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      Modification of the Streptococcus mutans transcriptome by LrgAB and environmental stressors

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          Abstract

          The Streptococcus mutans Cid/Lrg system is central to the physiology of this cariogenic organism, affecting oxidative stress resistance, biofilm formation and competence. Previous transcriptome analyses of lytS (responsible for the regulation of lrgAB expression) and cidB mutants have revealed pleiotropic effects on carbohydrate metabolism and stress resistance genes. In this study, it was found that an lrgAB mutant, previously shown to have diminished aerobic and oxidative stress growth, was also much more growth impaired in the presence of heat and vancomycin stresses, relative to wild-type, lrgA and lrgB mutants. To obtain a more holistic picture of LrgAB and its involvement in stress resistance, RNA sequencing and bioinformatics analyses were used to assess the transcriptional response of wild-type and isogenic lrgAB mutants under anaerobic (control) and stress-inducing culture conditions (aerobic, heat and vancomycin). Hierarchical clustering and principal components analyses of all differentially expressed genes revealed that the most distinct gene expression profiles between S. mutans UA159 and lrgAB mutant occurred during aerobic and high-temperature growth. Similar to previous studies of a cidB mutant, lrgAB stress transcriptomes were characterized by a variety of gene expression changes related to genomic islands, CRISPR-C as systems, ABC transporters, competence, bacteriocins, glucosyltransferases, protein translation, tricarboxylic acid cycle, carbohydrate metabolism/storage and transport. Notably, expression of lrgAB was upregulated in the wild-type strain under all three stress conditions. Collectively, these results demonstrate that mutation of lrgAB alters the transcriptional response to stress, and further support the idea that the Cid/Lrg system acts to promote cell homeostasis in the face of environmental stress.

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          Most cited references 68

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

              Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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                Author and article information

                Journal
                Microb Genom
                Microb Genom
                MGen
                Microbial Genomics
                Microbiology Society
                2057-5858
                February 2017
                28 February 2017
                : 3
                : 2
                Affiliations
                [ 1]Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida , Gainesville, FL 32611, USA
                [ 2]Bioinformatics, Interdisciplinary Center for Biotechnology Research, University of Florida , Gainesville, FL 32610, USA
                [ 3]Department of Oral Biology, College of Dentistry, University of Florida , Gainesville, FL 32610, USA
                [ ]Present address: Department of Health Outcomes and Policy, College of Medicine, University of Florida, Gainesville, FL 32610, USA.
                Author notes
                *Correspondence: Sang-Joon Ahn, sahn@ 123456dental.ufl.edu

                All supporting data, code and protocols have been provided within the article or through supplementary data files. Five supplementary figures and fourteen supplementary tables are available with the online Supplementary Material.

                Article
                mgen000104
                10.1099/mgen.0.000104
                5361627
                © 2017 The Authors

                This is an open access article under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

                Product
                Funding
                Funded by: National Institute of Dental and Craniofacial Research
                Award ID: R03 DE023603
                Funded by: National Institute of Dental and Craniofacial Research
                Award ID: R01 DE025237
                Categories
                Research Article
                Systems Microbiology: Transcriptomics, proteomics, networks
                Custom metadata
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                stress response, lrgab, heat-shock, vancomycin, s. mutans, oxygen

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