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      1970s and 'Patient 0' HIV-1 genomes illuminate early HIV/AIDS history in North America.

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          Abstract

          The emergence of HIV-1 group M subtype B in North American men who have sex with men was a key turning point in the HIV/AIDS pandemic. Phylogenetic studies have suggested cryptic subtype B circulation in the United States (US) throughout the 1970s and an even older presence in the Caribbean. However, these temporal and geographical inferences, based upon partial HIV-1 genomes that postdate the recognition of AIDS in 1981, remain contentious and the earliest movements of the virus within the US are unknown. We serologically screened >2,000 1970s serum samples and developed a highly sensitive approach for recovering viral RNA from degraded archival samples. Here, we report eight coding-complete genomes from US serum samples from 1978-1979-eight of the nine oldest HIV-1 group M genomes to date. This early, full-genome 'snapshot' reveals that the US HIV-1 epidemic exhibited extensive genetic diversity in the 1970s but also provides strong evidence for its emergence from a pre-existing Caribbean epidemic. Bayesian phylogenetic analyses estimate the jump to the US at around 1970 and place the ancestral US virus in New York City with 0.99 posterior probability support, strongly suggesting this was the crucial hub of early US HIV/AIDS diversification. Logistic growth coalescent models reveal epidemic doubling times of 0.86 and 1.12 years for the US and Caribbean, respectively, suggesting rapid early expansion in each location. Comparisons with more recent data reveal many of these insights to be unattainable without archival, full-genome sequences. We also recovered the HIV-1 genome from the individual known as 'Patient 0' (ref. 5) and found neither biological nor historical evidence that he was the primary case in the US or for subtype B as a whole. We discuss the genesis and persistence of this belief in the light of these evolutionary insights.

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          Most cited references15

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          Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci.

          Effective population size is fundamental in population genetics and characterizes genetic diversity. To infer past population dynamics from molecular sequence data, coalescent-based models have been developed for Bayesian nonparametric estimation of effective population size over time. Among the most successful is a Gaussian Markov random field (GMRF) model for a single gene locus. Here, we present a generalization of the GMRF model that allows for the analysis of multilocus sequence data. Using simulated data, we demonstrate the improved performance of our method to recover true population trajectories and the time to the most recent common ancestor (TMRCA). We analyze a multilocus alignment of HIV-1 CRF02_AG gene sequences sampled from Cameroon. Our results are consistent with HIV prevalence data and uncover some aspects of the population history that go undetected in Bayesian parametric estimation. Finally, we recover an older and more reconcilable TMRCA for a classic ancient DNA data set.
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            Many-core algorithms for statistical phylogenetics.

            Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of protein-coding sequence evolution with a resulting increase in phylogenetic accuracy. Unfortunately, due to the high number of codon states, computational burden has largely thwarted phylogenetic reconstruction under codon models, particularly at the genomic-scale. Here, we describe novel algorithms and methods for evaluating phylogenies under arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the large number of processing cores to efficiently parallelize calculations even for large state-size models. We implement the approach in an existing Bayesian framework and apply the algorithms to estimating the phylogeny of 62 complete mitochondrial genomes of carnivores under a 60-state codon model. We see a near 90-fold speed increase over an optimized CPU-based computation and a >140-fold increase over the currently available implementation, making this the first practical use of codon models for phylogenetic inference over whole mitochondrial or microorganism genomes. Source code provided in BEAGLE: Broad-platform Evolutionary Analysis General Likelihood Evaluator, a cross-platform/processor library for phylogenetic likelihood computation (http://beagle-lib.googlecode.com/). We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (http://beast.bio.ed.ac.uk/).
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              Counting labeled transitions in continuous-time Markov models of evolution.

              Counting processes that keep track of labeled changes to discrete evolutionary traits play critical roles in evolutionary hypothesis testing. If we assume that trait evolution can be described by a continuous-time Markov chain, then it suffices to study the process that counts labeled transitions of the chain. For a binary trait, we demonstrate that it is possible to obtain closed-form analytic solutions for the probability mass and probability generating functions of this evolutionary counting process. In the general, multi-state case we show how to compute moments of the counting process using an eigen decomposition of the infinitesimal generator, provided the latter is a diagonalizable matrix. We conclude with two examples that demonstrate the utility of our results.
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                Author and article information

                Journal
                Nature
                Nature
                Springer Nature
                1476-4687
                0028-0836
                November 03 2016
                : 539
                : 7627
                Affiliations
                [1 ] Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA.
                [2 ] Department of History and Philosophy of Science, University of Cambridge, Cambridge CB2 3RH, UK.
                [3 ] Departments of Biomathematics, Biostatistics and Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA.
                [4 ] Centers for Disease Control and Prevention, Atlanta, Georgia 30333, USA.
                [5 ] UCB, Brussels BE-1070, Belgium.
                [6 ] Laboratory of Infectious Disease Prevention, The New York Blood Center, New York, New York 10065, USA.
                [7 ] Department of Microbiology and Immunology, Rega Institute, KU Leuven-University of Leuven, Minderbroedersstaat 10, 3000 Leuven, Belgium.
                Article
                nature19827 NIHMS841864
                10.1038/nature19827
                5257289
                27783600
                938e6788-0463-4884-aff3-ebfdf5993ea2
                History

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