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      Container-aided integrative QTL and RNA-seq analysis of Collaborative Cross mice supports distinct sex-oriented molecular modes of response in obesity

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          Abstract

          Background

          The Collaborative Cross (CC) mouse population is a valuable resource to study the genetic basis of complex traits, such as obesity. Although the development of obesity is influenced by environmental factors, underlying genetic mechanisms play a crucial role in the response to these factors. The interplay between the genetic background and the gene expression pattern can provide further insight into this response, but we lack robust and easily reproducible workflows to integrate genomic and transcriptomic information in the CC mouse population.

          Results

          We established an automated and reproducible integrative workflow to analyse complex traits in the CC mouse genetic reference panel at the genomic and transcriptomic levels. We implemented the analytical workflow to assess the underlying genetic mechanisms of host susceptibility to diet induced obesity and integrated these results with diet induced changes in the hepatic gene expression of susceptible and resistant mice. Hepatic gene expression differs significantly between obese and non-obese mice, with a significant sex effect, where male and female mice exhibit different responses and coping mechanisms.

          Conclusion

          Integration of the data showed that different genes but similar pathways are involved in the genetic susceptibility and disturbed in diet induced obesity. Genetic mechanisms underlying susceptibility to high-fat diet induced obesity are different in female and male mice. The clear distinction we observed in the systemic response to the high-fat diet challenge and to obesity between male and female mice points to the need for further research into distinct sex-related mechanisms in metabolic disease.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-020-07173-x.

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          Most cited references69

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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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            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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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Contributors
                fuadi@tauex.tau.ac.il
                achatzi@bioacademy.gr
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                3 November 2020
                3 November 2020
                2020
                : 21
                : 761
                Affiliations
                [1 ]GRID grid.5216.0, ISNI 0000 0001 2155 0800, Division of Pediatric Hematology-Oncology, First Department of Pediatrics, , National and Kapodistrian University of Athens, ; Athens, Greece
                [2 ]GRID grid.11047.33, ISNI 0000 0004 0576 5395, Department of Biology, , University of Patras, ; Patras, Greece
                [3 ]GRID grid.12136.37, ISNI 0000 0004 1937 0546, Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, , Tel-Aviv University, ; Tel-Aviv, Israel
                [4 ]GRID grid.5361.1, ISNI 0000 0000 8853 2677, Division of Bioinformatics, , Medical University of Innsbruck, ; Innsbruck, Austria
                [5 ]e-NIOS PC, Kallithea, Athens, Greece
                [6 ]GRID grid.417975.9, ISNI 0000 0004 0620 8857, Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, ; Athens, Greece
                [7 ]GRID grid.83440.3b, ISNI 0000000121901201, Department of Genetics, , University College of London, ; London, UK
                [8 ]GRID grid.5173.0, ISNI 0000 0001 2298 5320, Institute of Computational Biology, Department of Biotechnology, University of Life Sciences and Natural Resources, Vienna (BOKU), ; Vienna, Austria
                [9 ]GRID grid.11478.3b, Centre for Genomic Regulation (CRG), ; Barcelona, Spain
                Article
                7173
                10.1186/s12864-020-07173-x
                7640698
                33143653
                5a5ddbf6-f947-4113-bb8d-8dbf7df085b2
                © The Author(s) 2020

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 29 July 2019
                : 21 October 2020
                Funding
                Funded by: Operational Program "Competitiveness, Entrepreneurship and Innovation 2014-2020"
                Award ID: "Innovative Nanopharmaceuticals: Targeting Breast Cancer Stem Cells by a Novel Combination of Epigenetic and Anticancer Drugs with Gene Therapy (INNOCENT)" (7th Joint Translational Call - 2016, European Innovative Research and Technological Development Projects In Nanomedicine) of the ERA-NET EuroNanoMed II
                Award Recipient :
                Funded by: Operational Program “Human Resources Development - Education and Lifelong Learning” Partnership Agreement (PA) 2014-2020
                Funded by: Hendrech and Eiran Gotwert Fund for studying diabetes
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 085906/Z/08/Z
                Award ID: 075491/Z/04
                Award ID: 090532/Z/09/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004375, Tel Aviv University;
                Funded by: FundRef http://dx.doi.org/10.13039/501100003977, Israel Science Foundation;
                Award ID: 429/09
                Award Recipient :
                Funded by: European Sequencing and Genotyping Infrastructure (ESGI) consortium
                Funded by: Israel Science Foundation
                Award ID: 1085/18
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006221, United States - Israel Binational Science Foundation;
                Award ID: 2015077
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001736, German-Israeli Foundation for Scientific Research and Development;
                Award ID: I-63-410.20-2017
                Award Recipient :
                Funded by: “BioS: Digital Skills on Computational Biology”, ΕRASMUS+, KA2: Cooperation for innovation and the exchange of good practices - Sector Skills Alliances
                Award ID: EACEA/04/2017
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Genetics
                collaborative cross,obesity,sex-differences,high-fat diet,qtl,rnaseq
                Genetics
                collaborative cross, obesity, sex-differences, high-fat diet, qtl, rnaseq

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