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      The within-host population dynamics of Mycobacterium tuberculosis vary with treatment efficacy

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          Abstract

          Background

          Combination therapy is one of the most effective tools for limiting the emergence of drug resistance in pathogens. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We address this question by studying the population dynamics of Mycobacterium tuberculosis within tuberculosis patients undergoing treatment with different combinations of antibiotics.

          Results

          By combining very deep whole genome sequencing (~1000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy has a clear impact on the population dynamics: sufficient drug pressure bears a clear signature of purifying selection leading to apparent genetic stability. In contrast, M. tuberculosis populations subject to less drug pressure show markedly different dynamics, including cases of acquisition of additional drug resistance.

          Conclusions

          Our findings show that for a pathogen like M. tuberculosis, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-017-1196-0) contains supplementary material, which is available to authorized users.

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          Most cited references56

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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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            ART: a next-generation sequencing read simulator.

            ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa and Applied Biosystems' SOLiD. ART also allows the flexibility to use customized read error model parameters and quality profiles. Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art.
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              The NumPy array: a structure for efficient numerical computation

              In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.
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                Author and article information

                Contributors
                guolongzhang@hotmail.com
                +41-61-284-8369 , sebastien.gagneux@unibas.ch
                cbarry@niaid.nih.gov
                qgao99@yahoo.com
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                19 April 2017
                19 April 2017
                2017
                : 18
                : 71
                Affiliations
                [1 ]ISNI 0000 0004 0587 0574, GRID grid.416786.a, Department of Medical Parasitology and Infection Biology, , Swiss Tropical and Public Health Institute, ; 4002 Basel, Switzerland
                [2 ]ISNI 0000 0004 1937 0642, GRID grid.6612.3, , University of Basel, ; 4001 Basel, Switzerland
                [3 ]ISNI 0000 0001 0125 2443, GRID grid.8547.e, Key Laboratory of Medical Molecular Virology of Ministries of Education and Health, Institutes of Biomedical Sciences and Institute of Medical Microbiology, School of Basic Medical Sciences, , Fudan University, ; Shanghai, 200032 China
                [4 ]ISNI 0000 0001 2164 9667, GRID grid.419681.3, , Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, NIAID, NIH, ; Bethesda, MD 20892 USA
                [5 ]ISNI 0000 0004 1937 1151, GRID grid.7836.a, Institute of Infectious Disease and Molecular Medicine, and the Department of Clinical Laboratory Sciences, Faculty of Health Sciences, , University of Cape Town, ; Rondebosch, 7701 South Africa
                [6 ]GRID grid.459614.b, , Henan Provincial Chest Hospital, ; Zhengzhou, 450003 Henan China
                [7 ]Sino-US International Research Centers of Tuberculosis, Zhengzhou, 450003 Henan China
                [8 ]Henan Public Health Clinical Center, Zhengzhou, 450003 Henan China
                Article
                1196
                10.1186/s13059-017-1196-0
                5395877
                28424085
                97629b01-d546-4ca0-8e9f-dfdf99d43dca
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 5 December 2016
                : 21 March 2017
                Funding
                Funded by: Division of Intramural Research, National Institute of Allergy and Infectious Diseases (US)
                Award ID: 2014DFA30340
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 91231115
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: 310030 166687
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 309540-EVODRTB
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Genetics
                tuberculosis,within-host evolution,combination therapy,drug resistance,whole genome sequencing

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