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      Genomic factors related to tissue tropism in Chlamydia pneumoniae infection

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          Abstract

          Background

          Chlamydia pneumoniae ( Cpn) are obligate intracellular bacteria that cause acute infections of the upper and lower respiratory tract and have been implicated in chronic inflammatory diseases. Although of significant clinical relevance, complete genome sequences of only four clinical Cpn strains have been obtained. All of them were isolated from the respiratory tract and shared more than 99% sequence identity. Here we investigate genetic differences on the whole-genome level that are related to Cpn tissue tropism and pathogenicity.

          Results

          We have sequenced the genomes of 18 clinical isolates from different anatomical sites (e.g. lung, blood, coronary arteries) of diseased patients, and one animal isolate. In total 1,363 SNP loci and 184 InDels have been identified in the genomes of all clinical Cpn isolates. These are distributed throughout the whole chlamydial genome and enriched in highly variable regions. The genomes show clear evidence of recombination in at least one potential region but no phage insertions. The tyrP gene was always encoded as single copy in all vascular isolates. Phylogenetic reconstruction revealed distinct evolutionary lineages containing primarily non-respiratory Cpn isolates. In one of these, clinical isolates from coronary arteries and blood monocytes were closely grouped together. They could be distinguished from all other isolates by characteristic nsSNPs in genes involved in RB to EB transition, inclusion membrane formation, bacterial stress response and metabolism.

          Conclusions

          This study substantially expands the genomic data of Cpn and elucidates its evolutionary history. The translation of the observed Cpn genetic differences into biological functions and the prediction of novel pathogen-oriented diagnostic strategies have to be further explored.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-015-1377-8) contains supplementary material, which is available to authorized users.

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          Most cited references38

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          GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

          J Besemer (2001)
          Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.
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            tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence

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              KaKs_Calculator 2.0: A Toolkit Incorporating Gamma-Series Methods and Sliding Window Strategies

              We present an integrated stand-alone software package named KaKs_Calculator 2.0 as an updated version. It incorporates 17 methods for the calculation of nonsynonymous and synonymous substitution rates; among them, we added our modified versions of several widely used methods as the gamma series including γ-NG, γ-LWL, γ-MLWL, γ-LPB, γ-MLPB, γ-YN and γ-MYN, which have been demonstrated to perform better under certain conditions than their original forms and are not implemented in the previous version. The package is readily used for the identification of positively selected sites based on a sliding window across the sequences of interests in 5’ to 3’ direction of protein-coding sequences, and have improved the overall performance on sequence analysis for evolution studies. A toolbox, including C++ and Java source code and executable files on both Windows and Linux platforms together with a user instruction, is downloadable from the website for academic purpose at https://sourceforge.net/projects/kakscalculator2/.
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                Author and article information

                Contributors
                Thomas.Weinmaier@univie.ac.at
                Jonathan.Hoser@helmholtz-muenchen.de
                Sebastian.Eck@medizinische-genetik.de
                Inga.Kaufhold@uksh.de
                Kensuke.shima@uksh.de
                TimStrom@helmholtz-muenchen.de
                Thomas.Rattei@univie.ac.at
                Jan.Rupp@uksh.de
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                7 April 2015
                7 April 2015
                2015
                : 16
                : 1
                : 268
                Affiliations
                [ ]Division of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, 1090 Vienna, Austria
                [ ]Department of Genome Oriented Bioinformatics, Technical University Munich, 85354 Freising, Germany
                [ ]Center for Human Genetics and Laboratory Diagnostics Dr. Klein, Dr. Rost and Colleagues, 82152 Martinsried, Germany
                [ ]Department of Molecular and Clinical Infectious Diseases, University of Luebeck, 23538 Luebeck, Germany
                [ ]Institute of Human Genetics, Helmholtz Center Munich, 85764 Neuherberg, Germany
                Article
                1377
                10.1186/s12864-015-1377-8
                4489044
                25887605
                37e9dc9a-b7e3-403a-a862-520f09f3f335
                © Weinmeier et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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
                : 22 December 2014
                : 21 February 2015
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

                Genetics
                chlamydia pneumoniae,genome assembly,comparative genomics,tissue tropism,snps,indels
                Genetics
                chlamydia pneumoniae, genome assembly, comparative genomics, tissue tropism, snps, indels

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