Host ecology determines the dispersal patterns of a plant virus

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.

Genealogical Working Distributions for Bayesian Model Testing with Phylogenetic Uncertainty

Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian phylogenetic inference. Approaches to estimate marginal likelihoods have garnered increased attention over the past decade. In particular, the introduction of path sampling (PS) and stepping-stone sampling (SS) into Bayesian phylogenetics has tremendously improved the accuracy of model selection. These sampling techniques are now used to evaluate complex evolutionary and population genetic models on empirical data sets, but considerable computational demands hamper their widespread adoption. Further, when very diffuse, but proper priors are specified for model parameters, numerical issues complicate the exploration of the priors, a necessary step in marginal likelihood estimation using PS or SS. To avoid such instabilities, generalized SS (GSS) has recently been proposed, introducing the concept of "working distributions" to facilitate--or shorten--the integration process that underlies marginal likelihood estimation. However, the need to fix the tree topology currently limits GSS in a coalescent-based framework. Here, we extend GSS by relaxing the fixed underlying tree topology assumption. To this purpose, we introduce a "working" distribution on the space of genealogies, which enables estimating marginal likelihoods while accommodating phylogenetic uncertainty. We propose two different "working" distributions that help GSS to outperform PS and SS in terms of accuracy when comparing demographic and evolutionary models applied to synthetic data and real-world examples. Further, we show that the use of very diffuse priors can lead to a considerable overestimation in marginal likelihood when using PS and SS, while still retrieving the correct marginal likelihood using both GSS approaches. The methods used in this article are available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.

Bayesian Inference Reveals Host-Specific Contributions to the Epidemic Expansion of Influenza A H5N1

Since its first isolation in 1996 in Guangdong, China, the highly pathogenic avian influenza virus (HPAIV) H5N1 has circulated in avian hosts for almost two decades and spread to more than 60 countries worldwide. The role of different avian hosts and the domestic-wild bird interface has been critical in shaping the complex HPAIV H5N1 disease ecology, but remains difficult to ascertain. To shed light on the large-scale H5N1 transmission patterns and disentangle the contributions of different avian hosts on the tempo and mode of HPAIV H5N1 dispersal, we apply Bayesian evolutionary inference techniques to comprehensive sets of hemagglutinin and neuraminidase gene sequences sampled between 1996 and 2011 throughout Asia and Russia. Our analyses demonstrate that the large-scale H5N1 transmission dynamics are structured according to different avian flyways, and that the incursion of the Central Asian flyway specifically was driven by Anatidae hosts coinciding with rapid rate of spread and an epidemic wavefront acceleration. This also resulted in long-distance dispersal that is likely to be explained by wild bird migration. We identify a significant degree of asymmetry in the large-scale transmission dynamics between Anatidae and Phasianidae, with the latter largely representing poultry as an evolutionary sink. A joint analysis of host dynamics and continuous spatial diffusion demonstrates that the rate of viral dispersal and host diffusivity is significantly higher for Anatidae compared with Phasianidae. These findings complement risk modeling studies and satellite tracking of wild birds in demonstrating a continental-scale structuring into areas of H5N1 persistence that are connected through migratory waterfowl.

Disentangling the impact of within-host evolution and transmission dynamics on the tempo of HIV-1 evolution

OBJECTIVE

To determine how HIV-1 risk groups impact transmitted diversity and the tempo of viral evolution at a population scale.

METHODS

We investigated a set of previously described transmission chains (n = 70) using a population genetic approach, and tested whether the expected differences in proportions of multivariant transmissions are reflected by varying proportions of transmitted diversity between men having sex with men (MSM) and heterosexual (HET) subpopulations - the largest contributors to HIV spread. To assess evolutionary rate differences among the different risk groups, we compiled risk group datasets for subtypes A1, B and CRF01_AE, and directly compared the absolute substitution rate and its synonymous and non-synonymous components.

RESULTS

There was sufficient demographic signal to inform the transmission model in Bayesian evolutionary analysis by sampling trees using env data to compare the transmission bottleneck size between the MSM and HET risk groups. We found no indications for a different proportion of transmitted genetic diversity at the population level between these groups. In the direct rate comparisons between the risk groups, however, we consistently recovered a higher evolutionary rate in the male-dominated risk group compared to the HET datasets.

CONCLUSION

We find that the risk group composition affects the viral evolutionary rate and therefore potentially also the adaptation rate. In particular, risk group-specific sex ratios, and the variation in within-host evolutionary rates between men and women, impose evolutionary rate differences at the epidemic level, but we cannot exclude a role of varying transmission rates.

Impact of spatial dispersion, evolution, and selection on Ebola Zaire Virus epidemic waves

Ebola virus Zaire (EBOV) has reemerged in Africa, emphasizing the global importance of this pathogen. Amidst the response to the current epidemic, several gaps in our knowledge of EBOV evolution are evident. Specifically, uncertainty has been raised regarding the potential emergence of more virulent viral variants through amino acid substitutions. Glycoprotein (GP), an essential component of the EBOV genome, is highly variable and a potential site for the occurrence of advantageous mutations. For this study, we reconstructed the evolutionary history of EBOV by analyzing 65 GP sequences from humans and great apes over diverse locations across epidemic waves between 1976 and 2014. We show that, although patterns of spatial dispersion throughout Africa varied, the evolution of the virus has largely been characterized by neutral genetic drift. Therefore, the radical emergence of more transmissible variants is unlikely, a positive finding, which is increasingly important on the verge of vaccine deployment.

Phylodynamic analysis of clinical and environmental Vibrio cholerae isolates from Haiti reveals diversification driven by positive selection

Phylodynamic analysis of genome-wide single-nucleotide polymorphism (SNP) data is a powerful tool to investigate underlying evolutionary processes of bacterial epidemics. The method was applied to investigate a collection of 65 clinical and environmental isolates of Vibrio cholerae from Haiti collected between 2010 and 2012. Characterization of isolates recovered from environmental samples identified a total of four toxigenic V. cholerae O1 isolates, four non-O1/O139 isolates, and a novel nontoxigenic V. cholerae O1 isolate with the classical tcpA gene. Phylogenies of strains were inferred from genome-wide SNPs using coalescent-based demographic models within a Bayesian framework. A close phylogenetic relationship between clinical and environmental toxigenic V. cholerae O1 strains was observed. As cholera spread throughout Haiti between October 2010 and August 2012, the population size initially increased and then fluctuated over time. Selection analysis along internal branches of the phylogeny showed a steady accumulation of synonymous substitutions and a progressive increase of nonsynonymous substitutions over time, suggesting diversification likely was driven by positive selection. Short-term accumulation of nonsynonymous substitutions driven by selection may have significant implications for virulence, transmission dynamics, and even vaccine efficacy.

IMPORTANCE

Cholera, a dehydrating diarrheal disease caused by toxigenic strains of the bacterium Vibrio cholerae, emerged in 2010 in Haiti, a country where there were no available records on cholera over the past 100 years. While devastating in terms of morbidity and mortality, the outbreak provided a unique opportunity to study the evolutionary dynamics of V. cholerae and its environmental presence. The present study expands on previous work and provides an in-depth phylodynamic analysis inferred from genome-wide single nucleotide polymorphisms of clinical and environmental strains from dispersed geographic settings in Haiti over a 2-year period. Our results indicate that even during such a short time scale, V. cholerae in Haiti has undergone evolution and diversification driven by positive selection, which may have implications for understanding the global clinical and epidemiological patterns of the disease. Furthermore, the continued presence of the epidemic strain in Haitian aquatic environments has implications for transmission.

Genetic characterization and clinical implications of human papillomavirus type 16 (HPV16) variants from northeastern Argentina

BACKGROUND

Human papillomavirus type 16 (HPV16) plays a central role in the development of cervical cancer. Worldwide studies indicate the existence of HPV16 variants that show different geographic distributions and oncogenic potential.

OBJECTIVE

Our goal was to describe the genetic variation of HPV16 isolates identified in urban women with different grades of cervical lesions living in northeastern Argentina.

STUDY DESIGN

We analyzed 116 HPV16-positive cervical samples (16 NLIM, 62 L-SIL, 16 H-SIL and 22 cervical cancer) from patients attending health centers in Misiones (Argentina) during 2006-13. HPV16 isolates were genetically characterized through PCR amplification and direct sequencing of 364 bp within the long control region, and the resulting sequences classified into variants based on phylogenetic analysis (lineages A, B, C and D). A potential association between HPV16 variants and lesion grade was evaluated through an odds ratio (OR) test. A temporal framework for the origin of HPV16 variants was assessed through coalescence analysis (BEAST v 1.7.5).

RESULTS

Phylogenetic analysis of HPV16 sequences showed that 92.1% of the samples clustered with lineage A, and 6.9% to lineage D. HPV16 variants from lineage D were more frequently associated with high-grade lesions and cancer (HSIL+) than lineage A variants at an OR of 13.8 (1.6-117.0). The time to most common recent ancestor (tMCRA) of all variants was 119,103 years before present (HPD 95%=48,486-197,239), a date consistent with the time frame for modern human evolution.

CONCLUSION

Our results suggest that HPV16 variants from lineage D may represent an additional risk factor for the development of cervical cancer in women living in northeastern Argentina. This study provides new information about viral isolates present in Argentina that will contribute to the monitoring of HPV16 infection in the vaccine era.

Streptococcus agalactiae clones infecting humans were selected and fixed through the extensive use of tetracycline

Streptococcus agalactiae (Group B Streptococcus, GBS) is a commensal of the digestive and genitourinary tracts of humans that emerged as the leading cause of bacterial neonatal infections in Europe and North America during the 1960s. Due to the lack of epidemiological and genomic data, the reasons for this emergence are unknown. Here we show by comparative genome analysis and phylogenetic reconstruction of 229 isolates that the rise of human GBS infections corresponds to the selection and worldwide dissemination of only a few clones. The parallel expansion of the clones is preceded by the insertion of integrative and conjugative elements conferring tetracycline resistance (TcR). Thus, we propose that the use of tetracycline from 1948 onwards led in humans to the complete replacement of a diverse GBS population by only few TcR clones particularly well adapted to their host, causing the observed emergence of GBS diseases in neonates.

Analysis of 41 plant genomes supports a wave of successful genome duplications in association with the Cretaceous-Paleogene boundary

Ancient whole-genome duplications (WGDs), also referred to as paleopolyploidizations, have been reported in most evolutionary lineages. Their attributed role remains a major topic of discussion, ranging from an evolutionary dead end to a road toward evolutionary success, with evidence supporting both fates. Previously, based on dating WGDs in a limited number of plant species, we found a clustering of angiosperm paleopolyploidizations around the Cretaceous-Paleogene (K-Pg) extinction event about 66 million years ago. Here we revisit this finding, which has proven controversial, by combining genome sequence information for many more plant lineages and using more sophisticated analyses. We include 38 full genome sequences and three transcriptome assemblies in a Bayesian evolutionary analysis framework that incorporates uncorrelated relaxed clock methods and fossil uncertainty. In accordance with earlier findings, we demonstrate a strongly nonrandom pattern of genome duplications over time with many WGDs clustering around the K-Pg boundary. We interpret these results in the context of recent studies on invasive polyploid plant species, and suggest that polyploid establishment is promoted during times of environmental stress. We argue that considering the evolutionary potential of polyploids in light of the environmental and ecological conditions present around the time of polyploidization could mitigate the stark contrast in the proposed evolutionary fates of polyploids.

Inferring heterogeneous evolutionary processes through time: from sequence substitution to phylogeography

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 propose an evolutionary approach that explicitly relaxes the time-homogeneity 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. [Bayesian inference; BEAGLE; BEAST; Epoch Model; phylogeography; Phylogenetics.]

Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2

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.

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.