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The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics. ispartof: Nature vol:544 issue:7650 pages:309- ispartof: location:England status: published

Motivation: Statistical methods for comparing relative rates of synonymous and non-synonymous substitutions maintain a central role in detecting positive selection. To identify selection, researchers often estimate the ratio of these relative rates () at individual alignment sites. Fitting a codon substitution model that captures heterogeneity in across sites provides a reliable way to perform such estimation, but it remains computationally prohibitive for massive datasets. By using crude estimates of the numbers of synonymous and non-synonymous substitutions at each site, counting approaches scale well to large datasets, but they fail to account for ancestral state reconstruction uncertainty and to provide site-specific estimates. Results: We propose a hybrid solution that borrows the computational strength of counting methods, but augments these methods with empirical Bayes modeling to produce a relatively fast and reliable method capable of estimating site-specific values in large datasets. Importantly, our hybrid approach, set in a Bayesian framework, integrates over the posterior distribution of phylogenies and ancestral reconstructions to quantify uncertainty about site-specific estimates. Simulations demonstrate that this method competes well with more-principled statistical procedures and, in some cases, even outperforms them. We illustrate the utility of our method using human immunodeficiency virus, feline panleukopenia and canine parvovirus evolution examples. Availability: Renaissance counting is implemented in the development branch of BEAST, freely available at http://code.google.com/p/beast-mcmc/. The method will be made available in the next public release of the package, including support to set up analyses in BEAUti. Contact: philippe.lemey@rega.kuleuven.be or msuchard@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Various factors determine the rate at which mutations are generated and fixed in viral genomes. Viral evolutionary rates may vary over the course of a single persistent infection and can reflect changes in replication rates and selective dynamics. Dedicated statistical inference approaches are required to understand how the complex interplay of these processes shapes the genetic diversity and divergence in viral populations. Although evolutionary models accommodating a high degree of complexity can now be formalized, adequately informing these models by potentially sparse data, and assessing the association of the resulting estimates with external predictors, remains a major challenge. In this article, we present a novel Bayesian evolutionary inference method, which integrates multiple potential predictors and tests their association with variation in the absolute rates of synonymous and non-synonymous substitutions along the evolutionary history. We consider clinical and virological measures as predictors, but also changes in population size trajectories that are simultaneously inferred using coalescent modelling. We demonstrate the potential of our method in an application to within-host HIV-1 sequence data sampled throughout the infection of multiple patients. While analyses of individual patient populations lack statistical power, we detect significant evidence for an abrupt drop in non-synonymous rates in late stage infection and a more gradual increase in synonymous rates over the course of infection in a joint analysis across all patients. The former is predicted by the immune relaxation hypothesis while the latter may be in line with increasing replicative fitness during the asymptomatic stage. ispartof: Virus Evolution vol:2 issue:2 ispartof: location:England status: published

Model-based phylogenetic reconstructions increasingly consider spatial or phenotypic traits in conjunction with sequence data to study evolutionary processes. Alongside parameter estimation, visualization of ancestral reconstructions represents an integral part of these analyses. Here, we present a complete overhaul of the spatial phylogenetic reconstruction of evolutionary dynamics software, now called SpreaD3 to emphasize the use of data-driven documents, as an analysis and visualization package that primarily complements Bayesian inference in BEAST (http://beast.bio.ed.ac.uk, last accessed 9 May 2016). The integration of JavaScript D3 libraries (www.d3.org, last accessed 9 May 2016) offers novel interactive web-based visualization capacities that are not restricted to spatial traits and extend to any discrete or continuously valued trait for any organism of interest. ispartof: Molecular Biology and Evolution vol:33 issue:8 pages:2167-2169 ispartof: location:United States status: published

Phylodynamic reconstructions rely on a measurable molecular footprint of epidemic processes in pathogen genomes. Identifying the factors that govern the tempo and mode by which these processes leave a footprint in pathogen genomes represents an important goal towards understanding infectious disease evolution. Discriminating between synonymous and non-synonymous substitution rates is crucial for testing hypotheses about the sources of evolutionary rate variation. Here, we implement a codon substitution model in a Bayesian statistical framework to estimate absolute rates of synonymous and non-synonymous substitution in unknown evolutionary histories. To demonstrate how this model can provide critical insights into pathogen evolutionary dynamics, we adopt hierarchical phylogenetic modelling with fixed effects and apply it to two viral examples. Using within-host HIV-1 data from patients with different host genetic background and different disease progression rates, we show that viral populations undergo faster absolute synonymous substitution rates in patients with faster disease progression, probably reflecting faster replication rates. We also re-analyse rabies data from different bat species in the Americas to demonstrate that climate predicts absolute synonymous substitution rates, which can be attributed to climate-associated bat activity and viral transmission dynamics. In conclusion, our model to estimate absolute rates of synonymous and non-synonymous substitution can provide a powerful approach to investigate how host ecology can shape the tempo of pathogen evolution. ispartof: MICROBIAL GENOMICS vol:2 issue:6 ispartof: location:England status: published

Swine influenza presents a substantial disease burden for pig populations worldwide and poses a potential pandemic threat to humans. There is considerable diversity in both H1 and H3 influenza viruses circulating in swine due to the frequent introductions of viruses from humans and birds coupled with geographic segregation of global swine populations. Much of this diversity is characterized genetically but the antigenic diversity of these viruses is poorly understood. Critically, the antigenic diversity shapes the risk profile of swine influenza viruses in terms of their epizootic and pandemic potential. Here, using the most comprehensive set of swine influenza virus antigenic data compiled to date, we quantify the antigenic diversity of swine influenza viruses on a multi-continental scale. The substantial antigenic diversity of recently circulating viruses in different parts of the world adds complexity to the risk profiles for the movement of swine and the potential for swine-derived infections in humans. DOI: http://dx.doi.org/10.7554/eLife.12217.001, eLife digest Influenza viruses, commonly called flu, infect millions of people and animals every year and occasionally causes pandemics in humans. The immune system can neutralise flu viruses by recognising the proteins on the virus surface, generically referred to as antigens. These antigens change as flu viruses evolve to escape detection by the immune system. These changes tend to be relatively small such that exposure to one flu virus generates immunity that is still effective against other related flu viruses. However, over time, the accumulation of these small changes can result in larger differences such that prior infections no longer provide protection against the new virus. Influenza A viruses infect a wide variety of birds and mammals. Viruses can also transmit from one species to another, which may result in the introduction of viruses with antigens that are new to the recipient species and which have the potential to cause substantial outbreaks. Pig flu viruses have long been considered to be a potential risk for human pandemic viruses and were the source of the 2009 pandemic H1N1 virus. Importantly, humans often transmit flu viruses to pigs. Understanding the dynamics and consequences of this two-way transmission is important for designing effective strategies to detect and respond to new strains of flu. Influenza A viruses of the H1 and H3 subtypes circulate widely in pigs. However, it was poorly understood how closely related swine and human viruses circulating in different regions were to one another and how much the antigens varied between the different viruses. Lewis, Russell et al. have now analysed the antigenic variation of hundreds of H1 and H3 viruses from pigs on multiple continents. The antigenic diversity of recent swine flu viruses resembles the diversity of H1 and H3 viruses observed in humans over the last 40 years. A key factor driving the diversity of the H1 and H3 viruses in pigs is the frequent introduction of human viruses to pigs. In contrast, only one flu virus from a bird had contributed to the observed antigenic diversity in pigs in a substantial way. Once in pigs, human-derived flu viruses continue to evolve their antigens. This results in a tremendous diversity of flu viruses that can be transmitted to other pigs and also to humans. These flu viruses could pose a serious risk to public health because they are no longer similar to the current human flu strains. These findings have important implications not only for developing flu vaccines for pigs but also for informing the development of more-effective surveillance and disease-control strategies to prevent the spread of new flu variants. DOI: http://dx.doi.org/10.7554/eLife.12217.002

Molecular phylogenetic and phylogeographic reconstructions generally assume time-homogeneous substitution processes. Motivated by computational convenience, this assumption sacrifices biological realism and offers little opportunity to uncover the temporal dynamics in evolutionary histories. Here, we extend and generalize an evolutionary approach that relaxes the time-homogeneous process assumption by allowing the specification of different infinitesimal substitution rate matrices across different time intervals, called epochs, along the evolutionary history. We focus on an epoch model implementation in a Bayesian inference framework that offers great modeling flexibility in drawing inference about any discrete data type characterized as a continuous-time Markov chain, including phylogeographic traits. To alleviate the computational burden that the additional temporal heterogeneity imposes, we adopt a massively parallel approach that achieves both fine- and coarse-grain parallelization of the computations across branches that accommodate epoch transitions, making extensive use of graphics processing units. Through synthetic examples, we assess model performance in recovering evolutionary parameters from data generated according to different evolutionary scenarios that comprise different numbers of epochs for both nucleotide and codon substitution processes. We illustrate the usefulness of our inference framework in two different applications to empirical data sets: the selection dynamics on within-host HIV populations throughout infection and the seasonality of global influenza circulation. In both cases, our epoch model captures key features of temporal heterogeneity that remained difficult to test using ad hoc procedures., 30 pages, 6 figure, 3 tables

BACKGROUND: Simulated nucleotide or amino acid sequences are frequently used to assess the performance of phylogenetic reconstruction methods. BEAST, a Bayesian statistical framework that focuses on reconstructing time-calibrated molecular evolutionary processes, supports a wide array of evolutionary models, but lacked matching machinery for simulation of character evolution along phylogenies. RESULTS: We present a flexible Monte Carlo simulation tool, called πBUSS, that employs the BEAGLE high performance library for phylogenetic computations to rapidly generate large sequence alignments under complex evolutionary models. πBUSS sports a user-friendly graphical user interface (GUI) that allows combining a rich array of models across an arbitrary number of partitions. A command-line interface mirrors the options available through the GUI and facilitates scripting in large-scale simulation studies. πBUSS may serve as an easy-to-use, standard sequence simulation tool, but the available models and data types are particularly useful to assess the performance of complex BEAST inferences. The connection with BEAST is further strengthened through the use of a common extensible markup language (XML), allowing to specify also more advanced evolutionary models. To support simulation under the latter, as well as to support simulation and analysis in a single run, we also add the πBUSS core simulation routine to the list of BEAST XML parsers. CONCLUSIONS: πBUSS offers a unique combination of flexibility and ease-of-use for sequence simulation under realistic evolutionary scenarios. Through different interfaces, πBUSS supports simulation studies ranging from modest endeavors for illustrative purposes to complex and large-scale assessments of evolutionary inference procedures. Applications are not restricted to the BEAST framework, or even time-measured evolutionary histories, and πBUSS can be connected to various other programs using standard input and output format. 13 pages, 2 figures, 1 table ispartof: BMC bioinformatics vol:15 issue:1 pages:133- ispartof: location:England status: published

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control., Author Summary What explains the geographic dispersal of emerging pathogens? Reconstructions of evolutionary history from pathogen gene sequences offer qualitative descriptions of spatial spread, but current approaches are poorly equipped to formally test and quantify the contribution of different potential explanatory factors, such as human mobility and demography. Here, we use a novel phylogeographic method to evaluate multiple potential predictors of viral spread in human influenza dynamics. We identify air travel as the predominant driver of global influenza migration, whilst also revealing the contribution of other mobility processes at more local scales. We demonstrate the power of our inter-disciplinary approach by using it to predict the global pandemic expansion of H1N1 influenza in 2009. Our study highlights the importance of integrating evolutionary and ecological information when studying the dynamics of infectious disease.

Since its isolation in 1966 in Kenya, rice yellow mottle virus (RYMV) has been reported throughout Africa resulting in one of the economically most important tropical plant emerging diseases. A thorough understanding of RYMV evolution and dispersal is critical to manage viral spread in tropical areas that heavily rely on agriculture for subsistence. Phylogenetic analyses have suggested a relatively recent expansion, perhaps driven by the intensification of agricultural practices, but this has not yet been examined in a coherent statistical framework. To gain insight into the historical spread of RYMV within Africa rice cultivations, we analyse a dataset of 300 coat protein gene sequences, sampled from East to West Africa over a 46-year period, using Bayesian evolutionary inference. Spatiotemporal reconstructions date the origin of RMYV back to 1852 (1791-1903) and confirm Tanzania as the most likely geographic origin. Following a single long-distance transmission event from East to West Africa, separate viral populations have been maintained for about a century. To identify the factors that shaped the RYMV distribution, we apply a generalised linear model (GLM) extension of discrete phylogenetic diffusion and provide strong support for distances measured on a rice connectivity landscape as the major determinant of RYMV spread. Phylogeographic estimates in continuous space further complement this by demonstrating more pronounced expansion dynamics in West Africa that are consistent with agricultural intensification and extensification. Taken together, our principled phylogeographic inference approach shows for the first time that host ecology dynamics have shaped the historical spread of a plant virus. ispartof: Virus Evolution vol:1 issue:1 ispartof: location:England status: published