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

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          Abstract

          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.

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          Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.

          Vladimir N. Minin, Erik W Bloomquist, Marc Suchard (2008)
          Kingman's coalescent process opens the door for estimation of population genetics model parameters from molecular sequences. One paramount parameter of interest is the effective population size. Temporal variation of this quantity characterizes the demographic history of a population. Because researchers are rarely able to choose a priori a deterministic model describing effective population size dynamics for data at hand, nonparametric curve-fitting methods based on multiple change-point (MCP) models have been developed. We propose an alternative to change-point modeling that exploits Gaussian Markov random fields to achieve temporal smoothing of the effective population size in a Bayesian framework. The main advantage of our approach is that, in contrast to MCP models, the explicit temporal smoothing does not require strong prior decisions. To approximate the posterior distribution of the population dynamics, we use efficient, fast mixing Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. In a simulation study, we demonstrate that the proposed temporal smoothing method, named Bayesian skyride, successfully recovers "true" population size trajectories in all simulation scenarios and competes well with the MCP approaches without evoking strong prior assumptions. We apply our Bayesian skyride method to 2 real data sets. We analyze sequences of hepatitis C virus contemporaneously sampled in Egypt, reproducing all key known aspects of the viral population dynamics. Next, we estimate the demographic histories of human influenza A hemagglutinin sequences, serially sampled throughout 3 flu seasons.
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            Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences.

            Beth Shapiro, Andrew Rambaut, Alexei J. Drummond (2006)
            Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior.
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              SpreaD3: Interactive Visualization of Spatiotemporal History and Trait Evolutionary Processes.

              Filip Bielejec, Guy Baele, Bram Vrancken … (2016)
              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.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 21 March 2017
                Publication date Collection: 2017
                Volume: 7
                Electronic Location Identifier: 44947
                Affiliations
                [1 ]Department of Infection and Immunity, King Faisal Specialist Hospital & Research Center , Riyadh, Saudi Arabia
                [2 ]Department of Microbiology and Immunology, Alfaisal University School of Medicine , Riyadh, Saudi Arabia
                [3 ]KU Leuven - University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research , B-3000 Leuven, Belgium
                [4 ]Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California , Los Angeles, USA
                [5 ]Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California , Los Angeles, USA
                [6 ]Section of Gastroenterology, Department of Medicine, College of Medicine, King Saud University , Riyadh, Saudi Arabia
                [7 ]Gastroenterology Unit, Department of Medicine, King Abdulaziz Medical City , Jeddah, Saudi Arabia
                [8 ]Gastroenterology Unit, Department of Medicine, King Faisal Specialist Hospital & Research Center , Riyadh, Saudi Arabia
                Author notes
                Article
                Publisher Item ID: srep44947
                DOI: 10.1038/srep44947
                PMC ID: 5359580
                PubMed ID: 28322313
                SO-VID: 5c6ebed4-f869-4f13-8ce2-e9f282508517
                Copyright © Copyright © 2017, The Author(s)
                License:

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                Date received : 14 October 2016
                Date accepted : 15 February 2017
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