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      Intracellular Staphylococcus aureus persisters upon antibiotic exposure

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          Abstract

          Bacterial persister cells are phenotypic variants that exhibit a transient non-growing state and antibiotic tolerance. Here, we provide in vitro evidence of Staphylococcus aureus persisters within infected host cells. We show that the bacteria surviving antibiotic treatment within host cells are persisters, displaying biphasic killing and reaching a uniformly non-responsive, non-dividing state when followed at the single-cell level. This phenotype is stable but reversible upon antibiotic removal. Intracellular S. aureus persisters remain metabolically active but display an altered transcriptomic profile consistent with activation of stress responses, including the stringent response as well as cell wall stress, SOS and heat shock responses. These changes are associated with multidrug tolerance after exposure to a single antibiotic. We hypothesize that intracellular S. aureus persisters may constitute a reservoir for relapsing infection and could contribute to therapeutic failures.

          Abstract

          Bacterial persister cells exhibit a transient non-growing state and antibiotic tolerance. Here, Peyrusson et al. provide evidence of metabolically active Staphylococcus aureus persisters within infected host cells exposed to antibiotics and analyse transcriptomic alterations associated with persistence.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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              Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

              The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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                Author and article information

                Contributors
                francoise.vanbambeke@uclouvain.be
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 May 2020
                4 May 2020
                2020
                : 11
                : 2200
                Affiliations
                [1 ]ISNI 0000 0001 2294 713X, GRID grid.7942.8, Pharmacologie cellulaire et moléculaire, Louvain Drug Research Institute, , Université catholique de Louvain (UCLouvain), ; Brussels, Belgium
                [2 ]ISNI 0000 0001 2353 6535, GRID grid.428999.7, Hub de Bioinformatique et Biostatistique – Département Biologie Computationnelle, , Institut Pasteur, USR 3756 CNRS, ; Paris, France
                [3 ]Institut Pasteur, Plate-forme Transcriptome et Epigenome, Biomics, Centre de Ressources et Recherches Technologiques (C2RT), Paris, France
                [4 ]Institut für Medizinische Mikrobiologie und Hygiene, Tübingen, Germany
                [5 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Institute of Technology, , University of Tartu, ; Tartu, Estonia
                Author information
                http://orcid.org/0000-0003-3980-4463
                http://orcid.org/0000-0002-5196-9431
                http://orcid.org/0000-0003-3909-5281
                http://orcid.org/0000-0002-0052-7991
                Article
                15966
                10.1038/s41467-020-15966-7
                7198484
                32366839
                817d545a-24d8-40e0-923e-86cd60b963ac
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 August 2019
                : 2 April 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001665, Agence Nationale de la Recherche (French National Research Agency);
                Award ID: ANR10‐NBS‐09‐08
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: SFB156
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002301, Eesti Teadusagentuur (Estonian Research Council);
                Award ID: PRG335
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                antibiotics,antibacterial drug resistance,cellular microbiology,pathogens
                Uncategorized
                antibiotics, antibacterial drug resistance, cellular microbiology, pathogens

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