the occupancy of E2F-DP and MuvB at most DREAM target genes decreases dramatically and many of those genes become upregulated. The retention of E2F-DP and MuvB at some target gene promoters in lin-35 null embryos allowed us to test their contribution to DREAM target gene repression. Depletion of MuvB, but not E2F-DP, in the sensitized lin-35 null background caused further upregulation of DREAM target genes. We conclude that the pocket protein functions primarily to support MuvB-mediated repression of DREAM targets and that transcriptional repression is the innate function of the evolutionarily conserved MuvB complex. Our findings provide important insights into how mammalian DREAM assembly and disassembly may regulate gene expression and the cell cycle.
| The 8-subunit DREAM transcriptional repressor complex contains 3 functional components that together control expression of cell cycle and developmental genes. How the E2F-DP transcription factor heterodimer, the pocket protein, and the highly conserved MuvB complex coalesce on chromatin and repress DREAM target genes has yet to be determined. We directly tested the prevailing model that the DREAM pocket protein links E2F-DP to MuvB and is required for gene repression. Using a protein null mutant of the sole C. elegans pocket protein LIN-35, we demonstrate that the pocket protein indeed links E2F-DP and MuvB, which aids in the stable occupancy of DREAM components near target genes. Depletion of additional DREAM components in lin-35 null worms revealed that the remaining chromatin-bound MuvB represses target genes. We conclude that the MuvB subcomplex mediates DREAM’s critical repressive function. Our functional genomics approach in the simplified C. elegans system reveals that the ancestral function of the pocket protein is to stabilize the innate repressive activity of MuvB, ensuring proper regulation of DREAM target genes through development.
| As embryonic cells develop and differentiate, a conserved transcriptional program establishes their ordered exit from the cell cycle into a resting phase, also called G0 or quiescence. The highly conserved DREAM (for Dp, Retinoblastoma (Rb)-like, E2F, and MuvB) transcriptional repressor complex mediates this cell cycle quiescence program [1–3]. DREAM contains 3 components: a “repressive” E2F-DP heterodimer (in mammals, E2F4/5-DP1/2), a retinoblastoma-like pocket protein (in mammals, p130 or p107), and a 5-subunit subcomplex called MuvB (in mammals, LIN9, LIN37, LIN52, LIN54, and RBAP48) [1, 4, 5]. Together, the 8-subunit DREAM complex negatively regulates cell cycle reentry by directly repressing key cell cycle genes [1, 2]. How DREAM assembly culminates in gene repression is not understood.
The prevailing model for DREAM complex activity is that DREAM assembly at promoters, driven by p130/p107 linkage of E2F-DP and MuvB, mediates gene repression. Biochemical analyses in mammalian cells have revealed that DREAM assembly is triggered by DYRK1A phosphorylation of the MuvB subunit LIN52, directing MuvB association with the pocket protein [6, 7]. The p130/p107 pocket domain simultaneously interacts with MuvB and the transactivation domain of repressive E2F-DP through separate binding interfaces, completing assembly of the complex [8]. To repress transcription, DREAM localizes to chromatin through E2F-DP and the MuvB subunit LIN54 binding to DNA sequence motifs called cell cycle-dependent elements (CDEs) and cell cycle genes homology regions (CHRs), respectively [9–13]. Loss-of-function analyses in mice support a central role for the pocket protein in DREAM complex activity, since both p130/p107 double knockout mice and E2F4/5 double knockout mice display neonatal lethality, which has been attributed to defects in chondrocyte proliferation [14–16]. However, no direct evidence has demonstrated how E2F-DP, the pocket protein, and MuvB coordinate to repress target genes.
Because of the transcriptional dynamics observed during the cell cycle, how each component, especially MuvB, contributes to gene repression remains obscure. Upon progression from G0 into the cell cycle, p130 is phosphorylated by CDK4/6-cyclin D and dissociates from DREAM [2, 7, 17]. The transcription factor B-Myb then binds MuvB, forming the Myb-MuvB (MMB) complex, which activates late cell cycle genes [18–22]. This dual role for MuvB in repressing and activating genes, depending on the cell cycle context, complicates studies that have attempted to address its role in DREAM gene repression. For example, in contrast to E2F-DP and pocket protein loss-of-function, loss of the MuvB subunit LIN9 in mice causes early embryonic lethality [23]. This phenotype is likely due to MuvB’s activating role in MMB in the late cell cycle and not its role in DREAM [23]. Therefore, how MuvB’s inclusion in the DREAM complex contributes to target gene repression remains an outstanding question [2].
In C. elegans, the homologous complex, called DRM, acts similarly to the mammalian DREAM complex on chromatin (Fig 1A). C. elegans possesses only one pocket protein, LIN-35, which functionally resembles p130/p107 [24]. C. elegans E2F-DP (EFL-1-DPL-1) resembles the repressive E2F4-DP1 heterodimer [25]. Importantly, C. elegans MuvB likely acts predominantly or solely in a repressive capacity, because C. elegans LIN-52 lacks the phosphorylation switch present in mammalian LIN52 and no C. elegans B-Myb homolog has been identified [7, 26]. A key role of the C. elegans DRM complex is to keep germline genes repressed in developing somatic cells, as mutations in 7 of the 8 subunits cause ectopic expression of germline genes in somatic cells [27, 28]. Thus, loss-of-function analyses of C. elegans DRM subunits are well suited to address how E2F-DP, the pocket protein, and MuvB coordinate on chromatin to repress DRM target genes.
Here we utilize a protein null mutant of the sole C. elegans pocket protein LIN-35 to test how each DRM component contributes to target gene repression. Using co-immunoprecipitation, we demonstrate that LIN-35 mediates E2F-DP and MuvB association. Using chromatin immunoprecipitation linked to high throughput sequencing (ChIP-seq), we demonstrate that E2F-DP and MuvB continue to localize to chromatin in lin-35 null embryos, but their occupancy is reduced globally. Thus, DRM components can assemble on chromatin in the absence of LIN-35 bridging E2F-DP and MuvB association, but LIN-35 is required for maximal DRM chromatin occupancy on all regulatory elements. We also determine that misregulation of many genes with promoter-bound DRM, or “DRM target genes,” begins in lin-35 null late-stage embryos. DRM target genes that retain some DRM chromatin occupancy in the absence of LIN-35 remain at least partially repressed; repression is mediated by MuvB, as revealed by upregulation of those target genes when MuvB components are depleted from lin-35 null mutants. Loss of MuvB activity in lin-35 null worms also results in sterility. Our findings highlight that MuvB's innate function is as a transcriptional repressor of DRM target genes and provide evidence for how this function is facilitated by LIN-35. These results shed light on how DRM components coordinate gene repression in worms, which has important implications for the current model of DREAM-mediated gene repression in mammals.
Chromatin association of the 8-subunit DRM complex occurs through the sequence-specific DNA-binding activities of E2F-DP and LIN-54 (Fig 1A) [11]. To begin to address how E2F-DP, LIN-35, and MuvB contribute to transcriptional repression, we re-evaluated each DRM subunit’s in vivo chromatin-bound landscape in C. elegans late embryos. Expanding upon our previously published 2 replicates of ChIP-seq data [29], we performed an additional biological replicate of each E2F-DP and MuvB subunit in wild-type (WT) late embryo extracts. Late embryos represent a predominantly somatic cell sample, as only 2 germ cells (Z2/Z3) are present in each 200- to 550-cell late embryo. LIN-53 was not included, because its chromatin localization was not as robust as the other MuvB subunits [29]. We applied a 1% Irreproducible Discovery Rate (IDR) threshold on peaks called from the 3 biological replicates to identify the high-confidence chromatin binding sites for each individual DRM subunit (S1 Table). The overlap of individual subunit peaks defined 1544 E2F-DP peaks (EFL-1 and DPL-1 overlap) and 1983 MuvB peaks (at least 3 of 4 subunit overlap). The overlap between E2F-DP, MuvB, and 2291 LIN-35 peaks defined 1418 high-confidence DRM binding sites that were used for all subsequent analyses (Fig 1B). We did not observe any appreciable chromatin association of individual subunits or sub-complexes independent of DRM in late embryos (S1 Fig). To determine whether DRM subunits preferentially assemble with other transcription factors on high occupancy target (HOT) regions, we compared our DRM peak regions with HOT regions observed in C. elegans embryos [30]. We observed that 20–30% of individual subunit peaks and 425 of the 1418 high-confidence DRM binding regions coincide with embryonic HOT regions (S1 Table). Overall, we conclude that E2F-DP and MuvB operate primarily within the context of the repressive DRM complex during late C. elegans embryogenesis, which represents an ideal stage for evaluating the mechanism of DRM action.
According to the current model of DRM complex assembly (Fig 1A), LIN-35 acts as a scaffold through concurrent interactions with EFL-1 and LIN-52 on opposite faces of the pocket domain [7, 16]. To address how LIN-35 contributes to DRM complex formation in C. elegans, we performed co-immunoprecipitation (co-IP) experiments from WT and lin-35(n745) late embryo protein extracts (Fig 2). The n745 allele introduces an early stop codon and is a protein-null allele [24]. EFL-1 immunoprecipitation successfully pulled down the MuvB components LIN-37 and LIN-54 from WT extracts but not from lin-35 null extracts. In reciprocal pull-downs, LIN-37 immunoprecipitation successfully pulled down EFL-1 and DPL-1 from WT extracts but not from lin-35 null extracts. As controls, the EFL-1 and LIN-37 IPs successfully pulled down their respective complex partners DPL-1 or LIN-54 from both extracts. These results were observed in 2 biological replicate experiments (S2 Fig) and demonstrate that E2F-DP and MuvB association requires LIN-35.
To test if loss of LIN-35 affects E2F-DP and/or MuvB chromatin localization, we performed ChIP-seq of E2F-DP (DPL-1 and EFL-1) and MuvB (LIN-9, LIN-37, LIN-52, and LIN-54) on 3 biological replicates of lin-35 null late embryos. LIN-35 ChIP-seq was performed on 2 lin-35 null biological replicates as a negative control. We observed a genome-wide decrease in E2F-DP and MuvB subunit chromatin occupancy in lin-35 null embryo extracts (Fig 3A, S3A Fig). To define the reproducible differences between lin-35 null and WT DRM subunit ChIP-seq, we used DEseq2 differential binding analysis [31]. Of the 1418 high-confidence DRM peaks identified in WT (Fig 1B, S1 Table), 866 peaks displayed significantly decreased chromatin occupancy by at least 1 E2F-DP/MuvB subunit in lin-35 null (Class I, FDR < 0.05) (Fig 3A). 71% of Class I peaks showed a significant decrease in chromatin occupancy of at least 3 of the 6 analyzed subunits in lin-35 null compared to WT, and 27% showed a significant decrease for all 6 subunits. Of the remaining 552 peaks that were not significantly decreased (Class II), 6 showed a significant increase in chromatin occupancy of at least one subunit; this corresponds to 0.4% of all high-confidence DRM peaks. Our data demonstrate that in the absence of LIN-35, the chromatin association of E2F-DP and MuvB is significantly reduced (Fig 3A, S3A Fig).
Although the 552 Class II peaks did not meet our significance cut-off for being reduced in lin-35 null compared to WT, these peaks appeared reduced in lin-35 (Fig 3A). In fact, the signal from 2 of the 3 lin-35 null ChIP-seq replicates suggested that LIN-35 loss greatly affected DRM subunit occupancy at all sites (S4 Fig). When we removed the remaining lin-35 null ChIP-seq replicate from the differential binding analysis, then 93% of all DRM peaks displayed significantly decreased chromatin occupancy by at least one DRM subunit, and 64% showed a significant decrease for all 6 subunits, in lin-35 null compared to WT. Taken together, our genome-wide analysis revealed that E2F-DP and MuvB chromatin occupancy is impaired globally in lin-35 null embryos.
We validated our ChIP-seq findings by ChIP-qPCR analysis of 8 Class I DRM peaks and 6 Class II peaks. In lin-35 null embryos, we observed a significant decrease in chromatin occupancy of the majority of DRM subunits at all 14 sites tested (Fig 3B, S3B Fig). Two representative Class II peaks within the mes-2 and cdk-1 promoters are shown in Fig 3B. Histone H3 lysine 4 trimethylation (H3K4me3) ChIP was used as a positive control; it confirmed that the observed reduction of DRM subunit chromatin occupancy in lin-35 null compared to WT late embryos was specific to DRM subunits (Fig 3B, S3B Fig). Similar to our ChIP-seq analysis, in lin-35 null embryos, the chromatin occupancy of the DRM subunits DPL-1, EFL-1, LIN-9, and LIN-37 was significantly enriched over the IgG negative control near 11 of the 14 promoter regions tested, including mes-2 and cdk-1 (Fig 3B, S3B Fig). Interestingly, the DRM complex occupies the promoter region of most of the genes that encode DRM subunits (S5A Fig), suggesting that the DRM complex represses its own expression. Indeed, the majority of DRM subunit transcripts were elevated in the 2 DRM mutants we tested (S5B Fig); protein levels were similar or slightly elevated in lin-35 null compared to WT late embryos, based on western blot analysis (S5C Fig). These results indicate that the observed decrease in DRM subunit chromatin occupancy was not driven by reduced protein levels. To extend this analysis beyond embryos, we also performed ChIP-qPCR to test DPL-1 and LIN-37 occupancy in L1 larvae. As in embryos, we observed a decrease in DPL-1 and LIN-37 occupancy in lin-35 null L1 larvae compared to WT L1 larvae (S5D Fig). Together, our ChIP analyses of DRM subunits in the lin-35 null indicate that E2F-DP and MuvB chromatin occupancy is significantly impaired but not eliminated at most genomic sites in the absence of the pocket protein.
We tested whether the DRM DNA binding motif is enriched in Class I targets and/or Class II targets. We performed de novo DNA motif discovery using MEME [32] on the 1418 high-confidence DRM peak regions and identified the characteristic hybrid CDE/CHR motif similar to what has been observed previously (Fig 3C) [33]. Additionally, we performed a search for the known E2F-DP binding motif (CDE, IUPAC code: BSSSSS) and LIN-54 binding motif (CHR, IUPAC code: TTYRAA), restricting our search to phylogenetically conserved motifs similar to an analysis performed on the mammalian genome (S6 Fig) [34]. The analyses together identified 719 peaks with the de novo CDE/CHR motif, 660 peaks with a conserved CDE motif, and 898 peaks with a conserved CHR motif (Fig 3C, S1 Table). However, the motif content of a DRM peak does not predict whether E2F-DP or MuvB remain more or less stably bound in the absence of LIN-35 (S6 Fig).
In mammalian cells, phosphorylation of the pocket protein triggers DREAM disassembly and reentry into the cell cycle [2, 7]. Activating E2F-DPs replace repressive E2F-DP, and MuvB forms the activating MMB complex to drive cell cycle gene expression [22, 35]. Since C. elegans E2F-DP alone can activate genes in the germline [36], we assessed whether the chromatin-bound landscape of the DRM components changes in lin-35 null embryos. We performed peak calling analysis on the lin-35 null ChIP-seq replicates, as described previously (S2 Table). For each subunit, roughly half of the WT peaks were lost in lin-35 null embryos (S7 Fig). After overlapping WT high-confidence DRM peaks with E2F-DP and MuvB peaks detected in lin-35 null embryos, we observed that 621 of the 1418 high-confidence DRM peaks in WT lose detectable E2F-DP and MuvB occupancy in lin-35 null embryos (Fig 3D). 564 of the 1418 high-confidence DRM peaks in WT retain appreciable E2F-DP and MuvB occupancy in lin-35 null embryos, underscoring that loss of LIN-35 impairs but does not eliminate DRM complex chromatin localization (Fig 3A). We did not observe any appreciable or consistent appearance of E2F-DP or MuvB at new sites (S7 Fig). Thus, loss of LIN-35 does not appear to result in de novo binding of E2F-DP or MuvB to new sites but rather destabilizes binding to their native sites, resulting in elimination of DRM occupancy from about half of the native sites.
To address the effects of LIN-35 loss on target gene transcription, we mapped the 1418 high-confidence DRM binding sites to likely gene targets and then analyzed transcript levels from those genes in lin-35 null mutants. 78% of all DRM binding sites map to within at least one gene’s promoter region, which we defined as 1000 base pairs upstream to 100 base pairs downstream (-1000 bp, +100 bp) of a transcriptional start site (Fig 4A, S3 Table). Of these promoter peaks, 688 DRM binding sites were observed within 677 single gene promoters, and 423 DRM binding sites were observed within bidirectional promoters for 844 genes. Of the remaining DRM binding sites, 148 were observed within an intron of 137 genes, 48 were observed within 1000 bp of 46 transcriptional termination sites, and 111 were observed in intergenic regions (Fig 4A, S3 Table).
To generate a list of high-confidence DRM targets, we assessed whether genes with DRM bound in their promoter region in WT (DRM target genes) were misregulated in previously published expression analyses that compared lin-35 null to WT in early embryos and L1 larvae [37] and compared lin-54(n2990) mutant to WT in mixed-stage embryos [33] (S3 Table) using microarray analysis. The lin-54 allele produces protein with impaired DNA-binding ability, which reduces its chromatin occupancy by ~50% [33]. Upregulated genes in both lin-35 null L1s and lin-54 mutant mixed embryos were significantly enriched for DRM target genes (Fig 4B). Our analysis defined 362 "high-confidence DRM targets" (bound by DRM and upregulated in DRM mutants), which we refer to as Group A DRM targets (Fig 4B, S3 Table, S8A Fig). The remaining "lower-confidence DRM targets" (1153 genes) we refer to as Group B DRM targets, because we do not have evidence that DRM represses these genes.
To investigate why the expression of many DRM target genes appears to be unaffected by disruption of DRM activity, we performed a Gene Ontology (GO) enrichment analysis of the 362 Group A genes and the 1153 Group B genes using DAVID (Fig 4C, S4 Table) [38, 39]. REViGO was applied to remove redundant GO terms (S9A Fig) [40]. Group A includes genes for cell cycle, while Group B lacks those genes; both groups include genes for development and reproduction (Fig 4C). DRM target genes marked by Class I peaks, Class II peaks, or HOT regions were not enriched for any particular GO term (S9 Fig). We speculate that the genes in Group A and Group B have distinct requirements for transcriptional activation: Group A genes, which include cell cycle genes that are likely expressed in all cell types, turn on when DRM activity is lost, while Group B genes do not turn on when DRM activity is lost, perhaps because they require additional transcriptional activators that are present only at specific developmental stages and/or in specific cell types.
Since developmental stage appears to greatly affect whether loss of DRM function causes target gene misregulation (Fig 4B, S8B Fig), we performed RT-qPCR analysis on lin-35 null and lin-54(n2231) mutant late embryos to match our ChIP-seq analysis. The lin-54(n2231) allele contains the same point mutation as lin-54(n2990), which impairs its DNA binding [33]. We included lin-54 late embryos to address whether the difference in number of genes significantly upregulated in lin-35 null early embryos and lin-54 mutant mixed-stage embryos (Fig 4B) is due to their different stages. We tested 4 candidates from Group A DRM target genes, which we expected to be upregulated in late embryos: set-21, cdk-1, air-1 and rad-51. All 4 candidates were significantly upregulated in both lin-35 null and lin-54 mutant late embryos as compared to WT late embryos (Fig 4D). Out of a total of 25 Group A genes tested, 22 were significantly upregulated in lin-35 null and/or lin-54 mutant late embryos (S5 Table). We also tested 5 Group B genes, genes not upregulated in the microarray analyses; 2 genes were significantly upregulated (S5 Table). Observing similar upregulation of genes in lin-35 null and lin-54 mutant late embryos suggests that the differences in number of genes upregulated in the microarray analyses are due to stage differences and that DRM target gene misregulation becomes detectable by late embryogenesis. Together with our ChIP-seq analysis, these results allow us to consider 2 possible mechanisms for how LIN-35 may function in DRM: 1) LIN-35 is essential for DRM-mediated repression, and loss of LIN-35 causes target gene upregulation or 2) LIN-35 is not essential for DRM-mediated repression, and loss of LIN-35 causes reduced chromatin occupancy by E2F-DP and MuvB, which in turn causes target gene upregulation.
We reasoned that if LIN-35 is essential for repression of DRM target genes, as the current model suggests, then further disruption of E2F-DP or MuvB should not cause further upregulation of DRM target genes. Conversely, if the reduced chromatin occupancy of E2F-DP or MuvB causes upregulation of DRM target genes, then further disruption of E2F-DP or MuvB should cause further upregulation of DRM target genes whose promoters retained those components. We tested these predictions on 4 DRM target genes that retained some E2F-DP and MuvB in lin-35 null late embryos (set-21, cdk-1, F01G4.4, and csc-1), and 4 DRM target genes that had no detectable E2F-DP or MuvB in lin-35 null late embryos (air-1, rad-51, kbp-3, and mes-4) (Fig 5). We confirmed that DRM subunit binding was retained or eliminated at 4 of these respective sites by ChIP-qPCR (S3B Fig). We performed RT-qPCR analysis on lin-35 null late embryos after two generations of RNA interference (RNAi) knockdown of efl-1, lin-9, or lin-54. The transcript levels of each targeted subunit were knocked down by at least 50% compared to empty vector control (S10A Fig). Knockdown of efl-1 had no effect on transcript levels from the 8 genes tested (Fig 5C and 5D); we also confirmed a previous report [41] that in the absence of LIN-35, E2F-DP does not activate germline genes in late embryos (S10 Fig). In contrast to efl-1, knockdown of lin-9 and lin-54 caused a significant increase in transcript levels from 3 of the 4 genes where MuvB retains some observable chromatin occupancy in lin-35 null late embryos and 0 of the 4 genes where MuvB chromatin occupancy is abolished in lin-35 null late embryos (Fig 5C and 5D). Testing of additional genes confirmed the above findings (S5 Table). Of 24 total genes to which MuvB remains bound in lin-35 null late embryos, 10 genes were further upregulated following knockdown of MuvB but not following knockdown of efl-1 (S5 Table). Of 6 total genes where MuvB occupancy is lost in lin-35 null embryos, 0 genes were further upregulated following knockdown of MuvB or efl-1. These results indicate that MuvB continues to repress DRM target genes in lin-35 null embryos, strongly supporting a role for MuvB as the mediator of DRM target gene repression.
The analyses described above show that E2F-DP and MuvB chromatin occupancy is reduced but not eliminated in the absence of LIN-35, even though E2F-DP and MuvB protein association is completely decoupled. In our RNAi RT-qPCR analysis, we observed that knockdown of MuvB in lin-35 null embryos resulted in further upregulation of DRM targets where MuvB retains some chromatin occupancy. While performing this experiment, we observed far fewer collected embryos in MuvB RNAi conditions compared to efl-1 and control RNAi. Indeed, the brood size of individual lin-35 null worms following knockdown of other DRM subunits was significantly reduced in efl-1 RNAi and severely reduced in lin-9 and lin-54 RNAi compared to empty vector controls (Fig 6A). Reduction in brood size was also seen after we introduced hypomorphic alleles dpl-1(n3643), which produces a truncated DPL-1 protein product [25], and lin-54(n2231) into the sensitized lin-35 null background. We observed a significant decrease in brood size in lin-35 null, dpl-1(n3643) hypomorphic, and lin-54(n2231) hypomorphic single mutants compared to WT (Fig 6B). We did not observe a significant reduction of brood size in lin-35(n745); dpl-1(n3643) double mutants compared to the lin-35(n745) single mutants (Fig 6B). In contrast, in lin-35(n745); lin-54(n2231) double mutants, we observed complete sterility. Together, these results indicate that further disruption of MuvB activity in lin-35 null worms causes a severe reduction in the production of viable progeny.
Importantly, null mutations of individual E2F-DP and MuvB subunits cause sterility due to a severe EMO phenotype (production of endomitotic oocytes) [33, 36, 42], whereas the brood size reduction observed in lin-35 null worms is associated with a moderate EMO phenotype (Fig 6C) [33]. E2F-DP null sterility is linked to its activating role in the germline, which is distinct from its role in DRM [36, 43]. However, MuvB null sterility is due to a defect in somatic sheath development [42]. We tested for EMO in lin-35 null worms depleted of other DRM subunits. In lin-35 null worms, RNAi knockdown of lin-9 or lin-54, but not of efl-1, caused an increase in severe EMO (Fig 6D). Similarly, lin-35; lin-54(n2231) double mutants, but not lin-35; dpl-1(n3643) double mutants, displayed an increase in severe EMO (Fig 6D). We conclude that further loss of MuvB repression in lin-35 null worms is catastrophic, reproducing the severe EMO phenotype previously observed only in null alleles that likely completely eliminate DRM activity.
Using genomic, genetic, and cell biological experiments on C. elegans lacking the sole pocket protein, we report evidence that MuvB mediates DRM target gene repression and that MuvB does not require LIN-35 to act as a transcriptional repressor. We demonstrate that loss of the sole C. elegans pocket protein LIN-35, which abolishes E2F-DP and MuvB association, results in a dramatic decrease in chromatin occupancy by both E2F-DP and MuvB and upregulation of many DRM target genes in late embryos. To determine how LIN-35 contributes to DRM gene repression, we depleted E2F-DP or MuvB subunits in lin-35 null animals. Depletion of MuvB, but not E2F-DP, causes further target gene upregulation and enhances the lin-35 null phenotype, indicating that MuvB directs repression of DRM target genes. Our study demonstrates that LIN-35 bridges E2F-DP and MuvB association, supporting their chromatin occupancy at target genes, and that MuvB functions innately as a transcriptional repressor of DRM target genes.
Studies of the mammalian DREAM DNA-binding motifs support the notion that MuvB mediates target gene repression. Most mammalian genes that are repressed in G0/G1 and activated in the later cell cycle contain a CDE, the binding motif for repressive E2F-DP, and a CHR, the binding motif for the LIN-54 subunit of MuvB [11]. Promoter analyses have shown that the CDE is not essential for DREAM binding; however, the presence of a CDE can enhance DREAM binding [12]. In contrast, the CHR is fully required for gene repression and activation at appropriate cell cycle stages [12]. Although our experimental approach does not eliminate the possibility that E2F-DP mediates repression of some target genes, taken together with the above motif analysis, our data support a model in which MuvB chromatin localization mediates DRM’s core repressive function.
Our assessment of the endomitotic oocytes (EMO) phenotype in the sensitized lin-35 null background may provide a clue as to how C. elegans E2F-DP contributes to DRM activity. EMO can result from germline dysfunction, e.g. defects in meiosis or fertilization, or somatic dysfunction, e.g. defects in somatic sheath cell formation or function [44, 45]. EMO and sterility have previously been observed in null mutants of dpl-1, efl-1, lin-9, and lin-54 [33, 36, 42, 43]. The dpl-1 and efl-1 null phenotype results from loss of E2F-DP’s transcriptional activation role in the germline, although some unknown somatic component is also involved [36, 43]. In contrast, the lin-9 null phenotype results from defects in somatic sheath development [42]. With E2F-DP decoupled from MuvB in lin-35 null worms, efl-1 knockdown resulted in significant reduction in fertility but no enhancement of the EMO phenotype. In E2F-DP null worms, we suspect that E2F-DP loss affects MuvB function in somatic cells but only when LIN-35 is present, and that effect is overshadowed by loss of E2F-DP activating function in the germline. Thus, we speculate that E2F-DP function in DRM is to support the association of MuvB with gene promoters, with the pocket protein acting as the intermediary.
In C. elegans, loss of DRM activity leads to ectopic expression of germline genes in somatic cells [27, 28]. Interestingly, in wild-type worms LIN-35 chromatin localization is dramatically reduced in the germline compared to somatic tissues [41]. Additionally, EFL-1 and DPL-1 are known activators of the germline oogenic program [36], and by ChIP-seq they localize to more genomic regions in the adult germline than in late embryos [41]. However, a previous study found no evidence for E2F-DP adopting an activating function in the absence of LIN-35 and directly activating germline gene expression in somatic cells [41]. Our analysis also found no evidence for C. elegans E2F-DP occupying new genomic sites in lin-35 null embryos that are normally only observed in the adult germline. Perhaps an additional event following loss of LIN-35 must occur before E2F-DP can localize to and activate a germline program. Our study further suggests that some DRM target genes require secondary regulatory factors in addition to loss of DRM for their activation. As an example, E2F-DP was recently shown to co-regulate some developmental genes with heat-shock factor (HSF) [46]. It is likely that additional regulatory networks overlap DRM functionality at certain DRM targets and coordinate their activation in specific tissues and times during development.
Our analysis of C. elegans DRM provides insight into how the mammalian DREAM complex components maintain cellular quiescence and promote the transition into the cell cycle. Upon exit from the cell cycle, p130/p107 promotes E2F-DP and MuvB association on chromatin, stabilizing MuvB-mediated transcriptional repression of important cell cycle gene targets (Fig 7A). We speculate that the effects on DRM activity observed in lin-35 null embryos mimic the events that immediately follow phosphorylation-mediated release of p130 from the mammalian DREAM complex, an event triggered by progression into the cell cycle (Fig 7B) [2, 7]. Loss of the pocket protein destabilizes the chromatin association of E2F-DP and MuvB, but remaining MuvB continues to repress some target genes. In mammalian cells, MuvB-mediated repression is likely overwhelmed by 1) the release of activating E2F-DP heterodimers from the retinoblastoma protein (pRb), which promotes progression into the early cell cycle [47] and 2) incorporation of MuvB into a transcriptional activation complex with B-Myb in the late cell cycle [20, 21]. Future work on how MuvB’s regulatory role is altered from repression to activation by B-Myb will help answer how precise temporal activation of cell cycle genes is achieved.
The MuvB complex’s dual transcriptional role in mammalian cells has obscured how it may contribute to mammalian DREAM-mediated gene repression. Recently, it was hypothesized that DREAM stably positions a nucleosome at the transcriptional start site of mammalian DREAM target gene promoters to prevent transcriptional activation [13]. A likely candidate for mediating DREAM-nucleosome interactions is the MuvB component RBAP48 (in C. elegans, LIN-53), a WD-repeat family protein known to bind histones when present in other complexes [24]. The RBAP48 ortholog in Drosophila has been shown to be required for dREAM-mediated gene repression [48]. Additionally, in C. elegans, LIN-53’s role may include facilitating deposition of the histone variant HTZ-1/H2A.Z within the body of target genes [29]. If MuvB positions nucleosomes through LIN-53/RBAP48, this would represent a novel mechanism for transcriptional repression that employs targeted non-enzymatic inhibition of transcriptional initiation.
Our demonstration of MuvB’s innate repressive role may provide clues as to how the DREAM complex components evolved to meet diverse biological needs. Phylogenetic analyses suggest that the activating E2F-DPs and pRb coevolved, diverging from their respective common ancestors, which are more similar to the repressive E2F-DPs and p130/p107 DREAM components [49, 50]. Our findings in C. elegans indicate that MuvB mediates the repressive functions of the worm DREAM complex, and can perform this activity in the absence of p130/p107 and a link to E2F-DP. Thus, if DREAM-associated gene repression represents the ancestral function of E2F-DP and pocket proteins, MuvB may have emerged prior to or coincident with E2F-DP and a pocket protein. Moreover, since C. elegans does not have a B-Myb homolog, MuvB’s repressive mechanism likely represents its ancestral function, with its activating role emerging more recently [26].
Compared to the strong tumor suppressor activity of pRb, functional redundancy in p130/p107 has obscured if and how they act as tumor suppressors [51]. Interestingly, all 3 mammalian pocket proteins are specifically targeted for degradation by the E7 oncoprotein in high-risk human papillovirus (HPV) [52, 53]. Concurrently, HPV16 E7 stimulates MMB assembly and gene activation [54]. These findings illustrate how cancer cells may drive cell cycle progression by promoting disassembly of DREAM through inactivation of p130/p107 while also coaxing MuvB into its activating function. Our results describe how the pocket protein stabilizes MuvB-mediated repression, revealing that loss of the pocket protein in cancer cells may not immediately or completely relieve MuvB repression. Cancer cell retention of the MuvB complex could allow for both oncogenic transformation through MMB-mediated activation of cell cycle genes [55] and escape from cytotoxic chemotherapy by induced reentry into quiescence through MuvB’s innate repressive activity [56]. We expect that future work on how MuvB both activates and represses its target genes will provide much-needed insights into how cancers hijack cell cycle control.
All strains were cultured at 20°C using standard methods, unless otherwise noted. N2 (Bristol) was used as wild type (WT). Mutant alleles that were used in this study are listed in S6 Table. For CoIP and ChIP experiments, late stage embryos were collected by bleaching gravid worms and aging embryos for up to 3.5 hours before freezing them in liquid nitrogen.
Following embryo collection, extracts were prepared and CoIP performed based on [5]. Frozen late embryos were ground using a mortar and pestle, resuspended in lysis buffer (25 mM HEPES pH 7.6, 150 mM NaCl, 1mM DTT, 1mM EDTA, 0.5 mM EGTA, 0.1% Nonidet P-40, 10% glycerol) with Complete EDTA-free protease inhibitors (Roche), and sonicated twice for 30 seconds. Lysates were clarified and precleared using Protein A Dynabeads (ThermoFisher, Waltham, MA). Protein concentrations of lysates were determined using a Qubit fluorometer (ThermoFisher). 15 μg of antibody was crosslinked to 100 μL Dynabeads using dimethyl pimelimidate in 0.2 M trimethylamine pH 8.2. Crosslinking was stopped using 0.1M Tris pH 8.0, and beads were washed with 0.1 M glycine pH 2.8 before being stored in phosphate buffered saline pH 7.2 with 0.05% Tween-20. 5 mg of protein lysate and 20 μL antibody-conjugated Dynabeads were incubated for 2 hours at 4°C. Each IP was washed with lysis buffer, eluted with 50 μL 2x SDS gel-loading sample buffer for 5 minutes at 98°C, and separated by SDS/PAGE. Antibodies used in CoIP and ChIP experiments are listed in S7 Table. Western blot analysis was performed using a 1:5,000 dilution of primary antibody and a 1:2,000 dilution of an appropriate HRP-conjugated secondary antibody. Serial western blot analysis was performed by stripping the blot with buffer containing 0.2M glycine (pH 2.2), 0.1% SDS, and 1% Tween-20 between antibody probings.
Following embryo collection, extracts were prepared and ChIP performed based on [29]. Frozen late embryos were ground, crosslinked for 10 minutes in 1% formaldehyde, and sonicated to an average size of 250 base pairs in FA buffer (50 mM HEPES/KOH pH 7.5, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate, 150 mM NaCl). Protein concentrations of lysates were determined using a Qubit fluorometer. Lysates were precleared with Protein A Dynabeads. ChIPs were performed with 1–2 mg of extract, and 2% of the extract was set aside for an input reference control. 1–5 μg of antibody were used for ChIP-seq analysis, and 0.5 μg of antibody were used for ChIP-qPCR analysis. ChIPs were incubated overnight at 4°C with 1% sarkosyl. Protein A Dynabeads equilibrated in 20 μL FA buffer were added and incubated for 2 hours at 4°C. ChIPs were washed with the following buffers: once with FA buffer containing 1 M NaCl, once with FA buffer containing 0.5 M NaCl, once with TEL buffer (10 mM Tris-HCl pH 8.0, 0.25 M LiCl, 1% NP-40, 1% sodium deoxycholate, 1 mM EDTA), and twice with TE buffer (10 mM Tris-HCl pH 8.0 and 1 mM EDTA). 2 elutions of 50 μL elution buffer containing TE plus 1% SDS and 250 mM NaCl were incubated at 55°C. Eluted ChIP and input samples were incubated with proteinase K for 1 hour at 55°C. Crosslinks were reversed overnight at 65°C. DNA was purified by phenol-chloroform extraction and ethanol precipitation using glycogen as a carrier. Quantitative PCR was performed using SYBR green reagents on a LightCycler 480 (Roche, Basel, Switzerland) or an Applied Biosystems ViiA 7 Real-Time PCR System (ThermoFisher) using primers specific to select promoter regions, which are provided in S8 Table, and normalized against signal from a negative control region on chromosome IV.
Sequencing libraries were prepared from genomic DNA fragments (input) or those obtained after ChIP using the TruSeq ChIP Sample Prep Kit (Illumina, San Diego, CA). Amplified libraries were size selected to obtain 200–500 bp fragments using Agencourt AMPure XP beads (Beckman Coulter, Brea, CA). After verifying library fragment sizes using a 2100 Bioanalyzer (Agilent, Santa Clara, CA), sequencing was performed using the Illumina HiSeq 2000/4000 platforms. Libraries were sequenced at the Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley. Sequencing reads were mapped to the ce10 reference genome using bowtie-1.1.2 [57], allowing a maximum of 2 reported alignments. ChIP-seq data were normalized to input using the signal extraction scaling (SES) method using deepTools and visualized using the UCSC genome browser [58–60]. Peak calling and data reproducibility checks were performed using the SPP peak caller and Irreproducible Discovery Rate (IDR) pipeline established by the ENCODE project [61, 62]. Peak overlaps and gene mapping were processed using HOMER [63], with gene transcriptional start sites (TSS) and transcriptional terminal sites (TTS) based on RefSeq annotations. Differential binding analysis was performed using the DiffBind package in Bioconductor (www.bioconductor.org) and the R statistical programming language [64]. Motif analysis was performed de novo using the MEME suite [32], or overlapping known motifs using HOMER and phastCons [65]. Motif sequence logos were generated using WebLogo [66]. Important quality control metrics are provided in S9 Table.
Bacteria from the Ahringer RNAi feeding library [67] expressing dsRNA against efl-1, lin-9, and lin-54 were sequence-verified and fed to lin-35(n745) worms. Progeny from gravid worms grown in liquid media were synchronized as L1 larvae and fed RNAi on NGM plates containing 1 mM IPTG and ampicillin. RNAi feeding was administered for 2 generations at 24°C, after which late embryos from 2nd generation gravid adults were collected in Trizol for RNA isolation and transcript analysis. A total of 1.5 μg RNA was DNAse treated and reverse transcribed using the High Capacity cDNA Kit (Applied Biosystems, Foster City, CA). qPCR was performed using SYBR green reagents on a LightCycler 480 (Roche) or an Applied Biosystems ViiA 7 Real-Time PCR System (ThermoFisher) using primers specific to a select gene set, which are provided in S10 Table. The relative quantity of experimental transcripts was calculated with act-2 as the control gene using the ΔCt method with efficiency correction. Statistical analysis of genome-wide expression data was performed using R, using the Quantile normalization and Robust Multichip Average (RMA) algorithm in the affy package from Bioconductor [68, 69]. To determine differential expression, moderated T Statistics were applied using the limma package, using q-value < 0.05 and fold change > 1.5 as the significance thresholds [70]. For EMO and brood size scoring, 2nd generation RNAi-fed L4 larvae were aged overnight and scored.
For endomitotic oocyte (EMO) scoring, L4 larvae were aged overnight, fixed using Carnoy’s solution, stained with the DNA intercalator DAPI, and scored blind. The definitions of severe EMO, moderate EMO, and no Emo are in the Fig 6 legend. Images were acquired using a Zeiss Axioskop (Oberkochen, Germany) and processed using Image J [71]. For brood size analyses, individuals were cloned to fresh plates every 24 hours starting at the L4 larval stage and all progeny were counted.
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10.1371/journal.pcbi.1002656 | Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability | Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well- known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.
| Proteins interact with each other in a network manner to precisely regulate complicated physiological functions of life. Diseases such as cancer may occur if the network regulations go wrong. In cancer research, network mining has been utilized to identify biomarkers, predict therapeutic targets, and discover new mechanisms for cancer development. Among these applications, the search for genes with similar expression patterns (co-expression) over different samples is particularly successful. However, few network mining approaches were systematically applied to different types of cancers to extract common cancer features. We carried out a systematic study to identify frequently co-expressed gene networks in multiple cancers and compared them with the gene networks found in multiple normal tissues. We found dramatic differences between networks from the two sources, with gene networks in cancer corresponding to specific traits of cancer. Specifically, the largest gene network in cancer contains many genes with cell cycle control and DNA stability functions. We thus predicted that a set of poorly studied genes in this network share similar functions and validated that most of these genes are involved in DNA break repair or proper cell division. To the best of our knowledge, this is the largest scale of such a study.
| Distinct types of human cancer share similar traits, including rapid cell proliferation, loss of cell identity, and the ability to migrate and seed malignant tumors in distal locations. Understanding these common traits and identifying the underlying genes/networks are key to gaining insight into cancer physiology, and, ultimately, to prevent and cure cancer. With cancer gene expression microarray datasets increasingly accumulated in central repositories, many bioinformatics data analysis methods have been developed to identify cancer related genes, characterize cancer subtypes and discover gene signatures for prognosis and treatment prediction. As an example, in breast cancer research, a supervised approach was adopted to select 70 genes as biomarkers for breast cancer prognosis [1], [2] and was successfully tested in clinical settings [3]. However, a major drawback of such an approach is that the selected gene features are usually not functionally related and hence, cannot reveal key biological mechanisms and processes behind different patient groups.
In order to overcome this hurdle to identify functionally related genes associated with disease development and prognosis, several approaches have been adopted. One such approach is gene co-expression analysis, which identifies groups of genes that are highly correlated in expression levels across multiple samples [4]–[9]. The metric to measure the correlation is usually the correlation coefficient (e.g., Pearson correlation coefficient or PCC) between expression profiles of two genes [4], [5], [10]. Using this approach, we were able to identify new gene functions in regulating cell mitosis in breast cancer [5], [11] by studying genes that have high correlation with the expression of the DNA repair protein, BRCA1.
By applying an advanced network mining algorithm, dense modules of highly co-expressed genes can be identified which can lead to the discovery of new gene functions, disease genes and biomarkers. For example, Horvath's group has developed a series of weighted gene co-expression network analyses using a hierarchical clustering based approach [6], [10], [12]–[15]. This method was applied to identify disease-associated genes such as ASPM in glioblastoma [7].
In this study, we hypothesize that studying clusters of frequently co-expressed genes in multiple types of cancers can shed light on the gene expression regulatory basis for common traits in cancer. We developed a workflow to test this hypothesis (Figure 1), and implemented a state-of-the-art weighted network mining algorithm called QCM (Quasi-Clique Merger [16]) to identify the gene co-expression clusters from the common cancer background using gene expression data from multiple types of cancers. Then, we further predicted the gene functions based on the networks we identified and their GO-term enrichment analysis, and validated our prediction using cell-based assays.
The QCM algorithm mines dense sub-network components in a weighted network. In contrast to traditional quasi-clique mining algorithms [4], [17], [18], QCM fully utilizes the weight of edges without turning them into un-weighted edges by a threshold cutoff. In addition, QCM returns dense sub-network components that allow overlaps of both vertices and edges. This feature makes it more appealing for mining biological networks than clustering algorithms. Thirdly and most importantly, QCM was proven mathematically to be able to generate high density sub-networks [16], which correspond to tightly co-expressed clusters of genes in our study.
Gene signatures or networks have been identified as predictive/prognostic biomarkers based on certain cancer type microarray data. However, few studies have been applied to identify cancer associated genes and therapeutic targets in multiple cancer types at the level of the functional gene module, in which gene clusters are functionally and possibly physically interacting with each other. It has been demonstrated by analyzing 507 co-expression modules and 665 gene signatures that co-expression network mining is a powerful tool to search for functional enriched modules [19]. Instead of using differential gene expression analysis, our approach is to directly mine frequent gene networks that are present in large- scale datasets of multiple cancer types, and compare them with those found in normal tissues to understand the pathways and networks that cause the major difference between cancer and normal tissue. In addition, it was reported that previous co-expression network searches often resulted in non-reproducible or poorly overlapped gene signatures/networks [20], which may have been due to arbitrary thresholds, results sensitive to parameter tuning, lack of generality or the lack of biological validation of the gene functions and interactions. We attempted to solve these problems by applying a weighted network mining algorithm to identify frequently presented co-expression gene networks on a common cancer background, and then further validated the findings with biological experimental evidence.
Our workflow to identify tightly clustered frequent co-expression networks was developed as follows (Figure 1): First, we selected a large number of gene expression datasets for 33 different types of cancers (originated from 16 tissue types, Table 1), including sarcoma, carcinoma, adenocarcinoma, leukemia, lymphoma, and brain cancer. As a comparison, we selected microarray datasets from nine different normal tissues. The datasets were selected such that the sample size in each dataset is above a minimal threshold to maintain the significance level of PCC computation (p-values<0.05 for PCC values larger than the threshold as described in Materials and Methods). In this study, all the selected datasets have at least 30 samples, which is comparable to other co-expression network studies [14], [21]. To avoid systematic bias between different microarray platforms, we further restricted our datasets to a single platform. All the selected datasets were generated using Affymetrix HU133 Plus 2.0 Genechip. Next, a total of 2.17×108 (20,827×20,826/2) gene-pair expression correlations (PCC) were computed within each dataset, and a frequency table was built for identified gene pairs with high correlation between their expression profiles in each dataset. The frequencies of highly correlated gene pairs were then used as weights to build a weighted gene co-expression frequency network (WGCFN). Third, we implemented QCM to identify high frequency gene co-expression networks in multiple types of cancers from the WGCFN for cancers and compared them to those identified in multiple types of normal tissues. In the final step, identified networks with similar members (overlaps above 30%) were merged iteratively to generate the final networks. This workflow runs parallel for the datasets from multiple cancer types and from multiple normal tissues.
The algorithm identified 111 gene co-expression networks (average network density 0.81±0.05) from cancer tissue gene expression microarray datasets before the merging step, and 70 networks for normal tissues (average network density 0.73±0.04) before the merging step. As a comparison, the average network density of 1000 randomly selected gene subsets (regardless of the subset size) was much lower as expected, and was close to the density of the overall network (0.0497, based on 20,827 genes).
We merged the networks with at least 30% similarity, obtained 18 distinctive networks in cancer datasets, and 6 networks in normal tissue datasets (Table 2, Table S1, Table S2). Despite the high diversity of cancer types, GO term enrichment analysis showed that the networks found from cancer datasets are highly enriched in elevated activities specific to cancer cells, such as cell proliferation, immune response, and cancer microenvironment construction, while the normal tissue networks are generally involved in housekeeping functions such as cell respiration, metabolism, and protein synthesis. For the networks that share similar GO term enrichment between cancer and normal tissue datasets, the cancer network generally includes most of the members of the normal tissue network but also contains many more genes. This indicates that the housekeeping functions in the cancer cell may exceed its normal range, allowing it to become more interconnected with other biological processes and pathways, which may contribute to the excessive uncontrollable growth of cancer cells.
As a comparison and the test for our QCM network mining workflow, we also applied the workflow described above (Figure 1) to the lung cancer samples of a single dataset (GSE18842, 46 samples), then to the normal lung tissue of the same dataset (45 samples). Similar observations from multiple cancer types vs. multiple normal tissue types also hold for the network mining results from single cancer type and the matching normal tissue. There are more and denser networks identified from lung cancer samples as compared with those from normal lung tissue. For the networks identified from lung cancer samples, they are enriched with functions related to cancer cells, such as DNA mismatch repair, immune response, and extracellular matrix (ECM) construction and organization (Table S4), whereas the networks from normal lung tissue are instead enriched with housekeeping functions such as protein synthesis, cell metabolism, and microtubule-based activity (Table S5). We also identified several immune response clusters from the normal lung tissue, presumably due to the fact that these normal lung tissue samples were obtained from the lung cancer patients and as a result, immune response signals induced from lung cancer can be spread to neighbor normal lung tissue. From this example, we conclude that the observations we have made in aggregate were also true in specific examples.
Network 1 is the predominant network identified consistently from cancer datasets regardless of the parameter setting (Figure 2, Table 2, Table S1, Table S3). By contrast, only a small portion of this network with looser connections was found from normal tissues (Figure 2, Table S2). Network 1 includes most of the genes that are frequently identified in a variety of gene signatures studies of the cancer microarray (Table 3, Table S1) [9], [22]–[28], and contains some less studied genes as well. The genes in this network are highly enriched in cell proliferation and genome stability maintenance functions such as cell cycle control/regulation, mitotic division, and DNA damage response (DDR). After querying the Ingenuity Knowledge Base for experimentally validated protein-protein interactions, we found that 99 out of the 412 gene products from Network 1 are connected to form a tight protein-protein interaction (PPI) network, as shown in Figure 3A (enrichment p- value 5.937E-217). Similarly, 33 out of the 57 genes from Network 6 are connected in a dense PPI network (enrichment p-value 1.564E-52, Figure 3B), which is involved in an extracellular matrix formation. In addition, we also tested this using a different PPI dataset obtained from the Protein Interaction Network Analysis platform (PINA). Null distributions were generated from repetitive 500 random selections of the same number of genes as networks 1 or 6 in PPI interaction database PINA. Next, z-scores of PPI hits in networks 1 and 6 were obtained from each distribution as described in the Materials and Methods section. Both networks 1 and 6 yielded very high z-scores (44.06 and 23.76 respectively), indicating highly enriched PPI in each network. This demonstrates that our QCM approach not only identifies a co-expression module that is highly enriched as a functional module, but also is capable of finding physically interacting networks, which confirmed the previous finding that the co-expression module can reveal those genes that form physically interacting modules [19].
We also isolated a gene network from cancer datasets that has very diverse GO terms but with no apparent theme (cancer Network 4 with 73 genes, Figure 2, Table 2, Table S1). Genes in this orphan network participate in functions including small molecule biochemistry, lipid metabolism, cell-to-cell communications, connective tissue development, etc. Interestingly, an almost identical network is also found in the normal tissue datasets (normal Network 3 with 60 genes, Figure 2, Table 2, Table S2). Inside this gene network, eight genes were involved in DNA damage response (SMG1, GTSE1, GTF1H3, PMS2P1, PMS2L2, XRCC2, DCLRE1C, and UACA) based on GO term enrichment analysis. PGF is involved in angiogenesis, epithelial cell growth, and the migration of mesenchymal stem cells [29], [30]. NEK9, HAUS2 are involved in mitotic spindle formation and centrosome integrity [31], [32]. However, a majority of the genes in this network are not closely connected with each other in the protein-protein interaction database from the most updated Ingenuity Knowledge Base at the time of the manuscript preparation. Instead, they either participate in diverse functions, which are not tightly linked to cancer, or have not been extensively studied. Using the gene set enrichment analysis tool TOPPGene, we found that within this network, 22 were down- regulated in poorly differentiated thyroid carcinoma, 13 were down-regulated in nasopharyngeal cancer, breast cancer and hepatocellular carcinoma (HCC), and 12 were up-regulated in the intrahepatic metastatic HCC versus primary HCC. However, it is not clear how these genes are functionally or physically interacting with each other, and the majority of them have not been linked with cancer development. These genes, along with other less studied members in this network, may be good targets for future cancer studies.
Since many gene signatures and biomarkers involved in cell cycle control and cell proliferation overlapped with genes in Network 1 from cancer datasets (Table 3), we tested their prognostic capability in breast cancer, ovarian cancer (OV) and glioblastoma (GBM) patients (Figure 4). Datasets were separated according to Network 1 (Figure 4A, C, E) or according to the Van't Veer 70-gene list (Figure 4B, D, F), and the survival of patients from each set were plotted up to 20 years. For patients from the NKI breast cancer dataset with mixed subtypes as well as the lymph node-positive (LN+) cohort, Network 1 separated the good and poor outcome groups comparably well as the Van't Veer 70-gene signature [1] (Figure 4A–D), and both passed the p-value significance threshold after Bonferroni correction, despite the fact that the two lists only shared five genes in common (CENPA, MCM6, ORC6L, PRC1, RFC4). However, for the ER-negative cohort, neither Network 1 genes (Figure 4E) nor Van't Veer 70-genes (Figure 4F) identified the individuals with longer survival. This suggests that the cell-proliferation network is less prognostic for the ER-negative cohort.
For GBM and OV cancer patients, in which prognosis studies based on microarray analysis are relatively scarce, we also tested the networks we identified from multiple cancer datasets. Network 1 genes failed to separate the good and bad outcome groups, even though certain cell proliferation genes are known to be associated with these cancers, such as ASPM in GBM [7] and BRCA1 and BRCA2 in OV [33]. Thus, a more sophisticated supervised feature selection approach is needed to improve the separation by selecting most relevant genes from this network [34]. However, Network 18 genes, which are enriched with cellular respiration function, had good prognosis power for GBM (p = 5.89E-3, Figure 4G) on the TCGA GBM dataset, while a recently published GBM 23-gene signature [28] failed to separate the good versus poor patient outcome using the same unsupervised K-means clustering approach on this dataset (Figure 4H). For OV patients, Network 17 genes, which have no significantly enriched GO-term (Table 2, Table S1), performed best among all the networks to separate the good and bad outcome groups (p = 3.39E-3, Figure 4I), comparable to an OV 19-gene signature when applied to the same OV dataset (Figure 4J) [34].
Genome instability, such as aneuploidy, due to hyperactive centrosome duplication (also called centrosome amplification) has been observed for decades in cancer cells [35], [36]. DNA repair proteins have recently been shown to localize and regulate the process as well [37]–[41]. Based on these findings, we then looked in Network 1 for genes with unknown functions to further study their roles in genome stability maintenance. Such genes/proteins have limited numbers of publications, or have not previously been shown to regulate centrosome duplication or homologous recombination. In addition, most genes we selected are absent from the validated PPI network in Figure 3A (red circles indicate the four genes present in the validated PPI network). By silencing the expression of target genes by transfection of siRNA, we screened for cells defective in homology-directed DNA repair (HR) or cells with supernumerary centrosomes. BRCA1 was used as a positive control, since its functions in homologous recombination and centrosome amplification have been known [37], [40], [42]–[45].
Out of the 13 genes we depleted with siRNA besides BRCA1, seven were significantly impaired for HR function (ASF1B, BARD1, CDCA3, DLGAP5, KIF14, MKI67 and ZWINT), and one was marginally impaired for HR function (NASP) (Figure 5A, Table 4). Four showed centrosome amplification (KIAA0101, KIF14, KIF23 and HMMR [5]) on the HeLa cell line and the breast cancer cell line Hs578T (Figure 5B, Table 4, Figure S2). Among these genes, BARD1 interacts with BRCA1 in the HR pathway [46], therefore the HR decrease upon BARD1 depletion was expected. BLM is an important genome stability maintenance protein with biochemical activity of a helicase, and BLM suppresses HR [47], [48]. The HR suppression activity of BLM explains why its depletion increased the cell activity of HR. HMMR (hyaluronan-mediated motility receptor), although directly interacts with BRCA1 and BRCA2, surprisingly does not affect HR activity in the cell after being depleted. However, HMMR depleted cells are known to exhibit centrosome amplification phenotype [5]. The depletion of KIAA0101 did not affect the HR activity, but centrosome amplification was observed. The unaffected HR activity upon KIAA0101 depletion was confirmed by a separate work published recently [49]. In that work, KIAA0101 was hypothesized to restrict HR activity. In our further study, KIAA0101 was shown to be over-expressed in breast cancer cells, and interacting directly with the BRCA1 protein [11]. This finding provided strong evidence that the cancer frequent co-expression network mining can be a powerful tool to direct gene function research, especially to facilitate the search for oncogenes and genes closely related to cancer cell activities.
The involvements of ASF1B, DLGAP5 and ZWINT in HR of the human cell are novel findings. ASF1B is a histone chaperone that facilitates histone deposition and histone exchange and removal during nucleosome assembly and disassembly [50], [51], [52], [53], [54]. DLGAP5, also called DLG7, is a potential cell cycle regulator that may play a role in carcinogenesis [55], [56], and it was identified in a gene co-expression analysis of multiple cancer datasets previously [9]. ZWINT is part of the MIS12 complex, which is required for kinetochore formation and spindle checkpoint activity [57], [58], and from these functions ZWINT would not be anticipated to function in HR. All four genes have never previously been shown to participate in DNA repair. The new discovery of those genes participating both in spindle/microtubule regulation and HR may explain the high frequency of hits of these genes in multiple gene expression profiling studies of cancer datasets (Table 3). We also tested HR upon TPX2 depletion, and decreased HR activity was observed. However, TPX2 depletion is lethal to cells, therefore it is difficult to determine whether the decrease of HR activity is due to the potential TPX2 function in DNA repair or due to cell death.
MKI67 (also called Ki67) has long been identified as a proliferation marker in breast tumor grading systems. However, the exact function of this protein remains obscure [59]. We found that depletion of MKI67 resulted in up to a five-fold reduction in HR (Figure 5A). This is the first demonstration that MKI67 is required for double-strand DNA break repair. This finding may provide direction for future study of MKI67 to elucidate its role in tumor proliferation.
KIF14 plays an important role in cytokinesis [60]. KIF23 is a plus-end-directed motor enzyme that moves anti-parallel microtubules in vitro. It localizes to the interzone of mitotic spindles. KIF14 and KIF23 directly interact with PRC1 within a complex that also contains KIF4A and KIF20A [60], [61]. KIF14 has been identified as a prognostic marker in breast and ovarian cancer in gene expression profiling studies [9], [62]. KIF23 was also up-regulated with three other genes in non-small cell lung cancer [63]. In our study, KIF14 and KIF23 depleted HeLa cells showed impaired HR, and increased centrosome amplification. However, we found the effect of KIF23 depletion on HR was complicated because its depletion caused cells to become resistant to plasmid transfection, which was confirmed through independent experiments (data not shown). As a result, the KIF23 depletion-induced genome instability is probably due to an indirect effect.
ASPM was hypothesized to regulate spindle formation and mitotic process based on sequence similarity (UniProt), but in our assay, ASPM depleted cells did not have the centrosome amplification phenotype. This indicates the ASPM's role in spindle regulation may be indirect or it participates in different pathways than the above ones.
It is clear that networks identified from cancers and normal tissues are very different. The former contain more tightly connected networks with more members, and with GO terms closely related to cancer-specific biological processes. By contrast, analysis from normal cells reveals fewer gene networks with fewer members that mostly comprise normal cell housekeeping functions. As described in [64], different cancers share common “hallmarks” such as replicative immortality, angiogenesis, invasion and metastasis. Then in [65], four additional properties were proposed as common hallmarks or characteristics for cancers including genome instability/mutation, tumor promoting inflammation, avoiding immune destruction and deregulating cellular energetics. In addition, tumor microenvironment also plays a pivotal role in cancer development. Interestingly, our findings are highly consistent with these common cancer properties. The predominant network identified from multiple cancer datasets is most enriched in genes involved in cell cycle control, genome instability and DNA repair functions (Network 1 with 412 genes), suggesting that regardless of the cancer types, the most active process in the cancer cell is cell proliferation, and genome instability is the enabling characteristics of cancer. Besides the cell cycle control and genome instability networks identified from cancer datasets, several immune/inflammation response networks and the type I interferon network were also identified which are potentially related to the tumor promoting inflammation and avoiding immune disruption characteristics. In addition, the tightly connected extracellular matrix network (Network 6 from cancer, Table 2) identified in cancer datasets supports the importance of tumor microenvironment in cancer development. Lastly, the lack of the cell metabolism network in cancer compared to the normal tissues (Network 1 from normal tissues, Table 2) implies disruption of normal cellular energetic processes. Overall, our results reveal that the common cancer hallmarks and characteristics involve highly coordinated transcriptomic activities. Many of the cancer network genes are differentially expressed in cancer vs. normal samples, and were identified using a differential expression analysis approach. In fact, cancer network 1 includes a high proportion of the cell proliferation genes identified from a differential expression study [22], [23] (Table 3). Some studies combined differential expression analysis with condition specific co-expression network mining [66]–[68], and identified cell cycle/cell proliferation networks in the cancer microarray datasets. Specifically in a smaller scale multiple cancer/normal microarray dataset study using differential co- expression approach, similar but smaller cell cycle networks were identified that were 100% included in our Cancer Network 1 gene list [67] (Table 3). However, the advantage of using a frequent co-expression network mining approach is that it combines datasets from multiple diseases instead of comparing two conditions and therefore, many microarray studies with few or no normal samples can still be integrated in our mining approach even though they are not suitable for differential expression analysis. Furthermore, the network genes identified from frequent co-expression analysis clearly groups genes into functionally and even physically interacting clusters, while differential expression analysis identifies isolated genes which need to be further clustered for functional analysis.
In normal tissues, the two biggest gene networks identified are involved in cell metabolism and protein synthesis; the members are mostly housekeeping genes (Table S2). Because our frequent co-expression network mining algorithm QCM uses gene-pair frequency as the edge weight, tissue-specific genes and networks do not get enriched in this network mining approach. The difference between cancer and normal tissue networks indicates that despite the different tissue sources and different cell types, cancer cells are more similar in their physiological activities, whereas normal cells are more distinct and specific to their own cell- type specific activities.
It has been found that several immune response gene co-expression networks are present in the multiple cancer microarray datasets [9], and this is confirmed in our results, in which the second largest network (Network 2 of 260 genes) is mostly involved in immune response. Protein synthesis is also an important part of cell proliferation, thus the third largest network found in cancer datasets is involved in protein synthesis. In addition, the cancer tissue microenvironment plays a key role in tumorigenesis, tumor development and metastasis. Our search also identified a network of 57 genes (Network 6) that are mostly collagen- related genes, which form an important part of the extracellular matrix and the cancer tissue microenvironment.
The cancer specific networks we identified showed strong prognostic power in breast cancer, glioblastoma, and ovarian cancer patients, especially the cell cycle/proliferation network (Network 1). It outperforms the 70-gene signature in the survival analysis of lymph-node positive cohort, and for a subset of this network (Network 1 before merging step), the performance is even better (Figure S1). However, it is likely that the large size of this network caused problems in the K-means algorithm, and hence the performance was impaired in the GBM and OV prognosis. Instead, smaller networks (Networks 17 and 18), each with only ten gene members, can be useful in GBM and OV prognosis.
It has been shown that chromosomal instability and aneuploidy are typical features of solid tumor cells (reviewed in [69], [70]). Mitotic genes from Drosophila have been used to predict survival for breast cancer patients [71]. Genes from Network 1 of cancer datasets are highly enriched for genome stability maintenance functions such as cell cycle, mitotic apparatus assembly and regulation as well as DDR and cell proliferation. The importance of this co- expression network in cancer has been confirmed by its significant overlap with a number of gene signatures for cell proliferation [9], [22], [23], mitotic division and chromosomal instability [25], [72] (Table 3). Among them, the key spindle formation regulator Aurora-A and TPX2 co- expression were observed in increased abundance in several cancer types (reviewed in [73]). This led us to examine genes in that cluster that have not been shown to be directly involved in DDR or genome stability maintenance in human cells. Genes that are verified to play roles in these functions are potential oncogenes. They may serve not only as candidates of biomarkers, but also as molecular targets of anti-cancer drugs, for example, Aurora-A inhibitors are already under clinical trials [74].
The QCM parameter β and γ initial settings affect the number of networks found and the size of networks. As described in the Materials and Methods, γ is the parameter controlling the selection of the first edge in each network, λ and t control the adaptive threshold of network density. Together these three parameters guarantee a lower bound of density for all networks. β is the threshold for merging networks. High γ generates fewer networks, and high β generates small and tight (high cluster density) networks. In order to obtain tightly clustered networks with relatively small size, we selected γ = 0.8 and β = 0.8 for cancer datasets, and γ = 0.7, β = 0.8 for normal tissue datasets (to accommodate the smaller sample size in each dataset and less total number of datasets available for normal tissues). However, when β and γ are set to 0.5 or above, the results are highly reproducible, which means the predominant networks we found from cancer datasets are always enriched with the same GO term, i.e., cell-cycle/cell proliferation network, immune response and protein synthesis, whereas the networks obtained from normal tissue datasets are always enriched with housekeeping functions such as cellular respiration and protein synthesis. The small set of core genes are identified with β and γ set to high values, as the values of the parameters decrease, more and more genes join the network, but the core genes and the enriched function are still preserved (Table S3, Figure S3). This suggests that the QCM algorithm is very robust in mining the frequent co-expression network in cancer microarray data. Furthermore, for all the γ settings above 0.5, we found very little overlap for the top three co-expression networks identified between cancer microarray datasets and the ones from normal tissue (see Table S3), which strongly suggests that the gene co-expression clusters found in cancer datasets are specifically involved in cancer-related functions and pathways, while the ones found in normal tissues are not.
As we have demonstrated, the QCM network mining approach can be applied to either single or multiple microarray datasets for co-expressed gene clusters. However, there are some intrinsic limitations not only for this QCM algorithm, but also for the co-expression network mining in general. In order to obtain a high level of significance for the Pearson correlation computing between each pair of genes, the dataset has to be in a relatively large size, and contain a good proportion of genes with significant signals readings and variations. Also due to the focus on gene expression correlation study, or transcriptome profiling study, any interaction in the non-transcriptional level, such as interactions in the post-transcription, translation and post-translation as well as DNA replication, will not be captured. This is the major limitation of the co-expression network mining approach per se. Another drawback exists in our current workflow is that we chose Pearson correlation to measure the correlation between any gene pair, which is fast in the computing step. However, in a biology system, the relationship between the expressions of two genes can be non-linear as well, therefore we plan to test an improvement to the method by incorporating the Spearman rank correlation and mutual information (MI) to further investigate and extract the non-linear correlated co- expression clusters among genes.
The NCBI Gene Expression Omnibus (GEO) was queried for cancer microarray datasets prepared from various types of primary tumor biopsy samples, with a sample size of 30 or more in a specific dataset (Table 1). This resulted in 27 cancer microarray datasets of 33 cancer types, including sarcoma, carcinoma, adenocarcinoma, leukemia, lymphoma, as well as brain cancer. For datasets containing normal tissue control samples, they were removed prior to further co-expression network mining. At the same time, we also queried the GEO database for various types of normal tissue microarray with sample sizes of at least 20 for any tissue type, resulting in 7 datasets composed of 9 types of normal tissues (Table 1). If a normal tissue dataset contained diseased tissue data, they were removed before running network mining. For datasets containing multiple tissue types, they were separated into different datasets before computing PCC. The cancer and normal tissue datasets were all from the Affymetrix GPL570 platform to avoid any platform related systematic errors among the datasets. The tissue and cancer types were carefully chosen to avoid bias towards a particular type of cancer or tissue. All datasets were pre-filtered to remove probes without gene annotation, and for genes with multiple probes, we selected the one with the highest expression values. This resulted in 20,827 probes/genes.
Each pair of genes from a specific cancer or normal tissue microarray dataset were computed for Pearson Correlation Coefficient (PCC), and only the gene pairs with high |PCC| values were retained for network construction. However, since the range of |PCC| values varies substantially among different datasets, we cannot select a uniform threshold on the |PCC| values. Instead, we adaptively set the threshold for |PCC| values to the top 5% (95 percentile) in each dataset to select the ones with high confidence (all the selected PCC have p-values less than 0.05). The frequency of such gene pairs in either cancer datasets or normal datasets was used as the edge weight for network mining using a greedy quasi-clique discovery algorithm called Quasi-Clique Merger (QCM) [16]. QCM is an iterative greedy algorithm. At the initial step, the edge with largest weight in the entire work is identified and its weight is designated as . Then for every iterative step, a new network is established with the first edge being the edge with the largest weight that is not contained in any previously established networks. In addition, the weight of this network cannot be smaller than (0<γ<1), otherwise the program stops. Once the first edge for a network is identified, new edges which can contribute most to the total density of the selected network will be added one a time. During this process, the density of selected network will gradually reduce. The process will stop if the edge of choice will drive the density of the network below an adaptive threshold defined by two parameters t and λ. Once the iteration is over, networks with overlap ratio above a re-defined threshold β will be merged iteratively and form a large network. The overlap ratio is defined as the ratio between the number of shared genes between two networks and the number of genes in the smaller network. The algorithm was implemented in C++, with the hierarchical clustering step omitted. The parameters were set as follows: t = 1.0, β = 0.8–0.9, λ = 2.0, γ = 0.5–0.9. The density of a weighted network with N vertices was defined as: with wij being the weight between vertices vi and vj (i = 1, 2, …, N; j = 1, 2, …, N; i≠j), normalized between 0 and 1. For randomly selected gene subsets, average gene subset size 10 and 400 were selected from the entire gene pool of Affymetrix HU133 2.0 Plus platform (GEO accession number GPL570), and the network density for each subset was computed using above formula. The random selections were repeated 1000 times for each size, and the average network density was calculated.
HeLa-DR13-9 cells (PuroR) and the pCBASce vector (AmpR) containing disrupted GFP gene and I-SceI were used in the assay as described in [42], [75]. Cells transfected with firefly gene GL2 siRNA were used as the negative control. For the experiment with target gene depletion by RNAi, 1 to 3 independent siRNA molecules were used for each gene (Table S6). The assay was repeated at least three times for each siRNA depletion. Two rounds of transfection were performed following Oligofectamine+siRNA protocol (Invitrogen). On Day 1, HeLa-DR cells (4×104 in a 2 cm2 well) were plated in media of DMEM with 1% Pen/Strep, 10% Bovine Serum and Puromycin final conc. of 1.5 g/ml. On Day 2, the first transfection was performed with 60 pmoles of siRNA with 1.5 µL of Oligofectamine. On Day 3, the cells were transferred to 10 cm2 well dishes. On Day 4, 100 pmoles of siRNA with 3 µg of pCBASCeI expression vector were co- transfected. On Days 5 to 7, the cells were trypsinized and those among 10,000 total cells that expressed green fluorescence were measured using a Becton Dickinson FACSCalibur instrument in the Ohio State Comprehensive Cancer Center's Analytical Flow Cytometry core lab. The pCAGGS vector was used as a control. Both pCBASce and pCAGGS were gifted from M. Jasin of the Memorial Sloan-Kettering Cancer Center.
The assay was done according to [11] on HeLa and Hs578T cell lines. 1 to 3 independent siRNA molecules were used for each gene (Table S6). siRNA and GFP-centrin plasmid [76] transfection was done using Lipofectamine 2000 (Invitrogen) according to the manufacturer's protocol, and cells were fixed 48 hours post- transfection. Either one or three different siRNA were transfected for a target gene. GFP- centrin2 marks centrioles, and these were counted by fluorescence microscopy using a Zeiss Axiovert 200 M microscope. The same GL2 siRNA transfected cells were used as the negative control.
The Breast Cancer dataset (NKI-295 dataset) and clinical information were obtained from the Netherlands Kanker Instituut (NKI) with 295 patients (226 ER+ and 69 ER−, 147 LN− and 148 LN+). The Glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) dataset was downloaded from the TCGA website (http://tcga.cancer.gov/). Among them, 345 patients from GBM and 156 from OV with valid vital status information were used.
For a selected gene list, the gene expressions of a patient form a vector. For testing datasets from different microarray platform, only matched genes from identified networks were used. We then used a K-means clustering algorithm (with distance set as correlation, repeated 100 times) to cluster patients into two groups. The survival time statistics were calculated by log rank and visualized in Kaplan-Meier survival curves [77]. If a patient's vital status is ‘LIVING’, ‘days_to _last_followup’ was used for the survival curve, otherwise, the ‘days_to_death’ was used.
GO enrichment on each the networks identified from QCM was analyzed by ToppGene Suite developed by the Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center (BMI CCHMC) (URL http://toppgene.cchmc.org). PPI networks were constructed using Ingenuity® Systems (IPA, http://www.ingenuity.com) with only validated physical protein-protein interactions extracted from the Ingenuity Knowledge Base using cancer network genes as input. PINA (Protein Interaction Network Analysis) data were used to compute the significance of protein-protein interactions in a specific network gene set. PINA integrates protein-protein interaction data from six curated public PPI databases and builds a comprehensive, non-redundant protein interaction dataset to look for interacting gene pairs [78]. For a cancer network being tested, we first query its genes in PINA database for known PPI relationship, and the significance of the number of hits in the PINA database was measured using hypogeometric test implemented in Matlab. Total of 73,472 gene pairs from PINA was used in the hypogeometric test. In addition, we also compared the tested cancer network with randomly selected networks. Specifically, we generated a randomly selected gene list (from the entire gene set of Affymetrix GPL570 platform) with the same number of genes as the cancer network, and then queried in PINA database for this random list and counted how many hits (known PPI relationships in PINA) can be detected. This random test was then repeated 500 times and the number of hits in the 500 tests was used to estimate a null distribution of PPI hits in PINA database. It was then used to compute the z-score for the number of hits for the two true cancer networks (network 1 and network 6). The z-score is the measurement of how many standard deviations the observed value is away from the mean, indicating the statistical significance of PPI enrichment in this case.
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10.1371/journal.pntd.0002011 | Implementation of a National Reference Laboratory for Buruli Ulcer Disease in Togo | In a previous study PCR analysis of clinical samples from suspected cases of Buruli ulcer disease (BUD) from Togo and external quality assurance (EQA) for local microscopy were conducted at an external reference laboratory in Germany. The relatively poor performance of local microscopy as well as effort and time associated with shipment of PCR samples necessitated the implementation of stringent EQA measures and availability of local laboratory capacity. This study describes the approach to implementation of a national BUD reference laboratory in Togo.
Large scale outreach activities accompanied by regular training programs for health care professionals were conducted in the regions “Maritime” and “Central,” standard operating procedures defined all processes in participating laboratories (regional, national and external reference laboratories) as well as the interaction between laboratories and partners in the field. Microscopy was conducted at regional level and slides were subjected to EQA at national and external reference laboratories. For PCR analysis, sample pairs were collected and subjected to a dry-reagent-based IS2404-PCR (DRB-PCR) at national level and standard IS2404 PCR followed by IS2404 qPCR analysis of negative samples at the external reference laboratory.
The inter-laboratory concordance rates for microscopy ranged from 89% to 94%; overall, microscopy confirmed 50% of all suspected BUD cases. The inter-laboratory concordance rate for PCR was 96% with an overall PCR case confirmation rate of 78%. Compared to a previous study, the rate of BUD patients with non-ulcerative lesions increased from 37% to 50%, the mean duration of disease before clinical diagnosis decreased significantly from 182.6 to 82.1 days among patients with ulcerative lesions, and the percentage of category III lesions decreased from 30.3% to 19.2%.
High inter-laboratory concordance rates as well as case confirmation rates of 50% (microscopy), 71% (PCR at national level), and 78% (including qPCR confirmation at external reference laboratory) suggest high standards of BUD diagnostics. The increase of non-ulcerative lesions, as well as the decrease in diagnostic delay and category III lesions, prove the effect of comprehensive EQA and training measures involving also procedures outside the laboratory.
| Buruli ulcer disease (BUD), the third most common mycobacterial disease worldwide, is treated with standardized antimycobacterial therapy. According to WHO recommendations at least 50% of cases should be laboratory confirmed by polymerase chain reaction (PCR). In a previous study PCR analysis of clinical samples from suspected BUD cases from Togo and external quality assurance (EQA) for local microscopy were conducted at an external reference laboratory in Germany. The relatively poor performance of local microscopy as well as time and effort associated with shipment of clinical samples abroad necessitated the availability of a local BUD reference laboratory and the implementation of stringent EQA measures. All processes in the laboratories as well as in the field were defined by standard operating procedures, microscopy conducted at regional facilities was subjected to EQA at national and external reference level, and PCR samples were analyzed in parallel at national and external reference laboratories. Inter-laboratory concordance rates of >90% and case confirmation rates of 50% (microscopy) and >70% (PCR) respectively suggest high standards of BUD diagnostics. Furthermore, an increase of non-ulcerative lesions and a decrease in diagnostic delay and category III lesions reflect the impact of comprehensive EQA measures also involving procedures outside the laboratory on the quality of BUD control.
| Buruli ulcer disease (BUD), caused by Mycobacterium ulcerans, is an infectious disease affecting skin, soft tissue and bones. If left untreated, extensive destruction of tissue followed by fibrous scarring and contractures may lead to severe functional limitations [1]–[6]. BUD is treated with rifampicin and streptomycin (or clarithromycin) for eight weeks if necessary followed by surgical interventions; the laboratory confirmation of clinically suspected BUD cases prior to treatment has become an integral part of clinical management. Whereas microscopy is an appropriate and cost-effective first-line test for peripheral laboratories, PCR is considered the method of choice and WHO recommends PCR confirmation of at least 50% of suspected BUD cases [3], [7]–[13]. Microscopy and various PCR assays have been successfully implemented in other endemic countries and case confirmation rates of 29–78% (microscopy) and 54–83% (PCR) were reported [10], [12]–[32].
Since the early 1990s, close to 2,000 BUD cases were reported from Togo. However, due to the lack of local diagnostic laboratory capacity, the majority of these cases remained unconfirmed [7], [13], [33]–[35].
From 2007 through 2010, a joint research project between the German Leprosy and Tuberculosis Relief Organization, Togo office, Lomé, Togo (DAHWT) and the Department of Infectious Diseases and Tropical Medicine (DITM), University Hospital, Ludwig-Maximilians-University, Munich, Germany, allowed the first systematic study on laboratory confirmation of BUD cases from Togo and proved the prevalence of BUD in South Togo (region “Maritime”). The study revealed a relatively poor performance of local Ziehl-Neelsen microscopy, suggesting the need for a stringent system for external quality assurance (EQA) including regular supervision of microscopy laboratories. Intensified training measures in the area of sample collection resulted in a PCR case confirmation rate of 70%. Effort and turnaround time associated with shipment of samples to an external reference laboratory, however, necessitated the availability of local laboratory capacities [13].
In the context of the EC-funded research project “BuruliVac” (FP7/2010–2013; grant agreement N° 241500), the implementation of a national reference laboratory for BUD in Togo was envisaged. Therefore, from January 2011 through April 2012, microscopy and PCR facilities were established at the “Institut National d'Hygiène” (INH), Lomé, Togo.
This study describes the approach to implementation of a national reference laboratory and analyzes the impact of intensified EQA and training measures on laboratory diagnosis and control of BUD in Togo.
Ethical clearance was obtained through the national Togolese ethics committee (“Comité de Bioéthique pour la Recherche en Santé”) at the University of Lomé (14/2010/CBRS) and the study was approved by the “Ministère de la Santé de la République Togolaise” Lomé, Togo (Ref. No. 0009/2011/MS/DGS/DPLET). All samples analyzed in this study were collected for diagnostic purposes within the EC funded research project “BuruliVac”. Written informed consent was obtained from all study participants.
This study constitutes a collaborative project between several Togolese and German institutions. Since 2007, the German Leprosy and Tuberculosis Relief Organization (DAHW) has supported the Togolese National Buruli Ulcer Control Program (“Programme National de Lutte contre L'Ulcère de Buruli – Lèpre et Pian” [PNLUB-LP]) in the area of training, laboratory confirmation and treatment of BUD. In this study, the main tasks of DAHWT, as partner of the “BuruliVac” consortium were field work, recruitment of study participants, and collection of diagnostic samples. The tasks of DITM – an accredited laboratory according to DIN EN ISO 15189 - as lead partner for all patient related activities of the “BuruliVac” project consisted of implementation of molecular diagnostic laboratory methods at the designated national Togolese BUD reference laboratory and standardization of all processes through on-site training, standard operating procedures (SOPs), and EQA of microscopy and PCR (by standard gel-based IS2404 PCR and IS2404 quantitative real-time PCR [qPCR]) including supervisory visits. Patients with suspected BUD were referred to peripheral health posts (“Unité de Soins Périphérique”, USP; operating on district level as point of care facilities with a catchment area of 5,000–9,000 inhabitants depending on the number of facilities per district), or a regional hospital (“Centre Hospitalier Régional [CHR] de Tsévié”, region “Maritime”, Togo, since 2007 national reference centre for BUD in Togo; catchment area: 2,599,955 inhabitants) for diagnosis and treatment; CHR conducted microscopic analysis. The “Institut National d'Hygiène” (INH), Lomé, Togo – a laboratory accredited by COFRAC (“Comité Français d'Accréditation”) according to NF EN ISO/CEI 17025 (version 2005) – constitutes the national Togolese reference laboratory for surveillance of transmissible, especially outbreak prone diseases, and has been nominated national reference laboratory for Buruli ulcer disease in 2010 [13]. In this study, INH resumed EQA for microscopy conducted at regional level and – after installation of a BUD PCR laboratory – PCR assessment of diagnostic samples by means of a dry-reagent-based PCR [21], [25], [29]. In March 2011, INH joined the WHO network for laboratory confirmation of BUD and – like DITM – participates in the annual program for external quality assessment of molecular detection of M. ulcerans in clinical specimens provided by the Mycobacteriology Unit, Microbiology Department, Institute for Tropical Medicine, Antwerp, Belgium, WHO Collaborating Centre for the diagnosis and surveillance of M. ulcerans infection [36].
In each of the six districts (Golfe, Ave, Zio, Yoto, Vo, Lac) of the region “Maritime”, five districts (“Direction de District Sanitaire” [DDS] 1–5) of the region “Lomé Commune” where BUD was proven to be endemic [13] and the four districts of the region “Central” (Blitta, Sotouboua, Tchaoudjo, Thamba), where BUD has been assumed to be endemic, outreach teams (“CLT teams”) consisting of district controllers (“Contrôleur Lèpre-TB-Buruli”, CLT), USP staff (“Infirmière du Centre Peripherique”, ICP) and community health workers (“Agent de Santé Communautaire”, ASC), and village nurses were formed and trained by experienced PNLUB-LP, CHR, DITM and DAHW staff. The main tasks of the CLT teams are supervision of USPs, as well as sensitization and screening activities in the field which are mostly conducted under participation of DAHW and CHR staff and in collaboration with PNLUB-LP and the non-governmental organization Handicap International. In particular the ASCs who are trained and continuously supervised by the respective CLTs constitute an integral part of the outreach activities. They organize quarterly sensitization activities and present educational films and information material in villages within proven or assumed areas of endemicity. Villagers are instructed to report to their local ASCs in case of wounds or other lesions suspicious for BUD, thus ASCs represent the primary contact person for the population on community level. Furthermore, ASCs organize regular screening programs in village schools to identify suspected BUD cases in the field. The final decision on referral of suspected BUD cases to USPs or CHR for further diagnosis and treatment lies with a superordinate “BUD team” consisting of medical staff (physician, nurse) from CHR, ASCs, and the regional CLT. Visits to field sites are conducted on demand of district CLT teams according to a schedule elaborated by the ASCs. A routine reporting system between ASCs, ICPs, CLTs and CHR staff has been established and to facilitate communication within and between CLT teams and BUD teams a mobile phone network has been implemented by DAHW in 2010.
Data collection was conducted by means of the WHO “BU01” form [3] and standardized project specific laboratory data entry forms (Form S1). All clinical, epidemiological and laboratory data including EQA results were entered in a web-based database specifically designed for the “BuruliVac” project.
Diagnostic samples were collected according to standardized procedures. Briefly, swabs were collected by circling the entire undermined edges of ulcerative lesions. Three millimeter punch biopsies and fine needle aspirates (FNA) were collected from the center of non-ulcerative lesions or from undermined edges of ulcerative lesions including necrotic tissue. To facilitate sampling, standardized specimen collection bags including swabs, biopsy punches, syringes and needles, slides, containers with transport media (700 µl [swab and punch biopsy samples], 300 µl [FNA samples] CLS [cell lysis solution, Qiagen, Hilden, Germany] for PCR samples) and data entry forms were provided to the study sites [13], [23], [25], [26], [29], [37]–[41].
Samples for PCR analysis were transported in CLS at ambient temperature in an upright position in custom-made specimen collection bags from the field to INH by DAHWT cars within a maximum of 48 hours following sample collection. Upon arrival of PCR samples at INH these were stored at 4–8°C until further processing. Slides for microscopy were transported in slide boxes at ambient temperature to CHR and subsequently to INH.
Direct smears for microscopy were prepared from swab and FNA samples at USPs or CHR and subjected to Ziehl-Neelsen staining at CHR. Slides were analyzed according to the WHO recommended grading system [42].
For PCR analysis DNA was prepared using the Gentra Puregene DNA extraction kit (Qiagen, Hilden, Germany) with minor modifications of the manufacturer's protocol [21].
Three IS2404 PCR formats (dry-reagent-based [DRB] IS2404 PCR [INH], standard gel-based IS2404 PCR and IS2404 qPCR [DITM]) were applied in this study. Briefly, for DRB-PCR the oligonucleotides MU5 and MU6 were lyophilized in reaction tubes. Illustra PuReTaq Ready-To-Go PCR beads (GE Healthcare, Munich, Germany) were added and dissolved in water before adding template DNA [21], [25], [26]. Standard IS2404 PCR was performed according to the protocol described by Stinear et al. [15], [17]. IS2404 qPCR was performed as recently described using a BioRad CFX96 real-time PCR detection system [27], [43]. All PCR assays included negative extraction controls, positive, negative (no template) and inhibition controls.
Implementation of diagnostic laboratory facilities at INH was accomplished in several phases. Before launching the national BUD reference laboratory at INH in January 2011, laboratory assessment of diagnostic samples from “BuruliVac” study participants was conducted at CHR (microscopy) and DITM (PCR) respectively (“initial phase” [phase I] from September 2010 through December 2010). To implement standardized BUD microscopy and PCR services at INH, all required equipment, reagents and consumables were shipped to Togo by DAHWT and installed under supervision of DITM staff from November through December 2010. Subsequently, the transitional phase (phase II) was initiated in January 2011. All relevant laboratory procedures were defined in SOPs (SOP S1–S4). An initial laboratory training workshop was held by DITM staff, and INH staff was familiarized with the principles of standardized documentation of samples and corresponding results (laboratory data entry forms, web-based database), the flow of information between the participating laboratories, and the principles of EQA as outlined below. Whereas during the transitional phase from January 2011 through April 2012 parallel diagnostic samples of all study participants were simultaneously subjected to PCR analysis at INH and DITM, the final phase (phase III) of PCR implementation (ongoing since May 2012) provides for diagnostic PCR conducted independently at INH accompanied by EQA on DNA extracts at DITM. (Figure 1)
During the initial phase EQA was conducted for microscopy only. Slides were read at CHR by two readers, forwarded to DITM for blinded re-reading [13], and both, CHR and DITM results were entered in the web-based database. In case of discordant results between CHR and DITM, slides were subjected to a second re-reading at DITM which determined the consensus result.
During the transitional phase CHR conducted the first reading of slides by two readers, entered a consensus result in a specific result form (Form S1), and forwarded slides and forms to INH (first controller) for blinded re-reading. INH consensus results were also determined by two readers and entered in a specific result form (Forms S2). Finally, CHR and INH results were entered in the web-based database by INH data managers. In case of discordant results the respective slides were re-read by both, CHR and INH staff, and a consensus result was determined. Subsequently, slides were forwarded to DITM (second controller) for blinded re-reading, and DITM results were entered in the web-based database. Slides with discordant results between DITM and INH were re-read by DITM and INH staff during DITM supervisory visits.
For EQA of PCR all clinical samples were collected in pairs and were simultaneously tested at INH (DRB-PCR) and DITM (standard IS2404 PCR, confirmatory IS2404 qPCR on negative samples). Results were entered in the web-based database. In case of discordant results both laboratories repeated PCR analyses. If the result did not alter, DNA extracts of the respective samples were exchanged and re-tested at both laboratories.
In accordance with a previous study on EQA for the laboratory diagnosis of BUD in Ghana [23] microscopy positivity rates (i.e. number of positive samples divided by the total number of samples tested) at CHR, INH, and DITM, PCR positivity rates at INH and DITM, rates of false negative and false positive results compared to DITM results and inter-laboratory concordance rates between CHR/INH/DITM for microscopy and INH/DITM for PCR were determined for the initial and transitional phases. In addition, case confirmation rates (i.e. number of laboratory confirmed BUD patients divided by the total number of suspected BUD cases) were determined for CHR (microscopy), INH and DITM (microscopy and PCR).
To assess the impact of the local reference laboratory and continuous EQA measures on BUD control, the clinical parameters “type of lesion”, “category of lesion”, and “duration of disease before clinical diagnosis” (i.e. the mean duration of disease in days based on the time from first recognition of clinical symptoms by patients and availability of the clinical diagnosis BUD) were analyzed and data obtained from the current study cohort from January 2011 through April 2012 after implementation of the national reference laboratory were compared to data obtained in a previous study from September 2007 through December 2010.
INH forwards all laboratory results directly to CHR, the subsequent reporting chain includes regional CLTs, district CLTs, ICPs, and ASCs. Laboratory confirmed BUD patients are subjected to treatment. In case of negative laboratory results in general the treatment decision is referred to the BUD team. For the purpose of documentation, lesions of all confirmed patients are photographed; the material is available for training and sensitization activities.
The study design was non-randomized and cross-sectional.
Approximative tests (χ2-tests) including analysis for linear trends in proportions and t-tests as parametric test were conducted using Stata software, version 9.0. (Stata Corporation, College Station, TX) and EpiInfo, version 3.3.2. (Centers for Disease Control and Prevention, Atlanta, GA). Significant differences were defined as not overlapping of 95 percent confidence intervals (95% CI) of proportions.
Altogether 16 workshops with 559 participants (“CLT teams” as well as other medical and paramedical staff) addressing clinical picture, laboratory diagnosis and treatment of BUD were held in the regions “Maritime” and “Central”. Since 2011, the CLT teams conducted sensitization activities in 1027 villages and screened a population of approximately 110,000. Out of 192 persons with lesions suspicious for BUD identified in the field, 82 suspected BUD cases were finally referred to USPs or CHR. (Table 1)
During the initial phase, 17 slides (swab, n = 6; FNA, n = 11) obtained from 16 suspected BUD cases (ten non-ulcerative lesions: one FNA sample per lesion; six ulcerative lesions, one swab sample per lesion and one additional FNA sample from one lesion with scarred edges) were analyzed at CHR and subjected to EQA at DITM.
During the transitional phase, 72 slides (swab, n = 24; FNA, n = 48) obtained from 66 suspected BUD cases (38 non-ulcerative lesions: one FNA sample per lesion; 28 ulcerative lesions: one swab sample each from 18 lesions, one swab and one FNA sample each from six lesions, one FNA sample each from four lesions) were analyzed at CHR and subjected to EQA at INH and DITM. (Table 2)
During the initial phase positivity rates of microscopy were 41.2% (7/17) at CHR and 47.1% (8/17) at DITM with 5.9% (1/17) false negative results from CHR, and an inter-laboratory concordance rate of 94.1% (16/17) between CHR and DITM.
During the transitional phase positivity rates of microscopy were 47.2% (34/72) at CHR, 48.6% (35/72) at INH and 55.6% (40/72) at DITM. The rate of false negative test results was 9.7% (7/72) at CHR and 6.9% (5/72) at INH, and 1 out of 72 slides (1.4%) was read false positive at CHR. Concordance rates between laboratories were 94.4% (68/72) for CHR/INH, 88.9% (64/72) for CHR/DITM and 93.1% (67/72) for INH/DITM.
The concordance rate between CHR and DITM for both phases was 89.9% (80/89). (Table 3)
During the initial phase, 35 samples (swab, n = 6; FNA, n = 16; punch biopsy, n = 13) obtained from 16 suspected BUD cases were subjected to standard PCR at DITM, all negative samples (n = 12) were additionally subjected to qPCR.
During the transitional phase, 99 sample pairs (swab, n = 33; FNA, n = 44; punch biopsy, n = 22) obtained from 66 suspected BUD cases were subjected to PCR at INH and DITM, which equals a mean rate of 3.0 (198/66) samples tested per patient. All negative samples (n = 30) were additionally subjected to qPCR. (Table 4)
During the initial phase the positivity rate of standard PCR at DITM was 65.7% (23/35). Confirmation of two out of 12 negative samples by qPCR provided an additional diagnostic yield of 5.7%.
During the transitional phase positivity rates of conventional PCR assays were 65.7% (65/99) at INH and 69.7% (69/99) at DITM. The rate of false negative test results at INH was 4.0% (4/99; 1 swab sample and 3 FNA samples), there were no false positive results, and the inter-laboratory concordance rate was 96.0% (95/99). Confirmation of 6 out of 30 negative samples by qPCR provided an additional diagnostic yield of 6.1%. (Table 5)
The case confirmation rates for microscopy were 31.3% (5/16) at CHR and 37.5% (6/16) at DITM during the initial phase, and 43.9% (29/66) at CHR, 47.0% (31/66) at INH, and 53.0% (35/66) at DITM during the transitional phase. In total 50.0% (41/82) of the suspected BUD cases were confirmed by microscopy. (Table 3)
The case confirmation rates for PCR were 75.0% (12/16) at DITM during the initial phase, and 71.2% (47/66) at INH and 78.8% (52/66) at DITM (including two cases additionally confirmed by qPCR) during the transitional phase. In total 78.1% (64/82) of the suspected BUD cases were confirmed by PCR. (Table 5)
Out of 64 laboratory confirmed BUD patients, 51.6% (33/64) had non-ulcerative lesions (plaque, n = 17; nodule, n = 10; papule, n = 1; edema, n = 5) and 48.4% (31/64) had ulcerative lesions, 48.4% (31/64) were male, and 48.4% (31/64) were in age group 5–14 years (age range 2–68 years, mean 18.1 years, median 13 years). Figure 2
The confirmed BUD patients originated from four districts of region “Maritime” (Yoto, n = 37; Zio, n = 22; Vo, n = 1; Golfe, n = 1), two districts of region “Plateaux” (Anié, n = 1; Ogou, n = 1) and one district of region “Savanes” (Dapaong, n = 1). The categories of lesions according to WHO classification [3] were as follows: 43.8% (28/64) category I, 40.6% (26/64) category II and 15.6% (10/64) category III. (Table 1)
All patients with suspected BUD (n = 82) who presented in Togo during the study period were included (no refusals to participate) and clinical samples were collected and analyzed from all of them. All laboratory confirmed BUD patients (n = 64) received a full course of treatment with rifampicin and streptomycin; in addition, six patients, despite negative laboratory results, were subjected to antimycobacterial treatment based on strong clinical suspicion of BUD. Although no regular outreach activities were conducted in region ”Plateaux” and ”Savanes“ patients from both regions were referred to CHR for treatment.
The number of patients with non-ulcerative lesions among all PCR-confirmed patients increased significantly (p<0.01) from 37.0% (as determined for the study cohort from 2007–2010, 119 patients) to 50.0% (current study cohort from January 2011 through April 2012, 52 patients).
Compared to the previous study category I lesions increased from 36.9% (95% CI: 28.3–45.6) to 44.2% (95% CI: 30.7–57.7), category II lesions increased from 32.8% (95% CI: 24.3–41.2) to 36.6% (95% CI: 23.5–49.6) and category III lesions decreased from 30.3% (95% CI: 22.0–38.5) to 19.2% (95% CI: 8.5–29.9).
The mean duration of disease before clinical diagnosis decreased from 51.8 (95% CI: 19.0–84.7) to 35.0 (95% CI: 23.5–46.5) days (no significant difference) among patients with non-ulcerative lesions, and significantly from 182.6 [95% CI: 119.2–245.9] to 82.1 [95% CI: 51.3–112.8] days among patients with ulcerative lesions. (Table 6)
Laboratory confirmation of suspected BUD cases, in particular by molecular diagnostic tests, plays a crucial role for clinical management, disease control and research on M. ulcerans.
To achieve the targeted PCR confirmation rate of more than 50% of suspected BUD cases worldwide, WHO has set up a network of external and local PCR reference laboratories [36]. Whereas until the early 2000s laboratory diagnostic services for endemic countries were mainly provided by external reference laboratories, until 2011 six African countries (Ivory Coast, Ghana, Benin, Cameroon, Central African Republic, Democratic Republic of Congo) installed their own reference laboratories upon increasing demand for local diagnostic capacity [6], [10], [11], [18], [20]–[26], [29], [30], [32], [37], [44]–[46]. Due to the absence of laboratory facilities a number of countries still require support from external reference laboratories; in general however, the role of external reference laboratories has shifted to development of improved laboratory techniques for application in endemic countries, technical support and training of local laboratory staff, as well as external quality assurance for newly established reference laboratories [6], [11], [21], [23]–[32], [37]–[40], [43].
As well known from other studies, the implementation of reference level laboratory facilities necessitates multiple provisions in terms of logistics, trained personnel and quality management [11], [23], [47], [48]. In the case of Togo, extensive preparatory work conducted in the context of previous research projects by DAHWT and DITM [13], vast expertise gained from a longstanding cooperation with partners in Ghana [21], [23], [25], [26], [29], [40], as well as continuous exchange of information with other “BuruliVac” partners [6], [32] facilitated the implementation of a national reference laboratory considerably.
Excellent technical skills of INH laboratory staff in conventional and molecular microbiological diagnostic techniques allowed starting laboratory training at an advanced level. All training activities took place at INH; basic laboratory training according to the concept of short-term “training of trainers” workshops in Europe as successfully applied by other external reference laboratories was not required.
In consideration of the existing quality management systems at DITM and INH, special emphasis was given to standardization of all relevant procedures. SOPs defined the interaction of the laboratory with external partners in the field and the external reference laboratory in Germany, as well as all processes within the laboratory, and granted a smooth workflow from the beginning of the project. Standardized documentation of all analyses and results in standardized laboratory forms and the project-specific web-based database facilitated rapid retracing of errors for local and external reference laboratory and allowed targeted training measures.
To measure the quality of diagnostics conducted at INH, we determined concordance rates between local and external reference laboratories. Compared to a previous study [13], the concordance rate for microscopic analysis between CHR and DITM (initial and transitional phase) increased from less than 70% to 90%, and the concordance rate between INH and DITM was over 90% during the transitional phase, suggesting a high standard of microscopy at both, CHR and INH. Compared to previous findings [13], also the case confirmation rate for microscopy increased from 30% (CHR) to 43% (CHR) and 47% (INH), respectively. Likewise, concordance rates between INH and DITM for PCR of swab and punch biopsy samples were over 95%. In this study, instead of testing the same sample subsequently at both laboratories, sample pairs were collected and one sample each was sent to DITM and INH to allow quality control for both, extraction efficiency and amplification. As already observed in other studies, parallel samples – even if collected from the same site of the lesion - may show an inhomogeneous distribution of mycobacteria and may increase the normal inter-laboratory variation regularly observed for weakly positive samples ([23], [49], unpublished data). Therefore, the findings suggest high quality of PCR conducted at INH. With 93% the inter-laboratory concordance rate for FNA samples was slightly lower which may be attributable to dividing FNA samples in two pieces for microscopy and PCR at INH (whereas the entire parallel sample was subjected to PCR at DITM). Consequently, also the case confirmation rate at INH was a little lower (71%) than at DITM (76%). Future EQA of PCR diagnostics is conducted on DNA extracts only, therefore both confounders (sample pairs and divided samples) are excluded.
In addition to conventional gel-based PCR, DITM applied IS2404 qPCR on negative samples which resulted in laboratory confirmation of two additional cases. As real-time PCR facilities are available at INH, implementation of IS2404 qPCR is envisaged for 2013. Laboratories in endemic countries without access to real-time PCR may consider forwarding at least samples from patients with strong clinical suspicion but negative conventional PCR result to an external reference laboratory for confirmatory IS2404 qPCR.
The study also attempted to measure the impact of local laboratory capacity and quality management on BUD control. The increase of the rate of non-ulcerative lesions by 13%, the significant reduction of the diagnostic delay by more than 100 days for patients with ulcerative lesions as compared to a previous study [13] and the reduction of category III lesions from 30.3% to 19.2% may be attributed to an extended quality management system also comprising patient related procedures outside the laboratory and intensified training measures.
Already during the previous study period from 2007 through 2010 CLTs, ICPs, ASCs and other field staff had been trained in 28 workshops with 152 participants. Since 2011, however, training measures achieved a roughly five-fold increase in coverage, and training of teams instead of individuals resulted in a multiplier effect in terms of knowledge transfer which became noticeable also in areas without regular outreach activities through referral of patients to CHR. The availability of trained CLT teams in 11 districts, in particular the ASCs, increased the coverage of sensitization activities and allowed to conduct extensive “information, education and communication” (IEC) campaigns under the guidance of DAHWT and PNLUB-LP in regions “Maritime” and “Central” accompanied by regular outreach activities to identify suspected BUD cases in the field. Finally, supervision of CLT teams by the CHR BUD team in terms of re-examining these patients provided continuous on-site training for CLT teams and enhanced the diagnostic skills of all field staff involved. Feed- back of laboratory results through a newly established reporting chain from INH to community level not only provides the basis for targeted case finding activities in the environment of confirmed patients, but is also conceived as confidence-building measure by ASCs as well as patients and their families. Altogether, the outreach system implemented in 2011 allowed to realize key components of BUD control in the field of early case detection, diagnosis and treatment as defined by the WHO [7], and more than 90% of BUD cases are currently detected through active case finding (opposed to roughly 60% in the previous study).
Whereas these outreach activities resulted in a constant flow of diagnostic samples from suspected BUD cases from peripheral health facilities in region “Maritime” via the regional hospital (CHR) to INH, and the first cases from region “Plateaux” and “Savanes” have been identified, to date no cases from region “Central” have been confirmed.
Since June 2012, a cooperation agreement between the “Faculté Mixte de Médécine et de Pharmacie” of the University of Lomé, Togo and the Faculty of Medicine of the Ludwig-Maximilians-University, Munich, Germany, has reinforced the existing diagnostic network through initiation of a collaboration with the “Laboratoire de Biologie Moléculaire et d'Immunologie” (BIOLIM), “Département des Sciences Fondamentales et Biologiques”. BIOLIM will support ongoing EQA measures in the field of quality control, academic and in-service training of local laboratory staff, thus contribute to maintaining sustainable standards in laboratory confirmation of BUD. Furthermore, access to a nationwide laboratory network established in the context of research on HIV and other infectious diseases conducted by BIOLIM will enable operational research on decentralised diagnostics and increase the efficiency of BUD control. [7], [48], [50]
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10.1371/journal.pntd.0006587 | Mapping dengue risk in Singapore using Random Forest | Singapore experiences endemic dengue, with 2013 being the largest outbreak year known to date, culminating in 22,170 cases. Given the limited resources available, and that vector control is the key approach for prevention in Singapore, it is important that public health professionals know where resources should be invested in. This study aims to stratify the spatial risk of dengue transmission in Singapore for effective deployment of resources.
Random Forest was used to predict the risk rank of dengue transmission in 1km2 grids, with dengue, population, entomological and environmental data. The predicted risk ranks are categorized and mapped to four color-coded risk groups for easy operation application. The risk maps were evaluated with dengue case and cluster data. Risk maps produced by Random Forest have high accuracy. More than 80% of the observed risk ranks fell within the 80% prediction interval. The observed and predicted risk ranks were highly correlated (ρ ≥0.86, P <0.01). Furthermore, the predicted risk levels were in excellent agreement with case density, a weighted Kappa coefficient of more than 0.80 (P <0.01). Close to 90% of the dengue clusters occur in high risk areas, and the odds of cluster forming in high risk areas were higher than in low risk areas.
This study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good surveillance tool to guide vector control operations.
| Dengue fever, the most prevalent mosquito-borne viral disease today, is caused by Dengue virus (DENV) and transmitted to human by Aedes mosquitoes, primarily the Ae. aegypti and Ae. albopictus. The key approach to mitigating dengue transmission is to control the Aedes population, and this often involve vector control strategies such as larval source reduction and preventive surveillance that are labour-intensive and require effective deployment of valuable resources. Spatial risk profiling of dengue transmission is therefore necessary to ensure the optimal utilization of limited resources, and achieving maximum impact of dengue vector control. Here, we developed a dengue risk map by stratifying the spatial risk of dengue transmission in Singapore. Random Forest was used to predict the risk rank of dengue transmission in 1km2 grids, and the predicted risk ranks are then categorized and mapped to color-coded risk groups. The dengue risk map is a good surveillance tool to guide vector control operations. Valuable resources can be deployed in a strategic manner, mitigating the spread of dengue transmission.
| Dengue is a viral infection caused by one of the four closely related yet antigenically distinct virus serotypes (DENV-1, DENV-2, DENV-3 and DENV-4), and transmitted by Aedes mosquitoes, primarily the Ae. aegypti and Ae. albopictus [1,2]. Infection confers lifelong immunity to the infecting serotype [3]. However, it increases risk for dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS), a deadly form that present with severe complications, in subsequent infections [4]. Since the publication of the GBD 2010, it was estimated that 390 million dengue infections occur each year globally, of which 500,000 develop into DHF [5,6]. Dengue poses a substantial public health threat globally, especially throughout the tropical and subtropical regions [7,8].
Located one and a half degrees north of the equator and lying in the dengue belt, Singapore is prone to dengue transmission, with all four dengue serotypes co-circulating and frequent introduction of new genotype virus [9]. Though intensive vector control efforts have successfully suppressed the Aedes population, from an Aedes house index of over 50% in the 1960’s to the present 1–2%, Singapore remains susceptible to dengue outbreaks [10–12]. The increased in human population density and the low herd immunity resulting from sustained period of low dengue transmission are factors that may have contributed to the resurgence of dengue in Singapore [13,14]. A significant amount of funding and resources has been allocated for dengue every year [15]. The estimated economic and disease burden of dengue were 9–14 disability-adjusted life years (DALYs) per 100,000 population and US$41.5 million per annum [16].
A dengue temporal model was developed in 2013 by the Environmental Health Institute, a research institute of the Singapore’s National Environment Agency (NEA) in collaboration with the National University of Singapore (NUS) to aid vector control measures. The model predicts trends and incidence up to 12 weeks ahead, providing early warnings of outbreak and facilitating public health response to moderate impending outbreak [17]. This model was able to accurately project an upward trend of dengue cases in 2013 and 2014, predicting the two major outbreaks [18]. NEA has been using the model in planning vector control and public communication [19]. However, a limitation of the model is the missing spatial resolution as it does not highlight areas with high risk of dengue transmission. Given that NEA’s key strategy in dengue control is preventive surveillance and larval source reduction, a labour-intensive activity that requires effective deployment of a limited pool of skilled vector control officers, spatial risk profiling of dengue transmission is thus necessary for effective deployment of resources, and achieving maximum impact.
In this paper, we describe a new approach for spatial risk stratification of dengue transmission in Singapore. Using Random Forest, we quantify the risk of dengue transmission in different areas and categorize them into different risk groups to guide the pre-emptive source reduction exercise conducted by NEA vector control officers. Predictive performance of the model is evaluated with both dengue cases and clusters.
Proposed by Leo Breiman, Random Forest is an ensemble machine learning method that uses an ensemble of decision trees [20]. In Random Forest, several (N = 1000) bootstrap samples are drawn from the training set data, and an unpruned decision tree fn(x), is fitted to each bootstrap sample. At each node of the decision tree, variable selection is carried out on a small random subset of the predictor variables, so as to avoid the “small n large p” problem. The best split on these predictors is used to split the node. The predicted response is obtained by averaging the predictions of all trees, i.e. 1N∑n = 1Nfn(x) (Fig 1). Random Forest was used to predict the percentile rank of dengue case count in 1km2 grids, with past dengue exposure (total number of cases in previous year, total number of cases in neighbouring grids in previous year and number of non-resident cases in previous year), human population (estimated population density), vector population (estimated ratio of Aedes aegypti mosquitoes out of all Aedes moquitoes—breeding percentage) and environmental data (vegetation index, connectivity index and ratio of residential area). The predicted percentile ranks are then categorized and mapped to four color-coded risk groups (RG1-4, lowest risk as RG1 and highest risk of dengue transmission as RG4) for easy operation application. Although administrative boundaries are more compatible with ground operation, 1km2 grids were used as study units as they are more consistent in area size and do not change over time. We use residential grids exclusively for the analysis and risk mapping. Random Forest analyses were performed using the randomForest package implemented in the R statistical language [21].
Data from 2006 to 2013 were used to parameterize the model, and performance of the model is evaluated with new dengue case data from 2014 to 2016. Apart from visually comparing the risk map and distribution of dengue cases, we applied the following quantitative metrics to evaluate the model: 1. correlation between predicted and observed percentile ranks, 2. coverage of prediction intervals, 3. summary statistics of the number of cases within each risk group, and 4. weighted (square) Kappa agreement coefficients of risk grouping.
In addition to using dengue case data, data on dengue cluster, which indicates possible transmission within the locality, were considered for model evaluation as well. We investigated the odds of clusters forming in high (RG 3 and 4) and low (RG 1 and 2) risk areas, and examined if transmission intensity, comprising of cluster’s growth rate, transmission duration and cluster size differ between high and low risk areas. Differences were analysed using Kruskal-Wallis tests.
Table 1 shows the various risk factors considered for the risk mapping. The risk factors were identified from literature review and examined with historical data [11,22,23]. All data (Dengue, Population and Entomological) were aggregated to the 1km2 grids. The time period used for all variables was January 2006 to December 2016, and their sources are:
Associations between covariates and dengue burden were examined through partial dependence plot. Consistent with our prior knowledge, all covariates are associated with dengue burden, as contrasted by the flat line partial effect of random noise (Fig 2). Among the covariates, the number of residential units, dengue burden in previous year and the breeding percentage in previous year are top-ranked in terms of variable importance (Fig 3), and impose a larger influence on model accuracy, relative to the other covariates. This, therefore, suggests that population density, dengue burden and abundance of Ae. aegypti are significant risk factors for dengue transmission.
The predicted percentile ranks were categorized and mapped to four color-coded risk groups based on the three quartiles so that the number of grids in each risk group is approximately the same. The distribution of risk groups is comparable in all three years, with high risk groups (RG 3 and 4) congregating in the eastern part of Singapore. When dengue cases were overlaid onto the risk maps, we observed good agreement between the cases and risk groups (Fig 4). Majority of the cases fell in risk group 3 and 4. There was strong positive correlation between the observed and predicted risk ranks, a correlation of 0.86 (P <0.01), 0.87 (P <0.01) and 0.88 (P <0.01) in 2014, 2015 and 2016 respectively. In addition, the risk level commensurate with case density. The predicted risk levels were in excellent agreement with the case density, a weighted Kappa coefficient of 0.814 (P <0.01) in 2014, 0.839 (P <0.01) in 2015 and 0.821 (P <0.01) in 2016. This is further supported by the increasing trend of dengue case count from risk group 1 to 4 (Table 2). Fig 5 shows the predicted percentile ranks and its 80% prediction interval. 82% and 83% of the observed percentile ranks fell within the 80% prediction interval in 2014 and 2015 respectively. In 2016, 81% of the observed percentile ranks fell within the 80% prediction interval. Overall, cases in 2015 have slightly better agreement with the risk map than in 2014 and 2016.
Evaluation of risk maps with 2014 to 2016 clusters data shows that the number of dengue clusters in high risk areas was almost 8 times the low risk areas (Fig 6). Each year, close to 90% of the dengue clusters were found in high risk areas, which represent 22% of Singapore land area and 50% of residential areas. The odds of cluster forming in high risk areas was higher than in low risk areas for all three years. The odds ratios were 11.1 (P <0.01), 14.6 (P <0.01) and 12.1 (P <0.01) for 2014, 2015 and 2016 respectively. Clusters were further stratified by the number of serotypes into single serotype and multiple serotypes clusters. High risk areas have a larger proportion of multiple serotypes clusters than low risk areas, and interestingly, 3-serotypes clusters were only present in high risk areas, especially in RG4 (Fig 6). Transmission intensity, comprising of cluster’s growth rate, transmission duration and cluster size were significantly different between single serotype and multiple serotypes clusters (P <0.01). Clusters with more serotypes present have a faster growth rate, longer transmission duration and larger cluster size (Table 3). The same characteristics were seen when we grouped the clusters by high and low risk areas. Though there were less clusters in low risk areas, the transmission intensity of clusters in these low risk areas was of no significant difference (P >0.1) when compared with those in high risk areas (Table 3).
Dengue has been endemic in Singapore since its first reported outbreak in 1901 [26]. Though the dengue temporal model is capable of predicting impending outbreaks, it does not indicate where the outbreak will be [17]. As a result, source reduction inspections are conducted on a frequency based on the risk level of the premises types (e.g. construction sites are of higher risk than apartment homes). Spatial risk mapping of dengue transmission is therefore essential for the prioritization and allocation of scarce resources especially manpower need to inspect premises.
Dengue risk map has been developed in many countries as a surveillance tool to enhance public health preparedness for dengue outbreak [27]. Statistical approaches such as logistic regression models, generalized linear models and general additive models were most commonly used to compute risk level and create dengue risk map [28–32]. Although very good predictive accuracy can be achieved from Random Forest, it has yet to be reported in the development of dengue risk map [27]. In this paper, we demonstrated the use of Random Forest, an ensemble learning method that has garnered much interest in the machine-learning community, to develop a dengue risk map with high accuracy and robustness. Studies have shown that Random Forest has excellent performance in classification tasks, and even outperforms its counterparts such as discriminant analysis, neural networks and support vector machines [33,34]. The methodology has several advantages over the traditional approaches, with the utmost advantage being highly tolerant to interactions among the input covariates. Dengue transmission is a multi-factorial stochastic process where often one risk factor is correlated with other risk factors, making it difficult to quantify the effect of a particular risk factor as well as to construct a risk map using classical modelling method such as regression.
The model ranked the overall risk of dengue transmission of different areas in a year and mapped the ranks as color-coded risk groups. By comparing the risk groupings of the grids over the years, NEA could identify recurring risk areas (i.e. grids that are persistently risk group 4 over the years) that are of concerns, fluctuating risk areas (i.e. grids that have fluctuating risk grouping over the years) and even potential risk areas that were not seen in the previous years (i.e. grids whose risk group change from 1 to 4). Evaluation using latest dengue case data showed the model had strong predictive capability. Strong positive correlation between the observed and predicted risk ranks, and an almost perfect agreement between the predicted risk levels and case density were observed. High risk areas are where clusters, in particular multiple serotypes clusters are most likely to occur. However, surprisingly, despite the difference in risk levels, there was no difference in the transmission intensity of clusters in high and low risk areas, and this may be attributed to the presence of small pockets of high Ae. aegypti population within the low risk areas. For instance, construction sites along Flora Road and Belgravia Drive had led to large dengue cluster of size 46 and 35 in traditionally low risk areas in 2014 and 2016 respectively. This, therefore, highlights the importance of ground inspections in identifying high risk sites in low risk areas.
The dengue risk map complements the dengue temporal model in allowing the operation department of NEA to prioritise vector control efforts. While the dengue temporal model provides the time component of when the next outbreak will be, it is thus now possible for NEA to deploy limited resources ahead of time, targeting at the places with high risk of transmission.
There are, however, some limitations to the use of Random Forest, the key on being the model not amenable to interpretation. The Random Forest is an ensemble method―it constructs many “weak” models and then combines them to achieve a “strong” model. There is no explicit formulae-form relationship between risk of dengue transmission and risk factors, making it virtually impossible to decompose a particular prediction output into contribution of risk factors. Understanding that the primary objective is to accurately stratify the risk of dengue transmission liberated us from concerns over interpretability. Nevertheless, the Random Forest model is able to offer some insights about dengue transmission by estimating importance and partial effects of variable at a macro level.
The dengue risk map has become an integral part of Singapore’s dengue control program. The dengue risk map would be generated at the start of each year, and NEA operations would use the risk map as a guide to prioritize resource allocation for dengue control and plan the preventive surveillance activities for the year. Dengue risk map has been used since 2015 by the operational division of NEA to guide targeted preventive interventions. Future work will include incorporating real time data to develop a spatio-temporal risk map.
This study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good tool to guide vector control operations, allowing targeted preventive measures before and in times of dengue outbreak. Valuable resources can then be deployed in a strategic manner, mitigating the spread of dengue transmission.
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10.1371/journal.pntd.0004434 | Population Genetics of Plasmodium vivax in Four Rural Communities in Central Vietnam | The burden of malaria in Vietnam has drastically reduced, prompting the National Malaria Control Program to officially engage in elimination efforts. Plasmodium vivax is becoming increasingly prevalent, remaining a major problem in the country's central and southern provinces. A better understanding of P. vivax genetic diversity and structure of local parasite populations will provide baseline data for the evaluation and improvement of current efforts for control and elimination. The aim of this study was to examine the population genetics and structure of P. vivax isolates from four communities in Tra Leng commune, Nam Tra My district in Quang Nam, Central Vietnam.
P. vivax mono infections collected from 234 individuals between April 2009 and December 2010 were successfully analyzed using a panel of 14 microsatellite markers. Isolates displayed moderate genetic diversity (He = 0.68), with no significant differences between study communities. Polyclonal infections were frequent (71.4%) with a mean multiplicity of infection of 1.91 isolates/person. Low but significant genetic differentiation (FST value from -0.05 to 0.18) was observed between the community across the river and the other communities. Strong linkage disequilibrium (IAS = 0.113, p < 0.001) was detected across all communities, suggesting gene flow within and among them. Using multiple approaches, 101 haplotypes were grouped into two genetic clusters, while 60.4% of haplotypes were admixed.
In this area of Central Vietnam, where malaria transmission has decreased significantly over the past decade, there was moderate genetic diversity and high occurrence of polyclonal infections. Local human populations have frequent social and economic interactions that facilitate gene flow and inbreeding among parasite populations, while decreasing population structure. Findings provide important information on parasites populations circulating in the study area and are relevant to current malaria elimination efforts.
| In Vietnam, Plasmodium vivax (P. vivax) is the second most frequent human malaria parasite and a major obstacle to countrywide malaria elimination. Knowing the local parasite structure is useful for elimination efforts. Therefore, we analyzed, with a panel of 14 microsatellite markers, 234 P. vivax mono infections in blood samples collected from 4 communities in central Vietnam. Genetic diversity in the population was moderate; a high occurrence of polyclonal infections and significant linkage disequilibrium were detected, suggesting inbreeding or recombination between highly related haplotypes. In addition, both genetic differentiation and population structure was low and only detected between communities at each side of the river. Those results suggest gene flow between study communities with the river defining a moderate geographical barrier. Future studies should determine how this genetic variation is maintained in an area of extremely low transmission.
| Vietnam has been extremely successful in decreasing the country’s malaria burden, thanks to the large scale implementation of control interventions such as insecticide-treated bed nets, indoor residual spraying, and prompt, free-of-charge diagnosis and treatment; the number of cases fell from 130,000 in 2004 to 27,868 in 2014 [1]. Malaria has been virtually eliminated from Northern and Southern Vietnam [1, 2]. In 2014, 80% of malaria cases occurred in nine “hot provinces” where annual incidence peaked at 3.1 cases per 1000, indicating a highly heterogeneous transmission, with hot spots of transmission (mostly in mountainous and forested areas) surrounded by areas of low transmission [1–5]. Vietnam aims at eliminating malaria by 2030 [6]. Such ambitious goal is threatened by P. vivax, whose characteristics (dormant liver forms that relapse weeks or months after clearance of the primary infection and gametocytes production before the occurrence of symptoms) together with the relative high occurrence of sub-patent and asymptomatic infections that remain undetected and thus untreated [3,7–10], make its transmission much more difficult to interrupt than that of P. falciparum. In addition, as already reported, elimination efforts are threatened by the emergence of drug resistance, for P. falciparum to artemisinin derivatives and partner drugs and for P. vivax to chloroquine (CQ) [2, 11, 12].
Current efforts to eliminate malaria are targeted to districts and communes reporting an increased number of malaria cases over time [2, 6]. In this context, understanding parasite genetic diversity and its population structure is relevant for (i) monitoring temporal changes in transmission following control efforts, (ii) elucidating the spatial distribution of parasite populations and predicting outbreaks, population resilience, and the spread of drug-resistant parasites, and (iii) identifying ecological and behavioral risk factors that can inform malaria control and elimination efforts [13–15].
A previous study conducted in Binh Thuan province in central Vietnam reported high levels of genetic diversity (average expected heterozygosity (He) = 0.86) and all infections being multi-clonal despite low transmission [13] (similar to what has been previously reported in South-East Asia) [16].
The aim of this study was to provide baseline data on the P. vivax parasite populations in four rural communities in the Vietnamese Quang Nam province.
Samples were collected from April 2009 to December 2010 in four communities (Fig 1) in the South Tra My district of Quang Nam, Central Vietnam during a prospective cohort study aiming to assess the short- and long-term efficacy of CQ and high-dose piperaquine (PQ) for the treatment of P. vivax mono-infections [12]. Detailed sociodemographic characteristics of the local population have been already reported elsewhere [3]. In 2009, the prevalence of malaria by light microscopy was 7.8%, while by polymerase chain reaction (PCR) prevalence was estimated at 22.6% (ranging from 16.4 to 42.5%), with a high proportion of P. vivax mono infections (43%). Sub-patent infections accounted for 58.7% of all infections, evidencing the existence of a substantial hidden human reservoir of malaria [3].
Malaria transmission is seasonal, and peaks during the rainy season (May to November). Based on data from the Provincial Malaria Station, between 2009 and 2013 the mean prevalence of P. falciparum, P. vivax, and mixed malaria cases for all age groups in the study area was 64.1%, 31.5%, and 4.4%, respectively [17–18]. The main malaria vectors in the area are Anopheles dirus sensu stricto and An. minimus, though An. vagus, An. aconitus, and An. philippinensis are also present [17–18].
A finger prick blood sample was collected at day 0 (before treatment) for diagnosis by light microscopy and two blood spots were collected on grade 3 filter paper (Whatman Ltd., Springfield Mill, Maidstone, United Kingdom) for molecular diagnosis and microsatellite (MS) genotyping.
The study was approved by the National Institute of Malariology, Parasitology and Entomology in Hanoi, the Ministry of Health of Vietnam, and the review boards of the Institute of Tropical Medicine and Antwerp University Hospital (UZA) in Antwerp, Belgium. Adult participants (in case of minors one of the parents/guardians) provided written informed consent.
Thick and thin film blood slides were stained with a 3% Giemsa solution for 45 minutes, and the number of asexual parasites was calculated following World Health Organization (WHO) guidelines [19]. Parasite density was estimated by dividing the number of asexual parasites for 200 white blood cells (WBCs) counted and expressed as the number of asexual parasites per microliter of blood, assuming 8000 WBC/μL. All blood slides were double-read by two technicians and in case of disagreement, slides were read by a third senior technician. The final results were expressed as the mean of the two closest results. A slide was declared negative if no parasites were found after counting 1,000 WBCs.
DNA was extracted from filter paper blood spots cut into 5-mm-diameter disks with the QIAamp DNA Micro Kit following the manufacturer’s recommendations (Qiagen, Hilden Germany). P. vivax mono infections were confirmed by species-specific multiplexed semi-nested PCR, as described by Rubio et al. [20]. All samples were genotyped with 14 MS (MS1-MS10, MS12, MS15, MS20, and PvSal1814) following previously described PCR protocols [13–14]. The PCR products of four MS were pooled and analyzed by capillary electrophoresis in a 3730 XL ABI sequencer (Applied Biosystems) and 1200 Liz was used as the internal size standard. Negative samples were repeated once.
Allele calling was performed using GeneMarker version 2.4.0. After pooling capillary electrophoresis fsa files from all samples, a standard cut-off value of 500 relative fluorescence units was defined and peaks below this limit were considered background noise. In addition, all samples were double-checked manually to confirm true alleles. At each locus the predominant allele (the one giving the highest peak) and minor alleles within at least two-thirds of the height of the predominant allele were scored [13, 15, 21]. Only predominant alleles were used to define haplotypes to ensure an unbiased estimate of minor allele frequency in polyclonal infections [15, 22–24].
Samples were defined as polyclonal if at least one locus presented more than one allele [13–14]. Polyclonal/locus (%) describes the percentage of samples identified as polyclonal by a given MS out of the total number of samples [13]. Multiplicity of infection (MOI), defined as the minimum number of different clones observed in a sample, was estimated by taking the maximum number of alleles at the two most polymorphic markers [13, 25]. Average MOI was defined as the sum of MOIs detected across all samples divided by the total number of samples. Average MOI and the proportion of monoclonal and polyclonal infections were compared with the Kruskal-Wallis and Pearson χ2 test, respectively. A value of p < 0.05 was considered significant. The predominant alleles in each sample were used to calculate the number of haplotypes by GenAlEx 6.5 [26]. Haplotypes that appear only once in the population were defined as unique haplotypes.
Genetic diversity, defined as the probability of observing different genotypes at a given locus in two unrelated parasites, was assessed by calculating the expected heterozygosity (He) for each community using the formula: He = [n/(n-1)*(1-∑pi2], where n is the total number of alleles and p is the allele frequency. He ranges between 0 and 1, with values close to 1 indicating high genetic diversity [27]. Allelic richness, defined as the number of alleles per locus independently of sample size, was calculated using FSTAT v2.9.3 [28]. He and allelic richness were compared between communities using the Kruskal-Wallis test. The presence of bias due to false assignment of predominant haplotypes was investigated by comparing He in the database containing all the alleles and the database containing the predominant alleles [29].
A standardized index of association (IAs), calculated with LIAN v3.5, was used to assess the presence of multilocus linkage disequilibrium (LD) in the parasite population [30]. The significance of the IAs estimate was assessed with Monte Carlo simulation using 10,000 random permutations of the data. To differentiate between clonal propagation and epidemic expansion, we compared LD in the predominant allele dataset and the unique haplotype dataset [31]. Pairwise LD was used to evaluate the physical linkage between loci located within the same contig using the G statistic in FSTAT v2.3.9 [28, 32].
Genetic differentiation between pairs of communities was estimated using a pairwise unbiased estimator of F-statistics FSTAT v2.3.9 with no assumption of Hardy-Weinberg equilibrium within samples [28, 32]. A matrix of p values corresponding to each pairwise FST was calculated after Bonferroni correction, with a value of p < 0.05 considered significant. Crude FST values were adjusted for sample size using Recode Data v. 0.1 [33] and standardized FST estimates were obtained by dividing crude FST values by adjusted FST values. FST estimates ranged from 0 (no genetic differentiation between communities) to 1 (full differentiation).
As a complementary approach, population structure was investigated using the software programs STRUCTURE v2.3.2 [34], CLUMPP [35], DISTRUCT [36], and GENODIVE [37]. STRUCTURE was used to identify clusters of genetically related samples. The number of clusters (K) was set from 1 to 10 with 10 replications per K, and 150,000 Markov Chain Monte Carlo steps after a burn-in period of 50,000 iterations using the admixture model. The loc-prior model was used for accurate inference of population and individual ancestry. Next, we used STRUCTURE HARVESTER v0.6.94 [38] to calculate the most likely number of K clusters. Additional data parsing and formatting of the STRUCTURE output was performed using CLUMPP and DISTRUCT [38–39]. CLUMPP permutes the clusters’ output by performing multiple replicate runs for the selected K. Samples with an average pairwise similarity (H value) of over 85% in one of the K clusters were considered to belong to that particular population; all other samples were considered admixed samples. DISTRUCT performs geographical displays of the aligned cluster assignment. We confirmed the optimal K, i.e. the K with the highest pseudo-F statistic, using AMOVA-based K-means clustering analysis in GENODIVE V2.0b23 (OS X 10.6 operating system). This method divides a number of individuals into an a priori assigned number of clusters (K) in such a way that minimizes within-group diversity and maximizes between-group diversity. The pseudo-F statistic was calculated by setting up simulated annealing runs with 150,000 steps and 50 algorithm repetitions to determine optimal clustering (highest pseudo- F-statistic).
eBURST v3 was used to identify clusters of closely related haplotypes, or haplogroups (HGs), which were defined as haplotypes sharing at least 9 loci from the 14 MS analyzed [40]. Haplotypes unrelated to any haplogroup (HG) were classified as singletons. The relationship between haplotypes following the defined K clusters was further analyzed by PHYLOViz [41]. Finally, to investigate the relationship between geographic and genetic distances in the study population, we performed principal coordinate analysis with the Mantel test for matrix correspondence in GenAlEx 6.5 [26]. Allele frequency was calculated in GenAlEx 6.5 using the predominant allele data set with 14 loci. The existence of a recent population bottleneck was investigated by evaluating the allele frequency distribution in the population (alleles at low frequencies are less abundant in populations with a recent bottleneck) [42–43].
In total, 234 individuals with P. vivax mono infection from the 260 recruited in the original study [12] were successfully genotyped and included in the analysis. Baseline characteristics of study participants are described in Table 1.
Successful genotyping, with at least 12 of the 14 MS, was achieved in 194 patients (82.9%). Allele data were successfully recovered in more than 83% of the samples for all MS except MS20 and Pvsal1814, for which successful amplification was achieved in 66% and 75% of samples respectively.
The MS characteristics are described in Table 2. Overall, genetic diversity was moderate, with an average He = 0.68 (95%CI 0.58–0.77) for all MS. MS3 and MS9 were the least polymorphic markers (He = 0.45 and 0.31 respectively), while MS10 and Pvsal1814 were the most polymorphic markers (He = 0.99 and 0.92 respectively), which were therefore used to calculate MOI. The number of alleles per MS ranged from 3 to 14. All MS had non-significant differences in He values in the database containing all alleles per locus and the predominant allele datasets ruling out bias in the construction of haplotypes from polyclonal infections (p = 0.68). The average number of alleles per locus was 5.5 (95%CI 3.82–7.17), the average number of alleles detected in a sample by any locus was 1.14 (95%CI 1.0–1.27) and the average allelic richness was 5.05 (95%CI 4.11–5.98).
The proportion of polyclonal infections, similar in all communities (p >0.05), was 71.4% (167/234) when all 14 MS were used, but 64.1% (141/220) (N = 220 as 14 samples had missing data for MS10 and Pvsal1814) when only Pvsal1814 and MS10, the most polymorphic markers were used. The same two MS were used to calculate MOI (based on 220 samples with completed data), whose mean in the four communities was 1.91 (95% CI 1.81–2.02), with no significant differences between communities (p = 0.52), age groups (MOI≤15years = 2.0 vs MOI>15years = 1.9, p = 0.50), gametocyte carriage (MOIgametocytes present = 1.93 vs MOIgametocytes absent = 1.80, p = 0.42), sex (MOImale = 1.89 vs MOIfemale = 1.95, p = 0.55), symptomatic vs asymptomatic (defined as fever at enrolment vs no fever at enrolment, MOI = 1.98 vs MOI = 1.80, respectively) (p = 0.10), season (MOIrainy season = 1.90 vs MOIdry seaoson = 1.97, p = 0.64), and ethnic minority (MOICadong = 1.89 vs MOIM’nong = 2.0, p = 0.46). No significant differences were found between the four communities for either level of He (p = 0.08) or allelic richness (p = 0.31).
We identified 101 haplotypes from 144 samples of which 84 haplotypes were defined as unique haplotype with complete genotyping data for 13 MS. MS20 was excluded because it had the lowest successful genotyping rate (66%). Of these haplotypes, 25.7% (26/101) were found in monoclonal infections and 16.8% (17/101) were found in both monoclonal and polyclonal infections; 6.93% of haplotypes (7/101) had a frequency of over 2 in 40 samples and the two most frequent haplotypes were detected in 7.6% (11/144) and 6.2% (9/144) of samples. One haplotype was shared between the four communities, 4 haplotype found in community 1 were also present in community 2 and one haplotype shared between community 3 and community 4.
To evaluate the existence of a recent population bottleneck we analyzed the allele frequency distribution in the population. Fig 2 shows an L-shaped distribution of allele frequencies, as would be expected from neutral evolution.
MS20 was also excluded from the LD analysis to maximize sample size and avoid bias due to an imbalanced number of samples between communities. Hence at least 25% of samples per community were included in the analysis. Significant LD was observed in each community (IAs ranged from 0.10 to 0.17) and in the overall study population (IAs = 0.113, p < 0.001). LD remained significant (IAs = 0.059, p < 0.001) when only the unique haplotypes were used. We then examined patterns of LD between pairs of MS (Fig 3). Pairwise LD was observed between loci located within the same contigs (MS4-MS5, MS7-MS8, and MS12-MS15) and also within different contigs. Even though lower pairwise LD was observed in communities 3 and 4, the fact that the overall LD was significant (p = 0.008) suggests the existence of a clonal parasite population.
We first compared the datasets containing only monoclonal infections (n = 67) with the predominant allele (n = 234) and found low genetic differentiation (FST = 0.05), indicating absence of bias. Then, we calculated FST values for pairwise genetic differentiation between the four communities (Table 3). We observed moderate genetic differentiation between community 4 and the other communities (FST = 0.15–0.18) and low differentiation for the other combinations (FST < 0.1) (n = 144), indicating that the parasite population in community 4 is moderately, although significantly, differentiated from parasite populations in communities 1, 2, and 3 (p = 0.008). Similar FST values were obtained when only unique haplotypes (n = 84) were used.
Structure analysis identified the most likely clusters in the population to be (i) K = 7 (ΔK = 9.4), (ii) K = 2 (ΔK = 5.2), and (iii) K = 3 (ΔK = 3.2) (n = 144). The AMOVA-based K-means clustering analysis identified K = 2 as the optimal number of clusters (pseudo-F = 30.5). We further analyzed the parasite population divided by K = 2 (cluster 1 and cluster 2) with CLUMPP and DISTRUCT (Fig 4), and found that 33.3% (48/144) of the samples (with complete haplotypes) observed in the study population belonged to cluster 1 and 20.1% (29/144) belonged to cluster 2 and 46.6% (67/144) were admixed samples. Community 4 had the highest proportion of admixed samples (62.1%), followed by community 3 (53.9%), while community 1 and 2 had similar rates (40.9% and 41.3% respectively). The proportion of admixed samples remained high when the number of clusters was set to K = 3 and K = 7 (48.6% and 38.9%, respectively). Of note, cluster 1 samples were absent from community 4, which supports a moderate degree of population structure between communities 1–3 and community 4. However, principal coordinate analysis failed to detect geographical clustering (per community) in the population.
Then, we investigated genetic relatedness, defined as haplotypes sharing at least 10/13 loci (n = 101) by eBURST. Eight different HGs and 13 singletons were identified. HG1 contained 51.5% (52/101) of all haplotypes in the four communities, while the other 7 HGs contained between 2.0% (2/101) to 11.9% (12/101) haplotypes. However, when relatedness was defined as haplotypes sharing at least 9/13 loci, only 2 HGs and 2 singletons were identified. HG1 included 95.0% (96/101) of all haplotypes detected. PHYLOVIZ analysis supported the existence of related haplotypes among all study communities (with slightly clustering of community 4 samples) (Fig 5A) and confirmed the absence of cluster 1 haplotypes in community 4 (Fig 5B).
We analyzed the genetic diversity and population structure of 234 P.vivax pre-treatment clinical isolates collected in a forested area of Central Vietnam between April 2009 and December 2010 [12]. We observed moderate levels of heterozygosity in all four study communities, with a high proportion of polyclonal infections and significant LD, suggestive of inbreeding across parasite populations circulating in the study communities. Genetic differentiation and population structure between study communities was low but present between villages at each side of the river defining a moderate geographical barrier to gene flow.
In this study we used eight MS (MS1, MS4, MS6, MS9, MS10, MS12, MS15, and MS20) with balanced diversity, three (MS2, MS5 and Pvsal1814) with unbalanced diversity, one (MS8) with significant excess diversity, and two (MS3 and MS7) with significant reduced diversity [25]. Mean He in the study population (He = 0.68) using those 14 MS was non-significantly different to He when only MS with balanced diversity (recommended for measuring population diversity) were used. Therefore, all MS were kept in the analysis to assess both diversity parameters and polyclonal infections, but only the two most polymorphic markers (MS10 and Pvsal1814) were kept to investigate MOI.
The mean He in our study population (He = 0.68) was similar to figures seen in areas of north-west Brazil with similar transmission intensities (He = 0.74 and He = 0.68) [43–44]; higher than those observed in South Korea (He = 0.43) [45] and the Loreto district, Peru (He = 0.37) [16], and lower than those seen in Sri Lanka (He = 0.89) [46], Pursat, Cambodia (He = 0.84) [16], and Binh Thuan, Central Vietnam (He = 0.88) [13]. In our study MS9, MS3, and MS7 displayed the lowest number of alleles per locus (n = 3) and MS9 and MS3 had the lowest He values (HeMS9 = 0.31 and HeMS3 = 0.45). MS3, MS7, and MS9 would therefore appear to be poorly informative markers in the study area and their use in future studies is not recommended.
Polyclonal infections were frequent (71.4%) when the results from 14 MS were combined and moderately lower (64.1%) when just Pvsal1814 and MS10 were used [47]. Mean MOI was 1.91, with similar MOI observed in symptomatic and asymptomatic study participants, possibly because of the high parasite density (mean 3,919/μL; 95%CI 2,852–4,986) detected in asymptomatic participants at day 0. In the literature, the proportion of polyclonal infections vary considerably depending on the MS markers used [16, 48], highlighting the need for a standardized methodology that allows comparison between studies and geographical regions. High proportions of polyclonal infections have also been reported in hypo-endemic areas in Sri Lanka (60%) [49], Colombia (60–80%) [48], the Amazon Basin in Brazil (50%) [43], and more recently, in a pre-elimination context in Sri Lanka (69%) [46]. It is noteworthy in a study carried out (1999–2000) in Binh Thuan province, central-south Vietnam, where the entomological inoculation rate was estimated at 1 infective bite/person/year, 100% of vivax infections were polyclonal with a mean MOI of 3.7 [13]. The high levels of genetic diversity and polyclonal infections in low transmission areas [13,23,48] can be, at least partially, explained by the unique biology of P.vivax which result in (i) a high prevalence of asymptomatic and low parasite density infections (which last longer because are difficult to detect, increasing the likelihood of repeated infections with divergent clones, resulting in increased polyclonality) and (ii) relapse from dormant liver stages (the reactivation of heterologous clones increases the likelihood of peripheral superinfections). Since a high proportion of study participants were asymptomatic at recruitment (59.0%) and poor adherence to PQ radical cure is known in the study area [3], the high proportion of polyclonal infections found in this study may reflect peripheral superinfection fed by heterologous clones from both relapses and reinfections.
Despite those high rates of polyclonal infections, we observed a significant LD (IAs = 0.113, p < 0.001) in the overall study population. Asexual clones present in one infection produce gametocytes that, taken by the vector, recombine during meiosis and generate new haplotypes in a process known as outcrossing. Consequently, the breakdown of pre-existing associations between unlinked loci would reduce LD to low levels [50] as opposed to recombination between gametes from the same parasite [51]. As transmission decreases, fewer parasite types will be present in the population and recombination will often occur between related parasites, increasing the level of inbreeding in the population. This is supported by the fact that 53.8% of all polyclonal infections were identified by multiple alleles at just one locus. Indeed, LD remained significant in the analysis using only unique haplotypes, indicating that it is a result of inbreeding rather than expansion of few haplotypes due to outbreaks or epidemics [31]. Closely related parasites in hypoendemic areas have been previously reported [52, 53]. In addition, inbreeding was further supported by overall significant pairwise LD [21,31]. LD combined with high levels of polyclonality has been reported in rural Amazonia [54] and more recently in Sri Lanka [46]. The authors of these studies offered two alternative interpretations for this phenomenon. First, the MS may not be strictly neutral (10/14 MS mapping to loci encoding either hypothetical or annotated proteins may be subject to natural selection) [22–23]. And second, replication-slippage events during mitotic (asexual) replication could result in the generation of new alleles due to the addition or deletion of repeats [49,55]. If the replication-slippage rate is higher than that of effective recombination (the probability of producing a recombinant genome), the clones generated would increase polyclonality, without altering LD.
It has been previously reported that replication-slippage events (and therefore number of alleles per locus and He) correlate positively with increasing repeat length and non-perfect repeats motifs, i.e. interrupted or compound motifs [25,56]. Pvsal1814 MS used in this study, which had an (AGA)44 motif structure with an interrupted/compound motif, He = 0.91 and 14 different alleles with frequencies ranging from 1.3% to 16%, identified 53.8% of all polyclonal samples in the study population. Indeed, inherent mutability in this MS has been described to produce excess diversity, which in turn is recommended to identify MOI [25].
We identified 101 haplotypes, of which 84 appeared only once in the population. Ninety percent of them were grouped in a single haplogroup (HG1), defined by identical alleles in at least 9/13 loci, indicating a high degree of relatedness among parasites across the communities. These results support the view that despite a high level of polyclonality, inbreeding among highly related haplotypes maintains LD.
The adjusted genetic differentiation was low between communities 1, 2, and 3 (FST < 0.05) and moderate when community 4 was included (FST = 0.15–0.18), indicating limited geographical boundaries between neighboring communities 1–3 but higher differentiation with the community across the river. In concordance with the FST values, the STRUCTURE analysis detected two main parasite populations. Two clusters of haplotypes, with a high proportion of mixture haplotypes (60.4%) were observed in all four communities. The fact that a majority of haplotypes found in community 4 belonged to cluster 2, which was the minor cluster in the other 3 communities, supports a certain degree of differentiation between communities 1–3 and 4. Moderate population differentiation between these communities can be explained by geographical proximity and socioeconomic relationships between the communities’ inhabitants as previously described [3]. Inhabitants of community 1–3 (located at one side of the river) belong to the Cadong ethnic group and therefore share some degree of kinship, facilitating social exchange. Conversely, community 4, whose inhabitants belong to the M’nong ethnicity, is located at the other side of the river with limited access during the rainy season.
Malaria incidence in the Quang Nam province has dropped by 78.0% over the last decade thanks to the implementation of efficient control strategies [1,17,57]. At the time of the study, malaria prevalence in the study area was 7.8% as assessed by light microscopy and 23.6% as estimated by PCR [3]. Therefore, the moderate-to-high levels of genetic diversity detected, together with the high polyclonality and low population structure are consistent with an epidemiological context of transition from moderate to low endemicity [58–59].
Future studies aiming at identifying changes in genetic diversity and population structure to support the development or improvement of control and elimination interventions should include isolates collected at several time points from all areas where malaria is prevalent (or has been recently eliminated). Ideally, a molecular surveillance system should be implemented within the existing network of sentinel sites for drug resistance across the country to support evaluation of interventions and improve response strategies at the provincial level.
Parasite populations with strong LD and the presence of gene flow could fuel the spread of resistant parasites in the event of the emergence of drug resistance, threatening current treatment efforts and achievements towards malaria elimination in Central Vietnam. Temporal analysis to investigate haplotype persistence and the risk of clonal expansion is urgently needed in order to inform decision makers.
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10.1371/journal.pntd.0006647 | Sphingosine-1-phosphate signaling in Leishmania donovani infection in macrophages | Sphingosine-1-phosphate (S1P) is a crucial regulator of a wide array of cellular processes, such as apoptosis, cell proliferation, migration, and differentiation, but its role in Leishmania donovani infection is unknown.
In the present study, we observed that L. donovani infection in THP-1 derived macrophages (TDM) leads to decrease in the expression of S1pr2 and S1pr3 at mRNA level. We further observed that Leishmania infection inhibits the phosphorylation of sphingosine kinase 1 (sphK1) in a time-dependent manner. Exogenous S1P supplementation decreases L. donovani induced ERK1/2 phosphorylation and increases p38 phosphorylation in TDM, resulting in a decrease in the intracellular parasite burden in a dose-dependent manner. On the other hand, sphK inhibition by DMS increases ERK1/2 phosphorylation leading to increased IL-10 and parasite load. To gain further insight, cytokines expression were checked in S1P supplemented TDM and we observed increase in IL-12, while decrease IL-10 expression at mRNA and protein levels. In addition, treatment of antagonist of S1PR2 and S1PR3 such as JTE-013 and CAY10444 respectively enhanced Leishmania-induced ERK1/2 phosphorylation and parasite load.
Our overall study not only reports the significant role of S1P signaling during L. donovani infection but also provides a novel platform for the development of new drugs against Leishmaniasis.
| Leishmania donovani is an intracellular parasite which is internalized by host macrophages by subverting several intracellular signaling events. During infection suppression of p38 MAPK and activation of ERK1/2 MAPK have been acclaimed for survival and proliferation of these protozoan parasites. In this study, we show novel signaling pathways that interact with these MAPK that further contributes to determine the final fate of the disease. Sphingosine-1-Phosphate (S1P) is a bioactive lipid that binds to a family of G-protein coupled receptors known as S1P receptors. TDM infected with Leishmania donovani showed a decrease in the expression of S1PR1-3. Moreover, the enzyme that catalyzes S1P production, Sphingosine Kinase 1, showed decreased activation in a time-dependent fashion in infected cells. Furthermore, exogenously supplementation of S1P clears intracellular parasite burden by a decrease in ERK1/2 phosphorylation and IL-10 at mRNA and protein level. On the other hand, S1P induces anti-leishmanial response by activating p38 phosphorylation and IL-12 at mRNA as well as at protein level. To further gain insight on the receptors subtypes involved in the anti-leishmanial response, we specifically blocked S1PR2 and S1PR3. In this study, we found a tremendous increase in the parasite load as a result of increased IL-10 secretion and ERK1/2 phosphorylation on combination of these inhibitor doses. Taking together, our study thus suggested the possible involvement of S1PR2-3 during Leishmania donovani infection in human macrophages. These findings thus elaborate our knowledge in understanding the interaction of signaling intermediates during Leishmania infection which may lead to the discovery of novel therapeutic interventions.
| Leishmaniasis is a neglected tropical disease that affects about 12 million people worldwide [1] It is caused by intracellular protozoa parasite Leishmania that invades macrophages and selectively impairs host’s critical signaling pathways for its successful intracellular growth and proliferation [2]. In particular, alteration in lipid metabolic pathways, lipid relocation, modification, and accumulation during Leishmania infection has been proven to be a critical step in the progression of disease [3]. In addition, Leishmania infection leads to increase in ceramide generation that further depletes cholesterol from the membrane and disrupts lipid rafts resulting in weak CD40 mediated signaling that leads to increased ERK1/2 phosphorylation and impaired antigen presentation to the T cells, worsening the diseased condition [4,5]. S1P signaling is emerging as a prominent regulatory pathway in cells that governs myriad of downstream signaling events [6]. The cascade begins with the generation of S1P from sphingosine by the action of sphK. Afterward, S1P translocates to the outer membrane and binds to its receptors, namely S1PR1-5, which triggers the small G-proteins associated with them [7]. These G-proteins then activates different signaling proteins resulting in numerous effectors functions. Being such a crucial regulator of cellular processes, it has been seen that this signaling pathway often dysregulated during several diseases [8,9]. In addition, the possible therapeutic strategy can be designed by carefully monitoring the impairment of this signaling during several diseased conditions [8,9].
S1P signaling has been well established in bacterial and viral diseases. Till date, no study addresses the role of S1P signaling in Leishmania donovani infection in human macrophages, The role of S1P in another intracellular pathogen such as Mycobacterium has been well studied in vitro and in vivo [10]. It was documented that S1P possess antimycobacterial properties such as reduction in intracellular growth by enhancement of phagolysosome acidification, induction of IFN-γ and enhanced antigen processing and presentation in monocytes [10,11]. Apart from this, it was shown that host sphingosine kinase regulates antimycobacterial responses and its inhibition leads to sensitization of RAW 264.7 macrophage to infection due to reduced expression of anti-mycobacterial effector functions such as pp38, inducible nitric oxide synthase (iNOS) and Lysosome-associated membrane protein 2 (LAMP 2) [12]. In Bordetella pertussis infection in mice, S1P mediated signaling through S1PR resulted in reduced pathology due to the infection [13]. Similarly, during Yersinia pestis infection, activation of S1PR1 mediated signaling by SEW2871 limits intra-nodal trafficking of infection [14]. Alveolar macrophages from sphK1-knockout mice showed an increased burden of intracellular fungal, Cryptococcus neoformans [15], which suggests a protective role of S1P against C. neoformans infection. Currently, the role of S1P is unknown during Leishmania donovani infection in human macrophages. Also, the involvement of S1PR mediated signaling during Leishmania infection is not studied till date. Hence, in our study, we examined the role of S1P signaling during Leishmania donovani infection that should be helpful for generation of host-directed therapies against Leishmaniasis.
To explore the role of S1P signaling in Leishmania donovani infection we first analyzed the expression of S1PR1-5 in infected and uninfected TDM. We next checked the phosphorylation of sphK1, the enzyme responsible for S1P production, in both TDM and human monocyte derived macrophages (hMDM). For further studies, we checked the phosphorylation of MAPK such as ERK1/2 and p38 in presence of S1P in infected and uninfected macrophages. In addition, cytokines such as Interleukin (IL) 10 and IL-12 were checked at mRNA and protein levels and parasite load was checked in infected macrophages upon S1P supplementation. For further confirmation of our results ERK1/2 and p38 activation was also studied upon inhibition of sphK or S1P supplementation in hMDM As observed earlier, the expression of S1PR2 and S1PR3 were decreased during the infection, we checked for ERK1/2 phosphorylation, cytokine secretion and parasite load in the presence of S1PR2 and S1PR3 inhibitors, JTE-013 and CAY10444, respectively.
The study was approved by Institutional Ethical Committee (IEC), Jamia Millia Islamia, New Delhi for the human subject participation. Each donor provided written informed consent for the collection of blood and subsequent analysis.
RPMI 1640, M199, Fetal Bovine Serum (FBS), and penicillin and streptomycin were purchased from Life Technologies. DMS (N-N Dimethyl-sphingosine), JTE-013 and CAY10444 were purchased from Cayman chemicals. S1P was purchased from Tocris. Antibodies such as ERK1/2, phospho-ERK1/2, p38, and phospho-p38 were purchased from Cell Signaling Technology. Phospho-sphingosine kinase 1 and total sphingosine kinase 1 antibody was purchased from ECM biosciences. ELISA kit for IL-0 and IL-12 were purchased from BD Biosciences.
The standard strain of L. donovani: (MHOM/IN/83/AG83) was maintained in M199 media (Life Technologies) with 25mM HEPES (Sigma) and supplemented with 10% heat-inactivated Fetal bovine serum (Life Technologies) with 1% penicillin and streptomycin (Life Technologies) at 22°C. Fourth to fifth-day culture was used to infect differentiated TDM or hMDM.
The THP-1 cell line was maintained in RPMI 1640 medium (Life Technologies) supplemented with 10% heat-inactivated FBS (Life Technologies) and 1% streptomycin-penicillin (Life Technologies) at 37°C in 5% CO2. PMA (phorbol 12-myristate 13-acetate; Sigma) was used for differentiation of THP-1 cells into macrophages by incubating cells for 24 hrs with 5 ng/ml PMA at 37°C in 5% CO2 in flat-bottom 6-well tissue culture plates (BD Biosciences).
Peripheral blood mononuclear cells (PBMCs) were isolated from buffy coats obtained from healthy donors (M.A Ansari Health Center, Jamia Millia Islamia, New Delhi, India) on Histopaque (Sigma). PBMCs were seeded on 6 wells plate and allowed to adhere. For differentiation of hMDM, non-adherent cells were removed by gentle washing and adherent cells were incubated with 5ng/ml GM-CSF (Pepro-Tech) at 37°C in 5% CO2 for 24 hrs and replenished with supplemented RPMI 1640 containing 10% FBS for 6 to 7 days.
Macrophages were harvested and distributed into six-well plates at 2 × 106 cells/well. Stationary-phase promastigotes were added to differentiated cells at an infection ratio of 1:10 to for 6 hrs initiate infection. Infected macrophages were further replenished with supplemented RPMI 1640 containing 10% FBS for additional 42 hrs for different studies.
1×105 cells per well were seeded onto 96-wells plate and was treated with PMA for diiferentiation for 24 h. Next day cells were washed and media was replaced with fresh media and cultured for additional 24 h for resting. After 24 h the cells were treated with different inhibitor used in the study DMS (5 μM), JTE-013 (10 μM), and CAY10444 (10 μM) for 42 hours. Cell viability was determined using the MTT cell viability assay. 3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide, MTT (Sigma–Aldrich) was applied at in dark following 4 h incubation at 37°C. The MTT containing medium was replaced with 100 μl of isopropanol-HCl (0.1N) and kept at 37°C for 10 min to solubilize the formazan crystals. The samples were transferred to 96-well plates and the absorbance of the converted dye was measured at 570nm. The percent cell viability of the control (non-treated) cells was taken as 100%.
RNA was extracted by Trizol (Sigma) as per user’s information and was quantified on Biophotometer (Eppendorf, Germany) and 1 μg of RNA was used to prepare cDNA. Levels of Il-10 and Il-12 expressions were determined in the treated and untreated IM by quantitative PCR (qPCR), with β-Actin taken as an endogenous control (Primer sequences in S1 Table). q-PCR was carried out in a final volume of 10μL in Lightcycler 480 (Roche). The reactions were carried out with an initial denaturation step of 10 minutes at 95°C, followed by 40 cycles of denaturation, for 15 seconds, at 95°C, and annealing/extension for 1 minute, at 60°C. Relative gene expression was analyzed by the Livak method.
For parasite load, TDM were seeded on sterile coverslips placed in 6 well culture plates. Infection was given at 1:10 for 6 hrs and infected cells were treated with different concentration of inhibitors/ S1P and cultured for next 42 hrs. Infected cells were washed with PBS and then fixed with ice-cold methanol for 10 min and air dried. Wells were immersed in Giemsa stain for 45 min and washed 2–3 times with PBS. At least 200 cells were observed from a minimum of 15 randomly selected fields for each condition to determine the average number of parasites per macrophage. The parasite load was calculated in percent for the different condition after taking parasite load in control as 100%.
After treatment, macrophages were washed twice with PBS and lysed with ice-cold lysis buffer (50 mM Tris-HCl, [pH 7.4], 150 mM NaCl, 1% Triton-X, 1 mM Sodium Orthovanadate, 10 mM Sodium Fluoride, 1X protease inhibitor cocktail (Cell Signaling Technology). Lysates were centrifuged at 14,000 × g at 4°C for 15 min, and the resulting supernatants were transferred to fresh tubes and stored at −80°C until required. 40–50 μg of protein were used for western blotting.
IL-10 and IL-12-specific enzyme-linked immunosorbent assay (ELISA) was performed to detect the level of secreted IL-10 and IL-12 in the cell-free supernatant obtained from different experiments using ELISA kits as per manufacturer’s instructions.
Immunoblots and PCR products were analyzed using ImageJ software, National Institute of Health, version 1.50i. The band intensity was calculated and normalized to corresponding control.
The results shown are representation from a minimum of three similar experiments which generated reproducible data. The statistical analysis was performed using GraphPad Prism, version 6.0 (GraphPad, San Diego, CA, USA). P-value of less than 0.05 was considered significant. The error bars of the values represent ± SD from the replicates. Tukey's multiple comparisons test and Student t-test were performed to ascertain the significance of the differences between the means of the control and the experimental groups.
The expressions of S1pr1-5 were checked upon Leishmania donovani infection in PMA differentiated human macrophages at mRNA level by semi-quantitative PCR. The macrophages were infection with Leishmania donovani promastigotes (multiplicity of infection [MOI] 1:10, macrophage to parasite) for 6 hours. The non-internalized parasite was removed by gentle washing 2–3 times with PBS. The macrophages were further for additional 42 hours. After the given time, mRNA was extracted and cDNA was prepared using 1μg RNA. In addition, we evaluated the fold change in the expression of these receptors using beta-actin as endogenous controls to normalize the S1P receptors gene expressions. We found the expression of S1pr1-3 in both infected and uninfected macrophages, however, S1pr3 was detected at very low level (Fig 1A). In our study, we found that there was a significant decrease in the expression of S1pr2 and S1pr3 in infected macrophages (Fig 1B), while there was a not significant change in the expression pattern of S1pr1 in infected macrophages. In addition, we couldn’t detect the expression of S1pr4 and S1pr5 in both infected and uninfected TDM (Fig 1A).
The level of sphK1 phosphorylation was checked in infected and uninfected TDM. The proteins were extracted from both the given experiment and analyzed by western blot for the detection of sphK1 phosphorylation. Total sphK1 was used as loading control for both the experiment. In our experiment, we observed that there was a significant decrease in the phosphorylation of sphK1 in the infected TDM (Fig 2A and 2B). Similarly, we also checked in the sphingosine kinase phosphorylation at various time intervals and we found a there was a time-dependent decrease in the phosphorylation level of sphK1 (Fig 2C and 2D). The decrease in the phosphorylation of sphK1 was also observed in hMDM 48h post infection (S1A and S1B Fig).
As sphK1 phosphorylation is necessary for S1P biosynthesis, we checked the effect of S1P supplementation on parasite load, pro-inflammatory and anti-inflammatory response. Firstly, we examined ERK1/2 and p38 activation in uninfected macrophages and infected TDM with L. donovani on S1P supplementation (10 μM) for 48 h. After given time, proteins were extracted and analyzed for the detection of phospho-ERK1/2 and phospho-p38. In our study, we found that S1P supplementation showed diminished ERK1/2 and increased p38 phosphorylation, as measured by western blot analysis (Figs 3A and S2A and S2B).
Next, we checked for the expression of cytokines such as Il-10 and Il-12 at mRNA level by both semi-quantitative PCR and real-time PCR. We found that S1P supplementation decrease Il-10 expression and increases Il-12 expression in infected macrophages (Figs 3B and S2C). In case of infected hMDM, we found that there was an increase in Il-12 expression upon S1P supplementation. However, we also registered a nonsignificant change in Il-10 expression at mRNA level (S4C Fig). We also determine the parasite load in presence of increasing doses of S1P and we found a dose-dependent decrease in the parasite burden in infected macrophages (Fig 3C). The most significant decrease in the parasite load was observed at maximum concentration i.e 10 μM (Fig 3C–3E).
To further validate our results, we checked for ERK1/2 and p38 phosphorylation levels cytokine expression, and parasite load in presence of sphingosine kinase inhibitor, DMS. In case of hMDM, DMS pretreatment increases ERK1/2 phosphorylation, while reduces p38 phosphorylation in infected hMDM (Figs 4A and 4B and S4A and S4B). Additionally, we found that DMS pretreatment in infected macrophages resulted in a significant increase in Il-10 expression, whereas decreased Il-12 expression was observed as compared to infected hMDM (S4C Fig). In uninfected and infected TDM pretreated with 5 μM DMS, we found an increase in ERK1/2 phosphorylation (Figs 5A and S3A). Additionally, cytokines expression were checked by both semi-quantitative PCR and real-time PCR and it was found that Il-10 expression was increased while Il-12 expression was decreased in DMS pretreated infected TDM (Figs 5B and S3B). We further evaluate the parasite load in presence of increasing dose of DMS and we found that DMS pretreatment leads to increase in parasite load in a dose-dependent manner with significant increase in parasite burden at 5 μM (Fig 5C–5E).
As shown in the previous experiment that the expression of S1PR2 and S1PR3 was decreased during Leishmania infection, we further checked for ERK1/2 phosphorylation and parasite load in presence of S1PR2 and S1PR3 specific inhibitors, JTE-013 and CAY10444, respectively [16,17]. We found that pretreatment with S1PR2 or S1PR3 inhibitors leads to a dose-dependent increase in the intracellular parasite load with a significant increase at higher dose i.e 10 μM (S5A and S5B Fig). In addition, ERK1/2 phosphorylation was also studied in JTE-013 (10 μM) and CAY10444 (10 μM) pretreated infected TDM alone and in combination. We found a significant increase in the phosphorylation of ERK1/2 in presence of these inhibitors, alone or in combination which further supports our study (Fig 6A and 6B). We further checked the parasite burden by pretreatment of both the inhibitors in combination or alone and we found that they both in combination contributed to further increase in the parasite burden (Fig 6C–6G).
We next investigated secretion of IL-12 and IL-10 upon modulation of S1P signaling by DMS, S1P supplementation, and S1PR2-3 inhibition, alone or in combination. As expected, we found an increase in IL-12 secretion, whereas IL-10 secretion was decreased upon S1P supplementation (Fig 7A and 7B). In contrast, DMS pretreatment leads to increased IL-10 and decrease IL-12 (Fig 7A and 7B). In addition, we observed that by blocking S1PR3 there was a significant increase in IL-10 secretion, however, not significant changes in IL-10 in presence of S1PR2 inhibition was observed. However, on combinational doses of S1PR2-3 inhibitors, there was a more significant increase in IL-10 secretion (Fig 7A). Interestingly, by blocking S1PR2 we notice a significant decrease in the secretion of IL-12 in infected TDM while no significant changes were observed by blocking S1PR3. Furthermore, the secretion of IL-12 was further decreased in infected macrophages that were treated with S1PR2-3 inhibitors in combination (Fig 7B).
S1P signaling is emerging as a novel therapeutic target for numerous infectious diseases. Many studies have acknowledged the fact that S1P signaling plays a critical role in numerous infectious diseases and hence careful manipulation of the signaling might provide a breakthrough therapy. Hence in this study, we, for the first time, examined the role of S1P signaling in one of the most neglected tropical disease, Leishmaniasis.
SphK1 is a cytosolic enzyme that on activation translocated to the plasma membrane leading to S1P production [18]. Earlier studied showed that sphK plays a critical role in viral infection. Non-structural proteins from bovine viral diarrhea virus (BVDV) have been shown to binds and inactivate sphK1 activity in a time-dependent manner, which favors viral growth by inhibition of apoptosis in Madin–Darby bovine kidney (MDBK) cells [19]. SphK1 inhibition was also reported in dengue virus (DENV) infection in HEK-293 cells [20]. It was demonstrated that thymocytes from Trypanosoma cruzi-infected mice showed a decrease in the activity of sphK1 and sphK2 at mRNA level [21]. S1P has been shown to regulate the phosphorylation of several MAPK (mitogen-activated protein kinase) including ERK1/2 and p38. S1P induces the phosphorylation of both ERK1/2 and p38 with maximum activation between 10–30 min of stimulation [22,23]. S1P induced activation of p38 is shown to be mediated by S1PR2 in SVEC endothelial cells line and pre-incubation with JTE-013 leads to inhibit the ability of S1P to induced p38 phosphorylation [23].
ERK1/2 and p38 phosphorylation, are established biomarkers for the progression and suppression of Leishmania infection [4,24]. Increase in ERK1/2 phosphorylation induces IL-10 production that has been associated with disease progression [4]. Moreover, it was found that IL-10 neutralization restores p38 activation and promotes parasite clearance, in contrary, IL-12 neutralization increases parasite burden [25–27]. Leishmania infection inhibits p38 phosphorylation that reduces Il-12 mRNA expression [28] and on the other hand, increase in p38 phosphorylation leads to IL-12 production that has been associated with disease suppression [4]. Altogether, IL-10 and IL-12 are considered to be important cytokines that regulate Leishmania donovani infection [29].
In this study, we observed that S1P results in decreased Leishmania induce ERK1/2 phosphorylation, while it increases p38 phosphorylation in macrophages. This further leads to induction of anti-leishmanial response by increase in IL-12 at mRNA and protein level, while a decrease in disease-promoting IL-10 at mRNA as well as in protein level, which altogether resulted in reduced parasite burden. DMS as a specific inhibitor of sphK which is reported by experiments done by other groups [30–32]. In our study, we also observed that DMS pretreatment inhibits sphK1 activity by inhibition of the phosphorylation of sphK1 in TDM (S6A Fig). Pharmacological inhibition of sphK by DMS or sphK1 specific siRNA has previously shown to decrease p38 phosphorylation in mouse macrophage cell line RAW264.7 [33]. Similarly, inhibition of sphK inhibition by same above approach in mice also has been shown to down-regulate pro-inflammatory cytokines [34]. These reports suggested regulation of MAPKs and cytokines by inhibition of sphK. We have shown that Leishmania donovani infection in human macrophages results in decreased sphK1 phosphorylation in a time-dependent manner. In addition, pharmacological inhibition of sphK1 by DMS have been shown to exacerbate the infection by increasing disease promoting ERK1/2 induced IL-10 secretion, while on the other hand, decreasing p38 activated IL-12 production.
Earlier, we showed that Leishmania infection significantly reduces the expression of S1pr2-3 in infected macrophages. To further gain insight on the role of these receptors mediated signaling, we checked the expression of ERK1/2 in presence of S1PR2 and S1PR3 specific antagonist JTE-013 (10μM) and CAY10444 (10 μM). Earlier studies have shown that inhibition of S1PR2 by JTE-013 results in increased ERK1/2 phosphorylation [35,36]. In our study, we found that there was an increase in the expression ERK1/2 on inhibition of S1PR2-3, alone or in combination. We also confirm this by checking the intracellular parasite burden and we found that combinational does of both the inhibitors increase the intracellular parasite burden in a very significant manner. Furthermore, we also observed increased IL-10 secretion in infected macrophages upon S1PR3 inhibition and combination doses of S1PR2 and S1PR3 inhibitor, but no observable changes were seen upon S1PR2 inhibition. Interestingly, inhibition of S1PR2 significantly decreases IL-12 secretion but no significant decrease was found upon S1PR3 inhibition. Notably, combinational doses of both these inhibitors significantly decrease IL-12 secretion and which may contribute to the increased parasite survival as observed by increase in parasite load. Hence this study suggests that S1PR2-3 inhibition reciprocally regulates IL-10/IL-12 balance during Leishmania infection which altogether makes macrophages more susceptible to Leishmania infection.
In summary, we showed that S1P signaling plays a protective role in Leishmania infection and S1PR2-3 can be considered as novel and attractive therapeutic target against leishmaniasis. Our study suggested S1P mediated signaling abolishes Leishmania induce ERK1/2 phosphorylation resulting in low parasite load, on the other hand, S1P induces activation of p38 pathway that leads to IL-12 production for further clearance of the intracellular parasite (Fig 8). Additionally, blockage of S1PR2-3 mediated signaling by specific inhibitors in alone and in combinational doses resulted in activation of the ERK1/2 pathway leading to IL-10 production and increase parasite load. Hence, our study thus provides a novel and important aspect of S1P signaling during Leishmania donovani infection that may be helpful for the generation of a new line of anti-leishmanial drugs.
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10.1371/journal.pcbi.1005651 | Possible roles of mechanical cell elimination intrinsic to growing tissues from the perspective of tissue growth efficiency and homeostasis | Cell competition is a phenomenon originally described as the competition between cell populations with different genetic backgrounds; losing cells with lower fitness are eliminated. With the progress in identification of related molecules, some reports described the relevance of cell mechanics during elimination. Furthermore, recent live imaging studies have shown that even in tissues composed of genetically identical cells, a non-negligible number of cells are eliminated during growth. Thus, mechanical cell elimination (MCE) as a consequence of mechanical cellular interactions is an unavoidable event in growing tissues and a commonly observed phenomenon. Here, we studied MCE in a genetically-homogeneous tissue from the perspective of tissue growth efficiency and homeostasis. First, we propose two quantitative measures, cell and tissue fitness, to evaluate cellular competitiveness and tissue growth efficiency, respectively. By mechanical tissue simulation in a pure population where all cells have the same mechanical traits, we clarified the dependence of cell elimination rate or cell fitness on different mechanical/growth parameters. In particular, we found that geometrical (specifically, cell size) and mechanical (stress magnitude) heterogeneities are common determinants of the elimination rate. Based on these results, we propose possible mechanical feedback mechanisms that could improve tissue growth efficiency and density/stress homeostasis. Moreover, when cells with different mechanical traits are mixed (e.g., in the presence of phenotypic variation), we show that MCE could drive a drastic shift in cell trait distribution, thereby improving tissue growth efficiency through the selection of cellular traits, i.e. intra-tissue “evolution”. Along with the improvement of growth efficiency, cell density, stress state, and phenotype (mechanical traits) were also shown to be homogenized through growth. More theoretically, we propose a mathematical model that approximates cell competition dynamics, by which the time evolution of tissue fitness and cellular trait distribution can be predicted without directly simulating a cell-based mechanical model.
| When genetically different cell populations are mixed, there is competition between cells such that losing cells are eliminated from a tissue. Such cell elimination is also observed during normal development in genetically-homogeneous tissues. In addition to the identification of key genes and molecular mechanisms related to these phenomena, the relevance of cell/tissue mechanics has been reported as a possible common mechanism of elimination. Here, we examined these mechanisms and possible functions of mechanical cell elimination (MCE) from the perspective of tissue growth efficiency and homeostasis. Using mechanical simulations of tissue growth processes, we identified key parameters of cellular mechanical/growth properties that determine elimination rates or cellular fitness (defined as the difference between cell division and elimination rate). Based on these results, we propose mechanical feedback mechanisms that could improve tissue growth efficiency and density/stress homeostasis. Furthermore, when cells with different mechanical traits are mixed, we found that MCE could drive a drastic shift in cell trait distribution, thereby improving tissue growth efficiency through the selection of cellular traits. With this, cell density, stress state, and phenotype were also shown to become homogenous. Our results will permit the elucidation of the mechanisms of intrinsic tissue defense against abnormal cells by their elimination through mechanical cell-cell interactions.
| In 1975, Morata and Ripoll analyzed the mosaic system of the Drosophila imaginal disc composed of wild type cells and mutant cells of ribosomal protein, and found that mutant cells underwent apoptosis and were eliminated from the tissue [1]. This was the first report of cell competition resulting from local cell-cell interaction. Subsequent work has shown that the competition phenomenon is widely present, not only in insects but also in vertebrates, and that the elimination of cells is realized through various processes such as cell death, phagocytosis, or live cell extrusion [2–4]. The process has close connections with important biological events such as tumor formation and tissue size regulation. Thus, it has attracted attention from a variety of fields [5,6]. As potential mechanisms of cell competition, related molecules and/or signaling pathways have been identified [7,8]. Moreover, recent reports have shown mechanical relevance as well as chemical or molecular mechanisms [7,9]; for example, Bielmeier et al. found that cells with mutations in genes that determine cell fate were extruded from a tissue by a common mechanical process [10]. In addition, de la Cova et al. reported that in the Drosophila imaginal disc, the effect of growth of clone did not reach beyond the AP compartment boundary [6], suggesting that cell elimination is influenced by mechanical constraints.
Interestingly, recent live imaging studies have shown that even when a population is genetically homogeneous, a non-negligible number of cells are extruded from developing tissues. For instance, it was reported that at pupal stages of Drosophila wing development, about 1000 cells are extruded when the number of cells constituting the wing tissue increases from 4000 to 8000, i.e. 20% of newly born cells are eliminated [11,12]. Similar live cell extrusion was observed around the midline of Notum closure [4,12]. In addition to epithelial development, in a culture system using MDCK cells, when cell density was artificially increased, some cells were excluded until the original density was restored [13]. In these cell elimination processes, there is no a priori program that selects which cells are lost, and these mechanisms were mostly explained in a mechanical context. On the other hand, Clavería et al. found that in early mouse development, competition is based on differences in the expression level of the Myc gene [14], identifying the existence of chemical signals which determine the relative merits between cells.
In this manner, the elimination of a portion of cells from a tissue or competition between cells is not necessarily due to a difference in genetic background. By regarding cell elimination from genetically-homogeneous cell populations as a form of broad-sense cellular competition, the experimental observations described above can be classified by the following criteria (Fig 1). The first criterion is whether the elimination is based on genetic differences or not, i.e. the focal cell population is “genetically-homogeneous” or “genetically-heterogeneous”. These are completely exclusive. Next, as a mechanism of elimination, the cases can be classified based on their mechanical relevance. Of course, this classification is not completely exclusive; for instance, cell-cell mechanical interactions might trigger the upregulation of previously identified cell death signaling pathways, while in another case, cell-cell chemical interactions through membrane-bound and/or secreted molecules might induce changes in mechanical cellular properties that enable the easy extrusion of cells from a tissue. Here, we used two kinds of tags, “mechanically-driven” and “non-mechanical” (or cases in which the mechanical relevance is unclear; Fig 1). [1,2,4,6,10–28]
In this study, we mainly focus on the mechanisms of mechanical cell elimination (MCE) from a genetically-homogeneous growing tissue for the following reasons. First, this issue is related to tissue growth efficiency, in other words, “how often do cells newly-born by proliferation contribute to tissue growth” and “how can tissues grow efficiently with less energy waste due to elimination?” Understanding the relationship between MCE and growth efficiency is important because tissues or individuals with higher growth efficiency have evolutionary advantages due to their faster growth (i.e., shorter time to reach their target sizes) and/or higher survival probability under environments with limited energy resources. Furthermore, as we will discuss later, cellular mechanical/growth parameters (also called cellular traits) generally show a distribution even if the genetic background of the cells is the same. Such phenotypic variation could induce cell-cell competition and change the distribution of cellular traits with an increase in cell population size. Since MCE in the presence of phenotypic variation can be regarded as a specific case of cell competition between populations of cells with different genetic backgrounds, examining what happens during the growth of such a mixed population of cells with different parameters will enhance our understanding of the mechanisms of cell competition (especially in relation to mechanical aspects). Secondly, it is likely that MCE is also related to tissue homeostasis. As shown in the density recovery experiment described above [4,13], if MCE is a kind of mechanical response to perturbations by extrinsic forces or cell division, it could function to maintain uniform cell density and/or stress distribution in a growing tissue. As one possible mechanism to maintain (local) cell density, the density or stress-dependent regulation of cell proliferation was proposed [29–32]. MCE could be another possible mechanism for cell density homeostasis. In addition, in the presence of phenotypic variation, competition could lead to the homogenization of cellular phenotypes. Lastly, from a more theoretical perspective, by focusing on the mechanical aspects, we can approach cell-elimination/competition independent of specific chemical signaling and gene regulation, the details of which are not well understood. Since the mechanical and growth parameters of cells are basically identical in a population, the rate of elimination due to mechanical cell-cell interactions can be uniquely determined for each population with a given set of mechanical parameters. The difference between the proliferation rate and elimination rate provides a net growth rate from which the fitness of the population can be quantified.
With this motivation, our aims are as follows. The first one is to clarify the dependence of mechanical/growth parameters on the cell elimination rate or net growth rate (fitness) and to find common geometrical/mechanical determinants of the elimination-rate/fitness. To achieve these aims, we introduce two quantities to define and measure fitness at the cellular and tissue levels. Regarding each cell population with a certain growth/mechanical trait as a “species”, the time derivative of its logarithmic growth curve defines the cellular fitness of the species. On the other hand, the tissue level fitness is defined as the average of cellular fitness between species weighted by the frequency of each species. When the traits of all cells in a tissue are identical (i.e., a pure population), the cellular and tissue level fitness are equivalent. In the presence of phenotypic variation, both cellular and tissue level fitness vary over time as a result of changes in the frequency distribution of cell types constituting the focal tissue through competition between them. After defining the fitness, by performing numerical simulations with a vertex dynamics model which is used in many studies of the mechanics of epithelial tissues [31,33–36], we examine the dependence of cell elimination rate or cellular fitness on mechanical/growth parameters of the cells. In contrast to experimental studies, we can independently control each parameter in the model, which is the biggest advantage of simulation-based studies. We show that the dependence could be summarized by how those parameters affected geometrical and stress heterogeneities within tissues, and that MCE functions to homogenize cell density and stress state within a tissue. Based on the simulation results, the second aim is to propose possible feedback mechanisms in which mechanical parameters of each cell are regulated depending on its stress state to improve tissue growth efficiency and homeostasis. These mechanisms could reduce the energy loss resulting from cell elimination, and homogenize cell density or tissue stress. Since the energy required for growth is proportional to the number of cells produced, the difference in the elimination rate becomes a greater advantage as tissue size increases. Interestingly, under the proposed feedback regulation, the geometrical and stress heterogeneities between cells were incompatible. By controlling geometrical heterogeneity, the elimination rate could be reduced but tissue stress heterogeneity increased (i.e. density homeostasis is achieved but stress homeostasis is impaired). In contrast, controlling the stress heterogeneity increased both geometrical heterogeneity and the elimination rate (i.e. stress homeostasis is achieved but density homeostasis is impaired). Finally, we examine what happens through competition in a population where cells with different mechanical traits are mixed. When daughter cells epigenetically inherit their parental traits (the degree of inheritance was quantified as heritability), the trait distribution within the tissue drastically changes with tissue growth, resulting in an increase in fitness at the tissue level. This clearly demonstrates that cell competition through MCE can improve tissue growth efficiency through the selection of mechanical cell traits, i.e. intra-tissue “evolution”. Furthermore, through selection, cell density, stress within a tissue, and cellular phenotype are homogenized, which is another possible role for competition through MCE. From a more theoretical perspective, we propose another differential equation model for competition dynamics that permits us to a calculate the approximate time evolution of tissue-level fitness and trait distribution. The model is useful for predicting the outcome when tissue size grows much larger, e.g., reaches a fully-developed size with ~106−107 cells, because direct simulations of a cell-based mechanical model require an immense amount of computation time.
Previous studies have introduced the concept of fitness to discuss the intensity of cell competition [8,37,38]. The main focus was relative survivability, that is, which population survives when different populations with different genetic backgrounds are mixed. In this study, we aim to reveal quantitative effects of cell competition through MCE on tissue growth dynamics. To do so, we now propose another quantitative measure of fitness, which differs from relative survivability.
In experimental observations and in tissue growth simulations, the growth curve of a developing tissue, i.e. temporal changes in the total number of cells within a growing tissue, g(t), can be locally approximated by an exponential function (during a certain period) [11,39,40]. Thus, its exponent, representing net growth rate or growth efficiency, defines the fitness of a focal tissue at each time t:
ϕ T i s s u e ( t ) = d d t log g ( t ) .
(1)
Assuming that growth dynamics is modeled by dg(t)/dt = (μ(t)-m(t))g(t), the fitness becomes simply μ(t)-m(t), where μ(t) is the tissue growth rate through cell proliferation. As stated before, in actuality, not all cells produced by division survive and contribute to the increase in tissue size. Some cells are lost from the tissue by extrusion or apoptosis, the rate of which (termed mortality) is represented by m(t). In this study, the cell elimination rate is regarded as a key factor in determining cell mortality within growing tissues. Rewriting the fitness as μ(t)(1-m(t)/μ(t)), the term (1-m(t)/μ(t)) or m(t)/μ(t) indicates the energy efficiency (the contribution ratio of produced cells to tissue growth) or the loss of energy (the waste of healthy cells). In this way, tissue fitness is determined by both the growth rate and energy efficiency at each time.
As an ideal situation, when a focal tissue is composed of cells with exactly the same traits in mechanical and/or growth properties (called a pure population), the fitness at the tissue level can also be regarded as the fitness of a cell with that focal trait, ϕCell = ϕTissue. In this case, as shown later, the value of fitness is almost constant (when the growth rate is sufficiently slow). Using cell cycle T, the tissue growth rate μ is given by (log 2)/T. Regarding the mortality m, when defining the cell elimination rate as the ratio of the number of eliminated cells to that of newly-born cells by cell division per unit increment of tissue size: ε = Neliminated/Nproduced, the relationship m = -(log(1-ε/2))/T holds (see the Models section for details). In the next three sections, we consider such a pure cell population to examine how and by what mechanisms cell mechanical/growth properties affect fitness, especially mortality through cell elimination.
Consider next a tissue that is composed of cells with different traits (called a mixed population). Here, the net growth rate at the tissue level and that at the cellular level for each population with each trait are different in general. Denoting the cellular fitness with i-th trait by ϕCell,i, the tissue fitness is given by:
ϕ T i s s u e ( t ) = ∑ i f i ( t ) ϕ C e l l , i ( t ) ,
(2)
ϕ C e l l , i ( t ) = d d t log g i ( t ) ,
(3)
where fi is the frequency of cells with i-th trait in the entire tissue and gi(t) is the growth curve of the population with i-th trait. Importantly, in a mixed population, cellular fitness for each trait is generally not constant but varies with time. This is because the elimination rate of cells with a certain trait depends on the traits of its surrounding cells that mechanically interact with one another, and because the distribution of cellular traits throughout the entire tissue changes with time through selection based on the differences in cellular fitness.
Focusing on developmental advantages, fitness at the tissue level is more important than the cellular fitness. In the remaining subsections, we show how mechanical feedback and competition between different traits improve tissue fitness or growth efficiency.
We started by systematically examining how the frequency of cell elimination depends on mechanical properties and the growth rules of epithelial cells using the vertex dynamics model (Fig 2A, also see the Models section). Specifically, we focused on tissue fluidity, cell division orientation, and proliferation rate. In the vertex dynamics model, each cell shape is represented as a polygon formed by linking several vertices, and each vertex moves so as to decrease energy function U of the system (see the Models section). U includes two parameters Λ and Γ (see the Models section for details). Λ is the coefficient for tension acting on a cell’s edge; stronger cell-cell adhesion and/or weaker contractility of actomyosin fibers along the edge correspond to a smaller value of Λ. The other parameter Γ is the coefficient for perimeter elasticity, the value of which is determined by the contractility of the actomyosin network over the apical surface of a cell [35]. Each cell has a clock representing the cell cycle. When the clock within a cell reaches a specific value T, the cell divides with an axis through its center and the clock is reset (note that the cell cycle includes slight stochasticity to avoid the synchronization of divisions; see the Models section for details). Regarding the division orientation, we modeled it as a random variable distributed around the shortest axis. By changing a single parameter in the distribution, the randomness of division orientation can be controlled (Fig 2B; also see the Models section). As a consequence of push-pull dynamics between cells through their divisions, a cell whose area is below a certain threshold (θT2 = 0.2) will be removed (called T2 process; Fig 2C; also see the Models section). As mentioned in the introduction, there have been some reports on MCE. However, currently there is little known about whether a threshold in cell area for MCE exists and what the threshold value is. The only exception is a study by Marinari et al. in which they showed that in the case of Drosophila notum development, cells whose area was less than ~25% of the initial area were eliminated [4]. According to this report, we set θT2 = 0.2. To confirm the generality of our results, we also examined cases with different values for θT2 (specifically, 0.05, 0.1, and 0.3), and our results (shown below) did not change qualitatively. In addition, as clarified in the later subsection, cell size is highly correlated with stress state, and thus our assumption on the criterion for MCE, the existence of a cell size threshold for MCE, also includes another criterion, the existence of a threshold for stress acting on a cell.
Tissue fluidity, i.e. the liquid-like behavior of a tissue, increases for smaller values of Λ and/or Γ [35]. Intuitively, in those situations, each cell moves so as to maintain its apical area as near to the natural value (a given constant, see the Models section) as possible, leading to easier deformation and more frequent cell-cell rearrangements when forces due to tissue growth act on the cell. In contrast, for larger values of those parameters, the force for isotropic shrinking increases, resulting in less fluidity (see also the Models section). In the vertex dynamics model, cell rearrangement is implemented by the reconnection of vertex networks called a T1-process (Fig 2C), and thus the distance threshold for the reconnection (θT1) also affects the tissue fluidity. Whether or not the value of θT1 itself is a controllable parameter is unknown. However, observations have shown that in some situations cell intercalation frequently occurs, and in other situations it rarely occurs and multicellular rosette structures are formed instead [41], suggesting the existence of mechanisms that regulate the frequency of intercellular rearrangement.
The upper panels in Fig 3A show the dependences of cell elimination rate ε and ϕCell on the three parameters affecting the tissue fluidity. These three parameters have the same tendency, although the degree of dependence on the T1-threshold θT1 is lower. With an increase in tissue fluidity, the elimination rate decreases or the fitness increases monotonically. In particular, the parameter dependence of ε can be approximated by a Gaussian-type function (Fig 3A), which is useful in calculating the time evolution of cellular/tissue fitness within a mixed cell population as will be shown later (see the final subsection). For a fixed parameter, the cell elimination rate ε and the cellular fitness ϕCell are nearly constant during tissue growth as long as cell density is regarded as constant (Fig 3B).
As described above, the randomness of cell division orientation was introduced as a tissue growth rule (Fig 2B). It was controlled by a single parameter, the variance of division orientation around the shortest axis of each cell. Unexpectedly, the division orientation has a clear effect on the cell elimination rate. When a cell divides along the shortest axis, the elimination rate decreases compared to situations in which division is randomly oriented (Fig 3A). In regards to the growth rate, as expected, the elimination rate becomes higher as it increases (Fig 3A). In actuality, this tendency has been observed in a biological system. During development of the Drosophila notum, cell elimination occurred more frequently in the tissue of the mutant with the higher growth rate [4].
As shown in the previous subsection, cell elimination naturally occurs as a consequence of tissue growth, and its rate depends on different mechanical/growth parameters. In order to find common factors for determining the elimination rates, we next searched for quantities whose values change with the same tendency as the elimination rate when mechanical/growth parameters change. Specifically, we focused on (i) cell shape regularity, which was quantified by elliptical approximation, (ii) the frequency of cell rearrangement (T1-process) and (iii) the variance in size between cells.
As shown in Fig 4A, only the variance in cell size has a high correlation with the elimination rate, indicating that cell size variance is the only geometrical determinant of the elimination rate. This is reasonable because cell elimination, i.e. the T2-process in the vertex dynamics model, is determined by cell size. However, it may be significant that the correlation with the remaining quantities (T1-frequency and cell shape regularity) is much lower. This can be interpreted as follows. Different mechanical and growth parameters affect the cell size variance (and thus cell elimination) in different ways (Fig 4). For example, for higher tissue fluidity (e.g., smaller Λ/Γ or larger θT1), the increase in T1-frequency reduces the cell size variance, leading to a decrease in the elimination rate (left panels in Fig 4A and 4B). In contrast, biased division-orientation along the shortest axis reduces the elimination rate even though the frequency of the T1-process is much lower (middle panel in Fig 4B). In this case, the increase in cell shape regularity instead of T1-frequency likely reduces the cell size variance (middle panel in Fig 4C). In regard to growth rate, as shown in Fig 4B, a higher growth rate decreases the T1-frequency and relative tissue fluidity is lower, which could cause an increase in the cell size variance and elimination rate. A higher growth rate also increases the spatial heterogeneity of cell density (Fig 5B); the density becomes higher in more central regions of tissue as observed in the development of the Drosophila wing imaginal disc [42,43]. As shown in Fig 5B, cell density has a clear positive correlation with size variance. More central regions have higher density, which inhibits smooth cell rearrangement and thus leads to an increase in cell size variance and elimination (right panel in Fig 5B). Taken together, cell size variance is regulated in different ways and determines the cell elimination rate.
Since the inverse of the cell area corresponds to local cell density, the above result can also be interpreted as the spatial heterogeneity in cell density determines the cell elimination rate. As expected, the heterogeneity of cell density around an elimination point (defined as the CV of the inverse of cell size in the region including the elimination point) decreases after its occurrence, demonstrating that cell elimination can recover the homogeneity in cell density (Fig 5C and 5D).
Since stress in a tissue drives its deformation, we next evaluated the stress state acting on each cell in a growing tissue. In this study, tissue is modeled not as a continuum but as a multicellular assembly, and thus Cauchy’s stress acting on each cell was evaluated by its microscopic and discrete representation. Among different representations hitherto proposed [44,45], we here adopted the two types of stress tensors used in recent papers on stress distribution in developing tissues [46,47] (see σ(A) and σ(B) in Fig 6A, and the Models section for details). The calculated tensors were characterized by the two scalars, stress magnitude σ1+σ2 and stress anisotropy σ1-σ2, where σ1 and σ2 (σ1>σ2) are the principal stresses (Fig 6B). Positive (or negative) values of σi represent a tensile (or compressive) stress. Correlation analysis with cell geometry showed that the stress magnitude and stress anisotropy are strongly correlated (>0.9) with cell size and cell shape anisotropy, respectively (Fig 6C, top and middle). This holds for cases with different mechanical/growth parameters and for either definition choice for stress tensor (Eqs (25) and (27) in the Models section). With regard to stress anisotropy, its direction (defined as the direction of the maximum principal stress) is perfectly consistent with the direction of cell shape anisotropy (Fig 6C, bottom). In this manner, in a pure population, cell geometry perfectly reflects the stress state acting on it. As seen later, when growth/mechanical properties vary through mechanical feedback or when a tissue is composed of cells with different properties, the correlation between cell geometry and stress state decreases somewhat (around 0.7).
We showed in the previous section that the cell elimination rate is determined by the variance in cell size or cell density. Thus, we can conclude that the spatial heterogeneity of stress magnitude, not that of stress anisotropy, is the main mechanical cause of cell elimination. To avoid misunderstandings, we emphasize that the heterogeneity described here is not driven by the difference in mechanical/growth properties between cells. Rather, in all simulations in Fig 6, all cells had the same parameter values (i.e., a pure population). The stress heterogeneity observed here is intrinsic to growing tissues.
To clarify the relationship between the heterogeneity of stress magnitude and tissue growth by cell division, we examined the change in stress states experienced by cells surrounding each dividing cell under different mechanical/growth rules. In all cases, the stress magnitude always decreased (became more compressed) on average. As expected, the average local stress change,
E [ ( σ 1 + σ 2 ) A f t e r d i v i s i o n − ( σ 1 + σ 2 ) B e f o r e d i v i s i o n ] ,
(4)
correlates well with the spatial heterogeneity in stress magnitude, and consequently with the cell elimination rate (Fig 6D). We also examined the change in local stress through the cell elimination event. In contrast to the case of cell division, cell elimination caused a release in stress on average. This suggests that MCE can be a mediator for homogenizing the tissue stress state (i.e., stress homeostasis) as well as actomyosin activity. This result is consistent with observations from previous experimental studies in which a potential role for cell elimination in the maintenance of homeostasis in epithelial tissues was shown [4,13]. Similar to the case of cell division, cell elimination only affected stress magnitude, not stress anisotropy.
Fig 7 shows a summary of the results obtained with a pure population in the above (2nd-4th) subsections. When tissue grows, cell division induces surrounding tissue compression, which increases the spatial heterogeneity in stress magnitude and cell size/density. The heterogeneity of cell size/density (or stress magnitude) is the common geometrical (or mechanical) trigger of MCE. Once cell elimination occurs, the compression due to cell division is released and the variance in local cell density also decreases. Thus, MCE functions as a mechanism for achieving density and stress homeostasis. On the other hand, from the perspective of energetic efficiency, reducing MCE events and increasing the contribution of newly-born cells to tissue growth can achieve target size with less energy resources, which can be achieved by higher tissue fluidity, division along the shortest axis, and lower growth rate.
In the above sections, to clarify the effects of cellular mechanical/growth parameters on the elimination rate or the loss of energy in the fitness function (see the first subsection), we assumed a pure population in which all cells had the same values for mechanical/growth parameters. However, in actuality, the values of these parameters can change among cells even if all of them have an identical genetic background. For example, cells can change their physical properties and/or growth rate through feedback depending on the stresses acting on them or mechanical environment. In addition, due to various noise sources such as intrinsic fluctuations in gene expression levels and extrinsic environmental noise [48,49], the parameter values can show a distribution among the cells. Here we examine the possibility of improving tissue growth efficiency (or tissue fitness ϕTissue) and homeostasis through mechanical feedback; in the next section we will examine the results of competition in a population where cells with different mechanical parameters are mixed.
Density- or stress-dependent growth regulation is a type of feedback that has been discussed extensively [29–31], although the molecular mechanism of mechano-sensing is not entirely clear. As a plausible example, we first examine how this feedback would affect ϕTissue. In particular, we modeled the clock of the cell cycle τα as a function of stress magnitude S = σ1+σ2:
d d t τ α ( t ) = { 0 ( S < S ¯ ) const. ( S ≥ S ¯ ) ,
(5)
where S ¯ is the mean stress magnitude before tissue growth starts. As mentioned before, when the clock becomes larger than the threshold T, cell division occurs. This stress-dependent growth regulation led to a large decrease in the elimination rate (Fig 8A) by preventing spatial heterogeneity in cell density, clearly demonstrating that this type of feedback can promote both tissue growth efficiency and homeostasis. However, it was not necessarily efficient in the sense of developmental speed, as it took much more time to reach a certain tissue size compared to cases without feedback (Fig 8B).
Developmental speed is an evolutionarily significant trait as well as growth efficiency and homeostasis. Our results in the previous sections suggest another possible means by which mechanical feedback could improve tissue growth efficiency and homeostasis: it could decrease the elimination rate while maintaining the growth speed. Here, we assume that cells can sense their own stress state or that of adjacent cells through their cytoskeletons or filopodia [50–52] and that, depending on the state, they can change local tissue fluidity by appropriately regulating their contractility and/or edge tension (i.e., Γ and Λ in the vertex dynamics model) through a change in intracellular localization of actin and adhesive molecules. As shown above, the elimination rate perfectly correlates with the variance in cell size or that of stress magnitude, and thus the proposed feedback mechanisms must be designed to decrease either of them. Specifically, we considered the following feedback systems;
d d t χ α ( t ) = c ( S α − S 0 ) − d ( χ α ( t ) − χ 0 )
(6)
or
d d t χ α ( t ) = c ( S α − S ¯ α ) − d ( χ α ( t ) − χ 0 ) ,
(7)
where χα(t) is the parameter for apical contractility or cell-cell edge tension of cell α (i.e., χα(t) = Γα(t) or Λα(t)), and χ0 (Γ0 or Λ0) is a cell-independent basal value. The magnitude of c indicates the feedback strength and d is the parameter determining the timescale of restoration to the basal value. When the value of c is positive, the feedback reduces cell size variance, whereas a negative value for c reduces the variance in stress magnitude. In the mechanism given by Eq (6), the apical contractility or cell-cell edge tension of a cell is regulated according to the difference of the stress magnitudes acting on the focal cell α from a reference value Sα—S0. In contrast, if cells can sense the stress state of neighboring cells as well as their own state, it is possible there could be another feedback mechanism dependent on the difference from the average of adjacent cells S α − S ¯ α, given by Eq (7).
The blue lines in Fig 8A and 8B show the results from growth simulations with either the feedback mechanism given by Eq (6) or (7). These mechanisms basically had the same effects on growth efficiency, i.e. elimination rate and growth speed. Importantly, we found that the reductions in cell size variance and stress variance were incompatible; positive and larger values of c decreased cell size variance (improved density homeostasis) but increased the stress variance (impaired the stress homeostasis), and vice versa (Fig 8C and 8D). As mentioned before, in the absence of feedback, the elimination rate is strongly correlated with the variance in cell size and stress magnitude. In the presence of the feedback, however, the elimination rate was negatively correlated with the variance in stress magnitude due to the incompatibility, although it is positively correlated with cell size variance (Fig 8C). In regard to the growth speed, the feedback with different strengths c returned almost the same value (Fig 8B). Consequently, to improve growth efficiency and density homeostasis by reducing the elimination rate and by keeping the growth speed normal, it is desirable that the parameter c in Eqs (6) and (7) has a positive value with which the feedback mechanism can reduce cell size variance. In contrast, the simulation results also shows that feedback with a negative value of c can achieve stress homogenization by actively inducing cell elimination.
Lastly, we considered the feedback to cell division orientation. As shown in Fig 3A, cell division along the shortest axis of a cell significantly suppressed the elimination rate. Since stress anisotropy perfectly correlates with cell shape anisotropy (Fig 6C), by reflecting the stress anisotropy to spatial arrangement of centrosomes that determine the division axis, tissue growth efficiency and density homeostasis could be improved. Importantly, through this feedback, the reductions in cell size variance and stress variance are compatible. This mechanism looks relatively simple, but it is an excellent way to improve tissue growth efficiency and maintain geometrical and mechanical homogeneity in a growing tissue. In the context of plant development, Alim et al. discussed the relationship between stress variability and division orientation [46].
Even in genetically identical cell populations, phenotypic variations in cellular traits (mechanical/growth properties) are inevitable. Here, we examined the results of mixing cells with different traits. For example, as an extreme situation let us consider two cases where (i) daughter cells perfectly inherit parental traits and (ii) traits of daughter cells are randomly distributed. In terms of evolutionary theory, the former is a case in which heritability h2 = 1, and the latter h2 = 0. Fig 9A shows the temporal evolution of frequency distribution f(t) = (f1(t), …, fN(t)) of cells with different values of a mechanical trait, Λ or Γ, where all cells have the same cell cycle time. In the case of h2 = 1, the distribution drastically changed with tissue growth and, as a result, the fitness increases at the tissue level (see Eq (2) for its definition; right panel of Fig 9A), clearly demonstrating that cell competition through MCE can improve tissue growth efficiency through the selection of mechanical cell traits. At the same time, cell size and stress were homogenized during growth (the right panel of Fig 9A). In contrast, when a trait is not inherited from a parent (i.e., h2 = 0), the frequency distribution of the cellular trait and tissue fitness never change, even though cells with traits giving higher cellular fitness survive.
How do the trait distribution f(t) and the tissue-level fitness ϕTissue change when tissue size becomes much larger, e.g. more than 106 cells (fully-developed size), or when the heritability h2 takes a value other than 1 and 0? Since directly simulating the vertex dynamics model requires an immense amount of computation time to generate such a huge number of cells, we sought a way to calculate the approximate time evolution of the competition process. To do so, we first modeled the process of inheriting the mechanical traits from the parental cell. Let us suppose that the focal trait has a discrete value of N-kinds and that the trait of the daughter cells is inherited from its parent with probability q and is uniformly and randomly chosen from all the possibilities with probability 1-q (Fig 9B). Then, the heritability h2 monotonously changes with the value of q (where q = 1 indicates perfect inheritance and q = 0 no inheritance). The black lines in Fig 9C show examples of tissue growth curves for different values of h2.
The time evolution of the number of cells with each trait, xi(t) (i = 1, …, N) is given by the following ordinary differential equation:
d x ( t ) d t = Q q Φ c e l l ( t ) x ( t ) ,
(8)
where x(t) = (x1(t), …, xN(t)). Qq is the matrix relevant to the heritability whose diagonal and off-diagonal components are q+(1-q)/N and (1-q)/N, respectively. ΦCell(t) is the matrix whose i-th diagonal component is the cellular fitness of the i-th trait ϕCell,i(t), and all of the off-diagonal components are zero.
In contrast to the pure population case examined in the previous sections, the cellular fitness ϕCell,i is generally not constant but changes with tissue growth as well as tissue-level fitness ϕTissue. This is because the elimination rate of each cell depends on mechanical properties of adjacent cells. For example, when the coefficient of line tension Λ on a cell edge is regulated by the amount of homophilic adhesive molecules and its value is different between two contacting cells, cell α and cell β, the effective value Λ ˜ of the parameter on the edge is regarded as max(Λα, Λβ) or simply approximated by the average (Λα + Λβ)/2 (Fig 10A; also see the Models section). This makes the elimination rate different from cases with a pure population.
Exceptionally, when the heritability is sufficiently large, cells with the same trait form a cluster, and thus ϕCell,i can be approximated as a constant value calculated in the pure population case (see Fig 9C for cases where h2 = 1, and Fig 10B for the spatial correlation of the cell trait). In this case, Eq (8) becomes a linear differential equation and thus can be solved analytically (approximation (i)):
x ( t ) = E x p [ Q q Φ c e l l t ] x ( 0 ) .
(9)
Using the frequency distribution,
f ( t ) = x ( t ) / ∑ i x i ( t ) ,
(10)
the time evolution of tissue fitness can be calculated from Eq (2). Fig 9C shows that this approximation (i) works well.
In contrast, when the heritability is not high, it is necessary to adjust mechanical parameters (and thus the cellular fitness value). For example, when Λ is adjusted by the max-function max(Λα, Λβ), the mean of its effective value for cells with the i-th trait is given as follows using the mean field approach,
Λ ˜ i ( t ) = Λ i ∑ k = 1 i f k ( t ) + ∑ k = i + 1 N Λ k f k ( t ) ,
(11)
where we assume Λi < Λj for i < j without loss of generality. Then, ϕCell,i(t) can be estimated by using the function for the elimination rate ε(Λ) obtained in the second subsection (Figs 3A and 10A):
ϕ C e l l , i ( t ) = log ( 2 − ε ( Λ ˜ i ( t ) ) ) / T .
(12)
Finally, by numerically solving a set of equations, Eqs (8) and (10)–(12), the time evolution of x(t), f(t), Λ ˜ i ( t ) and ϕCell,i(t) was obtained (approximation (ii)). Fig 9C shows that this solution is in good agreement with the results of a full simulation with the vertex dynamics model. As a special case, when the heritability is sufficiently small, h2 ≈ 0, since the trait distribution is almost uniform. Thus, fi(t) ≃ 1/N, Eq (10) can be approximated as follows:
Λ ˜ i ( t ) ≅ 1 N ( i Λ i + ∑ k = i + 1 N Λ k ) = const.
(13)
In this case, the cellular fitness ϕCell,i becomes constant (see Eq (12)) and thus, as in the other extreme case where h2 ≈ 1, Eq (8) becomes a linear equation and can be solved analytically. Again, Fig 9C shows the validity of this approximation.
Now, by using the above approximation approach, we can predict the time evolution of the trait distribution f(t) and the tissue fitness ϕTissue when the tissue size is very large. For example, when the number of cells within a tissue reaches 106, the total number of eliminated cells through tissue growth was 4×104 in the case where h2 = 1 and 26×104 for h2 = 0 (Fig 10C). In this manner, cell competition through MCE can considerably reduce the loss of energy and improve tissue growth efficiency when heritability is high. Although there are few studies of multicellular organisms and the extent to which a daughter cell epigenetically inherits parental trait, such epigenetic inheritance is observed in the persistence phenomenon in bacteria [53]. Thus, a non-negligible degree of heritability is expected in the developmental processes of multicellular organisms.
Above, we considered the situation in which the value of a trait, i.e. fluidity, is included in a certain finite range. In the absence of such a restriction, does the distribution of a trait continue to shift so as to increase tissue fluidity? And as a result, does the tissue fitness become higher? The answer is affirmative under the above settings of growth simulation, but in actuality, it is negative because of other tradeoffs not included in the simulation. For instance, as shown above, cells with smaller values of Λ have higher fitness. The smaller value of Λ means stronger adhesion between cells. Since cells with higher adhesion tend to have lower proliferation rates, in actuality, the value of Λ might affect not only cell mortality through elimination but also the cell proliferation rate [54]. To observe this, we performed tissue growth simulations where the cell proliferation rate was given as a monotonously increasing function of Λ. As shown in Fig 10D, the frequency distribution shifted so that it peaks at an intermediate value of Λ, reflecting the tradeoff between the contributions to tissue growth rate μ(t) and energy efficiency (1-m(t)/μ(t)). In this manner, even if phenotypic variability exists in the initial phase of development, mechanical cell traits can be homogenized (i.e., homogenization of phenotype) by cell competition through MCE during tissue growth.
In this study, we examined mechanical cell elimination, an intrinsic phenomenon in growing tissues, in a genetically-homogeneous tissue. First, we proposed novel quantitative measures to evaluate fitness in cell competition phenomena at the cellular and tissue levels. In a pure population where all cells have identical parameter values, cellular fitness was shown to be uniquely determined for each set of mechanical/growth parameters, and the analysis of simulation results demonstrated that the dependence could be summarized by how those parameters affect geometrical (specifically the variance in cell size) and mechanical (variance in stress magnitude) heterogeneity within tissues. This information has provided the basis for feedback regulation for improving tissue growth efficiency and homeostasis. Moreover, we showed that in the presence of phenotypic variation in mechanical cell properties, the distribution of a mechanical trait can shift with tissue growth depending on the heritability of a focal trait, resulting in the improvement of tissue growth efficiency and homeostasis. In addition, from a more mathematical perspective, we developed a theoretical model to approximate the time evolution of trait distribution and fitness. This model enabled us to predict the outcomes when tissue size was much larger without performing direct simulations of the cell-based model that would require an immense amount of computation time.
Our results clearly demonstrate the potential roles for MCE in tissue growth efficiency and homeostasis. One role is to promote geometrical and mechanical homogeneity of tissues by removing the cells in which stress and strain are concentrated due to cell division (Fig 7). This role as a homogenizer itself has also been discussed in previous experimental studies [4,13,15]. We quantitatively confirmed this homogenization process (Fig 5D), and also showed that the role as a homogenizer can be improved with appropriate regulation of mechanical/growth parameters depending on cellular stress state. Running the simulations in the presence of phenotypic variation, we showed that MCE could improve tissue growth efficiency by selecting cells with higher fitness, which is another potential role for MCE. This is important in an energetic sense. The energy required for tissue growth is proportional to the number of cells that are produced. Thus, improving tissue growth efficiency through competition during an earlier phase of development (when tissue size is still small) would provide a great advantage as the tissue increased in size. Along with the improvement of tissue growth efficiency, in the presence of phenotypic variation, MCE also homogenized or autonomously tuned cellular phenotype (mechanical parameters), as well as homogenization of cell density and stress state, through tissue growth. According to studies of the vertex dynamics model [31,43,55], there is a limited range of mechanical parameters that can reproduce some of the laws observed in experiments such as Lewis’s law (the relationship between cell size and the number of angles in a polygon representing a cell) and responses to mechanical stimuli [35,56,57]. Our result shows a possible mechanism by which cells could tune the values of their parameter. That is, MCE in growing tissues could select cells with desirable parameter values.
MCE in a mixed cell population with phenotypic variation can be regarded as a specific case of cell competition between populations of cells with different genetic backgrounds. Although specific signaling pathways that induce apoptosis and phagocytosis have been examined [7,8,37], the shrinkage of less fit cells through mechanical cell-cell interaction can be one of the first cues that induce those events, and the elimination of the cell would be supported by additional feedback regulation. As stated in the introduction, reports on MCE under genetically-heterogeneous conditions [10,15,21] support this possibility. If such a mechanical view is valid, as shown in Figs 9 and 10, the rate of elimination or cellular fitness would be determined by the difference in mechanical/growth parameters between cell populations and the frequency distribution of cells with different mechanical traits. This also suggests the possibility of promoting the elimination of a cell population (abnormal cells) and preventing tumor growth by appropriately regulating mechanical cellular properties through mechanical feedback. These will be important and interesting topics for future work.
The parameter dependence of the MCE rate shown in Fig 3A would be verifiable with current experimental settings. For example, suppressing cell-cell adhesion and promoting the rate of cell division would be experimentally feasible by manipulating related molecules. Under such conditions, an increase in cell size variance and elimination rate would be expected. In addition, based on our simulation results, the spatial heterogeneity of stress magnitude (defined as the trace of the stress tensor) would also be expected to rise. Applying the methods of force inference from cellular geometrical information (i.e., vertex network) [47,58,59], the tension on a cell-cell edge and relative value of the intracellular pressure could be calculated, with which it would be possible to estimate the spatial heterogeneity in stress magnitude. According to the results shown in Fig 4A, when the cell size variance is somewhat large (CV>0.2 for our simulation settings), the CV for cell size and the elimination rate are expected to have an almost linear relationship with changes in the different parameters, although the lower limit value for the linearity (i.e., CV = 0.2) may depend on the value of θT2. In this regard, however, it should be noted that we examined the responses (cell elimination rate) for situations where each parameter value was changed independently (Figs 3 and 4), although we do not know if this is experimentally possible or not. Furthermore, Fig 5B shows a high positive correlation between cell density and the CV for cell size, which could be verified in an artificially-densified cell culture system described in the introduction and/or in vivo systems such as Drosophila wing and notum development (by using the spatial difference in cell density).
As mentioned above, phenotypic variation between cells will naturally exist even in a genetically homogeneous cell population. As examined in the final subsection of the Results, in the presence of such variation, it should be possible to identify that cells with reduced expression levels of adhesive molecules and/or in which actomyosin activity is higher, are easier to eliminate, and that expression distribution would become more uniform. Again, these experiments would be possible in an artificially-densified cell culture system and/or in vivo systems such as Drosophila wing and notum development.
Although there are reports of MCE from both genetically-homogeneous tissues and genetically-heterogeneous tissues, there is little convincing evidence for any particular unambiguous cue that triggers elimination. In this study, we adopted cell area as a possible trigger for MCE based on the report by Marinari et al., which demonstrated in Drosophila notum development that cells with an area less than ~25% of their initial area were eliminated; to the best of our knowledge, this is the only report to quantitatively describe that trigger [4]. As shown in the 4th subsection of the Results, since cell size is highly correlated with the stress magnitude acting on a cell, our assumption on the criterion for MCE, i.e. the existence of a cell size threshold for MCE, also includes another potential criterion, the existence of a threshold for stress acting on a cell. Thus, our criteria for MCE include both geometrical and mechanical cell quantities, which we think are not so specific. On the other hand, Marinari et al. also reported a correlation between cell elimination and cell shape anisotropy during notum closure in Drosophila development, suggesting that a combination of multiple factors such as cell size and shape anisotropy (or the combination of stress magnitude and anisotropy), could be another candidate trigger for MCE.
In order to quantify the degree of competition, we adopted net growth speed or growth efficiency as a measure of tissue-level fitness. However, considering developmental processes, the spatial order between cells is also an important factor directly affecting the performance of pattern formation and tissue morphogenesis. For example, our results showed that the increase in tissue fluidity improves energy efficiency by suppressing cell elimination, but at the same time, spatial rearrangement between cells occurs frequently. This might make it difficult to realize robust tissue patterning. Thus, the frequency of cell rearrangement (quantified by the number of T1-processes) is a quantity that might be included in the fitness measure as well as tissue growth efficiency when considering patterning precision. Tamada et al. reported that in Drosophila axis elongation, the frequency of forming multicellular rosette structures is different in the presence or absence of the expression of Abl tyrosine kinase [41]. In addition, Levayer et al. showed that the efficiency of eliminating weaker cell populations is promoted by intermingling of cells with different genetic backgrounds [60]. These experimental studies suggest that the frequency of cell rearrangement or tissue fluidity would be controlled by cells. On the other hand, as mentioned before, the regulation of cell-cell adhesion is a possible mechanism to determine tissue fluidity. Adhesion and cell proliferation are related, and thus it would be generally impossible to control them independently. This means that growth speed and energy efficiency might be interdependent although we dealt with them as independent quantities in our model. In this manner, there are likely to be various tradeoffs in cellular/tissue behavior. In the future, by studying how cell competition contributes to cellular and tissue fitness in the presence of those tradeoffs, its biological significance will become clearer.
As given by Eq (1), in this study, we defined fitness at the tissue level, ϕTissue(t), by the time-derivative of the logarithm of the growth curve g(t) (g(t) is the total number of cells in a growing tissue at time t). As described in the first subsection of the Results, tissue fitness is generally a function of time, but in a pure population the growth curve can be well approximated by an exponential function with a constant exponent, μ-m, and thus tissue fitness is regarded as a constant, where μ is the tissue growth rate determined by the cell cycle time and m is the mortality determined by the rate of (mechanical) cell elimination. In contrast, in a mixed population, tissue fitness generally varies with time because the different cell types with different traits that comprise the entire tissue have different cellular fitness, and the net tissue growth rate depends on the frequency distribution of cellular traits at each time point. Denoting the number of cells with i-th trait at time t by gi (t), the tissue size or the total number of cells comprising the tissue, g(t), is given by:
g ( t ) = ∑ i = 1 N g i ( t ) .
(14)
Substituting this into Eq (1) which defines the tissue fitness, we obtain:
ϕ T i s s u e ( t ) = d d t log g ( t ) = d d t log ( ∑ i = 1 N g i ( t ) ) = 1 g ( t ) ∑ i = 1 N d g i ( t ) d t .
(15)
Using the frequency of cells with i-th trait, fi = gi (t)/g(t), this equation can be rewritten as:
1 g ( t ) ∑ i = 1 N d g i ( t ) d t = ∑ i = 1 N f i ( t ) g i ( t ) d g i ( t ) d t = ∑ i = 1 N f i ( t ) d log g i ( t ) d t .
(16)
With the definition of cellular fitness with the i-th trait, ϕCell,i(t), given by Eq (3), the above equation is equivalent to Eq (2). Further, decomposing the cellular fitness at time t into the growth rate (μi(t)) and mortality (mi(t)) of a cell population with i-th trait,
d log g i ( t ) d t = μ i ( t ) − m i ( t ) ,
(17)
the tissue fitness can be written as:
ϕ T i s s u e ( t ) = ∑ i = 1 N f i ( t ) μ i ( t ) − ∑ i = 1 N f i ( t ) m i ( t ) .
(18)
Defining the tissue growth rate (μ(t)) and mortality (m(t)) by the first and second term on the right-hand side, respectively, the tissue fitness in a mixed population can be also represented as μ(t)- m(t).
As described in the first subsection of the Results, assuming that growth dynamics g(t) is modeled by dg(t)/dt = (μ(t)-m(t))g(t), the tissue fitness becomes μ(t)-m(t), where μ(t) is the tissue growth rate and m(t) is mortality. In a pure population, the fitness is roughly regarded as a constant and determined by the cellular processes, division and elimination. When all cells have the same value (on average) for cell cycle time T and cells are never eliminated (i.e. mortality m = 0), the number of cells rises 2N -fold during the next NT hours (of course, the unit of time is arbitrary). Thus, fitting this cellular process to an exponential tissue growth curve, 2N = exp(μNT) or μ = (log2)/T holds. Next, let us consider a case with cell elimination. Since the elimination rate is defined as ε = Neliminated/Nproduced, during T hours, the increment for the number of cells added by division (i.e. Nproduced) is one but a cell is eliminated at rate ε. As a result, the number of cells rises (2-ε)N -fold over the next NT hours. Fitting these cellular processes to an exponential tissue growth curve, (2-ε)N = exp((μ-m)NT) holds. Substituting μ = (log2)/T into this equation, we finally obtain the relationship m = -(log(1-ε/2))/T.
To simulate epithelial tissue growth, we adopted a vertex dynamics model (Fig 2), The vertex dynamics model has been used to study animal epithelial tissue growth, such as Drosophila wing disc, Xenopus tadpole fin, Fundulus epiboly [31,33–36] and plant tissue growth, such as shoot apical meristem and leaf development [61–63].
In the vertex dynamics model, each cell is represented as a polygon formed by linking several vertices, and the motion of each vertex is determined to decrease the energy function U of the system as follows:
η d r i d t = − ∂ U ∂ r i ,
(19)
U = ∑ α Κ 2 ( A α − A 0 ) 2 + ∑ < α , β > Λ α β l α β + ∑ α Γ α 2 L α 2 ,
(20)
Λ α β = { max ( Λ α , Λ β ) ( Λ α + Λ β ) / 2 ,
(21)
where ri is the positional vector of the i-th vertex and η is a coefficient of viscous resistance. The first term of U indicates an area constraint. If the polygonal area Aα of a cell with constant volume is changed, the cell height adjusts. Under such deformation, the elastic energy can be described by a coefficient Κ and a natural area A0. The second term represents the line tension and/or cell-cell adhesion on a cell edge lαβ between cell α and cell β. Larger values of its coefficient Λαβ generate stronger tension or weaker cell-cell adhesion. Λαβ is a key parameter to regulate tissue fluidity, that is, how the tissue behaves like liquid when external forces are applied. For smaller values for Λαβ, the effect of area constraint is relatively large and each cell moves so as to maintain its apical area as near to the natural value as possible. In addition, a larger cell-cell contact length is preferable (i.e., the energy level is lowered). This leads to easier cell deformation and more frequent cell-cell rearrangements when forces (due to division of surrounding cells) act on the cell. In contrast, when Λαβ takes larger values, the force of isotropic shrinking shows a relative increase and a shorter cell-cell contact length is preferable, making cellular deformation and cell-cell rearrangement more difficult. Consequently, the tissue fluidity decreases. The third term of Eq (20) is the elastic energy of the cell perimeter Lα. Its coefficient Γα determines the magnitude of apical contractility. In the first three (2nd-4th) subsections of the Results, we considered the pure population case where all cells have the same mechanical properties. Thus Λαβ and Γα are simply referred to as Λ and Γ. In the last two subsections of the Results, it is possible that the coefficient of line tension Λ can vary between cells. The effective value of Λ at the contact surface between cells that have different values (Λα and Λβ) might depend on underlying molecular mechanisms. For example, when the coefficient of line tension is regulated by the amount of homophilic adhesive molecules, its effective value Λαβ on the focal edge is regarded as max(Λα, Λβ) or simply approximated by the average (Λα+Λβ)/2 (Eq (21)). Although the results obtained in this study did not change qualitatively, we chose to use the former, i.e., max(Λα, Λβ), for the simulations performed in the final subsection of the Results.
The model is usually used after non-dimensionalization that is done by introducing the following variables and parameters:
t ^ ≡ δ t , r ^ i ≡ r i A 0 , U ^ ≡ U Κ A 0 2 , η ^ ≡ η δ Κ A 0 , Λ ^ α β ≡ Λ α β Κ A 0 , Γ ^ α ≡ Γ α Κ A 0 , l ^ α β ≡ l α β A 0 , L ^ α ≡ L α A 0 .
Then Eqs (19)–(21) are represented as:
η ^ d r ^ i d t ^ = − ∂ U ^ ∂ r ^ i ,
(22)
U ^ = 1 2 ∑ α ( A ^ α − 1 ) 2 + ∑ < α , β > Λ ^ α β l ^ α β + ∑ α Γ ^ α 2 L ^ α 2 ,
(23)
Λ ^ α β = { max ( Λ ^ α , Λ ^ β ) ( Λ ^ α + Λ ^ β ) / 2 ,
(24)
where the hat symbol “^” indicates a dimensionless quantity. Λ ^ and Γ ^ are the non-dimensionalized free parameters representing the weights relative to area constraint of the line tension/adhesion and the apical contractility elasticity, respectively. We used (Λ ^, Γ ^) = (0.14, 0.04) as a reference parameter set. For simplicity, we omitted the hat symbol from the normalized quantities in every part except here.
Regarding cell division, we introduced a clock representing the cell cycle for each cell. The clock goes forward linearly with time. When the timer reaches a threshold T, a cell enters the mitotic phase of division. To prevent the synchronization of cell division, we included 20% randomness (uniformly-distributed) into the cell cycle time. During the mitotic phase, the natural area A0 in Eq (20) increases linearly with time, and the actual area of the cell (Aα in Eq (20)) expands to double the area just before entering the mitotic phase. When the area doubles, the cell divides with an axis through its center, then the cell cycle clock is reset to zero and a new cycle time T is chosen. We examined another method for cell division in which a cell does not have a mitotic phase, and the cell divided immediately when the clock reached the threshold described above. In this case, the simulation results were qualitatively the same as those in the case with the mitotic phase. This is consistent with the previous study [56]. Regarding the division orientation, we modeled it as a random variable obeying von Mises distribution f(θ;κ) around the shortest axis with a parameter κ controlling its variation (Fig 2B). The shortest axis was obtained from the elliptical approximation of a cell using its vertices (i.e., tricellular junctions). This modeling of division orientation was because of Hertwig’s rule [64]. Recently, Bosveld et al. [65] examined the mechanism of Hertwig’s rule by observing spatial distribution of tricellular junctions.
As a consequence of push-pull dynamics between cells in a growing tissue, cells rearrange spatially and cell elimination occurs. The rearrangement is implemented by edge reconnection as shown in Fig 2C. This process is called a T1-process in the physics of foam [66] and occurs when the edge length is less than T1-threshold θT1. The elimination is implemented simply by removing a cell whose area is less than T2-threshold θT2, which is called a T2-process. We used θT2 = 0.2 the value of which reflects experimental observations [4]. We also confirmed that our results did not qualitatively change when we used different values for θT2 (specifically, θT2 = 0.05, 0,1, 0.3). As shown in the 4th subsection of the Results, cell size is highly correlated with its stress magnitude defined below, and thus our assumption of the criterion for MCE, i.e. the existence of a cell size threshold for MCE, also includes another criterion, the existence of a threshold for stress magnitude acting on a cell.
All growth simulations started with 250 cells. Edges of the boundary have stronger line tension (3Λα) than inner edges for maintaining the circular shape of the entire tissue and preventing occasional complicated boundary shapes such as protrusions, etc. In calculating the statistics, we excluded the boundary cells, and we also confirmed that the line tension imposed at the boundary we used only minimally affected our results, especially pertaining to the cell elimination rate.
In this study, the stress within a tissue was evaluated by two types of stress tensors used in recent papers [46,47]. These tensors are discrete representations of Cauchy’s stress and are defined by using the forces acting on vertices that compose each polygonal cell. Our analysis is based on the assumption that cell deformation occurs at a sufficiently slow speed that viscous forces are negligible and cells can be considered quasi-static (the forces acting on each vertex were considered approximate to the mechanical equilibrium). In actuality, in our simulations each cell or vertex only moves in response to cell division, and the cell cycle time (for the reference case) is long enough compared to the relaxation time of the vertex dynamics model (except for cells around the division point, the movement of almost all cells is very slow). To date, several methods have been proposed for calculating the force/stress tensor in an epithelial cell population. For example, Chiou et al. and Brodland et al. have discussed in detail how to address stress within a tissue [58,59].
The force acting on vertex i of cell α is composed of pressure inside the cell, Pα and tensions on the two edges that are linked to the vertex, Tij,α, Tik,α. The edge tension involving the cell α was assumed to be half of the tension totally acting on the focal edge, Tij, and the remaining half was allotted to the other cell β that shares the edge, i.e., Tij = Tij,α+Tij,β = 2Tij,α (Fig 6A).
In one stress tensor (denoted by σ(A)α) [46], the force acting at each point along an edge was calculated by linear interpolation using the force vectors on the two vertices at the ends of the edge Fi and Fj:
σ ( A ) α = 1 A α sym [ ∮ c F ⊗ r ] ≅ 1 A α sym [ ∑ < i , j > ∫ 0 1 F i j ( λ ) ⊗ r i j ( λ ) d λ ] ,
(25)
where Fij(λ) = λ Fi+(1-λ)Fj and rij(λ) = λ ri+(1-λ)rj. ri and rj are the positional vectors of vertices i and j from the cell center. The force vector acting on each vertex is defined as:
F i = P α n j k + T i j , α r i j ‖ r i j ‖ + T i k , α r i k ‖ r i k ‖ ,
(26)
where njk is the normal vector to the segment connecting the adjacent vertices of the focal vertex (see also Fig 6A), and rij = rj−ri, rik = rk−ri. For details, see Alim et al. [46].
The other stress tensor (denoted by σ(B)α) was calculated using the pressure Pα, identity matrix I, tension Tij,α and positional vector rij from the focal vertex i to the adjacent one j [47]:
σ ( B ) α = − P α I + ∑ < i , j > T i j A α r i j ⊗ r i j ‖ r i j ‖ .
(27)
For both stress tensors σ(A)α and σ(B)α, Pα and Tij,α were calculated as follows:
P α = − ∂ U ∂ A α = − ( A α − 1 ) ,
(28)
T i j , α = ∂ U ∂ l i j = Λ α β 2 + Γ α L α .
(29)
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10.1371/journal.ppat.1005605 | The SH3BGR/STAT3 Pathway Regulates Cell Migration and Angiogenesis Induced by a Gammaherpesvirus MicroRNA | Kaposi’s sarcoma (KS)-associated herpesvirus (KSHV) is a gammaherpesvirus etiologically associated with KS, a highly disseminated angiogenic tumor of hyperproliferative spindle endothelial cells. KSHV encodes 25 mature microRNAs but their roles in KSHV-induced tumor dissemination and angiogenesis remain unknown. Here, we investigated KSHV-encoded miR-K12-6-3p (miR-K6-3p) promotion of endothelial cell migration and angiogenesis, which are the underlying mechanisms of tumor dissemination and angiogenesis. We found that ectopic expression of miR-K6-3p promoted endothelial cell migration and angiogenesis. Mass spectrometry, bioinformatics and luciferase reporter analyses revealed that miR-K6-3p directly targeted sequence in the 3’ untranslated region (UTR) of SH3 domain binding glutamate-rich protein (SH3BGR). Overexpression of SH3BGR reversed miR-K6-3p induction of cell migration and angiogenesis. Mechanistically, miR-K6-3p downregulated SH3BGR, hence relieved STAT3 from SH3BGR direct binding and inhibition, which was required for miR-K6-3p maximum activation of STAT3 and induction of cell migration and angiogenesis. Finally, deletion of miR-K6 from the KSHV genome abrogated its effect on the SH3BGR/STAT3 pathway, and KSHV-induced migration and angiogenesis. Our results illustrated that, by inhibiting SH3BGR, miR-K6-3p enhances cell migration and angiogenesis by activating the STAT3 pathway, and thus contributes to the dissemination and angiogenesis of KSHV-induced malignancies.
| Kaposi’s Sarcoma (KS), caused by infection of Kaposi’s sarcoma (KS)-associated herpesvirus (KSHV), is a tumor of endothelial cells characterized by angiogenesis and invasiveness. In vitro, KSHV-infected endothelial cells display an increased invasiveness and angiogenicity. KSHV encodes twelve precursor miRNAs (pre-miRNAs), which are processed into at least 25 mature miRNAs. However, the roles of these miRNAs in KSHV-induced tumor dissemination and angiogenesis remain unknown. Here, we investigated KSHV-encoded miR-K12-6-3p (miR-K6-3p) promotion of endothelial cell migration and angiogenesis, which are the underlying mechanisms of tumor dissemination and angiogenesis. We demonstrated that miR-K6-3p promoted cell migration and angiogenesis by directly targeting SH3 domain binding glutamate-rich protein (SH3BGR). Furthermore, we found that STAT3, which was negatively regulated by SH3BGR mediated miR-K6-3p-induced cell migration and angiogenesis. MiR-K6-3p downregulation of SH3BGR, hence relieved SH3BGR direct inhibition of STAT3 resulting in the activation of STAT3 and induction of cell migration and angiogenesis. These results identify miR-K6-3p and its the downstream pathway as potential therapeutic targets for the treatment of KSHV-associated malignancies.
| Kaposi’s sarcoma-associated herpesvirus (KSHV) is a gammaherpesvirus associated with AIDS-associated Kaposi’s sarcoma (KS), primary effusion lymphoma (PEL), and multicentric Castleman’s disease (MCD) [1]. KS is an angiogenic vascular tumor of endothelial spindle cells [2]. KS is characterized by vast aberrant proliferation of small vessels, lack of basement membrane, and excessive leakiness with microhemorrhages and hemosiderin deposition [3]. Although skin lesions are a common manifestation of KS, KS is also a highly disseminated tumor often observed as multifocal lesions and in visceral organs. KS spindle cells have increased invasiveness, which has been attributed to the enhanced expression of several matrix metalloproteinases (MMPs), including MMP-1, MMP-2, MMP-3, MMP-7, MMP-9, and MMP-13 [4–7]. In KS lesions, the majority of the spindle tumor cells are latently infected by KSHV; however, a small number of them also undergo spontaneous lytic replication. Lytic replication generates infectious virions for spreading to other cells and at the same time produces virus-encoded cytokines as well as induces cellular cytokines through viral lytic proteins or de novo viral infection, all of which could contribute to KS pathogenesis by promoting KS angiogenesis, inflammatory infiltration and tumor dissemination through autocrine and paracrine mechanisms [8].
MicroRNAs (miRNAs) are a class of ~22 nt long non-coding small RNAs involved in diverse cellular functions and in all phases of cancer development [9]. They act post-transcriptionally to regulate the expression of large numbers of genes by targeting the complementary gene sequences through its seed region. Usually, miRNAs bind to complementary sequences within the 3’ untranslated region (UTR) of a target gene resulting in mRNA degradation or down-regulation of translation [10]. KSHV encodes 25 mature microRNAs derived from 12 precursor miRNAs (pre-miRs), which are highly expressed during viral latency and in KS tumors [1, 11–16]. KSHV miRNAs not only promote viral latency by directly targeting viral genes or indirectly targeting cellular pathways [17–24], but also modulate apoptosis, cell cycle, cytokine production and secretion, immune evasion, epigenetics, cellular transformation, and angiogenesis by directly regulating KSHV and/or host genes [18, 25–43]. For example, KSHV miR-K6-5p represses breakpoint cluster region protein expression, enhances Rac1 activity, and increases in vitro angiogenesis [40]. Furthermore, miR-K2 and -K5 inhibit tropomyosin 1 and increase anchorage-independent growth and endothelial tube formation [42]. Besides angiogenesis, KSHV miRNAs are also involved in cell motility. Our recent study has shown that, by directly targeting G protein-coupled receptor (GPCR) kinase 2 (GRK2), miR-K3 promotes endothelial cell migration and invasion via activation of the CXCR2/AKT signaling pathway, which might contribute to the dissemination of KSHV-induced tumors [44].
SH3 domains are protein–protein interaction modules that recognize poly-proline motifs in a context dependent manner [45]. These SH3 domains containing adaptors have been implicated in diverse processes including mediation of signaling induced by growth factors, cytoskeletal regulation, vesicle trafficking, membrane dynamics, cell motility, endocytosis, and cell adhesion [45–47]. These processes are crucial in regulating different aspects of cancer cell homeostasis [47]. SH3 domain binding glutamate-rich protein (SH3BGR), which contains a highly conserved SH3 binding motif and a glutamic acid-rich domain at the COOH terminal [48], was initially identified to be involved in heart morphogenesis, and hence, in the pathogenesis of congenital heart disease (CHD) in Down syndrome (DS) [49]. Furthermore, SH3BGR was also implicated in obesity [50]. However, the role of SH3BGR in the pathogenesis of cancer remains unclear.
Because miR-K6-3p is expressed at high level in B cells latently infected by KSHV [51] and in KS tumors [52], we set out to examine the effect of miR-K6-3p on cell mobility and angiogenesis. We found that miR-K6-3p directly targeted SH3BGR to promote endothelial cell migration and angiogenesis. Furthermore, activation of the STAT3 pathway, which was negatively regulated by SH3BGR, contributed to miR-K6-3p-induced endothelial cell migration and angiogenesis. To our knowledge, this is the first report to describe the involvement of a viral miRNA in both cell migration and angiogenesis. Because of the high angiogenicity and invasiveness of KS, our findings reveal a novel mechanism by which KSHV miRNAs contribute to the pathogenesis of KSHV-associated tumors.
To examine the involvement of miR-K6-3p in endothelial cell motility and angiogenesis, we transduced HUVEC with the different MOIs of a lentivirus expressing miR-K6-3p. At MOI 1, miR-K6-3p-transduced HUVEC showed a miR-K6-3p expression level similar to that of KSHV (BAC16)-infected HUVEC (S1A Fig). Thus, we chose MOI 1 for the subsequent transduction experiments. Under this condition, over 94% cells were RFP-positive at day 3 or 4 post-transduction, indicating the successful lentivirus transduction (S1B and S1C Fig). Expectedly, miR-K6-3p markedly inhibited the activity of pGL3-miR-K6-3p sensor reporter, indicating that the miR-K6-3p expression construct was functional in HUVEC (S1D Fig). To examine the effect of miR-K6-3p on cell motility and invasion, Transwell migration and Matrigel invasion assays were performed with miR-K6-3p-expressing HUVEC. As shown in Fig 1A and 1B, HUVEC transduced with miR-K6-3p exhibited strikingly enhanced abilities of migration when compared with cells transduced with the vector control. However, miR-K6-3p did not induce cell invasion. To determine the effect of miR-K6-3p on angiogenesis, we first performed a microtubule formation assay. We found that ectopic expression of miR-K6-3p dramatically increased tube formation in HUVEC compared to control vector (Fig 1C and 1D). Furthermore, RT-qPCR was performed to detect several cytokines that are related to cell migration and angiogenesis. We found that ectopic expression of miR-K6-3p in HUVEC increased the expression levels of MMP1 and MMP13 mRNA transcripts as well as those of VEGFA and VEGFR2 (Fig 1E). Western blotting showed that miR-K6-3p strongly promoted the expression of VEGFA (Fig 1F).
To further confirm the effect of miR-K6-3p on angiogenesis, Matrigel plug assay was performed. By detecting the hemoglobin content in the plug, which represented the relative angiogenesis index, we found that miR-K6-3p significantly increased the angiogenesis in nude mice (Fig 1G and 1H). Hematoxylin and eosin (H&E) staining showed extensive dense neovascularization and hemorrhagic necrotic foci in the miR-K6-3p plugs. There were more smooth muscle actin (SMA)- and VEGFA-positive cells in plugs induced by miR-K6-3p (Fig 1I and 1J). qPCR was used to measure the transcriptional levels of cytokines in plugs. As expected, the levels of MMP1, MMP13, VEGFA, and VEGFR2 mRNAs were significantly elevated in plugs of miR-K6-3p-transduced HUVEC (Fig 1K). Taken together, these data suggest that miR-K6-3p promotes endothelial cell migration and angiogenesis.
To identify the targets of miR-K6-3p, we examined proteins differential expressed between miR-K6-3p- and mpCDH-transduced HUVEC by mass spectrometry analysis. Among the altered cellular proteins, 47 were downregulated by > 2.0 folds by miR-K6-3p (Table 1). Bioinformatics analysis with several programs including TargetScan, RNAhybrid, Findtar, and Pita, was then performed to predict the putative miR-K6-3p targets in these 47 proteins. As shown in Table 2, we predicted 3 proteins that might have miR-K6-3p putative binding sites in their 3'UTR. A luciferase reporter assay confirmed that miR-K6-3p only decreased luciferase activity of the SH3BGR 3’UTR reporter (Fig 2A). Both the protein and mRNA levels of SH3BGR were markedly down-regulated in miR-K6-3p-expressing HUVEC compared to cells expressing the control vector (Fig 2B and 2C). In KSHV-infected HUVEC, both protein and mRNA levels of SH3BGR were also dramatically reduced compared to the mock-infected control (Fig 2D and 2E). Consistent with these observations, there were less SH3BGR-postive cells in KS lesion compared to the normal skin tissue as shown by IHC staining (Fig 2F and 2G).
To confirm the specificity of miR-K6-3p targeting of SH3BGR, we first conducted 3'UTR luciferase reporter assay. As shown in Fig 3A, miR-K6-3p inhibited the reporter activity of SH3BGR 3'UTR in a dose-dependent fashion. Western blotting confirmed that miR-K6-3p attenuated the expression of SH3BGR protein in a dose-dependent manner (Fig 3B). We further mutated the putative miR-K6-3p-binding site in the SH3BGR 3'UTR (Fig 3C). Mutation of the putative binding site abolished the inhibitory effect of miR-K6-3p on the SH3BGR 3'UTR reporter activity, while a mutant mimic lacking the seed sequence failed to inhibit the SH3BGR 3'UTR reporter activity (Fig 3D). The mutant mimic designed to match the SH3BGR 3'UTR mutation (Fig 3C) exhibited a strong inhibitory effect on the mutant 3’UTR reporter (Fig 3D). Consistent with these results, a mimic of miR-K6-3p suppressed the expression of endogenous SH3BGR in HUVEC while a mutant mimic of miR-K6-3p lacking the seed sequence did not (Fig 3E). The expression level of SH3BGR in HUVEC transfected with miR-K6-3p mimic was similar to that of KSHV infection (Fig 3F).
We next determined whether SH3BGR mediated miR-K6-3p-induced cell migration and angiogenesis. MiR-K6-3p-expressing HUVEC were transduced with lentivirus-SH3BGR lacking the native 3’UTR sequence and further analyzed for migration and angiogenesis activities. Overexpression of SH3BGR significantly abolished miR-K6-3p-induced migration at 6 and 12 h post-seeding (Fig 4A and 4B). Meanwhile, overexpression of SH3BGR blocked tube formation of HUEVC induced by miR-K6-3p (Fig 4C and 4D). RT-qPCR showed that overexpression of SH3BGR decreased the expression of MMP13, VEGFA and VEGFR2, but not that of MMP1 (Fig 4E). Western-blotting confirmed the suppression of endogenous SH3BGR by miR-K6-3p (Lane 3 vs lane 1 in Fig 4F). Transduction with lentivirus-SH3BGR increased the expression level of SH3BGR, and inhibited VEGFA expression (Lanes 2 and 4 in Fig 4F). Furthermore, Matrigel plug assay showed that overexpression of SH3BGR not only inhibited miR-K6-3p-induced angiogenesis (Fig 4G and 4H), but also decreased SMA- and VEGFA-positive cells in plugs induced by miR-K6-3p-transduced cells (S2A and S2B Fig). Consistent with these observations, overexpression of SH3BGR reduced the expression of MMP13, VEGFA and VEGFR2 transcripts in miR-K6-3p-induced plugs (Fig 4I).
Since dysregulation of STAT3 is detected in many human cancers including KSHV-infected PEL and KS tumors [53–55], we asked whether STAT3 signaling was involved in miR-K6-3p induction of endothelial cell migration and angiogenesis. In agreement with the previous reports [53, 56, 57], HUVEC cells latently infected by KSHV had increased levels of STAT3 phosphorylation. Importantly, expression of miR-K6-3p alone in endothelial cells was sufficient to increase STAT3 phosphorylation (Fig 5A). Interestingly, there was a negative correlation between the expression level of SH3BGR and STAT3 phosphorylation (Fig 5A). Overexpression of SH3BGR in miR-K6-3p-expressing HUVEC inhibited STAT3 activation (Fig 5B), indicating that SH3BGR negatively regulated the STAT3 activity.
To determine the mechanism of SH3BGR negative regulation of STAT3 activation, we performed co-immunoprecipitation and found that SH3BGR interacted with STAT3 (S3A and S3B Fig and Fig 5C). Overexpression of SH3BGR increased the amount of SH3BGR-immunoprecipiated STAT3 and reduced the level of activated STAT3 (Fig 5C). Similar interaction of SH3BGR and STAT3 was also observed in STAT3-transfected HUVEC cells (Fig 5C). Consistently, overexpression of SH3BGR reduced the level of activated STAT3 in the cytosol and nucleus with simultaneous colocalization of SH3BGR and STAT3 detected by confocal microscope (Fig 5D and S3C Fig). In addition, knockdown of SH3BGR with shRNAs was sufficient to increase the phosphorylated STAT3 level (Fig 5D and S3C Fig). To examine whether SH3BGR may directly associate with STAT3, GST pull-down assays were performed. As shown in Fig 5E, GST-STAT3 was robustly associated with His-SH3BGR, whereas His-SH3BGR was not detected in the GST control pull-down complex. These data indicated that SH3BGR was directly associated STAT3 to inhibit its activation, and miR-K6-3p direct repression of SH3BGR relieved its inhibition of STAT3 phosphorylation, resulting in higher level of STAT3 activation.
To determine whether STAT3 mediates miR-K6-3p-induced cell migration and angiogenesis, we transduced miR-K6-3p-expressing HUVEC with a mixture of shRNAs to knock down STAT3 expression (shSTAT3; Fig 6A and S4 Fig). Transwell migration and microtubule formation assays showed that knockdown of STAT3 inhibited miR-K6-3p-induced migration and tube formation (Fig 6B and 6C). To further confirm these observations, AG490, an inhibitor of JAK2/STAT3 pathway, was used to treat miR-K6-3p-transduced HUVEC. AG490 not only decreased the level of phosphorylated STAT3 (Fig 6D) but also inhibited cell migration and tube formation (Fig 6E and 6F). Furthermore, Matrigel plug assay showed that knock-down of STAT3 by shSTAT3 inhibited miR-K6-3p-induced angiogenesis (Fig 6G). Knockdown of STAT3 in vivo also blocked miR-K6-3p induction of MMP13, VEGFA, and VEGFR2 (Fig 6H).
Together these data indicated that miR-K6-3p-induced cell migration and angiogenesis is mediated by activating the STAT3 pathway.
To further confirm that miR-K6-3p induced endothelial cell migration and angiogenesis by targeting the SH3BGR/STAT3 pathway, we infected HUVEC and obtained latent cultures of a BAC16 miR-K6_mut virus with miR-K6 deleted from the KSHV genome. RT-qPCR did not detect the expression of miR-K6 in cells infected with the miR-K6_mut virus while the expression of other miRNAs was not affected (S5A Fig). The levels of LANA in miR-K6-mut-infected cells were similar to that of cells infected by the wild type (WT) KSHV (S5B Fig). As shown in Fig 7A and 7B, the level of cell migration in HUVEC infected by the mutant virus was significantly lower than that of cells infected by the WT virus albeit it remained higher than that of uninfected cells. Similarly, deletion of miR-K6 decreased the level of KSHV-induced tube formation in HUVEC (Fig 7C and 7D). Mutant cells remained to have higher angiogenesis activity than that of uninfected cells. Consistent with these observations, cells infected by the mutant virus had decreased levels of MMP1, MMP13, VEGFA, and VEGFR2 mRNAs than those infected by the WT virus (Fig 7E). Importantly, cells infected by the mutant virus had SH3BGR expression at level similar to mock infected cells. However, cells infected by the mutant virus reduced STAT3 phosphorylation and VEGFA expression compared to cells infected by the WT virus (Fig 7F).
Considering that deletion of miR-K6 abolished the expression of both miR-K6-3p and miR-K6-5p, to show that the observed phenotypes were due to the absence of miR-K6-3p from the KSHV genome, we transfected WT KSHV-infected HUVEC with a specific miR-K6-3p inhibitor. As expected, inhibition of miR-K6-3p was sufficient to reduce cell migration and angiogenesis in WT KSHV-infected HUVEC (S6 Fig and Fig 6).
To further confirm the observations, we performed knock-down of SH3BGR with a mixture of shRNAs (shSH3BGR) in mutant cells. As expected, knock-down of SH3BGR in miR-K6_mut-infected HUVEC with shSH3BGRs increased the level of activated STAT3 compared to cells transduced with the control vector (Fig 8A and S7 Fig). Meanwhile, knock-down of SH3BGR partially increased cell migration and angiogenesis of the cells infected by miR-K6_mut virus (Fig 8B and 8C). Similarly, overexpression of STAT3 in miR-K6_mut-infected cells increased cell migration and angiogenesis (Fig 8D, 8E and 8F). Similar increase in cell migration and angiogenesis was also observed in the mutant cells following overexpression of miR-K6-3p (S8 Fig). Together these results show that, in the context of KSHV infection, miR-K6 activates the STAT3 pathway to promote cell migration and angiogenesis by targeting SH3BGR.
SH3BGR, a protein of 239 amino acids, contains a conserved N-terminal region, and a less conserved C-terminal region highly enriched in glutamic acid residues [58]. The N-terminal region contains a proline-rich sequence (PLPPQIF), which conforms both the SH3 binding motif (PXXP) [59] and the Homer EVH1 binding motif (PPXXF) [60]. SH3BGR is involved in the pathogenesis of Down syndrome congenital heart disease and obesity [49, 50]. However, whether dysregulation of SH3BGR expression is associated with the development and progression of tumors, including KS, remains unknown. In this study, we detected the expression of SH3BGR in the skin and endothelial cells. We revealed that SH3BGR suppresses cell migration and angiogenesis by interacting and inhibiting STAT3 activation. Furthermore, KSHV miR-K6-3p directly targets SH3BGR to activate the STAT3 pathway, resulting in the enhanced cell migration and angiogenesis.
STAT3 is constitutively active in a significant proportion of human solid tumors and regulates a number of important functions in tumorigenesis, including cell cycle progression, apoptosis, tumor angiogenesis, invasion and metastasis, and tumor cell evasion of immune system [54, 55, 61–64]. Recent studies suggest that persistent activation of STAT3 also plays a critical role in KSHV-associated tumors [65, 66]. For instance, KSHV latent infection in primary endothelial cells resulted in aberrant and chronic activation of STAT3, and activation of STAT3 enhanced KSHV latency [57, 67]. STAT3 signaling was shown to induce the pro-survival protein survivin, which was found to be an important factor in STAT3-mediated pro-survival effects in PEL cells [53]. De novo KSHV infection resulted in lymphatic endothelial cell reprogramming of blood endothelial cells by activating STAT3 in a gp130-dependent but vIL-6-independent manner [68]. Therefore, STAT3 signaling, in part, mediated via gp130, is important for the maintenance of KSHV latency and for the survival of latently infected cells. Here, we have demonstrated that ectopic expression of miR-K6-3p in HUVEC led to the increased STAT3 signaling while deletion of miR-K6 from the KSHV genome significantly decreased the phosphorylation level of STAT3 in KSHV-infected endothelial cells. Thus, miR-K6-3p contributes to aberrant STAT3 signaling during KSHV infection. Further, our co-immunoprecipitation and GST-pull down results indicated that SH3BGR physiologically interacted with STAT3 and inhibited STAT3 activation in normal endothelial cells. However, when endothelial cells were infected by KSHV, the expression of miR-K6-3p resulted in the downregulation of SH3BGR, which released STAT3 from SH3BGR inhibition and activated STAT3. The outcomes were enhanced cell migration and angiogenesis. Thus, our results revealed a novel role of STAT3 signaling in the metastasis and angiogenesis of KSHV-related tumors.
De novo KSHV infection of human endothelial cells results in an increased secretion of several growth factors, cytokines, chemokines, and angiogenic factors, including MMPs and VEGF, which are involved in cell motility [69, 70] and angiogenesis [71], respectively. Indeed, several MMPs have been reported to contribute to KS spindle cell migration and invasion [4–7]. Consistently, in the current study, MMP1 and MMP13 were significantly increased following ectopic expression of miR-K6-3p. Inversely, deletion of miR-K6 from the KSHV genome resulted in the reduced induction of MMPs by KSHV in endothelial cells. However, either overexpression of SH3BGR or knock-down of STAT3 did not change MMP1 level, indicating that miR-K6-3p may target the other signaling pathway rather than the SH3BGR/STAT3 axis to regulate MMP1.
Our results also showed that deletion of miR-K6 from the KSHV genome did not completely abolish the capabilities of migration and angiogenesis of KSHV-infected endothelial cells. This is more likely to reflect the fact that, besides miR-K6, KSHV encodes a number of proteins and miRNAs that may also contribute to tumor angiogenesis and metastasis. While miR-K6 was removed from the KSHV genome, other viral products could still exert their functions to regulate cell migration and angiogenesis. For example, KSHV-encoded latent nuclear antigen (LANA), G protein-coupled receptor (vGPCR), interleukin-6 (vIL-6), ORF-K1, and ORF-K15 have been shown to regulate cell migration and angiogenesis [72–79]. Similarly, our recent study also indicated that miR-K3 promotes endothelial cell migration and invasion [44].
Interestingly, either inhibition of SH3BGR or overexpression of STAT3 did not totally reverse KSHV-induced cell migration and angiogenesis. These results imply that, besides the SH3BGR/STAT3 pathway, miR-K6-3p might target the other cellular genes to induce cell migration and angiogenesis. In addition, both miR-K6-5p and miR-K6-3p were derived from the same precursor miRNA and miR-K6-3p might also be involved in KSHV-induced angiogenesis [40].
In summary, we revealed that miR-K6-3p promotes endothelial cell migration and angiogenesis by targeting the SH3BGR/STAT3 pathway (Fig 8G). These results constitute parts of the novel regulatory networks between KSHV miRNAs and their multiple targets. Considering the multiple functions and targets of viral miRNAs, further studies will be required to explore the molecular mechanisms by which the other KSHV miRNAs contribute to KSHV-induced malignancies.
The clinical section of the research was reviewed and ethically approved by the Institutional Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (Nanjing, China; Study protocol # 2015-SR-116). Written informed consent was obtained from all participants, and all samples were anonymized. All participants were adults.
The animal experiments were approved by the Institutional Animal Care and Use Committee of Nanjing Medical University (Animal protocol # NJMU/IACUC_2013-8-18-01). All animal care and use protocols were performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals approved by the State Council of People's Republic of China.
Primary human umbilical vein endothelial cells (HUVEC) were isolated from freshly obtained human umbilical cords by digesting the interior of the umbilical vein with collagenase (Sigma, St. Louis, MO, USA) and cultured in complete EBM-2 culture media (LONZA, Allendale, NJ, USA) [80]. HUVEC were used between passage 3 and 6. HEK293T were cultured as previously described [81]. ISLK-BAC16 cells and iSLK-BAC16△miR-K6 cells were maintained as previously described [82]. All cells were cultured at 37°C in a humidified, 5% CO2 atmosphere.
The construct pGL3-SH3BGR 3’UTR and the mutant SH3BGR 3'UTR construct were generated by cloning the full length of the SH3BGR 3’UTR sequence and mutant 3’UTR sequence into the downstream of the luciferase sequence in the pGL3-Control plasmid (Promega, Shanghai, China), respectively. Human SH3BGR gene with a Flag tag at the C-terminus was amplified using the cDNA of HUVEC as PCR templates and inserted into the lentiviral transferring plasmid pHAGE to generate recombinant pHAGE-SH3BGR as previously described [78, 83]. The miRNA (miR-K6-3p) expressing plasmid was constructed by two steps: First, the miR-30 precursor stem-loops plus RFP coding sequences were amplified from the pTRIPZ plasmid (Open Biosystems, AL, USA) and inserted into another lentiviral plasmid pCDH-CMV-MCS-EF1-copGFP (System Bioscience, CA, USA) to create a new lentiviral plasmid, which has GFP and RFP two signals and was designed as modified pCDH (mpCDH) [44]. Second, the precursor stem-loops of miR-K6-3p was amplified using primers (forward) 5’-CAG AAG GCT CGA GAA GGT ATA TTG CTG TTG ACA GTG AGC G-3’ and (reverse) 5’-CTA AAG TAG CCC CTT GAA TTC CGA GGC AGT AGG CA-3’ and cloned into mpCDH. Meanwhile, the mpCDH plasmid was also used for the short hairpin RNA (shRNA) expressing lentiviral vector. ShRNA complementary sequences to SH3BGR and STAT3 were listed in Table 3. The pCMV3-Flag-STAT3 construct containing STAT3 cDNA was purchased from Sino Biological Inc. (Beijing, China). The GST-STAT3 plasmid was constructed by inserting the full-length STAT3 open reading frame into pGEX-4T-3 (GE Healthcare, Piscataway, USA). Plasmid expressing SH3BGR fusion protein was constructed in the pET32a(+) vector (EMD Chemicals, CA, USA). In this study, the control of pHAGE-SH3BGR was named as pHAGE, and the controls of miR-K6-3p and all the shRNA were a modified pCDH (mpCDH for short).
Transfection of HUVEC was performed with the Effectence transfection reagent (Qiagen, Valencia, CA, USA), while the other transfections were performed with Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. For luciferase assay, HEK293T cells (1×105) were co-transfected with miRNA mimic, luciferase reporter DNA and Renilla vector pRL-TK (Promega, Madison, WI), and then harvested at 24 h post-transfection. Relative luciferase activity was assayed using the Promega dual-luciferase reporter assay system. Firefly activity was normalized to internal Renilla luciferase levels.
Anti-KSHV LANA rat monoclonal antibody (MAb) was purchased from Advanced Biotechnologies Inc.(Columbia, MD, USA) [81]. Anti-phospho-STAT3 (Y705) rabbit MAb, anti-STAT3 mouse polyclonal antibody (PAb), and anti-Flag M2 rabbit MAb were obtained from Cell Signaling Technologies (Beverly, MA, USA). Anti-SH3BGR mouse MAb, anti-GAPDH mouse MAb, anti-α-Tubulin mouse MAb, and horseradish peroxidase (HRP)-conjugated goat anti-mouse or anti-rabbit IgG were all purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Anti-His mouse MAb, anti-mouse immunoglobulin G (IgG) and anti-GST mouse MAb were from Beyotime Institute of Biotechnology (Nantong, Jiangsu, China). Western blotting analysis was performed as described previously [84, 85]. AG490, a JAK2 inhibitor, was purchased from Selleck Chemicals (Shanghai, China).
Wild type recombinant KSHV BAC16 and a KSHV mutant with miR-K6 deleted, BAC16△miR-K6, were previously described [24, 40, 82]
Production of KSHV BAC16 virus was performed according to the previous study [44].
To obtain the recombinant lentivirus, the virus-packaging cells HEK293T were seeded for 24 h later and then co-transfected with lentiviral plasmids, packaging vector psPAX2 and envelope vector pMD2.G as previously described [86]. The virus containing supernatants were collected 48 h after transfection.
Ten-fold serial dilution of lentivirus was prepared in PBS from 10−1 to 10−10. The HUVEC cells at 50% confluence was prepared in a 48-well culture plate. A 100 μl of each virus dilution was added in each well. The plate was incubated at 37°C for 3 h to allow adsorption. Then 100 μl of maintenance medium was added in each well and incubated at 37°C in 5% CO2 for 48 h. The number of fluorescent cells in the lowest dilution of virus was considered as an end point to calculate the virus titer.
Transwell migration assay was performed as previously described [44]. The number of migrated cells was determined by counting stained cells and the average cell number per field for each well was calculated. The counting was blinded by three individuals, including one who was blinded to the results. For each experiment, at least four replicate wells were used and the representative images were taken from five randomly selected fields of each well.
Total RNA was isolated from cells by Trizol reagent (Invitrogen, Carlsbad, CA, USA) and subjected to the Promega Reverse-Transcription Kit (Promega, Madison, WI) to obtain cDNA. The sequences of specific primers of RT-qPCR for several genes were listed in Table 4. Quantitative PCR (qPCR) was performed using SYBR Premix Ex Taq Kit (TaKaRa Biotechnology Co. Ltd., Dalian, China) according to the manufacturer’s instructions.
Mass spectrometry analysis was done as previously described [87]. Briefly, proteins were reduced, alkylated, and digested with trypsin. The resulting peptides were labeled with isobaric tandem mass tags (TMT 6-plex; Thermo Fisher Scientific Inc., San Jose, CA), mixed, and fractionated by strongcation exchange chromatography. Peptide digests were analyzed by nanoscalereversed phase liquid chromatography (Easy-nLC, Thermo Fisher Scientific Inc.) coupled online with an LTQ-OrbitrapVelos mass spectrometer (Thermo Fisher Scientific Inc.). Spectra were searched using Maxquant (version 1.2.2.5), and results were filtered to 1% FDR at the unique peptide level using the COMPASS software suite.
The KS clinical tissue specimens and the normal skin tissue specimens were provided by the First Affiliated Hospital of Nanjing Medical University for hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining. All the samples were formalin-fixed, parafin-embedded, and immunostained with the indicated antibodies as previously described [44, 86]. The results were processed and analyzed using Image-Pro Plus 6.0 image analysis system (Media Cybernetics, Silver Spring, MD). Five random fields were chosen under the microscope and further measured for area and intensity of the expression of target protein, with the expression level of target protein calculated based on average absorbance (gray).
Matrigel plug assay was performed as previously described [78, 86, 88]. Male athymic BALB/c nu/nu mice of 3-4-week-old (Nanjing Biomedical Research Institute of Nanjing University, Nanjing, China) were maintained under pathogen-free conditions. The cells were harvested at subconfluence, washed with phosphate-buffered saline and resuspended in serum-free medium. Cell aliquots (0.2 ml) were mixed with 0.4 ml of High Concentration Matrigel (BD Biosciences, Bedford, MA, USA), and the mixture was immediately injected subcutaneously into the right flanks of nude mice. The mice were killed 10 days after the injection, and the Matrigel plugs were removed from the mice. The hemoglobin content of the Matrigel was determined using Drabkin’s reagent kit (Sigma-Aldrich) according to the manufacturer’s instructions. The final hemoglobin concentration was calculated from a standard calibration curve after spectrophotometric analysis at 540 nm.
The microtubule formation assay was performed on micro-slide Angiogenesis ibiTreat (ibidi, Martinsried, Germany) coated with 10 ul of Matrigel (BD Biosciences) as previously described [88]. HUVEC was seeded at 5 x 103 cells per well with 50 μl serum-free basic medium. Tubule formation was quantified by counting the number of branching points and measuring the total length of the capillary tubes in at least three images using NIH Image software.
Immunoprecipitation was performed using a standard protocol. Briefly, cells were collected, rinsed twice with cold PBS, and lysed in Lysis/Wash buffer (150 mM NaCl, 1 mM EDTA, 5% glycerol, 1% NP-40, 25 mM Tris-HCl, pH7.4) supplemented with protease inhibitors, phosphatase inhibitors and phenylmethylsulfonyl fluoride (PMSF). Lysates were cleared by centrifugation at 11,000 g at 4°C for 10 min. Supernatants were incubated with 5 μg of the specified antibody overnight at 4°C followed by 50 μl of protein A-beads for 4 h at 4°C with gentle rotation. The beads were then pelleted at 5, 000 g for 2 min and washed 3 times in 1 ml ice-cold Lysis/Wash buffer containing 1mM PMSF and 50 g/ml aprotinin. Antibody-protein conjugates were eluted by boiling (5 min) and samples were then subjected to SDS-PAGE and immunoblotting as described above.
Immunolocalization was performed as previously described [78]. The stained cells were examined and photographed using a Zeiss Axiovert 200M laser scanning confocal microscope (Carl Zeiss, Freistaat Thuringen, Germany).
Purification of GST- or His-tagged proteins and GST pull-down assay were performed as previously described [89]. Briefly, GST fusion proteins were purified from bacteria using GST-BindTM Resin (Novagen, Darmstadt, Germany) according to the manufacturer’s protocol and resuspended in PBS containing 0.5% Nonidet P-40 and protease inhibitors. Equal amounts of GST or GST-STAT3 fusion protein were incubated with purified SH3BGR protein (His-tagged) for 2 h at 4 ℃ in binding buffer (50 mM Tris-HCl, PH 7.5, 100 mM NaCl, 0.25% Triton-X100, 35 mM 2-Me). Proteins were eluted and SH3BGR binding to GST-STAT3 fusion protein was then detected by immunoblotting with antibody against His.
Quantitative data were presented as mean ± SD Two-sided Student’s t-test was used to determine the significance between different treatment groups. P < 0.05 was considered statistically significant. All the experiments were repeated three times, unless otherwise stated.
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10.1371/journal.pcbi.1002473 | Ligand-Dependent Conformations and Dynamics of the Serotonin 5-HT2A Receptor Determine Its Activation and Membrane-Driven Oligomerization Properties | From computational simulations of a serotonin 2A receptor (5-HT2AR) model complexed with pharmacologically and structurally diverse ligands we identify different conformational states and dynamics adopted by the receptor bound to the full agonist 5-HT, the partial agonist LSD, and the inverse agonist Ketanserin. The results from the unbiased all-atom molecular dynamics (MD) simulations show that the three ligands affect differently the known GPCR activation elements including the toggle switch at W6.48, the changes in the ionic lock between E6.30 and R3.50 of the DRY motif in TM3, and the dynamics of the NPxxY motif in TM7. The computational results uncover a sequence of steps connecting these experimentally-identified elements of GPCR activation. The differences among the properties of the receptor molecule interacting with the ligands correlate with their distinct pharmacological properties. Combining these results with quantitative analysis of membrane deformation obtained with our new method (Mondal et al, Biophysical Journal 2011), we show that distinct conformational rearrangements produced by the three ligands also elicit different responses in the surrounding membrane. The differential reorganization of the receptor environment is reflected in (i)-the involvement of cholesterol in the activation of the 5-HT2AR, and (ii)-different extents and patterns of membrane deformations. These findings are discussed in the context of their likely functional consequences and a predicted mechanism of ligand-specific GPCR oligomerization.
| The 5-HT2A receptor for the neurotransmitter serotonin (5-HT) belongs to family A (rhodopsin-like) G-protein coupled receptors (GPCRs), one of the most important classes of membrane proteins that are targeted by an extensive and diverse collection of external stimuli. Recently we learned that different ligands targeting the same GPCR can elicit different biological responses, but the mechanisms remain unknown. We address this fundamental question for the serotonin 5-HT2A receptor, because it is known to respond to the binding of structurally diverse ligands by producing similar stimuli in the cell, and to the binding of quite similar ligands with dramatically different responses. Molecular dynamics simulations of molecular models of the serotonin 5-HT2A receptor in complex with pharmacologically distinct ligands show the dynamic rearrangements of the receptor molecule to be different for these ligands, and the nature and extents of the rearrangements reflect the known pharmacological properties of the ligands as full, partial or inverse activators of the receptor. The different rearrangements of the receptor molecule are shown to produce different rearrangements of the surrounding membrane, a remodeling of the environment that can have differential ligand-determined effects on receptor function and association in the cell's membrane.
| Serotonin 2A receptors (5-HT2AR) are a very well characterized family of G-protein coupled receptors (GPCRs) in the amine sub-class of rhodopsin-like class A GPCRs [1], [2]. The 5-HT2ARs are targeted by chemically and pharmacologically distinct classes of ligands which include antidepressants, anxiolytics, antiemetics, antipsychotics and anti-migraine agents. Notably, some agonists exhibit hallucinogenic properties [2], [3] that have been attributed to specific manners of activation of these receptors [4], [5]. Even when they share key structural features, such as the indole moiety of the non-hallucinogen 5-HT and the hallucinogen LSD, the 5-HT2AR ligands have been shown to be able to bind differently to the receptor molecule, and to exhibit different pharmacological properties [2], [6], [7], [8]. Understanding the relation between the different modes of binding of structurally diverse compounds in the 5-HT2AR binding site, and the pharmacological responses they elicit, has therefore been of great interest in the quest for understanding the function of the 5-HT2AR and especially its role in hallucinogenesis [5]. Important clues came from in vivo studies demonstrating that behavioral responses to different 5-HT2AR ligands correlate with distinct transcriptome fingerprints for the ligands [4]. However, while it remains unclear how ligand binding induces distinct conformational states of the 5-HT2AR, and how this can result in different pharmacological outcomes [5], the significant variability in receptor conformations that can be induced by different ligands has recently been demonstrated for the cognate β2-adrenergic receptor [9].
Structural evidence for differential effects of the GPCR ligands in relation to receptor function should be reflected in the variability of rearrangements in the key structural elements involved in the various activation states of the receptors, e.g., the structural motifs/functional microdomains (SM/FMs) [10] (see Figure 1A) that characterize GPCR activation [5], [11], [12], [13]. Specific SM/FMs have been reported from studies of a large variety of GPCRs [10], [14], [15], [16], [17], and their dynamic signatures include (i)-the flipping of the toggle switch W6.48 (Trp336, identified here by the Ballesteros-Weinstein generic numbering [18]) in the cluster of conserved aromatic residues in TM5 and TM6, (ii)-the opening/closing of the ionic lock between the DRY motif (D3.49–R3.50–Y3.51) and E6.30, involved in the movement of the intracellular (IC) end of TM6 away from TM3, and (iii)-the dynamics of the conserved NPxxY motif at the IC end of TM7 that connects as well to H8. These are elements of activation common to many GPCRs (see [5], [10], [11], [13], [14], [15], [16], [17], [19]), and their status in the X-ray structures of various GPCRs has been evaluated [12], [20], [21], [22], [23], [24], [25]. It is still unclear, however, how the binding of different ligands affects these elements of GPCR activation and how they connect to the mechanisms of the ligand-driven receptor oligomerization that has been shown to be critical for GPCR function [26], [27], [28], [29], [30], [31].
To shed new light on these central mechanistic questions from the perspective of ligand-dependent conformational states involved in the activation and oligomerization of GPCRs in their membrane environment, we performed large-scale molecular dynamics (MD) simulations of 5-HT2AR in complex with ligands exhibiting different pharmacological properties: the full agonist 5-HT, the partial agonist LSD, and the inverse agonist Ketanserin (KET) (Figure 2A). The simulation results show that the three ligands affect differently the dynamics of SM/FMs monitored in the simulations (Figure 1B), which achieve distinct conformations that are consistent with the pharmacological classification of these ligands. Moreover, the simulations show that the ligand-bound GPCRs produce differential responses in the lipid membrane surrounding the receptor, as reflected in the spatial pattern of the remodeling of membrane thickness. These trajectories reveal as well the modes and effects of direct receptor-cholesterol interaction. Recently we have described the development and implementation of a new method, CTMD (Combined conTinuum and Molecular Dynamics), for quantitative analysis of the membrane remodeling pattern based on MD trajectories [32]. With this method we account for both the membrane remodeling energy and the energy cost of any partial (incomplete) alleviation of the hydrophobic mismatch by this remodeling of the membrane. From the quantitative analysis with CTMD of the simulation results for the monomeric 5-HT2AR we identified ligand-specific local membrane perturbations that can produce different patterns of 5-HT2AR oligomerization driven by hydrophobic mismatch [32]. Our results lead to the prediction that the dimerization interfaces for 5-HT2AR oligomers will be different when the receptor binds ligands with different pharmacological properties (inverse agonist, partial agonist, or agonist), as suggested earlier [27]. Notably, the extent of membrane-driven oligomerization of a 5-HT2AR in the inverse agonist-bound state is predicted to be larger than in the agonist-bound state. These predictions are consistent with previous experimental findings on cognate GPCRs [27], [28], [31], supporting the link we identify here between ligand-dependent conformational changes in GPCRs and differences in local membrane perturbations.
The main dynamic rearrangements observed in the simulations of the 5-HT2AR when it binds each of the ligands, are described below with reference to the SM/FMs (Figure 1A) identified in this family of GPCRs [5]. The sequential order of the description is determined by the order in which these changes appear in the simulation trajectories of the 5-HT2AR bound to the full agonist 5-HT (Figure 1B).
Comparison of results in Figure 4 with Figure 3 brings to light the differences among the dynamic mechanisms connected with the binding of the three different ligands to the 5-HT2AR, as detailed below.
The nature of similarities and differences observed in the dynamics of the 5-HT2AR when it binds each of the three ligands was further evaluated with Combined Essential Dynamics (Comb-ED, see Methods) [42] performed on concatenated trajectories for 5-HT&LSD, 5-HT&KET, and LSD&KET, each combining the last 100 ns of the individual trajectories for the pair. The comparison of such combined trajectories by their projection along their first and second eigenvectors is shown in Figure 6A, which illustrates the differences in the conformational spaces sampled by the 5-HT2AR bound to different ligands. Clearly, along the first eigenvector, the conformational spaces sampled by the 5-HT-bound and LSD-bound receptor are seen to be more similar to each other than either one is to the space sampled by KET-bound 5-HT2AR (note that the first and second eigenvectors are different in each plot because the concatenated trajectories differ, so that the sampled spaces shown in the plots for any one ligand-bound receptor appear at different positions).
The comparison in Figure 6B–C shows the differences in a structural context by indicating where the largest differences occur, as monitored by the magnitudes of the projections on the first eigenvectors (color coded from red, green to blue representing magnitudes from large, median to small, respectively). Also evident in this figure is the greater similarity between the dynamics of the 5-HT and LSD-bound receptors (Figure 6B–C, top panel). Comb-ED analysis identifies only insignificant differences between the agonist- vs. partial agonist-bound states of the receptor, with some variations in the positioning of the juxta-membrane H8 and in TM4 (Figure 6B–C, top panel). However, the structure of 5-HT2AR in complex with either 5-HT or LSD is clearly distinct from that with KET bound, as seen in Figure 6B–C where the Comb-ED detects differences in TM5–6 (linked by IL3) and TM4 in the 5-HT vs. KET comparison (middle panel), and LSD vs. KET (bottom panel).
Differences between 5-HT2AR complexes with the inverse agonist, and those with the agonists 5-HT or LSD, are apparent as well for TM1, TM3 and H8 (Figure 6B–C, middle and bottom panels). Thus, in the KET-bound receptor, Comb-ED identifies the movement of TM5 and TM6 toward TM3 at the IC end, consistent with the observed closing of the ionic lock in the inverse agonist state (Figure 4D,F, right panel). Furthermore, differences are evident at the EC end of TM6 between KET- and 5-HT-induced conformations, in agreement with the different level of kink in TM6 around the P6.50 in the two systems (compare Figures 3A and 4A). In addition, in line with the observed differences in the dynamics of NPxxY motif (Figures 3F and 4E), the Comb-ED analysis in the KET-bound receptor detects the motion of H8 toward TM7 to close the angle between them, consistent with earlier studies of cognate GPCRs [22], [37], [43].
Based on the Comb-ED results suggesting structural differences as well in TM1 and TM4 between the states of 5-HT2AR stabilized by the three ligands (Figure 6B–C), we found different levels of tilt in TM1 and TM4 in the three states of the receptor. Thus, in 5-HT, LSD, and KET trajectories TM4 forms angles of 12°, 16° and 22°, respectively, with the membrane normal z axis; TM1 tilts so that in KET-bound compared to 5-HT-bound receptors its EC end is 3 Å closer to TM7 and its IC side is 1.5 Å farther from TM7. The differences in conformational changes of TM1 are consistent with the available X-ray structures of the activated GPCR, where a repositioning of the IC end of TM7 towards TM1 is reported in active β2AR [23] and opsin structures [20], [21]. As discussed below, these tilt differences in TM1 and TM4 are reflected in the response of the membrane to the interaction with the protein, and thereby can affect the ligand-regulated oligomerization of the 5-HT2AR.
The nature of the changes occurring in the transition from the “activated” 5-HT-bound state of the receptor, to the KET-bound “inactivated” state, is evidenced by the application of Comb-ED analysis to combined trajectories involving the KET-substituted simulation (started from an equilibrated 5-HT-bound receptor) (Figure 5B). Separately, two Comb-ED analysis were performed: One comparing the last 100 ns from the KET-substituted and the original KET-bound simulations, and the other comparing the KET-substituted and the 5-HT-bound simulation. The projections along the first eigenvector of these combined trajectories (Figure 5B) reveal the internal consistency of the results and show that, upon KET substitution, the 5-HT2AR structure deviated from the 5-HT-stabilized conformation and became similar to that stabilized by KET in our earlier simulation, with TM4 and TM6 helices changing the most. Consistent with the results in Figure 6, in the KET-substituted simulation the IC end of TM6 moved towards TM3, and TM4 became tilted.
In addition to Comb-ED analysis of pair-wise concatenated trajectories, we applied Comb-ED as well to all four trajectories (5-HT, LSD, KET, KET-substituted) concatenated together. The results (Figure S3 in Text S1) clearly show that KET-substitution transitions the receptor from the conformational states visited by 5-HT to those most visited when KET is bound in the receptor.
From the results of the comparative simulations we have identified two mechanisms of membrane re-organization in response to the conformational changes associated with the dynamics of the ligand-bound receptor: (i)-the direct interactions of the receptor with the Cholesterol (Chol) constituent of the membrane, and (ii)-the deformation of the membrane around the GPCR, which modulates the local thickness of the bilayer and the hydrophobic mismatch that can drive oligomerization of the 5-HT2AR [32].
The distinct conformational changes in the receptor produced by the binding of the different ligands (see above) produce different patterns of bilayer deformations around the receptor protein in complex with the different ligands (Figure 8). This difference is a result of the tendency of the lipids to minimize the hydrophobic mismatch at various TMs, i.e., the difference in the hydrophobic lengths presented to the membrane by the corresponding TMs in the different receptor complexes (see detailed discussion in [32]). Therefore, hydrophobic thickness profiles of membranes around 5-HT2AR in the simulated complexes with 5-HT, LSD, and KET, shown in Figure 8, reveal remarkable differences in the membrane organization around individual TMs in the three systems. For example, the membrane appears thinner around TM4 and TM6 in 5-HT (left panel) than in the KET simulation (right panel), whereas at TM1 the bilayer is thicker in the LSD (middle panel) than in the complexes with 5-HT or the KET.
We have developed a quantitative method (CTMD), for the analysis of such membrane deformations and the significant residual exposure to unfavorable hydrophobic-hydrophilic interactions at specific TMs that results from an incomplete alleviation of the hydrophobic mismatch [32]. When applied to the 5-HT2AR complexes discussed here, residual exposure [32] was found at TM4 for all three complexes, although the values were different possibly because the TM4 tilt is different in the KET, LSD and 5-HT trajectories (see above). Because the extent of the hydrophobic mismatch around the TM helices is considered to be a driving force for oligomerization [32], , we had compared the residual exposure energies at all TMs in the simulation results for the three complexes. At TM1 it was found to be substantial only in the KET simulation, consistent with the conformational changes we observed for TM1 in different systems (see above), and at TM5 it appeared to be relatively similar in all three complexes, but somewhat more pronounced in the 5-HT-bound structure; lastly, the residual exposure at TM6 is largest as well in the 5-HT trajectory, possibly due to the relatively straighter configuration of this helix in the 5-HT simulation (Figures 3–4). One possible mechanism to reduce the energy penalty for this residual exposure in the membrane-embedded receptor conformation produced by the binding of a particular ligand, is to bring together the TM domains where the residual exposure is largest. Therefore, we proposed [32] that this represents a membrane-determined energy drive for the association of the proteins in the membrane.
Consequently, our data in Table 2 of [32] suggests that if the hydrophobic mismatch is the driving force for receptor oligomerization, then the contact interfaces for oligomerization of the 5-HT2AR will be different in the complexes with 5-HT, LSD, or KET. According to this mechanism, ligands will not only regulate the extent of GPCR oligomerization, but will also influence which TM domains constitute the oligomerization interface. Thus, a comparison of residual surface area values at different TMs in 5-HT, LSD, and KET simulations implicates TM1, TM4 and TM5 as likely participants in the oligomerization interface of 5-HT2AR in complex with KET, TM4 and TM5 in the oligomerization interface of LSD-bound receptors, and TM5 (and possibly TM6, TM4 and TM2 as well) as the most likely participants in the oligomerization of 5-HT-bound serotonin receptor.
In addition, the results in Table 2 of [32] for the 5-HT and KET simulations imply that overall the inverse agonist KET will promote more extensive hydrophobic mismatch-driven oligomerization, since the residual surface area value summed over all TMs is about 90 Å2 higher for KET-bound 5-HT2AR than it is for 5-HT-bound receptor. This prediction is in excellent agreement with the experimental data on ligand-regulated oligomerization on β2AR [31], where in comparison to the agonist isoproterenol, the binding of an inverse agonist was suggested to promote tighter packing on β2AR protomers and/or to result in formation of higher-order oligomeric structures.
With regard to the validation of the ligand-dependent dynamic properties, it is important to note that similar residual exposure is observed in the two KET-bound simulations starting from very different initial conformations. Thus, the trend of large residual exposures at TM1, TM4, and TM5 of the KET system is also observed in the KET-substituted system (Table S3 in Text S1). Moreover, near the TMs where the hydrophobic mismatch is alleviated by the membrane remodeling (e.g., TM6), the membrane has similar thickness in both the KET and KET-substituted system (Figure S6 in Text S1).
The MD simulations of the 5-HT-, LSD- and KET-bound 5-HT2AR reported here provide the first molecular representation of the different effects that pharmacologically distinct ligands can have on the 5-HT2AR. The concepts of “functional selectivity” [49], [50] and “receptor bias” [51] are frequently being used to explain the increasingly common observation of differential responses elicited by different ligands from the same receptor (e.g., for 5-HT2AR see [4], [52]). However, no structural context had been identified for the distinct effects on the dynamics produced in the same GPCR by the binding of pharmacologically different ligands. Here we simulated the dynamics of the 5-HT2AR binding of such pharmacologically distinct ligands, and identified different effects on the SM/FMs of the receptor. These effects were shown to lead to different rearrangements that correspond to the different levels of activation known to be produced by these ligands. Notably, the differential effects were shown to be consonant with the pharmacological characterization of the three ligands as a full, partial and inverse agonist, respectively. To our knowledge, such inferences were obtained for the first time here from unbiased atomic MD simulations, but they are in line with the increasingly detailed experimental evidence about ligand-related functional selectivity [49], [50], [51], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], with the proposals of ligand-selective conformations in the 5-HT2AR [67] and the D2R [68], and with structural data indicating that GPCRs such as β2AR are stabilized in distinct conformational states by inverse, partial, or full agonists - respectively [12], [13].
In the current simulations, structural changes associated with SM/FM characteristics of an “activated state” of the 5-HT2AR appear in sub-microsecond trajectories. In contrast, experimentally determined GPCR activation timescales generally vary from microseconds (photoactivation of rhodopsin [69]) to seconds (β2AR in living cells [70]). We emphasize that the conclusions reached here do not require the simulations to have converged to an “active state structure” of the kind claimed for the constructs determined crystallographically. Indeed, a number of modes of activation proposed from experiment share this characteristic and can also be significantly faster [71], [72], [73]. But in general, there are many reasons for the time scale differences between our results and functional measurements. In particular, the simulated system is an idealized construct in that all interaction components are placed in optimal positions to be at or near their targets. Titratable groups are also assigned their final charge states, e.g., when the D3.49 and E6.30 are in the protonated form in some of the constructs. Interestingly, the specific protonation form does not determine whether the ionic lock is formed or not (see Figures 3–4, and Figure S5 in Text S1); rather, the determinant factor is seen from our results to be the nature of the dynamics induced by the binding of a specific ligand. But considering that inactive GPCR (β2AR) may be pre-coupled to G-protein Gs [31] and the protonation of E3.49 in rhodopsin (an activation step) depends on transducin [74], the degree of precoupling will likely play a role in the activation time. Moreover, the simulation conditions (such as pH, salt, lipid composition, and crowding) certainly do not mimic completely those surrounding the receptor in living cells (e.g., it is known that the highly flexible DHA chain of SDPC, included in the lipid mixture used here, facilitates GPCR activation [75]), and similar time-scale differences have been observed between computer simulations and experiments for other GPCRs [76], [77].
The response of the membrane environment to the different ligand-induced structural re-arrangements produces a reorganization of the membrane around the receptor. This is reflected in (i)-the involvement of Chol in direct interactions with the protein [43], [78], that was shown here to affect the dynamics of the SM/FMs, and (ii)-the membrane deformations around the TM bundle of a GPCR [48], [79], described here with the use of the CTMD method [32]. Because the different ligand-determined conformational changes in 5-HT2AR establish different patterns of local perturbations in membrane structure around the receptor complex, they were suggested to promote different ligand-dependent receptor oligomerization patterns through the hydrophobic mismatch between the TMs and the surrounding membrane [32]. This is supported by observations in the literature that: (i)- oligomeric associations of the dopamine D2R [27], 5-HT2CR [28], and the β2AR [31] is ligand-sensitive; and (ii)- GPCR self-assembly is regulated by the mismatch between the hydrophobic length of the TM segments of GPCRs and the hydrophobic thickness of the lipid bilayer, as suggested by both experimental results [80] and computational studies for rhodopsin [32], [48], [79]. Along these lines, the results presented here suggest that the dimerization interfaces of 5-HT2AR oligomers will be different for inverse agonist-, partial agonist-, or agonist-bound complexes, and moreover that the inverse agonist KET would promote more extensive 5-HT2AR oligomerization than the full agonist (5-HT). We note that these experimentally testable predictions regarding possible oligomerization interfaces were obtained by analyzing monomeric GPCRs in complex with different ligands, without the need to simulate the dimers or higher oligomers.
Several model systems of the serotonin 5-HT2AR receptor were studied with all-atom MD simulations in explicit models of the hydrated lipid membrane environment. The 5-HT2AR was simulated in complex with three ligands known to exhibit different pharmacological efficacies: the full agonist 5-HT, the partial agonist LSD, and the inverse agonist KET (Figure 2A).
The parameters for 5-HT were taken from an earlier study [7]. For LSD and KET, the results of geometry optimization and electrostatic potentials obtained from quantum mechanical calculations with the Gaussian program (Gaussian, Inc., Wallingford, CT) were used as input to the Restrained-ElectroStatic-Potential fit method [102] implemented in Antechamber [103] to derive charges. CHARMM topology and parameter files were then prepared with Antechamber using Restrained-ElectroStatic-Potential charges and GAFF force field. Force field parameter files for 5-HT, LSD and KET are included in Text S1. For protein, PALM, lipids etc., the all-atom CHARMM27 force field with CMAP corrections [100] was utilized (this approach is similar to a procedure used successfully in previous studies [104], [105]).
All MD simulations were performed with the NAnoscale Molecular Dynamics (NAMD) suite [106]. As established in similar studies in the lab (e.g., see [107]), the simulations were conducted under constant temperature and pressure conditions with anisotropic pressure coupling, and utilized PME for long-range electrostatics [108]. The Nose-Hoover Langevin piston method [106] was used to control the target pressure with the LangevinPistonPeriod set to 100 fs and LangevinPistonDecay set to 50 fs. All MD simulations were performed with rigidBonds allowing 2 fs time step.
All the simulated systems were equilibrated following a procedure described recently [109]. According to this protocol, the 5-HT2AR backbones and the heavy atoms of the ligands were initially fixed and then harmonically constrained, and water was prevented from penetrating the protein-lipid interface. Constraints were released gradually in four 300 ps-step MD simulations with decreasing force constants of 1, 0.5, 0.1 and 0.01 kcal/(mol·Å2), respectively. Following this equilibration phase, all three GPCR-membrane complexes were simulated for 350 ns.
The stability of the simulated complexes was monitored from the backbone RMSDs of the TMs in 5-HT2AR using the following definitions for TMs: L1.29–L1.59, A2.38–Y2.67, L3.24–N3.56, S4.38–V4.62, D5.35–K5.67, N6.29–I6.60, G7.32–F7.56 and K7.58–I7.68. As illustrated in Figure 1E, after initial equilibration, the RMSDs in all the three systems were stable and fluctuated around or below 2 Å. In all three simulations the ligands maintained key interactions with the receptor (Figure 1B–E), consistent with previous experimental data [2], [6], [7], [91], [92].
To quantify the changes in protein structure produced by the simulations we used various analysis tools. Analysis of protein structural data was carried out with Ptraj in AMBER 9 [110] and other tools discussed below. To quantify helix distortion parameters in the simulations, we used the Prokink package [111] implemented in Simulaid [112]. The correlation analysis on the time-dependent data of different variables, such as helix bend angles, face-shifts, as well as Chol-protein distances, was conducted following the procedure described in [43]. Briefly, the correlation analysis was carried out on two separate sets of dynamic variables. In the first, we followed the time-sequence of m = 8 selected variables that included proline kink and face-shift angles in TM6 and TM7, the minimum distances between the Chol at the EC end of TM6 and the residues on TM6 (I6.53, M6.57, I6.60, C6.61). In the second set, m = 12 dynamic variables were selected that included proline kink and face-shift angles in TM6 and TM7, the minimum distances between the Chol at the IC end of TM6–7 and the residues on TM6 and TM7 (K6.35, I6.39, F6.42, V6.46, L7.44, V7.48, V7.52, L7.55, F7.56).
For each set, we first studied pair-wise correlations between different variables, and constructed the matrix of coefficients of determination, R2 (Figure 7D of the main text) using Spearman's rank correlation test (see for instance Ref. [113]). In this method, given Nframes pairs of observations, (xi, yi), first the xi and yi values separately are assigned a rank, and then the corresponding difference, di between the xi and yi ranks is found for each pair. The R2 is then defined as:(1)Because it uses rankings, Spearman's method eliminates the sensitivity of the correlation test to the function linking the pairs of values and thus is preferred over parametric tests when no a priori knowledge exists on the functional relationship between xi and yi pairs.
To compare the conformational spaces of 5-HT2AR stabilized by the different ligands (i.e., 5-HT, LSD and KET), a Combined Essential Dynamics analysis [42], [114] was performed on Cα atoms of the protein using Gromacs 3.3 [115]. Essential dynamics analysis separates the configurational space into an essential subspace with a few degrees of freedom which describe overall motions of the protein that are likely to be relevant to its function, and a physically constrained subspace describing local fluctuations. The method is based on the diagonalization of the covariance matrix of atomic fluctuations defined by:(2)where xi are the three Cartesian coordinates of the carbon atoms Cα of the molecule under study, and the angular brackets denote averages over an ensemble of configurations and over the simulation time. The diagonalization of Eq. (3) yields eigenvectors that describe the directions of correlated positional changes in the molecule, whereas the eigenvalues indicate the total mean square fluctuation along these directions.
In the Comb-ED, the covariance matrix is calculated for two or more concatenated trajectories, which are fitted on the same reference structure. Given this construct, the eigenvectors resulting from Comb-ED do not represent the essential degrees of motion of the molecules, but rather reveal differences and/or similarities in the dynamical and structural characteristics of the compared simulated structures. To identify structural differences between 5-HT2AR stabilized by the three ligands, Comb-ED analysis was performed on 3 concatenated trajectories obtained by combining the trajectories for the pairs 5-HT-LSD, 5-HT-KET, and LSD-KET, each for the last 100 ns, respectively.
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10.1371/journal.ppat.1004031 | First Experimental In Vivo Model of Enhanced Dengue Disease Severity through Maternally Acquired Heterotypic Dengue Antibodies | Dengue (DEN) represents the most serious arthropod-borne viral disease. DEN clinical manifestations range from mild febrile illness to life-threatening hemorrhage and vascular leakage. Early epidemiological observations reported that infants born to DEN-immune mothers were at greater risk to develop the severe forms of the disease upon infection with any serotype of dengue virus (DENV). From these observations emerged the hypothesis of antibody-dependent enhancement (ADE) of disease severity, whereby maternally acquired anti-DENV antibodies cross-react but fail to neutralize DENV particles, resulting in higher viremia that correlates with increased disease severity. Although in vitro and in vivo experimental set ups have indirectly supported the ADE hypothesis, direct experimental evidence has been missing. Furthermore, a recent epidemiological study has challenged the influence of maternal antibodies in disease outcome. Here we have developed a mouse model of ADE where DENV2 infection of young mice born to DENV1-immune mothers led to earlier death which correlated with higher viremia and increased vascular leakage compared to DENV2-infected mice born to dengue naïve mothers. In this ADE model we demonstrated the role of TNF-α in DEN-induced vascular leakage. Furthermore, upon infection with an attenuated DENV2 mutant strain, mice born to DENV1-immune mothers developed lethal disease accompanied by vascular leakage whereas infected mice born to dengue naïve mothers did no display any clinical manifestation. In vitro ELISA and ADE assays confirmed the cross-reactive and enhancing properties towards DENV2 of the serum from mice born to DENV1-immune mothers. Lastly, age-dependent susceptibility to disease enhancement was observed in mice born to DENV1-immune mothers, thus reproducing epidemiological observations.
Overall, this work provides direct in vivo demonstration of the role of maternally acquired heterotypic dengue antibodies in the enhancement of dengue disease severity and offers a unique opportunity to further decipher the mechanisms involved.
| Dengue (DEN) is an arthropod-transmitted viral disease which affects approximately 390 million individuals in the tropical and subtropical world annually. DEN clinical manifestations range from mild febrile illness (dengue fever) to life-threatening dengue hemorrhagic/dengue shock syndrome (DHF/DSS). Epidemiological observations indicate that infants born to dengue immune mothers are at greater risk to develop the severe form of the disease (DHF/DSS) upon infection with any serotype of dengue virus (DENV). It was proposed that the presence of maternally acquired DENV specific antibodies cross react but fail to neutralize DENV particles, resulting in higher viremia that correlates with increased disease severity. Direct experimental evidence supporting this antibody-dependent enhancement (ADE) hypothesis has been missing. Furthermore, a recent epidemiological report has challenged the influence of maternally acquired antibodies in disease outcome. Here, we have developed a mouse model of ADE where DENV2-infected mice born to DENV1 immune mothers displayed enhanced disease severity compared to DENV2-infected mice born to dengue naïve mothers. This is a long-overdue direct experimental evidence of the role of maternally acquired antibodies in dengue disease outcome. It provides a unique opportunity to dissect the mechanisms involved in this phenomenon.
| Dengue (DEN) is the most prevalent arthropod-borne viral infection in the world [1]. Approximately 3 billion people who are living in the tropical and subtropical regions from Southeast Asia, the Pacific and the Americas are at risk of infection [1]–[3]. A recent meta-analysis using cartographic approaches estimates 390 million dengue infections per year including 96 million with clinical manifestations [4]. This number is more than three times higher than the previous dengue burden estimated by the World Health Organization [5]. With no licensed drug or vaccine, DEN represents a serious public health concern and economic burden for societies.
The etiological agent of DEN, dengue virus (DENV), belongs to the genus Flavivirus within the Flaviviridae family, which also includes Japanese encephalitis virus (JEV), West Nile virus (WNV), and yellow fever virus. DENV is an enveloped virus with a single-stranded, positive-sense 10.7 kb RNA genome. It is translated as a single polyprotein that is cleaved by viral and host proteases into three structural proteins (capsid [C], pre-membrane/membrane [prM/M] and envelope [E], and seven non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5) [6]. There are four antigenically distinct serotypes of DENV (DENV1-4) that may co-circulate in the same geographical area [1], [3]. The virus is primarily transmitted to humans by the highly urbanised Aedes aegypti female mosquito which has spread globally due to increased trade and travel [7].
Human infection with one of the four DENV serotypes can result in either asymptomatic or symptomatic disease; the latter presents itself in a wide spectrum of clinical manifestations, ranging from mild acute febrile illness to self-limiting classical dengue fever (DF) to the severe dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS) [8], [9]. The hallmark of DHF/DSS is the increased vascular permeability that results in fluid loss which may progress to hypovolemic shock. Clinical management of DHF/DSS patients consists of isotonic fluid resuscitation and blood/platelet transfusion when appropriate [8], [9]. Specific to DEN, viremia is transient (around 7–10 days) and development of the severe forms of the disease typically occurs during defervescence when the virus is almost cleared from the blood circulation [2].
Despite increasing interest from the scientific community worldwide, the mechanisms involved in DEN pathogenesis, and in particular DHF/DSS, remain unclear with increasing contradictory and controversial findings [10]. This is partly due to the lack of a robust animal model of DEN which recapitulates the clinical manifestations and disease kinetic as seen in dengue patients [11]. Thus currently, most of the knowledge on the mechanisms involved in dengue pathogenesis has been derived from both in vitro systems and epidemiological observations although a number of mechanistic hypotheses could be experimentally confirmed in vivo in mouse models [11].
Whereas infection with one DENV serotype confers an individual life-long protection against that particular DENV serotype, it does not cross-protect against the other DENV serotypes [2], [7]. Instead, epidemiological studies conducted worldwide over five decades have indicated that the vast majority of DHF/DSS cases occur upon secondary infection with a heterologous DENV serotype [12]–[15]. Mechanistically, these epidemiological observations could be explained by the antibody-dependent enhancement (ADE) of infection hypothesis, whereby actively (upon primary infection) or passively (through maternal transfer) acquired anti-DENV antibodies cross react but fail to neutralize heterotypic DENV particles [12]. Furthermore, it was also proposed that sub-neutralizing concentrations of homotypic DENV antibodies also trigger ADE [12]. Mechanistically, antibody-opsonized DENV gains entry into Fc receptor (FcR)-bearing cells such as monocytes resulting in increased viral replication which in turn triggers the massive release of inflammatory and vasoactive mediators that contribute to the disease severity [16]–[18]. In vitro studies using heterotypic or sub-neutralizing concentrations of homotypic immune sera or monoclonal antibodies and FcR-bearing cells have supported the ADE hypothesis, with higher virus production and higher levels of cytokines such as TNF-α or IL-1β compared to viral infection in absence of the antibodies [19]–[22]. In vivo, passive transfer of a monoclonal cross-reactive anti-DENV antibody in rhesus monkeys infected with DENV4 resulted in significantly increased viremia titers [23]. In an acute fatal mouse model of severe DEN, passive administration of heterotypic anti-DENV antibodies to DENV2-infected mice led to increased virus titers, cytokine storm, lower platelet counts, increased vascular permeability, intestinal hemorrhage and reduced survival rate, compared to the infected untreated control animals [24], [25].
Perhaps the strongest clinical evidence for a role for antibodies in DEN pathogenesis comes from the observation of increased risk of DHF/DSS in infants at 5–9 months of age where maternal antibodies against DENV wane to sub-neutralizing levels [26]–[30]. While infection with a heterologous DENV serotype in young children or adults would trigger anamnestic but cross-reactive responses from both B and T cells [29], [30], only maternal antibodies but not T cells cross the placenta to the infant. This indicates that disease enhancement cannot be due to other factors but sub-neutralizing levels of DENV antibodies in this case [31]. However, a more recent epidemiological study has challenged this hypothesis whereby no significant association was found between DENV3 ADE activity at illness onset and the development of DHF in infants compared with less severe symptomatic illness [32]. Thus, there is an urgent need to address this controversy experimentally as the ADE hypothesis is the most widely proposed explanation for clinical and epidemiological observations. This work describes a unique animal model of enhanced disease severity upon primary DENV infection of mice born to mothers immune to a heterotypic DENV strain.
All the animal experiments were carried out under the guidelines of the National Advisory Committee for Laboratory Animal Research (NACLAR) in the AAALAC-accredited NUS animal facilities (http://nus.edu.sg/iacuc/). NUS has obtained a license (#VR008) from the governing body Agri-Food & Veterinary Authority of Singapore (AVA) to operate an Animal Research Facility. The animal experiments described in this work were approved by the IACUC from National University of Singapore under protocol number 009/09. Non-terminal procedures were performed under anesthesia, and all efforts were made to minimize suffering of the animals.
D2Y98P-PP1 is a DENV2 strain derived from a 2000 Singapore clinical isolate (Genbank accession number #JF327392) [33]. DENV1 [Dengue 1 05K3903DK1 (Genbank accession number #EU081242)] was isolated from a patient during a DEN outbreak in Singapore in 2005 [34]. MT5 strain was derived from D2Y98P-PP1 virus through a single amino acid substitution (Phe->Leu) at position 52 of NS4B protein [33]. All the DENV strains were propagated in the Aedes albopictus cell line C6/36 (American Type Culture Collection [ATCC #CRL-1660]). C6/36 cells were maintained in Leibovitz's L-15 medium (GIBCO) supplemented with 5% fetal calf serum (FCS), and virus propagation and harvest were carried out as described previously [35]. Virus stocks were stored at −80°C. Virus titres of these virus stocks were determined by plaque assay in BHK-21 cells as described below.
UV-inactivation of DENV particles was performed using a handheld 6 watts shortwave UV lamp (product no. UVG-54) for 10 mins at a distance of 15 cm inside a biological safety cabinet. Plaque assay was performed to ensure that the DENV particles were inactivated.
Plaque assay was carried out in BHK-21 cells as described previously [35]. Briefly, 2×105 cells BHK-21 were seeded in 24-well plates (NUNC, NY, USA). BHK-21 monolayers were infected with 10-fold serially diluted viral suspensions ranging from 10−1 to 10−8. After 1 h incubation at 37°C and 5% CO2, the medium was decanted and 1% (w/v) carboxymethyl cellulose was added to the wells. After 4 days (D2Y98P-PP1) or 5 days (DENV1) incubation at 37°C and 5% CO2, the cells were fixed with 4% paraformaldehyde and stained with 1% crystal violet. The plates were rinsed thoroughly, dried and the plaques were scored visually and expressed as the number of plaque forming units (PFU). Triplicate wells were run for each dilution of each sample. The limit of detection for the plaque assay was set at 10 PFU per ml.
Blood samples were collected in 0.4% sodium citrate and centrifuged for 5 min at 6,000 g to obtain plasma. The presence of infectious viral particles was determined by real-time PCR as follows. DENV2 viral RNA was extracted from 100 ul of infected mouse serum using QIAviral RNA extraction kit (Qiagen), according to the manufacturer's protocol. Reverse transcription was then performed on 5 ul of total RNA using iScript cDNA synthesis kit (Bio-Rad Laboratories). Real-time PCR (RT-PCR) was performed on ABI Prism 7500 sequence detector (Applied Biosystem) over 40 cycles with an annealing temperature of 60°C using D2Y98P-PP1 NS4B primers as described previously [35]. Samples were run in triplicates. At least 2 independent experiments were conducted for the respective experiments. Data are expressed in Log10 PFU Equiv. DEN RNA/mL serum.
Virus loads in the organs from DENV-infected mice were determined by RT-PCR. Briefly, at Day 5 post-infection, mice were euthanized and perfused extensively with sterile PBS. Pooled left and right brachial and axillary lymph nodes, intestine, spleen, and liver were harvested and stored in RNAlater solution (Ambion). Within 24 hours, total RNA was extracted from 30 mg of the respective organs using Qiagen RNeasy kit, according to the manufacturer's protocol. Reverse transcription was performed on 1000 ng of total RNA using iScript cDNA synthesis kit (Bio-Rad Laboratories). RT-PCR amplification was conducted with ABI Prism 7500 sequence detector as described above. Samples were run in triplicates. 18s rRNA primers were also used as internal reference for data normalization. The following primers were used: NS4B forward primer (FP), 5′-AACCGAGATGGGTTTCCTGGAA-3′; NS4B reverse primer (RP), 5′-TTCAAACTTTGGATCATAGGGT-3′; 18srRNA (FP), 5′-CGGCTACCACATCCAAGGAA-3′; 18srRNA (RP), 5′-GCTGGAATTACCGCGGCT-3′. Data are expressed in Log10 PFU Equiv. DEN RNA/organ.
AG129 [129/Sv mice deficient in both alpha/beta (IFN-α/β) and gamma (IFN-γ) interferon receptors] breeders were obtained from B&K Universal (UK). They were housed and bred under specific pathogen-free conditions in individual ventilated cages. Six-week old female AG129 mice were infected with 106 PFU of DENV1 per mouse via the subcutaneous (sc) route (0.1 ml in sterile PBS) which led to asymptomatic infection. One week post-infection, after virus clearance, the females were allowed to mate with naïve 6-week old AG129 males and pups were weaned out 21 days later. Uninfected AG129 females were also used to give birth to naïve controls. At 2, 5 or 8 weeks of age (as indicated), mice born to DENV1 immune or naive mothers were administered with 103 PFU of DENV2 (D2Y98P-PP1 or MT5 strain) via the sc route (0.1 ml in sterile PBS). The clinical symptoms were scored as follows: 0 - at healthy state, 1 - signs of ruffled fur, 2 - hunched back, 3 –severe diarrhea, 4 - moribund stage, 5 - severe weight loss. The infected animals were monitored daily (clinical score between 0–2) and every 12 hours (clinical score from 3 onwards). Survival rate was derived from the number of mice that were euthanized at moribund stage as evidenced by severe diarrhea, lethargy, and sharp body weight loss as described previously [35], [36].
Mice were euthanized and tissues (intestines and liver) were harvested and immediately fixed in 10% formalin in PBS at the indicated time points. Fixed tissues were paraffin embedded and stained with Hematoxylin and Eosin (H&E).
Mouse blood samples were collected in K2EDTA and serum tubes (Greiner). Whole blood was immediately analyzed for cell counts using automated hematology analyzer Cell Dyn – 3700 (Abbott). Serum alanine (ALT) and aspartate (AST) aminotransferases levels were quantified using chemistry analyzer COBAS C111 (ROCHE).
Vascular leakage was assessed using Evans Blue dye as a marker for albumin extravasation as described previously [35]. Briefly, 0.2 ml of Evans blue dye (0.5% w/v in PBS) (Sigma Aldrich) were injected intravenously into the anesthetized mice. After 2 h, the animals were euthanized and extensively perfused with PBS. The tissues were harvested and weighed prior to dye extraction using N,N-dimethylformamide (Sigma; 4 ml/g of wet tissue) at 37°C for 24 h after which absorbance was read at 620 nm. Data are expressed as fold change in OD620nm per gram of wet tissue compared to control group or as absolute absorbance values per gram of wet tissue.
TNF-α, Interleukin-6 (IL-6), VEGFA and MMP-9 levels were quantified in the mouse serum using commercially available detection kits (R&D systems) and according to the manufacturer's instructions.
Six-week old female AG129 mice (n = 10) were sc infected with 106 PFU of DENV1 per mouse. Eight weeks post-infection, mice were euthanized and serum was collected by cardiac puncture. Serum was also obtained from age-matched uninfected control animals. Pooled sera from each group were heat-inactivated, titrated by ELISA and stored at −80°C until use.
Five-week old AG129 mice were ip administered with 100 µl/mouse of neat heat-inactivated DENV1 immune serum or naive serum. Mice were sc infected with 103 PFU of D2Y98P-PP1 24 h post-administration.
Five-week old AG129 mice born to DENV1 immune mothers were sc infected with 103 PFU of D2Y98P-PP1. At Day 1 and Day 2 post-infection, mice were injected intraperitoneally (ip) with 100 µg of anti-TNFα (eBioscience, Cat. no. 167322-85) or 100 µg Rat IgG1 K isotype control (eBioscience, Cat. no. 16-4301-85) per mouse as described previously [37].
The levels of systemic IgG antibodies against DENV1 or DENV2 were determined by enzyme-linked immunosorbent assay (ELISA). Briefly, 96-well plates (Corning costar, NY, USA) were coated overnight at 4°C with 105 PFU of UV-inactivated DENV1 or DENV2 in 0.1M NaHCO3 buffer at pH 9.6. Diluted (1/50) serum samples were added to the wells and incubated for 1 h at 37°C. HRP-conjugated goat anti-mouse IgG (H+L) (Bio-rad) secondary antibody was used at a 1∶3,000 dilution. Detection was performed using O-phenylenediamine dihydrochloride substrate SigmaFast (Sigma Aldrich) according to the manufacturer's instructions. The reaction was stopped upon adding 75 µl of 1M H2SO4 and absorbance was read at 490 nm using an ELISA plate reader (Bio-rad model 680).
For IgG isotypes analysis, secondary goat HRP-conjugated anti-mouse IgG1, IgG2a, IgG2b and IgG3 (Jackson Immuno Research) were used at a 1∶3,000 dilution.
Sera from 5-week old mice born to naïve or DENV1 immune mothers were inactivated at 56°C for 30 minutes. Sera diluted 1∶20 were incubated for 1 h at 37°C with DENV2 at a multiplicity of infection (MOI) of 10 before adding the suspensions to non-adherent human monocytes THP-1 cells in 96-wells flat bottom tissue culture plates (NUNC) as described previously [38]. At 72 h post infection, cells were harvested and clarified by centrifugation. The virus titer in culture supernatants was determined by plaque assay on BHK-21 cells.
Serial dilutions (2-fold, starting 1/10 dilution) of heat-inactivated serum from mice born to DENV1 immune mothers was performed in a 96-well sterile flat bottom plate with RPMI 1640 with 2% FBS (life technologies). Each dilution (100 µl) was incubated at 37°C for 1 hour with 100 µl containing approximately 50 PFU of DENV2. A positive control mix with virus alone was included. Plaque assay was then carried out in BHK-21 cells as described above and in triplicates in 24-well plates. The percentage of neutralization was determined by comparing the number of plaques obtained with each serum dilution to that obtained with the positive control. Data were plotted in Graphpad Prism (version 5.0a) and PRNT50 was determined by nonlinear regression as the serum dilution factor for which 50% reduction in the number of plaques was obtained.
The results were analyzed using the unpaired Student t test. Differences were considered significant (*) at p value <0.05.
Adult female AG129 mice were infected sc. with 105 PFU of a clinical isolate of DENV1 which led to asymptomatic transient viremia (Suppl. Fig. S1). At 7 days post-infection (p.i.), once the virus has been cleared from the circulation the mice were mated with dengue naïve AG129 adult males. At 5 weeks of age, the mice born to the DENV1 immune mothers were sc. infected with 103 PFU of D2Y98P-PP1, a DENV2 strain that triggers severe dengue associated with vascular leakage and death as reported by us previously [35], [36]. The survival rate indicated that DENV2-infected mice born to DENV1 immune mothers died much earlier (at day 6 p.i.) as compared to age-matched DENV2-infected mice born to dengue naive mothers (day 12 to 18 p.i.) (Fig. 1A). Furthermore, mice born to DENV1 immune mothers did not display the clinical symptoms progression that was typically seen for the first four days upon primary infection with D2Y98P-PP1 virus including ruffled fur, hunched back, and severe diarrhea (Fig. 1B) [36]. Instead DENV2-infected mice born to DENV1 immune mothers rapidly progressed into lethargy and eventually reached moribund stage at day 6 p.i. at which point the animals were euthanized. No signs of paralysis were observed in both DENV2-infected groups throughout the course of the experiment.
These observations indicate that DENV2-infected mice born to DENV1 immune mothers experienced enhanced disease severity, similar to observations made with dengue infected children born to dengue immune mothers [26]–[30].
Previous studies have reported a direct correlation between high viremia and disease severity [39], [40]. Viremia was thus determined by real-time PCR at day 3 and 5 p.i. in DENV2-infected mice born to DENV1 immune or naïve mothers. At day 3 p.i. comparable virus loads were measured in both DENV2-infected groups, whereas significantly higher virus loads were measured at day 5 p.i. in the DENV2-infected mice born to DENV1 immune mothers (Fig. 1C). Together, these results thus support that the presence of maternal DENV1 specific antibodies circulating in DENV2-infected mice led to an increased production of infectious virus particles.
Furthermore, increased vascular permeability is a hallmark of severe dengue in humans which is also observed in our DENV2 primary infection mouse model [35], [36]. Vascular permeability was thus measured in DENV2-infected mice born to DENV1 immune or naïve mothers at moribund stage using Evan's blue dye extrusion assay [35]. A significantly higher vascular leakage in the moribund mice born to DENV1 immune mothers was observed in all the organs tested compared to moribund mice born to naïve mothers (Fig. 1D).
Together, these results indicate that the enhanced disease severity observed in DENV2-infected mice born to DENV1 immune mothers correlate with higher viremia and greater vascular leakage compared to mice born to naïve mothers.
Enhanced disease severity in DENV infected children born to dengue immune mothers has been proposed to be mediated by maternally transferred non neutralizing cross-reactive antibodies [12]. In order to confirm the role of maternal antibodies in our mouse model of dengue disease enhancement, a passive immune transfer experiment was carried out whereby naïve 5-week old AG129 mice were injected ip. with naïve or DENV1 immune serum one day prior to DENV2 infection. Similar to what was observed with DENV2-infected mice born to DENV1 immune mothers, DENV2-infected mice passively transferred with DENV1 immune serum died much earlier than DENV2-infected mice administered with naïve serum, with survival means of 6 and 18 days, respectively (Fig. 2A). Furthermore, significantly higher virus loads at day 5 p.i. were detected in mice that received DENV1 immune serum compared to the control animals (Fig. 2B), thus reproducing the viremia pattern seen with DENV2-infected mice born to DENV1 immune mothers (Fig. 1C).
Together, these results support that the enhanced dengue disease severity observed in DENV2-infected mice born to DENV1 immune mothers is caused by maternal antibodies that are circulating in these animals at the time of DENV2 infection.
To further characterize the disease severity enhancement observed in DENV2-infected mice born to DENV1 immune mothers, we measured the viral load in various organs. Five days post-DENV2 infection, the animals were euthanized and perfused with PBS prior to organ harvest in order to avoid contamination by infectious DENV particles circulating in the blood that may artificially lead to over-estimation of the number of infectious particles detected in the organs, in particular in highly vascularized organs such as the spleen and liver. The results indicated that a significantly higher viral load was measured in the spleen but not in the lymph nodes, intestines and liver from DENV2-infected mice born to DENV1 immune mothers compared to DENV2-infected mice born to dengue naïve mothers (Fig. 3A). This data thus suggest that the antibody-mediated enhanced viral replication and output mainly occurs in the blood and spleen.
Furthermore, histological analysis showed no intestinal damage in DENV2-infected mice born to DENV1 immune or naïve mothers at moribund stage (Fig. 3B). However, signs of villi disintegration and detachment in some areas of the intestines were observed for 38% and 30% of the fields analyzed for each infected group respectively (Fig. 3B). Furthermore, no significant damage in the liver was observed for both DENV2-infected groups (Fig. 3B), with levels of alanine and aspartate transaminases not significantly different from the levels measured in the uninfected controls born to dengue naïve mothers (Fig. 3C).
In addition, various blood parameters were assessed over time post-DENV2 infection. The levels of white blood cells (WBC), lymphocytes (LYM), red blood cells (RBC), hematocrit (HCT) and platelets (PLT) were comparable between both animal groups at day 1, 3, and 5 p.i. (Fig. 4). Notably, drops in lymphocyte (LYM) and platelets (PLT) concentrations were detected at day 5 p.i. which is consistent with previous reports in dengue patients [2]. A delayed increase in the neutrophil (NEU) concentration was observed in the group of mice born to DENV1 immune mothers compared to mice born to naïve mothers. Also, remarkably, when comparing both DENV2-infected groups at moribund stage, corresponding to day 5 p.i. for mice born to DENV1 immune mothers and day 12 p.i. for mice born to naïve mothers, the level of all the blood parameters monitored were found significantly higher in the latter group.
Together, these results indicate that enhancement of disease severity in this mouse model cannot be explained by aggravated tissue damage or profound changes in blood parameters. The hematological differences observed between DENV2-infected mice born to DENV1 immune versus naïve mothers is likely due to the differential disease progression rate between both animal groups whereby DENV2-infected animals born to DENV1 immune mothers die much more rapidly than the mice born to naïve mothers which therefore does not allow observing in the former group further increase in concentration of some of the blood parameters.
The current paradigm of DEN-associated vascular leakage involves a number of soluble players including inflammatory cytokines and chemokines, as well as non-inflammatory soluble mediators, whereby elevated serum levels of these mediators have been correlated with disease severity [41], [42]. Key mediators including VEGF, MMP-9, IL-6 and TNF-α were measured and compared between both groups of DENV2-infected mice born to either DENV1 immune or naive mothers. A significantly higher level of VEGF was measured at Day 5 p.i. in DENV2-infected mice born to DENV1 immune mothers compared to uninfected controls (Fig. 5A). However, this level was not significantly different from that measured in DENV2-infected mice born to dengue naïve mothers. No significant differences in the levels of MMP-9 were observed between both DENV2 infected groups and compared to the uninfected controls (Fig. 5B). In contrast, the levels of IL-6 and TNF-α were significantly higher at day 3 and 5 p.i. in mice born to DENV1 immune mothers compared to those measured in mice born to naïve mothers. Furthermore, when comparing both DENV2-infected groups at moribund stage, mice born to DENV1 immune mothers displayed significantly higher levels of IL-6 and TNF-α than mice born to naïve mothers (Fig. 5C&D). Of note, the cytokine levels measured in age-matched uninfected mice born to naïve or DENV1 immune mothers were comparable (data not shown).
These results thus suggest that a greater inflammation reaction is triggered in DENV2-infected mice born to DENV1 immune mothers, as evidenced by higher levels of IL-6 and TNF-α.
Elevated levels of TNF-α in severe dengue patients have been measured [43]–[45]. Furthermore, the role of TNF-α in dengue disease severity has been demonstrated whereby the administration of anti-TNFα blocking antibodies delayed the fatal outcome of DENV-infected AG129 mice [25], [37], [46]. To confirm the role of TNF-α in our mouse model of enhanced dengue disease severity, DENV2-infected mice born to DENV1 immune mothers were administered with anti-TNFα blocking antibodies at day 1 and 2 p.i. Survival rates were monitored and indicated that anti-TNFα antibody treatment delayed the animals' death significantly compared to untreated animals and compared to mice treated with an isotype antibody control, with survival means of 13, 6 and 6 days respectively (Fig. 6A). Appearance of clinical symptoms was also delayed in the anti-TNFα antibody treated mice (Fig. 6B).
To further investigate the effects of anti-TNFα antibody treatment, viremia was determined at day 3 and 5 p.i. No significant difference in viremia was observed at day 3 and 5 p.i. between the anti-TNFα and isotype control-treated groups (Fig. 6C).
The involvement of TNF-α in dengue-associated vascular leakage has been shown in vitro [47], [48] but has yet to be directly demonstrated in an in vivo model of severe dengue. Here, vascular permeability was thus measured in both the anti-TNFα and isotype control treated groups at day 5 and day 10 p.i. The results indicated that the extent of vascular leakage was significantly lower at day 5 p.i. in the liver and kidneys from the anti-TNFα treated mice compared to the isotype control group (Fig. 6D). However, at day 10 pi. vascular leakage in the anti-TNFα treated mice was comparable to that measured in the isotype control group at day 5 p.i (Fig. 6D).
Altogether these results demonstrate that the anti-TNFα antibody treatment given at day 1 and 2 p.i. resulted in transient and partial control of vascular leakage which correlated with delayed death of the animals. This set of data provides a direct experimental evidence of a role for TNF-α in increased vascular permeability in an in vivo model of severe dengue.
Since primary infection with any of the 4 DENV serotypes primarily results in asymptomatic disease, we tested the ability of maternally acquired DENV1 antibodies to shift the disease outcome from asymptomatic to severe disease upon DENV2 infection. To do so, 5-week old mice born to DENV1 immune or naïve mothers were infected with a DENV2 mutant virus strain (namely MT5) that was derived from the lethal DENV2 D2Y98P-PP1 strain through a single amino acid substitution in NS4B protein [33]. This amino acid substitution resulted in lower viral replication efficacy which translated into asymptomatic transient viremia upon primary infection in dengue naïve adult AG129 mice ([33] and Fig. 7A). However, infection with 103 PFU of MT5 virus of 5-week old mice born to DENV1 immune mothers resulted here in lethal outcome for more than 70% of the animals (Fig. 7A). These animals displayed hunched back, progressive lethargy and diarrhea, but no paralysis. In addition, significant vascular leakage was measured in the liver, spleen and kidneys from these animals at moribund stage (Fig. 7B), supporting a correlation between disease severity and increased vascular permeability. However, surprisingly viremia measured at day 5 p.i. in MT5-infected mice born to DENV1 immune mothers was not statistically different from the viremia measured in MT5-infected mice born to dengue naïve mothers (Fig. 7C). We postulate that the lack of statistical difference may be due to the heterogeneity in both disease outcome and time of disease manifestations observed with the MT5-infected mice born to DENV1 immune mothers.
Thus, this set of data demonstrates that maternally acquired heterotypic DENV antibodies are able to shift dengue disease outcome from asymptomatic to severe, thereby recapitulating epidemiological observations.
To further characterize the nature and properties of the maternal DENV1 specific antibodies circulating in 5-week old mice born to DENV1 immune mothers, ELISA was carried out using UV-inactivated DENV1 or DENV2 particles as coating antigens. Expectedly, elevated absorbance readings were measured when probing for the presence of anti-DENV1 total IgG antibodies in the serum from 5-week old mice born to DENV1 immune mothers (Fig. 8A). In addition, serum from these mice cross-reacted significantly with DENV2 (Fig. 8A). Isotyping of the anti-DENV1 IgG antibodies revealed majority of IgG1 and IgG2a circulating in the serum of mice born to DENV1 immune mothers (Fig. 8B).
To further confirm the role in disease severity enhancement of the maternal antibodies circulating in mice born to DENV1 immune mothers at the time of DENV2 infection, an in vitro ADE assay was carried out as reported before [38]. Neat serum samples from 5-week old mice born to DENV1 immune or naive mothers were mixed with DENV2 prior to infection of Fcγ-R bearing macrophages THP-1. At 3 days p.i., the number of infectious particles produced in the supernatant was determined by plaque assay. Serum samples from mice born to DENV1 immune mothers clearly enhanced DENV2 production compared to the serum samples from mice born to dengue naïve mothers (Fig. 8C). These data thus confirmed the enhancing properties of the serum from 5-week old mice born to DENV1 immune mothers.
Epidemiological studies reported that specifically 5–9 months old infants born to dengue immune mothers are at risk of developing enhanced dengue disease severity upon primary DENV infection [12]. To explain this age window of disease enhancement, it was proposed that the level of maternally acquired DENV specific antibodies initially provides the baby with a pan-serotype protection against DENV infection. However, as the level of maternal antibodies wanes over time, the maternal antibodies no longer neutralize but instead enhance infection with a heterologous or even homologous DENV serotype. In an attempt to recapitulate in our mouse model this age-dependent disease protection versus enhancement status, DENV2 challenge was performed in mice born to DENV1 immune mothers at 2 weeks, 3 weeks, 5 weeks or 8 weeks of age. The survival rates showed that 2-week old mice born to DENV1 immune mothers were significantly protected against DENV2 challenge compared to age-matched animals born to dengue naïve dams (Fig. 9 panel A). In contrast, 3-week, 5-week and 8-week old mice born to DENV1 immune mothers died significantly earlier than age-matched animals born to dengue naïve mothers (Fig. 9 panels B–D). Furthermore, the level of maternal DENV1 IgG antibodies measured in 2-week old mice was significantly higher than the level measured in 3-week, 5-week and 8-week old animals (Fig. 10 panel A). Of note, comparable levels of anti-DENV1 IgG antibodies were measured in 3, 5 and 8 week-old animal groups which suggests that the half-life of these antibodies is longer than the average 2 weeks half-life previously reported for rodent IgG antibodies [49]. In contrast, cross-reactivity with DENV2 of the serum from mice born to DENV1 immune mothers clearly declined with age, suggesting that cross-reactive anti-DENV1 antibody species may have a shorter half-life than non cross-reactive species (Fig. 10 panel B). Finally, the neutralizing activity against DENV2 determined by PRNT showed that the serum from 2-week old mice displayed a strong neutralizing activity against DENV2 (PRNT50 above 50) whereas the PRNT50 measured with the sera from 3-, 5- and 8-week old mice were below 20 (Table 1). Therefore, both the ELISA and PRNT data correlate well with the disease outcomes observed upon DENV2 challenge in mice born to DENV1 immune mothers, and support the epidemiological observations in DENV-infected infants born to DENV immune mothers.
Advancement in understanding DEN pathogenesis has been largely hampered by the lack of a suitable animal model. Humans and mosquitoes are so far the only known natural hosts for DENV. Upon DENV infection, non-human primates develop viremia and produce neutralizing antibody responses, but they do not display overt clinical signs of disease [11]. Moreover ethic and economic considerations have greatly limited the use of non-human primates as animal model to study DEN. Immune competent mice are not susceptible to DENV infection [11]. Indeed, while DENV was shown to interfere and block IFN signalling in human cells [50], [51], it fails to do so in murine counterparts [52]. Accordingly, immune-compromised mice which lack type I and II IFN signalling pathways (AG129) were found permissive to infection with most of DENV lab strains and clinical isolates whereby transient viremia could be detected [11], [53]. However, clinical manifestations in these mice are DENV strain-dependent and range from none (majority of the DENV strains) to severe vascular leakage accompanied by thrombocytopenia [11]. Recently, our group has reported the subcutaneous infection of adult AG129 mice with a non mouse-adapted DENV2 strain, namely D2Y98P-PP1 [36]. In this model, the virus disseminates systemically and replicates transiently in the blood and a variety of organs. Severe vascular leakage develops over time which eventually leads to non-paralytic death of the infected animals after the virus has been cleared from the blood circulation [36]. However, the lack of functional type I & II IFN responses represents an important weakness for this dengue mouse model since dengue patients are generally immune competent. Data generated in this mouse model must therefore be interpreted with caution, and may not accurately reflect the situation in patients. A recent work has indicated the susceptibility of IFNAR (Type I IFN) KO mice to infection with a DENV2 strain [54]. It would be very interesting to test whether these mice would display maternal antibody-mediated enhancement of disease severity as seen in AG129 mice.
Leveraging on our model of primary DENV2 infection in AG129 mice, we report here a model of ADE that resembles the situation observed in humans, where infants born to DENV immune mothers have greater risk to develop severe dengue disease. This is the first report of an in vivo ADE model that demonstrates the role of maternally acquired heterotypic DENV antibodies in disease severity. Previous in vivo models of ADE have relied on the passive transfer of DENV specific antibodies (immune sera or monoclonal antibodies) followed by heterotypic DENV infection, a somewhat indirect and artificial approach [23]–[25]. Here, DENV2 infection of 5-week old AG129 mice born to DENV1 immune mothers displayed enhancement of dengue disease severity as evidenced by earlier time of death, increased virus loads in the circulation and in the spleen, and increased vascular leakage compared to DENV2-infected mice born to dengue naïve mothers. Similar disease pattern and kinetic was observed upon passive transfer of DENV1 immune serum to DENV2-infected mice, thus strongly supporting the role of DENV1 specific antibodies in disease severity enhancement. Furthermore, mimicking even more closely the situation in humans, using an attenuated DENV2 strain (MT5) derived from the lethal D2Y98P-PP1 strain, we showed that mice born to DENV1 immune mothers developed lethal dengue accompanied by vascular leakage upon MT5 infection whereas MT5-infected mice born to dengue naïve mothers did not display any clinical manifestation.
Importantly, we were able to observe in this mouse model the age-dependent susceptibility to severe disease as described in humans [12], whereby 2-week old mice born to DENV1 immune mothers were significantly protected upon DENV2 challenge, whereas 3, 5 or 8 week-old mice displayed disease severity enhancement. These disease outcomes correlated well with the levels of maternal DENV1 specific IgG antibodies and their neutralizing activity against DENV2 measured in the serum from the mice born to DENV1 immune mothers. These observations suggest that unlike humans, mice seem to have a rather large susceptibility window for disease enhancement due to the sustained presence of maternally acquired DENV1 antibodies at enhancing concentrations. This may indicate that the half-life of these maternal DENV1 IgG antibodies may be greater than the average half-life of 2 weeks previously suggested for rodents IgG antibodies [49]. In humans, a byphasic decay pattern has been reported for maternal dengue IgG antibodies with a half-life of 24–29 days between birth and 3 months, and 44–150 days after 3 months [55]. It is thus possible that in mice, maternally acquired DENV1 specific IgG antibodies may display a similar biphasic decay pattern.
Higher viremia was measured at day 5 p.i. in DENV2-infected mice born to DENV1 immune mothers compared to DENV2-infected mice born to dengue naïve mothers. This observation supports the findings from several epidemiological studies reporting that higher viremia was measured in DHF cases arising from secondary heterologous DENV infection [12], [39], [40]. Our model here supports experimentally that high viremia correlates with disease severity, although this dogma has recently been challenged by recent epidemiological studies where no correlation could be established between viremia levels and disease severity [32], [56].
Using in vitro ELISA and ADE assays, we clearly showed that the serum from mice born to DENV1 immune mothers cross-reacted with DENV2 particles and eventually enhanced DENV2 replication in Fc-R bearing cells. Thus, our ADE mouse model seems to support the ADE hypothesis. After an initial brief period of cross-reactive neutralization, possibly due to the high maternal antibody titer that co-ligates the inhibitory FcγRIIB [38], the virus-antibodies complexes facilitated infection of myeloid cells, such as monocytes, through activating Fc-R to result in increased virus replication and output [16]–[18]. In order for an antibody to be neutralizing and therefore protective in the presence of Fc-R-mediated entry, it must be of high enough affinity to be able to neutralize epitopes at the surface of the virus and must be in sufficient concentration [57]. Failure to fulfill either of these requirements results in enhancement of the disease. This idea can explain why antibodies induced by one DENV serotype, although protective against that serotype [58], may increase the risk of severe disease upon infection with a heterologous DENV serotype due to enhanced infection of FcR-bearing cells by sub-neutralized viral particles [59]. It was proposed that due to differences in the surface antigens between DENV serotypes, only some of the antibodies raised against one will react with another, and among these cross-reactive antibodies, some may have a reduced affinity for the second serotype. Therefore, during a heterotypic DENV infection, the threshold for neutralization is less likely to be reached and, consequently, ADE-mediated severe disease is more likely to occur.
At the intracellular level, it was proposed that FcR-mediated entry of DENV allows the virus to escape the type I and II IFN intracellular signaling pathways, thereby resulting in greater virus replication [18], [60]. However, higher viremia was seen in this ADE model that uses AG129 mice which are deficient for both type I and II IFN pathways. This suggests therefore that the enhanced viral output observed in this ADE mouse model results from the viral escape of type I and II IFN-independent antiviral mechanisms.
Elevated serum levels of an ever increasing list of soluble mediators (such as TNFα, IL-6, C5a, VEGF, MMP-9 and others) have been measured in DHF/DSS patients and represent hallmarks of severe dengue. The current paradigm of DEN-associated vascular leakage involves the combination of a plethora of soluble factors (including pro-inflammatory cytokines/chemokines and other non-inflammatory mediators) that act in concert and lead to the transient alteration of the vascular permeability [41], [42]. Surprisingly, only IL-6 and TNF-α levels were found higher in DENV2-infected mice born to DENV1 immune mothers compared to DENV2-infected mice born to dengue naïve mothers, despite the significantly higher vascular leakage measured in the former group. However, a more exhaustive list of mediators should be measured and this work is currently in progress in our laboratory.
Using neutralizing antibodies, we demonstrated the role of TNF-α in this ADE mouse model whereby anti-TNFα antibody treatment resulted in transient and partial control of vascular leakage in some organs which correlated with delayed death of the animals, while it had no impact on the viremia. The correlation between elevated systemic levels of TNF-α and dengue disease severity has been reported in a number of in vivo mouse model of severe dengue [24], [25], [35], [36], [37], [46], [54], [61], among which four of them have used anti-TNFα antibody treatment to demonstrate a reduced mortality rate [25], [37], [46], [61]. However, these studies failed to report an impact of the anti-TNFα treatment on vascular leakage specifically. Thus our work provides the in vivo direct evidence of a role for TNF-α in DENV-induced vascular leakage in an ADE model.
In addition to the cytokine storm theory (which has the weakness to be observed in other acute infections without leading to increased vascular permeability), other mechanisms have been proposed to play a role in DENV-induced vascular leakage. Among possible mechanisms, circulation of DENV specific antibodies capable of cross-reacting with host proteins has been reported and referred to as DENV-induced autoimmunity [62]. Specifically, Anti-NS1, anti-E, anti-prM and anti-C antibodies were shown to cross-react with endothelial cells, platelets, and some of the molecules involved in the coagulation and fibrinolysis pathways [62]. Involvement of these antibodies in enhanced vascular permeability observed in our ADE mouse model are currently being investigated in our laboratory.
In conclusion, this study is a long-overdue experimental demonstration of the ADE hypothesis proposed by Halstead as early as 1970 [12], [13]. This mouse model reproduces the natural route of maternal antibodies transfer from DENV1 immune mothers to their offspring and how they impact on dengue disease severity upon DENV2 infection. Despite the caveat of the absence of the type I and II IFN pathways, our mouse model offers a unique opportunity to study a number of aspects that are specifically involved in maternal antibody-mediated enhancement of dengue disease severity. For example, the dynamics of the IgG placental transfer, the role and importance of DENV specific antibodies acquired through breast milk, or the half-life of the transferred maternal antibodies can be studied in this model. Also very importantly, the impact of DENV vaccination of mothers on the offspring can be addressed. Furthermore, several studies have reported a higher neutralizing DENV antibody titer in cord blood versus maternal serum, suggesting a preferential movement across the placenta of some DENV antibody sub-species with greater avidity for DENV [63], [64]. This aspect can be specifically addressed in our model and the identification of the IgG antibody sub-species that preferentially cross the placenta might reveal very useful.
In addition we believe that that this mouse model offers a novel platform for therapeutic testing in the context of ADE that can be more relevant than the previously described ADE models which rely on the administration of enhancing concentrations of DENV specific antibodies prior to DENV infection. Indeed, while the latter rely on the administration of antibody doses that may be totally outside the range of concentrations that would be actually present in naturally infected dengue individuals, the maternal antibody-mediated ADE model avoids such bias. This aspect is of particular importance when testing the possible interference of pre-existing DENV specific antibodies on the protective efficacy of some therapeutic candidates in particular monoclonal antibodies that would compete with pre-existing circulating antibodies for binding to the virus particles.
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10.1371/journal.pgen.1005450 | Regulation of Gene Expression Patterns in Mosquito Reproduction | In multicellular organisms, development, growth and reproduction require coordinated expression of numerous functional and regulatory genes. Insects, in addition to being the most speciose animal group with enormous biological and economical significance, represent outstanding model organisms for studying regulation of synchronized gene expression due to their rapid development and reproduction. Disease-transmitting female mosquitoes have adapted uniquely for ingestion and utilization of the huge blood meal required for swift reproductive events to complete egg development within a 72-h period. We investigated the network of regulatory factors mediating sequential gene expression in the fat body, a multifunctional organ analogous to the vertebrate liver and adipose tissue, of the female Aedes aegypti mosquito. Transcriptomic and bioinformatics analyses revealed that ~7500 transcripts are differentially expressed in four sequential waves during the 72-h reproductive period. A combination of RNA-interference gene-silencing and in-vitro organ culture identified the major regulators for each of these waves. Amino acids (AAs) regulate the first wave of gene activation between 3 h and 12 h post-blood meal (PBM). During the second wave, between 12 h and 36 h, most genes are highly upregulated by a synergistic action of AAs, 20-hydroxyecdysone (20E) and the Ecdysone-Receptor (EcR). Between 36 h and 48 h, the third wave of gene activation—regulated mainly by HR3—occurs. Juvenile Hormone (JH) and its receptor Methoprene-Tolerant (Met) are major regulators for the final wave between 48 h and 72 h. Each of these key regulators also has repressive effects on one or more gene sets. Our study provides a better understanding of the complexity of the regulatory mechanisms related to temporal coordination of gene expression during reproduction. We have detected the novel function of 20E/EcR responsible for transcriptional repression. This study also reveals the previously unidentified large-scale effects of HR3 and JH/Met on transcriptional regulation during the termination of vitellogenesis and remodeling of the fat body.
| In addition to being vectors of devastating human diseases, mosquitoes represent outstanding model organisms for studying regulatory mechanisms of differential gene expression due to their rapid reproductive cycles. About 7500 transcripts are differentially expressed in four sequential waves during the 72-h reproductive period in the fat body, a critical reproductive organ. The major regulators for these waves of gene expression are the two very important insect hormones, 20-hydroxyecdysone (20E) and Juvenile hormone (JH), their respective receptors Ecdysone Receptor (EcR) and Methoprene-Tolerant (Met), amino acids and the orphan nuclear receptor HR3. These key regulators are responsible for activation and repression of co-regulated gene sets, at different time points, within the 72-h reproductive period. Importantly, this study, apart from providing an insight into the regulatory complexity involved in the temporal coordination of gene expression, also reveals the previously unidentified roles of 20E/EcR, JH/Met and HR3 during the 72-h period post blood meal.
| Numerous studies in model organisms have identified patterns of gene expression correlated with embryogenesis and development [1–9]. These studies have eloquently demonstrated the existence of a tight coordination between large gene cohorts and various stages of a developing organism on a spatiotemporal scale. In contrast, investigation of genomic profiles during reproduction has attracted much less attention. Blood-feeding animals such as mosquitoes, in addition to being vectors of numerous devastating human diseases, represent outstanding models because their reproductive events are synchronized by the intake of blood and occur within a short time span. Moreover, their reproduction is cyclic, with each cycle of egg development linked to a separate blood-feeding event. Previous studies have identified differential gene expression associated with blood feeding in the malaria mosquito Anopheles gambiae and the Dengue virus vector mosquito Aedes aegypti [10–12]. However, temporal control of gene expression patterns during blood-meal-activated mosquito reproduction is not yet completely understood.
The gonadotrophic cycle of a female mosquito is divided into two periods: pre- and post-blood meal. In the A. aegypti female, the pre-blood meal period, which in the first gonadotrophic cycle also includes post-eclosion (PE) development, lasts from at least 72 h until the mosquito takes a blood meal. It is controlled by juvenile hormone (JH) and its receptor Methoprene-tolerant (Met) [13, 14]. Both amino acid/Target of Rapamycin nutritional signaling and insulin are essential for activating post-blood-meal (PBM) events in the gut, ovaries and the fat body [15–19]. 20-Hydroxyecdysone (20E) is the main regulator of PBM events in the fat body, which produces yolk protein precursors (YPPs) for subsequent egg development [13, 20]. In the A. aegypti female, it takes 72 h to complete the entire PBM period.
During each gonadotrophic cycle, the fat body undergoes dramatic changes, shifting its functions from acting as a storage depot for lipid and carbohydrate reserves to becoming an immense protein-producing factory [20)]. At the end of the gonadotrophic cycle, it undergoes programmed autophagy and transforms itself back to reserve storage [21]. Hence, this tissue is particularly useful for studies of temporal coordination of gene expression. Our previous study [14] revealed gene expression patterns in the fat body during the pre-blood-meal period of the first gonadotrophic cycle in the A. aegypti female mosquitoes. We have shown that while metabolic genes are expressed early, those encoding transcription and translation machineries get activated later during this period. Moreover, we demonstrated that while the former group of genes is repressed by JH and Met, the latter is activated by these factors [14].
Here, we investigated the network of regulatory factors responsible for sequential gene expression in the PBM fat body. We show that systemic factors—JH, 20E and nutritional amino acids (AAs)—differentially regulate this gene-expression program. Moreover, our study has revealed that JH and 20E signaling in the PBM fat body is mediated by Met, EcR and HR3. Importantly, we report the previously unidentified role of JH in controlling gene expression during the PBM period. Furthermore, we have demonstrated the repressive function of 20E, which downregulates large cohorts of PBM genes in this mosquito tissue. Finally, we have shown that EcR mediates this repressive function. Taken together, our study provides new insights into the complexity of regulatory mechanisms responsible for temporal coordination of gene expression during reproduction in the female A. aegypti mosquito.
The goal of this study was to obtain detailed information about differential gene expression dynamics and to elucidate the regulatory networks governing the complex gene expression patterns following blood meal activation of reproductive events in female mosquitoes. We addressed these issues using the fat body, because it constitutes a tissue critical for female reproduction and is highly amenable for experimental studies in this organism, which lacks well-established genetics [20]. Custom-made Agilent microarray chips containing probe sets corresponding to 15,321 A. aegypti genes [14, 22] were used to examine the fat-body tissue samples collected at nine time points, spanning from 3 h to 72 h PBM. Differentially expressed gene (DEG) sets were established by comparing transcripts from each of the nine time points with that from the fat body of pre-blood-meal female mosquitoes, 72 h PE, using a minimum fold change of ≥1.75 (0.8 in a log2 scale) as the confidence threshold and a false-discovery rate (adjusted P value) of ≤0.01, similar to the criteria used by Zou et al., 2011, 2013. When applying this threshold, 7468 transcripts, which constituted almost half of the total number of genes probed in the A. aegypti genome, displayed differential expression during at least one of the nine time points within the 72-h period PBM, in the fat body of female Aedes mosquitoes (Fig 1A and see S1 Dataset). 3045 transcripts were found to be downregulated, 2938 upregulated and 1485 were found to be both up- and downregulated during different time points within the 72-h period PBM (Fig 1B and see S1 Dataset). Of these 1485 transcripts, 395 were downregulated to a greater extent than were upregulated (a difference of >1.75 between the fold changes), 306 were more upregulated than downregulated applying the same criterion (Fig 1B and see S1 Dataset), and 784 were almost equally up- and downregulated at different time points (a difference of <1.75 between the fold changes). More than 2500 transcripts displayed differential expression of greater than 5-fold (Fig 1B and see S1 Dataset).
Unlike during the PE period, when the number of DEGs increased consistently to reach a maximum during late PE (60–66 h) [14], the number of DEGs during the PBM period started to increase at around 12 h, reached a maximum between 18 h and 24 h, and then decreased sharply after 36 h PBM (S1 Fig).
Hierarchical clustering of the DEGs resulted in 12 different clusters (Fig 1A). Genes within certain clusters displayed similar expression profiles, barring minor variations. As a result, most of the genes could be categorized into four broad sets, depending on their expression profiles and the time of their maximal expression: early genes (EGs), early-mid genes (EMGs), late-mid genes (LMGs) and late genes (LGs) (Fig 1A). Transcript levels of EGs (Clusters 1, 11 and 6) were elevated by 3 h PBM, reached their maximum levels between 6 h and 12 h PBM, and declined between 18 h and 36 h, before getting slightly elevated again between 48 h and 72 h PBM (Fig 1C and see S2 Dataset). In comparison, EMG transcript levels (Clusters 8, 7, 4) did not show significant increase until 12 h PBM, reached the maximum levels between 18 h and 24 h, after which their expression declined by 36 h PBM (Fig 1C and see S2 Dataset). The genes that displayed a low expression prior to 24 h PBM followed by a sudden increase within 36–48 h PBM and a sharp decline post 48 h were grouped as the LMGs (Cluster 5; Fig 1C and see S2 Dataset). The LGs (Clusters 2 and 3) are those showing a decline in their expression following a blood meal, maintaining low expression during early-mid and late-mid periods, and showing maximum expression between 48 h and 72 h (Fig 1C and see S2 Dataset). Expression patterns of fat body genes were confirmed by means of quantitative real-time polymerase chain reaction (qRT-PCR) analysis; the profiles obtained by measuring the transcript levels of selected EGs, EMGs, LMGs and LGs, using qRT-PCR, displayed good correlation with the microarray data (S2 Fig).
Overall, our microarray analysis revealed an extremely high level of transcriptional activity in the fat body during the PBM period of the gonadotrophic cycle. Moreover, we were able to identify four major sequential waves of gene expression over the 72-h period PBM in the female fat body.
To understand the functional identity of genes expressed during the PBM period in the fat body, the EGs, EMGs, LMGs and LGs were examined by searching the inNOG (Insects non-supervised orthologous groups and their proteins) within the eggNOG v3.0 (evolutionary genealogy of genes: Non-supervised Orthologous Groups) database [23]. This analysis revealed that multiple changes occur in the functional identity of the fat body transcriptome over the duration of 72 h PBM. We observed that 40% of the EGs belong to cellular processes and signaling (CP&S) gene category, 30% to metabolism (MT) and 30% to information storage and processing (IS&P) (Fig 2A and see S3 Dataset). In the case of the EMGs, the percentage of MT genes increases to ~45%, while the percentages of the other two groups decreases (~35% CP&S; ~20% IS&P) (Fig 2A). This trend continues for the LMGs, for which almost 70% are MT genes, with about 10% IS&P genes and about 20% CP&S genes (Fig 2A). However, this trend completely reverses with the LGs, for which only about 15% of the total genes is represented by the MT genes, and both CP&S and IS&P constitute a little over 40% each (Fig 2A).
It was observed that while most MT genes are active during the early-mid and late-mid PBM periods, there is an enrichment of CP&S and IS&P during the early and late PBM periods. To examine the functional dynamics on a finer scale, we further sorted these genes into more-specific functional categories within the inNOG database (Fig 2B–2D and see S3 Dataset). The results of this analysis revealed a remarkable temporal separation of major functional gene categories over the 72 h PBM. Separation of IS&P genes into finer functional categories revealed that more than 70% of EGs are transcription (TR) and translation, ribosome structure and biogenesis (TRB) genes, where TRB alone constitutes more than 50% of the genes (Fig 2B). The percentage of TR genes increases in EMGs (>35%) and further in LMGs (55%), whereas, the TRB genes show an opposite trend, these genes seem to be significantly downregulated during the late-mid and late stages (Fig 2B).
Similarly, a closer look at the CP&S groups showed that while signal transduction mechanism (STM) genes constitute a little over 10% of CP&S amongst EGs, the percentage increases to ~45% in LMGs and decreases slightly in LGs (Fig 2C). Conversely, intracellular trafficking, secretion and vesicular transport (ITS&VT) show an exact opposite trend (Fig 2C). The percentage of genes related to posttranslational modification, protein turnover and chaperones (PMTC) decrease, whereas that of genes related to cytoskeleton increases over the course of the gonadotrophic cycle (Fig 2C).
Although MT genes account for most of the EMGs and LMGs, genes that belong to different functional sub-categories seem to be more prevalent in each gene set. While genes related to inorganic ion transport and metabolism (IIT&M) make up 25% of the early-mid MT genes, lipid transport and metabolism (LT&M) accounts for a similar amount of the late-mid MT genes (Fig 2D).
It is worth mentioning that in each of the four gene sets, ~40% of the genes belong to diverse or unknown functional classes, defined as having either insufficient information or no significant matches to other organisms.
Next, we investigated the regulatory signaling network responsible for the temporal dynamics of gene expression in the fat body during the PBM period of the mosquito gonadotrophic cycle. Previous studies have identified involvement of AAs, insulin and 20E in regulation of vitellogenic events in the mosquito fat body [15, 16, 24]. We used a combination of RNA interference (RNAi) and in-vitro organ culture techniques to elucidate the regulation of the temporal gene expression program in the fat body.
The average EG expression increased soon after a blood meal and reached its maximum between 6 h and 12 h PBM, just before the 20E titer started to peak (Fig 1C). The expression levels declined sharply with the increase in 20E titer, suggesting an inverse correlation between the 20E titer and EG expression. This is unlike the early genes in the 20E regulatory cascade where a positive correlation can be observed. We monitored the responses of three EGs (AAEL002269 –purine nucleoside phosphorylase; AAEL002488 –dead box ATP-dependent RNA helicase; and AAEL004345 –cysteinyl t-RNA synthetase) to treatments in in-vitro fat body culture (IVFBC) using qRT-PCR. The genes were selected on the basis of their high level of expression and similarity to the average profile (Fig 1C). Two of the three genes were IS&P genes, one from the RPM and one from the TRB sub-categories while the third was a nucleotide metabolism and transport gene. Each of these genes showed a differential expression of >2-fold. Tissues (fat bodies) collected at 72 h PE, when there is only a basal level of 20E, were placed in complete culture media and treated with either AAs alone or with AAs plus increasing concentrations of 20E (5x 10−8 M for 4 h and 10−6 M for 4 h); non-treated (NT) fat bodies in culture media served as the control. Total RNA was extracted, cDNA was made and qRT-PCR for the three EGs suggested that these genes were being upregulated by AAs and repressed by 20E (Fig 3A and S3A and S3B Fig).
To confirm the repression of these genes by 20E, we used the RNA-interference (RNAi) technique. Double-stranded RNA (dsRNA) was injected at 24 h PE to knock down EcR (S4A Fig), mosquitoes were blood fed 72 h post injection, and tissue was collected 24 h PBM. If these genes are being repressed by 20E then knocking down its receptor should remove the effects of repression. qRT-PCR for these genes with cDNA made from tissues collected from the EcR knocked-down mosquitoes confirmed that these genes are indeed repressed by 20E (Fig 3B and S3C and S3D Fig). Injecting dsRNA for the Luciferase gene (iLuc) served as the control.
Although the elevation of JH titer during the late PBM period has been reported (S1 Fig; [25, 26]), the role of this hormone in transcriptional regulation during the PBM period in the female mosquito is not entirely clear. To understand whether JH could play any regulatory role in EG expression during the late PBM period, we examined the effect of JH on the same genes using IVFBC and found that it had a moderate activating effect (Fig 3C and S3E and S3F Fig). The tissue used to check the effects of JH was collected at 24h PBM when the titer of JH was at the basal level, and incubated in a complete culture medium supplemented with either JH (10 μg/ml JH III) or the solvent (acetone) for 8 h.
To confirm this JH action, we knocked down the JH receptor, Met, by injecting a dsRNA (S4B Fig). Injections were done at 72 h PE, after the completion of the first preparatory phase; mosquitoes were blood fed 72 h post-injections and tissue was collected 72 h PBM. qRT-PCR for the same genes corroborated the activation by JH through its receptor Met, when the results demonstrated a decline in expression of the genes as a result of the Met knockdown (Fig 3D and S3G and S3H Fig). Next, we checked whether these genes were being activated by insulin, which has been reported to have a regulatory effect along with AAs on certain genes PBM [16, 17, 19, 27]. Exogenous insulin along with 20E has been shown to enhance AA-dependent activation of Vg expression in the isolated fat body [16]. The results suggested that insulin was not involved in the activation of these genes during the early PBM period.
Overall, our results have shown that the representatives of the EGs tested are activated by AAs at the early stage of the PBM period, are repressed by 20E/EcR in the mid stage and are activated again moderately by JH/Met at the end of the PBM phase. Involvement of insulin in the regulation of EGs tested could not be detected.
Transcript levels of the early-mid genes (EMGs) started increasing by 12 h, reached their maximum between 18 h and 24 h, and then declined drastically to basal levels by 36 h, staying low thereafter (Fig 1C). These genes show a positive correlation with the 20E titer during the PBM period. To examine the regulation of EMGs, we selected six genes, three well-known YPP genes (AAEL010434—Vitellogenin, AAEL007585—Cathepsin b and AAEL006563—Vitellogenic carboxypeptidase) and three others (AAEL014671- protease S51 alpha-aspartyl dipeptidase; AAEL001433 –FGF receptor activating protein; AAEL004398 –G-protein-coupled receptor). The latter genes were chosen on the basis of high expression and their similarity to the average EMG profile. Like the YPP genes, these three genes belong to the CP&S functional group and were upregulated by >8-fold. The expressions of genes were tested in the IVFBC with either AAs alone or AAs plus increasing concentrations of 20E for 8 h. The results showed that there is either minimal or no effect of AAs alone on these genes; however, all of these genes were activated by 20E in the presence of AAs (Figs 4A and 5A and S5A and S5B and S6A and S6B). To confirm the activation by 20E, these genes were tested using qRT-PCR with tissues (fat bodies) from EcR knocked-down mosquitoes (similar to those used for testing the EGs). The results corroborated that these genes are activated by 20E, as there was a decrease in transcript level for each of these genes in fat bodies from EcR-silenced mosquitoes (Figs 4B and 5B and S5C and S5D and S6C and S6D). The declines in these gene transcript levels correlate with the 20E titer drop in female mosquitoes, by about 30 h PBM. To test whether 20E was required for maintaining a high level of expression of EMGs, we modeled the 20E titer decrease in the IVFBC: the fat bodies from female mosquitoes 24h PBM were pre-incubated in the culture medium supplemented with AAs and changing concentration of 20E for 8 h (4 h each with two different concentrations, as described previously) and then incubated in a medium depleted of 20E for 3 h (three washes with complete culture medium every hour). The qRT-PCR results with cDNA made from these tissues showed a decline in the transcript levels of all six genes, further confirming the direct correlation between the 20E titer and the expression these genes (Figs 4C and 5C and S5E and S5F and S6E and S6F).
Expression levels of EMGs decline to their lowest levels by 36 h PBM when the orphan nuclear receptor HR3 has been reported to regulate transcriptional reprogramming of the fat body [28]. Therefore, we examined a possible effect of HR3 on these genes by RNAi interference. dsRNA was injected at 24 h PE to knock down HR3 (S4C Fig), the mosquitoes were blood fed 72 h post-injection and tissue was collected 36 h PBM. This RNAi experiment showed that the transcript levels of all tested genes were elevated, suggesting that they are indeed repressed by HR3 (Figs 4D and 5D and S5G and S5H and S6G and S6H). Therefore, as judged by testing six selected EMGs, the decrease in 20E titer and repression by HR3, constituted conditions responsible for the programmed decline in the transcriptional activity of EMGs.
It has been shown that insulin activates the YPP gene Vitellogenin in the presence of AAs and 20E in the A. aegypti fat body [16]. Our results confirmed a positive effect of insulin on expression of two other YPP genes, Vitellogenic carboxypeptidase (Fig 4E), and Cathepsin b (S5I Fig), along with that on the expression of Vitellogenin (S5J Fig). Surprisingly, we could not detect any activation by insulin of the other EMGs tested.
Since, the JH titer has been reported to rise again during the late PBM period, we wanted to check whether these genes are repressed by JH. The results suggested that JH has no repressive effect on these genes.
In summary, our findings indicate that representatives of the EMGs are activated by 20E and EcR, are downregulated by a declining 20E titer, and repressed by HR3. We also observed that insulin activated only a subset of EMGs, as tested here using the YPP genes.
The LMGs are a group in which the expressions of most of the genes are at low levels until 24 h PBM, after which they increase sharply and reach the maximal level between 36 h and 48 h PBM, declining thereafter (Fig 1C). Therefore, these genes appear to have a high level of expression only within a window when the titers of both 20E and JH are at low levels. We selected three genes on the basis of their high level of expression and similarity to the average LMG profile. All three genes (AAEL003568 –threonine dehydratase, AAEL010075—oxidoreductase and AAEL002638 –cytochrome 450) are metabolism-related genes and >10-fold upregulated at 36 h PBM when compared with their expression levels at 72 h PE. The effect of AAs in-vitro on these genes was not entirely consistent, because two of three genes tested were not affected (Fig 6A and S7A Fig), whereas the third (S7B Fig) showed activation by AAs. 20E repressed these genes in-vitro, which is consistent with their low level of expression up to 24 h PBM (Fig 6A and S7A and S7B Fig). EcR RNAi silencing (performed similarly to that described in the previous sections) confirmed the repression of these genes by EcR (Fig 6B and S7C and S7D Fig). Transcripts of the LMGs are elevated at the time when HR3 has been reported to be active in the mosquito fat body [28]. We hypothesized that HR3 is the factor responsible for upregulation of this gene set. When we conducted the HR3 RNAi silencing (as in previous section), it was indeed found that this nuclear receptor is responsible for the activation of the LMG representatives in the fat body (Fig 6C and S7E and S7F Fig). The LMGs showed a low level of expression between 48 h and 72 h PBM; therefore, we checked the effects of JH on the LMG representatives by IVFBC and found that these were repressed by JH (Fig 6D and S7G and S7H Fig). Met RNAi silencing revealed that these genes are repressed by JH through its receptor Met (Fig 6E and S7I and S7J Fig).
Overall, the results suggest that the LMG expression peak between 36 h and 48 h can likely be defined by repressive actions of 20E during the early PBM part and JH during the late PBM period. LMGs are activated by the reprogramming factor HR3 when the titers of both hormones are at relatively low levels.
The expression level of LGs starts to decline after a blood meal and remains low through the early-mid and late-mid phases PBM, before rising after 36 h and reaching maximal levels between 48 h and 72 h (Fig 1C). This pattern of LG expression has a positive correlation with the reported titer of JH during the PBM period [25, 26]. Our hypothesis was that 20E and JH determined temporal coordination of LG expression in which they are repressed by 20E during most of the PBM period and then activated by the rising titer of JH. In order to test this hypothesis, we selected three representative LGs (AAEL015143 –Glycine-rich ribosome binding protein; AAEL003352 –Ribosomal protein l7ae_E2; and AALE004328 –Origin recognition complex). We used the criteria described in the previous sections of high levels of expression and similarity to the average profile. All three genes were related to information storage and processing and were >20-fold downregulated when their minimum expressions were compared with their expressions at 72 h PE. Interestingly, IVFBC results showed all genes tested were downregulated by AAs and none by 20E (Fig 7A and S8A and S8B Fig). RNAi silencing of EcR corroborated that 20E does not have any repressive effect on the genes tested. In contrast, all of these genes were indeed activated by JH in-vitro (Fig 7B and S8C and S8D Fig). In-vivo RNAi confirmed their activation by JH through its receptor Met (Fig 7C and S8E and S8F Fig).
Thus, our experiments have shown that JH and Met play roles of major regulators for LG activation during the late PBM period. The AA pathway appears to be a repressor of these genes. Surprisingly, we found no role for 20E in the regulation of the genes tested from this group.
A characteristic feature of female mosquito reproduction is that it is cyclical, with each egg developmental cycle tightly linked to a separate blood feeding. Hence, we investigated whether the same genes are expressed during the late PE (LPE) and the late PBM periods, when the organism is preparing itself for a blood meal. We compared the LGs (all differentially expressed late genes based on the microarray data) with those expressed during the late PE period and are regulated by Met (LPE and iMet genes) [14]. We found that 111 late genes (S1 Table) appeared in both LPE (i.e., those are upregulated by >1.75-fold during the LPE period) and iMet downregulated (i.e., knockdown of Met during the PE period results in downregulation of >1.75-fold) gene sets (Fig 8A) and will be referred to as cyclical genes (CGs). Comparisons of functional groups constituting the LGs and CGs shows that—enrichment of the functional sub-categories in the two gene sets are markedly different (Fig 8B–8D and see S3 Dataset). We selected one gene each from the IS&P (AAEL001171- tRNA-dihydrouridine synthase), CP&S (AAEL002675 –arginase) and MT (AAEL001623—proteasome subunit) functional groups from within the 111 CGs and checked the effects of AAs, 20E and JH using IVBFC. IVBFC with AAs and changing concentrations of 20E demonstrated that these genes are repressed by AAs in-vitro (Fig 8E and S9A and S9B Fig), just like the PBM-specific LGs, whereas 20E may (S9A Fig) or may not (Fig 8E and S9B Fig) have a repressive effect. The activation by JH was evident from the IVFBCs with JH (Fig 8F and S9C and S9D Fig) and was confirmed by the in-vivo RNAi knock-down of the JH receptor Met (Fig 8G and S9E and S9F Fig). It is worth mentioning that Met dsRNA was injected at 72 h PE, after the completion of PE preparatory phase, and its effect was examined at 72 h PBM. We also found that one gene (AAEL003352) of the three, tested as PBM LGs, appeared in the list of LPE genes and was regulated by Met during the PE period.
Next, we checked the expression profiles of the representatives of CGs along with that of AAEL003352, post first (Fig 8H and S9G and S9H Fig) and second blood meal (Fig 8I and S9I and S9J Fig), after the mosquitoes completed the first reproductive cycle and had laid eggs. The expression profiles (Fig 8H and 8I and S9G–S9J Fig) demonstrated that these genes are indeed cyclical and are activated late during the second egg maturation cycle.
Our data suggests that EGs too can be up-regulated by JH to a lesser extent, probably at a later stage, therefore we compared the EGs with LPE and found that there are 164 genes that are common between the LPE, EGs and iMet-down sets (Fig 9A and S1 Table).
We also looked at the overlap between LMGs, representatives of which were found to be repressed by JH through Met, and the EPE and MPE sets, which according to Zou et al. [14], have a significant overlap with the iMet up (Met-repressed) sets and found 82 (Fig 9B and S1 Table) and 28 (Fig 9C and S1 Table) genes in common, respectively. We tested three of these genes (AAEL002781–Galactokinase, AAEL003347-CRAL/TRIO domain containing protein and AAEL000705 –Steroid dehydrogenase), common within the EPE, LMGs and iMet up sets, to check if these were repressed by JH through Met post blood meal. The qRT-PCR results with Met depleted samples (knock-down done at 72 h PE after the completion of the PE period) showed that these genes like in PE (as reported by Zou et al., 2013) are repressed by Met during the late-mid PBM period (Figs 9D and S10A–S10B). Since these are LMGs we tested the effects of AAs, 20E and EcR knockdown on these genes. The results showed that like in other LMGs, AAs may (S10C Fig) or may not (Figs 9E and S10D) have an effect on these genes, also these genes are repressed by 20E through EcR (Figs 9E and 9F and S10C–S10F).
In summary, the results suggest that there are sets of genes that are cyclically activated and repressed through the JH/Met pathway during both PE and PBM periods.
In this study, we have shown that certain key elements of the regulatory network mediating temporal gene expression in the fat body of a female mosquito, during blood-meal-activated reproduction, have dominant effects during specific periods. Unlike previous studies of the A. aegypti fat body transcriptome [29, 30] that have identified the differentially expressed genes at a single time point (24 h PBM), in this study, we have not only looked at the changes in the transcripts over a 72-h period (nine different time points) PBM, but have also demonstrated that there are key factors responsible for the differential expression of the genes. Our results have revealed the complexity of gene regulation in the fat body of female A. aegypti during this period, when the organism is undergoing massive physiological changes within a short time span. The inNOG database search reflects changes in the expression of genes that belong to the different functional groups during the four different stages (early, early-mid, late-mid and late) within the PBM period, with MT genes being highly active during the early- and late- mid PBM periods, between 18 h and 48 h, when blood is digested, yolk proteins are made and the fat body reprogramming for the next egg developmental cycle begins.
It has been well established that 20E is the major stimulus that upregulates YPP gene expression in mosquitoes, which, along with protein synthesis, positively correlates with 20E titers [20, 31]. In this study, we were able to demonstrate 20E-mediated activation of not only the YPP genes but also representatives of a super-group (EMGs) within which the YPPs fall (Fig 10). We have also shown that 20E represses representatives from groups of genes that are activated before the rise of the 20E titer (EGs), and after the decline of the 20E titer (LMGs) We also checked the response of representative genes from both of these groups to a lower concentration of 20E and found that there is either no effect or varying degrees of repressive effects (S11A–S11F Fig). To the best of our knowledge, this is the first study to report large-scale transcriptional repression by 20E and EcR. While the molecular mechanism of gene activation by 20E hierarchy is well established, little is known about repressive 20E action.
Although 20E is known to be the main regulator of vitellogenesis, by itself it is not sufficient to activate this process. Experiments have demonstrated that AAs are critical for the activation of YPP genes by 20E [15, 31], and we have shown here that AAs are not only essential for the activation of YPP genes by 20E, but can activate and repress gene sets without the assistance of 20E. Representatives of EGs were found to be activated, whereas those of LGs were found to be repressed by AAs (Fig 10).
Around 36 h PBM, when the titer of 20E has declined to basal levels and YPP synthesis has ceased, the fat body converts back to a nutrient storage and metabolism function until the next vitellogenic cycle is initiated [32]. It has been demonstrated previously that HR3 targets genes in the termination phase of the vitellogenic cycle and plays an essential role in programmed termination of the first cycle as well as in the entry into the second [28]. In this study, we demonstrated that the repressive effects of HR3 are not limited to vitellogenin or even the YPP genes. During the termination of vitellogenesis HR3 inhibits a good number of genes, mostly EMGs (Fig 10), which are activated by 20E between 18 h and 24 h PBM. Similarly, activation by HR3 is not restricted to the transcription factors in the 20E regulatory cascade, but affects a much larger set of genes (mostly LMGs) that are activated during the termination of vitellogenesis, between 36 h and 48 h (Fig 10). These effects of HR3 might either be direct or indirect through other transcription factors activated by HR3. For example, HR3 stimulates transcription of genes encoding EcRB and USPA isoforms [28]. In turn, they may participate in mediating HR3 action. This question requires further investigation. Thus, the critical role played by HR3 during the termination of the first vitellogenic cycle by switching on and off certain gene sets has been well established in this study.
JH is associated with changes in the fat body, during the pre-vitellogenic period, which allows the fat body to become responsive to signals that induce vitellogenesis [33–35]. The fat body becomes competent to respond to the steroid hormone 20E and to synthesize the massive amounts of yolk protein required for egg maturation [36]. In this study, we have identified the role of JH in transcriptional regulation during the late PBM (post vitellogenic) period and demonstrated that JH not only activates representatives of a group of genes (LGs), but also is responsible for repressing genes that are activated by HR3 during the termination of vitellogenesis (LMGs, Fig 10). Moreover, apart from activating genes in the late PBM period, it also regulates a set of genes (CGs) that are cyclical. The genes that are cyclically activated by JH follow the JH titer and probably play important roles in preparing the fat body for successive vitellogenic cycles. There are also genes that are cyclically repressed through Met during both PE and PBM periods. Thus, this work demonstrates that the role of JH in transcriptional regulation is not limited to the pre-vitellogenic stage; along with HR3, JH plays a key role in remodeling the fat body for the next egg developmental cycle, post vitellogenesis.
In contrast to observed effects of AAs, 20E, HR3 and JH, we were unable to detect any large-scale effects of insulin, other than activation of the YPP genes, within the fat body, during the PBM period. We were able to detect a synergistic activating effect of 20E and insulin in the presence of amino acids (previously reported by Roy et al. 2007) on genes tested other than Vitellogenin. Surprisingly, the other EMGs (of which YPPs is a subgroup) tested showed no significant activation by insulin. We also did not detect any activation of the EGs by insulin, either by itself or in the presence of amino acids and 20E, suggesting that activation by insulin is limited to the YPP genes during the early and early-mid PBM period.
Therefore, we can summarize, based on the microarray transcriptomic analysis that the PBM period can be divided broadly into four phases: early (0–12 h), early-mid (12-30h), late-mid (30–48h) and late (48-72h). Our study using in-vitro organ culture and RNAi depletion analyses also revealed the major regulators of gene expression during these four phases—AAs, 20E, HR3 and JH, respectively (Fig 10). Each of these factors is responsible for the activation and repression of different gene sets during the four distinct PBM phases, thereby successfully completing vitellogenesis, and the reprogramming of the fat body, for the next reproductive cycle (Fig 10). These results provide a clear insight into the complexity of gene regulation within this key mosquito tissue, thereby elucidating the coordination among the different key regulators in the orchestration of spatial and temporal gene expression patterns required during this critical phase of female mosquito reproduction. In-vitro fat body culture has its limitations as the dissected tissue loses communication with other tissues and therefore cannot receive the signals arising from those tissues. We have in most cases checked the effects of single factor at one time, and utilized RNAi mediated depletion of complementary factors for this analyses. The goal of this study was to identify how the major regulators affect the waves of expression during a specific time period. Although we have accomplished this primary goal, further study is required to identify other factors involved in the regulation of gene expression during these periods.
Mosquitoes of the A. aegypti wild-type UGAL strain were raised at 27°C and 80% humidity, as described previously [16]. The larvae were raised in non-crowded conditions [37,38] (200 in 750 ml distilled water per 9” x 12” pan) and fed 0.125 ml– 0.900 ml vol. of standard diet (equal parts of rodent diet, Lactalbumin and active dry yeast) between Day 0 and Day 4. Four pans of pupae were combined into one adult cage. Adult mosquitoes were fed continuously on water and 10% (wt/vol) sucrose solution. All dissections were performed in Aedes physiological solution (APS) at room temperature [16]. Blood feeding of all adult mosquitoes other than the Met knocked-down ones, was done with white rats. Adult Met knocked-down mosquitoes were blood-fed using White Leghorn chickens. All procedures for using vertebrate animals were approved by the UCR animal care and use committee.
Single-color hybridizations of custom-made Agilent microarrays with 15,321 A. aegypti genes were conducted at the University of Chicago Core Instrument Facility following standard protocols [22]. Three independent biological replicates were performed for each treatment. The raw image data were processed using the Agilent Feature Extraction Software [39]. Subsequent analysis steps including background correction, normalization and statistical analysis of DEGs, were performed in the statistical programming environment R using Bioconductor packages [40], as described previously [14]. DEGs were filtered and hierarchical clustering was performed using the same criteria used previously [14]. Discrete clusters were obtained by the same tree cutting approach described before [14].
The dataset for insects non-supervised orthologous groups (inNOG) and their proteins, from the eggNOG v3.0 (evolutionary genealogy of genes: Non-supervised Orthologous Groups) database was used for determination of the functional categories. 11318 Aedes aegypti genes are present in version 3.0 of the inNOG dataset that was downloaded. The EGs, EMGs, LMGs, LGs and CGs were mapped against the inNOG dataset with the help of the Microsoft (MS) Acess software. MS Excel was used to generate the stacked column bar graphs.
cDNAs were synthesized from 2 μg total RNA using the SuperScript III Reverse Transcriptase kit (Invitrogen). RNA was treated with DNase I (Invitrogen) before cDNA synthesis. PCR was performed using the Platinum High Fidelity Supermix (Invitrogen).
qRT-PCR was performed using the iCycler iQ system (Bio-Rad, Hercules, CA and an IQ SYBR Green Supermix (Bio-Rad). Quantitative measurements were performed in triplicate and relative expression (RE) was measured as RE = 2-ΔΔCt and normalized to the internal control of S7 ribosomal protein mRNA for each sample. Real-time data were collected from the software iCycler v3.0. Raw data were exported to Microsoft Excel and analyzed. P-values were calculated with the help of unpaired t test using the online version of GraphPad.
Sequences of all primers used for qRT-PCR analyses are shown in S2 Table.
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10.1371/journal.pgen.0030226 | Chromosome Structuring Limits Genome Plasticity in Escherichia coli | Chromosome organizations of related bacterial genera are well conserved despite a very long divergence period. We have assessed the forces limiting bacterial genome plasticity in Escherichia coli by measuring the respective effect of altering different parameters, including DNA replication, compositional skew of replichores, coordination of gene expression with DNA replication, replication-associated gene dosage, and chromosome organization into macrodomains. Chromosomes were rearranged by large inversions. Changes in the compositional skew of replichores, in the coordination of gene expression with DNA replication or in the replication-associated gene dosage have only a moderate effect on cell physiology because large rearrangements inverting the orientation of several hundred genes inside a replichore are only slightly detrimental. By contrast, changing the balance between the two replication arms has a more drastic effect, and the recombinational rescue of replication forks is required for cell viability when one of the chromosome arms is less than half than the other one. Macrodomain organization also appears to be a major factor restricting chromosome plasticity, and two types of inverted configurations severely affect the cell cycle. First, the disruption of the Ter macrodomain with replication forks merging far from the normal replichore junction provoked chromosome segregation defects. The second major problematic configurations resulted from inversions between Ori and Right macrodomains, which perturb nucleoid distribution and early steps of cytokinesis. Consequences for the control of the bacterial cell cycle and for the evolution of bacterial chromosome configuration are discussed.
| Genomic analyses have revealed that bacterial genomes are dynamic entities that evolve through various processes including intrachromosome genetic rearrangements, gene duplication, and gene loss or acquisition by gene transfer. Nevertheless, comparison of bacterial chromosomes from related genera revealed a conservation of genetic organization. Most bacterial genomes are circular molecules, and DNA replication proceeds bidirectionally from a single origin to an opposite region where replication forks meet. The replication process imprints the bacterial chromosome because initiation and termination at defined loci result in strand biases due to the mutational differences occurring during leading and lagging strands synthesis. We analyze the strength of different parameters that may limit genome plasticity. We show that the preferential positioning of essential genes on the leading strand, the proximity of genes involved in transcription and translation to the origin of replication on the leading strand, and the presence of biased motifs along the replichores operate only as long-term positive selection determinants. By contrast, selection operates to maintain replication arms of similar lengths. Finally, we demonstrate that spatial structuring of the chromosome impedes strongly genome plasticity. Genetic evidence supports the presence of two steps in the cell cycle controlled by the spatial organization of the chromosome.
| Genomic analyses have revealed that bacterial genomes are dynamic entities that evolve through various processes, including intrachromosome genetic rearrangements, gene duplication, and gene loss or acquisition by lateral gene transfer [1]. Nevertheless, comparison of bacterial chromosomes from related genera revealed a conservation of organization [2]. For example, the genetic maps of E. coli and Salmonella typhimurium that diverged from a common ancestor about 140 million years ago are extensively superimposable [1]. Multiple forces seem to shape the organization of bacterial chromosomes, and the imprinting of these processes on the chromosome is evident at different levels.
DNA replication initiated at oriC proceeds bidirectionally until the two replication forks meet. Replication initiation and termination at defined loci result in guanine/cytosine skew between leading and lagging strands due to the mutational differences [3–5]. In wild-type (wt) cells, replication arms coincide with the two compositional skewed halves of the chromosome, hence the name of replichore [6]. Initiation of replication occurs at oriC, the origin junction of replichores, and in most cases, the two replication forks are predicted to meet at the terminal junction of replichores where skew changes [7]. Biological processes may exploit these strand-biased sequences defining each replication arm as a target for selection pressure. Two examples of positive selection at the replichore scale have been well documented in bacteria; first, the octamer χ sequence involved in the RecBCD-mediated recombination process is overrepresented 3.5 times in one orientation along each replichore [8]. Second, FtsK-Orientating-Polar-Sequences (KOPS) are overrepresented on one DNA strand ([9], see below).
Beyond the replichore organization, processes affecting the genome organization at the gene level also shape chromosome structures, and two different parameters might be affected: orientation of gene transcription relative to replication, and location of genes relative to the origin of replication. Since replication and transcription occur simultaneously on the same DNA molecule, both head-on and co-oriented collisions are thought to occur in replicating bacteria. It has been originally proposed that highly expressed genes are preferentially positioned on the leading strand to allow faster DNA replication and reduce transcript losses that occur during head-on collisions [10]. In E. coli, 54% of coding sequences are found on the leading strand, and as for most bacterial species, highly expressed genes such as rRNA operons (rDNA) and genes encoding ribosomal proteins are transcribed in the direction of replication. However, at least in E. coli and Bacillus subtilis, essentiality, not expressiveness, selectively drives the gene-strand bias [11]. Another parameter thought to shape chromosome structure at the gene level involves the location of genes relative to the replication origin, and gene dosage effect may constrain this positioning. In fast-growing bacteria, the replication gene dosage effects are mainly associated with the elements of the translation and transcription machinery, i.e., rDNA, transfer DNA (tDNA), RNA polymerase, and ribosomal protein genes [12].
In bacteria, selection operates to maintain the two replichores of approximately equal length. In most cases, the size of the longest replichore corresponds to 50%–60% of the entire chromosome [13]. In E. coli, the constraint on the size of replication arms is ensured by the presence of ten Ter sites (TerA–J) scattered in two oppositely oriented groups in the terminal half of the chromosome ([14], Figure 1A). Each of the Ter sites binds Tus, the replication terminator protein, with a specific affinity. Each replication fork travels across the five Ter sites in the permissive orientation before it encounters a Ter site in the nonpermissive orientation and is blocked. The forks are thus trapped between oppositely oriented sites, defining a region called the replication fork trap. In conditions in which Tus blocks replication forks at ectopic Ter sites, creating a region impossible to replicate, the RecBCD pathway of homologous recombination and SOS induction are essential for viability [15–17]. The need for a high level of homologous recombination protein RecA and helicase UvrD accounts for the requirement of SOS induction for viability [18]. A detailed study has shown that forks blocked at Ter sites are stable; linear DNA molecules are formed upon arrival of a second round of replication forks and RecBCD-promoted recombination catalyzes the reincorporation of the double-strand DNA (ds-DNA) ends made by replication run off [17]. UvrD was proposed to enable replication forks initiated at recombination intermediates to progress across the Ter–Tus barrier [18].
Microscopy observations have shown that circular bacterial chromosomes are organized with a particular orientation within growing cells that preserves the linear order of loci on the DNA [19–23]. The E. coli chromosome consists of four structured macrodomains (MDs) and two nonstructured regions [24,25]. The Ori MD containing the origin of replication oriC is centered on migS, a centromere-like structure involved in bipolar positioning of oriC [26]. The Ori MD is flanked by two nonstructured (NS) regions called NSright and NSleft (Figure 1A). The Ter MD containing the replication fork trap is centered on the terminal replichore junction. The Ter MD is flanked by two MDs called the Right and Left MDs. The existence of the four MDs and two NS regions was deduced from genetic data showing that different MDs do not interact during cell growth, but interact with their adjacent NS regions [25]. Several important processes take place in the Ter MD. First, replication ends in the Ter MD because of the presence of the replication fork trap. Second, the replichore junction diametrically opposed to oriC is the region of the change in compositional skew defining the two replichores [7]. The site-specific recombination site dif is present near the replichore junction and allows the resolution of chromosome dimers into monomers; to be active, dif must be present in a zone of converging KOPS [9,27]. KOPS are recognized by FtsK which translocates the DNA directionally in order to align dif sites at the septum where XerCD can resolve chromosome dimers into monomers (for review, see [28,29]). Third, the Ter MD contains two Non-Divisible Zones (NDZ) refractory to inversions ([30], see below).
Genetic approaches have provided experimental evidence that some chromosome rearrangements are detrimental for growth or, in rare cases, refractory to inversions [30–37]. Using homologous recombination, intrareplichore inversions (Intra) of segments with one endpoint located in the 20%–30% region flanking the terminal replichore junction, i.e., the periphery of the Ter MD, have been shown to be reproducibly highly problematic or prohibited in E. coli (for review, see [38]). However, these regions are not refractory to inversions by the site-specific recombination system used here [25,37]. Inversions that split the Ter MD are detrimental for growth and delay cell division [37].
In a previous study, we have generated strains with chromosomes carrying inverted segments using the λ site-specific recombination system [25]. Interestingly, we noticed that strains carrying combinations of partner att sites located in the same regions of the chromosome have similar phenotypes upon inversion, and many of the inversions seemed to affect cell physiology. The results reported here allow us to define extents and limits to plasticity in the E. coli chromosome. The analysis of detrimental rearrangements allowed the identification of two types of chromosome inversions that, by changing MD organization, severely affect the progression of the cell cycle.
By using the site-specific recombination system of bacteriophage λ, we previously developed a genetic system that allows the construction and detection of genetic inversions in the E. coli chromosome [25]. We have constructed several series of strains containing one defined att site at a fixed position and its att partner site inserted at random locations; strains carrying combinations of partner att sites that could give rise to viable recombinants have been selected. Cassettes were designed to detect inversion between att sites: recombination between attL and attR restores lacZ integrity (Figure 1B). By providing a limiting amount of recombinase, we were able to reveal the existence of MDs that correspond to large regions that are insulated from each other in the cell (Figure 1A). By providing a high amount of recombinase, recombination between most of the combinations of att sites can be detected, and there is a good correlation between the frequency of collisions and the frequency of recombinants [25]. There was no correlation between the frequency of inversion and the physiological properties of cells with inverted configuration; inversions occurring at high frequency can be detrimental, whereas those occurring at low frequency can be neutral (see below). We now analyze in detail the properties of strains carrying chromosomes with the different inverted configurations.
To unravel the consequences of inverting a chromosomal segment on cell physiology, we performed a number of analyses aimed at detecting defects visible at the colony or cell level. The size of colonies from strains with the inverted chromosome (lacZ reconstituted, blue colonies) was compared to that of strains with the wt configuration (white colonies) in rich medium. The effect of these inversions on growth was also measured using a coculture assay in which strains with chromosomes in wt and inverted configurations were compared (Materials and Methods). To analyze the consequences of the inversion on the nucleoid morphology, cells grown in exponential phase were stained with DAPI, and nucleoids were observed by fluorescence microscopy (see Materials and Methods). The percentages of cells with different types of nucleoids were numbered according to the cell size. The number of chromosome origins was estimated by fluorescence-activated cell sorting (FACS) analysis. Viability of strains was tested in different genetic backgrounds affected in pathways related to DNA metabolism. We used recA mutants and since RecA is required for both homologous recombination and SOS induction, the requirement of SOS induction for viability was tested in lexA ind− (SOS− Rec+) and recA lexAdef (SOS+ Rec−) mutants. When RecA was required, we used mutants affected in the two RecA-dependent recombination pathways, i.e., RecBC and RecFOR, to identify the pathway involved. Measurements of SOS induction were performed in a sfiA background to avoid SfiA-dependent filamentation and inhibition of cell division [39]. SOS induction was quantified in culture by using a plasmid carrying the uidA gene encoding β-glucoronidase under the control of the PsfiA promoter (see Materials and Methods). In addition, the presence of a plasmid carrying a gfp gene under the control of the PSfiA promoter allowed the direct visualization of the induction of the SOS response at the cellular level. tus mutants were used to estimate the defects provoked by inverted Ter sites in various configurations. When recombinant colonies could not be obtained, PCR reactions probing the presence of recombination at the DNA level were used to check for the occurrence of attL–attR inversion and for the presumed lethality conferred by the inversion.
Ten Ter sites are found in the E. coli chromosome, which are bound in vitro by Tus with varying efficiencies [14]. The Kobs for Tus binding to the very strong TerB site is about 5 × 10−13 M, and the relative arrest activity was estimated to be around 95%. Although studies were not performed with other Ter sites, the effect of mutations in TerB mimicking the sequence of other Ter sites allows estimation of their respective strength as deduced from Tus–Ter binding affinity measurements and from measure of the replication arrest activity [14]. TerA–E and TerG are predicted to be very strong sites (arrest activity greater than 50%), TerH moderately strong (arrest activity around 33%), and TerF and TerI–J weak sites (arrest activity less than 20%). To estimate the respective strength of Ter sites in vivo in a strain producing wt levels of Tus, we have generated strains in which different Ter sites are inverted, and their properties have been analyzed (Figures S1 and S2, and Text S1). Altogether, the results indicate that efficiency of replication arrest at different chromosomal Ter sites correlates with the predictions based on in vitro affinities and on replication arrest activity of TerB mutant sites. They show that in conditions of wt level of Tus protein, blocking the two forks by the strong TerE and TerA sites renders RecBCD-dependent recombination essential for viability, as previously observed with TerA [15]. SOS induction is also essential in these conditions. The effect of the moderate site TerH in the inverted orientation was less severe, but still significant. Inverted weak TerI and TerJ sites do not appear to affect growth, suggesting they do not significantly impede replication (Figure S1 and Text S1).
On the E. coli chromosome, the two replichores are of similar size, suggesting that most replication forks meet within the replication fork trap diametrically opposite to the origin. To evaluate the requirements for the balance of replication arms, we analyzed strains in which inversion endpoints are in each replication arm, asymmetrically relative to oriC (interreplichore inversion [Inter], Figure 2A, Table 1). As the inverted region contains oriC, these inversions do not change the orientation of sequences or genes relative to replication. However, because the two endpoints are at different distances from oriC, the size of the replication arms are modified, one becoming greater and one smaller than 50% of the chromosome. Imbalance of 5%–10% for replication arms has no effect on colony morphology: the colonies with inverted configurations are similar to those with wt configuration (Figure 2B, 47% for the short arm and 53% for the long arm (47–53) in strain Inter R-L3 (Table 1), and 42–58 in strain Inter R-L5 (Table 1)). The effect of these inversions on growth was also measured using the coculture assay containing strains with either a chromosome in wt or inverted configuration: no defect was associated to this genetic rearrangement as the ratio of inverted to wt cells was close to one after 60 generations (Figure 2C and unpublished data). The cells and nucleoids of strains with either configuration were not distinguishable (Figure S3). When the imbalance reached 15% (36–64 in Figure 2B, strain Inter R-NSleft1 in Table 1), some defects became apparent. The recombinant colonies were smaller than noninverted colonies, and the ratio of inverted to wt cells after 60 generations was affected (0.13 ± 0.02), but the cells and nucleoids of the inverted configuration were similar to those of the wt configuration: only 2% of the cells appeared abnormal (Figure 2D). Around 20% of imbalance (30–70 in Figure 2B, strain Inter R-NSleft2), the size of colonies carrying the inversion was affected; in coculture assays, the ratio of cells with inversion to wt configuration was less than 0.01 (Figure 2C) and microscopic observation showed longer cells with abnormal nucleoids (14% of abnormal cells in Inter R-NSleft2, Figure S3). Above 20% of imbalance (23–77 and 18–82 in Figure 2B, strains Inter R-NSleft4 and Inter R-O1, respectively, in Table 1), colonies were barely visible, and more than 20% of cells displayed condensed nucleoids, i.e., a par phenotype, or grew as cells with unsegregated nucleoids (Figure 2D and Figure S3, respectively).
Interestingly, we noticed that all strains with an imbalance greater than 20%, i.e., with a replication arm smaller than 30% and the other larger than 70%, were dependent on RecA for viability (Figure 2B and Table 1). Recombinant colonies could be obtained in a recFOR background, but not in conditions inhibiting either RecBC DNA recombination or SOS induction, indicating that the RecBC homologous recombination pathway is required for viability in the presence of an imbalance of replication arms greater than 20% (Table 1). The dependence on RecA for viability was suppressed by a tus deletion, indicating that the impediment of replication forks by Tus at Ter sites is responsible for lethality in a recA background (Figure 2B). Finally, a 2- to 4-fold SOS induction was apparent in strains that required recA for viability (Table 1). The analysis performed with inverted Ter sites indicated that in cells expressing wt levels of Tus, the replication forks are stopped at the first strong Ter site in the nonpermissive orientation ([15] and Text S1). It implies that, when the imbalance is smaller than 20%, the two forks of a same replication round can progress to the replication fork trap. In contrast, RecBC-dependent recombination is solicited to restart the first fork that reaches a Ter site before the other fork can reach it when the imbalance of replication arms is larger than 20%. We propose that, in the conditions used, when the shorter replication arm is less than half the longer one, it is fully replicated twice before completion of replication of the longer arm, leading to the formation of DNA double-stranded ends. These double-stranded ends induce the SOS response and are lethal in the absence of RecARecBC-dependent homologous recombination. Many natural inversions in bacterial genomes are symmetrical with respect to replication origins and termini. Scatter plots of the conserved sequences between related species produce an X-shaped pattern, called X-alignment [2]. These rearrangements reveal that selection operates to maintain replichores of similar lengths; in most genomes, the size of the longest predicted replication arm does not exceed 60% of the chromosome [13]. By moving the position of the replication fork trap on the genetic map, we have been able to analyze the effect of varying the imbalance of replication arms. Remarkably, we did not observe negative effects when the imbalance was around 10%, in total agreement with the observed size distribution of replichores in different species. Some defects appeared when the imbalance reached 15%, and recombinational rescue of replication forks was required above 20%.
The analysis of interreplichore inversions affecting at the same time two MDs revealed that making hybrid MDs while keeping the wt replichore junction unaffected was well tolerated (Figure S3). We noticed that for similar levels of imbalance less than 20%, inversions involving endpoints located either in the NS regions or in the Left, Right, and Ter MDs (Figure S3 and Table 1) behave similarly: the growth of recombinant colonies was slightly affected, and recombinants were viable in a recA background (Table 1). It is only when the imbalance exceeded 20% that recombinant colonies were affected and their formation recA-dependent (Table 1). Altogether, these results suggest that in the context of interreplichore inversions, the effect of MD disorganization for the Left, Right, and Ter MDs can be well tolerated by the cell.
We noticed that large inversions inside a replichore (intrareplichore) with one endpoint in the Ori MD and the other in the NSright region gave rise to recombinants with no strong defects. Three examples of strains with such rearrangements are shown in Figure 3. These inversions encompass 916, 927, and 668 kb corresponding to 826, 828, and 607 genes, including four, three, and one rDNA operons, respectively (Figure 3A, strains Intra O-NSright1 to −3 in Table 1). Similar outcomes were obtained in the left replichore. For example, the inversion of a 982-kb–long segment that changes the orientation of 942 genes, including two rDNA operons and 34 ribosomal protein genes (strain Intra L-NSleft1 in Table 1) had no detectable detrimental effects (Figure 3B and unpublished data). The colonies of strains with the rearranged chromosome had the same size as those with the wt configuration (Figure 3B). The diagram shown in Figure 3C indicates that even the largest inversion has no detectable effect on nucleoid morphology. No strong defect was associated with these genetic rearrangements, because the ratio of inverted to wt configuration was above 0.75 after 60 generations in coculture assays (Figure 3D). Finally, colonies carrying these inverted configurations were viable in a recA background, indicating the absence of important DNA damage (Figure 3B). Altogether, these results indicate that the direction of replication can be inverted through hundred of genes, including rDNA genes, without important consequences for growth. Furthermore, the results show that inversions between Ori MD and the NS regions are well tolerated. Similar conclusions were obtained from the analyses of intrareplichore inversions between NS regions and the flanking Right or Left MD in the absence of active Ter sites (Figure S4 and Table 1). Therefore, gene orientation, gene dosage, and sequence skews appear to operate only as long-term positive selection determinants. Our results are in agreement with the evidence [11,40] that weakens the proposed influence of replication on gene orientation [41,42]. However, given the large size of bacterial populations, slightly deleterious effects that can be accredited to positioning rDNA and ribosomal protein genes on the lagging strand are most likely sufficient to eliminate such configurations from the population in long-term evolution.
In contrast to well-tolerated inversions described above, two types of intrareplichore inversions were highly detrimental for the cell: the first type involved endpoints located in the Ter and the Right MDs, and provokes the separation of the replication fork trap from the wt replichore junction. The second type involved inversion between endpoints located in the Ori MD and in the Right MD. Features of these two detrimental configurations are described in detail below.
Intrareplichore inversions with endpoints in the Right and Ter MDs (Figure 4A, strains Intra R-T1 to −3 in Table 2) generate a hybrid Right-Ter MD in which the orientation of TerA, TerD, and TerE is modified, creating a replication arms imbalance close to 35%–65% (see intra R-T1 in Figure 4B). These strains carry two zones of converging KOPS (Figure 4C): the normal one corresponding to the wt replichore junction, and a new one associated with the replication fork trap in the hybrid Right-Ter MD. Inversion severely affected the growth of colonies (Figure 4D). The observation of cells with the inverted configuration revealed the occurrence of a high proportion of abnormal cells: 27% of cells showed a par phenotype, 15% formed cells with unsegregated DNA, and 1% of cells were anucleate (strain Intra R-T1 in Figures 4E, 4F, and S5). Cells larger than 10 μm with a high amount of nonsegregated nucleoids were observed. FACS analyses indicated that the number of chromosomes in the large cells ranged from 16 to 32 (unpublished data). Other strains with intrareplichore inversions between Right and Ter MDs (Intra R-T2 and −3 in Figure 4 and Table 2) showed the same features (unpublished data).
The origin of the detrimental phenotypes caused by this chromosomal configuration was analyzed by testing different genetic backgrounds (Figure 4G). It was not possible to obtain viable recombinants in a lexA ind− background, i.e., in SOS-defective conditions. SOS induction was directly visualized by the use of a plasmid expressing gfp under the control of PSfiA promoter (Figures 4F and S5). Homologous recombination was also required because recombinants with the inverted configuration could not be obtained in a recA, recBC, or in a recA lexAdef background (i.e., in conditions of constitutive SOS induction, but in the absence of RecA-dependent recombination).
The phenotype and RecA-independence of interreplichore Right-Ter inversions (Figure S3, strains Inter R-T1 to −4 in Table 1) suggests that intermingling Right and Ter MDs cannot by itself be responsible for the growth defects of strains Intra R-T1 to −3 in the inverted configuration. Growth defects and RecA dependence for viability were suppressed in a tus background, indicating that the position of the displaced replication fork trap is responsible for the growth defects (Figure S5E). The detrimental effects can not originate only from imbalance of replication arms because the imbalance of replication arms is close to 35–65, a level that does not render RecA essential for viability in interreplichore inversions (Figure 2 and Table 1). Three other hypotheses that might account for the growth defects were tested below: the positioning of dif outside of the replication fork trap, the presence of two zones of converging KOPS, and the merging of replication forks far away from the wt replichore junction region.
In these intrareplichore Right-Ter inversions, the replication fork trap is separated from dif. It was previously reported that the dif site does not need to be present in the replication fork trap to be fully active because the insertion of a ectopic TerA* site near TerA, moving the replication fork trap away from the dif region, did not affect dif activity [43]. dif is active in any new replichore junction formed after inversion [9,27]. After deletion of dif from its normal position, we reinserted a 28-bp fragment corresponding to dif in the new replication fork trap, in the region where KOPS converge, far away from the wt replichore junction (Figure 4C, strain Intra R-T2 Δdif difRFT in Table 2). Strains carrying this inverted configuration still showed strong detrimental defects and were not obtained in a recA background (Table 2) even though insertion of dif in the new replication fork trap improved nucleoid distribution in a way suggesting dif activity, i.e., by removing a 12%–15% fraction of filaments (47% of abnormal cells instead of 64% in the absence of dif, and 50% when dif is present at its normal location; unpublished data). These results indicate that the absence of dif from the new replication fork trap is not responsible for the observed defects. These results were corroborated by the viability of Inter Right-Ter inversions in a recA background when dif was deleted (strain Inter R-T2 Δdif in Table 2), confirming that the RecA dependence for viability of Intra Right-Ter inversions does not result from the lack of dif in the replication fork trap.
To analyze the defects provoked by forming two zones of converging KOPS and by positioning the replication fork trap far from the dif region but without Right and Ter MDs intermingling, we constructed strains with an inversion that positioned the replication fork trap at the limit between the Ter and the Right MDs (Intra T1 in Figure 4C, and strains Intra T1 and Intra T2 in Table 2). Colony formation was not affected; cells and nucleoids were similar to those of the wt configuration, and strains carrying inversions were viable in a recA background (Figure 4C and 4D). These results indicate that as long as sequences belonging to the Ter MD remain together, merging of replication forks far away from the wt replichore junction in the presence of two zones of converging KOPS does not provoke important growth defects.
To determine whether merging of replication forks outside the Ter MD may be responsible for the detrimental effects of intrareplichore Right-Ter inversions, we generated two different genetic inversions in the Right MD (strain Intra R3 in Figure 4C, and strains Intra R3 and Intra R4 in Table 2) that inverted TerE in a strain in which TerA and TerD are deleted; inversion of the TerE region provoked replication to end in the Right MD, and generated two zones of converging KOPS (Figure 4C) and an imbalance of replication arms close to 35–65 (Table 2). Recombinant colonies were slightly affected compared to those with a wt configuration (Figure 4D); cells and nucleoids from both configurations were similar (unpublished data), and recombinants were viable in a recA background (Figure 4D). These results indicate that replication forks can merge in the Right MD without affecting viability.
Because none of the simple modifications in the chromosome structure can, by itself, account for the growth defect of intrareplichore Right-Ter inversions, we tested whether the defect was dependent on the length of the Right MD that separates the replication fork trap from the wt replichore junction in the Intra R-T1 configuration. We constructed strain Intra R-T4 (Figure 4C and Table 2). In this strain, the chromosome configuration is similar to Intra R-T1, Intra R-T2 and Intra R-T3 configurations, but the extent of sequences belonging to the Right MD that are embedded in the Ter MD is reduced (170 kb compared to 420 kb). Recombinants showed fewer defects; only a fraction of cells (13%) showed a par phenotype, and less than 1% formed cells with unsegregated nucleoids (Figure S6). Importantly, strains in the inverted configuration were viable in a recA background (Figure 4D). These results are in agreement with the hypothesis that the extent of Right MD DNA that separates the Ter region where fork merge from the replichore junction region is responsible for the observed defects. The combination of the embedding of Ter sequences in the Right MD and finishing replication within these Ter sequences is responsible for the deleterious effect. The shortening of the region of Right MD that separate the replication fork trap from the wt replichore junction region suppresses the defects. Together with the observed viability of recA- interreplichore inversions involving the Right and the Ter MDs (strains Inter R-T1 and R-T2 in Figure S3 and Table 1), these observations support the hypothesis that the requirement of RecA for the viability of intrareplichore Right-Ter inversions results from the separation of the replichore junction region from the region in the Ter MD where replication ends. It is therefore tempting to speculate that in deleterious configurations resulting from intrareplichore inversion, replication ends in the displaced part of the Ter MD, activities normally associated to the wt replichore junction region cannot be performed, and the cell cycle is affected. Altogether, these results suggest the existence of a tight temporal and/or spatial coupling between the end of DNA replication in the Ter MD and an unknown activity near the replichore junction region required to progress in the cell cycle. Further work will be required to determine whether proteins known to function near the terminal replichore junction, FtsK [44] and TopoIV [45], are involved in this coupling.
The second class of detrimental inversions corresponds to intrareplichore inversions that combine Ori MD with the Left or Right MD. Because most of the inversions between the Left and Ori MDs also induce a high imbalance of replication arms, we focused our study on the inversion between Ori and Right MDs. The detrimental effects of intermingling Ori and Right MDs were revealed by combining attL and attR sites inserted at various positions in the Right (14′, 17′, 19′, and 22′) and Ori (0.7′, 97′, 95′, 94′, 92′, 88′, 87′, and 86′) MDs (Figure 5A, Table 2, and unpublished data). Viable recombinants could be obtained only when the inversion involved sites located between 92′ and 0.7′ in the Ori MD, and they all showed strong growth defects (proximal Ori–Right combinations, strains Intra O-R1 to −5 in Figure 5A [indicated in grey] and 5B). In contrast, viable recombinants could not be obtained when the inverted fragment extended from the Right MD to 88′, 87′, or 86′ (distal Ori-Right combinations, strains Intra O-R6 to −8 indicated in black in Figure 5A). Proximal inversions invert the TerHI sites as inversions between Right MD and NSright region (control c in Figure 5A, Intra R-NSright3 in Table 1), whereas distal inversions invert both TerHI sites and the centromere-like sequence migS found at 89′. migS did not seem to be responsible for the difference observed between the two types of combinations since distal combinations did not give rise to viable recombinants in the absence of migS (unpublished data). In the absence of both TerH and TerI (ΔTerHI in Figure 5C), viable recombinant colonies with no strong growth defects could be obtained for proximal inversions, and they are viable in a recA background (strains Intra O-R1 to −3 in Figure 5C and Text S1). Distal inversions remained lethal on rich medium when both TerH and TerI were deleted (Figure 5D and unpublished data, and strains Intra O-R6 to −8 in Table 2), but viable colonies could be obtained on minimal growth medium (Figure 5D). These recombinants showed growth defects in minimal medium supplemented with casamino-acids (Figure 5D) and could not be propagated in rich medium (unpublished data).
The proximal Ori–Right inversions that gave rise to viable colonies were used for microscopy analysis (strains Intra O-R1 to −3 in Table 2). In the presence of TerH and TerI, we observed a predominant filamentation with DNA accumulating in nonsegregated nucleoids (e.g., 39% of filaments and 10% of par-like cells in the inverted configuration of Intra O-R3 in Figure 5E, and unpublished data). Analysis of the nucleoids of recombinant colonies deleted for TerH and TerI revealed a high percentage of normal cells. However, in the inverted configurations, a significant proportion of cells formed filaments (5%, 9%, and 22%, according to the strain, Figure 5F and 5G, and unpublished data). Remarkably, these filaments were different from those observed in all other rearrangements described in this study; they showed apparently segregated nucleoids with no division septa between DNA bodies (Figure 5G).
To visualize the defects responsible for the absence of viability in distal Ori–Right combinations in rich medium, cells obtained in minimal medium were grown in liquid rich medium and observed at different time points. After 180 min, cells with inverted configurations accumulated a fraction of abnormal cells (16% of filaments not observed in wt cells; Figure 5H and unpublished data), whereas after 300 min, most of the cells were elongated, with improperly compacted and segregated nucleoids (Figure 5H and 5I). Altogether, these results indicate that intermingling Right and Ori MDs interferes with nucleoid management and formation of the division septum. For both proximal and distal combinations involving Ori–Right MDs, the presence of longer cells with an increased number of segregated nucleoids indicates an inhibition in the formation of a septum of division. It is striking to note that inversions that move the Ori MD close to the Right MD (less than 50 kb from the Right MD; strain Intra O-NSright4 in Table 1, Figure S7) slightly affect cell physiology. It is therefore likely that the origin of the defect results from an antagonism between Ori and Right MDs rather than from simply moving Ori sequences on the genetic map. The observation that distal inversions were more problematic than proximal ones suggests that the deleterious effects are proportional to the length of the MD. In B. subtilis, the chromosome partitioning and sporulation protein Spo0J binds eight parS sites scattered in the 800-kb region flanking oriC [46]. Our results suggest the presence of similar specific sequences in the Ori MD. Imbedding of such putative sequences in the Right MD could be the reason for growth defects. In this regard, it is interesting to note that the NSright region separates Ori and Right MDs and could play a buffer role. We would like to speculate that mixing Ori and Right MD sequences would perturb proper segregation of Ori and Right MD, a step necessary to establish septum division. Further experiments will be necessary to determine whether the perturbation of the spatial control of cytokinesis affected by this type of inversion involves SfiA [39], MinCDE [47], SlmA [48], or other unidentified proteins.
Altogether, the results reported here give an important insight into the role of MDs in cell cycle control by chromosome configuration in E. coli (Figure 6). The Ter MD is involved in a process that spatially and/or temporally couples the end of replication in the Ter MD with a subsequent step near the replichore junction region. The antagonistic Ori and Right MDs are involved in a process coupling chromosome segregation and cytokinesis. Identification of determinants or factors that specify MDs should help us understand how MDs are involved in the control of these processes.
E. coli K12 strains are all derivatives of MG1655. Standard transformation and transduction procedures used were as described before [25]. Plasmids and strains with relevant genotypes are described in Table S1.
Conditions for inversion formation were as described previously [25].
SOS response was quantified by measuring the amount of β-glucoronidase [49] in sfiA cells transformed by a pBAD18-derived plasmid carrying the uidA gene fused to the sfiA promoter. Similar results were obtained in a sfiA+ background, but results were less variable in a sfiA background. SOS induction was visualized by using cells transformed by a P15A derivative carrying gfp under the control of the sfiA promoter (pZA-PsfiA-gfp).
The cultures were grown to optical density (OD) 0.2 at 30 °C and then processed for microscopy or flow cytometry. For the microscopy analysis, the cells were processed as described before [45]. For flow cytometry analysis, chromosome numbering was estimated by counting the number of replication origins using a rifampicin/cephalexin replication run-out [50]; aliquots were taken every 10 min over a period of 200 min. Cells were fixed in a 75% ethanol–PBS 1× solution, then washed in PBS 1×, treated with RNaseA, and the DNA was then stained with propidium iodide. The cells were analyzed on a Partec PASIII flow cytometer.
Strains carrying the inverted configurations were grown in coculture with the same strain carrying the wt configuration. A 1:1 mixture of the two strains was grown in serial cultures in LB medium at 30 °C for up to 70 generations. Every 10 generations, the relative numbers of both configurations were determined by plating. Experiments were performed in triplicate. |
10.1371/journal.pcbi.1004824 | Inferring Strain Mixture within Clinical Plasmodium falciparum Isolates from Genomic Sequence Data | We present a rigorous statistical model that infers the structure of P. falciparum mixtures—including the number of strains present, their proportion within the samples, and the amount of unexplained mixture—using whole genome sequence (WGS) data. Applied to simulation data, artificial laboratory mixtures, and field samples, the model provides reasonable inference with as few as 10 reads or 50 SNPs and works efficiently even with much larger data sets. Source code and example data for the model are provided in an open source fashion. We discuss the possible uses of this model as a window into within-host selection for clinical and epidemiological studies.
| Since the 1960’s researchers have observed that Plasmodium falciparum infections, the cause of most severe malaria, are frequently composed of several different strains of the parasite. In this work, the authors use Bayesian methods on whole genome sequence data to model the structure of these mixtures. Results from this method are consistent with previous approaches but provide finer resolution of these mixtures. As whole genome data in malaria research becomes increasingly common, this work will allow researchers to delve further into the within-host dynamics of the parasite, a much-discussed but previously difficult-to-access aspect of this disease.
| The protozoan parasite Plasmodium falciparum (Pf) is the cause of the vast majority of fatal malaria cases, killing at least half a million people a year [1–3]. The parasite’s ability to develop resistance to drugs and the rapid spread of that resistance across geographically-separated populations presents a constant threat to international control efforts [4–6]. While research has elucidated many genetic factors this process, much of the genetic epidemiology of the parasite—including the effective recombination rate and the rate of gene flow across populations—is still unclear [5, 7, 8].
Understanding the implications of multiplicity of infection (MOI), where multiple strains appear to be present within a single patient’s bloodstream, is a long-standing challenge [9–13]. While MOI-focused studies implicate MOI levels with a range of conditions, including clinical severity [14], age-specific severity [15–18], parasitemia levels during pregnancy [19], and other effects [20–23], there is no broad consensus about its role in controlling the course of an infection. Still, a wide variety of studies and genetic assays—most commonly through typing the MSP genes—show MOI as a regular feature of clinical Pf isolates [24–26].
WGS technologies applied to Pf extracted directly from infected patients’ bloodstreams provide an unprecedented window into the structure of genetic mixture within samples [27, 28]. Initial work on this data shifted focus from estimating MOI to analysis based on inbreeding coefficients [13, 29–31]. These metrics, a form of F-statistic, give an estimate of the departure of within-sample allele frequencies from those expected under a Hardy-Weinberg-type equilibrium with the ambient population. From this perspective, each patient’s bloodstream is seen as a subpopulation comprised of an admixture of all strains in the local environment, ranging from a perfectly random sampling of all nearby strains (panmixia) to the repeated sampling of just a single strain (unmixed).
The initial study applying WGS to clinical Pf isolates from eight countries on three continents showed the parasite to exhibit significant population structure at continental scales, with the amount of subpopulation structure varying significantly among regions [27]. Employing an F-statistic approach to measure the inbreeding coefficient from thousands of single nucleotide polymorphisms (SNPs), this work also argued that the degree of mixture varies significantly across populations, with highly mixed samples occurring relatively frequently in west Africa but only occasionally in Papua New Guinea. The authors suggest an association between increased levels of observed mixture and increased transmission intensity in the local environment. Transmission intensity, the rate at which individuals are infected with Pf, likely determines some part of the frequency of out-crossing within parasite populations and so may be critical to understanding gene flow and strategies for resistance control [32].
In this paper, we present a statistically rigorous model that synthesizes these two distinct and previously disparate approaches to analyzing Pf clinical mixtures: assessing the number of distinct genetic types within a sample (the MOI approach [31]) and measuring the degree of panmixia with respect to the local population (the panmixia approach [33]). The model makes two significant innovations: first, a reversible jump Markov Chain Monte Carlo (MCMC) implementation to capture uncertainty in the number of strains, and second the inclusion of a panmixia term to deal with unexplained variation in the mixture. This work possesses similarities in character to the COIL algorithm [34], but can capture more complex mixture structure and is geared toward analyzing WGS data (>1000 SNPs) rather than a small number of SNPs (∼50 SNPs).
This model centers around how the two sub-models—MOI and panmixia—contribute to the observed within-sample non-reference allele frequencies (WSAF) as they relate to the population-level non-reference allele frequencies (PLAF). For clarity, we will deprecate the use of non-reference in front of the term allele frequency, since they are all calibrated in this fashion. We will use the acronyms WSAF to denote the within-sample allele frequency and PLAF to denote population-level allele frequency to avoid confusion about the particularly allele frequency being indicated. The goal of the model is to explain observed ‘bands’ that emerge when examining SNPs WSAF as a function of their PLAF (Fig 1).
The model assumes (1) that the number of bands is a consequence of the number of distinct strains present within a sample, (2) that SNPs are unlinked, and (3) that unexplained variation is assumed to be due to a small fraction of genomes sampled under panmixia. To distinguish from an inbreeding coefficient—a similar but distinct concept—we refer to this fraction as a panmixia coefficient. The collection of WSAF bands then appears as a function of the finite mixture of the strains, with the slope in WSAF bands with respect to the PLAF explained by both the SNP distribution and the panmixia coefficient.
Fig 2 lays out how the consequent banding patterns can arise. In the simplest case, a sample is composed of a single, unmixed strain, and all SNPs exhibit a WSAF of zero or one (see Fig 2(a)), based on whether they agree with the reference. Consequently, the WSAF is independent of PLAF, leading to two flat bands at these values. We call these samples unmixed, since this is how a single strain with some divergence from the reference will appear. In the case where there are a finite number of strains mixed within a sample, then at a given SNP position some number of the strains will exhibit a reference allele and some a non-reference allele. The WSAF for that SNP is determined by the proportions of non-reference strains in the sample mixture. Observing many SNPs displays ‘bands’ of constant WSAF across the PLAF. Thus, for K component strains there are 2K possible combinations of biallelic states, leading to that number of bands.
A fraction of the Pf organisms present within the blood may not be from any of the dominant strains. We model these as randomly sampled from the local population according to simple panmixia. Observationally, this leads to a consistent change in the slope of each of the bands. To see this, consider an admixture of two distinct Pf populations: a single strain, representing 1 − α of the within-sample genomes, and the remaining α that we assume follow panmixia. The α tilt in the WSAF arises from the fact that for this proportion of organisms the probability of sampling non-reference allele is proportional to the PLAF (Figs 1(c) and 2(c)). Samples with high K appear to have additional tilt due to the higher probability of non-reference alleles occurring at high PLAF (Figs 1(d) and 2(d)).
The paper proceeds as follows. We first detail the structure of the WGS data, introduce some notation, and the essential mathematical structure of the model. We then present an extensive simulation study on the performance of the model, an application of the model to artificial laboratory mixtures, and an examination of its application to field isolates collected from northern Ghana. We conclude by discussing the strengths and weaknesses of the model, a means of experimental validation, and potential consequences for the etiology of clinical malaria.
The field WGS data come from Illumina HiSeq sequencing applied to Pf extracted from 419 clinical blood samples collected from infected patients in the Kassena-Nankana district (KND) region of Upper East Region of northern Ghana. Collection occurred over approximately 2 years, from June 2009-June 2011. The raw sequence reads for these data are accessible through the PF3K project https://www.malariagen.net/projects/parasite/pf3k. This includes data from the MalariaGEN Plasmodium falciparum Community Project on www.malariagen.net/projects/parasite/pf. On the website for this method, we provide read count data subsampled from the full data set. The artificial laboratory samples were sequenced and called per protocols given in [35]. The raw sequence data is available through the European Nucleotide Archive with the accessions available in the S1 Text.
The full sequencing protocol and collection regime are described in [27]. After quality control measures, all samples were examined, and following a documented protocol comparing against world-wide variation, 198,181 single-nucleotide polymorphisms (SNPs) were called [27]. These are exclusively coding SNPs found outside of the telomeric and subtelomeric regions that exhibit unusual structural properties. Each SNP xcall provides the number of reference and non-reference read counts observed at each variant position within the genome, ascertained against the the 3D7 reference [36]. These data were exhaustively examined for spurious heterozygosity and evidence of DNA contamination, with mixed calls verified using time-of-flight mass spectrometry at greater than 99% accuracy [27].
For this project, we further filtered the data. First, multiallelic positions were reclassed as biallelic. We then excluded positions that exhibited no variation within the KND samples, any level of missingness (no read counts observed), or minor allele frequency less than 0.01. To remove low quality samples, we removed those with more than 4,000 SNPs missing and fewer than 20 read counts, following an inflection point observed in S1 Fig. These cleaning measures left 2,429 SNPs in 168 samples. These SNPs exhibit desirable properties for model inference—high and consistent coverage across all samples—that could likely be expanded to non-coding or less stringent cleaning standards without issue. More than 95% of the remaining samples’ sequencing was completed without PCR amplification. We observe little apparent population structure among the samples, evidenced either by principal components analysis or a neighbor-joining tree of the pairwise difference among samples (S2 Fig). The data preparation scripts are available with the source code for the model, https://github.com/jacobian1980/pfmix/.
Following the data preparation and cleaning, our analysis begins with a set of N = 168 clinical samples, each composed of M = 2,429 SNPs. At each SNP j within each clinical sample i, we observe rij reads that agree with the reference genome and nij reads that do not agree. The total number of read counts in sample i at SNP j is then nij + rij. For a sample i, we write the complete data across all SNPs as D i = [ ( r i 1 , n i 1 ) , ⋯ , ( r i M , n i M ) ]. For each SNP j, we associate a PLAF pj. The collection of all pj we refer to as P.
Conditional upon the number of strains K, there are 2K bands, indexed by r = 1, ⋯, 2K. The full collection of bands we call Q, with qijr indicating the WSAF for sample i at SNP j in band r. The probability of a SNP lying within the distinct bands across the PLAF is specified by a mixture component λr, which is a function of the PLAF detailed below. The degree of panmixia in a sample is given by α, a value between zero and one. A complete list of the model parameters is given in Table 1.
Statistically, the model takes the form of a finite mixture model with the mixture components associated with individual bands [37, 38]. We take a Bayesian approach to inference and construct the model by giving an overall rationale for the decomposition of the posterior distribution, and then justify the appropriate choice of probability distributions for each of the terms [39].
We infer the model parameters using a standard reversible jump MCMC approach [40, 41] with one exception: we first calculate maximum-likelihood estimates (MLE) for P across all samples and then treat these as fixed when inferring the remaining parameters [42]. This choice is motivated by statistical expedience and computational speed: except for P, the parameters of the model are independent across samples and so this approximation enables the algorithm to infer parameters in parallel rather than jointly. This avoids the difficulties of performing inference on the number of strains within all samples simultaneously. Running in parallel also increases the computational speed of the implementation by at least an order of magnitude. Since the sample collection is large enough that the PLAF is nearly independent of any given sample, we do not expect this approximation to significantly bias inference.
For each SNP j, the MLE derives from treating the non- and reference reads within a sample as coming from a binomial distribution with parameter pj. This leads to:
p ^ j = ∑ i N n i j / ∑ i N ( n i j + r i j ) .
To infer the number of strains, K, for each sample, we employ a pair of complementary split/merge reversible jump MCMC moves. To specify the split move first not that in moving from K → K + 1 that the transformation only affects the parameter W. If we randomly select wk, 1 ≤ k ≤ K, then we can split this into two components, u ⋅ wk and (1 − u) ⋅ wk, where u is drawn from a uniform distribution. This establishes a diffeomorphism between parameters at K and K + 1 with Jacobian wk. The proposal ratio is (K2 − K)/K = K − 1. The acceptance ratio then is the product of the proposal ratio, Jacobian, the likelihood ratio, and the prior likelihood. The merge move randomly selects two states, k1 and k2, and merges them to k′ by setting w′ = wk1 + wk2. The Jacobian and proposals are the reciprocal of those for the split move.
Conditional on P and K, for each of the three parameters, α, W, and ν, we propose new values directly from the prior distribution. This leads to Metropolis-Hastings ratios almost solely dependent on the ratio between the likelihood and priors for the proposed state to those for the current. The inference scheme is implemented in set of scripts for the R computing language, and can be found under the Academic Free License at https://github.com/jacobian1980/pfmix/s. For a single sample with K = 5, a sufficiently long MCMC run takes approximately 10 minutes on a single high-performance computing core.
To demonstrate the efficacy of the model and our implementation, we present a simulation study examining the algorithm’s performance under a range of simulated data. We consider two distinct aspects of the inference: how well the model infers the number of strains, and, conditional upon that number, how well it infers the model’s other parameters. We simulate data from the model in the following way. Conditional upon the number of SNPs (M), panmixture coefficient (α), number of strains (K) and the sum of the read counts (C) we draw a vector of probabilities (W) from a uniform Dirichlet distribution. We combine the values of W in all possible permutations to create the 2K bands and assign the PLAF for the SNPs evenly from 1/M to 1, so that the j th SNP has PLAF j M. For each SNP, we first probabilistically select the band it occupies according to Eq (6). We then simulate read counts from the likelihood (Eq 5) with qijr per Eq (8). For all simulations, we set ν = 10. We run the simulation across the range of values for M, α, K and C given in Table 2. For each parameter set, we create 10 independent realizations.
We apply the algorithm to 18 artificial laboratory mixtures. These artificial samples were generated by taking stock of two standard Pf lines, DD2 and 7G8, and adding them together in the fixed proportions given in S1 Table, and completing then Illumina sequencing and variant-calling with using the same protocols as [27]. Samples had a median of 65 reads for the variants considered here. Complete sequencing protocols and laboratory methods detailed in [35] (data available at European Nucleotide Archive). Both strains have high-confidence reference sequences. We subsample 2000 SNPs from the 23,109 SNPs available for comparison based on non-reference WSAF. The results in S1 Table show very strong agreement between the laboratory and inferred mixtures. The inferred α for all samples was less than 0.001 and had Bayes factor for non-zero α as less than 1, indicating that the samples have little unexplained mixture observed relative to the field samples.
Applying the algorithm to the 168 high-quality samples from KND, we observe numbers of strains ranging from 1 to 7, with α falling between 0 and 0.14, and a moderate correlation between K and α (Fig 5). The largest subset of samples were unmixed, with K = 1 and α < 0.01, though the majority of samples exhibit low but noticeable levels of admixture, with K = 2, 3, 4 and 0.01 ≤ α ≤ 0.03. A small number of samples exhibit complex mixtures, with K > 4 and α typically greater than 0.02. These samples also exhibit the most variance in the posterior estimate of K, frequently ranging from 3 to 8. To examine the necessity of the panmixia model to capture unexplained variation in the field samples, we calculate a Bayes factor for each sample under the two models, M0: α = 0 and M1: α ≠ 0. Since this is a single parameter, we employ the Savage-Dickey ratio calculation as in [43]. We find that 78 samples give factors larger than 10, indicating strong evidence for M1, and 9 samples give factors larger than 100, indicating overwhelming evidence for M1.
To visually inspect the quality of the results, we generate figures for each of the samples showing the observed WSAF and PLAF data, the inferred model structure, and data simulated under the inferred model following the observed PLAF. We show examples of these plots for three typical samples in Fig 6. Nearly all samples (158/168), across all different mixture patterns, show strong visual correspondence between the observed and model-simulated data. Samples where PCR amplification was used (9 samples) exhibit no unusual features other than low values for α relative to the remaining samples. We also observe a strong correlation between the inferred number of components and an estimate for the inbreeding coefficient for each sample (Fig 7) [29]. These results are consistent with the high rate of MOI previously observed in Ghanaian clinicial samples [24, 44, 45].
In this work we show how to infer strain mixture within Pf isolates using WGS with two improvements over previous efforts: an additional model for unexplained variation based on a panmixia and a reversible jump implementation that accounts for uncertainty in the underlying number of strains. Simulations show that the model can perform accurate inference (MSE < 0.05 for strain proportions) with as few as 50 SNPs and 10 read counts per SNP. Simulations with more than 100 SNPs or at least 25 read counts give highly accurate results (MSE < 0.02). In artificial laboratory mixtures the model provides excellent agreement with baseline mixture. In field samples the model provides strong agreement with observed data and evidence based on Bayes factors indicates that some unexplained variation is present in a significant fraction of samples.
While the method works efficiently in practice, a number of possible improvements could strengthen its statistical performance. Most immediately, creating a full Bayesian approach rather than the parallelizing implementation here—while likely not improving the parametric inference for individual samples—would provide the full posterior distribution across all samples. The panmixia model is one of several possible approaches to dealing with additional within-sample variation that rigorous model comparison could reveal. The model also does not perform haplotype phasing to resolve the sequence of the underlying strains [46–48]. The analysis here suggests that a method for estimating haplotypes would be straight-forward for some samples but difficult for others (say, when α is greater than 0.05). Researchers may be particularly interested in whether, in these phased samples, particular stretches of the genome appear more or less frequently in the dominant strains than others, indicating structures of immunological or environmental selection. This is a natural avenue for statistical methods development.
The model makes a number of simplifying assumptions that may be violated in practice. The model presumes that SNPs are unlinked and consequently independent for the purpose of calculating the likelihood. Given the high recombination rate of Pf this assumption may hold for the majority of pairs of SNPs, but neglects correlations that appear locally (∼ 10 kB). However, we expect that this independence assumption serves to moderately weaken the inferential power of the model rather than cause any type of bias since it effectively fails to include possibly informative data. More problematic is the model’s implicit assumption of limited population structure. In the case of the KND samples, and perhaps in much of west Africa, this assumption appears supported [27, 49]. In other contexts, specifically southeast Asia, recent population bottlenecks and selection suggest that this assumption will be violated [50]. The consequences on this model inference are unknown but may be partially resolved with appropriate simulation studies.
The model will work with any technology capable of typing multiple variants and where the measurement of the fraction of non-reference variants is unbiased. It was developed for WGS data but is not specific to the sequencing employed and should work similarly for Illumina, 454 and Pacific Bioscience read technologies. As noted in the results, we observe that the small number of field samples where PCR amplification was used did not appear unusual other than exhibiting relatively low α values. This is could be due to preferential amplification of the dominant strains, suggesting that PCR-based approaches may obscure some aspects of natural infections. This model is not appropriate for data from RFLP assays or DNA microarrays without substantial modification.
In principle, the model can be explicitly tested against experimental mixtures more complex than those presented above. Laboratory facilities with the capacity to store many field strains (>100) could generate artificial samples in an experimental analog of our simulation procedure. Starting with N unmixed strains at equal dilution, they could create mixtures by first fixing the required sequencing volume as η, and the desired parameters for panmixia (α), number of component strains (K), and their mixture parameters, W. For the finite mixture component, they would then combine volumes of η · W from the K strains. For the panmixture component, they would then fix some large number but experimentally feasible number of strains (say 50) and randomly sample from all of them a volume of η/50. Combining these into a final sample and applying WGS sequencing, will yield data that we hypothesize will closely follow the integrated model outlined above, with ν capturing the experimental variation. Naturally, consistent results would indicate the sufficiency of the model but not its necessity, holding out the possibility of a more minimal description. These results could be further compared against other next-generation technologies—such as single-cell sequencing—that have been deployed to understand Pf clinical mixtures [51].
The model presents an important new tool for interrogating the biology of clinical Pf infections. The ability to measure the structure of strain mixture connects to many aspects of Pf epidemiology including seasonality, transmission intensity, outcrossing, and rates of gene flow. It also presents a means for clarifying the poorly detailed structure of intra-host infection dynamics, such as strain selection or density-dependent selection [52], by resolving how the model parameters change within the course of an infection or in response to drug intervention. This approach can serve as a means for researchers to empirically resolve these hypotheses.
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10.1371/journal.pntd.0007536 | Elucidating diversity in the class composition of the minicircle hypervariable region of Trypanosoma cruzi: New perspectives on typing and kDNA inheritance | Trypanosoma cruzi, the protozoan causative of Chagas disease, is classified into six main Discrete Typing Units (DTUs): TcI-TcVI. This parasite has around 105 copies of the minicircle hypervariable region (mHVR) in their kinetoplastic DNA (kDNA). The genetic diversity of the mHVR is virtually unknown. However, cross-hybridization assays using mHVRs showed hybridization only between isolates belonging to the same genetic group. Nowadays there is no methodologic approach with a good sensibility, specificity and reproducibility for direct typing on biological samples. Due to its high copy number and apparently high diversity, mHVR becomes a good target for typing.
Around 22 million reads, obtained by amplicon sequencing of the mHVR, were analyzed for nine strains belonging to six T. cruzi DTUs. The number and diversity of mHVR clusters was variable among DTUs and even within a DTU. However, strains of the same DTU shared more mHVR clusters than strains of different DTUs and clustered together. In addition, hybrid DTUs (TcV and TcVI) shared similar percentages (1.9–3.4%) of mHVR clusters with their parentals (TcII and TcIII). Conversely, just 0.2% of clusters were shared between TcII and TcIII suggesting biparental inheritance of the kDNA in hybrids. Sequencing at low depth (20,000–40,000 reads) also revealed 95% of the mHVR clusters for each of the analyzed strains. Finally, the method revealed good correlation in cluster identity and abundance between different replications of the experiment (r = 0.999).
Our work sheds light on the sequence diversity of mHVRs at intra and inter-DTU level. The mHVR amplicon sequencing workflow described here is a reproducible technique, that allows multiplexed analysis of hundreds of strains and results promissory for direct typing on biological samples in a future. In addition, such approach may help to gain knowledge on the mechanisms of the minicircle evolution and phylogenetic relationships among strains.
| Chagas disease is an important public health problem in Latin America showing a wide diversity of clinical manifestations and epidemiological patterns. It is caused by the parasite Trypanosoma cruzi. This parasite is genetically diverse and classified into six main lineages. However, the relationship between intra-specific genetic diversity and clinical or epidemiological features is not clear, mainly because low sensitivity for direct typing on biological samples. For this reason, genetic markers with high copy number are required to achieve sensitivity. Here, we deep sequenced and analyzed a DNA region present in the large mitochondria of the parasite (named as mHVR, 105 copies per parasite) from strains belonging to the six main lineages in order to analyze mHVR diversity and to evaluate its usefulness for typing. Despite the high sequence diversity, strains of the same lineage shared more sequences than strains of different lineages. Curiously, hybrid lineages shared mHVR sequences with both parents suggesting that mHVR (and DNA minicircles from the mitochondria) are inherited from both parentals. The mHVR amplicon sequencing workflow proposed here is reproducible and, potentially, it would be useful for typing hundreds of biological samples at time. It also provides a valuable approach to perform evolutionary and functional studies.
| The protozoan parasite Trypanosoma cruzi (Kinetoplastea: Trypanosomatidae) is the causative agent of Chagas disease. This parasite infects millions of people throughout its distribution in Latin America. Chagas disease can display a broad pathological spectrum, including potentially fatal cardiological and gastrointestinal dysfunctions [1].
T. cruzi is a monophyletic taxon showing a remarkable genetic heterogeneity, with at least six phylogenetic lineages formally recognised as Discrete Typing Units (DTUs), TcI–TcVI [2, 3]; and a seventh lineage, named TcBat [4–6]. The genetic diversity of T. cruzi was firstly revealed by Multilocus Enzyme Electrophoresis [7, 8] and posteriorly by very diverse techniques including Multilocus Sequence Typing (MLST) [9–12], microsatellite typing (MLMT) [13–18], target-specific PCR [19–21], PCR-RFLP [22, 23], PCR-DNA blotting with hybridization assays [24–26], and recently by amplicon deep sequencing [27, 28]. The different approaches have their own advantages and disadvantages and bring out the genetic diversity of T. cruzi at different levels. Approaches that allow direct typing from biological samples (blood, tissues, etc.), avoiding parasite culture, are more suitable for clinical and epidemiological studies. However, nowadays there is no methodologic approach with a good sensibility, specificity and reproducibility for direct typing on biological samples.
Because there is usually a low number of parasites in infected tissues or blood samples, genetic markers with high number of copies are required to achieve good sensitivity of detection [29]. In this regard, T. cruzi, as all the kinetoplastids, has a unique and large mitochondrion which contains a complex network of DNA, the kinetoplastic DNA (kDNA). The kDNA represents approximately 20–25% of the total cellular DNA in T. cruzi and consists of two kind of circular DNA molecules: maxicircles and minicircles. Maxicircles contain mitochondrial genes characteristic of other eukaryotes [30]. Minicircles are present in tens of thousands of copies [31]. Each of them is organized into four highly conserved regions located 90° apart each other, and an equal number of hypervariable regions (mHVRs) interspersed between the conserved regions [32]. The highly conserved regions of minicircles have been widely used as targets for molecular detection of T. cruzi DNA. The used primers show a good sensitivity and specificity [29] and amplify a region of about 330 bp that totally include the mHVRs present between conserved regions. This amplified region has been used in hybridization assays (mHVR probes) and DTU-specific hybridization was observed only between isolates belonging to the same genetic group [25, 26, 33–35]. This specificity observed in hybridization assays suggests the presence of DTU specific sequences and even genotype-specific sequences (i.e. sequences showing specificity at intra-DTU level). However, technical limitations that existed until a few years ago for sequencing these highly variable kDNA regions, prevented the identification of the sequences in which the specificity relies. Some attempts were made by cloning and sequencing some mHVRs [36, 37] but the limited number of studied sequences were not enough to obtain a complete picture of the genetic diversity of these sequences. Thus, the observed hybridization patterns between mHVRs continue being a black box system and the sequence diversity of T. cruzi mHVRs virtually unknown.
Beyond the potential utility for strain typing, studying mHVR diversity is also interesting because these sequences are involved in functions that are only known in kinetoplastids and in no other eukaryotic organism. mHVRs code for short RNAs called guide RNAs (gRNAs). gRNAs are involved on edition of several mitochondrially-coded mRNAs. This edition varies from addition of some Us to building almost the full open reading frame of the mRNA [38, 39]. In this sense, gRNAs can be inferred from sequences of the mitochondrial mRNAs and diversity on edition among strains can be addressed [40]. In addition, studying mHVR diversity can shed light on how such sequences evolve and how they are inherited.
Here, we propose an amplicon deep sequencing approach that allows an accurate knowledge of the sequence diversity of the hypervariable region of kDNA minicircles of T. cruzi and opens the possibility of functional and evolutionary studies. This approach can be also used as a typing method for hundreds of samples at time.
DNA from nine cloned T. cruzi strains belonging to the six main DTUs was examined in this study (Table 1). All the strains were typified by using an optimized Multilocus Sequence Typing scheme based on four gene fragments (HMCOAR, GPI, TcMPX and RHO1) according to Diosque et al. [7], in order to confirm DTU for each strain.
In order to amplify the minicircles hypervariable region, kDNA specific primers 121 (5’-ACACTCTTTCCCTACACGACGCTCTTCCGATCTAAATAATGTACGGG(T/G)GAGATGCATGA-3’) and 122 (5’-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGTTCGATTGGGGTTGGTGTAATATA-3’) were modified by adding an oligo adapter to be used in an Illumina platform. The mHVR libraries were generated by a one-step PCR performed in 5 μl reaction volumes containing 5 ng of DNA, 250 nM of each primer, 2 μM of barcode primers, 5 U of Fast Start High Fidelity Enzyme Blend (Roche), 0.50 μl of 10X buffer (supplied with the Fast Start High Fidelity Enzyme Blend), 25 nM of MgCl2 (Roche), 0.25 μl of DMSO (Roche), 10 mM of PCR grade nucleotide mix (Roche). The PCR reaction was carried out on a Veriti Thermal Cycler (Life Technologies) and ran as follow: an initial denaturation step (10 min at 95°C), 10 cycles (95°C for 15 seconds, 60°C 30 seconds, 72°C 1 min), 2 cycles (95°C for 15 seconds, 80°C 30 seconds, 60°C 30 seconds, 72°C 1 min), 8 cycles (95°C for 15 seconds, 60°C 30 seconds, 72°C 1 min), 2 cycles (95°C for 15 seconds, 80°C 30 seconds, 60°C 30 seconds, 72°C 1 min), 8 cycles (95°C for 15 seconds, 60°C 30 seconds, 72°C 1 min) and 5 cycles (95°C for 15 seconds, 80°C 30 seconds, 60°C 30 seconds, 72°C 1 min). Amplicons were then purified using the magnetic beads Agencourt AMPure XP-PCR Purification (Beckman Genomics, USA). The concentration of the purified amplicons was controlled using Qubit Fluorometer 2.0 (Invitrogen, USA). All libraries were validated using the Fragment Analyzer system (Advanced Analytical Technologies, USA). The average size of the mHVR amplicons was ~480bp. All samples were then pooled and prepared according to the manufacturer's recommendations (Illumina Protocols: Sequencing Library Preparation) and sequenced on an Illumina MiSeq using a 500 cycle v2 kit (Illumina, San Diego, USA) to produce amplicons of approximately ~480 bp in length (250 bp paired-end reads).
The raw data set has been deposited in the NCBI SRA database (BioProject ID: PRJNA514922).
A total of 22,092,382 paired reads were obtained by amplicon sequencing of the mHVR from nine strains belonging to six DTUs. A total of 14,766,753 sequences were retained (an average of ≈1.4 million of sequences per strain) after trimming low quality ends, merging paired reads (forward and reverse), elimination of chimeric reads and filtering by base quality (S1 Table). Surviving sequences were clustered according to different identity thresholds (85%, 90% and 95%) (Table 2, S2 and S3 Tables). The number of mHVR clusters for each strain was very similar using different thresholds (with differences less than 10% in all comparisons between 85% and 95% thresholds). However, clustering at 85% threshold returned few more mHVR clusters than clustering at 90% and 95% identity (See Table 2, S2 and S3 Tables). In addition, most clusters were highly divergent among them (S1 Fig). At any threshold, the number of mHVR clusters was variable among strains and DTUs (Table 2), ranging from 71 (Mncl2 –TcV) to 373 (X109/2 –TcIII) clusters. Additionally, strong intra-DTU variations in the number of clusters were observed in strains of TcI and TcII (Table 2). Finally, rarefactions of each dataset discarded that these differences among strains are the effect of different sequencing depths (Table 2, S2 and S3 Tables).
Strains belonging to TcIV, TcV and TcVI showed some dominant clusters containing a high proportion of reads (i.e. the cluster size) (Fig 1). The sum of the six most abundant clusters in TcIV, TcV and TcVI represent in all cases more than 50% of the clustered sequences (80.9% and 69.1% in the TcV strains LL014R1 and MNcl2, respectively; 58.7% in TcIV strain CANIIIcl1; and 52.5% in the TcVI strain LL015P68R0cl4). Even more, in LL014R1 and MNcl2 (TcV strains) the most abundant cluster represented the 29.7% and 17.8% of the total mHVR, respectively. Instead, none of the clusters present in TcI, TcII and TcIII strains represented more than 5.2%. This higher diversity in TcI, TcII and TcIII is also revealed by a higher Simpson diversity index than other DTUs (Table 2). Moreover, intra-DTU differences in mHVR cluster diversity were observed in TcII. Particularly, Tu18cl93 had relatively less cluster diversity than Esmeraldo (Table 2 and Fig 1).
As expected, shared mHVR clusters were mostly observed in strains belonging to the same DTU. However, the percentage of shared clusters was highly variable depending on DTU. TcV strains (LL014R1 and MNcl2) showed the higher proportion of shared clusters (97.3%; 72/74). However, we observed strong differences in the cluster sizes (Fig 2C) although a positive correlation was detected (correlation coefficient, r = 0.75) and some shared clusters were highly abundant in both strains (Fig 2C). TcI strains (PalDa20cl3 and TEV55cl1) shared 17.5% (83/475), and TcII strains (Tu18cl93 and Esmeraldo) shared 7.1% (33/466). Conversely, when we look for shared mHVR clusters between strains belonging to different DTUs, we detected none or few shared clusters (Fig 2D–2I and S2 Fig).
The Bray-Curtis dissimilarity between strains was calculated using mHVR clusters conformed at the different identity thresholds (85%, 90% and 95%). Such dissimilarities were used to analyze principal coordinates (PCoA) and to build UPGMA trees (Fig 3 and S3 Fig). Strains from the same DTU clustered together (Fig 3) despite the high dissimilarities between strains belonging to the same DTU (Fig 3C). These high dissimilarities between strains belonging to the same DTU determine that the three first axis in the PCoA explain just 49.1% of the variance. TcV strains clustered distant from other DTUs. TcIII and TcIV strains clustered near to each other. Interestingly, TcVI strain was placed between TcII and TcIII in the PCoA. Moreover, TcVI was clustered with TcII in the UPGMA tree (Fig 3C). Such results are not in agreement with the hypothesis of uniparental inheritance of the minicircles in the hybrid TcVI, which comes from hybridization between TcII and TcIII. Consequently, we analyzed shared clusters between TcII, TcIII and the hybrids DTUs (TcV and TcVI) in order to analyze the hypotheses of uniparental or biparental inheritance of minicircles. We used a 90% identity threshold in order to be more confident about the identity by descendance of the clusters. We observed that TcV and TcVI share 11/530 and 19/559 mHVR clusters with TcII, respectively. Likewise, TcV and TcVI shared 12/429 and 9/469 mHVR clusters with TcIII, respectively (Fig 4). Instead, TcII and TcIII share only 2 mHVR clusters between them out of a total number of clusters of 842 combining TcII and TcIII. These results suggest that minicircle inheritance is biparental. In addition, TcV and TcVI shared more mHVR clusters with their parental DTUs than between them (Fig 4) which is concordant with the hypothesis of independent origins of TcV and TcVI.
In order to test if parallel amplicon sequencing would be useful for simultaneous typing of hundreds of strains, we first evaluated rarefaction curves. In general, the minimum number of reads required to detect at least 95% of the observed clusters was 20,000 filtered reads. The only exception was MNcl2, which required 40,000 filtered reads. Increasing the number of reads per sample beyond 20,000 slightly increased the number of detected mHVR clusters (Fig 5A). In addition, we evaluated the minimum number of reads required to observe the right DTU assignment described in Fig 2. As few as 10,000 reads were enough to accurate clustering of the strains (Fig 5B and 5C) at 100% of the rarefactions.
Amplicon sequencing of the mHVR could be useful to identify intra-DTU clusters, particularly in TcV or TcVI where strains may have the same composition of mHVR clusters but with high differences in abundance of each one. In order to develop future methods to assign strains to intra-DTU clusters is pre-requisite that amplicon sequencing can be reproducible to determine mHVR cluster abundance. Consequently, we assessed reproducibility by sequencing and comparing two independent PCR reactions of the mHVR in LL015P68R0cl4 strain (TcVI). High correlation in cluster abundances in different PCRs of the same sample was observed (r = 0.999 for the three different identity thresholds) (Fig 5D).
Here, we made a deep amplicon sequencing of the hypervariable region of kDNA minicircles in the six main lineages (DTUs) of T. cruzi. To the best of our knowledge, this is the first time that these kDNA regions were sequenced at millions of reads of depth. Our results shed light on different and very interesting aspects of these intriguing DNA sequences. We accurately show the level of sequence diversity of mHVR within strains, between strains belonging to the same DTU, and between strains belonging to different DTUs. Although it was already known that mHVR were highly diverse [36], the magnitude of this diversity at the intra- and inter-DTU level has not been demonstrated with the high precision provided by an NGS approach, as we made here.
We propose a method for typing/elucidating intra-specific diversity of T. cruzi based on the deep sequencing of the hypervariable region of kDNA minicircles. The idea is based on the outdated but highly sensitive method of mHVR probes [25, 26, 35, 46–48]. Such probes are useful to detect T. cruzi diversity in biological samples. However, this methodology has the disadvantages of being technically cumbersome, relying on visual interpretation of bands and requiring representative strains of the diversity of T. cruzi in every assay (used as probes). The deep amplicon sequencing approach proposed here is reproducible and based on objective sequence data which can be stored in online databases. Also, the method is multiplexable for hundreds of samples at time and it would be directly applied to biological samples as the mHVR probes. The method may be potentially useful to address epidemiological questions about associations between intra-specific diversity and variability in clinical manifestations of the chronic disease or the different rates of congenital transmission in different endemic areas. Such questions have been unsuccessfully addressed using molecular markers with low resolution and/or low sensitivity on biological samples. We determined that around 20,000 filtered reads are enough to reveal most mHVR diversity in a strain and theoretically allowing for running hundreds of samples in a single run of a MiSeq with costs similar or lower than MLST. However, a wider set of strains belonging to the six main lineages must be studied. In addition, new bioinformatic methods of analysis will be required for a direct application of the method to biological samples.
In order to develop such typing method, we preliminarily analyzed and compared the diversity of mHVR sequences in reference strains of six DTUs and at millions of reads of sequencing depth. We observed that strains of the same DTU share more mHVR clusters than strains of different DTUs. However, unprecedented high differences in mHVR cluster composition was observed for strains of the same DTU with less than 20% of shared mHVR clusters in TcI and TcII. Instead, almost all mHVR clusters were shared between different TcV strains. In addition, the patterns of DTU specificity observed by using mHVR probes may be explained in TcV and TcVI by the presence of some shared and abundant clusters. Instead, considering the higher diversity and low abundance of clusters in TcI, TcII and TcIII, the global pattern of sequences is probably the responsible of specificity in the hybridization assays involving these DTUs.
Interestingly, our data revealed that diversity of mHVR sequences was variable even within a DTU. This was particularly evident in TcII, where the number of mHVR clusters in Esmeraldo strain doubled that of Tu18cl93. Such differences may be caused by long times in culture as it has been observed for other trypanosomatids [40, 49]. However, both strains were isolated in the eighties and although it is possible that they had different times in culture, such times would be not very different (i.e. not in the order of decades). According to this, we suppose that the observed difference in mHVR diversity between the two TcII strains is not due to long time in culture. In support of the hypothesis of no influence of the time in culture, we observed no differences in mHVR diversity between the two TcV strains examined, despite they have very different times of isolation and maintenance mode in the laboratory. One of them was isolated in the 1980s and subjected to long periods of maintenance in culture (Mncl2); and the other TcV strain (LL014R1) was isolated in 2008 and maintained in triatomine-mouse passages.
Our results also shed some light on the evolutionary mechanism determining the large genetic distances in mHVR sequences among strains and DTUs. The focus should be first placed on TcV strains which are identical according to MLST and which shared most mHVR clusters. Despite this, they strongly varied in relative frequencies of mHVR clusters. Such variations cannot be attributed to simple stochasticity of the PCR amplification because we observed good correlation between different PCR reactions from the same sample (Fig 5D). Consequently, it is probable that minicircle diversity is mainly driven by genetic drift. We propose that when two strains diverge, the frequencies of mHVR cluster varies stochastically, some clusters increasing their relative frequency and other decreasing it. The next step can be seen in strains of TcI which are more genetically distant than the TcV ones. Such TcI strains show clusters with high abundance in one strain and with very low (or null) abundance in the other one (look at most clusters located on the axes in Fig 2). Therefore, some clusters will be lost if such lost is not deleterious (i.e. replaced by a different mHVR class that codes a gRNA editing the same mRNA fragment). Thus, strains would diverge by variations in frequency of the mHVR classes faster than by changes in their sequences. These variations in the frequency of mHVR classes probably are not under selective pressure. mHVR frequency variations are apparently allowed because the effective edition of the mRNA is not dependent on the abundance of a minicircle [50, 51]. Variations in the frequency of mHVR classes have been also inferred for T. brucei and Leishmania [52] and by a theoretical study assuming random or partially random segregation of minicircles [53].
With the purpose of developing in the future DTU specific PCRs, we analyzed if different DTUs share common mHVR clusters. Telleria et al. [36] did not detected shared sequences between DTUs probably because the low sequencing depth. With a different approach, Velazquez et al. [37] detected that most abundant mHVR classes in CL-Brener (TcVI) were also present in other DTUs but in a considerably lower frequency. We detected shared mHVRs between different DTUs but we did not detect any sequence shared by the six DTUs. Interestingly, we observed shared clusters between TcVI and TcIII (2.1%). This is expected considering that TcIII is a parental DTU of the hybrid TcVI and maxicircle sequences of TcIII are closely related to the TcVI ones [54–58]. However, the TcVI strain also shared 2.5% of mHVR clusters with Esmeraldo strain (belonging to TcII, the other parental DTU of TcVI). Something similar is observed for the also hybrid DTU TcV (Fig 3). Instead, only 2 mHVR clusters were shared between TcII and TcIII strains (0.2%). This clearly suggests that although maxicircles have apparently uniparental inheritance in TcV and TcVI, minicircles were probably inherited from both parentals and some of them persisted for 60,000 years since hybridization [59]. Biparental inheritance of minicircles and maxicircles has been proposed for Trypanosoma brucei hybrids [60–62]. In this parasite, it has been observed that maxicircle and minicircle inheritance is biparental in hybrids. However, maxicircles (20–50 copies) are homogenized by genetic drift resulting in the loss of whole maxicircles of one parental in few generations. However, minicircles have much more copies and they resist the fixation effect of genetic drift for more time. Consequently, maxicircle inheritance is biparental and just seems to be uniparental due to genetic drift. As consequence of the biparental inheritance of minicircles, it has been proposed that such inheritance may help to preserve mHVR diversity in T. brucei preventing the effect of the drift, and even that T. brucei requires genetic exchange to prevent the deleterious effect of loss of essential minicircle classes [53]. Nevertheless, genetic exchange has remained elusive to be detected in T. cruzi. Experimental hybrids obtained by Gaunt and coworkers showed that maxicircles are from one parental but minicircles were not analyzed [63] and kDNA inheritance was still not addressed in more recent experimental hybrids [64]. In addition, the frequency of genetic exchange may be variable among different DTUs. TcV and TcVI (which display a clearly clonal genetic structure at population level) [9, 10, 12, 57] have very low mHVR diversity. Instead, TcI, TcII and TcIII, for which genetic exchange has been proposed in the nature [11, 13, 15, 65], have higher mHVR diversity.
Moreover, our data may help elucidate the origin of hybrid DTUs. It has been proposed that TcV and TcVI are the result of a single hybridization event between TcII and TcIII and both DTUs diverged posteriorly [66, 67]. However, the alternative hypothesis (two independent hybridization) gain weight in the last years. Particularly, Multilocus Microsatellite Typing (MLMT) and Multilocus Sequence Typing (MLST) analyses favored the two independent hybridizations hypothesis [57, 59]. Considering biparental inheritance, and assuming a single hybridization event, the two hybrid DTUs (TcV and TcVI) should share more mHVR classes between them than with the parentals. However, our analyses show the contrary with very few classes shared between TcV and TcVI (Fig 4). This result supports independent hybridizations for the origin of TcV and TcVI. Alternatively, because both DTUs would have lost many mHVR clusters, the high divergence among them may have been caused by simple stochasticity, although is less likely. Interestingly, if minicircle are biparentally inherited it is expected that they will behave like the nuclear genes. So, it is expected that nuclear phylogenies will be similar to the mHVR phylogeny and both discordant to maxicircle phylogeny in cases of hybridization or introgression. However, some hypotheses about events that occurred very distant in time (e.g. mitochondrial introgression in the origin of TcIII [57–58]) might not be addressed by mHVR-based phylogenies because the almost null number of shared mHVR clusters between some DTUs.
Concluding, massive amplicon sequencing of the mHVR is reproducible and suitable for typing hundreds of T. cruzi strains at time because few thousands of reads are required per sample. However, some drawbacks still need solution. The main problem in biological samples are mixed infections of different genotypes or DTUs which are very frequent [48]. However, such problem can be overpassed by developing new bioinformatic methods comparing mHVR composition of a sample against a reference mHVR database which should collect information about the diversity in the DTUs of T. cruzi. In addition, the develop of an online database where mHVR representative sequences are stored is needed. We are currently working on such items. In addition, some rare events of mitochondrial introgression observed in natural populations of T. cruzi lead to discordant typing between nuclear and maxicircle markers [16, 68, 69]. However, it is unknown the effect of mitochondrial introgression on minicircles. In this sense, a Multilocus deep Sequence Typing (MLdST) may be good alternative and a second step. The deep sequencing of amplicons of the mHVR plus satDNA (a 195 bp sequence with 105 sequences per genome) [70] may help elucidate such rare events and may increase sensitivity for typing on biological samples.
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10.1371/journal.ppat.1005141 | Serine Phosphorylation of HIV-1 Vpu and Its Binding to Tetherin Regulates Interaction with Clathrin Adaptors | HIV-1 Vpu prevents incorporation of tetherin (BST2/ CD317) into budding virions and targets it for ESCRT-dependent endosomal degradation via a clathrin-dependent process. This requires a variant acidic dileucine-sorting motif (ExxxLV) in Vpu. Structural studies demonstrate that recombinant Vpu/tetherin fusions can form a ternary complex with the clathrin adaptor AP-1. However, open questions still exist about Vpu’s mechanism of action. Particularly, whether endosomal degradation and the recruitment of the E3 ubiquitin ligase SCFβTRCP1/2 to a conserved phosphorylated binding site, DSGNES, are required for antagonism. Re-evaluation of the phenotype of Vpu phosphorylation mutants and naturally occurring allelic variants reveals that the requirement for the Vpu phosphoserine motif in tetherin antagonism is dissociable from SCFβTRCP1/2 and ESCRT-dependent tetherin degradation. Vpu phospho-mutants phenocopy ExxxLV mutants, and can be rescued by direct clathrin interaction in the absence of SCFβTRCP1/2 recruitment. Moreover, we demonstrate physical interaction between Vpu and AP-1 or AP-2 in cells. This requires Vpu/tetherin transmembrane domain interactions as well as the ExxxLV motif. Importantly, it also requires the Vpu phosphoserine motif and adjacent acidic residues. Taken together these data explain the discordance between the role of SCFβTRCP1/2 and Vpu phosphorylation in tetherin antagonism, and indicate that phosphorylation of Vpu in Vpu/tetherin complexes regulates promiscuous recruitment of adaptors, implicating clathrin-dependent sorting as an essential first step in tetherin antagonism.
| Counteraction of tetherin, a host antiviral protein that blocks viral release from infected cells, is an essential attribute of HIV-1 and its related viruses. The HIV-1 accessory protein Vpu binds to tetherin, preventing its incorporation into viral particles, and targets it for ubiquitin-dependent degradation. This involves mis-trafficking of tetherin by a Vpu-dependent mechanism through the engagement of clathrin adaptor proteins. Although structural evidence exists for Vpu and tetherin interacting with clathrin adaptor 1 (AP-1), evidence that it is required for Vpu-mediated tetherin counteraction is still lacking. Tetherin degradation by Vpu also requires an E3 ubiquitin ligase, SCFβTRCP1/2 that binds to phosphorylated serine residues in the Vpu cytoplasmic tail. Again, discrepancies exist about the importance of this interaction in tetherin’s counteraction. Here we show that Vpu phosphorylation, in combination with its physical interaction with tetherin, regulates interaction with both AP-1 and the other major cellular clathrin adaptor, AP-2. These interactions can be decoupled from SCFβTRCP1/2 recruitment, thus indicating clathrin-dependent mis-trafficking as a critical step in tetherin antagonism by Vpu. Additionally, the ability to interact both with AP-1 and AP-2 in a tetherin-dependent manner indicates a redundancy in host cofactors used by Vpu that explains disparate previous observations of its mechanism of action.
| Counteraction of the antiviral membrane protein tetherin (BST2/ CD317) is an essential attribute of primate lentiviruses, and is mediated by either the Vpu or Nef accessory proteins, or occasionally the viral envelope glycoprotein (reviewed in [1]). In their absence, tetherin restricts the release of virions assembling at the cell surface [2–6]. By virtue of its N-terminal transmembrane (TM) domain and C-terminal GPI anchor, partitioning of tetherin dimers into budding virions allows them to simultaneously span host and viral membranes resulting in accumulation of cross-linked virions on the plasma membrane (PM) [7,8]. In addition to physically limiting virion release, tetherin’s activity sensitizes infected cells to antibody-dependent cellular cytotoxicity [9–12], targets virions for endosomal degradation, and in the case of great ape tetherins, can directly induce the activation of proinflammatory NF-κB signaling [13–16].
Tetherin recycles to the PM via the trans-Golgi network (TGN) [17]. This requires a dual tyrosine-based sorting signal (YDYCRV in humans), which can interact with the clathrin adaptor AP-1. Lentiviral countermeasures physically interact with tetherin, often in a highly species-specific manner [1]. Through their action, tetherin incorporation into virions is blocked, and this is associated with its reduced cell surface levels. In the case of HIV-1 Vpu, a small membrane phospho-protein, physical interaction is mediated by the TM domains themselves [18–20]. HIV-1 Vpu targets human tetherin into an ESCRT-dependent endosomal degradation pathway [21,22]. This is an ubiquitin driven process and requires a highly conserved DSGNES motif in the Vpu cytoplasmic tail [23–25]. Phosphorylation of the serine residues (S52/53 and S56/57 in subtype B depending on the isolate) by casein kinase II (CKII) [26,27] recruits the β-TrCP1/2 subunits of a Skp1-Cullin1-F-Box (SCF) E3 ubiquitin ligase [28] that mediates direct ubiquitination of various residues in the tetherin cytoplasmic tail including an STS motif [29]. However, there is still debate as to whether the recruitment of the SCFβTRCP1/2 to the DSGNES motif in Vpu is required for counteraction of physical retention of virions by tetherin (hereafter also termed antagonism) as well as its final endosomal degradation. Much of this discrepancy may be attributable to whether assays are performed in virally infected cells or those transiently transfected with Vpu, tetherin or both [30]. While ESCRT-I appears to be dispensable in infected cells [21], evidence that the ESCRT-0 component HRS is required for tetherin antagonism suggests targeting to endosomal degradation plays a role [22]. Furthermore, mutations of the Vpu serine residues (so called 2/6 mutations) have intermediate phenotypes in tetherin antagonism suggesting degradation does not fully explain Vpu function[24,25,31]. Moreover this defect in antagonism is not recapitulated by siRNA depletion of β-TrCP1/2 [32]. Indeed evidence that the DSGNES motif might have a dual function in tetherin trafficking has been proposed [33]. This is consistent with our recent study of Vpu variation in patients where we found that naturally occurring variants in the NE of the DSGNES imparted tetherin-specific defects to Vpu without blocking its other SCF-dependent activity, dislocation of CD4 from the endoplasmic reticulum [34].
Vpu has been shown to block newly synthesized and/or recycling tetherin from trafficking to the cell surface [33,35]. This requires a variant of an acidic dileucine motif, ExxxLV, in the second alpha helix of the cytoplasmic tail of most HIV-1 group M clade Vpu [36]. Acidic dileucine sorting signals bind to the σ subunits of the major cellular clathrin adaptors AP-1 (trafficking from TGN to endosomes and vice versa) and AP-2 (clathrin-dependent endocytosis from the PM) (reviewed in [37]). In keeping with this, Vpu-mediated tetherin antagonism is entirely clathrin-dependent [36,38]. Mutation of the ExxxLV motif does not block Vpu/tetherin interactions, but reduces the efficiency of counteraction and inhibits degradation [36]. In particular ExxxLV is essential for counteraction of tetherin in CD4+ T cells upon interferon upregulation, and mutant phenotypes are exacerbated when tetherin lacks the YDYCRV motif [36]. A recent structural and biochemical study has demonstrated that the ExxxLV motif can bind canonically to the σ subunit of AP-1, whereas the YXXθ motif of tetherin can bind to the μ subunit of AP-1 [39]. In fusions of Vpu and tetherin cytoplasmic tails both motifs can occupy their respective binding sites simultaneously [39]. Some density in the structure also indicated other contacts between Vpu and AP-1μ, and together implied a mechanism whereby the formation of this ternary complex would modulate AP-1-dependent trafficking of tetherin to endosomes. However, whilst the localization of Vpu to the TGN suggested AP-1 as the major target, siRNA-mediated knockdown of AP-1 or expression in AP-1 -/- murine fibroblasts did not inhibit Vpu function [36]. Neither has physical interaction between AP-1 and the wild-type Vpu protein been demonstrated in living cells. Expression of tetherin fused at its N-terminus to the second helix of Vpu is excluded from budding virions at the PM in an ExxxLV-dependent manner [18]. Added to this, tetherin can be expressed as two isoforms, one of which lacks the YDYCRV motif and can be antagonized by Vpu to a certain extent without cell surface downregulation [13,40]. Likewise, Vpu has only a modest effect on tetherin endocytosis [25,35], and AP-2 knockdown also has little impact on antagonism, contrasting sharply with SIV Nef and HIV-2 envelopes [38,41,42].
AP1 binding to a non-canonical acidic dileucine motif in CI-M6PR has been associated with upstream serine phosphorylation by CKII previously [43]. Thus we hypothesized that the DSGNES in Vpu might regulate clathrin adaptor interaction independently of SCF recruitment. Here we provide evidence that this is indeed the case.
The importance of the SCFβTRCP1/2 E3 ligase and the ultimate degradation of tetherin to the counteraction of its physical antiviral activity by Vpu has been controversial. Since the discrepant studies were mostly performed under conditions of transient transfection of tetherin, provirus or both, and which have been shown previously to lead to artifactual effects on tetherin degradation [30], we re-examined these issues in HIV-1 infected 293T cells stably expressing surface tetherin at levels similar to those induced by type 1 interferon (Fig 1A). We have previously shown that an endosomal sorting-specific subunit of ESCRT-I, UBAP1, is essential for tetherin’s degradation but not for antagonism [21,36]. Despite efficient levels of knockdown, similarly efficient siRNA knockdowns of HRS (ESCRT-0) or UBAP1 had only minor effects on one-round yield of wild-type HIV-1 (HIV-1 wt) from 293T tetherin cells at an MOI of 0.8 (Fig 1B and 1C). As expected, knockdown of the core ESCRT-I subunit TSG101 destablized UBAP1 [44] and blocked all virion release because of its essential late-domain function [45], and all siRNA treatments also stabilized Vpu expression (Fig 1B). In keeping with this, cells infected at an MOI of 2, to ensure at least 90% infection, demonstrated that Vpu-induced degradation was blocked by all siRNA knockdowns (Fig 1D and 1E). These data therefore indicate that in infected cells expressing physiological levels of Vpu from an integrated HIV-1 provirus, the core ESCRT pathway and HRS are essential for Vpu-mediated tetherin degradation, but dispensable for counteraction of tetherin’s physical antiviral activity.
A previous study indicated that HRS interacted with Vpu in immuno-precipitates [22]. We confirmed this in transfected cells using myc-tagged HRS, and found that HRS truncations that removed its double-ubiquitin interaction motif (DUIM) inhibited this interaction (S1A and S1B Fig). Furthermore, point mutations in the DUIM that abolish ubiquitin-interaction (A266Q/ A228Q) [46], not putative ubiquitin-binding mutants in the VHS domain, completely abolished HRS/Vpu interactions in co-IPs (S1C Fig). Whilst formally possible that the DUIM is a direct binding site for Vpu, these data likely suggest that Vpu interactions with HRS are mediated indirectly through ubiquitination either of cargo, or associated factors in the degradation pathway.
We next similarly re-evaluated the effect of simultaneously knocking down β-TrCP1 and 2 on Vpu-mediated tetherin-degradation and tetherin-counteraction in infected cells. Again despite efficient knockdown, we saw little effect of this treatment on HIV-1 WT release (Fig 1F–1H). Of note, there was no evidence that β-TrCP1/2 knockdown reduced wild-type release to that of a viral mutant lacking the phosphorylated serines at positions 52 and 56 that are essential for β-TrCP1/2 recruitment (HIV-1 Vpu 2/6A). This was in contrast to a complete reversal of Vpu-mediated tetherin degradation by β-TrCP1/2 siRNAs in cells infected at an MOI of 2 (Fig 1H). Therefore whilst tetherin degradation by Vpu requires the SCFβTRCP1/2 complex, under conditions when it is sufficiently depleted to block this, there is no effect on Vpu-mediated tetherin antagonism.
Since the phospho-mutant of Vpu, Vpu 2/6A, has been shown to be partially defective for tetherin antagonism [23–25], we revisited whether this impairment could be uncoupled from the ubiquitin ligase. We recently showed that mutants of clade B Vpu lacking a conserved ExxxLV sorting signal (Vpu ELV) were also partially defective for tetherin antagonism because they could not traffic tetherin/Vpu complexes for endosomal degradation [36]. Notably, ELV mutant Vpu loses all residual activity against tetherin lacking the dual-tyrosine recycling motif, and a recent study demonstrated that the tetherin and Vpu cytoplasmic tails can assemble into a ternary complex with clathrin adaptor AP-1 [39]. In addition, hints in the structure suggested that residues 42 and 43 of the first helix of the cytoplasmic tail make a non-canonical contact with AP-1μ. We found similar Vpu mutants with tetherin-defective phenotypes in our patient cohort [34], and mutation of conserved L41I42/L45I46 in the first alpha helix to alanines in the NL4.3 provirus led to a profound defect in tetherin antagonism and degradation without preventing interaction (S2A–S2E Fig). Since the DSGNES motif is located in an acidic patch between helix 1 and the ExxxLV site, we hypothesized that Vpu phosphomutants may also be similarly defective for mis-trafficking tetherin. In one round virus infection assays in 293T/tetherin cells, LI/LI, ELV and 2/6A mutants all had similarly defective phenotypes for tetherin antagonism (Fig 2A and 2B). Interestingly, like the ELV mutant [36,39], both LI/LI and 2/6A mutants lost all their residual activity in cells expressing tetherin Y6,8A whereas release of the wild-type virus was only slightly affected. Moreover, as expected, all mutants were defective for tetherin degradation (Fig 2C). Examination of the localization of the three mutants in transfected HeLa cells revealed that, unlike the wild-type, 2/6A and LILI localized prominently to peripheral endosomal structures as well as the TGN (Fig 2D). This was similar to the localization expected for the ELV mutant [36], and quantification of coincidence with TGN46 revealed that all three mutants had a significantly reduced localization to the TGN consistent with a trafficking defect (Fig 2E). Importantly there was no significant additive effect of combined 2/6 and ELV mutations in full-length virus release from either the 293T/tetherin cells or primary CD4+ T cells (S3A–S3C Fig). Also these data could be recapitulated using a highly active primary Vpu (Vpu 2_87) isolate from our previous patient study [34] (S4A–S4D Fig). Treatment of 293T tetherin cells infected with wild-type HIV-1 with a CKII inhibitor, Tyrphostin, to mimic the 2/6A mutation showed a reduction of virus release only in the presence of tetherin, or more prominently, the Y6,8A mutant (Fig 2F and 2G). Western blot analysis of cell lysates transfected with HA-tagged Vpu expression vectors and run on an 8% PhosTag gel showed that in the presence of Tyrphostin, the smear of phosphorylated Vpu was reduced indicating inhibition of Vpu phosphorylation (Fig 2H). Together, these data therefore suggested that the defective tetherin antagonism of Vpu 2/6A may be due to phosphorylation-regulated trafficking of Vpu rather than ubiquitin ligase recruitment and degradation.
The current model for Vpu function is that it prevents tetherin trafficking to the PM from the TGN and sorts it into a clathrin-dependent endosomal trafficking pathway [1,47]. If our above hypothesis was the case, we reasoned that bypassing clathrin adaptors and linking Vpu directly to clathrin itself could functionally rescue all ELV, LI/LI and 2/6A mutants. To do this we appended the AQLISFD clathrin box (CB) from HRS or a mutated sequence, AQAASFD, lacking the leucine and isoleucines essential for clathrin interaction, to the C-termini of Vpu and the respective mutants (Fig 3A). Transient transfection of increasing doses of Vpu into 293T tetherin cells effectively rescued Vpu-defective HIV-1 viral release, and neither the clathrin box nor its mutant impaired wild-type Vpu function (Fig 3B and 3C). Remarkably, however, Vpu 2/6A, Vpu ELV or Vpu LI/LI function was almost fully restored by fusion of the clathrin box, whereas grafting the mutated sequence had no effect. All Vpu chimeras were well expressed, although as shown in Fig 3C, the apparent molecular weight of Vpu and its chimeras in SDS-PAGE did not reflect amino acid length. Similar results were obtained for a heterologous clathrin box (RNLLDLL) derived from GGA2 (available on request). The clathrin box also fully restored downregulation of tetherin from the surface of transiently transfected HeLa-TZMbl cells to all the mutants (Figs 3D and S5A–S5D). To show that this rescue of function was clathrin-dependent, we depleted clathrin membrane binding with the C-terminal fragment of the neuronal clathrin-adaptor AP180 (AP180c). As expected, rescue of wild-type Vpu-dependent virus release was inhibited by AP180c whereas residual viral release in the presence of tetherin was not [36]. In all cases, the same held true for clathrin box fusions (Fig 4A). Thus, direct linkage to the clathrin machinery was sufficient to rescue both Vpu 2/6A and the trafficking mutants. Moreover, in cells stably expressing the Vpu chimeras, no reduction of tetherin steady state levels was observed upon CB fusion to any of the chimeras (Fig 4B), nor was β-TrCP interaction restored to the 2/6A mutant fusion (Fig 4C), indicating this was independent of SCF and ESCRT function. Wild-type subcellular localization was restored to all mutants; 2/6A, ELV and LI/LI localization was significantly restored to TGN-associated compartments upon CB fusion (Fig 4D and 4E).
To further characterize these Vpu chimeras, we next examined whether they were functional against tetherin bearing tyrosine (trafficking) and serine/threonine (the proposed SCFβTRCP ubiquitination site [29]) mutations in the cytoplasmic tail. In the case of 293T tetherin-STS-AAA cells, the Vpu CB chimeras behaved as they did against the wild-type protein, effectively fully rescuing the 2/6A, LILI or ELV lesion (Fig 5A). Importantly, stable expression of an STS mutant tetherin had no detectable effect on the efficiency of counteraction by wild-type Vpu, and the CB addition had no effect, indicating that there is no reduction in Vpu antagonism when tetherin lacks the residues proposed to be important for ubiquitination. However, in the case of 293T tetherin Y6,8A cells, whilst Vpu wild-type and CB fusions remained active, the mutant chimeras remained completely defective (Fig 5B). These data imply that unlike the ExxxLV motif, the clathrin box addition is not dominant over the tetherin tyrosine-based sorting motif. This therefore suggests that tetherin sorting into clathrin-rich domains in the recycling compartment is essential for clathrin box chimera rescue, which then anchors the Vpu/tetherin complex. Subsequent endosomal trafficking, and importantly, any requirement for serine/threonine ubiquitination are downstream of this event. It also further reinforces the notion that the primary lesion in tetherin antagonism of the 2/6A mutant, like ELV and LI/LI, is at the level of clathrin-dependent sorting, not ubiquitin ligase recruitment.
Finally we examined mutations within the DSGNES motif itself. The consensus for a β-TrCP-binding site is DSGxxS, yet the N55/E56 in group M Vpu is almost universally conserved. We found rare mutations (N55H/E56G) in patients that displayed impaired tetherin antagonism despite retaining β-TrCP interaction [34]. Similarly, examination of a Vpu N55H/E56G mutation in the context of the NL4.3 Vpu revealed defects in tetherin counteraction in 293T tetherin cells (S3 and S4 Figs), which again could be rescued by a clathrin box fusion unless tetherin itself contained tyrosine mutations (Fig 5C and 5D). Together with the above data, these observations suggest that structural constraints or flexibility within the phosphoserine motif may underlie the reason why the 2/6A mutant is defective for tetherin mis-trafficking.
Our previous characterization of the ExxxLV motif and the data presented herein indicate that clathrin-dependent sorting of Vpu/tetherin complexes is an essential step in tetherin antagonism, prior to ubiquitin-dependent degradation. The demonstration that the ExxxLV motif of Vpu and the YDYCRV site in tetherin can form a ternary complex with AP-1 [39] is consistent with the cell biological observations that Vpu primarily blocks tetherin recycling and transit to the PM rather than stimulating its endocytosis [33,35]. However, demonstration that Vpu can interact with AP-1 in cells is lacking, and neither siRNA depletion of AP-1, nor deletion of γ-adaptin in murine fibroblasts, affects tetherin antagonism [36]. Clathrin adaptor interactions with their cargoes can sometimes (but not universally) be detected in yeast 2 or 3-hybrid assays or with recombinant proteins, but the relative weakness of their affinities often precludes direct demonstration of their interactions in vivo by conventional immunoprecipitations. To examine Vpu interaction with AP-1, we initially employed a proximity-based biotin ligase assay (S6A Fig). A consenus clade B Vpu or indicated mutant (note the phosphomutant S53,57A is labeled S3/7A), was fused to a myc-tagged E coli biotin ligase BirA-R113G, which itself does not compromise Vpu activity (S6B Fig). These constructs were then transfected into 293T or 293T tetherin cells. 6 hours after transfection the cells were incubated with free-biotin overnight in the presence of concanamycin A to block any tetherin degradation by the wild-type Vpu protein. Cell lysates were precipitated with streptavidin beads, and recovered proteins analyzed by Western blotting. Such treatment will lead to promiscuous biotinylation of proteins in close proximity with Vpu, potentially allowing us to detect interacting factors with weak affinities. As shown in S6C Fig, addition of biotin led to an accumulation of biotinylated proteins in cell lysates, including a strong band that is auto-biotinylation Vpu-BirA fusion itself. Importantly, β-TrCP was detected for all mutants tested in both 293T and 293T tetherin cells except the 2/6A mutant. Interestingly AP-1 γ-adaptin was detected only in streptavidin precipitates from 293T tetherin cells transfected with wild-type Vpu-BirA fusion, and not cells lacking tetherin expression. Furthermore, in 293T tetherin cells both ELV and LI/LI mutants failed to biotinylate AP-1. Interestingly, this was observed for the 2/6A mutant and also a Vpu A14L/W22A mutant that lacks tetherin binding. Thus, proximity-based tagging suggested Vpu does indeed interact with AP-1 in living cells. This appears to be dependent on tetherin binding and requires both the predicted AP-1σ binding site in Vpu, ExxxLV, and the non-canonical AP-1μ contact proposed to imparted by LI/LI. Furthermore, the lack of the 2/6A mutant to biotinylate AP-1γ suggests that Vpu phosphorylation is required to promote interaction, consistent with its cellular phenotype.
Whilst this data is strongly suggestive, it does not rule out that conformational changes in the mutants position the BirA in a context where AP-1 cannot be biotinylated. To strengthen these observations, we performed cross-linking immunoprecipitations in 293T tetherin cells transfected with HA-tagged Vpu or all of the above Vpu mutants. This revealed that AP-1γ could be detected in immunoprecipitates of Vpu-HA (Fig 6A). This was not detected for the A14L/W22A mutant, again indicating a requirement for tetherin interaction. A reduced amount of AP-1γ was detected in the 2/6A and ELV mutant immunoprecipitates, and this varied between replicates (see histogram below blot). Since tetherin’s YDYCRV motif also binds to AP-1 (Jia et al., 2014), we repeated the immunoprecipitations in 293T tetherin Y6,8A cells (Fig 6B). Whilst AP-1 precipitation was preserved for the wild-type protein, this effectively removed all detectable AP-1 interactions with any of the mutants, including the NE mutation between the two serines, indicating the reduced detection was due to tetherin/AP-1 interactions. To confirm these data, we also performed the same precipitations in 293T cells expressing a rhesus macaque tetherin to which HIV-1 Vpu cannot bind (Fig 6C), or parental 293T cells (S7A Fig) and found that no AP-1 could be detected under any conditions. These data also held true for the patient isolate Vpu 2_87 (S7B Fig). Therefore, these data demonstrate for the first time that Vpu does interact with AP-1 in vivo. Tetherin/Vpu TM-domain interactions are essential for this interaction, as are the predicted AP-1 binding sites in Vpu. Moreover, the lack of interaction of the 2/6A mutant indicates that phosphorylation of Vpu upstream of the ExxxLV regulates AP-1 interaction, and these data correlate well will the clathrin dependency presented in Fig 4.
The ExxxLV motif has the potential to bind to other clathrin adaptor σ subunits[39]. Since AP-1 depletion does not block Vpu function, we wondered whether Vpu interaction with the clathrin machinery might also occur through AP-2. We therefore analyzed the precipitations from cells expressing tetherin Y6,8A for the AP-2α adaptin subunit (Figs 6A, 6B and S7B). Surprisingly this could also be detected with the wild-type protein, but was absent for all the mutants, indicating ExxxLV also regulates this interaction. Thus, Vpu interacts promiscuously with both major cellular clathrin adaptors in a manner dependent on its ability to bind to tetherin. This is likely to account for why individual adaptor knockdowns fail to block Vpu function, and suggest that AP-2 might represent a compensatory clathrin-dependent trafficking mechanism for counteracting tetherin.
Finally, to provide direct evidence that it was phosphorylation of Vpu that permitted AP1/AP2 interactions, we repeated these immunoprecipitations in 293T tetherin Y6,8A cells treated with Tyrphostin (Fig 7). Under these conditions the ability of wildtype Vpu to interact with AP1 or AP2 was abolished, indicating that CKII-mediated phosphorylation for Vpu is required for recruitment of clathrin transport machinery.
In this study we have re-evaluated discrepancies in the literature regarding the role of SCFβTRCP1/2 and ESCRT in Vpu-mediated tetherin degradation and antagonism of its physical antiviral activity. We find that whilst essential for the former, they are dispensable for the latter in HIV-1 infected cells. We further show that phospho-serine mutants of Vpu have a distinct phenotype, displaying defects in tetherin antagonism because they cannot engage with clathrin-dependent trafficking pathways. We demonstrate that in cellulo Vpu/tetherin TM interactions induce Vpu binding to either clathrin adaptors AP-1 or AP-2. This interaction requires the ExxxLV trafficking motif, validating the recent structural study [39]. Importantly, phosphomutants of Vpu are also defective for clathrin adaptor engagement, implying that CKII-mediated phosphorylation not only regulates SCFβTRCP1/2 recruitment, but also regulates Vpu trafficking. Together these data clarify the role of the Vpu DSGNES motif in tetherin counteraction and provide strong evidence that sorting of Vpu/tetherin complexes into clathrin-rich domains of the endocytic pathway is the critical event in efficient tetherin antagonism. Furthermore, the observation that Vpu can interact both with AP-1 or AP-2 suggests a redundancy in adaptor protein requirement for tetherin counteraction that provides a plausible explanation for why depletion of either AP-1 or AP-2 is not sufficient to compromise Vpu function[36]. Thus potentially, tetherin/Vpu complexes that escape AP-1 in the TGN, and which traffic to the PM, can be retrieved by AP-2. Such a model would also rationalize why in some cases tetherin counteraction by Vpu can be observed with minimal evidence of surface downregulation [18,48].
Much of the discrepant literature regarding the mechanism of Vpu-mediated tetherin antagonism comes from experiments where tetherin, provirus and/or Vpu are transiently transfected into cells. Whilst these experiments are useful for understanding much of the biology of tetherin/HIV interactions, they are prone to artifacts when interpreting the cell biology and importance of Vpu-mediated degradation. Overexpression of tetherin or Vpu at non-physiological levels has been shown to induce ER-associated degradation [30]. This is not observed in infected cells, where tetherin is degraded in endosomes. Also, because of the nature of transient transfections, there is a huge variability of expression levels of the transfected components between cells within the culture. Under these conditions strong blocks to degradation may lead to tetherin accumulation, and an overwhelming of the endosomal system, giving the appearance of a direct inhibition of counteraction. By infecting tetherin-expressing cells at relevant multiplicities of infection, to ensure each cell has on average one productive infection event, these issues can be mitigated and this has allowed us to separate the requirement of the phospho-serine motif in counteraction from the recruitment of SCFβTRCP1/2 and the ESCRT machinery for degradation.
Our in cellulo data validates the structural and biochemical studies by Jia et al [39], in which AP-1 interaction requires the ExxxLV motif that occupies the acidic-dileucine binding site in AP-1σ. We also provide evidence that in cells, this motif can also bind to AP-2. Furthermore, the phenotype of our LI/LI mutant is consistent with the proposed non-canonical interaction of R44/L45 with AP-1μ suggested by densities in the crystal. However, because the constructs used by the authors to determine the structural requirements for AP-1/tetherin/Vpu interaction required artificial Vpu/tetherin fusions, they may not faithfully represent how AP-1 is initially recruited. Thus, the requirements for the DSGNES and Vpu/tetherin transmembrane domain interactions that we have uncovered in cells were not previously observed.
We propose a model whereby phosphorylation of Vpu regulates the AP interaction with the ELV motif (Fig 8). Whilst we cannot formally rule out that the phosphoserine directly contributes to AP-1 interaction itself, the lack of a significant additive phenotype in terms of virus release and AP-1 interaction makes this the most consistent explanation of our data. Furthermore there is precedence for phosphorylation upstream of certain acidic dileucine motifs interactions with the clathrin transport machinery [43]. In particular, a CKII phosphorylation upstream of a non-canonical RDDHLL site in the cation-independent mannose-6-phosphate receptor regulates its interaction with AP1. Another context-dependent feature of acidic dileucine signals is an adjacent acidic patch [37]. Interestingly, this feature is present in HIV-1 Vpu. Furthermore, the laboratory strain NL4.3 Vpu, which has a reduced anti-tetherin activity compared to most primary isolates, has a shorter acidic patch between the DSGNES and ExxxLV motifs [34]. The requirement for TM interactions in addition to the phospho-serines in “priming” Vpu for clathrin adaptor interaction would imply that tetherin binding contributes to conformational changes that are required for antagonism. Since β-TrCP binding does not require the presence of tetherin (or CD4), phosphorylation must be an independent event. However, whether β-TrCP and AP-1/2 binding can occur simultaneously or are mutually exclusive is unknown. Another interesting point to note is that the LI/LI mutation is more severely compromised than either the 2/6 or the ELV mutations in some contexts. As it also compromises AP binding, the non-canonical interaction of the R45,L46 with AP-1μ may also play an essential contextual role in positioning the ELV motif. This interaction may also explain why the residual activities of 2/6 and ELV mutations are sensitive to clathrin depletion.
Structural information on the Vpu cytoplasmic tail is limited at present. Partial NMR structures in solution and associated with lipids have been determined [49–52]. In a lipid environment, the ExxxLV is embedded within helix 2 of the cytoplasmic tail [52], but adopts an extended conformation in solution [51]. To bind to AP-1, the ExxxLV site cannot be helical. However, the lipid-associated structure has a very interesting feature: a highly conserved C-terminal tryptophan residue appears to pack against the DSGNES, almost as if locking the structure. Mutations in the W residue have context-dependent defects in tetherin antagonism depending on the Vpu used [34,53]. Importantly, NMR studies on the effects of serine phosphorylation suggests that it leads to conformational changes within the C-terminal region of the Vpu cytoplasmic tail that promotes βTRCP binding. In some studies [49,50], but not others [54], these conformational changes are consistent with an opening up of the ELV site. However, all these studies have thusfar been performed in the absence of target binding using soluble Vpu cytoplasmic tails, and so how representative they are of the wildtype protein is unclear. Furthermore, upregulation of SCYL2, a clathrin associated protein that modulates protein phosphatase 2A (PP2A), induces Vpu de-phosphorylation and reduces tetherin antagonism [55]. Thus, there is scope for regulated phosphorylation and subsequent dephosphorylation cycles in regulation of Vpu activity. We suggest that this would occur at the level of clathrin-dependent transport rather than SCFβTRCP1/2 interactions.
There is much indirect evidence consistent with AP-1 being the major clathrin adaptor used by Vpu. The block to tetherin transport to the surface, the predominant localization to the TGN, and of course the recent structure discussed above [33,35,36,39]. However, AP-1 knockdown is difficult to efficiently achieve and does not compromise Vpu function [36]. AP-1 has multiple orthologs for some of its subunits, and there is potential redundancy in the adaptor machinery allowing the cell to compensate for its absence [37]. Our observation that Vpu can interact also with AP-2 in an ExxxLV-dependent manner is therefore an important observation for several reasons. Firstly, it suggests that Vpu is promiscuous and if one adaptor is compromised, another can be used, explaining why neither AP-1 nor AP-2 have been unambiguously identified as Vpu cofactors [25,36]. Secondly, it might explain why in some studies, Vpu has been observed to induce a weak enhancement of tetherin endocytosis [35]. Artificial tetherin/Vpu linked chimeric proteins are excluded from budding virions, and this is dependent on the ExxxLV motif [18], which would be consistent with anchoring by AP-2 into clathrin-rich domains at the plasma membrane. The YDYCRV motif of tetherin cannot interact with AP-2μ as a YXXθ signal because of a steric clash of Y6 in the binding pocket [39]. The YDYCRV motif is essential for the “slow”, AP-1-dependent recycling of tetherin to the PM via the TGN [17,33]. Therefore, Vpu is likely to meet the majority of its target (newly synthesized and recycling tetherin) in the TGN. Since AP-1 has been proposed to regulate bidirectional traffic between the TGN and endosomal compartments [37], AP-1 is likely to be the major player in tetherin counteraction. However, the ExxxLV motif is dominant over the tetherin recycling motif [36]. Therefore we would predict that tetherin/Vpu complexes that escape re-routing in the TGN and make it to the PM would be excluded from virions and AP-2 would promote their endocytosis, much in the same way that SIV Nef proteins and HIV-2 envelopes antagonize tetherin [6,38,56]. More importantly, it also accounts for why Vpu still has some activity against the short tetherin isoform without appreciable cell surface downregulation [13,40]. The relative role of AP-1 and AP-2 will reflect the kinetics of their respective activities in different cell types. We suggest the combination of some or all of the above accounts for the variable importance that downregulation of tetherin from the PM has been given to its antagonism. The requirement for the ExxxLV and DSGNES motifs is not absolute when tetherin levels are low. At higher expression levels, such as upon IFN treatment of primary CD4+ T cells, they become essential for tetherin antagonism [36]. This residual function requires tetherin’s sorting motif, suggestive of competition between the clathrin-dependent trafficking and virion retention. Tetherin/Vpu interaction may simply tip this balance, reducing tetherin partitioning into virions sufficiently when its expression levels are low. It is this that we propose to augment via our clathrin box fusion rescue, locking the tetherin/Vpu complex into clathrin-rich domains in the recycling pathway from where they cannot be transited to the PM.
Decoupling tetherin degradation (which amongst primate lentiviruses is so far peculiar to HIV-1 group M Vpu) from subversion of trafficking (counteraction) suggests that the importance of the former might reflect downstream consequences of tetherin restriction. Enhanced antagonism of the long tetherin isoform by Vpu could be required because of its signal transduction or its ability to deliver retained virions to endosomes [14,40]. Our data shows that in stable tetherin expressing cells, STS mutations impart little resistance to Vpu and that they are still sensitive to Vpu-clathrin box fusions. Since neither LI/LI nor ELV mutations block binding of Vpu to β-TrCP or tetherin, ubiquitination may still occur on serine and threonine residues. However, its effect is likely to be subsequent to antagonism by clathrin-dependent mis-trafficking. Strong reduction of tetherin at the cell surface by Vpu coupled to endosomal degradation would therefore be a potent way of suppressing signal transduction, or blocking the routing of virions for degradation where they may encounter other host pattern recognition receptors or antigen processing machinery. These will be important attributes to maintain in vivo without necessarily being essential for physical antagonism of tetherin.
HEK293T cells were obtained from ATCC (American Tissue Culture Collection). 293T tetherin cell lines stably expressing human tetherin and mutants were previously described [4,57]. The HeLa-TZMbl reporter cell line, was kindly provided by John Kappes through the NIH AIDS Reagents Repository Program (ARRP). Cells were maintained in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal calf serum and Gentamycin (Invitrogen, UK). Wildtype HIV-1 NL4.3 (obtained from NIH-ARRP), a Vpu-defective counterpart and a codon optimized pCR3.1 Vpu-HA has been described previously [58]. The Vpu A14L/W22A, ELV, 2/6A, LILI and NE mutants in pCR3.1 Vpu-HA and in the NL4.3 proviral genome were generated by Quick-change site-directed mutagenesis PCR according to standard protocols using Phusion-II polymerase (New England Biolabs). A codon-optimised version of the previously described primary wild-type HIV-1 Vpu 2_87 [34] was HA-tagged and cloned into pCR3.1. The Vpu A15L/W23A, ELV, 2/6A, LILI and NE mutants were generated in pCR3.1 Vpu 2_87-HA by Quick-change site-directed mutagenesis as described above. Consensus B codon-optimised Vpu-myc-BirA-R188G fusion was synthesized (Life Technologies) and cloned into the lentiviral vector pAIP (kindly provided by A Cimarelli). The Vpu A15L/W23A, ELV, 2/6A and LILI mutants were generated by Quick-change site-directed mutagenesis as described above. The pCR3.1 myc-β-TrCP2 was previously described by [36] and the pCR3.1 myc-HRS expression vector was kindly provided by Juan Martin-Serrano [59].
Primary human CD4+ T cells were isolated from fresh venous blood drawn from healthy volunteers. CD4+ T cells were purified from total peripheral blood mononuclear cells (PBMC) isolated by lymphoprep (AXIS-SHIELD) gradient centrifugation using a CD4+ T cell Dynabeads isolation kit (Invitrogen). T cells were then activated for 48 h using anti-CD3/anti-CD28 magnetic beads (Invitrogen). The beads were then removed cells were then maintained in rhIL-2 (20 U/ml) (Roche).
For full-length HIV-1 WT, HIV-1 ΔVpu, HIV-1 Vpu LILI, HIV-1 Vpu ELV, HIV-1 Vpu LILI/ELV, HIV-1 Vpu 2/6A, HIV-1 Vpu 2/6A/ELV virus stocks pseudotyped with the Vesicular Stomatitis Virus Glycoprotein (VSV-G), 293T cells were transfected with 2 μg of proviral plasmid in combination with 200 ng of pCMV VSV-G. 48 hours post-transfection, the supernatant containing virions was harvested and endpoint titers were determined on HeLa-TZMbl cells as described previously [3].
For virus release assays using transient transfection, subconfluent 293T cells or derivatives were plated in 24 well plates and transfected with 500 ng of NL4.3 proviral plasmid, in combination with increasing concentrations of tetherin (0 ng, 25 ng, 50 ng and 100 ng) and fixed 25 ng of Vpu-HA or mutants using 1 μg/ml polyethyleneimine (Polysciences). Medium was replaced 8 hours post-transfection and cells and supernatants were harvested after 48 hours. The infectivity of viral supernatants was determined by infecting HeLa-TZMbl and assayed for β-galactosidase activity as previously described [36]. For biochemical analysis of physical virus particle release, supernatants were filtered (0.22 μm) (Merck Millipore) and pelleted through a 20% sucrose/ PBS cushion at 20,000 x g for 90 min at 4°C. Virion and cell lysates were subjected to SDS-PAGE and Western blotted for rabbit anti-HSP90 (Santa Cruz Biotechnologies), HIV-1 p24CA (monoclonal antibody 183-H12-5C; kindly provided by B Chesebro through the NIH ARRP), monoclonal mouse anti-HA.11 (Covance), polyclonal rabbit anti-HA (Rockland) and/ or Vpu (rabbit polyclonal; kindly provided by K. Strebel through the NIH ARRP [60]. For CK-II inhibition, we used Tyrphostin AG1112 (Sigma) reconstituted in DMSO at a concentration of 50 μM. Where indicated, Phos-tag (Wako Chemicals, Japan) and MnCl2 (Sigma) were added to the composition of 8% polyacrylamide gels to induce mobility shifts in phosphorylated proteins, to final concentrations of 25 μM and 50 μM, respectively.
1.5 x 105 293T tetherin cells were infected with VSV-G-pseudotyped HIV-1 WT, HIV-1 ΔVpu, HIV-1 Vpu LILI, HIV-1 Vpu ELV or HIV-1 Vpu 2/6A at an MOI of 2. The medium was replaced 4 hours after infection. 48 hours post infection cell lysates were harvested and subjected to SDS-PAGE and Western blotted for rabbit anti-HSP90 (Santa Cruz Biotechnologies) and polyclonal rabbit anti-tetherin antibody (kindly provided by K Strebel through the NIH ARRP) [48], and processed as described above.
293T tetherin cells were seeded at a density of 2 x 105 cells per well in a 12 well plate. After 6 hours, the first transfection was performed. For each well, 2 μl Dharmafect (Thermo Scientific) was added to 98 μl of Opti-MEM (Life Technologies), this solution was added to 5 μl of 20 μM siRNA in 95 μl of Opti-MEM according to manufactures protocol. For HRS knockdown, siRNA oligonucleotide against HGS targeting the CCGGAACGAGCCCAAGTACAA sequence (Qiagen) was used. For UBAP1 knockdown, siRNA oligonucleotide against UBAP1 targeting CTCGACTATCTCTTTGCACAT (Qiagen) was used. For TSG101 knockdown, siRNA oligonucleotide sequence CCUCCAGUCUUCUCUCGUCUU (Thermo Scientific) was used. For β-TrCP1 and 2 knockdown, SMARTpool siRNA against human BTRC and FBXW11 (Thermo Scientific) were used. A non-targeting siRNA was used as control (Thermo Scientific). The cells were re-seeded into a 24 well plate on day 2 and a second transfection was performed according to manufactures protocol. The cells were infected 3 hours post transfection with VSV-G-pseudotyped HIV-1 WT, HIV-1 ΔVpu at an MOI of 0.8. The infectivity of viral supernatants was determined by infecting HeLa-TZMbl as described above. Cell lysates and viral particles were subjected to SDS-PAGE, and Western blot assays were performed using a rabbit polyclonal anti-HRS (HGS) antibody (Millipore), a polyclonal rabbit anti-UBAP1 antibody (Proteintech) and a monoclonal mouse anti-TSG101 antibody (Abcam).
HeLa-TZMbl cells were transfected with 400 ng of pCR3.1 GFP and 400 ng of pCR3.1 Vpu-HA or indicated mutants. 48 hours post transfection the cells were harvested and stained for surface tetherin using a monoclonal anti-BST2 IgG2a antibody (Abnova) and a goat-anti-mouse IgG2a-Alexa633 conjugated secondary antibody (Molecular Probes, Invitrogen, UK). Tetherin expression on GFP positive cells was then analyzed using a BD FACSCanto II flow-cytometer (Becton Dickinson) and the FlowJo software.
For Vpu/HRS coIP, 293T tetherin cells were transfected with 700 ng of pCR3.1 myc-HRS or indicated mutants/truncations in combination with pCR3.1 Vpu-HA or indicated mutant or pCR3.1 GFP expression plasmids. 48 hours post transfection the cells were lysed in buffer containing 50 mM Tris pH 7.4, 150 mM NaCl, 200 μM sodium ortho-vanadate, 5 mM NEM, complete protease inhibitors (Roche) and 1% digitonin. After removal of the nuclei, the supernatants were immunoprecipitated with 5 μg/ml monoclonal mouse anti-myc antibody previously described (Kueck and Neil, 2012). Western blot assays were performed using a polyclonal rabbit anti-HA antibody (Rockland) and rabbit polyclonal anti-HRS (HGS) antibody (Millipore). For Vpu/tetherin coIP, 293T cells were transfected twice over 48 hours with siRNA oligonucleotide against UBAP1 targeting CTCGACTATCTCTTTGCACAT or Non-targeting siRNA was used as control (Dharmacon). The cells were then infected with VSV-G-pseudotyped HIV-1 WT, HIV-1 ΔVpu, HIV-1 Vpu LILI or HIV-1 Vpu A14L W22A at an MOI of 2. 48 hours post infection the cells were lysed on ice for 30 min in buffer containing 50 mM Tris pH 7.4, 150 mM NaCl, complete protease inhibitors (Roche) and 1% digitonin (Calbiochem). Immunoprecipitation was performed as previously described [36] and Western blot assays were performed using a rabbit anti-Vpu antibody polyclonal rabbit anti-tetherin antibody and polyclonal rabbit anti-UBAP1 antibody (Proteintech), and visualized by ImageQuant using corresponding HRP-linked secondary antibodies (New England Biolabs, UK).
Hela cells were grown on coverslips, transfected with 50 ng of pCR3.1 Vpu-HA or indicated mutant. 16 hours later cells were fixed in 4% paraformaldehyde/ PBS, washed with 10 mM glycine/ PBS, and permeabilized in 1% bovine serum albumin/ 0.1% Triton-X100/ PBS for 15 min. Cells were stained using anti-rabbit polyclonal HA antibody (Rockland) in combination with sheep anti-human TGN46 (AbD Serotec), followed by the appropriate secondary antibodies conjugated to Alexa 488 or 594 fluorophores (Molecular Probes, Invitrogen). Cells were mounted on glass slides using ProLong AntiFade- 4’,6-diamidino-2-phenylindole (DAPI) mounting solution (Molecular Probes, Invitrogen) and images were captured with a Nikon ESCLIPSE Ti inverted microscope. Z stacks were taken of all cells, images deconvolved using AutoQuant X3 and analyzed using the ImageJ software.
293T, 293T tetherin, 293T tetherin Y6,8A or 293 Rhesus tetherin cells were transfected with 8 μg GFP expression construct, pCR3.1 Vpu-HA or mutant thereof. Transfection media was changed 6 hours post transfection and cells incubated with 50 nM concanamycin. In the case of CKII inhibitor treatment, cells were treated with 50 μM final Tyrphostin 24 hours prior to harvesting. 48 hours post transfection, cells were trypsinised and washed in PBS. Cells were cross-linked with 0.05% HCHO/PBS for 10 min at 37°C. The cross-linking reaction was then quenched by incubating cells in 0.25 M glycine for 5 min. Cells were washed once in PBS before resuspension in lysis buffer (10 mM Hepes pH 7, 150 mM NaCl, 6 mM MgCl2, 2 mM DTT, 10% glycerol, 0.5% NP40, 200 μM sodium orthvanadate and 1x Complete protease inhibitors (Roche)). Cells were lysed on ice for 10 min followed by repeated sonication (3 x 10 s cycles with 20 s rests). The cell lysates were clarified by centrifugation at 1000 x g for 10 min and supernatants were immunoprecipitated with 5 μg/ml mouse monoclonal anti-HA.11 antibody (Covance) or rabbit polyclonal anti-HA antibody (Rockland) on Dynabeads protein G beads (Life Technologies) for 4 hours at 4°C. Beads were collected post incubation and washed 5 times in lysis buffer before cross-links were reversed in 1% SDS, 10 mM EDTA and 5 mm DTT at 65°C for 45 min. Western blot assays were performed using rabbit polyclonal anti-HA antibody (Rockland), polyclonal rabbit anti-tetherin antibody, mouse monoclonal anti-HA.11 antibody, mouse monoclonal anti-AP-1γ1 antibody (Sigma) and mouse monoclonal anti-AP-2α antibody (Sigma). Vpu/β-TrCP2 cross-linking IP was previously described by [36].
[61] 293T or 293T tetherin cells were transiently transfected with 8 μg empty BirA vector, Vpu-myc-BirA or relevant mutant constructs using polyethylenimine (PEI). Cells were incubated for 8 hours prior to changing medium and treated overnight with 100 nM Concanamycin A (Invitrogen) and 150 μM biotin (Invitrogen). Cells were washed twice in PBS and lysed in 1 ml lysis buffer (50 mM Tris pH 7.4, 500 mM NaCl, 0.4% SDS, 5 mM EDTA, 1 mM DTT and 1x Complete protease inhibitor (Roche)) before sonication. Triton-X-100 was added to a final concentration of 2% before further sonication and an equal volume of 50 mM Tris pH 7.4 was added to the cell lysates before clarification at 14,000 rpm for 5 minutes. Supernatants were incubated with 200 μl avidin agarose (Pierce) for 4 hours at 4°C. Beads were collected and washed four times in 1 ml lysis buffer before resuspension in 100 μl Laemmli-SDS sample buffer supplemented with free biotin. Cell lysates and precipitates were analysed by Western blot using HRP-conjugated streptavidin (Invitrogen), mouse monoclonal anti-myc antibody (Covance), rabbit monoclonal β-TrCP antibody (Cell signaling Technology) and mouse monoclonal anti-AP-1γ1 antibody (Sigma).
Permission to isolate primary human CD4+ T cells from healthy consenting donors was provided by the KCL Infectious Disease BioBank Local Research Ethics Committee, reference SN-1/6/7/9.
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10.1371/journal.pbio.2006128 | Highly diverged novel subunit composition of apicomplexan F-type ATP synthase identified from Toxoplasma gondii | The mitochondrial F-type ATP synthase, a multisubunit nanomotor, is critical for maintaining cellular ATP levels. In T. gondii and other apicomplexan parasites, many subunit components necessary for proper assembly and functioning of this enzyme appear to be missing. Here, we report the identification of 20 novel subunits of T. gondii F-type ATP synthase from mass spectrometry analysis of partially purified monomeric (approximately 600 kDa) and dimeric (>1 MDa) forms of the enzyme. Despite extreme sequence diversification, key FO subunits a, b, and d can be identified from conserved structural features. Orthologs for these proteins are restricted to apicomplexan, chromerid, and dinoflagellate species. Interestingly, their absence in ciliates indicates a major diversion, with respect to subunit composition of this enzyme, within the alveolate clade. Discovery of these highly diversified novel components of the apicomplexan F-type ATP synthase complex could facilitate the development of novel antiparasitic agents. Structural and functional characterization of this unusual enzyme complex will advance our fundamental understanding of energy metabolism in apicomplexan species.
| The mitochondrial F-type ATP synthase, which is a major source of ATP in eukaryotic cells, is a unique nanomotor. The enzyme consists of two subcomplexes called the F1 and FO sectors. The F1 sector is the site of ATP synthesis, while the FO sector couples proton translocation to the rotary motion of the enzyme. FO sector subunits also form the peripheral stalk (stator), which holds the nonrotating parts of the enzyme together. F1 sector subunits are highly conserved among eukaryotes, while most FO sector subunits appear to be diverse and remain unknown in many species, including in the important human pathogen Toxoplasma gondii. In this study, we have partially purified the T. gondii F-type ATP synthase and analyzed its proteome, using mass spectrometry. This resulted in the identification of 20 novel subunits of the enzyme, including extremely diversified key FO sector subunits. Many of these novel T. gondii proteins are conserved in related apicomplexan parasites, such as the malaria parasite, and thus might be good drug targets. Conservation of many of these proteins in related free-living alveolate species provides insights on their evolutionary history and can potentially facilitate further biochemical and structural studies on this unusual enzyme.
| The F-type ATP synthase is a ubiquitous nanomotor present on the inner membrane of mitochondria, chloroplast, and bacterial plasma membrane that catalyzes ATP formation from ADP and Pi [1,2]. In mitochondria, the energy required to drive the nanomotor is harnessed from the proton motive force and associated membrane potential, which are generated by the mitochondrial respiratory complexes [3,4]. The mitochondrial F-type ATP synthase complex consists of two subcomplexes called the F1 and FO sectors. The hydrophilic globular F1 sector includes three each of α and β subunits and a central stalk containing one each of γ, δ, and ε subunits. The site of ATP formation resides in the catalytic center located in the β subunit [1,2,4,5]. Unlike the F1 sector subunit components, which are well conserved across various eukaryotic phyla, many FO sector subunit components are highly diverse [6–9] and are not readily identified based on sequence similarity. The complete set of FO sector subunits have been mapped only in a few species [10,11]. For example, in the yeast Saccharomyces cerevisiae, the FO sector is composed of the hydrophobic membrane–spanning oligomeric subunit c (10–12 monomers), along with subunits a, b, d, f, 8, h, and OSCP (oligomycin sensitivity–conferring protein) [10]. In addition, other accessory subunits such as e, g, i, j, and k are also associated with the enzyme. Orthologs of all yeast FO sector subunits, except subunits j and k, are present in the mammalian counterpart (bovine enzyme), which additionally has subunits AGP and MLQ [11].
Numerous biochemical and structural studies have addressed the assembly and interaction between the various subunit components of F-type ATP synthases [1,12]. These studies have provided in-depth insights on the functional coupling of the proton motive force to ATP synthesis. The FO sector subunits c and a interact to form the proton channel; and proton translocation, which occurs along the interface of these two subunits, is coupled to the rotary motion of the c-ring and central stalk of the enzyme [13–16]. The peripheral stalk structure of the enzyme—comprised of FO sector subunits b, d, h (f6 in bovine), and OSCP—has the critical role of holding the α3β3 catalytic domain in place during the clockwise rotation of the central stalk. These events elicit conformational changes in the α3β3 catalytic domain, which then results in ATP synthesis [2,4,17,18]. FO sector subunits e and g are known to be involved in dimer assembly of the enzyme complex in yeast [19,20], which appears to facilitate cristae formation by the mitochondrial inner membrane [21–24].
However, despite the importance of the various FO sector subunits, only subunit c and OSCP can be detected from sequence conservation in the vast majority of unicellular eukaryotes, including in the alveolate infrakingdom, which includes the phylum Apicomplexa [25–27]. It is unlikely that the missing subunits are totally absent in these species. Plausible scenarios are that they have either diverged in sequence beyond recognition or that a completely new set of proteins have substituted for the missing subunits in these species. This is indeed the case in organisms such as Euglena gracilis [9], Trypanosoma brucei [7], Tetrahymena thermophila [8], and Chlorophyceae algae [6]. Hence, our interest was to characterize the complete subunit composition of the F-type ATP synthase from the apicomplexan parasite T. gondii.
T. gondii is an obligate intracellular protozoan parasite responsible for toxoplasmosis in humans and animals [28]. The parasite completes its life cycle in two host species; the definitive hosts are naïve feline species that support the sexual development of the parasite, and all warm-blooded animals are intermediate hosts and support its asexual development [29]. During asexual development, the parasites can reversibly differentiate between fast-growing virulent tachyzoites and slow-growing latent bradyzoites [30]. By virtue of their fast-growing nature, tachyzoites are metabolically more active, and glycolysis is the primary source for carbon and energy for optimal growth of this parasite [31–33]. However, recent findings have revealed that glucose is not an essential nutrient [34], and parasites survive in the presence of glutamine as an alternative nutrient source [35,36]. In the absence of glucose oxidation via glycolysis, oxidative phosphorylation is the only source for bulk cellular ATP. Evidence for an active oxidative phosphorylation in T. gondii exists [37–39], and treatment with atovaquone, a potent inhibitor of mitochondrial electron transport chain (mtETC), results in inhibition of ATP synthesis and parasite growth (EC50 < 50 nM) [40].
Very little is known about the structure and function of the F-type ATP synthase from T. gondii and other apicomplexan parasites. Comparative genomics has revealed that, while the five canonical F1 sector subunits—α, β, γ, δ, and ε—are readily identified in all apicomplexan parasites based on amino acid sequence conservation, only two FO sector subunits—c and OSCP—can be identified by sequence [25–27]. Characterization of F-type ATP synthase from solubilized mitochondria lysates of the human malaria parasite Plasmodium falciparum revealed that the enzyme assembles into monomer and dimer forms [26]. This suggests that a full complement of subunits, typical for the eukaryotic enzyme, is present in P. falciparum and likely in other apicomplexan parasites as well.
Here, we have investigated the subunit composition of the enzyme from T. gondii. We were able to identify and partially purify the F-type ATP synthase enzyme complex from solubilized mitochondrial preparation of T. gondii using blue native PAGE (BNP) separation, by immunoprecipitation (IP) and by chromatographic enrichment. Liquid chromatography–mass spectrometry (LC-MS) analysis of the enzyme preparations revealed the identity of the proteins associated with the T. gondii F-type ATP synthase. We have identified 20 novel proteins (of unknown function) as being bona fide subunit constituents of T. gondii F-type ATP synthase, based on consensus from multiple and independent experiments. Phyletic profiling revealed that orthologs for many of these proteins are present in most apicomplexan species, except in those Cryptosporidium spp. that possess a degenerate mitochondrion devoid of all proteins involved in oxidative phosphorylation [41]. Further, the phyletic profiles of these novel proteins reveal their origin to be ancient, probably even ancestral to alveolate radiation. The identification of these novel protein components of the apicomplexan F-type ATP synthase will facilitate further structural, functional, and inhibitor discovery studies on this important enzyme complex.
Comprehensive in silico analysis on the presence and absence of key enzymes involved in mitochondrial metabolism in apicomplexan species has been previously carried out [25,27,42]. These studies have highlighted the missing subunits of the F-type ATP synthase enzyme from these organisms. We have further confirmed these findings from our efforts to identify T. gondii orthologs for yeast and bovine F-type ATP synthase subunits. Orthologs for F1 sector subunits α (TGME49_204400), β(TGME49_261950), δ(TGME49_226000), ε(TGME49_314820), and γ(TGME49_231910) and FO sector subunits c (TGME49_249720) and OSCP (TGME49_284540) were readily identified from T. gondii. This suggested that all the subunits necessary for assembling the catalytic core (α3β3), central stalk (δ, ε, and γ), and the subunit c oligomer portions of the F-type ATP synthase (Fig 1A) are encoded by T. gondii. However, as reported previously [25,27], we were unable to identify the orthologs for critical FO sector subunits involved in proton translocation and formation of the peripheral stalk structure of the enzyme in T. gondii through routine bioinformatic analysis. This appears to be the case in all apicomplexan species and in other taxons within the alveolate infrakingdom, such as the dinoflagellates, ciliates, and chromerids (Fig 1B). It is likely that the missing FO subunits are either highly divergent or completely novel. This situation is not exclusive to alveolates, since many other unicellular eukaryotes also appear not to possess the corresponding orthologs for many of the yeast or bovine FO subunits. In fact, novel F-type ATP synthase–associated proteins (ASAPs) have been previously identified from Tetrahymena [8], Chlamydomonas [6], and Trypanosoma [7]. Therefore, we attempted to purify the F-type ATP synthase enzyme from isolated T. gondii mitochondria in native form and identify its protein composition by mass spectrometry.
To facilitate the purification of the T. gondii F-type ATP synthase enzyme in native form, we engineered in-frame genomic YFP-HA tags in the 3′ end of the respective genes encoding the F1 β and FO OSCP subunits (Fig 2A). The modified genes continued to be expressed under the control of their endogenous promoters. The correct insertion of the tags was confirmed by genomic PCRs (S1 Fig) from clonal isolates of the respective transgenic parasites. Expression of the desired YFP-HA tagged proteins and their mitochondrial localization were confirmed by western blotting (Fig 2B) and microscopy (Fig 2C), respectively. We then compared the ability of the parental and transgenic strains of the parasites to maintain total cellular ATP levels in the presence/absence of glucose. Although T. gondii tachyzoites are known to prefer glucose as the primary nutrient source, in the absence of glucose, they are known to switch to glutaminolysis for carbon and energy supply. In the latter case, ATP is obtained by the parasites via oxidative phosphorylation, and this can be inhibited using atovaquone [25,40,43]. Like the parental parasites, transgenic parasites expressing YFP-HA-tagged F1 β and FO OSCP subunits were capable of maintaining cellular ATP levels via oxidative phosphorylation in the absence of glucose, which was inhibited by atovaquone (Fig 2D). These results confirm that the modification of either F1 β or FO OSCP proteins with YFP-HA tag had no detrimental effect on the function of the enzyme, implicating that the structure of the tagged enzyme remained intact.
The fully assembled F-type ATP synthase is known to exist as dimers on the inner mitochondrial membrane, and this dimerization is known to influence the characteristic cristae formation by the membrane [21,24]. In the case of the human malaria parasite P. falciparum F-type ATP synthase, dimer formation has been observed previously [26]. To find out whether the T. gondii enzyme can assemble into dimers, we carried out BNP analysis of detergent-solubilized mitochondrial preparations. After resolving the samples on a 3%–12% gradient BNP gel capable of resolving a molecular weight range between 20 kDa to approximately 1,200 kDa (Fig 3A lane M), Coomassie staining revealed prominent bands at the sizes corresponding to dimeric and monomeric forms of the enzyme (Fig 3A lane A). This was further confirmed by western blotting after BNP separation using α-HA antibodies (Fig 3A lane B), which indicated that the dimer form of the enzyme is more abundant than the monomer form.
Next, we carried out in-gel activity assays to detect the ATPase activity associated with the isolated enzyme. Results from these assays confirmed that the ATPase activity was intact, for both dimer and monomer forms of the enzyme, after BNP separation (Fig 3B). We found that, although the dimer form of the enzyme was more abundant, its ATPase activity was less than that of the monomer form. This is supported by the fact that F1 ATPase activity is inhibited in vivo to minimize the risk of ATP hydrolysis and favor ATP synthesis [44]. Since the in-gel activity assay is an ATP hydrolysis assay, the dimer has less of this activity. Based on BNP mobility, we deduce the size of the dimer form of the enzyme to be 1–1.2 MDa and that of the monomer form to be approximately 600 kDa. This is in agreement with what has been reported previously for P. falciparum [26], S. cerevisiae [19], and bovine [45] enzymes and suggests that a full complement of FO subunits is present in T. gondii F-type ATP synthase. Therefore, we excised the regions in the BNP corresponding to the dimer and monomer forms of the enzyme and proceeded to identify the proteins in the gel band by LC-MS analysis (Fig 3A).
A standard protocol (see Methods section) was followed for in-gel LC-MS/MS analysis of the gel bands corresponding to the dimer and monomer forms of the enzyme identified by BNP. This was done on samples prepared from both TgATPβ–YFP-HA and TgATPOSCP–YFP-HA expressing transgenic parasite lines. Since the dimer form of the enzyme was more abundant than the monomer form after BNP separation, we had better success with LC-MS/MS analysis of the gel band corresponding to the dimer form of the enzyme. A total of 96 proteins were identified with high confidence from consensus data obtained from multiple in-gel LC-MS/MS analysis (S1 Table). Proteins corresponding to all F1 subunits and FO OSCP were detected, while FO subunit c was not detected in any of the experiments. In addition, many proteins of unknown function were also detected. However, due to comigration of several nonspecific proteins, such as myosin, in the area corresponding to the gel band processed for LC-MS/MS analysis, it is likely that some of these proteins of unknown function are not bona fide subunits of the F-type ATP synthase. A full list of the proteins identified from these samples, along with the details of the peptides detected, is given in S1 Table.
In order to specifically identify the protein subunits of F-type ATP synthase, we resorted to partially purifying the enzyme before LC-MS/MS analysis by chromatographic separation (Fig 4A and 4B) and IP (using the α-HA antibody). In contrast to BNP analysis, after size exclusion chromatography, we observed that the dimer form of the enzyme was less abundant than the monomer form (Fig 4B). This is likely due to stability issues with the dimer, which might progressively fall apart as monomers during the process of chromatographic separation. The fractions corresponding to the dimer and monomer forms of the enzyme were pooled and concentrated separately before processing for LC-MS/MS analysis. A total of 64 proteins were detected (Fig 5A) from the fractions corresponding to the monomer form of the enzyme, and we did not get reliable data from the dimer fractions, owing to very low protein concentration.
LC-MS/MS analysis on the enzyme enriched by IP resulted in the identification of a total of 29 proteins, of which 28 were also detected from either BNP or chromatography samples (Fig 5A). Details for the peptides detected from chromatography and IP samples are provided in S1 Table. It is notable that 19 proteins, including most F1 subunits and FO OSCP, were detected in samples prepared by all three methods. Out of the 29 proteins, only 3 were considered as nonspecific proteins and not related to F-type ATP synthase. Out of the remaining 26 proteins, 5 were known F1 subunits (α, β, δ, ε, γ), 1 was FO OSCP, and the remaining 20 were proteins of unknown function. It should be noted that we were unable to identify FO subunit c in any of our LC-MS/MS analyses, probably owing to its highly hydrophobic nature. Based on the consensus of proteins identified by the three different approaches, the F-type ATP synthase from T. gondii appears to be comprised of at least 27 protein subunits. We have coined the term “ATP synthase–associated proteins” (or “ASAPs”) to refer to the 20 novel subunit components of the enzyme identified in this study. A complete list of all T. gondii F-type ATP synthase subunits identified by mass spectrometry, along with their annotation from ToxoDB and the experiments from which they were detected, is given in the table in Fig 5B.
As shown in Fig 1, in addition to subunits c and OSCP, the FO sector consists of at least 6 other subunits in yeast, mammalian, and plant enzymes. Subunit a is essential for proton conductance, which it facilitates along with subunit c [13–15]. Subunit b forms the core of the stator structure, which is essential for holding the catalytic α3β3 structure in place during the rotary motion of the central stalk [18]. Subunit d is part of the stator structure. We attempted to find out which of the ASAPs correspond to these three subunits in T. gondii F-type ATP synthase. Since none of the ASAPs had any sequence identity to the yeast FO a, b, and d subunits, we resorted to conserved structure-based identification using pairwise comparison of profile hidden Markov models as implemented in HHPred [46,47]. For this analysis, we used the corresponding tool available from web-based MPI bioinformatics toolkit (toolkit.tuebingen.mpg.de). We analyzed each of the ASAPs using this tool and successfully identified hits for FO subunits a, b, and d based on previously known structures for these proteins from other species. The ASAP TGME49_310360 was identified as FO subunit a (Fig 6A), and we were also able to generate sequence alignments for the C-terminal domain of this protein, which showed the conservation of key amino acid residues arginine (important for proton translocation) and glutamine (yellow highlight in Fig 6B). Similarly, searches with TGME49_231410 and TGME49_268830 came up with hits for FO subunits b and d, respectively (S2 and S3 Figs).
From our ortholog identification attempts, and from previous studies [27,42], it was apparent that the F-type ATP synthase subunits missing from T. gondii were also missing from all other species grouped within the Alveolata infrakingdom. In fact, the novel subunit components identified from Tetrahymena (Alveolata; Ciliophora) enzyme were found to be unique to ciliates and not conserved in other alveolate organisms [8]. Therefore, we were interested in finding out whether the novel ASAPs identified in this study are unique to T. gondii F-type ATP synthase. We were able to identify orthologs for many of the ASAPs in 3 major alveolate taxons—Apicomplexa, Chromerida, and Dinoflagellata (Fig 7). A list of all orthologs identified from selected species belonging to these taxons are given in S2 Table. Out of the 20 ASAPs, 15 were conserved in all apicomplexan clades, except in the case of Cryptosporidium, in which only C. muris contained orthologs for 10 of these proteins. A few ASAPs were unique to the Coccidian clade. More importantly, all ASAPs, except one, were conserved in Chromerida, and at least 9 and 12 ASAPs were also conserved in Symbiodinium and Perkinsus, respectively.
To obtain further insights on the evolutionary origin of these proteins, we generated neighbor-joining phylogenetic trees for ortholog sequences of all F1 and FO subunits from representative species of Haemosporida, Piroplasmida, Coccidia, Cryptosporidiidae, Chromerida, Dinoflagellata, and Ciliophora (Fig 7 and S4 Fig). Stramenopile (Ectocarpus siliculosis, Phaeodactylum tricornutum, Pythium ultimum, Thalassiosira pseudonana) and Plantae (C. reinhardtii and A. thaliana) species were included as outgroups while constructing the phylogenetic trees for the highly conserved F1 subunits. Even though the topology of most trees did not reflect the expected evolutionary relationship between the included species, monophyletic grouping was observed in general at the taxon level, and importantly, this was evident for the conserved F1/FO subunits, as well as the novel ASAPs (S4 Fig). Thus, the evolutionary origin of the newly identified highly divergent ASAPs in Apicomplexa, Chromerida, and Dinoflagellata clades appears to be ancient.
Confirming the mitochondrial localization for the ASAPs is essential for validating their subunit membership in T. gondii F-type ATP synthase. We first predicted the presence of signal sequence for mitochondrial targeting using the Mitoprot tool [51] in 12 ASAPs (Fig 7). Five ASAPs, including TGME49_231410 (FO subunit b like protein), were previously shown to localize in the mitochondrion [50], and we experimentally confirmed the mitochondrial localization for another four (S5 Fig). Due to the functional importance of the F-type ATP synthase enzyme, it is reasonable to expect that the enzyme would be essential in T. gondii. This is indeed the case, and all known subunits and ASAPs (except ASAP-19 and 20; Fig 7) of the enzyme were found to be essential in a previous study [50]. In order to obtain further independent evidence for ASAPs as bona fide subunits of F-type ATP synthase, we carried out transcriptome coexpression correlation analysis using publicly available gene expression datasets for T. gondii and P. falciparum [52,53]. We assumed that the ASAPs interacting with one other would show significant pairwise correlation profiles in their expression levels. Strikingly, we found very high correlation of gene coexpression profiles among the F-type ATP synthase subunits, in comparison to coexpression with other unrelated gene pairs, in both T. gondii and P. falciparum transcriptome datasets (Fig 8). This finding further supports the fact that the novel ASAPs are indeed coexpressed together and are likely bona fide subunits of the unusual F-type ATP synthase enzyme from T. gondii.
Mitochondrial oxidative phosphorylation is an important source of ATP in most eukaryotic organisms and is facilitated by the multimeric F-type ATP synthase enzyme complex [1,2,5]. The enzyme consists of two functionally distinct F1 and FO sectors, which act in concert to convert the electrochemical energy into mechanical energy to facilitate ATP synthesis. The F1 sector α3β3 catalytic core is connected to the rotary motor formed by FO subunit c ring via the rotating central stalk structure [1,2,5]. The asymmetric structure of the γ subunit of the rotating central stalk induces conformational changes in the α3β3 catalytic core, which facilitates ATP synthesis. The stator structure—comprised primarily of the FO subunits b, d, h and OSCP, along with other accessory subunits—helps in holding the α3β3 catalytic core in place while the central stalk rotates [17]. Thus, all subunit components and structural features of this unique enzyme are critical for efficient ATP synthesis.
In addition to the core subunit composition described above, other accessory proteins participate in assembling the V-shaped dimer form of the enzyme complex [19]. The paired arrangement of the F1 complex (spaced by 12 nm) was observed in Paramecium inner mitochondrial membrane by freeze-fracture and deep-etching studies [54]. Subsequent studies on yeast, bovine, and other species confirmed the dimeric arrangement of the F-type ATP synthase complex by BNP, atomic force microscopy, and cryo-electron microscopy [19,55,56]. In fact, the dimer form of the enzyme appears to facilitate cristae formation by the inner mitochondrial membrane [21,22,24] and is implicated in maintenance of the mitochondrial membrane potential [57]. However, the identity of the proteins responsible for dimer formation has not been ascertained in most species. In case of the yeast enzyme, subunits e, g, and k were identified as dimer specific components, and studies with genetic mutants revealed that subunits e and g are essential in dimer formation [19,20]. Orthologs for these proteins are present in mammalian species as well.
BNP and size exclusion chromatography studies on purified yeast F-type ATP synthase revealed that the average molecular size of the dimer and monomer form of the enzyme is around 1 MDa and between 500–600 kDa, respectively [19]. Given the complex structure and assembly of the enzyme, the expected number of protein subunits in an intact dimer is around 20, based on yeast and mammalian enzyme compositions [10,11]. Interestingly, while the F1 subunits are well conserved across various taxons, the FO subunits, except c and OSCP, appear to be poorly conserved and in many instances are not readily identified from sequence. This is especially true for a variety of unicellular eukaryotes, including many free-living and parasitic protists, as evident from Kyoto Encyclopedia of Genes and Genomes (KEGG) data (kegg.jp). Comparative genomics studies revealed that orthologs for all yeast and mammalian FO subunits, except c and OSCP, are missing in the entire apicomplexan phylum [25–27]. In fact, along with Apicomplexa, this appears to be the case for all other taxons belonging to the Alveolata infrakingdom. A study on the subunit composition for F-type ATP synthase from the ciliate T. thermophila, an alveolate species, resulted in the identification of 13 novel proteins [8]. Surprisingly, these proteins were unique to ciliates, indicating the possible existence of a unique set of FO components in other alveolate species as well.
The P. falciparum enzyme was identified in dimer form with a molecular size of >1 MDa, confirming the presence of novel FO subunits [26]. This study also revealed that the enzyme is essential in P. falciparum. However, in the case of Plasmodium berghei, F1 subunit β was found to be essential for development in mosquito but not for survival of blood stage parasites in mammalian host [58]. Studies with metabolic mutants in T. gondii have revealed that mitochondrial oxidative phosphorylation is an important source of ATP, especially during glucose deprivation and in the absence of a glycolytic flux [34,35]. Mitochondrial ATP synthesis is also likely essential for formation and maintenance of tissue cyst forms of T. gondii in infected hosts. Despite the importance of F-type ATP synthase in apicomplexan parasites, not much is known about its subunit composition, structure, and function in these parasites.
In this study, we have successfully identified the subunit constituents of F-type ATP synthase from T. gondii, a model apicomplexan parasite. Intact monomer and dimer forms of the enzyme can be identified by BNP analysis from detergent-solubilized parasite mitochondria preparations. We have identified 20 novel proteins (ASAPs) as subunit constituents of the T. gondii F-type ATP synthase by LC-MS/MS analysis of partially purified enzyme using BNP, IP, and chromatographic techniques. While some of these proteins could be counterparts of the missing FO subunits, others are most likely accessory proteins required for dimer formation. Importantly, we were able to identify putative FO subunits a (TGME49_310360), b (TGME49_231410), and d (TGME49_268830) based on conserved structural features. Although the FO subunit a from yeast is known to have 6 transmembrane domains, the T. gondii protein has only 3. Nevertheless, the critical arginine residue, which is important for proton translocation [14], appears to be conserved (Fig 6B). The hallmark feature of FO subunit b from yeast and other species is the presence of an extended helix that spans the distance between the membrane and the α3β3 catalytic core. Although there was very poor sequence conservation, very high structural similarity (>97% probability in HHPred assignment) can be found between yeast and T. gondii FO subunit b proteins. FO subunit d has no transmembrane domain and is known to interact with FO subunit b via parallel/antiparallel coiled coil helical domains [2]. The putative T. gondii FO subunit d was highly similar in secondary structure features over the conserved regions with the bovine enzyme. Further follow up work on the 3D structure of the enzyme is needed to verify these findings.
All F1 subunits and FO c and OSCP subunits, along with 18 ASAPs, were found to be essential in T. gondii in a genome-wide clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated 9 (Cas9)-mediated gene-knockout screen [50] (Fig 7). In the same study, 5 ASAPs were localized to the mitochondrion. We interrogated the mitochondrial localization for all subunits of T. gondii F-type ATP synthase by first considering evidence from in silico prediction of mitochondrial localization signals, followed by experimental localization of selected ASAPs (Fig 7). Further, we found that the transcript coexpression for the subunits of F-type ATP synthase was highly correlated, independently in both T. gondii and P. falciparum transcriptome datasets (Fig 8), thus providing additional evidence for functional interaction between these proteins across apicomplexan parasite taxons. In summary, evidence from gene essentiality, subcellular localization, and transcript coexpression together provide independent levels of support for our identification of ASAPs as bona fide subunits of T. gondii F-type ATP synthase. Moreover, an independent and parallel study on the subunit composition of T. gondii F-type ATP synthase [59] identified 13 novel proteins, 10 of which are among the ASAPs reported here, thus further authenticating their association with the enzyme.
As can be expected for such an important protein complex playing a fundamental biological role, phylogenetic analysis revealed that most ASAPs identified from T. gondii are conserved across the phylum Apicomplexa. This suggests that the origin of a divergent F-type ATP synthase may predate the origin of apicomplexan parasites. In support of this conclusion, we find that many of the apicomplexan ASAPs are also conserved in two other alveolate phyla, Chromerida and Dinoflagellata. This scenario is intriguing from an evolutionary perspective, since the ciliates, which are basal alveolates, have a completely different and unique set of ASAPs [8].
Moreover, the mitochondrial genome- and organelle-associated metabolic functions have undergone extensive diversification and streamlining within the alveolate infrakingdom [27], with ciliates being distinct from others. For example, the ciliate T. thermophila mitochondrial genome is approximately 47 kb and encodes 45 protein-coding genes, including the F-type ATP synthase FO subunits c and a [8,60]. In contrast, the mitochondrial genomes of other alveolates—from phyla Apicomplexa, Chromerida, and Dinoflagellata—are highly reduced and encode only 3 (sometimes 2) protein-coding genes, none of which are F-type ATP synthase subunits [61–63]. This dramatic difference between the ciliates and other alveolates might signify an important bottleneck during evolutionary radiation within the alveolate infrakingdom. The origin of novel ASAPs in the common ancestor of Apicomplexa, Chromerida, and Dinoflagellata appears to have occurred following (or as a consequence of) this evolutionary bottleneck, either from de novo evolution or via lateral gene transfer.
It is striking that apicomplexan ASAPs are conserved across all plastid-bearing alveolate clades, suggesting a probable link between their origin and the ancestral red algal endosymbiont. The evolutionary history of the alveolate plastid, however, appears to be complex, with recent studies supporting serial higher order endosymbiotic events [64,65] rather than a single ancestral endosymbiotic event and subsequent vertical descent [66]. Regardless, the endosymbiont must have had its own mitochondrion and associated F-type ATP synthase, possibly constituted by the novel ASAPs. It is conceivable that, following the lateral transfer of genes from the endosymbiont genome into the host genome, the endosymbiont-derived ASAPs were targeted to the host mitochondrion and subsequently retained.
The conservation of apicomplexan ASAPs in other algal alveolates, such as the chromerids and Perkinsus, opens up the opportunity for studying the structure and function of this unusual enzyme from free-living alveolate species, from which the enzyme can be isolated in native conditions at higher yields and purity. Moreover, a deeper understanding of the structure and function of this enzyme will facilitate the discovery of novel antiparasitic compounds with pan-apicomplexan effect.
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10.1371/journal.pbio.2006613 | MEKK3 coordinates with FBW7 to regulate WDR62 stability and neurogenesis | Mutations of WD repeat domain 62 (WDR62) lead to autosomal recessive primary microcephaly (MCPH), and down-regulation of WDR62 expression causes the loss of neural progenitor cells (NPCs). However, how WDR62 is regulated and hence controls neurogenesis and brain size remains elusive. Here, we demonstrate that mitogen-activated protein kinase kinase kinase 3 (MEKK3) forms a complex with WDR62 to promote c-Jun N-terminal kinase (JNK) signaling synergistically in the control of neurogenesis. The deletion of Mekk3, Wdr62, or Jnk1 resulted in phenocopied defects, including premature NPC differentiation. We further showed that WDR62 protein is positively regulated by MEKK3 and JNK1 in the developing brain and that the defects of wdr62 deficiency can be rescued by the transgenic expression of JNK1. Meanwhile, WDR62 is also negatively regulated by T1053 phosphorylation, leading to the recruitment of F-box and WD repeat domain-containing protein 7 (FBW7) and proteasomal degradation. Our findings demonstrate that the coordinated reciprocal and bidirectional regulation among MEKK3, FBW7, WDR62, and JNK1, is required for fine-tuned JNK signaling for the control of balanced NPC self-renewal and differentiation during cortical development.
| Microcephaly is a neural developmental disorder characterized by significantly reduced brain size and variable intellectual disability. WD repeat domain 62 (WDR62) was identified as the second most common gene for autosomal recessive primary microcephaly (MCPH) in human. Here, we studied the underlying regulatory mechanism of WDR62 and the impact on generation of new neurons. We show that mitogen-activated protein kinase kinase kinase 3 (Mekk3), Wdr62, and c-Jun N-terminal kinase 1 (Jnk1) knockout (KO) mice have defects in the generation and maturation of neurons. We demonstrate that WDR62 stability is positively regulated by a mitogen-activated protein kinase kinase kinase (MAPKKK), MEKK3, but negatively regulated by the E3 ligase, F-box and WD repeat domain-containing protein 7 (FBW7). These positive and negative factors calibrate the strength of the activity of the JNK signaling pathway, which controls self-renewal and differentiation of neural progenitor cells (NPCs) during brain development. This finding improves our understanding of the molecular pathogenesis of MCPH.
| Establishment of the mammalian neocortex requires precise control of proliferation and self-renewal of neural progenitor cells (NPCs), as well as the differentiation of NPCs and neuronal migration [1–4]. Defects in these processes lead to brain disorders, including autosomal recessive primary microcephaly (MCPH) [5–8]. During cortical development, the balance between symmetric and asymmetric cell division of NPCs determines the size of the NPC pool for ongoing neurogenesis and ultimately brain size [9–16]. Disturbance in symmetric cell division leads to a reduction in the NPC pool and a falloff of neuron production [10,14,16–19].
MCPH is a neural developmental disorder characterized by significantly reduced brain size and variable intellectual disability [20,21]. Most of the 23 MCPH-associated genes (MCPH1-23) identified so far are predicted to be associated with the mitotic apparatus, such as centrosomes or mitotic spindle poles, at least during part of the cell cycle [6,22–33].
WD repeat domain 62 (WDR62) was identified as a causative gene of MCPH [22–24]. More than 50% of MCPH cases worldwide are caused by mutations in either abnormal spindle-like microcephaly-associated (ASPM) or WDR62 [7,21]. WDR62 has been reported to be a scaffold protein for the c-Jun N-terminal kinase (JNK) signaling pathway by forming a complex with MAP kinase kinases (MKKs) 4 and 7, and JNKs [34,35], similar to what we and other groups have demonstrated for the JNK pathway scaffold proteins such as Plenty of SH3s (POSH) and JNK-interacting proteins (JIPs) [36–39]. We and others have recently shown that WDR62 plays a role in NPC maintenance [40–42]. However, how WDR62 and JNK signaling are regulated for the control of neurogenesis and brain size during brain development is still not clear.
Here, we have identified 2 novel WDR62-interacting proteins: the MAP3K kinase, mitogen-activated protein kinase kinase kinase 3 (MEKK3), and the E3 ubiquitin ligase, F-box and WD repeat domain-containing protein 7 (FBW7). Using in vivo short hairpin RNA (shRNA) knockdown (KD), gene knockout (KO), and transgenic mice, we find that MEKK3, WDR62, and JNK1 play an important role in neurogenesis during cortical development. We demonstrate further that there is synergy between MEKK3 and WDR62 in the activation of JNK signaling while FBW7 negatively regulates the stability of WDR62 through specific phosphorylation of WDR62. Taken together, our findings have revealed the detailed mechanism regulating WDR62 protein levels via interaction with MEKK3 and FBW7, to control proliferation and differentiation of NPCs during brain development. Our study thus unravels a novel molecular mechanism underlying MCPH pathogenesis.
WDR62 serves as a scaffold for the JNK pathway [34,35] and is critical for the maintenance of NPCs during brain development [40]. MAP3Ks (MKKKs) are important for development and tissue homeostasis and act as central regulators of cell fate during development [43]. To identify potential WDR62 interacting proteins, especially the MKKK that acts upstream of JNK and plays a role in neurogenesis, we screened for neurogenesis-disturbing MAP3Ks with different shRNAs via in utero electroporation at embryonic day 16.5 (E16.5) in rat (Fig 1A and S1 Fig) [40,44]. We found that only MEKK3 (also named MAP3K3) depletion with 3 different shRNAs incurred defects very similar to those resulting from WDR62 KD [40], including a dramatic reduction of NPCs in the proliferative regions of the ventricular and subventricular zones (VZ and SVZ) (Fig 1A). KD of MEKK2 or MEKK4 did not disturb the distribution of cells in a similar way as WDR62 KD (S1 Fig). In addition, we have shown previously that KO or KD of another MKKK, TAK1, only affects the migration of newborn neurons [45].
MEKK3 is a serine/threonine kinase that can be activated by different signaling pathways. Previous studies showed that MEKK3 is essential for T-cell or cancer cell proliferation [46,47]. The similar defects induced by depletion of MEKK3 and WDR62 suggest that MEKK3, like WDR62, may control NPC proliferation and differentiation. To test this, we crossed the Mekk3flox/flox mice that we generated previously [46] with Nestin-Cre mice to obtain Mekk3flox/flox;Nestin-Cre conditional knockout (Mekk3 cKO) mice in which Mekk3 was deleted in the NPCs. We inspected E16.5 cortical slices with different progenitor cell markers including Pax6 and Sox2 (markers for radial glial cells or apical progenitor cells) and Tbr2 (a marker for intermediate or basal progenitor cells). The thickness of Pax6+, Sox2+, and Tbr2+ cortical layers was reduced significantly in Mekk3 cKO mice, indicating a decrease in NPCs (Fig 1B–1D). In addition, the thinner Pax6+ and Sox2+ cell layers were accompanied by broader cortical staining for Tuj1 (a marker for immature neurons) and decreased numbers of cells positive for phosphor-histone H3 (P-H3, a marker for mitotic activity), respectively (Fig 1B and 1C). Furthermore, we examined the effect of Mekk3 KO on cell-cycle exit index. Both Mekk3 cKO and their wild-type (WT) littermates were labeled at E16.5 with 5-bromo-2’-deoxyuridine (BrdU) to track cells undergoing DNA synthesis. Twenty-four hour later, Ki67 (a marker for proliferating cells) and BrdU+ cells were inspected in brain slices. We observed a substantial decrease in Ki67+ and BrdU+ cells and a significant increase in cell-cycle exit index (cells that had incorporated BrdU but were Ki67−) (Fig 1E), indicating an overall decrease in cell proliferation. Finally, we analyzed cell death in the Mekk3 cKO cortices and did not observe an apparent increase in cell death in the VZ/SVZ (S2 Fig). Taken together, these findings indicate that MEKK3 is required for NPC proliferation and differentiation during cortical development.
Because both MEKK3 and WDR62 are required for neurogenesis, we postulated that MEKK3 might interact with WDR62 to regulate JNK activity and neurogenesis. To test this hypothesis, constructs encoding MEKK3 and WDR62 were transfected into HEK293 cells individually or in combination, and reciprocal coimmunoprecipitation experiments revealed that MEKK3 interacts with WDR62 (Fig 1F). In addition, an anti-MEKK3 antiserum was able to pull down endogenous WDR62 from E14.5 mouse cortex (Fig 1G).
We went on to generate different truncation mutants of WDR62 and MEKK3 in order to identify the potential binding site in MEKK3 and WDR62 (Fig 2A). Using the glutathione-S-transferase (GST)-fused WDR62 truncation mutant (WDR62 C2, aa1018-1523), which includes the MKK and JNK binding domains [34,35], we performed in vitro GST pull-down assays and noticed that MEKK3 could bind to WDR62 C2, but not the WDR62 C1 (aa1314-1523) mutant or GST (Fig 2B). GST pull-down experiment on E14.5 mouse brain lysate was able to pull down endogenous MEKK3 (Fig 2C). These results indicate that WDR62 and MEKK3 association is direct and mapped to 1018–1314 at the WDR62 C-terminal.
Because the WDR62 MKK4/7 binding domain was mapped to aa1212-84 [35], the MEKK3 binding domain is likely to be located within aa1018-1212 on WDR62 unless there is an overlap with the MKK4/7 binding domain. Therefore, we characterized in more detail the potential MEKK3 binding motif on WDR62. As shown in Fig 2D, MEKK3 could interact with aa1018-1523, 1018–1314, and 1018–1212 of WDR62. This indicates that the binding motif for MEKK3 in WDR62 is located within aa1018-1212.
The domain structure of MEKK3 consists of a conserved kinase domain and a PB1 domain in the C- and N-terminals, respectively (Fig 2A). Through reciprocal coimmunoprecipitation analysis, we detected that WDR62 interacted with the C-terminal half of MEKK3, but not the N-terminal half of MEKK3 (Fig 2E and S3A Fig). In order to investigate whether a synergistic effect exists between WDR62 and MEKK3, as what has been shown previously for POSH and the MLK family members [36,37], WDR62 and MEKK3 were expressed either alone or in combination in 293 cells. As shown in Fig 2F, when WDR62 and MEKK3 were coexpressed, the level of JNK activity (phosphorylated form of JNK) was significantly enhanced compared to WDR62 or MEKK3 expressed alone. Interestingly, the levels of WDR62 and MEKK3 protein were also much higher when coexpressed (Fig 2F). This suggests that WDR62 and MEKK3 play a synergistic role in the activation of JNK signaling, likely by mutual stabilization of the two proteins.
To determine whether JNK1 is also involved in the regulation of WDR62 levels, downstream of MEKK3, WT JNK1 and a constitutively active form of JNK1 (CA JNK1) were expressed in 293 cells. As shown in Fig 2G, the WDR62 protein level was higher in WT JNK1-expressed cells, and even higher in CA JNK1-expressing cells compared with vector controls, in accordance with the level of JNK activity. Importantly, endogenous WDR62 protein levels were much lower in E16.5 Mekk3 cKO cortices (Fig 2H). However, KD or overexpression of MEKK3 had no significant effect on WDR62 mRNA levels (S3B and S3C Fig). Taken together, the above results indicate that MEKK3 and JNK1 regulate WDR62 expression at the post-transcriptional level.
Jnk1 and Jnk2 double-deficient mouse embryos develop exencephaly and die around E11–12 [48]. Jnk1 KO induced pluripotent stem cells (iPSCs) are impaired in their ability to develop into neural precursors in vitro [49]. We have shown previously that JNK1 KD and WDR62 KD cause similar defects during cortical development [40]. We therefore generated a Wdr62 null mutant [50] and investigated further the relationship between WDR62 and JNK activity during brain development. As shown in Fig 3A, the levels of JNK activity were significantly reduced in the Wdr62 mutant cortex as indicated by immunostaining and western blotting, which is consistent with previous findings in different systems [50,51]. We went on to inspect whether KO of Jnk1 would result in similar defects as KO of Wdr62 during cortical development. As observed in Wdr62 mutant mice (Fig 3B), Jnk1 KO brains at E16 also showed enlarged ventricles and a thinner cortex, especially in the VZ/SVZ (Fig 3C). In addition, we observed a significant decrease in Ki67+ cells and an increase in cell-cycle exit index in Jnk1 KO cortices (Fig 3D).
Because WDR62 regulates JNK activity, we postulated that WDR62 might regulate NPC proliferation and differentiation through JNK1. To test this hypothesis, we investigated whether the defects in Wdr62 mutants can be rescued by JNK1. We first generated conditional transgenic mice expressing CA JNK1 (S4 Fig). The transgenic mice were crossed with Nestin-Cre mice in order to express CA JNK1 in NPCs (JNK1 cTg, hereafter). As observed in cells, CA JNK1 expression increased JNK activity and WDR62 protein level in the cortex (S5A and S5B Fig). We next examined the effect of JNK1 activation on NPC development through BrdU labeling. As shown in Fig 3E, JNK1 cTg cortices showed a significant increase in BrdU+ and Ki67+ cells, while the cell-cycle exit index was comparable between JNK1 cTg and their WT littermates.
Because Wdr62 mutants were sterile [50], we used brain-specific Wdr62flox/flox;Nestin-Cre mice (Wdr62 cKO) for further investigations. Similar to our Wdr62 mutants, Wdr62 cKOs showed reduced brain weight, enlarged ventricles, and a thinner cortex (Fig 4A–4C and S5C and S5D Fig). By crossing Wdr62 cKO with JNK1 cTg mice, we were able to generate Wdr62 cKO, JNK1 cTg, and Wdr62 cKO;JNK1 cTg genotypes. Compared with WT littermates, Wdr62 cKO, but not JNK1 cTg, mice had decreased brain weight at P12. The brain weight of Wdr62 cKO;JNK1 cTg mice was increased compared with Wdr62 cKO mice, and comparable to that of controls (Fig 4B). This indicates that Wdr62 cKO-induced microcephaly can be rescued by increased JNK1 activity. Similarly, JNK1 cTg also rescued the reduced cortex thickness and enlarged lateral ventricle phenotypes in Wdr62 cKO mice (Fig 4C and S5D Fig).
Because Wdr62 deficiency leads to defects in NPC proliferation and differentiation, we investigated whether those defects could be rescued in Wdr62 cKO;JNK1 cTg mice. As shown in Fig 4D, the number of Pax6+ cells was significantly reduced in Wdr62 cKOs and significantly increased in JNK1 cTgs, while Wdr62 cKO;JNK1 cTg double mutants were comparable to controls. Moreover, we observed a significant increase in cell-cycle exit in Wdr62 cKOs but not in Wdr62 cKO;JNK1 cTg cortices (Fig 4E). Taken together, these findings indicate that WDR62 regulates NPC proliferation and differentiation through JNK1.
WDR62 and MEKK3 play a synergistic role in the activation of JNK. However, under physiological conditions, a negative regulatory mechanism likely exists to prevent cell death incurred by sustained JNK activation. We noticed that a JNK1-target phosphorylation site in WDR62, T1053 [52], is located within LPQTPEQE, a potential binding motif for the E3 ubiquitin ligase substrate recognition component FBW7. FBW7 plays an opposite role to WDR62 during brain development, promoting rather than antagonizing NPC differentiation [53,54]. This led us to postulate that FBW7 may interact with WDR62 to regulate WDR62 protein stability through the proteasomal pathway. WDR62 was transfected into HEK293 cells either alone or together with the 3 different isoforms of FBW7, FBW7α, β, and γ. Interestingly, WDR62 protein levels appeared lower when coexpressed with FBW7 α or γ in particular, and the reduction could be significantly blocked by MG132 (Fig 5A). We therefore performed a coimmunoprecipitation analysis and detected an interaction of WDR62 with FBW7α and γ but not FBW7β (Fig 5B). In addition, FBW7α interacted with the C-terminal half of WDR62 (WD40Δ) but not the N-terminal half of WDR62 that consists primarily of WD40 domains (Fig 5C). To examine whether FBW7 possesses E3 ligase activity towards WDR62, we assessed WDR62 ubiquitination both in vivo and in vitro. Both FBW7α expression in cells (Fig 5D) and purified FBW7α in an in vitro assay (Fig 5E) induced the ubiquitination of WDR62. To rule out the possibility that the smeared ubiquitin signals were from WDR62-associated proteins, we performed the immunoprecipitation of influenza hemagglutinin (HA)-tagged WDR62, blotted for WDR62, and detected significantly increased upper smear signal when coexpressed with FBW7α and γ but not FBW7β (Fig 5F). Reciprocal immunoprecipitation for HA-ub also pulled out more WDR62 in FBW7α overexpressed cells (Fig 5G). Importantly, WDR62 protein levels were significantly higher in E14.5, E15.5, and E17.5 Fbw7 cKO brains (Fig 5H and S6A and S6B Fig). However, deficiency of FBW7 had no significant effect on WDR62 mRNA level (S6B Fig). These results indicate that Fbw7 negatively regulates WDR62 protein stability through the proteasomal pathway.
Previous study indicates that FBW7 controls neural stem cell differentiation in midbrain [54]. We inspected the distribution of cells in cortex electroporated with Ctrl shRNA, Fbw7 shRNA, Wdr62 shRNA, and Wdr62 shRNA with Fbw7 shRNA. FBW7 KD led to reduced percentage of cells in the SVZ, intermediate zone (IZ), and cortical plate (CP), while WDR62 KD had the opposite phenotype. The phenotype was partially neutralized when Fbw7 shRNA and Wdr62 shRNA were cotransfected together (S6C and S6D Fig). This further supports the notion that WDR62 and FBW7 plays an opposite role in NPC development.
We went on to perform a cycloheximide (CHX) time course analysis and found that WDR62 was degraded more rapidly when coexpressed with FBW7α (Fig 6A). We also utilized a WDR62 mutant (PM6, L1299A/L1301A) that is unable to bind to and activate JNKs [55], and compared with WT WDR62, the PM6 mutant was very stable even when coexpressed with FBW7α (Fig 6A), suggesting that the interaction between JNK and WDR62 is important for the destabilization of WDR62.
Because JNK1 can induce the phosphorylation of WDR62 T1053 [52], we investigated the role of this modification in WDR62 stability. When WDR62 T1053 was mutated to alanine, its interaction with FBW7 decreased significantly compared with that of WT WDR62 (Fig 6B). We went on to make a phosphomimetic mutant, WDR62 T1053D, and found that it was less stable than WT WDR62, while WDR62 T1053A was more stable when cotransfected with FBW7α (Fig 6C and S7 Fig). We further examined the ubiquitination of WDR62 mutants induced by FBW7. When coexpressed with FBW7, an increased ubiquitination of WT WDR62 but not WDR62 T1053A was detected (Fig 7A and 7B). Consistently, the ubiquitination level of WDR62 T1053D was significantly higher than that of WT WDR62. WDR62 T1053A seemed to be more stable than WT WDR62 and could induce JNK activity more significantly when coexpressed with MEKK3 (Fig 7C).
JNK activity has been shown to increase during G2 and M phases of the cell cycle and decline after exiting M phase [56]. We arrested HeLa cells in anaphase with nocodazole and then released the cell-cycle arrest. As shown in Fig 7D, JNK activity declined after release from nocodazole arrest, as did the phosphorylation of endogenous WDR62 at T1053. Meanwhile, the level of WDR62 protein increased correspondingly. Taken together, our results indicate that the phosphorylation of WDR62 at T1053 by JNK is critical for its interaction with FBW7 and subsequent ubiquitination and degradation.
Several MCPH proteins (ASPM, WDR62, CDK5RAP2, CEP63, CEP135, CEP152, CPAP, MCPH1, and STIL) have been shown to play a role in neurogenesis [7,57–60]. Our study reveals the mechanisms that regulate the stability of MCPH-associated protein WDR62 and NPC proliferation and differentiation during brain development. Specifically, we demonstrate that MEKK3 interacts with WDR62 to stabilize WDR62 and regulates JNK activity in a synergic way. On the other hand, JNK activity also regulates the phosphorylation of WDR62 at T1053 in a feedback loop which facilities the recruitment of FBW7 degradation of WDR62 (Fig 8). In addition, KO of MEKK3 or JNK1 phenocopies WDR62 KO in the dysregulation of NPC development. Transgenic expression of JNK1 can rescue the defects of WDR62, indicating a critical role of JNK signaling pathway in cell fate determination and NPC maintanence.
Through a functional screen, we have found that MEKK3 KD induces very similar phenotypes as WDR62 KD [40], such as NPC depletion in the embryonic neocortex, suggesting defects in the maintenance of NPC proliferation and the occurrence of premature differentiation. This notion is supported by the significant decrease in cycling cells (Ki67+ and P-H3+) and an increase in cell-cycle exit index in the Mekk3 cKO embryonic neocortex. Meanwhile, we observed a considerable reduction in Pax6-, Sox2-, and Tbr2-positive NPCs accompanied by an increase in Tuj1-positive immature neurons in these mice. Thus, our findings indicate a role for MEKK3 in the proliferation and differentiation of NPC during neurogenesis.
The similar function of MEKK3 and WDR62 led us to explore their relationship and confirm their interaction in the embryonic brain. Interestingly, WDR62 and MEKK3 are likely to play a synergistic role in the activation of JNK signaling as well as in the elevation of each other’s protein levels. In addition, expression of JNK1 elevated WDR62 levels, while endogenous levels of WDR62 were much lower in Mekk3 cKO cortices. Therefore, we can postulate that MEKK3 regulates the protein level of WDR62 through JNK signaling.
Previous studies have shown that depletion or mutation of several MCPH proteins leads to the premature cell-cycle exit of NPCs and consequently to premature neuronal differentiation or cell death during neurogenesis [42,57,61,62]. Several MCPH proteins such as MCPH1 and ASPM have been shown to regulate the Chk1-Cdc25b and Wnt signaling pathways respectively to control brain size [62,63]. Our studies indicate that the JNK signaling plays a critical role in the normal function of WDR62. First, Jnk1 KO mice have phenotypes very similar to our Wdr62 KO and cKO mice, including premature differentiation of NPCs, enlarged lateral ventricles, and thinner cortices during cortical development. These defects in Wdr62 cKO mice can be largely rescued by the transgenic expression of CA-JNK1. In addition, mice with deletion of kinases upstream of JNK1 have phenotypes somewhat similar to Wdr62 and Jnk1 mutants. For example, brain-specific Mkk7 or Mkk4 KO mice display either enlarged embryonic brain ventricles or reduced brain size [64]. Taken together, all these studies imply that WDR62 cooperates with MEKK3, MKKs, and JNK1 in the regulation of brain development.
The E3 ligase FBW7 is important for normal brain development, and KO of Fbw7 inhibits NPC differentiation [54], the opposite defect of that caused by Wdr62 KO. We have confirmed the interaction between WDR62 and FBW7. Interestingly, WDR62 T1053, which is phosphorylated by JNK1 and localizes within the FBW7 binding motif of WDR62, is important for the interaction between WDR62 and FBW7. In addition, phosphorylation of T1053 is crucial for the regulation of WDR62 stability by FBW7, through ubiquitination and degradation of WDR62.
Previous studies have shown that WDR62 protein level is cell-cycle dependent [22,52]. However, the underlying mechanism is unknown. Two mutants—WDR62 L1299A/L1301A, which cannot bind to JNK, and WDR62 T1053A—are more stable than WT WDR62, indicating the involvement of JNK signaling in the regulation WDR62 expression. JNK activity increases during G2 and M phases of cell cycle [56]. Intriguingly, the level of WDR62 phosphorylated at T1053 declines after cells are released from nocodazole arrest (M anaphase). This is accompanied by the decline in JNK activity and the elevation of WDR62 level, indicating that phosphorylation of T1053 is negatively correlated with WDR62 stability. We would like to propose a model (Fig 8) that JNK activation at G2/M phase leads to WDR62 phosphorylation at T1053, which will recruit FBW7 to induce the ubiquitination and degradation of WDR62. As the cell cycle progresses, JNK activity declines, and newly synthesized WDR62 will accumulate. Through the interaction with MEKK3, WDR62 is stabilized and promotes activation of JNK at G2/M phase. Thus, JNK-induced phosphorylation of T1053 is also likely to play a critical role in recruiting FBW7 and the degradation of WDR62 during cell-cycle progression. How MEKK3 and JNK1 stabilize WDR62 and activate JNK needs to be explored in the future.
Taken together, our results support a model in which the scaffold protein WDR62 organizes a protein complex that includes MEKK3, MKKs, and JNK1 to control the proliferation and differentiation of NPCs during corticogenesis (Fig 8). The expression of WDR62 is fine-tuned both positively by MEKK3 and JNK activity and negatively by JNK-induced phosphorylation of WDR62 at T1053. Thus, the coordinated reciprocal and bidirectional regulation among WDR62, MEKK3, JNK1, and FBW7 fine-tunes JNK signaling to control the balance between proliferation and differentiation of NPCs and prevent superfluous cell death incurred by sustained JNK activation during brain development.
All animal procedures used in this study were performed according to protocols approved by the Institutional Animal Care and Use Committee at the Institute of Genetics and Developmental Biology (IGDB), Chinese Academy of Sciences (CAS) (protocol number: AP2016053).
The antibodies used for western blotting (for human) were as follows: GFP (abcam, ab290, 1:2,000), α-tubulin (CST, 3873s, 1:2,000), GAPDH (CST, 2118s, 1:2,000), Flag (MBL, M185, 1:2,000), Myc (MBL, M047-3, 1:5,000), HA (MBL, M180-3, 1:5,000), Phospho-JNK (CST, 9255, 1:1,000), and WDR62 (bethyl, A310-550A, 1:1,000). For mouse, they were as follows: WDR62 (abcam, 1:1,000), WDR62 antibody generated by MBL company using antigen VGQGGNQPKAGPLRAGTC, Phospho-WDR62 1053T (present from Dominic) [52], and MEKK3 (CST, 5727, 1:1,000). The antibodies used for immunostaining were Sox2 (abcam, ab97959, 1:1,000), Pax6 (Covance, PRB-278P, 1:400), Tbr2 (Millipore, ab2283, 1:1,000), β-III Tubulin/Tuj1 (abcam, ab7751, 1:1,000), γ-Tubulin (abcam, ab11316, 1:1,000), α-Tubulin (CST, 3873, 1:2,000), Phosph-Histone 3 (P-H3) (abcam, ab10543, 1:1,000), Nestin (abcam, ab6142, 1:1,000), GFP (abcam, ab13970, 1:1,000), Ki67 (abcam, ab15580, 1:1,000), BrdU (abcam, ab6326, 1:500), Phospho-JNK (abcam, ab124956, 1:1,000), activated-caspase3 (abcam, ab13847, 1:1,000). Nuclei were stained with DAPI (4’,6-diamidino-2-phenylindole) (Invitrogen).
Human WDR62 WT, WD40 and WD40Δ truncations, or WDR62 mutants were created by cloning WDR62 cDNA sequence into the pCMS.EGFP (modified) (Flag) or pCDNA3.1-HA vector. Full-length MEKK3, MEKK3 N- and MEKK3 C-terminal truncations were cloned into the pCMV-Tag2B vector. Full-length MEKK3 was also cloned into the pCMS.EGFP (modified) (Flag) or pCDNA3.1-HA vector. For GST pull-down assay, full-length WDR62, WDR62 C2 (1018-1523aa), or WDR62 C1 (1314-1523aa) were cloned into pGEX6p1. Flag-FBW7α, β, and γ in pCDNA3.1 were kindly provided by Dr. Clurman [65]. All constructs were verified by sequencing.
For cryosections, tissues were fixed in 4% PFA, cryoprotected in 30% sucrose, and frozen in tissue freezing medium (TFM). Sections (thickness of 20–50 μm) were used for immunofluorescence staining. Immunofluorescence staining was carried out essentially as described previously [44,67].
For single-pulse BrdU labeling, pregnant mice at defined pregnancy stages were injected intraperitoneally with 50 mg/g body weight of BrdU (Sigma-Aldrich) and were euthanized 12 to 24 hours after injection.
HEK293 cell culture, transfection, immunoprecipitation, and western blotting were performed as previously described [36]. Plasmids were transfected into HEK293 cells with VigoFect (VIGOROUS). For western blot of Figs 5F, 5G and 7B and S7 Fig, cells were treated with 20 μm MG132 for 4 hours before lyses (MG132 added). Densitometric analysis was performed using Image J software. The relative Integrated Density of western blot band was measured.
In utero electroporation was performed as previously described [67]. Pregnant Sprague Dawley rats were provided by the animal center of IGDB. Rat Mekk3 shRNA vector containing the following target sequences was used: shM1: 5’-GCCTTAGGATACTACTGTTA-3’; shM2: 5’-GCAGCAACATGATTGTGCA-3’; and shM3, 5’-GATCACAAAGACTACAATGA-3’. The human MEKK3 shRNA target sequence was 5’-GCAGAGTGACGTCAGAATC-3’. The rat Mekk2 shRNA target sequence was as follows: shRNA1: 5’-GAGCGAATTGTTCAGTATTA-3’; shRNA2: 5’-GAAGCAATGGCTGCCATCT-3’; and shRNA3: 5’-GCTGGATCCATTGTCTTTA-3’. The rat Mekk4 shRNA target sequence was as follows: shRNA1: 5’-GAGGAAGCTGGATCCAAATG-3’; shRNA2: 5’-GAGTATCATAAAGAAGTTG-3’; and shRNA3: 5’-GCCTTTATTTCAGCTTTAC-3’. The rat Fbw7 shRNA target sequence was 5’-CCTTCTCTGGAGAGAGAAA-3’.
Sections were imaged on an LSM 700 (Carl Zeiss) confocal microscope as described [40]. Cell counts were analyzed with Imaris X64 or ImageJ. All data were analyzed using Excel and Prism software (Graph Pad Software, La Jolla, CA). Tests used were unpaired t test or one-way ANOVA paired with Tukey post-test.
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10.1371/journal.pcbi.1003592 | Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning | Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/.
| Many genetic diseases in humans and animals are caused by combinations of single-letter mutations, or SNPs. When these mutations occur in a protein-coding region of a genome, they can have a profound effect on the protein's function and ultimately on a health-related phenotype. Recently, a growing number of evidence suggests that many of SNPs reside on or near the protein regions that are required for the interactions with other proteins. Some of these SNPs could rewire the protein-protein interactions altering the functions of the protein interaction complexes, while other SNPs are neutral to the interactions. Understanding the effect of SNPs on the protein-protein interactions is a challenging problem to solve, both experimentally and computationally. Here, we leverage the machine learning methods by training a computational predictor to tell apart the mutations that are harmful to protein-protein interactions from those ones that are not. We use these tools in two case studies of mutations affecting the protein-protein interaction networks centered around the genes associated with breast cancer and diabetes.
| Being one of the most prevalent types of genetic variation in humans, single nucleotide polymorphisms (SNPs) occur in both coding and non-coding regions of the genome and have been associated with a number of Mendelian diseases and complex genetic disorders [1], [2]. With the rapid advancement of DNA sequencing and genotyping technology, millions of SNPs have been determined [3], [4]. An average gene is estimated to have several non-synonymous missense SNPs (nsSNPs), each substituting an amino acid residue [5]. Nevertheless, our knowledge of SNPs that cause a disease is very limited. Understanding whether or not a mutation or a group of mutations induce changes of a molecular function is often the first step towards finding the missing link between the genetic variation and the disease.
Recent studies of disease networks have linked many nsSNPs with protein-protein interactions [6], [7]. Understanding how these mutations can rewire the interaction network mediated by proteins associated with the disease is critical in studying complex genetic disorders, such as cancer, autism, and diabetes [8]–[10]. Unfortunately, the interaction landscape determined by the genetic variants of the disease-associated genes is far from being fully reconstructed. Thus, computational methods can play an important role in modeling nsSNP-induced rewiring of a disease network.
The growing interest in understanding the relationship between a genetic variation and its functional effect on a protein has lead to a number of recent in-silico methods. A group of methods introduced the idea of computational mutagenesis to study the structure-function relationship [11], predict the changes in enzyme activity [12], [13], detect disease potential of a SNP [14], and characterize other functional effects [15]. Most recently, a number of computational alanine scanning methods were developed to study protein-protein interactions (PPIs) and protein-peptide interactions [16]. These methods aimed at finding residues in the interaction interface that would disrupt the interaction when mutated to alanine; they did it by estimating the relative free energy change (ΔΔG) between the wild-type and mutant PPI complexes. Another group of methods focused on predicting the effects of general nsSNPs on protein function and distinguishing them from functionally neutral mutations [17]–[30]. Finally, several works studied the effects of disease-associated nsSNPs on protein-protein interactions by investigating the changes in binding energy using force field and electrostatic calculations [31], [32] and understanding the structural effects caused by nsSNPs that lead to the disruption of PPI [6], [33]. However, in spite of the tremendous progress, developing an accurate approach that predicts the effect of an nsSNP on the protein function, including protein-protein interaction, remains an open problem.
The goal of this paper is to introduce a novel computational approach for the characterization of effects on PPIs caused by nsSNPs (nsSNP-induced effects). The idea of our approach is to consider prediction of such effects as a classification problem. Specifically, we defined three related classification problems that differ in the available input information and the types of nsSNP-induced effects to be identified and characterized. Leveraging the machine learning methodology, we formulated each of the three problems as the supervised and semi-supervised learning tasks. The comparative assessment of the independently built classifiers using a variety of the supervised and semi-supervised methods has demonstrated feasibility of the machine learning approach in addressing each of the above problems.
The problem of determining whether an nsSNP within a gene has any effect on a PPI mediated by the gene product is broken down into three related classification problems (Fig. 1). In the first problem, we assume that it is known that an nsSNP affects a biochemical function mediated by a PPI. Such a functional change may be a result of the nsSNP disrupting the interaction or, on the contrary, significantly increasing the binding affinity, which may cause for a transient complex to become permanent. Therefore, our goal in the first problem is to determine whether the nsSNP has a strengthening or weakening effect on the PPI. The second problem is to determine whether an nsSNP is likely to disrupt or preserve a PPI, without any prior knowledge on changes in the biochemical function mediated by the interaction. Finally, the third, most challenging, problem is to predict whether an nsSNP has one of three effects on a PPI, detrimental, neutral, or beneficial, again without any prior knowledge of the functional changes associated with the PPI Thus, the first and second problems are formulated as 2-class problems, and the third one as a 3-class problem.
For each problem, supervised and semi-supervised approaches are developed and assessed, and their performances are compared. The top classifiers are then integrated into a computational tool called the SNP-IN (non-synonymous SNP INteraction effect predictor) tool. The overall protocol of the training stage includes four steps (Fig. 2). First, the data on nsSNPs are collected, and each nsSNP is assigned to a class by comparing the difference of binding affinity between the mutant and wild-type protein-protein interactions. Second, the unlabeled data are obtained by generating a complementary set of all other possible mutations different from the wild-type residue and its mutations analyzed as in the first step. These mutations are generated for each residue from the interaction interface of the PPI being analyzed. Third, for each nsSNP, a feature vector is generated. Last, a set of supervised and semi-supervised classifiers are trained and evaluated; for each classification problem a single classifier is selected. During the prediction stage, the same set of features for a novel nsSNP is calculated, and the feature vector is used to classify the nsSNP.
Comprehensive analysis of the mutation effects on PPIs on a large scale by experiments is a difficult task. As a result, while several datasets have been used by the computational methods [34]–[36], no golden standard currently exists. Here, we use one of the largest such datasets, SKEMPI [35], which includes mutations on structurally-defined heterodimeric complexes that were experimentally characterized and extracted and manually curated from the literature. For each mutation, the database provides the changes in thermodynamic parameters and kinetic rate constants between the wild-type and mutant PPIs. From the initially collected set of 3,047 mutations occurring in 158 heterodimeric complexes, we keep 2,795 mutations after removing the redundancy, where the redundant mutations are defined as the same mutations obtained from different references. Finally, since in this work we focus on the effects caused by a single nsSNP, we filter out from the sets those entries that include multiple mutations, resulting in the final dataset of 2,079 single SNPs and 151 corresponding protein complexes (This training dataset is available for download at SNP-IN tool website: http://korkinlab.org/snpintool).
Next, each mutation is characterized as one of three interaction-associated types: beneficial, neutral, or detrimental. The types are assigned based on the difference, , between the binding free energies of the mutant and wild-type complexes. Specifically, we calculate , where and are the mutant and wild-type binding free energies, correspondingly. Each energy value is calculated as , where is the gas constant, is temperature, and is the known binding affinity. For our dataset, is obtained from the SKEMPI dataset at http://life.bsc.es/pid/mutation_database/datatable.html (column 7 for the mutant and column 8 for the wild-type). This value can also be calculated by , where , can also be found at the same link above. The beneficial, neutral, or detrimental types of mutations are then determined by applying two previously established thresholds to [35], [37], [38]:
Intuitively, a neutral mutation will not change the interaction's properties, whereas the beneficial mutation will significantly increase the binding affinity, and the detrimental mutation is expected to disrupt the associated PPI. Using these three mutation types, the labeled dataset for each supervised and semi-supervised classifier is formed (see subsection Training and evaluation of supervised and semi-supervised classifiers in Methods). We note that these mutation types are introduced to characterize the effect on a protein-protein interaction rather than the biological function associated with the interaction. For instance, an nsSNP that has a beneficial effect on protein-protein interaction may have a detrimental functional effect by transforming a transient complex to a permanent one.
Finally, the dataset of unlabeled mutations is generated for the semi-supervised learning classifiers. Specifically, for each of the 2,079 mutations, all other 18 possible mutations, excluding the original mutant and wild-type residues, are introduced at the same location in the corresponding complex as the original nsSNP. For these mutations, no values are available, thus they cannot be assigned a specific interaction-associated type. The final set includes 17,692 mutations (mutations for which some of the software packages failed to generate the features are excluded).
Each nsSNP in the labeled and unlabeled sets is represented as a 33-dimensional feature vector. To calculate the set of features, we first model the structure of the mutant PPI complex using FoldX [39], [40] and using the structure of the wild-type complex as a modeling template. Next, for each nsSNP a set of features is calculated for the modeled mutant complex as well as the wild-type native structure, and the difference of these features is included into the final feature vector.
Several software packages are used to generate the features (Table 1) [39], [41]–[45]. The first group consists of 22 energy terms calculated in FoldX: Total energy, Backbone Hbond, Sidechain Hbond, Van der Waals, Electrostatics, Solvation Polar, Solvation Hydrophobic, Van der Waals clashes, entropy sidechain, entropy mainchain, sloop_entropy, mloop_entropy, cis_bond, torsional clash, backbone clash, helix dipole, water bridge, disulfide, electrostatic kon, partial covalent bonds, Energy Ionisation, Entropy Complex [39]. The second group of three features includes energy terms (OPUS-PSP terms 1–3) calculated in OPUS-PSP [44]. Accessible surface area of the mutant amino acid residue is computed by NACCASS [41], as a descriptor to measure the changes on solvent accessibility during this mutation. The next feature, Interaction energy, is defined as the sum of interaction energies of the protein chain carrying the mutation against all other chains in the complex. Interaction energy for each pair of chains is also calculated in FoldX. The remaining features include three energy terms (Goap terms 1–3) from software Goap [45], Geometric score from Geometric tool [42], energy term from Dfire2 [46], and Decomplex energy score [43].
Two supervised and two semi-supervised approaches are implemented and compared. The supervised learning methods include Support Vector Machines (SVM) and Random Forrest (RF) classifiers, which have been consistently among the top performing methods for a number of bioinformatics tasks [47]–[49]. Random Forests have been shown to outperform other feature-based supervised learning approaches in bioinformatics and other domains [50]–[53], although in some cases they perform worse than SVM methods [48], [54]. The SVM approach, in addition to being among most widely used supervised learning methods in bioinformatics, lies in the core of the top performing semi-supervised learning algorithm [55]. For SVM, we assessed three popular kernels: (i) linear, (ii) polynomial kernel, , where d is degree of the polynomial, and (iii) radial basis function (RBF), . The polynomial kernel is then selected with d = 3 as the most accurate one, as it has the highest f-measure value. SVM models are implemented using the libSVM package [56] and the RF classifier is implemented in Weka software [57].
Semi-supervised learning has been only recently introduced to the field of bioinformatics [49], [58]–[61]. The basic idea is to rely not only on the labeled training data, but also to incorporate an additional, unlabeled, dataset (often of a significantly larger size) as a part of training to improve learning accuracy. We first apply semi-supervised learning by low density separation (LDS) [55], which is considered one of the most accurate semi-supervised methods [62]. The LDS approach relies on clustering to guide the unlabeled dataset by combining (i) graph-based distances that emphasize low density regions between clusters and (ii) optimization of the Transductive SVM objective function [63] which places the decision boundary in low density regions using gradient descent. Specifically, a nearest-neighbor graph G = (V,E) is first derived for both labeled and unlabeled feature vectors. Then a modified connectivity kernel is computed, defined as follows:where p is a path of length |p| from the set Pi,j of all paths connecting two feature vectors xi and xj, and is a parameterized ρ-path distance defined between the set of all labeled vectors on one hand and set of all vectors on the other hand. The computed kernel is then used to train an SVM in the supervised part of the algorithm [55].
Based on assessment of the supervised methods (see Leave-one-out cross validations subsection), the RF classifier shows superior performance over the SVM classifiers. Thus, we would like to further improve the accuracy of this approach, by developing a simple RF-based semi-supervised learning protocol that leverages self-learning heuristics [64]. First the protocol trains a supervised learning RF classifier. Next, this classifier is applied to the unlabeled dataset and assigns each unlabeled nsSNP to one of the classes. The newly labeled dataset is merged with the originally labeled datasets. Finally, the resulting labeled datasets are used to re-train the supervised RF method. We note that while several RF-based semi-supervised based methods have been recently introduced in pattern recognition and computer vision [65], [66], to the best of our knowledge, no RF-based semi-supervised method has been applied in a bioinformatics area.
Finally, to further improve the performance on the most difficult 3-class problem, we explore whether the classifier of the 3–class problem can benefit from the other two classifiers addressing one of the 2-class problems. Specifically, for the most accurate classifier of Problem 3 (selected based on the weighted f-measure), we calculate two additional features: the prediction results from the most accurate binary classifiers for Problems 1 and 2. To obtain these features, we use each of the two binary classifiers to generate the prediction value if it is a positive prediction, or one minus prediction value if it is a negative prediction and scale the value to be from 0 to 1.
The labeled set for a supervised classifier addressing the first 2-class problem includes mutations determined as beneficial as the first class (strengthening PPI) and mutations determined as detrimental as the second class (weakening PPI). Another labeled set corresponding to the second 2-class problem includes both beneficial and neutral mutations as the first class (preserving PPI), and detrimental mutations as the second class (disrupting PPI). Mutations in the final labeled set corresponding to the 3-class problem are naturally grouped into beneficial, neutral, and detrimental classes. For each semi-supervised classifier, we use the same labeled data as in the corresponding supervised classifier and the previously described unlabeled set of 17,692 nsSNPs (Table 2).
To evaluate all supervised and semi-supervised classifiers for each of the three classification problems, three assessment protocols were implemented. The first protocol was a standard leave-one-out (LOO) cross-validation protocol with the goal to compare the methods and select the most accurate classifier for each problem by utilizing each of the labeled datasets for the corresponding problem in both supervised and semi-supervised cases. For each problem, the class-based recall, precision and f-measures are calculated for each class. Next, overall performance of a classifier on the classification problem is assessed by the average accuracy and weighted f-measure scores as following:where NCi, fi, and Ni are the number of correctly identified class members, standard f-measure, and total number of class members in class i, correspondingly. A classifier with the highest weighted f-measure is selected for each problem and included into the SNP-IN tool web-server.
In the second protocol, we compare our top performing classifier with the only other published method for predicting the effect of nsSNPs on PPIs, BeAtMuSiC [31]. Unlike our approach, BeAtMuSiC relies on a set of statistical potentials derived from the structures of interacting proteins and does not use a supervised learning and, subsequently, a training set. Coincidentally, for the assessment of this method the authors used the same SKEMPI dataset as was used in SNP-IN tool LOO cross-validation, with a slightly different redundancy removal protocol. Thus, we compared the performances of BeAtMuSiC and SNP-IN tool on the overlapped dataset by calculating the Pearson correlation coefficient between the predicted scores and the experimental data for the latter predictor and comparing with the published score for the former method. The raw classification prediction score of the SNP-IN tool was used. We discuss the validity and potential shortcomings of this assessment protocol further in the paper.
In the last protocol, we assess the performance of SNP-IN tool by applying it to the datasets of 26th Critical Assessment of PRediction of Interactions (CAPRI) competition [67]. CAPRI is a community-wide competition in computational tasks related to characterization of the molecular structure of protein complexes. Recently, a new type of challenge was introduced with a goal to characterize the effect of mutation on protein-protein complexes. Specifically, there were two challenge targets (Target 55 and Target 56), each target was a designed influenza inhibitor interacting with hemagglutinin (HA) [68]. A comprehensive set of site-directed mutagenesis experiments was done for the residues located next to or inside the interaction interface for each target complex, and the effect of each point mutation on the binding affinity was evaluated by deep sequencing of mutants before and after binding [69]. During the competition, all CAPRI participants were asked to provide a score as the prediction of each mutation's effect on inhibitor-HA interactions. The three types of effects correspond to our 3-class problem and include detrimental, neutral and beneficial mutations. The correlations between predicted scores and experimental evaluations were calculated by using the Kendall's τ rank correlation coefficient (http://www.ebi.ac.uk/msd-srv/capri/round26/). Here, we apply the CAPRI assessment protocol to predictions of the effect of each point mutation in Targets 55 and 56 obtained by the 3-class classifier from SNP-IN tool.
Finally, the SNP-IN tool is applied to analyze nsSNPs in the PPI networks associated with human diseases in two case studies using the following protocol. First, the disease-associated nsSNPs and the corresponding genes are selected from dbSNP database [70]. Second, for each nsSNP, a PPI mediated by the mutated protein is identified, and its structural template is extracted from a recently published dataset by Wang et al [7]. Third, MODELLER [71] is used to build an accurate comparative model for each nsSNP-associated PPI complex. Last, SNP-In tool is used to predict nsSNP-induced loss/preservation of the PPI by characterizing the effect of that nsSNP on the PPI.
The SNP-IN tool was implemented as a web-server freely available at http://korkinlab.org/snpintool/ (Fig. 3). It allows users to upload a pdb file containing the structure of the studied PPI, and provide information about the nsSNP they would like to investigate. The server will then return the effects of the nsSNP predicted by the semi-supervised RF-SL classifiers for both 2- and 3-class problems.
Here, we provide a comparative assessment of the supervised and semi-supervised approaches with (i) each other, (ii) the only currently published method, and (iii) the results of a recent CAPRI competition. We also analyze the importance of contribution of each feature in each of the three classification problems. Finally, we report results of the application of SNP-IN tool to characterization of genomic variants in the PPI networks associated with two human diseases.
The importance analysis of all 33 features, carried out using InforGainAttributeEval function in Weka [72], showed that many features (Table 3) were equally important for all three classification problems. These are primarily the energy terms obtained from FoldX and OPUS. On the other hand, some features appeared to be important only for certain classification problems. For instance, Geometric score and Accessible Surface Area (ASA) were not important in the interaction disrupting/preserving classification problem, while the Goap energy terms were more important, compared with the other two problems. On the other hand, Electrostatics feature appeared to be more important for the 3-class problem than for the 2-class problems. Interestingly, while relative contribution of the features was different, all features without exception were informative in the vector representation: removing each of the features did not improve the prediction accuracy for any of the supervised methods. The importance analysis, thus, may be used to determine a higher priority when improving the accuracy of certain features, such as the FoldX and OPUS energy terms, which may be beneficial for all three classification problems.
To assess performance of the four classifiers, we applied a LOO cross-validation protocol (Table 4, Table S1). We started by testing the classifiers on the data for the first classification problem (strengthening/weakening mutations). Interestingly, for all four classifiers, predicting a weakening mutation was significantly more accurate than predicting a strengthening one. In addition, both the SVM supervised classifier and LDS semi-supervised classifier, which relied on transductive SVM (TSVM), performed worse than the RF-based supervised and RF-based semi-supervised learning methods. The top performing RF-based supervised classifier reached 0.87 in weighted f-measure and 0.89 in average accuracy.
The performance gap between the SVM-based and RF-based methods became even more apparent when assessing these methods on the 3-class problem (Problem 3). Specifically, very low recall and precision when classifying the beneficial nsSNPs made the difference between the weighted f-measures of SVM-based and RF-based methods to be close to 0.20 for both supervised and semi-supervised approaches (Table 4). The top performing method for this classification problem was the RF-based semi-supervised approach, with the weighted f-measure value of 0.70 and average accuracy of 0.72.
Based on the superior performance of the supervised and semi-supervised RF-based methods for the first 2-class and 3-class problems, we focused on evaluating only those two methods for the second 2-class problem (disruptive/preserving PPI mutations). We found that unlike the previous two classification problems, the performance of both methods on the two classes of this problem was more even (Table 4). Interestingly, the top performing RF-based semi-supervised approach for this problem (weighted f-measure is 0.78 and average accuracy is also 0.78) gained ∼0.04 in weighted f-measure, compared to the supervised approach. This was not observed in the other two classification problems where the difference between the RF-based supervised and semi-supervised classifiers was at most 0.02.
The results of cross-validation allowed us to select the top performing method for each problem, using weighted f-measure (Table 4). The top classifiers for the more generally applicable second and third classification problems were then integrated into the SNP-IN tool. The overall weighted prediction accuracies (0.72–0.89) and f-measures (0.70–0.87), as estimated by the LOO cross-validation protocol, suggest that each of the three problems is feasible when applying a machine learning approach. In addition, we observed that the performance of the classifiers on individual classes varies even in the case of the most accurate methods. To account for that in our evaluation, we calculated the Mathews correlation coefficient (MCC) score for the top-performing RF approaches (Table S1). The overall performance of the methods according to the MCC score was consistent with the performance evaluated based on the weighted f-measure.
While the thresholds for employed here are widely used by the community [35], [37], [38], other more conservative definitions for the beneficial/neutral/detrimental mutations exist. For instance, Bogan and Thorn [73] used a threshold of 2.0 kcal/mol to identify the residues that contributed to the interaction hot spots. We analyzed and compared the behavior of our top performing supervised and semi-supervised methods by defining beneficial, neutral, and detrimental effects using the more conservative thresholds of ±2.0 kcal/mol instead of ±0.5 kcal/mol, followed by retraining and evaluation of the methods for each problem (Table S2). Using the more conservative definition resulted in significantly unbalanced datasets (beneficial: 48, neutral: 1388, detrimental: 518), but the performance of the classifiers was similar, showing that our approach is adaptive to other definitions of interaction effects.
Lastly, by including the performance of the two 2-class classifiers as additional two features we were able to get a striking improvement of the most accurate RF self- learning classifier for the 3-class problem (Table 4, last row). Most significantly, we obtained 82% gain in the recall of classifying beneficial mutations (from 0.22 to 0.40), and 25% gain of the MCC score (from 0.49 to 0.61). Thus, integrating the intrinsic relationship between classification problems allowed us to significantly improve predictions for the most difficult 3-class problem. We note that there may be other, simpler, 2-level protocols where each of the three classes can be eliminated consecutively (e.g., classifying the detrimental nsSNPs vs. the rest at the first level, and classifying the neutral nsSNPs vs. beneficial ones at the second level). However, our protocol is less restrictive, since it does not make a classification decision for all three classes until the last level, where the performances of both 2-class classifiers are considered simply as additional numerical features and may or may not influence the final classification.
We next compared the performance of our top performing RF-based semi-supervised classifier to BeAtMuSiC, a recently published and the only publicly available tool, to the best of our knowledge [31]. The authors of BeAtMuSiC assessed their method by applying it to the SKEMPI set. Out of 3,047 entries in SKEMPI, they removed the redundant entries and entries with multiple mutations. The resulting set of 2,007 was used to calculate the predicted values and compare them with the original experimental measurements. Following our preprocessing protocol, we also removed redundant entries and entries with multiple mutations and then successfully predicted 1,954 mutations. Finally, comparing our set with the set of 2,007 entries used in BeAtMuSiC, we determined 1,897 entries shared between the two sets that we used for our comparative assessment.
We note that BeAtMuSiC is not a classifier, as it predicts the changes in binding affinity caused by an nsSNP. Therefore, instead of direct classification results, we used the classifier-calculated probability for an nsSNP to be of the preserving type; we expected this probability to correlate well with changes in the binding affinity. We also note that our RF-based classifier and all other classifiers were trained using the SKEMPI set. Therefore, for this comparative assessment we applied a LOO cross-validation protocol to train models and used predictions on the test examples from the same protocol to calculate the Pearson correlation coefficient [31]. As a result, the computed Pearson correlation coefficient between our prediction scores and experimental values from SKEMPI was 0.57, while the authors of BeAtMuSiC reported the correlation coefficient of 0.47.
As a final evaluation of our method, we applied the semi-supervised RF-SL classifier of SNP-IN tool to characterize all mutations of both CAPRI Targets, 55 and 56, and then scaled the probability of each classification to obtain the score of mutation effects on binding. Comparing to other participation groups in 26th round of CAPRI [74] and BeAtMuSiC applied for the same purpose [31], our RF-SL classifier from SNP-IN tool obtained a Kendall's tau coefficient with experimental results of 0.37 on target 55 and 0.25 on target 56. Both results were significantly better than those ones by either a CAPRI predictor or BeAtMuSiC (Table 5). The validation on the targets of the 26th round of CAPRI demonstrates that our semi-supervised RF-SL classifier is currently the best predictor of the mutation effects on PPIs.
The accuracy and computational performance of our approach allowed us to study the mutation-induced rewiring effects of protein-protein interaction networks mediated by disease genes. The rationale of this approach was as follows. All nsSNPs on the surface of a protein could be roughly organized in two groups with respect to their role in a PPI mediated by this protein. The first group included nsSNPs that were located inside the interaction interface, while the second group consisted of nsSNPs that are located outside interface (but might nevertheless rewire the PPI).
To demonstrate the applicability of our approach, we used it to study two disease PPI networks centered around the genes critically implicated in two complex genetic diseases, breast cancer and diabetes (Fig. 4). For each study, we used dbSNP [70] and a recently published INstruct database [75] to (1) select the disease-associated genes that form a PPI network, (2) select nsSNPs associated with the disease, and (3) determine whether any interactions from that network have homologous structural templates. To ensure the accuracy of the PPI data we used HINT database [76] that includes PPIs experimentally supported by one or more publications. We required for each PPI to be supported by at least two references. For each PPI with a known structural template we obtained a homology model (see Feature representation subsection in Methods), mapped known nsSNPs onto the modeled structure of the PPI and grouped them into the two groups discussed above. Finally, we run SNP-IN tool on each structurally resolved PPI and compared the obtained results with the known literature on the effects of those variants.
In this work, we developed a new approach, SNP-IN tool, that characterizes the effects of nsSNPs on protein-protein interactions. We introduced three related nsSNP effect classification problems and applied supervised and semi-supervised machine learning methods leveraging SVM and RF formalisms. The performance assessment of the classifiers allowed us to draw several conclusions regarding the nature of the studied problem and the machine learning methodology addressing it. First, we found that while many of the same nsSNP features play equally important role in all three classification problems, some problems appeared to be more challenging than the others. Second, we concluded that the random forest approach is better suited for this problem than the SVM approach: both RF-based supervised and semi-supervised methods significantly outperformed the corresponding SVM-based methods. Finally, we observed that the semi-supervised learning method did not always significantly outperform the supervised method. The comparative assessment showed the superior performance of SNP-IN tool on the CAPRI targets as well as over the only other published method, BeAtMuSiC. We note, however, that the latter comparison should be treated with caution, as it was done over the SKEMPI dataset that was used in LOO for SNP-IN tool. In contrast, BeAtMuSiC is not a machine learning approach, so it used this dataset exclusively for its assessment. Thus, while none of the assessed examples from SKEMPI were simultaneously used in training (due to design of LOO cross-validation protocol) and could not influence the classifiers, further more detailed assessment between these two methods must be done, when another large dataset is available.
Semi-supervised learning approaches have received growing attention from the bioinformatics community with their successful applications to several areas of bioinformatics and computational biology [47]–[49]. To the best of our knowledge, none of the currently existing semi-supervised approaches in bioinformatics have utilized random forest classifiers. Our simple RF-based semi-supervised classifier performed remarkably better than state-of-the-art transductive SVM and LDS based semi-supervised classifiers, suggesting that this could be a promising direction for addressing the biological classification problems that involve vector-based representations of highly heterogeneous features. Overall, limitation of the labeled data due to the difficulty of obtaining experimental binding affinities from the site-directed mutagenesis experiments renders semi-supervised approaches a powerful alternative to the supervised methods.
A related issue is predicting the effect of a non-synonymous SNP on a function carried by a protein product of the mutant gene, and specifically on a PPI mediated by this protein, has emerged as an important computational challenge. A problem of labeling nsSNPs as detrimental, neutral or beneficial, has been recently introduced for the first time at the 26th round of the CAPRI competition [88]. Considering the 3-class problem as the most comprehensive annotation for nsSNP effects on PPI, we have also introduced two other problems, each involving only 2 classes. While related, the problems are designed to characterize the genetic variation from different perspectives. One two-class problem, where an nsSNP is characterized as disrupting or preserving the associated PPI could be used to study the network rewiring caused by certain mutations, which in turn could be useful in pinpointing the causative SNPs. The other 2-class problem, where an nsSNP is labeled as either strengthening or weakening the interaction, is useful when characterizing molecular mechanisms behind a SNP that has been already linked to a functional change.
While an nsSNP occurring inside or in close proximity of an interaction interface will directly modify only one of the two interacting proteins, it is critical that our method takes into account the structural information of the entire interaction, including both binding sites forming the interaction interface. In this manner, the role of the interaction partner and its binding site is taken into consideration. For instance, it is possible that for a protein that competitively binds two other proteins through fully or partially overlapping binding sites, a mutation occurring in the overlapping region of these binding sites would disrupt one interaction but be neutral for another interaction. With hundreds of thousands of available interaction templates [89] and the advancement of comparative modeling, the requirement for structural information of the overall interaction makes an increasingly small impact on the coverage of SNP-IN tool.
Understanding functional roles of nsSNPs associated with diseases by studying the disease-centered PPI network has many challenges. Being among the first such methods, SNP-IN tool is yet to deal with some of them. One of the key challenges is accounting for the indirect effects of nsSNPs on the interactions, such as disabling a phosphorylation site that regulates a PPI, altering an allosteric site, or nsSNP-induced structural changes of a protein that affect the interaction. The difficulty of modeling such effects lies in the complexity of indirect mechanisms, as well as in the fact that the effect-causing SNPs may be relatively distant from the protein interaction interface they affect.
Another challenge is our ability to infer the functional importance of an nsSNP—and ultimately its contribution to the disease phenotype—from prediction of its effect on a PPI. For instance, the disruptive effect on a PPI predicted for an nsSNP that is either buried inside the interface or lies in its close proximity would indicate the true functional effect of the variation. However, predicting the neutral effect of a surface nsSNP that is in proximity to the interface does not necessarily mean that this genetic variation does not alter a biological function, as it could be a part of another functional site. On the other hand, an nsSNP that is buried inside the protein interaction interface is far less likely to be involved in the other function, e.g., belong to a DNA- or small ligand–binding site or a site of posttranslational modification. Thus, the predicted neutral effect of such genetic variation would indeed mean that it does not have any functional impact.
As a recent work by Wang et al showed [7], there are thousands of nsSNPs associated with the interaction interfaces, and more SNPs are being identified every year from new high-throughput studies [90]. Combined with the exponential growth of the number of PPI structures being experimentally solved [91], we expect that the coverage of SNP-IN tool will continue to grow, providing more insights into molecular mechanisms of complex genetic diseases. In addition, with the growing experimental knowledge about the cooperative effects of multiple nsSNPs on PPIs, we plan to expand the SNP-IN tool to multiple mutations as one of the next future steps. Even more challenging is a problem of computational estimation of the values upon structural changes in the protein interaction complex due to genetic variation. The classification of nsSNPs can be considered as a simplified, discretized, version of the latter problem. Based on the success of the current machine learning approach, we anticipate that the supervised and semi-supervised regression approaches will complement the classical biophysical methods to address this challenge.
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10.1371/journal.pntd.0004364 | A Rapid Screening Assay Identifies Monotherapy with Interferon-ß and Combination Therapies with Nucleoside Analogs as Effective Inhibitors of Ebola Virus | To date there are no approved antiviral drugs for the treatment of Ebola virus disease (EVD). While a number of candidate drugs have shown limited efficacy in vitro and/or in non-human primate studies, differences in experimental methodologies make it difficult to compare their therapeutic effectiveness. Using an in vitro model of Ebola Zaire replication with transcription-competent virus like particles (trVLPs), requiring only level 2 biosafety containment, we compared the activities of the type I interferons (IFNs) IFN-α and IFN-ß, a panel of viral polymerase inhibitors (lamivudine (3TC), zidovudine (AZT) tenofovir (TFV), favipiravir (FPV), the active metabolite of brincidofovir, cidofovir (CDF)), and the estrogen receptor modulator, toremifene (TOR), in inhibiting viral replication in dose-response and time course studies. We also tested 28 two- and 56 three-drug combinations against Ebola replication. IFN-α and IFN-ß inhibited viral replication 24 hours post-infection (IC50 0.038μM and 0.016μM, respectively). 3TC, AZT and TFV inhibited Ebola replication when used alone (50–62%) or in combination (87%). They exhibited lower IC50 (0.98–6.2μM) compared with FPV (36.8μM), when administered 24 hours post-infection. Unexpectedly, CDF had a narrow therapeutic window (6.25–25μM). When dosed >50μM, CDF treatment enhanced viral infection. IFN-ß exhibited strong synergy with 3TC (97.3% inhibition) or in triple combination with 3TC and AZT (95.8% inhibition). This study demonstrates that IFNs and viral polymerase inhibitors may have utility in EVD. We identified several 2 and 3 drug combinations with strong anti-Ebola activity, confirmed in studies using fully infectious ZEBOV, providing a rationale for testing combination therapies in animal models of lethal Ebola challenge. These studies open up new possibilities for novel therapeutic options, in particular combination therapies, which could prevent and treat Ebola infection and potentially reduce drug resistance.
| Studies to evaluate the effectiveness of candidate antiviral drugs to inhibit Ebola virus infection have been hampered by the availability and access to level 4 containment facilities. Using a mini-genome model system that generates Ebola virus-like particles that infect cells, we have been able to screen a panel of candidate drugs for antiviral activity, under normal level 2 containment. We compared the activities of 8 different antivirals from 3 drug classes, including drugs repurposed for the treatment of Ebola: type I interferons and nucleoside analogs. Our data indicate that IFN-ß is a potent inhibitor of Ebola virus, contributing to the decision to conduct a clinical trial of IFN-ß treatment for Ebola virus disease in Guinea. Moreover, we identified that 2 and 3 drug combinations inhibit Ebola replication when administered 24 hours post-infection. Drug combinations have important implications for clinical use, since lower doses of each drug are administered, potentially decreasing side-effects and, based on different mechanisms of action, there is less likelihood for the emergence of drug resistance. These studies set the stage for both preclinical and clinical evaluation.
| As of December 13, 2015, the current outbreak of Ebola virus disease (EVD) in West Africa has resulted in 28,633 cumulative cases and 11,314 deaths [1]. Two potential vaccine candidates, rVSVΔG-ZEBOV and ChAd3-EBO Z, have shown durable protection from lethal Ebola challenge in mice [2] and macaques [3] respectively, and are part of the phase II/III PREVAIL trial in Liberia and Guinea (https://clinicaltrials.gov/ct2/show/NCT02344407). Other potential therapeutics, such as convalescent plasma and the antibody cocktail ZMapp [4] have been approved for an emergency phase II/III trial in Guinea (https://clinicaltrials.gov/ct2/show/NCT02342171) and a phase I trial in Liberia (https://clinicaltrials.gov/ct2/show/NCT02363322), respectively. However, to date there is no licensed vaccine or treatment for EVD, although improvements in supportive care are increasing survival rates [5].
Repurposing antivirals used for other viral infections, based on knowledge of mechanisms of action, has prompted accumulating interest in the application of different nucleoside/nucleotide analogs and type I interferons (IFNs) for the treatment of Ebola virus disease (EVD). Experimental nucleoside analogs may have therapeutic efficacy for EVD, given the evidence of protection in primate and rodent disease models, 2–6 days after lethal Ebola or the related hemorrhagic Marburg virus challenges [6,7]. Favipiravir, a viral polymerase inhibitor, provides 100% protection when administered 6 days after challenge with a lethal dose of Ebola virus [6] and has been evaluated in the phase II/III JIKI trial in Guinea (https://clinicaltrials.gov/ct2/show/NCT02329054). TKM-100802, a cocktail of siRNAs targeting VP35 and L polymerase and brincidofovir (BCV), a viral polymerase inhibitor that has activity against dsDNA viruses such as adenovirus and cytomegalovirus [8], were also considered for treatment against EVD. The brincidofovir trial was halted, ostensibly because of projections of low recruitment.
Despite infecting different target cells, Ebola and HIV-1 share many similar features early in their replication cycle. Both are RNA viruses that package a viral polymerase (L for Ebola, RT for HIV-1) required for early replication in the cytosol of the host cell [9]. Homology-based structural prediction of the RNA-dependant RNA polymerase of Ebola indicates the polymerase contains conserved structural motifs in the catalytic palm subdomain similar to viral DNA polymerases [10], supportive of nucleoside analogs potentially inhibiting Ebola replication. Inhibiting HIV-1 reverse transcription with nucleoside analogs such as lamivudine (3TC, cytidine analog), zidovudine (AZT, thymidine analog) or tenofovir (TFV, adenosine monophosphate analog) is the basis for highly active antiretroviral treatment (HAART) [11,12]. Nucleoside analogs are on the WHO list of essential medicines and can be deployed in limited resource settings [13]. Moreover, AZT binds RNA through G-C and A-U bases [14], prompting us to evaluate whether these nucleoside analogs might also inhibit Ebola replication.
Type I IFNs mediate diverse biological effects, including cell type-independent antiviral responses and cell type-restricted responses of immunological relevance. IFNs inhibit viral infection by preventing viral entry into target cells and by blocking different stages of the viral replication cycle for different viruses. Moreover, type I IFNs have a critical role in linking the innate and adaptive immune responses to viral challenge. IFN-α/β expression occurs as the earliest non-specific response to viral infection. Indeed, viruses have evolved immune evasion strategies specifically targeted against an IFN response, confirming the importance of IFNs as antivirals. This immune evasion strategy is relevant when one considers the IFN response to Ebola infection [15]. Ebola proteins VP24 and VP35 inhibit host cell systems that lead to IFN production and also inhibit events associated with an IFN response [16–18]. VP24 blocks the binding of importins to phosphorylated STAT1, preventing STAT1 nuclear translocation required for transcription of interferon simulated genes [16]. VP35 binds viral dsRNA, preventing dsRNA degradation [17] and inhibits the phosphorylation of IRF-3 and the SUMOylation of IRF-3 and IRF-7, thereby limiting IFN production [18]. Despite these virally-encoded mechanisms to limit an IFN response to infection, different rodent and non-human primate studies provide evidence for IFN-induced partial protection: the effects of IFN-α/β treatment in lethal Ebola virus infection reduced viremia and prolonged survival [19–21]. Thus, a potential therapeutic effect for IFNs as monotherapy in EVD, or in combination with other anti-Ebola therapies, has not been resolved.
We employed an established mini-genome system to rapidly evaluate candidate drugs that could inhibit Ebola Zaire replication under BSL 2 conditions [22–24]. At the outset we established the experimental conditions for infection with replication and transcription-competent virus like particles (trVLPs), by examining luciferase activity under various transfection and drug treatment conditions, which included transfection with viral support protein plasmids (S1 Fig). We included treatment with maraviroc, a CCR5 inhibitor, that would have no effect on trVLP entry and infection, thereby serving as a negative control for subsequent treatment regimens.
In a first series of experiments, we examined the inhibitory effects of IFN-α (0.5μM/10,000 U/mL), IFN-ß (0.2μM/1,000 U/ml), TOR (5μM), CDF (100μM), FPV (100μM), and a combination of 3TC, AZT and TFV (5μM each) on trVLP infection of 293T cells (Fig 1). Specifically, the 293 T cells were treated with the different drugs at four different times relative to infection with trVLP, as indicated. We provide evidence that for each of the individual drugs and for the triple drug combination, at the doses indicated, trVLP infection of 293 T cells is inhibited when treatment is initiated at +2, +6 or +24 hours post-infection. Interestingly, TOR, an estrogen receptor modulator discovered in a high throughput screen as a potent inhibitor of Ebola [25], significantly reduced viral luciferase activity at all time-points tested. For IFN-α, IFN-ß, TOR and FPV treatments, maximal inhibition of trVLP infection was achieved when the cells were treated prior to challenge with trVLP. By contrast, pre-treatment with CDF at 100μM, 24 hours prior to infection with trVLP, resulted in enhanced infection.
In subsequent dose-response studies, we compared the inhibitory effects of IFN-α, IFN-ß, TOR, CDF, FPV, 3TC, AZT or TFV when administered 24 hours post trVLP infection (Fig 2). The data in Fig 2I summarize the IC50 dose for each drug. The IFNs exhibited the lowest IC50 values at 0.016μM for IFN-ß and 0.038μM for IFN-α. The data show a log-fold difference in IC50 values for IFN-α and IFN-ß when compared in terms of U/ml, the norm for antiviral activity measurements (Fig 2A and 2B). TOR had the next lowest IC50 (0.36μM) and completely inhibited infection at doses > 5μM (Fig 2C). TFV had an IC50 at 0.98μM. CDF, 3TC and AZT all exhibited similar IC50 values in the dose range 4.2–7.8μM, while FPV had the highest IC50 of the nucleoside analogs at 36.8μM. At their IC50 concentration, none of these drugs directly inhibited luciferase reporter activity (S2 Fig). We observed a relatively small antiviral dose range for CDF (1.5–25μM) (Fig 2D), beyond which the drug appeared to enhance viral infection (S3 Fig). In cell viability assays we observe that at doses >10μM CDF affect cell viability, confounding the interpretation of the effects of CDF on viral replication.
In an orthogonal assay to confirm these findings, we next measured viral replication and transcription by qRT-PCR, following trVLP infection. trVLP-infected cells were either left untreated, or treated with the different drugs 24 hours post-infection, then viral replication and transcription evaluated 24 hours later (Fig 3). All treatments, with the exception of TOR, significantly reduced the amount of genomic vRNA detected within cells (Fig 3A) and all treatments significantly reduced the synthesis of cRNA and mRNA isolated from infected cells (Fig 3B). Notably, IFN-ß treatment of trVLP-infected cells resulted in the greatest reduction in viral replication and transcription.
Next we examined the effectiveness of two and three drug combinations on trVLP infection. We first examined 28 two-drug combinations, using each drug at its IC50 value, and used the median-effect equation and combination index theorem [26] to determine drug synergy, additive or sub-additive effects (Fig 4A). Synergy is defined as greater than additive effect when drugs were combined (CI<1), additive as the effect expected when combining each drug (CI = 1) and sub-additive as a smaller than expected additive effect (CI>1). When administered 24 hours post-infection, many of the two-drug combinations showed strong synergism in inhibiting trVLP replication (Fig 4J), with IFN-β + 3TC demonstrating the greatest synergism (97.3% inhibition, CI = 0.028). 3TC was synergistic with all seven other drugs tested. Notably, when CDF was used in combination with FPV, AZT, TFV or IFN-α, it produced a sub-additive effect.
Next we tested all possible 56 three-drug combinations, using each drug at its IC50 value, to assess whether adding a third drug enhanced efficacy compared with two-drug combinations (Fig 4B–4I). This series of experiments served to validate our two-drug findings, as synergistic two-drug combinations such as IFN-β + 3TC and IFN-β + AZT, predicted strong synergy for the triple drug combination of IFN-ß + 3TC + AZT. As anticipated from the two-drug polygonogram, CDF was sub-additive when combined in three-drug combinations (Fig 4E). This was most evident even when CDF was administered in conjunction with two-drug combinations that had shown strong synergy, such as IFN-β + 3TC or FPV + TFV, further indicating that CDF diminishes the antiviral effects of other drugs. IFN-ß, 3TC, AZT and TFV all promoted strong synergism when included in triple drug combinations, with IFN-β + AZT specifically providing strong synergism when combined in three unique triple therapies.
From these two-drug and three-drug screens, we calculated the combination index (CI) and fractional inhibition (Fi) (Fig 4J and 4K). Many of the synergistic drug combinations (i.e. low CI) included one nucleoside analog and an IFN, while those drug combinations that were sub-additive all included CDF. IFN-β was predominant in the most efficacious two- and three-drug combinations. In particular, IFN-β + 3TC and IFN-β + 3TC + AZT consistently exhibited the strongest synergism and highest Fi when administered 24 hours post-infection. Refer also to S1 and S2 Tables.
In a final series of experiments, in order to validate our findings from the trVLP infection studies, we examined the antiviral effectiveness of IFN-ß, IFN-α, TOR, FVP, AZT, 3TC and TFV in 293T cells infected with ZEBOV (ZEBOV contained an eGFP reporter). CDF was excluded from these experiments. Initial dose-response studies were conducted at doses reflective of those used in the trVLP experiments in Fig 2. A higher dose of each drug was required to inhibit ZEBOV infection compared with trVLP infection (S4 Fig). Using the IC25 of each drug, we next evaluated 2 and 3 drug combinations for additive or synergistic effects against ZEBOV infection. All seven 2 drug combinations were synergistic (low CI) (Fig 5A), similar to the most synergistic combinations against trVLP in Fig 4J. IFN-β + 3TC proved to be the most synergistic 2 drug combination, analogous to trVLP infection. Of the most synergistic 3 drug combinations identified in the trVLP infection system, all seven exhibited synergy against ZEBOV infection, with IFN-β + 3TC + AZT and IFN-β + TOR + AZT exhibiting the strongest synergy (Fig 5B). The CIs determined from trVLP infection correlated well with those determined using ZEBOV infection; specifically, the correlation coefficients (R2 values) confirm this (Fig 5C and 5D).
In September 2014, the WHO hosted a conference to facilitate development of a global action plan to deal with the Ebola outbreak in West Africa. Delegates from affected West African countries, ethicists, scientists, health care providers, logisticians and representatives from different funding agencies were in attendance. A committee had been struck to evaluate the different vaccine candidates and therapeutic interventions being developed, which subsequently received an overwhelming number of submissions for consideration, and was hampered by an inability to compare antiviral effectiveness, since in vitro and pre-clinical in vivo model systems vary, treatment regimens vary from prophylaxis to post-exposure administration, and direct readouts of antiviral efficacy differ. Moreover, given the virulence and high mortality associated with EVD, all of these studies have been conducted under BSL 4 conditions, limiting the number of laboratories that can engage in these antiviral studies. Cognizant of these limitations, we employed the trVLP model system to compare the antiviral effectiveness of eight antiviral candidates from three drug classes. We evaluated their antiviral activities in the context of inhibition of Ebola replication, using this mini-genome model that allows for rapid comparisons among compounds under BSL 2 conditions. The tetracistronic minigenome represents the most sophisticated in vitro replication model of Ebola virus to date. trVLPs proceed through every replication step as wild-type Ebola virus, and have been tested in multiple cell lines. Using TOR, there has been some validation of the trVLP assay. Specifically, TOR has been evaluated in limiting Ebola virus infection of VeroE6 and HepG2 cells, and exhibited IC50 values of 0.2 μM and 0.03 μM, respectively [27], in line with the IC50 dose for TOR (0.36μM) observed with trVLP infection. Likewise, the IC50 identified in the trVLP system for FPV (36.8μM), is consistent with that of 67μM recorded using Ebola virus infection [6], suggesting that this Ebola mini-genome system has relevance for screening potential antiviral compounds. Indeed, our validation studies using ZEBOV (ZEBOV-eGFP) suggest that the trVLP infection model has utility as an in vitro screening assay when comparing different drugs as monotherapies or in 2 and 3 drug combinations.
As mentioned, the Ebola virus encodes in its genome factors that limit a type I IFN response to infection [16–18]. Yet, both rodent and non-human primate studies suggest that IFN-α and IFN-ß treatment can confer partial protection from infection, reducing viremia and prolonging survival [19–21], suggesting that it may be possible to override the inhibitory effects of the virus by treatment with IFN. At the outset, we conducted a series of experiments to compare the antiviral activities of IFN-α and IFN-ß in the trVLP infection system, and our findings suggest that whether treatment is administered prior to or post-infection, both IFN-α and IFN-ß exhibit antiviral activity. These findings only have relevance for the direct antiviral activities of these IFNs, since the effects of IFN-α or IFN-ß on immune modulation for viral clearance cannot be determined using this system. Nevertheless, these data contributed to the decision to conduct a clinical trial of IFN-ß treatment for EVD in Guinea.
We provide evidence that the nucleoside/nucleotide analogs 3TC, AZT, TFV, FPV and CDF inhibit Ebola trVLP replication in vitro. The results with 3TC are in contrast to published data that show no evidence for 3TC inhibiting Ebola virus infection in vitro [27]. These studies examined the antiviral effectiveness of 3TC when administered one hour prior to infection, in contrast to our studies that have focused on post-exposure protection. In cells, the kinetics of 3TC phosphorylation are such that a minimum of four hours are required for optimal activity, perhaps distinguishing why our 24 hour pre-treatment, specifically a combination treatment, offered protection. Post-exposure treatment with 3TC and the other nucleoside/nucleotide analogs we examined, would more likely reveal activity against viral RNA synthesis than pre-treatment. When comparing the IC50 values of each of the nucleoside analogs that we tested, TFV exhibited the lowest IC50 at ~1μM. Whether this reflects the fact that this adenosine monophosphate analog only requires two phosphorylation events to become an active drug versus three for the other nucleoside analogs, remains undetermined. Extensive published data reveal both the safety profiles [11,28,29] and the biodistribution of 3TC, AZT and TFV in the circulation and liver [30,31], the same compartments where Ebola infects monocytes, macrophages, dendritic cells, endothelial cells and hepatocytes. Moreover, drug interactions with other nucleoside analogs have been well studied: e.g. tenofovir disoproxil fumarate, when used alone or in combination with emtricitabine effectively prevents HIV-1 infection in antiretroviral pre-exposure prophylaxis (PrEP) [29].
Our studies also revealed that the active metabolite of brincidofovir, CDF, has a narrow therapeutic window of efficacy (6.25–25μM) when assessed in the trVLP assay, enhancing viral replication at higher doses when added either prior to or post-infection. In cell viability assays, CDF exhibits cytotoxicity at doses >10μM. These findings suggest that caution is required if CDF is to be considered further for the treatment of EVD, specifically that phase I/II trials define the safety profile of this drug for EVD.
Another advantage of this in vitro system is that it allowed us to evaluate various 2 and 3 drug combinations and demonstrates that combination treatments limit viral replication up to 97.3%. A benefit of combination treatment is the potential to limit/avoid the emergence of drug resistance. Interestingly, IFN-ß was predominant among all the 8 antivirals considered in terms of contributing very strong synergism in combination treatments: e.g. IFN-ß + 3TC; IFN-ß + 3TC + AZT. Using this system, we observe that FPV, when administered 24 hours post-infection, has an IC50 of ~ 37μM. To date, the phase II/III JIKI trial examining the efficacy of FPV against EVD has reported only modestly encouraging results. In our 2 drug combination treatment studies we show that, with the exception of CDF, whenever FPV is included, synergy occurs, effectively reducing the CI. It may transpire that for treating EVD, FPV is most effective in a drug combination regimen.
Viewed altogether, we present an in vitro Ebola trVLP screening system, that requires only level 2 biocontainment, which allowed us to compare the antiviral activities of 8 compounds, either alone or in combination. We provide evidence that IFNs are effective inhibitors of Ebola replication, with IFN-ß exhibiting greater efficacy over IFN-α, or when used in combination with nucleoside analogs. We infer from our data that whether IFN-ß treatment is administered 24 hours prior to, or up to 24 hours post-infection, reduced Ebola replication is achieved. As additional antiviral therapeutic candidates become available, we now have the capability to measure and compare their direct antiviral activities with the existing panel. This allows for rapid in vitro evaluation and the opportunity to prioritize antiviral candidates for further pre-clinical and clinical trial studies.
We employed an established mini-genome system to rapidly evaluate candidate drugs that could inhibit Ebola Zaire replication under BSL 2 conditions [22]. The mini-genome encodes 3 of the 7 Ebola proteins (VP24, VP40 and GP1,2) and a luciferase reporter gene. Expression plasmids for the remaining four Ebola nucleocapsid proteins (L, NP, VP30 and VP35) were also included during transfection. Cell culture conditions and virus infections were performed as previously described [22]. Briefly, 80,000 producer 293 T cells (American Type Culture Collection; ATCC, Rockville, USA) were seeded in individual wells of 24-well plates in 400μL Dulbecco’s Modified Eagle Medium (DMEM) containing 10% FBS, 1% penicillin and 1% streptomycin, and grown in 5% C02 atmosphere at 37°C. Cells were transfected with the viral replication protein plasmids (L, NP, VP30, VP35), a tetracistronic Ebola mini-genome and the T7 polymerase, using the CalPhos Mammalian Transfection Kit (Clontech Laboratories). 24 hours later, medium was replaced with 800μL DMEM with 5% FBS. The replication and transcription-competent virus like particles (trVLPs) were harvested 3 days later. Virus stock was frozen at -80°C.
For infection, 293 T target cells were seeded at 80,000 cells in 400μL of DMEM supplemented with 10% FBS. Target cells were then transfected with the four viral replication protein plasmids, as well as Tim-1, to allow efficient virus binding and entry. 24hr post-transfection, 25μL of trVLP stock was diluted in 600μL of DMEM with 5% FBS, warmed to 37°C for 30 min, then added to target cells. Medium was removed the following day and replaced with 800μL DMEM with 5% FBS. Four days post-infection, the medium was aspirated and cells were re-suspended in 200μL of 1x Renila Luciferase Assay Lysis Buffer (Renilla Luciferase Assay System, Promega). Lysates were assayed for luciferase activity.
We generated recombinant ZEBOV expressing enhanced green fluorescent protein (eGFP) from cDNA clones of full-length infectious ZEBOV, as previously described [32]. The eGFP reporter protein was expressed as an eighth gene, and the virus exhibited an in vitro phenotype similar to wild-type ZEBOV. Notably, in vivo, incorporation of GFP into wild-type ZEBOV results in some attenuation of disease [32]. All work with infectious ZEBOV was performed in biosafety level 4 (BSL4), at the National Microbiology Laboratory of the Public Health Agency of Canada in Winnipeg, Manitoba.
30,000 293 T cells were seeded in 96-well plates in 100μL DMEM with 10% FBS. 24 hours thereafter, the medium was replaced with 100μL DMEM with 10% FBS containing ZEBOV-GFP at an MOI of 0.1. 24 hours post-infection, the medium was removed and replaced with 200μL of DMEM with 5% FBS, or 190μL DMEM with 5% FBS and 10μL of single or combinations of drugs. eGFP fluorescence was measured 3 days post-infection using a Synergy HTX Multi-Mode Microplate Reader (BioTek).
For these experiments, we used toremifiene citrate (TOR; Sigma), cidofovir hydrate (CDF; Sigma) favipiravir (FPV, T-705; Cellagen Technology), lamivudine (3TC; Sigma) zidovudine (AZT), tenofovir (TFV) maraviroc (MVC; NIH AIDS Reagent Program), Infergen (IFN alfacon-1, Pharmunion Bsv Development Ltd.) or human interferon beta-1a (IFN-β, Avonex; Biogen).
Forty-eight hours after trVLP infection, medium was aspirated from 293 T cells that had either been left untreated or treated with the various drugs and total RNA extracted from cell lysates with 500μL of TRIzol (Thermo Fisher Scientific). cDNA synthesis was performed on 5 μg of total RNA, using the First-Strand cDNA Synthesis Kit (GE Healthcare Life Sciences), according to the manufacturer’s instructions. A 20 μl reaction also contained bulk first-strand cDNA reaction mix, DTT solution and 40 pmol of one of two trVLP specific primers [33]: vRNA forward (5’-GGC CTC TTC TTA TTT ATG GCG A -3’), or cRNA/mRNA reverse (5’-AGA ACC ATT ACC AGA TTT GCC TGA-3’). Both primers were synthesized by the Center for Applied Genomics (The Hospital for Sick Children, Toronto, Canada). Real-time qPCR reactions (25 μl) were conducted in duplicate, using the Rotor-Gene RG-3000 thermocycler (Corbett Research, Montreal, Canada). Each reaction contained 100 ng template cDNA, 12.5 μL 2 x SYBR Green PCR Master Mix (Applied Biosystems, Warrington, UK), 300 nM of both the forward (vRNA) and reverse (cRNA/mRNA) primers, and PCR grade H2O (Roche Diagnostics, Indianapolis, USA). Samples lacking reverse transcriptase (No RT) during first-strand cDNA synthesis served as negative controls. Cycling parameters were as follows: initial denaturation at 95°C for 10 min, followed by 40 cycles of amplification with 95°C for 15 seconds, 56°C for 30 seconds, and 60°C for 30 seconds. Biological triplicates in the drug-treated groups were normalized to the average Ct of infected cells given DMSO solvent alone, by the 2-ΔCT comparative CT method.
Dose-response cytotoxicity/viability assays were conducted in 293 T cells 4 days post-infection for each of the drugs examined, either alone or in the various combinations indicated, using the MTT assay as previously described [34].
Means were compared using a two-tailed, unpaired Student’s t test and corrected for multiple comparisons. For all figures, (*) denotes a p value <0.05, (**) denotes a p value <0.01 and (***) denotes a p value <0.001. Error bars shown are the standard error around the mean (SEM). Synergy between two and three-drug combinations, combination index (CI) and dose-reduction index (DRI) were calculated with CompuSyn Version 1.0 [26]. The coefficient of determination (R2) was determined for simple linear regressions.
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10.1371/journal.pntd.0000854 | Viral Etiology of Encephalitis in Children in Southern Vietnam: Results of a One-Year Prospective Descriptive Study | Acute encephalitis is an important and severe disease in children in Vietnam. However, little is known about the etiology while such knowledge is essential for optimal prevention and treatment. To identify viral causes of encephalitis, in 2004 we conducted a one-year descriptive study at Children's Hospital Number One, a referral hospital for children in southern Vietnam including Ho Chi Minh City.
Children less than 16 years of age presenting with acute encephalitis of presumed viral etiology were enrolled. Diagnostic efforts included viral culture, serology and real time (RT)-PCRs. A confirmed or probable viral causative agent was established in 41% of 194 enrolled patients. The most commonly diagnosed causative agent was Japanese encephalitis virus (n = 50, 26%), followed by enteroviruses (n = 18, 9.3%), dengue virus (n = 9, 4.6%), herpes simplex virus (n = 1), cytomegalovirus (n = 1) and influenza A virus (n = 1). Fifty-seven (29%) children died acutely. Fatal outcome was independently associated with patient age and Glasgow Coma Scale (GCS) on admission.
Acute encephalitis in children in southern Vietnam is associated with high mortality. Although the etiology remains unknown in a majority of the patients, the result from the present study may be useful for future design of treatment and prevention strategies of the disease. The recognition of GCS and age as predictive factors may be helpful for clinicians in managing the patient.
| Viral encephalitis is associated with high morbidity and mortality in Vietnam. However little is known about the causes of the disease due to a lack of diagnostic facilities in this relatively resource-poor setting. Knowledge about the etiologies and clinical outcome of viral encephalitis is necessary for future design of intervention studies targeted at improvement of clinical management, treatment and prevention of the disease. We report the viral agents, clinical outcome and prognostic factors of mortality of encephalitis in children admitted to a referral hospital for children in southern Vietnam. We show that about one third of the enrolled patients die acutely, and that mortality is independently associated with patient age and Glasgow Coma Scale on admission. Japanese encephalitis, dengue virus and enterovirus (including enterovirus 71) are the major viruses detected in our patients. However, more than half of the patients remain undiagnosed, while mortality in this group is as high as in the diagnosed group. This study will benefit clinicians and public health in terms of clinical management and prevention of childhood encephalitis in Vietnam.
| Acute encephalitis is associated with high morbidity and mortality and affects both children and adults. There have been few population-based studies, reporting incidences ranging between 3.5 and 7.4 cases per 100.000 patient-years [1]. Viruses are regarded as the most important etiological agents of encephalitis worldwide. However, in the majority of cases a specific infectious etiology cannot be found. Furthermore, the specific causes of the illness show considerable geographic and age dependent variation. In a population-based study in the United Kingdom, herpes simplex virus (HSV) was the most common virus diagnosed, and the proportion of cases with an identified etiology was significantly lower in children (33%) than in adults (45%) [2]. In the California Encephalitis Project, a confirmed or probable infectious etiology was found in only 16% of cases, with HSV-1 most commonly found in adult patients and enteroviruses in children [3].
In Southeast Asian countries like Cambodia and Vietnam, Japanese encephalitis virus (JEV) has been the leading reported cause of acute encephalitis in children, accounting for 31% to 45% of cases [4], [5]. However, the causes of the disease in the remaining cases were not extensively studied. Improved insight into the specific viral etiology and pathogen-specific clinical outcome of acute encephalitis in this region is essential to guide strategies for prevention and clinical management. We conducted a one-year prospective descriptive study of children with acute encephalitis who were admitted to a pediatric referral hospital in Ho Chi Minh City, Vietnam.
The Childrens's Hospital Number One (CH1) in Ho Chi Minh City is an 850-bed pediatric referral hospital for the southern provinces of Vietnam. Each year, the hospital admits between 400–800 patients less than 16 years of age with acute symptoms of fever and altered consciousness (including seizures). Of these patients, approximately 40% are considered to have acute encephalitis of viral origin based on clinical judgment of pediatricians at CH1.
Children admitted between January 1 and 31 December 2004 with suspected acute encephalitis of viral origin, based on the clinical judgment of admitting physicians, and with no preexisting neurological conditions or evidence of bacterial meningitis by microscopy or culture of cerebrospinal fluid (CSF) samples, and no febrile convulsion (defined by a single convulsion lasting less than 15 minutes with regaining of consciousness within 60 minutes in a child between 6 months and 6 years of age) were eligible for inclusion in the study after provision of written informed consent by the patient's parents or legal guardians. Tests of human immunodeficiency virus and tuberculosis infections were not performed when recruiting the patients.
Detailed demographic and clinical data, including routine blood and CSF hematology and chemistry laboratory investigations, were collected on case record forms at enrollment and during follow-up. Clinical outcomes were defined as death, full recovery, and severe, moderate or minor neurological sequelae, defined based on neurological examination, degree of independent functioning and controllability of seizures. For etiological investigations, acute and convalescent CSF, plasma and serum samples were obtained at enrollment and around 2 weeks later or at discharge. In addition, throat and rectal swabs were collected in viral transport medium (VTM) at enrollment.
Depending on clinical sample types in which evident of viral infetion was identified, the encephalitis etiology was considered as definitive, probable or possible cause. Details on the diagnostic interpretation are presented in table 2.
Chi-square test, Fisher exact test, independent samples t test and Wilcoxon rank-sum test were used to compare data between groups of patients when appropriate by using either SPSS for Windows version 14 (SPSS Inc, Chicago, USA) or statistical software R version 2.9.0 (http://www.r-project.org). In addition, to analyze potential prognostic factors for fatal outcome, univariate analyses (including linear regression) and logistic regression were applied.
This study was approved by the Institutional Review Board of CH1 and the OXford TRopical Ethics Committee (OXTREC), University of Oxford, UK. Written informed consent was obtained from a parent or guardian of each enrolled patients.
A total of 194 patients with a clinical syndrome of acute encephalitis of presumed viral etiology were enrolled between January 1 and December 31, 2004.
Details of clinical features and outcome of enrolled patients are presented in Table 3. Overall mortality was observed in 57/194 (29%) of the patient, while 48/194 (25%) had neurological sequelae at discharge, including: severe sequelae in 10%, moderate in 10% and mild in 5% (Table 3).
Patients were enrolled throughout the year. JE occurred throughout the year but there was a peak in March and during the months of June and July consistent with the rainy season. No clear seasonality was observed for other etiologies but numbers were small (data not shown).
Since detection of virus or antibodies may differentially be affected by the timing of specimen collection, we analysed diagnostic yields in CSF specimens by PCR and serology in relation to illness day at the time of sampling (Figure 1). Overall, the median illness day at admission was 4 days (IQR: 3–6). No significant differences in illness days at specimen collection were observed between patients with detectable virus or IgM in CSF and those with negative diagnostic results. However, there was a trend towards a shorter illness duration at time of sampling in PCR positive patients (median, IQR: 3.5, 3–4 versus 4.8, 3–6, P = 0.1) (Figure 1).
Of the 57 fatal cases, 28 patients (49%) died within 7 days after onset of illness (Table 6). Twenty-nine (51%) of fatal cases were between 0 and 1 years of age compared to 43 of 137 (31%) surviving patients. Univariate analyses were used to screen for potential factors associated with fatal outcome (Table 7), and demonstrated that the occurrence of convulsions on admission, the presence of limb weakness, GCS, and age were all significantly associated with fatal outcome, whereas illness day on admission, history of convulsion, and gender were not (Table 7). However, only GCS and age remained independently associated risk factors for fatal outcome in logistical regression analysis (Table 7).
Acute encephalitis is an inflammation of the brain parenchyma, most commonly caused by viruses and associated with substantial morbidity and mortality. Worldwide, reported mortality ranges between 0–11% [3], [4], [13]–[17], but was substantially higher in our study: 30% of children died during hospitalization, with about half of deaths occurring within 3–7 days after the onset of illness, and more than half affecting infants. Furthermore, 25% of surviving children suffered from mild to severe neurological sequelae at discharge (including: severe sequelae in 10%, moderate in 10% and mild in 5%). Several prognostic factors for death or severe outcome of acute encephalitis have been proposed [14], [15]. Whereas, similar to other studies, univariate analyses in our study suggested associations between fatal outcome and age, convulsions at admission and limb weakness, GCS and age remained the only independent prognostic factors for fatal outcome in logistic regression analyses.
Our study illustrates the challenge of identifying the causative agents in children with acute encephalitis. A confirmed or probable etiology was identified in only 41% of enrolled patients which is within the same range as reported in other etiology studies [3], [4], [13], [14], [18]. Worldwide, the causes of encephalitis vary between geographical regions. While JEV was the most common cause of encephalitis in children in Cambodia, enteroviruses and Tick-borne encephalitis virus were the two most common viruses found in young patients in the United States and Sweden, respectively [3], [4], [19]. A study in China revealed enteroviruses, mumps virus and rubella virus as frequent causes of encephalitis in children between 7 months and 13 years of age [20]. Our study identified JEV, enteroviruses and DENV as the most common etiologies in southern Vietnamese children, accounting for 26%, 9.3%, and 4.6% of cases, respectively. The high prevalence of JEV in our patients is in accordance with a previous study in Ho Chi Minh City reporting a prevalence of 45% in children with encephalitis [5]. Similar to previous studies, JEV was associated with high mortality (16%) [5], [21], [22]. Our observations emphasize the need for JE vaccination programs in Vietnam and other regions of Southeast Asia where JEV is endemic [4]. Widespread JE vaccination in developing countries like Vietnam is complicated by high costs and the requirement of multiple vaccine doses. In our study population, only 19.5% of patients had received at least one dose of JE vaccine. There was a trend of lower vaccination rates in JE patients, but this did not reach statistical significance (data not shown).
Enteroviruses are well established causes of aseptic meningitis and encephalitis in young children. While reported case fatality rates of enteroviral central nervous system (CNS) infections are relatively low (0–7%) [3], [13], mortality of confirmed or probable enteroviral encephalitis in our patients was high with 9 of 18 (50%) of patients dying during hospitalisation. However, a definitive diagnosis of enteroviral encephalitis was only established in 4 of these 18 patients, 2 of whom died. In the remaining patients enteroviral encephalitis was not confirmed by virus detection in CSF but suspected based on virus detection in throat and rectum and a clinical syndrome consistent with the diagnosis. Hence, a definitive causative role of enteroviruses in these patients remains unclear, particularly since a convincing link has not yet to be established between detection of enteroviruses in non-CNS sites and encephalitis [23]. In addition, beside enterovirus 71 (EV71), we have not determined whether our patients were infected with enterovirus serotypes of particularly high virulence, such as coxsackievirus B4 and echovirus 11 which have been associated with high case-fatality rates among neonates [24]. Six of 18 (33%) enteroviral infections in our patient group were caused by EV71. In accordance with reported high mortality of EV71 CNS infections, 3 of these patients died [25].
CNS manifestations are rare clinical complications of dengue but are reported with an increasing frequency in endemic areas [26]–[28]. Dengue was found in 4% of 378 pediatric patients with suspected encephalitis in southern Vietnam [27]. Similarly, DENV was identified in nearly 5% of our study patients. The precise pathogenesis of dengue-associated CNS manifestations remains unclear [27], [29]. Patients in this study experienced mild clinical symptoms of dengue without shock or bleeding and all made a full recovery. However, the detection of virus and specific antibodies in CSF suggest that invasion and viral replication in the CNS may play a role in at least a proportion of patients with dengue-associated neurological symptoms.
Me Tri virus is a variant of Semliki Forest virus belonging to the genus Alphavirus isolated from mosquitoes collected in Vietnam [10], [30]. Previous serologic surveillance suggested that this virus might be a frequent cause of encephalitis in young patients in Vietnam with a prevalence of 14% [30]. However, the virus was not detected in any of our patients. Likewise, human parechoviruses are an emerging cause of meningitis and encephalitis in children and infants [31], [32], but were not detected in our study patients. As a previous report suggested that human parechovirus infections exhibit a two-yearly incidence peak [33], the absence of parechovirus infections in our patients may be due to the fact that our study period covered only one year. HSV encephalitis was found in only one patient in our study which is not unexpected considering the young age of our study group.
Bacterial pathogens, including S. pneumoniae and H. influenzae type B, were detected retrospectively by PCR in 12 children (6%), illustrating the difficulties in distinguishing acute viral encephalitis and bacterial meningitis in children based on clinical judgment only. Indeed, the clinical syndromes of these children seemed indistinguishable from the remaining group of patients, although CSF white cell counts were higher as expected (Table 3). Of note, 4 children with bacterial meningitis had evidence of concurrent viral encephalitis based on the detection of virus (EV, CMV) or specific IgM (JEV) in the CSF. These observations suggest that testing for other pathogens should still be considered when a single pathogen has been identified in the CSF. While the clinical relevance of these coinfections remain unclear, it is tempting to speculate about the interplay between viral and bacterial CNS infections, for example by facilitating entry of one or the other into the CNS compartment.
In 59% of the children, no confirmed or probable viral etiology could be identified. Several factors may contribute to this relatively low diagnostic yield.
Firstly, a proportion of children may not have suffered from viral or other infectious CNS illnesses. Other conditions that may have presented in this manner may include pretreated pyogenic meningitis, toxins, atypical bacteria, mycobacterial and other unknown or un-tested bacteria or viruses. As mentioned before, bacterial meningitis was in fact diagnosed retrospectively by PCR in 6% of enrolled children.
A reliable case definition for acute viral encephalitis would be helpful for triage and clinical management of patients, especially in the absence of diagnostic support. When retrospectively using a predefined case definition consisting of a) fever history of less than seven days, b) an alteration or reduction of consciousness (Glasgow coma scale≤14), c) at least one of the following symptoms or signs: seizures (excluding febrile convulsions, defined as a single convulsion lasting less than 15 minutes in patients between 6 months and 6 years of age), focal neurological signs, neck stiffness or cerebrospinal fluid (CSF) pleocytosis, d) no evidence of bacterial infection in CSF specimens, and e) no alternative diagnosis for the clinical syndrome during admission, the viral diagnostic yield only modestly increased from 41 to 45%, whereas definitive/probable laboratory diagnoses of viral CNS infections were established in 20 of 62 patients (32%) who did not meet the criteria of the case definition. Overall clinical outcome and patients characteristics were similar between the two groups even though there were slight differences in terms of patient origins, clinical outcome, history of fever, limb weakness, neck stiffness, percentage of patient with a GCS below 9 and CSF cell count (Table 3). The last five are more likely due biases toward the criteria of predefined case definition.
Diagnostic yield was similar when the criterion of a fever history of less than seven days and febrile convulsions were left out. Diagnostic yield was higher when CSF results were included into this case definition (White cell count <1000 10e6/L, CSF/blood glucose ratio >50%, protein <0.45 g/L, CSF lactate <4 mmol/L): 49% and 37% in patients that did or did not meet the case definition, respectively. However, two thirds of all patients did not meet the case definition. Clearly, further studies are needed to optimize case definitions for viral encephalitis.
The limited detection of viral pathogens in our patients may have been due to clearance of virus from CSF at the time of clinical presentation, as is the case for JEV [34]. This may also explain why, except for dengue virus, flaviviruses were not detected in any of our patients, including those with serologically confirmed JEV infections. The importance of early sampling for reliable diagnostics is suggested by findings in our study: most of the patients were admitted relatively late in the course of illness, possibly in part because of referral delays, and a trend was observed towards shorter illness duration at the time of sampling in patients with positive virus-specific PCRs. The association between earlier sampling time and PCR positivity was statistically significant when analysis was restricted to 132 patients fulfilling our predefined case definition of acute viral encephalitis (3 days versus 4 days respectively, P = 0.03).
As diagnostic assays of some viral pathogens are not available in our laboratory, we did not exhaustively look for all potential infectious causes of encephalitis in Vietnam. Thus various etiologies (e.g. measles virus, henipaviruses and Banna virus) may have been missed. In addition, undiagnosed encephalitis may be caused by as yet unknown human or zoonotic pathogens. As socio-economic, ecological and environmental factors in Southeast Asia may favor the emergence of novel zoonotic or vector-borne pathogens [35], the circulation of novel pathogens in Vietnam is probable. Therefore, efforts to identify novel or previously unrecognized pathogens in these undiagnosed patients are essential for future prevention and treatment strategies.
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10.1371/journal.pntd.0004213 | siRNA-Mediated Silencing of doublesex during Female Development of the Dengue Vector Mosquito Aedes aegypti | The development of sex-specific traits, including the female-specific ability to bite humans and vector disease, is critical for vector mosquito reproduction and pathogen transmission. Doublesex (Dsx), a terminal transcription factor in the sex determination pathway, is known to regulate sex-specific gene expression during development of the dengue fever vector mosquito Aedes aegypti. Here, the effects of developmental siRNA-mediated dsx silencing were assessed in adult females. Targeting of dsx during A. aegypti development resulted in decreased female wing size, a correlate for body size, which is typically larger in females. siRNA-mediated targeting of dsx also resulted in decreased length of the adult female proboscis. Although dsx silencing did not impact female membrane blood feeding or mating behavior in the laboratory, decreased fecundity and fertility correlated with decreased ovary length, ovariole length, and ovariole number in dsx knockdown females. Dsx silencing also resulted in disruption of olfactory system development, as evidenced by reduced length of the female antenna and maxillary palp and the sensilla present on these structures, as well as disrupted odorant receptor expression. Female lifespan, a critical component of the ability of A. aegypti to transmit pathogens, was also significantly reduced in adult females following developmental targeting of dsx. The results of this investigation demonstrate that silencing of dsx during A. aegypti development disrupts multiple sex-specific morphological, physiological, and behavioral traits of adult females, a number of which are directly or indirectly linked to mosquito reproduction and pathogen transmission. Moreover, the olfactory phenotypes observed connect Dsx to development of the olfactory system, suggesting that A. aegypti will be an excellent system in which to further assess the developmental genetics of sex-specific chemosensation.
| Only adult female mosquitoes, which require blood meals for reproduction, bite humans and spread diseases. The genes that regulate development of sex-specific traits may therefore represent novel targets for mosquito control. Here, we examine the effects of silencing the sex-determination gene doublesex (dsx) during development of the human disease vector mosquito Aedes aegypti. Targeting of dsx resulted in decreased length of the female wing and proboscis, ovary and reproductive defects, and disruption of olfactory system development. Female lifespan, a critical aspect of mosquito pathogen transmission, was also significantly reduced in adult females following developmental targeting of dsx. The results of this investigation demonstrate that silencing of dsx during A. aegypti development disrupts multiple sex-specific morphological, physiological, and behavioral traits of adult females, a number of which are directly or indirectly linked to mosquito reproduction and pathogen transmission. The results obtained also connect Dsx to development of the mosquito olfactory system, suggesting that A. aegypti will be an excellent system in which to further assess the developmental genetics of sex-specific chemosensation.
| Most animal species display sexually dimorphic behaviors, the majority of which are linked to sexual reproduction [1]. Disease vector mosquitoes are excellent subjects for studies that explore the biological basis of sexual dimorphism. Only adult female mosquitoes, which require blood meals for reproduction, bite humans and transmit pathogens. Females differ from males in morphological, physiological, and behavioral traits that are critical components of their ability to spread diseases, including feeding behaviors, longevity, and susceptibility to infections. Researchers have therefore had a long-standing interest in the potential to manipulate genetic components of the sex determination pathway and sexual differentiation for vector control. Moreover, success of the sterile insect technique (SIT) and other genetic strategies designed to eliminate large populations of mosquitoes is dependent upon efficient sex-sorting of males and females, and many have argued that such sex-sorting, as well as insect sterilization itself, is best achieved through large-scale genetic or transgenic approaches (reviewed by [2, 3]). Although the genes that regulate sex-specification and development of mosquito sexual dimorphism may represent novel targets for vector control, most of these genes have not yet been functionally characterized in vector mosquitoes.
Research in Drosophila melanogaster identified a mutation in the doublesex (dsx) gene that transformed males and females into intersexes [4]. Subsequent molecular analyses demonstrated that the dsx gene encodes a key terminal transcription factor in the sex-determination pathway that controls Drosophila male and female sexual differentiation [5–7]. Drosophila dsx pre-mRNAs are spliced in a sex-specific manner [8, 9], generating male (DsxM) and female (DsxF) proteins with a common N-terminus and DNA-binding domain, but distinct male and female C-termini that differentially regulate sex-specific gene expression (reviewed by [10, 11]). Studies in diverse insects have demonstrated that although primary signals for sex determination vary within the insect order [12], all relay their signal through the sex-specific splicing of dsx, which plays a well-conserved role as a transcription factor that regulates expression of downstream target genes which contribute to sexual differentiation [13–25]. The roles of Dsx have been particularly well studied in a variety of beetle species [17, 18, 19, 21, 26]. For example, sex-specific Dsx splice forms are known to regulate sexually dimorphic exaggerated horn development in two species of beetles, Onthophagus taurus [18] and the rhinoceros beetle Trypoxylus dichotomus [19]. Dsx function was also characterized in the red flour beetle Tribolium castaneum, in which it is required for oocyte development, egg production, and egg hatching [17]. More recently, Dsx was shown to regulate sexually dimorphic mandible development in the stag beetle Cyclommatus metallifer [21]. Here, we examine the function of dsx during development of the disease vector mosquito Aedes aegypti, which exhibits innate sexually dimorphic behaviors that contribute to the transmission of dengue, yellow fever, and chikungunya viruses [27].
Salvemini et al. [28] detected male (dsxM) and female (dsxF) splice variants of dsx in A. aegypti. Recently, Hall et al. [29] described characterization of a male-determining locus (M-locus) gene, Nix, a male-determining factor (M factor) in A. aegypti that is required and sufficient to initiate male development, and which encodes a potential splicing factor. The absence of Nix shifts the alternative splicing of dsx toward the female-specific dsxF splice form, suggesting that Nix normally promotes splicing of dsxM. Although the sex-specific dsx splice forms likely direct sexually dimorphic mosquito development, functional analysis of dsx in A. aegypti is lacking. In a recent study from our laboratory [30], we detected sex-specific dsx expression in the pupal brain suggesting that sexually dimorphic neural development in A. aegypti may require dsx function. In support of this, a search of the A. aegypti genome sequence uncovered 732 Dsx consensus binding sites, 48 of which flank dimorphically expressed genes identified in male vs. female pupal head transcriptome microarray experiments [30]. A. aegypti genes flanked by Dsx consensus binding sites group under a number of significant gene ontology terms, many of which are linked to neurological processes or neural development, suggesting that Dsx may regulate sex-specific gene expression in the developing mosquito brain. To examine this, we [30] used small interfering RNA (siRNA)-mediated gene targeting to silence dsx during A. aegypti pupal development. In our study, siRNAs corresponding to different target sequences in exon 2, which is common to male and female splice variants, were injected into A. aegypti pupae. These targeting experiments demonstrated that Dsx is required for the regulation of sex-specific gene expression during A. aegypti neural development. The results of our initial investigation [30], in conjunction with studies performed in a variety of insects (reviewed by [24]), support the hypothesis that Dsx regulates the development of sex-specific characters in A. aegypti. Here we test this hypothesis by examining the impact of larval and pupal dsx silencing on the development of sex-specific traits in adult female mosquitoes.
Whyard et al. [31] recently used RNA interference (RNAi) to target the female-specific isoform of A. aegypti dsx (dsxF) during development. Their dsx targeting protocol differed from that which we used [30] in that they used longer pieces of dsRNA (as opposed to siRNA) targeting the two female-specific dsx exons (as opposed to exon two, which is common) that was delivered by soaking mosquito larvae in dsRNA or by feeding the larvae E. coli expressing dsx-targeting dsRNA (rather than through microinjection). Although the Whyard et al. [31] dsx targeting strategies resulted in highly male-biased populations of mosquitoes, the number of dsx dsRNA-treated larvae that developed into adults was halved relative to the negative controls, and with no significant increase in the number of males observed, suggesting that the majority of females simply failed to survive to adulthood, which did not permit analysis of sex-specific characters in these animals. Our ability to analyze late pupae microinjected with siRNAs targeting a separate region of the dsx gene (exon two) suggested that this targeting strategy [30], which differs from the Whyard et al. [31] procedure as noted above, may facilitate analysis of adult female phenotypes. Such analyses are of great interest given that adult females are responsible for transmission of pathogens that result in human diseases. Indeed, we found that use of a pupal microinjection procedure to deliver siRNA targeting exon 2 [30], as well as the use of chitosan nanoparticles [32–34] to deliver these same exon 2 targeting siRNAs to A. aegypti larvae, silenced dsx while permitting female survival. Use of these targeting strategies allowed us to examine adult female morphological, physiological, and behavioral phenotypes that result from developmental silencing of dsx.
The A. aegypti Liverpool-IB12 (LVP-IB12) strain (from D.W. Severson, Notre Dame, IN), from which the genome sequence [35] was generated, was used in this investigation. The mosquitoes were reared as described [36], except that an artificial membrane sheep blood (HemoStat Laboratories, Dixon, CA) feeding system was utilized. Mosquitoes were maintained in an insectary at 26°C, at ~80% humidity, and under a 12 hr light/12 hr dark cycle with 1 hr crepuscular periods at the beginning and end of each light cycle. Mosquito larvae were fed on a suspension of dried beef liver powder, and adults were provided cotton soaked with 10% sugar solution. Pupae were sexed on the basis of differing pupal tail morphology as described by Christophers [37]. Adults were sexed on the basis of external characters [37]. Sexes of dsx-silenced adults were further confirmed in a subset of animals through dissection to assess the presence of testes or ovaries.
In situ hybridization was performed as previously described [38]. Riboprobes corresponding to the following genes were synthesized according to the Patel [39] protocol: OR 2 (AAEL005999), OR 9 (AAEL006005), OR 62 (AAEL011796), and OR 123 (AAEL017537) and dsx (AAEL009114; probe corresponded to exon 2 which is common to males and females). At least 20 tissue specimens were processed for each in situ hybridization experiment, and at least two replicate experiments were performed. A sense riboprobe was used as a control in all hybridization experiments. Immunohistochemical staining was performed as described previously [33, 40] using Texas Red-X Phalloidin and TO-PRO-3 iodide, both which were obtained from Molecular Probes (Eugene, OR). Following processing, the tissues were mounted and imaged on a Zeiss 710 confocal microscope using Zen software, and images were analyzed with FIJI ImageJ and Adobe Photoshop CC 2014 software. For OR transcript analyses, mean gray values (average signal intensity over the selected area) were calculated for digoxigenin-labeled OR transcript signal in 25 control or experimental antennae combined from two replicate experiments. Data were statistically analyzed using one-way ANOVA followed by the Bonferroni post hoc test.
Targeting of dsx (AAEL009114) was performed as described previously [30]. Two siRNAs, dsx-KD A and dsx-KD B, that correspond to different target sequences in exon 2, which is common to both the male and female dsx splice variants, were used in dsx silencing experiments. The sequences of these siRNA duplexes, which were purchased from Integrated DNA Technology (IDT, Coralville, IA) and confirmed through BLAST searches to have no significant homology to A. aegypti genes other than dsx, are as follows:
Dsx-KD A: 5’ rCrArGrGrArArCrArGrArCrGrArCrGrArArCrUrUrGrUrCAA3’ / 5’rUrUrGrArCrArArGrUrUrCrGrUrCrGrUrCrUrGrUrUrCrCrUrGrArG3’, and Dsx-KD B: 5’rCrArArGrArUrCrGrCrUrGrGrArUrGrGrUrArArArGrArUGT3’ / 5’rArCrArUrCrUrUrUrArCrCrArUrCrCrArGrCrGrArUrCrUrUrGrCrG3’. All phenotypes were confirmed following knockdown (KD) with both dsx-KD A and dsx-KD B, suggesting that none of the phenotypes reported herein were the result of off-site targeting by either siRNA. A scrambled version of dsx KD B, an siRNA duplex lacking significant sequence homology to any genes in the A. aegypti genome, was used for control experiments:
5’rGrArArGrArGrCrArCrUrGrArUrArGrArUrGrUrUrArGrCGT3’ / 5’rArCrGrCrUrArArCrArUrCrUrArUrCrArGrUrGrCrUrCrUrUrCrCrG3’. None of the phenotypes reported were observed in control-injected animals, which were not significantly different than wild type animals for any of the phenotypes assessed.
siRNA was microinjected into pupae [30] as described previously. Chitosan/siRNA-mediated targeting of dsx was performed using the procedure described by Mysore et al. [33], which was adapted from Zhang et al. [32] and is described in detail in Zhang et al. [34]. Silencing of dsx was confirmed through in situ hybridization as discussed in the recent siRNA-mediated dsx gene targeting study [30]. To quantify knockdown levels, mean gray values were calculated as described [41] for digoxigenin-labeled dsx transcript signal in brains and antennae from minimally 20 control or experimental specimens combined from two separate replicate experiments. These data were statistically analyzed using one-way ANOVA followed by the Bonferroni post hoc test.
Wing length and area, proboscis, antenna, and maxillary palp lengths were assessed in females following chitosan/siRNA nanoparticle mediated targeting of dsx as described above. For these experiments, structures were dissected from sugar-fed ~10 day old adult female mosquitoes, mounted and analyzed with a Zeiss Axioimager equipped with a Spot Flex camera. Areas and lengths were measured using Fiji Image J software. Wing lengths were measured from the apical notch to the axillary margin, excluding the wing fringe as described in [37]. To minimize measurement errors, all appendage measurements were determined by a single researcher. Data from at least four replicate experiments were combined for statistical comparisons, which were performed using Graphpad Prism 6 software with one-way ANOVA followed by the Bonferroni post hoc test. Maxillary palp and antennal sensillary morphology was further assessed (following pupal microinjection of siRNA) through scanning electron microscopy (SEM) with an FEI-MAGELLAN 400 FESEM as previously described [42]. Briefly, female heads were placed in acetone for 24 hrs and subjected to critical point drying followed by sputter coating with gold/Iridium. Control vs. dsx-KD olfactory structures were visualized under SEM and assessed for numerical and structural anomalies. Data from replicate experiments were combined and statistically analyzed with one-way ANOVA followed by the Bonferroni post hoc test.
Blood feeding behavior was visually assessed through analysis of engorged female abdomens following plasma membrane blood feedings which were one hour in duration. The number of eggs produced per female (fecundity) and eggs produced per female that generated first instar larvae (fertility) were assessed as described by Hill et al. [43]. The number of fertile females, which served as evidence of successful mating, was also recorded. Survivorship, which was monitored in individual females following completion of the fecundity assays, was performed and analyzed as described by Hill et al. [43]. Ovary length was assessed in four day-old adults prior to blood feeding, as well as in 10 day post blood-fed females. The number of follicles was assessed in four day-old adults prior to blood feeding, while ovariole number and length was assessed five days post blood feeding. For these assays, ovaries were immunohistochemically processed as described above, mounted, and then analyzed with a Zeiss 710 confocal microscope using Zen software. Scanned images were analyzed using FIJI and Adobe Photoshop CC 2014 software. With the exception of lifespan, which was analyzed using Kaplan-Meier survival curves, data were analyzed using Graphpad Prism 6 software with one-way ANOVA followed by the Bonferroni post hoc test.
Introns as well as 5’ flanking sequences 0–5 kb upstream of the open reading frames of A. aegypti OR genes were exported from VectorBase [44]. These sequences were searched (using ClustalW) for the Clough et al. [45] Dsx consensus binding site sequence: VHHACWAWGWHDN. Sequences with no more than one mismatch are reported.
The following genes were studied in this investigation: dsx (AAEL009114), OR 2 (AAEL005999), OR 9 (AAEL006005), OR 62 (AAEL011796), and OR 123 (AAEL017537).
siRNA-mediated gene targeting, which was employed in a recent analysis of dsx function in the developing A. aegypti brain [30], was used to silence dsx during A. aegypti larval and/or pupal development. For larval silencing experiments, dsx-KD A, dsx-KD B, or control chitosan/siRNA nanoparticles were fed to larvae [34]. Silencing of dsx in chitosan/siRNA nanoparticle-fed or siRNA-injected animals was confirmed through in situ hybridization experiments. Quantification of dsx signal through mean gray value analyses in control vs. dsx-silenced animal tissues indicated that significant knockdown levels were achieved (S1 Fig). A summary of phenotypes assessed in this study and the delivery method used for analysis of each phenotype is provided in S1 Table. For analysis of structures/traits that develop in the late pupal stage, dsx-KD A or dsx-KD B siRNA was delivered through pupal microinjection, which generates more effective silencing at the late pupal stage, at which time some recovery of dsx expression is observed in animals that were fed chitosan/siRNA nanoparticles as larvae (S1 Fig).
Chitosan/siRNA larval dsx targeting experiments did not impact animal survival to adulthood (Table 1, n = 50 per control or experimental condition; four replicate experiments performed, P>0.05) nor impact our ability to distinguish male and female animals on the basis of their external morphology. However, as discussed further below, this chitosan/siRNA silencing of dsx in A. aegypti larvae led to morphological defects in the wing, proboscis, maxillary palp, and antenna. For analysis of structures/traits that develop in the late pupal stage, dsx-KD A, dsx-KD B, or control siRNAs were microinjected into female pupae [30]. As with the larval targeting experiments, survival to adulthood was not impacted in these microinjection dsx targeting experiments (n = 50 per control or experimental condition; three replicate experiments performed, P>0.05), and the mosquitoes could still be identified as females on the basis of the external morphology. However, as detailed herein, fertility and fecundity defects that correlated with ovary defects were observed in these animals. Olfactory phenotypes were also detected, and female lifespan decreased.
A. aegypti body size, which is larger in females, can be assessed through analysis of wing size, a proxy for adult body size [46, 47]. Wing areas (p<0.0001, Fig 1A) and lengths (p<0.0001, Fig 1B) are significantly decreased in females fed dsx-KD A or dsx-KD B siRNA nanoparticles as larvae. On average, the areas of dsx-KD A and dsx-KD B wings are 19% and 21% smaller than control-fed animal wing areas, respectively (Fig 1A). Likewise, wing lengths of dsx-KD A animals are 15% smaller than control-fed females, while the lengths of dsx-KD B adult females are reduced by 16% (Fig 1B).
Adult females also have an elongated proboscis that is critical for blood feeding. In comparison to control-fed animals, proboscis length is significantly reduced in adults fed with dsx-KD A (P<0.00001, Fig 1C) or dsx-KD B (P<0.00001, Fig 1C) nanoparticles as larvae. Proboscis lengths of dsx-KD A females are 11% smaller than control-fed females, while the lengths of dsx-KD B adult females are reduced by 18%. Lengths of the antenna (Fig 1D) and maxillary palp (Fig 1E) were also assessed, and both are significantly reduced in females fed with dsx-KD A or dsx-KD B nanoparticles (P<0.0001). The antennae of animals fed with dsx-KD A nanoparticles are 23% shorter than those of control-fed animals, while those fed dsx-KD B nanoparticles are reduced by 14% (Fig 1D). The maxillary palp of dsk-KD A animals is 11% reduced in length with respect to the control, while maxillary palp length is 18% shorter in dsx-KD B females (Fig 1E).
Fertility and fecundity were assessed in dsx KD vs. control A. aegypti females mated to wild type male mosquitoes. The number of eggs laid per female (fecundity) is significantly reduced (P<0.0001) in adult females that were injected as pupae with dsx-KD A or dsx-KD B siRNA as compared to control-injected females (Fig 2C). On average, females injected with dsx-KD A as pupae lay 16% fewer eggs than control-injected females, while those injected with dsx-KD B lay 21% fewer eggs (Fig 2C). Likewise, fertility (the percentage of hatched eggs) is significantly reduced (P<0.0001) in adult females that had been injected as pupae with dsx-KD A or dsx-KD B siRNA (Fig 2D). On average, the fertility of females injected with dsx-KD A or dsx-KD B females is 9% and 19% less, respectively, than that of control-injected females (Fig 2D).
To explore the cause of this reduced fertility and fecundity, mating, blood feeding behavior, and ovary histology were assessed in dsx-targeted females. In comparison to control females, no significant differences (P>0.05) were observed in the percentages of females that took blood meals (Fig 2A) or mated (Fig 2B) following larval nanoparticle or pupal microinjection delivery of dsx-KD A or dsx-KD B siRNA. Pre-blood meal ovaries were assessed at 4 days post eclosion, at which time the follicles are arrested until the mosquito takes a blood meal [48]. These experiments revealed defects in animals fed dsx-KD A or B siRNAs that were confirmed and quantified in females microinjected with dsx-targeting siRNAs as pupae (Fig 3). The length of pre-blood meal ovaries in dsx-KD A and dsx-KD B animals is significantly less than that of control-injected females (P<0.0001, Fig 3A, 3C1, 3C2 and 3C3). On average, the length of both dsx-KD A and dsx-KD B female ovaries is 26% shorter than control ovaries (Fig 3A). Five days post-blood meal, ovary length in 10–12 day old females that had been injected with dsx-KD A and dsx-KD B siRNA as pupae is also significantly less than that of control-injected females (P<0.0001, Fig 3B, 3D1, 3D2 and 3D3). On average, the length of dsx-KD A post-blood meal ovaries is 35% less than that of control ovaries, while dsx-KD B female ovaries are an average 34% shorter in length than control ovaries following blood meals (Fig 3B; P<0.0001). The number of ovarioles per ovary (Fig 3E) and ovariole length (Fig 3F) are also significantly reduced in post-blood meal ovaries following pupal injection with dsx-KD A or dsx-KD B siRNA (P<0.001).
Given the significantly shorter lengths of the female antenna and maxillary palp following silencing of dsx (Fig 1D and 1E), the olfactory systems of these animals were assessed in more detail. The lengths of the most abundant type of antennal sensilla, Trichoid sensilla (sTrichodea) were quantified through analysis of SEM images of the adult female antenna (Fig 4A and 4C). The length of these structures in females that had been injected with dsx-KD A or dsx-KD B siRNA as pupae is significantly less than that of control-injected females (P<0.0001, Fig 4C). On average, the length of dsx-KD A female antennal sTrichodea is 36% less than that of control sTrichodea, while dsx-KD B female sTrichodea are 34% shorter in length than control sTrichodea (Fig 4C). The lengths of Basiconic sensilla (sBasiconica) were assessed through analysis of SEM images of the adult female maxillary palp (Fig 4B and 4D). Likewise, the lengths of these structures are significantly reduced in females that had been injected with dsx-KD A or dsx-KD B siRNA as pupae (P<0.0001, Fig 4D). On average, the length of dsx-KD A female sBasiconica is 29% less than that of control sBasiconica, while dsx-KD B female sBasiconica are 37% shorter in length (Fig 4D).
Expression of several odorant receptor (OR) genes was assessed in A. aegypti adults. Bohbot et al. [49] reported female-specific expression of OR 62 and OR 123 that was detected through qRT-PCR studies. WhoIe-mount in situ hybridization detected expression of OR 62 (Fig 5A) and OR 123 (Fig 5D) transcripts in the antennae of adult females. Sequences that match the Dsx consensus binding site sequence [45] were identified upstream of the OR 62 and OR 123 open reading frames (S2 Table). Although it was not biochemically assessed whether Dsx can bind to these sequences, expression of both OR genes is disrupted in the antennae of adult females that had been microinjected with dsx-KD A (Fig 5B and 5E) or dsx-KD B siRNA (Fig 5 and 5F) as pupae. Mean gray scale analyses indicated that the OR 62 and OR 123 transcript signals in the antennae of dsx-KD A and dsx-KD B animals were significantly reduced in comparison to those of control-injected animals (P<0.0001; S3 Table). Likewise, sequences that match the Dsx consensus binding site sequence [45] were identified upstream of the OR 2 and OR 9 open reading frames (S2 Table). Adult female expression of both genes (Fig 5G and 5J) is disrupted in the antennae of females injected with dsx-KD A (Fig 5H and 5K) or dsx-KD B (Fig 5I and 5J) as pupae. Mean gray scale analyses for these expressions studies demonstrated that the OR 2 and OR 9 transcript signals in the antennae of dsx-KD A and dsx-KD B animals were also significantly reduced in comparison to those of control-injected animals (P<0.0001; S3 Table). Thus, dsx silencing resulted in multiple OR expression defects in adult female mosquitoes.
The impact of developmental targeting of dsx on adult female survival was also assessed. The median survival rates for animals injected with control, dsx-KD A, and dsx-KD B siRNA were found to be 40, 23, and 25 days, respectively. In comparison to control-injected animals, life span was significantly decreased in individuals injected with dsx-KD A (P<0.001) or dsx-KD B (P<0.001) siRNA (Fig 6). These data demonstrate that silencing dsx during pupal development results in a significantly shorter female adult lifespan.
Despite the long-standing interest in genes that regulate sex determination and sex differentiation in mosquitoes, functional genetic characterization of such loci has been a challenge. Here, siRNA-mediated gene silencing was applied for successful functional characterization of A. aegypti dsx, a terminal transcription factor in the insect sex determination pathway, during development of female mosquitoes. The adult phenotypes examined fell into four general categories that are discussed in more detail below: growth (Figs 1 and 3), reproduction (Figs 2 and 3), olfactory (Figs 4 and 5), and life span (Fig 6).
Body size is a sexually dimorphic trait in A. aegypti, in which females are larger than males [37]. Loss of dsx function resulted in significantly smaller wing size, a correlate for body size (Fig 1A and 1B). The impact of dsx silencing was observed in a number of other tissues, including the proboscis, antenna, maxillary palp (Fig 1C,1D, 1E and 1F), ovaries (Fig 3) and sensilla (Fig 4), all of which were significantly smaller with respect to control animals. Although one might have expected to observe a longer male-like maxillary palp in dsx-silenced adult females, this was not found to be the case. Interestingly, our preliminary data suggest that much like females, silencing of dsx in male mosquitoes results in decreased body size and appendage lengths. These findings suggest that Dsx may function in A. aegypti to positively regulate growth of both sexes. Dsx has been associated with growth in a number of other insects and in additional tissue types. For example, in a study of Nasonia species, Loehlin et al. [50] found that wing size reduction correlated with an increase in dsx wing expression levels that is specific to developing males of this species. Sex-specific Dsx splice forms are known to regulate sexually dimorphic exaggerated male horn development in two species of beetles, O. taurus [18] and the rhinoceros beetle T. dichotomus [19]. In male O. taurus, silencing of dsx reduced horn development in large males, while silencing of dsx in females resulted in induction of ectopic, nutrition-sensitive horn development in females that are otherwise hornless. Comparable results were obtained in T. dichotomus [19]. Dsx also regulates sex-specific mandible growth, which is exaggerated in Cyclommatus metallifer males [21].
Our recent study demonstrated that genes linked to the cell cycle are upregulated in A. aegypti females [30]. These genes, which are likely associated with increased growth of female tissues, may be direct or indirect targets of Dsx. In support of this notion, cyclin dependent kinase 4/6 (cdk4/6), a positive regulator of cellular growth in D. melanogaster [51, 52], is upregulated in the A. aegypti female pupal brain. Dsx consensus binding sites flank the A. aegypti cdk 4/6 gene, and sexually dimorphic expression of this gene in the pupal brain is disrupted by dsx knockdown [30]. Interestingly, Cyclin D, the Cyclin associated with Cdk4/6 in D. melanogaster [53], was recently identified as a Dsx target gene in Drosophila [45]. It is therefore likely that loss of Cyclin D-Cdk4/6 function may be at least in part responsible for the size differences observed upon silencing of dsx in both sexes of A. aegypti. Additionally, Gotoh et al. [21] identified a link between dsx and the juvenile hormone (JH) signaling pathway, demonstrating that female-specific splice variants of C. metallifer dsx contribute to the insensitivity of female mandibles to JH. It would therefore be interesting to determine if sex-specific Dsx splice forms regulate JH-responsiveness in A. aegypti.
Loss of Dsx function results in sterility in D. melanogaster [4] and Bombyx mori [20], and RNAi targeting of dsx during pupal development in T. casteneum was found to result in decreased fertility and fecundity [17]. Decreased female fertility and fecundity were observed when dsx was silenced during female A. aegypti pupal development (Fig 2). These differences correlated with decreased adult ovary length both pre- and post-blood meal, as well as a decreased ovariole length and number in blood-fed adult females (Fig 3). In D. melanogaster, Dsx was found to play early roles in the development of the female/male genitalia and analia, which are both derived from the larval genital imaginal disc. D. melanogaster Dsx regulates the anterior/posterior organizer to control growth of female or male genital primordia, and then it acts in a sex-specific manner to direct differentiation of each male or female primordium into the defined adult structures present in either sex [54]. Dsx may have similar roles in A. aegypti, and so it would be interesting to assess its function at the level of the genital primordia, the development of which has not yet been assessed. Here, we focused on later stages of ovary development, which in mosquitoes is dependent upon the acquisition of a blood meal, and which has been better characterized [48].
Vitellogenesis, the synthesis and secretion of yolk protein precursors (YPPs), is a critical event in the mosquito reproductive cycle that is activated in response to the blood meal [55, 56]. In D. melanogaster, which does not require a blood meal for reproduction, the vitellogenin subunit yolk protein-1 (yp1) and yp2 genes are targets of Dsx [57, 58]. Similarly, the T. casteneum YPP vitellogenin (vg) and vitellogenin receptor (vgr) genes were identified as targets of Tc Dsx [17]. Thus, one might speculate that Dsx regulation of YPP genes is conserved in insects. However, the A. aegypti YPP genes lack any obvious Dsx binding sites, and if Dsx regulates expression of these genes in A. aegypti, it is likely to do so indirectly. Interestingly, juvenile hormone (JH) is known to regulate expression of A. aegypti Vg-A [59]. Although we were unable to identify any obvious Dsx consensus binding site sequences associated with components of the JH signaling pathway in A. aegypti, Gotoh et al. [21] recently demonstrated that female-specific splice variants of C. metallifer dsx contribute to the insensitivity of female mandibles to JH. It would therefore be interesting to determine if links between Dsx and JH responsiveness exist in A. aegypti.
Dsx likely regulates A. aegypti female reproduction in many additional ways. Although we did not detect any significant impacts of Dsx silencing on membrane blood feeding behavior or the ability to mate in the laboratory setting (Fig 2A and 2B), it is not known how these mosquitoes would perform in the wild. Furthermore, Lee et al. [60] recently identified a Dsx-positive neuronal pathway in D. melanogaster that controls sperm ejection and storage. When the neuronal signaling pathway in the brain, which consists of Diuretic hormone 44 (Dh44) and its receptor (Dh44R1), is suppressed, the brain expedites sperm ejection from the uterus, resulting in decreased fecundity. Thus, Dsx signaling could have multiple impacts on female reproduction in A. aegypti. Gaining a fuller understanding of these impacts will likely require much more detailed knowledge of A. aegypti reproductive behaviors.
This investigation has linked Dsx with the expression of OR genes. This linkage, which to our knowledge has yet to be identified in other organisms, provides insight into the regulation of sex-specific olfactory development. Silencing of dsx during pupal development was found to disrupt expression of two female ORs, 62 and 123 (Fig 5). Although the functions of these ORs have yet to be characterized in A. aegypti, upregulation of the expression of these genes by DsxF may contribute to female-specific olfactory-driven behaviors. The detection of Dsx consensus binding site sequences upstream of the OR 62 and 123 open reading frames (S2 Table) suggests that their expression may be regulated directly by Dsx, but this has not yet been directly assessed. Dsx also positively regulates expression of ORs 2 and 9 (Fig 5). The function of A. aegypti OR 9 is not known. However, OR 2, which is well conserved among mosquitoes, is known to be activated by indole, a major volatile component of human sweat that is also implicated in oviposition site selection [61, 62].
To date, analysis of the development of sexual dimorphism in the olfactory system has largely centered on analysis of the roles of fruitless (fru), which also encodes a terminal transcription factor in the sex determination pathway that is spliced in a sex-specific manner [8]. Cachero et al. [63] performed a global search for sexually dimorphic structural differences in the Drosophila brain, as well as a saturating clonal analysis of Fru-positive neurons. They noted that the proportion of cells in the D. melanogaster brain that expresses Dsx is smaller and partially overlaps with Fru, an interesting observation given that Neville et al. [64] suggested that Drosophila Dsx and Fru may act together, either in a physical complex or through co-regulation of target genes, to control sex-specific neural development. It will therefore be interesting to functionally assess the roles of Fru during neural development in A. aegypti.
Drosophila researchers have begun to link Dsx and Fru to specific sexually dimorphic neural physiologies, neural circuitries, and behaviors, and so another challenge will be to further develop genetic technologies with the goal of being able to perform comparably technical analyses in mosquitoes. For example, in D. melanogaster, the activation of a set of Fru-positive olfactory receptor neurons (ORNS) that express OR67d, which responds to male pheromone cis-vaccenyl acetate, was found to inhibit male courtship of other males and induce female receptivity to other males. The ORNs expressing 67d converge to a single glomerulus, DA1, in both the sexes, but the projections from the DA1 glomerulus to the protocerebrum were found to be sexually dimorphic, suggesting that differential behaviors induced by this pheromone result from sex-specific neural circuitries [65]. Kohl et al. [66] further demonstrated that sex-specific wiring induces differential responses to cVA pheromone inputs, suggesting that different fru isoforms function as a bidirectional switch to activate different behaviors in males and females. In another study, reduction of Ecdysone receptor-A in FruM-positive neurons, which is associated with an increase in male-male courtship activity, was found to result in significant reduction in the size of two antennal lobe glomeruli, suggesting that EcR-A is required for establishment of male-specific neuronal architecture in the D. melanogaster olfactory system [67]. These findings suggest that in addition to differences in OR expression, changes in the overall neural circuitry responding to the odorant may induce dimorphic behaviors in males and females, and it will be interesting to examine this in A. aegypti in the future.
As discussed by Brady et al. [68], the survival of arthropod vectors is one of the most critical components of pathogen transmission. Increased survival results in the production of more offspring. It also increases the likelihood of the arthropod to become infected, to disperse over greater distances once infected, to survive long enough to transmit the pathogen, and to deliver a greater number of infectious bites during its lifespan. Thus, small changes in survival rates could have large impacts on pathogen transmission [69–72], and vector control strategies that shorten vector lifespan may represent new alternative control strategies [73–75].
The results of this study demonstrate that targeting dsx during female pupal development significantly reduces adult A. aegypti female lifespan (Fig 6). The genetic manipulation of sex-determination gene expression in D. melanogaster has been shown to impact lifespan [76]. While overexpression of dsxF during male development was lethal to males and females (with a limited number of female escapers), overexpression of dsxF in adults dramatically reduced the life span of both males and females. Overexpression of the male isoform of fru in males or females yielded similar results. Interestingly an RNAi line targeting fru reduced lifespan in D. melanogaster females only. Shen et al. [76] suggested that it would be interesting to examine potential interactions between the sex determination genes and the insulin/IGF1-like signaling (IIS) pathway or dietary restriction, both of which regulate lifespan in a sex-dependent manner. Furthermore, Tarone et al. [77] demonstrated that Yp expression is negatively correlated with longevity in D. melanogaster. Thus, as discussed above, any impact that Dsx might have on Yp expression could underlie the decreased longevity of A. aegypti dsx-targeted females. Finally, silencing dsx in A. aegypti was shown to result in decreased expression of p53 [30]. Overexpression of p53 in the D. melanogaster female nervous system results in increased life span [76]. It is tempting to speculate that downregulation of p53 expression following dsx silencing may contribute to the decreased lifespan observed in dsx-targeted A. aegypti females.
Female mosquitoes differ from males in several morphological, physiological, and behavioral traits that are critical to their ability to transmit diseases. The arthropod disease vector research community has therefore had a long-standing interest in the potential to manipulate sex determination and differentiation genes for controlling disease vectors. Our previous work [30] demonstrated that Dsx regulates sex-specific gene expression in the developing A. aegypti pupal nervous system. The present investigation extended these initial findings through assessment of the effects of developmental siRNA-mediated dsx silencing in adult females. Targeting of dsx resulted in decreased size of the female wing and proboscis (Fig 1). Decreased fecundity and fertility correlated with decreased ovary length, ovariole length, and ovariole number in females in which dsx was silenced during development (Figs 2 and 3). Targeting dsx also resulted in disruption of olfactory system development, as evidenced by reduced length of the female antenna and maxillary palp and their respective sensilla (Figs 1 and 4), as well as disrupted OR expression (Fig 5). Female lifespan, a critical aspect of mosquito pathogen transmission, was also significantly reduced in adult females following developmental targeting of dsx (Fig 6). These results demonstrate that developmental silencing of dsx in A. aegypti females, which disrupts development of multiple adult female traits linked directly or indirectly to reproduction and pathogen transmission, may be useful for vector control.
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10.1371/journal.pcbi.1002948 | Localized Lipid Packing of Transmembrane Domains Impedes Integrin Clustering | Integrin clustering plays a pivotal role in a host of cell functions. Hetero-dimeric integrin adhesion receptors regulate cell migration, survival, and differentiation by communicating signals bidirectionally across the plasma membrane. Thus far, crystallographic structures of integrin components are solved only separately, and for some integrin types. Also, the sequence of interactions that leads to signal transduction remains ambiguous. Particularly, it remains controversial whether the homo-dimerization of integrin transmembrane domains occurs following the integrin activation (i.e. when integrin ectodomain is stretched out) or if it regulates integrin clustering. This study employs molecular dynamics modeling approaches to address these questions in molecular details and sheds light on the crucial effect of the plasma membrane. Conducting a normal mode analysis of the intact αllbβ3 integrin, it is demonstrated that the ectodomain and transmembrane-cytoplasmic domains are connected via a membrane-proximal hinge region, thus merely transmembrane-cytoplasmic domains are modeled. By measuring the free energy change and force required to form integrin homo-oligomers, this study suggests that the β-subunit homo-oligomerization potentially regulates integrin clustering, as opposed to α-subunit, which appears to be a poor regulator for the clustering process. If α-subunits are to regulate the clustering they should overcome a high-energy barrier formed by a stable lipid pack around them. Finally, an outside-in activation-clustering scenario is speculated, explaining how further loading the already-active integrin affects its homo-oligomerization so that focal adhesions grow in size.
| Focal adhesions are complex, dynamic structures of multiple proteins that act as the cell's mechanical anchorage to its surrounding. Integrins are proteins linking the cell inner and outer environments, which act as a bridge that crosses the cell membrane. Integrins respond to mechanical loads exerted to them by changing their conformations. Several diseases, such as atherosclerosis and different types of cancer, are caused by altered function of integrins. Essential to the formation of focal adhesions is the process of integrin clustering. Bidirectional integrin signaling involves conformational changes in this protein, clustering, and finally the assembly of a large intracellular adhesion complex. Integrin clustering is defined as the interaction of integrins to form lateral assemblies that eventually lead to focal adhesion formation. The effect of the plasma membrane on formation of integrin clusters has been largely neglected in current literature; subsequently some apparently contradictory data has been reported by a number of researchers in the field. Using a molecular dynamics modeling approach, a computational method that simulates systems in a full-atomic scale, we probe the role of the plasma membrane in integrin clustering and hypothesize a clustering scenario that explains the relationship between integrin activation and focal adhesion growth.
| Focal adhesions are complex, dynamic structures composed of several proteins that act as the cell mechanical anchorage to the extracellular matrix (ECM). Integrins are the first signal receptors encountered in the cell mechanical micro-environment [1], [2], [3]. Integrin-mediated adhesion often occurs under forces such as fluid flow or myosin-mediated contractions that cells exert to sample the rigidity of their surroundings [4]. The surface density of integrins on the plasma membrane is ∼300 integrins/µm2 [5]. The lifetime of focal adhesions as distinct entities is in the order of 5–10 minutes. Within focal adhesions, integrins that are directly linked to the ECM show exchange rates on the order of 1–3 minutes [6]. The exchange rate is defined as the average time it takes for 50% of integrins in a focal adhesion to dissociate from the ECM and replace with new integrins [7].
Integrins are α-β hetero-dimeric receptors that consist of large extracellular domains (700–1000 residues), two transmembrane α-helices, and short cytosolic tails (50–70 residues) [2], [8], [9], [10]. Integrins transfer signals bidirectionally between the ECM and the cytoskeleton [6], [11], [12]. Signal transmission from the cell exterior to the cytoskeleton is called ‘outside-in signaling’, whereas ‘inside-out signaling’ occurs when a biochemical signal is relayed from the cytoskeleton, being converted afterward to a conformational change of the protein. Inside-out signaling is putatively triggered by separation of the two integrin subunits at their cytoplasmic and transmembrane domains, which follows association of talin to the integrin cytoplasmic β-subunit domain [12], [13]. A major restraint that holds integrins in an inactive mode is the interaction between transmembrane and cytoplasmic domains of the α- and β-subunits. Separation of these domains is sufficient to allow unbending of the ligand-binding headpiece and conformational changes that increase ligand-binding affinity [14], [15]. Conversely, binding of the ligand to the integrin ectodomain results in an extended conformation whereby the α- and β-subunit legs are separated [12], [15], [16].
Bidirectional integrin signaling involves conformational changes in the hetero-dimer, integrin clustering, and the assembly of a large intracellular adhesion complex [9], [17], [18]. Integrin clustering is defined as the interaction of hetero-dimers to shape lateral assemblies that eventually lead to focal complex formation [13], [15]. It has been known for two decades that integrin activity is regulated by its conformational changes [11], [19]. Although there is a strong correlation between integrin clustering and activation, how one leads to the other has remained elusive [8], [13], [18], [20], [21], [22]. A body of evidence has proposed that αIIbβ3 integrin activation, which triggers the clustering process, is regulated by α- and β-subunit homo-oligomerization [18], [20], [21], [22]. In opposition to this hypothesis, a series of Cys mutagenesis scanning experiments has suggested that neither inactive nor ligand-bound αIIbβ3 integrins form homomeric association [8]. Instead, it has been hypothesized that integrin clustering occurs as a result of binding of several integrins to multimeric ligands, and the transmembrane-cytoplasmic (TMC) domains do not play any significant role in the process [8]. Additionally, there is evidence that demonstrates it is hetero-dimerization of different integrin molecules that triggers the clustering phenomenon [8], [23].
Studies indicate that focal adhesions alter their size as a result of changes in the force they sustain [11], [24], a process termed “reinforcement” [25]. It has been reported that for focal adhesions larger than 1 µm2, traction force at the focal adhesion increases with its size linearly [24]. Current activation/clustering models do not provide an explanation for the force-recruitment correlation in integrins. In contrast, the switchblade model, which is a widely accepted functional model for integrin, limits the integrin's molecular conformations to three modes, namely passive, active, and ligand-bound [19], [26], [27]. The passive mode is corresponding to a bent integrin structure while integrin activation is marked by a global conformational change, which results in a stretched structure with a separation of α- and β transmembrane-cytoplasmic domains from each other. Finally, an opening between the α- and β-subunit heads gives rise to a ligand-bound conformation [26], [27]. Once the ligand-bound conformation is achieved, it is unclear how the reinforcement phenomenon takes place under larger loads.
The effect of the plasma membrane on integrin activation/clustering is largely neglected in previous experimental works, whereas molecular-level, computational studies of integrin are mostly focused on simulating the ectodomain [6], [11], [16]. In this study, we employ all-atom molecular dynamics techniques and conclude that the plasma membrane surrounding the integrin transmembrane domains plays an important role in the process of activation/clustering by forming a lipid pack around the transmembrane domains. Furthermore, by performing normal mode analysis on the full-length αIIbβ3 molecule, we show that the integrin ectodomain and TMC domains swing about a hinge-like region that links them together. Finally, we hypothesize a potential integrin clustering scenario that explains the seemingly contradictory results for integrin clustering mechanism, namely homo-oligomerization-based and multivalent ligand-based clustering models, elucidating the unexplained force-area correlation in focal adhesions as well. One of the major determining factors for feasibility of a reaction is the free energy change profile over the reaction coordinate. Therefore, we finally asked if integrin subunits were to oligomerize how the free energy of the system would change as the two monomers approached one another.
Normal mode analysis is a powerful method to detect softer regions in proteins and has proved useful in determining physiologically relevant motions of proteins as well as their compartmental breakpoints [28], [29], [30], [31]. Our normal mode analysis of the full-length integrin αIIbβ3 suggested that the protein's structure consists of a rather rigid ectodomain, hinged to two cantilever-like transmembrane-cytoplasmic (TMC) domains (see Fig. 1). Modes 7 to 10, corresponding to lowest non-zero deformation energies, show a clear rotation of the ectodomain and TMC domains about the hinge-like region (see Fig. S1). The soft linker region between the ectodomain and TMC domains hinders strong mechanical integrity of TMC domains with the extracellular domain. As expected, under an instantaneous excitation the ectodomain and TMC domains vibrate about the soft region in opposite directions at any given time. As mode number increases the molecule deformation energy is elevated rapidly, and consequently, contribution of higher modes to the molecule movement is insignificant (Fig. S2) [32]. Additionally, no homomeric interactions are reported to occur between integrin ectodomains [8]. Therefore, in order to reduce the computational cost of our simulations, in all molecular dynamics simulations solely the TMC α- and/or β-domains were modeled.
The first step to our molecular dynamics simulations was aimed at investigating the homomeric interactions between the two α-subunits when they are in an active state (i.e. α- and β-subunits are distant enough not to interact significantly). In order for integrin α-subunits to initiate focal adhesion formation through their homo-oligomerization, α-subunits should be able to readily bind/unbind each other. Two identical crystal structures of integrin αIIbβ3 TMC α-subunits (Gly955 to Glu1008) were carefully embedded in a plasma membrane patch, 5 nm apart from one another. The system was minimized and equilibrated for 0.5 ns and steered molecular dynamics (SMD) was exploited to drag one of the α-subunits toward the other for 1.2 ns with a constant velocity of 0.025 Å/ps, while the other monomer was left unconstrained. Importantly, the free monomer also started to move in the same direction soon after the steered one did. Furthermore, the overall force required to direct the steered monomer toward the free subunit showed an abrupt increase at distance 5.1 nm (see Fig. 2c), and electrostatic as well as van der Waals energies of the interaction between the two monomers were insignificant during a major part of the simulation (data not shown). This indicated that the two biomolecules did not ‘see’ each other throughout the simulation. Some minor positive interaction energy spikes were observed, which, based on the trajectory of the simulation, can be attributed to interactions between a GAMG sequence located at the very end of the extracellular side of the steered α-subunit, where it is truncated off the ectodomain (Fig. S3). Conducting the same simulation but without the GAMG motif demonstrated almost no interaction energy at any time (see Fig. S3). This negative control suggests that the emerging energy spikes were an artifact of the separation of the integrin transmembrane and extracellular domains that had left this flexible linker region unconstrained, allowing the GAMG sequence to reach out to the other monomer. Another site of interaction was the hydrophobic patch on the cytoplasmic side of the free monomer, which also extended out from the plasma membrane. We further explored the phenomenon by repeating the simulation using a geometry in which the steered monomer's orientation is 90° rotated about its longitudinal axis relative to the free molecule. This time the free monomer maintained a distance of ∼4 nm (data not shown).
To observe if the pulling rate has a significant effect on results, the simulation was performed for a longer time scale with a pulling velocity as low as 0.0025 Å/ps. The results were, in general, similar to the first simulation except that the fluctuations of the distance between the monomers fell in a narrower band. Nevertheless, as Fig. 2b depicts, the distance in both cases eventually approaches the same value (5–5.1 nm), which is the closest distance between centers of mass that the two monomers can reach without any constraints applied. We call this distance “final distance” hereinafter. The increase in the force level required to maintain the constant pulling rate also occurs at the distance of 5 to 5.1 nm.
Mimicking α-dimerization appeared unlikely if α-subunits are not constrained. Thus in the next round of simulations we fixed the non-steered monomer at the same four atoms (Cα of residues Ala956, Gly972, Ala 986, and Gly1007). The steered monomer was pulled on toward the unconstrained one at the same four atoms as before, which were selected such that they divide the monomer into similar length spans, and fixed atoms with smallest side chains are chosen to reduce the potential error caused by steering. The steered monomer approaching the constrained one, the pulling force rose again when the monomers were ∼5.1 nm apart. The four-point SMD was deemed incapable of mimicking the full dimerization process. The pulling mechanism used in our simulations assumed a dummy point that moved in the space with a constant velocity and pulled the steered points on the molecule, using dummy springs that are linking those points to the dummy point. In fact, when the α-monomer was being pulled from four points, the two points at the two ends of the monomer fell inside the water box while the two points in the middle were embedded in the lipid bilayer. As a result, when the lipid packs around the two monomers started to overlap, the two embedded points experienced a substantially higher resistance than the ones moving in the water box, and therefore fell behind the lipid embedded points. This initially led to unfolding of the steered monomer. We avoided this problem by releasing the two end points when the steered monomer approached the fixed one. Hence, the simulation was continued one more time with only two, membrane-embedded steering residues (i.e. Gly976 and Ala986) (Fig. 3). These two complementary simulations, therefore, conferred a profile of the overall force required to tow a single TMC α-domain toward the other constrained one. An overall SMD force of as much as 1.7 nN is required to eventually bind the two α-domains.
Observing the system trajectory from the top view (perpendicular to the membrane patch), it appeared that two cylinder-like batches of lipid chains surrounding the α-subunits moved along with the monomers. As shown in Fig. S4a, although a portion of lipid chains originally located in the proximity of the monomer disperses in different directions, the majority of lipid molecules within at least 10 Å of the transmembrane domain are more energetically favorable to remain attached to the monomer. In order to further quantify the phenomenon, we measured the distance between the monomer's center of mass and three lipid chains located at the rear side of the moving monomer, along the line that connects the two monomers, with different initial distances from the monomer. Intriguingly, the lipid chain that was originally located as close as ∼12 Å to the monomer's center of mass almost kept a constant distance with the monomer, which means it moves with a constant velocity along with the monomer. Nonetheless, the farther the lipid chain located from the monomer, the more slowly the lipid chain was dragged behind the monomer. This is visualized in Fig. S4b by monitoring the distance between the monomer and lipid chain.
It was speculated that “lipid packing” around the monomers might be the underlying reason for why the free monomer was “repelled” from the steered monomer. Thus, the atomic density (number of atoms per volume) of lipid around both α- and β-subunits was measured as a function of the lipid position, relative to the membrane patch center. Interestingly, as depicted in Fig. 4a and 4b, the atomic density of lipid in the region surrounding the two α-subunits is approximately 20% higher than the density of the free areas on the two sides of the membrane patch. However, the lipid arrangement around β-subunits does not show this much of a contrast. Although the density of the lipid between the two β-subunits is also higher than other regions, it appears that formation of a significantly denser lipid chain conformation between α-subunit TMC domains is favorable, which hinders oligomerization. A major portion of the transmembrane domain of α-subunit consists of hydrophobic residues, which makes it favorable for lipid chains to form a denser pack in the region defined by two α-subunits. Although it is challenging to verify this phenomenon experimentally, computational models allowed us to investigate this system in molecular detail.
To verify potential effects of the lipid bilayer, a control simulation was performed exclusively with two TMC α-subunits with no surrounding membrane. Interestingly, this time the repulsion of the free monomer was not observed, rather the two monomers simply approached each other and eventually associated together (see Fig. 4c). The pulling force remained constant until t = 900 ps, when the interaction between the two monomers began. The pulling force reached a negative minimum at t = 1000 ps when the helix surfaces were in contact and the hydrophobic attraction between the monomers was so strong that the steered monomer was effectively pulled back to keep the velocity constant (see Fig. 4c). Subsequently, the steered helix compelled the free one to move along with it, elevating the force back to the level exhibited prior to the interaction. It should be noted that the minimum separation reached by the α-monomers was ∼2 nm. Therefore, the average effective radius of each monomer, considering the steric repulsion caused by side chains, could be estimated to be ∼1 nm.
As mentioned earlier, a portion of the lipid side chains surrounding each monomer traveled along with the α-helix. In the simulation where a TMC α-domain was directed toward another free one, the van der Waals interaction energy between the two lipid cylinders, each with external radius of 2.5 nm, was computed (see Fig. 4d). The final distance in this case was 5.1 nm. As the system is geometrically symmetric relative to the monomers, it could be concluded that a lipid cylinder of 2.55 nm forms around each membrane-embedded α-monomer. Nonetheless, it is noteworthy that the cylinder is not perfect, meaning its radius differs along different directions. Since the effective radius of each α-monomer (considering the steric repulsion caused by side chains) is more than 1 nm the lipid cylinder average radius is estimated to be 1.5 nm. The result indicated a rise in the interaction level between the two semi-cylinders until t = 1000 ps. The interaction energy continued fluctuating about a plateau afterwards. At this moment, the two monomers were at their final distance and the steered monomer pushed off the free one via their lipid cylinders (Fig. 4d).
These results implied that the repulsion is caused by lipid packing around monomers. Given the highly hydrophobic nature of the membrane-embedded residues of the TMC α-integrin, when the two monomers are far enough from each other, local interactions between their surrounding lipid chains and their hydrophobic residues occupy their hydrophobic patches' binding sites, preventing the buried residues in the two monomers from interacting. Furthermore, these hydrophobic interactions form a dense pack of lipid chains around each monomer. Once the two lipid packs overlap, they are forced away due to steric repulsion. The friction of the two lipid packs with their surrounding lipids creates a barrier for each movement step of the pack. Therefore, the free monomer is pushed off intermittently rather than being shifted smoothly. Therefore, if the two monomers are to dimerized, external, mechanical work is needed to overcome the energy barrier that maintains the packed lipid chains together, letting the monomers pass through.
Proposed by some researchers as an appropriate choice to regulate integrin clustering/activation, the TMC α-domains should be able to associate and detach from one another readily [20], [21]. A dimerization test was carried out in order to characterize the α-domain homo-oligomerization process. Two α-domains were positioned in a plasma membrane patch as close as 2 nm apart. The system was minimized and equilibrated for 2 ns. Interestingly, at this time, the two monomers bonded to each other fairly rapidly (∼50 ps after the simulation started). Afterward, one monomer was fixed (at the same four points), while the other monomer was pulled away with a constant velocity (0.025 Å/ps). The force increased linearly with the steered monomer moving away from the fixed one. A maximum force of 1.6 nN was required to completely separate the monomers. Concomitant with the lipid barrier breakage, the plasma membrane thins out in the region between the two α-monomers (Fig. 5a). This phenomenon again affirms the movement of a significant mass of lipid with the steered monomer.
Another crucial element of integrin activation/clustering is β-subunit, which is another candidate reported to be an initiator for integrin clustering and focal adhesion formation consequently. We probed the effect of outside-in signaling on the TMC β-domain by conducting a simulation of two TMC β-domains (Gly684 to Thr762), one constrained at four atoms (Cα of Val695, Gly708, Ala728, and Ala742) while the other one is steered (at the same four points) toward it with a constant velocity of 0.025 Å/ps. Regions of the β-domains falling outside the membrane interacted first, trapping the system in a stable conformation. The stability of the oligomerized state was confirmed by removing the steering force after the dimer was formed and the system was equilibrated for 3 ns. It was observed that the system did not change its conformation significantly after this period of equilibration.
The process of β-dimerization took place before the monomers paired up completely, leaving some lipid chains trapped in between (Fig. 6a). In fact, the lipid packed around the monomers partially ruptured at 1.5 nN and let the β-monomer through. Lower density of hydrophobic residues in the cytoplasm-proximal side of the transmembrane domains forms a region of narrow lipid packing, further facilitating the interaction between the cytoplasmic domains of the β-monomers. Membrane-proximal, cytoplasmic residue W739 on the steered monomer and residues F727 and F730 on the constrained monomer interacted with the plasma membrane strongly enough to introduce a kink between the cytoplasmic and transmembrane domains of each monomer that signifies the partially hydrophilic region of the transmembrane protein within the cytoplasmic leaflet (Fig. 6a). Residue W739 of the steered monomer and residues F727 and F730 of the constrained monomer form hydrophobic bonds, shaping a stable conformation.
The relative position of Cα of Leu718 residues on the two monomers are depicted in Fig. 6a, which represents the geometric center of the lipid-embedded portions of the monomers. The distance drops linearly down to 36 Å and remains constant afterward, at a level close enough to allow the cytoplasmic and extracellular regions of the transmembrane domains to interact.
As β-domains are often reported to form trimers in focal adhesions, we placed them on vertices of an equiangular triangle with side length 2 nm. Trimerization occurred quickly within 50 ps. Again, we exploited steered molecular dynamics to separate one of the β-subunits from the trimer, exerting forces on four atoms (Cα of Val695, Gly708, Ala728, and Ala742). The steered monomer separated from other two at a maximum of 1.3 nN. In contrast to TMC α-domains, which have an entirely hydrophilic membrane-embedded compartment, these residues are sparse on the TMC β-domains cytoplasm-proximal compartment (starting from D723). By narrowing the lipid pack barrier, this forms a weak spot where the lipid barrier cuts open in a zipper-like fashion. In other words, the steered β-domain detachment initiates at the cytoplasm-distal region. As the steered molecule is further forced other bonds break one at a time. This appears in Fig. 5, where each peak represents a bond-breaking event.
In order to further evaluate possibility of integrin subunit homo-oligomerization we analyzed the free energy change of the system as one TMC subunit approached the other for either of α- and β-subunits (see Fig. 7.a). To model the system more accurately, rather than solely bringing the monomers close to each other, we included the effects of the linkage of β-subunits to the cytoskeleton through talin. We started from an initial step where two β-subunits are embedded in the plasma membrane patch, 5 nm apart. Then, we exerted the steering force only on the cytoplasmic domain of the moving monomer (i.e. Ala750-Thr762), while the other monomer was left to diffuse freely. The steering speed was kept constant at 0.025 Å/ps and the two cytoplasmic domains dimerized after 2 ns. The same protocol was employed to measure the free energy difference for α-dimerization.
To calculate the free energy of interaction we invoked the Jarzynski method for non-equilibrium transitions in the system phase space [33]. This method has been able to closely estimate the free energy difference in molecular dynamics simulations by sampling the system for a limited number of times (e.g. 10 times) [34]. To achieve reliable results from the Jarzynski method, sufficient number of identical systems should be minimized and equilibrated separately before they are steered [35]. We minimized and equilibrated the system for either of α- and β-subunit homo-oligomerization profiles 10 times for 0.5 ns, each starting from the same initial state. The systems were steered subsequently and the free energy was evaluated using a second-order expansion of the Jarzynski equality [33], [34]. The free energy profile of the system is shown in Fig. 7a. The free energy profiles of α- as well as β-subunit association are plotted against each other. The free energy of the system decreases gradually as either of the moving α- or β-subunit approaches the free subunit. The effect of the lipid pack disturbance is seen at the few local peaks that appear between 2 nm and 3.5 nm for both subunits, and finally, the free energy drops drastically as a result of the two monomers interacting and forming dimers. Interestingly, the global free energy minimum for the oligomerized states of the α- or β-subunits closely coincide. However, the α-monomer indicates a sharper drop in the free energy with about 10% higher free energy difference between its peak and valley. Furthermore, α-monomers require about twice as much activation energy as do β-monomers to overcome local free energy maxima. The free energy barrier that should be overcome by β-subunits to homo-oligomerize is about 400kBT. Although this value is too high for two free β-subunits to overcome, the mechanical work caused by their linkage to the cytoskeleton is likely to push β-subunits over this free energy bump, which would lead to oligomerization. On the other hand, α-subunits would require as high as 800kBT of activation energy to overcome the oligomerization energy barrier. Considering that no significant mechanical linkage between α-subunits and the cytoskeleton has been reported, the homo-oligomerization seems thermodynamically unlikely.
Although the role of integrin activation in integrin clustering and vice versa has been studied intensively over the past decade, the phenomenon still remains ambiguous in many aspects [8], [21], [22]. Some researchers suggested that monomer homo-oligomerization of transmembrane integrin α- and β-subunits triggers integrin clustering [21], [22]. However, other researchers provided evidence that homo-oligomerization of integrin subunits does not significantly take part in the process of integrin clustering [8]. Because real-time monitoring of single integrins proved challenging, previous studies utilized assays that involved collecting integrins from cells that are already lysed [8], [20], [21], [22], which implied neglecting effects of the plasma membrane. This study aimed at investigating effects of the plasma membrane on the integrin clustering phenomenon through a full-atom simulation.
Molecular dynamics has been widely recruited to model mechanistic behaviors of cytoskeletal proteins [36]. Particularly, Kalli et al. in their recent work provided evidence that molecular dynamics simulations are able to closely mimic the hetero-dimerization of transmembrane domains of integrin, when embedded in a lipid bilayer [37]. With no experimental techniques currently available to obtain dynamic, atomic-level insights into the integrin activation pathway, we employed steered molecular dynamics (SMD) simulations to derive these insights computationally. Because integrin has a massive structure usually only relevant-to-the-problem portion of integrin is truncated and modeled in molecular dynamics simulations [11], [26]. Also, it is believed that the membrane-proximal segment of the ectodomain is a soft region that links the transmembrane domain and the ectodomain. We conducted a normal mode analysis on the full-length integrin molecule to show the presence of a hinge-like region that minimizes the mechanical integrity of the extracellular and TMC domains of the integrin [2], [9]. Thus, we circumvented the extremely high computational cost of simulating an intact integrin embedded in a membrane, by including only the TMC domains in our simulations.
Inactive integrin α- and β-subunits are locked by two transmembrane interactions: Outer membrane clasp (OMC) and inner membrane clasp (IMC). The GXXXG motifs are thought to be sufficient for keeping the two hetero-dimers in contact [38]. Also these sequences allegedly play a significant role in the process of homo-oligomerization [21]. It has been demonstrated that transmembrane α-domains in an activated integrin tend to form dimers, while transmembrane β-domains are more inclined toward homo-trimerization [20], [21], [22]. Fluorescence microscopy studies demonstrated that there is no interaction between α- and β- cytoplasmic tails in the active state, and they are at least 10 nm apart [39]. Therefore, in all simulations we mimicked the ligand-bound mode by employing non-bonded TMC α- and/or β-domains as opposed to the inactive case where these domains are in a hetero-dimerized state. Ectodomains are associated with the ECM so they could not have a significant effect on homo-oligomerization [8].
In fact, diffusion of transmembrane proteins, including integrins, occurs significantly slower in vivo compared to how rapidly it would occur in artificial lipid bilayers. This is suggested to be caused by the physical blocking of their cytoplasmic tails by the cortical cytoskeleton [40]. Lepzelter and Zaman proposed the value of 0.25 nm2/µs for the free diffusion coefficient of integrin dimers [40]. Assuming a 2D random walk on the membrane, we can employ the Green's function of diffusion to correlate the integrin position variance from the movement origin with the diffusion time as follows:(1)where σ2, D, and t are respectively the position variance, diffusion coefficient, and diffusing time. Assuming a normal distribution, a single integrin traveling from the origin will fall within 3σ of the origin at all times with 99.7% certainty. The time scale is 2 ns for most of our simulations. Substituting these values in Eq. 1 yields a standard deviation of 0.0447 nm, consequently, 3σ = 0.1342 nm. Dividing this by the time scale of 2 ns gives a characteristic velocity of 0.0671 nm/ns. However, this is the molecule average speed from its hypothetical origin, rather than its instantaneous speed at each walking time step, which is what is “mechanically sensed” by its surrounding. Indeed, the instantaneous speed could be much greater than this. According to the central limit theorem, if the molecule takes steps of size Δx each in time span of Δt, variance of the position could be calculated as:(2)Comparing Eq. 1 and 2, noting that is simply the instantaneous (time step) speed for small time steps, and rearranging, we are left with the following expression for the instantaneous speed:(3)In which we denote the instantaneous speed with V. Substituting 0.25 nm2/µs and = 2 fs (the time step used in all simulations), the instantaneous speed is V = 22.36 nm/ns. We used a pulling velocity of 2.5 nm/ns, which is an order of magnitude less than the average instantaneous speed in order to avoid sudden sharp movements of the steered monomer that would otherwise impose a significant impulse to the free monomer.
Our simulations showed that a high level of hydrophobicity in the transmembrane α-domain combined with its perpendicular-to-membrane orientation forms a lipid pack of ∼1.5 nm thick around its effective surface. In addition, in the case where two α-subunits are to approach one another, the density of lipid chains was shown to be significantly higher in the region between the subunits. Although highly neglected in previous studies, this lipid shield forms a significant energy barrier of about 800kBT that should be overcome if two α-subunits are to dimerize. Hence, unless there is an external mechanical load that forces the monomers through the lipid pack, the lipid barrier simply hinders α-dimerization. Because, to the best of our knowledge, no mechanically effective binding of α-subunits with cytoskeletal proteins has been reported, it is unlikely for α-subunits to form homo-dimers even if they are in an active/ligand-bound state. Studies that reported α-subunit homodimerization have, in fact, observed integrins after the cell had been lysed, i.e. the plasma membrane would have been removed. We mimicked the experiments carried out by Li et al. [20] by observing the dimerization process in the absence of the plasma membrane (see Fig. 4). Our results showed that homo-oligomerization under such circumstances readily occurs. Transmembrane α-domain dissociation simulations corroborate this conclusion. Even if the homo-dimerization occurred, α-subunits would require ∼25% higher magnitude of force to dissociate than would their β-counterparts. Thus, α-subunit homo-oligomerization does not seem to be a readily available regulating event of integrin clustering. Although Wang et al. also performed homo-oligomerization and Cys scanning analysis in the absence of the cell plasma membrane, they utilized full-length integrin versus TMC domains used by Li et al [8], [20]. Insofar as integrin TMC domains are highly hydrophobic, in a hydrophilic milieu it is likely that the integrin TMC domains have bonded to hydrophobic patches on the integrin ectodomain, thereby blocking the homo-oligomerization sites on the TMC-domains. This could explain why homo-oligomerization was not observed by Wang et al.
On the other hand, transmembrane β-domains are α-helices longer than α-domains that also span the plasma membrane. They possess a large number of hydrophobic residues but the subtlety lies within a cytoplasm-proximal region of them. TMC β-domains are different from α-subunits in three distinct ways. First, TMC β-domains feature longer cytoplasmic regions that are less affected by the lipid packing phenomenon as their cytoplasmic compartment is significantly longer; second, in the resting as well as ligand-bound states β-domains maintain a 25° angle with the membrane surface normal, as opposed to α-domains that remain perpendicular to the membrane [2], [41], [42]; and finally, in β-domains the membrane-embedded region is not fully hydrophobic which gives rise to a narrowing of the lipid pack adjacent to the β-domains in the cytoplasmic leaflet of the plasma membrane. Unlike transmembrane α-domains that are more or less uniformly hydrophobic throughout their membrane-embedded length, a transmembrane β-domain length span of ∼2 nm in proximity of the cytoplasm experiences a combination of hydrophobic and hydrophilic residues that weakens the lipid packing effect around it. This region, starting from the residue D723, appears to rotate against the main body of the monomer shortly after it was equilibrated and this forms a kink that signifies this region. The rotation is caused by another hydrophobic interaction between the residue W739 and the plasma membrane. This interaction acts as a trigger for homo-dimerization. Although 1.5 nN of steering force was required to overcome the lipid pack and promote homo-oligomerization, this event could be considered physiologically feasible since it is widely established that talin head domains bind to the NPLY sequence on the β-cytoplasmic domain, a key bond that links integrins to actin filaments (Fig. 7). Other focal adhesion proteins such as α-actinin and kindlin are also capable of linking β-cytoplasmic domains to the cytoskeleton [15], [43].
Calculating the average density of the lipid bilayer around α- and β- TMC domains provides a reliable measure to compare the behavior of these two monomers when embedded in the plasma membrane (Fig. 4a and 4b). Intriguingly, the average density of lipid reaches its highest in the region between the α-domains (∼100 #/nm3), presenting a dense area of lipid that potentially hinders α-homo-oligomerization. Similar phenomenon occurs for β-domains except that the difference between the area enclosed by and outside the β-domains is no more than a few percents. It is noteworthy that the reported densities are averaged over five microstates of the system for each data point. Another piece of evidence was put forward by estimating the free energy changes along the reaction coordinate of β-β cytoplasmic domain homo-oligomerization. Importantly, an overall loss of free energy on the system was observed as the moving monomer approached the free one until it reached an energy bump at 2.5 nm. After the dimerization occurred, a drastic decrease in the system free energy was seen. This showed that the reaction is favorable and feasible if a source of energy exists that injects as much as 400kBT into the system such that it could overcome the energy barrier. As the average load sustained by an actin filament is reported to be ∼50 pN [5], an actin stretch of ∼34 nm would be sufficient to provide the amount of energy required for homo-oligomerization.
Cluzel et al. reported that activated integrin, immobilized ligands, presence of monomeric talin head domain, and phosphoinositole-4, 5-bisphosphate (PIP2) are necessary factors for integrin micro-clustering [13]. Presence of actin network, however, is not critical for de novo-formed integrin clusters. Along the same line, Tan et al. did not observe the linear regime of focal adhesion area growth with the force they exerted to the substrate, for nascent focal adhesions (i.e. less than 1 µm2 in area) [24]. Therefore, since homo-oligomerization is dependent on the presence of actins, it seems reasonable to assume neither TMC α- nor β-domains play an important role in promoting clustering in newly-formed focal adhesions. Nevertheless, TMC β-domains might be capable of mechanically regulating the clustering process when the focal adhesion is matured. Previous molecular dynamics simulations mapped out details of vinculin and α-actinin activation that is highly involved in the process of maturation [30], [31], [44], [45], [46]. An increase in the stretch level of the cytoskeleton, and particularly actin filaments that are already bonded to talin and other focal adhesion proteins and consequently to integrin β-subunits, might lead to a lateral gathering effect that brings β-subunits closer, thereby making it more probable for them to homo-oligomerize (Fig. 7b). Thus, one could hypothesize a model to link integrin activation and clustering as follows (see Fig. 7b). Individual active integrins absorb focal adhesion proteins, including talin, vinculin, etc., and eventually bind actin filaments. As the focal adhesion matures, adjacent protein hubs (∼40–50 nm apart) [47] gradually grow in size and unify. Increasing the tension in actin bundles, and consequently actin filaments that form the bundles, causes monomers to be aligned and dragged toward each other more vigorously. Homo-oligomerization interactions reinforce the binding by overcoming lipid barriers, which assembles larger numbers of integrin subunits in the focal adhesion.
Although current activation models explain how three conformations of integrin are associated with different signaling states, they fall short on elucidating alterations in the surface area of focal adhesions as the tension in focal-adhesion-binding actins grows in a 2D cell culture regime [9], [14], [15], [19], [22], [48]. Our speculated model, however, explains the phenomenon as follows. The higher the tension in an actin bundle grows, the more β-integrin homo-trimerization interactions likely occur. This in turn increases the area of the focal adhesion in a 2D culture, which implies that a larger number of active integrins are recruited if an elevated magnitude of force is to be borne by the corresponding actin bundle. The extended ectodomain of integrin and presence of a hinge-like region between the ectodomain and TMC domains enables integrin β-cytoplasmic tails to homo-oligomerize while their ectodomains are bonded to adjacent ECM ligands. The GXXXG sequences are binding sites for hetero-trimerization. As long as the cytoskeletal tension is present and integrins are stabilized in a homo-trimeric interaction, they are not expected to return to their resting state, where α- and β-domains are bonded at their IMCs and OMCs.
In summary, our simulations showed that TMC α-domains are unlikely to contribute to the process of integrin clustering. However, TMC β-subunits' geometry as well as composition make them an important candidate to drive the integrin clustering process. Previous experiments implicitly neglected energetic effects of the plasma membrane, obtaining apparently contradictory outcomes with lysed cells. Hence, we suggest future experimental studies of the field focus on clustering of integrin β-subunits over the course of the focal adhesion formation and collapse either in vivo or within artificial lipid bilayers. Furthermore, in order to obtain a thorough understanding of the focal adhesion growth, effects of other environmental factors, such as extracellular and cytoplasmic ion concentration, on integrin clustering should be examined.
Integrin αllbβ3 ectodomain (PDB ID: 3FCS chains A and B [49]) and transmembrane-cytoplasmic domains (PDB ID: 2KNC [50]) were downloaded from the Protein Data Bank (PDB) and combined to build the full-length integrin molecule, assuming a covalent bond between residues Cys959 and Gly955 in the α-subunit, and Gly684 and Gly690 in the β-subunit. The natural vibration frequencies of the full-length integrin molecule were determined using the normal mode analysis (NMA) software WEBnm@ [51]. The normal mode matrix, which is a function of integrin molecular structure, shows natural movements in flexible molecular regions and little movement in rigid regions. WEBnm@ uses the MMTK [52] software internally and computes natural frequencies using Hinsen's computational methods [53], which calculates approximate normal modes by determining the eigenvectors of the matrix of second derivatives of potential energy with respect to displacement of the Cα atoms of each residue. The potential energy function used for this calculation utilizes a Hookian potential between Cα atoms within an 8 Å cutoff distance. Because NMA represents movements resulting from the overall structure, the use of Cα atoms is sufficient for NMA calculations [54].
All molecular dynamics simulations are carried out with the program NAMD2.7 [36], using the CHARMM27 force field for lipids and CHARMM22 for proteins [55]. The transmembrane-cytoplasmic domains of the integrin αllbβ3 molecule are taken from Protein Data Bank (PDB ID: 2KNC). Integrin subunits are embedded in a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer, using the software VMD [56]. In all simulations, overlapping lipid chains are removed afterward and the membrane is solvated in an explicit water box with ionic NaCl concentration of 8 mM [57]. In all simulations integrin is assumed to be in an activated state as transmembrane-cytoplasmic domains of α- and β-subunits are apart. The temperature and pressure of the system are held constant at 1atm and 310K, using Langevin's piston and Hoover's method [36]. Coordinates, energy, and steering forces were recorded every 1 ps or 10 ps and the time step was 2 fs. The cutoff distance for non-bonded interactions was 1.2 nm. For all simulations, particle mesh Ewald (PME) method was used for electrostatic force calculations [36]. The cutoff distance for non-bonded interactions was 1.2 nm. The hydrogen atom bond length was constrained using SHAKE method. SHAKE method fixes bond lengths between large atoms and hydrogen atoms, preventing unnecessary calculation of irrelevant interactions [58].
In α-α and β-β homo-oligomerization simulations, the two monomers were initially positioned 5 nm apart within the lipid bilayer and the system was solvated and ions were randomly added to the system afterward. Subsequently, the system was minimized for 4 ps and equilibrated for 0.5 ns. The results were visualized using VMD [56]. The steered α-monomer was pushed at Cα of residues Ala956, Gly972, Ala 986, and Gly1007, whereas β-monomer is forced at Cα of residues Val695, Gly708, Ala728, and Ala742 with a constant velocity of either 0.025 Å/ps or 0.0025 Å/ps. The other monomer is either fixed at the same atoms or free to move.
To observe the α-dimerization interaction, two α-dimers are placed initially 2 nm apart and equilibrated for 1 ns. After bonds are formed and the system reached equilibrium, one monomer is constrained at the same four atoms while the other is pulled on from the atoms corresponding to the constrained one, away from the fixed monomer. β-trimerization is scrutinized by primarily placing three transmembrane-cytoplasmic β-domains on vertices of an equiangular triangle each 2 nm away from its adjacent monomers. The system is then equilibrated for 1 ns until the trimerization interaction assumes an equilibrium state. Then, one monomer is pulled while others are constrained. This was continued until the steered monomer was completely separated from its neighboring β-monomers. All dimerization and trimerization simulations were equilibrated for 1.5 ns to confirm the stability of interactions.
The density of the cytoplasmic compartments of lipid chains were measured for the entire length of the plasma membrane along the line that connected the two subunits embedded in the membrane. A 100×50 Å and a 100×150 Å lipid patch were used to embed α- and β-domains in, respectively. The lipid patches and integrin subunits were minimized and equilibrated. Nine (each one 100 ps after the previous one) and five (each one 500 ps after the previous one) snapshots of the system in equilibrium were averaged for α- and β-domains, respectively.
In order to calculate the free energy profile of the α-α and β-β homo-oligomerization of cytoplasmic domains, we invoked Jarzynski method [33]. This method is a powerful tool for calculating free energy profiles along reaction coordinates. It is applicable to non-equilibrium processes on the energy landscape of the system. The method employs the following formula to relate the free energy change of the system to the external work done on the system:(4)where β, ΔF, and W are 1/kBT, change in free energy, and external work, respectively. Provided the number of samplings is extremely high, the formula applies independent of the process speed. Nonetheless, reasonable approximations have been made using Jarzynski method for steered molecular dynamics, when the system has been sampled only 10 times. Therefore, we minimized and equilibrated the system 10 times including two integrin TMC subunits, 5 nm apart, embedded in a patch of plasma membrane. Afterwards, one subunit was steered toward the other from its cytoplasmic tail, while the other subunit was left unconstrained. The identical process of steering was repeated once starting from each of the 10 equilibrated systems. In order to avoid the bias toward the samples with smaller numbers, the free energy was calculated using the second order expansion of Eq. 4, as follows:(5)where M and Wi are number of samples and the work corresponding to the i-th sample.
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10.1371/journal.pntd.0005980 | Reappraisal of Leishmanin Skin Test (LST) in the management of American Cutaneous Leishmaniasis: A retrospective analysis from a reference center in Argentina | Leishmania (Viannia) braziliensis is the species most frequently implicated with cutaneous and mucosal leishmaniasis in the Americas; its diagnosis is based on the identification of amastigotes in lesions, which is limited by low parasite burden. Leishmanin Skin Test (LST) is a support tool for diagnosis, based on delayed type hypersensitivity responses to Leishmania antigens injected intradermally, used in endemic areas as a complement to diagnosis. A retrospective analysis of individuals evaluated for their first episode of tegumentary leishmaniasis at a reference center in Argentina during the period 2006–2015 was performed, with the goal of assessing its usefulness as a support tool in the diagnosis of leishmaniasis. Demographic, clinical and diagnostic work-up were analyzed in individuals with clinically compatible lesions, lesion`s smear and LST. A total of 733 cases that met the case definition were included in the analysis; 678 (93%) localized cutaneous cases, 50 (7%) with mucosal involvement and 5 (<1%) disseminated. Diagnostic confirmation was reached in 474 (65%) cases through positive smears from skin or mucosal lesions, with only 6 cases among this group having negative LST. Among smear negative cases, 190 were negative also by LST, but in 69 instances LST was positive. Across age groups, similar ratios of sensitivity between smear and LST were calculated. Lesions older than 21 days-old were found to correlate with positive results both for smear and LST significantly more than younger lesions. These findings support the clinical use of LST as a diagnostic complement for American Cutaneous Leishmaniasis across all age groups even in endemic areas. In this analysis, the correlation with smear was high. Standardization of this technique and further research into its most adequate preparation and utilization protocols across different sites will help in the management of suspicious clinical cases.
| American Cutaneous Leishmaniasis (ACL) is a vector borne Neglected Tropical Disease with skin ulcers as the main clinical manifestation. Diagnosis is suspected when characteristic lesions occur in patients in areas known to be endemic and confirmed with microscopic identification of amastigotes in samples obtained from lesions. The Leishmanin Skin Test (LST) is an aid in the diagnosis based on the elicitation of delayed type hypersensitivity responses upon exposure to Leishmania antigens injected intradermally into the patient´s forearm and measured as the induration size after 48 hours. This analysis of 733 cases, which is the largest series of cases evaluating the role of LST in ACL highlights and establishes the usefulness of this complementary diagnostic tool with a strong correlation with lesion smear and a demonstration that false positive responses due to previous exposure to the parasite are not of concern. The results support the use of LST in endemic areas to assist in guiding clinical decisions regarding treatment initiation.
| The leishmaniases are a group of vector borne neglected tropical diseases affecting a significant number of people in the tropical and subtropical regions of the world; this population is estimated in over 398 and 556 million people at risk of cutaneous leishmaniasis and visceral leishmaniasis respectively, in high burden countries [1]. As stated in a Technical Report gathered by WHO, there are dire needs for expansion of control programs and generation of research based evidence in all aspects of this group of diseases [2]. Leishmania (Viannia) braziliensis, which is distributed in the Americas, from Argentina to Mexico, has a significantly distinctive clinical behavior, being the most frequent cause of American Tegumentary Leishmaniasis (ATL), the clinical form that involves skin and/or mucosa in the Americas. Another feature of the leishmaniasis caused by L. (V.) braziliensis is the low burden of parasites in the lesions, which complicates the diagnosis through methods that depend on the timely identification of parasites from the lesions (direct methods) [3,4]; this scarcity is more pronounced in mucosal than in cutaneous lesions [5].
Northern Argentina has been endemic for tegumentary leishmaniasis in restricted areas since its initial descriptions in the 1910s, and remains endemic in those same areas, with evidence pointing towards urban transmission and the introduction of visceral disease due to L. (Leishmania) infantum mainly in the northeast but also in the northwest [6–8]. The Province of Salta, in the Northwest, has been the area of the country with the highest incidence of tegumentary leishmaniasis, with most cases originating in the Oran Department, where cutaneous and mucosal disease have been documented to be almost uniformly due to L. (V.) braziliensis; with rare cases due to L.(V.) guyanensis and L.(L.) amazonensis [9–11].
The diagnosis of tegumentary leishmaniasis relies mostly on the sum of epidemiologic background, clinically compatible lesions and confirmation through a direct test, which most frequently is a Giemsa stained smear from skin scrapping that is evaluated under immersion oil microscopy and identifies amastigotes with its characteristic kinetoplast. Alternative approaches include skin biopsies and needle aspirates for culture and most recently molecular biology approaches to detect Leishmania DNA from scrapings, aspirates and biopsies [12].
The Leishmanin Skin Test is a complementary tool for diagnosis that relies on the elicitation of delayed type hypersensitivity by the intradermal inoculation of Leishmania extracts, which provokes in the sensitized host, an immune response upon exposure to specific antigens and clinically manifests as an indurated nodule that can be seen and measured 48 hrs. after inoculation in the forearm. The LST was initially described by Montenegro and its principle is analogous to the Tuberculin test used for tuberculosis [13].
The purpose of this study was to perform a descriptive and comparative analysis of LST reactivity in suspected cases of ATL in an endemic area in a set of clinical and demographic variables.
The Instituto de Investigaciones en Enfermedades Tropicales (IIET) in the city of Oran (Oran Department), Northwestern Argentina, located at 23°08’S, 64°20’W, serves as a regional reference center for the diagnosis of leishmaniasis; patients are referred by local physicians or self-referred for the evaluation of lesions suspicious of leishmaniasis, which if epidemiologically and clinically compatible undergo diagnostic sampling through skin scrapping and LST. Upon completion of the diagnostic work-up, patients are referred to their physicians or the local reference Hospital.
An ad-hoc database was constructed with the information from the archives of the IIET, where in a de-identified manner, the following variables were included: age at presentation, sex, department of residence, lesion age, clinical form (cutaneous, mucosal involvement, disseminated), lesion/s location, largest lesion size, number of lesions, smear result, LST result. The case definition for this analysis was established as any individual with first episode of lesions compatible with ATL, which had a diagnostic work-up and results of smear and LST available for evaluation. The study was performed with cases seen at the IIET between January 1st, 2006 and December 31st, 2015. When smear results were >24hours older than the LST application, the case was excluded in order to prevent the bias induced by repeated smears in LST positive cases.
Clinical diagnosis was made on suspicious cutaneous and mucosal lesions and categorized as “Localized cutaneous” form when all lesions were in the skin without meeting the case definition for “Disseminated” form, which was defined as ≥10 lesions in ≥2 body areas [14]. Mucosal disease was defined as compatible lesions in mucosal tissues (whether without cutaneous lesions “Mucosal” or concomitant with skin lesions “Mucocutaneous”).
As per the Standard Operating Procedures, the lesions were processed as follows: lesions with clinical and epidemiologic features of ATL were investigated through scrapping of the border of the lesion. The obtained sample was allowed to dry, fixed, stained with 10% Giemsa and examined under immersion oil optic microscopy for the presence of amastigotes. Semi-quantification of the burden of amastigotes was carried out as follows: negative, no amastigotes on the entire slide; +, 1–10 parasites/ 1000 fields; ++, 1–99 parasites/ 100 fields; +++, ≥10 parasites/ 10 fields. LST was applied by injecting intradermally 0.1 ml of Leishmanin (40μg of protein/ml) into the forearm, and the reaction recorded prior to the initiation of treatment at 48 to 72 hours post application; indurations ≥5mm were considered positive [15].
LST was locally prepared with a soluble extract of promastigotes of L.(V.) braziliensis, obtained in culture from a patient of our region (strain MHOM/AR/03/OLO1). Parasites were identified through Multi Locus Enzyme Electrophoresis. During study period, two different lots of LST were employed; these, were produced from the same strain and following the same protocol.
The cultures were initially maintained in biphasic agar-blood supplemented with 20% defibrinated rabbit blood (UNSa animal facilities, Salta, Argentina) and LIT medium supplemented with 10% Fetal Bovine Serum (Sigma-Aldrich). Promastigotes were harvested at peak log phased growth, centrifuged at 2500 rpm for 10 minutes at 4°C and washed with saline solution. Afterwards, promastigotes were re-suspended at a concentration of 106 /mL in a phenol 5‰ solution; this solution was centrifuged at 3000 rpm for 15 minutes at 4°C. The obtained supernatant was sterilized with filters of 0.22μm pore (Merck Millipore, Billerica MA) and stored at -20°C until use.
With the database filled, conflicting entries clarified and quality control measurements approved, the data was locked and analyzed through Epidat (Xunta de Galicia, Spain). Dichotomous variables were analyzed through Chi square (X2) and the Yates`correction for continuity was added for calculations in groups with >40 cases; gamma and Taub-C tests were used for variables organized in ordinal categories; agreement between tests was also measured with the Kappa Index. Continuous variables were analyzed according to the underlying distribution of the data using Student`s T test or the Mann-Whitney´s non-parametric test. Correlations were estimated from Spearman´s rank correlation coefficients. Significance was defined at p values ≤0.05.
All the database was de-identified. The project was evaluated and approved by the Bioethics Committee of the Universidad Nacional de Salta as part of the research plan upon entry to CONICET Research Track of the first author (AJK).
The analysis included 733 cases that met the entry criteria. From 730 cases with information on the locality of residence, 665 (91.1%) were form Oran Department and another 43 (5.9%) from other Departments within Salta Province and 22 (3%) from other parts of Argentina or Bolivia. The male:female ratio was 3:1. All cases had information on age, with a median (inter quartile range) of 42 years old (IQR: 28–57) and no differences between females and males; only 24 cases (3.3%) corresponded to individuals ≤14 year-old. Description of clinical presentation is detailed in Table 1.
The diagnostic procedures showed that 474 of the 733 cases with clinically suspicious lesions (64.7%) had diagnostic confirmation through positive smears. When discriminated by the clinical form of presentation, the 474 positive smears came from 436 localized cutaneous, 34 mucosal and 4 disseminated cases. In this analysis, considering the whole study population, there was agreement between tests in 658 of 733 cases (89.8%) which includes 468 cases with both positive tests and 190 with negative results on LST and smear; disagreement in the remaining 75 cases included only 6 (0.8%) cases corresponding to positive smears with negative LST (5 cutaneous and 1 mucosal) and 69 cases with positive LST and negative smear; the Kappa index agreement between smear and LST was 0.76 (p <0.001) (Table 2). When analyzed in subgroups by clinical presentation, the agreement remained significant in all groups through the application of Kappa index and X2 with Yates correction, including the subgroup of disseminated cases with only 5 cases and a full agreement between tests (Table 2). Sex and age of the lesion were found not to be associated with clinical form; there was however a significantly higher proportion of positive cases among males both for LST and smear results. The prevalence of positive LST and smear results among the study population was stable across age groups, as was the relationship between LST and smear positivity (Table 3).
The LST response measured through the size of induration had a median (IQR) of 18mm (13–23) among smear positive cases and 0mm (0–0) among those with a negative smear. Comparing median values of induration size according to clinical form and smear results demonstrated significant differences between smear categories but also between cutaneous and mucosal lesions among smear negative groups (Fig 1). Correlations between parameters of disease severity and LST size of induration were evaluated through the analysis of correlation between induration size and the diameter of the largest lesion and with the semi-quantitation of the parasite burden in the smear; the size of the largest skin lesion, which was available for analysis in 657 cases, showed a significant positive correlation in the Spearman correlation test (p = 0.001). We also found a significant positive correlation between semi-quantitative parasite burden in the lesion and LST positivity (p<0.001), with all cases showing +++ lesions (75 cutaneous, 1 mucosal and 3 disseminated) having a positive LST. Besides the cut-off of 5mm of induration defining positivity of LST, the absolute induration size was also found to be significantly correlated with parasite burden (p<0.001).
The age of the lesion was analyzed in view of the influence that the time to mount an adequate immunologic response might pose on the performance of the LST. With a median time of lesion age of 30 days among patients with cutaneous lesions, a cut-off of 21 days was used to compare lesions defined as “recent” vs “old”. In this analysis, we found that among the 45 cases with mucosal involvement and information of lesion age, only 1 corresponded to “recent lesion”, as expected; therefore, we concentrated this analysis on localized cutaneous cases where we identified a total of 674 cases, 236 (35%) were recent and the other 438 (65%) late. Lesions older than 21 days had a significantly higher likelihood of having a positive LST (Table 4). When 30 days was used to separate the 2 groups of lesions, the difference was no longer statistically significant. Potential confounding variables like differences in time to seek for medical attention between males and females were ruled out, although there was a significant correlation between age of the patient and lesion age in Spearman’s correlation test (p = 0.02) among these 674 localized cutaneous cases. We also identified a statistically significant increase in the positive results in lesion`s smear when performed in the group of lesions older than 21 days (p = 0.001) among the same subgroup of cases. The localization of the lesion was evaluated for cases with localized cutaneous disease as a variable in reference to the results of LST and smear; among 341 cases with localized cutaneous lesions below the waist, Spearman`s correlation between LST and smear remained significant (p<0.001). In the subgroup of 117 cases of localized cutaneous lesions below the waist and <21 days of lesion age, 52 cases (44%) had confirmed amastigotes on smear of the lesion, of which 51 also were LST+, and another 10 cases (8.5%) were LST+ with negative smears.
The findings of this study highlight the usefulness and contribution of LST in the diagnosis of ATL in a highly endemic area. Currently, there is a lack of standardized procedures for LST preparation; studies are necessary to harmonize key aspects of this diagnostic method if a wider use for the management of suspected clinical cases is expected. Recently published guidelines on the management of leishmaniasis discourage the use of LST due to its unavailability in the US and Canada [12], a situation which coupled with its continuous use in countries endemic for ATL in Latin America, should encourage further research, standardization and production in order to make it widely available for clinical use and epidemiologic investigations.
This study, with a large sample size of over 700 patients, contributes to a better understanding of the characteristics of this type of diagnostic tool. This becomes relevant and important in low-resource settings where molecular amplification assays are not routinely available. These results also contribute as a complement in diagnosis even in well-equipped centers where PCR and microscopy might not be able to confirm the diagnosis.
To our knowledge, this is the largest study analyzing retrospectively the role of LST in clinical practice. The results are most significant at confirming the high sensitivity and specificity of this method even in a highly endemic area as ours, where although the exposure risk is significant for most of the population due to the rural and urban transmission of leishmaniasis [6], the correlation between lesion smears and LST was highly significant. This was seen for all the clinical forms of the disease, including localized cutaneous, mucocutaneous and disseminated leishmaniasis; showing an overall sensitivity of over 98% using lesion’s smears as the standard comparator. The maintenance of the ratio of positive LST to positive smears across age groups (Table 3), suggests that the group of cases with positive LST and negative smears represents a clinically significant contribution for the diagnosis of leishmaniasis, rather than LST sensitization due to exposure to the parasite but without clinical signs of infection. With 13% of the total LST positive cases having negative smears, although this most likely represents the limits of sensitivity of microscopy when done by expert observers [5,16], alternative diagnoses must be considered an included in the diagnostic work-up; some of them, like sporotrichosis, with unclear cross reactivity in LST responses [17]. Previous studies that used the LST as a marker of exposure/infection in the general population, have noted that the prevalence of LST+ cases increases with age at a disproportionally higher rate than the increases in the incidence of clinical disease [8], referring to the cumulative risk of exposure as a function of time, rather than just the poor sensitivity of the direct methods. The sensitivity of the LST found in this study is in accordance with previous studies, all of them informing sensitivities ≥90% [18,19]. The size of the induration was not found to be larger among cases with mucosal involvement in accordance with previous smaller studies [20] although other small studies found larger induration size in cases with mucosal involvement [21,22].
Previous studies have looked at the impact of lesion age in reference to the sensitivity of LST with results suggesting that LST was less sensitive in lesions of <1 month [20,23]; our study allowed us to determine that lesions of <21days old were significantly less likely to produce a positive LST (Table 4). This finding has significant clinical implications in order to define the best timing for the use of the LST. Still, among cases with lesions <21 days old, the most frequent diagnosis based on positive smears was leishmaniasis with 132 (56%) of 236 cases (Table 4). We also identified statistically significant differences in lesion smears, with lesions >21 days being more likely to render positive results; previous studies have noted decreasing sensitivity of the lesion smears in lesions >3 months old [5,23]. The most likely explanation for the presence of more cases with negative LST and smears in those lesions <21 days old and below the waist is that in those cases alternative etiologies that often resolve within 3 weeks are more frequent. The results on the analysis based on the localization of the lesions revealed that the presence of lesions above or below the waist does not alter the correlation between LST and smear. All this information supports the clinical conduct of indicating anti-Leishmania therapy when typical lesions and a positive LST are present despite the lack of parasitologic confirmation of leishmaniasis, in the context of an adequate epidemiologic background and a diagnostic evaluation that rules out other pathologies like vascular and traumatic ulcerative lesions, foreign-body reactions, superinfected insect bites, myiasis, impetigo, fungal and mycobacterial infections, sarcoidosis, and neoplasms for cutaneous lesions and paracoccidiodomycosis, histoplasmosis, syphilis, tertiary yaws, leprosy, rhinoscleroma, midline granuloma, sarcoidosis, and neoplasms for mucosal lesions [24].
The limitations of this study include its retrospective nature, the lack of a diagnostic gold standard, the lack of validated protocols that formally evaluate stability and reproducibility of the LST preparation, and the fact that the homology between the circulating strains in our area and the strain used to produce LST are likely to be optimal, although at least 2 genotypes of L. (V.) braziliensis have been documented to circulate in the study area [25]; therefore, the reproducibility of this results in a less than optimal situation need confirmation based on differences seen in previous studies [18]; despite this, epidemiologic studies performed in our region with LST produced in other regions were still useful and current developments are still unclear on the impact of the genetic difference between Leishmania parasites on various aspects of immunology and pathogenesis [8,26].
In summary, LST is an adequate and useful diagnostic complement for the diagnosis of ACL. Further work on its standardization and validation across geographic locations and including correlation with molecular biology methods in the diagnostic panel is warranted in view of its performance and ease of use in clinical settings.
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10.1371/journal.ppat.1005808 | Jagged1 Instructs Macrophage Differentiation in Leprosy | As circulating monocytes enter the site of disease, the local microenvironment instructs their differentiation into tissue macrophages (MΦ). To identify mechanisms that regulate MΦ differentiation, we studied human leprosy as a model, since M1-type antimicrobial MΦ predominate in lesions in the self-limited form, whereas M2-type phagocytic MΦ are characteristic of the lesions in the progressive form. Using a heterotypic co-culture model, we found that unstimulated endothelial cells (EC) trigger monocytes to become M2 MΦ. However, biochemical screens identified that IFN-γ and two families of small molecules activated EC to induce monocytes to differentiate into M1 MΦ. The gene expression profiles induced in these activated EC, when overlapped with the transcriptomes of human leprosy lesions, identified Jagged1 (JAG1) as a potential regulator of MΦ differentiation. JAG1 protein was preferentially expressed in the lesions from the self-limited form of leprosy, and localized to the vascular endothelium. The ability of activated EC to induce M1 MΦ was JAG1-dependent and the addition of JAG1 to quiescent EC facilitated monocyte differentiation into M1 MΦ with antimicrobial activity against M. leprae. Our findings indicate a potential role for the IFN-γ-JAG1 axis in instructing MΦ differentiation as part of the host defense response at the site of disease in human leprosy.
| Mycobacterial diseases, such as leprosy, continue to be serious causes of mortality and morbidity worldwide. They pose a unique treatment challenge due to their ability to modify the immune response in infected individuals. For example, in leprosy there are two distinct manifestations of the disease, each characterized by the immune response of the individual. One results in a more disseminated and severe form of the disease, lepromatous leprosy, and the other is a more limited form with marked antimicrobial activity, tuberculoid leprosy. These differences in the immune response can be characterized by the phenotype and activation state of the macrophage. We illustrate how the local endothelial microenvironment can “educate” macrophages, identifying Jagged1 and select small molecules that can regulate this pathway. Therefore, these studies identify a potential strategy to intervene in infection and inflammation, by targeting macrophage instruction at the site of disease. Through the integration of in vitro modeling and gene expression profiles at the site of disease, we found that Jagged 1 harnesses the endothelial microenvironment to instruct antimicrobial macrophage responses in leprosy.
| When circulating monocytes enter the site of disease, local cues from the tissue microenvironment direct their differentiation into specialized MΦ equipped for diverse tasks [1–3]. While classically activated M1 MΦ with antimicrobial activity promote host defense against intracellular pathogens, alternatively activated (M2) MΦ perform homeostatic functions including phagocytosis critical to tissue remodeling [1–6]. In leprosy, the divergence of MΦ functional programs correlate with the clinical disease spectrum [7–9]. In the self-limited, tuberculoid (T-lep) form of leprosy, disease lesions contain well-organized granulomas with M1 MΦ, expressing the MΦ marker CD209, but negative for the haptoglobin receptor CD163, yet armed with antimicrobial effector function [7]. By contrast, in the progressive, lepromatous (L-lep) form of leprosy, patient lesions are characterized by disorganized granulomas containing MΦ which co-express CD209 and CD163 but lack antimicrobial activity. Instead, these MΦ are programmed with phagocytic function, which results in the accumulation of host-derived lipids and favors mycobacterial growth [10, 11], and are therefore referred to as M2 MΦ. These data raise the question regarding the mechanisms by which clues from the microenvironment influence MΦ programming at the site of infection.
As a gatekeeper to circulating monocytes that enter disease lesions, the microvasculature is poised to deliver key differentiation cues. The very cells which allow monocytes to exit the blood and enter the site of disease, i.e. EC, were shown to trigger monocyte differentiation into MΦ [12], specifically of the M2 type [13]. Therefore, unstimulated EC have the ability to instruct M2 MΦ differentiation, yet the conditions that might alter EC to instruct M1 MΦ differentiation are not known. Here, we explore how the EC-monocyte interface can influence M1 MΦ differentiation, including upregulation of antimicrobial activity, in the context of leprosy as a human disease model.
Given the critical role the microvasculature plays in the transmigration of circulating leukocytes, it is poised to deliver instructive cues to monocytes entering the site of disease [12, 14, 15]. To investigate how the microvasculature, specifically EC, influence MΦ differentiation, we chose leprosy as a model, focusing on M1 and M2 MΦ that expressed CD209, and the relative expression of CD163, either low and high, reflecting the major MΦ phenotypes at the site of disease and endowed with distinct functional programs. We hypothesized that a resting microenvironment leads EC to instruct monocyte differentiation into M2 MΦ with phagocytic function; whereas, perturbations in the local microenvironment may direct monocytes to differentiate into M1 MΦ with antimicrobial activity (Fig 1A).
When human monocytes were cultured in the presence of several types of EC, but not vascular smooth muscle cells, we observed differentiation of monocytes into MΦ expressing CD209 (S1 Fig). As a first step to explore how the endothelial microenvironment may influence M1 vs. M2 MΦ differentiation, EC were treated with various regulatory cytokines before adding human peripheral blood mononuclear cells. After co-culture for 48h, monocyte differentiation was assessed by flow cytometry. For most of the cytokines used in the pre-treatment, EC triggered differentiation into a comparable percentage of M2 MΦ co-expressing CD209 and CD163 (Fig 1B). However, only IFN-γ-treated EC facilitated monocyte differentiation into the CD209+CD163neg M1 MΦ phenotype associated with host defense [7, 16]. These M1 MΦ also expressed higher levels of CD40 [17, 18] (Fig 1C and 1D), but not the dendritic cell marker CD1a (S2 Fig). To assess whether this effect was specific to the Type II IFN, IFN-γ, we also tested the Type I IFN, IFN-α, which failed to mediate similar EC-triggered MΦ differentiation (Fig 1E). Monocyte differentiation into CD209+ MΦ was dependent on direct contact with EC and did not occur when monocytes were treated with IFN-γ in the absence of EC (S3A and S3C Fig). While the addition of IFN-γ induces very low levels of CD209 on human monocytes, direct contact with ECs elicits the highest expression of CD209 on differentiating MΦ(S3A and S3C Fig). In addition, similar results were obtained with purified CD14+CD16neg monocytes, indicating the process was independent of lymphocytes (S3B Fig).
Next, we compared the MΦ derived from activated vs. resting EC for antimicrobial vs. phagocytic characteristics, given that our previous studies showed these programs to be divergent [7]. The M1 MΦ (CD209+CD163neg) induced by IFN-γ-treated EC, when compared to the M2 MΦ induced by resting EC (CD209+CD163+), were found to: i) take up less oxidized low density lipoprotein (oxLDL) (p <0.01) (Fig 1F), ii) produce greater amounts of pro-inflammatory cytokines in response to stimulation with a mycobacterial TLR2/1 ligand (p <0.05) (Fig 1G), and iii) express greater levels of the vitamin D antimycobacterial pathway genes Cyp27b1, VDR and cathelicidin [7] (Fig 1H). Therefore, IFN-γ, which is known to play a critical role in host defense against M. leprae and other mycobacterial pathogens [4, 8, 19, 20], licensed the EC to instruct monocyte differentiation into M1 MΦ programmed with upregulation of the vitamin D antimicrobial pathway.
IFN-γ is a potent inflammatory mediator that regulates an extensive gene program in EC [19–21]. We envisioned that small molecules that would facilitate EC-driven M1 MΦ differentiation may do so through convergence upon shared regulatory mechanisms (Fig 2). From a small molecule library generated by diversity-oriented synthesis (n = 642) [22], 24 compounds (3.7%) were identified which when used to treat EC, promoted M1 MΦ differentiation as measured by cell surface phenotype (Fig 3A). Two structurally distinct families, naphthyridines and tetrahydro-pyrrolo-triazolo-pyridazindiones (tptp) accounted for 13 (54%) of the “hits”, and subsequent experiments with compounds from each of these families confirmed that upon treatment of EC, they triggered differentiation of M1 MΦ (Fig 3B). As with IFN-γ, this effect was EC-dependent, since the compounds failed to directly trigger monocytes to become MΦ that express CD209 (S4 Fig). Among 81 naphthyridine analogs [22], 34 (42%) prompted EC to instruct monocyte differentiation into M1 MΦ (S5 Fig), indicating some specificity among naphthyridines. As with IFN-γ treated EC cultures, M1 MΦ derived from the compound-activated EC cultures were significantly less phagocytic than the M2 MΦ derived from the resting EC cultures (p < 0.0001) (Fig 3C and 3D). We chose the most effective compound (naphthyridine 105A10) for further analysis and found that 105A10-treated EC triggered MΦ that were also more responsive to TLR2/1 activation in terms of induction of pro-inflammatory cytokines (Fig 3E).
Having identified structurally diverse compounds that mimicked IFN-γ, we sought to use these compounds to explore the mechanisms by which EC trigger this differentiation. Since IFN-γ signaling is primarily through STAT-1, we sought to determine if active compounds from both families increased the phosphorylation of STAT-1 in treated EC. Active compounds from both tptp and naphthyridine families of compounds failed to induce phosphorylated STAT-1 (Fig 4A). To determine whether the various stimuli induce a common gene signature in EC, we measured the gene expression profiles in EC treated with either IFN-γ, IFN-α or one of four active small molecules (two naphthyridines: 105A9, 105A10 and two tptp family members: 104B11, 104C2). IFN-γ induced a broad profile (n = 3675 probes >1.25-fold induction), by comparison, the four compounds induced a more restricted profile, (range n = 1248–1935 probes >1.25-fold induction). A high proportion of the genes induced by the four compounds (24–29%) overlapped with the IFN-γ signature (hypergeometric p values for enrichment: 5.35 x 10−32 to 2.80 x 10−103, Fig 4B).
To identify the genes triggered in activated EC with relevance to leprosy, we overlapped three profiles: i) induced by IFN-γ in EC, ii) induced by at least one of the four small molecules in EC; and, iii) preferentially expressed at the site of disease in the self-limited T-lep vs. the progressive L-lep form of leprosy (Figs 2 and 5A). This analysis identified 166 candidate regulatory genes, of which 50 were induced by at least two of the four compounds (S1 Table).
We next tested the role of the 50 common genes in facilitating EC-directed M1 MΦ differentiation. EC were transfected with siRNA against each of these candidate genes, and then treated with IFN-γ to induce the M1 polarizing microenvironment, followed by co-culture with primary human PBMC. In this context, monocyte differentiation into CD163+ MΦ would reflect that the M1 MΦ-polarizing effect of IFN-γ treated EC was being inhibited by the siRNA. Across five separate experiments, eight genes significantly inhibited the effect that IFN-γ exerts on the EC-driven M1 MΦ phenotype (Fig 5B, S6 Fig). In parallel, the gene expression profiles of the 50 common genes were examined in leprosy lesions, with the premise that inverse correlation with CD163 expression may indicate a role in regulating M1 MΦ differentiation at the site of disease. Among the top eight candidate genes, JAG1 demonstrated the strongest inverse correlation with CD163 expression across the spectrum of leprosy lesions (r = -0.834, R2 = 0.6956, P<0.0001, T-lep lesions n = 10, L-lep lesions n = 6), with greater expression in T-lep vs. L-lep lesions (fold change 2.2, p<0.0002) (Fig 5B and 5C).
After confirming that JAG1 is induced on EC following stimulation with IFN-γ (S7 Fig), we then assessed JAG1 expression at the site of disease. JAG1 expression in leprosy lesions was validated by immunohistochemistry, which demonstrated that JAG1 was expressed within the dermis and the granulomas in T-lep, but not L-lep lesions (Fig 5E, S8 Fig). We also noted perivascular labeling of JAG1 in proximity to CD209+ MΦ (Fig 5F), as well JAG1 expression in the microanatomic locations in which M1 MΦ (CD209+CD163neg) were found (Fig 5F). In addition, there appeared to be JAG1 staining in the epidermis of both the T-lep and L-lep lesions which is consistent with the known role of JAG1 in keratinocyte differentiation and maturation [23, 24]. Blinded analysis of JAG1 immunohistochemical staining determined a significant (p = 0.0063) increased positive staining in T-lep sections, scores ranged from 0 (absent) to 4 (highly positive). Together, these data indicated that JAG1 expression correlated with M1 MΦ accumulation at the site of disease in leprosy.
We next investigated whether JAG1 could instruct the differentiation of monocytes into M1 MΦ with antimicrobial function. We found that soluble JAG1 (sJAG1) facilitated EC-driven M1 MΦ differentiation (Fig 6A and 6B). Furthermore, overexpression of JAG1 in EC, as well as addition of a JAG1 agonist peptide to the co-cultures, induced the differentiation of monocytes into the M1 MΦ phenotype (S9 Fig). In contrast, addition of sJAG1 to monocytes alone did not induce MΦ differentiation (S10 Fig). Given that JAG1 is known to activate Notch 1 signaling, we determined whether Notch-downstream genes were upregulated by the addition of JAG1 to the EC/monocyte co-cultures. In comparison to untreated EC, the addition of JAG1 led to the mRNA upregulation of three prototypic Notch-downstream genes in MΦ, HES1, SOCS3 and RBPJ (S11 Fig).
Differentiation of monocytes in the presence of sJAG1 and EC yielded M1 MΦ with decreased phagocytosis (Fig 6C) and heightened induction of vitamin D-dependent antimicrobial pathway genes (Fig 6D). To determine whether EC treated by either IFN-γ or JAG1 induced differentiation of monocytes into MΦ with antimicrobial activity, MΦ differentiated in the presence of treated EC were infected with live M. leprae, and the antimicrobial response measured according to the ratio of M. leprae RNA to DNA [25, 26] (Fig 6E). As compared to MΦ differentiated in the presence of resting EC (i.e. treated with media), the MΦ induced by culture with EC treated with either IFN-γ or sJAG1 showed significant antimicrobial activity. Therefore, when monocytes encounter JAG1 in the context of EC, a differentiation program is triggered, resulting in M1 MΦ, defined by a CD209+CD163neg phenotype and antimicrobial function. The presence of IFN-γ, JAG1-expressing EC and CD209+CD163neg MΦ in the self-limited form of leprosy suggests that the IFN-γ-JAG1-antimicrobial MΦ differentiation pathway contributes to host defense at the site of disease in leprosy.
Our understanding of MΦ immunobiology has been significantly advanced through understanding of the pathways by which microbial ligands and/or cytokines program monocytes to differentiate into M1 and M2 MΦ [27, 28]. However, it is not clear how local tissue signals can differentially program the MΦ response. Signals from endothelium are involved; this default pathway triggers M2 MΦ differentiation [13]. However, the mechanisms by which monocytes, upon entering the site of disease via the endothelium, are instructed to differentiate into M1 MΦ remain elusive [1–3]. Here, we hypothesized that if EC were to encounter the proper signals, the EC microenvironment would instruct monocytes to differentiate into M1 MΦ, equipped for host defense against intracellular pathogens at the site of disease. By studying leprosy as a model, we provide evidence that upregulation of JAG1 on endothelium instructs monocytes to differentiate into M1 MΦ with antimicrobial activity.
Our data indicate that the induction of JAG1 is involved in EC instruction of M1 MΦ differentiation. In addition, the concomitant induction of Notch 1-downstream genes including HES1, SOCS3 and RBP-J mRNAs was detected in the differentiated M1 MΦ. These findings are consistent with the known ability of JAG1 to signal via Notch 1 receptors [29], and with reports that Notch 1 signaling, via SOCS3 and RBP-J [30–32] through reprogramming of mitochondrial metabolism [33], contributes to M1 MΦ differentiation. Nevertheless, since JAG1 is known to signal via several distinct receptors [34, 35], further work is necessary to identify the physiologically relevant interactions responsible for EC-driven M1 MΦ differentiation. Not only does IFN-γ induce JAG1 on EC which can influence monocyte differentiation, IFN-γ also augments TLR-induced regulation of JAG1 expression in differentiated MΦ [36]. Further studies will be required to elucidate how JAG1 can contribute to MΦ differentiation, plasticity, function and proliferation at the site of disease [37].
In addition to the role of JAG1 in regulating innate immune responses via MΦ differentiation, evidence suggests a role for JAG1 in regulating adaptive T cell responses. Patients with Alagille syndrome, in which JAG1 mutations result in a multisystem disorder [34, 38], can exhibit altered Th1 responses [35], implicating JAG1 induced signaling in T cell differentiation. In vitro studies have also shown that JAG1 expression on keratinocytes promotes dendritic cell maturation, which could also influence T cell responses [39]. Therefore, the expression of JAG1 by resident cells in tissue can influence both innate and adaptive immune responses.
Under resting conditions, EC instruct monocytes to differentiate in M2 MΦ [13]. M2 MΦ are highly phagocytic, and are involved in clearing various biomolecules relevant for tissue repair, removal of excess metabolic products as well as clearance of debris. However, in the context of M. leprae infection, M2 MΦ can phagocytize the bacteria, but are unable to mount an antimicrobial response. Furthermore, these M2 MΦ take up host-derived lipids, providing necessary nutrients for mycobacterial growth [40]. Therefore, the induction of M1 MΦ is required for host defense against this intracellular pathogen, as these MΦ are weakly phagocytic but exhibit a strong antimicrobial response. One direct signal at the site of infection is production of IL-15, which directly triggers M1 MΦ differentiation. In addition, our data demonstrates that IFN-γ induces JAG1 expression on EC, which also facilitates differentiation of monocytes into M1 MΦ. In the self-limited form of leprosy, JAG1 expression is restricted to microanatomical regions of the granuloma enriched for M1 MΦ. Therefore, our findings support the concept that the IFN-γ-JAG1 axis is involved in the EC instruction of the antimicrobial MΦ response against M. leprae at the site of infection.
The ability to model how the microenvironment influences the immune response at the site of disease has become feasible because of advances in analyzing increasingly complex systems. We used a cell co-culture system in which we integrated small molecule screening with gene expression profiles to look for recurrent motifs in gene activation patterns associated with EC-triggering M1 MΦ. Since none of the molecular signals we identified recapitulate the antimicrobial MΦ phenotype on their own, our findings indicate that the emergent properties inherent to more complex heterotypic systems allowed for their discovery [41]. As such, this approach provides a strategy to identify potential drugs or biologic agents that would otherwise not be identified in experiments exploring direct effects on monocyte differentiation into antimicrobial MΦ. The identification of JAG1 and other small molecules that can harness the local microenvironment to augment innate immune responses at the site of disease may hold promise for combating intracellular pathogens.
IFN-γ and IL-4 (Peprotech) were used at 10ng/ml. IFN-α (PBL Interferon Source) was used at 10ng/ml. IL-15 (25ng/ml), IL-10 (10ng/ml), IL-5 (10ng/ml), fc-JAG1 (250ng/ml) and fc-control (250 ng/ml) were purchased from R&D Systems. JAG1 protein active peptide fragment (1μM) was purchased from Phoenix Pharmaceuticals. Small molecule compound libraries and analogs were synthesized in the Ohyun Kwon laboratory. Compounds were dissolved in DMSO and used at a final concentration of 10 μM.
Co-culture experiments were carried out as previously described [22]. In short, Primary human endothelial cells (EC) were plated to confluence in a 96 well plate. After adherence, endothelial cells were activated by indicated treatments for a period of 5 hours and subsequently washed 2–3 times to ensure removal of activation treatment. We then added human peripheral blood mononuclear cells (PBMC) at a ratio of 3 PBMC to 1 EC. Cultures were incubated at 37°C and 7% CO2 for a period of 48hrs. Human Umbilical Vein Endothelial Cells (HUVEC) were purchased from Lonza and used from passages 4–8. Peripheral blood mononuclear cells were isolated from healthy donors (UCLA Institutional Review Board # 92-10-591-31) using Hypaque Ficoll gradients (GE Healthcare).
Samples were retrieved by skin biopsy from patients with leprosy. The designation of tuberculoid leprosy (T-lep) and lepromatous leprosy (L-lep) was determined according to the criteria of Ridley and Jopling. Patient skin biopsies were performed at the time of diagnosis and subsequently embedded in OCT medium (Ames, Elkhart, IN), snap frozen in liquid nitrogen and stored at -80°C (24).
HUVEC were stimulated with IFN-γ and compounds (104 B11, 104 C2, 105 A9 and 105 A10) for 15 minutes and then stained according to manufacturer’s protocol for phosphorylated STAT-1. (N = 3)
Cells were harvested after 48 hours incubation at 37°Celsius in 7%CO2. Surface expression of protein was determined using specific antibodies: CD209 (Becton Dickinson), CD40 (Becton Dickinson), CD1a (Becton Dickinson), CD163 (R&D systems), Jagged1 (R&D systems), CD14 (Becton Dickinson) and IgG controls (Becton Dickinson). Phosphorylated STAT-1 levels were determined using Anti-Human phospho-STAT1 (eBiosciences). Cytometric Bead Arrays (CBA) were used to characterize TLR2/1R activated CD14+MΦ supernatants. CBAs were performed on 50μL of supernatant that was harvested after 24 hours of incubation. Supernatants were tested for the presence of MIP1-β, IL-6 and TNF-α. CBA Flex kits were obtained from Becton Dickinson and performed according to manufacturer’s recommendations. Samples were acquired using FacsCalibur and FacsVerse flow cytometers and FCS files were analyzed using FlowJo software.
PBMC/EC Co-cultures were harvested after 48 hours of incubation and CD14+MΦ were subsequently purified using a CD14 positive selection bead assay (Miltenyi Biotec) (purity > 95%). CD14+MΦ from each condition (DMSO, IFN-γ and 105A10) were plated in equal number in 96 well flat bottom plates and stimulated with 10μg/ml TLR2/1 ligand (EMC Microcollections). After 24 hours of stimulation supernatants were harvested and characterized by CBA for production of MIP1-β, TNF-α and IL-6.
cDNA was generated using iScript cDNA synthesis reagent (Biorad) following manufacturers guidelines. Primers (IDT) were used for determining mRNA expression of CYP27b, CAMP, VDR, and JAG1. SYBR Green PCR Master Mix (BioRad) was used for Real Time PCR reactions and data was normalized to h36B4 gene expression (IDT). Expression values were calculated as previously described [7].
CD14+MΦ from co-cultures were harvested and purified as previously mentioned. After purification, MΦ were plated in 10% FCS with 25-D3 (10−8 M) (Biomol) and incubated for 24 hrs. Cells were then harvested and analyzed for CAMP, VDR and Cyp27b1 gene expression by qPCR.
Viable bacteria stocks of M. leprae were obtained from Dr. James L. Krahenbuhl of the National Hansen's Disease Programs, Health Resources Service Administration, Baton Rouge, LA. For antimicrobial assays, Endothelial/PBMC co-cultures were set up as previously mentioned. After 48 hours of incubation CD14+ MΦ (>90% CD209+) were isolated from co-cultures for infection with M. leprae. Co-culture conditioned (Media, IFN-γ and sJAG1) CD14+ MΦ were cultured in RPMI with 10% FCS (Omega Scientific) in the presence of live M. leprae (MOI 10:1). Infected cells were subsequently stimulated with IFN-γ (10ng/ml) in the presence of 25-D3 (10−8 M) after 24 hours of infection. To measure antimicrobial activity in M. leprae-infected MΦ (5 days post infection) we followed the protocol as previously described [40, 42]. In short, qPCR was performed to determine levels of bacterial 16S rRNA and genomic element DNA (RLEP). Expression levels of h36B4 were also evaluated to determine infectivity between all the conditions. The M. leprae 16S rRNA and RLEP primers used were as previously described [25, 26].
DiI (1,1′-dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate)-labeled CuSO4-oxidized low density lipoprotein (Dil-Ox-LDL) from Intracel was added to co-cultures after 44 hours and further cultured for 4 hours in the presence of Dil-OxLDL to allow for uptake (50μg/ml). After incubation, Dil-OxLDL levels were determined within the CD209+ population of our stained cultures. To determine M. leprae uptake (S12 Fig), CD14+ MΦ were harvested from co-cultures as previously mentioned and infected with labeled M. leprae. After 24 hours of infection MΦ were harvested and stained for CD209, CD14 and CD163 expression.
Immunoperoxidase and immunofluorescence labeling were carried out on frozen patient tissue sections. For immunoperoxidase staining, samples were initially blocked with normal horse or goat serum prior to labeling with monoclonal antibodies (JAG1 (Abcam), vWF (AbD Serotec), CD163 (AbD Serotec) and appropriate isotype controls). Sections were then labeled with biotinylated horse anti-mouse IgG or biotinylated goat anti-rabbit IgG. After labeling, sections were counterstained with hematoxylin and visualized using the ABC Elite system (Vector Laboratories). In order to determine protein co-localization in tissue sections, two-color immunofluorescence and confocal microscopy were performed. For Immunofluorescence, sections were labeled with rabbit anti-human JAG1, anti-CD163 (IgG1), anti-CD209 (IgG2b), anti-vWF (IgG1) and appropriate isotype controls. Subsequently samples were labeled with isotype-specific, fluorochrome (A488 or A568)-labeled goat anti-mouse/rabbit immunoglobulin antibodies (Molecular Probes). Nuclei were stained with DAPI (4',6'-diamidino-2-phenylindole). Double immunofluorescence of skin sections was examined using a Leica-TCS-SP MP inverted single confocal laser scanning and a two-photon laser microscope (Leica, Heidelberg, Germany) at the Advanced Microscopy/Spectroscopy Laboratory Macro-Scale Imaging Laboratory, California NanoSystems Institute, University of California at Los Angeles [26]. Blinded review of IHC samples was carried out and positive staining was scored on the scale of 0 (absent) to 4 (highest staining) relative to isotype controls. Fishers exact test was used to determine significance.
siRNA transfections were carried out on 2x104 HUVEC in 96 well plates and 7x103 HUVEC in 384 well plates. siRNA for candidate genes, siControl and siGlow were obtained from Dharmacon as was the transfection reagent Dharmafect 4. siRNA transfections were performed according to manufacturer’s recommendations using 100nM concentration of siRNA. Decrease in message in transfected cells was confirmed by qPCR and protein expression. Ectopic expression cassettes for JAG1, GFP and M11-empty vector were obtained from Genecopoeia. Plasmid transfections were carried out on HUVEC that were grown to 80–90% confluence. HUVEC were harvested and transfected with 1μg DNA using the AMAXA transfection device and HUVEC Nucleofect kit (Lonza). To determine transfection efficiency, control cells were characterized for GFP production. In addition, surface expression of transfected JAG1 was confirmed by flow cytometry.
For microarrays performed on compound and cytokine treated HUVEC, ECs were seeded in 6 well plates at 1X106 cells/well. Single wells were stimulated for five hours with DMSO, IFN-γ, IFN-α and compounds 104B11, 104C2, 105A9 and 105A10 at concentrations noted earlier. After incubation, mRNA was harvested using Trizol (Invitrogen), followed by RNeasy Minelute Cleanup Kit (Qiagen). mRNA samples for all arrays were processed using the Affymetrix Human U133 plus 2 platform and analyzed as previously described [22].
Statistical significance (<.05) of experimental values was calculated using a paired two-tailed Student’s t-test. Hypergeometric p values were calculated using the online resource (http://systems.crump.ucla.edu/hypergeometric) (Tom Graeber laboratory, UCLA).
Patient samples were obtained with approval from the IRB of the University of California Los Angeles, the Institutional Ethics Committee of Oswald Cruz Foundation and the University of Southern California School of Medicine. All subjects were legal adults and provided written informed consent before participating in the study [26].
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10.1371/journal.pcbi.1003578 | Finding Novel Molecular Connections between Developmental Processes and Disease | Identifying molecular connections between developmental processes and disease can lead to new hypotheses about health risks at all stages of life. Here we introduce a new approach to identifying significant connections between gene sets and disease genes, and apply it to several gene sets related to human development. To overcome the limits of incomplete and imperfect information linking genes to disease, we pool genes within disease subtrees in the MeSH taxonomy, and we demonstrate that such pooling improves the power and accuracy of our approach. Significance is assessed through permutation. We created a web-based visualization tool to facilitate multi-scale exploration of this large collection of significant connections (http://gda.cs.tufts.edu/development). High-level analysis of the results reveals expected connections between tissue-specific developmental processes and diseases linked to those tissues, and widespread connections to developmental disorders and cancers. Yet interesting new hypotheses may be derived from examining the unexpected connections. We highlight and discuss the implications of three such connections, linking dementia with bone development, polycystic ovary syndrome with cardiovascular development, and retinopathy of prematurity with lung development. Our results provide additional evidence that plays a key role in the early pathogenesis of polycystic ovary syndrome. Our evidence also suggests that the VEGF pathway and downstream NFKB signaling may explain the complex relationship between bronchopulmonary dysplasia and retinopathy of prematurity, and may form a bridge between two currently-competing hypotheses about the molecular origins of bronchopulmonary dysplasia. Further data exploration and similar queries about other gene sets may generate a variety of new information about the molecular relationships between additional diseases.
| Understanding the roles that genes involved in normal human development can play in disease processes is an important part of predicting disease risk and designing novel treatment approaches. In this study, we have identified classes of disease that are associated with a surprisingly large number of genes involved in any of several tissue-specific developmental processes. To do so, we developed a novel approach whose strength comes from pooling genetic information across related diseases, overcoming problems ordinarily posed by limited information about individual gene-disease relationships. We demonstrate the method's efficacy both by examining its ability to highlight connections between gene sets and disease classes that are known to be related, and by demonstrating that the approach recovers expected broad classes of connections, such as those between heart development and cardiovascular disorders. However, by examining unexpected connections in this data set, we are able to develop new understanding of some surprising disease relationships, such as the one between dementia and osteoporosis. Such connections may lead to a better overall understanding of the role of development in lifelong health, as well as to the design of new methods to treat a range of diseases.
| The study of the health implications of developmental processes has now entered the genomic era. The recent sequencing of an entire fetal genome [1] has demonstrated the possibility of applying molecular methods to design novel prenatal diagnostics. The development of therapeutic approaches for personalized fetal treatment of developmental disorders is now on the horizon [2]. Genomic approaches are providing new insights into causes of and possible treatments for such widespread pediatric disorders as asthma [3] and autism [4]. A growing awareness that development may influence lifelong health risk [5], [6] has led to closer examination of the molecular links between developmental processes and disease at multiple life stages.
Despite considerable progress, our understanding of the molecular etiology of most complex diseases is still limited. Yet by combining weak signals from multiple genes, we may identify patterns that provide clinically significant insights into disease processes. We hypothesized that by examining the relationships between sets of genes related to specific developmental processes and reported disease genes, we could develop novel insights into developmental impacts on health. To test this hypothesis, we created a novel approach and tool to assess the overrepresentation of various developmental gene sets among groups of genes linked to specific diseases. Our approach derives its strength from combining signals of sets of genes and from pooling disease-gene links across disease subtypes using a hierarchical taxonomy of disease. We demonstrate that this pooling approach improves accuracy over a comparable enrichment-detection method without pooling. Our approach has the advantage of potentially generalizing incomplete disease gene data and overcoming variation in how genes are associated with specific disease terms, improving our ability to detect novel and interesting connections.
We note that a similar principle - that of pooling many weak signals to provide a stronger one - has led to the creation of many highly effective “gene-set analysis” methods for expression data [7], [8] and genome wide association data [9]. However, these approaches are inappropriate for assessing the overlap of disease-linked genes with genes involved in developmental pathways, because the members of our developmental gene sets cannot meaningfully be ranked by the strength of their participation in the set. Standard statistical enrichment methods such as the hypergeometric distribution might be more suitable, but their probabilities depend on inappropriate assumptions of gene independence [10]. Our approach avoids these problems.
The choice of a disease taxonomy for this analysis is vitally important, yet most existing hierarchies lack the molecular focus inherent in the proposed analysis [11]. We chose the MeSH hierarchy of diseases (category C) because it is widely used, it is relatively compatible with our disease-gene databases, and it represents diseases multiple times within different parts of the tree, thus potentially including somewhat molecularly homogeneous groupings [12]. For example, type 1 diabetes mellitus appears multiple times in the taxonomy under categories corresponding to nutritional and metabolic diseases, endocrine disorders, and immune system diseases. The MeSH disease taxonomy can be represented as a “forest” of disease terms (a collection of “trees,” in the computational sense [13]), with 26 top-level categories (Table S1) represented by “disease trees,” and more specific disease terms located at increased tree depths.
We derive our disease-gene links from two sources: OMIM, a curated collection of genes linked to human disease [14], and the Genopedia data from the database of Human Genetic Epidemiology (HuGE), whose disease-gene information is obtained primarily by computational literature curation, but includes manual review of both abstracts and index terms [15]. We then pool genes linked to descendants of a disease node in the MeSH trees, and we assess significance through permutation. Because of the current incomplete knowledge of gene-disease connections, enrichment of gene sets among genes linked to a specific disease node in the MeSH forest may not be detectable. By pooling gene links from related diseases, we are able to rescue some of these lost connections.
For this study, we focus on identifying connections to genes involved in developmental processes. The gene sets chosen were based on Biological Process terms from the Gene Ontology (GO), a hierarchically-organized collection of controlled-vocabulary functional annotation of genes and gene products [16]. However, given our interest specifically in developmental gene sets, we chose to use the gene sets from DFLAT, a prior collaboration of ours that aimed to expand human developmental annotation in the Gene Ontology framework [17]. Gene sets derived from the Gene Ontology that include the DFLAT annotation have been shown to improve the interpretability of gene expression data related to human development [18], so they are a reasonable choice for the analysis described here. We refer to the developmental gene sets whose links to disease are being investigated as the query gene sets.
Additional related work assesses significant enrichment of GO functional annotation terms in query gene sets using the directed-acyclic graph structure of the Gene Ontology. Such approaches adjust enrichment calculations by accounting for relationships between the genes at a given annotation node and those at the parent or child [19], [20]. But these methods are concerned with a different problem - that of spurious enrichment at higher levels of the GO hierarchy. Instead, the hazard in our case is false negatives that occur because of the incomplete knowledge of disease genes and the variable levels of precision used to map known disease genes to the MeSH forest. We therefore focus here on query sets representing top-level developmental processes (e.g., “heart development” rather than “atrial cardiac muscle cell development”), because highly specific terms typically include very few genes, rendering gene-set analyses powerless. Future efforts will include drilling down into specific developmental pathways. Yet even at this high level, our analysis identifies both expected links and several unexpected ones, the latter leading to individual novel hypotheses about surprising molecular connections that may affect future disease research.
To identify significant connections between gene sets and disease, we used a novel method of assessing overlaps between disease genes and the designated query gene sets. We first created a computational representation of the MeSH disease taxonomy in which each node represents a MeSH disease concept. We extracted and combined gene-disease links from the HuGE Genopedia database and from OMIM, and mapped the resulting 119,400 gene-disease links to the MeSH forest (see Methods). Taking advantage of the hierarchical representation of disease concepts in MeSH, we then created a version of the forest in which each disease node D contains any genes in the subtree rooted at D. For example, instead of identifying four lung development genes linked to neural tube defects, two to meningomyelocele, and three to spinal dysraphism, pooling them together identifies seven distinct lung development genes implicated in neural tube defects (Figure 1).
For this study we considered nine DFLAT gene sets, broadly representing development in brain, bone, heart, kidney, liver, lung, nerve, blood vessels, and skin. We identified the overlaps between each of these gene sets and the disease genes at each node of our MeSH tree by counting the number of genes in both. (Table S2 lists the query gene sets and their sizes.) Assessing the significance of these overlaps must account for gene set sizes and multiple testing. However, such adjustment is non-trivial because of the complex dependencies between the tests. (For example, any method that assumes the probability of enrichment at node D is independent of the probability of enrichment at D's parent or child is going to be wildly inaccurate.) We therefore use a permutation test (described in the Methods section) to assess the significance of each observed overlap, given the number of genes in the query set and the disease-gene mappings in the MeSH forest. This test produces a p-value at each node estimating the probability of seeing an overlap of the observed size at that node by chance.
Our hypothesis was that mapping disease genes to broader disease terms in the MeSH tree as described above would improve our power to detect actual enrichment by mitigating the effects of varying precision in gene annotation. However, it is also possible that pooling might lead to less-accurate results by incorrectly mapping genes to unrelated disease classes. Assessing which happens more frequently is challenging because the right answers are rarely known. Thus, to compare our pooling approach to a more traditional enrichment analysis, we performed the following experiment.
The intuition behind this experiment is that disease classes that are correctly linked to the query gene set should be more likely to be supported by withheld data from the same query set. So we use support by withheld data as a rough way to approximate correctness. Our “pooling” approach computes the significance of the query gene set's enrichment at disease node D by pooling data from the genes in the subtree rooted at D. For fairness, we chose (as the “traditional” method) to assess significance of linkage using exactly the same random permutations of gene labels, but counting only the genes directly linked to disease node D (rather than those linked to the node or any of its descendants).
We note that the traditional method used here is really just a randomized approximation to the classical hypergeometric calculation, but one that maintains the correlation structure of genes between different diseases. We have separately computed the hypergeometric probabilities (data not shown), and found them to give very similar overall results to those derived using permutation. Accordingly, we present just the permutation-based method, which is the most direct control for our pooling approach, in the comparison below.
We withheld 100 randomly chosen links, each connecting a gene in the query gene set to a specific associated disease. We recomputed enrichment at each disease node without the withheld links, using both the pooling method and the traditional one. Counting then allows us to estimate the probability that a randomly-chosen node found to be more significant under the pooling approach than the traditional approach would be supported by a randomly withheld link, and , the probability that a node more significant by the traditional method would be. (See Methods for further details.)
We repeated this experiment with a different set of 100 withheld links 100 times for each of the 9 developmental gene sets. Table 1 shows the average values of and for each of the development gene sets, and Figure 2 shows histograms of the distribution of - for all of the development gene sets. If is larger than , then the nodes that are more significant under the pooling approach tend to be more consistently supported by the withheld data, which is our proxy for correctness. In other words, when is larger, it suggests that the pooling method tends to make correct links appear more significant. For all nine query sets, we found that the averaged is greater than the averaged , suggesting that the pooling method is better able to identify true links between developmental gene sets and disease.
While it is relatively easy to provide a list, for each developmental gene set, of MeSH terms whose gene set enrichment p-value is below some cutoff, interpreting those lists is complex. Because enrichment calculations are based on subtrees, there is important information available at different scales, ranging from high-level overviews of the MeSH disease forest to specific enriched gene-disease links, their significance scores, and the genes involved. For these results to lead to new discoveries, we must select from this large collection of significant links a few that are surprising yet plausible. Doing this requires a considerable amount of domain knowledge in molecular medicine.
To facilitate data exploration by collaborators with such expertise, we developed a web-based tool that provides both an abstract and a detailed view of the associations (available at http://gda.cs.tufts.edu/development). For a high-level overview, we visualize each disjoint hierarchy of disease terms (i.e., each tree of the MeSH disease forest) in a simplified triangular form (Figure 3). Each significant disease association with the given gene set is represented as a dot in this triangle, whose color represents the degree of significance. This abstract view helps highlight the broad overall patterns of association between development gene sets and disease classes.
Clicking on a particular disease subtree leads to a detailed tree view (Figure 4). The tree visualization is implemented using Cytoscape Web [21]. Color again corresponds to significance, with darker nodes indicating more significant enrichment of the developmental gene set in the disease genes associated with the subtree rooted at that node. For clarity, this view by default only displays disease nodes significantly associated with the query gene set (and their ancestors in the chosen tree). However, users can adjust parameters to view the full tree if desired. Specific genes and p-values for individual links can be identified by selecting nodes in this view. The associated gene lists are easily selected and pasted into functional analysis tools for pathway identification.
In the next two sections, we describe some results from our initial explorations using this tool. The first section provides a sanity-check by demonstrating that we find the broad patterns of connections that one would expect, while the next shows that we can use this approach and the tool described here to make novel but plausible discoveries with potential clinical impact.
We first take a high-level view of all the results together. Generally speaking, one would expect to see connections between tissue-specific developmental gene sets and broad categories of diseases known to involve those particular tissues. For example, it seems likely that many cardiovascular disorders would be linked to a significant number of heart development genes. Figure 5 shows a heatmap of the relative “density” of disease terms significantly linked to each of the gene sets (see Methods) for several MeSH disease trees. We see high enrichment that essentially mirrors our expectations: bone development genes are over-represented in musculoskeletal disorders, brain development genes in nervous system disorders, heart development genes in cardiovascular disorders, etc.
There are a few interesting exceptions. For example, the percentage of nervous system disorders significantly enriched for nerve development genes is relatively high, but not quite high as the percentage of musculoskeletal diseases enriched for nerve development genes. This seems to be in part an artifact of the large number of distinct nervous system disorders listed in MeSH category C despite having little or no molecular information, artificially decreasing the normalized density values (the maximum density score in the C10 category is lower than the maximum score in any of the other MeSH disease trees shown in the figure).
The root node of MeSH category C4, “Neoplasms,” is significantly associated () with all of the developmental gene sets except for nerve and skin (the two smallest of the gene sets and therefore the least likely to have significant overlaps). This observation reflects the fact that the regulation of cell growth and differentiation that comprise normal developmental processes are typically disrupted and dysregulated during the onset of malignancy [22], [23]. A range of signaling proteins that play roles in directing both developmental processes and tumorigenesis are likely to blame for these interactions [24]–[26]. However, the specific signaling processes implicated in the different tumor types, as well as those known to be involved in developmental processes but not yet implicated in specific tumor types, may be of interest.
Similarly, given that the query gene sets are all involved in developmental processes, it is not surprising that the C16 MeSH subtree, described as “Congenital, Hereditary, and Neonatal Diseases and Abnormalities,” shows significant enrichment at the root node () for all of the tested developmental gene sets. A wide range of molecular developmental processes are implicated in this MeSH category. The density measurement shown in Figure 5 provides a broader way of assessing a similar property. The density measure for the C16 tree is above average (i.e., the z-score normalized density metric is positive) for each of the nine gene sets considered here.
By confirming that we find expected and reasonable high-level results, the observations in this section provide evidence of the efficacy of our approach.
Delving more closely into specific results, we identified several findings that seemed, at first glance, less predictable than those described above. Here we describe three such links. All of them identified surprising connections that, since our initial discovery of them using this approach, have been further supported by new publications.
We have introduced a new approach that identifies significant overlap of gene sets with groups of related diseases in a hierarchical disease taxonomy. To evaluate this approach, we implemented a tool that allows users to explore connections between disease subtrees in MeSH and several developmental gene sets. Our observations in this analysis have helped identify surprising molecular connections between disparate processes. They have also more generally served to validate the approach of pooling incomplete information about disease genes across related disorders to strengthen our ability to identify such connections. With a growing interest in research into the developmental origins of adult disease, this resource should prove a valuable source of information for generating hypotheses about such connections at the molecular level.
Our work has assumed only that query gene sets are lists of genes that share some common property [61]. However, for this study we have chosen query sets whose genes share common annotations in the Gene Ontology. An interesting future direction would be to consider the possibility of creating hierarchically-structured queries representing related query terms in the Gene Ontology's directed acyclic graph structure, while still looking for significant links to disease classes or subtrees in the MeSH forest.
While our implementation relies on a particular set of disease-gene information and a small group of developmental gene sets, the power of the approach will be best exploited by the inclusion of a more comprehensive set of disease-associated genes. One key limitation of the current approach is due to the nature of the available data linking genes to diseases. OMIM is an excellent resource created largely by computer-assisted manual review of the literature [14]. However, it is limited in scope and is curated by locus rather than by disease, so that even identifying all genes related to, for example, type 2 diabetes, can be complicated. Conversely, the HuGE database, which provides the majority of the disease-gene data used in this project, derives most of its information from computational screening of PubMed (along with some manual review) [62], [63]. This raises the possibility that, in addition to being incomplete, our gene-disease database may include a substantial number of false positives due not only to false-positive experimental results but also to inappropriate interpretation of the text. There is prior work on reducing the rate of false positives when mining such information from the literature [64], and the HuGE database creators worked to assess and improve accuracy [63], but any data set derived from computational literature analysis will always have this concern. On the other hand, the success of our initial analysis in identifying expected connections suggests that false positives are so far not interfering significantly with the use of this tool for discovery. Further improving the quality of the data and characterizing the impact of different types of noise on the results will be an important area to investigate in the future.
Finally, we note that while there are many disease taxonomies that are widely used for different purposes, there is growing dissatisfaction with most of them, in part because of the lack of a molecular representation of disease relationships [11]. Analyses such as ours may, as the data improve over time, lead to better understanding of molecular disease relationships across the board. Such knowledge is an important prerequisite for developing a truly molecular taxonomy of disease. We therefore hope that this work may ultimately contribute to the development of a new, more molecular disease taxonomy that is well suited to support translational research in the genomic era.
We assembled a combined set of disease-gene links for 11,831 genes using 116,117 human gene-disease associations from the Genopedia compendium in the HuGE database of Human Genetic Epidemiology [62] and 4,813 gene-disease associations from the OMIM database [65], both downloaded in November, 2013. Genes from the Genopedia database were mapped to their corresponding disease concepts in the MeSH hierarchy of medical subject headings (http://www.nlm.nih.gov/mesh/), using the Unified Medical Language System (UMLS) [66] as a thesaurus to identify synonymous diseases. To find MeSH terms that best correspond to the OMIM phenotypes, we used the MEDIC merged disease vocabulary, an ongoing toxicogenomics effort to map OMIM disease terms into the MeSH disease hierarchy, downloaded from the Comparative Toxicogenomics Database [67] in November, 2013. After removing one copy of the 1,530 duplicate associations found in both data sets, we were left with a total of 119,400 unique associations.
We estimate the distribution of the expected number of shared genes between the query gene set and the genes associated with a disease under the null hypothesis that there is no meaningful relationship between the query gene set and the disease class. We do so by randomly choosing gene sets of the query-set size from among all the genes in our MeSH tree. This is equivalent to randomly permuting the labels of the genes in the data to determine whether or not they are in the query set. Such permutation leaves the gene-disease connections intact and maintains the complex correlation structure of genes between related diseases. Assuming that is the observed size of the real overlap at disease node (i.e., the number of genes in the query gene set that are linked to node ), for each permuted query set we can then determine whether the number of genes at node in that random query set is larger than . We ran 10,000 permutations to compute a p-value at each node estimating the probability of seeing an overlap of the observed size at that node by chance.
Density of enrichment was computed between the 9 query gene sets and the 26 top-level MeSH disease categories, each represented by its own tree. Because many diseases are represented multiple times at different places in each tree, we first created a listing of all the unique MeSH disease terms in each tree. If different instances of the same disease in the same tree had different p-values, they were averaged. We then compared the p-values to the chosen significance cutoff of 0.005. The fraction of unique terms in the tree with lower significance was computed. This fraction represents the “density” of significant enrichment of the query gene set in the chosen MeSH category.
To create the heatmap, we z-score normalized the densities across each row (query gene set). To identify expected enrichment, we manually selected the 9 top-level MeSH disease categories thought to be most relevant to the 9 query gene sets (or to many/all developmental gene sets, as in the case of C4 - neoplasms and C16 - congenital, hereditary, and neonatal diseases and disorders).
We performed the following experiment to compare the accuracy of our proposed pooling approach to a comparable enrichment analysis using only the genes directly associated with a given disease term. To describe the experiment, we first introduce new terminology:
Assume that we are discussing only a single, fixed query gene set. Let be the set of all gene-disease links in our combined database: gene is associated with disease . For any disease node in the MeSH forest, let be the permutation-based significance score for enrichment of the query gene set among genes in associated with that node using the traditional method (only those genes directly linked to node ). Similarly, let be the analogous score for node under the pooling approach.
Then we will repeatedly randomly withhold some links from . Specifically, for the th random iteration, let be a randomly chosen set of 100 pairs from , such that is in the query gene set, and let We can then partition the disease nodes into those that are more significant under the pooling method (in the th iteration) and those that are more significant under the traditional method. Formally, let nodes , and let nodes . (Note that in the many cases where , the nodes contribute to neither set. Many of these are either leaves, or nodes with no associated genes under either method.)
We say a node is supported by gene-disease link from if a node corresponding to appears in the subtree rooted at . We can then determine the probability that a node in the set or is supported by some link in . Let indicator function if node is supported by a link in , and 0 otherwise. Then the probability that a node in is supported by is defined asand is defined analogously, using Finally, we average over all random trials to compute the averages and that are reported in Table 1. Figure 7 illustrates the process of calculating and with an example for the random trial.
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10.1371/journal.pcbi.0030063 | Integration of Genome and Chromatin Structure with Gene Expression Profiles To Predict c-MYC Recognition Site Binding and Function | The MYC genes encode nuclear sequence specific–binding DNA-binding proteins that are pleiotropic regulators of cellular function, and the c-MYC proto-oncogene is deregulated and/or mutated in most human cancers. Experimental studies of MYC binding to the genome are not fully consistent. While many c-MYC recognition sites can be identified in c-MYC responsive genes, other motif matches—even experimentally confirmed sites—are associated with genes showing no c-MYC response. We have developed a computational model that integrates multiple sources of evidence to predict which genes will bind and be regulated by MYC in vivo. First, a Bayesian network classifier is used to predict those c-MYC recognition sites that are most likely to exhibit high-occupancy binding in chromatin immunoprecipitation studies. This classifier incorporates genomic sequence, experimentally determined genomic chromatin acetylation islands, and predicted methylation status from a computational model estimating the likelihood of genomic DNA methylation. We find that the predictions from this classifier are also applicable to other transcription factors, such as cAMP-response element-binding protein, whose binding sites are sensitive to DNA methylation. Second, the MYC binding probability is combined with the gene expression profile data from nine independent microarray datasets in multiple tissues. Finally, we may consider gene function annotations in Gene Ontology to predict the c-MYC targets. We assess the performance of our prediction results by comparing them with the c-myc targets identified in the biomedical literature. In total, we predict 460 likely c-MYC target genes in the human genome, of which 67 have been reported to be both bound and regulated by MYC, 68 are bound by MYC, and another 80 are MYC-regulated. The approach thus successfully identifies many known c-MYC targets and suggests many novel sites. Our findings suggest that to identify c-MYC genomic targets, integration of different data sources helps to improve the accuracy.
| c-MYC is an important proto-oncogene that controls the expression of many other genes, and MYC regulation is deranged in many cancers. Identifying c-MYC target genes is one of the key steps to understand both the biological role and molecular mechanism of c-MYC action. Defining the complete list of c-MYC target genes and categorizing them as genes that are directly and indirectly modulated remains a challenge. Computational models also help us to understand the mechanisms modulating c-MYC function. We describe a method to predict where MYC will bind in the genome and which c-MYC binding sites will be biologically active. The method integrates multiple sources of data, including both genome sequence and functional annotations, to predict that 460 genes are direct c-MYC targets. These include many genes previously known to be c-MYC targets as well as 245 novel direct c-MYC targets. Using multiple, independent gene-expression datasets improves the sensitivity and specificity of the prediction and demonstrates significant tissue-specific variation in c-MYC action at different genes. Our study suggests that chromatin state plays an important role in modulating both c-MYC binding-site activity and the functional consequences of c-MYC binding.
| MYC plays a critical role in regulating cell proliferation, growth, apoptosis, and differentiation. Human malignancies are often associated with aberration of the c-MYC gene [1–3]. The diversity of its functions has been attributed to c-MYC's ability to activate or repress the transcription of an extensive array of target genes mediating a wide range of cellular activities [4–6]. c-MYC's actions are mediated by sequence-specific binding of the c-MYC protein, dimerized with its partner MAX, to DNA elements called E-boxes with the core sequence motif 5′-CACGTG-3′ [7–9]. Binding of the MYC–MAX heterodimer to a target gene can directly activate or repress transcription, but many E-boxes do not bind MYC, and in many experimentally confirmed cases, MYC binding is not associated with changes in gene expression. Identifying functional MYC binding sites and target genes is a critical step in understanding both the biological role and molecular mechanism of MYC action.
mRNA expression studies have identified many target genes activated or repressed by c-MYC in various animal and human cells or cell lines. The number of experimentally validated c-myc targets are expanding rapidly thanks to the use of high throughput methods [10–13]. The recent studies of Basso et al. [14] and Remondini et al. [15] suggest that the potential list of c-MYC targets could be much larger than what was previously anticipated. However, different experimental and theoretical studies give quite different findings, and much work remains to be done to define the complete set of c-MYC targets.
Gene expression studies alone cannot discriminate between direct and indirect targets of c-MYC action, although network-based inference of direct action has been proposed [14]. Recently, chromatin immunoprecipitation (ChIP) and whole genome scale analysis of methylation status have emerged as new sources of relevant data for the analysis of genomic regulatory elements.
Theoretical analysis complements and extends experimental study, and several researchers have attempted to predict c-MYC target genes using computational methods. By searching human transcript sequence, analyzing E-box location, and using genomic sequence evolutionary conservation, Schuldiner et al. [16] identified 12 putative targets, two of which were confirmed by subsequent experimental analysis. Zeller et al. [17] built a database of c-Myc responsive genes that have been reported in publications and supported by multiple lines of evidence. They then identified seven out of 12 candidate genes in this database using phylogenetic analysis, and they confirmed six of these predictions using ChIP. The use of evolutionary conservation is also supported by Haggerty [18] who categorized c-MYC targets into two classes: Class I, to which the majority of genes belong, has E-boxes that are evolutionarily conserved; and Class II, which includes genes with no region of homology at or flanking the genomic regions that exhibit MYC binding.
The promoter regions of c-MYC–regulated genes often contain E-box sequences that bind MYC with high occupancy. Li et al. [17] analyzed genomic binding sites for c-MYC in Burkitt lymphoma cells and found a strong correlation between MYC DNA binding and gene transcription, strengthening the view that high binding occupancy of a c-MYC site near a gene's promoter region is, to some extent, a sign of a c-MYC target gene. However, this is not a hard and fast rule, and many sites that bind MYC with high occupancy are not associated with c-MYC target genes. Fernandez et al. [19] performed a large-scale assay for genomic Myc binding sites in vivo by quantitative ChIP. They found that promoter E-boxes are distributed in two groups differing in MYC binding occupancy. The strongest DNA-sequence characteristic of high-affinity/high-occupancy targets was location of the E-box within a CpG island. This observation can be partially explained by the fact that most CpG dinucleotides in the mammalian genome are subject to cytosine methylation [20,21], but the methylation of the CpG dinucleotide in the consensus myc binding site sequence reduces the binding affinity of myc–max dimers for the target DNA [22,23]. Further, DNA methylation is often coupled to and associated with histone methylation and the formation of heterochromatin [24]. Recent work by Ernesto [25] shows that target sites are only recognized MYC by whether they are packaged in chromatin bearing high H3 K4/K79 methylation and H3 acetylation. This is true for both classic E-box (CACGTG) and alternative sequence sites.
With the abundant volumes of experimental data on c-MYC target gene expression and in vivo binding of MYC to the genome, there is an increasing need to integrate these information resources and to classify c-MYC responsive genes into direct and indirect targets. In this paper, we identified c-MYC target genes using a computational approach that draws on data from multiple sources including gene expression profiling, gene annotations, ChIP, sequence conservation, and sequence composition. First, we developed a computational model to predict the likelihood of CpG methylation. Next, we developed a computational strategy to predict which E-box sites were likely to be functional MYC binding sites. Finally, the binding predictions were integrated with gene expression and gene annotation data to identify direct and indirect c-MYC target genes. The performance of these tools was validated by comparison with multiple experimental datasets. Our method is able to successfully predict the occupancy of the binding sites as revealed by CHIP. Although this computational method was specifically built on c-MYC data, it also provides useful information on the binding of cAMP-response element-binding protein (CREB) [26], another transcription factor whose binding is sensitive to DNA methylation. After further integration with gene expression data from different tissues and datasets, and the Gene Ontology (GO) annotations for c-MYC targets, we identified 460 likely c-MYC target genes. Of these genes, 215 have been previously identified as MYC bound or regulated and 245 are novel. Our study shows that integrating multiple data sources improved the prediction specificity of MYC binding site prediction. Similarly, using gene expression profiles from several independent studies improved the sensitivity for target gene prediction. Our analysis suggests that much of the variation between microarray-based gene expression assays may be due to limitations of the technology. In addition, there appears to be a significant tissue-specific component to the responses of some c-MYC target genes.
The performance of the fifth-order Markov model for genomic methylation status was tested using an independent dataset from the Human Epigenome Project [27]. We found that 86.4% of these unmethylated sites fell within our predicted hypomethylation regions. In contrast, only 22% of the hypermethylated CpG sites were within the predicted hypomethylated regions (p < 2.2e−16). We applied this model to predict the CpG islands and hypomethylation regions on the human genome. On the human repeat masked genome NCBI35, we found that 0.71% of the human genome sequences were CpG islands and 1.07% of the genome sequences were predicted to be hypomethylated regions, with 82% of the CpG islands falling within hypomethylated regions. As anticipated, promoter regions had much higher percentages of CpG islands and hypomethylated regions than the whole genome. Based on our prediction results, 43% of the human genes had CpG islands and 46% of the genes had hypomethylated regions within 5 Kb upstream of their transcription start sites. In the 5 Kb upstream of the transcription start sites, 4.4% of the sequence was covered by a CpG islands and 6.7% of sequences were predicted to be hypomethylated.
In this step we identified sites in the human genome where MYC is expected to bind in vivo with high occupancy. Candidate MYC binding sites were identified by scanning the complete human genome sequencing using the TRANSFAC MATCH algorithm [28] and the MYC–MAX position specific weight matrix (PSWM) MA0059 [29] from the JASPAR database [30,31]. Sites that achieved a matrix score of 0.8 are referred to as motif matches. Four additional sources of data were used to define a subset of these motif matches that are likely to bind MYC in vivo: proximity to transcription start sites, proximity to CpG islands, predicted hypomethylation, and evolutionary conservation.
To train our algorithm, we began with the Fernandez et al. [19] data for genomic Myc binding sites in live human cells. This study examined MYC binding to more than 700 E-box sequences in vivo by quantitative ChIP. The authors found that promoter E-boxes were distributed in two groups that bound MYC at distinct frequencies. In the Fernandez et al. dataset, we only used sites where the PCR primers and the associated E-box could be mapped back to the human genome as an exact sequence match. Further, sites were excluded when the dataset contained multiple contradictory assay results. This filtering process resulted in a set of 493 binding sites where ChIP was measured in either U937 or HL60 cell lines (for 40% of the sites, data is available and consistent across both cell lines). A high quality training dataset was defined using sites where ChIP results were consistent across more than one assay. The training set had 43 nonredundant high-occupancy sites and 90 nonredundant low-occupancy sites.
Using a Bayesian network classifier, we integrated this high quality training subset of the ChIP dataset with our hypomethylation analysis and genomic sequence conservation data. The resulting classifier predicts which sites in the genome are likely binding MYC with high occupancy. In the training process, we looked for the most important factors determining MYC binding to DNA. The Kolmogorov–Smirnov test showed that the following factors differed significantly (p < 0.01) between the high- and low-occupancy sites: distance to transcription start site (Figure 1A), distance to nearest CpG island (Figure 1B), distance to nearest hypomethylation region (Figure 1C), phastCons scores [32] (Figure 1D), and distance to nearest chromatin acetylation island (Figure 1E). The CpG islands and hypomethylation regions near or overlapping with the above MYC binding sites were predicted with our Fast Motif Analyzer (FMA), described in the Materials and Methods section. The acetylation island information was derived from the Roh et al. high-resolution genome-wide mapping of Lys 9 and Lys 14 diacetyl histone H3 in resting and activated human T cells [33]. PhastCons scores identify evolutionarily conserved elements using a multiple alignment of genomic sequence weighted using a phylogenetic tree. The PhastCons score is a base-by-base conservation score that can be interpreted as the probability that each base is in a conserved sequence element [32]. This score is chosen as a measurement of sequence conservation over other alternatives because it is available through the widely used University of California Santa Cruz Genome Browser, and could be readily incorporated into our model.
Using these datasets and a supervised classification approach [34], we evaluated several algorithms for the prediction of high-occupancy MYC binding sites. The best classification results were obtained using a Bayesian network classifier (see Materials and Methods). This method assigned a probability for every site in the genome. We predicted a site to have high binding if this probability was above 0.5. The Bayesian network shown in Figure 2 gave the best results on the high-quality training dataset. A 10-fold cross-validation showed a precision of 0.88 and a recall of 0.98 on the high-occupancy binding sites prediction. When we applied this classifier to Fernandez's entire dataset, it correctly identified 130 of the 183 high-occupancy sites and 258 of the 310 low-occupancy sites.
To further verify that our algorithm was able to predict high-occupancy MYC binding sites, we evaluated performance of independently obtained test data. We considered binding sites within ±3 Kb of transcription start sites, because almost all of the sites in the training data are within this region. We used the MYC–MAX matrix MA0059 [29] from the JASPAR database [30] to identify putative MYC binding sites, and we applied the rules described above to sort these sites into groups predicted to bind MYC with high and low occupancy. Using TRANSFAC MATCH to analyze the complete human genome sequence, we identified 89,560 MYC sites within ±3 Kb of a transcription start site for 14,387 genes. From these candidate sites, our method classified 14,638 sites in 5,276 genes as likely to bind MYC with high occupancy.
We assessed the reliability of these predictions by comparing them with two independently published experimental datasets. One dataset is from Zeller et al. [35] using ChIP–PET to map genomic c-MYC binding sites in human B cells. This study the identified loci as PET sequence tag clusters with varying numbers of tags per cluster. More tags matching a cluster increases the reliability of binding site identification. Zeller's paper defined 964 PET-2+ clusters with two or more tags per cluster falling within 3 kb of a TSS. PET-2+ may contain a significant number of false positive identifications. Zeller et al. also defined 113 PET-3+ cluster with three or more tags per cluster and within 3 kb of a TSS. These are believed to be highly reliable identifications. For the second evaluation dataset, we used high-density oligonucleotide array ChIP–chip data from Cawley et al. [36]. Looking only at Chromosomes 21 and 22, Cawley et al. defined 181 high-occupancy MYC binding segments within 3 kb of a TSS.
In the experimental datasets, MYC binding is localized to a segment of genome, but not necessarily a single E-box. We refer to these segments as “MYC binding loci” and compare these with our predictions at the gene level. Most of the experimentally defined MYC binding loci are associated with a single gene. Table 1 compares the experimentally defined MYC binding loci that are within 3 Kb of a transcription start site with those that were predicted by our methods. First, it is apparent that the different experimental assays yield markedly different results. Only one of the 113 Zeller PET-3+ loci was also scored as a high-occupancy binding site by Fernandez et al. Similarly, only one of six Zeller PET-3+ loci on Chromosomes 21 and 22 was identified as a high-occupancy site by Cawley et al., and only four of the 181 binding loci identified by Cawley et al. were scored as high occupancy in the Fernandez dataset. Comparing our predictions with the experimental datasets, we see higher levels of agreement than the experimental datasets show among themselves. This is in part a consequence of the fact that our method predicted a larger number of loci (5,276) than are observed in the experimental datasets. Interestingly, the fractional overlap between our predictions and the Zeller et al. dataset increases as we consider more confident clusters. Whereas only about a quarter of the PET-2+ clusters contain a predicted high-occupancy site, half of the PET-3+ cluster and all of the PET-4+ clusters do. One-third of the loci identified by Cawley et al. on Chromosomes 21 and 22 contain a site predicted to bind MYC with high occupancy, and more than a quarter of our predicted high-occupancy sites on these chromosomes are confirmed by Cawley et al.
To extend the analysis to more tissues and cell types, we used data from the MYC Target Gene Database. We compared predictions for genes annotated as c-MYC targets with predictions for a collection of genes selected at random from the human genome. The MYC Target Gene Database [17] includes more than 1,000 putative MYC target genes reported to be either regulated or bound by Myc. These MYC target genes tend to have more MYC recognition sites than do randomly selected genes. Table 2 shows the performance of our algorithm in the prediction of high-occupancy binding sites. Each gene in Table 2 contains at least one putative MYC site predicted by a motif match. The MYC target genes are compared with a set of 1,000 randomly selected genes containing 6,259 MYC sites, which served as the control group. Second, we chose 589 genes showing myc binding from the MYC Target Gene Database that were not in Fernandez's dataset. Third, we chose 417 genes showing Myc regulation for which no binding data was available. The fourth set consisted of 98 genes in MYC Target Gene Database showing both myc binding and myc regulation that were not in Fernandez's dataset. For each group of genes, we used our algorithm to predict the occupancy of each MYC recognition site. Statistical significance was assessed using Fisher's exact test on each test group against the control group. We found that each of the putative c-MYC target groups had a significantly higher percentage of high-occupancy binding sites than the genes in the randomly selected group. This demonstrates that our algorithm is able to discriminate biologically functional high-occupancy sites from low-occupancy, presumably nonfunctional binding-site motif matches.
We next asked what genes were associated with the high-occupancy binding sites. Many genes have multiple MYC binding sites, but little information is available on how multiple MYC sites affect each other's binding. Therefore, in our study we treated all the genes having high-occupancy binding sites as potential MYC binding genes. Table 3 shows the result of these predictions. The results show that known c-MYC targets were predicted to bind MYC with a significantly higher frequency than random genes do. Thus, our method has significantly higher accuracy in discriminating the MYC binding genes than does motif match alone.
Although our method was built using c-MYC data, the attributes used in the model are general, so we anticipated that the method might also be informative for other transcription factors that are sensitive to DNA methylation. CREB has a CpG dinucleotide in its binding sequence and its binding is sensitive to DNA methylation. We applied our method to the analysis of CREB sites and compared our predictions with a previous ChIP–chip study [26] of 10,209 distinct promoters on the human genome that were predicted to have at least one cAMP-responsive element defined as a match to a simple cAMP-responsive element consensus-site algorithm. Forty of these sites were confirmed by manual ChIP assays. Applying our model to these 40 genes, we correctly classified 21 of the 23 high CREB occupancy genes with seven false positives (Table 4). The ChIP–chip assay identified 2,195 CREB high-occupancy binding sites near promoters. Our model correctly classified 1,713 (78%) of these high-occupancy promoters and gave 3,621 false positives (Table 5). However, many false positives could actually be true positives, because Zhang et al. pointed out in their paper that, although their ChIP–chip method had a high specificity, only 54% of the promoters occupied by CREB in their manual ChIP assay showed positive in their ChIP–chip assay. These predictions on CREB show that our method is useful for the analysis of other transcription factors that are sensitive to epigenetic factors.
Myc binding does not always imply c-MYC regulation, and a great deal remains to be learned about how the cell determines which genes are actually regulated by c-MYC under different conditions and in different tissues. To address this issue, we analyzed the co-expression pattern of genomic genes and c-MYC genes in two tissues where c-MYC is reported to play an important biological role: B cell lymphoma and prostate cancer.
The c-MYC gene is often deregulated in cancer and induces the expression of many c-MYC target genes. Notable examples include B cell lymphoma and prostate cancers. We selected two datasets for analysis, a human B cell dataset [14] of 336 samples and a human prostate cancer dataset [37] of 102 samples based on number of samples in the dataset, availability of the raw data, and thus use of a standard gene expression analysis platform, the Affymetrix HG-U95Av2 microarray. The raw data for both datasets were reprocessed with Bioconductor [38] and the RMA algorithm [39]. Figure 3 shows the RMA normalized log transformed expression signals of the three c-myc probe sets on the HG-U95Av2 GeneChip for each dataset. In this study we used two c-myc probe sets, “1973_s_at” and “37724_at”, because their expression signals demonstrated a strong and consistent correlation, whereas “1827_s_at” was not strongly correlated with either of the other two (see Figure 3). The Pearson's correlation coefficients between each c-myc probe set and every other probe set on the chip was calculated, and this value is referred to as the co-expression pattern of c-myc with the other genes. Figure 4 shows the distribution of these co-expression patterns for data derived from B cells and prostate cancers. Overall, 1,217 and 1,418 genes were found to be significantly correlated with c-MYC expression (FDR < 0.01) in B cells and prostate cells, respectively. Altogether, 2,233 genes were highly correlated with c-MYC in these two datasets; 403 genes were correlated with c-MYC expression in both datasets. Table S1 lists the MYC correlated genes identified in these two tissues.
Many known MYC target genes have functions involved in cell cycle progression, apoptosis, and cellular transformation; it is reasonable to hypothesize that unknown MYC targets will share many of these functions. We constructed a set of gene functions that were overrepresented in known MYC targets as represented in GO terms. Using the experimentally verified MYC targets in the MYC target database [17] and considering only the genes on the HG-U95Av2 chip, we found 144 known MYC targets to which 875 terms in the Molecular Function and Biological Process categories were applied. Because some GO terms are too general to be as informative, in subsequent analysis we only used the GO terms with fewer than 500 genes. Applying a hypergeometric test, we found 156 GO terms overrepresented in the MYC target gene sets (p-value < 0.025; Table S2). Among the top overrepresented GO terms are those related to cell cycle, biosynthesis, nucleic acid binding, and translational regulation. These findings correlate well with expectations based on the biology of c-MYC.
Using the predicted MYC high-occupancy binding sites, the c-MYC co-expressed genes in B cells and prostate cells, and genes annotated with GO terms that are overrepresented in known c-MYC targets, we applied a rule-based procedure to define likely c-MYC targets. For a gene to be labeled as a c-MYC target, it must meet four criteria. First, the gene must have at least one MYC high-occupancy binding site. Second, the signal variance of the gene's probe set must be greater than the mean variance across all probe sets in either the B cell or prostate cancer dataset. Third, the correlation coefficient must be greater than zero and the significance of gene's co-expression with c-MYC must be less than 0.01 in either the B cell or prostate cancer dataset. Fourth, the GO annotation for this gene must have at least one overrepresented c-MYC target-related GO term.
Applying these rules, we found 440 genes that meet our criteria (see Table S3). Comparing our findings with the Myc Target Gene Database [17], we found that in the literature, 128 of the predicted target genes are reported to bind MYC and 142 are reported to be regulated by MYC. This represents an independent validation of our findings because data from the MYC Target Gene Database was not included in our training data. Sixty-two of the predicted target genes were reported to be both bound and regulated by MYC; we successfully predicted 62 out of the 144 known MYC targets on HG-U95Av2 platform.
Among the predicted genes, 264 were correlated with MYC expression in B cells, 277 were correlated with MYC expression in prostate cancers, and 101 were correlated in both datasets. This high level of correlation further validates our predictions. Among the 62 previously identified targets, 42 correlate with MYC expression in the B cell dataset, 39 in the prostate dataset, and 19 in both. These findings show that many MYC targets exhibit some tissue specificity in MYC responsiveness. Figure 5 also shows that many of the known MYC targets in the MYC Target Gene Database [17] have different correlations of gene expression with MYC expression in the two tissues.
To further investigate c-MYC tissue specific responses, we examined seven additional microarray gene expression datasets from breast, lung, prostate, and leukemia cancers (Table 6). All these datasets used the Affymetrix HG-U95Av2 platform. Adding correlation of gene expression with c-MYC in these datasets, we were able to identify 20 additional MYC targets. In total, 460 c-MYC target genes were predicted including 215 in the MYC Target Gene Database that had previously been reported to be bound or regulated by MYC (Table S3); 144 were regulated by MYC and 132 were bound by MYC. Further, we found evidence in the literature to validate three additional MYC target gene predictions, ATF3 [40], HSP90A [41], and BAT1 [42]. Overall, 218 of our 460 predictions were validated, including 67 genes that have evidences for both binding and regulation. We believe that this is an underestimate for the true number of MYC targets because we only considered the 8,000 GO annotated genes on Affymetrix HG_U95Av2 platform; this is slightly more than one-third of the genes in the human genome.
We compared our predicted 460 genes from nine datasets with the 2,063 genes in the MYC subnetwork predicted by Basso et al. [14] and the 668 high-quality MYC direct responsive genes identified by Zeller et al. [35]. Figure 6 shows the overlaps between these datasets. We see that the overlap number is higher than would be expected purely by chance, but well below complete agreement. Comparing these datasets with the genes in the MYC Target Gene Database (Table 7), we find that our method shows a better specificity than the other two approaches, even without applying GO filtering. Using the GO functional annotation improved the specificity. We investigated the 45 targets identified by Basso et al. that were missed in our 460 predicted targets. Experimentally, these targets exhibit both binding and regulation. Thirteen targets were missed due to their relatively low expression variation or nonpositive correlation with c-MYC across samples, six targets did not have any c-MYC binding motif within 3 Kb of their transcription start sites, 15 targets were false negatives in high-occupancy sites prediction, and the other 11 targets did not have overrepresented GO terms.
Using a Bayesian model, we integrate genome sequence data and epigenetic information to identify myc recognition sites in the human genome likely to bind c-MYC with high occupancy. By combining the myc binding probability, gene co-expression data, and functional annotations, we predicted 460 c-MYC targets among the genes presented on Affymetrix HG-U95Av2 platform. The list of predicted c-MYC targets contains many genes found previously in the literature, but also 245 genes not previously identified as c-MYC targets. Our method only predicts upregulated c-MYC targets because downregulated c-MYC targets are not generally mediated by E-box binding. Among the 67 predicted genes that have already been observed to be bound and regulated by MYC, 61 are upregulated by MYC, five genes are reported to be downregulated, and one has evidence for both downregulation and upregulation. Thus, the predicted MYC targets agree well with previous observations. In addition to these 67 independently validated predictions, we also identify 148 genes in the MYC Target Database, 68 of which are bound by MYC in vivo and 80 of which are MYC regulated (Table 6). Among the 250 predicted novel targets, 27 correspond to MYC binding loci reported by Zeller et al. [35] and 11 of these correspond to highly reliable binding loci with PET-3+ clusters.
Different from previous studies predicting c-myc targets, our study integrated four sources of data (genomic sequence, gene expression, ChIP, and functional annotation) to improve the specificity of predictions. Tools such as TRANSFAC or MatInspector, which rely on motif matches alone to predict myc binding, have very high false positive rates. The improved specificity obtained by our integrated approach emphasizes the importance of epigenetic factors in modulating c-MYC binding to DNA. Although epigenetic status does vary with tissue type and other factors, recent high throughput studies show that tissue-specific variation in genomic methylation is limited [27]. Adding genomic acetylation islands data obtained on T cells to the model improved the prediction precision from 0.81 to 0.88 and the recall from 0.88 to 0.98 in cross-validation. Among the attributes considered in the model, we found that the distance to the nearest hypomethylation region is the most informative; this attribute alone could correctly identify 80% of the high- and low-occupancy sites in the cross validation. However, adding the additional attributes does improve performance, and considering all five attributes allows the model to correctly identify 95% of all the cases in cross-validation.
MYC is not the only transcriptional factor whose binding is sensitive to the epigenetic factors such as DNA methylation or chromatin acetylation. Because the attributes used in our model are general and the epigenetic factors could influence DNA binding through similar mechanisms, elements of our model may also be useful for other transcription factors. As a validation we applied our MYC binding prediction model to another transcription factor, CREB [26]. Like MYC, the CREB consensus binding sequence has a CpG dinucleotide and CREB binding is sensitive to DNA methylation. Our analysis shows that although our model was specifically built for the study of c-MYC, it is still able to correctly discriminate most of the high-occupancy binding sites and the majority of low-occupancy binding sites. Thus, our approach to modeling chromatin structure effects is transferable to other transcription factors. ChIP–chip technology can partially address the question of where transcription factors bind the genome, but with current technology the resolution of TF binding loci is limited and the data are error-prone. In addition, computational modeling can help to understand the complex transcriptional machinery. For example, adding or removing an attribute to the model and assessing the affects on performance is one way to evaluate the importance of this attribute on the regulation of the DNA binding by a transcription factor.
Combining different types of data offsets the shortcoming of each. Obviously, our predictions based on genome sequence alone cannot address tissue specific binding. However, by taking gene expression data of specific tissues into consideration, we restrict our identified targets to those functionally regulated by MYC in the context of certain tissues, which would be of real interest to the biological community. In addition, integration of different types of data also allows us to predict which DNA–protein binding sites are likely to trigger transcriptional regulation. Figure 4 shows that compared with the expression of all genes, a higher portion of genes from our MYC binding and function predictions are highly correlated with c-MYC expression. This demonstrates that our binding prediction and gene function analysis do identify bona fide c-MYC targets and are helpful in improving the specificity of prediction.
One of the limitations in our target gene analysis is that we only consider genomic sequences within 3 Kb of transcription start sites. This was done because almost all of the sites in our training data fall within this region, but there are some high-occupancy MYC target sites far from any known transcription start sites. It is possible that there are direct c-MYC targets where the only functional MYC sites are more than 3 Kb from the transcription start site. Increasing the search window for c-MYC recognition sites might improve the sensitivity of prediction, but such a change would also decrease specificity.
Another limitation of our method is that we only consider E-box–dependent MYC binding and regulation; MYC targets regulated through other mechanisms will not be identified. Transcriptional inhibition of MYC targets is often mediated by mechanisms unrelated to E-box binding [43–47]. Therefore, we only consider the MYC activation in our predictions and consider only the expression of target genes that is positively correlated with c-MYC expression. We do observe a minority of genes where there is a significant anti-correlation of gene expression with c-MYC expression. The analysis of these cases will be the subject of future work.
Distinguishing between direct and indirect targets of MYC is an important issue. In the co-expression analysis we found a large number of genes where expression levels showed positive correlations with c-MYC expression and yet lacked a high-occupancy MYC binding site, either predicted or experimental. We believe that many of the genes are not the direct targets of MYC, but it is difficult to exclude the possibility that they contain a functional MYC binding site not detected by our own or experimental methods.
MYC responses vary significantly between different tissues. Less than half of our predicted MYC targets show significant gene expression correlation with c-MYC in both B cells and prostate cancers. We analyzed seven additional independent microarray datasets (Table 6) where the c-MYC gene was deregulated and its probe-set signals showed large variance across samples. In these studies, many predicted MYC targets, including genes that have been experimentally verified as direct MYC targets, failed to show a strong correlation of target gene expression with MYC expression. The differences between these datasets cannot be explained by tissue of origin alone. For example, datasets 2, 3, and 4 are all derived from the prostate, but the c-MYC correlated targets from these three datasets do not agree more than those from different tissues (see Figures S3–S5). Some of this variation could be a result of technical variation in gene expression profiles between different laboratories and experiments.
This is one of the first studies to systematically analyze c-myc targets in multiple datasets and in multiple tissues; most previous studies focused on a single tissue or cell line. Our analysis confirms the well-known finding that c-MYC is deregulated in many cancers and has a direct influence on the expression of hundreds of other genes. One potential pitfall in using GO as a criterion for predicting MYC targets is the possibility of missing important groups of targets that do not fall into a specific GO category or are not annotated by GO at all. In Table S3 we also provide the list of 1,188 predicted targets without applying GO filtering in the prediction. A second concern is that it is difficult to define a test set that is totally independent of prior knowledge because we cannot exclude the possibility that GO annotators were aware of MYC regulation status in assigning gene annotations.
Looking at the target genes we predicted that were not in the Basso et al. prediction, we find variable levels of correlation with MYC expression across the different datasets that we have examined. Even genes that have been identified as c-MYC targets in published literature often have very different co-expression patterns with c-MYC in different microarray datasets. This is consistent with the view that many of the differences among these high throughput studies may result from experimental variation, the noise inherent in these approaches, and the effects of cell density or the number of culture passages [48]. Whether this reflects tissue-specific responses or technical variation in microarray data, it is apparent that a study focusing on any single dataset will be insufficient. As is shown in Table 6, using multiple datasets from different studies improved the power of the prediction.
Because the current study only predicted upregulated genes with MYC binding motifs close to the transcription start site, which are on the Affymetrix HG-U95Av2 array and which have GO annotations, we believe that the 460 targets identified here underestimate the number of direct MYC targets in the human genome. Our estimates for the number of MYC targets in the human genome are roughly consistent with the MYC database and the high-confidence Zeller [35] and Cawley [36] studies. They are not inconsistent with the larger numbers of c-MYC targets suggested by some other studies [12–15]. One explanation for this range of findings is that MYC binding in vivo is not a Boolean event and even strong MYC binding sites are unlikely to be occupied with unit stoichiometry. Thus, different studies may be applying different thresholds for defining a MYC target.
All the genes in the paper and their Entrez Gene IDs can be found in Tables S1 and S3.
The HG17 build of the human genome sequence was downloaded from the University of California Santa Cruz (UCSC) genome database. The transcription start sites were from the annotated transcription starts of RefSeq genes in the UCSC genome database (http://hgdownload.cse.ucsc.edu/goldenPath/hg17/bigZips/upstream1000.zip). PhastCons scores for multiple alignments of seven assemblies to the human genome hg17 were downloaded from http://hgdownload.cse.ucsc.edu/goldenPath/hg17/phastCons/mzPt1Mm5Rn3Cf1Gg2Fr1Dr1/. The human gene annotation information was downloaded from ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2refseq.gz, and the GO information was downloaded from ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz.
Known MYC target genes in previous literature were obtained from the MYC Target Gene Database [17] (http://www.myc-cancer-gene.org). The large-scale assay for genomic MYC binding sites in live human cells was obtained from the supplementary data of Fernandez et al. [19]. Chromatin acetylation data were obtained from the supplementary data of Roh et al. [33]. MYC binding loci on Chromosomes 21 and 22 [36] were downloaded from http://transcriptome.affymetrix.com/publication/tfbs/, and MYC binding data in human B cells using ChIP–PET were obtained from the supplementary data of Zeller et al. [35]. The CREB genomic binding loci [26] was downloaded from http://natural.salk.edu/CREB. Two microarray gene expression datasets were used in this study, the B cell dataset [14] (GSE2350) and the prostate cancer dataset [37] (http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi). The B cell dataset contains 336 samples of normal and transformed human B cells, and the prostate dataset contains 52 tumor and 50 nontumor prostate samples. The sources of other microarray gene expression profile datasets are listed in Table 6.
Bayesian network classification was performed using Weka 3.4.5 [34], which is available for download at http://www.cs.waikato.ac.nz/ml/weka/. When learning the Bayesian network, the best performance was obtained when using K2 as the search algorithm for local score metrics and using Simple Estimator with alpha at 0.5 to find the conditional probability tables. In addition, an empty network is set as the initial structure when learning the network.
The training data contained 43 high-occupancy sites and 90 low-occupancy sites from Fernandez's dataset [19]. These sites and their ChIP primers were mapped back to human genome using the UCSC In-Silico PCR tool. The five attributes for every site were derived from the genome sequence and annotations, including: distance to transcription start site, distance to nearest CpG island, distance to nearest hypomethylation region, nearest hypomethylation region score, and phastCons scores [32]. The CpG islands and hypomethylation status of sequence were predicted as described below. The distances in base pairs and the hypomethylation scores were transformed to natural log scale.
CpG islands were predicted by the criteria of Gardiner-Garden [49]. Hypomethylation regions were predicted using a fifth-order Markov model likelihood ratio test.
where Lnm is the likelihood ratio score that the hexamers in the interval from n to m are drawn from the frequency distribution for a training set of hypomethylated sequences relative to the likelihood that they are drawn from a frequency distribution describing the genome as a whole. si is the residue occurring at position i in the sequence, f(si+5 | si…si+4) is the frequency of finding residue si+5 given the preceding five residues si…si+4 in the hypomethylated DNA collection, and g(si+5 | si…si+4) is the corresponding frequency in the genome as a whole. The hypomethylation score for a region is the sum of the log likelihood ratio scores for all overlapping hexamers in the region, expressed as log base 2. This score measures how similar the hexamer content of a region is to the hexamer frequencies in the hypomethylated sequences training set described below. The boundaries for a predicted hypomethylation region are chosen to maximize this log likelihood ratio score, with constraints that each predicted region must score at least 23.1 bits and must not contain any subregion whose score is more negative than −14.5 bits. An optimized hexamer score cutoff is determined as shown in Figure S1.
This algorithm is implemented in the program FMA, a C++ program, which utilizes a hexamer model to predict hypomethylation regions. The output includes the exact locations of CpG islands, the fraction of CpG dinucleotides, and the exact location and score (log2Lnm) of each hypomethylation region. FMA also implements an efficient indexed motif search. In the first phase of the FMA search, each PSWM in the query set is analyzed and the most informative contiguous six-nucleotide core segment is identified. Next, a branch-and-bound strategy is used to enumerate all hexamers matching this core segment and able to be extended over the full PSWM to achieve a specified log likelihood threshold for the full match. Hexamers in this set are stored in a suffix tree. During the search, the target sequence is first scanned using the suffix tree to find core segment matches. These matches are then extended over the full PSWM to see if they achieve the specified log likelihood threshold. Using this approach, we are able to exhaustively search large target sequence libraries for matches to large query sets of PSWM.
The training data for hypomethylated human genomic sequences were obtained by aligning the hypomethylated sequence tags collected by Cross et al. [50,51] with the human genome sequence and extending to the nearest MseI site. These hypomethylated sequence tag sequences were obtained by digesting human genomic DNA from peripheral blood leukocytes with MseI, selecting fragments that failed to bind a methyl–CpG binding protein column, methylating these fragments in vitro, and subsequently selecting fragments that bound the methyl–CpG binding protein column. This yields a collection of genomic DNA fragments that were not methylated in vivo, but which contained a CpG dinucleotide that could be methylated in vitro. These fragments were subsequently cloned and subjected to end-sequence analysis. By aligning the end-sequence tag to genomic sequence and extending to the nearest MseI site, we reconstruct the sequence of the full hypomethylated DNA segment.
Validation data for hypomethylation predictions were obtained from the Human EpiGenome Project [27].
TRANSFAC was used to scan the human genome sequence for putative c-MYC binding sites with the MYC–MAX position weight matrix [MA0059] [29] from the JASPAR database [30].
The sequence attributes for each putative binding site were inputted into the Bayesian network classifier (see above) to predict the probability of MYC binding for each site. A site assigned a MYC binding probability above 0.5 was considered to be a high-occupancy site. Because almost all the sites in the training data were within ±3 Kb of transcription start sites, we limited the prediction of high-occupancy binding sites to this region for further studies.
The prediction of c-MYC binding genes was based on the high-occupancy c-MYC binding sites prediction. If a gene contained any high-occupancy binding site, it was considered a potential c-MYC binding gene.
The HG-U95Av2 platform annotation file was downloaded from Affymetrix. The raw data files for each dataset were firstly normalized with RMA in Bioconductor [38]. For each probe set, the signal variance across the samples was calculated. Only probe sets with a signal variance larger than the mean of all probe sets' variances were used in co-expression analysis. The Pearson's correlation coefficient r was then calculated for every pair consisting of a c-MYC probe set and another probe set. The significance (probability) of the correlation coefficient is determined using the t-statistic:
where r is the correlation coefficient and n is the sample size. The p-values from this multiple testing were adjusted to control the false discovery rate of Benjamini and Hochberg [52]. Only probe set pairs with an adjusted p-value less than 0.01 were considered to be significantly co-expressed. For a gene to be correlated with c-MYC expression, it must have at least one probe set with p-value less than 0.01 for both c-MYC probe sets “1973_s_at” and “37724_at.”
We analyzed genes on the HG-U95Av2 GeneChip. From the Myc Target Gene Database [17], we extracted the subset of these genes that were reported to be both bound and regulated by myc. For these genes, we collected the associated GO terms from the Molecular Function and Biological Process trees and tested for significant overrepresentation using a hypergeometric test implemented in the GOHyperG function Bioconductor [38] package GOstats. A GO term is claimed to be significant if the p-value is less than 0.025. |
10.1371/journal.pcbi.1006434 | A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria | We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) identifies phenotype-specific k-mers, (b) generates a k-mer-based statistical model for predicting a given phenotype and (c) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 459 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets obtained the F1-measure of 0.88 on the K. pneumoniae test set, 0.88 on the P. aeruginosa test set and 0.97 on the C. difficile test set. The F1-measures were the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets. PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).
| Predicting phenotypic properties of bacterial isolates from their genomic sequences has numerous potential applications. A good example would be prediction of antimicrobial resistance and virulence phenotypes for use in medical diagnostics. We have developed a method that is able to predict phenotypes of interest from the genomic sequence of the isolate within seconds. The method uses a statistical model that can be trained automatically on isolates with known phenotype. The method is implemented in Python programming language and can be run on low-end Linux server and/or on laptop computers.
| The falling cost of sequencing has made genome sequencing affordable to a large number of labs, and therefore, there has been a dramatic increase in the number of genome sequences available for comparison in the public domain [1]. These developments have facilitated the genomic analysis of bacterial isolates. An increasing amount of bacterial whole genome sequencing (WGS) data has led to more and more genome-wide studies of DNA variation related to different phenotypes. Among these studies, antibiotic resistance phenotypes are the most concerning and have garnered high public interest, especially since several multidrug-resistant strains have emerged worldwide. The detection of known resistance-causing mutations as well as the search for new candidate biomarkers leading to resistance phenotypes requires reasonably rapid and easily applicable tools for processing and comparing the sequencing data of hundreds of isolated strains. However, there is still a lack of user-friendly software tools for the identification of genomic biomarkers from large sequencing datasets of bacterial isolates [2,3].
While microbial genome-wide association studies (GWAS) can be successfully used in case of previously known genotype-phenotype associations caused by a single gene or only a set of few and specific mutations, more complex and novel associations would remain undetected. In addition, many bacterial species have extensive intra-species variation from small sequence-based differences to the absence or presence of whole genes or gene clusters. Choosing only one genome as a reference for searching for the variable components would be highly limiting.
Alternative approaches use previously detected genomic features, either single nucleotide variations or longer sequences, behind the phenotype to create and train models using those features as the predictors. Not only antibiotic resistance but wide range of other phenotypes can be predicted, e.g host adaptation in invasive serovars [4], needed minimum inhibitory concentrations of antibiotics [5] or virulence of the strains [6]. Using longer sequence regions, such as full genes in those models, requires assembled genomes as an input which adds data preprocessing step. The solution to avoid this is using k-mers, which are short DNA oligomers with length k, that enable us to simultaneously discover a large set of single nucleotide variations, insertions and deletions associated with the phenotypes under study. The advantage of using k-mer-based methods in genomic biomarker discovery is that they do not require sequence alignments and can even be applied to raw sequencing data.
In recent years several publications using different machine learning algorithms and k-mers for detecting the biomarkers behind different bacterial phenotypes have been published. Among the latest, short k-mers and machine learning (ML) has been used to create minimum inhibitory concentration prediction models in assembled Klebsiella pneumoniae genomes for several antibiotics [7]. PATRIC and RAST annotation services include prediction of antimicrobial resistance with the species- and antibiotic-specific classifier k-mers which are selected using publicly available and collected metadata and the adaptive boosting ML algorithms [8].
Though providing a framework or predictive models for a specific species with a certain phenotype, those studies have not been concentrating on the creation of a software easily applicable by a wider public without an access to extensive computing resources but still having the need for analyzing large scale bacterial genome sequencing data with a reasonable amount of computing time. Only few papers describe software which we were able to compare with PhenotypeSeeker.
The SEER program takes either a discrete or continuous phenotype as an input, counts variable-length k-mers and corrects for the clonal population structure [6]. SEER is a complex pipeline requiring several separate steps for the user to execute and currently has many system-level dependencies for successful compilation and installation. Another similar tool, Kover, handles only discrete phenotypes, counts user-defined size k-mers and does not use any correction for population structure [9]. The Neptune software targets so-called 'signatures' differentiating two groups of sequences but cannot locate smaller mutations, such as single isolated nucleotide variations, being the reason, it was not used in the comparison in current paper. The 'signatures' that Neptune detects are relatively large genomic loci, which may include genomic islands, phage regions or operons [10].
We created PhenotypeSeeker as we observed the need for a tool that could combine all the benefits of the programs available but at the same time would be easily executable and would take a reasonable amount of computing resources without the need for dedicated high-performance computer hardware.
PhenotypeSeeker consist of two subprograms: 'PhenotypeSeeker modeling' and 'PhenotypeSeeker prediction'. 'PhenotypeSeeker modeling' takes either assembled contigs or raw-read data as an input and builds a statistical model for phenotype prediction. The method starts with counting all possible k-mers from the input genomes, using the GenomeTester4 software package [11], followed by k-mer filtering by their frequency in strains. Subsequently, the k-mer selection for regression analysis is performed. In this step, to test the k-mers’ association with the phenotype, the method applies Welch’s two-sample t-test if the phenotype is continuous and a chi-squared test if it is binary. Finally, the logistic regression or linear regression model is built. The PhenotypeSeeker output gives the regression model in a binary format and three text files, which include the following: (1) the results of association tests for identifying the k-mers most strongly associated with the given phenotype, (2) the coefficients of k-mers in the regression model for identifying the k-mers that have the greatest effects on the outcomes of the machine learning model, (3) a FASTA file with phenotype-specific k-mers, assembled to longer contigs when possible, to facilitate an user to perform annotation process, and (4) a summary of the regression analysis performed (Fig 1). Optionally, it is possible to use weighting for the strains to take into account the clonal population structure. The weights are based on a distance matrix of strains made with an alignment-free k-mer-based method called Mash [12]. The weights of each genome are calculated using the Gerstein, Sonnhammer and Cothia method [13]. 'PhenotypeSeeker prediction' uses the regression model generated by 'PhenotypeSeeker modeling' to conduct fast phenotype predictions on input samples (Fig 1). Using gmer_counter from the FastGT package [14], the tool searches the samples only for the k-mers used as parameters in the regression model. Predictions are then made based on the presence or absence of these k-mers.
PhenotypeSeeker uses fixed-length k-mers in all analyses. Thus, the k-mer length is an important factor influencing the overall software performance. The effects of k-mer length on speed, memory usage and accuracy were tested on a P. aeruginosa ciprofloxacin dataset. A general observation from that analysis is that the CPU time and the PhenotypeSeeker memory usage increase when the k-mer length increases (Fig 2). Previously described mutations in the P. aeruginosa parC and gyrA genes were always detected if the k-mer length was at least 13 nucleotides. We assume that in most cases, a k-mer length of 13 is sufficient to detect biologically relevant mutations, although in certain cases, longer k-mers might provide additional sensitivity. The k-mer length in PhenotypeSeeker is a user-selectable parameter. Although most of the phenotype detection can be performed with the default k-mer value, we suggest experimenting with longer k-mers in the model building phase. All subsequent analyses in this article are performed with a k-mer length of 13, unless specified otherwise.
PhenotypeSeeker was applied to the dataset composed of P. aeruginosa genomes and corresponding ciprofloxacin resistance values measured in terms of minimum inhibitory concentration (MIC) (μg/ml), which is defined as the lowest concentration of antibiotic that will inhibit the visible growth of the isolate under investigation after an appropriate period of incubation [15]. We built two separate models using a continuous phenotype for one and binary phenotype for another. Binary phenotype values were created based on EUCAST ciprofloxacin breakpoints [16]. Both models detected k-mers associated with mutations in quinolone resistance determining regions (QRDR) of the parC (c.260C>T, p.Ser87Leu) and gyrA (c.248C>T, p.Thr83Ile) genes (Fig 3, S2 Table). These genes encode DNA topoisomerase IV subunit A and DNA gyrase subunit A, the target proteins of ciprofloxacin [17]. Mutations in the QRDR regions of these genes are well-known causes of decreased sensitivity to quinolone antibiotics, such as ciprofloxacin [18]. The classification model built using a binary phenotype had a F1-measure of 0.88, prediction accuracy of 0.88, sensitivity of 0.90 and specificity of 0.87 on the test subset (Table A in S3 Table). The MIC prediction model built using the continuous phenotype had the coefficient of determination (R2) of 0.42, the Pearson correlation coefficient of 0.68 and the Spearman correlation coefficient of 0.84 (Table M in S3 Table).
In addition to the P. aeruginosa dataset, we tested a C. difficile azithromycin resistance dataset (S2 Table) studied using Kover in Drouin et al., 2016 [9]. ermB and Tn6110 transposon were the sequences known and predicted to be important in an azithromycin resistance model by Kover [9]. ermB was not located on the transposon Tn6110. PhenotypeSeeker found k-mers for both sequences while using k-mers of length 13 or 16. Tn6110 is a transposon that is over 58 kbp long and contains several protein coding sequences, including 23S rRNA methyltransferase, which is associated with macrolide resistance [19]. The predictive models with all tested k-mer lengths (13, 16 and 18) contained k-mers covering the entire Tn6110 transposon sequence, both in protein coding and non-coding regions. In addition to the 23S rRNA methyltransferase gene, k-mers in all three models were mapped to the recombinase family protein, sensor histidine kinase, ABC transporter permease, TlpA family protein disulfide reductase, endonuclease, helicase and conjugal transfer protein coding regions. The model built for the C. difficile azithromycin resistance phenotype had a F1-measure of 0.97, prediction accuracy of 0.97, sensitivity of 0.96 and specificity of 0.97 on the test subset (Table A in S3 Table).
In addition to antibiotic resistance phenotypes in P. aeruginosa and C. difficile, we used K. pneumoniae human infection-causing strains as a different kind of phenotype example. K. pneumoniae strains contain several genetic loci that are related to virulence. These loci include aerobactin, yersiniabactin, colibactin, salmochelin and microcin siderophore system gene clusters [20–24], the allantoinase gene cluster [25], rmpA and rmpA2 regulators [26,27], the ferric uptake operon kfuABC [28] and the two-component regulator kvgAS [29]. The model predicted by PhenotypeSeeker for invasive/infectious phenotypes included 13-mers representing several of these genes. Genes in colibactin (clbQ and clbO), aerobactin (iucB and iucC) and yersiniabactin (irp1, irp2, fyuA, ybtQ, ybtX, and ybtP) clusters showed the most differentiating pattern between carrier and invasive/infectious strains (Fig 4; S2 Table). A 13-mer mapping to a gene-coding capsule assembly protein Wzi was also represented in the model. The model built for K. pneumoniae invasive/infectious phenotypes had a F1-measure of 0.88, prediction accuracy of 0.88, sensitivity of 0.91 and specificity of 0.78 on the test subset (Table A in S3 Table).
To measure the average classification accuracies of logistic regression models, all three datasets were divided into a training and test set of approximately 75% and 25% of strains respectively. A K-mer length of 13 was used, and a weighted approach was tested on binary phenotypes (Table 1). To reduce the influence of sequencing errors when using sequencing reads instead of assembled contigs as the input, we only counted 13-mers as being present in one of the input lists if they occurred at least 5 times in that input list. The PhenotypeSeeker prediction accuracy is not lower when using raw sequencing reads instead of assembled genomes, and therefore, assembly building is not required before model building. Our results with K. pneumoniae show that PhenotypeSeeker can be successfully applied to other kinds of phenotypes in addition to antibiotic resistance.
In our trials, the model building on a given dataset took 3 to 5 hours per phenotype, and prediction of the phenotype took less than a second on assembled genomes (Table 1). The CPU time of model building by PhenotypeSeeker depends mainly on the number of different k-mers in genomes of the training set. The analysis performed on our 200 P. aeruginosa genomes showed that the CPU time of the model building grows linearly with the number of genomes given as input (S1 Fig).
The memory requirement of PhenotypeSeeker did not exceed 2 GB if default parameter settings are used, allowing us to run analyses on laptop computers (S2 Fig) if necessary. The p-value cut-offs during the k-mer filtering step influence the number of k-mers included in the model and have a potentially strong impact on model performance. Tables A-E in the S1 Table show the effects of different p-value cut-offs on model performances.
We ran SEER and Kover on the same P. aeruginosa ciprofloxacin dataset and C. difficile azithromycin resistance dataset to compare the efficiency and CPU time usage with PhenotypeSeeker.
In the P. aeruginosa dataset, SEER was able to detect gyrA and parC mutations only when resistance was defined as a binary phenotype. In cases with a continuous phenotype, those k-mers did not pass the p-value filtering step. Since Kover's aim is to create a resistance predicting model, not an exhaustive list of significant k-mers, it was expected that not all the mutations would be described in the output. gyrA variation already sufficiently characterized the resistant strains set, and therefore, parC mutations were not included in the model. The same applies to the PhenotypeSeeker results with 16- and 18-mers. parC-specific 16- or 18-mers were included among the 1000 k-mers in the prediction model (based on statistically significant p-values) but with the regression coefficient equal to zero because they were present in the same strains as gyrA specific predictive k-mers.
In the C. difficile dataset, our model included the known resistance gene ermB and transposon Tn6110. We were able to find ermB with both SEER and Kover. We also detected Tn6110-specific k-mers with SEER while running Kover with 16-mers instead of 31-mers as in the default settings.
Regarding the CPU time, PhenotypeSeeker with 13-mers was faster than other tested software programs (3.5 hrs vs 14–15 hrs) without losing the relevant markers in the output (Table 2). Using 16- or 18-mers, the PhenotypeSeeker’s running time increases but is still lower than with SEER and Kover.
PhenotypeSeeker works as an easy-to-use application to list the candidate biomarkers behind a studied bacterial phenotype and to create a predictive model. Based on k-mers, PhenotypeSeeker does not require a reference genome and is therefore also usable for species with very high intraspecific variation where the selection of one genome as a reference can be complicated.
PhenotypeSeeker supports both discrete and continuous phenotypes as inputs. In addition, this model takes into account the population structure to highlight only the possible causal variations and not the mutations arising from the clonal nature of bacterial populations.
Unlike Kover, the PhenotypeSeeker output is not merely a trained model for predicting resistance in a separate set of isolates, but the complete list of statistically significant candidate variations separating antibiotic resistant and susceptible isolates for further biological interpretation is also provided. Unlike SEER, PhenotypeSeeker is easier to install and can be run with only a single command for building a model and another single command to use it for prediction.
Our tests using PhenotypeSeeker to detect antibiotic resistance markers in P. aeruginosa and C. difficile showed that it is capable of detecting all previously known mutations in a reasonable amount of time and with a relatively short k-mer length. Users can choose the k-mer length as well as decide whether to use the population structure correction step. Due to the clonal nature of bacterial populations, this step is highly advised for detecting genuine causal variations instead of strain-level differences. In addition to a trained predictive model, the list of k-mers covering possible variations related to the phenotype are produced for further interpretation by the user. The effectiveness of the model can vary because of the nature of different phenotypes in different bacterial species. Simple forms of antibiotic resistance that are unambiguously determined by one or two specific mutations or the insertion of a gene are likely to be successfully detected by our method, and effective predictive models for subsequent phenotype predictions can be created. This is supported by our prediction accuracy over 96% in the C. difficile dataset. On the other hand, P. aeruginosa antibiotic resistance is one of the most complicated phenotypes among clinically relevant pathogens since it is not often easily described by certain single nucleotide mutations in one gene but rather through a complex system involving several genes and their regulators leading to multi-resistant strains. In cases such as this, the prediction is less accurate (88% in our dataset), but nevertheless, a complete list of k-mers covering differentiating markers between resistant and sensitive strains can provide more insight into the actual resistance mechanisms and provide candidates for further experimental testing.
Tests with K. pneumoniae virulence phenotypes showed that PhenotypeSeeker is not limited to antibiotic resistance phenotypes but is potentially applicable to other measurable phenotypes as well and is therefore usable in a wider range of studies.
Since PhenotypeSeeker input is not restricted to assembled genomes, one can skip the assembly step and calculate models based on raw read data. In this case, it should be taken into account that sequencing errors may randomly generate phenotype-specific k-mers; thus, we suggest using the built-in option to remove low frequency k-mers. The k-mer frequency cut-off threshold depends on the sequencing coverage of the genomes and is therefore implemented as user-selectable. One can also build the model based on high-quality assembled genomes and then use the model for corresponding phenotype prediction on raw sequencing data.
PhenotypeSeeker was tested on the following three bacterial species: Pseudomonas aeruginosa, Clostridium difficile and Klebsiella pneumoniae. The P. aeruginosa dataset was composed of 200 assembled genomes and the minimal inhibitory concentration measurements (MICs) for ciprofloxacin. The P. aeruginosa strains were isolated during the project Transfer routes of antibiotic resistance (ABRESIST) performed as part of the Estonian Health Promotion Research Programme (TerVE) implemented by the Estonian Research Council, the Ministry of Agriculture (now the Ministry of Rural Affairs), and the National Institute for Health Development. Isolated strains originated from humans, animals and the environment within the same geographical location in Estonia and belonged to 103 different MLST sequence types (Laht et al., Pseudomonas aeruginosa distribution among humans, animals and the environment (submitted); Telling et al., Multidrug resistant Pseudomonas aeruginosa in Estonian hospitals (submitted)). Full genomes were sequenced by Illumina HiSeq2500 (Illumina, San Diego, USA) with paired-end, 150 bp reads (Nextera XT libraries) and de novo assembled with the program SPAdes (ver 3.5.0) [30]. MICs were determined by using the epsilometer test (E-test, bioMérieux, Marcy l'Etoile, France) according to the manufacturer instructions. Binary phenotypes were achieved by converting the MIC values into 0 (sensitive) and 1 (resistant) phenotypes according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints [16]. The resulted dataset consisted of 124 ciprofloxacin sensitive P. aeruginosa isolates (62%) and 76 ciprofloxacin resistant P. aeruginosa isolates (38%) and is deposited in the NCBI’s BioProject database under the accession number PRJNA244279 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA244279).
The C. difficile dataset was composed of assembled genomes of 459 isolates and the binary phenotypes of azithromycin resistance (sensitive = 0 vs resistant = 1), adapted from Drouin et al., 2016 [9]. The isolates originated from patients from different hospitals in the province of Quebec, Canada and the genomes were received from the European Nucleotide Archive [EMBL:PRJEB11776 ((http://www.ebi.ac.uk/ena/data/view/PRJEB11776)]. The dataset consisted of 246 azithromycin sensitive isolates (54%) and 213 azithromycin resistant isolates (46%).
The K. pneumoniae dataset was composed of reads of 167 isolates, originating from six countries and sampled to maximize diversity, and the binary clinical phenotype of human carriage status vs human infection (including invasive infections) status (carriage = 0 vs infectious = 1), adapted from Holt et al., 2015 [31]. The reads were received from the European Nucleotide Archive [EMBL:PRJEB2111 (https://www.ebi.ac.uk/ena/data/view/PRJEB2111)] and de novo assembled with SPAdes (ver 3.10.1) [30]. The dataset consisted of 36 isolates with human carriage status as phenotype (22%) and 131 K. pneumonia isolates with human infection status as phenotype (78%).
Abstractly, each test dataset was composed of pairs (x, y), where x is the bacterial genome x∈{A,T,G,C}*, and y denotes phenotype values specific to a given dataset y ∈ {0.008, …, 1024} (continuous phenotype) or y ∈ {0, 1} (binary phenotype).
All operations with k-mers are performed using the GenomeTester4 software package containing the glistmaker, glistquery and glistcompare programs [11]. At first, all k-mers from all samples are counted with glistmaker, which takes either FASTA or FASTQ files as an input and enables us to set the k-mer length up to 32 nucleotides. Subsequently, the k-mers are filtered based on their frequency in strains of the training set. By default, the k-mers that are present in or missing from less than two samples are filtered out and not used in building the model. The remaining k-mers are used in statistical testing for detection of association with the phenotype.
By default, PhenotypeSeeker conducts the clonal population structure correction step by using a sequence weighting approach that reduces the weight of isolates with closely related genomes. For weighting, pairwise distances between genomes of the training set are calculated using the free alignment software Mash with default parameters (k-mer size of 21 nucleotides and sketch size of 1000 min-hasehes) [12]. Distances estimated by Mash are subsequently used to calculate weights for each genome according to the algorithm proposed by Gerstein, Sonnhammer and Chothia [13]. The calculation of GSC weights is conducted using the PyCogent python package [32]. The GSC weights are taken into account while calculating Welch two-sample t-tests or chi-squared tests to test the k-mers’ associations with the phenotype. Additionally, the GSC weights can be used in the final logistic regression or linear regression (if Ridge regularization is used) model generation.
In the case of binary phenotype input, the chi-squared test is applied to every k-mer that passes the frequency filtration to determine the k-mer association with phenotype. The null hypothesis assumes that there is no association between k-mer presence and phenotype. The alternative hypothesis assumes that the k-mer is associated with phenotype. The chi-squared test is conducted on these observed and expected values with degrees of freedom = 1, using the scipy.stats Python package [33]. If the user selects to use the population structure correction step, then the weighted chi-squared tests are conducted according to the previously published method [34].
In the case of continuous phenotype input, the Welch two-sample t-test is applied to every k-mer that passes the frequency filtration to determine if the mean phenotype values of strains having the k-mer are different from the mean phenotype values of strains that do not have the k-mer. The null hypothesis assumes that the strains with a k-mer have different mean phenotype values from the strains without the k-mer. The alternative hypothesis assumes that the means of the strains with and without the k-mer are the same. The t-test is conducted with these values using the scipy.stats Python package [33], assuming that the samples are independent and have different variance. If the user selects the population structure correction step, then the weighted t-tests are conducted [34]. In that case, the p-value is calculated with the function scipy.stats.t.sf, which takes the absolute value of the t-statistic and the value of degrees of freedom as the input.
To perform the regression analysis, first, the matrix of samples times features is created. The samples in this matrix are strains given as the input and the features represent the k-mers that are selected for the regression analysis. The values (0 or 1) in this matrix represent the presence or absence of a specific k-mer in the specific strain. The target variables of this regression analysis are the resistance values of the strains. Thereupon, input data are divided into training and test sets whose sizes are by default 75% and 25% of the strains, respectively. The proportion of class labels in the training and test sets are kept the same as in the original undivided dataset. In the case of a continuous phenotype, a linear regression model is built, and in the case of a binary phenotype, a logistic regression model is built. The logistic regression was selected for binary classification task as it showed better performance on our datasets than other tested machine learning classifiers like support vector machine (with no kernel and with Gaussian kernel) and random forest. The performance of logistic regression models on our tested datasets in comparison to performance of other machine learning classifiers are shown in S3 Fig and in Tables A-L in S3 Table. The performance of linear regression model on P. aeruginosa dataset is shown in Table M in S3 Table. For both the linear and logistic regression, the Lasso, Ridge or Elastic Net regularization can be selected. The Lasso and Elastic Net regularizations shrink the coefficients of non-relevant features to zero, which simplifies the identification of k-mers that have the strongest association with the phenotype. To enable the evaluation of the output regression model, PhenotypeSeeker provides model-evaluation metrics. For the logistic regression model quality, PhenotypeSeeker provides the mean accuracy as the percentage of correctly classified instances across both classes (0 and 1). Additionally, PhenotypeSeeker provides F1-score, precision, recall, sensitivity, specificity, AUC-ROC, average precision (area under the precision-recall curve), Matthews correlation coefficient (MCC), Cohen’s kappa, very major error rate and major error rate as metrics to assess model performance. For the linear regression model, PhenotypeSeeker provides the mean squared error, the coefficient of determination (R2), the Pearson and the Spearman correlation coefficients and the within ±1 two-fold dilution factor accuracy (useful for evaluating the MIC predictions) as metrics to assess model performance. To select for the best regularization parameter alpha, a k-fold cross-validation on the training data is performed. By default, 25 alpha values spaced evenly on a log scale from 1E-6 to 1E6 are tested with 10-fold cross-validation and the model with the best mean accuracy (logistic regression) or with the best coefficient of determination (linear regression) is saved to the output file. Regression analysis is conducted using the sklearn.linear_model Python package [35].
Our models were created using mainly k-mer length 13 (“-l 13”; default). We counted the k-mers that occurred at least once per sample (“-c 1”; default) when the analysis was performed on contigs or at least five times per sample (“-c 5”) when the analysis was performed on raw reads. In the first filtering step, we filtered out the k-mers that were present in or missing from less than two samples (“—min 2—max 2”; default) when the analysis was performed on a binary phenotype or fewer than ten samples (“—min 10—max N-10”; N–total number of samples) when the analysis was performed on a continuous phenotype. In the next filtering step, we filtered out the k-mers with a statistical test p-value larger than 0.05 (“—p_value 0.05”; default).
The regression analysis was performed with a maximum of 1000 lowest p-valued k-mers (“—n_kmers; 1000”; default) when the analysis was done with binary phenotype and with a maximum of 10,000 lowest p-valued k-mers (“—n_kmers 10000”; default) when the analysis was performed with a continuous phenotype. For regression analyses, we split our datasets into training (75%) and test (25%) sets (“-s 0.25”; default). The regression analyses were conducted using Lasso regularization (“-r L1”; default), and the best regularization parameter was picked from the 25 regularization parameters spaced evenly on a log scale from 1E-6 to 1E6 (“—n_alphas 25—alpha_min 1E-6—alpha_max 1E6”; default). The model performances with each regularization parameter were evaluated by cross-validation with 10-folds (“—n_splits 10”; default).
The correction for clonal population structure (“—weights +”; default) and assembly of k-mers used in the regression model (“—assembly +”; default) were conducted in all our analyses.
SEER was installed and run on a local server with 32 CPU cores and 512 GB RAM, except the final step, which we were not able to finish without segmentation fault. This last SEER step was launched via VirtualBox in ftp://ftp.sanger.ac.uk/pub/pathogens/pathogens-vm/pathogens-vm.latest.ova. Both binary and continuous phenotypes were tested for P. aeruginosa and the binary phenotype in C. difficile cases. Default settings were used. Kover was installed on a local server and used with the settings suggested by the authors in the program tutorial.
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10.1371/journal.pntd.0001280 | Necator americanus and Helminth Co-Infections: Further Down-Modulation of Hookworm-Specific Type 1 Immune Responses | Helminth co-infection in humans is common in tropical regions of the world where transmission of soil-transmitted helminths such as Ascaris lumbricoides, Trichuris trichiura, and the hookworms Necator americanus and Ancylostoma duodenale as well as other helminths such as Schistosoma mansoni often occur simultaneously.
We investigated whether co-infection with another helminth(s) altered the human immune response to crude antigen extracts from either different stages of N. americanus infection (infective third stage or adult) or different crude antigen extract preparations (adult somatic and adult excretory/secretory). Using these antigens, we compared the cellular and humoral immune responses of individuals mono-infected with hookworm (N. americanus) and individuals co-infected with hookworm and other helminth infections, namely co-infection with either A. lumbricoides, Schistosoma mansoni, or both. Immunological variables were compared between hookworm infection group (mono- versus co-infected) by bootstrap, and principal component analysis (PCA) was used as a data reduction method.
Contrary to several animal studies of helminth co-infection, we found that co-infected individuals had a further downmodulated Th1 cytokine response (e.g., reduced INF-γ), accompanied by a significant increase in the hookworm-specific humoral immune response (e.g. higher levels of IgE or IgG4 to crude antigen extracts) compared with mono- infected individuals. Neither of these changes was associated with a reduction of hookworm infection intensity in helminth co-infected individuals. From the standpoint of hookworm vaccine development, these results are relevant; i.e., the specific immune response to hookworm vaccine antigens might be altered by infection with another helminth.
| Parasitic infections in humans are common in tropical regions and under bad housing and sanitation conditions multiple parasitic infections are the rule rather than the exception. For helminth infections, which are thought to affect almost a quarter of the world's population, most common combinations include soil-transmitted helminths, such as hookworm, roundworm, and whipworm, as well as extra-intestinal infections by schistosomes. In order to develop and test a hookworm vaccine in endemic areas, the understanding of the impact of multiple helminth infections (co-infection) on the immune response against hookworm in infected individuals is crucial. The authors report in their article, that several parameters of the cellular (T cell markers, cytokines, chemokines) and humoral immune response (e.g. IgG4 and IgE antibodies) against hookworm are significantly affected or modulated in individuals co-infected with hookworm, roundworm and/or schistosomes. These results imply that the immune response against components of a hookworm vaccine might be altered by previous contact with other helminth species in endemic areas.
| Helminth co-infection in humans is common in tropical regions [1], [2], where transmission of Ascaris lumbricoides, Trichuris trichiura, the hookworms (N. americanus or A. duodenale), and schistosomes often occur concurrently [3], [4]. Although co-infection is often the rule rather than the exception in endemic areas, most previous immuno-epidemiological studies of human helminth infection have focused on the immune response to a single helminth species (mono-infection) rather than the more common situation where an individual is infected with one or more different helminth species [5]. At our study site in Northeastern Minas Gerais State, Brazil, where co-infection with schistosomes and soil-transmitted helminths (STHs) is common [6], we have attempted to study the epidemiologic, immunologic, and genetic determinants of infection in individuals resident in these co-endemic areas [7]–[11].
Much of the previous information on the immunology of helminth co-infections has come from laboratory animal models, especially experimental rodent models. The majority of these studies show a competition between the co-infections, with one infection usually leading to the rapid expulsion of the other [12]–[16]. The immune mechanisms behind this effect are hypothesized to include cross-reactive antibodies (also referred to as “cross-protection”) [13], [16], a skewing towards Th2 cytokines (e.g., elevated IL-4), increased Th2-type antibody isotypes (e.g., elevated production of IgG1) [15], and mucosal mast cell activation [13]–[15]. However, conflicting animal studies report that co-infection increases infection intensities by down modulating Th2 cytokine responses, which in turn reduces intestinal inflammation, leading to slower worm expulsion and increased worm burdens in co-infected animals [17]. Possible explanations for these opposite findings, among others, might be differences in animal models, different combinations of parasite infections, and the different timing of co-infection (by timing of the primary versus the secondary infection).
The few studies on the human immune response in co-infected individuals are also contradictory. In one group of studies, helminth co-infection appeared to result in a synergistic effect among the infections, with infection with one helminth being associated with an increased risk of having a high intensity infection with another helminth [7]. However, other studies imply a cross-protective effect derived from co-infection: for example, individuals mono-infected with hookworm or A. lumbricoides develop antibodies that cross-react with antigens from S. mansoni [18]–[20]. In another set of studies, co-infection appeared to skew the immune response away from the helminth infection under study, e.g., the humoral and cellular immune responses to hookworm or Ascaris antigens are diminished in individuals resident in a schistosomiasis endemic area [21]. Along these same lines, studies have also demonstrated an upregulation of the immune response during helminth co-infection; e.g., increased production of inflammation markers to S. mansoni infection in children who are also infected with hookworms and/or Entamoeba species [22]. However, given the contradictory nature of these outcomes, the central question of whether multiple helminth infections drive host immune responses towards phenotypes different from those of a single infection still remains to be answered [23].
In our previous epidemiological study in Brazil, we showed synergistic effects among helminth co-infections in terms of egg counts [7], leading us to expect a similar synergistic effect on immune responses during helminth co-infection. In keeping with the results from experimental animal studies [12]–[16], we further hypothesized that hookworm co-infections with A. lumbricoides and/or S. mansoni would significantly alter the immune responses to crude hookworm antigen extracts, resulting in reduced Th2-type responses (IL-4, IL-5, IL-13), a reduced inflammatory response (e.g., lower TNF-α secretion), and an increase in the production of regulatory cytokines (e.g., IL-10). To test this hypothesis, we compared the cellular and humoral immune responses of individuals infected with hookworm alone (mono-infected) and individuals infected with hookworm and either A. lumbricoides, S. mansoni or both (co-infected).
The study was conducted in an area of the northeastern part of the state of Minas Gerais in Brazil that is endemic for S. mansoni and the STH as previously described [7]. The area of Americaninhas is divided into five rural sectors and a central municipality. The Fundação National de Saúde (the National Health Foundation) estimates the population to be approximately 1000 in the urban municipal center and another 1000 in the surrounding rural areas. Each house was assigned a unique household identification number (HHID), and each resident, a unique personal identity number (PID). Only individuals meeting the following inclusion criteria were included into the study: (1) resident in the study area over the last 24 months; (2) reporting not to have received anthelmintic treatment within the last 24 months; and (3) willing and able to give informed consent to study protocol. Individuals were not included if they: (1) attended school outside the study area; (2) worked full-time outside the study area; or (3) tested positive on a pregnancy test. Females found to be pregnant during the test were excluded from treatment during their pregnancy and received treatment for all helminth infections later. For parasitological exams, participants were instructed to deposit one fecal sample per day into each container and return the container to one of several collection points, where the sample was stored at 4°C. Fecal samples returned later than 48 h after date of distribution were not accepted, and new containers were issued. Presence of infection was determined by using the formalin-ether sedimentation technique. Individuals positive for any helminth in the formalin-ether sedimentation technique were asked to contribute two more samples over the course of two more days to be analyzed by Kato-Katz technique for assessment of eggs per gram of feces (infection intensity). Two slides were taken from each day's fecal sample for a total of four slides from each individual. Slides were examined within 45 minutes of slide preparation to avoid drying of hookworm eggs. The arithmetic means of the four slides was calculated and then converted to eggs per gram according to the Kato-Katz method [24].
Out of 1,332 consented participants in the study, two-hundred and fifty individuals were selected by simple random sampling for immunological assays. Random sampling was performed on an age, gender, and infection stratified sampling frame. In brief, individuals with a negative fecal exam were removed from the sampling frame; i.e., only persons with a positive fecal exam were included. The sampling frame was then divided into 10 mutually exclusive and exhaustive gender-based strata based using the following age intervals: <9, 10–19, 20–29, 30–39, and >40 years of age. Simple random sampling was performed independently in each stratum. Individuals who refused to enroll in this part of the study or who were not eligible were replaced by simple random sampling from the same stratum. The final stratified random sample was compared to non-participants for age, gender, and infection intensity, and no statistically significant differences (p>0.05) were found in terms of those variables between those individuals included in the survey and those not.
Individuals found to be infected with hookworm or other intestinal nematodes were treated with albendazole (400 mg). Participants with schistosomiasis were treated with praziquantel (50 mg/kg) under the supervision of the project physician.
In the present study, cellular and humoral immune responses from individuals with a hookworm mono-infection [9] were included, as well as from individuals co-infected with (a) hookworm and A. lumbricoides, (b) hookworm and S. mansoni, or (c) hookworm, A. lumbricoides and S. mansoni. After parasitological exams and before anthelminthic treatment, approximately 20 mL of blood was collected in heparinized tubes from children ≥6 years of age and adults for separation of peripheral blood mononuclear cells (PBMC) and 4 mL of blood in EDTA tubes for the immunological assays described below. The study was approved by the ethical review committees of The George Washington University (GWU, USA), the London School of Hygiene and Tropical Medicine (UK), the Centro de Pesquisas René Rachou FIOCRUZ and the Brazilian National Committee for Ethics in Research (CONEP), and all subjects provided written informed consent to participate in the study, or, in the case of minors, written informed consent was given by their parents or guardians.
Phenotyping of lymphocytes was performed as described elsewhere [9] and the following pairs of monoclonal antibodies (mAb), either conjugated with phycoerythrin (PE) or fluorescein isothiocyanate (FITC) were used: CD4(FITC)/CD25(PE), CD4(FITC)/HLA-DR(PE), CD4(FITC)/CD45RO(PE), CD4(FITC)/CD45RA(PE), CD8(FITC)/CD28(PE), CD8(FITC)/HLA-DR(PE), CD8(FITC)/CD45RO(PE), CD8(FITC)/CD45RA(PE), CD3(FITC)/CD69(PE), and CD19(FITC)/CD27(PE). Mouse IgG1 antibodies conjugated with FITC or PE served as isotype controls. Sample acquisition was done on a FACScan flow cytometer (Becton Dickinson, USA) and results for 10,000 events were analysed with BD Cell Quest™ software (Becton Dickinson, USA).
For the evaluation of humoral and cellular immune responses, soluble somatic antigen extracts were prepared from third-stage larvae (L3) and adult worms (AE) of Ancylostoma caninum. Excretory/secretory (ES) antigens were obtained from cultured A. caninum adult worms. The preparations were performed as described elsewhere [9]. For the detection of parasite-specific IgE antibodies, each of the hookworm antigens were diluted with carbonate buffer (pH 9.6) to a concentration of 5 µg/ml. High-binding ELISA plates (NUNC, Maxisorp, Fisher Scientific, USA) were coated with 100 µl of the diluted antigens and incubated overnight at 4°C. Plates were washed 5 times with washing buffer (phosphate buffered saline [PBS]/0.05% Tween-20; pH 7.2–7.4) and were then blocked for 1 hour at room temperature (RT) with 200 µl of blocking buffer (PBS/ 0.05% Tween-20/ 3% bovine serum albumin). Individual serum samples were diluted 1∶50 in blocking buffer, 200 µl were added in duplicate to the respective wells, and plates were incubated overnight at 4°C. On the following day, plates were washed 10 times with washing buffer. A 1∶1,000 dilution of anti-human IgE alkaline phosphatase-conjugated antibody (Pharmingen, USA) was prepared in PBS/0.05% Tween-20 and 100 µl were added to the wells. After another incubation of 90 minutes at RT, plates were washed 5 times and then 100 µl of p-nitrophenyl phosphate substrate was added to each well. Plates were incubated overnight at 4°C and the following morning the color reaction was read at 405 nm using an automated ELISA reader (SpectraMax 340 PC, Molecular Devices, USA) using SOFTmax Pro 5.2 for Windows (Molecular Devices) for data capture. Reference sera were assayed on each plate as positive and negative controls.
For detection of parasite-specific IgG subclasses, horseradish peroxidase-conjugated, anti-human IgG1, IgG3, and IgG4 (Zymed, USA) were used at a dilution of 1∶1000, as described above. As substrate, ortho-phenylene diamine was used and the color reaction was stopped with H2SO4 after incubation for 30 min at RT in the dark. Plates were read at 490 nm.
The separation of lymphocytes, their stimulation in vitro with different hookworm antigens and with the mitogen phytohemagglutinin (PHA), lymphocyte proliferation, as well as the secretion of several cytokines and chemokines after in vitro stimulation were performed as described elsewhere in detail [9]. Here we report the proliferation of lymphocytes after stimulation with the crude soluble hookworm antigens L3, AE, and ES. For in vitro cytokine or chemokine secretion, lymphocyte cultures were stimulated with the same antigens and with PHA, as described for proliferation assays, and the following analytes were measured: Interleukin (IL)-2, IL-4, IL-5, IL-10, IL-13, CXCL10, TNF-α, and IFN-γ.
The intensity of hookworm infection (as determined by fecal egg counts) was compared between groups by non-parametric Kruskal-Wallis test. Associations between Necator intensity of infection and antibody level against crude antigen extracts or Necator infection intensity and secreted cytokines/chemokines were analysed by Spearman's rank correlation. Analyses of these immune responses were done separately for the different co-infection combinations and then compared with hookworm mono-infected individuals. As the results among the different co-infection subgroups were found to be generally similar (see below, in particular Table 1 and Figure 1), we merged the various co-infections into a single group. For the chemokine and cytokine variables, analysis was done on the log-transformed variables, after replacing any zero values with 1. Immunological variables were compared by bootstrapping the geometric mean after adjusting for age by linear regression on the log-values. For the lymphocyte populations, the untransformed values were used and hence the arithmetic means were compared. The immunological variables were summarized using principal component analysis (PCA), via a projection-pursuit algorithm robust to departures of the data from normality [25], [26]. We then used biplots [27] to simultaneously show i) the contributions of each of the original variables to the first two principal components (the ‘loadings’), and ii) each person's value of the principal components (the ‘scores’). The bivariate score means and their 95% confidence ellipses [28] were calculated for the mono-infected and co-infected groups. These means were compared between infection groups by the multivariate Hotelling's T2 test [29]. PCA analysis was done for lymphocyte sub-populations, for antibody responses, and for chemokine and cytokine response to three hookworm antigen preparations (AE, ES, and L3) and a mitogen (PHA) Pairs of correlation coefficients by infection group were compared by first transforming the variable to a standard normal deviate via the Fisher Z transformation. No adjustment for multiple comparisons was made in these analsyses. Analyses were performed using S-PLUS version 6.2 or later (Insightful Corp, Seattle WA, USA) and R version 2.10 or later (R Foundation for Statistical Computing, Vienna, Austria). The PCA analysis used the ‘pcaPP’ package in R.
Of the 250 study participants who were randomly selected, 197 were infected with hookworm and were therefore included in the immunological assessments. Table 1 shows the demographic characteristics of individuals either mono-infected with N. americanus, co-infected either with A. lumbricoides or S. mansoni, or infected with all three helminth species. The median age in the co-infected groups was lower than in the mono-infected group, but the hookworm parasite load, estimated by the number of eggs per gram of feces, did not differ significantly between the four groups (Table 1 and Figure 1). Figure 1 shows the median fecal egg counts for the different groups, which covered a wide range of infection intensity.
We observed a statistically significant increase in CD4/HLA-DR and CD8/HLA-DR positive T-cells in co-infected individuals compared to mono-infected individuals. Other comparisons of surface markers on T and B cells between mono- and co-infected individuals were not significant (see Table 2). PCA was performed on these immunological parameters jointly in order to obtain a more complete and integrated picture of the immunological pattern and compare the weight of each parameter's contribution to the immune response. The first principal component (PC 1) was dominated by a contrast between CD4+/CD25+ (positive loading) and CD8+/CD28− T cells (negative loading). PC 2 is effectively an average of CD4/CD45RA and CD8/CD45RA positive memory T cells (see Figure S1).
In participants either mono-infected or co-infected, we found positive correlations between individual fecal egg counts and serum IgG4 antibody levels against all the hookworm crude antigen preparations tested: L3, AE and ES. Other isotypes, such as IgG1, IgG3, and IgE, were not strongly correlated with egg counts (see Table S1). For individuals with co-infections, the correlations between fecal hookworm egg counts and hookworm-specific IgG4 were significant for AE (rho = 0.40; p<0.001), ES (rho = 0.21; p = 0.007), and L3 (rho = 0.26; p = 0.001) antigen preparations.
Optical density values for hookworm-specific serum antibodies were measured and the age-adjusted ratio between mono- and co-infected individuals are shown in Table 3, where we observed significantly higher values for L3-specific IgG3, IgG4, and IgE, AE-specific IgG1, IgG4, and IgE, and ES antigen specific IgG1 and IgG4 responses in co-infected individuals compared to mono-infected individuals (Table 3).
Mean PC values for mono-infected and co-infected individuals, plus their 95% confidence intervals (ellipses), showed distinct segregation between these infection groups, with the mono-infected individuals having lower values of PC 1, which was dominated by IgG3 against AE antigen, and IgE against AE and ES antigens (Figure 2). PC 2 showed a contrast between i) IgG1 and IgG3 against AE antigen (positive loadings) and ii) IgE against AE and ES antigens (negative loadings).
Values for lymphocyte proliferation were indicated as stimulation indices, i.e. proliferation of antigen- or mitogen-stimulated cells divided by the proliferation of unstimulated control cultures. Analysis of lymphocyte proliferation did not result in any significant differences between mono- and co-infected groups (data not shown). Non-parametric correlations between individual PBMC secreted cytokine or chemokine levels and fecal hookworm egg counts were strongly negative for IL-10 in mono-infected participants and significantly different when compared with co-infected individuals, whether stimulated with L3 or AE (p = 0.032 for both comparisons), or ES antigen (p = 0.003, Table 4). Likewise, strong negative correlations were found for TNF-α in control cultures from mono-infected individuals or when cells were stimulated with ES, which were significantly different from the co-infected group (p = 0.002 and p = 0.04, respectively, Table 4). In individuals with co-infection, significant negative correlations between egg counts and CXCL10 secretion were found in cell cultures stimulated with L3 (p<0.05) or ES antigen (p<0.01), however without any significant differences when compared with mono-infected individuals.
Analysis of cytokine and chemokine production in PBMC after stimulation with L3 antigen resulted in a significantly higher production of CXCL10 in mono-infected individuals (Table 5). Also, in PBMC stimulated either with AE or ES crude antigen extracts, significantly higher concentrations of TNF-α or IFN-γ were observed in mono-infected individuals when compared with the co-infected group (Tables 6 and 7). Examples of PCA for antigen-specific cytokine and chemokine secretion are shown in Figures S2, S3. For AE, as well as for ES antigen stimulation of PBMC, the highest loadings for PC1 and PC2 with the same directions were obtained for both Th1- and Th2-type cytokines or chemokines.
This is the first study to comprehensively examine the hookworm-specific humoral and cellular immune response in individuals who are co-infected with other helminths in an area of high hookworm transmission. This is also the first study to examine the effect of co-infection on the immune response to crude hookworm antigen extracts from different stages of hookworm development (L3, AE, ES). Moreover, these effects were analyzed in an epidemiologically well-characterized group of individuals, where the spatial, genetic and demographic aspects of hookworm infection and co-infection have been intensively studied [7], [8], [10], [11]. Apart from non-parametric methods and comparisons of individual parameters, we also utilized principal component analysis for comparison of the immune responses to hookworm crude antigen extracts between mono- and co-infected individuals, enabling us to examine, and compare numerous mutually correlated immune variables in relation to the effects of mono- or co-infection status [30].
Our analyses showed that chronic co-infection with nematode and trematode species considerably alters the immune response to hookworm crude antigen extracts. Most interestingly, co-infection altered to a significant degree the antigen-induced secretion of inflammatory TNF-α and led to a further diminution of hookworm-specific IFN-γ and CXCL10 secretion, but did not alter production of IL-10 or the Type-2 cytokines, when compared to mono-infected individuals. In contrast to our previous study [9], we found that the immune response to hookworm infection was increasingly modulated in co-infected individuals, an alteration that did not lead to expulsion of one parasite species as shown in experimental co-infections of mice with S. mansoni and Trichuris muris [15].
These findings are extremely relevant for successful planning of a hookworm vaccine currently under development [31]. In areas endemic for hookworm, such as the one studied, co-infections with other helminth species like A. lumbricoides and Schistosoma are common. Our results show that Type 1 immune responses to hookworm are significantly altered by such co-infections, which might have implications for hookworm vaccine development, with recent hookworm vaccines focused on inducing a Th1 response [32] in order avoid problems with hookworm induced IgE.
The major emphasis of our immunological study was on T cells, i.e., the proliferation of T cells, activation of T cell subpopulations, and secretion of Th1- and Th2-type cytokines and chemokines. Changes in CD4 and CD8 T cell counts, together with increased activation of these T cell subpopulations, have already been reported for helminth infections [33]. We add to this literature the finding that percentages of activated CD4+ and CD8+ T cells increased with co-infection. We speculate that multiply-infected individuals have higher percentages of activated CD4+ and CD8+ T cells due to ongoing higher antigenic stimulation of the immune system by different helminth species and cross-reactive antigens. This is supported by in vitro experiments on naïve human PBMC stimulated with soluble egg antigen from S. mansoni (SEA), which showed an increase in the CD4+/HLA-DR+ cell population after in vitro priming and a further increase during recall responses [34].
Even though mean fecal egg counts in mono-infected patients were found to be in the range of those from co-infected individuals, correlations between hookworm egg counts and hookworm-specific IgG4 responses were stronger in co-infected patients, which might be attributed to the presence of antibodies that were cross-reactive with antigens from co-infecting helminth species [21], [35], [36]. Chronic infections with multiple helminth species might induce a stronger and ongoing antigenic stimulation of the host's immune system, which may lead to the expansion of antigen-specific B cells and the secretion of specific IgG4 antibodies, especially in co-infected individuals with increased hookworm infection. In support of this, a prior study with volunteers co-infected with hookworm, S. mansoni, and A. lumbricoides showed an increase in helminth antigen-specific total IgG antibodies when compared with the respective mono-infected groups [21]. In hookworm infections, the production of all antigen-specific IgG subclasses rises with ongoing infection [35] and hookworm-specific IgG4 has been proposed as a good marker for patent and chronic infections [35]–[37].
Analysis of cytokine and chemokine secretion patterns from mono-infected volunteers revealed no clear polarization into Th1 or Th2 type immune responses, but rather a mixed pattern [9]. Similar results were recently obtained for individuals co-infected with A. lumbricoides and T. trichiura [38]. However, in the co-infected group, we found a decreased TNF-α secretion, together with a further down-modulation of hookworm-specific IFN-γ production. Another study on co-infection detected elevated levels of pro-inflammatory cytokines and chemokines in co-infected children in response to S. mansoni adult worm antigen, whereas IFN-γ and IL-13 secretion patterns revealed no significant differences between individuals mono- and poly-infected with schistosomes, hookworm and Entamoeba species [22]. As opposed to A. lumbricoides and Trichuris trichiura co-infections [38], we were neither able to detect a positive relationship between hookworm antigen-induced IL-10 secretion and intestinal worminess, nor to detect negative associations between IL-10 and Th1/Th2-type cytokines. These described differences might be due to the presence of different parasite species and also due to a mixture of intestinal and extra-intestinal parasites.
Considerable antigen-induced IL-10 secretion has been described in individuals with hookworm infection [9], [39]. In the current study, IL-10 levels correlated inversely with fecal egg counts in mono-infected hookworm patients especially in response to ES. This strong negative correlation was ablated in co-infected individuals, most probably because A. lumbricoides and S. mansoni infections induce production of IL-10 themselves [39]. Even though there was an unexpected negative correlation between parasite load and IL-10 secretion of lymphocytes, the antigen-induced IL-10 secretion was significantly associated with mono-infected individuals, indicating its importance in immune regulation during hookworm infection.
This study has some important limitations. First, the cross-sectional study design, in which groups are compared from a single time point, does not allow causal inferences to be made. In addition, the small sample size may have limited our ability to detect small statistical differences between groups. Nor does the sample size allow for further stratification of the groups in order to explore other factors which may account for these differences. Age is likely to be among the most important of such confounding factors but was included as a covariate when testing for differences between groups. One positive aspect of the study design was the population-based sampling which should enhance the generalizability of the study.
In summary, individuals co-infected with other helminth species presented with a significantly different immune response when compared with mono-infected participants. These changes included a stronger activation of CD4+ and CD8+ T cells, lower secretion of Type 1 cytokines, and increased levels of IgG4 and IgE antibodies against somatic hookworm antigens (L3 and AE). Furthermore, positive correlations between egg counts and hookworm-specific IgG4 responses, as well as missing correlations between egg counts and regulatory (IL-10) and inflammatory (TNF-α) cytokines in co-infected individuals. This modulation of hookworm-specific cellular and humoral immune responses by co-infection with other helminth species will be an important consideration during clinical trials for hookworm vaccine testing. Although vaccination is obviously not the same as natural infection, the immunogenicity of hookworm antigens in a vaccine might be altered and adversely affected by infections with parasites such as S. mansoni and A. lumbricoides.
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10.1371/journal.pgen.1005975 | Gifsy-1 Prophage IsrK with Dual Function as Small and Messenger RNA Modulates Vital Bacterial Machineries | While an increasing number of conserved small regulatory RNAs (sRNAs) are known to function in general bacterial physiology, the roles and modes of action of sRNAs from horizontally acquired genomic regions remain little understood. The IsrK sRNA of Gifsy-1 prophage of Salmonella belongs to the latter class. This regulatory RNA exists in two isoforms. The first forms, when a portion of transcripts originating from isrK promoter reads-through the IsrK transcription-terminator producing a translationally inactive mRNA target. Acting in trans, the second isoform, short IsrK RNA, binds the inactive transcript rendering it translationally active. By switching on translation of the first isoform, short IsrK indirectly activates the production of AntQ, an antiterminator protein located upstream of isrK. Expression of antQ globally interferes with transcription termination resulting in bacterial growth arrest and ultimately cell death. Escherichia coli and Salmonella cells expressing AntQ display condensed chromatin morphology and localization of UvrD to the nucleoid. The toxic phenotype of AntQ can be rescued by co-expression of the transcription termination factor, Rho, or RNase H, which protects genomic DNA from breaks by resolving R-loops. We propose that AntQ causes conflicts between transcription and replication machineries and thus promotes DNA damage. The isrK locus represents a unique example of an island-encoded sRNA that exerts a highly complex regulatory mechanism to tune the expression of a toxic protein.
| As the function of conserved core-genome-encoded small RNAs (sRNA) reflects the basic lifestyle of bacteria, the function of non-conserved island-encoded sRNAs remains enigmatic. The island-encoded sRNA IsrK belongs to Gifsy-1 prophage of Salmonella. Here, we report a complex mechanism in which the IsrK RNA functions as both sRNA and mRNA to control the production of the toxic AntQ protein. The isrK promoter directs the synthesis of two distinct RNA species: a full-length translationally inactive target mRNA and the correctly terminated, shorter IsrK sRNA. IsrK sRNA binds the full-length inactive mRNA producing an antiterminator protein, AntQ, which interferes with transcription termination. Expression of antQ results in bacterial growth arrest and ultimately cell death. Fluorescence microscopy of E. coli and Salmonella expressing antQ revealed condensed chromatin morphology as observed upon exposure to DNA-damaging agents. We propose that expression of the phage antiterminator protein results in conflicts between transcription and replication machineries and thus facilitates DNA damage. In summary, the RNA regulator IsrK presents a new regulatory principle in which a horizontally acquired sRNA controls genome integrity.
| The first systematic searches for bacterial sRNA were based on bio-computational identification of conserved genes in intergenic regions [1]. The subsequent characterization of these conserved sRNAs identified them as important players in many adaptive and physiological responses. These conserved core-genome encoded regulatory sRNAs comprise many antisense RNAs of which a subset are cis-encoded whereas the majority acts on trans-encoded target mRNAs by limited base complementarity [2]. Most trans-acting base-pairing sRNAs of enteric bacteria require the RNA chaperone protein Hfq for both intracellular stability and for efficient annealing to target mRNAs [3]. However, the chromosomes of these bacteria are mosaics, composed of conserved collinear regions interspersed with unique genetic islands that were acquired horizontally via once-mobile genetic elements. Therefore, the early searches based on sequence conservation generally disregarded unique horizontally acquired sRNAs. However, subsequent global cDNA cloning, comparative genomic based expression screens, Hfq-bound and global transcriptomic screens detected short RNA species in non-conserved horizontally acquired regions as well as highly abundant short RNA species from the UTRs of protein-coding genes [4–8]. The function of these non-conserved sRNAs remains enigmatic yet promising, as they may inform new regulatory principles.
Members of the genus Salmonella carry numerous genomic islands that were acquired by horizontal transfer of phages, plasmids and transposons. These islands carry fitness and virulence genes that are integral to Salmonella pathogenicity, enabling the bacteria to adapt to different niches, invade intestinal cells and multiply within cells of the immune response [9]. In a previous study, we screened the horizontally acquired genomic islands of Salmonella typhimurium for non-conserved sRNA genes. Our analysis led to identification of 19 unique island-encoded sRNAs including the Gifsy-1 prophage encoded IsrK RNA [6]. The chromosome of Salmonella is lysogenic for a number of phages including Gifsy-1 and Gifsy-2, both of which carry genes implicated in Salmonella virulence [9, 10]. Bacteriophage Gifsy-2 carries the sodCI gene encoding a periplasmic superoxide (Cu,Zn)-dismutase, with a proposed role in Salmonella defense against killing by macrophages as well as a number of genes encoding type III secreted effectors [11–13]. Gifsy-1 carries multiple virulent factors such as gipA, which is involved in bacterial colonization of small intestine [14, 15]. To coordinate expression between core and island genes, bacteria often recruit sRNAs [16, 17]. For example, InvR from the major SPI-1 island represses the production of the core-encoded major outer membrane protein OmpD [18]. IsrE, the paralogue of RyhB, represents another example of a cross talk between genes of core and islands [6]. The island-encoded sRNA IsrE is regulated by Fur, a core-encoded repressor in response to iron-deplete conditions and contributes to control of the core-encoded iron regulon [6, 19]. Here we show that IsrK of Gifsy-1 prophage controls the expression of its genetic locus leading to growth arrest of Salmonella by acting as small and messenger RNA, in an Hfq-independent manner. The growth inhibition is caused by an increase in expression of a Q-like antiterminator protein (here denoted AntQ) that is encoded on the same locus.
AntQ belongs to Q proteins’ family of lambdoid phages. Bacteriophage λ Q protein is an operon specific transcription anti-termination factor required for expression of the phage late genes. Q protein joins the elongation complex at early stages of transcription and enables RNA polymerase to read-through the terminator located upstream of the phage late genes [20–23]. To join the elongation complex, Q interacts with a specific DNA sequence element as well as with RNA polymerase that is paused during early elongation. The binding of Q alters the functional properties of the transcription elongation complex interfering with termination signals [24, 25]. We find that although Q proteins are known to bind specific sites within phages, the function of the Gifsy-1 Q-like protein AntQ is not limited to Salmonella or phage DNA. By contrast AntQ promotes transcription elongation of core genome transcripts resulting in growth arrest and ultimately cell death.
The gene encoding IsrK sRNA is located within Gifsy-1 prophage. Upstream of IsrK is SL2579 encoding a Q-like anti-terminator protein (here denoted AntQ). The transcription start-site of antQ was mapped 1,600 bases upstream of antQ [8]. Downstream of isrK we noticed a putative ORF of 45 amino acids, followed by SL2578 encoding a predicted anti-repressor-like protein (denoted AnrP) and the previously identified sRNA encoding gene, isrJ (Fig 1A) [6]. To examine whether transcription of the downstream gene anrP is linked to IsrK, we constructed isrK-orf45-anrP-lacZ transcriptional fusions with and without the isrK promoter. The assays showed that IsrK promoter directs transcription of the downstream gene anrP. Interestingly, the corresponding translation fusion demonstrated that although the operon is transcribed, there is no translation of anrP mRNA (Table 1A).
We noticed that plasmid borne constitutive expression of isrK is toxic. Salmonella cells fail to yield any colonies when transformed with plasmids expressing isrK, constitutively (Fig 1B). To further investigate the toxic phenotype, the isrK gene was cloned under the inducible PBAD promoter and we followed bacterial growth in wild type and a strain deficient of the chromosomal isrK promoter. High levels of IsrK expressed in trans result in growth arrest of wild type Salmonella (Fig 1C). The growth inhibition is not observed when the chromosomal isrK promoter is deleted, suggesting that the genetic regulation leading to toxicity requires expression of the isrK locus in both cis and trans. In addition, we examined whether the IsrK-dependent toxicity involves lysis of the host by Gifsy-1 induction. To this end, we deleted Gifsy-1 genes SL2575 and SL2576 encoding proteins of phage lysis and phage lysozyme superfamily, respectively. Both mutant strains failed to yield any colonies when transformed with plasmids expressing isrK constitutively, indicating that the growth arrest phenotype does not involve Gifsy-1 induction and lysis of the host (S1 Fig).
Deletion mapping at the isrK locus to identify the cause of toxicity demonstrated that strains deleted for sequences upstream (antQ) or downstream (anrP) of isrK showed no growth inhibition, forming normal size colonies when transformed with a plasmid expressing constitutive high levels of IsrK, indicating involvement of both genes (S2A Fig). To define whether any of the above-mentioned genetic elements is toxic when expressed alone, in the absence of IsrK, we transformed strains deleted of the locus including the lysis genes up to antQ with plasmids expressing antQ or anrP from Ptac promoter under the control of the lacI repressor. Whereas cells transformed with plasmids expressing anrP formed regular colonies (S2B Fig), cells expressing antQ fail to grow, indicating that AntQ is sufficient for toxicity.
The growth arrest data indicated that toxicity involves IsrK RNA present in cis and expressed in trans, hence, we monitored the effect of IsrK expressed in trans on transcription and translation at the isrK locus. A northern blot probed with an isrK specific primer, detected a long transcript of ~ 900 nucleotides when isrK was expressed in trans (S3A Fig). Probing the northern blot with an isrJ specific primer demonstrated that the long transcript encompasses anrP, suggesting that transcription starting at the isrK promoter reads-through the isrK Rho-independent transcription termination signal downstream into orf45 and anrP. Analysis of shorter RNA species using isrK specific primer supports expression of the plasmid encoded isrK gene (S3B Fig, lanes 4–9), as well as chromosomally encoded short isrK form, indicating that transcription starting at the isrK promoter produces the IsrK sRNA as well as isrK operon mRNA (S3B Fig, lanes 1–3). In addition, a lacZ-transcription fusion starting at the isrK promoter (PisrK-isrK-orf45-anrP'-lacZ) showed that isrK expressed in trans increased the levels of the long transcript by ~8 fold. The anrP translation fusion also showed that isrK expressed in trans activates translation of anrP by 140 fold (Table 1B). Together these data demonstrate that RNA polymerase partially reads-through the isrK Rho-independent transcription termination signal downstream into orf45-anrP and that IsrK sRNA acting in trans causes a slight increase in the levels of the downstream polycistronic mRNA and activates anrP translation.
Gifsy anti-repressor proteins bind to and inactivate the lysogenic repressor, thereby leading to transcription of phage operons [26]. To learn about the correlation between increased levels of IsrK sRNA, the anti-repressor protein AnrP and the anti-terminator AntQ, we monitored antQ mRNA levels upon expression of isrK and anrP, using quantitative Real-Time PCR. This analysis showed that in trans expression of isrK resulted in increased antQ mRNA levels. Similarly, in trans expression of anrP led to higher antQ transcript levels (Fig 2A and 2B). To learn about the activity of the anti-repressor protein AnrP, we measured RNA levels of SL2581, the second gene of antQ operon. Similarly to antQ, SL2581 levels increased upon expression of isrK or anrP, indicating that AnrP activates transcription of the antQ operon most likely through activated transcription activity (S4 Fig). Together, the results indicate that IsrK activates expression of anrP, which in turn leads to AntQ synthesis. Furthermore, we monitored Gifsy-1 prophage induction upon expression of isrK and anrP (S5 Fig). Phage plating on a susceptible strain demonstrates that Gifsy-1 phage induction by IsrK requires an intact isrK locus, whereas Gifsy-1 induction by AnrP is independent of isrK locus. These results further support the regulatory cascade we present for the isrK locus and the biological relevance of this locus to phage development. We also observed oxidative stress dependent general phage induction (Materials and Methods and S5C Fig), upon which the levels of IsrK sRNA increase during the first minutes of exposure to hydrogen peroxide while antQ and SL2581 mRNA levels increase gradually (S3D Fig and S6 Fig).
To visualize the translation pattern of the downstream operon including orf45 and anrP, we integrated the coding sequence of the sequential peptide affinity (SPA) tag [27] into orf45 and anrP to generate C-terminal fusion proteins, in two separated strains. The western blot showed that IsrK expressed in trans increases translation of both orf45 and anrP (Fig 3).
orf45 carries two in frame initiation codons and a stop codon overlapping the initiation codon of anrP (AUGA). Nucleotide and amino acid conservation analysis demonstrated that the nucleotide sequence of orf45 is conserved among the enterobacteria (S7 Fig), whereas the amino acid sequence of orf45 varies (S8 Fig). The proximity of orf45 to anrP prompted us to examine their potential translation coupling. By mutating the initiation codons we found that translation starting at the first initiation codon leads to translation of anrP. Moreover, insertion of a stop codon proximal to the translation initiation site reduced anrP translation activation by IsrK (Table 1C). Together, our data indicate that isrK expressed in trans and orf45 translation are required to stimulate anrP translation.
To investigate the mechanism of the translational regulation of isrK-orf45-anrP by IsrK, we induced random mutations at this locus using PisrK-orf45-anrP-'lacZ translation fusion plasmid and screened for high-level expression mutants in the absence of in trans isrK. Two high-level expression mutants were found to carry mutations within isrK (G28A and G31A). Structural prediction analysis using RNA fold program (http://rna.tbi.univie.ac.at/) show that the wild type transcript (1–180 nt) forms one conformation (A) having a ΔG of -85.66 kcal/mol, whereas the mutated RNA forms an alternative conformation (B) with a predicted ΔG value of -84.21 kcal/mol (Fig 4A and 4B). Functional studies of translation fusions carrying mutations G28A or G31A showed that G28A and G31A, which are predicted to form the alternative structure B, increase the basal level of AnrP translation (Table 1D), indicating that structure B is translationally active, whereas structure A is translationally silent. To visualize the two isoforms and to learn about their ratios in the wild type RNA, we examined the RNAs on nondenaturing polyacrylamide gels. The native gels demonstrate that wild type RNA is found almost exclusively in one structure, while G31A and G28A mutant RNAs display two conformers of which one resembles the wild type conformation and the other represents the alternative structure B (Fig 4C).
In the inactive structure formed by wild type RNA (A), the middle part of IsrK (in purple) base pairs with ~ 30 nt long sequence of orf45 (in blue) forming helix b-I (Fig 4A and 4B). In this structure the ribosome-binding site of orf45 forms hairpin d-I. In the alternative structure (B), the middle part of IsrK forms an alternative hairpin (b-II), whereas the RBS of orf45 forms a new helix by pairing with its 3’-end (d-II). Mutations G28A and G31A are likely to destabilize structure A by disrupting the middle helix, but are predicted to have no effect on structure B. To examine base pairing, we modified helix b opposite to G28A and G31A to carry the corresponding complementary mutations C162U and C159U, respectively and when combined, would restore formation of the helix. RNA mutants carrying G28A/C162U or G31A/C159U exhibit one conformation, the same as wild type RNA (Fig 4A, 4B and 4C), indicating that G28A basepairing with C159U and G31A/C162U basepairing form structure A.
Functional studies of translation fusions carrying C162U and C159U showed that like mutations G28A or G31A, C162U and C159U mutants exhibited a high basal level of anrP translation (Table 1D). The basal levels decrease when these mutations are combined with the corresponding complementary mutations further indicating that structure A is translationally inactive, whereas structure B is translationally active. Because mutations C162U and C159U affect the stability of structure B in addition to A, they exhibit a higher basal level of translation than that observed for the opposite mutations (see below).
To affirm the differences between the two structures, we constructed mutations G114A and the corresponding complementary mutation C175U, both predicted to destabilize structure B with no effect on structure A. Given that wild type RNA is found almost exclusively in conformation A, these mutations only a mildly affected translation of anrP (S1 Table).
Fig 4A shows that in structure A, the middle part of the cis-encoded IsrK (in purple) binds a long sequence of orf45 (in blue). Given the complementarity between cis-encoded IsrK and orf45 and the influence of in trans expression of isrK on downstream translation, we explored the functional and structural consequences of IsrK binding to structures A and B, in trans. In binding to structure A, IsrK is predicted to compete with its own sequence for the binding of the middle helix (Fig 5). Binding of structure B by IsrK is predicted to destabilize the helix d-II that sequesters the RBS of orf45 (Fig 5). Mutational analysis supported binding of in trans IsrK to the cis-encoded isrK-orf45-anrP target mRNA. Mutations C159U and C162U are predicted to affect base paring with IsrK by replacing CG pairs with UG pairs (Fig 5). Functional studies of translation fusions of C159U and C162U mutant RNAs demonstrate that wild type IsrK weakly affected translation of anrP, indicating that stable binding of IsrK in trans is important for anrP translation activation and that destabilization of the helix formed between in trans IsrK and cis-encoded orf45 abrogates anrP translation control by IsrK (Table 1D). Similarly, mutation G173A is predicted to affect base paring with wild type IsrK by replacing a GC pair with an AC pair (Fig 5). Translation fusions studies of orf45 carrying G173A mutation demonstrated that wild type IsrK RNA is less effective in activation of anrP translation than an IsrK mutant carrying the corresponding complementary mutation C18U (Table 1E). Likewise, mutation C175U replaces CG pair with UG pair and anrP translation activation by wild type IsrK is less productive. Moreover, because of imperfect basepairing, IsrK activation of G173A is lower than that of C175U mutant (S1 Table).
The effect of IsrK acting in trans is visible in native gels. Incubation of wild type RNA with IsrK at 37°C results in minimal binding of structure A to IsrK (S8 Fig). In accordance, binding of structure A of isrK-orf45G31A RNA by IsrK is indistinct as opposed to binding of the structure B of this RNA mutant (S8 Fig). Pre-incubation of the target RNAs at 70°C to denature the structures, prior to their incubation with IsrK, facilitates binding by IsrK. Under these conditions, IsrK binds structure A, characteristic of wild type RNA and both structure A and B that are characteristic of isrK-orf45G31A (Fig 6A). No binding can be detected when isrK-orf45 wild type RNA is incubated with isrKG31A mutant further confirming that this mutant is inactive as supported by our cultivation experiments (Fig 6B, S10 Fig).
Since IsrK binds orf45 adjacent to its RBS, detection of 30S binding in real time (toe printing) is inconceivable. However, 30S binding at the RBS would protect the neighboring upstream and downstream sequences. To probe the accessibility of the sequence surrounding the RBS, we used dimethyl sulfate (DMS), which methylates unpaired adenosine and cytidine residues at N1 and N3 positions, respectively. Samples were incubated with and without 30S and/or IsrK prior to the addition of DMS. The modified sites were detected by primer extension after phenol extraction. A few nucleotides that surround the RBS of orf45 are susceptible to DMS modification in the presence of 30S ribosomes or IsrK (Fig 7 lanes 2,4). The same nucleotides are protected from DMS in presence of both IsrK and 30S (Fig 7 lane 6), indicating that wild type IsrK facilitates 30S binding to the RBS of orf45. 30S protection from DMS decreases much in the presence of isrKG31A mutant that is unable to bind orf45 (S11 Fig).
On the one hand, IsrK is part of the target isrK-orf45-anrP mRNA, and as such mutations in IsrK affect the structure this mRNA forms. For instance, G28A disrupts helix b in structure A, shifting the equilibrium towards the translationally active structure B. Therefore, mRNA carrying isrKG28A-orf45-anrP exhibits a high basal level of anrP translation. On the other hand, the short IsrK acts in trans to destabilize the cis-encoded translationally inactive target mRNA leading to anrP translation. Therefore, one mutation in isrK gene is predicted to yield two different phenotypes. We examined the effect of isrKG28A in cis and in trans and found that whereas, cis-encoded isrKG28A-orf45-anrP exhibits a high basal level of anrP translation; isrKG28A acting in trans is unable to activate anrP translation (Table 1F). In accordance, high levels of isrKG28A or isrKG31A expressed from the PBAD promoter have no effect on growth of wild type Salmonella (S10 Fig).
We have shown that toxicity involves expression control of anrP by IsrK that in turn induces transcription of the anti-terminator protein AntQ (Fig 2A and 2B). Considering the origin of antQ, i.e., Gifsy-1 prophage, we examined whether its toxicity is specific to Salmonella and/or phage genes. Accordingly, the influence of high levels of AntQ was investigated in two E. coli K-12 strains; wild type (MG1655) and MDS42 that is deleted of all genetic islands including prophages and insertion elements [28]. antQ expression repressed growth and decreased survival of both strains. At 40 minutes of induction, survival of wild type and MDS42 decreased by ~15 and ~30 fold, respectively, indicating that toxicity of the Q-like anti-terminator protein is not specific to Salmonella or phage DNA (S12 Fig). Moreover, the results suggest that AntQ protein has natural recognition sites within the core genome of these strains.
Bacteriophages Q antiterminator proteins interfere with transcription termination by binding specified sites at promoter regions and forming a persistent complex with RNA polymerase. This complex of RNA polymerase and Q protein can bypass terminators [24]. We examined changes in protein expression pattern of Salmonella upon exposure to AntQ using one-dimensional SDS-PAGE. Gel areas showing differences in the pattern of proteins because of AntQ were isolated and subjected to mass spectrometry (S13 Fig). Two proteins, whose expression increased, were selected for further analysis because of their score and annotation; the transcription termination factor Rho and the DEAD-box-containing ATP-dependent RNA helicase SrmB [29–32].
We suspected that expression of rho and srmB increased in response to transcription elongation related stress. Thus, we examined whether co-expression of antQ with these genes would abolish the toxic effect of AntQ. Survival assays show that co-expressing rho with antQ help to rescue cells from AntQ-mediated toxicity. After 40 minutes of induction, survival of cells expressing antQ alone dropped to ~8% of their original amount, whereas cells expressing both antQ and rho managed to maintain a high CFU count, indicating that Rho can halt the toxicity inflicted by AntQ. Likewise, survival assays in which srmB and antQ were co-expressed demonstrated that SrmB prevented AntQ toxicity (Fig 8A and 8B). Together the results show that proteins that harbor RNA helicase activity impede the toxic effects of transcription anti-termination.
Unregulated transcription elongation increases formation of DNA-RNA hybrids upstream of RNA polymerase (R-loops). The resulting R-loops may initiate DNA replication independently of oriC, leading to DNA damage [33, 34]. RNase H is an evolutionary conserved helicase that resolves R loops, thus protecting genomic DNA from breaks [30, 35]. Survival rates of cells co-expressing antQ and rnhA encoding RNase H were 10 fold higher than those expressing antQ alone, suggesting that the toxic effects of AntQ result from DNA damage due to the creation of R-loops (Fig 8C).
It is well documented that exposure of bacteria to detrimental stressful conditions impairing protein synthesis or causing DNA damage, triggers genome condensation [36]. We visualized the effect of antQ expression on chromatin morphology by florescence microscopy. Images of Salmonella expressing a control plasmid display, as excepted, chromatin spread over the entire cytoplasm. In contrast, images of Salmonella expressing antQ reveal condensed chromatin morphology. Likewise, exposure of Salmonella cells to nalidixic acid (NA), a pleiotropic drug that inflicts diverse DNA lesions (nicks, gaps, and DSBs) [36] resulted in genome condensation (Fig 9). Furthermore, E. coli cells expressing antQ or exposed to NA exhibit genome condensation, similarly to Salmonella, indicating that antQ toxicity is mechanistically conserved. The E. coli UvrD protein is a DNA helicase/translocase that functions in methyl-directed mismatch repair (MMR) nucleotide excision repair (NER) and more broadly in genome integrity maintenance [37]. Recent studies in E. coli have shown that UvrD can act as an accessory replicative helicase that resolves conflicts between the replisome and transcription complexes [37–39]. Using uvrD-yfp [40], we demonstrate in vivo localization of the fluorescently tagged uvrD to the nucleoid upon expression of antQ and upon exposure to DNA damaging agents (Fig 10). Together our data show that the function of the phage antiterminator protein is wide-ranging causing changes in bacterial chromatin morphology.
In this study we show that a subset of the IsrK sRNA transcripts reads through its transcription terminator to form a translationally inactive bi-cistronic mRNA. Concomitantly, short IsrK RNA acts in trans, interacting with the inactive transcript to promote formation of a translationally active structure, in which orf45 translation leads to anrP translation by translational coupling (model Fig 11). In bacteria, translational coupling provides a mechanism to coordinate expression of multiple proteins with adjacent or overlapping coding sequences. Ribosomes terminating translation of upstream ORF dissociate and re-initiate translation at the downstream RBS [41, 42]. Re-initiation is enabled due to ribosomes elongating along the upstream ORF to unfold mRNA structures that sequester the downstream ribosome-binding site. Such an example is PhrS sRNA that activates translation of pqsR mRNA by interaction with a sequence sequestering the RBS of an ORF upstream of pqsR [43]. We find that inserting a stop codon proximal to the translation initiation site of orf45 reduces IsrK-controlled anrP translation activation, indicating that translational coupling is necessary for AnrP synthesis. Given that the structural changes caused by IsrK and/or ribosome binding at the orf45 RBS do not seem to involve structural changes in the RBS of anrP, we suggest that the translational coupling between orf45 and anrP requires ribosome elongation from the RBS of the orf45 downstream to anrP.
A structural homolog of IsrK is SeqA RNA of P4-like phages [44]. In the lysogenic state P4 prevents expression of its own replication genes by premature transcription termination. The factor responsible for efficient termination is CI RNA that is generated by processing of a primary untranslated transcript. CI RNA acting as an antisense RNA leads to transcription termination by pairing with two complementary sequences, seqA and seqC located upstream and downstream of CI, respectively [45–47]. In Salmonella, transcriptome analysis revealed the existence of a stable non-coding RNA species downstream of IsrK (STnc1160) [8]. Our RNA analysis detected STnc1160 in wild type cells but not in a strain deleted of the isrK promoter, suggesting that STnc1160 is generated by processing of the readthrough transcript initiating from the isrK promoter (S3 Fig). It is possible that STnc1160 similarly to CI modulates transcription termination at IsrK Rho-independent terminator. Since IsrK and STnc1160 share complementary sequences, IsrK binding of STnc1160 renders it inactive as a termination factor leading to transcription readthrough and thus to grow arrest. However, in experiments of co-expression of isrK and orf45 (STnc1160) in which STnc1160 was constitutively expressed, IsrK mediated growth arrest was even more pronounced (S14 Fig).
In S3B Fig, we present a northern blot showing the levels of chromosomally and plasmid encoded isrK. It is interesting to note that in addition to short IsrK, our analysis revealed a stable transcript (isrK-orf45’) that is generated by processing of the long polycistronic transcript. isrK-orf45’ species is observed upon expression of plasmid-encoded isrK in wild type cells (lane 6), as well as upon expression of chromosomally-encoded isrK (see lane 1–3), indicating that the pattern detected with high level expression is valid with chromosomally-encoded isrK. In addition, the results demonstrating that Gifsy-1 phage induction by IsrK requires an intact isrK locus, whereas Gifsy-1 induction by AnrP is independent of isrK locus, further substantiate the regulatory cascade we present for the isrK locus and signify the biological relevance of this locus to phage activation. Moreover, we show that wild type cells grown in minimal medium to stationary phase exhibit prophage induction, whereas isrK promoter deletion mutant (ΔPisrK::frt) fails to produce phages (S15 Fig). These findings indicate that IsrK is an important player in initiating prophage induction. Concerning the conditions inducing isrK expression, we find that IsrK levels increase at stationary phase and under low Mg2+ conditions (S15 Fig). Salmonella global transcriptome analysis carried out by Kröger et al [8] shows that IsrK levels increase during conditions such as low Fe2+ shock, oxygen shock and growth in InSPI2 medium. In addition they find that the levels of the transcript encoding orf45 resulting by transcription elongation through the isrK transcription terminator (STnc1160) increase during low Fe2+ shock, InSPI2 and late stationary phase. Together, the data indicate that isrK short and long forms are produced under a variety of environmental conditions.
The majority of the sRNA genes are encoded within intergenic regions acting in trans to control expression of physically unlinked target genes. However, it is now increasingly appreciated that in addition to intergenic regions, many sRNAs originate from the 5’ or 3’ regions of coding mRNAs. Such examples are 3’ UTR derived sRNAs that are generated either by internal processing of the related mRNA, as in the case of RybD or produced as a primary transcript like MicL and DapZ [7, 48]. Generated from within protein coding loci, these sRNAs act in trans controlling expression of unlinked target mRNAs. Likewise, SreA and SreB originate from 5’ UTRs of two S-adenosylmethionine (SAM) riboswitches, and base pair with the unlinked prfA mRNA to repress translation [49]. In Staphylococcus aureus, SprA1AS is transcribed from the strand opposite to SprA1 target mRNA encoding pepA1 ORF. The antisense RNA SprA1AS acts in trans by base pairing with the 5’ domain of SprA1 to repress pepA1 translation by occluding its RBS [50]. Somewhat different is the archaeal RNA regulator, sRNA162. sRNA162 masks the RBS of MM2441 by binding MM2440-MM2441 mRNA internally [51]. Biochemical studies demonstrated that in addition to in trans binding of MM2441 RBS, encoded opposite of MM2442, the 5’-end of sRNA162 targets the 5’-untranslated region of the cis-encoded MM2442 mRNA. However, the regulatory outcome of this interaction is as yet unknown.
The mechanism of expression regulation of the isrK locus is unique, representing the first example of an RNA that acts as a small RNA on its own mRNA. On the one hand, IsrK is part of a translationally inactive target mRNA, whereas on the other hand the short RNA species acts in trans to enable translation of the target mRNA. Therefore one mutation within IsrK RNA yields two different phenotypes; when located in the long target mRNA it increases translation whereas the short mutant IsrK RNA can no longer activate translation.
Increasing evidences indicate that in prokaryotes and eukaryotes, common transcription-replication encounters lead to blockage of replication that is often accompanied by DNA damage and genome instability. In bacteria, because replication and transcription proceed simultaneously on the same template DNA, yet DNA replication forks move 10 to 30 times faster than do RNA polymerases, both co-directional, and head-on collisions appear to be unavoidable [34]. Transcription-replication conflicts may also result from stalled transcription elongation complexes, as they form stable barriers to the replication machinery. These complexes increase the production and/or the length of DNA-RNA hybrid structures within the transcription bubble, causing the region of complementary single-stranded DNA to loop out. The resulting R-loops may initiate DNA replication independently of oriC, leading to DNA damage [34]. We find that expression of the antiterminator protein results in bacterial growth arrest and ultimately cell death. Co-expression of antQ with transcription termination factor Rho rescues cells from the toxic effects of the antiterminator protein. Likewise, survival rates of cells co-expressing antQ and rnhA encoding RNase H were 10 fold higher than those expressing antQ alone. Given that RNase H protects genomic DNA from breaks by resolving R-loops, suggests that the toxic effects of AntQ result in part from DNA damage due to the creation of R-loops. In accordance, we find that expression of antQ affects bacterial chromatin morphology. Fluorescence microscopy images of E. coli and Salmonella expressing the antiterminator protein reveal condense chromatin morphology. Fluorescently tagged UvrD localizes to the nucleoid upon expression of the antiterminator protein as well as upon exposure to NA, a DNA-damaging agent. uvrD is a member of DNA helicase superfamily 1 and part of the SOS regulon. During SOS response the intracellular level of UvrD increases approximately three fold. UvrD functions in methyl-directed mismatch repair (MMR) nucleotide excision repair (NER) and genome integrity maintenance. Recent studies have demonstrated that UvrD contributes to genomic integrity by resolving conflicts between transcription and DNA repair complexes [37–39]. UvrD binds RNA polymerase in blocked transcription elongation complexes, forcing it to slide backwards along the DNA. This backwards sliding exposes DNA lesions that are out of reach allowing the nucleotides excision repair enzyme to access the site of damage.
A question remains, are the massive effects on chromatin structure detected upon IsrK/AntQ overproduction physiologically relevant? Quantitation analysis of antQ mRNA levels indicates that at around two hours upon exposure to in trans IsrK, during the time that the long transcript isrK-orf45-anrP is detected, the copy number of antQ is increased by 6 fold (2.5x10-2 per 16S rRNA). During the first nine minutes of exposure to hydrogen peroxide the copy number of antQ increases by 4 fold (1.2x10-2 per 16S rRNA). Thus, it is conceivable that the phenotype observed with overproduction of antQ is biologically relevant. Regardless, the toxic phenotype of antQ is intriguing, a phage encoded antiterminator protein known to facilitate transcription antitermination at a specific phage terminator, is in fact, wide-ranging capable of affecting bacterial core genome sites. Given the morphological appearance of cells expressing the antiterminator protein and that the toxic phenotype is reversed by opposing functions, including transcription termination and elimination of R loop, AntQ, by affecting transcription elongation causes DNA damage. Whether the phage exploits antQ to modulate vital bacterial machineries or bacterial cells use the Q-like antiterminator core genome native sites for self-inhibition upon phage infection remains to be addressed in further studies.
Salmonella Typhimurium SL1344 cells were grown at 37°C (200 rpm) in LB medium (pH 6.8). Ampicillin (100 μg/ml), Chloramphenicol (20 μg/ml) and kanamycin (40 μg/ml) were added where appropriate. Induction of PBAD promoter was obtained with arabinose (0.2%), whereas Ptac promoter was induced with IPTG as indicated. (List of strains, plasmids and DNA primers used in this study appear in S2–S4 Tables)
Gene deletion mutants were generated using the gene disruption method as described [52]. For construction of deletion mutants, chloramphenicol or kanamycin cassettes were amplified from plasmids pKD3 and pKD4, respectively [53]. The PCR product (5–10 μg) purified using the Wizard SV PCR clean-up system (Promega, Madison, WI) was introduced into arabinose treated LB5010 cells [54] carrying pKD46 cells [52] and chloramphenicol or kanamycin-resistant colonies were selected. The deletion mutation was transferred into a wild type SL1344 genetic background by transduction using the P22 bacteriophage. The resistance gene was eliminated using pCP20 [53]. In ΔanrP::kan, the chromosomal region flanked by genome coordinates 2759753 and 2760316 (GenBank entry CBW18679.1) was replaced by the kan gene using primers 1571 and 1572. anrP gene disruption was examined by PCR using flanking primers, 1528 and 1529. In ΔantQ::cat, the chromosomal region flanked by genome coordinates 2760589 and 2761266 (GenBank entry CBW18680.1) was replaced by the cat gene using primers 1573 and 1458. antQ gene disruption was examined by PCR using flanking primers, 1542 and 1486. In Δ(SL2575-SL2576)::cat and Δ(SL2575-SL2576)::kan the chromosomal region flanked by genome coordinates 2756646 and 2757568 (GenBank entry CBW18677.1 and CBW18676.1) was replaced by the cat gene using primers 1615 and 1616, or kan cassette using primers 2149 and 2150. (SL2575-SL2576) gene disruption was examined by PCR using flanking primers, 1617 and 1618. In SL1344 antQ::cat Δ(SL2575-SL2576)::kan double deficient mutant, a P22 lysate generated from ΔantQ::cat was transferred into Δ(SL2575-SL2576)::kan by transduction and the resistant genes were later eliminated using pCP20 to generate a mutant deficient of the entire region SL1344 antQ to (SL2575-SL2576)::frt. The disruption was examined by PCR using flanking primers, 1761 and 1618. To construct strains carrying SPA tags in the chromosome; SL1344 orf45-SPA-kan and SL1344 anrP-SPA-kan, primers were designed to amplify the sequential peptide affinity (SPA) tag together with the kanamycin resistance cassette from the plasmid pJL148, and flanked by 45 nt of sequence homologous to the insertion region, as described before. SL1344 orf45-SPA-kan and SL1344 anrP-SPA-kan were constructed using primers 2122, 2123 and 2209, 2210 respectively. The PCR products were purified from gels and used to transform SL1344 cells carrying pKD46 plasmid [52]. Insertions were confirmed by sequencing of PCR products generated using primers 1987, 2227 (198 nt of orf45-SPA-kan) and 839, 2227 (196 nt of anrP-SPA-kan). The products were sequenced using primer 2227. uvrD-yfp fusion [40] was transferred into strain RW118 [55] by P1 transduction.
To construct Ptac-isrK and PBAD-isrK, isrK sequence from its transcription start site plus 37 nt downstream of its transcription terminator was PCR amplified from SL1344 chromosomal DNA using primers 1364 and 1365 and cloned into the EcoRI and HindIII restriction sites of pRI and pJO244 respectively. To construct Ptac-antQ-lacI, antQ sequence from its ATG plus 22 nt downstream of its stop codon was PCR amplified from SL1344 chromosomal DNA using primers 1544 and 1510 and cloned into the EcoRI and SalI sites of pKK177-3-lacI. In this plasmid, antQ translation is directed by an artificial translation initiation signal found in the right position in pKK177-3-lacI. To construct Ptac-anrP-lacI, anrP sequence from its ATG plus 40 nt downstream of the stop codon was PCR amplified from SL1344 chromosomal DNA using primers 1893 and 1897 and cloned into the EcoRI and PstI sites of pKK177-3-lacI. In this plasmid, anrP translation is directed by an artificial translation initiation signal found in the right position in pKK177-3-lacI. To construct Prho-rho (p15A origin), a DNA fragment including both the promoter of Rho and the rho gene (-200 to 112 nt downstream of the stop codon) was PCR amplified from SL1344 chromosomal DNA using primers 1872 and 1862 and cloned into the BglII and HindIII sites of pACYC184. To construct PBAD-srmB (p15A origin), a DNA fragment containing srmB from nucleotide146 upstream of the ATG to nucleotide 2 downstream of the stop codon was PCR amplified from SL1344 chromosomal DNA using the primers 1884 and 1885 and cloned into the PstI and HindIII sites of pEF21. In this plasmid, srmB translation is directed by its own translation initiation signal. To construct PrnhA-rnhA (p15A origin), a DNA fragment including both the promoter of rnhA and the rnhA gene (-112 to 89 nt downstream of the stop codon) was PCR amplified from SL1344 chromosomal DNA using primers 1907 and 1908 and cloned into the XbaI and BamHI sites of pACYC184. To construct lacZ fusions in single copy plasmids (pBOG551 and pBOG552), the origin of replication of pRS551 and pRS552 [56] was replaced by the origin of replication of pZS*24 (pSC101*) [57]. The origin of replication was amplified using primers 2032 and 2042 and cloned into the PstI and SalI sites of pRS551 and pRS552 plasmids. To construct wild type fusions PCR fragments carrying PisrK-orf45-anrP were amplified from genomic DNA using primers 1512 and 1703, digested and cloned into the EcoRI and BamHI sites of pGEM3. The PisrK-orf45-anrP fragment was sub-cloned into pBOG551 and pBOG552 using the EcoRI and BamHI sites. All fusion mutants were constructed by transferring mutated PisrK-orf45-anrP fragments from pGEM3 into pBOG plasmids, as described above. To construct PLtetO-1-orf45 (p15A origin), we first deleted the luc gene of pZA31-luc by PCR using primers 1989 (KpnI) and 1990 (phosphorylated). Thereafter, orf45 sequence from its second ATG to its stop codon was PCR amplified from SL1344 chromosomal DNA using primers 1987 (KpnI) and 1988 (phosphorylated). The two PCR fragments (orf45 and pZA31) where then ligated.
To carry out random mutagenesis, 1 volume (1.5 μg) of plasmid DNA carrying PisrK-orf45-anrP-'lacZ (pSA81) was mixed with 5 volumes of phosphate solution (0.5 M NaH2PO4, 1 mM EDTA, adjusted to pH 6 with NaOH), and 4 volumes of hydroxylamine solution (1 M hydroxylamine hydrochloride (Fluka) in phosphate solution, adjusted to pH 6 with NaOH). The mixture was incubated at 65°C for 2 hours, then dialyzed overnight against TE buffer (10 mM Tris-HCl at pH 8, 1 mM EDTA) at 4°C and again for 2.5 hours. The mutagenized plasmid was used to transform MC4100 cells. Dark blue colonies were picked from LB plates containing 40 μg/ml 5-bromo-4- chloro-3-indolyl-β-D-galactopyranoside (x-gal) (Inalco). Plasmid inserts from selected colonies were sequenced [56].
Mutations A107C, AU119-120UA, G28A, C162U, G31A, C159U, G114A, G173A and C175U were generated by PCR using plasmid carrying PisrK-orf45-anrP (pSA77) and two tail-to-tail divergent primers of which one carried the desired mutation. The PCR product was gel purified, subjected to blunt end ligation and the mutated plasmid was digested with EcoRI and BamHI for sub-cloning to pBOG551 and pBOG552. The double mutants A107C/G121A, G28A/C162U, G31A/C159U and G114A/C175U were constructed using PisrK-orf45A107C-anrP (pSA77A107C), PisrKG28A-orf45-anrP (pSA77G28A), PisrKG31A-orf45-anrP (pSA77G31A) and PisrK-orf45G114A-anrP (pSA77G114A), respectively as template and two tail-to-tail divergent primers of which one carried the desired second mutation. The PCR product was gel purified, subjected to blunt end ligation and the mutated plasmid was digested with EcoRI and BamHI for sub-cloning to pBOG551 and pBOG552 [56].
Overnight cultures of S. typhimurium SL1344 or SL1344 carrying plasmids were grown from fresh transformation plates. Each strain was grown in duplicates. Starters were diluted 1/100 in 15 ml LB (125 ml Erlenmeyer flasks) and grown at 37°C (200 rpm). Arabinose (0.2%) was added at the time of dilution where indicated. One hour after dilution, IPTG was added to a final concentration of 0.2 mM to induce expression of antQ. Samples were taken prior to, 20 and 40 minutes after the addition of IPTG, diluted in 1X PBS and plated. Each sample was plated twice. Colonies were counted and percentage of survival rate was calculated.
Overnight cultures were diluted 1/100 in 20 ml LB medium supplemented with ampicillin and kanamycin, and grown to OD600 ~ 1.0. To induce IsrK, arabinose (0.2%) was added at the time of dilution. β-galactosidase activity was assayed as described [58].
To detect Gifsy-1 phage induction by AnrP, overnight cultures of S. typhimurium wild type, isrK promoter deletion mutant (ΔPisrK::frt), and Gifsy-1 lysis proteins deletion mutant ΔSL2575-SL2576::frt carrying Ptac-lacI or Ptac-anrP-lacI were diluted (1/100) in LB medium supplemented with 10 mM MgSO4 and ampicillin and grown at 37°C to OD600 ~ 0.3. Thereafter, IPTG (0.2 mM) was added to induce expression of anrP. At 2 hr after induction the cultures were treated with chloroform to release phage particles. 5μ of the supernatant were plated on LT2 (lambda sensitive) bacterial lawn made with soft agar. To detect Gifsy-1 phage induction by IsrK, wild type and ΔPisrK::frt mutant carrying PBAD and PBAD-isrK were diluted, grown and their phages plated as above. Arabinose (0.2%) was added at the time of dilution for induction of IsrK. To detect oxidative stress dependent phage induction [15, 59] H2O2 (0.1 and 0.5 mM) was added at OD600 ~ 0.3 and phages were plated as above.
To measure the effect of AnrP on expression at antQ locus, ΔSL2575-SL2576::frt cells carrying Ptac-lacI or Ptac-anrP-lacI were grown to OD600 ~ 0.5. Total RNA was extracted prior to and at 25 min of induction with IPTG (1 mM). To measure the effect of IsrK on expression at antQ locus, wild type cultures carrying PBAD and PBAD-isrK (treated with arabinose (0.2%) at the time of dilution) were grown and total RNA was extracted at time points as indicated. To measure expression at antQ locus upon phage induction, H2O2 (0.1 mM) was added at OD600 ~ 0.3. Total RNA was extracted prior to and upon exposure to H2O2 (as indicated) and cDNA was prepared for real time PCR. To monitor expression of chromosomally encoded-isrK, wild type and (ΔPisrK::frt) mutant strains were grown in LB medium to OD600 ~ 0.3, 0.6, 1.0 and for 8 hours or in low MgCl2 N-minimal medium to OD600 ~ 0.3 [6].
Overnight cultures of S. typhimurium SL1344 or SL1344 carrying plasmids were diluted and grown as described before. To isolate total RNA, the cultures were pelleted and re-suspended in 50 μl 10 mM Tris–HCl (pH 8) containing 1 mM EDTA. Lysozyme was added to 0.9 mg/ml and the samples were subjected to three freeze-thaw cycles. Total RNA was purified using TRI reagent (Sigma) according to the manufacturer’s protocol.
RNA concentrations were determined using a NanoDrop machine (NanoDrop Technologies). DNA was removed by DNase treatment according to the manufacturer’s instructions (RQ1 RNase free DNase, Promega). About 1 μg DNA-free total RNA was used for cDNA synthesis using MMLV reverse transcriptase and random primers (Promega). Quantification of cDNA was performed by real-time PCR using SYBR-green mix (Absolute SYBR GREEN ROX MIX, ABgene) with Rotor gene 3000A (Corbett) according to manufacturer’s instructions. Specific primer pairs were designed according to the Guidelines for Amplicon and Primer Design (http://www.tamar.co.il/tamar-laboratory-supllies/guidelines-amplicon-primer-design/). The level of 16S rRNA (rrsA) was used to normalize the expression data for each target gene. The relative amount of cDNA was calculated using the standard curve method. A standard curve was obtained from PCR on serially diluted genomic DNA as templates and was analyzed using Rotor-gene analysis software 6.0.
Overnight cultures of wild type cells carrying a control plasmid (Ptac) or an AntQ expressing plasmid were diluted 1/100 and grown at 37°C. IPTG (1 mM) was added at OD600 ~ 0.2 for AntQ induction. Samples were taken 30 and 60 minutes after exposure to IPTG, pelleted and then fluidized in 1X Laemmli sample buffer, heated at 95°C for 5 min and centrifuged for 5 min. 20 μl of each sample were analyzed by 12% SDS-PAGE (9 mA, 1X running buffer for 24 hours at 4°C). To visualize the proteins, the gel was stained for 30 minutes at 37°C (coomassie blue staining). The mass spec data of the band (see S13 Fig) were analyzed based on coverage, which represents the percentage of the protein that was sequenced; area, which describes the fraction of the specific protein out of the sample, and the number of unique peptides, found to only match this specific protein. The high scored proteins were considered for further analysis based on their relevant function in transcription elongation or termination.
Overnight cultures of SPA-tagged strains carrying control plasmid (PBAD) or IsrK expressing plasmid (PBAD–IsrK) were diluted 1/100 and grown shaking at 37°C. Arabinose (0.2%) was added at the time of dilution. Samples were taken at indicated OD600, pelleted and then fluidized in 1X Laemmli sample buffer, heated at 95°c for 5 min and centrifuged for 5 min. Samples of 3x107 cells were analyzed on SDS-PAGE (12% and 15% for ORF45-SPA and AnrP-SPA, respectively) [60]. The proteins were transferred to a nitrocellulose membrane (Invitrogen), the blots were blocked with skim milk (2.5% for 1 hour) and probed with FLAG M2-AP monoclonal antibody (Sigma-Aldrich) according to the manufacturer’s protocol. The tagged proteins were visualized using secondary antibody Anti-Mouse IgG-Alkaline Phosphatase (Sigma-Aldrich) based on Alkaline Phosphatase development protocol.
The RNAs; isrK-orf45 wild type (from transcription start site to nucleotide 217 within orf45 or to nucleotide 785 at the end of anrP) and mutants: isrKG31A-orf45 (217 nt), isrKG31A-orf45C159U (217nt), isrKG28A-orf45 (217nt), isrKG28A-orf45C162U (217nt) as well as IsrK sRNA (90 nt) wild type and G31A mutant were synthesized with phage T7 RNA polymerase (25 units; New England Biolabs) in 50 μl reactions containing 40 mM Tris-HCl (pH 7.9), 6 mM MgCl2, 10 mM diothiothreitol (DTT), 20 units RNase inhibitor (CHIMERx), 500 μM of each NTP and 200 ng of purified PCR templates carrying the sequence of the T7 RNA polymerase promoter. Synthesis was allowed to proceed for 2 hours at 37°C, and was terminated by phenol/chloroform extraction and ethanol precipitation in the presence of 0.3M ammonium acetate.
RNA samples (20 μg for detection of isrK and 30 μg for detection of STnc1160 and orf45) were denatured for 5 min at 65°C in 98% formamide loading buffer, separated on 8 M urea-6% polyacrylamide gels and transferred to Zeta Probe GT membranes (Bio-Rad Laboratories) by electroblotting. To detect IsrK RNA, the membranes were hybridized with [32P]-end-labeled isrK primer (1197) in modified CHURCH buffer [6]. STnc1160 and orf45 were detected using anti-STnc4100 labeled riboprobe synthesized using PCR template (2316 and 2317) as previously described [6]. Riboprobe hybridization buffer contained 50% formamide, 3.5% SDS, 250 mM NaCl, 82 mM Na2HPO4, 40 mM NaH2PO4 at pH 7.2. After 2 hours at 50°C, the membranes were treated for 20 min at 50°C in 2X SSC, 1% SDS, 20 min at 55°C in 1X SSC, 0.5% SDS and 20 min at 60°C in 0.5X SSC, 0.1% SDS. To detect isrK-orf45-anrP full length RNA, samples (20 μg) were denatured for 10 min at 65°C in MOPS loading buffer, separated on 1.2% agarose gels and transferred to Zeta Probe GT membranes by capillary transfer [6]. The membranes were hybridized in modified CHURCH buffer using end-labeled isrK (1197) or isrJ (1471) specific primers.
To detect binding of IsrK to its templates in vitro synthesized RNAs wild type isrK-orf45 and mutants (217 nt, 0.2 pmol) were incubated in 10 μl of Native Buffer (6.7 mM Tris-acetate (pH 7.4), 3.3 mM Na-acetate, 1 mM DTT and 10 mM MgCl2) for 3 minutes at 70°C and chilled on ice. Thereafter, in vitro synthesized IsrK or IsrKG31A RNAs were added as indicated in the Fig and incubated for 15 minutes at 37°C. The RNA samples were analyzed on 5% non-denaturing polyacrylamide gels (19:1) run at 50 volts in 20 mM Tris-HCl (pH 7.5), 60 mM KCl and 10 mM MgCl2 for 5–6 hours at 4°C as described before [56]. After its transfer to nylon membrane by electroblotting, the RNA was detected by probing with end-labeled orf45 specific primer (1948). To detect RNA conformations the template RNAs wild type and mutants (217 nt, 0.2 pmol) were incubated in Native Buffer (as above) for 15 minutes at 37°C and then analyzed as describe above. The RNAs were also analyzed on denatured gels (see Northern analysis) using end-labeled orf45 specific primer that detects isrK-orf45 templates (1948).
Template RNA synthesized in vitro (0.7–1 pmol) was incubated in 50 mM Na-cacodylate (pH 7.4), 10 mM magnesium acetate, 100 mM NH4Cl and 2.5 mM β-mercaptoethanol for 3 minutes at 70°C and chilled on ice for 10 min. Thereafter, in vitro synthesized wild type IsrK or IsrKG31A (7–10 pmol) was added and the mixture was incubated for 15 minutes at 37°C. Pre-activated (30 minutes at 37°C) 30S ribosomal subunits (1.2–2.4 pmol) were added for 5 minutes prior to the addition of uncharged fMet-tRNA (12 pmol). The binding reactions were incubated for 15 min before DMS (0.5 μl, diluted 1:10 in ethanol) was added. The modification reaction was allowed to proceed for 5 minutes. Reactions were stopped with phenol/chloroform and precipitated with ethanol in the presence of 0.3 M sodium acetate, 1 μl of Quick-Precip (Edge BioSystems) and 20 μg yeast RNA. The modification sites were detected by primer extension using MMLV reverse transcriptase (Promega) and end-labeled primer (1948).
Fluorescence microscopy was carried out as described previously [61]. In brief, 1–2 ml cells were centrifuged, washed with 1X phosphate buffered saline (PBS) and finally re-suspended in 10–100 μl of PBS. The membrane was stained with FM4-64 (Molecular Probes, Invitrogen) at a final concentration of 10 μM or 1 μg/ml, respectively. DNA was stained with DAPI (Sigma-Aldrich) at a final concentration of 2 μg/ml. Cells were washed twice before microscopic examination. Cells were visualized and photographed using Nikon Eclipse Ti-E inverted microscope equipped with Perfect Focus System (PFS) and ORCA Flash 4 camera (Hamamatsu photonics). Images were processed using NIS Elements-AR software.
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10.1371/journal.ppat.1006337 | Mapping of the Lassa virus LAMP1 binding site reveals unique determinants not shared by other old world arenaviruses | Cell entry of many enveloped viruses occurs by engagement with cellular receptors, followed by internalization into endocytic compartments and pH-induced membrane fusion. A previously unnoticed step of receptor switching was found to be critical during cell entry of two devastating human pathogens: Ebola and Lassa viruses. Our recent studies revealed the functional role of receptor switching to LAMP1 for triggering membrane fusion by Lassa virus and showed the involvement of conserved histidines in this switching, suggesting that other viruses from this family may also switch to LAMP1. However, when we investigated viruses that are genetically close to Lassa virus, we discovered that they cannot bind LAMP1. A crystal structure of the receptor-binding module from Morogoro virus revealed structural differences that allowed mapping of the LAMP1 binding site to a unique set of Lassa residues not shared by other viruses in its family, illustrating a key difference in the cell-entry mechanism of Lassa virus that may contribute to its pathogenicity.
| To infect, enveloped viruses need to fuse their membrane with the host membrane. Fusion is mediated by special glycoprotein machineries that must be triggered only at the right time and at the right place. A major cue that viruses utilize for triggering is acidic pH. Until recently, such pH-induced triggering was assumed to be the only mechanism used by the Arenaviridae family. However, Lassa virus, a notorious pathogenic member of this family, was shown to use the binding to an intracellular receptor named LAMP1 to potentiate its pH-induced triggering. This two-step mechanism was a surprising finding that raised critical questions regarding the cell-entry mechanisms of other viruses from this family. Here we used a structure-guided approach to investigate whether other Arenaviridae utilize LAMP1 for cell entry. We mapped the LAMP1 binding site on the Lassa-derived glycoprotein and confirmed its identity using grafting experiments. This mapping revealed the unique sequence signature needed for LAMP1 binding. Sequence analysis suggests that no other members of the Arenaviridae bind LAMP1.
| Receptor switching is a newly discovered event in the cell entry process of Lassa virus (LASV) [1], a zoonotic, enveloped, negative-strand RNA virus that belongs to the Arenaviridae family [2]. LASV is a devastating pathogen that causes severe hemorrhagic fevers with significant mortality in West Africa [3]. LASV locates its host cells by binding to its primary cellular receptor, α-dystroglycan [4, 5]. Then, through a process of macropinocytosis [6, 7], LASV is internalized and reaches a late endosomal compartment. In this acidifying environment, LASV changes its binding specificity and engages LAMP1, a ubiquitous protein of lysosomes and late endosomes [1]. A requirement for receptor switching has also been identified for Ebola virus, which binds to the Neimann-Pick C1 protein in the lysosome to infect cells [8, 9]. Receptor switching is thus an emerging theme for viral entry that may be relevant for other viruses as well.
LASV has a surface-displayed class-I trimeric glycoprotein spike complex that mediates receptor recognition and fusion of the viral and host-cell membranes at acidic pH [10, 11]. The spike complex consists of three copies of a single polypeptide chain that is cleaved twice to give a structured signal peptide, a receptor-binding module (GP1), and a trans-membrane module (GP2) [12]. We previously showed that a triad of histidines on GP1 is important for binding LAMP1 [13], and we further demonstrated that the binding of LAMP1 triggers the spike of LASV to catalyze membrane fusion by potentiating its response to pH [14]. Upon protonation in a weak acidic environment, the positively charged histidine triad functions to inhibit pre-mature triggering of the spike, an inhibition that LAMP1 overrides [14].
LASV is classified as an ‘Old World’ (OW) mammarenavirus [15]. The histidine triad is fully conserved among OW mammarenaviruses and thus may have a similar function in these viruses as well. A critical question is whether other OW mammarenaviruses are activated by LAMP1 binding during cell entry. Here we investigate this possibility and show that representative OW mammarenaviruses do not interact with LAMP1. We present a crystal structure of the GP1 domain from the Morogoro (MORV) OW mammarenavirus [16], which does not interact with LAMP1, and conduct a comparative structural analysis between GP1 of MORV and LASV (GP1MORV and GP1LASV, respectively) to identify structural differences related to the ability to bind LAMP1. Structure-guided mutagenesis assisted mapping of the LAMP1 binding site on GP1LASV, which was corroborated by grafting it onto GP1MORV. The binding site is located on the apex of the trimeric spike complex, suggesting critical attributes for the activation mechanism of the LASV spike complex by LAMP1. Moreover, the interaction surface includes a variable region of LASV that significantly differs from other OW mammarenaviruses. Thus, we conclude that switching to LAMP1 is unique for LASV.
To test whether other OW mammarenaviruses can interact with LAMP1, we produced the GP1 domain from lymphocytic choriomeningitis virus (LCMV) fused to an Fc portion of an antibody (GP1LCMV-Fc) and performed a pull-down assay using a total cell lysate of HEK293T cells, side by side with GP1LASV-Fc (Fig 1A). Unlike GP1LASV-Fc, GP1LCMV-Fc did not pull down endogenous LAMP1 (Fig 1A). We further used surface plasmon resonance (SPR) to test for potential weak interactions with the recombinant distal domain of LAMP1 (Fig 1B), which is sufficient for LASV to bind [1]. In contrast to GP1LASV-Fc, GP1LCMV-Fc was inert towards LAMP1 (Fig 1B). This observation agrees with previous indications that LAMP1 is not required for infection by LCMV [1]. A phylogenetic analysis based on the GPC sequences from representative OW mammarenaviruses shows that LCMV and LASV segregate to two different linages (Fig 1C). We thus asked whether an OW mammarenavirus that is genetically closer to LASV would be able to bind LAMP1. MORV is one of the closest OW mammarenaviruses to LASV. We therefore produced a GP1MORV-Fc protein and tested its ability to pull down endogenous LAMP1 (Fig 1A) or to interact with recombinant LAMP1 using SPR analysis (Fig 1B). Similarly to GP1LCMV-Fc, GP1MORV-Fc did not pull down LAMP1 from HEK293T cells, nor did it interact with recombinant LAMP1 immobilized on a sensor chip (Fig 1B).
Because GP1MORV did not bind LAMP1, we first sought to determine whether GP1MORV assumes a similar conformation to that previously observed for the LAMP1 binding-competent GP1LASV. In this regard, it was recently shown that the prefusion-conformation of GPCLCMV [17] greatly differs from the conformation of isolated GP1LASV [13] and inability to adopt a similar conformation to GP1LASV could thus account for the inability to bind LAMP1. To investigate that, we crystallized and solved the structure of GP1MORV to 2.6 Å resolution (PDB: 5NFF). We obtained crystals of GP1MORV (residues 73 to 235) at pH 4.0. Crystals belonged to a trigonal (P32) space group (Table 1). We found a molecular replacement solution using the coordinates of GP1LASV (PDB: 4ZJF) as a search model. The asymmetric unit contained a total of 16 copies of GP1MORV (Fig 1D), mostly differing in the extent of electron density observed for their N-linked glycans, which in some cases were stabilized by a nearby protein molecule. Electron density allowed us to model N-linked glycans at position 108 for 11 chains and at position 222 for 7 chains. Chain B was modeled with an N-linked glycan at position 118 as well. Due to crystal packing, chains differed in their average thermal factors (Fig 1D). The model was refined utilizing restraints between non-crystallographic symmetry-related molecules. We utilized simulated annealing and omission of selected regions during refinement to eliminate model bias. The final model of GP1MORV consists of residues 80–235 for all 16 chains and has Rwork/Rfree values of 17.4% and 20.7%, respectively (Table 1).
GP1MORV has a central β–sheet made of five β-strands, flanked by the domain termini on one side and helices and loops on the other (Fig 1E & 1F). The global structure of GP1MORV is similar to the previously determined structure of GP1LASV [13] (Fig 2A). The histidine triad in GP1MORV (His91, His92, and His228 according to GPCMORV numbering) adopts the same conformation as the triad in GP1LASV (Fig 2A). GPCMORV has a β-hairpin made of two β-strands (β5 & β6) stabilized by a disulfide bridge (D3) against α3 (Fig 1E). This β-hairpin is divergent in sequence (Fig 1F) and was previously termed the variable region of GP1LASV [13]. Comparing the solvent accessible surfaces and the calculated electrostatic potentials of the two proteins at pH 5.0 reveals some differences both in the charge distribution and in the surface geometry (Fig 2B). GP1LASV has a strong positive charge in the vicinity of the triad, whereas this charge on the GP1MORV surface seems milder (Fig 2B). On this side of the protein, GP1MORV lacks one negatively charged patch compared to GP1LASV and has a slightly more bulky surface near the histidine triad (Fig 2B). Overall, the structure of GP1MORV does not reveal major global structural differences that could account to the inability to bind LAMP1. However, the structural similarity together with a 70% amino-acid identity between GP1LASV and GP1MORV provides now a unique opportunity to elucidate the structural determinants of GP1LASV that are required for LAMP1 binding or of GP1MORV that preclude LAMP1 binding.
Previously we showed that mutations of histidines in the GP1LASV triad to tyrosines abrogated LAMP1 binding [13] and that LAMP1 requires a positively charged His230 for binding [14]. To map other regions that contribute to the LAMP1 binding site on GP1LASV, we compared the vicinities of the histidine triads of GP1LASV and GP1MORV (Fig 2C) and found several compositional as well as conformational differences between the two proteins. One is Met96 at the carboxy terminus of LASV strand β1, which corresponds to Gln95 in MORV. Other differences are in a loop connecting helix α3 with strand β5 (Fig 2C), designated L7 (Fig 1F). These include the small residues Ala195LASV, Gly197LASV and Gly198LASV, which are replaced by the more bulky Tyr194MORV, Ser196MORV and Asn197MORV, respectively (Fig 2C). To evaluate whether these variations affect LAMP1 binding, we mutated the residues in GP1LASV-Fc to their corresponding amino acids from GP1MORV and tested the ability of the mutated GP1LASV-Fc to pull down endogenous LAMP1 (Fig 2D) and to bind recombinant LAMP1 using SPR (Fig 2E). Whereas the M96Q mutation did not change the ability to bind LAMP1, mutations of the alanine and the first glycine residue in L7 almost completely abolished binding. Mutating Gly198LASV, at the end of L7, only slightly affected LAMP1 binding compared to WT GP1LASV (Fig 2D). Thus, the composition of the residues that makes L7 loop is critical for LAMP1 binding.
A closer look at L7 reveals that in addition to its side-chain composition, its main-chain conformation also differs between GP1LASV and GP1MORV (Fig 3A). In GP1LASV, the loop conformation is stabilized by a hydrogen bond between the side-chain hydroxyl of Tyr200LASV and the main-chain carbonyl of Gly197LASV (Fig 3A). In GP1MORV however, Leu199 replaces this tyrosine and cannot form a similar hydrogen bond. This in turn frees the main chain to rotate and to adopt the observed conformation (Fig 3A). Noteworthy, such main-chain conformational differences are evident in the crystal structure of GP1MORV but were not predicted using a standard homology modeling approach (S1 Fig), illustrating the advantage of having bona fide crystallographic information for such analyses. We established that the conformation of L7 is an important determinant for LAMP1 binding, as mutating Tyr200LASV to leucine prevented binding to and pull down of LAMP1 (Figs 3B & 2E). Importantly, the side chains of both Tyr200LASV and Leu199MORV are buried and face the protein cores, thus not likely to directly contact LAMP1. Our structural analysis further suggested that the observed conformation of L7 in GP1LASV might also depend on Leu188LASV, a core residue that is in a close proximity to Tyr200LASV (S2 Fig). Indeed, mutating Leu188LASV to methionine as found in MORV substantially affected the binding to LAMP1 (Figs 3B & 2E). Hence both the composition and the conformation of L7 loop are important for binding to LAMP1. The identification of L7 as a major element for binding suggests that the binding site extends from the histidine triad toward β5 (Fig 2C).
The β–hairpin made by β5 & β6 is the most variable region in GP1s of OW mammarenaviruses (Fig 1F). It is oriented such that β5 is highly exposed and β6 is partially buried (Fig 2A). Comparing the β–hairpins from GP1LASV and GP1MORV indicates differences in main-chain conformation, side chains that project to opposite directions, and divergent residue composition (Fig 3C). The β–hairpin in GP1LASV is one residue longer compared to GP1MORV, having Asn209LASV as the extra residue (Fig 3C). Ile201LASV, Ala202LASV, Asp204LASV, and Gly206LASV are exposed residues on β5, and mutating each of them individually to the corresponding residue based on the GP1MORV structure (to serine, proline, tyrosine, and isoleucine, respectively) strongly diminished the ability of GP1LASV-Fc to pull down endogenous LAMP1 (Fig 3B) or to bind recombinant LAMP1 (Fig 2E). Asp204LASV in this regard makes the extra negatively charged patch on the surface of GP1LASV that is not seen in GP1MORV (Fig 2B). Mutating Arg207LASV, which is located at the tip of the β–hairpin, to serine had only a mild effect on the ability to interact with LAMP1 (Figs 3B & 2E), possibly marking a boundary of the LAMP1 binding site. Likewise, deleting Asn209LASV, mutating Trp210LASV, or mutating Asp211LASV from β6 to threonine had either no effect or only weak effect on LAMP1 binding (Figs 3B & 2E). Following the observation that β5 is an important determinant for LAMP1 binding, we further analyzed the vicinity of β5 to identify residues that differ between GP1LASV and GP1MORV and could also participate in LAMP1 binding. Met192LASV from helix α3 and Ser216LASV from a loop connecting strands β6 and β7 (Fig 3D) also affected the pull-down of LAMP1 upon mutagenesis to arginine and alanine, respectively (Fig 3B). On the other hand, substituting Met214LASV with glutamine did not affect the pull-down of LAMP1.
The residues that were shown to be important for LAMP1 binding are mostly located at β5 and L7. Together with several residues from the close vicinity of L7 in the tertiary structure, the identified residues makes an elongated and continuous surface that stretches from the histidine triad to the tip of the β–hairpin (Fig 3E). The residues that were mutated and found to be unessential for pulling down LAMP1 are distributed around the continuous surface and mark the potential boundaries of the binding site (Fig 3E). A recent EM study provided low-resolution reconstructions of the complete spike complex of LASV in its pre-fusion conformation [11]. One of the reconstructions is of the spike complex at pH 5.0 [11], a pH that is compatible with LAMP1 binding. Examining three copies of GP1LASV docked into the EM density map reveals that the identified LAMP1 binding sites clusters on the apex of the trimer around its three-fold symmetry axis (Fig 3F). Such a configuration could potentially restrict the binding of more than a single LAMP1 molecule at the same time, as we further discuss below.
The functional mapping indicated that quite a few residues of GP1MORV are incompatible with binding to LAMP1. In an attempt to enable GP1MORV to pull down LAMP1, we created chimeric proteins by grafting critical elements from GP1LASV. We grafted the entire β–hairpin form GP1LASV (Fig 4A), trying to ensure a correct residue composition as well as main-chain conformation. In addition, we included in these chimeric proteins Arg191MORV to methionine and Ala214MORV to serine substitutions as the reciprocal mutations were identified to be critical (Fig 3B & 3D) as well as Tyr172MORV to alanine and Ser171MORV to tyrosine mutations that also contribute to LAMP1 binding (Figs 3B & 2E). In the structure of GP1MORV, Arg191 interacts with and compensates the charge of Asp188 from α3. We thus further substituted Asp188MORV with glutamine to avoid having an unpaired charged residue. We produced several mutated GP1 variants as Fc-fusion proteins by the addition of the various mutations in a sequential order according to the description above, performed pull down experiments for all variants, but failed to detect binding to LAMP1. Only when we included the Met187MORV to leucine substitution, which corresponds to the core residue that could potentially modulate the conformation of L7 (S2 Fig) were we able to generate a chimeric GP1MORV (GP1Chimera) capable of pulling down LAMP1 (Fig 4B). We used SPR to compare the binding of GP1Chimera to LAMP1 with the binding of GP1LASV (Fig 4C). Grafting the identified binding site on GP1MORV resulted in a remarkable gain of LAMP1-binding function with an estimated KD of ~520 nM (Fig 4C). Though somewhat weaker than GP1LASV binding of LAMP1 (KD of ~15 nM), this result indicates that we faithfully identified the LAMP1 binding site.
Next, we asked whether grafting the LAMP1 binding site onto a complete MORV spike complex would allow the formation of a functional spike and whether it could affect spike-mediated cell-entry. For this aim, we replaced a portion of the GPCMORV with the GP1Chimera to produce GPCChimera. By introducing a Flag-tag at the C’-terminus, we were able to verify that GPCChimera expresses similarly to the WT GPCs as seen in the high molecular weight band corresponding to unprocessed GPC (Fig 4D). The processing of the spike to produce GP2, however, was less efficient and more heterogeneous than seen for GPCMORV (Fig 4D). This observation suggests that GPCChimera fails to assume a fully native conformation, probably due to incompatibilities between the altered residues in GP1Chimera and the native GP2MORV protein. This would reduce the amount of properly processed and functional spikes displayed on GPCChimera-expressing cells. Indeed, when we tested the ability of GPC-expressing cells to form syncytia, we observed that cells expressing GPCChimera had almost no observable activity at acidic pH compared with cells expressing GPCMORV or GPCLASV (Fig 4E). Overall, the grafted sequence motif is compatible with the primed conformation of GP1 but is poorly compatible with other regions of the spike (likely GP2) when it adopts the native pre-fusion conformation. Thus, we were unable to evaluate the effect of a LAMP1 gain-of-function mutation on cell entry of MORV using the current GPCChimera. Though not informative regarding the effect of introducing LAMP1 binding function into a virus previously lacking it, the results of grafting in the context of the complete spike complex are nevertheless informative because they identify regions of GPC that are important for assembly of the native trimeric spike.
Previous studies have shown that LAMP1 is dispensable for cell entry by LCMV [1, 17]. Here we extended this notion and demonstrated that GP1LCMV does not interact with LAMP1 at acidic pH like GP1LASV (Fig 1A). A recent structure of the LCMV glycoprotein in a pre-fusion conformation [17] revealed a very different architecture for GP1LCMV compared with the LAMP1-compatible conformation observed for GP1LASV [13]. It was tempting to speculate that the GP1LASV structure represents a pH-induced “primed” conformation and that GP1LCMV could also adopt such conformation that might be important for the virus regardless of the use of LAMP1. However, an alternative interpretation of the available structural data would be that the two viruses have fundamental structural differences that explain the differences in ability to bind LAMP1. Thus, a critical question was whether other OW mammarenaviruses can adopt a similar “primed” conformation as seen for GP1LASV. The crystal structure of GP1MORV revealed that indeed it adopts this “primed” conformation as GP1LASV (Fig 2A). Therefore, “priming” is common to other OW mammarenaviruses, and inability to bind LAMP1 could not be explained by fundamental structural differences. Instead, side-chain compositional differences and local backbone conformational changes must dictate the ability to bind LAMP1.
We analyzed the various local structural differences between GP1LASV and GP1MORV (Figs 2A, 2C, 3A, 3C & 3D) and used them to guide the mapping of the LAMP1 binding site (Figs 2D, 2E & 3B). We found that the binding of LAMP1 by GP1LASV could be easily disrupted by individual point mutations in a set of multiple residues. The identified residues cluster in a continuous patch on the surface of GP1LASV near the histidine triad, forming a logical footprint for LAMP1 (Fig 3E). This cluster of residues is necessary and sufficient for binding, as grafting it into GP1MORV allowed binding to LAMP1 (Fig 4B & 4C). A central part of the identified LAMP1 binding site consists of strand β5 of GP1LASV, which is highly variable in OW mammarenaviruses (Fig 1F). No other OW mammarenavirus seems to have the required residue signature compatible with human LAMP1 (Fig 1F). Even Mopeia virus, which is the closest virus genetically to LASV (Fig 1C), lacks a LAMP1 compatible sequence. Examining representative LASV strains, however, reveals that they all share this unique residue signature (Fig 4F). We thus can conclude based on sequence analyses that LASV but no other OW mammarenaviruses can utilize human LAMP1.
An interesting related question is whether members of this family can utilize LAMP1 orthologs of their host species. It was previously shown that an N-linked glycan at position 76 of human LAMP1 is a critical determinant for binding of LASV, since chicken LAMP1 becomes a functional receptor for LASV when mutated to bear this glycan [1]. Thus, other putative determinants on the surface of LAMP1 that LASV requires in addition to the N-linked glycan at Asn76 must be evolutionary conserved. Considering the large evolutionary distance between humans and chickens, it is likely that the binding surface on LAMP1 would be further conserved in rodent orthologs that are evolutionary much closer to humans than chickens. Taken together, the apparent inability of OW mammarenaviruses to bind human LAMP1 strongly implies that they are also unable to utilize rodent-host orthologs of LAMP1.
Our previous work has shown that LAMP1 triggering greatly enhanced the efficiency of GPCLASV-mediated cell entry [14]. We proposed that binding to LAMP1 helps to insure coordinated triggering of multiple spikes at an optimal distance from the membrane [14]. For such coordinated triggering to work, the binding and the subsequent triggering must be fast processes. The mapping of the LAMP1 binding site provides additional support for this idea; based on the available low-resolution EM reconstruction of the LASV spike complex at pH 5.0 and the predicted orientation of GP1 within the complex [11], the three LAMP1 binding sites cluster near the 3-fold symmetry axis of the spike (Fig 3F). If the stoichiometry of LAMP1 binding is three LAMP1 molecules per viral spike complex trimer, the positioning of the binding sites implies that three globular LAMP1 domains cannot bind simultaneously without steric hindrance. Therefore, either a non-globular region of LAMP1 interacts with its binding site in GPC with a 3:3 stoichiometry, or else the stoichiometry is one LAMP1 per GPC trimer (S4 Fig). If the latter is the case then a single LAMP1 binding event might be sufficient for triggering. It should be noted that three binding sites rotated 120 degrees with respect to one another ensures that at least one binding site is favorably posed for interaction when a spike complex approaches a LAMP1 molecule, effectively increasing the on-rate for binding. Such a unique triggering mechanism that enhances the cell-entry efficiency of LASV might be a contributing factor for its pathogenicity, a possibility that will need to be addressed in the future.
Notably, in the EM study by Li S. et al. [11], the authors noticed extra densities near the cervixes of the trimer when the structure was reconstructed in the presence of LAMP1 distal domains. These densities were attributed to LAMP1 distal domains, suggesting a stoichiometry of 3 LAMP1 distal-domains per trimer [11]. Our mapping however does not agree with this observation. Analyzing the extra densities reveals that they are likely to be too small to accommodate the expected distal domain of LAMP1, raising the possibility that they were wrongly attributed to LAMP1. Further supporting this notion is the fact that Li S. et al. used a LAMP1 distal domain that was produced in the presence of kifunensin [11], an inhibitor that prevents the maturation of N-linked glycans. However, it was shown before that sialylation is critical for making LAMP1 a viable receptor for LASV [1]. This is further corroborated by our observation that GP1LASV fails to bind LAMP1 that was produced in the presence of kifunensin (S3 Fig). Thus, in the absence of fully matured sialylated glycans, a GPCLASV/LAMP1 complex is not likely to form.
The coordinate file and structure factors of GP1MORV were deposited to the protein data bank and are available under accession code 5NFF.
To express and purify GP1MORV we used the same methodologies as for GP1LASV [13]. Briefly, GP1MORV was expressed as a secreted protein using the baculovirus system in Tni (Trichoplusia ni) cells (Expression Systems). Media were collected and buffer exchanged to TBS (20 mM Tris-HCl pH 8.0, 150 mM sodium chloride) using a tangential flow filtration system (Millipore). Protein was captured using a HiTrap IMAC FF Ni+2 (GE Healthcare) affinity column followed by size exclusion chromatography purification with a superdex 75 10/300 column (GE Healthcare). For pull-down assays and SPR analyses, Fc-fused GP1s were expressed in monolayer HEK293T cells (ATCC), and LAMP1 distal domain fused to Fc was expressed in HEK293 cells adapted to suspension (Expression Systems). Transfections were done using linear polyethylenimine (PEI) (25 kDa; Polysciences). Media were collected after 5 days of incubation and supplemented with 0.02% (wt/vol) sodium azide and PMSF.
Screening for initial crystallization conditions was done with an 8 mg/ml stock of GP1MORV using a Mosquito crystallization robot (TTP Labs). Initial hits were identified using the PEGRx HT (Hampton) screen and were optimized manually. Crystals of GP1MORV were obtained at 20°C using sitting drop vapor diffusion in 26% PEG 8000, 100 mM sodium citrate pH 4.0, and 0.01% octylphenoxypolyethoxyethanol (IGEPAL). Crystals were then successively cryo-protected using 10% and 25% glycerol in reservoir solution. Details regarding data collection, structure solution and refinement are given in the Supplemental Information.
Media containing GP1-Fc were incubated with Protein A beads (Santa Cruz) for 1 hour at 4°C and washed 3 times with NETI buffer (50 mM Tris-HCl, 150 mM sodium chloride, 1 mM EDTA, 0.5% (vol/vol) IGEPAL). HEK293T cell extracts were prepared in NETI, adjusted to pH 5.0, and incubated for 1 hour at 4°C with the GP1-Fc beads. Proteins were eluted using TBS buffer and precipitated using acetone at -20°C. Pellets were recovered in sample buffer for SDS-PAGE and immunoblot analysis. Anti-LAMP1 antibody (CD107a) was obtained from Millipore and anti-human IgG from Abcam.
The binding of WT GP1LASV-Fc, GP1MORV-Fc, and their mutants to LAMP1distal-Fc was compared using a Biacore T200 instrument (GE Healthcare). Fusion proteins were batch purified using protein A beads in spin columns, and proteins were eluted in 50 mM sodium citrate pH 3, 0.25 M sodium chloride, 0.005% (v/v) tween and 0.02% sodium azide. Purified LAMP1distal-Fc was immobilized at coupling density of 1500 response units (RU) on a CM5 sensor chip (GE Healthcare) using primary amine coupling chemistry. One of the four flow cells on the sensor chip was mock-coupled using buffer to serve as a blank. Experiments were performed at 25°C in 50 mM sodium citrate, pH 5.0, 0.25 M sodium chloride, 0.005% (v/v) tween and 0.02% sodium azide. Sensor chip was regenerated using TBS buffer. GP1LASV-Fc WT, GP1MORV-Fc and their mutated variants were injected at a flow rate of 60 μL/min.
Partial charges were assigned using PDB2PQR at pH 5.0 [18], and electrostatic potentials were calculated using APBS tools [19] as implemented in the PyMOL Molecular Graphics System, Version 1.8 Schrödinger, LLC. Structural analyses and image generation were done using PyMol.
For phylogenetic analysis we used Phylogeny.fr [20]. The diagram was constructed based on GPC sequences obtained from UniProt: Lassa (P08669), Ippy (Q27YE4), Mobala (Q2A069), Dandenong (B1NX58), Mopeia (Q5S586), Morogoro (C6ZK00), Menekre (F1AM21), Gbagroube (F1AM06), Luna (K0IVJ1), LCMV (P09991), and Lujo (C5ILC1) viruses.
HEK293T cells were seeded in a 24-well plate pre-coated with poly-L-lysine (Sigma). Seeded cells were transfected 24 hours later with 0.2 μg DNA of the indicated GPC constructs using PEI. Twenty-four hours later, cells were rinsed once with HEK293T FM supplemented with 20 mM MES (Acros Organics) and titrated to pH 5.0 or 4.5. Cells were then incubated with the same medium for 10 min, followed by wash and incubation with HEK293T FM for 2 h at 37°C. Following incubation, cells were fixed in 3.7% formaldehyde solution (J.T. Baker) in phosphate buffered saline (PBS) (Biological Industries) for 10 min. Phase images of syncytia were taken at x10 magnification using a phase microscope, and their boundaries were automatically selected using the versatile wand tool of ImageJ.
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10.1371/journal.pcbi.1000038 | A Coarse-Grained Biophysical Model of E. coli and Its Application to Perturbation of the rRNA Operon Copy Number | We propose a biophysical model of Escherichia coli that predicts growth rate and an effective cellular composition from an effective, coarse-grained representation of its genome. We assume that E. coli is in a state of balanced exponential steady-state growth, growing in a temporally and spatially constant environment, rich in resources. We apply this model to a series of past measurements, where the growth rate and rRNA-to-protein ratio have been measured for seven E. coli strains with an rRNA operon copy number ranging from one to seven (the wild-type copy number). These experiments show that growth rate markedly decreases for strains with fewer than six copies. Using the model, we were able to reproduce these measurements. We show that the model that best fits these data suggests that the volume fraction of macromolecules inside E. coli is not fixed when the rRNA operon copy number is varied. Moreover, the model predicts that increasing the copy number beyond seven results in a cytoplasm densely packed with ribosomes and proteins. Assuming that under such overcrowded conditions prolonged diffusion times tend to weaken binding affinities, the model predicts that growth rate will not increase substantially beyond the wild-type growth rate, as indicated by other experiments. Our model therefore suggests that changing the rRNA operon copy number of wild-type E. coli cells growing in a constant rich environment does not substantially increase their growth rate. Other observations regarding strains with an altered rRNA operon copy number, such as nucleoid compaction and the rRNA operon feedback response, appear to be qualitatively consistent with this model. In addition, we discuss possible design principles suggested by the model and propose further experiments to test its validity.
| A bacterium like E. coli can be thought of as a self-replicating factory, where inventory synthesis, degradation, and management is concerted according to a well-defined set of rules encoded in the organism's genome. Since the organism's survival depends on this set of rules, these rules were most likely optimized by evolution. Therefore, by writing down these rules, what could one learn about Escherichia coli? We examined E. coli growing in the simplest imaginable environment, one constant in space and time and rich in resources, and attempted to identify the rules that relate the genome to the cell composition and self-replication time. With more than 4,400 genes, a full-scale model would be prohibitively complicated, and therefore we “coarse-grained” E. coli by lumping together genes and proteins of similar function. We used this model to examine measurements of strains with reduced copy number of ribosomal-RNA genes, and also to show that increasing this copy number overcrowds the cell with ribosomes and proteins. As a result, there appears to be an optimum copy number with respect to the wild-type genome, in agreement with observation. We hope that this model will improve and further challenge our understanding of bacterial physiology, also in more complicated environments.
| The rRNA (rrn) operons of E. coli have the important role of determining ribosome synthesis in the cell (c.f. [1]–[6] for reviews). These operons are unique in the sense that a wild-type (WT) E. coli cell carries seven copies of this operon per chromosome [7] (other bacteria have copy numbers ranging between 1 and 15 [8]). This copy number also appears to be selectively maintained. S. typhimurium for example, from which E. coli is thought to have diverged 120–160 million years ago [9], also has seven copies of this operon [10] and in evolution experiments of up to 104 generations no deviations from the WT copy number have been observed [11]. These findings raise the question of what underlying mechanisms, if any at all, fixed this copy number to be seven and not six or eight. In other cases it has been shown that the WT genome configuration maximizes fitness [12]–[14]. Thus, can it be shown that this copy number maximizes fitness?
In general, this question is hard to answer because the natural environment of E. coli is expected to vary both spatially and temporally [15],[16], thereby invoking complex physiological responses in the cell that are complicated to model. We therefore consider a much simpler scenario, where a resource-rich environment is spatially and temporally constant, and where the cell is at a state of balanced exponential steady-state growth [3], such that it has a well defined and reproducible growth rate and physiological state [17],[18]. In such an environment, cells that can outcompete their rivals will takeover the population and thus fix their genotype. Thus we refer to fitness in the narrow sense that previous authors have used [11],[14], i.e. it is the potential capacity of a cell in exponential growth to outcompete another strain population wise. Therefore, for an exponentially growing cell in a constant and rich environment, fitness would be, by definition, the growth rate of that cell.
Experimental evidence indeed suggests that for exponentially growing cells, cells with altered rRNA operon copy numbers have a lower growth rate. In a series of experiments, the Squires group has measured the growth rate and cell composition of seven strains of E. coli with rRNA operon copy numbers ranging from one to seven copies per chromosome [19]. All strains were grown in the same nutrient rich environment and measurements were performed on cells in exponential phase. These experiments show that cells with fewer than six rRNA operons have a considerable lower growth rate [19] (c.f. Figure 2A presented later in the text). For example, cells with five functional rRNA operons have a 21% lower growth rate than WT cells, while cells with only one functional rRNA operon have a 50% lower growth rate than WT cells [19]. In addition, a strain carrying extra rRNA operons on a plasmid exhibited a 22% reduction in growth rate relative to a WT control strain with a plasmid expressing nonfunctional rRNA [20].
To gain further insight into these findings, we sought to formulate a model of E. coli that could predict phenotype, such as growth rate and cell composition, directly from DNA related parameters, such as the rRNA operon copy number, while keeping the complexity of the model to a minimum. The model of E. coli proposed here differs from existing models of E. coli in several respects. Traditionally, E. coli has been monitored in different or changing environments [17], [21]–[23], and existing models have attempted to predict E. coli's response to such environmental perturbations [12], [23]–[26]. However, since disparate environments are expected to induce disparate genetic networks, we anticipate that such a strong perturbation will be difficult to capture in a simple model that attempts to predict phenotype from DNA related parameters (c.f. S2.3 in Text S1). Existing models of E. coli tend to fall into two classes. One class includes very complex models, involving tens to hundreds of equations [12],[24],[25], which do not lend themselves to simple interpretation. The other class involves simple and elegant models of E. coli that followed the Copenhagen school [22],[23],[26],[27] (see [17] for review). These classic models, however, do not relate genome to growth rate and composition, nor do they make reference to certain key physical processes in the cell elucidated since. Included in our current model are the relationships of genome to growth rate and cell composition, reflecting key physical processes now better understood, such as RNA polymerase (RNAp)-promoter interaction [3],[28], RNAp autoregulation [29], ribosome-ribosome binding site (RBS) interaction [30]–[33], mRNA degradation [34]–[40], DNA replication initiation [41]–[43] and macromolecular crowding (see below) [44]–[51]. In addition, we have attempted to find the middle ground in terms of complexity by coarse-graining, for simplicity, certain features of the cell: in the spirit of previous works [28],[52], the genome has been lumped into a small set of “gene classes” that represent all transcription and translation within the cell for the given environment. Similarly, the cell composition was reduced to a small set of variables accounting for the macromolecule content of the cell. The resulting type of model is referred to as a Coarse-Grained Genetic Reactor (CGGR).
Another point of difference with respect to existing models of E. coli is that in this model we take into account possible global biophysical effects resulting from the high volume fraction of macromolecules in E. coli, a state commonly termed “crowding” [45]. Formulating such a biophysical model for E. coli raises the basic question: is the macromolecular volume fraction, Φ = Vmacro/Vcell, inside E. coli constrained to be fixed or does it change for genetically perturbed cells? We have explored both of these possibilities in what we refer to as the constrained (Φ = const) and unconstrained (Φ≠const) CGGR models.
Using the CGGR modeling approach, we have modeled the seven strains engineered by the Squires group and have calculated their growth rate and their effective cellular composition. We were able to reproduce the experimental data within a model in which macromolecular volume fraction was allowed to change for genetically perturbed cells. These findings, along with other biological considerations, seem to favor the unconstrained CGGR model (see Discussion). According to this model, increasing the chromosomal rRNA operon copy number beyond seven will over-crowd the cytoplasm with ribosomes and proteins. Under such over-crowded conditions, we expect that binding affinities will weaken due to prolonged diffusion times. As a result, given this assumption, we show that the growth rate of an exponentially growing cell in a constant rich medium will not increase substantially beyond its WT growth rate when the rRNA operon copy number is increased beyond seven. Although we have not shown that the maximum in growth rate is a global maximum, since we only perturbed one genetic parameter, this result suggests that—at least for the case of a cell undergoing balanced exponential steady-state growth in a constant and rich medium—basic kinetic and biophysical considerations may have an important role in determining an optimal rRNA operon copy number (see Discussion).
Besides explaining the Squires data, the unconstrained CGGR model is qualitatively consistent with observations regarding nucleoid compaction in the inactivation strains and with the rrn feedback response originally observed by Nomura and coworkers (see Methods and Discussion). Thus, the CGGR model may offer an initial conceptual framework for thinking about E. coli as a whole system at least for the simplified environment considered. More complex genetic networks may subsequently be embedded into this model enabling one to analyze them in the larger, whole cell framework. Such a model may also help elucidate how E. coli works on a global scale by making experimentally testable predictions and suggesting experiments (see Discussion). We will also consider possible insights into the “design principles” of E. coli suggested by the CGGR model, such as intrinsic efficiency of resource allocation and decoupling of DNA replication regulatory mechanisms from cell composition.
Our goal is to formulate a model of E. coli that predicts phenotype, such as growth rate and the cell composition, from parameters directly related to the genome, while keeping complexity to a minimum. To reduce the complexity of this problem we coarse-grained both input parameters (the genome) and output parameters (the cell composition and growth rate). The genome (input) was lumped into four basic gene classes: RNA polymerase (RNAp), ribosomal protein (r-protein), stable RNA and bulk protein. The gene classes are represented by genetic parameters such as: genetic map locations, promoter strengths, RBS strengths, mRNA half-lives and transcription and translation times, all of which can be, in principle, linked to the DNA. Genetic parameters have been determined based on empirical data for the WT growth rate (or very close to it) and represent all transcription and translation within the cell at that growth rate (see Results for more details).
The cell composition (output) was reduced to the following five macromolecule classes: free functional RNAp, total RNAp, free functional ribosomes, total ribosomes and bulk protein. The bulk protein class represents all other cell building and maintenance proteins in the cell [53]. The concentration of these macromolecules, together with the growth rate constitutes six state variables that define the cell state. Table 1 gives an example of the observed WT cell state at 2.5 doub/h. In the Discussion we consider the applicability of this choice of coarse-graining.
After coarse-graining the cell, one can map the various feedback mechanisms that exist between these coarse-grained components, as illustrated in Figure 1A. Transcription of the various gene classes by RNAp [29] is depicted on the left, and translation of mRNA by ribosomes on the right. Ribosomes are shown to be assembled by combining rRNA with r-protein. r-proteins synthesis rate is regulated to match the rRNA synthesis rate [1] as indicated by the black arrow in Figure 1A.
RNAp naturally has positive feedback to all promoters, and ribosomes have positive feedback to all RBSs. In the case of RNAp, it has been shown [29] that the ββ′ subunits, which limit the production of RNAp (c.f. discussion in [17]), repress their own translation, and the functional, assembled RNAp holoenzyme represses transcription of ββ′. While the details of the RNAp autoregulation are still being elucidated, the latter finding suggests that the apparent fast response of the negative translational autoregulation of the ββ′ operon keeps the concentration of total RNAp fixed, at least approximately (for a detailed discussion see S2.1 in Text S1). The level of RNAp may also be modulated by guanosine 5′-diphosphate 3′-diphosphate (ppGpp) [3],[17],[28], however, since ppGpp levels were measured to be constant for strains with increased or decreased number of rRNA operons [20],[54], this modulation will not be relevant in this analysis (see discussion below).
Finally, there is the feedback arising from the translation-degradation coupling, indicated in Figure 1A by the dashed green line connecting ribosomes to mRNA degradation. Ribosomes bound to the RBS of mRNA protect the mRNA from degradation by preventing RNase E – thought to be the primary endonuclease in E. coli [35],[55] – from binding to the 5′ end of the mRNA and cleaving it (for recent reviews see [34],[38],[39]). RNase E is considered to be the partially or fully rate-determining step in the mRNA degradation process [35], [55]–[59]. Here we have modeled this effect by allowing only mRNA's with a vacant RBS to be cleaved [38],[39],[60]. This feedback manifests itself as dependence of mRNA half-life on the probability that the RBS is vacant, as suggested by observation [34],[56],[61] (see S2.7 in Text S1 and discussion further below).
We shall refer to the feedbacks depicted in Figure 1A as internal feedbacks. If the macromolecular volume fraction is allowed to change, then an additional internal feedback arises due to the fact that the binding affinities of RNAp and ribosomes to their corresponding binding sites may change due to crowding effects. This kind of dependence on the crowding state of the cell offers an additional feedback path not explicit in Figure 1A. We will elaborate on this point in the Results section. Also not explicit in Figure 1A is DNA replication that determines gene concentration. This issue will be further discussed below.
It has long been known that there is some form of feedback control on rRNA operons that responds to any artificial attempt to manipulate ribosome synthesis [1]–[6],[20],[62], yet the source of this feedback has remained controversial. Since we will be considering perturbations on the rRNA operon copy number, which affect ribosome synthesis, it is pertinent to identify any additional effectors that apply a feedback within the system.
Nomura and his coworkers noted that cells with increased number of rRNA operons did not exhibit a significant increase in rRNA transcription [62], i.e. the transcription per rRNA operon decreased by means of some feedback. Furthermore, the absence of this feedback in cells overproducing nonfunctional rRNA, and the observation of a feedback response in strains in which ribosome assembly was blocked, suggests that complete ribosomes are involved in the feedback response [62]. This became known as the “ribosome feedback regulation model” (c.f. discussions in [1],[3],[5]). Direct effect of ribosomes on rRNA transcription could not, however, be observed in vitro [62], and it was suggested by these authors that this regulation may be achieved indirectly [62]. Further experiments indicated that the feedback depends on translating ribosomes (or translational capacity) rather than free ribosomes [1],[63]. Later studies have demonstrated the feedback response for various other perturbations that attempted to artificially manipulate ribosome synthesis rate, including: increasing rRNA operon copy number [20],[64],[65], decreasing rRNA operon copy number [54], overexpressing rRNA from an inducible promoter [66], deleting the fis gene [20],[67] (see below), muting the rpoA gene coding for the α subunit of RNAp [20],[68] and more (c.f. [2],[20]). Since many of these perturbations [20], as well as perturbations in nutritional conditions [2],[69], correlated with changes in the concentration of ppGpp and nucleoside triphosphate (NTP), Gourse and his coworkers have proposed that NTP and ppGpp are the feedback regulators [6],[69]. In addition, these authors have suggested a model where translating ribosomes consume or generate NTP and ppGpp and thus are able to achieve homeostasis of rRNA expression on a rapid time scale [6],[69]. Yet these authors also point out that these effectors cannot explain the feedback response specifically associated with changes in rRNA gene dosage [64] (the perturbations considered in this study). In this case, it has been demonstrated that the small molecule ppGpp has no effect on rRNA synthesis rate both in the case where rRNA gene dosage was increased [20] or decreased [54] since ppGpp concentration remains constant in these strains (also indicating that tRNA imbalance was not a problem in those strains). In addition, feedback inhibition due to increased rRNA gene dosage was of the same magnitude in both wild-type cells and strains lacking ppGpp [70]. Similarly, the concentration of the small effector NTP was shown to be constant when decreasing or increasing the rRNA gene dosage [64]. In a different study, NTP concentration decreased by only a small amount (20%) when rRNA gene dosage was increased [20], such that those authors concluded that the small change in NTP concentration appears to be insufficient to account for the entire effect on transcription initiation. Due to these findings, Gourse and coworkers concluded that there may be additional mediators involved in feedback control of rRNA expression when altering the rRNA operon gene dosage [2],[20]. We show that internal feedbacks may account, at least partially, for the feedback response, although an additional effector may still be involved. In the Discussion we analyze model predictions and compare them to observations regarding this effect. We will also discuss the predicted feedback response in the context of Nomura and coworkers' feedback model and show that there appears to be no contradiction between the two.
In addition to the small molecules mentioned above, rRNA transcription is further modulated by transcription factors like Fis, HN-S and DskA, as well as the UP element [1], [2], [4]–[6],[69],[71], however there is currently no experimental evidence to suggest that these factors are linked to the feedback response to altered rRNA gene dosage. DskA, for example, a small molecule that binds to RNAp, is thought to amplify effects of small nucleotide effectors such as ppGpp and NTP [4],[6],[72]. DskA concentration, however, was found to be unchanged with growth rate and growth phase and therefore it apparently does not confer a novel type of regulation on rRNA synthesis [3],[72] and is thus considered to be a co-regulator rather than a direct regulator [4]. Fis stimulates rRNA transcription by helping recruit RNAp to the promoter through direct contact with the α subunit of RNAp, while the UP element, a sequence upstream of the promoter, binds the α subunit of RNAp and can greatly stimulate rRNA transcription [2], [4]–[6],[71]. Although Fis levels change throughout the growth cycle [4],[5], strains lacking Fis binding sites retain their regulatory properties [2],[5],[67] indicating that fis is not essential for regulation of rRNA transcription during steady-state growth [67], and perhaps just plays a role in control during nutritional shift-ups and onset of the stationary phase [5]. HN-S concentration changes with the growth phase of an E. coli culture [2],[69],[71] and is thought to be associated with regulation related to stress [15], particularly in stationary phase [4],[69]. Since there is currently no direct evidence that shows that any of these or other factors are associated with the feedback response to rRNA gene dosage perturbation, no such factors were included in the proposed models, yet future experiments may prove otherwise (c.f. Discussion).
The biochemical reactions that make up the feedback network illustrated in Figure 1A are approximated, for simplicity, by Michaelis-Menten type kinetics [3],[28],[30],[36],[37], as is illustrated in Figure 1B. These reactions include: stable RNA synthesis, bulk protein synthesis and bulk mRNA decay. Since ribosomes and the bulk of proteins in E. coli are stable on timescales of several generations [35],[73], their degradation can be neglected compared to the fast doubling time of the cell (∼30 min). We also do not need to explicitly consider r-protein synthesis since ribosome synthesis is limited by rRNA [1]. Finally we note that the free RNAp in these reactions may include RNAp bound nonspecifically to DNA and in rapid equilibrium with it [28] that may locate promoters by a type of 1-D sliding mechanism [74]. In the current model, all inactive RNAp was assumed to be associated with pause genes (c.f. [28] and e.g. Table S4) and thus inaccessible to promoters. However, it may be that some of these inactive RNAp molecules are just bound nonspecifically to the DNA [28] perhaps serving as an additional reservoir of RNAp.
Since the Squires strains were measured under steady-state conditions, we consider next the steady-state equations implied by Figure 1B.
The reactions in Figure 1B can be readily expressed as rate equations and analyzed at steady-state. Although the full derivation is rather lengthy (see S2.5 in Text S1), the final equations lend themselves to simple interpretation. The average transcription [3],[75] and translation [30],[31],[76] initiation rates are given by the usual Michaelis-Menten relations(1)where ni denotes the concentration of species i, and are the maximum transcription and translation initiation rates of the i-th gene class respectively, and Km,i and Lm,i are RNAp holoenzyme and 30S ribosome subunit binding affinities of the i-th gene class to their corresponding binding sites respectively, measured in units of concentration (see Table 2 for notation list and units). Using this notation, the RNA transcript synthesis rate per unit volume is vi = diVi (where di is the gene concentration of the i-th gene class) and the number of translations per mRNA is , where is the functional half-life of the i-th gene class mRNA. Therefore, the protein synthesis rate per unit volume of gene class i is viui. In this notation, the five equations of state take the form:(2)where tc,i and tl,i are the times to transcribe and translate the i-th gene class respectively, and τi is the average assembly time for component i (the boxes in Figure 1A). Equation (i) states that the total RNAp concentration is constant. This is due to our assumption that the negative autoregulation of RNAp is ideal. This somewhat naïve model for the autoregulation of RNAp can be, in principle, replaced with a more sophisticated model describing the steady-state response of the negative transcriptional and translational autoregulation of RNAp, once the details of this mechanism are known. Equations (ii) and (iii) are the bulk protein and ribosome synthesis equations respectively, assuming exponential growth, i.e. dilution at a rate of α = μln 2, where μ is the doubling rate. Note that vrrn is the total ribosome synthesis rate per unit volume. Finally, (iv) and (v) are conservation equations for RNAp and ribosomes within the cell. In Eq. (iv), these terms include (left to right): free RNAp, bound RNAp and immature RNAp (a modified version of Eq. (iv) was first derived in [28]). Similar terms exist in the ribosome conservation equation (v). The contribution of RNAp to the conservation equations was neglected since it constitutes less than 2% of the total protein mass [17]. Note that in the second term of (v), the number of bound ribosomes to the r-protein class is determined by the time it takes to translate all r-proteins and the total rrn transcription rate, due to the matching of r-protein synthesis rate and rRNA synthesis rate through regulation at the r-protein mRNA level [1]. The ribosome conservation equation (v) is equivalent to the previously derived result [3]: α = (Nribo/P)βrcp, where Nribo is the number of ribosomes per cell, P is the total number of amino acids in peptide chains, βr is the fraction of actively translating (bound) ribosomes and cp is the peptide chain elongation rate.
Explicit expressions for functional and chemical half-lives of bulk protein, and their dependence on the concentration of free ribosomes, can also be derived from Figure 1B, taking into account the negative autoregulation of RNase E (c.f. S2.5 and Eq. S15 in Text S1). For example, one can show that the functional half-life of bulk protein mRNA is given by , where is a genetic parameter denoting the functional half-life of bulk mRNA in the absence of ribosomes. Thus, mRNA half-life increases with the probability that the RBS is occupied. This relation reflects translation-degradation coupling trends observed between mRNA degradation and translation [34],[39], further discussed in S2.7 of Text S1.
To extract the cell composition from Eq. 2 we require an expression for the gene concentrations, di(μ), of the various gene classes. This expression is given by linking [43] the Cooper-Helmstetter model of DNA replication [77],[78] and Donachie's observations regarding the constancy of the initiation volume [41],[79]:(3)where Vini is the initiation volume, defined as the ratio of the cell volume at the time of replication initiation and the number of origins per cell at that time, mi,j represents the map location of the j-th gene in the i-th gene class relative to the origin of replication (0≤mi,j≤1), and finally C is the C period, the time required to replicate the chromosome (roughly 40 min). Recent observations and modeling of the replication initiation mechanism in E. coli [41],[42] suggest that the initiation volume is regulated to be fixed, and therefore it should be independent of genetic perturbations that do not target that regulation (Tadmor and Tlusty, in preparation). See S2.2 in Text S1 for further details. Thus, we can use Eq. 3 to predict the gene concentration for the genetically perturbed cells considered here.
Equation set 2 provides us with five equations of state. We now test whether these equations are consistent with observed WT cell states.
We wish to see whether given measured genetic parameters at a specified growth rate, we can reproduce the cell state, namely the growth rate of the cell and its coarse-grained composition (Table 1). For the case of growth at 2.5 doub/h, all genetic parameters, except the Michaelis-Menten parameters for translation initiation ( and Lm,bulk) are based on (1) previous estimates derived from empirical data for this growth rate [28], (2) global mRNA half-life measurements at 37°C in LB broth [40], and (3) gene lengths and map locations obtained from the sequenced genome of E. coli. These genetic parameters are summarized in Table S1. was set at several plausible values (above observed average translation initiation rates [3],[80],[81] and below the maximum limit where ribosomes are close-packed), with the remaining parameters estimated to minimize the mean square error (MSE) with respect to the WT cell state (Table 1). Errors in estimation of the cell state were no more than 6% of the observed WT cell state and within experimental error bounds of these measurements (∼15%; c.f. Table S3 for estimated genetic parameters and corresponding MSEs). Similar results were obtained for the cell state at 1 and 2 doub/h (see for example Table S3 for 1 doub/h). These results indicate that the equations in equation set 2 can be mutually satisfied for these growth rates. We also note that in all cases we found that Lm,bulk is of same order of magnitude as the concentration of free ribosome, nribo,free, indicating that the RBSs are not saturated by free ribosomes, in agreement with pervious studies [30]–[33]. Further details are given in S1.1.1 of Text S1.
In the series of experiments that we consider here, Asai et al. [19] have measured the growth rate and rRNA to total protein ratio of seven E. coli strains, with rRNA operon copy numbers ranging from one to seven per chromosome (Figure 2). Since all strains were grown in a constant environment of Luria-Bertani broth at 37°C (μ = 2.0 doub/h for the WT strain), the CGGR model is applicable. We first reconstructed the WT genetic parameters and the relevant physical constants (C periods and elongation rates) for a growth rate of 2 doub/h (c.f. Table S5 and S1.2 in Text S1 for a detailed account). Next, by analyzing the published lineage of these strains (Table S6) we derived the genetic parameters for each specific strain (Table S7). In S1.3 of Text S1 we explain which genetic parameters for the WT cells can be carried over to the inactivation strains and which parameters change, and how. The WT cell state at 2 doub/h is given in Table S2 and the genetic parameters at 2 doub/h for the WT cell and the inactivation strains are summarized in Table S5 and Table S7, respectively.
Given the genetic parameters, we set to solve Eq. 2 for the different strains. However, in order to solve for the CGGR cell state, which consists of six state variables, we need an additional relation which apparently does not arise from kinetic considerations. A hint to the solution may lie in the fact that so far we have neglected the function of the bulk protein and biophysical considerations such as macromolecular crowding.
The in vivo milieu of E. coli is extremely crowded with macromolecules [45] with typical values of macromolecule volume fraction Φ = Vmacro/Vcell of 0.3–0.4 [46]. Observations of WT E. coli in varying environments suggest that the macromolecular mass density of the interior of the cell is more or less a constant [23]. If we neglect the contribution of RNAp, mRNA and DNA (∼6% at 2.5 doub/h [17]) this is roughly equivalent to stating that(4)where vi is the volume occupied by a particle belonging to the i-th species (c.f. Table S2), and with potential contribution from “load genes” that express products not utilized by the cell and pose a pure burden, like antibiotic resistance for example. Equation 4, which balances bulk protein against ribosomes, leads to a contradiction: it appears from this model, that by genetic perturbations, e.g. by increasing the rRNA operon copy number, one could construct a cell composed almost entirely of ribosomes with no bulk proteins to support it, or vice versa. To resolve this difficulty we need to take into account the fact that some of the bulk proteins are required to support ribosome synthesis.
One possible resolution is to introduce a mechanism that would limit protein and ribosome synthesis when bulk protein density is reduced. For example, one could assume that the peptide chain elongation rate, cp, is given by , where h is some Hill coefficient, is the maximal elongation rate and Mbulk is a cutoff. Mbulk may depend on the environment, reflecting the dependence of cp on the environment [17]. Note that cp affects our system of equations through the translation times tl,i. This criterion along with Eq. 2 and Eq. 4 define the constrained CGGR model.
However, is the macromolecular volume fraction, Φ, really constant? The phenomenological evidence indicating that Φ is roughly constant has been obtained for WT cells in different environments, and not for a suboptimal mutant growing in a given environment like the Squires strains. Indeed, it has been proposed that Φ can vary by adjusting the level of cytoplasmic water to counter changes in the external osmotic pressure [82]. These observations suggest that Φ = const is apparently not a universal law in E. coli.
An alternative resolution, which does not hypothesize that Φ = const, could be to postulate a cost criterion, which states that the amount of ribosomes that the cell can produce is limited by resources, such as ATP, amino acids, etc., that are made available by the bulk proteins. Assuming that bulk protein concentration, nbulk, is proportional to its demand, i.e. to total ribosome concentration, nribo, and also to possible load protein concentration, nload, the criterion takes the form:(5)where ci are the costs and n0 is some minimal density of the cell (e.g. housekeeping proteins, membrane building proteins etc.), assumed to be more or less constant. cribo, for example, is defined as the number of bulk proteins per cell, Nbulk, required to increase the number ribosomes in the cell, Nribo, by one, given a fixed environment E (i.e. sugar level, temperature, etc.), a fixed cell volume and a fixed number proteins, Nj, expressed from all other genes (akin to the definition of a chemical potential):(6)In other words, to synthesize and support one additional ribosome per cell, in a constant environment, cell volume etc., according to this definition, would require an additional cribo bulk proteins per cell (cribo is dimensionless). An equivalent way to interpret Eq. 5 is to say that cribo is the capacity of a ribosome to synthesize bulk proteins: one additional ribosome added to the cell will synthesize cribo bulk proteins. Thus, at steady-state, cost and capacity are different sides of the same coin. The costs, ci, depend on the environment since the cost of producing and maintaining a ribosome in a rich environment is expected to be lower than the cost in a poor environment due to the availability of readymade resources that otherwise the cell would need to produce on its own. The hypothesized costs, ci, can therefore be thought of as effective environment-dependent genetic parameters and could, in principle, be estimated from knowledge of the genetic networks invoked in a given growth environment. Note that Eq. 4 is actually a special case of Eq. 5 for certain negative costs. Eq. 5 also crystallizes the difference between bulk proteins and load proteins: the latter are a burden for the former. The cost criterion together with Eq. 2 define the unconstrained CGGR model. The final equation set for both models is summarized in S2.6 of Text S1.
From an experimental point of view it should be possible to discern between the two hypotheses: one model (Eq. 5) predicts a positive slope for the nbulk vs. nribo curve, whereas the other model (Eq. 4) predicts a negative slope. In the Discussion we suggest how the cost criterion may naturally occur in the cell.
With the CGGR models at hand, we now compute the cell states for each of the seven strains used in the Squires rRNA operon inactivation experiments. We will use this data to fit for the unknown environment dependent parameter in each of the CGGR models: cribo for the unconstrained model and Mbulk for the constrained model. In the case of the unconstrained model, the predicted rRNA to total protein ratio was more sensitive to cribo than the predicted growth rate, with a best fit for the former at cribo≈38 bulk proteins per ribosome (for mean square errors refer to Figure S1). For comparison, a 70S ribosome is about 70 bulk proteins in mass. Note that cribo has a rather limited range of values since 0<cribo<nbulk/nribo≃101 via Eq. 5.
For the constrained CGGR model, the minimum Hill coefficient to yield a solution that did not diverge in growth rate for copy numbers greater than 7, which contradicts observation (see Introduction), was h = 2 (see e.g. Figure S2 for a fit with h = 1). For h = 2, Mbulk was chosen minimize the MSE with respect to the growth rate, which displayed a minimum, and the best fit was achieved for Mbulk≅7.4·106 molec/WT cell (for all MSEs c.f. Figure S1). Attempting to minimize the MSE with respect to the rRNA to total protein ratio resulted in a slightly lower MSE (though still higher than the MSE for the unconstrained model fit), however solutions diverged in growth rate for copy numbers greater than 7, again contradicting observation. Increasing the Hill coefficient so as to penalize the peptide chain elongation rate, cp, for higher copy numbers did not remedy this and growth rate continued to diverge for copy numbers greater than 7 (c.f. Figure S3) rendering such solutions inapplicable. Finally, increasing the Hill coefficient beyond 2 did not improve the overall MSE to either the growth rate or to the rRNA to total protein ratio (Figure S1). Thus the fit for the constrained model presented here represents the best fit, over all parameter range, which does not contradict observation.
Figures 2A and 2B show the observed growth rate and rRNA to total protein ratio plotted against the best fits of these models. In both cases, the fit to the observed data was reasonable, however the model for which macromolecular volume fraction, Φ, was not constrained gave an overall better fit indicating a preference for that model. Further evidence in favor of this model and against the constrained model will be considered in the Discussion. The deviation observed for the Δ6 strain may possibly be due to tRNA imbalance in this strain [19].
Whereas the macromolecular volume fraction Φ in the constrained model is, by definition, constant, the unconstrained CGGR model predicts that Φ increases with the number of rRNA operons with consequences on binding affinities (Figure 3). This increase in the macromolecular volume fraction is due to an increase in both ribosome concentration and bulk protein concentration due to the relation imposed by the cost criterion (Eq. 5; also c.f. Figure S5). Quite surprisingly, the fit to the Squires data depends very little on the crowding scenario chosen. This results from a self-adjusting homeostasis mechanism: it is the ratios of free RNAp and free ribosomes with respect to their corresponding binding affinities that govern the transcription and translation rates (Eq. 1). Hence, although the binding affinities change with Φ, the concentrations of free RNAp and free ribosomes counterchange to stabilize these ratios (see Figure S4 and S1.6 in Text S1). The efficiency of the homeostatic mechanism diminishes as the degree of crowding is increased above ∼0.4, as can be seen by comparing to predictions of the “no crowding” scenario, in which binding affinities were assumed to be independent of Φ (Figure 2A and 2B).
Due to translation-degradation coupling, bulk mRNA half-life was predicted to mildly increase with rRNA operon copy number for all models. In both crowding scenarios, bulk mRNA half-life increased from about 0.8 of the WT half-life to about 1.2 of the WT half-life. The increase in mRNA half-life is caused by the increase in the ratio of the RBS binding affinity and the concentration of free ribosomes with rRNA operon copy number (Figure S4). This ratio reflects the probability that a RBS is occupied, thereby protecting the mRNA from cleavage.
Increasing the rRNA operon copy number beyond 7 (at map location 0), we found that both CGGR models exhibit a shallow optimum plateau for of the growth rate in the range of 7–12 copies, with the maximum occurring at a copy number of 10–11 for the diffusion limited scenario, and 11–12 for the transition state scenario. In the case of the unconstrained models, overcrowding contributed to the formation of this maximum (e.g. there is no maximum in the unrealistic model where binding affinities are assumed to be independent of Φ). A striking difference between the models is in their predictions regarding the rRNA to total protein ratio. This ratio strongly diverges in the constrained model at high copy numbers because ribosomes are formed at the expense of bulk protein (see Discussion).
For the data of the Squires strains, the unconstrained CGGR can be approximated by a simplified three-state model involving only nribo, nbulk, and μ (c.f. S3 in Text S1):(7)where grrn and gbulk are effective genetic parameters that are estimated from the WT cell state. Equation (i) reflects ribosome synthesis, (ii) reflects bulk protein synthesis and (iii) is the cost criterion. Interestingly, in a different context of WT cells measured in varying environments, a relation similar to Eq. (ii) has been observed [21]. Solving Eq. 7 for the growth rate we obtain(8)In the limit grrn→∞, μ≤gbulk/cribo, suggesting that in the absence of crowding effects, growth rate would be limited by the production cost of a ribosome. To fit to experiments where the rRNA operon copy number is manipulated, we approximate that grrn→grrn·copy #/7. The best fit to the Squires data was obtained for cribo≃38.2±2.8, in agreement with the prediction of the full unconstrained model (see Figure 2A and 2B, and also Figure S1 for MSEs). Since the simplified model is unrealistic in the sense that it lacks crowding effects, growth rate continues to increase with rRNA operon copy number.
Ribosome efficiency has been previously defined as er≡βrcp = αP/Nribo [3],[19] where cp is the peptide chain elongation rate and βr is the fraction of actively translating ribosomes. For wild-type cells, βr = 80%, and is independent of growth rate [17]. Genetically perturbed cells may however respond differently [19]. For example, the simplified model predicts that er = αP/Nribo = ln 2(gbulkLbulk+μLr-protein), where Li is the length of gene class i and μ is given by Eq. 8. Since cp is assumed to be fixed in the unconstrained/simplified models, ribosome efficiency is therefore expected to decrease purely due to kinetic considerations. Crowding effects tend to either increase or decrease ribosome efficiency, depending on the scenario. In Figure 2C we plot the ribosome efficiency for the various crowding scenarios in the unconstrained CGGR model, for the constrained CGGR model and for the simplified three-state model. We see that the data points lie between the diffusion limited crowding scenario and the transition state crowding scenario, which possibly indicates that the in vivo crowding scenario is somewhere between being diffusion limited and transition state limited. Overall however, the diffusion limited model was a better predictor of ribosome efficiency than the transition state model and its deviation from the observed data points was on the order of the maximum error for these points (the maximum deviation from experimental data points is ∼10%, and although the error for the protein measurement was not stated in [19], the maximum error on ribosome efficiency was at least 9% based on the errors quoted in [19]). This result possibly indicates a preference for the diffusion limited scenario for the in vivo case (see Discussion). The solution for which binding affinities are independent of crowding (the “no crowding” scenario) also fits the data due to the proposed homoeostasis mechanism for Φ<∼0.4. The constrained model clearly deviates from the experimental points indicating, as we have seen before, that the constrained CGGR model is not applicable to E. coli. Finally, the simplified model appears to adequately trace the observed ribosome efficiency.
Figure 4 shows the initiation rate of a single rRNA operon, Vrrn, as a function of the rRNA operon copy number as predicted by the unconstrained model (Eq. 1). The solid lines represent models where the rRNA chain elongation rate was assumed to be constant (85 nuc/sec [17]; Table S5). Both unconstrained models exhibit an increase in rRNA expression per operon as copy number is decreased from 19 copies per chromosome down to 3 copies per chromosome (in the case of the diffusion limited model) and 5 copies per chromosome (in the case of the transition state model). This trend is in agreement with the feedback response mechanism, especially for the diffusion limited model (see Discussion). It has been shown that the rRNA chain elongation rate (but apparently not mRNA chain elongation rate) increases in inactivation strains from ∼90 nuc/sec in a WT strain to ∼135 nuc/sec in a strain with four inactivated rRNA operons [54], but remains constant in strains with increased rRNA gene dosage [65]. To check how these finding affect our predictions, we also included a model where rRNA chain elongation rate decreased linearly from 160 nuc/sec for one functional rRNA operon per chromosome, to 85 nuc/sec for the WT strain (dashed lines in Figure 4). Indeed, the feedback response seems to be stronger for the inactivation strains when assuming that rRNA chain elongation rate increases as more operons are inactivated. Quantitatively, for the diffusion limited model, rRNA expression from a single operon increased from 0.6 of the WT expression for 19 chromosomal rRNA operons to about 1.1 of the WT expression for 3 chromosomal rRNA operons. Finally, the “no crowding” scenario exhibited a milder feedback response due to departure from the homeostasis discussed earlier.
The goal of the coarse-grain genetic reactor (CGGR) approach is to attempt to link global phenotypes, such as growth rate and cell composition, directly to genetic parameters, while keeping the model as simple as possible by means of coarse-graining. The present CGGR models assumed the simplest type of environment, namely a spatially and temporally constant environment that is unlimited in resources. The models attempt to explain a series of experiments performed by the Squires group [19] in which growth rate and cell composition have been measured for seven E. coli strains with varying rRNA operon copy numbers. The genome of all seven strains has been coarse-grained, and their corresponding cell state was calculated based on the CGGR models.
We considered two possible CGGR models, one in which the macromolecular volume fraction is constrained to be fixed, and one in which macromolecular volume fraction is unconstrained. We have seen that the unconstrained CGGR model appears to give an adequate fit to experimental data, while the fit for the constrained CGGR model is rather poor (despite the latter having an additional degree of freedom). Yet beyond the fit of the unconstrained model to the Squires data, this model also appears to be consistent with additional observations regarding strains with altered rRNA operon copy numbers. For example, the unconstrained CGGR model predicts that growth rate decreases for higher rrn copy numbers, as indicated by observation. For comparison, the best fit of the constrained CGGR model actually predicted that growth rate increases for rRNA operon copy numbers greater than 7, contradicting observation. In addition, both models predict that the concentration of ribosomes (and ribosomes per cell) decreases with rRNA operon copy number (Figure S5), as was shown in measurements of an earlier set of inactivation strains engineered by the same group, with rRNA operon copy number ranging from three to seven [54]. Below we discuss further evidence in support of the unconstrained CGGR model: observations regarding the nucleoid size in strains with altered rRNA gene dosage appear to be consistent with the crowding predictions of this model. Finally, the unconstrained (diffusion limited) CGGR model is in agreement with the trend associated with the feedback response, and appears to be in qualitative agreement with measurements of this effect. The proposed model is also consistent with the feedback model proposed by Nomura and coworkers, as will be discussed further below. The constrained CGGR model, on the other hand, in addition to yielding an inferior fit to the Squires data, is also problematic from a biological standpoint. This model should predict that ppGpp levels rise due to a shortage in an essential factor such as charged tRNAs [3],[6],[89]. However, ppGpp levels were observed to be constant in similar rRNA inactivation strains with up to four inactivations [54]. In addition, the constrained model appears to be considerably more complicated than the unconstrained model in that it necessitates some kind of homeostasis mechanism for keeping the volume fraction fixed, to which there is no experimental evidence as far as we know, while the unconstrained model does not necessitate any additional biological mechanisms (see below). In fact, evidence from osmotically stressed cells indicates that the volume fraction of macromolecules can change quite considerably [82]. Indeed, these experiments indicate growth rate can be limited by crowding [82], just as predicted by the unconstrained CGGR model (see below).
Since the macromolecular volume fraction in the unconstrained CGGR model is not constant, we needed to consider crowding effects on association reactions such as transcription initiation and translation initiation. We investigated two possible crowding scenarios: one in which all association reactions are diffusion limited and one in which all association reactions are transition state limited and have been evolutionally tuned to be maximal at the WT volume fraction. Both crowding scenarios give an adequate fit to the growth rate and rRNA to total protein ratio data, thanks to the homeostasis mechanism involving free RNAp and free ribosomes. However, the diffusion limited model seems to give a slightly better fit when considering the feedback response and ribosome efficiency data, possibly indicating a preference for this model. Indeed, it has been proposed that the in vitro 30S-mRNA association may be diffusion rate limited since in vitro measured on rates are of the order of the diffusion limit [90]. In addition we have proposed a simplified version of the unconstrained CGGR model, which is a three-variable model and is included since it is an analytically solvable reduction of the more complicated six state model. We have shown, however, that since the simplified model does not take into account the physical effects of crowding, its predictions for strains with increased rRNA operon gene dosage is unrealistic. Hence the full unconstrained CGGR model is the biophysical model that we propose to be relevant for E. coli growing in balanced exponential steady-state growth in a rich medium.
Further support for the reduction of macromolecular volume fraction in the rRNA inactivation strains may perhaps be found in fluorescence images of the WT Squires strain vs. the Δ6 strain in which six rRNA operons have been inactivated Figure 4 in [19]). The nucleoid in the WT cells is seen to be much more compact than in the Δ6 strain, suggestive of lower entropic forces in the latter strain due to a lower degree of crowding [48]. Recent observations in strains in which six rRNA operons were entirely deleted from the genome (and not just inactivated as in [19]) show similar results, and also indicate that the compact structure of the nucleoid was recovered in strains in which rRNA is expressed solely from a high copy number plasmid with all other rrn operons entirely deleted from the genome (S. Quan and C. L Squires, personal communication). These results are consistent with crowding effects [48] predicted by the unconstrained CGGR model, effects that are absent in the constrained CGGR model.
While both unconstrained CGGR models exhibited a decrease in the expression of a single rRNA operon as rRNA gene dosage was increased, as is observed in the feedback response, in the case of the inactivation strains, the diffusion limited model appeared to be in better agreement with the feedback response than the transition state model (Figure 4). In the former model, rRNA expression from a single rRNA operon increased as rRNA operon copy number was decreased from 19 copies per chromosome to 3 copies per chromosome. The transition state model exhibited this dependence only up to an rRNA operon copy number of 5. The increase in the rRNA operon expression is due to an increase in the ratio of free RNAp concentration and the rRNA operon binding affinity (Figure S4). It is interesting to note that in the diffusion limited scenario, it is actually the changes in binding affinities, and not free RNAp, which correct for the observed trend of the feedback response, as free RNAp concentration is actually predicted to increase when the number or rRNA operons per chromosome is increased (Figure S4B). Furthermore, we found that a model in which the rRNA chain elongation rate increases when inactivating rRNA operons, as observed experimentally [54], exhibits a slightly stronger feedback response when inactivating rRNA operons than a model that assumes that this parameter is constant.
Quantitatively, in the case of the diffusion limited scenario with variable rRNA chain elongation rate, the rRNA expression from a single operon increased from 0.6 of the WT expression for 19 chromosomal rRNA operons to about 1.1 of the WT expression for 3 chromosomal rRNA operons. Although rRNA operon synthesis rate was not measured for the inactivation strains considered here, we can qualitatively compare these predictions to experiments with other strains. Strains in which four rRNA operons were inactivated exhibited a 1.4 to 1.5 increase rRNA operon expression relative to a WT background, where expression was measured as β-galactosidase activity from WT P1 promoter fragments fused to a lacZ reporter gene and normalized to expression from a WT background [64] (we are not aware of measurements for lower copy numbers). In a similar manner rRNA expression was shown to decrease by a factor of 0.65 to 0.8 with respect to the WT background in strains in which rRNA gene dosage increased by using plasmids expressing rRNA (the plasmid copy number was not specified) [64]. In a different study by the same group, the initiation rate in strains with increased rRNA operon copy number was obtained based on counting the number of RNAp bound to rRNA operons using electron microscopy and measurement of the rRNA elongation rate, and yielded 0.66 of the WT initiation rate [65]. Although the predicted feedback response for the inactivated strains is somewhat weaker than the response observed experimentally, the overall trend appears to be in qualitative agreement with the feedback response, i.e. as the rRNA operon copy number is increased, the transcription from a single rRNA operon decreases. We note however that the genetic makeup of the inactivated strains tested above differed from the inactivated strains of Asai et al. [91], especially in the respect that in the former strains, each inactivated rRNA operon expressed antibiotic resistance, which may have had adverse effects on the cell. The fact that the elicited feedback response is not as strong as the one observed experimentally in the inactivation strains may also possibly be a consequence of some of the simplifying assumptions made in this model (e.g. ideal RNAp autoregulation or the somewhat naïve crowding models assumed) or perhaps indicate the presence of an additional mediator (see below).
The unconstrained CGGR model also predicts that bulk mRNA transcription would be affected by the change in rRNA gene dosage since in the current model bulk RNAp binding affinity has the same response to changes in macromolecular crowding as the rRNA binding affinity. The effect may be, however, somewhat alleviated by the fact that bulk mRNA binding affinity is proposed to be about 3 times stronger than the P1 rRNA promoter (which is the major site for the feedback response [92]) at this growth rate (Table S4), thus closer to saturation, and can even be ∼30 times stronger in poor medium (Table 2 in [28]), although is has also been suggested that RNAp promoters may require the same or less RNAp than other RNA promoters for transcription [93]. Also, in principle rRNA and bulk promoters could respond differently to crowding. When measured experimentally, mRNA promoters did in fact exhibit some reduction when the feedback response was induced using increased rRNA gene dosage: while expression of a P1-lacZ fusion decreased by 0.45 relative to a control with WT rRNA gene dosage, spc or lacUV5 promoters fused to lacZ decreased by ∼0.8 relative to the same control [92]. Nevertheless, these results may also indicate that there is an additional mediator involved, which interacts specifically with the P1 rRNA promoter [92]. If this turns out to be the case, the influence of such an effector could be incorporated into the proposed model.
No molecule, however, has yet been implicated in the feedback response to a change in the rRNA gene dosage. In addition, experiments indicate that ribosomes appear not to be directly responsible for this feedback response (see Introduction). Therefore, it may be possible that for the type of perturbation considered here, the feedback response results, at least in part, from internal feedbacks inherent in the system. Various models have suggested that free RNAp is in one way or another limiting (e.g. [28] and also discussion in [3],[5]), yet it is not certain that changes in RNAp alone can account for the observed changes in rRNA expression due to changed rRNA gene dosage [1],[5],[65]. In the present work, we are only concerned with the response of the cell to changed rRNA operon copy number in a constant rich environment, where ppGpp concentration is constant. Therefore we do not attempt to explain how ppGpp modulates rRNA expression. In addition, we found that the model that best fits experimental data is one where both the concentration of free RNAp and the binding affinities of RNAp to its promoters are altered in response to changes in rRNA gene dosage. Therefore, according to this model, it is not the concentration of free RNAp which affects the transcription, as has been proposed in the past, but rather the ratio of the concentration of free RNAp to its binding affinity that determines transcription. In fact, we have seen that in the diffusion limited scenario, free RNAp concentration actually decreases as rRNA operon copy number is reduced, and it is the increase in the rRNA operon binding affinity that is responsible for the increased transcription of the rRNA operon (e.g. Figure S4B).
The notion that crowding can be an effector modulating transcription of the rRNA operons is consistent with the feedback model of Nomura and coworkers [1],[63] since only functional rRNA gets assembled into ribosomes, and together with supporting bulk proteins crowd the cell, thus contributing to the feedback response. Nonfunctional rRNA would be degraded away and hardly contribute to crowding or the feedback response. Finally, the notion that the feedback arises from the inherent internal feedbacks in the cell is consistent with the indirect aspect of the feedback response proposed by Nomura and coworkers [62].
Extrapolating to higher copy numbers suggests that the WT growth rate in a constant and rich environment is nearly maximal. In an experiment with increased rRNA gene dosage, where ppGpp concentration was shown to be constant, the growth rate of a strain carrying extra rRNA operons on a plasmid indeed decreased by 22% relative to a WT strain carrying a control plasmid expressing nonfunctional rRNA [20], in agreement with the trend predicted by the model. In another experiment with increased rRNA gene dosage, growth rate decreased relative to WT cells containing a control plasmid, and rRNA to total protein ratio was more or less constant (thus appearing to favor the unconstrained CGGR model) although the authors argue that there may be tRNA imbalance in these strains [94]. In addition, the unconstrained CGGR model predicted that ribosome and bulk protein concentration increase with rRNA operon copy number (Figure S5) thus leading to an increase in the macromolecular volume fraction (Figure 3, insert). This increase is due to the cost criterion hypothesis (Eq. 5), which correlated the concentration of bulk protein in the cell with the concentration of ribosomes.
The biophysical origin of the predicted upper limit on growth rate with respect to the rRNA operon copy number, suggested by the unconstrained CGGR model, is overcrowding of the cytoplasm with ribosomes and with bulk proteins supporting/synthesized by those additional ribosomes via the cost criterion relation (Eq. 5). As rRNA operon copy number is increased, the concentration of ribosomes and bulk protein increases (Figure S5) leading to an increase in macromolecular volume fraction in the cell (Figure 3, insert). In vitro experiments suggest that in a crowded environment diffusion times increase [47],[51],[87]. If in an overcrowded environment, when all reactions are thought to be diffusion limited [44], [51], [83]–[85], increased diffusion times cause binding affinities to weaken, then overcrowding will reduce the efficiency of transcription initiation and translation initiation (Figure 4 and Figure S4). This reduction in efficiency ultimately causes the growth rate to decrease at high rRNA operon copy numbers. In the scenario where binding affinities were assumed to be independent of the level of crowding in the cell (the ‘no crowding’ scenario in Figure 2A), growth rate continued to increase as rRNA operon copy number increased, indicating that the reduction in growth rate in the transition state and diffusion limited crowding scenarios was due to crowding effects. See also Figure S6 for a breakdown of the different contributions in the ribosome synthesis equation, Eq. 2iii. Interestingly, a similar phenomenon may be occurring in osmotically stressed cells. It has been shown experimentally that the growth rate of osmotically stressed cells is correlated with the amount of cytoplasmic water in those cells [82] leading those authors to propose that increased diffusion times of biopolymers due to crowding may be limiting growth rate. This conclusion appears to be in accord with our findings.
The fact that the maximum in growth rate is so shallow may suggest that in a natural environment for E. coli there are additional constraints in the system. In nature, E. coli is likely to experience chronic starvation conditions like in water systems, as well as fluctuating environments like in the host intestine [15],[16]. Indeed, it has been shown that E. coli's growth rate displays a more pronounced dependence on the rRNA operon copy number in a changing environment compared to a constant one [15], and that a high rRNA operon copy number enables E. coli and other bacteria to adapt more quickly to changing environments [15],[95],[96].
Finally we wish to point out that the optimum we have shown is only with respect to rrn copy number perturbations of a WT E. coli genome, and therefore may possibly not be a global one. A higher growth rate could perhaps be attained when considering perturbations of all genetic parameters.
The unconstrained CGGR model suggests possible insights into the design principles of E. coli. The model introduces the concept of a cost per gene class, akin to a chemical potential. In the absence of load genes for example, the cost criterion basically measures the number of bulk proteins needed to support the synthesis of ribosomes (or vice versa). This criterion implies that the cell is efficient: bulk protein is utilized to its full potential and is not stored as inventory for later use. This is true even for genetically perturbed (i.e. suboptimal) cells. A similar notion of efficiency was suggested by Ecker and Schaechter in the context of WT cells growing in different environments [21]. How then is the cost criterion realized by the cell? Perhaps the cost criterion is realized simply by virtue of internal feedback. If, for example, the rRNA operon copy number is slightly increased, resulting in a small increase in ribosome concentration, Δnribo, the transient deficit in bulk protein (−Δnbulk) will be compensated for, at steady-state, by the extra ribosomes when Δnbulk ( = criboΔnribo) bulk proteins are synthesized. nbulk therefore increases to the minimum concentration needed to sustain these excess ribosomes. Thus, the cost criterion obviates the need for a homeostatic mechanism for keeping Φ fixed. Nevertheless, direct experimental proof for the cost criterion is currently lacking.
An additional engineering principle suggested by the CGGR models is related to the DNA replication mechanism. Replication enters the model through the C period and the initiation volume (Eq. 3), both of which are regulated to be roughly constant [23],[41] and thus in principle unaffected by genetic perturbations (Tadmor and Tlusty, in preparation). Since this implies that gene concentrations do not depend strongly on growth rate (see Figure S7 and S2.2 in Text S1), this result suggests that the regulatory mechanism of replication initiation may be designed to be decoupled from the cell state. Such a scheme may simplify the task of engineering global regulation mechanisms such as the one responsible for rRNA regulation in different growth conditions or growth phases.
The CCGR models rely on many assumptions, the validity of which should be questioned. One possibility is that the coarse-graining has discarded “hidden variables”. Such variables may include, for example, the structure of the nucleoid and transcription factors associated with it (which can affect global transcription [97]), or the osmotic response of the cell [82]. In addition, strong genetic perturbations may lead to ribosome instability [98] and possibly induce a stress response with global effects. Other concerns may be possible additional factors regulating rRNA synthesis alluded to earlier, the validity of the assumptions regarding the function of the bulk protein and the existence of limiting resources even in a rich environment. In a resource limited environment for example, state variables related to the energy metabolism of the cell would probably come into play. Although, regarding limitation of resources, as was pointed out in the Introduction, it has been demonstrated experimentally that the concentration of NTP is constant or changes by only a small amount when altering the rRNA operon copy number [20],[64], and ppGpp is also constant in these strains [20],[54]. The latter observation suggests that the cell is not limited, for example, by the availability of amino acid, charged tRNAs or carbon [69],[89] (see also [6]). Another concern may be that some portion of the inactive RNAp, which was assumed to be inaccessible because of pausing, is actually nonspecifically bound to DNA [28] and might serve as an additional reservoir of RNAp for transcription initiation. With all these difficulties in mind, the advantage of the CGGR modeling approach is that it offers an initial conceptual framework for thinking about E. coli while making quantitative predictions. Such tests can be useful in identifying factors that have been left out in this round of coarse-graining and can be subsequently added. Examples of quantitative predictions include: (i) non-constancy of the macromolecular volume fraction in genetically perturbed cells (Figure 3, insert) (ii) state variables and their relations, e.g. the cost criterion (Figure S5) (iii) decay of binding affinities at high volume fractions (Figure 3 and Figure S8; raising the more general question of the nature of crowding effects on equilibrium constants) (iv) increase in bulk mRNA half-life with rRNA operon copy number. Yet another test to this model may be to increase rRNA gene dosage beyond the WT gene dosage, where the differences between the CGGR models is much more pronounced [28] (Figure 2B). Although the focus here was on altering the rRNA operon copy number, other genetic perturbations can be considered, like adding non-native proteins that only serve as a load on the cell. In such a case, in vivo diffusion times are expected to be increased due to increased crowding. Green fluorescent protein (GFP) diffusion coefficient did in fact appear to decrease in E. coli cells overexpressing GFP, however GFP dimerization may have contributed to this effect, as noted by Elowitz et al. [99]. Finally, the proposed model may suggest testable predictions for the effect of genetic noise on protein expression and growth rate. |
10.1371/journal.pntd.0000861 | Trachoma Prevalence and Associated Risk Factors in The Gambia and Tanzania: Baseline Results of a Cluster Randomised Controlled Trial | Blinding trachoma, caused by ocular infection with Chlamydia trachomatis, is targeted for global elimination by 2020. Knowledge of risk factors can help target control interventions.
As part of a cluster randomised controlled trial, we assessed the baseline prevalence of, and risk factors for, active trachoma and ocular C. trachomatis infection in randomly selected children aged 0–5 years from 48 Gambian and 36 Tanzanian communities. Both children's eyes were examined according to the World Health Organization (WHO) simplified grading system, and an ocular swab was taken from each child's right eye and processed by Amplicor polymerase chain reaction to test for the presence of C. trachomatis DNA. Prevalence of active trachoma was 6.7% (335/5033) in The Gambia and 32.3% (1008/3122) in Tanzania. The countries' corresponding Amplicor positive prevalences were 0.8% and 21.9%. After adjustment, risk factors for follicular trachoma (TF) in both countries were ocular or nasal discharge, a low level of household head education, and being aged ≥1 year. Additional risk factors in Tanzania were flies on the child's face, being Amplicor positive, and crowding (the number of children per household). The risk factors for being Amplicor positive in Tanzania were similar to those for TF, with the exclusion of flies and crowding. In The Gambia, only ocular discharge was associated with being Amplicor positive.
These results indicate that although the prevalence of active trachoma and Amplicor positives were very different between the two countries, the risk factors for active trachoma were similar but those for being Amplicor positive were different. The lack of an association between being Amplicor positive and TF in The Gambia highlights the poor correlation between the presence of trachoma clinical signs and evidence of C. trachomatis infection in this setting. Only ocular discharge was associated with evidence of C. trachomatis DNA in The Gambia, suggesting that at this low endemicity, this may be the most important risk factor.
ClinicalTrials.gov NCT00792922
| Trachoma is caused by Chlamydia trachomatis and is the leading infectious cause of blindness. The World Health Organization's (WHO) control strategy includes antibiotic treatment of all community members, facial cleanliness, and environmental improvements. By determining how prevalent trachoma is, decisions can be made whether control activities need to be put in place. Knowing what factors make people more at risk of having trachoma can help target trachoma control efforts to those most at risk. We looked at the prevalence of active trachoma and C. trachomatis infection in the eyes of children aged 0–5 years in The Gambia and Tanzania. We also measured risk factors associated with having active trachoma or infection. The prevalence of both active trachoma and infection was lower in The Gambia (6.7% and 0.8%, respectively) than in Tanzania (32.3% and 21.9%, respectively). Risk factors for active trachoma were similar in the two countries. For infection, the risk factors in Tanzania were similar to those for TF, whereas in The Gambia, only ocular discharge was associated with infection. These results show that although the prevalence of active trachoma and infection is very different between the two countries, the risk factors for active trachoma are similar but those for infection are different.
| Trachoma is caused by ocular infection with serovars A, B, Ba or C of the bacterium Chlamydia trachomatis. It is the leading infectious cause of blindness worldwide [1] with an estimated 40.6 million people suffering from active trachoma (trachomatous inflammation, follicular (TF) and/or intense (TI)) and 8.2 million having trichiasis [2]. As part of the “SAFE” (Surgery, Antibiotics, Facial cleanliness, Environmental improvement) trachoma control strategy, the World Health Organization (WHO) recommends mass antibiotic treatment annually for at least three years of all individuals in any district or community where the prevalence of TF in children aged 1–9 years is at least 10%. After three or more years of A, F and E interventions, the prevalence is reassessed and a decision is made regarding the need to continue or cease treatment [3]. Mass antibiotic treatment aims to clear infection from the community, most of which is found in children [4].
Trachoma is endemic in both The Gambia and Tanzania, with estimated active trachoma prevalences in children aged 1–9 years of 10.4% and 27%, respectively [5], [6]. Accordingly, they have both recently qualified for a donation of the antibiotic azithromycin for mass treatment by Pfizer via the International Trachoma Initiative. Given the different endemicities of these two countries, one in which trachoma is almost disappearing and one in which trachoma shows only modest signs of being reduced, the question of whether the same risk factors are predictive of trachoma is of interest. In addition, since the presence of trachoma clinical signs is often poorly correlated with that of ocular C. trachomatis infection [5], [7], [8], [9], [10], [11], the risk factors for these markers of trachoma may also differ.
Studies have shown that although young age is a common risk factor for active trachoma, other risk factors may be setting-specific. Furthermore, few studies have simultaneously reported risk factors for active trachoma and ocular C. trachomatis infection within the same setting [12], [13]. Information on risk factors can contribute to our understanding of trachoma transmission within the study area, and the targeting of trachoma control interventions can be aided through knowledge of risk factors.
We aimed to assess the prevalence of, and risk factors for, both active trachoma and ocular C. trachomatis infection pre-treatment in The Gambia and Tanzania, as part of the Partnership for the Rapid Elimination of Trachoma (PRET) cluster randomised controlled trial. The aims of PRET are to test the impact on the prevalence of active trachoma and ocular C. trachomatis infection, as detected by Amplicor PCR, after three years in communities mesoendemic for trachoma (between 20% and 50% TF) or hypoendemic (between 10% and 20% TF), when communities are randomised to different mass treatment population coverage levels and a different number of rounds of treatment, with a graduation rule if the prevalence of TF or detected ocular C. trachomatis infection falls below 5% (Stare et al. submitted).
The data presented here are from the baseline surveys of PRET, where data on the prevalence of TF and evidence of ocular C. trachomatis infection were collected, and risk factors for these outcomes were obtained in a standardised fashion.
The study methods have been described in detail elsewhere (Stare et al. submitted) and are summarised below. Reporting of the study has been verified in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist (provided as supporting information, Checklist S1).
The research was done in accordance with the declaration of Helsinki. Ethical approval was obtained from the London School of Hygiene & Tropical Medicine (LSHTM), UK, Ethics Committee; The Gambia government/Medical Research Council (MRC) Joint Ethics Committee, The Gambia; the Johns Hopkins Institutional Review Board; and the Tanzanian National Institute for Medical Research. Oral consent was obtained from the village leaders, and written (thumbprint or signature) consent from the child's guardian at the time of examination, which was signed by an independent witness.
In The Gambia, 48 census Enumeration Areas (EAs), designed to have similar population sizes of between 600–800 people, were randomly selected from within 4 strata consisting of the following districts: Foni Bintang and Foni Kansala in Western Region, and Central Baddibu and Lower Baddibu in North Bank Region (12 EAs per district) (Figure 1). In Tanzania, 32 communities (geographically distinct areas within a village with an average population of approximately 1500 people) were selected in Kongwa district, Dodoma region (Figure 2). Tanzanian communities were selected based on having an active trachoma prevalence above 20% in preliminary surveys and were therefore not randomly selected as they were in The Gambia.
A week-long workshop was conducted in February 2008 to standardise all fieldwork methods, including trachoma grading, photography, sample collection, form filling, facial cleanliness status grading, and data entry. For trachoma grading, graders were standardised against a senior grader (RB) every day by examining participants in the field. A kappa of >0.6 for TF grading was required between the senior grader and the graders in the final grading exam. All other procedures had to be performed correctly five times in the field under observation by senior investigators before certification was given. Fieldwork in The Gambia took place between 19th May 2008 and 29th July 2008. In Tanzania, data collection was between 15th May and 1st November 2008.
Data were entered into a customised database (MS Access v2007) developed at the Dana Center, Johns Hopkins University. Key fields were double-entered by different entry clerks. Reports of discrepant, missing or query entries were generated in the database and resolved by reference to the forms, or in some cases by return field visits. Further queries of data inconsistencies were produced in statistical packages (Stata v10, STATA Corp., College Station, TX, USA for the Gambian data; SAS v9.2, SAS Institute Inc., Cary, NC, USA for the Tanzanian data) prior to analysis. All queries were verified against the original paper forms. The analyses presented here were conducted using Stata, v10.
Baseline characteristics of household attributes and population size were summarised for both countries. Evidence of variability between communities (clusters) and households was assessed using random effects logistic regression models assuming a 3-level hierarchy to the data structure (community, household and individual) in null regression models.
Univariate associations with TF and ocular C. trachomatis infection in children aged 0–5 years were tested using random effects logistic regression, accounting for between-cluster and between-household variation (variance), comparing models with and without covariates using the likelihood ratio test (LRT). Multivariate model building for TF and C. trachomatis infection in both countries employed the same stepwise strategy; age and sex were considered a priori risk factors and included in all models. Covariates associated with TF or evidence of C. trachomatis infection at the 10% significance level in univariate analyses were added in turn (a forward stepwise approach) and covariates retained in the model if the LRT p-value was ≤0.1. In The Gambia, the final multivariate model also adjusted for district to account for sampling stratified by district.
In The Gambia, 5033 children aged 0–5 years were examined. In Tanzania, 3198 children were examined but ocular C. trachomatis data were missing from 76 of these children. In The Gambia, there were 9 households with missing data for awareness of a village face-washing education programme. In Tanzania, the number of missing values was 5 for household head education, 6 for time to water, 20 for latrine access, and 693 (of which 677 were recorded as “unknown”) for knowledge of a face-washing health education programme.
Community randomisation units were larger in Tanzania than in The Gambia, containing more, smaller households, as seen from the total population size and average household sizes (Table 1), although similar proportions of the total population were children aged under 10 years. Household heads in The Gambia had less formal education than in Tanzania, whereas latrines and water were less easily accessible in Tanzania. Around one third of households in both countries reported awareness of receiving community face-washing health education programmes.
The low prevalence of Amplicor positives in The Gambia provided little power for formal risk factor analyses (Table 5). Chi-squared tests of association suggested that ocular discharge was a possible risk factor for an Amplicor positive result (p = 0.044) and that prevalence varied by district (p<0.001). In Tanzania, Amplicor positivity was associated in univariate analyses with being aged ≥1 year, having ocular or nasal discharge, flies on the child's face, lack of household head education, and poor access to water or a latrine (Table 5). In multivariate models, being Amplicor positive was only significantly related to being aged 2–5 years, having discharge, and a head of household educational level of less than 7 years, and possibly poor access to water (Table 5). Other factors were not related to evidence of C. trachomatis infection.
As expected, the TF and Amplicor positive prevalences were lower in the Gambian than in the Tanzanian sample, with pre-treatment prevalences similar to reported national estimates [5], [6]. The Gambia and Tanzania have different trachoma control profiles. The Gambia is the smallest country on the African continent. Its National Eye Care Programme (NECP) has been in operation since 1986, which covered the whole country by 1996 [17]. There is also evidence of trachoma decline in the absence of specific control interventions but associated with improvements in sanitation, water supply, education, and access to health care in the villages [18]. In contrast, the national trachoma control programme in Tanzania started in 1999, with SAFE incorporated into the district health plan of 12 of the 50 trachoma-endemic districts by 2008 [19]. Mass azithromycin treatment in The Gambia began in November 2007, whereas in Tanzania distribution programmes started in 1999.
The risk factor data suggested similarities between the two countries in that TF was related to signs of an unclean face (ocular or nasal discharge), the level of household head education, and the child being aged ≥1 year. In the multivariate models, the association with access to water in The Gambia was weak and there was no association in Tanzania, suggesting that univariate associations were driven by confounding with education or facial cleanliness markers. Interestingly, there were additional risk factors for TF in Tanzania: being Amplicor positive, an index of crowding (the number of children per household), and flies on the children's faces. This may reflect that transmission of C. trachomatis is easier and more frequent in Tanzania, though we did not find an association between evidence of being Amplicor positive and crowding or flies on the face. It may be that through crowded conditions and exposure to flies, there is continual stimulation of the follicular immune response because of unhygienic living conditions, or the presence of a low level of inoculum that either does not lead to productive infection or a short lived infection.
Some level of education for the household head was a protective factor for TF in both settings. In The Gambia, few household heads had formal education, as has previously been documented [20], whereas Tanzanian household head formal education was better. A higher education level, a possible proxy for higher socio-economic status, has been associated with reduced risk of trachoma in other studies [21], [22]. In Egypt, low service utilisation has been associated with low educational and socio-economic level [23], which may be an explanation for the protective effect afforded by a greater number of years of household head education.
Knowledge of a health education programme in the village was not associated with TF in either setting. A third of Tanzanian households reported knowing of a health education programme, which is similar to that previously reported in the same Tanzanian district [24]. However, 693 households had missing data for this variable, based on the households reporting “don't know” to the question. Thus, this may not be the most reliable method of collecting data on the presence of health education programmes in communities since recall may be poor. The proportion of households in The Gambia aware of a health education programme was similar to that in Tanzania, despite village health workers not being required to conduct village-level hygiene sessions as they are in Tanzania. In Tanzania, the households that responded “don't know” may have done so if they did not consider the hygiene promotion provided by village health workers as formal health education. Health education programmes have previously been associated with clean faces when examined in clinics, indicating the potential impact that these programmes can have on facial cleanliness [24]. However, there is evidence that health education alone does not result in effective behaviour change [25], [26].
Both ocular and nasal discharge were associated with increased risk of TF in both settings. Discharge has been closely associated with active trachoma in many studies, including those from Tanzania and The Gambia [20], [27], [28], [29], but the causal relationship remains unclear. Infected discharge may aid transmission via fingers, flies or fomites. Discharge may however also be the consequence of trachoma, as inflammation of the conjunctiva could result in discharge being produced [20], [24].
Less than 10% of children had ocular discharge or flies on their face in Tanzania, although nearly half had nasal discharge. The association between TF and flies in Tanzania accords with previous risk factor studies from this country [13], [29], [30], [31]. In The Gambia, the prevalence of flies on the face was higher (14.7%), ocular discharge was similar (8.8%), and nasal discharge was less (32.9%), than in Tanzania. In The Gambia, flies were not associated with TF after adjustment for age, sex, ocular and nasal discharge, indicating that the univariate effect of flies was explained by discharge. This is consistent with the finding by Emerson et al. in The Gambia that those with discharge had twice the fly-eye contacts of those without [32]. In Tanzania, the strength of the individual associations of ocular discharge, nasal discharge and flies on the face with TF was slightly weaker in the multivariate model than in the univariate model. In particular, the association with flies showed only some evidence of an association once adjusted for ocular and nasal discharge, indicating that some of the univariate association was explained by the presence of discharge.
If the presence of either nasal or ocular discharge is combined into a single sign of an unclean face, then the prevalence of unclean faces in the sentinel children in Tanzania, 50.9%, was greater than in The Gambia, 37.5%, perhaps reflective of the higher rate of trachoma and infection in the Tanzanian setting. Thus, despite similar awareness of health education, The Gambia appears to have better facial cleanliness. However, in our study, facial status was recorded at the time of examination and could therefore reflect parents cleaning their children's faces before taking them for screening, leading to an under-estimate [24].
Latrine access was not associated with TF in either country. In Tanzania, household latrine access was 64.8%, indicating that latrine provision at these levels may be sufficient. The provision of latrines is expected to result in a reduction in flies because Musca sorbens (the putative trachoma vector) has been seen to preferentially breed in exposed human faeces [33], but is not found exiting latrines [34]. Latrine access in The Gambia was 89.6%, and the importance of latrine access has previously been demonstrated in this setting [35]. However, access to a latrine does not necessarily mean the latrine is used [36]. Previously in the same Tanzanian district of Kongwa, Taylor et al. found an association between lack of latrine access and TI. They also noted that only 59% of households had access to a latrine, and 23.4% of these were non-functional [37]. In a case-control study in the same Tanzanian district, Montgomery et al. observed that latrine use was greater in households that were trachoma-free than households containing a case of active trachoma, and this association remained after adjustment for potential confounders [22]. Thus, measurement of latrine use, rather than latrine access, may be a more valuable marker of the effect of latrines on trachoma prevalence.
In The Gambia, a primary water source more than 30 minutes away was weakly associated with increased risk of TF. Interestingly, time to fetch water was not a risk factor for TF in Tanzania. Poor access to water was recently not reported as a risk factor for active trachoma in a comparable study area of The Gambia [20], whereas in Tanzania it is often an independent risk factor for active trachoma [30], [38]. Montgomery et al. found that there was an unadjusted association between households with worse access to water and having a case of active trachoma [22]. Access to water in this district has not changed in the past 20 years, as West et al. (1989) documented that 79.8% of children came from households more than 30 minutes away from the water source [39], which is similar to the 76.9% found in our study. The lack of an association in Tanzania despite no apparent change in access to water indicates that interventions other than water supply improvements may now be more important for trachoma control in this area of Tanzania.
However, as with latrine access and use, access to water does not necessarily correlate with behaviour change. In the study by West et al. (1989), although there was a greater risk of having active trachoma and an unclean face with increased distance to water, these outcomes were not associated with the estimated amount of water brought into the household [39]. In a Gambian study, families with active trachoma were likely to use less water per person per day to wash children than families with no cases of active trachoma, after adjustment for family size, distance to water and socio-economic indicators [40]. If it is assumed that discharge is a causal factor in trachoma transmission, it is possible that a combination of improved water access and community health education may lead to cleaner faces and a consequent decline in trachoma. However, as previously noted, interpretation of clean face data is difficult, and a decline in unclean faces may not correlate with a decline in trachoma.
In Tanzania only, an increased risk of TF was associated with an increase in the number of children per household. In Tanzania, West et al. similarly found an association between the number of children per household and active trachoma [39], and in The Gambia Bailey et al. observed that an increase in the number of people per bedroom was associated with active trachoma [41]. The number of children in the household is used as a marker for crowding, but the two are not necessarily correlated, as a few people may live in a small house and many people may live in a large house. A study in Mali showed that although increased risk of active trachoma was associated with bedrooms in which there were more than 4 people, it was also associated with households containing less than 10 people compared with more than 10 [21]. Without an understanding of the household physical space, it is not possible to determine whether a larger number of people is a true marker of crowding.
Risk factors for evidence of infection differed between the countries, largely due to the relative absence of Amplicor positives in The Gambia and the associated lack of power to observe associations. In Tanzania, where active trachoma and being Amplicor positive were well associated, risk factors for TF and Amplicor positivity were similar, although the environmental factors of crowding and flies on the face were not related to being Amplicor positive. In The Gambia, since ocular discharge was the only factor associated with evidence of infection, it may be the key infection transmission route at this low endemicity. We have previously suggested that child-level factors should be the main target for control interventions in this setting [20], and that targeting children may eliminate the last reservoirs of infection.
As with any risk factor study reliant on a questionnaire, the validity of the data is prone to responder bias. The questionnaires were designed to be simple and appropriate for both countries for logistic and comparability reasons. We therefore asked a limited number of questions and did not support these with observational data, such as evidence of water or latrine use, which may be better measures of these trachoma risk factors [22], [40]. The major limitation of cross-sectional risk factor questionnaires is the inability to determine causality. Thus, although active trachoma and infection were both associated with ocular discharge in both countries, this does not necessarily mean that ensuring the absence of ocular discharge (such as through hygiene promotion) will aid trachoma control. Furthermore, determination of an unclean face may have been affected by the children's faces being examined at the time of screening, rather than in their household setting [24].
In the Gambian districts of Lower and Central Baddibu, the prevalence of clinical signs appears to have decreased in the absence of mass azithromycin treatment within the two years preceding this study. Only one village was previously mass treated in April 2006. The overall TF prevalence of 6.7% observed in this study is lower than the prevalence of 10.7% found in children aged 1–9 years in a survey conducted in 2006 [5]. In 2006, the prevalence of TF in 0–5 year-olds was 19.2% (19/99) in Lower Baddibu and 25.0% (23/92) in Central Baddibu, whereas the districts' respective prevalences in the same age-group are now 9.2% and 10.1%. This is not surprising as the NECP has been active since 1986, and over 85% of households have access to a latrine and a water source within 30 minutes. The overall prevalence of Amplicor positives in The Gambia in this study was only 0.8%, similar to the 0.3% observed in 2006 [5]. These results indicate that there is very little ocular C. trachomatis infection remaining in The Gambia. This may explain the lower TF prevalence in those aged under 1 year (1.6%) compared with the older age groups who have a prevalence exceeding 6%. However, the lower prevalence in the younger age group may be because they have less exposure, or the follicular signs of trachoma only appear after repeated or persistent infections and are therefore rare in children aged less than 1 year. The lower prevalence may also be due to the lack of ability to mount a follicular response to organism in very young children. In older children, there may also be some factor other than C. trachomatis infection causing follicles to appear, such as other organisms like Moraxella and adenovirus [7].
The difference between the two countries is further accentuated by the association between presence of clinical signs and evidence of infection. In The Gambia, the association was poor with only 0.9% of those with active trachoma being infected. In contrast, 38.9% of Tanzanian children with disease were Amplicor positive. Interestingly, of the few cases with evidence of infection in The Gambia, the majority did not fulfil the WHO criteria for active trachoma. This could indicate that there was contamination of samples or Amplicor false-positives. However, only one (0.4%) air control was positive, the laboratory passed its standardisation with the UCSF laboratory, and there were only 39 (0.8%) Amplicor positives in total, indicating that the risk of contamination and false-positives was low. The association in Tanzania was more conventional, with most Amplicor positive individuals being classified as also having active trachoma. Disease in Tanzania was more severe than in The Gambia with a larger proportion of children with clinical signs having TI, and these were more likely to be Amplicor positive. As seen in other studies, the prevalence of infection is associated with disease severity [10], [42], [43], [44]. It is likely that the distribution of mass treatment in Tanzania might increase the disconnect between infection and disease, as infection rapidly declines but clinical signs of trachoma persist [7], [11], [45], [46].
In summary, this study showed that despite different prevalences of active trachoma and evidence of infection between the Tanzanian and Gambian study sites, the risk factors for TF were similar. The risk factors for being Amplicor positive in Tanzania were similar to those for TF, whereas in The Gambia, only ocular discharge was associated with evidence of C. trachomatis DNA, suggesting that at this low endemicity, this may be the most important risk factor. The lack of an association between being Amplicor positive and having TF in The Gambia highlights the poor correlation between the presence of trachoma clinical signs and evidence of C. trachomatis infection in this setting.
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10.1371/journal.ppat.1003282 | Mycobacterium tuberculosis Responds to Chloride and pH as Synergistic Cues to the Immune Status of its Host Cell | The ability of Mycobacterium tuberculosis (Mtb) to thrive in its phagosomal niche is critical for its establishment of a chronic infection. This requires that Mtb senses and responds to intraphagosomal signals such as pH. We hypothesized that Mtb would respond to additional intraphagosomal factors that correlate with maturation. Here, we demonstrate that [Cl−] and pH correlate inversely with phagosome maturation, and identify Cl− as a novel environmental cue for Mtb. Mtb responds to Cl− and pH synergistically, in part through the activity of the two-component regulator phoPR. Following identification of promoters responsive to Cl− and pH, we generated a reporter Mtb strain that detected immune-mediated changes in the phagosomal environment during infection in a mouse model. Our study establishes Cl− and pH as linked environmental cues for Mtb, and illustrates the utility of reporter bacterial strains for the study of Mtb-host interactions in vivo.
| Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, a disease that remains a major global health problem. To ensure its long-term survival in the host, Mtb must be able to sense and respond to changes in its immediate environment, such as the pH differences that occur in the phagosome in which it lives. Knowledge of the external signals that Mtb recognizes during infection is critical for understanding the impact of the microenvironment on Mtb pathogenesis and persistence, and how Mtb interacts with its host cell. We show here that [Cl−] correlates inversely with pH as the phagosome matures, and identify [Cl−] as a novel cue that Mtb responds to, in synergism with pH. By constructing a Mtb strain that fluorescently reports on changes in [Cl−] and pH, we find using a mouse model of infection that environmental alterations in Mtb's phagosomal home are mediated at the local level by activities of the host immune system. Our study demonstrates how a pathogen can exploit linked environmental cues during infection, and shows the value of reporter bacterial strains for Mtb-host whole animal studies.
| Mycobacterium tuberculosis (Mtb) causes a chronic infection in approximately one third of the human population and remains an important public health problem [1]. The macrophage (MØ) is the major host cell for much of Mtb's life cycle, and a defining feature of Mtb's pathogenesis is its ability to arrest full maturation of the phagosome in which it resides [2], [3]. Indeed, Mtb mutants that fail to arrest phagosomal maturation have reduced survival during MØ infection [4]. However, Mtb remains subject to multiple stresses within the phagosome, which may act as important environmental cues for Mtb [5]. Proper sensing of such signals informs Mtb of its surroundings, allowing the bacterium to respond appropriately to ensure its survival and replication. Elucidating the cues that Mtb recognizes during infection, and the possible interplay between such signals, is critical for a complete understanding of the impact of the microenvironment on Mtb pathogenesis and persistence, and Mtb's interaction with fundamental host cell processes.
One environmental cue that has received particular attention is pH; the Mtb phagosome acidifies to an intermediate pH of 6.4 [3], [4], [6], and even in medium, the bacterium exhibits a profound transcriptional response to acidic pH [5], [7], [8]. The abolition of phagosome acidification during bacterial uptake by MØs, through treatment with concanamycin A, eliminates a majority of Mtb's transcriptional response, indicating the importance of pH as a signal for the bacterium in sensing and responding to its environment [5]. The process of acidification does not, however, proceed in isolation. Specifically, acidification (increase in [H+]) must be counterbalanced by efflux of other cations from the phagosome, and/or by the uptake of a counter anion. We hypothesized that Mtb might also take advantage of this counterbalancing factor as an environmental cue, expanding the sensitivity and dynamic range of its ability to define its immediate environment. Cell biological studies have established Cl− as a major counter anion during acidification of the endosome [9]–[11]. Several Cl− channels are known to be present on the endosomal membrane [12], [13], although it remains controversial which of these channels are involved in the counter-balancing of increased [H+] during endosomal maturation [14], [15]. More recent studies have also proposed efflux of cations, such as K+, as a counter mechanism to increased [H+] in the lysosome [16]. The existence of such mechanisms have not, however, been formally shown for phagosomes. In this context, it is of particular note that the Mtb phagosome has been reported to possess a high [Cl−] [17]. The impact of common ions and changes in their concentration on Mtb during infection is a concept that is just beginning to be appreciated [18]; however, much remains to be determined regarding their physiological significance.
In this study, we show that [Cl−] increases during phagosome maturation, mirroring a decrease in pH within the compartment. Mtb modulates its transcriptional profile in response to [Cl−], and reacts to the environmental cues of pH and [Cl−] in a synergistic manner, with the two-component regulatory system phoPR playing a central role in this response. By constructing a fluorescent reporter Mtb strain responsive to both Cl− and pH, we were further able to directly examine the microenvironment of Mtb during in vivo infection in a mouse model. Maturation of Mtb-containing phagosomes is known to be impacted by the immune status of the MØ. Infection of wild type versus immune-deficient interferon-γ−/− mice revealed differential induction of fluorescence in vivo, and demonstrated the influence of host immune pressure on the microenvironment in which Mtb resides. These data were further validated with a second Mtb reporter strain, expressing GFP under the regulation of the more fully-characterized hypoxia and nitric oxide-responsive dosR regulon [19]–[22]. The results confirm existing hypotheses concerning localized immune-mediated pressure within infection foci, and provide a new generation of tools to probe the fitness and viability of Mtb in in vivo infection models.
We first sought to establish the dynamics of [Cl−] during maturation of the phagosome with model particles. The fluorescent Cl−-sensitive, pH-insensitive compound 10,10′-Bis[3-carboxylpropyl]-9,9′-biacridinium (BAC) [9] was synthesized as a trifluoroacetate salt, and coupled to IgG beads. As previously reported, BAC fluorescence is quenched by Cl− in a concentration-dependent manner, and is unaffected by pH changes (Figure S1 in Text S1) [9]. To track [Cl−] changes during phagosomal maturation we attached Alexa Fluor 594 (AF594) as a calibration fluorophore to the BAC-IgG beads. These dual-color Cl− sensor beads were added to murine bone marrow-derived MØs and fluorescence measured in a microplate reader. We observed an increase in AF594/BAC fluorescence ratios over time, indicating an increase in [Cl−] as the phagosome matured (Figure 1A). This increase in [Cl−] was also observed with phagosome maturation in MØs derived from human monocytes (Figure 1B). To calibrate AF594/BAC ratios to actual [Cl−], we treated MØs that had phagocytosed Cl− sensor beads with bafilomycin A1 and the ionophores nigericin and monensin in buffers of known [Cl−]. By fitting a polynomial regression to the standard curve (Figure S2 in Text S1), we calculate that phagosomal [Cl−] reached a maximal concentration of ∼70–95 mM. As this is a population-based measurement, we note that this value range underestimates the [Cl−] that can be reached in individual phagosomes (see below).
We further examined the dynamics of [Cl−] increase during phagosome maturation by tracking individual beads during phagocytosis by live-cell time-lapse microscopy. These experiments showed that the switch from low [Cl−] to high [Cl−] occurred for most beads, although a subset remained in phagosomes with low [Cl−] (Figure 1C and Video S1). Imaging of populations of Cl− sensor bead-containing cells at given time points illustrated the heterogeneity in [Cl−] attained in individual phagosomes, with measurements indicating that a [Cl−] greater than 120 mM was reached in some phagosomes (Figure 1D). Similar results were obtained in MØs derived from human monocytes (data not shown). Cl− sensor beads present in media alone and imaged in parallel did not show significant changes in fluorescence, demonstrating that the decrease in BAC fluorescence observed in the phagocytosed beads has a biological basis and is not due to bleaching of the fluorescent signal during imaging (Video S2).
In examining these results, we noted that the increase in [Cl−] mirrored the kinetics of the decrease in phagosomal pH previously reported [23]. In order to quantify this correlation directly within a single experiment, we coupled BAC to IgG beads in combination with the red fluorescent pH sensor pHrodo, which exhibits an increase in fluorescence as pH decreases. Measurement of the fluorescence profile of the beads during phagocytosis by MØs showed the previously observed quenching of BAC signal indicative of increased [Cl−] as the phagosome matured (Figure 1E). pHrodo fluorescence on the same particles exhibited an inverse profile, increasing in intensity over time, signaling a decrease in pH (Figure 1E). Analysis of the phagocytosis of the BAC/pHrodo indicator beads by live-cell time-lapse microscopy further verified these results at the individual phagosome level (Figure 1F and Video S3). Similar profiles were observed in MØs derived from human monocytes (data not shown). We also verified that BAC/pHrodo beads imaged in media alone did not show such changes in fluorescence profile (Video S4).
Further support for the relation between [Cl−] and pH during phagosomal maturation was demonstrated by the failure of phagosomal [Cl−] to increase when MØs were treated with bafilomycin A1 (Figure S3A in Text S1). Similarly, addition of bafilomycin A1 to the MØs after phagosomes containing the Cl− sensor beads had initially been allowed to mature resulted in increased BAC fluorescence, indicating a reversal of the Cl− accumulation upon dissipation of the pH gradient (Figure S3B in Text S1). Together, these results demonstrate that [Cl−] increases during phagosomal maturation, and supports the proposed functional relationship between acidification of the endosomes and [Cl−] increase [9]–[11].
Mtb shows a marked transcriptional response upon exposure to acidic pH, and we have previously shown that almost half of the Mtb genes upregulated during an early stage of MØ infection are induced in a pH-dependent manner [5]. Given our results indicating [Cl−] increase during phagosomal maturation and the link between [Cl−] and acidification, we compared the transcriptional profiles of Mtb grown in regular 7H9 media to those grown in 7H9 media supplemented with 250 mM NaCl for 4 hours. The number of genes (32) upregulated on exposure to high [Cl−] was noticeably fewer than the hundreds previously reported to be induced under acidic pH (Table 1) [5], [7]. Strikingly however, a significant number of genes that were upregulated in the presence of high [Cl−] (18/32) were genes that also showed increased expression during exposure to acidic pH (Table 1).
The upregulated gene expression detected by microarrays was validated by semi-quantitative real time PCR (qRT-PCR) for several genes. These experiments were also carried out on samples exposed to acidic pH (pH 5.7), and showed data consistent with the microarray analysis (Figure S4 in Text S1). While our microarray platform allows for the global analyses of gene expression changes, it does have a flattened dynamic range [5], [24], and the qRT-PCR data indicate that the actual level of induction is considerably greater. These experiments indicate that Mtb responds transcriptionally to Cl−, and further reinforce the idea that pH and Cl− may function as interconnected environmental cues for Mtb during the course of infection.
To perform analyses of Cl− and pH as environmental cues for live Mtb, we utilized the microarray and qRT-PCR results to select candidate genes for construction of a reporter Mtb strain that would be responsive to both changes in [Cl−] and pH. We focused on the rv2390c-rpfD operon, which appeared particular promising as both genes in the operon showed robust induction under conditions of high [Cl−] or acidic pH (Figure S4 in Text S1). The promoter region of rv2390c was cloned upstream of GFP in a replicating plasmid, and transformed into Mtb CDC1551. This CDC1551(rv2390c'::GFP) reporter strain was then grown in media +/− 250 mM NaCl, buffered to pH 7.0 to study [Cl−] effects at neutral pH, or in media buffered at pH 5.7, without added NaCl. Using FACS analysis, we observed an increase in GFP fluorescence of CDC1551(rv2390c'::GFP) in conditions of high [Cl−] or acidic pH over time, with peak inductions of 7–9 fold over control in each instance (Figure 2A). To verify the Cl−-specificity of the response, we tested several other compounds for their ability to induce GFP fluorescence in CDC1551(rv2390c'::GFP), including KCl, arginine-HCl, Na2SO4, and sucrose, in media buffered at pH 7.0. Induction was observed with compounds containing Cl−, but not with Na2SO4 and sucrose, indicating that Cl− was the agent responsible for the increase in GFP signal, and suggesting that neither Na+ nor osmolarity were contributory factors (Figure S5 in Text S1). Induction of rv2390c'::GFP expression was also reversible, with GFP fluorescence returning to baseline levels within 5 days of removal of the high [Cl−] stimulus in log-phase bacteria (Figure S6 in Text S1). These data, along with the lack of induction observed with other stressors such as NO and hypoxia (Figure S7 in Text S1), argue for the usefulness of CDC1551(rv2390c'::GFP) as a specific reporter Mtb strain for the intraphagosomal cues of pH and Cl−.
To determine if Mtb's response to Cl− occurs in a concentration-dependent manner, we repeated the time-course induction assays with media containing different [Cl−] at pH 7.0. GFP fluorescence of CDC1551(rv2390c'::GFP) increased as [Cl−] rose, showing Mtb's ability to modulate its response to [Cl−] in a manner comparable to a rheostat (Figure 2B). In agreement with a previous study reporting Mtb's dynamic response to diminishing pH [24], we also observed increasing GFP signal with decreasing pH for CDC1551(rv2390c'::GFP) (Figure 2C). These results further demonstrate the usefulness of CDC1551(rv2390c'::GFP) as a reporter Mtb strain for Cl− and pH, and indicate that Mtb's response to these two environmental cues is fine-tuned by its environment.
To test whether Cl− and pH might act synergistically as intraphagosomal cues, we incubated CDC1551(rv2390c'::GFP) in media buffered at pH 5.7, with 250 mM NaCl. These conditions resulted in induction of GFP fluorescence to a level (>50 fold) much greater than merely the sum of the GFP signal obtained when the bacteria were grown in conditions with only one cue (high [Cl−] or acidic pH) (Figure 3A). qRT-PCR tests on several genes in wild type Mtb (WT) exposed to the different conditions confirmed the synergistic activity (Figure 3B).
This synergy implied cross-talk between regulatory circuits. In particular, we examined the role of the two-component regulator phoPR, a system previously shown to be required for expression of the acid and phagosome-regulated locus aprABC [24], and whose regulon significantly overlaps the list of genes regulated in a pH-dependent manner during MØ infection [5], [8]. We found that unlike WT, a phoP::Tn mutant carrying the rv2390c'::GFP reporter failed to induce GFP fluorescence during growth at acidic pH, supporting the critical role of phoP in regulating Mtb's response to pH (Figure 3C). Our experiments further indicated that phoP also played a role in regulating Mtb's response to Cl−, as induction of the GFP reporter signal during growth in high [Cl−] was reduced in the phoP::Tn mutant as compared to WT (1.5–2 fold vs. 7–9 fold) (Figure 3C). Intriguingly, GFP induction with the reporter phoP::Tn mutant in conditions of high [Cl−] at acidic pH (4 fold) was still greater than that observed with high [Cl−] alone, despite the lack of induction with acidic pH as a single signal (Figure 3C). qRT-PCR analyses on a ΔphoPR Mtb mutant, as well as a complemented ΔphoPR strain (phoPR*), confirmed these data. There was decreased induction of target transcript in conditions of high [Cl−] alone or high [Cl−] at acidic pH in the ΔphoPR mutant as compared to WT (3 vs. 5 fold and 12 vs. >50 fold respectively), and no increase in transcript at acidic pH for the mutant (Figure 3D). Genetic complementation restored transcript induction in the mutant to WT levels (Figure 3D).
These results implicate phoPR as a regulator that modulates Mtb's response to Cl−, while also indicating that it is merely one part of a regulatory circuit that impacts this response.
Having established that Mtb's response to Cl− and pH is interconnected in vitro, we next pursued these studies in the context of MØ infection by Mtb. To make use of the rv2390c'::GFP reporter for these intracellular studies, we first moved the construct into a replicating plasmid containing mCherry driven by the constitutive promoter smyc [24], [25], to generate the strain CDC1551(rv2390c'::GFP, smyc'::mCherry) (Figure S8 in Text S1). This allows visualization of all bacteria regardless of reporter induction levels, and an internal calibration of the GFP signal.
Activation of MØs prior to infection with Mtb is known to increase the maturation stage and lower the pH of the bacteria-containing vacuoles [26], [27], which should increase induction of GFP expression as a function of both pH and [Cl−]. Resting or activated murine bone marrow-derived MØs were infected with the reporter Mtb strain, and samples examined by confocal microscopy. We observed increased GFP fluorescence as the infection progressed, with significantly more induction of GFP signal in the activated MØs (Figures 4A and 4B). This difference in the microenvironment experienced by Mtb during infection of resting or activated MØ was even more starkly illustrated by pre-incubating the reporter Mtb in conditions of high [Cl−] prior to MØ infection. In this case, the inoculating bacteria had an increased level of rv2390c'-driven GFP expression at the start of infection, and exhibited an enhanced divergence in GFP signal between the resting and activated MØs (Figures 4C and 4D).
These experiments indicate that Mtb experiences different [Cl−] and pH during MØ infection, dependent on the activation status of the host MØ, and points to dynamic regulation of its gene expression in response to these environmental cues.
The MØ experiments above demonstrate the feasibility of using the CDC1551(rv2390c'::GFP, smyc'::mCherry) reporter strain to reveal important aspects of Mtb's microenvironment during infection. We sought to test the utility of this reporter system in a whole animal infection where the infection foci will likely present regional variation in immune responsiveness and heterogeneous levels of MØ activation. To probe if we could detect regional variation in immune-mediated modulation of infected MØs, we infected C57BL/6J WT or isogenic interferon-γ−/− (IFNγ−/−) mice with Erdman(rv2390c'::GFP, smyc'::mCherry) via intranasal inoculation. IFNγ−/− mice fail to properly activate their MØs on infection and are susceptible to Mtb, developing a disseminated infection that is fatal [28], [29]. The Erdman strain was used for these experiments, as it establishes robust infection in mice. In vitro tests show that the Erdman reporter strain responds similarly to both Cl− and pH (Figure S9 in Text S1).
Infected mice were sacrificed at 14 and 28 days post-challenge, and lung tissue examined by confocal microscopy. We observed significantly higher GFP fluorescence in the reporter strain in WT vs. IFNγ−/− mice at each time point examined (Figures 5A and 5B). In the case of IFNγ−/− mice, we also noted a disseminated infection, in agreement with previous studies (Figure 5A) [28], [29]. These results faithfully reproduce our MØ experiments since IFNγ−/− mice, which are unable to activate their MØs, exhibit reduced expression of the GFP reporter signal.
To further examine the impact of host immune pressure on determining Mtb's microenvironment, we used host inducible nitric oxide synthase (iNOS) expression as an indicator of immune activation in WT mice at 28 days post-infection. This allowed us to compare Mtb resident in regions with vs. without an active immune response, within a single infected WT host. A first observation was that most Mtb were located in iNOS-positive regions in the mouse lung tissue (Figures 5C and 5D). Significantly however, we found greater reporter GFP fluorescence in the bacteria residing in iNOS-positive regions vs. those located in iNOS-negative regions (Figure 5D). This result reinforces the concept that host immune pressure can impact substantially on the cues that Mtb responds to in its microenvironment, and that reporter Mtb strains can be exploited to shed light on the signals the bacteria are exposed to during in vivo infection. In particular in the context of the rv2390c'::GFP reporter, it suggests that Mtb experiences a microenvironment with higher [Cl−] and more acidic pH during infection of a host with an activated immune system. While the complex nature of in vivo infection means that it remains possible that there are yet other, unidentified, factors that also contribute to the differential induction of GFP fluorescence observed, the apparent specificity of the rv2390c'::GFP reporter supports the notion of [Cl−] and pH being at least two of the major drivers of the phenotype observed. This is also consistent with the increase acidification of Mycobacterium-containing phagosomes in activated MØs reported previously [4], [27], [30], and supports the contention that the bacteria are delivered live to a compartment that represents a more hostile environment.
In order to further validate the utility of reporter strains for studying Mtb infection, we performed additional experiments to examine the possibility of generating a second, independent reporter Mtb strain that would respond to different environmental cues from the rv2390c'::GFP reporter strain. In particular, we pursued in vivo studies with a hspX promoter-driven reporter strain. hspX is a much-studied Mtb gene often used as a marker of expression of the dos regulon, known to respond to hypoxia and NO [19]–[22]. As expected, in vitro, GFP induction of an Erdman(hspX'::GFP, smyc'::mCherry) reporter strain varied with O2 tension and NO (Figures 6A and 6B). Confocal microscopy analyses of lung tissue from mice infected with Erdman(hspX'::GFP, smyc'::mCherry) showed significantly greater induction of Mtb reporter GFP fluorescence in WT vs. IFNγ−/− mice at both 14 and 28 days post-infection (Figures 6C and 6D). We also observed much greater induction of hspX'-driven GFP signal at 28 days vs. 14 days post-infection, in accord with the reported time-frame of iNOS synthesis during Mtb infection in WT mice (Figures 6C and 6D) [31]. Immunofluorescent staining of iNOS further showed significantly higher hspX'-driven GFP fluorescence in Mtb residing in iNOS-positive vs. negative regions in WT mice (Figure 6E). Together with the Erdman(rv2390c'::GFP, smyc'::mCherry) results above, these experiments illustrate that both reporter Mtb strains reliably detect and respond to localized regions of immune activation in vivo, and support the usefulness of reporter Mtb strains for studies of Mtb-host interactions.
Our finding that Mtb can utilize Cl− as an environmental cue, in synergy with pH, is a first illustration of a pathogen exploiting interlinked host signals during phagosome maturation. Importantly, Mtb responds to these cues not just in vitro but also during in vivo infection, where these signals are modulated by immune activity of the host. Most studies on Mtb and its response to environmental cues have centered on in vitro assays and homogeneous bacterial cultures.
While these constitute an important foundation they provide little insight into how Mtb senses and responds to environmental cues in vivo, where the heterogeneity linked to location and immune activation is critical in determining the productiveness of the diverse subpopulations of Mtb present in an infected host [32]. In the current study we validated the two reporter strains for their ability to respond to stresses relevant to their survival in vivo. Using confocal microscopy and rigorous quantification of GFP fluorescence at the level of the individual bacterium, we were able to probe infected mouse tissue and demonstrate that: (1) GFP expression level was linked to immune activation by IFNγ, (2) bacteria in regions that stained positive for the activation marker iNOS exhibited higher levels of GFP expression, and (3) the heterogeneity amongst the bacterial population was as marked as predicted [32], and can only be revealed by panels of reporter bacteria such as the ones developed in this current study. We feel that these strains represent a new generation of tools to probe the fitness of Mtb in vivo. These strains should enable us to functionally dissect the TB granuloma to identify privileged regions of bacteria growth, or hostile areas of immune containment. We also predict that these strains will be valuable in probing for drug action and tissue penetrance, through enhanced stress, as one tries to improve drug availability in vivo.
Extending beyond Mtb, our results also have potential implications for other intracellular organisms that similarly experience compartments with a range of decreased pH, such as the bacteria Coxiella burnetti [33] and Brucella [34], and the parasite Leishmania [35]. Might these microbes also respond to Cl−, and is the ability to use Cl− and pH as synergistic cues a more widespread phenomenon? In bacterial studies, Cl− has largely been examined only within the context of salt tolerance and osmolarity. Few reports have studied Cl− itself in the context of bacterial-host interactions, although Radtke and colleagues proposed that increased [Cl−] aided Listeria monocytogenes phagosomal escape by increased activation of listeriolysin O [36].
Our study further raises the question of what roles other common ions might have on bacterial-host interactions. Although ions, such as iron, that serve as essential micronutrients and are actively sequestered by the host have long been recognized as important focal points for bacterial-host interactions [37], the possible impact of more common ions, like Cl−, remain largely unstudied. In addition to Cl−, we speculate that other common ions, such as K+, might also act as a signal for infecting bacteria. There are several known bacterial K+ transporters [38], and these also impact on important aspects such as pH and membrane potential [39]. Indeed, K+ transporter mutants in several bacterial species, including Mtb, have been reported to be attenuated in colonization of their host [40], [41]. We propose that further study of common ions and their possible role as environmental signals for microbes will yield many more as yet undiscovered aspects of the bacterial-host interface.
All animal procedures were conducted in strict compliance with the National Institutes of Health “Guide for the Care and Use of Laboratory Animals”. The animal protocol was reviewed and approved (protocol number 2011-0086) by the Institutional Animal Care and Use Committee, Cornell University, under the guidelines of the Association for Assessment and Accreditation of Laboratory Animal Care, US Department of Agriculture, and the Public Health Service guidelines for the care and use of animals as attested by the National Institutes of Health. All efforts were made to minimize suffering.
Bone marrow-derived MØs were isolated from C57BL/6J WT mice (Jackson Laboratories), and maintained in DMEM (Corning cellgro) containing 10% FBS (Thermo Scientific), 20% L-cell conditioned media, 2 mM L-glutamine, 1 mM sodium pyruvate and antibiotics (penicillin/streptomycin) (Corning cellgro), at 37°C in a 7% CO2 atmosphere. Monocytes isolated from peripheral blood mononuclear cells (Elutriation Core Facility, University of Nebraska Medical Center) were grown in DMEM containing 10% human serum (SeraCare Life Sciences), 2 mM L-glutamine, 1 mM sodium pyruvate and antibiotics, and allowed to fully differentiate into MØs before use in assays.
Generation of Cl− and Cl−/pH sensor beads are described in the Supplementary Materials and Methods. For plate reader assays, 2×105 MØs/well were seeded in a 96-well black plate (Corning Costar), and for confocal live-cell time-lapse microscopy assays, 4×105 MØs/well were seeded in a Lab-Tek II 8-chambered coverglass (Nalge Nunc International). MØs were washed 3x with pre-warmed assay buffer (PBS, pH 7.2, 5% FBS, 5 mM dextrose, 1 mM calcium acetate, 1.35 mM K2SO4, 0.5 mM MgSO4), and sensor beads added at ∼2–5 beads/MØ in assay buffer. Acquisition of data on a plate reader or by confocal imaging was initiated within 2–3 minutes of bead addition. A Molecular Devices Gemini EM fluorescence plate reader was used for bottom read signal detection (BAC – Ex. 365 nm/Em. 505 nm, AF594 – Ex. 590 nm/Em. 617 nm, pHrodo – Ex. 560 nm/Em. 585 nm), with 4 replicate wells/condition, and temperature control at 37°C. In experiments to establish a calibration curve, at the end of the assay (2 hrs) described above, the MØs were washed 3x with pre-warmed Cl−-free buffer (1.54 mM KH2PO4, 2.71 mM Na2HPO4, 69 mM Na2SO4, 5 mM dextrose, 1 mM calcium acetate, 1.35 mM K2SO4, 0.5 mM MgSO4), and then placed in buffer supplemented with specific [NaCl], 200 nM bafilomycin A1 (Sigma), 10 µM nigericin (Calbiochem), and 10 µM monensin (Enzo Life Sciences). After incubation to allow equilibration, the BAC and AF594 fluorescence signals were read on a plate reader as above.
For live-cell time-lapse microscopy, cells were imaged with a Leica SP5 confocal, equipped with a stage enclosed temperature control system. A 364 nm laser line was used for excitation of BAC fluorescence, a 594 nm laser line for Alexa Fluor 594 fluorescence, and a 543 nm line for pHrodo. Emission detection was set at +/−15 nm of the peak emission λ in each case. 10 z-slices over a 12 µm range were acquired at each time point, using the Leica Application Suite Advanced Fluorescence program. Volocity software (PerkinElmer) was used for analysis and tracking of individual beads.
The Mtb strain CDC1551 was the parental strain for all in vitro and MØ infection experiments. Strains used in mice infections were in the Erdman strain background. Routine culture of Mtb was as previously described [24]. The phoP::Tn mutant was from BEI (#NR-14776), and has been previously described [24]. Details of the construction of a CDC1551 ΔphoPR mutant and its complemented strain are described in the Supplementary Materials and Methods.
Log-phase Mtb (OD600∼0.6) was used to seed 10 ml cultures at OD600 = 0.3 in 7H9 media buffered at pH 7.0, +/−250 mM NaCl, in standing vented T-25 flasks. RNA samples were collected after 4 hours of treatment, and five biological replicates were tested. RNA isolation, amplification, labeling and analyses by microarrays were carried out as previously described [5]. This microarray dataset is available in the ArrayExpress database under accession number E-MTAB-1374, and on the TB Database website [42]. qRT-PCR experiments were conducted on cDNA generated from amplified RNA as previously described [24].
To generate CDC1551(rv2390c'::GFP), a 704 bp region immediately upstream of rv2390c was PCR amplified, placed in front of GFPmut2 [43] in a modified replicating plasmid pSE100 [24], and transformed into CDC1551. The rv2390c'::GFP, smyc'::mCherry reporter strain was constructed by cloning of rv2390c'::GFP into the replicating plasmid pCherry3 [25], and transformation into CDC1551 or Erdman. To construct the Erdman(hspX'::GFP, smyc'::mCherry) reporter, a 558 bp region upstream of the hspX start codon was PCR amplified and cloned upstream of GFPmut2 in the pSE100 vector. The hspX'::GFP fusion was then subcloned into the pCherry3 plasmid and transformed into Erdman. Selection in all cases was carried out on 7H10 agar containing 50 µg/ml hygromycin.
For broth assays, Mtb was grown in standing vented T-25 flasks, in 10 ml 7H9 medium buffered at specified pH, with addition of NaCl or other compounds as stated for each experiment. pH 7.0 medium was buffered with 100 mM MOPS, while pH 5.5–6.5 media were buffered with 100 mM MES. Appropriate antibiotics were added as necessary. NO assays were done in stirred, aerated, cultures and used the NO donor DETA NONOate (Cayman Chemicals) at 100 µM. Hypoxia experiments were conducted in 50 ml culture volumes in 125 ml duo-capped Erlenmeyer flasks (BD Biosciences) with stirring using a magnetic stir bar. Cultures were placed in a hypoxia chamber with adjustable O2 and CO2 controls (BioSpherix), set on a magnetic stirrer within a 37°C incubator. CO2 was set at 7%, while O2 levels were adjusted as required. For all in vitro assays, samples were fixed with 4% paraformaldehyde and GFP fluorescence read on a BD FACS LSR II. FACS data were analyzed using FloJo (Tree Star, Inc).
Infection of murine bone marrow-derived MØs with Mtb were carried out as previously described [24]. Where needed, MØs were activated by treatment with 100 U/ml IFNγ and 10 ng/ml LPS. For infection with CDC1551(rv2390c'::GFP, smyc'::mCherry) pre-induced with Cl−, the bacteria were grown in the presence of 250 mM NaCl for 6 days prior to MØ infection. Bacteria were at log-phase when MØs were infected. Samples were fixed, imaged and analyzed by confocal microscopy as described below.
All animal experiments were carried out in accordance with NIH guidelines, and with the approval of the Institutional Animal Care and Use Committee of Cornell University. C57BL/6J WT mice and their isogenic IFNγ−/− derivatives (Jackson Laboratories) were infected with 103 CFU of Erdman(rv2390c'::GFP, smyc'::mCherry) or Erdman(hspX'::GFP, smyc'::mCherry) via an intranasal delivery method. This was accomplished by lightly anesthetizing the mice with isoflurane and administering the bacterial inoculum in a 25 µl volume onto both nares. At sacrifice, the lungs were removed and fixed in 4% paraformaldehyde overnight.
For MØ infections, Mtb infected cells on glass coverslips were fixed overnight at indicated time points with 4% paraformaldehyde. Nuclei were visualized with DAPI (Invitrogen). For mouse infections, whole lung lobes were fixed overnight with 4% paraformaldehyde, and stored in PBS prior to processing. Details of sample processing and antibodies used for confocal microscopy imaging are described in the Supplementary Materials and Methods. Samples were imaged with a Leica SP5 confocal microscope, and z-stacks reconstructed into 3D using Volocity software. For quantification of reporter Mtb signal, the fluorescence voxel volume of each bacterium was measured via the mCherry channel, with the corresponding sum of the GFP signal for that bacterium simultaneously measured. Settings for the GFP channel were maintained during imaging of samples within experimental sets to allow comparison of values. At least 100 bacteria were quantified for each condition. Statistical differences between data sets were determined by a non-parametric Mann-Whitney test.
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10.1371/journal.pgen.1005817 | The Zinc-Finger Protein SOP1 Is Required for a Subset of the Nuclear Exosome Functions in Arabidopsis | Correct gene expression requires tight RNA quality control both at transcriptional and post-transcriptional levels. Using a splicing-defective allele of PASTICCINO2 (PAS2), a gene essential for plant development, we isolated suppressor mutations modifying pas2-1 mRNA profiles and restoring wild-type growth. Three suppressor of pas2 (sop) mutations modified the degradation of mis-spliced pas2-1 mRNA species, allowing the synthesis of a functional protein. Cloning of the suppressor mutations identified the core subunit of the exosome SOP2/RRP4, the exosome nucleoplasmic cofactor SOP3/HEN2 and a novel zinc-finger protein SOP1 that colocalizes with HEN2 in nucleoplasmic foci. The three SOP proteins counteract post-transcriptional (trans)gene silencing (PTGS), which suggests that they all act in RNA quality control. In addition, sop1 mutants accumulate some, but not all of the misprocessed mRNAs and other types of RNAs that are observed in exosome mutants. Taken together, our data show that SOP1 is a new component of nuclear RNA surveillance that is required for the degradation of a specific subset of nuclear exosome targets.
| Cells use various RNA quality control mechanisms to monitore the correct expression of their genome. Indeed, gene transcription can often generate faulty transcripts that are rapidly degraded to avoid possible deleterious effects to the cell. RNA degradation by the exosome is the main pathway for the removal of unwanted RNA in all kingdoms. Recognition of aberrant RNA involves a number of RNA binding proteins and other factors that target them for degradation by the exosome. Here, we used a genetic approach to identify proteins involved in the degradation of a mis-spliced RNA by the nuclear exosome in plants. Our screen identified two known components of nuclear RNA degradation pathway, namely the exosome core subunit RRP4 and the exosome-associated RNA helicase HEN2 that is required for the elimination of non-ribosomal RNAs by the nuclear exosome. Furthermore, we identified SOP1 as a novel putative exosome cofactor that is required for the degradation of some, but not all, of the substrates of HEN2.
| The synthesis of mRNA in eukaryotes is a complex multistep process, involving the transcription of DNA into RNA, capping, splicing of intronic sequences and maturation of the 3’ end of the messenger prior to export to the cytoplasm for translation into protein. Production of functional RNA can be impaired by either genetic mutation or incorrect processing; both can be deleterious for the cell and have been associated with various human diseases [1,2]. To prevent the production of potentially harmful RNA, eukaryotic cells employ numerous RNA surveillance mechanisms enabling the recognition and degradation of defective or aberrant RNA and thereby ensure quality control throughout the RNA production pipeline [3–5].
One of the principal contributors to RNA surveillance and quality control is the RNA exosome, a multi-subunit complex that provides the main 3’-5’ exoribonuclease activity in eukaryotic cells [6–8]. The exosome complex consists of a core complex of nine conserved proteins and associated ribonucleases. In addition, the exosome interacts with activator/adaptor complexes containing RNA helicases, RNA binding proteins or terminal nucleotidyl transferases that are required for exosome activity and are involved in substrate recognition. The composition of these activator/adaptor complexes varies between different intracellular compartments and also between species. In mammals, the nucleolar exosome complex interacts with the RNA helicase MTR4, the RNA binding protein ZCCHC7, and the terminal nucleotidyl transferase hTRF4 in a complex similar to yeast TRAMP complexes [9]. The human MTR4 is also present in the nucleoplasm where it is associated with the RNA binding proteins ZCCHC8 and RBM7 to form the so-called NEXT (Nuclear EXosome Targeting complex) complex [10,11]. NEXT targets promoter upstream transcripts, enhancer RNAs, 3’ extended small nucleolar RNAs (snoRNAs) and introns and is considered as a central activator/adaptor complex of exosome-mediated RNA surveillance.
The core exosome and many of its cofactors are conserved in plants [12–14]. In Arabidopsis (Arabidopsis thaliana), the nucleolar exosome is bound to AtMTR4, which in turn associates with ribosome biogenesis factors [14,15]. The nucleoplasmic exosome associates with HUA-ENHANCER2 (HEN2), an RNA helicase closely related to MTR4. HEN2 is part of a NEXT-like complex and required for the elimination of virtually all types of non-ribosomal exosome substrates including snoRNAs, a range of other non-coding RNAs and 3’ or 5’ extended mRNAs [14]. Downregulation of HEN2 also results in the accumulation of transcripts comprising exons and unspliced introns, suggesting that HEN2 targets also alternatively or mis-spliced mRNAs for degradation by the exosome. Hence, HEN2 appears to be the general cofactor of nuclear RNA surveillance in Arabidopsis.
Here, we report the identification of SOP1, a zinc-finger protein involved in nuclear RNA degradation. The sop1 mutation suppresses the developmental phenotype of a splice site mutation in the essential PAS2 gene. This splice site mutation results in the production of pas2-1 mRNA variants that undergo degradation by the nuclear exosome. In sop1 pas2-1 plants, selected pas2-1 mRNA variants are stabilised, thereby allowing the production of a functional PAS2 protein. In addition, loss of SOP1 results in the accumulation of splice variants generated from other gene loci, which also accumulate in hen2 and exosome mutants. Similarly to exosome mutants, loss of SOP1 counteracts the posttranscriptional silencing of a transgene (PTGS), indicating that SOP1 contributes to RNA surveillance. However, only a portion of HEN2 targets accumulate in sop1 mutants suggesting that SOP1 is involved in the degradation of only a subset of nuclear exosome targets.
PAS2 (At5g10480) encodes the 3 hydroxy acyl-CoA dehydratase necessary for fatty acid elongation by the elongase complex in the endoplasmic reticulum [16]. The very long chain fatty acids (VLCFA; 20 carbons and over) produced by the elongase complex are essential for plant growth as demonstrated by the loss of PAS2 in pas2 null mutants leading to embryo lethality [16]. However, the weak allele pas2-1, which harbors a point mutation affecting the splicing donor site of the eighth intron, allows viable embryogenesis and seedling development of the homozygous mutants [17,18]. The pas2-1 homozygous mutant has a strong developmental phenotype with rod-shaped cotyledons and an enlarged hypocotyl due to an increased number of cell layers. The mutant plants also suffer from defective organogenesis with fused-organs, e.g. leaves, stems and flowers which leads to sterility [17]. During multiple rounds of mutant proliferation, we isolated a pas2-1 homozygous natural variant that still showed the severe developmental pas2 phenotype at the seedling stage, but developed into the adult stage and produced seeds. Importantly, this fertile variant, named pas2-1YaYa (pas2-1Y) has the same genomic sequence of the pas2-1 gene. The putative second site mutation or epigenetic phenomenon that underlies the partial restoration of the pas2-1 phenotype in pas2-1Y has not yet been identified. However, the restoration of fertility in pas2-1Y made this natural variant an ideal starting point for a genetic screen to isolate supressors of the pas2-1 seedling phenotype from an ethyl methane sulfonate (EMS) mutagenized population. Suppressor plants were screened from individual progeny of M1 plants at the seedling stage based on the restoration of cotyledon organogenesis of pas2-1Y (Fig 1A and S1A Fig). We isolated eight suppressors of pas2 (sop) defining three complementation groups: four alleles for sop1, one allele for sop2 and three alleles for sop3 (S1A Fig). The three suppressors displayed almost wild type cotyledons and did not show any organ fusions, despite the presence of the splicing pas2-1 mutation.
The loss of 3-hydroxy acyl-CoA-dehydratase activity in pas2-1 mutants prevents the elongation of VLCFA with an acyl chain longer than 18 carbons [19,20]. In addition, loss of PAS2 activity results in the accumulation of 3-OH acyl-CoA intermediates [16] (Fig 1B and S1B Fig). To test if the suppression of pas2-1 developmental defects in the isolated suppressor plants was caused by restoration of VLCFA content, we compared the acyl-CoA pools in wild type, pas2-1, pas2-1Y and the suppressor plants. As compared to pas2-1, the pas2-1Y plants showed a partial restoration of fatty acid elongation, but with the persistence of 3-OH acyl-CoA intermediates indicating that PAS2 dehydratase activity was still impaired in these plants (Fig 1B and S1B Fig). By contrast, all the sop pas2-1Y suppressor lines had wild type levels of VLCFA, associated with an absence of detectable 3-OH acyl-CoA intermediates indicating a complete restoration of the acyl-CoA dehydratase activity (Fig 1B and S1B Fig). Since PAS2 provides the only acyl-CoA dehydratase activity in plants [16], these results indicated that the suppression of the pas2-1 phenotype in sop lines was achieved by restoration of PAS2 activity.
Next, we tested whether the sop1 mutation suppresses specifically the pas2-1Y phenotype or can also suppress the phenotype of other VLCFA-deficient mutants. For this purpose, we introgressed sop1-5, a knock-out allele, that harbours a T-DNA insertion in the At5g21580 locus which encodes the SOP1 protein (see below), into the original pas2-1 mutant as well as into pas1-2, pas2-4 and pas3-1 mutants [16,21,22]. Importantly sop1-5 suppressed the bona fide pas2-1 mutant (Fig 1E). Hence suppression of the pas2-1 phenotype by sop1 does not require the presence of the pas2-1yaya background and is caused by the loss of SOP1/At1g21580 function. By contrast, sop1-5 did not suppress VLCFA deficient pas1 and pas3 mutants (Fig 1E), indicating that sop1 is not a general suppressor of VLCFA deficiency. Moreover, sop1-5 was also unable to suppress the embryo lethality of a pas2-4 knock-out mutant, as no homozygous pas2-4 could be recovered from 24 F3 plants from the progeny of a pas2-4 +/- sop1-5 -/- parental plant (Fisher’s exact test, p = 0.0219). Thus sop1 specifically suppresses the pas2-1 mis-spliced allele, but does not compensate for a complete loss of PAS2 function.
Knowing that the pas2-1 allele harbors a point mutation affecting the splicing donor site of the eighth intron, we reasoned that the suppression of pas2-1 by sop mutations could be due to a restoration of the splicing defect. To test this hypothesis, we analyzed pas2-1 mRNA produced in the suppressor background by RT-PCR (Fig 1C and 1D). While a single band was obtained from WT plants, three bands were detected in pas2-1, pas2-1y and all three sop pas2-1 double mutants. This result indicates that the splicing defect of the pas2-1 mutant was not restored in pas2-1y or in the sop mutants. On the contrary, an accumulation of the largest splicing isoform was observed. When compared to pas2-1, pas2-1Ysop suppressor plants had also slightly higher levels of the PAS2-1 mRNA of wild type size, albeit at much lower levels than WT plants (Fig 1C and 1D). An identical repartition of PAS2 mRNA isoforms was observed when sop1-5 was introgressed in the pas2-1 background, while the sop1-5 mutation alone did not alter the expression of PAS2 mRNA in WT background (Fig 1D and S2B Fig). These data suggested that the sop mutations affect the production or the stability of specific mRNA isoforms generated from the pas2-1 locus.
To understand the splicing defects present in pas2-1 mutants, we cloned and sequenced the PAS2-1 RT-PCR products. Four different isoforms were identified (for sequence detail see S3 Fig). The longest isoform (PAS2-1LONG) corresponded to an incompletely spliced PAS2 mRNA, which retained the 8th intron leading to the production of an mRNA with a premature termination codon (PTC). The shortest (PAS2-1SHORT) PCR product lacked the 8th exon resulting in a direct fusion of Exon 7 to Exon 9, which results in a frame shift leading to the loss of the stop codon. The band with a size similar to wild type corresponded to a mix of two isoforms. One corresponded to a mispliced isoform (PAS2-1MIDb) that used a cryptic splicing donor site (GT) seven nucleotides upstream of the pas2-1 mutation, and also resulted in the loss of a stop codon. The second product present in the WT-size band corresponded to a correctly spliced PAS2-1 mRNA (PAS2-1MIDa) which retained the point mutation present in the pas2-1 allele resulting in a single amino-acid change in the PAS2 protein sequence (Gly199Ser). This latter isoform is the only isoform that is predicted to produce a full-length protein.
To investigate whether the enhanced level of one of the pas2-1 splicing variants could confer suppression, we expressed the different RNA isoforms under the endogenous PAS2 promoter in a pas2-1 mutant background. Beside wild type PAS2 protein, only its closest isoform PAS2-1MIDa was able to complement pas2-1 mutant (Fig 2A), suggesting that PAS2G199S encoded by PAS2-1MIDa RNA is a functional dehydratase.
The relative levels of the different mRNA isoforms present in WT, pas2-1, pas2-1y and pas2-1ysop1-1 plants were estimated with the number of RNAseq reads matching a ten nucleotide long sequence spanning the exon junction involved in each of the pas2-1 mRNA isoforms (S3C Fig, sequences in bold). In agreement with the RT-PCR results (Fig 1D), the quantification of RNA seq reads showed that the PAS2-1LONG isoform was the most abundant isoform in pas2-1Ysop1-1 (Fig 2C, 7.5-fold increase compared to pas2-1Y). Interestingly, the higher levels of the PAS2-1LONG RNA were associated with a mild increase of the PAS2MIDa (2.17-fold), but not PAS2MIDb RNA (1.01-fold). While the ratio of PAS2MIDa/PAS2MIDb was about 0.3 in pas2-1 and pas2-1Y, it raised to 0.7 in pas2-1Ysop1-1 thanks to the accumulation of PAS2MIDa. These data indicate that the restoration of acyl-CoA dehydratase activity in sop1 plants was due to higher levels of the PAS2-1MIDa compared to pas2-1 and pas2-1Y plants, which in turn led to the production of a functional PAS2G199S protein. Furthermore, our data suggest that that sop1 favours the production of the PAS2MIDa either directly by affecting the efficiency of pas2-1 splicing, or indirectly by stabilising the intron-retaining RNA isoform PAS2-1LONG, which in turn would improve the production of PAS2-1MIDa isoform.
To ascertain whether sop1 can affect the levels of mRNA isoforms generated from other splicing-defective loci, we crossed sop1-5 to ton2-12, a mutant harbouring a mutation in a splicing donor site (GT->AT of the first intron) of the TONNEAU2 (TON2) gene, encoding the regulatory subunit of the protein phosphatase 2A (PP2A) complex involved in the control of the orientation of the division plane [23]. The ton2-12 mutation results in the production of an mRNA isoform with similar features to pas2-1 (retained intron with PTC) and also leads to a strong developmental phenotype [23]. However, sop1-5 did not rescue the growth defect of ton2-12 mutants (Fig 3A), and did not affect accumulation of the ton2-12 intron-retaining RNA isoform (Fig 3B). We also queried intron-retention events in the sop1-1 mutant in our RNAseq data to identify other mis-spliced RNA. In addition to the expected accumulation of introns corresponding to alternative splicing events, we identified only one locus (At5g36880) accumulating an intron specifically in sop1-1 background. However, this intron retention was also associated with a point mutation of its 5’ intronic splice donor site in sop1-1 (S4 Fig). Similarly to the ton2-12 mutation, the intron-retaining transcript of At5g36880 did not accumulate in sop1-1. These results suggest that sop1 influences PAS2-1LONG mRNA accumulation, but does not have a general effect on the stabilisation of incompletely spliced mRNAs.
Our data indicate that the major effect of the sop mutations on pas2-1 mRNA isoforms is the accumulation of the intron containing PAS2-1LONG isoform (Figs 1D and 1E and 2C), suggesting that SOP1 affects either the production or the stability of this particular isoform. The PAS2-1LONG isoform is characterised by two molecular determinants: the retained intron and the presence of a premature termination codon (PTC, S3C Fig), the latter of which is known to trigger rapid RNA degradation via the non-sense mediated mRNA decay (NMD) pathway [24]. Therefore, we tested the hypothesis that the PAS2-1LONG isoform is a substrate for non-sense mediated mRNA decay [25–27]. The pas2-1 mutant was crossed with mutants of UPF1 (encoding an RNA Helicase) and UPF3 (encoding an RNA-binding protein), both key components of the NMD pathway. The resulting double mutants were analysed for both growth and accumulation of the PAS2-1LONG isoform. The results showed that neither pas2-1 upf1-5 nor pas2-1 upf3-1 double mutants suppressed the pas2-1 growth phenotype (Fig 3C) or showed enhanced levels of the PTC containing PAS2-1LONG RNA (Fig 3D). These results indicate that RNA degradation through NMD is not responsible for the low levels of PAS2-1LONG isoforms observed in pas2-1 mutants.
To identify the sop mutations, we first conducted a positional cloning of the suppressor mutations with a mapping population prepared from a cross between the pas2 sop mutants (Columbia accession) and Landsberg erecta accession. In addition to the segregation bias on Chromosome V due to the presence of the pas2-1 mutation (At5g10480), we identified 0.5-1Mb segregating regions on Chromosome I for SOP1 or SOP2 and Chromosome II for SOP3. Next generation sequencing of genomic DNA extracted from the suppressors pas2-1Ysop1-1, pas2-1Ysop2-1 and pas2-1Ysop3-1 identified the specific polymorphisms associated with each genotype and matching the coding sequence of genes present in the mapped regions of SOP loci (Fig 4A). For sop1-1, a unique single nucleotide polymorphism (SNP) in At1g21580 gene fulfilled these criteria and was further confirmed by sequencing three other alleles (all four sop1 alleles contained PTC). Similarly, an SNP was found in At2g06990 gene for sop3-1 and was confirmed with two other sop3 alleles (one missense mutation and two PTC). For sop2-1, a candidate SNP in At1g03360 gene was identified and confirmed by complementation of the pas2 sop2 mutant phenotype with the wild-type At1g03360 gene (S5B Fig).
Remarkably, all three SOP proteins are involved in RNA metabolism. SOP2 encodes Ribosomal RNA Processing 4 (RRP4), a core subunit of the RNA exosome required for the processing of rRNA, several snoRNA and the degradation of aberrant transcripts [12]. SOP3 encodes HUA-Enhancer 2 (HEN2), a RNA helicase homologous to MTR4, identified initially as a regulator of AGAMOUS splicing [28] and more recently as interacting with the nuclear exosome for the degradation of misprocessed mRNA and other types of non-ribosomal exosome targets [14]. SOP1 encodes a recently re-annotated large protein which was formerly annotated as two genes (At1g21570/AtC3H7 [29,30] and At1g21580, unknown protein). SOP1 contains five zinc-finger (ZnF) domains at its carboxy-terminus which may bind RNA [29].
While the exosome core complex is present in both nuclear and cytosol, HEN2 was shown to be a nuclear protein enriched in nucleoplasmic foci. We therefore compared the subcellular distribution of SOP proteins by expression of functional GFP fusion proteins in stable Arabidopsis transformants (S5A–S5C Fig). Confirming previous results, RRP4-GFP was detected in both the cytoplasm and nucleus, with a specific enrichment in the nucleoli (Fig 4B and 4C)[14,27], while HEN2-GFP was detected in nucloplasmic speckles, but also diffusely distributed in the nucleoplasm (Fig 4B and 4C)[14]. Interestingly, SOP1-GFP was not diffused in the nucleoplasm, but predominantly localized in nucleoplasmic speckles, similar to the foci labelled by HEN2-GFP (Fig 4B and 4C). Therefore co-localization of SOP1, SOP2/RRP4 and SOP3/HEN2 was assessed by co-expression of corresponding RFP and GFP fusion proteins. This experiment revealed that SOP1 indeed colocalized with SOP3/HEN2 in nucleoplasmic speckles while SOP2/RRP4 and SOP3/HEN2 colocalized diffusely in the nucleoplasm (Fig 4C). Those nucleoplasmic speckles were found throughout the nucleoplasm (S1 Movie) and presented a limited dynamic (S2 Movie) that was synchronous between SOP1 and HEN2 (S4E and S4F Fig). However, speckles containing exclusively SOP1 could also be occasionally observed (Fig 4C). These results reinforce the idea that SOP1 could be involved in similar functions than HEN2, namely the degradation of nuclear exosome targets.
Defects in either nuclear or cytosolic RNA quality control (RQC) functions generally result in increased post-transcriptional (trans)gene silencing (PTGS). The rationale is that RQC serves as a first layer of defense to eliminate aberrant RNAs. Thus, aberrant transgene RNA bypass the RQC defenses and enter into the PTGS pathway only when the RQC machinery is dysfunctional or when it is saturated by a large excess of aberrant transgene RNA [14,27,31–34]. In particular, it was shown that mutations in the exosome core component RRP4 strongly enhance PTGS [27]. Mutations in HEN2, but not in MTR4, also strongly enhance PTGS, indicating that the degradation of abberant transgene RNA in the nucleus involves the nucleoplasmic fraction of the exosome [14]. The GUS tester line Hc1, which triggers PTGS in only 20% of the population at each generation [31,35], is a sensitive tool for monitoring the effect of both enhancers and suppressors of transgene PTGS. To quantify the effect of the sop mutations on PTGS, the Hc1 line was crossed to the three sop mutants and plants homozygous for both the transgene and the sop mutations were analyzed. As reported previously for rrp4 and hen2 mutants [14,27], PTGS was strongly enhanced in sop2 and sop3 mutants (Fig 5A). Interestingly, the sop1 mutation also increased PTGS albeit to milder levels, suggesting that SOP1 is not essential, but indeed participates to RNA quality control.
To evaluate a possible role for SOP1 in RNA degradation by the nuclear exosome, we compared the accumulation of known exosome targets in sop1, sop2 and sop3 mutants by Northern blots or qRT-PCR. In agreement with previous results [12,14], only sop2/rrp4 mutants had elevated levels of 3’ extended pre-5.8S rRNA, a known target of the nucleolar exosome ([14,15] Fig 5B). By contrast, sop1 did not accumulate 5.8S rRNA precurors similarly to sop3/hen2 indicating that SOP1 is not involved in rRNA processing (Fig 5B). Among selected model targets of HEN2/SOP3 [14], sop1 had an effect on one mis-spliced mRNA and two 3’ extended mRNAs (Fig 5C). However, the effect of sop1 was weaker than the effect of hen2/sop3-1, a result corresponding to that observed for PTGS suppression (Fig 5A). Finally, unlike hen2/sop3, sop1 mutants did not accumulate stable non-coding RNAs, precursors of snoRNAs, or transcripts generated from intergenic repeats (Fig 5C). Collectively these data suggested that SOP1 is dispensable for some of the reported functions of the nuclear exosome, but could be involved in the degradation of RNAs that are also substrates of the nucleoplasmic exosome and HEN2.
To better understand the role of SOP1 in the accumulation of pas2-1 mRNA and RNA quality control, we aimed to identify other transcripts affected by sop1 mutation. Therefore, we compared the transcriptomes of WT, pas2-1Y and pas2-1Ysop1-1 plants by RNA seq. When comparing pas2-1Y to wild type plants, 424 genes were induced more than 2-fold while 414 genes were repressed. Consistent with the full restoration of the VLCFA-deficiency in pas2-1Ysop1 mutants (Fig 1B), the expression of most of these genes (93% and 44% for induced and repressed genes, respectively) was restored to wild type level in pas2-1Ysop1 mutants. However, our analysis identified 114 and 201 genes that were specifically up- or down-regulated in presence of the sop1 mutation (Fig 6A). Unlike hen2 or exosome mutants, which were shown to accumulate a large number of non-genic transcripts [12,14], the majority of the transcripts that were misregulated in sop1 were mRNAs (S1 Table) and likely include both direct targets of exosome-mediated degradation and secondary transcriptional responses. However, with the exception of the splicing factor SR34b (Fig 6B), which was reported to modulate the splicing of IRT1 (At4g19690, S1 Table, [36]), we did not identify obvious transcriptional cascades. Interestingly, a Go-term analysis revealed that many of the misregulated mRNAs in sop1 are involved in splicing or other RNA-related processes (Fig 6B, S1 Table). Since some of the upregulated RNA processing or splicing factors identified by the RNA seq analysis were predicted to undergo alternative splicing, we evaluated the levels of splicing isoforms by RT-PCR (Fig 6C). For each of HEN4 and U11-48k mRNAs, only one predominant splice form was detected but appeared to be more abundant in sop1, sop2 and sop3 mutants. For SRP30 and U2AF65a, two main RNA isoforms were detected. While the levels of the smaller isoforms were similar in all samples, the larger isoforms generated by intron retention accumulated upon mutation of SOP1, SOP2 and SOP3 (Fig 6C). These data are in line with the idea that incompletely spliced mRNAs are targeted for exosome-mediated RNA degradation, and that sop1 is involved in this process. As these alternatively spliced isoforms were not detected in NMD mutants (S6 Fig), their accumulation of in sop1, sop2 and sop3 is unlikely related to defects in non-sense mediated decay.
Finally, we analysed the upregulation of some of the candidate genes identified by RNA seq analysis by qRT-PCR in sop1, sop2 and sop3 mutants. For this experiment we used primer pairs located in the body of the mature RNA, but also primer pairs located in introns, or immediately upstream or downstream of the annotated mRNA, indicative of misprocessed mRNA with the typical features of bona fide exosome targets [14]. For all candidate targets tested, we detected a significant accumulation in sop1, sop2 and sop3 samples (Fig 6D). These data show that loss of sop1 does indeed affect the degradation of a subset of exosome substrates, including misprocessed mRNA and transcripts expressed from pseudogenes and some non-coding loci.
To conclude, our data identify SOP1 as a Zn-finger protein that co-localises with the exosome-associated RNA helicase HEN2 and participates in the degradation of a selective subset of nuclear exosome targets including misprocessed mRNAs. Taken together, our results indicate that SOP1 functions as a co-factor of nuclear RNA quality control by the nucleoplasmic exosome.
In this study, we elucidated the molecular basis of the strong decrease in 3-hydroxy acyl-CoA dehydratase activity in the pas2-1 mutant. In pas2-1 plants, a mutation of the last nucleotide in the penultimate exon of PAS2 (G1841A) prevent correct mRNA splicing leading to the retention of the last intron and to aberrant intron splicing donor site usage. This result in a low steady state levels of four different pas2-1 mRNA isoforms, of which only PAS2MIDa encodes a protein that retains 3-hydroxy acyl-CoA-dehydratase activity. Second site mutations in the exosome subunit RRP4 (in sop2), in the nuclear exosome cofactor HEN2 (in sop3) and in the Zn-finger protein SOP1/AT1G21580 (in sop1) result in the accumulation of the longest PAS2-1 mRNA isoform, which still contains the unspliced 8th intron. In addition, pas2-1 sop double mutants have, relative to single pas2-1 and pas2-1y plants, higher levels of the functional PAS2-1MIDa mRNA. These findings indicate that in pas2-1, the incompletely spliced PAS2-1LONG isoform is recognized by the nuclear RNA surveillance machinery and targeted to rapid degradation by the nuclear exosome. Therefore, impaired RNA degradation in pas2-1 sop could lead to stabilisation of PAS2-1LONG mRNA, allowing enough time for splicing to occur and resulting in an increased production of PAS2-1MIDa mRNA to eventually produce an active PAS2-1 (Gly199Ser) protein. In other words, slowing down degradation could allow unefficient splicing to occur, as previously reported [37,38].
The pas2-1 suppressor genetic screen identified two known components of the nucleoplasmic RNA surveillance machinery, HEN2/SOP3 and RRP4/SOP2, which confirmed the role of the exosome in the degradation of mispliced mRNAs. The G55E mutation in the sop2 allele affects an evolutionary strictly conserved residue of the exosome core subunit RRP4 [39]. Based on the crystal structure of the yeast EXO9-RRP6 complex, this residue is located close to the N-Terminal Domain (NTD) of RRP4 which forms the interface of the core complex with RRP6 [40]. Interestingly, Arabidopsis has three RRP6 isoforms with different subcellular localizations [41]. However, none of these isoforms has yet been shown to interact with the core exosome [12,14]. Hence, we can only speculate that the G55E exchange found in sop2 might possibly affect the interaction of the exosome core complex (EXO9) with homologues RRP6 or with other proteins that might bind to this part of the exosome surface in plants.
While SOP2/RRP4 and SOP3/HEN2 are known components of the nuclear RNA surveillance machinery, SOP1 is a previously uncharacterized protein. Loss of sop1 in pas2-1 background results in accumulation of the PAS2-1LONG isoform comparable to what is observed in pas2-1 sop2/rrp4 or pas2-1 sop3/hen2, suggesting that the underlying mechanism of pas2-1 suppression is similar in all three suppressor lines. Moreover, loss of sop1 results in accumulation of certain misprocessed mRNAs and other transcripts, all of which are also targets of HEN2 and the exosome. Lastly, sop1 enhances transgene PTGS, as previously observed for rrp4 and hen2 [14,27]. Collectively these findings indicate that SOP1 participates in exosome-mediated RNA degradation, which is consistent with its colocalization with HEN2 in nucleoplasmic speckles. However, not all of the targets detected in hen2 or exosome mutants accumulate also in sop1 mutants, suggesting that SOP1 participates in the degradation of only a subset of exosome targets. This idea is further supported by the fact that sop1 has a rather mild effect on PTGS when compared to sop2/rrp4 or sop3/hen2, and that the subcellular localization of SOP1 is restricted to nucleoplasmic speckles while HEN2 and RRP4 are also detected throughout the nucleoplasm and in the entire nucleus, respectively.
The recognition of RNA substrates by the yeast exosome is thought to involve so-called adaptor proteins. For example, the recognition of specific nucleolar RNA targets by the yeast exosome is mediated by the association of the HEN2-related RNA helicase MTR4 with Nop53 for the processing of pre-5.8S rRNA and UTP18 for the degradation of rRNA maturation by-products [42]. Similarly, two ZnF proteins have recently been shown to assist exosome-mediated RNA degradation in Schizosaccharomyces pombe. S. pombe possesses a functional homologue of Arabidopsis HEN2, named Mtl1 (for MTR4-like1), which interacts with the large Zn Finger protein Red1 in the so-called Mtl1-Red1 core of the NURS/MTREC (for Nuclear RNA silencing/Mtl1-Red1-core) complex [43–46]. Another submodule of NURS is the CBCA complex comprising the Cap-binding complex and Ars2 [45,46]. Futhermore, NURS comprises Iss10–Mmi1 and Pab2–Rmn1-Red5, the latter of which is also a Zn-Finger protein [44–46]. Interestingly, NURS is detected in nuclear speckles in S. pombe, resembling the localisation of HEN2/SOP3 and SOP1 in plants [44,45]. Similar to Arabidopsis HEN2, S. pombe Mtl1 is required for the exosome-mediated degradation of cryptic unstable transcripts, non-coding RNAs and misprocessed mRNAs [14,46,47] i.e. virtually all types of nuclear exosome substrates. In addition, S. pombe NURS mediates the elimination of meitotic mRNAs during mitosis [43–45]. The molecular basis for the recognition of meiotic trancripts in S. pombe, called Determinant for Selective Removal (DSR), has been identified as a repeated consensus sequence U(U/C)AAAC present in introns or 3’UTR [48,49]. Recently, Mmi1 has been shown to be co-transcriptionally recruited to unspliced transcripts containing the UNAAAC consensus sequence in retained introns [50]. No obvious DSR-like sequence was identified in SOP1-targets, such as PAS2-1LONG or AtU2AF65a shown to accumulate in sop1. The accumulation of SOP1 targets was shown by qRT-PCR in oligo-dT primed cDNA, indicating that targets SOP1 are oligoadenylated, as is the case for other targets of the nuclear exosome and HEN2 [14,51]. However, it is still unclear whether polyadenylation is a prerequisite of target recognition, or rather a consequence of target accumulation in absence of efficient degradation. Hence, the RNA features that are recognized by SOP1 remain to be identified.
Human and plant nuclear exosome targeting complexes show both common and distinct features when compared to the NURS complex in S. pombe. While humans have only a single homologue of the RNA helicase MTR4, both S. pombe and Arabidopsis employ two related RNA helicases in nucleolar and nucleoplasmic degradation processes. In contrast, the NEXT complexes that have been co-purified from humans and plants appear to be rather similar, as they contain related Zn-knuckle and RNA binding proteins [10,14], while sequence homologues of S. pombe Red1 or Red5 have not been found in plant or human exosome purifications as yet. In S. pombe, recruitment of Red1 to the exosome core complex requires RRP6 [46]. Although Arabidopsis has three RRP6-like proteins, to date none of them has been shown to interact with the exosome complex and we were not able to identify a sequence homologue of Red1 in Arabidopsis. By contrast, sequence comparison has identified SOP1 as the closest Arabidopsis homologue of S. pombe Red5, although the sequence homology is restricted to the Zn-Finger domain. The other domains present in SOP1 do not show similarity to known proteins outside plants. Whether SOP1 associates with other protein factors involved in the degradation of nuclear exosome targets remains to be studied.
The link between the exosome, targeting complexes involved in substrate recognition such as NEXT or NURS, and the CAP-binding complex is clearly conserved in S. pombe, humans and plants [10,14,45,47,52,53]. In humans and S. pombe, CBC is bound to Ars2, the Arabidopsis homologue of which, named Serrate, was implicated in RNA splicing and the degradation of unspliced mRNA and introns [54,55]. However, in S. pombe, the physical link between the exosome and the splicing machinery could also be mediated by a direct interaction of the RNA helicase Mtl1 with the spliceosome [46]. Interestingly HEN2, the plant homologue of Mtl1, was co-purified with MagoNashi, a component of the exon-exon junction complex deposited by the splicing machinery, while SOP1 was not yet detected in purifications of plant NEXT-like complexes [14]. It is therefore possible that parallel mechanisms, only some of which require SOP1, enable recognition and degradation of misspliced mRNAs in plants.
Arabidopsis thaliana Columbia (Col 0) accession was used throughout this study. Seedlings were grown on Arabidopsis medium [56] supplemented with 1% sucrose in long day condition (16h light) at 18–20°C. The suppressor screen was been performed on EMS-mutagenized individual pas2-1Y seeds. The progeny of 800 individual M1 plants were screened on petri dishes for restoration of cotyledons organogenesis on 7-day-old seedlings. The upf1-5, upf3-1 and ton2-12 mutants have been described previously [23,27,57]. sop1-5 (salk_019457) and pas2-4 (GABI_700G11) were obtained from the Nottingham Arabidopsis Stock Center.
Acyl-CoAs were extracted as described by [58] from 12-say old seedlings frozen in liquid nitrogen, and analysed using LC-MS/MS + MRM in positive ion mode. The LC-MS/MS + MRM analysis (using an ABSciex 4000 QTRAP Framingham, MA) was performed as described by [59], (Agilent 1200 LC system; Gemini C18 column (Phenomenex, Torrance, CA), 2 mm inner diameter, 150 mm length, particle size 5 μm). For the identification and calibration, standard acyl-CoA esters with acyl chain lengths from C14 to C20 were purchased from Sigma as free acids or lithium salts.
SOP1 genomic DNA was amplified from JAtY54C19 using Phusion polymerase (Life Technologies) and cloned in pDNR207 using Gateway Technology (Invitrogen). SOP1-GFP or SOP1-RFP fusions were generated by LR recombination in pMDC83 [60] or pH7RGW2 [61]. RRP4 cDNA in pDNR201 and RRP4-GFP have been described previously [27]. RRP4-RFP has been generated by LR reaction into pH7RWG2. HEN2-GFP has been described previously [14]. PAS2-1 isoforms were cloned by RT-PCR from pas2-1 mRNA into pDNR207 by Gateway BP reaction (Invitrogen). PAS2WT cDNA was published in [16]. The various PAS2 isoforms were cloned in a modified pB7FWG2 vector [61] carrying a 2Kb PAS2 promoter cloned in place of the 35S promoter (SpeI / HindIII). Plant transformations were performed using Agrobacterium C58 pMP90 by the floral dip method [62]. All primers used for construct cloning and plant genotyping are listed in S2 Table.
Total genomic DNA isolated from whole 12 day old seedlings was extracted using the DNeasy Plant mini kit (Qiagen) according to the manufacturer’s instructions. For genome sequencing of sop1-1, DNA was prepared into indexed fragment libraries with amplification and sequenced on an Illumina GAIIx instrument to a minimum of 30 M reads per sample, each with 76 nt read length. Using a custom Perl script, reads were trimmed to 65 nts to remove ends of biased composition and low quality. Reads were mapped to the TAIR10 genomic reference (www.arabidopsis.org) using GenomeMapper in the SHORE software suite [63]. Single nucleotide polymorphism (SNP) variants were determined using SHORE version 0.6 using a consensus minimum coverage of 3 reads. Overlap of SNPs with known genomic features, and functional consequences of SNPs were computed and summarized using FEATnotator [64]. Sequencing of sop2-1 and sop3-1 were performed using Illumina Technology (The Genome Analysis Center, Norwich), and mutations were identified using the MutDetect pipeline [65].
Total RNA were extracted from 12-day-old seedlings using RNeasy extraction kit (Qiagen) according to the manufacturer’s instructions. Reverse transcriptions were performed on 1μg RNA using reverse transcriptase (Fermentas). Quantitative Real-Time PCR (RT-qPCR) reactions were performed as in [14] and Northern Blot as in [15]. For transcriptome analysis, mRNA was enriched from total RNA using oligo(dT) capture (Invitrogen) and prepared into Illumina RNASeq libraries according to the manufacturer’s instructions. Sequencing was performed as paired reads of length 2 x 100 nt on an Illumina GAIIx instrument to minimum depth of 25 M read pairs (50 M reads) per sample. These were trimmed to 88 nt as above and mapped to the Arabidopsis TAIR10 genome reference using Tophat v 2.0.5 [66], with only uniquely mapped reads retained for further analysis. Read number aligned to annotated exon regions (TAIR10) for each annotated gene was computed using a custom Perl script. For genes with multiple isoforms, exons from the representative gene model (TAIR10) were used.
Differential expression between samples was analyzed pairwise using NOISeq ver. 2.0.0 [67], an R bioconductor package that uses read count data as input. NOISeq was used to simulate 5 samples within each condition (nss parameter), permitting 0.2% of total reads in each condition for each simulated sample (pnr parameter) and a variability (v parameter) of 0.02 in total sequencing depth of simulated samples. Normalization (norm parameter) was according to the RPKM calculation, and for genes with zero read counts, a pseudo count of 0.5 was used (k parameter) for computing RPKM. Correction factor for length normalization (lc parameter) was set to 1, indicating counts to be divided by a single order of length. The NOISeq pipeline was repeated for exons alone, and for full length genes (both exons and introns included).
RNAseq reads have been deposited in the NCBI short read archive (SRA) under the accession numbers listed in the BioProject PRJNA293799.
GUS activity was quantified as described before [68] using crude extracts from plant leaves and monitoring the quantity of 4-methylumbelliferone products generated from the substrate 4-methylumbelliferyl-b-D-glucuronide (Duchefa) on a fluorometer (Thermo Scientific fluoroskan ascent).
Imaging of fluorescent fusion proteins was performed on 7 day-old roots by confocal scanning laser microscopy on a Zeiss LSM710 microscope equipped with a 63X 1.20 NA water-immersion objective. Excitation of fluorophore were performed at 488nm for GFP and 561nm for RFP and emission settings were 500–550nm for GFP and 570–620nm for RFP. Multichannel confocal stacks were processed with ImageJ 1.49h for figure preparation.
The raw data of sop1-1 transcriptome analysis by RNAseq have been deposited to NCBI short read archive (SRA) accessible in the BioProject PRJNA293799. Data are also available in a user-friendly Jbrowse interface at http://sop1rna.inra.fr
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10.1371/journal.pcbi.1005270 | Novel non-invasive algorithm to identify the origins of re-entry and ectopic foci in the atria from 64-lead ECGs: A computational study | Atrial tachy-arrhytmias, such as atrial fibrillation (AF), are characterised by irregular electrical activity in the atria, generally associated with erratic excitation underlain by re-entrant scroll waves, fibrillatory conduction of multiple wavelets or rapid focal activity. Epidemiological studies have shown an increase in AF prevalence in the developed world associated with an ageing society, highlighting the need for effective treatment options. Catheter ablation therapy, commonly used in the treatment of AF, requires spatial information on atrial electrical excitation. The standard 12-lead electrocardiogram (ECG) provides a method for non-invasive identification of the presence of arrhythmia, due to irregularity in the ECG signal associated with atrial activation compared to sinus rhythm, but has limitations in providing specific spatial information. There is therefore a pressing need to develop novel methods to identify and locate the origin of arrhythmic excitation. Invasive methods provide direct information on atrial activity, but may induce clinical complications. Non-invasive methods avoid such complications, but their development presents a greater challenge due to the non-direct nature of monitoring. Algorithms based on the ECG signals in multiple leads (e.g. a 64-lead vest) may provide a viable approach. In this study, we used a biophysically detailed model of the human atria and torso to investigate the correlation between the morphology of the ECG signals from a 64-lead vest and the location of the origin of rapid atrial excitation arising from rapid focal activity and/or re-entrant scroll waves. A focus-location algorithm was then constructed from this correlation. The algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation with a spatial resolution of 40 mm, respectively. The general approach allows its application to any multi-lead ECG system. This represents a significant extension to our previously developed algorithms to predict the AF origins in association with focal activities.
| Atrial tachy-arrhythmias are associated with irregular excitation waves arising from re-entrant excitation, multiple wavelets or rapid focal activity. Identifying the origin of the irregular activity may be vital for diagnosis and treatment of the disorder. Where invasive and non-invasive methods provide approaches for such identification, both have associated disadvantages. In this study, we used a biophysically detailed model of the human atria and torso to develop an algorithm based on the correlation between the electrocardiogram (ECG) signal from a 64-lead vest and the location of rapid focal and re-entrant excitation. Using the properties of the atrial activation and the ECG signals, we developed a focus-location algorithm which is able to distinguish rapid focal activity from re-entrant scroll waves centred in the same location. Based on simulated data, the algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation, respectively, and 88% for distinguish focal and re-entrant excitation, with no false positives. Inherited from our previous algorithm, it is also easily generalizable to any multi-lead ECG system.
| Atrial tachy-arrhythmias, including atrial fibrillation (AF), atrial tachycardia (AT) and flutter (AFL), are the most common cardiac arrhythmias, predisposing to heart attack, stroke and even possible cardiac death [1,2]. All three are characterised by rapid and irregular electrical activation of the atria, with AF presenting the greatest complexity. Such rapid and irregular electrical activity of the atria is normally associated with one or more of the following abnormal excitation patterns: focal pacing (spontaneous rapid firing of non-pacemaker cells) [3,4], fibrillatory conduction of multiple wavelets [5] and re-entrant excitation scroll waves (i.e., rotors) [4,5].
Epidemiological studies have shown an increase in AF prevalence in the developed world associated with an ageing society, highlighting the need for effective treatment options [6,7]. Current treatment of AF involves the use of rate control, anticoagulation, cardioversion and ablation [8]. The restoration of sinus rhythm in the atria may improve cardiac function, however several drug treatments have limited efficacy in long term maintenance of sinus rhythm [6,9]. Developments aiming to reduce the critical mass required to sustain AF, such as catheter-based radio-frequency ablation therapy, have proven to be more effective in suppressing AF substantially [9], although multiple procedures may still be necessary due to high recurrence rates [10].
For a successful AF ablation, it is vital to know the origins (i.e., the driving sources) of AF prior to the procedure, because isolating the driving source from the rest of the atria is the primary goal of such therapy [9]. To identify such origins, both invasive and non-invasive techniques have been developed. These include the low density endo-surface mapping technique of 64-electrode basket catheters [11] and electrocardiogram imaging (ECGi) [12]. The main limitation of using an invasive method is that it might produce further complications during the surgery [13]. There is a pressing need to develop effective non-invasive methods to identify AF origins which might provide all of the necessary information prior to the surgery. The ECGi technology, based on the inverse problem solution [12], is a promising method in clinical diagnosis. However, current algorithms require further information to constrain the solution to achieve a reliable reconstruction of cardiac excitation waves due to the ill-posedness of the problem [14].
Recent studies have also developed algorithms to identify non-invasively the location of focal sources by using either the standard 12-lead [15–17] or multiple-lead (e.g. 64-lead) ECG systems [18,19]. The success rates of these algorithms range from 40 to 90%. Most are based on the correlation between the location of focal activity and the P-wave morphology or polarity [16–18]. Whereas they are useful in identifying the origin of focal excitation, current algorithms may not be applicable to identify re-entry or very rapid focal activity; at such rapid rates, regular and irregular fragmented waves are typically observed and thus determination of morphology or polarity of the main activation wave is non-trivial. Confounding the case for re-entry, atrial flutter waves or F-waves are also likely to be more fragmented and more complex in nature. It is also important to be able to distinguish very rapid focal activity from that of re-entry at a comparable rate, as the underlying maintenance mechanisms in these conditions are different and thus it is possible that different intervention may be required to terminate the arrhythmia.
The aim of this study is to go-beyond our previous studies [17–19] in identifying the origins of focal-related AF from body surface ECG to develop a novel algorithm based on rapid regular atrial waves in order to identify origins for both rapid focal and re-entrant activity from a multi-lead ECG system.
A previously validated biophysically detailed computational model of the three-dimensional (3D) human atria and torso [17,18,20] was used to simulate ectopic focal and re-entry conditions (Fig 1). The atrial model was segmented into the major anatomical structures and accounts for electrophysiological heterogeneity between these regions (Fig 1A) [21]. The model has been previously used and determined suitable for studying atrial arrhythmia mechanisms [17,22]. The atrial model was placed into a previously developed and validated male torso model which accounts for the segmented structure of lungs, liver, blood masses and spinal cord and the respective electrical conductivities (Fig 1B) [18,20]. A table of conductivity values used can be found in supplementary information (S1 Table). For further test, a previously developed and validated female torso geometry [18] was implemented (S1 Text). Both models have been used before to develop an algorithm to diagnose atrial ectopic origin from multi lead ECG systems [18]. Details of the atrial cell models and 3D simulation protocols can be found in Colman et al [22] or in S2 Text and single cell code S3 Text; details of the torso model development, validation and simulation protocols can be found in Perez Alday et al [18].
Ectopic focal and re-entrant excitations were initiated in different regions of the atria (Fig 2Ai). 2D views and 3D vtk files can be found in supplementary information S. In order to allow rapid excitation waves with rates at frequencies typical of AF/AT/AFL (i.e. 2.5–8 HZ [22,23]) to be sustained in the atria, parameters of the Colman et al. model of single human atrial myocytes were modified to incorporate experimentally observed AF-induced electrical remodelling of ion channels [22], which resulted in shortened AP (Fig 2A). To simulate ectopic focal activity, a sequence of external supra-threshold electrical pulses (with amplitude of 2nA and duration of 2-3ms) was applied to various locations across different regions of the atria (Fig 2Ai). Re-entrant excitation waves were initiated by a phase distribution method [24,25]. Although this is an artificial method for initiating re-entrant excitation, it allows the location of the centre of the rotor wave to be easily controlled. To avoid possible effects of the transition period of excitation waves on their kinetics due to the unphysiological initiation procedure, data after 1 second of initiation were analysed. In cases where re-entrant scroll waves were not localised to the initiation point, i.e. there was a degree of meander, a small non-excitation area (0.5 cm in radius) was incorporated around a specific region of the atria, in order to stabilise the rotor centre (Fig 2C). This allowed sustained re-entrant activity with its origin (i.e. tip) located in a specific region of the atria to be produced. The inclusion of a small area of non-excitable tissue did not produce a marked change in tissue’s volume or morphology of the measured potential on the body surface. In simulations, cases when re-entrant excitation waves had a significant degree of meander were used to test the ability of the algorithm to track the tip of the scroll waves spatio-temporally.
To test the algorithm’s ability to distinguish between focal and re-entrant activities centred on the same spatial locations, a set of focal stimuli simulations were matched in location and excitation rate to re-entrant simulations centre at multiple locations.
A boundary element method (BEM) was used to calculate the potential on the surface of the torso [26]. From the body surface potential (BSP), 64-lead ECG signals were obtained by selecting elements of the torso mesh corresponding to the position of the electrodes as described in previous studies [18,20]. The P-wave of the 64-lead ECG during control conditions matched the experimental data of multiple patients [18,27] (see S1 Fig), validating the development of the heart-torso model.
From the measured atrial-waves, the dynamical evolution of the spatial distribution and amplitude of the atrial-wave dipole was computed from the 64-lead ECG, following the same method as used in previous experimental studies [18,27]. The dipole pattern on the body surface was reconstructed by selecting the maximum positive potential value (positive pole) and the minimum negative potential value (negative pole) of the 64-lead ECG at every time step [27]. The amplitude and the spatial pattern of the atrial-wave dipole based on the 64-lead ECG changed with time as the atrial activation evolved. In the model, both the amplitude and the temporal evolution of the dipole location agreed with the experimental data [18] during control conditions (S2 Fig), further validating the model development.
In a previous study, we developed an algorithm to identify the location of atrial ectopic focal activity, using the polarity map on the body surface potential that was produced from a 64-lead ECG system, which was split into two sets of quadrants (anterior/posterior) [18]. The algorithm was based on the fact that a negative polarity P-wave in a certain lead implied an excitation wave propagating away from the positive electrode of that lead. Thus, the quadrant of the 64-lead electrode positions with the largest number of electrodes with negative P-waves would correlate directly to the origin of the focal excitation. The success rate of the algorithm was 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations.
However, the previous algorithm was only able to detect slow ectopic focal atrial activation from body surface potential mapping and cannot be applied directly to detect the origin of atrial excitation waves due to rapid focal or re-entrant activity because of the complexity of the body surface waveform, which produces fibrillatory waves. Determining the polarity of fast atrial waves is not trivial since it may consist of positive, negative and biphasic waves, depending on the time period investigated (Fig 2C(ii) and 2D(ii)). Furthermore, re-entrant and focal excitation patterns may present different characteristics of fragmented fast atrial waves, and the ability to distinguish between these types of excitation could provide valuable information for directing treatment. Thus, in order to apply our previously developed algorithm to both rapid focal and re-entrant excitation, new tools were developed. The first tool was to determine the polarity of the atrial wave associated with main atrial activation, in order to identify the location of the source. The second tool was to quantify the differences between focal and re-entrant activity. Further details of these algorithmic developments are provided below.
At slow pacing rates, it is straightforward to determine the polarity of individual P-waves: the long period of the diastolic phase means that the ECG signal remains at a baseline during this interval, with a clear deflection from the baseline corresponding to atrial activation during the systolic period (Fig 2B). This deflection is the P-wave, and may be positive, negative or biphasic (with both positive and negative portions). The duration (i.e., the time interval) of the P-wave corresponds to the time interval of atrial activation.
The challenge for determining the polarity of the atrial wave at rapid pacing rates is that the diastolic period is absent, leaving the ECG signal absent of a stable baseline. Therefore, there is no clear distinction between successive deflections (Fig 2C(ii) and 2D(ii)). Determination of the polarity of the atrial wave in such case is thus non-trivial; any polarity can be extracted from the same signal, depending on the time interval which is considered (Fig 2C(ii) and 2D(ii)). However, the polarity in the interval during which a large volume of the atrial mass is excited (i.e. main atrial activation) can be determined and is suitable for our algorithm. Thus the time interval corresponding to the main atrial activation must first be determined.
Analysis shows that the dipole signal provides sufficient information to determine the time interval of main atrial excitation (Fig 3). Fig 3 illustrates results for three different cases of atrial activation originating from the same location but with increasing complexity (i.e. slow focal pacing, rapid focal pacing, and re-entrant excitation).
At the slow rate, determination of the polarity of the P-wave is straightforward and can be seen to be positive in lead V1 (Fig 3A–black line). Note that the time interval of the P-wave indeed corresponds to the time interval of the atrial activation (Fig 3A(i) (ii)). Also, both positive and negative poles of the body surface dipole have one significant deflection, and the time interval of this deflection corresponds directly to the time interval of atrial activation and therefore the P-wave (Fig 3 –red and blue lines). The positive and negative dipole signals can be combined as a “dipole sum” (defined as the sum of the modulus of the negative and positive poles), giving a single signal with a significant deflection corresponding to the time interval of atrial activation (Fig 3 –green line).
At rapid rates where F-waves rather than P-waves are observed, there are no clear markers for the time interval of atrial activation in the ECG signal (Fig 3B and 3C–black line). The dipole sum, however, still presents a signal with one easily identifiable prominent deflection; the time interval of this deflection corresponds to the main atrial activation (Fig 3B(i),(ii)), even in the case of more fragmented atrial waves resulting from re-entrant activity (Fig 3C(i),(ii)). The portion of the atrial wave within this time interval therefore gives the polarity associated with the main atrial activation. In examples shown in Fig 3, the polarity is positive in lead V1 for all cases but the polarity will vary spatially across the body surface according to lead position.
Thus, by selecting the ECG segment corresponding to the main atrial activation (obtained from dipole sum, Fig 3 magenta/shaded regions), the polarity (positive, negative or biphasic) of each lead in this segment is determined.
Having identified the polarity of the fast atrial waves in each lead, the resulting 64-lead polarity distribution feeds directly into our original atrial source location algorithm [18].
Fig 4 demonstrate the correlation used by the algorithm to determine the origin of non-meandering atrial re-entrant activations, centred on the sino-atrial node (SAN) (left), right atrial appendage (RAA) (middle) and pulmonary veins (PV) (right). In each case, the time interval has been obtained by selecting the largest deflection in the dipole sum evolution pattern (Fig 4A- vertical dashed lines) as described in the previous section. Then, an atrial-wave polarity map is created (Fig 4B) from the time interval selection. Once the polarity map has been created (Fig 4A and 4B), the location of the source of the atrial activation can be found through the Perez Alday et al. algorithm [18] (S3 Fig), which associates the two set of torso quadrants (Qti) (Fig 4B) with the two set of atria quadrants (Qai) (Fig 4C (i)-(ii)).
Our simulations demonstrate that re-entrant excitation waves are characterised by more fragmented ECGs (Fig 3C) compared to focal activity (Fig 3A and 3B). This might be attributable to the fact that the wave propagation through the atria due to focal excitation is more uniform and symmetric (around the origin of excitation) than re-entrant excitation. Performing Fourier Transformation analysis (FFT) of the signal from lead V1, commonly used for AF analysis due to its large atrial signal [28] (closest is lead 15 in the 64-lead configuration), allows the fragmentation of the signal V1 to be quantified, providing a way to distinguish the cases of focal from re-entrant excitation waves, with the same excitation rate and origin (Fig 5). FFT MATLAB function was applied to analyze the simulated ECG signals. From the FFT, as would be expected the dominant frequency (DF) shows no marked difference between the focal and re-entrant cases due to the same activation rates. However, the re-entrant cases exhibited considerably more power at higher frequencies. To quantify this, the ratio of the area under the normalized power spectrum density (PSD) in the ranges 0 –(2 x DF) Hz and 0–50 Hz (AFFTr2DF) was calculated. The use of the threshold of 2xDF was chosen because it is the value at which the distinction between focal and re-entrant excitation was the most significant (S4 Text). The ratio showed dramatic differences between re-entrant and ectopic activations (Fig 5A, 5B and 5C). By plotting the AFFTr2DF against its DF for all simulations (Fig 5D), it was clear that a ratio of above 0.675 corresponded to focal activity, a ratio below 0.655 corresponded to re-entrant activity and a ratio in the range 0.655 to 0.675 could correspond to either (overlapping area in Fig 5D).
The new tools developed were integrated into a flow chart of the algorithm as illustrated in (Fig 6). The first step of the new algorithm was to compute the dipole sum from the body surface potential distribution. Then, by selecting the time interval corresponding to the largest peak in the dipole sum, which is attributable to a large volume of the atrial mass that has been excited, a polarity map can be created. The next step was to implement the previous algorithm we have developed [18] to identify the source of atrial activation based on the body surface potential distribution. The last step was to differentiate focal from re-entrant activities based on the spectral characteristics of the fast atrial waves.
By using a biophysically detailed computer model of human atria-torso and identifying the correct polarity of the fast atrial waves, we have developed a novel algorithm to locate the origin of atrial fibrillation in association with both of ectopic focal and re-entrant activity. The success rate of the algorithm was 92% and 75% for focal and re-entry activation, respectively. The properties of the FFT allowed re-entry and focal activation to be distinguished with a success rate of 88%.
The algorithm was also tested with a different torso geometry. During this test, dipole sum and AFFTr2DF values were obtained and the algorithm successfully identified the quadrant where the origin of the arrhythmia was located. Further information can be found in supporting information S1 Text.
It was also shown that when white noise was added with a signal to noise ratio of 10, the performance of the algorithm was not affected as the changes of the FFTAr values were small (S5 Text). However, further test need to be implemented.
FFT analysis was applied to experimental AF signals, where AFFTr2DF values were obtained to compare simulated and experimental data. Similar AFFTr2DF values were obtained with simulated and experimental data. Further information can be found in S6 Text.
Previous studies have been focused on differentiating ectopic activity against re-entry [29–32]. Most use atria-electrocardiograms to detect and characterize complex fractionated signals, FFT and DF atria maps [29,30,33]. The success rate of these algorithms is in the range of 60–80% [29,30,34], however, as it is an invasive method, it might unduly lengthen the ablation procedure [35]. By using a 12-lead ECG system, algorithms to detect ectopic activity have been developed [16,36], however, the success rates range within 55–78% [15,16,18] and it has been proved that the 12-lead ECG system does not produce enough information to identify the origins when fast atrial waves are presented or under re-entrant activity [15,18,32]. Other attempts have used multi-lead ECG systems and body surface mapping [31,32,37], to correlate to atrial DF or add extra information like phase mapping [32]. However, it has been difficult to validate the time interval, location and the source of the atrial activation when fast atrial waves are presented. Nevertheless, they are promising methods that can add extra useful information. Ours is the first attempt to distinguish the main activity and find the position of the focus and tip of the re-entry from a multi-lead ECG.
The present algorithm can also be used together with other invasive or non-invasive mapping methods, with potential to reduce procedures times in locating the origin of atrial arrhythmias.
The torso model lacks considerations of some other tissue types or organs (such as muscles, fat tissue, bowel, kidneys and spleen) that may affect the amplitude of simulated surface potentials. However, the absence of those tissues does not have a big effect on the polarity of the atrial-waves, which is the characteristic used in the present algorithm, as demonstrated previously [18].
In the algorithm, two-sets of quadrants were defined to cover the torso. The spatial resolution of the 8 quadrant was about 40mm. The finer spatial resolution with lower accuracy was 20mm. Though this provided roughly the site of the AF origin, it is the information needed for identifying which part of the atria for ablation (i.e., left or right atrium, the low or upper part of the atrium). The diameter size in current ablation procedures varies from 3mm to 10mm [9,13,38,39]. Therefore, the current algorithm can be used in clinical studies, but it may require further investigation and refinement to provide the essential information in all case.
In the present study, we only tested the effectiveness of the algorithm for detecting single atrial focal activity and a single centre of a rotor activity. However, a possible extension is to identify multiple wavelets, using the dipole evolution patterns. For that purpose, consideration of combined use of the present algorithm with vecto-cardiograms, phase relationships, correlation analysis and inverse problem reconstruction may be necessary, warranting further investigation.
A novel algorithm has been developed to locate the origins of rapid and irregular atrial excitation waves, associated with both ectopic focal and re-entrant activity. This represents a significant progress to previously developed algorithms to predict AF origins in association with focal activities.
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10.1371/journal.pmed.1002038 | Geographical Inequalities and Social and Environmental Risk Factors for Under-Five Mortality in Ghana in 2000 and 2010: Bayesian Spatial Analysis of Census Data | Under-five mortality is declining in Ghana and many other countries. Very few studies have measured under-five mortality—and its social and environmental risk factors—at fine spatial resolutions, which is relevant for policy purposes. Our aim was to estimate under-five mortality and its social and environmental risk factors at the district level in Ghana.
We used 10% random samples of Ghana’s 2000 and 2010 National Population and Housing Censuses. We applied indirect demographic methods and a Bayesian spatial model to the information on total number of children ever born and children surviving to estimate under-five mortality (probability of dying by 5 y of age, 5q0) for each of Ghana’s 110 districts. We also used the census data to estimate the distributions of households or persons in each district in terms of fuel used for cooking, sanitation facility, drinking water source, and parental education. Median district 5q0 declined from 99 deaths per 1,000 live births in 2000 to 70 in 2010. The decline ranged from <5% in some northern districts, where 5q0 had been higher in 2000, to >40% in southern districts, where it had been lower in 2000, exacerbating existing inequalities. Primary education increased in men and women, and more households had access to improved water and sanitation and cleaner cooking fuels. Higher use of liquefied petroleum gas for cooking was associated with lower 5q0 in multivariate analysis.
Under-five mortality has declined in all of Ghana’s districts, but the cross-district inequality in mortality has increased. There is a need for additional data, including on healthcare, and additional environmental and socioeconomic measurements, to understand the reasons for the variations in mortality levels and trends.
| Deaths among children younger than five years old have been declining worldwide for the past few decades. Children’s death rates in sub-Saharan Africa, although also declining, remain higher than in other regions.
Ghana has performed better than most other sub-Saharan African countries in terms of reducing child deaths, but we lack information on how much child mortality varies in different parts of Ghana.
Also, it is not known how factors such as low education of mothers or not having clean water or improved toilet facilities that increase the risk of illness and death among children vary in different parts of Ghana.
The purpose of our study was to provide information on changes in the child death rate, and the social and environmental factors associated with it, for all of Ghana’s 110 districts between the years 2000 and 2010.
Information from two national censuses in 2000 and 2010 and sophisticated statistical models were used to make estimates of the child death rate in each district for the years 2000 and 2010 and for change between these two years.
The risk of children dying before they reach age five was higher in northern Ghana than it was in the southern part of the country.
Child death rates went down in every district in Ghana between 2000 and 2010, but progress was slower in northern Ghana than in the southern part of the country.
More women and men in the country had completed primary school education in 2010 than in 2000, and more homes had access to clean water and improved toilet facilities and cleaner cooking fuels like liquefied petroleum gas.
Use of liquefied petroleum gas for cooking may reduce the risk of child death in Ghana.
There should be strategies to help districts where young children are at a relatively higher risk of dying, largely those in northern Ghana, to catch up with those districts with lower child mortality.
Some of the actions needed may be related to social and environmental factors like education, clean sanitation, and cooking fuels. But it is likely that there is an important role for healthcare, which was not investigated in this study.
It is important for Ghana and other sub-Saharan African countries to improve the measurement and monitoring of child mortality and its social, environmental, and healthcare risk factors at the community level.
| Mortality in children under 5 y of age has declined in most countries, with the decline accelerating since 2000 [1,2]. The pace of under-five mortality decline has been slower in sub-Saharan Africa (SSA) than in other regions, and SSA accounts for nearly one-half of global under-five deaths [1,3]. Poverty-related risk factors, including unimproved water and sanitation and household air pollution from solid fuels, are estimated to account for a large proportion of worldwide under-five deaths [4]. Parental, and especially maternal, education is also an important predictor of child survival [5]. These factors may also be associated with within-country disparities in under-five mortality [6,7].
Above and beyond global inequalities, there are important subnational inequalities in under-five mortality in relation to socioeconomic status and geography [6,8–14]. Although studies from health and demographic surveillance systems have reported child mortality at specific sites in some countries [15–18], very few studies have examined under-five mortality at fine spatial resolutions for entire countries, which is relevant for assessing community determinants and interventions [9,14,19,20]. Further, little is known about whether and how subnational variations and trends in under-five mortality are associated with social and environmental factors that have been found to be associated with child survival in individual-level studies [6,7].
Ghana has had one of the largest declines in under-five mortality in SSA [1,3]. Under-five mortality in Ghana dropped from 128 deaths per 1,000 live births in 1990 to 62 in 2015, a 52% reduction [1]. Ghana has also had one of SSA’s best economic performances, with its per-capita gross domestic product growing substantially between 2000 and 2013 [21]. Economic inequality, as measured by the Gini coefficient, increased slightly between 1998 and 2006 [21]. It is unclear whether the national improvement in child survival is benefiting all local communities, how it might be affecting within-country inequalities, and whether it is associated with socioeconomic and environmental improvements or whether it is driven by other factors.
In the analyses presented in this paper, we used geocoded data from two national censuses to estimate under-five mortality in Ghana at the district level in 2000 and 2010, and assessed variations and inequalities in under-five mortality in these two years and changes over the decade. We also analyzed the distributions of social and environmental risk factors of under-five mortality, and their associations with under-five mortality and its change. To our knowledge, this report is one of only a few high-resolution “small-area” studies of under-five mortality and its social and environmental risk factors, and the only one to assess change in small-area units over a period of a decade for an entire country. Small-area analysis helps reveal geographical inequalities in mortality and allows the benchmarking of outcomes in each district against the others.
We used 10% random samples of Ghana’s 2000 and 2010 National Population and Housing Censuses. Using a set of predefined questions, both censuses gathered information on a number of individual- and household-level variables related to socioeconomic factors (e.g., literacy and educational attainment for persons 11 y or older), living environment (e.g., household’s main water supply source, sanitation facility type, and cooking fuel type, as a commonly used metric for household air pollution [4,22]), and children's births and deaths for females 12 y or older. Each record in the census data had information on the census enumeration area (EA), the smallest geographical unit, with an average population of 750. The 2000 and 2010 censuses had nearly 26,000 and 38,000 EAs, respectively. Our analysis was conducted at the district level, the country’s second smallest level of subnational administrative divisions, with EAs mapped to the district of residence. We used districts as units of analysis because they are administrative units for resource allocation and for program implementation. Further, EAs were defined separately in each census and could not be mapped from one census to the other. There were 110 and 170 districts in the 2000 and 2010 censuses, respectively. We merged the 2010 districts, linking them to their original districts that had split since 2000, to create 110 common districts for our analyses. The 110 districts are administratively assembled into ten regions: Ashanti, Brong-Ahafo, Greater Accra, Central, Eastern, Northern, Western, Upper East, Upper West, and Volta.
We used the data to calculate the distribution of households or persons in each district for the following variables that are associated with child survival: household’s main source of cooking fuel (wood, charcoal, other biomass, kerosene, liquefied petroleum gas [LPG], electricity), type of sanitation (toilet) facility usually used by households (improved, unimproved), household’s main source of drinking water (improved, unimproved), maternal education (highest educational grade completed: none, primary, secondary or higher), paternal education (highest educational grade completed: none, primary, secondary or higher), and urban versus rural place of residence. We classified census responses on drinking water source and sanitation facility as improved versus unimproved based on WHO/UNICEF joint monitoring program categories for water supply and sanitation (http://www.wssinfo.org).
District under-five mortality estimates and social and environmental factors that may be associated with child survival for the index years 2000 and 2010 are summarized in Table 1. Median district under-five mortality declined from 99 deaths per 1,000 live births in 2000 to about 70 in 2010. In 2000, under-five mortality varied substantially among districts, ranging from about 75 to nearly 150 deaths per 1,000 live births (Fig 1). Under-five mortality was above 100 per 1,000 live births in over half of the districts in 2000. The majority of these districts were in the three northernmost regions of the country (Upper West, Upper East, and Northern). There was also high under-five mortality in areas along the coastline of the Western and Central regions. Under-five mortality declined in all districts between 2000 and 2010 (Fig 2), and was above 100 per 1,000 live births in only 13% of districts in 2010 (Fig 2A). In 2010, under-five mortality was below 70 deaths per 1,000 live births for nearly half of the districts, whereas no district had had under-five mortality this low in 2000 (Fig 1).
From 2000 to 2010, under-five mortality declined by over 40% in some southern districts, but the decline was less in the north, with nearly a third of districts having less than 20% reduction (Fig 2). Districts with the highest mortality in 2000 generally had a smaller decline, exacerbating existing cross-district inequalities, especially for relative inequalities. For example, the difference and the ratio of the highest and lowest percentile of district under-five mortality increased from 76 and 2.0 in 2000 to 78 and 2.5 in 2010, respectively.
From 2000 to 2010, the proportion of households without improved sanitation and drinking water decreased in all districts (Table 1). The share of households with improved drinking water was over 80% in 47 districts, and the share of households with improved sanitation was over 80% in 64 districts, in 2010; virtually no district had had this level of improved drinking water and improved sanitation in 2000 (Fig 3). Despite the improvements, the share of households with improved sanitation was less than 80% in all districts in the Northern, Upper East, and Upper West regions in 2010. There was also some reduction in the use of wood for cooking, largely replaced by charcoal, which emits less health-damaging particulate matter [22,28,29]. The proportion of households using LPG also increased from 6% in 2000 to 18% in 2010. In some districts in the Greater Accra region, over a quarter of households used LPG as the primary cooking fuel in 2010.
More women completed primary education and fewer were illiterate in every district in 2010 compared to 2000 (Fig 3). In over one-half of districts, including in all 24 districts in the northernmost regions, at least one-half of women had not attended school or had not completed primary education in 2000; by 2010, this was the case in 25% of districts. Despite improvements in basic literacy and primary education, the proportion of women who attained secondary or higher education did not change noticeably.
In multivariate analysis, higher use of LPG for household cooking was associated with lower under-five mortality after adjusting for other factors, with each 10% increase in households using LPG associated with a 11.1% (95% CI 3.0%–18.8%) decline in 5q0 (Fig 4). Associations for the other social and environmental variables were not consistent or were weak in the different analyses, although there were indications of beneficial effects from replacing wood with charcoal or kerosene, from improved sanitation and drinking water, and from having a higher share of mothers who had completed primary education.
After adjustment for socioeconomic and environmental factors, some unexplained spatial variation in under-five mortality remains (Fig 5), which represents unmeasured and/or unknown risk factors not already included in the model. Specifically, in 2010 the spatially structured part (Fig 5A) of the unexplained component of under-five mortality was higher in the northernmost and Greater Accra regions, and lower in the Western, Brong-Ahafo, Ashanti, and Eastern regions and parts of the Volta region (Fig 5A).
Our small-area analysis found that under-five mortality declined in all of Ghana’s districts between 2000 and 2010, but the size of this decline varied considerably across districts. The pace of decline was steeper in southern districts, where under-five mortality was lower in 2000, than in the north, exacerbating existing cross-district inequalities, especially for relative inequality. The decline in under-five mortality in Ghana appears to continue beyond 2010, as indicated by the latest national estimate by the UN IGME [2]. We also found improvements in education and household environment, including access to improved water and sanitation and cleaner cooking fuels, from 2000 to 2010. Higher use of LPG for cooking was associated with lower under-five mortality, but the associations of either the level or change in under-five mortality with other social and environmental variables were weak. We observed high unexplained variability in under-five mortality in parts of the country, especially in 2010. This unexplained variability does not readily point to any single factor that we are aware of. It is possible that a combination of unmeasured variables, including healthcare, socioeconomic status, natural resource availability, and local governance, played a role in this (unexplained) variability.
Like in our study, studies in Zambia, Papua New Guinea, and India found that under-five mortality declined in all or some districts over a similar time period, with large subnational variation in the magnitude of decline [9,14,19]. Unlike in our study, cross-district inequality decreased in Zambia over time, but the Indian study found a rise in cross-district inequality, as we did in Ghana. The association between parental, especially maternal, education and child survival was weaker in our data than in some other population-based studies [5,12,30–32].
The main strength of our study is its novel scope in analyzing under-five mortality, and its socioeconomic and environmental risk factors, at the small-area level. Small-area information allows benchmarking of subnational administrative units, both in terms of mortality levels and trends and in terms of the environmental and socioeconomic risk factors for mortality. This information can in turn identify districts that are performing well and those that are most in need of additional interventions related to the environmental and socioeconomic factors analyzed or to other determinants of child survival, as discussed below. We used geocoded data from two national censuses, which included every household in the country. We also used Bayesian spatial analysis, which balanced between unstable district-level estimates and simplified aggregate national estimates to obtain consistent and comparable under-five mortality estimates for all districts.
Our study also has a number of limitations. Due to the limited coverage of the vital registration system in Ghana, we relied on demographic models to estimate under-five mortality at the district level. While this approach is well tested and used by national and international agencies, it introduces uncertainty into our estimates. In particular, the estimates using the 2000 census were substantially different from the national estimates using other sources. We dealt with this issue by adjusting our estimates to be consistent with pooled estimates from all sources. This adjustment could introduce additional uncertainty into the district-level estimates. We could not separate child deaths into those during the neonatal versus subsequent periods. The social and environmental risk factors in our data were each measured using one or two questions in the census. This simplification of measurement may have affected the associations of these factors with under-five mortality. We had no information on healthcare access and interventions such as immunization, insecticide-treated nets, and nutritional supplementation, which are important factors for child survival [33–35]. Similarly, we had no information on within-country migration, which might be partially responsible for changes in specific districts over time.
The Millennium Development Goals, and the policy emphases and resources that followed them, have led to acceleration of under-five mortality decline in Ghana and other countries in SSA. These efforts seem to be benefiting all of Ghana’s districts, but the rate of progress has been slow and unequal across districts. There is therefore a need both to accelerate the decline and to put special emphasis on districts that have progressed more slowly. Experiences of more equitable decline in countries such as Niger, Brazil, and Bangladesh show that achieving this requires multisectoral approaches while maintaining a major role for the health system [33–35]. In particular, although we found weak associations between under-five mortality and some of the social and environmental factors we investigated, the role of these factors may have been partially masked by others, including changes in health services and health systems interventions, and hence there should be investments in education and household environments as a way to continue and accelerate improvements in child survival. At the same time, the weak association of child mortality with social and environmental factors, and the large body of evidence on the efficacy of healthcare and health systems interventions for child health, should motivate increasing the coverage of healthcare interventions such as antenatal care, immunization, insecticide-treated nets, and treatment of acute conditions such as pneumonia, malaria, and diarrhea throughout Ghana, with an emphasis on equity in access and utilization [33,36]. Finally, improving measurement and monitoring of under-five mortality and its risk factors and interventions at the subnational level are particularly important for all SSA countries, to better inform policies and programs that facilitate equitable progress.
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10.1371/journal.pntd.0007011 | A high throughput neutralization test based on GFP expression by recombinant rabies virus | The effectiveness of rabies vaccination in both humans and animals is determined by the presence of virus neutralizing antibodies (VNAs). The Rapid Fluorescent Focus Inhibition Test (RFFIT) is the method traditionally used for detection and quantification of VNAs. It is a functional in vitro test for assessing the ability of antibodies in serum to bind and prevent infection of cultured cells with rabies virus (RABV). The RFFIT is a labor intensive, low throughput and semi-quantitative assay performed by trained laboratorians. It requires staining of RABV-infected cells by rabies specific fluorescent antibodies and manual quantification of fluorescent fields for titer determination. Although the quantification of fluorescent fields observed in each sample is recorded, the corresponding images are not stored or captured to be used for future analysis. To circumvent several of these disadvantages, we have developed an alternative, automated high throughput neutralization test (HTNT) for determination of rabies VNAs based on green fluorescent protein (GFP) expression by a recombinant RABV and compared with the RFFIT. The HTNT assay utilizes the recombinant RABV ERA variant expressing GFP with a nuclear localization signal (NLS) for efficient quantification. The HTNT is a quantitative method where the number of RABV-infected cells are determined and the images are stored for future analysis. Both RFFIT and HTNT results correlated 100% for a panel of human and animal positive and negative rabies serum samples. Although, the VNA titer values are generally agreeable, HTNT titers tend to be lower than that of RFFIT, probably due to the differences in quantification methods. Our data demonstrates the potential for HTNT assays in determination of rabies VNA titers.
| The potency of rabies vaccine is demonstrated by the presence of virus neutralizing antibodies (VNAs) in serum. It is critical to evaluate immunologic status of individuals who work directly with rabies virus (RABV) (laboratorians) or at high risk of infection due to interaction with animals (veterinarians and animal control workers). In addition, rabies vaccination records and demonstration of VNAs in animals are mandatory before initiating pet travel to rabies-free counties or regions. Rabies VNAs are currently determined by the rapid fluorescent focus inhibition test (RFFIT) and the fluorescent antibody virus neutralization (FAVN) test, which measure the ability of antibodies to bind and prevent infection of RABV in vitro. Both assays require staining of infected cells using anti-rabies antibodies and manual observation of infected cells by a fluorescent microscope to determine VNA titers. In this study, we have developed a GFP reporter-based high throughput neutralization test (HTNT) for automated quantification of infected cells. This method has the advantages of allowing investigators to analyze and store the results, and can accommodate large sample sizes. Overall, the results from HTNT exhibited 100% correlation with that of RFFIT, albeit with differences in rabies VNA titer values due to quantification methods.
| Human rabies is a zoonotic disease transmitted predominantly through bites from infected animals [1]. Although, rabies is nearly 100% fatal after onset of symptoms, it is preventable by post exposure prophylaxis (PEP) when administered immediately and appropriately after a suspect exposure. The surrogate for protection against rabies virus (RABV) infection is the presence of virus neutralizing antibodies (VNAs) targeted against the RABV glycoprotein [2, 3]. VNAs play important roles in preventing the invasion of RABV into peripheral nerves at the site of exposure and subsequent transport to the brain [4]. Rabies vaccination confers complete protection against the disease and can be administered either pre- or post- exposure [4]. The advisory committee on immunization practices (ACIP) currently recommends as pre-exposure (Pr-E), a three-dose regimen administered at days 0, 7 and 21 or 28 [5]. For PEP, ACIP recommends administration of purified rabies immunoglobulin (RIG) at the wound followed by four doses of vaccination at days 0, 3, 7 and 14 after exposure [6]. While the prophylactically administered antibodies offer immediate protection, acquired immunity followed by vaccination provides long-term memory response. The Pr-E vaccination is intended for individuals who are at high risk for RABV infection, such as laboratory personnel and animal care providers like veterinarians, wildlife rehabilitators and animal control workers [7]. In addition to Pr-E vaccination, monitoring rabies VNA titers against the virus periodically is required to determine the level of immunity against RABV [8].
Nearly 99% of the estimated annual 59,000 human deaths worldwide are caused by dog bites [1]. Rabies control could be achieved by vaccinating 70% of dog population, particularly free roaming dogs, in order to break the RABV infection cycle and end the circulation of virus [9, 10]. In the United States (U.S.) and several other countries, circulation of canine (dog) RABV variant has been eliminated by comprehensive dog rabies vaccinations [11, 12]. The importation of unvaccinated dogs to the U.S., including a case of a RABV-infected dog by potential falsification of vaccination records, have been reported [13, 14]. To avoid re-introduction of canine RABV variants in previously eliminated regions, the World Organization of Animal Health (OIE) has enforced strict guidelines for importation of pets and other domestic animals [11, 15, 16]. Many countries require rabies vaccination records and demonstration of rabies VNA levels in pets before travel by either rapid fluorescent focus inhibition test (RFFIT) or fluorescent antibody virus neutralization (FAVN) tests or an extended period of quarantine in the absence of titer information [14].
Developed during the 1970s, RFFIT replaced the mouse neutralization test (MNT), which required demonstration of VNAs to protect infection in vivo. Due to the requirement of large numbers of mice, ethical considerations and long duration for MNT, an alternative in vitro test (RFFIT) was developed [17]. The RFFIT is a semi-quantitative method in which 20 microscopic fields are observed for the presence of fluorescent foci (RABV-infected cells) to determine rabies VNA titer. Similarly, FAVN test developed in 1998, utilizes a modified protocol for rabies VNA titer determination and has demonstrated similar results compared to RFFIT and MNT [18]. According to ACIP, complete neutralization of RABV at the 1:5 serum dilution, which corresponds roughly to 0.1 International Unit (IU) / ml is a prerequisite for rabies protective titer in humans [5]. A minimum titer for 0.5 IU/ml is required as a proxy for protection according to World Health Organization (WHO) requirements [19]. For animals, rabies neutralization titer should be 0.5 IU/ml or higher as per OIE guidelines [15].
The RFFIT is a labor intensive, low throughput assay requiring skilled personnel to perform, interpret and quantify results. While the number of fluorescent foci are recorded, the fluorescent images viewed in a microscope are not stored and hence cannot be re-analyzed. Because RFFIT involves observation of 20 fields (40%) and not the entire well, the choice of fields may vary with testing personnel. Considering these drawbacks, we intended to develop a high throughput and quantitative test with the ability to store and analyze the results long-term. The high throughput neutralization test (HTNT) described in the present study utilizes a recombinant RABV ERA variant that expresses green fluorescent protein (GFP). GFP reporter viruses are used in neutralization studies for several viruses, including RABV, wherein for quantification of virus infected cells are based on GFP expression from viral genome instead of staining the cells with antibodies against viral proteins [20, 21]. However, in order to improve automated detection and quantification in the HTNT, a nuclear localization signal (NLS) was added to the N-terminus of GFP [22, 23]. The overall procedure for both RFFIT and HTNT are very similar, however in HTNT the infected cells are quantified based on the ratio of GFP positive nuclei to total nuclei, to determine percent neutralization.
The high content screening (HCS) instrument, ArrayScan used in this study is an automated stage microscope which takes multiple images of an entire 96- or 384-cell plates to capture DAPI and GFP staining, respectively. The instrument also stores the images for future analysis. The results obtained by HTNT assay correlated 100% with RFFIT results for detection of rabies VNAs in serum samples. The rabies VNAs titers determined by HTNT based on the quantification of infected cells agreed with RFFIT values, although HTNT titers tended to be lower than that of RFFIT due to differences in quantification methods. Overall, this study demonstrates the utility of a GFP reporter-based assay for detection of rabies VNAs in an automated high throughput system.
The mouse neuroblastoma (MNA) and BSR (a clone of baby hamster kidney 21) cells (Centers for Disease Control and Prevention [CDC] collection) were cultured in E-MEM media supplemented with 10% fetal bovine serum (FBS), L-glutamine, essential vitamins, antibiotics (Penicillin and Streptomycin) and antimycotic (Amphotericin B). The RABV variant challenge virus standard -11 (CVS-11) was propagated in BSR cells [24].
The codon optimized Monster Green Fluorescent Protein (hMGFP) gene, which offers greater fluorescent intensity and lower cytotoxicity from Montastrea cavernosa, was obtained from Promega Corporation. The hMGFP gene was modified at the N-terminus by addition of NLS (NLS-hMGFP) for nuclear targeting and localization as previously described [24]. The NLS-hMGFP open reading frame was incorporated between the phospho- (P) and the matrix- (M) protein genes in RABV ERA genome (Fig 1A). After cloning and sequence verification, the full-length recombinant viral genomic cDNA was applied for virus recovery. In brief, the BSR cells grown in a six-well plate at ~ 90% confluency were transfected using a combination of 6 plasmids: a full-length viral genomic cDNA plasmid pERA-NLS-hMGFP at 3.0 μg/well, and five helper plasmids of pTN at 1.0 μg/well, pMP at 0.5 μg/well, pML at 0.5 μg/well, pMG at 0.5 μg/well (the plasmids expresses RABV encoded proteins N, P, L and G in trans) and pNLS T7 at 1.0 μg/well. Seven to 10 days after transfection, the recombinant ERA-NLS-hMGFP virus was recovered and further amplified in fresh BSR cells until virus titer reached at least 107 Focus Forming Unit (ffu)/ml.
BSR cells were seeded overnight on 12-well glass bottom tissue culture plates. The cells were infected with recombinant RABV ERA-NLS-hMGFP virus for 24 h at 37°C, fixed with 4% paraformaldehyde after infection and blocked with 10% FBS in 1X PBS for 15 minutes at room temperature (RT). Mouse monoclonal antibodies against N protein (anti-N mAb) were diluted in 1X PBS containing 10% FBS and incubated for 30 minutes at RT, followed by three washes with 1X PBS. Alexa Flour 594 conjugated anti-mouse (Molecular Probes) in 1X PBS was used as secondary antibodies and incubated for 30 minutes at RT. Cell nuclei were stained with DAPI (4’, 6-diamidino-2-phenylindole) and mounted with Prolong Antifade Mounting Media (Thermo Fisher Scientific). The cells were visualized using LSM 710 inverted confocal microscope (Zeiss) for DAPI, GFP and Alexa Flour 594 using respective filters.
Human sera used as negative controls were previously confirmed by RFFIT for absence of rabies VNAs. These historic patient specimens received at the CDC Rabies Laboratory for human antemortem rabies diagnostic testing were de-identified according to an IRB approved CDC protocol #7028.
Human sera positive for rabies VNAs, previously collected from rabies vaccinees as part of Occupational Health Clinic screens were de-identified and used for validation in the HTNT and RFFIT assays. Animal sera, either from control (unvaccinated) or vaccinated animals, primarily dogs and cats were provided by Nancy Laboratory for rabies and wildlife, ANSES, France.
The RFFIT [17] is utilized to measure the level of rabies VNAs against the RABV CVS-11 in human and animal serum samples. Eight 5-fold serial dilutions of heat-inactivated serum samples were incubated with RABV in 8-well tissue culture chamber slides for 90 min at 37°C. The titer of RABV CVS-11 virus used is 50 FFD50/0.1 mL. 200μL of MNA cells (5 X 105 cells/ml) were then added to every well containing the serum-virus mixture, which is comprised of 50μL of serum and 100μL of the CVS-11 virus and incubated for an additional 20 hours at 37°C with 0.5% CO2. Slides were then washed, fixed with acetone and stained with anti-rabies FITC (fluorescein isothiocyanate) immunoglobulin (Fujirebio Diagnostics, Inc) containing Evans blue (0.5% in PBS). Evans blue is a counterstain that provides a red background fluorescence to improve contrast. Twenty distinct microscopic fields per well were examined using a fluorescence microscope at X200 magnification to score the virus-infected cells (foci). From the number of positive fields per well, the rabies VNA titers are mathematically calculated using the Reed-Muench calculation [25]. The endpoint neutralization titer of the test serum is converted to international units (IU)/ml values by calibration against the endpoint neutralization titer of the U.S. Standard Rabies Immune Globulin (SRIG) (obtained from Food and Drug Administration, U.S.), which was measured in the same assay at 2.0 IU/ml.
HTNT to measure rabies VNAs in human and animal sera was optimized with theRFFIT protocol as a starting point. Starting at a 1:2.5 dilution, eight 5-fold serial dilutions of sera were made in 96-well plates. Specifically, 40μl of serum was added to 60μl of E-MEM media and 20μl of this mixture was transferred into 80μl of media to complete the 5-fold dilutions. ERA-NLS-hMGFP (50 FFD50/0.1ml) was pre-diluted 1:25 in E-MEM media (80μl), at a multiplicity of infection sufficient to infect 50–70% of cells, was added to the diluted sera (80μl) and mixed. Media and virus only controls were included on each plate and an SRIG positive control was included at least once per run. Samples were incubated for 90 min at 37°C, followed by addition of BSR cells at a concentration of 3.5x105 cells/ml in equal volume to the virus and serum mixture and moved to a black plate with transparent wells (Costar). All the samples and controls were run in duplicate to obtain average values. The plates were incubated for 20 h at 37°C, fixed with 4% paraformaldehyde for 15 min at RT, washed twice with PBS and incubated with 3 μM DAPI (nuclear stain) for 5–15 min. After three additional washes, PBS was added to each well, plates were sealed and stored at 4°C until reading on the ArrayScan reader.
The ArrayScan XTI HCS reader is an automated fluorescent microscope that acquires and records images in up to six separate fluorescent channels (ThermoFisher Scientific). The instrument takes multiple images starting at the center in a spiral fashion to cover entire surface of a well. The accompanying Cell Analysis Software records the size and fluorescent intensity of the imaged objects and further analysis is performed using specified criteria. In the protocol described here, the channels blue and green were used to identify DAPI-stained nuclei (blue) and ERA-NLS-hMGFP infected cells (green, GFP-expressing cells) respectively [23].
Rabies VNA titers are mathematically calculated using the Reed-Muench formula [25]. The serum end-point titer in the neutralization assay is described as the highest dilution factor with 50 percent reduction in the number of the fluorescent foci observed. The rabies VNA titers are determined using the method of Reed-Muench that calculate the difference between the logarithm of the starting dilution and the logarithm of the 50% end-point dilution (difference of logarithms) from the formula:
[50%−(infectivitynextbelow50%]/[(infectivitynextabove50%)–(infectivitynextbelow50%)]Xlogarithmofdilutionfactor
The RFFIT results can be expressed as a serum titer or in international units (IU). For the calculation of IU/ml, the 50% end point titer of the reference serum (diluted to 2 IU/ml) and that of test serum is used in the following formula:
NumberofIU/ml=(End-pointtiterofthetestserum/End-pointtiterofthereference)X2IU/mlinthereferenceserum
Total number of DAPI-stained cells and GFP-positive cells were counted using the HCS Studio Cell Analysis software. Infected cells were determined by establishing a fluorescence intensity threshold where cells with a total GFP intensity above the threshold (determined from the instrument) were considered GFP-positive responders. The percentage of GFP-positive responders per well was recorded and further analyzed using Microsoft Excel 2013.
Relative GFP-positive responder (RPR) values were then calculated by using the cell and virus only controls:
Relative%responders=[(Sample%responders)–(Cellonly%responders)]/[(Virusonly%responders)–(Cellonly%responders)]
The RPR were calculated for each serum dilution and the duplicates averaged. The RPRs below and above 50% were then used in the Reed-Muench method to calculate the 50% endpoint titer, in which RPR is used instead of percentage of fields used by RFFIT. Endpoint titers were converted to IU/ml as described above.
The results of HTNT are compared to the traditional RFFIT, to determine sensitivity and specificity measurements using RFFIT results as the true positives and true negatives. Sensitivity was calculated by the following formula [true positive/(true positive + false negative)]. Specificity was calculated using the following formula [true negative/(false positive + true negative)]. The 95% confidence intervals were calculated using the Clopper and Pearson method. Quantitative analyses were performed to compare the titer values between the two methods. Specifically, the differences between the RFFIT and HTNT IU/ml values for positive samples were analyzed using Bland-Altman plots [26]. Bland-Altman plots are constructed by plotting the differences between the IU/ml of the two methods against the IU/ml averages of both methods. The mean difference, or bias, and limits of agreement (+/- 1.96SD) are also plotted and utilized to evaluate the systemic differences between the two methods. The Pearson coefficient and concordance correlation coefficient (Lin’s coefficient) were used to measure the agreement of titers between methods. All analyses were performed using Microsoft Excel, GraphPad 6.0, and R 3.4.1.
The recombinant RABV ERA virus expressing hMGFP was generated by reverse genetics as described in methods (Fig 1A). The recombinant virus replicated similarly to wild type RABV in cell culture. To check the expression and localization of hMGFP, BSR cells were infected with RABV ERA-NLS-hMGFP for 24 h at 37°C, fixed and processed for confocal microscopy. The expression of nucleoprotein (N protein) from the RABV genome was monitored by staining with mouse anti-N monoclonal antibody (mAb) followed by secondary staining with goat anti-mouse IgG—Alexa Flor 594 conjugate. Fig 1B demonstrates the expression of hMGFP in infected cells, based on N protein staining. The localization of hMGFP was exclusively in the nucleus, overlapping with the nuclear stain DAPI designed to enhance fluorescent signal intensity and improve quantification.
RABV ERA-NLS-hMGFP was evaluated for rabies neutralization assay by HTNT as described in methods. The steps involved in HTNT are compared and contrasted with RFFIT procedures in Table 1. In the HTNT assay, the number of DAPI-stained nuclei denotes the total cell count, while GFP positive nuclei that co-localize with DAPI represented infected cells. Fig 2A shows representative HCS data. Around 8,000 cells out of 14,000 total cells were positive for GFP staining in virus only positive control condition demonstrating close to 50% infection. None or negligible GFP positive nuclei were observed in cell only negative control demonstrating specificity of GFP detection and quantification. The GFP expression and co-localization with DAPI observed in the virus only condition was specific to expression from RABV-infected cells, as evidenced by the complete inhibition when incubated with SRIG antibody at 1:5 and 1:25 dilutions. HTNT using the ArrayScan also detects concentration dependence of RABV VNAs in sera as illustrated by the increased infectivity of cells at higher serum dilutions. From the number of GFP positive cells in test vs controls, percent RABV infection was obtained for titer determination (Fig 2B).
The human sera samples were run by both HTNT and RFFIT and considered positive based on complete neutralization of RABV infection at the 1:5 dilution as recommended by the ACIP guidelines. Using the RFFIT results to categorize the samples as true positive and true negative, 100% sensitivity and specificity was obtained with HTNT (Fig 3). Of 135 human serum samples, 74 negative and 61 positive results were consistent between both assays. Similarly, for the 42 animal samples both RFFIT and HTNT had identical results (Fig 4). HTNT exhibited 100% sensitivity and specificity compared to RFFIT in the ability to completely neutralize RABV in 1:5 dilution for animal samples tested (Fig 4).
As positive and negative determinations were consistent in both assays, we next compared the rabies VNA titer values of positive sera obtained from RFFIT and HTNT. The IU/ml titer values determined by RFFIT and HTNT for human sera were plotted and the Pearson coefficient was calculated (Fig 5A). Statistically significant coefficient value (r = 0.88) was observed suggesting a strong linear relationship between the RFFIT and HTNT titer values. Because the Pearson correlation coefficient only measures the linearity of the relationship but not the agreement between the two methods, we also calculated the concordance correlation coefficient (CCC, Fig 5B). The CCC was moderately strong (0.77), indicating that the HTNT titers replicate RFFIT titers but the IU/ml values do not match entirely. To investigate the differences between the two measurements obtained from RFFIT and HTNT analyses, we used Bland-Altman plots. In the Bland-Altman plots, the differences of the titers obtained from the two methods were plotted against the average titers (Fig 5C) to determine bias (measure of the systemic difference between the two methods) and limits of agreement. The bias value 14.78 indicates that the HTNT titers are on average 14.78 IU/ml lower than RFFIT titers. Although samples with RFFIT titers (above 200 IU/ml) also had the highest values in the HTNT (above 30–60 IU/ml), overall HTNT titers were of lower magnitude resulting in bias. When the Bland-Altman plots were analyzed without these outliers, as expected, the bias measurement decreased to -1.10, indicating that there was a systemic difference of 1.1 IU/ml between RFFIT and HTNT values, with samples having slightly higher HTNT titers (Supplemental Fig 1).
We also compared the rabies VNA titer values (in IU/ml) of positive animal sera obtained from the two assays. The Pearson coefficient value was statistically significant (r = 0.96) demonstrating a strong linear relationship between the RFFIT and HTNT titer values (Fig 6A). The CCC was also higher at 0.97, indicating that the HTNT titers replicate RFFIT titers (Fig 6B). Similar to human sera, Bland-Altman plots for animal sera to determine the differences of the titers obtained from the two methods were plotted against the average titers (Fig 6C). The bias value of 1.95 (HTNT titer was lower than RFFIT by a factor of 1.95 IU/ml) observed with animal samples was much less than that of human sera, partly because of the absence of high titer samples. Since 0.5 IU/ml titer cut-off is required for demonstration of sufficient neutralizing activity and lower titers require rabies vaccination boosters (based on the WHO / OIE recommendations), we compared the titers of positive samples obtained by RFFIT and HTNT methods. The results demonstrated higher correlation for RVNA titers greater than 0.5 IU/ml for animal samples by both methods (S1 Table). Although 100% correlation was observed for human samples based on ACIP recommended complete neutralization at 1:5 cut-off, there were differences if 0.5 IU/ml was considered for minimal protective titer (S1 Table).
A subset of animal sera (N = 14) were tested three independent times on different days to measure reproducibility of both assays. Negative samples did not neutralize at the 1:5 dilution in any of the replicate runs in either HTNT or RFFIT methods. Fig 7 represents the titer of positive replicate values for both the RFFIT and HTNT assays. No significant differences in titer values were observed using either method. As SRIG was included as a control in every HTNT assay, we measured the titer value of SRIG to evaluate reproducibility across days. The median 50% endpoint dilution for SRIG replicates was 160 (95% Confidence interval of 128.9–172.1) with a coefficient of variation of 21.4% demonstrating the reproducibility and consistency of assay.
The presence of antibodies to bind and neutralize RABV in vitro is a proxy for determining successful vaccination against rabies. The demonstration of rabies VNAs are pre-requisites for certain personnel whose job duties possess a high risk of contracting RABV infection, such as laboratorians, veterinarians and animal handlers [7]. The traditional assays for determining VNA titers, such as RFFIT or FAVN, utilize wild type RABV and antibodies conjugated to fluorescent dyes to measure levels of RABV infection to quantitate neutralization [17, 18]. As an alternative, reporter-based virus neutralization methods, particularly GFP-based assays are utilized widely [22, 23, 27, 28]. The GFP reporter based assay utilizes recombinant virus expressing GFP under the control of viral promoter to determine and quantitate viral infected cells without the need for additional antibody staining against viral proteins or other staining methods, like crystal violet, to determine survival or clearance of infected cells. Recombinant RABV expressing a GFP reporter gene has been previously developed and utilized for the determination of VNAs [20, 21]. The results demonstrated significant correlation of the reporter-based assay VNA titer values with RFFIT results. In addition, assays using GFP reporter-based pseudo-type viruses (lentivirus or Vesicular stomatitis virus) expressing either RABV or other lyssavirus G proteins displayed concordance with traditional assays [29] [30].
In this study, we utilized RABV expressing GFP reporter with an NLS at the N-terminus for nuclear localization. The nuclear targeting offers two major advantages, (1) enhances fluorescence intensity and (2) enables highly accurate and automated quantification mechanism. GFP targeted to the nucleus as opposed to being distributed throughout the cell [20, 21] concentrates the protein to a smaller surface area. Because the shape of nucleus is more homogenous than the overall shape of cells, automated methods can be employed for more accurate quantification of cells that express GFP [23]. We used DAPI staining to count all nucleated cells in a given well and calculated the proportion of infected cells based on GFP and DAPI co-localization. Nearly 100% co-localization efficiency was achieved using NLS targeting with accurate quantification. Confocal microscopy demonstrated expression and co-localization of GFP and DAPI in the infected cells based on N protein staining (Fig 1B). The ArrayScan, a high content automated fluorescent microscope, used in this assay offers several advantages in detecting and analyzing neutralization data. The automated microscope scans and captures multiple images at different wavelengths to obtain DAPI and GFP signals of an entire well. In contrast, the standard RFFIT requires scanning only a subset of 20 fields (40%) from a well for anti-rabies staining. Further, HTNT does not require this additional anti-rabies staining step and relies on GFP fluorescence. In addition, the images captured by the microscope are stored for future analysis. The time required to read five samples (with 8 dilutions) are similar by both manual and HCS instrument. However, with the capability of reading either 96- or 384- well plates and addition of a plate stacker with the instrument, can greatly increase the high throughput capabilities, an advantage for testing large sample sizes.
In our assay, an average of 13,000 cells are counted in every well (based on DAPI staining) with nearly 50% of the cells expressing GFP from RABV infection in the absence of VNAs (Fig 2). Compared to RFFIT, the numbers (total and infected cells) obtained by HTNT are more accurate. These numbers are used to determine the percent of infected cells in the presence and absence of VNAs, generating more exact measurements of infection in the HTNT compared to RFFIT. Although detection methods are different, HTNT and RFFIT results correlated 100% in both sensitivity and specificity from a panel of both human and animal sera (Figs 3 and 4). Complete neutralization of RABV infection at the 1:5 dilution, either based on GFP or anti-rabies detection were consistent.
The correlation between HTNT and RFFIT was better at lower VNA titer values compared to higher titers, as the differences observed are amplified with high titer samples. The Bland-Altman plot determined higher bias value for human sera, primarily for samples with high titers (above 50 IU/ml) as exclusion reduced the difference between RFFIT and HTNT values (S1 Fig). Because titers in the animal panel were on the lower range, we did not observe a significant difference in Bland-Altman bias values in titers between the two assays. As RFFIT only accounts for fluorescence in 20 fields and not on the number of fluorescent foci (or infected cells) in each field, it is possible to have similar number of positive fields but may exhibit differences in number of RABV infected cells. On the contrary, HTNT determines the percent of infected cells at different dilutions across the entire well which tend to be lower than the RFFIT, and hence the titer values. One of the limitations of this HTNT assay is availability of the ArrayScan or other High Content Screen instruments in rabies testing laboratories. We are currently evaluating the HTNT assay using different platforms to determine the flexibility and ability for laboratories to adopt the HTNT based on specific availability and needs. In addition, we are comparing large sero-survey results obtained from RFFIT and HTNT, in order to exemplify the advantages of HTNT.
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10.1371/journal.pgen.1000561 | Defects in tRNA Modification Associated with Neurological and Developmental Dysfunctions in Caenorhabditis elegans Elongator Mutants | Elongator is a six subunit protein complex, conserved from yeast to humans. Mutations in the human Elongator homologue, hELP1, are associated with the neurological disease familial dysautonomia. However, how Elongator functions in metazoans, and how the human mutations affect neural functions is incompletely understood. Here we show that in Caenorhabditis elegans, ELPC-1 and ELPC-3, components of the Elongator complex, are required for the formation of the 5-carbamoylmethyl and 5-methylcarboxymethyl side chains of wobble uridines in tRNA. The lack of these modifications leads to defects in translation in C. elegans. ELPC-1::GFP and ELPC-3::GFP reporters are strongly expressed in a subset of chemosensory neurons required for salt chemotaxis learning. elpc-1 or elpc-3 gene inactivation causes a defect in this process, associated with a posttranscriptional reduction of neuropeptide and a decreased accumulation of acetylcholine in the synaptic cleft. elpc-1 and elpc-3 mutations are synthetic lethal together with those in tuc-1, which is required for thiolation of tRNAs having the 5′methylcarboxymethyl side chain. elpc-1; tuc-1 and elpc-3; tuc-1 double mutants display developmental defects. Our results suggest that, by its effect on tRNA modification, Elongator promotes both neural function and development.
| The efficiency of protein synthesis can be modulated by alterations of various components of the translation machinery. In translation, transfer RNAs act as adapter molecules that decode mRNA into protein and thereby play a central role in gene expression. In the tRNA maturation process, a subset of the normal nucleosides undergoes modifications. Modified nucleosides in the tRNA anticodon region are important for efficient translation. We found that, in the worm C. elegans, components of the Elongator complex are required for the formation of a certain set of tRNA modifications in the anticodon region. We observed a reduced efficiency of translation as well as a lower production of neurotransmitters in Elongator mutant worms. Elongator is conserved in eukaryotes, and mutations in a subunit of human Elongator cause a severe neurodegenerative disease, familial dysautonomia (FD). It is unclear in humans whether Elongator acts on the translational level through tRNA modification to regulate neuronal processes. Our observations in C. elegans, together with the role of yeast Elongator in translation, show that the function of Elongator in tRNA modification is conserved. Inactivation of Elongator may cause neuronal defects by affecting translation.
| Regulation at the level of translation is one important way in which gene activity is controlled in metazoans. Several different mechanisms have previously been identified by which translation can be regulated during development or memory formation [reviewed in 1]. During anterior-posterior patterning of the Drosophila embryo, the translation of hunchback mRNA in the posterior region of the embryo is inhibited by binding of a protein complex to the Nanos response element in the hunchback 3′UTR [2]. In Caenorhabditis elegans, developmental timing is controlled by the small temporal RNAs, lin-4 and let-7, which act by forming heteroduplexes with their target mRNAs and, at least in some cases, suppressing their translation [3]. Translation efficiency is also regulated by phosphorylation of translational components at the initiation and elongation steps [4],[5]. For example, during memory formation in mice, translation of ATF4 mRNA is regulated by phosphorylation of initiation factor eIF2α [6].
Another way in which the efficiency of translation can be modulated is by covalent modification of nucleosides in the anticodons of tRNAs. In the decoding of mRNA, modified nucleosides in the anticodon region, especially position 34 (wobble position) and position 37, have been suggested to be important for restriction or improvement of codon-anticodon interactions [7]–[10]. In S. cerevisiae, 25% of the tRNA species are covalently modified by the addition of either carbamoylmethyl (ncm) or methoxycarbonylmethyl (mcm) side chains to the 5′carbon of U34 [11]–[14]. A subset of these tRNAs contains a further modification on wobble uridines, addition of a thio group at the 2′position (Figure 1) [11],[13],[14]. In vivo, presence of an 5-carbamoylmethyluridine (ncm5U), an 5-methoxycarbonylmethyluridine (mcm5U) or an 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) improves reading of both A- and G-ending codons [14]–[16].
In S. cerevisiae, formation of ncm and mcm side chains present at 5′position of wobble uridines requires the Elongator complex [12], which is composed of six subunits Elp1p – Elp6p [17],[18]. Yeast cells lacking Elongator activity are viable but display multiple defects including those in PolII transcription and exocytosis [16], [18]–[22]. However, these defects all appear to result from a primary defect in tRNA modification [16]. Elongator complex is conserved in eukaryotes and has also been purified from humans [23]. Inactivation of Elongator subunits in multicellular organisms causes multiple defects including those in development, cell proliferation, cell migration and neuron projection [24]–[27]. Recently, Elongator in mice has been reported to acetylate α-tubulin [27]. However, it is presently unclear whether Elongator in higher eukaryotes functions directly in multiple processes or acts on a small number of targets whose absence leads to pleiotropic defects.
Mutations in the human homologue of yeast ELP1, IKBKAP/hELP1, have been shown to cause Familial Dysautonomia (FD), a genetic disorder primarily affecting the sensory and autonomic nerve systems [28]–[30]. Human IKAP/hELP1 protein is part of a complex of six proteins that also contains the human homologues of yeast Elongator proteins [23]. Whether Elongator in humans or other metazoans promotes tRNA modification has not been reported.
The aim of the present study was to investigate the function of the Elongator homologues, ELPC-1 and ELPC-3 in the nematode, C. elegans. In particular, we were interested to determine first, whether Elongator in metazoans is required for modification of wobble uridines, and second, whether C. elegans could be established as a model to study the role of Elongator in modulating translation within neurons and other tissues. We demonstrate that Elongator is required in C. elegans for the formation of modified nucleosides in tRNA, and that Elongator mutants have defects in neurological and developmental processes associated with reduced translation. We believe our results also have important implications for the etiology of FD disease.
Searches of the C. elegans protein sequence database with the yeast or human Elp1p and Elp3p sequences revealed that C. elegans contains single Elp1p and Elp3p homologues, named ELPC-1 and ELPC-3, which are encoded by Y110A7A.16 and ZK863.3 respectively [see Materials and Methods for an explanation of gene nomenclature]. To investigate the function of elpc-1 and elpc-3 in C. elegans, we used elpc-1(tm2149) and elpc-3(tm3120), deletion mutants kindly supplied by S. Mitani of the National Bioresource Project, Japan. The elpc-1(tm2149) deletion removes 275 bp of sequence spanning parts of exons 7 and 8 (Figure 2A), whereas the elpc-3(tm3120) removes 356 bp spanning the first half of exon 3 and contains as well an insertion of four nucleotides in the second half of exon 3 (Figure S1A). The elpc-3(tm3120) deletion removes part of a sequence sharing significant homology to the Radical S-adenosylmethionine (SAM) superfamily [31]. Members of this family of proteins contain an FeS cluster and use S-adenosylmethionine (SAM) to catalyse a variety of radical reactions. The Elp3p Radical SAM domain has been found to be required for iron binding in Methanocaldococcus jannaschi [32], and for integrity of the Elongator complex in yeast [33].
In yeast, Elp1p and Elp3p are required for the formation of mcm5 and ncm5 side chains of modified nucleosides mcm5U, ncm5U, ncm5Um and mcm5s2U present at the wobble position in tRNA [12]. To determine whether their homologues in C. elegans, ELPC-1 and ELPC-3, also function to promote wobble uridine tRNA modification, we examined if the mcm5U, ncm5U or mcm5s2U modified nucleosides were present in tRNA isolated from wild-type and elpc mutants. Total tRNA isolated from wild-type worms contained ncm5U and mcm5s2U nucleosides (Figure 2B and 2D, Figure S1B, S1D). However, no mcm5U was detected (Figure S2D), implying that modification of uridine in C. elegans tRNA differs in at least one respect from that in S. cerevisiae. In contrast to wild-type worms, no mcm5s2U or ncm5U nucleosides were present in tRNA isolated from elpc-1(tm2149) mutants (Figure 2C and 2E). Instead, 2-thio uridine (s2U) was detected in tRNA isolated from the elpc-1(tm2149) mutant but not from wild-type worms (Figure 2F and 2G). This nucleoside arose from a failure in the mutant to add the mcm5 side chain of the mcm5s2U nucleoside. The tRNA modification defect in the elpc-1(tm2149) mutant was rescued by elpc-1 activity provided by a transgene (Figure 2C and 2I). Thus, like yeast Elp1p, C. elegans ELPC-1 is required for the formation of mcm5 and ncm5 side chains in tRNA. Consistent with the tRNA modification defect in the elpc-1(tm2149) mutant, tRNA isolated from elpc-3(tm3120) mutants lacked the mcm5s2U and ncm5U nucleosides and instead contained s2U (Figure S1).
Synthesis of the s2 group of mcm5s2U in yeast requires Tuc1p [15], [34]–[38]. The homologue of Tuc1p in C. elegans is encoded by open reading frame F29C4.6 [39]. In this paper we will refer the F29C4.6 gene as tuc-1. We analyzed tRNA from tuc-1(tm1297) mutant worms by HPLC and confirmed that it lacked the mcm5s2U modification and instead had mcm5U, a nucleoside not normally found in C. elegans tRNA (Figure S2B, S2C, S2D, S2E). Furthermore, a transgene containing wild-type tuc-1 DNA restored formation of mcm5s2U in tRNA (data not shown). Consistently, tRNA isolated from an elpc-1(tm2149); tuc-1(tm1297) double mutant lacked both the 5′- and 2′ side-chains of wobble uridines and no ncm5U or mcm5s2U nucleosides were observed (Figure S3).
To investigate the expression pattern of C. elegans ELPC-1 in various tissues, we examined worm strains harboring a transgene encoding functional, full length ELPC-1 protein fused to GFP. The transgene contained 435 bp of the promoter region and all 11 introns (Figure 2A). The transgene rescued the tRNA modification defect in the elpc-1 mutants (Figure 2C and 2I). The fusion protein encoded by the transgene was preferentially detected in several tissues including the nervous system (Figure 3). However, its presence was not uniform. Within the nervous system, ELPC-1::GFP was seen predominantly in a pair of neurons that control egg-laying, the HSNs (Figure 3F and 3G), and in chemosensory neurons in the head (Figure 3A–3E). Within the latter class of neurons, the ELPC-1::GFP level was particularly high in the ASE, ADF and ASK pairs of neurons (Figure 3B–3E. For nomenclature of neurons, see Materials and Methods). Expression was seen both within the cell bodies (Figure 3B) and along the entire lengths of the neuronal processes (data not shown). Outside of the nervous system, a strong ELPC-1::GFP signal was seen in the pharynx (the feeding organ) (Figure 3A) and the vulva (Figure 3N and 3O), part of the egg-laying apparatus in the hermaphrodite. In all animals examined, ELPC-1::GFP expression was also seen in the two CAN cells (Figure 3H and 3I), which are associated with the excretory canals and are required for proper function of the excretory system. In all cells in which ELPC-1::GFP was seen, fluorescence was restricted to the cytoplasm (Figure 3A). The ELPC-3::GFP fusion was expressed in the same set of cells (data not shown).
In S. cerevisiae, defects in wobble uridine tRNA modification are associated with reduced translation efficiency [14]–[16],[40]. The yeast elp3 tuc1 double mutant, in which modifications at both the 5′and 2′positions of the uridine moiety are absent, is lethal [15]. To investigate the influence of wobble uridine modifications on the efficiency of translation in C. elegans, we examined the effect of elpc-1, elpc-3 and tuc-1 mutations on β-galactosidase expression in worms harboring a lacZ transgene driven by heat shock-responsive elements from the hsp16-1 gene. The induction of lacZ mRNA upon heat shock was not reduced in strains lacking wild-type elpc-1, elpc-3 or tuc-1 gene activity, or in elpc-1; tuc-1 double mutant worms (Table 1). However, β-galactosidase activity was 28% lower in protein extracts from heat shocked elpc-1; tuc-1 double mutants than in those from wild-type worms subjected to the same heat shock regime (Table 1). A modest (∼14–18%) but significant reduction in β-galactosidase activity was also seen elpc-1(tm2149), elpc-3(tm3120) or tuc-1(tm1297) single mutant worms (Table 1).
To monitor cell and tissue specific protein synthesis, we used an established technique, fluorescence recovery after photobleaching (FRAP) [41]. The rate of protein synthesis in different cells and tissues was measured using GFP reporters. We used gcy-5::gfp and mec-4::gfp which are expressed in ASER and 6 touch cell neurons respectively, and myo-3::gfp which is expressed in the body wall muscle. In all reporter fusions examined, photobleached wild type animals showed a significant recovery of GFP signal within 5 hours (Figure 4, Figure S4). However, animals with the elpc-1(tm2149) or elpc-3(tm3120) mutations had a slower GFP signal recovery, indicating a reduced rate of protein synthesis (Figure 4, Figure S4). Cycloheximide, an inhibitor of translation, was used to confirm that the recovered GFP signal was due to newly synthesized protein. In animals treated with cycloheximide, no significant recovery of GFP signal was observed (Figure 4, Figure S4). Together, these experiments demonstrate that an absence of uridine modification in tRNA is associated with a reduction in translation efficiency in C. elegans.
elpc-1 and elpc-3 single mutants were viable and fertile and they were able to move normally on the bacterial lawn. Furthermore, the chemosensory neurons in which ELPC-1::GFP and ELPC-3::GFP are strongly expressed are present at their normal positions and have normal morphology (Figure S5). Among these neurons (Figure 3A–3E), the ASE pair of sensory neurons is required for experience-dependent behaviors elicited by different salt concentrations [42]. Wild-type worms normally chemotax towards NaCl. However, pre-incubation in normal salt concentrations in the absence of nutrients elicits an aversion response to NaCl when worms are subsequently tested in chemotaxis assays [43]. In this salt learning assay, worms that have grown at normal salt concentrations and in the presence of abundant nutrients are first starved for four hours in the presence or absence of salt and then assayed for their chemotactic response to NaCl. Since we observed strong expression of ELPC-1 and ELPC-3 in ASE neurons (Figure 3B and 3C, data not shown), we tested elpc-1 and elpc-3 mutants in a salt learning assay. At 20°C, the mutants behaved as wild type (Figure 5A). At 25°C, wild-type worms exposed to 100 mM NaCl in the absence of nutrients moved away from NaCl, whereas elpc-1 or elpc-3 mutants treated in the same way continued to chemotax towards the NaCl (Figure 5C). In the elpc-1 mutant, this defect was partially rescued by the elpc-1::gfp construct (Figure S6). Thus, C. elegans elpc-1 and elpc-3 are required for an experience-dependent change in behavior. In contrast, in tuc-1 mutant worms no statistically significant changes were observed (Figure 5).
Ablation of the ASE neurons leads to an inability to chemotax towards certain water-soluble compounds including Na+, Cl−, lysine and biotin [44]. elpc-1 and elpc-3 mutants were able to chemotax both to water soluble and volatile compounds at all temperatures tested (Figure S7). When elpc-1 or elpc-3 mutants were grown at 20°C to the time at which the chemosensory neurons have developed and then shifted to 25°C, salt learning was abnormal (Figure 5B). Together, these observations suggest that the salt learning defect seen in elpc-1 and elpc-3 mutants is not caused by a defect in the development of the ASE chemosensory neurons or in their ability to detect salt.
Since neuronal function in metazoans is known to be dependent upon the ability to synthesize and secrete neurotransmitters and neuropeptides, we tested whether these processes were abnormal in C. elegans elpc-1 and elpc-3 mutants. One established assay for examining the synthesis and secretion of neuropeptides involves a heterologous fusion protein, ANF::GFP. The prodomain of a preproANF–GFP fusion protein can be used as a reliable fluorescent reporter of dense-core vesicle transport and exocytosis in rat PC12 cells, as well as in D. melanogaster and C. elegans neurons [45]–[47]. In C. elegans, ANF::GFP is secreted by neurons into the pseudocoelomic space from where it is rapidly cleared by three pairs of coelomocytes [47]. In elpc-1 and elpc-3 mutants, we observed a reduced accumulation of ANF::GFP in coelomocytes (Figure 6C and 6D), which could be caused by either less synthesis or reduced secretion of ANF::GFP from neurons. In both wild-type and elpc mutant worms carrying the ANF::GFP transgene, the fusion protein was visible in neurons, but the GFP signal was weaker in elpc mutants that was also reflected by western blot (Figure 6A and 6B). As there was no significant reduction of ANF::GFP mRNA in elpc mutants, the lower production of ANF::GFP was at the posttranscriptional level (Figure 6A and 6B).
To investigate whether elpc-1 and elpc-3 also affected extracellular levels of a neurotransmitter, we examined whether the mutants showed increased resistance to aldicarb, an inhibitor of acetylcholinesterase present in the synaptic cleft. Wild-type worms exposed to aldicarb immediately hypercontract and then die after a few hours because they are unable to reduce synaptic levels of acetylcholine secreted by neurons [48]. Mutants with reduced acetylcholine-mediated signaling are partially or completely resistant to the drug. Aldicarb-resistant mutants fall into two classes, those that have pre-synaptic defects resulting in reduced synthesis or secretion of acetylcholine and those in which the fault lies in the post-synaptic neurons [49]. elpc-1 and elpc-3 mutants showed greater resistance to aldicarb than that displayed by aex-6(sa24) (Figure 6E), which has been described previously as being partially resistant to the drug [50]. elpc-1(tm2149) mutant harboring the elpc-1::gfp transgene on an array behaved as wild type in the aldicarb assay (Figure S8). elpc-1 and elpc-3 mutant worms showed normal response to levamisole (Figure 6F), which activates the post-synaptic acetylcholine receptor [49], suggesting a defect in the pre-synaptic compartment. These results suggest that either less acetylcholine is produced in the neurons or less acetylcholine is released from the neurons in the elpc-1 and elpc-3 mutants.
Recently it was shown that mouse ELP3 protein can acetylate α-tubulin in vitro [27]. Thus one possibility is that the neural defects seen in mice with reduced Elongator activity is caused by aberrant α-tubulin function. Acetylation of α-tubulins in a wide variety of species occurs on a conserved lysine residue at position 40. In C. elegans, there is a single α-tubulin with a lysine at this position, MEC-12 [51]. To investigate whether Elongator in C. elegans promotes acetylation of α-tubulin, we examined acetylation in elpc-1 or elpc-3 mutants. As previously reported, an antibody that recognizes lysine 40-acetylated α-tubulin in various species, 6-11B-1, could detect the residue in extracts from wild-type worms but not those from the mec-12(e1607) mutant. However, we observed no reduction in the levels of acetylated MEC-12 in elpc-1 or elpc-3 mutants (Figure S9A). Furthermore, unlike elpc-1 or elpc-3 mutants, mec-12(e1607) is not aldicarb resistant (Figure S9B).
In humans, IKBKAP/hELP1 expression is not confined to the nervous system but is also seen in many other cell types [29],[52],[53]. In C. elegans, we also observed ELPC-1::GFP expression in several non-neuronal tissues (Figure 3). However, in an otherwise wild-type genetic background, although they grow slower than wild-type and had reduced fertility at 25°C (Table 2), the development of elpc-1 or elpc-3 mutants is not grossly disturbed. In yeast, elp1 and elp3 deletion strains are also viable. However, yeast cells lacking both ELP3 and TUC1, which therefore lack both mcm5 and s2 groups of tRNAs having the nucleoside mcm5s2U34, are not viable [15]. In the course of analyzing elpc-1; tuc-1 double mutant worms, we observed that the strain could be propagated at 15°C but not at 25°C. The elpc-1(tm2149); tuc-1(tm1297) double mutant hermaphrodites raised at 15°C for different periods of time were shifted to 25°C and then examined both for their own development and also for their ability to give rise to viable progeny. When 4th larval stage (L4) hermaphrodites were shifted to 25°C, they continued to develop and became fertile adults. However, the eggs they laid arrested development during embryogenesis (Figure 7A–7D). The arrest did not occur at one specific embryonic stage but rather at different stages in different embryos. Some embryos were arrested either prior to enclosure with relatively few cells (<100 cells) (Figure 7A); or at the 3-fold stage (Figure 7D). However, the majority were arrested during or immediately after morphogenesis (Figure 7B and 7C). Similar defects were seen in elpc-3; tuc-1 double mutants (Figure S10). Thus ELPC-1, ELPC-3 and TUC-1 likely function at multiple times during embryogenesis. No synthetic defects were seen in elpc-1; elpc-3 double mutants, suggesting that Elongator function is abolished in both elpc-1 and elpc-3 single mutants.
Temperature shift experiments with 1st or 2nd stage larvae (L1 or L2) also indicated a role for ELPC-1, ELPC-3 and TUC-1 in development of the vulva and for generation of germ cells. When L1 or L2 larval hermaphrodites containing both the elpc-1(tm2149) and tuc-1(tm1297) mutations were raised at 15°C and shifted to 25°C, they developed to become small sterile adults. Inspection of the shifted animals at high magnification indicated that vulval development was invariably abnormal (Figure 7I and 7J, Figure S10I, S10J). In wild-type worms, the three progenitors of the vulva, P5.p, P6.p and P7.p are induced to adopt vulval fates: they divide to give rise to 22 cells that together form a tube through which the eggs are laid in adult hermaphrodites. In the temperature-shifted elpc-1; tuc-1 and elpc-3; tuc-1 double mutants, the divisions of P5.p, P6.p and P7.p failed to occur properly and significantly fewer vulval cells were formed (Figure 7I and 7J, Figure S10I, S10J). At the L3 stage, when the vulval developmental fates are induced, expression of the elpc-1::gfp reporter was upregulated in P5.p, P6.p and P7.p as well as in their immediate descendants (Figure S11), suggesting that one or more of the signaling pathways mediating vulval cell fate specification controls elpc-1 expression. Inspection of the gonads of the temperature shifted double mutants revealed that the overall organization of the germline was relatively normal (data not shown). However, the oocytes completely failed to mature (Figure 7E and 7F, Figure S10E, S10F); the sperm were highly vacuolated and grossly abnormal (Figure 7G and 7H, Figure S10G, S10H). These observations imply that elpc-1 and elpc-3 also function in development of non-neuronal tissues.
The developmental defects in the elpc-1; tuc-1 double mutant were rescued by extrachromsomal arrays harboring the elpc-1::gfp transgene. When elpc-1(tm2149); tuc-1(tm1297) double mutant hermaphrodites raised at 15° were allowed to lay eggs at this temperature for two hours and the eggs subsequently shifted to 25°, the progeny invariably arrested either during embryogenesis or during early larval stages (n = 65). However, 60% (n = 40) of elpc-1(tm2149); tuc-1(tm1297); svEx808[elpc-1::gfp Punc-122::gfp] embryos raised grew to become adults with normal vulval development. 15% of these adults gave rise to some live larval progeny indicating partial rescue of both the germline defect and the requirement during early embryogenesis. A second array, svEx806[elpc-1::gfp Punc-122::gfp] also rescued although not quite as efficiently: 40% of embryos grew to become adults.
Here we show that the elpc-1 and elpc-3 genes, homologues to yeast ELP1 and ELP3, are required for formation of the ncm5 and mcm5 side chains present in the wobble nucleosides, ncm5U and mcm5s2U in C. elegans tRNA. Worms with mutations in elpc-1 or elpc-3 show a defect in a salt learning assay, associated with reduced expression of neuropeptide and slow accumulation of acetylcholine in the synaptic cleft. elpc-1::gfp and elpc-3::gfp reporters are strongly expressed in certain sensory neurons including ASE, required for salt learning. elpc-1 and elpc-3 mutant phenotypes are strongly exacerbated by mutations in tuc-1, which is required for the formation of 2-thio group in the mcm5s2U modified wobble nucleosides.
Although a requirement for the Elongator complex for the modification of wobble uridines in yeast tRNA is well documented [12], studies on the role of Elongator in this process in metazoans have not been previously reported. Our results demonstrating that ELPC-1 and ELPC-3 are required for the addition of mcm5 and ncm5 side chains to uridine residues in C. elegans tRNA imply that Elp1p and Elp3p function has been conserved in evolution. Our results also confirm, however, that differences exist in tRNA modification in eukaryotes. In yeast there are 13 tRNA species with a uridine at the wobble position. Of these, eleven contain the nucleoside ncm5U, ncm5Um, mcm5U or mcm5s2U [11]–[14]. In our analysis of C. elegans wild-type tRNAs, we found ncm5U and mcm5s2U but not mcm5U. This observation is consistent with an earlier investigation showing that mcm5U is not present in tRNAs isolated from calf liver [54]. For example, nucleoside 34 in from yeast has mcm5U [55], whereas that from calf liver has mcm5s2U [56]. These findings suggest that mcm5U might be absent from tRNAs in metazoans.
In yeast, Elongator was suggested to participate in three distinct cellular processes: transcriptional elongation, polarized exocytosis and formation of modified wobble uridines in tRNA [12],[21],[22]. Strong genetic evidence was provided that the pleiotropic phenotypes seen in yeast, including those in transcription and exocytosis, were caused by a translational dysfunction due to lack of mcm5 and ncm5 side chains at wobble uridines [16]. This suggests that the physiological relevant role of Elongator complex in this organism is in the formation of modified nucleosides in tRNA, i.e. wobble uridine tRNA modification is crucial for the translation of mRNAs that encode proteins important for transcriptional elongation and polarized exocytosis. Cellular localization studies primarily placed Elongator subunits in the cytosol in yeast, mouse and human cells [22], [23], [27], [57]–[60]. As modifications in the anticodon region normally take place in the cytosol [61], such a localization is consistant with a role in wobble uridine modification. In C. elegans, we did not observe any fluorescence of ELPC-1::GFP in the nucleus suggesting that Elongator in this organism functions in the cytosol.
In elpc-1 and elpc-3 mutants, we observe reduced expression of an ANF::GFP neuropeptide reporter. Given that ANF::GFP mRNA levels are normal in the mutants, the reduction in ANF::GFP accumulation could in principle be explained either by increased degradation of the protein or by decreased translation. Since tRNAs are intimately involved in protein synthesis, we believe it more likely that ELPC-1 and ELPC-3 affect ANF::GFP levels by promoting translation. Further evidence indicating a role for Elongator in translation is that the recovery of GFP signals after photobleaching in strains with gcy-5::gfp, mec-4::gfp and myo-3::gfp reporter genes is slower in Elongator mutants than in wild type. The effect of Elongator on translation is also consistent with the synthetic effects we observe in elpc-1; tuc-1 and elpc-3; tuc-1 double mutants. The reduction in accumulation of β-galactosidase activity in elpc-1 or elpc-3 single mutants (in which the mcm5 side chain of mcm5s2U containing tRNAs is absent) is similar to that seen in tuc-1 single mutants (in which the s2 side chain is absent). In the double mutants (in which both the 2′and 5′modifications are lost) the efficiency of translation is further reduced. An explanation for the reduced efficiency of translation in C. elegans worms lacking elpc-1 or elpc-3 activity is that the modifications of uridine residues at the wobble position aid codon-anticodon interactions [7]–[10]. Experiments in vivo with S. cereverisiae, suggest that the primary function of the mcm5U, ncm5U and mcm5s2U nucleosides is to improve binding to A- and G- ending codons, decoded by tRNAs containing these modified nucleosides [14]–[16]. For tRNAs normally modified at both the 2′and 5′positions, the absence of either modification (or both) did not lead to any obvious misreading of U- or C-ending codons [15],[16]. Thus, presence of modifications at wobble uridines in tRNAs appears to promote the rate of elongation during translation rather than its fidelity.
There are examples of tRNA modification mutants that show temperature sensitive (ts) phenotypes, suggesting a reduced functionality of the hypomodified tRNA at the elevated temperature [16],[62],[63]. In yeast, elp and tuc1 mutations result in hypomodification of and [12],[15]. In the anticodon loop, both tRNAs are rich in uridines that have a low stacking potential, and in , mcm5 and s2 of U34 are required for a canonical anticodon loop structure [64]. Therefore, we believe that the temperature sensitive phenotype observed in elpc and tuc-1 single mutants and enhanced in elpc-1; tuc-1 and elpc-3; tuc-1 double mutants is caused by destabilization of anticodons in hypomodified tRNAs, resulting in further weakening of codon-anticodon interactions.
The higher levels of expression of the elpc::gfp reporters within the nervous system of C. elegans is consistent with the finding that the most severe defects of elpc-1 or elpc-3 single mutants are observed in nervous system. It is interesting to note that a strong expression of Elongator subunits was also observed in the nervous system of mice [27]. A possible explanation for the greater requirement for Elongator in neurons is that neurons have markedly higher rates of protein synthesis than most other cell types [41],[65],[66]. It is also striking that in both C. elegans and mice, expression within the nervous system is not uniform. Perhaps different neurons have different rates of translation.
In C. elegans and other metazoans, neuronal function is dependent upon the ability to synthesize and secrete neurotransmitters and neuropeptides. In elpc-1 and elpc-3 mutants, the production of ANF::GFP neuropeptide is reduced at the posttranscriptional level. Thus Elongator mutations might cause the neurological defects by impairing the translation of neuropeptides. In addition, our findings that elpc-1 and elpc-3 mutants appear to have reduced levels of acetylcholine in the synaptic cleft suggest that Elongator is required for the production or secretion of neurotransmitter. Since elpc-1::gfp and elpc-3::gfp are expressed in a set of chemosensory neurons, the salt chemotaxis learning defect displayed by Elongator mutant worms is likely to be a consequence of inefficient communication among various neurons due to low production of neurotransmitters or neuropeptides. It is interesting to note that mutations in the human ELP1 gene, also called IKBKAP, cause the neurodegenerative disease, Familial Dysautonomia (FD) [28],[29]. Furthermore, association studies in humans have revealed that variants at the ELP3 locus confer increased risk for the neurodegenerative disorder Amyotrophic Lateral Sclerosis (ALS) [67]. Neuronal defects are also observed in Drosophila, Zebrafish and mouse with reduced function of ELP3 [27],[67].
Conflicting reports exist concerning the origin of the defects caused in human cells by a reduction in hELP1/IKAP levels [25],[26],[52],[68],[69]. Recently, in mice Elongator was suggested to catalyze α-tubulin acetylation [27]. However, our observations that acetylation is not obviously abnormal in C. elegans elpc-1 or elpc-3 mutants suggest that the neuronal defects observed in Elongator mutants in the worm are not caused by a failure to acetylate α-tubulin.
In contrast to the elpc-1 and elpc-3 mutants, tuc-1 mutants do not display defects in either the salt learning assay or in secretion of ANF::GFP. In yeast, the growth defects of Elongator mutants are more pronounced than those of the tuc mutants [15],[16]. One possible explanation for these differences might be that the absence of the s2 modification has less effect on codon-anticodon interactions than the absence of ncm5- and mcm5-groups. Alternatively, the effects on salt learning might be caused by reduced expression of a protein encoded by an mRNA rich in codons decoded by tRNAs harboring solely the 5′modification.
Previous studies on ELP1 and ELP3 function in vertebrates have focused on their roles in neurons. While we have shown that ELPC-1 and ELPC-3 are important for nervous system function in the worm, our results clearly demonstrate that they also act in non-neural tissues. Although their expression is far from ubiquitous, the expression of ELPC::GFP reporters is clearly not restricted to neurons. More importantly, the defects in temperature-shifted elpc-1; tuc-1 and elpc-3; tuc-1 double mutants indicate that Elongator is also involved in embryogenesis and vulval development. The phenotypes observed suggest that tRNA modification is a mechanism by which the efficiency of translation is modulated during metazoan development.
Our observations suggest that Elongator acts in neurological and developmental processes in C. elegans by modulating translation. An important task in the future is to identify the mRNAs whose translation is dependent on Elongator activity. Identification of these mRNAs might help in the understanding of the molecular mechanisms of Elongator-related human diseases.
The names of genes in the text have been given according to existing nomenclature rules for S. cerevisiae, C. elegans and humans. The yeast ELP1 gene encodes a protein, Elp1p; the equivalent gene in C. elegans, elpc-1 encodes ELPC-1; in humans, IKBKAP/hELP1 encodes IKAP/hELP1. The respective mutant alleles are elp1 (S. cerevisiae) and elpc-1(tm2149) (C. elegans). Neurons in C. elegans have three-letter names e. g., ASE. These names are not acronyms or abbreviations.
C. elegans worms were cultured and handled as described previously [70]. All strains were maintained at 20°C unless likewise indicated. All are derived from the wild-type strain, Bristol N2 [70]. For routine propagation, worms were maintained on nematode growth medium (NGM) plates [70]. The following mutations were used in this study. Linkage group (LG) I, tom-1(ok285) [71]–[73], aex-6(sa24) [50], lev-11(x11) [74]; elpc-1(tm2149), LG III, mec-12(e1605), mec-12(e1607) [75], LG IV, tuc-1(tm1297) [39]; LG V, elpc-3(tm3120). The transgenes used were ubIn5[hsp16::lacZ] [76], oxIs180[Paex-3::ANF::gfp] [47], svEx557[Pelpc-1::elpc-1::gfp], zdIs5 I[mec-4::gfp lin-15(+)] [77], svEx666[lin-25::HA myo-3::gfp], svEx806[elpc-1::gfp Punc-122::gfp], svEx808[elpc-1::gfp Punc-122::gfp], adEx1262[gcy-5::gfp lin-15(+)] [78]. The elpc-1, elpc-3 and tuc-1 deletion mutants were backcrossed eight times with wild-type N2 before use.
To generate the elpc-1::gfp fusion, the entire elpc-1 coding region together with 435 base pairs of DNA upstream of the start ATG was amplified by using primers 5′-AAAAGCATGCTCCGGTACGGTATGTGGC-3′ and 5′-AAAACTGCAGTGGGAAAACTGAAG CAAATGAA-3′. The PCR product was subcloned into pPD95.77 GFP expression vector between PstI and SphI sites.
A Leica DMRB microscope equipped with both Nomarski differential interference contrast and epifluorescence optics was used to view worms at high magnification. Images were captured with a Deltpix CCD camera and software (Deltapix, Copenhagen). Confocal microscopy was performed on a Leica TCS SP2 confocal microscope. Confocal images were captured using Leica confocal software.
Techniques described by Gaur et al. (2007) were used with minor modifications to isolate and analyze tRNA from C. elegans worms. For each strain, worms from twenty 9 cm culture plates containing mixed-stage populations of worms were used. After extensive washing with M9 buffer, the worm pellets were frozen in the liquid nitrogen and then thawed in the presence of 0.5 volumes of TRIzol (Invitrogen). A tissue-grinder (Kontes) was used to break open the worms. After extraction of the lysate with chloroform, followed by addition of isopropanol, total RNA was sedimented by centrifugation. tRNA was separated from other types of RNA by using methods described previously [79]. Purified tRNA was digested with Nuclease P1 for 16 h at 37°C and then treated with bacterial alkaline phosphatase for 2 h at 37°. The hydrolysate was analyzed by high pressure liquid chromatography with a Develosil C-30 reverse-phase column as described [79]. ncm5U, mcm5U, mcm5s2U and mcm5Um have all been found on wobble uridines in S. cerevisiae tRNA. We did not examine C. elegans tRNA for the presence of ncm5Um because P1 and BAP cannot digest the dinucleotide ncm5UmpX to nucleosides [80].
Total RNA was extracted with the aid of a BIO-RAD Aurum total RNA mini kit according to the instruction manual. Real-time PCR was carried out in 25 µl reaction mixes. iScript one-step RT-PCR kit with SYBR green (BIO-RAD) and the iCycler iQ Real-Time PCR Detection System (BIO-RAD) were used. The data were normalized to tbb-2 and ubc-2 mRNA levels. Six independent assays were performed for each strain analyzed.
For each strain analyzed, one 6-cm plate containing a population of well-fed worms was subjected to a 2 h heat shock at 33°. The worms were washed from the plate with M9 salt solution, sedimented, washed once in M9 and then once in breaking buffer (100 mM Tris-HCl, 1 mM DTT, 20% glycerol). After resuspension in 250 µl of breaking buffer containing Roche protease inhibitor cocktail, the worms were broken open by sonication. Five 2 sec pulses at maximum effect were used. The extracts were transferred to microcentrifuge tubes and worm debris was sedimented by centrifugation at 13,000 rpm for 15 min. β-galactosidase activity in the cleared extracts was measured using standard protocols [81].
The assay was performed as described in detail by Kourtis and Tavernarakis [41]. Worms carrying the gcy-5::gfp, mec-4::gfp or myo-3::gfp reporters were mounted on the agarose pad in the presence of levamisol and photobleached with light from an HBO 103W/2 mercury lamp (OSRAM). A 63× objective was used for photobleaching gcy-5::gfp and mec-4::gfp strains, a 20× objective for myo-3::gfp strains.
Salt chemotaxis assays were performed as described by Ward [82] and Bargmann and Horvitz [44]. All the assays were carried out at room temperature (ca. 21.5°C) on 9 cm agar plates containing 5 mM KH2PO4 pH 6.0, 1 mM CaCl2, 1 mM MgSO4 and 2% agar. N2, elpc-1(tm2149), elpc-3(tm3120) and tuc-1(1297) strains were maintained at 25°C for at least three generations prior to being assayed. The salt gradient with a peak 0.5 cm from one edge of the plate was formed overnight by placing a block of agar measuring approximately 5 mm in each dimension and containing 100 mM NaCl, 5 mM KH2PO4 pH 6.0, 1 mM CaCl2, 1 mM MgSO4 and 2% agar. In each single test, 80–100 young adult worms were washed three times in 5 mM KH2PO4 pH 6.0, 1 mM CaCl2, 1 mM MgSO4 and then placed in the center of the assay plates. Before the worms were placed on the assay plate, 1 µl of 0.5 M sodium azide was spotted both at the salt gradient peak and at the opposite side of the plate to capture the worms moving to those areas. The numbers of worms at different positions on the plate were counted every 10 min after the start of the assay. The formula was used to calculate the chemotaxis index. In this equation, A was the number of worms at the attractant area, C the number of worms at the control spot, and N the total number of worms placed on the plates. Each experiment was repeated at least 4 times. For chemotaxis assays with isoamyl alcohol, the odorant was dropped on the assay plate immediately prior to the addition of worms to the plate.
The assay was performed as described [43], with minor modifications. For each assay, adult worms were washed off the culture plates with chemotaxis washing buffer (5 mM KH2PO4 pH 6.0, 1 mM CaCl2, 1 mM MgSO4) and then washed three times in the same buffer. For the naive condition, worms were washed and then assayed immediately without further incubation. The other worms were conditioned respectively on nematode growth medium (NGM) plates containing 100 mM NaCl, or on NaCl-free NGM plates for 4 hours. After conditioning, worms were collected again and placed on the assay plates. After 30 min, the numbers of worms in the NaCl spot (A) and the control region (C) were counted. The index was calculated using the formula, .
ANF::GFP levels were measured by western blotting using an anti-GFP antibody (Clontech, JL-8). 50 L4 larvae of each genotype were collected, boiled in SDS sample buffer for 5 min and loaded onto a 10% SDS PAGE. Quantification of imaging pixel intensity was performed by NIH image J. To measure acetylated α-tubulin levels by western blot, protein was extracted from young adult worms. To avoid protein degradation, worms were suspended in ice-cold extraction buffer containing proteinase inhibitors and rapidly frozen in liquid nitrogen. The frozen pellets were ground to a powder in a mortar. 20 µg protein was loaded on the gel in each lane. The dilution of anti-lys40-acetylated-α-tubulin antibody (abcam, 6-11B-1) was 1∶1000, and of anti-α-tubulin antibody (Sigma, B-5-1-2) was 1∶2000.
The assays were performed as described by Mahoney et al. [49]. 25–30 worms were used for each genotype. The assay was performed blind in triplicate at room temperature (ca. 21.5°C). The worms were cultivated at 25°C prior to being assayed.
The assay was performed as described by Speese et al. [47]. Fluorescence confocal micrographs were made of coelomocytes. The intensity of GFP fluorescence in captured images in grey scale was measured with the aid of the NIH ImageJ software.
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10.1371/journal.pcbi.1004466 | Structural Bridges through Fold Space | Several protein structure classification schemes exist that partition the protein universe into structural units called folds. Yet these schemes do not discuss how these units sit relative to each other in a global structure space. In this paper we construct networks that describe such global relationships between folds in the form of structural bridges. We generate these networks using four different structural alignment methods across multiple score thresholds. The networks constructed using the different methods remain a similar distance apart regardless of the probability threshold defining a structural bridge. This suggests that at least some structural bridges are method specific and that any attempt to build a picture of structural space should not be reliant on a single structural superposition method. Despite these differences all representations agree on an organisation of fold space into five principal community structures: all-α, all-β sandwiches, all-β barrels, α/β and α + β. We project estimated fold ages onto the networks and find that not only are the pairings of unconnected folds associated with higher age differences than bridged folds, but this difference increases with the number of networks displaying an edge. We also examine different centrality measures for folds within the networks and how these relate to fold age. While these measures interpret the central core of fold space in varied ways they all identify the disposition of ancestral folds to fall within this core and that of the more recently evolved structures to provide the peripheral landscape. These findings suggest that evolutionary information is encoded along these structural bridges. Finally, we identify four highly central pivotal folds representing dominant topological features which act as key attractors within our landscapes.
| Folds are considered to be the structural units which make up the protein universe. Structural classification schemes focus on the assignment and organisation of protein domains into folds. However, they do not suggest how different folds might relate to one another in a global way. We introduce the concept of bridges through fold space: significant similarities between these units. We consider four alignment methods and a dynamic approach to placing these bridges. A greater consensus between these methods cannot be achieved by simply increasing the stringency with which edges are assigned. Instead, we emphasise the importance of considering consensus maps and only report results where there is agreement across all networks. It is possible that a study of the bridges may reveal evolutionary relationships. Based on a phylogenetic analysis of structures, we find that bridges consistently fall between folds which evolved at similar times. Moreover, the landscapes all consist of a core of older folds, with younger structures more often seen at the periphery. Finally we identify four pivotal folds in the landscapes. They contain topological motifs which unite disparate regions of fold space.
| The vast repertoire of proteins which exist in nature are testament to billions of years of evolutionary change. The nature of their relationships and how these have evolved are questions which continue to fascinate the scientific community [1–4]. Protein structure classification schemes such as SCOP [5] and CATH [6] partition the protein universe into different structural units known as folds or topologies. Yet relationships between these folds and topologies, and how they sit relative to each other in a global structure space, are largely undiscussed by these schemes. For example, it is highly unlikely that the current repertoire of folds evolved independently of each other [7]. The evolutionary trajectory of new folds may well be through the adaptation of already existing structures. In fact, recent studies have uncovered such distant relationships between different fold units [8]. This concept has implications for protein structure classification, and more broadly within the field of protein design. A global view of the protein universe which incorporates inter-fold relationships can provide examples of efficient and evolutionary viable transitions between very different structures. More particularly, how this universe has, and continues to, evolve can be used to simulate directed evolution approaches to protein design [9, 10].
Different techniques have previously been explored in order to generate global representations of protein structure space (see, for example, [11]). Commonly, these approaches utilise structural similarities between protein domains, which produce complex, multi-dimensional data structures. The process of deriving a global landscape from these data can vary and will inevitably involve assumptions about the nature of the underlying relationships and the extent to which structural alignments can reproduce them. For example, using multi-dimensional scaling or principal component analysis can produce lower dimensional embeddings of an array of similarity scores [12–21]. In these spaces, two or three dimensional maps can be visualised which approximate the similarity between any two structures as closely as possible by their distance on the reduced axes. An alternative is to use networks to capture relationships resulting from significant alignments [13, 22–30]. Unlike multidimensional scaling approaches, network constructions do not assume that structural similarity between protein domains is transitive [11]. On the other hand, they do require a score threshold to be set: above which an alignment is considered significant.
Networks and embeddings can both be built using a variety of inputs to the similarity score [14, 27, 30]. For example, sequence information can be used to provide the similarity score or supplement structural alignments [12, 14, 15, 29, 31], as can functional annotations [24, 25, 32].
Despite the fact that the above studies construct visualisations of the protein universe using a wide range of different methods, they present a generally consistent picture of this space. In particular, a striking partition between structures based on their secondary structure content is immediately evident [12–14, 17, 21, 26, 29, 32]. Broadly speaking, this partition agrees with the class level classifications of SCOP and CATH, which consist of all-α domains, all-β domains and mixed αβ domains. In SCOP, mixed domains are further split into the parallel stranded α/β class and the anti-parallel α + β class. Protein space representations reveal interesting relationships within and between these groups. Several studies comment on the densely clustered group of α/β structures [26, 31, 32], and the more dissipated α + β structures [12, 29], with all-α and all-β domains tending to congregate in between these two extremes [26]. Secondary structure seems to be one of the dominant forces within these spaces. Even when sequence signal alone is used to determine the global landscape, the main secondary structure classes, as well as other classes such as membrane proteins, divide along secondary structure lines [29]. Functional studies also point to the importance of secondary structure. For example, a recent paper exposed a functionally diverse region, at the centre of structure space, which largely overlaps with the α/β cluster [32].
Visualisations of structure space can also allow us to consider the distribution of protein domains across this space. In particular, highly dense and connected portions of the space can be used as evidence for a continuous landscape [28, 33–35]. This conclusion has implications for the very foundations of our understanding of proteins and their evolution. A continuous protein space indicates that a partitioning of protein structures into folds and topologies is itself meaningless, as these represent discrete units of structure. The initial motivation behind the concept of a discrete fold derives from the fact that structure is a highly conserved property during evolutionary change [36]. As highly correlated to its function, the structure of a protein tends to constrain the variation which is tolerable for that domain to remain operational. The abundant structural similarities between different folds however, have stimulated a debate as to whether this is truly the case [33, 37, 38]. A third view has also developed, that protein space displays both discrete and continuous characteristics [31, 33, 34, 39]. In particular, it has been argued that discrete and continuous paradigms of fold space do not necessarily contradict one another but form complementary descriptions of the evolutionary and structural landscapes respectively [39]. This distinction between structural and evolutionary relationships has also been implemented in the new SCOP2 prototype [40], which separates the hierarchical structure of traditional SCOP where evolutionary units superfamilies are contained within structural units of folds into two distinct categories. It is this dual view of fold space that we will adopt here, and in particular, supplement the discrete classification of domains into folds with summaries of their geometric similarities to other folds to establish a global landscape within which these folds sit. In doing so, it is important to note the assumptions this model makes. The first, which has been stated above, is that there is a duality within the underlying dynamics of the space, where both fold classifications and structural alignments between different folds are meaningful. The second is that the methods we use to capture the discrete units and the continuous relationships are correct. We use the SCOP classification to capture the collapse of the domain universe to discrete folds. While SCOP is well established in the literature, it is by no means the only such scheme. As we have mentioned, CATH describes a complementary scheme [6]. There are also other structural schemes such as FSSP [41], and purely sequence-based classifications, such as Pfam [42]. Similarly, there is no single established method for structural alignment and there are still many unsolved problems in the field [43, 44].
In this paper we present several possible sets of inter-fold relationships, which we term bridges through fold space. Each set of bridges is visualised as a network over 631 SCOP folds. To build these networks we have used four different structural alignment algorithms and with each several different thresholds of similarity. We find that with all methods, the resultant organisation of structure space is at least a partial relic of the alignment algorithm used to generate it. While structural alignment programs have continued to improve in quality over recent years, generating relevant alignments consistently remains an unsolved problem [44]. By examining the areas of consensus between these maps we can more easily identify features with a higher confidence than relying on a single method in isolation.
We show that such consensus spaces are vital to an appreciation of the underlying structural relationships between folds as, even at stringent threshold, a proportion of edges in a network will always be an artefact of the alignment method. Nevertheless, the different networks agree on a well defined partition of fold space into five principal community structures: all-α, all-β sandwiches, all-β barrels, α/β and α + β.
We have previously used a phylogenetic analysis of fold usage to estimate an evolutionary age for different folds [45, 46]. Age estimates relate to the emergence of a fold’s structural ancestor and are guided by its prevalence on a diverse set of completely sequenced genomes from across the tree of life. In a previous publication we found that different age estimates demonstrated particular preferences in terms of the properties exhibited by their fold structures [46]. Projecting these age estimates onto the structure space networks could provide the potential to examine the relationship between structure and evolution in a more global way. To explore this hypothesis, we examine properties of the nodes and edges of these networks in the context of their estimated age. In particular, we examine the difference in the age of two folds connected by bridges. We also look at the distribution of fold ages across the networks and how these relate to the centrality of a fold in the network’s architecture. Finally, we examine four highly pivotal ancient folds, each of which exhibit different topological properties which act as structural attractors between disparate regions of the network spaces.
Domain coordinate files for structures from the four main SCOP classes (all-α, all-β, α/β and α + β) were taken from the ASTRAL database (version 1.75) and filtered to < 40% sequence identity [47]. To ensure these structures were of sufficient quality we removed any file with an assigned aerospaci score of < 0.4, as suggested by Brenner et al. [47]. Due to the requirements of the structural alignment algorithms the dataset was further refined by omitting structures with only backbone Cα coordinates, and those which contained one or more chain breaks. Chain breaks were assigned using the Bio.PDB module in BioPython where successive Cα atoms were further than 4.3Å apart [48]. This resulted in a dataset of 4,098 domains, comprising 793 from the all-α class, 948 classified as all-β, 1,215 α/β domains and 1,142 from α + β. These domains represent a total of 631 folds.
Four different methods were used to generate structural alignments between domains in this dataset. These methods have all been previously published and are available as open source programs or code. They are Mammoth (MAMMOTH) [49], jFatcat (FATCAT) [50], TM-align (TM-ALIGN) [51] and Elastic shape analysis (ESA) [52]. These methods were chosen as computationally efficient yet methodologically dissimilar representatives from the wide array of structural alignment approaches. For each of these methods, 8 , 394 , 753 = ( 4098 2 ) pairwise comparisons were computed. Each method was run using the default parameters. ESA characterised each domain backbone as a curve of N points, where N is the average length of each pair of domains. FATCAT was run in flexible mode and TM-ALIGN used a TM-score normalised by the average length of the domains.
TM-ALIGN measures the strength of each alignment through the TM-score, and ESA generates an elastic metric. However, both MAMMOTH and FATCAT alignments produce multiple similarity scores for each alignment. For example, the MAMMOTH program generates a Z-score, E-value, TM score and PSI score. In these cases we chose the score that maximised the area under the ROC curve, when comparing how well each score correctly identified fold siblings under the SCOP classification. As a result of this analysis MAMMOTH alignments were summarised using the Z-score and FATCAT by the p-value (−ln(p)).
Networks were constructed from the pairwise comparisons by extracting those entries representing strong similarities between different folds. What constituted a strong similarity was determined by examining each score’s distribution and by assessing its ability to discriminate between SCOP folds. We employed a Bayesian analysis to each score, similar to that outlined in [53]. Explicitly, we considered the posterior probability that two domains were representatives of the same fold (F = 1) if their similarity S was measured above a candidate threshold s ¯:
P ( F = 1 | S > s ¯ ) = P ( S > s ¯ | F = 1 ) P ( F = 1 ) P ( S > s ¯ | F = 1 ) P ( F = 1 ) + P ( S > s ¯ | F = 0 ) P ( F = 0 )
The prior probabilities P(F = 1) and P(F = 0) were assumed to be the proportion of pairs in the domain dataset representing SCOP fold siblings and unrelated domains respectively. The conditional probabilities P ( S > s ¯ | F ) were estimated as the proportion of either the set of fold siblings or the set of pairs of unrelated domains in the dataset with similarity scores greater than s. Each pairwise alignment was thus associated with a posterior probability between zero and one, based on the relative strength of its score. For example, Fig 1a shows the relationship between the TM-scores of TM-ALIGN alignments and their posterior probabilities. For the purposes of this work we considered only scores associated with a probability ≥ 0.5. We varied this cutoff between 0.5 and 0.9 to show that both the network behaviour and our results remained robust to this choice. As suggested by the FATCAT team, we calculated the significance of comparisons involving all-α domains separately to those between the other SCOP classes. This resulted in two different thresholds at each posterior probability: one applicable to alignments involving an all-α domain and one for all other alignments. The scores which were used in this analysis and the effective cutoff equivalent to different probabilities are given in Supplementary S1 Table.
Networks of fold relationships were built by collapsing the 4098 × 4098 pairwise array of domains to a 631 × 631 array N of folds. Each entry of this array N(A,B) is characterised by the structural alignment between the pair of representative domains of folds A and B with the highest posterior probability. As the probability threshold decreased from 1 to 0.5 dynamic networks were constructed with folds as nodes and edges between two nodes where any two of their representative domains produced a similarity score which was associated with a probability above the threshold. For probability thresholds of 0.5, 0.6, 0.7, 0.8 and 0.9 static networks were also built. Furthermore at each of these thresholds we constructed consensus networks built from edges between folds appearing in all four networks at that threshold. In total 25 static networks were constructed representing the four alignment methods and their consensus at the five different probability thresholds. Fig 1b shows a simplified schematic of the fold network construction process from pairs of representative domains whose alignments correspond to a posterior probability of at least 0.9.
Weights were added to each edge and were used to represent a measure of the distance between the folds at its endpoints. The MAMMOTH Z-score and the FATCAT p-value are statistical values and we therefore felt they were inappropriate as quantitative distances between two structures. Instead, we used the TM score as edge weights in both the FATCAT and MAMMOTH networks. TM-scores are generated as part of MAMMOTH’s output, and we calculated an approximate TM-score from FATCAT’s opt_rmsd score and the domain lengths. The TM-score was also used as weights in the TM-ALIGN network and the inverse of the elastic metric was used in the ESA network. Weights in the consensus networks were calculated by first centering weights corresponding to an individual alignment method by dividing them by their mean. Consensus weights were then calculated by averaging the respective normalised weights.
Networks were visualised using Cytoscape [54]. Dynamic networks as the posterior probability on edges decreased from 1 to 0.5 were visualised as animations using the DynNetwork plugin. Static visualisations were calculated using a spring embedded layout, while the prefuse layout was used in the dynamic representations.
Community structures were detected using the Louvain method for non-overlapping communities in weighted networks [55]. Network analysis was performed using the tnet package [56] in R [57]. Shortest path lengths d(i,j) between nodes i and j were calculated as the minimum sum of reciprocal weights whk along the series of edges connecting the two nodes as proposed by Dijkstra [58]:
d ( i , j ) = min ( 1 w i h + … + 1 w h j )
The centrality of a node i was calculated using degree (CD(i)), closeness (CC(i)) and betweenness (CB(i)) measures in weighted networks as suggested by Opsahl et al. [56]. Respectively, they are defined:
C D ( i ) = ∑ j ∈ N w i j C C ( i ) = ∑ j ≠ i 1 d ( i , j ) C B ( i ) = ∑ j , k ≠ i σ j k ( i ) σ j k
where N is the set of nodes connected by a single edge to i, wij is the weight along the edge ij, d(i,j) is the shortest path length between nodes i and j as defined above, σjk(i) is the number of shortest paths between nodes j and k which go through i, and σjk is the total number of shortest paths between j and k. Closeness and Betweenness were calculated for nodes in each connected component separately. Central and peripheral sets of folds were identified for each measure as follows. Central folds were the top 30% of nodes ranked by their centrality measures. Peripheral folds defined by closeness were the bottom 30% of nodes ranked by closeness. Degree and betweenness measures followed a skewed distribution with large numbers of nodes calculated to have very low values and far fewer folds being assigned a high degree or betweenness. Therefore, folds with peripheral degree were those with either one or no neighbour in the network. Similarly, peripheral folds by betweenness were those with a betweenness value of zero.
Evolutionary age estimates were calculated for each fold following the method outlined in [45], and more recently in [46]. These ages use a parsimony algorithm on the predicted fold content of 1014 genomes from across the sequenced tree of life to predict a relative estimate of its structural ancestor. Ages are normalised to lie between zero and one where zero corresponds to a recent ancestor, while an age of one indicates an ancestral fold predicted to exist in the last universal common ancestor. All statistics are calculated assuming an underlying phylogeny of these species as traced from the NCBI taxonomy database [59]. Populations’ age distributions were compared using the Mann Whitney U test [60].
We constructed five dynamic networks representing the structural relationships between 631 well characterised SCOP folds from the four main classes (all-α, all-β, α/β and α + β). Each network summarised the results of the pairwise alignments of 4098 high quality structures representing these folds. Alignments were calculated using four different methods: MAMMOTH, FATCAT, TM-ALIGN and ESA. Four separate networks were built corresponding to each of these methods and one for their consensus, where nodes represented the 631 folds detailed above and edges represented a significant structural similarity resulting from the pairwise alignments. These edges can be seen as structural bridges through fold space, uniquely defining the resultant landscapes. The process of determining significant similarity was standardised across the different methods by the introduction of a posterior probability attached to each method’s score, which quantified its effectiveness in characterising fold level relationships (see Methods for details). Networks were drawn for each method at five different probability thresholds ranging from 0.5 to 0.9, representing increasingly stringent thresholds for determining similarity, and are shown in Fig 2. At each cutoff a consensus network was also built, capturing edges detected by all four methods. In each network bridges were weighted with a similarity measure appropriate to each method.
The networks were further visualised dynamically, as the cutoff for deducing similarity decreased in stringency. Movies displaying dynamic visualisations of these networks as the threshold varies can be found in the Supplementary Information (S1–S5 Videos). They capture both how the landscapes and their consensus are constructed and how robust their organisation is to the probability threshold. These movies can also be found online at http://www.stats.ox.ac.uk/research/proteins/resources#bridges. At high thresholds, the landscapes display an early organisation into disconnected clusters, predominantly of the same class. As the threshold decreases, these components coalesce into a giant component somewhere between a threshold of 0.9 and 0.8. As the threshold decreases still further the overall organisation of the networks remains relatively stable.
The landscapes of structural bridges described above represent similarities at the inter-, rather than the intra-fold level. As such, the method involves a collapse from the set of relationships across 4098 protein domains to those between their 631 different SCOP folds. This process of collapse is an important one as it imposes relationships between domains from an external classification scheme. It is also relevant in the context of comparing our networks to other fold space representations in the literature: some of which consider relationships between domains, and others those between folds. In order to illustrate the stages of the network collapse Fig 3 shows network representations of the TM-ALIGN alignments at a posterior probability threshold of 0.7. These relationships are collapsed first to the SCOP family level, then to superfamilies, and finally to folds. Evident at all stages of collapse is the distinction between the different secondary structure classes. Noticeable too is the relative similarity between the superfamily and fold networks, and a more striking visual difference between the network of domains and that of families. The differences between these early stages of collapse potentially derive from the effects of multiple domains representing a small number of families.
For each method the structural bridges at each probability threshold collectively determine a landscape for the global organisation of fold space. Some general network statistics relating to each construction can be found in S1 Fig. As the probability threshold increases, networks become less connected. The number of folds connected to another structure decreases (S1a Fig), and even within connected components, shortest path lengths connecting two folds increase (S1f Fig). The number of edges in the landscapes vary from 5,571 in the MAMMOTH network at a 0.5 threshold to 250 in the ESA network at a threshold of 0.9 (S1b Fig).
An important observation is the significant differences between the alignment algorithms, as well as their areas of agreement. In a single network generated from ESA, MAMMOTH or FATCAT alignments, about 50% of the edges were only identified by that method. For the TM-ALIGN networks, this proportion was somewhat lower at 20–30% of edges (S1c Fig). Moreover, this figure does not improve with increased stringency (i.e. increasing values of the posterior probability). In fact, as the similarity threshold increases, this proportion remains relatively constant, and even increases in the TM-ALIGN and ESA networks. In other words, networks constructed using different alignments remain the same distance apart regardless of similarity threshold. Taken in isolation, a proportion of edges in these networks will always be an artefact of the alignment method, emphasising the importance of considering a consensus network.
As mentioned previously, connected nodes congregate, in most cases, in a single dominant connected component up till a probability threshold > 0.8 (see also S1i Fig). The exception to this is the consensus network, where all-α folds are separated from the largest connected component. While there are separate smaller components within the networks (S1h Fig), the vast majority of nodes are either part of a single connected component or are completely unconnected to other structures. This observation supports previous work suggesting that the proportion of unconnected nodes in structure networks sets these structures apart from random models [23], and that fold space can be partitioned into either highly continuous or highly discrete sections [31]. It is also significant regarding the discussion of traversing fold space. A previous study emphasised the short path lengths between structures in fold space as indicative of a continuous space [28]. Within largest connected components we found that average path lengths were less than 5.5 (S1f Fig). While the increase of unconnected nodes is largely responsible for the lack of traversability of networks at higher probability thresholds, it is also interesting to note that, even within connected components, average path lengths between two folds increase. This indicates that the dynamic networks transition from more continuous, connected landscapes at lower probability thresholds to more unconnected spaces at higher thresholds, although both extremes contain densely connected regions and completely unconnected folds.
Previous results have indicated that α/β structures dominate the highly connected section of fold space [31]. Our results do not find such a dramatic distinction, with all four classes found within the connected component. We do however find that far fewer unconnected folds are α/β and, within the α/β cluster shortest path lengths are shorter than those of other classes.
Despite these differences, several properties remain conserved across every landscape. In general, and in concert with previous observations, the networks partition fold space into the four secondary structure classes. The α/β folds form densely packed clusters, as too, to a lesser degree, do the all-α folds. On the other hand, folds with anti-parallel β sheets, belonging to the all-β and in particular to the α + β classes are more dissipated throughout the space. Nevertheless, applying a community detection algorithm to these landscapes identifies five predominant communities with a higher density of structural bridges within each group, and sparsely connected externally. These communities can be generally defined as all-α, α/β, α + β, all-β sandwiches and all-β barrels by the prevailing population of folds within these clusters. Fig 4 shows the communities in the consensus network which include these five groups along with smaller communities resulting from the smaller connected components of the network. The all-β sandwiches and barrels tend to remain partitioned from each other even at the least stringent probability threshold of 0.5 and are often closer in the landscapes to the α + β community than they are to each other. While the majority of previous visualisations of fold space have noted a four class clustering into SCOP classes [17, 21, 26], one study also saw a division between all-β structures [13]. However, in this case β-meanders and β-zigzags were found to form the basis for this distinction. Meander structures connected to α + β structures and zigzags included both sandwiches and barrels. This is markedly different from the clusters we find here, where the basis of the division is strictly delineated by a domain’s characterisation as a barrel or sandwich. While some α + β folds appear within the sandwich and barrel clusters, in these cases they consist of well segregated α and β regions, with the β regions demonstrating the appropriate structural feature.
Edges in these networks represent, not simply the phenomenon of structural similarity between proteins, but structural bridges between folds: thought to be distinct and separate structural units. We projected fold age estimates, as calculated in [46], onto the folds in each network. These ages estimate the emergence, on a tree of sequenced life, of a fold’s structural ancestor. Each age estimate falls between zero and one, where an age of one represents an ancestral fold emerging at the root of the tree, and an age of zero signifies an ancestor at its leaves. We were thus able to consider the difference in age attributed to each of the bridges in our networks. As edges were undirected we considered the absolute difference in age of the endpoint folds to each edge (bridge). We investigated the distribution of age differences, comparing those of structural bridges to a background distribution of random pairs of dissimilar folds.
As described above, a large number of these bridges were identified by just a single alignment method so we examined separately the distribution of age differences on edges found on one, two, three and four networks to those found on none. Fig 5a shows a boxplot of these age differences on the set of networks built at a probability cutoff of 0.6. Distributions for the other networks are similar. Not only are the pairings of unconnected folds associated with higher age differences than bridged folds, but this difference increases with the number of networks displaying the edge. Bridges identified by at least two different methods had a median age difference of zero as opposed to the 0.25 of unconnected folds.
The prominence of each fold within these landscapes was calculated using three different centrality measures: degree, closeness and betweenness. For each measure on each network, we identified two populations: central and peripheral folds, and compared the age distributions of these two populations. In every network, including the consensus networks, and by all three of these measures central nodes were found to be significantly older than more peripheral nodes (see S2 Fig). This tendency was true regardless of how we partitioned the nodes. While the three measures produced rankings for the nodes which correlated positively with each other, they all define the concept of centrality slightly differently. Fig 6 illustrates these differences on the MAMMOTH network at a threshold of 0.6.
While these measures interpret the central core of fold space in varied ways they all identify the disposition of ancestral folds to fall within this core and the more recently evolved structures to provide the peripheral landscape.
A previous study found that clusters in structure networks could be associated with functional fingerprints [24]. Based on the assumption that older proteins will be represented by more popular clusters within the domain network, they found that older clusters were matched by a greater heterogeneity in function space. This concept of a functionally diverse ancestral core to structure space was also noted by [32] who found that this region of functional diversity largely derived from a cluster of α/β domains, which are known to be older folds [19, 45]. We show here that the central core to our networks of structural bridges is significantly older than the peripheral nodes. It is interesting to note that these central, older folds are not in fact dominated by the α/β class. In fact, folds from the four classes are almost equally represented in these sets. Despite this, our results agree in noting the importance of ancient protein folds within fold space.
The above network centrality analysis further exposed certain pivotal folds, which were calculated as highly central in all networks, including the consensus network. Here we examine four examples of pivotal folds: the long α-hairpin (a.2), the Immunoglobulin-like β-sandwich fold (b.1), the Flavodoxin-like fold (c.23) and the Ferrodoxin-like fold (d.58). These folds are all ancient, with a fold age of 1.0, and are represented strongly in proteins found right across the tree of life. S3 Fig shows the situation of each pivotal fold within the consensus network at a threshold of 0.5. a.2 and c.23 remain strongly central to the communities of all-α and α/β folds respectively. They are evident as central folds at the highest thresholds of the dynamic networks. On the other hand b.1 and d.58 together connect much more diverse neighbourhoods within the landscapes. In particular, they have an edge between them, and their shared neighbourhood incorporates 61 structural bridges connecting together four distinct communities in the network: the α/β, all-β sandwiches, all-β barrels and the α/β cluster. We visit these folds in more detail in S1 Text. In particular, specific topological features of each fold are specified as instrumental to their highly central positions. Such features include a left-handed α-hairpin, the greek key motif and the α-β-α switch.
We have proposed and constructed a dynamic network representation of fold space to capture variations in its organisation resulting from different methodologies and similarity thresholds. While a vast array of different techniques have been applied to visualise the structural organisation of the global protein universe, very little has been done to ensure such landscapes are robust to differences in the alignment methodology which generates them. We have shown that, in terms of network representations using four dissimilar methods, there are several disagreements as to where bridges between different folds in the global space lie. We also found that these disagreements cannot be overcome by simply increasing the threshold at which a structural bridge is determined for each method.
Nevertheless, the four different methods and their consensus networks do converge on certain properties of fold space. In particular, the consistent division between secondary structure classes into five predominant communities: all-α, α/β, α + β, all-β sandwiches and all-β barrels. Moreover, folds tend to fall either within a dense and easily traversable connected component, or are completely unconnected. As the probability threshold changes, the balance between these two populations shift as expected, although there remain significant numbers of each at both low and high thresholds.
Structural bridges could exist for a variety of reasons. It is possible they are the result of a misannotation of fold boundaries, or that fold space is wrongly assumed to be discrete. They may also be the result of convergent evolution to a particularly favourable confirmation. They could also represent the structural relic of an evolutionary transition from one fold to another. Whatever their cause, such inter-fold similarities are deserving of further study, to illuminate the overall structure and dynamic of naturally occurring fold space. Moreover, the significant number of these bridges, especially in consensus networks representing the agreement of all four methods, suggests that structural classification, while an important and useful construct, might be a misrepresention of the true nature of the protein universe.
Another feature of the core structure space is the population of ancestral folds at highly central positions within its landscape. Using each different alignment method separately, as well as in consensus, and at different levels of significance, we examined the age distributions of central and peripheral folds. We calculated the centrality of folds as nodes in each network using three different centrality measures, each with a different interpretation of the priority of a node within the landscape. In all these cases, key locations within the landscapes tend to be occupied by older folds than those at the periphery of the space. A previous study identified a functionally diverse core within fold space [61]. This core was predominantly characterised by α/β folds, which have also been identified as predominantly ancient [19, 45]. The central folds we identify here, on the other hand, represent all four SCOP classes, and form key structural bridges both within and between the class communities. To illustrate this diversity we identified four highly central pivotal folds. These folds represent dominant structural features, such as the greek key motif, the α-β-α switch and the α hairpin which act as key attractors within our landscapes.
Structural alignment in general remains an unsolved problem, and much has been written about the inaccuracies of current methodologies. For example a recent study demonstrated a high level of evolutionary inconsistency when comparing several alignment methods, including MAMMOTH, FATCAT and TM-ALIGN [44]. However, despite their limitations, these alignments can give us clues as to a global structure space, in ways in which common classification systems cannot. The representations we have included here cannot be claimed to be accurate depictions of this global space. However, there does appear to be a well defined core to this space where different alignment methods agree on the architecture and general properties of fold space. Moreover, the fact that structural bridges at the heart of this core consensus tend to fall between folds of similar age estimates lends support to the argument that evolutionary information may be encoded along these bridges.
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10.1371/journal.ppat.1006347 | ERRα negatively regulates type I interferon induction by inhibiting TBK1-IRF3 interaction | Estrogen-related receptor α (ERRα) is a member of the nuclear receptor superfamily controlling energy homeostasis; however, its precise role in regulating antiviral innate immunity remains to be clarified. Here, we showed that ERRα deficiency conferred resistance to viral infection both in vivo and in vitro. Mechanistically, ERRα inhibited the production of type-I interferon (IFN-I) and the expression of multiple interferon-stimulated genes (ISGs). Furthermore, we found that viral infection induced TBK1-dependent ERRα stabilization, which in turn associated with TBK1 and IRF3 to impede the formation of TBK1-IRF3, IRF3 phosphorylation, IRF3 dimerization, and the DNA binding affinity of IRF3. The effect of ERRα on IFN-I production was independent of its transcriptional activity and PCG-1α. Notably, ERRα chemical inhibitor XCT790 has broad antiviral potency. This work not only identifies ERRα as a critical negative regulator of antiviral signaling, but also provides a potential target for future antiviral therapy.
| As a member of the nuclear receptor superfamily involved in metabolism signaling, the precise role of ERRα in antiviral innate immunity remains to be clarified. Here, we showed that ERRα deficiency led to increased interferon production, resulting in enhanced resistance to viral infection both in vivo and in vitro. Mechanistically, viral infection induced TBK1-dependent ERRα stabilization, which in turn increased its binding to TBK1 and IRF3 to prevent the formation of a functional TBK1-IRF3 complex. The effect of ERRα on IFN-I production was independent of its transcriptional activity and PCG-1α. Notably, ERRα chemical inhibitor XCT790 has broad antiviral potency. Taken together, our results identified ERRα as an important negative downstream regulator of TBK1 in RLRs-TLRs signaling pathways and suggested a potential therapeutic target for viral infection.
| The innate immune system plays important roles in the detection and elimination of invading pathogens. The host senses viral and bacterial pathogen invasion via the recognition of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs), including membrane-bound Toll-like receptors (TLRs) and cytosolic sensory molecules, such as RIG-like receptors (RLRs) and NOD-like receptors (NLRs). These then activate a series of signal cascades, leading to the production of IFN-I and proinflammatory cytokines. Upon viral infection, TLRs detect pathogen nucleic acids in the lumen of endosomes, whereas RLRs, DAI, IFI16, LRRFIP1 and cGAS sense pathogen nucleic acids in the cytoplasm [1–5]. TLRs-mediated signaling pathways associate with the adaptor protein MyD88 and TRIF, while RLRs recruit MAVS and STING. Both pathways ultimately converge on the activation of TBK1 upon adaptor recruitment. Activated TBK1 then phosphorylates IRF3, IRF5 and IRF7, triggers their dimerization and nuclear translocation, and activates IFN-I expression. Secreted IFN-α/β further activates downstream signaling pathways to induce a wide range of antiviral genes and elicit cellular antiviral responses.
As a critical kinase involved in antiviral immunity, TBK1 activity must be tightly regulated to maintain immune homeostasis. Various mechanisms have been reported to positively or negatively regulate IFN-I production through effects on TBK1. Nrdp1 [6] and GSK-β [7] enhance TBK1 activity by catalyzing Lys63-linked polyubiquitination or promoting TBK1 self-activation, respectively. TAX1BP1, A20 and NLRP4 terminate antiviral signaling by promoting TBK1 degradation or disrupting the Lys63-linked polyubiquitination of TBK1 [8,9]. Affecting the formation of functional TBK1-containing complexes is another major mechanism that modulates antiviral immune responses. For example, HSP90 facilitates TBK1-IRF3 complex formation through TBK1 stabilization [10]. MIP-T3 and SIKE negatively regulate IFN-β production by inhibiting the formation of TRAF3-TBK1 and TBK1-IRF3 complexes [11,12].
ERRα is an orphan receptor that shares high sequence identity with nuclear receptors α/β (ERα and ERβ). Nevertheless, a functional analysis has indicated that the majority of genes regulated by ERRα are distinct from those controlled by ERα [13]. ERRα possesses a central zinc finger DNA binding domain (DBD), a conserved C-terminal domain with a putative ligand binding domain (LBD) and a less conserved N-terminal region [14]. Although the natural ligand of ERRα is unknown, ERRα activates the transcription of genes that are involved in mitochondrial function and energy metabolism [15–23]. However, the roles of ERRα may not be limited to the direct transcriptional regulation of metabolism. For instance, ERRα induces orientated cellular migration by promoting the transcriptional expression of TNFAIP1, which subsequently destabilizes RHOA [24]. Under hypoxic conditions, ERRα acts as a co-activator to enhance HIF-mediated hypoxic responses by associating with HIF1α [25]. Mice lacking ERRα produce fewer reactive oxygen species (ROS) in macrophages and are susceptible to Listeria monocytogenes (LM) infection in response to IFN-γ treatment [22]. A recent study showed that ERRα negatively regulates TLR-induced inflammation by promoting the expression of A20 [26]. Hwang and colleagues proposed that ERRα is important for providing a favorable metabolic environment that supports optimal cytomegalovirus replication [27].
In the present study, we found that the inhibition of ERRα yielded broad anti-viral activities. ERRα deficiency induced significantly higher levels of IFN-β and increased the expression of a panel of ISGs in response to viral infection. Mechanistically, viral infection stabilized ERRα expression, which in turn associated with TBK1 to impede the formation of the TBK1-IRF3 complex, IRF3 phosphorylation, IRF3 dimerization and the DNA binding affinity of IRF3. Therefore, ERRα is a feedback inhibitor of antiviral innate immunity.
An increasing amount of evidence has demonstrated the crosstalk between the innate immune response and metabolic pathways; however, the precise molecule that links the two systems remains to be clarified. As a member of the nuclear receptor superfamily involved in metabolic signaling, the precise role of ERRα in regulating antiviral innate immunity remains to be clarified. To evaluate the importance of ERRα in viral infection, we first infected wild type (WT) and ERRα-KO (ERRα-KO) mice with vesicular stomatitis virus (VSV). As shown in Fig 1A, the ERRα-KO mice were more resistant to VSV infection in the overall survival assay. VSV titers in sera, liver and lung isolated from ERRα-KO mice were also significantly reduced, compared to WT mice on day 3 post-infection (Fig 1B). We next infected WT type and ERRα-KO mice with herpes simplex virus type 1 (HSV-1), a DNA virus. As shown in Fig 1C, ERRα-KO mice were more resistant to lethal HSV-1 infection.
To further determine the role of ERRα in viral infection, we examined the effects of ERRα deficiency on the replication of various viruses in isolated and cultured cells. Bone marrow-derived macrophages (BMDMs) from the ERRα-KO mice showed lower VSV production than the cells from the WT mice did (Fig 1D and 1E and S1A Fig). Moreover, stable ERRα knockdown clone 2 (shERRα-2) in human 293T (S1B Fig) resulted in decreased VSV titers (Fig 1F) and enhanced cell viability in response to VSV infection (S1C Fig). Similarly, the expression of siRNA for ERRα (siERRα) in A549 cells also resulted in lower viral titers in the supernatant than transfection with control siRNA (siCtrl; Fig 1G). Conversely, 293T cells with overexpressed ERRα showed significantly increased VSV titers in the supernatant (S1D Fig). We next infected 293T cells with GFP-tagged Newcastle disease virus (NDV-GFP) and HSV. Based on quantification by fluorescence microscopy, flow cytometry and plaque assays, both NDV-GFP replication (Fig 1H and S1E and S1F Fig) and HSV-1 production (Fig 1I) was greatly reduced in the shERRα-2 cells. These data collectively suggest that ERRα deficiency confers resistance to viral infections both in vitro and in vivo.
A recent study reported that ERRα was required for the efficient production of cytomegalovirus progeny by providing a favorable metabolic environment. Here, we used microarray analysis to determine the expression of genes altered by ERRα inhibition. ShCtrl and shERRα-2 clones in the 293T cell line (S2A Fig) were analyzed by microarray assay 12 h after VSV infection. First, we subjected genes that exhibited 1.5-fold changes to FunNet analysis to determine the several top pathways regulated by ERRα upon viral infection. Based on this analysis, the top eight most significant downregulated KEGG pathways following ERRα inhibition were associated with metabolic pathways, Wnt signaling, and adherens junctions, which have been reported previously [28–30] (Fig 2A). Interestingly, the NLRs, TLRs and RLRs innate immune pathways were ranked as the top upregulated signaling pathways (Fig 2A). Specially, IFNB1 and several interferon-responsive genes, including IFIT1, IFIT2, IFIT3, IFIH1 and LILRB2, were induced at greater levels in infected cells in which ERRα was knocked down (Fig 2B). The increased expression of IFN-β and responsive genes by ERRα knockdown was validated by quantitative real-time PCR (qRT-PCR) on RNA samples prepared at various time points after VSV infection (Fig 2C–2E). Consistent with a previous report, RNA corresponding to genes that encode triacylglycerol metabolism and glycolytic proteins were downregulated by at least a factor of two following knockdown of ERRα, including CRAT, ACO2, LIPE, and BDH1 (S2B Fig) [22,24,31,32].
To further investigate the role of ERRα in innate immunity, we isolated primary BMDMs from WT or ERRα-KO mice and measured the expression of IFN-β expression in response to RLR-, cGAS- and DDX41-activating stimuli. We transfected 5’-triphosphate(5’-ppp) dsRNA, poly(I:C), poly(dA:dT), and cyclic diguanosine monophosphate(c-di-GMP) in the WT and ERRα-KO BMDMs. IFN-β secretion was significantly increased in the ERRα-KO BMDMs (Fig 3A). BMDMs from the ERRα-KO mice also produced significantly more IFN-β in response to poly(I:C), lipopolysaccharide (LPS) or flagellin incubation, which activate TLR3, TLR4 or TLR5, respectively (Fig 3A). Consistent with this result, ERRα-KO BMDMs showed upregulated IFNB1 mRNA expression in response to all the agonists tested (Fig 3B). VSV is a negative-strand ssRNA rhabdovirus that activates IFN-α/β through RIG-I [33]. VSV-induced IFN-β secretion and IFNB1 mRNA expression was also greatly enhanced in ERRα-KO macrophages in a time-dependent manner (Fig 3C and 3D). Therefore, ERRα is involved in negative regulation of the RLRs, DDX41 and TLRs signaling pathways. In reporter assays, shERRα-2 293T cells (Fig 3E) and siERRα A549 cells (S3A Fig) dramatically potentiated VSV-induced activation of the IFN-β promoter.
To further determine the role of ERRα in type I interferon induction in vivo, we infected WT and ERRα-KO mice with VSV. In keeping with our in vitro data, the induction of IFNB1 mRNA expression was greatly enhanced in the organs of ERRα-KO mice compared to WT mice infected with VSV (Fig 3F). Furthermore, we detected more circulating IFN-β in the blood of ERRα-KO mice on day 3 after VSV infection (Fig 3G). The lungs of ERRα-KO mice demonstrated significantly less inflammation, with reduced epithelial damage, mononuclear cell infiltrates and alveolitis (Fig 3H). Thus, ERRα functions as a negative regulator of type I interferon production upon viral infection.
Various activators, such as RIG-I, MAVS, TBK1, and IKKε, have been reported to be involved in the virus-triggered IRF3 activation pathway [34]. Overexpressed ERRα inhibited IFN-β (Fig 4A and S4A Fig), IRF3 (S4B Fig) and ISRE activation (S4C Fig) induced by these activators in a luciferase reporter assay. Overexpression of IRF3 in 293T cells potently activated the IFN-β and ISRE promoters, while as little as 0.01 μg of ERRα was sufficient to cause potent repression (>80%) of IFN-β (Fig 4B and S4D Fig) and ISRE (S4E Fig). The extent of the suppression increased with increasing amounts of ERRα, suggesting that ERRα inhibited the induction of IFN-β by IRF3 in a dose-dependent manner. The phosphorylation, dimerization and nuclear translocation of IRF3 necessary for the activation of IFNB1 transcription require IKKε and TBK1. Knockdown of ERRα expression significantly enhanced IFN-β promoter activation by TBK1 or IKKε (Fig 4C and S4F Fig).
Our observation that ERRα inhibited the IFN-I production by targeting TBK1 and IRF3 raised the possibility that ERRα might physically interact with these targets. To test this possibility, lysates with ectopic expression of TBK1 or IRF3 from 293T cells were incubated with GST or the GST-ERRα fusion protein. Both TBK1 and IRF3 could bind to GST-ERRα but not to GST (Fig 4D and 4E), demonstrating an in vitro interaction of ERRα with TBK1 and IRF3. To test whether ERRα bound to TBK1 and IRF3 in mammalian cells, Flag-ERRα was transfected together with HA-TBK1, HA-IRF3 or HA-IKKε. Immunoblotting analysis of anti-Flag immunoprecipitate with an anti-HA antibody showed a significant association between Flag-ERRα and HA-TBK1, HA-IKKε and HA-IRF3 (Fig 4F–4H). A far-western assay also revealed a direct interaction between ERRα and IRF3 (Fig 4I). Importantly, we visualized endogenous ERRα-TBK1 complex formation using an in situ proximity ligation assay (PLA). We observed few spots representing the ERRα-TBK1 complex in uninfected 293T cells, while the spots increased significantly at 9 hpi and began to reduce at 12 hpi (Fig 4J and 4K). A domain mapping experiment indicated that the N-terminal domain of TBK1 [35] (amino acids 1–510) was required for its interaction with the AF2 domain of ERRα (Fig 4L and 4M). These data suggest that the interaction between TBK1, IRF3 and ERRα is responsible for the ERRα-mediated inhibition of antiviral signaling.
To further investigate the inhibitory mechanism underlying the role of ERRα in antiviral immune signaling, we explored the effect of ERRα on the TBK1-IRF3 interaction and IRF3 dimerization triggered by viral infection. Notably, the introduction of overexpressed ERRα destroyed the binding between TBK1, IKKε and IRF3 (Fig 5A). XCT790 is a specific inhibitor of ERRα, which reduces ERRα expression [36] (S5A Fig). By using in situ PLA, we found that the number of spots representing the IRF3-TBK1 complex induced by stimulation with VSV was greatly increased in 293T cells treated with XCT790 (Fig 5B and S5B Fig). In addition, we generated 293T cells lacking ERRα using the CRISPR/Cas9 system (ERRαCRISPR-/-). As shown in Fig 5C, the TBK1-ERRα and TBK1-IRF3 interaction began to increase at 3 hpi. In the absence of ERRα, a significantly increased TBK1-IRF3 interaction was observed upon VSV infection (Fig 5C and S5C Fig).
Type I interferon gene transcription is mediated primarily through transcription factor IRF3, which is localized inside the cytoplasm of resting cells. Upon stimulation, IRF3 is activated by serine/threonine phosphorylation, which leads to dimerization, nuclear translocation and binding to recognition sequences in the promoters and enhancers of type I interferon genes. We next attempted to dissect the effect of ERRα on the activity of IRF3. Notably, the signals for VSV-induced IRF3 phosphorylation were significantly higher in BMDMs isolated from the ERRα-KO mice (Fig 5D). Because IRF3 phosphorylation promotes its dimerization, we measured the dimerization of IRF3 using native PAGE gels. Flag-V or Flag-ERRα plasmids were transfected into ERRαCRISPR-/- cells. As expected, IRF3 dimerization and IRF3 phosphorylation in response to VSV infection occurred at much higher levels in the ERRαCRISPR-/- cells than in the WT cells or in the ERRαCRISPR-/- cells rescued with ERRα (Fig 5E). Moreover, IRF3 dimerization was disrupted with the addition of ERRα (Fig 5F). Because the IRF3 dimer binds more strongly to DNA than does the IRF3 monomer, the influence of ERRα on IRF3 binding to the IFN-β promoter was measured using a ChIP assay. As shown in Fig 5G, the increased binding to the IFN-β promoter region by VSV infection was significantly blocked by overexpressed ERRα.
We then wanted to investigate whether the direct binding of ERRα to the N-terminal kinase domain of TBK1 has any functional relevance in ERRα-mediated type-I IFN inhibition. Consistent with the binding result, the AF2 domain of ERRα was required for the inhibition of VSV and TBK1-induced activation of the IFN-β promoter (S5D Fig and Fig 5E). TBK1-induced ISRE activation was also inhibited by the AF2 domain (S5F Fig). To further validate the role of the AF2 domain in regulating the production of IFN-β, we transfected HA-TBK1 along with Flag-vector, Flag-ERRα or Flag-ERRα deletion mutants into ERRαCRISPR-/- cells. TBK1-induced IFN-β activation was inhibited by WT ERRα, AF1 or DBD deletion mutant, but not by the AF2 deletion mutant (Fig 5H and S5G Fig). Consistently, viral growth in ERRαCRISPR-/- cells transfected WT ERRα, AF1 or DBD deletion mutant was greater than that in ERRαCRISPR-/- cells transfected with the AF2 deletion mutant (Fig 5I and S5H Fig). Based on these experiments, we concluded that the binding of ERRα inhibits antiviral signaling through direct physical binding with TBK1.
Our results indicated that ERRα prevented the formation of functional TBK1-IRF3 complex and inhibited the binding affinity of IRF3 to impede IFN-α/β activation. However, whether the transcriptional activity of ERRα is required for the inhibition of this process is unknown. Similar to other nuclear receptors, the DBD domain of ERRα consist of two zinc-finger motifs: the first zinc-finger is responsible for the recognition of specific DNA binding sites, and the second zinc-finger mediates homo-dimerization of the nuclear receptors. Because cysteine residues in the zinc-finger motifs are critical for zinc ion binding, an ERRα CA mutant was constructed by changing the cysteines at positions 79, 96, 115, and 121 to alanines in order to abolish its transcriptional activity. We found that the ERRα CA mutant lost its ability to activate the ERRα promoters (Fig 6A); however, this mutant retained its ability to inhibit the activation of IFN-β and ISRE to levels as potent as the WT (Fig 6B and 6C). TBK1-induced IFN-β activation was equally inhibited by the wild type ERRα and CA mutant (Fig 6D). We then transfected Flag-ERRα or the Flag-ERRα CA mutant into ERRαCRISPR-/- cells. Viral growth in ERRαCRISPR-/- cells transfected with WT and CA mutant ERRα were greater than that in ERRαCRISPR-/- control cells (Fig 6E).
PGC-1α usually acts as a transcriptional cofactor for ERRα in the regulation of metabolic signaling. A previous report revealed that substitution of the ERRα H8–H9 loop (amino acids 338–341, ERRα H8/9) with ERα amino acids 457–468 abolished its interaction with PGC-1α [37]. The ERRα point mutations D338A and Q262E also significantly reduced its binding to PGC-1α [37]. Coimmunoprecipitation and reporter assays indicated that all three mutants could still interact with TBK1 (Fig 6F) and inhibited TBK1-induced IFN-I activation (Fig 6D) to a similar extent as WT ERRα. ERRα negatively regulates TLR4 induced inflammation partially mediated by transcriptional activation of A20 [26]. To assess the role of A20 in ERRα-mediated antiviral signaling, we transfected Flag-ERRα into A20 knockout 293T cells generated by the CRISPR-Cas9 system (A20CRISPR-/-). We observed that the suppression abilities of ERRα on IFN-β activation and IRF3 phosphorylation in response to VSV infection were unchanged by A20 deletion (Fig 6G and 6H). Taken together, these data suggest that the negative effect of ERRα on innate immune signaling is independent of its transcriptional activity and its cofactor PCG-1α.
Because ERRα inhibited the production of interferons and regulated antiviral immunity, we examined whether ERRα was induced after viral infection. Protein levels of ERRα in 293T, BMDMs, THP-1, mouse embryonic fibroblasts (MEFs) and HeLa cells were increased significantly and rapidly following VSV infection (Fig 7A–7E). Moreover, ERRα expression was induced in THP-1 macrophages infected with HSV-1 (Fig 7F) or treated with LPS (Fig 7G). ERRα protein levels were also induced at 12 and 24 hpi in the lung, liver, and spleen of mice infected by tail vein injection of VSV, with the highest induction observed in the spleen (Fig 7H). Cytosolic redistribution of ERRα has been reported in response to HCMV infection [27]. Consistent with this result, nuclear-cytoplasmic fractionation of VSV-infected cells at different time points showed that ERRα was upregulated exclusively in the cytoplasm at 3 hpi, and this upregulation lasted until 24 hpi (Fig 7I). VSV infection also caused a mild upregulation of nuclear ERRα. Interestingly, nuclear ERRα migrated slower than the cytoplasmic form. The expression of ERRα mRNA did not change by VSV infection (Fig 7J). These results indicate that ERRα is stabilized by a post-transcriptional mechanism following viral infection.
Although the natural ligand of ERRα is unknown, ERRα can be activated by several cytokines and by PGC-1α. To determine the contribution of PGC-1α to viral-induced ERRα activation, we evaluated the contribution of PGC-1α to ERRα activation in response to viral infection. As shown in Fig 8A, PGC-1α knockdown cells also exhibited an induction of ERRα expression after VSV infection, indicating that other factors are involved in viral-induced ERRα stabilization. The association between ERRα and TBK1 prompted us to analyze the effect of TBK1 on ERRα expression in response to viral infection. When expression plasmids encoding TBK1 or IKKε were transfected into 293T cells with Flag-ERRα, a significant enhancement in the cellular abundance of ERRα was found (Fig 8B). Notably, the VSV-mediated expression of ERRα was completely inhibited by the TBK1 inhibitor BX795 (Fig 8C). In addition, we found that the induction of ERRα triggered by VSV infection was severely impaired in TBK1 defective cells (TBK1CRISPR-/-; Fig 8D). The half-life of ERRα was greatly reduced in the presence of BX795 (Fig 8E). QRT-PCR analysis showed no significant difference in the transcriptional level of ERRα following TBK1 overexpression (Fig 8F).
We next wanted to explore the role of TBK1 kinase activity on ERRα stabilization. Overexpression of WT TBK1, but not the TBK1 K38A kinase dead mutant (in which the ATP binding residue Lys38 was mutated to alanine), caused increased stabilization of ERRα (Fig 8G). ULD-mutated TBK1 (TBK1 L352A, I353A) which failed to activate the NF-κB, IFN-β and IRF3 promoter as shown previously [38], retained its ability to induce ERRα expression (Fig 8G). Therefore, TBK1 phosphorylation rather than the TBK1-mediated antiviral response is required for viral-mediated stabilization of ERRα. Consistent with the essential role of MAVS in TBK1 phosphorylation and activation, MAVS-KO MEFs failed to stabilize ERRα in response to VSV infection (Fig 8H). TBK1 overexpression also increased the expression of the ERRα target gene ERRE [39], as shown by the luciferase reporter assay (Fig 8I). These results indicated that TBK1 is required for viral induced ERRα stabilization.
To further delineate the mechanism of TBK1-mediated ERRα stabilization, Myc-ubiquitin was cotransfected with plasmids expressing Flag- ERRα together with HA-TBK1 or HA-TBK1 KD in the presence of proteasome inhibitors MG132, ERRα was then immunoprecipitated by anti-Flag antibody and blotted with anti-Ubi-K48 antibody. Immunoprecipitation assay showed that overexpression of WT TBK1, but not TBK1 KD mutant, led to a sharp reduction on the K48 ubiquitination level of ERRα (Fig 8J), suggesting that TBK1 phosphorylation modification might contribute to the stabilization of ERRα by inhibiting its K48-linked polyubiquitylation.
Next, we investigated the antiviral activity of XCT790, a synthetic antagonist of ERRα. First, the effect of XCT790 on IFN-I induction was explored. As shown in Fig 9A, the mRNA expression levels of VSV-induced IFN-β and the IFN-regulated gene products IFIT1 and IP-10 were significantly upregulated in XCT790-treated cells. Accordingly, treatment of 293T cells with XCT790 inhibited VSV production (Fig 9B) and VSV-G expression (with IFN-β and 25-HC as the positive control) (S6A Fig). XCT790 inhibited VSV-G protein expression in a dose-dependent manner (Fig 9C). A similar antiviral effect was observed in several human cell lines, including HeLa, A549, and primary isolated BMDMs (S6B Fig). A cytoprotective effect of XCT790 in 293T, HeLa, and A549 cells upon VSV infection was also observed (S6C Fig). To determine the breadth of the antiviral activity of XCT790, we tested the effect of XCT790 on various viruses. By quantifying NDV-GFP using flow cytometry and fluorescence microscopy analysis, we found that XCT790 inhibited NDV-GFP replication in 293T cells by over 90% (Fig 9D and S6D Fig). Treatment of A549 cells (S6E Fig) with XCT790 inhibited NDV-GFP expression by approximately 50%. XCT790 also inhibited HSV-1 production in 293T cells (Fig 9E). Treatment with the indicated dose of XCT790 reduced HBV surface antigen (HBsAg) and e antigen (HBeAg) expression by 50% (Fig 9F).
To further evaluate the antiviral role of XCT790 in vivo, we intravenously administered VSV in mice treated with XCT790 or DMSO. As expected, the XCT790-treated mice had a much lower virus load in the serum, liver and lung (Fig 9G). Taken together, our results demonstrate that ERRα chemical inhibitor XCT790 exhibits antiviral activity against several types of viruses.
Innate immunity and metabolism appear to be inextricably linked and are now known to regulate each other reciprocally [16,40–42]. Exciting new evidence is emerging with regard to the role of TLRs and NLRs in the regulation of metabolism and the activation of inflammatory pathways during the progression of metabolic disorders, such as type 2 diabetes [43] and Reye's syndrome [44]. Studies have also suggested that metabolites, such as 25-HC [45–47], NAD [48] (acting via deacetylases such as SIRT1 and SIRT2) and succinate [49] (which regulates hypoxia-inducible factor 1), regulate innate immunity. Additionally, extracellular overproduction of metabolites, such as uric acid and cholesterol crystals, is sensed by NLRP3, leading to activation of the inflammasome complex and the production of IL-1β [50,51]. In turn, some nuclear receptors reported to regulate metabolism, such as the glucocorticoid receptor (GR) [52], peroxisome proliferator-activated receptor γ (PPAR-γ) [53] and retinoid X receptor α (RXRα) [54], have been implicated in type I interferon regulation. The interplay between immunity and metabolic changes is a growing field of research.
This study investigated an unappreciated relationship between the host IFN-I response and ERRα, a member of the nuclear receptor superfamily involved in the transcriptional control of energy homeostasis. Several lines of evidence support this argument: (1) Viral infection led to increased ERRα expression both in vivo and in vitro. Further study showed that TBK1 was indispensable for viral-induced ERRα stabilization. (2) Overexpression of ERRα resulted in potent inhibition of virus-triggered IRF3 activation and IFN-β induction, while inhibition of ERRα by knockdown, chemical antagonist and knockout enhanced IFN-β production and increased resistance to VSV, NDV, HSV and HBV infections. (3) Mechanistically, ERRα disrupted the TBK1-IRF3 interaction and the homo-dimerization of IRF3 by interacting with TBK1, IKKε and IRF3, which are critical for virus-induced IRF3 activation and IFN-β induction. (4) The effect of ERRα on IFN-I production was independent of its transcriptional activity and PCG-1α. Therefore, our findings indicate that ERRα serves as a negative regulator downstream of TBK1 that attenuates the persistence of the antiviral state independently of its role in metabolic signaling.
TBK1 is a key Ser-Thr kinase involved in innate immunity that is activated by adaptors, such as STING, TRIF and MAVS [55]. Activation of TBK1 leads to adaptor phosphorylation, IRF3 activation and expression of IRF3-dependent genes that are important for the response to viral infection; thus, their activities are tightly regulated. In addition to ERRα, MIP-T3 and SIKE have been identified as two other physiological suppressors that negatively regulate IFN-β production by inhibiting the formation of functional TBK1 complexes [11,12]. SIKE and ERRα disrupted the TBK1-IRF3 association by targeting TBK1, while MIP-T3 disrupted the TRAF3-TBK1 association through its direct interaction with TRAF3. Although both SIKE and ERRα are associated with TBK1 under physiological conditions, only the ERRα protein level was significantly increased in response to VSV infection. Hence, our work indicated that TBK1 activation not only activated IRF3 but also activated ERRα to affect both IFN-I induction and metabolic signaling, indicating the unique status of ERRα among the cellular inhibitors of innate immunity. Hwang reported that ERRα provides a metabolic environment to promote the production of cytomegalovirus [27]. In our study, we also showed that ERRα upregulation can be detected as early as 3 hpi. We speculated that the upregulation of ERRα at the early stage of viral infection may present a general strategy by which the host produces the energy to counteract the stress; however, the pathogen hijacks the host cell metabolic environment. Thus, pharmacological targeting of ERRα to uncouple pathogens from their nutritional dependencies and the host negative innate immune response may prove to be an effective strategy for controlling pathogen spread.
ERRα functions downstream of PGC-1α and PGC-1β and controls the expression of genes involved in metabolism. The upregulation of ERRα and the unchanged expression of PGC-1α in response to viral infection imply that additional factors may be involved in the regulation of viral-induced ERRα upregulation. Here, we showed that ERRα was specifically stabilized in response to the virus infection downstream of TBK1. Sequence profiles of ERRα across mammalian species revealed several putative consensus TBK1 phosphorylation motifs. We speculated that ERRα might be phosphorylated and activated by TBK1. In support of this hypothesis, TBK1 kinase activity was required for ERRα activation. Recent reports have shown that MAVS phosphorylated by TBK1 relays its downstream signal to IRF3 for its phosphorylation and activation by TBK1. Interestingly, MAVS knockout cells displayed both defective basal and activated ERRα in response to VSV infection. However, the phosphorylation of ERRα by TBK1 and the roles of adaptors in TBK1-mediated ERRα activation require further investigation.
In a recent work, TBK1 was shown to be highly expressed in lung, breast and colon cancers, and subjects with tumors that highly express TBK1 have poor responsiveness to tamoxifen treatment and a high potential for relapse [35]. ERRα expression was markedly increased in neoplastic versus normal tissues, and ERRα-positive tumors were associated with more invasive disease and a higher risk of recurrence [56]. We established that ERRα associates directly with TBK1; thus, ERRα might affect cancer progression as a substrate of the TBK1 kinase in addition to cooperating with TBK1 in the regulation of innate immune signaling. Indeed, breast cancer patients with hypo-phosphorylated ERRα are more likely to respond to hormonal-blockade therapy and have a longer overall survival time than those with hyper-phosphorylated ERRα, which may be a direct consequence of TBK1-mediated ERRα phosphorylation [57]. Our studies also suggest the possibility that viral infection induced ERRα activation may be a tumor-promoting factor, especially in persistent infection, but further investigation is required. These findings influence our understanding of the complex relationship between innate immune effectors, metabolic regulators and the signaling events that drive tumor formation.
Here, we provided direct evidence indicating the critical role of ERRα in virus replication by modulating IFN-I induction independent of its transcriptional activity. In line with this finding, the inhibition of ERRα effectively reduced the yield of VSV, NDV, HSV and HBV and showed a promising cytoprotective effect in response to viral infection in multiple cell lines. As ERRα is a potential target for the treatment of breast cancer and metabolic disorders, several selective ligands against ERRα are being developed. Our studies thus suggest the potential new application of ERRα antagonists in the treatment of viral infection.
ERRα-KO mice on a C57BL/6J background were purchased from the Jackson Laboratory and maintained in specific pathogen–free conditions. All animals were handled in strict accordance with the Guide for the Care and Use of Laboratory Animals and the principles for the utilization and care of vertebrate animals, and all animal work was approved by the Institutional Animal Care Committee of Beijing Institute of Biotechnology. Animal experiments were performed in accordance with the regulations in the Guide for the Care and Use of Laboratory Animals published by the Ministry of Science and echnology of the People’s Republic of China. The protocol was approved by the ethics committee of Beijing Institute of Biotechnology (Permit Number: 2008–09).
Mammalian expression plasmids pCMV-Flag-ERRα and HA-STING were provided by Dr. Toren Finkel [58] and Hongbin Shu [59]. Expression plasmids for pEBB-HA-TBK1 and pEBB-HA-IKKε were gifts from Dr. Genhong Cheng [60]. Gal4-Luc and Gal4-IRF3 were obtained from Zhijian J. Chen [34]. The ERRE luciferase plasmid was a gift from Timothy F. Osborne. IRF3 and RIG-I cDNA were amplified from a human spleen library and subsequently cloned into CMV promoter-based vectors. Other tagged cDNA containing plasmids and mutants were constructed by PCR amplification based on these plasmids. IFN-β-Luciferase and ISRE-Luciferase reporter plasmids were purchased from Beyotime Corp. Other mammalian expression vectors encoding Flag-, Myc-, or HA-fusion proteins tagged at the amino terminus were constructed by inserting PCR-amplified fragments into pcDNA3 (Invitrogen) or pCMV (Clontech). Plasmids encoding GST fusion proteins were generated by cloning PCR-amplified sequences into pGEX4T-1 (Amersham Pharmacia Biotech). HuSH 29mer shRNA constructs against ERRα kit was purchased from OriGene Company. The sequence of effective shRNA that targeted ERRα is GCAAAGCCTTCTTCAAGAGGACCATCCAG. A non-effective 29-mer scrambled shRNA cassette in the same vector from the kit was used as a negative control. All plasmids were verified by restriction enzyme analysis and DNA sequencing.
Total RNA from the cells with or without virus infection was quantified by the NanoDrop ND-2000 spectrophotometer (Thermo Scientific), and the RNA integrity was assessed using the Agilent Bioanalyzer 2100 (Agilent Technologies). The sample labeling, microarray hybridization and washing were performed based on the manufacturer’s standard protocols. Briefly, total RNA were transcribed to double stranded cDNA, then synthesized into cRNA and labeled with Cyanine-3-CTP. The labeled cRNAs were hybridized onto the Agilent Human Gene Expression (8*60K, Design ID: 039494) microarray. After washing, the arrays were scanned by the Agilent Scanner G2505C (Agilent Technologies). Feature Extraction software (version 10.7.1.1, Agilent Technologies) was used to analyze array images to obtain raw data. Genespring was employed to complete the basic analysis of the raw data. First, the raw data were normalized with the quantile algorithm. Then, GO analysis and KEGG analysis were applied to determine the roles of these differentially expressed mRNAs.
BMDMs were isolated from WT and ERRα-KO C57BL/6 mice by culturing for 6 days in RPMI 1640 medium containing 10 ng/ml M-CSF (PeproTech). Twenty-four hours prior to infection, 1 x 106 cells were seeded into 12-well plates with RPMI 1640 containing 10 ng/ml M-CSF and 10% fetal bovine serum (FBS, HyClone).
Human cell lines 293T, HeLa, A549, mouse embryonic fibroblasts (MEFs) and HepG2.2.15 were routinely cultured in DMEM (Invitrogen) containing 10% FBS (HyClone). 293T, HeLa and A549 cell lines were obtained from ATCC. MEFs and HepG2.2.15 cells were gifted from Dr. Cheng Cao. Cells were maintained as monolayers in a humidified atmosphere containing 5% CO2 at 37°C. Lipofectamine 2000 reagent was used for transfection following the manufacturer’s protocol (Invitrogen). Stable cell lines were selected in 1 μg/ml puromycin for approximately 2 weeks. Individual clones were screened by standard immunoblotting protocols and produced similar results. The luciferase reporter assay was performed as described previously [61].
VSV and NDV were kindly provided by Dr. Cheng Cao. HSV-1 was donated by Dr. Wei Chen. Cells were infected with the virus at the indicted MOI for 1 h, and then the media was replaced with fresh media. For HSV-1 and VSV, supernatants were collected, and titers were measured by plaque assays using BHK21 cells [54,62].
Cell extracts were prepared, immunoprecipitated and analyzed as previously described [63]. An aliquot of the total lysate (5%, v/v) was included as a control for the interaction assay. Immunoprecipitation was performed with an anti-Flag M2 Affinity Gel (Sigma-Aldrich, A2220) and anti-ERRα (Epitomics, 2131–1). Western blotting was performed by HRP-labeled anti-Myc (Sigma-Aldrich, A5598), anti-HA (Sigma-Aldrich, H9658), anti-TBK1 (Epitomics, 3296–1), anti-ERRα (Epitomics, 2131–1), anti-IRF3 (pSer386) (Epitomics, 2346), anti-IRF3 (pSer396) (Cell Signaling Technology, 4947s), anti-A20 (ABclonal, A2127), anti-IKKε (ABclonal, A0244) or anti-α-Tubulin (Sigma-Aldrich, T6074) antibodies. The antigen-antibody complexes were visualized by chemiluminescence.
In a Far-Western assay, immunoprecipitates were separated by SDS-PAGE and then blotted onto nitrocellulose membranes. The membranes were subsequently incubated with purified GST-fusion proteins for 1 h at room temperature. The GST fusion proteins binding to nitrocellulose were probed with an anti-GST antibody.
qRT-PCR was performed in the iQ5 Real-time PCR System (Bio-Rad) using iTaq universal SYBR Green supermix (Bio-Rad). Each sample was analyzed in triplicate with GAPDH as the internal control. S1 Table lists the primer sequences used for different genes in this study.
Cells were lysed in NP-40 lysis buffer as previously described [65] and mixed with native loading buffer (250 mM Tris-HCl (pH 7.5), 50% glycerol and 0.007% xylene cyanol). The 8% native gel was pre-run with 25 mM Tris and 192 mM glycine with 1% deoxycholate (DOC) in the inner chamber for 30 min at 40 mA. Then, the samples were resolved for 60 min at 40 mA at 4°C [64]. The proteins from the native gel were transferred to PVDF membranes for immunoblotting analysis, as described above.
Fixed and permeabilized cells were incubated overnight at 4°C with the following pairs of primary antibodies: anti-ERRα (Epitomics, 2131–1), mouse mAb to IRF3 (BioLegend, 655701) or mouse mAb to TBK1 (Santa Cruz, sc-398366). The cells were washed and allowed to react with a pair of proximity probes (Olink Bioscience). The remainder of the in situ PLA protocol was performed according to the manufacturer’s instructions. The cells were examined by fluorescence microscopy (UlthaView VOX, PerkinElmer), and the Duolink Image Tool (Olink Bioscience) was used for quantitative analysis.
293T cells cultured in 24-well plates were transfected using Lipofectamine 2000 with 0.1 μg of reporter, 0.002 μg of the pRL control vector, and various amounts of the indicated constructs. After incubation for 24 h, the cells were harvested, and luciferase activity was analyzed using the Dual Luciferase Reporter Assay System (Promega). Total light production was measured with a TD-20/20 Single-Tube Luminometer (Turner BioSystems). All experiments were repeated at least three times.
Human and mouse IFN-β were quantified with IFN-β ELISA kits from Antigenix (4756) and Biolegend (3861), respectively.
The lungs from control or virus-infected mice were washed with PBS, and then fixed in 4% PBS-buffered paraformaldehyde for 12 h, embedded into paraffin, sectioned, stained with hematoxylin and eosin solution.
293T control cells or ERRα knockdown cell lines were infected by VSV for the indicated time and subjected to the ChIP assay using anti-IRF3 or control mouse IgG. The IFN-β enhancer region was amplified by PCR using specific primers as follows [65]: Sense, 5′-GAATCCACGGATACAGAACCT-3′, Antisense, 5′-TTGACAACA-CGAACAGTGTCG-3′. Amplification of the total input DNA was shown as an equal loading control. The experiment was performed as described in reference [65].
Significant differences were calculated using a paired Student’s t-test. *p < 0.05, **p < 0.01 and ***p < 0.001. Estimation of overall survival was performed using Kaplan–Meier analysis, and differences between curves were compared using log-rank tests.
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10.1371/journal.ppat.1004605 | The Molecular Basis for Control of ETEC Enterotoxin Expression in Response to Environment and Host | Enterotoxigenic Escherichia coli (ETEC) cause severe diarrhoea in humans and neonatal farm animals. Annually, 380,000 human deaths, and multi-million dollar losses in the farming industry, can be attributed to ETEC infections. Illness results from the action of enterotoxins, which disrupt signalling pathways that manage water and electrolyte homeostasis in the mammalian gut. The resulting fluid loss is treated by oral rehydration. Hence, aqueous solutions of glucose and salt are ingested by the patient. Given the central role of enterotoxins in disease, we have characterised the regulatory trigger that controls toxin production. We show that, at the molecular level, the trigger is comprised of two gene regulatory proteins, CRP and H-NS. Strikingly, this renders toxin expression sensitive to both conditions encountered on host cell attachment and the components of oral rehydration therapy. For example, enterotoxin expression is induced by salt in an H-NS dependent manner. Furthermore, depending on the toxin gene, expression is activated or repressed by glucose. The precise sensitivity of the regulatory trigger to glucose differs because of variations in the regulatory setup for each toxin encoding gene.
| Diarrheagenic illness remains a major disease burden in the developing world. Enterotoxigenic Escherichia coli (ETEC) are the leading bacterial cause of such disease; hundreds of millions of cases occur every year. The severe watery diarrhoea associated with ETEC infections results from the action of enterotoxins. The toxins target human gut epithelial cells and trigger the loss of water and electrolytes into the gut lumen. Oral rehydration therapy can counteract this process. Hence, glucose and salt solutions promote rehydration of the patient. In this work we show that the gene regulatory mechanisms controlling toxin expression respond directly to sugar and salt. Furthermore, we describe a molecular mechanism to explain these effects. Hence, we provide a starting point for the optimisation of oral rehydration solutions to reduce toxin expression over the course of an ETEC infection.
| ETEC are Gram negative bacteria that cause severe diarrhoea, known as non-vibrio cholera, in humans [1], [2]. First isolated in 1971, ETEC are responsible for 210 million infections annually, mostly in developing countries, leading to 380,000 deaths [3]. Disease results primarily from the action of two enterotoxins. The heat-labile toxin (LT) is similar in structure and function to cholera toxin [4], [5]. The heat-stable toxin (ST) mimics the human hormone guanylin [6]. Both toxins are secreted by ETEC during infection. Made up of two subunits, encoded by the eltAB operon, LT has the configuration AB5 [5], [7]. In the gut, LT binds to host cell GM1 gangliosides and is endocytosed [8], [9]. This triggers constitutive cAMP production in the affected cell [8]. The ST toxin, encoded by the estA gene, also interferes with cell signalling [6]. Hence, ST binds to the guanylate cyclase C receptor and stimulates overproduction of cGMP. The combined actions of LT and ST cause loss of H2O, and electrolytes, from epithelial cells into the gut lumen [4]. Oral Rehydration Therapy (ORT) is used to redress the resulting electrolyte imbalance and rehydrate the patient [10]. In its most simple form, ORT requires only an aqueous solution of glucose and salt. Hence, the availability of metabolites and cations are a central theme of ETEC mediated disease. The effect of ORT on human physiology is well understood: glucose and Na2+ are transported across the epithelial membrane, along with water, to promote rehydration [11]. Surprisingly, despite the existence of molecular mechanisms that allow bacteria to respond to these signals, the consequences for ETEC are unknown.
In E. coli, the transcriptional response to glucose is controlled by cAMP receptor protein (CRP) [12]. In the absence of glucose, intracellular cAMP levels increase and CRP binds DNA targets with the consensus sequence 5′-TGTGA-n6-TCACA-3′ [13]. Subsequently, gene expression is reprogrammed to make use of alternative carbon sources [14]. Note that the gene regulatory network managed by CRP includes many indirect pathways [14], [15]. Hence, CRP is also a pleiotropic regulator of transcription. Whilst indirect regulatory effects are difficult to characterise, genes that are directly controlled by CRP can be divided into distinct classes [12]. At Class II targets, CRP binds to a site overlapping the promoter -35 element and interacts directly with both the N-terminal and C-terminal domains of the RNA polymerase α subunit (αNTD and αCTD). At Class I targets, CRP binds further upstream and interacts only with αCTD. This interaction can be further stabilised by UP-elements, AT-rich DNA sequences, adjacent to the CRP site, that facilitate αCTD-DNA interactions [12]. At both classes of promoter, the various contacts enhance gene expression by stabilising the transcription initiation complex. Unsurprisingly, most genes regulated by CRP encode proteins involved in metabolism. However, in some bacteria, CRP has been co-opted as a virulence regulator [16].
The Histone-like Nucleoid Structuring (H-NS) factor is a component of bacterial nucleoprotein. Consequently, H-NS also influences gene expression on a global scale [17]. Briefly, H-NS targets sections of the genome with a low GC content [17]. Depending on H-NS conformation, the resulting nucleoprotein complexes can be filamentous or bridged in organisation [18]. Filamentous complexes favour gene regulation by excluding RNA polymerase, and transcriptional regulators, from their targets [19], [20]. Bridged complexes favour RNA polymerase trapping [21]. In all scenarios, it is thought that H-NS acts primarily to silence transcription [22]. The conformation of H-NS, and hence the way in which it modulates DNA topology, can be controlled by divalent cations. Consequently, H-NS mediated repression can be relieved by increased osmolarity [23]. Like CRP, H-NS has been incorporated into the virulence gene regulatory networks of many bacteria [17].
In this work we define the molecular trigger that controls toxin expression in ETEC. We show that CRP and H-NS are key regulatory factors. Strikingly, this allows ETEC to integrate extracellular signals of osmolarity and metabolism to control toxin production. Hence, we propose that ETEC toxicity responds directly to osmo-metabolic flux. Interestingly, the precise regulatory settings are different for each toxin encoding gene. The differences result from i) varying promoter configurations and ii) competition between CRP and H-NS for overlapping DNA targets. This is significant since fluctuations in osmolarity, and changes in the availability of metabolites, are central to ETEC infection and its treatment.
The prototypical ETEC strain H10407 reproducibly elicits diarrhoea in human volunteers and has a well-defined genome that shares 3,766 genes with E. coli K-12 [1]. Pathogenicity arises from 599 ancillary genes encoded by 25 discrete chromosomal loci and 4 plasmids. The plasmids, named p948, p666, p58 and p52, encode the enterotoxins. Derivatives of the estA gene are found on plasmids p666 (estA1) and p948 (estA2). A single copy of the eltAB operon is encoded by plasmid p666. We used Chromatin Immunoprecipitation (ChIP) coupled with next-generation DNA sequencing (ChIP-seq) to map CRP and H-NS targets across the ETEC H10407 genome. The binding profiles are shown in Fig. 1A. In each plot genes are illustrated by blue lines (tracks 1 and 2), DNA G/C content by a cyan and pink graph (track 3), H-NS binding is in green (track 4) and CRP binding is shown in orange (track 5). As expected, H-NS binding is inversely correlated with DNA G/C content (compare tracks 3 and 4). Similarly, CRP binding occurs in expected locations; 96% of the CRP binding sites are associated with the DNA logo shown in Fig. 1B (i.e. the known CRP consensus sequence (13–15)). We identified a total of 111 high-confidence CRP targets (Table 1). Of these targets 93% were present in the genome sequences of both ETEC H10407 and E. coli K-12. The most common location for CRP sites was in intergenic regions (66% of targets) whilst a smaller number of targets were found within genes (34%). Consistent with expectations, CRP sites were most frequently located ∼40.5 bp, or ∼92.5 bp, upstream of experimentally determined transcription start sites (TSSs). Surprisingly, CRP binding was restricted to the ETEC chromosome (Fig. 1Ai). Conversely, H-NS bound to chromosomal and plasmid loci (Fig. 1Ai), including all toxin encoding genes (Fig. 1Aii).
To better understand the lack of CRP binding to p948 and p666 we took a bioinformatic approach. CRP targets were aligned to generate a position weight matrix (PWM). The PWM was then used to search p948 and p666 for CRP sites. A continuum of over 100 potential CRP targets was identified. However, we recognise that the vast majority of these are likely to be false positives. Hence, we next sought to differentiate between genuine CRP sites and spurious predictions. To do this, predicted sites were scored, grouped, and ranked on the basis of their match to the PWM (Fig. 2A, S1 Table). Electrophoretic mobility shift assays (EMSA) were then used to measure binding of CRP to a target from each group so that a meaningful cut-off could be established. The result is illustrated graphically in Fig. 2B. The raw data are shown in S1A Fig. We found that predicted sites with a score<10 did not bind CRP. To assess the affinity of CRP for all predicted targets scoring >10 a second set of EMSA experiments was done (S1B Fig.). Hence, we identified a total of 5 potential CRP targets on p666 and p948. Interestingly, the estA1 and estA2 genes, which both encode ST, were amongst the 5 targets (Fig. 2C). Remarkably, all 5 of the plasmid borne CRP targets identified in silico, and bound tightly by CRP in vitro, were occupied by H-NS in vivo (Fig. 2C).
To understand if CRP could regulate ST production we focused first on estA2. This derivative of the toxin is more commonly associated with human disease and ETEC H10407 is somewhat unusual in also encoding estA1 [24]. The sequence of the estA2 regulatory region is shown in Fig. 3A. A 93 bp DNA fragment, containing the regulatory region, was cloned into the lacZ reporter plasmid pRW50 to generate a lacZ fusion (S2A Fig.). The estA2 TSS was then determined using mRNA primer extension analysis. We detected a single extension product, of 109 nucleotides (nt) in length (Fig. 3B). The position of the TSS is labelled “+1” in Fig. 3A. Promoter -10 (5′-TTAAAT-3′) and -35 (5′-TTGCGC-3′) elements were observed at the expected positions upstream of the TSS. Throughout this work we refer to this promoter, highlighted purple in Fig. 3A, as PestA2. To confirm CRP binding at the predicted site we used DNase I footprinting (Fig. 3C). As expected, CRP protected the predicted target from digestion. Additionally, CRP induced DNase I hypersensitivity in the centre of the site. Note that the CRP site is centred 59.5 bp upstream of the TSS and adjacent to an AT-rich sequence that may be an UP element (Fig. 3A). Thus, we hypothesised that PestA2 is a class I CRP activated promoter. To test our hypothesis we first determined whether CRP could indeed activate PestA2. To do this, we compared LacZ expression in M182Δlac and M182ΔlacΔcrp cells carrying the PestA2::lacZ fusion. The data show that loss of CRP results in a 3-fold decrease in LacZ expression from PestA2 (Fig. 3D). We next tested the ability of CRP to activate PestA2 in vitro. The 93 bp DNA fragment was cloned upstream of the λoop terminator in plasmid pSR. In the context of this construct a 112 nt transcript is generated by RNA polymerase from PestA2 in vitro. The amount of transcript can then be quantified by electrophoresis. The result of the analysis, with and without CRP, is shown in Fig. 3E. As expected, an intense band corresponding to the 112 nt transcript was observed. Production of the transcript was stimulated by CRP. Note that CRP had no effect on production of the 108 nt control RNAI transcript from the plasmid replication origin. Finally, we examined the AT-rich DNA sequence (highlighted blue in Fig. 3A) located between the CRP site and the promoter -35 element. We found that increasing the GC content of the putative UP-element altered migration of the 93 bp DNA fragment on an agarose gel, consistent with a change in DNA topology (S3A Fig.). Moreover, these changes to the UP-element rendered PestA2 insensitive to CRP in vivo and in vitro (S3B Fig.).
Promoters can be liberated from H-NS repression if separated from flanking, H-NS bound, DNA [25]. We reasoned that this might be why, when isolated on the 93 bp fragment, PestA2 was active and dependent on CRP. To test this logic we generated a further two PestA2::lacZ fusions using the pRW50 plasmid system. The additional PestA2 DNA fragments were both 460 bp in length and include the full estA2 gene that was entirely bound by H-NS in our ChIP-seq assay (Fig. 2C). The CRP site was ablated in one of the additional fragments by introducing point mutations that are predicted to disrupt CRP binding. The sequence of the DNA fragments is shown in S2A Fig. The lacZ fusions are illustrated graphically in Fig. 4A. Our expectation was that the longer 460 bp fragment would bind H-NS whilst the starting 93 bp fragment would not. To test this prediction we used ChIP. Thus, we compared H-NS binding to the different PestA2 containing fragments in vivo. Fig. 4B shows results of a PCR analysis to measure enrichment of the PestA2 locus. As expected, PestA2 was only enriched in anti-H-NS immunoprecipitates when in the context of the 460 bp fragment. Crucially, enrichment is specific because, in a set of control PCR reactions, there was no enrichment of the yabN locus in any immunoprecipitate.
Our ChIP analysis suggests that the 460 bp fragment containing PestA2 is subject to regulation by H-NS. To confirm that this was the case, the various pRW50 derivatives were used to transform M182Δlac and M182ΔlacΔhns cells. We then measured LacZ activity, driven by PestA2, in the transformants. Consistent with our expectations the data show that PestA2 is repressed 5-fold by H-NS only in the context of the 460 bp DNA fragment (Fig. 4C). Importantly, mutations in the CRP binding site abolish PestA2 activity in the absence of H-NS. Hence, the measured LacZ expression must be driven by PestA2 rather than any spurious promoters located within the estA2 gene. Taken together our ChIP-seq and LacZ activity data show that H-NS prevents CRP from activating PestA2.
The estA1 regulatory region, located on plasmid p666, contains a sequence similar to PestA2 (Fig. 5A). We expected that this sequence would be the estA1 promoter (PestA1). To test this expectation we created a 92 bp PestA1::lacZ fusion, equivalent to the 93 bp PestA2::lacZ fusion described above, and mapped the 5′ end of the resulting mRNA. As expected, the primer extension product was 109 nt in length (Fig. 5B). Hence, PestA1 and PestA2 use equivalent TSSs. However, we were surprised that the intensity of the PestA1 primer extension product increased in cells lacking CRP (Fig. 5B). Closer examination of the alignment in Fig. 5A shows that, whilst PestA1 and PestA2 are similar, there are differences in the sequence and position of key promoter elements. To try and understand which changes result in the aberrant behaviour of PestA1 we made a set of hybrid promoters. The hybrid constructs are derived from the CRP-activated estA2 promoter. In each hybrid, named PestA2.1 through PestA2.7, a region of PestA2 was replaced with the equivalent region from PestA1 (see underlined sequences in Fig. 5C). The ability of the different hybrid promoters to drive lacZ expression, with and without CRP, was then tested. The results are shown in Fig. 5D. Note that, in Fig. 5D, the composition of each hybrid promoter is indicated in the grid below the graph. For example, PestA2.1 is derived from PestA2 but contains the PestA1 CRP site. As expected, both PestA1 and PestA2 were able to drive lacZ expression but CRP had opposite effects. Moreover, maximal expression from PestA1 was 3-fold lower than from PestA2. Only PestA2.3 and PestA2.5, which both carried the same changes in the promoter -35 element, exhibited a reversed dependence on CRP. Hence, the PestA1 -35 element must be responsible for the altered CRP dependence. All other hybrid promoters exhibited an overall reduction in activity compared to the parent PestA2 construct. We conclude that this combination of changes results in the lower activity of PestA1. Note that both PestA1 and PestA2 were bound by H-NS in our ChIP-seq analysis (Fig. 2C). We reasoned that cloning PestA1, with flanking DNA, would reveal H-NS mediated repression. We generated a derivative of the PestA1::lacZ fusion where the downstream boundary was extended to include the entire estA1 gene (S2B Fig., Fig. 5Ei). As expected, transcription from PestA1 was repressed by H-NS in the presence of downstream DNA (Fig. 5Eii).
We next turned our attention to the LT toxin promoter (PeltAB) [26], [27]. Previously, Bodero and Munson [27] showed that transcription from this promoter was repressed by CRP. A mechanism for repression was proposed whereby CRP acted directly by binding three DNA targets overlapping PeltAB [27]. Even so, no CRP binding at PeltAB was identified by our ChIP-seq analysis (Fig. 6A). It is possible that this is because H-NS also excludes CRP from this locus (Fig. 6A). However, we also failed to identify CRP targets at PeltAB in our bioinformatic screen, even below the stringent cut-off (Fig. 2, S1 Table). In retrospect, this appears to be because all of three PeltAB CRP binding sites contain at least 4 mismatches to the consensus for CRP binding (Fig. 6A). Hence, we measured the affinity of CRP for PeltAB using EMSA assays. In parallel, we tested CRP binding to PestA2 as a control. As expected, CRP bound tightly to PestA2 at low concentrations (Fig. 6B, lanes 1–6). At high CRP concentrations further non-specific binding was observed (evidenced by a conspicuous “smear” in DNA migration in lane 7). In the equivalent experiment, with PeltAB, no specific binding of CRP was observed (lanes 8–13). However, non-specific CRP binding was again detectable at high protein concentrations (lane 14). Hence, CRP does not bind specifically to PeltAB. We hypothesised that previously observed changes in PeltAB activity, in cells lacking CRP, may occur indirectly. To test this, we cloned a 359 bp DNA fragment, containing PeltAB, into our pRW50 lacZ expression system. We also made a truncated 118 bp derivative of this construct where two of the three putative CRP targets were removed. A derivative of the truncated 118 bp construct, where the remaining CRP site was completely ablated by point mutations, was also made. The DNA sequences of the different constructs are shown in S2C Fig. They are illustrated graphically in Fig. 6Ci. Consistent with previous measurements, we found that transcription from PeltAB increased 2.5 fold in the absence of CRP. However, the response of PeltAB was identical when the CRP binding sites were removed (Fig. 6Cii). Hence, although CRP represses transcription from PeltAB, this must occur indirectly.
Given the configuration of H-NS binding at the eltAB locus (Fig. 6A) we reasoned that PeltAB would be repressed by H-NS in the presence of sufficient flanking DNA. As we had done previously for PestA1 and PestA2, we compared the binding of H-NS to PeltAB in the presence and absence of the downstream flanking sequence. The different DNA constructs are illustrated in Fig. 7A and results of ChIP experiments to measure H-NS binding are shown in Fig. 7B. As predicted, enrichment of PeltAB, in immunoprecipitations with anti-H-NS, was only observed in the presence of downstream DNA. Importantly, this enrichment was specific to PeltAB and not observed for the control locus yabN. Corresponding LacZ activities, for the different DNA constructs, measured in M182 or the Δhns derivative, are shown in Fig. 7C. Incorporation of flanking DNA downstream of PeltAB resulted in a 15-fold reduction in LacZ activity that was largely relieved in the absence of H-NS.
Given the established regulatory connections between CRP and glucose, and between H-NS and salt, we next measured changes in the activity of PestA1, PestA2 and PeltAB in response to glucose and salt. A complete description of assay conditions is provided in the Materials and Methods section. Briefly, to establish the range of conditions across which the promoters were able to respond, we examined the effect of titrating glucose or salt into the growth medium individually. In all experiments, we used the promoter::lacZ fusions that included downstream flanking DNA. This was to ensure that signals sensed by both CRP and H-NS could be integrated. As expected, the activity of PestA1 was low. Consequently, the effects of glucose and salt were negligible (S4A Fig.). Conversely, the activity of PestA2 was sensitive to both glucose and salt (S4B Fig.). Thus, lacZ expression driven by PestA2 was repressed by glucose (orange line) and enhanced by salt (green line). As expected, PeltAB activity increased in the presence of both salt and glucose, but induction by salt was more prominent (S4C Fig.). We hypothesised that, for PestA2, the inhibitory effect of glucose should override the stimulatory effect of salt. Our reasoning was that, although H-NS can repress PestA2, the promoter is ultimately dependent on CRP for activity. Hence, we examined the effect of adding salt and glucose, to cells carrying the PestA2::lacZ fusion, separately and in combination (Fig. 8A). As predicted, the inhibitory effect of glucose was dominant (Fig. 8Ai) and was still observed in the absence of H-NS (Fig. 8Aii). Conversely, the stimulatory effect of salt required H-NS (compare green bars in Fig. 8). Importantly, in a separate experiment, we also showed that the effect of glucose on PestA2 activity requires that the CRP site is intact (S4D Fig.). The combined effect of salt and glucose on PeltAB was more difficult to predict because CRP acts via an undefined, and indirect, mechanism. The result of the analysis (Fig. 8B) shows that the stimulatory effects of salt and glucose on transcription from PeltAB are not additive. Moreover, the stimulatory effect of glucose requires H-NS.
Examination of all sequenced ETEC genomes reveals slight variations in the sequence of the eltAB and estA2 promoter sequences (recall that ETEC H10407 is somewhat anomalous in also encoding estA1). Thus, we next sought to understand if our model for regulation of LT and ST expression was broadly applicable. We focused our efforts on ETEC E24377A since i) the genome has been sequenced and ii) a vast array of independently generated transcriptomic data are available for this organism [28], [29]. Using ETEC E24377A DNA as a template, we generated a 460 bp PestA2, and 1126 bp PeltAB DNA fragment. The sequences are shown in S2D Fig. The DNA fragments were cloned into pRW50 and the ability of the promoters to drive lacZ expression in response to CRP and H-NS was measured. As expected, transcription from PestA2 was repressed by H-NS and activated by CRP whilst PeltAB was repressed by H-NS (Fig. 9A). We observed no effect of CRP on transcription from PeltAB in the context of the 1126 bp ETEC E24377A fragment. This is not unexpected because CRP acts indirectly and these indirect CRP effects have only previously been observed in the context of short DNA fragments containing PeltAB that are not subject to direct repression by H-NS. We note that Sahl and Rasko previously examined the global transcriptome response of E24377A to glucose levels and bile salts [28]. In exact agreement with our model for toxin regulation, and the data in Fig. 9A, this study confirmed that i) salt induced expression of both toxins and ii) glucose inhibited expression of estA2 [28]. Fortuitously, changes in the ETEC E24377A transcriptome, prompted by ETEC attachment to human gut epithelial cells, have also been quantified comprehensively [29]. Briefly, in these experiments, ETEC were added to sets of Caco-2 intestinal epithelial cell tissue cultures. Over a time course, ETEC that had adhered to host cells were separated from non-adhered ETEC. The transcriptomes of adhered and non-adhered ETEC were then compared. By mining these data, we next sought to determine if our model was consistent with observed changes in the transcription of crp, hns, eltA and estA during host cell attachment. Briefly, our data predict that changes in estA expression should be directly correlated to changes in the level of CRP and inversely correlated with changes in levels of H-NS. Conversely, levels of eltA expression should be inversely correlated with levels of H-NS. The result of the analysis is illustrated in Fig. 9B. The data show that the relative levels of crp transcription in attached and unattached cells are similar (orange line). However, levels of hns transcription change dramatically (green line) 60 minutes after host cell attachment. As predicted by our model, levels of estA2 and eltA transcription (dashed lines) inversely track changes hns transcript levels. When undertaking this analysis we noticed that, although there was little change in the relative level of crp mRNA between attached and unattached ETEC cells, the absolute level of crp mRNA did fluctuate across the time course of the experiment and between biological replicates. Strikingly, when these absolute mRNA levels are compared there is a clear linear relationship between crp and estA2 expression (Fig. 9C). Note that in Fig. 9C the absolute level of hns mRNA has been added in parenthesis for each data point. Remarkably, the only two outlying data points in this plot correspond to the two samples with increased hns expression. We conclude that regulation of estA2 and eltA by CRP and H-NS is important during the attachment of ETEC to human intestinal epithelial cells, and that the regulatory control of ETEC toxins is conserved across different strains.
Taken together, our data suggest that CRP and H-NS form a regulatory switch that controls ETEC toxicity. We next sought to examine the effect of disabling the switch on virulence. This is not straightforward because no animal model faithfully mimics the disease caused by ETEC in humans. However, intranasal mouse models have been used as a proxy for measuring E. coli pathogenicity [30]. Importantly, pathogenic E. coli cause more severe disease in this model than non-pathogenic strains [30]. Furthermore, ETEC strains lacking genes encoding toxins and known colonisation factors are less virulent in this model [31]. We opted to disrupt the regulatory switch by removing the crp rather than the hns gene. This was a deliberate decision since E. coli strains lacking hns are severely attenuated for growth in laboratory conditions. Conversely, the crp null derivative of ETEC H10407 was only mildly compromised for growth in liquid culture. Hence, we compared pathogenicity of ETEC H10407, and the crp derivative, using the intranasal mouse model [30]. Note that the outcome of this experiment is difficult to predict since the effects of CRP on pathogenicity likely go far beyond the control of toxin expression. However, it is reasonable to assume that ETEC virulence should differ in cells lacking crp. The median survival of mice challenged with wild type ETEC was 53 hours and the mortality rate was 100%. Conversely, the median survival of mice challenged with Δcrp ETEC was 72 h and 20% of the mice survived (Fig. 9D). Thus, whilst the full extent to which CRP co-ordinates the ETEC virulence programme remains to be determined, CRP is clearly central to the pathogenic response.
We propose that toxin expression in ETEC can be controlled by osmo-metabolic flux. This is relevant to conditions in the small intestine (osmolarity equivalent to 300 mM NaCl) disease symptoms (the extrusion of cations and cAMP into the gut lumen) and treatment (the ingestion of solutions containing glucose and salt) [7]–[11], [32]. A molecular model, describing how the different signals are integrated, is illustrated in Fig. 10. Two gene regulatory proteins, CRP and H-NS, are central to our model. Hence, H-NS directly represses the expression of eltAB, estA1 and estA2 (pathways “a” and “b” in Fig. 10). For estA2 and eltAB this repression can be relieved, in an H-NS dependent manner, by increased osmolarity. At PestA2 CRP directly activates transcription by a Class I mechanism (pathway “c”). H-NS can interfere with this process by competing with CRP for binding at PestA2 (pathway “d”). Finally, CRP can indirectly repress expression of eltAB via an unknown pathway that is influenced by H-NS (“e”). Both pathways “c” and “e” are sensitive to glucose availability because of their dependence on CRP. We speculate that pathway “e” may include H-NS since the effects of salt and sugar on eltAB expression were epistatic (Fig. 8). Our model for H-NS repression of eltAB is consistent with previous work [26]. However, our conclusion that eltAB is indirectly repressed by CRP disagrees with a previous study [27]. Even so, we were able to faithfully reproduce most of the observations previously described by Bodero and Munson [27]. We note that Bodero and Munson previously suggested that CRP may bind targets at PeltAB with a 7, rather than 6, base pair spacer between the two CRP half sites. Such CRP targets have never been described amongst hundreds of known CRP regulated promoters. Furthermore, we found no such CRP sites in our ChIP-seq analysis. Given that these DNA sequences can be deleted, without negating the effect of CRP on PeltAB activity, the regulatory effect of CRP must be indirect.
Our model for regulation of ST and LT expression is pertinent to both ETEC mediated disease and its treatment. ST and LT trigger the extrusion of H2O, cations, and cAMP (the cofactor for CRP) from the small intestine into the gut lumen [4]–[9]. Furthermore, solutions of salt and glucose are consumed by patients to reverse this process [10], [11]. We speculate that, during infection, extrusion of electrolytes and cAMP into the gut lumen could create a positive feedback loop to drive toxin expression. Importantly, our model also suggests that ORT may provide benefits beyond stimulating rehydration of the patient. The concentration of glucose used in ORT is ∼10-fold higher than is required to repress estA2 expression. Hence, even if 90% of glucose present in ORT solutions is absorbed before reaching the site of infection, sufficient glucose should be present to down regulate toxin expression. Furthermore, even though salt is able to induce expression of estA2 and eltAB, the effect is only observed at concentrations far higher than those found in ORT solutions.
Our observation that estA1 and estA2 are oppositely regulated by CRP is intriguing given the similarities between the promoter sequences of these genes. Differential regulation is dependent on the promoter -35 element (Fig. 5). At Class I CRP regulated promoters an αCTD protomer sits between CRP and domain 4 of the RNA polymerase σ subunit, which is bound to the promoter -35 element [12]. Thus, one possible explanation is that changes in the -35 element result in subtle repositioning of σ. This could result in unproductive interactions between αCTD and σ when CRP is present.
Our data indicate that several strong CRP binding sites in the H10407 genome are occluded by H-NS. This strongly suggests that the CRP regulon has evolved to incorporate additional environmental signals through the action of H-NS. The repressive effect of H-NS on transcription has been widely described [23]. H-NS represses transcription predominantly by occluding the binding of RNAP or by trapping RNAP at promoters [20]. Recently, it was shown that H-NS occludes many binding sites for the CRP homologue, FNR, in E. coli [21]. Thus, occlusion of transcription factor binding sites appears to be a major function of H-NS, especially for CRP family proteins. Note that, in order to exclude CRP from target promoters, sites of H-NS nucleation and CRP binding need not overlap precisely. For example, at both estA1 and estA2, maximal H-NS binding is observed within the coding sequence of the gene (Fig. 2C). Despite this, H-NS oligomerisation across adjacent DNA is sufficient to prevent CRP binding.
In summary, our model provides a framework for better understanding ETEC mediated disease and its treatment. Moreover, our catalogue of CRP and H-NS binding targets provide a useful community resource for further studies of all E. coli strains. In particular, our ChIP-seq data for CRP report >50 targets not identified previously in E. coli K-12 and 8 ETEC-specific targets. Finally, our data show how very small changes in the organisation of gene regulatory regions can have major effects on gene expression, such that transcription responds differently to the same environmental cues.
ETEC strain H10407 is described by Crossman et al. [1]. The C-terminal crp-3×FLAG tag was introduced into the H10407 chromosome using the recombineering method of Stringer et al. [33]. Wild type E. coli K-12 strains JCB387 and M182 have been described previously [34], [35]. The Δhns M182 derivative was generated by P1 transduction of hns::kan from E. coli K12 derivative YN3144 (a gift from Ding Jin). Plasmids pRW50 and pSR are described by Lodge et al. [36] and Kolb et al. [37]. More detailed descriptions of strains and plasmids, along with the sequences of oligonucleotides, are provided in S2 Table.
Cultures were grown to mid-log phase in M9 minimal medium with 1% (w/v) fructose at 37°C. Targeted ChIP experiments (Fig. 4 and 6) were done exactly as described by Singh and Grainger [38] using PestA2 or PeltAB fragments cloned in pRW50 carried in strain M182. The ChIP-seq was done as described extensively by Singh et al. [25] using strain H10407. Briefly, H-NS and CRP-3×FLAG were immunoprecipitated using protein A sepharose (GE Healthcare) in combination with 2 µL of anti-H-NS or 2 µl of anti-FLAG respectively. After immunoprecipitation and washing, beads were resupended in 100 µL 1× Quick Blunting Buffer (NEB) with dNTPs (as specified by the manufacturer) and 2 µL Quick Blunting Enzyme Mix, and incubated for 30 minutes at 24°C with gentle mixing. After being collected by centrifugation, the beads were again washed and the associated DNA was A-tailed by resuspension of beads in 100 µL 1× NEB buffer #2 supplemented with 2 mM dATP and 10 units of Klenow Fragment (3′→5′ exo-; NEB). Following incubation for 30 minutes at 37°C, with gentle mixing, the beads were again collected and washed. Illumina adapters (1 µl NEXTflex ChIP-seq barcoded adapters; BioO Scientific) were added to beads resuspended in 100 µL 1× Quick Ligation reaction buffer and 4 µL Quick T4 DNA Ligase (NEB), and incubated for 15 minutes at 24°C with gentle mixing. After washing the beads, the DNA was the eluted into a fresh tube by addition of 100 µL ChIP elution buffer (50 mM Tris–HCl, pH 7.5, 10 mM EDTA, 1% SDS) and incubation at 65°C for 10 minutes. The eluate was collected by centrifugation for one minute at 4000 rpm. Crosslinks were reversed by incubation for 10 minutes at 100°C. Samples were purified by phenol extraction and precipitated with ethanol, 40 µg glycogen and 8.3 mM sodium acetate. DNA was pelleted for 15 minutes at 4°C at top speed in a microcentrifuge, washed with 70% ethanol, dried and resuspended in 11 µL H2O. After quantification by PCR each library was amplified, purified and resuspended in 20 µL H2O. Libraries were the sequenced using a HiSeq 2000 sequencer (Illumina; University at Buffalo Next Generation Sequencing Core Facility). Sequence reads were aligned to non-repetitive sequences in the E. coli H10407 genome using CLC Genomics Workbench and overall coverage was determined using custom Python scripts. Sequence reads have been submitted to the EBI ArrayExpress database and can be accessed using accession number E-MTAB-2917.
ChIP-seq peaks were identified as described previously [25]. We refer to these peaks as “high stringency” peaks. A second round of peak calling was performed in which the sequence read threshold values (i.e. the minimum number of sequence reads at a given genomic position that is required for a peak to be called) was reduced by 20%. We refer to these peaks as “low stringency” peaks. MEME [39] was used to identify enriched sequence motifs in the sequences from 50 bp upstream to 50 bp downstream of the high stringency peak centres. Thus, we identified a motif closely resembling the known CRP consensus site in many of the regions surrounding high stringency ChIP-seq peaks. These CRP site sequences are included in Table 1. Those high stringency peaks for which MEME did not identify a motif were used for a second round of analysis using MEME. This also identified a motif closely resembling the known CRP consensus site. These CRP site sequences are also included in Table 1. We used MEME to identify enriched sequence motifs in the low stringency peak list. This also identified a motif closely resembling the known CRP consensus site. These CRP site sequences are also included in Table 1. “High-confidence” ChIP-seq peaks listed in Table 1 include all the high stringency peaks but only those low stringency peaks for which we identified a motif using MEME. A complete list of all peaks, including low stringency peaks for which a motif was not identified by MEME, is provided in S3 Table. In order to assess the location of CRP sites with respect to TSSs we used the targets listed in Table 1. For each target the predicted sequence from MEME was used in a BLAST search against the E. coli K-12 MG1655 genome. All but 11 CRP sites in ETEC had a single perfect match in the E. coli K-12 chromosome. For each perfect match the distance from the centre of the CRP site to all transcription start sites was calculated. Transcription start site coordinates are from Kim et al. [40] and Cho et al. [41]. Distances between −200 and +100 were selected and all other distances were discarded. Distances were then grouped in bins of 5 bp each and the most common distance bins were identified. Note that, because the position of the CRP site was transposed onto the E. coli K-12 genome, the distance between CRP sites and TSSs
The PWM describing CRP binding sites was generated using the PREDetector software package and our previous list of 68 CRP binding sites in the E. coli K-12 genome [15], [42]. Subsequent bioinformatic screens of plasmids p666 and p948 were done by importing the relevant genbank files into PREDetector and running a binding site search with a cut-off of 7 using settings that did not exclude CRP sites within genes. The “score” for each site predicted by PREDetector increases if a closer match to the PWM is found. To generate the chromosome and plasmid maps shown in Fig. 1 we used DNA plotter software [43].
Data shown in Fig. 9B–C were extracted from the publically available datasets of Kansal et al. [29] that measure changes in the ETEC E24377A transcriptome upon contact with Caco-2 intestinal epithelial cells. The data are hosted under the GEO accession code GSE40427. For each assay condition (planktonic and attached ETEC cells) we extracted the signal intensity for microarray probe sets A1527 (crp), UTI189_C1433 (hns), D4754 (eltA) and D4048 (estA). The average signal intensity was calculated and the fold change in transcription in attached compared to planctonic ETEC cells was determined for each time point. The data in Fig. 9C show a comparison of absolute signal intensities for probe sets A1527 (crp) and D4048 (estA) compared for each of the two replicates obtained at 30, 60 or 120 minutes after attachment to host cells. Signal intensities obtained after 30 minutes growth in LB medium (three replicates) are also included in this analysis.
The CRP and σ70 purification was done exactly as described previously [44], [45]. RNA polymerase core enzyme was purchased from Epicenter. RNA polymerase holoenzyme was generated by incubating the core enzyme with an equimolar concentration of σ70 at room temperature for 20 minutes prior to use. H-NS was overexpressed in T7 express cells from plasmid pJ414hns. After overexpressing H-NS, cells were collected from the culture by centrifugation and resuspended in buffer A (20 mM Tris-HCl pH 7.2, 1 mM EDTA and 10% (v/v) glycerol) containing 100 mg/ml PMSF. Cells were lysed by sonication and the sample was cleared by centrifugation. The supernatent was loaded directly onto a Heparin column (Amersham) pre-equilibrated with buffer A. A linear NaCl gradient was applied and H-NS was found to elute at approximately 500 mM NaCl. The peak fractions were pooled and diluted 3-fold with buffer A. The sample was then loaded onto an S-FF column (Amersham) pre-equilibrated with Buffer A. A NaCl gradient was applied and H-NS eluted at approximately 550 mM NaCl. The H-NS containing fractions were then dialysed against a buffer containing 20 mM Tris HCl (pH 7.2), 300 mM KCl and 10% Glycerol (v/v)for storage at −80°C.
DNA fragments for DNAse I footprinting or EMSA assays were excised from pSR by sequential digestion with HindIII and then AatII. After digestion, fragments were labelled at the HindIII end using [γ-32P]-ATP and T4 polynucleotide kinase. DNAse I footprints and EMSA experiments were then done as described by Grainger et al. [45] except that cAMP was added to reactions at a concentration of 0.2 mM. Radio-labelled DNA fragments were used at a final concentration of ∼10 nM. Note that all in vitro DNA binding reactions contained a vast excess (12.5 µg ml−1) of Herring sperm DNA as a non-specific competitor. Footprints were analysed on a 6% DNA sequencing gel (molecular dynamics). The results of all footprints and EMSA experiments were visualized using a Fuji phosphor screen and Bio-Rad Molecular Imager FX.
Transcript start sites were mapped by primer extension, as described in Lloyd et al. [46] using RNA purified from strains carrying the 92 bp PestA1 or 93 bp PestA2 fragment cloned in pRW50. The 5′ end-labelled primer D49724, which anneals downstream of the HindIII site in pRW50, was used in all experiments. Primer extension products were analysed on denaturing 6% polyacrylamide gels, calibrated with size standards, and visualized using a Fuji phosphor screen and Bio-Rad Molecular Imager FX.
The in vitro transcription experiments were performed as described previously Savery et al. [35] using the system of Kolb et al. [38]. A Qiagen maxiprep kit was used to purify supercoiled pSR plasmid carrying the different promoter inserts. This template (∼16 µg ml−1) was pre-incubated with purified CRP in buffer containing 0.2 mM cAMP, 20 mM Tris pH 7.9, 5 mM MgCl2, 500 µM DTT, 50 mM KCl, 100 µg ml−1 BSA, 200 µM ATP, 200 µM GTP, 200 µM CTP, 10 µM UTP with 5 µCi [α-32P]-UTP. The reaction was started by adding purified E. coli RNA polymerase. Labelled RNA products were analysed on a denaturing polyacrylamide gel.
β-Galactosidase assays were done using the protocol of Miller [47]. All assay values are the mean of three independent experiments with a standard deviation <10% of the mean. Cells were grown aerobically at 37°C to mid-log phase in LB medium unless stated otherwise. For all experiments investigating the effects of glucose and salt M9 minimal medium was used so that the glucose and salt concentrations could be controlled more accurately. The amount of glucose is shown as percentage w/v. The addition of “salt” refers to a 3∶1 molar ration of NaCl to KCl. We have arbitrarily described 30 mM NaCl and 10 mM KCl as being a “1%” salt solution.
Strains of ETEC were grown in Luria Broth (LB) to an OD600 of 1.0. Groups of 10 mice (8–10 week old BALB/c) were infected intranasally with approximately 1×109 colony forming units of bacteria in 100 µl of inoculums according to Byrd et al. [30]. Mice were monitored daily for 6 days post-infection for weight and morbidity.
The protocol 12-02-015IBT “Oral Immunization of Mice with Enterotoxigenic: E coli (ETEC)” has been approved by the Noble Life Sciences IACUC committee. All animal care and use procedures adhere to the guidelines set by the Public Health Service Policy, U.S. Dept. of Agriculture (USDA) and the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health.
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10.1371/journal.pgen.1002174 | Caenorhabditis briggsae Recombinant Inbred Line Genotypes Reveal Inter-Strain Incompatibility and the Evolution of Recombination | The nematode Caenorhabditis briggsae is an emerging model organism that allows evolutionary comparisons with C. elegans and exploration of its own unique biological attributes. To produce a high-resolution C. briggsae recombination map, recombinant inbred lines were generated from reciprocal crosses between two strains and genotyped at over 1,000 loci. A second set of recombinant inbred lines involving a third strain was also genotyped at lower resolution. The resulting recombination maps exhibit discrete domains of high and low recombination, as in C. elegans, indicating these are a general feature of Caenorhabditis species. The proportion of a chromosome's physical size occupied by the central, low-recombination domain is highly correlated between species. However, the C. briggsae intra-species comparison reveals striking variation in the distribution of recombination between domains. Hybrid lines made with the more divergent pair of strains also exhibit pervasive marker transmission ratio distortion, evidence of selection acting on hybrid genotypes. The strongest effect, on chromosome III, is explained by a developmental delay phenotype exhibited by some hybrid F2 animals. In addition, on chromosomes IV and V, cross direction-specific biases towards one parental genotype suggest the existence of cytonuclear epistatic interactions. These interactions are discussed in relation to surprising mitochondrial genome polymorphism in C. briggsae, evidence that the two strains diverged in allopatry, the potential for local adaptation, and the evolution of Dobzhansky-Muller incompatibilities. The genetic and genomic resources resulting from this work will support future efforts to understand inter-strain divergence as well as facilitate studies of gene function, natural variation, and the evolution of recombination in Caenorhabditis nematodes.
| The nematode Caenorhabditis briggsae is increasingly used for comparisons with its more famous relative, C. elegans. To improve genomic resources for C. briggsae, we created two sets of inbred lines derived from crosses between diverged C. briggsae strains. High-throughput genotyping of these has improved the resolution of the recombination map and genome assembly. It also allows detailed comparisons of recombination both within and between species. Unexpectedly, we found that alleles from one parental strain were much more likely to be fixed on three of the six chromosomes in one of the sets of lines. One of these biases is caused by a pronounced developmental delay in F2 progeny that is seen in both reciprocal crosses, whereas the other two manifest in only one of the two cross directions. This indicates that the parental strains have diverged in both nuclear and nuclear-cytoplasmic interactions, either because of local adaptation or restricted gene flow across much of the genome.
| Caenorhabditis nematodes, first described over one hundred years ago [1], are easily cultured and have been employed since the 1960s as model organisms in a number of fields. C. briggsae exhibits many features desirable of a genetic model organism: a self-fertilizing hermaphrodite, presence of rare males for genetic crosses, and broods of hundreds that reach sexual maturity in a few days [2]. Sydney Brenner initially touted C. briggsae as the model system of choice for studying the genetic basis of cellular development, although he eventually championed the now-famous C. elegans [3], [4]. The many similarities between C. briggsae and C. elegans [5] led to confusion as to which strains belonged to which species until 1977 [6], and it seems C. briggsae could easily have been the more widely-studied species today.
More recent reports have revealed key ways in which C. briggsae differs from C. elegans. For example, genetic and phylogenetic studies have demonstrated that C. elegans and C. briggsae independently evolved self-fertile hermaphroditism by means of distinct genetic mechanisms [7]–[11]. Surprising differences also exist in their early embryonic patterning [12] and anatomy of the excretory system [13], [14].
C. elegans and C. briggsae also differ in their phylogeography. Global sampling of natural isolates suggests near-panmixia among C. elegans populations [15]–[24], while strong latitudinal population structure exists in C. briggsae [17], [25]–[28]. Thus, while sharing reproductive mode and cosmopolitan distribution, C. elegans and C. briggsae appear to migrate and interbreed at different rates, and as a result have differing levels of species-wide genetic variation [18], [26]. Despite its minimal population structure, however, C. elegans harbors a polymorphic (and potentially selfish) incompatibility locus that causes hybrid lethality [29]. Evidence of outbreeding depression in C. briggsae has also been noted [17], though its genetic structure is unknown.
The greater genetic and phenotypic variation in C. briggsae makes it useful for mapping loci affecting various traits, such as male tail development, vulva cell fate, and fecundity [17], [27], [30]–[33], and refutes an early criticism of Caenorhabditis “that the animal has few morphological and behavioral traits” [4]. Some of these studies sought to identify ecological correlates of phylogeography, such as temperature, that might explain the diversity exhibited among C. briggsae strains. However, no such correlations between geography, genotype, and phenotype have been made for C. elegans, and they might not exist [34], [35]. Thus, C. briggsae can be both a critical companion species for comparative analysis with C. elegans and also a potentially better choice for studies investigating the genetic architecture of ecological adaptation in nature. Both of these roles demonstrate the value of continued development of C. briggsae as a model system.
Research on C. briggsae has enjoyed a recent surge in popularity [e.g. 8], [17], [26], [31], [36] since its genome was sequenced [37]. The last decade has seen improvement of the genetic and genomic research tools available [37]–[40], but they still lag behind those for C. elegans. Initially motivated by a desire to improve C. briggsae as a genetic system, we produced and genotyped advanced-intercross recombinant inbred lines. Such cross designs have been employed in other species [15], [41]–[43] and are particularly useful for expanding genetic maps [44]. Such an improved map allows precise comparisons of recombination landscapes for homologous chromosomes. C. briggsae is similar to C. elegans in a number of genetic and population genetic characteristics (e.g. low effective population size [24], [28], frequent self-fertilization, equivalent genome size [37], and strong crossover interference [38]). This raises the possibility that variation in recombination rate might contribute to their different levels of DNA polymorphism [18], [26]. Previous studies suggest that a general chromosome-wide pattern of recombination rate domains is conserved between the two species [15], [38]. However, the low resolution and sparse density of genetic markers in the previous C. briggsae genetic map diminish the accuracy of such a comparison. Intra-species variation of recombination rates among wild-type strains has been examined in C. elegans [15], [45]; a comparison of intra-species (C. briggsae) and inter-species (C. elegans – C. briggsae) recombination profiles might reveal how recombination rates evolve over timescales as small as hundreds of thousands of years.
The stereotyped and discrete domains of recombination common to Caenorhabditis [15], [38] also aid identification of correlates of change in recombination rate. For example, inversions alter recombination when heterozygous, often suppressing (but not always absolutely) recombination within them [46]–[50] and increasing it around them [51]. Such rearrangements are also thought to contribute to adaptation and speciation [52]–[56]. A comparison of intraspecific genetic maps could clarify the relationship between inversions, adaptation and speciation in different populations.
In this study, we produced and genotyped two sets of C. briggsae recombinant inbred lines (RIL). One set was generated from the strains AF16 and HK104 using an advanced-intercross design (AI-RIL; Figure 1). Roughly half of these AI-RIL were established in one cross direction (AF16×HK104, where the first strain listed provides the male, by convention) and half in the other (HK104×AF16). [Note: when discussing both subsets of AI-RIL without respect to cross polarity, the notation “AF16/HK104” will be used]. The second set of RIL was generated from the strains AF16 and VT847 using an F2 cross scheme. The linkage maps derived from these two sets of RIL are suited for revealing differences in relative recombination rates. We also used the sets of RIL to detect selection occurring on hybrid genotypes and to identify inter-strain genetic incompatibilities, revealing the potential utility of C. briggsae for studying the process of incipient speciation in a highly selfing species.
The first-generation C. briggsae genetic map was produced by RIL generated by the selfing of F2 founders [38]. C. elegans chromosomes generally experience one recombination event per meiosis [57]. Assuming that C. briggsae is similar, F2 RIL contain few recombination breakpoints per chromosome, limiting their utility for making genetic maps [38]. We therefore created a set of advanced-intercross recombinant inbred lines (AI-RIL) for C. briggsae in order to improve the genetic map. We used six generations of mating prior to ten generations of selfing to decrease the size of haplotype blocks in the AI-RIL (Figure 1). The parental strains were C. briggsae AF16, the standard laboratory strain from India whose genome has been sequenced [37], and HK104, a divergent Japanese strain already used for SNP discovery and mapping [7], [16], [39], [58]. AF16 and HK104 are members of distinct tropical and temperate clades of C. briggsae [28], respectively, that diverged roughly 90,000 years ago [26].
180 AI-RIL and the parental strains were genotyped at 1,536 single nucleotide polymorphism (SNP) markers. 167 AI-RIL and 1,032 SNP markers passed quality control thresholds and inspections (Materials and Methods), resulting in 172,344 genotype calls for the AI-RIL (Table S1). After exclusion of lines apparently heterozygous at many markers (Materials and Methods), only three heterozygous genotype calls remain in the final genotype data set. The remaining genotypes were homozygous for one of the parental strains (67,286 AF16/AF16; 105,055 HK104/HK104). Homozygosity of the parental strains at each marker was confirmed directly (Table S1).
89 F2 RIL were produced by repeatedly selfing the offspring of VT847×AF16 F1 hybrids. VT847 is a C. briggsae isolate from Hawaii [30], part of the same clade of tropical isolates as AF16 [17]. These RIL were genotyped at the same 1,536 SNPs. Mostly because many of these SNPs are monomorphic between the parental strains, only 209 markers passed quality control. Again, the vast majority of genotype calls were homozygous for one of the parental strains (9,344 AF16/AF16; 9,184 VT847/VT847); 50 calls were heterozygous (Table S1, but see Materials and Methods). 132 markers were successfully genotyped in both sets of RIL.
Genetic maps of the five autosomes and X chromosome comprising the nuclear genome were estimated de novo from the final AF16/HK104 AI-RIL SNP genotype data set. Marker compositions and lengths of the maps are given in Table 1. The expanded AI-RIL genetic maps for autosomes range from 148.6 to 173.2 centimorgans (cM) in cumulative length; the X chromosome map length is 100.0 cM. The new C. briggsae genome assembly (see below) inferred from the genetic map allowed us to plot the recombination rate as a function of physical position (Marey maps; [59], Figure 2). This reveals the presence on each chromosome of small tip domains and larger central domains that host less recombination compared to the chromosome “arm” domains (Caenorhabditis chromosomes are holocentric [60]). As previously found in C. elegans and C. briggsae, the X chromosome domain boundaries are qualitatively less evident than those of the autosomes [15], [38].
Of the 1,031 C. briggsae SNPs used to produce chromosome assemblies (one marker was genetically mapped but not used in the chromosome assemblies), only 443 genetic intervals are defined, owing to the complete linkage of a number of SNPs. The average size of an interval is 101.3 kbp, with median size 43.8 kbp and maximum of 1.45 Mbp. The average marker spacing is 2.1 cM, with median spacing 1.2 cM and a maximum of 18.7 cM. We note that these values represent cumulative genetic distance defined for the AI-RIL, not per-meiosis distances. Normalizing each linkage group to the expected per-meiosis map length of 50 cM, the average marker spacing becomes 0.6 cM. The genotypes of the VT847×AF16 RIL were also used to estimate de novo genetic maps; the genetic positions of markers and the genotypes of the RIL are given in Table S1. The estimated genetic maps for autosomes range in length from 82.1–110.6 cM; the X map is 43.0 cM.
The number of autosomal recombination breakpoints captured by the C. briggsae AF16/HK104 AI-RIL constructed for this study ranged from zero to six with an average of 1.59 (Table 2), less than might be expected given the cross design. Nevertheless, in the AI-RIL, autosomes exhibit almost twice as many evident recombination events compared to our F2 RIL and to the F2 RIL used to create the previous C. briggsae genetic map version [38] (Table 2). The AI-RIL and F2 RIL reported here also almost double the observed number of recombination events on the X chromosome.
The 1,032 genetically mapped markers represent a four-fold increase in the number of markers used to produce C. briggsae genome assembly version cb3 [38] (Table 1). Combined with the increased number of recombination breakpoints afforded by the AI-RIL, the new genetic map facilitated the incorporation of unplaced sequence supercontigs, orientation of previously unoriented supercontigs, and identification and resolution of some existing assembly errors. Table 1 provides statistics on the new assembly, version cb4. Most notably, we have confidently ordered an additional 14 Mbp of sequence (13% of the genome), representing a 2.5-fold reduction in the amount of sequence unassigned to chromosomes and a 34-fold reduction in the amount of sequence unable to be ordered within chromosome assemblies. Importantly, 1.8 Mbp of sequence contained on 15 supercontigs has changed chromosomal assignment from cb3 to cb4. We also orient sequence contigs comprising 21 Mbp (20% of the genome). Additional details of the assembly are available in Text S1.
With an improved genome assembly, we re-evaluated the extent of chromosomal synteny between C. elegans and C. briggsae using a genome-wide plot of nucleotide conservation. By identifying only maximal unique matches (MUMs) in each comparison sequence, orthologous coding regions are predominantly identified (Figure 3, by comparison with plots of MUMs using translated nucleotide sequence, not shown). Extensive matches exist in the self-diagonal (comparisons between homologous chromosomes of C. elegans and C. briggsae), but relatively few off-diagonal (interchromosomal) MUMs are apparent. The center domains of the autosomes have extensive colinearity in MUMs, while synteny in the arms is much less apparent. Although syntenic blocks on the X are larger and comprise a larger proportion of the chromosome than on autosomes, the order of blocks on the X nevertheless differs between the species.
While interspecies inversions and translocations are evident in these chromosomal plots, the presence and extent of polymorphic inversions among C. briggsae strains is unknown. By comparing our AF16/HK104 AI-RIL linkage maps with the VT847×AF16 F2 RIL linkage maps, we sought evidence for such inversions. Because heterozygous inversions present in hybrids should suppress recombination [46], inversions are expected to manifest genetically as blocks of markers that are recombinant with each other in one linkage map and nonrecombinant in the other. For all 132 SNPs common to both genetic maps, we ordered the SNPs based on physical assembly position and then identified blocks of markers that exhibit this genetic signature of inversion (Table S2).
Twenty-one blocks of markers are nonrecombinant in the F2 RIL but resolved in the AI-RIL; in the AI-RIL, four nonrecombinant blocks are resolved in the F2 RIL. Most of the former are expected due to the overall shorter F2 RIL map, whereas the latter might be enriched for true recombination suppressors. For example, ChrIV markers cbv19538 and cb58228 acted as a point in the AI-RIL genetic map, but were 1 cM apart in the F2 RIL map of ChrIV normalized to 50 cM. These markers reside in high recombination arm B of ChrIV, where the normalized breakpoint density in the AI-RIL map is 5.66 breakpoints/cM. We thus expect to see 5.03 breakpoints between them, averaged over the 89 F2 RIL. Assuming that the breakpoints are Poisson-distributed with an expected value of 5.03, the observed value of zero is significantly different (p = 0.006). When Bonferroni-corrected for multiple tests, the genetic distance between these markers in the F2 RIL remains significant (p<0.05).
We estimated the physical and genetic size and recombination rate of each domain (Table 3). To allow comparisons between maps of different overall lengths, the recombination rates in the AI-RIL were normalized by adjusting the map length of each chromosome to the expected per-meiosis length of 50 cM (see Materials and Methods). Low synteny in the chromosome arms (Figure 3) precludes meaningful direct comparisons of arms between species. We therefore refer to the arms of C. briggsae chromosomes as “A” and “B” rather than “L” (left) and “R” (right) to prevent inappropriate inference of homology, and we compare arm attributes between C. briggsae and C. elegans using ratios of lengths and rates from one arm to the other. The homology of center domains is not ambiguous, so their values can be compared directly.
The center domains occupy more than a third of the physical length of each autosome (Table 3). However, they are relatively smaller in C. briggsae (comprising 40–46% of the total chromosome length in C. briggsae vs. 47–52% in C. elegans [15]). On the X chromosome in both species, the center domain occupies closer to a third of the chromosome length. Compared to their physical lengths, the genetic lengths of central domains are short compared to the arms in both species (but they still exhibit variation, e.g. ChrI, ChrV). Tip domains tend to occupy larger proportions of the chromosome length in C. elegans than C. briggsae. The absence of tip domains on the B arms of C. briggsae ChrII and ChrV could represent real diversity or be due to poor marker coverage in those regions.
The ratios of arm physical sizes are similar, ranging from 1.11–1.59 in C. briggsae and 1.12–1.77 in C. elegans (Table 3). However, arm genetic lengths vary more between species. For example, the ratio of genetic lengths of the two ChrII arms is 1.45 in C. briggsae, but 1.06 in C. elegans. Strikingly, genetic and physical length ratios do not always correlate. C. briggsae ChrIV arms have the largest asymmetry in physical length (1.59-fold) but the smallest in genetic length (1.17-fold). The opposite pattern is seen in C. elegans, whose ChrIV arms have a physical length ratio of 1.18 but a genetic length ratio of 1.82. Arm ratios for the X are similar between the two species.
Chromosomal attributes that dictate the sizes or boundaries of recombination domains are expected to co-vary in the two species. To identify candidate attributes, we compared three characteristics of homologous C. elegans and C. briggsae center domains: their genetic length, physical length and proportion of the chromosome physical length. We also examined the degree of asymmetry in arm pairs as measured by the ratios of their genetic and physical lengths. Of these, the fraction of the total physical chromosome length occupied by a given central domain in one species was the most predictive of the state for the homolog in the other (R2 = 0.8253).
To identify variation in the recombination domains on a shorter time scale, we compared their characteristics in the AF16/HK104 AI-RIL and VT847×AF16 F2 recombination maps (Figure 4). As the low marker density of the F2 VT847-based map precludes precise de novo determination of recombination domain boundaries, we used the boundaries determined for the AI-RIL for both maps (visual inspection of the F2 RIL Marey maps, Figure 4, indicates this is reasonable). The comparison reveals two ways in which apparent recombination rates vary across a given chromosome (Figure 4). First, while the genetic lengths of the two arm domains of a given autosome are generally symmetrical in the AF16/HK104 map (fold-change range 1.17–1.72), observed recombination is often heavily biased to one arm in the VT847×AF16 maps (fold-change range 1.41–7.09). Second, the genetic lengths of the center domains can differ between AF16/HK104 and VT847×AF16 (for ChrIII and ChrIV, over two-fold). Thus, the Marey map curves visibly differ in the two maps for ChrI, ChrIII, ChrIV, and ChrV.
In the crossing scheme used to produce the AI-RIL, each parental strain is expected to contribute half of the alleles at any autosomal locus; for ChrX, two-thirds of lines are expected to fix the allele of the hermaphrodite parent in the original cross (Figure 1). Deviation from the neutral-expected allele fraction value is called marker transmission ratio distortion (MTRD) and can indicate the action of selection on specific hybrid genotypes. We plotted the relationship between the proportion of lines fixed for the HK104 allele and the physical position of each marker in order to identify departures from the neutral expectations (Figure 2). For ChrI, ChrII and ChrX, in neither cross direction does allele fraction significantly deviate from expected. However, for markers on the remaining autosomes, significant MTRD towards the HK104 parental allele was common. On ChrIV and ChrV, significant departure from the expected allele fraction value occurred only in one cross direction. On ChrIV, the AF16×HK104 AI-RIL were biased (maximum allele fraction = 0.81; 7.3 Mbp significantly biased); on ChrV, the HK104×AF16 AI-RIL were biased (maximum allele fraction = 0.81; 7.4 Mbp significantly biased). We hypothesized that epistatic genetic interactions between one or more loci in the central recombination domains of ChrIV or ChrV and a factor dictated by cross direction produces the observed MTRD. To directly test for cross direction effects, we compared allele fractions between the crosses in these regions. For ChrV, the allele fraction values of three adjacent markers (Figure 2, asterisk) were significantly different (p<0.05 after Bonferroni correction), while no ChrIV markers met this standard.
The most extreme MTRD was on ChrIII. The majority of ChrIII markers were biased toward the HK104 allele in both cross directions (maximum allele fraction = 0.87; AF16×HK104: 8.2 Mbp and HK104×AF16: 7.6 Mbp significantly biased). Despite the MTRD, at no marker was the AF16 allele completely absent from the AI-RIL set. Line PB1149, which had the fewest number of AF16/AF16 calls (137 of 1,032), exhibits only six recombination breakpoints and is fixed for HK104 across all of ChrI, ChrII and ChrIII.
During production of the AI-RIL, we noticed that approximately 20% of F2 hybrids from crosses between AF16 and HK104 exhibit a pronounced developmental delay (Figure 5A and 5B; [17]). These delayed F2 take approximately four days to reach sexual maturity at 20°C, whereas P0s, F1s and most F2s reach sexual maturity in approximately three days. The delayed development of these F2s was associated with homozygosity for AF16 alleles in the central domain of ChrIII (Figure 5C–5F), consistent with the under-representation of AF16 alleles on ChrIII in the AI-RIL. The delay phenotype is reproducible in crosses between AF16 and HK104, but was not observed in VT847×AF16 F2 individuals during production of the F2 RIL (not shown). Furthermore, while a bias against AF16 alleles can be seen in the ChrIII genotypes of AF16×HK104 F2 RIL [38] (Figure 5G), no such bias is evident in the VT847×AF16 F2 RIL (Figure 5H).
Characterization of interchromosomal linkage disequilibrium (LD) in the lines could identify co-adapted loci that might affect hybrid fitness, enhance the utility of the AI-RIL, and determine whether X-autosome epistatic interactions explain the cross direction-specific MTRD for ChrIV and ChrV described above. D′, a measure of LD that ranges from zero to one and normalizes D for overall allele frequencies [61], was employed as the metric here (Figure 6). Very few regions of high interchromosomal D′ values common to both cross directions were observed in this analysis. However, discrete blocks of high D′ present only in one cross direction are seen, including a block containing markers with interchromosomal D′ = 1. In this case, in the AF16×HK104 cross, AI-RIL whose genotypes are AF16/AF16 at cb22151 (ChrIII) are never also AF16/AF16 at cb4013 (ChrIV). However, D′ is calculated under the assumption of Hardy-Weinberg equilibrium, which might not be appropriate for inbred lines. Indeed, this correlation is not significant (chi-square, p = 0.058), most likely due to the strong HK104-biased allele frequencies of the AI-RIL set. Similarly, in the opposite cross direction, no gametic class frequencies are significantly different from expected based on the allele frequencies at these markers (chi-square, p = 0.773). It is nevertheless interesting to note that the same block of ChrIII markers interacts with a small region of ChrV in one cross direction and with ChrIV in the other (Figure 6). These three blocks on ChrIII, ChrIV and ChrV overlap with (but are much smaller than) regions of significant MTRD (the blocks are identified by shading in Figure 2).
The previous C. briggsae genetic map was based on SNP genotyping of F2 RIL [38]. Because Caenorhabditis chromosomes generally experience one crossover event per meiosis [57], these RIL have very large haplotype blocks. While this did not hinder assignment of sequence supercontigs to linkage groups, it often prevented the supercontigs from being ordered and oriented within a chromosome [38]. The five additional generations of mating beyond F2 used to produce the AI-RIL (Figure 1) expanded the genetic map to 928.6 centimorgans total length, a 1.57-fold increase compared to the cb3 genetic map. In addition, we were able to substantially increase the map's resolution by more than tripling the number of scored SNPs (1,032) in almost twice (167) the number of inbred lines (Table 1). Our AI-RIL genetic map compares favorably with other contemporary maps in marker number (1,032) and density (0.6 cM average spacing when normalized to a 50 cM map length). Those recently estimated in the genera Bombyx, Apis, Nasonia, and Brassica contain between 1,000 and 2,000 markers, producing 0.3–2.05 cM average marker spacing [62]–[65].
Our map did not match the quality of the C. elegans AI-RIL-based genetic map [15], however. This map captured 3,629 recombination breakpoints over 1,588 cM, while our AI-RIL captured 1,494 breakpoints. Four explanations might account for this difference. First, our cross design did not achieve the maximum potential of an AI-RIL design because exchange of worms between the pools of intercrossing worms was not performed as in [15]. Second, we genotyped fewer lines (167 compared to 236). Third, pervasive selection against AF16 alleles that occurs over much of the genome in the AI-RIL might have caused rapid reduction of heterozygosity during line construction prior to inbreeding, resulting in fewer observable recombination breakpoints. Finally, any contribution of self-progeny to the mating pools during the sib-mating phase of line construction, for example matings of male cross-progeny with hermaphrodite self-progeny, would reduce the map length. Although several lines contained one or more chromosomes with no apparent recombination breakpoints, none lack AF16 alleles completely. We can thus be certain that no lines were inadvertently established wholly from self-fertilization. Despite these potential issues, our AI-RIL cross scheme was successful at improving the resolution of the C. briggsae genetic map length compared to the previous F2 RIL-based version, capturing approximately twice the number of recombination breakpoints (Table 2).
Because the X chromosome is hemizygous in males during outcrossing (Figure 1), its map length in our design is expected to be 2/3 the length of the autosomal maps. Indeed, the expanded AI-RIL X map length, 100.0 cM, is similar to the expected value of 110.5. Unexpectedly, however, significantly fewer than expected SNPs were genotyped on the X. Although we cannot rule out the possibility that the C. briggsae X chromosome has reduced SNP density compared to autosomes, the method by which SNPs were chosen for genotyping is the most likely cause (Materials and Methods). Because only two markers are required both to order and to orient each supercontig within a chromosome assembly, chromosomes with larger supercontigs would have had fewer total SNPs genotyped per unit of length. Indeed, supercontigs assigned to ChrX are significantly larger on average than autosomal ones (t test, P = 0.02448; Figure S1), a possibility that had been noted earlier [38].
The increased marker coverage of our genetic map allowed the incorporation of previously-unassembled genomic sequence supercontigs into the chromosomal assemblies and facilitated the genetic orientation of many supercontigs that were previously not oriented. Additionally, inconsistencies between the cb3 assembly [38] and the cb25 physical map [37], as well as three previously reported issues with cb3, have been resolved (Text S1).
The C. briggsae genome assembly is more complete than some recently-sequenced insect genomes, such as for Nasonia vitripennis [66], whose genome assembly comprises 63.6% of 312 Mbp of sequence based on a genetic map with more markers (1,255) but greater average inter-marker physical distance (249 kbp) [65]. The cb4 assembly now surpasses the Drosophila melanogaster genome assembly in completeness as well (version R5.33, flybase release FB2011_01 [67]). While 13.4% of the D. melanogaster genome sequence is unordered (half comprising unordered sequence from heterochromatic regions), the unordered content of C. briggsae has decreased from 15.9% (cb3) to 3% (cb4). However, compared to C. elegans, whose genome assembly is truly complete (i.e. containing no unordered sequence contigs, no gaps, and no uncalled bases), much work remains to complete the assembly of C. briggsae. The absence of heterochromatic centromeres and heteromorphic sex chromosome likely accounts for the relatively high quality of the Caenorhabditis assemblies.
Inter-species variation in recombination rate has been described in other taxa. In Helianthus, most intervals tested exhibited rate variation between species 0.75–1 million years (MY) diverged [68]. Variation among some Drosophila species also exists [69], but fine-scale recombination rates do not differ between others, suggesting lineage-specific and/or scale-dependent recombination rate variation [70]. Comparison of the C. elegans [15] and C. briggsae AI-RIL genetic maps reveals both conservation and variation in physical and genetic lengths of some recombination domains (Table 3). In both species, chromosome arms are clearly distinct domains that experience the vast majority of recombination events, and the distributions of arm recombination rates overlap, ranging from ∼2.5–8 cM/Mbp for autosomes. C. elegans arms tend to have slightly higher rates than C. briggsae, but C. elegans chromosomes also tend to be slightly smaller, so the elevated recombination rates likely reflect the necessity of fitting obligate recombination events into a shorter physical space.
Poor local synteny in the arm domains (Figure 3) prevented their direct comparison between species. We therefore compared the ratios of attributes for the two arms of a given homologous chromosome, assuming that aspects of the domains might be conserved despite mixing of the sequence content. For the AI-RIL-based genetic maps of both species, the ratios of arm physical or genetic lengths only exceeded two in one case, for the arm genetic lengths of C. elegans ChrI. The ratio of recombination rates of arms also occupied the same range, only once exceeding two (C. elegans ChrIV). However, this similarity should be interpreted carefully given the extent of intraspecies variation discussed below.
An additional caveat to the interpretation of the genetic parameters (map length and recombination rate) of the domains is that the values reported (Table 3) do not reflect recombination alone. Homozygosity resulting from selection acting on an allele during RIL construction would prevent the detection of future recombination events occurring in the domain and cause a deviation in the fixation of parental alleles in regions under selection. Evidence of such selection exists for chromosomes in C. elegans [15], [29]. In our C. briggsae AI-RIL, MTRD on ChrIII, ChrIV and ChrV also likely signifies the action of selection (discussed below). The regions experiencing MTRD are broad (Figure 2), but the arm whose allele fraction comes closest to the neutral expected value (IIIA, IVB, VB) is always genetically longer than the opposite arm. This matches the prediction that MTRD, possibly due to selection, results in a decrease in apparent recombination breakpoints and thus a reduction in genetic map length over part of a chromosome. In sum, each autosome exhibits a signature of selection, MTRD, in one of the two species. For this reason, the genetic values reported in Table 3 (both genetic length and recombination rate) might not represent the neutral recombination rate, especially for C. elegans ChrI and ChrII and for C. briggsae ChrIII, ChrIV and ChrV.
In contrast to map lengths, comparisons of physical attributes do not suffer from the influence of selection. The low recombination center domains, which have maintained greater synteny (Figure 3) over the roughly 18 MY since the common ancestor of C. elegans and C. briggsae [71], also revealed some size variation. Our findings concur with those from C. elegans, that the center domains are not precisely centered physically on the chromosome [15]. We find that, of the domain features tested, the proportion of total chromosome physical length occupied by the center domain is the most correlated between the species, suggesting that some aspect of relative physical position on the chromosome influences the positions of the center/arm domain boundaries.
Work in a number of taxa has shown that recombination rates can vary within a species. A recent study of the evolution of recombination rates within mice found evidence for widespread rate differences among members of the species complex across 19% of the genome [72]. A remarkable seven-fold difference in recombination fraction within a Drosophila species has been revealed [69], and a detailed study of maps from intraspecific crosses in Nasonia revealed a slight (1.8%) but statistically significant increase in recombination frequency compared with interspecific crosses on a genome-wide scale [73]. Our findings from C. briggsae fall in the middle of this range, with the apparent recombination rates in homologous arm domains varying up to 2.9-fold between the crosses.
Our AI-RIL and F2 RIL paired parental strains between and within, respectively, C. briggsae clades that are estimated to have diverged about 90,000 years ago [26]. Examination of Figure 4 reveals that, for some chromosomes (ChrII and ChrX), the genetic lengths of both center and arm domains are constant. In addition, for each chromosome, the arm with the larger AF16/HK104 genetic map length is always the genetically larger arm in the VT847×AF16 map. However, substantial divergence in the genetic lengths of both the center domains (ChrIII and ChrIV) and arm domains (ChrI, ChrIV and ChrV) exists. The most striking feature of the genetic map comparison is the divergence in arm length ratio for multiple autosomes in the VT847×AF16 F2 RIL (Figure 4). Taken at face value, these results suggest that recombination itself is unusually biased to one arm in this cross, but alternative explanations should be considered. For example, we did not quantitatively compare our VT847×AF16 F2 RIL Marey maps to those previously reported for AF16×HK104 F2 RIL [38] because of the many differences in genome assemblies and markers scored in the two studies. Instead, we used our AF16/HK104 AI-RIL maps for the inter-strain comparison. However, both AF16/HK104 maps exhibit symmetrical arm usage, and generally resemble each other (except for total genetic length) more than either resembles the VT847×AF16 F2 RIL map. This suggests that intra-species differences are not caused by an artifact related to comparison of different cross designs.
Strong selection against individual loci or recombinant haplotypes could also account for asymmetrical apparent recombination rates in the two arms. However, evidence for both of these is lacking for the chromosomes that have arm genetic length ratios >2 (ChrI, ChrIV, and ChrV; Figure 4). First, the strong effect of genetic drift in the F2 RIL implies that any hypothetical deleterious recombinant genotypes would have to be severely debilitating to strongly bias breakpoint capture to one arm, yet no class of morbid progeny was observed during line construction. Also, no strong MTRD is evident in the F2 RIL (Figure S2), suggesting an absence of selection on individual loci. We therefore conclude that real differences in recombination are the most likely explanation for the asymmetric arm breakpoint capture in the VT847×AF16 F2 RIL. This suggests that recombination rate can vary over short periods of time but does not necessarily correlate with genomic divergence.
Greater variation in broad-scale recombination rate within rather than between species has also been observed in Nasonia [73]. The diversity in rate among populations of C. briggsae was unexpected, particularly given the similarities in the above interspecies comparisons and previous assertions that the overall similarity of recombination pattern among species likely reflects conservation [25]. Our results suggest that although the physical sizes of high and low recombination domains are stable within C. briggsae, variation in the degree of bias in usage of one arm over another exists. Comparisons with more genetic maps from other C. briggsae and C. elegans strains will likely reveal more diversity and patterns relevant to the understanding of the forces shaping the evolution of recombination rate.
The comparison of C. briggsae genetic maps also revealed three blocks of markers with inverted genetic order relative to flanking markers in one cross (Table S2). Because the AI-RIL and F2 RIL genetic maps share one parental strain, a physical difference in marker order in one of the strains, for example by physical inversion, would not be expected to produce this genetic effect. Possible explanations for this discrepancy include multiple recombination events that accumulated in a small physical interval and resulted in inaccurate estimations of genetic positions, or unappreciated copy number variation that created genotyping artifacts. However, a similar local reversal of marker order was observed in a study describing the behavior of genetic markers associated with polymorphic inversions in Anopheles gambiae [49].
The stereotyped recombination domains for each linkage group have stimulated investigations into factors that might dictate their boundaries. Repeat density correlates with the domain structure [38] and is also associated with recombination rate differences in other species [72]. Likewise, inspection of Figure 3 suggests that many recombination domain boundaries are associated with loss of synteny. This finding suggests that local signals direct the locations of boundaries [15]. However, for both repeat content and synteny, it remains unclear whether these are causes or consequences of domain differences.
The molecular basis of the distribution of meiotic crossovers is only beginning to be understood. In C. elegans, DPY-28 acts in a classical condensin I complex to regulate the number and distribution of crossover events [74], [75]. In addition, loss of the chromatin protein XND-1 inverts the typical crossover distribution so that recombination occurs more frequently in the centers of chromosomes than in the arms [76]. Histone modifications on the arm and center domains are also distinct [77], suggesting an interplay between nucleosomes, condensins, and recombination in Caenorhabditis.
C. elegans chromosomes contain pairing centers: regions that promote homolog pairing and synapsis [78]. It has been suggested that these features might themselves have a cis effect on the distribution of recombination events, although their genetic locations in C. elegans do not perfectly correlate with recombination domain features [15]. Pairing centers might promote recombination in their vicinity, but this hypothesis cannot yet be tested in C. briggsae because no pairing centers have been characterized. Site-specific, perhaps cis-acting, segregating recombination rate modifiers, as are thought to exist in C. elegans [15] and mice [72], might also be responsible for observed variation. This might explain why variation in the extent of arm recombination asymmetry in the F2 RIL is restricted to a subset of chromosomes (Figure 4).
An earlier comparison of the C. elegans genome with C. briggsae assembly cb3, based on the positions of orthologous genes, revealed that the vast majority of rearrangements during divergence of these species were intrachromosomal and that syntenic blocks are larger on the X than on autosomes and also larger in center domains than on arms [38]. Our comparison using the cb4 assembly (Figure 3) qualitatively agrees with these previous findings. Specifically, syntenic blocks are longer in the low-recombining chromosome centers and are reduced or absent on the arms; the X chromosome exhibits the most structural similarity between the species. The relatively few off-diagonal sequence alignments (Figure 3) confirm the rarity of interchromosomal gene movement. We find no evidence of large interchromosomal translocations, although sequence divergence between C. elegans and C. briggsae might have obscured some that did occur.
Although the ortholog content of chromosomes is generally conserved (Figure 3, [38]), inter-arm movement has greatly eroded arm synteny between C. elegans and C. briggsae. Even the better-conserved center domains of chromosomes lack strict co-linearity. As a result, the relative orientation of the genetic and sequence maps of C. elegans and C. briggsae is basically arbitrary (Figure 3), especially for ChrII and ChrIII. The similarity of the recombination profiles of the chromosomes is therefore quite striking, reinforcing the impression that something other than gene content dictates the positions of recombination domain boundaries.
The comparison of two distinct C. briggsae genetic maps allowed us to ask whether the genetic signature of inversions exists. The strongest candidate region, within the B arm of ChrIV, provides the first genetic evidence of inversions distinguishing strains of C. briggsae. In this case, we conclude that an inversion of at most 666 kbp in HK104 relative to AF16 and VT847 likely exists. Given the hundreds of presumed translocations and/or inversions evident from the C. elegans and C. briggsae comparison (Figure 3) and the approximately 18 MY of divergence between the species [71], it is reasonable that a rearrangement distinguishing strains occurred during the divergence between the temperate and tropical clades of C. briggsae. The spacing of markers common to both the AI-RIL and F2 RIL genetic maps suggests that inversions up to 1 Mbp in size would often be undetectable in our analysis (particularly on the X chromosome). As in mice [72], it is possible that inversions unique to one strain or species are responsible for some of the recombination rate variation evident within and between species.
Large regions on ChrIII, ChrIV and ChrV in the AI-RIL preferentially fixed HK104 alleles to a degree not explained by sampling error alone (Figure 2), and nearly two-thirds of all AI-RIL marker genotypes are homozygous for the HK104 allele. Unintentional selection operating on hybrid genotypes during the intercross phase of RIL production is the most likely explanation for this widespread bias. In principle, selection could begin to cause MTRD as early as the F1 generation if a heterozygote-by-cross direction effect exists, but is not a factor here because there was no competition between cross directions during line production. More relevant here, selection on hybrid genotypes starting in the F2 generation would bias the transmission of parental alleles. We provide corroborating evidence for such F2 selection against AF16 alleles on ChrIII.
A modest bias of ChrIII toward HK104 was also evident in AF16×HK104 F2 RIL (Figure 5G) [38], presumably due to the acute developmental delay described here, but no MTRD was observed on ChrIV or ChrV. Our study should be more sensitive to incompatibilities because recombinant genotypes had substantial opportunity to compete against each other, whereas for the F2 RIL individual F2 were isolated immediately. This would be expected to allow genetic drift to dominate over all but the most severe fitness effects, such as that on ChrIII. Additionally, the AI-RIL cross scheme produced smaller haplotype blocks, perhaps separating co-adapted complexes of linked genes and creating more maladapted combinations of alleles than in F2 RIL. The difference in cross schemes might also explain the higher extinction rate of AI-RIL lines compared to the F2 RIL (59 of 240 vs. 1 of 112 lines).
Selection against a subset of hybrid genotypes is commonly ascribed to the presence of Dobzhansky-Muller incompatibilities (DMI) that arise when loci diverge in two strains experiencing reduced gene flow between them [79]–[81]. MTRD in hybrid Caenorhabditis genomes might also occur based on physical attributes of chromosomes regardless of the genes residing in the biased regions. In C. elegans males, homologous chromosomes differing by as little as 1 kb in length can segregate with biased frequencies, with the larger homolog included preferentially into the nullo-X gamete [82]. Homolog sizes could diverge between C. briggsae strains by expansion or contraction of repetitive sequences, which comprise over 22% of the genome [37]. Additionally, C. elegans isolates exhibit extensive copy number variation [83], suggesting that C. briggsae strains might as well. Meiotic drive can also produce MTRD [84]–[86]. However, selection against delayed development is sufficient to explain the ChrIII bias (see below), and neither size-based assortment bias nor meiotic drive would explain the cross-specific MTRD observed on ChrIV and ChrV. Thus, while these phenomena might occur to some extent, we conclude that they are not a major factor in determining AI-RIL genotypes compared to selection.
The F2 developmental delay phenotype associated with ChrIII (Figure 5) indicates that AF16 alleles at one or more loci in the central domain are dysfunctional when homozygous in a hybrid background. Delayed animals were unlikely to have been chosen for the next generation of the AI-RIL cross scheme, and this might entirely explain the MTRD seen on ChrIII (Figure 2). The lack of extensive LD between this distorted domain and other autosomal regions (Figure 6) suggests it interacts with HK104 alleles at multiple loci. Neither the delay phenotype nor MTRD on ChrIII (Figure 5H) were apparent during production of the VT847×AF16 F2 RIL, suggesting that the incompatibility does not exist in this cross. The phylogenetic and geographic relationships of AF16, HK104 and VT847 match the expectation that incompatibilities are more likely to arise between more divergent strains [28], [87], [88].
The smaller genetic map length of ChrIII relative to other autosomes in the AI-RIL (148.6 cM vs. 164.5–173.2 cM) might be another consequence of strong selection on ChrIII, as rapid loss of AF16 haplotypes reduces the opportunity for additional recombination events to produce detectable breakpoints. The ChrIII locus (or loci) responsible for the developmental delay phenotype is unlikely to be the same region of ChrIII involved in interchromosomal LD. The maximum MTRD for ChrIII occurs at roughly 5 Mbp, while the region of maximal D′ is limited to a small portion at 12 Mbp that also contains an unusual divergence of parental allele fixation between the two cross directions (Figure 2).
Although all autosomal loci in the F1 founders of the AI-RIL are heterozygous AF16/HK104, cross direction alters the source of maternal cytoplasm and ChrX allele frequencies (Figure 1). These genetic distinctions between cross directions raise the possibility that an epistatic interaction between autosomal and either X chromosome or mitochondrial genome (mtDNA) alleles in a hybrid might cause MTRD on that autosome in only one cross direction, as seen on ChrIV and ChrV (Figure 2). If the mitochondrial and nuclear genomes have co-evolved through compensatory changes [89], DMIs might be revealed when two strains or species hybridize [90]. In hybrid AI-RIL, cytonuclear epistasis might cause preferential transmission of the autosome involved that originated from the parental hermaphrodite. Negative cytonuclear epistatic interactions might eventually produce reproductive isolation [91], although it has been argued that incompatibilities will rarely lead to the formation of independent species [92].
Such a model of cytonuclear coadaptation fits the pattern of MTRD on ChrIV in AF16×HK104 AI-RIL. These lines contain HK104 mtDNA and are overrepresented for ChrIV HK104 alleles (Figure 2). ChrX could also drive this bias, but the lack of LD between ChrX and ChrIV rules out this possibility (Figure 6). A coadaptation model cannot explain the biased fixation of HK104 alleles on ChrV in the HK104×AF16 AI-RIL (Figure 2), which bear AF16 mtDNA. A plausible alternative model here is cytonuclear transgressive segregation, in which a synergistic interaction between the mtDNA of one strain and a nuclear allele of the other produces fitness greater than either parental strain [93]. Consistent with this, we again see no evidence of LD between ChrV and ChrX (Figure 6). We therefore favor cytonuclear epistatic interactions (either coadaptive or transgressive) as the most likely explanations for the cross direction-specific MTRD on ChrIV and ChrV.
Other studies have reported similar patterns of MTRD in hybrid crosses. In Mimulus, an interpopulation cross exhibits MTRD involving multiple linkage groups [94], and in an interspecies cross, bias against the maternal genotype is seen [95], much like the pattern of bias on C. briggsae ChrV (Figure 2) that we tentatively attribute to transgressive segregation of mitochondrial and nuclear loci. Such patterns of MTRD are often attributed to cytonuclear incompatibility (e.g. in Nasonia wasps [96] and a moss [97]). Further, regions exhibiting MTRD might be expected to overlap the positions of hybrid incompatibility loci, as found in a cross between Solanum species [98]. However, it is unclear at what point (i.e., at what allele fraction threshold) an interchromosomal epistatic interaction might be classified as an incompatibility. Only when two incompatible loci are tightly linked, such as in the case of the zeel/peel lethal system on C. elegans ChrI, would allele fraction values be expected to approach unity. Even in that case, the allele fraction of linked markers in C. elegans AI-RIL do not reach unity [29]. Given the limited evidence for the presence of an extreme (i.e. lethal) incompatibility between AF16 and HK104, at this point we conclude only that cytonuclear epistatic interactions are responsible for the MTRD on ChrIV and ChrV. This is further supported by the significant difference between allele fraction values for the two cross directions in a block of markers on ChrV (Figure 2, asterisk).
The nuclear genome encodes mitochondrial proteins, some of which interact with mitochondrion-encoded proteins involved in oxidative phosphorylation [90], [99]. The mitochondrial genome can co-adapt both with the nuclear genome [99] and with temperature [90], [100], and some hybrids in other taxa suffer from decreased oxidative phosphorylation efficiency [99], [101]. The mitochondrial genome of C. briggsae evolves rapidly [27] and is polymorphic for large deletions [102]. As this degree of mtDNA variation can impact fitness [27], [103], we propose that cytonuclear epistasis between AF16 and HK104 becomes evident when the mitochondrial genome is separated from co-adapted nuclear genes and/or provided nuclear alleles from a different strain. Similar incompatibilities have been discovered between many species (e.g. [104]–[106]) and can have complex genetic architecture [107]. Incompatibilities, cytonuclear or not, can contribute to speciation when hybrid fitness is sufficiently reduced [91], [108], [109].
Fecundity in Caenorhabditis can be affected by temperature [110], and the strains employed in this study experienced substantially different temperatures in nature. Strain AF16 was isolated in Ahmadabad, India, a lowland tropical city (23°N latitude) where the average annual temperature is over 30°C (http://www.fao.org/countryprofiles/Maps/IND/07/tp/index.html). In contrast, HK104 was isolated in Okayama, Japan, a more temperate locale (34°N latitude) with an annual mean temperature of only 14°C (http://www.data.jma.go.jp/obd/stats/data/en/smp/index.html). Our AI-RIL were raised at 20°C, a temperature possibly more optimal for temperate strains [111]. Thus, the bias for HK104 alleles (61% of genotypes) in the AI-RIL might reflect selection for temperature-adapted genes. Furthermore, although 120 lines in each cross direction were initiated, only 95 AF16×HK104 and 86 HK104×AF16 lines survived. Line extinction might reflect selection against hybrid genotypes specifically unsuited to 20°C. Repetition of the hybrid crosses at higher temperatures might yield different results, yet at 20°C under lab conditions, HK104 individuals produce fewer offspring over their lifetime than AF16 [110], [112]. This suggests that a temperature-dependent effect separate from total fecundity might explain the bias of HK104 alleles in the AI-RIL. Alternatively, line extinction might be due to generalized outbreeding depression between the strains [113]. The regions of significant MTRD coincide with the central recombination domains (Figure 2) and associated blocks of LD (Figure 6). Thus, selection on loci in the central domain, which will rarely be separated by recombination, can affect the population genetics of half of a chromosome [114]. While the recombination profile of Caenorhabditis chromosomes amplifies the population genetic signals of selection, the near-absence of recombination in the central domain is an obstacle to fine-scale mapping of loci under selection.
The genotyped AI-RIL described here serve as a powerful new resource for the mapping of divergent phenotypes, as has been accomplished using C. elegans RIL [35]. For example, they are being used to explore the genetic architecture of temperature tolerance of AF16 and HK104 (A. Cutter, pers. comm.) To continue improving resources for the study of C. briggsae, future efforts should identify genetic markers on remaining unassembled sequence supercontigs in order to incorporate them into the genome assembly. Further increasing the marker density might also identify yet more misassemblies that exaggerate the apparent genomic divergence between C. briggsae and related species.
More biologically, we note that the genetic structuring of C. briggsae strains by latitudinal zone [17], [25]–[28] is not seen in C. elegans. Whether the epistatic effects described here represent maladaptive loss of local adaptations in hybrids or more generalized incompatibilities, only a few intra-species hybrid incompatibility loci have been described at the molecular level in animals (reviewed in [108], [109]). Future efforts will focus on mapping the hybrid developmental delay locus on ChrIII and testing the hypothesis that cytonuclear epistasis exists among C. briggsae strains diverged roughly 100,000 years [26]. It has been known for some time that some species of Caenorhabditis are cross-fertile but post-zygotically reproductively isolated [115]–[118]. The recent identification of fertile interspecies hybrids between C. briggsae and C. species 9, which shared a common ancestor as recently as one million years ago [26], has facilitated the study of post-zygotic reproductive isolation [119]. Thus, C. briggsae provides unique opportunities to explore different stages of reproductive isolation in the nematode phylum.
Advanced-intercross recombinant inbred lines (AI-RIL) were produced from the C. briggsae strains AF16 from Ahmadabad, India [33] and HK104 from Okayama, Japan (H. Kagawa). Crosses between males and sperm-depleted hermaphrodites were established in both directions, and several mated (as determined by presence of a copulatory plug) hermaphrodite F1 produced a large F2 population. Three plugged F2 hermaphrodites (each having mated with one or more males) were chosen to found 120 lines from each cross direction. Generations F3–F7 were similarly founded by a population of three plugged hermaphrodites. The exact relatedness between mates thus varied, but should have been no closer than biparental full-sibs. During the F3–F7 generations, matings would have occurred between progressively more restricted genotypes, such that by F8 substantial homozygosity might have already existed. From F8–F17, the lines were intentionally inbred by complete selfing using a single virgin (L4 stage) founder hermaphrodite per generation. 95 lines were produced for the AF16×HK104 cross (male×hermaphrodite), and 86 for the HK104×AF16 cross. The disparity between the number of lines initiated and that produced was due to the extinction of lines. Additionally, one AF16×HK104 line was not genotyped.
F2 RIL were produced from AF16 and the C. briggsae strain VT847 from Hawaii [30]. Crosses between VT847 males and sperm-depleted AF16 hermaphrodites were performed as described [38]. Eighty-nine RIL were initiated from individual F2 hermaphrodites produced by sib-mated F1 individuals, then inbred by one L4 hermaphrodite per generation through F11.
DNA was extracted from AI-RILs with a QuickGene-Mini80 using the DNA tissue kit S (Fujifilm Corp., Tokyo, Japan).
The genotypes of 180 AI-RIL, 93 F2 RIL, and parental strains were obtained using the GoldenGate genotyping assay (Illumina, [120]). The DNA samples were genotyped with 1,536 single nucleotide polymorphism (SNP) marker assays distinguishing AF16 from HK104 and/or VT847 [39]. These SNP markers were chosen 1) on the basis of their distribution on sequence supercontigs in order to genotype at least one marker on as many of the largest supercontigs as possible, and also 2) to maximize the number of large supercontigs containing at least two markers, so that the supercontigs could be oriented. Because the chromosomal assignment of supercontigs containing the markers was not considered during marker selection, the genome-wide distribution of genotyped SNPs was expected to reflect the true distribution of SNPs. Autosomal and X chromosome supercontig lengths were analyzed via var.test and an unpaired two-sample t test in R.
Genotypes of pools of delayed F2 hybrids were determined through sequence analyses of PCR amplification products derived from Cbr-egl-5 and Cbr-mab-20. Forward and reverse primers for Cbr-egl-5 were (5′ to 3′) CCGAGATTCAGAAAACCCGAAG and CACTACAGTAAACCCCCTCAAGACC, respectively. Forward and reverse primers for Cbr-mab-20 were TGCTCTTCGGTTGGAATGCGAC and CGGTTTTTTGGTTTGATGGTGGG, respectively. Sequencing reactions for both genes were primed with the forward primers.
Raw GoldenGate assay data were analyzed with GenomeStudio 2008 (v. 1.0.2.20706) using the genotyping module (v. 1.0.10, Illumina). The data were required to exceed the following quality control thresholds in order to be analyzed. Numbers in parentheses represent the number of samples or assays not exceeding each threshold in the AI-RIL.
The 172,344 AI-RIL genotype calls (Table S1) were imported into Map Manager QTXb20 (v. 0.30) [121]. A genetic map for each of the six linkage groups (five autosomes and the X chromosome) was estimated using the following parameters: probability of incorporation into a linkage group 1×10−6, Haldane map function, and intercross linkage evaluation. The cb3 map, produced from F2 RIL, was estimated using self-RI linkage evaluation [38]. However, this approach infers per-meiosis recombination rates from breakpoints accumulated over multiple generations, and thus reports compressed map lengths inconsistent with the number of observed recombination breakpoints in the AI- RIL. Selecting intercross evaluation, similar to the approach of selecting backcross evaluation to estimate AI-RIL maps in [15], forces Map Manager QTXb20 to regard all breakpoints as occurring in a single meiosis. The resulting longer map lengths reflect the numbers of recombination breakpoints observed (Table 2) and are thus more directly comparable to other AI-RIL maps.
Map Manager QTXb20 was also used to estimate genetic maps using the 18,601 VT847×AF16 F2 RIL genotype calls (Table S1) with the same parameters as previously used for C. briggsae F2 RIL [38]. A strategy of relaxation of the probability of incorporation was employed to incorporate five markers into the six major linkage groups, as in [38]. As was the case for the AI-RIL, it was empirically determined that the presence of 50 heterozygote genotype calls prevented robust map estimation. Therefore, these calls were considered as missing data in Map Manager QTXb20 and are reported as such (“?”) in Table S1.
Map Manager QTXb20 reported the numbers of recombination breakpoints per linkage group used to calculate average breakpoint capture (Table 2). However, because it does not count breakpoints associated with heterozygote calls under self-RI linkage analysis, the counts were manually increased to account for breakpoints necessary to produce heterozygote genotypes.
We noticed an artifact introduced when map positions were calculated using Map Manager QTXb20: map positions were offset by one marker. Exports of some linkage maps gave the genetic position of the first marker in the map as non-zero; the position of the last marker in each map was never reported, and the last marker in any block of non-recombinant markers was always reported to have a map position different from the others in that block. Defining the position of the first marker in each linkage group as 0 centimorgans (cM) and then shifting each subsequent map position by one marker resolved these discrepancies. This artifact might explain why some markers in the cb3 linkage maps are nonrecombinant yet flank haplotype breakpoints and differ in allele fraction: the reported genetic positions of the markers might differ slightly from their true values. The orientations of linkage maps produced in this study were compared with the cb3 maps [38] and inverted when necessary to maintain the same relative map positions of markers.
Based on our new genetic maps and the locations of the SNP markers on sequence supercontigs, we first reassembled the genome from the cb25 supercontigs [37] and then compared this assembly with cb3 [38]. For a few supercontigs (see Text S1), the cb3 genetic maps contained more information than the cb4 maps. In these cases, we supplemented our data with data from cb3. Only where our data contradicted or improved upon the cb3 assembly did we make changes. Where necessary, cb25 supercontigs were split to resolve discrepancies between the genetic and physical order of markers (see Text S1). Figure S3 depicts the decision tree employed to resolve these discrepancies; the genetic and physical map data used to select locations at which to split supercontigs to resolve certain discrepancies are provided in Table S3. Genome assembly version cb4 is available at http://www.wormbase.org.
Each tip domain (two per chromosome) comprises the sequence between a chromosomal assembly terminus and the most internal genetic marker in the terminal block of non-recombinant markers. By definition, these domains have a recombination rate of zero. For the AI-RIL, the boundaries of the arm-center recombination domains were identified by segmented linear regression for each linkage group as in [15] using the “segmented” package implemented in R [122].
The genetic map positions of recombination domain boundaries were estimated for the AI-RIL by linear interpolation from the two markers flanking each boundary. The lower marker density in the F2 RIL genotype data set reduced confidence in the accuracy of boundaries estimated by segmented linear regression, so we imposed the physical positions of domain boundary estimates from the AI-RIL onto the F2 RIL genetic maps and estimated the genetic length of each domain as above. The recombination rates for C. elegans domains reported in Table 3 differ from those previously reported [15], which were rate estimates based on the slopes of segmented linear regression. Here, we calculated C. elegans domain genetic lengths, as above, from the interpolated genetic positions of domain boundaries (kindly provided by M. Rockman, unpublished; Table 3).
To facilitate comparison between maps, we used a unique correction factor for each linkage group to normalize the sum of estimated genetic lengths of the three domains to 50 cM, the expected per-meiosis length under selfing.
C. elegans (release ws185, the assembly version used to define recombination domain boundaries in [15]) and C. briggsae (cb4) genome sequences were first masked using RepeatMasker 3.2.9 with default parameters and the June 4, 2009 RepBase repeat libraries [123]. The masked sequences were then compared with MUMmer 3.22 [124] using nucmer to identify only maximal unique matches.
We compared the observed number of AI-RIL fixed for the HK104 allele to the expectation of 50% with a Bonferroni-corrected chi-square test. Because linked markers are not truly independent tests, the effective number of independent tests was estimated as follows: The autocorrelation parameter at lag = 1 was estimated for the allele fraction data within each recombination domain for each cross direction using the acf() function in the base package of R. The value of the autocorrelation parameter was then used to estimate the effective number of tests [125]–[127]. The significance threshold p = 0.05 was then Bonferroni-corrected by the genome-wide sum of effective number of tests for each cross direction and used to calculate the allele fraction value, plotted in Figure 2, at which a marker would reach genome-wide significance for deviation from the expected value.
To test for epistasis between cross direction and the ChrIV or ChrV center domain markers, the allele fraction values for both cross directions were compared using Fisher's exact test in R. The significance threshold p = 0.05 was then Bonferroni-corrected by the sum of the largest effective number of tests estimated above for the two center domains for both cross directions.
After identifying the relative genetic order of markers, the genotype data from each AI-RIL cross direction were imported separately into Haploview v. 4.2 [128]. With the Hardy-Weinberg p-value cutoff set at 0, intra- and inter-chromosomal linkage disequilibrium D′ values were plotted using the Standard color scheme (Figure 6). One pair of markers exhibiting D′> = 0.8 from each block of markers in interchromosomal LD was selected to test for significance using the chi-square test. Expected counts of AI-RIL fixed for the same parental allele at two loci were calculated according to the parental allele frequencies at each locus for each cross direction.
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10.1371/journal.ppat.1005237 | Modeling the Effects of Vorinostat In Vivo Reveals both Transient and Delayed HIV Transcriptional Activation and Minimal Killing of Latently Infected Cells | Recent efforts to cure human immunodeficiency virus type-1 (HIV-1) infection have focused on developing latency reversing agents as a first step to eradicate the latent reservoir. The histone deacetylase inhibitor, vorinostat, has been shown to activate HIV RNA transcription in CD4+ T-cells and alter host cell gene transcription in HIV-infected individuals on antiretroviral therapy. In order to understand how latently infected cells respond dynamically to vorinostat treatment and determine the impact of vorinostat on reservoir size in vivo, we have constructed viral dynamic models of latency that incorporate vorinostat treatment. We fitted these models to data collected from a recent clinical trial in which vorinostat was administered daily for 14 days to HIV-infected individuals on suppressive ART. The results show that HIV transcription is increased transiently during the first few hours or days of treatment and that there is a delay before a sustained increase of HIV transcription, whose duration varies among study participants and may depend on the long term impact of vorinostat on host gene expression. Parameter estimation suggests that in latently infected cells, HIV transcription induced by vorinostat occurs at lower levels than in productively infected cells. Furthermore, the estimated loss rate of transcriptionally induced cells remains close to baseline in most study participants, suggesting vorinostat treatment does not induce latently infected cell killing and thus reduce the latent reservoir in vivo.
| Combination antiretroviral therapy (cART) for HIV infection must be taken for life due to the existence of long lived latently infected cells. Recent efforts have focused on developing latency reversing agents to eliminate latently infected cells by activating HIV production. In this work, we assess the impact of a latency reversing agent, vorinostat, by fitting dynamic models to data from a clinical trial. Results show that vorinostat treatment induces HIV transcription transiently and that the sustained induction of HIV transcription may depend on the temporal impact of vorinostat on host gene expression. Our results also suggest that vorinostat treatment is not sufficient to induce killing of latently infected cells in a majority of HIV-infected individuals on cART.
| Treatment of HIV-infected individuals with combination antiretroviral therapy (cART) effectively suppresses HIV to levels below the limit of detection of conventional assays and substantially reduces morbidity and mortality of HIV infected patients [1]. However, it does not eradicate the virus and treatment is lifelong [2]. Therefore, developing novel therapeutics to cure HIV infection remains an important research priority [3,4]. A major barrier to cure is the presence of a population of long lived latently infected cells [4] that can persist indefinitely in patients treated with highly potent cART [5]. Recent efforts have focused on strategies that activate HIV production in latently infected cells. The idea, termed ‘shock and kill’ [6], is to first shock latently infected cells thereby activating HIV gene expression, such that the cells are then killed by viral cytopathic effects or immune-mediated cell death. Histone acetylation is one of several factors that regulate HIV transcription and is therefore important for establishing and maintaining latency [7]. Drugs such as histone deacetylase inhibitors (HDACi) enhance acetylation of both histones and proteins and thereby induce changes in gene transcription, including transcription of HIV [8].
Vorinostat, a histone deacetylase inhibitor licensed for the treatment of cutaneous T-cell lymphoma [9], has been shown to activate HIV transcription in resting memory CD4+ T-cells in vivo [10,11]. In a recent clinical trial, 20 HIV-1 infected individuals on suppressive cART were treated orally with 400 mg a day of vorinostat for 14 days and then followed for an additional 70 days. Overall, vorinostat induced a rapid and sustained increase of cell-associated unspliced (CA-US) HIV RNA [10]. However, the response pattern was highly variable among the participants. For example, in half of the participants, after an initial significant increase in CA-US HIV RNA, the level of CA-US HIV RNA decreased rapidly within 1–3 days before increasing again, and in 14 of the 20 participants, the level of CA-US HIV RNA continued to increase after vorinostat was stopped. These puzzling observations raise important questions about the temporal impact of vorinostat treatment on HIV transcription and the design of treatment strategies to eradicate the latent reservoir.
We constructed mathematical models to better understand the temporal changes in CA-US HIV RNA in individuals treated with vorinostat. Mathematical models have been widely applied to study viral dynamics in vivo [12–14]. They played an instrumental role in quantifying important parameters, such as the half-lives of virions and infected cells in vivo [14]. Recently, several models have been developed to understand the maintenance of the latent reservoir under cART treatment [15–17], the viral rebound time distribution after latency reversing agent (LRA) treatment [18], and the optimal time to start a LRA [19]. However, the dynamic response of HIV transcription in latently infected cells following treatment with a LRA has not been investigated. This question has important implications for future clinical trial design and optimizing treatment strategies to eliminate latently infected cells. Previous models have generally assumed that the HIV provirus in latently infected cells becomes fully activated following treatment with a LRA and that subsequent events will be identical to latently infected cells activated by normal immunological signals or through the T-cell receptor [18,19]. However, evidence suggests that current LRA treatments primarily activate HIV transcription and its impact on translation may be mild or minimal [20,21]. Here, we construct models that treat cells activated by a LRA and naturally activated cells separately. By fitting models to the clinical data, we show the complex dynamic response of latently infected cells to vorinostat can be explained. Furthermore, we use the models to quantify the extent to which vorinostat activates HIV transcription and induces cell death in vivo.
We first construct a mathematical model based on a previously published latency model by Rong et al. [15]. As the study participants were treated with suppressive cART for a medium of 5 years, we have chosen parameters in the Rong et al. model such that before vorinostat treatment the viral load is approximately 5 HIV RNA copies/ml, target cells levels are 750 T cells/μL similar to mean CD4 count in the 20 clinical trial subjects [10] and latently infected cell levels are 2/mL, which is approximately 2.7/million CD4 cells (roughly consistent with previous studies [22,23]). The major innovation in the direct activation model is that we assume latently infected cells become transcriptionally induced and express CA-US HIV RNA directly upon vorinostat treatment (Fig 1A; see Methods for full description of the model and Table 1 for parameter values). Thus, two equations are added to the Rong et al. model [15], one for the number of cells that have HIV transcription induced by vorinostat, LA, and one for the average amount of CA-US HIV RNA per transcriptionally activated cell, R. Also, cells that are transcriptionally induced by vorinostat are assumed to be in a different state than cells that are naturally activated.
We fitted this model to the clinical data collected during the entire 84-day study period (see Methods for the fitting procedure and S1 Table for best-fit parameter values). In general, the direct activation model does not explain the data well especially during the first 1–3 days’ treatment (S1 Fig) and the period after treatment stops (S2 Fig). First, in 10 of the 20 participants, the level of CA-US HIV RNA first increased upon initiation of vorinostat, and then decreased rapidly after the first 1–3 days, whereas the best-fit model curves in these patients have CA-US HIV RNA increasing continuously during vorinostat treatment. Second, in 14 out of the 20 participants, the level of CA-US HIV RNA increased at variable time points after cessation of vorinostat at day 14, whereas the model predicts that the level always decreases over time after cessation of vorinostat.
To better understand the initial peaking pattern observed in half of the study participants soon after the initiation of vorinostat, we fitted the model to data obtained during the first 7 days of treatment only (Fig 2). The peaking pattern was well described by the model in five participants (VOR001, VOR004, VOR010, VOR016, VOR018). In these individuals, the estimated loss rate of transcriptionally activated cells, dLA, ranged between 1.8 and 10 day-1 (see S2 Table), and is much greater than the death rate of productively infected cells, i.e. 1.0 day-1 [27]. Rates of decrease of CA-US HIV RNA levels higher than 1.0 day-1 were also apparent in 5 other participants (VOR008, VOR019 and VOR021-023). This decrease can potentially result from either death of cells transcriptionally activated by vorinostat or from shutdown of HIV transcription and loss of CA-US HIV RNA. We reason that this decline is not due solely to cell death, as it is unlikely that cells activated by vorinostat die at a faster rate than productively infected cells. Then why would HIV transcription shut down during vorinostat treatment?
Previous in vitro studies have shown the activation of HIV transcription is a transient stochastic process, and that the duration of this transient process is dependent on the strength of Tat transcriptional feedback [34–36], as well as the availability and regulation of many host factors that are necessary for transcriptional activation, such as the NAD-dependent deacetylase sirtuin-1, NF-κB, Yin Yang 1 and the positive transcription elongation factor, P-TEFb [37–41]. In latently infected cells, mostly memory T cells, these transcription factors are likely to be at low levels [42–44], whereas many host enzymes such as Murr1 (a gene product that restricts HIV-1 replication), human schlafen 11 and the lipid raft associated protein tetherin, actively inhibit HIV transcription initiation [45], mRNA translation [46], and viral release [47]. Therefore, before vorinostat treatment, the host factors/enzymes required for full HIV gene activation are most likely limiting in latently infected cells. After vorinostat treatment initiation, host genes undergo rapid differential regulation at 2, 8 and 24 hours [10]; however, the immediate impact of vorinostat treatment may not be sufficient to induce HIV gene transcription sustainably. This unfavorable cellular environment and rapid changes in gene expression may lead to very short transcriptional pulses of Tat activity and CA-US HIV RNA production. Without further production of CA-US HIV RNA, the rapid decrease observed in the data may be a result of the loss of US HIV RNA by degradation and by splicing.
Vorinostat treatment not only induces rapid changes in host gene expression but also induces changes after treatment cessation [10]. It is therefore plausible that the late increase in HIV transcription after vorinostat treatment is due to a longer-term impact on host gene transcription. To test this hypothesis, we extended the direct activation model to include a ‘transiently activated’ state (LT) and a ‘waiting’ state (LW), and denote this as the ‘delayed activation’ model. We assume that upon vorinostat treatment latently infected cells first get activated transiently, i.e., enter the transient activation state, LT, where CA-US HIV RNAs are produced for a short period of time. We assume the cells then enter a waiting state, LW, in which there is no CA-US HIV RNA production before becoming sustainably activated cells, LA (Fig 1B). The waiting state reflects the time needed for the transcriptional programs to produce sufficiently high levels of host factors necessary for transcriptional activation such that the cellular environment becomes favorable for sustained HIV transcription. We assume that the cells in the transiently activated state and in the waiting state die at the same rate as in the latent state (L) as transient activation is not likely to be strong enough to produce the shock needed for kill. The ordinary differential equations (ODEs) describing this model are given in the Methods.
Next, we tested whether the above hypotheses explain both the short-term and the long-term dynamics of CA-US HIV RNAs by fitting the delayed activation model to the full data set (see S3 Table for best-fit parameter values), and found that the delayed activation model describes the data much better than the direct activation model in a majority of participants (compare S3 and S4 Figs with S1 and S2 Figs, respectively). It successfully describes the initial pattern of CA-US HIV RNA change following initiation of vorinostat in most individuals as well as the dynamics of CA-US HIV RNA in 6 of the 14 individuals where the level of CA-US HIV RNA increased after cessation of vorinostat. For the other 8 patients, the delayed activation model does not predict the magnitude of the late increase in CA-US HIV RNA level at some time points (S4 Fig). We speculate that the discrepancy may arise from the assumption of an exponentially distributed residence time for latently infected cells in the waiting state before becoming sustainably activated (an assumption implicitly assumed in the ODE system). This assumption is valid when a single event is needed for the transition to sustained activation. However, it is likely that multiple events must occur before the transition to sustained activation, such as upregulation of several host factors, HIV RNA splicing and expression of tat and other regulatory proteins including rev.
We, thus, further modified our model to assume that cells in the waiting state have to go through several stages before becoming sustainably activated as in previous work describing the multiple events needed to drive an initially infected cell into viral production [48]. We denote this model the ‘multistage delayed activation’ model and the equations describing this model are given in the Methods. See Table 2 for a summary of the assumptions made with regard to the impact of vorinostat on latently infected cells in the three different models.
In this model, the LW state is divided into n identical sub-states, i.e. LW,1, LW,2, … LW,n. The transition rate from one sub-state to the next is set to nkw such that the average residence time in the overall waiting state is 1/kw. This model is equivalent to one in which we assume the transition out of the waiting state is stochastic with the delay described by a gamma probability distribution [49]. We let n change from 1 to 10, and fitted these 10 model variants to the clinical data from all 20 participants (S5 Fig). The fitting results show that this multistage delayed activation model describes the patterns of increases of CA-US HIV RNA after cessation of vorinostat as well as the initial peak following initiation of vorinostat (Fig 3 and S6 Fig; see S4 Table for best-fit parameter values).
We further performed model selection using the corrected Akaike information criterion (AICc) (see Methods). The direct activation model significantly underperformed compared to the delayed activation model and the multistage delayed activation model in 19 out of the 20 participants. The multistage delayed activation model was significantly better than the delayed activation model in 12 participants (Table 3). These results support the hypothesis that the immediate impact of vorinostat treatment is to activate HIV transcription for a short period of time (1–3 days), possibly due to the limited availability of many host cellular factors and that sustained activation may take longer to attain and a number of events (possibly in host cell transcriptional regulation) must occur before sustained HIV transcription becomes possible. As the number of events required varied among the participants, cells in different individuals may be in different states of latency. Also, host gene expression patterns, which can differ among individuals, may play a role in determining the length of the delay. Analyzing the effect of changing the number of waiting stages on the model fit to the data using AICc shows that for 17 out of the 20 participants, using a model with more than 7 stages would be a good choice in general (S5 Fig).
We next examined the best-fit parameter values of the multistage delayed activation model to assess the impact of vorinostat on latently infected cells in vivo. First, we find that the estimated values of α, the rate of CA-US HIV RNA production induced by vorinostat in latently infected cells, varies over a wide range (over 1.5 logs) among the 20 patients, suggesting the response to vorinostat is very heterogeneous across participants. In a majority of patients, the estimated values of α are smaller than the production rate of CA-US HIV RNA in productively infected cells, αI (Fig 4A; see Methods for calculation of αI). We then examined the estimated loss rate of transcriptionally activated latently infected cells, dLA, and found that in 12 participants the estimated loss rates are extremely low, close to the death rate of latently infected cells, dL (Fig 4B). Although the estimated loss rates are higher than dL in other patients, we were not able to distinguish whether the loss is through shutdown of HIV transcription or through cell death. Nonetheless, the low estimates of the loss rate in most participants suggest that vorinostat treatment does not induce killing of transcriptionally activated latent cells in vivo in a majority of individuals, and thus, according to this model, the reductions in reservoir size were minimal or absent in most participants.
We further tested the robustness of the parameter estimates to variations in our assumptions. First, we varied the values of two fixed parameter values that describe the intracellular dynamics of CA-US HIV RNAs, i.e. the rate of US HIV RNA export from the cell in the form of virions, ρ, which we initially assumed to be 0, and the combined rate of RNA splicing and degradation, μ. We find the parameter estimates are robust to changes in the value of ρ (S7 Fig), and that the estimated production rate of CA-US HIV RNAs, α, decreases approximately linearly with decreases in μ (S8 Fig). Thus, if the rate of CA-US HIV RNA loss is lower in cells activated by vorinostat than we have estimated, the estimated production rate of CA-US HIV RNA would also be lower. Last, we tested the robustness of our results to the assumption in the Rong et al. (15) model about how the latently infected cell population is maintained by employing a different model based on the work of Kim and Perelson [50] in which the latent population is maintained by homeostatic proliferation rather than asymmetric division (see Methods). We found the model fits to the CA-US HIV RNA data and the estimates of α and dL are largely unaffected (S9 Fig).
We have constructed mathematical models to describe the dynamics of CA-US HIV RNA in HIV-infected individuals on ART who received multiple doses of the HDAC inhibitor vorinostat. By fitting these models to a clinical dataset, we have assessed the dynamic response of latently infected cells to vorinostat and estimated the quantitative impact of vorinostat on the latently infected cell population.
Model analyses show that the multistage delayed activation model, can describe both the short-term and the long-term patterns of change in CA-US HIV RNA induced by vorinostat in most individuals. This model assumes that in response to vorinostat treatment, HIV transcription in latently infected cells is induced transiently. Afterwards, the cells rather than returning to their original latent state go through several waiting stages where CA-US HIV RNAs are not produced but host gene expression patterns may change before becoming sustainably induced. The sustained induction of HIV transcription may even occur after vorinostat treatment is stopped. The induction of HIV gene expression depends on the availability of the HIV Tat protein as well as many host factors [34–36,42–44]. In latently infected cells, the number of Tat proteins [34] and the host factors necessary for inducing HIV transcription, such as P-TEFb, are likely to be at low levels [42–44], and at the same time, the presence of inhibitory molecules, such as Murr1, human schlafen 11 and tetherin, prevent transcriptional activation [45–47] before and at the early stage of response to vorinostat treatment. A recent proteomics and transcriptomics study showed that after 24 h of vorinostat treatment of primary CD4+ T cells the expression of a large number of host genes and proteins as well as genes and proteins previously reported to be involved in HIV transcription was modulated, with some effects appearing to be stimulatory and others inhibitory for HIV reactivation [51]. Therefore, it is likely that the immediate impact of vorinostat on histone acetylation and host gene transcription lead only to a transient induction of HIV RNA transcription and sustained HIV transcription may depend on the longer-term impact of vorinostat on host gene transcription [10]. This delay in sustained transcriptional induction may explain the later increase in the level of CA-US HIV RNA after cessation of vorinostat seen in this study, and the observed refractory periods in response to multiple doses of vorinostat in another study [52]. Note that the effect of vorinostat on host genes may also include the generation of read through transcripts containing HIV RNA [53], but a recent report suggests such transcripts are a minor fraction of total gag RNA [54].
Analyzing the model, we found that the number of stages latently infected cells goes through the waiting state and the total waiting period before sustained induced transcription varied among individuals. This suggests that latently infected cells in different individuals may be in different states, possibly due to variations in Tat protein copy number, host gene expression or alternatively different degrees of chromatin silencing or configuration potentially dependent on the sites of HIV integration. This, in turn, would cause responses to vorinostat to be heterogeneous. Interestingly, the maximal fold increase of CA-US HIV RNA was strongly correlated with the basal level of CA-US HIV RNA before vorinostat treatment [10]. Thus, it is plausible that the basal level of CA-US HIV RNA serves an indicator of the status of latency in a patient and the ease of induction of transcription using LRAs, suggesting that future treatment strategies may be able to be tailored to individual patients.
We further assessed the impact of vorinostat on the rate of loss of cells in the sustained activated state. This estimated loss rate, which serves as an upper bound on the death rate of activated cells (as cells could lose their activated state), is extremely low in most individuals, suggesting that vorinostat treatment does not induce killing of transcriptionally activated cells in most participants. This is in agreement with several previous in vitro and ex vivo studies showing vorinostat activates HIV transcription in only a subset of cells and that this level of HIV transcription and protein expression does not lead to cell death [21,52,53,55,56]. Interestingly, a recent in vitro study showed that vorinostat treatment only has significant impact on HIV transcriptional activation, with the impact on translation being minimal, suggesting that HIV proteins may not be produced sufficiently to lead to virion production or to induce viral cytopathic or cytotoxic T cell mediated cell death [20]. Thus, new treatment strategies aiming at both transcriptional and translational activation of HIV may be needed to induce efficient killing of latently infected cells.
In the model, we have assumed that in each participant, latently infected cells are a homogeneous population and respond to vorinostat by going through two activation steps. However, because of limited data sampling there could have been additional transient activation steps that we were unable to detect. In addition, the latent state of individual infected cells in vivo may differ [57] and it is possible that within an individual, some cells go through a different number of activation steps or have different waiting periods before becoming sustainably activated. This may give rise to the minor discrepancies between the data and the model seen in some participants (VOR010, VOR018, VOR019, VOR021, VOR023 in Fig 3). Although a model that accounts for different responses of latently infected cell subpopulations or has additional activation steps might explain the data, such a model would have more unknown parameters than our current model with only a marginal improvement in model fit. Nonetheless, the possibility that there exist different cell populations in individual patients in terms of their response to vorinostat or additional activation steps cannot be excluded. Further experiments examining the dynamics of host factors/enzymes that are responsible for transcriptional activation and inhibition under LRA treatment could validate our model, improve our understanding of the impact of vorinostat, and ultimately aid the design of treatment strategies to eradicate the latent reservoir.
To conclude, our results suggest that vorinostat induces both immediate transient induction and delayed sustained induction of HIV transcription. Similar dynamic patterns of CA-US HIV RNA were also observed in clinical trials of the LRAs panobinostat and romidepsin [58,59], suggesting LRAs may induce both transient and delayed transcription activation in latently infected cells in general. Therefore, designing clinical trials with frequent longitudinal sampling during both treatment and the follow-up period would help quantify the impact of LRAs. To our knowledge, our work represents the first mathematical model to assess the impact of a LRA on the dynamics of CA-US HIV RNA in vivo. Our model can be easily adapted to study other LRAs as well as combinations of these agents once data are available. In addition, our model or variants of it could be used to assess the efficacy of different candidate treatments, such as those using anti-HIV monoclonal antibodies combined with LRAs, and ultimately suggest optimal drug combinations to eliminate latently infected cells in HIV-infected individuals.
As described by Elliot et al. [10], 20 chronically HIV-infected adults receiving at least three antiretroviral agents, having plasma HIV RNA < 50 copies per mL for at least three years (excluding single viral ‘blips’), a CD4+ T-cell count > 500 cells/μL and documented subtype B HIV-1 infection were recruited into a vorinostat trial (ClinicalTrials.gov, NCT01365065). Participants received vorinostat 400 mg orally once daily for 14 days. Levels of CA-US HIV RNA were measured in peripheral blood mononuclear cells at 0, 2, 8 and 24 hours, and on days 7, 14, 21, 28 and 84 (as well as on day 3 for participants VOR019-023). For each blood sample, four replicate q-PCR runs were performed to measure the levels of CA-US HIV RNA. The total number of data points in each patient ranges from 35 to 44.
The ordinary differential equations (ODEs) describing the model are:
dTdt=s−dTT−(1−εRT)βVITdIdt=(1−εRT)(1−f)βVIT−δI1.44+2(1−pL)aLdLdt=(1−εRT)fβVIT−dLL−aL+2pLaL−υLdLAdt=υL−dLALAdRdt=α−μR−ρRdVIdt=(1−εPI)[pVI+ρRLA]−cVIdVNIdt=εPI[pVI+ρRLA]−cVNIUS=RLA+US0
In this model, target cells, T, are produced at a constant rate, s, and die at per capita rate, dT. In the absence of cART, they are infected at per capita rate, βVI, where β is a rate constant and VI is the concentration of infectious virus. The effect of reverse transcriptase inhibitors (RTI) is to multiply the rate of infection by the factor 1-εRT, where εRT is the effectiveness of the RTI with 0 ≤ εRT ≤ 1. Under protease inhibitor treatment, a fraction, εPI, of produced viruses are non-infectious (VNI). Upon infection with viruses, a fraction, f, of infected cells becomes latently infected, and the remaining fraction, 1-f, becomes productively infected. Productively infected cells, I, die at per capita rate, δI1.44, where the power 1.44 models the effects of both viral cytopathicity and cell-mediated immune responses [60,61].
We assume that latently infected cells, L, when activated by antigen at rate a, undergo an asymmetric division in which a daughter cell remains in latency with probability, pL, and becomes productively infected and produces virus with probability, 1-pL [15]. Latently infected cells die at per capita rate, dL. We assume that vorinostat causes latently infected cells to move into a transcriptionally activated state, LA, at rate ν. The transcriptionally activated cells are lost at rate, dLA. We also assume there is a pharmacological delay, t0, in the effect of vorinostat upon treatment initiation such that the rate of activation remains 0 when t<t0, and it becomes ν when t0≤t≤14 days. After treatment is terminated, we assume the effectiveness declines exponentially as νe-w(t-14) for t>14 days, where w is the rate at which vorinostat is cleared from the system. We also model the population average of the amount of CA-US HIV RNA (R) within transcriptionally activated cells. We assume that CA-US HIV RNA is produced at a constant rate, α, once vorinostat becomes effective, i.e. when t≥ t0. CA-US HIV RNA is encapsidated and exported as virions at per capita rate, ρ, and lost by degradation and splicing at per capita rate, μ. Here, we assume that the transcriptionally activated cells do not produce mature viral particles, i.e. ρ = 0, because recent work shows that the production of virus from cells treated with vorinostat is minimal [20,53]. As other LRAs may induce viral production, we leave ρ in the model. Viruses are produced from productively infected cells at rate pv and from transcriptionally activated cells at rate ρR (note that ρ is set to 0 for vorinostat). Viruses are cleared at per capita rate c. The amount of CA-US HIV RNAs, US, is calculated as the sum of the basal level of CA-US HIV RNA, US0, and the average number of US HIV RNAs per activated cell, R, multiplied by the number of activated cells, LA.
In the delayed activation model, the equations describing the transient activation state, LT, the waiting state, LW, the transcriptionally activated state, LA, and the total number of CA-US HIV RNAs, US, are
dLTdt=υL−dLLT−kTLTdLWdt=kTLT−dLLW−kWLWdLAdt=kWLW−dLALAUS=R(LT+LA)+US0
where kT and kw are transition rate constants. The ODEs describing other variables are kept the same as in the direct activation model.
The ODEs describing the multistage waiting states are:
dLW,1dt=kTLT−dLLW,1−nkWLW,1dLW,idt=nkWLW,i−1−dLLW,i−nkWLW,i i=2,3,……,ndLAdt=nkWLW,n−dLALA
where the waiting state is divided into n identical sub-states, i.e. LW,1, LW,2, … LW,n. The transition rate from one sub-state to the next is set to nkw, so that the average residence time in the waiting state, 1/kw, is the same as in the delayed activation model. The ODEs describing other variables are the same as in the delayed activation model.
To test the assumption about how the population of latently infected cells is maintained, we modified the multi-stage delayed activation such that the latently infected cells proliferate at a constant rate as in Kim and Perelson [50] instead of asymmetric division in the main text. The ordinary differential equations (ODEs) describing the productively infected population and latently infected population are shown below and other terms in the model are kept the same as in the main text.
In this model, the latently infected cells becomes activated at per capita rate a and proliferate at per capita rate r. We set a = 0.0088 day-1 according to [62] and r = 0.0183 day-1, such that the half-life of the latent reservoir is 44 months as estimated before [63].
The fixed parameter values are based on prior work to yield a baseline state with a plasma viral load of 5 copies/mL and a latently infected cell population of ~2 cells/million cells (Table 1). The production and death rates of uninfected CD4+ T cells were chosen to yield a baseline CD4 count similar to the average level in patients in Elliot et al. [10]. Changes in the values of the fixed parameters that govern the dynamics of target cells and productively infected cells do not impact the estimation of the fitted parameters (S10 Fig). This is because cART is so effective that the contribution of new infections to the latent reservoir is negligible over the time period we study here. We also tested the robustness of the parameter estimates against changes in the fixed parameters that govern intracellular HIV transactivation in latently infected cells and to the structure of the model with regard to how latently infected cells proliferate (S7–S9 Figs).
The CA-US HIV RNA production rate in a productively infected cell, αI, is calculated based on the derivations in Ref. [28]. The basal transcription rate in eukaryotic cells is estimated to be approximately 40 nucleotides/s [64]. The HIV genome has around 9500 nucleotides. Thus, the basal production rate of CA-US HIV RNA production can be estimated as 40/9500≈0.0047 s-1≈406 day-1. In the presence of Tat, the transcription is upregulated to 100 fold [65]. Then, the production rate of CA-US HIV RNA in a productively infected cell, α0, can be calculated as approximately 40,000 transcripts day-1. This maybe a minimal estimate if productively infected cells live approximately one day while producing virus, as Chen et al. [32] have estimated that a productively infected cell produces between 40,000 and 55, 000 virions over its lifespan.
To fit the direct activation model to the data, we varied 5 unknown model parameters: the production rate of CA-US HIV RNA in a transcriptionally activated cell (α), the death rate of transcriptionally activated cells (dLA), the rate of transcriptional activation (ν), the initial delay of vorinostat effectiveness (t0) and the baseline US HIV RNA level (US0). To fit the delayed activation model and the multistage delayed activation model, we allow 2 additional parameters to be estimated (together with the 5 parameters in the direct-activation model): the transition rate, kT, from the transiently activated state to the waiting state, and the transition rate, kw, from one sub-state of the waiting state to the next.
To estimate the parameter values in each model, we first calculate the residual sum of squares (RSS) between model predicted log CA-US RNA level and log transformed data, and then minimize the RSS using the Nelder-Mead algorithm [66]. 1,000 individual fits were performed for each model starting from parameter values randomly sampled within biologically plausible ranges. The parameter values with the smallest RSS among the 1,000 fits are taken as the best-fit parameter values for each model.
We perform model selection using the corrected Akaike information criterion (AICc) to account for the low number of data points (ranges from 35 to 44) for each patient [67]. The AICc score is calculated as
AICc=nlog(RSSn)+2Knn−K−1
where n is the number of data points and K is the number of fitted parameters. When comparing models, the model with the lowest score is the best model, although small difference in AICc scores, e.g. ≤ 2, is not significant [67].
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10.1371/journal.pcbi.1000564 | Identifying Changes in Selective Constraints: Host Shifts in Influenza | The natural reservoir of Influenza A is waterfowl. Normally, waterfowl viruses are not adapted to infect and spread in the human population. Sometimes, through reassortment or through whole host shift events, genetic material from waterfowl viruses is introduced into the human population causing worldwide pandemics. Identifying which mutations allow viruses from avian origin to spread successfully in the human population is of great importance in predicting and controlling influenza pandemics. Here we describe a novel approach to identify such mutations. We use a sitewise non-homogeneous phylogenetic model that explicitly takes into account differences in the equilibrium frequencies of amino acids in different hosts and locations. We identify 172 amino acid sites with strong support and 518 sites with moderate support of different selection constraints in human and avian viruses. The sites that we identify provide an invaluable resource to experimental virologists studying adaptation of avian flu viruses to the human host. Identification of the sequence changes necessary for host shifts would help us predict the pandemic potential of various strains. The method is of broad applicability to investigating changes in selective constraints when the timing of the changes is known.
| Influenza A's natural reservoir is waterfowl. Sometimes avian virus genomic segments are able to shift to a human host, either in toto or by combining with those that underwent a previous host shift event. Such host shift events can cause worldwide pandemics in their immunologically naive hosts. In order for these host shifts to establish a stable lineage, the virus has to adapt to the new host. Identifying the changes that have occurred in the past can provide important clues about how this process happens, and how surveillance for new influenza threats should be targeted. Unfortunately, it is difficult to determine whether an amino acid has changed due to adaptation to the new host or whether the change occurred through random drift. Here we describe a novel phylogenetic approach to identifying locations where the nature of the selective pressure exerted on the location has changed corresponding to the host shift event. We identify a set of locations on a number of the genomic segments. The approach we describe is of wide applicability when the timing of the change of selective constraints is known in advance.
| Influenza A has the distinction of being an old disease, a recurring disease, and an ‘emerging’ disease. Influenza A viruses are found in humans as well as in other animals including swine, horses, sea mammals, and birds, of which waterfowl are considered the natural reservoir [1]. Subtypes of influenza A are distinguished by two surface glycoproteins; haemagglutinin (HA), the primary target of the immune response, and neuraminidase (NA). There are sixteen known types of haemagglutinin (H1 to H16) and nine of neuraminidase (N1 to N9), all found in waterfowl. Only H1, H2, H3 and N1, N2, however, are known to have caused epidemic disease in humans. The predominant forms of influenza A currently circulating in humans are H1N1 and H3N2.
There are two distinct problems represented by influenza. Firstly, the various subtypes currently in circulation in humans cause significant morbidity and loss of life. Secondly, periodically a subtype of influenza can make the shift from aquatic birds to humans, possibly through an intermediate host, resulting in a widespread pandemic in an immunologically-naïve population. These ‘antigenic shifts’ can occur either through the transfer of an entire virus from one host to another, or through a re-assortment process where genomic segments of the avian virus mix with genomic segments currently circulating in humans. In 1957 three virus segments (HA, NA, and PB1) from an avian-like source were combined with the other five segments already circulating in humans to create the H2N2 ‘Asian flu’ pandemic, while in 1968 two segments (HA and PB1) from an avian-like source were combined with the other six from the already-present human H2N2 virus to form the H3N2 ‘Hong Kong flu’ pandemic [2]. It has been suggested that the 1918 H1N1 ‘Spanish flu’ virus was the result of a single host-shift event from birds to humans [3]–[5] but this remains controversial [6]–[9]. In recent years, a number of different avian subtypes have caused sporadic human infections, including H5N1, H7N3, H7N7, and H9N2 [10]. While there is evidence for sporadic transmissions of these avian viruses between humans, the genetic changes necessary for widespread human-human transmission have, so far, seemingly not occurred.
A number of different proteins have been implicated in determining host ranges. Influenza haemagglutinin binds to sialic acid linked to galactose on the surface of the targeted cell; the differing nature of the sialic acid-galactose linkages in birds and humans (α2,3 sialic acid linkages in the bird gut, α2,6 sialic acid linkages of the upper human respiratory tract [11]) provides an important barrier to host shift events. A number of amino acid substitutions have occurred in human influenza haemagglutinin (e.g. Q226L and G228S in H2 and H3, E190N/D and G225E/D in H1) to adjust to the different receptors [12]–[16]. Neuraminidase, the protein responsible for cleaving the haemagglutinin from the receptor surface, also seems adapted to the particular sialic acid linkages, as well as for the pH and temperature of the host tissues [17]. Proteins in the viral replication complex (PA, PB1, PB2, and NP) have also been implicated in limiting host range by restricting replication and intra-host spread in mammals (for a review, see [18].) Of particular note is the PB2 gene, where one specific substitution, E627K, was identified and characterised experimentally as crucial for replication and intra-host spread in mammals [19]–[21].
As part of the widespread surveillance effort, it is important to understand the process of host shifts, and to identify the important changes that are necessary for the shift to occur, or that make the shift more likely. We currently have many examples of both avian and human viruses, so there have been a number of efforts at identifying ‘genetic signatures’ that characterise the virus as adapted to one or the other host. The most common method is to identify sites where the distribution of amino acids found in the virus in one host are sufficiently different from the distribution of amino acids found in the same site in viruses that affect the other host [22]–[24]. Unfortunately, there are two fundamental problems with this approach.
Firstly, the observed changes could represent the result of neutral drift rather than anything specific to the nature of the different hosts. As the human viruses are more closely related to each other than they are to the avian viruses, it would be expected that there would be characteristic amino acids found in the human lineages that are distinct from those found in the avian lineages because of the ‘founder effect’ [25], that is, the maintenance of the idiosyncratic properties of the particular virus that first infected humans. Comparisons of amino acid frequencies in viruses from the two hosts cannot easily distinguish between those that accidentally accompanied the host shift event and those that were actually associated with different selective constraints acting on the viruses in the two hosts.
The second related problem is the use of inappropriate statistical tests to identify when these two distributions are sufficiently different. The statistical tests used generally assume that each of the observed sequences represent a set of independent measurements. But the underlying phylogenetic relationships will generate correlations in the amino acids at a site, confounding the signal due to the host shift event. This can be demonstrated by considering Figure 1, which shows two possible situations where the avian viruses all have a leucine in a given position where all of the human viruses have a valine in the same position. In example A the results are statistically significant, in that the positions are independent, and it is unlikely that the simultaneous parallel changes in sequence occurred at random in the human viruses but not in the avian viruses. In example B there is much less statistical signal, as only one change of amino acid on the branch connecting the human and avian viruses is needed to explain the multiple observations. By neglecting the underlying phylogenetic structure, a single change of amino acid can be interpreted as a large number of independent events, grossly exaggerating the statistical significance.
A number of the published approaches to this problem suffer from the above problems. For example, both Chen et al. [22] and Miotto et al. [24] employed an information-based approach to identify sites where host-specific amino acids can be identified. Their computations of entropy (a measure of sequence diversity) and mutual information (the dependence of the observed residue distribution on host species) are based on considering every observed sequence as an independent data-point, ignoring correlations between the evolutionarily related sequences. Different distributions in the two hosts can be explained due to the founder effect described above, independent of any role these sites have in host adaptation. That is not to say that their results are incorrect, only that these problems make it impossible to determine their statistical significance. Finkelstein et al [23] looked at sites with a significantly higher degree of conservation in human lineages than avian lineages, and identified 32 markers within the M1, NP, NS, PA, and PB2 genes, 26 of them on the polymerase proteins NP, PA, and PB2. This analysis did not consider the phylogenetic relationships explicitly in their calculation of conservation, choosing instead to base their calculation on the frequency of the different amino acids observed in that site in the different hosts. While they employed strict tests for, for instance, multiple hypothesis testing, it is difficult to determine how much their results were affected by considering only frequencies of amino acids to represent the selective constraints, again ignoring the underlying phylogenetic relationships. It is known, for instance, that such counting methods produce very inaccurate amino acid frequencies compared with phylogenetically-based methods [26], and can not generally identify the rate of substitutions in the tree, but only the range of acceptable amino acids.
As described above, the differences in the distribution of amino acids at a given site between avian and human viruses might represent neutral drift or, more interestingly, a change in the underlying selective pressure applied to the virus by the host. Rather than characterising only the difference in observed amino acid distributions, we can instead look directly for evidence of changes in the selective constraints by modelling the phylogenetics explicitly. These selective constraint changes will result in differences in the substitution process, as mutations that arise in one virus or another will have different probabilities of achieving fixation. Thus, changes in selection constraints will manifest themselves as changes in the observed substitution rates. This also allows rigorous statistical methods, such as the likelihood ratio test, to be used to establish statistical significance.
The selective pressure acting on a site can be positive, negative, or neutral. Positive selection, also called adaptive, or more misleadingly [27] ‘Darwinian’, refers to the acceptance of advantageous mutations; negative, or purifying selection involves the rejection of deleterious mutations. Neutral selection pressure involves the chance acceptance of mutations that do not have a significant effect on the fitness. Both positive and negative selection pressure represent strong constraints on the amino acids at a given site; the difference is that during purifying selection the current amino acids generally fulfil these constraints so change is restricted, while during adaptive evolution the current amino acids are not well suited, generally due to changes in the constraints or a selective advantage for diversification, enhancing the rate of evolution until more appropriate residues are found.
Changes in the selective constraints can result in changes in the rate of substitutions at that location. If the initial amino acids do not match the current requirements of that site, there may be an adaptive burst of faster substitutions until the constraints are satisfied. Modifications of the stringency of the constraints, causing a given site to be more or less restricted, may cause a longer-term change in the substitution rate without necessarily causing an adaptive burst. Previous phylogenetic methods have generally focused on identifying changes in the absolute substitution rate [28]–[35] or ratio of non-synonymous to synonymous changes [36]–[38]. The latter method was used, for instance, to identify twelve sites on the influenza A nucleoprotein that seem to have undergone a change in selective constraints corresponding to the switch from avian to human host [39]. While these approaches are often useful, transient position-specific adaptive bursts are difficult to identify given the short duration of the effect. Sites can also undergo shifts in selective constraints without adaptive bursts or detectible changes in substitution rates, especially if the constraints in the two hosts overlap. Monitoring changes in the nature of the selective constraints has been much less common [40] and has not been applied to host shift events.
In this paper we investigate the use of a phylogenetic method to detect changes in selective constraints that considers not only changes in the magnitude of selection constraints, but also changes in its nature, represented as the relative propensity for the different amino acids. We do this by considering two different models for each site, a homogeneous model where the selective constraints are independent of host, the other a non-homogeneous model where the selective constraints depend upon the host. The likelihood ratio test can then determine the level of statistical support for rejecting the null hypothesis of no such dependence.
We start our analysis with a set of human and avian influenza viral sequences and the associated phylogenetic trees for each influenza gene. We consider the different haemagglutinin and neuraminidase serotypes (e.g. H1, H2, H3, N1, N2) separately. For each non-conserved site, we apply increasingly complicated substitution models, using the Likelihood Ratio Test (LRT) to evaluate the statistical support for each further complication.
The substitution models are defined by a symmetric exchangeability matrix S, the equilibrium frequencies of the twenty amino acids π, and a rate scaling parameter ν representing the relative substitution rate at that site compared with other sites. The simplest model, Model 1, consists of the WAG exchangeability matrix combined with the associated equilibrium frequencies for the different amino acids [41], with one adjustable parameter per site representing the scaling factor ν. We then consider Model 2 where the equilibrium frequencies of the amino acids are optimised individually for each site [26]. The likelihood ratio test demonstrated that the use of site-specific equilibrium frequencies was justified for all sites (P values ranging from 0.028 to 9.4×10−27).
We then created a non-homogeneous model, Model 3 where virus substitutions are modelled by one set of substitution rates in the avian host, and by a different set of substitution rates in the human host, as illustrated in Figure 2. The two different substitution models shared the WAG exchangeability matrix S and a site-specific rate-scaling factor ν, but now the equilibrium amino acid frequencies were both host- and site-specific. We identified sites with statistical support for different substitution rates in the two hosts, using a false discovery rate (FDR) method to account for multiple hypothesis testing [42]. We identified 172 sites with an FDR<0.05 (i.e. we would expect 5% of these sites to be false positives), and 518 sites with an FDR<0.20. We will refer to the 172 higher-confidence locations as ‘A sites’ and the remaining 346 lower-confidence locations as ‘B sites’.
We then considered if modelling differences in the equilibrium amino acid frequencies was adequate, or whether we should include host-dependent rate scaling factors as well. We implemented a more complicated model (Model 4) where the substitution rates were still defined with the WAG exchangeability matrix, but now both the equilibrium frequencies and the scaling factor ν were host- and site-dependent. Of the 2143 sites considered, few (37) had P values less than 0.05; after correcting for multiple hypothesis testing using the false discovery rate method, no site yielded any statistically significant improvement. The results described below will be based on Model 3 above.
The list of 172 ‘A’ sites (FDR<0.05) is shown in Table 1. Sites were found on all of the genes considered. Supporting Table S1 shows the list of the 518 ‘A’ and ‘B’ sites with FDR<0.20. Sites that have been identified experimentally are detected using this method, notably PB2 627. HA sites H1 190 and 225 and H3 228 are also identified. Sites H2 226 and 228 are significant at the weaker FDR<0.20 level, while H3 226 is not statistically significant.
To assess the performance of the technique describe here, we simulated each one of the 264 variable sites in the PB2 gene ten times (2,640 simulations in total). All sites were simulated using the same fixed tree topology. The 22 ‘A’ and ‘B’ sites identified as undergoing selective constraint changes (FDR<0.20) were simulated under the non-homogeneous model, using the parameters obtained by optimizing model 3. Similarly, the 242 locations with no evidence for change in selective constraints were simulated under the homogeneous model (model 2). We then applied the analysis described above to identify locations in the synthetic datasets that had undergone changes in selective pressure. On average, we observed that 1.5% of the locations identified with FDR<0.05 were false positives (false positive rate of 0.08%); this increased to 3.6% (false positive rate of 0.2%) for FDR<0.20. This indicates that the FDR values are, at least for PB2, likely conservative. Of the 22 locations modelled with changing selective constraints, 12.9 were identified with FDR<0.05 (false negative rate of 41%), with 16.2 identified with FDR<0.20 (false negative rate of 26%). The 13 ‘A’ sites were identified more consistently, with 10.1 found with FDR<0.05 and 11.0 found with FDR<0.20. This suggests that there remain more locations undergoing changes in selective pressure than are being identified with the procedure described here.
Our approach relies on the prior construction of an appropriate phylogenetic tree. In order to estimate the effect of phylogenetic uncertainty, we repeated the analysis of the PB2 gene segment with ten different phylogenetic trees obtained through non-parametric bootstrapping. The 13 ‘A’ sites were identified on 79% of the bootstrap trees with FDR<0.05 and identified on 90% with FDR<0.20. 85% of the 22 ‘A’ and ‘B’ sites were similarly identified on the bootstrap trees with FDR<0.20. Conversely, the bootstrap trees identified on average 2% (with FDR<0.05) and 6% (with FDR<0.20) of alternative locations that were not identified on the original tree. These might be false positives for the alternative trees, suggesting a similar amount of false positives on the original tree. Some of these locations, however, may be locations with changes in selective constraints, and thus represent false negatives for the original tree; most of these locations would have been so identified with a higher FDR threshold of 0.50, although these points represent only about 12% of the otherwise unidentified locations.
We constructed a simple model to help explain the lack of statistically significant improvement with adding host-specific scaling factors. This was based on considering a protein site where two amino acids (A and B) are present, where an organism with residue B has a fitness equal to 1−s relative to an organism with residue A. We used Kimura's fixation rate theory [43] to calculate the resulting substitution rates between A to B, and formulate these expressions in terms of a rate scaling factor ν and equilibrium frequencies πA and πB ( = 1−πA). We considered how ν, πA , and πB change as the relative fitness difference between A and B is altered. We also considered the overall rate at which substitutions occur in both directions, both for negative selection where the residues are at equilibrium (Γ−) as well as for positive selection (Γ+) where the location contains the unfavourable residue B. Figure 3 shows the dependence of πA, πB, ν, Γ−, and Γ+ (the latter three normalised by the mutation rate μ) on the relative fitness difference s (scaled by the effective population size Neff). As shown, under conditions of negative selection, increasing fitness differences result in a decrease in the overall rate of substitutions, but an increase in the rate-scaling factor. There is a relatively weak dependence of ν on s as long as the latter is not large relative to 1/Neff. Under conditions of positive selection, both quantities increase with larger fitness differences.
The theoretically predicted weak dependence of ν on selective pressure and the lack of statistical support for host-dependent values of this parameter indicate that ν is not a good measure of the degree of selective constraints. To generate a more appropriate measure, we calculated the relative entropy between the equilibrium frequencies and what would be expected under no selection, π0, estimating the latter by averaging the amino acid frequencies over our entire database. This measure of selective constraint magnitudes for the various sites in avian and human hosts are presented in Table 1, Supporting Table S1, and in Figure 4.
As described in the introduction, ignoring the underlying phylogenetic relationship often results in a gross over-estimation of statistical significance, as single evolutionary events are interpreted as a large number of independent measurements. Correspondingly, certain sites that have been identified by other methods that do not model the underlying phylogenetics lose their statistical significance when the phylogenetics is considered. For instance, site 271 in PB2 is identified as a significant site in three previous analyses [22]–[24]; human viral sequences are most commonly alanine at this site, while avian viral sequences are predominantly threonine, although alanine also occurs. When each sequence is interpreted as an independent event, there is strong statistical support for host-specific amino acid distributions at this site. All of the alanines in the human lineage, however, can be explained by a single threonine to alanine substitution. In contrast, in the avian influenza there were at least three independent threonine to alanine substitutions. The single example of the substitution in human influenza is not significant given the relative frequency of this transition in avian influenza. Indeed, the more complex Model 3 incorporating host-dependent substitution rates a P value of 0.095 compared with Model 2 that assumes no such host-dependence, with an unimpressive false discovery rate of 0.48 after the correction for multiple hypothesis testing. More threonine to alanine substitutions in the human lineage, even if that meant more human sequences with a threonine at this site, would have provided more statistical support. The statistical support would also have been larger if the various avian strains with an alanine at this site represented the result of a single substitution.
The sites that are identified are those with a significant statistical signal given the available data; other sites might be undergoing shifts in selective constraints that are not detected for different reasons. As with all appropriate statistical methods applied to this problem, we require adequate evolutionary time and a suitable substitution rate for the substitution patterns to be detectable. In particular, there has to be sufficient evolutionary time in both the avian and human lineages for the parameters in the substitution models to be sufficiently well defined in each so that the differences in selective constraints are detectible. This will require longer evolutionary time when the selective constraint changes are smaller. As shown in the phylogenetic trees (Supporting Figures S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, and S11), there is relatively little sequence evolution in the human H2 lineage; this is possibly the cause of the relatively few sites identified in this gene subtype. There are more H3 sequences, although most available avian H3 sequences are highly similar, reducing our ability to detect selective pressure changes in this gene subtype. In particular, we do not identify the H3 Q226L mutation whose importance has been determined experimentally, as the strict conservation of glutamine in the avian lineage is not highly informative given the lack of evolutionary divergence among the avian H3 sequences. Finally, the improvement in the log likelihood necessary for a given level of statistical significance is a function in the increase in the number of adjustable parameters between the two models, which is one minus the number of amino acids found in that location. Locations that are highly variable require more adjustable parameters, reducing the power of the likelihood ratio test. In particular, human H3 viruses contain glutamine, leucine, isoleucine, and valine at position 226, making identification of selective constraint changes at this location difficult.
The identified changes in selective constraints may not be the direct result of the host shift event. Selection constraint changes at one site might be a response to substitutions that occur at a different site, even if those changes were themselves the result of neutral drift. We have also assumed that the change in selection constraint occurs simultaneously with the host shift event. In reality this method has limited temporal resolution, and changes in the substitution rate occurring near the host shift event might also be identified.
We do not include ‘pre-selection’ in the model, that is, that the match between the avian sequence and the selective constraints in the human host does not influence the probability that that particular virus strain will undergo a shift to humans. This could be added to such a phylogenetic-based model by considering the probability that a host shift would occur on a given lineage as a function of the protein sequence. This would greatly increase the complexity of the model, increasing the number of adjustable parameters, reducing the statistical power of the method. It is important to note, however, that this would increase the number of false negatives, as these occurrences would look identical to founder effects. It is less likely that this process would produce false positives.
We have included information from the A/California/04/2009 (H1N1) sequences from the 2009 ‘Swine flu’ pandemic in Table 1. This strain represents a reassortment of avian-like European swine genes (M1, M2, NA) with a triple-reassortment strain previously circulating in swine containing segments originally from the classical swine (NP, NS, HA), human (PB1), and avian lineages (PA, PB2) [44]. Considering the locations identified with a false discovery rate of 5%, most segments originally from classical or European swine (NA, M1, NP, NS1) mostly matched the human selective constraints, suggesting a similarity between the constraints in humans and swine. The exception is the HA gene, where many locations seemed to match the avian selective constraints despite its classical-swine origin, possibly reflecting the slow rate of antigenic change of the classical swine haemagglutinin [45],[46]. In the segments more recently from the avian lineages (PA, PB2), most locations more closely matched the avian constraints, while PB2 684 and PA 356 more closely matched the human. Interestingly, by comparing with avian sequences, it appears that PB2 A684S and PA K356R substitutions, both involving changes from an avian-like to a human-like amino acid, occurred in the interval between the host shift to swine and the subsequent transfer to humans, suggesting that these changes might be related to the ability of these viruses to infect humans.
Most methods that look for changes in the substitution rates model this as changes in ν, the scaling parameter, or in the related ratio of synonymous to non-synonymous substitutions. In our analysis, we find that, when we allow the equilibrium frequencies π to vary, there is no statistically significant variation in ν. This seems initially counter-intuitive, as there are some sites where there seems to be substantial changes in the degree of conservation; in site 274 in N1, for instance, is almost universally tyrosine in avian viruses, while it varies between tyrosine, serine, and phenylalanine in human viruses. Yet the likelihood ratio test applied to this site rejects the inclusion of host-dependent scaling factors with a P value of 0.90, suggesting that the relationship between rate scaling factors and site variation are not simply related.
This observation motivated our simple model to try to gain insight into the relationship between equilibrium frequencies and rate scaling factors, by considering a protein site where two different amino acids, A and B, are found. We imagine that organisms possessing residue A at this location have a fitness advantage. Negative purifying selection would occur when the residues at this location are at their equilibrium value, while positive selection would occur when this location was filled by B, such as might occur when the selective pressure on the protein changes. By using Kimura's theory of fixation probability [43], we can calculate the values of the rate scaling factor ν, the overall rate of substitutions for purifying (Γ−) and positive selection (Γ+), and the equilibrium frequencies of A (πA) and B (πB), as a function of the different finesses provided to an organism with the two different possible amino acids at that location, as described in the Methods section below. Normalised values of ν, Γ−, and Γ+ are plotted as a function of 2Neff s in Figure 3. As shown, ν varies surprisingly little with s as long as s is not much more than 1/Neff. This explains why including a host-specific ν never yielded statistically significant improvements with our data. When we consider adaptive substitutions, larger values of s correspond to higher selective constraints, larger values of ν, and faster evolution. The situation is quite different with purifying selection. As might be expected, larger values of s (corresponding to larger degree of purifying selection) result in a slower substitution rate, but this actually corresponds to larger values of ν. The reason why most phylogenetic programs use an inverted relationship, where larger values of ν correspond to faster substitution rates, is that they do not consider the value of π appropriate for each site. By assuming that the same values of π apply to all sites, a more extreme distribution of equilibrium frequencies, resulting in a decrease in the number of substitutions, is interpreted as a reduction in ν although this parameter is, in fact, increasing
The magnitude of the selective constraints for the various sites in avian and human hosts are presented in Table 1, Supporting Table S1, and in Figure 4. It is interesting to note the number of positions under changing selection constraints where the magnitudes of the selection constraints are relatively constant. Such sites would be difficult to detect by looking for changes in the substitution rate, especially in cases where the distributions of amino acids found in the two hosts have significant overlap.
The methods described here are applicable for a wide range of problems involving changes in selective constraints. There are two particular factors, however, that make the technique especially well suited for influenza. Firstly, the branch along which the selective pressure changes can be identified a priori. Secondly, it is important to generate appropriate phylogenetic trees for the position under consideration. Generation of such trees can be complicated when there is incongruence between different locations. For influenza, incongruence between the various genomic segments results from the process of reassortment, where chimeric viruses containing genomic segments of different origin result from multiple infections. We are able to address this issue by considering each different genomic segment independently, constructing gene-specific phylogenetic trees. A more difficult problem is intra-gene homologous recombination, where different regions of a single genomic segment have different phylogenies. Such recombination is either extremely rare or non-existent in influenza (as well as other negative RNA viruses), and has never been observed experimentally [47]–[49].
We have assumed that the transitions from avian to human hosts did not go through an intermediate species, such as swine. There is no evidence of involvement of swine in the 1957 Asian flu and 1968 Hong Kong flu host shift events. Based on his analysis of the 1918 Spanish flu sequences and the relative timing of the 1918 influenza outbreaks in swine and humans, Taubenberger concluded that the Spanish flu transferred in toto from birds to humans and from humans to swine [3]–[5], although this conclusion has been challenged [6]–[9]. If an intermediate host species were involved, it would not be expected to affect the results if the selective constraints at any location in this intermediate host were to resemble either that of avian or human viruses, as this would only change the timing of the shift from one selective constraint to another. If there were an intermediate host and the selective constraints at some locations in this intermediate host were strong and substantially different from either avian or human viruses, the amount of evolutionary time in this intermediate host were sufficiently long, and the evolutionary time in humans sufficiently short so that the new equilibrium is not attained, the results of these calculations could be affected.
There are two other important assumptions made in this work. Firstly, we assume that the selective constraints in human and avian viruses are constant, and that each location can be considered independently. We do not consider, for instance, that there may be different selective constraints in low-pathogenic and high-pathogenic avian viruses, or that compensatory changes can occur elsewhere in the protein or even in other proteins. The observation (both here and experimentally [12]–[16]) that different hemagglutinin subtypes undergo different patterns of change of selective constraints indicates that this assumption is not strictly valid.
For the following discussion we assume the evolution of a viral protein along a phylogenetic tree with two different host lineages, avian and human, where we consider the root of the tree to exist somewhere in the avian lineage. The evolution of amino acids in a site along a phylogenetic tree can be modelled as a continuous Markov process, described by a 20×20 substitution matrix Q. (Standard phylogenetic modelling techniques are described in [50].) In order to provide for time reversibility (that is, the expected number i to j transitions equalling the expected number of transitions from j to i), this is commonly represented as where S is a symmetric matrix representing the exchangeability of amino acids i and j, πj is the equilibrium frequency of amino acid j () and ν is a scaling parameter that accounts for the overall rate of substitution at the site. S encodes the underlying codon structure as well as the relative similarities of the physicochemical properties of the amino acids, while the equilibrium frequencies represent the relative propensities for each of the amino acids at that site. We can calculate the likelihood of the data at this site given the model using Felsenstein's pruning algorithm [51],[52].
We first consider a standard substitution model where S and π are given by the WAG substitution matrix [41], where each site in the set of proteins is characterised by a distinct substitution rate scaling factor ν whose value is determined by maximising the log likelihood given the sequence data at that site and the input phylogenetic tree. This we refer to as Model 1. We then considered the appropriateness of modelling each site in the set of proteins with a distinctive set of equilibrium amino acid frequencies [26], what we refer to as single-site homogeneous Model 2. We adjust the values of π simultaneously with ν to maximise the likelihood. To avoid over parameterisation, we still use WAG S values for all sites. The tree topology is assumed fixed, and branch lengths are the same for all sites. In order to reduce the number of adjustable parameters, πi = 0 for any amino acids not found at that site. As the equilibrium frequencies of the amino acids not observed are set to zero, this results in an increase in the number of parameters equal to the number of amino acids present at that site minus one (due to the constraint that the equilibrium frequencies must sum to one). We then use the likelihood ratio test to see if site-dependent equilibrium frequencies can be justified with the data. As described in the Results section, the site-dependence of the equilibrium frequencies could be justified for all sites.
Now let us imagine that upon inspection of the phylogenetic tree, we notice that amino acid preferences at a particular site seem different in the two host clades. We can incorporate this observation into our model by using two distinct Q matrices to describe the evolution of this site in the different hosts, as illustrated in Figure 2. For the reservoir avian host we write and for the new human host where π and π′ represent the equilibrium amino acid frequencies at that site in avian and human viruses, respectively. (In principle we could also have S depend upon the host, but this would result in a large increase in the number of adjustable parameters. We will consider host-dependence of ν below.) The host shift event is defined as the midpoint of the branch connecting the common ancestor of the human viruses with its parent node. We can now calculate a new likelihood for this site using the same fixed topology, again adjusting π, π′, and ν to maximise the likelihood. We call this the single site non-homogeneous model, Model 3. Again, the increase in the number of adjustable parameters for Model 3 relative to Model 2 equals the number of amino acid types observed at that site minus one. Because the Model 2 is nested inside Model 3, we can again use the likelihood ratio test to test the hypothesis of different selective constraints in different hosts at that site.
In general, for a protein with N variable sites, we could repeat the procedure above for each site in the alignment, and perform N likelihood ratio tests. This would generate a list of those sites that show statistically different amino acid compositions, and hence distinctive selective constraints, in the different hosts. Following the calculation of the statistical significance for each site we can then use standard false discovery rate (FDR) methods to account for multiple hypothesis testing [42].
Finally, we consider if, in addition to host-dependent equilibrium frequencies, we also have statistical evidence for host-dependent rate scaling factors. We again use for the reservoir avian host but now use for the new human host where ν and ν′ represent the rate scaling factors at that site in avian and human viruses, respectfully. Again, Model 3 is nested inside Model 4 with an increase of one adjustable parameter, meaning that the statistical support for this extra factor can be evaluated with the likelihood ratio test. We do not observe support for this extra parameter in any of the sites after adjusting for multiple hypothesis testing.
Human and avian viral sequences were collected from the NCBI Influenza Virus Resource [53]. Due to the frequency of reassortment, we cannot assume that the phylogenetic relationships for the various genomic segments are similar; they must be treated independently, including creating genetic-segment specific phylogenetic trees. The sequences for the various segments were treated as independent data sets, with separate datasets for the H1, H2, H3, N1, and N2 genes. Clusters of highly similar sequences (approximately >99.5%) were culled as to reduce the overall number of sequences to around 400 per dataset. It is common to find sporadic transmissions between avian, human, and other (e.g. swine) hosts; we eliminated all sequences resulting from such transmissions (e.g. human H5N1 sequences), leaving us with a single connected set of avian sequences and separate monophyletic human clades corresponding to the host shift events of 1918 (H1, N1, internal genes), 1957 (H2, N2, PB1), and 1968 (H3, PB1).
In order to generate more accurate phylogenetic trees, the culled sequences were aligned at the amino acid level (MUSCLE, [54]), with these alignments then used to create nucleotide codon alignments (PAL2NAL, [55]). The phylogenetic tree topologies were then created for the nucleotide data using PhyML ([56]; HKY85 model [57], Gamma-distributed rates). The resulting trees are included as Supporting Figure S1–S11. Because amino acid distances are needed for the models developed here, branch lengths were then re-optimised for this fixed tree topology using the corresponding amino acid data (PAML [58],[59], WAG substitution matrix [41], Gamma-distributed rates). The analysis was then performed with each gene set, based on the phylogenetic tree for the genomic segment in which the gene is located. A computer program written in Java that implements and optimises the various models described above is available from the authors.
The determinations of changes in selective constraints at each site is a separate hypothesis to be evaluated, so we must address the multiple-hypothesis question, that is, if we ask a suitably large number of statistical questions we are likely, at random, to obtain some statistically-significant results. We use the false discovery rate method, that is, specifying for each site the false positive rate that would have to be tolerated in order for that result to be statistically significant, following the Benjamini and Hochberg estimator [42]. We first choose an acceptable false discovery rate δ. If P(k) is the k-th smallest P value for a set of n sites, we choose the largest value of k so that . As different genes are evolving in different circumstances, we would not expect the fraction of sites in each gene undergoing changes in selective constraints to be the same. Combining all of the genes together in one dataset would result in an increase in false positives for the genes with fewer changes in selective constraints, and an increase in false negatives for the genes with more changes in selective constraints. For this reason we analyse the false discovery rate for each gene individually. Table 1 and Supporting Table S1 list, for each site, the smallest possible acceptable false discovery rate that would result in that site being labelled as statistically significant. These should not be interpreted as the probability that that given site is a false positive.
Each site was simulated under the homogeneous (Model 2) and non-homogeneous (Model 3) models 10 times using the program Evolver [59] using the estimated tree topology and the WAG+F substitution matrix [50]. For each site, the tree was scaled according to the site-specific estimated rate-scaling parameter ν. Simulation under the non-homogeneous model was performed in two steps: the avian part of the tree was simulated using a randomly generated root sequence following the avian equilibrium frequencies for that location. The avian subtree contained a host shift tip that served as the root of the human clade. The human subtree was then simulated according the human equilibrium frequencies using the simulated avian sequence at the host shift.
The PB2 sequence was bootstrapped 10 times and tree topology re-estimated for each boot sample. The homogeneous and non-homogeneous models were optimised for the observed data at each location, and the LRT was performed again for each one of the 10 new tree topologies so as to assess the effect of tree topology uncertainty on the identification of adaptive sites.
Consider a protein site where two amino acids, A and B, are found. Let us imagine that that A is the more advantageous amino acid, that is, organisms with A at this site have a higher fitness, while organisms with B at this site has relative fitness . Let us also imagine that the mutation rate from A to B μAB is equal to the reverse mutation rate μBA = μ. We imagine a number of different lineages that have diverged, each with effective population size Neff. Assuming that the mutation rate relative to the population is reasonably small, A or B will become fixed in each lineage. For haploid organisms, the probability that A would become fixed in a given lineage is given by [43](1)where we have recognised that this probability is simply the equilibrium frequency of A in the ensemble of diverged organisms, with .
The substitution rate of A by B is just the mutation rate μ times the fixation probability, given by Kimura's formula for small s [43].(2)We can compare these expressions with as used in phylogenetic analyses. As we are only dealing with two different residues, is a simple multiplicative constant and can be set equal to one, resulting in . Equating these two expressions for QBA and solving for ν yields(3)(4)
Similar results are obtained, as would be expected, when we express .
We can now consider the cases of neutral, adaptive (positive), and purifying (negative) selection. Neutral selection is simply the case when Neffs is small and . For both neutral and negative selection, we can consider the overall rate at which substitutions occur, given by , which is equal to Neff μ in the case of neutral selection. Positive selection involves the situation where we are not at equilibrium, but rather, at least in this case, we have the less-fit residue occupying the given position. In this case, assuming again that A is the favoured residue, .
We characterise the selection constraints by how far the equilibrium amino acid frequencies π differ from what would be expected under no selection π0 through the relative entropy, defined as(5)which is, as is desired, zero when π equals π0. Unfortunately, it is difficult to estimate π0, as there is little of the virus genome that is not under some degree of selective constraints. We estimate π0 by averaging the amino acid frequencies over our entire database, with the expectation that specific selection constraints will, at least approximately, average out.
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10.1371/journal.pcbi.1007269 | Non-trophic interactions strengthen the diversity—functioning relationship in an ecological bioenergetic network model | Ecological communities are undeniably diverse, both in terms of the species that compose them as well as the type of interactions that link species to each other. Despite this long recognition of the coexistence of multiple interaction types in nature, little is known about the consequences of this diversity for community functioning. In the ongoing context of global change and increasing species extinction rates, it seems crucial to improve our understanding of the drivers of the relationship between species diversity and ecosystem functioning. Here, using a multispecies dynamical model of ecological communities including various interaction types (e.g. competition for space, predator interference, recruitment facilitation in addition to feeding), we studied the role of the presence and the intensity of these interactions for species diversity, community functioning (biomass and production) and the relationship between diversity and functioning.Taken jointly, the diverse interactions have significant effects on species diversity, whose amplitude and sign depend on the type of interactions involved and their relative abundance. They however consistently increase the slope of the relationship between diversity and functioning, suggesting that species losses might have stronger effects on community functioning than expected when ignoring the diversity of interaction types and focusing on feeding interactions only.
| The question of how species diversity contributes to the functioning of ecological communities has intrigued ecologists for decades, and is especially relevant in the current context of species extinctions. Ecological communities are not only diverse in terms of the species that compose them but also in terms of the way they interact with each other: for example, species compete for space and for food, eat and facilitate each other. The diversity of ways species interact has rarely been taken into account in the study of ecological communities, although widely acknowledged. Here we show that the diversity of interaction types matters: it affects species diversity, community functioning and the relationship between them by strengthening this relationship. This means that when the diversity of interaction types is taken into account, species losses have stronger impacts on the functioning of ecological communities. Our results therefore suggest that species loss may have more important consequences than expected based on classical models that do not take the diversity of interaction types into account.
| Despite the wide recognition of the coexistence of multiple interaction types linking species in nature [1–3], research on ecological networks has been massively dominated by studies on a single interaction at a time (e.g. trophic, competitive or mutualistic; e.g. [4–6]). The implications of the diversity of interactions for ecological community dynamics and resilience remains therefore largely unknown, despite a recent growing interest in the ecological literature [7–10].
Among interaction types, feeding has massively dominated the literature [2], leading to the analysis of the structural properties of food webs on data sets and to the use of modeling to investigate the functional consequences of these structures (e.g. [4, 11–16]). Early on, Arditi and colleagues [17] proposed to integrate non-trophic interactions in such dynamical models as modifications of trophic interactions (so-called ‘rheagogies’). Building on that idea, Goudard and Loreau [18] investigated the effect of rheagogies on the relationship between biodiversity and ecosystem functioning (BEF) in a tri-trophic model. They showed that ecosystem biomass and production depended not only on species richness but also on the connectance and magnitude of the non-trophic interactions.
Several studies have investigated the role of incorporating specific interactions in food webs. For example, incorporating interspecific facilitation in a resource-consumer model allowed species coexistence in communities of plants consuming a single resource [19]. This increase in species diversity also happens in ecological communities with higher trophic levels including both trophic and facilitative interactions [3]. In the same model, intra- and inter-specific predator interference increased species coexistence as well in multi-trophic webs, although to a lesser extent than facilitation among plants [3].
More generally, the joint effect of several interaction types is expected to affect community functioning and stability. Extending May’s work, Allesina and Tang [20] showed that communities including a mixture of mutualistic and competitive interactions with equal probability were less likely to be stable than random ones (i.e. where interactions between species are randomly chosen), themselves being less stable than predator–prey communities (i.e. in which interactions come in pairs of opposite sign). Using a similar approach, Suweis and colleagues [21] explored the effect of mixing mutualistic and predator-prey interactions on stability, and showed that, without making any further hypothesis, increasing the proportion of mutualistic interactions tend to destabilize the community. Conversely, in a spatially explicit model including both mutualism and antagonism, Lurgi et al. [9] found that increasing the proportion of mutualism increased the stability of the communities. Addressing the relationship between structure and stability, Sauve et al. [8] showed that the role of nestedness and modularity—structural properties that were shown to promote stability in their single interaction types networks (more specifically in mutualistic networks for nestedness and in antagonistic networks for modularity)—was weakened in networks combining mutualistic and antagonistic interactions. Note that this result contrasts with Allesina and Tang [20]’s result on community matrices who showed that, for mutualistic interactions, nested matrices were less likely to be stable than unstructured matrices.
Combining dynamical models with an empirical network analysis including all known non-trophic interactions between the species of intertidal communities in central Chile [22], Kéfi et al. [10] found that the specific ways in which the different layers of interactions are structured in the data increased community biomass, species persistence and tend to improve community resilience to species extinction compared to randomized counter-parts. More recently, García-Callejas et al. [23] used a dynamical model to investigate the effect of the relative frequency of different interaction types on species persistence and showed that persistence was more likely in species-poor communities if positive interactions were present, while this role of positive interactions was less important in species-rich communities.
Altogether, these studies suggest that the joint effect of several interaction types could alter fundamental properties of ecological systems—such as species coexistence, production and community stability—with however a clear lack of consensus on how. So far, most studies have addressed these questions with specific subsets of non-trophic interactions [3, 8, 18, 19], in small species modules [24, 25], in networks with limited numbers of trophic levels [19] or with unrealistic trophic structure [18]. Only a few studies have extended these approaches to complex networks of interactions with a diversity of interaction types (see e.g. [9, 23, 26]). We therefore still lack a clear view on the overall role of the diversity of interaction types per se for species diversity and community functioning, and especially how they may affect the relationship between diversity and functioning.
In the 90ies, because of the raising awareness of the increase in species extinction rates, the long-lasting interest on the origin and maintenance of species diversity shifted toward the study of the consequences of biodiversity, and especially of its loss, for ecosystem functioning [27]. This became an entire sub-field of ecology referred to as ‘Biodiversity and Ecosystem Functioning’ (so-called BEF) and lead to decades of experimental and theoretical research investigating how diversity affects functioning (see [28–33] for reviews). Results of experimental studies suggests that more diverse communities generally produce more biomass than less diverse ones [34, 35]. Theoretically, the question has been addressed as well; models have long focused on plant communities (i.e. a single trophic level) (e.g. [36]), but have more recently started to expand these investigations to more complex, realistic communities (e.g. [37–39]). Until now, as far as we know, studies had not specifically investigated the role of the diversity of interactions types on the shape of the BEF.
Here, using a bioenergetics resource-consumer model in which broad categories of non-trophic interactions were introduced [3], we systematically investigated the functioning of ‘multiplex’ ecological networks, i.e. how multiple interactions (their abundance and intensity) affect species coexistence, community functioning (biomass and production), and the relationship between diversity and functioning. Our model includes, in addition to the consumer-resource interactions, competition for space among sessile species, predator interference, refuge provisioning, recruitment facilitation as well as effects that increase or decrease mortality.
In what follows, we use NTI(s) to refer to non-trophic interaction(s).
We ran community dynamics with or without NTIs, and evaluated the relative difference in community characteristics at steady state obtained in the presence compared to in the absence of each NTI. This allowed comparing the effects of the different NTI types (for a range of interaction intensities; see Methods). At steady state, the measured characteristics of the communities were: species diversity (the number of species which survived at steady-state, i.e. whose biomass was above a threshold level), total biomass (the sum of the biomass of all surviving species at steady state) and total production (the sum of the intrinsic growth of basal species and food uptake minus respiration of consumers, over all surviving species; first term in Eq (4) in Methods, ‘The dynamical model’).
The following NTIs were introduced, one at a time, in the consumer-resource model: i) predator interference, which can occur between two predators which share at least one prey, ii) refuge provisioning which can happen if a species protects another from its predator (e.g. affecting the attack rate of the predator), iii) competition for space which occurs predominantly between sessile species, iv) recruitment facilitation which happens when some species increase the recruitment of new plants in the community (e.g. by habitat amelioration), v) increases in mortality when some species decrease the survival of others (e.g. because of whiplash) and vi) decreases in mortality when some species increase the survival of others (e.g. by improving the local environmental conditions).
We found that interference had a negative effect on diversity and community production and a weak (negative) effect on biomass (1st row of Fig 1). Through time, interference decreased the consumption of some of the predators; this initially favored some of the basal and intermediate species (that were less consumed), and eventually lead to the extinction of some of the intermediate and top predators, as well as to a decrease in their total biomass (1st rows in S1 and S2 Figs). Primary producers, some of which were relieved from consumers, exhibited a slight gain in biomass.
Refuge provisioning had similar overall effects, but with a larger effect on biomass than the one of interference (2nd row of Fig 1). In this case, species benefiting from refuges remained in the system but were less accessible resources. This lead to a loss of biomass and subsequent extinctions of some consumers (which could not access their prey; especially top predators), while their resources remained under protection and gained a bit of biomass (except for those whose protector went extinct) (see S1 and S2 Figs, 2nd row).
Competition for space had a strong negative effect on all variables (which affected all trophic levels), while recruitment facilitation had a positive effect on all community characteristics (but affected only consumer and predator species; 3rd and 4th rows of Fig 1 and S2 Fig). Through time, these effects tend to first affect the basal species, then the intermediate and eventually the top predators (see S1 Fig).
Modifications of mortality rates produced very weak effects overall. Increasing mortality had a negative effect on diversity and biomass and no effect on production (5th row of Fig 1). Decreasing mortality had a very weak positive effect on diversity and biomass and no effect on production (6th row of Fig 1).In what follows, we did not consider decreases in mortality (also referred to as ‘positive effects on mortality’) which was the weakest of all NTIs considered overall and focused instead on the five remaining NTIs, namely interference among predators, refuge provisioning, competition for space, recruitment facilitation, and increase in mortality (also referred to as ‘negative effects on mortality’).
Overall, the most influential NTIs among the ones studied were competition for space and recruitment facilitation, in terms of both diversity and functioning (see slopes linking the parameter values to see the extent of the effects in Fig 1). The effect of competition for space was two orders of magnitude larger than those of all the other NTIs (namely interference, refuge provisioning, recruitment facilitation and increase in mortality). Regarding biomass, the effect of competition for space was two orders of magnitude larger than the one of recruitment facilitation, which was itself an order of magnitude larger than the one of all the other NTIs (namely interference, refuge provisioning, and increase in mortality).
For all NTIs except for competition for space, effects seemed to be stronger on intermediate and top trophic levels at steady state (see S2 Fig). Regarding species diversity, this was partly due to the fact that plant species already all persisted with trophic interactions only, and—besides competition for space (and to a much lesser extent increases in mortality)—the other NTIs were not able to lead to plant extinctions, because their effects either corresponded to a decrease in plants consumption (interference, refuge) or to a positive effect on plants (recruitment facilitation, decrease in mortality). Therefore, the NTIs studied here had very little opportunities for affecting plant species diversity. Conversely, the NTIs studied had more leverage on intermediate and higher trophic levels where species did not all persist in webs with trophic interactions only, and where they could therefore either increase or decrease species diversity. Regarding biomass, effects seemed to first affect basal species but then climb up the food web to eventually affect the top predators more strongly (see S1 Fig).
This first set of simulations helped us categorize the NTIs studied into ‘positive’ (i.e. beneficial; recruitment facilitation) vs ‘negative’ (i.e. detrimental; interspecific predator interference, refuge provisioning, competition for space and increase in mortality) based on their effect on diversity.
Next we mixed the five remaining NTIs together, with NTI intensities picked at random within predefined ranges to study the joint effect of the NTIs considered. These predifined ranges were chosen so that each NTI increases or decreases the diversity of the system by 2.5% to 10% compared to the case without NTI (see Methods and Fig 1). Pre-defining these ranges for each of the NTI taken individually allows to put all NTIs on comparable grounds.
Not unexpectedly, the effect of the presence of the NTIs depended on the relative number of links of the different NTIs and on their intensities. When all interaction types were together with an equal proportion of positive and negative NTIs, networks with NTIs tended to have a smaller species diversity than networks without NTIs (Fig 2A). In other words, NTIs lead to extinctions of species compared to simulations run with feeding interactions alone. There were also quite a few number of cases where the net effect on diversity was null.
There was nonetheless a large fraction of cases where NTIs tended to enhance species diversity; these were clearly cases where beneficial NTIs were present and strong (orange and red areas on Fig 2B). It was noteworthy that the NTI values were all chosen at random for each of the simulations, so all combinations of intensity values were possible and present across simulations, but our results showed that positive effects of NTIs on diversity always happened when the beneficial NTI (recruitment facilitation) was strong while the detrimental NTIs were weaker.
Now fixing the intensities of all NTI links to their maximum value (corresponding to a 10% effect on diversity ratio; see Fig 1) and focusing on their relative abundance, we found that a greater number of recruitment facilitation links tend to favor positive effects on diversity while increasing the number of interference, refuge or competitive links pushed toward negative effects on diversity (S3 Fig).
How did these effects on species diversity translate into community functioning? Using the previous simulations where NTI intensities were picked at random, we found that both in food webs (i.e. in ecological networks without NTIs) and in ecological networks with NTIs, the relationship between species diversity and biomass at steady state was positive (Fig 3A and 3B; this was also the case for production: see S4 Fig)Strikingly, in presence of NTIs, the relationship was significantly stronger than in their absence (ANCOVA p-value<1e-16; comparing slopes in Fig 3A and 3B). We checked that this result was robust to changes in the value of major parameters of the model (namely the Hill exponent which determines the shape of the functional response, the parameter expo which determines how species body mass depends on their trophic level, and the capture coefficient a0 of consumers; S5 Fig).
Plotting the biomass ratio (i.e. the variation in biomass with NTIs compared to without NTIs) as a function of the species diversity ratio suggested that when NTIs contributed to a gain in species, this generally translated into a gain in biomass as well (Fig 3C). Actually, networks with NTIs tended to gain biomass (compared to networks without NTIs) even when there was no gain (or even a weak loss) in diversity (see boxes at diversity ratios of -0.1 and 0 in Fig 3C).When there was a small loss of diversity in presence of NTI (-0.1–0), the remaining species took advantage of these extinctions and gained biomass. When there was a gain in species diversity compared to the case without NTIs, this was often happening because of the presence of beneficial NTIs (Fig 2B), and those beneficial links lead to a considerable increase in biomass as well. There was, however, a large variability around these trends due to the fact that each simulation corresponded to a different combination of NTI intensities.
Using a bioenergetic model in which six types of NTIs were incorporated, we found that these NTIs in isolation and jointly affected significantly species diversity and community functioning (biomass and production), consistently with previous studies addressing the role of the diversity of interaction types in module or network contexts [3, 10, 17–20, 23].
Overall, when taken together and with a balanced number of beneficial and detrimental interactions (as defined by their individual effects on diversity), the presence of NTIs tended to have a slightly negative effect on species diversity. This is in agreement with Goudard and Loreau [18] who studied NTIs that are modifications of feeding links; with equal numbers of positive and negative effects, they found a decrease in the total number of species when NTIs are incorporated. In our case, we did not expect this result since the range of NTI intensities spanned was selected such that each interaction type had equivalent effects on diversity when taken individually. Yet, despite controlling for both the number and the intensities of NTIs, the joint effect of the NTIs, when simultaneously incorporated in the model, tended to be negative for the species diversity of the resulting communities. It is noteworthy that the only beneficial NTI studied in this model, recruitment facilitation, operates on a single trophic level, namely the plants. When considered alone (i.e. without any other NTI), we showed that the positive effect of recruitment facilitation on diversity of the entire networks was entirely due to indirect effects on consumer species (trophic level > 2, S2 Fig). Thus, the negative joint effect of NTIs on diversity suggests that recruitment facilitation has less leverage on diversity than the other NTIs, and more specifically, that the fact that its positive effect on diversity comes about only by indirectly affecting species on higher trophic levels, does not allow it to compensate for the more direct negative diversity effects of some of the detrimental NTIs.
Surprisingly, we found interference between predators to have a negative impact on diversity, which contrasts with other studies reporting stabilizing effects on population dynamics and positive effects on diversity [12, 49, 50] when interference is included via a Beddington-DeAngelis functional response [51, 52]. However, these studies included interference either as a purely intra-specific effect or assumed that intra-specific interference was stronger than inter-specific interference. Here, intra-specific interference was present in all simulations at a fixed value, and our aim was to study the effect of changes in the intensity of inter-specific interference. In this sense, our result that interference reduces diversity is reflecting classic results from competition theory, namely that competition is destabilizing if it is stronger between species than within species [53]. As interference was only included for predators that are already competing for at least one common prey species, the decline in diversity can be attributed to an increased effect of the competitive exclusion principle [54]. It has to be noted, however, that the complex network structure of trophic and non-trophic interactions provides a plethora of niches, which reduces the direct applicability of this principle [13].
Interestingly, we found that NTIs affected the relationship between diversity and functioning, and this result seems to be robust to changes in the value of key parameters of our model. Despite the array of possible effects from the NTIs, individually and combined, the relationship between species diversity and biomass was found to be significantly stronger in networks with NTIs than in networks without them. Again, this was not necessarily expected since simulations were run with all NTIs together, whose intensity values were picked at random in a range such that their effects on diversity was controlled; we could therefore have expected, e.g. compensatory or negative effects since most of the NTIs studied here tended to have negative effects on diversity (Fig 1). The effect of NTIs on the slope of the diversity-biomass relationship means that when species-rich networks gain even more species, that goes with a disproportionately higher gain in biomass in the presence than in the absence of NTIs. This also means that, conversely, species-poor communities lose more biomass with additional species loss with than without NTI. This is due to the fact that species-rich communities are communities in which beneficial interactions are present and strong, while species-poor communities are communities where detrimental NTIs operate. This result is interesting in that it suggests that species loss may have stronger consequences on community functioning than expected if ignoring non-feeding interactions. Further work is needed to see if this result extends to other models as well as to real ecosystems.
Of course, our study presents a number of limitations. The strongest NTIs studied here, namely competition for space and facilitation for recruitment, both affect mainly plants in our model. It is therefore unclear whether these NTI types appear to exert stronger effects because plants are the affected species. This could be a topic of further investigations.
Moreover, we have focused on a selection of six NTIs that are the major ones found to occur in the Chilean web [10, 22], but other interaction types not present in this data set are known to be frequent and important in nature. Examples include parasitism, effects on resource availability, plant dispersal or animal movement. Further work could introduce these other interactions in a single framework.
We also assumed here the intensity of the interactions between pairs of species to be constant through time. Some interactions may however be context-dependent (beyond the biomass or abundance of other species). For example, adaptive inducible defenses are a form of phenotypic plasticity that affects the strength of predator-prey interactions [55]. Changes in morphological, behavioral, or life-historical traits in response to chemical, mechanical or visual signals from predators have been reported in the literature for a number of organisms [56]. These responses can moreover occur with a lag, given that the expression of defenses may involve considerable time, relative to the organisms life-cycle [57]. The intensity, and even the type of interactions, could also change with e.g. changes in abiotic factors such as climate.
We have no information regarding the relative importance or intensities of the different interaction types. We proposed a way of putting all interaction types on equal footing regarding their effect on species diversity. This is however a debatable choice—we could for example have chosen to make NTIs comparable regarding their effect on biomass. In nature, it is likely that interaction intensities are not equivalent and that some of them are much stronger than others. Making progress along these lines requires experimental work aiming at quantifying different interactions types, which involves a number of challenges [1].
In this study, NTIs were plugged in the food web randomly although with a number of constraints based on our knowledge of the Chilean web [10, 22]. We did not explicitly investigate the role of the structure of the NTI network, despite the fact that previous studies have suggested that it might play an important role [10, 58]. This remains a difficult task since it can be necessary to take into account the dependency between the structure of the different layers (e.g. NTI types, as observed in [10]); however this is a promising avenue of future research.
Previous studies have used the community matrix approach focused on net effects between species to investigate the role of the diversity of interaction types [7, 20]. This approach has a number of advantages, including the fact that it allows analytical predictions and generalizations. Here, we chose to focus on a more mechanistic approach, starting from the mechanism of the NTIs without assuming their net effect. For example, we had initially assumed that refuge provisioning would be a beneficial NTI, meaning that it would have a positive effect on species diversity. We however found the opposite in the model simulations—indeed, refuge provisioning protects prey from their consumers but it also deprives consumers from their resource and it seems that this latter effect has stronger consequences at the community scale. In a dynamical model, Gross [19] showed that interspecific facilitation among plants allowed the maintenance of species diversity despite the fact that the net effects measured among plants remained negative. These results highlight that insights gained from the analysis of few-species systems cannot be easily translated into the dynamics of complex communities. Focusing on the net effects between species may conceal important coexistence mechanisms when species simultaneously engage in both detrimental and beneficial interactions and stresses the importance of working with mechanistic models to better understand the consequences of NTIs for community diversity and functioning. Nonetheless, a more mechanistic approach implies, for each of the NTI identified, to model it in one specific way, matching our knowledge of how these interactions operate in nature (in our case here, having the Chilean web in mind [10, 22]). For example, regarding refuge provisioning, alternative ways of how it could affect trophic interactions are certainly conceivable. It could increase the Hill coefficient of all predators of the protected prey, e.g. to mimic the fact that prey at low density would become better protected, but if prey density is too high, the predators would still see them. This could have a different effect on diversity than found here.
Our study is a step toward getting a better understanding of the dynamics of multiplex ecological networks (i.e. including several interaction types among a set of species), and more precisely of the role of NTIs on community functioning. Our model results suggest that, when simultaneously included, and assembled according to simple rules reflecting observations in nature, NTIs tend to mechanically strengthen the BEF, making the dependency between the number of species present in the community and the functioning of this community (in terms of biomass or production) stronger. This result has important consequences for predicting the consequences of species loss on community functioning.
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10.1371/journal.pntd.0001687 | Immunodominant Antigens of Leishmania chagasi Associated with Protection against Human Visceral Leishmaniasis | Protection and recovery from visceral leishmaniasis (VL) have been associated with cell-mediated immune (CMI) responses, whereas no protective role has been attributed to humoral responses against specific parasitic antigens. In this report, we compared carefully selected groups of individuals with distinct responses to Leishmania chagasi to explore antigen-recognizing IgG present in resistant individuals.
VL patients with negative delayed-type hypersensitivity (DTH) were classified into the susceptible group. Individuals who had recovered from VL and converted to a DTH+ response, as well as asymptomatic infected individuals (DTH+), were categorized into the resistant group. Sera from these groups were used to detect antigens from L. chagasi by conventional and 2D Western blot assays. Despite an overall reduction in the reactivity of several proteins after DTH conversion, a specific group of proteins (approximately 110–130 kDa) consistently reacted with sera from DTH converters. Other antigens that specifically reacted with sera from DTH+ individuals were isolated and tandem mass spectrometry followed by database query with the protein search engine MASCO were used to identify antigens. The serological properties of recombinant version of the selected antigens were tested by ELISA. Sera from asymptomatic infected people (DTH+) reacted more strongly with a mixture of selected recombinant antigens than with total soluble Leishmania antigen (SLA), with less cross-reactivity against Chagas disease patients' sera.
Our results are the first evidence of leishmania proteins that are specifically recognized by sera from individuals who are putatively resistant to VL. In addition, these data highlight the possibility of using specific proteins in serological tests for the identification of asymptomatic infected individuals.
| One of the most striking features of infection by Leishmania chagasi is that infection leads to a spectrum of clinical outcomes ranging from asymptomatic infection to active disease. The existence of asymptomatic infected people has served as an incentive to believe that an effective vaccine is possible, but unfortunately no successful immunological characterization of such cases was obtained. Patients recovered from visceral leishmaniasis show a similar immunological profile to asymptomatic infected individuals and both exhibit a strong cell-mediated immune response against Leishmania antigens and are resistant to disease. Since the past decade several approaches were undertaken to try to shed light on the immunological profile associated with such “resistance” to infections, notwithstanding antigenic recognition profile associated to resistance to infection was not successfully explored. In the present manuscript we describe a specific IgG recognizing pattern associated with resistant individuals (asymptomatic infected people and recovery patients to visceral leishmaniasis). These data highlight the possibility of using specific proteins in serological tests for the identification of asymptomatic infected individuals.
| Visceral Leishmaniasis (VL) is a potentially fatal disease caused by infection with Leishmania chagasi in the New World and Leishmania donovani or Leishmania infantum in the Old World [1]. Infection leads to a spectrum of clinical outcomes ranging from asymptomatic infection to active disease. The anti-Leishmania immune response during asymptomatic infection is characterized by a low serological and positive cellular response, which is demonstrated by a positive delayed-type hypersensitivity skin response (DTH+) [2]. Patients with active VL, on the other hand, present a strong positive serological and a negative cell-mediated immune (CMI) response with low IFN-γ production [3]. Treated patients that recover from illness (accounts for 90%) only exhibit a positive DTH response long after treatment [4]. Epidemiological studies in Brazil showed that a positive DTH response is a marker of protection against VL [5].
Serological diagnosis of L. chagasi during asymptomatic infection is complicated by low antibody titers and frequent cross-reactivity with other diseases [6]. In addition, serologic markers of recovery or resistance to infection have not been characterized. There is no effective and safe vaccine approved for human use against any form of visceral leishmaniasis despite the obvious need and considerable effort that has been made.
In the present report, we compared the reactivity against total protein extracts from L. chagasi of sera obtained from either DTH positive patients (asymptomatic or treated and recovered individuals) or symptomatic patients. A serological pattern associated with DTH positivity was observed in both asymptomatic individuals and in recovered patients. In addition, the recombinant version of select antigens appeared to be a valuable tool for the serological identification of asymptomatic patients.
This study was approved by the Research Ethics Committee of the Federal University of Maranhão University Hospital, Brazil (localities where the field study was performed). All clinical investigations were conducted according to the Declaration of Helsinki. Written, informed consent was obtained from all participants or legal guardians.
Leishmania chagasi (MHOM/BR00/MER/STRAIN2) promastigotes were cultured in Schneider's medium supplemented with 10% inactive FBS, 2 mM L-glutamine, 100 U/mL penicillin, and 100 µL/mL streptomycin.
Sera were obtained at two distinct settings as described below: The patients were classified as VL (pre-treatment, samples obtained during active disease previous to treatment) and post-treatment (samples from treated and cured patients). All patients were from the Maranhão Federal University Hospital. Diagnosis was confirmed by identification of Leishmania sp. in Giemsa-stained smears of bone marrow aspirates (parasitological test). The study was conducted from August 2000 to July 2002 and information on the individuals has been previously reported [4], [7].
Sera were stored at −20°C without thawing. All patients received adequate treatment. DTH skin reactivity assays (Montenegro test) were performed with SLA prepared as described elsewhere [8].
Parasites in logarithmic growth phase were washed twice with PBS, lysed with Laemmli's Buffer [9], sonicated, heated at 95°C for 5 min and centrifuged for 15 min at 12,000×g at 4°C. Extracts from 106 parasites were loaded by line into an acrylamide gel. For 2D electrophoresis, parasites were lysed at 4°C with 200 µL of Buffer A (0.5% Nonidet P40, 0.1 mM PMSF, 1 mM DTT, 10 mM Tris-HCl, pH 7.4) followed by addition of 200 µL of phenol and vortex. The samples were centrifuged at 6,000×g for 5 min, and the aqueous phase was discarded. The proteins were precipitated by adding 1 mL of 0.1 M ammonium acetate in absolute methanol and centrifuged at 6,000×g for 10 min at 4°C. The resultant pellet was washed with 80% acetone and dried. The proteins were solubilized for 3 h at 30°C in RP3 Buffer (7 M urea, 2 M thiourea, 4% CHAPS, 40 mM Tris-HCl pH 8.8, 0.5% ampholytes, pH 4–7), followed by 15 min of centrifugation at 12,000×g at 4°C. The amount of proteins in the supernatants was quantified using the Quick Start Bradford Protein Assay (BioRad. USA), and the proteins were then stocked at 20°C.
Samples containing 250 µg L. chagasi protein extract in 200 µL RP3 Buffer supplemented with DTT (50 mM) were applied by rehydration to 11 cm IPG strips (pH 4–7). Isoelectric focusing (IEF) was performed using a Multiphor II electrophoresis unit (GE Healthcare, UK) at 3,500 V for 15,000 Vh. Subsequently, the IPG strips were reduced (130 mM DTT) and alkylated (135 mM iodoacetamide) for 15 min in equilibration buffer (0.375 M Tris-HCl, pH 8.8, 6 M urea, 20% vol/vol glycerol, 2% wt/vol SDS). The second dimension was run on home-casted SDS-PAGE gels (10% or 8% wt/vol polyacrylamide) at 50 V for 30 min and then at 160 V until the dye front reached the bottom of the gel. With the separation in the second dimension, the proteins were visualized by staining with PlusOne™ Silver Staining Kit or Colloidal Coomassie staining (GE Healthcare, UK).
The electrophoresed proteins were transferred to nitrocellulose membranes (GE Healthcare, UK) and were stained with Ponceau S. Membranes were blocked with 5% non-fat dried milk powder in wash solution (PBS and 0.05% Tween 20). The membranes were probed with sera (1∶1000 or 1∶500), and an anti-human-IgG Phosphatase Alkaline (PA) immunoconjugate (Sigma-Aldrich. Germany) was used as a secondary antibody (1∶2000). To measure the recognition of sera by IgG subclasses after incubation, mouse anti-human-IgG1, IgG2, IgG3 and IgG4 were employed. After three washes with wash solution, an anti-mouse-IgG PA immunoconjugate was used (1∶2000). Specific IgG-PA binding was measured with Western Blue® Stabilized Substrate for Alkaline Phosphatase (Promega, USA).
To match antigen spots in Western blots with the corresponding protein spot in the Coomassie gels, the blot coordinates were defined after the Ponceau S staining pattern of the blot filter was aligned with the spot pattern of the Coomassie gel. Only perfect overlap (position and form) between blot-spot and Ponceau S staining-spot was accepted (mapped spot). Spots in Western blots without overlap with Ponceau S or Coomassie spot were defined as unmapped spots.
The protocol that was used for in-gel digestion was based on that in a previous publication [10]. Briefly, gel pieces were distained twice for 30 min at 25°C with 25 mM ammonium bicarbonate/50% (w/v) acetonitrile, dehydrated in acetonitrile, dried, and treated with trypsin (20 µg/mL, Promega, USA) in 25 mM ammonium bicarbonate pH 7.9 at 37°C for 16 h. Digests were extracted from gel pieces with 50% (v/v) acetonitrile/water and 0.1% (v/v) formic acid and subsequently combined and vacuum-dried. The concentrated digests were mixed with 0.5 µL of α-cyano-4-hydroxycinnamic acid matrix (10 mg/mL) in 50% (v/v) acetonitrile/0.1% (v/v) trifluoroacetic acid and were spotted onto a MALDI target plate.
Mass spectrometric analysis was performed using MALDI ToF/ToF-MS/MS (matrix-assisted laser desorption ionization time of flight/time of flight-mass spectrometry) on a Shimadzu instrument (model Axima Performance). MS data were acquired in the m/z range of 700 to 4,000, with an accelerating voltage of 20 kV, delayed extraction, a peak density of maximum 50 peaks per 200 Da, a minimal S/N ratio of 10 and a maximum peak at 60. MS/MS data were acquired in the mass range of 60 Da to each precursor's mass, with a minimum S/N ratio of 10, a maximum number of peaks set at 65 and a peak density maximum of 50 peaks per 200 Da.
LaunchPad 2.8.4 (Shimadzu Biotech) was used to submit the combined MS and MS/MS data to the MASCOT protein search engine version 2.2 using the National Center for Biotechnology Information (NCBI) protein database. The search parameters were as follows: no restrictions on protein molecular weight; one tryptic missed cleavage allowed; peptide mass tolerance in the searches was 0.2 Da for MS spectra and 0.8 Da for MS/MS spectra. Carbamide-methylation due to treatment of sulfhydryl with iodoacetamide and oxidation of methionine and tryptophan were specified in MASCOT as fixed and variable modifications, respectively.
For expression of antigens identified by MALDI ToF/ToF-MS/MS, coding regions were amplified by polymerase chain reaction (PCR) and were subcloned into the pQE30 expression vector (Qiagen, Germany). The following primers were employed for amplification: Enolase, forward: 5′-CGGGATCCATGCCGATCCAAAAGGTTTAC-3′ and reverse: 5′-CCAAGCTTTTACGCCCAGCCGGGGTAG-3′; S-adenosylmethionine synthetase, forward: CGGGATCCATGTCTGTCCACAGCATCCTC, and reverse: 5′-CCCAAGCTTTTACTCGACCATCTTCTTGG-3′; Alpha tubulin, forward: 5′-CGGGATCCATGCACACAGACACGCACGC-3′, reverse: 5′-GGGGTACCCCTTCGCTTCACTATTTTTG -3′; Heat shock protein 70, forward: 5′-CGGGATCCATGTCGTCTACCAACGCCATC-3′, reverse: 5′-CCCAAGCTTTTAGTCAACGTCTTCGGCG-3′; Heat shock 70, mitochondrial precursor, forward: 5′-CGGGATCCATGTTCGCTCGTCGTGTG-3′, reverse: 5′-GGGGTACCTCAACTATTACCTGAGTAGG-3′ and heat shock protein 83-1, forward: 5′-CGGGATCCATGACGGAGACGTTCGCGTT-3′, reverse: 5′-CCCAAGCTTTCAGTCCACCTGCTCCATGC-3′. Underlined sequences in primers indicate restriction sites for cloning.
Recombinant antigens were over-expressed in E. coli cultures, transformed with serial pQE30s by the addition of 2 mM isopropyl β-D-1-thiogalactopyranoside (IPTG), followed by 3 h at 37°C incubation. Non-native bacterial lysates were subjected to Ni-nitrilotriacetic acid agarose columns chromatography (Qiagen, Germany). Purification was performed according to the manufacturer's instructions.
Soluble Leishmania antigen (SLA) from L. chagasi and recombinant purified proteins were diluted in PBS buffer to 1 µg/100 µL, and then 100 µL of each sample were placed into wells of 96-well microtiter plates (Probind; Falcon, Becton Dickinson, USA) and were incubated overnight at 4°C. Wash solution (PBS 1× with 0.5% Tween 20) was used three times for 10 min at room temperature. To block wells, a blocking solution (PBS plus 0.5% Tween 20 and 5% non-fat milk) was used for 1 h at room temperature. Serum samples diluted at 1∶100 in blocking solution were added at 100 µL/well, and plates were then incubated for 2 h at room temperature. A new round of washes was performed as indicated previously, followed by an incubation of 1 h at room temperature with a 1∶2000 dilution of alkaline phosphatase-conjugated anti-human IgG antibody (Sigma-Aldrich, Germany). Antibody excess was removed by four rounds of 10 min washes using wash solution. The plates were developed using a chromogenic solution of p-nitrophenylphosphate in sodium carbonate buffer (pH 9.6) with 1 mg/mL MgCl2. The absorbance was recorded at 405 nm.
After treatment, VL patients develop anti-Leishmania CMI, as evidenced by positive Montenegro reaction followed by decreases in titers of IgG against Leishmania total protein [2]. However, even a year after treatment and curing disease, anti-Leishmania antibodies are still present in the sera [4]. To understand if this reduction in IgG recognition is associated with changes in antigen specificity, we screened sera from patients before and after DTH conversion for reactivity with L. chagasi total protein by Western blot. A decrease in the number of proteins with reactivity was observed in sera from VL patients after their recovery, and new reactivity patterns were observed after DTH conversion (Figure 1A). Next, we compared the reactivity pattern of sera from symptomatic VL patients (DTH−), recovered patients (DTH+), asymptomatic donors (DTH+) and uninfected volunteers from the same endemic area (Figure 1B). Sera from post-treatment and asymptomatic patients both of which were DTH+, showed weak or no reactivity to a majority of the bands. A high mass proteins group (approximately 110–130 kDa) was detected in all naturally resistant patient samples and in 60% of post-treatment DTH+ sera tested (Figure 1; Supplementary Figure S1; Baseline characteristics of the study population used in Table S1). To determine if any IgG subclass was involved in the differential reactivity of sera between groups, IgG1, IgG2, IgG3 and IgG4 binding was tested. Only IgG1-antibody showed significant reactivity that was similar to that of the total IgG pattern in all cases (Table S1).
To characterize the reactivity patterns associated with protection against VL, 200 µg of total L. chagasi protein was 2D electrophoresed, electrotransferred to nitrocellulose membranes and tested against serum samples. After the second dimension (using 10% or 8% SDS-PAGE gel), approximately 250 spots were obtained that had high resolution and reproducibility (Figure S2). Membranes were tested using a pool composed of the five more reactive sera from each group, and IgG interactions were detected. Strikingly, we observed significant differences between groups in the 2D Western blot (Figure 2). Results were summarized in Table 1. In VL, post-treatment VL and asymptomatic patients, 58, 62 and 33 spots were detected respectively (Table 1). It is remarkable that the ratio between mapped spots versus total spots reactions were higher when we used sera from asymptomatic (14/33) than those found we used sera from VL (15/58) or Post-VL (17/62) groups. Each group showed specific mapped spots, whereas two mapped spots (202 and 204) were recognized by all groups. In asymptomatic individuals, spots mapped were not detected in the 110–130 kDa range, but a specific non-mapped signal was detected (delineated in a quadrant in Figure 2). This signal was also recognized by sera from a post-treatment VL sample; however it was not observed when we used VL patient sera.
MALDI ToF/ToF-MS/MS analysis of the 24 identified spots (of which 6 spots were specifically recognized by VL patient sera, 8 spots by post-treatment VL sera, 8 spots by asymptomatic patient sera and 2 spots recognized by all groups) resulted in the identification of 14 (58.3%) proteins. These results, combined with MASCOT protein identification, are summarized in Table 2.
We identified the following two proteins that specifically reacted with sera from VL patients (DTH−): Mitochondrial 70 kDa heat shock protein (MPT70, spot 205A) and paraflagellar rod protein 1 (PFR1, spot 207B). Additionally, eleven proteins specifically reacting with sera from DTH+ patients were identified. Five proteins reacting with sera from post-VL treatment patients (translation elongation factor 1-beta [LieEF1B, spot 55], eukaryotic initiation factor 4A [LieIF4A, spot 141A], enolase [spot 149], S-adenosylmethionine synthetase [MAT2, spot 149A] and adaptor gamma-1 chain [spot 189]) and 6 proteins with specific reactivity with sera from asymptomatic infected patients (disulfate isomerase [PDI, spot 148A], alpha- and beta-tubulin [spots 151A and 151B, respectively], vacuolar ATP synthase subunit B [spot 157] and 83 kDa heat shock protein [HSP83, spot 210]) were identified. Finally, the two proteins recognized by all groups (spots 202 and 204) were both identified as 70 kDa heat shock protein (HSP70). The putative functions and immunological properties of the identified proteins were retrieved from published literature and also from the L. infantum Genome Project database (www.genedb.org).
To analyze whether the identified proteins were indeed reactive with sera from the respective groups and could be used in a serological test for potentially asymptomatic L. chagasi infected individuals, we measured reactivity of the following recombinant proteins (Figure S3 for supporting information) by ELISA: alpha-tubulin, enolase, MAT2, and HSP83, which were reactive with DTH+ individuals sera; MPT70, which reacted with VL patients' sera and HSP70, which reacted with sera from both groups (Figure 3). Recombinants antigens reacted with some of the serum samples from VL (DTH−), post-treatment (DTH+) and asymptomatic patients (DTH+) (Figures 3 A–C). However, when we normalized the titers of IgG reactive with antigens from SLA, we observed that asymptomatic patients presented higher specific reactivity with DTH+ antigens (Figure 3D–F). We then used a combination of recombinants proteins composed of enolase, MAT2, alpha-tubulin and HSP83 (MIX) to test the sensitivity in the sera from asymptomatic patients and their specificity against sera from patients with Chagas disease, which presents high cross-reactivity with Leishmania antigens [11]. We verified that asymptomatic individuals had a higher reaction with MIX than SLA, and sera from patients with Chagas disease showed lower cross-reactivity (Figure 4).
The decrease in anti-Leishmania IgG titers during conversion to DTH+ in effectively treated VL patients has been previously associated with the development of cell-mediated immune responses [4]. Herein, we show that decreased antibody levels in VL-related states and positive DTH reactions are associated with a reduction in the reactivity against most of the parasite proteins and, more relevant, the recognition of previously unrecognized proteins. These observations indicate that important changes in the humoral response occurs during DTH conversion and reveals a reactivity pattern associated with recovery and natural resistance to VL that is characterized by high-mass group proteins (approximately 110–130 kDa). These high-mass proteins did not react with sera from VL patients, indicating that some specific immunodominant antigens are specifically associated with DTH conversion and putatively with protection against VL. Unfortunately, high-mass group proteins were not 2D-resolved, and their identification was not possible; however, 2D-recognizing map analyses of sera from VL patients and DTH+ individuals (post-VL or naturally resistant) showed other immunodominant antigens whose recognition was DTH status-dependent. A lower number of reactive spots were observed in sera from DTH+ individuals living in the endemic area who are presumably naturally resistant. It is noteworthy that sera from post-VL patients (DTH+) reacted with a larger number of proteins, which suggests that post-VL patients likely share antigenic recognition patterns with both VL patients and naturally resistant individuals which reinforces the idea that some antigens are specifically associated with positive DTH response and therefore protection against VL.
Proteomic analysis revealed the identification of 14 antigens, including proteins not previously reported as antigenic, such as adaptor gamma-1 chain, MPT70 and PFR1. Among the identified antigens associated with the post-VL subset, four of them (LieIF4A, LieEF1B, enolase and MAT2) have been reported as highly expressed in drug-resistant strains [12]–[14] and have been implicated in the survival of the parasite inside the host [15], [16]. For the six identified antigens that reacted with sera from the VL naturally resistant people (PDI, alpha- and beta-tubulin, vacuolar ATP synthase subunit B and HSP83), drug resistance implications and host parasite survival properties have been previously described too [13], [17]–[21]. The stress state imposed on the parasite in a DTH+ environment (resulting either from drug treatment or natural resistance) can induce the overexpression of these proteins, and this fact may explain their immunodominant antigenicity in these individuals. It is notable that LieIF4A protein was described as antigen reactive with sera from VL patients infected by L. donovani from India [25] and we found a clear reactivity against this protein using sera from post-VL patients. It could be interpreted as yet another indication that IgG pattern recognition in post-VL patients preserved reactivity against some antigens of active illness. In addition, it was already observed that a Th1 activation response, associated with healing, was induced in cutaneous leishmaniasis patients' PBMC by recombinant LieIF4A [26]. We consider the hypothesis that some correlation may exist between the antigenicity of DTH+ recognized proteins and its capacity to induce anti-Leishmania cellular responses. On the other side, it is possible that the highly immunogenic proteins, such as HSP70 [22]–[24], are implicated in its recognition by all groups.
To determine if the immunodominant antigens identified could serve as serological markers, some of them were expressed as recombinant proteins, purified and then employed as antigen in ELISA (Figure 3). When we normalized to SLA, enolase exhibited a remarkable increase in reactivity against sera from post-VL group respect to other antigens. Moreover, an increased enolase recognition was observed using sera from asymptomatic people, strengthening the idea that antigens recognized by post-VL sera could also be reactive in VL patients and resistant people sera. In the other hand, alpha tubulin and HSP83, showed a remark recognition increase from asymptomatic donors' sera. Meanwhile, MTP70 that was identified as a VL-specific protein did not react with any groups. There are four not identical copies of MPT70 in L. infantum genome (www.genedb.org), and only one of them was cloned to obtain MPT70 recombinant protein. Future studies with the other 3 variants will shed light about the real serological properties of these proteins. Finally, and as expected, HSP70 reacted with several serum samples from all groups.
Lastly, we proved that a mixture of DTH+ antigens (enolase, MAT2, alpha-tubulin and HSP83) had higher reactivity with sera from asymptomatic individuals than SLA and lower cross-reactivity with sera from patients with Chagas disease. Our results are the first description of several antigens that are immunodominant in DTH+ individuals (post-VL and naturally resistant people). Future studies using these antigens may help to identify potent serological tools that could be useful for determining patient disease status as well as new anti-Leishmania vaccine candidates.
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10.1371/journal.ppat.1001161 | Retention and Loss of RNA Interference Pathways in Trypanosomatid Protozoans | RNA interference (RNAi) pathways are widespread in metaozoans but the genes required show variable occurrence or activity in eukaryotic microbes, including many pathogens. While some Leishmania lack RNAi activity and Argonaute or Dicer genes, we show that Leishmania braziliensis and other species within the Leishmania subgenus Viannia elaborate active RNAi machinery. Strong attenuation of expression from a variety of reporter and endogenous genes was seen. As expected, RNAi knockdowns of the sole Argonaute gene implicated this protein in RNAi. The potential for functional genetics was established by testing RNAi knockdown lines lacking the paraflagellar rod, a key component of the parasite flagellum. This sets the stage for the systematic manipulation of gene expression through RNAi in these predominantly diploid asexual organisms, and may also allow selective RNAi-based chemotherapy. Functional evolutionary surveys of RNAi genes established that RNAi activity was lost after the separation of the Leishmania subgenus Viannia from the remaining Leishmania species, a divergence associated with profound changes in the parasite infectious cycle and virulence. The genus Leishmania therefore offers an accessible system for testing hypothesis about forces that may select for the loss of RNAi during evolution, such as invasion by viruses, changes in genome plasticity mediated by transposable elements and gene amplification (including those mediating drug resistance), and/or alterations in parasite virulence.
| RNAi interference pathways play fundamental roles in eukaryotes and provide important methods for the analysis of gene function. Occasionally RNAi has been lost, precluding its use as a tool, as well as raising the question of what forces could lead to loss of such a key pathway. Genomic and functional studies previously showed that within trypanosomatids protozoans RNAi was absent in both Leishmania major and Trypanosoma cruzi. The genome of L. braziliensis, a member of the early diverging Leishmania subgenus Viannia, retained key genes required for RNAi such as an Argonaute. We demonstrated that in fact L. braziliensis shows strong RNAi activity with reporter and endogenous genes affecting flagellar function. These data suggest that RNAi may be productively applied for functional genomic studies in L. braziliensis. We mapped the evolutionary point at which RNAi was lost in lineage leading to Leishmania and Crithidia, and establish that RNAi must have been lost at least twice in the trypanosomatids, once on the lineage leading to T. cruzi and independently following the divergence of the Viannia subgenus from other Leishmania species. Lastly, we discuss hypotheses concerning the forces leading to the loss of RNAi in Leishmania evolution, including viral invasion, increased genome plasticity, and altered virulence.
| In metazoans, RNAi interference and related pathways play many key roles including regulation of mRNA levels and translation, chromatin silencing, programmed DNA rearrangements, genome surveillance, and defense against invading viruses. The phylogenetic distribution of key genes required for RNA interference such as Argonaute and Dicer suggests that this pathway may have been present in the common eukaryote ancestor [1]. However the situation for eukaryotic microbes is complex: some have active RNAi pathways, others lack RNAi genes and activity, and demonstration of RNAi has proven elusive in some species bearing reasonable homologs of canonical genes such as Argonaute [2]–[7].
The trypanosomatid protozoa comprise three major lineages, broadly grouped as the African trypanosomes (Trypanosoma brucei), South American trypanosomes (T. cruzi) and a lineage encompassing a number of genera associated with insects or plants, ultimately leading to the mammalian parasite Leishmania [8]. Functional and genome sequencing data have shown that species within the African trypanosome lineage such as T. brucei contain an active RNAi pathway and genes, including an Argonaute “slicer” (AGO1; [2]) and two Dicers (DCL1 and DCL2; [9], [10]). In contrast, T. cruzi, L. major and L. donovani lack these activities and associated genes [11]–[14]. However the genome of L. braziliensis (subgenus Viannia) contains orthologs of T. brucei AGO1, DCL1 and DCL2 [15], suggesting this group might retain a functional RNAi pathway. Given the uncertainties of extrapolating from RNAi genes to functions noted in other eukaryotic microbes [2]–[4], we sought to establish whether the RNAi machinery functions in L. braziliensis, and explored its utility as a genetic tool. Furthermore, we made evolutionary comparisons to map when the RNAi pathway was lost, and we discuss potential selective forces impacting on the parasite that may have contributed to the demise of RNAi during Leishmania evolution.
Dicer is required to process long dsRNA to small interfering RNAs (siRNAs), which in trypanosomes are 24–26 nt long [16]. A convenient marker of RNAi activity is siRNA formation from endogeneous retroelements [17], and Northern blot analysis of L. braziliensis RNAs revealed the presence of small RNAs of the expected sizes arising from the retroelement SLACS, similar to T. brucei siRNAs (Fig. S1; [16]).
We then developed a green fluorescent protein (GFP)-based RNAi reporter assay for siRNA formation, as well as target mRNA and protein levels. Initially we experienced unexpected difficulty in L. braziliensis transfection, when using episomal constructs previously developed in one of our labs that function effectively in many Leishmania species, and in many laboratories [18]. The basis for this effect is not definitively known, as addressed in the discussion, but we suspect it is due to the tendency of episomal vectors to be transcribed from both strands, which in an RNAi-proficient species would strongly inhibit episomal gene expression [11], [13]. Thus in all studies reported here, transfection was accomplished following integration of DNA constructs into the ribosomal small subunit RNA (SSU) locus, using the appropriately digested DNA from pIR1SAT-based vectors, or derivatives thereof [19]. In trypanosomatids, processing of polycistronic RNA precursors by 5′ trans-splicing and 3′ polyadenylation produces capped mRNAs that can direct protein synthesis [20].
First we generated a GFP ‘stem-loop’ (long hairpin) construct, containing two copies of an AT-rich GFP reporter (GFP65) in an inverted orientation separated by a short loop (Fig. 1A). This GFP stem-loop construct (GFP65-StL) was flanked by Leishmania sequences required for efficient 5′ and 3′ end mRNA formation, and was expressed following integration into the parasite small subunit ribosomal RNA locus (SSU rRNA; Fig. 1A) in L. braziliensis strain M2903.
Northern blot analysis with a GFP65 probe showed that expression of GFP65-StL gave rise to a variety of products (Fig. 1D, lane 2). The largest of these likely correspond to unprocessed transcripts, while the smaller ones likely correspond to degradation products, which could occur irrespective of whether RNAi pathways are active. Importantly, abundant levels of 24–26 nt siRNAs were seen (Figs. 1B and 1E). In contrast, similarly small RNAs were not detected with probes to the SAT drug resistance marker, which is not found in an inverted repeat (data not shown). These data suggested that L. braziliensis expresses a robust Dicer-like activity.
We used two GFP reporters, one encoded by the AT-rich ORF (GFP65) used in the GFP65-StL construct above, and the second by a GC-rich ORF (GFP+). These genes differ in most 3rd codon positions, but their protein products only differ by a single amino acid. Alignment of these genes showed that the longest tracts of identical nucleotides were less than 14 nt (Fig. S2). GFP65 or GFP+ was then expressed separately following integration into the SSU rRNA locus, in wild-type (WT) L. braziliensis or the GFP65-StL transfectant that produces GFP65 siRNAs.
As expected, expression of GFP65 or GFP+ led to high levels of GFP mRNA and protein in WT lines, as did expression of GFP+ within the GFP65-StL transfectant (Fig. 1D, F, G). In contrast, clonal lines arising from introduction of GFP65 into the GFPST-StL transfectant showed only trace amounts of GFP65 mRNA (Fig. 1D), and the level of GFP protein was below the limit of detection by western blotting (<1% in these studies; Fig. 1G) or flow cytometry (Fig. 1C). These data established that GFP65-derived dsRNA mediated selective ablation of the AT-rich GFP65 but not the GC-rich GFP+.
Similar studies were carried out with a luciferase (LUC) reporter, expressed alone or in combination with a LUC stem-loop construct, revealing strongly-reduced LUC expression (90–300 fold; Fig. S3, and other studies below).
We then tested the activity of the RNAi pathway on several endogenous genes. In transient transfections performed using several protocols and dsRNAs synthesized in vitro against the L. braziliensis α-tubulin, Northern blot analysis showed at best a 63% decrease in α -tubulin mRNA (Fig. 2A). This contrasts with T. brucei where such protocols readily yield >95% reduction in tubulin mRNA expression [21]. This perhaps reflects the lower efficacy of transient transfection attained thus far in Leishmania [11].
Since inducible expression systems were unavailable, we focused on stably expressed ‘stem-loop’ constructs targeting a panel of nonessential genes in L. braziliensis, including ones mediating synthesis of the abundant glycoconjugate lipophosphoglycan (LPG1, LPG2, LPG3; [22]), hypoxanthine-guanine phosphoribosyltransferase (HGPRT), or the genes PFR1 and PFR2, which encode major components of the paraflagellar rod, a component of the trypanosomatid flagellum required for motility [23]. These StL-transfectants showed a variable decrease in mRNA levels when estimated by qPCR, ranging from no effect (LPG1) to more than 10-fold reduction (LPG2, LPG3; Fig. 2B). However, Northern blot analysis showed a nearly complete absence of LPG2 mRNA (Fig. 2C), suggesting that the qPCR values are likely underestimates, possibly due to the presence of RNA degradation intermediates able to act as templates (these are evident in Fig. 2C). Despite the reductions in mRNA levels, LPG levels were at best only 3-fold lower in the LPG2-StL or LPG3-StL transfectants, with considerable clonal variability (Fig. 2E; data for LPG3-StL not shown). This suggests that L. braziliensis requires only low levels of LPG biosynthetic proteins, similar to the relatively small effects of RNAi on trypanosome glycoconjugate biosynthetic genes [24]. Both HGPRT mRNA and protein levels showed 3–4 fold decreases in HGPRT-StL transfectants (Fig. 2B, D).
One of the earliest reports of stable phenotypic modulation by RNAi in trypanosomes involved down regulation of a paraflagellar rod protein [25], [26]. The paraflagellar rod is a complex assembly of proteins required for motility, which in trypanosomatids includes two major proteins, termed PFR1 and PFR2 in Leishmania [23], [27], [28]. Introduction of PFR1-StL or PFR2-StL constructs into L. braziliensis yielded viable transfectants that grew normally, but lacked the paraflagellar rod, as visualized in longitudinal or transverse EM sections, and exhibited motility defects (Fig. 3). These phenotypes closely resemble those seen in L. mexicana PFR1 and PFR2 gene deletion mutants [23].
Multiple attempts to introduce ‘stem-loop’ α- or β-tubulin constructs were unsuccessful, as anticipated for essential genes (not shown). Collectively, the strength of the RNAi effect for these phenotypic reporters suggests that RNAi may function sufficiently well to assess the functions of many genes in L. braziliensis.
In other organisms RNAi is mediated by the combined activity of a number of proteins, ultimately converging on the endonucleolytic ‘slicer’ activity of the Argonaute protein, which is encoded by the single AGO1 gene in trypanosomes and L. braziliensis [15], [17]. To establish a critical role for L. braziliensis AGO1 in RNAi, we employed the seemingly counterintuitive approach of ‘RNAi of RNAi genes’, where introduction of dsRNAs targeting RNAi pathway genes inhibits RNAi activity, albeit not to the same level seen in null RNAi pathway gene knockouts [17], [29]–[31]. To facilitate comparisons of the efficacy of RNAi, we developed a single RNAi ‘self reporter’ construct which simultaneously expressed two mRNAs, one encoding a luciferase ORF (LUC) and a second encoding a luciferase ORF stem-loop (LUC-StL). This minimized experimental variability and the number of transfections required, allowing the assessment of RNAi efficacy by the introduction of a single construct. When introduced into WT L. braziliensis, the ‘LUC RNAi self reporter’ (LUC-SR) showed low levels of luciferase activity, about 4-fold over background and comparable to that obtained with lines expressing LUC and LUC-StL independently after successive transfections (Fig. 4). In contrast, introduction of the LUC reporter alone resulted in activities nearly 1000-fold over background (Fig. 4).
We then introduced a construct expressing an AGO1 stem-loop (AGO1-StL) into the LUC RNAi reporter line (LUC-SR). These transfectants showed an average of 100-fold increased luciferase expression relative to LUCSR transfectants, signifying a considerable reduction in the efficiency of RNAi (Fig. 4). As expected from studies in other organisms cited above, inhibition of RNAi activity was partial, as these values were still about 10-fold less than seen in WT cells transfected with the LUC reporter construct alone (Fig. 4). These data thus implicate AGO1 as an essential component of the RNAi pathway of L. braziliensis.
We explored the prevalence of RNAi pathways in other Trypanosomatid species by comparative genomics. PCR assays detected AGO1 and/or DCL1 genes in all isolates of the Leishmania subgenus Viannia tested (L. braziliensis, L. guyanensis, L. panamensis) but not in Leishmania (Sauroleishmania) tarentolae, L. mexicana, L. major or L. donovani (data not shown). Partial genome sequencing of a close non-parasitic ‘outgroup’, Crithidia fasciculata revealed AGO1, DCL1 and DCL2. To confirm the presence or absence of a functional RNAi pathway, we expressed the GFP65-StL RNA in L. tarentolae, L. mexicana, L. panamensis, L. guyanensis and Crithidia fasciculata, and monitored siRNA formation by Northern blotting. Consistent with the observed distribution of RNAi pathway genes, GFP siRNAs were made only in Crithidia, L. guyanensis and L. panamensis (Fig. 5, S4). Transfection with the GFP reporters showed strong reductions in GFP expression in L. panamensis, comparable to that seen with L. major in Fig. 1 (data not shown), and we show in a later section that RNAi is active in L. guyanensis using a luciferase reporter The level of GFP expression in Crithidia with the Leishmania vectors used was too low to utilize for quantification of the strength of RNAi by flow cytometry (data not shown).
Association of these findings with the trypanosomatid evolutionary tree (Fig. 6A) through evolutionary parsimony identified a single point when the RNAi pathway was lost during evolution, located after the divergence of members of the subgenus Viannia from the remaining species complexes (Fig. 7). Importantly, this corresponds precisely to the point when RNAi genes were lost in evolution, as deduced by comparative genomics and evolutionary parsimony. Inspection of the sequenced Leishmania genomes shows that all RNAi-deficient Leishmania now contain only remnant, highly degenerate pseudogenes (AGO1) or have undergone gene deletion (as revealed by ‘synteny gaps’ for DCL1 and DCL2) for known trypanosomatid RNAi genes. Since species retaining only a partial set of intact RNAi genes have not been reported, from these data we cannot identify which essential RNAi pathway gene was lost first at this distant point in Leishmania evolution. Presumably, once a gene critical for RNAi activity was inactivated, the remaining genes of the pathway become superfluous and fall prey to evolutionary drift, as seen in many other metabolic pathways during evolution.
RNAi pathways were probably present in the common eukaryote ancestor [1], and the evolutionary relationships of the available trypanosomatid RNAi pathway proteins closely resemble those of housekeeping protein-based phylogenies (shown for AGO1 and DCL1 in Fig. 6 B–D). While the L. braziliensis AGO1 gene is not syntenic with that of T. brucei [15], [32] the congruency of the RNAi gene and ‘housekeeping’ gene phylogenies renders the possibility of lateral gene transfer and/or independent acquisitions unlikely. Thus, RNAi most likely was lost twice independently in trypanosomatids, once in the lineage leading to T. cruzi, and a second time in the lineage leading to Leishmania, subsequent to the divergence of most Leishmania groups from the non-parasitic species Crithidia fasciculata and the Leishmania subgenus Viannia (Fig. 7).
We and others have speculated that one of the forces contributing to the loss of RNAi in eukaryotic microbes may be invasion or loss of RNA viruses [13], [33]. Significantly, dsRNA viruses termed LRVs are found in many (but not all) strains and/or species from the Leishmania subgenus Viannia, including L. braziliensis [34], [35]. We reasoned that studies of the efficacy of RNAi in extant Leishmania bearing or lack LRVs could provide some insight into their potential roles in evolution.
Using specific PCR primers for LRVs we showed that the L. braziliensis strain M2903 used here lacked LRVs, consistent with previous reports [36], [37]. Unfortunately methods for the introduction and/or cure of LRV from Leishmania are not well developed, precluding tests of isogenic L. braziliensis engineered to harbor the LRV virus. Similarly, just one isogenic virus-free derivative of an LRV-containing Leishmania has been described; L. guyanensis is closely related to L. braziliensis (Fig. 7), and a virus-free derivative arose fortuitously in the course of other studies [38]. The efficiency of RNAi in these lines was evaluated by introduction of the luciferase RNAi reporter (LUC-SR) described earlier, relative to transfectants expressing only LUC. Multiple clonal lines were obtained, and LUC expression was measured in six randomly selected lines (Fig. 8A). Importantly, the level of luciferase expression seen in the lines expressing only LUC were comparable between the closely related Viannia species M2903 L. braziliensis and M4147 L. guyanensis (Fig. 8A). All lines and transfectants were shown to retain or lack the LRV1-4 by RT-PCR tests as expected (Fig. 8B).
While the RNAi pathway was active in the LRV+ L. guyanensis M4147, its efficiency was only about 30-fold (3.8% LUC-SR vs. LUC), compared to the 300-fold reduction seen in the virus free L. braziliensis M2903 (0.34% LUC-SR/LUC; Fig. 8A). The WT LRV+ LgM4147 strain also showed reduced efficiency of RNAi relative to M2903, in studies using successively transfected GFP reporter and GFP-StL constructs (data not shown). Significantly, the LRV-free line LgM4147/pX63HYG showed a similar 30-fold efficiency of RNAi in these studies (3.3% LUC SR/LUC). These data suggest that the reduced RNAi efficiency seen in L. guyanensis M4147 does not require the continued presence of the virus.
Our studies have established that L. braziliensis possesses a functional RNAi pathway, which enables the down-regulation of a variety of reporter and endogenous genes when assayed at the mRNA or protein levels. RNAi of AGO1 was used to confirm a requirement for the sole argonaute gene AGO1 in this process. As seen in many organisms, strong reductions in mRNA expression were seen, often accompanied by phenotypic changes, albeit of variable strength. As anticipated, it was not possible to introduce stem-loop constructs for essential genes such as α- or β-tubulins. Studies of such genes will require the development of inducible expression systems in Leishmania, which while promising have not yet reached the point of utility attained in trypanosomes.
Strong phenotypes were produced by the knockdown of two genes implicated in flagellar motility and paraflagellar rod synthesis (PFR1 and PFR2), closely approximating the phenotypes seen in gene deletion mutants in L. mexicana [23]. In contrast, at best only weak phenotypes were produced by knockdowns of three LPG biosynthetic genes, in keeping with findings in trypanosomes where it has proven difficult to down-regulate expression of genes implicated in glycoconjugate synthesis far enough to attain phenotypic effects. Overall, the results to date suggest that the range in efficacy of RNAi knockdowns, as judged by various phenotypic criteria, is comparable to that seen in trypanosomes and other organisms, and thus is likely to be similarly useful in the systematic analysis of Leishmania gene function in the future.
Given the importance of RNAi pathways in many fundamental aspects of eukaryotic biology, it is unsurprising that it has been lost relatively few times during evolution. While the critical roles of RNAi in metazoan gene regulation would likely select strongly against such attenuation, eukaryotic microbes lacking RNAi have arisen sporadically [1], [2]. This in turn raises the question of under what circumstances RNAi might occur. We consider three working hypotheses for selective pressures that may act independently or in concert to drive this loss in Leishmania.
We proposed previously that viral pressure could act as a selective force for the loss of RNAi in Leishmania evolution [11], [13]. In one scenario, invasion by LRVs at some point in Leishmania evolution could lead to an attenuation of the RNAi response, as many RNA viruses are prone to attack by cellular RNAi pathways [39]. Attenuation could be achieved through down regulation of the RNAi pathway by the host cell, or through viral genes targeting key RNAi pathway activities. While some RNA viruses encode inhibitors of RNAi, no studies have been undertaken as yet for Leishmania LRVs. The challenge for this model is to explain what forces would prompt cells to favor RNA virus retention over disruptions arising from perturbation or loss of the RNAi pathway. Interestingly, LRV infection has been proposed to be advantageous to Leishmania, possibly by modulating host immune responses in a way beneficial to parasite survival [40], [41]. In support of this hypothesis, recently we have obtained preliminary in support of the proposal that LRV-containing L. guyanensis show increased survival and pathogenicity (L-FL, KO, S. Hickerson and SMB, unpublished data; N. Fasel, personal communication). Selection for the presence of LRV able to promote parasite survival could thus provide a selective force promoting down-regulation of RNAi activity targeting RNA viruses.
While one cannot perform experimental tests in the ancestral Leishmania, one prediction is that in extant species or strains now harboring Leishmania LRVs, attenuation of the RNAi response may occur. Here we compared the efficacy of RNAi seen in the virus-free L. braziliensis M2903 used in the majority of our studies with a closely related species L. guyanensis that bears the cytosolic dsRNA virus LRV1-4 [35], [36] (Fig. 8). While the RNAi pathway remained highly active in the LRV-infected L. guyanensis, its activity as assayed with LUC or GFP reporters was attenuated ∼10-fold relative to that seen in virus-free L. braziliensis (Fig. 8A). Although tools for the introduction of LRV are not well-developed, one line of L. guyanensis has been described which was cured of LRV [38]. Notably the efficiency of RNAi in the virus free line was similar to that of the LRV1-4 containing line (Fig. 8A), showing that the attenuated RNAi response did not require the continued presence of virus. This implies that attenuation occurred through a down-regulation of the cellular RNAi pathway occurred in the LRV-bearing L. guyanensis. If a similar process occurred in the evolutionary lineage leading to extant RNAi-deficient Leishmania species, it could in turn have facilitated a later transition to a complete loss of RNAi activity. Future development of methods for more readily introducing and curing LRV infections will permit further tests of these hypotheses, as will the advent of RNAi-deficient lines of Leishmania braziliensis and other Viannia species. However, the data already in hand are consistent with the possibility of a biologically relevant interplay between parasite RNAi pathways and viral infection during evolution, as seen in viral infections of metazoans.
A second selective force arises from consideration of the impact of genome plasticity in Leishmania. The ability of mobile elements to produce mutations and genomic rearrangements are well known, and in trypanosomes and other eukaryotes RNAi pathways may help protect against such events [42]–[44]. Importantly, the RNAi-competent L. braziliensis genome contains several classes of mobile elements, including retrotransposons, while RNAi-deficient L. major and L. infantum appear to lack active transposons [15]. While the forces leading to the loss of mobile elements are unknown, their departure could have freed the parasite from the need to maintain activities including RNAi which act to mitigate their effects.
Gene amplification is another important form of genomic plasticity in Leishmania, often occurring in the form of extra-chromosomal circular DNAs associated with drug resistance [45], [46]. In contrast, extra-chromosomal gene amplifications have not been seen in T. brucei, a difference potentially attributable to its active RNAi pathway [11], [13] since circular amplicons tend to be transcribed from both strands [47]. Consistent with this model, extrachromosomal gene amplifications are uncommon in RNAi-proficient L. braziliensis [48], and we found that transfections with a variety of circular DNAs were generally unsuccessful, causing us to rely exclusively on integrative constructs in this work. This does not imply that episomal circular DNAs will never arise in RNAi-proficient species; but when found, their transcription will be subject to RNAi effects and/or they will contain cis-acting elements that confer a high degree of strand specificity [49]. These requirements might act to constrain the emergence of episomal elements in RNAi-proficient species.
Thus the loss of RNAi could be seen as ‘freeing’ the genome of RNAi-deficient Leishmania from several constraints limiting genome plasticity. In this regards, loss of RNAi may be viewed as ‘mutator’ phenotype, similar to the ‘ARMed’ phenotype described recently in the malaria parasite Plasmodium falciparum or the high mutability phenotypes associated with elevated bacterial virulence in humans [50], [51].
Lastly, loss of RNAi may have been selected directly through effects on Leishmania virulence during evolution. The RNAi machinery affects gene expression at multiple levels, and its loss could lead to profound changes in parasite biology that could alter parasite virulence. Such direct alterations in gene expression may act in concert with the genomic alterations described above. The Leishmania subgenus Viannia is an early diverging clade within the genus [52], and these species exhibit a number of distinct features including the nature of the immune response in the mammalian host, the composition of their surface glycocalyx, and their behavior within the sand fly vector [8], [53]. Any such systematic differences between the RNAi-proficient Viannia subgenus and the RNAi-null Leishmania species groups could potentially reflect changes associated gene expression mediated by the RNAi pathway.
Our findings provoke the question of whether the RNAi machinery could be transplanted from L. braziliensis into its close RNAi-deficient relatives. This would be useful given the extensive previous work on species such as L. major and L. donovani, as well as providing a tool for understanding the RNAi machinery. This feat was recently accomplished in Saccharomyces cerevisiae, which required only the introduction of Argonaute and Dicer from the closely related species S. castellii [33]. However, reintroduction of RNAi in L. major or L. donovani may require restoration of a more extensive suite of genes. While only three RNAi genes have been confirmed in trypanosomatids (an Argonaute and two Dicers) [9], [10], [17], preliminary data suggest a requirement for at least two additional genes (E. Ullu and C. Tschudi; unpublished data). Importantly, all 5 genes are absent in the genomes available for RNAi-deficient Leishmania species. In other eukaryotes the RNAi machinery includes as many as 9 proteins or more [15], [31], [54]. Another obstacle may be the tendency of RNAi-deficient species such as L. major to transcribe the antisense chromosomal strand at low levels [55], as well as to synthesize antisense transcripts [56], [57]. This suggests the possibility that introduction of an active RNAi pathway into L. major could be lethal [11], [58]. Thus re-introduction of RNAi into RNAi-deficient Leishmania species will be a challenging task; nonetheless, efforts to introduce this suite of genes from RNAi proficient L. braziliensis are underway.
In summary, we have shown that the RNAi pathway is functional in Leishmania braziliensis. These data provide some optimism for the application of RNAi approaches as a tool for the study of these predominantly asexual organisms, by forward and reverse genetic approaches. While less experimentally developed, L. braziliensis has the potential to emerge as an attractive model, and the advent of RNAi-based tools should provide a further stimulus for this effort. In the long term, delivery of siRNAs targeting essential parasite genes may prove an effective route to chemotherapeutic treatment of RNAi-proficient Leishmania. Lastly, the Leishmania provide an attractive system for testing hypotheses about forces leading to the evolutionary loss of RNAi, including the role of viral pressure, changes in genome plasticity, and virulence. As drug resistance mediated by gene amplification is one manifestation of gene plasticity, these findings have practical implications to parasite chemotherapy.
RNA extraction procedures and Northern analyses were carried out as described [16]. The 5′UTR of L. braziliensis α-tubulin mRNA plus the first 317 nt of the ORF were PCR-amplified from genomic DNA and inserted between the HindIII and XbaI sites of plasmid vector pPD19.36, which contains two opposing T7 RNA Polymerase promoters [59]. The synthesis of dsRNA was according to Ngo et al. [21]. The same DNA was used as a probe in the α-tubulin Northern. PCR products of GFP+ or GFP65 ORFs were used as probes for the GFP Northerns. A portion (nt 3160 to nt 4482) of the L. braziliensis SLACS (LbrM08−V2.0700) was PCR-amplified with primers (LBSLACS1399F: 5′-GCCAGAGAGGTGGTGAGGGTG and LBSLACSORFa-R: 5′-GAGCTCGAGAAAGGTCCACCACCCCGA) from M2903 genomic DNA and TA cloned to generate a sense radiolabeled RNA probe for Northern analysis of small RNAs. For LPG2 (LbrM20_V2.2700) the probe was a PCR fragment (nt 1 to nt 411) amplified with primers SMB3219 and SMB3220 (Table S1).
Leishmania total RNA was isolated using the Trizol reagent (Invitrogen), treated with DNAse and purified using MEGAclear columns (Ambion). Reverse transcription (RT) was performed according to the manufacture instructions using Superscript III First-Strand reverse transcriptase (Invitrogen) in a 20 µl reaction containing 1µg purified RNA. Controls containing the same amount of RNA but lacking reverse transcriptase or template were used to rule out DNA or other contamination. For test RNAs, primers were designed to amplify ∼100 bp amplicons within the target ORF but outside of the stem-fragment, and tested using L. braziliensis gDNA. PCRs were performed using the SYBR Green (Applied Biosystems) and the ABI PRISM 7000 Sequence Detection System instrument (Applied Biosystems). PCR amplifications were performed as follows: 50°C for 2 min and 95°C for 10 sec then followed by 40 cycles of 95°C for 15 sec, 60°C for 1min. The generation of specific PCR products was confirmed by melting curve analysis and agarose gel electrophoresis. Each primer set was individually tested for four StL transfectants (2 for StL-F and 2 for StL-R; except 4 for LPG3-StL-F). All samples were performed in triplicate. Control samples of H2O were included in each experiment. Amplification of SSU rRNA was used as internal control to normalize the parallel reaction of target amplicons.
L. braziliensis M2903 (MHOM/BR/75/M2903), L. guyanensis M4147 (MHOM/BR/75/M4147) and L. panamensis WR120 (MHOM/PA/74/WR120) were obtained from Diane McMahon-Pratt (Yale University), L. braziliensis strain M2904 from Angela Cruz (U. Sao Paulo Riberao Preto), L. tarentolae strain TarII was obtained from M. Ouellette and B. Papadopoulou (U. Laval), L. mexicana (MNYZ/BZ/62/M379) from David Russell (Cornell University), and Crithidia fasciculata Cf-C1 from Larry Simpson (UCLA). The LRV-bearing strain of L. guyanensis M4147 (MHOM/BR/75/M4147) and a virus free derivative M4147/pX63-HYG [38] were obtained from Jean L. Patterson (Southwest Foundation for Biomedical Research, San Antonio, Texas). The identities of all Viannia strains used were confirmed by partial and/or complete sequencing of the AGO1 or other genes (not shown).
Viannia species were grown in freshly prepared Schneider's Insect Medium (Sigma-Aldrich Cat. No. S9895) supplemented with 10% heat-inactivated fetal bovine serum, 2 mM L-glutamine, 500 units penicillin/ml and 50 µg/ ml− streptomycin (Gibco Cat No. 5070). Other Leishmania and Crithidia were propagated in M199 medium supplemented with 10% heat-inactivated fetal bovine serum, hemin, adenine, biopterin and biotin [60].
For each transfection, 10 ml of log phase L. braziliensis were resuspended in 100 µl human T-cell Nucleofector solution (Amaxa Cat No. VPA-1002) mixed with 5 µl of 4 µg/ µl of α-tubulin dsRNA or control dsRNA and subjected to nucleofection with an Amaxa Nucleofector with program U-033 using the kit's cuvette. The transfection mixture was transferred immediately to 10 ml of complete medium and kept in 28°C for 3 hrs. RNA from 9 ml cells was taken for Northern blot analysis with an α-tubulin hybridization probe.
Stable transfections were performed using the high voltage (1400V) procedure described previously [11]. Following electroporation organisms were grown in drug-free media overnight, and then plated on semisolid media [60] to obtain clonal lines. For selections using the SAT marker, parasites were plated on 50–100 µg/ml nourseothricin (clonNAT, Werner BioAgents, Germany), and with the PHLEO marker, parasites were plated on 0.2–2 µg/ml phleomycin (Sigma). After colonies emerged (typically <2 weeks) they were recovered and grown to stationary phase in 1 ml media, and passaged thereafter in 10 and 0.1 µg /ml nourseothricin and phleomycin, respectively. The plating efficiency of untransfected L. braziliensis M2903 ranged from 60–95% and the transfection efficiency from 50–220 colonies / 20 µg DNA.
The generation of whole genome shotgun sequence data from Crithidia fasciculata strain Cf-C1 by 454 sequencing technology will be described fully elsewhere. Blast searches using L. braziliensis AGO1 were used to identify homologous sequences, which were then assembled manually into several large contigs. PCR primers were designed to amplify missing gaps, and the 5′ end of the mRNA was obtained by RT-PCR using a forward miniexon primer (CFSLB 5′-AAGTATCAGTTTCTGTACTTTATTG) and reverse CfAGO1 specific primer (SMB2895: 5′-AAGCAGTTCGTCTCCACCGTCACCTG). Then a nested PCR was done with CfSLB and CfAGO1 primers (SMB 2894: 5′- GTGATGCCGCCCTCCTCGCGGTCACG). The PCR products were TA cloned and sequenced. The CfAGO1 sequence was deposited in GenBank (EU714010). We noted a polymorphism in the CfAGO1 sequence, introducing a stop codon yielding a truncated protein terminating after amino acid 198. The consequences of this polymorphism (if any) have not been investigated further. The sequence of the L. guyanensis M4147 AGO1 ORF was determined by direct sequencing of the PCR amplicon obtained with primers B2468 (5′-ATGTTGGCGCTAAACGCAGGTTC) and B2469 (5′- CTACAGGTAGTGCATCGTGGGGC), and deposited in GenBank (accession number FJ234150).
RT-PCR reactions were performed as described above, with two sets of primers to detect LRV viruses described previously [38] (set 1, primers SMB2472/2473 and set 2, primers SMB3850/3851 (Table S1).
The constructs used in this work are derivatives of pIR1SAT (B3541) [11] or pIR1PHLEO (B4054, this work), which have two expression sites (XbaI/SmaI, site a, and BglII, site b). High fidelity thermostable polymerases such as recombinant Pfu DNA polymerase (Stratagene) were used for PCR, and constructs were confirmed by restriction mapping and sequencing of all relevant regions. Unless otherwise indicated, all constructs were digested with SwaI and the linear SSU-targeting fragment purified for subsequent transfection by electroporation.
pIR1PHLEO (B4054) was created by replacing the SAT marker of pIR1SAT with the PHLEO marker (M. Cunningham, unpublished data). pIR1PHLEO-GFP+(a) (B5793), pIR1PHLEO-GFP65(a) (B5779) and pIR1-GFP65*(a) (B5959) were constructed by generating ORF cassettes of the respective genes and inserting into the XbaI (a) site. The GFP+ ORF was taken from pXG-GFP+ (B2799), GFP65 from pXG-GFP65 (B2355), and GFP65* was obtained by site-specific mutagenesis of pIR1PHLEO-GFP65 (QuickChange Multi Site-Directed Mutagenesis, Stratagene), changing nt 193 from T to A, resulting in a S65T mutation. A luciferase (LUC) ORF was amplified using pGL3-basic (Promega) as template, with primers adding flanking BglII sites, and a CCACC initiation sequence preceding the initiation codon. The modified LUC ORF was inserted into pGEM-T (Promega) yielding pGEM-Luciferase (B6033); the LUC ORF was then extracted by BglII digestion and inserted into the BglII site of B5959 to create pIR1PHLEO-GFP65*(a)-LUC(b) (B6034).
pIR1SAT-GFP65-StL(b) (B4733) was described previously [11]. For other StL constructs, we assembled a stem-loop consisting of the target gene sequences in inverted orientation, separated by a PEX11-MYC(3) loop/stuffer fragment used previously in pIRGFP Stem-Loop (B4733), and inserted this into either the ‘a’ or ‘b’ expression sites of pIR1SAT. In these constructs the ‘stem’ sequences were organized either in divergent or convergent orientations (DIV or CONV) relative to the target gene sequence, and the stuffer fragment similarly could be in a ‘sense’ or ‘antisense’ orientation relative to PEX11 (F or R). The specific target genes and regions studied included LPG1 (LbrM25_V2.0010, nt 11–592); LPG2 (LbrM20_V2.2700, nt 411–1021); LPG3 (LbrM29_V2.0780, nt 1657–2236); HGPRT (LbrM21_V2.0990, nt 127–626); α-tubulin (LbrM13_V2.0190, nt 736–1309); β-tubulin (LbrM33_V2.0930, nt 470–1004); PFR1 (LbrM31_V2.0160, nt 900–1593); PFR2 (LbrM16_V2.1480, nt 951–1644), AGO1 (LbrM11_V2.0360, nt 247–1070) and LUC (LUC+ from Promega pGL3-Basic, nt 281–788). These steps yielded constructs pIR1SAT-LPG1-StL (b,DIV,R)(B6128), pIR1SAT-LPG1-StL (b,DIV,F) (6132), pIR1SAT-LPG2-StL(b,DIV,R) (B6137), pIR1SAT-LPG2-StL(b,DIV,F) (B6138), pIR1SAT-LPG3-StL(b,DIV,F) (B6140), pIR1SAT-HGPRT-StL(b,DIV,F) (B6136), pIR1SAT-HGPRT-StL(b,DIV,R) (B6135), pIR1SAT-PFR1-StL(b,DIV,F) (B6294), pIR1SAT-PFR2-StL(b,DIV,F) (B6282), pIR1SAT-αTub-StL(b,DIV,F) (B6283), pIR1SAT-βTub-StL(b,DIV,F) (B6295) and pIR1SAT-LUC-StL(b,CONV,F) (B6185), or pIR1SAT-LUC-StL(b,DIV,F) (B6190).
A single construct enabling tests of RNAi activity was generated by inserting the LUC ORF into the ‘b’ site and a LUC Stem-Loop into the ‘a’ site of a modified pIR vector (pIR2SAT-LUC-StL(a)-LUC(b) (B6386). This construct is referred to as the ‘LUC RNAi self reporter’ or ‘LUC SR’. For RNAi studies of AGO1, an analogous construct was made with a HYG marker (pIR2HYG-LUC-StL(a)-LUC(b), strain B6447). A pIR1SAT-LbrAGO1-StL(b) construct was used for RNAi tests (B6524).
Western blots were performed as described elsewhere using anti-GFP (Abcam Cat No. 6556, 1∶2500) or anti-L. donovani HGPRT antiserum (1∶5000; J. Boitz and B. Ullman, Oregon Health Sciences University) as the primary antibody, and detected using goat anti-rabbit IgG as the secondary antibody (1∶10000, Jackson ImmunoResearch Laboratories, Inc. catalog number 111-035-003). Parasites expressing GFPs were analyzed using a Becton-Dickenson FACS Calibur, using fluoroscein excitation/emission parameters. LPG was purified and quantitated from L. braziliensis lines grown in logarithmic phase (4–5×106 cells/ml) as described [61]. Purified LPG was subjected to western blotting with antisera CA7AE which recognizes the Gal(β1,4)Man(α1-P) repeat units of the L. braziliensis LPG [62].
106 logarithmic phase promastigotes were suspended in 200 µl media containing 30 µg/ ml of luciferin (Biosynth AG) and added to a 96-well plate (Black plate, Corning Incorporated, NY, U.S.A.). After 10 min incubation, the plate was imaged using a Xenogen IVIS photoimager (Caliper LifeSciences), and luciferase activity quantitated as photons/sec (p/s).
Promastigotes were fixed in 2% paraformaldehyde/2.5% glutaraldehyde (Polysciences Inc., Warrington, PA) in 100 mM phosphate buffer, pH 7.2 for 1 hr at room temperature. Samples were washed in phosphate buffer and postfixed in 1% osmium tetroxide (Polysciences Inc., Warrington, PA) for 1 hr. Samples were then rinsed extensively in water prior to en bloc staining with 1% aqueous uranyl acetate (Ted Pella Inc., Redding, CA) for 1 hr. Following several rinses in water, samples were dehydrated in a graded series of ethanol solutions and embedded in Eponate 12 resin (Ted Pella Inc.). Sections of 95 nm were cut with a Leica Ultracut UCT ultramicrotome (Leica Microsystems Inc., Bannockburn, IL), stained with uranyl acetate and lead citrate, and viewed on a JEOL 1200 EX transmission electron microscope (JEOL USA Inc., Peabody, MA).
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10.1371/journal.pcbi.1002832 | Phylogenetic Diversity Theory Sheds Light on the Structure of Microbial Communities | Microbial communities are typically large, diverse, and complex, and identifying and understanding the processes driving their structure has implications ranging from ecosystem stability to human health and well-being. Phylogenetic data gives us a new insight into these processes, providing a more informative perspective on functional and trait diversity than taxonomic richness alone. But the sheer scale of high resolution phylogenetic data also presents a new challenge to ecological theory. We bring a sampling theory perspective to microbial communities, considering a local community of co-occuring organisms as a sample from a larger regional pool, and apply our framework to make analytical predictions for local phylogenetic diversity arising from a given metacommunity and community assembly process. We characterize community assembly in terms of quantitative descriptions of clustered, random and overdispersed sampling, which have been associated with hypotheses of environmental filtering and competition. Using our approach, we analyze large microbial communities from the human microbiome, uncovering significant variation in diversity across habitats relative to the null hypothesis of random sampling.
| Microbial diversity analyses have revolutionized our knowledge of the microscopic world, from terrestrial and marine to human and urban environments. This growing field rests on the evolutionary relatedness of organisms, and at its frontier is the inference of ecological processes from phylogenetic diversity. However, the rapidly reducing cost of sequencing means that computational analysis of phylogenetic data is becoming increasingly intractable. We develop a new analytical method to address this issue, providing a computationally-efficient way to compare local phylogenetic diversity to a sample from a regional pool of organisms, under a given ecological process. Our approach has both pragmatic and far-reaching applications. Until now investigators have lacked even an analytical method to compare the diversity of unequally-sized communities without throwing data away, while on a deeper level our theory provides a new framework for connecting phylogenetic data to a wide range of ecological processes. As an application of our approach, we use our methods to distinguish between random, clustered and overdispersed sampling for human microbiome habitats. Finally, we identify a new, phylogenetic analogue of the widely used taxonomic measure of diversity, the Species Abundance Distribution, and we find that it has consistent behavior across microbiome habitats.
| Microbial ecology has been advancing at a rapid pace, but understanding the processes driving microbial community structure remains a challenge [1]. The first step towards identifying these processes has been to document microbial biodiversity, and the use of phylogenetic methods has been stimulated by the abundance of genomic data harvested from microbial communities [2]. Phylogenetic measures of diversity have been proposed as a more accurate representation than taxonomic diversity of community trait and functional diversity, and are therefore a potentially more relevant starting point for quantifying and understanding microbial communities [3]. Phylogenetic approaches have been used to quantify microbial diversity along environmental [4] and elevational gradients [5], [6], across distinct habitat types [7]–[10] and for different experimental treatments, for habitats ranging from marine and freshwater, to soil, indoor and outdoor air, to the human body [11].
Despite this success in documenting patterns of phylogenetic diversity in a broad range of contexts, our ability to translate these patterns into processes has been hampered by a lack of phylogenetic theory. The difficulty in formulating quantitative hypotheses impacts even the simplest of questions: which of two microbial communities is more phylogenetically diverse? If one community is larger than the other, we cannot answer this basic question without a theoretical hypothesis for the way we expect phylogenetic diversity to increase with community size. More generally, the lack of quantitative, analytical theory has made it difficult to address the relative importance of ecological processes of environmental selection, competition, dispersal and stochasticity in a given community. We have qualitative hypotheses and computational approaches to assess the impact of these processes, but there is no overarching, analytical framework within which to compare them.
Phylogenetic theory can address both of these issues: the pragmatic problem of comparing phylogenetic diversity in different communities, and the larger question of inferring ecological processes from phylogenetic patterns. In this manuscript we develop a way to cast many different assembly processes in a common framework, centering around the comparison of a local community of co-occuring organisms with a sample from a regional, metacommunity of organisms. We draw from the sampling theory of taxonomic diversity [12]–[14] and our analysis rests on a new way of characterizing a metacommunity phylogenetic tree, which we term the Edge-length Abundance Distribution. This distribution is a new phylogenetic analogue of the classic taxonomic Species Abundance Distribution.
As a proof-of principle application of our framework, we focus on publicly-available human microbiome data [11] and explore three questions centering around the comparison of microbiome phylogenetic diversity with a null hypothesis of random sampling from a metacommunity. First we document patterns across different microbiome habitats and different subjects, and identify a power-law pattern for the Edge-length Abundance Distribution. Species Abundance Distributions have been proposed to have a log-normal distribution in many ecological communities [15], [16], including microbial systems [17], and our work puts this observation on a new, phylogenetic footing. Second, we examine the effect of metacommunity scale on distinguishing different community assembly hypotheses [18], [19]. We compare the diversity of body habitats to a null hypothesis of random sampling from two different definitions of the metacommunity, one significantly larger than the other. We see a clear impact of metacommunity size on the phylogenetic diversity of body habitats relative to the null hypothesis. Finally, we address the question of whether the microbiome of a given human subject is consistent with the null hypothesis of random sampling, and find that while whole microbiome diversity for a given subject is typically much lower than a random sample from the metacommunity, this hides a wide range of different behaviors for the distinct habitats within that subject. We think of this as exploring the impact of local community resolution within the human body: different levels of resolution reveal more complexity.
Phylogenetic Diversity (PD) has been defined as the total branch length connecting all organisms in a phylogenetic tree, and provides a natural phylogenetic analogue of taxonomic diversity [20]. Similarly, the UniFrac distance measure quantifies the overlap in phylogenetic branch length of two communities, and tells us how similar or different those two communities are [21]. PD and UniFrac provide convenient measures of microbial diversity that do not rely on the ability to identify or enumerate microbial species, and are also amenable to exploring phylogenetic versions of classic biogeographical patterns of alpha and beta diversity.
Comparing observed patterns of phylogenetic diversity to the patterns expected under various null models provides both a normalization to take into account the difference in sample sizes across different habitats or treatments [22], [23], and also a connection between patterns of phylogenetic diversity and the processes underlying them. Given information on the evolution of ecologically important traits [24], [25] and the phylogenetic relatedness of organisms in local communities, an ‘ecophylogenetic’ framework can potentially provide insights into the relative importance of processes such as dispersal, competition, filtering, or drift [26]–[30]. So far, these approaches to evaluating phylogenetic diversity against null models have relied on ‘brute force’ sampling, where a randomizing algorithm is used to sample tips from a phylogenetic tree, and the process is repeated many times to infer expected phylogenetic diversity for a sample of a given size. This approach becomes intractable for large phylogenetic trees, and must also be repeated for each additional hypothesis and sample size.
We have developed a new, analytical approach to address these problems. Our conceptual framework links a local sample of individual organisms, a regional pool or metacommunity, and processes connecting these two scales. This perspective has a long history in ecological theory [31]–[33], and has been recently advocated as an appropriate framework for the ecology of symbiont systems [34]. Figure 1 outlines the conceptual overview for this local and metacommunity framework, with explicit examples in the context of the human microbiome. Within our framework, the community assembly processes linking these local and regional scales could be either mechanistic, directly drawing on a process such as dispersal; or phenomenological, characterizing general patterns in statistical terms. The phylogenetic diversity literature has most often focused on the latter, comparing observed PD with the hypotheses of random, clustered and overdispersed sampling. We therefore focus on these sampling schemes in this manuscript, but our framework is potentially generalizable to specific community assembly mechanisms such as dispersal limitation [14]. The methods described in this paper have been implemented in software and will be available in version 1.5 of the picante R package [35].
The human body is host to multiple microbial communities, whose combined total outnumbers our own cells by at least a factor of 10 [11], and a community ecology perspective may be essential for a full understanding of the impact of the human microbiome on human health [38]. We now apply our framework to such microbial communities, collected from 7 human subjects across 26 distinct habitats. This data provides an ideal testing ground to demonstrate the utility of our theoretical framework: the largest metacommunity tree we sample from has tips, each tip representing an individual sequence sampled in the study, which would render computational null model approaches intractable. Also, the different community types, subjects and scales allow us to address how our community assembly hypotheses depend on the choice of local community and metacommunity.
In this manuscript we have developed a new, analytical method to quantify and distinguish different hypothesis for the phylogenetic structure of ecological communities. Our approach centers around a new characterization of phylogenetic tree shape, which we term the Edge-length Abundance Distribution (EAD), and we find that this distribution is analogous and complementary to the Species Abundance Distribution (SAD) in taxonomic sampling theory. We observe that the EAD follows a roughly power-law distribution across a number of communities within the human microbiome. Power-law patterns in the distribution of branch lengths have been observed before in phylogenetic trees [41], but while intriguing, the relevance of these patterns was not clear. What makes this pattern different is that, just like the SAD and taxonomic diversity, the definition of the EAD is not arbitrary: it has an essential role in connecting local and regional diversity. An important next step will be to identify what kinds of ecological mechanisms and constraints can give rise to particular types of Edge-length Abundance Distribution, including approximately power law distributions.
We applied this theoretical framework to investigate whether phylogenetic diversity (PD) of local communities from the human microbiome is greater or less than the expected PD for randomly drawn samples from a metacommunity. Local community PD lower than random has been associated with the hypothesis of environmental filtering, while local community PD greater than random (phylogenetic overdispersion) has been associated with competition and competitive exclusion [27]. Taking as our local communities the set of organisms sampled from a single habitat from a single human subject, we observed a wide range of variation across habitats and across subjects in terms of deviation from the random hypothesis. On the other hand, it is not clear yet how to quantitatively connect these deviations to ecological processes, indicating that sampling schemes with a direct connection to ecological mechanism may in the longer term be more relevant than the phenomenological sampling schemes we have explored in this manuscript.
Our results set the scene for a much more rigorous investigation of these issues, and we see three main future directions. For the first time, our framework has made the phylogenetic analysis of large microbial metacommunities analytically tractable, and we find that metacommunity size is highly relevant in our proof-of-principle analysis of human microbiome communities. This confirms the expectation that our conclusions about community assembly depend crucially on the definition of the metacommunity, and indicates the need for a very careful definition of the metacommunity to fully understand the processes structuring local phylogenetic diversity. Second, we have adapted tools from taxonomic sampling and applied them in a phylogenetic context, but this provides just the first steps towards developing a comprehensive theoretical toolbox for distinguishing hypotheses and predicting patterns. Directly characterizing ecological mechanisms, for example dispersal limitation, in terms of our phylogenetic sampling theory will provide a clearer connection between ecological process and phylogenetic patterns.
Finally, we have focused here on the total phylogenetic diversity of local communities [20]. This is the analogue of looking at total species richness alone as a way to distinguish between different hypotheses. In studies of taxonomic diversity, species abundances have provided a way to distinguish quantitatively between different types of community [15], [16], [42], [43], to extrapolate taxonomic diversity to scales far beyond our samples [17] and to connect taxonomic pattern and mechanistic processes more clearly [16], [33], [44]–[46] than using species richness alone. We do not yet have the overarching phylogenetic theory with which we can distinguish between environmental selection, competition, dispersal and stochasticity. But the application of our framework with mechanistically-based sampling schemes has the potential to put phylogenetic diversity on this same quantitative footing as taxonomic diversity, potentially allowing us to extrapolate PD from local samples to much larger scales, and to distinguish between different ecological hypotheses more effectively.
Our methods are integrated into the body of the manuscript, primarily in the Results section. Additional methods and derivations are included in our Supporting Information Text S1.
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10.1371/journal.pgen.1003851 | A Critical Role for PDGFRα Signaling in Medial Nasal Process Development | The primitive face is composed of neural crest cell (NCC) derived prominences. The medial nasal processes (MNP) give rise to the upper lip and vomeronasal organ, and are essential for normal craniofacial development, but the mechanism of MNP development remains largely unknown. PDGFRα signaling is known to be critical for NCC development and craniofacial morphogenesis. In this study, we show that PDGFRα is required for MNP development by maintaining the migration of progenitor neural crest cells (NCCs) and the proliferation of MNP cells. Further investigations reveal that PI3K/Akt and Rac1 signaling mediate PDGFRα function during MNP development. We thus establish PDGFRα as a novel regulator of MNP development and elucidate the roles of its downstream signaling pathways at cellular and molecular levels.
| Craniofacial anomalies, including cleft lip and palate, are frequent birth defects. Although these are often associated with defects in neural crest development, the more severe phenotypic manifestations of midline defects is facial clefting, which is poorly understood. In this work, we show that the facial clefting phenotype of PDGFRα mutants is not associated with a defect in neural crest cell specification but rather a subsequent defect in the medial nasal process (MNP), a facial primordium derived from the frontonasal prominence. We further show that this defect is associated with alterations in both cell proliferation and cell migration, and that PI3K and Rac1 signaling are essential to maintain a normal level of cell proliferation. Last, we provide evidence that Rac1 regulates cell migration at the level of cell motility as well as chemotaxis under the regulation of PDGFRα. We thus establish PDGFRα as a novel regulator of MNP development and elucidate the roles of its downstream signaling pathways at cellular and molecular levels.
| Neural crest cells (NCCs) are a transient and multipotent cell population unique to vertebrates. During development, NCCs give rise to a broad variety of cell types, which contribute to the formation of the peripheral nervous system, cardiac outflow tract, pigment cells, and the majority of craniofacial bones and cartilages [1]–[4]. Alterations of cranial NCC (cNCC) development often lead to craniofacial malformations, one of the most prevalent birth defects [5]. These facts underscore the significance of understanding the mechanisms regulating NCCs during craniofacial morphogenesis.
At the onset of craniofacial development, the facial primordium is composed of five prominences surrounding the stomodeum: the frontonasal prominence (FNP) at the rostral region, two paired maxillary processes in the middle, and a pair of mandibular processes at the caudal end [6], [7]. These primordia are populated predominantly by NCC derived cells, surrounding a mesodermal core and covered by the overlying ectoderm. The ectoderm then thickens and invaginates to form two bilateral nasal placodes, dividing the FNP into the medial nasal process (MNP) and a pair of lateral nasal processes (LNP). The MNP and bilateral maxillary processes contribute together to form the upper lip [8]. In mammals, the MNP further develops into the philtrum and the nasal septum, which later forms the nasal cartilage and bone [9]. Disruption of the MNP usually causes a variety of craniofacial defects, ranging from mild hypoplasia of the nasal bones to complete midfacial clefting. A number of genes regulate maxilla and mandible development, but it remains largely unknown how MNP development is controlled at the molecular and cellular level. Vital dye labeling studies reveal that the NCCs giving rise to different facial prominences share distinct origins along the rostral-caudal axis: NCCs from the diencephalon and anterior mesencephalon give rise to the MNP and LNP, while those originating from the posterior mesencephalon and hindbrain give rise to the maxilla and mandible [10], [11]. These results suggest that the MNP and other prominences may be regulated through different mechanisms.
Multiple genetic factors have been implicated in cranial NCC (cNCC) development. Among these, growth factor signaling pathways are essential for induction, proliferation, survival and migration [12]–[14]. BMP, FGF and Wnt signaling together mediate induction of cNCCs from neural ectoderm [13], [15]. cNCC proliferation and survival are under the control of BMP, FGF and TGFβ signaling, and migration of the cNCCs at the caudal level is regulated by BMP, Wnt, Semaphorin and Ephrin signaling [13]. Growth factors act via binding and activation of their cell surface receptors, which in turn engage multiple intracellular signaling pathways. It remains to be elucidated how these intracellular signaling pathways mediate the receptors' function, especially in developmental contexts.
Platelet Derived Growth Factor (PDGF) signaling plays essential roles in development and disease [16]–[19]. In mammals, PDGF signaling can be activated by four PDGF ligands (A, B, C and D) operating through two receptor tyrosine kinases, PDGFRα and β [17], [20]. Activation of PDGFRs leads to phosphorylation of intracellular tyrosines and docking of intracellular effectors, which in turn engage downstream signaling cascades including the MAPK, PI3K, PLCγ, STAT and Src pathways. Previous studies from our laboratory and others have shown that PDGFRα and its downstream signaling pathways are crucial for cardiac and cranial NCCs [21]–[24]. PDGFA/PDGFRα signaling has also been implicated in cell migration in zebrafish palatogenesis [25]. However, the precise mechanisms by which PDGFRα regulates cNCCs and MNP development still remain to be elucidated. To this end, we have carried out a detailed study of PDGFRα NCC conditional knockout embryos. Our work reveals novel roles of PDGFRα in regulating NCC migration, and for PDGFRα engaged PI3K signaling in MNP neural crest cell proliferation. Moreover, we show that Rac1 and PI3K signaling mediate these processes under the control of PDGFRα.
To understand how PDGFRα and its downstream signaling pathways regulate NCC development and craniofacial morphogenesis, we generated NCC-specific PDGFRα conditional knockout (cKO) embryos by intercrossing PDGFRαfl/fl and Wnt1Cre transgenic mice [21], [26]. Morphological differences first became visible at E11.5, as the medial nasal processes (MNP) of cKO littermates remained separated by an obvious gap relative to the control embryos (Fig. 1A, B). By E13.5, cKO embryos lacked the philtrum and the primary palate (Fig. 1C, D), both of which are derived from the MNP. This observation was confirmed by histological analysis that showed the absence of the primary palate and philtrum in the cKO embryos (Fig. 1E, F). At E18.5, skeletal preparations showed that PDGFRαfl/fl; Wnt1 Cre embryos exhibited a significant cleft (6 out of 6) and shortening of nasal cartilage (88±4.8% relative to control) and premaxilla (81.2±2.0% relative to control) (Fig. 1G, H). Skeletal analysis of cKO mutants also revealed malformation of the neural crest derived basisphenoid, alisphenoid and pterygoid bones (Fig. 1G, H) [1], [27], [28]. Other cNCC derived structures, such as the mandible, developed normally in the cKO mutants. No cKO embryos survived past birth.
To further understand how PDGFRα signals during MNP development, we analyzed the expression of PDGFRα and its ligands. Using PDGFRαGFP/+ knockin reporter mice [29], we observed broad GFP expression in the facial structures at E10.5 (Fig. 2A). Coronal sections of PDGFRαGFP/+ embryos at different stages revealed that PDGFRα is exclusively expressed in mesenchymal cells of the future facial structures, which are derived predominantly from cNCCs [1]. At E10.5, PDGFRα appears to be expressed equally in both the MNP and the LNP (Fig. 2B); however by E11.5, PDGFRα expression is reduced in the LNP relative to the MNP mesenchyme (Fig. 2C). PDGFA and PDGFC encode two endogenous ligands that bind specifically to PDGFRα, and inactivation of the two genes together recapitulates the PDGFRα null mutant phenotype [30]. In situ hybridization showed that these genes exhibit overlapping expression in the MNP: PDGFA is expressed in the MNP and LNP epithelium and PDGFC is expressed in both the epithelium and mesenchyme (Fig. 2D–G). The expression patterns of PDGFRα and its ligands indicate paracrine and possibly autocrine PDGFRα signaling during MNP development.
To examine the role of PDGFRα in the MNP, we first analyzed cell proliferation and apoptosis in developing embryos. BrdU labeling revealed that in control embryos (n = 9), 40±3% of MNP mesenchymal cells and 35±2% of LNP mesenchymal cells were proliferating at E11.5. In cKO embryos, MNP mesenchymal cell proliferation was decreased by 30% relative to littermate controls, while LNP mesenchymal cell division was maintained a comparable level (Fig. 3A, B and E). No ectopic apoptosis was identified in cKO MNP cells (data not shown). Since the MNP and LNP are derived from the FNP during embryogenesis, we further traced these defects to the FNP. We found that at E9.5, cKO FNP mesenchymal cell proliferation was significantly downregulated by 19% (Fig. 3C–E), and no ectopic cell apoptosis was identified (data not shown). We also assayed expression of genes critical for MNP development in E10.5 embryos. Among them, Six1 and Alx4 expression remained unaltered in cKO and control embryos (Fig. S1), while Alx3 expression was downregulated in the cKO MNP mesenchyme, but not in the mandible (Fig. 3F, G). Because Alx3 is essential for MNP cell survival and no ectopic apoptosis was found in the cKO MNP (data not shown) [31], the reduction of Alx3 mRNA might be caused by a decrease in cell number in the cKO MNP. A similar change in gene expression has been observed in Pax6 mutant embryos, which showed a migration defect of MNP progenitor cells [32]. Together, these data suggest that PDGFRα regulates cell proliferation in the FNP and specifically in the MNP.
PDGFRα regulates cell fates and behaviors through a number of downstream signaling pathways. Previous studies in our laboratory showed that PI3K signaling is the major effector of PDGFRα in craniofacial development. Loss of PDGFRα-mediated PI3K signaling alone caused cleft palate with incomplete penetrance, but inactivation of PDGFRα together with PDGFRβ-mediated PI3K signaling caused facial clefting similar to PDGFRα null mutant embryos [24]. To examine the role of PI3K signaling in MNP development, we first analyzed the phenotype of PDGFRαPI3K/PI3K mice. PDGFRαPI3K/PI3K embryos exhibited a shortened nasal septum at E13.5 (Fig. 4A, B) and shortened nasal bones at E18.5 (Fig. 4C, D). The mutant nasal cartilage was 10% shorter than the heterozygous control and the premaxilla was 14% shorter than the control (n = 5, p<0.05). As with the cKO MNP, BrdU labeling results revealed that a decrease in cell proliferation in the MNP of PDGFRαPI3K/PI3K embryos at E11.5 (Fig. 4E–G). To further substantiate these results, we extended these studies to Mouse Embryonic Palatal Mesenchymal cells (MEPMs). Although MNP cells would be the ideal material at this point, we were not able to maintain MNP cells in culture beyond passage 1. Similar to MNP mesenchymal cells, MEPMs originate from cNCCs, exhibit stable PDGFRα expression and response to PDGFA stimulation (Fig. S2) and are thus a robust tool to study PDGFRα function. PDGFA treatment significantly increased cell proliferation in WT MEPMs, but failed to do so in PDGFRαPI3K/PI3K MEPMs (Fig. S3). Conversely, PDGFRαPI3K/PI3K MEPMs exhibited a small decrease in cell proliferation (data not shown). Together these results indicate that PI3K signaling is essential for PDGFRα-regulated neural crest derived cell proliferation.
Lineage studies have revealed that MNP cells are predominantly derived from NCCs [1], [2]. To be able to trace NCCs in the conditional mutants, we introduced the R26R Cre reporter allele [33] into the PDGFRαfl/fl; Wnt1Cre background. CKO and control embryos were age matched by counting the number of somites. Lineage tracing showed that at E8.5 LacZ reporter expression was comparable in cKO and control embryos (Fig. 5A–D, cKO n = 7, ctrl n = 11), indicating that PDGFRα is not critical for neural crest specification. This observation was confirmed by quantifying the expression of neural crest marker genes Sox10 and Ap2α in E8.5 embryos using RT q-PCR (Fig. 5E, n = 3). By E9.5 however, R26R expression was attenuated in the cKO FNP as compared to the control (Fig. 5F–I, cKO n = 4, ctrl n = 15). Consistent with the above observations, Sox10 expression was also disrupted in E9.5 cKO embryos (Fig. 5J, K). At E10.5, cKO embryos showed fewer NCCs in pharyngeal arches III to VI (Fig. 5L, M, cKO n = 5, ctrl n = 7) and abnormal bifurcation of the NCCs streams migrating to these pharyngeal arches was observed in some embryos (Fig. S4A, B). Skeletal elements derived from these structures including the hyoid bone, the stapes, and the styloid process were severely deformed or missing in cKO embryos at E18.5 (Fig. S4C–H, n = 6). The cell lineage tracing results, along with the altered gene expression signature and defects in skeletal development indicate that PDGFRα is essential for NCCs to migrate in normal numbers and populate craniofacial regions.
To analyze the function of PDGFRα in NCC migration, explant cultures were established from the cranial neural tube (anterior to the first pharyngeal arch) of cKO and control embryos. To uncouple potential cell migration defects from alterations in cell proliferation, explants were plated on fibronectin in the presence of mitomycin C. The explant cultures exhibited a significant decrease in emigration of cKO NCC cells (Fig. 6A–D, n = 3). Moreover morphometric analysis indicated that primary cKO NCC cells were significantly smaller (33.1%) than control cells (n = 50), and exhibited fewer lamellipodia (Fig. 6E, F), which are required for cell migration. The cKO NCCs also exhibited an increased nuclear-cytoplasmic ratio (12.7% in cKO cells and 4.3% in wild type cells), as well as fewer focal adhesions (34.5 per cKO cell and 89.1 per wild type cell, n = 50; Fig. 6 G).
These results suggest that PDGFRα is essential for neural crest cell motility, possibly by regulating cytoskeletal architecture. Alternatively, PDGFA/PDGFRα might also regulate NCC migration by regulating cell guidance [25], [34]. To distinguish between these possibilities, we carried out further experiments in primary MEPMs at passage 1. PDGFA acted as a chemo-attractant of primary MEPMs in transwell assays (Fig. 7A). In addition, PDGFA treatment sped up the wound healing rate of MEPMs (Fig. 7B). These data indicate that activation of PDGFRα plays dual roles in neural crest derived cell migration, by stimulating chemotaxis and by regulating cell motility.
To investigate if PI3K signaling plays a role in NCC migration, we generated PDGFRPI3K/PI3K; Wnt1Cre; R26R+/− embryos. Lineage tracing showed no obvious cNCC migration defects in PDGFRαPI3K/PI3K; Wnt1Cre; R26R+/− embryos (Fig. S5, n = 6). Transwell assays revealed that PDGFRαPI3K/PI3K MEPMs respond and migrate towards a source of PDGFA at a level comparable to wild type cells (Fig. 7C). In addition, the wound healing speed of PDGFRαPI3K/PI3K MEPMs remains comparable to that of heterozygous cells in a scratch assay (Fig. 7D). In summary, these results indicate that PI3K signaling engaged by PDGFRα is not essential for cell migration, in contrast to its role in regulating proliferation of neural crest derived cells during development.
The abnormal morphology of cKO NCCs indicates other signaling might be essential to regulate the cytoskeleton downstream of PDGFRα. Rho GTPases constitute a group of major regulators of cell migration that mediate actin reorganization, and lamellipodia and filopodia formation [35], [36]. PDGF and PI3K/Akt signaling have been shown to phosphorylate guanine nucleotide-exchange factors (GEFs), which in turn prompt the formation of GTP-bound, active small GTPases such as RhoA, Cdc42 and Rac1 [37]–[40]. Inactivation of Rac1 in NCCs caused facial clefting, strikingly resembling the PDGFRα cKO phenotype [41], [42], suggesting Rac1 to be a potential mediator of PDGFRα in NCC development. Rac1 activity was attenuated in lysates of E10.5 cKO MNP cells (Fig. 8A), and we observed a reduction in the expression of phosphorylated cofilin 1, an actin depolymerization enzyme required for cNCC development (Fig. 8 B) [43]. PDGFA stimulation facilitates phosphorylation of cofilin in MEPMs (Fig. 8C), indicating that PDGFRα can regulate Rac1 activity in neural crest derived cells. Consistent with an important role for Rac1 in mediating PDGF driven functions, treatment of MEPMs with the Rac1 specific inhibitor NSC 23766 blocked PDGFA stimulated proliferation and wound healing (Fig. 8D, E). Further examination revealed that inactivation of Rac1 affected lamellipodia formation at the leading edge of migrating MEPMs, reminiscent of the phenotype of PDGFRα deficient MEPMs (Fig. 8F–K). Inhibition of Rac1 activity in MEPMs also led to smaller size (31% of untreated cells, n = 50), fewer focal adhesion complexes (22.1 per treated cell vs. 83.2 per untreated cell, n = 50), and increased nuclear-cytoplasmic ratio (9% in treated cells vs. 5% in untreated MEPMs, n = 50) (Fig. 8L). Taken together, these results indicate a prominent role for Rac1 in the regulation of PDGF-induced cell migration and proliferation.
Facial clefting is a rare birth defect and its etiology remains poorly understood. In this work, we show that the facial clefting phenotype of PDGFRα mutants is not associated with a defect in NCC specification but rather a subsequent defect in the medial nasal process (MNP), a facial primordium derived from the frontonasal prominence (FNP). We further show that this defect is associated with alterations in both cell proliferation and cell migration, and that PI3K and Rac1 signaling are essential to maintain a normal level of cell proliferation. Last, we provide evidence that Rac1 regulates cell migration at the level of cell motility as well as chemotaxis under the regulation of PDGFRα.
A previous study from our laboratory had shown that the facial clefting observed in PDGFRα mutants was of neural crest origin, using chimeric analysis and conditional mutagenesis with the Wnt1Cre driver [21]. Although global defects in cell proliferation and migration were not documented, chimeric analysis identified a role for PDGFRα in development of the pharyngeal arches, which we now show by lineage tracing are deficient in cNCCs by E10.5 in cKO embryos. In this work, we have considerably refined the analysis by examining specific rather than overall craniofacial subregions. We were thus able to document disruption of cell migration in the FNP by cell lineage analysis at E9.5, and of cell proliferation in the MNP of cKO embryos by E10.5. Therefore, both the previous study and the present work identify a crucial role for PDGFRα in NCC development.
We found that PDGFRα exhibits a strong expression pattern in the MNP mesenchyme at different stages. PDGFA is expressed in the MNP epithelium, and PDGFC is expressed in both the MNP epithelium and the mesenchyme, consistent with paracrine or autocrine PDGFRα signaling during craniofacial development. Prior genetic evidence from our lab, using point mutations in the PI3K binding sites in the PDGFRs, has implicated PI3K as the key signaling pathway regulating craniofacial development [24]. We also found that that PI3K signaling regulates p44/42 MAPK (data not shown). Strikingly, mice carrying a neural crest-specific deletion of Erk1/Erk2 display facial clefting [23]. p44/42 MAPK can also be engaged by other pathways than PI3K, and by other RTKs that are critical for craniofacial development including Fgfrs, Eph receptors, EGFR, Ror or Ryk. Although these RTKs share some similar intracellular domains and engage overlapping signaling pathways, their impact on craniofacial development might reflect tissue-restricted expression patterns of receptors and ligands, as well as engagement of unique combinations of downstream signaling cascades. It will be important to understand if the phenotypic differences associated with different RTKs are due to dosage variation of PI3K signaling, involvement of other unique signaling pathways, or a combination of both.
Neural crest cells form through delamination of cells at the lateral plate border of the neural tube that undergo an epithelial to mesenchymal transition. There is extensive evidence that cell-cell contacts through intricate lamellipodial and filipodial extensions play critical roles in regulating how cells exit the neural tube and migrate to their proper destination (for a review, see [13]). Small GTPases, including Rho, Rac and Cdc42 are well known to regulate such cell behaviors, but also cell proliferation and gene transcription under the regulation of multiple RTKs [44]. Recent gene targeting studies showed that inactivation of Rac1 or Cdc42, or overexpression of a dominant negative Rho kinase in mouse NCCs causes a severe facial clefting phenotype, which strikingly resembles PDGFRα homozygous mutant embryos [21], [22], [41], [42], [45]. In particular, Rac1 deficient NCCs exhibit decreased proliferation, abnormal cell morphology, as well as disrupted lamellipodia formation [42], very similar to the defects we have observed in PDGFRα deficient NCCs and MEPMs. The notion that PDGFRα might be a major regulator of Rac1 activity is further supported by our present work and other studies that show that PDGFA stimulates Rac1-GTP levels in a variety of biological settings [46]–[48]. It has been suggested that EGFR could be the major effector of the Rac1 mutant phenotype [41], as EGF has been used as an agonist of Rac1 activity in a variety of in vitro studies. However EGFR null mutant mice exhibit a much milder craniofacial phenotype with only a cleft palate. In addition, gene expression data showed that EGFR and Rac1 only partially overlap in ectodermal cells during early stages of craniofacial morphogenesis (Emage, www.emouseatlas.org), whereas PDGFRα shows a broad expression pattern similar to Rac1 in cNCCs at E10.5. Functionally, PDGFRα deficient NCCs exhibit abnormal morphology and defective formation of lamellipodia and focal adhesions. These lines of evidence point to a major role for Rac1 in mediating PDGFRα functions in NCC development and craniofacial morphogenesis.
The Mount Sinai School of Medicine Institutional Animal Care and Use Committee (IACUC) approved all animal work and procedures used in this study. The Mount Sinai animal facility is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC).
PDGFRαfl/fl, PDGFRαGFP/+, PDGFRαPI3K/+, Wnt1Cre and R26R mice have been described previously [21], [24], [26], [29], [33]. PDGFRαfl/fl, PDGFRαPI3K/+ and Wnt1Cre mice were maintained on a 129S4 co-isogenic genetic background, and PDGFRαGFP/+ and ROSA26R mice were kept on a C57BL/6J co-isogenic background. Mice and embryos used in lineage tracing studies were maintained on a mixed genetic background.
For histology, staged embryos were dissected in ice-cold PBS, fixed in Bouin's fixative, dehydrated through a graded series of ethanol washes, and embedded in paraffin. Sections were cut at 10 µm for hematoxylin and eosin staining. Skeletal analysis was performed on E18.5 embryos as described [22]. Craniofacial morphometry was performed as described in Fig. S6. For immunostaining, embryos were fixed in 4% PFA overnight at 4°C, dehydrated in 30% sucrose/PBS and embedded in OCT. Cryosections were prepared at a thickness of 10 µm. Immunostaining was performed according to standard protocols using antibodies to PDGFRα (1∶60; Santa Cruz), vinculin (1∶200, Sigma), BrdU (1∶500, DSHB), Cleaved-Caspase 3 (1∶200, Cell Signaling Technology) and rhodamine phalloidin (0.2 µM, Biotium). For in situ hybridization, embryos were dissected in ice-cold PBS, fixed in 4% paraformaldehyde (PFA), dehydrated through graded ethanol washes, and embedded in paraffin. Coronal sections were cut at 10 µm and in situ hybridization was performed as described [49]. X-gal staining was performed as described [33].
To measure cell proliferation rates in vivo, BrdU was injected intraperitoneally into pregnant females at a dosage of 50 µg per gram of body weight. Embryos were dissected after 1 hour, fixed in 4% PFA and processed for cryosections and immunostaining using standard protocols. BrdU labeled cells were counted in a random area in the defined mesenchyme at comparable levels in mutant and control samples. Three continuous sections were counted from each of triplicate samples. The result of BrdU labeling was presented as percentage of BrdU-positive cells against total nuclei labeled by DAPI. Student's t-test was used to determine statistical significance.
Neural tubes anterior to the first pharyngeal arch were dissected from E8.5 embryos. Following two brief washes in ice-cold PBS, heads were sagitally split into two equal halves. The tissue was incubated in 0.5% trypsin/2.5% pancreatin in PBS for 5 minutes on ice, and then in DMEM with 10% FBS for 10 minutes to stop the reaction. The head mesenchyme was carefully dissected and isolated with fine-tipped Dumont #5 tweezers, and the neural tube was transferred to fibronectin-coated cover slips in a 6 well plate. For NCC emigration assays, neural tubes were cultured in DMEM/F12 with 10% FBS for 6 hours. The explants were then treated with low serum media (DMEM/F12 containing 0.5% FBS) with 10 µg/ml mitomycin C for two hours. The culture medium was replaced with low serum media and maintained for 24 hours. Results were recorded at 8 hours and 24 hours respectively. The explants were then removed and the emigrating cells were subjected to immunostaining.
Primary Mouse Embryonic Palatal Mesenchymal cells (MEPMs) were isolated as described [50]. For scratch assays, MEPMs at passage 1 were seeded on fibronectin-coated cover slips in 6 well plates at a density of 100,000 cells per well. After reaching 70–80% confluency, cells were starved for 24 hours in DMEM containing 0.5% FBS. In some experiments, serum starved cells were pretreated with 30 µM Rac1 inhibitor NSC23766 (R&D Systems) for 3 hrs before stimulation with 30 ng/ml PDGFA. The scratch was mechanically created using a sterile P200 pipette tip and washed twice with starving medium to remove cell debris. The wound area was then photographed at marked positions (3 different fields per well). Cells were allowed to migrate for 12 hours at 37°C before the same fields were recorded. All experiments were performed in triplicate. Scratch results were measured with Image J software (NIH, Bethesda, USA) and analyzed using the extension package MiToBo [51].
For transwell assays, cell culture inserts for a 24-well plate (Fisher) with a pore size of 8 µm were coated with fibronectin. P1 MEPMs were trypsinized, washed, and suspended in serum free medium at a concentration of 5×106 cells/ml. 300 µl of cell suspension was added to the insert chambers immersed in 500 µl medium with 10% FBS, 0.5% FBS, or 0.5% FBS with PDGFAA. After incubation at 37°C and 5% CO2 for 3.5 hours, the inserts were fixed in 3.7% formaldehyde and stained in Mayer's hematoxylin solution. Filters of the inserts were then isolated with a scalpel and mounted. The numbers of cells on the bottom were counted. Data were recorded from 9 high power fields from three independent experiments.
Western blot analysis was carried out as described previously [24], using primary antibodies from Abcam: anti-cofilin (phospho-S3) and from Cell Signaling Technology: anti-cofilin, anti-p44/42 MAPK, anti-phospho p44/42 MAPK, anti-Akt, anti-phospho-Akt (Ser473). Chemical inhibitors for MAPK (U0126, Promega), PI3K/Akt (LY294002, Stemgent) and Rac1 (NSC23766, R&D systems) were used in lysate generation for western blot analysis and the Rac1 activity assay, following the manufacturers' instruction. Rac1 activity analysis was performed using the colorimetric based G-LISA Rac1 Activation Assay Biochem Kit (Cytoskeleton Inc.).
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10.1371/journal.pgen.1008142 | An integrative association method for omics data based on a modified Fisher’s method with application to childhood asthma | The development of high-throughput biotechnologies allows the collection of omics data to study the biological mechanisms underlying complex diseases at different levels, such as genomics, epigenomics, and transcriptomics. However, each technology is designed to collect a specific type of omics data. Thus, the association between a disease and one type of omics data is usually tested individually, but this strategy is suboptimal. To better articulate biological processes and increase the consistency of variant identification, omics data from various platforms need to be integrated. In this report, we introduce an approach that uses a modified Fisher’s method (denoted as Omnibus-Fisher) to combine separate p-values of association testing for a trait and SNPs, DNA methylation markers, and RNA sequencing, calculated by kernel machine regression into an overall gene-level p-value to account for correlation between omics data. To consider all possible disease models, we extend Omnibus-Fisher to an optimal test by using perturbations. In our simulations, a usual Fisher’s method has inflated type I error rates when directly applied to correlated omics data. In contrast, Omnibus-Fisher preserves the expected type I error rates. Moreover, Omnibus-Fisher has increased power compared to its optimal version when the true disease model involves all types of omics data. On the other hand, the optimal Omnibus-Fisher is more powerful than its regular version when only one type of data is causal. Finally, we illustrate our proposed method by analyzing whole-genome genotyping, DNA methylation data, and RNA sequencing data from a study of childhood asthma in Puerto Ricans.
| In this research, we developed a statistical approach using a modified Fisher’s method (denoted as Omnibus-Fisher) to combine separate p-values of association testing for a trait and SNPs, DNA methylation markers, and RNA sequencing, calculated by kernel machine regression into an overall gene-level p-value to account for correlation between omics data. We further extended the method to an optimal version in order to consider all possible disease models.
| Because of major advances in high-throughput biotechnologies, large amounts of omics data have been collected to study the biological mechanisms underlying complex diseases at different levels, such as genomics, epigenomics, and transcriptomics. Such different types of omics data can help us understand a disease from several perspectives. However, each of the arrays or sequencing technologies is designed to collect a specific type of omics data, such as SNPs, DNA methylation markers, and RNA sequencing. Thus, the association between a complex disease and one type of omics data is usually tested individually, but this strategy is suboptimal and has some disadvantages. Researchers often find that only a small proportion of disease variation can be explained by one type of omics (e.g., genetic) data, leading to “missing heritability” [1]. Moreover, molecular variants identified by different studies usually suffer from poor reproducibility [2, 3]. Most importantly, only partial information is used for each individual analysis. Therefore, in order to better characterize biological processes and increase the consistency of variant identification, omics data from separate platforms need to be integrated and analyzed. Integrating information from different biological datasets has the potential to yield better insight into causal mechanisms of complex diseases than that from individual omics datasets.
Although integrative analysis of omics data is clearly needed, the complexity of disease mechanisms, the large number of collected molecular variables, and relatively small datasets can make such analysis quite challenging. Bersanelli et al. [4] summarized a list of existing statistical approaches for integrative analysis. When developing integrative analysis methods, two inevitable issues are: 1. handling a large number of variables and 2. dealing with data from relatively small studies. In genetic studies, to handle a large number of genetic variants in a gene, gene-based approaches [5–15] have been developed to evaluate the joint effects of genetic variants in the same gene on the disease of interest. Of the existing methods, the sequence kernel-machine-based associations test (SKAT) [16, 17] is a powerful, flexible, and computationally efficient test. In this kernel machine (KM) approach, the test statistic follows a mixture of chi-square distributions, and thus p-values can be computed analytically and quickly without using resampling techniques. Although gene-based tests were originally developed for genetic studies, the same concept can be applied to studies of multi-omics data. Another issue is small sample size, especially for epigenomic or transcriptomic data. For example, large-scale genome wide association studies (GWASs) have been widely conducted for genetic studies for many years, so researchers usually have hundreds or thousands of genotyped samples. However, genome- wide methylation studies are more recent, and thus researchers often have a small number of samples. Moreover, incomplete samples may be wasted when using methods requiring complete samples (e.g., methods incorporating multi-omics data variables into one regression model). In this scenario, methods combining multiple p-values can be applied to make full use of data. For example, the p-values for association testing of a disease and SNPs, DNA methylation markers, and RNA sequencing data are be calculated separately, and then these separate p-values can be appropriately combined into one final p-value.
In order to test for overall gene-level significance, we here present an approach to use a modified Fisher’s method (denoted as Omnibus-Fisher) to combine separate p-values for association testing of a disease or trait and SNPs, methylation markers, and RNA sequencing data calculated by KM regression into an overall gene-level p-value accounting for correlation between omics data. This method can be applied to either samples with all three types of omics data or samples with one or two types. To account for all possible disease models, we further extend the modified Fisher’s method to an optimal test by using perturbations. In our simulation studies, we show that a usual Fisher’s method has inflated type I error rates when directly applied to correlated omics data. In contrast, our Omnibus-Fisher test preserves the expected type I error rates when employed in correlated omics data. Moreover, the Omnibus-Fisher method has increased power compared to its optimal version when the true disease model involves all of SNPs, methylation markers, and RNA sequencing data. On the other hand, the optimal Omnibus-Fisher method is more powerful than its regular version when only one type of data is causal. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping, DNA methylation, and RNA sequencing data from a study of childhood asthma in Puerto Ricans.
When applied to samples with independent SNPs (G), methylation markers (M), and RNA sequencing data (E), all of the methods used (i.e., the Fisher’s methods with and without considering p-value covariance [Omnibus-Fisher and usual Fisher], and the optimal test with p-values from Omnibus-Fisher as inputs [optimal Omnibus-Fisher]) had empirical Type I error rates close to the nominal level (Fig 1A and Table 1). When the usual Fisher’s method without considering covariance was applied to G and E correlated data, the Type I error rate was inflated (Fig 1B and Table 1). In contrast, the optimal and regular Omnibus-Fisher methods with considering covariance retained the desired Type I error rates as evidenced by the patterns observed in the QQ plots shown in Fig 1B and Table 1. Similar results were observed when extending to 100,000 datasets for evaluation (S1 Table).
When we compared the power of the statistics on the samples with independent G, M and E (Fig 2), the power of optimal Omnibus-Fisher was consistent higher than that of the regular Omnibus-Fisher method when G was the only causal factor, but when G, M and E were all causal factors, the optimal methods had lower power. This was expected because the optimal methods automatically searched for the appropriate disease model; in contrast, the regular Omnibus-Fisher assumed that G, M and E were all causal factors. Thus, when the simulation matched the assumption of the regular version method, they performed better than the optimal version and vice versa. However, when G and M were causal factors, no methods were consistently better than another. Furthermore, similar patterns were observed, when evaluated using the samples with G and E correlated (Fig 3). Since the causal SNPs in G were correlated with E, GM causal was equivalent to G, M and E causal. Note that the usual Fisher’s method is not included in Fig 3 because of its inflated Type I error rate with correlated data.
We used the proposed optimal Omnibus-Fisher statistic and its regular version to analyze the Puerto Rican childhood asthma data from WBCs for associations between asthma status and 14,808 genes with all SNPs, DNA methylation markers, and gene expression, with adjustment for age, gender and first two principal components calculated based genotypes. In addition, batch effect and cell type composition were also adjusted for DNA methylation and RNA sequencing data. We found that ZPBP2 was the most significant gene from both optimal (P = 1.40×10−5) and regular (P = 3.39×10−5) Omnibus-Fisher tests, although it didn’t reach a Bonferroni corrected significance level (P = 3.38×10−6) (Fig 4). The ZPBP2 region from chromosome 17q21 has been consistently replicated as an asthma-susceptibility locus across diverse ethnic groups [18–28] including Puerto Ricans [29] and this region regulates its gene expression in Puerto Ricans [30]. In a meta-analysis of GWAS in Puerto Ricans [29], the only region associated with asthma was the ZPBP2 locus and the current genotypic dataset was analyzed as a part of the data. This gene could be served as a positive control in asthma genetic studies. In the optimal Omnibus-Fisher test, the significance of ZPBP2 as well as GSDMB was mainly driven by their genetic effect (P = 2.89×10−6 for ZPBP2 and 2.36×10−6 for GSDMB). Moreover, five additional genes (KAT2A, HIST1H1C, NFRKB, C14orf178 and ZNF213-AS1) were suggestively associated with asthma (P < 0.0001 [Table 2]) from the regular Omnibus-Fisher test. Of these genes, KAT2A had moderate effects for SNPs, DNA methylation, and RNA expression separately, which could be overlooked by a single type of data analysis. The results also indicate that the optimal Omnibus-Fisher test was more powerful than its regular version when the significance was driven by one type of data. Conversely, when statistical significance was driven by two or three types of data, the regular Omnibus-Fisher had overall better power than the optimal version. These observations were generally consistent with the simulation results. Here, the optimal Omnibus-Fisher test does not outperform its regular version that assumes all types of omics data are in the disease model. Since only three types of omics data were analyzed in this study, it was still fine to assume they all were in the disease model. However, when more omics data are analyzed, the optimal test could be more useful than simply assuming all types of data are causal. We additionally output the p-value correlation for each gene across the whole genome (S1 Fig): 1. between SNPs and DNA methylation markers, 2. between SNPs and expression genes, and 3. between DNA methylation markers and expression genes.
Analysis of the WBC genome-wide data with 1,116 samples and 14,808 genes took ~108.8 hours on a single computing node with a 3 GHz CPU and 4 GB memory. Using a computer cluster with multiple nodes, we anticipate that genome-wide data analysis should be finished within hours using our proposed methods.
In this work, we developed an Omnibus-Fisher statistic using a kernel machine (KM) regression framework, which can be employed to test overall gene-level significance by combining separate p-values of association testing for a disease and SNPs, methylation markers, and expression genes, accounting for correlation between omics data. The separate p-values are calculated by gene-based KM regression. The gene-based analysis methods can improve power by testing a set of variants jointly and by reducing the multiple testing penalty. In addition, the method using a gene as the unit can easily combine different types of omics data that are mapped to the same gene and thus easily interpret the results. Since we do not know the exact disease model in reality, the extended optimal Omnibus-Fisher test can account for all possible disease models. Moreover, our proposed tests can be applied to either samples with all three types of omics data or with one or two types. In other words, samples with incomplete data can still contribute to the test statistic. The information about whether the different types of omics data are from the sample can also be accounted.
In the simulation studies, we showed that using a usual Fisher’s method on correlated omics data results in an inflated Type I error rate, while the modified Fisher’s method, Omnibus-Fisher, had the correct Type I error rate because it considered the omics data correlation in the model. The Omnibus-Fisher method achieves better power performance compared to its optimal version when the true disease model involves all of SNPs, RNA expressions and DNA methylations. On the other hand, the optimal Omnibus-Fisher method has better power than its regular version assuming all types of data are causal when only one type of data is actually causal. Our real data study also shows that the regular Omnibus-Fisher test has better power than the optimal test, when two or three types of data contribute to the combined p-value. Because we only consider three types of omics data in this study, assuming they all are causal could be still acceptable. However, when more omics data are analyzed, we believe that the optimal test would be more powerful for most genes than simply assuming all types of data are causal. Nevertheless, both the optimal and regular Omnibus-Fisher tests are able to detect genes with moderate separate effects, which could be overlooked by single type of data analyses.
Although the optimal Omnibus-Fisher test uses perturbation to consider the correlation between omics data and search for the optimal disease model, the genome-wide data analysis could be completed within hours using multiple CPUs (e.g., one CPU for each chromosome). We adapt a stepwise manner to implement perturbation (e.g., more iterations for smaller p-values) so as to save computation times when calculating large p-values. Thus, the majority of the computation time is used by genes with small p-values. However, if a large number of genes are highly associated with the phenotype, the optimal test may be infeasible due to computational intensity. In such case, the regular test is recommended. Although the regular test also involves resampling technique to calculate covariances between different types of omics data, it only requires a small number of resampling (e.g., the default setting is 200 times).
Our method framework is general and flexible. Both continuous and binary traits for independent samples can be analyzed. Covariates can be easily incorporated into the model and different covariates can be used for different omics data. The regular and optimal version of Omnibus-Fisher algorithms were implemented in R (http://www.r-project.org) and the R package (https://cran.r-project.org/web/packages/OmnibusFisher/index.html) is available.
We used KM regression to calculate the gene-level p-values for association testing of a disease and SNPs, methylation markers, and expression genes. First, we test the effect of SNPs. Let there be n subjects with q genetic variants. The n × 1 vector of the continuous trait y follows a linear model:
y=Xβ+Gγ+ε,
when the phenotypes are binary, y follows a logistic model:
logitP(y=1)=Xβ+Gγ
where X is an n × p covariate matrix, β is a p × 1 vector containing parameters for the fixed effects (an intercept and p– 1 covariates), G is an n × q genotype matrix for the q genetic variants of interest where an additive genetic model is assumed (i.e., coded as 0, 1, or 2 representing the copies of minor alleles) for illustration, γ is a q × 1 vector for the random effects of the q genetic variants, and ε is an n × 1 vector for the random error. The random effect γj for variant j is assumed to be normally distributed with mean zero and variance τwj; thus, the null hypothesis H0: γ = 0 is equivalent to H0: τ = 0, which can be tested with a variance component score test [17] in the mixed model. The random variable ε is assumed to be normally distributed, and is uncorrelated with γ:
γ∼N(0,τW)
ε∼N(0,σE2I),
where W is a predefined q × q diagonal weight matrix for each variant and may use W = I when lacking of prior information, and σE2 is the error variance.
Following the same rationale as in the derivation of the SKAT score statistic [31–33], the test statistic is:
Q=(y−Xβ^)′GWG′(y−Xβ^)/σ^E2,
when phenotypes are continuous, and
Q=(y−μ^)′GWG′(y−μ^)
when phenotypes are binary, where β^ is the vector of estimated fixed effects of covariates under H0 and μ^=logit−1(Xβ^).
Under the null hypothesis, the linear model is y = Xβ + ε, and the estimates are
Σ^=σ^E2I=var(y−Xβ^)I
β^=(X′X)−1X′y
P0=I−X(X′X)−1X′;
the logistic model is logit P(y = 1) = Xβ, and the estimates are
Σ^=diag(μ^∙(1−μ^))
β^=(X′Σ^−1X)−1X′Σ^−1y
P0=Σ^−Σ^X(X′Σ^X)−1X′Σ^.
The statistic Q is a quadratic form and follows a mixture of chi-square distributions under H0. Thus,
Q∼∑i=1qλiχ1,i2,
where λi are the eigenvalues of the matrix P012GWG′P012 [34] for both continuous and binary traits. The p-values can be calculated by numerical algorithms, such as Davies’ method [35] and Kuonen’s saddlepoint method [36], which are both included in the R package.
Analogously, the gene-level effects of DNA methylation markers and expression genes can be tested by replacing Gγ with Mρ and Eη, M is an n × k matrix for the k methylated loci, ρ is a k × 1 vector for the random effects of the k methylated loci, E is an n × g matrix for the RNA expression, and η is a g × 1 vector for the random effects of the RNA expression. When using microarray platform, multiple probes could map to the same gene and each probe has an expression value, which result in more than one expression value for one gene. Here, g is the number of probes for one gene. When using RNA sequencing platform, one gene can always have one expression value (i.e., E is an n × 1 vector and η is a scalar), although it is also possible to obtain the transcript (i.e., isoform) level expression values. The null hypothesis is ρ = 0 for testing DNA methylation markers and η = 0 for testing expression genes. It is worth to note that all three models can have the same or different null models.
In order to have one single p-value to represent the significance of a gene, we propose an approach to test if the trait is associated with any SNP, DNA methylation marker, and RNA sequencing variant. This could help researchers to screen out potentially interesting genes. Thus, after obtaining the three p-values for SNPs, DNA methylation markers, and expression genes, respectively, we used a modified Fisher’s method [37] to combine the three p-values to one. In Fisher’s method, let pi (i = 1, 2,…, w) be independent p-values obtained from n hypothesis tests. Under the null hypothesis that p-values follow a Uniform(0, 1) distribution, the combined test statistic is equal to T=−2∑i=1wln(pi) that follows χ2w2. However, within a gene, these p-values are correlated, thus the generalized Fisher’s method cannot be used directly. To address this issue, we consider a Satterthwaite approximation by approximating a scaled T statistic with a new chi-square distribution [38].
cT≈χv2,wherec=vE(T),v=2[E(T)]2Var(T),
E(T)=E(−2∑i=1wln(pi))=2wand
Var(T)=var(−2∑i=1wln(pi))=4w+2∑i<jcov(−2ln(pi),−2ln(pj))
where w = 3 for SNPs, DNA methylation markers, and expression genes. The covariance part takes the correlations of p-values into account and can be empirically estimated by perturbations. The perturbation details are described in the following section.
If the disease risk only depends on SNPs and the model with SNPs, DNA methylation markers, and expression genes is used, then the testing power will lose. Since in reality we do not know the underlying true disease model (e.g., only SNP effect, both SNP and RNA variant effects, or all SNP, RNA variant, and DNA methylation marker effects; totally 7 combinations), it is difficult to choose the correct model. Thus, it is desirable to develop a method accommodating all possible disease models to maximize power. This can be achieved by using the minimum p-value of all possible models (7 combinations) as a new test statistic. Then, perturbation can be used to calculate the final p-value.
The perturbation-based approach was described in Wu et al. [39]. For continuous phenotypes, with large n, under H0 the (y−Xβ^)/σ^E are approximately standard normal. Then each Q=(y−Xβ^)′GWG′(y−Xβ^)/σ^E2 is essentially comprised of a vector of standard normal variables sandwiching a square matrix. Thus, we can perturb each Q by replacing (y−Xβ^)/σ^E with a new, common vector of normal values to generate new score statistics. Following a similar procedure as described in Urrutia et al. [40]:
1. Calculate the p-values for SNPs (G), DNA methylation (M) and RNA expression (E) separately (i.e., pG(0),pM(0), and pE(0)) by KM regression.
2. For l ∈ {G, M and E}, compute Λl = diag(λl,1,⋯,λl,ml), and Vl = [vl,1,⋯,vl,ml] where λl,1 ≥ λl,2 ≥⋯≥ λl,ml are the ml positive eigenvalues of P0l12DlWlDl′P0l12 with corresponding eigenvectors vl,1,⋯,vl,ml, where Dl ∈ {omics data matrices G, M and E}. For example, the aforementioned P012GWG′P012 is for G.
3. Generate r(b)=[r1(b),⋯,rn(b)]′ with each rj(b)∼N(0,1). This indicates that one subject has one rj(b). If the subject has all G, M and E, the same rj(b) will be used for G, M and E, respectively. Thus, whether G, M and E come from the same subjects or different subjects are considered.
4. For l ∈ {G, M and E}, rotate r(b) using the eigenvectors to generate rl(b)=Vl′r(b).
5. Compute Ql(b)=rl(b)′Λlrl(b) for each l and obtain a corresponding p-value, pl(b).
6. Repeat (3)-(5) B times to obtain pG(1),pG(2),⋯,pG(B),pM(1),pM(2),⋯,pM(B) and pE(1),pE(2),⋯,pE(B) for some large number B.
7. Calculate the covariance between pG, pM and pE by using pG(b),pM(b), and pE(b) for b ∈ {0, 1,…, B}.
8. Calculate the joint p-values of SNPs, DNA methylation and RNA expression (i.e., for b ∈ {0, 1,…, B}, pGM(b),pGE(b),pME(b), and pGME(b)) by Omnibus-Fisher considering p-values covariance.
9. For l* ∈ {G, M, E, GM, GE, ME, and GME}; b ∈ {0, 1,…, B}, set p(b)=min1≤l*≤L*pl*(b).
10. The final p-value for significance is estimated as
p=B−1∑b=1BI(p(b)≤p(0))
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10.1371/journal.pntd.0000859 | Insecticide Susceptibility of Phlebotomus argentipes in Visceral Leishmaniasis Endemic Districts in India and Nepal | To investigate the DDT and deltamethrin susceptibility of Phlebotomus argentipes, the vector of Leishmania donovani, responsible for visceral leishmaniasis (VL), in two countries (India and Nepal) with different histories of insecticide exposure.
Standard WHO testing procedures were applied using 4% DDT and 0.05% deltamethrin impregnated papers. The effect of the physiological status (fed and unfed) of females on the outcome of the bioassays was assessed and the optimal time of exposure for deltamethrin was evaluated on a colony population. Field populations from both countries were tested.
Fed and unfed females responded in a similar way. For exposure time on field samples 60 min was adopted for both DDT and deltamethrin. In Bihar, knockdown and mortality with DDT was respectively 20 and 43%. In Nepal almost all sand flies were killed, except at the border with Bihar (mortality 62%). With 0.05% deltamethrin, between 96 and 100% of the sand flies were killed in both regions.
Based on literature and present data 4% DDT and 0.05% deltamethrin seem to be acceptable discriminating concentrations to separate resistant from susceptible populations. Resistance to DDT was confirmed in Bihar and in a border village of Nepal, but the sand flies were still susceptible in villages more inside Nepal where only synthetic pyrethroids are used for indoor spraying. The low effectiveness of indoor spraying with DDT in Bihar to control VL can be partially explained by this resistance hence other classes of insecticides should be tested. In both countries P. argentipes sand flies were susceptible to deltamethrin.
| Visceral leishmaniasis (VL), also know as kala azar, is one of the major public health concerns India, Nepal and Bangladesh. In the Indian subcontinent, VL is caused by Leishmania donovani which is transmitted by Phlebotomus argentipes. To date, Indoor Residual Spraying (IRS) campaigns have been unable to control the disease. Vector resistance to the insecticides used has been postulated as one of the possible reasons explaining this failure. A number of studies in the region have shown a variable degree of resistance to DDT in areas where this insecticide has been widely used for IRS (mainly India). However there is no coordinated and standardized program to monitor resistance to insecticides in the region. In this study we tested P. argentipes susceptibility to DDT and deltamethrin in VL endemic villages in India and Nepal. The results confirmed the DDT resistance in India and in a border village of Nepal. P. argentipes from both countries were in general susceptible to deltamethrin, an insecticide used in some long lasting insecticidal nets.
| Approximately 200 million people are at risk of visceral leishmaniasis (VL) – also known as kala-azar – in Bangladesh, India and Nepal [1]. In South East Asia, VL is caused by Leishmania donovani Laveran & Mesnil (Kinetoplastida: Trypanosomatidae) which is transmitted by Phlebotomus argentipes Annandale & Brunneti (Diptera: Psychodidae), the only incriminated vector in the region [2]. VL is fatal if untreated and current control measures rely on diagnosis and treatment of cases and Indoor Residual Spraying (IRS) to reduce or interrupt transmission in the affected communities. In India, two annual rounds of DDT spraying at 1 mg/m2 have been conducted in VL endemic districts since more than two decades [3]. In Nepal, the use of DDT to control VL was stopped in 1995 and IRS has been based since on synthetic pyrethroids (i.e alphacypermethrin or lambdacyhalothrin) targeting communities reporting at least one VL case in the previous year [4]. In Bangladesh vector-control activities are practically inexistent [5]. The use of Long Lasting Insecticidal Nets (LN), deltamethrin, alphacypermethrin or permethrin based [6], have been postulated as an alternative or complimentary approach as the current vector control strategies are failing to control VL in the region [7], [8]. Among other reasons, P. argentipes resistance to the insecticides used in the national programs may explain the lack of effect observed, particularly in India and Nepal. In a recent review, Ostyn et al. [8] reviewed the published reports on P. argentipes susceptibility to different insecticides in the Indian subcontinent since 1978. The results of this review show that DDT resistance has been reported in India since early 1990's but the results were variable and patchy. P. argentipes were consistently susceptible to DDT in Nepal and Bangladesh but the number of reports from those two countries was limited. Studies in the region showed susceptibility to deltamethrin, except for a report from Pondicherry, India [9]. However the methodologies used in those surveys were not standardized (i.e. insecticide concentration and time of exposure varied) and none of the studies applied the same protocol in different regions simultaneously.
In this paper we present the results of two studies on P. argentipes susceptibility to insecticides. First, a laboratory test to asses the influence of the physiological status of the sand fly on insecticide efficacy and to standardize the time of exposure to deltamethrin for field assays. Secondly, a field study was carried out to assess P. argentipes resistance to DDT and deltamethrin in VL endemic villages in India and Nepal.
The protocol study was approved by the ethical review boards from the London School of Hygiene and Tropical Medicine, University of Antwerp, Rajendra Memorial Research Institute and B.P. Koirala Institute of Health Sciences. Written informed consent was obtained from the head of the household where the sand flies were collected.
No significant differences were observed between unfed (E) and fed (F) sand flies for knockdown and mortality (Fig. 2) for DDT (Chi Square KD: p = 0.46; mortality: p = 0.99) and for deltamethrin (Chi Square: KD: p = 0.17; mortality: p = 0.12). For DDT only 38% mortality (N = 248; 11 replicates) was observed indicating DDT resistance in this colony population. For deltamethrin knockdown increased with time of exposure and mortality after 60 min exposure reached 99% (N = 193; 9 replicates). The exposure time of 60 min with a concentration of 0.05% was further adopted for testing the field populations.
For Bihar all replicates performed on specimens coming from the eight study villages were put together as no difference occurred in knockdown and mortality among the study sites. For DDT, knockdown was of 20% and only 43% died after 24 h (1 h exposure, N = 211; 16 replicates) suggesting DDT resistance. Deltamethrin 0.05% induced a knockdown of 86% and a mortality of 100% (N = 162; 8 replicates) (Fig. 3).
In Nepal, the results were presented by village as there were differences between study sites. DDT resistance was only observed in one of the villages (i.e. Amahibelha) (KD 51%, mortality 62%, N = 113; 6 replicates) while full susceptibility was observed in the other three sites. For deltamethrin, knockdown fluctuated between 85 and 93% and mortality between 96–99% (Fig. 3). Mean mortalities and knockdown rates were very similar to the rates calculated on total specimens tested.
Results of the bioassays are provided in detail as Dataset S1.
No discriminating concentrations or time to kill all susceptible specimens have been established for sand flies as is the case for malaria vectors [10]. Based on literature data [8], [15] 4% DDT and 1 h exposure seems to be an acceptable discriminating concentration. The sand fly colony of Patna can then be considered as resistant to DDT, as well as the wild population in the study area of Bihar. This DDT resistance in the colony of P. argentipes in RMRI is not surprising as it is regularly mixed with wild specimens and cannot be considered a reference strain. Previous data (1998–1999) in the area [16] showed a patchy distribution of DDT resistance (mortality between 100 and 71%). The observed mortality of around 40% in present study could suggest an increasing trend in DDT resistance in Bihar. However, dose or time response assays are needed to compare the levels of resistance between the different populations [17]. In Nepal, DDT resistance was only observed in the study site of Amahibelha (mortality 62%), a location close to the border with Bihar (Fig. 1), while P. argentipes was susceptible in the other 3 more inland located study sites. In Nepal the use of DDT for IRS was stopped in early 1990's and from 1995 the IRS policy was mainly based on the use of pyrethroids (mainly alphacypermethrin) but only in villages with VL cases [4]. This underlines once more that DDT resistance in P. argentipes has been mainly attributed to indoor spraying with this insecticide and its frequency of application [8], [18], but the use of sublethal doses as consequence of poor management and supervision of the IRS control programs may also enhance the selective pressure.
As no fully susceptible reference strain of P. argentipes was available, it was not possible to estimate a discriminating concentration with deltamethrin. Deltamethrin 0.05% is the discriminating concentration established for anopheline vectors, but it is not obvious to extrapolate this to sand flies or P. argentipes. In Brazil, bioassays with 0.05% deltamethrin were used and a clear difference between the insecticide susceptibility of two sand fly populations was observed [17]. In that study the sand fly population without previous specific insecticide exposure, a Lethal Time 50% (LT50) of 25 min was obtained and all sand flies died after one 1 h. In the population exposed to sand fly control measures using pyrethroids, LT50 was significantly higher (40 min) and the mortality was only 62% after 1 h [17]. Bioassays performed on the colony population of RMRI indicate a LT50 lower (<15 min) than the one observed in the most susceptible population in Brazil. One hour exposure induced a knockdown of around 70% and a mortality of 99% and these exposure conditions were further maintained for testing field populations. Similar results were obtained for the field populations (KD: 81–92%; mortality 95–100%) suggesting, and contrasting with the Brazilian study, a relatively good susceptibility to deltamethrin of the wild P. argentipes populations of Nepal and Bihar. Moreover, in P. argentipes, there is no indication of DDT-deltamethrin cross resistance, commonly found in anophelines where target Kdr resistance is present [19]. So far only metabolic mechanisms have been reported in sand flies [15], [17] and acetylcholinesterase and esterase-based insecticide resistance mechanisms have been observed in P. argentipes of Sri Lanka which probably arose from IRS with Malathion of the Anti-Malaria Campaign [20].
The limited but significant reduction (25%) of P. argentipes densities induced by mass use of deltamethrin-based long lasting insecticidal nets (LNs) observed in a trial conducted recently in the same areas in India and Nepal [21] cannot therefore be explained by a low susceptibility to deltamethrin but resides in the behavior of vector. Indeed P. argentipes, although known as being endophilic, are mainly opportunistic blood feeders and feed in a significant proportion on bovines[22]. Hence, this will reduce the mass effect of LNs on P. argentipes populations. The current failure to control the transmission of L. donovani in the region relying on IRS with DDT can be partially explained by the resistance to this compound and other insecticides should be evaluated to replace it. However, the first requirement for a successful control program remains the quality of IRS implementation.
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10.1371/journal.pgen.1005349 | In vivo Modeling Implicates APOL1 in Nephropathy: Evidence for Dominant Negative Effects and Epistasis under Anemic Stress | African Americans have a disproportionate risk for developing nephropathy. This disparity has been attributed to coding variants (G1 and G2) in apolipoprotein L1 (APOL1); however, there is little functional evidence supporting the role of this protein in renal function. Here, we combined genetics and in vivo modeling to examine the role of apol1 in glomerular development and pronephric filtration and to test the pathogenic potential of APOL1 G1 and G2. Translational suppression or CRISPR/Cas9 genome editing of apol1 in zebrafish embryos results in podocyte loss and glomerular filtration defects. Complementation of apol1 morphants with wild-type human APOL1 mRNA rescues these defects. However, the APOL1 G1 risk allele does not ameliorate defects caused by apol1 suppression and the pathogenicity is conferred by the cis effect of both individual variants of the G1 risk haplotype (I384M/S342G). In vivo complementation studies of the G2 risk allele also indicate that the variant is deleterious to protein function. Moreover, APOL1 G2, but not G1, expression alone promotes developmental kidney defects, suggesting a possible dominant-negative effect of the altered protein. In sickle cell disease (SCD) patients, we reported previously a genetic interaction between APOL1 and MYH9. Testing this interaction in vivo by co-suppressing both transcripts yielded no additive effects. However, upon genetic or chemical induction of anemia, we observed a significantly exacerbated nephropathy phenotype. Furthermore, concordant with the genetic interaction observed in SCD patients, APOL1 G2 reduces myh9 expression in vivo, suggesting a possible interaction between the altered APOL1 and myh9. Our data indicate a critical role for APOL1 in renal function that is compromised by nephropathy-risk encoding variants. Moreover, our interaction studies indicate that the MYH9 locus is also relevant to the phenotype in a stressed microenvironment and suggest that consideration of the context-dependent functions of both proteins will be required to develop therapeutic paradigms.
| African Americans have a disproportionate risk for developing chronic kidney disease compared to European Americans. Previous studies have identified a region on chromosome 22 containing two genes, MYH9 and APOL1, which likely accounts for nearly all of this difference. Previous reports provided strong statistical evidence implicating APOL1 as the major contributor to nephropathy risk in African Americans, driven by two coding variants, termed G1 and G2. However, other groups still report statistical evidence for MYH9 association in kidney disease, and animal models have demonstrated biological relevance for MYH9 function in the kidney. Here, we show that suppressing apol1 in zebrafish embryos results in perturbed kidney function. Importantly, using this in vivo assay, we show that the G1 variant appears to cause a loss of APOL1 function, while the G2 variant results in an altered protein that may be acting antagonistically in the presence of normal APOL1. We also report a genetic interaction between apol1 and myh9 under anemic stress, which is consistent with our previous findings in sickle cell disease (SCD) nephropathy patients. Finally, we provide functional evidence in vivo that the G2-altered APOL1 may be interacting with MYH9 to confer nephropathy risk.
| Chronic kidney disease (CKD) is an acute public health problem world-wide. Within the United States alone, it affects up to 14% of the adult population and is associated with both high costs and poor clinical outcomes[1]. Compared with European Americans, African Americans have a disproportionate risk for several forms of CKD, including human immunodeficiency virus (HIV)-associated nephropathy, focal segmental glomerulosclerosis (FSGS), hypertension-attributed CKD, and sickle cell disease nephropathy (SCDN), all of which contribute to a four-fold increased risk of the most severe stage of CKD, end-stage renal disease (ESRD)[1–5]. A genomic region on chromosome 22q12 likely accounts for almost all of this racial disparity. This region contains two genes, non-muscle myosin heavy chain IIA (MYH9; Entrez, 4627) and apolipoprotein L1 (APOL1; Entrez, 8542), both of which have been associated with increased risk among African American patients with nondiabetic nephropathy[5–11]. Initial admixture mapping and subsequent fine mapping studies focused on MYH9[8, 9, 11]. However, due to the inability to identify variants in MYH9 that alter protein sequence, the major source of genetic association has been attributed to APOL1, located 14 kb downstream of MYH9[6]. Two APOL1 alleles, G1 (encoding p.S342G and p.I384M in cis) and G2 (encoding p.N388del:Y389del), comprise one of the strongest genetic signals ever reported in complex human disease (odds ratios ranging from 10.5 to 16.9)[6, 7]. Additionally, these alleles alter the protein to confer resistance to Trypanosoma brucei rhodesiense, offering a potential evolutionary explanation for the increased occurrence observed among individuals of African ancestry[6].
Despite these genetic findings and the association of this locus with increased risk of multiple forms of CKD, there is a dearth of functional data to inform directly whether MYH9 or APOL1 is the driver of this genetic association. In mice, homozygous Myh9 knockouts die at an early embryonic stage[12], and heterozygotes appear viable without any detected abnormalities[13]. However, subsequent studies have demonstrated that knock-in mutants display renal glomerulosclerosis, while podocyte-specific deletion of Myh9 may predispose mice to glomerulopathy[14–16]. In zebrafish, myh9 is required for the normal development of the glomerulus; morpholino (MO)-induced myh9 suppression results in non-uniform podocyte foot processes and glomerular basement membrane thickening[17]. In contrast, the possible relevance of APOL1 to CKD is derived primarily from in vitro work: cellular localization studies of APOL1 in nondiabetic kidney disease patient biopsies suggest an implication in arteriopathy[18, 19], while overexpression of APOL1 and its risk alleles enhance podocyte necrosis in vitro [20].
Nephropathy is a major contributor to early mortality in patients with sickle cell disease (SCD)[21, 22]. SCDN is a clinically well-characterized pathology that includes glomerular hypertrophy, hyposthenuria, tubular dysfunction, proteinuria, and overall progressive renal failure[23]. We reported previously an association of both MYH9 and APOL1 variants as independent risk factors for proteinuria in a SCD study population[5]. Additionally, when glomerular filtration rate (GFR) in SCD patients was modeled as a function of the previously reported MYH9 risk haplotype and the APOL1 recessive model, we observed a significant interaction between the two genes, suggesting that APOL1 and MYH9 may act together to induce SCDN[5]. However, as with other forms of CKD, well-characterized in vivo model systems are needed to understand both the individual effects of APOL1 relevant to disease, and also the potential interaction of APOL1 with MYH9 in the context of anemic stress as observed in SCD.
Here, we used zebrafish as an in vivo model to study the consequences of gene perturbation and potential synergistic effects of APOL1 and MYH9 in kidney disease. Although the zebrafish pronephros is a simplified kidney, the structure and function of the larval glomerulus is similar to that of humans and represents a tractable model in which to study apol1 (RefSeq: NM_001030138) and myh9 (RefSeq: NM_001098177.2)[24, 25]. In this report, we provide insight into the role of apol1 in glomerular development and pronephric filtration in zebrafish embryos, as well as the effects of APOL1 G1 and G2 allelic expression. Moreover, we provide functional evidence for an interaction between myh9 and apol1 under anemic stress conditions. Overall, these data implicate both MYH9 and APOL1 as significant biological contributors to non-diabetic nephropathy and intimate context-dependent roles in disease pathology.
The apolipoprotein L family of genes evolved rapidly in humans and some non-human primates[26, 27]. However, using BLAST and reciprocal BLAST searches against the D. rerio and H. sapiens genomes, we identified a single D. rerio locus encoding a protein of unknown function (chr2:37,674,122–37,676,731 Zv9; NCBI Ref: NP_001025309.1; 38% identity, 46% similarity on the amino acid level) as a possible unique functional ancestral ortholog to the human apolipoprotein L family (Fig 1A–1D). To explore the function of this transcript in developing zebrafish, we first asked whether the candidate apol1 ortholog is expressed in a temporal manner amenable to transient assays of renal development and function. RT-PCR analysis of cDNA generated from wild-type (WT) whole-larval total RNA collected at three days post-fertilization (dpf) and 5 dpf showed expression at time points corresponding to the formation of the pronephros. Additionally, we detected apol1 expression in flow-sorted podocyte fractions harvested from glomeruli of pod::NTR-mCherry adult zebrafish (Fig 1E) [28].
To test the effects of apol1 suppression, we designed a translation-blocking morpholino (MO; Gene Tools, LLC) targeting the candidate zebrafish apol1 locus (apol1-MO) and we injected increasing doses into embryos at the one to four cell stage (n = 49–65 embryos/injection; repeated three times). Masked scoring for morphological defects at 5 dpf revealed a dose-dependent increase of the percent of larvae displaying pericardial and yolk sac edema, a phenotype that has been implicated previously in glomerular filtration defects[24, 30] (Fig 2A–2C). Co-injection of WT APOL1 human mRNA (GenBank Accession: BC112943.1; 100 pg/nl) rescued significantly the edema caused by apol1 suppression (p<0.0001; Fig 2D), arguing not only that the phenotype was unlikely to be a non-specific toxic effect of the MO, but also that the zebrafish locus we targeted is the ortholog of the human transcript. Importantly, co-injection of human mRNA encoding other human apolipoprotein L members (APOL2, APOL3, APOL4, APOL5, and APOL6) with apol1 MO did not rescue the edema formation of apol1 morphants (S1 Fig). Additionally, we observed a significant decrease in endogenous APOL1 protein expression in apol1-MO injected zebrafish embryos (p = 0.026), which is restored to normal levels upon co-injection with wild-type human APOL1 mRNA (S2 Fig). Furthermore, as an additional test of the specificity of apol1 perturbation to edema formation, we induced microdeletions in exon 3 of apol1 using the CRISPR/Cas9 system[31, 32] (Fig 3A–3C). Injection of guide RNA and CAS9 protein into one-cell stage embryos reproduced the edema phenotype (scored in founders, F0) seen in apol1 morphants (n = 26–38 embryos/injection, repeated three times; p<0.001; Fig 3D).
To test whether the generalized edema phenotype was relevant to nephropathy, we assessed the integrity of the glomerular filtration barrier in apol1 morphants and F0 mutants as described[30]. First, we injected 70-kDa FITC-labeled dextran into the cardiac venous sinus of larvae at 48 hours post-fertilization (hpf). After injection, the eye vasculature was imaged at 24 and 48 hours post-injection (hpi; Fig 2E and 2F). We quantified the average fluorescence intensity (ImageJ) and calculated changes in intensity at 48 hpi relative to the 24 hpi measurements. apol1 morphant larvae display a significant reduction in circulating 70-kDa dextran compared to controls (n = 26; p = 4.44x10-4; MO vs. control; Fig 2E and 2F), consistent with the occurrence of proteinuria. Importantly, this phenotype was also reproduced in apol1 CRISPR/Cas9 larvae (Fig 3E). Upon co-injection of WT APOL1 human mRNA, the increased dextran clearance in apol1-MO larvae was rescued significantly and fluorescence intensity returned to levels indistinguishable from controls (n = 28; p = 7.75x10-4, MO vs. MO + mRNA; Fig 2E and 2F).
Next, we evaluated the cellular organization and patterning of the developing glomerulus in the context of apol1 suppression. We performed transmission electron microscopy (TEM) of ultrathin sections of zebrafish larvae at 5 dpf in WT and apol1 morphants and mutants, with myh9 morphants as a positive phenotypic control. In agreement with previous studies[17], myh9 morphant larvae exhibit focal bulges and glomerular basement membrane (GBM) thickening in comparison to controls, as well as the presence of microvillus protrusions, a defining characteristic of proteinuria (S3 and S4 Figs). Notably, apol1-MO injected larvae display a similar glomerular ultrastructure compared with myh9 morphants. Naked patches of GBM are apparent throughout the glomerulus, indicative of extensive podocyte effacement (Figs 2G, 2H, and S4). However, we did not observe GBM thickening as evident in myh9-MO injected larvae (S3 Fig). In areas in which we did observe foot process formation, podocyte protrusions were irregular and inhibited slit diaphragm development (Figs 2G, 2H, and S4). We also noted the formation of microvillus protrusion in the urinary space of apol1 morphants. Similarly, apol1-CRISPR/CAS9 injected embryos display an aberrant glomerular ultrastructure, as evident by podocyte foot process effacement (Fig 3F). Co-injection of orthologous WT human mRNA in apol1 morphants rescued these glomerular ultrastructure defects (Fig 2I). Together, these data represent compelling in vivo evidence implicating APOL1 in renal function.
Initial reports associating APOL1 variants with kidney disease in African Americans identified two independent sequence variants, termed G1 and G2, which reside in a 10-kb region in the last exon of the gene[5–7, 10]. The G1 allele consists of two nonsynonymous coding variants in perfect LD, rs73885319 and rs60910145, while the G2 variant consists of a six base pair deletion that removes amino acids N388 and Y389 (~21% and ~13% allele frequency in African Americans, G1 and G2 respectively; Fig 1D). Therefore, we evaluated the ability of each of the G1 and G2 alleles to rescue apol1-MO injected zebrafish larvae. APOL1 G1 (I384M/S342G) and G2 allelic constructs were generated from a WT APOL1 human cDNA clone, transcribed, and co-injected with apol1-MO in zebrafish embryos (100pg/nl). Importantly, each APOL1 allelic construct produces a stable protein detectable by immunoblotting when co-injected with apol1-MO (S2 Fig). apol1 morphants co-injected with either APOL1 G1 (I384M/S342G) or G2 human mRNA did not display significant rescue of edema formation in developing embryos compared to apol1-MO injected embryos alone (Fig 4A and 4B). In addition, we also co-injected each individual G1 variant (I384M and S342G) into apol1 morphant embryos. APOL1 message encoding either p.I384M or p.S342G were individually able to rescue significantly the edema caused by apol1 suppression (Fig 4C and 4D) suggesting that the cis effect of both variants in the same haplotype is required to confer pathogenicity. When APOL1 G2 mRNA was injected alone, a significant number of embryos developed edema in comparison to sham-injected controls (n = 52–63 embryos/injection; repeated three times; p = 0.012; Fig 4B); no edema was observed with injection of 100pg APOL1 G1 mRNA alone (Fig 4A). Additionally, dextran clearance assays demonstrated that neither APOL1 G1 or G2 mRNA were able to rescue glomerular filtration defects caused by apol1 suppression, while APOL1 G2 mRNA injected alone caused significant filtration defects compared to controls (n = 12–21; p = 0.003, Control vs. G2 mRNA; Fig 4E and 4F). Finally, when we injected embryos with APOL1 G2 titrated with increasing concentrations of APOL1 WT mRNA, we observed a significant reduction of edema formation in developing embryos (Fig 4G) suggesting that this allele is conferring a dominant negative effect on protein function.
We also examined the glomerular ultrastructure of apol1 morphants co-injected with either APOL1 G1 or G2 human mRNA using TEM. However, we did not observe any noticeable improvement in glomerular ultrastructure abnormalities at 5 dpf (S5 Fig). In concurrence with our observations of gross morphological defects, embryos injected with G2 mRNA alone also display glomerular aberrations and microvillus protrusions (Fig 4H) similar to myh9 and apol1 morphants (Figs 2H and S4); no abnormalities were seen in larvae injected with G1 mRNA alone (Fig 4I). These data provide direct evidence for a functional consequence of the human APOL1 G1 and G2 risk alleles, and suggest that they confer loss-of-function and dominant negative effects, respectively.
Although recent studies have provided statistical evidence implicating APOL1 variation in nondiabetic nephropathies[7, 33, 34], MYH9 risk variants are still associated with chronic kidney disease (CKD) in non-African American populations[35] and in sickle cell disease nephropathy[5]. As such, our group and others have hypothesized that these genes may be co-regulated to induce nephropathy risk; in fact, when we modeled glomerular filtration rate in sickle cell patients as a function of the previously reported MYH9 risk haplotype and an APOL1 recessive model, we observed a significant interaction between the two genes[5]. Therefore, we tested for functional interaction effects between apol1 and myh9 in zebrafish, an experimentally tractable model for investigating additive and synergistic effects[36–40]. First, we co-injected both apol1-MO and myh9-MO into embryos and we scored for gross morphological defects at 5 dpf. Under this co-suppression model, we observed no significant differences in edema formation when compared to batches injected with either MO alone (Fig 5A), even when individual MO concentrations were reduced to subeffective doses (Fig 5B). Next, we tested the possibility that suppression of either apol1 or myh9 in zebrafish could be rescued significantly by the co-injection of the reciprocal human mRNA. myh9-MO was co-injected with human APOL1 WT mRNA (100pg/nl) and apol1-MO was co-injected with human MYH9 WT mRNA (100pg/nl). However, we were unable to rescue the suppression phenotypes of either apol1 or myh9 with the human mRNA of the reciprocal gene (S6 Fig).
Our hypothesis for an interaction between APOL1 and MYH9 was based on data derived from SCD patients. Thus, we posited that myh9 and apol1 may only interact under additional biologic stress, such as anemia or hemolysis. Accumulating evidence suggests that both anemia and hemolysis, which are key features of SCD pathophysiology, impact renal function; in particular, hemolysis appears to be associated with both microalbuminuria and hyperfiltration[41, 42]. While a zebrafish model of SCD does not exist currently, suppression of ATPase inhibitory factor 1 (atpif1α), a mitochondrial protein, produces profound anemia in zebrafish by interfering with heme synthesis through decreased catalytic efficiency of ferrochelatase[43]. The resultant effect of low hemoglobin and hematocrit stresses the kidney because of the organ’s high oxygen consumption. Consistent with the original report[43], we observed a dose-dependent reduction in hemoglobin with increasing concentrations of the atpif1a MO (atpif1α-MO), as measured by o-dianisidine staining of whole MO-injected larvae at 4 dpf. Strikingly, we found a significantly more severe nephropathy phenotype in an anemic context as indicated by accelerated dextran clearance, with co-suppression of apol1 and myh9 under atpif1α-MO induced anemia (n = 12–19 embryos/injection; p<0.001 for myh9/apol1 MOs vs. myh9/apol1/atpif1a MOs; Fig 5C and 5E). Importantly, neither morphant alone resulted in a more severe phenotype under atpif1α-MO induced anemia (e.g. myh9-MO vs. myh9-atpif1α-MO; p = 0.78; or apol1-MO vs. apol1-atpif1α-MO; p = 0.90; Fig 5E). Furthermore, these observations were reproducible using an independent and non-genetic induction of anemia. Butafenacil, an inhibitor of protoporphyrinogen oxidase, causes loss of hemoglobin following exposure during early zebrafish development[44]. In a butafenacil-induced anemic context (0.195 μM treatment at 48 hpf), we observed a similar effect upon co-suppression of apol1 and myh9 (n = 17–23 embryos/injection; p<0.001 for myh9/apol1 MOs vs. myh9/apol1 + 0.195 μM butafenacil; Fig 5D and 5F).
To dissect further the possible genetic interactions between myh9 and apol1, we tested whether suppression of endogenous apol1 or ectopic expression of mutant human APOL1 could alter expression of myh9 in zebrafish embryos. We monitored myh9 expression in zebrafish larvae using quantitative real-time PCR in the context of apol1 suppression, and G1 or G2 expression, as well as apol1/APOL1 modulation in conditions of anemia induced by atpif1α-MO injection at 5 dpf (Fig 6A) and 3 dpf (Fig 6B). We observed a significant decrease in myh9 expression when zebrafish embryos were injected with the proposed dominant-negative APOL1 G2 allele alone (21% reduction; p = 0.043; Fig 6B), suggesting that the mutant protein may be suppressing myh9, either directly or indirectly, to induce nephropathy. Furthermore, zebrafish embryos co-injected with APOL1 G2 mRNA and atpif1α-MO display an even greater reduction in myh9 expression compared to controls (46% reduction; p = 0.0013; Fig 6B), and a significant reduction of myh9 expression compared to APOL1 G2 mRNA alone (p = 0.0297; Fig 6B), suggesting that the altered APOL1 (p.Asn388_Tyr389del) protein has a more pronounced effect on myh9 expression in the context of anemic stress. We also observed a significant increase in myh9 expression in APOL1 G1/atpif1α-MO vs. APOL1 G1 injected embryos (Fig 6A), however, neither of these conditions induced nephropathy. To determine whether this effect was specific to myh9 or was a general effect on transcripts expressed in the glomerulus, we also assessed expression levels of other nephropathy-associated genes during apol1/APOL1 modulation and atpif1α induced anemia. We observed no significant differences in expression of genes implicated in familial focal segmented glomerulosclerosis, including anln[45], trpc6b[46], and wt1a[47] upon apol1/APOL1 modulation (S7 Fig), suggesting that APOL1 G2 regulation may be specific to myh9.
Based on the observations that APOL1 G2 expression has the ability to decrease myh9 expression in vivo, we next attempted to rescue APOL1 G2 defects by co-injecting human WT MYH9 mRNA. We injected a constant amount of APOL1 G2-encoding message (100pg) with increasing amounts of human MYH9 mRNA (100pg, 150pg, and 200pg) and scored larvae live for generalized edema at 5dpf. However, we did not observe a significant reduction of edema in APOL1 G2/MYH9 co-injected embryos (Fig 5C), suggesting that compensation with MYH9 message alone is not sufficient to account for the deleterious effects of the G2 variant, possibly because APOL1 G2 has a trans effect on other loci in the genome or is acting to perturb cellular pathways[20].
In recent years, multiple lines of statistical evidence have implicated the MYH9/APOL1 locus on chromosome 22q12.3 with nondiabetic end-stage renal disease, focal segmental glomerulosclerosis, HIV-associated nephropathy, lupus nephritis, SCDN, and diabetic nephropathy in patients of recent African ancestry and European Americans[5–10, 33, 35, 48–50]. Additionally, APOL1 has been associated with an increased burden of cardiovascular disease in African Americans participating in the Jackson Heart Study[51]. Compelling statistical evidence in human cohorts points to the G1 and G2 alleles of APOL1, rather than MYH9 variation, as the most likely contributors to nephropathy risk. Nonetheless, functional studies of the MYH9 locus provide biological evidence for its role in the kidney, including perturbed glomerular development in myh9 morphant zebrafish[14–17]. Here, we have identified a functional ortholog of human APOL1 in zebrafish and, using transient genetic manipulation, provide functional evidence demonstrating apol1 involvement in both kidney development and filtration.
Although the human APOL gene cluster has undergone recent natural selection in primates[26, 27], we report the identification of a functional APOL1 ortholog in the zebrafish genome and its implication in renal function. Specific detection of the zebrafish apol1 protein product with the human APOL1 antibody, rescue of kidney defects in apol1 morphant embryos with human APOL1 mRNA, as well as recapitulation of renal phenotypes with an apol1-CRISPR/CAS9 F0 mutant, provide evidence that zebrafish apol1 is indeed functionally relevant to its human ortholog with respect to its role in the glomerulus. Furthermore, no other human mRNA in the human apolipoprotein L family ameliorated kidney defects induced by apol1 knockdown, supporting further its functional orthology to human APOL1. Nonetheless, it is unclear whether the zebrafish APOL1 protein serves all functions of its human counterpart, especially given the lack of a secretory domain in the zebrafish APOL1 peptide (Fig 1A).
Suppression and genome-editing of apol1 in zebrafish and three independent phenotypic scoring paradigms support a role for apol1 in nephropathy; we observed severe edema formation with concomitant glomerular filtration defects and severe podocyte loss. Complementation of apol1 suppression with APOL1 CKD risk alleles (G1 and G2) failed to ameliorate these observed defects. Notably, complementation of each individual variant of the G1 haplotype (I384M and S342G) rescued significantly nephropathy phenotypes caused by apol1 suppression, suggesting that both variants must be present in cis to confer risk. This is concordant with initial reports on the lytic potential of APOL1 recombinant proteins on T. b. rhodesiense, in which APOL1 variants with either S342G or I384M alone were less lytic than if both were present together[6].
Strikingly, injection of human APOL1 G2 mRNA alone resulted in significant edema formation in 5dpf zebrafish larvae as well as perturbed glomerular filtration and ultrastructural defects. Our expression data suggest that this could arise from myh9 suppression induced by the altered APOL1 protein harboring the G2 variant. The G2 deletion lies in the SRA-binding domain of APOL1 (Fig 1B and 1D). Therefore it is plausible that disruption of this region of the protein may either prohibit proper binding of APOL1 to its usual partners, or perhaps permit new interactions that induce nephropathy. Further studies are needed to elucidate the functional impacts of the altered APOL1 protein to nephropathy. We also report for the first time functional evidence of a genetic interaction between myh9 and apol1. Intriguingly, this interaction was only observed in the presence of anemic stress, consistent with our previous genetic association findings in human SCD patients[5].
An immediate question remains regarding the mechanism by which apol1 suppression is inducing kidney injury. Early studies revealed APOL1 mRNA expression in the placenta, lung, and liver, with specific cell-type expression found in endothelial cells and possibly macrophages[26]. More recent studies, however, have characterized the cellular localization of APOL1 in human kidney sections to podocytes, proximal tubules, and arteriolar endothelial cells[18]. These data are consistent with our observation of apol1 morphants and mutants exhibiting extensive podocyte loss and suggest that apol1 is necessary for the development and/or maintenance of glomerular podocytes. Interestingly, it has been shown that APOL1 may cause toxic renal effects through programmed cell death pathways leading to glomerulosclerosis[52, 53]. Thus, apol1 suppression could dysregulate autophagic pathways, causing podocyte malformation, thereby promoting the susceptibility of the pronephros to glomerular injury.
Initial studies implicating MYH9 in nondiabetic nephropathy failed to identify coding variants associated with renal outcome[8, 9], and since the nearby nonsynonymous variants identified in APOL1 provided stronger statistical association[5–7], it was hypothesized that APOL1 variation represents the true attribution to renal disease risk. In fact, it has been shown in multiple studies that controlling for the APOL1 risk alleles (G1-G2) attenuates significantly the effect of MYH9 SNPs[6, 33]. However, recent reports still demonstrate statistical association of MYH9 in nondiabetic nephropathy[5, 35] and previous in vivo modeling studies provide further evidence for the role of Myh9 in glomerular development and glomerulosclerosis[14–17]. As such, our group and others have postulated that complex genetic models may exist in this region, including the possibility of MYH9-APOL1 gene interaction[5, 10]. Our observation of exacerbated glomerular filtration in the context of anemic stress provides biological evidence in support of this hypothesis. Because knockdown of each of myh9 and apol1 independently impairs proper pronephric development and filtration, it is plausible that their encoded proteins are functioning in separate pathways to induce kidney dysfunction. However, these effects only appear to become additive under an additional stress (anemia). The associated variants alone may not be sufficient to induce nephropathy progression, while under low hemoglobin and hematocrit levels, additive effects between MYH9 and APOL1 may become apparent and result in a more drastic reduction in renal function, along with the observed significantly high early mortality rates among SCD nephropathy patients[21, 22, 41, 54].
Furthermore, we provide evidence suggesting that the functional consequences of APOL1 variation may not be acting in a strictly recessive manner as had been previously suggested[5–7, 55]. Our data demonstrate that APOL1 G1 (I384M/S342G) confers loss of proper APOL1 function in the developing zebrafish kidney, while APOL1 G2 is acting in a dominant-negative manner to induce nephropathy, possibly through suppression of myh9. These data indicate that the risk conferred by the APOL1/MYH9 locus is likely to be governed by a more complex model than recessive patterning as suggested previously.
In summary, our study demonstrates the essential role of both apol1 and myh9 in the development of the pronephric glomerulus and proper renal filtration in zebrafish. We report comprehensive in vivo causal evidence of apol1 involvement in kidney decline, and we provide the first in vivo evidence of a potential dominant-negative effect of the APOL1 G2 allele. Further, we have shown that the presence of the G2 allele decreases significantly the expression of myh9. Similar to the common haplotype on 10q26 that influences age-related macular degeneration underscored by complex regulatory events of neighboring genes ARMS2 and HTRA1, our data highlight further the importance of comprehensive evaluation of functional consequences at a susceptibility locus[56]. Taken together, these data provide essential biological insight into the mechanisms by which MYH9 and APOL1 confer disease risk and progression in human nondiabetic nephropathies.
We maintained WT zebrafish stocks (Ekkwill, Ekkwill x AB F1 outcross, or pod::NTR-mCherry[28] according to standard zebrafish husbandry procedures. Embryos were obtained from natural matings of adult fish.
Complementation assays were designed essentially as described[57]. Briefly, a MO was designed by Gene Tools, LLC (Philomath, OR) to target the translation initiation site of zebrafish apol1 (NM_001030138) (apol1-MO), (5’-AGTCGTCCAGCCATTCCATGAGGGT-3’). A translation-blocking morpholino (MO) targeting zebrafish myh9 and a splice-blocking MO targeting zebrafish atpif1a were described previously[17, 43]. APOL1 G1 and G2 allelic constructs were synthesized from a WT APOL1 human ORF clone (GenBank: BC112943) using site-directed mutagenesis (Stratagene, QuikChange II), subsequently transcribed (mMESSAGE mMACHINE, Life Technologies, Ambion) into capped mRNA and co-injected with apol1-MO into zebrafish embryos at the one-to-four cell stage (WT, 100pg/ nl; G1, 100pg/nl; G2, 100pg/nl). Controls were injected with phenol red. A WPI pneumatic pico pump microinjector was used for MO and mRNA injection to deliver 1 nl/embryo. After injection, embryos were maintained at 28°C in embryo medium.
48 h.p.f. larvae were anesthetized in 1.0% tricaine and placed laterally in agarose wells. 70 kDa FITC-conjugated dextran (LifeTechnologies, 3.0nl/embryo) was injected into the cardiac venous sinus and larvae were transferred to embryo medium for recovery after injection. The eye vasculature of individual fish was imaged at 24, and 48 hours after dextran injection using a Nikon AZ100 fluorescent microscope and Nikon NIS Elements AR software. The average fluorescence intensity was measured across the eye (ImageJ) and changes in intensity relative to the 24 h.p.i measurements were calculated for comparison. GraphPad Prism version 6.03 (GraphPad Software, San Diego, CA) was used for statistical analysis of relative intensity.
Glomeruli from pod::NTR-mCherry adult zebrafish were manually dissected and dissociated in 0.5% trypsin/collagenase. Dissociated cells were then filtered through a 70μm strainer and filtered again through a 30μm strainer. Cell-sorting was done on a Beckman Coulter Astrios instrument for mCherry (610nm). Sorted cells were placed in RLT Buffer (Qiagen) and RNA was extracted using the RNeasy Micro Kit (Qiagen).
Total RNA from zebrafish embryos was extracted with TRIzol Reagent (Life Technologies) and cDNA was reverse transcribed using QuantiTect Reverse Transcription Kit (Qiagen). The following primers were used for amplification: actb1, Fwd: TTGTTGGACGACCCAGACAT, Rev: TGAGGGTCAGGATACCTCTCTT; nphs2, Fwd: CCTTCGCTAGCATTCCAGAC, Rev: GCAGCTCTGGAGGAAGATTG; wdr81, Fwd: ATGGAGAGAAAAACATGGAGGA, Rev: AAGGAGAAAACCTGGAAGAACC; apol1, Fwd: GACTTTCGATTAAGTGAAACTCAGAGAGA, Rev: GTTATGGTAGCTACACCTCCCACAGCGCTG; myh9 (qRT), Fwd: GGAAAAACCGAAAACACCAA, Rev: CAATATTGGCTCCAACGATGT; anln (qRT), Fwd: TTTGACCTTCACCACCACATT, Rev: TTTGGTGTGATTGCCTTTGA; wt1a (qRT), Fwd: ATGGCCAAACTGTCAGAAGAA, Rev: TTATTTCCTGCCGTTTCTGTG; trpc6b (qRT), Fwd: GGCACCATGAGCCAGAGCCCGGCGTTCGGG, Rev: CTAAGGTGGGCCCATTGGCACTTAAGAAAA. qRT-PCR was performed on a ABI Prism 7900HT instrument and cycle threshold values were computed using SDS 2.3 software (Applied Biosystems). Relative expression was calculated against actb1 in each sample and compared against sham-injected controls to determine significant differences in expression.
5 dpf embryos were anesthetized in 1.0% tricaine and then fixed in 4.0% gluteraldehyde in 0.1M Na2PO4 buffer containing 0.12mM CaCl2 at 4°C overnight. Fixed larvae were washed in 1X PBS, washed in 1X phosphate buffer, postfixed in 2% osmium tetroxide for 2 hours, and dehydrated through a graded acetone series. Embedding was performed with Epoxy 812. Sections were cut on a Leica-Reichert Ultracut E ultramicrotome and semithin sections (1.0μm) were collected and stained with toluidine blue. 90nm ultrathin sections were placed on copper grids and contrasted with 4.0% uranyl acetate for 10 minutes. Grids were incubated in lead citrate (Reynolds Lead) for 3 minutes and then examined on a Phillips CM12 electron microscope. Images were taken with an AMT XR61 camera.
apol1 gRNA was produced by synthesizing and annealing two oligonucleotides, gRNA F: TAGGGTTGCAGGCCAACCAGTCCT and gRNA R: AAACAGGACTGGTTGGCCTGCAAC. The annealed oligos were then ligated to a T7cas9sgRNA2 vector by performing the ligation and digestion in a single step in a thermal cycler as described [31]. 2 μL of the reaction was used for transformation. Prior to transcription, the gRNA vector was linearized with BamHI. gRNA was transcribed using the MEGAshortscript T7 kit (Life Technologies, AM1354) and purified using alcohol precipitation. A total of 100pg of apol1 gRNA and 200pg of CAS9 protein (PNA Bio) was co-injected into individual cells of one-cell stage embryos. For T7 endonuclease I assay, genomic DNA was prepared from 1 dpf embryos as described [58]. A short stretch of the genomic region (~270–280 bp) flanking the apol1 gRNA target site was PCR amplified from the genomic DNA (Fwd: TGTGTGAAGGATGCATTTGTT, Rev: TGGGATAATGTATGGGAGAATG). The PCR amplicon was then denatured slowly and reannealed to facilitate heteroduplex formation. The reannealed amplicon was then digested with 5 units of T7 endonuclease I (New England Biolabs) at 37°C for 45 minutes. The samples were resolved by electrophoresis through a 3.0% agarose gel and visualized by ethidium bromide staining.
Whole embryo protein lysates were collected at 2 dpf by homogenizing anesthetized embryos immersed in RIPA Buffer (50 mM Tris, 150 mM NaCl, 0.1% SDS, 0.5% sodium deoxycholate, 1% Triton X 100, protease inhibitor (Roche, cat. no. 11697498001)). 100 mg protein was loaded into individual wells of a Mini-PROTEAN TGX Precast Gel (Bio-Rad) and a western blot was performed as described [59]. Blots were incubated overnight at 4°C with anti-APOL1 antibody (1:1000; Abcam, EPR2907, ab108315). The membranes were subsequently washed in PBST (0.1% Tween 20) and incubated for 1 hour at room temperature with anti-rabbit IgG conjugated to horseradish peroxidase (1:20,000; GE Healthcare, NA934V). ACTIN antibody (1:1000, Santa-Cruz, cat. no. sc-8432) was used as a loading control.
All animal protocols were reviewed and approved by the Duke University Institutional Animal Care & Use Committee (IACUC; protocol A229-12-08).
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10.1371/journal.pcbi.1000359 | Polymorphism Data Can Reveal the Origin of Species Abundance Statistics | What is the underlying mechanism behind the fat-tailed statistics observed for species abundance distributions? The two main hypotheses in the field are the adaptive (niche) theories, where species abundance reflects its fitness, and the neutral theory that assumes demographic stochasticity as the main factor determining community structure. Both explanations suggest quite similar species-abundance distributions, but very different histories: niche scenarios assume that a species population in the past was similar to the observed one, while neutral scenarios are characterized by strongly fluctuating populations. Since the genetic variations within a population depend on its abundance in the past, we present here a way to discriminate between the theories using the genetic diversity of noncoding DNA. A statistical test, based on the Fu-Li method, has been developed and enables such a differentiation. We have analyzed the results gathered from individual-based simulation of both types of histories and obtained clear distinction between the Fu-Li statistics of the neutral scenario and that of the niche scenario. Our results suggest that data for 10–50 species, with approximately 30 sequenced individuals for each species, may allow one to distinguish between these two theories.
| One purchases 100 wineglasses and 100 pairs of pants. After one year, 10 glasses and 10 pants survive. What can be said about the relative quality of the survivors? Well, clothes “die” as a result of accumulated wear; the surviving items are of better quality. The breaking of a wineglass is an external, random event: here the survivors are not the best, but the luckiest. To tell apart the superior from the fortunate, one should examine the development over time: the number of surviving items decays exponentially with time for the glasses and follows a sigmoid curve for the pants. An ongoing argument among macroecologists deals with similar issues. Adaptive theories suggest that the frequent species are the fittest, while the neutral theory explains the observed frequencies as a result of demographic stochasticity, assuming all species to have the same fitness. The histories suggested by the two scenarios are clearly different, but how can one probe the prehistoric abundance of species? In fact, past abundance is reflected in current genetic variance within a population. Here, we present a new technique, based on the Fu-Li F-statistic, which allows one to distinguish between niche and neutral scenarios and to resolve this important debate.
| One of the most interesting peculiarities of mother nature is the large variance in abundance of otherwise similar species. In the tropical rainforest, for example, there are differences of 4–5 orders of magnitude in the observed abundance of tropical trees [1]–[5]. Moreover, the abundance distribution admits a fat tail, which may be described by power-law or log-normal statistics. This observation is somewhat puzzling, as on the basis of evolutionary mechanisms and the competitive exclusion principle one expects the survival of only a few, most fit, species.
The simplest explanations for this phenomenon are based on “niche theory” [5]–[7]. This theory suggests that the abundance differences reflect fitness, or competitive ability variations. Strong species defeat the weak, and thus their population is large; weaker species survive due to geographical variations (regions where their fitness is better), symbiosis with strong species, or spatio-temporal fluctuations of the environmental conditions. Mathematically speaking, the system may be described by a series of coupled differential equations of, say, the Lotka-Volterra type, where each of the species undergoes logistic growth, but the growth rate and the carrying capacity are determined by the abundance of other species. The actual abundance distribution reflects a stable fixed point of this set of equations. In a fixed environment, thus, the abundance ratios among species are fixed up to demographic stochasticity; if the deterministic equations predict population size for certain species, one should expect temporal fluctuations proportional to . The observed frequency at present reflects the intrinsic fitness of that species, and thus one conjectures a similar community structure in past generations.
Another theory that gained much popularity in the last decade is the neutral theory of species abundance. It assumes [1],[2] that the fitness differences between species are negligible, and that the system is controlled solely by demographic stochasticity. The underlying dynamic that controls the abundance of different trees in the tropical forest is similar to the dynamic that governs surname frequency. The fact that there are many “Smith”s but only a few “Maruvka”s does not reflect (we hope) the undesirable features of the infrequent surname, but rather the stochastic inheritance, appearance (mutation) and “death” of surnames along genealogic lineages [8].
Within the framework of the neutral model, demographic stochasticity may be described as a multiplicative random walk along the abundance line. Multiplicative random processes are known for many years as the underlying mechanism behind fat-tailed statistics, e.g., firm size distributions [9],[10]. In fact, niche models for species abundance, like MacArthur's broken stick [6] or May's independent factor explanation [7] , are also based on some sort of multiplicative process. The difference we intend to extract here is that in the neutral scenario this random process characterizes the actual time evolution of species abundance, while the adaptive theories assume such a process in the fitness/resource space. Thus, if niche-based theories are correct, the real-time stochastic birth-death process is biased towards the observed (present) frequency . If the neutral theory is right, the random walk is almost unbiased (a tiny bias towards extinction is related to the mutation rate), and the species frequency undergoes huge fluctuations. An illustration of the temporal abundance fluctuations for the two scenarios is given in Figure 1.
Confronting the different models on the basis of current community structure data poses a very difficult statistical problem [5], [11]–[13]. Even in the presence of a reliable datasource, distinguishing between the various fat-tailed distributions (e.g., zero-sum multinomial, multivariate Poisson lognormal, broken stick distribution, etc.) is a demanding task. The noisy measurement of relative abundance in ecosystems renders this analysis even harder to accomplish. On the other hand, it would be very easy to recognize the underlying mechanism if the history of the frequency variations was given, as seen in Figure 1. Unfortunately, the Neanderthal men were too busy to conduct large-scale surveys of species abundance. In order to gather the relevant information one must seek out traces of the past in the present, i.e., the genetic polymorphism of the community.
In this work we present an experimental method that extracts these differences and allows one to distinguish between the two scenarios. It requires the collection of a large amount of genetic data from the current population, in particular noncoding DNA from either haploid (mtDNA, Y-chromosome or cpDNA) or diploid sequences. Intuitively, the genetic diversity of these sequences should reflect the history of the species abundance; one expects different results for a more or less fixed population (as suggested by the niche theory), than for a strongly fluctuating population with bottlenecks and high prevalence times (as suggested by the neutral theory). Here we quantify this concept, explain how to distinguish between the two scenarios, and demonstrate our results in a numerical experiment using “DNA sequences” obtained from simulated data with different histories.
Our technique is limited by two time scales. The sequence mutation time sets its resolution, as no reliable conclusion may be drawn on the basis of only a few mutations. The abundance history may be recovered for timescales that are much larger than the typical time needed for a single mutation to appear in the whole sequence. The time to the most recent common ancestor sets, of course, the maximal timescale. For an almost fixed population (niche scenario) of size , the most recent common ancestor of any typical collection of sampled individuals appears about generations before present. This implies that our method, which uses the “structure” of the phylogenetic tree, enables differentiation between scenarios if the abundance differences were substantial in the last generations. Accordingly, our techniques are not limited to small, local ecosystems but are applicable to the metacommunity as well, since the “time horizon” to the past is proportional to the abundance.
A similar idea, utilizing the differences in assumed history to distinguish between the two hypotheses, was suggested by Ricklefs [14] (see also similar approach used in [15]). Relying on data from passerine birds, Ricklefs compared the species' lifetime (i.e., the time elapsed since the species first appeared) and its contemporary abundance. Under the assumption of neutrality, the average species' lifetime is almost linearly proportional to the current abundance (technically, this is the first passage time [16] of a multiplicative random walk started at ). According to Ricklefs, [14] the species' actual lifetime (obtained from genetic divergence data) is much shorter than expected by Hubbell's neutral theory. His method, however, requires prior knowledge of the current population size and mutation rate, two parameters that may be difficult to obtain. Here we suggest a method that only requires knowledge of the genetic variability.
Before we discuss the polymorphism analysis itself, let us add an important comment. Restricting our considerations to “pure” adaptive/neutral histories, like those demonstrated in Figure 1, is clearly a simplification. In reality , one should expect, for example, larger fluctuations for an ecosystem that obeys the rules of the niche theory, due to the effects of environmental stochasticity. We do assume, however, that these fluctuations either conserve the species abundance ratio (i.e., are not species specific) or are relatively weak. If environmental fluctuations cause rapid shifts in the relative species frequency, the conceptual meaning of the “niche theory” becomes unclear and the difference between the two scenarios is not so interesting. Throughout this work we therefore assume that the effect of environmental stochasticity is weak and yields only minor corrections to the niche/neutral predictions. In the final section we return to this issue, and discuss in detail the various types of environmental stochasticity, together with their identification using genetic polymorphism data.
We tried a number of methods in order to distinguish between the genetic polymorphism of the two scenarios, and found that the most efficient one is Fu & Li F-statistic [17]. Originally, this method was developed in order to measure the similarity of a given phylogenetic tree to the one expected from the Kingman Coalescent Model [18],[19]. It was used by Sjödin [20] to measure when fluctuations in the population size cancel the similarity with the Kingman Coalescent. Here we used this method in order to distinguish between the two scenarios of fluctuating populations.
The Fu-Li F-statistic compares the sum of the lengths of the external branches to the average internal branch length. Under the correct scaling, these lengths should be the same, if the assumptions of the Kingman Coalescent Model (fixed population size, small sample size, and neutrality of mutations) are fulfilled. Therefore, in the Wright-Fisher process, for example, the value of the F-Statistic is zero. In a growing population, this value is negative, and for a shrinking population it is positive.
Basically, the Fu-Li F-statistic compares the features of the recent past, which affect the external branch length, to the features of the far past, affecting the internal branches. Thus, it emerges as a suitable technique for distinguishing between the two scenarios. In the niche scenario, the population in the past is similar to the population in the present, so the statistic should be approximately zero. For a neutral scenario, the population in the present differs from the past population; in most cases, the population in the present is larger than the population in the past (this is an interesting feature of a multiplicative random walk with an absorbing state, see [21]). Therefore, one expects that the statistic for that scenario will admit a broad distribution with a negative average.
The F-statistic is defined by:(1)where is the sample size, is the average number of pairwise nucleotide differences (the average being over all possible pairs in the sample), S is the number of segregating sites, is the number of singletons (mutations that appear in only one individual in the sample), and and are constants given the sample size .
We also worked out the Fu and Li D-statistic for the same datasets. The results were similar to those of the F-statistic but the resolution obtained from the F-statistic was better and is therefore preferable.
We performed many numerical experiments simulating the niche scenario and the neutral scenario, and calculated the F-Statistic for each realization. We then produced the probability distribution of the F-Statistic for the two types of histories. As can be seen in Figure 2 the F-statistic differs in the two scenarios; both the width of the distribution and its average are not the same, as expected. An important feature of these statistics is that they do not depend on the species' abundance, only on their history.
Given real DNA sequences from several species, this difference in the distributions can be used to determine whether the species followed the Niche history or the Neutral history, and end the ongoing debate between these two hypotheses.
Since we do not currently have enough DNA sequences from many different species, we did not try to check the common method of test, that given a few data points can distinguish between two similar distributions, rather we give here only a rough estimation for the number of species needed to distinguish between the two hypotheses. For species, the standard error of the F-statistics is , and in order to discriminate between the two scenarios this quantity should satisfy:(2)where are the averages of the F-statistic of the neutral and niche scenarios respectively. For a sample size of individuals per species, as in Figure 2, the required number of species necessary to decide between the two theories is 10–50.
While our analysis until this point assumed one independent community, i.e. meta-community [2], our approach can also be applicable to local communities. For local communities (like those described by an island-mainland model), in cases of weak migration, the abundance fluctuations for neutral population are still much larger than those expected from the niche theory and one can still distinguish between the two scenarios. The migration is “weak” when the relaxation time towards the metacommunity's relative abundance is large relative to the time scale associated with the demographic stochasticity - this is the limit considered by [22]. Moreover, if there is a possibility to recover the migration rates from the abundance data (e.g., in the case of a few local communities coupled to the same metacommunity, such that the parameter used in [1],[2] is the same for all islands), one can calculate the Fu-Li statistic for different local communities with different migration rates. This statistic should approach the neutral case as the migration rate gets smaller.
In this section we present a short survey of other methods we examined to distinguish between niche and neutral histories. At the end of the day we concluded that the Fu-Li method is superior if no information is given beyond the genetic data. Yet, in the presence of other pieces of information, one of the following techniques may be preferable to the Fu-Li method.
As we have mentioned above, the “pure” niche/neutral scenarios considered here are an idealization that may be true in some cases (e.g. small communities), but in other cases one should expect large fluctuations in the abundance of a species due to environmental stochasticity. Indeed, for certain ecosystems (like phytoplankton) some degree of environmental fluctuations has been suggested in order to explain, in the framework of niche theory, how the system overcomes the competitive exclusion principle [23]. It is thus important to consider the conceptual and the practical implications of this stochasticity in both frameworks. We stress that the following discussion is relevant not only to the polymorphism-based technique presented here, but also to the niche-neutral debate in general, including the analysis of the observed species abundance ratios.
One fundamental observation is that all theories become practically neutral in the limit of large, fast and independent environmental fluctuations. If the fitness of a species varies tremendously in time, and is uncorrelated with the (also fluctuating) fitness of other species, and if the correlation time of these fluctuations approaches zero, the fitness differences are averaged out and the adaptive dynamics is equivalent to the neutral one. Niche theories are meaningful only if (at least) one of the three conditions mentioned above is not satisfied: the environmentally-induced fitness fluctuations should be either weak, slow, or correlated.
If the effect of environmental stochasticity is weak, the corrections to the predictions of the “pure” theory are relatively small. In that case one may use our Fu-Li technique, expecting only small deviations from the predictions for the idealized case. In other occasions, however, one can find evidence for large perturbations (e.g., climate changes) that affect the ecosystem. Here the timescale is important: one may try to relate observed quantities (abundance ratios, genetic polymorphism) to the predictions of an adaptive theory only if the characteristic time needed for the ecosystem to reach demographic equilibrium is much shorter than the typical period between environmental shifts. The Fu-Li statistic in that case will fit our predictions if the genetic time horizon, , is smaller than the characteristic period between environmental transitions.
Even if these conditions are not satisfied and the Fu-Li statistic (as well as the species abundance ratio) fails to follow the pure scenario predictions one may still uses other polymorphism based methods. The basic challenge, now, is to discriminate between the neutral scenario suggested by [24], with a neutral drift superimposed on the overall carrying capacity fluctuations, and between two competing niche scenarios. One can imagine an adaptive ecosystem that is subject to uniform (correlated) pressure, such that the abundance of all species shrinks or grows in the same proportions (the relative abundance is conserved and is independent of the total population size). On the other hand, the pressure may be uncorrelated (niche-selective), not affecting all species in the same manner, in which case the abundance ratio is time-dependent [23],[25].
As suggested above, polymorphism data may be used in order to calculate , the number of lineages as a function of time, and from this quantity can be calculated [note that the rate of disappearance of lineages in the Wright-Fisher coalescence model is proportional to the abundance ]. This technique may be used in order to extract past abundance ratios for different species. With the abundance history at hand one can distinguish between the three different possibilities. In the case of niche scenarios with uniform pressure, one expects the abundance-ratio to be fixed in time. The neutral scenario suggests that the abundance ratio is not varying but for different species is correlated, i.e., a global catastrophe resulted in a decrease of all the species and vice versa. An uncorrelated historic abundance (i.e., both abundance of any species and the abundance ratio fluctuate in time) corresponds to niche-selective pressure. Thus, even in the case of large environmental stochasticity, more sophisticated genomic techniques can be used to differentiate between the two histories.
Most of the empirical tests suggested for the niche-neutral debate rely on snapshots, such as via comparison of the predicted and the observed species abundance ratio. Some authors did consider historic abundance data [25]–[27], but the populations they dealt with are relatively small. Moreover, these authors tested only the neutral hypothesis against the data; in order to have a well defined niche theory, one must clarify the relative weight given to the stochasticity in comparison with the deterministic part of the dynamics. In this work, we suggest the use of current genetic polymorphism as an indicator for past abundance fluctuations. We believe that due to the fast-paced development of sequencing techniques, this data will be available for analysis in the near future. By sequencing more and more individuals from different species, one may use our technique to improve the quality of the results in any ecosystem and for a large time horizon. As explained in the last section, the results may be used as a test for both niche and neutral scenarios, and may allow one to establish a “mixed” theory, comparing the importance of stochasticity vs. deterministic dynamics.
We have gathered our data from a simulation of the Wright-Fisher model with discrete generations. We initiate the system with individuals, each carrying a “genome” of 1000–10000 sites. At each generation, any of the individuals produces offspring, where is a random number generated from a Poissonian distribution with an average of 2. Each of the offspring carries the exact DNA sequence of its ancestor with probability , and mutates at a single, randomly chosen site with probability . From all of the offspring in a generation, only are selected at random to survive, where is the (time dependent) carrying capacity. For niche histories, fluctuate around with , as expected for a population with a well-defined carrying capacity subject to demographic stochasticity. For neutral histories, the population size follows a Markovian process: given , the carrying capacity of the last generation, is chosen at random from a Poissonian distribution with average . In order to compare the predictions of the two theories for a species given the current abundance , we have created first the sequence starting from and go backwards in time up to . We then simulate the genealogic process from past to present, and obtain the Fu-Li statistics using the algorithm presented in [28]. For a different simulation technique has been used: the genealogic tree has been generated from the sampled population at present using the “ball in a box” procedure [29], an implementation of the Wright-Fisher process. The number of “boxes” changes from generation to generation according to the above mentioned procedure for the corresponding scenario. As seen in Figure 2 below, the Fu-Li statistic obtained using the two procedures are essentially the same.
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10.1371/journal.pntd.0002027 | How Effective Is School-Based Deworming for the Community-Wide Control of Soil-Transmitted Helminths? | The London Declaration on neglected tropical diseases was based in part on a new World Health Organization roadmap to “sustain, expand and extend drug access programmes to ensure the necessary supply of drugs and other interventions to help control by 2020”. Large drug donations from the pharmaceutical industry form the backbone to this aim, especially for soil-transmitted helminths (STHs) raising the question of how best to use these resources. Deworming for STHs is often targeted at school children because they are at greatest risk of morbidity and because it is remarkably cost-effective. However, the impact of school-based deworming on transmission in the wider community remains unclear.
We first estimate the proportion of parasites targeted by school-based deworming using demography, school enrolment, and data from a small number of example settings where age-specific intensity of infection (either worms or eggs) has been measured for all ages. We also use transmission models to investigate the potential impact of this coverage on transmission for different mixing scenarios.
In the example settings <30% of the population are 5 to <15 years old. Combining this demography with the infection age-intensity profile we estimate that in one setting school children output as little as 15% of hookworm eggs, whereas in another setting they harbour up to 50% of Ascaris lumbricoides worms (the highest proportion of parasites for our examples). In addition, it is estimated that from 40–70% of these children are enrolled at school.
These estimates suggest that, whilst school-based programmes have many important benefits, the proportion of infective stages targeted by school-based deworming may be limited, particularly where hookworm predominates. We discuss the consequences for transmission for a range of scenarios, including when infective stages deposited by children are more likely to contribute to transmission than those from adults.
| Large donations of drugs to treat soil-transmitted helminths (STHs, intestinal worms) means that many more school-aged children will be treated, improving their well-being and development. These children will have to be repeatedly treated since reinfection will occur due to contaminated environments in the absence of improvements in hygiene and sanitation. Repeated treatment of school-aged children may have the added benefit of reductions in levels of infection for the whole community. This will in part be determined by the proportion of the total worms harboured or eggs output by school-aged children, a product of how heavily infected school-aged children are and how many school-aged children there are in the community. In one setting school-aged children output as little as 15% of hookworm eggs whereas in another setting they harbour up to 50% of roundworms. Thus, whilst school-based programmes may have important health benefits, the community-level impact on transmission could be limited unless school-aged children over-contribute to infection. We use mathematical models to show that if children contribute more infective stages to the environment which adults are exposed to than adults do, the reductions in transmission resulting from treating children will be larger, but may still be limited.
| In January 2012, a high-level meeting brought together 13 pharmaceutical companies and the global health community in London, UK to announce a new public-private partnership to eliminate or control the seven preventable neglected tropical diseases (NTDs) by 2020, based largely on a new NTD roadmap from the World Health Organization (WHO) [1]. The pledge by pharmaceutical companies to sustain and extend donation programmes facilitates a large portion of the necessary supply of drugs and other interventions to help achieve this goal [2]. Such commitments raise the question of how best to use these resources to induce maximum impact, given that many treatments for NTDs must be administered repeatedly to individuals living in endemic areas due to re-exposure to infection and the absence of fully protective acquired immunity.
The most common NTDs worldwide are the soil-transmitted helminths (STH: Ascaris lumbricoides, Trichuris trichiura and the hookworms, Necator americanus and Ancylostoma duodenale), with an estimated 5.3 billion people worldwide, including 1.0 billion school-aged children, living in areas of stable transmission for at least one STH species [3]. STHs are easily treated with one of four drugs: albendazole and mebendazole, and to a lesser extent, levamisole, and pyrantel pamoate [4]–[5]. However, reinfection commonly occurs [6] due to the inability of the human host to mount protective immunity to reinfection by intestinal helminths [7]–[8], combined with inadequate hygiene and sanitation to restrict or eliminate re-exposure in environments continuously contaminated with the egg or larval free-living transmission stages of these parasitic worms [9].
Following treatment, average worm loads in the population return to their pre-treatment equilibiria in a monotonic manner. The exact dynamics will depend on a number of density-dependent processes that influence parasite reproduction, infection and mortality (in part related to the build-up of a degree of acquired immunity), plus the relatively long life expectancies of established worms in the human host (measured in years) [10]. It will also depend on the proportion of worms in the entire human community in a defined location which are exposed to treatment in a particular control programme.
Deworming programmes for the STHs are often centred on school delivery because of the large burden of morbidity and concomitant developmental consequences for these children [11]–[13], as well as relative ease of access to children in poor rural areas through schools and the cost-effectiveness of school-based deworming [14]–[15]. A number of countries have programmes which additionally include adults, as part of lymphatic filariasis treatment campaigns providing mass treatment with albendazole and ivermectin (or diethylcarbamazine), with associated large impacts on transmission [16]. However, there are large areas where this STHs are not co-endemic for lymphatic filariasis and these areas the WHO-recommended treatment strategies prioritise school-aged children, but also recommend preventive chemotherapy of preschool children, women of childbearing age and adults at high risk [4]. A number of countries are currently implementing only school-based deworming [11]. The large donation of 600 million doses per year announced in the London Declaration almost completely covers the estimated 610 million school-aged children in need of preventive chemotherapy [1]–[2], [11], [17], but it does not cover pre-school children, women of child-bearing age or treatments more than once a year. This paper therefore examines the effectiveness of school-targeted programmes in restricting transmission within the larger community of pre-school children, school-aged children and adults. We use analytical methods deriving from the description of the transmission dynamics of these parasitic worms [18] and demographic plus school attendance information to calculate what fraction of the total population is treated. We also use mathematical models to discuss the impact of school-based programmes on transmission, including scenarios in which infective stages deposited by children are more likely to contribute to transmission than those from adults. We discuss how the impact of a treatment programme could affect the infection dynamics in the population as a whole depending on the, as yet unknown, details of between age-group mixing.
The effect of treating school-aged children on the overall transmission dynamics of the parasites depends on a number of factors. These include the fraction of the total worm population harboured by these age groups, the fraction of the eligible school-aged children who attend school and receive treatment, how other age groups are exposed to the eggs or infective larvae produced by school-aged children and vice versa, and drug efficacy. Data exist for the first two factors, which we discuss below. However there is limited evidence to facilitate the exploration of various assumptions such as random or non-random exposure of age groups to the infective stages produced by other age groups in a defined human community. Drug pharmacokinetics and efficacy are also reasonably well documented for the commonly used drugs for STHs and schistosomes (mebendazole [19], albendazole [20]–[22] and praziquantel [23]). In this analysis we estimate the fraction of worms among school-aged children and then discuss the likely impact of this level of coverage on transmission using a suite of infectious disease models. We first outline the parasitological measures and data on which these estimates will be based.
Monitoring and evaluation of the impact of community-based preventive chemotherapy programmes is based on two epidemiological measures, the prevalence and the intensity of infection. Prevalence represents the fraction or percentage of the population infected and is typically stratified by factors such as age and gender. The intensity of infection or worm burden for STHs is typical measured indirectly by counts of eggs expelled in faeces (eggs per gram of faeces or EPG) and is similarly typically stratified by age and sex. Less commonly, worm burden may be measured by the worms expelled in total faecal output over a defined period post curative chemotherapy [24]–[26]. The two epidemiological measures are statistics of the probability distribution of worm numbers per person. These distributions are typically highly aggregated in form, with the variance exceeding the mean in value. They are well described by the negative binomial probability model [27]. For this distribution the relationship between prevalence (as a proportion) and mean intensity is given by:(1)Here, the negative binomial parameter varies inversely with the degree of parasite aggregation within the human population and for values in excess of five the distribution is approximately random in form with the variance approaching the mean in value. Some typical estimates of the magnitude of are recorded in Table 1.
A plot of the relationship between and for the negative binomial is presented in Figure 1. It is clear from this figure that the prevalence is a very poor measure of the impact of community-based chemotherapy. Large changes in average worm load as a consequence of treatment will only have a small effect on prevalence unless the mean worm burden is low (i.e. when transmission is very low). For example, in a study in Myanmar two villages with mean EPGs of about 4000 and about 400, an order of magnitude difference in intensity, had almost no difference in prevalence (Figure 1B [28]) and so highly effective treatment of the high intensity village, reducing the burden by a factor of 10, might be viewed as a failed programme if only prevalence were monitored.
The cost and difficulty of monitoring intensity, as opposed to prevalence, is of course greater. However, if monitoring is to have any value, intensity must be measured in some fraction of the treated population. The issue of how best to sample to gain an accurate picture of the impact of treatment, while attempting to keep sample size and the concomitant costs low, requires careful thought. The underlying distribution of parasite numbers per host is central and given its heterogeneity, small sample sizes will not provide robust measures of trends [29]. One compromise is to monitor impact in a subset of age classes – one of children and one in the adult age groups, to see how the treatment of the school-aged children impacts on transmission to adults [30].
The greatly expanded deworming programmes seen in many regions of the world in recent years have not been accompanied by systematic recording of treatments delivered and the associated impact on prevalence and intensity. Recently, however, some progress has been made on the generation of open access databases recording global and national spatial distributions of helminths based on estimates of the infection prevalence, as illustrated, by the Global Atlas of Helminth Infection (http://www.thiswormyworld.org [12]) and the Global Neglected Tropical Diseases database (http://www.gntd.org [31]). The Global Atlas of Helminth Infection will be expanded in the near future to include measures of the intensity of infection and treatments delivered. The website will also be extended to encourage the electronic deposition of data on STHs collected in association with the current expanded efforts on community-based control using mass or school-aged targeted anthelminthic treatment. Such data are collected by a number of excellent NTD programmes but is rarely subject to detailed analysis on trends in transmission [32].
The present absence of international databases on treatment of STH and impact of such treatment does make analysis of questions concerning the optimal delivery strategy for community-based programme somewhat challenging. We therefore base our analyses on a small number of available well-designed studies that record prevalence and intensity of infection, stratified by age and sex, before and after various treatment programmes. The age-profiles used are from studies of A. lumbricoides in Myanmar [33], India [24] and Iran [34]; T. trichiura in St Lucia [35]; hookworm in Uganda [36] and Vietman [37] and, for comparison, studies of Schistosoma mansoni in Uganda [38] and Brazil [39].
Epidemiological studies that record the mean intensity of infection stratified by age, when combined with demographic plus school enrolment data, provide information on the fraction of the total worm population exposed to treatment. Along with measures of drug efficacy, this in turn gives the fraction of the total worm population removed by school-aged targeted chemotherapy. The importance of this fraction to the overall transmission dynamics of the target parasites cannot be overstated – and in current control programmes it is an unmeasured parameter.
If is the proportion of the total population in age class a, the proportion of the human population in the school age classes 5 to 14 years of age, is given by:(2)The proportion of the total worm population harboured at time by people between the ages of 5 to 14 years, , is then given by:(3)where is the mean worm burden in age-group at time .
If the measure of intensity of infection is eggs per gram of faeces (EPG), then the proportion of egg output produced by school-aged children is:(4)Here is the density-dependent egg output function, which gives the expected egg output for an individual with mean worm burden for age and negative binomial aggregation parameter . In practice egg output is usually measured, rather than mean worm burden, so we can approximate this by(5)where the mean egg output in age-group at time . We calculate these fractions for some example datasets on parasite distributions, together with demographic and school enrollment data.
Demographic data from various countries where STHs are endemic provides an initial template to assess these issues [40]. Table 2 records the fraction of various populations in the school ages of 5 up to 14 years. In general, within countries where helminth infections are endemic, the fraction of the total population in the school going groups is between 11% and 30%.
UNESCO and the World Bank provide data on school enrolment by sex, location (urban or rural) and country. Recent data are recorded in Figure 2 for rural and urban areas for a selection of countries [41] and in Table 6 for enrollement of female students. Over the past decade there has been a steady increase in school enrolment in most countries throughout the world. Progress has been less good in poor rural areas by comparison with urban districts in developing countries. Generally, the most recent data (2005 and beyond) suggest figures in the 80% to 90% range for most urban areas, but with a range of 20% to 60% in some sub-Saharan African countries in rural areas. There is often a gender bias in many poor countries, with attendance figures for females lower than those for males in the primary and secondary school enrolment data. Poor attendance could severely reduce the population-level impact of school-based deworming. Conversely however, there is anecdotal evidence that there may be higher attendance to schools for deworming days due to awareness of the health benefits. These effects have not yet been quantified, to our knowledge.
For a given coverage of a school-based programme, as defined by proportion of estimated treatment of a proportion of worms , , or egg output, , the impact on transmission will depend on the particular dynamics of the parasite. Here we outline general insights on the non-linear effects of limited coverage on transmission. We then use heterogeneous mixing models to investigate how different mixing patterns between adults and children will affect the impact of targeted programmes.
Simple theory provides some important general insights into the factors controlling helminth transmission and the impact of community-based chemotherapy [18]. For directly transmitted helminths with a free living larval or egg stage outside the human host, the basic dynamics of the system can be described by the following differential equation,(6)where 1/μ is human life expectancy, 1/μ1 is adult parasite life expectancy, M is the mean number of worms in the population, f(M) is the mean egg output per gram of stool, given a mean worm burden of M, the dispersal parameter, k, and fecundity coefficient, z:(7)The basic reproductive number defined as the average number of female worm offspring that survive to reproduce in the absence of density-dependent constraints (ignoring the complexities of mating probabilities and age structure in the human population) is given by equation 8(8)Here, denotes the fraction of female worms, the per capita egg production rate, the proportion of female worms that survive in the human host to reproductive maturity, the fraction of eggs or larvae that survive to the infective state, is human life expectancy, is the adult worm life expectancy in the human host, is infective stage life expectancy and is the per capita transmission coefficient for the infective stage. Table 3 records published estimates of for various helminth species.
A rough approximation of the growth rate of the parasite population post extensive treatment, again ignoring the effects of density-dependence and worm mating probabilities, is given by:(9)Where () is the parasite life expectancy in the human host. Equation 9 is based on the assumption that is much shorter than human life expectancy, which is true for all STHs (see Table 4).
Ignoring age structure in the human population, the effect of treatment on the effective reproduction number, , is described by(10)where is the interval between successive rounds of treatment and is the efficacy of the drug, defined as the proportion of worms killed by the drug. The critical fraction of the human population that must be treated, , to reduce the effective reproductive number to less than unity in value (assuming that the breakpoint in transmission is close to zero due to the aggregated distribution of worms per person [27]) is given by:(11)And thus, the fraction is simply determined by , , and adult worm life expectancy . We simulate these approximations for a range of parameter values to illustrate the non-linearities in these relationships.
These approximations for homogenous mixing models are very useful to illustrate the impact of limited coverage on transmission. To gain a more accurate picture of how the treatment of just school-aged children influences the overall transmission dynamics of the parasite, the impact of not only age structure but also exposure to infective stages produced by all age groups in the population must be taken into account. The homogeneous model described above can be extended to an age-structured model to describe the characteristic age-profiles of intensity [18]. Such models usually assume that exposure to infective stages across all age classes is random and independent of which age class produces the eggs or infective larvae. However, the spatial structure of egg deposition and infective stage development arising from one age group, plus their concomitant contact with other age groups, is in reality unlikely to be random in a defined community. Instead, it may be more likely that infective stages produced by school-aged children are deposited in areas closer to habitation, and hence acquired by all age groups of the population, while those arising from adults are less likely to come into contact with children. Therefore the model should include heterogeneous mixing [42].
A simple way to mimic non-random contact is to stratify the population into two age groups, namely; school-aged children (5–14 years), and the rest (0–4 and ≥15 years combined for simplicity, although patterns may also be different between these two groups), and assume different contact patterns with the infective stages within and between these larger age groupings. Such a stratification of hosts has the further advantage of facilitating the modelling of school-based treatment programmes. We assume that the child and adult age groups have negative-binomially distributed worm distributions with the same shape parameter, k, but different means, Mc and Ma, respectively. The means evolve independently according to the degree of contact of each group with a common infectious ‘reservoir’. The model equations are (extending the approach by Chan et al. [42])(12)The quantity, , is the per capita infectiousness of the shared reservoir. The parameters and determine the strength of contact with the reservoir for children and adults respectively. The dynamics of the infectious reservoir are described by the following equation(13)The parameters in equation 13 are as defined earlier. The parameters and represent the proportion of the population in the two age classes and and the fraction of egg output that enters the reservoir from children aged 5–14 years and other age groups, respectively.
We investigate the effect of regular school-based treatment on the evolution of worm burdens in the community for three scenarios:
It should be noted that under scenario C there will be higher intensity of parasites in children at equilibrium, or baseline, as a consequence of their higher exposure to infection.We use the mathematical model described above (equations 12 and 13) to simulate how the mean intensity of infection would vary for a setting with a given mean intensity and treatment coverage under each of these different scenarios. We run the mathematical model for A. lumbricoides and hookworm, which have contrasting transmission dynamics, to illustrate the importance of age-dependent mixing in the success of an age-targeted treatment programme.
The proportion of worms or egg-output in school-aged children, as calculated using equations 3 and 5 above, is best illustrated by reference to a set of published studies covering the main parasites in different country settings. The first example is that of A. lumbricoides in Myanmar from a study by Thein-Hliang et al. [33]. The demography and age intensity data are presented in Figure 3. Demographic surveys for the year of the study reveal that treatment of all children in the 5–14 years age range would expose roughly 49.4% of the worm population to anthelminthics. In terms of the fraction of the parasites killed by a school-based programme assuming a drug efficacy of 95% [5] and school attendance of 95% on the days of treatment gives an overall estimate of 44.5% of the total worm population removed by one round of treatment. Calculations for a study by Elkins et al. [24] of A. lumbricoides in Tamil Nadu in India yield a very similar figure for the percentage of worms potentially removed by the treatment of children attending school. Calculations for hookworm (largely N. americanus) in Uganda from a study by Pullan et al. [36] are significantly different, as shown in Figure 4. For this population only 15% of the egg-output is generated by school-aged children. A drug efficacy of 95% and school attendance figure of 85% in a rural region (see Figure 2), produces an estimate of 12% of egg output treated by one round of treatment. These contrasting figures for A. lumbricoides and hookworm illustrate the importance of the shape of the age intensity profile to both the efficiency of school-aged targeting and the degree to which such treatment programmes will impact on overall transmission in a population. Analyses of epidemiological studies of Schistosoma mansoni produce figures similar to A. lumbricoides, given marked convexity in the intensity by age profiles (age intensity data from Kabatereine et al. [38]). T. trichiura is somewhat intermediary between A. lumbricoides and hookworm with a degree of convexity but significant worm loads in adults [35], [43]–[44]. The more convex the age intensity profile is, the greater the impact of school-based deworming on overall transmission, provided the peak intensity lies within the age ranges 5–14 years. The continued rise of hookworm intensity in adult age classes yields a low fraction of the total worms in the treated classes.
The calculations presented in Figures 3 and 4 and Table 5 are approximations to the values defined in equations 3 and 5, based on the assumptions that human population size is constant but with an age distribution as documented by the US Bureau of Population and [40], and that the effects of density-dependent fecundity () are negligible. Polynomial fits are employed to calculate integrals of the functions and respectively from cross sectional studies of the intensity of infection, and age pyramids (both sexes combined) from national demographic data. The calculations are conservative estimates since they are based on the assumption that all who are eligible attend school. In practice, the percentages recorded in Table 5 must be multiplied by the school attendance data from recent UNESCO studies [45].
The impact of the parasite coverage levels presented in Table 5 on transmission under the assumption of homogeneous mixing can be seen using the approximations to the critical treatment coverage, (derived above in relation to the parasite life expectancy, , and the basic reproduction number, , as in Figure 5 (equation 11)) and the impact of increasing on the mean worm load, , and the prevalence of infection, (Figure 6, equation 6). Figure 6 reveals that the mean worm load decays approximately linearly as the fraction treated rises, while prevalence only begins to fall steeply as the fraction treated approaches the critical value and effective transmission ceases. Note that from Figure 5 the critical fraction treated reaches 1 for high /low parasite life expectancy. This indicates the parasite may be very difficult to eradicate by treatment with the given efficacy ( = 0.9) when is large (∼>4, estimated values for particular parasites in Table 3) and is short (∼<1.2 years, values in Table 4) e.g.for A. lumbricoides and T. trichiura in high transmission settings.
Further insight on the effect of targeting school-aged children can be gained by considering differential mixing patterns between children and the rest of the population, as outlined above. The results of example simulations are presented in Figures 7 and 8, where the worm burdens in school-aged children and other age groups (where applicable) and averaged across the community are presented for different modelled scenarios, helminths and treatment intervals. The columns of the figures correspond to the scenarios A (homogeneous population), B (homogeneous mixing) and C (heterogeneous mixing). The heterogeneous mixing (scenario C) results in a higher worm burden in the children than in the adults, as is seen in several settings (note this model does not include any immunity). All models have the same mid-range value of 3. Treatment in the homogeneous model is made comparable with the heterogeneous model by setting coverage to . We have simulated these scenarios for A. lumbricoides, with a life expectancy of 1 year (Figure 7) and hookworm with a life expectancy of 2.5 years (Figure 8).
The most striking feature of Figure 7 and Figure 8 is the very modest impact of treatment of children on transmission, even at high levels of efficacy (95%) and coverage (85%). The treated children do have large benefits in terms of periods free of worms or with low worm burdens. However, the effect of treating children on worm burdens in the larger community is small. This reflects the proportion of the worm population actually reached by treatment, even though the chosen value (30%) is at the high end of school-attending fraction of the population (Table 5). Decreasing the treatment interval (bottom row in each figure) has only a moderate effect. The two group equal mixing model (scenario B) shows the direct effect of school-based treatment on school-aged children and also the indirect effect on adults through the reduction in infectious material in the community. The rate of bounce-back after treatment is slightly reduced in the heterogeneous model as compared to the homogeneous one (scenario B versus scenario A). This means that homogeneous descriptions (e.g. the homogeneous model, A) of non-uniform treatment regimens (targeted at some portion of the population) will always underestimate the time to recover to pre-treatment levels.
Scenario C mimics what we believe to be a more realistic epidemiological scenario, with school-aged children contributing twice as much infectious material as other age groups and also being twice as exposed, resulting in higher worm burdens in school-aged children. This is more likely to be observed for A. lumbricoides (e.g. Figure 3) than for hookworm (e.g. Figure 4). As such, the effect of treatment on the school-aged group is quite pronounced, but the impact at the community level, sometimes termed the ‘herd impact’ is only marginally improved, due again to the small proportion of worms treated. These simulations highlight the importance of mixing patterns in determining the effectiveness of school-based treatment programmes.
School-based approaches to deworming children have many advantages in terms of ease of access in urban and rural regions and the ability to link with other nutritional, health and education initiatives in order to try and minimize delivery and logistic costs. Advocacy for this approach to the control of STHs and the morbidity they induce has been made by many over the past decade [1], [30], [46]. However, with increased drug donations to support such programmes, it is now crucial to evaluate the benefits and disadvantages of such an approach. The limitations of this approach has already been implicitly acknowledged in the WHO recommendations to additionally target pre-school children, women of child-bearing age and high risk adults where possible [4] and previous identification of adults as a possible reservoir of infection [47]. However, there are many countries where only school-based deworming is currently under consideration [11]. Of particular importance in this context is the impact of school-based treatment on transmission of the parasites in the entire community, including the pre-school and adult age groups. In particular, sustained transmission (and thus production of infective stages) in other age groups will influence the frequency of treatment in school-aged children required to sustain infection at very low levels.
A limited number of field-based studies of mass chemotherapy have suggested that adult age groups who do not have access to treatment still benefit from school-based deworming as a result of its impact on the overall intensity of transmission within the population [30], [33], [48]. The treatment of a few (heavily infected individuals) can impact on the effective reproductive number and therefore reduced exposure to infective stages in those untreated. This effect is analogous to the concept of herd immunity in community-based vaccination programmes where vaccination reduces transmission to those still susceptible to infection. In the context of worms and chemotherapy, the herd impact arises from the reduction in the output of infective stages in faeces that result in the contamination of the environment in which the community of all age groups lives. A similar indirect protection among untreated individuals is the impact upon older children and adults seen after age-targeted (1–10 year olds) large-scale treatment of azithromycin against trachoma [49].
We employed two approaches to examine the impact of school deworming programmes on infection within the entire community, the ‘herd impact’ of the treatment programme. The first was empirical and based on the calculation of the proportions of the population in the school attendance age groups, the fraction of these age groups who attend school and the fraction of the total worm population harboured by in these school-aged children. The results of a set of calculations are summarised in Table 5. It should be noted that the age-profile and population pyramids used here were not exactly matched, and therefore the calculations will not be precise for a particular point in time. It highlights the need for more recent data for these pathogens, as highlighted in recent articles [50]–[51]. In many of the low-income settings in which STHs are highly prevalent, the population pyramid is approximately exponential in shape, meaning that from 20–30% (Table 2, Figure 3A and Figure 4A) are of school age. However, for some STHs, such as A. lumbricoides, the highest intensity infections are in this age-group (Figure 3B) and therefore a large proportion of worms are targeted by school-aged treatment (up to 50% in our examples, Table 5). These predictions are similar to empirical findings by Bundy et al. [30] who evaluated the impact of age-target chemotherapy on community transmission on the island of Montserrat, West Indies, where T. trichiura, which has similar transmission dynamics to A. lumbricoides, was the predominant species and had a prevalence of 12% (A. lumbricoides and hookworm occurred at <2%). The authors found that 4-monthy treatment of 2–15 year olds had a subsidiary effect on intensity of untreated 16–25 year olds. In contrast to A. lumbricoides and T. trichiura which exhibit age-convexity in intensity, hookworm intensity tends to monotonically increase with age (e.g. Figure 4B), as has been seen in several studies [52]–[54], and therefore a smaller proportion of worms or egg output are targeted by school-aged treatment (<10% in one of our examples, Table 5).
The different age profiles for these helminth species are a result of differing behaviour patterns, force of infection, heterogeneous exposure and, arguably, immunity and genetic pre-disposition. The details of the biology which generate these patterns do not need to be understood for the calculation of the proportion of worms treated. However, to estimate the impact of treating children on transmission in the larger community we need a combination of additional studies and novel modelling analyses. Our simulations (Figures 7 and 8) show the importance of understanding the nature of the interaction between school-aged children and the rest of the community in order to optimise treatment programmes in the longer term. It is possible that school-aged deworming will give little benefit outside the children being treated and that intensifying either the treatment coverage within this age-group and/or increasing the frequency of treatment (bottom rows in Figures 7 and 8) may not lead to the desired benefits in the larger community. In this case, other strategies will be needed to increase the potential for long-term control and, if the breakpoint can be crossed by combinations of interventions, eventual elimination of these helminths.
As the schematic, Figure 9, shows, the impact of school-based treatment programmes on transmission in the larger community is diluted by a number of effects. The benefits of deworming for the affected children are many, but if we are to plan for long-term control and, in the longer term, elimination of these pathogens we need to consider strategies that will reduce transmission from year to year, as is already being discussed in some settings [55], particularly for schistosomiasis [56]. We also require an understanding of the species mix in each setting so as to tailor the design of interventions according to the underlying transmission dynamics. As this paper shows, there are many outstanding data gaps and needs for new modelling studies both to understand the dynamics of transmission under such programmes, and to design optimal treatment strategies for the future.
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10.1371/journal.ppat.0030062 | Impact of Mycobacterium ulcerans Biofilm on Transmissibility to Ecological Niches and Buruli Ulcer Pathogenesis | The role of biofilms in the pathogenesis of mycobacterial diseases remains largely unknown. Mycobacterium ulcerans, the etiological agent of Buruli ulcer, a disfiguring disease in humans, adopts a biofilm-like structure in vitro and in vivo, displaying an abundant extracellular matrix (ECM) that harbors vesicles. The composition and structure of the ECM differs from that of the classical matrix found in other bacterial biofilms. More than 80 proteins are present within this extracellular compartment and appear to be involved in stress responses, respiration, and intermediary metabolism. In addition to a large amount of carbohydrates and lipids, ECM is the reservoir of the polyketide toxin mycolactone, the sole virulence factor of M. ulcerans identified to date, and purified vesicles extracted from ECM are highly cytotoxic. ECM confers to the mycobacterium increased resistance to antimicrobial agents, and enhances colonization of insect vectors and mammalian hosts. The results of this study support a model whereby biofilm changes confer selective advantages to M. ulcerans in colonizing various ecological niches successfully, with repercussions for Buruli ulcer pathogenesis.
| Mycobacterium ulcerans is the etiologic agent of Buruli ulcer, a necrotic skin disease affecting humans living close to wetlands in tropical countries. This mycobacteria resides in water where it could colonize many ecological niches such as aquatic plants, herbivorous animals, and water bugs. The latter were shown to be able to transmit the bacteria to mammalian hosts. Here, we described that the bacilli could be structured with a thick envelope called the extracellular matrix (ECM). This peculiar coat contains in small vesicles a toxin named mycolactone, the main virulence factor of M. ulcerans. The ECM confers to the mycobacterium increased resistance to antimicrobial agents and plays a role in virulence. Indeed, a bacteria with ECM is more potent for colonization of insect vectors and mammalian hosts compared to bacteria. Unraveling the regulation of the production of the ECM together with the export of mycolactone will be an important step in developing new pharmacological approaches for the treatment of Buruli ulcer, which has been greatly handicapped by the lack of effectiveness of the current antibiotics.
| Mycobacterium ulcerans is the etiologic agent of Buruli ulcer, a necrotic skin disease affecting humans living close to wetlands in tropical countries. The natural history and transmission of this mycobacteria are still obscure. Epidemiological studies suggest that swampy areas, and more specifically, aquatic environments, are the main ecosystems inhabited by M. ulcerans [1–7]. Many aquatic insects in this environment are predators that feed on herbivorous organisms, such as snails, which, after grazing on plants covered by M. ulcerans, act as passive hosts. It is conceivable that, following ingestion of prey contaminated with M. ulcerans, certain carnivorous aquatic insects might then transmit the bacteria to humans. We previously demonstrated that predatory aquatic insects, such as Naucoris cimicoides, ingest M. ulcerans–containing prey under laboratory conditions and are both hosts and vectors of this mycobacterial species [8–11]. Indeed, they are able to deliver invasive bacteria to laboratory mice, whose tails were exposed to the infected insects, with lesions developing 30 to 90 d later precisely at the point where the bite occurred.
M. ulcerans is the only Mycobacterium species that localized within the salivary glands of these aquatic insects, where it can both survive and actively multiply without damaging insect tissues [8,10]. Furthermore, it has recently been shown that the lipid toxin mycolactone, the sole known virulence factor responsible for Buruli ulcer [12], is essential for the colonization of the salivary glands and that mycolactone-deficient mutants do not multiply in N. cimicoides [10].
Another striking feature of M. ulcerans is its ability to assemble into a biofilm, as first seen on the surface of aquatic plants [3]. Biofilms for human bacterial pathogens such as Pseudomonas aeruginosa, Haemophilus influenzae, and Vibrio cholerae have been well-studied [13] and consist of discrete bacteria surrounded by an extracellular matrix (ECM) [14,15]. Typically, the ECM shapes the bacterial network and can be crossed by channels, which play a critical role in water and nutrient circulation, as well as in interbacterial communication via quorum-sensing [16].
Biofilm formation confers a selective advantage for persistence under diverse environmental conditions and for resistance to antimicrobial agents, and also facilitates colonization of the host by the bacteria [13]. With regard to mycobacterial species, mutants of M. avium and M. smegmatis impaired in biofilm formation are less able to invade and translocate through bronchial epithelial cells and to form smegma in mice, respectively [17,18]. Molecular events involved in biofilm formation have already been reported in several studies undertaken on the genetically tractable M. smegmatis [19]. Recently, the GroEL1 chaperone was shown to be involved in mycolic acid biosynthesis during biofilm formation.
Here, we show that the ECM of M. ulcerans differs from known biofilms since it is associated with only the outermost cell layer as opposed to classic biofilms in which all cells are surrounded by the matrix. Biochemical characterization of the ECM was performed and its role in pathogenesis at the different stages of the currently known life cycle investigated. Taken together, these findings provide insight into the factors that promote persistence in diverse environmental niches and infectivity of M. ulcerans to various hosts.
Given the complexity of the life cycle of M. ulcerans, systematic examination was undertaken of the ultrastructure of the bacterium by scanning electron microscopy. Large clusters of M. ulcerans that were covered with a biofilm-like structure were detected in biopsy samples from patients with confirmed Buruli ulcers (Figure 1A). An analogous biofilm structure was also found in bacteria isolated from lesions from mice experimentally infected with M. ulcerans. According to the relative magnetic bead size, the surface of the recovered biofilm-like structure is estimated at about 200 μm × 50 μm, suggesting that one structure could harbor up to 105 bacteria. Further analysis of the same samples by Ziehl–Neelsen staining also revealed that, for each biofilm-like structure, there were less than ten free individual bacilli (unpublished data). A biofilm-like structure was also seen with M. ulcerans cultured in 7H9 broth, with or without Tween 80, and in 7H11 and 7H12 with or without PANTA (antimicrobial mixture) (unpublished data). The same amount of ECM was recovered from bacteria grown under all these culture conditions.
Strains 1615 and 1G897 were examined at different time points, and bacilli-containing clusters were evident as early as day 10 (Figure 1B-1). From days 35 to 45, large cell aggregates measuring more than 100 μm were detected (Figure 1B-3). Higher-magnification micrographs revealed that entire clusters were surrounded by an abundant biofilm-like structure, the ECM (Figure 1B-4). All M. ulcerans clinical isolates that were cultured under the same conditions displayed the same biofilm-like structure, including the mycolactone-deficient mutant of 1615, mup045 (unpublished data). In contrast, other environmental or pathogenic mycobacteria, such as M. chelonei, M. fortuitum, M. kansasii, and M. tuberculosis, grew in vitro without displaying significant ECM despite their exhibiting large clusters of cells (Figure S1). M. marinum, the progenitor of M. ulcerans [20], formed discrete packets of cells that were quite distinct from the clusters, but displayed no ECM (Figure S1-3). These data show that the ECM is a peculiar feature of M. ulcerans colonies.
Subsequent analysis by transmission electron microscopy (TEM) revealed that the ECM covers only the outermost bacterial layer, its thickness was estimated to range between 4 and 40 μm (Figure 2A, dotted circled area). Strikingly, very little matrix was found within the bacterial network (Figure 2B, arrows). This contrasts with classical biofilm, in which the bacteria are each individually surrounded by the matrix [14,15].
Furthermore, scanning microscopic pictures of M. ulcerans strains revealed the presence of vesicles on the surface of the ECM after 35 to 45 d of incubation (Figure 3A). The diameter of the vesicles varied between 50 and 200 nm (Figure 3B). Vesicles were isolated by ultracentrifugation from the wild-type bacterial ECM as well as from the mycolactone-deficient mutant (unpublished data). Together with ECM, vesicles could also be recovered from biopsies of mouse lesions by immunomagnetic separation (Figure 3C). Furthermore, the vesicles could be isolated independently from the ECM fraction by performing ultracentrifugation and were thus considered separately in the following analysis.
To determine whether this matrix influences bacterial phenotype, comparison of the growth rate of M. ulcerans either harboring ECM, or from which ECM was carefully removed, was undertaken. No difference in colony-forming unit (CFU) counts of bacteria using Löwenstein–Jensen slants, and in metabolic activity using the Bactec radiometric method, was found with or without ECM removal from M. ulcerans (Figure 4), suggesting that biofilm does not confer a selective advantage for bacterial growth in vitro. In our culture conditions, the ECM re-forms in 2 wk.
The ECM fraction was isolated from broth-cultured M. ulcerans by mechanical disruption combined with Tween 80 detergent treatment, as typically used for other mycobacteria [21,22]. Fifteen seconds are sufficient for complete removal of ECM from bacteria (Figure 4A). We then compared the effect of the treatment on the cultivability of the treated bacteria. The same amount of CFUs was obtained for M. ulcerans with or without ECM (Figure 4B), showing that this mechanical disruption does not impair the cultivability of the bacteria. Furthermore, we checked whether this treatment modifies bacterial permeability. To this end, the level of potassium release by bacteria was monitored. No significant levels of potassium were released after mechanical vortexing for up to 60 s compared to untreated bacteria (Figure 4C). In addition, the presence of KatG, a catalase-peroxidase that is cytosolic or membrane-associated, was not detected by Western blot analysis in samples that had been treated for 15 s and 30 s (Figure 4D). Altogether, these data show that ECM can be efficiently isolated with a 15-s mechanical disruption, and this argues against major contamination of ECM by lysed bacteria.
To determine whether ECM plays a role in protecting bacteria from toxic compounds in the environment, the susceptibility of M. ulcerans, with and without ECM, toward chlorine and two common antibiotics was tested. This could be done since removal of ECM did not alter the growth of M. ulcerans, as shown above (Figure 4B). The minimum inhibitory concentration (MIC) of rifampin was <0.0625 μg/mL for M. ulcerans devoid of ECM compared to 0.5 μg/mL for M. ulcerans with ECM, a significant increase in susceptibility (p = 0.01). In contrast, the MIC for amikacin was identical under both experimental conditions. To test the susceptibility of M. ulcerans to chlorine, the bacteria were incubated in solutions of different concentrations. The concentrations of chlorine required to kill 108 M. ulcerans cells, with or without ECM, were 100 and 40 mg/l, respectively (p = 0.05). Taken together, these results suggest that the ECM protects the M. ulcerans population from noxious agents.
Coomassie blue staining of an SDS-PAGE gel of the ECM fraction shows that the protein composition differs between isolated ECM and that of a whole bacterial lysate (Figure 5, lanes 1 and 2). By subsequent use of two-dimensional (2-D) gel electrophoresis or liquid chromatography combined with mass spectrometry (LC/MS), 84 proteins were identified within the ECM fraction (Table 1) and classified in seven out of 11 different functional categories used for pathogenic mycobacteria. As many as 70 (83%) of the proteins fell into four classes: 14 in virulence, detoxification, and adaptation; 10 in lipid metabolism; 32 in intermediary metabolism and respiration, and 14 in the conserved hypothetical protein class (Tables 1 and S1). AhpC, AhpD, and SodA are major players in the oxidative stress defense, whereas DnaK, GroEL1, GroEL2, GroES, HtpG, Hsp18, ClpB, ClpC1, and Clp1 are specialized in heat shock responses. Of particular interest, the GroEL1 chaperone was recently shown to modulate synthesis of mycolates during biofilm formation [19]. Strikingly, a large number of proteins essential for intermediary metabolism and respiration were identified in the ECM, which has not been reported in other bacterial biofilm studies. Among them, pgi-, pgk-, gltA2-, and glcB-encoded proteins are involved in glycolysis, the tricarboxylic acid cycle, or even the glyoxylate shunt.
Furthermore, the protein pattern of isolated ECM—as classified by class or molecular weight—is significantly different from that of the secreted proteins, indicating that the ECM is an independent and specific bacterial compartment. Interestingly, besides chaperones, common mycobacterial antigens such as the antigen 85 family (FbpA, B, C, D), Mpt64, and Wag31 were not identified in the ECM by the proteomic analysis. However, it is well known that Buruli ulcer patients develop humoral responses to several M. ulcerans antigens [23,24]. To further check for the presence of M. ulcerans antigens within the ECM, this fraction was probed with the serum of 30 patients diagnosed with Buruli ulcer. Using Western blotting, no ECM-reactive IgG antibodies were detected in the serum of all patients, irrespective of the disease stage (Figure 5A). In contrast, the serum contained IgG antibodies that bound to many components of the bacterial lysate (Figure 5A). A more sensitive ELISA method was used to search for IgG antibodies recognizing M. ulcerans proteins in different fractions. Sera from all patients reacted with the whole bacterial lysate, cytosolic, membrane, and vesicle fractions, but not with the ECM fractions (Figure 5B). Indeed, no ECM-reactive IgG antibodies were detected in 53% (16/30) of the cases.
Biochemical analysis of ECM revealed a complex mixture of carbohydrates and lipids, with the total amount of carbohydrates in the ECM estimated to be as high as 2 mg per 109 bacteria, a value 3-fold higher than that measured for the same number of bacterial cells lacking ECM but submitted to the same treatment. Fluorescence microscopy confirmed the abundance of carbohydrates, which are mainly localized in the ECM of the bacterial cluster (Figure 6A) Moreover, M. ulcerans aggregates have a high affinity for calcofluor, suggesting that β-glucans were a major component of ECM (unpublished data). Thin-layer chromatography (TLC) analysis of the sugar constituents of the ECM showed that glucose and mannose were the main monosaccharides found in this fraction (unpublished data).
Strikingly, the ECM of M. ulcerans wild-type strain 1615 contained the bulk of the mycolactone, with as much as 0.2 mg per gram of bacteria (Figure 6B-1). In addition to the toxin, the lipid content of the ECM from M. ulcerans wild-type strain 1615 consisted mainly of phosphatidylinositol mannosides (PIM2, PIM5, PIM6), phospholipids (phosphatidylethanolamine, phosphatidylinositol, cardiolipin), triacylglycerol, phthiodiolone diphthioceranates, and two unidentified apolar compounds (Figure 6B-2). Using the CS-35 monoclonal antibody, lipoarabinomannan was detected in ECM. Interestingly, in spite of their reported abundance in the outermost layers of the cell envelope of some M. ulcerans strains [25], no trehalose dimycolates were detected in the matrix. Further comparative analysis show that the type and quantity of lipids of the ECM were similar in the transposon mutant mup045, where they were impaired in mycolactone production, and the wild-type strain.
The vesicles could be isolated independently of the ECM fraction by performing ultracentrifugation. By subsequent use of LC/MS, 57 proteins were identified within the purified vesicle fraction (Table 2). Among them, only six proteins were also found in ECM, whereas 51 were also present in the membrane fraction, suggesting that vesicles are more likely to derive from the membrane compartment. Strikingly, the polyketide synthases MlsA1 and MlsB required for mycolactone synthesis are also present in the vesicles, and further lipid analysis showed the presence of mycolactone there (unpublished data).
Surprisingly, vesicles recovered from wild-type bacteria displayed cytotoxic activity on bone marrow–derived mouse macrophage cultures. Indeed, 24 h after vesicle addition, 80% of the macrophages were lysed, and similar cytotoxicity was seen with HeLa and Cos cells (Figure 7). In parallel, mycolactone purified from M. ulcerans was noticeably much less cytotoxic than the vesicles that were prepared and purified from an equal amount of the same culture, suggesting that the supramolecular organization of mycolactone affects its biological activity (Figure 7). It has also been found that, owing to its hydrophobic nature, the toxin aggregates, thus giving non-linear dose-response curves [26]. In addition, vesicles isolated from mup045 did not cause cytotoxicity, regardless of the amount added (unpublished data), demonstrating that the vesicles' toxicity is likely due to the presence of mycolactone. Altogether, these data suggest that vesicles trigger mycolactone export, thus enhancing toxicity.
To determine whether the ECM affects bacterial virulence within the host, we infected mice in the tail with M. ulcerans, with or without ECM, and examined the inoculation site for bacterial load, the time of onset of clinical symptoms, and symptom severity. The clinical evolution was significantly different (p = 0.008) using the two bacterial populations: lesions occurred much earlier in mice inoculated with 103 bacilli covered by ECM (35 ± 5 d versus 58 ± 7 d). In addition, all mice (10/10) inoculated with bacteria covered with ECM showed cutaneous lesions. In contrast, seven of ten mice inoculated with bacteria lacking ECM displayed similar lesions. The results are consistent with a role for the ECM containing vesicles as a reservoir of toxin.
We investigated whether the ECM plays a role in the colonization of aquatic insects by M. ulcerans, as we have recently shown that the early trafficking events involve translocation of the bacillus from the head capsule to the coelomic cavity containing hemolymph [11]. To evaluate the role of ECM during the translocation, Naucoris aquatic insects were first fed with prey that had been inoculated with M. ulcerans, with or without ECM. After 6 h, the insect hemolymph was extracted and presence of M. ulcerans DNA was determined by real-time PCR. As shown in Table 2, no M. ulcerans DNA could be detected in samples from insects infected with M. ulcerans lacking ECM, whatever the initial bacterial load. In contrast, in the case of bacteria with ECM, M. ulcerans DNA corresponding to up to 5 × 104 bacteria was readily detected. It should be recalled that both types of bacteria inoculated into the prey remain cultivable in this setting. Thus, in our experimental model of aquatic bug infection, M. ulcerans lacking ECM was apparently unable to colonize the insect vector, suggesting that the ECM is required during translocation.
Using scanning electron microscopy, no sign of ECM was found on bacilli in the salivary glands of N. cimicoides that were previously infected with M. ulcerans covered by ECM (Figure 8A). However, the latter was observed on insect setae, through which the infection occurs via penetration of the prey [10]. Furthermore, incubation of M. ulcerans having ECM with salivary gland extracts from N. cimicoides resulted in rapid and complete degradation of ECM [11]. A likely explanation is that hydrolytic enzymes interact with the ECM and trigger its disintegration.
We have previously shown that M. ulcerans can be naturally recovered from the environment as a biofilm on the surface of aquatic algae [3]. However, this biofilm structure completely differs from that of the ECM as seen in in vitro culture in the absence of algal extract and host lesions (Figure 8B). In the presence of algal extracts, ECM could still be present, though in amounts undetectable by electron microscopy. No proteins from the ECM fraction isolated from cultures performed in the presence of algal extracts were detected by LC/MS analysis. Additionally, biochemical composition analysis showed that while significant amounts of mycolactone and (glyco)phospholipids (cardiolipin, phosphatidylethanolamine, phosphatidylinositol, and phosphatidylinositol mannosides) were detected in the ECM from control bacteria, there was no evidence for the presence of such material in algae-treated bacteria (unpublished data). Moreover, when these bacterial clusters were inoculated in fresh standard culture media, or when intermediate hosts, such as aquatic snails, were fed with these bacterial clusters, the ECM was synthesized within a few days (unpublished data).
Taken together, the absence of ECM in specific conditions, i.e., in culture medium complemented by crude algal extract or in the salivary gland of water bugs, strongly suggests regulation of ECM formation by external factors.
We investigated the presence of the extracellular matrix within M. ulcerans biofilms in the different settings of the currently known lifecycle. We show that M. ulcerans forms biofilms on the surface of aquatic plants [3], and, in addition, this is surrounded by an ECM in hosts such as snails, mice, and humans. We also demonstrated that M. ulcerans with ECM is more potent for insect and mammalian host colonization, which is typical for bacterial biofilms in general [13]. In contrast to other bacterial biofilms [14,15], M. ulcerans ECM is devoid of bacteria and is a thick layer only in contact with the outermost layer of M. ulcerans. While scanning electron and fluorescence microscopy unambiguously showed the abundance of ECM on the external surface of the aggregates, no ECM could detected within the network of bacteria in aggregate. This observation could not be supported by a statistical analysis, as we encountered severe difficulties for generating a large number of sections for TEM analysis from the same sample. We observed that during the fixation step, the araldite resin penetrated inefficiently into the bacterial aggregates, even when high pressure fixation was used. This phenomenon is likely to be due to the thickness of the ECM, which may also create a microaerophilic atmosphere that has been found more suitable for M. ulcerans growth in hostile conditions [27]. Similar to other biofilms, the ECM of M. ulcerans seems to be crossed by channels, whose role requires further investigation [16].
Some of the lipids/lipoglycans identified in the M. ulcerans ECM, such as lipoarabinomannan, PIM, and phthiocerol diesters, are known to play important roles in the permeability barrier of the cell envelope, as well as in the virulence of other mycobacterial species [28–30]. The ECM also contains significant quantities of carbohydrates, with glucose being the main monosaccharide constituent. The abundance of this polysaccharide in M. ulcerans suggests that it might be structurally related to the capsular D-glucan of M. tuberculosis [31]. Assuming that the bacteria use the ECM as a source of carbon and energy, there may be a relationship between the presence of carbohydrates and enzymes involved in glycolysis and the tricarboxylic acid cycle in the ECM. It has already been shown that ectoenzymes or exoenzymes of biofilms could be involved in the complex process of conversion of non-assimilable into assimilable molecules, as seen in Cellulomonas flavigena [32].
One striking feature of the ECM is its large variety and abundance of proteins. It could be argued that the presence of such a large amount of proteins results from bacterial lysis. However, the method used for preparing the ECM has also been used to investigate the architecture of the cell envelope of other mycobacteria without causing significant lysis [21], as confirmed by our control experiments (Figure 4C and 4D). Among the abundant proteins in the ECM were several chaperones, and the genes encoding chaperones like DnaK, GroEL, GroES and other oxidative stress response proteins are known to be over-expressed in different biofilms (Staphylococcus aureus, P. aeruginosa) [33,34], and GroEL1 was recently reported to be involved in biofilm formation in M. smegmatis [19]. Surprisingly, many enzymes required for the biosynthesis or catabolism of lipids and sugars were recovered from the ECM, whereas most of them are usually located exclusively in the cytoplasm of planktonic bacteria. Their presence within the M. ulcerans ECM suggests that they may participate in the formation or maturation of the ECM, although their functionality remains to be addressed.
Another general feature of the biofilm matrix of bacteria is its role as a diffusional barrier interfering either with the transport kinetics or the modification of extracellular molecules. We showed that ECM-coated M. ulcerans bacteria are resistant to rifampin, but not to amikacin. Similarly, biofilm-grown cells of M. avium in catheters are also resistant to rifampin as well as to clarithromycin [35]. In contrast, rifampin activity was not reduced for slime producing S. epidermidis [36]. The difference in susceptibility of ECM-harboring M. ulcerans to amikacin and rifampin could be explained by ECM acting as a selective barrier or trap for rifampin, or it may even contain enzymes that hydrolyze rifampin.
The ECM is likely to function as a resistance barrier to the host immune system. In Staphylococcus biofilms, the matrix was shown to interfere with macrophage phagocytic activity [37] and to prevent antibodies from reaching the bacterial cell surface [38]. On the one hand, M. ulcerans escaping from immune recognition could be due to the inaccessibility of the surface antigens to the host immune system. On the other hand, mycolactone has already been shown to limit phagocytic activity [26,39] as well as to cause the death of macrophages and other cells via apoptosis [12,40]. Uncoupling the specific effects of ECM from those of mycolactone at the cellular level is currently being investigated.
Usually, toxins of Gram-positive bacteria accumulate in the cytoplasm in a precursor form or are secreted without accumulation. In Gram-negative bacteria, apart from the endotoxin lipopolysaccharide, the same processes are observed. The presence of mycolactone in an external reservoir was proposed previously when the toxin was found associated with suspended lipids [41]. We provide further evidence by showing that mycolactone accumulates mainly in the ECM, which may play the role of a reservoir and amplify the pathogenicity of the bacteria.
Furthermore, the toxin is secreted in specialized vesicles, which are cytotoxic for a variety of cells, both phagocytic and non-phagocytic, suggesting that mycolactone-containing vesicles do not recognize a specific receptor. Similarly, vesicles have already been shown to be secreted by some bacteria and exported to reach their target for delivering virulence factors to host cells [42]. The fact that mycolactone is sequestered within vesicles suggests that treatment prospects based on the design of neutralizing antibodies against this polyketide toxin would likely be inefficient.
Although we showed that ECM has an important role for insect vector colonization and M. ulcerans translocation to the coelomic cavity, no ECM is found on the bacteria in this particular compartment. This could be due to two different processes: either hydrolysis by salivary enzymes, or down-regulation of matrix production by external factors. For instance, no matrix was produced when M. ulcerans catabolized carbohydrates present in algal material, whereas growth in lipid-rich medium such as 7H9 with oleic acid induced copious amounts. Generating a mutant lacking ECM will help decipher the molecular mechanisms involved in ECM production, although inhibiting its synthesis with plant extracts may be a useful alternative. Unraveling the regulation of the production of the ECM together with the export of mycolactone will be an important step in developing new pharmacological approaches for the treatment of Buruli ulcer, which has been greatly handicapped by the lack of effectiveness of the current antibiotics.
The strains of M. ulcerans 1G897 [43] and 1615 [12] (Trudeau Collection Strain) were originally isolated from human skin biopsies from French Guyana and Malaysia, respectively. Each clinical isolate was inoculated in the mouse tail. Forty days later, M. ulcerans bacteria were recovered from the infected tissue and seeded onto Löwenstein–Jensen slants (Bio-Rad, http://www.bio-rad.com) for an additional 45 d before being aliquoted and stored at −80 °C [44]. The mup045 mutant (MU1615::Tn118) has a transposon insertion located in the ketosynthase gene mup045, leading to undetectable levels of mycolactone in culture [26,39] The mycobacterial strains M. chelonei (6B0139), M. fortuitum (10B0345), M. kansasii (11B0014), M. marinum (8B0432, clinical isolate from a French patient), and M. tuberculosis H37Rv were used as controls.
Frozen aliquots of each strain were first inoculated onto 7H11 solid medium supplemented with 10% OADC (oleic acid, dextrose, catalase; Difco, Becton-Dickinson, http://www.bd.com) and Tween 80 0.05% (Sigma, http://www.sigmaaldrich.com). Then, 35 d later, exponentially growing bacteria from agar plates were harvested in 7H9 broth supplemented with 10% OADC and Tween 80 0.05% (Sigma) at 105 bacteria/mL. Titration of the intitial inoculum was performed by the Shepard and Rae method [45]. The cultures were performed in 200 mL in a disposable polystyrene cell culture flask (EasYFlask; Nunc, http://www.nuncbrand.com) with gentle agitation (30 rpm) at 30 °C for 35 d, which corresponds to the end of exponential phase of bacterial growth. Regarding the experiment performed with the algal extract, mycobacteria were inoculated at 105 bacteria/mL in 200 mL of 7H9 broth supplemented with 10% OADC, Tween 80 0.05%, and a crude extract of Rhizoclonium sp. algae obtained as previously described [3].
Samples were fixed for 30 min in 0.1 M cacodylate buffer (pH 7.2) containing 2.5% glutaraldehyde for 1 h at 4 °C, then left to stand for 12 h at 20 °C in cacodylate buffer. Specimens were progressively dehydrated and then metallized prior to examination by scanning electron microscopy on a JEOL 6301F field emission microscope. For transmission microscopy, the bacteria were embedded in Araldite (Fluka, St. Quentin Fallavier, France; http://www.sigmaaldrich.com/Brands/Fluka___Riedel_Home.html). After dehydration, thin sections were stained with uranyl acetate and Reynold's lead citrate and then examined on a JEOL 120 EX electron microscope.
The fresh broth was incubated overnight and cells were harvested by centrifugation (8,000g for 10 min), and washed twice in PBS. The resulting pellets were fixed in 2.5% (w/v) glutaraldehyde, in cacodylate buffer for 2 h in the dark at room temperature. Cells were washed three times in cacodylate buffer (0.1 M [pH 6.8]), postfixed for 2 h in the dark in 1% (w/v) osmium tetroxide (Sigma), 0.05% and then washed twice each in cacodylate buffer and in water. Bacteria were dehydrated through a graded ethanol series of 50%, 60%, 70%, 80%, and 95% for 5 min each, and then washed twice for 15 min each in 100% ethanol, then twice for 15 min each in propylene oxide. The bacteria were finally embedded in Araldite (Fluka). Resin was replenished the next morning and samples were left to cure at 60 °C overnight. Blocks were thin-sectioned on a Reichert–Jung microtome and mounted on copper grids. Sections were poststained with uranyl acetate and Reynold's lead citrate. Microscopy was performed on a JEOL 120 EX electron microscope.
A biopsy from an Ivory Coast patient and from mouse tail lesions were surgically excised from skin. The tissue specimens were minced with disposable scalpels in a Petri dish and ground with a Potter–Elvehjem homogenizer, size 22 (Kimble/Kontes, http://www.kimble-kontes.com), in PBS/Tween 80 0.05%. For isolation of M. ulcerans, the human and mouse tissue specimens were processed by immunomagnetic separation to isolate bacilli. Two types of immunomagnetic particles were used: 2.8-μm-diameter immunomagnetic particles precoated with sheep anti-rabbit IgG (Dynald) for the human sample, and 1-μm-diameter immunomagnetic particles precoated with goat anti-rabbit IgG (Interchim, http://www.interchim.com) for the mouse sample. Firstly, coating of the immunomagnetic particles (104) was carried out for 2 h at 37 °C with agitation with a rabbit polyclonal antibody raised against whole PFA-fixed M. ulcerans [8] at 20 μg in a total volume 200 μl of PBS (pH 7.2) containing Tween 80 0.05%. Secondly, 0.1 g/mL of tissue homogenate was added to the coated immunomagnetic particles and incubated with bidirectional mixing at 4 °C for 12 h. Finally, particles were washed six times for 3 min each with PBS containing Tween 80 0.05%.
A 35-d-old M. ulcerans shaking culture (200 mL) was washed three times with 20 mM Tris-HCl (pH 7.5) (3,000g for 30 min at 4 °C). The mycobacterial pellet was then resuspended in the same buffer supplemented with antiprotease Complete EDTA free cocktail (Roche, http://www.roche.com) to obtain 109 bacilli/mL. Thirty glass beads (4 mm in diameter) were added to the suspension and vortexed for 15 s. The mycobacterial suspension was then centrifuged at 8,000g for 10 min at 4 °C. The ECM fraction consists of the supernatant collected after centrifugation and filtration through a 0.45-μm filter. The supernatant was further fractionated into vesicles that were recovered in the pellet after ultracentrifugation at 40,000g for 3 h, then washed three times in 0.1 M Tris-HCl (pH 7.5) with Tween 80 0.05%.
The pellet containing whole bacteria was then suspended in the same buffer and the mycobacteria were broken with 106-μm acid washed glass beads (Sigma) for 5 min at speed 30 using a bead beater (Mixer Mill MM301; Retsch GmbH, http://www.retsch.com) at 4 °C. The bacterial lysate consisted of the supernatant obtained after removal of unbroken cells and cell debris by centrifugation at 8,000g at 4 °C. The soluble cytosolic proteins were subsequently obtained by ultracentrifugation of the bacterial lysate (70,000g, 90 min, 4 °C). The pellet, consisting of membrane proteins, was washed with 200 μl of 50 mM Tris-HCl (pH 7.5) to remove residual cytosolic contaminants and then resuspended in 50 mM Tris-HCl (pH 7.5). For all fractions, quantification was performed by measuring protein concentration using a Bio-Rad protein assay. Regarding the secreted proteins, the shaking liquid culture was performed in the absence of albumin and prepared as previously reported [46]. Briefly, the culture filtrate was recovered after filtration through 0.22-μm-pore-size filters (Millex GP; Millipore, http://www.millipore.com), followed by concentration using a filter with a 3-kDa cutoff (Centricon; Millipore).
Bacterial suspension preparation and ECM removal were carried out exactly as described above. After recovery by centrifugation at 5,000g for 10 min, the number of CFU was determined by inoculating 10-fold dilutions of the bacterial suspension onto three Löwenstein–Jensen slants (Bio-Rad) incubating for 6 wk at 30 °C. In addition, the bacterial dilutions were inoculated into Bactec 12B vials (Becton-Dickinson) containing Middlebrook medium with 14C-labeled palmitic acid as a carbon source. Substrate consumption generates 14CO2 in the airspace of the sealed vial. The BACTEC TB-460 instrument detects the amount of released radioactivity and records it as a growth index (GI) on a scale from 0 to 999. The vials were incubated at 30 °C, and every 5 d the GI was recorded.
To test bacterial permeability, kinetics of vortexing with glass beads for the ECM removal step was performed at 15-s intervals for up to 3 min. After removal of unbroken cells and debris by filtration (0.45 μm), quantification of K+ in the supernatant was monitored on BM/Hitachi 917 apparatus following manufacturer protocol. As a control, boiled M. ulcerans suspension was used for maximum K+ release. In addition, KatG detection was performed by Western blot analysis, as this enzyme is known to be localized in the membrane and cytosol. To this end, supernatant of bacterial culture pelleted after treatment by glass bead (60 μg) was loaded on a sodium dodecyl sulfate (SDS)–polyacrylamide gel (4%–12%) (Bio-Rad) and the separated bands were transferred onto a 0.45-μm nitrocellulose membrane (Amersham, http://www.amersham.com). After blocking with 5% skimmed milk in PBS, the membrane was incubated with serum from rabbit anti-KatG 1:100 [47] in PBS containing Tween 80 0.05% (Sigma) for 90 min at 37 °C. After two washes with PBS containing Tween 80 0.05%, sheep anti-rabbit IgG (heavy and light chains) peroxidase-conjugated antibodies (Amersham) at 0.5 μg/ml and 0.5 μg/ml DAB (Interchim) was used for detection of the bands.
Protein fractions were analyzed by 1- or 2-D gel electrophoresis followed by a combination of matrix-assisted laser desorption/ionization mass spectrometry peptide mass fingerprinting (MALDI-MS PMF) and liquid chromatography/electrospray ionization mass spectrometry (LC-MS/MS). The spots or bands of interest were excised from the gel and treated automatically using a Probest/P50MS robotics system (Genomics Solutions, http://www.genomicsolutions.com). The tryptic peptide mixture was then extracted by 10% formic acid, desalted using Micro C18 Zip Tips (Millipore), and eluted with 0.5 μl of alpha-cyano-4-hydroxy-cinnamic acid (Sigma). The samples were analyzed by MALDI-MS on a Voyager DE STR (PerSeptive Biosystems, http://www.appliedbiosystems.com) equipped with a nitrogen laser (337 nm). To search the M. ulcerans ORF database “BuruList” (http://genolist.pasteur.fr/BuruList), monoisotopic masses were assigned using a local copy of the MS-Fit3.2 part of the Protein Prospector package (University of California Mass Spectrometry Facility, San Francisco; http://prospector.ucsf.edu). The parameters were set as follows: no restriction on the isoelectric point of proteins, 50 ppm as the maximum mass error, and one incomplete cleavage per peptide. Eleven different samples were analyzed.
Protein digest fractions of samples were also analyzed by reverse phase LC-MS/MS. As peptides eluted off the C18 Pepmap column (LC-Packings, http://www.dionex.com), they were introduced on line into a QSTAR XL instrument (MDS-Sciex; Applied Biosystems, http://www.appliedbiosystems.com) and were analyzed using data-dependent switching between MS and MS/MS modes. The ProID (MDS-Sciex; Applied Biosystems) program was used to interpret the LC-MS/MS data by searching against BuruList [48]. The search parameters were as follows: 1) 0.2-Da mass error tolerance for both MS and MS/MS; 2) one missed cleavage of trypsin specificity was allowed. Peptide matches with significant homology (confidence score > 95) were considered as identified peptides. Proteins identified by a single peptide were validated by manual inspection of the MS/MS spectra.
The Buruli Patients group consisted of 30 patients recruited from the Centre de Diagnostic et de Traitement de l'Ulcère de Buruli in Pobè, Benin, and were included in a sero-epidemiological study, for which written consent had been obtained [9]. Nine out of 30 patients presented early clinical signs without ulceration (four nodules, two oedema, three plaques), ten patients with limited ulceration (<6 cm), and 11 with extensive ulceration (>10 cm). Diagnosis of M. ulcerans infection was by Ziehl–Neelsen staining of material taken from swabs of the lesions or directly from the biopsy for the early form and confirmed by PCR for M. ulcerans–specific IS2404 DNA [6]. The participants, who had given their written consent, were enrolled as volunteers in the study, the protocols of which were approved by the Ministry of Health in Benin. Serum was prepared from 8 ml of blood from each participant and tested for potential HCV and HIV exposure using Access HIV-1/2 automated immunoassay (MDA/98/58) and Sanofi Diagnostics Pasteur Access anti-HCV automated immunoassay (plus update on five other anti-HCV assays [MDA/96/26]).
Proteins (10 μg) from ECM, bacterial lysate, membrane, and cytosolic fractions were coated onto 96-well Nunc Maxisorb plates by incubation overnight at 4 °C in 100 μl of PBS containing Tween 80 0.05%. The coated plates were then incubated with PBS containing 5% skimmed milk at room temperature for 2 h. After three washes in PBS/Tween 80, the samples were incubated for 1 h at 37 °C with human serum diluted 1:200 in PBS/Tween 80. After three further washes, plate-bound human immunoglobulins were detected using peroxidase-conjugated goat anti-human IgG (γ chain) antibodies (Sigma) and OPD (Dako, http://www.dako.com). The diluted sera were tested in triplicate and the average absorbance at 650 nm was expressed in optical density units.
Proteins (60 μg) from lysates or ECM fractions were run in an SDS–polyacrylamide gel (4%–12%) (Bio-Rad), and the separated bands were transferred onto a 0.45-μm nitrocellulose membrane (Amersham). After blocking with 5% skimmed milk in PBS, the membrane was incubated with serum from humans diluted 1:100 in PBS containing Tween 80 0.05% for 90 min at 37 °C. After two washes with PBS containing Tween 80, anti-human IgG (γ chain) peroxidase-conjugated antibodies (Sigma) at 1:2,000 and 0.5 μg/ml DAB (Interchim) was used, respectively, to detect human IgGs bound to the different bands.
Bacteria were fixed in 2.5% (w/v) formaldehyde in PBS buffer and surface carbohydrates labeled with Texas red hydrazide (Molecular Probes, http://probes.invitrogen.com) or with calcofluor white M2R (Sigma).
Bacteria were then stained by DAPI, and labeled carbohydrates were visualized directly using a Zeiss Axioskop 20 fluorescence microscope and the AxioVisionLE 4.2 SP1 program (http://www.zeiss.com) used to perform the 3-D reconstruction.
ECM obtained by treating bacteria with Tween 80 0.05% and glass beads was extracted by phase partitioning. The aqueous layer from the H2O/CHCl3/CH3OH partition was concentrated, the polymers precipitated overnight at 4 °C with six volumes of cold ethanol, and the precipitates collected by centrifugation at 14,000g for 1 h. The lipoarabinomannan content of this fraction was analyzed by SDS-PAGE and immunoblotting using the CS-35 antibody as described [49]. The interphase derived from the partition experiment was extracted three times with water before precipitating the polymers with ethanol and submitting them to acid hydrolysis in 2 M trifluoroacetic acid for 2 h at 120 °C. The monosaccharide constituents of this fraction were then analyzed by TLC, and their migration profile was compared to that of known standards.
Total lipids from bacterial cells treated with Tween 80 0.05%, or untreated, were extracted as described [50] and analyzed by TLC on silica gel 60–precoated plates F254 (Merck, http://www.merck.de). Extraction of mycolactone and cytotoxicity tests was performed according to George et al. [40] either using the bacterial pellet (cells without ECM) or ECM [13]. The carbohydrate content of the ECM material was measured by a colorimetric method [51].
Bone marrow–derived macrophages were obtained by seeding 105 bone marrow cells from 8-wk-old C57BL/6 mice per well in RPMI 1640 supplemented with 10% heat-inactivated fetal calf serum and 10% L-cell-conditioned medium. Culture medium was changed at day 4 and just before adding mycolactone at day 7. HeLa cells and Cos cells (American Type Culture Collection, http://www.atcc.org) were cultured in Dulbecco's modified Eagle's medium supplemented with 10% heat-inactivated fetal calf serum. Proliferating cells were seeded in 96-well microtitration plates at a density of 105 cells/well, which were further incubated for 24 h at 37 °C under 5% CO2 in air before each assay. Various concentrations of vesicles or mycolactone in ethanol were added (2 μl/well). The mycolactone used as reference was purified as previously reported [12]. After 24 h incubation in the above conditions, cytotoxicity was then assessed by addition of 20 μl of dimethylthiazolyl diphenyl tetrazolium bromide solution (MTT, Sigma) (7.5 mg/mL) to each well and further incubated for 4 h at 37 °C to allow the formation of formazan. Formazan crystals were then dissolved with 100 μl of 10 % SDS in 10 mM HCl. The optical density of each well was measured at 595 nm using a Multiwell plate reader. The values given are the average of two replicates and are representative of four independent experiments. The 50% inhibition concentration was determined by curve fitting.
Adult N. cimicoides water bugs were collected and housed as described previously, then fed with grubs of Phormia terrae novae (Verminière de l'Ouest, http://www.verminieredelouest.fr) that had been inoculated beforehand with M. ulcerans, with or without ECM, in 30 μl by using a 25-gauge needle.
Six hours after feeding, the insect hemolymph was collected with an insulin syringe [10]. Pooled hemolymph (100 μl) was added to 100 μl of cold distilled water. The samples were washed three times by centrifugation (14,000g for 15 min) in distilled water and resuspended in 50 μl of 50 mM NaOH and heated at 95 °C for 15 min. Real-time PCR was performed using brilliant SybrGreen Q PCR mix (Stratagene, http://www.stratagene.com) containing Taq polymerase, 2.5 mM MgCl2, 100 μM (each) deoxynucleoside triphosphate and 20 pM primers. The primers were MLF (5'- CCCTTCGACGTCATCAAGAAA −3′) and MLR (5'- CCGACTGACCGATGAGCAA −3′), leading to amplification of a 63-bp region of the mls genes [52]. After 15 min at 95 °C, the DNA was amplified by 30 cycles of 45 s at 95 °C; 1 min at 61 °C, and 45 s of elongation at 72 °C on an MX3000P apparatus (Stratagene). The dissociation curve was performed between 55 °C and 95 °C.
The MIC of rifampin and amikacin, inhibiting >99% of the bacteria, was determined as previously described [53]. To measure chlorine susceptibility, ∼108 bacteria, with or without ECM, were suspended in solutions containing a range of chlorine concentrations (20–200 mg per liter). After 60 min at 25 °C, residual chlorine was neutralized with sodium thiosulfate [54] and bacterial viability determined by inoculation onto Löwenstein–Jensen slants.
Suspensions (30 μl) containing 5 × 103 bacteria, with or without ECM, were injected subcutaneously into the tail of ten female Balb/c mice (Charles River Laboratories, http://www.criver.com). Mice tails were examined weekly over 6 mo.
The non-parametric Mann-Whitney U test was used for statistics. A p-value of < 0.05 was considered significant. |
10.1371/journal.pgen.1005767 | Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies | False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.
| Genome-Wide Association Studies (GWAS) can reveal genetic-phenotypic relationships, but have limitations. To control false positives, population structure and kinship are incorporated in a fixed and random effect Mixed Linear Model (MLM). However, because of the confounding between population structure, kinship, and quantitative trait nucleotides (QTNs), MLM leads to false negatives, missing some potentially important discoveries. Here, we present a new method, Fixed and random model Circulating Probability Unification (FarmCPU). FarmCPU performs marker tests with associated markers as covariates in a fixed effect model and optimization on the associated covariate markers in a random effect model separately. This process enables efficient computation, removes the confounding, prevents model over-fitting, and controls false positives simultaneously. FarmCPU controls false positives as well as MLM with reductions in both false negatives and computing times. Researchers will not only be able to analyze big data, but will also have greater success with fewer mistakes when mapping genes of interest.
| Genome-Wide Association Studies (GWAS) use direct statistical tests as opposed to direct genetic inferences carried out in linkage analyses. Associations between a genetic marker and a phenotype happen for many reasons in addition to the genetic linkage between the tested genetic markers and functional causal polymorphisms[1–4]. Population structure and kinship among individuals are two common indirect, non-causal associations that lead to false positives[5–7]. The most effective strategy to eliminate false positives is either 1) fitting population structure as covariates in a General Linear Model (GLM)[8], or 2) fitting both population structure and each individual’s total genetic effect as covariates in a Mixed Linear Model (MLM)[9] to make adjustments for testing markers.
Population structure is normally represented by proportions of individuals belonging to subpopulations, commonly known as the Q matrix[10,11], or by principal components (PCs)[8,12,13] derived from genetic markers covering the whole genome. Because subpopulations in the Q matrix are fitted as fixed effects, the statistical tests on genetic markers (S) can be performed with GLM, one marker at a time. The model can be conceptually presented as y = Q+S+e, where y and e are phenotype and residuals, respectively. This model is also known as the Q model.
Similarly, the entire set of genetic markers can be used to derive a kinship (K) matrix to define the relationship among individuals. Total genetic effects of individuals are fitted as random effects with variance and covariance structure defined by K. Conceptually, MLM with both Q and K can be written as y = Q+K+S+e and is also known as the Q+K model [9]. Previous studies demonstrated that both the Q and Q+K models control false positives better than naïve models such as the t-test, which only fits the testing markers[8,9]. In general, the Q+K model performs better than the Q model or the K model alone when they can not be inclusively represented each other[9,14].
Compared with GLM, MLM is much more computing intensive. Many algorithms have been developed to reduce the computational burden, including EMMA[15] (Efficient Mixed-Model Association), EMMAX[16] (EMMA eXpedited), P3D[17] (Population Parameters Previously Determined), GEMMA[18] (Genome-Wide Efficient Mixed-Model Association), FaST-LMM[19] (Factored Spectrally Transformed Linear Mixed Model), and GRAMMAR-Gamma[20] (fast variance components-based two-step method). However, the statistical power of these algorithms remains the same as the regular MLM.
Another problem with MLM is that its advantage disappears for complex traits when they are associated with population structure. The MLM method was compared with a naïve test (without control over population structure and kinship) in an association study on 107 traits from 199 Arabidopsis thaliana individuals genotyped at 250,000 Single Nucleotide Polymorphisms (SNPs)[21]. Both the MLM and naïve methods revealed the known genes without obvious inflation of P values in statistical tests on traits associated with disease resistance, development, and ionomics. However, for traits of flowering time, the naïve method encountered inflated P values; consequently, the signals of known flowering time genes were indistinguishable from the background noise. In contrast, the MLM method controlled inflation well, but the signals of known genes also faded into the background, similar to the naïve method. Thus, for complex traits associated with population structure such as flowering time, incorporating Q and K in a MLM controls P-value inflation well, but also weakens the real associations.
Two strategies have been developed to solve the confounding problem and improve statistical power for MLM methods. The first strategy, the Compressed MLM (CMLM), clusters individuals into groups and fits genetic values of groups—rather than genetic effects of individuals—as random effects. The CMLM method improves statistical power compared to regular MLM methods[17]. Furthermore, the Enriched CMLM (ECMLM), continually improves statistical power by optimizing the group kinship definition, rather than using the average kinship algorithms constantly[22].
The second strategy changes the definition of kinship among individuals. Only the associated genetic markers are used as pseudo Quantitative Trait Nucleotides (QTNs) to derive kinship instead of all, or a random sample of genetic markers. Pseudo QTNs are expected to closely track some of the causative QTNs, and are selectively used to derive kinship for a specific testing marker. Whenever a pseudo QTN is correlated with the testing marker, it is excluded from those used to derive kinship. In the FaST-LMM-Select method, a pseudo QTN is considered correlated if it is within a 2Mb interval on either side of the testing marker[23]. Instead of using a 2Mb interval, the Settlement of MLM Under Progressively Exclusive Relationship (SUPER) method applies a threshold on Linkage Disequilibrium (LD) between the pseudo QTNs and the testing marker. Selectively including and/or excluding pseudo QTNs to derive kinship for a specific testing marker improves statistical power compared to deriving a overall kinship from all, or a random sample of genetic markers[24].
Both above strategies conduct genetic marker tests one at a time. However, testing multiple markers simultaneously is more advantageous, and can be done by fitting pseudo QTNs in addition to the testing markers in a stepwise MLM, named Multi-Locus Mixed-Model (MLMM)[25]. The overall kinship derived from all available markers is used to define the variance and covariance structure of individuals' genetic effects. After the pseudo QTNs have converged in the final stage of the regression, the P values of pseudo QTN markers are calculated from the MLM with all pseudo QTNs as covariates. Then, genetic markers are tested one at a time with all pseudo QTNs included as covariates in a MLM. The MLMM method outperforms the regular MLM.
Our objective was to develop an improved statistical method that completely eliminates the confounding, and simultaneously improves statistical power and reduces computing time.
Herein, we present a new statistical method that was inspired by the ongoing developments in GWAS analyses, especially the modifications that have improved statistical power. With these developments, statistical methods have been advanced from the naïve method (e.g., t-test) to GLM[8], from MLM[9] to CMLM[17], from FaST-LMM-Select[23] to SUPER[24], and from single marker testing to multiple loci testing (MLMM)[25]. The improvements in statistical power reflect two types of adjustments for testing genetic markers. The first type of adjustment controls false positives and increases power by fitting covariates such as Q, K, and pseudo QTNs. The second type of adjustment reduces confounding issues by either refining how K is derived from all the markers, or selectively including or excluding pseudo QTNs based on their relationship with the testing markers (Fig 1A).
With the only exception on the naïve method, all the above methods incorporate the first type of adjustment. However, only a few methods incorporate the second type of adjustment. For example, CMLM replaces individuals’ genetic effects with groups’ genetic effects. MLMM adds pseudo QTNs as covariates, which are adjusted by using a step-wise regression procedure. The FaST-LMM-Select and SUPER methods selectively include pseudo QTNs to derive kinship for a specific testing marker. However, the confounding between testing markers and covariates still remains a problem. For example, MLMM retains the kinship un-adjusted. FaST-LMM-Select removes markers in kinship that are adjacent (within 2Mb) to testing markers[23]. Yet, a common biological phenomenon is that LD exists at further distances, even across chromosomes. SUPER takes LD into account across the whole genome. However, the exclusion of confounding is limited by the LD threshold[24].
To address the residual confounding problem, our idea was to divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM), and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. For the convenience of illustration, the associated markers were named as pseudo Quantitative Trait Nucleotides (QTNs). To avoid model over-fitting problem in FEM, pseudo QTNs were estimated by REM, where the pseudo QTNs are used to define kinship. FEM and REM are used iteratively until no change on pseudo QTNs. The P values of testing markers and pseudo QTNs are unified at each iteration. Simultaneously, our method completely controls false positives, eliminates confounding, and improves computational efficiency through the following four strategies:
The first strategy gives the benefits of efficient computation and the elimination of confounding between kinship and testing markers. The second strategy applies the first type adjustment on the testing markers. The third strategy incorporates a marker map into the estimation of pseudo QTNs by using the SUPER method. The pseudo QTNs are derived through a maximum likelihood method in REM and then used to derive kinship among individuals. Regardless of the number of pseudo QTNs, genetic variance and residual variance are the only unknown parameters. The limited number of parameters avoids the problem of model over-fitting. The fourth strategy enhances the MLMM's algorithm for calculating the P values of pseudo QTNs. Because all pseudo QTNs are examined for each genetic marker tested, we identify and use only the most significant P value among all tests for each pseudo QTN.
Our proposed method requires that the FEM and REM proceed in an iterative fashion. The FEM tests markers, one at a time, and uses a set of pseudo QTNs as covariates. The model can be written as:
yi=Mi1b1+Mi2b2+…+Mitbt+Sijdj+ei
(1)
where yi is the observation of the ith individual; Mi1, Mi2,…, Mit are the genotypes of t pseudo QTNs, initiated as an empty set; b1, b2, …, bj are the corresponding effects of the pseudo QTNs; Sij is the genotype of the ith individual and jth genetic marker; dj is the corresponding effect of the jth genetic marker; and ei is the residuals having a distribution with zero mean and variance of σe2.
Each of the testing markers receives a P value except those designated as pseudo QTNs and used as covariates. Initially, these pseudo QTN markers are assigned “NA" (Not Available) for their P value. As each pseudo QTN is examined for each testing marker, the NA is replaced with the most significant P value for that pseudo QTN, which becomes the P value of its corresponding marker. We call this process substitution (Fig 1B).
After substitution, every marker has its own P value. The P values and the associated marker map are used to update the selection of pseudo QTNs by using the SUPER algorithm[24] in a REM as follow:
yi=ui+ei
(2)
where yi and ei stay the same as in Eq (1) and ui is the total genetic effect of the ith individual. The expectations of the individuals’ total genetic effects are zeros. The variance and covariance matrix of the individuals’ total genetic effects is G=2Kσa2, where σa2 is an unknown genetic variance and K is kinship derived from the pseudo QTNs.
The set of pseudo QTNs that maximizes the likelihood of the REM, Eq (2), is used to replace the pseudo QTNs in the FEM, Eq (1). The iteration stops when no change occurs in the estimated set of pseudo QTNs. We named this method Fixed and random model Circulating Probability Unification (FarmCPU). The FarmCPU procedure is further detailed in the online methods section.
In addition to its potential for increasing statistical power, FarmCPU has two other benefits. First, FarmCPU is computationally efficient. Marker testing is conducted by a FEM that has a computing time complexity linear to the number of markers and individuals. Second, P values for non-pseudo QTN markers are not inflated. All markers influential to phenotype are included in the model, either as pseudo QTNs or as markers associated with pseudo QTNs. Because association tests on all markers are performed with pseudo QTNs as covariates, significant P values are not expected for non-pseudo QTN markers.
By performing association tests on real and simulated data and comparing results to current methods, we demonstrated FarmCPU's improved statistical power, increased computational efficiency, and ability to control false positives, i.e. Type I error.
We analyzed real data to demonstrate new findings and overlaps with known associated loci by using FarmCPU. We simulated data to examine the null distribution and statistical power under different levels of Type I error and False Discovery Rate (FDR). Simulated data were also used to examine FarmCPU's computational efficiency in response to variations in number of markers and sample size.
We reanalyzed a published dataset and performed enrichment study on candidate genes to validate the associated loci. When we reanalyzed the 107 traits of 199 Arabidopsis thaliana samples genotyped at 250,000 SNPs[21] with FarmCPU and three other methods (naïve, GLM, and MLM), we were able to repeat the previous results by using the naïve and MLM methods (Fig 2A). FarmCPU not only controlled inflation of P values well, but also identified new loci and known associated loci, especially for flowering time (S1 File).
To validate the associated loci on flowering time, we extracted the known candidate genes and conducted an enrichment study. We divided the whole genome into small regions (10,000 base pairs) and categorized each region into either a gene region containing at least one candidate gene or a non-gene region containing no candidate genes. We calculated an enrichment coefficient as the ratio between the numbers of gene regions versus non-gene regions. An enrichment coefficient of 1 is expected for a random association. For the top association, the enrichment coefficient equaled 2.4 for the naïve and GLM methods, 3.8 for the MLM method, and 8.9 for the FarmCPU method (Fig 2B). For the top ten hits, the averaged enrichment coefficients were 1.7, 2.3, 2.8, and 4.0 for naïve, GLM, MLM, and FarmCPU, respectively.
We compared FarmCPU with other six methods selected from different categories. These methods are: (1) naïve method (t-test); (2) GLM[8]; (3) MLM[9,26]; (4) CMLM[17]; (5) FaST-LMM-Select[23], and (6) MLMM[25]. Except FarmCPU and t-test, all the other methods included the first three PCs as covariates [14]. We examined datasets from multiple species, including Arabidopsis thaliana[21], human[27,28], maize[29], mouse[30], and pig[31]. The results are summarized in Fig 3 and S1–S4 Figs and S1–S6 Tables.
FarmCPU outperformed other methods with respect to controlling inflation of P values, identifying new associated markers, and overlapping with known loci. Taking flowering time at 16°C in Arabidopsis thaliana as example, the P values were overwhelmingly inflated under the naïve method (Fig 3). More than 4,000 markers associated with flowering time at a threshold of 1% after Bonferroni multiple test correction. One-half of the markers had P values that deviated from expectation. Thus, the naïve method was unable to distinguish the real signals from the background noise. GLM reduced the inflation, however, 10% of markers still had P values that deviated from expectation. The MLM, CMLM, and Fast-LMM-Select controlled inflation well, but identified no associated markers above the threshold of 1% after Bonferroni multiple test correction. MLMM not only controlled inflation well, but also identified two associated loci above a threshold of 1% after Bonferroni multiple test correction. Besides the two loci identified by MLMM, FarmCPU identified another three associated loci. The new identified loci included the known gene FLOWERING LOCUS C (FLC)[32] that controls flowering time in Arabidopsis thaliana (S1 Table).
We examined null distribution of FarmCPU compared with two other extreme methods. One is the naïve method, t-test, which is expected to exhibit inflation of P values. The other is the MLM method, which controls inflation well. Three datasets with different level of population stratification were used to examine null distribution. The first is Arabidopsis thaliana with connected subpopulations. The second is the East Asian lung cancer dataset with mild-isolated subpopulations. The third is the WTCCC1 controls dataset with distinct-isolated subpopulations. The plots of the first three PCs are displayed in S5 Fig. The null distributions are summarized in Fig 4, and S7–S9 Tables. Null distributions were investigated under three confounding level settings:
Genetic markers were classified into the ones on QTN-area and non-QTN area to evaluate statistical power under different levels of FDR and Type I error. The markers on non-QTN areas were used to derive null distribution. For a specific level of Type I error, power was defined as the proportion of QTNs detected. For each level of power, the corresponding FDR was defined as the proportion of false positives (See Materials and Methods section for details). FarmCPU was compared with other common methods under different scenarios, including levels of non-genetic effect, complexity of genetic architecture, and variation of applications such as incorporating PCs.
In addition to improved statistical power, FarmCPU is also computationally efficient. We theoretically analyzed the computing time complexity and measured the actual performance for datasets with specific number of markers and sample size. The factors impacting computing time were investigated to further improve computational efficiency.
False positives can be reduced by fitting covariates to adjust the association tests on markers. The common covariates are population structure and kinship among individuals. However, the confounding between these covariates and testing markers also produce false negatives. The iterative usage of the fixed effect and random effect models in the FarmCPU method integrates both the markers and the covariates together by optimizing the covariates and using substitution. Testing markers in a fixed effect model makes FarmCPU computationally efficient. The optimization of pseudo QTNs in a random effect model involves only two parameters (genetic and residual variance components) in addition to the number and size of bins of the SUPER GWAS method. Therefore, the problem of model over-fitting is much less compared to including pseudo QTNs and testing markers in the same model. SUPER's bin method takes the map information into account, which effectively reduces redundancy among pseudo QTNs.
The REM part of FarmCPU has converging problem for the optimization of genetic and residual variance components when one of the components is near zero. This issue is common for a trait with extremely low heritability or a permuted phenotype that has zero heritability. In this case, no pseudo QTNs are associated with the trait. Fortunately, this situation can be detected statistically under a threshold (e.g. 1%) after multiple test correction through the fourth step of the FarmCPU procedure (see online methods). The multiple test correction can be performed with Bonferroni method at lowest computing time. Permutation test costs more time with benefit of improved power (see details in S28 Fig).
Non-genetic effects cause false positives, especially when they are not correlated with population structure. In this case, it is hard to capture them unless indicators can be identified to capture the non-genetic effects. When the non-genetic effects are correlated with population structure, fitting population structure as covariates reduce false positives. We tested the performance of FarmCPU and MLM when environmental effects were added on phenotypes (using the Chinese, Japanese, and Korean datasets) to contribute an additional 25% of the phenotype variance. These added levels of environmental effects meant that the non-genetic effect was about 170 times greater than the QTN effect. Even in this situation, FarmCPU without fitting PCs as covariates still outperformed MLM that incorporated PCs as covariates (S14 and S15 Figs). Fitting PCs as covariates in FarmCPU further improved statistical power (S16 Fig).
Compared with MLMM, FarmCPU uses an improved version for calculating the P values of the pseudo QTNs. MLMM calculates P values using all pseudo QTNs as covariates in the model and excludes testing markers, an approach we named “onsite”. Instead, FarmCPU uses the most significant P value out of each pseudo QTN in conjunction with the tests on all markers. We named this process "substitution". As demonstrated, substitution dramatically improves statistical power (S21 Fig).
FarmCPU's improved statistical power and control on false positives generate a fresh look to the Manhattan and QQ (“Q” stands for Quantile) plots (Fig 3). The skyscrapers over the Manhattan, New York skyline become helicopters above the Manhattan, Kansas skyline. The new appearance of the Manhattan plot better illustrates the trend of increasing marker density. When marker density is high enough, spikes are no longer surprising. Most interesting, and most relevant for data interpretation, is the strongest association in each region of the plot. The QQ plot from FarmCPU is shaped like a hockey stick with a long shaft that joins the observed and expected P values together for the majority of markers. The blade of the hockey stick indicates the associations for the markers with observed P values that deviated from the expectation.
High marker densities and increased sample sizes, driven by the reduction of genotyping cost, are producing big datasets for analysis[41]. Most statistical methods were developed to solve big data with a focus on either marker size or sample size, but not both. FarmCPU is computationally efficient for both sample size and marker density. Among MLM methods, the CMLM and FaST-LMM methods work best with large sample sizes; the P3D/EMMAX and GRAMMAR-Gamma (Software: GenABEL R package, v 1.8–0) methods work best with high marker densities. For example, a dataset with 10,000 individuals and 10,000 markers freezes a computer running GRAMMAR-Gamma, but only takes 3 minutes with FaST-LMM (Software: FaST-LMM v 2.07). In contrast, a dataset with 1,000 individuals and 1,000,000 markers freezes a computer running FaST-LMM, but only takes 10 minutes for GRAMMAR-Gamma. For a dataset with 10,000 individuals and 1,000,000 markers, computers freeze with both FaST-LMM and GRAMMAR-Gamma. However, FarmCPU solves this dataset in less than four hours (S26 Fig).
URL: The FarmCPU software package (source code, user manual, demo data, and tutorials) is available at http://www.ZZLab.net/FarmCPU.
Our proposed method uses the Fixed Effect Model (FEM) and the Random Effect Model (REM) iteratively. The FEM is employed to test m genetic markers, one at a time. Pseudo QTNs are included as covariates to control false positives. Specifically, the FEM can be written as follows:
yi=Mi1b1+Mi2b2+…+Mitbt+Sijdj+ei
(3)
where yi is the observation on the ith individual; Mi1, Mi2,…, Mit are the genotypes of t pseudo QTNs, initiated as an empty set; b1, b2, …, bj are the corresponding effects of the pseudo QTNs; Sij is the genotype of the ith individual and jth genetic marker; dj is the corresponding effect of the jth genetic marker; ei is the residual having a distribution with zero mean and variance of σe2.
The REM is employed to optimize the selection of pseudo QTNs from markers based on their testing statistics (i.e., P values) and positions by using the SUPER algorithm[24]. Mathematically, the REM can be written as follow:
yi=ui+ei
(4)
where yi and ei stay the same as in Eq (3) and ui is total genetic effect of the ith individual. The expectations of the individuals’ total genetic effects are zeros. The variance and covariance matrix of the individuals’ total genetic effects is G=2Kσa2, where σa2 is an unknown genetic variance and K is the kinship matrix defined by pseudo QTNs.
The iterative usage of the FEM (1) and the REM (2) is specifically described by the following steps:
We used previously published datasets from multiple species that included Arabidopsis thaliana, human, maize, mouse, and pig.
We used two datasets of Arabidopsis thaliana. The first dataset includes 199 samples, with 216,130 SNPs and 107 phenotypes[21]. When phenotypes had less than 100 records, a minor allele frequency (MAF) of 0.05 was set to filter the SNPs. The second dataset includes 1,179 samples with 214,545 SNPs. One sample was removed because one-half of the genotypic data is missing (URL: http://archive.gramene.org/db/diversity/diversity_view). The kinship matrix and principal components were calculated by GAPIT[42] using 10% of SNPs sampled randomly.
One human dataset, “WTCCC1 controls dataset”, ID # EGAD00000000002, was obtained from EMBL-EBI (The European Molecular Biology Laboratory–The European Bioinformatics Institute)[38]. Respecting the privacy of individual level data, the data is only available under the permission of MalariGen Data Access Committee. This dataset contains 1,500 samples. All samples were genotyped by the Affymetrix_500k SNP Chip and 495,473 markers were used in our simulation study (URL: https://www.ebi.ac.uk/ega/datasets/EGAD00000000002). The kinship matrix and principal components were calculated by GAPIT using 10% of SNPs sampled randomly.
The other human dataset, “East Asian lung cancer dataset”, ID # phs000716.v1.p1, was obtained from dbGaP[27]. Respecting the privacy and intentions of research participants, the data is only available under the permission of NIH (National Institute of Health) and Intramural NCI (National Cancer Institute). The authors applied and got the data through dbGaP Authorized Access. A total of 8,807 samples were used that contain 4,962 lung cancer cases and 3,845 controls. All samples were genotyped by the Illumina Human610_Quadv1_B and Human660W-Quad_v1_A platforms and each sample has 629,968 SNPs (URL: http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000716.v1.p1). The kinship matrix and principal components were calculated using 10% randomly sampled or total SNPs by GAPIT and PLINK, respectively.
The maize genotype dataset includes 2,279 inbred lines, each with 681,258 SNPs. The phenotype is flowering time measured as days to silk[29] (URL: http://www.panzea.org/!#genotypes/cctl). The kinship matrix and principal components were calculated by GAPIT using 10% of SNPs sampled randomly.
The mouse genotype dataset has 1,940 samples (1000 males and 940 females from a heterogeneous stock mice population owned by the Welcome Trust Centre for Human Genetics) with 12,226 SNPs. The phenotype is weight growth intercept[30]. The kinship matrix and principal components were calculated by GAPIT using all SNPs.
The pig genotype dataset has 820 samples (412 Large White and 408 crosses from Large White and Landrace) with 64,212 SNPs. The phenotype is last rib back-fat thickness[31]. All SNPs were used to build the kinship matrix and principal components.
We used real genotype datasets from human and Arabidopsis thaliana to simulate genetic effects and generate phenotypes by adding residual effects. The QTNs underlying these phenotypes were randomly sampled from the real genotypes. The QTN effects followed a geometric distribution with an additive effect of parameter a. The effect of the ith QTN was ai. The parameter a was set to 0.9, 0.95, and 1 as described in previous studies[9,17]. Phenotype was simulated as: y = additive effect + residual effect. Additive effect was calculated as: additive effect = QTN matrix * QTN effects. The residual effect, following a Gaussian distribution with mean of 0 and variance of σe2, was calculated as: σe2=(1−h2)σa2/h2, where σa2 is the variance of additive effect and h2 is heritability. Simulations were performed using a variety of QTN numbers and heritability values, and with QTNs included and excluded from the genotypic data for association tests. For each combination of factors, simulations were repeated either 100 or 1,000 times, specified for each experiment.
Statistical power, Type I error, and FDR were examined simultaneously in association tests on simulated phenotypes with known QTNs, using the method described by Segura et.al[25] and two methods from our previous studies—SUPER[24] and Enriched CMLM[22]. A QTN was considered identified if a positive marker was within a prescribed interval distance (e.g. 50 kb). Power was defined as the proportion of QTNs identified at a threshold of Type I error or FDR. Markers were used to derive the null distribution of negative control if no QTN was within the interval. The null distribution of Type I error was derived from the non-QTN markers. FDR was defined as the proportion of the non-QTN markers among the positive markers.
The flowering time candidate genes from the database reported by Atwell et. al, (2010, Nature) were used to evaluate the associated SNPs on 23 flowering time traits in Arabidopsis thaliana. The whole Arabidopsis thaliana genome was divided into gene regions and non-gene regions. The genes and their extensions, 10,000 base pairs on either side, were considered gene regions with a total length of 4,552,815 base pairs (3.9% of whole genome). The remaining areas were considered non-gene regions with total length of 114,616,742 base pairs (96.1% of whole genome). The average hit per base pair was defined by number of associated SNPs divided by total length. The ratio of average hit on gene regions to the average hit on non-gene regions was used as the enrichment coefficient. The random hits were expected to have an enrichment coefficient of 1.
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10.1371/journal.ppat.1003273 | A Ubiquitin-specific Protease Possesses a Decisive Role for Adenovirus Replication and Oncogene-mediated Transformation | Adenoviral replication depends on viral as well as cellular proteins. However, little is known about cellular proteins promoting adenoviral replication. In our screens to identify such proteins, we discovered a cellular component of the ubiquitin proteasome pathway interacting with the central regulator of adenoviral replication. Our binding assays mapped a specific interaction between the N-terminal domains of both viral E1B-55K and USP7, a deubiquitinating enzyme. RNA interference-mediated downregulation of USP7 severely reduced E1B-55K protein levels, but more importantly negatively affected adenoviral replication. We also succeeded in resynthesizing an inhibitor of USP7, which like the knockdown background reduced adenoviral replication. Further assays revealed that not only adenoviral growth, but also adenoviral oncogene-driven cellular transformation relies on the functions of USP7. Our data provide insights into an intricate mechanistic pathway usurped by an adenovirus to promote its replication and oncogenic functions, and at the same time open up possibilities for new antiviral strategies.
| Adenoviral infections can result in severe outcomes leading to mortality especially in children undergoing immunosuppressive therapies. Unfortunately, no specific anti-adenoviral treatments are available to treat disseminated adenoviral infections. We have set out to identify host factors promoting adenoviral growth and could identify the cellular protein Ubiquitin-specific protease 7 (USP7) being central to adenoviral infection. Here we show that USP7 interacts with the viral protein E1B-55K, a central regulator of adenoviral replication and adenoviral oncogene-mediated cellular transformation. We demonstrate that USP7 ensures stability and/or proper expression levels of adenoviral proteins at early and late time points of infection. Consistent with this, small-molecule inhibitors of USP7 showed efficient reduction of capsid protein levels and viral progeny numbers. Thus, USP7 inhibition might be a useful treatment option in the context of disseminated adenoviral infections. Moreover, we were also able to show that adenoviral oncogene-mediated cellular transformation can be hampered by USP7 disruption. In summary, this study shows that two different adenoviral disease mechanisms can be inhibited by targeting one host cellular factor.
| Human adenoviruses constitute a group of more than 60 adenovirus types. In general, adenoviruses cause self-limiting infections of the eye, or gastrointestinal and respiratory tract, which can lead to epidemic keratoconjunctivitis, diarreah, and severe acute respiratory diseases [1]–[9]. However, with increasing prevalence of transplantations with concomittant downregulation of the immune system (such as in bone marrow transplations), the frequency of disseminated adenoviral infections is also rising in immuno-compromised patients, resulting in high mortality rates [10], [11]. Unfortunately, no specified antiviral treatments or wide-spread vaccination strategies are currently available to counteract adenoviral outbreaks in an efficient manner [12], [13].
For successful infection, adenoviruses, like other viruses, must circumvent certain antiviral defense mechanisms. In this regard, the ubiquitin proteasome system (UPS) adopts a central position in aiding viral infections. For example, HSV-1, HPV-16/18 and EBV have been shown to use strategies which involve targeting cellular proteins with antiviral functions, such as p53, for proteasomal degradation using viral encoded or components of cellular E3 ubiquitin ligases [14]–[17]. Adenoviruses use two viral regulatory proteins, E4orf6 and E1B-55K, to exploit cellular factors to form an SCF-like E3 ubiquitin ligase complex promoting p53, Mre11, Bloom helicase (BLM), DNA ligase IV, integrin alpha 3 and Tip60 polyubiquitination followed by subsequent proteasomal degradation [18]–[23].
In contrast to all the functions involving adding ubiquitin moieties to target substrates, viral exploitation of the reverse mechanism in host cells has become increasingly important over the past few years. Deubiquitination is mediated by deubiquitinating enzymes (DUBs), and the replication of several viruses has been shown to either benefit from, or be inhibited by certain DUBs. Liao and colleagues demonstrated that Usp11 specifically inhibits influenza virus infection [24], whereas Perry and coworkers have shown that Usp14 is necessary for efficient viral replication of a panel of viruses, including norovirus, encephalomyocarditis virus, Sindbis virus, and La Crosse virus [25]. Among those DUBs, USP7 (herpesviral associated ubiquitin-specific protease [HAUSP]) was the first to be associated with viral infection, through interacting with herpesviral ICP0 [26]. Since then, more herpesviral regulatory proteins have been found to use the functions of USP7 for their own benefit. For example, EBV EBNA1 utilizes USP7's properties to stimulate its DNA-binding activity, to initiate disruption of PML proteins, to reduce p53 steady-state levels or to enhance the deubiquitination of histone H2B resulting in EBV oriP transcriptional activation. Furthermore, KSHV LANA probably interacts with USP7 in order to regulate latent viral genome replication [27]–[30]. Since cellular DUBs obviously represent an important family of proteins used by viral proteins, studies are underway to develop specific inhibitors of these enzymes.
Like herpesviruses, adenoviruses also encode several proteins that bind to and manipulate key cell growth regulatory proteins to promote viral replication. The adenoviral protein E1B-55K is a multifunctional phospho-protein performing central roles during productive infection, including viral mRNA transport and degradation of cellular components (e.g. p53 and Mre11), using the ubiquitin proteasome system (UPS) [21], [22]. Moreover, E1B-55K is able to induce cellular transformation of primary cells in cooperation with the adenoviral protein E1A [31], [32]. Although adenoviruses are known to be closely involved in manipulating proteins of the UPS especially through E1B-55K, to date the activity of cellular DUBs during adenoviral infection remains enigmatic and has not been studied so far.
Here, we demonstrate that the adenoviral protein E1B-55K interacts with the cellular DUB USP7. We found that USP7 is relocalized in a time-dependent manner during adenoviral infection even though independent of E1B-55K. To our interest, USP7 knockout/knockdown and inhibitor assays demonstrate that expression and/or stability of E1B-55K is strongly dependent on the presence and functions of USP7. In addition, it became evident that USP7 promotes viral growth by regulating expression and/or stability of additional adenoviral proteins. We also illustrate that adenovirus oncogene-induced transformation relies on the presence and function of USP7. Therefore, we demonstrate for the first time that general HAdV5 functions strongly depend on the availability and functions of the cellular protein USP7.
E1B-55K plays key regulatory roles during adenovirus infection. This is mainly achieved through interactions with several binding partners directly or indirectly involved in p53 regulation or DNA damage response, a common strategy employed by almost all known viruses to promote viral replication and hinder antiviral defense mechanisms [33].
To discover more about the functions of E1B-55K, we profiled cellular interaction partners of E1B-55K using a yeast two-hybrid system. With the N-terminal region of HAdV5 E1B-55K protein as a bait, we identified several positive clones encoding USP7 (two positive “hit”-sequences are displayed, Figure S1).
Next, we wanted to verify the results from the yeast two-hybrid screen in vitro. Therefore, as summarized in Figure 1A, we generated GST fusion proteins including full-length, and a series of truncated or alternative splicing variants of E1B-55K protein and evaluated their interaction with cellular USP7 by GST pull-down experiments. GST purification of intact full-length E1B-55K protein was inefficient since it proved unstable when expressed in bacteria (Figure 1B, lower panel, lane 3). However, the combination of full-length protein and its bacterial degradation products could precipitate USP7 (Figure 1B, upper panel, lane 3). Besides the full-length protein, all the fusion products containing the first 79 residues of E1B-55K precipitated USP7 regardless of their C-terminal extensions (Figure 1B, upper panel, lanes 4, 5, 7 and 8). Taken together, these data demonstrate that the N-terminal 79 amino acid region of E1B-55K is necessary and sufficient for binding to USP7 in vitro.
Previous investigations had revealed that the USP7 protein can be roughly divided into four domains [34]–[36]: the N-terminal TRAF-like domain (TD; residues 53–208), central catalytic domain (CD; residues 208–560), and two C-terminal structural domains (C1 and C2; residues 600–870 and 885–1061, respectively) (Figure 1C). To identify which of these domains interacts with E1B-55K, we generated GST fusions corresponding to these regions and carried out GST pull-down experiments in wt (H5pg4100)-infected H1299 cell extracts (Figure 1D). Neither the central CD, nor the C-terminal domains C1 and C2 interacted with E1B-55K; however, the N-terminal segment of USP7 (residues 1–215) was found to strongly and specifically precipitate E1B-55K (Figure 1D, lane 3).
To further investigate the USP7 interaction, endogenous USP7 was immunoprecipitated from p53-negative H1299 cells transfected with a plasmid encoding wt pE1B-55K and stained for coprecipitated E1B-55K (Figure 1E, lanes 1 and 2). Additionally, H1299 (p53-negative) and A549 (p53-positive) cells were mock-infected and infected with E1B minus (H5pm4149) or wt (H5pg4100) virus. Subsequent USP7 immunoprecipitation experiments confirmed USP7-E1B-55K interactions in both cell lines (Figure 1B, lanes 5 and 8) whereas control immunoprecipitation experiments with an unspecific IgG2a antibody proved to be negative for E1B-55K as well as USP7 precipitation (Figure 1B).
To verify this interaction in living cells, flow cytometry-based FRET (Foerster's Resonance Energy Transfer) analyses were employed as described by Banning and colleagues [37], with USP7-CFP serving as a donor chromophore and YFP-E1B-55K as an acceptor chromophore (scheme, Figure 1F). When both proteins interact, excitation of the CFP chromophore results in secondary excitation of the YFP chromophore leading to FRET signal emission displayed in the corresponding FRET gate (Figure 1G, panel b or d). False-positive signals are excluded by using different controls, as indicated, together with an appropriate gating strategy (Figure 1G, panel a and c). This assay revealed ca. 17% FRET-positive cells (FRET+; Figure 1G, graph + panel d), indicating that USP7 and E1B-55K also interact in living cells. Although FRET+ cell levels were relatively low compared to the positive control (fusion of CFP and YFP resulting in “constant” FRET emission; CFP-YFP), possibly explained by CFP/YFP-tag interference and/or competition between endogenous and exogenous USP7, nevertheless, FRET+ cells scored significantly more than the negative controls (CFP cotransfected with YFP; compare Figure 1G, panels a and d).
Taken together our results establish E1B-55K as a new specific interaction partner of USP7 which could also be confirmed in living cells.
To determine if adenoviral infection affects USP7 subcellular localization in a time-dependent manner, we performed extensive time course immunofluorescence studies. A549 cells were mock-infected or infected at an MOI of 20 FFU per cell with wt HAdV5 virus (H5pg4100) and then methanol-fixed at indicated hours post infection (h p.i.; Figure 2A to D). USP7 and E1B-55K (E1B) were detected with specific monoclonal antibodies, and visualized using double-label immunofluororescence microscopy. In uninfected cells (mock), USP7 localized diffusely in the nucleus with a few prominent dot-like structures (Figure 2A). However, upon wt adenoviral infection (H5pg4100), USP7 localization changed dramatically. Several different relocalization patterns of USP7 were observed, which were categorized for each investigated time point with respect to E1B-positive cells (percentages of each category are denoted in Figure 2B to D). In general, USP7 increasingly accumulated into dense, ring-like structures over time during adenovirus infection. Interestingly, as infection proceeds USP7 colocalization with E1B-55K increasingly correlates with these ring-like structures (merge). This is especially displayed in cells with USP7 relocalization of categories 5 and 6.
However, the initial step of USP7 redistribution is probably independent of E1B-55K, since USP7 relocalization could be observed before detection of E1B-55K, and in the absence of colocalization (Figure 2C, panels E–H and I–L) and during infection with a virus lacking all E1B functions (Figure S2, panels D–F). Nevertheless, USP7 redistribution in the nucleus forming ring-like structures as seen in categories 5, 6 and 7 was observed in the majority of patterns analyzed up to 24 hours post infection (Figure 2C, panels M–T; 2D panels M–X).
Strikingly, the relocalization pattern of USP7 during wt adenoviral infection (H5pg4100) strongly resembled staining patterns of the adenoviral E2A protein (also called DBP, Figure 2E and F) [38]–[41]. E2A is a single-stranded DNA (ssDNA) binding protein involved in adenoviral genome replication and can be found not only colocalized with sites of viral ssDNA, but also surrounding sphere-shaped sites of double-stranded DNA, and is thus a marker for sites of both transcription and replication [42], [43].
To test whether USP7 is relocalized to sites of viral DNA replication and transcription, in situ costainings of E2A and USP7 were prepared to detect significant colocalization of both proteins in a time-dependent manner (from 8–48 h p.i. data not shown). In a similar approach as above, A549 cells were infected at an MOI of 20 FFU/cell and methanol-fixed at different time points. Upon analyzing the staining patterns of USP7 in E2A-positive cells, it was evident that nearly all the cells displayed USP7 staining patterns strongly correlating with the E2A-stained structures which is exemplified in Figure 2E or 2F (16 h p.i. or 24 h p.i.).
Altogether, these observations demonstrate that time-dependent USP7 relocalization is strictly related to the formation of viral replication centers, where interaction with E1B-55K probably occurs. Moreover, this points to a functional exploitation of the cellular DUB like it has been shown for a number of other cellular proteins relocalized to adenoviral replication centers (e.g. BLM, RPA32, Mre11, ATR, ATRIP, E1B-AP5 [44]–[46]).
To examine the role of USP7 in adenoviral infection, we analyzed the effects of reducing USP7 steady-state levels and inhibiting USP7 on E1B-55K protein levels and adenoviral replication.
We generated USP7 knockdown and corresponding control cell lines (using H1299 and A549 as parental cell lines) and synthesized a small-molecule compound [47] (here called “HBX”; Figure 3A) which was previously shown to inhibit USP7 [48]. Moreover, we utilized a second USP7 inhibitor (HBX41108), which is a derivative of HBX, to support specificity of our assays [47], [48].
To assess optimal conditions for the inhibitor assays, growth behavior and viability of the cells were tested under mock infection conditions plus inhibitor treatment. In a first attempt to characterize HBX, MTS-based proliferation assays were carried out on cell lines used in our experiments. As shown in Figure S3, sigmoidal dose response curves were generated for three inhibitor treatment durations (24, 48 and 72 h) with several dilution rows performed at least in triplicate. The summarized GI50 values are represented in Figure 3B and reveal that HBX administration in the micromolar range results in growth inhibition.
Previous reports demonstrated that loss of USP7 through knockout leads to decreased proliferation of the respective cell line [49]. Therefore, since USP7 plays a critical role in cell proliferation, it was necessary to determine inhibitor treatment conditions that did not significantly inhibit cell growth. Otherwise it would be difficult to distinguish between cell growth defects or specific compound-mediated effects negatively influencing virus yield. First, time-of-addition experiments after mock infection were performed to determine tolerable inhibitor concentrations leading to insignificant cell growth inhibition. In effect, it turned out that 15 hours of inhibitor treatment prior to cell harvest worked best for all investigated cell lines. For example, A549 cells exhibited no statistically significant decrease in cell number compared to untreated cells 24 hours post mock infection (h p.m.i.) at both HBX concentrations (Figure 3C). However, a significant reduction in cell numbers was observed 48 h p.m.i. after HBX application at both concentrations (∼25% reduction). Nevertheless, trypan blue exclusion to determine the number of viable cells displayed no significant cytotoxic effect on A549 cells either 24 or 48 h p.m.i., meaning that cell cytotoxic effects could be excluded in subsequent experiments (Figure 3D). Similarly, H1299 cells experienced no cell growth defect after HBX treatment 24 h p.m.i., but underwent ca. 25% reduction 48 h p.m.i. (Figure 3E). However, again the number of dead cells did not increase after HBX incubation compared to untreated cells (Figure 3F).
Previous reports have shown that inhibitors of USP7 such as HBX41108 (Figure 4A) induce, among others, p53 protein accumulation and a decrease in Mdm2 protein levels [48], [50]–[52]. This is because, upon USP7 inhibition, Mdm2 deubiquitination/stabilization is heavily decreased and auto-ubiquitination of Mdm2 increased with subsequent lower p53 turnover and p53 accumulation [35].
In order to assess whether HBX exerts similar effects on Mdm2 and p53, the two cell lines mainly used in this study were treated with HBX and HBX41108 for 24 or 15 hours (assay conditions). As expected, both compounds induce an increase in the steady-state protein levels of p53 in A549 cells (Figure 4B, lanes 2 and 3; Figure 4D, lanes 3, 4, 7 and 8). However, a decrease of Mdm2 protein levels could not be detected in this cell line either owing to deregulation of the USP7-p53-Mdm2 pathway or low/too short inhibitor treatment duration. Nevertheless, a decrease in Mdm2 protein levels could be detected in H1299 cells (Figure 4C, lanes 2 and 3; Figure 5C, lanes 3 and 4).
USP7 knockdown (kd) cell lines were generated as an additional tool for this study. The A549-derived USP7 knockdown cell line APU6 (Figure 5A, lane 3) displayed slower growth rates than the parental cell line (data not shown). However, another cell line also transfected with the shRNA plasmid construct against USP7 and derived from A549 cells, APU5, with slow growth comparable to APU6 cells presents normal USP7 levels (Figure 5A, lane 1). To exclude the possible influence of slower growth, this APU5 cell line was used as a control (Figure 4E). Similarly, the USP7 knockdown cell line HU5 derived from H1299 cells (Figure 5A, lane 6) possessed almost identical growth rates compared to the control cell line HC2 (Figure 4F) and the H1299 parental cell line (data not shown).
In summary, it was possible to establish two USP7 knockdown cell lines with corresponding control cells, and find suitable conditions for HBX treatment in infection experiments where cell growth and viability were not significantly affected. Nonetheless, cell growth defects after USP7 inhibition were observed at later stages of mock infection, which corresponds to previously described effects of USP7 on cell proliferation. Therefore, these effects were expected and taken into consideration in subsequent experiments (by normalizing the virus yield per cell and subsequently normalizing yield without inhibitor treatment). Additionally, as expected, both compounds HBX and HBX41108 were able to induce an increase in p53 protein levels but also a decrease in Mdm2 protein levels indicating specific inhibitory effects exerted upon USP7.
When we transfected E1B-55K expression constructs along with an expression construct for EYFP (YFP) into the USP7 knockdown and corresponding control cell lines, we detected severely reduced E1B-55K steady-state levels in the USP7 knockdown background (HU5) without affecting expression levels of the control plasmid encoding YFP (Figure 5A, lane 6). However, a slight reduction of YFP was detected in APU6 cells (Figure 5A, lane 3). Therefore same samples were reanalyzed by Western blot (Figure 5A, lower panel) with double amounts of APU6 (lane 3) in comparison to APU5 lysates (lanes 1 and 2). Here, in APU6 USP7 knockdown cells (lane 3) E1B-55K protein levels still displayed strong reduction in comparison to APU5 cells with normal USP7 protein levels (lanes 1 and 2). To support the knockdown data, we treated A549 and H1299 parental cell lines with the USP7 inhibitors HBX (Figure 5B; Figure 5C; Figure 5D) or HBX41108 (Figure 5C, lane 4). Neither DMSO nor HBX/HBX41108 affected steady-state protein levels of USP7, but in HBX/HBX41108-treated cells protein levels of transfected E1B-55K were severely reduced, comparable to the knockdown experiments (Figure 5B, lanes 6 and 9). Moreover, coimmunoprecipitation of E1B-55K by USP7 was reduced after treatment with HBX (Figure 5B, lane 9) and HBX did not display reduction of cotransfected GFP (Figure 5D).
Additionally, transfecting an E1B-55K expression construct into HCT116 USP7 knockout cells (USP7 KO) also resulted in strong reduction of E1B-55K protein levels (Figure 5E, upper panel). An identical result was obtained when HCT116 lysates (Figure 5E, lower panel, lanes 1 and 2) were compared to double amounts of USP7 KO lysates (Figure 5E, lower panel, lane 3) by immunoblotting. The same effect was obtained after infection of the respective cell lines (Figure 5F). Corresponding p53 staining showed significantly increased p53-levels despite infection with wt (H5pg4100) virus at an MOI of 50 FFU per cell indicating insufficient adenoviral E3 ligase activity due to low E1B-55K protein levels (Figure 5F).
The stabilizing effect of USP7 upon E1B-55K was further supported by cycloheximide chase assays demonstrating a reduced half-life of E1B-55K in the USP7 knockdown background (Figure S4A) and after HBX or HBX41108 treatment (Figure S4B and C).
Taken together, USP7 knockdown/knockout or inhibition led to greatly reduced E1B-55K protein levels, indicating a stabilizing role of USP7 for E1B-55K.
Herpesviruses, like HSV-1 and KSHV rely on the functions of USP7 to efficiently promote virus growth or genome replication [29], [53]. In contrast, the role of cellular DUBs in adenovirus replication has not been investigated.
In a first step to evaluate the influence of USP7 on adenovirus infection, the generated USP7 kd cells APU6 and HU5 (79.5% and 86.6% knockdown efficiency respectively; Figure 6A and S5A) were infected with wt virus (H5pg4100), and the synthesis of early and late viral proteins, as well as the production of progeny virions were compared to those of the control cell lines at different time points (Figure 6 and S5).
First, the effect of USP7 depletion on the synthesis of early viral proteins E1A, E1B-55K and E2A was assayed by immunoblotting. Surprisingly, being the first gene products expressed, E1A proteins showed a defect in accumulating protein levels in APU6 and HU5 cells compared to the USP7+ counterparts. Similarly, E2A levels were also detected to be slightly lower in these cells than in the control USP7+ APU5 and HC2 cells. Consequently, when the expression pattern of E1B-55K was investigated, a significant defect was observed not only in the expression time, but also in the amounts of this protein (Figure 6A and S5A).
The expression of late structural proteins was also investigated in the knockdown cells. As expected, the observed inefficient synthesis of the early viral proteins resulted in delayed accumulation of late structural proteins in both APU6 and HU5 cells compared to the USP7+ counterparts during wt infection (H5pg4100). Late structural protein synthesis was either delayed in USP7 kd lines (e.g. pIII in HU5, or minor capsid proteins in APU6), or these proteins did not accumulate to the parental cell line levels (e.g. pVI in HU5 or pII in both knockdown lines) (Figure 6A and S5A).
Moreover, in order to investigate whether USP7 inhibition leads to effects similar to USP7 knockdown, infected H1299 and A549 cells were subsequently treated with inhibitor HBX. USP7 protein steady-state levels were not affected after inhibitor treatment, either in infected or mock-infected cells in both cell lines (Figure 6B and D; Figure S5C and D). Similar to the knockdown experiments, a reduction of E1B-55K and structural capsid proteins could be detected after HBX treatment in both cell lines (Figure 6B and S5C, each lane 3). Less E1B-55K was further confirmed by quantifying E1B-positive cells after HBX treatment of infected cells using immunofluorescence microscopy (Figure S6). However, decreased E1B-55K could only be observed 24 h p.i., but not 48 h p.i., consistent with the immunofluorescence quantification data (compare Figure 6B and S5C with Figure S6B and D). This may suggest that functional inhibition of USP7 cannot overcome the likely high transcription-translation activity at this stage of infection (at least for the early protein E1B-55K). Interestingly, E1A levels seemed to increase whereas E2A levels only show a slight decrease in A549 cells, and L4-100K protein levels displayed a modest decrease after HBX incubation (Figure 6B and S5C, each lane 3). It is probable that differences between both approaches (knockdown vs. inhibition) may reflect variable efficiencies of functional inhibition.
However, overall, knockdown or inhibition of USP7 led to reduced steady-state protein levels of various adenoviral proteins.
Virus yield experiments performed in both USP7 kd and their respective control cell lines, demonstrated 76.3% or 72.5% reduced viral progeny numbers 24 h p.i. in APU6 or HU5 (Figure 6C and S5B). At 48 h p.i. the virus yield was still reduced by 40.4% (APU6) or 26% (HU5), implying that USP7 is biologically significant for efficient adenovirus infection, even at the late stage of infection.
More importantly, USP7 KO cells, devoid of any USP7 function, were infected with wt virus (H5pg4100) along with the respective control cell line HCT116. The USP7 KO cells were kept in 15%FBS containing medium to compensate for the growth defect this cell line exhibits in comparison to HCT116 cells [49], [54]. Nearly identical numbers of wt-infected HCT116 and USP7 KO cells were harvested at 24 h p. i. (Figure 7A) and virus yield was determined (Figure 7B). As expected, the number of infectious virus progeny particles was severely diminished up to 95.9% (Figure 7B) even though a relatively high MOI of 50 FFU per cell was used. These results strongly support the findings that USP7 is required for efficient adenovirus infection.
As with the knockdown experiments or in the USP7 KO background, inhibitor treatment (15 h before assaying) strongly impaired virus growth in A549 and H1299 cells 24 h p.i. (A549 = 80.4% and H1299 = 91.4%, Figure 6E and S5E). Even at a later time point (start 33 h p.i. with harvest 48 h p.i.) structural capsid proteins (Figure 6B and S5C, each lane 6) and virus progeny numbers (Figure 6E and S5E) were significantly reduced (27.5% A549 and 44.1% H1299). This clearly supports the notion that USP7 may exert its effects not only during early, but also at late times of infection. Moreover, the degree of virus growth inhibition was comparable to that after USP7 knockdown (compare Figure 6E and C). To exclude off-target effects and to demonstrate specificity towards USP7 virus yield was determined in cell lines expressing an shRNA against GFP (A549shGFP and H1299shGFP) and compared to the control cell lines APU5 and HC2 (Figure 6F and G). No significant reduction in virus progeny production was observed in A549shGFP and H1299shGFP cells, respectively (Figure 6F and G). Furthermore, another USP7 inhibitor, HBX41108, demonstrated similar efficacy in reducing adenoviral progeny numbers as HBX (Figure 6F, HBX 58.8% reduction, HBX41108 66% reduction; Figure 6G, HBX 98.7% reduction, HBX41108 80.3% reduction).
Next, our knockdown cell lines were treated with both USP7 inhibitors. In APU6 cells neither HBX nor HBX41108 could further significantly reduce virus yield (Figure 6F). This indicates that the effects observed in our hands are specific to USP7 inhibition. Interestingly, in HU5 cells further reduction of progeny virus numbers could be achieved, but this reduction is comparable to that after HBX treatment in the HC2 control cell line (Figure 6G) indicating that remaining USP7 activity might be better exploited by HAdV5 in the H1299 background of HU5 (Figure 6G, compare HC2 + HBX with HU5 + HBX/HBX41108 and HU5 + DMSO).
In conclusion, USP7 probably exerts global positive effects upon adenoviral protein steady-state levels, which become visible when USP7 functions are artificially compromized. As expected, those general decreases in viral protein steady-state levels led to severely reduced progeny virion production, meaning that USP7 plays a pivotal role in adenoviral infection.
Together with adenoviral E1A, E1B-55K possesses the ability to transform primary rodent cells [31]. To clarify the potential role of USP7 in cell transformation mediated by adenovirus E1A and especially E1B-55K proteins, we used USP7 specific RNAi (shUSP7) and the USP7 inhibitors HBX and HBX41108 in transformation assays.
Primary baby rat kidney (Brk) cells were transfected with plasmids encoding E1A in combination with E1B-55K and shUSP7 (Figure 8A). Consistent with previous results [32], [55], E1A alone had more restricted focus forming activity, but cotransfecting the cells with E1B-55K expression plasmids increased the number of foci three to four-fold. As expected, in the presence of shUSP7, E1B-55K had little effect in cooperative focus formation, suggesting a strong requirement for USP7 in E1A/E1B-55K-mediated cell transformation. Additionally, shUSP7 had no significant effect on sole E1A-induced focus formation ruling out off-target effects (Figure 8A).
To investigate in detail how USP7 shRNAs might inactivate cell transformation by adenovirus oncogenes, a panel of transformed monoclonal Brk cell lines was established from E1A/E1B-55K (AB), and E1A/E1B/shUSP7 (ABshU) transformed foci. An shUSP7 cotransformed cell line (ABshU729, ca. 25% USP7 kd efficiency; Figure 8B, lane 5) was characterized by immunoblot in comparison to the Brk1 cells (a spontaneously transformed rat cell line derived from primary Brk cells) and reference cell lines transformed with E1A/E1B-55K plus empty vector constructs for shRNAs (AB718–720; Figure 8B, lanes 2–4). In accordance with the transfection data in the USP7 knockdown cell lines, E1B-55K expression was detected in ABshU729 cells, although reduced in comparison to reference AB cells. In all of the established cell lines E1A protein was found to be presented in similar amounts. Thus, it can be concluded that the influence of shUSP7 on the transformation process mainly affects the functions of E1B-55K.
The USP7 inhibitor HBX was applied in similar transformation assays to substantiate the role of USP7 in adenoviral oncogene-mediated cell transformation processes (Figure 8C). Plasmid-based transformation of primary rodent cells with E1A and E1B encoding plasmids was visualized by crystal violet staining of cell foci (representative plates in Figure 8D). Quantification of several experiments revealed a marked reduction in cell foci number upon HBX treatment (Figure 8C) similar to the shRNA experiments (Figure 8A). Interestingly, applying the inhibitor reduced foci formation to that of E1A-induced transformation alone, again suggesting that the effect of HBX treatment was specifically exerted upon E1B-55K. DMSO control-treated cells showed no significant change in foci formation compared to untreated cells (Figure 8C).
To test the ability of another compound against USP7 functions, HBX was applied in parallel with HBX41108. In effect, both compounds exerted almost identical efficacy in reducing the focus forming activity of E1A-E1B indicating similar specificity upon USP7 (Figure 8E). More importantly, application both compounds displayed no further reduction in foci formation activity after sole E1A transfection. This should rule out mere detrimental effects upon cell growth being responsible for reduced foci formation after HBX or HBX41108 treatment (Figure 8F).
It is notable that different to the previous transformation assays with inhibitor application (Figure 8C), in Figure 8E and F inhibitors were applied only after foci were already visible to reduce the overall time of inhibitor incubation. This might explain the lower efficiency in reducing E1A–E1B focus formation activity compared to the assay in Figure 8C.
Since interaction between USP7 and E1B-55K was only shown in transformed human cells, it was necessary to demonstrate that this binding also occurs in transformed rat cells. Indeed, it was possible to coprecipitate E1B-55K from E1B-plasmid transfected Brk1 cells, indirectly implying that this interaction also plays an important role in this setting (Figure 8G, lane 2).
These results clearly demonstrate the important role of USP7 in adenoviral oncogene-mediated transformation processes and show that shRNA or small-molecule inhibitor treatment can efficiently reduce the number of transformed cells in the experimental set-ups.
Since USP7 was first discovered as a herpesviral interacting protein by Meredith and colleagues [26], numerous studies have contributed to our knowledge about this deubiquitinating enzyme (DUB) and defined its important role in not only herpesviral diseases but also cancer-related processes. Until now, four herpesviruses have been described to be associated with USP7, namely Herpes simplex virus type 1 (HSV-1), Epstein-Barr virus (EBV), Kaposi's sarcoma-associated herpesvirus (KSHV) and Human cytomegalovirus (HCMV). Here, to this list we add, for the first time, a virus from the family of Adenoviridae and provide evidence that USP7 functions promote efficient adenovirus replication and oncogenicity.
We demonstrate direct association between the adenoviral E1B-55K protein and cellular USP7 by several assays. Functionally, USP7 is very significant for adenoviral replication and oncogene-mediated cellular transformation of Brk cells. Interestingly, not only E1B-55K protein levels seem to be dependent on USP7 functions, but also several other adenoviral proteins, too. For example, E2A protein levels were also diminished upon USP7 knockdown and partly after inhibitor application (Figure 6 and S5). Moreover, E2A colocalized with USP7 indicating USP7 being relocalized to adenoviral replication centers (Figure 2) and also providing a possibility of E2A-mediated USP7 relocalization since E1B-55K seems to be dispensable for this phenotype. In a functionally different context, this can have implications in gene expression control like it has been shown for USP7 in its activities upon EBNA1 (facilitating binding of EBNA1 to latent viral genome oriP elements) and p53 (supporting sequence-specific DNA binding activity) [28]. Therefore, it can be speculated that USP7 exerts similar functions on E2A. Nevertheless, it should be considered that just three viral and three cellular elements have been found to be sufficient for efficient adenovirus DNA replication in vitro: Adenovirus DNA-polymerase, precursor- or preterminal protein (pTP), E2A (also called DNA binding protein [DBP]), cellular NFI, NFII, and topoisomerase I [56]. In this respect, USP7 would have supportive rather than essential functions, and we are now investigating possible roles of USP7 for E2A activity.
Many efforts have been invested to find new drugs against DUBs or other proteins related to the ubiquitin-proteasome system (UPS). Aberrantly regulated DUBs are described to be involved in specific human diseases such as cancer and neurodegenerative disorders [57]. Since USP7 is as yet the only DUB discovered to be directly connected to both cancer and infectious diseases, it is very enticing to find suitable inhibitors that can be used efficiently and specifically against USP7. In a patent from 2006, Hybrigenics described several cyano-indenopyrazine substances that exerted functional inhibition of USP7 [47]. One of these Hybrigenics substances was resynthesized (due to the lack of commercially available inhibitors; here called HBX) and used in this study to perform inhibitor assays on adenovirus-infected cells in order to investigate the functional consequences of the USP7-E1B-55K interaction and prove that USP7 inhibition, like USP7 knockdown or knockout, can efficiently reduce virus replication. In the course of our studies, a derivative of HBX, HBX41108, was released and this compound was also implemented in our assays to compare efficacy of both compounds supporting specificity upon USP7 in diverse assays of this study.
Both USP7 knockdown and inhibitor application severely reduced E1B-55K protein levels, and in the knockdown setting as well as after USP7 inhibitor application the half-life of E1B-55K was significantly shorter (Figure S4A and S4B). In trying to reveal the precise mechanism underlying USP7-mediated E1B-55K stabilization, we invested much effort in demonstrating first, ubiquitination of E1B-55K and second, subsequent deubiquitination by USP7. Unfortunately, our efforts were not successful. As for now, there is no report that has investigated possible ubiquitination of E1B-55K which also involves identification of the respective E3 ligase. So, only after clarifying these two questions deubiquitination by USP7 can be tackled. Moreover, it is far from clear that stabilization of E1B-55K is mediated through deubiquitination and degradation by ubiquitination, although some indications may lead to that assumption and our study might support this theory. However, considering known functions of USP7 in gene expression control through regulation of histone proteins and the known relationship between adenoviral gene expression activity and the chromatinization of the adenoviral genome inside the nucleus of infected cells, it is also likely that USP7-mediated E1B-55K stability might be exerted through a mechanism other than direct deubiquitination of E1B-55K [58]–[60].
In keeping with the functions exerted by E1B-55K in the adenoviral life cycle, many defects in virus replication, such as reduced late protein production, can be explained by decreased functionality of this protein and the complexes it forms during productive replication. For example, late adenoviral mRNA transport is carried out by a complex comprising E1B-55K and E4orf6 [61]–[66]. As a result of reduced complex formation, it is very likely that these mRNA species do not accumulate sufficiently, which is eventually reflected by lower adenoviral capsid protein production (Figure 6 and S5).
However, during the course of this study, it became clear that USP7 not only specifically targets E1B-55K, but also exerts positive effects on other early proteins (and late proteins). To our surprise E1A and E2A steady-state protein levels were also negatively affected by USP7 knockdown and inhibition. However, certain differences between knockdown and inhibition were observed. For instance, E1A protein levels are clearly lower in knockdown versus the control cell line (Figure 6A and S5A), but showed no differences or even increased after inhibitor treatment depending on the cell type treated (Figure 6B and S5C). While it remains enigmatic why increased E1A levels do not lead to higher protein levels of those genes regulated by early viral promoters (e.g. E2A, Figure S5C), it is much easier to understand that lower E1A protein levels lead to reduced activation of viral early promoters, with a subsequent delay in protein expression of the respective genes.
Interestingly, Fessler and Young demonstrated that lowered expression from the major late promoter (MLP) leads to increases in the expression of early genes among them E1A and E1B (the mechanism of this phenomenon has not been clarified in detail yet) especially when MLP expression is hampered at late times of infection [67]. This might explain the contrast between USP7 knockdown and inhibition in relation to the E1A and E1B-55K reduction (compare Figure 6A and S5A with Figure 6B and S5C). Knockdown is a permanent condition in our assays leading to the assumption that USP7 is needed for proper E1A expression in the initial phase of the adenoviral replication cycle. However, inhibitor treatment in our assays starts earliest 9 h p.i. and compromising late gene expression/late protein stability at this time point through USP7 inhibition rather increases than decreases E1A levels and might also explain why E1B-55K reduction is attenuated in contrast to the single transfection assays. But how can USP7 affect late gene expression? Taking into account that USP7 not only affects E1B-55K but also, for example, E2A levels it is probable that E2A functions in promoting DNA replication might be hampered which in turn lead to the observed negative effect on late gene expression [67], [68].
Another aspect to consider, while knockdown affects protein levels in toto, inhibitor treatment might affect only one function of a protein without affecting other functions at all. Due to USP7's multi-domain structure, its functions are not only carried out by the enzymatic domain [36], [69]. However, the specific effects of both approaches (knockdown and inhibition) upon other viral proteins such as E1B-55K or structural proteins clearly suggest that USP7 functions are necessary during the whole course of infection. In this context, USP7, having enzymatic activity, represents a potent target for small-molecule inhibitors. Our results clearly indicate that adenoviral progeny virions can be reduced in a significant manner (up to over 90%) even after an established infection using an inhibitor of USP7, results that could, at least qualitatively, also be confirmed with RNAi experiments.
Strikingly, both approaches to disrupting USP7 functions also diminished the ability of adenoviral oncogenes to induce cellular transformation. It is not sure but possible that shUSP7 induced low levels of E1B-55K which might explain the fewer counted cell foci in this set-up (Figure 8B). Similarly, addition of the USP7 inhibitors HBX (Figure 8C and E) and HBX41108 (Figure 8E) also lowered the number of transformed cell foci.
Considering that USP7 is already known to be involved in tumor pathways, the observed phenotypes may be explained by two possible scenarios: First, p53 is activated, accumulates and promotes antiproliferative activities due to the instability of its negative regulator Mdm2. It has been shown that Mdm2 is the primary target of USP7-mediated stabilization. Thus, inhibition of USP7 in this setting might lead to reduced Mdm2 levels. Second, increased Daxx proapoptotic activity supports cell death. As in the case of p53, Daxx levels increase due to missing negative Mdm2 regulation after USP7 inhibition. Additionally, in transformation settings, Daxx functions are antagonized by E1B-55K, and as shown in Figure 5 E1B-55K levels definitely depend on functional USP7. Indeed, it was possible to demonstrate an E1B-55K-USP7 interaction in rat (equivalent to human) cells (Figure 8G), indirectly supporting a direct relationship between USP7 and E1A-E1B-55K-mediated cellular transformation. Therefore, similar USP7-dependent mechanisms may play an important role during adenoviral lytic infection and adenoviral-oncogene-mediated transformation processes, emphasizing the extraordinary relationship between USP7 and E1B-55K.
In summary, for the first time, one cellular protein can be linked to efficiently reducing adenovirus yield and virus-mediated cellular transformation. Therefore blocking the activity of USP7 could potentially be used to treat adenovirus infections. Particularly, pediatric patients undergoing allogenic stem cell transplantation are vulnerable to disseminated adenovirus infections, leading to a high mortality rate [11]. Hence, there is a need for potent antiviral therapeutics against adenoviruses that allow suppression of the virus at different stages in the replication cycle [12]. USP7 represents a striking drug target.
A549 and H1299 cells were cultivated as described [70]. HCT116 and HCT116 USP7 double-knockout cells (USP7 −/− [KO]; kind gifts of Dr. Bert Vogelstein) were grown in McCoy's 5a Medium (GIBCO) supplemented with 10% or 15% (USP7 KO) fetal bovine serum (FBS, PAA), 100 U of penicillin, and 100 µg of streptomycin per ml. Primary Brk (baby rat kidney) cells and the Brk-derived cell line Brk1 [31] were grown in Dulbecco's modified Eagle medium (DMEM, PAA) supplemented with 5 to 10% FBS, 100 U of penicillin, and 100 µg of streptomycin per ml in a 5% CO2 atmosphere at 37°C. APU5 and APU6 (USP7 knockdown, kd) cells were grown under the same conditions as Brk cells, and HC2 and HU5 (USP7 kd) cells were grown under the same conditions as H1299 cells. Additionally, all the knockdown and control cell lines were also grown under constant puromycin challenge (2 µg/ml, Calbiochem). Infections with wt (H5pg4100) and E1B minus (H5pm4149) adenoviruses and subsequent virus yield experiments were carried out as described earlier [70]. Infection with E1B minus2 (dl1520) was carried out like described earlier [39]. Virus yield was calculated as described earlier [70] in virus particles per cell (FFU/cell), these results were normalized and presented as a function of untreated or control cells. This calculation allowed negative cell growth effects exerted by HBX or HBX41108 to be neglected. In indicated experiments HBX, HBX41108 and DMSO was added 15 hours before cell harvest at denoted concentrations or incubated for 24 hours. Transfections with plasmid DNAs used PEI as a transfecting agent and were carried out as described earlier [70]. Other experiments included cycloheximide addition (SIGMA) and harvest at indicated time points (end concentration 10 µM). The USP7 inhibitor HBX (example 1) was synthesized as described earlier [47].
Indirect immunofluorescence staining and image capturing was carried out as described earlier [70]. Processing and layout of images were accomplished using Adobe Photoshop and Illustrator CS4 software tools. Statistical analyses were all performed with Microsoft Excel 2007 and GraphPad Prism 5. Western blot band intensities were analyzed with ImageJ 1.45s.
Coimmunoprecipitation (Co-IP) assays were performed with the anti-USP7 (3D8 or 6E6) and, as a control, a non-specific IgG2a monoclonal rat antibody (1–2 µg/sample) was used. Protein analyses, Western blots and antibody usage in general were also carried out as described earlier [70]. The USP7 antibodies 3D8 and 6E6 were used for Western blots as a 1∶10 dilution in phosphate-buffered saline (PBS) containing 0.05% Tween 20 (AppliChem) and 1% nonfat dry milk. Following steps as referenced above.
2.5×105-1.0×106 H1299 cells were seeded into 6-well plate wells or 10 cm dishes (Sarstedt) transfected with pECFP-C1, pEYFP-C1, pEYFP-ECFP (pEYFP-ECFP is a fusion protein; the first three plasmid constructs were kind gifts from Dr. Carina Banning), YFP-E1B-55K (YFP-55K) and USP7-CFP in different combinations as indicated. 24–48 hours post transfection cells were harvested and assayed. Following steps were performed as previously described [37], [71].
The GST fusion proteins E1B-55K, E1B 156R, E1B 93R, E1B 83–188, E1B 93R 1–82, E1B 1–162, USP7 TD, USP7 CD, USP7 C1 and USP7 C2 were expressed and purified as described earlier [70]. For the GST pull-down assays equal amounts of fusion proteins were incubated with a defined quantity of cell lysate. This mixture was then incubated for 2 h at 4°C on a turning rotor. The proteins bound to the Glutathione Sepharose (GE Healthcare) were subsequently precipitated by centrifugation (6500 rpm, 5 min, 4°C), six times washed with PBS or lysis buffer, centrifuged and boiled in 25 µl of SDS sample buffer. The protein samples were then analyzed by SDS-PAGE and Western blotting. Input of recombinant proteins was analyzed by Coomassie brilliant blue staining (CB).
1.5×103 cells were seeded per 96-well plate well (Falcon). 12–20 hours later, culture medium with different compound concentrations (concentration series in triplicates) was added to cells, replacing the old medium. As controls, untreated and compound solvent (DMSO) treated cells were used. For all compound and solvent treated cells, the final concentration of DMSO (usually 0.05%) was equal. Cells were incubated for different time points with compound, usually 24, 48 and 72 h and then cell proliferation was measured with the Promega CellTiter 96 Aqueous One Solution Cell Prolifertation Assay (MTS = (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium)) according to manufacturer's instructions. The resulting color reaction was measured with a plate reader at 490 nm (BioTek SynergyMx).
Transformation assays were carried out as described earlier [72], [73]. In addition, the USP7 inhibitor HBX and DMSO were also included in the growth medium as indicated. Transformation assay in Figure 8C was carried out with inhibitor addition 4–6 days after transfection. Transformation assay in Figure 8E and F were carried out with inhibitor addition after first foci were visible.
To establish stable rat cell lines, foci were isolated using a glass cloning cylinder (5 mm diameter) circling single colonies. The cells within the cylinders were trypsinized with 100 µl of trypsin/EDTA solution. When the cells were detached from the dish, they were transferred into the wells of a 24-well plate (Falcon) containing DMEM with 10% FBS. These cells were grown for several weeks and expanded to obtain monoclonal cell lines.To establish stable monoclonal USP7 knockdown cell lines from A549 and H1299, these cells were seeded onto 6-well dishes and transfected with pSuper-shUSP7 (shUSP7) or empty vector using PEI. One day after transfection, fresh media containing 2–3 µg/ml of puromycin (Sigma) was added to the transfected cells. Three days later, the cells were split in a ratio of 1∶30, and seeded onto two 150 mm-diameter tissue culture dishes (Falcon). Fresh media containing puromycin were added to the cells every 3–4 days to select the stably transfected ones. Three weeks after splitting, foci were chosen and isolated as above to establish monoclonal cell lines and expanded. Puromycin was always included in the growth medium of these cells. HC2 contains the pSuper.retro.puro empty vector and HU5 is stably transfected with shUSP7. APU5 and APU6 are both stably transfected with shUSP7 whereas USP7 knockdown is only detected in APU6.
HAdV5 E1B-55K protein: AP_000199.1. Human USP7 (HAUSP) protein: NP_003461.2.
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10.1371/journal.pgen.1005784 | Mutations of the Calcium Channel Gene cacophony Suppress Seizures in Drosophila | Bang sensitive (BS) Drosophila mutants display characteristic seizure-like phenotypes resembling, in some aspects, those of human seizure disorders such as epilepsy. The BS mutant parabss1, caused by a gain-of-function mutation of the voltage-gated Na+ channel gene, is extremely seizure-sensitive with phenotypes that have proven difficult to ameliorate by anti-epileptic drug feeding or by seizure-suppressor mutation. It has been presented as a model for intractable human epilepsy. Here we show that cacophony (cacTS2), a mutation of the Drosophila presynaptic Ca++ channel α1 subunit gene, is a particularly potent seizure-suppressor mutation, reverting seizure-like phenotypes for parabss1 and other BS mutants. Seizure-like phenotypes for parabss1 may be suppressed by as much as 90% in double mutant combinations with cacTS2. Unexpectedly, we find that parabss1 also reciprocally suppresses cacTS2 seizure-like phenotypes. The cacTS2 mutant displays these seizure-like behaviors and spontaneous high-frequency action potential firing transiently after exposure to high temperature. We find that this seizure-like behavior in cacTS2 is ameliorated by 85% in double mutant combinations with parabss1.
| Seizure disorders, such as epilepsy, are a serious health concern because of the large number of patients affected and a limited availability of treatment options. About 10% of the population will have at least one seizure during their lifetime, and 1% will experience persistent, recurrent epileptic seizures. Moreover, for about one-third of patients, epilepsy is intractable with seizures that are not controlled with any available drugs. Genetic seizure suppressors are modifier mutations that are capable of reverting seizure susceptibility to wild type levels when combined with seizure-prone mutants in double mutant individuals. Suppressors are valuable in providing an experimental approach that can provide insight into mechanisms underlying seizure susceptibility. Also, they identify novel gene products that may be targets for therapeutic drug development. In the present study we show that a severe seizure phenotype of the Drosophila paralyticbss1 (parabss1) mutant is 90% suppressed by the N-type calcium channel mutation cacophonyTS2 (cacTS2). The effect of suppression is not restricted to parabss1, but cacTS2 can also revert seizure-like phenotypes of other Drosophila mutants like easily-shocked (eas) and slamdance (sda). Thus, cacTS2 acts as a highly potent, general seizure suppressor mutation. A surprising finding in this study is co-suppression: parabss1 also suppresses a seizure phenotype in cacTS2 mutants induced by elevated temperature. A current view of complex diseases such as epilepsy, is that multiple genes and environmental factors can each contribute small, additive effects that can summate to produce a disease state when some threshold value is exceeded. Our findings indicate that different pathogenic ion channel mutations can sometimes form therapeutic combinations with effects that mask one another.
| Human seizure disorders are a substantial neurological health problem because of the large number of affected individuals, and heterogeneity underlying the many syndromes. An estimated 1% of the U.S. population, nearly 3 million Americans, is affected by the more than 40 different syndromes that comprise the epilepsies [1,2]. Seizures occur because of an imbalance in excitation and inhibition: excitation can be excessive, inhibition can be inadequate, or both. The resulting seizure activity involves large numbers of neurons firing uncontrollably and synchronously, usually in a rhythmic pattern. Multiple and different molecular aspects of electrical signaling appear to be responsible for the triggering of seizures at the site of initiation or focus, their subsequent spread from the focus to adjacent regions of nervous tissue, and their eventual termination.
In this study, we examine the contribution of basic synaptic transmission to seizure-susceptibility in a Drosophila model using mutations of the cacophony (cac) gene, responsible for neurotransmitter release. The cac gene encodes the α1 subunit of the Drosophila voltage-gated presynaptic Ca++ channel, homologous to the mammalian N-type channel [3–7]. The allele used here, cacTS2, shows conditional and reversible phenotypes dependent on temperature: a behavioral paralysis phenotype and a loss of neurotransmitter release phenotype [5–7]. At restrictive high temperatures, evoked synaptic currents are markedly reduced in cacTS2 mutants, returning to wild-type levels when temperature is lowered to permissive temperatures.
We report here that the cacTS2 mutation affects seizure susceptibility in complex ways including seizure-sensitivity and seizure-resistance, under different conditions. As reported [8], cac temperature-sensitive mutants display spontaneous seizure-like activity when shifted to restrictive temperature. We find here that at permissive temperature, cacTS2 is a seizure-resistant mutation and a potent seizure-suppressor. In double mutant combinations with bang-sensitive (BS) seizure-sensitive mutants, cacTS2 is one of the strongest seizure-suppressors that we have identified in the fly, to date. In particular, the cacTS2 mutation is found to ameliorate seizure-like phenotypes in homozygous parabss1, a Na+ channel gain-of-function mutation, the most severe of Drosophila seizure-sensitive mutations [9,10] and resembling, in some aspects, Na+ channel loss-of-function mutations responsible for intractable epilepsy [11,12]. The cacTS2 mutation is a good suppressor of parabss1 phenotypes comparable to malelessnapts and stronger than heat-treated shibirets1 and gilgamesh (Table 1)[13,14,15,16].
At restrictive temperatures, cacTS2 exhibits complex phenotypes including TS seizure-like activity, synaptic failure and paralysis. We found that all cacTS2 phenotypes are reciprocally suppressed in double mutant combination with parabss1. Suppression of TS seizure-like behaviors in cacTS2 by a Na+ channel mutation indicates that the combination of two ion channel alleles involved in epilepsy can have beneficial clinical effects when present in the same individual organism: that is, each of the two mutations co-suppresses seizures caused by the other, similar to observations reported for mouse [17].
The behaviors of cacTS2 mutants are unexceptional at room temperature (24°C): feeding, grooming, and mating behaviors appear normal. Overall activity levels are unaltered: flies are neither sluggish nor hyperactive. The cacTS2 mutants show no bang-sensitive (BS) behavioral paralysis phenotype and are unaffected by mechanical stimulation. Using the adult giant fiber (GF) neurocircuit as a proxy for holo-nervous system function, the electrophysiology phenotype for cacTS2 at room temperature generally resembles wild-type [18]. Thus, single pulse stimulation of the GF produces evoked potentials and synaptic currents in the dorsal longitudinal muscle (DLM) that are normal in appearance (S1 Fig) [5], have a threshold of 0.96 ± 0.12 V (mean ± s.e.m., n = 9) and a latency of 1.1 ± 0.04 msec (mean ± s.e.m., n = 5).
Seizure-like electrical activity in cacTS2 mutants can be evoked with high-frequency stimuli (HFS; 0.5 msec stimuli at 200 Hz for 300 msec; Fig 1A), similar to discharges observed for other Drosophila mutants [19,20]. Large HFS voltages were characteristically required to evoke seizures at room temperature indicating that cacTS2 behaves as a seizure-resistant mutant. Seizure threshold for cacTS2 was (58.3 ± 1.0 V HFS, mean ± s.e.m., n = 13), nearly twice that of Canton-Special wild type flies (24.64 ± 2.83 HFS, mean ± s.e.m., n = 10; Fig 1A–1C). The seizure threshold for cacTS2 is comparable to previously-reported seizure-resistant mutants such as paralyticts1, ShakerKS133 and shakingB2 that have high seizure thresholds and can also act as seizure-suppressor mutations in double mutant combinations with BS mutants [19].
Several cac alleles, especially cacTS2 and cacNT27, are notable for their temperature-sensitivity (TS) with essentially normal behavior and neurology at room temperature which is permissive; and displaying complicated neurological phenotypes at high temperature (>38°C) which is restrictive [5,8]. The shift from permissive to restrictive temperature, causes a transient period of nervous system hyperexcitability lasting several seconds [8], followed by a prolonged period of hypoexcitability with synaptic failure and behavioral paralysis [5]. The hyperactive period is characterized by spontaneous seizure-like behaviors: leg-shaking, abdominal twitching, wing scissoring, and proboscis extensions. These are accompanied by spontaneous seizure-like firing of the DLM motor neurons in electrophysiology recordings (Fig 1D), similar to that described previously [8]. Seizure-like activity for cacTS2 at elevated temperature is interesting considering the seizure-resistant phenotype observed at room temperature. The spontaneous seizure-like DLM activity generally resembles that observed during evoked seizure-like activity (Fig 1). We were unable to determine a reliable evoked seizure-threshold for cacTS2 at restrictive temperature. Immediately following the shift to restrictive temperature, the seizure-threshold is still high, resembling the threshold of cacTS2 at room temperature. Spontaneous seizure-like activity ensued soon after the shift to restrictive temperature and their nearly continuous occurences made it difficult to distinguish them from stimulus-evoked seizures. Taken together, these findings indicate that cacTS2 is apparently a seizure-resistant mutant at room temperature, changing to a seizure-sensitive mutant after shift to restrictive temperature.
In order to test for genetic interactions, we constructed double mutants between cacTS2 and different BS mutants. This resulted in suppression of the BS phenotype as exemplified by hemizygous double mutant male flies cacTS2/Y;;sda that showed 12% behavioral BS paralysis (indicating 88% phenotypic suppression, n = 147; P < 0.0001, chi- square test; Fig 2A; Table 1) at room temperature (24°C), compared to the sda single mutant control flies which showed 100% BS paralysis (P < 0.0001). This finding of suppression at room temperature was a little unexpected since cacTS2 has previously been described as a temperature-sensitive mutation and permissive temperature phenotypes have not been reported. This result may be related to the observation above that cacTS2 is seizure-resistant at room temperature; heretofore the only other difference from wild type that we have seen at room temperature.
In order to examine if temperature has an effect on BS suppression, we examined double mutants at elevated temperatures within the nominally permissive range, that is below 38°C, to avoid cacTS2 behavioral paralysis. We found that seizure-suppression by cacTS2 is increased at elevated temperatures. In hemizygous double mutant flies cacTS2/Y;;sda, a brief heat shock (HS; 3 min at 30°C), completely suppressed all sda bang-sensitivity (0% paralysis, 100% suppression, n = 93; P < 0.0001, chi-square test; Fig 2A; Table 1). A similar brief HS delivered to control single mutant sda flies had no effect on bang-sensitivity: 100% of control flies continued to show BS paralysis (P < 0.0001).
The cacTS2 mutation is a general seizure-suppressor, not limited to suppression of BS phenotypes in sda mutants: modest seizure-suppression is also observed for eas mutants. In hemizygous double mutant male flies eas cacTS2/Y, BS was 90% (10% suppression, n = 147; P = 0.0002, chi- square test; Fig 2B; Table 1) at room temperature compared to 100% BS in eas single mutant controls. In eas, suppression by cacTS2 was also increased with exposure to elevated temperature. In hemizygous double mutant flies eas cacTS2/Y, bang-sensitivity was 54% (46% suppression, n = 93; P = < 0.0001, chi-square test; Fig 2B; Table 1) following a brief HS (3 min at 30°C). HS had no effect on the bang sensitivity of eas single mutant control flies: 100% of the control flies showed BS paralysis (P < 0.0001). Thus, cacTS2 acts as a general suppressor of BS behavior, reverting phenotypes of both sda and eas BS mutants in double mutant combinations. Some suppression occurs at room temperature, although suppression increases with increases in temperature within the permissive temperature range. Previous studies have also shown that BS phenotypes in sda mutants are easier to suppress than for eas mutants [21,22].
We investigated genetic interactions between cacTS2 and parabss1 by constructing the appropriate double mutant combinations. Previous studies have found that seizure-like phenotypes are difficult to suppress in parabss1 mutants, that carry a gain-of-function voltage-gated Na+ channel defect [10 (Table 2)]. We find here that cacTS2 is an effective suppressor of parabss1 behavioral phenotypes. For hemizygous double mutant males (genotype: parabss1 cacTS2/Y), BS paralysis was 36% (64% suppression; n = 658; P < 0.0001, chi-square test) at room temperature (Fig 2C; Table 1). Suppression by cacTS2 was also increased at elevated temperature. After a brief HS, BS paralysis decreased to 13% (87% suppression; n = 650; P < 0.0001, chi-square test) in hemizygous double mutants (Fig 2C; Table 2). Homozygous double mutant females (genotype: parabss1 cacTS2) showed 23% BS paralysis at room temperature (77% suppression) which decreased to 8% (92% suppression) following HS (Table 2). In control parabss1 flies, there was no effect of HS: 100% of flies showed BS paralysis (P < 0.0001; Table 2). The cacTS2 mutation is an especially effective suppressor of parabss1/+ heterozygote behavioral phenotypes. In double mutant flies (genotype: parabss1 cacTS2/para+ cacTS2) BS paralysis was 2% (98% suppression) at room temperature, compared to 62% BS paralysis seen in control parabss1/+ heterozygotes without the cacTS2 suppressor (n = 60; P < 0.0001, chi-square test; Fig 2D; Table 2).
The salient consequence of cacTS2 suppression is the increased percentage of flies escaping BS paralysis, but flies that undergo paralysis are also influenced by the suppressor: observed as a reduction in the time required for recovery. Control parabss1/Y mutant males when paralyzed ordinarily have a long recovery time 195 sec. In contrast, paralyzed flies carrying the suppressor (genotype: parabss1 cacTS2/Y) have about a four-fold reduction in the time to recovery 46 sec (n = 45; P < 0.0001, unpaired student t-test). Moreover, cacTS2 reduced the refractory time period for hemizygous double mutant male flies parabss1 cacTS2/Y. Double mutants show a shorter refractory time period of 17 min, compared to 25 min for parabss1 single mutant flies (n = 34; P = 0.0005, unpaired student t-test).
Seizure suppression by cacTS2 is also observed in evoked seizure-like neuronal activity recorded electrophysiologically. This analysis shows an unusual seizure-suppression of parabss1 by cacTS2. Immediately following HS, there is considerable suppression of parabss1 (Fig 3A–3D), but this suppression is transient and short-lasting (Fig 3D). Thus, immediately following the HS (3 min at 30°C), seizure-threshold for parabss1 cacTS2 double mutants is high (51.6 ± 1.2 V HFS, mean ± s.e.m., n = 7; P < 0.0001, ANOVA test; Fig 3D). This is greater than the seizure threshold for the parabss1 by about a factor of ten, and greater than wild type seizure-threshold, by nearly a factor of two; flies with seizure-thresholds in this range are seizure-resistant mutants (Table 3) [14]. Seizure-threshold quickly decreases when maintained at room temperature and the steady-state seizure-threshold of parabss1 cacTS2 double mutants at room temperature is (3.82 ± 0.2 V HFS, mean ± s.e.m., n = 10), similar to the parabss1 single mutant (3.2 ± 0.1 V HFS, mean ± s.e.m., n = 8; Fig 3A). The time course of the threshold change is difficult for us to determine with our present electrophysiology protocols, but it appears similar to changes in BS behavior following HS, about 5–7 min.
Electrophysiology analysis also shows cacTS2 suppression of other BS mutants. The cacTS2;;sda double mutant has a seizure-threshold of (20.5 ± 2.42 V HFS, mean ± s.e.m., n = 7) following HS and tested at room temperature, about 3-fold higher than the threshold of the sda single mutant (6.2 ± 0.3 V HFS, mean ± s.e.m., n = 5, P < 0.0001, unpaired student t-test; Fig 3E). That is, in the double mutant, there is a reversion of BS electrophysiology by cacTS2 to nearly the wild-type range of seizure-threshold. The cacTS2 mutation also suppresses eas seizure-like activity. Hemizygous double mutant flies eas cacTS2/Y have a seizure-threshold of (8.7 ± 1.1 V HFS, mean ± s.e.m., n = 11) following HS, about two-fold higher than the low threshold of the eas single mutant (3.8 ± 0.1 V HFS, mean ± s.e.m., n = 6; P < 0.01; unpaired student t-test; Fig 3D; Table 3).
To further study cac seizure-suppression, we generated loss-of-function cac genotypes using cacRNAi to knockdown cac expression; these were tested for BS mutant suppression. Flies utilizing a pan-neuronal GAL4 driver and one copy of cacRNAi were viable and had largely normal behavior. Similar to the cacTS2 mutation, cacRNAi suppressed BS behavioral phenotypes in double mutant male parabss1 flies. Males (genotype: elavc155-GAL4 parabss1/Y;;UAS-cacRNAi/+) showed BS paralysis in 64% of flies (36% suppression; n = 212; P < 0.0001, chi-square test; Fig 4A) at room temperature. Suppression of BS by cacRNAi was more effective in eas mutants. Males (genotype: elavc155-Gal4 eas/Y;;UAS-cacRNAi/+) showed BS paralysis in 15% of flies (85% suppression, n = 74; P < 0.0001, chi- square test; Fig 4B). Although cacRNAi was effective at suppressing BS phenotypes, in other respects it was different than cacTS2 mutations because it did not cause temperature-sensitive phenotypes. Thus, male cacRNAi flies (genotype: elavc155-GAL4/Y;;UAS-cacRNAi/+) at 38°C showed netiher spontaneous seizure-like behaviors no behavioral paralysis.
Double mutant parabss1 cacTS2 flies were examined following a temperature shift from room temperature to 38°C, the restrictive temperature for cacTS2. Interestingly, some temperature-sensitive phenotypes, prominent in the cacTS2 single mutant, were reduced in the double mutant, apparently suppressed by the presence of parabss1 in the double mutant combination. In parabss1 cacTS2 flies the TS spontaneous seizure-like electrophysiological phenotype was greatly reduced (Fig 5A and 5B). Electrophysiological recordings from cacTS2 single mutants show the number of spontaneous seizure-like discharges was 10 ± 1 spontaneous discharges/HS (mean ± s.e.m., n = 10; HS = 3 min at 38°C. Fig 5A and 5C). In contrast, recordings from parabss1 cacTS2 double mutants show 2.5 ± 0.42 spontaneous discharges/HS (mean ± s.e.m., n = 20, P = 0.0003, unpaired student-t test; Fig 5B and 5C). In addition to the number of spontaneous discharges being reduced, there also appeared to be a reduction in discharge duration (Fig 5A and 5B). The temperature-sensitive behavioral paralysis phenotype of cacTS2 was also suppressed by parabss1 (Fig 5D). For the cacTS2 single mutant, 100% of flies undergo paralysis when the temperature is increased from room temperature to 38°C, as described in previously [5]. In contrast, for parabss1 cacTS2 double mutants, only 20% of flies are paralyzed at 38°C (80% suppression, n = 76; P < 0.001, chi-square test, Fig 5D).
We find that cacTS2 is a general seizure-suppressor mutation, reverting neurological phenotypes of several BS mutants: sda, eas, and parabss1. Suppression of parabss1 is especially notable because it is a BS mutant that has previously been difficult to modify by suppressor mutation [21,22] or antiepileptic drug [23–27]. Recently, directed efforts to target parabss1 by suppressors have identified two: gilgamesh (gish) and shibirets (shits), although both appear somewhat weaker than cacTS2 [15–16 (Table 1)]. Suppression by gish is unusual because it is selectively effective against parabss1/+ heterozygotes; gish does not suppress homozygous parabss1. Also, gish does not suppress other BS mutants, such as sda or eas. [16]. For shits, suppression of parabss1 is not evident at room temperature, but occurs with increased temperature that causes interference with endocytosis during synaptic vesicle recycling [15]. BS suppression reported here for cacTS2 is comparable or better than for gish and shits.
The major questions arising from this study are: how does cacTS2 suppression work? And what is it about the cacTS2 mutation that makes it such an effective suppressor of parabss1 primary phenotypes, BS behavior and seizure threshold? A complete answer to these questions remains unclear from the experiments we are able to perform here, but leads to speculation about mechanisms of seizure, and about how seizure-suppression might be accomplished. The cacTS2 allele behaves as a recessive loss-of-function mutation with reduced neurotransmitter release at the neuromuscular junction and paralysis at high temperature [5]. Also at high temperature, the mutant displays considerable spontaneous seizure-like activity seen in muscle fiber recordings [8]. At first, it might appear that this seizure-like activity is inconsistent with the cacTS2 phenotype of reduced transmitter release, especially if this reflects an overall reduction in excitability. The cacTS2 seizure-like activity must be due to spontaneous action potential bursting in adult and larval motor neurons; the activity recorded in the muscle fiber is reflecting seizure-like motor neuron firing. We suggest that this motor neuron firing may be due to a loss of inhibitory synaptic activity impinging on them, possibly causing some type of post-inhibitory rebound excitation within the motor neurons. That is, as excitatory transmitter reduction by temperature is observed at the neuromuscular junction, synaptic inhibition that ordinarily limits motor neuron firing is concurrently reduced leading to spontaneous seizure-like activity observed in muscle.
About fifteen mutations have been identified previously as seizure-suppressors [reviewed in 22]. Some of these suppressors encode well-studied gene products that have not heretofore been associated with neuronal signaling or membrane excitability such as the de-ubiquitinase USP9X [28] and DNA topoisomerase I [29]. Some of the seizure-suppressor genes encode neuronal signaling molecules that have allowed us to consider previously three likely mechanisms for how seizure-like activity might be suppressed by second-site mutations; here, suppression by cacTS2 suggests to us a fourth mechanism. Previously, we found that:
The signaling molecules responsible for the process of chemical synaptic transmission are a potentially rich source for identifying seizure-suppressor mutations. Seizure-suppressors are most logically expected from among mutations enhancing inhibitory GABAergic synaptic transmission or mutations diminishing excitatory cholinergic transmission. Some other mutations are most logically expected to enhance seizure phenotypes such as mutations decreasing GABAergic function or enhancing cholinergic transmission. It is more difficult to anticipate the effect on seizures of mutations affecting general synaptic transmission properties, that is, molecules that are common to both excitatory and inhibitory synaptic processes. The cacTS2 mutation examined here is such a mutation, the cac gene encodes the α1, primary structural subunit of the voltage-gated Ca++ channel responsible for triggering regulated synaptic vesicle release at both excitatory and inhibitory synapses [4,34]. Thus, it was surprising that cacTS2 was not only a seizure-suppressor mutation, it was one of the most effective suppressors that we have identified. Because of this, we propose that cacTS2 suppression may work via a somewhat different mechanism than we have observed previously, generally, using neurocircuitry to cause seizure suppression. We presume that the suppression works by interfering with chemical synaptic transmission in many or most circuits in the fly. Modest interference in synaptic transmission at room temperature is sufficient to suppress weak BS mutants, such as sda. Stronger disabling of synaptic transmission following a heat pulse is necessary to suppress the stronger BS parabss1.
We thought it possible to identify specific circuits responsible for suppression by the differential GAL4/UAS expression of cacRNAi. Our initial attempts expressed cacRNAi selectively in excitatory interneurons (cha-GAL4 driver), or inhibitory interneurons (GAD-GAL4 driver). Expression of cacRNAi in different interneuronal populations was a little less effective than pan-neuronal expression, but differences were small (S2 Fig). From this limited investigation, we do not find indications for specific circuits suppressing seizures or, if they exist, how we might go about discovering them. We entertain the interesting possibility that cac suppression may not be due to the disabling of particular circuits, but is a general block of seizure-like activity by an overall poorly-transmitting nervous system. It remains surprising that such a putative mechanism of seizure-suppression would be especially effective at reverting parabss1 seizure phenotypes which are severe.
The cac gene is one of the most interesting Drosophila neurological genes. The gene is predicted to encode 15 annotated transcripts and 14 unique polypeptides. Numerous mutations have been identified (72 alleles) and functions ascribed to different subsets of cac mutations [35–37]. Male courtship song alteration is one of the canonical phenotypes of cac exemplified by the original cacS mutation. Subsequently cacTS2 and cacNT27 were also shown to have alterations in courtship song [37]. All of the mutations in this subset show motor defects, seizure-like activity, and behavioral paralysis. These mutations and several other cac alleles in this subset all fail to complement each other. The cacTS2 mutation is due to a mis-sense mutation that is thought to alter Ca++-dependent inactivation [38]. Thus, although cacTS2 is recessive, it could behave as a gain-of-function mutation. Nevertheless, RNAi experiments presented here show that cac loss-of-function can cause seizure-suppression. However, flies carrying cacRNAi show neither seizure-like activity nor paralysis, suggesting these phenotypes could be due to gain-of-function phenotypes of cacTS2. These issues remain to be determined in future experiments.
Another interesting finding in this study is the co-suppression by parabss1 of the cacTS2 spontaneous seizure-like phenotype induced by high temperature. We presume that this must be due to a loss of spontaneous motoneuron spiking, since activity in the DLM muscle fiber reflects post-synaptic potentials from neuromuscular transmission. The mechanism responsible for this loss of motoneuron spiking is unclear; there are not previously described functions of parabss1 that easily account for it. The parabss1 sodium channel mutation causes gain-of-function phenotypes and leads to hyper-excitability in neurons. It is this hyper-excitability that makes parabss1 mutants more prone to seizures. The cacTS2 mutation causes a less functional Ca2+ channel and hence a decrease in release of neurotransmitter. So, bringing two defective ion channels with different effects on membrane excitability effects leads to the suppression of epilepsy. This Drosophila suppression resembles seizure-suppression findings in mice [17]. Double mutant mice carrying mutations in two epilepsy genes, Cacna1and Kcna1a showed improvement in both absence epilepsy and limbic seizure phenotypes caused by these mutations [17].
Drosophila strains were maintained on standard cornmeal-molasses agar medium at room temperature (24°C). The cacophony (cac) gene is located on the X chromosome at 10F-11A on the cytological map and encodes a voltage-gated Ca++ channel α1 subunit implicated in neurotransmitter release [3–8]. The cacTS2 allele is a recessive mutation caused by a substitution (P1385S) at the C-terminus [4]. This position is adjacent to an EF hand motif thought to be involved in calcium dependent inactivation. The cacTS2 mutation causes temperature-sensitive paralysis: apparently due to a reduction, and then loss of synaptic current as the temperature is raised from permissive to restrictive values [4]. The paralytic (para) gene is located at map position 1–53.5 and encodes a voltage-gated Na+ channel [39–40]. The allele use here is a bang-sensitive (BS) paralytic mutation, parabss1, previously named bss1[14] It is the most seizure-sensitive of fly mutants, the most difficult to suppress by mutation and by drug, and has been proposed as a model for human intractable epilepsy [10]. The parabss1 allele is a gain-of-function mutation caused by the substitution (L1699F) of a highly conserved residue in the third membrane-spanning segment (S3b) of homology domain IV [10]. The easily shocked (eas) gene is located at 14B on the cytological map and encodes an ethanolamine kinase [41]. The BS allele used in this study is easPC80, which is caused by a 2-bp deletion that introduces a frame shift; the resulting truncated protein lacks a kinase domain and abolishes all enzymatic activity [22]. The slamdance (sda) gene is located at 97D and encodes an aminopeptidase N. The allele used in this study is sdais07.8 caused by a 2-bp insertion in the 5’ untranslated region [42]. The UAS-cacRNAi line was obtained from Bloomington Drosophila Stock Center. The insert for UAS-cacRNAi is located on the 3rd chromosome.
The double mutants used in this study were constructed by standard genetic crosses and then verified for the presence of both the BS mutation (sda, eas or parabss1), as well as cacTS2. The presence of cacTS2 in the homozygous double mutant stock was verified by testing for behavioral paralysis after heat shock (37°C for 5 min), which is characteristic for this mutation; BS mutants do not paralyze under such conditions. The presence of the homozygous BS mutation in the double-mutant stocks was verified by backcrossing each double mutant stock to females of the appropriate BS genotype. The progeny from those crosses, which should be homozygous for the BS mutation and heterozygous for cacTS2, were then tested for the BS behavioral phenotype. All of the genotypes arising from the back cross phenotypically resembled BS homozygous flies. The lack of any obvious effects among the different genetic backgrounds tested also indicated the alterations in seizure-sensitivity reported here were due to the homozygous presence of cacTS2 in the double mutant combinations and not likely due to nonspecific genetic background differences.
Behavioral testing for BS paralysis was performed on flies 3d after eclosion, as described previously [16]. Flies were anesthetized with CO2 before collection and tested the following day. For testing, 10 flies were placed in a clean food vial and stimulated mechanically with a VWR vortex mixer at maximum speed for 10 s. The parabss1, eas, and sda mutants ordinarily show 100% penetrance of BS paralytic behavior with this test. Suppression by cacTS2 was initially manifest as a reduced percentage of BS behavioral paralysis in the double mutant compared to the single BS mutant. Recovery from BS paralysis was determined by counting the number of flies standing at different intervals following stimulation. Recovery time was the time at which 50% of flies had recovered from paralysis. For genotypes that display partial penetrance of BS paralysis, only those flies that displayed paralysis were used for recovery time analysis. For BS behavioral analysis, pools of flies are combined for each genotype from among the separate trials (in total, n ≈ 100 for each genotype). For analyses using heat shock (HS), a single fly was placed in a clean food vial and tested the following day. The vial was submerged in a water bath (30°C for 3 min), rested at room temperature (24°C for 30 seconds), and then tested for BS behavioral paralysis. For construction of double mutant stocks, flies were tested similarly for the presence of the cacTS2 mutation except that water bath temperature was 37°C, and the assay was temperature-sensitive behavioral paralysis.
In vivo recording of seizure-like activity and seizure threshold determination in adult flies was performed as described previously [16]. Flies 2–3 days post-eclosion were mounted in wax on a glass slide, leaving the dorsal head, thorax, and abdomen exposed. Stimulating, recording, and ground metal electrodes were made of uninsulated tungsten. Seizure-like activity was evoked by high-frequency electrical brain stimulation (0.5 msec pulses at 200 Hz for 300 msec) and monitored by dorsal longitudinal muscle (DLM) recording. During the course of each experiment, the giant fiber (GF) circuit was stimulated by single-pulse electrical brain stimulation and monitored continuously as a proxy for holobrain function. For each genotype tested n ≥ 10.
Chi-square tests were used to compare the penetrance of seizures. Student’s t-test and ANOVA were used to compare recovery times and seizure thresholds across genotypes, as appropriate. For ANOVA analysis, where the null hypothesis was rejected by the overall F ratio, multiple comparisons of data sets were performed by Fisher’s least significant difference with t-test significance set at P < 0.05. For Figures (1–3 and 5) error bars represents standard error of the mean, and statistical significance is indicated by * P < 0.01, ** P < 0.001 and *** P < 0.0001.
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10.1371/journal.pgen.1001259 | Ancestral Regulatory Circuits Governing Ectoderm Patterning Downstream of Nodal and BMP2/4 Revealed by Gene Regulatory Network Analysis in an Echinoderm | Echinoderms, which are phylogenetically related to vertebrates and produce large numbers of transparent embryos that can be experimentally manipulated, offer many advantages for the analysis of the gene regulatory networks (GRN) regulating germ layer formation. During development of the sea urchin embryo, the ectoderm is the source of signals that pattern all three germ layers along the dorsal-ventral axis. How this signaling center controls patterning and morphogenesis of the embryo is not understood. Here, we report a large-scale analysis of the GRN deployed in response to the activity of this signaling center in the embryos of the Mediterranean sea urchin Paracentrotus lividus, in which studies with high spatial resolution are possible. By using a combination of in situ hybridization screening, overexpression of mRNA, recombinant ligand treatments, and morpholino-based loss-of-function studies, we identified a cohort of transcription factors and signaling molecules expressed in the ventral ectoderm, dorsal ectoderm, and interposed neurogenic (“ciliary band”) region in response to the known key signaling molecules Nodal and BMP2/4 and defined the epistatic relationships between the most important genes. The resultant GRN showed a number of striking features. First, Nodal was found to be essential for the expression of all ventral and dorsal marker genes, and BMP2/4 for all dorsal genes. Second, goosecoid was identified as a central player in a regulatory sub-circuit controlling mouth formation, while tbx2/3 emerged as a critical factor for differentiation of the dorsal ectoderm. Finally, and unexpectedly, a neurogenic ectoderm regulatory circuit characterized by expression of “ciliary band” genes was triggered in the absence of TGF beta signaling. We propose a novel model for ectoderm regionalization, in which neural ectoderm is the default fate in the absence of TGF beta signaling, and suggest that the stomodeal and neural subcircuits that we uncovered may represent ancient regulatory pathways controlling embryonic patterning.
| Echinoderms (sea urchins, starfish, etc.) are marine invertebrates that share a close ancestry with vertebrates. Their embryos offer many advantages for the analysis of transcriptional circuits that control developmental programs. During early development of the common sea urchin Paracentrotus lividus, a signaling center located within the ventral ectoderm sends two key signals, Nodal and BMP2/4, that control patterning of the embryo along the whole dorsal-ventral axis. How this signaling center works is not understood. We have conducted a large-scale functional analysis of the genes responsible for patterning of the ectoderm along the dorsal-ventral axis. We identified direct targets of Nodal and BMP2/4 and identified several key regulators that mediate the effects of these factors and drive essential and probably ancient regulatory circuits that together constitute a transcriptional program controlling morphogenesis of the embryo. In addition, we uncovered a striking parallel between the mouse embryo and the sea urchin embryo by showing that in both models a neurogenic ectoderm is the default state of ectoderm differentiation in the absence of Nodal and BMP signaling. Our results support the idea that inhibition of Nodal and BMP signaling was probably an ancient mechanism to specify neural cells in the ancestor of vertebrates.
| It is becoming increasingly apparent that most developmental processes are controlled by dozens or hundreds of regulatory genes assembled into complex gene regulatory networks (GRNs), rather than by a small number of master genes. By describing the functional relationships between these genes, GRNs allow integration of various levels of information on the activity of transcription factors and signaling pathways that regulate developmental processes. Over the last few years, a number of GRNs have been elucidated, including regulatory networks that drive specification of germ layers or organs in various organisms [1]–[7].
Sea urchin embryos offer many advantages for GRN analysis [8]. Unlike vertebrates, sea urchin embryos have a relatively small number of cells (about 800 cells in a gastrula) are fully transparent, and their embryos, available in huge number, develop rapidly as free-swimming larvae. A panoply of techniques is available for the functional analysis of developmental genes including treatments with pharmacological inhibitors and exogenous ligands, microinjection of antisense morpholino oligonucleotides for gene loss of function, and overexpression of mRNA for gain of function. Analysis of the first full sea urchin genome sequence from Strongylocentrotus purpuratus has revealed that echinoderms have a vast genetic repertoire but a low level of genetic redundancy, with almost all developmental regulatory genes being present as single copy [9]. Furthermore the sea urchin embryo has a rich history of experimental embryology and a wealth of biological knowledge is available on various aspects of its development. Finally, echinoderms occupy a basal position within the deuterostome lineage and are more related to chordates than most other invertebrate phyla. These various properties mean that echinoderms are a key phylum to study the evolution of developmental mechanisms and to understand the evolutionary origin of certain features of the chordate body plan. Axis specification has been extensively studied in the sea urchin [10]. Pioneer studies on endomesoderm patterning have shown that it is possible to dissect a complex GRN without the use of classical genetics by combining cis-regulatory and functional analysis, embryological, cell biological and genomic/computational approaches [11]. However, while considerable knowledge is available regarding the functional relationships between genes controlling specification of the territories along the animal vegetal axis, much less was known until recently on the genes that regulate ectoderm patterning and morphogenesis of the embryo along the dorsal-ventral axis. This gap started to be filled recently by the identification in Paracentrotus lividus of the TGFβ Nodal, Univin, and BMP2/4 as key regulators of ectoderm patterning [12]–[15]. Nodal is expressed zygotically, starting at the 32-cell stage. Its expression is initially very broad then it is rapidly restricted to a discrete sector of the ectoderm that corresponds to the presumptive ventral ectoderm. The restricted expression of nodal is so far the earliest known regional difference in zygotic gene expression detectable along the dorsal ventral axis. However, experiments performed at the beginning of the century have shown that as early as the 8-cell stage, respiratory gradients, visualized by mitochondrial cytochrome oxidase activity, prefigure the dorsal-ventral axis of the early embryo [16]. In addition, orientation of the dorsal-ventral axis can be biased by using respiratory inhibitors or by culturing embryos in hypoxic conditions [17]–[19]. Recent studies reported that mitochondria are asymmetrically distributed in some batches of eggs of Strongylocentrotus purpuratus with the ventral side displaying the highest concentration, and that microinjection of purified mitochondria can bias orientation of the dorsal-ventral axis [20], [21]. A possible link between the transcriptional activation of nodal and these redox gradients is suggested by the finding that the stress activated kinase p38 is required for nodal expression [22]. An attractive model therefore emerges in which an asymmetry in the distribution of mitochondria may generate a redox gradient, which would activate p38 anisotropically leading to the spatially restricted expression of nodal. However, strong experimental evidence supporting this model are presently lacking and experimental manipulations that perturb the redox gradient have very modest effects on the spatial expression of nodal [21] (Thierry Lepage unpublished results). If the role of redox gradients in the establishment of nodal expression is still unclear, in contrast, the role of a reaction diffusion mechanism, which involves a short range Nodal positive autoregulation and a long range inhibition mechanism by the Nodal antagonist Lefty, is probably essential to convert a subtle initial anisotropy into a sharply defined pattern [12].
Overexpression of nodal strongly ventralizes the embryos and largely mimics the effects of treatments with nickel chloride [23], knockdown of Nodal function using morpholinos or by overexpressing lefty, completely eliminates dorsal-ventral polarity and results in embryos with disorganized skeletal elements, no mouth and a straight archenteron. The same, strongly-radialized, phenotypes are obtained by blocking translation of the univin transcript which encodes a Vg1/GDF1 ortholog expressed maternally [14], suggesting that Univin may either act upstream of nodal expression or that it may heterodimerize with Nodal as suggested in vertebrates [24], [25]. Intriguingly, in the absence of Nodal, not only is the expression of ventral marker genes such as brachyury, goosecoid or lefty abolished, but the expression of dorsal marker genes such as tbx2/3 and of the novel transmembrane protein 29D is suppressed as well [13]. As a consequence, most of the ectoderm (except the ectoderm surrounding the animal and vegetal poles) of Nodal morphants differentiates into a thick ectoderm consisting of cuboidal ciliated cells that morphologically resembles the neurogenic ectoderm of the ciliary band. Injection of synthetic mRNA encoding either Nodal or an activated Nodal receptor into one blastomere of Nodal morphant embryos at the 8-cell stage is sufficient to rescue both the ventral and the dorsal side of these embryos, indicating that a distinct relay molecule specifies dorsal fates. This relay molecule was recently identified as BMP2/4, which is transcribed in the ventral ectoderm downstream of Nodal signaling, has a strong dorsalizing activity when overexpressed, and mediates the “rescue” of dorsal structures when Nodal signaling pathway is ectopically activated in a cell-autonomous manner in a Nodal loss of function background [26]. Furthermore, despite its ventral transcription, BMP2/4 has been shown to trigger receptor mediated signaling exclusively on the dorsal side of the embryo. Based on this series of findings, a basic model for sea urchin embryo dorso-ventral patterning emerges in which the dorsal ectoderm is induced by BMP2/4 signals emanating from the opposite side of the embryo. The ventral side produces inducing factors such as Nodal and BMP2/4 but it is also a source of inhibitors such as Lefty, which restricts Nodal signaling to the ventral side, and Chordin, which prevents BMP2/4 signaling in the ventral ectoderm. In the absence of lefty function, Nodal signaling is unrestricted and propagates throughout a large belt of cells surrounding the embryo while in the absence of chordin, ectopic BMP2/4 signaling occurs on the ventral side and causes abnormal patterning of the embryo [12], [26]. Therefore, in the sea urchin as in vertebrates patterning of the embryo critically relies on sequential inductive events mediated by Nodal and BMP2/4 and on the interplay between ligands and their antagonists. However, in the sea urchin embryo, both the ligands (Nodal and BMP2/4) and their antagonists (Chordin and Lefty) are co-expressed in the ventral ectoderm, which may represent a D/V organizer, and D/V patterning requires translocation of BMP2/4 from the ventral side where it is produced to the dorsal side where it activates its receptor.
Another pathway that plays a crucial role in ectoderm patterning is the Wnt pathway. Wnt signaling from the vegetal pole region is required to restrict formation of the animal pole domain. The animal pole domain is a small ectodermal territory made of thick ciliated ectoderm that forms in the apical region of the embryo. This six3 expressing neurogenic territory appears to be specified at mesenchyme blastula stage and is thought to be resistant to Wnt and TGF beta signaling [10], [13], [27], [28]. When the Wnt pathway is blocked by overexpression of cadherin or of a dominant negative form of TCF, the animal plate expands towards the vegetal pole and most of the ectoderm differentiates into neuroectodem, which contains scattered serotonergic neurons normally restricted to the animal plate region [27]. In contrast, inhibition of Nodal/Vg1/Activin signaling with a pharmacological inhibitor of the Nodal receptor causes formation of a thickened ciliated ectoderm, but this ciliated ectoderm does not appear to be specified as animal plate ectoderm since serotonergic neurons remain localized to the animal pole in these embryos. Instead, this ectoderm may have a ciliary band like identity as first proposed by Duboc et al. [13]. This idea is supported by the finding that the ectoderm of Nodal morphants abundantly expresses the ciliary band marker tubulinß3 [13] and by the presence of ectopic neurons as revealed by staining for the pan-neural marker synaptotagmin [27]. However, more in depth analysis of the specification state of this ectoderm in the absence of Nodal signaling is required to further test this idea.
Deciphering the gene regulatory network that controls patterning of the ectoderm is of special importance for several reasons. The first reason is that patterning of all three germ layers relies on the activity of a signaling center located in the ventral ectoderm and analyzing how this signaling center works is essential to understand how dorsal ventral polarity of the embryo is established. Another reason is that, despite a wealth of information available on establishment of D/V polarity during normal and regulative development, the GRN that controls specification of the main ectodermal territories (ventral ectoderm, dorsal ectoderm and ciliary band) remains incompletely described and the molecular mechanisms involved in regionalization of the embryo along the D/V axis in normal and perturbed embryos have just started to be investigated [29]. A third reason to study the D/V GRN comes from the basal evolutionary position of echinoderms within the deuterostome superclade, and of the notion that studying D/V axis formation in echinoderms will contribute to better understand the evolution of the patterning mechanisms that shaped the deuterostome body plan. Indeed, recent studies have shown that this GRN relies extensively on cell interactions mediated by TGF beta family members such as Nodal, Univin/Vg1 and BMP2/4, molecules that play crucial roles during vertebrate development [13], [14], [26]. Finally, since major morphogenetic processes such as mouth formation, skeleton formation and elongation of the arms and apex of the larva occur along the D/V axis, dissecting the D/V GRN offers the promise to study how morphogenetic processes are encoded in the genomic program of development. This will help to fill the gap that presently exists between our understanding of cell fate specification and our knowledge of how genes work together to regulate morphogenesis.
We previously described the core of the GRN that acts downstream of Nodal and is responsible for patterning of the ectoderm along the dorsal-ventral axis [13]. We showed that on the ventral side, Nodal acts at the top of this GRN by regulating the expression of lefty, bmp2/4, goosecoid and brachyury while on the dorsal side BMP2/4 activates the expression of tbx2/3. Although the functional relationships between these key genes was elucidated in this initial study, recent molecular screens conducted by us (Thierry Lepage unpublished) and others [30] revealed that many more downstream genes are likely involved in patterning of the ectoderm along the dorsal ventral axis. A large scale effort to dissect the ectoderm GRN in S. purpuratus was recently published by Su and colleagues who used the nanostring technology to monitor the effects of gene perturbations [29]. However, this technique, which measures RNA concentrations in whole embryos, lacks the spatial resolution that is required to analyze the changes in the complex spatial expression patterns of many developmental genes.
To understand better how the ectoderm of the sea urchin embryo is patterned by Nodal and BMP2/4 signals and to expand our provisional GRN, we conducted a large-scale study. Using a combination of gain of function and loss of function studies, and taking advantage of the amenability of Paracentrotus lividus embryos to detailed phenotypic analyses and in situ hybridization studies, we analyzed at high spatial resolution the expression and regulation by Nodal and BMP2/4 of 18 transcription factors and 8 signaling molecules that displayed a restricted expression along the D/V axis. Using an assay with recombinant proteins, we identified direct targets of Nodal and BMP2/4. Finally, by conducting a large-scale analysis of the epistatic relationships between these genes, we were able to start ordering them into a hierarchy and to identify key regulators acting downstream of Nodal and BMP2/4. Not only our results uncover novel and probably ancient regulatory circuits that drive morphogenetic processes such as mouth formation and neural induction, but they elicit a model for patterning of the ectoderm in which two successive inductive events regionalize the ectoderm into three territories: the ventral ectoderm that is specified by Nodal, the dorsal ectoderm that is specified by BMP2/4 and the neurogenic ectoderm of the ciliary band, which forms between the ventral and the dorsal ectoderm in a region protected from Nodal and BMP signaling. In addition, these findings highlight a striking parallel between the mouse embryo and the sea urchin embryo by showing that in both models a neurogenic ectoderm is the default state of ectoderm differentiation in the absence of Nodal and BMP signaling. Our analysis provides a picture of this GRN significantly different from that proposed by Su et al. in S.purpuratus and stresses the importance of the spatial resolution level in the analysis of gene regulatory networks in early embryos.
To elucidate the gene regulatory network that controls specification and patterning of the ectoderm in Paracentrotus, we first performed large scale in situ hybridization screens. In addition to a random screen initiated several years ago, which allowed us to characterize the expression of 4000 randomly selected cDNAs (Thierry Lepage unpublished), we screened a P. lividus EST database against S. purpuratus sequences encoding transcription factors and signaling molecules and analyzed the expression of all those that were expressed during development of the sea urchin embryo [30]–[34] (Table 1). This allowed us to assemble a list of 36 genes displaying a robust expression in either the ventral ectoderm, the dorsal ectoderm or in the ciliary band territory (Table 1) (Figure 1A, 1B). Genes expressed in the animal pole domain were largely excluded from this analysis since most of them do not display a restricted expression along the D/V axis. The expression patterns of a number of the genes presented in this study had previously been described at various degrees in S. purpuratus [30]–[34] but they had never been described in Paracentrotus. In addition, the expression of several genes analyzed here, including smad6, gfi1, id, admp2, BMP1, and oasis has not been described previously in either species.
The earliest asymmetrically distributed transcript that we identified in the in situ screens is the maternal transcript encoding mitochondrial cytochrome oxidase, with cleavage stage embryos frequently displaying a graded distribution of transcripts in the presumptive ectoderm (Figure 1A1). This asymmetrical distribution of a mitochondrial transcript likely reflects the asymmetrical distribution of mitochondria previously reported by Coffman and colleagues [20], [21]. At the zygotic level, the first signs of tissue regionalization within the ectoderm are seen at 64/128 cell-stage with nodal and lefty transcripts starting to accumulate in the presumptive ventral territory (Figure 1A2,3) [12], [13]. The second wave of zygotic genes displaying a restricted expression along the D/V axis starts at the prehatching blastula with bmp2/4 and goosecoid starting to be transcribed in the ventral ectoderm rapidly followed by fgfr1, chordin and nk2.2 at the swimming blastula stage (Figure 1A4–8) [15], . In Paracentrotus, there is no known example of genes displaying a restricted expression in the dorsal ectoderm before the swimming blastula stage. The first genes to be expressed in the dorsal ectoderm are nk2.2 and tbx2/3, whose expression increases abruptly in the presumptive dorsal territory after hatching (Figure 1A8,9) [31], [40], [41]. These genes are therefore good candidates as immediate early targets of Nodal or BMP2/4 signaling and are likely to play an early role in specification of these territories. Soon after ingression of the primary mesenchyme cells, when the embryo acquires its bilateral symmetry, a third wave of zygotic genes starts to be expressed. This includes the largest number of genes such as foxA, brachyury, foxG, Delta, NK1, in the ventral ectoderm (Figure 1A10–14) [34], [42]–[45], onecut/hnf6 and fgfA (Figure 1A15–16) [46]–[48] in the lateral ectoderm and glypican5, irxA, hox7, dlx, smad6, msx, id, oasis, admp2 and cyIII in the dorsal ectoderm (Figure 1A17–26) [26], [30], [31], [36], [39]. Based on the timing of their expression, genes in this category are likely secondary targets of Nodal or BMP2/4 signaling.
Starting at the early gastrula stage, additional genes start to be expressed with a restricted pattern along the D/V axis, with ptb transcripts accumulating in the ventral ectoderm (Figure 1A27), bmp1, deadringer (dri), otx, and rkhd being expressed in a broad domain encompassing the ventral ectoderm and ciliary band territory (Figure 1A28–31) [13], [49]–[52], and gfi1, pax2/5/8, wnt8, univin transcripts starting to be expressed in the presumptive ciliary band (Figure 1A32–36) [32], [36], [48], [53]. Similarly, atbf1, unc4, wnt5 start to be expressed in the dorsal ectoderm at the early gastrula stage. (Figure 1A37–39). Finally, at prism stage, tubulinß3 transcripts accumulate in the presumptive ciliary band while transcripts encoding the sea urchin specific transmembrane protein 29D accumulate in the presumptive dorsal ectoderm (Figure 1A 40,41) [13], [54].
At the mesenchyme blastula stage, foxG (also known as Brain factor1 or Bf1) is expressed in two broad ventro-lateral stripes that largely overlap with the goosecoid expression territory (Figure 1A12), while Delta is first expressed in the ectoderm in a cluster of cells at the animal pole as well as in individual cells, possibly neurons, first on the ventral side then on the dorsal side, within the vegetal part of the foxG expression domain. At the prism/early pluteus stage, the pattern of foxG resolves into a thin belt of cells on the ventral side of the presumptive ciliary band (Figure 1A42) [34] while Delta expression now occurs in a salt and pepper pattern within the ciliary band and facial ectoderm (Figure 1A43) [55].
For simplification we divide the ectoderm into three main territories along the dorsal-ventral axis, however there are additional regional differences in gene expression that show that more than three regions can be defined (Figure 1C). For example, the homeobox gene nk1 is expressed in the ventral-vegetal ectoderm in a region fated to become the ventral supra-anal ectoderm (Figure 1C1). Similarly, several dorsally expressed genes such as msx, id, oasis, admp2 or unc4 are strongly expressed in the dorsal-vegetal region fated to become the dorsal supra-anal ectoderm (Figure 1A22–25; 38; Figure 1D2,3). Thus, the ectoderm near the vegetal pole is divided into at least two sub domains along the D/V axis. Gene expression patterns also revealed that the ventral and dorsal ectodermal regions are progressively regionalized into different domains. This is best illustrated by the dynamics of goosecoid expression. goosecoid and brachyury are initially co-expressed within the ventral ectoderm (Figure 1A5,11), but during gastrulation, the expression domain of goosecoid is progressively cleared from the center of the ventral ectoderm (Figure 1C3). While goosecoid expression is progressively shifted at the periphery of the ventral ectoderm, forming a belt of cells abutting the ciliary band, brachyury and foxA remain expressed at the center of the ventral ectoderm, where the stomodeum will form (Figure 1C2). Similarly, analysis of gene expression within the dorsal ectoderm revealed the existence of nested patterns, with genes like nk2.2, tbx2/3 and dlx (Figure 1D4, Figure 1A,9,20) being expressed in a broader domain than genes like msx, wnt5 or smad6 (Figure.1A22,29,39; 1D3,4 see also [26]) and genes like irxA being expressed in a sub domain of the dorsal ectoderm that excludes the dorsal apex (Figure 1D1). Finally, sub regions can also be recognized within the ciliary band territory starting at the early gastrula stage, with genes like fgfA, vegf, pax2/5/8 and sprouty being expressed in the ventral lateral region (Figure 1A33,34; Figure 1D6 and data not shown) [48], [56], genes like onecut/hnf6 or gfi1 being expressed in the entire presumptive ciliary band territory (Figure 1C5; Figure 1D5), and genes like foxG, which in vertebrates is expressed in and required for specification of the ventral telencephalon [57], [58], being expressed in a ventral subdomain of the ciliary band (Figure 1A42).
Interestingly, several genes whose expression is later confined to the ciliary band are initially expressed much more broadly in the ectoderm (Figure 1E). This is particularly apparent for glypican5, fgfA, univin, and wnt8, which are expressed in a large belt of ectodermal cells at blastula stage and also for the neural marker onecut/hnf6 which is first expressed ubiquitously, then in a broad ventro-lateral domain, and only later in the ciliary band (Figure 1E1–6) [26], [46]–[48]. This suggests that the expression of these ciliary band marker genes is initiated by broadly distributed transcription factors and later repressed on the ventral and/or dorsal sides by additional factors. As a first step to dissect the ectoderm gene regulatory network, we analyzed the regulation of these broadly expressed ciliary band genes. Since SoxB1 plays a critical role in ectoderm patterning in the sea urchin [59] and in the specification and maintenance of neural regions in vertebrates [60], we tested if SoxB1 is required for expression of ciliary band marker genes (Figure 1F). Injection of morpholinos against SoxB1 abrogated the expression of most markers of the neurogenic ectoderm of the ciliary band including onecut, gfi1, foxG, egip, fgfA, pax2/5/8, univin, wnt8 and strongly affected the spatial expression of dri and otx [14] (Figure 1F1–20). This result supports the idea that transcription of at least a subset of ciliary band marker genes is initiated by broadly distributed transcription factors such as SoxB1 and later restricted to the ciliary band by zygotic factors induced by Nodal and/or BMP signaling.
We next tested how Nodal and/or BMP2/4 regulate the expression of the 36 genes identified in the in situ screen. We focused on Nodal and BMP2/4 since previous studies showed that these two ligands are essential for specification and patterning of the ventral and dorsal territories. We first analyzed the effects of overexpressing nodal or bmp2/4 on the expression of ectodermal markers. Embryos were injected with nodal or bmp2/4 mRNA and the expression of the ventral, dorsal, or ciliary band markers was monitored at different stages. In most cases, results were confirmed by treatments with recombinant mouse Nodal or BMP4.
Overexpression of nodal mRNA or treatments with recombinant Nodal protein dramatically expanded the expression of nodal, bmp2/4, chordin, lefty, goosecoid and brachyury as reported previously (Figure 2) [13], [26]. Overexpression of Nodal also expanded the ectodermal domain of expression of foxA and fgfr1 at mesenchyme blastula stages. Similarly, the expression domain of nk1, which is normally restricted to the ventral vegetal ectoderm, became radial in nodal overexpressing embryos. Genes expressed in the ciliary band behaved differently depending on the gene. In the case of deadringer, bmp1 and univin, which are expressed in the ciliary band and in the ventral ectoderm, overexpression of nodal expanded their expression to the whole ectoderm. In the case of wnt8, which is expressed in two broad lateral stripes at gastrula stages, as well as in the case of fgfA and its downstream target pax2/5/8, which are expressed in the ventral sub domain of the ciliary band, all expression was eliminated by exogenous nodal. However, in the case of foxG, egip, onecut/hnf6, gfi1, otx, exogenous nodal suppressed expression in most of the ectoderm except in the animal and/or vegetal most domains of the ectoderm. Overexpression of nodal increased the number of ventral-vegetal cells that normally express Delta at the early gastrula stage and, at 48h, produced ventralized embryos in which most Delta expressing cells were located at the animal pole and in the vegetal most ectoderm. Largely similar phenotypes were obtained following treatments with nickel chloride (Figure S3) although we noted intriguing differences in the behavior of a few genes including wnt8, univin, fgfA and pax2/5/8, in response to these perturbations. Overall, these data are consistent with the idea that in nodal-overexpressing or nickel treated embryos, radially expressed Nodal promotes specification of ventral ectodermal fates and suppresses specification of the ciliary band in a large equatorial region but not in the animal pole region or in the ectoderm surrounding the blastopore. One likely reason that may explain why the vegetal ectoderm is refractory to Nodal overexpression or to nickel treatment is that in these embryos, Nodal signaling is restricted to the equatorial region [13]. The vegetal ectoderm may therefore be protected from Nodal activity by Lefty which is thought to diffuse farther than Nodal [12], . Consistent with this idea, in Nodal treated embryos and in nickel treated embryos, nodal expression expands to a large belt of cells in the equator and a ciliary band differentiates in the vegetal most ectoderm while in lefty morphants, which also display unrestricted Nodal signaling, ciliary band marker genes such as tubulinß3 and onecut/hnf6 are expressed in the animal pole region but not in the vegetal ectoderm (Figure 2) [12]. Taken together, these results suggest that a Lefty dependent inhibition of Nodal signaling is required for ciliary band formation in the vegetal pole region. Finally, as expected, overexpression of nodal eliminated the expression of all the dorsal marker genes we tested including, nk2.2, tbx2/3, smad6, msx, atbf1, wnt5, admp2, unc4, hox7, dlx, and 29D (Figure 2).
Reciprocally, overexpression of bmp2/4 or treatments with recombinant BMP4 protein eliminated expression of all the ventral marker genes we tested including nodal, bmp2/4, chordin, goosecoid, foxA, lefty (not shown), brachyury, and nk1 (Figure 3). As in the case of nodal overexpression, misexpression of bmp2/4 or of the activated Alk3/6 BMP receptor (Alk3/6QD) [26] strongly suppressed the expression of the ciliary band markers such as bmp1, foxG, onecut/hnf6, otx, gfi1, tubulinß3, egip, dri, univin, wnt8, fgfA and pax2/5/8. However, unlike in the case of nodal overexpressing or nickel treated embryos, which conserved expression of ciliary band markers in the animal pole and in vegetal ectodermal regions, overexpression of bmp2/4 or of the activated type I BMP receptor (Alk3/6QD) efficiently eliminated the expression of all the ciliary band markers at the animal pole and in the vegetal most ectoderm as well as the expression of animal pole specific markers such as foxQ2 highlighting the very strong antagonism existing between high level of BMP2/4 signaling and specification of the animal pole and ciliary band cell fates. Finally, misexpression of BMP2/4 dramatically expanded the expression of all the dorsal marker genes including tbx2/3, smad6, nk2.2, wnt5, oasis, msx, irxA, dlx, atbf1, hox7, unc4, admp2, id and 29D.
We next sought to determine which genes are direct targets of Nodal and BMP2/4 signaling. Based on the timing of expression of the ventral or dorsal markers genes, it was expected that only a subset would be direct targets of Nodal or BMP2/4 signaling. For example, only lefty, bmp2/4, chordin, goosecoid, nk2.2, fgfr1 and tbx2/3 are expressed at swimming blastula stage, the expression of most of the other starting only at mesenchyme blastula stage. We therefore tested whether the ventral marker genes are transcribed in direct response to Nodal and whether the dorsal marker genes are transcribed in direct response to BMP2/4 signaling or if transcription of these genes requires protein synthesis. To achieve this, we treated embryos at the hatching blastula, mesenchyme blastula or gastrula stages with recombinant mouse Nodal or BMP2/4 proteins in the presence or absence of a protein synthesis inhibitor (Figure 4), and analyzed the expression of all the ventral and all the dorsal marker genes.
Short treatments with recombinant Nodal protein at blastula stage strongly induced expression of nodal, lefty, bmp2/4, chordin, goosecoid, nk2.2 and fgfr1 throughout most of the ectoderm (Figure 4A). These effects were observed even in the presence of a translational inhibitor suggesting that these genes are direct targets of Nodal signaling. In contrast, short treatments with Nodal at either mesenchyme blastula or gastrula stages failed to induce any ectopic expression of the other ventral genes such as foxA (Figure 4A) foxG, nk1, or deadringer (data not shown), which are expressed in the ectoderm starting at or after mesenchyme blastula. This suggests that these genes are indirect targets of Nodal signaling that cannot be induced during the short interval of the treatment. Interestingly, in the case of brachyury, a weak but consistent broadening of the ectodermal domain of expression was observed following treatment with Nodal. However, this effect was abolished by treatment with the protein synthesis inhibitor, consistent with this gene being an indirect target of Nodal signaling. Similarly, among all the dorsal marker genes we tested, 3 genes were strongly induced by treatments with BMP2/4, even in the presence of protein synthesis inhibitors. These were tbx2/3, nk2.2 and smad6 (Figure 4B). Short treatments with high doses of BMP2/4 failed to induce expression of irxA, dlx, msx, atbf1, hox7, id, unc4, oasis, wnt5, admp2 or glypican 5 (data not shown) suggesting that these genes may be indirect targets of BMP signaling. The very good correlation between the results of this induction assay and the timing of expression of the downstream targets of Nodal and BMP2/4 indicates that this assay predicts with good confidence the direct, and probably also the indirect, target genes of these ligands at swimming blastula stage. It should be kept in mind however, that at later stages, this assay does not allow to rule-out completely the existence of a direct input from Nodal or BMP2/4 to downstream target genes. An alternative explanation for the fact that several genes appear to be refractory to induction by recombinant Nodal or BMP4 proteins is that after swimming blastula stage, the ventral and dorsal ectoderm may no longer be competent to switch their gene regulatory networks to a state that supports expression of dorsal or ventral genes respectively.
We next attempted to determine if the activity of Nodal and BMP2/4 accounts for the restricted expression of all of the ventral and all the dorsal genes. Embryos were injected with a nodal morpholino and the expression of ventral, dorsal or ciliary band markers analyzed at successive stages (Figure 5). Expression of all the ventral marker genes that we tested including, bmp2/4, goosecoid, fgfr1, nk1, chordin, brachyury, foxA and lefty disappeared in the Nodal morphants, consistent with previous results (Figure 5B) [13], [29], [38]. Injection of the nodal morpholino also largely prevented expression of foxG, confirming that this gene is induced downstream of Nodal signaling [29]. We also found that in Nodal morphants, the expression of all dorsal markers genes was strongly downregulated in most of the ectoderm, with responses falling into two categories: for some genes, e.g. glypican5, oasis, msx, dlx, hox7, wnt5, smad6, or unc4, expression completely disappeared in the Nodal morphants (Figure 5C). Others, e.g. tbx2/3, id, irxA, nk2.2, atbf1, admp2 and 29D displayed residual expression in the vegetal-most ectoderm and/or in the PMCs indicating Nodal-independent expression of these genes in the presumptive dorsal vegetal ectoderm.
A striking result was obtained when we analyzed the expression of ciliary band markers in the nodal morphants (Figure 5D). The expression of most ciliary band markers dramatically expanded to most of the ectoderm following inhibition of Nodal signaling. This was the case for fgfA, bmp1, univin, wnt8, otx, pax2/5/8, onecut/hnf6, gfi1, dri, as well as of the late ciliary band marker tubulinß3 and the ciliary band antigen 295. Importantly, expression of Delta, which at pluteus stages identifies individual neurons of the facial ectoderm and ciliary band region [26], [55], was expanded to the whole ectoderm in Nodal morphants, strongly suggesting that most of the ectoderm is converted into neurogenic ectoderm in these embryos. Largely similar results were obtained using a pharmacological inhibitor of the Nodal receptor [62] (Figure S4). Taken together, these results show that Nodal signaling is essential for expression of all the ventral and of all the dorsal marker genes within the ectoderm. In the absence of Nodal, expression of all the ventral and dorsal marker genes is abolished and ciliary band genes are ectopically expressed throughout most of the ectoderm.
We also examined the effect of knocking down BMP signaling on the expression of the ventral, dorsal and ciliary band markers (Figure 6). As expected, we found that expression of all the ventral markers that we tested was independent of BMP2/4 signaling: nodal, bmp2/4, chordin, brachyury or foxA were expressed at similar levels and in similar domains in the controls and in the alk3/6 morphants (Figure 6B). Removing BMP2/4 or Alk3/6 function affected the expression of dorsal marker genes in a way very similar to that caused by removing Nodal: expression of most genes including wnt5, atbf1, hox7, msx, dlx, smad6, tbx2/3, unc4 was abolished while for irxA, nk2.2 and id, residual expression was still observed in the vegetal most ectoderm on the presumptive dorsal side (Figure 6C). These results confirm that expression of all the dorsal ectodermal genes stringently relies on BMP2/4 signaling and that in the absence of Nodal or BMP2/4 signals, no other signals compensate for the lack of these inducers. Again, a striking result was observed when we analyzed the expression of ciliary band markers in the bmp2/4 or Alk3/6 morphants. For all of them, including gfi1, onecut/hnf6, otx, deadringer, pax2/5/8, foxG, wnt8, fgfA, univin, bmp1 and tubulinß3, loss of BMP2/4 signaling caused a dramatic ectopic expression in the dorsal ectoderm (Figure 6D). This ectopic expression transformed the normally bilateral expression domains of fgfA, pax2/5/8, foxG, gfi1, univin, and wnt8 into a horseshoe shaped domain covering the lateral and dorsal regions and caused the expression domain of deadringer and otx to become radial. These results reveal that in addition to promoting specification of dorsal cell fates, an essential function of BMP2/4 signaling is to repress ciliary band gene expression within the dorsal ectoderm.
To establish the functional hierarchy between key ventral, dorsal and ciliary band genes, we designed morpholinos against 17 transcription factors and 8 signaling molecules expressed within the ectoderm with a restricted pattern along the dorsal-ventral axis. Among these 25 morpholinos, 19 (alk4/5/7, alk3/6, brachyury, bmp2/4, chordin, foxA, foxG, fgfA, goosecoid, irxA, lefty, tbx2/3, dlx, msx, nodal, onecut/hnf6, soxB1, univin, wnt8) gave a clearly recognizable morphological phenotype (Figure 5–9). The expression of 15 transcription factors (goosecoid, brachyury, foxA, nk1, nk2.2, tbx2/3, msx, smad6, hox7, irxA, onecut, gfi1, dri, pax2/5/8, foxG) and 8 signaling factors (nodal, bmp2/4, fgfA, chordin, wnt8, univin, wnt5, glypican5) was analyzed at different stages in the 17 morphant backgrounds while in the case of nodal and bmp2/4 morphants we analyzed the expression of an additional set of 17 marker genes (Tables S1, S2). In addition, we overexpressed a subset of genes encoding transcription factors (goosecoid, foxA, foxG, deadringer, nk2.2, tbx2/3, msx, smad6) and signaling molecules (nodal, bmp2/4, chordin) and analyzed the expression of ventral, dorsal and ciliary band genes in these embryos. Since many of the genes identified in our screens including brachyury, foxA, otx, smad6, tbx2/3, wnt5, oasis, univin, wnt8, rkhd, ptb, fgfA, Delta are expressed not only in the ectoderm but also in the mesendoderm and since many other markers such as atbf1, irxA, nk2.2 or egip, oasis, wnt5, glypican5, wnt8, Delta, otx or bmp1 are expressed in more than one region and sometimes in both the ventral and dorsal ectoderm, we used in situ hybridization rather than QPCR to monitor the consequences of the perturbations. In situ hybridization is usually not used as the primary technique in large-scale projects such as gene regulatory network analysis since it is time and effort consuming and requires large numbers of injected embryos. However, we believe it is the only technique that provides the necessary spatial resolution to accurately analyze the expression of genes with complex expression patterns in perturbed embryos. Furthermore, when used with appropriate controls, in situ hybridization can provide a good estimate of the level of expression in perturbed embryos compared to controls. To provide a temporal view of the consequences of these perturbations and avoid secondary effects, the expression of the genes analyzed in response to nodal or bmp2/4 overexpression was examined at two different stages, soon after the onset of their restricted expression, and at a later stage, most often early or late gastrula stage depending on the gene analyzed.
Information derived from these perturbations analyses was combined with earlier results to build a provisional gene regulatory network. The main features of this gene regulatory network are described below.
Low levels of goosecoid transcripts are present maternally then their abundance increases sharply at swimming blastula stage, shortly after the peak of Nodal expression [35] (Figure S2). Expression of lefty, chordin, bmp2/4, fgfr1 and goosecoid, was unchanged in the goosecoid morphants consistent with these genes being direct targets of Nodal signaling and with previous studies [38] (Figure 7A and data not shown). Interestingly, at gastrula stages, strong ectopic expression of wnt8, univin and foxG was detected in the ventral ectoderm of goosecoid morphants indicating that one function of Goosecoid is to repress expression of these three genes in the ventral ectoderm between blastula and gastrula stages. In contrast, ectodermal expression of foxA and brachyury, two likely indirect targets of Nodal required for mouth formation, was lost in the goosecoid morphants, consistent with the lack of stomodeum in these embryos (Figure 7A) [42]. Reciprocally, overexpression of goosecoid caused a dramatic expansion of foxA and brachyury (Figure 7B). Therefore, in the sea urchin as in vertebrates, brachyury and foxA are targets of Nodal signaling but unlike in vertebrates, in the sea urchin, they are not primary targets of Nodal since their expression depends on the zygotic expression of goosecoid [63]–[65]. Overexpression of goosecoid also expanded the expression of deadringer as reported previously by Bradham et al. [22], [66]. In contrast, the two dorsal marker genes hox7 and msx failed to be expressed in the goosecoid overexpressing embryos consistent with previous studies showing that goosecoid overexpression suppresses expression of dorsal genes such as tbx2/3 and spec1 [35], [40]. Overexpression of goosecoid also abolished the expression of all the other ciliary band genes that we tested including wnt8, univin, foxG, egip, gfi1 and onecut/hnf6. Taken together these observations suggest that goosecoid plays a double function, first by allowing expression of stomodeal genes such as foxA and brachyury and second by suppressing the expression of ciliary band and dorsal genes.
Once goosecoid and foxA have been turned on, Brachyury and FoxA cross regulate each other so that brachyury maintains foxA expression while foxA promotes brachyury expression (Figure 7C, 7D). When the function of either of the two genes was blocked with a morpholino, expression of the other gene was lost and the resulting embryos developed without a stomodeum. The role of these cross regulatory interactions between brachyury and foxA may be to stabilize and lock the specification of the ventral ectoderm that has been initiated by Nodal as described in the endomesoderm GRN, for example between the transcription factors hex and tgif [7].
Inhibition of tbx2/3 function strongly perturbed establishment of dorsal-ventral polarity resulting in embryos with a rounded shape, which lacked ventral arms and had a strongly reduced dorsal region (Figure 8A). Molecular analysis revealed that ventral markers such as chordin, foxA or brachyury were expressed in tbx2/3 morphants, albeit with reduced levels compared to controls (Figure 8A). A similar slight reduction was observed for the ciliary band markers onecut/hnf6, fgfA and pax2/5/8. In contrast, inhibition of tbx2/3 function abolished the expression of several dorsal genes encoding transcription factors including msx, dlx, irxA and atbf1 while the expression of other genes such as smad6, glypican5, oasis and wnt5 appeared unaffected. These results identify tbx2/3 as a key regulator of dorsal gene expression downstream of BMP2/4.
Since loss of BMP2/4 or Alk3/6 signaling causes ectopic expression of ciliary band genes in the dorsal ectoderm, it follows that in unperturbed embryos, a transcriptional repressor must act in the dorsal ectoderm downstream of BMP2/4 to prevent expression of ciliary band genes. Of the four transcription factors expressed in the dorsal ectoderm that we tested, only in the case of one of them did we observe robust ectopic expression of a ciliary band gene. This gene is irxA. In embryos injected with morpholinos against the irxA transcript, onecut/hnf6 expression was strikingly expanded in the dorsal ectoderm (Figure 8B). This effect was very robust and the territory in which the ectopic expression of onecut/hnf6 was observed was congruent with the expression territory of irxA. Interestingly, a small number of embryos injected with irxA morpholinos later developed with a thickened ectodermal region on the dorsal side that resembled an ectopic ciliary band (Figure 8B). This suggests that IrxA is a repressor of ciliary band genes downstream of BMP2/4.
onecut/hnf6 is of one of the earliest marker genes expressed in the presumptive ciliary band. onecut/hnf6 morphants developed with a slightly reduced D/V axis but they clearly displayed a D/V polarity and a well-developed ciliary band (Figure 8C). Nevertheless, we found that the expression of several marker genes of the ciliary band was affected in the onecut/hnf6 morphants. A reduced level of expression in the onecut/hnf6 morphants was observed in the case of pax2/5/8, foxG and dri while in the case of gfi1, no expression was detected. onecut/hnf6 is thus an upstream regulator of gfi1. Gfi proteins are conserved in C. elegans (Pag3), Drosophila (Senseless) and mice (Gfi1). In all three species, these zinc finger proteins play conserved roles in neural development [67]. Mice mutant for gfi1 are deaf and ataxic while flies mutant for senseless lack sensory organs indicating that Gfi proteins regulate sensory organ development [67], [68]. One can therefore anticipate that Gfi1 likely plays a role in neural development in the sea urchin embryo as it does in vertebrates and in flies. Since gfi1 is downstream of onecut, the ciliary band network therefore appears to be composed of at least two layers of zygotic factors.
In this study, taking advantage of the detailed phenotypic analyses and robust in situ hybridization procedures available in Paracentrotus lividus, we analyzed with a high level of spatial resolution the expression, the regulation and the function of most of the zygotic transcription factors and signaling molecules displaying restricted expression within the ectoderm of the sea urchin embryo. This analysis allowed us to assemble a gene regulatory network, the D/V GRN, which describes the regulatory interactions between these genes and provides a framework for understanding the developmental program responsible for patterning the embryo along the dorsal-ventral axis. Several interesting conclusions emerged from the resultant GRN. First, it provides a clear demonstration that the activities of Nodal and BMP2/4 account fully for the spatially restricted expression of all the known genes of this network: Nodal controls the expression of all the genes expressed specifically in the ventral ectoderm, and through BMP2/4, the expression of all the genes expressed specifically in the dorsal ectoderm. Both overexpression of these ligands and corresponding loss of function experiments produced very strong, all or none, effects consistent with the idea that Nodal and BMP2/4 are critical inputs that drive the D/V GRN. It should be noted that despite their essential roles, Nodal and BMP2/4 are certainly not the only ligands involved in D/V patterning of the ectoderm and other ligands more broadly expressed likely cooperate with Nodal and BMP2/4 to specify the ventral and dorsal regions. In particular, Nodal may bind to its receptor as a heterodimer with Univin, a GDF1/Vg1 ortholog, as shown in other models [24], [25] while BMP2/4 may heterodimerize with BMP5/8 to specify the dorsal ectoderm as shown in vertebrates and in Drosophila [69], [70]. Nevertheless, the key roles played by Nodal in this GRN together with the essential function of Nodal factors in D/V axis formation in vertebrates and basal chordates [71] reinforce the hypothesis that an ancestral function of Nodal may have been in the regulation of D/V axis formation in deuterostomes.
A second key conclusion emerging from our D/V GRN is that in the sea urchin, Goosecoid is a key upstream element of a small regulatory circuit that controls mouth formation. In vertebrates ectopic expression of goosecoid promotes cell migration and induces incomplete secondary axes while loss of function studies implicate goosecoid in the function of the Spemann organizer and head formation [72]. The function of goosecoid during development of other deuterostome embryos has not been studied. In the sea urchin, previous studies reported that both overexpression and loss of function of goosecoid strongly perturbed establishment of the dorsal-ventral axis, however the target genes of goosecoid were not known and the role of this repressor within the ventral ectoderm remained largely unclear [35], [38], [40]. Our finding that goosecoid is a direct target of Nodal signaling strongly suggested that this gene could play a key role in specification of the ventral ectoderm downstream of Nodal. We have shown that Goosecoid likely regulates the expression of deadringer and foxG in the ventral ectoderm. Furthermore, we demonstrated that Goosecoid plays a critical role in mouth formation by regulating downstream target genes such as the stomodeal genes brachyury and foxA. This raises the possibility that an ancestral function of goosecoid may have been in the regulation of stomodeum formation. Consistent with this idea, goosecoid is expressed in the stomodeal region in both protostomes and deuterostomes and is co-expressed with brachyury and foxA in the oral region of cnidarians [73]. Since Goosecoid is a transcriptional repressor [74], this suggests that zygotic goosecoid activates foxA and brachyury by repressing the expression of a transcriptional repressor, the identity of which is presently unknown (Figure 10). Similar double repression mechanisms have been described in different GRNs. For example, in the sea urchin the skeletogenic mesoderm GRN, the repressor pMar has been proposed to repress hes-C as well as unidentified repressors to allow expression of genes specific of the PMC lineage [75], [76]. Similarly Schnurri, represses the expression of brinker to allow the expression of Dpp target genes in Drosophila imaginal discs [77]. One candidate for a repressor acting downstream of goosecoid is the transcriptional repressor ZEB1/Smad Interacting Protein 1 (Sip1) [78]. In the sea urchin embryo, Sip1 is expressed early in the presumptive ectoderm and its expression is downregulated at blastula stage, coincident with the onset of goosecoid expression [31] (see Figure S2 and S5). Experiments are currently being carried out in different labs to test this hypothesis.
Another important function of Goosecoid appears to be in the repression of ciliary band and dorsal genes. Overexpression of goosecoid potently repressed expression of ciliary band markers. Furthermore, knockdown of Goosecoid function caused ectopic expression of univin, wnt8 and foxG in the ventral ectoderm. However, additional repressors likely cooperate with Goosecoid in this repression since inhibition of goosecoid function, unlike inhibition of irxA on the dorsal side, was not sufficient to derepress ciliary band markers genes such as onecut within the ventral ectoderm.
Tbx2/3 has a special status amongst dorsal genes since it is one of the earliest zygotic genes expressed on the presumptive dorsal side [40], [41]. Previous studies had shown that tbx2/3 is expressed dynamically in a broad dorsal territory in all three germ layers and that its expression is regulated by BMP signaling [13], [26], [40], [41]. Indeed we showed that tbx2/3 is a direct target of BMP2/4 signaling in the ectoderm and that its function is required for expression of several dorsally expressed transcription factors such as msx, dlx, irxA and atbf1. Intriguingly, previous studies in Paracentrotus failed to detect any D/V polarity defect in tbx2/3 morphants [40]. In contrast, we found that tbx2/3 is essential for D/V axis formation in this species. The reasons for this discrepancy are unclear. Interestingly, in vertebrates, tbx2 is also a target of BMP4 signaling during D/V patterning of the optic cup [79]. Similarly, in hemichordates, which are positioned phylogenetically as the sister phylum of echinoderms, tbx2/3 is a target of BMP2/4 suggesting that key genes that drive the D/V GRN are conserved in these two closely related phyla [80]. In vertebrates, tbx2 and tbx3, unlike brachyury, which is a transcriptional activator, act as transcriptional repressors due to the presence of a strong repressor domain in their C-terminal region [81], [82]. It is therefore possible that the sea urchin Tbx2/3 protein also functions as a transcriptional repressor and that, like Goosecoid, it stimulates gene expression by relieving the repressive action of a transcriptional repressor. The identity of this hypothetical transcriptional repressor is presently unknown.
One of the most important findings of this study is the identification of irxA as a gene which acts downstream of BMP signaling to repress the ciliary band gene onecut. We previously reported that inhibition of BMP2/4 or Alk3/6 function causes an expansion of the presumptive ciliary band territory towards the dorsal side, and that this expansion is accompanied by the ectopic expression of the neural gene onecut/hnf6 [26]. On the basis of this result we anticipated that one function of the BMP pathway in the dorsal ectoderm was to repress ciliary band gene expression and we postulated the existence of a BMP2/4 dependent repressor of ciliary band genes. We have now identified IrxA as one such repressor based on the following evidence. First, we showed that irxA expression is regulated by BMP2/4 signaling. Second, we showed that blocking irxA translation with morpholinos caused a robust ectopic expression of onecut in a sector of the dorsal ectoderm that coincides with the expression domain of irxA. Finally, it is established that Irx proteins can function as repressors by recruiting the Groucho Co-repressor [83], [84]. Since irxA is downstream of tbx2/3 in the GRN, we might predict that blocking tbx2/3 function should also result in ectopic expression of ciliary band genes. Surprisingly, we never observed ectopic expression of ciliary band marker genes in tbx2/3 morphants. This observation is consistent with previous GRN studies, which reported that direct target genes are more strongly affected than indirect target genes or in other words, that when a perturbation affects the driver gene, it causes stronger effects on target genes than when the perturbation affects genes further upstream in the pathway [29]. However, the simplest explanation is that our tbx2/3 morpholino may not be completely effective and that residual irxA expression may prevent ectopic expression of onecut in these embryos.
In vertebrates and in Drosophila, irx genes are involved in neural development [85]. In Xenopus for example, irx1 promotes neural development by repressing bmp4 expression in the neural plate. It was therefore surprising to find that in the sea urchin embryo, irxA acts downstream of BMP2/4 to negatively regulate neural marker genes. Nevertheless, the identification of irxA as a BMP2/4 dependent repressor of ciliary band gene expression strongly supports our proposal that the default state of the ectoderm in the absence of TGF beta signaling is the ciliary band and that the ectoderm is patterned by two successive inductive events that repress the ciliary band fate on the ventral and dorsal sides.
The results obtained in this study largely support this idea that the default state of the ectoderm in the absence of Nodal and BMP signaling is a ciliary band-like ectoderm that expresses a number of neural genes and that Nodal and BMP2/4 restrict this ciliary band fate by specifying the ventral and dorsal ectoderm. The first hint that the default state of the ectoderm in the absence of TGF beta signaling is the ciliary band is that several genes whose expression is later restricted to the ciliary band territory are expressed throughout the ectoderm at earlier stages. This is for example apparent for fgfA, univin and wnt8, which are expressed in a belt of cells that includes most of the presumptive ectoderm at blastula stages. The expression of fgfA, univin and wnt8 is subsequently repressed on the ventral and dorsal sides during gastrulation thereby restricting the expression of these genes to the ciliary band domain. Several additional lines of evidence support the idea that the default state of the ectoderm in the absence of TGF beta signaling is a ciliary band and neural fate and that alternative ectodermal fates must be induced by active signaling. First, overexpression of both nodal and bmp2/4 strongly antagonized the expression of ciliary band and neural markers such as onecut, foxG and gfi1, with bmp2/4 leading to a very potent inhibition of ciliary band formation. Second, in the lefty morphants the ciliary band failed to form while in the absence of Nodal and BMP2/4 signaling, the ventral and dorsal ectodermal regions were not specified and most of the ectoderm differentiated instead into a thickened ciliated ectoderm that resembled the ciliary band ectoderm and expressed all tested ciliary band markers. These ciliary band markers were de-repressed throughout the ventral and dorsal ectoderm in the nodal morphants while in the absence of BMP2/4, which acts as a dorsal inducer, or of alk3/6, which is required to transduce BMP2/4 signals, only specification of the dorsal ectoderm was perturbed and ectopic expression of these ciliary band genes was detected only on the dorsal side. A third argument is that the presumptive ciliary band territory is also a region in which fgfA and vegf are expressed and where MAP kinase activity is high [48], [56], [94]. Studies in vertebrates have shown that the activity of the MAP kinase ERK inhibits both BMP signaling and neuralization by phosphorylating Smad1 in the linker region thereby preventing its nuclearization. We thus predict that during normal development of the sea urchin embryo, the high MAP kinase activity present in the lateral ectoderm promotes neural fates within the presumptive ciliary band by inhibiting the activity of pSMAD1/5/8 and pSMAD2/3. Thus, in the absence of Nodal and BMP signaling, signals such as FGFA that are normally present at the level of the lateral ectoderm are ectopically expressed in the ventral and dorsal regions where they may promote ectopic neuron formation [26], [27]. One last but crucial argument that supports our model of the ciliary band as a default state of the ectoderm in the absence of TGF beta signaling is that we identified irxA and possibly Goosecoid as repressors of a subset of ciliary band genes downstream of Nodal or BMP signaling. One read-out of Nodal and BMP2/4 signaling therefore appears to be active repression of the ciliary band fate as we had predicted [26].
Yaguchi and colleagues previously demonstrated that in the absence of Wnt signaling, most of the ectoderm differentiates as a neurogenic ectoderm that expresses markers of the animal pole [27]. Since many ciliary band genes are also expressed in the animal pole, it could be argued that the ectopic expression of ciliary band marker genes observed following inhibition of Nodal or BMP signaling also reflects an expansion of the animal pole domain. This can be ruled out for several reasons. First, we showed that the expression of animal pole markers such as foxQ2, is unaffected in Nodal morphants or in embryos treated with a pharmacological inhibitor of the Nodal receptor. Second, Yaguchi et al. showed that the number and location of serotonergic neurons of the apical organ are unaffected by inhibition of Nodal signaling. Importantly, we showed that pax2/5/8, which is expressed in the vegetal part of the ciliary band but not in the animal pole region behaved exactly like the other ciliary band marker genes and was strongly derepressed in the ventral and dorsal ectoderm of Nodal morphants. Taken together these observations indicate that the lateral ectoderm of the prospective ciliary band, not the animal pole domain, is expanded in the Nodal morphants.
Our study suggests that specification of the ciliary band is likely initiated by a combination of maternal factors such as SoxB1 and by zygotic factors such as FGFA, Otx and Onecut/Hnf6 whose expression is initiated independently of the Nodal and BMP2/4 signals (Figure 10). These zygotic genes initially show a broad expression in the ectoderm, which then becomes restricted to the presumptive ciliary band by the activity of transcriptional repressors such as Goosecoid and IrxA expressed in the ventral or dorsal ectoderm downstream of Nodal or BMP2/4. Collectively our results suggest that the neural ectoderm of the ciliary band forms in a territory that is devoid of Nodal and BMP2/4 signaling (Figure 11). On the dorsal side, inhibition of BMP signaling appears to be sufficient to trigger formation of the ciliary band as was observed in BMP2/4 or Alk3/6 morphants or in embryos injected with low doses of smad6 mRNA. Similarly, on the ventral side, inhibition of Nodal signaling is sufficient to initiate formation of a ciliary band since BMP signaling does not occur on the ventral side but on the dorsal side [26]. In this case, ectopic neural differentiation likely results from inhibition of ventral differentiation. This highlights that, in the sea urchin ectoderm, preventing ventral cells to differentiate downstream of Nodal signaling promotes neural differentiation just as efficiently as inhibiting BMP signaling on the dorsal side. Similarly, in zebrafish embryos, inhibition of Nodal signaling causes the transfating of prospective mesendodermal cells into neural cells [95], [96] and in the mouse, lack of Nodal signaling causes precocious neural differentiation [97]. Therefore, in the sea urchin embryo like in vertebrate embryo models, neural differentiation can result both from inhibition of BMP signals as well as from inhibition of other signals that regulate the fate of early blastomeres and allocate cells to embryonic territories and germ layers.
In summary, our results show that in the sea urchin embryo, the neurogenic territory of the ciliary band is not induced by an interaction between the ventral and dorsal territories as previously suggested [98], but that it represents the default state of the ectoderm in the absence of Nodal and BMP signaling. Nodal and BMP2/4 may therefore be regarded as factors that are required to prevent premature differentiation of ectodermal cells into neural cells as much as factors that are required for specification of the ventral and dorsal ectoderm.
Another recent GRN analysis of ectoderm specification in S. purpuratus was performed using nanostring technology [29]. A comparison of the architecture of the gene regulatory networks derived from this study and ours reveals the expected similarities but also some major differences. A common central element in the architecture of both networks is the critical dependence of dorsal genes on non-autonomous signaling by BMP2/4, a feature already proposed previously [13]. Another point of convergence is that both studies pointed to goosecoid and tbx2/3 as important early zygotic genes downstream of Nodal and BMP2/4: both studies identified brachyury as a downstream target of Goosecoid, and dlx and irxA as downstream targets of Tbx2/3. Finally, both studies identified foxG and deadringer as downstream targets of Nodal.
The first important difference in the architecture of the two proposed networks is that whereas our study defines the default state of the ectoderm in the absence of Nodal and BMP signals as a ciliary band-like ectoderm, the network proposed by Su et al. largely ignores formation of the ciliary band. Another important difference between the two studies concerns the dependence of ventral genes on Nodal. Su et al. argued that only part of the oral ectoderm specification system is downstream of Nodal [29]. According to the authors, a number of regionally expressed genes including onecut/hnf6, otx2, lim1, and foxA, are activated “specifically in the oral ectoderm…exactly the same with or without nodal”, leading them to speculate that hypothetical Nodal independent early oral ectoderm signals regulate these genes in the ventral ectoderm. We do not agree with this interpretation, since from our in situ analysis, it is clear that these genes cannot be considered as oral-specific markers. Furthermore, we showed that the expression of onecut/hnf6, otx2, lim1, and foxA in the presumptive ectoderm region of Nodal morphants was not regionalized, consistent with the absence of any oral territory in these embryos. The expression of onecut/hnf6 and otx2 is first initiated in a territory much larger than the ventral ectoderm, before subsequently becoming restricted to either to the ciliary band (onecut/hnf6) or to a broader territory that also includes the ventral ectoderm (otx2). We thus interpret the continued expression of onecut/hnf6 and otx2 in the ectoderm as reflecting adoption of a ciliary band character by the entire ectoderm. Concerning foxA, the nodal-independent detection of the mRNA reported by Su et al is undoubtedly due to the abundant expression of this gene in a distinct endodermal territory, which, unlike the oral ectoderm expression, is largely Nodal-independent. The foxA example highlights the importance of using methods that allow spatial resolution to analyze the expression of genes with complex expression patterns in epistasis experiments.
According to Otim and colleagues and Su and colleagues, two genes, onecut/hnf6 and deadringer, play essential roles in the DV GRN. Using an “unconventional morpholino” that targeted a sequence 660 bp downstream of the first ATG but that did not target a splice junction, Otim et al. reported that “inhibition” of hnf6/onecut function eliminated D/V polarity and caused a radialized phenotype that strikingly resembled the Nodal loss of function. Using the same reagent, Su et al. expanded this analysis and further argued that a positive regulatory input from onecut/hnf6 is required for the expression of several key regulators such as nodal, goosecoid, lefty, chordin, and bmp2/4 [29], [46]. These results are highly surprising since morpholinos are predicted to be ineffective at blocking translation when they target sequences after the first 25 bases following the initiator ATG [99], [100]. Using two different and more conventional morpholinos targeting the 5′ leader or the translation start site of the P. lividus hnf6/onecut transcript, we were unable to reproduce either the striking hnf6/onecut morphant phenotypes originally reported by Otim and colleagues or the effects on nodal, goosecoid, lefty, chordin, and bmp2/4 reported by Su and colleagues. It is therefore very unlikely that onecut/hnf6, which is expressed only transiently within the ventral ectoderm, plays the crucial role proposed by these authors in this gene regulatory network. Regarding deadringer, Su et al. found that deadringer morphants display a much reduced expression of ventral genes such as goosecoid, NK1 and hes as well as a strongly reduced expression of dorsal genes such as irx, nk2.2 and tbx2.3. Again, these results are surprising since the published cDNA sequence of deadringer used by Su et al. to design their morpholino as well as the associated predictions of the translation start site of the protein are probably incorrect and correspond to a truncated protein sequence as suggested by our sequence analysis of the genomic S. purpuratus deadringer locus and the analysis of the deadringer cDNAs in Paracentrotus (Figure S1). In addition, using two different morpholinos against the P. lividus deadringer transcript, we were unable to reproduce the published drastic effects of deadringer morpholinos on the expression of ventral and dorsal marker genes. It is therefore also unlikely that deadringer plays the role that it had been previously attributed in the S. purpuratus GRN.
Finally, it has been argued that specific aboral differentiation genes such as CyIIIa and spec1 are transcriptionally activated in the aboral ectoderm long before late blastula and that this implied the existence of an early asymmetry in the aboral ectoderm that affected transcriptional activity. Su et al. postulated that this asymmetry may be a redox gradient that would directly regulate the transcriptional activity of aboral genes such as CyIIIa and tbx2/3. Our results oppose this view. In Paracentrotus, the ectodermal expression of tbx2/3 is essentially lost following inhibition of Nodal or BMP2/4 signaling. While it is true that a residual tbx2/3 expression is observed in the Nodal morphants at gastrula stage, this expression is restricted to the vegetal most regions and therefore likely reflects the response of this gene to signals that act along the animal-vegetal axis rather than response to a redox gradient along the D/V axis. Furthermore, in Paracentrotus, expression of CyIII genes is first ubiquitous and only becomes restricted to the dorsal ectoderm at mesenchyme blastula stage (see Figure S5), coinciding with the nuclear translocation of pSmad1/5/8 in dorsal cells. In other words, we never observed any marker gene that was expressed specifically in the dorsal ectoderm before the onset of BMP signaling i.e. at late blastula stage. Our observations therefore do not support the view that the asymmetrical CyIIIa or tbx2/3 expression is driven by an early red-ox gradient, at least not in Paracentrotus, but suggest that their expression is more likely driven by differential Nodal and BMP signaling along the dorsal-ventral axis.
A comparison of the mechanisms of neural induction in different species reveals both similarities and divergences regarding the signaling pathways involved. In Xenopus, inhibition of both Nodal and BMP signaling appears to be essential for neural induction, although FGF signaling is likely implicated in the early steps of this process [101], [102]. Similarly, in mammals, both Nodal and BMP signaling have been involved in neural differentiation, the strongest evidence being that most epibast cells of mouse embryos mutant for nodal or bmpr1 display widespread and precocious expression of anterior neural markers [97], [103]. In the chick and in zebrafish, there is strong evidence that FGF signaling regulates neural induction partly through the regulation of expression of BMP ligands and of BMP antagonists [104], [105], [106]. In contrast in ascidians, which are basally branching but divergent chordates, FGF signals are the key players in neural induction by directly regulating the expression of neural markers such as otx [107]–[109]. Inhibition of BMP signaling does not appear to play a role in this process [110] while Nodal plays a distinct, inductive role in patterning of the neural plate [111]. Similarly, in hemichordates, which together with the echinoderms form a sister group of the chordates and have a diffuse neural system, BMP signaling does not appear to play a role in the choice between neural and epidermis [80].
Our experiments in the sea urchin embryo show that inhibition of Nodal and BMP signaling is central to neural induction in echinoderms and that in the absence of Nodal or BMP signaling, most cells of the ectoderm differentiate into a neurogenic ectoderm. Since BMP signaling also regulates neural differentiation in insects [112] and annelids [113], it appears likely that inhibition of Nodal and BMP signaling may have been an ancestral mechanism to specify neural cells not only in deuterostomes but also perhaps in bilateria, and thus that the neural specification mechanisms used in ascidians and hemichordates have diverged during evolution.
Although in the sea urchin inhibition of Nodal causes the ventral ectoderm to adopt ultimately a neurogenic ectodermal fate, it should be kept in mind that our experiments also suggest that Nodal may have an early and positive role in specification and/or patterning of the neurogenic territory of the ciliary band since we showed that Nodal promotes the expression of Delta in a subpopulation of ciliary band cells and drives the early expression of the neural gene foxG. Therefore, in the sea urchin as in chordates, in addition to its general inhibitory role on neural induction, Nodal may also play a positive role in specification and/or patterning of the neural territory [111], [114], [115].
In conclusion, this large scale, systematic GRN analysis has allowed us to identify a number of key gene regulatory interactions and to build a provisional gene regulatory network describing specification of the three main ectodermal territories of the sea urchin embryo. It has not only uncovered key and probably ancient regulatory sub circuits that drive morphogenesis of the ectoderm, but has also allowed us to propose a new model of how specific regions of the ectoderm are induced over a default state, and of how the ectoderm is patterned by successive rounds of induction by TGF beta ligands. This relatively simple model captures most of the results derived from the functional analyses of Nodal and BMP2/4 in the sea urchin embryo and provides testable predictions for futures studies. Finally, our study illustrates the power of the GRN based approaches which can provide a global perspective on a set of genes regulating a biological process, explaining how this process works and what happens when it fails.
Adults sea urchins (Paracentrotus lividus) were collected in the bay of Villefranche-sur-Mer. Embryos were cultured as described previously [116], [117]. When required, fertilization envelopes were removed by adding 2mM 3-amino-1,2,4 triazole 1 min before insemination to prevent hardening of this envelope followed by filtration through a 75µm nylon net. SB431542 (10 µM in sea water) was diluted from stocks solutions in DMSO, and embryos incubated in 24 well plates protected from light. In controls experiments, DMSO was added at 0.1% final concentration. NiCl2 was used at 0.5 mM. SB431542 and nickel treatments were performed continuously starting 30 min after fertilization. Continuous treatments with recombinant mouse Nodal (1µg/ml) and BMP4 proteins (0.5 µg/ml) (R&D) started at the 16-cell-stage and used embryos lacking the fertilization envelope. We verified with a set of 10 genes that RNA overexpression and recombinant proteins produced equivalent effects for both Nodal and BMP.
To determine if marker genes are direct or indirect targets of Nodal or BMP4 signaling, embryos at the swimming blastula/late blastula, early mesenchyme blastula stage or at gastrula stage from which the fertilization envelope had been removed were treated for 2h with recombinant proteins in the presence or absence of protein synthesis inhibitors. To block protein synthesis, puromycin or emetine was added at a final concentration of 360µM (200µg/ml) or 5µM (10µg/ml) respectively using stock solutions prepared in DMSO. In control experiments embryos were treated with 0.1% DMSO or with Puromycin at 200µg/ml or emetine at 10µg/ml. Development of the treated embryos was usually arrested 30 min after addition of the inhibitor, an indication of the effectiveness of the reagent and after 3–4h, all the treated embryos underwent a massive and brutal apoptosis, an effect characteristic of treatments with protein synthesis inhibitors. In the case of nodal, bmp2/4, lefty, goosecoid, fgfr1, chordin, nk2.2, tbx2.3, treatments were performed at the swimming blastula stage. In the case of nk1, foxA, brachyury, foxG, dlx, hox7, id, irxA, glypican5, cyIIIa, admp2, smad6 and msx, treatments were performed at the early mesenchyme blastula sage. In the case of deadringer, atbf1, msx, wnt5, irxA and dlx treatments were also performed at gastrula stage. Short treatments with Nodal or BMP4 failed to induce ectopic expression of any marker gene at gastrula stage suggesting that most of the genes expressed at this stage are indirect targets of Nodal and BMP2/4 or alternatively that at this stage, ectodermal territories are resistant to respecification by exogenous Nodal or BMP4.
Most of the genes analyzed in this study were discovered in the course of a random in situ hybridization screen using cDNA libraries from various stages (T. Lepage unpublished). Additional marker genes were discovered in a second in situ screen aimed at analyzing the expression profiles of all the transcription factors and signaling molecules expressed during early sea urchin development [30] using a Paracentrotus lividus EST library (http://goblet.molgen.mpg.de/cgi-bin/webapps/paracentrotus.cgi). When the isolated clones were incomplete, full-length cDNA sequences were obtained either by screening cDNA libraries with conventional methods and sequencing the corresponding clones. In certain cases, 5′RACE was performed using the Smart RACE kit (Clontech) to obtain the 5′ sequences. A list of all the Paracentrotus transcripts analyzed in this study with a summary of their temporal and spatial expression patterns is provided in Table 1 together with the corresponding accession numbers and original references describing these genes. Note that in the case of deadringer, the sequence of the Paracentrotus lividus clones diverged significantly from the published Strongylocentrotus purpuratus sequence. The published S. purpuratus deadringer transcript is predicted to encode a 490 amino acid protein. However, all the 13 independent deadringer cDNA clones that we sequenced encoded a protein 100 amino acids longer on the N-terminal side. Furthermore, translation of the S. purpuratus genomic sequence upstream of the predicted first ATG revealed the presence of a much longer open reading frame compared to the published deadringer protein sequence that encoded a protein highly similar to the deduced protein sequence from Paracentrotus (see Figure S1). This indicates that the previously published deadringer mRNA sequence was probably incorrect on the 5′ end and that the predicted deadringer protein sequence deduced from this mRNA was truncated. Since morpholinos fail to block translation when their target sequence is located after the first 25 bp following the initiator ATG [100], the conclusions derived from previous functional studies of deadringer in S. purpuratus, which relied on a truncated sequence, are probably erroneous.
For each gene of the network, a detailed analysis of the expression pattern was performed using digoxygenin labeled probes and in some cases, the temporal expression was analyzed by Northern blotting to verify maternal expression and to determine the exact onset of zygotic gene expression (Figure S2). In situ hybridization was performed following a protocol adapted from Harland [118] with antisense RNA probes and staged embryos. For marker genes expressed in ventral or dorsal territories at early stages, and for genes with complex expression profiles, double in situ hybridization was performed to confirm the orientations of the expression pattern. In this case, the two probes were hybridized and developed simultaneously. Probes derived from pBluescript vectors were synthesized with T7 RNA polymerase after linearization of the plasmids by NotI, while probes derived from pSport were synthesized with SP6 polymerase after linearization with SfiI. Control and experimental embryos were developed for the same time in the same experiments. Two color in situ hybridization was used following the procedure of Thisse et al. [119].
For overexpression studies the coding sequence of the genes analyzed was amplified by PCR with the Pfx DNA polymerase (Invitrogen) using oligonucleotides containing restriction sites and cloned into pCS2 [120]. Capped mRNAs were synthesized from NotI-linearized templates using mMessage mMachine kit (Ambion). After synthesis, capped RNAs were purified on Sephadex G50 columns and quantitated by spectrophotometry. RNAs were mixed with Tetramethyl Rhodamine Dextran (10000 MW) or Texas Red Dextran (70000 MW) or Fluoresceinated Dextran (70000 MW) at 5 mg/ml and injected in the concentration range 100–800µg/ml. The nodal, bmp2/4, fgfA, univin, alk3/6QD, and chordin pCS2 constructs have been described in Duboc et al. (2004), Röttinger et al. (2008), Range et al. (2007) and Lapraz et al. (2009). The pCS2 goosecoid construct is described in [40]. RNA derived from the following additional constructs were made (the cloning sites are indicated in parenthesis): pCS2foxA (ClaI-XbaI); pCS2deadringer (EcoRI-XhoI); pCS2foxG (ClaI-XhoI); pCs2smad6 (EcoRI-XbaI); pCS2pax2/5/8 (BamHI-XhoI); pCS2tbx2/3 (BamHI-XhoI); pCS2msx (BamHI-XhoI); pCS2nk2.2 (BamHI-XhoI).
Morpholino antisense oligonucleotides were obtained from GeneTools LLC (Eugene, OR). The nodal, BMP2/4, Alk4/5/7,Alk3/6, univin, lefty and soxB1 morpholinos are described in [12]–[14], [26]. Since morpholinos can have side effects or display toxicity or produce variable reductions in gene activity [121], we designed and tested several morpholinos for each gene. A pair of morpholinos that did not display toxic effects was selected for further use (a morpholino was considered toxic if it caused developmental arrest during cleavage or a massive cell death at the onset of gastrulation when injected at low doses (0.1–0.3 mM)). In the cases of nodal, bmp2/4, alk3/6, Alk4/5/7, univin and soxB1, the efficiency of the morpholino to downregulate the expression of previously characterized targets genes was systematically assessed in control experiments [13], [14], [26]. The phenotypes observed for nodal, bmp2/4, brachyury chordin, foxA, fgfA, goosecoid, irxA, lefty, tbx2/3, dlx, msx, onecut/hnf6, soxB1, univin, wnt8 morpholinos were considered specific since they were confirmed with a separate, non-overlapping morpholino. In the case of alk3/6, alk4/5/7 and nodal, a rescue experiment had previously been performed demonstrating the specificity of these reagents [13], [14], [26]. The phenotypes observed were always consistent with the zygotic expression pattern of the targeted genes and with previous well-established functional data [13], [14], [26], [35], [42], [122]. We did not observe inconsistent phenotypes among several knockdowns except in one case, in which knocking down Tbx2/3, an upstream regulatory gene of irxA, did not cause the same effect on the IrxA target gene onecut/hnf6 as knocking down irxA itself suggesting that the tbx2/3 morphant phenotype is a hypomorphic phenotype and not a null. In the case of the ventrally expressed genes nodal and bmp2/4, we observed strong non autonomous effects consistent with the demonstrated translocation of BMP2/4 from the ventral to the dorsal ectoderm and with the role of BMP2/4 as relay downstream of Nodal [26]. In contrast, we never observed strong effects on the expression of ventral markers by morpholinos targeting genes expressed dorsally. In three cases, (dlx, msx, foxG) a morphological phenotype was consistently observed but molecular analysis failed to detect significant perturbations in the expression of the genes analyzed. Other morpholinos pairs (deadringer, hox7, nk2.2, oasis, wnt5) gave very weak or not always reproducible phenotypes. Molecular analysis on embryos injected with these morpholinos failed to detect significant and reproducible changes in gene expression in any of the ventral, dorsal or ciliary band markers genes that we tested. In a few cases, (atbf1, klf2/4) all the morpholinos synthesized were highly toxic and were not studied further. The loss of function phenotypes of 29D, tubulinß3, egip, CyIIIa, admp2, fgfr1, pax2/5/8, unc4, nk1, id, rkhd and ptb and otx were not analyzed in this study and these genes were only used as markers in the following experiments. The sequences of all the morpholino oligomers used in this study are listed below. The most efficient morpholino of each pair is labeled with a star.
alk4/5/7 Mo 1: TAAGTATAGCACGTTCCAATGCCAT
alk3/6: Mo1: TAGTGTTACATCTGTCGCCATATTC
brachyury Mo1: AGCATCGGCGCTCATAGCAGGCATA
brachyury Mo2*: CTGGCAGAAGATGTACTTCGACGAT
bmp2/4 Mo1*: GACCCCAGTTTGAGGTGGTAACCAT
bmp2/4 Mo2: CATGATGGGTGGGATAACACAATGT
chordin Mo1*: GGTATAAATCACGACACGGTACATG
chordin Mo2: CGAAGATAAAAACTTCCAAGGTGTC
deadringer Mo1: TGCTCGCGGTAACAAGTGATTCCAT
deadringer Mo2: TTATATGGCAAAGGACTTCTACAGC
dlx Mo1: CCCACGTCAAATGAATACATCAACA
dlx Mo2: AAACACGTTTAGAATCCTCACGACT
fgfA Mo1: ACTTTCATCCATTTTCGCTTTCATG
fgfA Mo2*: ACACATTTTGGATACTTACAGCTCC
foxA Mo1: CATGGGTTCCTCCTTGAAATCCACG
foxA Mo2*: TGAAAGATTAAAGTAGCACAGTCAG
foxG Mo1*: TCCGATGAATGTGCATGAAAAACTG
foxG Mo2: CTTCTTGCTAAATACCAAGTTGGAG
goosecoid Mo1*: TGTCTGGAAGGTAATAGTCCATCTC
goosecoid Mo2: AGATCAGAGCTAACCACTTAGGACG
hnf6/onecut: Mo1: AGCCGCTGGACCTCAAACGCGAAGA
hnf6/onecut Mo2*: AAAATGATAATGTGGTCTCCGTCGC
hox7 Mo1: TGACGAAATACGAACTCGAACTCAT
hox7 Mo2: ACCACTTCATTAATAGCCAAAACCT
irxA Mo1: ATTGTGGATAACTGCTCGTCGTCAT
irxA Mo2: TTGTTGAAATCAACTTTGAGACGAT
Lefty Mo1: GGAGCGCCATGAGATAATTCCATAT
Lefty Mo2: GGAGATGGGCAAAATATGAAGATAC
msx Mo1: CGACTTGATGGAAGAAAATTATTCC
msx Mo2 : TTATCGCTTTAAGAATGACCAAGGA
NK1 Mo1: AAGCATTGAGAATCCCTAAAACTGC
NK1 Mo2: CATGTGCTCTGTTCAGACGGTCAAC
nk2.2 Mo1: ATCAACATTCATACGATGTCTCTAT
nk2.2 Mo2: ATAGTTAATTCCACACCACCCACTT
nodal Mo1*: ACTTTGCGACTTTAGCTAATGATGC
nodal Mo2: ATGAGAAGAGTTGCTCCGATGGTTG
tbx2/3 Mo1: TCGACGAACCACCAAATCTTGAGCA
tbx2/3 Mo2* : TCGGCAAAAGCCTCCGAGTCCAAAT
Oasis Mo1: CTCTTCACCTAAAAGCCCATCCATG
Oasis Mo2: CCAATTTGGGCCGTAGTCGAGGGAC
soxB1 Mo 1*: GACAGTCTCTTTGAAATTAGACGAC
soxB1 Mo2: GAAATAAAGCCAAAGTCTTTTGATG
univin Mo1*: ACGTCCATATTTAGCTCGTGTTTGT
univin Mo2: GTTAAACTCACCTTTCTAAACTCAC
wnt8 Mo1: GAACAACTGCCGTAAAGATATCCAT
wnt8 Mo2*: AACAGTCCAAATATGAAGTTCAAAC
As a control for defects related to injection and egg quality, we used morpholinos directed against the hatching enzyme gene: 5′-GCAATATCAAGCCAGAATTCGCCAT-3′ or against the Nemo like kinase transcript -5′-TCGGAGGCAGACCAGCAGCGAGAAA-3′. Embryos injected with either of these morpholinos at 1mM normally develop into pluteus larvae. Morpholinos oligonucleotides were dissolved in sterile water and injected at the one-cell stage together with Tetramethyl Rhodamine Dextran (10000 MW) at 5 mg/ml. For each morpholino a dose-response curve was obtained and a concentration at which the oligomer did not elicit non-specific defect was chosen. Approximately 2–4 pl of oligonucleotide solution at 0.5 mM were used in most of the experiments described here. For morphological observations, about 150–200 eggs were injected in each experiment. To analyze gene expression in the morphants a minimum of 50–75 injected embryos were hybridized with a given probe. All the experiments were repeated at least twice and only representative phenotypes observed in more than 80% of embryos are presented.
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10.1371/journal.pgen.1002497 | Contrasting Properties of Gene-Specific Regulatory, Coding, and Copy Number Mutations in Saccharomyces cerevisiae: Frequency, Effects, and Dominance | Genetic variation within and between species can be shaped by population-level processes and mutation; however, the relative impact of “survival of the fittest” and “arrival of the fittest” on phenotypic evolution remains unclear. Assessing the influence of mutation on evolution requires understanding the relative rates of different types of mutations and their genetic properties, yet little is known about the functional consequences of new mutations. Here, we examine the spectrum of mutations affecting a focal gene in Saccharomyces cerevisiae by characterizing 231 novel haploid genotypes with altered activity of a fluorescent reporter gene. 7% of these genotypes had a nonsynonymous mutation in the coding sequence for the fluorescent protein and were classified as “coding” mutants; 2% had a change in the S. cerevisiae TDH3 promoter sequence controlling expression of the fluorescent protein and were classified as “cis-regulatory” mutants; 10% contained two copies of the reporter gene and were classified as “copy number” mutants; and the remaining 81% showed altered fluorescence without a change in the reporter gene itself and were classified as “trans-acting” mutants. As a group, coding mutants had the strongest effect on reporter gene activity and always decreased it. By contrast, 50%–95% of the mutants in each of the other three classes increased gene activity, with mutants affecting copy number and cis-regulatory sequences having larger median effects on gene activity than trans-acting mutants. When made heterozygous in diploid cells, coding, cis-regulatory, and copy number mutant genotypes all had significant effects on gene activity, whereas 88% of the trans-acting mutants appeared to be recessive. These differences in the frequency, effects, and dominance among functional classes of mutations might help explain why some types of mutations are found to be segregating within or fixed between species more often than others.
| Genetic dissection of phenotypic differences within and between species has shown that mutations affecting either the expression or function of a gene product can contribute to phenotypic evolution; mutations that alter gene copy number have also been shown to be an important source of phenotypic variation. Predicting when and why one type of mutation is more likely to underlie a phenotypic change than another remains a pressing challenge for evolution biology. Understanding the relative frequency and properties of different types of mutations will help resolve this issue. To this end, we isolated 231 mutants with altered activity of a focal gene. Mutants were classified into one of four functional classes (i.e., coding, cis-regulatory, trans-acting, or copy number) based on the location and nature of mutation(s), or lack thereof, within the focal gene. Mutant effects on focal gene activity were assessed in both haploid and diploid cells. These data identified differences in the frequency, effects, and dominance (relative to the wild-type allele) among functional classes of mutants that help explain patterns of genetic variation within and between species.
| Mutations are the ultimate source of genetic variation, thus understanding the properties of new mutations is important for both medical and evolutionary genetics. Large-scale sequencing surveys have recently measured mutation rates for different types of DNA lesions (e.g., transitions, transversions, indels, rearrangements, duplications) in a variety of organisms [1]–[3], but little remains known about the genetic properties of these mutations or their effects on the activity of individual genes. Although not often incorporated into population genetic models of the evolutionary process, differences in the frequency and properties of different types of mutations can influence evolutionary paths [4]–[6].
From the perspective of a single gene, mutations affecting its activity can be divided into four functional classes: [nonsynonymous] coding mutations that alter the sequence of the encoded RNA or protein gene product, cis-regulatory mutations that alter (typically, non-coding) sequences that regulate the gene's expression in an allele-specific manner, trans-acting mutations that alter coding or cis-regulatory sequences of other genes in the genome and affect activity of the focal gene via a diffusible gene product, and copy number mutations resulting from duplications or deletions that change the number of copies of the focal gene in the genome. As the raw material of evolutionary change, all of these types of mutations have the potential to become polymorphisms segregating at an appreciable frequency within a species and/or substitutions fixed between species, yet studies identifying the genetic basis of trait differences suggest that some types of changes underlie phenotypic differences more often than others (reviewed by [7]–[9]).
The apparent inequality in the contribution of different types of mutations to phenotypic evolution is often explained by invoking differences in pleiotropy (i.e., the number of traits affected by a mutation) among functional classes. Increased pleiotropy is assumed to increase the chance that a mutation has deleterious effects on fitness and will be disfavored by natural selection. One example of this is that coding mutations are commonly expected to be more pleiotropic (and hence have lower average fitness) than cis-regulatory mutations ([7], [9], [10], but see [11]). Although undoubtedly important, pleiotropy is only one factor influencing the probability that a certain type of mutation is fixed. For example, the direction and magnitude of a mutation's effect on gene activity and whether or not the mutation is dominant to the wildtype allele are also expected influence the evolutionary trajectories of new mutations in diploid populations. Of course, these factors matter only after a mutation has occurred, thus mutation rates can influence the evolutionary process as well [7], [12]–[14]. The frequency, effects, and dominance of new mutations have all been predicted to vary among functional classes of mutations [15]–[17], but little data has been available to test these predictions [13], [15].
To directly compare these parameters among functional classes of mutations, we systematically isolated and quantitatively characterized over 200 mutations in Saccharomyces cerevisiae affecting activity of a focal gene. To make this experiment feasible, we used a mutagen to elevate the mutation rate and studied mutations affecting activity of a reporter gene expressing Yellow Fluorescent Protein (YFP) that could be scored quantitatively in thousands of living cells per second using flow cytometry. Expression of this heterologous fluorescent protein was controlled by native S. cerevisiae promoter and terminator sequences (which allowed us to interrogate endogenous S. cerevisiae transcriptional regulatory networks) and the mutagen was expected to cause mutations relatively uniformly across the genome.
Using this experimental system, we measured the proportion of cells with new mutations that altered activity of the reporter gene and used this proportion to estimate the spontaneous mutation rate for this phenotypic change. We then isolated 231 mutants with altered activity of the reporter gene and subjected them to further characterization, including determining the relative frequency of different types of mutations, comparing their effects on reporter gene activity, and assessing their dominance relative to the wildtype allele. These data revealed differences in the frequency, effects, and dominance among coding, cis-regulatory, trans-acting, and copy number mutations that are expected to influence the relative contribution of different types of mutations to phenotypic variation within and between species.
To characterize the spectrum of mutations affecting activity of a focal gene, we screened mutagenized cells containing a fluorescent reporter gene and quantified cellular fluorescence using flow cytometry. Mutagenesis was performed using ethyl methanesulfonate (EMS), and the increase in mutation rate was controlled by titrating exposure of cells to this chemical. The reporter gene was constructed by fusing the coding sequence of the Venus variant [18] of YFP to the S. cerevisiae CYC1 terminator [19], and placing them both under the control of 5′ intergenic sequence of the S. cerevisiae TDH3 gene. This chimeric transgene (PTDH3-YFP) was integrated into a pseudogene on the first chromosome of S. cerevisiae, where integration of fluorescent reporter genes has previously been found to have no measurable effect on fitness (B. Williams, personal communication). For each cell, the activity of PTDH3-YFP was measured as YFP fluorescence per unit of “forward scatter” (FSC); FSC is proportional to cell size [20] and is linearly related to YFP fluorescence (Figure 1A). In the absence of amino acid changes in YFP, cellular fluorescence is expected to be linearly related to YFP protein abundance, as has been shown for the related Green Fluorescent Protein [21].
To determine the frequency of mutations that affected PTDH3-YFP activity, which is expected to reflect the genome-wide mutational target size for this phenotype, we measured YFP fluorescence in each cell of a mixed sample containing both EMS-treated and untreated cells. Cells that were not exposed to EMS were considered control cells and labelled with Cy5 (a fluorescent dye), but otherwise processed identically to the EMS-treated cells. Comparing YFP fluorescence between >25,000 control cells labelled with Cy5 and >20,000 unlabelled control cells showed that Cy5 labeling had no significant effect on the measurement of YFP fluorescence phenotypes (P = 0.9, t-test).
The EMS-treated population displayed an approximately equal increase of cells with YFP fluorescence levels in both tails of the distribution (Figure 1B, 1C), suggesting that mutations increasing and decreasing fluorescence occurred at similar rates. The increase in cells with both high and low fluorescence was taken as the frequency (f = 0.0298) of EMS-induced mutants with altered activity of PTDH3-YFP, whereas the remaining cells were assumed not to carry any mutations affecting PTDH3-YFP activity. Assuming mutations affecting PTDH3-YFP activity were Poisson distributed, the inferred frequency of genotypes without a relevant mutation (P0 = 1−f = 0.9702) suggested an average of 0.0303 mutations affecting PTDH3-YFP activity per genome in the EMS-treated cells. Given this mutation rate, a Poisson process predicts that 2.94% of the EMS-treated cells should have exactly one relevant mutation (P1), whereas just 0.04% of the EMS-treated cells (1% of all mutants) should have more than one mutation affecting PTDH3-YFP activity (P>1).
To estimate a spontaneous mutation rate for PTDH3-YFP activity from this mutagen-treated population, we measured the frequency of canavanine resistance mutants in the same EMS-treated population, and found that it was 5737-fold higher than the spontaneous canavanine resistance mutation rate reported by [22]. Assuming that a similar proportion of sites affecting canavanine susceptibility and PTDH3-YFP activity were targeted by EMS, our data suggest a spontaneous mutation rate for quantitative changes in PTDH3-YFP activity of 0.0303/5737 or 5.3×10−6 per haploid genome per generation (Text S1). An alternative estimate of the spontaneous mutation rate, based on the number of empirically confirmed mutants in each tail of the EMS-treated distribution (described in the next section), was also calculated and is presented in Table S4.
To isolate individual cells with abnormal PTDH3-YFP activity for further characterization, cells exhibiting YFP fluorescence less than a minimum threshold near the 1st percentile of control cells and greater than a maximum threshold near the 99th percentile of control cells (Figure 1B) were collected using FACS. Exact sorting thresholds for the nine replicate sorting experiments are shown in Table S1. On average, cells with YFP fluorescence similar to that of the lowest fluorescing 0.82% and highest fluorescing 0.64% of the control population were sorted. These threshold levels of YFP fluorescence resulted in sorting cells from the lowest fluorescing 1.21% and highest fluorescing 1.04% of the mutagenized population, suggesting that (1.21–0.82)/1.21, or 32.2%, of EMS-treated cells sorted from the low-fluorescence tail and (1.04–0.64)/1.04, or 38.5%, of EMS-treated cells sorted from the high-fluorescence tail were mutants. In all, 864 FACS “events” (i.e., cells or other particles) were sorted from each tail of the EMS-treated subpopulation, and 864 FACS events were sorted from each tail of the control population, for a total of 3456 FACS events arrayed individually on solid media. The percentage of sorted events that formed colonies was similar between the EMS-treated and control populations (68% vs 70%, P = 0.26, Fisher's Exact test), suggesting that EMS-induced mutations severely limiting growth were rare among cells sorted from this population. A slightly larger, and statistically significant, difference was observed, however, between the percentage of sorted events from the high- and low-fluorescing tails that formed colonies for both EMS-treated and control cells (65% vs 70% for mutagenized cells, and 67% vs 72% for control cells; P = 0.03 in each comparison, Fisher's Exact Test). The similar asymmetry observed in the mutagenized and control populations suggests that it was not caused by the EMS treatment.
Each colony was used to inoculate a liquid culture, and YFP fluorescence was measured in at least 5,000 cells from each of these clonal cultures by flow cytometry. The YFP fluorescence phenotype of each culture was calculated as the median YFP/FSC ratio of all cells within a fixed range of FSC values (Figure 1A). To determine the effect on PTDH3-YFP activity of any mutation(s) present in a recovered genotype, we calculated the difference in YFP fluorescence between each genotype and the mean YFP fluorescence of replicate control cultures, and then divided it by the standard deviation of YFP fluorescence phenotypes among the replicate control cultures. This value is a test statistic known as a Z-score (Z), and reflects the magnitude and direction of each genotype's effect on YFP fluorescence relative to the starting (unmutagenized) genotype as well as the likelihood that this effect is significantly different from 0. Given our experimental design, only mutations that prevented colony formation on solid media, slowed growth in liquid culture enough to preclude sampling 5,000 cells, or had effects on YFP fluorescence below our detection limits should have been systematically eliminated from our collection.
Genotypes isolated from the EMS-treated population with |Z|>2.58 were considered mutants and subjected to further analysis. This statistical threshold corresponds to a 99% confidence interval for the mean of the control population, and implies that all genotypes considered mutants showed a change in YFP fluorescence supported by a [two-tailed] p-value<0.01. On the basis of this statistical cut-off, 231 (22%) of the 1064 liquid cultures derived from the EMS-treated colonies were considered mutants (Table S2). By contrast, only 16 (1%) of the 1137 cultures derived from the control colonies exceeded the |Z| = 2.58 significance threshold (Table S2); these 16 isolates are not included in the collection of mutants discussed below. In addition to these changes in median YFP fluorescence, 18.6% of EMS-treated genotypes classified as mutants, 4.4% of EMS-treated genotypes not classified as mutants, and 1.8% of genotypes isolated from the control population showed significant changes in the variance of YFP fluorescence (Figure S1). Although changes in both the median and variance of YFP fluorescence might or might not be caused by the same mutation, the elevated proportion of genotypes with altered variance among EMS-treated genotypes classified as YFP fluorescence mutants suggests that they might often be one and the same.
Mutants affecting PTDH3-YFP activity were identified solely on the basis of their YFP fluorescence phenotype, thus we expected them to include genotypes with (1) mutations in the coding sequence of PTDH3-YFP, (2) mutations in cis-acting sequences of PTDH3-YFP, (3) mutations outside of the known cis-regulatory and coding sequences of PTDH3-YFP that putatively have trans-acting effects on the cell's YFP fluorescence phenotype, and (4) duplications or deletions changing the copy number of PTDH3-YFP (copy number variants, CNVs).
To identify genotypes with mutations in the coding and cis-regulatory sequences of PTDH3-YFP, we sequenced the entire PTDH3-YFP transgene in each of the 231 mutants. 16 independently isolated genotypes were each found to contain a mutation predicted to change an amino acid or introduce a stop codon in YFP; two of these observed mutations were found in two genotypes each (Figure 2A). Additionally, one mutant was found to contain a synonymous mutation within the YFP coding region (Figure 2A). Four mutants had mutations within the cis-regulatory promoter region of PTDH3-YFP, two of which carried the same mutation (Figure 2A). None of the 231 mutants had a mutation in the CYC1 terminator (Figure 2A), nor did any contain more than one mutation in the entire PTDH3-YFP gene (i.e., TDH3 promoter, YFP coding sequence and CYC1 terminator). Cases where the same mutation was found in two mutants could have resulted from recurrent mutation or common ancestry, although the experiment was designed to minimize the potential for recovering clonally related mutants (see Text S1) and in at least one case (described below) the shared mutation exists on different genetic backgrounds, suggesting independent origins.
EMS is not generally thought to induce changes in copy number [23], [24], but spontaneous duplications are common in S. cerevisiae [1]. Therefore, we tested for changes in the copy number of PTDH3-YFP by mating each haploid mutant to a closely related genotype (of the opposite mating type) in which the YFP coding sequence in PTDH3-YFP was replaced with the coding sequence for a Cyan Fluorescent Protein (CFP, 95% amino acid sequence identity with YFP [25]). Pyrosequencing was then used to compare the relative frequency of YFP and CFP alleles in genomic DNA extracted from each of the resulting diploid genotypes. 22 (10%) of the 221 mutants tested showed evidence of PTDH3-YFP duplications (Figure 2B); 10 mutants were not analyzed because they either failed to produce diploids or showed evidence of contamination. 16 genotypes isolated from the control population with |Z|>2.58 were also tested, and 5 (31%) showed evidence of more than one copy of PTDH3-YFP. This high frequency of copy number variants in the control population is consistent with the idea that copy number variants in the EMS-treated population also resulted primarily from spontaneous duplications. This in turn suggests that duplications are the most common type of spontaneous mutation affecting PTDH3-YFP activity, given that we estimated the frequency of point mutations was elevated ∼5700-fold by EMS in our screen.
On the basis of these data, we divided the 221 mutants tested for copy number variation into four classes (Table S3): the 16 mutants containing a mutation affecting the amino acid sequence of YFP were classified as “coding”; the 22 mutants containing a duplication of PTDH3-YFP were classified as copy number variants, or “CNV”s; the 4 mutants containing a mutation in the TDH3 promoter were classified as “cis-regulatory”; and the 179 mutants that had neither a cis-regulatory or non-synonymous mutation in PTDH3-YFP nor a change in its copy number were classified as “trans-acting”. This large trans-acting class of mutants is expected to include coding and noncoding changes in genes other than PTDH3-YFP that regulate its transcription and post-transcriptional processing as well as mutations that impact elements of the cell that affect fluorescence per unit cell size (i.e., FSC) (e.g., pH [26]). Epigenetic changes are also possible. Of the 10 mutant genotypes that we were unable to test for PTDH3-YFP copy number, none showed any sequence differences in PTDH3-YFP, indicating that they could be either CNVs or trans-acting mutants (Table S3). Because of this ambiguity, these 10 genotypes were excluded from the comparisons among mutant classes described below.
Mutations affecting the amino acid sequence of YFP or the number of copies of PTDH3-YFP were assumed to explain the mutant phenotypes of genotypes in which they occur; however, we were less confident of this assumption for the promoter mutations. Therefore, we empirically tested whether each of the three mutations identified in the promoter region was (1) sufficient to alter YFP fluorescence and (2) sufficient to recreate the YFP fluorescence phenotype of the mutant genotype(s) that harbored it. Site-directed mutagenesis was used to introduce each mutation into the ancestral (unmutagenized) genotype, and YFP fluorescence was analyzed in a haploid population of these genetically modified cells using flow cytometry. In all three cases, populations of cells containing one of these promoter mutations showed in a significant change in YFP fluorescence relative to cells with the ancestral promoter (P<0.05, Mann-Whitney-Wilcoxon (MWW); Figure 2C). For three of the four genotypes containing a promoter mutation, this mutation was sufficient to recapitulate the change in YFP fluorescence (Figure 2C), showing that the promoter mutation was solely responsible for the observed mutant phenotype. The one exception was a genotype that carried the same promoter mutation as another strain; in this case, the promoter mutation only partially recreated the mutant's YFP fluorescence (Figure 2C), indicating that this genotype (mutant 4Q4E4) contained more than one mutation affecting YFP fluorescence.
As described above, the Z-score calculated for each sorted genotype describes the magnitude and direction of its effects on PTDH3-YFP activity relative to the control (unmutagenized) genotype. To determine the relative frequency of mutations that increased and decreased YFP fluorescence, we examined the sign of the Z-score for each of the 231 mutants with |Z|>2.58 and found that 162 (70.1%) showed increased fluorescence (Z>0). When alternative thresholds of either |Z|>1.96 or |Z|>1.645, corresponding to p<0.05 and p<0.1, respectively, were used to identify mutants, 68% showed increased fluorescence. This excess of mutants with increased YFP fluorescence was surprising given the similar increases in cell number observed in both tails of the full EMS-treated population (Figure 1B, 1C). As described above, differences in colony formation rates were observed between the high- and low-fluorescing tails; however, they are unlikely to explain the apparent excess of high-fluorescing mutants: 65.3% of FACS events sorted from the low-fluorescing tail formed colonies and 38.5% of this group were expected to be mutants, whereas 70.1% of FACS events sorted from the high-fluorescing tail formed colonies and 32.2% of these were expected to be mutants, suggesting that about half (52.7%) of recovered mutants should increase fluorescence. This discrepancy might instead result from a nonuniform distribution of mutants within each tail, such that sorting from a slightly larger tail at the low-fluorescing end of the distribution (1.22% vs 1.04% of cells in the EMS-treated population) resulted in a lower proportion of sorted cells being classified as mutants.
Comparing the distributions of Z-scores among the four mutational classes showed differences in both the magnitude and direction of effects among groups (Figure 3). For example, none of the 16 coding mutants increased fluorescence, compared to 21 (95%) of the 22 CNVs, 2 (50%) of the 4 cis-regulatory mutants, and 131 (73%) of the 179 trans-acting mutants. Considering only the magnitude of the change in YFP fluorescence caused by each mutant (|Z|), we found statistically significant pairwise differences among coding, CNV, and trans-acting mutants (P≤0.02 in all 3 comparisons, MWW test). With only four cis-regulatory mutants recovered from our screen, we had little power to detect differences in comparison with other classes, and all three pairwise tests involving this class failed to reach statistical significance (P≥0.15 in all cases, MWW test). Overall, coding mutants had the largest effect on gene activity (median |Z| = 48), followed by cis-regulatory mutants (median |Z| = 8.1) and CNVs (median |Z| = 8.0), and finally trans-acting mutants (median |Z| = 4.6).
We isolated mutants in haploid cells so that we could recover recessive mutations; however, wild populations of many eukaryotes, including S. cerevisiae, tend to be diploid. To determine how the haploid mutant genotypes in our collection act in diploid cells, we again crossed each mutant genotype with the reference strain containing PTDH3-CFP that was used to identify CNVs. YFP and CFP fluorescence was measured in at least 9,000 diploid cells for each mutant genotype using flow cytometry. Z-scores describing YFP and CFP fluorescence were calculated for each mutant by comparing their fluorescence to that of replicate populations of control diploid cells resulting from mating the ancestral, unmutagenized PTDH3-YFP haploid genotype to the reference haploid genotype containing PTDH3-CFP. We successfully tested all 16 coding mutants, all 4 cis-regulatory mutants, 21 of the 22 CNVs, and 171 of the 179 trans-acting mutants for their effects on YFP and CFP fluorescence in diploid cells.
To assess the dominance of each mutant relative to the reference strain (i.e., its ability to affect PTDH3-YFP activity in heterozygous, diploid cells), we compared the Z-scores for YFP fluorescence of each mutant from haploid and diploid cells (Figure 4A). We found that the effects of coding, CNV, and cis-regulatory mutants in diploid cells (median |Z| = 96, 15, and 10, respectively) were more similar to their effects in haploid cells (regression coefficients (b) of 1.67, 1.50, and 1.34, respectively, from a model II regression) than were those of trans-acting mutants (median |Z| = 1, b = 0.12). These data show that as a group, the trans-regulatory mutants were much more recessive than mutants from any of the other classes. This was also seen using a threshold of |Z| = 2.58 to classify mutants as recessive (i.e., no significant effect on YFP fluorescence in diploids): none of the coding, CNV, or cis-regulatory mutants were called recessive, whereas 151 (88%) of the 171 trans-acting mutants tested were.
To determine whether a mutant genotype had similar effects on both alleles of the reporter gene present in diploid cells, we compared the effects of each genotype on [diploid] YFP and CFP fluorescence (Figure 4B). This analysis showed that CNVs had the largest effect on CFP fluorescence (median |Z| = 4.2 compared to median |Z| = 1 for all other mutant classes). Surprisingly, 15 of the CNVs showed a significant decrease in CFP fluorescence despite a significant increase in YFP fluorescence. Of the coding mutants tested, the majority (14 of 16) showed no significant effect on CFP fluorescence (|Z|<2.58), as expected. The remaining two showed small increases in CFP fluorescence (Z = 2.6 and 2.8, respectively) despite showing decreases in YFP fluorescence (Z = −167 and −28). These genotypes might harbor amino acid changes that alter the emission spectrum of the mutant YFP protein and cause it to overlap that of CFP. Mutants classified as cis-acting were also not expected to alter CFP fluorescence, and three of the four cis-acting mutants did not (|Z| = 0.75, 1.1, and 0.68). The one cis-regulatory mutant that showed an effect on CFP fluorescence (1Q4D11, whose mutant phenotype in haploids appeared to be caused solely by the identified promoter mutation, Figure 2C) decreased both CFP and YFP fluorescence (Z = −2.7, P = 0.006), suggesting transvection [27]. Finally, non-recessive mutants in the trans-acting class were expected to have similar effects on both YFP and CFP fluorescence in diploid cells, and 13 (65%) of the 20 trans-acting mutants with |Z|>2.58 for YFP fluorescence in diploid cells also showed a significant (|Z|>2.58) effect on CFP fluorescence in the same direction. Considering all mutants in all classes, the relationship between CFP and YFP fluorescence in diploid cells was strongest for the trans-acting mutant class (Figure 4B; b = 1 compared with b = 0.00, 0.37, and 0.08 for coding, CNV, and cis-regulatory mutants, respectively).
This study provides a systematic survey and functional analysis of mutations affecting activity of a reporter gene (PTDH3-YFP) in S. cerevisiae. By comparing unmutagenized and mutagenized subpopulations of a clonal culture, we estimated a spontaneous mutation rate for activity of PTDH3-YFP of 5.3×10−6 per haploid genome per generation, which, in S. cerevisiae, is intermediate between spontaneous mutation rates reported for single gene loss-of-function phenotypes (10−6–10−8, [22], [28]–[32] and more complex organismal phenotypes such as growth rate (10−3, [33]), and suggests that PTDH3-YFP activity is controlled by a moderate number of genes. Further characterization of 231 mutants with changes in PTDH3-YFP activity revealed differences in the relative frequency, effects, and dominance of different types of mutations (Table 1) that are expected to influence their likelihood of contributing to phenotypic evolution.
Before discussing the evolutionary implications of our results, it is important to consider how the use of the fluorescent reporter gene PTDH3-YFP and chemical mutagen EMS might cause our data to differ from a spectrum of spontaneous mutations affecting activity of an endogenous gene.
Activity of the chimeric PTDH3-YFP reporter gene was regulated by the native S. cerevisiae TDH3 promoter and CYC1 terminator sequences. The CYC1 terminator controls proper 3′ end formation of mRNA by binding to factors such as Rat1p and Sen1p that are important for the termination of many genes transcribed by DNA polymerase I and II in S. cerevisiae [34]. The TDH3 gene encodes isozyme 3 of glyceraldehyde-3-phosphate dehydrogenase [35], is not required for viability under normal culture conditions [36], and is transcribed during growth on both fermentable and non-fermentable carbon sources [37] with minimal fluctuations during the cell cycle [38]. The TDH3 promoter exemplifies regulatory principles shared by many eukaryotic genes. For example, it includes both activating [39] and repressing [37] sequences that bind transcription factors such as Gcr1p, Gcr2p, Hsf1p, Pho2p, and Rap1p [40], [41]. TDH3 is one of the ∼19% of genes in the S. cerevisiae genome whose promoter has a TATA box; this is important to note because the expression of such genes appears to be more mutable than genes whose promoters lack this sequence [17]. Promoters with simple repetitive sequences have also been shown to have increased evolvability of gene expression in yeast [42], but the TDH3 promoter appears to lack such sequences. Differences in promoter and terminator sequences among genes are expected to cause differences in gene-specific mutational spectra, but we do not expect the regulatory mutation spectrum recovered for PTDH3-YFP to be fundamentally different from that of an endogenous gene such as TDH3.
Unlike the regulatory sequences of PTDH3-YFP, its coding sequence was not native to yeast: it encoded a Yellow Fluorescent Protein derived from the Green Fluorescent Protein originally isolated from Aequorea victoria [43]. The Venus variant of YFP used in this study was previously optimized to speed maturation, improve stability, and minimize sensitivity to environmental changes such as pH and chloride concentration [18]. This optimization might explain why none of the YFP coding mutants we recovered showed increased fluorescence and suggests that mutants affecting YFP fluorescence by altering the cellular environment might be rare. Native yeast proteins might not always have such optimal activity; however, nonsynonymous mutations are generally thought to decrease a protein's function more often than they increase it, suggesting that the YFP coding sequence is not extremely unrealistic in this respect. The length and GC-content of the YFP coding region are also expected to influence the coding mutation rate measured in this study by affecting the mutational target size: at 238 amino acids, YFP is near the 20th percentile for the length of native S. cerevisiae proteins [44], and its GC-content of 35.56% is similar to the median GC-content for all S. cerevisiae genes of 39.95% (Figure S2).
The use of the chemical mutagen EMS is perhaps the most artificial element of our experimental design, although we are not the first to use it to make inferences about spontaneous mutations [45]–[48]. EMS predominantly causes G/C to A/T transitions [23], [24] and thus generates a subset of possible spontaneous mutations. However, G/C to A/T transitions are tied with G/C to T/A transversions as the most common type of spontaneous point mutation in yeast [1]. We anticipate that many types of point mutations will have similar distributions of genetic and phenotypic effects, although it will be interesting to test this hypothesis in future work. More importantly, we expect the proportion of sites targeted by EMS to be similar for coding, cis-regulatory, and trans-acting mutations, suggesting that comparing EMS-induced mutations among these classes reveals differences that should also be observed for spontaneous point mutations. We do anticipate, however, that spontaneous mutations involving more than one base-pair (e.g., insertions/deletions (indels), segmental duplications, chromosomal rearrangements) will have different distributions of effects than single point mutations, and this was observed when we compared mutants with (presumably spontaneous) duplications of PTDH3-YFP to those with single copies of the PTDH3-YFP gene. Consequently, we believe that the spectrum of mutational effects described in this work provides a reasonable approximation of the spectrum of mutational effects caused by different types of spontaneous point mutations, but might not be representative of other types of DNA lesions.
For any mutation, the likelihood of fixation depends upon the probability that the mutational event occurs and the probability that, once it occurs, it becomes fixed within a population. This latter probability depends upon the phenotypic effects of the mutation (specifically, its impact on fitness) and (for diploid organisms) dominance. Mutations that arise more frequently have more opportunities to become fixed; mutations with larger effects on fitness should either be removed from or fixed within a population faster than mutations with smaller effects on fitness [49]; and adaptive mutations that are recessive are less likely to fix in a diploid population than equally adaptive mutations that are not recessive [50]. As described above, we observed differences in the frequency, effects (on YFP fluorescence), and dominance of different types of mutations affecting PTDH3-YFP activity. Below, we discuss how these differences might influence the evolutionary trajectories of (i) coding and regulatory mutations, (ii) cis-regulatory and trans-regulatory mutations, and (iii) copy number variants.
This study provides an unprecedented survey of the functional characteristics of new mutations; however, our understanding of mutational properties remains far from complete, even for the PTDH3-YFP reporter gene. For example, the effect of a mutation on fitness is what matters most for evolution, but we measured only the effects of mutations on YFP fluorescence. If fluorescence were an adaptive trait, a relationship between mutational effects on PTDH3-YFP activity and fitness is expected, but the effects of each mutation on other traits (i.e., pleiotropy) will also influence fitness, complicating this relationship. Similarly, we assessed the dominance of mutant effects on YFP fluorescence, but dominance at the level of a single gene's activity might not always translate to dominance at the level of higher-order phenotypes, which are more likely to be the targets of natural selection.
The unique collection of mutants described here provides a rare opportunity to address these issues, however, by mapping each of the mutations affecting PTDH3-YFP activity, engineering them individually into the ancestral genetic background, and directly measuring pleiotropy (quantified as the number of genes in the genome that change expression in response to the mutation) and fitness under different conditions (quantified as the effect of the mutation on relative growth rate). Determining the identity of mutations responsible for these mutant phenotypes will also allow us to assess their distribution within the genome and among factors expected to influence PTDH3-YFP activity and to compare the overall contributions of coding and non-coding changes to the mutational spectrum for PTDH3-YFP. Integrating these data with those presented here, as well as performing similar analyses of reporter genes using promoters from other S. cerevisiae genes, should greatly improve our ability to predict the types of genetic changes most likely to contribute to phenotypic evolution under different conditions.
An abbreviated version of the materials and methods follows. Complete materials and methods, including calculation of the spontaneous mutation rate, are included as Supporting Information (Text S1).
Chemical mutagenesis with Ethyl Methane Sulfonate (EMS) was performed as previously described [67], except that the volume of the cell suspension was doubled to 2 ml, the cell density was reduced by 50% to 6×107 cells/ml, the concentration of EMS was reduced by 75% to 7.5%, and the time of exposure was reduced by 25% to 45 minutes. Following mutagenesis, control and mutagen-treated cells were cultured at 30–32°C for 42 hours in arginine dropout liquid media (Synthetic Complete media lacking arginine) [67].
The canavanine resistance mutation rate in the EMS-treated population was calculated by comparing colony forming units on arginine dropout plates with and without 60 mg/l canavanine sulfate (Sigma-Aldrich, St. Louis, MO) [67].
Prior to analysis and sorting, cells from the control culture were stained with Cy5 (GE Healthcare, Piscataway, NJ) so that they could be distinguished from EMS-treated cells when analyzed simultaneously. Aliquots of both populations were mixed together in Phosphate Buffered Saline (PBS) for analysis and sorted in a FACSaria flow cytometer/cell sorter (BD Biosystems, San Jose, CA). The sorting and analysis of the mixed suspensions was restricted to a FSC-defined subset of events to reduce the influence of non-cell particles. In each of nine consecutive sorting runs, 96 events were collected from each tail of the EMS-treated and control populations, for a total of 384 events collected in each sorting run. Thresholds used for sorting during each run are presented in Table S1. Sorted events were arrayed onto YPD agar plates [67], then incubated at 30°C for 28 hours.
High-throughput parallel liquid culturing of genotypes was performed by inoculating from a colony or patch and culturing for 24 hours at 30°C in 96-well deep well plates in YPD. Saturated cultures were diluted 100× into arginine dropout liquid and cultured at 30°C for at least 2 doublings until the density reached 0.5–1.0×107 cells/ml.
Haploid YFP fluorescence and FSC were evaluated by a C6 flow cytometer (Accuri, Ann Arbor, MI); diploid YFP, CFP, and FSC were evaluated by a FACSaria flow cytometer (BD Biosciences, San Jose, CA). After log-transformation of FSC and fluorescence values, filters were applied to cull events with extreme FSC values; generally, these filters corresponded to approximately the 20th and 80th percentiles of FSC. The fluorescence phenotype of each genotype was defined as its median YFP/FSC or CFP/FSC ratio. This ratio was converted to a Z-score using the mean and standard deviation calculated from at least 10 (and up to 143) replicate control cultures containing cells with the unmutagenized, ancestral genotype.
PCR primers hybridizing to DNA sequences shared by YFP and CFP that flanked a position of dissimilarity were used to amplify a small region of DNA for analysis. Using the PSQ96 pyrosequencer (Qiagen, Valencia, CA), an internal sequencing primer was hybridized to these amplified fragments and extended to a diagnostic position that differed between YFP and CFP, allowing the relative frequency of YFP and CFP alleles to be compared in heterozygous diploid cells.
After introducing each promoter mutation into the unmutagenized ancestral genotype using site-directed mutagenesis, we quantified YFP fluorescence using the C6 flow cytometer (Accuri, Ann Arbor, MI). Two 80,000-event samples were collected for genotypes into which the mutations identified at positions −255, −240, and −140 had been introduced into the unmutagenized progenitor, a strain in which a wildtype copy of the promoter had been re-introduced in parallel, and the four mutant genotypes in which the promoter mutations were originally detected (including the two different isolates with the mutation at −255). Taking the median YFP/FSC as the fluorescence phenotype of each culture, we compared the genotypes with site-directed promoter mutations to the reengineered wildtype control, and the genotypes with site-directed promoter mutations to the mutant(s) in which it was originally observed using MWW tests.
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10.1371/journal.ppat.1005725 | Molecular Mechanisms for Drug Hypersensitivity Induced by the Malaria Parasite’s Chloroquine Resistance Transporter | Mutations in the Plasmodium falciparum ‘chloroquine resistance transporter’ (PfCRT) confer resistance to chloroquine (CQ) and related antimalarials by enabling the protein to transport these drugs away from their targets within the parasite’s digestive vacuole (DV). However, CQ resistance-conferring isoforms of PfCRT (PfCRTCQR) also render the parasite hypersensitive to a subset of structurally-diverse pharmacons. Moreover, mutations in PfCRTCQR that suppress the parasite’s hypersensitivity to these molecules simultaneously reinstate its sensitivity to CQ and related drugs. We sought to understand these phenomena by characterizing the functions of PfCRTCQR isoforms that cause the parasite to become hypersensitive to the antimalarial quinine or the antiviral amantadine. We achieved this by measuring the abilities of these proteins to transport CQ, quinine, and amantadine when expressed in Xenopus oocytes and complemented this work with assays that detect the drug transport activity of PfCRT in its native environment within the parasite. Here we describe two mechanistic explanations for PfCRT-induced drug hypersensitivity. First, we show that quinine, which normally accumulates inside the DV and therewithin exerts its antimalarial effect, binds extremely tightly to the substrate-binding site of certain isoforms of PfCRTCQR. By doing so it likely blocks the normal physiological function of the protein, which is essential for the parasite’s survival, and the drug thereby gains an additional killing effect. In the second scenario, we show that although amantadine also sequesters within the DV, the parasite’s hypersensitivity to this drug arises from the PfCRTCQR-mediated transport of amantadine from the DV into the cytosol, where it can better access its antimalarial target. In both cases, the mutations that suppress hypersensitivity also abrogate the ability of PfCRTCQR to transport CQ, thus explaining why rescue from hypersensitivity restores the parasite’s sensitivity to this antimalarial. These insights provide a foundation for understanding clinically-relevant observations of inverse drug susceptibilities in the malaria parasite.
| In acquiring resistance to one drug, many pathogens and cancer cells become hypersensitive to other drugs. This phenomenon could be exploited to combat existing drug resistance and to delay the emergence of resistance to new drugs. However, much remains to be understood about the mechanisms that underlie drug hypersensitivity in otherwise drug-resistant microbes. Here, we describe two mechanisms by which the Plasmodium falciparum ‘chloroquine resistance transporter’ (PfCRT) causes the malaria parasite to become hypersensitive to structurally-diverse drugs. First, we show that an antimalarial drug that normally exerts its killing effect within the parasite’s digestive vacuole is also able to bind extremely tightly to certain forms of PfCRT. This activity will block the natural, essential function of the protein and thereby provide the drug with an additional killing effect. The second mechanism arises when a cytosolic-acting drug that normally sequesters within the digestive vacuole is leaked back into the cytosol via PfCRT. In both cases, mutations that suppress hypersensitivity also abrogate the ability of PfCRT to transport chloroquine, thus explaining why rescue from hypersensitivity restores the parasite’s sensitivity to this antimalarial. These insights provide a foundation for understanding and exploiting the hypersensitivity of chloroquine-resistant parasites to several of the current antimalarials.
| Originally identified as the protein responsible for conferring resistance to the ‘wonder-drug’ chloroquine (CQ) [1, 2], the Plasmodium falciparum ‘chloroquine resistance transporter’ (PfCRT) has become a key player in the malaria parasite’s steadily expanding resistance to drugs [3–5]. The isoforms of PfCRT that confer CQ resistance (PfCRTCQR) render the parasite less susceptible to many other compounds [6–10], but also simultaneously induce hypersensitivity to a subset of structurally-diverse molecules [6, 10–18]. This phenomenon, whereby resistance to one drug causes hypersensitivity to another, is known as ‘inverse susceptibility’ or ‘collateral sensitivity’ and has been observed in a wide range of pathogens and cancer cells [19–22]. The growing awareness of the propensity of drug-resistant pathogens to exhibit hypersensitivity to one or more other drugs has sparked interest in the potential for exploiting this Achilles’ heel to combat existing drug resistance and to delay the emergence of resistance to new drugs [19]. However, it is not known how PfCRTCQR isoforms induce hypersensitivity to certain drugs and, more generally, much remains to be understood about the molecular mechanisms that underpin collateral sensitivity in pathogens and cancer cells [20–22].
The ability of PfCRT to affect the activity of so many compounds is likely to be a product of its location at the membrane of the parasite’s digestive vacuole (DV) [1, 11]; an acidic compartment in which many types of antimalarials accumulate and/or act. The parasite takes up hemoglobin and digests it within this compartment in order to grow within its host erythrocyte. This process releases heme monomers that are detoxified via conversion into the inert crystal hemozoin. Quinoline-type antimalarial drugs, including CQ, quinine, and quinidine, concentrate within the DV via ‘weak-base trapping’ [23], where they exert an antimalarial effect by binding to heme and arresting its detoxification [24–27]. Resistance to these quinolines is associated with reductions in the accumulation of the drugs within the DV [8, 11, 28] and we have previously obtained direct evidence of this phenomenon being due, at least in part, to the ability of PfCRTCQR isoforms to efflux CQ, quinine, and quinidine from this compartment. This was achieved by expressing a PfCRTCQR isoform from Dd2 parasites (PfCRTDd2) and the wild-type protein (PfCRT3D7) in Xenopus laevis oocytes. PfCRTDd2 was found to possess significant CQ, quinine, and quinidine transport activity, whereas PfCRT3D7 did not [29–31]. These findings were consistent with a number of biochemical studies that had provided indirect evidence of drug transport via PfCRTCQR isoforms. For example, PfCRTCQR isoforms had been linked to the efflux of CQ and quinine from parasite-infected red blood cells [32, 33], and PfCRTCQR was also implicated in the quinoline-induced efflux of protons from the DV of CQ-resistant parasites [34–36]. Moreover, the expression of PfCRTDd2 at endosomal membranes within Dictyostelium discoideum reduced the accumulation of CQ and quinine within these vesicles, consistent with the mutant protein mediating the transport of these two drugs [37, 38]. Aside from modulating the parasite’s susceptibility to diverse pharmacons, PfCRT fulfills an essential [39, 40] but currently unresolved physiological function in the parasite.
In this study, we investigated the mechanistic basis of PfCRT-induced drug hypersensitivity by characterizing the isoforms of PfCRT carried by nine different parasite lines (Table 1). These lines were generated by Cooper, Johnson, and colleagues [11, 12, 14, 15] and were produced by applying CQ, quinine, and/or amantadine pressure to either the CQ-sensitive strain 106/1 or the CQ-resistant strain K1. CQ-resistant strains, including K1, are hypersensitive to amantadine [13, 15]—a weak-base antiviral drug that is thought to accumulate in acidic organelles [41]. Like all PfCRTCQR isoforms identified to date, the K1 variant of PfCRT (PfCRTK1) contains a mutation at position 76, where a positively-charged lysine (K) residue is replaced by an uncharged residue (usually threonine; T) (S1 Fig). The K76T mutation is necessary (but not sufficient) to enable PfCRT to mediate the transport of protonated CQ [30, 42]. The version of PfCRT carried by 106/1 retains 76K but is otherwise identical to PfCRTK1. Pressuring 106/1 parasites with CQ resulted in the CQ-resistant lines 106/176T, 106/176I, and 106/176N, which carried either PfCRTK1 or PfCRTK1 with an isoleucine (I) or an asparagine (N) at position 76 (76I-PfCRTK1 and 76N-PfCRTK1, respectively) [11]. The latter two lines displayed unexpected drug responses: 106/176I was hypersensitive to quinine and 106/176N was sensitive to quinidine [11, 12]. The subsequent selection of 106/176I parasites with quinine resulted in the reintroduction of a positively-charged residue (C72R, Q352K, or Q352R) into 76I-PfCRTK1 that suppressed the parasite’s hypersensitivity to quinine but also re-sensitized it to CQ [12]. This ‘reciprocal collateral sensitivity’ was also observed when K1 parasites or the 106/176I line were pressured with amantadine [14, 15]; the resulting parasites gained mutations in PfCRT (including S163R and V369F), were no longer hypersensitive to amantadine, but were rendered sensitive to CQ.
We compared the abilities of these PfCRTK1 variants to transport CQ, quinine, quinidine, and amantadine when expressed in Xenopus oocytes and complemented the amantadine work with assays that detected the PfCRT-mediated transport of the drug in the parasite. Our findings indicate that the hypersensitivity of the 106/176I line to quinine arises from the drug exerting two antiplasmodial effects—its normal anti-hemozoin activity as well as potent inhibition of PfCRTCQR, which likely kills the parasite by blocking the normal function of the transporter. By contrast, the hypersensitivity of CQ-resistant parasites to amantadine is due to its PfCRTCQR-mediated efflux from the DV into the cytosol, where it appears to gain better access to its antiplasmodial target. In both cases, the mutations that suppress hypersensitivity cause a substantial or complete reduction in the capacity of PfCRTCQR for CQ transport, thus explaining why rescue from hypersensitivity reinstates the parasite’s sensitivity to CQ.
We used a previously-described version of the PfCRTK1 coding sequence [42] to generate the isoforms of pfcrt carried by the 106/1 and K1 parasite lines (Table 1). This version of the pfcrt sequence has been codon-harmonized for expression in Xenopus oocytes and encodes a retention motif-free form of PfCRT that expresses in a functional form at the oocyte plasma membrane [42]. We conducted immunofluorescence assays (Fig 1A) to confirm the localization of each of the PfCRTK1 variants to the oocyte plasma membrane and used a semiquantitative western blot analysis [42] to establish that they were present at comparable levels in the oocyte membrane (Fig 1B). Hence, any differences in drug transport activity between these isoforms of PfCRT can be attributed to differences in their transport properties rather than differences in expression.
To understand the mechanisms underlying the inverse drug susceptibilities of the 106/1- and K1-derived lines, we characterized the transport properties of the respective PfCRTK1 variants in Xenopus oocytes. A key advantage of the oocyte system is that it allows the transport activity of PfCRT to be studied directly and in isolation, without confounding effects such as the binding of drugs to heme or interactions of the compound with other parasite targets or transporters. The direction of [3H]drug transport in this system is from the acidic extracellular medium (pH 5.0–6.0) into the oocyte cytosol (pH 7.1–7.2 [30]), which corresponds to the efflux of protonated drug from the acidic DV (pH 5.0–5.5 [43, 44]) into the parasite cytosol (pH 7.3 [45]). Noninjected oocytes take up [3H]CQ, [3H]quinine, and [3H]quinidine to low levels via simple diffusion of the neutral species of the drug (Fig 2A–2C); this represents the background level of [3H]drug accumulation [29, 30]. We found that none of the PfCRTK1 isoforms carried by CQ-sensitive exhibited CQ transport activity when expressed in oocytes (Fig 2A). By contrast, the expression of PfCRTK1 variants from CQ-resistant parasites caused significant increases in the accumulation of CQ. Indeed, we observed a positive correlation between the ability of a PfCRTK1 variant to transport CQ and the magnitude of in vitro CQ resistance exhibited by the respective parasite line (R2 value of 0.856; S2A Fig). We conclude that the mutations which suppress the quinine-hypersensitivity of the 106/176I line (C72R, Q352K, or Q352R) simultaneously re-sensitize the parasite to CQ by abrogating the protein’s capacity to efflux CQ from the DV. Likewise, a mutation that suppresses the hypersensitivity of 106/176I parasites to amantadine (V369F), also caused a marked reduction in the ability of 76I-PfCRTK1 to transport CQ, which explains why the 106/176I,369F line displayed a relatively low level of resistance to CQ.
Similar results were obtained when we examined the uptake of [3H]quinine and [3H]quinidine (Fig 2B and 2C and S2 Fig). That is, the capacities of the different PfCRTK1 isoforms to transport quinine or quinidine generally correlated with the in vitro responses of the parasite lines to these drugs. Key exceptions included the quinine hypersensitivities of the 106/176I and 106/176I,369F lines, which were not readily reconciled with the low quinine transport activity of 76I-PfCRTK1 and the lack of quinine transport via 76I,369F-PfCRTK1, respectively. The modest capacity of 76I-PfCRTK1 for quinine transport was nonetheless abolished by the introduction of C72R, Q352K, or Q352R. Another exception was the moderate ability of 76N-PfCRTK1 to transport quinidine, which was at odds with the sensitivity of 106/176N parasites to this drug.
Together, these findings revealed a strong relationship between the magnitude of CQ, quinine, or quinidine resistance exhibited by a given 106/1 or K1 parasite and the capacity of its PfCRT protein for mediating the efflux of the respective drug from the DV. However, the mechanism underpinning the quinine-hypersensitivity caused by the introduction of T76I into PfCRTK1, and why the subsequent addition of C72R, Q352K, or Q352R negates this response, remained unclear.
We undertook experiments to determine whether the kinetics of 76I-PfCRTK1- and 76N-PfCRTK1-mediated transport could explain why these proteins cause unexpected responses to quinine and quinidine, respectively. All of the PfCRTK1 isoforms we had identified as possessing significant CQ, quinine, or quinidine transport activity (Fig 2A–2C) were included for study. The resulting Michaelis-Menten plots (S3 Fig) yielded a Michaelis–Menten constant (Km) and maximum velocity (Vmax) for each drug and PfCRT isoform (Table 2). Across all three drugs, the Km values increased in the order 76I-PfCRTK1 < PfCRTK1 < 76N-PfCRTK1, whereas the Vmax values increased in the order 76I-PfCRTK1 < 76N-PfCRTK1 ≤ PfCRTK1. Thus, we conclude that T76I increases the affinity of PfCRTK1 for its quinoline substrates, but that this change is accompanied by a significant decrease in its maximum rate of transport. By contrast, T76N consistently decreased the affinity of PfCRTK1 for its quinoline substrates, and either had little effect on the Vmax (quinine) or caused a marked reduction in the Vmax (CQ and quinidine). Interestingly, the introduction of V369F into 76I-PfCRTK1 significantly reduced the protein’s affinity for CQ (from 247 to 415 μM) and further reduced its maximum rate for CQ transport (from 29.3 to 13.8 pmol per oocyte/h), while also abrogating its ability to transport quinine and quinidine (Fig 2B and 2C). Our finding that 76I,369F PfCRTK1 is a very low-affinity and low-capacity transporter of CQ is again consistent with the modest level of CQ resistance exhibited by the 106/176I,369F parasite line.
Remarkably, we found that 76I-PfCRTK1 possesses an extraordinarily high affinity for quinine and an extraordinarily low Vmax for quinine transport; its Km was ~70 times lower, and its Vmax ~115 times lower, than the value obtained for PfCRTK1. This combination of kinetic properties indicates that 76I-PfCRTK1 binds extremely tightly to quinine and only occasionally translocates the drug. Thus, quinine will clog the binding site of 76I-PfCRTK1, which should greatly diminish the ability of the protein to transport its natural substrate. These findings suggest that quinine-hypersensitivity results from quinine exerting two killing effects in 106/176I parasites—the inhibition of hemozoin formation and the inhibition of PfCRT’s normal physiological role (which is essential for parasite survival [39, 40]). The low level of quinine transport mediated by 76I-PfCRTK1 was not detected following the introduction of C72R, Q352K, Q352R, or V369F (Fig 2B). Of these mutations, Q352R and Q352K each fully return 106/176I parasites to quinine-sensitive status, C72R causes a substantial but incomplete rescue from quinine-hypersensitivity, and V369F confers a modest but significant suppression of the parasite’s hypersensitivity to quinine (Table 1). Taken together, our data indicate that the insertion of a positively-charged residue into the substrate-binding cavity of 76I-PfCRTK1 greatly diminishes or abolishes its interaction with quinine (and its ability to transport CQ and quinidine), thereby reversing the parasite’s hypersensitivity to quinine (and simultaneously re-sensitizing it to CQ and quinidine). Our finding that these effects are achieved to a much lesser degree by V369F, which instead introduces a bulky hydrophobic residue into 76I-PfCRTK1, indicates that electrostatic repulsion is the key mechanism underpinning the dramatic decrease in the protein’s ability to bind and translocate protonated quinolines.
Our kinetic analyses also revealed that 76N-PfCRTK1 is unique in being both a low-affinity and a low-capacity transporter of quinidine. By comparison, PfCRTK1 mediated the low-affinity but high-capacity transport of quinidine, and 76I-PfCRTK1 was a high-affinity, low-capacity transporter of quinidine. Thus, the sensitivity of 106/176N parasites to quinidine is likely due to the inability of 76N-PfCRTK1 to decrease the concentration of quinidine within the DV to sub-toxic levels.
We recently reported [42] that the CQ transport activity of PfCRTK1 is ~20% higher than that of PfCRTDd2, even though the two proteins differ only at position 356 (Table 1). However, the I356T mutation also demonstrated epistasis. That is, depending on the nature and number of the mutations already present, its introduction increased, decreased, or had no effect on the ability of PfCRT to transport CQ [42]. We therefore investigated whether the introduction of I356T decreases the ability of PfCRTK1 variants to transport quinine and quinidine. We measured the uptake of [3H]quinine or [3H]quinidine in oocytes expressing either a PfCRTK1 variant (PfCRTK1, 76N-PfCRTK1, or 76I-PfCRTK1) or its I356T counterpart (PfCRTDd2, 76N-PfCRTDd2, or 76I-PfCRTDd2). Consistent with our previous observation with CQ transport, we found that the rate of quinine and quinidine uptake mediated by PfCRTK1 was 17–35% greater than that measured for PfCRTDd2 (S4 Fig). Moreover, we observed the same relationship, only considerably more exaggerated, between the 76N variants of PfCRTK1 and PfCRTDd2 and, in the case of quinidine transport, between 76I-PfCRTK1 and 76I-PfCRTDd2. The single exception to this pattern was quinine transport via 76I-PfCRTK1, which showed a 30–40% decrease relative to its PfCRTDd2 counterpart. Our findings establish a key role for position 356 in the attainment of a high level of quinoline transport activity, but also confirm the epistatic nature of this position.
We next sought to understand why CQ-resistant parasites are hypersensitive to amantadine and how certain mutations in PfCRT (e.g., T76K, S163R, or V369F) countercheck this response. Given that we had already ascertained that the PfCRTK1 variants carrying T76K or S163R do not possess significant quinoline transport activity (Fig 2A–2C), we focused our initial investigations on 76I,369F-PfCRTK1, as it mediates a detectable level of CQ transport. To test the possibility that hypersensitivity to amantadine is due to it clogging the substrate-binding cavity of PfCRT, and that this effect is alleviated by the addition of V369F, we compared the ability of unlabeled amantadine to inhibit the transport of [3H]CQ via 76I-PfCRTK1 and 76I,369F-PfCRTK1 (Fig 3A). Somewhat surprisingly, there was little difference in the resulting half-maximum inhibitory concentrations (IC50s; listed in Fig 3A). Moreover, these IC50s were much higher than those obtained in the oocyte system for established inhibitors of PfCRTCQR, such as the quinine dimer Q2C (1.4 ± 0.2 μM [46]), saquinavir (13 ± 1 μM [47]), and verapamil (30 ± 3 μM [30]). Since amantadine is a relatively low-affinity inhibitor of both 76I-PfCRTK1 and 76I,369F-PfCRTK1, it is unlikely that the hypersensitivity of CQ-resistant parasites to amantadine is due to it exerting an anti-PfCRTCQR effect.
This result led us to examine whether amantadine hypersensitivity is instead a consequence of significant changes in the ability of PfCRT to transport amantadine. We found that oocytes expressing PfCRTK1 or 76I-PfCRTK1 showed a marked increase in [3H]amantadine accumulation relative to noninjected oocytes (Fig 3B and 3C). By contrast, oocytes expressing 76K-PfCRTK1 or 163R,356V-PfCRTK1 failed to take up [3H]amantadine above the background level of accumulation, and 76I,369F-PfCRTK1 displayed only a very low (but statistically significant) level of amantadine transport activity. Moreover, [3H]amantadine transport via PfCRTK1, 76I-PfCRTK1, and 76I,369F-PfCRTK1 was inhibited by the PfCRTCQR inhibitors verapamil and saquinavir. These findings suggest that hypersensitivity to amantadine arises from the ability of PfCRTCQR isoforms to transport amantadine, and that mutations such as T76K, S163R, and V369F reverse this response by substantially decreasing or abolishing amantadine transport activity.
The signal-to-background ratio we obtained for [3H]amantadine transport in the oocyte system was around 2 (Fig 3B), which is relatively modest when compared with that obtained for quinine and quinidine transport (which typically produce ratios of 4–7; e.g., Fig 2B and 2C and S4 Fig) or CQ transport (which typically produces a ratio of 8–25; e.g., Figs 2A and 3D and S5 Fig). We therefore interrogated the amantadine transport properties of PfCRT further by conducting a series of trans-stimulation experiments. Many transporters reorientate more quickly from one face of the membrane to the other when a substrate is bound compared with when the transporter is empty. Transporters that display this characteristic can be trans-stimulated; in the case of PfCRTCQR, the uptake of [3H]CQ from the external solution will be accelerated by the presence of an unlabeled substrate on the cytosolic face of the membrane. We therefore measured the uptake of [3H]CQ into oocytes which had been microinjected with a buffer control or with buffer containing unlabeled amantadine, spermine, or histidine. Spermine (a polycation) and histidine (a weak base) do not appear to interact with PfCRTDd2 (S5 Fig and Martin et al. [30]) and were therefore included as extra negative controls. In addition, oocytes expressing an unrelated P. falciparum transporter (the nucleoside transporter PfNT1 [48]) were included as a further negative control. We found that amantadine did not affect the accumulation of [3H]CQ in noninjected oocytes or oocytes expressing 76K-PfCRTK1, PfCRT3D7, or PfNT1 (Fig 3D). Moreover, neither spermine nor histidine altered [3H]CQ uptake in any of the oocyte types. By contrast, amantadine trans-stimulated the transport of [3H]CQ into oocytes expressing PfCRTK1, PfCRTDd2, 76I-PfCRTK1, or 76I,369F-PfCRTK1, albeit to different extents; the respective increases in the rate of CQ influx (in pmol per oocyte/h) were 6.4 ± 0.2, 6.7 ± 0.3, 0.68 ± 0.04, and 0.43 ± 0.02. We extended this analysis by measuring the concentration-dependence of the trans-stimulation of PfCRTK1, 76I-PfCRTK1, and 76I,369F-PfCRTK1 (Fig 3E and 3F and S6 Fig). A least-squares fit of the Michaelis-Menten equation to the data yielded apparent kinetic parameters for the trans-stimulatory effect of amantadine (listed in Fig 3). These results revealed that the addition of V369F to 76I-PfCRTK1 causes a 6.2- to 7.2-fold increase in the concentration of amantadine required for the half-maximal trans-stimulation of the transporter.
Taken together, our work shows that PfCRT variants from parasites that are hypersensitive to amantadine possess the ability to transport this drug, and that the introduction of mutations that suppress amantadine hypersensitivity either abolish (T76K and S163R) or substantially decrease (V369F) the protein’s capacity for amantadine transport. These findings led us to propose that amantadine, which should sequester within the DV via weak-base trapping, exerts its main antiplasmodial effect outside of the DV and that the PfCRT-mediated leak of the drug back into the cytosol results in amantadine hypersensitivity. We therefore utilized a set of parasite assays to test this hypothesis in situ.
The function of PfCRT can be investigated in its native environment by employing an assay that indirectly detects the movement of protonated drugs out of the DV [34–36]. This method uses a fluorescent pH-sensitive probe to measure an outward leak of protons, which manifests as an increase in the rate of alkalinization of the DV. A drug-induced proton leak arises when a weak-base drug enters the acidic DV in its unprotonated form and is effluxed in its protonated form. We applied this assay to a set of P. falciparum transfectants (the C2GC03, C4Dd2, and C67G8 lines [2]) that are isogenic except for their pfcrt allele, which encodes either PfCRT3D7 (C2GC03) or a PfCRTCQR isoform of the protein (PfCRTDd2 in C4Dd2 and PfCRT7G8 in C67G8). CQ was included as a positive control and consistent with previous observations [34–36, 46], it increased the rate of DV alkalinization in the CQ-resistant C4Dd2 and C67G8 lines and was without effect in the CQ-sensitive C2GC03 line (Fig 4). We obtained similar results with amantadine and also showed that verapamil inhibits the amantadine-associated leak of protons from the DV of C4Dd2 and C67G8 parasites. These findings confirm that amantadine accumulates within the DV via weak-base trapping and also provide in situ evidence of the ability of PfCRTCQR isoforms to transport amantadine back into the cytosol.
If amantadine hypersensitivity results from the PfCRT-mediated efflux of the drug from the DV, we would expect that inhibitors of PfCRTCQR would reduce this response. We tested this hypothesis by determining the susceptibility of the isogenic lines to amantadine in the absence and presence of verapamil or chlorpheniramine [49] (another established inhibitor of PfCRTCQR). These experiments, which included CQ as a control as well as CQ-resistant (Dd2) and CQ-sensitive (3D7) reference strains, entailed using a fluorescence-based method to measure parasite growth in the presence of increasing concentrations of amantadine or CQ. The resulting IC50s (Table 3) provided two salient findings. First, the data verified the striking hypersensitivity of CQ-resistant parasites to amantadine. Secondly, in all cases this hypersensitivity was partially suppressed by the PfCRT inhibitors. Thus, we conclude that the hypersensitivity of the 106/176I line, and of other CQ-resistant parasites, to amantadine arises from the PfCRTCQR-mediated redistribution of the drug from the DV into the cytosol, where it gains better access to its main antiplasmodial target.
Our work provides mechanistic explanations for the patterns of inverse susceptibility induced by PfCRT. First, we confirmed our previous observation [42] of there being a positive correlation between the capacity of a given PfCRT isoform for mediating CQ transport and the magnitude of CQ resistance achieved by the respective parasite (S7A Fig) and extended this relationship to include, with the notable exception of the QN-hypersensitive lines, a positive correlation between the quinine or quinidine transport activity of PfCRT and the parasite’s in vitro responses to these drugs (Fig 5A and S7B Fig). Moreover, we showed that in most cases, the isoforms of PfCRTK1 that possessed CQ transport activity also transported quinine and quinidine and vice versa. Together, these findings confirm a common role for PfCRT in reducing the accumulation of CQ, quinine, and quinidine within the DV and explain the tendency of CQ-resistant parasites to exhibit decreased susceptibilities to quinine and quinidine. However, our results resolve this phenomenon further by providing a fundamental insight into PfCRT-induced drug phenotypes: whether a given isoform of PfCRT alters the parasite’s susceptibility to a drug, and to what extent and in which direction, depends on the kinetics of the drug’s transport via PfCRT.
A key example of this principle is the quinine-hypersensitivity conferred by 76I-PfCRTK1. Our work reveals that the introduction of 76I into PfCRTK1 has the remarkable effect of transforming the protein into an exceedingly high-affinity, low-capacity transporter of quinine. These highly abnormal kinetic properties will cause the drug to clog PfCRT’s substrate-binding cavity, which should block the transport of the natural substrate and thereby prevent the protein from fulfilling its essential physiological role (Fig 5A). Thus, we propose that hypersensitivity to quinine results from the drug exerting at least two killing effects in 106/176I parasites—anti-hemozoin and anti-PfCRTCQR. This hypothesis provides mechanistic explanations for two previously abstruse observations: (1) despite dramatic differences in their quinine responses, 106/176I and 106/1 parasites accumulate similar levels of quinine [11] and (2) quinine and CQ produce a synergistic interaction in 106/176I parasites, whereas a slightly antagonistic interaction occurs in the 106/176T line [12]. In the case of the first observation, the lack of a difference in quinine accumulation concurs with the exceedingly low capacity of 76I-PfCRTK1 for transporting quinine. In regard to the second phenomenon, the synergistic interaction in 106/176I is readily reconcilable with the ability of quinine to adhere to the binding cavity of 76I-PfCRTK1, as this should both inhibit the normal function of the protein and block the efflux of CQ from the DV. The slightly antagonistic effect observed in the 106/176T parasites is likely to result from the two drugs competing to bind to heme (CQ is the more potent inhibitor of hemozoin formation [25, 27]) and/or other differences in their activities (e.g., CQ also appears to inhibit the glutathione-dependent degradation of heme, whereas quinine is thought to lack this activity [50]).
Cooper and colleagues reported a third perplexing characteristic of the 106/176I line that may likewise arise from quinine’s ability to block the normal function of 76I-PfCRTK1. The presence of verapamil typically re-sensitizes CQ-resistant parasites to CQ (and to quinine and quinidine) and this resistance-reversing effect was evident for all three drugs in the 106/176T and 106/176N lines [11, 12]. It was also apparent in the 106/176I parasites, but only in the CQ and quinidine treatments. In the quinine treatment, verapamil exerted the opposite effect; i.e., it decreased the sensitivity of the 106/176I line to quinine and thus partially suppressed the parasite’s hypersensitivity to this drug [11, 12]. One possible explanation for this highly unusual observation is a scenario in which verapamil competes with quinine for binding to 76I-PfCRTK1, and that the natural substrate is better able to access the translocation pore when verapamil is bound relative to when quinine is bound. Given that the substrate-binding site of PfCRTDd2 has been shown to behave as a large polyspecific cavity that can bind at least two molecules simultaneously [29], it is conceivable that verapamil and quinine interact with the binding cavity differently and that verapamil presents somewhat less of an obstacle to the transport of the natural substrate via 76I-PfCRTK1 than does quinine. Such a scenario would provide a mechanistic explanation for the verapamil-induced reduction in quinine’s activity against the 106/176I line.
We recently reported that CQ-resistant parasites are hypersensitive to dimers of quinine, and that this appears to be due to these molecules inhibiting both heme detoxification and PfCRTCQR function (the quinine dimers are potent inhibitors of PfCRTDd2 but are not translocated by the transporter) [46]. Hence, the anti-PfCRTCQR activity of quinine in the 106/176I parasites is not simply an intriguing but extraneous biological oddity; it signifies that one of the parasite’s key modulators of drug resistance is itself a druggable antimalarial target.
Our findings suggest that less dramatic changes in the kinetics of transport via PfCRT can also significantly affect the parasite’s response to a drug. 76N-PfCRTK1 decreases the parasite’s susceptibility to CQ and quinine while being without effect on its response to quinidine [11, 12, 14]. A simple interpretation of these observations would be that 76N-PfCRTK1 transports CQ and quinine but fails to recognize quinidine. However, we found that 76N-PfCRTK1 maintains the ability to transport quinidine, but that its affinity for the drug is slightly reduced, and that its maximum rate of quinidine transport is significantly reduced, relative to the kinetics of quinidine transport via PfCRTK1. The low-capacity, low-affinity nature of quinidine transport via 76N-PfCRTK1 will limit the protein’s ability to reduce the accumulation of the drug within the DV and hence could explain why 76N-PfCRTK1 has little net effect on the parasite’s susceptibility to quinidine (S7B Fig).
The hypersensitivity of CQ-resistant parasites to amantadine was first reported several decades ago [13], but the molecular basis for this phenomenon has remained unknown. We demonstrate that amantadine hypersensitivity cannot be explained by an anti-PfCRTCQR effect and is instead attributable to a second mechanism. Our work shows that amantadine sequesters within the acidic environment of the DV and that the hypersensitivity of CQ-resistant parasites to amantadine results from the ability of PfCRTCQR isoforms to efflux the drug from the DV into the parasite cytosol (Fig 5B). These findings indicate that amantadine acts on a target outside of the DV and that it exerts its antiplasmodial activity to greater effect when proteins such as PfCRTDd2, PfCRT7G8, PfCRTK1, and 76I-PfCRTK1 leak the accumulated drug back into the cytosol. The antiviral target of amantadine is a proton channel [51] and it is possible that the drug likewise targets an essential cation channel or transporter in the parasite.
All of the mutations that introduced a positively-charged residue into PfCRTK1 abolished its ability to transport CQ, quinine, quinidine, and amantadine. Hence, in addition to confirming the importance of electrostatic repulsion in preventing interactions between PfCRT and protonated drugs, our results suggest that the transporter’s ability to transport protonated quinolines out of the DV (and thereby confer quinoline resistance) is fundamentally connected to its ability to transport protonated amantadine out of the DV (and thereby induce amantadine hypersensitivity). Furthermore, the detection in field isolates of T76K, C72R, or S163R in otherwise PfCRTCQR isoforms [5] indicates that these types of changes could be occurring in the parasite population in response to selection forces exerted by other drugs and/or because, in the absence of CQ, these parasites are fitter than their CQ-resistant counterparts (most PfCRTCQR isoforms impart a fitness cost [52–55]). Such a phenomenon appears to have taken place in French Guiana; Pelleau and colleagues [56] recently showed that another mutation that introduces a positive charge into PfCRTCQR (C350R) emerged in a CQ-resistant population following the withdrawal of CQ and is attributed with reinstating these parasites to CQ-sensitive status.
Our findings also provide a foundation for understanding and exploiting clinically-relevant cases of reciprocal collateral sensitivity. Most of the current treatments for uncomplicated malaria are combination therapies that pair an artemisinin derivative (all of which are metabolised into dihydroartemisinin) with a quinoline-related partner drug—of which the most widely used is lumefantrine [57]. Cases of severe malaria, as well as Plasmodium-infected pregnant women, are typically treated with combinations that include an antibiotic, such as clindamycin. It is, therefore, worth noting that several in vitro studies have observed isoforms of PfCRTCQR to induce hypersensitivity to lumefantrine (with 1.5–3.2-fold decreases in the IC50 [16, 17, 56, 58–61]), artemisinin and/or dihydroartemisinin (with 1.9–3.7-fold decreases in the IC50 [6, 11, 17, 58, 59, 62]) as well as to a number of antibiotics [10, 63–67]—including clindamycin [10, 63, 65, 68]. Moreover, multiple clinical trials undertaken in malarious regions throughout the world have associated the wild-type protein (i.e., PfCRT3D7) with significant reductions in the parasite’s susceptibility to artemether-lumefantrine [16, 69–71], such that (1) the administration of artemether-lumefantrine was found to cause significant selection of CQ-sensitive parasites carrying PfCRT3D7 and (2) the presence of parasites carrying PfCRT3D7 prior to artemether-lumefantrine treatment was associated with an increased risk of recrudescence. It is possible that the hypersensitivity of CQ-resistant parasites to lumefantrine, the artemisinins, and/or to the antibiotics results from one of the mechanisms we describe in this study. For instance, the antibiotics are either known or expected to act on targets outside of the DV [63, 72, 73], but due to their weak-base nature, these drugs will sequester within the acidic environment of this organelle. Hence, it is plausible that the antibiotics gain greater access to their antimalarial target by being transported into the cytosol via PfCRTCQR. For these antibiotics as well as for lumefantrine and the artemisinins, and for the many other pharmacons that display enhanced activity against CQ-resistant parasites, the set of assays outlined in our study offer the means to determine which of the two mechanisms are involved, or whether an altogether different mechanism is responsible. Such insights have the potential to contribute to the formulation of rational approaches for maintaining and extending the useful lifespan of many antimalarials by exploiting the opposing selection forces they exert upon PfCRT.
The coding sequences of the different isoforms of PfCRT were generated via site-directed mutagenesis using the primer pairs listed in S1 Table and an approach described previously [42]. The mutations were introduced into a codon-harmonized version of the PfCRT sequence that had been inserted into the pGEM-He-Juel oocyte expression vector [74]. This sequence encodes a version of PfCRT that is free of endosomal-lysosomal trafficking motifs and which is therefore expressed at the plasma membrane of Xenopus laevis oocytes [30, 42]. All of the resulting coding sequences were verified by sequencing (undertaken by the ACRF Biomolecular Resource Facility, ANU). The plasmids were linearized with SalI (ThermoFisher Scientific) and 5’-capped complementary RNA (cRNA) was synthesized using the mMessage mMachine T7 transcription kit (Ambion), and then purified with the MEGAclear kit (Ambion).
Ethical approval of the work performed with the X. laevis frogs was obtained from the Australian National University Animal Experimentation Ethics Committee (Animal Ethics Protocol Number A2013/13) in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes. Oocytes were harvested and prepared as described in full elsewhere [9]. Briefly, sections of ovary were harvested from adult female frogs (purchased from NASCO) via a minor surgical procedure and single, de-folliculated oocytes were prepared using collagenase D (Roche). Stage V-VI oocytes were microinjected with cRNA (20 ng per oocyte) encoding PfCRT or PfNT1 and were stored at 16–18°C in OR2+ buffer (82.5 mM NaCl, 2.5 mM KCl, 1 mM MgCl2, 1 mM Na2HPO4, 5 mM HEPES, 1 mM CaCl2, and 50 μg/mL gentamycin; pH 7.8).
The preparation of oocyte membranes and the semi-quantification of PfCRT protein was carried out using a protocol described in detail elsewhere [42]. Protein samples prepared from oocyte membranes were separated on a 4–12% Bis-Tris SDS-polyacrylamide gel (Life Technologies) and transferred to a Protran 0.45 μM nitrocellulose membrane (Amersham, GE Healthcare Life Sciences). The membranes were probed with rabbit anti-PfCRT antibody (concentration of 1:4,000; Genscript) followed by horseradish peroxidase-conjugated goat anti-rabbit antibody (1:8,000; Life Technologies, cat. no. 656120). Validation of the specificity of the anti-PfCRT antibody has been published in detail elsewhere [42]. The PfCRT band for each variant was detected by chemiluminescence (Pierce), quantified using the Image J software [75], and expressed as a percentage of the intensity measured for the PfCRTK1 band. Total protein staining was used to evaluate sample loading and efficiency of transfer as outlined previously [42]. Between five and seven independent experiments were performed (on oocytes from different frogs), and in each experiment measurements were averaged from two independent replicates.
Immunofluorescence analyses were performed on oocytes three days post-injection using a method adapted from Weise et al. [76]. Unless specified otherwise, both the incubation and wash steps were conducted at room temperature with gentle shaking or rotation. The volume of the incubation solution was 500 μL and the washes were performed with 1 mL of the specified solution. Six oocytes from each treatment type were fixed for 30 min in a solution of phosphate-buffered saline (PBS) and 4% v/v paraformaldehyde and then washed (10 min) three times in PBS. The oocytes were permeabilized with 100% methanol for 20 min (-20°C, without shaking) and washed (10 min) three times in PBS. A blocking solution (4% w/v bovine serum albumin (BSA), 2% v/v normal goat serum (Life Technologies), and 0.1% v/v Triton X-100 in PBS) was applied for 2 h, after which the oocytes were incubated overnight at 4°C in a second blocking solution (4% w/v BSA and 2% v/v normal goat serum in PBS). The samples were then incubated for a further 4 h at room temperature before the blocking solution was replaced with a solution containing the rabbit anti-PfCRT antibody (1:100 in 1.5% w/v BSA and 0.01% v/v Triton X-100 in PBS) and the samples were incubated for 4 h at room temperature and then overnight at 4°C. Three 10-min washes were performed in PBS supplemented with 1.5% w/v BSA and all of the remaining steps were undertaken in the dark (and at room temperature). The Alexa Fluor 488 donkey anti-rabbit antibody (1:500; Molecular Probes, cat. no. A-21206) was incubated with the samples for 4 h in PBS supplemented with 4% w/v BSA and 2% v/v normal goat serum, after which three 10-min washes were performed in PBS.
The oocytes were post-fixed with paraformaldehyde (3.7% v/v in PBS) for 30 min, washed twice (15 min) in 2 mL of PBS, and then dehydrated with a series of incubations in solutions of increasing ethanol content. The solutions (in order of administration) were: 30% v/v ethanol in PBS, 50% v/v ethanol in PBS, 70% v/v ethanol in ultrapure water, 90% v/v ethanol in ultrapure water, and 100% ethanol. In each case, the samples were briefly washed in the ethanol-containing solution before a 15-min incubation was performed. A further two incubations in 100% ethanol were conducted before the oocytes were embedded in an acrylic resin using the Technovit 7100 plastic embedding system (Kulzer). Briefly, the samples were incubated with 500 μL embedding solution (50% v/v Technovit 7100 in 100% ethanol) for 2 h, after which the oocytes were incubated overnight in 500 μL of a second embedding solution (1% w/v Technovit 7100 ‘hardener 1’ in Technovit 7100). A further two incubations (≥2 h each) were performed in the second embedding solution before all of the solution was removed and 800 μL of a third embedding solution (6.66% v/v Technovit 7100 ‘hardener 2’ and 1% w/v Technovit 7100 ‘hardener 1’ in Technovit 7100) was added. After the samples had set (≥4 d), a microtome was used to obtain ~4 μm slices, which were dried on microscope slides. Coverslips with a drop of ProLong Gold Antifade Mountant (Life Technologies) were placed over the slices and sealed with nail polish.
Images of the slices were obtained with a Leica Sp5 inverted confocal laser microscope (Leica Microsystems) using the 63x objective. Excitation was achieved with a 488 nm argon laser and the emissions were captured using a 500–550 nm filter. Images were acquired using the Leica Application Suite Advanced Fluorescence software (Leica Microsystems). At least two independent experiments were performed (on oocytes from different frogs) for each oocyte type, within which slices were examined from a minimum of three oocytes. All of the slices taken from oocytes expressing a PfCRT variant displayed a fluorescent band above the pigment layer (i.e., consistent with the localization of PfCRT to the plasma membrane) that was not present in noninjected oocytes.
The radiolabeled drugs were purchased from American Radiolabeled Chemicals ([3H]CQ, [3H]quinine, and [3H]quinidine) or Moravek ([3H]amantadine). The uptake into oocytes of [3H]CQ (0.25 μM; 20 Ci/mmol), [3H]quinine (0.25 μM; 20 Ci/mmol), [3H]quinidine (0.25 μM; 20 Ci/mmol), or [3H]amantadine (0.146 μM; 137 mCi/mmol) was measured 3–6 days post-injection. Unless specified otherwise, the drug influx assays were conducted over 1.5–2 h at 27.5°C and in the presence of a low concentration of the unlabeled drug (CQ, 15 μM; quinine and quinidine, 1 μM; amantadine, 50 μM). The reaction buffer was either ND96 pH 5.0 ([3H]quinine, [3H]quinidine, and [3H]amantadine transport assays) or ND96 pH 6.0 ([3H]CQ transport assays) and contained 96 mM NaCl, 2 mM KCl, 1 mM MgCl2, and 1.8 mM CaCl2 supplemented with either 20 mM homo-PIPES (pH 5.0 buffer) or 10 mM MES and 10 mM Tris-base (pH 6.0 buffer). For each treatment, 10 oocytes were transferred to a 5 mL polystyrene round bottom tube (Falcon) and washed twice with 3.5 mL of ND96 buffer, with the residual buffer removed by pipette. Influx commenced with the addition of 100 μL of ND96 buffer supplemented with the radiolabeled and unlabeled drug and, where specified, an unlabeled inhibitor (e.g., verapamil, saquinavir, or CQ). The assay was terminated by removing the reaction buffer with a pipette and washing the oocytes twice with 3.5 mL of ice-cold ND96 buffer. Each oocyte was transferred to a separate well of a white 96-well plate (NUNC or PerkinElmer), incubated overnight at room temperature in 30 μL of 10% SDS, and mixed on an orbital shaker the following day for approximately 5 min. The lysed oocyte was then combined with 150 μL of MicroScint-40 microscintillant (PerkinElmer), the plate covered with a TopSeal-A (PerkinElmer), and the radioactivity measured with a PerkinElmer MicroBeta2 microplate liquid scintillation analyzer.
Note that noninjected oocytes and oocytes expressing PfCRT3D7 take up [3H]CQ to similar (low) levels via simple diffusion of the neutral species of the drug; this represents the ‘background’ level of [3H]CQ accumulation in oocytes (refer to Martin et al. [30] for full data and discussion). There is also no detectable difference in the accumulation of [3H]quinine between noninjected and PfCRT3D7-expressing oocytes, nor does the accumulation of [3H]quinidine differ between these two oocyte types, and this background level of uptake has likewise been attributed to simple diffusion [29, 30].
The kinetic parameters for CQ, quinine, and quinidine transport via different isoforms of PfCRT (Table 1 and S3 Fig) were determined in SigmaPlot Windows Version 11.0 (Systat Software Inc.) by a least-squares fit of the Michaelis-Menten equation (v = Vmax substrate]/(Km + [substrate]) to the data.
The IC50 values presented in Fig 3A were determined in SigmaPlot by a least-squares fit of the equation y = ymin + [(ymax—ymin)/(1 + ([inhibitor]/IC50)c] to the data, where y is PfCRT-mediated CQ transport, ymin and ymax are the minimum and maximum values of y, and c is a fitted constant. PfCRT-mediated CQ transport was calculated by subtracting the uptake measured in the control oocytes (oocytes expressing 76K PfCRTK1) from that in oocytes expressing 76I-PfCRTK1 or 76I,369F-PfCRTK1.
A subset of experiments measured the ability of unlabeled amantadine to trans-stimulate the uptake of [3H]CQ into the oocyte. Immediately prior to the commencement of the experiment, the oocytes (days 3–4 post-injection of the cRNA) were microinjected with either amantadine (estimated intracellular concentrations of 1, 2.5, 5, 10, 15, or 20 mM) or a control treatment (buffer, spermine, or histidine; the estimated intracellular concentrations of spermine and histidine were 5 and 10 mM). The volume injected was 50 nL and the intracellular concentrations were calculated using previous estimates of the volume of stage V-VI oocytes (~400 nL [77]). The resealed oocytes were then incubated at 16–18°C in OR2+ buffer for approximately 5 min and the influx of [3H]CQ was measured as described above. For each amantadine concentration, the rate of CQ influx above that measured in the relevant buffer-injected control was calculated and a least-squares fit of the Hill equation (y = Vmax[amantadine]in/(Kmn + [amantadine]in) to the data was performed in SigmaPlot, where y is the reaction velocity and n is the Hill coefficient. This analysis yielded Hill coefficients of 1.63 ± 0.12, 1.49 ± 0.07, and 1.46 ± 0.05 for PfCRTK1, 76I-PfCRTK1 and 76I,369F-PfCRTK1, respectively. These values indicated that CQ and amantadine either bind independently of one another, or are slightly cooperative (i.e., the binding of one drug enhances the affinity of the transporter for the second drug). The Michaelis-Menten equation was then fitted to the data to derive the kinetic parameters for the trans-stimulation of [3H]CQ transport by amantadine.
In most cases, at least five independent experiments were performed (on different days and using oocytes from different frogs), and within each experiment measurements were made from 10 oocytes per treatment.
The use of human blood in this study was approved by the Australian National University’s Human Research Ethics Committee. The CQ-sensitive strain ‘3D7’ (isolated from the Netherlands but probably of African origin [78]), the CQ-resistant strain ‘Dd2’ (isolated from Southeast Asia), and three pfcrt transfectant lines [2] (C2GC03, C4Dd2, and C67G8) were cultured and synchronized as described previously [79, 80]. In the C4Dd2 and C67G8 lines, the wild-type pfcrt allele of the CQ-sensitive ‘GC03’ strain has been replaced with the pfcrt allele from Dd2 or from the CQ-resistant ‘7G8’ strain (isolated from Brazil), respectively. C2GC03 is a CQ-sensitive recombinant control that retains the wild-type pfcrt allele (i.e., PfCRT3D7). C67G8 contains an additional mutation (I351M) in PfCRT that is not present in 7G8 parasites [36]. The parasite lines were maintained in the presence of the selection agents blasticidin (5 μM; Sigma-Aldrich) and WR99210 (5 nM; Jacobus Pharmaceuticals). These selection agents were not present during the experiments.
Saponin-isolated trophozoite-stage parasites containing the membrane-impermeant pH-sensitive fluorescent indicator fluorescein-dextran (10,000 MW; Life Technologies) in their DVs were prepared as outlined elsewhere [36]. The isolated parasites were washed and suspended in a saline solution (125 mM NaCl, 5 mM KCl, 1 mM MgCl2, 20 mM glucose, 25 mM HEPES; pH 7.1) at a density of 1–3 × 107 cells/mL. The fluorometry experiments were performed as described previously [35]. The pH of the DV was monitored at 37°C using a PerkinElmer Life Sciences LS50B fluorometer with a dual excitation Fast Filter accessory (excitation 490 and 450 nm; emission 520 nm). The experiments entailed monitoring the alkalinization of the DV upon addition of the V-type H+-ATPase inhibitor concanamycin A (100 nM; Sigma-Aldrich), in the presence or absence of the drugs of interest. Half-times for DV alkalinization (t1/2) were calculated as outlined elsewhere [34]. In all cases, five independent experiments were performed on different days.
Parasite proliferation was measured in 96-well plates using a fluorescent DNA-intercalating dye [81] and a protocol described in detail previously [82]. Briefly, cell suspensions containing erythrocytes infected with ring-stage parasites (hematocrit and parasitemia of approximately 2% and 1%, respectively) were incubated at 37°C for 72 h. The samples were frozen, thawed, and then processed by the addition of 100 μL (per well) of SYBR Safe DNA Gel Stain (Molecular Probes; 0.2 μL/mL) in a lysis buffer (20 mM Tris, 5 mM EDTA, 0.008% w/v saponin, and 0.08% v/v Triton X-100; pH 7.5). The fluorescence emanating from each well was measured immediately using a Tecan Infinite M1000 PRO microplate reader (excitation 490 nm; emission 520 nm) and the average fluorescence from wells containing the highest concentration of the drug was subtracted from the resulting values. The level of parasite proliferation in the presence of each drug concentration was expressed as a percentage of the proliferation measured in the absence of the drug for which the IC50 was being determined. The IC50s were determined in SigmaPlot by a least-squares fit of the equation y = a/ [1 ([drug]/IC50)c] to the data, where y is the percent parasite proliferation, a is the maximum change in the percent parasite proliferation, and c is a fitted constant. In all cases, five independent experiments were performed (on different days), and within each experiment measurements were averaged from three replicates.
All errors cited in the text and shown in the figures represent the SEM. Statistical comparisons were made using one-way ANOVAs in conjunction with Tukey’s multiple comparisons test.
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10.1371/journal.pgen.1006915 | The population genetics of human disease: The case of recessive, lethal mutations | Do the frequencies of disease mutations in human populations reflect a simple balance between mutation and purifying selection? What other factors shape the prevalence of disease mutations? To begin to answer these questions, we focused on one of the simplest cases: recessive mutations that alone cause lethal diseases or complete sterility. To this end, we generated a hand-curated set of 417 Mendelian mutations in 32 genes reported to cause a recessive, lethal Mendelian disease. We then considered analytic models of mutation-selection balance in infinite and finite populations of constant sizes and simulations of purifying selection in a more realistic demographic setting, and tested how well these models fit allele frequencies estimated from 33,370 individuals of European ancestry. In doing so, we distinguished between CpG transitions, which occur at a substantially elevated rate, and three other mutation types. Intriguingly, the observed frequency for CpG transitions is slightly higher than expectation but close, whereas the frequencies observed for the three other mutation types are an order of magnitude higher than expected, with a bigger deviation from expectation seen for less mutable types. This discrepancy is even larger when subtle fitness effects in heterozygotes or lethal compound heterozygotes are taken into account. In principle, higher than expected frequencies of disease mutations could be due to widespread errors in reporting causal variants, compensation by other mutations, or balancing selection. It is unclear why these factors would have a greater impact on disease mutations that occur at lower rates, however. We argue instead that the unexpectedly high frequency of disease mutations and the relationship to the mutation rate likely reflect an ascertainment bias: of all the mutations that cause recessive lethal diseases, those that by chance have reached higher frequencies are more likely to have been identified and thus to have been included in this study. Beyond the specific application, this study highlights the parameters likely to be important in shaping the frequencies of Mendelian disease alleles.
| What determines the frequencies of disease mutations in human populations? To begin to answer this question, we focus on one of the simplest cases: mutations that cause completely recessive, lethal Mendelian diseases. We first review theory about what to expect from mutation and selection in a population of finite size and generate predictions based on simulations using a plausible demographic scenario of recent human evolution. For a highly mutable type of mutation, transitions at CpG sites, we find that the predictions are close to the observed frequencies of recessive lethal disease mutations. For less mutable types, however, predictions substantially under-estimate the observed frequency. We discuss possible explanations for the discrepancy and point to a complication that, to our knowledge, is not widely appreciated: that there exists ascertainment bias in disease mutation discovery. Specifically, we suggest that alleles that have been identified to date are likely the ones that by chance have reached higher frequencies and are thus more likely to have been mapped. More generally, our study highlights the factors that influence the frequencies of Mendelian disease alleles.
| New disease mutations arise in heterozygotes and either drift to higher frequencies or are rapidly purged from the population, depending on the strength of selection and the demographic history of the population [1–6]. Elucidating the relative contributions of mutation, natural selection and genetic drift will help to understand why disease alleles persist in humans. Answers to these questions are also of practical importance, in informing how genetic variation data can be used to identify additional disease mutations [7].
In this regard, rare, Mendelian diseases, which are caused by single highly penetrant and deleterious alleles, are perhaps most amenable to investigation. A simple model for the persistence of mutations that lead to Mendelian diseases is that their frequencies reflect an equilibrium between their introduction by mutation and elimination by purifying selection, i.e., that they should be found at “mutation-selection balance” [4]. In finite populations, random drift leads to stochastic changes in the frequency of any mutation, so demographic history, in addition to mutation and natural selection, plays an important role in shaping the frequency distribution of deleterious mutations [3].
Another factor that may be important in determining the frequencies of highly penetrant disease mutations is genetic interactions. The mutation-selection balance model has been extended to scenarios with more than one disease allele, as is often seen for Mendelian diseases [8,9]. When compound heterozygotes have the same fitness as homozygotes for the disease allele (i.e., there is no complementation), the combined frequency of all disease alleles can be modeled similarly as the bi-allelic case, with the mutation rate given by the sum of the mutation rate to each disease allele [8]. In other cases, a disease mutation may be rescued by another mutation in the same gene [10–12] or by a modifier locus elsewhere in the genome that modulates the severity of the disease symptoms or the penetrance of the disease allele (e.g. [13–15]).
For a subset of disease alleles that are recessive, an alternative model for their persistence in the population is that there is an advantage to carrying one copy but a disadvantage to carrying two or none, such that the alleles persist due to overdominance, a form of balancing selection. Well known examples include sickle cell anemia, thalassemia and G6PD deficiency in populations living where malaria exerts strong selection pressures [16]. The importance of overdominance in maintaining the high frequency of disease mutations is unknown beyond these specific cases.
Here, we tested hypotheses about the persistence of mutations that cause lethal, recessive, Mendelian disorders. This case provides a good starting point, because a large number of Mendelian disorders have been mapped (e.g., genes have already been associated with >56% of Mendelian disease phenotypes; [17]). Moreover, while the fitness effects of most diseases are hard to estimate, for recessive lethal diseases, the selection coefficient is clearly 1 for homozygote carriers in the absence of modern medical care (which, when available, became so only in the last couple of generations, a timescale that is much too short to substantially affect disease allele frequencies). Moreover, assuming mutation-selection balance in an infinite population and no effects in heterozygotes would suggest that, given a per base pair (bp) mutation rate u on the order of 10−8 per generation [18], the frequency of such alleles would be u, i.e., ~10−4 [4]. Thus, sample sizes in human genetics are now sufficiently large that we should be able to observe completely recessive, lethal disease alleles segregating in heterozygote carriers.
To this end, we compiled genetic information for a set of 417 mutations reported to cause fatal, recessive Mendelian diseases and estimated the frequencies of the disease-causing alleles from large exome datasets. We then compared these data to the expected frequencies of deleterious alleles based on models of mutation-selection balance in order to evaluate the effects of mutation rates and other factors in influencing these frequencies.
We relied on two datasets, one that describes 173 autosomal recessive diseases [19] and another from a genetic testing laboratory (Counsyl [20]; <https://www.counsyl.com/>) that includes 110 recessive diseases of clinical interest. From these lists, we obtained a set of 44 “recessive lethal” diseases associated with 45 genes (S1 Table), requiring that at least one of the following conditions is met: (i) in the absence of treatment, the affected individuals die of the disease before reproductive age, (ii) reproduction is completely impaired in patients of both sexes, (iii) the phenotype includes severe mental retardation that in practice precludes reproduction, or (iv) the phenotype includes severely compromised physical development, again precluding reproduction.
Based on clinical genetics datasets and the medical literature (see Methods for details), we were able to confirm that 417 Single Nucleotide Variants (SNVs) in 32 (of the 44) genes had been reported with compelling evidence of association to the severe form of the corresponding disease and an early-onset, as well as no indication of effects in heterozygote carriers (S2 Table). By this approach, we obtained a set of mutations for which, at least in principle, there is no heterozygote effect, i.e., for which the dominance coefficient h = 0 in a model with relative fitness of 1 for the homozygote for the reference allele, 1-hs for the heterozygote, and 1-s for the homozygote for the deleterious allele, and the selective coefficient s is 1.
A large subset of these mutations (29.3%) consists of transitions at CpG sites (henceforth CpGti), which occur at a highly elevated rates (~17-fold higher on average) compared to other mutation types, namely CpG transversions, and non-CpG transitions and transversions [18]. This proportion is in agreement with previous estimates for a smaller set of disease genes [21] and for DMD [22].
Allele frequency data for the 417 variants were obtained from the Exome Aggregation Consortium (ExAC) for 60,706 individuals, of whom 33,370 are non-Finnish Europeans [23]. Out of the 417 variants associated with putative recessive lethal diseases, three were found homozygous in at least one individual in this dataset (rs35269064, p.Arg108Leu in ASS1; rs28933375, p.Asn252Ser in PRF1; and rs113857788, p.Gln1352His in CFTR). Available data quality information for these variants does not suggest genotype calling artifacts (S2 Table). Since these diseases have severe symptoms that lead to early death without treatment and these ExAC individuals are healthy (i.e., do not manifest severe Mendelian diseases) [23], the reported mutations are likely errors in pathogenicity classification or cases of incomplete penetrance (see a similar observation for CFTR and DHCR7 in [24]). We therefore excluded them from our analyses. In addition to the mutations present in homozygotes, we also filtered out sites that had lower coverage in ExAC (see Methods), resulting in a final dataset of 385 variants in 32 genes (S2 Table).
Genotypes for a subset (91) of these mutations were also available for a larger sample size (76,314 individuals with self-reported European ancestry) generated by the company Counsyl (S3 Table). A comparison of the allele frequencies in this larger dataset to that of ExAC suggests that the allele frequencies for individual variants are concordant between the two datasets (Pearson’s correlation coefficient of 0.79, S1 Fig) and that the overall distributions do not differ appreciably (Kolmogorov–Smirnov test, p-value = 0.23). Thus, both data sets appear to reflect the general distribution of these disease alleles in Europeans. In what follows, we focused on ExAC, which includes a greater number of disease mutations.
To generate expectations for the frequencies of these disease mutations under mutation-selection balance, we considered models of infinite and finite populations of constant size [3] and conducted forward simulations using a plausible demographic model for African and European populations [25] (see Methods for details). In all these models, there is a wild-type allele (A) and a deleterious allele (a, which could also represent a class of distinct deleterious alleles with the same fitness effect) at each site, such that the relative fitness of individuals of genotypes AA, Aa, or aa is given respectively by:
The mutation rate from A to a is u; we assume that there are no back mutations.
For a constant population of infinite size, Wright [26] showed that under these conditions, there exists a stable equilibrium between mutation and selection, when the selection pressure is sufficiently strong (s>>u). In particular, when the deleterious effect of allele a is completely recessive (h = 0), its equilibrium frequency q is given by:
q=u/s.
(1)
For a finite population of constant size, Nei [3] derived the mean (Eq 2) and variance (Eq 3) of the frequency of a fully recessive deleterious mutation (h = 0) based on a diffusion model, leading to:
q¯=Γ(2Nu+1/2)2NsΓ(2Nu),
(2)
σq2=u/s−q¯2,
(3)
where N is the diploid population size and Γ is the gamma function (see Simons et al. [1] for a similar approximation).
In a finite population, the mean frequency, q¯, therefore depends on assumptions about the population mutation rate (2Nu). If the population mutation rate is high, such that 2Nu>>1, q¯ is approximated by
q¯≈u/s,
(4)
which is independent of the population size and equal to the equilibrium frequency in an infinite population, i.e., the right hand side of Eq (1). The important difference between the two models above is that in a finite population, there is a distribution of frequencies q (because of genetic drift), whose variance is given in Eq (3), rather than a single value, as in an infinite population.
In contrast, when the finite population has a low population mutation rate (2Nu<<1), the mean allele frequency, q¯, is approximated by:
q¯≈u2πN/s,
(5)
which depends on the population size [3].
We note that Nei [3] assumed a Wright-Fisher model, in which there is no distinction between census and the effective population sizes. However, when the two differ, it is the effective population size that governs the dynamics of deleterious alleles, so the N in the analytical results in fact represents the effective population size. In humans, the mutation rate at each bp is very small (on the order of 10−8 [18]) and the effective population size not that large, even recently [27,28], so the second approximation should apply when considering each single site independently.
The expectation and variance of the frequency of a fatal, fully recessive allele (i.e., s = 1, h = 0) are then given by:
q¯=u2πN,
(6)
and
σq2=u−q¯2=u(1−2πNu)≈u.
(7)
This single site model implicitly ignores the existence of compound heterozygosity in modeling the strength of selection acting on an individual site.
Although an infinite population size has often been assumed when modeling deleterious allele frequencies (e.g. [5,29–32]), predictions under this assumption can differ markedly from what is expected from models of finite population sizes, assuming plausible parameter values for humans. For example, the long-term estimate of the effective population size from total polymorphism levels is ~20,000 individuals (assuming a mutation rate of 1.2 x 10−8 per bp per generation [18] and diversity levels of 0.1% [33]). In this case and considering a mutation rate of 1.5 x 10−8 for exons (which have a higher mutation rate than the rest of the genome, because of their base composition [34]), the average deleterious allele frequency in the model of finite population size is ~23-fold lower than that in the infinite population model (Fig 1).
Because the human population size has not been constant and changes in the population size can affect the frequencies of deleterious alleles in the population (e.g. [2,35]), we also simulated the population dynamics of disease alleles under a plausible demographic model for European populations based on Tennessen et al. [25]. The original model assumes a genome-wide mutation rate of 2.36 x 10−8 per bp per generation, when current, more direct estimates are approximately two-fold smaller [18,34,36]. We therefore rescaled the demographic parameters of the Tennessen et al. model, based on a mutation rate of 1.2 x 10−8 [18] (see Methods). Assuming a mutation rate of 1.5 x 10−8 per bp (as recently estimated for exons [34]), the mean allele frequency of a lethal, recessive disease allele obtained from this model was 7.10 x 10−6, ~1.33-fold higher than expected for a constant population size model with Ne = 20,000 (Fig 1). The mean frequency seen in simulations instead matches the expectation for a constant population size of 35,651 individuals (see Methods and S2A Fig). Increasing the effective population size in a constant size model is not enough to capture the dynamics of disease alleles appropriately, however. For example, if simulation results obtained under the Tennessen et al. [25] demographic model are compared to those for simulations of a constant population size of Ne = 35,651, the mean allele frequencies match, but the distributions of allele frequencies are significantly different (Kolmogorov-Smirnov test, p-value < 10−15; S2B and S2C Fig). These findings thus confirm the importance of incorporating demographic history into models for understanding the population dynamics of disease alleles [5,37,38]. In what follows, we therefore tested the fit of the more realistic demographic model [25] (and variants of it) to the observed allele frequencies.
The mutation rate from wild-type allele to disease allele, u, is a critical parameter in predicting the frequencies of a deleterious allele [4,39]. To model disease alleles, we considered four mutation types separately, with the goal of capturing most of the fine-scale heterogeneity in mutation rates [27,36,40,41]: transitions in methylated CpG sites (CpGti) and three less mutable types, namely transversions in CpG sites (CpGtv) and transitions and transversions outside a CpG site (nonCpGti and nonCpGtv, respectively). In order to control for the methylation status of CpG sites, we excluded 12 CpGti that occurred in CpG islands, which tend not to be methylated and thus are likely to have a lower mutation rate [36] (following Moorjani et al. [42]). To allow for heterogeneity in mutation rates within each one of these four classes considered, we modeled the within-class variation in mutation rates according to a lognormal distribution (see details in Methods and [27]).
For each mutation type, we then compared the mean allele frequency obtained from simulations to what is observed in ExAC, running 100,000 replicates. To this end, we matched simulations to the empirical data with regard to the number of individuals sampled and number of mutations observed of each mutation type and focused the analysis on the largest sample of the same common ancestry, namely Non-Finnish Europeans (n = 33,370) (Fig 2A). We found significant differences between empirical and expected mean frequencies for nonCpGtv (30-fold higher on average; two-tailed p-value < 1 x 10−4; see Methods for details) and nonCpGti (15-fold higher on average, two-tailed p-value < 1 x 10−4), but only marginally so for CpGtv (5-fold higher on average, two-tailed p-value = 0.08). The mean frequency for CpGti is also somewhat higher than expected, but insignificantly so (1.17-fold higher on average, two-tailed p-value = 0.59). Intriguingly, the discrepancy between observed and expected frequencies becomes smaller as the mutation rate increases (Fig 2B).
Two additional factors that we have not included in our model should further decrease the predicted frequencies of disease alleles. Given that frequencies in ExAC are already unexpectedly high, these factors would only exacerbate the discrepancy between observed and expected frequencies of deleterious alleles. First, we have ignored the effects of compound heterozygosity, the case in which combinations of two distinct pathogenic alleles in the same gene lead to lethality. This phenomenon is known to be common [43], and indeed, in the 320 cases in which we were able to obtain this information, 58.44% were initially identified in compound heterozygotes. In the presence of compound heterozygosity, each deleterious mutation will be selected against not only when present in two copies within the same individual, but also in the presence of lethal mutations at other sites. Since the purging effect of selection against compound heterozygotes was not modeled in simulations, we would predict the frequency of a deleterious mutation to be even lower than shown (e.g., in Fig 2A).
In order to model the effect of compound heterozygosity in our simulations, we re-ran our simulations, but focusing on a gene rather than a single site and so considering the sum of frequencies of all known recessive lethal alleles within a gene. In these simulations, we used the same set-up as in the site level analysis, except for the mutation rate, U, which is now the sum of the mutation rates uj at each site j that is known to cause a severe and early onset form of the disease [8] (S2 Table; see Methods for details). This approach does not consider the contribution of other mutations in the genes that cause the mild and/or late onset forms of the disease, and implicitly assumes that all combinations of known recessive lethal alleles of the same gene have the same fitness effect as homozygotes. Comparing observed frequencies of disease alleles for each gene to predictions generated by simulation, about a fourth of the 27 genes for which we implemented the gene-level analysis (see Methods) differ from the expected distribution at the 5% level, with a clear overall trend for observed frequencies to be above expectation (S4 Table; Fig 3; Fisher’s combined probability test p-value = 6 x 10−8).
This finding is even more surprising than it may seem, because we are far from knowing the complete mutation target for each gene, i.e., all the sites at which mutations could cause the disease. If there are additional, undiscovered sites in the gene at which mutations are fatal when carried in combination with a known recessive lethal mutation, the purging effect of purifying selection on the known mutations will be under-estimated in our simulations, leading us to over-estimate the expected frequencies of the known mutations in simulations. Therefore, our predictions are, if anything, an over-estimate of the expected allele frequency, and the discrepancy between predicted and the observed is likely even larger than what we found.
The other factor that we did not consider in simulations but would reduce the expected allele frequencies is a subtle fitness decrease in heterozygotes, as has been documented in Drosophila for example [44]. To evaluate potential fitness effects in heterozygotes when none had been documented in humans, we considered the phenotypic consequences of orthologous gene knockouts in mouse. We were able to retrieve information on phenotypes for both homozygote and heterozygote mice for only eight out of the 32 genes, namely ASS1, CFTR, DHCR7, NPC1, POLG, PRF1, SLC22A5, and SMPD1. For all eight, homozygote knockout mice presented similar phenotypes as affected humans, and heterozygotes showed a milder but detectable phenotype (S5 Table). The magnitude of the heterozygote effect of these mutations in humans is unclear, but the finding with knockout mice makes it plausible that there exists a very small fitness decrease in heterozygotes in humans as well, potentially not enough to have been recognized in clinical investigations but enough to have a marked impact on the allele frequencies of the disease mutations. Indeed, even if the fitness effect in heterozygotes were as small as h = 1%, a 79% decrease in the mean allele frequency of the disease allele is expected relative to the case with complete recessivity (h = 0) (S3 Fig).
To investigate the population genetics of human disease, we focused on mutations that cause Mendelian, recessive disorders that lead to early death or completely impaired reproduction. We sought to understand to what extent the frequencies of these mutations fit the expectation based on a simple balance between the input of mutations and the purging by purifying selection, as well as how other mechanisms might affect these frequencies. Many studies implicitly or explicitly compare known disease allele frequencies to expectations from mutation-selection balance [5,29–32]. In this study, we tested whether known recessive lethal disease alleles as a class fit these expectations, and found that, under a sensible demographic model for European population history with purifying selection only in homozygotes, the expectations fit the observed disease allele frequencies poorly: the mean empirical frequencies of disease alleles are substantially above expectation for all mutation types (although not significantly so for CpGti), and the fold increase in observed mean allele frequency in relation to the expectation decreases with increased mutation rate (Fig 2). Furthermore, including possible effects of compound heterozygosity and subtle fitness decrease in heterozygotes will only exacerbate the discrepancy.
In principle, higher than expected disease allele frequencies could be explained by at least six (non-mutually exclusive) factors: (i) widespread errors in reporting the causal variants; (ii) misspecification of the demographic model, (iii) misspecification of the mutation rate; (iv) reproductive compensation; (v) overdominance of disease alleles; and (vi) low penetrance of disease mutations. Because widespread mis-annotation of the causal variants in disease mutation databases had previously been reported [23,45,46], we tried to minimize the effect of such errors on our analyses by filtering out any case that lacked compelling evidence of association with a recessive lethal disease, reducing our initial set of 769 mutations to 385 in which we had greater confidence (see Methods for details).
We also explored the effects of having misspecified recent demographic history or the mutation rate. Based on very large samples, it has been estimated that population growth in Europe was stronger than what we considered in our simulations [47,48]. When we considered higher growth rates, such that the current effective population size is up to 10-fold larger than that of the rescaled Tennessen model, we observed an increase in the expected frequency of recessive disease alleles and a larger number of segregating sites (S4 Fig, columns A-E). However, the impact of larger growth rate is insufficient to explain the observed discrepancy: the allele frequencies observed in ExAC are still on average an order of magnitude larger than expected based on a model with a 10-fold larger current effective population size than the one initially considered [25] (S4 Fig). In turn, population substructure within Europe would only increase the number of homozygotes relative to what was modeled in our simulations (through the Wahlund effect [49]) and expose more recessive alleles to selection, thus decreasing the expected allele frequencies and exacerbating the discrepancy that we report.
To explore the effects of error in the mutation rate, we considered a 50% higher mean mutation rate than what has been estimated for exons [34], beyond what seems plausible based on current estimates on human mutation rates [42]. Except for the mean mutation rate (now set to 2.25 x 10−8), all other parameters used for these simulations (i.e. the variance in mutation rate across simulations, the demographic model [25], absence of selective effect in heterozygotes, and selection coefficient) were kept the same as the ones used for generating S4 Fig, column A. The observed mean frequency remains significantly above what those predicted and qualitative conclusions are unchanged (S4 Fig, column F).
Another factor to consider is that for disease phenotypes that are lethal very early on in life, there may be partial or complete reproductive compensation (e.g. [50]). This phenomenon would decrease the fitness effects of the recessive lethal mutations and could therefore lead to an increase in the allele frequency in data relative to what we predict for a selection coefficient of 1. There are no reasons, however, for this phenomenon to correlate with the mutation rate, as seen in Fig 2B.
The other two factors, overdominance and low penetrance, are likely explanations for a subset of cases. For instance, CFTR, the gene in which some mutations lead to cystic fibrosis, is the farthest above expectation (p-value < 0.004; Fig 3). It was long noted that there is an unusually high frequency of the CFTR deletion ΔF508 in Europeans, which led to speculation that disease alleles of this gene may be subject to over-dominance ([51,52], but see [53]). Regardless, it is known that disease mutations in this gene can complement one another [10,11] and that modifier loci in other genes also influence their penetrance [11,14]. Consistent with variable penetrance, Chen et al. [24] identified three purportedly healthy individuals carrying two copies of disease mutations in this gene. Similarly, DHCR7, the gene associated with the Smith-Lemli-Opitz syndrome, is somewhat above expectation in our analysis (p-value = 0.056; Fig 3) and healthy individuals were found to be homozygous carriers of putatively lethal disease alleles in other studies [24]. These observations make it plausible that, in a subset of cases (particularly for CFTR), the high frequency of deleterious mutations associated with recessive, lethal diseases are due to genetic interactions that modify the penetrance of certain recessive disease mutations. It is hard to assess the importance of this phenomenon in driving the general pattern that we observe, but two factors argue against it being a sufficient explanation for our findings at the level of single sites. First, when we removed 130 mutations in CFTR and 12 in DHCR7, the two genes that were outliers at the gene level (Fig 3; S4 Table) and for which there is evidence of incomplete penetrance [24], the discrepancy between observed and expected allele frequencies is barely impacted (S5 Fig). Moreover, there is no obvious reason why the degree of incomplete penetrance would vary systematically with the mutation rate of a site, as observed (Fig 2B).
Instead, it seems plausible that there is an ascertainment bias in disease allele discovery and mutation identification [52,54,55]. Unlike missense or protein-truncating variants, Mendelian disease mutations cannot be annotated based solely on DNA sequences, and their identification requires reliable diagnosis of affected individuals (usually in more than one pedigree) followed by mapping of the underlying gene/mutation. Therefore, those mutations that have been identified to date are likely the ones that are segregating at higher frequencies in the population. Moreover, mutation-selection balance models predict that the frequency of a deleterious mutation should correlate with the mutation rate. Together, these considerations suggest that disease variants of a highly mutable class, such as CpGti, are more likely to have been mapped and that the mean frequency of mapped mutations will tend to be only slightly above all disease mutations in that class. In contrast, less mutable disease mutations are less likely to have been discovered to date, and the mean frequency of the subset of mutations that have been identified may tend to be far above that of all mutations in that class.
To quantify these effects, we modeled the ascertainment of disease mutations both analytically and in simulations. A large proportion of recessive Mendelian disease mutations were identified in inbred populations, likely because inbreeding leads to an excess of homozygotes compared to expected under random mating, increasing the probability that a recessive mutation would be discovered as causing a disease. Therefore, we modeled ascertainment in disease discovery in human populations with a plausible degree of inbreeding (see Methods). As expected, we found that for a given mutation type, the probability of ascertainment increases with the sample size of the putative disease ascertainment study (na) and the average inbreeding coefficient of the population under study (Fa); in addition, the average allele frequency of mutations that have been identified is always higher than that of all existing mutations, and the discrepancy decreases as the ascertainment probability increases (Table 1). Furthermore, comparison across different mutation types reveals that a higher mutation rate increases the probability of disease mutations being ascertained (Table 1 and S6 Fig) and decreases the magnitude of bias in the estimated allele frequency relative to the mutation class as a whole (Table 1). In summary, among all the possible aforementioned explanations for the observed discrepancy between empirical and expected mean allele frequencies, the ascertainment bias hypothesis is the only one that also explains why it is more pronounced for less mutable mutation types (Fig 2B).
One implication of this hypothesis is that there are numerous sites at which mutations cause recessive lethal diseases yet to be discovered, particularly at non-CpG sites. More generally, this ascertainment bias complicates the interpretation of observed allele frequencies in terms of the selection pressures acting on disease alleles. Beyond this specific point, our study illustrates how the large sample sizes now made available to researchers in the context of projects like ExAC [23] can be used not only for direct discovery of disease variants, but also to test why disease alleles are segregating in the population and to understand at what frequencies we might expect to find them in the future.
In order to identify single nucleotide variants within the 42 genes associated with lethal, recessive Mendelian diseases (S1 Table), we initially relied on the ClinVar dataset [56] (accessed on June 3rd, 2015). We filtered out any variant that is an indel or a more complex copy number variant or that is ever classified as benign or likely benign in ClinVar (whether or not it is also classified as pathogenic or likely pathogenic). By this approach, we obtained 769 SNVs described as pathogenic or likely pathogenic. For each one of these variants, we searched the literature for evidence that it is exclusively associated to the lethal and early onset form of the disease and was never reported as causing the mild and/or late-onset form of the disease. We considered effects in the absence of medical treatment, as we were interested in the selection pressures acting on the alleles over evolutionary time scales rather than in the last one or two generations, i.e., the period over which treatment became available for some of diseases considered. To evaluate the impact of treatment, we decreased s from 1 to 0 (i.e., we assumed a complete absence of selective effects due to treatment) in the last three generations and compared the mean allele frequencies across 100,000 simulations implemented with or without this readjustment in selection coefficient. Because of the stochastic nature of the simulations, we repeated this pairwise comparison 10 times in order to get a range of expected increase in allele frequencies. We observed only a minor increase in the mean allele frequency (2.6% at most) across the 10 replicates. This simulation procedure corresponds to a scenario in which there is an extremely effective treatment for all diseases for the past three generations, which is an overestimate of the effect and length of treatment for the disease set considered.
Variants with mention of incomplete penetrance (i.e. for which homozygotes were not always affected) or with known effects in heterozygote carriers were removed from the analysis. This process yielded 417 SNVs in 32 genes associated with distinct Mendelian recessive lethal disorders (S2 Table). Although these mutations were purportedly associated with completely recessive diseases, we sought to examine whether there would be possible, unreported effects in heterozygous carriers. To this end, we used the Mouse Genome Database (MGD) [57] (accessed July 29th, 2015) and were able to retrieve information for both homozygote and heterozygote mice for eight out of the 32 genes (all of which with a homologue in mice) (S5 Table).
In addition to the information provided by ClinVar for each one of these variants, we considered the immediate sequence context of each SNV, to tailor the mutation rate estimate accordingly [18]. To do so, we used an in-house Python script and the human genome reference sequence hg19 from UCSC (<http://hgdownload.soe.ucsc.edu/goldenPath/hg19/chromosomes/>).
The Exome Aggregation Consortium (ExAC) [23] was accessed on August 9th, 2016. The data consist of genotype frequencies for 60,706 individuals, assigned after Principal Component Analysis to one of seven population labels: African (n = 5,203), East Asian (n = 4,327), Finnish (n = 3,307), Latino (n = 5,789), Non-Finnish European (n = 33,370), South Asian (n = 8,256) and “other” (n = 454) [23]. We focused our analyses on those individuals of Non-Finnish European descent, because they constitute the largest sample size from a single ancestry group. We note that, some diseases mutations, for instance, those in ASPA, HEXA and SMPD1, are known to be especially prevalent in Ashkenazi Jewish populations, which could potentially bias our results if Ashkenazi Jewish individuals constituted a great portion of the sample we considered. However, this sample includes only ~2,000 (~6%) Ashkenazi individuals (Dr. Daniel MacArthur, personal communication).
From the initial 417 mutations, we filtered out three that were homozygous in at least one individual in ExAC and 29 that had lower coverage, i.e., fewer than 80% of the individuals were sequenced to at least 15x. This approach left us with a set of 385 mutations with a minimum coverage of 27x per sample and an average coverage of 69x per sample (S2 Table). For 248 sites with non-zero sample frequencies, ExAC reported the number of non-Finnish European individuals that were sequenced, which was on average 32,881 individuals [23]. For the remaining 137 sites, we did not have this information. Nonetheless, the coverage across all samples is reported and does not differ significantly between the two sets of sites (by a Kolmogorov-Smirnov test, p-value = 0.90; S7 Fig). We therefore assumed that mean number of individuals covered for all sites was 32,881 and used this number to obtain sample frequencies from simulations, as explained below.
A second genetic dataset was obtained from Counsyl (<https://www.counsyl.com/>). Counsyl is a commercial genetic screening laboratory that offers, among other products, the “Family Prep Screen”, a genetic screening test intended to detect carrier status for up to 110 recessive Mendelian diseases in couples that are planning to have a child [20]. A subset of 294,000 of its customers was surveyed by genotyping or sequencing for “routine carrier screening”. This subset excludes individuals with indications for testing because of known personal or family history of Mendelian diseases, infertility, and consanguinity. It therefore represents a more random (with regard to the presence of disease alleles), population-based survey. For these individuals, we had details on self-reported ancestry (14 distinct ethnic/ancestry/geographic groups) and the allele frequencies for 98 mutations that match those that passed our variant selection criteria described above, of which 91 are also sequenced to high coverage in the ExAC database (S2 Table). We focused our analysis of this dataset on the 76,314 individuals of self-reported Northern or Southern European ancestry.
We modeled the frequency of a deleterious allele in human populations by forward simulations based on a crude but plausible demographic model for human populations from Africa and Europe, inferred from exome data for African-Americans and European-Americans [25]. To this end, we used a program described in [1]. In brief, the demographic scenario consists of an Out-of-Africa demographic model, with changes in population size throughout the population history, including a severe bottleneck in Europeans following the split from the African population and a rapid, recent population growth in both populations [25]. As in Simons et al. [1], we simulated genetic drift and two-way gene flow between Africans and Europeans in recent history.
The original demographic model was inferred using a mutation rate u of 2.36 x 10−8 per bp per generation [25,58]. More recent estimates, based on direct resequencing of human pedigrees, instead point to mutation rates about 50% smaller than that [18,34,36]. To incorporate what is believed to be a more accurate mutation rate estimate, we rescaled all demographic and time parameters in the original Tennessen et al. [25] model by a factor of 1.97, based on the difference between the mutation rate considered in the original study and that of Kong et al. [18] (which is similar to that found in other studies [48]). We refer to this model as the rescaled Tennessen model and rely on it throughout.
Negative selection acting on a single bi-allelic site was modeled as in the analytic models. Allele frequencies follow a Wright-Fisher sampling scheme in each generation according to these viabilities, with migration rate and population sizes varying according to the demographic scenario considered. Whenever a demographic event (e.g., growth) altered the number of individuals and the resulting number was not an integer, we rounded it to the nearest integer, as in Simons et al. [1]. A burn-in period of 10Ne generations with constant population size Ne = 14,328 individuals was implemented in order to ensure an equilibrium distribution of segregating alleles at the onset of demographic changes in Africa, 11,643 generations ago.
In contrast to Simons et al. [1], our simulations always start with the ancestral allele A fixed and mutation occurs exclusively from this allele to the deleterious one (a), i.e., a mutation occurs with mean probability u per gamete, per generation, and there is no back-mutation. However, recurrent mutations at a site are allowed, as in Simons et al. [1].
When implementing the simulations, we considered a mean mutation rate u of 1.5x10-8 per bp, per generation, as has been estimated for exons [34], as well as mutation rates for four distinct mutation types (CpGti = 1.12 x 10−7; CpGtv = 9.59 x 10−9; nonCpGti = 6.18 x 10−9; and nonCpGtv = 3.76 x 10−9) estimated from a large human pedigree study [18]. While these four categories capture much of the variation in germline mutation rates across sites, a number of other factors (e.g., the larger sequence context or the replication timing) also influence mutation rates, introducing heterogeneity in the mutation rate within each class considered [27,36,40,59]. To allow for this heterogeneity as well as for uncertainty in the point mutation rates estimates, in each simulation, when compared to human exome data (Figs 1, 2, S4 and S5), instead of using a fixed rate u, we drew the mutation rate M from a lognormal distribution with the following parameters:
log10M|u∼N(log10u−σ22ln(10),σ2)
(8)
such that that E[M] = u. σ was set to 0.57 (following [27]).
For each mutation type, we then proceeded as follows:
To assess the significance of the deviation between observed and expected mean, we obtained a two-tailed p-value, defined as 2 x (r+1)/(100000+1), where r is the number of simulated allele frequencies that were greater or equal to that of the empirical mean [60], for each mutation type separately.
A well-known source of heterogeneity in mutation rate within the CpGti class is methylation status, with a high transition rate seen only at methylated CpGs [21]. In our analyses, we tried to control for the methylation status of CpG sites by excluding sites located in CpG islands (CGIs), which tend to not be methylated [42]. The CGI annotation for hg19 was obtained from UCSC Genome Browser (track “Unmasked CpG”; <http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/cpgIslandExtUnmasked.txt.gz>, accessed in June 6th, 2016). BEDTools [61] was used to exclude those CpG sites located in CGIs. We note that the CpGti estimate from [18] includes CGIs, and in that sense the average mutation rate that we are using for CpGti may be a very slight underestimate of the mean rate for transitions at methylated CpG sites.
Unless otherwise noted, the expectation assumes fully recessive, lethal alleles with complete penetrance. Notably, by calculating the expected frequency one site at a time, we are ignoring possible interaction between genes (i.e., effects of the genetic background) and among different mutations within a gene (i.e., compound heterozygotes). These assumptions are relaxed in two ways. In one analysis (S3 Fig), we considered a very low selective effect in heterozygous individuals (h = 1%), reasoning that such an effect could plausibly go undetected in medical examinations and yet would nonetheless impact the frequency of the disease allele. Second, when considering the gene-level analysis (Fig 3), we implicitly allowed for compound heterozygosity between any pair of known lethal mutations [8]. For this analysis, we ran 1000 simulations for a total mutation rate U per gene that was calculated accounting for the heterogeneity and uncertainty in the mutation rates estimates as follows: (i) For j sites in a gene known to cause a recessive lethal disease and that passed our filtering criteria (S2 Table), we drew a mutation rate uj from the lognormal distribution, as described above; (ii) We then took the sum of uj as the total mutation rate U; (iii) We then ran one replicate with U as the mutation parameter, and other parameters as specified for site level analysis. Because the mutational target size considered in simulations is only comprised of those sites at which mutations are known to cause a lethal recessive disease, it is almost certainly an underestimate of the true mutation rate—potentially by a lot. We note further that by this approach, we are assuming that compound heterozygotes formed by any two lethal alleles have fitness zero, i.e., that they are identical in their effects to homozygotes for any of the lethal alleles. Moreover, we are implicitly ignoring the possibility of complementation, which is (somewhat) justified by our focus on mutations with severe effects and complete penetrance (but see Discussion). Since we were interested in understanding the effect of compound heterozygosity, for this analysis, we did not consider the five genes in which only one mutation passed our filters (BCS1L, FKTN, LAMA3, PLA3G6, and TCIRG1).
To calculate the probability of ascertaining a recessive, lethal mutation, we assumed that all currently known disease mutations were identified in a putative ascertainment study of sample size na in a population with an inbreeding coefficient of Fa. Under this model, we can estimate Pasc, the probability of ascertaining a disease mutation, as following:
For a disease allele (denoted as a) at frequency q in the present population, if we randomly sample an individual with inbreeding coefficient of Fa, the probabilities of the three genotypes are:
P(AA)=Fa(1−q)+(1−Fa)(1−q)2,
(9)
P(Aa)=(1−Fa)2q(1−q),
(10)
P(aa)=Faq+(1−Fa)q2.
(11)
Thus, if na unrelated individual are surveyed, the probability of not seeing any homozygote for the disease allele (which is the same as the probability of not being ascertained in this set) is:
Pnasc=(1−P(aa))na.
(12)
Therefore, the probability of ascertainment is
Pasc=1−Pnasc=1−(1−P(aa))na=1−[1−Faq−(1−Fa)q2]na=1−[(1−q)(1+q−Faq)]na,
(13)
which is an increasing function with regard to q (the population allele frequency), Fa (the inbreeding coefficient of the population under study) as well as na (the sample size of the putative ascertainment study) (S6 Fig).
We also demonstrate the relationship between the probability of ascertainment and mutation rate using simulations of ascertainment bias implemented according to the following steps:
These simulations were meant to illustrate the likely impact of ascertainment bias, rather than to precisely describe the disease mutation identification process or to quantify the expected effect. Notably, we performed these simulations for single sites, so the criteria for ascertainment in step 3 did not include the possibility of compound heterozygotes, despite the fact that an estimated 58.4% of the disease mutations included in our study were initially identified in compound heterozygotes. However, this simulation framework could readily be extended in this direction and it would not change our qualitative conclusion.
|
10.1371/journal.ppat.1000141 | Phosphoinositide-3 Kinase-Akt Pathway Controls Cellular Entry of Ebola Virus | The phosphoinositide-3 kinase (PI3K) pathway regulates diverse cellular activities related to cell growth, migration, survival, and vesicular trafficking. It is known that Ebola virus requires endocytosis to establish an infection. However, the cellular signals that mediate this uptake were unknown for Ebola virus as well as many other viruses. Here, the involvement of PI3K in Ebola virus entry was studied. A novel and critical role of the PI3K signaling pathway was demonstrated in cell entry of Zaire Ebola virus (ZEBOV). Inhibitors of PI3K and Akt significantly reduced infection by ZEBOV at an early step during the replication cycle. Furthermore, phosphorylation of Akt-1 was induced shortly after exposure of cells to radiation-inactivated ZEBOV, indicating that the virus actively induces the PI3K pathway and that replication was not required for this induction. Subsequent use of pseudotyped Ebola virus and/or Ebola virus-like particles, in a novel virus entry assay, provided evidence that activity of PI3K/Akt is required at the virus entry step. Class 1A PI3Ks appear to play a predominant role in regulating ZEBOV entry, and Rac1 is a key downstream effector in this regulatory cascade. Confocal imaging of fluorescently labeled ZEBOV indicated that inhibition of PI3K, Akt, or Rac1 disrupted normal uptake of virus particles into cells and resulted in aberrant accumulation of virus into a cytosolic compartment that was non-permissive for membrane fusion. We conclude that PI3K-mediated signaling plays an important role in regulating vesicular trafficking of ZEBOV necessary for cell entry. Disruption of this signaling leads to inappropriate trafficking within the cell and a block in steps leading to membrane fusion. These findings extend our current understanding of Ebola virus entry mechanism and may help in devising useful new strategies for treatment of Ebola virus infection.
| Each year, filoviruses such as Ebola virus claim many human lives and decimate gorilla populations in Africa. Infection results in an acute fever often associated with profuse internal and external bleeding and death rates of up to 90%. Due to these symptoms and high pathogenicity, these viruses have been heavily publicized in the media. The first step of infection is entry, where the virus is taken up and penetrates into the cell, from which it spreads throughout the body. While it is known that the cell must engulf the virus by the process of endocytosis, we know little about how the virus triggers this event. Here, we use a novel technology to measure penetration of Ebola virus into the cell in real time and show that Ebola virus stimulates phosphoinositide-3 kinase, a signaling molecule known to induce endocytosis. Importantly, drugs that interfere with this signaling inhibit infection by Ebola virus and block virus spread. This work provides a mechanistic insight into how Ebola virus manipulates the cell to start an infection, may explain part of virus induced pathogenesis, and provides a potential way to treat this deadly disease.
| Ebola virus, a member of the family Filoviridae, is an emerging infectious agent that causes severe and often fatal hemorrhagic fever in humans and nonhuman primates. In many outbreaks, especially those caused by Zaire Ebola virus (ZEBOV), mortality rates close to 90% have been reported [1]. Currently, no vaccine or therapy is available for Ebola virus hemorrhagic fever. Significant mortality rate, high transmissibility and lack of therapeutic and preventive measures make Ebola virus a potentially serious public health threat.
Ebola viruses are filamentous enveloped viruses. The envelope contains two virally-encoded glycoproteins, GP1 and GP2, which together serve as the primary viral determinant for entry into host cells. The two glycoproteins are produced from a precursor protein (GP), which is cleaved by a furin-like endoprotease to generate surface-bound protein GP1 and the transmembrane protein GP2, the two proteins, remain associated by a disulfide bridge after cleavage [2]. As with many enveloped viruses, entry of ZEBOV into cells likely involves virus particles binding to host cell receptor(s), followed by endocytosis and trafficking through vesicular compartments, and finally fusion of the virus membrane to that of the endocytic vesicle. This results in release of the viral nucleocapsid into the cytoplasm where the subsequent steps of the replication cycle take place [3]. GP1 is believed to mediate interaction with the host cell receptor, while GP2 is involved in membrane fusion [3]. Membrane fusion is believed to involve a GP2 structural rearrangement triggered by low pH in an endocytic compartment. In addition, intracellular processing of GP1 by endosomal cathepsins is also a prerequisite to membrane fusion [4]. Thus, an important aspect of ZEBOV entry involves endocytic trafficking into the cell.
Recent work has shown that many viruses exploit host cell molecules and signaling pathways to facilitate various steps of the entry process. A critical role of focal adhesion kinase and protein kinase C was described for endocytosis and endosomal sorting of West Nile virus in mosquito cells [5], and recently, Rho A and its upstream tyrosine kinases were implicated in endocytosis and trafficking of poliovirus [6]. However, for most viruses, especially enveloped viruses, information on the requirements of cell signaling for entry is limited. Important advances have been made regarding the entry of Ebola virus and the role of the envelope glycoproteins in cell attachment and endocytosis (reviewed in [7]). However, our understanding of the role of cell signaling in virus entry remains limited.
The phosphoinositide-3 kinase (PI3K) pathway is an important cell signaling pathway that regulates diverse cellular activities including proliferation, differentiation, apoptosis, migration, metabolism, and vesicular trafficking [8]. PI3Ks (OMIM#601232; Online Mendelian Inheritance in Man database: http://www.ncbi.nlm.nih.gov/sites/entrezdbomim) are a family of lipid kinases that are divided into three classes according to their structure and substrate specificity. Of these, class I PI3Ks are the most widely studied. They signal through cell surface protein tyrosine kinase (PTK) or G-protein coupled (GPC) receptors. Activation of PI3K results in phosphorylation of phosphatidylinositol-bis phosphates to produce phosphatidylinositol-triphosphates, which serve as potent second messengers for downstream signaling. Akt-1 (OMIM#164730), is a key downstream intermediate in PI3K-dependent signaling. A variety of molecules are directly or indirectly regulated by Akt, and serve as downstream effectors to carry out diverse PI3K-regulated responses [9].
Regulation of vesicular trafficking is one of the oldest recognized functions of PI3Ks [10]. PI3Ks influence a variety of intracellular trafficking events that include cargo selection, vesicle formation, vesicle movement and membrane fusion. This is often through stimulation of actin turnover [11]. Rac1 (OMIM#602048), along with other Rho family GTPases, is a key effector in this process [12]. Therefore the regulation of Rac1 by class 1 PI3Ks, as seen in many cell types [13]–[16], provides a mechanism to couple receptor-ligand interaction to induction of endocytosis as well as other actin-mediated functions in the cell. Bacterial pathogens have been shown to take advantage of this mechanism by stimulating phagocytosis and internalization through PI3K activation [17],[18]. Similarly, interaction of the non-enveloped adenovirus with receptors on the cell surface was shown to activate Rac1 and Cdc42 (OMIM#116952) in a PI3K-dependent manner, and this was required for virus uptake into endosomes [19]. However, similar dependencies for enveloped viruses have not been described. Here we investigated the role of PI3K cell signaling pathway in cellular entry of ZEBOV. The findings indicated that ZEBOV induces activation of PI3K pathway prior to or during entry and that activity of class 1A PI3Ks is critical for entry into host cells. Rac1 GTPase was found to be an important downstream effector in regulating ZEBOV entry. The impact of inhibiting PI3K, Akt or Rac1 was similar, causing an aberrant accumulation of ZEBOV particles in intracellular vesicles, indicating a role of the PI3K-Akt-Rac1 pathway in vesicular trafficking of virus particles.
Several viruses utilize the PI3K-Akt pathway to support replication in host cells [20]–[23], however, involvement of this pathway in early events of infection such as entry has not been conclusively demonstrated for enveloped viruses. We investigated the effect of LY294002 (a highly specific inhibitor of PI3K) on infection by wild type ZEBOV. To determine if the drug was acting early or late in the infection cycle, cells were exposed to drug only during the first 2 h of incubation with the virus. Subsequently, the drug and the unbound virus were removed, and infection was allowed to continue. As compared to untreated cells, the infection of ZEBOV was significantly (nearly 10-fold) reduced in cells treated with LY294002 (p<0.01). In contrast, LY294002 exhibited no significant effect (p>0.05) on infection by vesicular stomatitis virus (VSV). A similar level of inhibition of ZEBOV infection was observed when a specific Akt inhibitor was used (Figure 1). These data suggested that the PI3K-Akt pathway plays a role in one or more of the early events in the ZEBOV infection cycle.
While the above data indicated that the activity of PI3K is important for early event(s) in ZEBOV replication, it was unclear if the basal level activity of the PI3K-Akt pathway was sufficient for infection or if ZEBOV itself was capable of inducing this pathway to promote infection. To address this question, phosphorylation of Akt-1 was measured in cells after incubation with ZEBOV. Akt is a major downstream effector of the PI3K pathway and is phosphorylated (activated) following activation of PI3K. Therefore, Akt phosphorylation is often used as an indirect, but reliable measure of PI3K pathway activation [21]–[23]. Serum-starved HEK293 cells were incubated with medium containing no serum (negative control), medium containing 10% fetal bovine serum (positive control), or γ-radiation-inactivated ZEBOV or VSV. Compared to the negative control, ZEBOV caused a marked (>2-fold) increase in Akt phosphorylation within 30 min and actually surpassed the level observed after serum stimulation of cells (Figure 2). In contrast, VSV had no significant effect on phosphorylation of Akt-1 over this time interval. These data suggested that ZEBOV actively and strongly induces the PI3K pathway very early during the infection process. Since γ-radiation-inactivated virus had been used, it was likely that this stimulation was the product of direct GP interaction with cell receptors.
The early dependence of ZEBOV infection on PI3K and Akt activity, and a significant induction of Akt phosphorylation by γ-radiation-inactivated (replication-incompetent) ZEBOV suggested that the PI3K pathway is likely involved at an early step in infection, most likely entry.
To investigate if the PI3K-Akt pathway played a role in ZEBOV entry or was required for some other early, but post-entry step, we adapted a previously described contents mixing assay that allowed rapid and quantitative measurement of entry of diverse enveloped viruses up to and including the point of membrane fusion [24]. The assay, which was originally based on virus pseudotypes, measures release of a recombinant nef-luciferase protein, encapsulated within virus particles. The nef peptide serves to non-specifically target luciferase to cell membranes at the time of particle budding and so, incorporates luciferase within the membrane of new virus particles. After cell-virus membrane fusion, the luciferase becomes accessible to its substrates, previously loaded into cells, and light is emitted. Here the assay was adapted for use, first with ZEBOV GP pseudotyped particles and then with ZEBOV virus-like particles. Both types of virus particles give a measure of GP function but VLPs, which share a filamentous structure with native ZEBOV are likely a better model system for wild type virus. In either case, this is the first time that this assay technology has been adapted to ZEBOV. Each assay was applied to determine if the PI3K-Akt pathway played a role in entry of ZEBOV.
To assess their efficiency in an entry assay, luciferase-containing pseudotypes with ZEBOV envelope glycoproteins (EVP) or VSV-G protein (VSVP), and VLPs carrying ZEBOV envelope glycoproteins (ZEBO-VLP) or VSV-G protein (VSV-VLP) were produced, sucrose purified and tested on cells. As compared to particles devoid of envelope glycoproteins, a strong signal was obtained for each of EVP, VSVP, ZEBO-VLP and VSV-VLP (Table 1, column 2). The relatively large difference in the signal between EVP and VSVP correlated to differences in pseudotype virus titer, as determined by standard infection assays with a GFP reporter gene (Table 1, column 3) and likely reflects the potency of the VSV-G relative to ZEBOV GP.
Both EVP and ZEBO-VLPs were then further validated for specificity of entry into cells. In each case the activity of the pseudotyped particles reflected that of the VLPs. ZEBOV neutralizing antibody (KZ52) significantly (∼70%) blocked entry of both EVP and ZEBO-VLP, (Figure 3A) and correlated to that reported for inhibition of infectious ZEBOV infection at the concentration of the antibody used here [25] while entry of VSVP was unaffected. Secondly, ammonium chloride (NH4Cl) and bafilomycin A1, two well-known inhibitors of endosomal acidification, both inhibited the pH-dependent entry of EVP and VSVP, while they had no effect on entry of a Friend murine leukemia virus pseudotype (FrVP), a pH-independent virus. Similarly, entry of ZEBO-VLP was also inhibited by NH4Cl (Figure 3B). Thirdly, a detailed examination of the entry kinetics of EVPs and ZEBO-VLPs (Figure 3C) revealed that the peak of the entry signal was preceded by a pronounced lag and occurred much later than that for VSV-G bearing particles. This timing was similar to that reported previously for a pseudotype infection assay [26]. Of note, both pseudotypes and VLPs entered cells with similar behavior and timings, indicating that the GP dictated the uptake kinetics and pathway used by the virus particle, more than the particle shape. In subsequent studies, the majority of the presented data are from VLPs but similar outcomes were seen with pseudotyped particles and are shown for comparison.
To investigate if the PI3K-Akt pathway played a role during the entry steps of ZEBOV infection, LY294002 and Akt inhibitor were tested in the entry assay. In each case, cells treated with the inhibitors exhibited significant reduction in the entry of ZEBOV GP bearing particles but not for particles bearing VSV-G (Figs. 4A, B). As a further independent test, a dominant-negative mutant of the p85 regulatory subunit (OMIM#171833) of class 1A PI3K (Δp85α) was used to inhibit PI3K-mediated signaling. Δp85α retains the ability to bind phosphotyrosine residues on upstream receptors that signal through PI3K but lacks the ability to interact with the PI3K catalytic domain [27]. Cells were transiently transfected with either a control plasmid (pcDNA3) or plasmid expressing Δp85α (pcDNA3:Δp85α) and entry assays performed using ZEBO-VLPs or VSV-VLPs (Figure 4C). Entry of ZEBO-VLPs was significantly inhibited in cells expressing Δp85α as compared to that in cells transfected with the empty vector, indicating a role for this isoform. In contrast, the entry of VSV-G bearing particles was similar in both cell types (Figure 4C).
Δp85α inhibits activity of those PI3K heterodimers that contain the α-isoform of the p85 regulatory subunit. To test the potential involvement of other isoforms, entry assays were performed using pik3R1 (OMIM#171833) knockout (p85α-, p50α-, p55α-deficient) or pik3R2 (OMIM#603157) knockout (p85β-deficient) mouse embryonic fibroblasts (MEF). Due to lower sensitivity of the VLP-based assay system in these cells, the experiments were performed using the pseudotyped virus-based assay. Furthermore, because of intrinsic resistance of corresponding wild-type MEFs to infection by VSVP, FrVPs were again used as a control. Compared to wild-type MEFs, EVP entry was reduced by >50% in the homozygous knockout cells for pik3R1, while entry in pik3R2 knockout cells was reduced by 75%. No significant effect on the entry signal of the FrVP was observed in either cell type (Figure 4D). These data indicate that both p85α and p85β containing PI3K heterodimers are involved in entry of ZEBOV.
The above findings strongly indicated that inhibition of PI3K-Akt pathway blocked ZEBOV infection up to or including the membrane fusion step. However, this could be due to impaired virus binding to cells or inhibition at a post-binding step such as endocytosis, trafficking, or membrane fusion. To further define the mechanism by which PI3K controls ZEBOV entry, virus binding to cells was measured. Cells were pretreated with LY294002 or Akt-1 inhibitor and pseudotyped viruses were bound for 10 min. Unbound virus was then washed away and cells with residual bound particles were lysed using non-ionic detergent to release virus encapsulated luciferase. Compared to DMSO-treated (control) cells, no significant difference was observed in luciferase activity in samples that were treated with either LY294002 or Akt-1 inhibitor, indicating that ZEBOV pseudotype binding to cells was unaffected (Figure 4E). Similarly, no significant difference was observed among p85 wild-type, pik3R1−/− or pik3R2−/−MEFs in their capacity to bind the pseudotyped particles (data not shown). This suggested that inhibition of the PI3K-Akt pathway does not influence levels of accessible receptor on the cell surface. Therefore, the PI3K-Akt pathway most likely plays a role in one or more post-binding steps involved in entry.
Among the possible downstream effectors regulated by PI3K-Akt pathway, mTOR (OMIM#601231) and Rac1 are most likely to influence the entry process. mTOR, being a positive regulator of translation [28], could potentially stimulate synthesis of factors needed during entry. However, a role of mTOR in ZEBOV entry was ruled out as rapamycin, a potent inhibitor of mTOR, had no effect on EVP entry (data not shown).
Rac1, through its regulation of actin polymerization plays a vital role in various steps involved in endocytosis. In many cell types, activity of Rac1 is regulated by the PI3K-Akt pathway [13]–[16]. Therefore, a specific Rac1 inhibitor was tested. A dose dependent inhibition of ZEBO-VLP was observed peaking at >90% inhibition at 400 µM without affecting VSV-VLP entry (Figure 5A). Furthermore, transient expression of a dominant-negative Rac1 mutant (Rac1-T17N) in cells reduced entry by >80% (Figure 5B). The extent of ZEBO-VLP entry inhibition corresponded to the number of cells expressing the dominant-negative Rac1 (Figure 5C, left panel).
The above findings provided evidence that PI3K-Akt and Rac1 pathways play a role in ZEBOV entry; however, it remained unclear if the two pathways were linked or acted independently. To investigate this, cells were made to express a constitutively active form of Rac1 (Rac1-G12V) in the presence of the Akt inhibitor. The effects of the inhibitor were significantly reversed (Figure 5B) by the mutant, indicating, that PI3K-Akt and Rac1 act sequentially in a pathway that controls entry of ZEBOV, with PI3K-Akt acting upstream of Rac1. Again, the extent of the effect of constitutively active Rac1 was proportional to the number of cells expressing the mutant protein (Figure 5C, middle panel). Similar data were obtained when EVPs and VSVPs were used in the assay (data not shown).
Inhibitors of PI3K, Akt and Rac1 had no significant effect on EVP binding to cells (Figure 4E, and data not shown) indicating that the action of the PI3K-Akt-Rac1 pathway was likely important for endocytosis, trafficking and/or membrane fusion of ZEBOV. To examine this, ZEBOV particles were labeled with a red fluorescent dye (Alexa Fluor594) and incubated with HEK293 cells in the absence or presence of LY294002, Akt inhibitor or Rac1 inhibitor. Cells were also stained for F-actin to visualize the cell cytoskeleton, and analyzed by confocal microscopy. In untreated cells, particles were found distributed evenly throughout the cell cytoplasm. In contrast, after treatment with each inhibitor, particles accumulated in clusters (Figure 6A). The drugs were also tested with Vero-E6 cells, a widely used, ZEBOV permissive cell line. Again, in untreated cells, individual virus particles were distributed throughout the cell cytoplasm (Figure 6B). Interestingly, image analysis of serial z-sections revealed that most of the individual particles were adjacent to small actin bundles (Figure 6C, left panel, arrow heads) supporting a requirement for actin involvement in ZEBOV movement through the cell cytoplasm. Treatment with inhibitors of PI3K, Akt-1 and Rac1 each gave a similar outcome to that seen in the HEK293 cells, with clustering of virus particles within a cytosolic compartment (Figure 6B), possibly of endocytic origin. These observations suggest that in the absence of PI3K activity, virus particles are taken into a vesicular compartment, but further trafficking is blocked.
This study describes a novel role for the PI3K cell signaling pathway in cellular entry of ZEBOV. A number of viruses utilize the PI3K-Akt cell signaling pathway to promote various steps in their replication cycle, such as regulation of gene expression and genome replication. Some bacteria and a few non-enveloped viruses also utilize this pathway to trigger their invasion and endocytosis into cells [22], [29]–[32]. This report provides evidence that the PI3K pathway plays a critical role in cellular entry of ZEBOV. A previous report suggested that PI3K is involved in early events in Influenza virus infection [33]. However, a detailed analysis of the mechanism of action was not performed and a subsequent study using the same cell type and virus strain failed to show a requirement of PI3K activity for Influenza virus entry into cells [34]. Thus, the present report is the first to show involvement of the PI3K-Akt pathway in entry of an enveloped virus.
The data obtained using the knockout cells provided information on which subtype of PI3K was important. Inhibition of pseudotyped ZEBOV entry into pik3R1 knockout (p85α-, p50α-, p55α-deficient) or pik3R2 knock out (p85β-deficient) cells suggested that class IA PI3Ks played a prominent role. Entry inhibition was more pronounced in pik3R2 knockout cells than in the pik3R1 knockout cells. This was somewhat surprising given that p85α is a major PI3K regulatory subunit, and deletion of the pik3R1 gene has a greater phenotypic impact, including perinatal lethality of homozygous mice accompanied with extensive hepatocytic and brown fat necrosis, enlarged skeletal muscle fibers, calcification of cardiac tissue and impaired B-cell development and proliferation [35],[36]. However, a few responses such as T-cell proliferation, and insulin-dependent tyrosine phosphorylation of insulin receptor substrate-2 were increased in pik3R2 knockout mice [37],[38], indicating that each subtype may have specialized roles in specific cells and tissues. Indeed, each binds different sets of proteins [39]. Individual disruption of pik3R1 or pik3R2 gene was insufficient to confer complete inhibition of ZEBOV entry and may be due to a partial redundancy in the function of each or that other PI3K isoforms may also be involved.
Given the relatively slow entry kinetics of ZEBOV, compared to VSV, the rapid phosphorylation of Akt by ZEBOV suggests that induction of the PI3K pathway may be related to a very early event in the entry process, such as receptor/co-receptor engagement. The class 1A PI3Ks are mainly activated by membrane-bound receptor tyrosine kinases (RTKs) [40]. Recently, Axl, Dtk, and Mer (Tyro3 family RTKs) were shown to serve as important entry factors for Ebola and Marburg viruses [41]. It was suggested that these molecules serve to promote endocytosis of ZEBOV particles; however, the exact mechanism and downstream effectors remained unclear. Interestingly, the inhibition of PI3K or Akt caused virus particles to aberrantly accumulate within the cell cytoplasm. In many cell types tyro3 family members trigger PI3K activation, and physical association of PI3K with Axl, Dtk and Mer has also been demonstrated [42]. ZEBOV interaction with the tyro3 RTK may then directly or indirectly trigger activation of PI3K and downstream effectors leading to virus endocytosis. However, more detailed analyses are required to further test this model.
In further studying the mechanism of action, Rac1 was found to be an important downstream effector. PI3K-Akt is one prominent activation pathway for Rac1 in many cell types [13]–[16]. Treatment with PI3K, Akt or Rac1 inhibitors all led to similar intracellular accumulation of ZEBOV particles (Figure 6), signifying a common block in one of the stages of ZEBOV uptake into cells. A similar clustering of internalized EphA2 receptor in endocytic vesicles was also observed after treatment with inhibitors of PI3K, Akt or Rac1 [43]. The likely role of Rac1, was through its regulation of actin polymerization, which plays a pivotal role in a variety of actin-dependent cellular processes such as membrane ruffling, receptor-mediated endocytosis and vesicular trafficking [12]. Indirect evidence suggesting that control of actin polymerization may be important for ZEBOV infection came from observations that: (i) internalized ZEBOV particles were in close proximity to actin bundles or filaments; (ii) inhibitors of PI3K, Akt and Rac1 all caused similar changes in F-actin characterized by loss of membrane ruffling and focal adhesions (data not shown); and (iii) agents that perturb actin dynamics significantly inhibit EVP entry [26].
Early activation of the PI3K-Akt pathway by ZEBOV may also have implications in pathogenesis of Ebola virus hemorrhagic fever. A profound inflammatory response is a key feature of the disease. Macrophages are among the primary targets of ZEBOV infection and respond by producing a number of proinflammatory cytokines and chemokines including TNF-α, IL-6 and IL-8 [44]. This appears to occur in the absence of virus replication as Ebola virus-like particles (VLPs) stimulate the same set of cytokines and is dependent on the presence of the Ebola virus envelope glycoproteins [45]. It follows that Ebola virus envelope proteins may play a vital role in the proinflammatory response induced during the infection. There is also evidence that the PI3K-Akt pathway contributes significantly toward regulation of each of these cytokines [46]–[49]. ZEBOV-induced activation of the PI3K-Akt pathway could then directly contribute to the proinflammatory response. Another hallmark of ZEBOV infection is hemorrhage due to increased vascular permeability. Vascular dysregulation has been attributed to both direct invasion and replication of ZEBOV in vascular endothelial cells, and to action of ZEBOV-induced proinflammatory cytokines, especially TNF-α, on vascular endothelial cells [50]. Also, PI3K-Akt pathway activation does lead to increased vascular permeability [51]. Thus, whether the mechanism of vascular dysregulation is through virus replication or action of cytokines, ZEBOV-induced PI3K activation has ability to affect both mechanisms, and thereby can potentially influence and partly explain a mechanism of ZEBOV pathogenesis.
The PI3K pathway is vital for regulation of diverse cellular activities, including growth, survival, differentiation and motility. There is mounting evidence that aberrant regulation of the PI3K pathway is central to development and/or progression of many forms of cancer [40]. As a result, considerable effort is currently being focused on developing therapeutic strategies targeting various components of this pathway, including targeting specific isoforms and subunits of the PI3K holoenzyme [52]. Earlier this year a PI3K inhibitor entered phase 1 clinical trials indicating that such drugs are becoming available [53]. The finding that the PI3K pathway is also essential for entry of ZEBOV is therefore highly relevant for design of new therapeutic strategies and provides new potential opportunities where PI3K inhibitors developed for cancer treatments may become equally useful for treatment ZEBOV infection.
HEK293 and HEK293FT human fibroblast-derived cells were purchased from ATCC and Invitrogen, respectively. HEK293-mCAT-1 are a clonal derivative of HEK293 cells that express the mCAT-1 protein which serves as a receptor for ecotropic murine leukemia viruses (MLV), such as the Friend 57 strain of MLV (Entrez nucleotide #X02794). These cells have similar morphology and growth properties compared to the parental HEK293 cells. Inhibition of ZEBOV entry by PI3K inhibitors was also confirmed in HEK293 cells with similar outcomes (data not shown). Vero-E6 cells were also purchased from ATCC. HEK293, FT and Vero-E6 cells were maintained in Dulbecco's modified Eagle's (DMEM) medium supplemented with 10% fetal bovine serum (Gemini Bioproducts, GA), 1% non-essential amino acids (Sigma, MO) and 1% penicillin-streptomycin solution (Sigma, MO). HEK293FT cells were used for pseudotype production and were maintained in the presence of geneticin at 0.5 mg/ml. Mouse embryonic fibroblasts (MEFs) were isolated from embryos that were heterozygous or homozygous for knockout of the PI3K-family related genes pik3R1 and pik3R2. Immortalized cells from these embryos were provided by Dr. Lew Cantley (Harvard Medical School, MA) and were cultivated in the above medium.
ZEBOV-specific KZ52 monoclonal antibody was a gift from Dr. Dennis Burton (Scripps Research Institute, LaJolla, CA). Anti-Akt-1 and anti-phospho-Akt-1 antibodies were purchased from Cell Signaling Technologies (Danvers, MA). Anti-HA (12CA5) was used as a non-specific control monoclonal antibody (Roche, IN).
All plasmids were prepared using Qiagen kits or by CsCl gradient centrifugation following standard procedures. The plasmids encoding HIV-1 gag and polymerase (pLP1), HIV-1 Rev (pLP2) and VSV-G envelope glycoprotein (pLP-VSVG) were purchased from Invitrogen. Construction of the plasmids encoding packageable enhanced green fluorescent protein (pLenti-EGFP) and Nef-luciferase fusion protein (pCDNA3-nef-luc) has been described previously [24]. Plasmid encoding ZEBOV matrix protein (VP40) (Entrez gene#NC002549) and envelope glycoproteins were kindly provided by Dr. Christopher Basler (Mount Sinai School of Medicine) and Dr. Paul Bates (University of Pennsylvania), respectively. The plasmid encoding envelope protein of the Friend 57 strain of MLV (pFr-Env) has been described previously [54].
HEK293FT cells were grown to approximately 80% confluence in 10-cm diameter dishes. The cells were simultaneously transfected with plasmids: (i) pLP1 (3 µg), pLP2 (2 µg), pLenti-EGFP (2 µg), pcDNA3-Nef-luc (1.5 µg) and one of the following envelope protein constructs pLP-VSVG, 2 µg; pFr-Env, 5 µg; pEbola-GP, 0.5 µg to yield pseudotyped viruses with VSV, Friend 57 MLV or ZEBOV envelope glycoproteins respectively. Transfection was by calcium-phosphate precipitation [55]. After overnight incubation, culture medium was replenished and the plate was incubated for a further 36 h. At this time, the cell culture supernatant was collected and filtered through a 0.45-µm pore size cellulose-acetate filter to remove cell debris. Virus in the filtrate was pelleted by centrifugation through a 20% (w/v) sucrose cushion in PBS. Centrifugation was for 3.5 h at 25,000 rpm in SW28 rotor at 4°C. The virus pellet was resuspended in 0.01 volume of DMEM, aliquoted and stored at −80°C until used.
ZEBOV-VLPs were produced by co-transecting HEK293 cells with plasmids encoding ZEBOV matrix protein (VP-40), Zaire Ebola virus (ZEBOV) envelope glycoproteins, and Nef-luciferase fusion protein using the calcium phosphate method. For VSV-VLP, plasmid encoding Ebola virus glycoproteins was replaced with one encoding VSV-G. Cell culture supernatant was collected 48 h after transfection and cell debris was cleared by centrifugation (1,200 rpm for 10 min at 4°C). Subsequently, VLPs were purified by centrifugation (25,000 rpm in SW28 rotor for 3.5 h at 4°C) through a 20% (w/v) sucrose cushion in PBS. The VLP pellet was resuspended in 0.01 volume of DMEM, aliquoted and stored at 4°C. Assays were performed within 2–3 days after purification of VLPs.
Zaire Ebola virus (Mayinga strain), was cultivated on Vero-E6 cells by infection at an MOI of 0.1. Culture supernatants were collected after 10 d and clarified by centrifugation at 2000×g for 15 min. Virus titer was determined by serial dilution in Vero-E6 cells. Cells were incubated with virus for 1 h and then overlaid with 0.8% (w/v) tragacanth gum in culture medium. 10 d post-infection cells were fixed with formalin, and stained with crystal violet so that plaques could be counted. All experiments with ZEBOV were performed under biosafety level 4 conditions in the Robert E. Shope BSL-4 Laboratory, UTMB.
HEK293-mCAT-1 cells were grown in a 96-well plate to approximately 50% confluence. Serial 5-fold dilutions of virus stocks were prepared in DMEM and 50 µl of each dilution was added to cells. After overnight incubation, the medium was replenished and the incubation continued until GFP expressing cells were apparent (2 d post-infection). The total number of GFP-positive colonies was counted in each well using an inverted epifluorescence microscope and the titer of stock virus was calculated.
Cells were removed from plates by trypsin treatment, pelleted by centrifugation and then resuspended in fresh medium. HEK293 cells (106) were mixed with Nef-luciferase containing pseudotyped virus or VLPs in a volume of 0.2 ml and incubated at 37°C on a rotating platform for indicated time intervals. Exposure of cells to low temperatures (4°C) was avoided as this is known to temporarily disrupt endocytosis and receptor trafficking upon return to 37°C. To remove excess virus particles, cells were pelleted by centrifugation at 200×g for 5 min, supernatant containing unbound virus was discarded, and the cell pellet was washed 3 times with DMEM. The final cell pellet was resuspended in 0.1 ml of luciferase assay buffer lacking detergent (Promega, WI) and luciferase activity measured using a Turner Design TD 20/20 luminometer and expressed as counts/sec.
For antibody inhibition assays, the luciferase-containing pseudotyped virus or VLPs were incubated with antibody for 1 h prior to incubation with target cells, which was performed in the continued presence of antibody.
To study drug activity on virus entry, cells were pre-treated for 1 h, followed by incubation with pseudotyped virus or VLPs in the continued presence of the drug. Virus entry was then measured as described above.
For dominant-negative or constitutively-active mutants, control plasmid (pcDNA3) or plasmid encoding the modified cDNA was transfected into HEK293-mCAT-1 cells by calcium phosphate precipitation as described above. Cells were used for entry assays 36 h after transfection.
HEK293 cells were grown to confluence and then serum-starved for 12–14 h. Radiation-inactivated wild type ZEBOV (Entrez Genome#15507) or VSV (Entrez Genome#10405) (sucrose purified and resuspended in serum-free medium) was then added at a calculated MOI of 5. For positive control, cells were treated with 10% fetal bovine serum in medium, while the negative control samples received serum-free medium. All samples were incubated at 37°C for times indicated. After the incubation, cell lysates were applied to 10% polyacrylamide gels and resolved proteins transferred to a nitrocellulose membrane by electroblotting. After blocking the membrane in 5% milk powder in TBST, blots were incubated overnight with anti-phospho-Akt-1 antibody at 4°C, washed and incubated with HRP-conjugated secondary antibody for 1 h. The membrane was then washed and developed using ECL chemiluminescence substrate (GE life sciences, Piscataway, NJ) and imaged. Subsequently, the same membrane was stripped and re-probed for total Akt-1 using an anti-Akt-1 antibody. Band densitometry was performed using ImageJ analysis software [56].
ZEBOV was grown on Vero-E6 cells to a titer of 106 pfu/ml. Virus-containing culture supernatant was clarified by pelleting cell debris at 2000×g for 15 min. The virus remaining in the supernatant was then pelleted through 20% sucrose in 10 mM HEPES, pH 7.4 by centrifugation at 100,000×g for 3 h. The virus pellet was resuspended in 140 mM NaCl in 10 mM HEPES, pH 7.4 and inactivated by gamma-radiation (5 Mrad). Protein content of the virus pellet was determined using a BCA protein assay kit (Pierce, Rockford, IL). An equal volume of 0.1 M sodium phosphate, pH 8.0 was added and protein concentration adjusted to 2 mg/ml by further addition of this buffer. Of this, 0.1 mg of total protein was labeled with 0.05 mg of Alexa Fluor594 carboxylic acid, succinimidyl ester (Invitrogen). The reaction was allowed to proceed for 2 h at room temperature at which time it was quenched by addition of 0.1 volume of 0.1 M glycine. The samples were then dialyzed overnight against PBS at 4°C and then again overnight against DMEM. The virus suspension was then aliquoted and stored at −80°C.
HEK293-mCAT-1 or Vero-E6 cells were cultivated overnight on chambered coverglass (Nunc, Rochester, NY) at a density of 50%. The following day, cells were incubated with fluorescently-labeled ZEBOV for 3 h. For analysis of drug action, cells were pretreated for 1 h prior to virus addition as described above. Cells were then washed three times in DMEM and fixed in 3.5% fresh paraformaldehyde in PBS. After one wash in PBS residual paraformaldehyde was neutralized by addition of 0.1 M glycine buffer, pH 7.4 and cells were permeabilized using 0.1% Triton X-100 for 1 min at room temperature. Cells were stained for F-actin using Alexa488-conjugated phalloidin (Invitrogen) for 15 min at room temperature. Cells were imaged using a Leica DMIRB inverted microscope with a 100× oil immersion lens or a Zeiss LSM 510 confocal microscope in the UTMB optical imaging core.
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10.1371/journal.pntd.0000611 | Distinct Genetic Diversity of Oncomelania hupensis, Intermediate Host of Schistosoma japonicum in Mainland China as Revealed by ITS Sequences | Oncomelania hupensis is the unique intermediate host of Schistosoma japonicum, which causes schistosomiasis endemic in the Far East, and especially in mainland China. O. hupensis largely determines the parasite's geographical range. How O. hupensis's genetic diversity is distributed geographically in mainland China has never been well examined with DNA sequence data.
In this study we investigate the genetic variation among O. hupensis from different geographical origins using the combined complete internal transcribed spacer 1 (ITS1) and ITS2 regions of nuclear ribosomal DNA. 165 O. hupensis isolates were obtained in 29 localities from 7 provinces across mainland China: lake/marshland and hill regions in Anhui, Hubei, Hunan, Jiangxi and Jiangsu provinces, located along the middle and lower reaches of Yangtze River, and mountainous regions in Sichuan and Yunnan provinces. Phylogenetic and haplotype network analyses showed distinct genetic diversity and no shared haplotypes between populations from lake/marshland regions of the middle and lower reaches of the Yangtze River and populations from mountainous regions of Sichuan and Yunnan provinces. The genetic distance between these two groups is up to 0.81 based on Fst, and branch time was estimated as 2–6 Ma. As revealed in the phylogenetic tree, snails from Sichuan and Yunnan provinces were also clustered separately. Geographical separation appears to be an important factor accounting for the diversification of the two groups of O. hupensis in mainland China, and probably for the separate clades between snails from Sichuan and Yunnan provinces. In lake/marshland and hill regions along the middle and lower reaches of the Yangtze River, three clades were identified in the phylogenetic tree, but without any obvious clustering of snails from different provinces.
O. hupensis in mainland China may have considerable genetic diversity, and a more complex population structure than expected. It will be of significant importance to consider the genetic diversity of O. hupensis when assessing co-evolutionary interactions with S. japonicum.
| The intermediate host of Schistosoma japonicum in Asia is the snail Oncomelania hupensis, which can be separated phenotypically into ribbed- and smooth-shelled morphotypes. In China, the typical morphotype is ribbed-shelled, with its distribution restricted to mainland China. Smooth-shelled snails with varix are also distributed in China, which are considered to belong to the same subspecies as the ribbed-shelled snails. In this study we investigate the genetic variation among O. hupensis from different geographical origins using combined complete ITS1 and ITS2 regions of nuclear ribosomal DNA. Snails including ribbed-shelled and smooth-shelled (but with varix on the shell) from the lake/marshland region of the middle and lower reaches of the Yangtze River, and smooth-shelled snails from mountainous regions of Sichuan and Yunnan provinces, were genetically distinct with no shared haplotypes detected. Furtheremore, the snails from Sichuan and Yunnan provinces were clustered in separate clades in the phylogenetic tree, and three clades were observed for snails from the middle and lower reaches of the Yangtze River. The population diversity of O. hupensis in China is thus considered large, and evolutionary relationships in the host-parasite system of O. hupensis-S. japonicum may be of interest for further research.
| The snail Oncomelania hupensis, the only intermediate host of Schistosoma japonicum, has been found in China, and also in Japan, Philippines and Indonesian island of Sulawesi. Over the past a few decades, the taxonomy of O. hupensis has been a dispute due to the variation in morphological characters such as shell sculpture, operculum etc. [1]–[3]. Phenotypically, O. hupensis can be separated into ribbed- and smooth- shelled morphotypes. In China, the typical morphotype of O. hupensis is ribbed-shelled, and its distribution is restricted to Yangtze River basin. Smooth-shelled snails are also distributed in mainland China, but are considered as the same species and subspecies of O. hupensis [1]–[4]. Oncomelania snails reported in other Far East countries are smooth-shelled, and have been considered either as subspecies of O. hupensis or independent species in this genus [5]–[8].
Based on shell form, biogeographical and allozyme data, Davis et al. [1] distinguished all of the O. hupensis in mainland China into three subspecies: O. hupensis subsp. robertsoni, O. hupensis subsp. tangi and O. hupensis subsp. hupensis. O. hupensis robertsoni which has a small, smooth shell but with no varix, is found in Sichuan and Yunnan provinces. O. hupensis tangi, which has a smooth shell but with thick varix, is found in Fujian province and Guangxi autonomous region, separated geographically from the Yangtze River, and extensive control measures have brought this subspecies to near extinction [9],[10]. However, Zhou et al. [11] separated the O. hupensis guangxiensis out from O. hupensis subsp. tangi based on allozymes and amplified fragment length polymorphism (AFLP) [12],[13] , which was verified recently by Li et al. [14] with internal transcribed spacer (ITS) and 16S fragments. O. hupensis hupensis is the most widely distributed subspecies of Oncomelania and lives primarily at low altitude but a few populations live in hilly areas in the drainage area of the Yangtze River in mainland China. It has varix, no matter whether the shell is smooth or ribbed, but most populations have ribbed-shell. O. hupensis hupensis has the same shell growth allometry as O. hupensis robertsoni but has a longer shell on average [1],[2].
The genetic diversity of O. hupensis in China has also been a focus over last two decades, and some results have been controversial. Spolsky et al. [15], by using cyt b gene, found considerable genetic diversity in O. hupensis in China, and using AFLP, Zhou et al. [12],[13] detected significant positive correlation between genetic and geographical distances for 25 populations of O. hupensis collected in China. With allozyme data, Davis et al. [1] showed that one smooth-shelled population from Zhejiang province could be considered genetically identical to a population of O. hupensis robertsoni from Sichuan province. Also, using an allozyme approach, Zhou et al. [16] and Qian et al. [17] found that smooth-shelled populations were clustered separately with ribbed-shelled populations in middle and lower reaches of the Yangtze River. With mitochondrial cytochrome oxidase subunit 1 (cox1) gene, Wilke et al. [3] showed that smooth-shelled individuals clustered together with ribbed-shelled ones, all collected in the middle and lower reaches of the Yangtze River, suggesting that all smooth- and ribbed- shelled populations of Oncomelania throughout the middle and lower Yangtze River basin belong to the subspecies O. hupensis hupensis. With the 16S RNA and ITS sequences respectively, Li et al. [14] recently found four and three branches in the phylogenetic trees, with the four branches representing O. hupensis robertsoni from Sichuan and Yunnan provinces, O. hupensis guangxiensis from Guangxi Karst region, O. hupensis hupensis from the middle and lower reaches of Yangtze River, and those from littoral and hill regions in Fujian province which was recognized as O. hupensis tangi [1]. However, the report by Li et al. [14] contained only a small number of specimens. Comprehensive analyses on the genetic diversity of these snails and the relationship between O. hupensis hupensis in the middle and lower Yangtze River basin and the smooth-shelled O. hupensis robertsoni in areas of upper Yangtze River have not been carried out with more samples collected on a much larger geographical scale.
In this study, the intermediate hosts of S. japonicum were collected from 29 localities in 7 provinces, comprising almost all uncontrolled endemic areas of schistosomiasis in mainland China. O. hupensis hupensis and O. hupensis robertsoni were obtained from localities in the middle and lower reaches of the Yangtze River, and from Sichuan and Yunnan Provinces in the upper Yangtze River, respectively. Highly variable internal transcribed spacer regions (ITS) of nuclear ribosomal DNA were sequenced for individual O. hupensis snails in order to examine the genetic diversity of O. hupensis hupensis and O. hupensis robertsoni in mainland China, and to find out the relationship between their geographical distribution and the genetic variation of these snails in China on the basis of phylogenetic analysis. The evolutionary implication of the intermediate host genetic diversity was then discussed.
The diagnosis of subspecies of O. hupensis followed that of Davis et al. [1]. O. hupensis hupensis and O. hupensis robertsoni were collected from October 2005 to October 2006 from endemic areas in Anhui, Hubei, Hunan, Jiangxi, Jiangsu, and in Sichuan and Yunnan provinces in mainland China, respectively (Table 1). Geographical information concerning these sample localities is listed in Table 1 and indicated in Fig. 1 using Google Earth with editing in Photoshop. Snails were collected with forceps from the field and brought back to laboratory, where they were cleaned after one month captivity, and then checked microscopically to ensure that schistosome-uninfected snails were selected for the experiment. The head-foot muscle of each snail was dissected individually under a microscope after being washed in 0.3% NaCl solution, and then preserved in 95% ethanol.
The total genomic DNA of individual snails was extracted using a standard sodium dodecyl sulfate-proteinase K procedure [18]. Each individual sample was incubated and thawed in 200 µl extraction buffer (50 mM Tris-HCl, 50 mM EDTA, 100 mM NaCl, 1% SDS, 100 µg/ml proteinase K), at 56°C for 2 h with gentle mixing. DNA in solution was extracted using standard phenol/chloroform purification, followed by 3 M sodium acetate (pH 5.2) and ethanol precipitation. Pellets of DNA were washed in 70% ethanol, air-dried, and resuspended in 20 µl TE (pH 8.0). Polymerase chain reaction (PCR) was used to generate a fragment spanning ITS1-5.8S-ITS2 between the forward primer OHITSF (5′- ATTGAACGGTTTAGTGAGGTCC -3′) and the reverse primer OHITSR (5′- CATTCCCAAACAACCCGACTC -3′) based on available GenBank sequences AY207042, AF367667 and U93228. The PCR protocols were 94°C for 3 min followed by 30 cycles of 94°C for 30s, 58°C for 30s, and 72°C for 90 s and then a final elongation step at 72°C for 10 min. The amplified products were purified on a 1.0% agarose gel stained with ethidium bromide, using the DNA gel extraction kit (Omega Bio-Tek). The purified PCR product was then cloned into pMD18-T vector (TAKARA) and sequenced using ABI PRISM BigDye Terminators v3.0 Cycle Sequencing (Applied Biosystems). The DNA sequences were deposited in the GenBank database under accession numbers FJ600745 to FJ600909 inclusive.
Sequences were aligned using ClustalX v1.83 [19] at default settings followed by manual correction in SEAVIEW [20]. DNAsp version 4.0 [21] was used to define the haplotypes.
Genetic variation within and between two subspecies were estimated by calculating nucleotide diversity (π) and haplotypic diversity (h) values in Arlequin3.11 [22] and DNAsp. Selective neutrality was tested with Tajima's D [23] and Fu's F test [24].
Phylogenetic relationships were conducted on the aligned sequences of combined ITS1-ITS2 rDNA sequences. We performed a wide array of phylogenetic analyses using different methods: neighbor joining (NJ), maximum parsimony (MP), maximum likelihood (ML) and Bayesian inference (BI). NJ and MP were implemented in PAUP* 4.0b10 [25] using heuristic searches and tree bisection-reconnection branch-swapping. Nodal support for the MP phylogenetic tree was estimated through bootstrap analysis using 1000 replicates, and with 10 random sequence additions per each step bootstrap replicates. ML analysis was conducted in PHYML 2.4.4 [26], also with 1000 replicates bootstrap. GTR+I+G was determined as the best-fit model of sequence evolution for each dataset by using the Akaike informative criterion implemented in Modeltest 3.7 [27]. BI was carried out with MrBayes 3.1 [28] under the best-fit substitution model. Analyses were run for 2×106 generations with random starting tree, and four Markov chains (with default heating values) sampled every 100 generations. Posterior probability values were estimated by generating a 50% majority rule consensus tree after the first 2000 trees were discarded as part of a burn-in procedure. All phylogenetic trees were rooted using Lottia digitalis as outgroup.
Mismatch distribution of the number of differences between all possible pairs of haplotypes were calculated using DNAsp, and tested against the expected values of a recent population expansion with 1000 bootstrap replicates. Within-species genetic structure was phylogenetically evaluated by constructing unrooted parsimony networks of haplotypes using TCS version 1.21 [29]. Net nucleotide divergence (Dxy) between two subspecies was calculated with the Tamura-Nei gamma correction model using MEGA 4 [30].
The complete ITS-5.8S-ITS2 fragments, including portions of the 3′ end of the 18S and 5′ start of the 28S, were sequenced for individual snails. The 3′ part of the 18S, 5′ part of the 28S and 5.8S of all specimens are completely identical. The ITS1 and ITS2 regions ranged from 412 to 441 bp and from 402 to 426 bp, respectively. The alignment of the combined ITS1–ITS2 sequences resulted in a total of 889 characters, including gaps, with 190 variable sites and 71 parsimony informative sites. A total of 93 haplotypes were identified from 165 individuals. 31 haplotypes were found in multiple individuals and 62 haplotypes were represented by single individuals (Table 1). The haplotype and nucleotide diversity for all sequences sampled were 0.974±0.004 and 0.023±0.002, respectively.
For O. hupensis hupensis, 80 haplotypes were identified from 130 individuals in 23 localities of five provinces along middle and lower reaches of Yangtze River. The haplotype and nucleotide diversity were 0.960±0.022 and 0.017±0.008, respectively. For O. hupensis robertsoni, 13 haplotypes identified from 35 individuals of 6 localities in Sichuan and Yunnan provinces. The haplotype and nucleotide diversity were 0.916±0.023 and 0.028±0.014, respectively.
When we classified all geographical populations into two subspecies, the genetic distance between O. hupensis hupensis from five provinces along the middle and lower reaches of Yangtze River and O. hupensis robertsoni from mountainous regions of Sichuan and Yunnan provinces was apparent (Fst = 0.810, P<0.001) and the gene flow was limited (Nm = 0.117, P<0.001), indicating that the diversity between the two subspecies is significantly obvious.
In neutrality analyses, strong selection has been observed in O. hupensis robertsoni either with Tajama's D or Fu's F test (P>0.1). Although limited deviation has been observed for O. hupensis hupensis (Fs = −12.51, P = 0.011). Except a real departure from neutrality, the same pattern can be obtained after a recent population expansion when equilibrium between gene flow and drift has not yet to be reached [31],[32].
Through mismatch distribution analysis, the observed (empirical) distribution of haplotype pairwise differences followed a multimodal, ragged pattern, deviating significantly from the expected curve reflecting population expansion (P = 0.002) (Fig. 2). This pattern suggests that O. hupensis has already differentiated genetically in mainland China, which in turn verified the diversity between O. hupensis hupensis and O. hupensis robertsoni. In contrast, O. hupensis hupensis displayed a smooth unimodal mismatch distribution, which is consistent with the expected values of an expanding population, supporting the latter possibility in the neutral analyses for O. hupensis hupensis, that is, O. hupensis hupensis has a recent population expansion while equilibrium between gene flow and drift has not yet to be reached.
Tree topologies generated by different building methods using NJ, ML, MP and BI were similar. Two distinct clades (clades A and B) were supported by high posterior probability or bootstrap values at key nodes (Fig. 3, ML tree). Clade A includes all haplotypes from five provinces including Anhui, Hubei, Hunan, Jiangxi and Jiangsu along the middle and lower reaches of the Yangtze River, and within this clade, a deep divergence was observed and it is quite obvious that three subclades, shown as A1, A2 and A3 can be recognized; but there is no distinct geographical relationship or phenotype characters, and posterior probabilities were low amongst the subclades. Clade B contains only haplotypes from mountainous regions in Sichuan and Yunnan provinces, and two subclades (subclade B1+B2 and subclade B3) were formed and supported by high posterior probabilities, which represent haplotypes from Sichuan and Yunnan provinces, respectively, except one shared haplotype from SCms and YNws populations in Sichuan and Yunnan provinces, respectively.
The haplotype network constructed by statistical parsimony had similarity at least to some extent to the phylogenetic tree, especially in that the haplotype networks between samples from lake/marshland and hill regions in five provinces along the middle and lower reaches of Yangtze River and those from mountainous regions of Sichuan and Yunnan provinces were so diversified (Fig. 4). But, haplotypes from the middle and lower reaches of Yangtze River were mixed into a reticulate topology of evolution, forming into cluster A, which was reflected as clade A in the phylogenetic tree (Fig. 3). It was, however, impossible to further group these haplotypes. For haplotypes from Sichuan and Yunnan provinces, three separate clusters were detected (Fig. 4), which are completely consistently with the subclades B1, B2 and B3 in the phylogenetic tree (Fig. 3).
Based on the substitution rates for invertebrate ITS sequences ranging from 0.4% to 1.2%/Myr [33]–[35], it is estimated that the divergence between O. hupensis hupensis and O. hupensis robertsoni is about from 2±0.29 to 6±0.15Ma (Dxy = 0.048±0.0070).
This study demonstrated distinct genetic differentiation of O. hupensis from 29 geographical populations collected from 7 provinces in mainland China, accounting for most ecological habitat types for O. hupensis in endemic areas of China. Phylogenetic analyses revealed two distinct well-supported clades: One included all samples from lake/marshland and hill regions in five provinces along middle and lower reaches of the Yangtze River, the other one included samples from mountainous regions of Sichuan and Yunnan provinces. The average genetic divergence between the two clades is up to 0.81 based on Fst, which is considered to be ‘very great’ by following the views of Wright [36]. Furthermore, the haplotype network revealed no connection between O. hupensis hupensis populations from lake/marshland and hill regions and O. hupensis robertsoni populations from mountainous regions, which also confirmed the genetic diversity of O. hupensis in mainland China geographically. The significant genetic differentiation was also reflected in the multimodal distribution in the mismatch analysis.
The genetic diversity of Oncomelania in China was previously examined by using COI [3],[37], Cytb [15], 16S rDNA [37] sequences and other methods such as AFLP [12],[13], and it has been shown that O. hupensis hupensis and O. hupensis robertsoni are genetically different. As revealed in the phylogenetic tree and haplotype network in the present study, O. hupensis robertsoni from Yunnan province differed genetically from those in Sichuan province, despite a shared haplotype from YNws and SCms which may need some further research. Li et al. [14], also using ITS sequences, found that O. hupensis robertsoni from Sichuan and Yunnan provinces were clustered into separate clades, although they were included in a larger clade, as observed in the present study. ITS, flanking sequences emanated from non-coding rDNA region, has a relatively fast evolutionary rate, and can be employed for investigating genetic differentiation and phylogeny of closely related species [38],[39]. In consideration of the ITS potential for heterozygote analysis [40], the large amount of samples used in the present study may stabilise the estimation of genetic variation and give more statistical confidence in the results [12],[41].
In other studies (data not shown here), we found that the complete mitochondrial DNA sequences had 10.3% genetic distance between O. hupensis hupensis and O. hupensis robertsoni, which may also reveal high genetic diversity between these subspecies. This information, to some extent, confirms the existence of wide genetic diversity for O. hupensis in mainland China. Although direct molecular evidence has not been previously available for the genetic diversity of O. hupensis, several authors [1]–[4],[11],[14] have considered that O. hupensis in mainland China can be separated into several subspecies, for example, O. hupensis hupensis from middle and lower reaches of Yangtze River, and O. hupensis robertsoni from mountainous regions of Sichuan and Yunnan provinces. It can then be concluded that these two subspecies differ not just in phenotypes and ecological habitats, but also genetically. Cross et al. [42] and He et al. [43] even showed that O. hupensis from different regions differed in their susceptibility to the same strain of Schistosoma japonicum, which may also have been reflected in genetic diversity of the snail intermediate hosts.
Ecological habitat and geographical distance were found to have some impact on genetic diversity of O. hupensis in mainland China [e.g. 14]. It has been suggested that O. hupensis evolved during its dispersal down the Yangtze River system, which would lead to genetic distance increasing with geographical distance [3]. Zhou et al. [12],[13] also found significant spatial genetic structure among 25 snail populations from 10 provinces in mainland China using AFLP, which was also verified by Li et al. [14] using ITS and 16S markers with a total of 30 individuals investigated in 13 localities. The habitats of O. hupensis in the middle and lower reaches of the Yangtze River include lake/marshland regions and hill regions, both of which have extensive physical connections with the Yangtze River through channels or in low floodplains beside the Yangtze River. With frequent floodings of the Yangtze River, snails in these habitats can be dispersed and subsequently deposited widely in various localities. The accumulation of mixed sources of snails can then generate genetically diversified populations of snails, leading to the existence of various haplotypes as observed in the present study. As found by Wilke et al. [3], ribbed-shelled snails and smooth-shelled snails but with varix on shell in the middle and lower reaches of Yangtze River were also clustered together in the phylogenetic tree. Whether this is the effect of potential heterozyges for ITS or not needs to be further investigated. The three subclades within the clade containing all samples, including those smooth-shelled snails with varix obtained in the middle and lower reaches of the Yangtze River may also indicate the genetic diversity of O. hupensis hupensis; it is therefore necessary to further investigate the genetic diversity of these snails by using more powerful tools and by covering more areas in the region.
In Sichuan and Yunnan provinces in the upper reaches of the Yangtze River, O. hupensis robertsoni are distributed in mountainous areas, and are not subjected to flood influence as much as in the middle and lower reaches of the river [44]. It is interesting to see that a relatively lower number of haplotypes were found in this region as compared with O. hupensis hupensis. Overall, these mountainous populations were genetically different from the populations in the middle and lower reaches of the river, as shown by phylogenetic trees, haplotype networks and genetic distance analyses. It thus appears likely that there has been certain degree of isolation for these mountainous populations. Wilke et al. [37] also found the diversity trend of O. hupensis robertsoni by COI and 16S rRNA sequences. It may also be possible that continuous control efforts, such as routine molluscicides in China, which have been used to control snails for about fifty years, might have imposed some effect on population genetics of these snails [45]. The diversity found in populations from Sichuan and Yunnan provinces may also need to be further clarified by obtaining more samples and by using more powerful molecular markers such as microsatellites.
About the origin and evolution history of Oncomelania, Davis [46] proposed a Gondwanan origin for the Pomatiopsidae, with rafting to mainland Asia via the Indian Craton after break-up of Gondwanan and colonization of South-East Asia and China. It is hypothesized [16],[47] that Oncomelania snails, arrived in southwestern China from Indian before the second (major) Tibetan orogeny (2.5 Ma), then evolved and spread down their respective river systems, to mainland of China, Indonesia and Philippines. Although mutation rate calibrations using fossil data is impossible here, many studies have demonstrated the confidence that molecular data can provide reasonable estimates of divergence time. Our data suggested that the two subspecies began to diverse as early about 2–6 Ma based on the invertebrate ITS substitution rate range. We did not find any strong molecular and fossil evidences about Oncomelania evolution, but the reported Oncomelania fossil found in Guangxi (1 Ma) by Odhner in 1930 and geological movement make this diversification time reasonable. It provides a new insight into the Oncomelania evolution history although the substitution rate needs to be verified with new fossil and molecular data in future study.
Davis et al. [48] speculated that, as Oncomelania snail populations form have diverged genetically, so must their associated schistosomes or else become regionally extinct. East Asian schistosomes and snails in the Pomatiopsidae have been considered as the only example of schistosome-intermediate host snail coevolutionary model [49], and a recent study also revealed that S. japonicum in mainland China can be highly genetically diverse, especially between populations from the lake/marshland lowland localities and populations from highland mountainous localities [50]. The continuous dispersal of the snails, probably as well as their schistosome parasites, in the middle and lower reaches of the Yangtze River may have considerable epidemiological, medical and evolutionary implications for the schistosome-snail system and schistosomiasis, as also suggested by Ross et al. [9]. It would be interesting, and necessary, to understand the population genetic diversity of the parasites and their intermediate hosts in greater detail throughout their distributions.
In summary, by cloning ITS1–ITS2 sequences, it has been shown that O. hupensis is highly genetically diverse. This clear and distinct genetic diversity in snail intermediate hosts may have strong implications in genetic diversity of schistosomes in China, and further studies on comparative phylogeography of the host-parasite system and also on their population genetics are necessary to understand the complexity of host-parasite population structures and evolutionary, if not co-evolutionary, relationships.
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10.1371/journal.pbio.2006229 | Low-intensity electromagnetic fields induce human cryptochrome to modulate intracellular reactive oxygen species | Exposure to man-made electromagnetic fields (EMFs), which increasingly pollute our environment, have consequences for human health about which there is continuing ignorance and debate. Whereas there is considerable ongoing concern about their harmful effects, magnetic fields are at the same time being applied as therapeutic tools in regenerative medicine, oncology, orthopedics, and neurology. This paradox cannot be resolved until the cellular mechanisms underlying such effects are identified. Here, we show by biochemical and imaging experiments that exposure of mammalian cells to weak pulsed electromagnetic fields (PEMFs) stimulates rapid accumulation of reactive oxygen species (ROS), a potentially toxic metabolite with multiple roles in stress response and cellular ageing. Following exposure to PEMF, cell growth is slowed, and ROS-responsive genes are induced. These effects require the presence of cryptochrome, a putative magnetosensor that synthesizes ROS. We conclude that modulation of intracellular ROS via cryptochromes represents a general response to weak EMFs, which can account for either therapeutic or pathological effects depending on exposure. Clinically, our findings provide a rationale to optimize low field magnetic stimulation for novel therapeutic applications while warning against the possibility of harmful synergistic effects with environmental agents that further increase intracellular ROS.
| Repetitive low-intensity magnetic stimulation has been used in the treatment of disease for over 50 years. Associated benefits have included alleviation of depression, memory loss, and symptoms of Parkinson disease, as well as accelerated bone and wound healing and the treatment of certain cancers, independently of surgery or drugs. However, the cellular mechanisms underlying these effects remain unclear. Here, we demonstrate that repetitive magnetic field exposure in human cells stimulates production of biological stress response chemicals known as reactive oxygen species (ROS). At moderate doses, we find that reactive oxygen actively stimulates cellular repair and stress response pathways, which might account for the observed therapeutic effects to repetitive magnetic stimulation. We further show that this response requires the function of a well-characterized, evolutionarily conserved flavoprotein receptor known as cryptochrome, which has been implicated in magnetic sensing in organisms ranging from plants to flies, including migratory birds. We conclude that exposure to weak magnetic fields induces the production of ROS in human cells and that this process requires the presence of the cryptochrome receptor.
| Weak electromagnetic radiation (μT-mT), which increasingly pollutes our environment, has been associated with dual and seemingly contradictory effects on human health. On the one hand, possibly deleterious public health consequences have elicited considerable debate on safety and exposure limits to electromagnetic field (EMF) radiation [1–4]. On the other hand, weak magnetic fields have been applied as therapeutic tools, notably in the form of pulsed electromagnetic fields (PEMFs), which have shown benefits in a broad range of regenerative medicine therapeutics, as well as in the alleviation of depression, reducing symptoms of Parkinson disease, and reducing memory loss [5–10]. Such PEMFs also affect nonexcitable tissues [7,9] and are below firing threshold for neurons [11,12] consistent with magnetic field effects and thereby activation of a biological magnetoreceptor. The current challenge is therefore to identify these putative magnetosensor(s) and to propose a mechanism that may explain the seemingly disparate effects of EMFs in medicine and in public health.
A possible class of biological magnetoreceptor [13] are the cryptochromes, which are conserved flavoprotein receptors [14] implicated in magnetosensing in organisms ranging from plants to migratory birds [14–16]. Cryptochrome receptors undergo redox reactions in the course of their activation that lead to the synthesis of reactive oxygen species (ROS) [17–19]. ROS are global regulators that are implicated in numerous cellular signaling functions related to response to stress and ageing and are toxic at high concentrations [20–23]. In mammalian cells, cryptochromes are both cytosolic and nuclear proteins that have been characterized for a role as core components of the circadian clock [24, 25] but that are not known to respond to external magnetic fields. However, recombinant mammalian cryptochromes expressed in a heterologous Drosophila system are reported to confer magnetic sensitivity in behavioral assays in flies [16], and they were recently proposed to play a role as sensors of low EMFs in the onset of childhood leukemia [26]. This raises the question of whether cryptochromes could be implicated in magnetic sensitivity in humans.
To explore this question, we chose to use PEMF exposure as a source of magnetic stimulation because it has demonstrated therapeutic effects on a wide variety of mammalian cell types [5–10]. To determine whether cryptochromes are implicated in PEMF effects, we first established whether a known magnetosensitive cryptochrome can mediate a response to a PEMF signal in a well-established magnetically sensitive model system. We used the fruitfly Drosophila melanogaster, which display a natural behavioral avoidance response to static magnetic fields [16]. Adult flies were placed on square petri plates to lay eggs for 24 hours and were subsequently removed. The ensuing hatched larva migrated freely over the plate for several days before choosing a location to attach to and form sessile pupa for metamorphosis. These pupae were located randomly around the perimeter of the plate, with preference for the corners (Fig 1). We tested magnetic sensitivity, with a coil generating continuous PEMF at 10 Hz, with peak amplitude of 1.8 mT at the level of the larvae (S1 and S2 Figs), placed underneath one of the 4 corners of the petri plate (see Materials and methods). Fly larvae grown under these conditions avoided the corner of the petri plate above the PEMF device (Fig 1A) compared to the other corners. Both Canton S (WTS) and Oregon (WTO) wild-type fly strains showed this avoidance response (Fig 1A and 1B) in blue light (which activates Drosophila cryptochrome; Fig 1B) but not in red light (which does not activate Drosophila cryptochrome; S3 Fig). As a control, a 1.0 mm mu-metal plate, which blocks static or low-frequency magnetic fields, was inserted between the magnetic coil and the petri plate containing the fly larvae. In these conditions, larvae did not show the avoidance response (S3 Fig). As a further control, we tested a coil in which the wire had been wound in an antiparallel fashion in order to cancel the magnetic field without altering the current in any way (see Materials and methods); this was also ineffective in causing an avoidance response. We next observed that fly mutants deficient in cryptochrome (cryb and cry02; [27]) did not avoid the PEMF, confirming a role for cryptochrome in this response. Finally, we tested transgenic fruitflies expressing the human cryptochrome-1 (HsCry1) protein in Drosophila cryptochrome-deficient strains as described previously [16, 27]). HsCry1 expression indeed restored the behavioral avoidance response to PEMF in flies lacking their endogenous cryptochrome (Fig 1B). These results indicate that PEMF can be detected by insects through the action of either Drosophila (DmCry) or human (HsCry1) cryptochrome, consistent with the response to static magnetic fields in this organism [16].
A possible mechanistic basis for this fly avoidance response was suggested by recent observations that ROS are byproducts of cryptochrome activation [17, 28] linked to signaling [29, 30]. Furthermore, at high concentrations, ROS are toxic metabolites implicated in oxidative stress and ageing, which damage cell membranes, nucleic acids, and proteins [20], consistent with the behavioral avoidance response. In contrast, at physiological concentrations, ROS are reported to have beneficial effects [20, 23], consistent with the observed therapeutic effects of PEMF [5–10]. To determine whether the PEMF signal stimulates formation of ROS, Spodoptera frugiperda (Sf21) insect cell cultures overexpressing DmCry [28] were stimulated by PEMF in blue light for 15 minutes in the presence of the ROS label, {5-(and-6)-chloromethyl-2’,7’-dichlorofluorecein diacetate} (DCFH-DA) [17, 28]. Confocal image analysis revealed a marked increase in fluorescent signal in PEMF-treated cells compared to unstimulated cultures (Fig 1C). In contrast, no visible effect of PEMF stimulation was observed in Sf21 cells lacking DmCry (S4 Fig). These data indicate that PEMF stimulation leads to intracellular accumulation of ROS and that this effect requires Drosophila cryptochrome.
Although flavin binding affinity is reportedly poor for vertebrate cryptochromes in vitro [31], they nevertheless confer light-sensitive phenotypes in expressing transgenic flies [16, 27] and undergo light-sensitive conformational change in the avian retina [15, 32], indicating that flavin is bound in vivo. Moreover, vertebrate-type cryptochromes are shown to undergo photoreduction and flavin radical formation in whole cell cultures, using an EPR spectroscopic approach [33]. These properties are consistent with the capacity to undergo flavin redox state interconversion and to form ROS, as do other cryptochromes [17, 28, 34]. We therefore tested for ROS induction following PEMF stimulation of human embryonic kidney 293 (HEK293) cells, grown in darkness for 48 hours in the presence or absence of PEMF (Fig 2). After incubation, the extracellular media were scored for secreted hydrogen peroxide (H2O2), a byproduct of ROS formation, using the Amplex Ultra Red fluorescence detection substrate as described [35]. The concentration of ROS was significantly elevated in media from PEMF-treated cell cultures compared to controls (Fig 2). To evaluate toxicity of prolonged exposure to PEMF, we counted cells at the end of the exposure period (see Materials and methods). A marked decrease in cellular growth was observed in PEMF-exposed HEK293 cultures compared to untreated controls, consistent with the toxicity of accumulated ROS (Fig 2). To assess a possible effect of cryptochrome on this response, short hairpin RNA (shRNA) lines with double HsCry1 and HsCry2 mRNA knockdown were constructed (see Materials and methods, S5 Fig) and similarly analyzed. These shRNA lines deficient in both HsCry1 and HsCry2 showed no significant effect of PEMF either on cell growth or on ROS secretion (Fig 2A and 2B), in marked contrast to wild type. Therefore, these magnetic field effects appear to involve cryptochrome function and formation of ROS in human cells.
We further analyzed PEMF effects on mammalian cells using fluorescence imaging to detect multiple ROS forms. As observed for the Sf21 insect cell experiments above (Fig 1C), HEK293 cells were incubated in the presence of DCFH-DA at 37 °C for 15 minutes in the presence or absence of PEMF (Fig 3). Fluorescent ROS labeling increased significantly in PEMF-stimulated cells compared to unstimulated control cell cultures. ROS staining can be seen in both nuclear and cytosolic compartments, with areas of concentration in nuclear speckles (nucleoli) and vesicular structures (E.R and Golgi), consistent with subcellular localization of mammalian cryptochromes [36].
To further confirm the involvement of cryptochrome in this response, we examined cells from murine cryptochrome mCry1/mCry2 double knockout mice [37]. Specifically, we analyzed immortalized mouse embryonic fibroblast (MEF) cell cultures from wild-type and mCry1/mCry2 double knockout lines, using the ROS fluorescence imaging techniques used for the HEK cell cultures. A marked induction of intracellular ROS after 15 minutes of PEMF stimulation was observed in wild-type MEF cells (Fig 3, middle panels), equivalent to those observed for the HEK293 human cell cultures (Fig 3, upper panels). However, mCry1/mCry2 null mutant cell cultures treated in an identical manner (Fig 3, lower panels) showed no visible increase in ROS labelling. Taken together, these data show that cryptochrome is necessary for PEMF-induced ROS formation in mammalian cells.
To further define the effects of PEMFs and relate them to therapeutic consequences observed in humans [5–10], we performed microarray analysis of gene expression in HEK293 cells cultured with or without 3 hours of PEMF stimulation (S1 and S2 Tables). Several hundred genes were up-regulated or down-regulated by PEMF stimulation. Of these transcripts, a significant proportion encoded proteins localized to nuclear, Golgi, and endoplasmic reticulum (ER) compartments (S5 Table). Significantly, bioinformatic gene ontology (GO) analysis of biochemical function showed enrichment in oxidoreductase function consistent with increased production of ROS (see Materials and methods, S6 Table). Furthermore, promoter analysis of PEMF-induced genes indicated that a majority (75%) contained promoter elements known to interact with ROS-responsive transcription factors. These data are consistent with stimulation of ROS-responsive genes following PEMF exposure (S7 Table). Furthermore, they parallel the imaging data of these HEK293 cells, which showed enhanced localization of ROS to the nuclear, Golgi, and ER compartments, whereas transcription of proteins localized to these compartments are particularly enriched among PEMF-regulated genes (S5 Table). Thus, the induction of ROS by PEMF is indicated by two entirely independent and complementary approaches: imaging and transcriptome analysis.
A widely held paradigm for cryptochrome magnetosensing involves a radical pair-based mechanism, whereby the singlet/triple interconversion rates of unpaired radicals formed in the course of cryptochrome redox chemistry can be altered by static magnetic fields [13]. This provides a mechanism whereby cryptochrome reaction rates and product yields, including of H202 and other ROS formed during the cryptochrome redox cycle [17,28], can be altered by magnetic fields. Recent experiments probing the light dependence of magnetic orientation in birds have pinpointed cryptochrome flavin reoxidation as the likely step for radical pair formation leading to magnetic sensitivity [32,38]. Such flavin reoxidation, which occurs independently of light, involves reaction of cryptochrome-bound reduced flavin with molecular oxygen and fulfills the criteria of radical pair formation during magnetoreception [39]. Nonetheless, in the case of both avian and drosophila cryptochromes, the initial formation of reduced flavin requires light (by the process of flavin photoreduction) [34,38]. This explains the requirement for light in establishing magnetic sensitivity in flies and birds because reduced flavin is required for the magnetically sensitive redox reaction (reoxidation) to ensue [38].
By contrast, mammalian-type cryptochromes appear to function independently of light in their role in the circadian clock and as negative regulators of transcription [14,24,25]. However, mammalian-type cryptochromes reportedly occur in a partially reduced redox state in vivo even in dark-adapted cell cultures [40]. As a consequence, they would retain the characteristics to respond to magnetic fields by a mechanism whereby flavin reoxidation is stimulated, with an ensuing burst of ROS synthesis consistent with our observations. We also note that, although there has been overwhelming evidence for a radical pair-based magnetic sensing mechanism involving vertebrate cryptochromes [13], the possibility of unrelated cry-dependent magnetosensing mechanisms cannot be excluded. For example, a recently suggested interaction of cryptochrome with the putatively magnetosensitive MagR protein could also be consistent with our data [41], whereas reported magnetic sensitivity mediated through a C-terminal overexpression construct of Drosophila cryptochrome [42] also suggests alternative magnetosensors impacting on a cry-based magnetosensing mechanism.
A mechanism based on regulation of ROS can explain both the beneficial and deleterious effects of magnetic stimulation that have so long puzzled the field. For example, proposed deleterious effects [1–4,26] of low-frequency EMFs could result from elevated ROS, which inform about exposure to magnetic fields either in human treatment or in public health. This result is furthermore consistent with past suggestions that the lifetimes and reactivity of 02 and ROS (both paramagnetic species) may be affected by magnetic fields in living systems [5]. However, prior speculation has focused exclusively on ROS generated via metabolic pathways of the mitochondrial electron transfer chain or via cell membrane–associated NADPH oxidases. Here, we implicate a flavoprotein receptor and signaling molecule, which is suitably positioned within the nucleus [36], to induce localized changes in ROS concentration and/or reactivity in close proximity to redox-sensitive and/or ROS-regulated nuclear signaling molecules. We note that the prolonged PEMF signal (S1 and S2 Figs) used in the current study has no therapeutic application and is apparently harmful to cell cultures over long periods. However, a range of alternate frequencies and amplitudes of PEMF signal have been empirically derived that provide proven physiological benefits involving cellular repair and healing [5–12]. These beneficial PEMF effects are compatible with modulation of intracellular ROS within a therapeutic range resulting in stimulation of ROS responsive cellular defense and repair mechanisms [20,23].
In conclusion, from a public health perspective, our work shows that exposure to even such low levels of magnetic fields as those generated by PEMF devices have definite physiological consequences. It should be noted that peak output at less than 1.8 mT is within an order of magnitude of emissions by household electronic devices and of current safety guidelines for exposure to EMF in humans [1–4]. In keeping with our results, it has also been shown that the low-level man-made EMFs emitted from electrical equipment in public buildings can disrupt orientation in birds, a process that has also been linked to both cryptochromes and magnetoreception [43]. Although current epidemiological studies have not provided conclusive evidence of EMF-induced pathology in humans [1–4], our results raise the possibility of synergistic harmful effects with other environmental or cellular factors that stimulate intracellular ROS [5,20]. More refined epidemiological studies taking these factors into consideration are therefore essential for a true assessment of long-term impact of EMFs on public health.
The pulsed magnetic field was generated by a commercially available device (EC10701; GEM Pty Ltd., Perth, Western Australia) used for the treatment of musculoskeletal disorders. During Drosophila behavioral tests, PEMF was applied continuously at a frequency of 10 Hz, with the coil 1 cm below the petri plate. Peak magnetic intensity at the experimental distance was 2 mT.
The parameters of the PEMF signal were verified by measurement of the current as presented in S1 and S2 Figs. The coil was 9 × 5.5 cm and 200 turns and produced a maximum magnetic field intensity 1 cm above the coil of 1.8 mT.
Fly strains used were as follows: wild-type Canton S, wild-type Oregon-R, cry02 and cryb (described in [27]). Transgenic strains tim-gal4;cry02 and UAS-Hscry1;cry02 were crossed to generate the heterozygote HsCry1-expressing strain as described in Vieira and colleagues [27]. Light sources and growth conditions on complete media were as previously described [27]. For the larvae migration studies (pupal distribution), adult drosophila were transferred to a square plate (12.5 cm × 12.5 cm) containing complete rich medium and were allowed to lay eggs. After a period of 24 hours, the adults were discarded and the plates placed under the indicated light conditions (blue or red light) for 3 days at 23 °C. Subsequently, a single corner of each plate was exposed to PEMF from underneath for an additional 5 days. Temperature differential between corners was less than 0.5 °C. The control condition was established by shielding the plate from the PEMF device with 1.0 mm mu-metal sheeting, which was measured to reduce magnetic field signal by 85%. Once pupal development was complete, the distribution of the now nonmotile pupae could be readily scored by counting the number of pupae in a defined area of the plate. As the pupae showed a preference for the corners of the plates, we evaluated 3.12 × 3.12 cm2 areas over each of the 4 (PEMF treated versus untreated) corners and compared the corners that had received no PEMF treatment with those exposed to PEMF. Statistical methods were as follows: for each experimental condition, a total of between 8 and 10 plates were analyzed (n = 8–10). The number of drosophila counted in each corner was expressed as the percentage of the total number of pupae ± SEM per plate. All statistical tests were carried out using SPSS (version 20, IBM Corporation, NY). Data were analyzed for normality (Shapiro-Wilk test, p < 0.05), so the differences between the PEMF and mean of the 3 non-PEMF corners per plate were compared using Kruskal-Wallis analysis of variance and Mann-Whitney-U post hoc where appropriate. The α value was set to p < 0.05.
Further details of the behavioral experimental setup are presented in S3 Fig as follows: the position of the plate containing drosophila growth media and growing larva under which the PEMF coil was placed (upper left) is designated as the position “1,” the “test” corner. The PEMF coil was at a distance of 1 cm from the bottom of the test plate containing the drosophila larvae. The temperature at all 4 corners was measured, and the PEMF device did not cause any change in temperature from the other corner positions of the plate. Equivalent-size squares at each of the other corners (designated positions 2, 3, and 4) serve as the internal “control” positions to the PEMF stimulated “test” position. The number of pupae that were deposited beneath the PEMF coil was compared to the number of pupae deposited within an equivalent volume at each of the other 3 corner positions.
Additional controls to the behavioral experiments are shown in S3B and S3C Fig. The PEMF device was shielded from the test plate using mu-Metal sheeting of 1.0 mm thickness. Under these conditions (S3B Fig), no significant avoidance of the PEMF corner position was detected. In addition, response to PEMF was scored in red light, which does not activate insect (Drosophila) cryptochrome (S3C Fig). In this case also, no avoidance of the PEMF was observed.
Preparation of DmCry-expressing and control (Spa1)-expressing insect cell cultures was performed as described in Arthaut and colleagues [28]. For imaging experiments, Sf21 cells were seeded at a density of 400,000 cells in a 3.5 cm2 observation chamber. After incubation at RT for 2 hours for cell attachment, Sf21 cells were incubated in 40 mM potassium phosphate buffer (pH 6.4) containing 12.5 μM DCFH-DA (Molecular Probes, Life Technologies, Grand Island, NY) for 15 minutes in the dark, rinsed 2 times in phosphate buffer, and were then exposed to blue light with or without PEMF for 15 minutes and observed with an inverted Leica TCS SP5 confocal microscope using a 40× objectif. Green fluorescence from DCFH-DA and differential interference contrast (DIC) were excited at 488 and 561 nm wavelengths, respectively. Emission fluorescence intensities and DIC were detected using a photomultiplier between 498 and 561 nm, and a transmission photomultiplier, respectively. Two channels were recorded sequentially. Z series projections were taken using ImageJ software (W. S. Rasband, ImageJ). As a control for these experiments, control cell cultures that did not express DmCry were used (S4 Fig). These cells did not show induction of ROS in response to PEMF.
By using the InvivoGen siRNA Wizard tool, shRNA sequences targeting human CRY1 (NM_004075.4) and CRY2 (NM_021117.3) were selected, and a pair of complementary (sense and antisense) oligonucleotides were designed for each sequence as follows:
shCRY1 sense (5’GTACCTCGGAACGAGACGCAGCTATTAATCAAGAGTTAATAGCTGCGTCTCGTTCCTTTTTGGAAA 3’); shCRY1 antisense (5’AGCTTTTCCAAAAAGGAACGAGACGCAGCTATTAACTCTTGATTAATAGCTGCGTCTCGTTCCGAG3’); shCRY2 sense (5’ACCTCGTACGTATGTCACCTTCACTATCAAGAGTAGTGAAGGTGACATACGTACTT3’); shCRY2 antisense (5’CAAAAAGTACGTATGTCACCTTCACTACTCTTGATAGTGAAGGTGACATACGTACG3’) (complementary sequences of the hairpin are underlined). Complementary oligonucleotide pairs were PAGE-purified, and 25 μM of each were annealed by incubation in 0.1 M NaCl at 80 °C (2 minutes) followed by slow (1 °C per minute) cooling to 35 °C. The resulting double-stranded DNA fragments were cloned into the same psiRNA-DUO-GFPzeo plasmid according to the manufacturer’s instructions using a two-step procedure (InvivoGen). Briefly, the psiRNA-DUO plasmid was digested with Acc65I and HindIII restriction enzymes and ligated with the first insert (shCRY1 annealed oligonucleotide pairs). The resulting construct was transformed into Escherichia coli GT115 cells (InvivoGen), and positive colonies were selected using Fast-Media Zeo X-gal (5-bromo-4-chloro-3-indolyl-β-d-galactopyranoside) (InvivoGen). The plasmid containing shCRY1 was subsequently digested with BbsI restriction enzyme and ligated with the second insert (shCRY2 annealed oligonucleotide pairs). The resulting construct was transformed into E. coli GT115 cells (InvivoGen), and positive colonies were selected using Fast-Media Zeo 5-bromo-4-chloro-3-indolyl-β-D-glucuronic acid, cyclohexylammonium salt (X-gluc) (InvivoGen). The obtained psiRNA-Cry1Cry2-GFPzeo expression plasmid was used for transfection of HEK cells, and psiRNA-LucLac-GFPzeo encoding shRNA for the silencing of a prokaryote gene (InvivoGen) was used to transfect HEK cells as nonsilencing control. Stable transfectants were selected in complete medium containing 300 μg/ml Zeocin (InvivoGen). Expression of HsCry1 and HsCry2 was verified by qPCR (S5 Fig).
HEK293 cells were grown and maintained in Eagle’s Minimum Essential Medium (EMEM), supplemented by 10% fetal bovine serum. The cells were cultured in 75 cm2 flasks to expand cell number. After reaching confluence, the cells were seeded in 12-well plates. The volume of medium totaled 1 mL. Medium was then changed every 2 days. The cultures were incubated in a 5% CO2 atmosphere at 37 °C in the same incubator (Fisher Scientific; Model 5). The temperature and CO2 levels were monitored daily and were maintained at 37 °C and 5%, respectively. All experiments were conducted in the same incubator. To control for location in the incubator and any associated electromagnetic noise or other spatial variation, the orientation of experimental and control cultures were periodically reversed, and 0.3 mm mu-Metal shielding was applied between PEMF-treated and control cell culture dishes within the incubator. Cells were seeded and allowed to rest for 4 hours under the same background conditions, at which time the magnetic exposures began. This time is denoted as t0. Fluorometric detection of H2O2 production was performed using the horseradish peroxidase-linked Amplex Ultra Red (Invitrogen) fluorometric assay. HEK cells were seeded at a concentration of 25.0 × 104 cells per well in a 12-well plate and were exposed to PEMFs for the duration of the experiment. Medium was aspirated off, and cells were then washed with PBS and incubated for 2 hours with DMEM containing 2% FBS, 0.2 units/ml horseradish peroxidase, and 10 μM Amplex UltraRed (AUR). Resorufin fluorescence was measured by a Varian Cary Eclipse spectrofluorimeter. Cellular number and resorufin fluorescence were measured at the same termination points. H2O2 production was normalized to cell count. H2O2 calibration curves with HRP-AUR in PEMFs did not show any difference compared to control, thus demonstrating that PEMFs do not interact with the detection system.
Human HEK and MEF cells were grown in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal calf serum (FCS) and 2 mM l-glutamine in a 95% air–5% CO2 incubator at 37 °C. For intracellular localization of ROS, living HEK or MEF cells were seeded on cell observation chambers and incubated in 40 mM potassium phosphate buffer (pH 7) containing 12.5 μM DCFH-DA (Molecular Probes) for 15 minutes in the incubator at 37 °C, during which they were either exposed or not to PEMF. Cells were rinsed for 15 minutes in the potassium phosphate buffer solution and were observed with an inverted Leica TCS SP5 confocal microscope equipped with a 95% air–5% CO2−37 °C thermostatic observation chamber and using a 63× objective. Green fluorescence from DCFH-DA and DIC were detected as previously described (section 5). For quantitation of intensity, using LEICA TCS software, the region of interest (ROI) corresponding to cells were dawn and mean fluorescent intensity (MFI) measured in each ROI.
Human HEK cells were grown in DMEM supplemented with 10% FCS and 2 mM l-glutamine in a 95% air–5% CO2 incubator at 37 °C. Cells were seeded into multiple 3.5 cm2 round cell culture dishes and were grown under identical conditions for 48 hours. Prior to confluence, cell culture dishes were treated with 3 hours of continuous PEMF in the absence of light (test condition). Control cell cultures were harvested prior to application of PEMF. Triplicate PEMF treated and control cell cultures were then harvested into liquid nitrogen, and total RNA was extracted by RNEasy RNA extraction kit (Promega, Inc.) and related protocols. Microarray gene expression and analysis using Agilent affymatrix technology was performed by IMGM Laboratories GmbH, Martinsried, Germany.
As a control to eliminate the possibility of artifact due to temperature and/or vibrational factors generated by the pulsed field device, we designed and built a modified PEMF coil in which the wire was folded in half before precision winding to achieve an antiparallel current travelling in opposite directions within the same coil during activation. This antiparallel coil had the same wire length and dimensions as the test coil used in our experiments, and it was driven by the same pulsed field generator device and with the identical current—which, because of the antiparallel winding of the coil, ran simultaneously in opposing directions within the coil. The signal measured in S7 Fig shows that, whereas there are residual spikes in the antiparallel field coil (panel B) that could not be cancelled, these spikes are less than 0.01 seconds in duration in comparison to the magnetic signal in the original PEMF coil, which lasts for 0.5 seconds (panel A). The residual spikes are not visible on panel D because they are too short lived for the detection limit for the instrument at this time scale. We conclude that these residual spikes are of negligible duration compared to the signal given out by the intact coil and are demonstrably too brief to trigger a biological response. As a result, we achieved a significant reduction (cancelling) of the pulsed magnetic field (S7 Fig) while keeping all other parameters (electric current driven by the pulsed field device) the same.
We tested the effect of the cancelled PEMF field on the behavioral avoidance response of wild type (Canton S) fly pupa according to the methods used for Fig 1. We measured the number of pupa in the corner of square petri plates exposed to the antiparallel (cancelled PEMF field) coil compared to the test coil (generating the PEMF signal) placed beneath the plate corner (S8 Fig). The flies did not show an avoidance response to the cancelled field (antiparallel coil), indicating that the magnetic field of the PEMF was indeed triggering the response.
We next evaluated the effect of the cancelled magnetic field coil on the stimulation of ROS in mammalian cell culture experiments. Using both HEK and MEF cell cultures, we observed that significant stimulation of ROS formation occurred only in response to PEMF but not to the antiparallel, PEMF-cancelled magnetic field (S9 Fig).
In conclusion, these data indicate that there was no discernable artifact introduced into our experiments through the operation of the pulsed field device and that positive results required the presence of the magnetic field.
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10.1371/journal.pgen.1000713 | E Unibus Plurum: Genomic Analysis of an Experimentally Evolved Polymorphism in Escherichia coli | Microbial populations founded by a single clone and propagated under resource limitation can become polymorphic. We sought to elucidate genetic mechanisms whereby a polymorphism evolved in Escherichia coli under glucose limitation and persisted because of cross-feeding among multiple adaptive clones. Apart from a 29 kb deletion in the dominant clone, no large-scale genomic changes distinguished evolved clones from their common ancestor. Using transcriptional profiling on co-evolved clones cultured separately under glucose-limitation we identified 180 genes significantly altered in expression relative to the common ancestor grown under similar conditions. Ninety of these were similarly expressed in all clones, and many of the genes affected (e.g., mglBAC, mglD, and lamB) are in operons coordinately regulated by CRP and/or rpoS. While the remaining significant expression differences were clone-specific, 93% were exhibited by the majority clone, many of which are controlled by global regulators, CRP and CpxR. When transcriptional profiling was performed on adaptive clones cultured together, many expression differences that distinguished the majority clone cultured in isolation were absent, suggesting that CpxR may be activated by overflow metabolites removed by cross-feeding strains in co-culture. Relative to their common ancestor, shared expression differences among adaptive clones were partly attributable to early-arising shared mutations in the trans-acting global regulator, rpoS, and the cis-acting regulator, mglO. Gene expression differences that distinguished clones may in part be explained by mutations in trans-acting regulators malT and glpK, and in cis-acting sequences of acs. In the founder, a cis-regulatory mutation in acs (acetyl CoA synthetase) and a structural mutation in glpR (glycerol-3-phosphate repressor) likely favored evolution of specialists that thrive on overflow metabolites. Later-arising mutations that led to specialization emphasize the importance of compensatory rather than gain-of-function mutations in this system. Taken together, these findings underscore the importance of regulatory change, founder genotype, and the biotic environment in the adaptive evolution of microbes.
| Experimental evolution of asexual species has shown that multiple genotypes can arise from a single ancestor and stably coexist (e unibus plurum). Although facilitated by environmental heterogeneity, this phenomenon also occurs in simple, homogeneous environments provisioned with a single limiting nutrient. We sought to discover genetic mechanisms that enabled an E. coli population founded by a single clone to become an interacting community composed of multiple clones. The founder of this population contained mutations that impair regulation of acetate and glycerol metabolism and likely favored the evolution of cross-feeding. Adaptive clones share cis- and trans-regulatory mutations shown elsewhere to enhance fitness under glucose limitation. Certain mutations that distinguish adaptive clones and underlie evolution of specialists were compensatory rather than gain-of-function, and all that we detected resulted in gene expression changes rather than protein structure changes. Evolved clones exhibited both common and clone-specific gene expression changes relative to their common ancestor; the pattern of gene expression in the dominant clone cultured alone differed from the pattern observed when it was cultured with variants feeding on its overflow metabolites. These findings illuminate the roles played by founder genotype, differential gene regulation, and the biotic environment in the adaptive evolution of bacteria.
| Evolutionary biologists have long sought to understand mechanistically how adaptive genetic variation arises and persists. Experimental studies using model organisms such as Drosophila [1]–[3] and C. elegans [4]–[6] transformed the search for such mechanisms from a retrospective to a prospective endeavor. But, long generation times, sexual recombination and practical limits on lab population size make higher eukaryotes imperfectly suited to study the tempo, trajectory and mechanisms by which evolution occurs in asexual species and in the somatic cells of sexual organisms. There, new genetic variation is limited by the rate of mutation supply and, in bacteria, also by the incidence of horizontal gene transfer. Fortunately, evolution in asexual species and cells can be studied using microbial models [7],[8]. Early microbial studies helped lead to two generalizations concerning the emergence and persistence of genetic variation in large, asexual populations. First, over ecological time and in the absence of spatial structure and differential predation, competition for the same limiting resource selects for one fittest variant, an insight that came to be known as the “competitive exclusion principle” [9],[10]. Second, over evolutionary time variation arising by mutation is subject to “periodic selection” leading to a succession of genotypes each more fit than its immediate predecessor [11]–[13]. These generalizations led to the expectation that large, clonal populations evolving under resource limitation should exhibit limited genetic variation.
Experimental evidence now suggests otherwise. Multiple genotypes which arise from a single ancestral clone can coexist over evolutionary time; in other words, out of one comes many (e unum pluribus). This phenomenon has been documented in spatially and temporally unstructured chemostats [14],[15], in temporally-structured batch cultures [16]–[20], and in spatially-structured microcosms [21]. In each setting, the emergence and persistence of polymorphism in the absence of sexual recombination seems to require that cohabitants exploit alternative ecological opportunities (i.e., unoccupied niche space), and/or accept trade-offs between being a specialist and a generalist (as reviewed in [22], also see [23]. In serial dilution batch culture multiple growth parameters can come under selection [reviewed in 24]. Different clones may arise that have reduced lag time, increased maximum specific growth rate, or enhanced capacity to survive at high cell densities in the presence of low nutrients. Periodic changes in population density and nutrient levels may bring balancing selection to bear on these different phenotypes, especially if antagonistic pleiotropy precludes evolution of one fittest genotype having all of these advantageous traits. In spatially structured environments selection may favor mutants better adapted to particular regions or better able to colonize microhabitats formed at the boundaries between such regions. In continuous nutrient-limited environments (e.g., chemostats), theory [25]–[27] predicts that selection will favor clones better able to scavenge the limiting resource or more efficiently convert that resource to progeny. Ultimately, the outcome of the “evolutionary play” in any of these “ecological theaters” will depend on founder genotype, the complexity of genetic pathways which lead to different adaptive strategies, as well as the propensity of key steps along those pathways to undergo mutation and to act pleiotropically.
Only recently have we begun to discover genetic mechanisms that explain how balanced polymorphisms arise and persist in large, asexual populations. In serial batch culture, differences in the activity of the global regulator RpoS help explain co-existence of two E. coli isolates with different propensities to survive extended stationary phase [28]; the precise genetic basis for these activity differences, however, remains obscure. In a spatially structured microcosm founded by a single clone of Pseudomonas fluorescens, a methylesterase structural mutant arose and persisted because the resulting change in exopolysaccharide production enabled the mutant to colonize the air-broth interface [29]. Finally, in glucose-limited chemostats polymorphic E. coli populations repeatedly evolved, in part owing to local regulatory mutations that affect expression of a single operon (acs-actP-yjcH) [30]. When adaptive clones from one such population were grown in monoculture, strain-specific differences in ca. 20% of identifiable proteins expressed suggested the presence of other mutations with highly pleiotropic effects [31]. Thus, regardless of experimental system, uncertainty remains as to whether either regulatory or structural mutations consistently deliver greater fitness increments, which category of mutation better explains the maintenance of diversity, and whether one type is more likely to precede the other in an evolutionary sequence leading to balanced polymorphism.
Theoretical considerations have led some to argue that the major phenotypic changes which underlie adaptive radiation are more likely due to regulatory than to structural mutations [32],[33 and refs. therein]. This argument is based on the perception that changes in coding sequences are more likely to have large pleiotropic effects than changes in the expression of those sequences, in particular changes that arise from the mutation of cis-regulatory elements affecting single genes. In effect, this type of regulatory mutation enables selection more easily to “tinker” (sensu Jacob, 1977), as it provides a mechanism to alter functionality in one process while still preserving the role of pleiotropic genes in others [34]. Also, and this fact too often goes unappreciated, a discrete cis-regulatory mutation preserves the capacity to restore the ancestral pattern of expression via compensatory or back-mutations. The proposition that regulatory mutations play a greater role in adaptive diversification has been criticized on empirical and theoretical grounds by Hoekstra and Coyne who point out the vastly greater number of examples where adaptation is attributable to structural rather regulatory mutations, as well as the facts that cis-acting elements offer much smaller targets for mutation than ORFs, and that in many species pleiotropic effects arising from structural mutations may be buffered by gene duplication [35].
While the “cis-regulatory hypothesis” is based largely on a consideration of multicellular eukaryotes, tends to be focused on events that transpire during plant and animal development, and requires what we view to be an artificial distinction between physiological and morphological adaptation, it nevertheless provides a useful framework in which to make predictions about how adaptive diversification might occur in ‘simpler’ species. In bacteria, both types of mutations can be evolutionarily significant. Structural mutations in the HopZ family of Type III secreted effector (T3SE) proteins play a major role in pathoadaptation by Pseudomonas syringae to its plant hosts [36]; likewise, T3SE mutations underlie host immune suppression by Yersinia and Xanthomonas [37]. On the other hand, pathoadaptation leading to an intracellular lifestyle in Samonella enterica results from a cis-regulatory mutation, specifically, acquisition of a binding site for pathogenicity island-2 regulator SsrB [38].
We sought to address the issue of structural versus regulatory change in Escherichia coli by investigating an experimental population first described by Helling et al. [14]. This population was founded by a single clone and evolved in an aerobic, glucose-limited chemostat at constant dilution rate (D = 0.2 h−1) and constant temperature (30°C). Helling et al. inferred from fluctuations in a neutral marker that adaptive mutations occurred about once every 100 generations. At the time they concluded their experiment (765 generations) they could distinguish four strains on the basis of colony size and ampicillin sensitivity (see Table 1). Three of these phenotypes were shown to stably co-exist in reconstruction experiments, wherein the majority clone strain, CV103, was followed in rank order of abundance by CV116 and CV101 [15]. Each strain exhibited a characteristic pattern of protein expression, as determined by 2D protein gel electrophoresis, when grown by itself in glucose-limited chemostats; as a group, evolved clones significantly differed from their common ancestor at ∼160 expressed proteins of ∼700 that could be resolved [31].
Relative to their common ancestor, all evolved clones showed enhanced uptake of the glucose analogue 14C-α-methylglucoside (αMG), and CV103 accumulated significantly more α–MG than any other clone [14], even though its yield was less than the other adaptive clones. Equilibrium glucose concentration (the amount detectable in a culture of actively dividing cells at steady state) was an order of magnitude less in CV103 than in CV101 chemostats and less than half that observed in CV116 (see Table 1). Unlike CV101 and CV116, however, CV103 left metabolizable carbon in the chemostat, effectively creating niches conducive to the evolution of cross-feeding. The other strains filled those niches, efficiently scavenging overflow metabolites below detection limit [15]. Acetate-scavenging strains were subsequently observed in 6 out of 12 independent evolutionary populations founded by cells of similar genetic background grown under similar conditions [39].
The Helling and Adams population is a classic example of how adaptive evolution occurs in the context of niche diversification. Because the approximate number of fixed adaptive mutations is few and the number of significant changes in protein expression is many, this population is well-suited for identifying mutations that exert large effects, and determining whether those mutations occur at loci that encode enzymes in metabolism, at trans-acting loci that encode proteins which regulate expression of multiple enzymes, or at cis-acting sequences that control how activators and repressors act on single genes. We tested the hypothesis that enhanced uptake and assimilation of the primary resource, glucose, results from one (or few) early-arising mutations in trans-acting global regulators, and that specialization on secondary resources arises from later-arising mutations in key structural loci or in their cis-acting sequences. Lastly, we anticipated that in comparing the consortium's expression profile to that of individual members grown in monoculture, we would discover emergent properties of the system not apparent using a purely reductionist approach.
Transcriptional profiling of evolved strains in monoculture reveals ∼180 genes significantly altered in expression. Many shared increases and decreases are attributable to shared mutations in rpoS and the maltose operon operator mglO. Expression differences that distinguish isolates occur mainly in the majority clone, CV103. Many of these genes are regulated (or are predicted to be regulated) by cAMP receptor protein (CRP) and/or the global stress regulator CpxR. The “community” expression profile is strikingly similar to the monoculture profiles of the three sub-dominant clones, suggesting that biochemical interactions among clones alter CRP-CpxR regulation. We identified in the founder regulatory mutations in genes required for acetate and glycerol catabolism that likely predispose this system to the evolution of cross-feeding. Among adaptive clones, we found shared mutations in rpoS and mglO, and mutations that distinguish clones from one another at pacs, malT and glpK. Taken together, our results suggest that both cis- and trans-regulatory changes underlie adaptive diversification in a simple, unstructured, resource-limited environment, and that founder genotype and chemical interactions among clones not only facilitate co-evolution, but also strongly impact their respective patterns of gene expression.
Table 1 summarizes previously published phenotypic data on the Helling et al. strains which are germane to interpretation of the expression and sequencing Results presented below [14],[15].
To assess the level of large-scale genetic variation between the ancestor and the evolved clones, we performed rep-PCR fingerprinting and array-CGH. BoxA1R rep-PCR fingerprints were indistinguishable (see Figure S1). However, a-CGH revealed a deletion of ∼30 Kb in the majority clone, CV103 (Figure 1). A total of 27 genes were lost by the deletion, 12 of which have no known function. Of the remaining 15, 3 have a predicted function based on homology to previously characterized genes and 12 are involved in a variety of cellular processes including transcription, arginine biosynthesis, anaerobic respiration, nitrogen metabolism and glycoprotein biosynthesis.
We used DNA microarrays to assess global transcriptional patterns of individual strains in glucose-limited chemostats. Evolved clones were grown to steady state (∼14 generations) under conditions identical to those under which they evolved (D = 0.2 h−1, 30°C). In each case, steady state transcript levels were estimated in relation to the ancestral strain JA122 grown in parallel under identical conditions. Relative to the common ancestor, expression of 6.8% (∼279 genes) of the measurable transcriptome was at least 2-fold increased or decreased in the evolved isolates (Figure S2). This number favorably compares with an early proteomic analysis of these strains grown in chemostat monoculture wherein Kurlandzka et al. [31] found that ∼160 expressed proteins of the ∼700 they were able to resolve differed between evolved clones and their common ancestor.
Using 1-class SAM, we identified 90 genes whose expression was significantly up- or down-regulated in all clones when grown in chemostat monoculture (Figure 2, Table S1). The 21 up-regulated genes, representing 9 unique transcription units, are primarily involved in carbon catabolism. The remaining 69 down-regulated genes from 58 transcription units belonged to a variety of MultiFun classes including carbon metabolism, building block/macromolecule biosynthesis, transport and adaptation to osmotic stress.
To ascertain how the transcriptional profiles of evolved clones differ from one another we performed a 4-class SAM analysis (Figure 3, Table 2, Table S2). Aside from the anticipated overexpression of acs-yjcHG in CV101, the transcription patterns of CV101, CV115 and CV116 appear remarkably similar. By contrast, CV103 differs from the other three at a number of loci, and accounts for the great majority (∼93%) of the significant differences that distinguish adaptive clones. When we adjusted δ (a tuning parameter that can be manually adjusted) to reflect a natural break in the data, we found that a total of 91 genes from 64 transcription units significantly differ in steady state expression levels in at least one isolate at a false discovery rate of 0%. These genes tend to fall into three MultiFun classes: metabolism, cell structure and transport. Under the category of metabolism, forty-four genes from twenty-seven transcription units vary in their relative expression patterns. The metabolism-building block biosynthesis subclass contained the most independent transcription units (8/27), including acs-yjcHG (acetyl CoA synthetase). Conspicuously absent is mRNA transcribed from the NRZ operon (narZYWV), which is deleted in CV103. This operon is normally induced during stationary phase and appears to be actively transcribed in the ancestor but slightly down-regulated in CV101, CV115 and CV116 (Figure 3, [49]. However, the fitness effect of this deletion is currently unknown (Figure 1).
Reconstruction experiments demonstrated that three of the evolved strains could stably coexist in continuous culture as a consortium, and that their coexistence was made stable by cross-feeding [15]. When limited on 0.0125% glucose, the consortium reproducibly apportioned as ∼70% CV103, 20% CV116 and 10% CV101 at steady state. To better understand mechanisms underlying stable coexistence we interrogated the consortium transcriptome.
In general, we observe that genes significantly up or down in the 1-class SAM monoculture analysis behave similarly when clones are co-cultured (Figure 4). Furthermore, consortium profiling extends the results of our monoculture analyses to include other members of operons previously identified by 1-class SAM. For example, malK and malM (which are co-transcribed with lamB), as well as malF, G and S from two separate, but similarly regulated transcription units each show increased expression when cells are cultured as a consortium (Figure 4D).
Several transcripts significantly up-regulated in the consortium, including genes for a second glycerol-3-phosphate transporter/phosphodiesterase, glpTQ, part of the G3P-dehydrogenase, glpA, and fumarase genes, fumA and fumC, were not scored as significantly up-regulated in the monoculture 1-class SAM using the highly stringent method of hand-tuning δ (Figure 4D, Figure S3). However, the majority of these were considered significant when a strict 0% FDR cutoff was applied. Those that do not meet this criterion are marked with a “†” in Figure 4.
When we compared the consortium's transcriptional profile to the 4-class SAM (Figure 5) we were surprised to find that many of the transcripts that distinguished CV103 from the other evolved clones in monoculture had expression patterns similar to CV101, CV115 and CV116, even though reconstruction experiments show that CV103 always emerges as the numerically dominant consortium member [14],[15]. To ascertain whether this phenomenon was a general feature of the dataset, we looked at transcript levels across all samples for genes that were either (A) significant in the consortium analysis but not in the monoculture experiments, or (B) significant in the monoculture experiments but not in the consortium profile. For this comparison, we used the highly stringent method of hand tuning δ to determine significance cutoff. In both cases, the vast majority of genes that were differentially regulated in CV103 monoculture (and thus distinguished this isolate from the other clones) again had transcript levels that closely matched CV101, CV115 and CV116. While this analysis is limited by the fact that the individual contributions of isolates cannot be dissected from the consortium RNA pool, the sheer number of transcripts that follow this trend strongly suggests that CV103 has a different gene expression profile in the shared metabolic environment of the consortium than when it is grown in isolation.
Three genes (lamB, acs and flgB) with different relative expression levels were selected for quantitative reverse transcriptase PCR on RNA isolated from chemostat monocultures. PCR results for all three closely approximated array results with correlation coefficients ranging from 0.78–0.99 (see Figure S4).
To place our results in the context of previously published work and to uncover mutations which may contribute to the transcriptional profiles of the adaptive clones, we sequenced 13 candidate genes and their corresponding regulatory elements (Table 3, for primers see Table S3). Selection of candidate genes was guided by the observation that members of the evolved polymorphism had differentiated from one other and their common ancestor with respect to glucose, acetate and glycerol metabolism [15].
Genetic polymorphism pervades most populations, and various balancing mechanisms have been invoked to explain how diverse genotypes can be stably maintained over successive generations [see 83]. Some, such as differential selection on the sexes or on different life stages do not apply to bacterial populations, while others, such as differential selection in space or time not only do, but can be empirically tested in the laboratory [22],[84],[85]. The conditions of continuous nutrient limitation in a well-mixed chemostat do not restrict the supply of mutations to a microbial population. But competitive exclusion and periodic selection could make the appearance of polymorphism an artifact of discretely sampling a continuous process. Nevertheless, multiple genotypes demonstrably persist in this simplest of laboratory environments [14],[57],[86]. The mechanisms which sustain their co-existence can be said to fall into two general categories: negative density-dependent interactions such as clonal interference [e.g.86],[87],[88], and positive density-dependent interactions such as those described by Helling et al. [14]. Because basic ecological theory predicts that the latter of these is more stable [89], we propose that the system we are studying can serve as a general model for how biodiversity arises in clonal species, how many arise from one (e unibus plurum).
Our approach has been to combine microarray-based comparative genome hybridization, transcriptional profiling, and targeted gene sequencing to understand mechanistically how multiple genotypes arise and coexist in a simple unstructured environment [14],[15],[31],[39]. Previous studies showed that coexistence arose from cross-feeding interactions in which the limiting resource was incompletely metabolized by the dominant clone, effectively creating secondary resources for niche specialists. When co-evolved clones were grown separately they differed from their common ancestor in ca. 20% of identifiable expressed proteins [31]. This observation coupled with the apparent fixation of no more than 8 adaptive mutations [14] suggested that global regulatory mutations were at least partly responsible for adaptive phenotypes. The genetic basis for sub-dominant clone CV101's ability to scavenge acetate was shown to be a regulatory mutation altering expression of the acetyl Co-A synthetase operon [39]. Completely unknown, however, are the genetic mechanisms that could explain why all adaptive clones are better at assimilating glucose than their common ancestor, why the dominant clone, CV103, does not re-assimilate residual metabolites, and how CV103 and CV116 can stably coexist. Also unknown are whether data obtained by analyzing clones separately can explain their behavior as a consortium, and how the founder genotype might have set the evolutionary trajectory taken by this population.
Our results show that all evolved clones share a common regulatory response to long-term glucose limitation. In general, genes involved in the phosphotransferase system, glycolysis, the pentose-phosphate pathway and mixed acid fermentation are down-regulated whereas TCA cycle genes are up-regulated (Figure S3). At first glance it may seem that reduced expression of glycolytic genes would be disadvantageous under glucose limitation. However, consistent with the energy conservation hypothesis [e.g. see 90], it may be economical for chemostat-grown cells to synthesize the minimum level of enzymes needed to process a limiting nutrient whose residual concentration has become vanishingly low. Strikingly similar changes in central metabolic gene expression have been reported for E. coli in batch culture as well as for Baker's yeast following adaptive evolution in long-term, aerobic, glucose-limited chemostat culture [18],[91],[92]. The generality of this phenomenon across replicate experiments within the same species, as well as across Domains, suggests that microbes may have limited options for increasing fitness in environments where glucose is the sole carbon source. However, new evolutionary opportunities may arise in the form of other carbon sources released during glucose metabolism.
Our 1-class microarray analysis and sequencing results indicate that changes in levels of the stationary-phase sigma factor, σS, expected from the shared C→T transition at nucleotide 97, account for many genes being significantly down-regulated in all strains. Most of these changes are consistent with the expression profiles of an rpoS knockout batch-cultured in rich medium: there, relative to wild type, all central metabolic pathways including the TCA cycle were down-regulated during early stationary phase, while the TCA cycle was strongly up-regulated during exponential phase [44]. At steady state under continuous nutrient limitation, bacterial growth approximates late exponential/early stationary phase in batch culture [26]. It is tempting to speculate that the pattern of expression we observe for genes in central metabolism is what might observe if an rpoS knockout were grown under our experimental conditions. And indeed, experiments to test this hypothesis are planned. Alternatively, up-regulation of TCA cycle genes in our strains may result from altered σs activity caused by incomplete suppression of the rpoSAm mutation, translation of truncated σS, or the effect of yet-to-be identified regulatory mutation(s).
In addition to shared global expression patterns for central metabolic genes, our microarray results show that evolved isolates also up-regulate genes involved in moving glucose across the outer and inner membranes. Increased transcription of the inner membrane Mgl galactose ABC-transporter (which also transports glucose) is common response to continuous glucose limitation [42],[93], and our experimental system is no exception. This regulatory adjustment is easily accounted for by a mutation present in all of the evolved isolates in the mgl operator sequence that presumably interferes with GalS-mediated suppression of mgl transcription [42],[93]. Similarly, increased movement of glucose into the periplasm in the evolved isolates is undoubtedly due in part to overexpression of the LamB glycoporin, another hallmark feature of E. coli adaptation to glucose limitation [41],[94].
In Ferenci and colleagues' experiments, adaptive overexpression of LamB (which is part of the malT regulon) results from mutations in the mal repressor Mlc and/or its activator MalT [41],[93]. Sequencing of mlc and its associated regulatory region failed to uncover mutations in any of our evolved clones. We did find a mutation in the gene encoding MalT, but its distribution was limited to CV101, CV115 and CV116 and its location was unique relative to other MalT mutations characterized as constitutive. It is surprising that this mutation does not occur in CV103 considering that, on average, CV103 has 3–6 fold higher transcript levels of lamB than the other three strains (significant in a between-subjects t-test, p = 0.0007).
While the superior glucose scavenging ability of CV103 may be attributed to its increased LamB expression relative to other evolved clones, this increase cannot be explained by inactivation of Mlc or by a constitutive mutation in MalT, as neither occurs in this strain. We also failed to recover mutations at ptsG, and we did not detect increased OmpF expression, both of which have been observed to enhance glucose uptake in other evolution experiments [57]. While increased LamB expression in all the evolved isolates is almost certainly due to defective rpoS, the rpoS mutation is shared and cannot account for among-strain differences [47]. CV103 does lack the Ala→Glu substitution at aa 53 in MalT (total length, 901 aa), a positive regulator of lamB. Based on the distribution of mutations in rpoS, galS, acs and glpK, it is highly probable that this mutation occurred in the common ancestor of CV101 and CV116 prior to specialization of CV101 on acetate, but after the divergence of CV103 (see Figure 6). Other mutations in the N-terminal portion of MalT which have been reported to arise in glucose-limited chemostats result in its constitutive expression [41]. However, despite the relatively large number of such mutations which have been characterized (at least 16), none is in the same position or motif as the one we report here [41],[68]. Interestingly, adaptation to long-term glucose limitation in batch culture can select for mutations that partially or fully inactivate MalT, one of which does occur in the same helix as our mutation [95]. If the malT mutation shared by CV101 and CV116 results in a weakened activator, and consequently less LamB, there exists the intriguing (although highly speculative) possibility that in our experiment, down-regulation of glucose influx through LamB could provide an advantage to minority clones that specialize on excess excreted carbon. Additional experiments will be needed to determine whether the malT mutation shared by CV101 and CV116 explains their diminished lamB expression, relative to CV103. Alternatively, it may be that CV103 has higher levels of endogenous maltotriose inducer, or harbors as yet unidentified mutations that affect lamB transcription and/or glucose uptake via other routes. Whether physiological or genetic, the mechanism underlying two-fold differences in the expression of this key transporter promises to be unique and interesting, and will be the subject of future investigations.
The constitutive overexpression of acetyl-CoA synthetase that enables CV101 to capture overflow acetate from the dominant clone has a clearly documented mutational basis that has been re-confirmed by our microarray and sequencing results. This mutation is selectively favored because the dominant clone, CV103, poorly recovers acetate produced via glycolysis [14], a phenotype that manifests as high equilibrium acetate concentration when CV103 is grown in chemostat monoculture (see Table 1) and absence of Acs activity when CV103 is grown in either batch or chemostat culture [15]. Given that the ancestor, JA122, has a weakened acs promoter, and that acetate is scavenged at low concentrations almost exclusively via the acs pathway, the acetate defect in CV103 could be explained by this genetic predisposition compounded by increased catabolite repression of acs arising from increased glucose transport. The rate of glucose uptake, equilibrium acetate concentration, and acetyl CoA synthetase measurements of CV116 under glucose limitation support this contention since all are intermediate between JA122 and CV103 (see Table 1). Moreover, when cells are grown in the presence of acetate and glycerol CV116 exhibits ancestral levels of Acs specific activity while CV103 Acs activity is negligible [15]. Thus, acs is neither appropriately activated nor repressed in CV103.
Regulation of acs expression in E. coli is quite complex, integrating signals from the TCA cycle, glyoxylate bypass pathway, and phosphotransacetylase/acetate kinase (pta/ackA) acetate dissimilation pathway [71],[73],[96]. acs expression can also be regulated by growth phase via Fis [97] or via the PTS system by means of cAMP-CRP [72]. Previous work indicated no defect in the regulation or structure of ackA [15]. In the present study, we sequenced the promoter and full structural gene for acs as well as the other enzyme in the dissimilation pathway, pta. With the exception of the first 17 base pairs of pta (which were not sequenced), we found no mutations. Thus, the genetic basis for loss of acs activity in CV103 remains obscure.
Increased glycerol uptake coupled with the observation that addition of glycerol increases the equilibrium frequency of CV116 co-cultured with CV103 led to the conclusion that CV116's success in the chemostat was due, at least in part, to glycerol cross-feeding [15]. Sequencing of the glycerol kinase gene (the rate limiting step in extracellular glycerol metabolism) identified a mutation in CV116 not found in the other isolates. However, given that this was a silent substitution resulting in a codon change from an abundant to a rare tRNA, and given that the surrounding sequence bears little similarity to a glycerol repressor (glpR) binding site, it is difficult to argue that that this mutation has adaptive significance. We therefore next examined glpR and were surprised to find a mutation that was not only present in the ancestor but was present in the E. coli progenitor strain from which JA122 was derived. This mutation has been characterized by other groups, and results in constitutive expression of the glycerol regulon [76]. Many GlpR-regulated genes did not show appreciable expression differences on our microarrays, as would be expected if they were also upregulated in the ancestor. But three genes did show modestly increased transcript levels across all evolved isolates at the 0% FDR level: the glycerol-3-phosphate transporter (glpT), the glycerophosphoryl diester phosphodiesterase (glpQ), and the anaerobic glycerol-3-phosphate dehydrogenase (glpA). These genes are partially under the control of GlpR, but they also have additional regulators not shared by other genes in the glycerol regulon. It appears likely that the superior ability of CV116 to recover and metabolize extracellular glycerol-3-phosphate is related to the increased expression of glpT, but the reason that it is able to scavenge glycerol better than CV101 and CV103 is unresolved. Catabolite repression, and/or glycolytic intermediate feedback due to increased glucose consumption may modulate GlpT activity post-transcriptionally in CV103.
Transcriptional profiling of the consortium RNA pool led to the unexpected observation that, in monoculture, CV103 has a different pattern of gene expression than when co-cultured with CV101 and CV116. The genes primarily affected are those that distinguish CV103 from the other clones in the 4-class SAM analysis, suggesting that a global regulatory mechanism is responsible for the shift in expression. Two global regulators dominate the 4-class SAM analysis, CRP and CpxR; together these explain expression patterns for nearly half the transcription units which distinguish CV103. CRP is known or predicted to influence the expression of 23% of CV103-specific transcription units, though none of these are under the exclusive control of CRP. While CpxR controls a smaller proportion of CV103-specifc transcription units, (19%), most of these are solely regulated by CpxR. Thus, CpxR regulation underlies much of CV103's expression pattern in monoculture; this effect is reversed when CV103 is co-cultured with the subdominant clones.
One dramatic environmental difference between the glucose-limited CV103 monoculture environment and the consortium environment is the concentration of extracellular acetate. When CV101 is present, acetate is efficiently scavenged and cannot accumulate. CpxR in its phosphorylated form mediates a global response to extracytoplasmic stressors such as high osmolarity, misfolded outer membrane protein, or alkaline pH (as reviewed in [98]). CpxR is normally activated by its sensor kinase CpxA, but it can also be phosphorylated in a CpxA-independent manner, albeit at a rate of phosphotransfer much lower than that which occurs between the sensor kinase and its response regulator. Although there have been no reports of a direct connection between extracellular acetate concentration and CpxR activation, CpxR can be phosphorylated by acetyl-P, the high-energy intermediate of the Pta/AckA pathway that accumulates during exponential phase growth on glucose and/or a proposed sensor kinase (SKx) that is connected to the Pta-AckA pathway [99]–[102]. Regardless of the precise molecular nature of the interaction, it seems clear that CpxR activation is intimately connected to acetate dissimilation. We previously reported that the Km for acetate kinase in CV103 and CV116 was lower than that of JA122 and CV101 [15]. Given the low equilibrium acetate concentration in the chemostat, it was concluded that this decrease in Km should not significantly affect acetate uptake or secretion. However, alterations in acetate kinase activity, increased acetate secretion, or reduced acetate uptake could conceivably affect the overall performance of the Pta-AckA pathway and thus influence intracellular levels of acetyl-P and/or some yet-to-be-identified effector molecule [102]. Such interactions could be reasonably postulated to elicit a CpxR-mediated transcriptional response when extracellular acetate concentrations increase (as in CV103 monoculture).
Shared mutations in rpoS and mglD strongly support the hypothesis that competition for the limiting nutrient, glucose, was the primary selective force operating in the chemostat prior to metabolic divergence of CV101 and CV116 [15]. Increased glucose consumption coupled with acetate and glycerol secretion by CV103 created a favorable environment for the evolution of clones that could efficiently consume these two overflow metabolites. While screening for mutations that contributed to the emergence of cross-feeding populations, we unexpectedly encountered ancestral regulatory mutations in both the acetate and glycerol metabolic pathways that affect the induction of acetyl CoA synthestase (the primary acetate scavenging pathway) and the glycerol regulon repressor GlpR. As a result, it appears that the ancestor is unable to efficiently recover excreted acetate and constitutively overexpresses the glycerol regulon. We believe that these two mutations in the ancestor profoundly influenced the evolutionary outcome of these experiments (as well as the replicate evolution experiments reported in [39], which showed similar qualitative results). Impaired acetate scavenging by the progenitor of CV103 undoubtedly accelerated or predisposed the evolution of a strain that could efficiently utilize this substrate. We cannot argue that acetate scavenging clones would not have eventually arisen from a purely “wild-type” inoculum, but the repeatability of their emergence as well as the precise way in which they were invariably generated (activation of acs by reversion of the ancestral mutation or IS element insertion) suggests that there was strong selective pressure for changes at the acs locus. The influence of the ancestral GlpR mutation is less clear: Overexpression of the glycerol dissimilation pathway could affect the excretion of glycerol-3-phosphate by CV103 or enhance the ability CV116 to recover it. In either case, it seems unlikely that the presence of the GlpR mutation is mere coincidence.
Overall, the influence of mutations with global and small-scale regulatory effects on the evolution of the consortium is clear (see Figure 6). The first steps in adaptation to limiting glucose occurred via mutations that increase glucose consumption: inactivation of the stationary-phase sigma factor σS and modification of the glucose/galactose transporter MglBAC repressor binding site. Mutations at these same trans- and cis-acting elements have been previously shown to confer fitness advantages under glucose-limitation, and rpoS mutants are commonly found in natural E. coli populations [46]. Subsequently, mutation of the maltose operon activator (MalT) and deletion of the chromosomal region that contains the NarZ nitrate reductase resulted in two distinct lineages: CV103 and the progenitor of CV101, CV115 and CV116. Strain CV101 acquired the ability to scavenge excreted acetate via the insertion of an IS30 element in the promoter of the acetyl CoA synthetase gene. Two other mutations of unknown effect, the loss of the plasmid pBR322 and a silent mutation in the glycerol kinase gene glpK, further delineated the glycerol-scavenging strain CV116.
The founder effect is generally disregarded in microbial evolution experiments because immense population sizes enable a pool of variants to be rapidly generated by mutation and also buffer against severe genetic bottlenecks. The results presented here suggest that microbial evolution experiments are influenced by founder genotype and that such influences can promote evolution of stable polymorphisms.
At least one mutation instrumental in the evolution and maintenance of cross-feeding (the acs IS30 insertion) was compensatory rather than neomorphic. Thus, the exploration of new biochemical opportunities required recovery of old functions, in addition to the development of novel traits. These observations may not be confined strictly to experimental systems as many natural microbial populations (such as those that cause nosocomial or chronic infections) are also founded by clones. For example, chronic Pseudomonas aeruginosa infection of the lungs of cystic fibrosis patients frequently originates from one or a few isolates that undergo clonal expansion over the course of many years [103],[104]. Common targets of selection during adaptation of these clones to the CF lung environment are regulatory: mutations in the aminoglycoside efflux pump regulator mexZ can enhance antibiotic resistance and mutations in lasR, a regulator of quorum sensing, may influence biofilm formation during infection. Similarly, Helicobacter pylori infections, the cause of most gastric ulcers, are often initiated in early childhood and persist throughout the lifetime of an untreated individual [105]. In both cases, mechanistic understanding of microbial adaptation is essential to successful implementation of novel therapeutic regimens.
Transcriptional profiling and targeted gene sequencing expanded and confirmed certain aspects of our understanding of the mechanisms that drive adaptation and diversification. All identified nonsynonymous mutations were regulatory in nature, but not strictly confined to global regulators. Initial selection in the chemostat favored mutations that enhance competitive acquisition of the limiting resource (such as those in rpoS and mgl), but ancestral regulatory mutations like those in acs and perhaps glpR explain much of the unique behavior of this system. The transcriptional effect of some adaptations was apparent even when consortium members were grown in isolation, while the expression of others appeared to depend on the metabolic activity of sibling clones. Finally, even under strong selection, at least one of the most beneficial mutations served to restore a lost function, thereby creating a stable cross-feeding interaction between adaptive clones.
The advantages of E. coli as a model organism for experimental evolution lie in its ease of cultivation, large population sizes, rich history of investigation, and perceived simplicity of adaptive response. An attempt to understand how one E. coli clone adapts to a single environmental factor led to the unexpected discovery that out of one can come many (e unum pluribus), and that biological diversity can evolve and endure even under the simplest conditions.
The mutations which we have so far discovered that help to explain this phenomenon localize to transcription factors or cis-regulatory regions, emphasizing the profound influence of differential gene regulation on adaptive evolution. Out of necessity, previous efforts to analyze this microbiological consortium relied upon the assumption that the sum of the individual units was mechanistically equal to the behavior of the whole. And indeed, detailed analysis of each member in isolation provided useful information about both their shared evolutionary history and individual adaptive strategies. However, treating the intact consortium as a single unit revealed a transcriptomic behavior that was clearly different from a simple aggregation of its “atomized” parts (sensu Gould and Lewontin, [106]). Future experiments which rely on advances in whole genome sequencing, cell labeling and cell sorting will enable us to dissect the consortium into its individual components prior to analysis, and precisely identify the characteristics that define each clone's adaptive strategy. The challenge of deconvoluting individual metabolic responses in this system underscores the complexity of even a simple three-membered “community.” Our finding that the sum activities of the community do not strictly equal its parts makes clear that experimental microbial evolution is a powerful tool to study the evolution of emergent properties in complex biological systems.
Escherichia coli JA122, CV101, CV103, CV115 and CV116 were stored at −80°C in 20% glycerol (See Table 1). Davis minimal media was used for all liquid cultures with 0.025% glucose added for batch cultures and 0.0125% for chemostats [107]. Inocula for chemostat cultures were prepared by growing isolated colonies from Tryptone Agar (TA) plates in Davis medium for 16–20 hours at 30°C, pelleting the cells at 2000× g and resuspending the pellet in fresh medium. A portion of this suspension was used to inoculate chemostats to a density that approximated the expected steady-state density. Chemostats contained Davis minimal media with 0.0125% glucose and were maintained at 30°C at a dilution rate of ≈0.2/hr for 70 hours (∼14 generations). A600 readings and spread plate cell counts were taken at regular intervals to monitor growth and cell densities at 70 hours were between 1.5 and 2.5×108 cells mL−1. At the end of each chemostat run, three aliquots of 40 mL of culture were rapidly filtered onto 0.2 µm nylon membranes, flash-frozen in liquid nitrogen and stored at −80°C for RNA extraction.
For transcriptional profiling, each strain was grown in triplicate on three different occasions with independently prepared batches of media. To reduce the effect of variation in media preparation, cultures of ancestral JA122 were grown concomitantly, such that each experimental chemostat had a corresponding reference control fed off of the same media reservoir.
Genomic DNA was extracted from cells grown in batch culture using a modification of methods described [108]. Subsequent to DNA precipitation, spun pellets were re-suspended in 1XTE (10 mM Tris, 1 mM EDTA, pH 8.0) containing 50µg/mL DNAse-free RNAse A and incubated at 37°C for 30 minutes. Samples were re-extracted once with phenol∶chloroform (3∶1), once with phenol∶chloroform (1∶1) and twice with chloroform and then precipitated with EtOH using standard techniques. Following re-precipitation, the DNA was dissolved in TE.
Total RNA was extracted using an SDS lysis/hot phenol method developed by the Dunham lab http://www.genomics.princeton.edu/dunham/MDyeastRNA.htm. Briefly, frozen filters were mixed with 4 mL lysis solution (10 mM EDTA, 0.5% SDS, 10 mM Tris pH 7.4) and vortexed to remove cells. An equal volume of acid phenol (pH 4.5) was added and the mixture was incubated at 65°C for 1 hour with frequent mixing. The entire extraction was transferred to a phase-lock gel tube (5Prime Inc., Gaithersburg, MD) and centrifuged according to the manufacturer's instructions. The aqueous layer was extracted twice more with chloroform∶isoamyl alcohol (24∶1) and precipitated with ethanol. Pellets were dried and dissolved in RNase free water, treated with 0.1U/µl RQ1 RNase-free DNase at 37°C for 1 hour (Promega, Madison WI), then further purified using the Qiagen RNeasy Mini kit. RNA quality was assessed on agarose denaturing gels as well as using a Bioanalyzer (Agilent Technologies) and quantified spectrophotometrically.
Microarrays were produced using full-length open reading frame PCR products generated with the Sigma-Genosys ORFmers primer set and reaction conditions and cycling parameters recommended by the manufacturer (Sigma-Genosys, The Woodlands, TX). This set contains primer pairs for all 4290 known and hypothetical ORFs in E. coli K12 MG1655. PCR reactions were repeated and pooled as necessary to obtain at least 3 µg of DNA and pooled reactions were ethanol precipitated, resuspended and further purified using a Qiagen MinElute96 UF PCR purification kit. Purified PCR products were run on agarose gels for quantification and to verify PCR product size. 192 PCR products were excluded because they were either the wrong size, produced multiple product bands, or failed to amplify after repeated attempts. An additional 19 ORFs amplified poorly and consequently were spotted at lower levels on the arrays, but were retained in the analyses (see Table S4). Products were standardized to each contain 2 µg (except as noted in Table S4), dried, and dissolved in 10 µl 3× SSC for printing. Arrays were printed onto Corning Gaps II aminosilane-coated slides using a 48-pin Stanford-UCSF style arrayer at the Stanford Functional Genomics Facility (Stanford, CA).
Microarray expression profiling and CGH were performed using protocols developed at the J. Craig Venter Institute (http://pfgrc.jcvi.org/index.php/microarray/protocols.html) with the following modifications. For a-CGH, 5 µg of genomic DNA was sonicated to an average fragment length of 2–5 kb using a Branson Digital Sonifier at 11% amplitude for 1.1 seconds and a final concentration of 0.5 mM, and 1∶1 aa-dUTP∶dTTP labeling mixture was used in the Klenow reaction. For expression profiling, 20 µg of total RNA was reverse transcribed using 9 µg of random hexamer and 0.83 mM 1:1 aa-dUTP:dTTP labeling mixture. Slides were blocked (using 5× SSC, 0.1% SDS, 1% Roche Blocking Reagent) prior to hybridization as described (http://www.genomics.princeton.edu/dunham/MDhomemadeDNA.pdf) (Roche Applied Science, Mannheim, Germany). Hybridized arrays were scanned using an Axon 4000B scanner (Molecular Devices, Sunnyvale, CA).
qRT-PCR was performed using the Step-One Plus Real-Time PCR System (Applied Biosystems (ABI), Foster City, CA). Primers and probes were designed using the default parameters with Primer Express 3.0 and purchased from Integrated DNA Technologies (IDT, Coralville, IA). A 2 µg aliquot of total RNA was treated with RNAse-free DNAse to remove residual DNA and subsequently reverse transcribed using the ABI High Capacity cDNA Reverse Transcription Kit, after which 1 µl of cDNA was added to 1X TaqMan Gene Expression Master Mix containing 900 nM each primer and 250 nM probe and cycled using the universal cycling program for the StepOne system. Relative amounts of each transcript were calculated using the ΔΔCt method using mdaB as an endogenous control [109]. The sequences of the primers and probes used are shown in Table S5.
a-CGH images were processed using a combination of GenePix Pro 6.0, the TIGR TM4 software suite available at (www.tm4.org), and Microsoft Excel [110]. Image analysis and spot filtering was done in GenePix and a-CGH spots were considered acceptable if they: (1) passed the default flag conditions imposed by the software during spot finding; (2) had an intensity∶background ratio >1.5 and overall intensity >350 in the reference channel; and (3) had an intensity∶background ratio >1.0 in the experimental channel. GenePix files were converted to TIGR MEV format using Express Converter. Ratios were normalized using total intensity normalization and replicate spots were averaged using TIGR MIDAS software. Results were viewed using Caryoscope 3.0.9 (caryoscope.stanford.edu). One a-CGH comparison was performed for each experimental isolate using the ancestor JA122 as the reference genome.
For transcriptional profiling, spots were considered acceptable if the regression R2 was >0.6, or the sum of the median intensities for each channel minus the median background was >500. Spots that contained saturated pixels in both channels were excluded from the analysis, but spots that were saturated in only one channel were flagged and retained. Again, GenePix results were converted to TIGR MEV format using Express Converter and ratios normalized and averaged using TIGR MIDAS. Results were viewed and analyzed using TIGR MeV. Three comparisons, including one dye-flip pair, were performed for each biological replicate for a total of nine comparisons for each strain with the exception of CV116 which only had eight comparisons due to a technical failure. Genes that did not have acceptable spots for 2 out of the 3 biological replicates were excluded from downstream analyses. For each biological replicate, reference RNA was prepared from independent JA122 as described above.
Significance Analysis of Microarrays [111] (SAM) was used to examine expression differences between strains using a multi-class comparison consisting of four groups. Similarities among strains were identified using one-class SAM and differences between the strains were examined using a 4-class SAM. δ cutoffs were either (1) assigned visually, a strategy in which the tuning parameter (δ) was adjusted manually to reflect a natural break in the plot of observed vs. expected d-values from a line with slope = 1 (which resulted in a FDR of 0%), or (2) set at the 0% FDR threshold (i.e. the highest δ value that gave a median false discovery rate of 0%). In all cases, these settings resulted in q-values of 0. The default settings for all other parameters were retained. The average (mean) log2 ratios for biological and technical replicates were calculated after SAM analysis using Microsoft Excel.
Pair-wise Pearson correlation coefficients between array and qRT-PCR expression data were calculated as in [112] using Microsoft Excel.
Trancription unit, regulon and operon information was collated from the EcoCyc Database at http://www.ecocyc.org [63]. Predicted regulatory binding site information was obtained via TractorDB (http://www.tractor.lncc.br) [64].
Data are available through the NIH GEO database under accession number GSE17314.
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10.1371/journal.pcbi.1000307 | Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy | The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination.
| What mathematicians call the “labeling problem” underlies difficulties in interpreting many classes of complex biological data. To derive valid inferences from multiple, noisy datasets, one must consider all possible combinations of the data to find the solution that best matches the experimental evidence. Exhaustive searches totally outstrip current computer resources, and, as a result, it has been necessary to resort to approximations such as branch and bound or Monte Carlo simulations, which have the disadvantages of being limited to use in separate steps of the analysis and not providing the final results in a probabilistic fashion that allows the quality of the answers to be evaluated. The Probabilistic Interaction Network of Evidence (PINE) algorithm that we present here offers a general solution to this problem. We have demonstrated the usefulness of the PINE approach by applying it to one of the major bottlenecks in NMR spectroscopy. The PINE-NMR server takes as input the sequence of a protein and the peak lists from one or more multidimensional NMR experiments and provides as output a probabilistic assignment of the NMR signals to specific atoms in the protein's covalent structure and a self-consistent probabilistic analysis of the protein's secondary structure.
| Labeling a set of fixed data with another representative set is the generic description for a large family of problems. This family includes clustering and dimensionality reduction, an approach in which the original dataset is represented by a set of typically far lower dimension (the representative set). The representative set, often the parameter vector that signifies a set of data points, can be simply the cluster mean (center) or may include additional parameters, such as the cluster diameter. The labeling problem is important, because it is encountered in many applications involving data analysis, particularly where prior knowledge of the probability distributions is incomplete or lacking.
A challenging instance of the labeling problem arises naturally in nuclear magnetic resonance (NMR) spectroscopy, which along with X-ray crystallography is one of the two major methods for determining protein structures. Although NMR spectroscopy is not as highly automated as the more mature X-ray field, it has advantages over X-ray crystallography for structural studies of small proteins that are partially disordered, exist in multiple stable conformations in solution, exhibit weak interactions with ligands, or fail to crystallize readily [1], provided that the NMR signals can be assigned to specific atoms in the covalent structure of the protein. The labeling problem known as the “assignment problem”, has been one of the major bottlenecks in protein NMR spectroscopy.
Protein NMR structure determination generally proceeds through a series of steps (Figure 1). The usual approach is first to collect data used in determining backbone and aliphatic side chain assignments (front-end labeling). These assignments are then used to interpret data collected in order to determine interatomic or torsion angular constraints (back-end labeling) used in structure determination.
The front-end “labeling process” associates one or more NMR parameters with a physical entity (e.g., nucleus, residue, tripeptide, helix, chain); the back-end “labeling process” associates NMR parameters with constraints that define or refine conformational states. In reality, the distinction between front-end and back-end is artificial. Strategies have been developed that use NOESY data for assignments [2],[3] or for direct structure determination without assignments [4]. In addition, as demonstrated recently, structures of small proteins can be determined directly from assigned chemical shifts by a process that largely bypasses the back-end [5],[6]. Ideally, all available data should be used in a unified process that yields the best set of assignments and best structure consistent with experiment and with a probabilistic analysis that provides levels of confidence in the assignments and atomic coordinates.
The usual approach to the solution of the problem of assigning labels to subsets of peaks (spin subsystems) assembled from multiple sets of noisy spectra is to collect a number of multidimensional, multinuclear datasets. After converting the time domain data to frequency domain spectra by Fourier transformation, peaks are picked from each spectrum for analysis. Methods have been developed for automated peak picking or global analysis of spectra to yield models consisting of peaks with known intensity, frequency, phase, and decay rate or linewidth [7],[8]. In the ideal case, the resulting peak-lists identify combinatorial subsets of two or more covalently bonded nuclei by their respective frequencies (Figure 2). These subsets must be “assembled” in a coherent way to “best” correspond to specific atoms in the amino acid sequence of the protein. In practice, peak lists do not report on all nuclei (because some peaks are missing), and “noise peaks” (peaks incorrectly reported as true peaks) are commonplace. In the examples analyzed here (Table 1), the level of missing peaks varied between 9% and 38%, while the level of noise peaks varied between 10% and 135%. The large number of false positives as well as false negatives typically present in the data result in an explosion of “ambiguities” during the assembly of subsets.
A common feature among prior approaches has been to divide the assignment of labels into a sequence of discrete steps and to apply varying methods at each step. These steps typically include an “assignment step” [9]–[12], a secondary structure determination step [13]–[15], and a “validation step” [16]. The validation step, in which a discrete reliability measure indicates the possible presence of outliers, misassignments, or abnormal backbone chemical shift values, is sometimes omitted. Other steps can be added, or steps can be split further into simpler tasks. For example, backbone and side chain assignments frequently are carried out sequentially as separate processes. Some approaches to sequence-specific assignment rely on a substantially reduced combinatorial set of input data by assuming a prior subset selection, e.g., prior spin system assembly [17],[18]. The specification of conformational states can be added as yet another labeling step. For example, backbone dihedral angles can be specified on a grid (e.g., 30° intervals) as determined from chemical shifts [19], coupling constants and/or NOEs [20], or reduced dipolar couplings [21].
The NMR assignment problem has been highly researched, and is most naturally formulated as a combinatorial optimization problem, which can be subsequently solved using a variety of algorithms. A 2004 review listed on the order of 100 algorithms and software packages [22], and additional approaches are given in a 2008 review [23]. Prior methods have included stochastic approaches, such as simulated annealing/Monte Carlo algorithms [24]–[26], genetic algorithms [27], exhaustive search algorithms [17], [28]–[30], heuristic comparison to predicted chemical shifts derived from homologous proteins [31], heuristic best-first algorithms [32]–[34], and constraint-based expert system that use heuristic best-first mapping algorithm [35]. Of these, the most established, as judged from BMRB entries that cite the assignment software packages used, are Autoassign [10] and GARANT [27].
Similarly, a wide range of methods have been used to predict the protein secondary structural elements that play an important role in classifying proteins [36],[37]. Prior approaches to assigning a secondary structure label to each residue of the protein have included the Δδ method [38], the chemical shift index method [14], a database approach (TALOS) [19], an empirical probability-based method [39], a supervised machine learning approach [40], and a probabilistic approach that utilizes a local statistical potential to combine predictive potentials derived from the sequence and chemical shifts [13]. Recently, a fully automated approach to protein structure determination, FLYA, has been described that pipelines the standard steps from NMR spectra to structure and utilizes GARANT as the assignment engine [41]. The FLYA approach demonstrates the benefits of making use of information from each step in an iterative fashion to achieve a high number of backbone and side chain assignments.
Our goal is to implement a comprehensive approach that utilizes a network model rather than a pipeline model and relies on a probabilistic analysis for the results. We reformulate the combinatorial optimization problem whereby each labeling configuration in the ensemble has an associated but unknown non-vanishing probability. The PINE algorithm enables full integration of information from disparate steps to achieve a probabilistic analysis. The use of probabilities provides the means for sharing and refining incomplete information among the current standard steps, or steps introduced by future developments. In addition, probabilistic analysis deals directly with the multiple minima problem that arises in cases where the data does not support a single optimal and self-consistent state. A common example is a protein that populates two stable conformational states.
The PINE-NMR package described here represents a first step in approaching the goal of a full probabilistic approach to protein NMR spectroscopy. PINE-NMR accepts as input the sequence of the protein plus peak lists derived from one or more NMR experiments chosen by the user from an extensive list of possibilities. PINE-NMR provides as output a probabilistic assignment of backbone and aliphatic side chain chemical shifts and the secondary structure of the protein. At the same time, it identifies, verifies, and, if needed, rectifies, problems related to chemical shift referencing or the consistency of assignments with determined secondary structure. PINE-NMR can make use of prior information derived independently by other means, such as selective labeling patterns or spin system assignments. In principle, the networked model of PINE-NMR is extensible in both directions within the pipeline for protein structure determination (Figure 1): it can be combined with adaptive data collection at the front or with three-dimensional structure determination at the back end. Such extensions should lead to a rapid and fully automated approach to NMR structure determination that would yield the structure most consistent with all available data and with confidence limits on atom positions explicitly represented.
In addition to its application to NMR spectroscopy, the PINE approach should be applicable to the unbiased classification of biological data in other domains of interest, such as systems biology, in which data of various types need to be integrated: genomics (DNA chips), proteomics (MS analysis of proteins), and metabolomics (GC-MS, LC-MS, and NMR) data collected as a function of time and environmental variables.
The fundamental idea of PINE is to embed the original assignment problem into a higher dimensional setting and to use empirically estimated compatibility (or similarity) conditions to iteratively arrive at an internally coherent labeling state. These conditions are embodied in the form of a parameterized Hamiltonian (energy function) that evolves at each iteration step. In the quasi-stationary regime, this construction yields clusters, defined as subsets of chemical shift data with assigned labels. The clusters have strong intra-cluster links and highly localized inter-cluster couplings. We view each possible cluster of related experimental data in the domain as a “site” that is to be potentially labeled. More specifically, our goal is to discover (learn) the map f that relates the “domain” (set of subsets of data) to the “codomain” (set of subsets of labels):where X = [x1, x2, …, xm] is the set of data values available from all experiments, and L = [L1, L2, …, Ln] is the set of labels associated to the chemical shifts. At first it may appear that this map is trivial, because one protein has precisely one set of correct chemical shifts. However, breaks in the backbone sequential data, incompleteness of experimental peak lists, and the presence of many noise peaks renders the discovery of a deterministic one-to-one map to the sequential labels unpromising. Rather than discovering a single map, we opt to find a set of maps, each with its associated probability. More directly, we choose to associate subsets of labels from the list L to subsets of data from the list X, each with a commensurate probability:
In order to formulate the computational problem, we require that the labels for data values satisfy constraints that arise from the system of neighborhoods built around each data value. The system of neighborhoods is a dynamic state variable that co-evolves with the probability values. We assign an initial set of labels, L, with associated weights to each input data point, S (e.g., chemical shift) and introduce a measure of similarity based on distances between “neighboring points” (Figure 3). Typically, in our starting configuration, the possible labels for each data value far exceed the number of sites. The set of labels contains the “null” label to allow for the case where a data element cannot be labeled.
The approach used to measure the global compatibility or support for the specific labeling of site S at iteration step m is to aggregate the compatibilities over versions of individual evidences by applying a variation of the belief propagation algorithm [42]. The evidence for assignment is weighted by the probability of each “neighbor” being correct, and the probabilities at stage m can be updated by replacing them by the new weight configuration state. As probabilities evolve, the information content of changing configurations is monitored for the optimally “informative” state. The resulting model is analogous to the random cluster Fortuin and Kasteleyn (FK) model [43]. In practice, a straightforward implementation leads to densely connected networks with noisy weights and no principled way to control the iteration steps.
To implement the intuitively appealing ideas presented above that are designed to find the optimal state in the form of marginal probabilities, we have devised an iterative approach that utilizes topology selection followed by a variation of belief propagation algorithm [42] and subsequent adjustment of initial weights and topology. This topology selection step plays a key role in achieving robust and computationally efficient results.
We proceed by analogy to FK [43]. Let G = (V,E) be any general graph, with e∈E an edge in G, and ν∈V a vertex. The set of assignments (or labels) for each vertex is designated by [1,2,…,q]. The “configuration energy” of this system is encoded in the partition function:(1)
In this formula, the outside sum is performed over the configuration states of the system represented by the map λ, and the inside product measures the compatibility of the vertex labels joined by the edge e. Each edge is weighted by the factor and has end-point vertices , and δ is the compatibility measure of end-point vertices configuration. By defining and , Eq 1 can be rewritten as:(2)
In the setting of statistical physics, the Boltzmann weight of a configuration is , where H (the sum in the exponential) represents the energy of the configuration and β is a parameter called the inverse temperature. Because the weights are assumed to be positive, they can be interpreted probabilistically (after normalization by Z) as a probability measure on the states for the graph G where N is the number of vertices.
In the standard random-cluster model, the neighborhood structure, or topology, of the graph is prescribed, and the objective is to find the ground state for a given set of weights by varying the “spin”, or labeling, configurations. In our case, we are determining the ground state ensemble and the topology of the model at the same time. At each iteration step i, we define Ai, a subset of the graph G, where , and evaluate the partition function for this subset. We evolve the topology of the graph at each iteration by the addition and removal of edges and by refining the edge weights toward the optimum topology as described in the algorithm section. A local Bayesian updating procedure updates the weights, and the local rate of change of weights is used to modify the corresponding local topology of the graph. On the subsequent iteration, our algorithm reintegrates these local modifications in the context of the entire network and attempts to establish a new quasi-stationary state.
The algorithm must address two critical challenges. The data that describe edge weights and states in Eq 2 are derived from empirical relationships that involve noisy data, and, therefore, a straightforward deterministic search of the resulting combinatorial space would be infeasible. In addition, the computational complexity of the resulting problem grows rapidly with the number of labels and the topology of the graph; thus, a suitable starting and evolving representation of the topology, and a corresponding approximation algorithm is the key to obtaining a robust solution to this problem.
The probabilistic construction used in PINE-NMR belongs to the general class of graphical models in which dependencies among random variables are constructed ahead of the inference task. In cases where the graph of dependencies is acyclic, there are powerful and efficient algorithms that correctly maximize the marginal probabilities through collecting messages from all leaf nodes at a root node [44]. When the graph is not acyclic, current algorithms for graphs with cycles often reach oscillatory states, converge to local maxima, or achieve incorrect marginals due to computational difficulties. Approaches have been described in the literature for dealing with a single loop condition [45] or for converging under alternative free energy approximations [46],[47]. “Tree-based reparameterization” algorithms [48] have been described as a general approach that iteratively reparameterizes the distributions without changing them on the subtrees in the original graph. These algorithms, which are geared toward addressing the approximation of marginals in the presence of loops, represent trade-offs among robustness, accuracy, computational speed, and efficiency of implementation. Our modification provides a simple extension that can be described as an adaptive form of coarse-to-fine approximation. We start with a “coarser topology” and explore more refined factorizations of the probability distribution and look for stable fixed points. In our adaptive approach, the extension of the state space (embodied in the algorithm) plays a critical role. In intuitive terms, the additional degrees of freedom (null states) provide “room for change” for existing distributions as the topology is being refined. The internal working of the stepwise factorization of the probability distribution requires a coarse estimate on the initial threshold that reduces the connectivity degree of the graph. In our case, this approximation is arrived at using a combination of theory and empirical investigation.
Figure 4 presents an overview of the probabilistic network implemented in PINE-NMR. Sets of probabilistic influence sub-networks are combined into a larger influence network, and each sub-network may have its own computational model used to perform the inference task. The entire probabilistic network is constructed by considering the conditional dependencies of the sub-networks. The actual implementation of PINE-NMR entails a fairly complicated network with more than 30,000 lines of code in Matlab and other supporting scripting language. A descriptive and stepwise version is given below.
1. Read input data and check for errors. If errors are found, report errors and abort.
2. Align the 1H, 15N, and 13C dimensions of all spectra independently.
3. Generate spin systems (Figure 5).
4. Estimate the b factor and c factor, which are the measures of data quality defined as follows:In calculating any of the above formulas, only the fields with choices are considered. For example if none of the experiments provided by the user has HA information, HA fields are not used in the calculation.
5. If (b<0.4 or c factor<0.2) # comment: Report low data quality to the user and stop. The low data quality check can be manually overridden through user requests. However, low “quality factors” are strong indicators of “highly incomplete” data and the web service discourages the use of results from low quality data.
6. Otherwise, set K = 0 (iteration counter).
Repeat:
7. K = K+1; (iteration counter).
8. Triplet amino acid typing:
9. Derive the backbone assignment network weights based on amino acid typing scoring, connectivity experiments, latest backbone assignment, and possible outlier detections from the last iteration (Figure 6):T is a threshold value for the connectivity score, which is defined as, c*max_connectivity_score, c is the quality factor defined in 5, and Pk-1(xn(i)) is the probability of assigning xn(i) to triplet residue n in the iteration k−1.
10. Select the network topology; calculate the threshold for removing low-weight edges from the network based on the quality of the data, use:
11. Apply the belief propagation algorithm [42] to find the marginal probabilities Pkn(xn(j)) of assigning triplet spin systems xn(j) to triplet (tripeptide) residues n.
12. Given the marginal probabilities of the triplet residue assignments, derive the probabilistic assignment of the individual backbone atoms.
13. Detect and remove the outliers in the backbone assignments [16].
14. Derive the secondary structure of each amino acid based on the formula: (5)pn(s|xn(j)) is the probability of residue n to be in the secondary structure state s given the assignment xn(j) derived from the method described in [13], and Pkn(xn(j)) is the assignment probability of triplet residue with the center residue n, to triplet spin system xn(j). The summation is over all the possible choices of tripeptides in the protein sequence.
15. If no convergence, probabilities are the average probability of last three iterations. “No convergence” indicates the presence of “nearby” local minima.
16. For every amino acid, generate an energetic model network and apply the Belief Propagation [42] to derive final probabilistic side chain assignments as described in supplementary material Protocol S1.
17. Report the final probabilistic assignments: backbone, side chain, secondary structure prediction, and possible outliers. The output can be specified to conform to variety of formats, including Xeasy, SPARKY, and NMR-STAR (BMRB).
The input to PINE-NMR consists of the amino acid sequence and multiple datasets known as peak lists (chemical shifts) obtained separately from selected, defined NMR experiments. The peak lists consist of sets of real-valued two-dimensional, three-dimensional, or four-dimensional vectors, denoted by lXij∈Rl l = 2,3,4. The dimension of the data is denoted by l, the index j indicates that the observation is from the jth dataset, and the index i denotes the ith observation within the dataset. To compare data from different experimental sets (different j) that have shared subspaces (signals from nuclei in common), we consider only the common subspace. This allows us to omit the index l in subsequent formulas. The similarity (or nearness) is used to build an initial system of neighborhoods. The approximate starting value for similarity is given a probabilistic interpretation by using Eq 3 (Basic Algorithm: 3.a) to compare each datum (peak) Xij with the reference datum (peak) Xmn. The peaks in the most sensitive experiments in the dataset (normally 15N-HSQC or HNCO) are used as the initial reference set. We define a common putative object, called the spin system (Figure 6), by aligning the peaks along the common dimensions and by registering them with respect to reference peaks according to Eq 3. The total number of states of the spin system is equal to the combinatorial set of all label choices including the null state. The preservation of all neighborhood information at this step is particularly important for the analysis of data from larger proteins in which noise peaks and real peaks are closely interspersed.
The spin-system scoring step is used to integrate the spin system sub networks by assigning a score to each possible label that can be associated to a spin system. This process makes use of empirical chemical shift probability density functions, calculated from combined BMRB (chemical shift) and PDB (coordinate) data from proteins of known structure, for each atom of every amino acid type in three label states: α-helix, β-strand, and neither helix nor strand (other) [13]. The general form of the score is obtained by computing the probability of a chemical shift X having the label n (residue number) as described in Basic Algorithm: 8.a. This approach connects amino acid typing and secondary structure state determination through a conditional dependency model. The successive application of weighted measures (Basic Algorithm), leads to the definition of a complex network of relationships and weights among correlated sets of information at the global level (Figure 3). This process establishes an initial system of neighborhoods (Figure 2). Whenever an initial set of probabilities is unavailable, a uniform distribution is assumed as the starting state.
The challenge is to address the computationally demanding problem of deriving the backbone and side chain assignments from amino acid typing and other experimental data (connectivity experiments) according to the model described above. Rather than modeling the assignment of labels to individual peaks, or assigning spin systems to a single amino acid, we generate triplet spin systems and label them to overlapping triplets of amino acids in the protein sequence (Figure 5). The selection of tripeptides instead of single residues reduces the complexity of the graph by eliminating a substantial number of labeling choices; however, it may introduce additional noise to the network due to possible erroneous spin system assembly. Given the trade-off between noise level and network complexity, we found that triplets yielded the optimum choice among other combinations of residues. However, the resulting network of weights and relationships has a complex topology in which a large fraction of relationships (edges) arise entirely from noise in the data, and the resulting random field is not amenable to a straightforward implementation. To overcome this problem, we determine, from spin system scoring and connectivity constraints, an initial topology and the sets of weights for the backbone (Figure 6 and Basic Algorithm: 9) and side chain assignments (Protocol S1). The topology ordinarily is dependent on the weights and a set of parameters (thresholds). These values typically are noisy and incomplete and are contaminated by false positives and false negatives. Our goal is to evolve the initial state of the system toward an “optimally coherent” state without the need for any manual parameter settings by carefully managing the selection of network topology. An initial topology for the network is determined by removing all edges with potential weights below a threshold value. The threshold value is calculated (Basic Algorithm: 10) automatically by approximating the level of success achievable by each threshold (Figure S1). For a fixed set of edge values, this function is generally unimodal and defines the appropriate threshold for the starting state. At each threshold, a variation of the belief propagation algorithm [42] operates on the dense multigraph to effectively prune many edges and to derive the posterior probabilities that define clusters (or labels). After each iteration step, the posterior probabilities of all label assignments are utilized to determine local topology modifications and new edge weights.
Secondary structure labels are dependent variables derived from prior chemical shift assignments. Each chemical shift assignment has an associated probability, and we derive the probabilities for the assignment of secondary structure labels from a normalized and weighted sum of associated probabilities. After computing the probability of each residue n to be in each of three conformational states (s = helix, strand, other) by the method described in [13] for different assignment configurations, the overall secondary structure probability is calculated by Eq 5 (Basic Algorithm). Note that this step involves a shift in the point of view from chemical shift centric to residue centric.
Posterior probabilities derived in each iteration of the assignment process are used as local prior probabilities in the next round of assignment, provided that (1) the assignment has not been detected as an outlier, (2) the assignment of chemical shift is correlated with the assignment of secondary structure consistent with known empirical distributions, and (3) the assignment is consistent with established connectivity constraints.
If one or more of the above conditions are not met, the results are deemed inconsistent because the resulting probabilities appear as outliers of the marginals supported by the current graph topology. This view is driven by the notion that the equilibrium of our fictitious system is the fixed point of the energy functional, with the factorization induced by our graph. In order to reach the consistent state, scores are re-evaluated and a new local score is computed for the next iteration; a new topology is generated, and the computational steps are repeated. The iteration process continues until a stationary or quasi-stationary state is reached, i.e., when the topology of the network and the labeling probabilities do not vary significantly. The iteration process leads to “self-correction” through appropriate adjustments to the topology of the underlying network in order to preserve maximum information.
PINE-NMR is designed to analyze peak lists derived from one or more of a large set of NMR experiments commonly used by protein NMR spectroscopists. This set (listed on the PINE-NMR website) currently includes data types used for backbone and aliphatic side chain assignments. (PINE-NMR will be expanded in the future to handle aromatic side chain assignment.) To test the software, we asked colleagues at the Center for Eukaryotic Structural Genomics (CESG) and the National Magnetic Resonance Facility at Madison (NMRFAM) to provide subsets of data from projects that had led to structure determinations with assigned chemical shifts deposited in the BMRB [49]. We wanted the assignments to have been refined and vetted in light of a structure determination, because we took the BMRB deposited values to be “correct”. In most cases, the input data supported the determination of both backbone and aliphatic side chain assignments. In some cases, the input data supplied supported only the determination of backbone assignments. The peak lists were provided by the persons submitting the data without any specification for the peak picking software, threshold, or other parameters.
Table 1 summarizes the PINE-NMR results for all datasets provided. The input datasets are indicated along with the size of the protein. A backbone or side chain assignment was scored as “correct” if the top ranked (highest probability) PINE-NMR assignment corresponded that in the BMRB deposition. The assignment accuracy is given as the number of “correct” assignments divided by the total number of assignments supported in theory by the input data expressed as a percentage. “The “correct” (BMRB) assignments had the benefit of additional information coming from NOESY data and filtering with respect to structure determination. Also listed in Table 1 is the backbone “assignment coverage” achieved by PINE-NMR (defined as the total number of correct backbone assignments in comparison to the total backbone assignments in the corresponding BMRB deposition expressed as a percentage).
The secondary structure accuracy reported in Table 1 compares the PINE-NMR result with the secondary structure of the deposited three-dimensional structure as determined by the DSSP software [50]. It can be seen that the accuracy of the PINE-NMR results correlates with the data quality factor. The outlier count is defined as the number of C′, Cα, or Cβ atoms detected as possible outliers in the final assignment by the LACS method [16].
In the majority of cases, the assignment accuracy was above 90% for backbone resonances and above 80% for aliphatic side chain resonances. Two cases in Table 1 yielded assignment accuracies below 90%. In the case of the 177-residue protein (At5g01610), the lower performance was due to the poor quality of data from a highly disordered region of the protein. A human expert was unable to go beyond the PINE-NMR assignments, and additional data were required to complete the protein structure determination. In the case of the 299-residue protein (At3g16450), its stereo array isotope labeling (SAIL) pattern [51] gave rise to chemical shift deviations that degraded expected matches. In this case the performance of PINE-NMR could be improved by incorporating corrections for the deuterium isotope effects on the chemical shifts.
An illustration of the improvement achieved by combining information comes from comparing the assignment accuracy results from PINE with those from PISTACHIO [12] (Table 1). PISTACHIO is an automated assignment tool developed earlier that does not make use of inferred secondary structure or outlier detection implemented in PINE-NMR. The results from PINE-NMR also are superior to those achieved by iterative pipelining of the individual assignment (PISTACHIO [12]), secondary structure determination (PECAN [13]), and outlier detection (LACS [16]) steps (results not shown). The tests of PINE-NMR shown in Table 1 are highly stringent, in that minimal information is provided. Separate tests (results not shown) demonstrate that the performance is improved if the input peak lists have been pre-filtered to correspond to spin systems.
The results of website users provide a separate measure of the performance of PINE-NMR. Since July, 2006, users have analyzed more than 1,300 sets of chemical shift data. Without access to the final structures and chemical shift assignments for these proteins, these results could not be analyzed, as in Table 1, with regard to correct assignments and secondary structure. Instead, we used the results from Table 1 to estimate the empirical conditional probability of incorrect labeling in the user PINE-NMR output: P(incorrect label| plabel = x). Assignments with a probability higher than 0.95 generally were found to be correct (Table 1). Using the data submitted to the PINE-NMR web site, we selected a representative sample of proteins with numbers of residues and data quality factors similar to those in Table 1. We then used the empirical estimate of accuracy to analyze the results from these proteins (Table S1). The outcome was in substantial agreement (in a statistical sense) with the results shown in Table 1. Of particular note are two proteins submitted to PINE twice (the proteins with 120 residues and 160 residues in Table S1). In each case, after an initial submission of the data, the user provided additional experimental data prior to another round of analysis. The additional data improved the empirical estimate of accuracy and led to additional assignments at improved levels of confidence.
The level of accuracy and completeness achieved in favorable cases by a single automatic PINE-NMR computation was sufficient for the initial downstream steps of structure determination. For example, the PINE assignment output for ubiquitin, which was obtained from the input of automatically picked peak lists from HSQC, HNCO, CBCA(CO)NH, HNCACB, C(CO)NH, H(CCO)NH, HCCH-TOCSY, HBHA(CO)NH, and C13-HSQC spectra, along with 15N-NOESY and 13C-NOESY spectra for this protein were provided as input to the Atnos [52]/Candid [53] program. The only manual step in the structure calculation was the determination of cross β-strand hydrogen bond constraints for the amino acid residues shown to be in β-sheet by the PINE analysis of secondary structure (an effort taking only about one hour). Hydrogen bond constraints for α-helical regions were introduced based on the results of the PINE secondary structure analysis. The resulting 20 conformers that best fit the input data had an rmsd of 1.1 Å for backbone atoms and 1.7 Å for all heavy atoms (0.8 Å for backbone residues and 1.3 Å for all heavy atoms in ordered residues as analyzed by PSVS [54]. This structure had a backbone rmsd of 1.23 Å from the highly refined ubiquitin structure determined from NMR data deposited in the PDB (1d3z). Without the manual hydrogen bond constraints the structure had a backbone rmsd of 2.77 Å from the 1d3z structure.
The level of assignments achieved by PINE-NMR for small proteins meets or exceeds the assignment levels that led to successful structure determination of small (under 100 residue) proteins from chemical shift data alone [5].
PINE-NMR also can be useful for semi-automated analysis of larger proteins that require for structure determination the collection of additional data such as dipolar couplings, manual NOESY assignments, or aromatic side chain assignments. We have developed PINE-NMR in ways that enable expert input, for example, by specifying a selective labeling scheme, pre-assigned cluster labels, pre-assigned spin systems, or pre-assigned cluster labels for subsets of the data. For pre-assigned cluster labels, PINE-NMR can act as a verification tool, for example, by checking their internal consistency with peak lists or by detecting chemical shift referencing problems or outliers (the LACS report). The software performs spectral alignment, detects excessive noise peaks, uncovers experimental inconsistencies, recognizes the insufficiency of input data, and identifies nomenclature conflicts.
The latest version of PINE-NMR is available for public use through a webserver at http://pine.nmrfam.wisc.edu. The PINE-NMR server offers complete backbone and side chain chemical shift assignment, secondary structure determination, and possible referencing error or outlier detection. The server supports a variety of convenient input and output formats, including Sparky, Xeasy, and BMRB (NMR-STAR). PINE-NMR also accepts prior information that reflects experimental information the user wishes to specify, such as fixed input (pre-assigned labels), selective labeling pattern, or assembled spin systems in cases where segments of the protein have been labeled by other means.
Application of the PINE algorithm to the NMR assignment problem has led to a tool that is capable of analyzing data in a self-correcting manner without the need for the user to manipulate any parameters in the software. The public availability of PINE-NMR through an online server has made it possible for a variety of users to test its accuracy and robustness. The PINE algorithm reformulates an otherwise intractable network of interactions within the context of an energy minimization problem. To address the high computational complexity of the minimization problem, we have devised a local approximation algorithm with reliable global properties. To address the non-convexity of the energy functional and the potential of “getting stuck” in local minima, we perform successive approximations with increasingly more complex energy functionals and with the reweighting of solutions.
Our evolution and selection of the initial network topology of PINE-NMR emerged through the examination of two quantities: (1) the estimated conformity across all datasets with respect to a single reference dataset (b factor), and (2) the estimated conformity between pairs of datasets that contained complementary information (c factor). These quantities, which are calculated as described in the Basic Algorithm, were found to be generally dependent on the size of the protein and the number of false positive and false negatives in the input data. In intuitive terms, the combination of these quantities measures the degree of conformity between the vertex and edge potentials in the network model. The numerical approximation of this quantity (in analogy to quantity called a matching polynomial) is encoded in the fourth root of the product of b and c. For example, when pairs of data in the dataset have low conformity measures, the network topology (e.g. change in the edge set) is strongly influenced by label assignments. These same quantities are also used in the computation of the quality factor and the predicted number of residues assigned (Table S1). After a user submits input data to the server, PINE-NMR performs a preliminary evaluation. If factors b and c do not satisfy the required threshold, PINE reports the problem to the user and suggests possible remedies. Otherwise the assignment process continues. Typically the datasets that yielded high-quality assignments in PINE-NMR had b factors equal to 0.65–0.85 and c factors equal to 0.4–0.6.
The impact of topology selection can be investigated computationally by running simulations that test the computational complexity (running time) and accuracy of the results as a function of increasing network complexity. For small proteins, where the number of false positives and negatives is small, increasing network complexity leads asymptotically to higher accuracy (Figure S1A). The network energy remains stable as more edges are added, and the computational complexity drops sharply as soon as an “essential network topology” is achieved. For larger proteins, increasing network complexity initially leads to higher accuracy, but accuracy falls off at the highest levels of complexity (Figure S1B). The most accurate label assignments are achieved when the cardinality of the edge set for the network is small. Therefore, selecting a more complex network of interactions not only is computationally inefficient but may also lead to decreased accuracy. Inaccuracies within more complex networks tend to propagate. Specifically, high complexity neighborhoods with large numbers of edges were found to degrade the accuracy of their neighbors, and, although this effect typically is local, it also can have long-range impact. These findings reinforce the importance of selecting good initial topology and underscore the advantages of local, as opposed to global, topology modification.
In practical terms, additional knowledge about the structure of a protein can improve the data interpretation. For example, NMR experts often use their experience and knowledge of similar structures or structural folds to make decisions – this knowledge is often hard to codify in an algorithm. In some instances, the bias is subtle. For example, the use of data from BMRB in order to generate simulated peaklists that are to be subsequently assigned is afflicted with bias, because the data in BRMB are highly likely to be associated with a known structure and, therefore, higher information content (sharper localization of parameters according to Bayes' formula).
One of the challenges in protein NMR spectroscopy is to minimize the time required for multidimensional data collection and analysis without sacrificing the quality of the resulting protein structure. We are in the process of coupling PINE-NMR to (HIFI-NMR) [55], an innovative approach that uses adaptive reduced dimensionality NMR data collection. For 3D triple-resonance experiments of the kind used to assign protein backbone and side chain resonances, the probabilistic algorithm used by HIFI-NMR automatically extracts the positions (chemical shifts) of peaks with considerable time-savings compared with conventional stepwise approaches to data collection, processing, and peak picking. The combination of HIFI- and PINE-NMR will support fully automated, probabilistic, NMR data collection and analysis through assignments, determination of secondary structure and backbone dihedral angles. We are currently developing protocols for including H(C)CH-COSY, CCH-TOCSY and common four dimensional NMR experiments in the PINE-NMR network. Our future plans also include the inclusion of NOESY data, which will extend side chain assignments to aromatic residues [56] and support assignments of larger proteins [3].
The core computational model of PINE should be applicable to other problems where automated clustering is needed. For example, when DNA microarray data are used to explore all genes of an organism in order to detail their biochemical networks, automated clustering of gene networks can provide unbiased information about the underlying biology.
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10.1371/journal.pntd.0001017 | Trypanosoma brucei Glycogen Synthase Kinase-3, A Target for Anti-Trypanosomal Drug Development: A Public-Private Partnership to Identify Novel Leads | Trypanosoma brucei, the causative agent of Human African Trypanosomiasis (HAT), expresses two proteins with homology to human glycogen synthase kinase 3β (HsGSK-3) designated TbruGSK-3 short and TbruGSK-3 long. TbruGSK-3 short has previously been validated as a potential drug target and since this enzyme has also been pursued as a human drug target, a large number of inhibitors are available for screening against the parasite enzyme. A collaborative industrial/academic partnership facilitated by the World Health Organisation Tropical Diseases Research division (WHO TDR) was initiated to stimulate research aimed at identifying new drugs for treating HAT.
A subset of over 16,000 inhibitors of HsGSK-3 β from the Pfizer compound collection was screened against the shorter of two orthologues of TbruGSK-3. The resulting active compounds were tested for selectivity versus HsGSK-3β and a panel of human kinases, as well as in vitro anti-trypanosomal activity. Structural analysis of the human and trypanosomal enzymes was also performed.
We identified potent and selective compounds representing potential attractive starting points for a drug discovery program. Structural analysis of the human and trypanosomal enzymes also revealed hypotheses for further improving selectivity of the compounds.
| Over 60 million people in sub-Saharan Africa are at risk of infection with the parasite Trypanosoma brucei which causes Human African Trypanosomiasis (HAT), also known as sleeping sickness. The disease results in systemic and neurological disability to its victims. At present, only four drugs are available for treatment of HAT. However, these drugs are expensive, limited in efficacy and are severely toxic, hence the need to develop new therapies. Previously, the short TbruGSK-3 short has been validated as a potential target for developing new drugs against HAT. Because this enzyme has also been pursued as a drug target for other diseases, several inhibitors are available for screening against the parasite enzyme. Here we present the results of screening over 16,000 inhibitors of human GSK-3β (HsGSK-3) from the Pfizer compound collection against TbruGSK-3 short. The resulting active compounds were tested for selectivity versus HsGSK-3β and a panel of human kinases, as well as their ability to inhibit proliferation of the parasite in vitro. We have identified attractive compounds that now form potential starting points for drug discovery against HAT. This is an example of how a tripartite partnership involving pharmaceutical industries, academic institutions and non-government organisations such as WHO TDR, can stimulate research for neglected diseases.
| Human African trypanosomiasis (HAT) and the lack of effective therapy constitute a health concern in 36 countries of sub-Saharan Africa [1]. The disease affects predominantly poor populations and transmission has been attributed to exposure during activities such as agriculture, animal husbandry, or hunting [2], which are the major means of livelihood in endemic regions. Following acute infection, the disease progresses to a chronic phase ultimately with invasion of the brain. This can happen within a month of initial infection, or alternatively can take years, depending on the parasite sub-species [3]. Four drugs, Eflornithine, Suramin, Pentamidine and Melarsoprol, are currently licensed for the treatment of HAT [4], [5]. Unfortunately, these are toxic and difficult to administer, limiting therapeutic choices [6]. Thus, new therapies for HAT are urgently needed.
Protein kinases, estimated to represent over 30% of all drug discovery programs, remain one of the most studied drug targets for a number of human and animal diseases [7]–[10]. More than 500 protein kinases have thus far been identified, many of which are linked to disease processes [11]. Of particular interest here is a serine/threonine glycogen synthase kinase -3 (GSK-3), which plays a role in the regulation of glycogen metabolism [12], [13], WNT signaling [14], cell cycle regulation [15], [16] and other processes. HsGSK-3 has been investigated as a drug target for several diseases including Alzheimer's disease [17], neurodegeneration and oncogenesis [18]. Two isoforms of GSK-3 exist in human cells, HsGSK-3 alpha and HsGSK-3 beta. These human isoforms display a high degree of sequence identity with only one amino acid difference (Glu196 in HsGSK-3 alpha and Asp133 in HsGSK-3 beta) in the ATP binding domain [19], [20].
Previous studies [21] demonstrated that the causative agent of HAT, Trypanosoma brucei, expresses two proteins (TbruGSK-3 short and TbruGSK-3 long) with homology to HsGSK-3. The shorter protein isoform was shown to be essential for parasite growth and viability and inhibitors of TbruGSK-3 short were found to kill mammalian-stage T. brucei. The authors concluded that evolutionary variations in the ATP binding domain of TbruGSK-3 short, relative to HsGSK-3 beta, might allow for designing parasite selective inhibitors.
HAT drug development is challenged by the disproportionately small commercial interest and investment in developing new anti-parasite agents relative to other human diseases like cancer [22]. This study involved a collaborative Public-Private Partnership (PPP) facilitated by WHO TDR between researchers at University of Washington, USA, University of Antwerp, Belgium, and Pfizer Global Research Development, Sandwich, UK, to find specific inhibitors of T.brucei using a target-based high throughput screening (HTS) approach. This is an example of how drug development for neglected diseases can be stimulated by the PPP approach.
A panel of 16,540 putative inhibitors previously associated with projects at Pfizer targeting HsGSK-3 was screened against the recombinant TbruGSK-3 short. Selected hits were counter-screened against HsGSK-3β. Kinase panel specificity and anti-parasitic screening were also conducted. Compounds identified in this study provide useful starting points for further chemical optimisation.
Recombinant TbruGSK-3 short (accession number Tb10.161.3140) was produced at the University of Washington [21] and shipped to Pfizer, Sandwich, UK for testing. Kinase-Glo reagent (Promega) was used as previously described [23]. This luciferase coupled assay, which provides a luminescent quantification of ATP consumed during the kinase reaction, was modified to a 384-well plate screening format. A selected library of 16,540 compounds comprising known HsGSK-3 beta inhibitors and close structural analogues was screened at 10 µM final assay concentration. Assay plates were prepared by dispensing 0.2 µL of compound (dissolved in 100% DMSO) from master plates into white 384-well plates (Greiner bio one). Primary screening was conducted in a 20 µL reaction volume. Enzyme was added to each well to a final concentration of 3.8 nM in a volume of 10 µl using a Multidrop Combi dispenser (Thermo Scientific) and the plates were incubated for 15 minutes at room temperature (RT). Glycogen synthase peptide 2 (BioGSP2; Sigma) and ATP, were dissolved in 20% acetonitrile and 1 M Tris-HCl pH 7.6 respectively, then diluted in assay buffer to a final concentration of 3.2 µM BioGSP2 and 2 µM ATP. The assay buffer consisted of 25 mM Tris-HCl pH 7.5, 10 mM MgCl2, 5 mM DTT, 0.1 mg/mL BSA, 2 U/mL Heparin and 10 µM EDTA. The reaction was initiated by adding 10 µL of the substrate mixture to each well and allowed to proceed at RT for 2 h. Twenty microlitres of Kinase-Glo reagent was added to quench the reaction. Luminescence was measured after 1 h at a 100 millisecond/well integration time using the Acquest Multimode plate reader (Molecular Devices). Each plate included a positive control (4 µM GW8510, Sigma) and negative control (1% DMSO). Hit compounds were further titrated using a through-plate IC50 format with a maximum concentration of 25 µM. The data was analysed using Pfizer SIGHTS software and visualised using Spotfire software (TIBCO). Five separate 384-well plates were screened in duplicate to assess the assay reproducibility.
Human GSK-3 beta inhibition data (IC50) for many of the compounds were recovered from Pfizer data files. If historical data were not available, the compounds were tested in an assay using 10 nM HsGSK-3 beta (Invitrogen) using Omnia Kinase Assay (Invitrogen) according to the manufacturer's instructions. The reaction volume was 20 µL and a range of compound concentrations were tested, up to a maximum of 40 µM. Briefly, 5 µl of HsGSK-3 beta was dispensed into black 384 assay plates (Greiner bio one) containing 0.2 µl of compounds. The enzyme was incubated with the compounds for 15 minutes at 30°C then 15 µl of substrate mixture was added to each well to commence the reaction. The substrate mixture consisted of 2 µL each of 2× kinase reaction buffer, 10 µM Omina peptide substrate, 0.2 mM DTT and 10 µM ATP, and 7 µL of ultra pure water. The reaction was allowed to proceed for 30 minutes at 30°C. Increase in fluorescence levels indicating peptide phosphorylation by the enzyme was monitored using an Envision (PerkinElmer) with λex 360/λem 485 nm and the data were analysed using Pfizer software SIGHTS and Spotfire (TIBCO).
Compounds with TbruGSK-3 short IC50<100 nM were tested for their ability to inhibit the proliferation of T. brucei (blood stage form). Cytotoxicity testing against human fetal lung fibroblast MRC-5 cell line was also performed. Both assays were carried out with compound concentrations up to 64 µM at the Laboratory for Microbiology, Parasitology and Hygiene, University of Antwerp (www.ua.ac.be). Briefly, T. brucei trypomastigotes (Squib-427 strain, suramin-sensitive) were cultured in Hirumi-9 medium supplemented with 10% fetal calf serum at 1.5×104 trypomastigotes per well. Following 72 hours incubation, parasite growth was assessed fluorimetrically by addition of resazurin. For cytotoxicity evaluation, 104 MRC-5 cells/well were seeded onto the test plates containing the pre-diluted compounds and incubated at 37°C with 5% CO2 for 72 hours. Cell viability was determined fluorimetrically after addition of resazurin [24], [25]. Single point kinase panel screening was also conducted on selected compounds at 10 µM by Invitrogen (www.Invitrogen.com) and University of Dundee, UK (www.dundee.ac.uk).
The crystal structure of human GSK-3 beta complexed with staurosporine (pdb entry 1q3d) was used as the basis for modelling work. Selected compounds were docked into the crystal structure of HsGSK-3 beta on the basis of binding modes of related known ligands. The binding-site residues were aligned and the residues that differ between human and TbruGSK-3 in the catalytic pocket were highlighted with different colours. Images were created using the Pfizer molecule-modelling package MoViT.
A high throughput 384-well assay was developed for Tbru GSK-3 short which measures ATP depletion following phosphorylation of the peptide substrate BioGSP-2. The previously identified inhibitor of TbruGSK-3 short, GW8510 [21], was used as a positive control. The assay yielded Z and Z' scores of 0.2 and 0.8, respectively, indicating excellent quality [26]. Assay reproducibility in HTS format was confirmed by duplicate testing of 5 separate 384-well plates which produced an identical number of hits (Figure 1A).
A collection of 16,540 compounds targeting HsGSK-3 beta were selected from Pfizer compound library and screened against TbruGSK-3 short at a concentration of 10 µM. In order to capture all potential actives, compounds conferring above 40% inhibition were considered hits, giving an overall hit rate of 8.6% (Figure 1B). Hits were titrated in the screening assay, revealing 1,317 hits with IC50<25 µM. Of these confirmed hits, 362 compounds had IC50<1 µM and 35 compounds had IC50<100 nM. The IC50 data against HsGSK-3 beta were either recovered from Pfizer records or the titration was conducted on selected compounds. A comparative analysis of inhibitor potencies between TbruGSK-3 short and HsGSK-3 beta is presented in Figure 1C. A majority of the compounds exhibited greater potency against the human enzyme which is not surprising, since the initial library was primarily made up of compounds that had been optimized for binding to HsGSK-3 beta. Compounds were clustered with an in-house algorithm that carries out single-linkage clustering, whereby any pair of compounds sharing a Tanimoto similarity value of 0.7 (calculated using Daylight fingerprints) were placed in the same cluster (Daylight Chemical Information System Inc). These hits were expanded by selecting near neighbour analogues from the Pfizer compound libraries and further titrating them against both HsGSK-3 beta and TbruGSK-3 short. Two compounds, 0181276 and PF-04903528, were found to show 7-fold selective inhibition of TbruGSK-3 short compared to HsGSK-3 beta (Table 1 and Figure 2).
Seventeen compounds with TbruGSK-3 short IC50 values of <100 nM (regardless of selectivity) were tested for their ability to inhibit the proliferation of mammalian-stage T. brucei. Specificity for the parasite was investigated by testing against the human fetal lung fibroblast MRC-5 cell line (Table 1 and Figure 2). Ten compounds showed in-vitro inhibition of T. brucei proliferation with EC50s of <1 µM and 6 had EC50s of 1–3 µM. Several of the most potent compounds also showed potent inhibition of the MRC5 cell line. However, six compounds showed at least a 5-fold window between T. brucei activity and activity on MRC5 cells, particularly CE-317112 which had 35-fold selectivity (Table 1). In general, potent inhibition of TbruGSK-3 enzyme activity correlated with potent activity against the whole parasite. However, CE-160042 which was a potent inhibitor of TbruGSK-3 enzyme activity, showed no inhibition of the whole parasite (EC50 >25 µM). We subsequently discovered that this compound showed no detectable cell permeability in a standard CaCo2 cell flux assay used routinely in drug discovery (data not shown) and therefore the lack of activity is most likely due to the compound failing to reach the target within the parasite.
Human kinase inhibitors often inhibit more than one kinase leading to safety issues. In order to understand the kinase inhibition profile of TbruGSK-3 inhibitors, 13 of the compounds were screened at 10 µM against a panel of approximately 40 human kinases. One of the compounds, CE-160042, was highly specific and only inhibited HsGSK-3 beta (Figure 3). PF-4279731 and 0180532 were also relatively specific showing >50% inhibition of only 2 and 4 other kinases, respectively. The remaining compounds were active against more than 10 other kinases.
Previous modelling of the Tbru GSK-3 active site identified a number of residues that differ between the human and parasite enzyme that could potentially be exploited to achieve selective inhibition. Using the published enzyme structures [21], the predicted binding modes of two of our compounds were examined (Figure 4). This demonstrated that of the previously reported binding site differences, only one, T.bru M101/Hs L132 is in close proximity to the compound binding site and therefore is likely to be the key residue for achieving selectivity. The modelling suggests that greater selectivity could be achieved by making compounds with substituents that have improved interaction with methionine compared to leucine at this position.
We have exploited knowledge of the essentiality of TbruGSK-3 short and the availability of a large number of HsGSK-3β inhibitors to initiate a drug discovery program for Human African Trypanosomiasis. Over 16,000 compounds were screened against TbruGSK-3 short isoform and compounds of interest were tested against HsGSK-3 beta, whole parasites and human cells. Specificity against a panel of approximately 40 human kinases was also evaluated. We identified 2 compounds with approximately 7-fold selectivity for TbruGSK-3 short over HsGSK-3 beta: PF-04903528 and 0181276. One of these, 0181276 was also relatively specific against the wider human kinase panel. CE-160042 was not selective against the parasite enzyme, but was completely selective for HsGSK-3 beta and showed no significant inhibition of any other kinases. In addition, CE-317112 showed a 35-fold safety window relative to the cytotoxicity control. Together, these compounds represent an attractive starting point for medicinal chemistry with a focus on further improving selectivity for a drug discovery program.
Using structural modelling, we have shown that improved selectivity may be possible by exploiting the T.bru M101/Hs L132 active site difference. Given that this is a relatively small difference, highly selective compounds may be difficult to obtain, however it is encouraging that our intitial screening has identified compounds with 7-fold selectivity. Previous studies suggest that in vivo inhibition of mammalian GSK-3 causes no significant changes in body weight, food consumption or any associated adverse effects, as judged by histopathology or blood chemistry analyses [27], [28]. Therefore, low levels of specificity may be tolerated. However, mouse knock-out studies of GSK-3 beta have shown embryonic lethality due to liver degeneration and changes in bone development [29], [30]. Consequently, non-selective inhibitors would not be safe for use in pregnant women, infants and young children. Therefore, selective inhibitors of the parasite enzyme would be highly desirable and the availability of the GSK-3 structural models provides a powerful tool for structure assisted compound design which could guide synthesis of more selective compounds, based on the initial 7-fold selective compounds we have identified.
This early drug discovery collaboration was facilitated by WHO TDR and demonstrates the power of such public private partnerships in bringing together the drug discovery expertise of pharma companies, the detailed target knowledge from academia and access to parasite biological assays from expert screening centers to accelerate drug discovery for neglected tropical diseases. Our most promising compounds are disclosed to accelerate the pace of drug development for HAT.
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10.1371/journal.pcbi.1003078 | Coordination of Rapid Sphingolipid Responses to Heat Stress in Yeast | The regulatory roles of sphingolipids in diverse cell functions have been characterized extensively. However, the dynamics and interactions among the different sphingolipid species are difficult to assess, because de novo biosynthesis, metabolic inter-conversions, and the retrieval of sphingolipids from membranes form a complex, highly regulated pathway system. Here we analyze the heat stress response of this system in the yeast Saccharomyces cerevisiae and demonstrate how the cell dynamically adjusts its enzyme profile so that it is appropriate for operation under stress conditions before changes in gene expression become effective. The analysis uses metabolic time series data, a complex mathematical model, and a custom-tailored optimization strategy. The results demonstrate that all enzyme activities rapidly increase in an immediate response to the elevated temperature. After just a few minutes, different functional clusters of enzymes follow distinct activity patterns. Interestingly, starting after about six minutes, both de novo biosynthesis and all exit routes from central sphingolipid metabolism become blocked, and the remaining metabolic activity consists entirely of an internal redistribution among different sphingoid base and ceramide pools. After about 30 minutes, heat stress is still in effect and the enzyme activity profile is still significantly changed. Importantly, however, the metabolites have regained concentrations that are essentially the same as those under optimal conditions.
| Sphingolipids play dual roles by serving as components of membrane rafts and by regulating numerous key cell functions. Although sphingolipids have been studied extensively, the details of their functioning are difficult to understand, because their synthesis, pathways of inter-conversion, and utilization constitute a complex, dynamically changing system. We analyze the role of yeast sphingolipids in the response to heat stress. Data show that the profile of these lipids changes almost immediately with the initiation of the stress, but it is a priori unclear how this response is organized. Using experimental data, a sophisticated dynamic model, and a novel optimization strategy, we show how changes in enzyme activities are temporally organized. Intriguingly, the results show that the cells take up as much material as possible in the first few minutes of heat stress and then shut down entry and exit routes of the biosynthetic pathway system. After about 30 minutes, when heat stress is still in effect, the enzyme activity profile is still significantly changed, but the metabolites have regained concentrations that are essentially the same as those under optimal conditions. The results demonstrate how novel insights are achievable with an effective combination of experimental and theoretical research.
| Cells and organisms are regularly exposed to small fluctuations in their environments and have developed effective mechanisms of tolerance. Stronger perturbations lead to stresses, which are not as easily tolerated and require the cells to mount well-coordinated, multi-scale responses. These stress responses are very intriguing, because they offer superb windows into the complex strategies and mechanisms with which cells manage to live and thrive in a changing world. Heat is a particularly useful artificial stressor for microorganisms as it is easily applied and measured, and because cells and organisms have regularly experienced changes in temperature throughout evolution and developed very effective defenses.
It is well known that yeast reacts to modest heat stress with responses at several levels of its biological organization [1]–[3]. Numerous genes are up or down regulated within a few minutes, heat shock proteins are mobilized, transcription factors relocate between the cytosol and nucleus, the protective disaccharide trehalose begins to accumulate to high concentrations, and the metabolic profile of sphingolipids undergoes drastic changes. All these changes commence essentially immediately after a sufficient shift in temperature and may last for an hour or more. Some of these alterations, in turn, are known to serve as signals effecting secondary responses, for instance, by activating transcription factors and stress elements that trigger the expression of genes associated with heat stress.
An interesting aspect of the collective cellular responses is the fact that they occur at distinct time scales. Some are effective immediately, while others require involvement of the entire sequence of gene expression, transcription, translation and protein modification before the end result takes effect. We are slowly beginning to understand how these multi-scale responses are coordinated, but many details are still unclear. To gain further insight into the complexity of the response, Fonseca and collaborators recently presented strategies for designing mathematical models capable of capturing the multi-scale nature of heat stress responses in yeast [4], [5]. In particular, combining experimental information and computational techniques, these authors analyzed the trehalose heat stress system and demonstrated how it is organized at different biological levels and in different time domains.
In this article, we focus on the particular dynamic roles of sphingolipids in the heat stress response of the baker's yeast Saccharomyces cerevisiae. Specifically, we investigate how the cell establishes the observed alterations in sphingolipid profiles within a few minutes of heat stress. It is clear that these altered metabolite profiles are the result of changes in the activities of some or all enzymes of sphingolipid metabolism. We demonstrate that critical changes in activity can be inferred with a novel computational approach that uses measured times series of different sphingolipid concentrations, combined with a customized optimization strategy and a dynamic model that we have been developing and fine-tuning over the past decade [6]–[8].
Any substantial increase in temperature has a direct effect on the macromolecules in a cell. Among them, proteins and lipids are most strongly affected. Nucleic acids can denature upon exposure to heat, but this process requires much higher temperatures of about 75°C–100°C [9], which are outside the realm of tolerable heat stress.
Heat affects proteins in three ways. First, high temperature can modulate their synthesis from gene expression. In this context, Castells-Roca and colleagues investigated transcription rates and the stability of various mRNAs in S. cerevisiae following a temperature shift from 25°C to 37°C, and concluded that both were affected [10]. Second, processes of protein inactivation are temperature dependent. And third, heat can change a protein's folding state, which in turn may affect its function, as well as its removal by the proteasome. In particular, if the protein is an enzyme, its activity is influenced directly by its ambient temperature, according to an empirical relationship commonly called the Arrhenius effect or the Q10 effect.
Lipids are major constituents of membranes, and although the effects of heat are not completely understood, it appears that changes in temperature have an impact on membrane stiffness and fluidity [11]. Jenkins and coworkers [12] were among the first to connect sphingolipids to heat stress responses in yeast, demonstrating that these lipids play several particularly important roles (see also [13]–[17]). They subdivided the heat stress response into two phases. During the first phase, the cell needs to gain thermotolerance, which is at least partially accomplished with an accumulation of trehalose and the induction of heat shock proteins. Furthermore, the cell arrests its cell cycle in G0/G1, and this arrest lasts for approximately one hour, during which time there is no growth. Once thermotolerance is achieved, the cell culture starts growing again in the second phase of the response, even if the temperature is still elevated.
The first response phase is directly associated with two distinct features of sphingolipids. First, the structural characteristics of complex sphingolipids, together with sterols, contribute to the physical organization of specific membrane microdomains within membranes, called lipid-rafts. These rafts are known to be associated with membrane fluidity, protein compartmentalization, and protein sorting and trafficking through membranes (e.g., [18]–[20]). As core components of rafts, sphingolipids are thus directly involved in organizational structures with potential signaling functions, and alterations in these functions are effective at a short time scale [21].
The second role of sphingolipids in the early heat stress response is their capacity to serve as bioactive signaling molecules. This signaling function influences the regulation of the cell cycle response, nutrient uptake, and the synthesis of proteins, which can have important secondary effects, especially if heat shock proteins are not available to serve as protectors of other proteins [22], [23]. Indeed, the groups of Ferguson-Yankey and Meier demonstrated that sphingolipid synthesis is required for an efficient initiation of translation, especially during heat stress [24], [25]. Specifically, the translation rate is increased if sphingoid bases are synthesized and accumulate. Jenkins and collaborators [26] and Dickson and co-workers [13] showed that ceramides and other simple sphingolipids, such as dihydrosphingosine and phytosphingosine (DHS and PHS), accumulate during heat stress in yeast. It appears that the short-term signaling role of sphingolipids is biphasic. In the first phase, sphingoid bases are required to regulate translation of heat shock mRNAs, a process that depends strongly on Pkh kinase, but not on Ypk kinases, which act downstream of Pkh. The second phase consists of a general increase in translation, which is dependent on the function of heat shock proteins. Without these heat shock proteins, the cell would run a severely elevated risk of protein aggregation or misfolding [25].
Sphingolipids also play roles over a longer time horizon. It has been known for a while that DHS induces the expression of a STRE-LacZ reporter gene, suggesting that the global stress element STRE can be activated by sphingolipid signals [13]. In particular, genes associated with the important trehalose stress response contain multiple copies of STRE. Knock-outs or overexpression of genes coding for the synthesis of dihydrosphingosine-1-phosphate (DHS-1P) show changes that resemble thermotolerant and heat sensitive yeast phenotypes, indicating that DHS-1P is an important regulator of heat stress [27]. Phytosphingosine-1-phosphate is involved with the regulation of genes required for mitochondrial respiration [28]. More generally, modulations in any of the sphingolipid enzymes cause ripple effects that change the concentrations of many sphingolipids and, possibly, the expression of a variety of genes. Futerman and Hannun [29] summarized the long-term signaling mechanisms of simple sphingolipids including sphingosine-1-phosphate, sphingosine, ceramide and ceramide-1-phosphate in yeast.
Taken together it is evident that sphingolipids exert important roles within the coordinated heat-stress responses of a cell, and that these roles are pertinent over short and long time horizons. However, it is so far unclear how the cell is able to establish an appropriate sphingolipid profile very quickly in response to heat stress. To answer this question, we propose a computational analysis based on observed heat stress time courses and a dynamic model of sphingolipid biosynthesis and degradation that allows us to investigate the dynamic profiles of critical enzymes involved in the sphingolipid pathway.
If changes in enzyme activities, for instance in response to heat, could be measured directly, the altered values could readily be entered into computational model equations [7], [8], and solving the equations would show the time trajectories of all pertinent metabolites. Our task here requires the opposite task, which is much more complicated. Namely, we ask: can we infer from the metabolite time courses which enzymes have to be altered dynamically, and by how much, in order for the model to generate the observed time-dependent metabolic profile? The optimization-simulation strategy proposed for this analysis, as detailed in the Methods section, answers this question and reveals for the first time how the activities of key sphingolipid enzymes are adjusted by the cell during the heat stress response. Specifically, we performed over 4,400 Monte-Carlo optimizations with random instantiations and selected from among these the 2,004 best models, based on the sum of squared errors (SSEs). To test the validity of these results, we also used the Akaike Information Criterion (AICc) for model selection and found that models selected based on SSEs and AICc were highly similar. Specifically, over 99% of the models identified through SSE were also identified with AICc; Text S1 contains further information on this comparative analysis. The thus selected models yielded dynamic trends for each sphingolipid enzyme during the heat stress response, These sets of individual trajectories reveal interesting insights. Namely, the trajectories collectively form tight, time-dependent activity ranges for those enzymes that control the influx to, and efflux from, the core of the sphingolipid biosynthetic pathway system. In other words, these enzymes always exhibit essentially the same dynamic activity patterns, independent of the randomly initialized start values. Most of the enzymes at the periphery of the pathway system, by contrast, exhibit widely varying activity profiles that are thus not identifiable from the available metabolic time series data. These results are described and discussed in detail in the following sections.
As a first validation of the collective results, we calculated the average of each computationally inferred enzyme activity at each time point and entered it into the pathway model (see Methods section) to check whether we were able to recoup the observed sphingolipid dynamics. The reconstructed sphingolipid dynamics indeed matches the original data quite well (Figure 1). This good match is by no means a priori guaranteed, because it is known that averages of parameter values from different good data fits do not necessarily correspond to good data fits themselves [30]. The averaged model was subsequently used for further interpretations of our results.
As a second, independent validation experiment, we explored changes in the concentrations of the complex sphingolipids IPC, MIPC, and M(IP)2C with the computationally inferred enzyme activities after a shift in temperature. In contrast to the profiles of simple sphingolipids (Figure 1), these trend lines are essentially flat, indicating that the complex sphingolipids do not change much during the heat stress response (Figure 2). This finding is directly consistent with experimental data [12] that were not used in our optimization.
As a final validation approach, and quasi as a negative control, we fixed those key enzymes that were inferred to have tight activity ranges (X34, X36, X41, X43, X50, X54, X57 and X59, see Figures 3 and 4) at their nominal steady-state values and optimized all other enzyme activity profiles with the same methods as before. The resulting fit (Figure S7) is not good and much inferior to that in Figure 1; further details regarding this negative control are given in Text S1.
More interesting than these overall validation results are the trends in the individual enzyme activities (Figures 3–8). Each panel in each of these figures shows grey lines, which are often so dense that they seem to form shaded areas. Each line is one of 2,004 simulated trend lines and represents the computationally inferred activity of the given enzyme at time points 1, …, 30, given a random initialization at t = 0. The red line in each panel shows the mean of the trend lines, while the dotted blue lines enclose 95% of the grey trend lines. The collective results from these panels are visualized in a different manner in Figure 9, where they are superimposed on the sphingolipid pathway system.
The first enzyme of interest, serine palmitoyltransferase (SPT; X57) is the key bottleneck through which all de novo biosynthesis must pass (see red zone in Figure 9). The results show that the computationally inferred solution has SPT activity increasing briefly and then converging essentially to zero within a few minutes (Figure 3). This pattern is seen in essentially all 2004 simulations with random initial settings (see Methods Section. The representation of fold changes seems most intuitive. However, the same results are also presented in Text S1 on a log2 scale, which stretches reduced activity levels. Changes in the subsequent, very fast step (3KDHS reductase; X27) are less defined. A possible explanation is that the substrate of this reaction is toxic [31] and therefore never present in large concentrations, so that the capacity of the enzyme is not limiting. As a consequence, this enzyme activity does not contribute much to the error function that is to be minimized.
Similarly well defined as SPT are enzymes that catalyze the redistribution of material within the core of sphingolipid metabolism as well as the steps of sphingolipid removal (blue zone in Figure 9). These enzyme activities again rise quickly but approach a very small value shortly after (Figure 4). The very long chain fatty acid synthase and elongase (ELO1p; X59) is responsible for the delivery of fatty acid-CoA to the sphingolipid system, while sphingosine-phosphate lyase (X50) and GPI remodelase (X43) are the only true exit routes out of central sphingolipid metabolism. The remaining enzymes in this group redistribute material within the pathway. Ceramide synthase (X34) shows the same pattern as X59, X50, and X43, while sphingoid base kinase (X36), sphingoid-1-phosphate phosphatase (X41), and 4-hydroxylase (X54) exhibit the same initial phase, but begin to rise more or less strongly after about 25 to 28 minutes of heat stress. These late increases in activity apparently indicate the first consequence of heat-induced gene expression. Among these enzymes, sphingoid-1-phosphate phosphatase (X41) shows the strongest peak at 28 to 30 minutes by far. This enzyme is known to be a key regulator of sphingolipid metabolism and, in particular, of stress responses [15]. It plays an important role in regulating the crucial balance between ceramide and phosphorylated sphingoid base levels and thereby modulates later stress responses.
The two alkaline ceramidases exhibit rather different patterns. As with the previous enzymes, the activity of dihydroceramide alkaline ceramidase (dihydro-CDase; X29), which converts dihydroceramide into dihydrosphingosine, decreases to almost zero, but much later and in a less defined manner. By contrast, the activity of phytoceramide alkaline ceramidase (Phyto-CDase; X53) shows tight trends consisting of three peaks, before returning to normalcy after about 30 minutes (Figure 5). These differences indicate that there is no “symmetry” between the function of dihydro- and phyto-forms of sphingolipids.
The activity patterns of enzymes associated with complex sphingolipids are different; they are shown in Figure 6 (green zone in Figure 9). They all indicate a sustained level of hyper-activity for about 20 minutes, before becoming very low between about 20 and 28 minutes. These enzymes are inositol phosphorylceramide synthase (IPC synthase; X33), mannosyl inositol phosphoceramide synthase (MIPC synthase; X35), and mannosyl di-inositol phosphorylceramide synthase (M(IP)2C synthase; X55), as well as inositol phosphosphingolipid phospholipase C (IPCase; ISC1 X51), which returns IPC, MIPC and M(IP)2C to the dihydroceramide (DHC) and phytoceramide (PHC) pools.
The remaining enzyme activities are not identifiable with our analysis. Some appear to be essentially unchanged throughout the measurement period of 30 minutes, during which the temperature remains elevated. Examples are fatty acid synthase (X52), acetyl-coenzyme A carboxylase (X60), and synthase (X63) (Figure 7; yellow zone in Figure 9). Other enzyme activity patterns (X26, X39, X42, X44, X46, X40, X45, X49, X31, X32, X38, and X56) exhibit larger degrees of variation (Figure 8; pink and tan zones in Figure 9). On average, each pattern exhibits an individual Q10 effect, and subsequently stays more or less constant, decreases somewhat, or continues to increase slightly, but the trends are not clear. One reason for the large variability in these trends may be that the available metabolite data are not informative enough. It is also to be expected that the different processes catalyzed by these enzymes allow for a large degree of redundancy. For instance, serine is not only used in the SPT reaction, but also for the production of phosphoserine and in the serine hydroxymethyl transferase reaction, so that computationally inferred excesses in one reaction may be compensated numerically by a lower activity of one of the other two. Finally, as we discussed elsewhere [7], it is possible that these processes are not as well modeled as those at the core of sphingolipid biosynthesis, because they also participate in other pathway systems, such as phsopholipid or ergosterol metabolism.
The computationally inferred patterns in enzyme activities are collectively depicted as colored zones in Figure 9. Most interesting are the red and blue zones, which control the influx to, redistribution within, and efflux out of the core of sphingolipid bipsynthesis. The green zone contains the complex sphingolipds, which provide material for activities in the blue zone, even though their concentrations do not change much throughout the thirty minutes of heat stress (see Figure 2). The yellow, pink, and tan zones at the periphery contain fatty-acid CoAs, serine compounds and phospholipids, respectively. These are necessary for sphingolipid biosynthesis, but also for other pathways. Due to their multiple roles, they are presumably not modeled comprehensively, and their enzyme activities are not identifiable with the data and methods used here.
The trend lines, as well as their averages, collectively suggest that the sphingolipid heat stress response is achieved with quite moderate changes in many enzymes rather than very extensive changes in just a few key enzymes. This result is consistent with earlier studies in the context of the diauxic shift, which implied that cells probably satisfy altered metabolic demands with many small, rather than a few large, adjustments [6], [32]. While it is impossible to identify the true advantage of this strategy unambiguously, the avoidance of large changes in any of the system components might be expected to reduce the risk of undesired side effects in neighboring pathways.
All trends in enzyme activities follow distinct patterns, which are the results of a balance among three forces induced by the shift in temperature from 30°C to 39°C: first, an essentially immediate increase in activity to a level of up to about four times the baseline, according to the enzyme's (typically unknown) Q10 value, which quantifies the Arrhenius effect (see Table S3); second, a possibly diminished activity due to partial protein unfolding and/or an altered half-life of the corresponding protein and/or mRNA; and third, changes in enzyme activity due to regulation and/or gene expression. These forces may be active to different degrees in overlapping time windows.
The three forces lead to different activity patterns. Most striking is the set of enzymes controlling the influxes and effluxes associated with the core of sphingolipid biosynthesis. Their pattern of heat responses consists of enzyme activities that first exhibit a Q10 effect, which is subsequently counteracted by deactivation mechanisms that could be due to changes in RNA amounts, changes in half-lives or degradation rates of proteins or mRNAs, post-translational modifications, or heat induced gene depression [33]. Thus, after a few minutes, these enzyme activities essentially disappear.
Without any computational analysis, the measured data directly show which sphingolipids are apparently needed under heat stress at different points in time. Measured as absolute quantities, PHS increases by far the most in concentration, whereas PHS-P increases most relative to its baseline value. Interestingly, both adjustments are much stronger than in the corresponding dihydro-forms. For instance, the concentration of DHS-P remains very low throughout the observation period of 30 minutes (Figure 10). DHS reaches its modest peak earlier than PHS and PHS-P, whereas PHC reaches its peak later. It is difficult to discern the rationale for this timing and the differences in peak heights.
What the computational analysis shown here suggests is how these observed adjustments are implemented by the cell. Initially, de novo biosynthesis increases quickly, but only for the first three or four minutes. The model actually allows us to quantify and compare the total amount of biosynthesis under optimal and heat stress conditions. Namely, we can record in the dynamic simulation the total production of 3-KDHS, while computationally omitting its degradation (Figure 11). Under optimal conditions, and with a constant influx of palmitate and serine, this accumulation is linear (blue line), and considering consumption as well, the concentration of 3KDHS is constant (results not shown). By contrast, under heat stress, the accumulation is faster for the first few minutes (red line), but it is increasingly reduced subsequently. Considering consumption as well, the concentration of 3KDHS decreases (results not shown).
In the next five to ten minutes, the patterns diverge strikingly. Probably most intriguing, both the input to, and the exit from, central sphingolipid metabolism are almost completely shut down. During this time period, the cell not only counteracts the unavoidable Q10 effect in SPT, but down-regulates this enzyme to a mere residual amount, as shown in top left panel of Figure 3. Similarly, the exit routes through the lyase and remodelase steps lose activity about 5 minutes into the heat stress (Figure 3). The second step of de novo biosynthesis, KDHS reductase, is less dramatically affected (right panel in Figure 3), but deprived of substrate. This substrate deprivation appears to be safer than enzyme down-regulation, as 3KDHS is toxic [34] and any accumulation could be dangerous.
The computational deductions imply that de novo sphingolipid biosynthesis appears to be up-regulated only for the first few minutes [12]. To establish the needed changes in sphingolipid profile under heat stress, the cell appears to absorb and process residual substrate as vigorously as possible, but subsequently seems to count on the much more reliable use of existing complex sphingolipids for the generation of signaling molecules such as PHS, PHS-P and, to a lesser degree, DHS and DHC, and on a subsequent redistribution among the simple sphingolipid pools. This conclusion is based on the inferred reduction in biosynthesis after about five minutes, the shutting off of the lyase and remodelase steps, as well as three additional observations. First, IPCase (Figure 6) is strongly upregulated in a sustained manner for about 15 minutes. Second, the hydroxylase, which converts DHC into PHC and DHS into PHS, loses almost all activity throughout the measured time period (Figure 4). Third, processes leading to the synthesis of complex sphingolipids, including IPC synthase and the synthesis of PI and DAG, are down-regulated after about 15 minutes (Figure 6), thereby slowing down the genesis of new complex sphingolipids from simple sphingolipids. Several of the enzymes associated with complex sphingolipids begin to become active again about 28 minutes into the heat stress, which may be a consequence of changes in gene expression.
After 30 minutes, the six measured sphingolipid concentrations essentially return to their baseline levels. In stark contrast, the enzyme system has not returned to its original state, and several enzymes still exhibit an activity that is quite distinct from the profile under optimal temperature conditions. Thus, the cell, which is still under heat stress, is regaining a close resemblance of normalcy with respect to its metabolites, but this state is achieved with a significantly different flux and enzyme profile.
In this work, we have proposed a computational approach to analyze heat stress response strategies in yeast. Specifically, we have inferred how cells adjust their enzyme activities within sphingolipid metabolism, which has been demonstrated in numerous earlier reports as a heat sensitive signaling system. Using experimental measurements of metabolite concentrations following a shift in temperature, combined with a detailed dynamical model, we computationally inferred adjustments in enzyme activities that appear to be both sufficient and necessary for mounting the observed metabolic response. Rather than computing a single solution to the inverse task, we computed a comprehensive ensemble of over 4400 independent solutions and selected from among them the best 2004 solutions, based on SSE and AICc metrics. These 2004 solutions led to very similar trends in the activities of key enzymes, although not of enzymes at the periphery of the pathway system.
The computed results suggest, first, that the response to heat is not achieved by drastic changes in a few “key” enzymes, but that numerous enzymes are involved. Second, the dynamic alterations in activities differ substantially in both, magnitude and timing, as well as in the general shape of the enzyme activity trends throughout the observed 30-minute time window following the initiation of heat stress. The main surprise in our results is the deduction that the changes in sphingolipid profile are apparently not achieved by sustained increases in de novo biosynthesis but through a brief initial spike, followed by the retrieval of simple sphingolipids from membrane-associated complex sphingolipids, as well as a complicated redistribution scheme among the different ceramide and sphingosine forms. While this strategy was not expected, its seems to have merit, because the cell cannot be sure that new resources are quickly available for de novo synthesis of sphingolipids, while complex sphingolipids such as IPC, MIPC and M(IP)2C are integral components of membranes and therefore always available, with the possible exception of the most deprived situations. Thus, it seems that the cell sacrifices some of its membrane structures and recreates them once the stress situation is under control. This sacrifice, however, is not very substantial, as the concentrations of complex sphingolipids change very little during the heat stress response (Figure 2). These results are consistent with experimental finding of Jenkins et al. [12], who studied different roles of sphingolipids during the heat stress response. Using isotope labeling, they showed that sphingoid bases and ceramides increase early on via de novo synthesis, but that IPC, MIPC and M(IP)2C remain essentially constant over a period of more than one hour. Wells et al. [17] also studied the formation of ceramide in response to heat stress and, using labeled phosphosphingolipids, and concluded that ceramide formation from IPC, MIPC, and M(IP)2C through the IPCase reaction was unlikely. However, the concentration profiles these authors observed were very different from those obtained by Cowart et al. [28], which we used here. In particular, under Wells' 39°C treatment, ceramide remained elevated at a level five times its baseline throughout the two-hour measurement period. Outside the fact that these authors studied a temperature shift from 24°C to 39°C, the differences in concentration profiles to those used here (Figure 1; Cowart et al. [28]) remain unexplained.
Although the computational results were obtained without any particular assumptions, some uncertainties are associated with the fact that many of the intermediate sphingolipids had not been measured and that the mathematical approach may not have revealed the one truly optimal solution. For instance, all results are obtained from large-scale simulations with a dynamical model that has been validated to some degree but could certainly be improved. Given the present data, it is unlikely that further simulations of the same type as shown here would lead to different results. However, if other metabolite concentrations could be measured, or if it were possible to determine some internal metabolic fluxes independently of the metabolite concentrations, the degree of reliability of our results would greatly increase.
The study presented here elucidates a systemic strategy with which the cell establishes the observed sphingolipid profile, but it does not address the specific roles of the various sphingolipids in the heat stress response. Interestingly, some of the simple sphingolipids that are known to have signaling roles do not change all that much, while others do. In particular, DHS, which activates the stress element STRE in the expression of stress related genes, maximally rises to only about twice its normal level, about 5 minutes into the heat stress. Apparently, this increase is sufficient. By contrast, PHS-P, which was recently identified as an important gene regulator, rises to a level that corresponds to almost 10 times its baseline level and exhibits a sustained response that lasts over 20 minutes. PHS rises to a four-fold level. No direct signaling role is known, and it may just be that this compound is needed as a precursor of PHS-P.
The experiments generating the data used here exposed the cells to persistent heat stress. At the end of the 30-minute observation period, all six key sphingolipids have essentially returned to their normal levels, except for DHC, which still seems to be very slightly elevated. By contrast, many of the enzyme activities are not “back to normal.” Expressed differently, the cell manages to mount a strong transient response, which is known to lead to longer-term genomic responses. Subsequently, within a total of just 30 minutes, it is able to adjust its catalytic machinery to the persistent heat conditions in such a manner that the fluxes exhibit a distinctly different activity pattern which, nevertheless, re-establishes a favorable metabolic state that is remarkably close to that under optimal conditions.
Our focus on sphingolipids sheds light on just one aspect of the well-coordinated, complex responses with which yeast adjusts to a new environmental condition. Nonetheless, this particular aspect is of special interest, as the roles of sphingolipids and their biosynthetic pathways have been preserved throughout evolution, from yeast to humans, where they are involved in numerous differentiation and disease processes (e.g., [35]–[38]).
The data, previously obtained in one of our labs, were described in the literature (see Supplements of [28]). They consist of duplicate 30-minute time courses of six key sphingolipids, collected following a step increase in temperature from 30°C to 39°C. Specifically, changes in metabolite concentrations were measured at baseline (t = 0; normal temperature) and at 5, 10, 15, 20, 25, and 30 minutes of heat stress. We used these measurements, averaged the duplicates, and then applied a smoothing spline technique to interpolate the trend of each time course so that concentration values at 31 time points (0, 1, …, 30 minutes) became available for each sphingolipid. The smoothed transients are shown as absolute concentrations in Figure 11 (see also Figure 1 for fold changes, which shows the smoothed data as symbols, along with a model fit based on averaged enzyme activities). For our computational analysis we used relative changes in each sphingolipid with respect to the baseline steady state before heat stress, which we directly obtained from the time series measurements, and scaled these with steady-state values, which were described in earlier work [8], to obtain actual concentrations.
The biosynthesis, metabolic conversions, and degradation of sphingolipids constitute a complex, highly regulated pathway system (Figure 9) that exceeds intuitive capabilities and suggests computational modeling for quantitative systemic analyses. Over the past decade, we have developed a series of such models using a General Mass Action (GMA) formulation within the modeling framework of Biochemical Systems Theory (BST) [6]–[8], [39]. Because these models have been described in detail elsewhere, we can keep their description here to a minimum.
The simple and complex sphingolipids, as well as other pertinent metabolites, are represented in the model as dependent variables, each of which satisfies an ordinary differential equation (ODE). Each ODE contains representations of the processes that produce or degrade this metabolite. According to the tenets of BST, each process is represented as a product of power-law functions, which consists of a rate constant and of every variable directly affecting this process, raised to an exponent, called a kinetic order. Variable names and equations are presented in Text S1 and an SBML implementation can be found in the file Model S1.
As an example for how to design a system equation, consider the dependent variable , which represents dihydrosphingosine (DHS). This metabolite is generated from three possible sources. First, KDHS reductase () catalyzes the reduction of 3-keto-dihydrosphingosine (KDHS; ). The formulation of this process consists of a rate constant , which is multiplied by , raised to the kinetic order , and by , raised to the kinetic order . Thus, the reduction process is modeled as . Second, DHS can be produced from dihydrosphingosine-1-phosphate (DHS-P; ), a process catalyzed by sphingoid 1-phosphate phosphatase (). In analogy to the first process, this step is represented with its own rate constant, as well as the substrate and enzyme, which are both raised to appropriate kinetic orders. Third, dihydroceramide alkaline ceramidase () converts dihydroceramide (DHC; ) into DHS, and this process is formulated in an analogous manner. DHS is subject to three possible metabolic fates, namely through the ceramide synthase reaction toward DHC, through the 4-hydroxylase reaction toward phytosphingosine (PHS), and through the sphingoid base kinase reaction toward DHS-P. Taken together, the ODE equation describing the dynamics of DHS contains three influx terms and three efflux terms as shown in Eq. (1).(1)All differential equations for dependent variables are formulated in this manner. Values for all parameters were determined from the literature [8], [40]. The complete model consists of 25 ordinary differential equations, including those representing the six key sphingolipids of interest here, namely dihydrosphingosine, dihydroceramide, dihydrosphingosine 1-phosphate, phytosphingosine, phytosphingosine 1-phosphate and phytoceramide. The model furthermore contains 41 independent variables, which represent enzyme activities and metabolites such as ATP, palmitate, acetate and phosphoserine, which were assumed to be constant or considered unaffected by the dynamics of the pathway system. The model was rigorously tested and validated against data not used for model construction [7]. It was also recently combined with a model of the sterol pathway, which has relevance for the composition of membrane rafts [39]. An SBML version of the model can be found in zip file Model S1.
As stated at the beginning of the Results section, it is our task to infer from the measured metabolite time courses which enzymes have to be altered dynamically, and by how much, in order for the model to generate the observed time-dependent metabolic profile? Mathematically, this inverse problem is underdetermined and furthermore complicated by the fact that the pathway is described by a system of nonlinear differential equations, as discussed before. If we were only concerned with a baseline steady state and the move of the system to a new steady state appropriate for heat stress conditions, we could use methods of linear algebra and pseudo-inverses, as we have demonstrated elsewhere [32]. However, here we are interested in the entire trajectories between stimulus (i.e., the beginning of heat stress) and the cell's metabolic adjustments over 30 minutes.
We solved this dynamic inverse problem with an iterative, piecewise optimization approach. Specifically, we estimated optimal enzymatic profiles by minimizing the distance between the smoothed sphingolipid data and the simulation results at each time point, with 1-minute time intervals, from 0 to 30 minutes. At each time point, the optimization engine searched for the best set of enzyme activities, which were modeled as independent variables. To satisfy the specified objective function, we algorithmically minimized the distances between the six observed sphingolipid concentrations and the solutions produced by each trial set of independent variables. We executed this strategy 4144 times, using different random values for initial settings. We then selected the 2004 best models based on residual errors (SSEs). In order to test the performance of this metric, we also selected models based on the Akaike criterion (AICc), and both criteria produced very similar results. Please see Text S1 for a detailed comparison of results using these two criteria. Subsequently, scanning all solutions throughout the 30-minute time period yielded dynamic alteration profiles in all enzymes as well as corresponding metabolite profiles that were consistent with the observed profiles throughout the experimental time period. Further details of this procedure are presented in the Text S1.
Each optimization run produced a dynamic enzymatic profile throughout the time period from 0 to 30 minutes. Due to the randomization of initial values and to the fact that the system is underdetermined, the solutions from different runs were different. Thus, instead of searching for a single unique solution, we studied an entire large ensemble of solutions and asked whether the solutions would reveal consistent trends of enzymatic profiles with in the potentially large solution space. Indeed, the overall result of this strategy was a set of surprisingly tight ranges for the key enzymes of sphingolipid biosynthesis.
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10.1371/journal.pgen.1005001 | Discovery of CTCF-Sensitive Cis-Spliced Fusion RNAs between Adjacent Genes in Human Prostate Cells | Genes or their encoded products are not expected to mingle with each other unless in some disease situations. In cancer, a frequent mechanism that can produce gene fusions is chromosomal rearrangement. However, recent discoveries of RNA trans-splicing and cis-splicing between adjacent genes (cis-SAGe) support for other mechanisms in generating fusion RNAs. In our transcriptome analyses of 28 prostate normal and cancer samples, 30% fusion RNAs on average are the transcripts that contain exons belonging to same-strand neighboring genes. These fusion RNAs may be the products of cis-SAGe, which was previously thought to be rare. To validate this finding and to better understand the phenomenon, we used LNCaP, a prostate cell line as a model, and identified 16 additional cis-SAGe events by silencing transcription factor CTCF and paired-end RNA sequencing. About half of the fusions are expressed at a significant level compared to their parental genes. Silencing one of the in-frame fusions resulted in reduced cell motility. Most out-of-frame fusions are likely to function as non-coding RNAs. The majority of the 16 fusions are also detected in other prostate cell lines, as well as in the 14 clinical prostate normal and cancer pairs. By studying the features associated with these fusions, we developed a set of rules: 1) the parental genes are same-strand-neighboring genes; 2) the distance between the genes is within 30kb; 3) the 5′ genes are actively transcribing; and 4) the chimeras tend to have the second-to-last exon in the 5′ genes joined to the second exon in the 3′ genes. We then randomly selected 20 neighboring genes in the genome, and detected four fusion events using these rules in prostate cancer and non-cancerous cells. These results suggest that splicing between neighboring gene transcripts is a rather frequent phenomenon, and it is not a feature unique to cancer cells.
| Genes are considered the units of hereditary information; thus, neither genes nor their encoded products are expected to mingle with each other unless in some disease situations. However, the genes are not alone in the genome. Genes have neighbors, some close, some far. With RNA-seq, many fusion RNAs involving neighboring genes are being identified. However, little is done to validate and characterize the fusion RNAs. Using one prostate cell line and a discovery pipeline for cis-splicing between adjacent genes (cis-SAGe), we found 16 new such events. We then developed a set of rules based on the characteristics of these fusion RNAs, and applied them to 20 random neighboring gene pairs. Four turned out to be true. The majority of the fusions are found in cancer cells, as well as in non-cancer cells. These results suggest that the genes are “leaky”, and the fusions are not limited to cancer cells.
| The traditional thinking is that gene fusions and their fusion products are caused by chromosomal rearrangement at the DNA level. However, a few isolated reports of RNA trans-splicing provide support for additional mechanisms for fusion RNA production in humans [1–4]. In prostate cancer, the SLC45A3-ELK4 fusion has recently gained attention because of its biomarker potential [5–7]. Interestingly, the fusion RNA is neither a product of chromosomal rearrangement, nor is it generated via RNA trans-splicing. With the two parental genes located next to each other, and transcribing the same strand, SLC45A3-ELK4 is produced by a mechanism of read-through, or cis-splicing between adjacent genes (cis-SAGe) [7,8].
Traditionally, true cis-SAGe events have been considered very rare in mammalian systems. In recent years, several studies using the EST database and RNA-sequencing approaches have identified fusion RNAs involving neighboring genes, which were named “transcription-mediated gene fusions”, “tandem chimerism” and “conjoined genes” by various groups [9,10]. However, no effort has been made to characterize these fusions in terms of their generating mechanism. Theoretically, fusions involving same-strand neighboring genes could be products of interstitial DNA deletion, RNA trans-splicing, or cis-SAGe. As a result, it is still unclear whether other examples of cis-SAGe truly exist, and how widespread the phenomenon actually is.
In this study, we first noticed a high percentage of potential cis-SAGe fusion RNAs in prostate cancer, as well as in their matching normal cases. We then used LNCaP cells as a model, and by integrating RNA sequencing data and CTCF silencing, we identified close to one hundred fusions. Bioinformatics analyses and experimental evidence support 16 of these fusions as true cis-SAGe events that are similar to SLC45A3-ELK4. By characterizing the 16 parental gene pairs, we developed a set of rules, which were then used to identify novel fusion RNAs between neighboring genes. Interestingly, most of these fusion RNAs are also present in non-cancerous cells, indicating that cis-SAGe is not unique to cancer cells.
RNA-seq and various software tools have been used to identify fusion events in prostate cancer [5,11–13]. We used SOAPfuse [14] to analyze RNA-sequencing data [15] from 14 pairs of matched prostate normal and cancer samples. Over 300 fusion events were observed in both the normal and cancer groups. We categorized these fusion RNAs into three groups: fusions involving parental genes located on different chromosomes (INTERCHR), fusions involving neighboring genes transcribing the same strand (INTRACHR-SS-0GAP), and other fusions with parental genes on the same chromosome (INTRACHR-OTHER) (Fig. 1A). On average, around 30% of chimeric RNAs are in the category of INTRACHR-SS-0GAP (33% in cancer, 29% in normal groups), candidates for cis-SAGe. Interestingly, the normal and cancer pairs tend to have similar percentages of these fusions (ranging from 9% to 80%) (Pearson’s R = 0.6) (Fig. 1B). As these normal samples are adjacent histologically “normal” tissues from prostate cancer patients, they are not true non-neoplasia. We then analyzed RNA sequencing data from four non-cancer donors’ prostate tissues [16]. The percentage of INTRA-SS-0GAP fusions were also close to 30% (33%, 33%, 25% and 38%) (S1 Fig.). These findings suggest that cis-SAGe may be a more frequent event, and that it occurs both in normal and cancer cells. To gain a better understanding of the process, we decided to use the LNCaP prostate cancer cell line as a model.
Previously, we showed an inverse correlation between the fusion RNA SLC45A3-ELK4 expression and transcription factor CTCF binding to the insulators located at the two parental gene boundaries [7]. Consistent with a negative role in regulating cis-SAGe, silencing CTCF resulted in an induction of SLC45A3-ELK4 fusion expression in LNCaP cells (Fig. 1C). In contrast, CTCF has been shown to facilitate the juxtaposition of interchromosomal regions [17], thus facilitating trans-splicing events. Indeed, silencing CTCF resulted in a down-regulation of a trans-spliced fusion RNA JAZF1-JJAZ1 [7], suggesting that changes in CTCF expression can have opposite effects on a fusion RNA depending on its generating mechanism. We reasoned that more cis-SAGe fusions could be uncovered based on their response to CTCF silencing.
RNA extracted from LNCaP cells transfected with siRNA against CTCF (siCTCF), or negative control siRNA (si-) were processed, and sequenced by two different companies using the Illumina Hi-seq platform to generate two output sequences: paired-end 50-nucleotide and 101-nucleotide in read length (S1 Table.). Nearly 100 million and 50 million raw reads were yielded from each sample respectively. We used FastQC to confirm the quality of the raw fastq sequencing data, and SOAPfuse software to detect fusion transcripts. To identify cis-SAGe events, we applied the following criteria outlined in Fig. 1D: 1) fusion RNAs involving neighboring genes transcribing the same strand (INTRACHR-SS-0GAP); 2) absence of interstitial DNA deletion in-between the two fused exons; 3) presence of CTCF binding site at gene boundaries; 4) chimeric RNAs induced by siCTCF; and 5) presence of intergenic transcript.
In order to identify more fusion events, we combined the reads from 50 bp read-length with those of 101 bp read-length. A total of 95 fusions (64 unique parental gene pairs) were identified from the si—and siCTCF samples (Fig. 2A, S2 Fig., and S2 Table.). Illustrated by Circos plots, it is obvious that the majority of the fusions are intrachromosomal (Fig. 2B). Out of the 95 fusions, 56 are composed of exons belonging to the INTRACHR-SS-0GAP category. There are only 13 interchromosomal fusions. The remaining 26 are fusions joining genes on the same chromosome on different strands (DS), or with more than one gene in between (INTRACHR-OTHER) (Fig. 2C). cBioPortal query on TP53 showed only 14.7% alteration in TCGA in the prostate cancer set. Consistently, LNCaP cells contain wild type TP53 [18]. This could be the reason for the relatively lower incidence of interchromosomal fusions.
We chose 71 fusions (including 48 INTRACHR-SS-0GAP, 12 INTERCHR and 11 INTRACHR-OTHER) to validate by RT-PCR. The PCR products were gel-purified, and sequenced by Sanger sequencing. A total of 62 were confirmed (one example of each category in S3 Fig., S4 Fig., and S5 Fig.). Interestingly, a higher percentage of the INTRACHR-SS-0GAP was confirmed by this method (95.8%), compared with 90.9% for INTRACHR-OTHER, and 50% for INTERCHR. We then focused on the 46 INTRACHR-SS-0GAP fusions that are composed of unique parental gene pairs as candidate cis-SAGe fusions.
Immediate neighboring genes. When viewed on the hg19 Assembly of UCSC Genome Browser, we found that 8 out of the 46 candidate pairs have other gene transcripts in between (one example in S6A Fig.), raising concerns about whether or not they are truly immediate neighboring genes (Table 1). To avoid complications, we then selected the remaining 38 pairs to continue the validation.
Absence of interstitial deletion. As one common mechanism for generating INTRACHR-SS fusions is the deletion of the sequence in-between the fused exons, we examined the copy number variation in LNCaP cells deposited on GEO database (GSM947411). We did not find convincing evidence for interstitial deletions of the genomic DNA in-between the fused exons of any of the candidates (Fig. 3A and Table 1), supporting mechanisms other than chromosomal deletion.
CTCF binding in between parental genes. We used two methods to evaluate the evidence for CTCF binding in-between the 38 pairs of parental genes: 1) visual examination of CTCF binding sites generated by ENCODE on the UCSC genome browser (Fig. 3B and Table 1), and 2) searching for CTCF binding site on the Insulator Database, CTCFBSDB. For fusions CHCHD10-VPREB3F, EIF3K-ACTN4, DTD2-HEATR5A, and VAMP1-CD27-AS1 (S6B Fig.), there was no evidence of CTCF binding in-between the parental genes. Among these, CHCHD10-VPREB3F and VAMP1-CD27-AS1 were eliminated based on the “neighboring genes” criteria. After the above three steps of elimination, 36 fusions that have at least one CTCF binding site in-between the parental genes were left for further validation.
Induced by siCTCF. To evaluate the induction by CTCF silencing, we performed qRT-PCR for the remaining 36 candidates (examples shown in Fig. 3C). We found that 16 fusions could be induced by silencing CTCF, 5 down-regulated and 16 unchanged (fold change greater than or equal to 1.5) (S7 Fig. and Table 1).
Detection of intergenic transcripts. cis-SAGe is essentially alternative splicing between the exons of neighboring genes. Therefore, transcripts in-between the two genes that are involved in cis-SAGe should be present. To further confirm the 16 candidate fusions that are up-regulated with siCTCF, we used RT-PCR to detect intergenic transcripts. DNase treatment eliminated most, if not all, DNA contaminates in the RNA samples manifested by the absence of signal in the “no reverse transcriptase” control (examples in S8 Fig.). Intergenic transcripts were detected in all 16 candidates. To further confirm the generating mechanism, we used antisense primers of downstream parental genes for reverse transcription, and detected by PCR all 16 intergenic transcripts (Fig. 3D). These results argue against the possibility that the intergenic transcripts detected are produced by antisense transcripts. For one fusion, CLN6-CALML4, we could also amplify and sequence confirm the primary transcript spanning from the last exon of CLN6 to the first exon of CALML4 (S9 Fig.).
We hypothesized that these intergenic transcripts are more likely to be induced by siCTCF like the cis-SAGe fusions. Indeed, qRT-PCR results showed that 12 out of the 16 had obvious induction when CTCF was silenced (S10 Fig.).
Relative expression of the fusion to parental genes. To gain insight on the relative level of the fusion RNAs to the parental genes expression, we converted the number of junction reads of the fusion RNAs to FKPM (fragments per kilobase of transcript per million fragments sequenced). Five fusions contribute to a significant portion of the total 5′ parental gene expression (>10%). Similarly, seven fusions are above 10% of the total expression of 3′ parental genes (Fig. 3E).
We noticed that the FKPMs of these 32 parental genes range from 0 to 122 (Fig. 4A). Considering the highest FKPM in the RNA-seq is above 2800, it is thus unlikely that the cis-SAGe fusions are non-specific side products, all due to overwhelming quantities of parental gene transcripts. The expression of several fusions contributes significant portions of parental genes (Fig. 3E), further supporting the argument. In addition, the fact that all of these 16 fusion RNAs were induced by siCTCF, yet many parental genes were not, suggests that the cis-SAGe events are actively regulated by additional mechanisms other than their parental genes’ expression.
Previous analyses reported that in the human genome, roughly 25,000 genes are composed of over 200,000 exons, with a typical gene containing 8.7 exons, and an average exon length of 174.5bp [19]. We used Genes and Gene Prediction Tracks from hg19 Assembly to download gene features, including exon number. The density of genes containing certain numbers of exons are plotted in Fig. 4B. In the genome, a high percent of genes have a single exon (12%). Another peak represents genes with about five exons (~ 8%). The overall distribution of exon numbers of the cis-SAGe parental genes is different from this whole genome analysis, especially for the 5′ parental genes (p = 2.445e-06) (S11 Fig.). Notably, none of the cis-SAGe parental genes are single-exonic.
In order to investigate whether the distance between the neighboring genes plays a role in the generation of cis-SAGe fusions, we analyzed the distribution of the distances between neighboring genes transcribing the same strand within the genome. The median distance between such neighboring genes in the genome is around 54 Kb (Fig. 4C). We found a strong statistical difference between the 16 pairs of cis-SAGe parental genes and the whole genome analyses, as the longest distance out of the 16 pairs is less than 30kb (p = 2.703e-05).
It is known that longer introns tend to facilitate alternative splicing, as RNA pol II has a higher chance of pausing on longer introns [20]. We compared the intron size involved in cis-SAGe fusions (for 5′ genes, the intron after the fused exon; for 3′ genes, the intron before the fused exon) with the hg19 genome (Fig. 4D). No statistical difference was noted (p = 0.7769, and 0.1353 for 5′ and 3′ genes respectively).
We then used CisFinder [21] to identify potential DNA motifs in the fused exons and immediate introns. No consistent motif in the introns, or in the 5′ and 3′ exons was found in the 16 pairs.
To determine whether a certain exon is favored for a cis-SAGe event, the positions of the exons immediately next to the fusion junction site, relative to the parental genes were plotted (Fig. 4E). No obvious “hot exonic position” was observed for the 5′ parental genes, but strikingly, more than 68% of fusion junctions have been observed occurring at the second exon of the 3′ parental genes (11 out of 16) (Fig. 4E). If the participation of the exons are totally random, the chances of having 11 out of the 16 fusions involving the 2nd exon of the 3′ gene is very low, illustrated by a simulation test (p = 1.718378e-05) (S12 Fig.). On the other hand, the bias towards shorter intergenic distance shown earlier does support the frequent usage of the 2nd exon, as it is the closest exon that has a splicing acceptor sequence. However, arguing against a totally non-specific model, there are three fusions that use the first exon of their 3′ parental genes (TMED4-DDX56, NUDT14-JAG2, and PRIM1-NACA), and no common splicing acceptor site (AG or AC), which account for over 99.98% of known splicings [22], was found in the sequence before these first exons.
Considering that the distance between the spliced exons may be one key factor, we went back to examine the distribution of 5′ exon positions counting backward. Impressively, we found that 50% of the fusions actually used the second-to-last exon of the 5′ parental genes (p<0.001)(Fig. 4E). When the top combinations of 5′ and 3′ exon usage were plotted, we noticed a strong bias towards the “2–2”, that is “second-to-last” of the 5′ gene fused to the second exon of the 3′ gene (Fig. 4F).
Out of the 16 fusion RNAs, five fusions have their junction sites fall into the UTR region, thus not changing protein-coding sequence (NR) (Fig. 5A). In six fusions, the protein coding sequence of the 3′ genes use a different reading frame than the 5′ gene (out-of-frame). In the remaining five, the reading frame of the 3′ gene is the same as the 5′ gene (in-frame). The number of in-frame fusions versus. that of the out-of-frame fusions (5 vs. 6) is higher than random (1 vs. 2), but the small number prevents further generalization at this stage. We then fractionated LNCaP cells, and measured the relative amount of these 16 fusion RNAs in the nuclear versus cytoplasmic fractions. Interestingly, the NR and out-of-frame fusions (10/11) were enriched in the nuclear fraction (nuclear/cytoplasma >1), indicating potential non-coding roles. In contrast, four out of five in-frame fusions showed more or equal amounts in the cytoplasmic fraction, possibly functioning as traditional protein coding mRNAs (Fig. 5A).
For one fusion RNA, ADCK4-NUMBL, we were able to obtain two siRNAs that specifically target the fusion transcript (Fig. 5B). They both significantly knocked down the fusion RNA, and had no obvious effect on the ADCK4 parental transcript. si-AN2 caused a slight reduction of the NUMBL parental transcript, whereas si-AN1 even caused an induction of NUMBL. We didn’t notice any obvious change in cellular proliferation rate, but cell motility was significantly reduced when LNCaP cells were transfected with these siRNAs (Fig. 5C)
To further validate these cis-SAGe fusions in other systems, we performed RT-PCR for the final 16 fusions in LNCaP, PC3, a castration-resistant prostate cancer cell line, and RWPE-1, a non-cancer prostate epithelial cell line (Fig. 5D). The majority of the fusion RNAs can be detected in two or more lines. ZNF592-ALPK3 and LMAN2-MXD3 were only detectable in RWPE-1 cells, but hardly detectable in the two cancer cell lines. In contrast, three fusions, PROM2-KCNIP3, BAIAP2L2-SLC16A8, and D2HGDH-GAL3ST2 had higher expression in the two cancer cell lines than in RWPE-1 cells.
We also performed STAR alignment [23] of the RNA-seq data from 14 clinical prostate cancer cases [15]. With IGV analyses [24], 11 out of the 16 cis-SAGe fusions were also found in this dataset (Fig. 5E). Using this method, we found 12 fusions in the matched normal group. Of note, most fusions were not cancer specific.
Next, we wondered whether or not the rules generalized from the 16 fusions could lead us to discover novel cis-SAGe fusions. We summarized the following four rules for cis-SAGe fusions (Fig. 6A): 1) neighboring genes transcribing the same strand; 2) with an intergenic distance less than 30kb; 3) 5′ genes actively transcribing and 4) favoring a configuration of the second-to-last exon in the 5′ fused to the second exon in the 3′ parental genes. According to hg19 genome assembly, there are 9478 pairs of neighboring genes transcribing in the same strand, and that are within a 30kb distance. We then downloaded RNA-seq data for prostate samples [25], and selected 20 random pairs with the 5′ genes expressed in prostate (FPKM>1). Using primers annealing to the second-to-last exon in the 5′ and the second exon in the 3′ gene, we successfully amplified four pairs in LNCaP cells. Sanger sequencing revealed an exact exon-exon splicing pattern (Fig. 6B). All four pairs were also detected in RWPE-1, or LHS, both non-cancer prostate epithelial cell lines (Fig. 6C).
Traditionally, fusion RNAs containing exons of neighboring genes have been considered rare in mammalian cells, with only a handful of examples experimentally identified [26]. However, in our initial analyses of paired—end RNA-sequencing data from the 14 pairs of normal and prostate cancer cases, we found a high percentage of fusion RNAs that are candidates for cis-SAGe (averaging around 30%). This observation is consistent with other in silico analyses and sequencing efforts [9,10,27]. However, the majority of the reported fusion RNAs have not been validated, and their generating mechanisms remain unknown. In fact, some other studies have attributed many such chimeras to experimental artifacts [28], raising questions about whether these fusions are even real. In our previous study, we reported SLC45A3-ELK4 as the first verified cis-SAGe event [7]. Here, we applied a set of criteria and identified 16 additional fusions that are generated by this cis-SAGe mechanism.
CTCF is a highly conserved zinc finger protein that binds to insulator sequences in the genome [29]. Insulators between the neighboring genes act as boundaries to protect a gene against the encroachment of adjacent, inactive, condensed chromatin, or against the activating influence of distal enhancers associated with other genes [30]. CTCF plays diverse regulatory functions, including transcriptional activation/repression, insulation, imprinting, and X chromosome inactivation [29]. Here we manipulated CTCF levels to enhance certain cis-SAGe events. To estimate that 25% of the fusions (16 out of a total number of 64 (unique parental gene pairs)) could be considered as cis-SAGe events may still be an underestimation. It is very likely that some cis-SAGe events are not regulated by CTCF. It is also possible that not all cis-SAGe events have CTCF binding in the experimental conditions we used, and/or are not induced by siCTCF. Thus, the study is aimed to discover CTCF-sensitive cis-SAGe events. Of note, this is also an artificial system created only to enhance some cis-SAGe signals in a cell line system. cis-SAGe is likely to be a complex event regulated by multiple factors. At this moment, the role of CTCF in regulating global cis-SAGe events in clinical prostate cancer is not clear.
It has been reported that CTCF could also facilitate the formation of chromosomal translocation by bringing distant genes into close proximity [31,32]. As we noted before, silencing CTCF resulted in a reduced expression of a trans-spliced chimera JAZF1-JJAZ1 in endometrial cells [7]. The discarded fusions for this study are likely to be candidates for RNA trans-splicing or chromosomal rearrangement, especially the ones that are down-regulated with siCTCF.
Neither statically significant trends in intron size, nor consistent motifs were found in the final 16 fusions, possibly due to the small number of the these cis-SAGe events. However, the parental genes tend to be multi-exonic, and we found a strong preference for shorter intergenic distance. This may partially explain the biased involvement of the second exon in the 3′ parental gene, and the second-to-last exon in 5′ gene. Even though this configuration only applies to about half of the final 16 fusions, we were able use this rule to discover four novel fusion RNAs in 20 randomly selected neighboring genes. Interestingly, these four fusions were also found in at least one non-cancer prostate cell line.
Traditionally, fusion RNAs were thought to be uniquely expressed in cancer cells, and sought after as ideal biomarkers. Some of the fusions we found here did show differential expression between prostate cancer cells and non-cancerous cells, and are potential biomarkers. However, the finding of many new chimeras in normal clinical samples, as well as in non-cancerous cell lines suggests that these events also happen physiologically. For cis-SAGe fusions, this means that genes are more “leaky” than we previously thought.
Prostate cancer cell lines LNCaP (androgen-dependent) were grown in RPMI1640 (Hyclone) media supplemented with 10% FBS and 1% 100 x Pen/Strep (Hyclone). siRNA against luciferase gene was used as control siRNA [33]. CTCF siRNA was purchased from Invitrogen and as described before [7]. The two siRNAs against ADCK4-NUMBL targeting sequences were TCCGCCCTTGGTTTCAAAG, and GGGUCCGCCCTTGGTTTCA. siRNA transfection was carried out using Lipofectamine RNAiMAX (Life Technologies) following the manufacturer’s protocols. Cellular fractionation was carried out according to manufactures’ protocol (NE-PER Nuclear and Cytoplasmic Extraction Kit, Thermo).
LNCaP cells transfected with si-negative control, or siRNAs against ADCK4-NUMBL were cultured for 3 days to obtain 80–90% monolayer confluency. A wound was created by scraping the cells using a 10ul plastic pipette tip, and the medium was replaced with fresh medium. Images were captured immediately after the scratch and six hours later. Cell migration was qualitatively assessed by the size of the wounds at the end of the experiment.
Cells transfected with si- or siCTCF were harvested 3 days after transfection. The RNA was extracted with TRIzol reagent (Life Technologies) following the manufacturer’s instruction. To assure the high quality RNA for next generation sequencing, RNA was further cleaned using the RNeasy kit (Qiagen). The mRNA in total RNA was converted into a library of template molecules suitable for subsequent cluster generation using the reagents provided in the Illumina TruSeq TM RNA Sample Preparation Kit. Millions of unique clusters on flow cells were loaded into the Hiseq 2000 platform and processed for RNA sequencing.
The two samples, the negative control and siCTCF samples were sequenced by the Illumina Hi-seq platform with paired ends to reach 100 million reads with 50bp read lengths (HudsonAlpha Insitute, Huntsville, AL), or 50 million reads with 101bp read lengths (Axeq, Seoul, Korea). To check the quality of the raw data, the software FastQC was used. The deep sequencing data was mapped to Human genome version hg19, and analyzed using the SOAPfuse software [14]. The identified chimeric RNAs were presented using Circos as previously described [34]. STAR align was used to identify the final 16 fusions in the clinical prostate cancer samples downloaded from the European Bioinformatics Institute.
Fusion candidates from SOAPfuse analyzed RNA-seq data were validated at the RNA level by real-time PCR. Quantitative RT-PCR was performed using the ABI Step One Plus real time PCR system (Applied Biosystems) following the manufactures’ instructions. We designed specific primer pairs (S3 Fig.) for the fusion candidates, intergenic transcripts and randomly selected 20 pairs, with each primer targeting one parental gene. Following RT-PCR and gel electrophoresis, all purified bands were sent for Sanger sequencing.
Multi Experiment Viewer (Mev, 4.9 version) software suite was used to generate the heat map containing the visualization of the expression levels of parental genes that are involved in the 16 fusions [35]. IGV analysis was used to visualize fusions in the clinical samples as described before [24].
For intergenic distance, exon numbers, and intron size, the Kolmogorov-Smirnov test [36] was used to decide if the distributions of the 16 pair parental genes were different from those of the whole human genome, by calculating the maximum distance between the sample and population empirical/cumulative distribution function (cdf). In each case, two hypotheses were tested, H0: the distribution of the 32 parental genes follows a normal human genome distribution versus H1: it does not follow the specified distribution.
To test if the total number of fusions use exon2 of the 3′ parental genes is statistically significant, we ran a simulation, in which 10,000 random samples of fusions for the 16 3′ parental genes’ exons were created. We then counted the total number of exon2 fusions in each simulation and plotted the results, creating an approximate binomial distribution of sample size n = 16. We then calculated the probability underneath the binomial distribution in which there are 11 or more exon2 fusions, Pr (number of exon2> = 11), and found the probability to be 1.718378e-05. Thus concluding that getting 11 exon2 fusions is significant.
The Raw and processed RNA-sequencing data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE63487.
The prostate clinical datasets from 14 patients were downloaded from EBI (European Bioinformatics Institute) (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-567) [15].
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10.1371/journal.pgen.1004890 | Altered Chromatin Occupancy of Master Regulators Underlies Evolutionary Divergence in the Transcriptional Landscape of Erythroid Differentiation | Erythropoiesis is one of the best understood examples of cellular differentiation. Morphologically, erythroid differentiation proceeds in a nearly identical fashion between humans and mice, but recent evidence has shown that networks of gene expression governing this process are divergent between species. We undertook a systematic comparative analysis of six histone modifications and four transcriptional master regulators in primary proerythroblasts and erythroid cell lines to better understand the underlying basis of these transcriptional differences. Our analyses suggest that while chromatin structure across orthologous promoters is strongly conserved, subtle differences are associated with transcriptional divergence between species. Many transcription factor (TF) occupancy sites were poorly conserved across species (∼25% for GATA1, TAL1, and NFE2) but were more conserved between proerythroblasts and cell lines derived from the same species. We found that certain cis-regulatory modules co-occupied by GATA1, TAL1, and KLF1 are under strict evolutionary constraint and localize to genes necessary for erythroid cell identity. More generally, we show that conserved TF occupancy sites are indicative of active regulatory regions and strong gene expression that is sustained during maturation. Our results suggest that evolutionary turnover of TF binding sites associates with changes in the underlying chromatin structure, driving transcriptional divergence. We provide examples of how this framework can be applied to understand epigenomic variation in specific regulatory regions, such as the β-globin gene locus. Our findings have important implications for understanding epigenomic changes that mediate variation in cellular differentiation across species, while also providing a valuable resource for studies of hematopoiesis.
| The process whereby blood progenitor cells differentiate into red blood cells, known as erythropoiesis, is very similar between mice and humans. Yet, while studies of this process in mouse have substantially improved our knowledge of human erythropoiesis, recent work has shown a significant divergence in global gene expression across species, suggesting that extrapolation from mouse models to human is not always straightforward. In order to better understand these differences, we have performed a comparative epigenomic analysis of six histone modifications and four master transcription factors. By globally comparing chromatin structure across primary cells and model cell lines in both species, we discovered that while chromatin structure is well conserved at orthologous promoters, subtle changes are predictive of species-specific gene expression. Furthermore, we discovered that the genomic localizations of master transcription factors are poorly conserved, and species-specific losses or gains are associated with changes to the underlying chromatin structure and concomitant gene expression. By using our comparative epigenomics framework, we identified a putative human-specific cis-regulatory module that drives expression of human, but not mouse, GDF15, a gene implicated in iron homeostasis. Our results provide a resource to aid researchers in interpreting genetic and epigenetic differences between species.
| Red blood cell (RBC) production (erythropoiesis) is one of the best understood examples of lineage commitment and cellular differentiation [1]–[4]. This process begins as multipotent hematopoietic stem cells (HSCs) differentiate into lineage committed erythroid progenitors, losing multipotency in intermediate progenitor cell populations. Early erythroid progenitors then differentiate into morphologically distinct early erythroid precursors, termed proerythroblasts (ProEs). The ProEs subsequently undergo terminal erythroid differentiation into mature RBCs that enucleate, contain a significant concentration of hemoglobin, and have highly elastic cytoskeletons [3]. This differentiation process is governed by a number of transcription factors (TFs) that dynamically coordinate a complex transcriptional gene regulatory network (GRN). Importantly, much of our knowledge of this GRN has been derived from mouse models of erythropoiesis [1]–[3]. Extrapolation from mouse models of terminal erythroid differentiation to humans has historically been straightforward, grounded in the nearly identical morphology of mature RBCs and their precursors between species [4]–[6]. While there are many well-known examples of species-specific differences in erythroid GRNs, such as developmental variation of β-like globin gene expression, the divergent role of BCL11A during developmental hemoglobin switching, and differences in cis-regulatory modules (CRMs) regulating GATA1 transcription [7]–[9], a marked global divergence in the expression profiles of the erythroid lineage was only recently described by systematic comparative analyses of human and murine erythroid transcriptomes [10], [11].
Indeed, these recent studies independently identified a large global divergence in temporal patterns of gene expression between human and mouse at critical, canonical stages of terminal erythroid differentiation [10],[11]. While many erythroid specific pathways and genes were generally conserved, such as the heme biosynthetic pathway, cytoskeletal proteins, and master TFs of erythropoiesis (e.g. GATA1, NFE2, and KLF1), significant differences in the timing and expression levels of certain constituent genes were observed (e.g. TAL1) [10]. In some pathways, such as the mitogen-associated protein kinase (MAPK) pathway, gene profiles were markedly divergent between species during differentiation [11]. These differences have many important implications for integrating the extensive information on erythropoiesis gained from mouse models to better understand human erythropoiesis and how this process goes awry in human disease. For example, congenital dyserythropoietic anemia type II (CDA II) is caused by recessive mutations in SEC23B, but the phenotype could not be recapitulated in mouse models [12]–[14]. The expression of SEC23A, a SEC23B paralog, varied between mice and humans, suggesting a reason for these divergent phenotypes. Moreover, these expression differences were accompanied by variation in TF occupancy proximal to SEC23A in erythroid cell lines suggesting that species-specific differences in transcription may be due to evolutionary divergence in TF occupancy and the epigenome [10]. However, the conservation or divergence of chromatin structure and TF occupancy between human and murine erythropoiesis has only been characterized in a few specific regions, and, to the best of our knowledge, we are not aware of any studies that measure the extent to which there is divergence or conservation across the genome [7], [12]. We have therefore undertaken a comparative epigenomic study to systematically analyze the global conservation of histone modifications and master transcriptional regulators necessary for erythroid differentiation. We map these epigenomic marks in both human and murine primary ProEs as well as in the model erythroid cell lines of human and mouse, K562 and G1E/G1E-ER (herein referred to as G1E), respectively. We compare these marks in the context of orthologous genes as well as across conserved regions of both genomes. Finally, we integrate high-quality stage-matched gene expression profiling (RNA-seq) of each cell type to investigate functional intra- and inter-species differences across the epigenome.
Our results suggest that chromatin structure and function is generally well conserved both between species and in erythroid cell models, although certain modifications are under greater constraint than others. In contrast, only ∼25% of the occupancy sites of most TFs are conserved between species, whereas we observed a 2-fold increase in conservation rates for erythroid cell models, validating K562 and G1E cell lines as species-specific model systems for studying such TFs. Nevertheless, we find that CRMs co-occupied by KLF1, GATA1, and TAL1 are significantly more conserved than any lower order combination of these factors and are strictly localized near highly-expressed genes that play a key role in defining erythroid cell state, suggesting that these regions are under strong evolutionary constraint to regulate common features of mammalian erythropoiesis. Moreover, although we show that chromatin structure is largely conserved between similar developmental cell-types across species, subtle changes in chromatin structure are associated with transcriptional divergence. Based on multiple lines of evidence, we suggest that evolutionary changes in transcription are partially driven by large-scale loss or gain of master TF occupancy that associate with changes to the underlying chromatin structure. In addition, these results provide a resource that can aid in translating findings from mouse erythropoiesis to the analogous process in humans.
For each species, we compiled chromatin immunoprecipitation high-throughput sequencing (ChIP-seq) data sets of histone modifications (H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K27me3, H3K36me3) and master TFs of erythropoiesis (GATA1, TAL1, KLF1, NFE2) at the ProE stage of erythroid differentiation (S1 and S2 Table) [15]–[25]. The vast majority of ChIP-seq data was available at the ProE stage, and this is known to be an important time point where a variety of epigenetic changes occur to mediate alterations in the transcriptional landscape [19], [20], [26], [27]. Additionally, we compiled and analyzed ChIP-seq data from erythroid cell lines, K562 (human leukemia cell line) and G1E/G1E-ER (mouse erythroid cell lines that are derived from Gata1-null erythroid cells containing an estrogen-inducible Gata1 transgene; herein G1E).
We initially leveraged the compiled data to investigate local chromatin structure and TF occupancy across 15,506 orthologous gene bodies with a one-to-one mapping because local chromatin structure is largely indicative of transcription status and interspecies TF occupancy differences [28], [29]. Overall, our observations are concordant with prior data suggesting that the functions of histone modifications, indicated by similar histone intensity profiles and the percent of genes present near each, are well conserved between humans and mice (Fig. 1 left panel, S1 & S2 Fig.) [30]. For example, the signal intensity of H3K4me3, generally regarded as a mark of transcriptional activation, was present in ∼50% of genes in both species and its intensity peaked at the transcription start site TSS (Fig. 1A), while the pattern of H3K27me3, a mark of transcriptional repression, was conserved overall but was present in a lower number of genes (∼20%) (Fig. 1C, S2 Fig.).
When we compared TF (GATA1, KLF1, TAL1, NFE2) occupancy profiles across gene-bodies identical to the above analysis of histone modifications, we discovered that for each TF, normalized occupancy intensities varied significantly more between species than histone modifications (S3 Fig.). One hypothesis for this observation is that certain TFs such as TAL1 are not as abundant or active in mouse versus human ProEs, although our expression data suggests that TAL1 is highly abundant at this stage in both species [11]. More likely hypotheses are that technical differences in ChIP protocol between labs explain most of the observed difference or that the differences are truly biological. A thorough analysis supporting these alternative hypotheses is detailed in the materials and methods.
To quantify the potential divergence in chromatin structure and TF occupancy, we compared relative histone modification intensity across the proximal promoter regions of a smaller set of 6596 orthologous genes with canonical transcripts in both species. We included erythroid cell lines in this analysis and used human ProEs as the primary cell type against which all others were compared to assess inter- and intra-species conservation of promoter epigenetic structure. Generally, histone promoter modifications were highly conserved between the two species and replicate experiments were highly correlated (middle and right columns of Fig. 1, S3 Table). H3K4me3, H3K4me2, and H3K9ac were highly conserved between all intra- and inter-species cell types, although modifications in K562 were most correlated with those in human ProEs (Fig. 1A,B,D). Interestingly, H3K27me3 was more conserved in mouse ProEs than K562 cells (Fig. 1C). The observed divergence of H3K27me3 in the leukemic K562 cell line is consistent with the fact that H3K27me3 modifications are frequently dysregulated during oncogenesis [31]. H3K36me3 and H3K4me1 were moderately conserved and more strongly correlated between cell types than between species, although these marks show the weakest enrichment at the TSS (S1 Fig. and S3 Table).
When we compared TF occupancy intensity, GATA1 and TAL1 intensity in human ProEs was moderately correlated with that of K562 cells, but not with mouse ProEs or G1E cells (S4 Fig.). In contrast, KLF1 and NFE2 occupancy was weakly to moderately correlated across all cell-types (S4 Fig.). Importantly, K562 cells proved a significantly better model of promoter TF activity in comparison with primary human erythroid promoters than mouse ProEs.
Comparing erythroid cell lines directly, the two classes of cell types showed the weakest correlation, consistent with their respective derivation from primary cells (S3 Table). G1E cells showed similar correlations to mouse ProEs and were moderately to strongly correlated with mouse ProEs across all modifications (S3 Table). Interestingly, these results suggest that active promoters, marked by H3K4me3, H3K4me2, and H3K9ac, are under strict evolutionary constraint and that conservation of these histone modifications is necessary for transcription that defines cell state across species. Overall, these data add to the increasing evidence that inter-species epigenetic differences are larger than intra-species differences – at least for cells that take on a similar global cellular state [29].
Promoters are only one piece of the total regulatory landscape, so we extended our analysis and performed a global cross-species comparison of chromatin structure and master TF occupancy to better understand patterns in epigenomic evolution. We investigated conservation of global occupancy patterns for all four master regulators of erythropoiesis. Briefly, we derived robust TF occupancy peaks and lifted narrow summits from the mouse genome to the human genome to assess conservation. We note that in this section, when we discuss conservation, we are primarily referring to “conservation of TF occupancy sites” between species or cell types.
During the 75 million years of evolution separating the two species, ∼75% of master regulator (GATA1, TAL1, and NFE2) occupancy sites were lost between humans and mice (Fig. 2A, “mapped”). In stark contrast, greater than 60% of KLF1 occupancy peaks were conserved between species. Interestingly, we observed that in ∼25% of lost TF peaks, new human-specific occupancy sites were created for each TF in nearby regions (+/- 5 kbs, henceforth known as “compensatory” occupancy sites), a phenomenon that has been described between human and mouse hepatocytes, adipocytes, and closely related Drosophila species (Fig. 2A, “mapped +/- 5 kb”) [32]–[34]. Nevertheless, although we observed that large numbers of TF occupancy sites were lost between species, each master regulator is far more conserved than expected by chance (p<10−5 for each, permutation test) and canonical TF binding motifs were nearly identical for each TF across species (Fig. 2D). These findings suggest that the exact genomic location of each TF occupancy site may not be as functionally important as its presence in a broader genomic region and highlight the idea that some presumed cis-regulatory modules (CRMs) may have at most small functional effects.
We also considered the differences in peaks called between species by mapping human peaks to mouse peaks (Fig. 2B). We observed a similar ranking of TF conservation, although the percentages were overall much lower, reflective of the greater number of occupancy sites called in humans. These percentages represent a lower bound on the true percentage of conserved peaks, while those shown in Fig. 2B are a better estimate of the true conservation of TF occupancy rate. As a sensitivity analysis, we investigated conservation of only the strongest 25% of TF occupancy peaks, providing an upper bound on the conservation estimate for each TF (S5 Fig.).
In juxtaposition to mouse sites, TF occupancy sites in K562 cells and human ProEs were highly concordant: ∼50% of occupancy sites were identical, and only a small percentage of compensatory peaks were observed (Fig. 2C). Importantly, the upper bound of the conservation estimates for human and mouse ProEs are still below the standard estimates for human ProEs and K562 cells. Overall, these data suggest that while select master regulators, such as KLF1, are under strong constraint, most master regulators, including GATA1, TAL1, and NFE2, are under weak to moderate constraint. Second, these data suggest that although TF occupancy sites are often lost during evolution, functional effects from these losses are partially buffered by the emergence of compensatory occupancy sites, a possibility that we validate in subsequent analyses. Our findings, both globally across the genome and in promoter regions, support the idea that intra-species TF occupancy is more conserved than inter-species TF occupancy.
In contrast to GATA1, TAL1, and NFE2 binding motifs, KLF1 motifs (SP1 or CACC) were not centrally enriched around the summit of KLF1 occupancy sites (although enrichment for the canonical motifs were observed across the entire peak). A thorough investigation of enriched motifs in KLF1 occupancy sites revealed that GATA1 and GATA1/TAL1 motifs were proximally, but not centrally, enriched in KLF1 peaks in human (Fig. 2D, Fig. 3A–B, S1–S8 Dataset). Furthermore, KLF1 motifs were recovered in both GATA1 and TAL1 peaks for both human and mouse, suggesting that regions co-occupied by KLF1, GATA1, and TAL1 are true CRMs under stricter evolutionary constraint than regions occupied by each factor alone (Fig. 3A).
To address this hypothesis, we mapped combinatorial occupancy regions of these three factors from the mouse to the human genome (S6A Fig.). We discovered that when one or more TF overlapped, the region was more likely to be conserved (p<10−5 for each, Fig. 3D). Confirming our hypothesis, ∼35% of regions co-occupied by GATA1, KLF1, and TAL1 in mice were also co-occupied by all three factors in humans, a result far more likely than by chance and a higher rate of conservation than any other grouping (p<10−5, Fig. 3C–D). Our observation that certain co-occupied TFs are more conserved than individual TFs is consistent with similar findings across closely related mammalian species [35].
This result suggests that CRMs co-occupied by all three master regulators are important for the regulation of highly conserved processes during erythropoiesis. Confirming this, we found that the majority of these regions localize to and may act as enhancer elements for a number of genes important for erythropoiesis including: β-globin, heme biosynthetic enzymes, red cell membrane and surface proteins, and master regulators of erythropoiesis (Fig. 3E–F). Additionally, the importance to erythropoiesis of many of the genes proximal to these constrained enhancers is unknown, providing a short list of potential new regulators of erythropoiesis under strict evolutionary constraint (S4 Table). Overall, these observations validate the full extent to which KLF1, in conjunction with GATA1 and TAL1, regulates many facets of mouse and human erythropoiesis [18], [36], [37].
Considering the divergence in TF occupancy sites between species, we investigated the extent to which underlying chromatin structure was associated with the observed loss or gain of different master TF occupancy sites. We undertook a comprehensive approach to annotate all regions of the genome by utilizing a hidden Markov model (HMM) to infer 15 biologically meaningful chromatin “states” in ProEs and for K562 cells, each comprised of multiple different histone modifications with varying “strengths” (i.e. frequencies) for every 200 bp region across both genomes (Fig. 4A, S7 Fig., S8 Fig., S9 Fig., S5 Table, see materials and methods for details) [38].
To facilitate comparisons across species, master regulator occupancy sites were grouped according to conservation. “Conserved” occupancy sites were defined as occupancy sites present in both mouse and human orthologous genomic regions, “lost” or “mouse-specific” were present in mouse but not in human, “compensatory” were gained in human proximal to a lost occupancy site, “gained” or “human-specific” were present in human but not in mouse, and “strongly gained” sites are the top 10% of human-specific occupancy sites.
We observed that conserved occupancy sites were most significantly enriched for active chromatin states that include strong enhancers and promoters (state 5, 6, 8, 10, 11, 12) (Fig. 4B). These states were also enriched at compensatory, gained, and strongly gained (human-specific) occupancy sites, but not at lost (mouse-specific) occupancy sites (Fig. 4C–F, p<10−5 for each comparison versus lost). Importantly, we observed that active regulatory states are more enriched for both conserved and strongly gained TF occupancy sites than for compensatory or all gained sites (Mann-Whitney test, p<10−5), but we did not observe a difference in enrichment between conserved and strongly gained sites (p = 0.56) or between compensatory and gained sites (p = 0.54). This pattern of regulatory chromatin enrichment was replicated in K562 cells, which themselves appear to have a similar chromatin state to human ProEs, supporting the functionality of these definitions (S10A–E,G Fig.).
In mouse ProEs, conserved TF occupancy sites were also enriched at active chromatin states and mouse-specific occupancy sites, while no enrichment was observed at strong human-specific sites, suggesting first that conserved TF occupancy sites are functional and preserve strong regulatory chromatin structure across millions of years of evolution (S10F Fig.). However, it also suggests that there is a dramatic change in chromatin structure at orthologous human- and mouse-specific TF occupancy sites (4F Fig.).
To determine if functional changes in transcription are associated with alterations in TF occupancy during the course of evolution, we investigated occupancy near species-specific genes (see materials and methods). We discovered that human-specific genes are significantly enriched for human-specific (gained and strongly gained) TF occupancy (Fig. 4G). Corresponding to this observation, mouse-specific genes are significant enriched for mouse-specific (lost) TF occupancy sites. Surprisingly, these genes are also enriched for conserved and compensatory occupancy sites, a finding that we investigate more thoroughly below.
Although the direction of causality is difficult to determine, we suggest that master TFs partially drive epigenomic evolution at orthologous genomic regions by mediating changes to the underlying chromatin structure. Indeed, it has been shown in corresponding null cell lines that the addition of master TFs, such as GATA1, can remodel chromatin structure to increase transcription of certain genes, but our results suggest that master regulators play a far more important global role in chromatin remodeling during evolution [39]. Alternatively, de novo chromatin remodeling may impair the ability of TF complexes to bind, resulting in the transcriptional changes observed.
We sought to understand the functional consequences of the observed epigenomic differences by quantifying the extent to which changes in chromatin structure and master regulator occupancy explain transcriptional divergence between species during terminal erythroid differentiation. We verified, using time-series RNA-seq data of gene expression, that intra-species transcription is indeed more conserved than inter-species transcription (S11 Fig.) [11]. For example, the gene expression profiles of late stage human OrthEs are more similar to early stage human ProEs than they are to mouse OrthEs (S11 Fig.). We observe that the matching early progenitor states (ProEs and BasoEs) are more similar to their species-specific erythroid cell model, K562 or G1E, than to corresponding stages across species (S11 Fig.).
Intensity of epigenomic marks around TSSs has been shown to explain up to ∼50% of gene expression, providing a simple framework to globally investigate species-specific differences in transcription [40]. We derived a naïve predictive model of transcription in ProEs based upon total epigenomic mark intensity in promoter regions using linear regression with an L1 penalty. Our derived models of ProE gene expression learned across both species using six histone modifications and four TFs performed well: without over fitting, these models are able to explain between 58% and 61% of the variation in gene expression for each species based upon the coefficient of determination (R2; Fig. 5A-B,D). Models learned independently on each species resulted in similar parameters and were unable to perform better, confirming that transcriptional “rules” are strongly conserved across species (S6 Table). This model remained highly predictive throughout terminal erythroid differentiation, providing further evidence that most epigenetic modifications are dynamically determined at the ProE stage (S6 Table). Interestingly, in this model, chromatin modifications and not TF-occupancy, were most predictive of gene expression (H3K9ac, H3K4me3, H3K27me3, H3K36me3, and GATA1 in order of importance, Fig. 5E).
Having confirmed the biological significance of our model, we applied it to model differences in gene expression between species based upon changes in epigenetic marks. Utilizing this approach, we are able to explain 18% of the changes in gene expression between species. Considering that most genes are not differentially expressed between species, we applied our model to only species-specific expressed genes (see materials and methods). In this case, we are able to explain 34% of the variation in gene expression between species based solely upon promoter epigenetic mark (Fig. 5C).
Although our promoter model was highly predictive and elucidated functional biological divergence, we further address the possibility that transcriptional changes are also associated with TF occupancy without restricting our analysis to only promoter regions. Specifically, we investigated the hypothesis that the evolutionary loss or gain of TF occupancy at CRMs is indicative of changes in nearby gene expression. We summarized time-series gene expression profiles for each of the categories of TF conservation that we defined previously (conserved, gained and strongly gained (human-specific), lost (mouse-specific), compensatory, and two additional subcategories of conserved occupancy sites, Fig. 5F and S12 Fig.).
Across all TFs, genes proximally occupied by at least one conserved TF were expressed at significantly higher levels across all cell states, from ProE to orthochromatic erythroblasts (OrthEs) (Fig. 5F and S12 Fig.). The sequential ordering by differential gene expression of groups associated with different TF occupancy (conserved > strongly gained > compensatory > gained > lost) is identical to the ordering of these groups based upon their association with active regulatory states. Furthermore, limited evidence suggests that while gene expression is most similar between groups at terminal stages, loss or gain of master TF occupancy may affect the timing of gene expression, resulting in subtle differences in expression during differentiation (S12 Fig.).
Applying this method to cross species differences in expression, we discover that genes proximally occupied by a strongly gained TF site show human-specific expression during terminal erythroid differentiation (Fig. 5G, S13 Fig.). Interestingly, the genes that show the strongest mouse-specific expression are, first, occupied by one or more TF sites that are conserved across species but, second, have one or more mouse-specific TF occupancy site. These findings suggest that the gains and losses of TF occupancy sites are associated with changes in transcription across species. Moreover, we remark on the observation that even though a single TF may be conserved across species, changes in the occupancy of other TFs at nearby regions may have large functional effects, similar to previously reported results [35].
As a general principle, our observations show that conserved TF occupancy across species is associated with both strong gene expression and active regulatory states. Indeed, while this principle has been shown for conserved GATA1 DNA binding motifs on a small scale, we have confirmed this principle for multiple master regulators with biochemical data across both genomes [41]. Slightly attenuated patterns are observed for species-specific TF occupancy, while orthologous genomic regions of lost TF occupancy show little enrichment for active regulatory states and are indicative of low gene expression. Furthermore, these data suggest that not all species-specific occupancy sites have an immediately observable function: only strong human-specific occupancy was clearly associated with actively transcribed genes.
To illustrate specific features of epigenomic conservation and divergence during cellular differentiation, we examined a few well-known regulatory regions involved in erythropoiesis, leveraging our framework to gain further insight into the physiological relevance of these differences. We first describe two regions of general epigenomic conservation with subtle, but important, differences.
We investigated the well studied locus control region (LCR) of the developmentally regulated β-like globin genes [42]. In both species, the LCR consists of 5 closely spaced regulatory regions, termed hypersensitive sites (HSs), directly upstream of the embryonic and adult β-like globin genes (Fig. 6). Each region in the LCR has been shown to loop to developmental stage-specific β-like globin genes to promote high-level gene expression [43], [44]. Here, we investigate the conservation of TF-occupancy and chromatin state assignment at the first four HSs.
We observe that the TF-occupancy profiles of these HSs are strongly conserved across species (Fig. 6A–B). In particular, GATA1 and TAL1 bind strongly to each HS. Canonical DNA motifs conserved across 46 vertebrates and present in both human and mouse genomes can be identified (Fig. 6C). Interestingly, we observe that the 1st and 3rd HSs are two of the highly constrained CRMs co-occupied by GATA1, TAL1, and KLF1, confirming that these HSs are under strict evolutionary constraint. We observe stronger KLF1 intensity at HS1 compared to HS2 in human, but stronger KLF1 intensity at HS2 versus HS1 in mouse. Additionally, we observe increased NFE2 occupancy at HS3 in humans compared to the same region in mouse. Furthermore, the first four HSs in the LCR are associated with strong/poised enhancer states in humans whereas they are associated with strong/weak enhancer states in mice. Finally, although GATA1 and TAL1 occupy HS4 across species, the specific binding sites in this HS appear to be different for human and mouse (Fig. 6C). Overall, chromatin structure and TF occupancy at the β-globin LCR is largely conserved, but subtle differences may have effects on stage-specific transcriptional patterns.
We next turned our focus to the large 2nd intron of BCL11A containing an erythroid specific enhancer that, when disrupted in mouse cell lines, reduces BCL11A transcription and has been suggested to underlie common genetic variation of this key globin switching factor [45], [46]. This enhancer is occupied by GATA1 and TAL1 in humans and contains a GATA1/TAL1 motif that is partially disrupted by the minor allele of the common polymorphism, rs142707 (degenerative TAL1 motif is CAT for the wildtype and CAG for the minor allele, S14A–C Fig.). Although this binding motif is conserved across species, the guanine minor allele in humans is the ancestral allele present in other primates and mice (S14C Fig.). We observe that GATA1 and TAL1 are also enriched at this site in mouse ProEs, suggestive of a conserved function for this enhancer element (S14A Fig.). Nevertheless, we more broadly observe divergent patterns of TF occupancy, histone modifications, and gene expression across species, suggestive of functional differences at this locus (S14A–B,D Fig.). This finding emphasizes that caution must be applied when investigating and interpreting results from single TF occupancy or HS site data alone rather than a comprehensive approach that includes multiple factors and histone modifications across a broader region.
Next, we focus on two examples that show substantial divergence across the epigenome. Recessive mutations in SEC23B have been implicated in congenital dyserythropoietic anemia type II (CDA II), but an erythroid phenotype could not be recapitulated in mouse models [12]–[14]. One hypothesis for this observation is that while SEC23A is not expressed in similar human cell-types, Sec23a is expressed in mouse and is functionally able to compensate for the absence of Sec23b, resulting in the absence of a phenotype in Sec23b knockout mice [10], [47]. We therefore investigated these potential differences in transcriptional regulation.
We observed no clear differences in TF occupancy or histone state at SEC23B, and this gene is similarly expressed between species (Fig. 7E, S15A–B Fig.). Thus, we focused on SEC23A. While we observed some small differences in TF occupancy between species, the most striking difference is that the local region surrounding human SEC23A is in a general state of heterochromatin (state 13) or polycomb repression (state 14), whereas the region around mouse Sec23a is comparatively open for transcription (Fig. 7G–H). Expanding out to a small region around SEC23A, three homologous genes are present in both species and exhibit similar species-specific chromatin states as well as similar gene expression pattern corresponding to their matching SEC23A/Sec23a gene (Fig. 7F–H). This suggests that transcription in the local region around SEC23A is repressed in humans whereas the homologous region around mouse Sec23a is significantly more transcriptionally permissive. This finding not only provides evidence for why knockout of Sec23b in mouse does not recapitulate the human disease phenotype, but also highlights a principle of epigenomic divergence: in concordance with our simplified promoter model (Fig. 5A–C), the local genomic region has transitioned during evolution from a low/active state in mouse to a repressed state in humans, and transcription has been blunted as a result. Alternatively, the reverse possibility may have occured: for an unknown reason transcription has decreased in this region, driving the chromatin changes that are observed.
Finally, we investigated a locus of interest where a gain of TF occupancy in a non-homologous genomic region is associated with species-specific gene expression (observed globally in Fig. 5F–G). Growth differentiation factor 15 (GDF15) is one of the most highly expressed genes in human differentiating erythroblasts but is absent in mouse erythroblasts (Fig. 7A) [11], [48]. GDF15 has been suggested to play an important role in the regulation of iron homeostasis as a result of changes in the extent and effectiveness of erythropoiesis [49]. Patients with β-thalassemia and other diseases characterized by ineffective erythropoiesis show increased levels of GDF15 expression [50]. By analyzing epigenomic patterns at this locus, we identified a species-specific difference in chromatin structure at the GDF15 locus: human GDF15 in ProEs has a strong promoter, whereas mouse Gdf15 in ProEs has a poised promoter (Fig. 7B–C). Most importantly, while we identified some TF occupancy near mouse Gdf15, we identified a novel, putative CRM occupied by GATA1, TAL1, KLF1, and NFE2 upstream of human GDF15 that is absent from the larger region that encompasses mouse Gdf15 (Fig. 7B–D). Comparing the underlying genomic sequence of this putative enhancer across species, we found that this region is highly conserved in primates, but not in mice (Fig. 7D), suggesting that human GDF15 expression may be driven by this element that is absent from mouse.
While numerous studies have been performed to understand how epigenomic modifications play a role in mediating cellular differentiation, only a limited number of studies have examined how these modifications have been altered during the course of evolution [29], [32], [34], [51]–[53]. Here, we have used erythropoiesis as a model of cellular differentiation to study how epigenomic modifications can underlie evolutionary changes in gene expression. We performed a systematic comparative analysis of occupancy for six histone modifications and four master TFs in both human and mouse primary ProEs as well as in erythroid cell lines, integrating our results with high quality gene expression data. Models based upon promoter marks were highly predictive of gene expression and were nearly identical for both human and mouse ProEs. While we observed that chromatin modifications, at least in promoter regions, were generally conserved across species, subtle differences in H3K9ac, H3K4me3, H3K27me3, and H3K9ac were associated with differential gene expression between species. This finding partially accounts for the previously reported divergence in gene expression during terminal erythroid differentiation across species [10], [11].
We found that only ∼25% of GATA1, TAL1, and NFE2 occupancy sites present in mouse ProEs were conserved in human ProEs; however, the loss of these sites is often offset by the acquisition of nearby compensatory TF occupancy sites. To some degree, compensatory sites appear to buffer transcriptional changes that occur from the original loss. This finding is consistent with the reported conservation and compensatory action of master regulators in other cell types that are found between closely related species [33], [34], [53]. In juxtaposition to other master regulators, KLF1 occupancy was highly conserved between human and mouse, approaching the conservation rates of TFs in closely related species of insects [52], [53]. Acting in combinatorial fashion with GATA1 and TAL1, we show that CRMs co-occupied by these three TFs are under strong evolutionary constraint and localize to genes that play a key role in defining erythroid cell state.
The critical role of these TFs in defining erythroid cell state is highlighted by human genetic studies that have identified causal mutations for various forms of anemia in GATA1 and KLF1 [54]–[58]. We suggest that disruption of these modules in either human or mouse progenitor cells would severely compromise terminal erythroid differentiation, and thus these regions may harbor non-coding polymorphisms in humans that underlie human erythroid disorders. In particular, polymorphisms that disrupt a GATA1, TAL1, or KLF1 binding motif in conserved or strong species-specific occupancy sites would be leading candidates for causal mutations in these disorders. For example, we identified that GATA1 and TAL1 co-occupy the first intron of UROS in both human and mouse. While coding mutations in UROS have been identified in over 50% of patients with congenital erythropoietic porphyria, rare mutations that disrupt a constrained GATA1 binding site in the first intron of UROS have been found in patients lacking a putative coding mutation [59]. Indeed, in an era when whole genomes of patients with various diseases can be readily sequenced, identification of causal mutations is frequently a difficult problem [60], [61], and results from this study could help prioritize variants identified by such approaches. Furthermore, in Mendelian diseases where a pathogenic coding variant is not immediately identifiable, targeted sequencing of conserved TF occupancy sites near causal genes could prove useful as an inexpensive and likely high-yield approach in comparison to whole genome sequencing.
In contrast to mouse ProEs, we found that TF occupancy in K562 cells is strongly conserved with human ProEs. We suggest that for certain erythroid disorders, K562 cells may more faithfully recapitulate features of the disease than primary mouse cells, particularly in cases where epigenetic or transcriptional regulation may be disrupted. The framework we have created provides an opportunity to prospectively ascertain the extent of conservation between mice and humans for various aspects of the transcriptional landscape underlying erythroid differentiation.
In this study, we confirmed and uncovered multiple principles of epigenomic conservation. We found that conserved TF occupancy between species is strongly associated with active regulatory regions and strong transcriptional activity during terminal erythroid differentiation. Similarly, the strongest human-specific TF occupancy sites were also associated with regions of active regulation and strong transcription. When extrapolating on information gained from TF occupancy in mouse ProEs, it is important to consider not only that ∼75% of regions are not conserved in humans, but also that regions of lost TF occupancy exhibit reduced regulatory modifications as well as, on average, reduced transcription of genes across all stages of terminal erythroid differentiation. As a result, we emphasize the importance of using such a comparative framework when examining whether findings from mouse models of erythropoiesis may have relevance to human blood production.
We have used our framework to interrogate specific regulatory regions as well as genes important in erythropoiesis to illustrate and provide vignettes for the principles that we identified on a more global scale. In particular, the results we present provide evidence that human GDF15 is actively transcribed and contains unique CRMs not found near mouse Gdf15, consistent with its reduced expression in mouse erythroid cells. This finding is important when interpreting the role of Gdf15 in mouse models, and further investigation on the epigenetic regulation of GDF15 may help explain variation in iron and erythroid homeostasis between mice and humans. In other cases the epigenomic landscape is more conserved, such as at the β-globin or BCL11A gene loci, although subtle variation may explain the species-divergent gene expression patterns that are observed.
Some limitations should be considered when interpreting the results of our study. First, while we included over 50 ChIP-seq datasets in our analysis, there are other histone modifications (e.g. H3K27ac), TFs (e.g. ZFPM1 and SPI1/PU.1) non-coding RNAs, and methylation patterns that may be important for understanding species-specific differences in erythropoiesis. Furthermore, we cannot exclusively rule out the possibility that certain peaks are “hyper-ChIPable” due to a lack of IgG control datasets, although recent work provides convincing evidence that this consideration, while critical in yeast, is far less of a concern in complex metazoans [62]–[64]. Finally, although the ProE stage is ideal to investigate epigenetic changes that occur to mediate alterations in the transcriptional landscape [19], [20], [26], [27], we did not investigate temporal changes in epigenomic marks during earlier or more terminal stages of differentiation where species-specific differences may be more pronounced [34].
We have made all of our results publically available as filetypes that are quickly loaded into standard genome browsers (IGV and UCSC Genome Browser). These data could guide investigators in choosing appropriate model systems for studying blood diseases or other aspects of erythropoiesis as well as aid in the interpretation of their results. Overall, our comparative epigenomics approach has successfully explained a significant portion of the transcriptional divergence observed during erythroid differentiation in mice and humans.
Hg19 and mm10 were used throughout the entire analysis as reference genomes for all human and mouse cell types, respectively. Orthologous genes were defined using Ensembl mouse to human ortholog matching and were downloaded from BioMart; genes which matched one: many were excluded from all analyses, resulting in 15506 one: one orthologous genes used for analysis. A smaller subset of orthologous genes (6596) with well-defined canonical transcripts from RefSeq was used for all quantitative promoter analyses. To compare mm10 to hg19, the UCSC liftOver tool was used to lift coordinates over from one genome to another with one: one matching and 10% sequence conservation required. PAVIS was also used to annotate genomic regions such as TF-occupancy peaks based upon proximity to known genes [65].
ChIP-seq datasets were either downloaded from NCBI GEO or from the ENCODE project's homepage (S1 Fig.). SRA files were transformed to FASTQ using FASTQ -dump from the NCBI SRA toolkit (https://www.ncbi.nlm.nih.gov/books/NBK158900/). Raw reads were aligned to the hg19 and mm10 genomes using Bowtie v0.12.9 with options “-v 2 -m 3 —strata –best” [66]. The BEDTools suite was used for multiple operations, comparisons, and intersections of all resultant BED files [67]. Reads were extended to a fragment length of 200bps, normalized to million-mapped-reads, and control input (in million-mapped-reads) were subtracted. In all quantitative analyses, reads were log2 scaled and read into R 3.0. NGSplot was used to plot normalized average intensity curves across 15506 orthologous genes (-2000 from TSS to +2000 after TES) for all ChIP-seq datasets [68].
TF-occupancy peaks were initially called using MACS 1.4 to estimate fragment size [69]. When replicates were present (e.g. GATA1 and TAL1, S1 Fig.), MM-ChIP was used to combine and robustly call peaks from datasets across multiple laboratories and technical conditions to create sets of high-quality peaks [70]. The top n-percentile of each set of peaks was defined based upon the total number of mapped reads present in the peak region. When regions/peaks were lifted over from one species to another, the denominator used was always the number of regions which mapped to the new genome successfully from the original genome, while the numerator was the number of mapped regions that overlapped with the target region in the new genome. If peaks from a single TF were lifted across genomes, only a narrow region (+/− 50 bps around summit) was mapped to reduce the probability of incorrect mappings due to non-functional decrease in sequence similarity at the far edges of peak regions called by MACS. DNA motif enrichment was performed using MEME-ChIP in the MEME Suite with standard options [71]. E-values are reported as corrected p-values in all figures. Enrichment of combinatorial TF occupancy was assessed using 100,000 permutations across the genome with the Genomic Association Tester [72]. Chromatin states were estimated for 200 bp bins spanning both genomes using a Hidden Markov Model (ChromHMM) [38], [73]. We settled on a 15 state model learned on all three cell types together, although we examined models ranging from 12 to 20 states (Fig. 4A and S7 Fig.). Biological relevance for each state was assigned based upon frequency of chromatin marks and functional enrichments similar to previous studies [73]. For example, the “Active Transcription” state is marked exclusively by H3K36me3 and is enriched primarily for exonic regions whereas the “Strong Promoter” state is marked by H3K4me2 and H3K4me3 but not H3K4me1 and is enriched for TSSs. The final model was highly conserved between models derived exclusively from mouse ProEs, human ProEs, and K562 cells (Fig. 4A, S8 Fig., S9 Fig.). Enrichment of chromatin states across regions of TF occupancy was compared using the “OverlapEnrichment” command in ChromHMM.
We performed multiple analyses to address the possibility that ChIP protocol differences may underlie the TF occupancy differences observed in analyses such as promoter differences and peak calling. First, we note that we were able to recover large sets of peaks (>5000 peaks) for each TF in each species, suggesting that immunoprecipitation was nominally successful. Indeed, western blots in human and mouse cell types suggest that these antibodies are specific for both human and mouse TFs (http://genome.cse.ucsc.edu/cgi-bin/hgEncodeVocab?ra=encode%2Fcv.ra&term=%22GATA1_(SC-266)%22 and 2TAL1_(SC-12984)%22). Importantly, these peaks were all significantly enriched for the TF canonical motif (Fig. 2D). This result is not surprising, given that human and mouse GATA1 and TAL1 share >86% similarity in amino acids based upon the Ensembl database. Furthermore, when we investigated occupancy site conservation between species by performing a sensitivity analysis on only the strongest 25% of peaks, the conservation rate of the least conserved TF (TAL1) showed a similar increase to that of the most conserved TF (KLF1), suggesting limited bias between species (S5 Fig.). Based upon this evidence, we believe that the most likely reason for the observed difference in absolute occupancy between species is that certain aspects of the protocols used vary between species and introduce bias such that weaker peaks in mouse ProEs may not be as readily observed in these cases. Alternatively, the difference in the number of occupancy sites could be a true biological observation. Regardless of the case, our estimates for conservation of TF occupancy scale with the absolute number of peaks called in mouse. In other analyses, we normalized between human and mouse to account for these differences.
Single-end RNA-seq datasets of primary erythroblasts were downloaded from NCBI GEO (GSE53983) [11]. K562 and G1E RNA-seq data was publically available from ENCODE and was also downloaded NCBI GEO (GSE40522) and from the ENCODE website (http://genome.ucsc.edu/ENCODE/). Five human time points (ProE, early BasoE, late BasoE, PolyE, OrthoE) and four mouse time points (ProE, BasoE, PolyE, OrthoE) with three replicates each were used in this analysis. Similar to ChIP-seq data processing, SRA files were transformed to FASTQ using FASTQ-dump. RNA-seq data was processed with the Tuxedo Tools Suite using the same options as recent protocols, except CuffQuant and CuffNorm were used to derive normalized (FPKM; fragments per kilobase of transcript per million mapped reads) and raw count data for each transcript [74]. In particular, raw reads were aligned to the genome using TopHat v2.0.10. Cufflinks v2.1.0 was used to assemble transcripts, CuffMerge was used to merge all annotations (separated by species), and CuffQuant and CuffNorm were used to output data at the gene, TSS, and promoter level that was then imported to R. All Tuxedo Suite tools were run using standard options as indicated [74].
For predictive analyses of ProE gene expression, we first quantile normalized our epigenomic profiles to account for any species-specific biases in intensity and integrated this dataset with stage-matched RNA-seq data. In order to derive predictive models of transcription for each species without over fitting parameters, we performed standard linear regression with L1-penalization (i.e. lasso regression, [75]). In this model, the β value of each predictor is shrunk towards zero until an optimum solution is reached; variables that add little predictive value are excluded (β = 0). Subsequently, 100-fold cross validation is performed for different lambda “penalization” values and a lambda one standard error lower than the best model was chosen to prevent over-fitting. In R, glmnet was used to perform L1-penalized linear regression [75].
We used the Integrative Genomics Viewer (IGV) to view epigenomic mark intensity files [76]. Bed files of aligned reads were extended to a fragment size of 200 bps and intensity files (bigwig) were created using UCSC Genome Browser tools. Enrichments were shown on a log scale unless otherwise noted and a cut-off of 20 bps was used as a lower bound in all representations unless otherwise noted. Phastcons nucleotide substitution rate score (0 to 1) and the primate and mouse (mm10) to human (hg19) alignments from the multiz 46-vertebrate multi-alignment were also used as available for import from the IGV servers [77], [78].
We have made aligned and processed ChIP-seq data for all six histone modifications, four transcription factors, and derived chromatin states for all cell-types available on GEO at GSE59801. Gene expression data processed in our pipeline is also available as aligned reads and as FPKM and counts for each gene. Furthermore, robustly defined transcription factor occupancy peaks and information regarding conservation, gain, or loss across species as discussed in the analyses is also made available in the same location.
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10.1371/journal.pgen.1000007 | Genome-Wide Assessments Reveal Extremely High Levels of Polymorphism of Two Active Families of Mouse Endogenous Retroviral Elements | Endogenous retroviral elements (ERVs) in mice are significant genomic mutagens, causing ∼10% of all reported spontaneous germ line mutations in laboratory strains. The majority of these mutations are due to insertions of two high copy ERV families, the IAP and ETn/MusD elements. This significant level of ongoing retrotranspositional activity suggests that inbred mice are highly variable in content of these two ERV groups. However, no comprehensive genome-wide studies have been performed to assess their level of polymorphism. Here we compared three test strains, for which sufficient genomic sequence is available, to each other and to the reference C57BL/6J genome and detected very high levels of insertional polymorphism for both ERV families, with an estimated false discovery rate of only 0.4%. Specifically, we found that at least 60% of IAP and 25% of ETn/MusD elements detected in any strain are absent in one or more of the other three strains. The polymorphic nature of a set of 40 ETn/MusD elements found within gene introns was confirmed using genomic PCR on DNA from a panel of mouse strains. For some cases, we detected gene-splicing abnormalities involving the ERV and obtained additional evidence for decreased gene expression in strains carrying the insertion. In total, we identified nearly 700 polymorphic IAP or ETn/MusD ERVs or solitary LTRs that reside in gene introns, providing potential candidates that may contribute to gene expression differences among strains. These extreme levels of polymorphism suggest that ERV insertions play a significant role in genetic drift of mouse lines.
| The laboratory mouse is the most widely used mammal for biological research. Hundreds of inbred mouse strains have been developed that vary in characteristics such as susceptibility to cancer or other diseases. There is much interest in uncovering differences between strains that result in different traits and, to aid this effort, millions of single nucleotide differences or polymorphisms between strains have been cataloged. To date, there has been less emphasis placed on other sources of genetic variation. In this study, we have conducted a genome-wide analysis to examine the level of polymorphism of mouse endogenous retroviral sequences (ERVs). ERVs are derived from infectious retroviruses that now exist in the genome and are inherited as part of chromosomes. Unlike in humans, genomic insertions of ERVs cause many new mutations in mice but their extent of variation between strains has been difficult to study because of their high copy numbers. By comparing genomic sequences of four common mouse strains, we found very high levels of polymorphism for two large active families of ERVs. Moreover, we documented nearly 700 polymorphic ERVs located within gene introns and found evidence that some of these affect gene transcript levels. This study demonstrates that ERV polymorphisms are a major source of genetic variability among mouse strains and likely contribute to strain-specific traits.
| The laboratory mouse is the model of choice for mammalian biological research and a plethora of mouse genomic resources and databases now exist [1]. Notably, fueled by availability of genomic sequence for the common strain C57BL/6J (B6)[2], several groups have documented genetic variation among strains using single nucleotide polymorphisms (SNPs) [3]–[5]. Surveys of mouse polymorphism due to segmental duplications or copy number variations have also recently been published [6],[7]. Such resources are invaluable in trait mapping, in tracing strain origins and in genotype/phenotype studies. However, to date, genome-wide studies to document other types of genetic variation have been lacking. For example, long terminal repeat (LTR) retrotransposons/endogenous retroviral elements (ERVs) are known to be highly active in inbred mice, causing ∼10% of spontaneous mutations [8], but relatively little is known about the level of polymorphism of such sequences. Southern blotting and extensive genetic mapping has clearly demonstrated that ERVs related to murine leukemia virus (MLV) are highly polymorphic [9]–[11], but such techniques are feasible only for low copy number ERVs which constitute a very small fraction of ERVs and LTR retrotransposons in the mouse genome. Due to the array-based technology employed, the largest mouse polymorphism study performed by Perlegen focused only on SNPs and was not designed to detect insertional ERV polymorphisms [5].
Compared with a single nucleotide difference, genetic variation due to insertion of an ERV obviously has a much greater probability of affecting the host. Not only is the absolute change in the DNA much larger, but the inserted ERV sequences also carry powerful transcriptional regulatory elements that can influence host genes. The phenotypes of most mouse germ-line mutations caused by ERV insertions result not from simple physical disruption of coding regions, although this does occur, but rather from transcriptional abnormalities mediated by ERVs located in introns or near the affected genes [8]. It is also well appreciated that retroviruses can activate oncogenes or growth control genes leading to malignancy [9],[12],[13], and indeed, are used as tags to identify genes involved in cancer [14],[15]. Determining the extent of mouse ERV polymorphism is therefore critical in understanding how ERVs contribute to diversity and disease susceptibility among inbred strains.
The retroviral-like Intracisternal A Particle (IAP) and the MusD/Early Transposon (ETn) families are two high copy number ERVs responsible for most of the insertional germ-line mutations described in mice. IAP elements have been extensively studied since the early 1980s [16] and cause both germ line mutations as well as oncogene or growth factor gene activation in somatic cells [8],[9],[12],[17],[18]. ETn elements were also originally reported in the early 1980s as a non-coding transposon-like sequence expressed in early embryogenesis [19]–[21] and capable of causing new mutations. It is now known that ETns represent a non-coding subclass of the retroviral-like MusD elements [22],[23], which provide the proteins in trans necessary for ETns to retrotranspose [24]. Thus, throughout this study, this group is referred to as ETn/MusD elements.
According to a list compiled at the end of 2005 [8], six strain polymorphisms and 26 mutations due to insertions of IAP elements have been documented. Four polymorphisms and 19 mutations due to insertions of members of the ETn/MusD family have also been reported. Genomic hybridization and PCR methods have demonstrated that the IAP [25]–[27] and ETn/MusD [28] families are polymorphic among strains, but the extent of this variation and the potential consequences on phenotype are unknown. The goal of this work was to conduct a genome-wide assessment of the level of insertional polymorphism of the IAP and ETn/MusD families. A second goal was to identify polymorphic ERVs with the highest probability of affecting host genes. By comparing only the few strains for which sufficient genomic sequence is available, we found high levels of insertional polymorphism for both the IAP and ETn/MusD families. Moreover, we detected 695 polymorphic members of these families located within genes, and found evidence that some of these affect gene transcription. Such polymorphisms represent a substantial source of genetic variability among inbred strains and may play a major role in strain-specific traits.
As the first step to assess the ERV polymorphisms in mice, we conducted a survey of the overall copy numbers of IAP and ETn/MusD elements in the well-sequenced, assembled B6 genome using BLAST (see Materials and Methods for details). For the IAP family, we detected 2595 full-length or partly deleted elements plus 2477 solitary LTRs, for a total of 5072. ETn/MusD elements are less numerous than IAPs, with 1873 sequences in the B6 genome, 1457 of which are solitary LTRs. In accord with previous studies [29], our results indicated that solitary LTRs, the result of recombination between the 5′ and 3′ LTRs of proviral forms, are typically more common than full length ERVs.
For mouse strains other than B6, sufficient whole genome shotgun sequence traces (see Figure S1) are available for only three of them: A/J, DBA/2J, and 129X1/SvJ (referred to hereafter as the three test strains). To identify all traces containing IAP or ETn/MusD sequences, we used specifically designed ERV probes (see Figure S2 and Table S1) to screen the trace archives of the three strains with local sequence alignment. Sequences flanking the ERV segment in each trace were then used to map the region to a unique position in the assembled B6 genome and to combine redundant traces (see Materials and Methods). This screening method identified 1659, 1509 and 1379 ETn/MusD elements that could be assigned a unique location in A/J, DBA/2J and 129X1/SvJ, respectively. Similarly, for the IAP elements, we identified 4696 elements in A/J, 4320 in DBA/2J, and 3878 in 129X1/SvJ. As discussed above, our genomic survey detected 1873 MusD/ETn elements and 5072 IAPs in assembled B6 genome. The lower ERV numbers detected in the three test strains compared with B6 is likely mainly due to incomplete sequence coverage of the traces available for each strain. Another factor that contributes to the loss of detectable ERV insertions is inability to map the trace to a unique location, usually because the flanking non-ERV portion is too short, composed of other types of repeats, or is located within duplicated genomic regions. To determine the approximate fraction of elements from each of the three test strains that are not detectable due to incomplete sequence coverage or other reasons, we determined how many elements in the assembled B6 genome could be found with our method using randomly sampled sets of WGS traces from the B6 trace archive database. Using numbers of B6 traces equivalent to that available for A/J (11,646,236), DBA/2J (7,998,826) and 129X1/SvJ (5,998,950), we detected 83.8%, 77.9% and 68.6% of the 1865 ETn/MusD insertions present in the assembled B6 genome (Figure S3). Thus, it seems reasonable that approximately 16.2%, 22.1% and 33.4% of the ERVs present in the three test strains are not found due to incomplete coverage or mapping difficulties. Moreover, this B6 trace sampling experiment also allowed us to conservatively estimate the false discovery rate of this procedure to be ∼0.4% (see Materials and Methods).
As outlined in Figure 1 and described fully in Materials and Methods, we designed a four-phase screening process to identify polymorphic ERVs. In the first phase, probes derived from known ERV sequences were used to screen the B6 assembled genome and a collection of ETn/MusD or IAP elements in B6 was obtained. In the second phase, illustrated in Figure 1A, we determined if the ERVs identified in the three test strains were also present in B6 by checking for existence of such ERV sequences at corresponding loci in the assembled B6 genome. In the third phase, represented in Figure 1B, we included the dataset of all ERVs present in B6 and determined the presence of these ERVs in the three test strains. To achieve this, we retrieved the 5′ and 3′ flanking sequences from elements present in the assembled B6 genome, obtained those flanking segments that could be uniquely mapped to the genome and then identified sequence traces from the test strains that contain these flanking segments. The traces were then checked for presence of the ERV. In the final phase, a similar strategy was applied to the polymorphic ERV insertions found in each test strain (but not in B6), and the existence of corresponding ERVs in the other two test strains was assessed. The combination of these strategies allowed us to compile lists of ERV genomic locations and the polymorphism status of each ERV in the four strains. Due to inability to uniquely map many ERV flanking regions to the short, unassembled sequence traces, the status of many elements present in the assembled B6 genome could not be computationally determined in the test strains (see below). In addition, as discussed above, incomplete sequence coverage of the test strains results in an “unknown” status for a proportion of ERVs in each test strain.
In spite of these limitations, we identified a large number of polymorphic ERVs (Figure 2). Of all IAP elements detected in at least one strain, 2143 were present in all four strains while 3394 elements were scored as polymorphic (absent in at least one of the four strains), giving an overall polymorphic fraction of 61.3%. For ETn/MusD elements, 1087 were mapped as present in all four strains and 375 could be scored as absent in one or more strains, a polymorphic fraction of 25.6% of all the elements having a determinable status. Another 1767 IAP and 660 ETn/MusD elements present in the assembled B6 genome could not be mapped to the test strain traces due to incomplete trace coverage or repetitive flanking regions, so their polymorphic status could not be computationally determined. These high levels of insertional polymorphism were obtained by considering just four strains and despite the fact that the status of many elements could not be ascertained in some strains. Thus, the numbers of polymorphic ERVs among inbred mice must be significantly higher.
Previous studies on human ERVs have shown that they are less prevalent in gene introns than expected by chance, likely due to selection against LTR elements found in genes [30]–[32]. Although they can affect genes at significant distances [9],[13],[17], retroviral elements or LTRs in introns are more likely to impact expression by introducing powerful transcriptional regulatory elements and splice sites [32]. Moreover, genomic analyses in several species have shown that ERVs/LTRs in introns are more likely to be oriented antisense to the enclosing gene [30]–[34]. Since retroviruses show no orientation bias upon insertion into genes (i.e. 50% in sense direction) [34],[35], this antisense bias is likely the result of stronger negative selection against sense-oriented elements. Indeed, of the 19 cases of ETn elements known to disrupt gene expression in various new mutations, 16 are oriented in the same direction as the gene [8],[32], indicating that sense-oriented elements are much more likely to perturb gene expression, causing a detectable phenotype, and being subject to negative selection. Although the original integration site preferences for ERVs are generally unknown, two studies have mapped small sets of fresh (unselected) insertions of IAP and ETn/MusD elements in retrotransposition assay systems and the data are consistent with a fairly random pattern of integration and no strand bias upon insertion into transcriptional units [24],[36].
Given that the genomic distributions of ERVs fixed in a species are strongly shaped by selection, we predict that recently inserted ERVs will display genic distributions different from their older cousins. To test this prediction, we compared the distributional properties of a subset enriched for the youngest ERVs with that of ERVs common to all four strains. To obtain the youngest elements, we chose those present in only one strain and which could be computationally scored as absent in the other three strains. Many of these likely still represent older polymorphic elements due to the fact that lab strains are genetic mixtures of subspecies of Mus [3]–[5],[37]. However, this group will contain all the truly young elements that inserted after strain divergence. As shown in Figure 3, these datasets enriched for the “youngest” elements are more likely to be found in genes (Figure 3A) and in the sense orientation within genes (Figure 3B), compared with elements shared between all four strains. The higher prevalence in genes and reduced intronic orientation bias displayed by ERV subsets enriched for the youngest elements suggests that some are deleterious but have inserted very recently and have not been eliminated by selection.
Our bioinformatics screens identified 623 polymorphic IAP elements and 72 polymorphic ETn/MusD elements located within genes in one or more of the four strains. Complete lists of these elements and their locations with respect to the B6 genome are given in Tables S2 and S3. These tables list in which of the four strains each element was computationally detected by our screens. As discussed above, the question marks in the Tables are mainly due to mapping difficulties or incomplete sequence coverage of the trace databases. A subset of these elements was analyzed using genomic PCR on DNA from a panel of mouse strains (including B6 and the three test strains) with primers flanking the insertion site to verify the insertion status. For this analysis, we chose all 28 cases of ETn/MusD elements found in A/J gene introns but absent in B6, and 12 cases of ETn/MusD elements present in B6 gene introns but scored as absent in A/J (Table 1). For the 28 cases of elements computationally detected in A/J (cases 1–28 in Table 1), the ETn insertion in the dysferlin (Dysf ) gene (case #9) is the only previously reported case and occurred 20–30 years ago in the A/J breeding stocks [38]. For the set of 12 elements present in B6 (cases 29–40 in Table 1), the ETn element in the Wiz gene (case #40) has also previously been reported as polymorphic [28].
In total, these 40 selected cases and four strains generated an experimental space of 160 predictions. As shown in Table 1, columns with a strain name followed by a “(p)” indicate that data in these columns are computational predictions of the existence of the ERV insertions in the corresponding strain. After excluding 16 undeterminable instances (denoted as “?”s in these columns in Table 1), we computationally determined the presence of these ERV insertions in all four strains with a total number of 144 predictions. For 140 of these, our computational predictions precisely matched the experimental confirmation of ERV insertion status using genomic PCR, demonstrating a high accuracy of our bioinformatics screens. In one instance, (case #39 in DBA), the PCR failed so we could not test our prediction. Therefore, only three cases showed anomalous PCR results that did not match our bioinformatics predictions. One of these cases was #24 in Table 1, where we predicted an ETn/MusD insertion in an intron of the Sytl3 gene in A/J mice. Using PCR, we found no evidence for this insertion in the A/J DNA sample used. We then reexamined the A/J sequence dataset and found orthologous sequence traces both with and without this particular ERV element (Figure 4). The most likely explanation for this finding is that this ERV represents a very recent insertion present in a heterozygous state in the A/J genomic DNA used to generate the trace sequence data. Since the rate of ETn/MusD retrotransposition in A/J is relatively high compared with other strains [8], it is not surprising that individual A/J mice will have occasional “private” insertions. The second anomalous case was #34 of an ETn/MusD LTR found in the B6 genome within the Cadm4 gene, and confirmed as present in all tested strains by PCR (Table 1). Our computational screens correctly scored this LTR as present in DBA/2J and 129X1/SvJ but scored it as absent in A/J. Upon further examination of the sequence data, we found that one of the two available A/J sequence traces mapping to this location is an artifact since it contains a segment of unknown origin. The other trace is also unusual as segments of it map to two locations several kb apart. Thus, this case can be explained by artifactual sequence traces, demonstrating that the trace archives and, therefore, our dataset are not without errors. The last inconsistent case was #37, an insertion located within the Slfn8 gene and predicted as present in both B6 and DBA. In this case, the PCR verification in DBA showed that the element is not present. Since both the computational and experimental results were clear yet contradictory, we do not have a definitive explanation for this case, although it is possible the trace is not of DBA origin. In any event, this case was regarded as a false positive. In several instances, the PCR data also allowed us to assign a definite insertion status to elements in test strains that could not be predicted in silico due to incomplete sequence coverage of the traces (see Table 1).
As expected, some of these insertions are not specific to a single strain. This finding indicates that many of the polymorphic ERV insertions arose prior to divergence of common inbred strains or represent even older polymorphisms due to different origins of chromosomal segments in the genomes of today's lab mice. For the 28 cases present in A/J but absent from B6, the short A/J sequence traces do not contain the entire ERV, but length of the inserted element could be estimated from the size of the genomic PCR product for 25 of these cases (see last column in Table 1). In 15 cases, the size matches that expected for an ETn element of 5.5–6 kb, whereas two appear to be full length MusD elements of 7.5–7.8 kb and one is likely a partly deleted element (case #10). Seven are solitary LTRs (320–400 bp), so the nature of the original insertion cannot be determined since the LTRs of ETnII elements and MusDs are extremely similar [22],[39]. For the 12 elements present in the assembled B6 genome, seven are solitary LTRs, one is a partial element and four are ETn elements based on size and sequence. The element in the Wiz gene is a longer ETn variant [28]. The preponderance of polymorphic ETn elements over MusD was expected, given that most published mutagenic insertions of this family are of the ETnII subfamily [8],[28].
Since ERVs/LTRs can affect gene transcription via a variety of mechanisms, some of the polymorphic ERVs detected here may contribute to gene expression differences between strains, possibly leading to phenotypic differences. However, the factors that determine whether transcription of a gene will be affected by a nearby or intragenic ERV insertion are not understood and are likely complex. Thus, it is not possible to estimate what fraction of the polymorphic insertions documented here may have functional consequences. Nonetheless, we can predict which cases may be more likely to affect gene expression. In the majority of documented cases where a new mutagenic ETn/MusD insertion causes significant transcriptional defects, the element has been located within an intron in the sense orientation and disrupted splicing patterns of the gene [8]. Thus, we predict that ETn elements within introns and oriented in the same direction as the enclosing gene have a relatively high probability of affecting mRNA processing. Moreover, compared with older insertions, the youngest, polymorphic subsets of these elements are potentially more likely to impact host gene expression, as selection may still be operating in these cases.
Based on the above reasoning, we chose a subset of cases to examine further using the following criteria: First, since the consequences of IAP insertions can involve LTR bidirectional promoter effects [8] which are more complicated and difficult to predict, we focused on ETn/MusD insertions. Second, we chose intronic ETn elements oriented in the same direction as the gene. Third, we chose elements verified as present in A/J and lacking in B6 using genomic PCR (see Table 1). Seven such cases exist, involving ETns in the Dnajc10, Dysf, Opcml, Prkca, A2bp1, Mtm1, and Col4a6 genes. We performed RT-PCR on RNA from A/J mice using primers from the gene exon upstream of the ETn insertion, coupled with primers from within the ETn, chosen to detect the most frequently reported types of ETn-mediated transcriptional fusions from the literature [8]. Sources of RNAs were chosen based on known expression patterns of the gene. As shown in Figure 5, chimeric transcripts were detected for all five of the genes tested, namely Dnajc10, Prkca, Mtm1, Opcm1 and Col4a6. The sense-oriented ETn element found in the Dysf gene in A/J has already been shown to cause similar splicing defects [38] and we did not examine A2bp1. In most cases, the splice sites used in the ETn element in the examples analyzed here were analogous to those characterized in known mutagenic cases. However, for Prkca, this analysis showed that the insertion is a member of the ETnI subfamily, as opposed to ETnII, and revealed usage of cryptic splice acceptor sites not previously documented. (see Figure S4 for sequences of splice sites). It should be noted that the subset of chimeric transcripts shown in Figure 5 is likely an underestimate, since a limited number of clones were sequenced and not all transcript variants would have been detected with the primers used. This RT-PCR analysis demonstrates that these ETn elements cause patterns of aberrant splicing similar to those documented in cases of known mutations due to new ETn integrations. However, further quantitative analyses are required to determine the significance of these splicing abnormalities in affecting overall levels of gene expression. Such in depth experimental investigations for each case are beyond the scope of the present study.
We also surveyed microarray data on gene expression differences in inbred strains available through the Gene Expression Omnibus [40] (http://www.ncbi.nlm.nih.gov/geo/). We examined all cases listed in Table 1 for correlations between presence of the insertion and differences in gene transcript levels compared with strains lacking the insertion (see Materials and Methods). Specifically, we analyzed the microarray data of Zapala et al. [41] (NCBI GEO accession GSE3594) that includes data on gene expression in 10 tissues profiled in A/J, B6, C3H/HeJ, DBA/2J and 129S6/SvEvTac mice. For the Dnajc10 gene, tissue-wide reduction in expression was noted in A/J mice relative to the other four strains (p<10−4, Binomial distribution) (Figure 6). Microarray data available through the GeneNetwork web site (http://www.genenetwork.org/) also showed that transcript levels of this gene in A/J are much lower than in all other tested strains, based on whole brain, cerebellum, hippocampus and eye datasets (Figure S5). Dnajc10 has a sense-oriented ETn element in the third intron in A/J and the related A/WySn mice, but no other tested strain (Table 1). This gene (also termed ERdj5) encodes an endoplasmic reticulum (ER) chaperone protein induced during ER stress and is likely involved in protein folding [42],[43].
Another gene for which significant differences in expression correlate with presence of an ETn element is Opcml. No data is available from the Zapala et al. study on this gene but datasets accessed through GeneNetwork show that transcript levels in A/J, the only tested strain carrying an ETn insertion (Table 1), are significantly lower than in any other strain in cerebellum, whole brain, hippocampus and eye, the only tissues where A/J microarray information is available for this gene (Figure S6). Opcml (Opioid binding protein/cell adhesion molecule-like), also termed Obcam, is a member of the IgLON gene family and encodes a synaptic neural cell adhesion molecule [44],[45]. Loss of expression and/or promoter hypermethylation of this gene has been reported in some human cancers, suggesting that it may play a tumor suppressive role [46],[47]. We performed Northern blot analysis on total RNA from A/J and B6 cerebellum using a probe derived from the exon upstream of the insertion site and results are shown in Figure 7A. The ∼6.5 kb band corresponding to Opcml full length mRNA is markedly decreased in A/J compared with B6. A similar reduction in Opcml RNA was also observed in A/J using an exon probe downstream of the insertion site (data not shown). The two bands at 3–3.5 kb are due to cross-hybridization to another gene, neurotrimin (Hnt), which is a closely linked member of the IgLON family and highly related to Opcml in the region used as a probe [48]. We also performed semi-quantitative RT-PCR on total RNA from A/J and B6 cerebral hemispheres using primers from Opcml exons just upstream and downstream of the ETn insertion site and found an approximately 4.6-fold reduction in the correctly spliced Opcml RNA in A/J relative to B6 (Figure 7B). These results confirm the microarray data (Figure S6) and show that presence of the ETn insertion correlates with a substantial decrease in full length, correctly spliced Opcml mRNA. While there could be other reasons for the reduced transcript levels, such patterns suggest that the ETn element in these two genes significantly affects expression by causing aberrant splicing (as shown in Figure 5) allowing only a minor fraction of normal transcripts to be produced.
For all other cases from Table 1, including the other genes with insertions that cause aberrant splicing detected by RT-PCR (Figure 5), available microarray data was either inconsistent or did not show a clear relationship between presence of the insertion and altered levels of transcripts. These findings suggest that, in most cases, the ETn insertion has no significant effect on expression. This result is not surprising since thousands of ERVs or LTRs have become fixed during evolution in human and mouse genes [32], indicating that they can reside within introns without a functional impact. However, as illustrated by the Dysf case, the microarray data should be treated with caution. It has been convincingly shown by Northern analysis that A/J mice with the ETn insertion lack full length Dsyf mRNA and protein in skeletal muscle [38]. However, the available microarray data for Dysf is limited to cerebellum and whole brain, neither of which shows abnormally low transcript levels in A/J (data not shown). There could be several reasons for this discrepancy but it illustrates that wet lab approaches are necessary to properly evaluate each case.
Besides causing gene splicing defects similar to ETns, it is well established that IAP LTRs can also promote ectopic gene transcription in cases of somatic oncogene activations and germ line mutations [8],[9],[12],[17],[18]. Moreover, a few mutations caused by IAP-driven aberrant gene expression have been shown to act as metastable epialleles, exhibiting variable expressivity among genetic identical mice linked to the variable epigenetic state of the IAP LTR [17],[49]. In a recent study, Horie et al [50] identified transcripts from 11 loci in 129 strain embryonic stem cells that initiate in an IAP LTR and read into flanking sequence, in five cases giving rise to chimeric RNAs between an intronic IAP and the enclosing gene. In six of the 11 loci analyzed, the IAP element was not present in the B6 genome, prompting the authors to postulate that variations in IAPs may contribute to strain-specific traits [50]. We have not yet functionally examined any cases of polymorphic IAPs identified here to look for LTR-initiated fusion gene transcripts, but it is likely that numerous such cases exist.
Although mice and humans have similar overall numbers of old retroviral-related sequences in their genomes [2], recent levels of activity of these elements are vastly different in the two species. In humans, only about a dozen ERV loci are known to be polymorphic, and no mutations due to ERV insertions have been documented [51]. In mouse, however, ERVs/LTR retrotransposons continue to retrotranspose and are a significant source of new mutations as discussed above. Here we have used the available DNA sequence from four inbred strains to conduct an assessment of the level of insertional polymorphism of the currently active IAP and ETn/MusD ERV families. Despite mapping limitations and incomplete sequence coverage, we identified 3394 IAP and 375 ETn/MusD elements that are polymorphic among the four strains, resulting in polymorphic fractions of 61.3% and 25.6%, respectively. This is the first genome-wide determination of the extent of polymorphism of these ERV families. Given that this study was based on only a few strains, the total numbers of polymorphic elements must be substantially higher and represent a large source of genetic variation among inbred strains.
Among the polymorphic copies, 623 IAPs and 72 ETn/MusD elements reside in gene introns. In all five cases of sense-oriented ETn elements in A/J introns that we examined, evidence for gene splicing disruption was found by RT-PCR and, for two genes, further evidence of lower gene expression in A/J mice was observed through surveys of microarray data. While most polymorphic ERVs likely have little effect on host genes, we found that the prevalence within genes and the intronic orientation bias exhibited by polymorphic ERV subsets enriched for the youngest elements are distinctly different from that of older elements. This observation suggests that some of the former are deleterious but have not yet been eliminated by selection due to their short time in the genome or the controlled breeding environment of laboratory mice. Indeed, new insertions of these elements could play a significant role in genetic drift and inbreeding depression of mouse lines [52]. We propose that a comprehensive effort to document ERV and other transposable element polymorphisms among multiple inbred strains would complement SNP data and greatly contribute to our understanding of mouse genetic history and genotypic and phenotypic variation.
The NCBI Trace Archive (http://0-www.ncbi.nlm.nih.gov.library.vu.edu.au/Traces/trace.cgi) included a total number of 195,993,571 traces from 38 mouse strains/classes as of May 2007. However, the majority of these traces were obtained by CHIP-related resequencing techniques, which exclude most repetitive sequences. In this study, we used only sequence traces obtained by whole genome shotgun (WGS) sequencing, which are unbiased in their content of repetitive elements. Three mouse strains (A/J, DBA/2J, 129X1/SvJ) were chosen to compare to the assembled B6 genome, [version mm8 at the UCSC Genome Browser website (http://genome.ucsc.edu)], since these were the only strains with sufficient traces sequenced by shotgun-related strategies (Figure S1).
RefSeq gene annotations were retrieved from the RefGene annotation table (version mm8, April 2007) downloaded from the UCSC Genome Browser. When an ERV insertion was found in a genomic region with multiple overlapping annotations, the one with the smallest gene size was chosen to improve specificity. We also calculated the genomic coverage of annotated RefSeq genes in the mouse genome (used as the ‘expected value’ in Figure 3A) based on the same annotation table. After merging overlapping RefSeq annotations and removing redundancies, we calculated the total coverage of genic regions in the mouse genome as 31.58%.
Three types of probes were designed based on template ERV sequences (only the type-1 probe is shown in Figure 1, step A1). For IAP, probes were based on a recently inserted polymorphic IAP 1Δ1 element (accession #EU183301) [53]. For ETn/MusD, probes were based on a mutation-causing ETnII element (accession #Y17106) [54]. MusD and ETnII elements are on average over 90% identical in the regions of the probes. To capture ETnI elements, which differ from ETnII/MusD elements in the 3′ part of the LTR and 5′ internal region [21],[22], we used a representative ETnI element (accession #AC068908). As shown in Figure S2, the type-1 probe included the full-length LTR and a small fragment of the internal ERV sequence; type-2 included only the full LTR; type-3 was only the first/last 60 bp of the 5′/3′ LTR. More information about probe design is summarized in Table S1.
We conducted a survey of both ETn/MusD and IAP insertions in the B6 genome using the 60 bp type-3 probes because they are in regions of low divergence between family members (data not shown), ensuring that all ERVs of each group will be detected. The probes were aligned to the B6 genome using the WU-BLAST 2.0 program, and any hit above our cut-off threshold was scored as an ERV insertion. To keep both sensitivity and specificity as high as possible, we designed an experiment to optimize the parameters of alignment identity and length of the aligned region and the results suggested a value of 80% for both parameters. To obtain an estimation of the sizes and numbers of ETn/MusD and IAP elements, all mapped ERV fragments (LTR termini) were merged into one individual element if they met the following criteria: 1) on the same chromosome; 2) in the same orientation; 3) within 10 kb from each other.
The standalone version of the WU-BLAST v2.0 program (Gish, W. 1996–2004 http://blast.wustl.edu/) was used to make local alignments between ERV probes and mouse traces in the NCBI trace archive database (step 2 in Figure 1A). Our threshold parameters for BLAST were 80% for sequence identity and 80% for length of the aligned region. A usable ERV-containing trace consists of two parts – a non-ERV flanking sequence and the target-ERV sequence. All ERV-containing traces with a flanking portion shorter than 30 bp were discarded. Once identified, a chimeric tag was constructed by taking the whole flanking portion appended with a small tail of its target-ERV sequence (Figure 1A, step 3). We required the target-ERV tail of the tag to be 1/5 of the flanking portion in length, and a maximum of 50 bp.
The ERV-containing traces were then mapped to the assembled B6 genome. Here we used the chimeric tags derived from the previous step as the input query for BLAT [55] and mapped them to the B6 genome (version mm8) (Figure 1A, step 4). We also estimated the sequencing error rate of the mouse traces as about 5% (data not shown). We therefore defined criteria for a significant mapping as follows: 1) it should be the highest mapping score among all BLAT hits; 2) the best hit should be at least 2% higher in identity and 10% longer in mapping length compared to the second hit; 3) the alignment identity between the chimeric tag and the target locus needed to be greater than 90%; 4) the length of aligned region needed to be more than 70% of the tag length. Once a significant BLAT mapping site was identified, it was straightforward to check for presence of the ERV in the B6 genome based on alignment of the small target-ERV tail of the chimeric tag. If the BLAT mapping included more than 2/3 of the target-ERV tail, it was considered a common insertion also present in B6; if the mapping included less than 1/3 of the target-ERV tail, it was scored as absent from B6. Situations in between these two boundaries were extremely rare and were discarded.
All sequences in the B6 genome with a length of 35 bp flanking both the 5′ and 3′ end of each detectable ERV element were aligned back to the B6 genome with BLAT and only those with a unique location were retained. Next, all these 35-bp-flanking-sequences were used as queries of the WU-BLAST program and all traces from the test strains containing such flanking sequences were collected (Figure 1, step B2). A minimum identity of 90% and a minimum mapping length of 80% were required. Because of incomplete genomic coverage of traces of test strains, many ERV flanking regions in B6 have no corresponding traces in the trace archive database and, therefore, their polymorphism status could not be determined (denoted as “?” in Tables S2 and S3). However, for ERVs in B6 with unique flanking sequence found in one or more test strain traces, presence of the ERV in test strains was determined by assessing identity between the ERV sequence in B6 and the sequence adjacent to the flanking sequence in the trace of the test strain. Here we used an implementation of the Needleman-Wunsch algorithm [56] to align the two sequences. We required a minimum identity of 90% and an alignment length of at least 35 bp to score the ERV as present in the test strain.
Using a similar strategy as above, we also assessed the polymorphism status of ERV insertions found in a test strain but not in B6. Using their locations with respect to the B6 genome, probes based on flanking genomic sequences were built and the trace archive database was searched to check if traces with the same flanking sequences were present for other test strains. All qualified traces obtained from other test strains were aligned to sequences of corresponding ERV families based on the same mapping criteria used above, and the existence of such ERV elements in other test strains was determined. Here we used exemplar ERV sequences instead of using the ERV portion in the original ERV-containing traces because, for some traces, the ERV portion was too short (less than 35 bp) to make an effective alignment.
The ERV numbers found in the three test strains are lower than the numbers detected in the assembled B6 genome. Incomplete sequence coverage and the inability to map the trace to a unique location are responsible for most of the loss of detectable ERV insertions. To estimate the fraction of ERVs that were not detected in each test strain, we applied our screening method using random samples of the unassembled B6 traces and plotted an ERV detection curve based on this simulation (Figure S3). Since the sequence quality of the B6 trace archive is generally lower than that of the three test strains, the sampling process was based only on B6 traces with less than 1% “N”s. Sample trace datasets of different sizes were constructed into simulative trace databases, and the corresponding numbers of B6 ERV insertions detected with these datasets were plotted in Figure S3. Independent random sampling was applied twice for datasets smaller than 12 million traces.
A second purpose for performing the screening simulations with B6 traces was to evaluate the accuracy of our screening method. Theoretically, all insertions found in the simulation assays in the B6 traces should be detected in the B6 reference genome. However, we did find a few cases of insertions cataloged as “polymorphic”, meaning they are from the B6 traces and were mapped to a significant locus in the B6 reference genome where no such insertion was found. One of the possible explanations for this is the fact that the assembly of the B6 genome is not perfect, especially in repetitive regions. Indeed, only 49 of the 54 non-ecotropic murine leukemia viruses (MLV) known to be present in B6 can be found in the mouse B6 assembly [57]. Nonetheless, we considered all the “polymorphic” cases in each simulation assay to be false positives and derived a conservative estimation of the accuracy of our screening method, resulting in an average false discovery rate of 0.4%±0.1%.
The presence of an insertion was tested by amplifying genomic DNA from the following strains: SWR/J, C3H/HeJ, Balb/cJ, B6, A/J, DBA/2J, 129X1/SvJ and A/WySn. All strains or DNA were from the Jackson Laboratory. Primers (see Table S4) flanking the potential insertion sites were used to amplify specific sequences from 75 ng of genomic DNA in a 25ul reaction with Phusion DNA polymerase (New England Biolabs). Cycling conditions were as per the manufacturer's instructions with annealing temperatures of between 55–65°C and extension times between 20 seconds and 4 minutes. PCR products were visualized on agarose gels. In some cases, amplification with the flanking primers did not produce a product, so one flanking primer and one LTR primer was used to confirm presence of an insertion. Therefore, in these cases, the size of the ERV insertion could not be estimated. In two cases, marked as “F” in Table 1, the PCRs were unsuccessful in one of the strains, suggesting a structural rearrangement or the presence of other polymorphisms that prevented amplification with the primers used. Some products were sequenced directly on Minelute (Qiagen) gel purified PCR fragments using the BigDye Terminator v3.1 Cycle Sequencing Kit (ABI) in an ABI PRISM® 3730XL DNA Analyzer system.
RNA from mouse tissues was extracted using RNeasy RNA isolation kit (Qiagen) according to manufacturer's recommendations. The presence of native transcripts using primers located in exons flanking the intron with the ETn insertion was confirmed with the following primer pairs: Col4a6-up-ex-s and Col4a6-down-ex-as; Dnajc10-up-ex-s and Dnajc10-down-ex-as; Mtm1-up-ex-s and Mtm1-down-ex-as; Opcml-up-ex-s and Opcml-down-ex-as; Prkca-up-ex-s and Prkca-down-ex-as. Then, RT-PCRs designed to look for chimeric transcripts between gene exons and the intronic ETn were performed. To search for transcripts utilizing the 2nd and 3rd splice acceptor sites in the LTR (see Figure 6), cDNA from A/J tissues specified in parentheses was amplified using a common ETn primer located downstream of the LTR, IM_3as, and the following upper exon-specific primers: Col4a6-up-ex-s (eye), Dnajc10-up-ex-s (testis), Mtm1-up-ex-s (lung), Opcml-up-ex-s (cerebral hemisphere) and Prkca-up-ex-s (eye). The same exon-specific primers and cDNA were used for the search of transcripts utilizing the first splice acceptor site, this time with the LTR-specific primer located upstream of the first PolyA site, MusD2_7130as. For Dnajc10, an additional PCR was performed with an upstream exon primer and a primer located at the very end of the LTR, IM_LTR_2as.
Semi-quantitative RT-PCR for the Opcml gene was performed with a series of A/J and B6 cerebral hemisphere cDNA dilutions, using primers in the exons upstream and downstream of the intronic ETn insertion, Opcml-ex2-s and Opcml-ex3-as. For Gapdh, primers Gapdh_ex6F and Gapdh_ex7R were used. Opcml and Gapdh fragments were amplified from cDNA dilutions of 1/20, 1/40 and 1/80 (Figure S7A). For each dilution, the intensity of the resulting band was quantified using ImageQuant LT (GE Healthcare) software and graphed as the intensity of Opcml relative to Gapdh (Figure S7B). The average and standard deviation among all experiments are displayed (Figure 7B). All primer sequences for RT-PCR experiments are listed in Table S4.
RNA from A/J and B6 cerebellum was used. For each lane, 6 mg of RNA was denatured, electrophoresed in 1% agarose 3.7% formaldehyde gel in 1×MOPS buffer, transferred overnight to a Zeta-probe nylon membrane (Bio-Rad) and baked at 80°C. A probe specific for the Opcml exon upstream of the ETn insertion was synthesized by PCR using primers Opcml-ex2-s and Opcml-ex2-as and labeled with 32P using a Random Primers DNA Labeling System (Invitrogen). Membranes were prehybridized in ExpressHyb (BD Biosciences) for 4 hours at 68°C, hybridized overnight at the same temperature in fresh ExpressHyb, washed according to manufacturer's instructions and exposed to film.
We obtained mRNA expression microarray data of Zapala et al [41] (NCBI GEO accession GSE3594) and considered 10 tissues profiled in A/J, B6, C3H/HeJ, DBA/2J and 129S6/SvEvTac mice. We averaged the expression values for a given probeset replicated within the same strain and tissue and examined the probeset expression rank in two ways. First, we determined each strain's expression rank across genes within a given tissue, and second, the inserter strain's expression rank for a given gene was determined across tissues.
The National Center for Biotechnology Information (NCBI) Nucleotide database (http://www.ncbi.nlm.nih.gov/sites/entrezdbNucleotide) accession number for the ETnII element used for probe design and to align in Figures 5 and S4 is Y17106. The ETnI element used for probe design is located in a BAC clone with accession number AC068908. The accession number for the IAP element used in probe design is EU183301. |
10.1371/journal.pgen.1005863 | UPF2-Dependent Nonsense-Mediated mRNA Decay Pathway Is Essential for Spermatogenesis by Selectively Eliminating Longer 3'UTR Transcripts | 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.
| 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.
| 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.
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10.1371/journal.pcbi.1004017 | Metadynamics Simulations Reveal a Na+ Independent Exiting Path of Galactose for the Inward-Facing Conformation of vSGLT | Sodium-Galactose Transporter (SGLT) is a secondary active symporter which accumulates sugars into cells by using the electrochemical gradient of Na+ across the membrane. Previous computational studies provided insights into the release process of the two ligands (galactose and sodium ion) into the cytoplasm from the inward-facing conformation of Vibrio parahaemolyticus sodium/galactose transporter (vSGLT). Several aspects of the transport mechanism of this symporter remain to be clarified: (i) a detailed kinetic and thermodynamic characterization of the exit path of the two ligands is still lacking; (ii) contradictory conclusions have been drawn concerning the gating role of Y263; (iii) the role of Na+ in modulating the release path of galactose is not clear. In this work, we use bias-exchange metadynamics simulations to characterize the free energy profile of the galactose and Na+ release processes toward the intracellular side. Surprisingly, we find that the exit of Na+ and galactose is non-concerted as the cooperativity between the two ligands is associated to a transition that is not rate limiting. The dissociation barriers are of the order of 11–12 kcal/mol for both the ion and the substrate, in line with kinetic information concerning this type of transporters. On the basis of these results we propose a branched six-state alternating access mechanism, which may be shared also by other members of the LeuT-fold transporters.
| Membrane proteins are crucial for the communication of the cell with the environment. Among these, symporters are in charge of the transport of molecules (like sugars, amino acids, osmolytes) inside the cells, exploiting the concentration gradient of an ion to perform the task. Here we investigate by atomistic simulations the transport mechanism of the Sodium-Galactose symporter. Our results allow constructing a detailed and quantitative model of the release process of the two ligands. Surprisingly, we find that the galactose is released to the cytosol independently from the ion, unambiguously indicating that the coupling in their transport mechanism is associated to the steps preceding the release process. A large family of symporters shares the same fold and potentially the same transport mechanism. As such our results are important also because they can provide insights on common mechanistic features of these transporters.
| Secondary active transporters are membrane proteins involved in the translocation of small organic molecules across the cellular membrane using the energy stored as transmembrane electrochemical gradient of ions (mostly Na+ or H+). Sodium symporters in particular use this alkali metal ion to cotransport a variety of substrates (sugars, amino acids, neurotransmitters, nucleobases) against their chemical concentration [1], [2]. These symporters share a common structural core domain called ‘LeuT-fold’ [3]–[9] and they play a crucial role in the physiology of the brain, intestine, kidney, thyroid and skin, representing thus the target for therapeutic intervention in the treatment of depression, diabetes, obesity, etc [1], [2]. The proposed transport mechanism of this kind of secondary active transporters is called ‘alternating access mechanism’ [10]. According to this mechanism, the symporters undergo a large conformational change switching from an outward-facing conformation, where the ligands bind from the extracellular medium, to an inward-facing conformation, where the ligands are released into the cytosol.
In this work we focused on the Sodium-Galactose Transporter (SGLT), a sodium symporter that accumulates sugars, like glucose or galactose, into cells. In humans this process is very important for a correct intestinal absorption and renal reabsorption, and it is nowadays a promising field for the development of a new class of drugs for the treatment of type 2 diabetes [11]. For Vibrio parahaemolyticus the crystal structure of the inward-facing conformation of the bacterial homologue of SGLT (vSGLT) was solved by Faham et al. [6] with galactose (Gal) bound inside the protein. While in the human transporter the substrate transport is driven by two Na+ ions, only one ion is required in the bacterial homologue. Yet, the Na+ was not solved in the X-ray structure (PDB 3DH4) and a plausible ion-binding site, corresponding to Na2 site, was proposed, on the basis of a structural comparison with the LeuT structure [4] and by mutational analysis. Subsequent, molecular dynamics (MD) simulations studies suggested that this crystal structure represents an ion-releasing state of the transporter [12]–[14]. Thus, in a previous work, using MD and metadynamics (MTD) simulations, we identified a possible ion-retaining state of the vSGLT [15].
The dissociation mechanism of galactose has also been investigated by molecular simulations studies which showed that Gal release occurs either spontaneously or by applying an external force [13], [14], [16]. These studies lead to contradictory conclusions on a possible gating role of Y263, on the exact conformational state of the transporter (open or occluded) captured crystallographically and on the free energy profile of Gal release [13], [14], [16]. Namely, Zomot et al. showed that Gal exited the protein only by using steered molecular dynamics (SMD), after the rotameric transition of the side-chain of Y263, which, according to this study, acts as a gate. A second gate represented by Y269 was also encountered later on along the path [13]. Consistently with these findings, Watanabe et al. showed that the sodium exit triggers the substrate release after the new rotameric conformation acquired by Y263 and that Gal has to overcome very small barriers ( kcal/mol) along its exit pathway [14]. A different scenario was instead provided by Li and Tajkhorshid in 2011. By combining MD and SMD simulations, they identified a curved translocation pathway for Gal release. In this path Gal moves around Y263, requiring no gating event. This study pointed out that the crystal structure represents an open state of the transporter [16]. Unfortunately, experiments do not help solving the puzzle, as data on these controversial points as well as on order of dissociation of the two ligands are incomplete [17].
We here perform extended bias exchange metadynamics simulations (BE-MTD) [18]–[20] aimed at establishing the reciprocal influence of the Na+ and Gal in their dissociation mechanism and at characterizing the kinetics and thermodynamics of the process. Our study shows that (i) the Na+/Gal interplay along the dissociation path is minimal and it is limited only to the initial displacement of both Na+ and Gal from their binding sites; (ii) the dissociation of both Na+ and Gal occurs with free energy barriers of about 11–12 kcal/mol, and the rate limiting step is associated to conformations in which Na+ and Gal are more than 10 Å far apart from their binding sites; (iii) no gating role can be assigned to Y263. Simulation of the Y263F mutant reveals a rather significant change in the binding site of Gal, confirming that this residue has an important functional role, even if it does not act as a gate.
The setup is the same we used in our previous work [15]. The model of vSGLT was built using the chain A of the 3 Å resolution crystal structure (PDB accession code 3DH4 [6]). The first helix, partially solved, was reconstructed from a more recent crystal structure (PDB code 2XQ2 [14], 2.7 Å resolution). The missing atoms of side chains of residues K124, V185, R273, K454, K547 were built using SwissPDBViewer [21] application. Residues S31 to L46, located between transmembrane helix (TM) 1 and TM2, and residues Y179 to A184, between TM5 and TM6, were built using Loopy [22] program. We here number the helices like in Ref [6]. The final monomeric structure contained 539 residues (S9 to K547). The protein was embedded in a pure, pre-equilibrated 1-palmitoyl-2-oleilphosphatidylcholine (POPC) lipid model (kindly supplied by T. A. Martinek) [23], [24] using the gmembed [25] tool of GROMACS4 [26] and then it was oriented following OPM [27] database model. Afterward the system was neutralized and solvated with TIP3P [28] water molecules (80969 total atoms in a box size of 97.6×96.7×85.1 Å3). Simulations were carried out with GROMACS4 [26] package using Amber03 [29] force field for protein, GAFF [30] for galactose and for membrane the parameters supplied by T. A. Martinek [23]. For more details, see Supplementary Information (S1 Text).
The starting point of this study was the structure of vSGLT in an ion-retaining state obtained from our previous MTD simulations [15]. In order to study the binding/dissociation of galactose and its coupling with the binding/dissociation of sodium ion by using BE-MTD, we exploited nine different collective variables using the Plumed plugin [31]. The novelty of this technique with respect to standard MTD is that a large number of collective variables can be employed at the same time, allowing thus the study of complex (bio)chemical processes, as in this case the simultaneous dissociation of both Na+ and Gal. Seven of the CVs were reserved to the Gal. In particular, to assess the controversial role of Y263 we used 1) a combination of two dihedral angles of Y263 (C-Cα-Cβ-C and Cα-Cβ-C -Cδ) using the alpha-beta similarity keyword of Plumed [31] and 2) the hydrogen bonds (H-bonds) between Y263 and N64. To focus on the dissociation of Gal from its binding site we used: 3) the distance of galactose from its binding site (represented by the center of mass (COM) of selected residues, see S1 Table); 4) the H-bonds between galactose and its binding site; 5) the H-bonds between galactose and the likely exiting pathway [13], [14], [16]; 6) the radius of gyration of a group of atoms belonging to the galactose binding site; 7) a path collective variable, which describes the progression of the galactose along its exit channel [32]. This variable, introduced in the simulation after a spontaneous exit of the galactose was observed biasing the Gal dissociation process with the other variables, is defined by a set of 7 reference conformations. Two additional variables were used to characterize the release mechanism of Na+: 8) the distance between the sodium ion and its binding site (defined by the COM of selected residues, see S1 Table) and 9) the coordination number between sodium and four residues of the binding site (carbonyl oxygens of I65 and A361 and hydroxyl oxygens of S364 and S365).
Initially, each walker was biased by only one CV. After 740 ns, in order to improve the statistics and allowing a faster convergence of the free energy profile (see S2 Text) more walkers were added biasing the same CVs. In this way, we simulated a total of 1400 ns using at most 16 walkers. Parameters like gaussians width (ds), intervals, walls were added/changed and adapted for a better and faster convergence of the simulation (see S2 Text, S1 Table). Simulations of ligands dissociations were also performed in the absence of Na+ in order to assess the role of the ion. Moreover, we have performed an additional simulation starting from the Y263F mutant to clarify the role of this residue in shaping the free energy landscape. At this scope we elongated the BE-MTD simulation mutating Y263F (for a total of 15 ns*16 CVs = 240 ns), maintaining all the CVs and their parameters used in the wild type (WT) simulation (S3 Text).
All structural and free energy analyses were performed using METAGUI [33], a VMD [34] interface for analyzing metadynamics and molecular dynamics simulations.
The error of the free energy profiles was calculated as the standard deviation of two different time averages of the biased potential in the first and the second part of the converged interval of the simulation.
In order to assess the role of selected residues along the ligands dissociation paths we calculated the average interaction energies at the relevant minima and transition states. We considered the van der Waals and coulombic interactions. We remark that this analysis is qualitative and is meant only to provide a picture of the role of selected residues in the relative stabilization/destabilization of transition states and minima, as shown in other studies [35], [36].
H-bond analysis was performed using Plumed [31].
We first investigated the dissociation path of Gal. The projection of the free energy along the path collective variable, representing the progression of Gal along its exit channel, is reported in Fig. 1. For sake of clarity, we add a subscript G for the states relative to the Gal exit path, and a subscript Na for those relative to the ion path. Initially, Gal is in the deepest minimum (Min 1G), which is the same binding site identified in the crystal structure (the RMSD of the heavy atoms of residues within 4 Å of Gal with respect to the conformation in the crystal structure is 1.0 Å (±0.1)). The substrate is stabilized by an extended H-bond network with E88, Q428, Q69, E68, N64 (S2 Table). Residue Y263 (OH) interacts with N64 (HN ). There are 2-3 water molecules in the binding site, interacting with the substrate. While Gal is in its binding site, Na+ is coordinated by three water molecules and three residues (A62, I65 and S365). This corresponds to the ion-retaining binding site of the inward-facing conformation of vSGLT identified in our previous work [15]. In analogy with our previous study we name this site LC1.
The next free energy minimum along the exit path of the substrate is Min 2G, where, consistently with the suggestion of Li et al. [16], Gal undergoes a lateral displacement toward a position in which it is only partially shielded by the ring of the Y263. From this point, the substrate will find its way out by rotating about 90 degrees (assuming a conformation in which its ring is roughly parallel to the protein axis) and continues his progression along a curved path beyond Y263. This residue is at the edge of the hydrophilic cavity and the presence of water molecules confers flexibility to it, which hence is able to accommodate to the passage of Gal (S1 Fig.). In this new position, several water molecules enter the binding site, while the substrate (C6-O) interacts with T431 (HO ) and, through a water bridge, with N142. In this minimum residue E68 assumes a new rotameric conformation. Indeed, its side chain, initially heading toward the Gal binding site, rotates toward the Na+ binding site, making one or two H-bonds with S66, a conserved residue across the SSS family (E68 (O 1) with S66 (HN) and E68 (O 2) with S66 (HO )). The interactions of N64 (O) with galactose (HO-C2) and N64 (HN ) with Y263 (OH) are still present, even if characterized by large fluctuations. A qualitative analysis of the interaction energies between the substrate and selected residues shows that in the first two minima the van der Waals interactions regard the substrate and Y263, while the electrostatic interactions involve Gal-N64 (S3 Table).
Afterwards, the substrate, hydrated by 4–5 water molecules, enters into a narrow cavity created by the residues N267, Q268, W134, T431, V434, transiently interacting with N142 and Y262 (Min ). The H-bonds of E68 (O 1) with S66 (HN) and E68 (O 2) with S66 (HO ) contribute to stabilize Gal in this minimum (S2 and S3 Tables). Y263 and N64 become very flexible as they can not form reciprocal H-bonds.
After overcoming a transition state (), where the substrate is partially hydrated, Gal finds another minimum (Min ). Here, it is almost fully hydrated, and surrounded by S368, V185, and the TM2-I, TM9, TM6, above loop TM5-6 and it is right below the sodium binding site, inside the hydrophilic cavity of the transporter. Gal (C1-OH and C2-OH) makes H-bonds with D189 (O ) and Gal (C6-OH) with A184 (carbonyl oxygen). Residue D189 is highly conserved throughout the SSS family and it has been experimentally seen to play an important role for a correct Na+-Gal cotransport and cation selectivity [37], [38]. We see here that it is also involved in the exit path of galactose, contributing to the stabilization of this minimum. Here, the aromatic ring of Y263 maintains an orientation similar to the crystal structure, while the sidechain of N64 assumes a new conformation, pointing toward the carboxylic group of E68. The H-bond between E68-S66 is present also when Gal is in this minimum (S2 Table).
In order to leave this site, moving deeper in the hydrophilic cavity, Gal has to overcome a transition state (TS2G) with ΔF# of 11.9±0.4 kcal/mol with respect to the minimum, which corresponds to the largest free energy barrier of the exit pathway. The breaking of the H-bonds between Gal and D189 contributes to the barrier, as suggested by the interaction energies among Gal and D189 along the path (S2 and S3 Tables).
At , the substrate is at the protein surface and, although being hydrated, it is still interacting with a few surface residues forming a H-bond (Gal (C3-OH) with N371 (O ) and hydrophobic interactions with other residues (G181, L182 (on loop TM5-6), V396 (TM10), N371 and T375 (TM9) (S2 and S3 Tables).
Remarkably, the free energy barriers associated to the exit path of Gal are significantly higher than those calculated by Watanabe et al. [14]. This is most probably due to the fact that our simulations start from a stable ion-retaining state of the transporter, and since a subtle cooperativity between Na+ and Gal is observed at the very beginning of the path, MD runs starting from the crystal structure, as those of Watanabe et al. [14], which corresponds to an ion-releasing state, may lead to simulate a different dissociation process.
Since Y263F mutation has been observed to impair the transport mechanism, to further check the controversial role of Y263 in the dissociation of the substrate, we performed a BE-MTD simulation of the mutant using the same setup of the WT simulation. Looking at S2 Fig., we can clearly see a different profile, where the second minimum becomes the global one, more stable and broader than the first minimum. In short, Min1-Y263F corresponds to Min1-WT, while Min2-Y263F is broad and thus characterized by different configurations of Gal (containing among them Min2-WT minimum). Their relative free energy has changed, meaning that Y263 decides in this transporter the relative stability of the minima characterizing the releasing path of the substrate. Thus, this mutation has a role in reshaping the free energy surface of Gal exit path. The stabilization of the other minimum does not seem to influence the barrier height significantly, but it may hamper Gal from assuming a position necessary to undergo the inward-outward conformational change, affecting in turn the overall transport cycle, in line with experimental findings [14]. We remark that the change in the free energy profile of the releasing of Gal caused by this mutation does not imply a gating role for this residue.
The dissociation path of Na+ is characterized by an overall free energy barrier of 11.1 (±0.7) kcal/mol and by the presence of a few faint metastable states (see Fig. 2). The most stable ion binding site is LC1 in which the ion is coordinated by three water molecules and three residues (A62, I65 and S365). As soon as the ion starts moving toward the cytoplasm, it loses its coordination with I65; then, it approaches the mouth of the binding site keeping the interaction with A62 and coordinating S364 and D189. This latter is often found to bind Na+ along the exit. This state was called PC in our previous work [15].
In this configuration the side chain of E68 rotates from a configuration in which it heads toward the Gal binding site to a new conformation in which it forms hydrogen bonds with S66. In State the ion, at almost 5 Å from its binding site, has overcome D189, moving deeper into the hydrophilic cavity, and it is fully hydrated. Then, it continues descending into the cavity coordinating G181 (loop 5–6) and S368 (on TM9) and four water molecules (State ). Remarkably, D189 interacts with the ion through water bridges, accompanying it from LC1 to State , confirming its important role in the exit pathway of [15], [37], [38]. After interacting with L182 (loop 5-6) (State ), it reaches State , which is at approximately 1.8 Å from the binding site. Here, Na+ is coordinated by a few residues of loop 1–2 and by 3–4 water molecules. It finally overcomes , where it is still transiently coordinated by D43, R400, and even by a POPC molecule. Thus, the total free energy barrier is due to cumulative energy cost of small structural changes accompanying the Na+ release without the formation of any stable intermediate. After the Na+ is quite delocalized, in a vestibule mainly formed by loops TM9–10, TM1–2 and loop TM5–6. It is important to note that the two ligands, starting from different binding sites, exit the protein through the same hydrophilic cavity (characterizing the inward-facing conformation) communicating to the cytoplasm.
In order to investigate possible cooperative effects in the release mechanism of Na+ and Gal, in Fig. 3 we report a projection of the free energy surface (FES) as a function of two CVs, the distance between the ion and its site and the path variable of Gal. It is possible to note that the deepest minimum for both exit pathways is the same, i.e. . Thus, it is labeled as Min 1. A zoom of the free energy landscape in the region close to the binding sites is also reported. The shape of the free energy landscape clearly suggests an interplay between the two ligands. Indeed, upon displacement of Gal from its binding site to move toward Min , the Na+ loses its coordination with I65 and moves toward a site with a reduced number of coordinating residues, the PC site (state ). The residue linking the two binding sites is the E68. Indeed, upon Gal displacement from Min 1, E68, initially heading toward the Gal binding site, rotates toward the Na+ binding site, establishing one H-bond with S66 (HN) (S3 Table), a conserved residue across the SSS family. This functional rotation of E68 is also observed between the holo (PDB 3DH4) and the apo (PDB 2XQ2) forms of the vSGLT crystals. These results are in line with the previous hypothesis suggesting that the departure of Na+ from its stable putative ion-retaining site, LC1, toward the PC site triggers the conformational changes at the basis of Gal displacement from the binding site, heading to the second metastable minimum of the path (Min ). However, the free energy barrier associated to this initial displacement is very small and the highest barriers lay further along the dissociation path of Na+ and Gal. This fact, along with the overall L-shape of the free energy for large values of the collective variables (see Fig. 3), indicates that the rate limiting steps of the release of the ion and the substrate are independent. Indeed, the values of the two CVs (the path collective variable of Gal and the distance Na+ - binding site) at the highest transition state () of Gal exit are 6.2 and 3 Å. While, those at the of Na+ exit are 2.2 and 21 Å. Namely, at the transition state of Gal, Na+ is close to its binding site, and viceversa. In order to quantitatively verify this point, we computed the free energy of Gal exit in absence of Na+. The free energy profile of Gal dissociation in the absence of Na+ is practically identical to the profile in the presence of Na+, confirming unambiguously this important result (Fig. 3, panel C).
In this work we used BE-MTD to study the binding/dissociation mechanism of the two ligands of vSGLT symporter. We observed that the minimum free energy exit pathway of the galactose does not involve any rotameric transition of the side-chain of Y263. Indeed, as already observed [16], Gal circumnavigates the so-called inner gate Y263 and proceeds along the hydrophilic cavity. However, our simulation of the mutant points to a possible functional role of Y263 in determining the relative stability of the minima observed along the Gal exit path. The global free energy minimum for the mutant and for the WT turns out to be different.
The main barriers characterizing the releasing mechanism are of the order of 11–12 kcal/mol for both the ion and the substrate. These barriers are significantly higher than those reported by Watanabe et al. [14]. This is possibly due to the fact that the free energy space explored in our case includes an occluded state of the transporter, with both ligands stable in their binding sites, while the work of Watanabe et al. [14], starting from a different structure, may simulate a different process, the departure of the ion from an ion-releasing state. As also mentioned in our previous work, our starting structure is only a possible candidate of an ion retaining state. However, this binding site of Na+ is the same identified in our previous work [15], by independent metadynamic simulations, demonstrating the reliability of our results. This ion-retaining site is also consistent with the observation of Faham et al. [6] pointing to an important role of S365 for the Na+-dependent transport of Gal. Moreover, Loo et al. [39] observed that a mutation of residue S392 of Na2 site of hSGLT1 (that works with stoichiometry 2 Na+: 1 sugar) affects the binding of both sugar and the second ion. In this case, a straightforward comparison with the corresponding S364 of vSGLT is not possible due to the different number of ions needed to activate Gal transport (stoichiometry 1 Na+: 1 sugar).
Concerning the reliability of the mechanism we propose, we remark that our barriers are affected by errors of the order of 0.7 kcal/mol due to the convergence of our BE-MTD simulations. Moreover, like in all the simulations based on classical molecular dynamics, our results could be affected by systematic errors due to the force field. These errors can alter the value of the barriers, but are unlikely to affect our main finding. Namely the fact that Na+ and Gal release are independent. Finally, we remark that we have not attempted computing the binding free energy ΔF of the two ligands. The value of the free energy at the maximum distance from the binding site is not a quantitative estimate of the ΔF, due to residual interactions of the ligands with the surface of the protein in the final states of our free energy profiles. The aim of our work was to reveal the interplay of Na+ and Gal during their dissociation mechanism focusing on the free energy barriers rather than on the binding free energies.
As we already underlined, the barriers calculated in this study are large, possibly of the same order of magnitude of those associated with the inward-outward conformational switch. Indeed, the transition rate associated with the crossing of our barriers is approximately 1 s−1 (assuming prefactor of 10−8 s−1). This value is in line with kinetic experimental data. Indeed, the galactose turnover in vSGLT was estimated to be around 0.4 s−1 by Turk et al. [40], or on the order of tens of ms in other works [11], [41]–[43]. Lapointe and coworkers measured the turnover rate of hSGLT1, finding values of 8 s−1 or, being near Vmax conditions, 13 s−1. Moreover, a transition rate of 50–60 s−1 was found for hSGLT1 [17], [42]. These values correspond to a free energy barrier in the range of 11–15 kcal/mol, consistently with our results.
Importantly, our simulations provide for the first time direct insights on the possible cooperativity between Na+ and Gal for their release mechanism toward the cytoplasm. A small interdependence is observed only at the very beginning of the ligands release process, with residue E68 playing a central role in the communication between the two binding sites. Remarkably, this intercommunication occurs far from the point of the free energy profile associated to the highest free energy barriers. Our simulations, carried out in the absence of Na+, reveal that the whole free energy profile of Gal exit is essentially unaffected, Fig. 3 (green line, panel C). The lack of a marked cooperativity in the release mechanism of Gal and Na+ from the binding site is at first sight surprising. However, it is likely that the cooperativity might be associated to the first steps of the transport cycle, when the symporter, in the outward-facing conformation, binds the sodium ion and then the substrate, and their binding triggers the outward-to-inward facing conformational change, as observed in the LeuT-fold superfamily [8], [44]–[47].
Due to this non-cooperativity in the Gal and Na+ release mechanism from the inward-facing conformation of vSGLT and to the almost identical rate limiting free energy barriers, we propose to extend the six-state kinetic model introduced by Wright and coworkers [11], [48] by adding one more state, Fig. 4 (blue region). Indeed, we suggest that, from the ligand bound inward-facing conformation, the transporter can follow independently two paths for Gal and Na+ release. The very similar free energy barriers observed for the Na+ and Gal dissociation from the inward-facing conformation may be in part responsible for the difficulties encountered experimentally in providing a detailed and clear picture for this part of the transport path of hSGLT [11], [17], [48].
We also observe that the crystal structure of the inward-facing conformation in the apo form of (PDB code 2XQ2) [14] differs in the presence of a kink of a few degrees in TM2-I (intracellular half) and the side chain of E68 heading toward S66. Remarkably, these structural features are observed during the Gal release pathway, suggesting that starting from a Na+ occluded and holo state of vSGLT (PDB code 3DH4) [6], we are able to visit these structural features of the apo state captured crystallographically (PDB code 2XQ2) [14].
An important process associated to this transporter is the permeation of water, whose precise mechanism is still under debate [49], [50]. The two mechanisms considered more viable are the active cotransport [49], [51], where water flux is coupled to ion/solute flux, or the passive permeation [52], where the accumulation of the solutes near the intracellular side of the membrane during solute transport induces a flux of water as a response to the local osmotic gradient. A detailed analysis of this controversial issue is beyond the scope of our study. However, in line with the passive mechanism [53], [54], we observe that: (i) the water molecules permeate easily through the whole protein during the releasing process; (ii) the water is free to enter from the cytoplasm into the hydrophilic cavity and then into the binding sites. Consistently with Ref. [45], [55], in our simulations water molecules help the breaking of the H-bonds that keep the ligands bound to the protein, playing an important role in the whole releasing pathway. Their presence at the edge of the Gal binding site confers, indeed, flexibility to Y263, facilitating the initial displacement of the substrate (S1 Fig.).
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10.1371/journal.pcbi.1002165 | Monkeys and Humans Share a Common Computation for Face/Voice Integration | Speech production involves the movement of the mouth and other regions of the face resulting in visual motion cues. These visual cues enhance intelligibility and detection of auditory speech. As such, face-to-face speech is fundamentally a multisensory phenomenon. If speech is fundamentally multisensory, it should be reflected in the evolution of vocal communication: similar behavioral effects should be observed in other primates. Old World monkeys share with humans vocal production biomechanics and communicate face-to-face with vocalizations. It is unknown, however, if they, too, combine faces and voices to enhance their perception of vocalizations. We show that they do: monkeys combine faces and voices in noisy environments to enhance their detection of vocalizations. Their behavior parallels that of humans performing an identical task. We explored what common computational mechanism(s) could explain the pattern of results we observed across species. Standard explanations or models such as the principle of inverse effectiveness and a “race” model failed to account for their behavior patterns. Conversely, a “superposition model”, positing the linear summation of activity patterns in response to visual and auditory components of vocalizations, served as a straightforward but powerful explanatory mechanism for the observed behaviors in both species. As such, it represents a putative homologous mechanism for integrating faces and voices across primates.
| The evolution of speech is one of our most fascinating and enduring mysteries—enduring partly because all the critical features of speech (brains, vocal tracts, ancestral speech-like sounds) do not fossilize. Furthermore, it is becoming increasingly clear that speech is, by default, a multimodal phenomenon: we use both faces and voices together to communicate. Thus, understanding the evolution of speech requires a comparative approach using closely-related extant primate species and recognition that vocal communication is audiovisual. Using computer-generated avatar faces, we compared the integration of faces and voices in monkeys and humans performing an identical detection task. Both species responded faster when faces and voices were presented together relative to the face or voice alone. While the details sometimes appeared to differ, the behavior of both species could be well explained by a “superposition” model positing the linear summation of activity patterns in response to visual and auditory components of vocalizations. Other, more popular computational models of multisensory integration failed to explain our data. Thus, the superposition model represents a putative homologous mechanism for integrating faces and voices across primate species.
| When we speak, our face moves and deforms the mouth and other regions [1], [2], [3], [4], [5]. These dynamics and deformations lead to a variety of visual motion cues (“visual speech”) related to the auditory components of speech and are integral to face-to-face communication. In noisy, real world environments, visual speech can provide considerable intelligibility benefits to the perception of auditory speech [6], [7], faster reaction times [8], [9], and is hard to ignore—integrating readily and automatically with auditory speech [10]. For these and other reasons, it's been argued that audiovisual (or “multisensory”) speech is the primary mode of speech perception and is not a capacity that is simply piggy-backed onto auditory speech perception [11].
If the processing of multisensory signals forms the default mode of speech perception, then this should be reflected in the evolution of vocal communication. Naturally, any vertebrate organism (from fishes and frogs, to birds and dogs) that produces vocalizations will have a simple, concomitant visual motion in the area of the mouth. However, in the primate lineage, both the number and diversity of muscles innervating the face [12], [13], [14] and the amount of neural control related to facial movement [15], [16], [17], [18] increased over time relative to other taxa. This ultimately allowed the production of a greater diversity of facial and vocal expressions in primates [19], with different patterns of facial motion uniquely linked to different vocal expressions [20], [21]. This is similar to what is observed in humans. In macaque monkeys, for example, coo calls, like the /u/ in speech, are produced with the lips protruded, while screams, like the /i/ in speech, are produced with the lips retracted [20].
These and other homologies between human and nonhuman primate vocal production [22] imply that the mechanisms underlying multisensory vocal perception should also be homologous across primate species. Three lines of evidence suggest that perceptual mechanisms may be shared as well. First, nonhuman primates, like human infants [23], [24], [25], can match facial expressions to their appropriate vocal expressions [26], [27], [28], [29]. Second, monkeys also use eye movement strategies similar to human strategies when viewing dynamic, vocalizing faces [30], [31], [32]. The third, indirect line of evidence comes from neurophysiological work. Regions of the neocortex that are modulated by audiovisual speech in humans [e.g., 8,33,34,35,36,37], such as the superior temporal sulcus, prefrontal cortex and auditory cortex, are similarly modulated by species-specific audiovisual communication signals in the macaque monkey [38], [39], [40], [41], [42], [43]. However, none of these behavioral and neurophysiological results from nonhuman primates provide evidence for the critical feature of human audiovisual speech: a behavioral advantage via integration of the two signal components of speech (faces and voices) over either component alone. Henceforth, we define “integration” as a statistically significant difference between the responses to audiovisual versus auditory-only and visual-only conditions[44].
For a homologous perceptual mechanism to evolve in monkeys, apes and humans from a common ancestor, there must be some behavioral advantage to justify devoting the neural resources mediating such a mechanism. One behavioral advantage conferred by audiovisual speech in humans is faster detection of speech sounds in noisy environments—faster than if only the auditory or visual component is available [8], [9], [45], [46]. Here, in a task operationalizing the perception of natural audiovisual communication signals in noisy environments, we tested macaque monkeys on an audiovisual ‘coo call’ detection task using computer-generated monkey avatars. We then compared their performance with that of humans performing an identical task, where the only difference was that humans detected /u/ sounds made by human avatars. Behavioral patterns in response to audiovisual, visual and auditory vocalizations were used to test if any of the classical principles or mechanisms of multisensory integration [e.g. 47,48,49,50,51,52,53] could serve as homologous computational mechanism(s) mediating the perception of audiovisual communication signals.
We report two main findings. First, monkeys integrate faces and voices. They exhibit faster reaction times to faces and voices presented together relative to faces or voices presented alone —and this behavior closely parallels the behavior of humans in the same task. Second, after testing multiple computational mechanisms for multisensory integration, we found that a simple superposition model, which posits the linear summation of activity from visual and auditory channels, is a likely homologous mechanism. This model explains both the monkey and human behavioral patterns.
All experiments and surgical procedures were performed in compliance with the guidelines of the Princeton University Institutional Animal Care and Use Committee. For human participants, all procedures were approved by the Institutional Review Board at Princeton University. Informed consent was obtained from all human participants.
Nonhuman primate subjects were two adult male macaques (Macaca fascicularis). These monkeys were born in captivity and provided various sources of enrichment, including cartoons displayed on a large screen TV as well as olfactory, auditory and visual contact with conspecifics. The monkeys underwent sterile surgery for the implantation of a head-post.
The human participants consisted of staff or graduate students (n = 6, 4 males, mean age = 27) at Princeton University. Two of the subjects were authors on the paper (CC, LL). The other four human subjects were naïve to the purposes and goals of the experiment.
We would like to briefly explain here why we chose to use avatars. First, it is quite difficult to record monkey vocalizations which only contain mouth motion without other dynamic motion components such as arbitrary head motion and rotation— which themselves may lead to audiovisual integration [54]. Second, start and end positions of the head from such videos of vocalizations, at least for monkeys, tend to be very variable which would add additional visual motion cues. Third, we wanted constant lighting and background and the ability to modulate the size of the mouth opening and thereby parameterize visual stimuli. Fourth, the goal of this experiment was to understand how mouth motion integrated with the auditory components of vocalizations and we wanted to avoid transient visual stimuli. Real videos would not have allowed us to control for these factors; avatars provide us with considerable control.
Experiments were conducted in a sound attenuating radio frequency (RF) enclosure. The monkey sat in a primate chair fixed 74 cm opposite a 19 inch CRT color monitor with a 1280×1024 screen resolution and 75 Hz refresh rate. The 1280×1024 screen subtended a visual angle of ∼25° horizontally and 20° vertically. All stimuli were centrally located on the screen and occupied a total area (including blank regions) of 640×653 pixels. For every session, the monkeys were placed in a restraint chair and head-posted. A depressible lever (ENV-610M, Med Associates) was located at the center-front of the chair. Both monkeys spontaneously used their left hand for responses. Stimulus presentation and data collection were performed using Presentation (Neurobehavioral Systems).
Experiments were conducted in a psychophysics booth. The human sat in a comfortable chair approximately 65 cm opposite a 17 inch LCD color monitor with a 1280×1024 screen resolution and 75 Hz refresh rate. The 1280×1024 screen subtended a visual angle of 28 degrees horizontally and 24 degrees vertically. All stimuli were centrally located on the screen and occupied an area of 640×653 pixels. All stimulus presentation and data collection were performed using Presentation (Neurobehavioral Systems).
Our audiovisual detection experiment is an extension of the classical redundant signals paradigm. In such experiments, it is common to observe that RTs to multisensory targets presented simultaneously are faster than unisensory RTs. This effect is usually termed the “redundant signals effect”. One important class of explanations for the redundant signals effect is the “race model”. According to the race model (or a “parallel first terminating” model), redundancy benefits are not due to an actual integration of visual and auditory cues. To illustrate, assume that the time to detect and respond to a single target—e.g., the facial motion--varies according to a statistical distribution. Similarly, the time to detect and respond to the auditory-only vocalization also varies according to a statistical distribution. Whenever, both facial motion and the vocalization are presented together, the stimulus that is processed faster in a given trial determines the response time. As the RT distributions typically overlap with one another, slow processing times are removed. Thus, RTs to redundant stimuli are always faster than those for the single stimuli. A standard way to test whether this principle can explain RT data is to use the race model inequality [57], which posits that the cumulative RT distribution for the redundant stimuli never exceeds the sum of the RT distributions for the unisensory stimuli. That is, if FAV (t), FV (t) and FA (t) are the estimated cumulative distributions (CDF) of the RTs for the three different modalitiesthen one cannot rule out race models as an explanation for the facilitation of RT. On the other hand, if this inequality is violated in a given data set, then parallel processing cannot completely account for the benefits observed for multisensory stimuli and an explanation based on co-activation is necessary. We computed the CDFs of our conditions and then computed the difference between the actual CDF of the audiovisual condition and the CDF predicted by the race model. The maximum positive point of this difference was taken as the index of violation, R. Positive values of R means that the race model is rejected. If this value is 0, then the race model is upheld.
Several models of audiovisual integration have been proposed over the years, but superposition models are simple and possess considerable explanatory power. Here we briefly describe the model, and detailed explanations are available elsewhere [63], [64]. We first consider the case of single modality trials (visual or auditory). We assume that the onset of the stimulus (i.e. visual mouth motion or the auditory vocalization) induces a neural renewal counting process (for examples, action potentials or spikes, but it could be any event which is counted) that counts up to a critical number of events, c. The assumption is that, as soon as a critical number of events, c, have been registered at some decision mechanism, a response execution process, M, which consumes an amount of time with a mean µM, is started. The main postulate of the superposition model is that in the audiovisual condition the renewal processes generated by either the visual and the auditory signals are superposed, thereby reducing the waiting time for the critical count. Specifically, if NV (t) and NA (t) are themselves counting processes for the visual-only and auditory-only conditions, and the two stimuli are presented simultaneously, that is with 0 lag, then the new process for the audiovisual stimulus is given as
It is immediately apparent that this audiovisual process will reach the critical level of c counts faster than the individual auditory and visual processes. If the auditory-only and visual-only stimuli are presented with a lag of say τ, as in our case with visual mouth motion preceding the auditory vocalization by τ milliseconds, then the process becomes,
To specify this model fully and test and fit to data, one must specify an inter-arrival distribution. Usually this is assumed to be exponential in nature that leads to a homogenous Poisson counting process. For τ = 0, the waiting time for the cth event is well defined and follows a gamma distribution with mean c/λ and variance c/λ2, where λ (λ>0) is the intensity parameter of the Poisson process. For example, the auditory and visual-only RT would then be
The mean audiovisual RT would be given by the following simple expression. It is the waiting time for the cth with the visual and auditory rates summed and is given as follows.
When this model is to be applied when there are differences in the SOAs, that is, τ>0, the waiting time for the cth event is no longer gamma distributed and instead follows a more complicated distribution. Fortunately, this model has been completely explicated and published expressions are already available [63], [64]. The audiovisual RT in this case is the expected value of the waiting time to the cth count.
Monkeys were trained, and humans were asked, to detect auditory, visual or audiovisual vocalizations embedded in noise as fast and as accurately as possible. This task was similar to a redundant signals paradigm [57], and was designed to approximate a natural face-to-face vocal communication event in close encounters. In such settings, the vocal components of the communication signals are degraded by environmental noise. The face and its motion, on the other hand, are usually perceived clearly. In the task, monkeys responded to ‘coo’ calls that are affiliative vocalizations commonly produced by macaque monkeys in a variety of contexts [65,66, Figure 1A]; humans were asked to detect the acoustically similar vowel sound /u/ (Figure 1B). All vocalizations had five different levels of sound intensity and were embedded in a constant background noise. The signal-to-noise ratio (SNR) ranged from −10 dB to +22 dB relative to a background noise of 63 dB. For dynamic faces, we used computer-generated monkey and human avatars (Figures 1C, D). The use of avatars allowed us to restrict facial motion to the mouth region, ensure constant lighting and background, and to parameterize the size of the mouth opening while keeping eye and head positions constant. The degree of mouth-opening was in accordance with the intensity of the associated vocalization: greater sound intensity was coupled to larger mouth openings by the dynamic face (Figure 1E). Two coos and two /u/ sounds were paired with two monkey and human avatars, respectively, and this pairing was kept constant. Furthermore, species-stimuli pairings were kept constant: monkeys only saw and heard monkey vocalizations, and humans only saw and heard human vocalizations. Previous psychophysical and fMRI experiments have successfully used computer-generated human avatars to probe the processing of audiovisual speech [54], [67], [68], [69].
During the task, one avatar face would be continuously on the screen for a block of trials (n = 60); the background noise was also continuous (Figure 1F). In the “visual only (V)” condition, this avatar would move its mouth without any corresponding auditory component; that is, it silently produced a coo for monkey avatars or an /u/for human avatars. In the “auditory-only (A)” condition, the vocalization normally paired with the other avatar (which is not on the screen) is presented with the static face of the avatar. Finally, in the “audiovisual (AV)” condition, the avatar moves its mouth accompanied by the corresponding vocalization and in accordance (degree of mouth opening) with its intensity. Each condition (V, A, or AV) was presented after a variable interval (drawn from a uniform distribution) between 1 and 3 seconds. Subjects indicated the detection of an event (visible mouth motion, auditory signal or both) by pressing a lever (monkeys) or a key (humans) within 2 seconds following the onset of the stimulus. Every 60 trials, a brief pause was imposed, followed by a new block in which the avatar face was switched on the screen, and the identity of the coo or /u/ sound used for the auditory-only condition was switched as well.
We measured the accuracy of the monkeys and humans detecting vocalizations in the audiovisual, auditory-only and visual-only conditions. Figure 2A shows the detection performance of Monkey 1 averaged over all sessions (both coo calls) as a function of SNR for the three conditions of interest. In the case of the visual-only condition, the size of mouth opening has a constant relationship with the auditory SNR and it is thus shown on the same x-axis. In the auditory-only condition, as the coo call intensity increased relative to the background noise, the detection accuracy of the monkey improved. In contrast, modulating the size of the mouth opening in the visual-only condition had only a weak effect on the detection accuracy. Finally, the detection accuracy for audiovisual vocalizations was mildly enhanced relative to the visual-only condition and with very little modulation as a function of the SNR. The same pattern was seen for Monkey 2 (Figure 2B). When the data was pooled over all the SNRs, accuracy was significantly better for both monkeys in the audiovisual condition compared to either unisensory condition (paired t-tests, Monkey 1: AV vs V, t (47) = 3.77, p<.001, AV vs A, t (47) = 19.94, p<.001; Monkey 2: AV vs V, t (47) = 15.85, p<.001, AV vs A, t (47) = 8.1, p<.001).
This general pattern was replicated in humans (n = 6). Figure 2C shows the performance of a single human observer on this same task detecting the /u/ sound. Excluding the lowest SNR value in the auditory-only condition, accuracy is almost at ceiling for all three stimulus conditions. The average accuracy over the 6 human subjects as a function of SNR is shown in Figure 2D. Performance pooled across all SNRs was maximal for the audiovisual condition and was enhanced relative to the auditory-only condition (t (5) = 2.71, p = 0.04). It was not significantly enhanced relative to the visual-only condition (t (5) = 0.97, p = 0.37). The lack of enhancement relative to the visual-only condition is likely because the visual-only performance itself was close to ceiling for humans.
In both species, the similarities in detection accuracy for visual-only and audiovisual conditions (Figures 2A–D) suggest that they were perhaps not integrating auditory and visual signals but instead may have adopted a unisensory (visual) strategy. According to this strategy, they used visible mouth motion only for both the visual and audiovisual conditions, and used the sound only when forced to do so in the auditory-only condition. We therefore examined the reaction times (RTs) to distinguish between a unisensory versus an integration strategy. Figures 3A, B show the mean RT as a function of the SNR and modality computed by pooling RT data from all the sessions for Monkeys 1 and 2. RTs for the auditory-only vocalization increased as the SNR decreased (i.e. the sound was harder to hear relative to the background). In contrast, RTs to the visual-only condition only increased weakly with an increase in the mouth opening size — a result consistent with the accuracy data. Although the audiovisual accuracy was only modestly better than the visual-only accuracy (Figure 2A,B), audiovisual RTs decreased relative to both auditory-only and visual-only RTs for several SNR levels. To illustrate, a non-parametric ANOVA (Kruskal-Wallis) computed for Monkey 1, which compared the ranks of the RTs for the auditory-only, visual-only and audiovisual conditions for the highest SNR (+22dB), was significant (χ2 = 490.91, p<.001). Post-hoc Mann-Whitney U tests revealed that the RT distribution for the audiovisual condition was significantly different from the auditory-only distribution and the visual-only distribution for all SNRs; that is, RTs in the audiovisual condition were faster than visual and auditory RTs. In Monkey 2, the audiovisual RT distribution was different from the auditory-only distribution for all SNRs (p<0.001), and was significantly different from the visual-only distribution for all but the lowest SNR (−10 dB, p = 0.68). It is notable that at the highest SNR (+22 dB; largest mouth opening), the RTs of both monkeys seem more like the auditory-only RTs, while at the lowest SNR (−10 dB; smallest mouth opening), the RTs seem to be more similar to the visual-only RTs.
Humans also show a RT benefit in the audiovisual versus unisensory conditions, with a similar, but not identical, pattern to that observed in monkeys. Figure 3C shows the average RTs of a single human subject as a function of the SNR. Similar to monkeys, decreasing the SNR of the auditory-only condition leads to an increase in the RTs, and RTs for the visual-only condition were only weakly modulated by the size of the mouth opening. For a range of SNRs, the audiovisual RTs were faster than auditory- and visual-only RTs. Figure 3D shows the average RTs over all 6 subjects. Paired t-tests comparing audiovisual RTs to auditory-only and visual-only RTs reveal that they were significantly different in all but the lowest SNR condition (p = 0.81 for the −10 dB condition, p<0.05 for all other conditions, df = 5). Though the RT patterns from human participants seem dissimilar to the monkey RT patterns (e.g., in monkeys the auditory-RT curve crossed the visual-only RT curve but for humans there was no cross over), we can show that the two species are adopting a similar strategy by exploring putative mechanisms. We do so in the next sections.
Our analysis of RTs rules out the simple hypothesis that monkeys and humans are defaulting to a unisensory strategy (using visual in all conditions except when forced to use auditory information). Another hypothesis is that a “race” mechanism is at play [59]. A race mechanism postulates parallel channels for visual and auditory signals that compete with one another to terminate in a motor or decision structure and thereby trigger the behavioral response. We chose to test this model to ensure that the observers were actually integrating the faces and vocalizations of the avatar. A simple physiological correlate of such a model would be the existence of independent processing pathways for the visual mouth motion and an independent processing pathway for the auditory vocalization. In the race scenario, there would be no cross-talk between these signals. Race models are extremely powerful and are often used to show independent processing in discrimination tasks [70], [71], [72]. In our task, independent processing would mean that in the decision structure, two populations of neurons received either auditory or visual input. These two independent populations count spikes until a threshold is reached; the population that reaches threshold first triggers a response. Such a model can lead to a decrease in the RTs for the multisensory condition, not through integration, but through a statistical mechanism: the mean of the minimum of two distributions is always less than or equal to the minimum of the mean of two distributions.
Figure 4A shows a simulation of this race model. The audiovisual distribution, if it is due to a race mechanism, is obtained by taking the minimum of the two distributions and will have a lower mean and variance compared to the individual auditory and visual distributions. Typically, to test if a race model can explain the data, cumulative distributions of the RTs (Figure 4B) are used to reject the so-called race model inequality [51], [57]. The inequality is a strong, conservative test and provides an upper bound for the benefits provided by any class of race models. Reaction times faster than this upper bound mean that the race model cannot explain the pattern of RTs for the audiovisual condition; the RT data would therefore necessitate an explanation based on integration.
Figure 4C plots the cumulative distributions for RTs collected in the intermediate SNR level and for ISIs between 1000 and 1400 ms for Monkey 1; the prediction from the race model is shown in grey. We used this ISI interval because, in monkeys only, the ISI influenced the pattern of audiovisual benefits (see Text S1 and Figure S2). Maximal audiovisual benefits were for ISIs in the 1000–1400 ms range. The cumulative distribution of audiovisual RTs is faster than can be predicted by the race model for multiple regions of RT distribution, suggesting that the RTs cannot be fully explained by this model. To test whether this violation was statistically significant, we compared the violation from the true data to one using conservative bootstrap estimates. Several points for the true violation were much larger than the violation values estimated by bootstrapping (Figure 4D). Audiovisual RTs are therefore not explained by a race model. For the entire range of SNRs and this ISI for the monkeys, maximal race model violations were seen for the intermediate to high SNRs (+5, +13 and + 22 dB; Figure 4E). For the softer SNRs (−10,−4 dB), a race model could not be rejected as an explanation. The amount of race model violation for the entire range of ISIs and SNRs is provided in Figure S3. For both monkeys, longer ISIs resulted in weaker violations of the race model and rarely did the p-values from the bootstrap test reach significance.
For humans, we observed similar robust violations of the race model. Figure 4F shows the average amount of race model violation across subjects as a function of SNR. Since humans showed much less dependence on the ISI, we did not bin the data as we did for monkeys. Similar, to monkeys, maximal violation of the race model was seen for loud and intermediate SNRs. For 3 out of the 5 SNRs (+22, +13, +5 dB), a permutation test comparing maximal race model violation to a null distribution was significant (p<0.05). In conclusion, for both monkeys and humans, a race model cannot explain the pattern of RTs at least for the loud and intermediate SNRs.
These results strongly suggest that monkeys do integrate visual and auditory components of vocalizations and that they are similar to humans in their computational strategy. In the next sections, we therefore leveraged these behavioral data and attempt to identify a homologous mechanism(s) that could explain this pattern of results. Our search was based on the assumption that classical principles and mechanisms of multisensory integration [48], [49], [50], [51], [73], originally developed for simpler stimuli, could potentially serve as starting hypotheses for a mechanism mediating the behavioral integration of the complex visual and auditory components of vocalizations.
The first mechanism we tested was whether the integration of faces and voices demonstrated in our data followed the “principle of inverse effectiveness” [49], [50]. This idea, originally developed to explain neurophysiological data, suggests that maximal benefits from multisensory integration should occur when the stimuli are themselves maximally impoverished [49], [50], [74], [75]. That is, the weaker the magnitude of the unisensory response, the greater would be the gain in the response due to integration. In our case with behavior, this principle makes the following prediction. As the RTs and accuracy were the poorest for the lowest auditory SNR, the benefit of multisensory integration should be maximal when the lowest auditory SNR is combined with the corresponding mouth opening. Our metric for multisensory benefit was defined as the speedup for the audiovisual RT relative to the fastest mean RT in response to the unisensory signal (regardless of whether it was the auditory- or visual-only condition). The principle of inverse effectiveness would thus predict greater reaction time benefits with decreasing SNR for both monkeys and humans. Figures 5A and B plot this benefit as a function of SNR for Monkeys 1 and 2. For monkeys, the maximal audiovisual benefit occurs for intermediate SNRs. The corresponding pattern of benefits for humans is shown in Figure 5C. For humans, this benefit increases as the SNR increases and starts to flatten for the largest SNRs. This pattern of benefits reveals that the maximal audiovisual RT benefits do not occur at the lowest SNRs. This is at odds with the principle of inverse effectiveness [49], [50]. If our results had followed this principle, then the maximal benefit relative to both unisensory conditions should have occurred at the lowest SNR (lowest sound intensity coupled with smallest mouth opening). Neither monkey nor human RTs followed this principle and therefore it cannot be a homologous mechanism mediating the integration of faces and voices in primates.
One potential caveat is that we are testing the principle of inverse effectiveness using absolute reaction time benefits whereas the original idea was developed using proportional referents. Thus, we re-expressed the benefits as a percent gain relative to the minimum of the auditory and visual reaction times for each SNR. We observed that, even when converted to a percent benefit relative to the minimum reaction time for each SNR, the inverted U-shape pattern of gains for monkeys (Figures S4A, B), as well as increasing gain with SNR for humans (Figure S4C), was replicated. Thus, whether one uses raw benefits or a proportional measure, RT benefits from combining visual and auditory signals could not be explained by invoking the principle of inverse effectiveness.
If inverse effectiveness could not explain our results, then what other mechanism(s) could explain the patterns of reaction time benefits? Monkey performance at intermediate SNRs (where the maximal benefits were observed; Figures 3A, B), the visual-only and auditory-only reaction time values were similar to each other. Similarly, for humans at intermediate to large SNRs (where maximal benefits were observed for humans), the visual-only and auditory-only reaction time values were similar to one another. This suggests a simple timing principle: the closer the visual-only and auditory-only RTs are to one another, the greater is the multisensory benefit. A similar behavioral result has been previously observed in the literature, albeit with simpler stimuli, and a mechanism explaining this behavior was (somewhat confusingly) dubbed “physiological synchrony” [51], [73]. According to this mechanism, developed in a psychophysical framework, performance benefits for the multisensory condition are modulated by the degree of overlap between the theoretical neural activity patterns (response magnitude and latency) elicited by the two unisensory stimuli [51], [73]. Maximal benefits occur during “synchrony” of these activity patterns; that is, when the latencies overlap. To put it another way, maximal RT benefits will occur when the visual and auditory inputs arrive almost at the same time.
To test this idea, we transformed the benefit curves shown in Figures 5A-C by plotting the benefits as a function of the absolute value of the difference between visual-only and auditory-only RTs. That is, instead of plotting the benefits as a function of SNR (as in Figures 5A–C), we plotted them as a function of the difference between the visual-only and auditory-only RTs for each SNR. If our intuition is correct, then the closer the auditory- and visual-only RTs are (i.e., the smaller the difference between them), then the greater would be the benefit. Figure 6A plots the benefit in reaction time as a function of the absolute difference between visual- and auditory-only RT for monkeys 1 & 2. The corresponding plot for humans is shown in Figure 6B. By and large, as the difference between RTs increase, the benefit for the audiovisual condition decreases with the minimum benefit occurring when visual- and auditory-only RTs differ by more than 100 to 200 milliseconds. Thus, physiological synchrony can serve as a homologous mechanism for the integration of faces and voices in both monkeys and humans.
Although the original formulation of the principle suggested “synchrony”, it seemed too restrictive. The reaction time data—at least for integrating faces and voices—suggest that there is a range of reaction time differences over which multisensory benefits can be achieved. That is, there is a “window of integration” within which multisensory benefits emerge. We use the term “window of integration” as typically defined in studies of multisensory integration: It is the time span within which auditory and visual response latencies must fall so that their combination leads to behavioral or physiological changes significantly different from responses to unimodal stimuli. Such windows have been demonstrated in physiological [49], [76] as well as in psychophysical studies of multisensory integration[48], [77]. To explore the extent of this “window of integration”, we elaborated upon the analysis shown in Figures 6A and B to the whole dataset of sessions and SNRs. For all the sessions and SNRs (48 sessions and 5 SNRs for 2 monkeys), we computed a metric that was the difference between the mean visual-only and auditory-only RTs. This gave us 480 values where there was a difference between visual and auditory RTs and, corresponding to this value, the benefit for the audiovisual condition. After sorting and binning these values, we then plotted the audiovisual benefit as a function of the difference between the mean visual-only and auditory-only RTs. Figure 6C shows this analysis for monkeys. Only in an intermediate range, where differences between unisensory RTs are around 100 – 200 ms, is the audiovisual benefit non-zero—with a maximal benefit occurring at approximately 0 ms. In addition, this window is not symmetrical around zero. It is 200 ms long when visual RTs are faster than auditory RTs and around 100 ms long when auditory-only RTs are faster than visual-only RTs. We repeated the same analysis for humans and the results are plotted in Figure 6D. For humans, a similar window exists: when visual reaction times are faster than auditory reaction times then the window is approximately 160 ms long. We could not determine the extent of the window because, in humans, auditory RTs were never faster than visual RTs.
To summarize, combining visual and auditory cues leads to a speedup in the detection of audiovisual vocalizations relative to the auditory-only and visual-only vocalizations. Our analysis of the patterns of benefit for the audiovisual condition reveals that maximal benefits do not follow a principle of inverse effectiveness. However, the principle of physiological synchrony that incorporates a time window of integration provided a better explanation of these results.
The principle of physiological synchrony with a time window of integration provides an insight into the processes that lead to the integration of auditory and visual components of communication signals. The issue however is that although this insight can be used to predict behavior, it does not have any immediate mechanistic basis. We therefore sought a computational model that could plausible represent the neural basis for these behavioral patterns. We specified two criteria for the model based on our results. First, audiovisual RTs should be faster than auditory- and visual-only RTs. Second, it should be consistent with, and perhaps subsume, the principle of physiological synchrony with a time window of integration—benefits accrued by combining visual and auditory cues should occur when the visual- and auditory-only RTs are almost equal to one another. If these two criteria are validated, then the model would be a straightforward homologous mechanism.
Superposition models are one class of integration models that could incorporate our criteria [53], [63], [64]. According to these models, activation from different sensory channels is linearly combined until it reaches a criterion/threshold and thereby triggers a response. We will use a model formulation based on counters for simplicity [63]. According to this counter model, the onset of a stimulus would lead to a sequence of events occurring randomly over time. Let N (t) denote the number of events that have occurred by time t after stimulus presentation. After the number of counts reaches a criterion, c, it triggers a response. Let us assume that there are separate counters for visual and auditory conditions, NV (t) and NA (t). During the audiovisual condition, a composite counter, NAV (t) = NA (t) + NV (t), comprised of both the visual and auditory signals, counts to the criterion, c (Figure 7A). This composite, multisensory counter would reach the criterion faster than either of the unisensory counters alone. Figure 7B shows that a computer simulation of a counter composed of superposed activity from both visual and auditory cues would reach criterion faster than the unisensory ones alone.
Using the RT data from Monkey 1, we set the parameters of the superposition model for the auditory- and visual-only RTs and then used the model to estimate the audiovisual RTs (Figure 7C). From this, the model produced audiovisual RTs that were faster than both the auditory-only and visual-only RTs—like the pattern of results we observed for monkeys (Figures 3A, B). As Figure 7C shows, except for the lowest SNR, there is a good one to one correspondence between the model's prediction of audiovisual RTs and the actual raw data. Thus, this model can at least generate the patterns of reaction times observed in response to audiovisual vocalization.
We next estimated the benefits in RT for the audiovisual condition relative to the visual-only and auditory-only condition from the simulated model (Figure 7D). The benefit curve has the same inverted U-shaped profile as the real patterns of benefit shown in Figure 5A. We repeated this analysis for the human RTs and the pattern of results is shown in Figure 7E–F. Figure 7E shows the predicted reaction time of the average participant as a function of SNR along with actual data. The predicted reaction times are very similar to the actual RTs observed for humans in Figure 3D. As with the monkey behavioral data, the fits performed worst for the softest SNR. Like the benefit patterns shown in Figure 5C, the benefit for the AV condition increases as SNR increases (Figure 7F). This replication by the model of the pattern of monkey and human data—faster audiovisual RTs and maximal benefit when visual and auditory RTs are well matched—suggests that a superposition model is a viable homologous mechanism.
The goal of our study was threefold. First, do monkeys integrate the visual and auditory components of vocalizations? Second, is monkey behavior similar to that of humans perfoming an identical task? Third, is there a homologous mechanism for the processing of audiovisual communication signals? We trained monkeys and asked humans to detect vocalizations by monkey and human avatars, respectively, in a noisy background. We measured their accuracy and reaction times. We found that monkeys do integrate the visual and auditory components of vocalizations (as measured by faster reaction times for the audiovisual relative to the unisensory conditions). Similar speedups in reaction times were observed also for human subjects. Rejection of the race model demonstrated that the behavioral patterns must be explained by an integrative process (one requiring the use of both unisensory channels together to drive behavioral change), and not one based on competing independent unisensory channels. We then tested whether classical principles of multisensory integration could serve as homologous mechanisms for the integration of faces and voices. The “principle of inverse effectiveness” failed to explain the data for either primate species. Both monkey and human RTs were better explained by the principle of “physiological synchrony” that incorporated a time window of integration. We found that a simple computational model positing the linear superposition of activity induced by visual and auditory cues could explain the pattern of results in monkeys as well as humans. Critically, its explanatory power was such that it could explain the small differences in behavior observed for monkeys and humans. Furthermore, the superposition model is completely consistent with the principles of physiological synchrony with a time window of integration. The superposition model, therefore, is an excellent candidate for a homologous mechanism used by monkeys and humans to integrate faces and voices.
Monkeys and humans share many homologous mechanisms for the production of vocalizations [22]. In humans, these production mechanisms deform the face in such a manner that facial motion enhances the detection and discrimination of vocal sounds by receivers [6], [7], . Often this enhanced behavior takes the form of decreased reaction times to audiovisual versus unisensory presentations of speech [8], [9]. While nonhuman primates could theoretically use the same or very similar facial motion to enhance their auditory perception, there has been no evidence of this to date. Several studies demonstrated that, like human infants, monkeys and apes can match facial expressions to vocal expressions [26], [27], [28], [29], and that eye movement patterns generated by viewing vocalizing conspecifics is similar between monkeys and humans [30], [31], [32]. None of these nonhuman primate studies, however, demonstrated a behavioral advantage for perceiving audiovisual vocalizations over unisensory expressions. Demonstration of such an advantage is necessary to invoke the hypothesis that a multisensory integration mechanism for communication signals is homologous across species. In the current study, we provide the first demonstration that monkeys exhibit a behavioral advantage for audiovisual versus unisensory presentations of vocalizations. The patterns of both accuracy and reaction time benefits were similar to humans performing an identical task.
Although we have emphasized throughout the similarities in the patterns of behavior for monkeys and humans, it is important to note that there were also differences. The most important difference was that humans were consistently faster for the visual-only vocalization compared to the auditory-only vocalization across the range of auditory intensities. Monkeys, on the other hand, responded faster to some auditory-only conditions versus visual-only conditions across the range of intensities. These differences ultimately led to differences in the amount of integration. Such differences could potentially arise due to the differences in auditory stimuli (/u/ sounds in humans vs coo calls in monkeys) or the amount of attentional engagement. We have suggested acoustic equivalence of “coos” and /u/ vocalizations, but they are not communicatively equivalent. Coos are common vocalizations in monkeys with behavioral significance including a positive emotional valence. In contrast, the /u/ sound we used with humans does not have any behavioral significance. With regard to engagement, we trained our monkeys using standard operant conditioning techniques. This meant the use of timeouts as negative reinforcement whenever the monkeys made false alarms. As a result, when compared to human performance, monkeys may adopt a more conservative criterion for the detection of these sounds to avoid false alarms. Despite these caveats, it is worth emphasizing that positing a linear superposition of visual and auditory signals reconciled these dissimilar results from the two species.
Two other design features of our study are worth pointing out before we discuss the broader implications of our results. First, we used a fixed delay between the mouth motion and the onset of the vocalization. Under natural conditions, delays between onset of mouth opening and sound onset, which we term time-to-voice (TTV), are wide ranging and can vary from utterance to utterance and speaker to speaker [5]. At the neural level, different TTVs modulate the degree of integration in local field potential signals recorded from the upper bank of the superior temporal sulcus of monkeys [40]. Thus, how this variable would affect behavioral integration of faces and voices in monkeys and humans is not tested in our experiments or in any other study.
A second design feature that we used consisted of the presence of a static face on the screen during the auditory-only vocalization. This face was also identity-incongruent with the auditory vocalization. Thus, both of these features could potentially slow down auditory-only RTs by creating confusion: the face doesn't move when it should during a vocalization and/or the face doesn't match the identity of the voice. However, we believe this concern is mitigated by the more naturalistic conditions that our design mimics and more pressing problems that it avoids. Our paradigm is naturalistic in the following sense: faces in noisy, cocktail-party like scenarios do not appear and disappear. Furthermore, monkeys like humans can recognize indexical cues in vocalizations (cues that indicate body size, age, identity, etc) and match them to faces [82], [83]. Thus, in our paradigm, it is not odd to hear one individual's voice while seeing another individual's face, a typical occurrence under natural conditions. The key to the face-voice integration is combining motion of the face to the correct, corresponding voice. If we did not present a static face during the auditory-only condition and observed an audiovisual benefit, then the benefits could be attributed to differences in overall attention or arousal (a frequent criticism of physiological studies of AV integration). Moreover, if we adopted a design where audiovisual vocalizations involved the sudden onset of a face followed by its mouth motion, then any RT benefits for audiovisual compared to auditory-only vocalizations would be uninterpretable: we could not be sure if it was due to the integration of facial motion with the sound or from the integration of the sound with the sudden onset of the face.
Whatever influences our design may actually have on our participants' RTs; we can model the outcome of hypothetically faster RTs that may arise with a study design that did not use a static, incongruent face in the auditory-only conditions. Since our data demonstrate that the principle of physiological synchrony with a time window of integration, we can actually perform a thought experiment to see what would happen if our auditory RTs are sped up. Simply put, the result would be that the point at which visual and auditory RT curves cross will be at a different SNR and this point of crossing would be the new point of maximal integration. Figure S5 shows that if we sped up all auditory RTs by 40, 80 and 120 ms in the model relative to the original data, the point of maximal integration shifts to lower SNRs.
We demonstrated that combining visual mouth motion with auditory vocalizations speeds up reaction times in monkeys and humans. Faster reaction times to multisensory signals compared to unisensory signals are a frequent outcome in human psychophysical studies [51], [57], [58], [59], [84], [85], [86], [87]. The first such demonstration, nearly a hundred years ago, showed that there was a speedup in responses for bi- and tri-modal stimuli compared to unimodal stimuli [87]. Since then, this seminal result has been replicated in a variety of settings almost always with the use of simple stimuli [51], [57], [85], [88], [89], [90], [91]. In particular, shortened reaction times are observed in response to multimodal stimuli using both saccades and lever presses as dependent measures [85], [92]. Physiologically, there are similar results. Neurons in the superior colliculus of anaesthetized cats respond faster to audiovisual compared to auditory and visual stimuli [93]. Our results confirm that similar behavioral advantages exist when combining the visual and auditory components of complex social signals encountered in everyday settings.
While there are certainly similarities in the integration processes for simple and complex signals like speech, there are also differences. An important issue which has been repeatedly demonstrated is that there are differences in the window of integration for simple versus complex stimuli[94]. For the integration of simple stimuli, tolerance of asynchrony between visual and auditory cues is very small leading to a narrow window of integration [94]. In contrast, for speech stimuli, observers are able to tolerate very large asynchronies and still bind them into a common percept[47]. We return to this issue later in the Discussion.
For both monkeys and humans, we found that the maximal benefit obtained by combining visual and auditory cues was for intermediate values of SNR. This is at odds with the principle of inverse effectiveness [49], [50]. This idea was originally formulated in the context of electrophysiological experiments and suggests that the maximal benefit (greater proportional response magnitude) from multisensory stimulus inputs would be achieved by combining visual and auditory cues that, individually, elicit weak responses. Support for the inverse effectiveness rule is also evident at the behavioral level in both monkeys and humans in detection tasks involving simple stimuli [85], [92], [95], [96]. If this principle held true for detecting vocalizations, then we would have observed maximal reaction time savings for the lowest SNR, with the benefit decreasing with increasing SNR. On the contrary, monkeys' detection of vocalizations generated a non-monotonic curve with peak multisensory benefits occurring at intermediate SNRs. For humans, the multisensory benefit increased with increasing SNRs. Thus, for the multisensory integration of vocalizations (with reaction times as a behavioral measure), neither in monkeys nor in humans does the principle of inverse effectiveness explain the behavior. Other results from the speech processing literature support our assertion. For example, in studies of speech intelligibility, maximal benefits gained by integration of auditory speech with visual speech are found when the auditory speech is presented in an intermediate, versus high, level of noise [7], [81]. Similarly, the McGurk effect occurs even under clear listening conditions (i.e., noisy signals aren't required to generate the illusory percept) [10], and vision can boost the comprehension of extended auditory passages even under excellent listening conditions [97].
As mentioned before, there are several studies which claim to support this principle in behavior [75], [85], [92], [95], [96], [98], [99], [100], so why do we not see support for the principle of inverse effectiveness in our data or in other studies [51], [57], [88], [92], [101]? We think that this principle is sensitive to the way multisensory stimuli are parameterized and tested in different experiments. In particular, the choice of stimuli, levels of intensity and the pairing of stimuli could all affect whether this principle will be apparent in the resultant data. To illustrate what we mean, we tested two hypothetical scenarios, where inverse effectiveness can be observed using RTs and compare it to a scenario resembling our experimental data. For each scenario, we constructed auditory and visual RTs to have a certain profile with respect to different intensity levels. Then, given that the superposition model is an excellent explanation of our RT data, as well as RTs to simple stimuli[53], [62], [63], [64], [86], we used it to simulate the expected audiovisual reaction times for these same intensity levels. We then examined if the multisensory benefits were consistent with the principle of inverse effectiveness or not. The first scenario is a case wherein RTs to both senses increase with decreases in intensity level, but at every intensity level, they are still roughly equal to one another (Figure S6A). In this scenario, RTs to visual and auditory stimuli increase with decreasing intensity and visual and auditory RTs are largely similar at every intensity level. Keeping with multisensory integration, audiovisual RTs are faster than both auditory-only and visual-only RTs. Critically, in line with our intuition, the multisensory benefit increases with the decrease in SNR — and is thus consistent with the principle of inverse effectiveness (Figure S6B).
We can also outline a second scenario where this principle would be observed to be in action. This is the case when the stimuli are such that the RT of one modality approaches the RT of the other modality only for the lowest intensity levels. Figure S6C shows a simulation of this scenario. The auditory-only RTs are much faster than the visual-only RTs for the highest intensity levels. However, as the stimulus intensity decreases, the auditory- and visual-only RTs approach each other. Again, audiovisual RTs are faster than auditory- and visual–only RTs. Like the previous scenario, as intensity decreases, the benefit increases and is thus consistent with the principle of inverse effectiveness (Figure S6D). A recent study showing support for inverse effectiveness had visual and auditory RTs closely following this scenario [99]. The third scenario is one that is a simulation of our data (Figure S6E). In this case, visual RTs do not change much with intensity level, but auditory RTs increase with a decrease in intensity. Audiovisual RTs are again faster than auditory and visual-only RTs. Critically, these data result in a pattern of benefits that is non-monotonic and takes the form of an inverted U; it is not consistent with the principle of inverse effectiveness (Figure S6F).
In summary, given that the superposition model is an excellent fit to data, simulations of this model using the scenarios above suggest that observing the principle of inverse effectiveness in behavior is to some extent dependent upon the way the parameters of the stimuli that are used in an experiment. Different multisensory stimuli (speech versus non-speech) as well as the choice of intensity levels are bound to have different effects on multisensory benefit. Thus, the principle of inverse effectiveness may be operational only under some situations. We would however note that, this framework of superposition only explains the inconsistencies about inverse effectiveness in RT output. A similar careful analysis is needed to explain accuracy of subjects as well as performance in tasks such as localization [75], [98].
We showed that maximal benefits from integration of visual and auditory components of communication signals occurred when the reaction times to visual and auditory cues are themselves very similar to one another. This is consistent with the idea of “physiological synchrony”, a principle proposed to explain behavioral data. The principle of physiological synchrony was first formulated based on psychophysical experiments using punctate, simple stimuli [51], [73]. In these experiments, it was noted that maximal multisensory benefits occurred when the stimulus-onset asynchrony between visual and auditory stimuli was adjusted to be equal to the difference between visual-only and auditory-only RTs. That is, “synchrony” was defined by theoretical neurophysiological activity (with reaction times as a proxy) rather than physical synchrony defined by the stimulus-onset asynchrony. According to this idea, performance benefits for the multisensory condition are modulated by the degree of temporal overlap between the theoretical neurophysiological activity patterns elicited by the two unisensory stimuli [51], [73]. Maximal benefits occur during synchrony of these neural activity patterns; that is, when their latencies over-lap.
It is worth repeating that this notion of physiological synchrony is a behavioral construct derived by considering RTs. RTs are a simple but powerful metric for indexing this behavior. However, they are the output of a complex mixture of sensory processing, motor preparation, temporal expectation, attention and other cognitive processes. Thus, the physiological synchrony mechanism, although it explains patterns of behavior using RTs to sensory stimuli does not necessarily predict that the integration is occurring in a purely sensory circuit. The neural locus where integration is taking place is not known. Sensory, premotor and/or motor circuits involved in multisensory processing are very likely all involved in generating behavioral responses during this task.
We found that there was a time window within which differences in reaction times between visual and auditory signals could lead to integration. This notion of a “temporal window of integration” is a recurring concept in behavioral and neurophysiological experiments of multisensory integration [48], [76], [77], [84], [89], [102], [103]. For example, participants perceive the McGurk effect when the stimulus-onset asynchrony between visual and auditory cues is in a window approximately 400 milliseconds wide, beyond which the illusion disappears [48]. Similarly, studies of orienting responses to audiovisual stimuli using saccades show that speedup of saccadic RTs occur in a variety of experimental settings within a time window of 150–250 ms [77], [84], [89], [104], [105]. Finally, neurophysiologically, maximal integration in multisensory neural responses in the superior colliculus is observed when the stimulus onset asynchrony is adjusted such that the discharge patterns to visual and auditory signals themselves overlap with each other [76].
We showed that a simple computational model of integration—a linear superposition model—explained the behavioral patterns observed for the integration of audiovisual vocalizations by monkeys and humans. The main tenet of this model is that the information from the two unisensory channels is integrated at a specific processing stage by the linear summation of channel-specific activity patterns. Superposition models have been successfully used to predict the reaction times of observers in other multisensory detection tasks, albeit with much simpler stimuli [53], [62], [63], [64], [86]. Physiologically, support for this principle was suggested in studies of the sensitivity of multisensory neurons in superior colliculus [76]. Our results suggest that this model can be readily extended to the integration of visual and auditory components of vocalizations, at least during behaviors involving speeded detection. Indeed, invoking this mechanism reconciled the observed dissimilarity in RTs from monkeys and humans. In addition, it automatically subsumes the principle of physiological synchrony and generates appropriately asymmetric time windows of integration. Whether this model works well for other tasks such as multisensory spatial orientation [75], [98], is an open question. Nevertheless, for the task presented in this study, i.e. the detection of vocalizations in noise, it is a parsimonious homologous mechanism.
That a linear, additive model could provide a good explanation for the detection of audiovisual vocalizations might seem irreconcilable with typical notions of multisensory integration that emphasize “super-additivity” or non-linear responses [49], [50]. Recent studies, however, report that multisensory neurons can integrate their inputs in an additive manner both in terms of spiking activity [See for e.g. 50,52,106], as well at the level of synaptic input [107]. Our emphasis on the superposition model as a homologous mechanism has another important implication. First, there are a remarkable number of nodes on which visual and auditory inputs that are sensitive to faces and voices, respectively, could converge. Any or all of these sites could be responsible for the behavioral advantage we report here. For example, neurons in the amygdala and association areas such as the upper bank of STS and prefrontal cortex respond to both the visual and auditory components of vocalizations. In some cases, we know that they integrate these vocalization-related cues [40], [42], [43], [108], [109]—at least during the passive reception of these signals. For example, in keeping with the linear superposition model we posited here, approximately 7% of ventrolateral prefrontal cortical neurons integrate visual and auditory components of vocalizations linearly [43].
The superposition model subsumes the time window of integration. The basis of superposition models is that they require activity patterns to overlap with one another and add together to generate benefits. Thus, activity patterns that overlap with one another have a higher probability of leading to integration, whereas activity patterns that do not overlap will not lead to integration. This implies that the measured window of integration is going to depend on the inherent statistics of the visual and auditory signals and the response profiles to the two signals in some neural structure on which they converge. The narrowness and the latency of these response profiles will thus determine the window of integration. Thus, in any given experiment, choices of the strength and duration of these visual and auditory signals would automatically result in corresponding changes in latencies and response profiles. A flash is highly likely to be processed in primary visual cortex and a moving face through a combination of face- and motion-sensitive neural structures. A similar argument can be made for auditory stimuli. Thus, unless the response profile(s) in some integrative structure(s) mediating detection of these various stimuli are identical, the windows of integration are bound to be different for simple stimuli such as flashes and tone pips versus more complex audiovisual vocalizations and speech signals. This might be a partial explanation for one of the best known findings in the multisensory literature — asymmetric broad windows for speech [47], [48], versus the small windows for simple stimuli [94] .
Finally, the superposition model is similar in many respects to a Bayesian model of bimodal integration. For example, in models developed by Ernst and colleagues [110], [111], maximal benefit due to bimodal discrimination occurs when the difficulty of each modality is roughly equated [112]. This is remarkably similar to the notion of physiological synchrony. Thus, Bayesian models could, presumably, be adapted to explain the reaction times and would also subsume the time window of integration concept. However, the advantage the superposition model has is that its neurophysiological implementation is immediately apparent. Bayesian models, in contrast, are usually more abstract, and it is unclear what their neural implementation would look like.
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10.1371/journal.pgen.1005721 | Origins of De Novo Genes in Human and Chimpanzee | The birth of new genes is an important motor of evolutionary innovation. Whereas many new genes arise by gene duplication, others originate at genomic regions that did not contain any genes or gene copies. Some of these newly expressed genes may acquire coding or non-coding functions and be preserved by natural selection. However, it is yet unclear which is the prevalence and underlying mechanisms of de novo gene emergence. In order to obtain a comprehensive view of this process, we have performed in-depth sequencing of the transcriptomes of four mammalian species—human, chimpanzee, macaque, and mouse—and subsequently compared the assembled transcripts and the corresponding syntenic genomic regions. This has resulted in the identification of over five thousand new multiexonic transcriptional events in human and/or chimpanzee that are not observed in the rest of species. Using comparative genomics, we show that the expression of these transcripts is associated with the gain of regulatory motifs upstream of the transcription start site (TSS) and of U1 snRNP sites downstream of the TSS. In general, these transcripts show little evidence of purifying selection, suggesting that many of them are not functional. However, we find signatures of selection in a subset of de novo genes which have evidence of protein translation. Taken together, the data support a model in which frequently-occurring new transcriptional events in the genome provide the raw material for the evolution of new proteins.
| For the past 20 years scientists have puzzled over a strange-yet-ubiquitous genomic phenomenon; in every genome there are sets of genes which are unique to that particular species i.e. lacking homologues in any other species. How have these genes originated? The advent of massively parallel RNA sequencing (RNA-Seq) has provided new clues to this question, with the discovery of an unexpectedly high number of transcripts that do not correspond to typical protein-coding genes, and which could serve as a substrate for this process. Here we have examined RNA-Seq data from 8 mammalian species in order to define a set of putative newly-born genes in human and chimpanzee and investigate what drives their expression. This is the largest-scale project to date that tries to address this scientific question. We have found thousands of transcripts that are human and/or chimpanzee-specific and which are likely to have originated de novo from previously non-transcribed regions of the genome. We have observed an enrichment in transcription factor binding sites in the promoter regions of these genes when compared to other species; this is consistent with the idea that the gain of new regulatory motifs results in de novo gene expression. We also show that some of the genes encode new functional proteins expressed in brain or testis, which may have contributed to phenotypic novelties in human evolution.
| New genes continuously arise in genomes. Recent evolutionary 'inventions' include small proteins that have functions related to the adaptation to the environment, such as antimicrobial peptides or antifreeze proteins, which have independently evolved in different groups of organisms [1,2]. A well-studied process for the formation of new genes is gene duplication and subsequent sequence divergence [3,4]. However, in recent years another important mechanism for the birth of new functional genes has been discovered- de novo gene emergence [5–7]. As deduced by comparisons to the genomic syntenic regions in other species, these genes derive from previously non-genic regions of the genome [8–14]. Genes that have recently evolved de novo are characterized by their lack of homologous genes in other species and, contrary to duplicated genes, they can evolve without the limitations which constrain sequences that have high similarity to a pre-existing gene [15]. Despite their recent origin, it has been shown that de novo Drosophila genes can quickly become functionally important [13,16].
Species or lineage-specific genes, which are often called orphan genes, have been described in a wide range of organisms, including yeast [9,17,18], primates [12,19–21], rodents [10,11,22], insects [8,23–25], and plants [26,27]. These studies based on annotated protein-coding genes have revealed that orphan genes tend to have a simple gene structure, a short protein size, and are preferentially expressed in one tissue [28,29]. As orphans lack homologues in other species, many of these genes are likely to have arisen de novo. Some of these proteins have been functionally characterized. One example is the hominoid-specific antisense gene, NCYM, which is over-expressed in neuroblastoma; this gene inhibits the activity of glycogen synthase kinase 3β (GSK3β), which targets NMYC for degradation [30].
Massively parallel RNA sequencing (RNA-Seq) has revealed that a large fraction of the genome extending far beyond the set of annotated genes is transcribed [31,32] and possibly translated [33–37]. Many genes that are annotated as long non-coding RNAs (lncRNAs) are lineage-specific and display high transcriptional turnover [38,39]. The high transcriptional activity of the genome provides abundant raw material for the birth of new genes. Indeed, the use of transcriptomics data has led to the discovery of an unexpectedly high number of recently emerged genes in yeast [33] and Drosophila [40,41]. As most of these genes show little evidence of selection, they have been called 'protogenes' [33]. The products resulting from the expression of protogenes become exposed to natural selection. If useful, they will be retained and continue to evolve under selective constraints [29,42,43].
Here we use transcriptomics data from four mammalian species to quantify the amount of transcription that is human and/or chimpanzee-specific and investigate the molecular mechanisms driving the expression of these transcripts. The data is used to assemble transcripts and identify both annotated and novel genes. The majority of de novo genes originate from regions with conserved genomic synteny in macaque. Analysis of these regions reveals that the expression of the genes is associated with the gain of novel regulatory motifs in the promoter region and U1snRNP splice sites downstream of the transcription start site. We also show that at least a subset of the newly evolved genes is likely to encode functional proteins.
We used strand-specific sequencing of polyadenylated RNA (polyA+ RNA-Seq) from several tissues from human, chimpanzee, macaque, and mouse, to perform transcript assembly with Cufflinks [44]. The total number of RNA-Seq datasets was 43, of which 26 were generated in this study and the rest were public datasets from previous studies [20,38,45]. The set of tissues sampled included testis and brain; these tissues have been found to be enriched in de novo genes [20,46]. In this study, we will use the term 'gene' to refer to the set of transcripts merged into a single locus by Cufflinks. Any genome unmapped reads were assembled de novo with Trinity for the sake of completeness [47].
Subsequently, we selected transcripts longer than 300 nucleotides (nt). This excluded any sequencing artifacts resulting from one single amplified paired end read (2x100 nt). We also filtered out all genes with a per-base read coverage lower than 5 to ensure transcript completeness (see Materials and Methods). A negative control lacking reverse transcriptase in the library construction step (RT-) indicated that the probability of a transcript to have resulted from DNA contamination was very low, virtually 0 in the case of multiexonic transcripts. To ensure a highly robust set of transcripts we filtered out intronless genes. This also removed possible promoter- or enhancer associated transcripts (PROMPTS and eRNAs). As a result of this process, we recovered 99,670 human, 102,262 chimpanzee, 93,860 macaque and 85,688 mouse transcripts merged in 34,188 human, 35,915 chimpanzee, 34,427 macaque, and 31,043 mouse gene loci. This included a large fraction of the long multiexonic genes annotated in Ensembl plus a significant number of non-annotated genes (Fig 1a). The number of annotated genes was much higher in human and mouse than in chimpanzee and macaque, mostly due to differences in the number of annotated lncRNAs. About 48% of the human genes not annotated by Ensembl matched genes assembled in recent large-scale RNA-Seq studies [38,48] (S1 Fig). Unsurprisingly, novel genes were shorter and expressed at lower levels than annotated genes (Fig 1b and 1c, respectively). In humans, unannotated genes represented 0.5–2% of the transcriptional cost depending on the tissue, as measured in terms of sequencing reads.
Next, we used BLAST-based sequence similarity searches [49] to identify the subset of de novo genes that could have originated in human, chimpanzee, or the common ancestor of these two species since the divergence from macaque (hominoid-specific genes). These genes lacked homologues in other species after exhaustive searches against the transcript assemblies described above, the transcript assemblies obtained using previously published non-stranded single read RNA-Seq data for nine vertebrate species [50], Ensembl gene annotations for the same set of species, and the complete expressed sequence tag (EST) and non-redundant (nr) protein databases from NCBI. We also employed genomic alignments to discard any transcripts expressed in syntenic regions in other species that could have been missed by BLAST (S2 Fig). This pipeline identified 634 human-specific genes (1,029 transcripts), 780 chimpanzee-specific genes (1,307 transcripts), and 1,300 hominoid-specific genes (3,062 transcripts). Taken together, the total number of candidate de novo genes was 2,714 (5,398 transcripts) (Fig 2a). The rest of genes will be referred to as conserved genes.
As we used strand-specific RNA sequencing, we could unambiguously identify a large number of antisense transcripts. Many of them were located within intronic regions (38.31%) and others partially overlapped exonic regions of other genes (10.62%). The rest of de novo transcripts were located in intergenic regions (51.07%). These percentages were similar for human, chimpanzee, and hominoid-specific genes (Fig 2b). Eight de novo genes from human and/or chimpanzee matched annotated protein-coding genes (S1 Table). One example was GTSCR1 (Gilles de la Tourette syndrome chromosome region, candidate 1), encoding a 137 amino acid long protein with proteomics evidence. Curiously, the human protein-coding genes in this set, including GTSCR1, were annotated as long non-coding RNAs (lncRNAs) in a subsequent Ensembl version (77). About 20% of de novo genes matched annotated lncRNAs or sequence entries in the 'EST' or 'nr' databases (Fig 2c). De novo transcripts had a similar distribution along the chromosomes than the rest of assembled transcripts (S3 Fig).
Transcripts from de novo genes were shorter and expressed at lower levels than those from conserved genes (S4 Fig). These biases have also been noted in young annotated primate protein-coding genes [12,20]. In general, de novo genes were located in regions with conserved synteny in macaque (> 75% S5 Fig), the proportion being similar to that observed for phylogenetically conserved genes. De novo transcripts were enriched in transposable elements; about 20% of their total transcript length was covered by transposable elements, whereas only 8% was covered in conserved genes (S6 Fig). An enrichment in transposable elements was previously observed in primate-specific protein-coding genes [12] as well as in lncRNAs in general [51].
We determined which genes were expressed in different human and chimpanzee tissues using the RNA-Seq data. The vast majority of de novo transcripts were expressed in testis (93.8–94.5%), as were transcripts from phylogenetically conserved genes (Fig 2d). In contrast, in brain, liver and heart, transcripts from de novo genes were underrepresented when compared to transcripts from conserved genes. This enrichment in testis has also been observed for mammalian lncRNAs [38,45,52]. It does not appear to be the result of increased capacity to detect weakly expressed genes in this tissue, as deduced from the overall distribution of gene expression values in testis compared to other tissues (S7 Fig). It was previously reported that young human protein-coding genes were enriched in the brain [46], but we did not detect a similar bias in our data.
As a result of the aforementioned differential expression patterns, de novo genes were twice as likely to show testis-restricted expression than the rest of genes (94.1%-96.4% as opposed to ~64% of all assembled transcripts, see Material and Methods). The use of gene expression data from GTEx, although limited to human annotated transcripts, produced consistent results (S8 Fig). The majority of de novo genes were detected in all or nearly all the 60 individuals with testis sequencing data in GTEx [53], indicating that they are expressed in a stable manner in the population (Fig 2e).
Divergent transcription from bidirectional promoters is widespread in eukaryotic genomes [54,55] and leads to the expression of numerous transcripts in antisense orientation, most of them poorly conserved in other species and generally lacking coding potential [56]. It has been proposed that the reuse of existing promoters can be a driving force of new gene origination [57]. We searched for bidirectional promoters by scanning the genome for transcription start sites of antisense transcripts at a distance < 1 Kb. Our hits had an average distance between the two TSSs of about 100 bp, consistent with the presence of a bidirectional promoter (S9 Fig). However, de novo genes were not enriched in bidirectional promoters with respect to the rest of genes (20% versus 29.81%), indicating that this is not the predominant mechanism for de novo gene formation.
Comparison of GC content in the region surrounding the TSSs clearly revealed that de novo genes are more A/T-rich than conserved annotated genes (S10 Fig). We searched for overrepresented transcription factor binding sites in the promoters of de novo genes using the programs PEAKS [58] and HOMER [59] (Fig 3a and 3b). With PEAKS we identified a clear enrichment of sites for CREBP, RFX, and JUN in the first 100 bp upstream of the TSS (p-value < 10−5, motif frequency > 20% higher than in other sequence bins). While CREBP (cAMP-responsive element binding protein) and JUN (transcription factor AP1) are general transcriptional activators, RFX (regulatory factor X) has been associated with expression in testis [60,61]. With HOMER we identified the same three motifs as well as two additional motifs (M1, M2) enriched in the first 100 bp upstream of the TSS. M1 and M2 matched the transcription factor TFIIB (RNA polymerase II complex) downstream element (BREd), which has the consensus sequence G/A-T-T/G/A-T/G-G/T-T/G-T/G [62].
We argued that, if the expression of de novo human and chimpanzee genes was at least partly due to the co-option of genomic sequences as active promoters, we should observe a lower frequency of the relevant TFBS in the corresponding syntenic regions in macaque. This is exactly what we found for the five motifs mentioned earlier, whereas no differences in motif frequencies existed for conserved genes (Fig 3c, S11 Fig). This was consistent with the gain of new transcription factor binding sites in the hominoid branches after the split from macaque in the de novo genes. We also noted that the occurrence of transposable elements (Fig 3d) tended to decrease near the TSS of all gene classes except for endogenous retrovirus-derived long terminal repeats (LTRs), which on average overlapped 13% of the proximal promoters of de novo genes compared to 5% in conserved genes. Further analyses indicated that LTRs tend to contribute CREB motifs (Fig 3e).
Transcription elongation is highly dependent on the presence of U1 small nuclear ribonucleoprotein recognition sites downstream of the TSS, whereas poly(A) sites (PAS) cause transcription termination [63]. The sequences bound by U1 correspond to 5’ splice sites (5’ss). As in standard multiexonic mRNAs, de novo genes showed enrichment of U1 sites and depletion of PAS downstream of the TSS. As U1 sites suppress the effect of PAS sites, we predicted that if transcription elongation is restricted to hominoids, we should see an underrepresentation of U1 sites in the corresponding macaque syntenic regions, but not necessarily of PAS sites. We indeed observed this pattern in de novo genes, whereas no differences were detected for conserved genes (Fig 3f). This is consistent with the idea that the gain of U1 sites contributes to the stabilization of de novo genes.
Most de novo genes were not annotated in the databases and their coding status was unclear. We analyzed two coding properties in de novo genes as well as in other sequences: ORF length and ORF coding score. The latter score was based on hexanucleotide frequencies in bona fide sets of coding and non-coding sequences (see Methods). The median length of the longest ORF of each de novo gene was 52 amino acids. De novo predicted proteins were shorter than proteins encoded by annotated coding RNAs (codRNA) with the same transcript length distribution as the set of de novo genes, and comparable to ORFs from similarly sampled intronic sequences (Fig 4a and 4b). In contrast, the coding score of the longest ORF was higher in de novo genes than in intronic ORFs (Wilcoxon test, p-value < 10−10) and comparable to the score for proteins shorter than 100 amino acids in the set of annotated protein-coding genes.
Next we searched for experimental evidence of proteins produced by de novo genes. We employed mass-spectrometry data from a recent study [64], limiting the searches to the same tissues we used for transcript assembly to increase specificity (testis, brain, heart, and liver), and also searched in Proteomics DB [65]. We identified uniquely mapping peptides in 6 de novo genes; 1 human and 5 hominoid-specific genes (Table 1). All 6 were expressed in testis; one was preferentially expressed in heart. In addition, we detected signatures of translation in 5 human and 10 hominoid-specific de novo genes using available ribosome profiling sequencing data from human brain [66]. Overall, 21 de novo genes had evidence of translation.
Closer inspection of the genes with experimental protein evidence showed that their size (median 76 amino acids) and coding potential (median 0.0414) were in line with the values observed in the rest of de novo genes (Fig 4c and 4d). Specific examples of proteins encoded by de novo genes are shown in Fig 4e and 4f. Two thirds of the ORFs in these genes were truncated in the syntenic region in macaque and none of them were detected in the syntenic region in mouse, consistent with absence of the proteins in these species (S12 Fig). These genes showed significant signatures of purifying selection (Table 2); this was assessed by calculating the fraction of nucleotide substitutions in different gene regions (introns, exons, ORF) with respect to the corresponding macaque syntenic genomic sequences. We tested whether the sequences had a lower number of substitutions than sequences evolving in a neutral or nearly neutral manner (introns), which would indicate purifying selection. We have to consider that this is a conservative test, as selection is not expected to have acted in the macaque branch in de novo genes, and positive selection may increase the number of substitutions counteracting the effect of negative selection. Despite this, signatures of purifying selection could be clearly distinguished in ORFs from the de novo genes with evidence of translation when compared to intronic regions (Fisher-test, p-value < 0.005), as it occurs in coding sequences encoding functional proteins (Table 2). In contrast, in de novo genes in general there was not a significant decrease in the number of substitutions in the longest ORF when compared to neutrally evolving sequences, suggesting that the majority of these transcripts do not encode functional proteins.
We performed a large-scale transcriptomics-based investigation on the emergence of new genes in hominoids. Our strategy was annotation-independent, which allowed us to recover many novel (non-annotated) genes and compare species for which the level of annotation varies greatly. The approach was entirely different from that employed in previous studies in which the initial datasets were composed of annotated protein coding genes in humans that lacked homologous proteins in other species [12,19–21]. We instead focused on new transcriptional events and subsequently analyzed the properties of the transcripts including coding potential and purifying selection signatures. We assembled the transcriptomes from different species to account for differences in the level of annotation, being able to recover a large number of genes likely to have originated very recently.
We employed a polyadenylated RNA sequencing strategy that was based on a combination of high sequencing depth and strand-specific sequencing, with an average of 115 Million mapped reads per sample. After performing exhaustive sequence similarity searches, we identified 2,714 genes which were specific of human, chimpanzee, or their hominoid ancestor. This is more than one order of magnitude greater than the number of human or primate-specific genes reported in previous studies [12,19–21]. The de novo origin of these genes is supported by the lack of genes expressed in the corresponding syntenic genomic regions of closely related species. We employed a carefully chosen per-base read coverage threshold, which allowed for the full recovery of complete sequences while permitting the detection of transcripts which were expressed at low levels. Our analysis was based on multiexonic genes but we have to consider that many recently evolved genes may not have yet acquired the capacity to be spliced, as shown by several examples in Drosophila [41]. Therefore, there are probably many more de novo genes than those studied here. The de novo genes constituted about 4% of all expressed multiexonic genes in human and chimpanzee. This fraction is consistent with similar transcriptomics-based studies in insects [40,24]. As these genes are short and expressed at low levels, their associated transcriptional cost is relatively small. De novo genes showed characteristic promoter and splicing signals and were expressed in a consistent manner across different individuals. However, they had very weak purifying selection signatures in general. This is interesting because it means that even if these genes are expressed in a stable manner, many of them are likely to lack functionality and thus can be considered protogenes [33].
The proportion of de novo genes with conserved genomic synteny in macaque was comparable to that of conserved genes. Given the low number of nucleotide differences in neutrally evolving regions between these two species (~ 6%), we could reliably use syntenic alignments to examine transcription-related sequence features. Relative to the corresponding genomic regions in macaque, we found an enrichment of transcription factor binding sites and U1snRNP motifs in de novo genes in human and chimpanzee; this is consistent with the idea that the gain of regulatory motifs underlies de novo gene origination. This scenario had been proposed for the formation of a new gene in mouse [7,10] but until now it had not been considered at a genome-wide scale. Interestingly, in addition to general activators and polymerase II binding sites we found an enrichment in RFX motifs in de novo gene promoters. Although there are several members of the RFX transcription factor family that bind to similar sequences, many of the sites in our sequences may be recognized by RFX2, which is highly expressed in testis and has been involved in spermiogenesis [61].
Several studies have found an excess of genes of very recent origin when compared to older gene classes [40,24]. This suggests that many young genes are subsequently lost, which is consistent with the relatively constant number of genes observed in a taxon. Our finding that signatures of purifying selection are generally very weak for de novo genes is indeed consistent with a scenario in which many of these genes are dispensable. However, a subset of genes with evidence of translation do display significant signatures of purifying selection, indicating that they correspond to functional genes. Studies in Drosophila indicate that directional selection determines the fate of some de novo genes from the very early stages [41]. While we focused primarily on possible coding functions, some of the genes may have also acquired non-coding functions. This is especially relevant in the case of antisense transcripts which can potentially influence the expression of the transcript in the opposite orientation [67]. It is important to consider that the annotations alone may not suffice to differentiate between coding and non-coding transcripts, as many annotated lncRNAs may translate short peptides according to ribosome profiling data [34,36,37]. LncRNAs tend to have small open reading frames and display limited phylogenetic conservation [37,68] and it has been previously proposed they may act as precursors of new protein-coding genes [13,21,37]. An interesting observation was that the coding score of de novo genes was clearly non-random. One possible explanation is that natural selection rapidly eliminates transcripts that produce toxic peptides [35], as one could expect such peptides to often have unusual amino acid compositions.
Here we detected 20 putative new human proteins using ribosome profiling from brain tissue [66]. Considering that the expression of most de novo genes was restricted to testis for which no ribosome profiling data has yet been published, we expect this number to increase substantially in the future. Mass-spectrometry has important limitations for the detection of short peptides [69], but we could nevertheless detect 8 putative proteins, mostly from testis. Our results indicate that the expression of new loci in the genome takes place at a very high rate and is probably mediated by random mutations that generate new active promoters. These newly expressed transcripts would form the substrate for the evolution of new genes with novel functions.
Chimpanzee and macaque samples were obtained from the Primate Bio-Bank of the Biomedical Primate Research Center (BPRC). BPRC offers state-of-the-art animal facilities (AAALAC accredited) and is fully compliant with regulations on the use of non-human primates for medical research. BPRC's Primate Tissue Bank is one of the biggest non-human primate banks in Europe and it is involved in the framework of the EuprimNet Bio-Bank (www.euprim-net.eu). The EUPRIM-Net Bio-Bank is conducted and supervised by the scientific government board along all lines of EU regulations and in harmonization with Directive 2010/63/EU on the Protection of Animals Used for Scientific Purposes. The animals used for tissue collection in all cases are diagnosed with cause of death other than their participation in this study and without any relation to the tissues used.
Human and mouse total RNA was purchased from Amsbio. Chimpanzee and macaque total RNA was extracted using a miRNeasy Mini kit from tissue samples obtained at the Biomedical Primate Research Centre (BPRC, Netherlands). Mouse samples were from a pool of 3 males and 3 females (Balb/C strain).
Libraries were prepared using the TruSeq Stranded mRNA Sample Prep Kit v2 according to the manufacturer’s protocol. PolyA+ RNA was purified from 250–500 mg of total RNA using streptavidin-coated magnetic beads (AMPure XP) and subsequently fragmented to ~300 bp. cDNA was synthesized using reverse transcriptase (SuperScript II, Invitrogen) and random primers. We did not add reverse transcriptase to one of the human testis replicate samples to use it as a control for DNA contamination (RT-). The strand-specific RNA-Seq library preparation was based on the incorporation of dUTP in place of dTTP in the second strand of the cDNA. Double-stranded DNA was further used for library preparation. Such dsDNA was subjected to A-tailing and ligation of the barcoded Truseq adapters. Library amplification was performed by PCR on the size selected fragments using the primer cocktail supplied in the kit. Sequencing was done with an Illumina HiSeq 2000 sequencer in a paired-end configuration (2x100 nt) according to the manufacturer’s instructions. Library preparation and sequencing were done at the Genomics Unit of the Center for Regulatory Genomics (CRG, Barcelona, Spain).
The polyA+ RNA-Seq included 96 sequencing datasets for 9 different species: 43 strand-specific paired end data (~3 billion reads) and 53 single read data (~3.2 billion reads). The strand-specific data was employed for the assembly of reference transcripts for human, chimpanzee, macaque, and mouse (Fig 1 for a summary of results). For comparative purposes, we used the same tissues and number of biological samples for human and chimpanzee (liver, heart, brain, and testis; two biological replicates per tissue). For macaque and mouse, we added available strand-specific RNA-Seq data from other tissues: adipose, skeletal muscle for macaque [20], and ovary and placenta for mouse [38,45]. The single read data corresponded to 5 primate species (human, chimpanzee, gorilla, orangutan, and macaque) and 4 additional vertebrates (mouse, chicken, platypus, and opossum) in 6 different tissues (brain, cerebellum, heart, kidney, liver, and testis) [50]. While these experiments were based on single reads and had lower coverage than the strand-specific RNA-Seq data, they were used to increase the number of species with expression data for sequence similarity searches. More information about the samples can be found in S1 Dataset. Sequencing data generated for this study is deposited in the Gene Expression Omnibus under accession number GSE69241.
RNA-Seq sequencing reads underwent quality filtering using Condetri (v.2.2) [70] with the following settings (-hq = 30 –lq = 10). Adapters were trimmed from filtered reads if at least 5 nucleotides of the adaptor sequence matched the end of each read. In all experiments, reads below 50 nucleotides or with only one member of the pair were not considered. We retrieved genome sequences and gene annotations from Ensembl v. 75 [71]. We aligned the reads to the correspondent reference species genome with Tophat (v. 2.0.8) [72] with parameters –N 3, -a 5 and –m 1, and including the correspondent parameters for paired-end and strand-specific reads whenever necessary. Multiple mapping to several locations in the genome was allowed unless otherwise stated.
We performed gene and transcript assembly with Cufflinks (v 2.2.0) [44] for each individual sample. Per-base read coverage and FPKM (fragments per kilobase of transcript per million mapped fragments) values were calculated for each transcript and gene as described by [44]. We only considered assembled transcripts that met the following criteria: a) the transcript was covered by at least 4 reads, b) Abundance was higher than 1% of the most abundant isoform of the gene and, c) <20% of reads were mapped to multiple locations in the genome.
Subsequently, we used Cuffmerge [44] to build a single set of assembled transcripts for each species, always keeping the strand-specific and the single read based RNA-Seq experiments separate. We compared our set of assembled transcripts with gene annotation files from Ensembl (gtf format, v.75) with Cuffcompare [44] to identify transcripts corresponding to annotated genes. This included the categories ' = ' (complete match), 'c' (contained), 'j' (novel isoform), “e”, and “o” (other exonic overlaps in the same strand). Genes for which none of the assembled transcripts matched an annotated gene were labeled ‘novel’. In human, 82% of the total annotated protein-coding and 44.5% of the non-coding genes (lincRNA, antisense and processed transcripts) were recovered.
Additionally, we ran Trinity [47], which reconstructs transcripts in the absence of a reference genome, with all unmapped reads in each species (read length > = 75 nucleotides). Before running Trinity, unmapped reads were normalized by median using Khmer (parameters –C 20, -k 20, -N 4). This allowed the recovery of any transcripts falling into non-assembled parts of the genome. We selected transcripts with a minimum size of 300 nucleotides.
We obtained a set of reference transcripts from the strand-specific RNA-Seq data using a per-nucleotide read coverage > = 5. This choice was based on the relationship between read coverage and the percentage of fully reconstructed annotated coding regions (CDS, longest one per gene) for the subset of genes mapping to annotated protein coding genes (Ensembl v.75) using only the categories ' = ' and 'c' in Cuffcompare (18,694 protein-coding genes). For values higher than 5 there was no substantial increase in the percentage of fully reconstructed CDS (coverage > = 5: 87.8%; coverage > = 10: 88.5%; coverage > = 20: 89.4%). The selection was based on coding regions and not complete transcripts because of the prevalence of alternative transcription start sites in many annotated transcripts, causing uncertainty in the latter parameter [73]. Very similar results were obtained for CDS shorter than 500 nucleotides or genes with only one annotated CDS, indicating that protein length or gene complexity has little effect on the suitability of this threshold.
Transcript assembly with the RT- control (see above) resulted in 22,803 different sequences that presumably corresponded to genomic DNA contamination, resulting from regions resistant to DNAse treatment. Except for the reverse transcriptase, all other reagents were added in the same concentration as in the other samples. Therefore, the number of contaminant fragments must be considered an upper boundary, as in a normal RNA-Seq experiment these fragments are probably sequenced much less efficiency as they have to compete with the genuine RT products. The sequences obtained in the RT- control did not contain any introns and the majority of them were shorter than 300 nucleotides (98.58%).
Reference transcripts were classified into three categories depending on their location with respect to transcripts from other genes: a) Intergenic: Transcripts that did not overlap any other assembled locus. b) Overlapping intronic: Transcripts located within introns of other assembled genes on the opposite strand. c) Overlapping antisense: Transcripts partially or completely overlapping exons from other assembled genes on the opposite strand.
We downloaded long interspersed element (LINE), short interspersed element (SINE), and long terminal repeat (LTR) annotations in the human and chimpanzee genomes from RepeatMasker (same genome versions than in Ensembl v.75) [74]. We used BEDTools [75] to identify any overlap between transcripts and/or genomic elements.
We downloaded human-chimpanzee, human-macaque, human-mouse, chimpanzee-macaque and chimpanzee-mouse pairwise syntenic genomic alignments, obtained by blastz [76], from UCSC. We developed an in-house Python script to recover syntenic regions corresponding to a given human or chimpanzee transcript, or to regions upstream and downstream of a human or chimpanzee transcription start site (TSS), using these alignments.
We scanned the human and chimpanzee genomes to identify transcripts with bidirectional promoters. We recovered any antisense pairs in which the distance between the two TSSs was < 1 kb). We estimated that 29.81% of the conserved genes and 20% for de novo genes were expressed from bidirectional promoters. This was significantly higher than the number expected by chance (5,31%, Binomial Test, p-value << 10−5). The location of different types of genes in the human chromosomes was visualized with Circos [77].
We developed a pipeline to identify de novo genes in human and chimpanzee based on the lack of homologues in other species. We first selected multiexonic transcripts from the reference transcriptome assemblies. Then, we performed exhaustive sequence similarity searches against sequences from other species with the BLAST suite of programs. Subsequently, we searched for overlapping transcripts in genomic syntenic regions.
Sequence similarity searches, using reference human or chimpanzee transcripts as query, were performed against the complete transcriptome assemblies from the nine different vertebrate species, gene annotations from Ensembl v.75 for the same species, and the EST and non-redundant protein “nr” [78] NCBI databases. We employed both BLASTN and TBLASTX programs [49], with an E-value threshold of 10−4. All BLAST searches were performed with the filter of low-complexity regions activated; we discarded all transcripts for which self-hits were not reported. Species-specific genes were those for which no transcripts (or transcripts of any paralogs) had sequence similarity hits to transcripts in any other species. To identify synteny-based homologues we took advantage of the existing pairwise syntenic genomic alignments from UCSC. We used data from human, chimpanzee, macaque, and mouse. If two transcripts overlapped (> = 1bp) in a syntenic region we considered it as evidence of homology. We reclassified the de novo genes accordingly.
We identified 634 human-specific genes (1,029 transcripts) and 780 chimpanzee-specific genes (1,307 transcripts). In the case of hominoid-specific genes we allowed for hits to gorilla and orangutan in addition to human and chimpanzee; this yielded 1,300 hominoid-specific genes (3,062 transcripts). About one third of them (221 genes and 1,016 transcripts) were reference transcripts in both species (multiexonic, coverage > = 5) and the rest were identified via the complete transcriptome assemblies, EST, and/or nr databases. Due to the fact that not all of these genes were detected as reference transcripts in both species the number of hominoid-specific genes is different for human and chimpanzee (604 and 916, respectively). Annotation files of de novo genes in GTF format are available at Figshare, http://dx.doi.org/10.6084/m9.figshare.1604892 (human) and http://dx.doi.org/10.6084/m9.figshare.1604893 (chimpanzee).
We analyzed the patterns of tissue expression in assembled transcripts, considering a transcript as expressed in one tissue if FPKM > 0. We measured the number of tissue-restricted transcripts using a previously proposed metric [79]:
τ=∑i=1i=n(1−xi)n−1
Where n is the number of tissues and xi is the FPKM expression value of the transcript in the sample normalized by the maximum expression value over all tissues. We classified cases with a τ > 0.85 as preferentially expressed in one tissue or as tissue-restricted.
For de novo genes annotated in Ensembl v.75 we obtained expression data from the GTEx project, which comprises a large number of human tissue samples. We used this data to calculate the number of genes showing tissue-restricted expression as well as the number of testis samples with detectable expression of a given gene.
We searched for significantly overrepresented motifs in de novo and conserved genes using computational approaches. We employed sequences spanning from 300 bp upstream to 300 bp downstream of the transcription start site (TSS). Redundant TSS positions were only considered once. With PEAKS [58] we identified three TRANSFAC motifs [80] enriched in de novo genes, corresponding to CREB, JUN, RFX. HOMER [59], a tool for motif discovery, also detected these motifs plus two additional motifs (M1, M2). The five motifs were enriched in the first 100 bp upstream of the TSS (p-value < 10−5, minimum 30 motif occurrences and enrichment > 20% when compared to other regions). M1 and M2 matched the transcription factor TFIIB (RNA polymerase II complex) downstream element (BREd), which has the consensus sequence G/A-T-T/G/A-T/G-G/T-T/G-T/G [62].
For graphical representation of the results, we computed the relative motif density in 100 bp windows upstream and downstream of the TSS in human and chimpanzee, and the corresponding genomic syntenic regions in macaque and mouse. We used MEME [81] to scan the sequences for the occurrence of motifs (matches to weight matrices with a p-value < 10−5). The average number of motif occurrences (motif density) was normalized to values between 0 and 1, where 1 corresponded to the highest density of a given motif in a sequence window.
It has been previously proposed that new genes tend to gain new U1 sites and lose PAS sites as they become more mature [63]. We used MEME with the same parameters as described above to search for U1 (U1 snRNP 5’ splice site consensus motif) and PAS (poly-adenylation signals) sites 500 bp upstream and downstream of the TSS (see supplementary material for weight matrices). PAS motifs found < 500bp downstream of a U1 site were not considered since the PAS effect is abolished by snRNPs bound to these U1 motifs at such distances.
We defined an open reading frame (ORF) in a transcript as any sequence starting with an ATG codon and finishing at a stop codon (TAA, TAG or TGA). In addition we require it to be at least 75 nucleotides long (24 amino acids), which is the size of the smallest complete human polypeptide found in genetic screen studies [82].
In each ORF we computed a coding score based on hexamer frequencies in bona fide coding and non-coding sequences [37]. Specifically, we first computed one coding score (CS) per nucleotide hexamer:
CShexamer(i)=logfreqcoding(hexamer(i))freqnon−coding(hexamer(i))
The coding hexamer frequencies were obtained from all human transcripts encoding experimentally validated proteins. The non-coding hexamer frequencies were calculated using the longest ORF in intronic regions which were selected randomly from expressed protein-coding genes. The hexamer frequencies were computed separately for ORFs with different lengths to account for any possible length-related biases (24–39, 40–59, >60 amino acids). Next, we used the following statistic to measure the coding score of an ORF:
CSORF=∑i=1i=nCShexamer(i)n
Where i is each hexamer sequence in the ORF, and n is the number of hexamers considered.
The hexamers were calculated in steps of 3 nucleotides in frame (dicodons). We did not consider the initial hexamers containing a Methionine or the last hexamers containing a STOP codon. Given that all ORFs were at least 75 nucleotides long, the minimum value for n was 22.
In coding RNAs (CodRNA all) the annotated ORF was selected for further analysis. To account for any possible bias due to transcript length, we randomly selected a subset of protein-coding transcripts (CodRNA short) with the same transcript length distribution as the de novo transcripts. In sequences with no annotated coding sequence (introns and transcripts from de novo genes), we chose the longest ORF considering all three possible frames. The only exception was when the longest ORF in another frame had a higher coding score than expected for non-coding sequences (0.0448 if ORF < 40 aa; 0.0314 if 60 aa > length ORF > = 40 aa; 0.0346 if length ORF > = 60 aa; p-value < 0.05) or if it was longer than expected for non-coding sequences (> = 134 aa, p-value < 0.05). In this very small number of cases (3.4%) we selected this other ORF.
We downloaded data from ribosome profiling experiments in human brain tissue [66]. Ribosome profiling reads were filtered as described previously [37]. We then used Bowtie2 [83] to map the reads to the human assembled transcripts with no mismatches. We considered each strand independently since the RNA-Seq data was strand-specific. RNA-Seq reads from the same experiment were also mapped to de novo transcripts to determine how many of them were expressed (FPKM > 0). Because of the low detectability of ribosome association at low FPKM expression values [37], two ribosome profiling reads mapping to a predicted ORF were deemed sufficient for the signal to be reported.
We used available mass-spectrometry data from human frontal cortex, liver, heart, and testis [64,65] to identify any putative peptides produced by de novo genes. Mass-spectrometry data was analyzed using the Proteome Discoverer software v.1.4.1.14 (Thermo Fisher Scientific, United States) using MASCOT v2.5 [84] as a search engine. The database we used contained the human entries in SwissProt [85], the most common contaminants, and putative peptides derived from the translation of transcripts from de novo genes. Carbamidomethylation for cysteines was set as fixed modification whereas acetylation in protein N-terminal and oxidation of methionine were set as variable modifications. Peptide tolerance was 7 ppm in MS and 20mmu in MS/MS mode, maximum number of missed cleavages was set at 3. The Percolator [86] algorithm implemented in the Proteome Discoverer software was used to estimate the qvalue and only peptides with qvalue < 0.01 and rank = 1 were considered as positive identifications. Lastly, we considered unique peptides matching young transcripts by using BLAST with short query parameters to search the candidate peptides against all predicted ORFs in assembled transcripts. Additionally, we searched for any matching peptides in Proteomics DB [65]. We found 6 de novo genes with proteomics evidence; two of them were annotated in Ensembl as lncRNAs and expressed in ≥55 testis samples from GTEx. Details of the results can be found in the supplementary material.
We estimated the number of substitutions per Kb in human-macaque genomic alignments with the maximum likelihood method ‘baseml’ from the PAML package [87] with model 4 (HKY85). We only analyzed transcripts with complete synteny in both species. We compared the number of substitutions with respect to sequence length in different sequence sets using the Fisher exact test.
The analysis of the data, including generation of plots and statistical test, was done using R [88].
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10.1371/journal.pntd.0001514 | Molecular Mechanisms of Drug Resistance in Natural Leishmania Populations Vary with Genetic Background | The evolution of drug-resistance in pathogens is a major global health threat. Elucidating the molecular basis of pathogen drug-resistance has been the focus of many studies but rarely is it known whether a drug-resistance mechanism identified is universal for the studied pathogen; it has seldom been clarified whether drug-resistance mechanisms vary with the pathogen's genotype. Nevertheless this is of critical importance in gaining an understanding of the complexity of this global threat and in underpinning epidemiological surveillance of pathogen drug resistance in the field. This study aimed to assess the molecular and phenotypic heterogeneity that emerges in natural parasite populations under drug treatment pressure. We studied lines of the protozoan parasite Leishmania (L.) donovani with differential susceptibility to antimonial drugs; the lines being derived from clinical isolates belonging to two distinct genetic populations that circulate in the leishmaniasis endemic region of Nepal. Parasite pathways known to be affected by antimonial drugs were characterised on five experimental levels in the lines of the two populations. Characterisation of DNA sequence, gene expression, protein expression and thiol levels revealed a number of molecular features that mark antimonial-resistant parasites in only one of the two populations studied. A final series of in vitro stress phenotyping experiments confirmed this heterogeneity amongst drug-resistant parasites from the two populations. These data provide evidence that the molecular changes associated with antimonial-resistance in natural Leishmania populations depend on the genetic background of the Leishmania population, which has resulted in a divergent set of resistance markers in the Leishmania populations. This heterogeneity of parasite adaptations provides severe challenges for the control of drug resistance in the field and the design of molecular surveillance tools for widespread applicability.
| Drug resistance is a serious problem that strikes at the core of infectious disease control. The mechanisms developed by pathogens to become resistant against existing drug treatments have been studied for many years but these studies have frequently scrutinized a few lines of the pathogen and rarely is it known whether the mechanisms identified occur in all pathogen populations present in endemic regions. In this study we assessed the diversity amongst drug-resistant parasites which emerged under treatment pressure in a natural parasite population. An extensive molecular and phenotypic characterisation of a collection of Leishmania donovani parasites isolated from leishmaniasis patients revealed that the parasites which are resistant to treatment have heterogeneous characters. The results provide evidence that how a parasite develops resistance under treatment pressure depends upon its genetic background. These findings provide key insights into the challenge that drug resistance poses for the control of infectious diseases like leishmaniasis.
| Leishmania, a protozoan parasite transmitted by phlebotomine sand flies, causes a neglected infectious disease commonly referred to as leishmaniasis. The species Leishmania (Leishmania) donovani is the causative agent of visceral leishmaniasis (VL) or kala-azar, the most severe form of leishmaniasis which is lethal if left untreated [1], [2]. Pentavalent antimonials, such as sodium stibogluconate (SSG), have been the first-line treatment for leishmaniasis worldwide for more than 70 years. However, this therapy is challenged by emergence of resistant parasites [3]–[6], a phenomenon best documented in the Indian subcontinent with SSG-resistant L. (L.) donovani being reported in both India [7] and Nepal [8]. In the late 1990s it was reported that parasite resistance to antimonials consistently correlated with SSG treatment failure in the Indian province Bihar [7], [9], and appeared to cause up to 60% treatment failure in that region [5]. In contrast, in the neighbouring Nepalese VL endemic region VL patients infected with SSG-resistant parasites were found to have only a 25% risk of SSG treatment failure [8].
Pentavalent antimonials are considered to have a dual action mode whereby (i) the administered formulation sodium stibogluconate stimulates the infected macrophages (mΦ) to impose oxidative/nitrosative stress on the intracellular parasites [10], [11], and (ii) the reduced form of the drug, trivalent antimony (SbIII), acts directly on the parasite by perturbing its redox-balance [12]–[14]. Figure 1 shows a schematic overview of the cellular processes known to be affected or involved in the mode of action of antimonials. The adaptations that Leishmania requires to resist this complex drug pressure are poorly understood. It has been shown that SSG-resistant L. (L.) donovani clinical isolates have a polyclonal origin, which could be explained by frequently and independent events of SSG-resistance emergence throughout the endemic region [15]–[17]. It is not known how variable or invariable the associated molecular changes are at each emergence event although results from studies investigating antimonial resistance in different L. (L.) donovani clinical isolates have suggested a very heterogeneous situation. Many different approaches have been used to study various clinical isolates and this has led to the tentative identification of multiple putative markers of SSG-resistance including heat shock proteins [18], histones [19], surface proteins [20]–[22], and various transporters [20], [21], [23]–[25]. The up-regulation of antioxidant pathways in SSG-resistant parasites has been most frequently reported and is thought to entail possible cross-resistance to oxidative/nitrosative stress [21], [23], [26]–[29]. It is currently not known whether any of these putative markers is universal to all SSG-resistant parasites and the number of clinical isolates included in individual studies so far has been too limited to make conclusions on the homogeneous or heterogeneous character of the SSG-resistant parasite population on the Indian subcontinent [21], [23], [26]–[28].
The aim of this study was to address whether or not SSG-resistant Leishmania emerging in a particular endemic region evolved via similar molecular adaptations. For this purpose, we designed a comprehensive characterisation of the parasite pathways implicated in SSG/SbV/SbIII metabolism using a collection of parasite strains isolated from Nepalese VL patients. The resulting data suggest that the SSG-resistant Leishmania parasites in this single endemic region have heterogeneous molecular and phenotypic features related to drug resistance. Our findings highlight the necessity of molecular typing of emerging drug resistant parasite populations in order to fully understand the nature, complexity and consequences of the appearance of drug resistance.
Written informed consent was obtained from the patients and in case of children from the parents or guardians. Ethical clearance was obtained from the institutional review boards of the Nepal Health Research Council, Kathmandu, Nepal and the Institute of Tropical Medicine, Antwerp, Belgium.
Parasites were isolated from bone marrow aspirates from confirmed VL patients recruited at the B.P. Koirala Institute of Health Sciences, Dharan, Nepal between 2002 and 2003 as described before [8] with a success rate of approximatively 60%. All clinical isolates were obtained at time of diagnosis, before SSG treatment, except for 3 isolates (BPK164/1, BPK177/0 and BPK181/12) which were obtained at time of adjudged treatment failure. The methods used for species typing, genotyping and in vitro antimonial testing of the clinical isolates were performed as described previously [8]. Briefly, all isolates were typed as L. (L.) donovani based on a CPB PCR-RFLP assay [30] and were further genotyped using kDNA PCR-RFLP [15], Illumina whole genome sequencing [16] and typing of 130 SNPs on the Sequenom platform [17]. Genotyping of the parasite strain collection used in this study revealed 2 major genetic clusters, designated here as population A and B. Population A is very homogenous and geographically clustered, while population B is heterogeneous compared to population A and geographically scattered [15]. Selected parasite isolates were cloned using the micro-drop method [31], to obtain homogenous working parasite populations for study, which we further refer to as clones. All clones were confirmed to be L. donovani by CPB PCR-RFLP analysis. All strains were infectious for macrophages in vitro [8] and 12 of them were previously studied for infectivity in vivo, showing that all are infective to BALB/c mice [32]. The in vitro susceptibility of a strain for SSG or SbIII is expressed as an activity index (A.I.), which is defined as the ratio of the ED50 of a tested strain versus the ED50 of the SSG-sensitive reference strain included in each assay. Isolates with an A.I. of 1–2 were considered sensitive to the tested drug, while isolates with an A.I. of 3 or higher were considered resistant. The details of all isolates and derived clones included in this study are given in Table 1.
Promastigotes were grown on modified Eagle's medium [33] (Invitrogen) supplemented with 20% (v/v) heat inactivated foetal calf serum (PAA laboratories GmbH) pH 7.5 at 26°C. The cultures were initiated by inoculating parasites at day 4 stationary phase into 50 mL culture medium to give a density of 5×105 parasites/mL; the resulting inoculated medium was equally distributed over 10 culture flasks. Every 24 hrs for 8 consecutive days, parasite density was determined to record parasite growth. Monitoring of parasite growth and concomitant sampling at various time-points was repeated for production of DNA extracts for sequencing, mRNA extracts for gene expression profiling, protein extracts for protein profiling, metabolite extracts for thiol profiling and in vitro oxidative and nitrosative stress testing.
Promastigote pellets were harvested during stationary phase of promastigote growth and washed 3 times in PBS. Three flasks of 5 mL cultures were needed for the required yield of 7–10 µg DNA. DNA isolation was performed from the dry pellets with the QIAamp DNA Mini Kit, according to manufacturer's instructions. From whole-genome sequencing data [16], the sequences of the 11 target genes and 500 bp upstream/downstream were retrieved and aligned to check for sequence polymorphism; read-depth analysis and SNP calling were done as described elsewhere [16] and the 11 targets all proved to be single-copy per haploid genome.
A promastigote pellet of each studied strain was harvested every 24 hrs for 8 consecutive days of in vitro promastigote growth. RNA was isolated, analysed and reverse transcribed as described before [34]. The resulting cDNA was 10 times diluted and used for quantitative PCR of 11 genes as described elsewhere [34]. Briefly, the mRNA levels of the 11 genes were determined at the 8 different time-points during promastigote growth in 1 quantitative experiment. The analysis of 11 genes in 144 samples could be performed as 1 integrated experiment by performing inter-plate calibrations (based on 3 samples included in each quantitative PCR-run) and centralised data-management with the qBase software package [35].
Duplicate pellets of 1×108 promastigotes were harvested for each line at 4 different time-points during promastigote growth, washed in PBS and lysed by repeated aspiration in ice-cold lysis buffer (0.25 M sucrose, 0.25% triton X-100, 10 mM EDTA) containing a protease inhibitor mix (10 µM E-64, 2 mM 1,10-phenanthrolin, 4 µM pepstatin A, 1 mM phenylmethylsulfonylfluoride). Cell debris was removed by centrifugation at 12000× g for 10 min at 4°C. The soluble protein fraction was fractionated on a 12% SDS-PAGE and electroblotted to nitrocellulose membranes (Amersham Biosciences). Membranes were blocked for 2 hrs at room temperature in TBS containing 5% milk and 0.2% Tween 20. Immuno-blotting of proteins of interest was done overnight with the primary antibodies anti-TR (trypanothione reductase) and anti-MST (mercaptopyruvate sulfurtransferase). Protein loading per well was assessed with anti-OPB (oligopeptidase B) antibody which was shown to have a stable expression throughout Leishmania promastigote growth [36]. The secondary antibodies used were rabbit anti-IgG and antibodies were detected using peroxidise-linked anti-rabbit IgG and ECL reagents (Pierce). The Western blots were digitally analysed using the software package Image J 1.38× (NIH, USA). The background of all blots was subtracted automatically before measuring the intensity of specific bands. Integrated densities for each band were determined for each protein of interest and its corresponding loading control. The ratio of the band intensity of the protein of interest versus the band intensity of the corresponding loading control was used as relative protein expression level and allowed comparison with other samples. All duplicate samples for each of the 4 time-points of 1 particular line were analysed in 1 blot.
Thiol levels were quantified by HPLC as described previously [37] with minor modifications. Briefly, triplicate cultures of 2.5×107 promastigote were harvested at 4 different time-points during promastigote growth and were prepared independently for thiol quantification. Following cold washing steps to remove extracellular medium, each pellet was resuspended in 40 mM HEPPS in 2 mM EDTA, pH 8.0 containing 0.7 mM TCEP and incubated for 45 min at room temperature during which cellular thiols were reduced by the agent TCEP. The reduced thiols were subsequently derivatised by adding 50 µL 2 mM monobromobimane, a fluorescent thiol-specific reagent, and immediate heating for 3 min at 70°C. After briefly cooling on ice and a deproteinisation step, the resulting supernatant with the derivatised thiols was kept for HPLC analysis. Separation and quantification of the derivatised thiols was done by high pressure liquid chromatography (HPLC, Dionex). The 3 thiols cysteine (CSH), glutathione (GSH) and trypanothione (T(SH)2) could be confidently identified in Leishmania extracts through comparison with chromatograms of commercially available cysteine (Sigma), glutathione (Sigma) and trypanothione (BACHEM). These commercially available thiols were also used to prepare standard solutions containing all 3 thiols in variable concentrations but with a total thiol concentration comparable to the content of a Leishmania lysate. The obtained thiol mixtures were used to determine (i) the correlation between fluorescence peak area and thiol concentration and (ii) the linear range of detection. A volume of 25 µL of each prepared derivatised Leishmania extract was injected on the column for thiol quantification. A mobile phase consisting of 0.25% acetic acid (solvent A) and 100% acetonitrile (solvent B) was applied to separate the different thiols. Gradient elution started with 100% solvent A and 0% solvent B. After 40 min, solvent B was increased to 8% for 60 min, to 15% for the subsequent 10 min, and finally to 50% for 1 min. The system returned to the initial solvent composition with 0% solvent B for the remaining 10 min of the run. Thiols were detected as they ran off the column by a fluorescence spectrophotometer (excitation 365 nm, emission 480 nm).
Promastigote susceptibility for 3 different oxidative/nitrosative stresses (Table 2) was assessed using Alamar blue (AB) assays as described elsewhere [38], with minor modifications. Briefly, at 3 different time-points during promastigote growth, parasites were plated in quadruplicate in 96-well plates at an initial density of 5×105 parasites/ml for 48 hrs incubation experiments. Each well with 100 µl parasite culture was topped with 100 µl drug solution prepared in 6 different dilutions (Table 2). The following drugs were used: Perdrogen hydrogen peroxide 30 WT % (Sigma-Aldrich), S-Nitroso-N-acetylpenicillamine (SNAP, Biomol International), and potassium antimonyl tartrate trihydrate (SbIII, Sigma-Aldrich). Quadruplicate untreated controls and blanks were included in each test plate. The culture plates were sealed and incubated at 26°C. AB reagent (Invitrogen) was added 24 hrs before the end of drug incubation to test cell viability, and AB fluorescence was read at the end of the drug incubation using 555 nm excitation wavelength and 585 nm emission wavelength.
AB fluorescence data from treated and non-treated cultures was used to calculate the IC50 by sigmoidal regression analysis (with variable slope) using GraphPad Prism v.5.02.
The change of all measured variables (RNA/protein/thiol/IC50) was evaluated (i) in relation to the SSG-phenotype and (ii) in relation to time during in vitro growth with a repeated measures two-way ANOVA (ANOVA p-value indicated under each heatmap) followed by Bonferroni post-tests (significance indicated by an asterisk under each time point). Since experiments were repeated at different time-points during promastigote growth, the analysis was done with matching by time-points. Statistical analysis was done with GraphPad Prism v.5.02 from GraphPad Software, Inc. Heatmaps were plotted in R (www.r-project.org) using the plotting tools of the packages gplots and RColorbrewer.
We selected 19 L. (L.) donovani clinical isolates with documented differential in vivo and in vitro SSG-susceptibility [8]: 8 of those isolates are in vitro sensitive to SSG (SSG-S) and 11 are in vitro resistant to SSG (SSG-R). Within this particular collection of L. (L.) donovani clinical isolates, kDNA fingerprinting showed there to be 2 distinct genotypic populations (here designated as population A and population B) differentiated by the highest minicircle sequence dissimilarity [15]. Population A (eight isolates) which is genetically very homogeneous and found in only 2 Eastern Nepalese districts, Sunsari and Morang (Figure 2), is thought to result from a recent clonal expansion [15]. In contrast, population B (eleven isolates) is much more heterogeneous at kDNA level than population A [15], and was found in the entire VL endemic region of Nepal (Figure 2). The higher homogeneity of population A (versus population B) was confirmed by whole genome sequencing [16] and genome-wide SNP typing [17]. PCA analysis revealed that population A clusters separately from population B (see fig. 1 in ref [16]) due to 35 homozygous SNPs - 19 coding, 11 non-synonymous and 8 synonymous. Populations A and B should be considered at present as model populations, as the parasite isolation success rate is only around 60% we cannot yet exclude the possibility that parasites with other genetic backgrounds occur in the region. Table 1 gives an overview of the geographical, clinical and biological characteristics of all parasite isolates (and derived clones) included in this study.
We comparatively characterised in vitro promastigotes of these L. (L.) donovani isolates (and derived clones) on the level of DNA sequence, RNA expression, protein expression, thiol content and oxidative/nitrosative stress phenotype. For the last 4 experiments, we monitored and sampled the promastigote cultures during 8 days in vitro growth, a process which consists of a logarithmic phase (day 1 to day 4) during which the parasites multiply, and a stationary phase (day 5 to day 8) during which metacyclogenesis (the development of the metacyclic promastigotes that are infectious to humans) occurs. The strains studied here all had a comparable in vitro growth dynamic (Figure 3). The purpose of this time-course analysis was two-fold: (i) to detect quantitative differences in parasite features at specific stages of growth, and (ii) to detect differential regulation of parasite features over time.
The gene expression levels of 11 target genes, selected on the basis of their reported relevance in the parasite's antioxidant response to SSG or pathways known to be affected by SSG (Figure 1), were assessed using quantitative PCR with 18 L. (L.) donovani clinical isolates (Table 1). The detailed results of the gene expression profiling experiments can be found in dataset S1.
Comparison of the expression profiles of all SSG-S and all SSG-R isolates identified 3 genes (CBS, GCS and MST) with a significantly different expression profile in the 2 groups of isolates (Table 3). However, a more detailed analysis clarified that the expression levels of these 3 genes do not represent molecular markers of all SSG-R parasites, but rather characterise SSG-R parasites in either population A or B (Table 3 and visualised in the heatmaps of figure 4):
Thus the SSG-R isolates are marked by an enhanced gene expression during metacyclogenesis, however in each case in just 1 of the 2 populations studied. This heterogeneous profile for expression of these genes in the SSG-R isolates contrasts with the more homogenous profiles of the SSG-S isolates (Figure 4). The hypothesis arising from these experiments, that SSG-R isolates in the 2 populations have heterogeneous adaptations, instigated further molecular and biochemical comparison of the isolates. To avoid working with mixed parasite isolates for this investigation, we produced clones (derived from a single parasite picked from the isolate culture and grown as independent culture) of selected clinical isolates which were confirmed to be representative for population A and population B by whole genome sequencing and SNP typing [16], [17]. These clones (characteristics shown in Table 1) were used for the remainder of the study.
Single nucleotide polymorphisms (SNPs) in the DNA sequence of the 11 studied genes (Figure 1) could modify performance of the encoded proteins (SNPs in coding regions) or could underlie the observed changes in gene expression (SNPs in non-coding regions), although such SNPs had not been reported previously in the context of antimonial resistance in Leishmania. In our study, the coding DNA sequence of the 11 genes of interest was determined by Illumina sequencing for 11 different parasite clones (5 SSG-S and 6 SSG-R, Table 1). All coding sequences were found to be 100% identical. Further screening of the neighbouring non-coding upstream and downstream sequences of all genes revealed that the upstream regions (500 bp) of the 11 genes were also completely conserved among the 11 clones. Some SNPs were found in the downstream regions (500 bp) of the genes coding for TR, MST and ACR2, but the SNP pattern did not correlate with either the genotypic diversity (populations A and B) or the SSG-susceptibility. The sequencing data are detailed in dataset S2.
Our protein analysis experiments focussed on MST and TR. The gene encoding MST was the one showing the most significant differential gene expression-profile between SSG-S isolates and SSG-R isolates (in pop. B) of all the genes analysed (Table 3). TR, on the other hand, did not have significantly different gene expression-profiles in the SSG-S and SSG-R isolates tested here, but has been implicated as a target of SbIII, the active form of SSG (Figure 1) [12], [14], [39]. We determined by Western blot analysis the relative protein levels within the measurable range at 4 different days during the 8-day promastigote growth for 6 clones of each population (Table 1).
The digitally integrated protein expression levels of TR and MST are visualised in the heatmaps of figure 5; the full dataset is given in dataset S3. MST expression-profiles were overall similar within population A (P = 0.450) and within population B (P = 0.469). The protein profiles of TR are also comparable amongst the clones of population A (P = 0.964), but in population B the SSG-R clones were found to have an average 3.9-fold lower protein level compared to the SSG-S clones (P = 0.018, Figure 5). Bonferroni post-tests indicated that this difference was statistical significant at 3 out of 4 time-points tested.
Thiols are the central metabolites of Leishmania's redox metabolism [40], and in this study were quantified at 4 different days during the 8-day promastigote growth for 6 clones of each population (Table 1). The resulting thiol quantities are in the same range as reported by other L. (L.) donovani studies [41]. The complete thiol quantification data are given in dataset S4.
The thiol levels in the 2 populations are visualised in the heatmaps of figure 6. The trypanothione and cysteine profiles are generally comparable for all tested clones. We also did not find a correlation between glutathione levels and SSG-susceptibility of the tested clones. However there was a significant overall difference in glutathione levels between population A and B, with the clones of population B having an average 1.5-fold higher glutathione level compared to those of population A (P = 0.0004). Although the levels detected were significantly different, it is not possible with the current knowledge to know whether or not such a small variation is of any biological significance. Interestingly, differences were found between the cysteine and glutathione levels in log and stationary phase promastigotes, however as this was not central to this study it was not investigated further
In vitro oxidative and nitrosative stress exposure of promastigotes was chosen as a phenotypic approach to verify if the molecular modifications leading to L. (L.) donovani SSG-resistance confer a modified tolerance to oxidative/nitrosative stress, which has been hypothesised from many other studies [21], [23], [26]–[29]. We exposed 6 clones of each population (Table 1) to 3 different oxidative/nitrosative stresses: (i) hydrogen peroxide (H202), a powerful oxidising agent; (ii) S-nitroso-N-acetylpenicillamine (SNAP), an NO donor imposing a nitrosative stress; and (iii) SbIII, a metalloid compound closely related to SSG which imposes an indirect oxidative/nitrosative stress by binding thiols and inhibiting the enzyme TR [12]–[14]. The stress susceptibility of the clones was determined on 3 consecutive days (days 5 to 7) during the stationary phase of the 8-day promastigote cultures. Table 2 summarises the conditions of the performed in vitro susceptibility tests.
The results of the stress susceptibility tests are shown in heatmaps in figure 7 which visually highlight one common finding for all 3 stresses: all clones of population A have a comparable stress-susceptibility, while in population B the SSG-R clones have a significantly different stress-susceptibility profile compared with the SSG-S clones. The susceptibility differences between SSG-S and SSG-R in population B, however, are not of the same nature and magnitude for the 3 compounds used. The difference is most pronounced for H202, with the tested SSG-R clones having a stable 1.6-fold lower (on average) IC50 compared with the tested SSG-S clones (P = 0.0003). The difference in SNAP susceptibility between the 2 phenotypes (P = 0.0004) varied during growth. The SNAP-tolerance of the SSG-R clones increased more steeply during stationary phase compared with the SSG-S clones. Consequently the difference between the 2 phenotypes was less significant as stationary phase progressed, and was almost non-existent by the end of stationary phase (day 7). The SbIII-susceptibility difference between the 2 phenotypes (P = 0.029) also changed during stationary phase. At the beginning of stationary phase, the SSG-R clones had a 2.3-fold lower SbIII tolerance compared with the SSG-S clones. However, the SbIII IC50 values of the SSG-S clones decreased drastically as stationary phase progressed, while those of the SSG-R clones remained approximately stable. The result is that the SSG-R clones had an average 41-fold higher SbIII tolerance compared with SSG-S clones by day 7. This SbIII tolerance difference at the end of stationary phase (when more infectious parasite forms are present) is consistent with the difference in SSG susceptibility of the subsequent intracellular amastigote life-stage. It is also of note that the steep drop in SbIII tolerance observed during stationary phase of the SSG-S clones in population B was not seen in any of the clones of population A, thus represents a significant phenotypic difference between the 2 populations (P = 0.041).
The primary aim of our study was to assess and attempt to correlate the molecular and phenotypic heterogeneity in parasite populations under drug treatment pressure. For this purpose we characterised a sample of L. (L.) donovani isolates that belong to 2 genetically distinct populations circulating in the VL endemic region of Nepal. In order to guarantee (i) reliable comparison of data collected in for numerous parasite lines and (ii) meaningful integration of data collected with various experimental approaches, it was of critical importance to use an experimental design based on highly standardised protocols. It is extremely difficult to achieve such standards when studying intracellular amastigotes, the clinical relevant form of the parasite, which need to be generated by in vitro macrophage infections [34]. Axenic amastigote culture techniques have been developed to overcome in part this limitation, but can only be used with parasites that are well-adapted to in vitro culture which is normally not the case for clinical isolates [42], [43]. Hence we chose to work with promastigotes, the form of the parasite that resides in the insect vector, which can be relatively easily and consistently cultured in vitro. We also chose to perform time-course analyses based on samples taken at different days during promastigote growth, an approach which we have applied previously to increase sensitivity and robustness in experimental studies of Leishmania [34]. Recent studies showed a dramatic down-regulation of gene expression from stationary phase promastigote to amastigotes, highlighting the functional importance of the former stage and supporting the hypothesis of a pre-adaption for intracellular life as an amastigote [44], [45], we cannot assume that the SSG-R features identified here in stationary phase promastigotes are also present in the corresponding amastigote stage. This limitation curtails confirmation of the role of the identified putative SSG-R markers in the resistance mechanisms active in amastigotes, nevertheless, as discussed below in detail, the identified promastigote characteristics provide significant new insights regarding the nature of the SSG-resistant phenotype in natural Leishmania populations.
We identified in this study a number of molecular and phenotypic features that correlate with the SSG-R phenotype. However, none of those markers were found to be common for all SSG-R lines included in this study (overview in Table 4) but were specific for either population A or B.
The SSG-R isolates of population A were characterised by elevated mRNA abundance of CBS and GCS in comparison to the SSG-S isolates of population A (Table 3 and Figure 4), but the underlying genetic polymorphism of these mRNA differences could not be identified as SNPs in the neighbouring untranslated regions (UTRs) could not be correlated to the differential gene expression profiles between SSG-S and SSG-R strains. This does not, however, exclude the possibility for changes in trans-regulatory elements. CBS and GCS encode key enzymes for the biosynthesis of the thiols cysteine and glutathione, respectively, which confer protection against oxidant stresses (Figure 1) [27], [40], [46], [47]. However in our sample, we did not detect a concomitant increase of thiol levels (Figure 6), nor an increased oxidative/nitrosative stress tolerance of the SSG-R clones compared to the SSG-S clones of population A (Figure 7). This is in contrast to what was reported for Indian L. (L.) donovani, where increased GCS gene expression linked to increased thiol levels in the promastigote stage have been frequently described in SSG-R clinical isolates [21], [23], [27], [29]. At this point it is unclear what other role(s) increased CBS and GCS mRNA abundance could play in SSG-resistance of population A.
In population B, the SSG-R isolates were found to have a significantly up-regulated gene expression of the sulfurtransferase MST (Table 3, Figure 4). However, we could not identify the underlying genetic polymorphism (mutations in the UTR's did not correlate with SSG-susceptibility) and protein profiling demonstrated that this mRNA difference is not translated to the level of protein (Figure 5). Hence, like the enhanced CBS/GCS gene expression in population A, it is not clear what role up-regulated MST gene expression plays in the SSG-resistance mechanism in population B. At the protein level, the enzyme TR was found to be present in significant lower quantities in SSG-R clones compared to SSG-S clones (Figure 5). This differential protein profile presumably results from distinct post-transcriptional regulation in the 2 phenotypes since upstream TR mRNA levels of all population B isolates were similar (Table 3). A decrease of the enzyme TR signifies a decrease of an important antimonial target in the parasite, and could thus represent the basis of the SSG-resistance mechanism of population B. It is nevertheless surprising to find decreased levels of TR as part of a successful Leishmania adaptation since the enzyme TR has a central role in the protection against oxidants in the host [48]–[50] (Figure 1). One would expect that the observed four-fold decrease in TR protein would interfere with the parasite's capacity to manage oxidative/nitrosative stress. The stress susceptibility tests of this study indeed revealed that in population B, the tested SSG-R clones have a significant different oxidative/nitrosative stress tolerance compared to the SSG-S clones (Figure 7). The reduced TR levels and associated modified oxidative/nitrosative stress management are a prominent tandem hallmark of antimonial-resistance in population B, but not in population A where TR protein levels and likewise responses to oxidant stresses were generally conserved.
The distinct profiles of SSG-R parasites in the 2 studied populations provide strong evidence that the evolved molecular mechanisms leading to the SSG-resistant phenotype differ between the 2 populations (Table 4). The key question that arises is which factors drive this divergent adaptation to antimonial treatment.
Several studies hypothesised that stress factors inherent to the natural environment of Leishmania (e.g. oxidative stress from the host cell, environmental arsenic contamination) can influence the mechanisms of adaptation to antimonial pressure [6], [51], [52]. Differential exposure to such environmental stress factors theoretically could result in differential molecular changes in SSG-R parasites. However, it seems unlikely that environmental differences have been the driving force in shaping the SSG-R heterogeneity in the studied parasite sample, since the 2 studied populations originate from the same endemic region and are likely to have been exposed to similar environmental stresses.
Based on the findings of this study, we hypothesize that differences in genetic background were a major driving force in the development of heterogeneous SSG-R phenotypes. The parasite's genetic background determines the parasite's intrinsic capacity to cope with antimonial stress, and this intrinsic capacity is likely to vary to some extent between distinct genetic populations. For instance, the distinct genetic background of the 2 populations studied here is manifested as population differences in antimonial detoxifying metabolites (glutathione) and antimonial stress response (SbIII IC50, Table 4). We postulate that the success of emerging mutations to confer antimonial-resistance to a particular parasite population can depend on the specific antimonial metabolising character of that population. For instance, a particular mutation might be successful in one population and unsuccessful in another population because the intrinsic antimonial metabolism of the 2 populations is differently affected by that same mutation. Our results imply that the mutation(s) which underlie the tandem TR/oxidant stress-feature of SSG-R in population B have a beneficial effect in combination with the population B background. As this TR-related mutation has not surfaced in population A, it appears that the same beneficial effect is not attained (or, at least, not attained as readily) in combination with the population A background. Drug-resistance studies on other pathogens including Plasmodium falciparum and Mycobacterium tuberculosis also reported that the effect of specific drug-resistance mutations can depend on the strain genetic background [53], [54]. It has been proposed that this relation between genetic background and drug-resistance mutations reflects a form of epistasis. Epistasis occurs when the phenotypic effect of a mutation changes depending on the presence or absence of other mutations in the same genome [55]. In the context of drug-resistance, epistasis is thought to become manifest when a particular drug-resistance mutation has a different fitness effect depending on the strain's genetic background [56], [57].
A picture emerges in which each genetically distinct population can (and perhaps is likely to) develop a SSG-resistant phenotype with a different molecular basis. This completely transforms current perceptions of Leishmania drug resistance, in which it is frequently presumed that there is a single dominant molecular feature which underlies the SSG-R phenotype encountered in the field. The multitude of clinical L.(L.) donovani resistance markers already reported in the literature [20]–[23], [26]–[29] appeared incoherent, and sometimes incongruent (e.g. SSG-R markers of Indian vs Nepalese strains), but our data suggest that those findings should be re-interpreted taking into account the genetic background of the various studied parasite samples. Further studies are needed to investigate in what way the various identified SSG-R molecular features interact with the genetic background of the respective natural populations to convey the antimonial resistant phenotype. Additional isolates with a different genetic background from those of populations A and B (if any should be found) will also be considered in future studies. Epidemiological surveillance of Leishmania drug resistance is currently impeded by the lack of molecular-based tools to monitor the emergence and spreading of antimonial resistant parasites [2], [58], [59]. It now seems that the putative high degree of molecular heterogeneity in SSG-resistant parasites needs to be included as a key factor when designing molecular tools to monitor SSG-resistance.
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10.1371/journal.pgen.0030205 | Forty Years of Erratic Insecticide Resistance Evolution in the Mosquito Culex pipiens | One view of adaptation is that it proceeds by the slow and steady accumulation of beneficial mutations with small effects. It is difficult to test this model, since in most cases the genetic basis of adaptation can only be studied a posteriori with traits that have evolved for a long period of time through an unknown sequence of steps. In this paper, we show how ace-1, a gene involved in resistance to organophosphorous insecticide in the mosquito Culex pipiens, has evolved during 40 years of an insecticide control program. Initially, a major resistance allele with strong deleterious side effects spread through the population. Later, a duplication combining a susceptible and a resistance ace-1 allele began to spread but did not replace the original resistance allele, as it is sublethal when homozygous. Last, a second duplication, (also sublethal when homozygous) began to spread because heterozygotes for the two duplications do not exhibit deleterious pleiotropic effects. Double overdominance now maintains these four alleles across treated and nontreated areas. Thus, ace-1 evolution does not proceed via the steady accumulation of beneficial mutations. Instead, resistance evolution has been an erratic combination of mutation, positive selection, and the rearrangement of existing variation leading to complex genetic architecture.
| Adaptation is not always a straightforward process, and often results from natural selection tinkering with available variation. We present in this study just such a tortuous natural selection pathway, which allows the mosquito Culex pipiens to resist organophosphorous insecticides. In the Montpellier area, following the use of insecticide to control mosquito populations, a high-resistance allele of the insecticide target enzyme appeared. But this allele also displayed strong deleterious side effects. Recently, several duplicated haplotypes began to spread in natural population that put in tandem a susceptible and a resistant allele. We show that the duplicated haplotypes actually display reduced side effects compared to the resistant allele when in the heterozygous state, but also new and strong costs in the homozygote. This pattern leads to an unexpected equilibrium between four different alleles across treated and nontreated areas. The story of resistance in C. pipiens is indeed far from a slow progression toward a “perfect” adaptation. Rather, selection for resistance to insecticide is a long process of trial and error leading to an uncommon genetic architecture.
| Adaptation is often envisioned as a slow and regular improvement, a view embodied by Fisher's geometrical model of adaptation, whereby mutations fix if they bring the current phenotype closer to an optimum [1,2]. However, the trajectory towards the optimum can take a truly tortuous path [3], as adaptation uses almost any allele that brings it closer to the optimum, regardless of its negative side effects. “Evolution is a tinkerer,” as emphasized by Jacob [4], may indeed be more than a pretty metaphor. That beneficial mutations often have deleterious pleiotropic effects is well established (i.e., they generate a fitness cost [1,5–8]). Pleiotropy causes an “evolutionary inertia” [9] whereby beneficial mutations often only ameliorate the side effects of the last beneficial mutation. This process of “amelioration” [10] can follow a scenario a la Fisher [11] with modifiers or compensatory mutations occurring at different loci, or with allele replacement at the same locus (Haldane [12]). Both cases have been reported (e.g., [10,13–16]). However, it is perhaps less often appreciated that pleiotropy can have more dramatic consequences. First, the pleiotropic effects of beneficial mutations may be more complex than simply deleterious. Second, they may trigger the evolution of the genetic architecture and gene number. This paper illustrates these two aspects with the tortuous path taken during the evolution of insecticide resistance in Culex pipiens mosquitoes in southern France.
In natural populations of the mosquito Culex pipiens, various resistance genes have been selected over the course of ∼40 y of control using organophosphorous (OP) insecticides (see [17] for a review). OP insecticides kill by inhibiting acetylcholinesterase (AChE1) in the central nervous system. The genetic basis of OP resistance involves two main loci, the super-locus Ester and the locus ace-1, both of which have major resistance alleles. Strong direct and indirect evidence indicate that the resistance alleles have a “fitness cost”; i.e., they are selected against in the nontreated areas (see reviews in [17,18]). At the Ester locus, the different resistance alleles are not identical and one has slowly replaced the other [15,16].
At the ace-1 locus, the resistance allele present worldwide, ace-1R, displays a single amino acid mutation (G119S), changing the glycine at position 119 into a serine. This large effect point mutation confers high resistance towards OP insecticides due to lower affinity and has arisen independently in several mosquito species [19–21]. However, this mutation also exhibits a strong deleterious side effect: G119S is less efficient at degrading acetylcholine (ACh) than the susceptible variant of AChE1. Overall, G119S causes a more than 60% reduction in enzymatic activity in the absence of insecticide [22], which alters the optimal functioning of cholinergic synapses of the central nervous system and probably causes the various developmental and behavioral problems that have been identified in individuals carrying ace-1R [23–25]. Field surveys also quantified the overall fitness cost of this allele in the wild. Fitness values are 1 and 0.89 for a susceptible and resistant homozygote, respectively, in the absence of insecticide (i.e., when only the cost applies [26,27]). However, in a treated habitat, only ace-1R-carrying individuals survive.
A few years after the appearance and spread of ace-1R, several duplications of the ace-1 gene appeared in wild populations, each involving a resistant and a susceptible copy of the ace-1 gene on the same chromosome [28–30]. We refer to these new duplicated haplotypes as ace-1D. The ace-1 copies involved in these duplicated haplotypes are barely distinguishable from local single-copy alleles. In particular, within a population, the sequence of the local ace-1D resistant copy is always identical to the sequence of the single-copy ace-1R allele [30]. In the Montpellier area of Southern France, the appearance of a duplicated haplotype was indirectly inferred and traced back to at least 1993 [29]. More recently, sequence data of ace-1 confirmed the presence of duplication, but surprisingly also revealed the presence of two distinct duplicated haplotypes in Montpellier area (ace-1D2 and ace-1D3) [30]. Other independent duplications were also found in the Caribbean (Martinique, ace-1D1) and Philippines (Palawan, ace-1D4) [30].
Gene duplication is an important type of mutation because it fosters the evolution of new functions [12,31–33]. In the case of ace-1, a strong genetic constraint drives resistance evolution, as the degree of resistance and the ability to degrade ACh trade off; OP and ACh molecules compete for the same active site of AChE1. Duplication may be a way to disentangle the two functions, i.e., by improving synapse signalling and mosquito's fitness while maintaining resistance.
Thus, our working hypothesis is that the spread of ace-1 duplications is driven by the increased AChE1 activity in individuals carrying ace-1D compared to those carrying ace-1R. In the last survey eight y ago [29], indirect estimation indicated a rapid initial increase of ace-1D, suggesting a strong selective advantage of ace-1D over ace-1R (in the range of 3%–6% of ace-1R fitness [29]). Based on these previous findings, we expected the duplicated haplotype to quickly replace ace-1R. The aim of this study was to test this prediction and to understand more precisely the fitness relationship between the various genotypes (single resistance or susceptible alleles, duplicated haplotypes and their heterozygotes) in treated and nontreated areas. Surprisingly, we show that ace-1D did not replace ace-1R. Several experiments indicated that duplications, while favored when heterozygous, exhibit very strong deleterious effects when homozygous. Our work thus highlights a novel scenario of adaptive evolution and trade-offs that hinge on the genetic architecture underlying the expression of resistance variation.
In the following, and according to the nomenclature used in Labbé et al [30], the duplicated haplotypes ace-1D2 and ace-1D3 will be denoted D2 and D3, susceptible copies being denoted D2(S) and D3(S), and resistant copies, D2(R) and D3(R), respectively. D refers to either D2 or D3. The single copy resistant allele (ace-1R) will be denoted as R. Finally, single susceptible alleles (ace-1S) will be denoted S. The only S allele present in the strains used in laboratory experiments and originating from the susceptible reference strain SLAB [34] will be denoted SSLAB.
We first analyzed the frequency variation of R and D (= D2 + D3) across treated and nontreated areas around Montpellier from 1986 to 2002, using a purely descriptive model (see Methods). As in previous analyses [26,27,29], we found that their frequency showed clinal variation: R and D are more frequent in the treated than the nontreated area (Table 1). The straightforward interpretation of these clines is that R and, to a lesser extent, D have a selective advantage in the presence of insecticide over S, but that they have a fitness cost in its absence. Migration leads to the smooth decline of frequency in the nontreated area and prevents fixation of resistance in the treated area. We also confirm that D was rare until around 1993, when it started to rapidly increase in frequency. Surprisingly, D frequency stopped increasing around 1996 and reached a plateau at about 22% (this holds when only summers clines are considered, analysis not shown) (Figure 1; Table 2);.
However, this analysis does not distinguish D2 and D3 duplications. To assess the frequency and the distribution of D2 and D3 along the cline, five populations were sampled in 2005, on the same transect as for cline analysis, two in the treated area (Maurin and Lattes), one at the limit between the two areas (Distill), and two in the nontreated area (Viols and Ganges, Figure 2). Molecular tests available allow detection of D2 and D3 only when SSLAB is the only S allele present. Resistant females from these samples were thus mated with SLAB-TC males (S/S) and their offspring analyzed (see Methods)., Despite the large crossing (700 females from each sample), the number of egg rafts was very low (this is usually observed for the C. p. pipiens subspecies, P. Labbé and M. Weill, personal observation), and some females were able to lay eggs several times, so that no reliable frequencies could be estimated. Nevertheless, molecular analyses revealed that both duplications are now segregating in these populations (Table 3).
This plateau frequency pattern strongly suggests that equilibrium has been reached among the four alleles segregating in these populations, S, R, D2, and D3. The next step in the analysis was therefore to infer, from the clinal patterns observed in the last years (1999–2002), the different genotypic fitnesses (in treated and nontreated areas) that could correspond to this equilibrium. With four alleles, there are ten diploid genotypes and therefore nine relative fitnesses to estimate in each habitat. Since we do not have access to the respective frequencies of D2 and D3, some of these parameters cannot be estimated separately (see Methods). We report fitness estimates that assume that the two duplications have identical fitness effects. We developed a maximum-likelihood analysis by combining exact simulations with an optimization routine. We used previous estimation for migration: C. pipiens displays a migration of 6.6 km.generation−1/2 [26]. This analysis indicated that the stable cline we observe could be expected at equilibrium between migration and selection. To achieve this equilibrium, the different genotypes should display relative fitness, as reported in Table 4. Since the equilibrium situation seems to have been reached around 1995–1996, it therefore suggests that both duplications spread before that date. Importantly, this equilibrium requires a situation of double overdominance: heterozygotes involving duplication, i.e., (D2/S), (D3/S), (D2/D3), (D3/R), and (D2/R), must have a higher fitness than R, D2, and D3 homozygotes in absence of insecticide. It also requires that R and S homozygotes are the best homozygote genotypes in the treated and nontreated area, respectively. With such a fitness scheme, each duplication could be maintained independently even if they have identical fitness effects. In addition, the presence of both allows them to reach a higher total frequency. More precisely, the overall D frequency when both are present should be intermediate between the frequency they could reach alone and twice this frequency, due to the overdominance of the (D2/D3) genotype.
The last step in the analysis was to confirm experimentally this possible pattern of double overdominance. To do so, we followed the relative success, over a single generation in the laboratory, of different pairs of genotypes in the absence of insecticide. In a first set of experiments, we showed that (D2/S) and (D3/S) largely outperformed (D2/D2) and (D3/D3) in terms of survival, development time, and fertility (Figure 3; Tables 5, 6 and 7). Homozygotes for both duplications indeed show an extremely low fitness with high mortality at pupation and emergence along with low fertility, such that it was nearly impossible to fix a strain for these duplications. By contrast, we showed that heterozygotes involving the two duplications (D2/D3) were as fit as (D3/S) (Figure 3; Table 5). Finally, we compared the fitness of (D3/S) and (D3/R) with R homozygotes (Figure 4; Table 5). Here again, we found that the heterozygotes outperformed the homozygotes, although less strikingly. We were not able to perform this last comparison with the D2 duplication because the survival and fertility of (D2/D2) homozygotes are too low to maintain a laboratory strain fixed for this duplication. Overall, these experiments directly confirm the fitness relationship deduced from the clinal pattern observed in the field. Homozygotes for either D2 or D3 have a severely reduced fitness, but heterozygotes for the two duplications, (D2/S), (D3/S), (D2/D3), and (D3/R) perform well.
This study shows how ace-1, one of the two major genes involved in resistance to OP insecticide in the mosquito Culex pipiens, evolved in the last 40 y of control using insecticides. The evolution of resistance to insecticide in C. pipiens does not follow a classical scenario whereby a beneficial mutation with deleterious side effects spreads and is followed by a steady “amelioration” process correcting for these side effects.
About 10 y after the beginning of OP treatments (1977), the major resistance allele ace-1R, which is beneficial in the treated area but has strong deleterious pleiotropic effects in absence of insecticide, appeared and spread [17,18]. Because of this fitness cost and incoming gene flow from the nontreated area favored by the absence of insecticide treatment in winter, it did not fix but remained polymorphic with a clinal pattern across treated and nontreated areas [26,27,35]. In the early nineties, at least one and probably two duplications involving a resistance and susceptible ace-1 copy, started to spread and replace ace-1R [29,30]. Our lab experiments indicate that these two duplications are both severely deleterious when homozygous, but that heterozygotes involving either ace-1S or ace-1R alleles do not exhibit deleterious side effects. After ∼1999, the pooled frequency of the duplications does not vary significantly, suggesting that the four “alleles” (ace-1S, ace-1R, ace-1D2, and ace-1D3) reached a stable equilibrium. The stability of the duplicated haplotype frequencies since 1996 suggests that both duplications occurred and spread before 1996. This latter conclusion is tentative, since our data may not be accurate enough to detect the small perturbation of the frequency equilibrium caused by the spread of one of the duplications after 1996, due to the indirect estimation of duplicated haplotype frequency.
Clearly, however, considerable polymorphism in this system is maintained by both overdominance and migration. What is the mechanism by which overdominance operates? First, a duplicated haplotype restores AChE1 activity while maintaining resistance [30]. It therefore combines resistance with no deleterious pleiotropic effect caused by a deficit of AChE1 activity. An excess of AChE1 activity may be deleterious as well, but the one caused by the duplication is mild. For this reason, duplications certainly exhibit at least marginal overdominance over treated and nontreated areas. Duplications could be described as “generalist” haplotypes that perform well in both habitats, compared to the “specialist” alleles ace-1S and ace-1R. Second, through lab experiments, we found, surprisingly, that both ace-1D2 and ace-1D3 duplications cause very strong deleterious effects when homozygous. However, these effects disappear in ace-1D2/ace-1D3 heterozygotes. The simplest explanation for this observation is that each duplication occurred independently rather than being generated by recombination (which may not be surprising, given the high duplication rate at this locus [30]), and that they carry distinct recessive sublethal mutations not necessarily related to ace-1–mediated resistance. Distinct unequal crossing over could have generated each duplication and in the same time disrupted different genes close to ace-1. Alternatively, different sublethal recessive mutations could have hitchhiked with the initial spread of each duplication. In all cases, these explanations require that the duplications cannot get rid of these deleterious mutations by recombining. It therefore suggests that recombination is very low around or within the duplicated haploypes or that they are situated on chromosomal inversions (and thus behave like a “balancer region” in analogy with the balancer chromosomes used in laboratory Drosophila). The fact that we never observed in laboratory crosses a recombinant separating the two ace-1 duplicated copies [30] is consistent with this last hypothesis. We consider this explanation plausible since duplication events involve an important modification of the genome, which can easily disrupt other genes or regulatory regions [31,33,36].
This example of adaptation involves three successful steps (the formation of ace-1R, ace-1D2 and ace-1D3), each of them being driven by natural selection. However, each of these three steps presents severe deleterious pleiotropic effects. The deleterious pleiotropic effect of ace-1R may be unavoidable if changing the AChE1 active site to decrease affinity towards OP insecticide necessarily also leads to a lower affinity towards ACh. However, the occurrence and spread of duplications that are sublethal when homozygotic is more perplexing. For instance, another ace-1 duplication, ace-1D1 seems to have spread and be almost fixed on the island of Martinique [37], indicating that ace-1 duplications do not necessarily involve strong deleterious pleiotropic effects when homozygous (confirmed by laboratory analyses, P. Labbé and M. Weill, unpublished data). However, once a duplication with a recessive cost has spread, even partially, in a population, selection is likely to be less effective at replacing it with a better one because the fate of a beneficial mutation is mostly determined when it is rare and therefore by its heterozygous effect [38]. Since ace-1D2 or ace-1D3 duplications enjoy almost no fitness cost when heterozygotic, any new duplication has a low chance to spread in these populations (although migration bringing fitter haplotypes could help escaping this apparent dead end). Thus, selection has taken the mosquito populations on a difficult path indeed.
More generally, we may wonder if such a tortuous adaptive trajectory is frequent in nature. Clearly, gene duplications may solve many genetic trade-offs and chromosome rearrangements such as inversions may strongly perturb the genetic architecture. This type of situation may be more common with strong selective pressure and strong pleiotropy, whereas less intense selection may select for more subtle variation (e.g., see [39]). Nevertheless, insecticide resistance may not be an exception, since other selective pressures appear to be intense as well (e.g., parasitism [40,41]) and thus could favor similar complex genetic responses. The new molecular tools available will allow deeper investigation of adaptation genetics evolution, and thus will help to settle the issue of the frequency of the kind of complex patterns uncovered by our study.
In the long term, selection may produce exquisite adaptations, but this study lays bare that even impressive adaptations are likely to have begun with a process of trial and error that seems to be anything but optimal. It appears that natural selection is forced to tinker with available variability, despite the costs, rather than build impressive and cost-free adaptations that are wholly novel.
Data used in cline analysis were collected in the Montpellier area since 1986 along a transect across the treated and the nontreated areas (Figure 2). Published data from the summers of 1986, 1991, 1995, and 1996, spring of 1993, and winters of 1995 and 1996 [26,29,35] were used to perform the overall analysis. This was complemented with unpublished samples: summer of 1999, 2001, and 2002, spring of 1996 and 2000, and winter of 1999 and 2002 (complete dataset in Table S1). For illustration, we also indicate the frequency observed in a single population in 1984 near the coast, but this population was not included in the cline analysis. Five populations were also sampled on the same transect in 2005 (Maurin, Lattes, Distill, Viols, and Ganges) to assess the distribution of the two duplicated haplotypes in the Montpellier area.
In laboratory experiments, different strains were used to compare life history traits. Two reference strains were used: SLAB, the susceptible reference strain (homozygote for the allele SSlab [34]), and SR, homozygote for ace-1R (R), but with the same genetic background as SLAB [23]. Two other strains, named MAURIN-D and BIFACE-D and respectively harboring ace-1D2 (D2) and ace-1D3 (D3), were also used. The duplicated strains originate from MAURIN and BIFACE strains [30] backcrossed for more than 15 generations with SLAB (method in [23]). These strains are not homozygous but contain three genotypes: (SSLAB/SSLAB), (D/SSLAB), and (D/D) (D2 and D3 for MAURIN-D and BIFACE-D, respectively).
Egg rafts resulting from the cross of homozygous males and females originating from BIFACE-D were used to constitute a strain homozygous for each duplicated haplotype, BIFACE-DFix. SLAB-TC strain (SLAB strain cured from Wolbachia bacteria [25]) was used for crosses with field samples to avoid incompatibility phenomena.
Identification of ace-1 phenotype. For each mosquito, the head was used to establish the phenotype at the ace-1 locus, using the TPP test [42], based on enzymatic activity of AChE1 in the presence or absence of insecticide. Single copy allele homozygotes (S/S) and (R/R) are easily detectable using this test. However, the heterozygotes (R/S) and the genotypes involving a duplicated haplotype (heterozygotes: (D/S) and (D/R) and homozygotes: (D/D)) can not be distinguished (they all display a [RS] phenotype). Moreover, this test does not allow distinction between the two duplicated haplotypes D2 and D3. The pooled frequency of D2 and D3 can be estimated from the apparent excess of [RS] phenotypes caused by the presence of the duplications [26]. This method assumes Hardy–Weinberg proportions for the different genotypes and is therefore not as accurate as direct observation of the different genotypes. However, it has been shown to correctly estimate D frequency in field samples where duplication frequency was independently estimated using crosses [26]. Note that no molecular test is currently available to directly detect the duplicated haplotypes in natural populations, as their D(R) and D(S) copies are not different from single copy alleles (R and S) present in the same sampling sites.
Clines description. We first analyzed the spatial variation in allele frequency across the treated and nontreated areas for each sample independently (i.e., for a given year and season) using a purely descriptive model. We estimated the pooled frequency of both duplications. More specifically, we assumed that the frequency (denoted p) of each resistance allele (indicated by the subscript i = R or D) at time j followed a scaled negative exponential as follows:
where x is the distance from the coast and hij, bij and aij are the estimated parameters. hij measures the frequency of resistance allele i on the coast (i.e., at x = 0) at time j. The parameters bij and aij describe rates of decline of the frequency of allele i (at time j) with distance and with the square of distance from the coast, respectively. We allow for a flexible clinal shape because it tends to vary with season [35].
The second step in the analysis was to compare frequency patterns across years and seasons. For this purpose, we fitted all samples simultaneously to measure the variation of clines trough time using the method developed in Labbé et al. [16]. More specifically, we assumed that hij values changed smoothly as a logistic function of time (measured in months)
where t1j is the number of months after January 1986 when the date of sampling is before January 1986 + t* and is t* otherwise. t2j is the number of months after January 1986 + t*. αi, βi, γi, and t* are estimated parameters. The overall change in frequency over the 1986–2002 period is measured for each resistance allele i by αi and βi, which measure the rate of frequency change between 1986 and 1986 + t* and between 1986 + t* and 2002, respectively. t* was introduced to allow for changes in the rate of allele replacement. Parameter γi is related to the initial frequency hi0 of each allele i (hi0 = Exp(γi)/[1 + Exp(γi)]). Expected phenotypic distributions were computed using allelic frequency and assuming Hardy–Weinberg proportions in each location (see [16]). The phenotype was considered to be a three-state random variable ([RR], [RS], and [SS]). The log-likelihood of a sample was computed from the phenotypic multinomial distribution and maximized using the Metropolis algorithm (see [26,27,35]). Models were compared using F-tests in order to correct for overdispersion. Deviance was also corrected for overdispersion to find the support limits of each parameter [43,44].
Fitness estimation of the different genotypes. In order to determine the fitness of the different genotypes that would yield the stable clines observed over the period 1999–2002, we also used a maximum-likelihood approach. For a given set of fitness values, we obtained by simulation the distribution of genotypes at equilibrium at different distances from the coast. From this distribution, we computed the expected frequency of [RR], [RS], and [SS] for each population in our dataset. The likelihood was then computed and maximized in the same way as with the descriptive models above. The simulation was performed using a stepping stone across treated and nontreated areas using the dispersion kernel that has been previously estimated for C. pipiens as described in Lenormand et al. [26] but with a treated area of 16 km that reflects the treatment practices over this period (P. Labbé, unpublished data). Because we do not have estimates for the relative frequency of the two duplications, we could not estimate their relative fitness. We therefore report estimates assuming that the two duplications have the same fitness effects. The fitness of a given genotype was modelled with two components: a fitness cost c (expressed in both treated and non treated areas) and a reduction in survival due to the presence of insecticide in the treated area s. The full parameter range was constrained to reflect that s should be lower (or equal) for genotypes with an increasing number R copies.
At first it may be surprising that polymorphism with more than two alleles could be maintained at migration—selection equilibrium in a situation with only two habitats (treated and nontreated areas). First, even with haploid selection, the number of alleles that can be maintained is larger than the number of habitats if dispersal is localized (as confirmed by our simulations, see also [45]). Second, particular overdominance relationships among alleles (such as the ones we find) can also increase the number of alleles that can be maintained at equilibrium.
Crosses. Larvae were sampled in five populations along the sampling transect (Maurin, Lattes, Distill, Viols, and Ganges; Figure 2) and reared in the laboratory. They were exposed to a dose of insecticide that kills all [SS] individuals (25 × 10−6 M of Propoxur). About 700 resistant females from each population were crossed with about 800 males of SLAB-TC strain (SSLAB/SSLAB). They were repeatedly blood fed each week until they died. Egg rafts were collected every day, isolated, and reared to the third instar. They were then exposed to a dose of insecticide that kills all [SS] individuals in order to discard all susceptible field alleles resulting from (D/S) or (R/S) mothers. DNA was extracted from a pool of ∼20 survivors of each egg raft. Molecular tests described below were used to detect the presence of each resistance haplotype (R, D2, and D3). Four types could be identified: genotypes (D2/D3) and (R/R) and phenotypes [D2] (i.e., (D2/D2), (D2/S), or (D2/R)), and [D3] (i.e., (D3/D3), (D3/S), or (D3/R)).
Molecular tests.
Using partial ace-1 sequences of each haplotype [30], we designed RFLP tests to discriminate D2, D3, R, and SSLAB. PCR amplification of a 458 bp fragment of exon 3 using the primers CxEx3dir 5′-CGA CTC GGA CCC ACT CGT-3′ and CpEx3rev 5′-GAC TTG CGA CAC GGT ACT GCA-3′ was performed (30 cycles, 93 °C for 30s, 55 °C for 30s, and 72 °C for 1min). The amplified fragment was then digested in parallel by different restriction enzymes. First, the fragment was cut twice by the enzyme BsrBI only when SSLAB is present, generating three fragments (127 bp, 141 bp, and 190 bp; Figures S1 and S2), all the other alleles being cut only once (two fragments of 127 bp and 331 bp; Figures S1 and S2). Second, the fragment is cut by the enzyme EagI only when D2(S) is present, generating two fragments (150 bp and 308 bp; Figures S1 and S2). Third, the fragment is cut by the enzyme HinfI only when D3(S) is present, generating two fragments (102 bp and 354 bp; Figures S1 and S2; note that there is a HinfI site in the primer CxEx3dir, subtracting 2 bp in each fragment; Figure S1). Discriminating between the resistance and susceptible copies is possible using the test provided by Weill et al. [20]: the G119S mutation providing resistance creates a site for the enzyme AluI.
Larval mortality: (D/D) versus (D/S). Trials between (D/D) and (D/S) individuals were performed in triplicate. Larvae of different genotypes were reared in competition under the same environmental conditions (food, temperature, etc.). They were selected at the first instar stage using Propoxur at a concentration of 25 × 10−6 M, which eliminates only (S/S) individuals. Adults were collected during the first and the second wk after the first adult emergence (∼30 individuals each wk). They will be respectively referred as early (first wk) and late (second wk) emerging adults. Genotype frequency was measured at second larvae instar and both adulthood stages. Three trials were conducted: (i) (D3/S) versus (D3/D3), (ii) (D2/S) versus (D2/D2), and (iii) (D3/S) versus (D2/D3). The different genotypes were obtained respectively from (i) a cross between males and females from BIFACE-D (progeny genotypes (D3/D3), (D3/SSLAB), and (SSLAB/SSLAB)), (ii) a cross between males and females from MAURIN-D (progeny genotypes (D2/D2), (D2/SSLAB), and (SSLAB/SSLAB)) and (iii) a cross between males from BIFACE-DFix and females from MAURIN-D and the reverse cross (progeny genotypes (D2/D3) and (D3/SSLAB)). Each sample was analyzed using the BsrBI-based RFLP test to determine the proportion of individuals of genotype (D3/SSLAB) (in the first and third trials) or (D2/SSLAB) (in the second trial).
Larval mortality: (D/S) or (D/R) versus (R/R). Trials were conducted between (D/R) or (D/S) individuals and individuals homozygote for the single resistance allele (R/R). Trials were performed in triplicates with 500 first instar larvae of each genotype reared under the same environmental conditions (food, temperature, etc.). In each replicate, early and late emerging adults were collected as indicated above. Two trials were carried out: (i) (D3/R) versus (R/R) and (ii) (D3/S) versus (R/R). The different genotypes were obtained from (i) a cross between females from BIFACE-DFix and males from strain SR to obtain (D3/R) individuals, and (ii) a cross between females from BIFACE-DFix and males from SLAB to obtain (D3/SSLAB) individuals. (R/R) individuals were directly obtained from strain SR. Each sample was analyzed using the AluI-based RFLP test to determine the proportion of individuals of genotype (D3/R) (first trial) or (D3/SSLAB) (second trial).
Fertility. Different crosses were realized in order to determine the fertility of individuals carrying a duplicated allele at the homozygous or heterozygous state ((D/D) or (D/S)). In each case, the proportion of females laying eggs and the proportion of hatching eggs rafts was recorded. For each series of cross, about 50 males were mated independently with five females each, all from the same strain. Five days after mating, the genotype of the males was determined using the BsrBI-based RFLP test. Females were then grouped according to the male genotype, blood fed, and kept without access to laying substrate. Six days later, they were allowed to lay eggs individually. The females were genotyped either after they laid eggs or less than 6 h after their death. Two series of crosses were realized (i) between males and females from BIFACE-D (genotype (D3/D3) or (D3/SSLAB) after selection), and (ii) between males and females from MAURIN-D (genotype (D2/D2) or (D2/SSLAB) after selection), to assess the fertility of individuals carrying ace-1D3 and ace-1D2, respectively.
Statistical analysis. For the larval mortality analysis, the following generalized linear model (GLM) was fitted, with binomial error: DD = Time + Replicate + Time.Replicate, where DD represents the proportion of the (D/D) genotype in the population (i.e., (D3/D3), (D2/D2), and (D2/D3) for the first, second, and third crosses, respectively), Time is a factor indicating when the sample was taken, Replicate is a factor indicating the three containers in which the experiment was replicated, and Time.Replicate is the interaction between the two factors. Two analyses were performed: (i) one to test for a difference in mortality between (D/D) and (D/S) individuals (in that case, Time was 2nd instar or emerging adult), and (ii) the second to test for a difference in development time between (D/D) and (D/S) individuals (in that case, Time was early emerging or late emerging adults). A similar model was used to analyze the proportion of (D3/S) and (D3/R) in trials versus (R/R). These models were simplified according to Crawley [46]: significance of the different terms was tested starting from the higher-order terms using F-test. Nonsignificant terms (p > 0.05) were removed. Factor levels of qualitative variables that were not different in their estimates (using F-test) were grouped as described by Crawley [46]. This process yielded a minimal adequate model.
The fertility of males and females of different genotypes was analyzed by comparing the proportion of females laying eggs and the proportion of hatching egg rafts among the different types of cross. The number of females laying eggs (Ne) and the number of hatching egg rafts (Nh) were analyzed using GLM with binomial error: Male + Female + Male.Female, where Male and Female are factors indicating male (or female) genotype. These models were simplified as above. All analyses were performed using R software (v 2.0.1., http://www.r-project.org).
The National Center for Biotechnology Information (NCBI) GenBank database (http://www.ncbi.nlm.nih.gov/sites/entrez?db=Nucleotide) accession numbers for ace-1 are AJ489456 and AJ515147. |
10.1371/journal.ppat.1000442 | CAR-Associated Vesicular Transport of an Adenovirus in Motor Neuron Axons | Axonal transport is responsible for the movement of signals and cargo between nerve termini and cell bodies. Pathogens also exploit this pathway to enter and exit the central nervous system. In this study, we characterised the binding, endocytosis and axonal transport of an adenovirus (CAV-2) that preferentially infects neurons. Using biochemical, cell biology, genetic, ultrastructural and live-cell imaging approaches, we show that interaction with the neuronal membrane correlates with coxsackievirus and adenovirus receptor (CAR) surface expression, followed by endocytosis involving clathrin. In axons, long-range CAV-2 motility was bidirectional with a bias for retrograde transport in nonacidic Rab7-positive organelles. Unexpectedly, we found that CAR was associated with CAV-2 vesicles that also transported cargo as functionally distinct as tetanus toxin, neurotrophins, and their receptors. These results suggest that a single axonal transport carrier is capable of transporting functionally distinct cargoes that target different membrane compartments in the soma. We propose that CAV-2 transport is dictated by an innate trafficking of CAR, suggesting an unsuspected function for this adhesion protein during neuronal homeostasis.
| Adenoviruses commonly cause subclinical morbidity in the ocular, respiratory, and gastrointestinal tracts, and less frequently, adenovirus-induced disease can be fatal for newborns and immunocompromised hosts. In addition, adenoviruses can reach the central nervous system (CNS) and cause associated encephalitis and tumours. On the flip side, during the last two decades, adenovirus vectors have become powerful tools to treat and address diseases of the CNS. Despite the fact that axonal transport of adenoviruses was reported more than 15 years ago, nothing was known concerning how adenoviruses access the CNS. The characterization of their interactions with brain cells was therefore long overdue. In this study, we describe the axonal trafficking of an adenovirus that preferentially infects neurons and reaches the CNS through long-range axonal transport. We show that this adenovirus exploits an endogenous vesicular pathway used by the adhesion molecule CAR (coxsackievirus and adenovirus receptor). Our study characterizes this endogenous route of access, which is likely to be crucial to neuronal survival, neurodegenerative diseases, gene transfer vectors, and adenovirus-induced morbidity.
| Adenoviridae is a family of greater than 150 nonenveloped double-stranded DNA viruses that infect all vertebrate classes. Whilst adenoviruses (Ads) are commonly associated with respiratory, ocular and gastrointestinal tract infections, many serotypes cause clinical manifestations in other tissues, including the central nervous system (CNS) [1]–[4]. Interest in Ad biology has been rekindled by at least two events: Ads have re-emerged as life-threatening pathogens in immunosuppressed hosts and young military recruits [5], and they are currently the most common viral vectors used in clinical gene transfer trials. Importantly, Ad infections can be lethal in immunocompromised patients due to genetic defects (SCID), during haematopoietic stem cell transplants or by pharmacological agents (e.g. during solid organ transplant) [2].
For brain-directed gene transfer, Ad vectors, in particular canine serotype 2 (CAV-2) [6] have unique characteristics. In the CNS of rodents, dogs and primates (including human tissue ex vivo), CAV-2 vectors preferentially transduce neurons and undergo efficient axonal transport ([7]; our unpublished data). We previously demonstrated that following interstriatal injections in rodents, CAV-2 was transported to afferent structures such as the contralateral and ipsilateral cortex, substantia nigra, thalamus and basal nuclei of Meynert [7]–[9]. In addition, following injection into the mouse gastrocnemius, CAV-2 preferentially transduced motor neurons of the sacral dorsolombar rachis [7]. CAV-2 vectors also lead to >1 year in vivo transgene expression in rodent CNS [8],[9] without accompanying immunosuppression. In addition to their potential in addressing fundamental neurobiological questions [9]–[11], these molecular tools could also be used for treatment of neurodegenerative disorders [12].
Although there are a handful of exceptions, most Ad attachment and trafficking studies have used epithelial-like cells and serotypes from human subgroup B, C and D (e.g. Ad2, 5, 35 and 37). Many human serotypes, as well as CAV-2, bind with high affinity to the coxsackievirus and adenovirus receptor (CAR) [13]–[16], a widely expressed cell adhesion protein involved in tight junction formation in epithelial cells and myocardial cells, and highly expressed in the developing brain [17]–[21]. Many CAR-tropic Ads are endocytosed in clathrin- and Rab5-associated pathways in epithelial cells [22]–[24]. Following receptor-mediated internalisation, subgroups C Ads are thought to undergo a stepwise disassembly, starting with detachment of the fibre from the virus at the cell surface, followed by a passage through early endosomal compartments in which acidification serves as a disassembly trigger [25],[26]. Although the mechanism is poorly understood, intra-endosomal signals likely release vertex proteins, which may lead to protein VI-mediated membrane lysis [27] and escape of the virion into the cytosol [25]. The metastable virions may then be targeted via dynein and microtubule-dependent mechanisms towards the nucleus in some cell types [28]–[30].
In spite of initial reports demonstrating that Ad vectors can be transported retrogradely in neurons in vivo [31],[32], little is known concerning their brain cell receptors, the endosomal compartment(s) entered during trafficking or the determinants for their long-range transport. Axonal transport is crucial for neuronal differentiation and homeostasis, which depend on the efficient long-distance delivery (up to 1 meter in humans) of signals and cargoes [33]. This pathway relies mainly on the microtubule-based motors kinesins and cytoplasmic dynein, and their coordination with F-actin-based motors [33],[34]. Alterations in components of the axonal transport machinery are associated with a growing number of neurodegenerative conditions, including Alzheimer's, Parkinson's, Huntington's and motor neuron diseases [33],[35]. In spite of its importance, we are only beginning to understand how the machinery of axonal transport is regulated.
The dual nature of Ads as ubiquitous pathogens and potential gene transfer vectors for the CNS, imposes an in-depth analysis of the molecular mechanisms involved in the virus-neuron interaction. Here, we characterised the binding, internalisation and axonal transport of an Ad that preferentially infects neurons. Our data suggest that the neuronal binding of CAV-2 is CAR-dependent and its internalisation involves clathrin-coated pits and the small GTPase Rab5. In contrast to the established paradigm of Ad trafficking in epithelial cells, long-range CAV-2 transport in axons is mainly vesicular, and depends on the sequential maturation of transported endosomes, which switch from Rab5 to Rab7. We found that CAV-2 axonal motility is bidirectional, with a bias for the retrograde direction. Carriers of CAV-2 also transported tetanus toxin and neurotrophin receptors and surprisingly still contained CAR. We also demonstrated that similarly to whole virions the fibre knob (FK) protein could be found in CAR+ organelles. We therefore propose that the intrinsic neuronal properties of CAR are responsible for the efficient trafficking of CAV-2 in neurons. More globally, our data demonstrates that distinct receptor-mediated endocytic events determine the sorting of diverse cargoes to nonacidic vesicles, which are then recruited in a Rab7-dependent manner to the long-range retrograde transport pathway, in a process that allows selected pathogens to reach the CNS.
CAV-2 vectors preferential infect neuronal cells in vivo and in mixed brain cell cultures, however the binding determinants responsible for this tropism have not been addressed. Although the 150 Ad serotypes can bind numerous co-receptors [36],[37], our previous studies suggested that CAR is the main receptor for CAV-2 [16],[38]. To study the neuronal link between CAR and CAV-2, we incubated Cy3-labelled CAV-2 virions (CAV-Cy3) with primary spinal cord motor neurons (MNs) on ice to allow binding, but prevent internalisation. Cells were then fixed and stained for endogenous CAR. Interestingly, CAR was found in two distinct compartments in MNs. In addition to a plasma membrane localisation seen also in sparse epithelial-like cells copurifying with MNs, CAR was also found in a large intracellular pool (Figure S1A). We found that >70% of CAV-Cy3 colocalised with CAR on neurites in MNs and dorsal root ganglia neurons (DRG) (Figures 1A, B and S1B). Moreover, when MNs were pre-incubated with saturating concentrations of recombinant fibre knob (FK), the adenovirus protein responsible for CAR binding, and then treated with CAV-2, virion uptake was reduced by 76% compared to control (Figure 1C).
We then examined the early steps of CAV-2 entry in MNs by transmission electron microscopy (TEM). At 1 minute post-internalisation, electron dense CAV-2 virions were associated with structures resembling clathrin-coated pits, often present at cell-to-cell contacts (Figures 1D, S1C and data not shown). By indirect immunofluorescence, we also found extensive colocalisation between clathrin heavy chain and CAV-2 (Figure S1D). These results are in good agreement with previous reports showing that in epithelial cells, CAR-tropic Ads undergo clathrin-associated endocytosis, and are consistent with our current understanding of CAV-2 internalisation in these cells [16],[39]. We next assessed CAV-2 internalisation in MNs. To this end, we again incubated MNs with CAV-Cy3 on ice and then replaced the medium with warm medium to induce internalisation. Cultures were incubated at 37°C for 45 minutes, then shifted back to 4°C and incubated with anti-Cy3 antibody to detect surface-bound virions. We found that MNs internalised >75% of CAV-2 under these conditions (Figure 1E).
Upon internalisation in epithelial cells, most CAR-tropic Ads are believed to rapidly exit endosomal compartments to reach the cytoplasm [30] from where the capsid may interact directly or indirectly with cytoplasmic dynein [29], and be transported towards the nucleus. To determine if a similar process was also at the basis of the axonal transport of CAV-2, virions were incubated with MNs at 4°C then shifted to 37°C, fixed at different times and then visualized by TEM. At 2 to 5 minutes post-internalisation, the majority (>90%) of the virions were inside intact endosomal membranes (Figure 1F). Surprisingly, this pattern did not change significantly (∼90%) 30 to 45 minutes post-internalisation, when live imaging of CAV-2 axonal transport was optimal (3 independent experiments, 97 virions in total; see below). At these later time points, membrane-enveloped virions could be detected close to structures morphologically similar to microtubule tracks (Figure 1G, black arrow). Together these results suggest that CAV-2 binds CAR, is endocytosed in clathrin-coated pits and, unexpectedly, remains within endosomal compartments associated with microtubules in MNs.
The above results prompted us to characterise the motility of intracellular CAV-2 using established vesicular transport markers by live cell imaging. Initially, we incubated CAV-Cy3 with primary MNs, and axons were then imaged by confocal microscopy. Using this approach, we detected bidirectional transport of CAV-2 (Figure 2A and B, Video S1). Whilst the majority (87%) of motile virions were transported towards the soma, some (13%) showed anterograde movement (Figure 2C, lower quadrant). In addition, some single virions changed direction during imaging (Figure 2A and B, asterisk and red dotted line), suggesting that either CAV-2 structures associates with molecular motors of different polarity or that dynein-dependent bidirectional transport [40] influences its kinetic properties. Bidirectional CAV-2 transport, with a preference for retrograde motility, was also found in cultures of embryonic DRG (data not shown), suggesting a similar mechanism in sensory neurons. The kinetics of transport were analysed by determining the speed distribution profile of CAV-2 in MNs (Figure 2D). CAV-2 retrograde transport appeared to be bimodal with peaks at 0.60 and 1.30 µm/s (Figure 2D, blue line), which is consistent with fast retrograde transport [41]. In contrast, the anterograde transport profile was more discontinuous (Figure 2D, red line).
While characterising CAV-2 transport kinetics, we noticed a delay in the onset of long-range axonal transport. Although our results suggested that CAV-2 is rapidly internalised (<5 min; Figure 1F), we detected primarily oscillatory movements at early times post-internalisation (Figure S2, top panel). Only after 25 minutes were we able to detect long-range movements (Figure S2, middle panel), with robust vectorial transport beginning after ∼30 minutes (Figure S2, middle and lower panels).
In contrast to the efficient escape from endosomes by CAR-tropic Ads, our TEM data showed that the majority of CAV-2 remained trapped in vesicles when axonal transport is most efficient. To directly address the possibility that CAV-2 axonal transport is mediated by a membrane compartment, we co-incubated MNs with CAV-Cy3 and AlexaFluor647-dextran, which is a fluid phase marker used to identify endocytic organelles. Consistent with our TEM observations, we found the majority (∼75%) of virions were co-transported with dextran (Figure 2E and F). These data suggest that CAV-2 uses a vesicular transport pathway to reach the MN soma.
The stable association of CAV-2 with the endosomal lumen is inconsistent with the canonical mechanism regulating productive CAR-tropic Ad infections, and may represent a key determinant for efficient axonal transport of CAV-2. Because the exit of Ads from endosomes is triggered by the acidification of their lumen, CAV-2 might enter nonacidic pH compartment(s) allowing its stable sequestration during axonal transport. To test this hypothesis, we assessed the association of CAV-2 with a fragment of tetanus toxin (TeNT HC), which is internalised via a clathrin-dependent mechanism coupled to axonal retrograde transport and is sorted to carriers characterised by neutral pH [42],[43]. To this end, we co-incubated MNs with CAV-Cy3 and fluorescently-labelled TeNT HC [41]. In fixed samples, CAV-Cy3 colocalised with TeNT HC in axons and somas (Figure 3A). Furthermore, using live-cell imaging we found that more than 85% of CAV-2 was co-transported with TeNT HC (Figure 3B and Video S2). Our previous work showed that TeNT HC carriers also contain neurotrophins and their receptors [44]. Accordingly, CAV-2 carriers were also positive for the neurotrophin receptor p75NTR (data not shown).
To directly assess the pH of the transport carriers containing CAV-2, MNs were incubated with CAV-2 covalently labelled with carboxyfluorescein (CAV-FC), a probe previously used to measure the pH of endosomes reached by Ads during endocytosis [45]. CAV-FC-infected MNs were incubated with the ionophores nigericin and monensin, exposed to L15 media at different pHs, and the ratio of the emission intensities upon sequential excitation at 458 and 488 nm was determined. Under these conditions, the calibration curve of the pH-dependent fluorescence of CAV-FC was obtained (Figure 3C). We then assayed the pH of CAV-FC-containing structures in neurites compared to cell bodies (Figure 3D). Consistent with the co-transport of CAV-2 with TeNT HC, we found that the majority of axonal CAV-FC was within a pH-range of 6 to 7 (Figures 3D). Interestingly, we detected numerous acidic (pH<6) CAV-FC structures in the soma, whereas only very few axonal CAV-FC could be observed at or below pH 6 (Figure 3D).
To test the presence of CAV-2 in nonacidic structures in axons using an alternative approach, MNs were incubated with CAV-Cy3, AlexaFluor647-dextran and Lysotracker-488, a probe that is sequestered in acidic compartments. Consistent with the above results, axonal CAV-2/dextran-positive carriers were Lysotracker-488-negative (Figure 3E). Furthermore, our quantitative analyses of the extent of colocalisation between CAV-2 and lysotracker confirmed the higher association of virions in acidic organelles in cell bodies of MNs versus neurites (Figure 3F). Taken together, these data demonstrate that the majority of CAV-2 is retrogradely transported in axons inside a nonacidic vesicular compartment, which is also used by endogenous ligands, receptors and other pathogens.
Progression along the endocytic pathway is tightly regulated in time and space. In many cell types, the classical endosomal pathway involves early endosomes containing Rab5, which then mature into late endosomes characterised by the presence of Rab7 on their cytosolic face [46]. Because axonal transport of TeNT HC requires the sequential activities of Rab5 and Rab7 [44], we asked if these small GTPases were also associated with CAV-2 transport. MNs were incubated with CAV-Cy3 for 5 or 45 minutes, fixed and stained for endogenous Rab5 and Rab7. At 5 minutes post-internalisation, we found numerous Rab5/CAV-2 structures lacking Rab7, both in axons (Figure 4A) and in cell bodies (data not shown), demonstrating that the virions associated with early Rab5+ endosomes immediately after internalisation. However, at 45 minutes post-internalisation we detected virions mainly in Rab7+ structures (Figure 4B). Quantitative analysis of the distribution of Rab5, Rab7 and CAV-2 showed that at 5 minutes post-internalisation, 40% of CAV-2 was in Rab5+ compartments whereas at 45 minutes post-internalisation, only 11% of the virions colocalised with Rab5. In contrast, at 45 minutes 44% of virions colocalised with Rab7, and 16% were Rab5/Rab7 double positive (Figure 4C). These ratios are in good agreement with the colocalisation between transported TeNT HC and Rab7 [44].
To address the functional relationship between CAV-2 transport and Rab7 activity, we microinjected MNs with plasmids expressing GFP-tagged fusion proteins of either wild-type Rab7 (GFP-Rab7WT) or its dominant-negative N133I mutant (GFP-Rab7N133I) [47]. The axonal transport of CAV-2 was then assayed using live-cell imaging in GFP and GFP-Rab7 expressing neurons. In agreement with the degree of colocalisation observed with the endogenous protein, CAV-Cy3 colocalised with GFP-Rab7WT in somas (Figure 4D) and axons (32%; 5 independent experiments, 107 virions in total) (Figure 4E and F). Furthermore, the GTPase activity of Rab7 was essential for axonal transport of CAV-2 since overexpression of GFP-Rab7N133I strongly impaired CAV-2 movement (Figure 4G and H), compared to overexpression of GFP or GFP-Rab7WT (Figure 4G and H). In agreement with previous reports [48], the inhibitory effect of GFP-Rab7N133I is linked to its expression levels. As a consequence, sub-threshold GFP-Rab7N133I expression did not alter the axonal transport of CAV-2 (Figure 4H; outlier in the GFP-Rab7N133I sample). Conversely, strong overexpression of GFP-Rab7WT caused a partial, yet not significant, inhibition of this process (Figure 4H). These results suggest that Rab5 to Rab7 vesicular maturation is required for CAV-2 progression along the axonal endocytic pathway.
Axonal transport is mainly powered by the microtubule-dependent motors cytoplasmic dynein and kinesins [34]. To further understand the determinants of bidirectional CAV-2 transport, we stained MNs previously incubated in the presence of CAV-Cy3 with antibodies specific for subunits of motor complexes. Dynein heavy chain (Figure 5A) and p50/dynamitin, a subunit of the dynein-dynactin complex (data not shown), were associated with more than 60% of virions, suggesting that this ubiquitous retrograde motor plays a major role in the axonal transport of CAV-2. Secondly, we found a lower, albeit significant, colocalisation of virions with the heavy chain of kinesin-1 (KHC) (Figure 5B). Although these data do not exclude the possibility that the bidirectional transport of CAV-2 is due uniquely to dynein, they favour the likelihood that both cytoplasmic dynein and kinesin play a role in this process. To directly demonstrate the involvement of these motor proteins in CAV-2 transport, we overexpressed p50/dynamitin, a treatment that disrupts endogenous dynein-dynactin complex [49]. In p50/dynamitin-expressing MNs, CAV-2 transport was strongly inhibited (Figure 5C and F) compared to GFP-expressing cells (Figure 5E). Similarly, overexpression of the tetratricopeptide (TPR) domain of kinesin light chain 1 [50] also reduced the frequency of motile virions (Figure 5D and F), suggesting that the axonal transport of CAV-2 require coordination between plus and minus-end microtubule motors.
Although the binding of Ads to CAR may induce downstream signalling [51], CAR's role in Ad infection has been considered primarily as a docking site prior to integrin-mediated internalisation. Consistent with this, deletion of CAR cytoplasmic tail had no significant effect on Ad internalisation in epithelial cells [52]. Yet, CAV-2 is one of a handful exceptions in the Adenoviridae family: the external capsid, in particular the penton base, does not contain a recognisable integrin-interacting motif [16],[53],[54]. Therefore, we asked whether CAV-2 and CAR were associated during endocytosis and the subsequent axonal transport. As mentioned previously, CAR staining in MNs showed a plasma membrane as well as an intracellular localisation (Figure S1A). After 45 minutes post-internalisation, 80% of axonal CAV-2 was found in CAR+ structures (Figure S3). Furthermore, upon incubation of MNs with TeNT HC and CAV-2, followed by an acid wash to remove extracellular-bound ligands whilst preserving internalised probes [44], anti-CAR immunostaining revealed high colocalisation levels of CAR, CAV-2 and TeNT HC in neurites (∼70%; Figure 6A). The colocalisation of CAV-2 and TeNT HC in axonal carriers prompted us to use a biochemical approach based on TeNT HC-coupled to superparamagnetic nanobeads to isolate these transport vesicles [44]. Using western blot analysis, we detected an ∼250-fold enrichment of CAR in these organelles (Figure 6B), further supporting the notion that CAR and CAV-2 co-inhabit a pool of axonal transport vesicles.
To directly monitor CAR neuronal trafficking, we used fluorescently-labelled CAV-2 fibre knobs (FK-Cy5 and FK-Cy3) to visualise CAR entry and transport in MNs. Initially, we tested the specificity of labelled-FK binding to CAR by transfecting CAR-negative NIH 3T3 cells with a plasmid encoding a GFP-CAR fusion protein. Transfected cells were then incubated with FK-Cy5 and fixed. We found that only GFP+ cells bound FK-Cy5, strongly supporting a CAR-specific binding of the CAV-2 fibre knob FK-Cy5 (Figure S4A). Consistently, preincubation of MNs with unlabelled FK blocked FK-Cy5 labelling (Figure S4C). When MNs were incubated with FK-Cy5 followed by acid wash, FK and CAR colocalised in discrete puncta (>95%, Figure 7A), suggesting that this viral protein and its cellular receptor are linked during endocytosis. Furthermore, FK-Cy5 was retrogradely transported in the same carriers as TeNT HC and displayed a bidirectional transport similar to CAV-2 (Figure 7B), suggesting that CAR-mediated binding and internalisation is coupled to axonal transport. Accordingly, we also found FK-Cy5 in GFP-Rab7+ axonal carriers (data not shown).
To further understand the role of CAR in CAV-2 binding and endocytosis, we took advantage of a CAR-ablated FK variant (FKm), which bears a single-point mutation in the CAR binding site [15]. We incubated MNs on ice with labelled-FK or FKm. In these conditions, FKm was not able to bind MNs (Figure S4B). Together, these results strongly suggest that in neurons, CAR can be endocytosed and trafficked bidirectionally in axons, and that this protein may dictate internalisation and subsequent axonal transport of CAV-2.
The above results suggest that Ads take advantage of an innate trafficking of CAR to access the CNS. This prompted us to investigate its intracellular dynamics in vivo. Sciatic nerve ligation represents a powerful system to study axonal dynamics. To specifically monitor CAR axonal transport, we injected FK-Cy3 in the tibialis anterior and gastrocnemius muscles of C57BL/6 mice after ligation of the sciatic nerve. Eight hours post-injection, we examined the distributions of CAR and FK-Cy3. Consistent with our hypothesis, CAR accumulated inside axons in both proximal and distal parts of the ligation site (Figure 8A). However, only distal sections showed a clear signal correspondent to retrogradely-transported FK-Cy3 (Figure 8A, right panel). Intra-axonal CAR was also observed by staining for CAR in transverse sections of unligated sciatic nerve (Figure 8B). CAR distribution was not significantly affected by the presence of FK-Cy3 since similar CAR staining patterns were also observed in the absence of FK (Figure 8A left panel, B, and data not shown). These data suggests that CAR undergoes constitutive bidirectional transport in sciatic nerve in situ.
A better understanding of the interactions between adenovirus and neurons was essential and overdue. To our knowledge, this is the first study to address the determinants of Ad neurotropism and axonal transport. Axonal transport has been described for a handful of viruses, including rabies, herpes simplex type I (HSV-1), measles, West Nile and poliovirus. Although less common than the above pathogens, both human and canine Ad serotypes are associated with brain pathologies [3],[4]. Notably different mechanisms of axonal transport have been described: direct interaction with molecular motors for HSV-1 and rabies viruses [55] versus endosomal trafficking for poliovirus [56].
Our proposed model goes partially against the paradigm derived from prototype Ad trafficking studies performed in epithelial cells. We propose that the recognition of CAV-2 on the neuronal surface is primarily CAR-dependent. Internalisation involves CAR and clathrin-coated vesicles that acquire the early endosomal marker Rab5, yet apparently does not induce capsid disassembly and endosomal escape. These latter axonal vesicles mature into Rab7+ compartments that still contain CAR, and have the advantageous characteristic of being nonacidic. After a lag phase, long-range transport of CAV-2 entrapped in vesicular organelles becomes sustained and bidirectional, probably involving the concerted action of dynein and kinesin. Crucially, our data also suggest an innate function of CAR in axons dictating CAV-2 transport.
Endocytic progression is required for Ad infection and has been shown to differ mechanistically for different Ad serotypes [28]. The lag phase observed before the onset of CAV-2 axonal transport, which is not seen in epithelial cells infected by CAV-2 or Ad2/5 [30],[39], was also similar to that observed for TeNT HC and p75NTR [44]. Although further studies will be needed to pinpoint the underlying causes of this delayed onset, a likely explanation is that it is due to cargo sorting and/or endosome maturation. The association of CAV-2 initially with Rab5+ early endosomes and then with a transport compartment containing Rab7 is also similar to TeNT HC trafficking. Interestingly, Rab7 effectors RILP and ORP1L can mediate the recruitment of cytoplasmic dynein to endosomes in HeLa cells [57]. Whether Rab7 also directly recruits the dynein complex in axons is unknown, but might explain why, by reaching organelles containing Rab7, CAV-2 undergoes efficient axonal transport. Although other serotypes can reach Rab7+ compartments [45], there appears to be a functional difference between some of those found in axons and epithelial cells, one difference being that a population of Rab7+ endosomes in axons have lumens that are neutral. Using a marker described to traffic inside pH-neutral carriers (TeNT HC), CAV-2 linked to a pH-sensitive dye [43] and Lysotracker, we showed that in contrast to virions in the cell body that can reach acidic organelles (pH 5–5.5), the majority of axonal CAV-2 carriers had a pH ranging from 6 to 7. These data, combined with previous report of the pH of axonal organelles [58] demonstrate that the presence of Rab5 and Rab7 offer no indication of the pH of the endosomes or other organelles under investigation. Neurons appear to differ in the regulation of endosomal acidification that occur in their axons versus cell body. By entering nonacidic organelles in axons, CAV-2 could remain stably and efficiently associated with long-range carriers until delivered to the soma, where endosomal acidification could occur, triggering the exit from these compartments.
In light of these results, it is tempting to speculate that human Ad serotype 5 (HAd5), which can be retrogradely transported in vivo [31],[32] and escapes endosomes when the pH drops below 6 in epithelial-like cells [45], could take advantage of a similar protective endocytic pathway to reach the neuronal cell body. Interestingly, when HAd5 and CAV-2 vectors were mixed and co-injected in the rodent brain, both are capable of axonal transport to afferent regions. However CAV-2 vectors are 50–100 fold more efficient when transgene expression is used as a readout at distal sites [7]. Does HAd5 use a pathway similar to CAV-2? There are notable similarities and differences between HAd5 and CAV-2 that may affect their axonal transport. In the case of CAR as a binding site, our data have consistently suggested that CAV-2 is “CAR-tropic” while other studies have reported that HAd5 uses CAR, as well as other cell surface molecules for binding and internalisation [37]. CAV-2 is also more thermostable than HAd5 (unpublished data). A priori, we would predict that if an HAd5 virion binds CAR it could be taken up and transported in a manner similar to that seen by CAV-2. Using real time confocal microscopy we detected fast axonal transport of HAd5 in primary neurons (our unpublished data) suggesting, but not demonstrating, similarities in transport. We do not know if the increased thermal stability of CAV-2 versus HAd5 plays a role during vesicular maturation at, for example, the axon soma interface. The interaction with integrins via the HAd5 penton base may also make the HAd5 capsid more sensitive to disassembly triggers in the lumen of a Rab7 vesicle in axons.
The motility of CAV-2 showed an average retrograde speed above 1 µm/s, consistent with fast axonal transport. Notably, we found a minor population of CAV-2 and FK carriers undergoing bidirectional transport. Similar bidirectional transport was detected using FK to monitor CAR trafficking in axons. This feature is not unique to Ad: HSV-1 shows bidirectional transport with a bias for the retrograde direction during infection and displays a preferential anterograde transport during the phase of egress [55],[59]. However, bidirectional HSV-1 transport is via direct recruitment of motors to the viral capsid. The association of CAV-2 and CAR with organelles undergoing bidirectional movement is particularly interesting because the regulation of bidirectional transport is still poorly understood. In this regard, CAR- or CAV-2-containing endosomes could represent an ideal tool to address how vesicular cargo coordinates the recruitment of both classes of microtubule-dependent molecular motors, or how a main retrograde motor, such as cytoplasmic dynein, may switch to an anterograde direction [40]. Dynactin may be a potential regulator of kinesin- and dynein-driven transport since it is able to simultaneously bind these two classes of motors. Interestingly, p50/dynamitin, a subunit of the dynactin complex, colocalised with CAV-2, and p50/dynamitin overexpression inhibited the axonal transport of virions. The observed impairment of CAV-2 transport by inhibition of either cytoplasmic dynein or kinesin-1 suggests that coordination between these two classes of motors is necessary to ensure efficient axonal retrograde transport, as previously observed for TeNT HC carriers and mitochondria (reviewed in [33]).
A priori, one could envisage that the internalised cargo, via its interaction with specific integral membrane proteins, dictates the directionality of the transport. In this light, although TeNT HC and CAV-2 share a high number of axonal carriers, together they move exclusively in the retrograde direction. In contrast, anterograde moving organelles contain CAR and CAV-2, but lack TeNT HC. This observation suggests the existence of discrete sorting steps during internalisation or en route endosomal maturation, which alter the ability of transported endosomes to recruit or activate anterograde and/or retrograde motor complexes. This may be achieved by engaging specific adaptor proteins able to co-ordinate motor complex activity, as in the case of huntingtin, which controls the directionality of vesicular carriers in cortical neurons via an Akt-dependent phosphorylation switch [60].
Although CAR is the main receptor for many Ad serotypes, little is known regarding its intracellular dynamics in neurons. In addition to a plasma membrane targeting, we found that CAR is also present on an internal vesicular pool. By means of competition experiments, we showed that the binding to CAR is an essential step for the entry of CAV-2. CAV-2 and its recombinant FK are taken up in CAR-containing vesicles, suggesting that the virus and its receptor could be endocytosed together and then co-transported. Notably though, our assays do not address whether fibres detach from the capsid, which is an early step in virion disassembly in epithelial cells [28]. Given the average size of CAV-2+ vesicles (100–110 nm) versus the CAV-2 icosahedra core (∼90 nm [61] plus the projecting fibres (30 nm)), the most obvious prediction is that the fibres would be detached. However, the CAV-2 fibre shaft, in contrast to other Ads [36], is particularly flexible due to the presence of two hinges [61]. This added suppleness may allow the fibre to fold over whilst still attached to CAR in the lumen of the endosomes.
By using fluorescently-labelled CAV-2 FK, we also demonstrated that CAR undergoes endocytosis and bidirectional transport in cultured MNs and in sciatic nerve axons. These findings introduce a paradigm shift for the CAR-mediated endocytosis of Ads. As mentioned above, the available in vitro evidence is that CAR functions as a primary attachment site and that integrins are responsible for virus internalisation via the interaction with motifs in the Ad penton base. The homotrimeric FK could bind three CAR D1 domains simultaneously [13],[15],[62]. In this light, it will be critical to determine if the FK induces clustering of CAR, which in turn triggers internalisation of ligand-receptor clusters, or if other mechanisms are involved. Interestingly, the affinity of the CAV-2 FK to CAR is 5 to 7-fold times greater than that of HAd5 knob-CAR and the highest reported for an Ad [15].
The roles of CAR as an adhesion molecule and key component of tight junctions are well established [18]. Although CAR is highly expressed in the developing brain [17], its neuronal function(s) remains speculative. Based on its direct interaction with actin, a potential role of CAR in neurite outgrowth has been proposed [63]. Recently, this association has been extended to several cytoskeletal components, suggesting a more general role of CAR in actin and microtubule dynamics [63],[64]. Notably, our ex vivo and in vivo data demonstrate that CAR is found inside axons even in absence of an exogenous “ligand”, and also link CAR directly or indirectly to the axonal transport machinery. Together, our observations suggest that CAV-2 is taking advantage of an axonal trafficking pathway involving CAR and that allows virions to be efficiently transported to the CNS.
The nature and regulation of axonal transport pathways are of crucial interest since their impairment has been linked to several neurodegenerative disorders. In this context, some Rab7-associated axonal organelles may be the hallmark of a long-range, vectorial axonal transport. Because CAV-2, like TeNT, is able to reach this compartment, it may have a preferential and efficient access to the CNS. Indeed, this Rab7+ nonacidic axonal compartment may offer ultimate protection against degradation during long-range transport, allowing pathogens, virulence factors, as well as endogenous molecules, to be delivered intact to the cell body of neurons.
All experiments were carried out under license from the UK Home Office in accordance with the Animals (Scientific Procedures) Act 1986 and following approval from the Cancer Research UK Ethical Review Committee.
Labelling reagents, AlexaFluor488-Lysotracker, AlexaFluor647-dextran, carboxyfluorescein and AlexaFluor-conjugated secondary antibodies were from Invitrogen. Mouse monoclonal anti-CAR antibody (MoAb.E(mh); a gift from Steven Carson, University of Nebraska) was used at 1∶500 in western blot analyses. Rabbit polyclonal anti-CAR antibodies (1∶300) (Ab1605; a gift from Joseph Zabner, University of Iowa), monoclonal anti-Rab5 (1∶200; Synaptic System), polyclonal anti-Rab7 (1∶200) [44], polyclonal anti-FK (1∶300) [65], anti-DHC (1∶100; Santa-Cruz) anti-p50/dynamitin (1∶200; BD Bioscience), anti-KHC (1∶100; Chemicon) were used in immunofluorescence (IF) studies. Monoclonal anti-Cy3 (1∶200; Abcam) was used on live cells. Anti-MBP was purchased from Boehringer (Mannheim, Germany). p50/dynamitin and TPR construct were kindly provided by Michael Way (CRUK, London). The plasmid expressing GFP and CAR was a gift from Joseph Zabner. Paramagnetic Fe-beads were purchased from G. Kisker GbR. Rat spinal cord MNs were purified from E13.5 embryos as described previously [43] and used from day in vitro 5 onwards.
CAV-Cy3 was prepared from the E1-deleted vector CAVGFP [66] by direct post-purification labelling with Cy3 [39]. CAV-Cy3 has a physical particle (pp) to infectious unit (IU) ratio of 25∶1 [66]. The vector was propagated, purified, and titred as previously described [7],[66]. Multiplicities of infection are in pp/cell.
For internalisation assays, MNs were incubated with CAV-Cy3 on ice and either fixed or shifted to 37°C for 45 minutes, back on ice, incubated with anti-Cy3 to label cell-surface virions and then fixed. Indirect immunofluorescence (IF) experiments were performed as follow. After fixation, MNs were permeabilised with 0.1% Triton X-100 for 5 minutes at room temperature (RT), followed by blocking with 3% bovine serum albumin (BSA) for 1 hour at RT. Primary and secondary antibodies were diluted in blocking solution and incubated sequentially for 1 hour at RT. Samples were then mounted with Mowiol (Harco) and imaged by confocal microscopy. For live cell experiments, MNs were incubated with CAV-Cy3 and AlexaFluor488-TeNT HC or AlexaFluor647-dextran or AlexaFluor488-Lysotracker at 37°C, washed with DMEM containing 30 mM HEPES-NaOH, pH 7.4 and imaged. Live and fixed samples were imaged by confocal microscopy (Zeiss LSM 510 equipped with a 63×, 1.4 NA Plan Apochromat oil-immersion objective). Images were processed using Zeiss LSM 510 software. For live cell imaging, 100–150 frames were acquired (0.2 frames/s) and analysed as previously described [42]. Kymographs were generated using MetaMorph (Molecular Devices). Vertical single line-scans through the thickness of each process were plotted sequentially for every frame in the time series. Acid wash was performed to release proteins bound to the cell surface by incubating the cells for 5 minutes at room temperature in 100 mM citrate-NaOH, pH 2.0, 140 mM NaCl and washed with PBS. Virus binding was quantified using the spot count option of the Imaris software and normalized to the total amount of membrane measured by voxel counting using ImageJ.
CAV-2 was directly labelled with carboxyfluorescein according to a previous report [45]. Briefly, carboxyfluorescein can be used as intracellular pH sensor by measuring the ratio of emission intensities upon sequential excitation at 458 and 488 nm (I488/I458). CAV-CF-infected MNs were imaged live and after obtaining the calibration curve (with MNs treated with 10 µg/ml of nigericin and monensin+L15 at various pHs), axonal versus somatic particles emission intensities were analysed. Intensities and ratios were measured using imageJ (version 1.37).
Magnetic isolation of TeNT HC carriers was performed as previously described [44]. Quantification of CAR enrichment in carriers by western blot was performed using ImageJ.
TeNT HC was isolated and labelled as previously described [44]. CAV-2 FK (residues 358–542) construct was cloned into pPROEX HTb (Life Technologies), expressed with a cleavable His6-tag, and purified as previously described [15]. The CAV-2 FKs were dialysed in PBS 0.1 M Na2CO3 pH 9.3 and labelled using Cy5 mono-reactive dye pack (Amersham Bioscience) for 45 minutes at RT. The elution of labelled protein was performed with 2 ml of PBS using NAP5 column (GE Healthcare) pre-equilibrated with 10 ml PBS. The final dye/protein ratio (∼2.4) was determined using a NanoDrop ND-100 spectrophotometer.
For TEM analysis, MNs were incubated for various time periods with CAV-Cy3. Cells were washed twice with 0.2 M Sorensen's buffer and fixed with 2.5% glutaraldehyde (Agar) in Sorensen's buffer, containing 70 mM sucrose for 1 h at 4°C. After washing, MNs were post-fixed with 1% osmium tetroxide for 30 minutes, washed, dehydrated in an ascending ethanol series and embedded in araldite over 2 days. Thin sections were stained with methanolic uranyl acetate and lead citrate. Sections were imaged with a JEOL 1010 transmission electron microscope.
Under isoflurane anaesthesia (National Veterinary Services, Stoke on Trent, UK), an incision was made along the left flank of adult C57Bl/6 mice to expose their sciatic nerve, which was ligated at the mid-thigh level. Immediately following ligation, the tibialis anterior and gastrocnemius muscles were exposed and FK-Cy5 (6 µg in 8 µl) was slowly injected intramuscularly using a Hamilton microsyringe. The needle was left in place for 1 minute to prevent leakage. The wound was sutured and the animals were allowed to recover. After approximately 8 hours, the mice were terminally anaesthetized with sodium pentobarbitone and perfused transcardially with 4% PFA (TAAB) in 0.1 M PBS. The ligated sciatic nerve was removed, post-fixed for 4 hours in the same fixative and then cryoprotected in 30% sucrose in PBS. The animals were housed in a controlled temperature and humidity environment and maintained on a 12 hour light/dark cycle with access to food and water ad libitum.
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10.1371/journal.pcbi.1002360 | Early Warning Signals for Critical Transitions: A Generalized Modeling Approach | Critical transitions are sudden, often irreversible, changes that can occur in a large variety of complex systems; signals that warn of critical transitions are therefore highly desirable. We propose a new method for early warning signals that integrates multiple sources of information and data about the system through the framework of a generalized model. We demonstrate our proposed approach through several examples, including a previously published fisheries model. We regard our method as complementary to existing early warning signals, taking an approach of intermediate complexity between model-free approaches and fully parameterized simulations. One potential advantage of our approach is that, under appropriate conditions, it may reduce the amount of time series data required for a robust early warning signal.
| Fisheries, coral reefs, productive farmland, planetary climate, neural activity in the brain, and financial markets are all complex systems that can be susceptible to sudden changes leading to drastic re-organization or collapse. A variety of signals based on analysis of time-series data have been proposed that would provide warning of these so-called critical transitions. We propose a new method for calculating early warning signals that is complementary to existing approaches. The key step is to incorporate other available information about the system through the framework of a so-called generalized model. Our new approach may help to anticipate future catastrophic regime shifts in nature and society, allowing humankind to avert or to mitigate the consequences of the impending change.
| Critical transitions are sudden, long-term changes in complex systems that occur when a threshold is crossed [1]. Many systems are known to be at risk of such transitions, including systems in ecology [2], climate research [3], economics [4], sociology [5] and human physiology [6]. Examples of critical transitions in ecology include shifts in food web composition in shallow lakes [7], degradation of coral reefs [8], degradation of managed rangelands [9], and desertification [10].
Warning signals for impending critical transitions are highly desirable, because it is often difficult to revert a system to the previous state once a critical transition has occurred [2], [11]. If an accurate mathematical model of the system is available then critical transitions can be predicted straight-forwardly, either by numerical simulation or by direct computation of the dynamical thresholds. For real world complex systems, however, sufficiently accurate models are in general not available, and predictions based on models of limited accuracy face substantial difficulties [12]. Recent research has therefore focused on model-free approaches that extract warning signals from observed time series [13]. Two of the most widely used approaches are increasing variance [14] and autocorrelation [15], both of which are caused by critical slowing down [16]. Other approaches consider warning signals based on skewness [17], flickering [18] and spatial correlation [19].
One strategy for improving the quality of an early warning signal, which to our knowledge has not been explored, is to utilize other information that may be available. This other information may take the form of other time series data, for example in ecological applications birth rates as well as population sizes, or additional knowledge about the system, such as that the top-predator mortality is likely to be linear. This highlights the need for intermediate approaches, which can efficiently incorporate available information without requiring a fully specified mathematical model.
In the present Letter, we propose an approach for the prediction of critical transitions based on the framework of generalized modeling [20], [21]. The approach allows available information to be used, subject to certain limitations on the quality and availability of the information. Our results indicate that in the cases considered here, the approach can reduce the amount of time series data required or increase the quality of the prediction. We demonstrate the applicability of the proposed approach by considering a simple one-population model, a previously studied fisheries model and a tri-trophic food chain.
Suppose that a system has been identified as being at risk of a critical transition. Even if very little specific information is available, the dynamics can generally still be captured by a so-called generalized model [20]. Such a model captures the structure of the system, without restricting it to specific functional forms.
To formulate a generalized model we first identify important system variables (say, abundance or biomass of the populations in the system) and processes (for example, birth, death, or predation). As a first step, the generalized model can then be sketched in graphical form, such as in Fig. 1 below. This graphical representation is sometimes called a causal loop diagram [22].
To obtain a mathematical representation of the model we write a dynamical equation for each variable . In these equations we represent the processes by general functions. For instance we can model a single population subject to gains and losses by an ordinary differential equationor as a discrete-time mapNote, that in contrast to conventional models, we do not attempt to describe the processes G and L by specific functional forms. Instead, we use unspecified functions and as formal placeholders for the (unknown) relationships realized in the real system.
We assume that before the critical transition, the system can be considered in equilibrium. We emphasize that this does not require the system to be completely static or closed in a thermodynamic sense, but that, on the chosen macroscopic level of description, the system can be considered at rest. For example, the system may be undergoing stochastic fluctuations of a fixed distribution around a stable fixed point. Additionally, the system is subject to a slowly changing external parameter that puts it at risk of undergoing a critical transition. The system is therefore at equilibrium only on a certain timescale. In the following we refer to this timescale as the fast timescale, while the dynamics of the whole system, including the slow change of the external parameter, takes place on the slow timescale.
Using the definitions above critical transitions can be linked to instabilities (bifurcations) of the fast subsystem [23]. For detecting these instabilities we construct the Jacobian matrix, a local linearization of the system around the steady state [24]. A system of ordinary differential equations (ODEs) is dynamically stable if all eigenvalues of the Jacobian have negative real parts, whereas a discrete time map is stable if all eigenvalues have an absolute value less than one. Critical transitions are thus signified by a change in the external parameter causing at least one of the eigenvalues to cross the imaginary axis (ODE) or a unit circle around the origin (map).
To warn of impending critical transitions we monitor the eigenvalues of the Jacobian of the fast subsystem, which usually change slowly in time. A warning is raised if at least one of the eigenvalues shows a clear trend toward the stability boundary (for ODEs, zero real part; for maps, absolute value of one). The Jacobian itself can be computed directly from the generalized model, but will contain unknown terms reflecting our ignorance of the precise functional forms in the model. Previous publications [20] have shown that these unknowns can be treated as well-defined parameters with clear ecological interpretations. In the present applications we estimate the unknowns in the Jacobian matrix from short segments of time series data. Thereby, a pseudo-continuous monitoring of the eigenvalues of the fast subsystem is possible.
The generalized model that is constructed should reflect existing knowledge about the structure of the system. It should contain terms that represent relevant and observable processes (or relevant processes whose magnitudes can be deduced from other processes, as we will see below). The generalized model should also have a structure that permits bifurcations that are relevant for the system; if not, the generalized model cannot be used to anticipate those bifurcations.
We note that with given time series data estimating the generalized model parameters is simpler than estimating the entries of the Jacobian matrix directly, because the generalized model already incorporates structural information about the system. Further, many of the parameters in the generalized model may already be available in a given application, because they refer to well-studied properties of the species, such as natural life expectancy or metabolic rate.
We applied the proposed approach to three case studies, focusing on a generic population with an Allee effect, a fisheries example, and a tri-trophic food chain.
Allee effects, that is, positive relationships between per-capita growth rate and population size, are postulated in many populations and have been conclusively demonstrated in some [25]. A population with an Allee effect can suddenly transition from a stable, non-zero population size to unconditional extinction [26].
We supposed that an early warning signal was desired for a population in which a slowly increasing death rate (for example the spread of a new disease, the appearance of a new predator, or habitat destruction) was pushing the population towards a critical transition associated with an Allee effect. We assumed that regular observations of the population size and birth rate were available.
Accordingly, we constructed the generalized model(1)where and are the birth and death rates of the population, respectively, and represents the external factor pushing the system towards the critical transition. We refer to the population and birth rate observations as and , taken at times , . (Observations of the death rate would also be acceptable in place of the birth rate.)
From the generalized model of Eq. (1), we constructed the Jacobian (in this 1-D system, also the eigenvalue) of the system(2)near its steady state, where the prime denotes the derivative with respect to . To calculate the changing values of the eigenvalue as the external parameter changes, we need to estimate the gradients and from our time series observations of and .
We calculated as follows. Since the birth rate and the population have been directly observed, could therefore be computed immediately, where we use the notation . (These one-sided derivative estimators involve a loss in accuracy but allow the eigenvalues to be estimated at the most recent observation time, which is important when attempting to predict an imminent transition.) A discretization of Eq. (1) gives . We cannot calculate in the same way as , because also depends on . Instead, we make one additional assumption: That the mortality is linear in (although the coefficient of this linearity may change with ). Then we can estimate . (Suppose . Then .) Finally, the eigenvalue .
To test the early warning signal, we simulated a simple model (given in the Supporting Information as Text S1) of an Allee effect with additive noise. A critical transition occurred, causing subsequent extinction of the population (Fig. 2). The challenge we addressed is predicting the critical transition from a limited number (here, fifteen) of observations of population size and birth rate. We emphasize that we did not utilize any information on the functional forms of processes employed in the simulation, so that the prediction is based solely on the 15 observations and the assumed structural information (that is, one population subject to gains and losses). By estimating the parameters of the generalized model as described above, we determined the eigenvalues of the Jacobian as a function of time (Fig. 2b). A clear increase in the eigenvalue is detectable well before the critical transition, giving ample warning of the impending collapse.
Due to a phenomenon called bifurcation delay [23], the population size did not start to change rapidly until well after () the bifurcation point (). As previously observed by Biggs et al. [27], management action to reverse the change in bifurcation parameter may successfully avert the critical transition even after the fast subsystem's bifurcation has occurred, if still within the range of the bifurcation delay. In the case of Fig. 2b, the eigenvalue trend is directed more towards the last possible time that successful management action can be taken than towards the time of the actual bifurcation.
Our second case study focuses on an example from fisheries. Increased harvesting of piscivores can induce a shift from the high-piscivore low-planktivore regime to a low-piscivore high-planktivore regime [28]. Many fisheries are suspected to have undergone such transitions [29], [30]. Based on the causal loop diagram (Fig. 1), we formulated a discrete-time generalized model, describing the piscivore and planktivore populations at the end of each year, in the spirit of stock-assessment modeling (see Text S1). Thereby detailed modeling of the intra-annual dynamics was avoided.
To test the warning signal, we generated time series data with a detailed fishery model by Biggs et al. [27], which was developed from a series of whole-lake experiments [31]. We describe this model more fully in Text S1, but note here that the model differs significantly from our generalized model by a) accounting for the intra-annual dynamics and b) containing an additional state variable denoting the juvenile piscivore population. These discrepancies were intentionally included to reflect the limited information that would be available for the formulation of the generalized model in practice. In simulations the detailed model showed a transition to a low-piscivore high-planktivore regime as the harvesting rate was increased (Fig. 3a).
From this simulation, we recorded the simulated piscivore and planktivore density and piscivore catch at the end of each year. Because the simulated data was very noisy we estimated the Jacobian's eigenvalues after smoothing the recorded data (see Text S1). In addition to the time series data, the information on the natural adult piscivore mortality and reproduction rate and the planktivore influx from refugia were required (see Text S1). This type of information can be reasonably well estimated for most systems. We confirmed that our predictions (reported below) are not sensitive to the specific values used. Indeed, a simple approach for estimating these parameters is to recognize that the initial state, before the critical transition, is stable. In a number of test trials we confirmed that any reasonable combination of parameters used that corresponded to an initially stable steady state provided an early warning signal comparable to the results reported below.
An estimate of the Jacobian eigenvalues for the fisheries example is shown in Fig. 3b. As the system approaches the critical transition we observe that an eigenvalue approaches one, which signifies a critical transition for discrete time systems. This result is compared to the variance early warning signal computed by Biggs et al. [27], which uses a much more densely sampled time series including intra-annual dynamics. The comparison shows that the approach proposed here produces a signal of similar quality (although possibly too early), while requiring significantly less time series data. Further, comparison with a variance signal using the same amount of time-series data as the generalized model shows that the generalized model-based signal is a much clearer early warning signal in this case. In particular, the variance signal only rises during or after the transition.
For our final example we consider a tri-trophic food chain. In ecological theory food chains play a role both as a prominent motif appearing in complex food webs and as coarse-grained models. Using generalized models, a general Jacobian for a continuous-time model of the tri-trophic food chain can be derived (see Text S1 and Gross et al. [32]).
We generated example time series data using a set of three ordinary differential equations that modeled a producer biomass, , predator biomass, , and top predator biomass, , as described in Text S1. We included additive noise terms in the equations, and if any biomass decreased to zero we suppressed the noise term so that the corresponding population remained extinct. We simulated these equations while increasing the mortality rate of the top predator. The resulting time series, for the chosen combination of parameters, show a slowly changing steady-state followed by a sudden transition to large oscillations, and a sudden collapse of all three populations (Fig. 4).
To provide an early warning signal for the transition we recorded time series of the three biomasses and the top-predator's death rate, and estimated the parameters of the generalized model from smoothed segments of these time series. Even for the smoothed data we find that one of the eigenvalues is very noisy and sometimes positive. We believe that the presence of this eigenvalue reflects the response of the prey to fluctuations on the higher trophic levels and therefore exclude this value from our analysis. As is increased toward the onset of oscillations, two eigenvalues show a clear increase toward zero real part (Fig. 4). The two eigenvalues approach zero as a complex conjugate eigenvalue pair, which is indicative of the system undergoing a Hopf bifurcation [24], which in turn generally implies a transition from stationary to oscillatory dynamics. The early warning signal constructed here, consisting of the approach of this eigenvalue pair towards the imaginary axis, warned of the transition to an oscillatory state significantly before the transition occurred. These large oscillations combined with stochastic fluctuations then led rapidly to extinction.
Supercritical Hopf bifurcations, to which class the bifurcation in the present system belongs, are by themselves not critical transitions. The detection of Hopf bifurcations is nevertheless of interest. First, subcritical Hopf bifurcations are indeed true critical transitions. Second, even supercritical Hopf bifurcations have long been associated in ecology with rapid destabilization and extinction of populations [33], a chain of events that we characterize as a critical transition and that we observed to occur in the present system. We also note that although to linear order sub- and super-critical Hopf bifurcations cannot be distinguished, generalized modeling can be extended to higher orders where these cases can be identified [34].
In this Letter we proposed an approach for anticipating critical transitions before they occur. In particular we showed that generalized modeling of the system can facilitate the incorporation of the structural information that is in general available.
We demonstrated the proposed approach in a series of three case studies. The first example showed that in simple systems even very few time points can be sufficient for clean prediction of the critical transition. The second example posed a hard challenge, where test data was generated by a model that differed considerably from the generalized model. Yet even in this case the generalized model significantly reduced the amount of data needed for predicting the transition. The third and final example demonstrated the ability of the proposed approach to distinguish between different types of critical transitions (in this case, through the presence of a complex conjugate pair of eigenvalues approaching the imaginary axis).
In all case studies we found that the proposed approach can robustly warn of critical transitions in the presence of noise. We believe that the performance of the approach under noisy conditions can be further improved by subsequent refinements. Such refinements could include combination with dynamic linear modeling [14], utilization of a parameter transformation (to ‘scale’ and ‘elasticity’ parameters) previously proposed for generalized models [20], or the use of optimized sampling procedures.
Two important rules for constructing the generalized model are as follows. First, there must be sufficiently few unobservable processes (represented by placeholder functions in the generalized model) that their magnitudes can be inferred from balancing the observable processes. For example, in the Allee effect study, the unobserved process was estimated by . Second, where a process is a function of variables (although in the cases studied here was never larger than 2), our method at present requires assumptions or other knowledge about the dependence of the process on of those variables. This requirement could be relaxed in future work, although probably at the cost of requiring more time series data.
An advantage of the proposed approach is that it generally becomes more reliable closer to a critical transition, where rates of change of state variables and other observables are generally larger, which may lead to better sampling, although noise will also increase close to the transition due to critical slowing down. In such situations statistical methods such as variance may become more difficult to estimate as the time series becomes less stationary, for example since detrending becomes more difficult. On the other hand, the model-free statistical approaches may be more useful where little knowledge is available about the system, or where trends in the means of observed quantities are strongly obscured by noise. In these respects, the proposed approach provides a tool complementary to established statistical methods, each method with its own domain of utility.
One limitation shared by both our and the statistical early warning methods is that large noise can bias the estimation of the respective warning signals. In our case, an asymmetric distribution of fluctuations can bias the estimation of the underlying steady state. That our approach effectively involves derivatives of time series can increase the sensitivity to high observation noise or otherwise poor-quality data. Another important assumption in our present treatment (that is also shared by the statistical approaches) was that the dynamics of the fast subsystem could, at least at some level of description, be considered as stationary. Let us emphasize that this is not a strong assumption because even systems that are primarily non-stationary, such as the fisheries example, can be modeled as stationary if a suitable generalized modeling framework is chosen. Furthermore, ongoing efforts aim at extending the framework of generalized modeling to non-stationary dynamics, which may lead to a further relaxation of that assumption in the future [35].
A thorough statistical analysis of the generalized modeling and the statistics-based approaches is another topic for future work. Such a study would help to quantify under exactly what conditions the generalized modeling approach can operate effectively and offer advantages compared to statistics-based approaches.
In summary, we used generalized models to efficiently incorporate available information about a system without requiring detailed knowledge about that system. Our intermediate-complexity method provides an early warning signal approach complementary to existing statistics-based methods. In the cases studied here, our method could provide early warning signals with significantly less time series data than statistical approaches. Thereby the proposed approach can, under suitable conditions and with good quality data, contribute to the warning of critical transitions from a realistic sampling effort.
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10.1371/journal.pcbi.1000639 | An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer | Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks.
| Intensive research on cancer has led to an understanding of many individual genes that may be important for the initiation and progression of tumors. However, since cancer is a progressive disease that results from accumulation of multiple mutations likely acting in concert, individual markers can only provide limited insights into cellular mechanisms that underlie tumorigenesis. For this reason, recent studies focus on identification of “sub-network markers”, that is, functionally associated genes that exhibit coordinate changes in molecular expression during cancer progression. However, expression of genes is most frequently interrogated at the mRNA level, which captures functional activity of genes only to a limited extent. Screening of protein expression, on the other hand, provides information on the abundance of functional gene products, but its scale is often limited compared to screening of mRNA expression. In this article, we develop a proteomics-driven computational method that searches for sub-network markers in human colorectal cancer, based on a seed of differentially expressed proteins identified by proteomic screening. Our results show that significant changes in the expression of these proteins is likely to be associated with coordinate changes in the expression of the genes whose products are functionally associated with these proteins. This analysis leads to novel insights in the synergistic processes that underlie tumorigenesis.
| Colorectal cancer (CRC) is the second leading cause of cancer death in adult Americans [1]. Interest in this complex disease is represented by a very mature body of research, much of it at the genomic level. Yet the identification and verification of proteins that have a functional role in the patho-physiology of CRC remains an important goal as proteins directly mediate the functions dysregulated in the disease. Modern, high-throughput proteomic methods provide one way of profiling the significant changes in protein expression of tumor samples with respect to control, using tissue biopsies obtained from patients diagnosed with this disease [2]–[5].
Proteomic screening techniques are particularly useful for furthering the understanding of the mechanisms that underlie complex phenotypes like CRC, in that they provide information at the post-translational level. However, due to various biological and experimental constraints (e.g., ascertainment bias and physical properties of proteins), proteomic methods may screen only a limited fraction of proteins and protein isoforms present in cells and tissues. We propose that this limitation may be mitigated through the integration of proteomic data with genome scale data sources, such as measurements of gene expression. In addition, protein-protein interaction (PPI) databases, which are rapidly growing in terms of both the quality and quantity of their annotations, provide another source of genome scale data integration [6]. Such integrative approaches can potentially lead to functional inference at the systems level, through identification of pathways and molecular sub-networks that are implicated in CRC.
In support of this approach, a recent review by Ideker and Sharan [7] summarizes studies that indicate that genes with a role in cancer tend to cluster together on well-connected sub-networks of protein-protein interactions. This suggests a hypothesis that the synergistic expression of multiple cancer-related genes at the level of mRNA can co-regulate the expression of proteins in their immediate “network neighborhood”. These differentially expressed proteins may be captured by expression proteomics experiments, thus their network neighborhood should provide an ideal starting place to search for sub-networks with a possible role in the disease.
The effectiveness of network-based approaches to the identification of multiple disease markers has been demonstrated in the context of various diseases, including Huntington's disease [8], the inflammatory response [9], and human breast cancer [10]. Furthermore, it was recently shown that “differentially expressed sub-network markers” were more accurate predictors of metastasis in breast cancer (compared to single gene markers) [11]. However, existing approaches are generally limited to mRNA expression data in terms of quantification of molecular expression, which captures post-transcriptional activity only to a limited extent [12],[13]. Consequently, inclusion of protein expression data in the search for sub-network markers has the potential to improve the effectiveness of systems biology approaches [14]. However, it remains largely unknown how a network-based approach may be enhanced when starting with proteomic data.
In this paper, we propose a novel computational approach that takes into account certain topological features of the interactome, namely connectivity and proximity, for searching the neighborhoods of proteomic targets to find significant sub-networks implicated in CRC. In doing so, we partly overcome (i) the bias inherent in proteomic profiling experiments, particularly those that are gel-based, which are typically limited to capturing changes only in relatively abundant proteins and (ii) the noise, missing data, and ascertainment bias in PPI data. This is accomplished by assessing the functional association between proteins based on the quantification of the statistical significance of network crosstalk through information-flow based modeling of the PPI network and development of a reference model that takes into account the network connectivity of proteomic targets. We hypothesize that identification of candidate sub-networks with a significant association to proteomic targets can reveal proteins that are not detected to be differentially expressed at the level of the proteome, but whose activity in the network may play a key role in maintaining the phenotype. Consequently, the proposed framework provides a means for expanding proteome expression data to infer a role for proteins that exhibit significant crosstalk to the proteomic targets. The flow of the proposed computational framework is illustrated in Figure 1.
A key objective of this study is to systematically elaborate a proteomics-driven approach as a sound method for inferring small sub-networks implicated in complex phenotypes, and ultimately make these methods practically available to a wider community of researchers working in this area. For this purpose, we ground our approach on the hypothesis that the observed fold change of the proteomic targets may be associated with the synergistic dysregulation of their interacting partners at the level of mRNA. From a computational perspective, our hypothesis is based on the premise that sub-networks which exhibit significant association with the proteomic targets should also show a significant change in activity between control and cancer. To test this hypothesis, we first score each protein in the network based on their crosstalk with the proteomic targets. In order to account for noise, incompleteness of data, and ascertainment bias, we also develop novel methods for assessing the significance of these “crosstalk scores”. Then, for each proteomic target, we identify a candidate sub-network that is composed of its interacting partners with significant crosstalk scores. Subsequently, using an information theoretic measure, we evaluate the synergistic differential expression of these candidate sub-networks between control and disease, based on changes in mRNA expression obtained from microarray experiments performed on tissue biopsies collected from a cohort of patients with CRC. Finally, using the sub-networks that exhibit significant synergistic dysregulation as features, we develop classifiers to predict disease class across different data sets.
The proposed computational approach for assessing functional association between proteomic targets and other proteins uses a random-walk based algorithm. Recently, Kohler et al. [15] and Chen et al. [16] used similar network algorithms to prioritize candidate disease genes implicated by linkage analysis in a variety of human diseases. Vanunu and Sharan [17] developed a global, propagation-based method that exploits information on known causal disease genes and PPI confidence scores. Their method more accurately recovered known disease gene relationships compared to several other extant methods. In contrast to these applications and rather than using raw scores obtained by such information flow based algorithms, we develop reference models to assess the statistical significance of these scores, with a view to identifying proteins that are significantly associated with proteomic targets. Furthermore, our biological hypothesis, which drives our approach, is that targets (proteomic or genomic) significant for the CRC phenotype may reside in or near cancer hotspots in the network, and thus present an ideal starting place to search for high-value sub-networks associated with the disease. Therefore, our computational approach does not rely on canonical disease-related genes or proteins; rather, it is a global, unbiased search that tries to identify network interactions statistically significant with respect to all targets in an experimentally-derived set.
Our previous work in this area [5] was limited in scope due to the lack of access to the topology of the commercial PPI we employed. This prevented us from assessing the importance of topology for sub-network generation, which is the primary focus of our computational approach in this study. Likewise, our network scoring and statistical hypothesis testing were all greatly limited in the previous work due to incomplete access to an unpublished microarray data. For the same reason we were practically prevented from iteratively adjusting network search parameters in the commercial software that would have generated a large list of candidate sub-networks for scoring.
Here we describe a new network search method for finding high-value candidate sub-networks associated with CRC. To overcome the limitations of the previous study and to permit independent evaluation of our methods, we utilize a public PPI (HPRD) and public microarrays (Gene Expression Omnibus) to evaluate performance using two independent sets of proteomic targets obtained by 2D-PAGE that are also publically available. We compare this result to that obtained using a set of CRC driver gene mutants as seeds for the network search. The basis for this test is the hypothesis that if mutated gene products map to cancer hotspots on the network, they would be similarly useful as seeds for our network search algorithm. To reveal the practical utility of our integrative approach, and to extend it beyond merely a theoretical computational framework, we validate by western blot several targets in a sub-network predicted by our method to be dysregulated, using a cohort of tissue biopsies not used in the original proteomic screen. Finally, we employ a cross-validation approach to compare the disease classification performance of the proteomic-versus genomic-derived sub-networks.
Our results show that the proposed proteomics-driven approach, as it integrates a variety of biologically relevant data, can identify significant sub-networks implicated in a complex phenotype, i.e. CRC. The definition of terminology frequently used in this paper is provided in Table 1.
We searched the PPI network obtained from the Human Protein Reference Database (HPRD) for CRC-implicated sub-networks using two distinct sets of proteomic targets from Nibbe et al. [5] (n = 67) and Friedman et al. [2] (n = 55). Both sets contain significant targets of CRC obtained by a proteomic screen using tissue biopsies (tumor and matched controls) obtained from twelve and six patients, respectively (see Proteomic Methods for details of the screen performed in our lab). We call these targets proteomic seeds. The HPRD PPI network was downloaded from the HPRD website on September 2008 and contained 35023 binary interactions between 9299 proteins, as well as 1060 protein complexes consisting of 2146 proteins. We integrated the binary interactions and protein complexes using a matrix model (e.g., each complex is represented as a clique between the proteins in the complex), to obtain a PPI network composed of 42781 binary interactions among 9442 proteins. 60 of the proteomic seeds from the data of Nibbe et al. had at least one interaction in HPRD, while 37 of the seeds from the data of Friedman et al. had at least one interaction in HPRD. 14 of the proteins in the two seed sets were common.
For every protein in HPRD, our procedure assigns a score based on the protein's proximity and connectivity to all the seeds (see Materials and Methods). If the score is not significant (p<0.001) but the protein directly interacts with one or more of the seeds, we call it an interactor, whereas a crosstalker is any protein whose score is significant. Note that a crosstalker is generally (but not necessarily always) an interactor since a significant crosstalk score for a protein indicates that it is in the network neighborhood of one or more of the seeds, however, there are many interactors that do not qualify as crosstalkers. Overall, this procedure revealed 233 crosstalkers for Nibbe seeds, and 210 crosstalkers for Friedman seeds.
Subsequently, for each proteomic seed in each set, a candidate sub-network consisting of its interactors, termed the interactor sub-network, was obtained, resulting in a total of 55 interactor sub-networks (46 for Nibbe seeds exclusively, 23 for Friedman seeds exclusively, and 14 additional sub-networks for both). Similarly, for each seed in both sets, a crosstalker sub-network was obtained. Thus, for every seed there are two corresponding sub-networks, an interactor sub-network and a crosstalker sub-network. The proteins in an interactor sub-network are merely characterized by their direct interactions with the corresponding proteomic seed. By contrast, proteins in a crosstalker sub-network are characterized by their degree of functional association with all proteomic seeds.
We evaluated the individual differential gene expression of each crosstalker identified using the Nibbe and Friedman proteomic seeds using two microarray datasets obtained from GEO (GSE10950 & GSE8671). GSE8671 represents 64 experiments using mRNA isolated from tissue biopsies obtained from 32 patients (matched tumor and adjacent normal mucosa) performed on an Affymetrix GeneChip (Human U133 Plus 2.0). Similarly, GSE10950 represents 48 experiments on matched tissue biopsies (24 patients) performed on an Illumina array (Human ref-8, v2.0).
The cumulative distribution of individual differential expression scores for proteomic seeds, (and a seed of CRC driver genes discussed later), as well as all proteins in the network computed as described in the Materials and Methods section, is shown in Figure 2 (please see the Materials and Methods section for details on how differential expression is quantified). As seen in the figure, we found no significant difference in the distribution of individual differential expression of the crosstalkers, as compared to the distribution of differential expression of all proteins in the HPRD network. This observation indicates that at the level of individual genes, significant network crosstalk with proteomic seeds in CRC is not associated with transcriptomic dysregulation in CRC.
For the purpose of discussion we will refer to a sub-network by the proteomic seed that induced the sub-network (e.g. TCP1). For each version of each sub-network we computed the mutual information (MI) of each sub-network between control and tumor using the mRNA expression data from microarrays GSE10950 and GSE8671 (see Computational Methods), and we used this score to estimate the significance of the various networks in differentiating the phenotype (Figure 1). The comparison of mutual information for the two versions of each sub-network associated with the Nibbe seed is shown in Figure 3. We plotted the results only for those (crosstalker) sub-networks where the mutual information exceeded 0.35 (approximately 1σ from random mean). The purpose of this analysis is to understand how the synergy of each crosstalker sub-network compares to that of its corresponding interactor sub-network. The MI and significance scores for all sub-networks can be found in Supplemental Table S1.
Of the 46 candidate sub-networks associated with Nibbe proteomic seeds, 10 unique interactor sub-networks (green squares) exhibited significant MI scores. For five of these sub-networks (CCT2, TCP1, SYNCRIP, HNRPF and HNRPH1) the crosstalker version of the sub-networks was found to have enhanced MI on one or the other microarray datasets. Two crosstalker sub-networks (red diamonds), CCT2 and TCP1, show improvement over their corresponding interactor sub-network on both arrays. Notably, on GSE10950, the mutual information score of the TPI1 crosstalker sub-network is significant, while the corresponding interactor sub-network failed to show significance.
Figure 4 shows the corresponding plots for the Friedman proteomic seeds. Here, seven unique interactor sub-networks have significant MI scores; two of them (ANXA3 and PSMA6) were common to both sets of microarray data. For the Friedman seeds, the crosstalkers for candidate sub-network TUBA1B showed dramatically increased mutual information compared to its interactor network. Furthermore, four other crosstalker sub-networks (associated with MYL9, GARS, ANXA3 and GSTP1) all revealed much higher synergy compared to their corresponding interactor sub-networks, two of which (MYL9, GSTP1) failed to show significance on either array. We discuss a possible explanation for these findings in the Discussion section.
Figures 5a and 5b show unions of crosstalker sub-networks associated with the Friedman and Nibbe seeds, respectively, for which the synergy was higher than the corresponding interactor sub-network. The graphs reveal that many proteomic seeds reside within or near dense sub-networks of crosstalkers.
We observed that several of the sub-networks generated using the two proteomic seed sets contained proteins in common. In particular, certain sub-units of the TCP1 complex exhibited marked crosstalk in the sub-network induced by CCT2 in the Nibbe seed, and TUBA1B in the Friedman seed (Figure 4). In addition, we had previously shown [5] that certain sub-units of this complex (CCT3, CCT5, and CCT7) were also significant for the late-stage CRC phenotype, as revealed by a similar network scoring methodology but using a commercial PPI unrelated to HPRD.
TCP1 (or TCPα) is a hetero-oligomeric complex comprised of two stacked ring structures, each composed of eight known subunits and plays a functional role in maintaining the CRC phenotype. Specifically, it was shown [18] to be required for the proper biogenesis of PLK1, a kinase that has a critical role in cytokinesis. However, other than their role as sub-units in the formation of the TCP complex little is known about the independent role, if any, of these sub-units in CRC [19]. Consequently, these targets present an opportunity for follow-on mechanistic studies. For this reason, we verified the protein expression of TCP1, CCT3, CCT5, CCT7, and PLK1 by western blot in a separate cohort of three patient sample pairs not used in screening phase, and compared this to the average expression at the level of mRNA (Figure 6). Consistent with our hypothesis, the data indicate co-regulation at the level of mRNA and protein, but also reveal the wide variability of expression of these targets among individual patients. CCT3 and CCT7 were dramatically over-expressed in two patients (507 and 534), but less so in patient 540, which was similar to the pattern for PLK1.
Although these data show that proteomic seeds are well-suited for identifying synergistically dysregulated sub-networks, we wished to investigate the power of genetically identified seed sets in discovering significant sub-networks. As CRC is commonly thought to be caused by the accumulation of somatic mutations, a number of cancer research labs have collaborated to conduct whole genome sequencing to identify the genes thought to be “drivers” in cancer, i.e. those represented by the set of genes that appeared most frequently mutated in a robust cohort of clinical biopsies. The results of one such study on human breast and colon cancer were recently reported by Sjöblom et al. [20]. We hypothesized that the gene products of the CRC driver genes reported in this study would be located at hotspots in the interactome. Further, if the mutations lead to dysregulation of neighboring genes at the level of mRNA, then the seed should reveal significant sub-networks using our method. Additionally, since there is less bias in PCR sequencing and high genome coverage, at least as compared to proteomic profiling, we supposed that driver gene seeds (n = 42) might be superior both in terms of the number and significance of the sub-networks identified.
As shown in Figure 7, when scored by GSE8671, only four significant sub-networks were found. Strikingly, for every one of them, only the crosstalker sub-networks were significant. Using GSE10950, seven sub-networks of crosstalkers were significant, including all four found on GSE8671. For all but two of the sub-networks (P2RX7, OBSCN), the crosstalkers show substantially higher synergistic differential expression as compared to their interactor counterparts. Notably, APC, a tumor suppressor gene widely viewed as the “gate-keeper” in CRC, was associated with a significantly dysregulated sub-network with respect to both arrays, and of all the genes in the driver seed it was found to be mutated in the highest percentage (90%) of the clinical samples. This expected finding may be viewed as a positive control for our analytical method.
In terms of the overall number of significant sub-networks identified, however, there was no apparent improvement using the driver gene seed set versus either proteomic seed set. Additionally, a number of the significant crosstalk sub-networks identified by the proteomic seeds show markedly higher synergy (MI>0.60) than all but one (EVL) of the sub-networks found by the driver gene seed.
We evaluated the quality of the crosstalker versus interactor sub-networks in terms of their ability to classify tumor versus control on the microarrays, using an SVM-based classifier in a cross-validation approach (see Materials and Methods). The significant sub-networks in each group were first ranked by MI, and the features were valued by superposing the mRNA expression values of each gene in the sub-network. When trained on GSE10950 and validated on GSE8671, proteomic crosstalkers outperformed the interactor sub-networks (both proteomic and genomic) when the number of features used to train the classifier was three or less. Beyond three features, both the proteomic interactor and CAN (candidate CRC driver genes) crosstalker sub-networks outperformed the proteomic crosstalkers (Figure 8a). Performance was similar when the training and validation sets were reversed, although the performance of proteomic crosstalkers dropped when more than two sub-networks were used for classification (Figure 8b). The raw classification data are provided in Supplemental Table S1.
We have shown that proteomic targets showing significant expression changes for a complex phenotype, such as CRC, provide valuable inputs for our algorithms designed to discover phenotypically significant sub-networks with connectivity and proximity to these targets. In addition, certain crosstalker sub-networks, when scored with respect to phenotype by the measure of mutual information, display significant differential synergistic expression at the level of mRNA with respect to the seed targets. When these implicated sub-networks contain proteins with no known role in the disease, they present new opportunities for follow-on mechanistic experiments to verify the in silico inference of biological significance in the disease. This point cannot be over-emphasized, because in our view the promotion of a candidate, disease-associated sub-network to an functional sub-network with a validated role in disease must be accomplished by wet lab experiments.
As mentioned in the previous section, with respect to the proteomic seeds, a number of the same sub-networks showed significance (>1σ from background) when scored by either GSE10950 or GSE8671. With respect to the driver gene seed, every sub-network that showed significance when scored by the GSE8671 array was also found to be significant when scored by the GSE10950 array. One explanation for why the sub-networks with respect to a given set of proteomic seeds did not show complete redundancy between arrays is that the microarrays represent experiments performed on different pathologic stages of CRC tumors, very early stage in the case of GSE8671 (adenoma) versus a more established tumor in GSE10950 (primary). The pathologic stage of the proteomic samples in the Nibbe seed was homogenous late stage CRC (Duke's D) while the Friedman seed was a mix of mid to late stage samples (Duke's B–D). This highlights a potential limitation of an integrated –omics approach, namely, it is often difficult to establish an optimal match of the biology underlying the measures made at the level of the proteome and transcriptome. However, in our case, if the sub-networks become dysregulated early in the disease and have a role in maintaining the phenotype through later stages, this limitation can turn into an opportunity for development of hypotheses regarding the mechanisms of the progression of CRC. In particular, the complete overlap of crosstalk sub-networks between arrays observed with the driver gene seed indicates the synergistic activity of these sub-networks may be independent of pathologic stage.
We also noted that only a relatively small fraction of the seeds induced significant sub-networks, either interactors or crosstalkers, and this was the case for both the proteomic and the genomic seeds. One potential explanation for this observation is that current human PPI networks capture only a very small fraction of all protein relationships in the human interactome [21], and therefore cannot be expected to reveal a significant sub-network for every experimentally determined seed. As these networks improve, we expect their value in uncovering interesting biology will only grow.
The classification performance indicates that experimentally-derived proteomic disease targets combined with our network search algorithm can discover high-valued sub-networks for mechanistic in vivo verification. This was consistent with our hypothesis, and supports the claim that a proteomic seed can identify sub-networks that provide additional pathways of interest (e.g CCT2, TCP1). To strengthen this claim, in an independent cohort of patient biopsies, we validated the differential expression of several targets in the TCP-1 sub-network, predicted by our model to be coordinately dysregulated.
The genomic seed showed excellent classification performance, and crosstalkers were superior in most instances to their corresponding interactor sub-networks, consistent with our computational hypotheses. When three or more features were used to train the classifier they were also better than the proteomic crosstalkers. However, this result is not entirely unexpected as the proteomic data has low coverage and may lack key seeds and thus may lack important sub-networks. However, the favorable classification performance of the genomic-derived sub-networks may be viewed as a positive control for this experimental approach. Alternatively, it is unlikely that all relevant sub-networks are regulated at the level of transcription, and this may reduce the number of significant sub-networks discoverable by our approach. Never-the-less, the approach can be generalized to many proteomics expression data sets to discover novel sub-networks dysregulated in many complex diseases.
In many classification applications, high dimensionality is an important problem and it is often desirable to be able to choose a small number of features that will provide reasonable performance (to overcome “curse of dimensionality”). In this respect, the classification performance provided by only a few sub-networks is indeed very promising, in that “crosstalk to proteomic targets” may actually provide a shortcut to the identification of a compact set of useful sub-network features. As our classification experiments were carried out in a cross-classification setting, the high accuracy of classification using up to three sub-networks indicates that the most significant crosstalker sub-networks were highly reproducible. Reproducibility is an important concern in classification applications, since if the sub-network features that are used are not reproducible across datasets, this will result in over-fitting. In this regard, the use of proteomic data can also be considered a tool for obtaining useful biological insights for feature selection.
The computational framework for integrating proteomic, transcriptomic, and interactomic data to discover sub-networks implicated in complex phenotypes is shown in Figure 1. As seen in the figure, we first identify disease targets with significant differential expression with respect to control, via proteomic screening as described above. Once these targets, called proteomic seeds, are identified, we map these seeds on the PPI network obtained from HPRD to identify proteins that are functionally associated with the proteomic seeds.
In order to develop biologically sound measures to quantify the functional association between proteins, we develop information flow based algorithms to compute crosstalk scores, which capture network proximity and connectivity to proteomic seeds. We discuss this procedure in Subsections A and B. In order to account for experimental artifacts, incompleteness of data, and ascertainment bias, we use Monte Carlo simulations to assess the significance of the crosstalk scores computed by these algorithms. Our statistical evaluation scheme is based on a reference model that captures the basic characteristics of the proteomic seeds, in terms of the number of seeds and their degree distribution. This procedure is described in Subsection C.
Subsequently, for each proteomic seed, we construct two “candidate sub-networks”: (i) sub-network induced by all interacting partners of the seed protein, (ii) sub-network induced by the interacting partners that have significant crosstalk scores (in our experiments, we use a p-value cut-off of 0.001 to determine “significant crosstalkers”). Finally, we evaluate the mutual information score of each candidate sub-network with respect to the phenotype of interest (in this paper, CRC), using mRNA expression data for test and control samples. For this purpose, we use an established information-theoretic scheme that quantifies synergistic differential expression in terms of the mutual information between the aggregate expression of the sub-network and disease classes across samples. This procedure is explained in Subsection D. In order to assess the statistical significance of synergistic differential expression, we also use Monte Carlo simulations based on reference models that accurately capture the basic topological characteristics of each sub-network. This procedure is explained in Subsection E. We then use identified sub-networks to develop classifiers for predicting disease class in CRC. This procedure is explained in Subsection F.
Systematic studies of differentially expressed genes in certain phenotype classes show that these genes are related to each other in molecular networks, composed of protein-protein interactions, transcriptional regulatory interactions, and metabolic interactions [22]. In one of the early algorithmic studies, Ideker et al. [23] develop a method for identifying differentially expressed metabolic sub-networks with respect to GAL80 deletion in yeast. This method is based on searching for connected groups of enzymes within the yeast metabolic network, such that the aggregate differential expression of genes coding these enzymes is statistically significant. Variations of this method prove useful in identifying multiple gene markers implicated in a variety of diseases, including prostate cancer [24], melanoma [25], and diabetes [26]. Building on these results, information theoretic schemes for assessing synergistic differential expression are also shown to be effective in network based disease classification [11],[27].
While differential network analysis is effective in identifying multiple gene markers, most of the existing methods utilize network information to primarily find the genes that are connected, hence potentially related to each other. In other words, these approaches do not take into account network topology, connectivity patterns, or degree of connectivity between proteins. This is because (i) much of the available network information is noisy and incomplete [28], therefore, connectivity patterns cannot be interpreted as well-defined wiring schemes, and (ii) network models (particularly, high-throughput protein-protein interactions) provide only a high-level qualitative description of the information flow in the cell. However, several studies show that variations in molecular expression can be interpreted in terms of network topology (e.g, subunits of a protein complex are co-expressed significantly over a time course [29], functional similarity of proteins correlates with proximity in a network of interactions [30],[31].
Motivated by these considerations, we develop network-based scoring schemes to quantify the crosstalk between proteomic seeds and the rest of the proteins in a network of interactions. Based on the premise that synergistic changes in transcriptional expression may be associated with significant changes in proteomic activity, we expect that proteins that demonstrate significant crosstalk with proteomic seeds will be good candidates for being implicated in the phenotype of interest. In order to assess the crosstalk between a group of proteomic targets and any other protein in the network accurately, we develop information flow based algorithms, as discussed in the next section.
Let G = (V,E) be a network of protein interactions, where V consists of the proteins in the network, and an undirected edge uv∈E represents an interaction between proteins u∈V and v∈V. For convenience, we also define N(v) as the set of interacting partners of protein v∈V, i.e., N(v) = {u∈V: uv∈E}. Let S⊆V be the set of proteomic seeds, i.e., the proteins that are identified by proteomic studies to exhibit significant fold change with respect to the phenotype of interest. Our objective is to compute a score α(v) for each protein v∈V, to quantify the network crosstalk between v and the proteins in S. Here, network crosstalk is used as an indicator of functional association between proteins.
In order to develop a biologically sound measure of network crosstalk, we rely on the following observations: (i) Functional similarity between two proteins, as measured by semantic similarity of Gene Ontology annotations [32], is significantly correlated with their network proximity, as measured by the shortest path (number of hops) between these proteins [30],[31]. (ii) Existence of multiple alternate paths between two proteins is an indicator of their functional association, since functional multiple paths are often conserved through evolution owing to their contribution to robustness against perturbations, as well as amplification of signals [33].
To incorporate both the number of hops and multiple alternate paths into the assessment of crosstalk between proteins, we use an information flow based algorithm based on random walks with restarts [34]. This algorithm can be considered a generalization of Google's well-known page-rank algorithm [35]. Furthermore, a special case of the proposed crosstalk score, when |S| = 1, is a network proximity measure [34] known to be closely related to commute distance and effective resistance [36] in graphs. Similar graph-theoretic measures are also used to identify functional modules in PPI networks [37], annotation of protein function [38], and prioritization of disease genes [15]–[17].
We assign crosstalk scores to all proteins in the network for a given S by simulating a random walk as follows. The random walk starts at a randomly chosen protein in S. At each step, when the random walk is at some protein v, it either moves to an interacting partner of v with probability 1−r, or it restarts at a protein in S with probability r. Here, the parameter 0≤r≤1 is called the restart probability (in our experiments, we use r = 0.5). For each move, the interacting partner to be moved to is selected uniformly at random from N(v). However, the move probabilities can also be adjusted to reflect the confidence of each interaction, so that more reliable interactions contribute more to the quantification of crosstalk. In other words, one can define the probability of a move from v to u as P(u,v) = w(u,v)/Σu′∈N(v) w(u′,v) if u∈N(v), 0 otherwise. Here, w(u,v) denotes the reliability of the interaction between u and v. Similarly, for each restart, the protein to be restarted is selected uniformly at random from S. These probabilities can also be adjusted to reflect the significance of the fold change of each protein in S, so that proteins with more significant fold change are considered as more reliable seed proteins. In other words, one can define the probability of restart at u∈V as ρ(u) = zP(u)/Σu′∈S zP(u′) if u∈S and 0 otherwise. Here, zP(u) denotes the z-score of the fold change of u with respect to the phenotype of interest, based on proteomic screening.
Based on this random walk model, we define the crosstalk between the proteins in S and each protein v∈V as the relative amount of time spent at v by such an infinite random walk, or equivalently, the probability that the random walk will be at protein v at a randomly chosen time step after the random walk proceeds for a sufficiently long time. More precisely, let αt denote a |V|-dimensional vector, such that αt(v) is equal to the probability that the random walk will be at protein v at step t, where ∥αt∥1 = 1 (here, ∥.∥1 denotes the 1-norm of a vector, defined as the sum of magnitudes of its elements). Let P denote the stochastic matrix derived from network G = (V,E), i.e., P(u,v) = 1/|N(v)| if uv∈E, 0 otherwise. Then, we have(1)where ρ denotes the restart vector with ρ(u) = 1/|S| for u∈S, and 0 otherwise. Then, letting α0 = ρ, the vector containing the crosstalk scores for each node in the network is given by α = limt→∞ αt. Observe that this formulation lends itself to an iterative algorithm to compute crosstalk scores efficiently, where each iteration requires O(|E|) time, since P is a sparse matrix with 2|E| non-zero entries.
Note that, when r = 0, α is equal to the eigenvector of P that corresponds to its largest eigenvalue (with numerical value 1), i.e., α(v) is exactly equal to the page rank of v in G for all v∈V. Therefore, the crosstalk score of a protein is not only an indicator of its connectivity and proximity to seed proteins, but it is also influenced by the centrality of the protein in the network. In order to account for such sources of bias, as well as the choice of parameter r (in our experiments, we use r = 0.5), we adjust the crosstalk scores statistically as we discuss in the next section.
Due to variability in physical properties of proteins and other experimental artifacts, it is likely that there will be significant ascertainment bias in the selection of proteomic seeds, as well as the availability of interaction data for each protein [39]. Indeed, our results show that the seed proteins extracted by proteomic screening are likely to be highly connected in the PPI network derived from HPRD. More specifically, the 60 proteins that are identified to have significant fold change (p<0.01) in late stages of human colorectal cancer have 24.1 interactions in HPRD on an average, while the average degree of a protein in the HPRD network is 9.1. Consequently, highly connected proteins in the network are likely to be assigned artificially high crosstalk scores just by chance. Since available network data is often incomplete and prone to ascertainment bias, these effects are likely to amplify the ascertainment bias and skew the results toward well-studied proteins. However, we are very interested in finding those proteins that are relatively less characterized but may provide novel insights into phenotype. Therefore, the crosstalk scores described above need to be assigned significance scores based on reliable statistical models.
In order to deal with such experimental and data-related sources of bias, we use a reference model that captures the degree distribution of seed proteins accurately. Namely, for a given seed set S, we generate a random instance S(i) representative of S as follows. For every protein u∈S, we create a bucket B(u) of proteins in the network, such that ∪u∈S B(u) = V and B(u)∩B(u′) = ∅ for all u, u′∈S. Here, protein v∈V is assigned to bucket B(u) if |N(v)−N(u)|≤|N(v)−N(u′)| for all u′∈S and ties are broken randomly. Then, we construct S(i) by choosing one protein from each bucket uniformly at random, so that |S(i)| = |S|. Observe that each bucket consists of proteins that have similar number of interactions with a particular seed protein; therefore, each seed protein is represented in S(i) by exactly one protein in terms of its number of interactions. Consequently, the expected total degree of the proteins in S(i) is likely to be very close to the total degree of the proteins in S. Once a random instance S(i) is generated, we compute the corresponding crosstalk vector α(i) by letting ρ(i)(u) = 1/|S(i)| for u∈S(i), and 0 otherwise.
Repeating this procedure n times, where n is sufficiently large (we use n = 1000 in our experiments), we obtain a sampling {α(1), α(2), …, α(n)} of the null distribution of crosstalk scores, with respect to seed sets that are representative of S in terms of their size and degree distribution. We then estimate the mean μS = Σ1≤i≤nα(i)/n and standard deviation σS2 = Σ1≤i≤n(α(i)−μS)2/(n−1) of the null distribution of crosstalk scores for S using this sample. Subsequently, we compute adjusted crosstalk scores(2)for each protein v∈V. These adjusted crosstalk scores represent the statistical significance of the crosstalk between each protein and the proteins in the seed set, accounting for the centrality of the protein the network, as well as the degree distribution of seed proteins.
Once all proteins in the network are scored according to their crosstalk with proteomic seeds, we construct candidate sub-networks as follows:
Formally, the set of candidate sub-networks is defined as C(S) = {N(u):u∈S}∪{N*(u):u∈S}, where N*(u) = {v∈N(u): zS(v)>z*}. Here, z* denotes the cut-off for adjusted crosstalk scores to be considered significant. In our experiments, we use z* = 3.45, to reflect a p-value cut-off of 0.001, under the assumption of normally distributed crosstalk scores.
For each candidate sub-network Q in C(S), we quantify the synergistic expression of the proteins in Q using an information-theoretic scheme developed by Chuang et al. [11]. Namely, for protein v∈V, let e(v) denote the properly normalized m-dimensional mRNA expression vector, provided by genome-scale transcriptomic screening of m disease and control samples. Let c denote an m-dimensional binary vector indicating the phenotype class of each sample, such that c(i) = 1 if the ith sample is diagnosed with the disease, 0 otherwise. Furthermore, define the aggregate expression vector e(Q) for the sub-network induced by set of proteins Q as(3)
Then, the synergistic differential expression φ(Q) of the genes coding for proteins in Q with respect to the phenotype of interest is given by the mutual information between e(Q) and c, i.e.,(4)Here, e(Q) denotes a discrete-valued vector obtained by quantizing e(Q) into k bins, H(x) denotes the entropy of a discrete-valued vector x over a finite alphabet A, i.e., H(x) = Σa∈A−p(a)log(p(a)), and p(a) = |{i:x(i) = a}|/m (in the context of our problem, A represents the set of bins). In this paper, we use k = 6, since this value of k was found to provide reasonable estimates for mutual information in our experiments.
Finally, we assess the statistical significance of synergistic differential expression for each candidate sub-network. In order to do so, for a given Q∈C(S), we generate a null distribution for synergistic differential expression of sub-networks that reflect the topological properties of Q. Since Q is composed of proteins that are connected to each other via a single protein (that is, the corresponding proteomic seed), the null distribution should also be derived from sub-networks that consist of the same number of proteins in Q, which are connected to each other through a single protein in the network. Therefore, we first construct a bag D of proteins in the network with degree at least |Q|, i.e, D = {v∈V:|N(v)|≥|Q|}. Subsequently, we choose a protein v from D uniformly at random. Finally, we choose |Q| proteins uniformly at random from N(v) to construct a random instance Q(i) representative of Q. Repeating this procedure n times (in our experiments, we use n = 1000) and computing φ(Q(i)), we obtain a null distribution of synergistic differential expression for sub-networks similar to Q. Observe that, only the size of Q(i) depends on Q in this procedure. For this reason, in our experiments, we do not explicitly generate a null distribution for each Q∈C(S). Rather, we generate a null distribution for sub-networks of size 2, 4, 8, 16, 32, 64. Then we interpolate the mean and standard deviation of synergistic differential expression for these distributions, to obtain a curve that characterizes the behavior of synergistic differential expression with respect to sub-network size.
In order to assess the reproducibility of discovered subnetworks across different data sets and evaluate the potential of the proposed framework for feature selection in classification of CRC, we perform cross-classification experiments. In these experiments, we use the aggregate expression profiles (e(Q)) of crosstalker and interactor subnetworks associated with Nibbe and CAN seeds as features for classification. For this purpose, in each experiment, we select the crosstalker (or interactor) subnetworks with synergistic differential expression (φ(Q)) one standard deviation above random mean, according to a specific mRNA expression data set (e.g., GSE8671). Assume that there are K such subnetworks. Then, for each k≤K, we use the k subnetworks with maximum φ(Q) to train an SVM classifier on the same data set (GSE8671), using Matlab's svmtrain function. Subsequently, we use this classifier to predict the class (tumor vs. normal) of each sample on a different data set (e.g., GSE10950), using Matlab's svmclassify function. We evaluate the performance of the classifier using the harmonic mean of precision (selectivity) and recall (sensitivity), known as the F-measure, defined asHere, precision is the fraction of true positives among all samples classified as tumor and recall is the fraction of tumor samples called accurately by the classifier among all tumor samples.
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10.1371/journal.pgen.1007472 | Duplication of a Pks gene cluster and subsequent functional diversification facilitate environmental adaptation in Metarhizium species | The ecological importance of the duplication and diversification of gene clusters that synthesize secondary metabolites in fungi remains poorly understood. Here, we demonstrated that the duplication and subsequent diversification of a gene cluster produced two polyketide synthase gene clusters in the cosmopolitan fungal genus Metarhizium. Diversification occurred in the promoter regions and the exon-intron structures of the two Pks paralogs (Pks1 and Pks2). These two Pks genes have distinct expression patterns, with Pks1 highly expressed during conidiation and Pks2 highly expressed during infection. Different upstream signaling pathways were found to regulate the two Pks genes. Pks1 is positively regulated by Hog1-MAPK, Slt2-MAPK and Mr-OPY2, while Pks2 is positively regulated by Fus3-MAPK and negatively regulated by Mr-OPY2. Pks1 and Pks2 have been subjected to positive selection and synthesize different secondary metabolites. PKS1 is involved in synthesis of an anthraquinone derivative, and contributes to conidial pigmentation, which plays an important role in fungal tolerance to UV radiation and extreme temperatures. Disruption of the Pks2 gene delayed formation of infectious structures and increased the time taken to kill insects, indicating that Pks2 contributes to pathogenesis. Thus, the duplication of a Pks gene cluster and its subsequent functional diversification has increased the adaptive flexibility of Metarhizium species.
| In fungi, gene clusters that synthesize secondary metabolites are hotspots for the generation of fungal metabolic diversity through gene duplication, but their ecological importance remains poorly understood. Metarhizium species are adapted to life as insect pathogens, plant symbionts and saprophytes, enabling the function of individual genes to be studied in diverse fungal lifestyles. We discovered that a duplication of a Pks (polyketide synthase) gene cluster in Metarhizium species has facilitated its ecological opportunism. Sequence diversifications occurred in the promoter regions, the intro-exon structures, and the coding sequences of the two Pks paralogs, and they synthesize different secondary metabolites, have different expression patterns, and are regulated by different signaling pathways. PKS1s involved in synthesis of conidial pigments and tolerance to several abiotic stresses. The Pks2 gene is involved in formation of infectious structures (appressoria), enabling these fungi to kill insects faster. This Pks gene cluster duplication event may have been important for the adaptation of Metarhizium species to diverse environments.
| Metabolic gene clusters are hotspots for the generation of fungal metabolic diversity through gene duplication, but the ecological importance of these gene clusters remains poorly understood [1]. Gene clusters that biosynthesize secondary metabolites (SMs) are particularly challenging, because they are often lineage-specific and their enzymatic activities are often poorly characterized [1]. Type I polyketides are common in fungi; they are usually synthesized by gene clusters that include polyketide synthase (Pks) genes [2, 3]. Fungi often have multiple Pks gene clusters as a result of gene duplication (typically) and horizontal gene transfer (less often) [1, 4–6]. After gene duplication, further diversification of Pks gene clusters might occur via lineage-specific duplication and loss events, or via functional divergences in response to ecological pressures [3, 4]. Functional analyses have shown that the SMs synthesized by some Pks gene clusters have important biological functions. For example, melanin allows some fungi to tolerate adverse environmental conditions, and allows other pathogenic fungi to infect hosts [7–9]. However, little is known about the relationship between the evolutionary diversification of Pks gene clusters and ecological adaptation in fungi.
The ascomycete genus Metarhizium is found worldwide, from the arctic to the tropics, and occupies an impressive array of environments including forests, savannahs, swamps, coastal zones, and deserts [10]. This worldwide distribution is largely attributed to the diverse lifestyles of Metarhizium species, and their tolerance to a broad range of environmental stresses including UV radiation and extreme temperatures [11–13]. Metarhizium has versatile lifestyles: it is a pathogen of arthropods, a saprophyte, and a colonizer of rhizosphere and plant roots [14]. The genomes of seven Metarhizium species (Metarhizium robertsii, M. brunneum, M. anisopliae, M. guizhouense, M. majus, M. acridum, and M. album) have previously been published [15]. A comparative genomic analyses of species in this genus indicated that host shift and speciation in Metarhizium were coupled with various evolutionary mechanisms including horizontal gene transfer and gene duplication. A significant relationship between SM-synthesizing gene clusters and infection structure (appressorium) formation suggested that the SMs produced by Metarhizium species might be pathogenicity factors [15]. The seven available Metarhizium genomes contain between 10 to 20 Pks genes, and in some species there is evidence of lineage-specific expansion [2, 15]. Few studies have focused on Pks genes in Metarhizium. To date, only Mr-Pks1 (herein referred to as Pks1) and Mr-Pks2 (herein referred to as Pks2) in a single species (M. robertsii) have been identified and investigated [16, 17]. Although it was shown that Pks1 was involved in conidial pigmentation, the biological functions of Pks2 have not been determined [16, 17]. The SMs synthesized by the PKS1 and PKS2 proteins also remain unidentified.
Here, we found that two Pks gene clusters in Metarhizium species were formed through the duplication of an ancient Pks gene cluster and following gene losses. Subsequent diversification in coding sequences, gene structures and promoter regions resulted in the two Pks paralogs (Pks1 and Pks2). These paralogs have different biological functions: they have different expression patterns, and encode proteins that synthesize different SMs. We found that PKS1 is involved in synthesis of an anthraquinone derivative. Pks2 is related to entomopathogenicity, while Pks1 facilitates tolerance to UV radiation, and heat and cold stress.
Using the PKS1 (MAA_07745) and PKS2 (MAA_03239) protein sequences in M. robertsii as queries, we performed a reciprocal BLASTP against the NCBI Fungal database (taxid: 4751). The best hit of PKS1 is different from that of PKS2 in each of five other Metarhizium species (M. brunneum, M. anisopliae, M. guizhouense, M. majus, and M. acridum) [15]. However, the best hit of PKS1 is the same as that of PKS2 in the basal Metarhizium species (M. album) and in the non-Metarhizium species. When the best hits of the M. robertsii PKS1 and PKS2 in M. album and the non-Metarhizium species were used as queries for reverse BLASTP against the M. robertsii protein database, the best hit was either PKS1 or PKS2. Based on this reciprocal BLASTP analysis, we speculated that PKS1 and PKS2 in Metarhizium species might result from gene duplication. To confirm this speculation, we performed phylogenetic analysis and predicted gene duplication with Metarhizium’s PKS1s and PKS2s, and their best hits (e-value cutoff 1e-05) from other Ascomycota species (S1 Table).
In M. brunneum, M. anisopliae, M. guizhouense, M. majus, and M. robertsii, the Pks1 gene is adjacent to EthD [17]. In GenBank, however, the corresponding genomic region in M. acridum and M. album was annotated as a single gene encoding a protein containing all of EthD and part of PKS1. Using qRT-PCR (quantitative reverse transcription polymerase chain reaction), we found that the transcription level of the EthD gene region was dramatically different from that of Pks1 in M. acridum and M. album, suggesting that the Pks1 and EthD regions were not contained within a single gene (S1 Fig). Further manual annotations and RT-PCR (reverse transcription PCR) analyses indicated this region in M. acridum and M. album contained two genes: Pks1 and EthD (S1 Fig). We have deposited the sequences of these newly determined Pks1 genes in GenBank (M. acridum Pks1, GenBank accession number: MG385100; M. album Pks1, GenBank accession number: MG385101).
As reported in previous studies [5, 16], domains are usually used for phylogenetic analysis of PKSs. We thus used the Batch Search program provided by PFAM (http://pfam.xfam.org/) to analyze the domain structures of 37 PKSs from 31 fungal species (S1 Table). Eight types of domains (S1 Table) were identified in the PKSs, six of which were found in all the PKSs. These six domains are KS-N (N-terminus of β-ketoacyl synthase) (PF00109), KS-C (C-terminus of β-ketoacyl synthase) (PF02801), AT (acyltransferase) (PF00698), PS-DH (polyketide synthase dehydratase) (PF14765), PP-binding (Phosphopantetheine attachment site) (PF00550), and TE (thioesterase) (PF00975). Because whole KS domains are typically used for phylogenetic analysis [5, 16], we used the protein regions (designated as KS domain below) that contained KS-N and KS-C domains. Sequences corresponding to homologous domains across all 37 PKSs were aligned with MUSCLE [18] and used to construct Maximum Likelihood (ML), Bayesian Inference, and Neighbor-Joining phylogenetic trees (Figs 1A and S2). For the trees constructed based on the KS domains, all analyses recovered a major clade of PKS1 and PKS2 proteins with high support (93% for ML; 81% for NJ; 0.988 for Bayesian Inference) (Fig 1A). This major clade was divided into two well-supported clades (Fig 1A). One clade (100% for NJ and ML, 1.0 for Bayesian Inference) contained PKSs from the six Metarhizium species, including M. robertsii’s PKS2 [16]. We thus designated this as the PKS2 clade. The other clade (85% for ML, 90% for NJ and 1.0 for Bayesian Inference), designated as the PKS1 clade, contained PKSs from seven Metarhizium species including M. robertsii’s PKS1 [17]. Phylogenetic analyses based on the other four domains (AT, PP, PS-DH and TE) generated trees with similar topologies to the KS domain tree (S2 Fig). We further compared the topology of the obtained KS domain tree with alternative KS domain trees using CONSEL [19]. The approximately unbiased (au) test showed that the obtained tree was the best supported. The alternative hypothesis, where the PKS1 and PKS2 clades were forced into a sister relationship, was statistically (P < 0.05) rejected (S3 Fig, S2 Table, S1 Dataset). The placement of the PKS2 clade outside of the major clade containing PKS1s and PKS2s was also statistically (P < 0.05) rejected (S3 Fig, S2 Table, S1 Dataset). The results of the seven other tests (np, bp, pp, kh, sh, wkh and wsh) available in CONSEL were consistent with the AU test: the obtained tree was the most well-supported and alternative trees were statistically (P < 0.05) rejected (S3 Fig, S2 Table).
Gene duplication and loss events were then predicted with the NOTUNG [20]. To this end, we first constructed the species tree of the 31 fungal species presented in Fig 1A (S4 Fig, S2 Dataset). To reduce bias resulting from weakly supported branches (< 90%) in the ML tree of the KS domains (Fig 1A); the tree was rearranged with NOTUNG. Using NOTUNG with a duplication-loss (DL) model or a duplication-transfer-loss (DTL) model, the rearranged and the raw ML trees were each separately reconciled with the species tree. For the DL model with default parameters (1.5 for duplication and 1.0 for a loss), reconciliations of the species tree with the raw or rearranged ML trees both estimated gene duplication events at five nodes (Figs 2, S5 and S6). The gene duplication event that generated the Pks1 and Pks2 genes in Metarhizium species could have occurred at node n3 and n4. The gene duplication event at the node n4 was the latest one, and it is more likely that this event generated Metarhizium’s Pks1 and Pks2 genes (Fig 2). Therefore, the duplication event that produced Pks1 and Pks2 might have occurred in the common ancestor of Metarhizium and Trichoderma (Fig 2), implying that one of the resulting two paralogs was lost in M. album and T. reesei (Figs 2, S5 and S6). Reconciliation assays using the DL model with other parameters generated the same results as that with the default parameters (S5 and S6 Figs). Using the DTL model with several parameter combinations, reconciliation assays also showed that Pks1 and Pks2 in Metarhizium species resulted from gene duplication, and that the duplication event could have occurred in the common ancestor of Metarhizium and Trichoderma (S3 Dataset).
As previously reported, there are 20 PKSs in M. robertsii [2]. We performed phylogenetic analysis combining the KS domains of the 37 PKSs previously analyzed (Fig 1A) with the 18 additional PKSs in M. robertsii. The resulting tree (S7 Fig) showed that the 18 M. robertsii PKSs (excluding PKS1 and PKS2) formed clades basal to the major clade containing the 37 PKSs previously analyzed (Fig 1A; including Metarhizium PKS1s and PKS2s). This result further indicated that the PKS1s and PKS2s in Metarhizium species were two paralogs resulting from gene duplication.
We next examined the genomic context, i.e. the genes upstream and downstream on the chromosome, of Pks1, Pks2 and their homologs in the other fungal species we had used for phylogenetic analysis. Basal to the Metarhizium clade was a clade including the Eurotiomycetidae species Aspergillus fumigatus, A. clavatus, Talaromyces marneffei, and Penicillin oxalicum (Fig 1A). It has been previously shown that the Pks gene Alb1 of A. fumigatus is contained within a cluster of genes (Alb1, Arp1, Arp2, Abr1, Abr2, and ayg1) encoding DHN-melanin biosynthesis proteins [8]. We also identified this gene cluster in A. clavatus and T. marneffei (Fig 1B). In P. oxalicum, these six genes were divided into three groups widely separated in the genome; each group contained two physically linked genes (Fig 1B). In the six Metarhizium species possessing the Pks2 gene, homologs of Arp1 and Arp2 were adjacent to Pks2. We designated this gene cluster as Pks2-gc. Only the homologs of Abr2 clustered with Pks1 homologs in the seven Metarhizium species, T. reesei, F. graminearum, and C. fioriniae. In these species, other genes are inserted between the homologs of Pks1 and Abr2, including the homolog of EthD; EthD now forms part of the Pks gene cluster in M. robertsii [17]. We designated the Metarhizium gene cluster containing the Pks1 gene as Pks1-gc. This cluster included Abr2, EthD and Pks1. In the other fungal species shown in the phylogenetic tree (Fig 1A), no homologs of Arp1, Arp2, Abr1, Abr2, and ayg1 were found in the vicinity of Pks1 and Pks2 homologs (Fig 1B). Gene clusters similar to Pks1-gc and Pks2-gc were absent in the fungi basal to the clade containing Aspergillus, Penicillium, Talaromyces, Metarhizium, Fusarium, Colletotrichum and Trichoderma fungi (Fig 1A).
We next constructed single gene phylogenies of Abr2, Arp1, and Arp2 in the fungi with gene clusters similar to Pks1-gc or Pks2-gc. The individual gene phylogenies had topologies nearly congruent with that of the Pks gene phylogeny (S8 Fig), suggesting that all genes in the two clusters could have followed similar evolutionary paths.
Based on our domain analysis results, we drew schematic domain structures for PKS1s and PKS2s in Metarhizium species and their homologs in other fungi with gene clusters similar to the Pks1-gc and Pks2-gc (Fig 3A). Except for M. acridum’s PKS2 that lacks a SAT domain, the PKS1s and PKS2s contain a SAT domain, a KS domain, an AT domain, a PS-DH domain, two PP-binding domains and a TE domain (Fig 3A). Homologs of PKS1 and PKS2 in T. reesei, T. marneffei, P. oxalicum, A. fumigatus and A. clavatus had the same domain structures as PKS1 and PKS2 in M. robertsii. The homologs in F. graminearum and C. fioriniae differed from M. robertsii’s PKS1 and PKS2 in having only one PP-binding domain. Additionally, F. graminearum had a KA-C domain not found in the PKS1s or the PKS2s in Metarhizium species (Fig 3A).
We next investigated the exon-intron structure of Pks1, Pks2 and their homologs in fungi with gene clusters similar to the Pks1-gc or Pks2-gc. The exons, introns, 5'UTRs and 3'UTRs were predicted with Gene Structure Display Server v2.0 (CBI, Peking University, China). For each gene, the introns predicted were the same as those annotated in NCBI (The accession numbers of the analyzed genes are shown in S1 Table). All Pks1 genes in Metarhizium had the same exon-intron structure with seven introns (Fig 3A). Pks2 in M. acridum had four introns; Pks2 in all other Metarhizium species had six introns (Fig 3A). The homolog of Pks1 and Pks2 in T. reesei had the same exon-intron structure as Pks1 in Metarhizium species, but homologs in other non-Metarhizium fungi had different exon-intron structures (Fig 3A).
We then used the ratio of non-synonymous to synonymous rate ratio (Ka/Ks) to calculate the extent of selection pressures on the full-length sequences of Metarhizium’s Pks1 and Pks2 genes, and their individual domains. The Ka/Ks value of the full-length sequences was 2.3 (Fig 3B), suggesting that Pks1 and Pks2 genes were under positive selection for beneficial mutants. The selection pressures acting on the domains varied, with the KS, AT, PP-binding, and PS-DH domains under positive selection and the TE domain under purifying selection (Fig 3B). Analysis of the Pks1 genes separately produced a Ka/Ks value of 0.1 (Fig 3B), indicating that purifying selection dominates, but this only held true for the PS-DH and TE domains. The KS, AT and PP-binding domains were under positive selection (Fig 3B). When the Pks2 genes were analyzed separately, the Ka/Ks value was 1.4, consistent with overall positive selection (Fig 3B). Only the TE domain in Pks2 genes was under purifying selection, the other four domains were all under positive selection (Fig 3B). Among the six Pks2 genes identified in Metarhizium species, M. acridum Pks2 gene had the largest Ka/Ks value (3.5) (S9 Fig).
Using protein sequence alignment, we analyzed the amino acid variation in PKS1 and PKS2 domains from Metarhizium species, and their homologs in other fungi with Pks1-gc and Pks2-gc like gene clusters (S4 Dataset). In the KS, PP-binding, PS-DH and AT domains, conserved amino acid residues specific to PKS1 were identified, while their corresponding sites in the PKS2s were changed to other conserved amino acid residues. In the KS domain, six conserved consensus motifs were previously characterized in fungal pigmentation PKSs [21]. An amino acid difference in one of the six motifs was found between PKS1s and PKS2s: the conserved motif sequence was DPGQRL in the PKS1s and DPAQRL in the PKS2s.
We also analyzed the promoter regions of the Pks1 and Pks2 genes. Phylogenetic analysis of the promoter sequences (645 base pairs, S5 Dataset) recovered a clade of Pks1 promoters (Fig 4). The promoter of Pks2 in M. acridum clustered with the Pks1 promoters, but the five other Pks2 promoters formed a separate clade (Fig 4). We next looked at the overrepresented motifs (motifs that are found in two or more species) in the Pks1 and Pks2 promoters using MEME (http://meme-suite.org/); see details of the overrepresented motifs in S5 Dataset. The Pks1 promoters in the generalist species (M. robertsii, M. anisopliae, and M. brunneum) and the intermediate host range M. guizhouense had the same motif structure. However, three of these overrepresented motifs (Motif 9, Motif 10, and Motif 14) were absent in M. majus, a species that also has an intermediate host range (Fig 4). The motif structures of the Pks1 promoters in the two specialist species (M. acridum and M. album) differed substantially, both from each other and from the other five Metarhizium species (Fig 4). The three generalists and the two species with intermediate host ranges had 13–17 overrepresented motifs, but only four of these motifs were identified in M. acridum and only six in M. album. Furthermore, three of the four overrepresented motifs in M. acridum had a different directionality as compared to the motifs in the other species. Similarly, the motif structures of the Pks2 promoters in generalist and intermediate host range species were the same. However, eight of the overrepresented motifs identified in the generalists were not found in the Pks2 promoter of the specialist M. acridum (Fig 4).
Because the promoter regions of Pks1 and Pks2 genes were diversified, we investigated whether they have different expression patterns in Metarhizium species. Previously, we found that the Pks1 gene was highly expressed during conidiation in M. robertsii [17]. Our previously published RNA-seq analyses [22] and the qRT-PCR analyses conducted here showed that M. robertsii Pks2 was upregulated in appressoria-forming germlings on locust cuticle relative to hyphae grown in nutrient-rich SDY (Sabroud dextrose broth plus 1% yeast extract) (Fig 5A). We used qRT-PCR to test whether Pks1 and Pks2 genes in the other Metarhizium species had the same expression patterns.
Consistent with the gene expression patterns observed in M. robertsii, Pks1 expression was upregulated in mycelia conidiating on PDA (potato dextrose agar) as compared to non-conidiating mycelia (Fig 5A). Except for M. acridum, Pks1 expression in SDY or on the locust cuticle was the same in Metarhizium species (Fig 5A). Pks1 gene expression in the appressoria-forming germlings of M. acridum was 500-fold greater than that in SDY (Student’s t test, n = 3, P < 0.01). Pks2 gene expression in conidiating M. acridum mycelia was significantly greater than in non-conidiating mycelia (Student’s t test, n = 3, P < 0.01); but in the other five Metarhizium species, no significant (Student’s t test, P > 0.05) difference in Pks2 gene expression was observed between conidiating and non-conidiating mycelia (Fig 5A). Compared to mycelia grown in SDY, Pks2 genes in all six Pks2-containing Metarhizium species (M. robertsii, M. brunneum, M. anisopliae, M. guizhouense, M. majus, and M. acridum) were significantly upregulated in appressoria-forming germlings on locust cuticle (Student’s t test, P < 0.01) (Fig 5A).
In previous studies, we reported that several key signaling pathways were involved in conidial pigmentation and appressorium formation in M. robertsii [14, 17, 22], and Hog1-MAPK was shown to regulate Pks1 during conidiation [17]. Using qRT-PCR, we further compared Pks1 gene expression during conidiation in the wild-type strain (WT) and several signaling mutants. The expression level of Pks1 in the WT was significantly higher than in ΔMr-OPY2 and ΔMero-Slt2 (P < 0.05 for both), and ΔMero-Slt2 expressed more (P < 0.05) Pks1 than ΔMr-OPY2 (Fig 5B). This suggested that Pks1 was positively regulated by Mr-OPY2 and Slt2-MAPK. Similarly, we compared Pks2 gene expression between the WT and the same set of signaling mutants during appressorium formation on locust cuticle. Pks2 expression by the WT was significantly greater than ΔMero-Fus3, but lower than ΔMr-OPY2 (n = 3, P < 0.05, Tukey’s test in one-way ANOVA) (Fig 5C), suggesting that the Pks2 gene is positively regulated by Fus3-MAPK and negatively regulated by Mr-OPY2.
Except for M. album that produces nearly white conidia, Metarhizium species produce conidia with pigments ranging from light to dark green (Fig 6). Previously, we constructed Pks1 KO (knock out) mutants of M. robertsii [17]. Here, we successfully constructed Pks1 KO mutants for four other species (M. anisopliae, M. brunneum, M. guizhouense, and M. acridum) (S10 Fig). Inability to clone the very large Pks1 genes into a plasmid for Agrobacterium tumefaciens-mediated fungal transformation precluded complementation of the KO mutants. We therefore selected three independent KO isolates for each mutant. As these three isolates did not differ in any subsequent analyses, we only present data for one isolate/mutant in the main text; data for the two other isolates are shown in the supplementary figures and tables. RNAi (RNA interference) was used to KD (knock down) Pks1 in M. majus and M. album (S10 Fig). Three independent isolates for each KD mutant were selected for further analysis. As these three isolates did not differ in any subsequent analyses, we only present data for one isolate per mutant in the main text, and data for the two other isolates are shown in the supplementary figures and tables.
As with the M. robertsii Pks1 mutant, Pks1 KO mutants of M. anisopliae, M. brunneum, M. guizhouense, and M. acridum all produced red conidia (Figs 6 and S11). The conidial color of the Pks1 KD M. majus mutant was lighter than that of its parental WT strain (Figs 6 and S11). Conidia of the Pks1 KD M. album mutant were almost white, identical in color to the conidia of its parental WT strain (Figs 6 and S11).
Conidial pigments have long been thought to be involved in abiotic stress tolerance in fungi [13, 17]. Therefore, we investigated the involvement of Pks1 in tolerating UV radiation and temperature stresses. Under optimal conditions (26°C in 1/2 SDY), the deletion of Pks1 had no impact on conidial germination in M. anisopliae, M. brunneum, and M. guizhouense, as indicated by the GT50 (time taken for 50% of the conidia to germinate) (S3 Table). However, compared to their respective WT strains, GT50 was significantly reduced in the Pks1 mutants of M. robertsii, M. majus, and M. album (Student’s t test, n = 3, P < 0.05), and was significantly increased in the Pks1 mutant of M. acridum (Student’s t test, n = 3, P < 0.05) (S3 Table). In previous studies [e.g. 23], relative germination inhibition (defined in the Materials and Methods) has been used to show fungal tolerance to abiotic stresses. Similar to M. robertsii [17], the deletion of Pks1 significantly reduced the UV tolerance of M. anisopliae and M. brunneum (Student’s t test, n = 3, P < 0.05) (Tables 1 and S4). Knocking out or knocking down Pks1 had no impact on UV radiation tolerance in M. guizhouense, M. acridum, or M. album (Tables 1 and S4). Compared to their respective WT strains, heat stress tolerance was significantly (P < 0.05 for all) reduced in the Pks1 mutants of M. robertsii, M. guizhouense, and M. album (Tables 1 and S4). Cold stress tolerance was reduced only in the Pks1 mutant of M. album (Tables 1 and S4). In contrast to all other Metarhizium species, the Pks1 mutant of M. majus germinated significantly faster than the WT strain under UV and cold stress (Student’s t test, n = 3, P < 0.05) (Tables 1 and S4).
M. robertsii is a model organism for the study of entomopathogenicity [17]. We therefore knocked out Pks2 in this species to investigate its involvement in pathogenicity. The conidial pigmentation of the Pks2 KO mutant (ΔPks2) did not differ from that of the WT (S12 Fig). ΔPks2 was not different from WT in tolerance to abiotic stresses including UV radiation, heat and cold stress (S5 Table). Compared to the WT, the LT50 (the time taken to kill 50% of insects) value of ΔPks2 was significantly increased (P < 0.05, Tukey’s test in one-way ANOVA) (Fig 7A). In addition, compared to the WT, appressorial formation in ΔPks2 was delayed on a hydrophobic surface (Fig 7B). However, the turgor pressure of the ΔPks2 appressoria was the same as that of the WT (S13 Fig). Fluorescent staining with Calcofluor white Brightener 2B showed that the fluorescent intensity of the ΔPks2 appressoria did not differ from that of the WT, suggesting that deletion of Pks2 did not alter cell wall structure or the composition of the appressoria (S13 Fig).
Our results suggest that Pks2 is an important factor in pathogenicity. The Pks2 and Arp2 genes in the Pks2-gc are lacking in the basal specialist M. album. We postulated that the absence of the complete Pks2-gc in M. album was related to development of host specificity. To test this hypothesis, we constructed a M. album strain that expressed the Pks2 and Arp2 of M. robertsii (S14 Fig). Bioassays showed that, similar to WT M. album, M. album expressing Pks2 and Arp2 was still unable to infect G. mellonella (Lepidoptera) or Drosophila melanogaster (Diptera), indicating that the Pks2-gc is not sufficient to broaden the host range of M. album.
We were unable to identify the SMs synthesized by M. robertsii PKS1 and PKS2 just by comparing the SM profiles of the Pks1 and Pks2 KO mutants with WT. We thus introduced the Pks1 and Pks2 genes of M. robertsii into A. nidulans, under control of the constitutive promoter gpdA of the A. nidulans glycerol-3-phosphate dehydrogenase gene (Fig 8A), as this fungus and promoter have been used previously for heterologous expression of Pks genes [9, 24]. Successful insertion of Pks1 and Pks2 into the genome of A. nidulans was confirmed with PCR (S15 Fig). Compared to the control strain (with an empty expression vector inserted), HPLC (high-performance liquid chromatography) analysis did not identify any new peaks in the transformant expressing Pks2, but did identify a new peak at about 18 min in the transformant (designated as TYPZ26) expressing Pks1 (Fig 8B). This peak indicated a possible compound (designated as Compound I) produced by PKS1. We fermented the transformant TYPZ26 on a large scale (in 10 L batches) to obtain sufficient Compound I for characterization. After semi-preparative reverse-phase HPLC separation, Compound I was purified. The molecular formula of Compound I was determined to be C15H10O7, based on HR-ESI-MS (m/z 315.0503 [M+H]+) (Figs 8C and S16). Analysis of the 1H (S17 Fig) and 13C NMR spectroscopic data (S18 Fig, S6 Table) for compound I showed that its structure was consistent with that of 1-acetyl-2,4,6,8-tetrahydroxy-9,10-anthraquinone [25], indicating that compound I was an anthraquinone derivative. As we were unable to identify a PKS2-derived SM in A. nidulans strain expressing Pks2, we investigated whether PKS2 synthesized the same SM as PKS1. We did this by introducing into the ΔPks1 mutant a Pks2 gene driven by the constitutive promoter Ptef of the translation elongation factor gene tef in Aureobasidium pullulans [26]. qRT-PCR analysis showed that the expression of Pks2 in three independent isolates of the resulting strain ΔPks1׃׃Pks2OE was over 50-fold greater than the ΔPks1 mutant (Student’s t test, P < 0.01) (S19 Fig). However, the three isolates of the ΔPks1::Pks2OE strain produced the same red conidia as ΔPks1 (S19 Fig), and HPLC analysis showed that they did not synthesize Compound I (S19 Fig), suggesting that Pks2 does not complement ΔPks1 and, therefore, that PKS1 and PKS2 synthesize different SMs.
We report here that the two Pks gene clusters (Pks1-gc and Pks2-gc) in Metarhizium species resulted from gene cluster duplication. Phylogenetic analyses of the Pks genes and gene duplication predictions showed that the ancestral gene cluster likely duplicated in an ancestral Hypocrealean fungus. The resulting two gene clusters have been retained in the Metarhizium fungi, but one has been lost in non-Metarhizium fungi, which have only one gene cluster similar to Pks1-gc and Pks2-gc. The basal specialist M. album lacks Pks2 and Arp2, indicating that it has lost Pks2-gc.
In Metarhizium species, Pks1-gc and Pks2-gc contained three genes. The other hypocrealean species T. reesei also had three genes in the Pks gene cluster. In contrast, A. fumigatus and other species basal to hypocrealeans have six genes in their Pks gene clusters. This discrepancy may have arisen from gene loss after gene cluster duplication in the ancestral Hypocrealean fungus. Gene loss is an established evolutionary mechanism for the diversification of Pks gene clusters after gene duplication [3, 4].
Our phylogenetic and genomic synteny analyses indicated that Pks1 and Pks2 genes in the Pks1-gc and Pks2-gc like gene clusters of Hypocremycetidae and Eurotiomycetidae were more closely related than homologs of Metarhizium’s Pks1 and Pks2 outside the clusters. Therefore, the phylogeny of the Pks genes is incongruent with previously published species-level phylogenies [27]. A possible explanation is that the common ancestor of the Hypocremycetidae and Eurotiomycetidae had an ancestral gene cluster resembling Pks1-gc and Pks2-gc, which has been retained in some descendants (such as Aspergillus and Metarhizium), but broken up in others (such as Neurospora crassa and Magnaporthe oryzae). We identified a possible intermediate separation of the Pks gene cluster in P. oxalicum: the six physically linked genes found in Aspergillus species were divided into three groups of two genes in P. oxalicum. Pks genes within gene clusters may have been subject to similar selection pressures, whereas selection pressures on their unclustered homologs diverged, resulting in the incongruence between the Pks gene phylogeny and the fungal species-level phylogeny. Alternatively, the common ancestor of Hypocremycetidae and Eurotiomycetidae may have lacked the Pks gene cluster, and this cluster has formed independently in the Hypocremycetidae and Eurotiomycetidae, which had the gene cluster. This seems less likely, because the chance that an identical gene cluster would develop independently in in such distantly related fungi is low.
Our results indicate that the two Pks paralogs in Metarhizium have diversified in several different ways. The promoter regions of Pks1 and Pks2 diversified, which could be attributed to their having different gene expression patterns. Consistent with which, we found that different upstream signaling pathways regulated Pks1 and Pks2. Exon-intron structure has also diversified, as indicated by the difference in intron number between Pks1 and Pks2. Changes in exon-intron structure may also affect expression patterns and splicing [28].
Pks2 did not complement the Pks1 deletion mutant in M. robertsii, suggesting that these two paralogs synthesize different SMs. Therefore, mutations in the coding sequences of the two paralogs (Pks1 and Pks2) in the Metarhizium genus resulted in neofunctionalization. Pks1 was highly expressed during conidiation in all seven Metarhizium species we tested; Pks2 was highly expressed during cuticle penetration in the six Pks2-containing Metarhizium species. In contrast to other Metarhizium species, the two Pks genes in M. acridum were both highly expressed during conidiation and cuticle penetration, which might explain the different biological features of M. acridum. Compared to other Metarhizium species, M. acridum shows higher tolerance to abiotic stress [29]. Although a couple of the Metarhizium Pks1 gene domains are under positive selection, purifying selection dominates over most of their length. In contrast, Metarhizium Pks2 genes are under positive selection, but it remains to be determined whether mutations retained by such selection diversified the biochemical functions of PKS2s.
Anthraquinone derivatives have been widely applied in industry. Many are used as fabric dyes and additives to mordant [30]. Anthraquinone derivatives are not highly toxic and have various pharmacologically-relevant effects, including anti-inflammatory, antiviral, antimalarial, antifungal, hypotensive and analgesic, antioxidant, and moderately antitumoral [31]. Anthraquinone derivatives have been found in the fungus G. lavendula [25]. Here we found that M. robertsii’s PKS1 synthesized an anthraquinone derivative. This anthraquinone derivative was successfully produced in A. nidulans on a large scale for future assays of its biological activity. Previous studies have shown that the homologs of M. robertsii PKS1 synthesize SMs other than anthraquinone including pentaketide in Colletotrichum orbiculare and Pestalotiopsis fici [9, 32], hexaketide in Exophiala dermatitidis [33], and heptaketide in A. fumigatus PksP/ALB1[34]. The Pks1 gene cluster in Metarhizium species is potentially involved in the synthesis of a previously unreported pigment in fungi. Because disrupting Pks1 in Metarhizium species resulted in red conidia, the pigment synthesized by the cluster Pks1-gc could be combined (or react) with the red pigment to form the characteristic green pigment in the WT strain.
The Pks1was highly expressed during conidiation in M. album, but this fungus produced nearly white conidia. This could be due to functional diversification of Pks1 in M. album. Alternatively, Pks1 may only contribute to pigmentation when the red pigment is produced by other genes that might be absent in M. album.
Fungal tolerance to abiotic stress is multifactorial [13], and conidial pigments act with other components to tolerate abiotic stresses [35]. The contributions of conidial pigments to abiotic stress tolerance vary among Metarhizium species [35]. This is supported by our functional characterization of the conidial pigmentation gene Pks1 in seven Metarhizium species. In five Metarhizium species, Pks1 was involved in the tolerance to at least one of the tested abiotic stressors (UV radiation, cold, and heat). However, in M. acridum, the deletion of Pks1 had no impact on tolerance to the tested abiotic stresses, while knocking down Pks1 increased germination rates in M. majus exposed to UV radiation and cold stress.
In summary, we reported that a gene cluster duplication and subsequent diversification resulted in two Pks gene clusters in the genus Metarhizium. The resulting two PKSs synthesize different SMs. Pks1 is highly expressed during conidiation and contributes to conidial pigmentation that provides protection from UV radiation, heat and cold stress. UV radiation and heat stress are the major factors for controlling Metarhizium’s population in nature [13, 36]. The Pks2 gene is a pathogenicity factor that facilitates infection of insects by M. robertsii. Efficient infection of insects is also important for the survival of Metarhizium in the environment because entomopathogenicity enables Metarhizium to escape competition from other microbes and build up population levels above the carrying capacity of the rhizosphere [12]. Therefore, duplication and subsequent diversification of a Pks gene cluster increased the adaptive flexibility of Metarhizium species.
Metarhizium robertsii ARSEF2575, M. album ARSEF1941, M. majus ARSEF297, M. guizhouense ARSEF977, M. brunneum ARSEF3297, and M. anisopliae strain ARSEF549 were obtained from the Agricultural Research Service Collection of Entomopathogenic Fungi. M. acridum CQMa 102 was a gift from Prof. Yuxian Xia at the Chongqing University China. The deletion mutants of the gene Fus3-, Slt2-, and Hog1-MAPK and Mr-OPY2 were previously reported [14, 22]. Escherichia coli strain DH5α was used for plasmid construction. Agrobacterium tumefaciens AGL-1 was used for Metarhizium transformation. The A. nidulans strain LO8030 was used for the heterologous expression of the Pks genes as previously described [9, 24]. Saccharomyces cerevisiae strain BJ5464-NpgA was used as the host for DNA assembly [37]. More information about the fungal strains, bacterial strains, and plasmids is given in S7 Table.
M. robertsii PKS1 or PKS2 were used as queries for BLASTP analysis in NCBI, and the protein sequence of the best hit (e-value cutoff 1e-05) in an Ascomycota species was retrieved for phylogenetic analyses. The domains of the obtained PKSs were determined using the Batch Search program provided by PFAM (http://pfam.xfam.org/). Based on these results, the domain sequences were manually extracted from the PKSs. For phylogenetic analyses of the domains, domain sequences were aligned using MUSCLE v3.7 with default parameters [18]. Protein alignments were manually refined and end-trimmed to eliminate poor alignments and divergent regions. Unambiguously aligned positions were used to construct a ML tree with MEGA 6.0 (gap treatment: use all sites; model of evolution: WAG+G; 100 bootstrap replications) [38]. We also constructed an NJ tree with default parameters (gap treatment:pairwise deletion; 1000 bootstrap replications) in MEGA 6.0. A Bayesian inference tree was constructed with MrBayes v3.2.5 as described [39]; the model of evolution was WAG + G. For each Bayesian analysis, four Metropolis-coupled chains were used. Each analysis ran for 5,000,000 generations, with sampling every 1000 generations (‘mcmc ngen = 5000000 samplefreq = 1000’). The analysis was considered finished when the average standard deviation of the split frequencies was 0.01 or less. The first 25% of all trees were removed as burn-in.
To evaluate the confidence of all topology hypotheses of the KS domain tree, site-wise log likelihoods of all alternative topologies were calculated with PhyML-3.1[40]. Then, the site-wise log likelihoods file was used as input to estimate the P-values for each alternative hypothesis using the Approximately Unbiased (au) test, the Bootstrap Probability (np, bp) test, the Posterior Probabilities (pp) test, the Kishino-Hasegawa (kh) test and the Shimodaira-Hasegawa (sh) test implemented in the program CONSEL [19].
The protein sequences of Abr2, Apr1 or Apr2 in the Pks1-gc or Pks2-gc gene clusters were obtained as described below (Microsynteny analysis of Pks1 and Pks2), and their phylogenetic analyses were conducted as described for the PKS domains.
The tree of the 31 fungal species analyzed in Fig 1A was constructed using the best scoring single-copy genes as previously described [5]. Previously, 23 genes were used [5], but here only 20 orthologs (S8 Table) were successfully retrieved from the 31 fungal species with BLASTP (e value cutoff e-05) using Saccharomyces cerevisiae genes as queries. Therefore, the 20 single-copy genes were used to construct the species tree. The ortholog protein sequences were aligned using MUSCLE 3.7 [18], which was then manually refined and end-trimmed to eliminate poor alignments and divergent regions. The resulting alignments were concatenated (S2 Dataset) to construct an ML tree with MEGA 6.0 (gap treatment: use all sites; model of evolution: LG+G+I; 100 bootstrap replications; ML heuristic method: Nearest-Neighbor-Interchange), an NJ tree with default parameters (gap treatment: pairwise deletion; 1000 bootstrap replications) in MEGA 6.0. A BI tree was conducted with MrBayes v3.2.5 using the LG+G+I model. For each analysis, we ran four Metropolis-coupled chains for 5,000,000 generations, sampling every 1000 generations (‘mcmc ngen = 5000000 samplefreq = 1000’). The analyses finished with an average standard deviation of split frequencies of 0.01 or less. The first 25% of trees were removed as burn-in.
To avoid overestimating duplications in the KS domain ML tree that had several nodes with weak sequence support, the tree was rearranged using NOTUNG v. 2.9 [20]. The standard parsimony weight parameters of NOTUNG were used: 1.5 for duplication and 1.0 for loss. We used 90 as the bootstrap cut-off value for weak branches.
The rearranged and raw ML trees were then reconciled with the species tree to predict gene duplication and loss using NOTUNG using duplication-loss (DL) or duplication-transfer-loss (DTL) model [20]. For each model, several parameter combinations were used.
The microsynteny of the genomic regions flanking Pks1 and Pks2 genes was manually analyzed using BLASTP. We analyzed the 20 genes flanking each Pks gene to identify homologs (BLASTP, e-value cutoff 1e-05) to the four genes (Abr2, Apr1, Apr2 and EthD) comprising the Pks1-gc or Pks2-gc in M. robertsii [17], and the five genes (Abr1, Abr2, Apr1, Apr2 and Ayg1) comprising the Pks (alb) gene cluster in A. fumigatus [8].
Based on the coding sequences of Pks1 and Pks2 genes and their protein sequences from the seven Metarhizium species, the Ka/Ks ratio was calculated as previously described [28]. Briefly, protein sequences were aligned with MUSCLE v3.7 [18], which guided the alignment of the coding sequences with PAL2NAL [41]. Based on alignments of coding sequences and protein sequences, we calculated Ka, Ks, and the Ka/Ks using the MYN algorithm of the KaKs_Calculator v2.0 [42].
Fungal total RNA was extracted with TRIzol reagent (Life Technologies, USA). Non-conidiating and conidiating mycelia were prepared as previously described [17]. Briefly, 100 μl of conidial suspension (1×107 conidia/ml) was evenly spread on a PDA plate (diameter = 90mm, BD, USA) and incubated at 26°C. Mycelia at 2 d and 5 d post incubation were collected as non-conidiating and conidiating mycelia, respectively.
Gene expression during saprophytic growth was compared to cuticle penetration. For saprophytic growth, conidia (1×106 conidia/mL) were grown at 26°C for 36 h in SDY. For cuticle penetration, appressoria-forming germlings on the hindwings of Locusta migrattoria manilensis were prepared as previously described [14].
qRT-PCR analysis was conducted as previously described [22]. Complementary DNAs (cDNAs) were synthesized with ReverTra AceqPCR RT Master Mix (Toyobo, Japan). Quantitative RT-PCR analysis was performed using Thunderbird SYBR qPCR Mix without ROX (Toyobo, Japan). Act and tef were used as internal standards [43]. The relative normalized gene transcription level was computed using the 2-ΔΔCt method [44]. All qRT-PCR assays were repeated three times with three technical replicates per repeat. Primers used in this study are listed in S9 Table.
Gene knockout based on homologous recombination was conducted as previously described [45]. Around 1kb of DNA fragment corresponding to the N-terminus of a PKS protein was deleted.
Gene knockdown using RNA interference was performed as previously described [46] with modifications. In brief, to construct a knockdown vector, the promoter region [645bp (base pair)] of the target gene was amplified with PCR using High-fidelity Taq DNA polymerase (Toyobo, Japan), and cloned into the plasmid pPK2-bar-GFP [14] to produce the plasmid pPK2-bar-GFP-Pro. To produce the genetic dsRNA, a 30 bp sense and anti-sense sequences (30bp) corresponding to the target gene were added to the forward and reverse primers to amplify the loop DNA fragment (150bp) with PCR. The PCR product was then cloned downstream of the promoter of the target gene in the plasmid pPK2-bar-GFP-Pro to produce the RNAi plasmid pPK2-bar-GFP-RNAi, which was then transformed into the WT Metarhizium species via A. tumefaciens AGL1. Transformants were selected based on herbicide resistance and the presence of GFP. Gene knockdown was confirmed with qRT-PCR. The loop DNA fragment (S9 Table) is part of the GUS (β-glucuronidase) gene, and has no similarities to the genomes of the Metarhizium species investigated in this study.
Assays of UV tolerance were conducted as previously described [17]. Briefly, conidia were exposed to a weighted 312 nm (280–320 nm) UV-B wavelength at 0.2 J cm-2 in a Bio-Sun++ chamber (Vilber Lourmat, Marne-la-Vallée, France). Irradiated conidia were incubated at 26°C, and conidial germination was observed every 2 h using an inverted microscope (Leica, Germany). Tolerance to heat and cold stress was assayed by measuring conidial germination in 1/2 SDY every 2 h at 37°C and 15°C, respectively. The control temperature was 26°C. The relative germination inhibition of a given stressor on each strain was calculated as (Gc-Gt)/ Gc [23], where Gc and Gt denote the GT50 of the stressed and unstressed conidia, respectively. All assays were repeated three times with three replicates per repeat.
Bioassays were conducted by applying a conidial suspension (1× 107 conidia/ml) topically to the last instars of G. mellonella larvae (Ruiqingbait Co., Shanghai, China) as described [47]. Insect mortality was recorded daily. Bioassays were repeated three times with 40 insects per repeat.
For appressorial assays, conidia were inoculated on the hydrophobic surfaces of a Petri dish (Corning, USA) as previously described [22]. Turgor pressure in appressoria was measured as previously described [48]. Fluorescent staining of appressoria using Calcofluor Brightener White 2B was performed as previously described [14].
To construct the Pks1 heterologous expression vector, SOE (splicing by overlapping extension)-PCR and yeast-based assembly approaches were used [49]. First, the constitutive gpdA promoter was introduced into the plasmid pYH-WA-pyrG as described [24] to form pYH-wA-pyrG-gpdA. Second, two PCR fragments with overlapping regions (250 bp), corresponding to the genomic region of the coding sequences of Pks1 or Pks2, were amplified from M. robertsii genomic DNA. The two fragments and the NheI-digested pYH-WA-pyrG-gpdA were purified and transformed into S. cerevisiae BJ5464-NpgA using an S. c. EasyComp Transformation Kit (Invitrogen, USA). PCR was used to screen for yeast colonies containing the target plasmids. Target plasmids were isolated using a Zymoprep (D2001) Kit (Zymo Research, USA), and confirmed with restriction enzyme digestion and sequencing. Target plasmids were linearized with SwaI and transformed into the WT A. nidulans strain LO8030 to create transformants expressing Pks1 or Pks2. The insertion of the Pks1 or Pks2 gene into the genome of A. nidulans was confirmed with PCR using a Taq Mix kit (Tiagen Biotech, China).
To profile SMs, A. nidulans strains were cultivated at 25°C in 20 mL liquid LMM (lactose minimal medium). After 4 days of still cultivation, materials were extracted with ethyl acetate /methanol/acetic acid (89:10:1). The organic phase was dried in a vacuum and the residue was dissolved in 5 mg/mL methanol (MeOH) for HPLC analysis. Analytical HPLC was conducted with a flow rate of 1 mL/min using a linear gradient of 20% to 100% MeOH (0–20 min), 100% MeOH (20–25 min), and 20% MeOH (25–30 min). Analytical HPLC was performed on a Waters HPLC system (Waters e2695, Waters 2998, Photodiode Array Detector) using an ODS column (C18, 250 × 4.6 mm, YMC Pak, 5 μm).
For fermentation and SM semi-preparation, A. nidulans was cultivated in 10 L of liquid LMM media at 25°C for 4 d. Liquid culture and mycelia were extracted together three times with methanol/ethyl acetate (10:90). The organic phase was dried under reduced pressure, and the residue was then resuspended in MeOH׃hexane (1׃1) to remove all lipid components by discarding the hexane phase; this treatment was performed three times. The MeOH phase was dried under reduced pressure. The resulting residue was re-solubilized with MeOH and applied to an ODS column, and then eluted with MeOH using a gradient solvent system that ranged from 35% to 100% MeOH (250ml per gradient solvent). The target compound (Compound I) was detected in the 60% and 65% fractions, which were then combined and dried under reduced pressure. The residues were then solubilized with MeOH for a semi-preparation HPLC that was performed using an ODS column [HPLC (YMC-Pack ODS-A, 10 × 250 mm, 5 μm, 3 mL/min)]. The target peak for Compound I was solubilized in DMSO-d6 for NMR and LC-MS analysis. We performed LC-MS on an Agilent Accurate-Mass-QTOF LC/MS 6520 (Agilent Technologies, USA). NMR spectra (1H, 13C) were recorded on a Bruker Avance-500 spectrometer using tetramethylsilane as an internal standard. Chemical shifts were recorded as δ values.
The genomic regions corresponding to the coding sequences of Pks2 and Arp1 in M. robertsii were cloned using PCR with high fidelity Taq DNA polymerase (Toyobo, Japan). The genomic clone of the Pks2 gene was inserted downstream of the constitutive promoter Ptef in the plasmid pPK2-Sur-Ptef [14], to produce the plasmid pPK2-Sur-Ptef-Pks2 with the herbicide resistant Sur gene. The genomic clone of the Arp1 gene was inserted downstream of the constitutive promoter Ptef in the plasmid pPK2-Bar-Ptef [14], to produce the plasmid pPK2-Bar-Ptef-Arp1 with the herbicide resistant Bar gene. pPK2-sur-Ptef-Pks2 was then transferred into A. tumefaciens, and transformed into either the M. robertsii Pks1-deletion mutant [17] or wild-type M. album. Overexpression of Pks2 in M. robertsii was confirmed with qRT-PCR. The pPK2-Bar-Ptef-Arp1 plasmid was transformed into the M. album strain expressing the Pks2 gene to produce a strain expressing Pks2 and Arp1 simultaneously. The expression of both Pks2 and Arp1 in M. album was confirmed with RT-PCR.
M. acridum Pks1: MG385100; M. album Pks1: MG385101.
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10.1371/journal.pbio.2006057 | Cross-resistance is modular in bacteria–phage interactions | Phages shape the structure of natural bacterial communities and can be effective therapeutic agents. Bacterial resistance to phage infection, however, limits the usefulness of phage therapies and could destabilise community structures, especially if individual resistance mutations provide cross-resistance against multiple phages. We currently understand very little about the evolution of cross-resistance in bacteria–phage interactions. Here we show that the network structure of cross-resistance among spontaneous resistance mutants of Pseudomonas aeruginosa evolved against each of 27 phages is highly modular. The cross-resistance network contained both symmetric (reciprocal) and asymmetric (nonreciprocal) cross-resistance, forming two cross-resistance modules defined by high within- but low between-module cross-resistance. Mutations conferring cross-resistance within modules targeted either lipopolysaccharide or type IV pilus biosynthesis, suggesting that the modularity of cross-resistance was structured by distinct phage receptors. In contrast, between-module cross-resistance was provided by mutations affecting the alternative sigma factor, RpoN, which controls many lifestyle-associated functions, including motility, biofilm formation, and quorum sensing. Broader cross-resistance range was not associated with higher fitness costs or weaker resistance against the focal phage used to select resistance. However, mutations in rpoN, providing between-module cross-resistance, were associated with higher fitness costs than mutations associated with within-module cross-resistance, i.e., in genes encoding either lipopolysaccharide or type IV pilus biosynthesis. The observed structure of cross-resistance predicted both the frequency of resistance mutations and the ability of phage combinations to suppress bacterial growth. These findings suggest that the evolution of cross-resistance is common, is likely to play an important role in the dynamic structure of bacteria–phage communities, and could inform the design principles for phage therapy treatments.
| Phage therapy is a promising alternative to antibiotics for treating bacterial infections. Yet as with antibiotics, bacteria readily evolve resistance to phage attack, including cross-resistance that protects against multiple phages at once and so limits the usefulness of phage cocktails. Here we show, using laboratory experimental evolution of resistance against 27 phages in P. aeruginosa, that cross-resistance is common and determines the ability of phage combinations to suppress bacterial growth. Using whole-genome sequencing, we show that cross-resistance is most common against multiple phages that use the same receptor but that global regulator mutations provide generalist resistance, probably by simultaneously affecting the expression of multiple different phage receptors. Future trials should test if these features of cross-resistance evolution translate to more complex in vivo environments and can therefore be exploited to design more effective phage therapies for the clinic.
| Natural microbial communities are comprised of complex networks of species interactions, wherein each species may be engaged in ecological interactions with many other species [1–3]. In this community context, the evolutionary response of a focal species to a given pairwise species interaction can promote an ‘evolutionary cascade’ through the adjacent interacting species [4,5]. For bacteria–phage interaction networks, we expect that the impact of a given phage resistance mutation will depend on the connectivity of that bacterial host within the community network [6] and the degree of cross-resistance provided by the mutation against other phage species in the network [7]. Whereas the statistical structure of interactions in bacteria–phage networks has been well studied [8], the structure and underlying genetic basis of cross-resistance networks remain poorly understood. This considerably limits our ability to predict how cross-resistance evolution affects bacteria–phage communities across different environmental, agricultural, and clinical contexts.
The extent of cross-resistance provided by a given resistance mutation is likely to depend on the genetic correlations between bacterial resistance traits selected by the different phage species. Cross-resistance is likely for cases of positive genetic correlation; for example, binding to shared receptors can cause synergistic pleiotropy between specific resistances—for instance, a mutation in the lipopolysaccharide (LPS) biosynthesis pathway is likely to promote cross-resistance to other phages that also adsorb to LPS [9,10]. In contrast, cross-resistance is less likely if there is antagonistic pleiotropy, in which resistance to one phage increases susceptibility to an alternative phage (for example, through replacement of a clustered regularly interspaced short palindromic repeat [CRISPR] spacer [11]), or no genetic correlation—for instance, if the phages bind to different receptors and accumulation of multiple resistance mutations is therefore required [12]. Because individual resistance mechanisms frequently incur fitness costs by impairing the normal functioning of the molecule acting as the phage receptor, accumulation of multiple resistance mechanisms may be limited by their combined fitness costs, particularly if there is negative epistasis among the fitness costs of resistance mutations [7,13]. Even though pleiotropic costs should limit the evolution of generalist resistance, cross-resistance is commonly observed [14,15]. Most of this evidence is, however, based on relatively simple phage communities, and it is less clear how the range of cross-resistance provided by different resistance mutations is related to the magnitude of fitness costs in more complex bacteria–phage networks.
Understanding the structure of bacterial cross-resistance to phage infection also has important applied implications with relation to phage therapy, i.e., the use of phages as antimicrobials to treat bacterial infections [16]. Phage cocktails (i.e., combinations of different phages) have been shown to delay the evolution of resistance in bacteria, both in vitro [17] and in vivo [18], compared to challenge with a single phage. Effective phage cocktails often contain phages that target different bacterial receptors (for example, [19,20]), and as a result, multiple resistance mutations in different receptor genes are required to provide resistance to all the phages present in the cocktail [21]. The requirement for bacteria to accumulate multiple resistance mutations is thought to enhance the evolutionary robustness of phage cocktails because there is a lower probability of resistance emerging. Furthermore, resistance to multiple phages is likely to be associated with greater fitness costs assuming additivity of fitness costs associated with each resistance mutation [7]. These assumptions may not apply, however, if very generalist cross-resistance is available via a single mutation affecting the expression of multiple phage receptors. This suggests therefore that minimising the potential for cross-resistance could be a key feature of effective phage cocktail design. However, this has not been tested experimentally.
Here we determined the network structure and genetic basis of cross-resistance against a collection of 27 phages infecting the opportunistic human pathogen, P. aeruginosa. The cross-resistance network contained both symmetric (reciprocal) and asymmetric (nonreciprocal) interactions, forming two cross-resistance modules defined by high within- but low between-module cross-resistance. Within cross-resistance modules, resistance mutations targeted distinct phage receptors, whereas between-module cross-resistance was caused by mutations targeting a global regulator likely to control the expression of multiple phage receptors. The range of cross-resistance provided by a mutation was not correlated to its fitness cost, except that global regulator mutations causing between-module cross-resistance were costlier than mutations causing within-module cross-resistance. Furthermore, the degree and symmetry of cross-resistance predicted the ability of phage combinations to suppress bacterial growth and the frequency of resistance mutations. Together, our data suggest that an understanding of cross-resistance interactions could help to predict the impact of resistance evolution on host–parasite community structure and aid the rational design of therapeutic phage cocktails.
To determine the extent of cross-resistance, we tested 263 spontaneous resistant mutants of P. aeruginosa PAO1 selected against each of 27 phages (i.e., 10 resistant mutants were selected against each focal phage; 7 mutants were discarded because of persistent phage contamination) for their ability to resist infection by all other phages (cross-resistance). Resistance was determined by measuring relative bacterial growth (RBG) of each spontaneous mutant in the presence versus absence of each phage where mutants were classified as resistant if their RBG exceeded a binary resistance threshold (RBG = 0.798; calculated as the 95% confidence interval of a normal distribution modelled over the peak of resistance within the complete RBG distribution [S1 Fig; S1 Data]). We observed variation in the pattern and range of cross-resistance among spontaneous resistance mutations (Fig 1; S2 Fig). First, the degree of cross-resistance selected by the different focal phages varied extensively, ranging from conferring resistance against fewer than 10% to up to 80% of all phages (S2 Fig; Kruskal-Wallis χ226 = 66.6, p < 0.0001). Second, both focal resistance and cross-resistance phenotypes varied considerably between independently evolved resistant mutants selected against the same focal phage (S2 Fig). Together, these results suggest that the magnitude of cross-resistance depends on the focal resistance selected and that multiple resistance mechanisms may exist against the same focal phage, resulting in different levels of cross-resistance between independent replicate mutants.
The evolution of broad, generalist cross-resistance could be constrained if it was associated with relatively higher costs compared to more specialised resistance or if mutations providing cross-resistance concomitantly provided only weak resistance against the focal phage. In contrast, while all resistance mutations selected against focal phage were costly (Fig 2A; one-sided t tests: all p < 0.005), we observed no overall relationship between the range of cross-resistance provided by resistance mutations and their associated costs (Fig 2A; linear mixed effects model: t236 = −0.655, p = 0.513). Moreover, we observed a positive relationship between the strength of focal resistance and the range of cross-resistance provided by resistance mutations (Fig 2B; linear mixed effects model: t245 = 15.09, p < 0.0001). These results suggest that the evolution of cross-resistance is unlikely to be constrained by trade-offs due to associated fitness costs or the strength of the focal resistance.
Network analysis of the directional cross-resistance frequency (CRF) of all pairwise phage combinations produced a cross-resistance network with two distinct modules (Fig 1). Within each module, all possible pairwise phage combinations were connected by some degree of cross-resistance, whereas cross-resistance between the two modules was more limited (narrower arrows between nodes denote low frequency of cross-resistance interactions) and observed only between a small proportion of all the potential phage pairs (8/92, i.e., around 8.7%, Fig 1). Within modules, asymmetric (i.e., nonreciprocal) cross-resistance was more common within module 1, whereas module 2 was dominated by symmetric (i.e., reciprocal) cross-resistance (note that the ‘other’ node in module 2 of Fig 1 contains a subset of 194 mutants providing consistently strong symmetric cross-resistance against 20 phages). The high degree of symmetric cross-resistance observed in module 2 could not be explained simply by the genetic similarity of the focal phages as estimated from their random amplification of polymorphic DNA (RAPD) PCR banding patterns (S4 Fig; S3 Data). Between-module cross-resistance was always asymmetric and typically from module 1 to module 2 (Fig 1). This network structure was robust to the binary threshold value used to classify resistance, although using lower thresholds led to increased numbers of asymmetric connections between modules (S3 Fig). Symmetric cross-resistance is likely to occur when both phages select for similar modifications to a shared receptor, whereas asymmetric cross-resistance could result if phages selected for different modifications to a shared receptor that varied in the extent of disruption or for entirely different resistance mechanisms that varied in the extent of generalism. To study this at the genetic level, we next obtained whole-genome sequences for resistant mutants selected against a subset of 10 focal phages that represented all nodes of the cross-resistance network (S5 Fig).
We obtained whole-genome sequences for 22 independent spontaneous resistant mutants of PAO1 selected against 10 focal phages to identify mutational changes associated with specific phage resistance profiles (Fig 3; S5 Fig). Cross-resistance within module 2 was associated with mutations in LPS biosynthesis genes wzy and wbpL, whereas cross-resistance within module 1 was associated with mutations in various genes encoding type IV pilus biosynthesis (Fig 3; S1 Table). These included genes encoding mechanical components of the type IV pilus, such as the motor proteins PilB and PilT, and enzymes involved in type IV pilus biosynthesis and assembly such as PilD, a prepilin peptidase. These data confirm that cross-resistance modules were determined by distinct phage adsorption cell-surface receptors—specifically, the LPS for module 2 phages and the type IV pilus for module 1 phages. We confirmed distinct receptor usage by testing the ability of all 27 phages to infect an unpiliated pilB transposon mutant: whereas module 1 phages were unable to form plaques on the unpiliated host, module 2 phages infected the unpiliated mutant at the same efficiency as they infected the piliated wild-type PAO1 host (all p > 0.1; S6 Fig; S4 Data). Between-module cross-resistance was associated with mutations in genes encoding the transcriptional regulators RpoN and PilS (Fig 3; S1 Table). This suggests that more generalist phage resistance required changes in bacterial gene regulation, which are likely to have broader-scale effects on the bacterial phenotype than mutations affecting structural genes performing steps in biosynthetic pathways. In addition, weaker between-module cross-resistance was associated with a mutation of the prepilin peptidase–encoding gene pilD (S7 Fig); here the likely mechanism of between-module cross-resistance is less clear.
To test if the different classes of resistance mutations identified by sequencing were associated with different magnitudes of fitness cost, we estimated the fitness of each of the genome-sequenced strains relative to PAO1 in the absence of phage. Between-module cross-resistance was associated with higher fitness costs than within-module cross-resistance (Fig 4A; ANOVA with post hoc Tukey test: module 1, p = 0.009; module 2, p = 0.015), but this was entirely due to far-higher fitness costs caused by resistance mutations in the rpoN gene compared to resistance mutations in either type IV pilus or LPS biosynthesis–associated genes (Fig 4B). Thus, between-module cross-resistance mutations in global regulators that are likely to disrupt many cellular functions are highly costly in the absence of phage, which may limit their long-term survival in bacterial populations.
We hypothesised that the degree and symmetry of cross-resistance between a pair of phages would predict the frequency of resistance evolution against phage combinations. Specifically, we predicted the highest frequency of resistance mutation would occur against pairs selecting for symmetric cross-resistance, followed by asymmetric cross-resistance, and lowest for no cross-resistance. To test this, we first estimated the frequency of resistance mutations against phage pairs relative to individual phages for all possible combinations of the subset of 10 phages representing all nodes of the cross-resistance network (S5 Fig). One phage (PA5P2) was excluded from further analysis because the absolute resistance mutation frequencies observed against this phage were unfeasibly high (approximately 1.7 × 10−3 for PA5P2 alone; S8 Fig). This could possibly indicate that a physiological mechanism of resistance against PA5P2 infection exists, in addition to the LPS-associated mutational mechanism observed in the sequenced resistant clone (Fig 3; S1 Table).
We found a positive relationship between the cross-resistance index (CRI; a nondirectional measure of cross-resistance) and the relative mutation frequency (linear regression R2 = 0.280, F1,106 = 42.7, p < 0.0001). Moreover, consistent with our hypothesis, the relative mutation frequency was highest for phage pairs that selected for symmetric cross-resistance (Fig 5; symmetrical versus asymmetrical p = 0.001; symmetrical versus none p < 0.0001) and lowest for phage pairs that selected no cross-resistance (Fig 5; asymmetrical versus none p < 0.0001). Thus, cross-resistance per se increased the frequency of resistance mutations, with the effect being strongest when cross-resistance was symmetric.
Consistent with the observed resistance mutation frequencies, phage pairs that selected no cross-resistance suppressed the growth of PAO1 most effectively during 24 h, whereas the effect of phage pairs that selected symmetric cross-resistance rarely differed from the best-performing individual phage (S9 Fig; S6 Data). Two phages did not conform to this pattern: Firstly, phage PA10P2 alone was sufficient to completely suppress bacterial growth, and all pairs including this phage were highly effective regardless of the symmetry of the cross-resistance. Secondly, phage pairs containing PA2P1 consistently performed poorly, irrespective of the symmetry of cross-resistance. These results suggest that the structure of cross-resistance predicts the performance of phage combinations but that strong phage identity effects can override this by either increasing or decreasing the efficacy of a phage combination more than expected by cross-resistance alone.
We analysed the network structure and underlying genetic basis of cross-resistance evolution in the bacterium P. aeruginosa PAO1 selected against 27 phages. Our data show that spontaneous resistance mutations against a focal phage commonly provide cross-resistance against other phages. The cross-resistance network was highly modular, containing two cross-resistance modules with high within- but weak between-module cross-resistance interactions. At the genetic level, cross-resistance modules were defined by shared mutational targets encoding biosynthesis of phage adsorption receptors (LPS or type IV pilus, respectively), whereas between-module cross-resistance was associated with mutations targeting regulatory genes. The strength, direction, and symmetry of cross-resistance between phage pairs predicted both the frequency of resistance mutation and the efficacy with which the phage pair suppressed bacterial growth: the highest-performing phage combinations were those that selected no cross-resistance, whereas the lowest-performing combinations selected symmetric cross-resistance. Taken together, these data suggest that cross-resistance will commonly shape the dynamic structure of bacteria–phage communities and that it is likely to be an important predictor of the robustness of phage therapy to resistance evolution. Further experiments will be required, however, to test whether cross-resistance predicts the efficacy of phage cocktails in more complex in vivo environments.
Our finding that cross-resistance was common for our phage collection suggests that resistance evolution events may frequently disrupt the structure of bacteria–phage interaction networks. Cross-resistance evolution has the effect of reducing connectance at the whole-community level more than would be expected if all interactions were strictly pairwise. The effect of connectance on community stability is dependent on underlying species interaction network architecture; reduced connectance can increase stability in trophic networks by enhancing modularity but may reduce stability in networks with nested structures [22]. Bacteria–phage networks typically have a nested-modular network structure [23,24]; thus, the impact of cross-resistance-mediated reduced connectance is likely to depend on precisely where in the network cross-resistance interactions occur. It seems reasonable to assume that modularity in bacteria–phage interaction networks may be caused by the same mechanism that causes cross-resistance modularity—namely, shared phage receptors. If this assumption is correct, then cross-resistance evolution may frequently lead to the collapse of isolated nested modules without destabilising the broader interaction network. Resistance in our experiments arose largely through mutations affecting the expression or biosynthesis of surface receptors. Notably, PAO1 lacks a CRISPR system, and it is likely that CRISPR-mediated phage resistance would cause cross-resistance to only very closely related phages that are sequence identical for the genomic region targeted by the newly integrated spacer. In systems where both resistance mechanisms occur, the mechanism by which resistance evolves is thought to depend on ecological conditions, with surface receptor modification favoured in high-resource environments with high phage densities [25], suggesting that in such systems, the structure of cross-resistance evolution may be highly ecologically contingent.
While resistance mutations were costly, we found no overall relationship between the range of cross-resistance provided by resistance mutations and their associated fitness costs, suggesting that the evolution of cross-resistance is unlikely to be constrained by fitness trade-offs except for rare cases of very generalist between-module cross-resistance (Fig 4B). This finding is somewhat surprising, since previous studies of pairwise bacteria–phage interactions and interactions between bacteria and multiple phage species have reported that broader resistance ranges are associated with higher costs [7,26]. In contrast to these studies, however, we did not allow broad resistance ranges to evolve via accumulation of multiple sequential mutations but instead measured the effects of single spontaneous resistance mutations on cross-resistance. It seems likely therefore that higher costs of generalist phage resistance described previously arise from negative epistasis between multiple resistance mutations rather than from inherent costs of cross-resistance itself. At a community level, cross-resistance could limit the ability of phages to maintain bacterial diversity via density dependent killing [27]. This could be mitigated by migration between local communities, promoting invasion of novel phages to which resistance is absent in the local community, or through phage counteradaptation. For example, phages have been shown to switch hosts in multihost environments [4] and expand host range through spontaneous mutation [28,29]. An important caveat is that fitness costs measured in simple lab environments are likely to underestimate the full extent of the pleiotropic effects of resistance mutations in more complex in vivo environments relevant to phage therapy. For example, loss of the type IV pilus is likely to be highly detrimental in vivo where type IV pilus–mediated motility, attachment, and biofilm formation play important roles in pathogenesis [30].
The modular structure of the cross-resistance network was determined by the shared phage receptors modified by resistance mutations. Within cross-resistance modules, mutations targeted biosynthesis of specific surface receptor targets for phage binding (S1 Table), specifically the type IV pilus in module 1 or the LPS biosynthesis in module 2. Resistance to the same phage could be provided by mutations to different genes in the same pathway. For example, mutations selected against phage PA5P2 affected PilD (a peptidase that processes prepilins; [31]), PilQ (which is involved in assembly and transport; [32]), and PilT (a motor protein that powers pilus retraction; [33]). By contrast, mutations that provided very generalist between-module resistance targeted the regulatory genes rpoN and pilS. RpoN is an alternative sigma factor that regulates transcription of approximately 700 genes, influencing a diverse range of functions, including motility (via both type IV pilus–and flagella-associated genes), quorum sensing, mucoidy, and biofilm formation [34]. PilS is part of a two-component regulatory system that promotes pilus expression by activating RpoN [35]. The diverse regulatory function of RpoN makes it difficult to identify the specific mechanism of generalist phage resistance. However, RpoN regulation of LPS-associated genes has been identified in P. aeruginosa (rfaD; [34]) and shown to directly influence LPS expression in Salmonella enterica (via rfaH; [36,37]). It is possible, therefore, that global regulatory mutations affecting the expression of multiple phage receptors could be typical for very generalist phage resistance. Crucially, only less than 10% of resistance mutants possessed generalist cross-resistance against both LPS and pilus binding phages, which suggest that these mutations are rarer. This is intuitive, since there are many more mutational targets in each biosynthesis pathway compared to the single copy of the rpoN gene. Moreover, the evolutionary success of global regulator-mediated resistance may be limited by the extensive pleiotropic effects of such mutations on the bacterial phenotype. Consistent with this, rpoN mutants suffered the greatest impairment in growth rate of all the observed resistance mutations (Fig 4B). Between-module cross-resistance could be achieved at lower cost through mutations affecting pilS, which were no more costly than other resistance mutations in type IV pilus–associated genes, suggesting that loss of PilS-mediated activation of RpoN may have been less disruptive to the cell than loss of RpoN itself. Because RpoN controls expression of important virulence-related functions such as quorum sensing and biofilm formation, phage combinations that select for these mutations may concomitantly drive reduced virulence.
We observed that asymmetric cross-resistance was common within our cross-resistance network. Reducing the threshold used to define resistance further increased asymmetric connections between modules (S3 Fig), suggesting that asymmetric cross-resistance may often be rather weak. While the mechanistic basis of symmetric cross-resistance appears conceptually straightforward—the two phages select for similar modifications to and/or loss of a shared receptor (for instance, via LPS modification [15])—the situation is likely to be more complex for asymmetric cross-resistance. We propose two potential routes to asymmetric cross-resistance: First, phages may select qualitatively different mechanisms of resistance offering different degrees of generality; for example, one phage may select for mucoidy [38,39], masking a number of different phage receptors thus providing cross-resistance, whereas the other phage may select for modification of a specific receptor only and limited cross-resistance. Second, resistance mutations for each phage may target different points in a biosynthesis pathway, such that mutations affecting the start of the pathway will provide greater cross-resistance than those affecting targets downstream [40]. Consistent with the latter mechanism, within module 1, the observed mutations in rpoN, which are likely to result in an unpiliated phenotype, provided complete cross-resistance within module 1, whereas mutations to genes lower down the pilus biosynthesis pathway (for instance, pilB and pilT encoding motor proteins that control extension and retraction of the pilus respectively; [33]) provided cross-resistance to only half of the module 1 phage. Understanding the mechanistic basis of cross-resistance in general, and the symmetry of cross-resistance in particular, should be a target of future research.
Our findings show that cross-resistance and its symmetry predict the efficacy of phage combinations both in terms of the frequency of resistance mutation and the efficiency of suppression of bacterial growth. Frequencies of resistance mutation were the highest for phage pairs with symmetric cross-resistance and the lowest for phage pairs that showed no cross-resistance, suggesting either multistep mutational changes or rarer generalist resistance mutations were required in the latter scenario. Consequently, phage pairs that exerted no cross-resistance often completely supressed bacterial growth, whereas failure to supress bacterial growth was more common for phage pairs that promoted some degree of cross-resistance evolution. Our analysis also identified individual phage strains that increased (or decreased) the performance of phage combinations more than predicted by cross-resistance alone. Although we observed an overall positive association between the strength of focal resistance and the strength of cross-resistance, in some cases, focal resistance caused by a mutation was quantitatively weaker than the cross-resistance(s) or undetectable. Since spontaneous resistant mutants have not had the opportunity to specialise their resistance against the focal phage, it is perhaps unsurprising that stronger cross-resistance can arise by chance. More puzzling are the cases of undetectable focal resistance despite there being observable cross-resistance and a resistance mutation identified in the genome sequence. This phenomenon was limited to particular phages: ϕKZ (pilR mutation), PA5P1 (wzy mutation), and PT7 (pilS, pilT, and pilJ mutations). Although we do not understand the mechanism underlying missing focal resistance, it is possible that this could be caused by extremely high rates of phage evolution to overcome the resistance mutation during the RBG assay, or particular phages being able to use an alternative surface receptor [41]. This suggests that other properties of phage life history are likely to affect their usefulness in phage combinations and that predictions based on cross-resistance networks could be sensitive to strong phage identity effects.
P. aeruginosa is a common cause of opportunistic infections, frequently of burn wounds, and is also the major pathogen associated with chronic infections of the cystic fibrosis (CF) airway [42–45]. High-level antibiotic resistance frequently makes P. aeruginosa CF infections nonresponsive to antibiotic treatments and consequently very difficult to eradicate [46]. As a result, phage therapy has been suggested as a potential alternative or complementary treatment [47–49]. Phage therapy has shown promising results against P. aeruginosa in both artificial CF lung sputum–like environments and murine models [50]. However, clinical trials of phage therapy on P. aeruginosa–colonised burn wounds have proved inconclusive thus far [51,52]. Our results suggest that unknown patterns of cross-resistance selected by the phages used in therapeutic cocktails could account for some degree of variation in the efficacy of phage therapies in these studies. Using combinations of phages that do not select for cross-resistance could potentially improve the efficacy and robustness of phage cocktails, increasing their ability to suppress bacterial growth by limiting resistance evolution. While caution is required when making inferences from simple lab experiments to far more complex in vivo environments, our data suggest that analysis of cross-resistance networks could aid the design process for improved therapeutic phage cocktails and warrants future in vivo experimental tests.
A total of 27 different phage strains (S2 Table) that were able to infect P. aeruginosa PA01 strain were used. Four of the phage strains have been previously characterised and are known to be phylogenetically, structurally, and serologically different [53,54] and promote bacterial resistance evolution to differing degrees [14]. The remaining 23 phage strains were isolated at the same time and location (sewage water treatment facility, Jyväskylä, Finland; for isolation protocols and infectivity ranges, see [55]). Sequence data are available for only two of these strains.
The genetic similarity of all phage strains was characterised using RAPD PCR that uses a set of primers (S3 Table) to amplify random sections of DNA, giving a unique PCR banding pattern for each distinct phage genotype. Phage DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany), and then a PCR was performed on each phage DNA sample (27 phages), with each primer (0.8 μm final primer concentration; S3 Table) under the following conditions: 4 initial cycles of 94 °C for 45 s, 30 °C for 120 s, and 72 °C for 60 s, followed by 25 cycles of 94 °C for 5 s, 30 °C for 30 s, and 72 °C for 30 s, ending with 72 °C for 10 min. PCR products were run on 1% Agarose gels for 30 min at 200 V. A difference matrix based on these banding patterns (i.e., the proportion of bands that two phages do not have in common) was used to make a neighbour-joining tree (R package ‘ape’, [56]).
All bacterial cultures were grown in 6 ml King’s media B (KB) in 30-ml glass microcosms with loose-fitting plastic lids and incubated at 37 °C with orbital shaking (200 rpm). Phage cultures were prepared by inoculating frozen stocks into 30-ml microcosms containing 6 ml KB with 60 μl of PA01 overnight culture (approximately 109 cells ml−1). Following overnight incubation at 37 °C, shaken, phage stocks were isolated by filtration (0.22 μm) and stored at 4 °C.
To select spontaneous phage-resistant mutants, a modified fluctuation test was used [57]. To establish 135 independent subpopulations of PAO1, we selected a single colony and incubated for 8 h before diluting by 1 in 10 into individual wells of 96-well microplates containing 200 μl of KB medium. Following overnight incubation, each of the bacterial populations was exposed to one of the 27 phages. Specifically, the overnight bacterial cultures were diluted by 10−2 directly into 200 μl of a phage stock solution, giving a multiplicity of infection of approximately 100 phage particles per bacterial cell and 5 independent bacterial populations per phage strain. From each bacteria–phage mixture, 100 μl was plated on KB solid agar and incubated overnight. Two colonies per plate were then restreaked onto KB agar plates and grown overnight to remove phage particles. We then picked a single colony from each streak plate to give 10 resistant mutants per phage strain (270 in total), which were then grown overnight in KB before preparing glycerol stocks (40% glycerol) and storing at −80 °C. These overnight cultures were also filter sterilised (0.22 μm) and plated on KB soft agar (0.8%) containing ancestral PA01 to check for any remaining phage particles. If phages were detected, phage-free stocks were created by restreaking the resistant mutant from glycerol stocks and repeating the last step. For seven replicates, we were unable to isolate phage-free stocks; therefore, these replicates were excluded from the analysis, leaving 263 resistant mutants in total.
To assess the extent of cross-resistance conferred by resistance against individual phage strains, all 263 resistance mutants were assayed against each of the 27 phage strains individually. Cross-resistance assays were performed in 96-well microplates (final volume of 150 μl) in KB media, at an approximate multiplicity of infection of 10 phage particles per bacterial cell. The RBG [58] was calculated by comparing absorbance readings (600 nm at t = 0 and t = 8 h) in the presence and absence of phage (Eq 1). RBG is a quantitative measure of bacterial resistance in which 1 indicates equal growth in the presence and absence of phage (i.e., complete resistance), and 0 indicates zero growth (i.e., complete susceptibility).
For phage i, bacteria j:
RBGij=[Abs600(t=8h)-Abs600(t=0h)]ij[Abs600(t=8h)−Abs600(t=0h)]controlj
(1)
Cross-resistance range describes the proportion of phages to which each resistant bacterial replicate mutant is resistant to, using a resistance threshold (RBG = 0.798) calculated as the 95% confidence interval of a normal distribution modelled over the peak of resistance within the complete RBG distribution (S1 Fig).
To determine the fitness costs associated with different cross-resistance profiles, the growth of all resistant mutants was measured in the absence of phage and compared to growth of the ancestral PA01 strain. Bacterial cultures were inoculated directly from glycerol stocks into 150 μl of KB media in 96-well microplates. Absorbance at 600 nm was measured every 30 min for 24 h (37 °C, shaken) to create a growth curve for each resistant mutant. Because of variation in the type of fitness costs observed (i.e., increased lag, reduced maximum OD, and reduced growth rate; S10 Fig), we used the integral of each growth curve as a combined measure of the effect of resistance mutations on bacterial growth. The integral of the growth curve correlates well with each of the other growth parameters (S11 and S12 Figs). The integral of each growth curve gives the total growth for each bacterial strain; dividing this value by the average integral for the ancestral PA01 strain gives an estimate of relative fitness.
Cross-resistance interactions between two phage strains can be quantified as the proportion of resistance mutants screened against one phage that display cross-resistance (RBG above 0.798) against the second phage, giving a directional metric of cross-resistance strength (CRF). To enable comparison of pairwise phage interactions, a nondirectional CRI was used as the mean of the two directional CRF values.
CRF can be used to construct an interaction network showing all directional cross-resistance interactions within a phage community. Firstly, an adjacency matrix is produced, containing directional CRF values for all possible phage pairs. The R package ‘igraph’ [59] was used to convert the adjacency matrix into a network graph (a list of all the realised links in the network and their associated weights; graph.adjacency function), which can then be plotted (plot.igraph function) as a directional weighted network. In the network, each node represents a single phage strain, and the directional connections are weighted by CRF, showing the frequency of cross-resistance against each phage. A community-detection algorithm (cluster_edge_betweenness function in the ‘igraph’ R package [59]) was used to identify the phage strains within the cross-resistance network that formed modules. This edge-betweenness algorithm [60,61] finds the optimum community structure of a given network by assigning a ‘betweenness’ value to every link in the network based on the frequency with which the link is used to create pathways between all possible pairs in the network. High ‘betweenness’ values indicate links between poorly connected modules. By removing these links in a stepwise manner (recalculating ‘betweenness’ values each time), the algorithm can define modules within the community. The subnetwork of the 10 phages used in further analysis was extracted from this full network.
Modified fluctuation tests [57] were used to estimate bacterial mutation frequencies against either individual phage strains or combinations of two phage strains. Three microcosms were inoculated from single colonies of the ancestral PA01 strain. After overnight incubation, each microcosm was subcultured into 55 wells of a 96-well microplate, diluting by 10−1 to a final volume of 200 μl, and then allowed to grow overnight at 37 °C in a static incubator. Concurrently, stock solutions of 10 phage strains (a subset representing each node within the CRF network; S5 Fig) were prepared (as above). Phage combinations were assembled, consisting of each phage alone (100 μl) and 1:1 mixtures of each possible phage pair (final volume 100 μl), to give 55 different phage combinations. One independent 200 μl PA01 culture from each of the three replicate microplates was then diluted 100-fold into each of the phage solutions, giving a multiplicity of infection of approximately 100 phage particles per bacterial cell, and incubated for 30 min at 37 °C in a static incubator.
Initial bacterial cell density was estimated by plating serial dilutions of 6 random 200 μl PA01 cultures per replicate microplate. The number of phage-resistant spontaneous mutants was then calculated by plating 60 μl of each bacteria–phage mixture onto solid KB agar to give colony-forming units per ml (CFU/ml). The ratio of phage-resistant mutants to initial bacterial cell density provides an estimate of the mutational frequency (MF, Eq 2) against each phage combination, and then comparison to the individual phage strains gives relative mutational frequency (RMF, Eq 3).
For phage suspension i, bacteria j:
MFi=[CFU/ml]ij[CFU/ml]controlj
(2)
For phage pair i1 and i2,
RMF=MFi1i2MFi1.MFi2
(3)
To determine the ability of phage combinations to suppress growth of the ancestral PA01 strain, bacterial growth was measured over 24 h in the presence of individual phage strains and all possible pairwise phage combinations of 10 phage strains. This phage subset contains all 4 phages from module 1 and 6 module 2 phages (S5 Fig) and is comprised of asymmetric (N = 11) and symmetric (N = 13) cross-resistance interactions, as well as pairwise interactions, which promote no cross-resistance (N = 21). Individual colonies of ancestral PA01 were inoculated into KB media and, following overnight incubation, were transferred to fresh KB media in 96-well microplates, diluting 10-fold. Phage suspensions were added at an approximate multiplicity of infection of 100 for both individual phage and pairwise phage combination treatments (prepared from phage stock solutions with a 1:1 ratio). Absorbance at 600 nm was measured every 30 min for 24 h during incubation at 37 °C with regular orbital shaking to produce growth curves for PA01 in the presence of each individual phage strain and all possible pairwise phage combinations within the phage subset (S5 Fig), each replicated three times.
To assess the genetic basis of cross-resistance, we randomly chose one resistant mutant screened against each phage within the cross-resistance subnetwork (S5 Fig), along with additional mutants representing symmetrical and asymmetrical cross-resistance profiles within resistance module 1, to be sequenced (22 independent spontaneous mutants in total). Bacteria were sequenced using the Illumina MiSeq platform, followed by bioinformatic analysis as follows: reads were aligned using Burrows-Wheeler Aligner [62], SNPs and small indel variants were called by GATK HaplotypeCaller [63], and then gene information was added using SNPeff [64]. Variants were filtered for quality by the following parameters: coverage of >20 reads per base pair and frequency of alternative allele in >80% of reads. The quality of each variant was further assessed visually using an alignment viewer (igv; [65]). Additionally, called variants occurring in all 22 sequenced mutants were discarded, as these represent mutations present in the ancestral PAO1 compared to the available reference strain used (accession ID AE004091). All sequence data have been uploaded to the European Nucleotide Archive (accession ID PRJEB27828).
To confirm that distinct cell surface receptors are required for infection by module 1 phages compared to module 2, we tested the ability of all 27 phage strains to infect a pilB transposon mutant (PW8623 pilB-G07::ISlacZ/hah; P. aeruginosa Two Allele Library) versus wild-type PAO1. Bacteria lawns were prepared as follows: three colonies were selected for each bacterial strain, inoculated into KB media (6 ml), and grown overnight at 37 °C, shaken; 200 μl of each culture was added to 12 ml of soft KB agar (0.6% agar) and poured over set standard KB agar (1.2% agar) in a 120-mm square petri dish to form a bacterial lawn. Filtered phage stocks were serially diluted, and each dilution was spot plated (5 μl) onto a lawn of each bacteria. Plates were incubated at 37 °C for 24 h, and then phage plaques were counted and density calculated as plaque-forming units per ml.
All analysis was conducted in R [66]. Resistant mutants originating from the same subpopulation were treated as paired replicates to prevent pseudoreplication. This means for 263 resistant mutants, we have 133 independent replicates. Variation in cross-resistance range between different focal phages was analysed using the nonparametric Kruskal-Wallis test, after averaging within subpopulations. To test for associations between cross-resistance range and focal resistance or relative fitness, linear mixed effects models (R package ‘lmerTest’ [67]) were used, with subpopulation included as a random effect. Variation in relative fitness between mutants with different network-level cross-resistance (i.e., within/between modules) was analysed using a one-way ANOVA followed by post hoc testing (Tukey test). Comparison of phage densities between transposon mutant (pilB) and wild-type hosts was performed using a linear mixed effects model, with bacteria and phage treated as interacting fixed effects. Statistical analysis of the effect of cross-resistance interaction type (i.e., symmetric/asymmetric) on RMF data was performed using the Kruskal-Wallis test, followed by post hoc testing (pairwise Mann-Whitney U) to compare interaction types.
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10.1371/journal.pgen.1000951 | Characterization of Oxidative Guanine Damage and Repair in Mammalian Telomeres | 8-oxo-7,8-dihydroguanine (8-oxoG) and 2,6-diamino-4-hydroxy-5-formamidopyrimidine (FapyG) are among the most common oxidative DNA lesions and are substrates for 8-oxoguanine DNA glycosylase (OGG1)–initiated DNA base excision repair (BER). Mammalian telomeres consist of triple guanine repeats and are subject to oxidative guanine damage. Here, we investigated the impact of oxidative guanine damage and its repair by OGG1 on telomere integrity in mice. The mouse cells were analyzed for telomere integrity by telomere quantitative fluorescence in situ hybridization (telomere–FISH), by chromosome orientation–FISH (CO–FISH), and by indirect immunofluorescence in combination with telomere–FISH and for oxidative base lesions by Fpg-incision/Southern blot assay. In comparison to the wild type, telomere lengthening was observed in Ogg1 null (Ogg1−/−) mouse tissues and primary embryonic fibroblasts (MEFs) cultivated in hypoxia condition (3% oxygen), whereas telomere shortening was detected in Ogg1−/− mouse hematopoietic cells and primary MEFs cultivated in normoxia condition (20% oxygen) or in the presence of an oxidant. In addition, telomere length abnormalities were accompanied by altered telomere sister chromatid exchanges, increased telomere single- and double-strand breaks, and preferential telomere lagging- or G-strand losses in Ogg1−/− mouse cells. Oxidative guanine lesions were increased in telomeres in Ogg1−/− mice with aging and primary MEFs cultivated in 20% oxygen. Furthermore, oxidative guanine lesions persisted at high level in Ogg1−/− MEFs after acute exposure to hydrogen peroxide, while they rapidly returned to basal level in wild-type MEFs. These findings indicate that oxidative guanine damage can arise in telomeres where it affects length homeostasis, recombination, DNA replication, and DNA breakage repair. Our studies demonstrate that BER pathway is required in repairing oxidative guanine damage in telomeres and maintaining telomere integrity in mammals.
| It has been proposed that oxidative DNA damage compromises telomere integrity. However, neither the types nor the consequences of oxidative DNA damage in telomeres have been fully explored. Oxidative base lesions, especially 8-oxoG and FapyG are among the most common oxidative DNA damage. These guanine lesions are excised by a DNA glycosylase, OGG1, and subsequently replaced by normal guanines via BER pathway. Telomeric DNA is rich in guanine, and guanine base is prone to be oxidized to 8-oxoG and FapyG. To study if 8-oxoG and FapyG accumulate in telomeres and affect telomere integrity in mammals, we utilized a mouse model that was genetically deleted for the Ogg1 gene (it is thus defective in 8-oxoG and FapyG repair). We found that oxidative guanine lesions accumulated in telomeres in Ogg1 deficient mouse cells, which was associated with multiple telomere defects. These findings demonstrate that oxidative guanine damage can perturb telomere integrity and that BER pathway actively participates in oxidative base repair in telomeres in mammals. Our studies will be beneficial for uncovering the role of oxidative DNA damage and BER in mammalian telomeres.
| Telomeres are chromosome end nucleoprotein structures that are composed of telomere associated proteins and TTAGGG repeats in mammals [1]. Telomeres cap chromosome ends and prevent them from being recognized as broken DNA. Dysfunctional telomeres, emanating from loss of telomere repeats and/or loss of protection by telomere-associated proteins, are recognized by many DNA damage response proteins, including γH2AX, 53BP1, and ATM that form telomere dysfunction-induced foci (TIF) and induce cellular senescence or apoptosis [2]–[3].
Telomere length homoeostasis is maintained through interplay among telomerase extension, telomere recombination, telomere replication, and telomere capping [1]. Telomerase, a ribonucleoprotein complex, replenishes replication dependent-telomere repeat loss and is essential in telomere length maintenance [1]. Telomere associated proteins also play a key role in telomere length regulation and capping. Mammalian telomeres are coated by a telomere protein complex, referred as shelterin. Shelterin includes telomere binding proteins TRF1, TRF2, and POT1 [3] that negatively control telomere length in cis by limiting the access of telomerase to the ends of individual telomeres [4]–[7]. Reduced telomere-bound TRF1 promotes telomere lengthening in human cells [4]–[5], but telomeres that are severely or completely stripped off the protective telomere protein complex result in telomere uncapping and evoke ATM or ATR dependent DNA damage response, nucleolytic degradation and undesirable recombination [8]–[15]. Efficient telomere replication also requires the telomere associated proteins, e.g. TRF1 and WRN [16]–[19].
Oxidative stress has been proposed to be a major cause of telomere shortening in cultured cells [20]. For instance, normoxia, hyperoxia (40% oxygen), and mitochondrial dysfunction-induced reactive oxygen species (ROS) accelerate telomere shortening and severely reduce proliferative lifespan of human somatic cells in vitro; while these phenotypes are delayed when cells are grown in hypoxia or in the presence of antioxidants [20]. Interestingly, human cells with long telomeres show increased sensitivity to hydrogen peroxide, but not to etoposide and bleomycin, supporting the notion that telomeres are particularly vulnerable to oxidative damage [21]. These studies suggest that oxidative stress causes telomere shortening or damage; however it is unclear which types of oxidative DNA damage arise in telomeres and how they compromise telomere length and integrity. Previous studies have demonstrated that oxidative stress causes single strand breaks (SSBs) in telomeric DNA [22]. Thus, telomere shortening could arise from SSBs. Oxidative stress has also been shown to induce oxidative base damage in telomeric oligonucleotides in vitro [23]–[25], and 8-oxoG at double-stranded telomeric nucleotides attenuates binding by TRF1 and TRF2 [26]. It is unclear if oxidative base damage has any impact on telomere length and integrity in mammalian cells.
Oxidative DNA damage, resulting from ROS, increases with age and can accumulate as a variety of oxidative modifications in purines and pyrimidines [27]–[28]. Oxidized bases may lead to mutagenesis, block DNA replication, or alter the affinity of DNA binding proteins, which can, in turn, attenuate cell viability or promote tumorigenesis [29]–[31]. BER is the primary DNA repair pathway for the repair of non-bulky damaged bases, and the initial step in BER is base removal by a DNA glycosylase. Several DNA glycosylases with distinct, but overlapping substrate specificities have been characterized, and OGG1 primarily excises 8-oxoG and FapyG paired with cytosine in duplex DNA [30]–[31]. OGG1 is well conserved from bacterial to mammals, implying its significant functional importance in maintaining genome integrity [27]–[28]. If 8-oxoG is unrepaired, it becomes highly mutagenic, because it can pair with adenine and lead to GC to TA transversions after two rounds of replication [32]–[34]. The removal of adenine opposite 8-oxoG is via the adenine-specific mismatch DNA glycosylase, MYH [35]. Mice lacking these repair genes exhibit an increased spontaneous mutation rate and a marked increase in tumor predisposition [35]–[38]. In addition, Ogg1 and Myh deficient murine cells are sensitive to oxidative stress [39]–[41]. These studies are consistent with the idea that oxidative base lesions contribute to genome instability, neoplastic transformation, and cell death.
Ogg1 deficiency causes an increase in 8-oxoG and FapyG lesions in the mouse genome [29], [36], [42]. This genetic model therefore allows us to study whether these unique oxidative guanine lesions can affect telomere integrity. Here, we present evidence that deletion of the mouse Ogg1 gene attenuates telomere integrity via multiple ways. Thus, interfering with telomere integrity may be one of the mechanism(s) by which oxidative base damage leads to genome instability.
The generation of Ogg1 null mice was described elsewhere [36]. Ogg1−/− mice were further backcrossed into C57BL/6 background. Wild type and Ogg1−/− mice were derived from heterozygous (Ogg1+/−) breeders. Primary MEFs were isolated from 13.5 day embryos of Ogg1+/− female bred with Ogg1+/− male and cultured in Dulbecco's Modified Eagle Medium containing 10% fetal bovine serum. Splenocytes were prepared from mouse spleens, cultured in RPMI 1640 with 10% FBS and 0.1% 2-mercaptoethanol, and stimulated with 50 µg/ml Escherichia coli LPS serotype O111:B4 (Sigma-Aldrich) and 50 ng/ml mouse IL-4 (R&D Systems). Bone marrow cells were flushed from femurs and tibias and cultured with Iscove's modified Dulbecco's medium (IBCO-BRL) supplemented with 20% fetal calf serum (Hyclone) in the presence of interleukin 6 (200 U/mL; Peprotech) and stem cell factor (100 ng/mL; Peprotech). To decrease or enhance oxidative stress, mouse cells were cultured in 3% oxygen (SANYO O2/CO2 incubator, MCO-18M) or 20% oxygen or in the presence of paraquat. All animal experiments were carried out according to the “Guide for the Care and Use of Laboratory Animals” (National Academy Press, USA, 1996), and were approved by the Institutional Animal Care and Use Committee of National Institute on Aging.
The telomere fluorescence in cell populations of spleen, bone marrow, and primary MEFs was measured by Flow cytometry and FISH (Flow-FISH) according to previously published protocol [43]. A telomere specific FITC conjugated (CCCTAA)3 PNA probe (0.3 µg/ml, Panagene) was used.
Quantitative FISH (Q-FISH) was performed as previously described [44]–[46]. Metaphase spreads were prepared from freshly isolated or subcultured mouse bone marrow cells, activated splenocytes, and primary MEFs. Briefly, mice were injected with 100 µl of 0.5% colchicine intraperitoneally for approximately 30 minutes before being sacrificed. Bone marrow cells were then collected by flushing 1ml of PBS through femurs and tibias. Cultured mouse cells were incubated with 0.1 µg/ml colcemid for 2–6 hr at 37°C to allow mitotic cells to accumulate. Metaphase spreads were obtained by incubating colchicine- or colcemid- treated mouse cells in 0.075 M KCl for 15 minutes in 37°C, followed by fixing cells in ice-cold 3∶1 methanol and glacial acetic acid and dropping the fixed cells onto slides. Metaphase spreads were hybridized with Cy3-labeled (CCCTAA)3 (0.3 µg/ml, Panagene), washed, and then counterstained with 4,6 diamidino-2-phenylindole (DAPI). For the detection of telomere signal intensity in G and C strands, metaphase spreads were initially hybridized with FITC-labeled (CCCTAA)3 PNA probes (0.3 µg/ml, Panagene). The free-(CCCTAA)3 probe were washed off the slides, and then hybridized with TRAMA-labeled (TTAGGG)3 (0.3 µg/ml, Panagene). Images were captured using Cytovision software (Applied Imaging) on a fluorescence microscope (Axio2; Carl Zeiss); followed by quantification of telomere fluorescence signals using the TFL-Telo software (a kind gift from Dr. Peter Lansdorp). For histograms and box-plots, data from different mice of each genotype were scored and R statistical package (http://www.r-project.org/) along with R.utils package and Biobase package (http://www.bioconductor.org/) were used. The frequencies of telomeres within a given range of telomere signal intensities were plotted against the telomere signal intensity using arbitrary units. Metaphases from different mice of each genotype were scored for chromosomal and telomeric abnormalities as previously described [45]–[46].
Telomerase activity was measured by Biomax Telomerase Detection Kit (Biomax) according to the manufacturer's recommendations. Briefly, mouse cell extracts were added to a pre-mix for quantitative telomerase activity in a real-time PCR reaction. MyiQ Single-Color Real-Time PCR Detection System (Bio-Rad) was used to perform the reactions, where each sample was done in triplicates and performed according to the manufacturer's instructions. HeLa cell extracts were used as positive control. Tert knockout mouse cell extracts and RNase-treated HeLa cell extracts were used as negative controls. Relative telomerase activity was expressed as log of CT value.
CO-FISH was used to measure T-SCEs and telomere lagging or leading strand loss [16], [47]. Briefly, mice were injected with 3∶1 ratio of BrdU/BrdC (Sigma) at a final concentration of 1×10−5 M intraperitoneally for approximately 20 hours, and subsequently with 100 µl of 0.5% colchicine for approximately 30 minutes before being sacrificed. Bone marrow cells were then collected by flushing 1ml of PBS through femurs and tibias. MEFs were cultured in medium containing a 3∶1 ratio of BrdU/BrdC (Sigma) at a final concentration of 1×10−5 M for 24 hours, and colcemid (0.1 µg/ml) was added 4 hours before harvest. Metaphase spreads were prepared from mouse bone marrow cells or MEFs, stained with Hoechst 33258, exposed to UV light, and digested with exonuclease III to remove newly synthesized DNA strands. Hybridization and wash conditions were identical to those described for Q-FISH. FITC-labeled (CCCTAA)3 and TRAMA-labeled (TTAGGG)3 PNA probes were used for the detection of lagging and leading strand, respectively. A chromosome with more than two telomeric DNA signals by both FITC-labeled (CCCTAA)3 and TRAMA-labeled (TTAGGG)3 PNA probes was scored as T-SCE positive. A chromosome with loss of one or two telomeric DNA signals by either FITC-labeled (CCCTAA)3 or TRAMA-labeled (TTAGGG)3 PNA probes was scored for telomere lagging or leading strand loss.
TEL-FISH was performed as described previously [46] with minor modifications. Briefly, cells were fixed in 1∶1 methanol∶acetone (Sigma) at −20°C for 10 minutes, permeabilized with 0.5% NP-40, and blocked in 1% Bovine serum albumin (BSA) (IgG-free, Sigma). Cells were first immunostained with a rabbit anti-γH2AX antibody (16193, Upstate Biotechnology), a rabbit anti-53BP1 antibody (BN 100–304, Novus Biologicals), or a mouse anti-XRCC1 antibody (X0629, Sigma) overnight at 4°C followed by Alexa 488-labeled secondary antibody (1∶500; Molecular Probes) for one hour at 37°C. Slides were washed with PBS for 15 minutes, fixed in 2% paraformaldehyde at room temperature for 10 minutes, dehydrated through ethanol series, and air-dried briefly. Slides were then hybridized to a TRAMA-labeled (CCCTAA)3 PNA probe (Panagene), then counterstained with DAPI. Z-stack images were captured and deconvoluted using Axiovision 4.6.3 software on a fluorescence microscope (Axiovert 200M; Carl Zeiss).
Identification of oxidative base lesions in telomeres was performed as previously described [48] with modifications. In brief, DNA was isolated from mouse liver or primary MEFs by salting out. 4 µg of DNA was treated with HinfI and RsaI restriction enzyme at 37°C overnight. The reaction was heated at 65°C for 15 minutes and then divided into two equal portions; one was treated with 8 units of E. coli formamidopyrimidine-DNA glycosylase (Fpg) (New England Biolabs) and another was treated with a mock buffer at 37°C for 30 minutes. Fpg was inactivated by heating at 60°C for 15 minutes. Genomic single-stranded DNA fragments were separated on 1% alkaline agarose gel according to their sizes, treated with UV light, then transferred to a nylon membrane. Single-stranded telomere DNA fragments were detected by 32P-labeled (CCCTAA)4 probe and visualized by autoradiography. ImageQuant software was applied in quantifying DNA cleavage in mock and Fpg-treated samples. A grid object was created as a single column with multiple rows and was placed over the lane corresponding to the molecular size markers. The density measurement was conducted in each row in which each marker was recorded. The mean length (ML) was calculated as a center of mass and expressed in kb: ML = Σ (MWi × ODi)/Σ (ODi), in which MWi is the length of the telomeric DNA at each row and ODi is the densitometer output at each row. The frequencies of Fpg-sensitive lesions in a sample were calculated based on ML values in Fpg- and mock-treated samples: lesions = (ML untreated/ML treated)−1, in which ML is expressed in kb. Fold-changes in each sample were further normalized with respect to the number of Fpg-sensitive lesions in a control.
Ablation of OGG1 function in S. cerevisiae can cause telomere elongation [49]–[50]. Since OGG1 is conserved from S. cerevisiae to mice, we investigated the impact of Ogg1 deficiency on telomere length in mice. Mouse bone marrow cells were freshly isolated from 1–3 month old mice and analyzed by Q-FISH. Compared to the wild type, Ogg1−/− mouse bone marrow displayed higher mean and median telomere signal intensities (Figure 1). Similar results were obtained from bone marrow cells from 12 month old mice (Figure S1). This observation was further confirmed by Flow-FISH, showing that telomere signal intensity was moderately increased in Ogg1−/− mouse bone marrow cells from young and old animals (Figure 2A). Freshly isolated splenocytes from >3 month old Ogg1−/− mice also displayed higher telomere signal intensity than those of age-matched wild type mice (Figure 2A). Additionally, we examined telomere length in wild type and Ogg1−/− primary MEFs cultivated in 3% O2 that mimics the in vivo oxygen level in mice. Telomere signal intensity was moderately increased in Ogg1−/− primary MEFs as shown by Flow-FISH (Figure 2B) and, to a lesser extent, by Q-FISH (Figure 3).
Next, we examined if deletion of Ogg1 could affect telomere capping in vivo. Freshly isolated mouse bone marrow cells and primary MEFs cultivated in 3% O2 were examined for the frequencies of chromosome end-to-end fusions and telomere signal free ends (SFEs). Ogg1−/− mouse cells did not show any chromosome end-to-end fusions. The incidence of SFEs was low and not significantly different between wild type and Ogg1−/− mouse cells. Furthermore, Ogg1−/− mouse cells did not display spontaneous chromosomal abnormalities, e.g. chromosome breaks or fragments (Table 1). These data suggest that ablation of OGG1 function does not lead to telomere uncapping and chromosomal instability, but moderate telomere lengthening in mouse tissues and primary cells that are subjected to low levels of oxidative stress.
To determine if high oxidative stress has the same or different impact on telomere length, primary wild type and Ogg1−/− MEFs were cultivated in 20% O2 or in the presence of 0.5 µM of paraquat (an oxidant). After six passages, they were evaluated for telomere length by Q-FISH and Flow-FISH. Surprisingly, under these conditions Ogg1−/− MEFs showed reduced telomere signal intensity, in comparison to wild type MEFs (Figure 2B and Figure 3). In addition to overall reduction in telomere signal intensity, Ogg1−/− MEFs had increased number of chromosomes and chromatids without detectable telomere signals (referred to SFEs and sister telomere loss, STL, respectively) (Figure 3 and Table 1). Notably, wild type MEFs also showed reduced telomere signal intensity after prolonged exposure to 20% O2 and 0.5 µM paraquat, in comparison to 3% O2. Nevertheless, they had less degree of telomere loss than Ogg1−/− MEFs (Figure 3). Similarly, subcultured Ogg1−/− mouse splenocytes and bone marrow cells showed reduced telomere signal intensity than wild type splenocytes, after being exposed to 20% O2 for the period of three days or to 200 µM paraquat for 16 hours, respectively (Figure 2C, Figure S2, and Figure S3). Collectively, these results suggest that high oxidative stress increases telomere attrition in Ogg1−/− mouse cells.
8-oxoG in telomeric DNA attenuates binding by telomere binding proteins [26], which may consequently evoke undesirable telomere recombination [14]–[15]. On the other hand, Ogg1 deficiency may hamper telomere recombination [51]. We thus examined the frequencies of telomere sister chromatid exchange (T-SCE) in wild type and Ogg1−/− mouse cells using CO-FISH (Figure 4A and 4B, and [47]). Freshly isolated Ogg1−/− bone marrow cells showed moderately increased T-SCEs (1.12±0.25% and 2.82±0.57% T-SCEs/chromosome in wild type and Ogg1−/−, respectively, p<0.001) (Figure 4C). In 3% O2 primary Ogg1−/− MEFs displayed slight yet insignificant increase in T-SCE events (2.35±0.08% and 3.28±0.35% T-SCEs/chromosome in wild type and Ogg1−/− respectively, p = 0.06) (Figure 4C). In 20% O2 primary Ogg1−/− MEFs, however, had fewer T-SCEs than the wild type (5.98±0.52% and 9.43±0.51% T-SCEs/chromosome in Ogg1−/− and wild type, respectively, p<0.001) (Figure 4C). These results suggest that deletion of Ogg1 may induce or inhibit telomere recombination, possibly depending on the level of oxidative stress.
Telomerase plays a key role in telomere elongation [1]. Telomere lengthening in ogg1-deleted S. cerevisiae is dependent on telomerase [50]. We therefore examined if telomerase activity was altered in Ogg1−/− mice. No detectable differences in telomerase activity were observed between wild type and Ogg1−/− mouse bone marrow cells by qT-PCR TRAP assay (Figure S4). Telomere lengthening is therefore unlikely through enhanced telomerase activity in Ogg1−/− mice; however, we cannot exclude the possibility that there is an increased accessibility of telomerase to telomeres in Ogg1−/− mice.
Previous studies suggest that oxidative stress-induced SSBs could result in telomere shortening [22]. In addition, oxidative base damage in the vicinity of DNA breaks can impose hindrance for resolving DNA ends [51]–[54]. Under high oxygen tension, Ogg1−/− mouse cells displayed telomere attrition (Figure 2 and Figure 3). It is unclear if oxidative stress-induced DNA strand breaks can accumulate in telomeres due to unrepaired oxidative base lesions and contribute to telomere attrition in Ogg1−/− mouse cells. We thus examined the frequencies of genomic and telomeric DNA strand breaks in wild type and Ogg1−/− primary MEFs cultivated in 20% O2. γH2AX and XRCC1 are known to form foci at the sites of double strand breaks (DSBs) and SSBs, respectively [55]–[56], and formation of γH2AX and XRCC1 foci were therefore used as markers for DSBs and SSBs in the genome and telomeres.
γH2AX foci were detected in late passage wild type and Ogg1−/− MEFs by indirect immunofluorescence. A greater fraction of Ogg1−/− MEFs showed >3 γH2AX foci compared to the wild type (approximately 12% wild type and 38% Ogg1−/− MEFs, respectively) (Figure 5A). γH2AX foci were detected in telomeres in both wild type and Ogg1−/− MEFs by TEL-FISH, and the latter had approximately 2-fold more telomeric γH2AX foci (Figure 5B and 5C). To further clarify telomeric γH2AX foci, we examined the formation of 53BP1 foci in telomeres by TEL-FISH [57]. The 53BP1 foci were also detected in telomeric DNA in Ogg1−/− MEFs (Figure S5). Similarly, XRCC1 foci were found in the genome in both wild type and Ogg1−/− MEFs (Figure 5D), and Ogg1−/− MEFs had higher occurrence of telomeric XRCC1 foci (Figure 5E and 5H). This phenotype was further enhanced when MEFs were treated with 10 µM hydrogen peroxide for 24 hours (Figure 5F and 5G). Under low oxygen tension (i.e. 3% O2), the frequencies of γH2AX and XRCC1 foci were low, and no detectable difference was observed between wild type and Ogg1−/− MEFs (data not shown). Collectively, these results support the notion that oxidative stress can increase DSBs and SSBs in the genome and telomeres when Ogg1 is deleted.
CO-FISH has been applied to detect defects in telomere lagging and leading strand loss, and it is proposed that such loss is caused by defects in lagging or leading strand synthesis (Figure 4A and 4B, and [58]–[59]). Because oxidative stress can increase DNA strand breaks in Ogg1−/− MEFs, it is possible that these DNA strand breaks may block telomere DNA replication and contribute to telomere attrition in Ogg1−/− MEFs. We therefore examined the frequencies of telomere lagging and leading strand loss in wild type and Ogg1−/− primary MEFs by CO-FISH. No significant difference in leading and/or lagging strand loss was detected between wild type and Ogg1−/− MEFs under low oxygen tension (3% O2); however, under high oxygen tension (20% O2), more telomere loss was found in the lagging strand in Ogg1−/− MEFs (1.80±0.37% and 3.50±0.44% lagging strand losses/chromosome in wild type and Ogg1−/− MEFs, respectively, p<0.001) (Figure 4D and Figure S6). These results indicate that oxidative stress-induced oxidative DNA lesions (possibly DNA strand breaks with adjacent oxidized guanines) may preferentially affect lagging strand DNA synthesis in telomeres in Ogg1−/− MEFs.
Aside from telomere lagging or leading strand synthesis defect, other factors (e.g. DNA strand breaks and nucleolytic degradation in a telomere strand) may also contribute to the loss of telomeric repeats in a telomere strand. To distinguish these possibilities, we employed a two-color telomere-FISH that detects telomere signals in G and C strands of a chromatid (Figure 6A and 6B). In 20% O2, loss of telomere signals in both G and C strands of a chromatid (or loss of a chromatid) was detected in wild type and Ogg1−/− MEFs, with higher frequencies in the latter (1.57±0.47% and 3.32±0.11% telomere chromatid losses/chromosome in wild type and Ogg1−/− MEFs, respectively, p<0.001) (Figure 6B and 6C). However, loss of telomere signal intensity in one of the telomere strands, either G or C strand was also evident in wild type and Ogg1−/− MEFs (Figure 6B and 6C), but G-strand loss appeared to be more prominent and was approximately 2-fold higher in Ogg1−/− MEFs (Figure 6C). Thus, telomere loss occurred in either one strand or both strands of a chromatid in Ogg1−/− MEFs. These results suggest that besides telomere replication defects, DNA breakage/degradation-mediated strand loss may have occurred in telomeres in Ogg1−/− MEFs. Because only the G-rich strand of telomeric DNA can harbor oxidized guanines, this may explain why oxidative DNA damage induces telomere attrition with strand bias.
Guanine has a lower oxidation potential compared to other bases, and triple guanines, composed of the mammalian telomere repeats, have an even lower oxidation potential [60]–[61]. Consistently, it has been found that triple guanines in telomere repeats are prone to oxidative damage in vitro [23]–[25], [62]. To determine the level of guanine oxidation in telomeres in vivo, genomic DNAs from wild-type and Ogg1 deficient mouse liver and primary MEFs were digested with restriction enzymes and then examined for their sensitivity to E. coli Fapy DNA glycosylase (Fpg). Fpg excises oxidized guanines, resulting in abasic sites that are further processed by the lyase activity of Fpg to create SSBs [27]–[28]. The extent of increased smaller single stranded telomeric DNA fragments is proportional to the amount of Fpg-sensitive lesions present within the telomeric DNA and can be extrapolated to estimate the number of lesions (Figure 7A and Figure S7, and [48]).
To validate the method, genomic DNA was treated in vitro with increasing concentration of hydrogen peroxide (H2O2) plus Cu2+. Higher doses of H2O2 treatment caused detectable increase of Fpg-sensitive lesions (Figure 7B), demonstrating that the method is feasible in estimating Fpg-sensitive lesions in telomeres. Next, we measured Fpg-sensitive lesions in telomeres in mouse livers derived from wild type and Ogg1−/− mice. The level of telomeric Fpg-sensitive lesions was not significantly changed in the hepatocytes from 2- and 8-month old wild type mice; however, telomeric Fpg-sensitive lesions were elevated in the hepatocytes from 8-month old Ogg1−/− mice (Figure 7C). We also measured oxidative guanine lesions in primary MEFs during prolonged culture under low and high oxygen tensions (3% O2 and 20% O2). Higher levels of Fpg-sensitive lesions were observed in primary MEFs under high oxygen tension, and Ogg1−/− MEFs harbored more lesions than wild type MEFs (Figure 7C). Collectively, these results indicate that ablation of OGG1 function can increase oxidative guanine lesions in telomeres in mouse tissues with aging or in primary MEFs during prolonged culture or under oxidative stress conditions.
To further verify if OGG1 participates in oxidative guanine repair in telomeres in vivo, early passage wild type and Ogg1−/− primary MEFs were exposed to 500 µM hydrogen peroxide for 60 minutes and then allowed to recover for 8 hours. Immediately after hydrogen peroxide treatment (0 hour), high levels of Fpg-sensitive lesions were detected in wild type and Ogg1−/− MEFs, compared to untreated MEFs. Eight hours after removal of hydrogen peroxide, Fpg-sensitive lesions were significantly reduced in wild type MEFs; in contrast they remained at a higher level in Ogg1−/− MEFs (Figure 7C and 7D). Thus, Ogg1−/− MEFs were inefficient in the repair of hydrogen peroxide-induced Fpg-sensitive lesions in telomeres, while wild type MEFs repaired these lesions with high efficiency. These results demonstrate that OGG1 is involved in the repair of oxidative guanine lesions in telomeres in vivo.
BER is the primary DNA repair pathway for the repair of oxidative base lesions. Here, we studied the impact of Ogg1 deficiency on telomeres in mammalian cells. We found that ablation of OGG1 function resulted in increased oxidative guanine lesions in telomeres in mice with aging or in primary MEFs during prolonged culture or cultivated in a high oxidative environment. In addition, lack of Ogg1 led to telomere length alteration that was dependent on the level of oxidative stress. Furthermore, deletion of Ogg1 caused altered recombination, increased DNA strand breaks, and preferential strand loss in telomeres. Our data support that oxidative guanine lesions affect telomere integrity and that the OGG1-initiated BER pathway plays an important role in telomere base damage repair and telomere maintenance in mammals.
Ogg1 deficient mouse cells showed moderate telomere lengthening under low oxygen tension (e.g. in tissues or 3% O2); however, they displayed accelerated telomere shortening under high oxygen tension (20% O2) or with paraquat treatment. This observation suggests that the level and types of oxidative DNA damage in telomeres may affect the outcome of telomere length. Several possibilities may contribute to the telomere length alteration in Ogg1 deficient mouse cells.
8-oxoG can directly disrupt telomeric DNA binding by TRF1 and TRF2 [26], and unrepaired 8-oxoG can lead to GC to TA transversions [33]. The affinity of telomere binding proteins to telomeric DNA is sequence-specific and can be altered by mutations in telomeric DNA [63]. Thus, both base lesions and base lesion-induced mutations may affect the association of telomere binding proteins to telomeres. Opresko et al have previously shown that the level of 8-oxoG in telomeres adversely affects binding by telomere binding proteins [26]. Thus, the number of oxidative base lesions and mutations may determine the severity of telomere binding protein depletion in telomeres. It is known that reduced binding or severe loss of telomere binding proteins in telomeres can lead to different telomere phenotypes; the former causes telomere lengthening and the latter results in telomere uncapping [3]. Thus, when few base lesions affect telomeric DNA repeats, they may moderately reduce telomere binding proteins in telomeres, which could liberate the negative regulation of telomere binding proteins on telomerase and consequently increase telomerase-dependent telomere repeat additions. Our studies in S. cerevisiae support this notion, in which telomere lengthening in ogg1-deleted S. cerevisiae is dependent on telomerase-mediated telomere elongation [50]. On the other hand, once oxidized bases accumulate to a certain level in telomeres, they may severely deplete telomere binding proteins in telomeres and result in telomere uncapping. Uncapped telomeres can become targets for nucleolytic degradation and hence cause telomere shortening.
When exposed to 20% O2 or hydrogen peroxide, Ogg1 deficient mouse cells showed increased incidences of SSBs and DSBs in telomeres, evident by XRCC1 and γH2AX foci formation in telomeres. These DNA strand breaks can represent an obstacle for DNA replication. Furthermore, base lesions in the vicinity of DNA strand breaks can somehow accelerate end resection, possibly by stimulating an endonuclease activity close to the breaks [64]. As a result, these DNA defects may ultimately lead to telomere shortening in Ogg1 deficient mouse cells. However, fewer DNA strand breaks may also arise in Ogg1 deficient mouse tissues and partially inhibit DNA replication, which could consequently enhance telomerase pathway and induce telomere elongation [65]–[66]. DNA strand breaks in Ogg1 deficient mouse cells may arise in telomeres by several means. High oxygen tension has been shown to cause detectable levels of SSBs in telomeres, especially in the G-strand [22]. Since the presence of oxidative guanine damage in the vicinity of DNA breakages may impose a hindrance to the resolution of DNA ends [51]–[54], they may possibly inhibit repair of DNA strand breaks in telomeres. If adenine is incorporated opposite unrepaired 8-oxoG, removal of adenine by the MYH DNA glycosylase and subsequent abasic site processing can lead to SSBs in the C-strand [40]. SSBs may also be indicative of increased partial repair products of back-up DNA glycosylase activity, e.g. Neil1 [67].
Previous reports demonstrate that telomere lagging strand loss can be detected in WRN and FEN1 mutant cells via CO-FISH, which may reflect lagging strand telomere synthesis defects [58]–[59]. Because oxidative guanine lesions are located at G-strand in telomeres, they may preferentially inhibit repair of SSBs in this strand. As a result, lagging strand telomere synthesis may be affected. Indeed, Ogg1 deficient MEFs showed an increase in telomere lagging strand loss by CO-FISH analysis. This result suggests that oxidative guanine damage and/or its negative effect on the repair of telomere strand breaks may perturb lagging strand DNA synthesis in telomeres. Telomere lagging strand loss may also result from SSBs in G-strand along with loss of distal telomeres or nucleolytic degradation. In fact, the two-color Q-FISH showed that telomeric G-strand loss can occur alone without a loss of its complementary C-strand in Ogg1−/− MEFs, supporting the latter possibility.
Besides changes in telomere length, the incidence of T-SCEs either increased or decreased in Ogg1 deficient mouse cells, which was reversely associated with the level of oxidative stress. Several possibilities may account for the altered telomere recombination in Ogg1 deficient mouse cells. First, variable levels of oxidative base damage may have different impact on recombination activity. For example, recombination rates are substantially increased in BER deficient yeast cells harboring low levels of oxidative DNA damage in the genome, and it has been postulated that a moderately damaged genome could promote illegitimate recombination that serves as a compensatory response in order to tolerate oxidative DNA damage [68]. However, high density of oxidative base lesions can inhibit RAD52 annealing activity and thus result in reduced recombination resolution [51]. Second, OGG1 can inhibit RAD52 strand annealing and exchange activity [51], and removal of OGG1 would therefore relieve this inhibition and activate the recombination pathway. Third, oxidized guanines may affect telomere recombination by disrupting shelterin's association to telomeres. Previous studies demonstrate that telomere binding proteins prevent telomeres from becoming substrates for HR, and deletion of telomere binding proteins invokes T-SCE events [10], [15]. Because oxidative base lesions in telomeric DNA can attenuate binding by telomere binding proteins [26], it is possible that reduced binding of telomere binding proteins to telomeres may promote telomere recombination. Finally, increased telomere sister chromatid exchanges may associate with recombination repair of stalled or broken replication forks that might occur at the sites of oxidative bases and/or DNA strand breaks in telomeres.
We found that oxidative guanine lesions in telomeres were elevated in older animals or in primary MEFs cultivated under oxidative stress conditions. Thus, oxidative damage on guanine bases can increase in telomeres in aging or by environmental oxidative stress. The level of Fpg-sensitive lesions in telomeres was increased in Ogg1−/− mouse liver and primary MEFs, in comparison to their wild type counterparts. These results indicate that OGG1 is involved in repairing oxidative guanine lesions in telomeres in vivo. This view was further supported by the evidence that Ogg1−/− MEFs were defective in the repair of hydrogen peroxide-induced Fpg-sensitive lesions in telomeres.
Oxidative stress-induced SSBs can cause telomere shortening in mammalian cells [20]. Here, we report that another form of oxidative DNA damage, oxidative base lesions can induce either telomere lengthening or shortening, depending on the level of oxidative stress. In a given organism, telomere length is maintained via a balance between telomere elongation and shortening [1]. It is possible that moderate oxidative base damage may favor the pathways for telomere lengthening (e.g. telomerase), while extensive oxidative base damage may attenuate telomere capping, telomere recombination, telomere replication, and the resolution of DNA strand breaks and ultimately result in telomere attrition (Figure 8). Telomere shortening has been linked to human aging and cancer development. Perhaps extensive base damage occurs in individuals with the conditions, such as defective BER and increased ROS levels (for example, chronic inflammation), which may consequently lead to accelerated telomere attrition, thus contributing to premature aging and cancer formation.
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10.1371/journal.pgen.1000763 | Elevated Levels of the Polo Kinase Cdc5 Override the Mec1/ATR Checkpoint in Budding Yeast by Acting at Different Steps of the Signaling Pathway | Checkpoints are surveillance mechanisms that constitute a barrier to oncogenesis by preserving genome integrity. Loss of checkpoint function is an early event in tumorigenesis. Polo kinases (Plks) are fundamental regulators of cell cycle progression in all eukaryotes and are frequently overexpressed in tumors. Through their polo box domain, Plks target multiple substrates previously phosphorylated by CDKs and MAPKs. In response to DNA damage, Plks are temporally inhibited in order to maintain the checkpoint-dependent cell cycle block while their activity is required to silence the checkpoint response and resume cell cycle progression. Here, we report that, in budding yeast, overproduction of the Cdc5 polo kinase overrides the checkpoint signaling induced by double strand DNA breaks (DSBs), preventing the phosphorylation of several Mec1/ATR targets, including Ddc2/ATRIP, the checkpoint mediator Rad9, and the transducer kinase Rad53/CHK2. We also show that high levels of Cdc5 slow down DSB processing in a Rad9-dependent manner, but do not prevent the binding of checkpoint factors to a single DSB. Finally, we provide evidence that Sae2, the functional ortholog of human CtIP, which regulates DSB processing and inhibits checkpoint signaling, is regulated by Cdc5. We propose that Cdc5 interferes with the checkpoint response to DSBs acting at multiple levels in the signal transduction pathway and at an early step required to resect DSB ends.
| Double strand DNA breaks (DSBs) are dangerous chromosomal lesions that can lead to genome rearrangements, genetic instability, and cancer if not accurately repaired. Eukaryotes activate a surveillance mechanism, called DNA damage checkpoint, to arrest cell cycle progression and facilitate DNA repair. Several factors are physically recruited to DSBs, and specific kinases phosphorylate multiple targets leading to checkpoint activation. Budding yeast is a good model system to study checkpoint, and most of the factors involved in the DSBs response were originally characterized in this organism. Using the yeast Saccharomyces cerevisiae, we explored the functional role of polo kinase Cdc5 in regulating the DSB–induced checkpoint. Polo kinases have been previously involved in checkpoint inactivation in all the eukaryotes, and they are frequently overexpressed in cancer cells. We found that elevated levels of Cdc5 affect the cellular response to a DSB at different steps, altering DNA processing and overriding the signal triggered by checkpoint kinases. Our findings suggest that Cdc5 likely regulates multiple factors in response to a DSB and provide a rationale for a proteome-wide screening to identify targets of polo kinases in yeast and human cells. Such information may have a practical application to design specific molecular tools for cancer therapy. Two related papers published in PLoS Biology—by Vidanes et al., doi:10.1371/journal.pbio.1000286, and van Vugt et al., doi:10.1371/journal.pbio.1000287—similarly investigate the phenomenon of checkpoint adaptation/overriding.
| Saccharomyces cerevisiae cells suffering a double stranded DNA break (DSB) activate a robust Mec1-dependent checkpoint response when DSB ends are processed to expose single-stranded DNA (ssDNA), and progression through the cell cycle is arrested prior to anaphase. Several well conserved factors are recruited at the DSB lesion, and contribute to the activation of a signaling pathway based on sequential phosphorylation events driven by the upstream kinases Tel1/ATM and Mec1/ATR which, in turn, activate the transducer kinases Rad53/Chk2 and Chk1 [1],[2]. The checkpoint response is influenced at several levels by kinases such as CDK1, CKII and Polo-like Cdc5, all involved in promoting key events throughout an unperturbed cell cycle, supporting the notion that the cellular response to DNA damage is tightly linked to cell cycle events [3]. The intensity of the DSB-induced checkpoint response correlates to the amount of the ssDNA that is accumulated at DSB lesions [4]. 5′-to-3′ nucleolytic processing of DNA ends is dependent upon several factors, including CDK1 and the nucleases Mre11, Sae2, Dna2 and Exo1 [5]. Moreover, the checkpoint is a reversible signaling pathway which is turned off when DNA lesions are repaired, thus permitting the resumption of cell cycle progression [6]. Different types of phosphatases (Pph3, Ptc2 and Ptc3) dephosphorylate and inactivate Rad53 and other checkpoint kinase targets [7]. Further, mutations in several DNA repair genes, including SAE2, KU70/80, RAD51, RDH54, SRS2, affect the inactivation of the DSB-induced checkpoint response [7],[8]. These observations suggest that the attenuation, as well the activation, of the checkpoint pathway are related to the metabolism of DSB ends, in a way that is not yet completely understood. It is also known that the checkpoint response can be attenuated when an irreparable DNA lesion is formed in the cell, leading to adaptation to DNA damage. Checkpoint inactivation during recovery and adaptation to DNA damage is a phenomenon described also in higher eukaryotes [6]. The functional role of adaptation is not completely understood; however, it was suggested that it may be partly responsible for chromosomal rearrangements, genome instability and tumorigenesis [6],[9]. Interestingly, the well conserved family of Polo-like kinases (Plks) has been involved in checkpoint adaptation and/or recovery both in budding yeast and vertebrates [10]. Cdc5 is the only polo kinase expressed in yeast, whereas higher eukaryotes usually express three or four Plks [11]. However, only Plk1, which is the most extensively studied, is a true mitotic kinase homolog to the Drosophila Polo kinase [11]. In yeast, CDC5 is an essential gene and the point mutation cdc5-ad (a Leucine-to-Tryptophan substitution at residue 251, within the kinase domain) causes the inability to adapt to one irreparable DSB lesion and to turn off Rad53 kinase [12],[13]. However, cdc5-ad cells can recover from checkpoint when the DSB is repaired, suggesting that adaptation and recovery are two genetically separate processes [14]. A corresponding cdc5-ad mutation in Plks has not yet been isolated in mammals; however, it was found that Plk1 depletion severely blocks checkpoint recovery and adaptation [10],[15],[16], and rapidly causes cell death in cancer cells [17],[18]. Based on the fact that the DNA damage checkpoint pathway is well conserved in all the eukaryotes, it is reasonable to expect that the functional role of Cdc5 in budding yeast and of Plk1 during adaptation (and perhaps in recovery) may be conserved. Polo-like kinases contain in the C-terminal region of the protein a polo box which mediates the interaction of Plks with substrates previously phosphorylated by CDK or MAPK kinases [19]. Indeed, Cdc5 targets multiple substrates during an unperturbed cell cycle [20] and could functionally interact with several checkpoint proteins as well. In vertebrates, polo kinases regulate the DNA damage checkpoint acting on multiple factors. They phosphorylate Claspin [21]–[24], a Chk1 kinase regulator, and the Fanconi-Anemia protein FANCM [25], promoting their degradation and checkpoint inactivation. Further, Plk1, Plk3 and Plk4 interact with and phosphorylate Chk2, the ortholog of Rad53 in human cells, likely influencing its activity [26]–[28]. Interestingly, yeast Cdc5 is phosphorylated and inhibited in a Mec1- and Rad53-dependent manner [29], and several studies indicate that in mammals Plk1 activity is inhibited by ATM/ATR-signaling in response to DNA damage [30]–[33]. Further, the DNA damage checkpoint regulates Plk1 protein stability in response to DNA damage in mitosis [34]. It was also shown that Aurora kinase A phosphorylates and re-activates Plk1 to promote recovery from DNA damage [35]. Altogether, these informations suggest that the DNA damage checkpoint inhibits Plk1, thus contributing to block cell cycle progression in response to DNA damage; however, the re-activation of Plk1 is a crucial event of a feedback regulatory loop in the inactivation of the DNA damage checkpoint during recovery and adaptation.
Therefore, the activity of Plks must be finely regulated during the DNA damage checkpoint response, and it is worth mentioning that the expression of a constitutively active Plk1 protein variant overrides the G2/M arrest induced by DNA damage [30]. Indeed, Plks are frequently overexpressed in tumor cells with uncontrolled proliferation and genome instability [36]–[39], and high level of Plk1 is predictive of a bad prognosis in several cancers [40]–[44].
To further characterize the functional link between Plks and the DNA damage checkpoint and, possibly, to understand why Plks are frequently overexpressed in cancer cells, we used budding yeast as a model system to study DNA damage related events in the presence of high levels of Cdc5.
Here, we show that overproduction of Cdc5 impairs the Mec1-signaling pathway in response to an inducible DSB lesion, altering phosphorylation of Ddc2, Rad9, Rad53 and other Mec1 targets. We also found that elevated levels of Cdc5 slow down DSB ends processing, although it does not prevent the formation of ssDNA, which triggers the recruitment of checkpoint factors. Consistently, we observed that overexpression of Cdc5 does not alter the loading of the apical Mec1 kinase checkpoint complex and recruitment of the checkpoint mediator Rad9, but surprisingly it physically interact with the checkpoint inhibitor Sae2, inducing its hyper-phosphorylation and an increased and persistent binding onto a DSB lesion.
We propose that high levels of polo kinase Cdc5 override Mec1-induced checkpoint response to DSB lesions, likely by regulating multiple factors, previously phosphorylated by CDK1, involved in both DSB processing and checkpoint signaling. Our work may represent a simple model to further understand why polo kinases are frequently overexpressed in cancer cells.
DNA damage checkpoints represent a barrier to oncogenesis; in fact, loss of these surveillance mechanism is a characteristic of early tumor development [45]. Several evidences indicate that Plks are targets of the DNA damage checkpoint in all the eukaryotes [29]–, suggesting a functional model in which the DNA damage checkpoint inhibits Plks to maintain a cell cycle block at the metaphase to anaphase transition. Indeed, numerous cancer cells have been reported to display overexpression of Plks, and this may contribute to their transformed phenotype [36]–[39].
In budding yeast, overproduction of the polo kinase Cdc5 in cdc13-1 mutant cells with uncapped telomeres has been reported to override the checkpoint-dependent cell cycle block in the G2 phase of the cell cycle [46],[47]. We found that overproduction of Cdc5 impairs the replication checkpoint, which delays S phase in the presence of the alkylating agent MMS (methylmetane sulfonate, Figure 1A). Indeed, Figure 1A shows that MMS treated wild type cells accumulate in S phase for a very long period (1C<DNA<2C), while Cdc5 overproducing cells rapidly go through the replication phase and reach a G2/M DNA content (2C). Moreover, the DNA damage-induced phosphorylation of Rad53 is essentially abolished in Cdc5 overproducing cells treated with zeocin, an agent causing DSBs (Figure 1B).
We have to assume that, although the DNA damage checkpoint inhibits Cdc5 [29],[46], contribuiting to block cell cycle in the presence of DNA damage, when CDC5 is placed under the control of the GAL1 promoter, the DNA damage-induced inhibition on overproduced Cdc5 is not complete. This is likely due to the elevated Cdc5 levels, which are higher than the endogenous amount (see also Figure S1), leading to the override of the checkpoint response. Indeed, it was previously shown that the overproduction of Cdc5, which is a finely regulated protein [29], causes severe phenotypes during an unperturbed cell cycle [48]–[51].
In order to expand the analysis on the crosstalk between polo kinases and checkpoint pathways, and possibly to understand why overexpression of Plks is often found in tumor cells characterized by uncontrolled proliferation and genome instability, we analysed the effects of elevated Cdc5 levels on the DSB-induced checkpoint cascade in S. cerevisiae. We took advantage of a standard yeast genetic system (JKM background) in which one irreparable DSB can be induced at the MAT locus by expressing the site-specific HO nuclease [8]. We overexpressed wild-type CDC5 and the two cdc5-ad and cdc5-kd mutant alleles (adaptation-defective and kinase-dead alleles, respectively [51]) from the galactose-inducible promoter and examined Rad53 phosphorylation and in situ auto-phosphorylation activity, which are routinely used as markers of DNA damage checkpoint activation [52]. To prevent variations due to cell cycle differences, we first arrested cells with nocodazole in mitosis, a cell cycle stage in which the DSB-depended checkpoint can be fully activated [12], and subsequently added galactose to induce Cdc5 overproduction and HO-break formation, while maintaining the cell cycle block. Figure 2A shows the FACS profiles of the cell cultures. We observed that overproduction of Cdc5 impairs the accumulation of hyper-phosphorylated Rad53 forms and prevents Rad53 auto-phosphorylation activity in response to DSB formation (Figure 2B). Interestingly, overproduction of the protein variants Cdc5-kd or Cdc5-ad did not significantly interfere with Rad53 phosphorylation and activation, suggesting that the kinase activity of Cdc5 and its capacity to interact with specific target(s) are required to override the DSB-induced Rad53 activation.
In vertebrates, polo kinases regulate the DNA damage checkpoint response by affecting the signal transduction pathway at different levels; interestingly, Chk2, the homologue of Rad53 in human cells, interacts with and is phosphorylated by the polo kinases Plk1, Plk3 and Plk4 [26]–[28].
Therefore, we tested whether the overproduction of Cdc5 might override Rad53 activation by targeting directly the Rad53 protein and/or by acting on other upstream checkpoint factors.
We failed to co-immunoprecipitate Rad53 and Cdc5, when expressed at endogenous levels or by using the polo box of Cdc5 in a standard GST pull down assay; however, we retrieved Rad53 with overproduced Cdc5 (Figure S2). Considering such physical interaction, we analyzed how overproduction of Cdc5 might affect the events leading to full activation of Rad53, which involves a two steps-based mechanism: an in trans phosphorylation event mediated by PIKKs, followed by auto-phosphorylation [53]. In theory, Cdc5 might affect any of these events required to activate Rad53. We analysed the effect of Cdc5 overexpression on the PIKKs-dependent phosphorylation of Rad53 by taking advantage of the catalytically inactive rad53-K227A mutant. Such protein can be phosphorylated in trans by the upstream kinases, but does not undergo auto-phosphorylation in the presence of DNA damage [52], allowing us to separate and discriminate the two steps.
In nocodazole blocked cells, induction of a single irreparable HO cut induced Mec1-dependent phosphorylation of the Rad53-K227A protein variant (Figure 3A). As expected, the corresponding phosphorylated bands of Rad53-K227A protein were not visualized by western blot using the monoclonal antibody (Mab.F9) which is specific for the auto-phosphorylated and active Rad53 isoform [54]. Moreover, the same phospho-specific antibody did not significantly detect Rad53 in wild type cells responding to DSB when Cdc5 is overproduced, confirming the results of the in situ kinase assay (Figure 2B). A residual shifted band of Rad53, visualized in CDC5 overexpressing cells through the highly sensitive Mab.EL7 antibody (both in Figure 2B and Figure 3A, and in other figures below), could reflect low levels of Rad53 activation not detected by the antibody against the active form; this is consistent with the residual Rad53 activity in the in situ analysis in Figure 2B. In any case, it is unlikely that this remaining Rad53 activity is sufficient to maintain a full checkpoint response, since overproduction of Cdc5 functionally overrides the cell cycle block in the presence of DNA damage.
Significantly, Cdc5 overproduction abolished DSB-induced in trans phosphorylation of the Rad53-K227A variant (Figure 3A). This result rules out the hypothesis that Cdc5 may override the DSB-induced checkpoint acting only on the auto-phosphorylation step of Rad53 activation, and suggests that CDC5 overexpression likely impairs the Mec1-dependent in trans phosphorylation and activation of Rad53.
The residual Rad53 phosphorylation and activity in the presence of high levels of Cdc5 might suggest that the upstream Mec1 kinase, which is mainly responsible of the Rad53 activation in the presence of a single DSB in wild type cells [55], is strongly but not fully inhibited. Alternatively, Mec1 may still be functional as a kinase, but impaired in fully trans-activating Rad53. To test more directly the activity of the upstream kinase Mec1, we analysed the phosphorylation state of its interacting subunit Ddc2, the ortholog of human ATRIP, and that of the checkpoint mediator Rad9, which are known to be directly phosphorylated by Mec1 [1]. Cells were arrested with nocodazole and CDC5 overexpression and induction of a single unrepairable DSB were induced by galactose addition (Figure 3B). Western blot analysis indicate that phosphorylated isoforms of Ddc2 and hyper-phosphorylated Rad9 (indicated by the arrow in Figure 3) accumulated after the formation of the HO cut in wild type cells, as expected; however, overexpression of Cdc5 reduced the DSB-induced hyper-phosphorylated form of both Ddc2 and Rad9, suggesting that the activity of Mec1 kinase is strongly impaired in the presence of high level of Cdc5. A careful analysis of the blot shown in Figure 3B or in analogous experiments indicates that reduced levels of phosphorylated Rad9 isoforms are present in CDC5 overexpressing cells, suggesting that Mec1 could still retain a flebile activity toward Rad9 and Rad53, as discussed above. In addition, it is known that Rad9 is a target of multiple kinases [56] and we cannot rule out the possibility that the residual phosphorylation of Rad9 observed in cells with elevated levels of Cdc5 may be due to other kinase(-s), including Cdc5 itself.
Taken together the results shown in Figure 1, Figure 2, and Figure 3 indicate that Cdc5 activity overrides the DSB-induced checkpoint by influencing an early step of the Mec1 signaling pathway, likely reducing the functionality of Mec1 activity. However, it is possible that Cdc5 may target multiple substrates, including the Mec1 interactor Ddc2, the checkpoint mediator Rad9, whose role in promoting Mec1-to-Rad53 signaling is well established, and Rad53 itself, thus counteracting the checkpoint signaling pathway at several levels.
Robust Mec1 and Rad53 activation is not triggered by the DSB itself, but requires multiple interconnected events following the formation of the lesion, including the generation of nucleolytic-dependent 5′-to-3′processing of the DNA ends and recruitment of various DNA repair and checkpoint factors onto the long stretches of the generated ssDNA [4].
Therefore, we investigated whether Cdc5 may control Mec1 signaling by affecting DSB processing. We measured the kinetic of ssDNA formation after a single unrepairable DSB in cells overexpressing CDC5. Cells were arrested in mitosis, to prevent cell cycle-dependent effects on resection [57], and samples were collected at various time points after HO nuclease induction (Figure 4). The kinetic of production of ssDNA regions in genomic DNA was tested by the loss of restriction sites distal to the HO-cut site which leads to the accumulation of undigested ssDNA fragments detectable with a strand-specific probe after alkaline electrophoresis (see the scheme of the unprocessed and processed DNA locus in Figure 4A). CDC5 overexpressing cells reproducibly exhibited a slower DSB resection, measured by the kinetic of appearance of DNA fragments, which correlated with a reduced phosphorylation of Rad53 (Figure 4B–4D). However, we found that, although the kinetic of DSB ends resection was delayed, high levels of Cdc5 do not prevent the generation of a long ssDNA track (25 kb) which is required to repair the DSB in a specific yeast genetic background [14] by the single-strand annealing process (Figure S3).
We previously identified a role for the checkpoint mediator Rad9 in inhibiting the kinetic of DSB ends resection, likely by generating a non-permissive chromatin configuration around the DSB and/or interfering with the action of nucleases [58]. Therefore, we analyzed the Rad9 contribution in delaying DSB processing in CDC5 overexpressing cells. Wild-type or rad9Δ cells, with or without GAL1::CDC5, were arrested in mitosis by nocodazole treatment and the same experiment described in Figure 4B was performed. We found that the kinetic of appearance of ssDNA fragments was accelerated in rad9Δ strains, despite the high levels of Cdc5 kinase (Figure 5A and 5B). Moreover, the faster DSB resection in CDC5 overexpressing rad9Δ cells also correlated with a modest increase in Ddc2 phosphorylation (Figure 5C); however, the phosphorylated state of Ddc2 did not reach the same level found in wild-type and rad9Δ cells, suggesting that overproduction of Cdc5 impaired Mec1-dependent signaling also in a rad9Δ background. These results suggest that elevated levels of Cdc5 may slow down DSB processing through the action of the Rad9-dependent barrier on resection [58], likely targeting Rad9 itself or other factors involved in this mechanism. Interestingly, many of the proteins involved in DSB ends processing (i.e. Rad9, Dna2, Xrs2 and Sae2) are phosphorylated by CDK1 [59],[60] and inspection of their protein sequence reveals that they may be potential targets of Cdc5.
Hence, Cdc5 may influence the DSB response acting on multiple factors, affecting DSB processing and Mec1-signaling; moreover, the possibility that Cdc5 might specifically regulate Rad53 by influencing its interaction with the checkpoint mediator Rad9 cannot be excluded.
Since high levels of Cdc5 did not prevent the generation of long ssDNA regions but inhibit Mec1-signaling, we tested, by chromatin immunoprecipitation (ChIP), whether overexpression of CDC5 affected the recruitment of checkpoint factors onto the HO-induced DSB lesion in nocodazole-arrested cells. Sheared chromatin from formaldehyde crosslinked cells taken at different time-points after galactose addition was immunoprecipitated to recover checkpoint proteins (i.e. Ddc2, Ddc1, Dpb11, Rad9) carrying the MYC or HA epitope tags at their carboxyl-terminal end. Quantitative multiplex PCR was then used to monitor co-immunoprecipitation of DNA fragments located either 66 kb centromere-proximal to the MAT locus (CON) or 1 kb away from the HO-cut site (DSB) (Figure 6A).
Ddc2 and Ddc1 association at the DSB was not significantly affected in CDC5 overexpressing cells blocked by nocodazole treatment (Figure 6B and 6C). The Mec1 interacting factor Ddc2 and Ddc1, one of three subunits of the stable PCNA-like 9-1-1 checkpoint complex, are recruited early onto a DSB lesion [61]–[63]. We, therefore, assume that Cdc5 overproduction does not prevent the recruitment of upstream checkpoint protein complexes onto damaged DNA. This observation also confirms that elevated levels of Cdc5, while delaying resection, do not prevent the generation of ssDNA (see Figure 4, Figure 5, and Figure S3) which is required for the recruitment of checkpoint factors [4].
Similarly, we found that overproduction of Cdc5 did not prevent the localization near the DSB of Dpb11 (Figure 6D), the yeast ortholog of TopBP1, which, together with the 9-1-1 complex, stimulates the Mec1 kinase activity [64].
Moreover, when we tested by ChIP analysis the binding of the checkpoint mediator Rad9, we found that also its localization onto the DSB was not altered in CDC5 overexpressing cells (Figure 6E).
Taken together, the ChIP analyses of checkpoint factors at a DSB site indicate that high levels of Cdc5 kinase do not significantly interfere with the binding of checkpoint proteins to a processed DSB.
We then tested the DSB binding of Sae2, which is a protein regulated by CDK1 [60] and PIKKs [65] after DNA damage and is involved in DSB processing [5] and checkpoint inactivation [66],[67]. Surprisingly, while in wild-type cells Sae2 loading was not significantly enriched at the HO-cut site (Figure 7A), likely because of its dynamic and transient binding to DSBs [67], Sae2 localization near the break greatly increased in CDC5 overexpressing cells (Figure 7A). To test whether Cdc5 may specifically target Sae2 influencing its binding onto DSBs, we analysed the level and modification of Sae2 by western blotting following DSB formation. In nocodazole-blocked cells, induction of the HO cut caused PIKKs-dependent phosphorylation of Sae2 at the same time-points at which Rad53 phosphorylation was observed (Figure 7B). Interestingly, although high levels of Cdc5 impair Rad53 phosphorylation, they seem to cause hyperphosphorylation of Sae2. Infact, in CDC5 overexpressing cells we observed the appearance of a ladder of slower migrating forms of Sae2 (Figure 7B), which are abolished by in vitro treatment with λ phosphatase (Figure 7C), indicating that they are due to phosphorylation events of Sae2. We then found that overproduction of Cdc5 induces Sae2 hyper-phosphorylation in untreated cells and in nocodazole-blocked cells without the HO-cut formation (Figures 7D), supporting the idea that Sae2 might be a direct target of Cdc5. Indeed, as mentioned above, Sae2 protein sequence reveals several sites that could be bound and/or phosphorylated by Cdc5 (Figure 8A). The C-terminus of Cdc5, like other Polo-like kinases, contains a phospho-serine/phospho-threonine binding domain called the Polo-box Domain (PBD) [19]. The PBD is known to bind Plk substrates after they have been “primed” by a preliminary phosphorylation by another protein kinase [19]. Interestingly, the putative PBD binding motif of Sae2 has been previously shown to be phosphorylated by CDK1 [60], making it a perfect candidate for mediating the interaction between Sae2 and Cdc5. Indeed, by a 2-hybrid assay we found that the PBD of Cdc5 interacts with Sae2 (Figure 8B), and a recombinant GST-PBD fusion protein, purified from E. coli, precipitated Sae2-3HA from yeast extracts (Figure 8C).
Taken together, the results shown in Figure 7 and Figure 8 indicate that Cdc5, through its PBD, interacts with Sae2, causing its hyper-phosphorylation and accumulation at the DSB (see also a model in Figure 8D). It is interesting to point out that CtIP, the functional ortholog of Sae2 in human cells, was found to be associated to chromatin following DNA damage and its chromatin binding is promoted by phosphorylation and ubiquitination [68]. Indeed, recent evidences indicate that CtIP and Ctp1 (the CtIP counterpart in S. pombe [69]), are recruited to DSB sites through their interaction with Nbs1 [70]–[72], a subunit of Mre11 complex, and BRCA1 [73],[74]. Moreover, CtIP is phosphorylated and regulated by CDK1 [74],[75]. In yeast, Sae2 is involved both in promoting an early step of DSB ends resection [5] and in inactivating checkpoint signaling during recovery and adaptation [66],[67], although the exact role of Sae2 in these processes is not yet fully understood. Interestingly, the overproduction of Sae2 also causes the overriding of the Mec1-signaling [66], while deletion of SAE2 gene prevents switching off of the checkpoint [65],[66].
One possible working model (Figure 8D), which needs to be verified, predicts that the increased and persistent binding of Sae2 to a DSB, induced by overproduction of Cdc5, may affect both DSB resection and Mec1-signaling. It is tempting to speculate that even physiological levels of Cdc5 may regulate Sae2 during recovery and adaptation, contributing to switch off the checkpoint signal. It is also possible that Sae2 is regulated by Cdc5 only when this kinase is expressed at elevated levels, leading to the checkpoint overriding. Indeed, such situation is frequently observed in cancer cells, when Plks are overexpressed [36]–[39], suggesting that what we found in yeast may represent a model for a pathological condition in human cells. Future works, requiring the analysis of sae2 mutations in the sites regulated by Cdc5, may help to discriminate between the two possibilities.
In conclusion, in the present study we further explored the role of the polo kinase Cdc5 in attenuating the DNA damage checkpoint in budding yeast. We found that overproduction of Cdc5 affects different parameters of the cellular response to an inducible DSB: i) it overrides Mec1 signaling and prevents the phosphorylation of various Mec1 targets (Rad53, Rad9, Ddc2); ii) it causes a slower resection of DSB ends in a RAD9-dependent manner; iii) it binds Sae2 protein, causing its hyper-phosphorylation and leading to its increased and persistent binding onto DSB.
The emerging scenario suggests that Cdc5 may target multiple factors involved in various aspects of the cellular response to DSB lesions and DNA damage checkpoint signaling. Indeed, Cdc5 is a fundamental regulator of cell cycle progression and targets many proteins throughout a normal cell cycle [20]. Most of the Cdc5 substrates are proteins previously phosphorylated by CDK1, which is the principal regulator of the DSB-induced response, regulating DSB processing, recombination and checkpoint signaling [57]. Here we found that high levels of Cdc5 separately affected Mec1 signaling and DSB processing, leading us to speculate that Cdc5 may regulate multiple targets in response to DNA damage, including factors phosphorylated by CDK1. In support of such hypothesis, Plks phosphorylate, in vertebrates, several proteins involved in various aspects of the DNA damage response, such as FANCM [25], Claspin [21]–[24], Chk2 [26]–[28], MCM5 [76], MCM7 [77] and others. Moreover, our findings on the functional role of Cdc5 in responding to a DSB in yeast rise the possibility that Plks may also regulate CtIP.
Recently, a proteome-wide screening led to the identification of novel Cdc5 targets in a normal cell cycle [20]; we believe that a similar approach is promising to identify Cdc5 targets regulated in response to DSBs. Good experimental evidence indicates that the functional role of Cdc5 in the DNA damage response is evolutionary conserved and the outputs of such a screening may provide important information for new cancer therapy strategies, targeting Plks and their substrates with specific tools.
Strains are listed in Table S1. All the strains were constructed during this study, and all were derivatives of JKM (MATα, hmldelta::ADE1, hmrdelta::ADE1 ade1-100, trp1delta::hisG, leu2-3, leu2-112, lys5, ura3-52, ade3::GAL::HO), with the exception of strain Y38, which was generated from strain Y5 (YMV80, matΔ::hisG1, hmlΔ::ADE, hmrΔ::ADE1, lys5, ura3-52, leu2::HOcs, ade3::GAL::HO, his-URA3-5′Δleu2-is4). To construct strains, standard genetic procedures for transformation and tetrad analysis were followed. Y38 and Y210 were obtained by integration of ApaI-digested plasmid pJC57 (pGAL1::CDC5-3HA) at the URA3 locus. Y215 was derived by integration of ApaI-digested pJC59 (pGAL1::CDC5-3myc) at URA3 locus. Y220 was obtained by integration of ApaI-digested plasmid pJC62 (pGAL1::cdc5-K110A-3HA) at URA3 locus. Y222 was obtained by integration of ApaI-digested plasmid pJC69 (pGAL1::cdc5-L251W-3HA) at URA3 locus. Deletions and tag fusions were generated by the one-step PCR system [78]. The yeast two-hybrid assay was performed using the B42/lexA system with strain EGY48 (Mata his3 ura3 trp1 6lexAOP-LEU2; lexAOP-lacZ reporter on plasmid pSH18-34) as the host strain [79]. Bait plasmid pEG202-PBD340–705 for the two-hybrid assay, expressing lexA fusion with polo box domain of Cdc5, was obtained by amplifying the corresponding coding sequence of CDC5 gene (aa 340 to 705) from genomic DNA and ligating the resulting fragment into pEG202 (kind gift from R. Brent). Prey plasmids pJG4-5-Swe1173–400 and pJG4-5-SAE2, expressing B42 activating domain fusions, were obtained by amplifying the corresponding coding sequence of SWE1 (aa 173 to 400) and SAE2 (full length) from genomic DNA and ligating the resulting fragments into pJG4-5.
The TCA protein extraction and the western blot procedures have been previously described [29]. Rad53, Rad9, Sae2-HA, Ddc2-HA, Ddc1-myc, Dpb11-myc, Cdc5-HA, Cdc5-myc were analysed using specific monoclonal or polyclonal antibodies: anti-Rad53 Mab.EL7 and Mab.F9 monoclonal [54], anti-HA 12CA5 monoclonal, anti-myc 9E10 monoclonal, anti-Rad9 polyclonal (a kind gift from N Lowndes's lab).
It was performed as previously described [52].
Yeast whole cell extracts were prepared by FastPrep (MP Biomedicals) in NP-40 lysis buffer (1% NP-40, 150 mM NaCl, 50 mm Tris (pH 7.5), 1 mM dithiothreitol (DTT), 60 mM β-glycerophosphate, 1 mM NaVO3, cocktail proteases inhibitors (Roche)). HA-tagged proteins were immunoprecipitated using anti HA monoclonal antibody (12CA5) conjugated to protein G Agarose.
GST and GST-PBD were induced in BL21 E. coli cells as previously described [80] and conjugated to glutathione-Sepharose 4B beads (GSH beads, Amersham). Yeast whole cell extracts, prepared as indicated above, were incubated with GST or GST-PBD GSH beads and rotated for 1 hour at 4°C. Samples were washed three times with NP-40 buffer, boiled in SDS-based sample buffer, and analyzed by Western blotting analysis.
Crude extracts were prepared as described [52], and resuspended in λ phosphatase buffer with or without 4000 U of λ phosphatase (Biolabs). Samples were incubated 30 min at 30°C and resuspended in Laemmli buffer.
Cells grown in YEP-raffinose 3% medium at 28°C to a concentration of 5×106 cells/ml were arrested with nocodazole (20µg/ml). A DSB was produced by adding 2% galactose and inducing the production of the HO endonuclease. The maintenance of the arrest was confirmed by FACS analysis and monitoring of nuclear division. Genomic DNA was isolated at intervals, and the loss of the 5′ ends of the HO-cleaved MAT locus was determined by Southern blotting [14],[81],[82]. To visualize the kinetics of resection, the graphs shown in Figure 4C and Figure 5B display, for each strain and for each ssDNA fragment (r1–r7), the time of the first appearance in the blot. In particular, since the appearance of a ssDNA fragment signal in the gel was due to the loss of the internal SspI sites, we represented the length of the minimal resection for each time point in the graph (see scheme in Figure 4A). All the experiments have repeated al least 3 times. In the corresponding figures, one representative example is shown with its graphic representation.
ChIP analysis was performed as described previously [83],[84]. Multiplex PCRs were carried out by using primer pairs complementary to DNA sequences located 1 kb from the HO-cut site at MAT (DSB) and to DNA sequences located 66 kb from MAT (CON). Gel quantitation was determined by using the NIH Image program. The relative fold enrichments of DSB-bound protein were calculated as follow: [DSB_IP/CON_IP]/[DSB_input/CON_input], where IP and Input represent the amount of PCR product in the immunoprecipitates and in input samples before immunoprecipitation, respectively.
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10.1371/journal.pgen.1004880 | Syd/JIP3 and JNK Signaling Are Required for Myonuclear Positioning and Muscle Function | Highlighting the importance of proper intracellular organization, many muscle diseases are characterized by mispositioned myonuclei. Proper positioning of myonuclei is dependent upon the microtubule motor proteins, Kinesin-1 and cytoplasmic Dynein, and there are at least two distinct mechanisms by which Kinesin and Dynein move myonuclei. The motors exert forces both directly on the nuclear surface and from the cell cortex via microtubules. How these activities are spatially segregated yet coordinated to position myonuclei is unknown. Using Drosophila melanogaster, we identified that Sunday Driver (Syd), a homolog of mammalian JNK-interacting protein 3 (JIP3), specifically regulates Kinesin- and Dynein-dependent cortical pulling of myonuclei without affecting motor activity near the nucleus. Specifically, Syd mediates Kinesin-dependent localization of Dynein to the muscle ends, where cortically anchored Dynein then pulls microtubules and the attached myonuclei into place. Proper localization of Dynein also requires activation of the JNK signaling cascade. Furthermore, Syd functions downstream of JNK signaling because without Syd, JNK signaling is insufficient to promote Kinesin-dependent localization of Dynein to the muscle ends. The significance of Syd-dependent myonuclear positioning is illustrated by muscle-specific depletion of Syd, which impairs muscle function. Moreover, both myonuclear spacing and locomotive defects in syd mutants can be rescued by expression of mammalian JIP3 in Drosophila muscle tissue, indicating an evolutionarily conserved role for JIP3 in myonuclear movement and highlighting the utility of Drosophila as a model for studying mammalian development. Collectively, we implicate Syd/JIP3 as a novel regulator of myogenesis that is required for proper intracellular organization and tissue function.
| A common pathology found in numerous cases of muscle diseases, including congenital myopathies and muscular dystrophies, is aberrantly located nuclei within individual multinucleated muscle cells. However, whether or not mispositioned myonuclei are a cause or consequence of muscle disease states is currently debated. Here, we take advantage of the model organism, Drosophila melanogaster, which shares the conserved myofiber found in mammalian systems, to identify Syd as a novel regulator of myonuclear positioning. We show that Syd is responsible for mediating the activities of Kinesin and Dynein, two motor proteins that exert forces to pull myonuclei into place. Moreover, we demonstrate that Syd-dependent myonuclear positioning also requires intracellular signaling from the JNK MAPK cascade to direct when and how myonuclei are moved into proper position. This work thus identifies developmental cues that direct proper muscle morphogenesis, suggesting that cases of muscle disease may result from a failure to achieve initial spacing of myonuclei. Supporting this notion, we find that loss of Syd impairs muscle function, but resupplying Syd restores proper myonuclear spacing and muscle function. These findings are particularly important as mispositioned myonuclei gain traction as a potential contributing factor in cases of muscle disease.
| The intracellular location of the multiple nuclei within muscle cells has recently gained traction as a potential contributing factor to muscle disease. Improper myonuclear position strongly correlates with muscle disease [1], [2] and muscle weakness [3]–[5]; yet, the mechanisms of myonuclear movement and positioning have only recently begun to emerge.
Recent work in Drosophila melanogaster has identified myonuclear positioning as a microtubule-dependent process [4], [6], requiring both Kinesin-1 (Kinesin) and cytoplasmic Dynein (Dynein), the plus- and minus-end directed microtubule motor proteins, respectively [4], [5], [7]. Specifically, two spatially distinct Kinesin- and Dynein-dependent processes position myonuclei [5], [7]. In the first, Kinesin and Dynein exert forces directly on the nucleus: Kinesin extends the front of the myonucleus in the direction of travel, and Dynein is necessary for the retraction of the trailing edge of the myonucleus to complete a translocation step [7]. In the other, Kinesin transports Dynein to the cell cortex near the ends of the muscles where Dynein then pulls microtubule minus-ends and the attached myonuclei into place [5], [7]. Disruption of either pathway leads to mispositioned myonuclei [5], [7], but how Kinesin and Dynein are both used in two spatially segregated mechanisms, and whether their actions in these two different locations are connected or interdependent, is not known.
In many cellular contexts, adaptor proteins specify a variety of motor protein functions. Adaptors either recruit or restrict motors to particular cellular locations [8]–[11], mediate specific motor-cargo interactions [12]-[20], ensure proper temporal and spatial activation of motor function [11], [21]–[23], or enhance motor processivity [24]–[26]. Additionally, adaptor proteins that influence motor function respond to distinct stimuli. Some adaptor proteins regulate motors in response to physical changes of the cell brought on by mechanical strain [27], while others respond to environmental cues via the induction of signaling cascades [13], [28]–[30]. Thus, adaptors are ideal candidates to direct the activities of Kinesin and Dynein during myonuclear positioning.
The JNK signaling cascade is one of three classical mitogen-activated protein kinase (MAPK) cascades conserved across eukaryotes [31]–[37]. Each cascade consists of a MAPK kinase kinase → MAPK kinase → MAPK signaling module that impacts various cellular functions in response to specific stimuli. One function of the JNK MAPK signaling cascade is to phosphorylate JNK-interacting proteins (JIPs), a class of adaptor proteins that regulate Kinesin and Dynein activity in neurons [13], [38]–[41]. While all JIPs interact with both Kinesin and Dynein [14], [21], [38], [39], each adaptor has unique motor-binding domains [13], [38], [42], [43]. JIP1/2 proteins contain a shared motor-binding domain, while JIP3/4 family members have separate Kinesin- and Dynein-binding domains that facilitate binding to both motors simultaneously; thus, different JIPs coordinate motor functions via distinct mechanisms [13], [21], [38], [41]–[44]. Given these features, the JIP proteins could regulate the two Kinesin- and Dynein-dependent pathways governing myonuclear positioning.
To address this possibility, we examined the role of the JIP3 ortholog, Sunday Driver (Syd), during Drosophila muscle morphogenesis. We find that Syd responds to the activation of the JNK signaling cascade to regulate myonuclear positioning by specifically promoting Kinesin-dependent localization of Dynein to the muscle ends. Furthermore, we propose that Kinesin and Dynein are both initially perinuclear and that Syd specifies a subset of Kinesin to relocate Dynein to the muscle cell cortex to initiate cortical pulling of myonuclei. Finally, we demonstrate that disrupted JNK signaling or loss of Syd decreases muscle function, indicating that proper regulation of the JNK signaling cascade and the JIP adaptor proteins is not only necessary for intracellular organization but also critical for muscle function.
We hypothesized that the adaptor protein, Sunday Driver (Syd), may regulate one, or both, of the spatially distinct Kinesin- and Dynein-dependent pathways that impact myonuclear positioning from 1) the nucleus, and 2) the muscle end [5], [7]. In neurons, Syd physically interacts with both Kinesin and Dynein to coordinate motor activity and promote axonal transport (Fig. 1A) [39], [40], [45]. Because Syd has only been studied in the central nervous system (CNS), we immunostained for Syd and quantified the immunofluorescence intensity of the signal specifically in the Lateral Transverse (LT) muscles (Fig. 1B). Importantly, we compared Syd intensity to that of Tropomyosin, which did not significantly vary between genotypes (S1A-B Figs.) and therefore served as an internal immunostaining control. Intensities were plotted as a function of position (Fig. 1C), and both the peak intensity value (Fig. 1D) and the area under the curve (Fig. 1E), indicating the maximum and total fluorescence, respectively, were used as measures of Syd protein levels.
These analyses demonstrated that Syd is highly expressed in the cytoplasm of muscle cells with no discernable regions of distinct accumulation (Fig. 1B–E, S1A–B Figs.), similar to observations of Syd in other tissues [39], [45]. This signal was lost in two different syd mutants (sydA2 and sydZ4) and in embryos that were depleted of Syd via GAL4/UAS-mediated [46] expression of Syd-RNAi specifically in the muscles (Fig. 1A–E, S1A–B Fig.). These analyses indicate that Syd is expressed in muscle tissue at the correct time and place to influence myonuclear positioning.
To determine whether myonuclear positioning was disrupted in syd mutants, we measured the distance between the myonuclei and the ends of the muscles at embryonic stage 16 (16 h After Egg Laying, AEL) as previously described [5], [7]. At this developmental stage, the myonuclei in controls reside in two groups near the dorsal and ventral ends of the muscles. However, in both sydZ4 and sydA2 mutants, the myonuclei were located significantly further from the muscle ends relative to controls (Fig. 2A,C), suggesting that Syd is required for proper myonuclear positioning.
To confirm that the role of Syd in myonuclear positioning is muscle autonomous, we assessed myonuclear position when Syd-RNAi was expressed in the muscles (Dmef2-Gal4), tendons (Stripe-Gal4), or CNS (Elav-Gal4). Expression of Syd-RNAi specifically in the muscles phenocopied syd mutants, while RNAi-mediated depletion of Syd in either the tendons or the CNS had no effect on myonuclear position (Fig. 2B,D). These data indicate that Syd impacts myonuclear position in a muscle autonomous manner.
Finally, we rescued syd-related myonuclear positioning defects with GAL4/UAS-mediated expression of JIP3 (also known as mSyd2 and JSAP1). JIP3 is the mammalian ortholog of Syd, bearing 69% similarity and 42% identity to Drosophila Syd. Expressing JIP3 in the mesoderm using twist-Gal4 (Fig. 1A–E, S1A–B Fig. and S2A Fig.) rescued myonuclear positioning in syd mutants (Fig. 2A,C). Additionally, because mammalian JIP3 and Drosophila Syd do not align in the regions targeted by Syd-RNAi (S2B–E Fig.), we co-expressed JIP3 and Syd-RNAi in the muscles and rescued the effects of RNAi-mediated Syd depletion on myonuclear position (Fig. 2B,D). Collectively, these data emphasize the conservation of this protein across species and confirm that Syd is required in muscle tissue for proper positioning of myonuclei.
Myonuclear positioning defects in syd mutants are similar to those in both Kinesin heavy chain (Khc) and Dynein heavy chain (Dhc64C) homozygous mutants [4], [5], [7]. Given that Syd physically interacts with Kinesin and Dynein [39], [40], [45], we tested whether Syd genetically interacts with Kinesin and/or Dynein during myonuclear positioning by examining doubly heterozygous embryos. Importantly, the myonuclei are properly positioned in embryos heterozygous for Khc8 (null), Dhc64C4-19 (null), and either syd allele (S3A Fig.) [7]. However, double heterozygotes of Khc8/+; syd/+ and Dhc64C4-19,+/+,syd exhibited mispositioned myonuclei (Fig. 3A–C), suggesting that Syd regulates myonuclear positioning in a Kinesin- and Dynein-dependent manner. Reciprocal crosses to control for maternal loading effects produced identical results (S3B–C Fig.). These data suggest that Kinesin, Dynein, and Syd work in a common pathway to position myonuclei.
Adaptors often regulate motors by modulating motor localization. We therefore examined Kinesin (S4A–D Fig.) and Dynein (Fig. 4A–D) localization in control and syd mutant embryos, focusing on Kinesin concentrated near the nucleus and Dynein accumulation at the muscle ends, as we previously identified these localization patterns as necessary for proper myonuclear positioning [5], [7]. The intensity of Kinesin or Dynein immunofluorescence was measured relative to that of Tropomyosin, which remained constant between genotypes (S1C–D Fig and S4E–F Fig.), and values were plotted as a function of position. The location of peak intensity indicated where the motor was enriched, while the amplitude of peak intensity and the integrated total fluorescence approximated protein levels. The location and levels of Kinesin immunofluorescence were similar in both controls and syd mutants (S4A–F Fig.), suggesting that Kinesin is properly localized near the myonuclei in syd mutants. In contrast, Dynein was reduced at the ends of the muscles in syd mutants compared to controls (Fig. 4A). This reduction was not due to a decrease in the amount of Dynein present (Fig. 4C,D), but rather, the distribution of Dynein was shifted towards the nucleus in syd mutants (Fig. 4B). Consistent with previous reports [7], Dynein was similarly mislocalized in Khc8 mutants (Fig. 4A–D). Together, these data suggest that Syd does not affect Kinesin localization but is required for Kinesin-dependent localization of Dynein to the muscle ends in Drosophila.
Importantly, inefficient transport of Dynein is not due to defects in the microtubule network, as gross microtubule organization is comparable to controls in both Khc8 [7] and syd mutants (S5A–C Fig.). Therefore, we hypothesized that, as a result of its ability to bind to Kinesin and Dynein [39], [40], [45], Syd mediates an association between the two motors to facilitate Kinesin-dependent localization of Dynein to the cell cortex at the muscle ends to promote proper myonuclear positioning.
This hypothesis implies that the intracellular distribution of Syd would be disrupted in Khc8 mutants but unaffected in Dhc64C4–19 mutants. Indeed, Syd was aberrantly enriched near the nucleus in Khc8 mutants, but Syd was found throughout the cytoplasm in controls and Dhc64C4–19 mutants (Fig. 5A–D, S1E–F Fig.). These data argue that the localization of Syd is Kinesin-dependent, but Dynein-independent.
These data are consistent with a role for Syd in the cortical pulling mechanism of myonuclear positioning in which Kinesin transports Dynein to the muscle ends, and cortically-anchored Dynein pulls microtubules and the attached nuclei towards the muscle ends [5], [7]. To determine whether Syd functions in this pathway, we tested whether Syd functionally interacts with factors shown to be required for this pathway: Dynein light chain (Dlc90F), Raps, and CLIP-190. We also examined Kinesin light chain (Klc), a regulator of the Kinesin motor. Myonuclear position was unaffected in Klc8ex94, Dlc90F05089, raps193, or clip190KG06490 single heterozygous embryos (S3A Fig.). However, embryos doubly heterozygous for syd and each of the aforementioned alleles exhibited significantly mispositioned myonuclei (Fig. 3A–C; S3B–C Fig.), suggesting that Syd functionally interacts with each factor in the cortical pathway. Syd did not interact with two different alleles of lis1, a regulator of Dynein that does not impact myonuclear positioning in Drosophila [5], highlighting the specificity of the observed genetic effects (Fig. 3A–C; S3B–C Fig.). Together, these data suggest that Syd works with Khc, Klc, Dhc64C, Dlc90F, Raps (Pins), and CLIP-190 in a common pathway to position myonuclei. Moreover, these factors impact cortical pulling of myonuclei [5], [7], suggesting that Syd also affects this process.
A role for Syd in one pathway does not exclude its participation in parallel pathways that work towards the same goal. Kinesin and Dynein also exert forces directly on the front and back of myonuclei, respectively, to promote myonuclear movement [7]. This leads to dynamic nuclear shape changes in which myonuclei transition between spherical and elongated nuclear outlines during translocation. Using previously described time-lapse imaging techniques [7], we found that syd mutants exhibited myonuclear shape changes similar to controls, and that the myonuclei in syd mutants maintained the correct leading edge during translocation (S6A–D Fig.). These data indicate that Syd does not impact the activities of Kinesin and Dynein that influence nuclear dynamics to promote myonuclear positioning [7].
Together with previously documented physical interaction data [39], [40], [45], these new findings collectively suggest that Syd specifically mediates Kinesin-dependent localization of Dynein to the muscle ends to promote cortical pulling of myonuclei [5], [7]. Syd contains a JNK-binding domain (JBD; Fig. 1A, purple) [40], which mediates binding to JNK/p-JNK [39] and facilitates JNK-dependent phosphorylation of Syd/JIP3 [47]. Furthermore, Syd and related JIPs respond to JNK signaling in neurons to promote cargo binding and influence Kinesin-dependent axonal transport [38], [39], [41], [47], [48]. Thus, we hypothesized that Syd could respond to induction of the JNK signaling cascade to impact myonuclear positioning in muscle tissue.
In Drosophila, the JNK signaling cascade is composed of Tak1 (MAPKKK), Hep (MAPKK), and Bsk (MAPK), the Drosophila ortholog of mammalian JNK [49]. We used the GAL4/UAS system to deplete Tak1, Hep, or Bsk specifically in the muscles, and in each case, the myonuclei were significantly mispositioned further from the muscle ends (S3D Fig.), mimicking syd, Khc, and Dhc64C mutants. Furthermore, muscle-specific expression of dominant negative Bsk (Bsk-DN), which cannot be phosphorylated [50], similarly affected myonuclear position (Fig. 6A,B), indicating that loss of JNK signaling disrupts myonuclear positioning.
Interestingly, myonuclear position was also sensitive to the levels of JNK signaling. Overactivation of the cascade with muscle-specific expression of Hep-Act, a constitutively active phosphomimetic form of Hep [50], similarly resulted in mispositioned myonuclei (Fig. 6A,B), indicating that disrupted regulation of JNK signaling negatively impacts myonuclear positioning.
We next tested whether JNK signaling requires Syd to position myonuclei. bsk/+; syd/+ double heterozygotes had mispositioned myonuclei similar to syd homozygous mutants and embryos with disrupted JNK signaling (Fig. 6A,B; S3A Fig.), suggesting that Syd and Bsk (JNK) work in a common pathway to position myonuclei. Furthermore, embryos expressing Bsk-DN resembled both Khc8 and syd mutants, with both Dynein (Fig. 6C–F, S1G–H Fig.) and Syd (Fig. 6G-J, S1I–J Fig.) aberrantly enriched near the nucleus. These data argue that JNK signaling is required for the cortical pulling pathway of myonuclear positioning.
The effects of overactive JNK signaling were more intriguing. In embryos expressing Hep-Act, Dynein was often properly located at the muscle ends, resembling controls; however, in many instances, Dynein was found more diffusely throughout the cytoplasm (Fig. 6C–F, S1G–H Fig.). When averaged together, these data generated an intermediate distribution curve (Fig. 6D, green). In contrast, Syd consistently becomes aberrantly enriched at the muscle ends in embryos expressing Hep-Act (Fig. 6G–J, S1I–J Fig.). Collectively, these data support that JNK signaling is indeed required to promote Kinesin- and Syd-mediated localization of Dynein to the muscle ends. These data also suggest that improper regulation of JNK signaling affects the maintenance of both Dynein and Syd at the muscle ends.
Syd likely transduces JNK signaling through its JBD to influence Kinesin-dependent transport of Dynein. However, the syd alleles result in premature coding truncations that produce N-terminal fragments of Syd (Fig. 1A) [45]. Thus, the JBD as well as the Kinesin- and Dynein-binding domains are present in syd mutants; yet, Dynein still fails to localize to the muscles ends, suggesting that the C-terminus of Syd is critical for motor transport. Indeed, immunostaining with an N-terminal Syd antibody demonstrated that truncated Syd was enriched near the nucleus in syd mutants despite proper JNK signaling in these genetic backgrounds (Fig. 6G–J, S1I–J Fig.). These data demonstrate that, although no known domains have been identified in the C-terminus of Syd, the C-terminus is required for Kinesin- and JNK signaling-dependent transport of Syd, and therefore Dynein, to the muscle end.
We next investigated whether Syd and/or JNK signaling was necessary for muscle function by quantifying stage L3 larval locomotion using previously described tracking techniques [4], [5], [51]. However, consistent with previous reports [45], only 14–17% of syd mutants hatch into larvae (Fig. 7A), and the survivors die soon after hatching, preventing assessments of L3 larval velocity. In contrast, while muscle-specific depletion of Syd using RNAi also leads to decreased viability, 30% of embryos hatch and survive to adulthood, and this lethality can be fully rescued with muscle-specific expression of JIP3 (Fig. 7B). Similarly, while genetic mutations in JNK signaling components leads to embryonic lethality [49], muscle-specific expression of Bsk-DN or Hep-Act does not impair viability at larval stages of development (Fig. 7C). Thus, GAL4/UAS-mediated tissue-specific loss and gain of Syd and JNK signaling was used to assess larval locomotion.
We found that larvae lacking Syd in the muscles crawled significantly slower than controls, but expression of JIP3 in the muscles rescued these locomotion defects (Fig. 7D). Additionally, although overactivation of the cascade was more deleterious, any disruption in JNK signaling negatively affected locomotive ability (Fig. 7D). Importantly, no changes were detected in the muscle innervation sites or the number of boutons at the neuromuscular junctions (S7A–C Fig), both of which, if altered, would be evidence of impaired synaptic transmission [52], [53]. Together, these data indicate that larvae lacking Syd or proper JNK signaling in the muscles exhibit decreased locomotion due to muscle-specific defects independent of communication between the muscles and the CNS.
We next examined myonuclear position in the Ventral Longitudinal (VL) muscles by two methods: 1) the Nearest Neighbor analysis described previously [4] to quantify the degree of clustering amongst adjacent myonuclei, and 2) a Longest Gap analysis to measure the greatest longitudinal span of the muscle devoid of nuclei. Importantly, both of these measures were normalized to muscle size to account for variation between genotypes (Fig. 7I). These analyses demonstrated that muscles depleted of Syd exhibit greater degrees of myonuclear clustering and longer spans of muscle tissue devoid of myonuclei compared to controls (Fig. 7E–G). Again, expression of JIP3 in these backgrounds restores all values to control levels (Fig. 7E–G). Importantly, the number of nuclei per VL muscle remains constant across genotypes (Fig. 7H), indicating that clustered nuclei and large muscle regions lacking nuclei are not due to gains or losses of myonuclei.
The effects of disrupted JNK signaling on myonuclear position were more interesting. Both loss and gain of JNK signaling led to significant defects in myonuclear position by Nearest Neighbor assessment (Fig. 7F); however, concomitant defects in muscle length in these backgrounds were also evident (Fig. 7I), indicating that JNK signaling is required for multiple aspects of muscle development. We previously observed a similar developmental relationship between myonuclear positioning and muscle length/growth [5], highlighting that these two processes are closely co-regulated. Interestingly though, we do not observe similar defects in muscle length in larvae lacking Syd, suggesting that Syd functions downstream of JNK signaling to specifically impact myonuclear positioning. Given the well-characterized role of Syd as a JNK interacting protein (JIP) [39]–[41], [54], these data suggest that, while JNK signaling is required for multiple aspects of muscle development, Syd may be a key link that facilitates JNK signaling-mediated regulation of myonuclear position.
We have used the Drosophila musculature to elucidate the cellular mechanisms and signaling pathways that impact myonuclear position in vivo. The stereotypic distribution of evenly spaced myonuclei is disrupted in embryos and larvae mutant for Sunday Driver (Syd), an adaptor protein known to regulate Kinesin and Dynein activity in neurons [39], [40], [45]. Here, we show that Syd is expressed and required in muscle tissue to regulate motor activity during myonuclear positioning. Moreover, while Kinesin and Dynein are known to influence myonuclear position via two spatially distinct mechanisms [5], [7], we demonstrate that Syd specifically regulates these motors in the context of cortical pulling at the muscle end without affecting motor activity at the nuclear surface. Syd is a member of the JNK-interacting protein (JIP) family, and we demonstrate that JNK signaling is required for Kinesin- and Syd-dependent localization of Dynein to the muscle ends, which facilitates Dynein-based cortical pulling of myonuclei (Fig. 8). Moreover, in the absence of Syd, both Kinesin and Dynein accumulate near the nucleus, where the motors are known to influence nuclear shape changes/dynamics to promote myonuclear translocation. Syd has no impact on nuclear dynamics; thus, we propose that, via instructive JNK signaling, Syd specifies and activates a population of Kinesin at the nucleus to relocate Dynein to the muscle ends to initiate cortical pulling of myonuclei (Fig. 8, numbers 2 and 7). Finally, we show that loss of Syd and disruption of JNK signaling impairs locomotive ability, indicating that Syd-dependent myonuclear positioning is critical for muscle function.
The finding that JNK signaling is required for this process is novel and significant. Prior to this study, work to elucidate the process of myonuclear positioning focused on identifying factors required for the physical mechanics of moving myonuclei through the cell. However, the present work newly identifies intracellular signals that regulate these activities. Specifically, we show that JNK signaling is required for Kinesin- and Syd-dependent localization of Dynein to the muscle ends. What remains unclear is whether JNK-mediated phosphorylation of Syd [39], [47] induces/permits Dynein binding to Kinesin or if a trimolecular Kinesin-Syd-Dynein complex forms prior to JNK signaling-dependent activation of Kinesin motor activity, which, in turn, transports Dynein to the muscle ends and leads to cortical pulling and proper positioning of myonuclei.
Regardless, our data are consistent with Syd being necessary to relay this instructive JNK signal. Specifically, the C-terminus of Syd is critical for this transport, as Kinesin, Dynein, and truncated Syd are all found near the nucleus in syd mutants, which express N-terminal fragments of Syd. This finding was initially unexpected because the minimal domains of Syd required for binding to Kinesin, Dynein, and JNK are all located in the N-terminus (Fig. 1A) [40]. Furthermore, the N-terminus of Syd is sufficient to support Kinesin-based transport in vitro [40], [41]. However, the C-terminus of Syd is more highly conserved than the well-annotated N-terminus [45], and biochemical analysis of JIP3 demonstrates that upstream components of the JNK MAPK signaling pathway bind to C-terminal regions of JIP3 [47], [54]. Consistent with these data, we show that mutants lacking the C-terminus of Syd fail to promote proper transport of Dynein to the muscle ends despite normal JNK signaling in these backgrounds. This argues that both the N- and C-termini of Syd are necessary to relay instructive cues from the JNK signaling cascade, which leads to proper myonuclear positioning.
Interestingly, while JNK signaling is indeed necessary for myonuclear positioning, we also demonstrate that constitutively active JNK signaling impairs this process. Despite the inconsistent phenotype, overall decreased amounts of Dynein are found at the muscle ends when Hep-Act is expressed in the muscles. This suggests that overactive JNK signaling either mildly impairs Dynein transport, or alternatively, prevents the maintenance of Dynein at the muscle end. We favor the latter interpretation based on the observation that increased levels of Syd are found at the muscle end when JNK signaling is overactivated. This suggests that efficient transport, and likely excess transport, indeed occurs in embryos expressing Hep-Act. This activity would transport Dynein to the muscle ends; thus, overactive JNK signaling likely inhibits the maintenance of Dynein at this location.
It is not clear how this inhibition could occur; however, one possibility is that failure to dephosphorylate Syd may impair the ability of Dynein to associate with ARF6-GTP, a membrane-bound protein that binds to JIP3/4 proteins [21]. ARF6-GTP and Klc compete for binding to JIP3, and phosphorylated JIPs preferentially bind and activate Kinesin [38]. However, JIP3–ARF6-GTP interactions enhance binding between ARF6-GTP and Dynactin, a well-known Dynein-interacting protein [21]. Thus, in our model, once the Kinesin-Syd-Dynein complex reaches the cell cortex, perhaps Syd must lose the JNK signal and become dephosphorylated to dissociate from Kinesin and bind to ARF6-GTP. This may facilitate hand-off of Dynein to ARF6-GTP, which would aid in securing Dynein to the muscle end.
Although the details of this final step remain unclear, our model (Fig. 8) likely represents a common mechanism by which Syd and other JIP3 orthologs act. Analogous to the proposed manner in which Syd selects certain Kinesin complexes to initiate transport, the C. elegans ortholog of Syd/JIP3, UNC-16, was found to exhibit similar gatekeeper characteristics by designating specific complexes to initiate axonal transport in neurons [55]. Furthermore, once specified, UNC-16 also mediates Kinesin-dependent transport of Dynein to the ends of nerve processes [14], [48]. Similarly, mammalian JIP proteins mediate Kinesin-driven transport of many large cargoes, including Dynein [21],[38],[41]. Finally, Syd, UNC-16, and JIP3 directly bind to Kinesin heavy and light chains, and all three orthologs bind to p50 and p150Glued, components of the Dynactin complex, a well-known regulator of Dynein [14], [21], [22], [39], [40]. Together with our data, these observations collectively suggest that this mechanism of JIP3-mediated motor coordination (Fig. 8) is well conserved across species and tissues.
These findings are interesting given that Kinesin and Dynein can directly interact in vitro [56]; however, adaptors such as Syd and other JIP proteins are likely required to mediate motor protein interactions to direct specific motor functions in vivo. Consistent with this notion, structural differences between the JIP1/2 subfamily [13], [42] and the JIP3/4 subfamily of proteins [13], [21], [43], [54] impact how each JIP binds to Kinesin and Dynein and influences motor activity [14], [21], [38], [41]. Although these different JIPs can cooperate towards a single goal [57], they often have unique roles and cannot fully compensate for loss of another in vivo [20], [22], [30], [58], [59].
In Drosophila, Syd is the only ortholog of the JIP3/4 family of proteins. Similarly, there is only one ortholog of the JIP1/2 family of proteins, Aplip1. Thus, in the context of myonuclear positioning, it is tempting to speculate that while Syd modulates motor activity to promote cortical pulling of myonuclei, perhaps Aplip1 affects the ability of the motors to regulate nuclear dynamics. Furthermore, perhaps JNK signaling is the necessary switch that shifts motor function from one mechanism to the other. Indeed, work in Drosophila neurons shows that activation of the JNK signaling cascade disrupts the association between Aplip1 (JIP1) and Kinesin [30], and here we show that JNK signaling is required for coordinated Kinesin-Syd (JIP3) function in the cortical pulling pathway of myonuclear positioning.
Regarding the biological relevance of Syd-dependent mechanisms, we demonstrate that Syd and proper regulation of JNK signaling are critical for muscle function. RNAi-mediated loss of Syd specifically in the muscles leads to decreased larval velocity, and similar locomotive dysfunction is observed in larvae with muscle-specific disruptions in JNK signaling. Consistent with previous reports [4], [5], we show that these locomotive defects are not due to impaired communication with the CNS, but rather correlate with mispositioned myonuclei. These analyses further revealed that muscle-specific disruptions of the JNK signaling cascade additionally impair muscle length/growth, which can also affect locomotion [5]. These findings indicate that JNK signaling is, not surprisingly, necessary for multiple aspects of muscle development. However, that similar concomitant defects are not observed in larvae lacking Syd argues that the role of Syd is more specific to mechanisms of myonuclear positioning. Finally, muscle-specific expression of mammalian JIP3 simultaneously restored myonuclear spacing and rescued larval crawling defects in larvae lacking Syd. These data highlight the high degree of conservation across species, emphasize the muscle autonomous role of Syd, and reiterate the strong correlation between mispositioned myonuclei and decreased muscle output observed previously [4]–[6]. In sum, we have identified that JNK signaling and the motor adaptor, Syd, are required for influencing specific functions of Kinesin and Dynein that lead to proper myonuclear positioning and muscle function, which has significant implications for muscle cell organization, development, and disease.
All stocks were grown under standard conditions. Stocks used: apterousME-NLS::dsRed [60], Df(3L)sydA2 and sydZ4 [45], Khc8 [61], Dhc64C4−19 [62], Klc8ex94 [63], Dlc90F05089 [64], lis1K11702 [65], lis1G10.14 [66], raps193 [67], twist-Gal4 [68], Dmef2-Gal4 [69], Stripe-Gal4 (gift from T. Volk), Elav-Gal4 (gift from E. Lai), UAS-GFP-JIP3 (this study), bsk1 and bsk2 [49], UAS-Bsk-DN and UAS-Hep-Act [50]. Bloomington Drosophila Stock Center: clip190KG06490 (14493), UAS-Tak1-RNAi (33404, 35180), UAS-Hep-RNAi (28710, 35210), UAS-Bsk-RNAi (31323, 32977, 35594, 36643), UAS-mCherry-RNAi (35785). Vienna Drosophila RNAi Center: UAS-Syd-RNAi KK109225 (v101459), UAS-Syd-RNAi GD12383 (v35346). Mutants were balanced and identified using CTG (CyO, Twi-Gal4, UAS-2xeGFP) and TTG (TM3, Twi-Gal4, UAS-2xeGFP) [70].
Mouse JIP3 construct: pEGFP-C1/mSyd2 (gift from L. Goldstein) [45].
For Drosophila rescue experiments, mSyd2 (later re-annotated as JIP3) was amplified from the pEGFP-C1/mSyd2 construct using the following primers and cloned into the pUAST vector containing GFP: mSyd2 Forward: 5′-CACCGAATTCATGGAGATCCAGATGGACGAGGGA-3′; mSyd2 Reverse: 5′-GAATTCCTCAGGGGTGTAGGACACCTGCCA-3′.
All constructs were sequenced and verified, and pUAST-GFP/mSyd2 DNA was injected into Drosophila embryos using transposable-element-based insertion methods (Genetic Services, Inc.) to generate flies carrying UAS-GFP-JIP3 on either chromosome II or III that were used in experiments.
Whole mount embryo staining was performed as described [60]. For Dynein localization measurements, embryos were fixed with 10% formalin diluted 1∶1 in heptane for 20 minutes, then rinsed three times in PBS containing 0.3% Triton X-100, then fixed with 4% EM-grade paraformaldehyde in PBS diluted 1∶1 in heptane for 20 minutes. In all cases, embryos were devitellinized by vortexing in a 1∶1::methanol:heptane solution. Larvae were dissected in ice cold HL3.1 as previously described [71] and fixed with 10% Formalin (Sigma, HT501128-4L). Dynein antibody incubations were performed in PBS supplemented with 0.2% BSA and 0.15% Triton X-100. All other antibody incubations were performed in PBS supplemented with 0.1% BSA and 0.3% Triton X-100. Embryos and larvae were mounted in ProLong Gold (Invitrogen) for fluorescent immunostainings. Antibodies were preabsorbed (PA) as described [72] where noted and used at the indicated final dilutions: rabbit anti-dsRed (1∶400, Clontech, 632496), rat anti-Tropomyosin (PA, 1∶500, Abcam, ab50567), mouse anti-GFP (PA, 1∶200, Clontech, 632381), mouse anti-Dhc (1∶50, Developmental Studies Hybridoma Bank), mouse anti-Tubulin (1∶500, Sigma T9026), rabbit anti-Khc (1∶200, Cytoskeleton Inc., AKIN01), mouse anti-Discs large (1∶200, Developmental Studies Hybridoma Bank), rabbit anti-N-terminal-Syd (SN1) and rabbit anti-C-terminal-Syd (7704) (1∶300) (gifts from V. Cavalli and L. Goldstein) [45]. Alexa Fluor 488-, Alexa Fluor 555-, and Alexa Fluor 647-conjugated fluorescent secondary antibodies (1∶200), Alexa Fluor 546-conjugated Phalloidin (1∶100), and Hoechst-33342 (1 µg/1ml) were used for fluorescent stains (Invitrogen). Fluorescence projection images were acquired on a Leica SP5 laser scanning confocal microscope equipped with a 63× 1.4 NA HCX PL Apochromat oil objective and LAS AF 2.2 software unless noted otherwise. Maximum intensity projections of confocal Z-stacks were rendered using Volocity 6.1.1 Visualization software (Improvision). All resulting 2D projection images were cropped using Adobe Photoshop CS6.
Analysis was performed as described [5].
High magnification projection images of a set 2 µm depth-size were acquired as described [5], ensuring that the entire Z-stack was acquired from completely within the muscle fiber. Any projection images with a slice including the muscle cell membrane or any area outside the bounds of the muscle were discarded to eliminate variation in background fluorescence. Immunofluorescence intensity was assessed across defined regions using ImageJ (NIH). In all cases, boxed regions were unbiasedly selected in the Tropomyosin/nuclear projection image and transferred to identical regions in other channels. For Kinesin, a box of set dimensions was used. For Dynein and Syd, a box of set width and varying length was used to measure immunofluorescence between the end of the muscle and the nearest nucleus. Immunofluorescence intensity of Kinesin, Syd, or Dynein, was compared to Tropomyosin intensity, and the ratios were multiplied by 100. For Kinesin, average ratios were then plotted as a function of raw position. For Dynein and Syd, the total distance was normalized to 100 to account for myonuclear positioning defects, and average ratios were plotted as a function of normalized position (from muscle end to the nearest nucleus). The area under the curves (total fluorescence) were calculated in Microsoft Excel. The greatest pixel intensity denoted the peak fluorescence intensity.
Analysis was performed as previously described [5] using ImageJ software (NIH) and projection images acquired at a 3X optical zoom on a Zeiss LSM510 laser-scanning confocal microscope equipped with a 63×1.4 NA HCX PL Apochromat oil DIC objective and ZEN 2009 software.
Experiments and analyses were performed as described [7].
Larval speed was assessed as previously described [5]. Tracked larvae were dissected, stained, and analyzed as described above. Using projection images, internuclear distance was assessed in muscles VL1, VL2, and VL4 using ImageJ (NIH) to measure the distance between each nucleus and the nearest neighboring nucleus. The greatest span of muscle devoid of nuclei was measured in the same manner. All distances were normalized to muscle length (measured from projection images using ImageJ). The number of nuclei per muscle, number of NMJs per muscle, and number of boutons per NMJ were quantified manually using a Leica SP5 laser-scanning confocal microscope with a 20×0.7 NA HCX PL Apochromat oil objective to view the muscles.
Embryos were collected at 25°C on yeasted apple juice agar plates using overnight lays. Stage 15–16 embryos lacking the balancer (where applicable) were staged by development of the gut and hand-selected for analysis. Embryos were counted, transferred to a lightly yeasted apple juice agar plate, and raised at 22°C overnight. The following day, L1 larvae were counted and transferred to vials of standard fly food at 22°C. Eight to twelve days later, the number of pupal cases and adults present in the vials were quantified. All values were normalized to 100%.
Protein and nucleotide alignments were performed using Clustal Omega and ClustalW2, respectively (EMBL-EBI).
All data sets were analyzed similarly. Error bars represent standard deviations calculated in Excel. Traditional pairwise comparisons using the Student's t-test were used to compare experimental values to control values. Additionally, Analysis of Variance (ANOVA) was used to compare the same data in the context of an entire experiment (three or more values per comparison), taking into account variations in multiple means and standard deviations amongst genotypes within a given experiment. With one-way ANOVA, multiple groups of data were compared simultaneously to determine significance (for example, comparing homozygous mutants to both wild-type and heterozygous controls). Values reaching a threshold of statistical significance in both the Student's t-test and the more stringent ANOVA assessment were considered significant and noted with asterisks in the relevant graphs. All analyses were performed using Prism 6.0.
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10.1371/journal.pntd.0002929 | Transcriptomic Evidence for a Dramatic Functional Transition of the Malpighian Tubules after a Blood Meal in the Asian Tiger Mosquito Aedes albopictus | The consumption of a vertebrate blood meal by adult female mosquitoes is necessary for their reproduction, but it also presents significant physiological challenges to mosquito osmoregulation and metabolism. The renal (Malpighian) tubules of mosquitoes play critical roles in the initial processing of the blood meal by excreting excess water and salts that are ingested. However, it is unclear how the tubules contribute to the metabolism and excretion of wastes (e.g., heme, ammonia) produced during the digestion of blood.
Here we used RNA-Seq to examine global changes in transcript expression in the Malpighian tubules of the highly-invasive Asian tiger mosquito Aedes albopictus during the first 24 h after consuming a blood meal. We found progressive, global changes in the transcriptome of the Malpighian tubules isolated from mosquitoes at 3 h, 12 h, and 24 h after a blood meal. Notably, a DAVID functional cluster analysis of the differentially-expressed transcripts revealed 1) a down-regulation of transcripts associated with oxidative metabolism, active transport, and mRNA translation, and 2) an up-regulation of transcripts associated with antioxidants and detoxification, proteolytic activity, amino-acid metabolism, and cytoskeletal dynamics.
The results suggest that blood feeding elicits a functional transition of the epithelium from one specializing in active transepithelial fluid secretion (e.g., diuresis) to one specializing in detoxification and metabolic waste excretion. Our findings provide the first insights into the putative roles of mosquito Malpighian tubules in the chronic processing of blood meals.
| The Asian tiger mosquito Aedes albopictus is a vector of several medically-important arboviruses and one of the most invasive mosquito species in the world. Existing control measures for mosquitoes are presently being challenged by the emergence of resistance to insecticides that target the nervous system. Thus, it is necessary to identify novel physiological targets to guide the development of new insecticides. We recently demonstrated that the ‘kidneys’ (Malpighian tubules) of mosquitoes offer a valuable, new physiological target for insecticides. However, our understanding of how this tissue contributes to the chronic metabolic processing of blood meals by mosquitoes is limited. Here we characterize the changes in transcript expression that occur in the Malpighian tubules of adult female A. albopictus with the goal of identifying key molecular pathways that may reveal valuable targets for insecticide development. We find dramatic changes in transcript accumulation in Malpighian tubules, which 1) provide new insights into the potential functional roles of Malpighian tubules after a blood meal, and 2) reveal new potential molecular pathways and targets to guide the development of new insecticides that would disrupt the renal functions of mosquitoes.
| The Asian tiger mosquito Aedes albopictus is considered one of the most invasive mosquito species in the world; since 1979 it has spread to over 28 countries outside of its native range in Asia and Southeast Asia, aided by the international trade of used automobile tires [1], [2]. Within the United States, the mosquito has spread to at least 36 states and models of its potential for range expansion in the northeastern United States within the next few decades are alarming [3]. Moreover, this species is a known or suspected vector of several medically important arboviruses, including chikungunya, dengue, eastern equine encephalitis, La Crosse, West Nile, and yellow fever [4]. Thus, A. albopictus is an emerging threat to global health for which effective control measures need to be developed.
Historically, mosquitoes have been controlled through the use of insecticides that target the nervous system (e.g., carbamates, organophosphates, organochlorines, and pyrethroids). However, resistance to these control agents is limiting their efficacy. In particular, the yellow fever mosquito Aedes aegypti exhibits high levels of resistance to insecticides in certain parts of the world, and there is concern that A. albopictus will soon develop such resistance [5]. Thus, it is important to identify new control agents that target novel physiological systems in mosquitoes to help combat the emerging threat of insecticide resistance.
A recent study by our group demonstrated that the renal excretory system (Malpighian tubules) of mosquitoes represents a valuable new physiological target for insecticides [6]. The Malpighian tubules produce urine via transepithelial fluid secretion, which is mediated by the coordinated actions of a V-type H+-ATPase along with several ion transporters, ion channels, and water channels [7]. In adult female A. aegypti mosquitoes, the Malpighian tubules play an especially important role in the post-prandial diuresis when the mosquito excretes urine during and after the engorgement of vertebrate blood [8]. The diuresis lasts for up to two hours after feeding and excretes a significant fraction of the ingested Na+, K+, Cl−, and water from the blood [8]. Once this diuresis ends, the female mosquito will continue to digest and metabolize the blood meal over the next two days to nourish the development of her eggs. The physiological importance of the Malpighian tubules during this time is unknown, but they presumably play a critical role in excreting excess nitrogenous wastes and other metabolites that are generated during the processing of the protein-rich meal [9], especially within the first 24 hours when ∼75–90% of the ingested protein is digested [10], [11].
Other groups have documented the effects of ingesting blood on the transcriptomes of adult female A. aegypti and Anopheles gambiae mosquitoes [12], [13], [14], [15], [16], including more focused studies on how blood feeding influences tissue-specific transcriptomes in the antennae, fat body, midgut, and salivary glands of these species [17], [18], [19], [20]. However, no previous studies have examined the effects of blood-feeding on the transcriptome of mosquito Malpighian tubules.
The goal of the present study was to characterize the global changes in transcript expression that occur in the Malpighian tubules of A. albopictus during the first 24 h after female mosquitoes consume a blood meal (using RNA-Seq), with the aim of identifying key metabolic pathways and transcripts that are activated or suppressed in the renal tubules during the processing of the blood meal. We found that blood feeding elicits dramatic, time-dependent changes to the Malpighian-tubule transcriptome of A. albopictus. A functional cluster analysis of the differentially-expressed transcripts revealed a potential functional transition of the tubule epithelium after blood feeding from one specializing in active transepithelial fluid secretion to one specializing in detoxification and metabolic waste excretion.
A. albopictus eggs were obtained from the Malaria Research and Reference Reagent Resource Center (MR4) as part of BEI Resources Repository, NIAID, NIH (ALBOPICTUS, MRA-804, deposited by Sandra Allan). Eggs were raised to adults using a protocol similar to that described for A. aegypti [21] with the exception that larvae were fed pulverized TetraMin flakes (Melle, Germany). Adult females between 5 to 10 days post-eclosion were used for the present study.
The experimental design consisted of two treatments, blood fed (BF) and non-blood fed (NBF) females at three different time points. In brief, the BF mosquitoes were fed on heparinized rabbit blood for 30 minutes (see details below) and collected at 3 h, 12 h, or 24 h after feeding. These time points occur after the post-prandial diuresis, which ends within 2 h after feeding [8]. Moreover, one or more of these time points has been commonly used in other studies examining the effects of blood feeding on gene expression in mosquitoes [13], [14], [15], [16], [17], [18], [19], [20], [22]. NBF females were only offered a 10% sucrose solution and dissected at similar time points to serve as controls. Each treatment/time point was replicated three times using females from different cohorts (i.e., 3 biological replicates per time point).
For each experimental treatment, 90 adult female mosquitoes were transferred to two small 32 oz. cages (45 females per cage) without access to a sucrose solution for 24 h prior to offering them blood or sucrose. To one cage, a membrane feeder (Hemotek, Blackburn, UK) was used to feed the mosquitoes warmed blood (37°C), which consisted of heparinized rabbit blood (purchased from Hemostat Laboratories, Dixon, CA) supplemented with adenosine 5′-triphosphoric acid (disodium salt; Sigma, St. Louis, MO) at a concentration of 0.01 g/ml. A solution of 10% lactic acid was applied to the membrane surface as an attractant. Females were given access to the blood for a period of 30 min before the feeder was removed from the cage. In the other cage, the mosquitoes were given access to cotton balls soaked with 10% sucrose for 30 min. At 3 h, 12 h, or 24 h after removing the feeder or cotton balls, the cage was refrigerated on ice to immobilize the mosquitoes.
Before dissecting the mosquitoes that were offered blood, their abdomens were visually examined to confirm their engorgement. The alimentary canal of each mosquito was then extracted by tugging on the last segment of the abdomen with fine forceps under Ringer solution. The Ringer solution consisted of (in mM): 150 NaCl, 3.4 KCl, 1.7 CaCl2, 1.8 NaHCO3, 1.0 MgCl2, 5 glucose, and 25 HEPES (pH 7.1). The Malpighian tubules were isolated from their attachment to the alimentary canal and immediately immersed in 50 µL of TRIzol Reagent (Life Technologies, Carlsbad, BA) in a sterile 1.5 ml microcentrifuge tube on ice. A total of 200 Malpighian tubules (from 40 females) were pooled for a given replicate.
Altogether, Malpighian tubules were isolated from 1) mosquitoes fed a blood meal at 3 different time points (3 h, 12 h, 24 h; 40 mosquitoes at each time point) and 2) mosquitoes not fed a blood meal at similar time points (40 mosquitoes each). A total of 3 biological replicates was obtained for each time point in both the BF and NBF groups, resulting in 18 sets of tubules for RNA isolation and cDNA library preparation (triplicates each of 3 h BF, 12 h BF, 24 h BF, 3 h NBF, 12 h NBF, and 24 h NBF).
Total RNA was extracted from each set of Malpighian tubules immediately after they were isolated from the mosquitoes using the method of Chomczynski and Sacchi [23]. The resulting RNA was treated with TURBO DNA-free (Life Technologies) to remove genomic DNA and then purified with a RNA Clean & Concentrator-5 kit (Zymo Research, Irvine, CA), according to the manufacturer's protocol.
The purified RNA was initially measured for quantity and quality using a NanoDrop 2000c Spectrophotometer (Thermo Fisher Scientific, Waltham, MA). Samples with a concentration <20 ng/µl or poor absorbance ratios (i.e., 260/280 value <1.6; 260/230 value <1.6 or >3.0) were discarded. RNA quality was further assessed using the Experion Automated Electrophoresis System (Bio-Rad, Hercules, CA). Only samples with RNA Quality indicator values of 7.5 or higher were used. The concentration of RNA was determined with a Qubit 2.0 Fluorometer (Life Technologies).
Total RNA (565 ng) was used to synthesize adaptor-indexed double-stranded cDNA libraries using the TruSeq DNA Sample Prep Kit V2, Set A and B (Illumina, San Diego, CA). The size chosen for libraries was ∼270 bp. The quality of the synthesized libraries was evaluated using the Agilent 2100 Bioanalyzer High Sensitive DNA Chip (Agilent Technologies, Santa Clara, CA) and the quantity determined using the Qubit 2.0 Fluorometer (Life Technologies).
The 18 resulting cDNA libraries were diluted to 18 nM and pooled to generate a multiplexed cDNA library (using 18 unique indexed adapters) of 36 fM. The pooled library was sequenced using the Illumina HiSeq 2000 platform at the Ohio State University Comprehensive Cancer Center (Columbus, OH). Demultiplexing was performed with CASAVA 1.8.2. FASTQ files were generated from the ‘basecall’ files. All single-end reads were submitted to the NCBI sequence read archive (accession number SRP034701). The sequencing of all 18 libraries generated over 232 million single-end raw reads (∼13 million reads per sample) (Table S1).
The MCIC-Galaxy pipeline was implemented for preprocessing, filtering, and data analysis [24]. The raw reads were first analyzed with the “FASTQC” tool (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to assess quality. Adapters were removed using CUTADAPT [25] with an error rate of 0.1 and a minimum overlap length of 6. Reads were trimmed for length and quality using the “Trim the reads” tool, version 1.2.2, (Phred threshold score <20; read length <20 bp), discarding all miscalled bases, but not duplicates or polyA tails, because reads were aligned on a reference transcriptome. The number of reads retained after removing adapters, low quality, and short reads was >194 million, which is ∼84% of the total number of original 232 million reads.
After the preprocessing and filtering, reads were aligned to the following two reference transcriptomes using “Burrow-Wheeler Aligner”, version 1.2.3 [26]: 1) A. aegypti (www.vectorbase.org; AaegL1.4, v1.00; 18,769 sequences) [27] and 2) A. albopictus (www.ncbi.nlm.nih.gov;transcript shotgun archive accession numbers JO845359-JO913491, 68,413 sequences) [28]. The number of reads that aligned to unique and redundant transcripts in the reference transcriptome was determined for each sample using “Count Features” tool, version 0.91. The dataset was filtered to contain only transcripts with a minimum of five mapped reads for any two replicates in at least one treatment/time combination. Only the subset of transcripts meeting this criterion was used in subsequent analyses.
Considerably more reads mapped onto the A. albopictus transcriptome (∼9.6 million reads/sample; 75% reads mapped) compared to the A. aegypti transcriptome (∼2.4 million reads/sample; 18.5% reads mapped). However, the A. albopictus transcriptome assembly and annotation is incomplete and contains redundancies (i.e., there are several transcript identification numbers corresponding to the same transcript), which limited our ability to accurately identify and quantify specific individual transcripts of interest (e.g., aquaporins, NH3 detoxification enzymes). Moreover, mapping onto the A. aegypti transcriptome resulted in the detection of 9,813 non-redundant transcripts, which is within the range of ∼7,000–18,000 transcripts detected in RNA-seq studies of A. aegypti isolated tissues or whole animals [12], [17], [29], [30]. Thus, we use the A. aegypti transcriptome and its transcript nomenclature for our downstream analyses.
For quality assessment, “Count Features” output was implemented to examine the dispersion of the biological replicates (libraries per treatment). Counting reads were normalized calculating the RPKM (reads per kilobase per million reads). Pearson's correlation and Principal Component Analysis graphics were generated to assess the similarity of the replications for each condition.
The read counts generated using “Count Features” output were submitted to the “DESeq” pipeline, version 1.0.0 [31], to identify transcripts with a significant differential expression between treatments/time points. This tool is based on the negative binomial distribution, with normalized libraries by size factor (developing an estimate effective library size) [31]. The comparisons made were a NBF treatment against BF treatments at three time points (3 h, 12 h and 24 h). All of the transcripts with a significant expression change were filtered by a FDR-adjusted P value threshold of 0.05, and their log2 fold-change value was recorded.
The DAVID v6.7 annotation clustering module [32] was used to classify differentially expressed transcripts into functional groups. Clustering analysis was carried out for the subset of transcripts that had showed sustained up- or down-regulation (i.e., at least two consecutive time points of differential expression). The DAVID is currently not compatible with A. aegypti transcript IDs. Thus, the differentially-expressed transcripts were first converted to A. gambiae transcript IDs using tBLASTx (E-value <10−20). Then, enrichment of GO and other annotation terms in candidate sub-lists were explored using the functional annotation clustering tool. This clustering method condenses the input transcript list into functionally related transcripts (annotation clusters), taking into account the similarity of their annotation profiles based on multiple annotation sources (e.g. GO terms and Interpro keywords).
The clusters are assigned an enrichment score, which represents the minus log-transformed geometric mean of the modified Fisher Exact (EASE) Scores within the cluster. Significantly enriched annotation clusters were defined as those containing a minimum enrichment score of 1.3, because the –log (0.05) = 1.3. Thus, an enrichment score of >1.3 corresponds to a P<0.05. The enrichment score is based on the following parameters: Similarity Term Overlap = 3; Similarity Threshold = 0.7; Initial Group Membership = 3; Final Group Membership = 5; Multiple Linkage Threshold = 0.3. Once the significantly-enriched functional clusters were identified, the A. gambiae transcript IDs within them were converted back to their respective A. aegypti transcript IDs.
We also performed a preliminary DAVID clustering analysis using reads mapped to the existing A. albopictus reference transcriptome, despite its incomplete assembly and annotation. Notably, there was a good correlation between the enriched functional clusters identified using this reference and those using the A. aegypti reference. For example, the thioredoxin, glutathione S-transferase, vitamin binding, cofactor metabolic process, oxidative phosphorylation/ATP synthesis, protein biosynthesis, glycoylsis, and ATPase activity clusters were enriched in both DAVID analyses. Thus, our decision to use the A. aegypti transcriptome, which allows for better identification and quantification of specific transcripts (see above), does not compromise our ability to identify enriched functional pathways.
As explained in the Methods, Malpighian tubules were isolated from mosquitoes at 3 different time points after being fed a blood meal (3 h, 12 h, 24 h). In parallel, Malpighian tubules were isolated from mosquitoes that were not fed a blood meal at similar time points. A total of 3 biological replicates were obtained for each time point in both the blood fed (BF) and non-blood fed (NBF) groups.
A principal component analysis (PCA) of the Malpighian-tubule transcriptomes derived from the 18 sequenced cDNA libraries (triplicates each of 3 h BF, 12 h BF, 24 h BF, 3 h NBF, 12 h NBF, and 24 h NBF) revealed distinct clustering of samples by treatment and time (Figure 1). Notably, all of the NBF samples clustered tightly with one another, regardless of time. Consistent with this observation, a GLM (general linear model) two-way ANOVA comparing the RPKM values among the NBF samples revealed no significant differences (F-value = 0.731; Table S2), which indicates that time does not affect transcript accumulation in the NBF tubules. The PCA also showed that the BF samples clustered separately from their respective NBF controls and each other (Figure 1). Consistent with this observation, an ANOVA comparing the RPKM values among the BF samples revealed significant differences among all of the samples (F-value = 0.017; Table S3), which indicates that time affects transcript accumulation in the BF tubules.
Below, we dissect the quantitative and qualitative differences among the NBF and BF samples in more detail. For simplicity and consistency, we selected the 24 h NBF samples as a universal control, because 1) an ANOVA found no significant transcript differences across all the time points for the NBF samples (see above) and 2) the 24 h NBF samples exhibited the lowest internal variation among the NBF samples (Figure 1). As described in the Methods, we used an assembled transcriptome for A. aegypti as a reference given its superior annotation and nominal redundancy compared to the available transcriptome for A. albopictus, which is still in its early stages of development.
DESeq was used to search for transcripts differentially expressed between the 24 h NBF control and the BF treatments at each time point. Using the A. aegypti transcriptome as a reference, a total of 1,857 non-redundant transcripts was found to be differentially expressed over all of the time points (∼10% of the A. aegypti transcriptome). Table 1 shows that the Malpighian tubules from the BF mosquitoes were characterized by progressive increases in differential expression throughout the time series. At each time point, the differentially expressed transcripts consist of similar numbers of up- and down-regulated transcripts.
We next aimed to identify enriched, functional pathways within the differentially expressed transcripts using a DAVID functional clustering analysis [32], [33]. We focused our analysis on transcripts that exhibited ‘sustained’ changes in differential expression after blood feeding, which we defined as those significantly up- or down-regulated for at least two consecutive time periods. Based on the A. aegypti transcriptome, a total of 669 transcripts met our ‘sustained’ criterion, consisting of 340 up-regulated transcripts and 329 down-regulated transcripts. As shown in Table 2, the DAVID analysis revealed a significant enrichment (enrichment score > 1.3) of 1) nine functional groups among the sustained, up-regulated transcripts and 2) six functional groups among the sustained, down-regulated transcripts. The identities of the transcripts that comprise the functional groups of Table 2 and their respective heat maps of differential expression are shown in Figures S1–S15.
Cursory interpretations of the changes to these broad functional groups and the transcripts within them suggests that blood feeding promotes the expression of transcripts associated with 1) antioxidants and detoxification, 2) proteolytic activity, 3) amino acid metabolism, and 4) cytoskeletal dynamics. On the other hand, blood feeding appears to suppress the expression of transcripts associated with 1) oxidative metabolism, 2) active transport, and 3) mRNA translation. Below, we discuss these interpretations in more detail with the caveat that transcript levels may not necessarily reflect protein abundance, biochemical activity, or physiological function. Thus, we consider our interpretations as the building of hypotheses that will require testing in future studies using functional genetic, biochemical, and physiological techniques.
The mosquito Malpighian tubule epithelium is well-studied, because of its remarkable capacity for active transepithelial fluid secretion, which mediates the post-prandial diuresis. The vacuolar (V-type) H+-ATPase is the ultimate energizer of transepithelial fluid secretion in the epithelium [34]. This proton pump resides in the luminal brush border of principal cells where it is situated in close proximity to mitochondria that fuel the pump with ATP [35], [36]; the pump is a multisubunit protein consisting of two sectors: 1) a catalytic, cytosolic V1 sector and 2) a H+-translocating, membrane-bound V0 sector [37]. Inhibiting the pump or the production of ATP in the epithelium effectively inhibits fluid secretion [34], [38]. Thus, the enrichment of the ‘oxidative phosphorylation/ATP synthesis’, ‘ATPase activity’, ‘glycolysis’, and ‘sugar/inositol transporter’ functional clusters among the transcripts that exhibited a sustained down-regulation (Table 2) caught our attention.
Listed prominently among the down-regulated transcripts in the ‘oxidative phosphorylation/ATP synthesis’ (Figure S1) and ‘ATPase activity’ (Figure S2) functional clusters are those encoding subunits of the V-type H+-ATPase. Remarkably, as shown in Figure 2, fourteen transcripts encoding subunits of the V-type H+-ATPase exhibit a sustained down-regulation after blood feeding, while only one shows a sustained up-regulation (AAEL003743-RA). Furthermore, a manual search of all the differentially-expressed transcripts (including those not considered ‘sustained’) revealed three other transcripts associated with the V-type H+-ATPase that are down-regulated transiently at one or two non-consecutive time points (i.e., AAEL002464-RA, AAEL010819-RA, AAEL010819-RB in Figure 2). Among all of these down-regulated transcripts, nine encode subunits of the V1 sector of the V-type H+-ATPase, seven encode subunits of the V0 sector, and one encodes an accessory protein (Figure 2). The only up-regulated transcript encodes subunit ‘a’ of the V0 sector (a.k.a. vha100-1).
The above changes to transcripts of the V-type H+-ATPase in the Malpighian tubules of A. albopictus contrast with those previously reported in the midgut of A. aegypti after blood feeding [19]. In our study, nearly all subunits of the V-type H+-ATPase exhibited a down-regulation in Malpighian tubules after blood feeding (Figure 2), whereas Sanders et al. found that transcripts encoding V-type H+-ATPase subunits exhibited an up-regulation in the midgut of A. aegypti at 12 h and 24 h after blood feeding [19]. These contrasting results suggest that the regulation of V-type H+-ATPase expression in response to blood feeding is tissue dependent in mosquitoes.
Notable among the transcripts that exhibited a sustained down-regulation after blood feeding in the ‘oxidative phosphorylation/ATP synthesis’ (Figure S1) and ‘glycolysis’ (Figure S3) functional clusters are those encoding enzymes associated with glycolysis, the citric acid cycle, and ATP synthesis, such as phosphofructokinase, pyruvate kinase, enolase, 2-oxoglutarate dehydrogenase, glycerol-3-phosphate dehydrogenase, and acetyl-CoA synthetase. In addition, within the ‘oxidative phosphorylation/ATP synthesis’ (Figure S1) and ‘sugar/inositol transporter’ (Figure S4) functional clusters are transcripts encoding putative SLC2-like sugar transporters, which import glucose into cells for use as a fuel to generate ATP. Thus, in Malpighian tubules, blood-feeding leads to a decrease in the abundance of transcripts encoding enzymes associated with the catabolism of glucose and synthesis of ATP, which is consistent with the aforementioned decrease in abundance of transcripts encoding subunits of the V-type H+-ATPase subunits.
Also notable among the transcripts listed in the ‘oxidative phosphorylation/ATP synthesis’ (Figure S1) functional cluster are those encoding ion transport mechanisms that are known or hypothesized to play a role in the transepithelial secretion of ions by mosquito Malpighian tubules. We discuss these mechanisms below.
The other functional clusters enriched among the transcripts that exhibited a sustained down-regulation are related to the translation of mRNA (i.e., protein biosynthesis and translational elongation; Table 2). These transcripts consist primarily of ribosomal protein subunits and translation/elongation factors that exhibit a down-regulation at 12 h and 24 h after a blood meal (Figure S5 and Figure S6), which suggests that most newly-translated proteins in the tubules in response to blood feeding are synthesized within 12 h. Furthermore, the data suggest that during the chronic processing of blood meals (24–48 h after blood feeding) the capacity of the tubules for de novo protein synthesis may decrease. These observations in the Malpighian tubules of A. albopictus are similar to results of previous studies in the midgut and fat body of A. aegypti, where blood feeding led to a down-regulation of transcripts encoding ribosomal protein subunits and/or translation factors within 12 h to 24 h after a blood meal [17], [19], [52].
Hemoglobin is the most abundant protein in mammalian blood, and its digestion leads to the production of heme, a highly toxic metabolite that causes cell and tissue damage via oxidative stress and/or the disruption of plasma membranes [53]. Mosquitoes utilize a variety of mechanisms to detoxify heme and limit its absorption into the hemolymph. The first line of defense is found in the midgut, which secretes a peritrophic matrix that encapsulates the ingested blood cells and may sequester more than half of the amount of heme in a typical blood meal [54], [55]. Additional mechanisms for heme detoxification include the 1) enzymatic degradation of heme by heme oxygenase (HO) to produce biliverdin, Fe2+, and carbon monoxide [56], 2) chelation of heme by xanthurenic acid (XA), which is a product of the kynurenine pathway of tryptophan catabolism [57], [58], and 3) binding and/or catabolism of heme by glutathione S-transferases (GSTs) [59], [60]. Furthermore, protection against heme-induced, free-radical damage in mosquitoes can be mediated by: 1) antioxidant enzymes, such as glutathione peroxidase (GP), thioredoxin peroxidase (THP), thioredoxin reductase (THR), superoxide dismutase (SOD), and catalase (CAT); 2) antioxidant proteins, such as thioredoxin (TH); and 3) small molecule antioxidants, such as glutathione and uric acid [53].
Thus, we were intrigued by the enrichment of the ‘thioredoxin’ and ‘glutathione S-transferase’ functional clusters among the transcripts that exhibited a sustained up-regulation after blood feeding (Table 2; Figures S7–S8). Furthermore, we noticed that the ‘cofactor metabolic process’ (Figure S9) and ‘vitamin biosynthetic process’ (Figure S10) functional clusters contained several transcripts associated with putative antioxidant and detoxification mechanisms, and that the ‘ATPase/AAA+ type’ (Figure S11) functional cluster contained several transcripts encoding putative ATP-binding cassette (ABC) transporters, which play key roles in insect metabolite/xenobiotic excretion [61]. Below, we discuss the up-regulation of transcripts after a blood meal within the aforementioned functional clusters in the context of 1) the prevention of heme-induced oxidative cell and tissue damage and 2) the detoxification/excretion of heme and heme-related metabolites.
Another functional cluster enriched among the transcripts that exhibited a sustained up-regulation is the ‘proteasome complex’ (Table 2), which is related to the degradation of protein. These transcripts consist entirely of those encoding proteasome or protease regulatory subunits and most exhibit an up-regulation at 3 h and 12 h after a blood meal (Figure S12), which suggests that the tubules enhance their molecular capacity for proteolytic activity early after a blood meal. Enhanced proteolytic activity within the tubule epithelium may lead to the degradation of 1) proteins encoded by the transcripts that are down-regulated after blood feeding, and/or 2) proteins that may experience oxidative damage from heme. An increase of proteasome activity would also be expected to result in an increase of free amino-acids available for the synthesis of new proteins [73]—perhaps those associated with the ‘thioredoxin’ and ‘glutathione S-transferase’ functional clusters mentioned above.
Consistent with the enrichment of the ‘proteasome complex’ functional cluster among up-regulated transcripts, which is expected to increase the abundance of free amino acids (see above), there is a corresponding enrichment in the ‘amine biosynthetic process’ cluster (Table 2). The transcripts within this cluster consist primarily of those encoding enzymes associated with amino-acid catabolism and/or biosynthesis, such as cysteine dioxygenase, 2-amino-3-ketobutyrate coenzyme A ligase, glutamine synthetase, phosphoserine phosphatase, ornithine decarboxylase, and phosphoserine aminotransferase (Figure S13). Similar (as well as redundant) transcripts are also found in the ‘vitamin binding’ functional cluster, such as alanine-glyoxylate aminotransferase and alanine aminotransferase (Figure S14). Notably, the up-regulation of these transcripts occurs at 12 h and 24 h, appearing to follow the up-regulation of transcripts associated with the proteasome (Figure S12). Thus, the potential increase in the availability of amino acids derived from proteasome activity in the tubules may be followed by an increased molecular capacity to breakdown and/or convert amino acids into other products.
The aforementioned up-regulation of glutamine synthetase and alanine aminotransferase drew our attention to a potential role of the Malpighian tubules in the handling of ammonia. As mosquitoes metabolize a protein-rich blood meal, they face a potentially toxic accumulation of ammonia in their hemolymph and tissues from the catabolism of proteins and amino acids in the blood meal. Glutamine synthetase and alanine aminotransferase play prominent roles in detoxifying the ammonia (see below and Figure 6).
In brief, to prevent the build-up of toxic ammonia, mosquitoes have a remarkable capacity to fix and assimilate it into free amino acids (e.g., alanine, proline, glutamine) via a series of biochemical reactions catalyzed by enzymes, such as glutamine synthetase (GS), glutamate dehydrogenase (GDH), glutamate synthase (GltS), alanine aminotransferase (ALAT), and pyrrolidine-5-carboxylate synthase (P5CS) and reductase (P5CR) (Figure 6) [9], [74], [75], [76]. Moreover, glutamine can serve as a substrate for the production of uric acid through a pathway that includes xanthine dehydrogenase (XDH) among other enzymes [9], [76]. Once uric acid is produced, it can be excreted directly or further converted into allantoin, allantoic acid, and urea through a series of biochemical reactions catalyzed by urate oxidase (UO), allantoinase (ALLN), and allantoicase (AALC), respectively (Figure 6) [9], [76].
Figure 7 shows the transcripts associated with this pathway in Malpighian tubules that are differentially expressed after blood feeding. Namely, two transcripts encoding GS, one transcript encoding ALAT, and one transcript encoding XDH exhibit a sustained up-regulation after blood feeding. Three transcripts encoding a GDH and two transcripts encoding ALAT are each transiently up-regulated at 12 h, whereas one transcript encoding GltS is transiently down-regulated at 12 h (Figure 7). The up-regulation of these transcripts occurs at 12 h and/or 24 h after blood feeding, which coincides with the putative availability of amino acids from increased proteasome activity in the tubule (see above), as well as a period of intense protein digestion of the blood meal in the midgut [10], [11].
Interpreting these molecular findings in the context of the ammonia detoxification pathway suggests that the Malpighian tubules detoxify ammonia by converting it to glutamate via GDH and/or glutamine via GS (Figure 6). Given the up-regulation of ALAT, it is reasonable to propose that any newly-formed glutamate is converted into alanine (Figure 6). This putative handling of ammonia is similar to that reported for the midgut of A. aegypti, which fixes and assimilates ammonia into glutamine and alanine, as opposed to the fat body which fixes and assimilates ammonia into glutamine and proline [77]. The fate of the alanine in Malpighian tubules is unknown, but it is possible that it is shuttled to the fat body for conversion into proline, which can then be shuttled to the flight muscle for use as an energy source [78].
The fate of glutamine in Malpighian tubules is also unknown, but it is possible that it is converted into uric acid for excretion, as indicated by 1) the sustained up-regulation of XDH, and 2) the transient down-regulation of GltS, after blood feeding (Figure 7). Since transcripts encoding UO, ALLN, and ALLC were not differentially expressed after blood feeding (data not shown), the uric acid may be directly secreted by the tubules for excretion (perhaps by an ABC transporter in Figure 5), or it may be retained by the epithelium for use as an antioxidant to combat potential oxidative damage due to heme. Consistent with the former notion, uric acid is excreted by mosquitoes after a blood meal [79]. Likewise, uric acid is excreted by tsetse flies and reduviid bugs following a blood meal [80], [81], [82].
The remaining functional cluster enriched among the transcripts that exhibited a sustained up-regulation is related to cytoskeletal dynamics (i.e., ‘tubulin, GTPase domain’) (Table 2). The transcripts in this functional cluster consist primarily of those encoding components of microtubules, such as tubulin chains (alpha and beta), dynein light chains, and microtubule-associated proteins (Figure S15). Another related transcript populating this cluster is one encoding a putative Rab GTPase (AAEL006091-RA); Rab GTPases play important roles in regulating microtubule-mediated trafficking of vesicular cargo [83]. These changes suggest that blood feeding leads to a more dynamic microtubule-based cytoskeleton, which may be associated with the intracellular trafficking of vesicles and/or organelles. At least one study has found that the actin cytoskeleton of principal cells plays a key role in modulating diuretic fluid secretion in A. aegypti Malpighian tubules [84].
Putative changes in the microtubule-based cytoskeleton would also be consistent with a potential functional transition of the epithelium after blood feeding. For example, if the capacity of the tubule for diuresis indeed decreases, as expected by the down-regulation of transcripts associated with the V-type H+-ATPase and other ion/water transport mechanisms (Figures 2–3), then enhanced vesicular trafficking may play a key role in the endocytosis and degradation of these membrane-bound proteins. Likewise, if the capacity of the tubules for detoxification and metabolite excretion indeed increases, as expected by the up-regulation of transcripts associated with heme and ammonia detoxification/excretion (Figures 4–6), then the cytoskeletal dynamics may facilitate the movements of newly synthesized membrane-bound transporters that mediate the excretion of metabolites, such as ABC transporters.
It is also possible that a more dynamic microtubule cytoskeleton would facilitate the movements of organelles within the epithelial cells. For example, retraction of mitochrondria from the apical microvilli in principal cells is associated with a decrease of fluid secretion in Malpighian tubules isolated from pupal stages of mosquitoes [85]. Thus, similar microtubule-mediated movements of mitochondria may occur during the chronic processing of blood meals (24–48 h after blood feeding) to further contribute to a putative decreased capacity for diuresis.
The present study provides the first transcriptomic analysis of the Malpighian tubules of a mosquito after a blood meal, and is also the first to be conducted in A. albopictus after blood feeding. The results reveal molecular changes in transcript accumulation in the tubule epithelium within the first 24 h after a blood meal that suggest a remarkable functional transition of the epithelium from one dedicated to electrolyte and fluid excretion to one dedicated to detoxification and metabolite processing (Figure 8). Moreover, the results uncover new putative roles of the Malpighian tubules in the chronic processing of blood meals after the post-prandial diuresis ends ∼2 h after a blood meal [8]. Thus, the tubule epithelium may represent an even more valuable target for the development of novel insecticides than has been previously appreciated. The next important step to complete is to validate the hypothesized functional transition of the epithelium after a blood meal using biochemical and physiological approaches.
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10.1371/journal.pcbi.1005717 | Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing | Alternative splicing is known to remodel protein-protein interaction networks (“interactomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing.
| Protein-protein interaction networks have been extensively used in systems biology to study the role of proteins in cell function and disease. However, current network biology studies typically assume that one gene encodes one protein isoform, ignoring the effect of alternative splicing. Alternative splicing allows a gene to produce multiple protein isoforms, by alternatively selecting distinct regions in the gene to be translated to protein products. Here, we present a computational method to predict and analyze the large-scale effect of alternative splicing on protein-protein interaction networks. Starting with a reference protein-protein interaction network determined by experiments, our method annotates protein-protein interactions with domain-domain interactions, and predicts that a protein isoform loses an interaction if it loses the domain mediating the interaction as a result of alternative splicing. Our predictions reveal the central role of alternative splicing in extensively remodeling the human protein-protein interaction network, and in increasing the functional complexity of the human cell. Our prediction method complements ongoing experimental efforts by predicting isoform-specific interactions for genes not tested yet by experiments and providing insights into the functional impact of alternative splicing.
| Protein-protein interaction (PPI) networks (also known as interactome networks) have been extensively studied in systems biology to understand genotype-phenotype relationships and several have been constructed for different model organisms such as human, yeast and bacteria [1–10]. The increase in the number of interactions reported by independent studies has led to the construction of large databases of experimentally determined PPIs, such as IntAct [11] and BioGRID [12]. In the case of human interactome mapping, some studies have used systematic yeast two-hybrid (Y2H) screening to generate large-scale, high-quality maps of the human binary interactome network [13], whereas other studies have used mass spectrometry to generate catalogues of protein complexes in human cells [14, 15]. The utility of these PPI networks can be further enhanced by annotating nodes and edges with structural domain information [16–21]. Despite their success, current network biology studies typically make the assumption that one gene encodes one protein isoform, and ignore the effect of alterative splicing (AS).
It is estimated that more than 100,000 AS events occur in pre-mRNA transcripts of human multi-exon genes [22, 23] and that over two-thirds of human genes contain one or more alternatively spliced exons [22, 24, 25]. During evolution, the expansion of the proteome by AS correlates positively with the increase in species complexity [26, 27]. In human, splicing events occur frequently in a tissue specific manner [22, 28] and in regions located on the surfaces of proteins, which are candidates for mediating molecular interactions [29, 30]. Moreover, AS occurs more often in transcripts which encode proteins that are involved in a high number of interactions and, through alternative inclusion or exclusion of exons, creates or eliminates protein-protein interactions [28, 31]. Given the known impact of AS on protein function [32], and its strong association to disease [33], efforts to systematically relate this functional impact to its role in remodeling the human interactome recently culminated in the large-scale mapping of a human isoform interactome [34]. Subsequent analysis of this experimentally mapped isoform interactome showed that different isoforms of the same gene having different interaction profiles with other proteins tend to behave as products of different genes in terms of function, disease phenotype and tissue expression, proving that AS contributes to the functional complexity of different human cell types by creating or eliminating isoform interactions. However, due to the challenging nature of these experiments, the current human isoform interactome is far from complete where protein isoforms encoded by less than 5% of the human genome were successfully tested for PPIs. Hence, there is a great need to complement experimental efforts with the development of computational methods for accurate prediction of isoform interactions. Such computational predictions also enable us to assess the general applicability of insights gained from the size-limited experimental datasets on the human isoform interactome.
In this paper we present a computational method, named DIIP: Domain-based Isoform Interactome Prediction, that predicts the isoform interactome from an experimentally determined reference interactome, with application to human (Fig 1). Starting with experimentally determined interactions between reference proteins, we map known structural domains onto proteins and known domain-domain interactions (DDIs) onto PPIs, and construct a domain-resolved reference interactome where PPIs are annotated with DDIs. Next, we construct an isoform interactome by expanding this domain-resolved reference interactome to include interactions predicted for alternative isoforms of reference proteins. Specifically, for each interaction involving a reference protein, an alternative isoform is predicted to maintain the interaction if the DDI mediating the interaction is retained, and predicted to lose the interaction if the DDI mediating the interaction is lost. We apply our computational method to predict isoform interactomes from two human reference interactomes, the high-quality HI-II-14 reference binary interactome [13] and the larger interactome from IntAct [11]. We find extensive network remodeling by alternative splicing: ~22% of genes with two or more isoforms in the predicted isoform interactome have at least one isoform losing an interaction, and ~18% of isoform pairs encoded by the same gene in the isoform interactome have different interaction profiles. In addition, we find that compared to protein pairs interacting with the same subset of isoforms of the same gene, protein pairs interacting with different subsets of isoforms of the same gene tend to be more divergent in terms of function, disease phenotype and tissue expression. Our predicted isoform interactome explores a different part of the isoform space than the experimentally mapped isoform interactome of Yang et al. (2016) [34]. Despite this minimal overlap, our results are consistent with the results of Yang et al. (2016), indicating that these results are broadly applicable to the human isoform interactome. Finally, using the experimentally mapped interactome of Yang et al. (2016) as a benchmark dataset, we show that our computational framework for predicting isoform interactions is of high-quality, performing better than random expectation. All together, our results show that our computational method complements experimental efforts, and that integrating structural domain information with PPI networks provides insights into the functional impact of AS on different human cell types through remodeling of the human interactome network.
We used the 11,557 DDIs combined from the 3did database of three-dimensional interacting domains [35] and the DOMINE Database of Protein Domain Interactions [36] which were inferred from Protein Data Bank (PDB) entries to construct two domain-resolved reference interactomes for human. The first domain-resolved reference interactome was constructed by annotating with DDIs the high-quality HI-II-14 human reference binary interactome [13] which consists of 13,427 interactions between 4,303 proteins. The resulting domain-resolved interactome consists of 917 proteins and 901 annotated interactions (S1 Table). The second domain-resolved reference interactome was constructed by annotating with DDIs the larger human interactome from IntAct [11] (retrieved May 2016) which consists of 9,023 proteins and 29,775 interactions reported by two or more experiments. The resulting domain-resolved reference interactome consists of 2,944 proteins and 4,363 annotated interactions (S2 Table).
From each domain-resolved reference interactome, we predicted an isoform interactome that includes experimentally determined interactions between reference proteins, as well as predicted interactions for the alternative isoforms of these proteins, where the isoform data are obtained from UniProt [37]. We predicted isoform interactions using the following rule: Given an experimentally determined interaction between two reference proteins annotated with one or more DDIs, we predict that an alternative isoform of one protein loses its interaction with the other protein if the isoform interaction loses all of the above-mentioned DDI annotations, otherwise the interaction is predicted to be retained. The predicted HI-II-14 isoform interactome consists of the 901 experimentally determined reference interactions, 2,185 predicted retained interactions and 303 predicted lost interactions involving the 917 reference proteins and their 1,227 alternative isoforms (S3 Table), whereas the predicted IntAct isoform interactome consists of the 4,363 experimentally determined reference interactions, 12,651 predicted retained interactions and 1,709 predicted lost interactions involving the 2,944 reference proteins and their 4,471 alternative isoforms (S4 Table). The lost interactions are spread among a large number of genes: 22.4% (130 of 580) of genes with two or more isoforms in the predicted HI-II-14 isoform interactome have at least one isoform losing one or more interactions, whereas 21% (402 of 1,911) of genes with two or more isoforms in the predicted IntAct isoform interactome have at least one isoform losing one or more interactions. Widespread remodeling of the human interactome by AS can also be seen at the level of isoform pairs. 18.8% of isoform pairs encoded by the same gene in the predicted HI-II-14 isoform interactome have different interaction profiles, whereas 16.5% of isoform pairs encoded by the same gene in the predicted IntAct isoform interactome have different interaction profiles. Altogether, these observations highlight the extensive role of AS in preserving or eliminating isoform interactions, hence creating different interaction profiles for different isoforms of the same gene.
Next, we focused on protein pairs interacting with the same target protein in the reference interactome, and classified these protein pairs into those interacting with the same subset of isoforms of the same target gene, and those interacting with different subsets of isoforms of the same target gene (Fig 2A). The vast majority of protein pairs considered here belong to only one category; protein pairs belonging to more than one category for different target genes are extremely rare, and are excluded from further analysis. In the predicted HI-II-14 isoform interactome, we identified 1,437 protein pairs interacting with the same subset of isoforms of the same gene, and 63 protein pairs interacting with different subsets of isoforms of the same gene. In the predicted IntAct isoform interactome, we identified 20,685 protein pairs interacting with the same subset of isoforms of the same gene, and 2,669 protein pairs interacting with different subsets of isoforms of the same gene. Therefore, AS is capable of creating a wide variety of isoform interaction profiles for proteins interacting with the same target protein.
We retrieved Gene Ontology (GO) associations from the UniProt-GOA database [38] and constructed a GO association profile for each protein in the reference interactome. We then used the Jaccard similarity index to calculate GO similarity for each protein pair in which at least one protein is GO annotated. Finally, we systematically calculated and compared GO similarity for different types of protein pairs: those interacting with the same subset of isoforms of the same gene, those interacting with different subsets of isoforms of the same gene (as defined in the previous section), as well as those interacting with protein products of different genes only.
In the HI-II-14 isoform interactome, compared to protein pairs interacting with the same subset of isoforms of the same gene, protein pairs interacting with different subsets of isoforms of the same gene are less similar in molecular function, biological process, as well as all three GO categories combined (p < 10−5 each), but the similarity is not significantly different in cellular component (p = 0.25) (two-sided bootstrap test with 100,000 resamplings; Fig 2B). On the other hand, compared to protein pairs interacting with protein products of different genes, protein pairs interacting with different subsets of isoforms of the same gene are more similar in cellular component (p = 2 x 10−3), but less similar in molecular function (p < 10−3). However, the difference is not statistically significant in biological process (p = 0.054) and all three GO categories combined (p = 0.71) (two-sided bootstrap test with 1,000 resamplings; Fig 2B). In the IntAct isoform interactome, compared to protein pairs interacting with the same subset of isoforms of the same gene, protein pairs interacting with different subsets of isoforms of the same gene are less similar in molecular function, biological process, cellular component, as well as all three GO categories combined (p < 10−5 each, two-sided bootstrap test with 100,000 resamplings; Fig 2C). On the other hand, compared to protein pairs interacting with protein products of different genes, protein pairs interacting with different subsets of isoforms of the same gene are more similar in biological process, cellular component, as well as all three GO categories combined, but less similar in molecular function (p < 10−3 each, two-sided bootstrap test with 1,000 resamplings; Fig 2C).
Altogether, our results show that protein pairs interacting with different subsets of isoforms of the same gene tend to be more divergent in biological function than protein pairs interacting with the same subset of isoforms of the same gene. In addition, they tend to be less divergent in biological function than protein pairs interacting with protein products of different genes, albeit to a lesser degree. Notably, for the GO molecular function aspect, protein pairs interacting with different subsets of isoforms of the same gene can be as divergent as protein pairs interacting with protein products of different genes. Our results therefore demonstrate that AS increases the functional complexity of human cells by remodeling the human interactome network.
Disruptions in AS events in human are associated with a wide range of known diseases [33]. To investigate the extent to which protein pairs interacting with different subsets of isoforms of the same gene are associated with different disease phenotypes, we retrieved gene-disease associations from the DisGeNET database [39, 40] and constructed a disease annotation profile for each human reference protein. Because the fraction of human proteins with disease annotations is small, we further constructed a disease subnetwork profile for each human reference protein where a protein belongs to a specific disease subnetwork if that protein or any of its interaction partners in the unbiased, high-quality HI-II-14 reference binary interactome is annotated with the disease. We then used the Jaccard similarity index to calculate the fraction of disease subnetworks shared by pairs of reference proteins each annotated with at least one disease subnetwork, where two proteins share a specific disease subnetwork if each of the two proteins or any of its interaction partners in the HI-II-14 reference binary interactome is annotated with that disease. Finally, we systematically calculated and compared disease subnetwork sharing for different types of protein pairs: those interacting with the same subset of isoforms of the same gene, those interacting with different subsets of isoforms of the same gene (as defined previously), and those interacting with protein products of different genes only.
In the HI-II-14 isoform interactome, protein pairs interacting with different subsets of isoforms of the same gene (n = 47) tend to share a smaller fraction of disease subnetworks than protein pairs interacting with the same subset of isoforms of the same gene (n = 1,241) (p < 10−5, two-sided bootstrap test with 100,000 resamplings; Fig 3A). In addition, protein pairs interacting with different subsets of isoforms of the same gene tend to share a larger fraction of disease subnetworks than protein pairs interacting with protein products of different genes (p = 0.024, two-sided bootstrap test with 1,000 resamplings; Fig 3A). In the IntAct isoform interactome, protein pairs interacting with different subsets of isoforms of the same gene (n = 196) also tend to share a smaller fraction of disease subnetworks than protein pairs interacting with the same subset of isoforms of the same gene (n = 3,226) (p < 10−5, two-sided bootstrap test with 100,000 resamplings; Fig 3B), as well as a larger fraction of disease subnetworks than protein pairs interacting with protein products of different genes (p < 10−3, two-sided bootstrap test with 1,000 resamplings; Fig 3B).
All together, our results show that compared to protein pairs with identical isoform interaction profiles, protein pairs with different isoform interaction profiles tend to be more divergent in disease phenotype, consistent with the experimental results of Yang et al. (2016) [34]. Our results therefore demonstrate that by remodeling the human interactome network, AS creates divergence in disease phenotype among protein pairs interacting with different isoforms of the same gene.
Since tissue expression strongly correlates with biological function and disease phenotype [41, 42], we investigated the extent to which protein pairs interacting with different subsets of isoforms of the same gene diverge in tissue expression, in addition to divergence in biological function and disease phenotype. We used the Illumina Body Map 2.0 RNA-Seq dataset [43] to quantify gene expression in 16 different body tissues, and calculated tissue co-expression using Pearson’s correlation coefficient for pairs of reference proteins with both expression levels simultaneously quantified in at least 8 tissues. In the HI-II-14 isoform interactome, protein pairs interacting with different subsets of isoforms of the same gene (n = 62) tend to be less co-expressed than protein pairs interacting with the same subset of isoforms of the same gene (n = 1,182) (p = 5.5 x 10−3, two-sided bootstrap test with 100,000 resamplings; Fig 4A). In the IntAct isoform interactome, protein pairs interacting with different subsets of isoforms of the same gene (n = 2,609) also tend to be less co-expressed than protein pairs interacting with the same subset of isoforms of the same gene (n = 19,678) (p < 10−5, two-sided bootstrap test with 100,000 resamplings; Fig 4B), and more co-expressed than protein pairs interacting with protein products of different genes (p < 10−3, two-sided bootstrap test with 1,000 resamplings; Fig 4B). Our results show that protein pairs interacting with different subsets of isoforms of the same gene tend to be more divergent in tissue expression than protein pairs interacting with the same subset of isoforms of the same gene, consistent with the experimental results of Yang et al. (2016) [34].
Here, we present case studies of two types of protein pairs predicted by our method to interact with different subsets of isoforms of the same gene. We first looked at the epidermal growth factor EGF and the growth factor receptor-bound protein GRB2, which were predicted by our method to interact with different subsets of isoforms of the epidermal growth factor receptor EGFR (UniProt ID: P00533) (Fig 5A). Our method predicted that EGF interacts with EGFR (P00533) and its three shorter alternative isoforms (P00533-2, P00533-3, P00533-4), whereas GRB2 was predicted to interact with EGFR (P00533) and lose interaction with all its alternative isoforms. All of these predictions are consistent with experiments. By interacting with EGFR, EGF and GRB2 carry out different functions in protein signaling. EGF activates P00533 by binding to its extracellular ligand binding (LB) domain, hence inducing autophosphorylation of its protein kinase (PK) domain [44]. GRB2 binds to the tyrosine-phosphorylated PK domain of P00533 through its SH3 domain, hence connecting growth factor stimulation to other intracellular signalling pathways [45]. P00533 consists of three main parts: the extracellular domain (exons 1–16) containing the LB domain, the transmembrane (TM) domain (exons 16–18), and the intracellular domain (exons 18–24) containing the PK domain. The second isoform (P00533-2) contains a large part of the extracellular domain which retains LB function [46], whereas the third and fourth isoforms (P00533-3 and P00533-4) contain the whole extracellular domain [47]. All three alternative isoforms of P00533, however, lack the TM domain and the intracellular domain. Therefore, EGF interacts with all four isoforms of EGFR on their LB domains, whereas GRB2 interacts with P00533 on its PK domain and loses interaction with the other three truncated isoforms. These three truncated isoforms have different tissue expression patterns [48], are expressed in different cancers [49–51], and may play a role in supressing cell growth by inhibiting EGFR [50, 51].
We also looked at the protein pair XIAP and APAF1 that were predicted by our method to interact with different subsets of isoforms of CASP9 (UniProt ID: P55211) (Fig 5B). Our method predicted that APAF1 interacts with CASP9 (P55211) and its alternative isoforms P55211-2 and P55211-3, and loses interaction with P55211-4. All of these predictions are consistent with experiments. Our method also predicted that XIAP interacts with CASP9 (P55211) and its alternative isoforms P55211-2, P55211-3 and P55211-4. All of these predictions are supported by experiments with the exception of XIAP’s zinc finger-mediated interaction with P55211-3, which retains the CARD domain known to interact with zinc fingers [52]. XIAP and APAF1 are known to carry out antagonistic functions in activating and inhibiting apoptosis. This is also true for their CASP9 isoform partners. The CASP9 gene encodes four isoforms: P55211 (CASP9-alpha) which has the longest sequence (416 residues), P55211-2 (CASP9-beta) which lacks a central large sequence segment (residues 140–289), P55211-3 (CASP9-gamma) which lacks the catalytic domain (residues 139–416) and only has the caspase recruitment domain (CARD) (residues 1–92), and P55211-4 that lacks the CARD domain but has the catalytic domain. XIAP inhibits apoptosis by binding to the catalytic domain of P55211 through its BIR3 domain thus inhibiting its catalytic activity [53]. On the other hand, APAF1 activates apoptosis by forming an apoptosome complex with P55211 through CARD-CARD interaction [54]. The isoforms of CASP9 also play different roles in apoptosis due to their different interaction profiles. P55211-3 which lacks the catalytic domain containing the active site for catalysis interacts with APAF1 through its CARD domain, interfering with the formation of the apoptosome and therefore functioning as an endogenous inhibitor of apoptosis [55]. Similarly, P55211-2 functions as an endogenous inhibitor of apoptosis by interacting with APAF1 through its CARD domain while at the same time losing a large part of its catalytic domain, even though it retains some residual interaction with XIAP [56]. On the other hand, P55211-4, which lacks the CARD domain, interacts with XIAP but does not interact with APAF1, suggesting that it may not inhibit apoptosis. Moreover, the interaction of P55211-4 with the apoptosis inhibitor XIAP suggests that it may promote apoptosis, contrary to P55211-2 and P55211-3.
Overall, these two case studies highlight specific mechanisms for how AS-mediated remodeling of interactions of the isoforms of the same gene leads to divergence in their biological function and disease phenotype, and illustrate that our computational method is capable of identifying biologically relevant isoform-specific interactions.
Here, we empirically assessed the quality of our isoform-interaction prediction method by validating it against the experimental dataset of Yang et al. (2016) [34], which is the only genome-wide isoform interactome dataset available so far. However, the experimental dataset of Yang et al. (2016) is small in size for our purpose of validation: there are only 310 reference interactions between reference proteins, among which we were able to annotate only 34 reference interactions with known DDIs. We then predicted isoform-specific interactions from these 34 DDI-annotated reference interactions using our method, and compared our predictions with experiments. In terms of predicting interaction loss events, we obtained a true positive rate (TPR) of 0.33 (9 out of 27 experimental interaction loss events are correctly predicted), and a false positive rate (FPR) of 0.2 (3 out of 15 experimental interaction retention events are incorrectly predicted). A random predictor would give a TPR equal to the FPR whereas a TPR that is larger than the FPR indicates that predictions are better than random. Our observed TPR is larger than the observed FPR, however the difference is not statistically significant due to small sample size (p = 0.48, two-sided Fisher’s exact test). To increase the sample size, we expanded the 34 reference interactions with full DDI annotations in the dataset of Yang et al. (2016) by including 226 additional reference interactions with partial DDI annotations for a total of 260 domain-annotated reference interactions. A reference interaction has a partial DDI annotation if a reference protein with multiple isoforms contains an interacting domain of a DDI whereas its interaction partner does not contain the other interacting domain of the DDI. An alternative isoform of the reference protein is then predicted to lose the interaction if it loses all of its interacting domains, otherwise the interaction is retained. Using this method, we predicted isoform-specific interactions from these 260 domain-annotated reference interactions, and compared our predictions with experiments. In terms of predicting interaction loss events, we obtained a true positive rate (TPR) of 0.31 (53 out of 171 experimental interaction loss events are correctly predicted) and a false positive rate (FPR) of 0.10 (12 out of 117 experimental interaction retention events are incorrectly predicted). Similar to the rates obtained above, the observed TPR here is three times larger than the observed FPR and the difference is statistically significant due to much larger sample size (p = 2.6 x 10−5, two-sided Fisher’s exact test), indicating that our prediction method is of high-quality, performing significantly better than random predictions. Since the benchmark experimental dataset may contain errors, the actual TPR of our method is expected to be even higher than the observed TPR.
Our computational method for predicting isoform-specific interactions is reliable because it does not aim to predict new PPIs from scratch. Rather, it starts with the annotation of experimentally determined PPIs with known DDIs, and predicts the retention or loss of PPIs based on the retention or loss of the annotated DDIs in different isoforms. All these individual steps are expected to generate high-quality predictions; thus our overall isoform-interaction predictions are expected to be of high quality as well. Indeed, the high quality of our predictions is confirmed by experimental validations. At the same time, our method is limited in that it can only predict isoform interactions from known reference PPIs. If a reference protein does not have any interactions in the reference interactome, it is not possible to predict interactions for any of its alternative isoforms. Therefore, the size of the isoform interactome predicted by our method is ultimately constrained by the size of the reference interactome.
Since our method predicts isoform interactions based on experimentally determined interactions between reference proteins, it is important to ensure the high quality of these experimentally determined interactions. Interactions in the HI-II-14 reference binary interactome are of high-quality since they were subjected to multiple screening and several other quality-control measures. On the other hand, interactions in the IntAct reference interactome were curated from different sources with varying quality. Furthermore, IntAct may contain indirect interactions between proteins in the same complex. To ensure the high quality of the IntAct-derived interactions, we only included physical interactions in IntAct reported by at least two independent experimental studies. In addition, our DDI-annotated interactome significantly enriches for direct binary interactions and filters out indirect interactions, as indirect interactions are much less likely to be annotated with DDIs than direct binary interactions.
For reference PPIs with multiple DDI annotations, our method imposes a strict requirement that for an interaction to be lost, all DDI annotations of that interaction must be lost, otherwise the interaction is retained. Assuming that the interactivity of different domains is independent of each other within the same protein, this strict requirement maximizes the accuracy of predicted lost interactions, at the cost of possibly reducing the accuracy of predicted retained interactions. It should be noted that this is not a significant problem in our study, as about half of the PPIs in the HI-II-14 and IntAct domain-resolved interactomes have only one DDI annotation (51% and 46%, respectively).
While our predicted isoform interactomes reveal extensive network remodeling by AS, the ratio of remodeled protein pairs (i.e., the ratio of the number of protein pairs interacting with different subsets of isoforms of the same gene to the number of protein pairs interacting with the same subset of isoforms of the same gene) is different in the two predicted isoform interactomes (12.9% in IntAct, and 4.4% in HI-II-14). This difference is due to systematic differences in the types of interactions reported in the two reference interactomes and in the fraction of protein hubs between the two domain-resolved interactomes. Since proteins with significant medical and scientific interests are intensely studied in the literature, protein hubs are much better annotated with domains in the literature-curated IntAct interactome than in the systematic HI-II-14 interactome. Indeed, protein hubs with vertex degree >23 (~5% of proteins in both binary interactomes) are 3.4 times more likely to have a domain-annotated interaction in the IntAct interactome than in the HI-II-14 interactome, whereas average proteins are only 1.6 times more likely to have a domain-annotated interaction in the IntAct interactome than in the HI-II-14 interactome. As a result, the fraction of protein hubs is significantly larger in the IntAct domain-resolved interactome than in the HI-II-14 domain-resolved interactome (11.1% in IntAct and 5% in HI-II-14, for hubs with vertex degree >5). A larger fraction of protein hubs in the IntAct domain-resolved interactome is the major cause for the observed larger ratio of remodeled protein pairs in the IntAct isoform interactome, since protein pairs interacting with different subsets of isoforms of the same gene can only be created by isoforms that lose some but not all interactions (which is more likely to occur for isoforms of a protein hub with many interactions), and cannot be created by isoforms that lose all interactions at once (which is more likely to occur for isoforms of a protein non-hub with few interactions). Indeed, the observed difference (12.9% in IntAct, and 4.4% in HI-II-14) in the ratio of remodeled protein pairs between the two isoform interactomes is almost completely eliminated (6.1% in IntAct, and 5.2% in HI-II-14) upon the removal of all protein hubs with vertex degree >5. This observed difference does not significantly bias our isoform-specific interaction predictions for the following reasons: we predict isoform-specific interaction retention/loss one isoform at a time based on sequence information only; no significant differences are observed between the two isoform interactomes (IntAct and HI-II-14) in terms of the fraction of isoforms per gene losing at least one interaction (8.2% and 8.6%) and the fraction of isoform pairs per gene with different interaction profiles (14.2% and 15.3%); we only draw conclusions from comparing protein pairs in terms of their biological properties, and from observations that are consistent between the two isoform interactomes.
Detailed sequence information is available for some reference proteins, but not others. When the precise sequence information is not available, we chose the reference isoform designated by UniProt to represent each reference protein in the reference interactomes. These UniProt-designated reference isoforms are typically either the longest isoform (~88%) or the most prevalent isoform; hence they are most likely the ones used in different experimental studies to map the reference interactions. In the unlikely event that some reference proteins in the reference interactomes are represented by a different isoform than the one designated by UniProt, our method only requires the knowledge that a reference PPI exists, and can still work well without knowing the exact sequence of the reference protein. Given the experimental knowledge that a reference PPI exists, our method predicts that an alternative isoform loses this interaction if and only if the isoform loses all possible interacting domains present in the UniProt-designated reference isoform with typically the longest sequence, which in half of the cases is just one interacting domain. Hence, our predictions of interaction loss and retention remain very reasonable even in the presence of minor discordances in reference isoform annotations.
In summary, we developed a domain-based computational method for predicting an isoform interactome from a reference interactome by integrating structural domain information with experimentally determined interactions. Our predictions reveal extensive remodeling of the human interactome network by AS: ~22% of genes with two or more isoforms in the predicted isoform interactome have at least one isoform losing an interaction, and ~18% of isoform pairs encoded by the same gene in the isoform interactome network have different interaction profiles. Our isoform-interaction prediction framework is of high quality as it performs significantly better than random predictions when assessed by experimental data. In addition, our predicted isoform interactome is larger and probes a different part of the isoform space than the experimental isoform interactome of Yang et al. (2016) [34]. In terms of the space of genes with at least two isoforms tested for interactions, our predicted isoform interactomes cover ~4 times larger gene space than Yang et al. (2016). Only ~19% of the gene space covered by our predicted HI-II-14 isoform interactome is covered by Yang et al. (2016), and only ~8% of the gene space covered by our predicted IntAct isoform interactome is covered by Yang et al. (2016). Despite this minimal overlap, the biological insights provided by our predicted isoform interactome are largely consistent with Yang et al. (2016). Compared to protein pairs interacting with the same subset of isoforms of the same gene, protein pairs interacting with different subsets of isoforms of the same gene tend to be more divergent in biological function, disease phenotype, and tissue expression. Thus, our computational study complements large-scale experimental efforts on mapping the human isoform interactome, and highlights the broad applicability of AS-mediated interactome remodeling as a driving force for the functional divergence of different isoforms encoded by the same gene.
Domain-domain interactions (DDIs) were retrieved from the 3did Database of Three-Dimensional Interacting Domains [35] (retrieved May 2017) and the DOMINE Database of Protein Domain Interactions [36] (retrieved Oct 2015). For DOMINE DDIs, we kept the 6,634 DDIs inferred from Protein Data Bank (PDB) entries, and excluded those DDIs predicted by computational methods. We then combined the 6,634 PDB-inferred DDIs from DOMINE with the 10,593 PDB-inferred DDIs from 3did. After removing duplicates, we obtained a total of 11,557 DDIs. To annotate PPIs with DDIs, we first annotated proteins in the reference interactome with structural domains. Gene Entrez IDs in the HI-II-14 reference interactome were mapped to Swiss-Prot IDs using the Retrieve/ID mapping tool provided by UniProt [37]. After removing self-interactions, 4,091 genes with unique SwissProt IDs were then used for further analysis. Swiss-Prot IDs for genes in the IntAct reference interactome were provided by the IntAct database. We then retrieved protein sequences from UniProt and scanned them for Pfam domains using HMMER hmmscan [57] with an E-value cutoff of 10−5. After annotating each protein in the reference interactome with its structural domains, each PPI in the reference interactome was annotated with a DDI if one of the interacting proteins was annotated with an interacting domain of the DDI and the other interacting protein was annotated with the other interacting domain of the same DDI. Only PPIs annotated with at least one DDI were included in the domain-resolved interactome.
Alternative isoforms of each reference protein in the domain-resolved reference interactome were annotated with structural domains by retrieving their sequences from UniProt and scanning them for Pfam domains using HMMER hmmscan [57] with an E-value cutoff of 10−5. Then, for each interaction between two reference proteins in the domain-resolved reference interactome annotated with one or more DDIs, we predicted that an alternative isoform of one protein loses its interaction with the other protein if the isoform interaction loses all the above-mentioned DDI annotations. If the isoform interaction keeps at least one of the DDI annotations, the interaction was predicted to be retained.
We retrieved Gene Ontology (GO) associations from the UniProt-GOA database [38] (retrieved Feb 2016), which provides a set of 16,329 controlled hierarchical GO terms split into three categories: 3,812 molecular function terms, 11,042 biological process terms, and 1,475 cellular component terms. GO terms were mapped onto reference proteins using the Swiss-Prot IDs associated with each GO term. We quantified GO similarity between two proteins by calculating the Jaccard similarity index of their GO association profiles, which is defined as the number of GO terms shared by both proteins divided by the number of GO terms associated with at least one protein. Similarly, we calculated molecular function similarity, biological process similarity and cellular component similarity between two proteins by calculating the Jaccard similarity index of their GO association profiles using the corresponding GO entries.
We retrieved gene-disease associations from the DisGeNET database [39, 40] (retrieved July 2016), which integrated data from UniProt [37], ClinVar [58], Orphanet (http://www.orpha.net), CTD [59], and the GWAS Catalog [60]. Diseases were mapped onto reference proteins by mapping disease-associated gene names to Swiss-Prot IDs using the mapping table provided by DisGeNET. To calculate the fraction of disease subnetworks shared by two proteins, we included in each protein’s disease association profile all diseases associated with that protein and its first-degree neighbors in the HI-II-14 reference binary interactome. We then used the Jaccard similarity index to calculate the fraction of disease subnetworks shared by the two proteins, where two proteins share a specific disease subnetwork if each of the two proteins or any of its interaction partners in the HI-II-14 reference binary interactome is annotated with that disease.
We used the RNA-Seq dataset of Illumina Body Map 2.0 [43] (retrieved Jan 2016), normalized using log2 transformation, to quantify gene expression levels in 16 human body tissues: adipose, adrenal, brain, breast, colon, heart, kidney, leukocyte, liver, lung, lymph node, ovary, prostate, skeletal muscle, testis and thyroid. Gene expression profiles were mapped onto reference proteins in the IntAct reference interactome by mapping the protein Swiss-Prot IDs to gene names using the Retrieve/ID mapping tool provided by UniProt [37]. Gene expression profiles were mapped onto reference proteins in the HI-II-14 reference interactome using gene names provided by the original HI-II-14 dataset. Tissue co-expression of each pair of reference proteins was calculated as Pearson’s correlation coefficient of their gene expression profiles.
The quality of our computational method was assessed by the experimental isoform interactome dataset of Yang et al. (2016) [34], which consists of 985 interactions and 763 non-interactions between reference proteins taken from the human ORFeome V8.1 database [61] and newly-cloned isoform sequences. 310 of these interactions involve an ORFeome protein and a newly-cloned reference isoform sequence (reference-reference), and the rest of the interactions involve an ORFeome protein and a newly-cloned alternative isoform sequence (reference-alternative). We annotated all isoforms in this experimental dataset with structural domains by scanning their sequences for Pfam domains using HMMER hmmscan [57] with an E-value cutoff of 10−3. The 11,557 PDB-inferred DDIs from 3did [35] and DOMINE [36] were then used to annotate the reference-reference interactions. A protein-protein interaction was given a full DDI annotation if one protein was annotated with an interacting domain of a DDI, and its interaction partner was annotated with the other interacting domain of the same DDI. A protein-protein interaction was given a partial DDI annotation if one protein with multiple isoforms was annotated with an interacting domain of a DDI, even if the interaction partner was not annotated with the other interacting domain of the same DDI.
We have created a web tool called “DIIP: Domain-based Isoform Interactome Prediction” that allows users to query our predicted isoform interactome for isoform-specific interactions of a protein of interest. In addition, our web tool gives users a second advanced option to predict isoform-specific interactions using our isoform interactome prediction method (DIIP) from interactions provided by the user. Interactions provided by the user do not need to be part of our predicted isoform interactome. The web tool can be accessed at the following URL: http://bioinfo.lab.mcgill.ca/resources/diip. The code used for predictions and analysis is available at the following URL: http://github.com/MohamedGhadie/isoform_interactome_prediction.
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10.1371/journal.pntd.0005323 | Characterizing Antibody Responses to Plasmodium vivax and Plasmodium falciparum Antigens in India Using Genome-Scale Protein Microarrays | Understanding naturally acquired immune responses to Plasmodium in India is key to improving malaria surveillance and diagnostic tools. Here we describe serological profiling of immune responses at three sites in India by probing protein microarrays consisting of 515 Plasmodium vivax and 500 Plasmodium falciparum proteins with 353 plasma samples. A total of 236 malaria-positive (symptomatic and asymptomatic) plasma samples and 117 malaria-negative samples were collected at three field sites in Raurkela, Nadiad, and Chennai. Indian samples showed significant seroreactivity to 265 P. vivax and 373 P. falciparum antigens, but overall seroreactivity to P. vivax antigens was lower compared to P. falciparum antigens. We identified the most immunogenic antigens of both Plasmodium species that were recognized at all three sites in India, as well as P. falciparum antigens that were associated with asymptomatic malaria. This is the first genome-scale analysis of serological responses to the two major species of malaria parasite in India. The range of immune responses characterized in different endemic settings argues for targeted surveillance approaches tailored to the diverse epidemiology of malaria across the world.
| Although malaria deaths have fallen by 60% worldwide since 2000, the disease remains a significant public health problem. India has the highest burden of malaria in the South-East Asia Region, where Plasmodium vivax and Plasmodium falciparum are its main causes. While the two major malaria parasite species co-occur in India, their proportion varies across the country. Antibodies in an individual indicate current or past Plasmodium infection, and can be used to identify suitable vaccine candidates, as well as develop novel tools for malaria surveillance. We present the results of a pilot study undertaking the first large-scale characterization of antibody responses to ~1000 Plasmodium antigens at three field sites in India using high-throughput protein microarray technology. Individuals from the eco-epidemiologically diverse sites showed reactivity to 265 P. vivax and 373 P. falciparum antigens, regardless of infection status. Further comparison of individuals with symptomatic and asymptomatic malaria revealed the most immunogenic Plasmodium antigens, as well as antigens that were recognized with greater intensity in individuals that were asymptomatic at the point of sample collection. These results are a valuable addition to existing data from other malaria endemic regions, and will help to expand our understanding of host immunity against the disease.
| The burden of malaria in India has halved over the last 15 years, yet India continues to account for over 70% of malaria cases in South East Asia [1]. The ‘National Framework for Malaria Elimination in India 2016–2030’ has two aims: eliminating malaria throughout the country by 2030 and maintaining malaria–free status in areas where transmission has been interrupted [2]. Long-lasting insecticide-treated bed nets and artemisinin combination therapy have greatly helped to reduce malaria incidence in India. However, as transmission declines, the proportion of asymptomatic and submicroscopic infections tends to rise in a population [3]; these infections can contribute to malaria transmission [4], but they remain undetected by the standard diagnostic and surveillance tools. In order to eliminate malaria, it will be critical to develop accurate and sensitive methods for diagnosis and surveillance of asymptomatic and submicroscopic malaria infections.
The human immune response to the malaria parasite Plasmodium is multi-faceted, involving both the humoral and cell-mediated response pathways. CD8+ effector T cells can kill intra-hepatocytic stages [5], while merozoites and intraerythrocytic stages are primarily controlled by antibody-mediated responses such as interference with invasion of naïve erythrocytes, increased clearance of antibody-bound erythrocytes, and antibody-dependent cellular cytotoxicity mechanisms [6, 7]. The importance of antibody-based responses against Plasmodium was first demonstrated by passive transfer of antibodies from a clinically immune adult to a symptomatic child, which conferred protection from severe disease [8, 9]. Antibodies are generated rapidly to several parasite antigens immediately following infection, boosted upon subsequent infections, and are able to persist for several years after parasite clearance [10, 11]. Despite being exposed to multiple infections, individuals living in endemic areas do not acquire sterile immunity to malaria; instead, they develop non-sterile transmission- and age-dependent protection from clinical disease, known as ‘naturally acquired immunity’ (NAI). Several studies have highlighted the role of antibody-based response in NAI, encompassing protection from infection (anti-parasite immunity) and severe clinical symptoms (anti-disease immunity). The acquisition of natural immunity has been extensively demonstrated for P. falciparum, and more recently for P. vivax, in regions of both high and low transmission [12, 13]. Interestingly, NAI is acquired more rapidly to P. vivax infection than to P. falciparum [12], which, hypothetically, could be attributed to the differing biology of the two parasite species, such as the ability of P. vivax to maintain a dormant state within hepatocytes [10]. Additionally, there may be a differential contribution of antibody responses to natural immunity against P. vivax and P. falciparum [14–19].
Antibodies in an individual can be indicative of recent exposure to Plasmodium parasites, current infections, or past infections, and can therefore be used to identify suitable candidates for vaccine development, and to develop tools that can estimate malaria transmission levels [20] or monitor the efficacy of treatment programs [21]. Antibody-based responses can be measured using techniques that assess responses to one or very few antigens, e.g., immunofluorescent antibody test, enzyme-linked immunosorbent assay etc., or that detect antibodies to several antigens simultaneously, such as genome-scale protein microarrays [20]. Previous attempts to characterize the antibody response to Plasmodium among Indian populations have been sparse [22], with only a few studies describing the targeted response to well-established vaccine candidates [23–26]. Protein microarrays have been used to characterize antibody responses in populations from malaria endemic regions in Africa, South East Asia, and South America [27]; however, there have been no such studies in India to date. To capture the diverse eco-epidemiology in India, we probed protein microarrays comprising ~500 P. vivax and ~500 P. falciparum antigens, with sera from individuals at three sites in India–Raurkela in the eastern state of Odisha, Nadiad in the north-west state of Gujarat, and Chennai in the southeastern state of Tamil Nadu. Although the two major malaria parasite species co-occur at all three sites, they differ in their prevalence. Historically, P. vivax has been predominant in Chennai and Nadiad, whereas P. falciparum has been predominant in Raurkela. The objectives of our study include: 1) to provide a descriptive analysis of seroreactivity profiles of individuals against Plasmodium antigens at three epidemiologically diverse malaria endemic sites in India; 2) to examine the relationship between age and the acquisition of antibodies against Plasmodium antigens; 3) to identify the most immunogenic Plasmodium antigens based on antibody responses at three sites in India; and 4) to identify antigens recognized with greater intensity by individuals with asymptomatic malaria.
Ethical approval to conduct this study was obtained from New York University Institutional Review Board (Study #i10-00173) and the Ethics Committee of the National Institute of Malaria Research, India. All project staff completed Protection of Human Research Subjects training prior to beginning the study, and clinical samples were collected after informed consent was obtained from all participants. For all adult patients, informed written consent was obtained from literate patients, and oral consent (documented by a thumb print) was obtained from illiterate patients. For child participants, assent was obtained from the participant, in addition to written or oral consent from their parent or legal guardian.
Samples were collected as part of epidemiology studies at three sentinel sites in India: [1] Chennai City in the state of Tamil Nadu; [2] Nadiad town and surrounding villages in the state of Gujarat; and [3] Raurkela town and surrounding villages in Odisha, during January 2013-April 2015. These field sites are part of a National Institutes of Health-funded International Centers of Excellence for Malaria Research (ICEMR).
Chennai is the largest city in the southern state of Tamil Nadu, with a population of over 7 million in 2011. Located on the eastern coast of India, it has a tropical wet and dry climate, with a rainy season focused between mid-October to mid-December. Chennai accounts for 55.6% of all malaria cases in Tamil Nadu, and had an annual parasite index (API) of 1.79 in 2013 [28]. Entomological inoculation rate (EIR) values are not available. P. vivax is the dominant species, and although transmission is perennial, malaria cases peak between July and October. Subjects were enrolled at the Besant Nagar Malaria Clinic, or in cross-sectional surveys conducted in the Besant Nagar catchment area, which is composed of middle- and upper- class urban dwellings, a few slums, and a large coastal fishing community.
Nadiad, with a population of 0.2 million in 2011 is located in the Kheda district in Gujarat state. Rainfall is primarily received between June and September. Nadiad is hypo-endemic for both P. vivax and P. falciparum species, with slightly higher prevalence of P. vivax, and an average API of 2.5 (range 0.87–4.12) and EIR of 0.05–0.21 [29]. The National Institute of Malaria Research (NIMR) Malaria Clinic enrolled subjects attending Nadiad Civil Hospital. In addition, subjects were enrolled in cross-sectional surveys conducted in rural areas in the vicinity of Nadiad town.
Raurkela, with a population of over 0.5 million, is located in Sundargarh district close to the northern border of the state of Odisha, and has a tropical climate, with high temperatures and heavy rainfall between June-September and December-January. P. falciparum is the dominant species in Raurkela, and it has the highest EIR (7.3–127) and API (average = 20, range 5.1–43.5) of the three field sites in our study [29]. Subjects were enrolled at a clinic set up in a suburb of Raurkela, and in cross-sectional surveys conducted in rural areas in the vicinity of Raurkela.
Sample collection and processing at these sites has been described in detail elsewhere [29–31]. To summarize, individuals between 1–69 years were enrolled after informed consent, and whole blood was collected to generate a blood smear for malaria diagnosis and to measure hemoglobin levels. The remaining blood was separated into plasma and red blood cell (RBC) fractions: DNA was extracted from the RBC fractions for species-specific detection of Plasmodium [32], and plasma samples were stored at -80°C.
Plasma samples from 353 individuals that were randomly selected from clinic and cross sectional studies at our field sites were utilized for this study (Table 1). A total of 236 individuals were diagnosed as malaria-positive and 117 individuals were diagnosed as malaria-negative by microscopy and PCR. Individuals ≤ 15 years were categorized as children, and those > 15 years were categorized as adults. Of the 236 malaria-positive individuals diagnosed by PCR, 147 individuals with documented fever (body temperature ≥ 37.5°C) or a history of fever in the past 48 hours were categorized as symptomatic; 89 individuals without documented fever or history of fever in the past 48 hours were categorized as asymptomatic. Pooled plasma samples from 20 healthy semi-immune adults from Sepik, Papua New Guinea, collected in 2004 and where transmission of P. falciparum and P. vivax was equally high, were used as positive controls, and pooled plasma samples from unexposed individuals in the United States were used as negative controls.
The Pf/Pv500 protein array (Antigen Discovery Inc., Irvine, CA) comprises a total of 515 P. vivax and 500 P. falciparum antigens, expressed in cell-free in vitro transcription/translation (IVTT) reactions. Accession numbers and description of the antigens are based on the P. vivax Salvador I and P. falciparum 3D7 genome annotation from the PlasmoDB database [33]. Complete array information is publicly available through the NCBI Gene Expression Omnibus database with accession number GPL18316. Sample processing and microarray probing was performed as described elsewhere [27].
Microarray spot intensities were quantified using the ScanArray Express software (Perkin Elmer, Waltham, MA). The non-specific background signal for each spot was calculated as the median intensity of sample-specific no-DNA IVTT control spots. For data normalization, the raw intensity values for IVTT proteins were divided by the median intensity of corresponding IVTT controls and log2 transformed to generate median-normalized fold-over-control (FOC) values. To generate the heat map, median-subtracted intensity values were obtained by subtracting the median intensity of IVTT controls from the raw intensity of IVTT protein spots. Significance Analysis for Microarrays [34] was carried out by comparing the median-normalized intensity of antibody binding to Indian samples with unexposed controls from the United States, to determine antigens that are specifically recognized in the Indian cohort. Individual plasma samples were considered seropositive for a particular antigen if the corresponding log2(FOC) ≥ 1. The breadth of antibody response was determined as the number of antigens an individual or group of plasma samples were seropositive to, based on the above criteria. Comparison of the breadth of responses across various groups was carried out using the Kruskal Wallis test with p-values adjusted using Dunn’s correction for multiple testing. Analysis of differential antibody reactivity between groups was performed by a two-sample T test assuming unequal variance and corrected for false discovery rates using the Benjamini-Hochberg method. Statistical analyses were carried out using Microsoft Excel, R v3.0.1, and Prism v7.0. All data have been made publicly available through PlasmoDB [33].
We used the Pf/Pv500 protein microarray containing 515 P. vivax and 500 P. falciparum antigens to evaluate the seroreactivity of 236 malaria-positive and 117 malaria-negative individuals (353 total) from three study sites, Raurkela (n = 168), Nadiad (n = 114) and Chennai (n = 71), in India. Table 1 provides characteristics of the individuals and study sites.
Comparison of antibody responses between all plasma samples identified 638 antigens recognized with significantly higher intensity in Indian samples versus unexposed U.S. controls. All subsequent analyses were carried out using this subset of 265 P. vivax and 373 P. falciparum antigens. The global profile of antibody binding to Plasmodium antigens at the three sites is shown (Fig 1). The overall seropositivity or breadth of response against P. falciparum antigens was higher compared to P. vivax antigens.
To analyze observed differences in seroreactivity across the three Indian sites in more detail, we compared the breadth of response to P. vivax and P. falciparum antigens in adults. Malaria-positive adults from Nadiad showed significantly higher breadth of response to P. vivax antigens compared to malaria-positive adults from Raurkela (p < 0.03) and Chennai (p < 0.0001). Malaria-negative adults from Nadiad also showed higher levels (p < 0.03) of seropositivity to P. vivax than adults from Chennai (Fig 2A). In the case of P. falciparum antigens, malaria-positive adults from Chennai had significantly lower seroreactivity compared to malaria-positive adults from Raurkela (p < 0.0001) and Nadiad (p < 0.002); a similar observation was made upon comparison between malaria-negative adults from Chennai with Raurkela (p < 0.002) and Nadiad (p < 0.03; Fig 2B).
We compared the breadth and intensity of antibody response between malaria-positive children (age ≤ 15 years) and adults (age > 15 years) to understand age-based differences in acquired immunity (limited to Raurkela and Nadiad due to insufficient numbers of children sampled in Chennai). Adults showed higher breadth of response against P. vivax (p < 0.0001; Fig 3A) and P. falciparum (p < 0.03; Fig 3A), as well as greater intensity of antibody binding against P. vivax (p < 0.002; Fig 3B) and P. falciparum (p < 0.03; Fig 3A) when compared to children.
We identified highly immunogenic Plasmodium proteins across all study sites. For every antigen, we calculated the average reactivity and breadth of response in malaria-positive and malaria-negative samples at each site. We identified 26 Plasmodium antigens with average log2(FOC) ≥ 1 among malaria-positive samples at all sites; these antigens were also recognized by most of the malaria-negative samples at each site. This list of 14 P. vivax and 12 P. falciparum antigens represents the most immunogenic Plasmodium antigens that are indicative of current or past malaria infections (Table 2). In the case of P. vivax, merozoite surface proteins, ETRAMP, SFT2, and a number of hypothetical proteins are recognized with the greatest intensity. Among the P. falciparum antigens recognized with the greatest intensity of antibody binding are members of the PfEMP1 family, PTP5, AMA1, and HSP70.
Based upon the presence or absence of fever at the time of enrollment and up to 48 hours earlier, Plasmodium-positive individuals were further categorized as symptomatic or asymptomatic. We compared the breadth and intensity of antibody response to Plasmodium antigens in adults classified into these two categories. Of the 174 malaria-positive samples, 121 were diagnosed by both PCR and microscopy, and 50 were detected only by PCR, i.e., were submicroscopic. Analysis of the 121 microscopy- and PCR-positive adults revealed that asymptomatic adults infected with P. falciparum showed significantly higher breadth (p < 0.03; Fig 4A) and intensity (p < 0.03; Fig 4B) of antibody response than symptomatic adults. In addition, we observed that the asexual parasitemia levels in asymptomatic adults were much lower (p < 0.002; Fig 4C) compared with symptomatic adults infected with P. falciparum. In the case of P. vivax, no significant differences in the breadth (Fig 4A) and intensity (Fig 4B) of antibody response were observed between adults with asymptomatic and symptomatic infection. The levels of asexual parasitemia were also comparable between asymptomatic and symptomatic adults infected with P. vivax (Fig 4C).
We were interested in identifying antigens that have significantly higher reactivity in sera from asymptomatic individuals compared to symptomatic individuals. We used log2(FOC) values to identify antigens that were differentially reactive between asymptomatic and symptomatic adults (p < 0.05, with Benjamini–Hochberg correction for false-discovery rate). Asymptomatic adults infected with P. falciparum displayed significantly higher reactivity to several P. falciparum antigens compared to symptomatic adults. We have identified 19 P. falciparum antigens that were recognized with 2-fold greater intensity in asymptomatic adults in India (Table 3). On the other hand, no P. vivax antigens showed significantly higher reactivity in asymptomatic compared to symptomatic individuals infected with P. vivax.
We present the first genome-scale analysis of seroreactivity to Plasmodium antigens in the Indian population using protein microarray technology. The three field sites in our study are distributed across eco-epidemiologically diverse regions in India, with differing prevalence of P. vivax and P. falciparum. We observed a broad response to P. vivax and P. falciparum antigens at all three sites, regardless of malaria infection status. Overall levels of antibody binding were greater in P. falciparum antigens compared to P. vivax antigens, as previously reported in regions with co-endemic occurrence of P. vivax and P. falciparum [35]. Our Indian sample cohort showed significant seroreactivity to 265 P. vivax and 373 P. falciparum antigens compared to unexposed US controls.
In the case of P. falciparum, we observed that decreasing prevalence corresponded with a decrease in the breadth of antibody response. Malaria-negative adults showed a similar pattern to malaria-positive adults at all sites, reflecting the background seroreactivity due to parasite exposure. The breadth of response to P. falciparum antigens was significantly lower in adults from Chennai compared to both Raurkela and Nadiad; although the mean breadth of response appeared to be lower in Nadiad compared to Raurkela, the decrease was not statistically significant.
We did not observe a correlation between the breadth of antibody response and species prevalence for P. vivax. Malaria-positive adults from Nadiad showed significantly larger breadth of response than adults from both Chennai and Raurkela; this was not observed in malaria-negative adults. We were puzzled that regardless of their malaria infection status, the seroreactivity of individuals from Chennai was similar to Raurkela, which has much lower prevalence of P. vivax. Since we began the study in 2012, the incidence of malaria has gone down in India, particularly in the state of Tamil Nadu where Chennai accounts for ~55% of the malaria burden [28]. Thus, the reduced seroreactivity of Chennai individuals may be reflective of reduced transmission levels during the period of sample collection for our study. Interestingly, some Indian states, including Odisha and Gujarat, witnessed a spike in malaria cases in 2014 [36]. Therefore, the prevalence of P. vivax and P. falciparum in Raurkela and Nadiad may have been higher than expected during the course of our study. Hypothetically, P. vivax seroreactivity may also be affected by differential rates of P. vivax relapse; sites with a higher relapse rate may exhibit greater priming of the immune response. However, we do not have data on relapse rates at our three sites to address this conclusively. Additionally, the breadth of response could be affected by differences in the total number of individuals and the proportion of symptomatic and asymptomatic infections between the sites.
The breadth and magnitude of immune response have been known to increase with age, as a consequence of repeated exposure to the parasite. We compared the responses of children and adults from Nadiad and Raurkela to determine the influence of age on the immune response against malaria. The breadth and intensity of response against P. vivax as well as P. falciparum antigens was significantly higher in adults than in children.
Antigens recognized with the highest intensity by sera from both symptomatic and asymptomatic malaria patients may serve as indicators of exposure. Data from other ICEMR studies [27] have highlighted the need to identify country-specific indicators of exposure, as they may vary depending on the epidemiology of malaria in different parts of the world. As the prevalence of the two major Plasmodium parasite species varies across India, we were interested in identifying highly immunogenic antigens from all three sites that could be used to develop a serological assay for countrywide routine surveillance. It is also important to elucidate the kinetics of antibody acquisition and maintenance [37] in order to distinguish between recent versus past exposure. Markers of exposure in sera from children may be better indicators of recent exposure, as adults may have had several exposure events, confounding the evaluation of responses from recent exposure. Several of the top immunogenic P. vivax proteins in the Indian population, such as ETRAMP, MSP10, MSP8, and hypothetical proteins such as PVX_117680, PVX_083560, PVX_097730, PVX_110935, PVX_118705 have also been identified as highly immunogenic in other studies [38–42]. Among the top immunogenic proteins in P. falciparum identified in our study, members of the PfEMP1 family, PTP5, MSP10 and HSP70 were also identified as immunogenic or recognized with greater intensity in asymptomatic malaria in other ICEMR studies [13, 37, 43, 44]. Invasion-related proteins AMA1 and SYN6 were also highly immunogenic [45].
Asymptomatic individuals may not be protected from malaria parasite infection, but they may possess immunity against symptomatic disease. Our data indicate that adults with asymptomatic P. falciparum infection have lower average asexual parasitemia, but higher breadth as well as intensity of response, than adults with symptomatic infection. Asymptomatic P. falciparum infection was also associated with significantly higher seroreactivity to several P. falciparum antigens as compared to symptomatic infection, and these antigens may serve as novel vaccine candidates in addition to the limited repertoire of candidates currently being developed. A majority of the antigens associated with clinical immunity in P. falciparum infections are either exported to the infected red blood cell during the intraerythrocytic stages of parasite development (PfEMP1, RESA, ETRAMP, PHISTc, Hsp70-x, GEXP18) or present on the merozoite surface (MSP2, MSP4, MSP11, SERA4). These proteins are exposed to the host immune system for the longest duration of the infection, facilitating the development of a strong immune response. Some of these antigens play essential roles in vital processes such as invasion (RON2), making them promising vaccine candidates. GCN5 and LSA3, previously associated with protection from experimental challenge with sporozoites [46, 47], and invasion-related proteins, RON2 and RH2b, were recognized more strongly by asymptomatic individuals in our study MSP2, MSP4, MSP11, PHISTc, Rh2b, PfEMP1, LSA3 and SERA4 were also associated with protection from symptomatic disease in other malaria-endemic regions [13, 35, 43] demonstrating that the antigens associated with asymptomatic infection are common across populations, which is encouraging for the development of a vaccine.
Comparison of serological profiles of adults with symptomatic or asymptomatic malaria suggests that immunity to P. falciparum is associated with a broad and intense response to several antigens, but with low parasitemia levels. In contrast, adults with asymptomatic and symptomatic P. vivax infection had comparable breadth and intensity of antibody response, as well as similar asexual parasitemias. In addition, asymptomatic P. vivax infection was not associated with higher seroreactivity to specific P. vivax antigens. Based on these findings, it appears that unlike P. falciparum, antibody-mediated immune responses may have a much lower contribution in asymptomatic P. vivax infections. P. vivax is thought to be more pyrogenic than P. falciparum, since it stimulates a stronger fever-inducing cytokine response despite frequently presenting at a lower parasite burden than P. falciparum [48]. However, Goncalves et. al., propose that regulatory cytokines, such as IL-10 may play a more critical role in protecting P. vivax patients from severe clinical complications, while a strong inflammatory response may be critical in controlling parasite density in P. falciparum infections [14].
Our pilot study was conducted in tandem with other protein microarray projects within the ICEMR program and using the same Pf/Pv500 protein array [13, 27, 35, 43]. Together, these studies facilitate a global comparison of seroreactivity to over 1000 Plasmodium antigens, and provide a means to identify highly immunogenic proteins and antigens recognized more strongly in asymptomatic individuals, which may subsequently be used for the development of vaccines and routine surveillance tools. We acknowledge the limitations of using this array, since the antigens have been produced from sequence information of decades-old single reference isolates (P. falciparum 3D7 and P. vivax Salvador I). Our future studies will include country-specific redesign of the arrays using sequence data from diverse parasite strains circulating at our study sites, in order to capture regional diversity in antigenic genes. In addition, appropriate study design will be extremely important to tease out the complexities of the host immune response to malaria. In particular, prospective cohort studies, with careful monitoring of parasite transmission and patient serology, are likely to be the most informative. In conclusion, these data have broadened our understanding of naturally acquired immunity against P. vivax and P. falciparum in India, and will contribute to global malaria control and eradication measures.
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10.1371/journal.pgen.1001345 | Environmental Sex Determination in the Branchiopod Crustacean Daphnia magna: Deep Conservation of a Doublesex Gene in the Sex-Determining Pathway | Sex-determining mechanisms are diverse among animal lineages and can be broadly divided into two major categories: genetic and environmental. In contrast to genetic sex determination (GSD), little is known about the molecular mechanisms underlying environmental sex determination (ESD). The Doublesex (Dsx) genes play an important role in controlling sexual dimorphism in genetic sex-determining organisms such as nematodes, insects, and vertebrates. Here we report the identification of two Dsx genes from Daphnia magna, a freshwater branchiopod crustacean that parthenogenetically produces males in response to environmental cues. One of these genes, designated DapmaDsx1, is responsible for the male trait development when expressed during environmental sex determination. The domain organization of DapmaDsx1 was similar to that of Dsx from insects, which are thought to be the sister group of branchiopod crustaceans. Intriguingly, the molecular basis for sexually dimorphic expression of DapmaDsx1 is different from that of insects. Rather than being regulated sex-specifically at the level of pre–mRNA splicing in the coding region, DapmaDsx1 exhibits sexually dimorphic differences in the abundance of its transcripts. During embryogenesis, expression of DapmaDsx1 was increased only in males and its transcripts were primarily detected in male-specific structures. Knock-down of DapmaDsx1 in male embryos resulted in the production of female traits including ovarian maturation, whereas ectopic expression of DapmaDsx1 in female embryos resulted in the development of male-like phenotypes. Expression patterns of another D. magna Dsx gene, DapmaDsx2, were similar to those of DapmaDsx1, but silencing and overexpression of this gene did not induce any clear phenotypic changes. These results establish DapmaDsx1 as a key regulator of the male phenotype. Our findings reveal how ESD is implemented by selective expression of a fundamental genetic component that is functionally conserved in animals using GSD. We infer that there is an ancient, previously unidentified link between genetic and environmental sex determination.
| Sex determination is a fundamental biological process that can be broadly divided into two major categories. In genetic sex determination (GSD), sex-specific differentiation results from intrinsic genetic differences between males and females, whereas environmental sex determination (ESD) relies on environmental signals to induce male or female sex determination. In contrast to model organisms that utilize GSD system, environmental sex-determining organisms are poor genetic models. Therefore, although candidate genes involved in ESD have been found in vertebrates, their functions have remained largely unknown, impairing our understanding of ESD and making the comparison of sex-determining genes between both systems difficult. Here, we report the identification of a gene responsible for the production of males during environmental sex determination in the crustacean Daphnia. This gene is homologous to the Doublesex gene that is functionally conserved in animals that use GSD. Expression of Doublesex was increased primarily in male-specific structures. Gain- and loss-of-function analyses established that Daphnia Doublesex gene is a major effector that regulates the male phenotype in Daphnia. We infer that there is an ancient, previously unidentified link between genetic and environmental sex determination.
| Sex determination is a fundamental biological process. It affects not only the sexual differentiation of gonads, but also the development of most organs, and leads to sex-specific differences in behavior, physiology and morphology. Organisms have evolved a variety of different sex-determining systems [1], [2] that can be broadly divided phenomenologically into two categories: genetic and environmental [3]. Genetic sex determination (GSD) is attributed to the genetic segregation of genes, often residing on sex chromosomes that initiate alternate sex-determining developmental pathways. Environmental sex determination (ESD) is initiated by environmental cues that presumably trigger alternative genetic signals, which regulate male or female sex-determining genes [4]. Although GSD is a more prevalent system in animals, ESD is also phylogenetically widespread, occurring in such diverse taxa as rotifers, nematodes, crustaceans, insects, fishes, and reptiles [5]. Environmental cues involved in ESD include temperature, photoperiod, nutrition, and population density [5]. Temperature is the most widely studied environmental cue, particularly in the case of reptiles where the temperature at which the egg is incubated determines sex [6]. ESD has arisen repeatedly during evolution [7], which may imply the adaptive significance of this system in environments [8].
It has long been suggested that selection forces drove the transition between GSD and ESD [9], [10]. Previous experiments using a temperature-sensitive mutation created artificially in Drosophila melanogaster and Caenorhabditis elegans also demonstrated how GSD could rapidly evolve into ESD as a consequence of a mutation in a single control gene [11], [12]. In addition, orthologs of some genes involved in GSD have been examined in species that use ESD, especially in temperature-dependent sex-determining reptiles [13]. Some of those are expressed in the gonads during the temperature-sensitive period [13]. These observations have led to the hypothesis that both ESD and GSD should have the same origin and share similar genetic components in their sex-determining pathways [4]. However, a complete functional analysis of temperature dependent ESD has not yet been performed. Therefore, analyzing function of genes involved in ESD and unraveling the sex-determining pathways are crucial to understand the origin and evolution of sex-determining pathway.
The water flea Daphnia magna is a branchiopod crustacean, which is a common inhabitant in fresh water ponds in Europe and Asia. D. magna is known to switch between parthenogenetic and sexual reproduction when environmental quality declines [14]. Normal, healthy populations are entirely female. However, shortened photoperiod, a lack of food and/or increased population density, lead to the clonal production of males that are genetically identical to their sisters and mothers. First instar male juveniles are easily distinguished from the females by their elongated first antennae [15]. During maturation, daphnids undergo morphological sexual differentiation of various somatic tissues including the first thoracic leg that is armed with the copulatory hook in males, which becomes larger in the fifth instar [16]. Gonads develop and finally settle at both sides of the gut during embryogenesis in both males and females [17]. It has been reported that the gonads exhibit morphological sex differences in the first instar juveniles [18], [19]. The appearance of males allows sexual reproduction to occur [20], [21] when females begin producing haploid eggs requiring fertilization.
Recently, we and others have shown that juvenile hormone analogs (JHA) induce male production in cladoceran crustaceans without environmental cues [22], [23]. Interestingly, exposure of D. magna to JHA at the stage corresponding to the environmentally-sensitive period for the other cladoceran Moina sex determination [24], reliably produces exclusively male broods, suggesting that juvenile hormone could be a key molecule for understanding environmental sex determination [22], [25], [26]. Together with the growing genome and transcriptome resources for Daphnia [27]-[30], this system is ideal to study genes responsible for ESD.
Mechanisms underlying genetic sex-determining pathways have been extensively studied in model organisms such as D. melanogaster, C. elegans and mouse and key genes have been identified [31]-[33]. A Doublesex (Dsx) gene was originally identified in D. melanogaster as a critical transcription factor considered to be at the end of sex determination cascades in GSD, that directly targets genes conferring sexually dimorphic traits [34]. Dsx contains two conserved domains: one is the Dsx/Mab-3 (DM) domain at the N-terminus that is evolutionarily conserved even in vertebrates [35] and another is the oligomerization domain at the C-terminus [36]. Genes encoding DM-domain (DM-domain genes) were discovered to play a related role not only in C. elegans [37], [38], but even in vertebrates [39]. In contrast, results from numerous studies have shown that other genetic sex-determining genes are widely diverse among species [1], [2], [40].
To understand the molecular and evolutionary relationships between GSD and ESD, we analyzed the function of two Dsx genes from D. magna using gene manipulations that we have developed [41]. We provide evidence that one of the homologs, termed DapmaDsx1, plays an important role in directing the major sexually dimorphic development of D. magna. Intriguingly, the function of Dsx is significantly conserved between Daphnia and genetic sex-determining insect species, which are thought to be the sister group of branchiopod crustaceans [42]; however, the factors that regulate Dsx gene expression are independently co-opted in each lineage. Our functional demonstration that Dsx controls sexual dimorphism in an environmental sex-determining organism supports the hypothesis that genetic and environmental sex determination are similar at their most fundamental level.
In an effort to understand environmental sex determination, we previously identified three DM-domain genes from D. magna and showed that two of the three DM-domain genes have sex dimorphic gene expression pattern in adult gonads [43]. However, none of these DM-domain genes exhibited sexually dimorphic expression patterns during embryonic development, suggesting that they are not involved in sex determination (Figure S1). This result prompted us to search for other DM-domain genes that might be involved in Daphnia sex determination. Two additional DM-domain genes were found in the D. magna EST database [27] and we cloned and sequenced cDNAs encoding each.
These newly identified DM-domain genes showed greater sequence similarity at the amino acid sequence level to known insect Dsx genes than to the previously identified Dsx-related genes. Therefore, these were designated Daphnia magna Dsx (DapmaDsx). One of the DapmaDsx genes (DapmaDsx1) encodes a protein of 330 amino acids from two mRNAs (DapmaDsx1-α and DapmaDsx1-β) that differ only in their 5'UTR (Figure 1A and Figure S2). The other (DapmaDsx2) encodes a protein of 314 amino acids (Figure S3). The predicted protein products of these two genes share 38% overall amino acid identity and both contain a DM-domain (Figure 1B). In addition, both genes also have an oligomerization domain that is characteristic for insect Dsx homologs (Figure 1C). Dimerization, which enhances specific DNA binding, is mediated by an extensive non-polar interface conserved within oligomerization domain. Importantly, in DapmaDsx2, two of three non-polar amino acids important in formation of the interface are substituted with the acidic amino acid, aspartic acid (Figure 1C). Phylogenetic analysis of these DM-domain genes confirmed that Daphnia Dsx genes are most closely related to the insect Dsx genes, but that the two Daphnia genes are paralogs that duplicated, forming a tandem gene cluster after the divergence of insects and crustaceans (Figure 1A and Figure S4).
We next examined expression levels of DapmaDsx genes during development by quantitative real time PCR. Expression of both DapmaDsx1 and DapmaDsx2 genes increased over 72 h exclusively in male embryos (Figure 2A, dsx1, dsx2). This temporal expression correlates well with the development of sexually dimorphic organs (e.g., gonads and first antennae), in which morphological sex differences are observed at 72 h after ovulation when hatchlings begin to swim out from the brood chamber. This temporal expression pattern suggests that one or both of the Daphnia Dsx genes might play a critical role in sexual differentiation. To examine whether the two different DapmaDsx1 promoters are used in a temporally independent manner, the expression levels of DapmaDsx1-α and DapmaDsx1-β mRNAs were evaluated. Both mRNAs increased only in males during early development, suggesting that both promoters function primarily during male development (Figure 2A, dsx1-α). DapmaDsx1-β mRNA could be detected immediately post-ovulation in males and females (Figure 2A, dsx1-β), suggesting that maternal DapmaDsx1-β mRNA is transferred to ovulated eggs.
We next examined whether DapmaDsx genes are expressed in male specific structures. Both DapmaDsx1 and DapmaDsx2 genes were highly expressed in the testis (Figure 2B). Whereas DapmaDsx1-α mRNA was expressed exclusively in the testis, DapmaDsx1-β mRNA was expressed in both testis and ovary. Whole mount in situ hybridization showed that DapmaDsx1 and DapmaDsx2 could both be detected in the first antennae and the first thoracic segments (Figure 2C and 2D), both of which are known to show sexually dimorphic characteristics. DapmaDsx2 is also expressed in female first thoracic segments though apparently more weakly than in males. Male-specific expression was also observed in the compound eye, whose sex difference has not been reported in this species to date. Taken together, these male specific expression patterns are regulated temporally and spatially, supporting strongly the involvement of DapmaDsx genes in male differentiation in Daphnia.
The sexually dimorphic expression of DapmaDsx mRNAs led us to hypothesize that the expression level of the DapmaDsx transcripts could mediate sex determination. To test this hypothesis, we established a technique to introduce exogenous genes into ovulated eggs and developed a dsRNA-based gene knockdown technique for D. magna [41]. Eggs induced to become males by fenoxycarb exposure to the mother during a critical stage of oocyte development [22], [26] were injected with Dsx-specific, or control dsRNAs, grown to the swimming juveniles and evaluated the phenotypes.
At the third instar stage, microinjection of the DapmaDsx1-specific dsRNAs resulted in development of the shortened first antenna whose length was the same as that of females in all of the DapmaDsx1-dsRNA-injected juveniles (Figure 3A). At the fifth instar stage, we dissected the feminized daphnids and found that the first thoracic appendage lacks a hook used in clasping the females and has a female-like long filament instead (Figure 3B). Correspondingly, repression of DapmaDsx1 resulted in the development of ovaries during which oocytes accumulate yolk granules and lipid droplets as well as those of wild-type females (Figure 3C). In contrast, microinjection of DapmaDsx2-dsRNAs did not induce the formation of either female-like somatic or gonadal tissues (Figure 3A–3C, Table 1).
The quantity of DapmaDsx1 and DapmaDsx2 mRNA was decreased to 40% and 20%, respectively, relative to control-dsRNA-injected embryos. Reduction of transcripts from either of the two DapmaDsx genes did not change the mRNA level of the other (Figure 3D), suggesting that the DapmaDsx genes do not regulate each other's expression or stability. We confirmed the specificity of the knockdown by using non-overlapping dsRNAs for each DapmaDsx gene (Figure S5). As expected, only DapmaDsx1 RNAi induced the same phenotypic changes (Table 1). Since the size of testes appears to be somewhat reduced following the DapmaDsx2-dsRNA injection (Figure 3C), we cannot rule out some low-level requirement for DapmaDsx2 function. However, these results suggest that DapmaDsx1 is necessary for sex determination.
To ask whether expression of DapmaDsx1 or Dsx2 might be sufficient to trigger male development in females, we developed a technique for transient transgenesis in Daphnia embryos by microinjection of capped, polyadenylated mRNAs into ovulated eggs. When the DapmaDsx1 or DapmaDsx2 mRNA was introduced into female eggs, only DapmaDsx1 mRNA partially induced elongation of the first antenna in females at 72 h after microinjection (Figure 4 and Table 2). Unfortunately, due to the transient nature of this method, the masculinization of the first antennae was observed only in the first instar juveniles, whose gonads were too small to evaluate the sex-reversal. Taken together, these data show that although they are similar in amino acid sequence and expression pattern, DapmaDsx1 and not DapmaDsx2 plays a primary role for male trait development.
Recent phylogenetic analyses [44]-[46] and developmental genetics [47], [48] suggest that insects may be a sister group to branchiopod crustaceans, a group that includes daphnids (water fleas) and brine shrimp Artemia [42]. As it is known that insect Dsx genes express sex-specific variants in their coding sequences, we next examined whether the sex-specific splicing of the Daphnia Dsx1 gene also occurs. Only a single amplified cDNA could be detected from either male or female by RT-PCR with primers to amplify a coding sequence, although PCR products obtained from females were very faint (Figure 5A). We sequenced the cDNA fragments and confirmed no sex differences of the fragments.
Further, to find altered DapmaDsx1 mRNA length and abundance, we used a northern blot with an antisense probe hybridized to the coding region. In male adults, three transcripts of approximately 4 kb, 2.8 kb, and 2 kb were detected. Of the three, 2.8 kb transcripts were the most abundant. In contrast, only faint 2 kb transcripts were detected in female adults (Figure 5B). As length differences between 5'UTR-α and -β were only 112 bases, one explanation for large differences of the transcript lengths might be the 3'UTR lengths. To determine the 3'end of DapmaDsx1 mRNAs, 3'RACE was performed. We identified four tandem polyadenylation sites located downstream of the stop codon and found that alternative usage of those sites was correlated well with production of the three DapmaDsx1 transcripts. The canonical AAUAAA signal was used for 2.8 kb transcripts, the variant AAUAUA for the 4-kb, and for the shortest 2 kb transcripts the two variant signals were used (AAGAAA or AAUUGA, Figure 5D, Figure S2). Interestingly, RT-PCR analysis with primers to discriminate between 5'UTR-α and -β showed that female 2 kb transcripts were only DapmaDsx1-β mRNAs with the shortest 3'UTR that presumably were supplied to eggs as maternal mRNAs (Figure 5C).
We report here that gain- and loss-of-function analyses in environmental sex-determining Daphnia have allowed us to characterize the function of DapmaDsx1 in sexual dimorphism and provide insight into the molecular relationship between GSD and ESD. We and others previously showed that exposure to juvenile hormone analogs reliably produces male Daphnia [25], [49]. This finding enabled us to examine genes in embryos directed to either male or female. Together with the growing genome and transcriptome resources and gene manipulation techniques for Daphnia [27]-[30], , this species is a first crustacean model that provides novel insights into understanding evolution of the sex-determining pathway.
The molecular mechanisms of genetic sex determination have been well studied in a few model organisms, such as the mouse, fruit fly and nematode. DM-domain genes are highly conserved and involved in sexual differentiation of these species [38]. The DM-domain gene was also identified as a sex-determining gene in some populations of fish of the genus Medaka [51]. Moreover, recently, molecular analyses of GSD in the frog Xenopus laevis [52] and chicken Gallus gallus [53] demonstrate deep conservation of DM-domain genes in GSD. To our knowledge, this study is the first in vivo demonstration that the Dsx gene, a fundamental genetic component that is functionally conserved in animals using GSD, can also implement ESD. Interestingly, in reptiles with temperature-dependent sex determination, the Dsx ortholog, Dmrt1 is regulated by temperature [54]-[56]. These indicate that DM-domain genes also play an important role in environmental sex determining organisms, supporting the hypothesis that both ESD and GSD have the same origin and share similar genetic components in their sex determining pathways.
DapmaDsx1, one of two Dsx homologs from Daphnia, shares several important characteristic features of Drosophila Dsx protein to function as a major effector of sexual differentiation [40], [57], [58]. First, DapmaDsx1 protein is composed of two domains, the phylogenetically conserved DM-domain [35] and the insect-specific oligomerization domain. In contrast, the DapmaDsx2 protein appears to be unable to regulate sexual differentiation and contains mutations at important amino acids of oligomerization domain [36], suggesting that this domain might be necessary for establishing sexual dimorphic traits. Second, male-specific expression of DapmaDsx1 is regulated temporally and spatially during development. Although it has long been believed that Drosophila Dsx gene was cell-autonomously expressed in all cells [59], Robinette et al. [60] recently reported that Drosophila also exclusively expressed Dsx in sexually dimorphic tissues and cells. Third, and perhaps most importantly, knock-down of DapmaDsx1 in male embryos resulted in the production of female traits including ovarian maturation whereas ectopic expression of DapmaDsx1 in female embryos resulted in partial masculinization of the first antennae. These results suggest that Dsx gene expression in sexually dimorphic tissues is a key process to induce sexual differentiation in crustacean Daphnia and insects.
In the fruit fly D. melanogaster, Dsx is spliced in the coding region by the Tra protein in a sex-dependent manner [34]. The female-specific RNA produced by alternative splicing is a functional mediator of Tra activity [61]. The female-specific splice variant of the Tra homologs encodes a functional protein not only in the Mediterranean fruit fly [62] and the house fly [63], but also in the honeybee Apis mellifera [64]. In all insect species studied to date (except the silkworm Bombyx mori) [65], Tra regulates sex-specific splicing of Dsx, which produces different mRNAs and proteins, resulting in sex-specific transcriptional activation and repression [66] (Figure 6). Sex-specific splicing of the Dsx gene by the Tra protein might be ancestral in insects. In contrast, DapmaDsx genes do not encode sex-specific Dsx proteins, but instead exhibit sexually dimorphic differences in the abundance of its transcripts. Interestingly, Daphnia has a homolog of the Tra protein but the D. magna Tra gene does not display any detectable sexually dimorphic differences in expression or splicing patterns [26]. We also performed knock-down of the D. magna Tra gene, but could not find any effect for development of sexually dimorphic traits (data not shown). This is consistent with the apparent lack of a sex-specific splicing in the Dsx1 gene. Although it is not yet clear if juvenile hormone directly activates the transcription of DapmaDsx genes, this remains an interesting possibility for future study. We found several motifs in promoter regions of the Dsx1 and Dsx2 genes, which resemble JH-responsive elements previously reported in D. melanogaster [67] and D. magna [68] (data not shown), suggesting that these motifs possibly function as elements to regulate the JH-dependent gene expression. The detection of unfavorable environmental conditions by Daphnia could be transmitted to the endocrine system, leading to the release of juvenile hormone to convey the environmental signals to sexually dimorphic cells. This would be a simple and elegant type of sex determination cascade. Understanding the molecular nature by which the transcription of the DapmaDsx genes is regulated remains an important future goal that will greatly enhance our understanding of not only sex determination, but also invertebrate hormonal systems.
Interestingly, expression of the DapmaDsx1 gene utilizes alternative polyadenylation at tandem poly(A) sites, which can yield transcripts that have identical protein-coding sequences but different 3'UTR sequences. Alternative polyadenylation is often associated with tissue- or developmental stage-dependent gene expression [69], [70]. We found that female DapmaDsx1-β mRNAs exclusively use the most promoter-proximal polyadenylation signals. The presence of alternative polyadenylation sites in Dsx genes has been reported in three insects, D. melanogaster, the phorid fly Megaselia scalaris and the mosquito Anopheles gambiae, indicating that regulation of the 3'UTR length might be a common mode to regulate expression of Dsx genes.
Despite having last shared a common ancestor with insects about 400 million years ago [42], [71] and differences of the initial cue to determine sex, the DapmaDsx1 maintained the domain structure essential for establishing sexual dimorphism, while regulation of its expression by other factors became complex and diverse. This is consistent with the prediction that new signals are co-opted upstream of a cascade during the course of evolution [72], [73]. Thus, we have established that there were no boundaries between GSD and ESD in evolution of sex-determining genes at their most fundamental level.
The Daphnia magna strain (NIES clone) was obtained from the National Institute for Environmental Studies (NIES; Tsukuba, Japan) and maintained as described previously [25]. In order to obtain male embryos, adult D. magna (about 2 weeks of age) were treated with a synthetic juvenile hormone mimic, fenoxycarb (1 µg/L), and eggs ovulated into the brood chamber were collected.
The amino acid sequence of the Drosophila melanogaster Dsx gene was retrieved from NCBI database (http://www.ncbi.nlm.nih.gov/) and used to search the D. magna EST database for related sequences. Two EST sequences were identified to have similarities with the Drosophila Dsx gene. The harvested daphnids were briefly washed and homogenized using the Physcotron NS-310E (Nichion, Tokyo, Japan). Total RNA was extracted with TRIzol reagent according to the manufacturer's protocol (Invitrogen, Carlsbad, CA, USA). Poly (A)+ RNA was isolated from purified total RNA using Fast Track (Invitrogen) and converted to cDNA using Superscript III and random primers (both Invitrogen) according to the manufacturer's protocol. cDNAs corresponding to the EST sequences were obtained by PCR, and full length cDNAs were obtained by RACE (Cap Fishing, SeeGene, Seoul, South Korea) using the oligonucleotide sequences shown in Table S1. Sequence data from this article have been deposited with the DDBJ/EMBL/Genbank Data Libraries under Accession No. AB569296, AB569297, AB569298.
A phylogenetic tree of DM domain genes including newly cloned D. magna Dsx genes was constructed using amino acid sequences of DM-domain genes used in the previous study [43] and insect Dsx genes listed in Table S2. A multiple alignment was constructed using Clustal W [74] with the following settings (pairwise alignment parameters: gap opening penalty 6.00, gap extension penalty 0.21, identity protein weight matrix; multiple alignment parameters: gap opening penalty 10.00, gap extension penalty 0.24, delay divergent cutoff 30%, gap separation distance 4). Phylogenetic reconstruction was performed using the p-distance algorithm and the neighbor-joining method implemented in MEGA version 4 [75]. The phylogenetic tree was rooted to vertebrate DMRT7 outgroups (mouse; NP_082008, bovine; NP_0010332710, data not shown).
Embryos were obtained from D. magna at two weeks of age. Ovulation occurred just after molting and was assigned to be 0 h. The embryos were collected 18 h, 42 h and 72 h after ovulation. Gonads were isolated and specific mRNAs were quantified as described previously [43]. The oligonucleotide sequences for PCR were indicated in Table S3.
Templates for the probe preparation were synthesized by PCR using gene-specific primers containing the T7 polymerase promoter sequence at their 5'-ends (Table S4). DIG-labeled probes were prepared as described by Butler et al. [76] and subjected to alkaline hydrolysis. Whole mount in situ hybridization was performed as described by Sagawa et al. [17]. Both antisense and sense probes were used to confirm the specificity of staining.
Double-stranded RNA was synthesized using the MEGAscript high yield transcription kit (Ambion, Austin, TX, USA). Templates were prepared by PCR using gene-specific primers with the T7 polymerase promoter sequence at their 5'-ends (Table S5). The synthesized RNAs were purified using phenol/chloroform. Following ethanol precipitation, the RNA was resuspended in DNase/RNase-free distilled water (Invitrogen, Tokyo, Japan) and annealed [41]. Sequences corresponding to each dsRNAs were shown in Figure S2. dsRNA lengths were: Dsx1-#1, 778 bp: Dsx1-#2, 579 bp: Dsx2-#1, 703 bp: Dsx2-#2: 448 bp.
DSX1-α, DSX1-β and DSX2 cDNAs were subcloned into pCS2 vector and used for RNA synthesis. To synthesize the control RNA, pEGFP-C1 vector was used. In vitro transcription with T7 RNA polymerase and poly-A tail addition were performed according to the manufacturers' protocol using commercial kits [mMESSAGE mMACHINE, and Poly(A) Tailing kit, respectively, Ambion]. Templates were prepared by PCR using primers corresponding to the 5'- and 3'-ends of the mRNA sequences. The T7 polymerase promoter sequence was attached to the 5' end of the forward primer.
Eggs were obtained from D. magna at two weeks of age just after the ovulation and placed in ice-cold M4 media [77] containing 40 mM sucrose (M4-sucrose). The synthesized dsRNA (1 mg/ml) containing 1 mM Chromeo 494 fluorescent dye (Active Motif Chromeon GmbH, Tegernheim, Germany) or mRNA was injected and incubated in a 96-well plate for appropriate times [41]. Equal molar amounts of DSX1-α, DSX1-β and Dsx2 mRNA were injected for the gain-of-function study. Injection volume was approximately 0.3 nL.
Embryos were dissected off the yolk, and photographed with a Zeiss Axioplan 2 Imaging microscope (Zeiss, Oberkochen, Germany). Adults and juveniles were observed and photographed using a Leica MZ APO dissecting microscope (Leica Microsystems Heidelberg GmbH, Mannheim, Germany). mRNA-injected juveniles were directly observed using an environmental scanning electron microscope (XL30 ESEM; Philips, Hillsboro, OR, USA).
3 µg and 10 µg of male and female poly (A)+ RNA were used respectively. The RNAs were separated by electrophoresis on a 1.0% formaldehyde–agarose gel and then transferred to positively charged nylon membranes (Hybond-N+; GE Healthcare, Little Chalfont, England). RNA probes were prepared with a DIG RNA labeling kit (Roche Diagnostic GmbH, Manheim, Germany). Primers to amplify templates for the probe preparation were (5'-3'): forward: AAGAATTGTCCGTGGGGGCAC and reverse: TAATACGACTCACTATAGGGAAAGTTTGGTGTAGGGAG. The membranes were hybridized with DIG-labeled RNA probes for 11 hr at 68 °C with DIG easy hyb (Roche Diagnostic). DIG-labeled RNA was detected with an alkaline phosphatase-conjugated anti-DIG antibody using CDP star (Roche Diagnostic) according to the manufacture's protocol.
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10.1371/journal.pbio.1000101 | Visualisation and Quantification of Morphogen Gradient Formation in the Zebrafish | During embryonic development, signalling molecules known as morphogens act in a concentration-dependent manner to provide positional information to responding tissues. In the early zebrafish embryo, graded signalling by members of the nodal family induces the formation of mesoderm and endoderm, thereby patterning the embryo into three germ layers. Nodal signalling has also been implicated in the establishment of the dorso-ventral axis of the embryo. Although one can infer the existence of nodal gradients by comparing gene expression patterns in wild-type embryos and embryos in which nodal signalling is diminished or augmented, real understanding can only come from directly observing the gradients. One approach is to determine local ligand concentrations in the embryo, but this is technically challenging, and the presence of inhibitors might cause the effective concentration of a ligand to differ from its actual concentration. We have therefore taken two approaches to visualise a direct response to nodal signalling. In the first, we have used transgenic embryos to study the nuclear accumulation of a Smad2-Venus fusion protein, and in the second we have used bimolecular fluorescence complementation to visualise the formation of a complex between Smad2 and Smad4. This has allowed us to visualise, in living embryos, the formation of a graded distribution of nodal signalling activity. We have quantified the formation of the gradient in time and space, and our results not only confirm that nodal signalling patterns the embryo into three germ layers, but also shed light on its role in patterning the dorso-ventral axis and highlight unexpected complexities of mesodermal patterning.
| One of the earliest events in vertebrate embryonic development is the patterning of the embryo into three germ layers: the ectoderm, mesoderm, and endoderm. Morphogens are signalling molecules that act in a concentration-dependent manner to induce the formation of different cell types. Members of the nodal family are thought to form a morphogen gradient in the developing zebrafish embryo and to be essential for pattern formation. Mesoderm and endoderm are believed to develop due to high levels of nodal signalling, while cells experiencing the lowest concentrations of nodal signalling become ectoderm. Although this idea is widely accepted, the formation of a nodal morphogen gradient has never been observed directly, and we have therefore used two different approaches to visualise the intensity of nodal signalling within individual cells. Our approaches have allowed us to visualise a gradient of nodal signalling activity in the developing zebrafish embryo. Quantification of the levels of nodal signalling experienced by individual cells confirms that nodal signalling patterns the animal-vegetal axis of the zebrafish embryo and, in contrast to previous studies, also suggests that it plays a role in patterning the dorso-ventral axis of the zebrafish embryo.
| During embryonic development, secreted molecules known as morphogens generate a concentration gradient of positional information that instructs developing tissues to adopt particular cell fates [1]. One example of this phenomenon is the patterning of the early zebrafish embryo by the nodal/TGF-β signalling pathway [2]. Thus, the zebrafish nodal ligands squint (sqt) and cyclops (cyc) are expressed in the most marginal cells of the developing zebrafish embryo and homozygous mutations in both sqt and cyc result in embryos that lack all endoderm and mesoderm, apart from a few somites in the tail [3]. Similar phenotypes are achieved through the loss of nodal signalling by mutation of both maternal and zygotic one-eyed-pinhead (MZoep) or by misexpression of the nodal antagonist lefty [4,5].
Misexpression of sqt and cyc in the zebrafish animal pole indicate that cyc acts only over short distances, whereas sqt functions as a morphogen and exerts its effects over long distances to induce target gene expression [2]. High levels of nodal signalling activate goosecoid (gsc) expression, whereas lower levels activate no-tail (ntl). Thus, gsc is expressed in cells near a source of sqt, and ntl is expressed in cells further away.
The correct regulation of ntl is essential for patterning of the zebrafish embryo, because homozygous mutations in ntl disrupt mesoderm and notochord formation [6]. The same tissues are disrupted in Xenopus embryos lacking Brachyury (Xbra) [7], and ectopic expression of Xbra in isolated animal regions converts ectodermal cells into a mesodermal fate [8]. Consistent with the requirement of nodal signalling for mesoderm formation, ntl expression fails to initiate in embryos with diminished nodal signalling [3–5].
Together, these experiments suggest that nodal family members form a gradient that induces target gene expression and specifies mesoderm and endoderm. Activation of the nodal signalling pathway within a cell results in the phosphorylation of Smad2, which then interacts with Smad4 [9]. The resulting Smad2/4 complex translocates to the nucleus where it activates the transcription of target genes. To visualise the formation of a nodal gradient, we have first made use of transgenic embryos expressing a Smad2-Venus fusion protein under the control of a ubiquitous promoter: nodal signalling causes such constructs to enter the cell nucleus [10]. In addition, however, we have exploited the greater signal-to-noise ratio afforded by the technique of bimolecular fluorescence complementation (BiFC) [11]. In this approach, the N- and C-terminal halves of a fluorescent protein are brought into proximity by interactions between the two unrelated proteins to which they are fused. They can then assemble into a functional fluorescent protein that can be detected by conventional microscopy. This approach has previously been used to visualise, in a quantitative manner, interactions between Smad2 and Smad4 in the Xenopus embryo [12]. Yolk autofluorescence in Xenopus prevented a proper study of endogenous signalling events [12], but in this Research Article, we show that the technique is effective in the zebrafish embryo.
Our data allow us to follow in space and time the formation of a gradient of nodal signalling activity within the developing zebrafish embryo. The results illustrate the dynamics of gradient formation, and in contrast to previous studies [13], clearly demonstrate a role for nodal signalling in dorso-ventral patterning, explaining why target genes such as gsc are only expressed in dorsal marginal cells. Our data also highlight the complexities of ntl regulation and of the formation of the border between dorsal mesendoderm and the neural plate.
In a preliminary attempt to investigate nodal signalling levels during zebrafish development, we generated transgenic embryos that express a Smad2-Venus fusion protein under the control of a ubiquitous promoter. During zebrafish development, marginal cells are thought to receive the highest levels of endogenous nodal signalling while cells at the animal pole experience low levels, if any [14]. As predicted, nuclei of animal pole cells of transgenic embryos were only weakly fluorescent (Figure 1A), but expression of a constitutively active version of the TGF-β receptor Taram-A-D (Taram-D*) [15], caused strong nuclear fluorescence (Figure 1B). We note that in unstimulated cells, Smad2-Venus appeared to be concentrated at the centrosomes, and fluorescence accumulates in the nucleus shortly before nuclear envelope breakdown, only to disperse about a minute later, and then weakly associate with the mitotic apparatus (Video S1).
In an effort to improve the signal-to-noise ratio in such experiments, we turned to BiFC. When zebrafish embryos were injected with the N- and C- terminal halves of a modified form [12] of the fluorescent protein Venus [16], no fluorescence was observed, demonstrating that these fragments are suitable for BiFC experiments in this species (Figure 1C, C'). We therefore created fusions of the N- and C- terminal halves of Venus with the N termini of zebrafish Smad2 and Smad4, respectively, to create VNSmad2 and VCSmad4. When these constructs were expressed in the zebrafish embryo, ntl expression was unaffected in 90% of cases (n = 124), and in the remaining embryos, expression was normal in the marginal zone with weak ectopic expression in animal pole cells (unpublished data). These experiments demonstrate that our Smad BiFC constructs are suitable reagents for the analysis of endogenous nodal signalling.
Consistent with the experiments described above, we observed no nuclear BiFC fluorescence in animal pole cells of embryos injected with VNSmad2 and VCSmad4 (Figure 1D). As in the Xenopus embryo [12], however, and in contrast to the behaviour of Smad2-Venus, intense fluorescence appeared to be associated with chromosomes during cell division (Figure 1D, arrows, and Video S2). This is discussed below. When embryos received injections of both Smad BiFC constructs and the constitutively active version of Taram-A-D [15], strong nuclear fluorescence was observed in animal pole cells (Figure 1E). Similar results were observed when embryos were co-injected with RNA encoding our Smad2/4 BiFC constructs and the TGF-β ligand sqt (unpublished data). Activation of the TGF-β signalling pathway results in the phosphorylation of receptor-regulated Smads in their C-terminal SXS motifs [9]. Deletion of the SXS phosphorylation site in the VNSmad2 construct (VNS2ΔSXS) abolished TGF-β induced nuclear fluorescence (compare Figures 1E and 1F).
Together, these experiments demonstrate that our Smad2-Venus transgenic embryos and Smad BiFC constructs report the activation of the TGF-β signal transduction pathway in the zebrafish embryo.
We first investigated endogenous nodal signalling in zebrafish embryos at 5–6 hours post fertilisation (hpf), when they express ntl and experience endogenous nodal signalling [17]. Observation of Smad2-Venus transgenic embryos at 6 hpf revealed a gradient of nuclear fluorescence that was high at the margin and decreased towards the animal pole (Figure 2A), indicating that there is a gradient of nodal signalling in the developing embryo. This impression was confirmed by use of Smad2/4 BiFC, where high levels of nuclear fluorescence were observed in marginal cells, with intensity gradually decreasing as distance from the margin increased (Figure 2B). This pattern of nuclear fluorescence was not observed in embryos injected with BiFC constructs lacking the TGF-β phosphorylation site (VNS2ΔSXS/VCS4) (Figure 2C), in embryos expressing the nodal antagonist lefty [4] (96%; n = 25) (Figure 2D), or in MZoep embryos (100%; n = 15, unpublished data).
Smad2-Venus transgenic embryos do not exhibit detectable nuclear fluorescence in the yolk syncytial layer (YSL) of the embryo (Figure 2A), and nor do we observe Smad2/4 BiFC fluorescence in YSL nuclei of embryos co-labelled with a fluorescent histone marker (Figure 2E). These observations have allowed us to use Volocity software (Improvision) to quantify nuclear Smad2-Venus and Smad2/4 BiFC fluorescence intensity from the margin to the animal pole at different stages, defining the average intensity and average position of the YSL nuclei as zero (Figure 3A and 3B). We found that the most marginal nuclei, nearest the YSL, had the greatest Smad2-Venus fluorescence (Figure 3A) and the greatest Smad2/4 BiFC (Figure 3B). The nuclear fluorescence decreased in cells closer to the animal pole, some 200 μm away. Interestingly, nuclei positioned close to each other frequently had very different levels of nuclear Smad2-Venus and Smad2/4 BiFC fluorescence (Figure 3A and 3B; see also Figure 2A and 2B). One possibility is that these differences reflect local variation in effective nodal concentrations. Alternatively, there may be cell cycle–dependent variations in signal level associated with the intense fluorescence during cell division (Figure 1D, arrow, and Video S1).
We went on to investigate the spatial and temporal patterns of Nodal signalling by allowing embryos to continue development after imaging and then noting the positions of the imaged cells relative to the shield. This analysis exploited the superior signal-to-noise ratio of the Smad2/4 BiFC technique (see Figures 1 and 2). In preliminary experiments, analysis of lateral nuclei revealed that cells have higher levels of Smad2/4 signalling at 6 hpf compared to 5 hpf (Figure 3C; blue points are 5 hpf and red are 6 hpf). To improve our understanding of the spatio-temporal aspects of these signalling events, we calculated the average nuclear Smad2/4 BiFC intensity in 25-μm intervals from the margin towards the animal pole in several different embryos (Figure 3D–3H). We defined regions as dorsal, lateral, or ventral if the imaged cells were positioned within the dorsal quarter, lateral two quarters, or ventral quarter of the embryo, respectively. This analysis was performed for dorsal, lateral, and ventral cells at 5 and 6 hpf. As observed in individual embryos (Figure 3C), equivalently positioned cells have greater nuclear BiFC intensities at 6 hpf compared with 5 hpf, consistent with the idea that these cells experience increasing levels of nodal signalling during this period (Figure 3D–3F). When dorsal, lateral, and ventral cells were compared, we observed that lateral and ventral cells experience near identical levels of nodal signalling but dorsal cells experience higher levels (Figure 3G and 3H).
To place our observations in the context of normal development, we studied the expression profile of the nodal target gene ntl (Figure 4A–4C). ntl is first activated on the dorsal side of the embryo at 4 hpf (Figure 4A). Expression then spreads laterally, and by 5 hpf transcripts are detectable 3–5 cells deep throughout the margin (Figure 4B). By 6 hpf the ntl expression domain has doubled, and is now approximately 12–14 cells deep (Figure 4C). The expansion of the ntl domain observed over this period reflects the increasing levels of nuclear BiFC fluorescence and of Smad2/4 signalling (Figure 4D compare blue and black trend lines).
Our BiFC results show that the highest levels of Smad signalling occur at the dorsal side of the zebrafish embryo near the margin, where gsc is expressed (Figures 3G, 3H, and 4D, compare red and black trend lines). Consistent with this observation, work in zebrafish and Xenopus indicates that activation of gsc requires higher levels of nodal or activin-like signalling than are required to induce Brachyury [2,18]. These results suggest, in contrast to previous proposals [13], that a gradient of nodal signalling specifies the dorso-ventral axis of the zebrafish embryo. To explore this point in more detail, we expressed increasing amounts of sqt in the embryo. Our results showed that as levels of sqt increased, the domain of gsc expression expanded both animally and ventrally, as exogenously introduced sqt supplemented levels of the endogenous protein (Figure 5B-5D and 5F).
In an effort to correlate, in a quantitative manner, sqt signalling with Smad2/4 BiFC and gsc expression, we injected embryos at the one-cell stage with increasing amounts of sqt mRNA. At 6 hpf, we then measured Smad2/4 nuclear BiFC in the animal pole cells of some of the embryos and processed the remaining embryos for gsc expression. Injection of 1–4 pg of sqt mRNA resulted in an expansion of the gsc expression domain, but few embryos expressed gsc at the animal pole (Figure 5G, boxed area). Injection of 5 pg of sqt mRNA resulted in a significant increase in the percentage of embryos that expressed gsc in animal pole cells (Figure 5H), suggesting that the threshold for activation of gsc lies between 4 and 5 pg of sqt mRNA. Quantification of nuclear Smad2/4 BiFC fluorescence in the animal pole cells of injected embryos demonstrated that as the levels of sqt increased, so did fluorescence intensity (Figure 5I). Based on these data, our results indicate that the threshold for the activation of gsc expression is represented by a nuclear Smad2/4 BiFC intensity between 0.60 and 0.65 (Figure 5I). The only cells to experience endogenous levels of Smad2/4 BiFC that exceed this threshold are dorsal marginal cells (Figure 3H).
Our results are consistent with the idea that nodal signalling patterns the dorso-ventral axis of the zebrafish embryo as well as the animal-vegetal axis. But is nodal signalling the prime mover for dorso-ventral patterning in the zebrafish, or do sqt and cyc act downstream of BMP family members? Embryos lacking BMP signalling become dorsalised and fail to form ventral tissues [19], so it is possible that the dorso-ventral axis is first established by the ventral activation of the BMP signal transduction pathway, and it is this that directs the spatial distribution of nodal signalling and the dorsal activation of genes such as gsc. In this model, all dorso-ventral patterning would depend on BMP signalling, so to address the idea we injected embryos at the one-cell stage with a dominant negative BMP receptor (dnBMPr) and then studied the expression of gsc. Injected embryos became elongated (Figure 6A and 6B; 190/203 elongated at 4-somite stage) and appeared strongly dorsalised [20]; by 24 hpf almost all had died, with the survivors displaying slightly weaker dorsalised phenotypes (9 = c4, 9 = c3, and 7 = c2). gsc expression was unaffected in embryos injected with RNA encoding dnBMPr (Figure 6C–6F), indicating that the establishment of dorso-ventral patterning and the spatial distribution of nodal signalling is independent of BMP signalling.
Our understanding of the role of morphogen gradients during development is based largely on experiments that monitor gene expression after an increase or decrease in the concentration of a putative morphogen. This approach has the benefit of simplicity, but it is difficult to infer from the results obtained the shape of a morphogen gradient or the dynamics of its formation, because different genes respond differently to different morphogen concentrations, and because there may be interactions between gene products that refine their expression domains [21]. As an alternative, it is possible to observe the behaviour of tagged morphogens, including members of the TGF-β family [22–24], but these may not reflect accurately the behaviour of the endogenous inducers, and it is also possible that effective gradients of inducers are created by inverse gradients of an inhibitor [25,26]. We addressed these problems by using BiFC [11], a technique that permits quantitative assessment of levels of nodal signalling [12]. Our results demonstrate that the early zebrafish embryo experiences a gradient of nodal signalling levels, with cells at the margin experiencing the highest levels of nodal signalling and cells positioned away from the margin and towards the animal pole experiencing lower levels. Highest levels of signalling are experienced by cells at the dorsal margin of the embryo, where gsc is expressed.
Our results are consistent with the idea that nodal signalling patterns the animal-vegetal axis of the zebrafish embryo, with changes in the distribution and intensity of Smad signalling being reflected in changes in the spatial expression pattern of the nodal target gene ntl (Figure 4). In addition, we observe that over expression of sqt causes the expression domain of gsc to extend towards the animal pole (Figure 5E and 5F).
However, in contrast to previous conclusions based on cell lineage and gene expression experiments [13], our data also suggest that nodal signalling plays a role in patterning the dorso-ventral axis of the zebrafish. In particular, we note that there are higher levels of Smad2/4 BiFC fluorescence in dorsal regions than in lateral and ventral regions (Figures 3G, 3H, and 4D) and that gsc, whose expression requires higher levels of nodal signalling than does ntl [2], is expressed in these regions of elevated fluorescence. Consistent with this model, our correlation of Smad2/4 BiFC intensity with ectopic gsc expression (Figure 5H and 5I) demonstrates that the only cells to go above the gsc threshold are dorsal marginal cells. In addition, we note that over half of the cells of the prospective endoderm, a tissue whose formation also requires high levels of nodal signalling, are located dorsally [27].
If high levels of nodal signalling are indeed required for dorsal fates and lower levels for lateral and ventral tissues, then increased nodal signalling should produce a ventral shift in dorsal fates and loss or attenuation of nodal signalling should result in a dorsal shift of ventral fates. Consistent with this model, increased nodal signalling expands the expression domain of gsc in a ventral direction (Figure 5A–5D) and loss of nodal signalling results in a dorsal shift of the ventral marker gata2 [5]. Similarly, fate mapping experiments demonstrated that cells fated to become pronephros and midbrain, which in wild-type embryos are located in ventral and lateral positions respectively, shift towards the dorsal side of sqt-/-;cyc+/- embryos [13]. However, some ventral markers, such as spt and vox, are not expanded dorsally in embryos with reduced nodal signalling [13]. It is likely that these genes are regulated by BMP family members [5]; if BMP signalling is attenuated, ventral tissues fail to form and embryos become dorsalised [19]. Significantly, we found that the expression of a dominant negative BMP receptor had no effect on the expression of gsc (Figure 6C–6F). This suggests that the elevated levels of nodal signalling at the dorsal side of the embryo occur independently of BMP signalling. Previous work has demonstrated that Wnt/β-catenin signalling is also required for the specification of dorsal cell fates and that ectopic activation of β-catenin induces the expression of gsc [13,28]. However, sqt is not expressed in embryos with disrupted β-catenin signalling, and β-catenin cannot induce gsc expression in sqt mutant embryos [13,28]. Together with our correlation of nodal signalling and gsc expression, these results indicate that the effects of β-catenin are mediated by nodal signalling.
In combination with the results described above, our data therefore indicate that patterning of the zebrafish dorso-ventral axis involves high levels of BMP signalling in ventral tissues and high levels of nodal signalling in dorsal regions, effectively setting up a double gradient. It is also possible, as in the Xenopus embryo [29], that BMP ventralises the embryo only after the onset of gastrulation.
As discussed above, the dynamic expression pattern of ntl (Figure 4B and 4C) reflects the spatial changes in nodal signalling that occur in the margin of the zebrafish embryo between 5 and 6 hpf (Figure 4D). Expression of both cyc and sqt declines between 5 and 6 hpf [13], so it is likely that the increased level of signalling experienced by cells positioned away from the margin at 6 hpf derives from nodal ligand that has traversed cell tiers 1–6 during this period.
At 6 hpf, Smad signalling extends farther towards the animal pole in dorsal regions of the embryo than in lateral and ventral regions (Figure 3H), yet ntl is expressed in approximately the same number of cell tiers throughout the margin, and does not spread into prospective neural tissue at the dorsal side [30]. This suggests that ntl expression is repressed in the prospective neural plate, perhaps, as in Xenopus, in a Sip-1–dependent manner [31,32]. It is possible that the Smad signalling that occurs in the neural plate provides positional information to this tissue; injection of increasing concentrations of lefty results in the gradual loss of hindbrain structures, whereas prospective forebrain tissues are converted into hindbrain structures following expression of cyc [4].
At 5 hpf, the expression of sqt and cyc is uniform throughout the margin of the zebrafish embryo [13], so what might cause the activation of Smad signalling to be higher in dorsal regions? Evidence suggests that the duration of signalling as well as the concentration of the morphogen may determine cell fate [17,33,34], and it may be significant that expression of sqt both commences on the dorsal side of the embryo and persists for longer in this region [13]. The elevated level of Smad2/4 BiFC in dorsal regions may therefore reflect both signal intensity and signal duration in the developing embryo.
Our observations of transgenic embryos expressing Smad2-Venus indicate that Smad2 is associated with the centrosome, and comparison with results obtained with Smad1 [35] suggest that this might represent Smad2 that is destined for degradation. This is under investigation. We also noted that Smad2-Venus entered the nucleus shortly before nuclear envelope breakdown, and in this respect, its behaviour resembled that of cyclin B1, which translocates to the nucleus after phosphorylation by Polo-like kinase 1 [36–38]. We do not yet know if the translocation of Smad2-Venus is regulated by phosphorylation, but if it were, this newly phosphorylated Smad2 might then be able to associate with Smad4 and form a complex on the chromosomes. We do not understand the significance of such an association, although one possibility is that it ensures an equal distribution of Smads between daughter cells, as is thought to occur for Sara-containing endosomes in the developing fly wing [39].
The Smad2-Venus fusion was generated by PCR amplification of Venus and cloning into a pCS2-Smad2 plasmid, thus generating a fusion of Venus to the N terminus of Smad2. The Smad2-Venus fusion was then subcloned into a miniTol vector containing the Xenopus EF1α, mcFos promoter. Transgenic embryos were generated by injecting embryos at the one-cell stage with 15 pg of Smad2-Venus miniTol plasmid and with 12.5 pg of transposase RNA. Injected embryos were raised to adulthood and then outcrossed to generate stable transgenic lines.
All constructs were injected in volumes of 2 nl into the yolk of zebrafish embryos at the one-cell stage, and embryos were then incubated at 28 °C. Where stated, embryos were injected with 100 pg of histone CFP [12], 50 pg of VNSmad2, 50 pg of VCSmad4, 300 pg of Lefty (Antivin) [4], 1 pg of Taram-A-D, 2.5–10 pg of sqt [2], or 800 pg of truncated dominant negative BMP receptor [40]. Zebrafish Smad2 and Smad4 open reading frames were amplified by PCR, cloned into the BiFC constructs [12], and sequenced. The VNS2ΔSXS construct was created by introducing a stop codon into the VNS2 plasmid using PCR based mutagenesis with the primers 5′-TTAGGACATACTTTAGCAGCGTACGGAGGGGGAGCCCATC- 3′ and 5′-GATGGGCTCCCCCTCCGTACGCTGCTAAAGTATGTCCTAA-3′. All RNA was synthesised using SP6 mMessage mMachine according to the manufacturer's instructions (Ambion). Whole mount in situ hybridisation was performed essentially as described [41], using probes specific for ntl [30] and gsc [42].
For imaging, embryos were de-chorionated and embedded in 0.3% agarose. Images were obtained with Perkin Elmer spinning disc and Olympus FV1000 inverted confocal microscopes using 40× lenses. All quantifications were performed by sequential imaging of CFP and Venus fluorescence using the Olympus FV1000 microscope. Ten 1-μm Z sections of the cells nearest the lens (based on focal plane) were imaged. Following imaging embryos were incubated at 28 °C until 6–7 hpf. The agarose dish was then placed in hot water to melt the agarose, the embryos were removed from the agarose using forceps, and the positions of the imaged cells in relation to the shield was noted. Individual Z sections were used for the quantification of animal pole cells. Fluorescence intensity was quantified using Volocity software (Improvision). Individual nuclei were identified using a protocol to mark objects with intensities between 10 and 100% in the CFP (histone) channel. Quantifications were analysed using Microsoft Excel. For each image, the nuclei of the YSL were identified and the average distance and intensity of these nuclei was subtracted from all nuclei in that image. Video S1 was made using the Perkin Elmer spinning disc microscope.
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10.1371/journal.pgen.1001301 | Prevalence of Epistasis in the Evolution of Influenza A Surface Proteins | The surface proteins of human influenza A viruses experience positive selection to escape both human immunity and, more recently, antiviral drug treatments. In bacteria and viruses, immune-escape and drug-resistant phenotypes often appear through a combination of several mutations that have epistatic effects on pathogen fitness. However, the extent and structure of epistasis in influenza viral proteins have not been systematically investigated. Here, we develop a novel statistical method to detect positive epistasis between pairs of sites in a protein, based on the observed temporal patterns of sequence evolution. The method rests on the simple idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic. We apply this method to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a non-epistatic null distribution, we detect substantial amounts of epistasis and determine the identities of putatively epistatic pairs of sites. In particular, using sequence data alone, our method identifies epistatic interactions between specific sites in neuraminidase that have recently been demonstrated, in vitro, to confer resistance to the drug oseltamivir; these epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today. This experimental validation demonstrates the predictive power of our method to identify epistatic sites of importance for viral adaptation and public health. We conclude that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites with , which would normally not be identified as positively selected, can facilitate viral adaptation through epistatic interactions with their partner sites. The knowledge of specific interactions among sites in influenza proteins may help us to predict the course of antigenic evolution and, consequently, to select more appropriate vaccines and drugs.
| Epistasis describes non-additive interactions among genetic sites: the consequence of a mutation at one site may depend on the status of the genome at other sites. In an extreme case, a mutation may have no effect if it arises on one genetic background, but a strong effect on another background. Epistatic mutations in viruses and bacteria that live under severe conditions, such as antibiotic treatments or immune pressure, often allow pathogens to develop drug resistance or escape the immune system. In this paper we develop a new phylogenetic method for detecting epistasis, and we apply this method to the surface proteins of the influenza A virus, which are important targets of the immune system and drug treatments. The authors identify and characterize hundreds of epistatic mutations in these proteins. Among those identified, we find the specific epistatic mutations that were recently shown, experimentally, to confer resistance to the drug Tamiflu. The results of this study may help to predict the course of influenza's antigenic evolution and to select more appropriate vaccines and drugs.
| Influenza A is among the most extensively studied viruses, owing to its importance as a human pathogen [1]–[6]. With a large, public database of genetic sequences, influenza viruses also offer a model system for studying molecular evolution in general. The evolution of influenza viruses is characterized by frequent reassortment events within subtypes [3], [7] as well as high rates of amino-acid substitutions in the viral surface proteins hemagglutinin (HA) and neuraminidase (NA) [8]–[10]. Such high evolutionary rates reflect both the poor fidelity of the viral polymerase [10], and the strong selection pressures to evade the human immunity [8], [9], [11]–[13] and, more recently, to develop drug resistance [14]–[16].
Numerous experimental studies and statistical analyses of genetic and antigenic data have identified sets of residues in HA and NA proteins, the so called epitopes, that are bound by human antibodies [17]–[20]. As a consequence, the epitopic sites tend to evolve especially quickly, in order to evade immunity [8], [21], [22]. Moreover, several recent studies have suggested lists of amino acids at specific residues in HA that evolved under positive selection over the past 40 years [23]–[25].
In addition to escaping human antibodies, several other selective forces act on hemagglutinin. As with any functional protein, HA must maintain its stability and its function – namely, to bind the sialic acid receptor of host cells and subsequently mediate membrane fusion [17], [18], [20], [26]. Thus, antibody escape mutations must not compromise these properties. Yet, numerous studies of protein evolution in vitro [27]–[29] as well as studies in bacteria [30] and viruses [20], [31], [32] have shown that beneficial mutations are often pleiotropic: in addition to their original beneficial effect, they cause some, usually negative, side effects on other protein properties, such as stability [28], [33]. These negative effects can typically be alleviated or compensated by other mutations, making certain combinations of mutations substantially more beneficial than single mutations alone [34], [35]. This phenomenon is known as positive epistasis between mutations [36]. Epistasis can also be negative, if a combination of mutations confers a smaller fitness gain than would be expected under additive effects of the individual mutations [36].
Epistasis is commonplace in eukaryotes [37]–[39], bacteria [30], [40], [41], and viruses [31], [34], [35], [42], and it plays an important role in the evolution of immune escape and drug resistance in various pathogens [35], [43]–[46] including influenza [16], [32]. Surprisingly, the extent of epistatic interactions in influenza proteins has not been systematically quantified or utilized. Yet, the knowledge of such interactions might provide a powerful tool for predicting future antigenically important substitutions and, consequently, for selecting better vaccine strains.
Numerous methods have been developed for detecting epistasis between mutations, based on sampled genetic sequences [47]. Early methods were based on the idea that co-evolving pairs of sites in a protein should leave a typical signature in a sequence alignment, which can be detected using quantities such as mutual information [48]–[50]. However, such methods ignore the phylogenetic relationships among sequences and so are justified only if the divergence times between samples are very large [51]. Various corrections for the phylogenetic non-independence have been proposed [52]–[54], and their performance has been shown to be satisfactory in some cases [55]–[57]. Nevertheless, methods that explicitly take account of the phylogeny are preferable [58]. Several such methods have been proposed recently [42], [59]–[66]. Most of them attempt to detect unusually frequent co-occurrences of substitutions at pairs of sites on individual branches of the phylogeny. This approach is conservative since it detects only those positively epistatic pairs of sites for which a mutation at one site increases the beneficial effect of a mutation at the second site so dramatically that one mutation could not fix without the other one [42]. Such strong epistasis can occur, for example, when one mutation confers a strongly deleterious effect that is compensated by a second mutation. However, a mutation at one site in a protein may lead to only a moderate increase in the beneficial effect of a mutation at another site, so that the latter substitution occurs at an accelerated rate, but it does not necessarily appear exclusively on the same branch of the phylogeny [59], [62], [64], [65]. In other words, substitutions at positively epistatic pairs of sites are likely to be temporally clustered [67]. In this paper, we exploit this idea to design an “epistasis statistic” that allows us to detect a broad class of epistatically interacting pairs of sites.
In essence, for each ordered pair of sites in a protein we measure the amount of phylogenetic time that typically elapses between a substitution at the first site and a subsequent substitution at its partner site. The epistasis statistic is defined as a decreasing function of this time interval. Thus, pairs in which the substitution rate at the second site tends to be increased after a substitution at the first site will have a larger value of the statistic. We obtain the null distribution of this statistic for all pairs simultaneously, by randomly shuffling the identities of substitutions on the phylogeny. We show that the number of site pairs in the surface proteins of the human influenza A/H3N2 virus with large values of the epistasis statistic significantly exceeds the null expectation—thus, influenza surface proteins evolve under substantial positive epistasis. We characterize the epistatically interacting sites we have inferred in terms of their overall patterns of evolution, protein locations, and functional significance. For type-1 neuraminidase, we compare the identities of the epistatic sites we have inferred with those that have been experimentally verified. We discuss the implications of our results both for practical issues surrounding influenza's antigenic drift and drug resistance, and for broader issues surrounding protein evolution in general.
We reconstructed the phylogenetic trees for HA and NA proteins (subtypes H3N2 and H1N1) and inferred the nucleotide sequences at internal nodes by maximum likelihood as described in “Materials and Methods”. In order to detect pairs of codon sites in a protein that have evolved under positive epistasis we used the “epistasis statistic” described in “Materials and Methods”.
Briefly, the epistasis statistic considers an ordered pair of sites, the first of which is called the “leading site” and the second is called the “trailing site”. The epistasis statistic tends to be large for pairs of sites in which a non-synonymous substitution at site tends to quickly follow a non-synonymous substitution at site , and for which substitutions at the trailing site occur in multiple lineages (see schematic in Figure 1). We measure time between a pair of non-synonymous substitutions as the number of synonymous substitutions that occur between them. Since we are interested in positive epistasis and would like to detect only those pairs of substitutions in which the second substitution is beneficial, we excluded all substitutions at terminal branches, because many such substitutions are likely to be deleterious. We also discarded all sites that experienced fewer than two substitutions at the internal branches (Table 1).
The epistasis statistic depends on the parameter that sets the timescale over which substitutions contribute information. Pairs of substitutions that are separated by times much shorter than contribute significantly to the epistasis statistic, whereas pairs of substitutions that are separated by times much longer than do not. We set to be equal to the average time , measured in the number of synonymous substitutions, that elapses between two non-synonymous substitutions randomly sampled from a phylogeny (see Text S1 and Table 1). For each phylogeny (H1, N1, H3, and N2) and its corresponding value of , we computed the epistasis statistic for all qualifying ordered pairs of sites and, for each such pair, we computed the distribution of the epistasis statistic under the non-epistatic null hypothesis (see “Materials and Methods” for details). We then selected all pairs of sites whose nominal was smaller or equal to 0.01. In H3, we identified 333 site pairs with a nominally significant epistasis statistic; we identified 225 such pairs in H1, 205 such pairs in N1, and 188 such pairs in N2 (see Table 1, and Table S1). Examples of epistatic site pairs in HA and NA are shown in Figure 2 and Figure 3.
We computed the false discovery rate (FDR) as well as the overall for the observed number of significant pairs (see “Materials and Methods”). Although the FDR in all proteins was high, around 60%, the observed number of nominally significant pairs was much larger than would be expected by chance (, see Table 1). Reducing the nominal cutoff somewhat reduced the FDR but also disproportionately reduced the number of inferred positives (see Figure S1).
We tested the sensitivity of our method with respect to the choice of the timescale parameter, in the range from to , as well as to uncertainty in phylogeny and internal node reconstruction. The results remained qualitatively similar to those reported here (see Text S1, Figure S2, and Table S4). As a negative control, we performed 100 simulations in which sites evolved independently (i.e. without epistasis) along a given phylogenetic tree (see “Materials and Methods”). In 52 out of 100 simulations, the number of significant pairs at the cutoff 0.01 was smaller than expected, in 47 cases this number was larger that expected, but not significantly so. In only one simulation out of 100 was this number larger than expected and significant (). We are therefore confident that our method indeed detects epistatic pairs of sites, and it does not systematically report more false positives than our FDR computation indicates.
Having obtained a list of pairs of sites with putative epistatic interactions (Table S1, Figure 2 and Figure 3), we inspected the properties of these pairs, compared to an appropriate null set. In particular, we compared the true, epistatic pairs to the pairs that had appeared as nominally significant in the 400 “fake data sets” produced by permutation (see “Materials and Methods”). Thus, we asked whether the pairs that we detected as epistatic differed systematically from the false positive pairs. We investigated three types of properties: the average dN/dS value at epistatically interacting sites, their location in the protein with respect to known epitopes (for H3N2 only), and the distances between interacting sites. For comparison of physical and linear distances we also excluded sites that were not present in the resolved crystal structure (see “Materials and Methods”). The results are summarized in Table 2 and Table 3, and discussed below.
The dN/dS values at the leading sites among the putative epistatic pairs were not significantly larger, on average, than at the leading sites among pairs identified under permutation. However, the average dN/dS value at the leading sites was less than one, which is usually interpreted as evidence of purifying selection. Therefore, without an analysis of epistasis, many of the leading sites would not have been identified as experiencing positive selection, even though they may play a critical role in facilitating adaptation in co-ordination with substitutions at their partner sites.
By contrast to the leading sites, the dN/dS values at the trailing sites were significantly larger than the null expectation, and they exceeded one on average (with the exception of N1). Thus, the trailing sites exhibit the characteristic signature of positive selection [68], even though the positive selection they experience was likely made possible (or, at least, more likely) by preceding substitutions at their corresponding leading sites. In other words, many of the positively selected substitutions that have occurred in HA and NA may have been facilitated by previous substitutions at epistatically interacting sites. In previous work, we have identified 25 sites in H3 at which certain specific amino acids evolved under directional positive selection [25]. Interestingly, 22 of these sites appear to be involved in epistatic interactions: 10 sites appear as leading sites, 7 sites appear as trailing, and 5 sites appear as both.
We also studied the location of epistatic sites with respect to the known antigenic regions of the influenza surface proteins, for H3 and N2. In both proteins, the leading site in an epistatic pair was more likely to fall within an antigenic epitope than under the null expectation (and significantly so in H3). The trailing site was slightly more likely to fall outside of known epitopes, despite the fact that the dN/dS ratio was typically greater than 1 at such sites. Thus, pairs of sites in which the leading site was in an epitope and the trailing site was not in an epitope, were typically overrepresented (although not significantly so). This suggests that the leading sites may often be directly involved in antigenic escape and the trailing sites may subsequently compensate for deleterious (e.g. destabilizing) side effects of the initial mutation. In some cases, however, both the leading and trailing sites of an epistatic pair fall within epitopes (47% of pairs in H3, 9% of pairs in N2). In such cases, for H3, the leading and trailing sites were significantly more likely to fall in different epitopes from each other, than expected – suggesting that substitutions across multiple epitopes may be particularly important for antigenic escape, at least in hemagglutinin of the H3 subtype. This observation reflects the widely held belief that antigenic change in hemagglutinin typically requires multiple substitutions spread across multiple epitopes [21], [26].
How far apart are the leading and trailing sites of epistatic pairs? Surprisingly, neither the average linear (sequence) distance nor the physical distance between the leading and the trailing sites in an epistatically interacting pair was significantly smaller than would be expected among false positive pairs.
Finally, we investigated the timing of consecutive substitutions at the leading and trailing sites in epistatic pairs. On average, both across pairs and across consecutive substitutions, a substitution at a leading site in H3 was followed by a consecutive substitution at its corresponding trailing site 3.7 years later (see Text S1 for details). Similarly, in H1 the mean time between consecutive substitutions was 5.8 years; 4.4 years in N1; and 4.2 years in N2. In all cases, the mean time between consecutive substitutions exceeds two years – which suggests that the observation of a substitution at the leading site of a known epistatic pair may provide useful predictive value for anticipating a subsequent substitution at its corresponding trailing site, within the time-frame required for selecting a seasonal vaccine strain [69].
Our analyses of substitution patterns suggest that positive epistasis is prevalent among sites in HA and NA. However, it is important to verify experimentally that the identified pairs of sites indeed show non-additive fitness effects. Fortunately, such verification has recently been performed for two specific pairs of sites in type-1 neuraminidase.
Currently circulating variants of the seasonal H1N1 subtype are resistant to the drug oseltamivir, which inhibits neuraminidase [15]. Resistance to this drug is conferred by the mutation at site 275, which is referred to as the “H274Y” mutation in the literature [15]. However, this mutation is known to be strongly deleterious in the absence of the drug [70]. Recently, Bloom et al. demonstrated that mutations R222Q and V234M restore the drug-resistant mutant's fitness in vitro [16], for seasonal H1N1. They also observed that mutations R222Q and V234M were fixed in the seasonal H1N1 population prior to the emergence of the H275Y mutation, and thus they likely acted as epistatic “permissive mutations” for the emergence of drug resistance in competent viruses.
Our statistical analysis of epistasis in N1, based on patterns of sequence evolution alone, is remarkably concordant with the experimental findings of Bloom et al. In particular, our analysis indicates that sites 222 and 234 interact strongly with site 275 (see Table S1). Moreover, among the top 10 most significantly epistatic pairs in N1 there are 6 other pairs that involve the drug-resistance site 275 as the trailing site; the leading sites in these pairs are 214, 287, 329, 354, 382, and 344. In all cases the subsequent mutation at site 275 is . Therefore, aside from sites 222 and 234, our analysis predicts that these six additional sites may be permissive mutations that, in combination with H275Y, produce competent, drug-resistant viruses. Although no epistasis between two of these sites (214 and 382) and site 275 was found experimentally [16], one of the mutations (D344N) has subsequently been shown to help counteract the decrease in total surface-expressed activity associated with the mutant neuraminidase ([71] and Jesse Bloom, personal communication), and it, along with 224 and 234, may have played a role in the emergence of oseltamivir resistance in seasonal H1N1 viruses before 2009.
Although further experimental validation is required, the remarkable concordance between our statistical inferences and experimentally verified epistatic interactions [16] suggests that patterns of sequence evolution contain extremely useful information about a protein's fitness landscape. In the case of oseltamivir resistance, this information is highly specific and of significant import to public health.
The oseltamivir resistance mutation, H275Y, was known in advance of the drug's widespread introduction. Moreover, this prior knowledge was used by Bloom et al. [16] to focus their experimental search for an epistatic partner to site 275. Nonetheless, our method of identifying epistatic pairs from sequence data implicates site 275 – without any prior knowledge of its role in drug resistance – as extremely important in the adaptive evolution of N1, especially in combination with sites 222, 234, and six other leading sites (Table S1). This demonstrates the practical, predictive power of our method for inferring the specific, epistatic interactions that shape viral adaptation. Thus, our method may, in the future, help us identify sites important for drug resistance or antigenic drift, even when no prior experimental data are available.
Finally, we note that our analysis implicates sites 222 and 234, which have been verified as important epistatic partners of the oseltamivir resistance site 275, as significant epistatic leading sites even when we restrict our data set to those viral isolates prior to the introduction of oseltamivir. In particular, based on sequence data prior to 2001, our method identifies sites 222 and 234 as participating in epistatic interactions with sites other than 275 (see Table S1). Thus, sites 222 and 234 may be structurally important and experience epistatic interactions even in the absence of selection for oseltamivir resistance.
We have developed a statistical method to detect positive epistasis between pairs of sites in a protein, based on patterns of thoroughly-sampled sequence variation. The essential idea underlying this method is simple: a substitution at one site should rapidly follow a substitution at another site if the sites interact epistatically. We applied this method to identify putative epistatic pairs in the influenza surface proteins hemagglutinin and neuraminidase, and we found a highly significant number of interacting pairs. We characterized the properties of the leading and trailing sites identified as epistatic. Finally, we validated our approach by comparison to experimentally verified epistatic interactions in neuraminidase, with significant implications for public health.
This study sheds some light on methodological and empirical questions in molecular evolution generally, as well as practical questions about influenza viral evolution in particular. Methodologically, it is instructive to compare our approach to identifying epistasis with other techniques in the literature. Over very long timescales, interacting sites in a protein have been identified by inspecting multiple sequence alignments, ignoring the phylogenetic relationship among the sequences being compared. Such an approach is justifiable over timescales so long that each site may be treated independently, and indeed it has proven successful at identifying epistasis in proteins conserved across all domains of life [51]. However, such techniques are not justified for shorter timescales, because correlations between sites may arise simply as the result of linkage and shared ancestry [58]. Although techniques exist to control for phylogeny in such tests [52]–[54], it is preferable to leverage the phylogeny in the design of a more powerful statistic for epistasis – which is the approach we have taken here.
Even among the techniques that account for phylogeny, methods differ in their power to detect epistasis. Some methods will be more powerful in some contexts, and others in other contexts – depending upon the structure of epistasis among sites, the selection coefficients involved, and the density of sampling. Most existing methods that utilize phylogenetic information assume that epistatic substitutions will co-occur along the same branch of the phylogeny [42], [60], [63], [64]. This assumption will not always be met, however, if the selective advantage conferred by a substitution at the trailing site is only moderate; in such cases, substitutions at trailing sites will occur at an accelerated rate but they may likely fall on subsequent branches in the phylogeny. To demonstrate this point, we applied the method of Poon et al [64], implemented in the HyPhy package [72], to the same data set of influenza sequences. That method detected 4 to 10 times fewer epistatically interacting pairs of sites than our method did, at the same false discovery rate (see Text S1 and Tables S2 and S3). Importantly, the method by Poon et al. failed to detect epistatic interactions between sites 222 and 234 and the drug-resistance site 275 in neuraminidase subtype N1, even though those pairs were highly ranked by our method and those epistatic interactions were confirmed experimentally. Although a thorough comparison between various existing methods is beyond the scope of this paper, we believe that the additional power of our method to detect epistasis in the influenza data arises because we allow for time lags between substitutions at interacting sites.
The epistasis statistic developed here is admittedly ad-hoc, compared to systematic, likelihood-based methods for jointly inferring phylogeny and epistasis under Markov substitution models [73]–[75]. At the same time, the vast dimensionality associated with substitution models incorporating pairwise epistasis, of order for a sequence of length , is daunting; whereas the frequentist statistic defined here seems to perform quite well. The strong performance of our approach likely arises from our ability to infer ancestral states reliably, due to the high-resolution sampling of influenza sequences.
Our method has several important shortcomings. One drawback is that it requires a large number of substitutions per site in order to discriminate between truly interacting site pairs and pairs that sustain substitutions in close succession just by chance. Moreover, even if the protein evolves rapidly, as influenza surface proteins do, the false discovery rate is still very high. Our method will likely perform much worse for proteins that evolve slowly or have been sampled sparsely.
Two other concerns are problematic for our approach, as well as most other methods of detecting epistasis from phylogenetic data. Such approaches generally suffer from the inability to weed out spuriously epistatic pairs, which leads to high false discovery rates. There are at least two sources of spuriously significant pairs: hitchhiking and coordinated temporal variation in selection pressures across sites. Imagine, for example, that sites and interact epistatically and that multiple substitutions at site in independent lineages rapidly follow a single substitution at site . Then site pair would be detected by our method. However, if the variant that carries the leading mutation at site also, by chance, happens to carry a mutation at site (which is not epistatic with ), then mutation hitchhikes to fixation together with mutation and so the site pair may also be detected as epistatic. In fact, mutation at site may be advantageous while mutation at site may be a neutral or slightly deleterious mutation that hitchhikes to fixation together with , but then “permits” the beneficial mutation at site . It may be possible to reduce the false discovery rate by designing statistics that consider only those site pairs for which consecutive substitutions involve multiple independent substitutions at the leading site as well as the trailing site.
Coordinated temporal variation in selection pressures across sites is another source of potential false positives under this and other tests of epistasis. Consider, for example, sites 391 and 73 in H3 illustrated in Figure 2. Substitutions at site 73 appear to quickly follow substitutions at site 391 in the early 1990's. Apart from epistasis, the clustering of substitutions at these two sites could be explained if both sites independently experienced positive selection during this time period, and otherwise negative selection. However, if this explanation were the dominant one for the observed clustering of substitutions, then, for each nominally significant ordered pair of sites, we would expect its inverse pair also to be nominally significant, on average. Yet, we do not find a single nominally significant pair whose inverse pair is also nominally significant, even though consecutive substitutions do occur in the direct and reverse order (see for example, Figure 2 and Table S1). It is unlikely that this observation is caused by insufficient sampling. Indeed, in H3, there are typically more than 6 substitutions (at internal branches) at either a leading or trailing site in an identified epistatic pair, and similarly for the other proteins. Moreover, many sites appear in our lists as both leading and trailing. Thus, leading and trailing sites exhibit similar number and pattern of substitutions, and there is plenty of power to detect a significant epistasis statistic in both directions. This suggests that the excess of significant pairs we observe is likely caused by epistasis, rather than coordinated temporal variation in selection pressures.
Another shortcoming of our method is that it aims to detect epistasis only between pairs of sites, whereas interactions among residues in a protein are certainly more complex. This may be a cause of our large false discovery rate. Imagine, for example, three sites, , , and , such that pairs and interact epistatically, but the pair does not. If substitutions at site quickly follow substitutions at site and if substitutions at site quickly follow substitutions at site , our method may detect the pair as epistatic, even though there is no direct epistatic interaction between these sites. Indeed, in our list of putatively epistatic pairs, we find 133 of such “circular” triplets in H3, 41 triplets in N2, 81 triplets in H1, and 71 triplets in N1. In order to discriminate truly epistatically interacting site pairs from spurious pairs, it may be possible to modify the Bayesian graphical models recently used for detecting epistasis in HIV [64], [66] to incorporate a time lag between consecutive substitutions.
Finally, although our method detects epistatic interactions between pairs of sites, it does not determine which specific mutations at those sites were epistatic. Extending our permutation technique to incorporate the information about specific mutations may prove difficult, but in many cases it is unnecessary. Often we can a posteriori identify the specific mutations that led to a significant value of the epistasis statistic for a pair of sites. For example, the drug-resistance site 275 is identified as trailing with many leading sites in N1 (see Table S1), but the specific substitutions at site 275 are all in fact identical: H275Y.
Methodological issues aside, our results on epistasis in HA and NA have several important practical implications for our understanding of influenza evolution. We have demonstrated that our method reliably infers a critically important oseltamivir resistance site, as well as the associated leading sites at which initial mutations are required for the production of a viable, drug-resistant virus. Remarkably, we can identify some of the leading sites (222 and 234) even when restricted to sequence data prior to the introduction of the drug. This degree of specificity and accuracy may prove helpful in preparing for resistance to other drugs that may be developed, or in predicting the emergence of oseltamivir resistance in the recent type-1 swine neuraminidase responsible for the 2009-10 influenza pandemic.
In addition to NA, we have also detected substantial amounts of epistasis in HA, including in the known epitopes, likely associated with antigenic drift. Knowledge of specific pairs of sites that interact epistatically in HA may improve our ability to predict future antigenic variants, and thus to calibrate vaccine strain choices accordingly. Previous studies of HA antigenic evolution have focused almost exclusively on those sites with the strongest signatures of positive selection, e.g. elevated dN/dS ratios [8], [21], [22]. However, our results suggest that this approach will inevitably miss many sites of genuine importance to adaptation, and will implicate others that are not directly involved in antigenic escape. In particular, we have seen that the leading site of an epistatic pair often falls within an epitope, but it also often exhibits . In contrast, the trailing site typically falls outside of an epitope and it exhibits significantly elevated . This observation appears counter to our expectation that epitopic sites have elevated dN/dS values and non-epitopic sites have depressed dN/dS values. However, not all epitopic sites experience elevated dN/dS values at all times because different epitopes may be immunodominant at different times [76], [77]. Thus, an average dN/dS value at a site may be well below 1 even if this site occasionally evolves under strong immune selection [78].
The patterns of epistatic interactions we have detected suggest the the following speculative model for the evolution of influenza surface proteins. If an epitope is immunodominant at a certain period of time, the pressure for antigenic escape is so strong that the leading site, of antigenic importance, substitutes despite a negative side-effect (e.g. diminished protein stability or function). This side-effect is subsequently compensated by a substitution at another, relatively unconstrained, site, in an epitope or not. It is possible that such unconstrained sites may act as “global suppressors”, i.e., compensate the destabilizing effects of many mutations [79], [80]. If there is a constant need for compensation (caused by antigenically important mutations), such compensating sites will continually evolve under positive selection and will exhibit . Under this scenario, the roles of the two sites would both be misinterpreted by an analysis based on dN/dS alone that neglects epistasis. In particular, our observations imply that mutations at sites with may sometimes be extremely important for antigenic adaptation, even though they have been largely ignored in compilations of antigenically relevant sites [8], [11], [21]. Conversely, some mutations at sites with may be unimportant for antigenicity per se, but are positively selected simply to compensate for prior antigenic escape mutations with deleterious side effects. Another potential mechanism of epistasis in influenza surface proteins could be the dynamic balance between mutations that simultaneously influence receptor binding avidity and antigenicity, as suggested recently by Hensley et al. [81].
Some of the epistatic pairs that we detect consist of an apparently neutral but “permissive” mutation at the leading site followed by a highly advantageous mutation at the trailing site, such as the pair of mutations V234M and H274Y in N1 previously identified by Bloom et al [16] and also detected by our method (Table S1). This observation is consistent with the idea that neutral or nearly neutral substitutions can facilitate adaptation at partner sites that might not otherwise have been available—a concept that has received much attention in theoretical studies of adaptation [82], [83] and of influenza evolution in particular [84].
Finally, we have observed that epistatic residues do not tend to be significantly closer to each other in the folded protein structure than would be expected by chance – a result that we expect to hold generally, and which suggests that structural influences on epistasis are probably not as straightforward as simple proximity of residues. In the future, it will be important to investigate, by computation or experiment, whether epistatic partner sites are compensating for protein stability even if they are distant from each other in a folded protein structure.
We downloaded all HA and NA coding region sequences of human influenza A virus subtypes H1N1 and H3N2 that were available in the NCBI's Influenza Virus Resource [85] in June 2010. The amino acid sequences were aligned using Clustal W ver. 1.83 [86] and the alignments were reverse translated using PAL2NAL [87]. Occasional gaps in the alignments were filled if more than 70 percent of sequences agreed on the nucleotide at the gap position; otherwise the sequence with a gap was excluded from further analysis. To test some aspects of our method, we used a smaller HA data set (subtype H3N2) which was downloaded in April 2009. To investigate whether our method detected any of the known epistatic site pairs in type-1 NA prior to the introduction of oseltamivir, we also used a truncated data set of N1 sequences with all sequences isolated subsequent to 2001 removed. All used alignments are available upon request.
In computing the epistasis statistic we excluded all substitutions at terminal branches, and we discarded all sites that experienced fewer than 2 substitutions at the internal branches.
We also downloaded the HA and NA crystal structures from the RCSB Protein Data Bank. In computing the linear (sequence) and physical distances between residues we excluded all residues that were not resolved in the crystal structures. We used the distance between the alpha-carbon atoms as a proxy for the physical distance between residues.
We reconstructed the maximum likelihood phylogenetic trees for HA and NA using PHYML [88] under the GTR substitution model with the four-category discrete approximation of the gamma distribution for the substitution rates. We reconstructed the nucleotide sequences at the internal nodes of the phylogeny using maximum likelihood algorithm in PAUP* 4.0b10 [89]. For each codon site, we identified whether it experienced at least one synonymous and/or non-synonymous substitution on each branch of the reconstructed phylogeny. In those rare cases in which a codon experienced more than one substitution of the same kind (synonymous or non-synonymous) on a branch, we did not record the number of substitutions, in order to simplify computations.
Consider an ordered pair of sites in the protein of interest. In order to detect a positive epistatic interaction for this pair, we designed a statistic that detects the acceleration of non-synonymous substitutions at site , which we call the trailing site, after the occurrence of a non-synonymous substitution at site , which we call the leading site.
First, we obtained a strict temporal order in which non-synonymous substitutions at sites and occurred on the phylogenetic tree. Such an order is not actually known if the phylogeny contains one or more branches on which both sites have experienced a non-synonymous substitution. We say that such branches are temporally unresolved with respect to the pair . Since we do not know in which order the sites in the pair have experienced substitutions on a temporally unresolved branch, we assume that both orders are equiprobable. If there are branches on the phylogenetic tree that are temporally unresolved with respect to the pair , there are a total of equally likely distinct strict temporal orders of substitutions on the tree with respect to this pair.
Next, for each strict temporal order of substitutions , , at sites and , we find all pairs of substitutions that are consecutive along the tree. Substitution at the trailing site (in this case ) and substitution at the leading site (in this case ) form a consecutive pair if has occurred in the lineage ancestral to and no other substitution at either site has occurred in the lineage between them. This notion is illustrated in Figure 1. If is a consecutive pair, we also say that substitution is consecutive to substitution . For each consecutive pair , substitution is called initial and substitution is called subsequent.
Before defining the epistasis statistic we introduce some notation. We denote the fact that branch is ancestral to branch by (“ precedes ”) or by (“ follows ”); if and denote the same branch, we naturally write . We denote the number of synonymous substitutions that occurred on branch by . We measure time between the initial substitution and the subsequent substitution of a consecutive pair as the expected number of synonymous substitutions that occurred between them. More precisely, if substitutions and occurred on branches and respectively, then(1)
The sum in this expression is taken over all branches on the lineage connecting branches and . Note that can be zero if no synonymous substitutions occurred between substitutions and .
Let denote the set of all consecutive substitution pairs at site pair found on the phylogenetic tree with the order of substitutions . We define the epistasis statistic as(2)where is a time-scale parameter that we specify in the “Results” section. The choice of an exponential function of is arbitrary. We expect that any monotonic decreasing function of would yield similar results. Note that if , i.e. if sites and never experience a non-synonymous substitution on the same branch, the strict temporal order of substitutions with respect to the ordered pair is unique. In this simple case, the epistasis statistic is equivalent to
If we take the time-scale parameter to be infinite, the statistic simply equals the number of consecutive substitutions for the ordered pair . We also define if all sets are empty for the pair . In other words, the epistasis statistic is zero for pairs of sites that never experience consecutive substitutions.
The value of the epistasis statistic is large if substitutions at the trailing site often follow substitutions at the leading site and if the time-lag between initial substitutions at the leading site and subsequent substitutions at the trailing site is typically small (compared to ). We therefore expect that pairs of sites that evolve under positive epistasis will have a larger value of the epistasis statistic.
If there were no epistatic interactions between sites and no temporal variation in selection pressures then we would expect the non-synonymous substitutions at each site to be distributed randomly on the phylogenetic tree. In order to obtain the distributions of the epistasis statistic under this null hypothesis for all ordered pairs of sites, we utilize the following straightforward permutation procedure. We shuffle all non-synonymous substitutions on the phylogenetic tree while keeping two sets of marginal quantities preserved: (a) for each branch of the phylogeny, we preserve the number of non-synonymous substitutions that occurred on that branch and (b) for each site, we preserve the total number of non-synonymous substitutions that occurred at that site on the tree. Condition (a) ensures that any possible temporal biases in the sampling of viral isolates, which would apply equally to all sites, are preserved in the null distribution. Condition (b) ensures that the overall selective constraint on each site is preserved. Synonymous substitutions are unaffected by this shuffling procedure.
Although this permutation procedure is conceptually simple, its computational implementation is challenging. A priori, it is unclear how to efficiently sample the space of possible substitution configurations while preserving the aforementioned marginals. This problem can be rephrased as follows. We can represent the phylogenetic tree with non-synonymous substitutions as an matrix, where is the number of branches on the phylogeny and is the number of sites, so that each cell in the matrix is either 1 or 0 depending on whether or not the given site experienced a non-synonymous substitution on the given branch. Thus, we would like to randomly permute the entries of this matrix while preserving the row and column sums. This problem is equivalent to the problem of obtaining the null distribution for the matrix of associations between individuals across a set of observations, which has been extensively studied in the ecology literature [90]–[92]. The method typically employed in ecology to sample the space of matrices that satisfy the constraint on the marginals is called the “swap method” and is based on the idea of swapping the entries of certain specific submatrices in a way that does not violate the constraints. This method, although computationally efficient, generates matrices that are not independent [91]. An alternative “fill method” permutes the matrix entries and simply discards those resulting matrices that do not satisfy the constraints [92]. This method can be prohibitively computationally expensive if many matrices are discarded, but it guarantees independent sampling.
We employed the “fill method” and found that only about between 0.05% and 5% of matrices are accepted, yet this did not present a serious computational limitation. We generated valid permutations per protein, which required about 10 minutes on a desktop computer.
We compute the value of the epistasis statistic and its associated nominal for many thousands of site pairs. We therefore need to quantify the fraction of false positives among the observed nominally significant pairs [93]. Because the values of the epistasis statistic for different site pairs are not independent, we estimate the distribution of the number of false positives in the data through bootstrap by designating 400 out of permutations generated by the procedure described above as “fake data sets”. For each such fake data set, which represents one draw from the null hypothesis, we computed the number of nominally significant site pairs. This allowed us to estimate the full distribution of the number of false positives in the data. In particular, we recorded the expected number of false positives in our data, which is typically referred to as “false discovery rate” (FDR), and overall for the total number of positives actually observed.
To ensure that our method does not detect epistatic interactions when there are none, we have performed detailed simulations of sequence evolution along a phylogenetic tree, with independent sites, as described in [25]. Briefly, the simulation algorithm takes as input a phylogenetic tree with branch lengths equal to the number of nucleotide substitutions and the nucleotide sequence at the root of the tree; it outputs the nucleotide sequences at all internal and terminal nodes. The sequence at a node is generated recursively, given that the sequence at the parental node is already known, using the following stochastic procedure. If the branch length connecting the focal node to the parental node is , then mutations are randomly distributed along the parental sequence proportionally to the entries in the nucleotide mutation matrix and the codon-specific dN/dS values. We used the HA phylogenetic tree to perform this simulation as well as to infer the nucleotide mutation matrix and the codon-specific dN/dS values [25]. Using this simulation algorithm, we generated 100 independent sequence data sets. On each of them, we performed the analysis described above with permutations, 100 of which were considered “fake data sets”.
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10.1371/journal.pgen.1008175 | A statistical model for reference-free inference of archaic local ancestry | Statistical analyses of genomic data from diverse human populations have demonstrated that archaic hominins, such as Neanderthals and Denisovans, interbred or admixed with the ancestors of present-day humans. Central to these analyses are methods for inferring archaic ancestry along the genomes of present-day individuals (archaic local ancestry). Methods for archaic local ancestry inference rely on the availability of reference genomes from the ancestral archaic populations for accurate inference. However, several instances of archaic admixture lack reference archaic genomes, making it difficult to characterize these events. We present a statistical method that combines diverse population genetic summary statistics to infer archaic local ancestry without access to an archaic reference genome. We validate the accuracy and robustness of our method in simulations. When applied to genomes of European individuals, our method recovers segments that are substantially enriched for Neanderthal ancestry, even though our method did not have access to any Neanderthal reference genomes.
| Recent analyses of modern human genomes have shown that archaic hominins like Neanderthals and Denisovans contribute a few percentage of ancestry to many populations. These analyses rely on having accurate reference genomes from these archaic populations. Due to the difficulty in sequencing these genomes, we lack a complete collection of reference genomes with which to identify archaic ancestry. Here, we develop a method that identifies segments of archaic ancestry in modern human genomes without the need for archaic reference genomes. We systematically evaluate the accuracy and robustness of our method and apply it to modern European genomes to uncover signals of introgression which we confirm to be from a population related to Neanderthals.
| Admixture, the exchange of genes among previously isolated populations, is increasingly being recognized as an important force in shaping genetic variation in natural populations. Analyses of large collections of genome sequences have shown that admixture events have been prevalent throughout human history [1]. These studies have shown that modern human populations outside of Africa trace a small percentage of their ancestry to admixture events from populations related to archaic hominins like Neanderthals and Denisovans [1, 2, 3]. Further, studies of the functional impact of archaic ancestry have suggested that Neanderthal DNA contributes to phenotypic variation in modern humans [4, 5].
Central to these studies is the problem of archaic local ancestry inference—the pinpointing of segments of an individual genome that trace their ancestry to archaic hominin populations. Methods for archaic local ancestry inference leverage various summary statistics computed from modern and ancient genomes. For example, at a given genomic locus, individuals with archaic ancestry are expected to have low sequence divergence to an archaic genome [6]. A number of summary statistics [7, 8, 9] as well as statistical models that combine these statistics [2, 10, 11, 12] to infer archaic local ancestry have been proposed.
These methods are most effective in settings where reference genomes that represent genetic variation in the archaic population are available. For example, the analyses of Neanderthal [6, 10] and Denisovan admixture events [13] relied on the genome sequences from the respective archaic populations. In a number of instances, however, the archaic population is either unknown or lacks suitable reference genomes. Several recent studies have found evidence for archaic introgression in present-day African populations from an unknown archaic hominin [14, 15, 16] while analysis of the high-coverage Denisovan genome has suggested that the sequenced individual traces a small proportion of its ancestry to a highly-diverged unknown archaic hominin [10].
One of the most widely used statistics for identifying archaic ancestry is the S*-statistic [9], which identifies highly diverged SNPs that are in high linkage disequilibrium (LD) with each other in the present-day population as likely to be introgressed. The S*-statistic is attractive as it can be applied even where no reference genome is available. However, the power of the S*-statistic tends to be low in the reference-free setting [3] and its accuracy depends on a number of parameters that need to be fixed in advance.
Here, we introduce a new statistical method, ARCHaic Introgression Explorer (ArchIE), that combines several population genetic summary statistics to accurately infer archaic local ancestry without the need for a reference genome. ArchIE is based on a logistic regression model that predicts the probability of archaic ancestry for each window along an individual genome. The parameters of ArchIE are estimated from training data generated using coalescent simulations. Our proposed method has several advantages. First, the model can incorporate a variety of statistics that are potentially informative of archaic ancestry. This flexibility allows the model to be applied to the reference-free setting (the setting that is the focus in this paper). However, the model can be extended to also incorporate reference genomes when available, even when these reference genomes might be from distant representatives [10] or from low-coverage samples [17, 18]. Second, our use of a statistical model allows us to efficiently estimate model parameters that optimize desired objective functions such as the likelihood. This property allows the model to be adapted to admixture events with different time depths or admixture fractions as well as to infer other population genetic parameters of interest. Indeed, recent studies have shown that statistical predictors that combine weakly-informative summary statistics can substantially improve a number of population genetic inference problems [19, 20, 21].
We show that ArchIE obtains improved accuracy in simulations over the S*-statistic (as well as the recently proposed S’ method [22]) while being robust to demographic model misspecifications that can cause the distribution of features and archaic ancestry labels in the training data to differ from the test data. We apply ArchIE to Western European (CEU) genomes from the 1000 Genomes project and show that the segments inferred to harbor archaic ancestry have an increased likelihood of being introgressed from Neanderthals even though no Neanderthal genome was used in the inference. These segments recover previously observed features of introgressed Neanderthal ancestry: we observe a decreased frequency of these segments in regions of the genome with stronger selective constraint [23] as well as elevated frequency at the BNC2 and OAS loci that have previously been reported to harbor elevated frequencies of Neanderthal ancestry [2, 3].
Our method, ArchIE, aims to predict the archaic local ancestry state in a given window along an individual haploid genome. This prediction is performed using a binary logistic regression model given a set of features computed within this window. Estimating the parameters of this model requires labeled training data i.e., a dataset containing pairs of features and the archaic local ancestry state for a given window along an individual genome. To obtain labeled training data, we simulate data under a demographic model that includes archaic introgression, label windows as archaic or not, compute features that are potentially informative of introgression, and estimate the parameters of our predictor on the resulting training data (Fig 1A, Methods). While our method is general enough to be applicable to non-human populations, we describe the demographic model in terms of a modern human-archaic human demographic history.
We simulate training data using a modified version of the coalescent simulator, ms [24], which allows us to track each individual’s ancestry. We use the demographic model from Sankararaman et al. 2014 [2] (See Table 1). In this model, an ancestral population splits T0 generations before present (B.P.) forming two populations (archaic and modern human in the case of the Neanderthal-human demography). The modern human population subsequently splits into two populations Ts generations B.P., one of which then interbreeds with the archaic population (referred to as the target population) while the other does not (the reference population). We simulate one haploid genome (haplotype) in the archaic population, 100 haplotypes in the target population and 100 haplotypes in the reference population (thus, a target population consists of 50 diploid individuals). We sample the archaic haplotype at the same time as the modern human haplotypes, but the statistics we calculate do not rely on features of the archaic genome. We simulate 10,000 replicates of 50,000 base pairs each (bp), resulting in 1,000,000 training examples. We use a window of length 50 Kb because that is the mean length of the introgressed archaic haplotype after Ta = 2, 000 generations based on the recombination rate assumed in our simulations.
We summarize the training data using features that are likely to be informative of archaic admixture. Since we are interested in the probability of archaic ancestry for a given focal haplotype, we compute features that are specific for the focal haplotype. First, for the focal haplotype, we calculate an individual frequency spectrum (IFS), which is a vector of length n, the haploid sample size of the target population. Each entry in the vector is the number of mutations on the focal haplotype that are segregating in the target population with a specific count of derived alleles. Due to the accumulation of private mutations in the archaic population, we expect the IFS to capture the excess of alleles segregating at frequencies close to the admixture fraction in the introgressed population. This statistic is closely related to the conditional site frequency spectrum [25].
Next, we calculate the Euclidean distance between the focal haplotype and all other haplotypes, resulting in a vector of length n. Under a scenario of archaic admixture, the distribution of pairwise differences is expected to differ when we compare two haplotypes that are both modern human or archaic versus when we compare an archaic haplotype to a modern human haplotype. We also include the first four moments of this distribution, i.e., the mean, variance, skew, and kurtosis. These summaries of haplotype distance are similar to the D1 statistic used in Hammer et. al. [14].
The next set of features rely on a present-day reference human population that has a different demographic history compared to the target population. The choice of the reference can alter the specific admixture events that our method is sensitive to: we expect the method to be sensitive to admixture events in the history of the target population since its divergence from the reference. While our method can also be applied in the setting where no such reference population exists, in the context of human populations where genomes from a diverse set of populations is available [1], the use of the reference can improve the accuracy and the interpretability of our predictions. Given a reference population, we compute the minimum distance of the focal haplotype to all haplotypes in the reference population. A larger distance is suggestive of admixture from a population that diverged from the ancestor of the target and reference populations before the reference and target populations split. This feature shares some similarities with the D2 statistic from Hammer et. al. [14].
We also calculate the number of SNPs private to the focal haplotype, removing SNPs shared with the reference, as these SNPs are suggestive of an introgressed haplotype. Finally, we calculate S* [9], a statistic designed for detecting archaic admixture by looking for long stretches of derived alleles in high LD.
Using these features, we train a logistic regression classifier to distinguish between archaic and non archaic segments. In our training data, we define archaic haplotypes as those for which ≥ 70% of bases are truly archaic in ancestry and non-archaic as those for which ≤ 30% are archaic in ancestry. We discard haplotypes that fall in-between those values in the training data resulting in 988,372 training examples.
We tested the accuracy of ArchIE by simulating data under a demography reflective of the history of Neanderthals and present-day humans [2]. We evaluated the ability of ArchIE to correctly predict the archaic ancestry at each SNP along an individual haplotype. Since ArchIE predicts archaic ancestry within a window, we simulated a 1 Mb segment, applied ArchIE in a 50 Kb window that slides 10 Kb at a time, and predicted archaic ancestry at a SNP by averaging predictions across all windows that overlap the SNP (Methods). We compute Receiver Operator Characteristic (ROC) and Precision Recall (PR) curves by varying the threshold at which we call a SNP archaic and calculating the true positive rate (TPR), false positive rate (FPR), precision, and recall (Fig 2).
We compared ArchIE to an implementation of the S*-statistic from Vernot and Akey using their hyper parameter choices [3] and to S’, a new method for reference-free inference of archaic ancestry [22] (Methods). At a 2% admixture fraction, ArchIE outperforms the S* and S’ statistics across all thresholds (Fig 2A and 2B). At a precision of 0.80, i.e., false discovery rate of 20%, ArchIE obtains a recall of 0.21, S* obtains a recall of 0.04, and S’ obtains a recall of 0.09. The area under the ROC curve (AUROC) is 0.94 (±0.008) for S*, 0.84 (±0.01) for S’, and 0.97 (±0.005) for ArchIE and the area under the PR curve (AUPR) is 0.47 for S* (±0.031), 0.28 (±0.032) for S’, and 0.60 (±0.05) for ArchIE (All standard error were estimated using a block jackknife [26] using 1 Mb blocks). We also note that while the ROC curves are similar, the PR curves show a large difference, indicative of the utility of PR curves in problems where there is an imbalance in the frequencies of the two classes.
We also evaluated the ability of ArchIE to call archaic haplotypes. Since haplotypes can range from having none of their ancestry to being entirely from the archaic population, we called haplotypes archaic if they contain ≥ 70% archaic ancestry or not archaic if they contain ≤ 30%. We see that again, ArchIE has larger AUPR (0.53 for ArchIE, 0.38 for S*) and AUROC (0.97 for ArchIE, 0.94 for S*) compared to S* (S4 Fig).
We examined the absolute value of the standardized weights learned by ArchIE to understand the features that contribute substantially to its predictions. Examining single features, we find that the minimum distance between the focal haplotype and each of the reference haplotypes, as well as the skew of the distance vector have the largest weights (Fig 3B). Intuitively, a larger distance to a reference population should indicate archaic ancestry. The next largest single statistic was the skew of the distance vector, which was negatively correlated with archaic ancestry. Under a simple scenario of admixture, we expect a bi-modal distribution of pairwise distances. However, when there is little archaic ancestry, the distribution will be unimodal resulting in a negative relationship between skew and archaic ancestry. The IFS contains mostly negative weights, suggesting that these features do not make a substantial contribution to the model predictions (Fig 3A).
As a further check, we wanted to determine how the performance of the model changes when trained on subsets of the features. First, since the “skew” feature has a large standardized absolute weight, we trained a model based only on this feature (S5 Fig). We find that accuracy greatly decreases, indicating that the model does best when it combines multiple features that are informative of archaic introgression. However, when we train only on the number of private SNPs or only on the minimum distance to the reference population, we see improved accuracy indicating that these features are informative of archaic ancestry independent of other features. When we take a combination of three features (skew, number of private SNPs, and minimum distance to the reference population), this model is still able to discern archaic from non-archaic haplotypes with slight decreased accuracy relative to the full model (S5 Fig). Finally, we tested the contribution of the reference population to the accuracy of ArchIE. We trained the logistic regression without using any features that rely on the reference and found that model still retains reasonable accuracy (AUPR = 0.36) to identify archaic ancestry (S5 Fig). This suggests that ArchIE is useful even in scenarios where a reference population is not available.
ArchIE relies on simulating data from a model with fixed demographic and population genetic parameters. In practice, these parameters are unknown and are inferred from data with some uncertainty. Thus, we wanted to determine the sensitivity of our method to demographic uncertainty. An exhaustive exploration of demographic uncertainty is challenging given the number of parameters associated with even the simplest models. As an alternative to an exhaustive exploration, we systematically perturbed each parameter at a time, simulated data using the perturbed model, and evaluated the performance of our classifier (trained on the unperturbed parameters corresponding to the Neanderthal demographic history).
ArchIE remains accurate when many aspects of the demography are misspecified, but has reduced precision or recall under some scenarios (Fig 4, S1 Fig). The most significant decrease in accuracy (in terms of recall and precision at a fixed threshold) arises when the reference population size is decreased or the split time of the reference and the target is increased. In this setting, the reference genomes are more drifted and hence, less representative of the ancestral population. We also compared the accuracies of ArchIE to S* across these perturbations and found that ArchIE remains relatively accurate across these settings (S1 Table).
We also tested the effect of variation in mutation rate (μ) and recombination rate (r) since we trained our model using fixed values of these parameters (μ = 1.25 × 10−8, r = 1 × 10−8). To evaluate how ArchIE performs on real data, we simulated test data randomly drawing pairs of μ and r from a distribution chosen to match local recombination and mutation rates along the human genome (see Methods). The overall AUPR is reduced (0.31, S1i Fig), the log10 fold changes in precision and recall are −0.30 and +0.19 suggesting that ArchIE is relatively robust to variation in mutation and recombination rates.
In addition, we tested the impact of the window size and found that reasonable choices of window size do not substantially impact the performance (S2 Fig). We also assessed the impact of sample size by simulating 30 haplotypes (15 diploid individuals), representing a modestly sized genomic dataset, and found a reduction in power as expected (AUPR = 0.45) (S3 Fig).
We tested the sensitivity of ArchIE to recent and ancestral structure in the demographic model. We simulated data under two scenarios of structure, one where 25% of the target population separates immediately after the target and reference population split, 2499 generations ago, and rejoins the generation prior to the archaic admixture, 2001 generations ago (S6A Fig). We refer to this as the recent structure scenario. Additionally, we simulated data where 25% of the population in N0 separates 12,000 generations ago and rejoins the ancestral population right before the target and reference populations split (2600 generations ago, S6B Fig). We refer to this as the ancestral structure scenario. We observe that for both scenarios, the fraction of SNPs detected as archaic is 0, suggesting that ArchIE is robust to introgression due to either recent or ancient structure at reasonable calling thresholds. We caution, however, that a more detailed exploration of structured demographic models is necessary.
To identify segments of archaic ancestry in modern human populations, we applied ArchIE to genomes of European individuals in the 1000 Genomes Project [27]. We used all unrelated individuals from a European (CEU) population as our target population (99 diploid individuals) and all unrelated individuals from an African (YRI) population as a reference (108 diploid individuals) and calculated the summary statistics described above. We applied ArchIE in non-overlapping 50 Kb windows. We evaluated the average percent of windows inferred as archaic as a function of the calling threshold (Fig 5A). Applying a threshold corresponding to a precision of 0.80 in simulations, we inferred 2.04% (block jackknife SE = 0.6% using 1 Mb blocks) of the genome as confidently archaic. This proportion is in line with proportion of Neanderthal ancestry from previous analyses [2, 6, 10] suggesting that the segments of archaic ancestry inferred by ArchIE likely correspond to segments of Neanderthal ancestry.
To further investigate whether the haplotypes inferred as confidently archaic by our model are enriched for introgressed Neanderthal variants, we computed a Neanderthal match statistic (NMS) defined as the number of shared variants between an individual haplotype and the Altai Neanderthal reference genome sequence [10] divided by the total number of segregating sites in that window (see Methods). We see that the archaic regions confidently inferred by ArchIE have a higher NMS suggesting that the archaic ancestry segments identified by our method are likely to represent introgressed Neanderthal sequence (we reject the null hypothesis that the difference in NMS is zero for archaic vs non-archaic haplotypes with a P value = 1.7 × 10−3 via 100 Kb block jackknife). Further, as we make the calling threshold more strict, we see an increase in the mean NMS for the archaic haplotypes (Fig 5B).
We also compared the performance of ArchIE, S’, and S* on real data from CEU Europeans. For each of these methods, we computed a matching rate with the Altai Neanderthal genome, defined as the fraction of SNPs called archaic that match the Altai Neanderthal sequence divided by the total number of SNPs called archaic. At a detection rate of ≈ 1%, S’ has a matching rate of 0.73 while ArchIE has a matching rate of 0.91 (S9 Fig; see S1 Text for details). Comparing with the S* calls released from [28], we found a match rate of ≈ 50% at a detection rate of ≈ 0.5%, consistent with results reported from the authors.
We then focused on two genomic regions that have been shown to harbor introgressed Neanderthal haplotypes at elevated frequencies: the BNC2 gene (Chromosome 9:16,409,501-16,870,786) [2] and the OAS gene cluster (Chromosome 12:113,344,739-113,357,712) [7]. ArchIE detects substantially increased frequency of archaic ancestry in both these genes (Fig 5C and 5D).
Finally, we analyzed the correlation between a measure of selective constraint of a given genomic region (B-value [23]) and frequency of confidently inferred archaic segments in the CEU population in the same region. Sankararaman et al. 2014 [2] describe a relationship where more constrained regions (lower B-value) have a lower frequency of archaic ancestry. We observe the same trend where more neutral regions (B-value ≥ 750) contain more archaic ancestry than constrained regions (B-value ≤ 250) consistent with selection against the archaic ancestry (P value = 7.86 × 10−9 via block jackknife; Fig 5E).
These analyses suggest that ArchIE obtains results concordant with those from a previous reference-aware method [2]. We caution, however, that the observed concordance can be inflated due to any biases shared by the two methods.
A key challenge in detecting the contribution of deeply-diverged populations (both deeply-diverged modern as well as archaic hominin populations) to the ancestry of present-day human populations arises from the lack of accurate representative genomes for these populations. Here, we present a statistical model (ArchIE) for detecting regions of archaic local ancestry without the need for an archaic reference sequence. ArchIE combines weakly informative signals computed from present-day human genomes using a logistic regression model. The parameters of the model are estimated from data simulated under a specific demographic model. Using simulations, we show that ArchIE obtains improved accuracy over other approaches for reference-free local ancestry inference. While the accuracy of ArchIE will depend on how similar the demographic model used for training is to the true demographic model, our empirical results suggest that ArchIE is relatively robust even when the true demographic model differs from the assumed model. Applying ArchIE to genomes from the CEU population in the 1000 Genomes project data, we detect 2.03 ± 0.6% archaic ancestry (at a threshold that corresponds to a false discovery rate of 0.2). We find that segments confidently labeled as archaic by ArchIE are enriched for Neanderthal ancestry.
One advantage of our approach is that the learning algorithm is general allowing it to be applied broadly to diverse inference problems as well as input summary statistics while its simplicity allows for a transparent interpretation of the features and the model.
There are several limitations of our methodology, however. First, we require some knowledge of the demographic history of the target, reference and archaic populations. We have shown that ArchIE is robust to some demographic misspecification, but it is most powerful when the simulated demography is close to the true one. Second, we rely on the data being phased. Switch-errors in phasing will reduce the power of ArchIE, which can be a problem when applying the method to less-well studied populations. In principle it is possible to use ArchIE on unphased data, calculating features on the diploid individual level rather than the haplotype level, though we do not explore that here. Third, the use of a fixed-size window ignores long-range as well as variable-length dependence among the features. Models that account for this dependency can be expected to yield improved accuracy. An example of such an approach is a recently published method that uses a hidden Markov model (HMM) that models the distribution of private variants [12]. Combining such models with the framework outlined here has the potential to yield improved accuracies. Fourth, the use of a linear model is likely to underfit the true function between features and outputs. It is possible to train more expressive models like deep neural networks, which can learn and capture non-linear relationships between features and tend not to suffer from the curse of dimensionality [19]. These methods have been used to great success in tasks such as image classification [29] and we anticipate their use in population genetics could improve predictive power. Preliminary results applying deep learning to this problem with the features used here are promising, motivating future work (S1 Text, S7 and S8 Figs). ArchIE relies on a careful choice of features as input. These hand crafted features are informed by population genetics theory, similar to other methods that have been proposed in population genetics [19, 20, 30, 31, 14]. Automatically learning features from genetic data is direction of high interest. Finally, while several methods [9, 12, 22] have been proposed to infer aspects of archaic ancestry without access to reference genomes, these methods are typically evaluated using simulations. Assessing the accuracy of these methods on real data remains challenging. Extrapolating simulation results to accuracy on real data depends on choices of the inference problem, population genetic models, parameters used for training and testing, genomic features used as input, and accuracy metrics of interest. A comprehensive comparison of these methods across a range of demographic histories and evolutionary forces is an important topic for future work.
In conclusion, our method improves on previous methods for reference-free inference of archaic ancestry by combining informative summary statistics in a statistical learning framework. We anticipate that this method will be informative not only in human populations where questions about admixture with other hominins abound, but also in other species and systems where pervasive admixture has shaped the distribution of genetic variation.
We simulated training and test data sets using a modified version of ms [24] that tracks the ancestry of each site in each individual genome. Using a previously proposed demographic model relating modern humans and Neanderthals [2], we sampled 100 haplotypes from the target, and 100 haplotypes from the reference over a region of length 50 Kb. We use a constant mutation rate μ = 1.25 × 10−8 and a recombination rate r = 1 × 10−8.
The general demography is as follows: an archaic population of size Na splits from a population of size N0, T0 generations before present (B.P.). Then, at TS, two populations split off from the ancestral population that then have effective population sizes N1 (termed the reference) and N2 (termed the target) respectively. Then, at time TA, the archaic population migrates into the target with an admixture fraction m. See Fig 1 for a graphical outline.
Each simulation at a given locus generates 100 haplotypes in the target. For each haplotype, we calculate the following classes of summary statistics: individual frequency spectrum, distance vector to all haplotypes within the test population as well as the first four moments of this vector, minimum distance to haplotypes in the reference population, the number of private SNPs, and the S*-statistic.
The individual frequency spectrum is created as follows: given a sample of n haplotypes, for each haplotype j, we construct a vector X of length n where entry Xi counts the number of derived alleles carried on the focal haplotype j whose derived allele frequency is i. For example, the first entry counts the number of singletons present in haplotype j, the second entry counts the number of doubletons and so on until n.
The distance vector is a vector of length n where entry i is the Euclidean distance from haplotype j to haplotype i over all sites, where j is the focal haplotype and i is the haplotype being compared.
The minimum distance to haplotypes in the reference population is computed as the minimum Euclidean distance from the focal haplotype to all haplotypes in the reference population.
The number of private SNPs is calculated as the number of SNPs the focal haplotype contains that are not present in the reference population.
This results in 208 features per example (a 50 Kb window for a single haploid genome), with 100 examples per locus and 10,000 loci resulting in 1,000,000 examples for training before filtering haplotypes with intermediate levels of admixture.
We used the “glm” function in R to construct a logistic regression model using the family = binomial(“logit”) option. We used the predict function to obtain a prediction and converted it to a probability using the “plogis” function.
Due to the process of recombination, the ancestry of a haplotype may vary along its length. On the other hand, ArchIE predicts a single ancestry state for a haplotype across a specified window. We evaluate the ability of ArchIE to predict the ancestry at each SNP along a haplotype by simulating sequences of length 1 Mb and applying ArchIE in 50 Kb windows, sliding by 10 Kb at a time. We average the predictions that each SNP on a haplotype receives across all windows that overlap the SNP to obtain the predicted archaic ancestry. We compare the predicted and the true ancestry state at each SNP along a haplotype.
We evaluated the performance using Precision-Recall (PR) curves as well as receiver operator characteristic (ROC) curves. We calculated precision (equivalently 1− the false discovery rate), recall (equivalently sensitivity) and false positive rates as:
R e c a l l ( t ) = T P ( t ) T P ( t ) + F N ( t ) S e n s i t i v i t y ( t ) = P r e c i s i o n ( t ) = T P ( t ) T P ( t ) + F P ( t ) F a l s e p o s i t i v e r a t e ( t ) = F P ( t ) F P ( t ) + T N ( t )
Here TP(t) is the number of true positives at threshold t, FN(t) is the number of false negatives at threshold t, FP(t) is the number of false positives at threshold t and TN(t) is the number of true negatives at threshold t. We summarize these results by reporting the recall at a fixed value of precision as well as by computing the area under the precision recall curve (AUPR) and the area under the ROC curve (AUROC). We compute the AUPR using the method of Davis and Goadrich [32]. We compute standard errors of the AUPR and AUROC using a block jackknife [26] where we drop a single 1 Mb region and recompute the statistics.
We compared ArchIE to the S* [9] and S’ [22] statistics. We calculate S* in a cohort of 100 haplotypes from the target population. Then, we convert the S* scores into a rank between [0-1] using the empirical cumulative distribution. We use a 50 Kb sliding window (10 Kb stride) across the 1 Mb region, averaging the score for a SNP.
We use a similar strategy for S’. However, since S’ predicts archaic ancestry in a sample of individuals rather than on the haploid genome level, we use an algorithm to convert sample predictions to haploid genome predictions. We run S’ on the sample. Then, at some S’ score threshold, we find the longest stretch of SNPs at that score or higher and interpolate the scores across genotypes, building haplotypes when individuals have the archaic allele. Then, for each SNP, we evaluate whether the SNP is archaic or not and calculate the number of true positives, false positive, true negatives, and false negatives. We repeat this procedure across thresholds and calculate the precision, recall, and false positive rates.
We examined the robustness of ArchIE to a specified demographic model by systematically perturbing one parameter at a time, simulating a dataset, and evaluating ArchIE’s performance. We doubled and halved the parameters, except when doing so would produce a demographic model that is not sensible.
We evaluated the robustness of ArchIE to mutation and recombination rate variation by calculating local rates at 50 Kb windows and then randomly drawing combinations of the rates and simulating data. Mutation rates were calculated by estimating Watterson’s θ [33] from the number of segregating sites within 50 Kb windows across 50 randomly sampled west African Yoruba genomes from the 1000 Genomes Project Phase 3 release and calculating the mutation rate: μ = θw/4NeL where we set Ne = 10, 000. Recombination rates were estimated from the combined, sex-averaged HapMap recombination map [34].
We validated our method using the Neanderthal introgression scenario as a test case. We downloaded phased CEU genomes from the 1000 Genomes Phase 3 dataset [27] and calculated the features mentioned above in 50 Kb windows. For each individual haplotype, we inferred the probability that the window is archaic. We then intersected our calls with the 1000 Genomes strict mask using BEDtools v2.26.0 [35], removing regions that are difficult to map to, measured as having less than 90% of sites in the callability mask.
We calculated a Neanderthal match statistic (NMS) for focal haplotype i in a window as the fraction of alleles at which the the focal haplotype matches the Altai Neanderthal [10] genome:
N M S i = S i N i + H i
Here Si denotes the number of alleles that match between the focal haplotype and the Neanderthal genome within the window. Since the Neanderthal genome is not phased, we count sites as matching if it contained at least one single matching allele or more. Ni denotes the number of Neanderthal mutations, including both homozygous and heterozygous sites. Hi denotes the number of human mutations within the window.
In order to test whether there is more Neanderthal matching in archaic haplotypes compared to non-archaic haplotypes, we computed the difference in NMS between the two classes of haplotypes at each window and test the hypothesis that the mean of this statistic averaged across the genome is zero. Specifically:
Δ N M S , i = N M S ¯ a r c h , i - N M S ¯ n o n - a r c h , i N M S i ¯
For each window i, we compute ΔNMS,i, defined as the difference between the mean NMS for archaic (N M S ¯ a r c h , i) and non-archaic (N M S ¯ n o n - a r c h , i) haplotypes divided by the mean NMS of all haplotypes (N M S i ¯) to control for mutation rate heterogeneity. We require a minimum of 90% callable sites within the window. We compute the mean of ΔNMS,i over all windows i as the genome-wide estimate and test if this estimate is significantly different from zero. To compute significance, we use a block jackknife and drop non-overlapping 100 Kb windows and recalculate the genome wide difference in means.
In order to assess the relationship between background selection and inferred archaic ancestry, we use the B-values from McVicker et al. 2009 [23] and intersected them with our calls. For visualization, we binned the B-values into 4 bins, [0-250], (250-500], (500-750], and (750-1000].
We tested for significant differences in allele frequency between the lowest and highest bins using a block jackknife using a 50 Kb block size.
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10.1371/journal.pmed.1002215 | Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach | The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain.
We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination.
We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases.
| Genetic sequence variants explain only a modest proportion of the variation in body mass index (BMI) and cardiometabolic disease in the general population.
There is limited understanding of the link of DNA methylation—a well-characterized epigenetic modification—with BMI and cardiometabolic disease in the general population.
We conducted a cross-sectional analysis of the association of BMI with leukocyte DNA methylation at over 400,000 sites in the genome among 7,798 community-dwelling adults.
We identified associations between BMI and methylation at 83 replicated sites (including 50 novel sites) and concurrent differences in expression in whole blood of genes overrepresented in lipid metabolism pathways.
Using genetic sequence variants to model exposure to differential DNA methylation and tissue-specific gene expression, we found differential methylation and expression of SREBF1 to be implicated in BMI, adiposity-related traits, and coronary artery disease.
Using genetic sequence variants to model exposure to differences in BMI, we found a substantial proportion of the differentially methylated sites (16 of 83) to be downstream of BMI.
Evidence is accumulating that epigenetic modifications, such as DNA methylation, are related to obesity-related diseases in the general population.
We provide support for a role of genomic regulation of a lipid metabolism transcription factor, SREBF1, in adiposity and coronary artery disease.
Mendelian randomization approaches can help prioritize relevant loci for future functional studies, but the cross-sectional observational nature of our study limits definitive causal inference.
| Obesity is highly prevalent in developed nations [1] and contributes to a substantial burden of morbidity and mortality [2,3]. Despite advances in the understanding of genetic variants, lifestyle factors, and gene–environment interactions associated with obesity [4–7], much of the interindividual variation in body weight remains unexplained by measurable lifestyle and genetic factors. DNA methylation, one of the most frequent and well-characterized epigenetic modifications, reflects at the molecular level a wide range of environmental exposures and genetic influences [8]. By stabilizing chromatin structure and altering gene expression, DNA methylation has the potential to affect an individual’s susceptibility to obesity (see review in [9]). Further, changes in the methylation of DNA may occur secondarily to obesity and may consequently influence the development of adiposity-related diseases such as diabetes, dyslipidemia, hypertension, and cardiovascular disease. Large gaps in knowledge remain as to how human epigenetic modifications relate to obesity and its sequelae.
Epigenetic biomarkers represent a largely untapped precision medicine resource to guide therapy decisions using an individual’s epigenetic profile obtained from blood samples [10]. Identification of clinically relevant epigenetic loci in blood holds the potential to create a foundation upon which to base future functional studies and trials to test epigenetically guided clinical decision making for cardiometabolic diseases. In addition, we may gain novel insights into the molecular underpinnings of obesity and adiposity-related diseases through the study of differentially methylated DNA loci in blood. Doing so may lead to the identification of biologically relevant therapeutic targets.
The present study provides results of an epigenome-wide association study (EWAS) of body mass index (BMI) in over 3,700 participants from the Framingham Heart Study (FHS) and the Lothian Birth Cohorts (LBCs) of 1921 and 1936 (LBC1921 and LBC1936). We conducted independent external replication in over 4,000 individuals from the Atherosclerosis Risk in Communities (ARIC), Genetics of Lipid Lowering Drugs and Diet Network (GOLDN), and Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) cohort studies. We examined the functional relevance of the identified loci by interrogating the known trans-tissue regulatory functions and concomitant changes in gene expression in blood. In addition, we explored the clinical relevance of the findings for adiposity-related diseases with genetic instrumental variable (IV) analyses using bidirectional and two-step trans-tissue Mendelian randomization (MR) approaches [11–13].
The study includes two major components. First, we conducted an EWAS of BMI. Second, BMI-related differentially methylated loci were taken forward for further analyses to better understand the magnitude of association, regulatory annotation, functional implications, and clinical relevance (Fig 1). The discovery/replication design and secondary models for the BMI EWAS were defined a priori (S1 Text). Downstream analyses to characterize the discovered loci were outlined a priori, but the final approach was primarily driven by the findings and concurrent advancements in the field.
The FHS protocols and participant consent forms were approved by the institutional review board of Boston University School of Medicine. Ethics permission for the LBC1921 was obtained from the Lothian Research Ethics Committee (Wave 1: LREC/1998/4/183). Ethics permission for the LBC1936 was obtained from the Multi-Centre Research Ethics Committee for Scotland (Wave 1: MREC/01/0/56) and the Lothian Research Ethics Committee (Wave 1: LREC/2003/2/29). Written informed consent was obtained from all discovery cohort (FHS and LBC) and replication cohort (ARIC, GOLDN, and PIVUS) participants.
Data for the discovery phase of this investigation were drawn from the FHS offspring cohort [14] and the LBCs of 1921 and 1936 [15–17]. As previously described [14], the FHS offspring cohort was initially recruited in 1971 and included 5,124 offspring (and their spouses) from the FHS original cohort [18]. The eligible sample for this investigation was from the 3,021 participants in the FHS offspring cohort who attended the eighth examination cycle from 2005 to 2008. The LBC1921 and LBC1936 samples derive from the Scottish Mental Surveys of 1932 and 1947, respectively, when nearly all 11-y-old children in Scotland completed an IQ-type test in school. The LBC studies provided follow-up of surviving participants, most of whom were living in the Lothian region (Edinburgh city and outskirts) of Scotland. The current study draws upon the older-age baseline examinations of 551 participants in LBC1921 recruited in 1999–2001 and 1,091 participants in LBC1936 recruited in 2004–2007.
Height and weight were measured in each study using established protocols as described in detail in the S1 Methods. BMI was calculated as weight (in kilograms) divided by height (in meters) squared.
DNA from whole blood samples was collected at the same examination assessment as the anthropometric and covariate measurements in both studies. DNA methylation, assayed with the Infinium HumanMethylation450 BeadChip [19] (Illumina), was available for 2,846 FHS participants and 1,518 LBC participants (514 from LBC1921 and 1,004 from LBC1936). Details of rigorous quality control, normalization procedures, and exclusions of non-autosomal probes, cross-hybridizing probes, and probes with underlying single nucleotide polymorphisms (SNPs) are described in S1 Methods. Each discovery and replication cohort conducted cohort-specific preprocessing pipelines that allowed each cohort to address study-specific technical and batch effects. This design allowed for the selection of true biological signals independent of bias introduced from uniform processing methods. After quality control in the discovery cohorts, there were 402,358 shared CpG (cytosine-phosphate-guanine) methylation probes available for analyses in 2,377 FHS and 1,366 LBC participants (446 from LBC1921 and 920 from LBC1936). Final sample size was determined by the number of community-based participants in the discovery cohorts who consented to genomic studies and who had available DNA and methylation assays passing quality control measures. In the FHS, SNP data were obtained from the Affymetrix 550K Array imputed to the 1000 Genomes Project reference panel, as previously reported [20]. The LBC samples were genotyped using the Illumina Human610-Quad v1.0 genotyping platform and imputed to the 1000 Genomes Project reference panel as well. Gene expression in blood was available in the FHS and was measured using the Affymetrix Human Exon 1.0 ST GeneChip as described in S1 Methods.
In the FHS, linear mixed effects regression models were conducted to test the association between site-specific DNA methylation and BMI. The primary model was adjusted for age, sex, family relatedness (random effect), and surrogate variables (to account for differential cell proportions and technical effects) [21], with BMI as the independent variable of interest and DNA methylation (inverse-normal transformed) as the dependent variable. In the LBC, linear regression models were conducted adjusting for age, sex, and white blood cell counts, with each DNA methylation probe (residual taken forward from a generalized linear model with a logistic link function adjusting for technical and batch effects) as the dependent variable and BMI as the independent variable of interest. Further analytical details for the discovery cohorts are described in S1 Methods. In both cohorts, secondary models were conducted: (1) additionally adjusting for smoking status, (2) restricted to participants with BMI 18–35 kg/m2 in order to avoid confounding due to frailty or morbid obesity and obesity-related diseases, and (3) testing for age and sex interactions. Results from the FHS and LBCs were meta-analyzed using methods that weighted the p-value by sample size [22]. Directional consistency of statistically significant cohort-specific effects was confirmed for all methylome-wide significant findings from the discovery meta-analysis. We focused our analyses on the resultant test statistic and direction of effect from the independent variable of interest (BMI) as the cohort-specific linear regression coefficients were not directly comparable due to the differences in the preprocessing approach between cohorts. The threshold for statistical significance in the discovery phase was defined by Bonferroni correction for multiple testing to be 0.05/405,000 (p-value < 1.2 × 10−7). A flowchart of analyses conducted is presented in Fig 1.
The methylome-wide significant CpGs from the FHS and LBC meta-analysis were taken forward to external replication in three independent cohorts that used the same methylation microarray: the ARIC study, using whole-blood-derived DNA from 2,096 participants of African ancestry; the GOLDN study, using DNA derived from CD4+ cells from 992 participants of European ancestry; and the PIVUS study, using whole blood-derived DNA from 967 participants of Swedish ancestry. Description and analytical methods of the replication cohorts are supplied in S1 Methods. Replication cohorts also conducted cohort-specific preprocessing. Replication was examined within each cohort individually and then in a meta-analysis of all three replication cohorts (using p-value-weighted methods and ensuring directional consistency as described above). The threshold for statistically significant replication was determined by Bonferroni correction to be 0.05 divided by the number of CpGs taken forward from discovery.
In order to demonstrate whether the DNA methylation and BMI association results were independent of genetic variants influencing methylation (methylation quantitative trait loci [meQTLs]), we conducted sensitivity models in the FHS for the replicated BMI-related CpGs conditional on the top cis-meQTL (selected by lowest p-value; ±500 kb from the CpG) for each replicated CpG. The approach to identify cis-meQTLs for the BMI-related CpGs is described in S1 Methods.
In order to determine the magnitude of variation in BMI contained within the studied epigenetic signatures in blood, we examined the variation captured in three ways. First, we examined the increase in model R2 starting from the baseline covariate-only linear regression model, with BMI as the dependent variable, when adding nonredundant (|r| < 0.7) replicated CpGs as independent variables in order of decreasing statistical significance. We conducted this analysis in two discovery test sets: (1) methylome-wide significant CpGs in the FHS only were tested in the LBCs and (2) replicated nonredundant CpGs from the BMI EWAS were tested in one of the replication cohorts, PIVUS. Due to differences from the discovery cohorts in ethnicity (African ancestry in ARIC) and cell line (CD4+ cells in GOLDN), we conducted the variation analyses only in PIVUS. Second, we created an additive composite measure of the same nonredundant statistically significant replicated CpGs weighted by effect size. The composite methylation measure was generated for each individual by summing the product of the methylation beta-value and the cohort-specific effect size (including direction of effect) for each of the nonredundant replicated CpGs. The distribution of BMI and prevalence of obesity (BMI ≥ 30 kg/m2) was assessed across deciles of the additive weighted composite measure in the PIVUS cohort. Third, the change in BMI and odds of overweight (BMI 25–29.9 kg/m2) and obesity were tested in age- and sex-adjusted linear and logistic regression models for each standard deviation (SD) change in the additive weighted composite measure in the PIVUS cohort. The weighted summation of the composite methylation measure was converted to SD units (mean = 0, SD = 1) to enhance interpretability of results. As some of the cross-sectional differential methylation changes were expected to be secondary to BMI differences, the purpose of these analyses was not to develop a biomarker or risk predictor for cross-sectional BMI measures but to determine if a large proportion of variation in BMI and obesity, and hence obesity-related cardiometabolic risk, is reflected in the blood DNA methylation patterns. Further analyses examine the molecular pathways that are affected and attempt to infer which methylation changes are causally influencing BMI, which are secondary to BMI differences, and which have relevance for clinical disease outcomes.
We analyzed whole blood gene expression data in the FHS to identify which BMI-related differentially methylated CpGs demonstrated association with altered gene expression. The replicated CpGs were tested using linear mixed effects models for association, with the expression level of the corresponding gene in whole blood (based on annotation by the manufacturer) as the dependent variable and DNA methylation as the independent variable, adjusted for age, sex, and technical and batch effects (further details in S1 Methods).
We studied the Gene Ontology (GO) biological process, molecular function, and cellular component pathways (release 2016-08-22) of the genes identified in the BMI EWAS using the PANTHER (protein annotation through evolutionary relationship) overrepresentation test [23]. Secondarily, we restricted analysis to the higher certainty genes shown to have altered whole blood gene expression in association with BMI-related differential methylation, as described in the previous section. If multiple probes were annotated to the same gene, then the gene was included only once (unweighted). As the methylation array covers 99% of RefSeq genes, the background universe of genes tested was not restricted. Results were corrected for multiple testing within each category.
In addition, we used eFORGE v1.2 (http://eforge.cs.ucl.ac.uk/) [24] to identify if the replicated CpGs were enriched in DNase I hypersensitive sites (DHSs) (markers of active regulatory regions) and loci with overlapping histone modifications (H3Kme1, H3Kme4, H3K9me3, H3K27me3, and H3K36me3) across available cell lines and tissues from Roadmap Epigenomics Project, BLUEPRINT Epigenome, and ENCODE (Encyclopedia of DNA Elements) consortia data [25–27].
IV analyses using SNPs as IVs for (1) DNA methylation, (2) gene expression, and (3) BMI were conducted in order to infer potential causal relationships between EWAS findings, BMI, and adiposity-related diseases (the series of analyses conducted is outlined in Table 1). The detailed approach is provided in S1 Methods. In brief, differences in methylation and expression were modeled using quantitative trait loci (QTLs), thus leveraging the contribution of genetic variation to epigenetic traits to infer causal relations. Blood QTL IVs were selected as the single top SNP methylation or expression association (by lowest p-value) in the FHS with replication in the external cohorts or public datasets. As QTLs vary in effect in different tissue types, we selected tissue-specific methylation and expression QTLs to examine tissue-specific effects (details in S1 Methods). To model the effect of BMI on methylation (reverse causation), the IV for BMI was assembled as an additive weighted genetic risk score from the 97 genome-wide significant SNPs from the Genetic Investigation of ANthropometric Traits (GIANT) consortium 2015 genome-wide association study (GWAS) results [7]. A sensitivity analysis utilizing a single SNP in the FTO (fat mass and obesity associated) locus as the IV for BMI was conducted to examine an IV less prone to pleiotropy bias but also less powerful to detect potential causal relations.
Forward MR, using the two-stage least squares method, tests the causal relation of differential methylation with BMI. SNP IVs that implicated a causal effect of differential methylation on BMI from the forward MR (Bonferroni-corrected and, secondarily, nominal causal p-value < 0.05) were tested in the trans-tissue two-step MR. The trans-tissue two-step MR was implemented to further break down the relationship between DNA methylation and BMI and to infer whether the hypothesized mediator (gene expression in multiple tissues) is influenced by the exposure (DNA methylation) and, second, whether the mediator (gene expression in multiple tissues) affects the outcome (BMI). SNP IVs that implicated a causal effect of differential methylation and expression on BMI were tested for associations with adiposity-related phenotypes from published GWAS results. Finally, the reverse MR was conducted to test the causal relation of BMI with downstream changes in DNA methylation.
The discovery sample included 3,743 individuals: 2,377 from the FHS and 1,366 from the LBCs (n = 446 from LBC1921 and n = 920 from LBC1936). The FHS, LBC1921, and LBC1936 cohorts were older adults (mean [SD] age 67 [9], 79 [1], and 70 [1] y, respectively) and had similar sex distribution (50%–60% female) and proportion of current smokers (8%–11%) (Table 2).
The interindividual variation in BMI and distribution of obesity captured in the BMI EWAS findings was evaluated. Regressing BMI on the 77 nonredundant (inter-probe correlation |r| < 0.7) CpGs from the 83 replicated CpGs identified in the BMI EWAS revealed that 18% of the interindividual variation (adjusted R2) in BMI is captured by differential methylation beyond age and sex in the external replication cohort PIVUS (S6 Fig). This proportion is similar to that observed when examining a completely independent discovery test set using the 75 CpGs that were methylome-wide significant in the FHS discovery cohort (no replication), which accounted for 17.5% of the interindividual variation in BMI (adjusted R2) beyond age and sex in the LBCs. Creating an additive weighted composite measure of the 77 nonredundant replicated CpGs and examining the distribution of BMI and obesity (BMI ≥ 30 kg/m2) across deciles of the measure demonstrated that the median BMI increased in a graded manner from 22 to 34 kg/m2 and the prevalence of obesity rose from 0% to 50% (Figs 2 and S7). For each SD increase in the composite DNA methylation measure in the PIVUS replication cohort, BMI increased by 1.63 (standard error 0.13) kg/m2 (p = 3.7 × 10−34). The odds ratios for obesity (BMI ≥ 30 kg/m2) and overweight (BMI 25–29.9 kg/m2) compared to the reference group (BMI < 25 kg/m2) were 2.8 (95% CI 2.3–3.5; p = 1.6 × 10−25) and 1.9 (95% CI 1.6–2.2; p = 2.5 × 10−18), respectively, for each SD increase in methylation measure in age- and sex-adjusted models.
We examined the association of DNA methylation at the 83 replicated BMI-related CpGs with gene expression among 2,246 FHS participants, in order to determine which genes in blood may be influenced by differential methylation of the BMI EWAS CpGs. Of the 83 replicated CpGs, annotated gene expression from whole blood was available for 62 CpG–gene expression pairs (three transcript results were unavailable on the microarray, and 18 CpGs were intergenic). There were significant associations (p-value < 8 × 10−4; 0.05/62) between differential DNA methylation and gene expression in whole blood for 19 CpG–gene expression pairs, representing ten unique gene transcripts (ABCG1, CPT1A, SREBF1, LGALS3BP, DHCR24, PHGDH, SARS, NOD2, CACNA2D3, and SLC1A5), with almost all of the CpG–gene expression pairs (18/19; 95%) demonstrating an inverse association of methylation with expression (S7 Table). There were significant three-way associations (CpG versus BMI; CpG versus gene expression; gene expression versus BMI) for 11 CpGs with seven unique annotated genes (Table 4). Five of the seven genes (71%) with significant three-way associations between CpG–gene expression–BMI are known to exhibit cardiometabolic phenotypes in murine gene knockout models [37–44].
Successive genetic IV analyses were conducted to infer causal relations between differential methylation, gene expression, and BMI, followed by evaluation of the modeled epigenetic changes on adiposity-related traits using GWAS results (Table 1).
In this analysis of the association of BMI with differential methylation of blood-derived DNA, we provide robust evidence of a connection between replicable epigenetic signaling at 83 CpGs and BMI. We also demonstrate the correlation of BMI-related differential methylation with the altered expression of ten genes in whole blood that are overrepresented in lipid metabolism pathways. Among the 83 replicated BMI-related CpGs, one differentially methylated locus (cg11024682) at the lipid metabolism transcription factor SREBF1 demonstrated evidence of a causal effect on BMI; genetically predicted exposure to differential methylation and expression of SREBF1 was found to be associated with BMI and other adiposity traits, glycemic traits, dyslipidemia, and coronary artery disease. In contrast, we found that a substantial proportion (16 out of 83 [19%]) of the BMI-related differentially methylated CpGs identified in this EWAS are likely a consequence of BMI (i.e., downstream signals).
A substantial proportion (~18%) of interindividual variation in BMI is captured by the replicated differentially methylated CpGs in blood. The magnitude of BMI difference (~12 kg/m2 between the highest and lowest deciles) equates to substantial health risks; for example, each 5-kg/m2 increase in BMI in the general population is associated with a 30% increase in mortality [62].
Our results suggest that epigenetic biomarkers hold the potential to improve risk prediction and help tailor therapy choices to prevent or treat cardiometabolic diseases. For example, at the population level, BMI is an effective measure of average future cardiometabolic disease risk [63], but it is insufficiently predictive at the individual level. Regardless of causality, blood-based biomarkers can be useful for prognostic or diagnostic purposes. Further research is required to determine whether refining BMI-related risk by incorporating epigenetic biomarkers can improve risk prediction and help guide treatment decisions.
Previous in silico methods of identifying putative epigenetically regulated obesity genes highlighted SOCS3 (suppressor of cytokine signaling 3) and RARA (retinoic acid receptor alpha) [84], both of which were identified in the FHS-LBC meta-analysis (p = 2.7 × 10−11 for cg27637521 in SOCS3 and p = 1.3 × 10−8 for cg13274938 in RARA). An association study of DNA methylation and BMI in 459 individuals from the Cardiogenics Consortium identified an association of methylation at three CpGs intronic to HIF3A (hypoxia inducible factor 3A) in blood and adipose cells with BMI [28]. We found modest associations of differential methylation and expression at the HIF3A locus with BMI in our study. However, the associations were stronger in younger individuals in the FHS, suggesting that the connection may be less apparent at older ages.
At a nominal causal p-value < 0.05, we found that many (16 [19%]) of the replicated CpGs are downstream of BMI. This is consistent with recent findings from longitudinal methylation data and bidirectional MR in the Avon Longitudinal Study of Parents and Children [85] that BMI-related HIF3A methylation is likely secondary to differences in BMI.
There is substantial overlap between the identified BMI-related CpGs and reported CpG–metabolite associations in blood from 1,814 participants in the KORA cohort (Kooperative Gesundheitsforschung in der Region Augsburg) [86] (S13 Table). Notably, ceramides and sphingolipids—known to have altered levels among obese individuals and implicated in the development of the metabolic syndrome [87–89]—were identified. In addition, the BMI-related differentially methylated CpG (cg03725309) at the SARS locus, as discussed above in the serine metabolism section, was found to be associated with blood levels of serine.
Of note, none of the CpGs associated with BMI was near genes previously identified in GWASs of obesity-related traits, such as FTO (fat mass and obesity associated) or MC4R (melanocortin 4 receptor). We hypothesize that many of the replicated differentially methylated loci reflect novel pathways involved in the regulation of adiposity or adiposity-related diseases. Long-range interactions of DNA methylation with known obesity-related loci, however, may exist [90]. Further work to understand the role of the novel loci in relation to adiposity is also required. In addition, combining information from DNA methylation with genetic markers identified from DNA sequence variation may allow for improvements in risk prediction previously not possible with sequence variants alone [91].
Many of the significant loci from the discovery phase (73 of 135) were replicated in African-Americans from the ARIC study [30]. Similarly, many of the BMI-related differentially methylated CpGs identified in this study were also reported in relation to BMI in people of Arabic ancestry [34]. In GWASs, failure to replicate across racial/ethnic groups may be due to differences in allele frequencies and linkage disequilibrium patterns. In contrast, the high rate of replication of DNA methylation results for BMI in individuals of European and African and other ancestries suggests that shared environmental exposures or changes secondary to differences in BMI, and not genetic variation, may underlie many of the associations. Further work is needed to identify environmental factors that promote or mitigate disease-relevant obesity-related epigenetic dysregulation. Our analyses that conditioned on top meQTLs showed minimal attenuation, suggesting that the association between differential methylation and BMI is largely independent of genetic variants near the reported CpGs.
Our study has several limitations. Results from MR analyses utilizing genetically predicted methylation and expression levels do not prove causation but provide supportive evidence. The results of the MR analyses are based on numerous assumptions, for example, that there are not alternative pathways through which the SNP IV may act on BMI (i.e., pleiotropic effects). The MR assumptions cannot be tested directly and may bias the results. The forward MR results did not reach Bonferroni-adjusted significance thresholds for multiple testing; however, validation of the nominally significant results in the larger GIANT consortium supports our findings. We avoided the use of multi-SNP score IVs as we had already identified adequate single SNP meQTLs and using multi-SNP score IVs would have further risked introducing bias due to pleiotropy. The meQTLs for the MR analyses were derived in the FHS and the outcome was tested within the same cohort, which can potentially result in bias toward significance. The MR analyses, using the blood meQTL IV, suggest an inverse relationship between the predicted methylation of the SREBF1 locus and BMI, the reverse of the observed relationship, which can be interpreted as a null result. This finding is potentially explained by different directions of effect of QTLs in alternate tissues, which was supported by examining the association of genetic variants in blood versus other metabolically active tissues in the GTEx Project resource. Unfortunately, there are limited datasets of meQTLs in various tissues to explore this further. The observation of associations of BMI with methylation at the same CpG in different directions of effect in blood versus adipose-derived DNA has been previously reported at BMI-related CpG sites [30]. For SREBF1, we presume that the metabolic consequences of altered methylation and the effect on BMI occur in tissues other than blood, such as the adrenal gland, with the methylation changes in blood that we were able to detect representing a biomarker of trans-tissue differential methylation [92]. In addition, it is possible that positive and negative feedback loops can result in regulation of the same gene to be both a causal and a downstream effect of adiposity. We would not be able to discern this scenario from the observational cross-sectional data in this study.
An alternate methylation assay would be required for clinical purposes as the current microarrays are unsuitable in a clinical setting. Future research would be required for technical validation for clinical purposes. Our study supports blood cells as a useful accessible tissue for epigenetic biomarker discovery in large population studies. However, our study would not be able to detect tissue-specific methylation changes occurring in non-blood cell lines (e.g., neuron-specific epigenetic modifications in relation to BMI). Many of our top CpGs replicated in the GOLDN study, which assessed DNA methylation in a single blood cell type (CD4+), suggesting that the associations we detected are not likely to be due to confounding by blood cell heterogeneity. Many of the genes associated with BMI-related differential methylation were known to have a role in adiposity and cardiometabolic traits from murine knockout models; however, the universe of knockout models is likely enriched for the study of adiposity and cardiometabolic traits, and we could not directly test whether our results identified more than expected. Our study was conducted among older-age adults, and the findings may not be generalizable to younger ages.
We provide the results of a large EWAS of BMI in almost 8,000 individuals that identified 83 replicable DNA methylation loci and evidence of complementary transcriptomic differences that were enriched for gene products involved in lipid metabolism. The genetic IV analyses prioritize the SREBF1 locus for future functional studies to further define the causal relation with adiposity, insulin resistance, obesity-related dyslipidemia, and coronary artery disease. Our findings provide a foundation for further research to determine if individualized epigenetic profiles can be used to guide clinical decision making and improve health outcomes. Our findings may have additional clinical and therapeutic relevance if other loci that are differentially methylated in relation to BMI represent attractive targets for the treatment or prevention of obesity and adiposity-related diseases.
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10.1371/journal.pgen.1001261 | Self-Mating in the Definitive Host Potentiates Clonal Outbreaks of the Apicomplexan Parasites Sarcocystis neurona and Toxoplasma gondii | Tissue-encysting coccidia, including Toxoplasma gondii and Sarcocystis neurona, are heterogamous parasites with sexual and asexual life stages in definitive and intermediate hosts, respectively. During its sexual life stage, T. gondii reproduces either by genetic out-crossing or via clonal amplification of a single strain through self-mating. Out-crossing has been experimentally verified as a potent mechanism capable of producing offspring possessing a range of adaptive and virulence potentials. In contrast, selfing and other life history traits, such as asexual expansion of tissue-cysts by oral transmission among intermediate hosts, have been proposed to explain the genetic basis for the clonal population structure of T. gondii. In this study, we investigated the contributing roles self-mating and sexual recombination play in nature to maintain clonal population structures and produce or expand parasite clones capable of causing disease epidemics for two tissue encysting parasites. We applied high-resolution genotyping against strains isolated from a T. gondii waterborne outbreak that caused symptomatic disease in 155 immune-competent people in Brazil and a S. neurona outbreak that resulted in a mass mortality event in Southern sea otters. In both cases, a single, genetically distinct clone was found infecting outbreak-exposed individuals. Furthermore, the T. gondii outbreak clone was one of several apparently recombinant progeny recovered from the local environment. Since oocysts or sporocysts were the infectious form implicated in each outbreak, the expansion of the epidemic clone can be explained by self-mating. The results also show that out-crossing preceded selfing to produce the virulent T. gondii clone. For the tissue encysting coccidia, self-mating exists as a key adaptation potentiating the epidemic expansion and transmission of newly emerged parasite clones that can profoundly shape parasite population genetic structures or cause devastating disease outbreaks.
| The parasites Toxoplasma gondii and Sarcocystis neurona have lifecycles that include a sexual stage in a definitive host and an asexual stage in intermediate hosts. For T. gondii, laboratory studies have demonstrated that the sexual stage can serve the dual purpose of producing new, virulent genotypes through recombination and promoting expansion of single clones via self-mating. Self-mating and other life history traits of T. gondii, including transmission of asexual stages among intermediate hosts, are assumed to account for the clonal population genetic structure of this organism. However, the relative contributions of sexual recombination and self-mating verses other life history traits in causing disease outbreaks or in shaping Toxoplasma's population genetic structure have not been verified in nature, nor have these traits been extensively examined in related parasites. To address this knowledge gap, we conducted population genetic analyses on T. gondii and S. neurona strains isolated from naturally occurring outbreaks affecting humans and sea otters, respectively. Our results identify self-mating as a key trait potentiating disease outbreaks through the rapid amplification of a single clone into millions of infectious units. Selfing is likely a key adaptation for enhancing transmission of recently emerged, recombinant clones and reshaping population genetic structures among the tissue-cyst coccidia.
| Population genetic studies of pathogenic microbes have been paramount to our understanding of disease resulting from emerging and re-emerging infectious organisms [1]. Studies performed to determine the relative contributions of drift and recombination in the production of genetic diversity have identified that most pathogens have methods to alter, exchange and acquire genetic material that are intimately associated with pathogenicity [1], [2]. For viral pathogens, enhanced levels of drift, genomic reassortment [3], and incorporation of host genes [4] have all been linked to emergence of virulence. Likewise, horizontal gene transfer between bacterial species has facilitated assimilation of pathogenicity islands, plasmids, prophages, and other insertional elements essential for disease and drug resistance phenotypes [5]–[9]. For eukaryotic pathogens, meiotic sex serves an analogous purpose functioning to alter the genetic make-up, and therefore the biologic and virulence potential of strains [10]–[17]. A general paradigm describing disease epidemics for many pathogens is that genetic diversification, complemented by the acquisition of traits that enhance relative fitness and facilitate clonal expansion, leads to the emergence of novel, virulent genotypes. Just as the life history traits for generating genetic diversity vary widely among pathogen types, it is often the case that the mechanistic basis for subsequent clonal expansion of pathogenic strains is unique on a taxonomic level. Determining the mechanisms and contribution of these life history traits to disease is important for focusing prevention and treatment strategies to the most relevant pathogen strains and life cycle stages.
For the cyst-forming coccidia, which comprise a diverse group of parasites belonging to the phylum Apicomplexa, complex lifecycles that include both sexual and asexual stages have led to unusual population genetic structures for several species. For the widespread zoonotic pathogen, Toxoplasma gondii, the majority of strains infecting birds and mammals throughout North America and Europe are comprised of just three clonal lineages which exist as successful clones from a genetic out-cross [14], [18]. These three lineages have apparently emerged only recently due to an enhanced fitness that facilitated their ability to effectively outcompete other genotypes [13], [19]–[21]. Likewise, the veterinary pathogen Sarcocystis neurona possesses a surprisingly simple population genetic structure punctuated by the dominance of a few clonal lines in North America [22]–[25]. Similar clonal structures have been reported for other parasitic protozoa that possess sexual cycles [26] but identifying the precise genetic mechanisms that have led to the emergence of distinct clones among the different species in nature remains enigmatic.
In combination with population genetic data, the contributions of sexual out-crossing and clonal expansion as factors governing the emergence and eventual dominance of distinct, disease-producing clones have largely been inferred from laboratory studies of T. gondii among the cyst forming coccidia. Prior experiments demonstrated that a sexual cross between mouse-avirulent strains can produce genotypes representing a range of virulence in the mouse model, including some progeny several logs more virulent than the parents [14]. This study identified that natural out-crosses likely produce at least some virulent genotypes, which may subsequently have potential to emerge through clonal amplification to cause extensive disease [20], [21]. Clonal propagation is possible since T. gondii can effectively bypass the sexual stage in felid definitive hosts and cycle, presumably indefinitely, among intermediate hosts. This can occur horizontally via oral transmission through carnivory among intermediate hosts [19], [20] or vertically by transplacental transmission [27]–[30]. Toxoplasma gondii can also functionally bypass genetic diversification during the sexual stage by self-mating in the definitive host. Self-mating (also termed selfing, uni-parental mating, or self-fertilization) occurs when a single parasite clone can give rise to both male and female gametes capable of undergoing fertilization and producing viable offspring [31], [32]. In other words, no predetermined mating types are apparent and the end result is effectively clonal expansion via sex and meiosis.
Despite these important laboratory studies, the implications of these life-history traits and their relative effects on population genetic structures, especially in the context of virulence and disease outbreaks, have not been extensively studied in T. gondii or other cyst forming coccidia in a natural setting. Parasite life stages that are most important for causing mass-morbidity and mortality may be revealed through review of past, large-scale T. gondii-associated human outbreaks. For eleven reports of T. gondii-associated disease outbreaks in immune-competent people, eight events, including the four most devastating that caused disease or death in hundreds of individuals, were attributed to the oocyst form of the parasite, which is only produced during the sexual life cycle stage in the definitive feline host [20]. Furthermore, an outbreak of the related veterinary pathogen Sarcocystis neurona that resulted in the death of nearly 1.5% of the threatened Southern sea otter population over the course of a single month is thought to have resulted from exposure to infectious sporocysts originating in the definitive opossum host [33]. Circumstantial evidence, such as a complete lack [34] or much reduced [35]–[37] prevalence of T. gondii in certain island environments without cats, also gives weight to the importance of the definitive host stage in the parasite life cycle. Similarly, S. neurona has not been identified outside of its definitive host range in the Americas. The apparently profound importance of this stage in the lifecycle of not just T. gondii, but other related parasites, warrants further study to determine the influence it could impart to shaping parasite population genetic structures and which genetic mechanisms inherent to this life stage (i.e. selfing or out-crossing) are more likely to precede a disease outbreak in nature.
To determine the genetic basis governing the exposure, evolution, and emergence of virulent genotypes during natural outbreaks linked to sexual stages of these parasitic protozoa, we tested whether epidemic isolates exist as: 1. a diverse array of multiple, novel genotypes that are the products of an out-crossing event in the definitive host, or 2. epidemic clones of a single genotype derived via selfing in the definitive host. To distinguish between these two possibilities, high resolution genetic typing was used to characterize parasite strains associated with a T. gondii outbreak in humans [38] and a S. neurona outbreak in sea otters [33], both of which were associated with unusually high levels of morbidity and mortality. The population level genetic studies presented here argue that selfing in the definitive host plays a central role in the epidemic expansion of newly emerged, recombinant parasite strains, thus potentiating clonal outbreaks caused by tissue cyst-forming coccidia.
A microsatellite-based typing scheme using the markers B17, B18, TgMa, TUB2, W35 [39], and M95 [40] was applied to determine the molecular genotypes of T. gondii isolates associated with a human water-borne outbreak in Brazil. This outbreak, which occurred over a short time span in 2001, was linked to oocyst-contamination of a municipal water supply in the town of Santa Isabel do Ivai and resulted in infection and symptomatic disease in hundreds of people [38]. Initial genetic typing analyses performed on two T. gondii strains isolated from the water cistern implicated as the source of the outbreak [38], as well as isolates from chickens [41] and cats [42] from the immediate environment were limited to PCR-RFLP at a single locus, SAG2, leading to the conclusion that the outbreak strain was a canonical Type I strain (see below). Later, more extensive analysis by PCR-RFLP [43] and DNA sequencing on a limited set of markers [44] showed that the outbreak-associated strains from the water cistern were clonal and non-archetypal. The majority of people who seroconverted during the outbreak also possessed a serologic profile consistent with infection by the outbreak clone, and the outbreak genotype appeared to be highly prevalent in the surrounding environment immediately following the outbreak event, infecting 4/11 chickens (TgBrCk98, TgBrCk101–103) and 1 cat (TgCatBr85) [44] (Figure 1).
To determine the extent of genetic relatedness among the outbreak-associated strains, high resolution MS typing and DNA sequencing using markers distributed on 11 of the 14 chromosomes was applied. This dataset distinguished the two water cistern, outbreak-associated strains at the genetic level from all others present in the environment, except for one chicken isolate (TgCkBr103) (Figure 1). Unfortunately, insufficient DNA remained from the cat isolate, TgCatBr85, which precluded testing whether it was genetically identical to the cistern isolates.
Utilizing the MS typing scheme confirmed the conclusion that the causal agent was a unique, emergent T. gondii strain with a potential for enhanced virulence. The additional typing provided in the current study refined the conclusions of previous studies in two key aspects. First, the much higher level of resolution provided by the markers used and the sequence level analysis imparts a higher level of confidence to the conclusion that the outbreak was in fact clonal. The possibility that the outbreak-associated clones are not genetically identical in lieu of additional typing cannot be excluded, but several facts strongly argue against this: 1. The 18 markers were distributed across all but three of the 14 chromosomes; 2. MS markers are prone to rapid evolution and therefore provide high resolution; 3. Strains from Brazil are genetically divergent from archetypal lines, as evidenced by the segregation of alleles amongst strains in Figure 1, and hence, less prone to linkage disequilibrium effects. Furthermore, only a single, oocyst-derived clonotype was isolated from independent filters collected from two different water-holding tanks providing additional evidence that these isolates resulted from self-mating rather than a genetic out-cross.
Second, this study refines previous work on the Santa Isabel outbreak by showing that the outbreak strain was actually rare in the surrounding environment, opposed to the high prevalence reported previously [44]. Moreover, close examination of the environmental isolates reveals that many of them, including those previously identified as the outbreak clone, and the outbreak clone itself, resemble recombinant progeny; only two allelic types are present that segregate independently across the loci examined (see TgCkBr98, 99, 100, 101, 102, 103, TgCatBr85 and Outbreak 1 and 2 in Figure 1). These data argue that prior to the outbreak, the epidemic clone was produced by a genetic out-cross and was subsequently expanded by self-mating. This confirms that the more extensive resolution provided by the current study was necessary to truly distinguish an epidemic clone in a region known to contain a diverse array of T. gondii genotypes, including many that are apparently siblings of this strain [44]. This result also speaks to the important role selfing in the definitive host can play; allowing a single, emergent genotype of low environmental prevalence to rapidly rise to dominance in the surrounding population by infecting several hundreds of hosts over a short time span.
Collectively these data support high-resolution genotyping schemes as important tools for detecting informative genetic signatures in this parasite species. Initial population genetic studies showed that T. gondii strain diversity was comprised of three main clonal groups: Type I, II, and III [18]. As a result of these early studies, many broader population genetic studies have since relied on typing at only one or just a few loci to classify strains as type I, II, or III. However, it is now apparent that strains from diverse geographic locales and host species are more often infected with strains bearing unique alleles or allelic combinations, so relying on a few markers is insufficient for robust conclusions [20]. The first quantitative analysis testing the accuracy of single locus typing found a very low predictive value for the loci analyzed to correctly identify strain genotype [45]. Indeed, results presented in the current study, when compared with results from more limited genetic studies of the same strains conducted previously [38], [41]–[44], provide a clear illustration of the value more extensive genetic typing can have in refining conclusions. This is especially relevant in outbreak investigations where variations in parasite genotype can be highly informative for explaining disease manifestation. High-resolution genetic typing appears to be critical for eliminating preconceived biases in epidemiologic investigations to ensure accurate discernment of disease-associated T. gondii strains and to recognize clonal outbreaks.
These results validate the utility of testing for epidemic clones from prospective and retrospective studies of T. gondii disease outbreaks [20]. In support of this, Dumar and colleagues applied a similar typing scheme to a T. gondii outbreak in Suriname and discovered that all five patients from whom they isolated parasites were infected with the same, previously undiscovered genotype [46]. Importantly, the outbreak in Suriname was another waterborne outbreak attributable to human exposure by infectious oocysts, further evidencing selfing in the definitive host as a key mechanism for allowing clonal expansion of virulent genotypes, ultimately resulting in disease epidemics.
Since parasite genetic material from past T. gondii outbreaks in humans is in limited supply for the majority of cases, we sought to further assess the role of self-mating in disease outbreaks by examining an epizootic of the related veterinary pathogen, Sarcocystis neurona, infecting the Southern sea otter (Enhydra lutris nereis) of California. As a threatened species, the Southern sea otter population is well monitored and accounted for by conservation groups, creating a unique opportunity to investigate infectious disease in a natural setting. Sea otters are also aberrant hosts for many terrestrial pathogens that can be washed to sea and their high susceptibility to many of these pathogens allows them to serve as a sentinel species for pathogens circulating in the adjacent terrestrial environment [44]. During April, 2004, the highest monthly mortality rate ever recorded in nearly 30 years of data collection occurred among Southern sea otters [33]. Over the course of approximately one month, at least 40 sea otters stranded dead or dying along an 18 kilometer stretch of coast within the 500–600 kilometer Southern sea otter range. Sixteen otters were in sufficient condition to allow for complete post-mortem analysis inclusive of PCR assessment and microscopic examination of tissues. Among these otters, the major cause of death for 15 of the 16 examined animals was S. neurona-associated brain and/or systemic disease [33].
Preliminary genetic analysis using only four polymorphic markers against parasite strains infecting a subset of these otters (n = 7) suggested they were genetically homogenous [25]. However, the limited polymorphism present in the markers used, and lack of information about the population genetic structure of S. neurona in California prevented a confident conclusion that they represented an epidemic clone. The present study developed and applied a battery of higher resolution, polymorphic microsatellite and gene-coding markers to type S. neurona strains. Additional samples were included, encompassing 12 S. neurona strains from otters that died during the outbreak, as well as additional strains from other geographic locations and/or time periods. The high number of sea otter deaths associated with this epizootic provided a unique opportunity to test whether self-mating, as identified in the human T. gondii outbreaks, could explain the genetic origin for the S. neurona strains that caused the outbreak. In addition, genetic data from the current study was combined with S. neurona typing data reported by Rejmanek et al. [23] to determine the population genetic structure of S. neurona in California spanning 15 years of study.
Sequence-level analysis of five surface antigen (Ag) genes (SnSAG1, 3, 4, 5, and 6) [25] and nine microsatellite (MS) markers (Sn2–Sn5, Sn7–Sn11) [23], [25] identified 12 Ag types and 33 MS types among 87 S. neurona-infected samples based on the allele combinations detected at each locus (Table 1; See Table S1 for complete strain and typing information). Seventy-four of the 87 samples were from mammals in California; other states represented include Georgia (n = 2), Illinois (n = 1), Missouri (n = 3), Washington (n = 5), and Wisconsin (n = 2). Combining Ag and MS alleles could distinguish 35 total genotypes, but for this study these typing schemes were analyzed independently because of the likelihood that these parts of the genome are under different selection pressures and subject to differing evolutionary processes [2]. The majority (56/87) of S. neurona strains were classified as either Ag type I or Ag type II (Figure 2A). Certain MS types were also over represented in the sample set, with MS types ‘a’, ‘c’, and ‘g’ accounting for 47/87 samples (Figure 2B). Importantly, 11/12 S. neurona strains from sea otters stranding during the mortality event in 2004 were an exact genetic clone at each marker analyzed (Ag type I, MS type ‘c’). The remaining outbreak sample (Ag type I, MS type ‘d’) differed from the other outbreak strains by only a single stepwise mutation at MS marker Sn4 (Table S1).
Since this and all previous studies of S. neurona have found a high level of sequence homology among strains [22]–[25], [47], we chose to analyze strain relatedness with the eBURST algorithm [48], [49]. This program helps eliminate confounding effects that low sequence diversity and moderate levels of recombination can have on other methods of intra-specific sequence analysis, such as clustering, dendrograms, and phylogenetic trees, as demonstrated in [22]–[24], by only focusing on single clones and their most recent descendents [48]–[50]. We adapted the MS data for the nine markers that permit simultaneous comparison of all strains (Sn2–Sn5, Sn7–Sn11) to serve as a multi-locus typing scheme. This typing scheme, which is based on the number of repeats at each locus, was amenable to use with this program. Using the default settings, which group isolates based on the premise that they are single locus variants (SLVs), or share 8 out of 9 alleles, we identified 8 clonal complexes (CC1–8), only 3 of which contained more than two genotypes, and 8 singletons (genotypes differing by 2 or more alleles from all others) (Table 1; Figure 3). Intriguingly, just two clonal complexes, CC1 and CC2, accounted for almost 64% (56/87) of the strains analyzed in this study (Figure 2C). This result held true even when correcting for bias introduced by the outbreak event by removing these samples from the data set, as 44/75 samples (59%) still belonged to CC1 or CC2.
All SLVs identified in this study differed by a single stepwise (i.e. a single di-nucleotide repeat) mutation, which supports the assumption that the eBURST groupings represent clonal complexes in which allelic variation is a result of mutation/drift and not recombination (Table S1) [50]. The only exceptions to this were SLVs ‘l’ and ‘o’, members of CC3, that differed by 3 di-nucleotide repeats at MS Sn11. These isolates were from a sea otter in California and a horse from Missouri so the greater number of stepwise mutations detected may be a result of extended geographic isolation, thus allowing time for more drift to occur (Table S1). A single mutation event that resulted in multiple stepwise mutations is also plausible.
Since recombination appeared to be rare between clonal complexes based on MS markers, we decided to overlay the results of the Ag typing analysis on the eBURST output (Figure 3). The results were consistent with previous claims of an intermediate population structure for S. neurona [22]–[25], [47] in that both clonal propagation and sexual recombination were supported. All members of CC1 and 29/30 members of CC2 possessed an identical Ag type (Ag types I and II, respectively). In contrast, all MS types in CC3 and CC8 possessed a distinct Ag type. There were also two cases (MS types ‘x’ and ‘bb’) where the same MS type was identified with two distinct Ag types (Ag types VII and VIII) and the reverse scenario also occurred where the same Ag type (VI) characterized three clonal complexes based on MS types (CC4, CC5, CC6), all of which could potentially indicate recombination events (Figure 3).
Overall, these data support a population structure that is highly clonal, though evidence for recombination is present as well. This intermediate population structure is similar to that described for T. gondii, though definitive conclusions will require a sample set less biased towards diseased animals [2]. It is worth noting here that the population structure of the organisms described in this study is, like all population genetic structures, only as resolved as the markers allow. For example, finer resolution can be achieved by applying the marker SnD2 from Rejmanek et al. [23] to SO4711, SO4786 and O7 to show that they are different strains. What this does not change, though, is that these strains are members of the same clonal complex and that resolution at this level is sufficient to identify an outbreak clone and to document geographic partitioning of strains along the California coastline (see below). This level of resolution is more robust to the possibility of strand slippage and evolution of new alleles during PCR that could make identical clones appear distinct with finer levels of resolution. An example of this may have occurred with SO4387, identified in this study as MS type ‘g,’ but by Rejmanek et al. [23] as MS type ‘i.’ These types differ by a single repeat at MS Sn9 (Table S1). It is also possible that this otter was co-infected with two closely related strains. Consistent identification of SLVs in many samples increases the confidence that they represent truly different strains. The outstanding potential these microsatellite markers have for more robust strain resolution, if interpreted cautiously, can facilitate addressing more specific questions, such as the identity and point source of an epidemic clone.
The majority of strains (72/87; 83%) evaluated in this study were collected from two distinct 200 km stretches along the California coast or the adjacent terrestrial environment (Table S1; Figure 4). As such, we utilized this subset of the data to examine the temporal stability of strains and their geographic and host distribution in central California.
The total time period covered by the strains analyzed in this study is 15 years (1994–2009). Sample sizes were not evenly distributed across each year and some years (1996–1998) had no representative samples, so it is likely that genotype life spans are underestimated. Despite this, at least one clonal complex, CC2, appears to be very stable in nature over time, exhibiting a lifespan encompassing the entire length of this study. CC2 was sampled during 12 of the 13 years for which a sample was collected (Table S2). Within this complex, Ag type II, MS type ‘g’ had a lifespan of the full time period examined (15 years) and was the longest lived of any Ag or MS type (Figure 5; Table S2). The other clonal complexes present in California, CC2, CC3, CC6–CC8, appeared to be stable as well, with life spans ranging from 5–8 years (Table S2). Collectively these data provide supporting evidence for S. neurona's ability to propagate clonally. However, it will be important to test whether or not these allelic combinations appear more often than would be expected by chance to confirm clonal propagation as more sequencing data becomes available from strains collected from non-diseased animals and the position of the markers in the genome is identified [51]. Interestingly, the genotype associated with the outbreak, Ag type I, MS type ‘c’, was only found during 2004 (Figure 5). These samples were all associated with otters dying during the epizootic in April, 2004, except for two samples that were obtained from sick otters in the same area four months after the event ended (Table S1). The implications these observations may have for strain virulence are discussed below.
On visual inspection, it appeared that the genetic composition of S. neurona strains from the Monterey Bay area was distinct from the southern strains obtained in or near Morro Bay (Figure 4; Table 1). We further tested this hypothesis by conducting χ2 analysis on the proportion of the majority clonal complexes (CC1 and CC2) that comprised each population. There was a highly significant difference between northern and southern strains (Figure 6). Significance remained when analysis was restricted to sea otter samples, in order to eliminate any confounding effects due to host species, because all southern strains were from sea otters (Figure 6). This conclusion is consistent with data reported previously on S. neurona strains from coastal California [24], [25], but contrasts with the conclusions of Rejmanek et al. [23].
We also sought to identify a potential terrestrial source for S. neurona strains present in the marine environment. Experimental evidence for the model organism, T. gondii, supports a route of infection for sea otters through ingestion of S. neurona sporocysts that were washed to the ocean in contaminated fresh water and then concentrated in the otters' filter-feeding invertebrate prey [52]–[54]. Implicating opossums as the ultimate terrestrial source of infection is supported by comparing the prevalence of the majority clonal complexes (CC1 and CC2) in sea otters and opossums in the northern, Monterey Bay area study site (the only locale from which opossum samples were obtained). Strain prevalence differences between these groups were not statistically different, suggesting that monitoring strain types in coastal dwelling opossums will be predictive of genotypes infecting adjacent marine dwelling otters (Figure 6). Observational data from the outbreak noting an abundance of razor clams and evidence of sea otter movement into the area for feeding (i.e. accumulation of broken shells on the shore) just prior to the event, further support this model of land-to-sea parasite transfer [33]. Sea otters very rarely consume known intermediate hosts of S. neurona [55], leaving the ingestion of sporocysts as the most biologically plausible route for sea otter infection regardless of the land-to-sea transport mechanism, and strongly supporting the conclusion that this outbreak originated from a selfing event in the opossum host.
Disease is a complex manifestation of the interplay between intrinsic pathogen factors (i.e. pathogen genotype) and numerous external factors, including dose, host immune status, and environmental conditions such as weather that can influence transmission. Delineating the relative contribution of each of these factors to a given disease outbreak is a difficult process, as is illustrated by the outbreaks described in this study. It is plausible that the S. neurona strain associated with the 2004 epizootic is intrinsically more virulent than other strains since it was only identified during the time period surrounding the outbreak and may have been too virulent for continued propagation. Also, the majority of otters infected died within 24–48 hours of stranding and had high IgM titers [33]. The rapid rise and subsequent fall of a virulent strain type is a phenomenon noted in many outbreaks of a diverse array of pathogens from viruses (e.g. Influenza virus [3]) to bacteria (e.g. Leptospira interrogans [56]) to fungi (e.g. Coccidioides immitis [57]). However, this phenomenon may also be attributable to sampling biases [2] or environmental factors [57] making the assumption that the virulent genotype is not adaptive inaccurate. Equally in the case of the sea otter outbreak, numerous external factors, including concurrent infection with other pathogens and domoic acid poisoning, abundant food source with potential for contamination with sporocysts, and a large rainstorm preceding the event that could have increased sporocyst deposition, may have played a contributing role in conferring this S. neurona strain with a virulent phenotype [33].
Similarly, the T. gondii strain implicated in the 2001 Brazil outbreak appeared to rise in prevalence during the outbreak but then decline over time in the local environment [44]. This was also a unique, newly identified genotype that caused symptomatic disease in 155 immune-competent individuals—an unusual phenomenon for this normally asymptomatic parasite. Importantly, though, ∼270 other individuals with access to the same water cistern seroconverted during this time with no overt signs of disease [44], invoking a role for environmental and host factors in this outbreak.
A striking character of both these outbreak events is the key role self-mating in the definitive host served as a catalyst allowing virulent pathogen genotypes to rapidly reach high levels under the right conditions to precipitate a disease epidemic.
Epidemic clonality associated with sporocyst or oocyst ingestion strongly suggests that self-mating in the definitive host was the key event leading to these outbreaks. Selfing in the definitive host has been confirmed experimentally for T. gondii [31], [32] but only indirectly assumed for S. neurona [58]. Prior to this study, rigorous genetic characterization of selfing events in nature were lacking and the question as to whether a productive sexual out-cross or a selfing event precedes an outbreak linked to oocysts or sporocysts had not previously been tested.
Early population genetic studies using limited, poorly resolved markers identified a paucity of mixed strain T. gondii or S. neurona infections in nature and these data have previously been interpreted to suggest that most definitive host infections would be by a single strain and therefore out-crossing would be rare in nature [59]. However, more recent studies using unbiased, multi-locus typing schemes have consistently identified mixed strain infections among natural intermediate hosts suggesting that prey species of definitive hosts are more frequently harboring mixed strain infections than previously envisaged [60]–[72]. Hence, the lack of mixed strain infections identified in earlier studies may simply reflect the techniques used, such as bioassay or limited genetic typing, that were biased toward certain strains and likely missed multiple infections and the true diversity of genotypes present.
As more high resolution, multilocus genetic markers are being applied against previously characterized strains of T. gondii, an increasing number are being re-classified as recombinants, defined as products of sexual out-crossing events, including strains previously linked to outbreaks [20]. Given the virulent nature of the two outbreaks examined here, and the evidence that out-crossing between two avirulent, haploid parents can produce progeny with enhanced virulence [14], we originally hypothesized that out-crossing might explain the genetic origin and expansion of the outbreak strains, rather than self-mating. Intriguingly, close examination of the environmental isolates surrounding the T. gondii outbreak supported this hypothesis because the epidemic clone was one of many progeny produced by a local genetic out-cross. However, the available evidence indicated that, while out-crossing certainly preceded the outbreak, it was the subsequent selfing event that was responsible for the epidemic expansion and transmission of the virulent clone that caused the outbreak. Certainly this dataset argues that sex and self-mating combined to produce the T. gondii clonal outbreak. Further typing of additional outbreaks is warranted to examine whether or not an out-cross is independently sufficient to cause an epidemic attributable to multiple, recombinant progeny.
This two-step process of local epidemic expansion via a sexual out-cross followed by clonal propagation of a few progeny with enhanced adaptations or virulence is reminiscent of the process envisioned on a larger scale for the pandemic rise of the archetypal T. gondii clones (Types I, II, and III), also found to be the progeny of an out-cross [13], [14]. Documenting this process in real time at a local level has provided key insight into mechanisms that account for clonal propagation in nature. It was previously proposed based on laboratory studies that clonal dominance of archetypal T. gondii strains was attributable to an enhanced ability for oral transmission through carnivory, a hypothesis which certainly warrants further investigation in natural settings [19]. However, recent studies have since shown that this trait does not operate as originally proposed [73], [74]. These findings raised the possibility that other life history traits may likewise be important in perpetuating clones.
In this light, it is worth noting that all aspects of the parasite lifecycle that promote clonal propagation, namely selfing, oral transmission through carnivory, and transplacental transmission, contribute in part to clonality in the population structure. However, when considering their relative roles, the advantage in fecundity the sexual stage can impart during a selfing event to a single parasite genotype, as documented in this study, provides strong evidence this mechanism is likely the major contributor to localized or regional clonal dominance of certain strains. The basic reproductive number (R0), or number of secondary infections a single infected individual will cause, is many orders of magnitude greater in the definitive host (which releases millions of environmentally stable, infectious propagules capable of waterborne or aerosolized transmission [75]) compared to an intermediate host (in which the infectious units produced can only be passed to those directly feeding on tissues). Oocysts or sporocysts can also successfully infect intermediate hosts at much lower doses (even a single oocyst) than tissue cysts [76], [77]. Oocyst deposition therefore exists as a potent mechanism for causing widespread epidemics and establishes a plausible rationale for explaining how selective sweeps can occur among these heterogamous pathogens. Determining what factors govern whether these sweeps occur on a local, and presumably more frequent, epidemic level or reach pandemic proportions are important subjects for future research.
Our results also confirm that fecal contamination of food and water sources represents a major threat to human and animal health, hence targeting the definitive host or the oocyst stage of these parasites is an excellent first-step strategy to disrupt transmission. This conclusion is further supported by studies showing the importance of the definitive host stage for maintaining continued transmission of this parasite in island communities [34]–[37] and how local vaccination of definitive feline hosts can significantly reduce T. gondii infection rates [78].
The scope of explanatory power for this selfing model can also be extended to other highly clonal, cyst forming parasites, including the clonal outbreak linked to S. neurona and likely other pathogenic Sarcocystis spp. and Neospora spp. This finding is significant since many aspects of the T. gondii life cycle have previously been proposed to be unique to this species among the tissue encysting coccidia, including its broad host range inclusive of nearly all warm-blooded vertebrates and its ability to be transmitted through carnivory among intermediate hosts [19]–[21,] (but also see: [79], [80]–[83]). Notably, selfing has also been demonstrated in more distantly related Apicomplexan parasites, including Eimeria spp. and Plasmodium spp. [31]. In addition, the processes of homothalism and same-sex mating identified in fungi serve the analogous purpose of clonal propagation via a mechanism more generally thought to serve in genetic recombination and out-crossing [84]. This suggests that selfing, as a genetic mechanism of clonal propagation, has potential to play a pivotal and previously under-recognized role for a diverse array of eukaryotic pathogens in the expansion of genotypes that cause disease epidemics and/or emerge as highly successful clonotypes to rapidly alter population genetic structures.
Work in California was conducted under United States Fish and Wildlife Service (USFWS) permit MA 491 672724-9 issued to United States Geological Survey Biological Resource Discipline (USGS492 BRD). Harbor seal carcasses were gathered and samples processed as part of Northwest Marine Mammal Stranding Network activities authorized under Marine Mammal Protection Act (MMPA) Stranding Agreements (SA), and Section 109(h) (16 U.S.C. 1379(h)). Additional specimens were acquired under MMPA Section 120, and the National Marine Fisheries Service (NMFS) MMPA Research Permit 782–1702.
Parasite DNA was obtained either from infected host tissues or parasite isolates maintained in tissue culture as described previously [25]. Samples were analyzed using a typing scheme that included the surface antigen markers: SnSAG1, SnSAG3, SnSAG4, SnSAG5, SnSAG6 [25] and 9 microsatellite markers Sn2–Sn5 and Sn7–Sn11 originally described by Asmundsson and Rosenthal [85] but applied as modified in Wendte et al. [25] and Rejmanek et al. [23]. Three additional microsatellite markers were designed by the following method: Publically available Sarcocystis neurona expressed sequence tags (ESTs) were downloaded from the NCBI dbEST database (http://www.ncbi.nlm.nih.gov/dbEST) and the S. neurona Gene Index (maintained by the Computational Biology and Functional Genomics Laboratory at the Dana Farber Cancer Institute, http://compbio.dfci.harvard.edu/tgi/) databases. The downloaded ESTs were assembled into contigs using the SeqMan (Lasergene) application. Contig sequences were then processed with the MISA microsatellite identification program (http://pgrc.ipk-gatersleben.de/misa/) with the following repeat parameters: definition (unit size-minimum repeats): 2-12, 3-7, 4-5, 5-4, 6-3, 7-3, 8-2, 9-2, 10-2, 11-2, 12-2, 13-2, 14-2, 15-2; interruptions (maximum difference between 2 simple sequence repeats): 25.
Approximately 50 microsatellites of sufficient length and/or complexity were identified. Three (Sn1520, Sn1863 and Sn515) of these markers were not previously published and possessed sufficient non-redundant flanking sequence to allow for nested primer design and produced robust size-polymorphic PCR amplification products. Primers were validated as described [25] and found to be specific and sensitive for S. neurona DNA in tissues (data not shown). The primers designed are as follows: Sn1520 Fext- GGGGCAGAACCATCGTAGTA, Rext- GTGAAGCATTTCCCCTACGA, Fint- GGCGGTAGTCACTTGCTGA, Rint- GTGGGAGAAGACGGTCGTTA; Sn1863 Fext- CATGGCGTGCGTTAACTAAA, Rext- CGTACAAACACACGCTCCAC, Fint- CCATTCATCGACAGCGACTA, Rint- TGAGACAGCCGTCAAACACT; Sn515 Fext- CTTCTAGCGGCTGTTTCTCC, Rext- TCTGTGTGGGTGTGGAAGTC, Fint- GACCCCCTCTCTGCTTCTCT, Rint- ACGCAAATGCGAACATATCA. Representative sequences for each allele at each locus were placed in GenBank under the following accession numbers: Sn1520: HM851251, HM851252, HM851253, HM851254, HM851255; Sn1863: HM851256, HM851257, HM851258, HM851259; Sn515: HM851249, HM851250. PCR, DNA sequencing and analysis were conducted as described previously, except, to control for bias in scoring results, random sample IDs were assigned to samples before sequencing so that sequence analysis for some loci was blinded [25].
For this study, S. neurona DNA from 15 sea otters and 4 harbor seals was analyzed. Additionally, samples from 21 sea otters, 2 harbor seals, 3 horses, and 2 raccoons previously described by Wendte et al. [25] at the SnSAG antigen loci and MS Sn9, were further typed in this study at the remaining 10 MS loci. Finally, S. neurona DNA from 21 sea otters, 1 porpoise, 4 horses, 13 opossums, and 1 cat that was previously typed by Rejmanek et al. [23] at SnSAG3, SnSAG4, and MS markers Sn2–Sn5 and Sn7–Sn11 were combined with the data in this study for a total sample set that included 87 samples from 57 sea otters, 6 harbor seals, 2 raccoons, 13 opossums, 7 horses, 1 porpoise, and 1 cat. In all, 75 of the 87 samples were from California. Other states represented include Georgia (n = 2 samples), Illinois (n = 1), Missouri (n = 3), Washington (n = 4), and Wisconsin (n = 2). Some overlap existed between the samples typed in this study and those reported by Rejmanek et al.: samples SO4387, SO4413, H1, H2, and H3 in this study are reported as SO1, SO2, Horse 1, Horse 2, and Horse 3 in Rejmanek et al. [23], respectively. Complete information about the sample origins is found in Table S1.
Toxoplasma gondii isolates from a water cistern (n = 2), chickens (n = 11), and one cat associated with a human waterborne toxoplasmosis outbreak [44], as well as laboratory strain CEP were typed at microsatellite loci B17, B18, TgMA, TUB2, W35 [39] and M95 [40]. Markers were PCR amplified and sequenced to assign alleles as for S. neurona markers [25]. Representatives of each microsatellite allele at each locus were placed in Genbank under accession numbers: B17: HM851260–67; TgMA: HM851268–73; W35: HM851274–77; M95: HM851278–81.
Because different parts of the genome are likely under different selective pressures, all S. neurona samples were categorized by an antigen (Ag) type designated by roman numerals and a microsatellite (MS) type indicated by a lowercase letter designation. Ag types were defined by the presence/absence of mutually exclusive antigen genes (SnSAG1, SnSAG5, or SnSAG6) and the inheritance pattern of alleles at SnSAG3 and SnSAG4 [23], [25]. MS types were assigned on the basis of allele combinations defined by the number of di- or tri- nucleotide repeats at each locus (Sn2–Sn5 and Sn7–Sn11, Sn1520, Sn1863). Sn515 was a complex repeat in which each isolate possessed one of two alleles. Samples from the study by Rejmanek et al. [23] were not typed at the SnSAG1-5-6 loci, but were placed into Ag groups based on the allelic profile at SnSAG3 and SnSAG4 and by the Ag group their MS type was associated with in samples typed at all markers. For example, based on the alleles at SnSAG3 and SnSAG4, sample SO4 (Table S1) could be placed either in Ag type II or V, but its MS type was only found associated with Ag type II in samples where all markers were typed, making this the most likely, though not definitive, Ag type designation. The S. neurona strains assessed by Rejmanek et al. [23] were also not typed at MS markers Sn1520, Sn1863, and Sn515. Presumptively classifying these samples into MS types based on alleles at Sn2–Sn5 and Sn7–Sn11 is likely accurate, though, since these three markers did not provide additional resolution to MS types for the 46 additional S. neurona strains described in this study.
The alleles present at MS markers Sn2–Sn5 and Sn7–Sn11 were used for creation of a multi-locus sequence typing scheme by which all isolates could be compared. The numerical designation of alleles allowed the detection of which MS types formed clonal complexes using the eBURST program [48]. Default settings were used which grouped MS types on the basis of sharing alleles at 8 of the 9 markers analyzed.
To assess T. gondii isolates for clonality, MS alleles were combined with previously published DNA sequence analysis at three genetic loci, PCR-RFLP or DNA sequencing at 10 loci, and serologic analysis as described by Vaudaux et al. [44].
Statistical analyses were performed using GraphPad Prism 5 and χ2 values were considered significant at P = 0.05.
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10.1371/journal.pntd.0007576 | ParaDB: A manually curated database containing genomic annotation for the human pathogenic fungi Paracoccidioides spp. | The genus Paracoccidioides consists of thermodymorphic fungi responsible for Paracoccidioidomycosis (PCM), a systemic mycosis that has been registered to affect ~10 million people in Latin America. Biogeographical data subdivided the genus Paracoccidioides in five divergent subgroups, which have been recently classified as different species. Genomic sequencing of five Paracoccidioides isolates, representing each of these subgroups/species provided an important framework for the development of post-genomic studies with these fungi. However, functional annotations of these genomes have not been submitted to manual curation and, as a result, ~60–90% of the Paracoccidioides protein-coding genes (depending on isolate/annotation) are currently described as responsible for hypothetical proteins, without any further functional/structural description.
The present work reviews the functional assignment of Paracoccidioides genes, reducing the number of hypothetical proteins to ~25–28%. These results were compiled in a relational database called ParaDB, dedicated to the main representatives of Paracoccidioides spp. ParaDB can be accessed through a friendly graphical interface, which offers search tools based on keywords or protein/DNA sequences. All data contained in ParaDB can be partially or completely downloaded through spreadsheet, multi-fasta and GFF3-formatted files, which can be subsequently used in a variety of downstream functional analyses. Moreover, the entire ParaDB environment has been configured in a Docker service, which has been submitted to the GitHub repository, ensuring long-term data availability to researchers. This service can be downloaded and used to perform fully functional local installations of the database in alternative computing ecosystems, allowing users to conduct their data mining and analyses in a personal and stable working environment.
These new annotations greatly reduce the number of genes identified solely as hypothetical proteins and are integrated into a dedicated database, providing resources to assist researchers in this field to conduct post-genomic studies with this group of human pathogenic fungi.
| The genus Paracoccidioides comprises fungi responsible for Paracoccidioidomycosis (PCM), a neglected tropical disease prevalent in South America that has been shown to affect approximately 10 million people and has great medical/social impact, since available treatments are poorly effective, frequently leading to relapses, chronic infections and sequelae. Genomic information available for five reference Paracoccidioides isolates could greatly assist researchers in developing new chemotherapeutic approaches against PCM, but usefulness of such data is limited, since ~60–90% of Paracoccidioides protein-coding genes (depending on isolate) are described as responsible for hypothetical proteins, without any functional/structural description. Such elevated number of hypothetical proteins is unexpected and probably derives from annotations performed solely by automated computing pipelines. This problem can be minimized by manual curation, when expert reviewers determine the functional designation of each gene, after comparing results derived from several reference databases. This work describes an effort to review the functional assignment of >40,000 genes, annotated across the five Paracoccidioides genomes mentioned above, which reduced the number of hypothetical proteins to ~25–28%, contributing to significantly increase quality and usefulness of such genomic information. These data have been compiled in a relational database named ParaDB, constituting an important resource for researchers in the field.
| The genus Paracoccidioides includes a series of thermodymorphic fungi responsible for causing a neglected tropical disease known as paracoccidioidomycosis (PCM), which represents one of the most prevalent systemic mycoses in Latin America [1]. In fact, approximately 10 million people have been estimated to be infected by these fungi, which are distributed in large areas of Brazil, Argentina, Colombia, Venezuela, Ecuador and Paraguay [1,2,3,4,5]. The genus Paracoccidioides was originally proposed in 1908, containing a single species, called P. brasiliensis [6]. Subsequent studies led to the characterization of several isolates, from different geographical regions, that display significant genetic variability, as well as differences in many biological characteristics, such as adaptability to laboratory culture, virulence and the ability to induce different host responses [7]. These isolates were initially distributed into five distinct subgroups (Pl, S1, PS2, PS3 and PS4) that have been unofficially considered cryptic P. brasiliensis species over the years [8,9]. In 2009, the P1 subgroup was classified as a new species, called P. lutzii, since its isolates display deeper genetic divergence, when compared with representatives of the other subgroups, which remained classified as members of the P. brasiliensis species complex [7,10]. More recently, the four subgroups within the P. brasiliensis complex have also been described as different species: P. brasiliensis (subgroup S1), P. americana (subgroup PS2), P. restrepiensis (subgroup PS3) and P. venezuelensis (subgroup PS4) [11].
Genomic studies involving Paracoccidioides spp. started to be developed in 2003, by large-scale sequencing/characterization of Expressed Sequence Tags (ESTs) obtained from the isolate Pb18, which is the main representative of P. brasiliensis (S1 subgroup) [12,13]. Subsequently, functional studies, based on the information derived from these EST analyses, demonstrated the potential of genomic approaches to increase our knowledge regarding the genetic bases that determine virulence in these fungi, as well as to provide information that may contribute to the development of new alternatives for the control and treatment of PCM [14,15,16]. These pioneering studies motivated the development of complete genome projects, which led to the characterization of draft genomes of three Paracoccidioides isolates: Pb01, Pb03 and Pb18 (representatives of P. lutzii, P. americana and P. brasiliensis, respectively) [17]. This work represented an important milestone to the genetic study of this group of fungi, providing clues that helped us to better understand the evolution of the genus Paracoccidioides, as well as a series of genomic characteristics that differentiate some of the abovementioned species/subgroups. However, the sequencing of these three isolates was performed using Sanger technology, generating assemblies with large contig numbers and presenting several regions with low quality consensus sequences, which led to the development of incomplete and inaccurate genomic annotations (v1) for these fungi [17]. Later on, these same isolates were submitted to a new sequencing, using Illumina's NGS platform, in order to produce more complete and precise assemblies [18]. Moreover, a re-annotation analysis performed with such assemblies allowed recovery of a large number of genes that were missed by the original annotation (v1), and this second annotation (v2) was more consistent across the three reference genomes (Pb18, Pb03, and Pb01). Finally, these analyses were extended to contemplate the genomes of additional isolates, representing P. restrepiensis (isolate PbCnh) and P. venezuelensis (isolate Pb300), providing reference genomes and annotations for isolates representing all five species/subgroups of the genus Paracoccidioides [19].
Currently, genomic data from these five Paracoccidioides isolates can be obtained from several generic databases, such as GenBank [20] and Ensembl [21], as well as from some fungal specific databases, such as MycoCosm [22] or FungiDB [23], but the genomic annotations provided through all these repositories are inconsistent and display an unusually large number of protein-coding genes described as responsible for hypothetical proteins. For example, GenBank and RefSeq describe ~62% of all Pb01 genes in association with hypothetical proteins and this proportion is even larger (up to 88%) in the genomes of Pb18, Pb03, Pb300 and PbCnh. A similar situation is observed in other databases, such as Ensembl and FungiDB, which provide the same annotation data found in GenBank/RefSeq. On the other hand, MycoCosm presents an alternative annotation for Pb18, in which a smaller proportion of genes (~68%) is described as associated with hypothetical proteins. However, MycoCosm does not present any information regarding other Paracoccidioides isolates (except for Pb03, but these data are based on outdated sequencing information, as they relate to the first version of the Pb03 genome, described by [17]). All these discrepancies, as well as the overall low level of functional gene categorization observed among Paracoccidioides isolates may partly derive from the fact that the abovementioned databases have not been submitted to appropriate manual curation, since they are dedicated to providing genomic information for a large number of organisms that may share little genomic similarity or phylogenetic proximity.
Thus, to improve and standardize the current genomic functional annotations of the main Paracoccidioides isolates, coding sequences (CDSs) derived from the latest genomic assemblies obtained for Pb18, Pb03, Pb01, PbCnh and Pb300 [18,19] were initially submitted to comparative BLAST analyses against a series of databases, including generic functional databases (InterPro, Pfam and Swiss-Prot) [24,25,26] and fungal-specific, manually-curated databases (Saccharomyces Genome Database, Candida Genome Database and Aspergillus Genome Database) [27,28,29].
Information derived from all these BLAST analyses were compiled in spreadsheets, along with specific Gene Ontology (GO) classifications [30,31]. This metadata was used to develop a manually curated consensus annotation for each of these Paracoccidioides genomes. As a result of this process, the number of genes described in association with hypothetical proteins has been reduced to ~25–28%, in all isolates. The information derived from this reannotation effort has been compiled in a publicly available database named ParaDB (available at http://paracoccidioides.com) [32], aimed at centralizing up-to-date genomic annotations for the major representatives of the five species/subgroups that compose the genus Paracoccidioides. Using a friendly graphical interface, ParaDB allows users to browse and download functional information for any set of genes from any of the abovementioned Paracoccidioides genomes. The ParaDB webpage also provides search tools based on keywords or DNA/protein sequence similarity, as well as fully reannotated genome files, in multi-fasta or General Feature (GFF3) formats, which may greatly assist researchers in a variety of large-scale, post-genomic studies with this important group of human pathogenic fungi. Finally, the entire ParaDB environment has been configured in a Docker service [33], which has been submitted to both the GitHub and Open Science Framework repositories, ensuring long-term data availability to researchers. This service can be downloaded and used to perform fully functional local installations of the database in alternative computing ecosystems, allowing users to conduct their data mining and analyses in a personal and stable working environment.
Files containing annotated protein coding sequences (CDS genomes) of the Paracoccidioides isolates were downloaded from NCBI, using the following accession numbers: Pb18 (RefSeq# GCF_000150735.1), Pb03 (GenBank# GCA_000150475.2), Pb300 (GenBank# GCA_001713645.1), PbCnh (GenBank# GCA_001713695.1), and Pb01 (RefSeq# GCF_000150705.2). The CDSs from these genomes were compared against each other, in order to identify all groups of orthologous genes (OGs) shared by two or more of the isolates, with the aid of the software OrthoFinder [34], using the software´s default parameters. Paralogous genes present within the same OG group were compared by multiple alignment, using Clustal Omega 1.2.4 [35]. The input parameters were set as follow: Output guide tree: false; Output distance matrix: false; Dealign input sequences: false; mBed-like clustering guide tree: true; mBed-like clustering iteration: true; Number of iterations: 0; Maximum guide tree iterations: -1; Maximum HMM iterations: -1. The Nexus-formatted matrix generated by Clustal Omega was then used to estimate genealogical relationships with the aid of Bayesian inference, using Mr. Bayes 3.0 [36]. The analysis involved 1,000,000 iterations, with savings at every 100th tree, 1,100,000 generations, in four heated Monte Carlo Markov chains (MCMCs), with 0.5 annealing temperature, 100 000 MCMC generation burn-in and a 16-category C distribution. A consensus tree was generated after burn-in, using a 50% majority rule, which allowed discrimination between orthologous and co-orthologous genes in the different OG groups. This list of orthologues was then used as a guide to ensure consistent annotation of equivalent genes throughout the five Paracoccidioides isolates, during the reannotation process (see below).
Several genes that transcribe non-coding RNAs (ncRNAs) have been identified and annotated in the genomes of Pb01 and Pb18 (the only formal datasets available for ncRNAs in Paracoccidioides spp.). Thus, their sequences were downloaded from the Ensembl Fungi database ftp site [21] and orthologues for each of these ncRNA genes were mapped in the genomes of Pb03, PbCnh and Pb300, using Bwa-MEM, version 0.7.17.1 [37], running in a local Galaxy environment [38], using the default software parameters. The resulting BAM alignment files were converted to BED files, with the aid of BAM-to-BED Converter, version 2.27.1 [39,40], also using default software parameters, which facilitated organizing and comparing the predicted ncRNAs across all Paracoccidioides spp. isolates. Finally, information regarding these ncRNAs was incorporated into GFF3 files (see below), with the aid of BED-to-GFF Converter [41], version 2.0.0, also using default parameters. All ncRNAs (along with their respective annotations) received identification codes consistent with the ones currently employed to describe Gene_IDs in each Paracoccidioides genome, but containing the designation NC (for non-coding) as a suffix. Thus, ncRNAs mapped in the genome of Pb18 received Gene_IDs starting from PADGNC_00001, while ncRNAs for Pb01, Pb03, Pb300 and PbCnh received Gene_IDs starting from PAAGNC_00001, PABGNC_00001, ACO22NC_00001 and GX48NC_00001, respectively. Genes responsible for transcribing additional ncRNAs, including tRNAs and rRNA genes (18S, 28S and 5S rRNAs) had been previously described in the original annotations of the Paracoccidioides spp. genomes [18] and sequences for such elements were available from the "rna_from_genomic" fasta files downloaded from GenBank/RefSeq [20]. These genes were also matched to their respective orthologues, using the same procedure described above, but we chose not to change their respective gene IDs (i.e.: they were not labeled with the designation NC), in order to respect their current GenBank/RefSeq IDs.
The overall process employed for reannotating the Paracoccidioides genomes is schematically shown in S1 Fig. Initially, all CDSs from Pb18 were individually submitted to comparative BLAST analyses against InterPro, Pfam and Swiss-Prot. Next, these CDSs were BLASTed against the manually-curated fungal databases SGD (Saccharomyces Genome Database), CGD (Candida Genome Database) and AspGD (Aspergillus Genome Database) [27,28,29]. All BLAST analyses employed high stringency criteria, which included as cutoff, an E-value < e-10, to identify orthologous genes containing functional descriptions among these databases. Information derived from all these BLAST analyses were compiled in a spreadsheet, along with Gene Ontology (GO) data regarding Pb18 genes, obtained from the Database for Annotation, Visualization and Integrated Discovery (DAVID), version 6.8 [42]. GO data were downloaded through the GO Direct option, in order to reduce the redundancy of terms, typically observed in GO analyses. Next, the metadata regarding each CDS was independently analyzed by three expert reviewers, in order to determine a consensus annotation term (ParaDB Annotation) for each CDS, which should be consistent with all the information derived from DAVID and from the BLAST searches previously performed. In a second round of annotation, a fourth reviewer compared the results of the three independent analyses and determined a final ParaDB Annotation term for each CDS. Finally, all information regarding the ParaDB Annotation, obtained for each Pb18 CDS, was transferred to the orthologues present in any of the Paracoccidioides isolates, using the list of orthologous genes (available at http://paracoccidioides.com/paracoccidioides-orthologous/) as a guide.
Next, all CDSs present in the genome of Pb01, which did not contain an orthologue in Pb18, were submitted to the same analysis procedure described in the previous paragraph. The same process was successively repeated with the remaining CDSs from Pb03, Pb300 and PbCnh, generating thorough and consistent annotations for all Paracoccidioides genomes.
In a subsequent annotation step, all CDSs that remained identified as hypothetical proteins were submitted to additional BLAST analyses, using a less stringent cutoff (E-value < e-5), essentially as described above. These CDSs were incorporated into the ParaDB database (see below) with a flag (E-value < e-5) highlighting the lower stringency criterion used to determine their respective functional annotation.
The information derived from the reannotation process described above has been compiled in a relational database named ParaDB, aimed at centralizing up-to-date genomic annotations for the major representatives of the five species/subgroups that compose the genus Paracoccidioides. ParaDB is available at the URL http://paracoccidioides.com and provides users with tabular archives describing each CDS and ncRNA sequences, including their final ParaDB Annotation consensus description, along with information derived from all databases evaluated during the present study. Additional information regarding these elements in all five Paracoccidioides isolates can also be downloaded (as nucleotide, or amino acid sequences) through multi-FASTA files, carrying strings that show, for each element, their respective locus_tag ID, protein_product ID and consensus_description (ParaDB Annotation). Updated General Feature Format (GFF3) files for each genome are also available through ParaDB, to assist researchers interested in performing large-scale OMICs analyses with the Paracoccidioides spp. genomes. These GFF3 files have been built upon the original GFF3 files available in RefSeq (Pb18 and Pb01) and GenBank (Pb03, Pb300 and PbCnh), by replacing the original GenBank/RefSeq annotations with the respective ParaDB Annotation, with the help of MS Excel. Information regarding the ncRNAs obtained from Ensembl were introduced in the GFF3 files using the BED-to-GFF Converter, as described above.
The ParaDB environment is based on the Database Management System (DBMS) MySQL and was developed in Docker [33]. Management configuration of the DB was made using Rancher [43], a robust Docker systems management tool, widely used in data center environments and other complex computing ecosystems. The Rancher cluster created to manage ParaDB resources is hosted in a cloud computing environment at CloudatCost [44]. Currently, the environment provides a total of 200 GB of disk space, in solid state drives (SSDs), and 20 GB of random-access memory (RAM). These resources are distributed in 10 virtual CPUs (vCPUs), in a Kubernetes cluster framework [45] (see S2 Fig for details). The ParaDB user interface has been developed in PHP language, using Wordpress [46] and tools to assist in keyword/BLAST searches were implemented with the help of Wordpress plugins and widgets [47].
A Docker service, carrying the Docker virtualization containers necessary to run ParaDB can be downloaded from https://cloud.docker.com/u/paradb/, allowing users to perform fully functional local installations of ParaDB, in different computational environments, given the platform-agnostic nature of Docker systems (see below).
To guarantee consistent genomic annotation of protein-coding genes among Paracoccidioides isolates, orthologous genes shared by two or more isolates were initially identified with the aid of the software OrthoFinder [34] and the Paracoccidioides spp. pan-genome derived from this analysis, containing all protein-coding sequences within the group, is shown in S3 Fig (a complete description of this pan-genome can be found in the ParaDB website, at http://paracoccidioides.com/paracoccidioides-orthologous/). Overall, the five isolates display a protein-coding pan-genome composed of 8365 groups of orthologous genes (OG) and share a core genetic pool that consists of 6396 OGs (~75%), reinforcing the close phylogenetic relatedness among members of this group of human pathogenic fungi.
Surprisingly, very few OGs could be found in association with only one isolate. In fact, no exclusive OGs were found in the genomes of Pb3 and Pb300, while Pb18 carries only 2 exclusive OGs of this type (OG0000046 and OG000738) and PbCnh displays only one (OG0006453). Even Pb01, which represents P. lutzii, the most distantly related species within the Paracoccidioides genus, contained only 2 exclusive OGs carrying protein-coding genes (OG0000018 and OG0006445). Not surprisingly, most of these OGs carry genes that typically display structural variations even among closely-related species, since they may perform similar biochemical functions, but interact with alternative substrates: OG0000018 and OG0000046 contain a series of Ser-Thr Protein Kinases, while OG0006445 and OG0006453 contain a series of plasma membrane ATP-binding cassette (ABC) transporters (OG0007385 contain genes that remained identified as hypothetical proteins). Currently, it is not possible to establish whether these genes contribute any kind of adaptive/biological specificities for the different Paracoccidioides isolates.
GenBank/RefSeq [20] also contained information regarding a series of non-coding RNAs from the five Paracoccidioides spp. isolates, including tRNAs and rRNA genes (18S, 28S and 5S rRNAs) (see Methods). All isolates displayed a similar number of tRNA genes, capable of providing all amino acids required for protein synthesis (see OG0008368 to OG0008478, at http://paracoccidioides.com/paracoccidioides-orthologous/). A similar situation was observed with the 5S rRNA genes (OG0008365), but the 18S and 28S rRNAs were present in significantly different copy numbers across the genomes of each isolate and could not be found in the genome of isolate PbCnh, probably reflecting problems with the currently available genomic assemblies (see OG0008366 and OG0008367, at http://paracoccidioides.com/paracoccidioides-orthologous/). A total of 32 additional ncRNA genes, responsible for transcribing small RNAs (sRNAs), small nuclear RNAs (snRNAs, including the spliceosome RNAs), small nucleolar RNAs (snoRNAs), the Telomerase RNA Component (tercRNA) and the Signal Recognition Particle RNA have also been mapped in the genomes of all five isolates, using as reference, a list of ncRNAs identified in the genomes of Pb18 and Pb01, available from the Ensembl Fungi database [21] ftp site. All Paracoccidioides spp. isolates share a common set of such elements, whose orthologues were easily identified in all genomes, with the aid of the short read Bwa-MEM aligner, as described in Methods (see OG0008479 to OG0008510, at http://paracoccidioides.com/paracoccidioides-orthologous/).
To prevent using different nomenclature while annotating corresponding genes across the Paracoccidioides genomes, the reannotation process of protein-coding genes was carried out as described in Methods. Thus, genome reannotation of Paracoccidioides spp. was initially performed with Pb18 and the annotation data obtained for all genes in this isolate were propagated to the corresponding genes present in the remaining Paracoccidioides spp. genomes, using the list of orthologous genes, described above, as a guide. Next, genes present in the genome of Pb01, which did not contain an orthologue in Pb18, were submitted to the same procedure and the same process was successively repeated with the remaining genes from Pb03, Pb300 and PbCnh, generating consistent annotations for all Paracoccidioides spp. genomes. As a result, specific functions and/or structural descriptions could be assigned to 6003 out of 8390 protein coding genes mapped in the genome of Pb18, reducing the proportion of genes described in association with hypothetical proteins to 28,5% (2386 genes) (Fig 1). In a second reannotation step, all CDSs that remained identified as hypothetical proteins were submitted to a new BLAST analysis, using a less stringent cutoff (E-value <e-5), allowing functional identification of additional 241 CDSs in Pb18, further reducing the proportion of hypothetical proteins in this isolate to 25.5% (2145 genes) (Fig 1). Reannotation of the remaining Paracoccidioides spp. genomes provided similar results with the other isolates, reducing the number of CDSs associated with hypothetical proteins to ~29–25% (with E-values < e-10 and < e-5, respectively), thus increasing the proportion of genes with functional/structural identification to ~71–75% (with E-values < e-10 and < e-5, respectively) in all cases, significantly improving the annotations of Paracoccidioides genomes, in comparison to the annotations currently available in any public biological data repository (see Fig 1).
Results from the functional reannotations shown in Fig 1 were compiled in ParaDB, a relational database developed to centralize standardized functional genomic annotation from all five reference isolates of the genus Paracoccidioides. ParaDB (available at http://paracoccidioides.com) presents a simple and intuitive interface, through which such information is made available (S4 Fig). Initial access to the annotation data can be made by the “Databases” button at the center of the webpage, or through a specific pull-down menu, available at the upper right corner of the main ParaDB webpage (S4A Fig). The “Full Database” option directs users to specific annotation data for each of the Paracoccidioides isolates under study (S4A Fig). In the “Full Database” mode (S4B Fig), users have initial access to a table that displays each Paracoccidioides spp. CDS (identified by numeric codes that correspond to their original GenBank/RefSeq IDs) and their respective ParaDB consensus functional/structural designation. Genes responsible for transcribing ncRNAs, which were not present in the GenBank/Refseq CDS files can also be accessed from these table and carry the suffix NC (non-coding) in the gene IDs assigned to them during our reannotation effort (see Methods). Information regarding data derived from all databases employed in the comparative analyses described above can be accessed by clicking on the (+) symbol, available in each of the CDS cells. Alternatively, such information can be accessed by clicking the “Columns” button on the upper right corner of the table and selecting the desired databases (S4B Fig). In either case, links are provided to direct users to the orthologous genes found in the fungal-specific databases (SGD, CGD and AspGD), where a variety of additional information can be found.
Users may also download the entire annotation files using the “Downloads” button, available from the pull-down menu (S4 Fig). The download site also allows users to access multi-FASTA files (containing either nucleotide or amino acid sequences) for each Paracoccidioides strains and/or their respective General Feature (GFF3) format files, which may be of great assistance for a series of downstream analyses, such as the evaluation of data derived from large-scale gene expression experiments involving microarray hybridization, or RNA-seq, for example.
ParaDB can be accessed and browsed directly on the web, at the URL http://paracoccidioides.com, through a variety of platforms, including personal computers of any kind, as well as mobile devices, such as cell phones and tablets, operating under either android or iOS operating systems. However, it is also possible for users to download and install a fully functional version of the database in their own personal computers, avoiding problems derived from low internet trafficking, host server instability, or communication restrictions, due to the presence of firewalls in local servers, for example. To accomplish that, ParaDB was developed in Docker [33], allowing configuration of the entire ParaDB environment in a Docker service, which has been submitted to both GitHub (https://github.com/paracoccidioidesdb) and Open Science Framework (https://osf.io/3sq97/) repositories, ensuring long-term data availability to researchers. This service can be downloaded and used to perform local installations of the database in alternative computing ecosystems, allowing users to conduct their data mining and analyses in a personal and stable working environment. Once installed in a local host, ParaDB will operate from two single containers, called ParaDB-Web and ParaDB-BLAST. Both containers can be consistently interchanged and deployed across different platforms, regardless of hardware and/or operating system (OS) specificities. This implementation is designed to ensure continued and full availability of ParaDB, independently of the original installation in our servers, allowing users to maintain mirrors of the entire database in their local environments. Additionally, the container concept allows the ParaDB infrastructure to be easily scalable, ensuring that hardware resources are provisioned whenever the computational environment reaches its limitations.
Hardware requirements for a local installation of ParaDB are reduced, and a personal computer (or notebook), running on Linux (recommended), containing 2 GB of RAM and 5 GB of available disk space can be used as host for installing a local version of ParaDB. The installation process is extremely simple and only requires previous installations of Docker [33] and Docker Compose in the host machine [48]. Once these components are available, only two steps are required to start a ParaDB environment in the host. In the first step, a Docker-Compose file is downloaded from the GitHub servers to the host machine. In the second step, the Docker Compose is executed, downloading and installing the images/containers of the standard ParaDB modules, starting the service. When Linux is the host machine's operating system, the following commands must be run on the terminal:
$ git clone https://github.com/ParacoccidioidesDB/paradb.git
$ cd paradb
$ docker-compose up -d
During the deployment process, some ports and disk volumes will be automatically configured on the host machine. Details about the ports and volumes created are available on the ParaDB website. In a standard implementation, ParaDB will use the local host address (IP: 0.0.0.0; HOST: http://localhost) as the default address for internal links. A video demonstrating the entire ParaDB implementation process in a local Linux environment is available at the URL http://paracoccidioides.com/local-install/.
Finally, it is worth mentioning that the ParaDB infrastructure can also be used by independent researchers to develop genome annotation projects involving other closely related fungi and its source code is freely available at: https://cloud.docker.com/u/paradb/.
Databanks dedicated to the storage of genomic data from Paracoccidioides spp. started to be designed since the pioneering EST analyses conducted with isolate Pb18 [12,13] and the information provided by these datasets greatly assisted the scientific community in a large number of projects, which contributed to improve our knowledge about this important group of human pathogenic fungi (recently reviewed by [49,50]). However, these original EST databases were gradually abandoned or deactivated after the release of the first draft genomes obtained for isolates Pb18, Pb03 and Pb01 [17]. At this time, data regarding these draft genomes were deposited in a database dedicated to members of the genus Paracoccidioides, as part of the Fungal Genome Initiative (FGI), developed by the Broad Institute of Harvard University and the Massachusetts Institute of Technology. This centralized database became the major source of information for subsequent genomic work on Paracoccidioides spp., as it contained a great deal of genomic data from these three isolates (including gene functional annotations, chromosome locations of loci and comparative evolutionary analyses of multiple gene sets), as well as tools for searching and downloading genetic sequences and other additional information. However, maintenance of the FGI databases, as well as their respective web interfaces, was discontinued in 2015 and, up to this moment, no other database has been able to reproduce a centralized and efficient environment for genomic analysis on Paracoccidioides spp.
Efforts were initially made to incorporate Paracoccidioides genomic data into other sites that support comparative analysis of fungal genomes, including MycoCosm and FungiDB [22,23]. FungiDB is a subsection of the EuPathDB family of databases, maintained by the Welcome Trust and NIH. It was designed to combine and make available a plethora of biological information, obtained from a wide variety of microbial eukaryotes. FungiDB [23] includes data from both pathogenic and non-pathogenic fungi and provides information about multiple genomes and gene records, which can be compared and downloaded with the aid of user-friendly browsers. It also integrates genomic data with comments and supporting evidence from the scientific community (including PubMed IDs, images, phenotypic information, etc.) and offers tools for integrating and mining diverse Omics datasets. MycoCosm (supported by JGI/DOE) offers a large collection of fungal genomes, along with interesting web-based tools for alternative types of genome-scale analyses [22].
However, in spite of the effective resources made available through these repositories, the genomic data regarding Paracoccidioides isolates that can be currently found in these databases are discrepant, apparently due to absence of manual curation and/or to the confusion generated by the publication of a second version of Paracoccidioides genomes (and their respective annotations) [18,19]. For example, detailed analysis of the information available from MycoCosm [22] shows genomic data only for isolates Pb18 and Pb03. However, Pb18 data refer to version 2 of the genome [18], while Pb03 data refer to version 1 [17]. This represents a serious problem for studies involving Pb03, since annotations referring to genomes v1 and v2 display only a portion of common genes [18]. Additionally, the Pb18 genome has ~68% of its CDSs described solely as responsible for encoding hypothetical proteins. EuPathDB/FungiDB [23], on the other hand, presents data for the three Paracoccidioides isolates, but only contemplate the genomic information described by [17]. Moreover, their annotations display puzzling results, since the genome of Pb01 displays ~62% of its CDSs identified as hypothetical proteins, while this proportion increases to ~87–88% in the genomes of Pb18 and Pb03. These results represent an unexpected discrepancy, especially when confronted with the comparative analysis of orthologues described herein and shown in S2 Fig. Actually, a closer analysis shows that the data contained in FungiDB [23] appear to have been incorporated directly from NCBI (GenBank/RefSeq) [20] or Ensembl [21], without receiving any manual curation to enhance their accuracy. GenBank/RefSeq and Ensembl are large databases dedicated to providing genomic information for a large number of organisms, relying mostly on automated pipelines to perform genomic annotations. However, these automated pipelines perform comparisons against a large number of independent databanks, resulting in large amounts of data, which are often too complex to be automatically summarized by computer algorithms, requiring manual evaluation by expert reviewers, in order to establish a consensus nomenclature for each analyzed sequence and ensure greater efficiency in the functional identification of the genes present in an organism [51,52,53,54]. Unfortunately, manual curation of genomic data is a time-consuming process and the large number of genomes currently deposited in generic databases, such as GenBank/RefSeq [20] and Ensembl [21], causes many of them to be displayed solely as the result of automated annotation pipelines [55,56].
As expected, manual curation of the Paracoccidioides genomes, as shown herein, reduced the proportion of genes described in association with hypothetical proteins from ~90%, in most isolates, to < 30%, in all organisms under study. Moreover, the information regarding these newly annotated genomes have been standardized and made available through a single public database, centralizing the genomic data for the main representatives of the group. Similar improvement in genomic annotation has also been verified with the human pathogenic fungus Candida albicans, whose genome has been submitted to manual curation. In fact, the C. albicans reference genome (RefSeq #GCF_000182965.3) displays ~39% of its genes annotated as responsible for hypothetical proteins, while a manually-curated reannotation, made available through the Candida Genome Database (CGD) project [28] reduced the proportion of hypothetical proteins to only ~21% [57].
Unfortunately, microorganisms responsible for neglected diseases tend to attract less attention from the scientific community and, as a result, their genomic annotations are often described with considerably lower accuracy, as manually curated reannotations are rarely performed. For example, the genome of the fungus Blastomyces dermatitidis, strain ER-3 (etiologic agent of blastomycosis), available through GenBank (accession #GCA_000003525.2), shows ~ 50% of its genes described in association with hypothetical proteins. Similar scenarios can be verified with the genomes of Cryptococcus neoformans var. grubii H99 and Coccidioides immitis RS, responsible for cryptococcosis and coccidioidomycosis, respectively, which present 47–49% of their CDSs described in association with hypothetical proteins (see RefSeq accessions #GCF_000149245.1 and GCF_000149335.2). A natural consequence derived from such lack of accuracy is that it is often difficult to use the information derived from these genomic data in large-scale post-genomic studies, such as in silico functional/metabolic reconstructions and transcriptome/proteome analyses, which could greatly contribute to improve our knowledge regarding the general biology of these fungi, or the molecular basis of their pathogenicity mechanisms. In this sense, the work described in this manuscript provides manually curated and standardized genomic annotations for the main representatives of all five species of Paracoccidioides spp., placing members of this genus in a unique position, when compared with many dimorphic fungi, responsible for neglected mycoses. These new annotations greatly reduce the number of genes identified solely as hypothetical proteins and are integrated into a dedicated database that provides different search/analyses tools to facilitate the development of future post-genomic studies with this important group of human pathogenic fungi.
It must also be highlighted that the data available from ParaDB should provide adequate genomic coverage of protein-coding genes to support in silico metabolic analyses in Paracoccidioides spp., since the current genomic assemblies display sizes between 29 and 32 Mb, encoding approximately 8000 to 9000 proteins. Thus, gene density in these genomes is 1 CDS/~3.5 kb, which is close to the values observed in well-annotated fungal genomes, such as the cases of Aspergillus spp. (1 CDS/~3.1 kb) [29] and Candida spp. (1 CDS/~2.3 kb) [28]. Thus, the current Paracoccidioides annotations are likely to have identified most (if not all) protein-coding genes present in these fungi, especially when compared with other neglected fungi, such as H. capsulatum (1 CDS/~4 kb) [58] and B. dermatitidis (1 CDS/~5.7 kb) [59]. However, information regarding the presence of non-coding elements in Paracoccidioides spp. is still scarce and such elements have only recently begun to be unraveled [60]. We expect further versions of ParaDB to incorporate more information on these elements, which can be more efficiently characterized through the analysis of transcriptome data.
Finally, the work presented in this manuscript proposes a pioneering and effective alternative to ensure that the data and resources provided by ParaDB shall remain available in a continued and reproducible way, by providing users with the possibility of installing fully functional mirrors of the database in their own working environments. This was accomplished by developing the entire ParaDB environment in Docker, which allowed the creation of a ParaDB Docker service that was deposited in both GitHub https://github.com/paracoccidioidesdb [61] and Open Science Framework repositories (https://osf.io/3sq97/), two of the world´s largest web-based hosting servers for open source software. The ParaDB image can be freely downloaded and deployed in any kind of local computer, with little infrastructure requirements. The Docker project is providing a new and promising virtualization strategy that consumes a considerably low amount of disk space (when compared to Virtual Machines) and offers the advantage of being platform-agnostic, since it relies on the configuration of containers, which can be consistently interchanged and deployed on different computing environments, regardless the specificities of their hardware and/or operating system [33]. In recent years, this type of technology has been increasingly employed to generate bioinformatics-related software and services to a large variety of research facilities, employing the concepts of Platform as a Service (PaaS) and Software as a Service (SaaS), as a strategy to assist in replicability and reproducibility of data analysis across laboratories [62,63,64,65,66,67,68]. In this context, ParaDB is the first initiative that tries to develop a Docker system with a biological database, carrying genomic information of pathogenic microorganisms, thus introducing the concept of Database-as-a-Service (DBaaS), as a strategy to guarantee long term availability of biological data and resources.
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10.1371/journal.pbio.3000156 | Scrutinizing assortative mating in birds | It is often claimed that pair bonds preferentially form between individuals that resemble one another. Such assortative mating appears to be widespread throughout the animal kingdom. Yet it is unclear whether the apparent ubiquity of assortative mating arises primarily from mate choice (“like attracts like”), which can be constrained by same-sex competition for mates; from spatial or temporal separation; or from observer, reporting, publication, or search bias. Here, based on a conventional literature search, we find compelling meta-analytical evidence for size-assortative mating in birds (r = 0.178, 95% CI 0.142–0.215, 83 species, 35,591 pairs). However, our analyses reveal that this effect vanishes gradually with increased control of confounding factors. Specifically, the effect size decreased by 42% when we used previously unpublished data from nine long-term field studies, i.e., data free of reporting and publication bias (r = 0.103, 95% CI 0.074–0.132, eight species, 16,611 pairs). Moreover, in those data, assortative mating effectively disappeared when both partners were measured by independent observers or separately in space and time (mean r = 0.018, 95% CI −0.016–0.057). Likewise, we also found no evidence for assortative mating in a direct experimental test for mutual mate choice in captive populations of Zebra finches (r = −0.020, 95% CI −0.148–0.107, 1,414 pairs). These results highlight the importance of unpublished data in generating unbiased meta-analytical conclusions and suggest that the apparent ubiquity of assortative mating reported in the literature is overestimated and may not be driven by mate choice or mating competition for preferred mates.
| Research on mate choice in birds has attracted much attention, partly because many birds form monogamous pair bonds like humans do. Human mate choice is characterized by the phenomenon of “like attracts like,” meaning that partners resemble each other in multiple ways (“assortative mating”). Assortative mating is also frequently reported for birds, but it is unclear whether this in turn implies that birds also have preferences for a similar partner. Here, we show that a range of methodological issues may provide a simpler and more accurate explanation for the frequent observation of assortative mating in birds. First, studies that report assortative mating may achieve greater visibility than studies that yield no such finding. Hence, the scientific literature may be biased toward positive results. Second, in field studies, it is logistically impossible to measure all birds accurately and under standardized conditions. Hence, fluctuations in, for instance, environmental conditions may induce a spurious similarity between partners when these are measured together in space or time. After accounting for such methodological issues, we conclude that mate preferences for a similar partner may be less common than previously thought.
| Members of a pair often resemble each other. For instance, in humans, partners have similar political attitudes [1, 2], level of education [3, 4], and body height [4–6]. In fact, assortative mating appears to be common across all animal taxa and across all phenotypic traits that have been investigated [7]. However, in most cases, the underlying processes that lead to mate similarity remain unclear.
Similarity of pair members, quantified as the strength of the correlation between their trait values, may arise via at least three biological mechanisms. (1) Mate choice and mating competition; one or both sexes may prefer phenotypes similar to their own (“like attracts like”). This may lead to the more frequent formation and enhanced stability of assortative pair bonds. (2) Spatial and temporal autocorrelation; individuals with different phenotypes may be separated in space and time, such that at the population level, even random mating would lead to partner similarity (“like meets like”). For instance, in high-quality habitats, individuals may grow larger than in poor habitats. If individuals from different habitats are less likely to meet (“nonpanmixis”), e.g., because of low mobility [8], a population-wide pattern of assortative mating may arise in the absence of choice for an assortative partner. Similarly, in migratory species, individuals that resemble each other in particular traits may have a higher probability to form a pair simply because they arrive at the breeding grounds closer in time (e.g., older individuals might arrive earlier, leading to assortative mating for age [9]). (3) Phenotypic changes over time; females and males may mate randomly for a certain phenotype but become similar to their partner over time (“become alike”) [10]. For instance, in humans, a positive correlation in body mass between couples may arise because they share the same food [11, 12]. Because the three biological mechanisms can act together and can also interact with other processes, their relative importance may be difficult to tease apart [13, 14]. Furthermore, if testing assortative mating that comes about via mate choice and mating competition is the goal of the study, then the other “biological” mechanisms could be considered as confounds [15]. Also, it should be noted that not all mechanisms may be relevant for all phenotypes (e.g., “becoming alike” does not apply to assortative mating with regard to age).
Besides the influence of biological processes [16], estimates of the strength of assortative mating can be confounded by three main methodological issues. (1) Observer bias; data sets often consist of measurements from multiple observers and may be taken over long periods. This introduces heterogeneity and temporal autocorrelation in the data because observers can differ in their measurements [17], and they may unconsciously change their measuring technique over time. Hence, spurious trait correlations between pair members may arise when pair members are typically measured by the same observer and on the same day (see our simulation in S1 Fig). Such methodological heterogeneity may be difficult to distinguish from biological heterogeneity in space and time. (2) Reporting bias; estimates found in the literature will be inflated when statistically significant estimates are more likely reported than nonsignificant ones [18, 19]. (3) Search bias; if a meta-analysis is based on a literature search with keywords like “assortative,” the strength of assortative mating may be overestimated because it is less likely null results are mentioned in the abstract of a publication [20]; hence, such searches may preferentially yield a subset of studies that have detected significant assortative mating. Similarly, when screening relevant publications, taking estimates from related studies that are being cited may also discriminate against null findings because studies with null findings tend to get cited less often than studies with significant and, hence, typically larger effects [21].
Assortative mating is often investigated with a focus on mate choice and mating competition [22, 23]. However, a systematic review of assortative mating in arthropods [13] suggests that mechanisms other than “mating preferences for phenotypic similarity” are predominantly responsible for the mating patterns observed in these invertebrates (such as mate availability and physical constraints). Whether mate choice and mating competition or other processes underlie assortative mating in vertebrates such as birds is unclear, particularly because birds often form strong social pair bonds for which the compatibility of partners may determine reproductive success [24].
Here, we quantify the strength of assortative mating in birds and assess how assortative mating estimates change with increasing control of the confounding factors discussed above. We then discuss biological implications of our findings and propose ways to minimize confounding effects if the aim is to investigate assortative mating due to mate choice and mating competition. For practical reasons (data availability), we focus primarily on assortative mating for size in birds, yet our approach is relevant for most phenotypic traits and taxa. In birds, body size is measured and reported in a variety of ways (body mass, wing length, tarsus length, bill length, body length, and first principal component of different measures). Thus, our measure of body size includes all size-related morphological measures but not measures of ornament size (e.g., plumage patch area or ornamental feather length) and of body condition (e.g., mass residuals over tarsus length), which were categorized separately.
First, we compare published estimates of the strength of assortative mating with estimates from our previously unpublished data from nine long-term field studies that are a sample of the species studied in the literature. This allows assessment of the effect of search and reporting bias, which should only affect the published data set. Second, we use the previously unpublished data set to explore causes of assortative mating patterns that are not due to mate choice and mating competition, such as effects of observer bias and spatial and temporal nonindependence. Finally, we present an analysis of experimental data from five studies of assortative mating in captive Zebra finches, Taeniopygia guttata [24, 25]. In these experiments, we took standardized measurements of all birds before randomly allocating them to experimental aviaries. So far, this is the only avian study for which we can estimate the strength of assortative mating among individuals that encountered each other, excluding all known confounding factors.
Overall, the published literature showed striking evidence for positive assortative mating across all trait categories (ranging from r = 0.156 to 0.434; none of the 95% CI overlap zero) (Fig 1 and S1 Table). The two random effects, “Study” and “Species,” explained only 9% and 6% of the variance, respectively. Compared to other traits, assortative mating for body size was the second weakest (r = 0.178, 95% CI 0.142–0.215, 83 species, 35,591 pairs), but it was the most frequently studied trait (54% of all estimates).
The previously unpublished data from nine long-term field studies also showed a clear yet weaker tendency for positive assortative mating by size, whereby the magnitude depended on how the data were analyzed (ranging from r = 0.067 to 0.103) (S2 Fig and S2, S3 and S4 Tables).
When repeatedly measured individuals were represented by a randomly selected single measure (i.e., model of “random alignment” of male to female measure), the strength of assortment was weak (mean r = 0.067, 95% CI 0.042–0.093, n = 16,545 pair–trait combinations) (S2 Fig and S2 Table). When average measures per individual were used (“average model”), estimates of assortment were only slightly higher (mean r = 0.080, 95% CI 0.054–0.105, n = 16,545) (S2 Fig and S3 Table). Finally, when using the male and female measures taken closest to the presumed time of pair formation (“nearest model”), the estimate of assortative mating was highest (mean r = 0.103, 95% CI 0.074–0.132, n = 16,611) (S2 Fig and S4 Table). The estimates from the “nearest” model were significantly higher than those from the “random alignment” model (paired t test for 32 species–traits, t31 = 4.50, P = 0.0001) and higher than estimates from the “average” model (t31 = 3.32, P = 0.002).
In the previously unpublished data set, levels of apparent assortative mating were significantly higher when measurements on the two members of a pair had been taken by the same observer (r = 0.075, 95% CI 0.033–0.118, t = 3.45, P = 0.0006, n = 22 estimates, 34,672 pair–trait combinations) than when measurements came from different observers (r = 0.023, 95% CI −0.022–0.067, t = 1.01, P = 0.3, n = 22 estimates, 24,771 pair–trait combinations; tsame versus different observer = 2.48, P = 0.01) (Fig 1 and S5 Table: model 4).
Similarly, estimates for assortative mating for size were significantly higher when measurements on the two members of a pair had been taken within the same month (within 30 days: r = 0.110, 95% CI 0.079–0.142, t = 6.92, P < 0.0001, n = 32 estimates, 51,995 pair–trait combinations) than when the partners had been measured more than 30 days apart (r = 0.014, 95% CI −0.013–0.041, t = 0.98, P = 0.3, n = 31 estimates, 20,729 pair–trait combinations; tsame versus different time = 5.7, P < 0.0001) (Fig 1 and S5 Table: model 5).
Finally, estimates of size-assortative mating were significantly higher when partners had been measured at the same site (0.073, 95% CI 0.046–0.100, t = 5.30, P < 0.0001, n = 32 estimates, 26,542 pair–trait combinations) than when they were measured at different sites (0.017, 95% CI −0.009–0.044, t = 1.28, P = 0.2, n = 32 estimates, 44,112 pair–trait combinations; tsame versus different location = 3.90, P < 0.0001) (Fig 1 and S5 Table: model 6).
Data from the five experiments on Zebra finches showed an overall size-assortative mating close to zero (r = −0.020, 95% CI −0.148–0.107; weighted mean of 13 estimates based on 1,414 pair–trait combinations) (S6 Table and S8 Data). Note that the statistical power for detecting an effect of r = 0.20 was >0.99 for each of the three size phenotypes (mass, tarsus, and wing length).
The strength of assortative mating decreased with increasing control for confounding factors from published (Web of Science search: r = 0.184; Cited studies: r = 0.140) to previously unpublished (r = 0.107) to experimental data (r = −0.004). However, in such a joint model, CIs around the estimates were relatively wide despite high sample sizes (Fig 1 and S7 Table). The random effect “Study” explained 7% of the variance, while the other two random effects (“Species” and “Trait”) explained zero variance (S7 Table).
Estimates of assortative mating from the literature data declined significantly with increasing sample size (test for asymmetry in the funnel plot t = −2.76, P = 0.006, n = 449) (Fig 2). According to the estimated regression line (Fig 2), correlation coefficients from the “Published data” reached the same magnitude as those from the previously “Unpublished data” at a sample size of n = 822 pairs.
Our meta-analysis of published estimates of assortative mating shows strong evidence for positive assortative mating in birds across different phenotypes (Fig 1). This is consistent with a recent meta-analysis of assortative mating across the whole animal kingdom [7]. However, using data sets of body size from different sources as an example, our study reveals that seemingly robust effects may disappear when controlling for multiple non-mate–choice mechanisms (Fig 1). Given the higher flexibility in other phenotypic traits, such as physiology and behavior, this problem is likely pervasive. Hence, our study questions the ubiquity and importance of assortative mating due to mate choice and mating competition and suggests that the meta-analytical results cannot be taken as evidence for mate choice for a similar partner (“like attracts like”). In the following, we discuss the effects of each confounding factor, and when possible, we suggest ways to avoid the bias.
A recent analysis of assortative mating across the animal kingdom [7] did not find clear evidence for publication bias, as indicated by significant asymmetry in the funnel plot, while in our data set of published studies, the funnel plot asymmetry was statistically significant (Fig 2). This could indicate either that publication bias is limited or that tests for asymmetry in the funnel plot are sometimes inefficient in detecting it [26]. Publication bias is expected because (a) most studies emphasize positive findings rather than null results [27] despite statistical power being low (selective reporting [28]), (b) incomplete reporting of nonsignificant outcomes is widespread [29], and (c) the few meta-analyses that compared published and unpublished effect sizes (including this one) found that published effects were larger than unpublished ones (Fig 1) (e.g., [30–32]). In this context, the increased availability of open access data [33] might allow an unbiased quantification of effect sizes (particularly when the published data were part of a study addressing a different question), which might help to put the existing knowledge into perspective.
In field studies, it is practically impossible to implement the optimal measuring design of experimental studies (all individuals measured in random order by the same observer in the same environment on the same day before mate choice starts). This means that multiple observers, multiple environments, and multiple time points introduce heterogeneity into the data, which is partly of methodological and partly of biological origin. Such heterogeneity may (1) partly blur existing patterns of assortment due to sexual selection, thereby leading to underestimation, and (2) induce spurious correlations between pair members when these are often measured by the same observer and close in time and space, leading to overestimation of assortment due to sexual selection. As our simulation shows, the effect of an overestimation of assortative mating clearly exceeds the effect of underestimation when (1) levels of true assortment are relatively low and (2) a large fraction of the pair members are measured by the same observer (see S1 Fig). In our empirical data, we found the expected difference between the “same” versus “different” observer conditions, as the simulation showed (Fig 1), and conclude that our “nearest” model tended to overestimate true levels of assortment because for 80% of the pairs, the partners had been measured by the same observer (S1 Fig).
When it is not practically feasible that all measurements are taken by a single observer, observer bias can be controlled for by calculating correlations between pair members after statistically removing observer effects (see [34] for a solution). Measurement error may also fluctuate in time and thereby induce temporal autocorrelation and, hence, an apparent similarity of pair members (see next paragraph). Finally, observers may have preconceptions about assortative mating, such that measurements suffer from confirmation bias [35]. This is perhaps less likely for data sets that were collected without hypotheses about assortative mating in mind (such as ours). Preconception bias can be solved by blinding observers to the hypothesis of interest [36]; this is already ensured when the members of a pair are measured by independent observers or at different points in time.
Our results show that the estimated strength of size-assortative mating is higher when individuals are measured within the same month or at the same site (Fig 1). Such results may arise for both biological and technical reasons. For example, measures of plumage coloration may show temporal autocorrelation because plumage color gradually changes after moult due to wear or because of changes in the white balance used for calibration of hand-held photo spectrometers [37]. Also, depending on the research question, effects that induce temporal and spatial autocorrelation can be of biological interest (e.g., for ecologists), or it can be a confounding factor (e.g., if the interest lies in studying mate choice and mating competition). In the latter case, one can use bivariate mixed models to disentangle the confounding effects of space or time, such that the correlation at the pair level will more closely reflect assortative mating due to mate choice and mating competition [34, 38].
We found that mate choice is an unlikely driver of size-assortative mating in birds (Fig 1). The observed effect sizes were much lower than, for instance, the level of assortative mating for body height in humans (r = 0.23, [6]), for which body height is only of secondary importance for mate choice [39]. To robustly examine whether assortative mating arises from (mutual) mate choice and mating competition rather than from other processes (e.g., “like meets like” or “become alike”), an experimental approach is needed in which all individuals can be measured before they mate (see “experimental study” in Methods). Further, such an experimental approach is inevitably needed when groups of individuals (potential mates) are spatially and temporally segregated (nonpanmixis). This is because evidence for the role of mate choice requires knowledge about the potential partners available during pair formation (who encountered whom). Also, an experimental approach allows disentangling scenarios in which separation in space or time according to certain phenotypic traits is already a consequence of choosiness (i.e., individuals separate in space because they have already been rejected, e.g., during courtship displays).
Often, however, an experimental approach is not feasible. In that case, a targeted analysis of binary mating decisions observed in the field (e.g., evidence for rejection versus acceptance of an individual) may provide some clues. In the end, knowledge of a study system and the consideration of possible confounds (“like meets like” and “become alike”) may help separating mate choice and mating competition from other causal factors.
Assortative mating for certain phenotypic traits may arise from various biological processes and methodological issues. We thus argue for careful consideration of mechanisms and confounding effects. Our results show (a) that assortative mating can arise through many processes other than mate choice and mating competition [8, 13, 40] and (b) that it might be less strong than suggested by meta-analyses of the published literature [7]. In this respect, the value of unpublished data sets deserves greater appreciation.
This study was carried out under the license of Max Planck Institute for Ornithology Animal Care; the Animal Experimentation Committee of the University of Groningen; Mexican authorities, including the administration of Isla Isabel National Park, the National Commission for Protected Natural Areas (CONANP), and the Secretary of Environment and Natural Resources (SEMARNAT, permit number SGPA/DGVS/08333/10). Field protocols of Barn swallow were noninvasive and adhered to the laws and guidelines of the Czech Republic (Czech Research Permit number 6628/2008-10001). All protocols were approved by the Animal Care and Use Committees at the Czech Academy of Sciences (041/2011 and 09/2015) and Charles University in Prague (4789/2008-30). The procedures required to collect the data of Tawny owl, which all fall under the ringing licenses of the authors, as provided by the relevant local authorities. Data collection on Western bluebird was carried out in accordance with the recommendations and guidelines approved by University Institutional and Animal Care and Use Committees and under state and federal permitting guidelines.
We also assessed assortative mating for size experimentally, using captive populations of Zebra finches (Taeniopygia guttata). As a rule, in each experiment, all birds were measured by a single observer prior to their release into breeding aviaries. Measurements were taken in an order that was independent of allocation to aviaries. This excludes systematic observer error as well as spatial and temporal heterogeneity. To minimize the “scale-of-choice-effect” (when mate choice exists at a smaller scale than that of the investigator’s sampling, while the trait is heterogeneously distributed at the true scale-of-choice) [8, 15, 59], we analyzed the degree of assortative mating within aviaries, hence comprising only the birds that were available for pairing at the time of release. To avoid selective reporting, we summarize all available information from our laboratory (partly published in [25]), comprising five experiments that largely fulfill the above criteria (minor violations of criteria are described in the S1 Text; decisions about inclusion were made blind to the outcome of the analyses).
The five experiments involved both a domesticated population (experiments 1–3; [25]) and a recently wild-derived population (experiments 4–5 [24]; details are given in the S1 Text). In the domesticated population, we released six males and six females in each aviary (n = 6, 6, and 72 aviaries in experiments 1–3, respectively), while in the wild-derived population we used 20 males and 20 females per aviary (n = 4 and 2 aviaries in experiments 4 and 5, respectively). The formation of pair bonds was deduced from observations of pair-bonding behavior (allopreening, sitting in body contact, or visiting a nest box together). We only considered monogamous pair bonds (cases of polygyny and polyandry were excluded) but allowed sequential bonds (initial pairing followed by divorce and repairing; for details see [25]). In total, we recorded 510 pair bonds (n = 44, 35, 342, 58, and 31 in experiments 1–5, respectively). For the domesticated population, we measured body mass, tarsus length, and wing length; for the recently wild-derived population, we measured body mass and tarsus length. Thus, we had data for 13 experiment–trait combinations (3 x 3 plus 2 x 2). For each of these combinations, we calculated the strength of assortative mating (Pearson’s r) within each experimental aviary. To summarize the multiple estimates for the same trait from multiple aviaries within an experiment, we Fisher’s z-transformed the r-values and calculated a weighted average (weighted by [n − 3]0.5, in which n is the number of pairs, requiring a minimum of four pairs within an aviary). The 13 average estimates (based on 1,414 pair–trait combinations; morphological data missing for 27 pair–trait combinations) were back-transformed to correlation coefficients to allow comparison with all other results.
To compare the results across the different methods, we modeled the Pearson’s r estimates as a function of “data source” (fixed effect with four levels: “Web of Science search,” “Cited studies,” previously “Unpublished data,” and “Experimental data”). “Species” and “Study” were added as random effects to account for nonindependence of data. Moreover, different measures of size might yield different levels of assortment, so we fitted “Trait” as a third random effect—seven levels, reflecting the following categories: (1) body mass, (2) principal components of size, (3) bill measurements, (4) head measurements, (5) leg measurements, (6) tail measurements, and (7) wing measurements. Each Pearson’s r estimate was weighted by (n − 3)0.5, in which n is the total number of unique pairs contributing to the estimate. The analysis included 392 r-values from “Web of Science search” (based on 26,076 pairs), 57 r-values from “Cited studies” (based on 9,515 pairs), 32 r-values from the previously “Unpublished data” (based on 16,611 pairs), and 13 r-values from “Experimental data” on Zebra finches (based on 1,414 pairs). For the previously “Unpublished data,” we used estimates from the “nearest model 3” because this method is most frequently used in the published literature. Hence, differences between “Web of Science search,” “Cited studies,” and previously “Unpublished data” estimates should reflect search and reporting bias rather than methodological differences.
To examine the estimates from the literature (“Web of Science search” and “Cited studies”) for signs of publication bias, we plotted all 449 correlation coefficients over their sample size (number of pairs) and tested for asymmetry in “funnel plots” using the package “meta” in R 3.1.1 [60]. For comparisons and contrasts between published and previously unpublished data sets, the same plotting was done for previously unpublished data as well.
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10.1371/journal.pbio.1001438 | Crag Is a GEF for Rab11 Required for Rhodopsin Trafficking and Maintenance of Adult Photoreceptor Cells | Rhodopsins (Rhs) are light sensors, and Rh1 is the major Rh in the Drosophila photoreceptor rhabdomere membrane. Upon photoactivation, a fraction of Rh1 is internalized and degraded, but it remains unclear how the rhabdomeric Rh1 pool is replenished and what molecular players are involved. Here, we show that Crag, a DENN protein, is a guanine nucleotide exchange factor for Rab11 that is required for the homeostasis of Rh1 upon light exposure. The absence of Crag causes a light-induced accumulation of cytoplasmic Rh1, and loss of Crag or Rab11 leads to a similar photoreceptor degeneration in adult flies. Furthermore, the defects associated with loss of Crag can be partially rescued with a constitutive active form of Rab11. We propose that upon light stimulation, Crag is required for trafficking of Rh from the trans-Golgi network to rhabdomere membranes via a Rab11-dependent vesicular transport.
| Animals sense light through receptors called Rhodopsins. These proteins are typically localized to stacked membranes in photoreceptors. In flies, upon light exposure, Rhodopsin undergoes conformational changes and becomes active as metarhodopsin. Metarhodopsin then initiates a signaling cascade that activates the photoreceptor cell. To deactivate the light response, metarhodopsin is converted back into Rhodopsin by absorption of another photon of light. Under certain conditions, metarhodopsin cannot be converted back to Rhodopsin, and it is then endocytosed and degraded. Rhodopsin then needs to be synthesized and delivered back to the membrane stacks. Here, we show that the Calmodulin-binding protein Crag is required for the delivery of newly made Rhodopsin to the membrane stacks. Loss of Crag leads to the accumulation of Rhodopsin in the cytosol, followed by shrinkage of membrane stack volume, and, eventually, photoreceptor cell degeneration. We also show that Crag activates a target protein, Rab11, which mediates the vesicular transport of Rhodospin to the membrane. Finally, we document that the human homolog of Crag, DENND4A, is able to rescue the loss of Crag in flies, suggesting that DENND4A functions in a similar process in vertebrates.
| The Drosophila phototransduction pathway has been extensively studied [1], and most known members of the pathway were isolated in forward genetics screens by means of electroretinogram (ERG) and phototactic assays [2]–[6]. Since vision is not an essential sense, previous screens were performed on homozygous viable mutants. This strategy was highly successful and led to the characterization of numerous proteins that play a critical role in light responses. However, the phototransduction cascade is likely to also rely on components that are shared with other processes that are essential for viability. We therefore initiated a large mosaic ERG screen of lethal mutations on the X chromosome. Here, we report the characterization of Calmodulin-binding protein related to a Rab3 GDP/GTP exchange protein (Crag).
The Crag gene was first identified in a biochemical screen for photoreceptor Calmodulin (CaM)–binding proteins [7]. Crag contains a CaM binding site and interacts with CaM in a calcium-dependent manner [7],[8]. Mutations in the Crag gene were later shown to affect the epithelial architecture and polarized localization of basement membrane proteins including Perlecan, Laminin, and Collagen IV [8]. Based on sequence homology, the N-terminus of Crag contains three conserved domains: uDENN, DENN, and dDENN, and belongs to the DENN (differentially expressed in neoplastic and normal cells) protein superfamily [9]. The first DENN family member was identified in a screen for variable mRNA expression in neoplastic cells [10], and 18 genes encoding DENN domain proteins are present in the human genome. DENND4A, DENND4B, and DENND4C are the human homologs of Crag, and their cellular function in mammalian systems has not been characterized. Many DENN-domain-containing proteins have been found to interact directly and function as guanine nucleotide exchange factors (GEFs) of various Rab proteins [11]. The DENN/MADD protein was found to be a GEF for Rab3 and Rab27 [12],[13], whereas Connecdenn was found to be a GEF for Rab35 [14]. Furthermore, a genome-wide survey revealed that most DENN proteins are GEFs [15]. However, none of the DENN proteins were identified as GEFs for Rab11.
Rabs are localized to distinct intracellular membranes [16],[17], switch between the inactive (GDP-bound) and active (GTP-bound) conformational state, and respond to various signaling cues. In the active state, Rab proteins interact with their effectors and regulate vesicle trafficking at numerous different steps [16]. GEFs bind to inactive Rabs and facilitate the exchange of GDP for GTP, thereby activating the Rabs.
Rab11 has been shown to affect many cellular processes. It mediates protein recycling by regulating membrane transport from recycling endosomes [18]. It is present in the trans-Golgi network (TGN) and post-Golgi vesicles, where it is required for membrane transport from the TGN to the plasma membrane [19]. In polarized MDCK cells, Rab11 is required for apical recycling and basolateral-to-apical transcytosis of immunoglobulin receptors [20]–[22]. In motile cells, Rab11 is required for transport of integrin to the leading edge [23]. During cellularization of Drosophila embryos, Rab11 is required for basolateral membrane growth [24]. Rab11 has been shown to bind to a subunit of the exocyst complex, Sec15, which regulates polarized vesicle transport in epithelial cells and neurons [25]–[27]. However, despite Rab11's important cellular functions, no GEF for Rab11 has been identified to date.
Rhodopsins (Rhs) are light sensors in Drosophila and vertebrate photoreceptor cells. Rh1 is the major Rh in Drosophila and is present in R1–R6 photoreceptor cells. Upon absorption of a photon (580 nm), Rh1 undergoes a conformational change to an active form, metarhodopsin (metaRh), which in turn signals through a G-protein-coupled cascade that triggers the opening of the transient receptor potential (TRP) channel and leads to the depolarization of photoreceptor cells [1]. Besides its sensory role, Rh1 is required to form a rhabdomere terminal web, a meshwork of F-Actin cables, which is proposed to play a supporting role in the highly stacked rhabdomeric membranes [28],[29]. A complete loss of Rh1 causes a collapse of the rhabdomere membrane at ∼90% of pupal development [30].
During development of photoreceptors, Rh1 is synthesized and matures in the endoplasmic reticulum, after which it is transported to the rhabdomeres via the Golgi. Impairment of its maturation process leads to severe photoreceptor degeneration [31],[32]. Rab11 has been shown to be required for the post-Golgi trafficking of Rh1 to the apical rhabdomere membrane during the development of the photoreceptors. Rab11 colocalizes with Rh1 in the sub-rhabdomere region in vesicles, and reducing Rab11 activity causes defects in rhabdomere morphogenesis and accumulation of Rh1-positive vesicles in the cytosol [33],[34]. However, a role for Rab11 in adult photoreceptor cells and the phototransduction pathway has not been documented.
In adult flies, the conversion of metaRh1 back to Rh1 upon light exposure mainly occurs on the rhabdomere membrane upon absorption of a second photon (580 nm) [35]. In addition, some Rh1 is endocytosed and degraded through a lysosomal pathway, to scavenge spontaneously activated or phosphorylated metaRh, thereby preventing photoreceptor degeneration [34],[36]. As a consequence, newly synthesized Rh1 is delivered back to rhabdomeres to maintain Rh1 homeostasis as well as the overall rhabdomere morphology. However, it is unclear how this process is regulated in response to light exposure. Our data indicate that Crag and Rab11 play an essential role in the regulated transport of Rh1 to the rhabdomere membrane upon light stimulation and Ca2+ influx.
To isolate novel genes that are involved in visual transduction, we performed an F3 forward genetic screen on the Drosophila X chromosome [37]. Mutations were induced using low concentrations (7.5–15 mM) of ethylmethane sulfonate on an FRT-containing X chromosome, and 33,887 stocks were screened for lethal mutations. A collection of 5,859 X-linked lethal mutations was established, and mutant clones in the eye were generated with ey-FLP [38]. We then performed ERG recordings on mutant photoreceptor cells of 3-wk-old flies and screened for aberrant ERGs. Hundreds of mutations were isolated, and rough mapping was performed through rescue of the lethality using X-chromosome duplications [39]. Mutations rescued by the same duplication were crossed inter se, and complementation groups were established. Here we report the characterization of one of these complementation groups. This complementation group, XE10, consists of three alleles (A, C, and D), and homozygous animals die as second or third instar larvae. ERGs of 3-wk-old XE10 mutant eye clones exhibit a reduction in both the amplitude of depolarization and the size of “on–off” transients when compared to control flies (Figure 1A and 1B).
To identify the gene that is mutated in the XE10 complementation group, we performed duplication and deficiency mapping [39],[40] and narrowed the candidate region to a ∼120-kb interval. We performed complementation tests between the XE10 alleles and known lethal mutations in the region, and found a lethal insertion PBac{WH}CG12659f07899 [41] inserted in the first exon of the Crag gene that fails to complement all three XE10 alleles for larval lethality (Figure 1C). The XE10 mutations also fail to complement a previously isolated null allele of Crag, CragCJ101 [8]. A Crag genomic construct rescues the lethality and phenotypes associated with all XE10 mutations (blue in Figure 1D), showing that we have identified novel alleles of Crag. We identified a missense mutation in CragA (C1371S), a nonsense mutation in CragC (R798STOP), and a point mutation affecting a splice donor site in CragD (Figure 1F). Lethal phase analyses indicate that the A and C alleles are severe loss-of-function or null alleles, whereas D is a hypomorphic allele (Figure 1E). We observe no immunoreactivity in CragC clones of L3 larval eye imaginal discs using an antibody recognizing Crag, whereas in wild-type cells, the antibody reveals cytoplasmic punctae (Figure 1G). Crag is a homolog of the human DENND4A, DENND4B, and DENND4C proteins. Expression of a UAS–human DENND4A construct using the ubiquitous daughterless-GAL4 driver rescues the lethality caused by loss of Crag (Figure 1E), indicating that Crag and DENND4A have conserved functions.
Since Crag mutant clones exhibit reduced ERG amplitude in 3-wk-old flies, we first examined whether the development of the visual system is affected by Crag mutations. To assess the morphology of photoreceptors, we performed cross-section and transmission electron microscopy (TEM) analysis of the eyes of newly eclosed flies. The data show that Crag mutant photoreceptors have normal rhabdomere morphology at day 1 (Figure 2B), indicating that the photoreceptors develop properly. To examine axonal targeting of the photoreceptors, we immunohistochemically stained the terminals of R7 and R8 in the medulla for Chaoptin, a photoreceptor-membrane-specific protein [42]. As shown in Figure S1, the two-layer projection patterns of R7 and R8 photoreceptors in the medulla in control and Crag mutant cells are indistinguishable. We then performed TEM in the lamina to determine whether R1–R6 photoreceptors target properly. Crag mutant photoreceptors form cartridges with the normal complement of photoreceptor terminals and synapses (Figure S2A–S2D). These data indicate that the photoreceptors display proper axonal guidance and synapse formation. Hence, the aberrant ERGs of Crag mutant clones are not likely due to developmental defects.
To determine whether Crag mutations cause photoreceptor degeneration, flies were kept either in a 12-h on/off light (∼1,800 Lux) cycle or in constant darkness, and ERGs were recorded at different time points. Flies carrying eye-specific clones of the CragC or CragCJ101 null alleles (Figure S3A and S3B) exhibit normal ERG responses at day 1 when kept in the dark, indicating that the photoreceptors develop and function properly (Figure 2A, 2C, and 2D). To further examine whether phototransduction is affected by Crag mutations in dark-raised flies, we performed intracellular recording of single photoreceptor cells. The data show that Crag mutant photoreceptors exhibit normal response to light stimulation when compare to controls (Figure S3C–S3E).
However, both the ERG amplitude and the size of on–off transients become smaller with age when the flies are reared in a 12-h on/off light cycle, whereas the ERGs of wild-type controls are unaffected in aged flies (Figure 2A, 2C, and 2D). Interestingly, Crag mutant clones still exhibit a normal ERG response when kept in the dark for 2 wk. The phenotypes of aged flies kept in a 12-h on/off light cycle are fully rescued by the genomic rescue construct (Figure 2A). Moreover, when the flies are exposed to constant light, ERG amplitudes of Crag mutant clones are severely affected, and a 90% reduction of ERG amplitude is observed in Crag clones after only 5 d. After 2 wk in constant light, the ERG responses are completely abolished in Crag mutant cells (Figure 2A). Since Rh1 is the major light sensor of Drosophila photoreceptors, we also measured the Rh1 levels of flies exposed to light for different periods of time (Figure S4). The data show that the Rh1 levels of Crag mutant clones gradually decrease when the flies are aged in a light/dark cycle but not when they are kept in the dark. These data show that mutations in Crag cause a light- and age-dependent disruption of photoreceptor cell function.
To determine whether the photoreceptor cells undergo neurodegeneration, we performed cross-section and TEM analysis of the fly eye. Crag mutant photoreceptors have normal rhabdomere morphology in newly eclosed animals, but upon 2 wk of light exposure, the rhabdomeres become severely damaged (Figure 2B and 2E). Since the sizes of the on–off transients are reduced in Crag mutant cells upon light exposure, we also examined the morphology of the photoreceptor terminals after 14 d of incubation in the light/dark cycle. The morphology of the terminals is also disrupted in Crag mutant clones, and subcellular organelles such as capitate projections, mitochondria, and active zones are barely recognizable (Figure S2F). The wild-type controls, as well as mutant flies that carry a genomic rescue construct (data not shown), maintain a normal morphology of photoreceptors upon light exposure (Figures 2B, 2E, and S2E). These data show that Crag mutations indeed cause light-induced photoreceptor cell degeneration.
Since loss of Crag causes a light-dependent degeneration, we hypothesized that Crag in photoreceptor cells may play a role in phototransduction. Upon light stimulation, Rh1 undergoes a conformational change to metaRh, which consequently triggers the opening of TRP channels through G-protein-coupled signaling and leads to the depolarization of photoreceptor cells [1]. InaD contains five PDZ domains and serves as a scaffold protein to allow many players in the pathway to form a signaling complex [43]–[45]. We performed immunostainings of major phototransduction proteins with a whole mount protocol to assess whether Crag is required for their subcellular distribution. Rh1, TRP, and InaD are all properly localized in Crag mutant photoreceptors in flies kept in dark, as the staining patterns are similar to those of wild-type cells (Figure 3A). These data are in agreement with the functional data, as Crag mutant clones exhibit normal ERG responses in newly eclosed flies or flies kept in darkness for several weeks. Upon 5 d of exposure to a light/dark cycle, these proteins are still properly localized in control photoreceptors. However, in Crag mutant cells, Rh1 is massively accumulated in the cytosol (Figure 3B). In contrast, a cytosolic accumulation of TRP and InaD is not observed (Figure 3B). Notably, with the whole mount staining protocol, a crescent shaped distribution pattern of these proteins is observed at the base of the rhabdomeres, which is consistent with previous studies [36],[46]–[48]. In contrast, a uniform distribution of these proteins in the rhabdomeres has been observed in immunostainings of thin sections [49]–[52]. We therefore also performed immunostainings in cross-sections of photoreceptors and observed a rhabdomeric localization of Rh1, InaD, and TRP in flies kept in dark (Figure 3C). Similarly, the data also show that light stimulation induces a cytosolic accumulation of Rh1, but not TRP and InaD, in Crag mutant cells (Figure 3D). The difference in localization between the two protocols is probably due to a different accessibility of the antibodies, since rhabdomeres are highly packed membrane stacks. These data suggest that Crag is involved in Rh1 transport upon light stimulation but not during photoreceptor development.
Since Rh1 is a transmembrane protein, its accumulation in the cytosol of Crag mutant cells upon light stimulation would imply an accumulation of membrane structures. We therefore examined the ultrastructure of the light-stimulated photoreceptor cells by TEM. After 5 d of 12-h on/off light stimulation, Crag mutant cells exhibit a massive accumulation of vesicles in the cytosol (25.2±8.4 in wild type; 186.8±21.3 in Crag mutants: n = 10, p<0.001) when compared to controls (Figure 3E and 3F). The accumulation of Rh1 as well as vesicles in the cytosol indicates a defect in vesicular trafficking of Rh1 in Crag mutant cells.
During photoreceptor development, Rh1 is delivered into rhabdomeres through a Rab11-mediated vesicle transport [34]. In adult flies, a subpopulation of metaRh is endocytosed and degraded [36],[46], and this Rh1 loss should be replenished with newly synthesized Rh1 to maintain homeostasis. The accumulation of vesicles in Crag mutant photoreceptors could be due to an increase in endocytosis, a decrease in the clearance of the endocytosed vesicles, or a failure to secrete newly synthesized vesicles. With white light stimulation, Rh1 is internalized at a very slow rate, as determined by Western blots and immunostainings (Figure S5A and S5B). Therefore, we kept flies for a 6-h period in blue light to determine which aspect of Rh1 trafficking is affected by Crag. Blue light converts Rh1 to metaRh, and metaRh requires orange light (580 nm) to be converted back into Rh1. In the absence of orange light, blue light triggers massive endocytosis and degradation of metaRh [53] (Figure 4A). Western blot data show that exposure for 6 h to blue light leads to a similar decrease of Rh1 in both wild-type and Crag mutant cells (Figure 4B, upper panel), indicating that endocytosis and degradation of metaRh is not significantly affected in Crag mutant photoreceptors. Moreover, upon 24 h of recovery in the dark, there is a significant increase in Rh1 levels in both wild-type and Crag mutant cells, indicating that the de novo synthesis of Rh1 is also not affected in Crag mutant cells. Previous studies have shown that internalized Rh1 may form insoluble aggregates that are not detectable in Western blot [36]. We therefore homogenized fly heads in SDS buffer containing 1 M urea and performed dot blots to assess total Rh1 levels. The data confirm that Rh1 is indeed degraded upon 6 h of blue light stimulation in both wild-type and Crag mutant cells (Figure 4B, lower panel). In summary, Crag does not appear to affect endocytosis, degradation, or synthesis of Rh1.
We further examined Rh1 dynamics upon blue light stimulation with the whole mount staining protocol. As shown in Figure 4C, 6 h of blue light stimulation triggers massive endocytosis of Rh1 in both wild-type and Crag mutant photoreceptors. After 18 h of recovery in the dark, cytosolic Rh1 is reduced and the crescent shaped pattern is re-formed in the wild-type photoreceptors, whereas in Crag mutant photoreceptors, Rh1 remains accumulated in the cytosol. Furthermore, the accumulation of Rh1 in Crag mutants persists even after 36 h of recovery.
Blue light exposure drives photoreceptors into prolonged depolarizing afterpotential (PDA) [35]. To test whether the accumulation of Rh1 in Crag mutant cells is caused by a PDA, we exposed the flies to blue light followed by orange light for 20 min to terminate the PDA. We then kept the flies for 18 h in the dark and examined the Rh1 distribution (Figure S5C). Rh1 still accumulates in the cytosol of Crag mutant photoreceptors but not in controls, indicating that Rh1 localization defects are not caused by PDA.
We next performed TEM analysis of photoreceptors during the course of the blue light treatment and the recovery (Figure S6). The data indicate that electron densities in the cytosol correlate with the Rh1 levels in the cytosol. Interestingly, after 36 h of dark recovery, the rhabdomeres start to break down in Crag mutant cells, as revealed by Actin staining and TEM analyses (Figure S6). The data suggest that persistent accumulation of Rh1 in the cytosol leads to the breakdown of rhabdomeres and the degeneration of photoreceptor cells.
Since the degradation of Rh1 is unaffected in Crag mutant cells, we hypothesized that the accumulation of Rh1 in the cytosol may be the result of a defect in transport of newly synthesized Rh1 to the rhabdomeres. We therefore stained the photoreceptors with a trans-Golgi marker, Peanut agglutinin [54], and observed an expansion of the TGN in Crag mutant photoreceptors (Figure 4D). Moreover, a portion of the accumulated Rh1 colocalizes with the trans-Golgi marker. These data, along with the vesicle accumulation observed by TEM, suggest that Crag is required for post-Golgi trafficking of Rh1 to the rhabdomeres. In summary, loss of Crag leads to accumulation of newly synthesized Rh1 in post-Golgi vesicles, a breakdown of rhabdomeres, and, eventually, photoreceptor degeneration.
Crag possesses three DENN domains and may serve as a GEF for a Rab protein. To identify its potential target(s), we performed a screen using a collection of 31 Rab dominant negative (DN) transgenic lines [55] (Figure S7). To bypass the requirement of some Rabs for the development of photoreceptor cells, we used Rh1-GAL4, which is expressed late in photoreceptor formation and in adult photoreceptors, to drive expression of the Rab-DNs. To lower expression levels we also raised the flies at 18°C prior to eclosion. We then aged the flies in a 12-h light/dark cycle or darkness for 21 d and performed ERGs. Expression of only Rab11-DN (S25N) causes a light-dependent reduction of ERG amplitude in 3-wk-old flies exposed to light, similar to that in Crag mutants (Figures 5A, 5B, and S7). To further confirm that loss of Rab11 activity in the adult eye leads to a light-dependent photoreceptor degeneration, we expressed two Rab11 double-stranded RNA constructs [34],[56] in the adult eye using Rh1-GAL4 and found that they also cause a reduction of ERG amplitude upon light stimulation (Figure 5A and 5B). In contrast, expression of wild-type or constitutively active (CA) Rab11 does not cause a phenotype (Figure 5A and 5B). Next, we performed TEM after light and dark exposure of photoreceptors with Rab11 knockdown. Light exposure disrupts the photoreceptor cell morphology in these cells, whereas dark incubation causes no obvious effects after 3 wk (Figure 5C). These data indicate that knockdown of Rab11 leads to a light-induced photoreceptor degeneration similar to that of loss of Crag. Hence, Rab11 is a potential target of Crag.
To assess whether Crag and Rab11 physically interact, we generated tagged Crag (FLAG) and Rab11 (HA) expression constructs and first examined their protein localizations in Drosophila S2 cells. Crag colocalizes with Rab11 when both are co-expressed (Figure 6A). When Rab7 and Crag are co-expressed, the large overlapping punctae observed when Rab11 and Crag are co-expressed are not obvious (Figure 6B). We then performed co-immunoprecipitation (co-IP) using an antibody against HA to pull down Rab11, and found that Crag is co-precipitated with Rab11, indicating that Crag is a binding partner of Rab11 (Figure 6C). GEFs bind to the GDP-loaded Rabs to promote the release of GDP, whereas binding of GTP to Rabs diminishes the binding of the GEFs. Hence, GEFs have a higher affinity for GDP-bound forms of Rabs than for GTP-bound forms. We therefore performed co-IP between Crag and the DN (mostly GDP-bound) or CA (mostly GTP-bound) form of Rab11. The results show that Crag binds preferentially to the Rab11-DN, and weakly to Rab11-CA (Figure 6C and 6D). We next mapped the Rab11 binding domain of Crag by generating a series of deletion constructs. The constructs containing the three DENN domains co-immunoprecipitate with Rab11, whereas those lacking DENN domains do not co-immunoprecipitate with Rab11 (Figure 6E). These data suggest that Crag may be a GEF for Rab11.
To determine whether Crag possesses GEF activity, we performed in vitro activity assays. Unfortunately, expression of the 180-kDa Crag protein in Escherichia coli produces insoluble protein in inclusion bodies. We therefore purified the protein using a baculovirus expression system in insect cells. For the GEF assay we preloaded the Rab proteins with fluorescence-labeled BODIPY-GDP, and then added excessive unlabeled GDP with or without the potential GEF and measured the release rate of BODIPY-GDP from the Rabs. Since the human homolog of Crag, DENND4A, was shown to exhibit GEF activity against Rab10 [15], we first performed the GEF assay with Rab10. As shown in Figure 6F, Crag strongly promotes GDP release from Rab10, indicating that Crag and DENND4A are functionally conserved and that the purified Crag protein is a GEF in vitro. However, Rab10 is not expressed in the adult eye [57], and expression of Rab10-DN does not cause a light-dependent degeneration (Figure 5). Hence, the degeneration phenotypes associated with Crag mutations are unlikely to be caused by defects in Rab10 activation.
Next, we performed the GEF assay for Crag against Rab11 and Rab5. As shown in Figure 6G and 6H, Crag indeed facilitates the GDP dissociation from Rab11 but not from Rab5. However, the kinetics of the reaction with Rab11 is slow compared to that of Crag against Rab10. We hypothesized that the slow kinetics is due to the molecular properties of purified Rab11. We therefore added 10 mM EDTA to GDP-preloaded Rab11 to examine its release kinetics. EDTA absorbs Mg2+ from the Rab proteins and induces a rapid release of GDP [58]. Although the kinetics for GDP release is significantly accelerated, it is relatively slow when compared to EDTA-triggered GDP release of Rab5 (Figure 6G and 6H). We concluded that the kinetics of Rab11 in vitro is generally slow. Since Crag binds to CaM in a calcium-dependent manner [7],[8], and since cellular Ca2+ levels increase during photoactivation, we next examined whether CaM regulates Crag activity. In the presence of CaM and Ca2+, the exchange rate is indeed increased (Figure 6G). Hence, it's possible that Crag activity is enhanced during light stimulation upon a Ca2+ influx in photoreceptor cells. These data, together with our in vivo observations, indicate that Crag is a GEF for Rab11.
We next examined the interactions between Crag and Rab11 in vivo. We first performed immunostaining of Crag and Rab11. Crag and Rab11 colocalize in punctate structures (Figure 7A), indicating that they may physically interact in vivo. The subcellular localization of Rab proteins often depends on their activation status. When bound to GTP, Rab proteins bind to various effectors that help target the proteins to the proper membrane compartments. Hence, many GEFs regulate the subcellular localization of Rabs. To determine whether Crag is required for the proper subcellular localization of Rab11, we exposed the flies to 12 h of light stimulation and performed immunostaining of Rab11 and Rh1. In control photoreceptors, Rab11 colocalizes with Rh1 in numerous punctae, and many of the punctae are in close vicinity to the rhabdomeres, providing further evidence for a role for Rab11 in regulating Rh1 transport. In Crag mutant photoreceptors, Rab11 exhibits a more diffuse pattern, less punctae are observed, and the punctae are rarely localized in the vicinity of rhabdomeres (Figure 7B). These data show that Crag is indeed required for the proper localization of Rab11 in adult photoreceptors.
To determine whether Rab11 functions downstream of Crag, we overexpressed a CA form of Rab11 (Rab11-CA), which does not require a GEF for its activation, in Crag mutant cells. If Crag functions as a GEF for Rab11, expression of Rab11-CA may rescue the defects associated with the loss of Crag. Hence, we aged the flies for 3 wk in a 12-h light/dark cycle and examined photoreceptor function with ERGs. As shown in Figure 7C–7E, Rab11-CA expression in Crag mutant cells partially rescues the ERG phenotypes. We also expressed Rab10-CA in the Crag clones and observed no significant rescue (data not shown). To further determine whether the Rab11-CA is able to also suppress the light-induced morphological defects of photoreceptors associated with the loss of Crag, we performed TEM. The data show that the rhabdomeres are much better preserved upon light stimulation in Crag mutant cells expressing Rab11-CA than in those expressing a GFP control (Figure 7F). In addition, we compared the time course of photoreceptor degeneration using ERG recordings in flies that contained Crag mutant clones and/or expressed a DN mutation for Rab11 in the eyes (Figure S8). The data show that no significant additive or synergetic effect is observed when Crag and Rab11 are impaired, suggesting that they are involved in the same pathway required for photoreceptor maintenance. In summary, these data indicate that Rab11 functions downstream of Crag genetically, and suggest that Crag regulates Rh1 transport via Rab11 in Drosophila photoreceptors.
Here, we show that Crag is a novel GEF for Rab11 and that it is required for the post-Golgi transport of Rh1 to the rhabdomeres during light activation (Figures 7G and S10). This regulated transport of Rh1, which is independent of Rh1 transport during the development of the photoreceptors, replenishes the loss of Rh1 induced by light stimulation. Loss of Crag leads to accumulation of secretory vesicles in the cytosol of photoreceptor cells, and eventually leads to a light- and age-dependent photoreceptor degeneration.
During development of photoreceptors, Rh1 and other phototransduction proteins are synthesized in the endoplasmic reticulum and transported to the rhabdomeres to build functional photoreceptors. Some molecular players, including Rab11 and XPORT, have been shown to play a role in this process [32],[34]. Upon light activation Rh1 is converted to metaRh (Figure S10A). MetaRh is then converted back into Rh1 on rhabdomere membranes via absorption of another photon, allowing the maintenance of Rh1 levels in the rhabdomere [35]. In wild-type photoreceptors, a portion of metaRh is phosphorylated and endocytosed [46], and it has been proposed that internalization of metaRh promotes the clearance of dysfunctional proteins and serves as a proofreading mechanism (Figure S10B). Internalized Rh1 is then degraded through an endosomal/lysosomal pathway [36]. Obviously, the gradual loss of Rh1 in wild-type photoreceptors leads to the necessity to constitutively synthesize Rh1 and replenish the rhabdomeric pool. This is nicely illustrated with the loss of retinol dehydrogenase (RDH), which is required for the regeneration of the chromophore of Rh1. Loss of RDH leads to progressive reduction in rhabdomere size and light-dependent photoreceptor degeneration [50].
Our data show that Crag is required to maintain homeostasis of Rh1 upon light stimulation. Loss of Crag leads to Rh1 accumulation in the cytosol and, eventually, retinal degeneration in the presence of light. Mutations in genes that affect metaRh1 turnover, such as Calmodulin and arrestin 2 [52],[59], lead to prolonged deactivation time of the photoresponse. Since both ERGs and single-cell recordings of Crag mutant photoreceptors are normal, it is unlikely that Crag is involved in the recycling of metaRh1 to Rh1. To test whether Crag is required for transport of newly synthesized Rh1 in adult photoreceptors, we exposed the flies to blue light to trigger massive endocytosis and degradation of Rh1, and then measured the new synthesis and transport of Rh1 back to the rhabdomeres over time. Crag is not required for the synthesis of Rh1. However, in Crag mutants, the newly synthesized Rh1 accumulates in the cytosol. We propose that Crag is required for the delivery of newly synthesized Rh1 to the rhabdomeres and that loss of Crag leads to a gradual reduction in the size of rhabdomeres and to degeneration of the photoreceptor cells (Figure S10C and S10D). Indeed, the time course and morphological features of degeneration associated with loss of Crag are very similar to the phenotypes observed in RDH mutants, further supporting that Crag is involved in the Rh1 synthesis/delivery pathway.
Rab11 has been implicated in various intracellular membrane trafficking processes. Its diverse functions in different membrane compartments are mediated through its downstream effectors in a context-specific manner; many of these functions have been identified in previous studies [60]. However, GEFs for Rab11 in any context have not yet been identified. Our in vivo and in vitro data provide compelling evidence that Crag is a GEF for Rab11. First, in Drosophila S2 cells, Crag colocalizes and physically interacts with Rab11. Second, Crag preferably binds to the GDP-bound form of Rab11, and the DENN domains are required for binding. Third, Crag is required for the proper localization of Rab11 in photoreceptors upon light stimulation. Fourth, loss of Crag or Rab11 leads to a similar light-induced photoreceptor degeneration. Fifth, expression of Rab11-CA partially rescues the degeneration caused by Crag mutations. Finally, an in vitro GEF assay shows that Crag facilitates the release of GDP from Rab11. It has been previously established that Rab11 is essential for photoreceptor cell development and Rh1 transport during pupal stages [34]. However both rhabdomere morphology and Rh1 localization are normal in Crag clones in newly eclosed flies. Similarly, initial deposition of TRP is also not affected by Crag mutations, in agreement with previous findings that Rh1 and TRP are co-transported to the rhabdomeres during their development [32]. Interestingly, cytosolic localization of TRP is not observed in Crag mutant photoreceptor cells exposed to light, suggesting that during light stimulation, Rh1 and TRP dynamics are distinct. Indeed, internalization of TRP upon light stimulation has not been reported in previous studies. Our data therefore indicate that other GEFs must exist for Rab11 during photoreceptor development, and that Crag is specifically required for Rab11 GDP/GTP exchange during light activation in adult flies. In addition, Crag may function as a GEF for Rab10 in other processes and cells, such as polarized deposition of basement membrane proteins in follicle cells.
The biochemical assay shows that the kinetics of Crag GEF activity is slow when compared to the GEF activity of other DENN-domain-containing proteins such as the Rab35 GEF [14]. Crag exhibits GEF activity against Rab10 with much faster kinetics than against Rab11, indicating that the slow kinetics may be due to properties of Rab11. This is further supported by the slow kinetics of EDTA that triggers GDP release of Rab11. It's possible that the GDP/GTP exchange of Rab11 requires other co-factors besides its GEF, as, for example, documented for Rab6 [61],[62].
CaM is a ubiquitously expressed calcium sensor [63]. In the Drosphila photoreceptor cells, photoactivation leads to influx of Ca2+ and activation of CaM. It has been shown that CaM is required for the termination of the photoresponse in several steps, including TRP inactivation and conformational change of metaRh [59],[64]. Crag contains a CaM binding site and interacts with CaM in a calcium-dependent manner [7],[8]. In our in vitro GEF assay, the presence of CaM and Ca2+ indeed enhances the GEF activity of Crag. Hence, it is possible that a light-induced increase of intracellular Ca2+ level enhances Crag activity via CaM binding. The activation of Crag/Rab11 then may serve to replenish rhabdomeric Rh1, whose loss is also induced by light stimulation.
In vertebrate rod cells, polarized transport of Rh is mediated by post-Golgi vesicles that bud from the TGN and fuse with the base of the outer segment [65],[66]. Rab11 has been detected on rhodopsin-bearing post-Golgi vesicles in photoreceptors [67],[68]; however, it has not yet been shown that Rab11 is required for Rh trafficking. DENND4 proteins are highly similar to Crag. Here we showed that expression of the UAS–human DENND4A construct not only rescues the lethality but also rescues the light-induced photoreceptor degeneration caused by loss of Crag (Figure S9), showing that the molecular function of DENND4A is also conserved. Moreover, three different subtypes of Usher syndrome, an inherited condition characterized by hearing loss and progressive vision loss, have been mapped to the vicinity of the DENND4A locus at 15q22.31 [69]–[71]. Hence, DENND4A may also function through Rab11 in human photoreceptors, and loss of DENND4A may lead to photoreceptor degeneration.
ERG recordings were performed as previously described [38]. In brief, adult flies were glued to a glass slide, a recording probe was placed on the surface of the eye, and a reference probe was inserted in the thorax. A 1-s flash of white light was given, and the response was recorded.
Single-photoreceptor recordings were performed as previously described [72]. In brief, a small hole was cut on the cornea of the eye, and the hole was sealed by Vaseline. A reference probe was placed at the back of the head, and a recording probe was inserted into the retina through the previously cut hole. The membrane potential was monitored by AXOCLAMP-2B (Axon Instruments). When the membrane potential dropped to below −60 mV, the fly was given a 10-ms white light stimulation (white LED, 7,000 mcd, super bright LEDs), and the response was recorded.
Electron microscopy of the photoreceptor and the lamina was performed as described [73]. In brief, fly heads were dissected and fixed at 4°C in 4% paraformaldehyde, 2% glutaraldehyde, 0.1 M sodium cacodylate, and 0.005% CaCl2 (pH 7.2) overnight, postfixed in 1% OsO4 for 1 h, dehydrated in ethanol and propylene oxide, and then embedded in Embed-812 resin (Electron Microscopy Sciences). Thin sections (∼50 nm) of the photoreceptor and lamina were stained in 4% uranyl acetate and 2.5% lead nitrate, and TEM images were captured using a transmission electron microscope (model 1010, JEOL) with a digital camera (US1000, Gatan). For quantification, the sizes of rhabdomeres are determined in Image J.
For cross-sections, fly heads were bisected, fixed in 4% paraformaldehyde for 3 h, dehydrated in acetone, embedded in LR white resin (Polysciences), and sectioned. Immunostaining was performed as described by [51]. For whole mount staining of fly heads, heads were fixed in 4% formaldehyde upon removal of the proboscis. The photoreceptors were dissected and fixed for 15 min. Standard immunostaining procedures were then performed, as previously described [74]. Images were obtained with a Zeiss LSM 710 confocal microscope. Antibodies were as follows: rat anti-Crag [8], 1∶200; mouse monoclonal anti-Rab11 (BD Biosciences), 1∶20; rabbit anti-Rab11 [34], 1∶1,000; mouse anti-Rh1 [75], 1∶50; rabbit anti-TRP [76], 1∶100; rabbit anti-InaD [45], 1∶200; rabbit anti-Arr2 [52], 1∶200; biotin-conjugated Peanut agglutinin (Vector Labs), 1∶1,000; Alexa 488–conjugated phalloidin (Invitrogen), 1∶200; and Alexa 405–, Alexa 488–, Cy3-, or Cy5-conjugated secondary antibodies (Jackson ImmunoResearch), 1∶200.
Flies were kept in a box with a blue LED (465 nm) light panel (∼1,000 lux) for 6 h with or without 24 h recovery in dim white light. For Western blots, fly heads were separated, homogenized, and incubated with SDS loading buffer (50 mM Tris-HCl [pH 6.8], 2% SDS, 10% glycerol, 1% β-mercaptoethanol, 12.5 mM EDTA, and 0.02% bromophenol blue) for 20 min at room temperature. For dot blots, fly heads were homogenized in SDS buffer with 1 M urea, as previously described [36]. Sample buffer was applied to natural cellulose membrane and air dried, followed by antibody detection. A polyclonal rabbit anti-Rh1 antibody (1∶2,000) was used to detect Rh1 [34].
S2 cells were cultured in Schneider's media with 10% fetal bovine serum at room temperature and transfected using Lipofectamin LTX (Invitrogen). For immunostaining, mouse anti-FLAG (M2, Sigma) and rat anti-HA (Roche) antibodies were used to detect Crag (FLAG) and Rab (HA) proteins. For co-IP, cells were harvested 40 h after infection and lysed with lysis buffer (50 mM Tris-HCl [pH 8.0], 100 mM NaCl, 1 mM EDTA, 1% Nonidet P-40, and Complete Protease Inhibitor Cocktail Tablets [Roche]). For comparing the binding affinity of Rab11-DN and Rab11-CA with Crag, EDTA was removed from the lysis buffer. Then the cell lysates were incubated with anti-HA affinity gel (Sigma) for 3 h in lysis buffer at 4°C. The anti-HA gel was pelleted and analyzed by Western blot using anti-HA (Roche), 1∶2,000, or anti-FLAG (M2, Sigma), 1∶1,000, followed by HRP-conjugated secondary antibodies (Jackson ImmunoResearch), 1∶5,000.
Crag cDNA tagged with GST (GE Healthcare) was cloned into a pOPINJ vector [77]. The construct was transfected into SF9 cells (Invitrogen), along with Bsu36I-digested BacPAK6 DNA. Recombined viruses were harvested from the medium and amplified with a second round of infection. To express Crag protein, Hi-5 cells (Invitrogen) were infected with the virus and cultured for 40 h before they were harvested and lysed. Crag protein was then purified from the cell lysates with glutathione sepharose 4B (GE Healthcare). Rab5, Rab10, Rab11, and CaM proteins were generated using the same protocol.
GEF assays were performed as described previously [61]. For preloading with GDP, Rab5, Rab10, and Rab11 were incubated in 110 mM NaCl, 50 mM Tris-HCl (pH 8.0), 1 mM EDTA, 0.8 mM DTT, 0.005% Triton X-100, and 50 µM BODIPY-GDP (Invitrogen) for 60 min at 30°C. 10 mM MgCl2 was then added to stop the reaction. 0.1 uM preloaded Rab proteins were then incubated with or without 0.05 µM Crag in a 110 mM NaCl, 50 mM Tris-HCl (pH 8.0), 12 mM MgCl2, 0.8 mM DTT, and 2 mM GDP solution. To test whether CaM regulates Crag function, 0.05 µM CaM and 1 mM CaCl2 were added to the above reaction. As a positive control, preloaded Rab11 was incubated with 10 mM EDTA in the above solution. The fluorescence intensity was recorded automatically by a FLUOstar OPTIMA plate reader (BMG Labtech) every 30 s over a 2-h period.
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