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Persistent HIV-1 replication maintains the tissue reservoir during therapy

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      Abstract

      Lymphoid tissue is a key reservoir established by HIV-1 during acute infection. It is a site of viral production, storage of viral particles in immune complexes, and viral persistence. Whilst combinations of antiretroviral drugs usually suppress viral replication and reduce viral RNA to undetectable levels in blood, it is unclear whether treatment fully suppresses viral replication in lymphoid tissue reservoirs. Here we show that virus evolution and trafficking between tissue compartments continues in patients with undetectable levels of virus in their bloodstream. A spatial dynamic model of persistent viral replication and spread explains why the development of drug resistance is not a foregone conclusion under conditions where drug concentrations are insufficient to completely block virus replication. These data provide fresh insights into the evolutionary and infection dynamics of the virus population within the host, revealing that HIV-1 can continue to replicate and refill the viral reservoir despite potent antiretroviral therapy.

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      Most cited references 57

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      A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

      The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/.
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        BEAST 2: A Software Platform for Bayesian Evolutionary Analysis

        We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
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          Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

          A new statistical method for estimating divergence dates of species from DNA sequence data by a molecular clock approach is developed. This method takes into account effectively the information contained in a set of DNA sequence data. The molecular clock of mitochondrial DNA (mtDNA) was calibrated by setting the date of divergence between primates and ungulates at the Cretaceous-Tertiary boundary (65 million years ago), when the extinction of dinosaurs occurred. A generalized least-squares method was applied in fitting a model to mtDNA sequence data, and the clock gave dates of 92.3 +/- 11.7, 13.3 +/- 1.5, 10.9 +/- 1.2, 3.7 +/- 0.6, and 2.7 +/- 0.6 million years ago (where the second of each pair of numbers is the standard deviation) for the separation of mouse, gibbon, orangutan, gorilla, and chimpanzee, respectively, from the line leading to humans. Although there is some uncertainty in the clock, this dating may pose a problem for the widely believed hypothesis that the pipedal creature Australopithecus afarensis, which lived some 3.7 million years ago at Laetoli in Tanzania and at Hadar in Ethiopia, was ancestral to man and evolved after the human-ape splitting. Another likelier possibility is that mtDNA was transferred through hybridization between a proto-human and a proto-chimpanzee after the former had developed bipedalism.
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            Author and article information

            Affiliations
            Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60011, USA.
            Institute for Emerging Infections, Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK.
            Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
            Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60011, USA.
            Centro de Investigação em Biodiversidade e Recursos Genéticos Universidade do Porto, Vairão, Portugal.
            Department of Medicine, University of California, San Diego, CA 92093, USA.
            Center for Infectious Disease Research, Korean National Institutes of Health, Osong, Korea.
            Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60011, USA.
            Department of Surgery, University of Minnesota, Minneapolis, MN, 55455 USA.
            Antiviral Pharmacology Laboratory, University of Nebraska Medical Center, College of Pharmacy, Omaha, NE 68198, USA.
            Division of Infectious Diseases, University of Minnesota, Minneapolis, MN 55455, USA.
            Department of Infectious Diseases, King's College London, Guy's Hospital, London, UK.
            Centre for Immunology, Infection and Evolution, University of Edinburgh, Edinburgh, UK.
            Department of Microbiology, University of Minnesota, Minneapolis, MN 55455, USA.
            Institute for Emerging Infections, Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK.
            Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60011, USA.
            Author notes
            Correspondence and requests for materials should be addressed to Steven Wolinsky
            Contributors
            Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60011, USA.
            Institute for Emerging Infections, Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK.
            Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
            Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60011, USA.
            Centro de Investigação em Biodiversidade e Recursos Genéticos Universidade do Porto, Vairão, Portugal.
            Department of Medicine, University of California, San Diego, CA 92093, USA.
            Center for Infectious Disease Research, Korean National Institutes of Health, Osong, Korea.
            Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60011, USA.
            Department of Surgery, University of Minnesota, Minneapolis, MN, 55455 USA.
            Antiviral Pharmacology Laboratory, University of Nebraska Medical Center, College of Pharmacy, Omaha, NE 68198, USA.
            Division of Infectious Diseases, University of Minnesota, Minneapolis, MN 55455, USA.
            Department of Infectious Diseases, King's College London, Guy's Hospital, London, UK.
            Centre for Immunology, Infection and Evolution, University of Edinburgh, Edinburgh, UK.
            Department of Microbiology, University of Minnesota, Minneapolis, MN 55455, USA.
            Institute for Emerging Infections, Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK.
            Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60011, USA.
            Journal
            0410462
            6011
            Nature
            Nature
            Nature
            0028-0836
            1476-4687
            28 December 2015
            27 January 2016
            4 February 2016
            27 July 2016
            : 530
            : 7588
            : 51-56
            26814962
            4865637
            10.1038/nature16933
            NIHMS746503

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