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Transcriptome responses of Streptococcus mutans to peroxide stress: identification of novel antioxidant pathways regulated by Spx

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      Abstract

      The oxidative stress regulator Spx is ubiquitously found among Gram-positive bacteria. Previously, we reported identification of two Spx proteins in Streptococcus mutans – SpxA1 was the primary activator of oxidative stress genes whereas SpxA2 served a backup role. Here, we used RNA sequencing to uncover the scope of the H 2O 2 (peroxide)-stress regulon and to further explore the significance of Spx regulation in S. mutans. The transcriptome data confirmed the relationship between Spx and genes typically associated with oxidative stress, but also identified novel genes and metabolic pathways controlled by Spx during peroxide stress. While individual inactivation of newly identified peroxide stress genes had modest or no obvious consequences to bacterial survival, a phenotype enhancement screen using the ∆ spxA1 strain as background for creation of double mutants revealed that four of the five genes inactivated were required for stress survival. Physiological and biochemical assays validated, at least in part, the transcriptome data indicating that SpxA1 coordinates transcriptional changes during peroxide stress that modify global metabolism and facilitate production of antioxidants. Collectively, our findings unraveled the scope of the peroxide stress regulon and expand the repertoire of oxidative stress genes in S. mutans, shedding new light on the role of Spx regulation.

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      Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

      Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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        Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection

        We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants—the ‘Keio collection'—provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).
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          The molecular mechanisms and physiological consequences of oxidative stress: lessons from a model bacterium.

           James Imlay (2013)
          Oxic environments are hazardous. Molecular oxygen adventitiously abstracts electrons from many redox enzymes, continuously forming intracellular superoxide and hydrogen peroxide. These species can destroy the activities of metalloenzymes and the integrity of DNA, forcing organisms to protect themselves with scavenging enzymes and repair systems. Nevertheless, elevated levels of oxidants quickly poison bacteria, and both microbial competitors and hostile eukaryotic hosts exploit this vulnerability by assaulting these bacteria with peroxides or superoxide-forming antibiotics. In response, bacteria activate elegant adaptive strategies. In this Review, I summarize our current knowledge of oxidative stress in Escherichia coli, the model organism for which our understanding of damage and defence is most well developed.
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            Author and article information

            Affiliations
            ISNI 0000 0004 1936 8091, GRID grid.15276.37, Department of Oral Biology, University of Florida College of Dentistry, ; Gainesville, FL 32608 USA
            Contributors
            jlemos@dental.ufl.edu
            Journal
            Sci Rep
            Sci Rep
            Scientific Reports
            Nature Publishing Group UK (London )
            2045-2322
            22 November 2017
            22 November 2017
            2017
            : 7
            29167560 5700188 16367 10.1038/s41598-017-16367-5
            © The Author(s) 2017

            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/.

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