Inferring the disruption of rabies circulation in vampire bat populations using a betaherpesvirus-vectored transmissible vaccine.

Transmissible vaccines are an emerging biotechnology that hold prospects to eliminate pathogens from wildlife populations. Such vaccines would genetically modify naturally occurring, nonpathogenic viruses ("viral vectors") to express pathogen antigens while retaining their capacity to transmit. The epidemiology of candidate viral vectors within the target wildlife population has been notoriously challenging to resolve but underpins the selection of effective vectors prior to major investments in vaccine development. Here, we used spatiotemporally replicated deep sequencing to parameterize competing epidemiological mechanistic models of Desmodus rotundus betaherpesvirus (DrBHV), a proposed vector for a transmissible vaccine targeting vampire bat-transmitted rabies. Using 36 strain- and location-specific time series of prevalence collected over 6 y, we found that lifelong infections with cycles of latency and reactivation, combined with a high R0 (6.9; CI: 4.39 to 7.85), are necessary to explain patterns of DrBHV infection observed in wild bats. These epidemiological properties suggest that DrBHV may be suited to vector a lifelong, self-boosting, and transmissible vaccine. Simulations showed that inoculating a single bat with a DrBHV-vectored rabies vaccine could immunize >80% of a bat population, reducing the size, frequency, and duration of rabies outbreaks by 50 to 95%. Gradual loss of infectious vaccine from vaccinated individuals is expected but can be countered by inoculating larger but practically achievable proportions of bat populations. Parameterizing epidemiological models using accessible genomic data brings transmissible vaccines one step closer to implementation.

Fast and inefficient star formation due to short-lived molecular clouds and rapid feedback.

The physics of star formation and the deposition of mass, momentum and energy into the interstellar medium by massive stars ('feedback') are the main uncertainties in modern cosmological simulations of galaxy formation and evolution1,2. These processes determine the properties of galaxies3,4 but are poorly understood on the scale of individual giant molecular clouds (less than 100 parsecs)5,6, which are resolved in modern galaxy formation simulations7,8. The key question is why the timescale for depleting molecular gas through star formation in galaxies (about 2 billion years)9,10 exceeds the cloud dynamical timescale by two orders of magnitude11. Either most of a cloud's mass is converted into stars over many dynamical times12 or only a small fraction turns into stars before the cloud is dispersed on a dynamical timescale13,14. Here we report high-angular-resolution observations of the nearby flocculent spiral galaxy NGC 300. We find that the molecular gas and high-mass star formation on the scale of giant molecular clouds are spatially decorrelated, in contrast to their tight correlation on galactic scales5. We demonstrate that this decorrelation implies rapid evolutionary cycling between clouds, star formation and feedback. We apply a statistical method15,16 to quantify the evolutionary timeline and find that star formation is regulated by efficient stellar feedback, which drives cloud dispersal on short timescales (around 1.5 million years). The rapid feedback arises from radiation and stellar winds, before supernova explosions can occur. This feedback limits cloud lifetimes to about one dynamical timescale (about 10 million years), with integrated star formation efficiencies of only 2 to 3 per cent. Our findings reveal that galaxies consist of building blocks undergoing vigorous, feedback-driven life cycles that vary with the galactic environment and collectively define how galaxies form stars.

Prevalence and risk factors for Q fever, spotted fever group rickettsioses, and typhus group rickettsioses in a pastoralist community of northern Tanzania, 2016-2017.

BACKGROUND: In northern Tanzania, Q fever, spotted fever group (SFG) rickettsioses, and typhus group (TG) rickettsioses are common causes of febrile illness. We sought to describe the prevalence and risk factors for these zoonoses in a pastoralist community. METHODS: Febrile patients ≥2 years old presenting to Endulen Hospital in the Ngorongoro Conservation Area were enrolled from August 2016 through October 2017. Acute and convalescent blood samples were collected, and a questionnaire was administered. Sera were tested by immunofluorescent antibody (IFA) IgG assays using Coxiella burnetii (Phase II), Rickettsia africae, and Rickettsia typhi antigens. Serologic evidence of exposure was defined by an IFA titre ≥1:64; probable cases by an acute IFA titre ≥1:128; and confirmed cases by a ≥4-fold rise in titre between samples. Risk factors for exposure and acute case status were evaluated. RESULTS: Of 228 participants, 99 (43.4%) were male and the median (interquartile range) age was 27 (16-41) years. Among these, 117 (51.3%) had C. burnetii exposure, 74 (32.5%) had probable Q fever, 176 (77.2%) had SFG Rickettsia exposure, 134 (58.8%) had probable SFG rickettsioses, 11 (4.8%) had TG Rickettsia exposure, and 4 (1.8%) had probable TG rickettsioses. Of 146 participants with paired sera, 1 (0.5%) had confirmed Q fever, 8 (5.5%) had confirmed SFG rickettsioses, and none had confirmed TG rickettsioses. Livestock slaughter was associated with acute Q fever (adjusted odds ratio [OR] 2.54, 95% confidence interval [CI] 1.38-4.76) and sheep slaughter with SFG rickettsioses case (OR 4.63, 95% CI 1.08-23.50). DISCUSSION: Acute Q fever and SFG rickettsioses were detected in participants with febrile illness. Exposures to C. burnetii and to SFG Rickettsia were highly prevalent, and interactions with livestock were associated with increased odds of illness with both pathogens. Further characterisation of the burden and risks for these diseases is warranted.

Using sulfur stable isotope ratios (δ34 S) for animal geolocation: Estimating the delay mechanisms between diet ingestion and isotope incorporation in tail hair.

RATIONALE: Metabolism and diet quality play an important role in determining delay mechanisms between an animal ingesting an element and depositing the associated isotope signal in tissue. While many isotope mixing models assume instantaneous reflection of diet in an animal- tissue, this is rarely the case. Here we use data from wildebeest to measure the lag time between ingestion of 34 S and its detection in tail hair. METHODS: We use time-lagged regression analysis of δ34 S data from GPS-collared blue wildebeest from the Serengeti ecosystem in combination with δ34 S isoscape data to estimate the lag time between an animal ingesting and depositing 34 S in tail hair. RESULTS: The best fitting regression model of δ34 S in tail hair and an individual- position on the δ34 S isoscape is generated assuming an average time delay of 78 days between ingestion and detection in tail hair. This suggests that sulfur may undergo multiple metabolic transitions before being deposited in tissue. CONCLUSION: Our findings help to unravel the underlying complexities associated with sulfur metabolism and are broadly consistent with results from other species. These findings will help to inform research aiming to apply the variation of δ34 S in inert biological material for geolocation or understanding dietary changes, especially for fast moving migratory ungulates such as wildebeest.

Relationship between animal health and livestock farmers' wellbeing in Ghana: beyond zoonoses.

INTRODUCTION: Livestock production is a key livelihood source for many people in developing countries. Poor control of livestock diseases hamper livestock productivity, threatening farmers' wellbeing and food security. This study estimates the effect of livestock mortalities attributable to disease on the wellbeing of livestock farmers. METHODS: Overall, 350 ruminant livestock farmers were randomly selected from three districts located in the north, middle and southern belts of Ghana. Mixed-effect linear regression models were used to estimate the relationship between animal health and farmer wellbeing. Farmer wellbeing was assessed using the WHOQOL-BREF tool, as the mean quality-of-life in four domains (physical, psychological, social, and environmental). Animal health was assessed as annual livestock mortalities to diseases adjusted for herd size, and standardized in tropical livestock units to account for different ruminant livestock species. We adjusted for the potential confounding effect of farmers' age, sex, educational attainment, farmland size, socio-economic status, perception of disease risk to herd, satisfaction with health, previous experience of disease outbreaks in herds, and social support availability by including these as fixed effects, and community as random effects, in a pre-specified model. RESULTS: Our results showed that farmers had a median score of 65.5 out of 100 (IQR: 56.6 to 73.2) on the wellbeing scale. The farmers' reported on average (median) 10% (IQR: 0 to 23) annual herd mortalities to diseases. There was a significantly negative relationship between increasing level of animal disease-induced mortality in herds and farmers' wellbeing. Specifically, our model predicted an expected difference in farmers' wellbeing score of 7.9 (95%CI 1.50 to 14.39) between a farmer without any herd mortalities to diseases compared to a (hypothetical) farmer with 100% of herd mortalities caused by diseases in a farming year. Thus, there is a reduction of approximately 0.8 wellbeing points of farmers, for the average of 10% disease-induced herd mortalities experienced. CONCLUSIONS: Disease-induced livestock mortalities have a significant negative effect on farmers' wellbeing, particularly in the physical and psychological domains. This suggests that veterinary service policies addressing disease risks in livestock, could contribute to improving the wellbeing of livestock dependent populations, and public food security.

Evaluating the potential of whole-genome sequencing for tracing transmission routes in experimental infections and natural outbreaks of bovine respiratory syncytial virus.

Bovine respiratory syncytial virus (BRSV) is a major cause of respiratory disease in cattle. Genomic sequencing can resolve phylogenetic relationships between virus populations, which can be used to infer transmission routes and potentially inform the design of biosecurity measures. Sequencing of short (<2000 nt) segments of the 15 000-nt BRSV genome has revealed geographic and temporal clustering of BRSV populations, but insufficient variation to distinguish viruses collected from herds infected close together in space and time. This study investigated the potential for whole-genome sequencing to reveal sufficient genomic variation for inferring transmission routes between herds. Next-generation sequencing (NGS) data were generated from experimental infections and from natural outbreaks in Jämtland and Uppsala counties in Sweden. Sufficient depth of coverage for analysis of consensus and sub-consensus sequence diversity was obtained from 47 to 20 samples respectively. Few (range: 0-6 polymorphisms across the six experiments) consensus-level polymorphisms were observed along experimental transmissions. A much higher level of diversity (146 polymorphic sites) was found among the consensus sequences from the outbreak samples. The majority (144/146) of polymorphisms were between rather than within counties, suggesting that consensus whole-genome sequences show insufficient spatial resolution for inferring direct transmission routes, but might allow identification of outbreak sources at the regional scale. By contrast, within-sample diversity was generally higher in the experimental than the outbreak samples. Analyses to infer known (experimental) and suspected (outbreak) transmission links from within-sample diversity data were uninformative. In conclusion, analysis of the whole-genome sequence of BRSV from experimental samples discriminated between circulating isolates from distant areas, but insufficient diversity was observed between closely related isolates to aid local transmission route inference.

Towards the elimination of dog-mediated rabies: development and application of an evidence-based management tool.

BACKGROUND: International organizations advocate for the elimination of dog-mediated rabies, but there is only limited guidance on interpreting surveillance data for managing elimination programmes. With the regional programme in Latin America approaching elimination of dog-mediated rabies, we aimed to develop a tool to evaluate the programme's performance and generate locally-tailored rabies control programme management guidance to overcome remaining obstacles. METHODS: We developed and validated a robust algorithm to classify progress towards rabies elimination within sub-national administrative units, which we applied to surveillance data from Brazil and Mexico. The method combines criteria that are easy to understand, including logistic regression analysis of case detection time series, assessment of rabies virus variants, and of incursion risk. Subjecting the algorithm to robustness testing, we further employed simulated data sub-sampled at differing levels of case detection to assess the algorithm's performance and sensitivity to surveillance quality. RESULTS: Our tool demonstrated clear epidemiological transitions in Mexico and Brazil: most states progressed rapidly towards elimination, but a few regressed due to incursions and control lapses. In 2015, dog-mediated rabies continued to circulate in the poorest states, with foci remaining in only 1 of 32 states in Mexico, and 2 of 27 in Brazil, posing incursion risks to the wider region. The classification tool was robust in determining epidemiological status irrespective of most levels of surveillance quality. In endemic settings, surveillance would need to detect less than 2.5% of all circulating cases to result in misclassification, whereas in settings where incursions become the main source of cases the threshold detection level for correct classification should not be less than 5%. CONCLUSION: Our tool provides guidance on how to progress effectively towards elimination targets and tailor strategies to local epidemiological situations, while revealing insights into rabies dynamics. Post-campaign assessments of dog vaccination coverage in endemic states, and enhanced surveillance to verify and maintain freedom in states threatened by incursions were identified as priorities to catalyze progress towards elimination. Our finding suggests genomic surveillance should become increasingly valuable during the endgame for discriminating circulating variants and pinpointing sources of incursions.

Transmission ecology of canine parvovirus in a multi-host, multi-pathogen system.

Understanding multi-host pathogen maintenance and transmission dynamics is critical for disease control. However, transmission dynamics remain enigmatic largely because they are difficult to observe directly, particularly in wildlife. Here, we investigate the transmission dynamics of canine parvovirus (CPV) using state-space modelling of 20 years of CPV serology data from domestic dogs and African lions in the Serengeti ecosystem. We show that, although vaccination reduces the probability of infection in dogs, and despite indirect enhancement of population seropositivity as a result of vaccine shedding, the vaccination coverage achieved has been insufficient to prevent CPV from becoming widespread. CPV is maintained by the dog population and has become endemic with approximately 3.5-year cycles and prevalence reaching approximately 80%. While the estimated prevalence in lions is lower, peaks of infection consistently follow those in dogs. Dogs exposed to CPV are also more likely to become infected with a second multi-host pathogen, canine distemper virus. However, vaccination can weaken this coupling, raising questions about the value of monovalent versus polyvalent vaccines against these two pathogens. Our findings highlight the need to consider both pathogen- and host-level community interactions when seeking to understand the dynamics of multi-host pathogens and their implications for conservation, disease surveillance and control programmes.

Deep Sequencing of Foot-and-Mouth Disease Virus Reveals RNA Sequences Involved in Genome Packaging.

Nonenveloped viruses protect their genomes by packaging them into an outer shell or capsid of virus-encoded proteins. Packaging and capsid assembly in RNA viruses can involve interactions between capsid proteins and secondary structures in the viral genome, as exemplified by the RNA bacteriophage MS2 and as proposed for other RNA viruses of plants, animals, and human. In the picornavirus family of nonenveloped RNA viruses, the requirements for genome packaging remain poorly understood. Here, we show a novel and simple approach to identify predicted RNA secondary structures involved in genome packaging in the picornavirus foot-and-mouth disease virus (FMDV). By interrogating deep sequencing data generated from both packaged and unpackaged populations of RNA, we have determined multiple regions of the genome with constrained variation in the packaged population. Predicted secondary structures of these regions revealed stem-loops with conservation of structure and a common motif at the loop. Disruption of these features resulted in attenuation of virus growth in cell culture due to a reduction in assembly of mature virions. This study provides evidence for the involvement of predicted RNA structures in picornavirus packaging and offers a readily transferable methodology for identifying packaging requirements in many other viruses.IMPORTANCE In order to transmit their genetic material to a new host, nonenveloped viruses must protect their genomes by packaging them into an outer shell or capsid of virus-encoded proteins. For many nonenveloped RNA viruses the requirements for this critical part of the viral life cycle remains poorly understood. We have identified RNA sequences involved in genome packaging of the picornavirus foot-and-mouth disease virus. This virus causes an economically devastating disease of livestock affecting both the developed and developing world. The experimental methods developed to carry out this work are novel, simple, and transferable to the study of packaging signals in other RNA viruses. Improved understanding of RNA packaging may lead to novel vaccine approaches or targets for antiviral drugs with broad-spectrum activity.

Identification of Low- and High-Impact Hemagglutinin Amino Acid Substitutions That Drive Antigenic Drift of Influenza A(H1N1) Viruses.

Determining phenotype from genetic data is a fundamental challenge. Identification of emerging antigenic variants among circulating influenza viruses is critical to the vaccine virus selection process, with vaccine effectiveness maximized when constituents are antigenically similar to circulating viruses. Hemagglutination inhibition (HI) assay data are commonly used to assess influenza antigenicity. Here, sequence and 3-D structural information of hemagglutinin (HA) glycoproteins were analyzed together with corresponding HI assay data for former seasonal influenza A(H1N1) virus isolates (1997-2009) and reference viruses. The models developed identify and quantify the impact of eighteen amino acid substitutions on the antigenicity of HA, two of which were responsible for major transitions in antigenic phenotype. We used reverse genetics to demonstrate the causal effect on antigenicity for a subset of these substitutions. Information on the impact of substitutions allowed us to predict antigenic phenotypes of emerging viruses directly from HA gene sequence data and accuracy was doubled by including all substitutions causing antigenic changes over a model incorporating only the substitutions with the largest impact. The ability to quantify the phenotypic impact of specific amino acid substitutions should help refine emerging techniques that predict the evolution of virus populations from one year to the next, leading to stronger theoretical foundations for selection of candidate vaccine viruses. These techniques have great potential to be extended to other antigenically variable pathogens.

Dynamics of a morbillivirus at the domestic-wildlife interface: Canine distemper virus in domestic dogs and lions.

Morbilliviruses cause many diseases of medical and veterinary importance, and although some (e.g., measles and rinderpest) have been controlled successfully, others, such as canine distemper virus (CDV), are a growing concern. A propensity for host-switching has resulted in CDV emergence in new species, including endangered wildlife, posing challenges for controlling disease in multispecies communities. CDV is typically associated with domestic dogs, but little is known about its maintenance and transmission in species-rich areas or about the potential role of domestic dog vaccination as a means of reducing disease threats to wildlife. We address these questions by analyzing a long-term serological dataset of CDV in lions and domestic dogs from Tanzania's Serengeti ecosystem. Using a Bayesian state-space model, we show that dynamics of CDV have changed considerably over the past three decades. Initially, peaks of CDV infection in dogs preceded those in lions, suggesting that spill-over from dogs was the main driver of infection in wildlife. However, despite dog-to-lion transmission dominating cross-species transmission models, infection peaks in lions became more frequent and asynchronous from those in dogs, suggesting that other wildlife species may play a role in a potentially complex maintenance community. Widespread mass vaccination of domestic dogs reduced the probability of infection in dogs and the size of outbreaks but did not prevent transmission to or peaks of infection in lions. This study demonstrates the complexity of CDV dynamics in natural ecosystems and the value of long-term, large-scale datasets for investigating transmission patterns and evaluating disease control strategies.

Relative abundance of Mycobacterium bovis molecular types in cattle: a simulation study of potential epidemiological drivers.

BACKGROUND: The patterns of relative species abundance are commonly studied in ecology and epidemiology to provide insights into underlying dynamical processes. Molecular types (MVLA-types) of Mycobacterium bovis, the causal agent of bovine tuberculosis, are now routinely recorded in culture-confirmed bovine tuberculosis cases in Northern Ireland. In this study, we use ecological approaches and simulation modelling to investigate the distribution of relative abundances of MVLA-types and its potential drivers. We explore four biologically plausible hypotheses regarding the processes driving molecular type relative abundances: sampling and speciation; structuring of the pathogen population; historical changes in population size; and transmission heterogeneity (superspreading). RESULTS: Northern Irish herd-level MVLA-type surveillance shows a right-skewed distribution of MVLA-types, with a small number of types present at very high frequencies and the majority of types very rare. We demonstrate that this skew is too extreme to be accounted for by simple neutral ecological processes. Simulation results indicate that the process of MVLA-type speciation and the manner in which the MVLA-typing loci were chosen in Northern Ireland cannot account for the observed skew. Similarly, we find that pathogen population structure, assuming for example a reservoir of infection in a separate host, would drive the relative abundance distribution in the opposite direction to that observed, generating more even abundances of molecular types. However, we find that historical increases in bovine tuberculosis prevalence and/or transmission heterogeneity (superspreading) are both capable of generating the skewed MVLA-type distribution, consistent with findings of previous work examining the distribution of molecular types in human tuberculosis. CONCLUSION: Although the distribution of MVLA-type abundances does not fit classical neutral predictions, our simulations show that increases in pathogen population size and/or superspreading are consistent with the pattern observed, even in the absence of selective pressures acting on the system.

Mobile Phones As Surveillance Tools: Implementing and Evaluating a Large-Scale Intersectoral Surveillance System for Rabies in Tanzania.

Katie Hampson and colleagues describe their experience of developing and deploying a large-scale rabies surveillance system based on mobile phones in southern Tanzania.

Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study.

Epidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in northern Tanzania as a case study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics.

Predicting the public health benefit of vaccinating cattle against Escherichia coli O157.

Identifying the major sources of risk in disease transmission is key to designing effective controls. However, understanding of transmission dynamics across species boundaries is typically poor, making the design and evaluation of controls particularly challenging for zoonotic pathogens. One such global pathogen is Escherichia coli O157, which causes a serious and sometimes fatal gastrointestinal illness. Cattle are the main reservoir for E. coli O157, and vaccines for cattle now exist. However, adoption of vaccines is being delayed by conflicting responsibilities of veterinary and public health agencies, economic drivers, and because clinical trials cannot easily test interventions across species boundaries, lack of information on the public health benefits. Here, we examine transmission risk across the cattle-human species boundary and show three key results. First, supershedding of the pathogen by cattle is associated with the genetic marker stx2. Second, by quantifying the link between shedding density in cattle and human risk, we show that only the relatively rare supershedding events contribute significantly to human risk. Third, we show that this finding has profound consequences for the public health benefits of the cattle vaccine. A naïve evaluation based on efficacy in cattle would suggest a 50% reduction in risk; however, because the vaccine targets the major source of human risk, we predict a reduction in human cases of nearly 85%. By accounting for nonlinearities in transmission across the human-animal interface, we show that adoption of these vaccines by the livestock industry could prevent substantial numbers of human E. coli O157 cases.

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data.

BACKGROUND: RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial. RESULTS: We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site's coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone. CONCLUSIONS: These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Prediction and characterization of novel epitopes of serotype A foot-and-mouth disease viruses circulating in East Africa using site-directed mutagenesis.

Epitopes on the surface of the foot-and-mouth disease virus (FMDV) capsid have been identified by monoclonal antibody (mAb) escape mutant studies leading to the designation of four antigenic sites in serotype A FMDV. Previous work focused on viruses isolated mainly from Asia, Europe and Latin America. In this study we report on the prediction of epitopes in African serotype A FMDVs and testing of selected epitopes using reverse genetics. Twenty-four capsid amino acid residues were predicted to be of antigenic significance by analysing the capsid sequences (n = 56) using in silico methods, and six residues by correlating capsid sequence with serum-virus neutralization data. The predicted residues were distributed on the surface-exposed capsid regions, VP1-VP3. The significance of residue changes at eight of the predicted epitopes was tested by site-directed mutagenesis using a cDNA clone resulting in the generation of 12 mutant viruses involving seven sites. The effect of the amino acid substitutions on the antigenic nature of the virus was assessed by virus neutralization (VN) test. Mutations at four different positions, namely VP1-43, VP1-45, VP2-191 and VP3-132, led to significant reduction in VN titre (P value = 0.05, 0.05, 0.001 and 0.05, respectively). This is the first time, to our knowledge, that the antigenic regions encompassing amino acids VP1-43 to -45 (equivalent to antigenic site 3 in serotype O), VP2-191 and VP3-132 have been predicted as epitopes and evaluated serologically for serotype A FMDVs. This identifies novel capsid epitopes of recently circulating serotype A FMDVs in East Africa.

High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutations.

Several human genetic diseases are associated with inheriting an abnormally large unstable DNA simple sequence repeat. These sequences mutate, by changing the number of repeats, many times during the lifetime of those affected, with a bias towards expansion. These somatic changes lead not only to the presence of cells with different numbers of repeats in the same tissue, but also produce increasingly longer repeats, contributing towards the progressive nature of the symptoms. Modelling the progression of repeat length throughout the lifetime of individuals has potential for improving prognostic information as well as providing a deeper understanding of the underlying biological process. A large data set comprising blood DNA samples from individuals with one such disease, myotonic dystrophy type 1, provides an opportunity to parameterize a mathematical model for repeat length evolution that we can use to infer biological parameters of interest. We developed new mathematical models by modifying a proposed stochastic birth process to incorporate possible contraction. A hierarchical Bayesian approach was used as the basis for inference, and we estimated the distribution of mutation rates in the population. We used model comparison analysis to reveal, for the first time, that the expansion bias observed in the distributions of repeat lengths is likely to be the cumulative effect of many expansion and contraction events. We predict that mutation events can occur as frequently as every other day, which matches the timing of regular cell activities such as DNA repair and transcription but not DNA replication.

A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data.

We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate unobserved infections missing from the sample. Our proposed framework addresses these shortcomings, enabling reconstruction of partially observed transmission trees and estimating the number of cases missing from the sample. Analyses of simulated datasets show the method to be accurate in identifying direct transmissions, while introductions and transmissions via one or more unsampled intermediate cases could be identified at high to moderate levels of case detection. When applied to partial genome sequences of rabies virus sampled from an endemic region of South Africa, our method reveals several distinct transmission cycles with little contact between them, and direct transmission over long distances suggesting significant anthropogenic influence in the movement of infected dogs.

Revisiting the diffusion approximation to estimate evolutionary rates of gene family diversification.

Genetic diversity in multigene families is shaped by multiple processes, including gene conversion and point mutation. Because multi-gene families are involved in crucial traits of organisms, quantifying the rates of their genetic diversification is important. With increasing availability of genomic data, there is a growing need for quantitative approaches that integrate the molecular evolution of gene families with their higher-scale function. In this study, we integrate a stochastic simulation framework with population genetics theory, namely the diffusion approximation, to investigate the dynamics of genetic diversification in a gene family. Duplicated genes can diverge and encode new functions as a result of point mutation, and become more similar through gene conversion. To model the evolution of pairwise identity in a multigene family, we first consider all conversion and mutation events in a discrete manner, keeping track of their details and times of occurrence; second we consider only the infinitesimal effect of these processes on pairwise identity accounting for random sampling of genes and positions. The purely stochastic approach is closer to biological reality and is based on many explicit parameters, such as conversion tract length and family size, but is more challenging analytically. The population genetics approach is an approximation accounting implicitly for point mutation and gene conversion, only in terms of per-site average probabilities. Comparison of these two approaches across a range of parameter combinations reveals that they are not entirely equivalent, but that for certain relevant regimes they do match. As an application of this modelling framework, we consider the distribution of nucleotide identity among VSG genes of African trypanosomes, representing the most prominent example of a multi-gene family mediating parasite antigenic variation and within-host immune evasion.