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      The rearing environment persistently modulates mouse phenotypes from the molecular to the behavioural level

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          Abstract

          The phenotype of an organism results from its genotype and the influence of the environment throughout development. Even when using animals of the same genotype, independent studies may test animals of different phenotypes, resulting in poor replicability due to genotype-by-environment interactions. Thus, genetically defined strains of mice may respond differently to experimental treatments depending on their rearing environment. However, the extent of such phenotypic plasticity and its implications for the replicability of research findings have remained unknown. Here, we examined the extent to which common environmental differences between animal facilities modulate the phenotype of genetically homogeneous (inbred) mice. We conducted a comprehensive multicentre study, whereby inbred C57BL/6J mice from a single breeding cohort were allocated to and reared in 5 different animal facilities throughout early life and adolescence, before being transported to a single test laboratory. We found persistent effects of the rearing facility on the composition and heterogeneity of the gut microbial community. These effects were paralleled by persistent differences in body weight and in the behavioural phenotype of the mice. Furthermore, we show that environmental variation among animal facilities is strong enough to influence epigenetic patterns in neurons at the level of chromatin organisation. We detected changes in chromatin organisation in the regulatory regions of genes involved in nucleosome assembly, neuronal differentiation, synaptic plasticity, and regulation of behaviour. Our findings demonstrate that common environmental differences between animal facilities may produce facility-specific phenotypes, from the molecular to the behavioural level. Furthermore, they highlight an important limitation of inferences from single-laboratory studies and thus argue that study designs should take environmental background into account to increase the robustness and replicability of findings.

          Abstract

          The phenotype of an organism results not only from its genotype but also the influence of its environment throughout development. This study shows that common environmental differences between animal facilities can induce substantial variation in the phenotype of mice, thereby highlighting an important limitation of inferences from single-laboratory studies in animal research.

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          Most cited references136

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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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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: VisualizationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                PLOS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                21 October 2022
                October 2022
                21 October 2022
                : 20
                : 10
                : e3001837
                Affiliations
                [1 ] Animal Welfare Division, Vetsuisse Faculty, University of Bern, Bern, Switzerland
                [2 ] Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
                [3 ] MWSchmid GmbH, Glarus, Switzerland
                [4 ] Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
                [5 ] Department of Behavioural Biology, University of Münster, Münster, Germany
                [6 ] Institute for Laboratory Animal Science and Central Animal Facility, Hannover Medical School, Hannover, Germany
                [7 ] Institute of Anatomy, Division of Functional Neuroanatomy, University of Zürich, Zürich, Switzerland; Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
                [8 ] Department of Behavioural Biology, Osnabrück University, Osnabrück, Germany
                Universidade Federal de Santa Catarina, BRAZIL
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-8818-2974
                Article
                PBIOLOGY-D-22-01111
                10.1371/journal.pbio.3001837
                9629646
                36269766
                f8303969-da5d-4066-9560-775c8bb1aa14
                © 2022 Jaric et al

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

                History
                : 18 May 2022
                : 20 September 2022
                Page count
                Figures: 4, Tables: 0, Pages: 33
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: 310030_179254
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003006, Eidgenössische Technische Hochschule Zürich;
                Award ID: Core funding
                Award Recipient :
                This study was supported by the Swiss National Science Foundation (grant 310030_179254) to H.W and core funding from ETH Zürich to the Laboratory of Microbiome Research headed by S.S. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Meta-Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Rodents
                Mice
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Rodents
                Mice
                Biology and Life Sciences
                Cell Biology
                Chromosome Biology
                Chromatin
                Biology and Life Sciences
                Genetics
                Epigenetics
                Chromatin
                Biology and Life Sciences
                Genetics
                Gene Expression
                Chromatin
                Biology and Life Sciences
                Psychology
                Behavior
                Animal Behavior
                Social Sciences
                Psychology
                Behavior
                Animal Behavior
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbiome
                Biology and Life Sciences
                Genetics
                Genomics
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Microbiology
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Genetics
                Phenotypes
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Biology and Life Sciences
                Psychology
                Behavior
                Social Sciences
                Psychology
                Behavior
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Custom metadata
                vor-update-to-uncorrected-proof
                2022-11-02
                The ATAC-seq data are available from the NCBI Gene Expression Omnibus (GEO) database under accession number GSE191125. The 16S rRNA gene sequencing data are available from the European Nucleotide Archive (ENA) under accession number PRJEB49361. All other relevant data supporting the findings of this study are available within the article and its supporting material. The code files for the ATAC-seq analysis are available at: https://github.com/MWSchmid/Jaric-et-al.-2022. The codes used for analysis of behavioural and physiological responses and randomization are available at the Figshare repository https://doi.org/10.6084/m9.figshare.21088642.

                Life sciences
                Life sciences

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