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Intra-continental spread of human invasive Salmonella Typhimurium pathovariants in sub-Saharan Africa

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      Abstract

      A highly invasive form of non-typhoidal Salmonella (iNTS) disease has been recently documented in many countries in sub-Saharan Africa. The most common Salmonella enterica serovar causing this disease is Typhimurium. We applied whole-genome sequence-based phylogenetic methods to define the population structure of sub-Saharan African invasive Salmonella Typhimurium and compared these to global Salmonella Typhimurium isolates. Notably, the vast majority of sub-Saharan invasive Salmonella Typhimurium fell within two closely-related, highly-clustered phylogenetic lineages that we estimate emerged independently ~52 and ~35 years ago, in close temporal association with the current HIV pandemic. Clonal replacement of isolates of lineage I by lineage II was potentially influenced by the use of chloramphenicol for the treatment of iNTS disease. Our analysis suggests that iNTS disease is in part an epidemic in sub-Saharan Africa caused by highly related Salmonella Typhimurium lineages that may have occupied new niches associated with a compromised human population and antibiotic treatment.

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      Most cited references 72

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      Basic local alignment search tool.

      A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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        The Sequence Alignment/Map format and SAMtools

        Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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          Is Open Access

          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            Author and article information

            Affiliations
            [1 ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton Cambridge, UK. CB10 1SA
            [2 ]Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
            [3 ]Department of Clinical Infection, Microbiology and Immunology, Institute for Infection and Global Health, University of Liverpool, Liverpool, UK
            [4 ]Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya
            [5 ]Malawi-Liverpool-Wellcome Trust Clinical Research Program, University of Malawi College of Medicine, Blantyre, Malawi
            [6 ]Department of Microbiology, College of Medicine, University of Malawi, Blantyre, Malawi
            [7 ]Department of Gastroenterology, Institute of Translational Medicine, Liverpool University, Liverpool, UK. L69 3GE
            [8 ]Health Protection Agency, Laboratory for Gastrointestinal Infections, Centre for Infections, London, United Kingdom
            [9 ]Norwich Medical School, University of East Anglia, Norwich, United Kingdom
            [10 ]Cellular and Molecular Medicine, School of Medical Sciences, University of Bristol, Bristol, United Kingdom
            [11 ]Division of Paediatric Infectious Diseases, Department of Paediatrics and Human Development, Michigan State University, East Lansing, MI 48824, USA
            [12 ]National Hospital Abuja, Plot 132 Central District (Phase II), Garki Abuja, Nigeria
            [13 ]Barcelona Centre for International Health Research (CRESIB, Hospital Clínic-Universitat de Barcelona), Barcelona, Spain
            [14 ]Centro de Investigação em Saúde de Manhiça (CISM), Manhiça, Mozambique
            [15 ]Instituto Nacional de Saúde, Ministerio de Saúde, Maputo, Mozambique
            [16 ]Novartis Vaccines Institute for Global Health S.r.l. (NVGH), Via Fiorentina 1, 53100 Siena, Italy
            [17 ]MRC Centre for Immune Regulation, School of Immunity and Infection, College of Medicine and Dental Sciences, University of Birmingham, Birmingham, B15 2TT, UK
            [18 ]Center for Vaccine Development, University of Maryland, Baltimore, HSFI 480, 685 West Baltimore St., Baltimore, MD 21201, USA
            [19 ]Department of Medicine, University of Maryland, Baltimore, HSFI 480, 685 West Baltimore St., Baltimore, MD 21201, USA
            Author notes
            [20]

            These authors contributed equally to this work

            [§ ]Corresponding author
            Journal
            9216904
            2419
            Nat Genet
            Nat. Genet.
            Nature genetics
            1061-4036
            1546-1718
            24 September 2012
            30 September 2012
            November 2012
            01 May 2013
            : 44
            : 11
            : 1215-1221
            23023330
            3491877
            10.1038/ng.2423
            EMS49913

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            Funding
            Funded by: Wellcome Trust :
            Award ID: 098051 || WT
            Categories
            Article

            Genetics

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