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      Broad time‐dependent transcriptional activity of metabolic genes of E. coli O104:H4 strain C227/11Φcu in a soil microenvironment at low temperature

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          Abstract

          In the current study, metabolic genes and networks that influence the persistence of pathogenic Escherichia coli O104:H4 strain C227/11Φcu in agricultural soil microenvironments at low temperature were investigated. The strain was incubated in alluvial loam (AL) and total RNA was prepared from samples at time point 0, and after 1 and 4 weeks. Differential transcriptomic analysis was performed by RNA sequencing analysis and values obtained at weeks 1 and 4 were compared to those of time point 0. We found differential expression of more than 1500 genes for either time point comparison. The two lists of differentially expressed genes were then subjected to gene set enrichment of Gene Ontology terms. In total, 17 GO gene sets and 3 Pfam domains were found to be enriched after 1 week. After 4 weeks, 17 GO gene sets and 7 Pfam domains were statistically enriched. Especially stress response genes and genes of the primary metabolism were particularly affected at both time points. Genes and gene sets for uptake of carbohydrates, amino acids were strongly upregulated, indicating adjustment to a low nutrient environment. The results of this transcriptome analysis show that persistence of C227/11Φcu in soils is associated with a complex interplay of metabolic networks.

          Abstract

          RNAseq analysis was used to study the changes in the differential transcriptomic profile of Escherichia coli O104:H4 strain C227/11Φcu in agricultural soil microenvironments at time point 0, and after 1 and 4 weeks at low temperature. Especially genes associated with stress response and primary metabolism genes were particularly affected at time points 1 and 4 weeks, as well as genes and gene sets for uptake of carbohydrates and amino acids, indicating adjustment to a low nutrient environment.

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                herbert.schmidt@uni-hohenheim.de
                Journal
                Environ Microbiol Rep
                Environ Microbiol Rep
                10.1111/(ISSN)1758-2229
                EMI4
                Environmental Microbiology Reports
                John Wiley & Sons, Inc. (Hoboken, USA )
                1758-2229
                29 August 2023
                December 2023
                : 15
                : 6 ( doiID: 10.1111/emi4.v15.6 )
                : 582-596
                Affiliations
                [ 1 ] Department of Food Microbiology and Hygiene, Institute of Food Science and Biotechnology University of Hohenheim Stuttgart Germany
                [ 2 ] Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany
                Author notes
                [*] [* ] Correspondence

                Herbert Schmidt, Department of Food Microbiology and Hygiene, Institute of Food Science and Biotechnology, University of Hohenheim, Germany.

                Email: herbert.schmidt@ 123456uni-hohenheim.de

                Author information
                https://orcid.org/0000-0001-6046-4198
                Article
                EMI413198
                10.1111/1758-2229.13198
                10667640
                37644642
                8a7091f5-447e-4832-8ae0-dfb777df015d
                © 2023 The Authors. Environmental Microbiology Reports published by Applied Microbiology International and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 March 2023
                : 16 August 2023
                Page count
                Figures: 7, Tables: 7, Pages: 15, Words: 10730
                Funding
                Funded by: Bundesanstalt für Landwirtschaft und Ernährung , doi 10.13039/501100010771;
                Award ID: 2819HS004
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                December 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.5 mode:remove_FC converted:24.11.2023

                Microbiology & Virology
                Microbiology & Virology

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