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      Human T cell epitopes of Mycobacterium tuberculosis are evolutionarily hyperconserved

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          Mycobacterium tuberculosis is an obligate human pathogen capable of persisting in individual hosts for decades. To determine whether antigenic variation and immune escape contribute to the success of M. tuberculosis, we determined and analyzed 22 genome sequences representative of the global diversity of the M. tuberculosis complex (MTBC). As expected, we found that essential genes in MTBC were more evolutionarily conserved than non-essential genes. Surprisingly however, most of 491 experimentally confirmed human T cell epitopes showed little sequence variation and exhibited a lower ratio of non-synonymous to synonymous changes than essential and non-essential genes. These findings are consistent with strong purifying selection acting on these epitopes, and imply that MTBC might benefit from recognition by human T cells.

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          Most cited references 53

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          MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0.

          We announce the release of the fourth version of MEGA software, which expands on the existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. Version 4 includes a unique facility to generate captions, written in figure legend format, in order to provide natural language descriptions of the models and methods used in the analyses. This facility aims to promote a better understanding of the underlying assumptions used in analyses, and of the results generated. Another new feature is the Maximum Composite Likelihood (MCL) method for estimating evolutionary distances between all pairs of sequences simultaneously, with and without incorporating rate variation among sites and substitution pattern heterogeneities among lineages. This MCL method also can be used to estimate transition/transversion bias and nucleotide substitution pattern without knowledge of the phylogenetic tree. This new version is a native 32-bit Windows application with multi-threading and multi-user supports, and it is also available to run in a Linux desktop environment (via the Wine compatibility layer) and on Intel-based Macintosh computers under the Parallels program. The current version of MEGA is available free of charge at (
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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:
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              MrBayes 3: Bayesian phylogenetic inference under mixed models.

              MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types-e.g. morphological, nucleotide, and protein-and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.

                Author and article information

                [1 ]Medical Research Council, National Institute for Medical Research, London, NW7 1AA, UK
                [2 ]New York University School of Medicine, New York, NY 10016, USA
                [3 ]The Institute for Systems Biology and the Bill and Melinda Gates Foundation, Seattle, WA 98102, USA
                [4 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [5 ]Research Centre Borstel, Molecular Mycobacteriology, 23845 Borstel, Germany
                [6 ]Mycobacteria Reference Laboratory (CIb-LIS), National Institute for Public Health and the Environment, 3720 BA Bilthoven, The Netherlands
                [7 ]Swiss Tropical and Public Health Institute, 4002 Basel, Switzerland
                [8 ]University of Basel, 4003 Basel, Switzerland
                Author notes
                [* ]To whom correspondence should be addressed. sebastien.gagneux@ , joel.ernst@


                I.C., J.D.E. and S.G. designed the study; P.M.S., S.N., K.K. and S.G. contributed sources of M. tuberculosis DNA and demographic information; I.C., J.C. and J.G. performed DNA sequencing and bioinformatics; I.C., P.M.S., J.D.E. and S.G. wrote the manuscript with comments from all authors.

                Nat Genet
                Nat. Genet.
                Nature genetics
                1 May 2010
                23 May 2010
                June 2010
                01 December 2010
                : 42
                : 6
                : 498-503
                20495566 2883744 10.1038/ng.590 UKMS29888

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                Funded by: Medical Research Council :
                Award ID: U117588500(88500) || MRC_



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