Bram Vrancken

The genealogical population dynamics of HIV-1 in a large transmission chain: bridging within and among host evolutionary rates.

Transmission lies at the interface of human immunodeficiency virus type 1 (HIV-1) evolution within and among hosts and separates distinct selective pressures that impose differences in both the mode of diversification and the tempo of evolution. In the absence of comprehensive direct comparative analyses of the evolutionary processes at different biological scales, our understanding of how fast within-host HIV-1 evolutionary rates translate to lower rates at the between host level remains incomplete. Here, we address this by analyzing pol and env data from a large HIV-1 subtype C transmission chain for which both the timing and the direction is known for most transmission events. To this purpose, we develop a new transmission model in a Bayesian genealogical inference framework and demonstrate how to constrain the viral evolutionary history to be compatible with the transmission history while simultaneously inferring the within-host evolutionary and population dynamics. We show that accommodating a transmission bottleneck affords the best fit our data, but the sparse within-host HIV-1 sampling prevents accurate quantification of the concomitant loss in genetic diversity. We draw inference under the transmission model to estimate HIV-1 evolutionary rates among epidemiologically-related patients and demonstrate that they lie in between fast intra-host rates and lower rates among epidemiologically unrelated individuals infected with HIV subtype C. Using a new molecular clock approach, we quantify and find support for a lower evolutionary rate along branches that accommodate a transmission event or branches that represent the entire backbone of transmitted lineages in our transmission history. Finally, we recover the rate differences at the different biological scales for both synonymous and non-synonymous substitution rates, which is only compatible with the ‘store and retrieve’ hypothesis positing that viruses stored early in latently infected cells preferentially transmit or establish new infections upon reactivation.

SpreaD3: Interactive Visualization of Spatiotemporal History and Trait Evolutionary Processes

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.

Distinct Effects of T-705 (Favipiravir) and Ribavirin on Influenza Virus Replication and Viral RNA Synthesis

ABSTRACT T-705 (favipiravir) is a new antiviral agent in advanced clinical development for influenza therapy. It is supposed to act as an alternative substrate for the viral polymerase, causing inhibition of viral RNA synthesis or virus mutagenesis. These mechanisms were also proposed for ribavirin, an established and broad antiviral drug that shares structural similarity with T-705. We here performed a comparative analysis of the effects of T-705 and ribavirin on influenza virus and host cell functions. Influenza virus-infected cell cultures were exposed to T-705 or ribavirin during single or serial virus passaging. The effects on viral RNA synthesis and infectious virus yield were determined and mutations appearing in the viral genome were detected by whole-genome virus sequencing. In addition, the cellular nucleotide pools as well as direct inhibition of the viral polymerase enzyme were quantified. We demonstrate that the anti-influenza virus effect of ribavirin is based on IMP dehydrogenase inhibition, which results in fast and profound GTP depletion and an imbalance in the nucleotide pools. In contrast, T-705 acts as a potent and GTP-competitive inhibitor of the viral polymerase. In infected cells, viral RNA synthesis is completely inhibited by T-705 or ribavirin at ≥50 μM, whereas exposure to lower drug concentrations induces formation of noninfectious particles and accumulation of random point mutations in the viral genome. This mutagenic effect is 2-fold higher for T-705 than for ribavirin. Hence, T-705 and ribavirin both act as purine pseudobases but profoundly differ with regard to the mechanism behind their antiviral and mutagenic effects on influenza virus.

Simultaneously estimating evolutionary history and repeated traits phylogenetic signal: applications to viral and host phenotypic evolution

Phylogenetic signal quantifies the degree to which resemblance in continuously-valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research, and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias. To incorporate the observation process and phylogenetic uncertainty in a model-based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that model continuous trait evolution as a Brownian motion process and incorporates Pagels λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators, and demonstrate the use of our coherent framework to address several virus-host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution.

Contribution of Epidemiological Predictors in Unraveling the Phylogeographic History of HIV-1 Subtype C in Brazil

ABSTRACT The phylogeographic history of the Brazilian HIV-1 subtype C (HIV-1C) epidemic is still unclear. Previous studies have mainly focused on the capital cities of Brazilian federal states, and the fact that HIV-1C infections increase at a higher rate than subtype B infections in Brazil calls for a better understanding of the process of spatial spread. A comprehensive sequence data set sampled across 22 Brazilian locations was assembled and analyzed. A Bayesian phylogeographic generalized linear model approach was used to reconstruct the spatiotemporal history of HIV-1C in Brazil, considering several potential explanatory predictors of the viral diffusion process. Analyses were performed on several subsampled data sets in order to mitigate potential sample biases. We reveal a central role for the city of Porto Alegre, the capital of the southernmost state, in the Brazilian HIV-1C epidemic (HIV-1C_BR), and the northward expansion of HIV-1C_BR could be linked to source populations with higher HIV-1 burdens and larger proportions of HIV-1C infections. The results presented here bring new insights to the continuing discussion about the HIV-1C epidemic in Brazil and raise an alternative hypothesis for its spatiotemporal history. The current work also highlights how sampling bias can confound phylogeographic analyses and demonstrates the importance of incorporating external information to protect against this. IMPORTANCE Subtype C is responsible for the largest HIV infection burden worldwide, but our understanding of its transmission dynamics remains incomplete. Brazil witnessed a relatively recent introduction of HIV-1C compared to HIV-1B, but it swiftly spread throughout the south, where it now circulates as the dominant variant. The northward spread has been comparatively slow, and HIV-1B still prevails in that region. While epidemiological data and viral genetic analyses have both independently shed light on the dynamics of spread in isolation, their combination has not yet been explored. Here, we complement publically available sequences and new genetic data from 13 cities with epidemiological data to reconstruct the history of HIV-1C spread in Brazil. The combined approach results in more robust reconstructions and can protect against sampling bias. We found evidence for an alternative view of the HIV-1C spatiotemporal history in Brazil that, contrary to previous explanations, integrates seamlessly with other observational data.

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.

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.

The epidemic dynamics of hepatitis C virus subtypes 4a and 4d in Saudi Arabia

The relatedness between viral variants sampled at different locations through time can provide information pertinent to public health that cannot readily be obtained through standard surveillance methods. Here, we use virus genetic data to identify the transmission dynamics that drive the hepatitis C virus subtypes 4a (HCV4a) and 4d (HCV4d) epidemics in Saudi Arabia. We use a comprehensive dataset of newly generated and publicly available sequence data to infer the HCV4a and HCV4d evolutionary histories in a Bayesian statistical framework. We also introduce a novel analytical method for an objective assessment of the migration intensity between locations. We find that international host mobility patterns dominate over within country spread in shaping the Saudi Arabia HCV4a epidemic, while this may be different for the HCV4d epidemic. This indicates that the subtypes 4a and 4d burden can be most effectively reduced by combining the prioritized screening and treatment of Egyptian immigrants with domestic prevention campaigns. Our results highlight that the joint investigation of evolutionary and epidemiological processes can provide valuable public health information, even in the absence of extensive metadata information.

Implications of hepatitis C virus subtype 1a migration patterns for virus genetic sequencing policies in Italy

BackgroundIn-depth phylogeographic analysis can reveal migration patterns relevant for public health planning. Here, as a model, we focused on the provenance, in the current Italian HCV subtype 1a epidemic, of the NS3 resistance-associated variant (RAV) Q80K, known to interfere with the action of NS3/4A protease inhibitor simeprevir. HCV1a migration patterns were analysed using Bayesian phylodynamic tools, capitalising on newly generated and publicly available time and geo-referenced NS3 encoding virus genetic sequence data.ResultsOur results showed that both immigration and local circulation fuel the current Italian HCV1a epidemic. The United States and European continental lineages dominate import into Italy, with the latter taking the lead from the 1970s onwards. Since similar migration patterns were found for Q80K and other lineages, no clear differentiation of the risk for failing simeprevir can be made between patients based on their migration and travel history. Importantly, since HCV only occasionally recombines, these results are readily transferable to the genetic sequencing policy concerning NS5A RAVs.ConclusionsThe patient migration and travel history cannot be used to target only part of the HCV1a infected population for drug resistance testing before start of antiviral therapy. Consequently, it may be cost-effective to expand genotyping efforts to all HCV1a infected patients eligible for simeprevir-based therapies.

Quantifying Next Generation Sequencing Sample Pre-Processing Bias in HIV-1 Complete Genome Sequencing

Genetic analyses play a central role in infectious disease research. Massively parallelized “mechanical cloning” and sequencing technologies were quickly adopted by HIV researchers in order to broaden the understanding of the clinical importance of minor drug-resistant variants. These efforts have, however, remained largely limited to small genomic regions. The growing need to monitor multiple genome regions for drug resistance testing, as well as the obvious benefit for studying evolutionary and epidemic processes makes complete genome sequencing an important goal in viral research. In addition, a major drawback for NGS applications to RNA viruses is the need for large quantities of input DNA. Here, we use a generic overlapping amplicon-based near full-genome amplification protocol to compare low-input enzymatic fragmentation (Nextera™) with conventional mechanical shearing for Roche 454 sequencing. We find that the fragmentation method has only a modest impact on the characterization of the population composition and that for reliable results, the variation introduced at all steps of the procedure—from nucleic acid extraction to sequencing—should be taken into account, a finding that is also relevant for NGS technologies that are now more commonly used. Furthermore, by applying our protocol to deep sequence a number of pre-therapy plasma and PBMC samples, we illustrate the potential benefits of a near complete genome sequencing approach in routine genotyping.