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      The Role of Salmonella Genomic Island 4 in Metal Tolerance of Salmonella enterica Serovar I 4,[5],12:i:- Pork Outbreak Isolate USDA15WA-1

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          Abstract

          Multidrug-resistant (MDR; resistance to >3 antimicrobial classes) Salmonella enterica serovar I 4,[5],12:i:- strains were linked to a 2015 foodborne outbreak from pork. Strain USDA15WA-1, associated with the outbreak, harbors an MDR module and the metal tolerance element Salmonella Genomic Island 4 (SGI-4). Characterization of SGI-4 revealed that conjugational transfer of SGI-4 resulted in the mobile genetic element (MGE) replicating as a plasmid or integrating into the chromosome. Tolerance to copper, arsenic, and antimony compounds was increased in Salmonella strains containing SGI-4 compared to strains lacking the MGE. Following Salmonella exposure to copper, RNA-seq transcriptional analysis demonstrated significant differential expression of diverse genes and pathways, including induction of at least 38 metal tolerance genes (copper, arsenic, silver, and mercury). Evaluation of swine administered elevated concentrations of zinc oxide (2000 mg/kg) and copper sulfate (200 mg/kg) as an antimicrobial feed additive (Zn+Cu) in their diet for four weeks prior to and three weeks post-inoculation with serovar I 4,[5],12:i:- indicated that Salmonella shedding levels declined at a slower rate in pigs receiving in-feed Zn+Cu compared to control pigs (no Zn+Cu). The presence of metal tolerance genes in MDR Salmonella serovar I 4,[5],12:i:- may provide benefits for environmental survival or swine colonization in metal-containing settings.

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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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            Gene Ontology: tool for the unification of biology

            Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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              Fitting Linear Mixed-Effects Models Usinglme4

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                Author and article information

                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                30 October 2020
                November 2020
                : 11
                : 11
                : 1291
                Affiliations
                [1 ]USDA, ARS, National Laboratory for Agriculture and the Environment, Agroecosystems Management Research Unit, Ames, IA 50011, USA; brian.kerr@ 123456usda.gov (B.J.K.); shelby.curry@ 123456biomin.net (S.M.C.)
                [2 ]USDA, ARS, National Animal Disease Center, Food Safety and Enteric Pathogens, Ames, IA 50010, USA; julian.trachsel@ 123456usda.gov (J.M.T.); daniel.shippy@ 123456yahoo.com (D.C.S.); sathesh.sivasankaran@ 123456usda.gov (S.K.S.); crystal.loving@ 123456usda.gov (C.L.L.); brunelle@ 123456arborbiosci.com (B.W.B.); shawn.bearson@ 123456usda.gov (S.M.D.B.)
                [3 ]Genome Informatics Facility, Iowa State University, Ames, IA 50011, USA
                [4 ]Animal Science Department, Iowa State University, Ames, IA 50011, USA; ngabler@ 123456iastate.edu
                Author notes
                [* ]Correspondence: brad.bearson@ 123456usda.gov ; Tel.: +1-515-294-0209
                [†]

                These authors contributed equally to this work.

                [‡]

                Current address: Arbor Biosciences, Ann Arbor, MI 48103, USA.

                Author information
                https://orcid.org/0000-0001-7033-7424
                https://orcid.org/0000-0002-2996-0334
                https://orcid.org/0000-0002-9842-368X
                https://orcid.org/0000-0001-7283-4782
                Article
                genes-11-01291
                10.3390/genes11111291
                7716197
                33142960
                f9ce0863-67a5-425e-83a8-a2b3f7b0747f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 September 2020
                : 27 October 2020
                Categories
                Article

                salmonella,metal tolerance,mobile genetic element,conjugation,copper

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