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      Determination of the two-component systems regulatory network reveals core and accessory regulations across Pseudomonas aeruginosa lineages

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          Abstract

          Pseudomonas aeruginosa possesses one of the most complex bacterial regulatory networks, which largely contributes to its success as a pathogen. However, most of its transcription factors (TFs) are still uncharacterized and the potential intra-species variability in regulatory networks has been mostly ignored so far. Here, we used DAP-seq to map the genome-wide binding sites of all 55 DNA-binding two-component systems (TCSs) response regulators (RRs) across the three major  P. aeruginosa lineages. The resulting networks encompass about 40% of all genes in each strain and contain numerous new regulatory interactions across most major physiological processes. Strikingly, about half of the detected targets are specific to only one or two strains, revealing a previously unknown large functional diversity of TFs within a single species. Three main mechanisms were found to drive this diversity, including differences in accessory genome content, as exemplified by the strain-specific plasmid in IHMA87 outlier strain which harbors numerous binding sites of conserved chromosomally-encoded RRs. Additionally, most RRs display potential auto-regulation or RR-RR cross-regulation, bringing to light the vast complexity of this network. Overall, we provide the first complete delineation of the TCSs regulatory network in  P. aeruginosa that will represent an important resource for future studies on this pathogen.

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          Most cited references78

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

              The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                18 November 2021
                28 October 2021
                28 October 2021
                : 49
                : 20
                : 11476-11490
                Affiliations
                Université Grenoble Alpes , CNRS, CEA, IBS UMR 5075, Team Bacterial Pathogenesis and Cellular Responses, 38044 Grenoble, France
                Université Grenoble Alpes , CNRS, CEA, IBS UMR 5075, 38044 Grenoble, France
                Université Grenoble Alpes , CNRS, CEA, EMBL, ISBG UAR 3518, 38044 Grenoble, France
                Université Grenoble Alpes , CNRS, CEA, IBS UMR 5075, 38044 Grenoble, France
                Université Grenoble Alpes , CNRS, CEA, IBS UMR 5075, 38044 Grenoble, France
                Université Grenoble Alpes , CNRS, CEA, IBS UMR 5075, Team Bacterial Pathogenesis and Cellular Responses, 38044 Grenoble, France
                Université Grenoble Alpes , CNRS, CEA, IBS UMR 5075, Team Bacterial Pathogenesis and Cellular Responses, 38044 Grenoble, France
                Author notes
                To whom correspondence should be addressed. Tel: +41 44 633 32 45; Email: jtrouillon@ 123456ethz.ch
                Correspondence may also be addressed to Sylvie Elsen. Email: sylvie.elsen@ 123456ibs.fr
                Author information
                https://orcid.org/0000-0003-1675-0896
                https://orcid.org/0000-0003-2034-7944
                https://orcid.org/0000-0002-2580-764X
                https://orcid.org/0000-0003-4611-4719
                Article
                gkab928
                10.1093/nar/gkab928
                8599809
                34718721
                9ff19384-5ca6-4559-ab84-b3bcaba646ff
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 28 September 2021
                : 24 August 2021
                : 28 July 2021
                Page count
                Pages: 15
                Funding
                Funded by: French National Research Agency, DOI 10.13039/501100001665;
                Award ID: ANR-15-IDEX-02
                Funded by: ANR, DOI 10.13039/501100001665;
                Award ID: ANR-15-CE11-0018-01
                Funded by: Grenoble Alpes University Graduate School;
                Award ID: ANR-17- EURE-0003
                Funded by: Fondation pour la Recherche Médicale, DOI 10.13039/501100002915;
                Award ID: DEQ20170336705
                Categories
                AcademicSubjects/SCI00010
                Data Resources and Analyses

                Genetics
                Genetics

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