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Hepatic gene expression variations in response to high-fat diet-induced impaired glucose tolerance using RNAseq analysis in collaborative cross mouse population

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Abstract

Hepatic gene expression is known to differ between healthy and type 2 diabetes conditions. Identifying these variations will provide better knowledge to the development of gene-targeted therapies. The aim of this study is to assess diet-induced hepatic gene expression of susceptible versus resistant CC lines to T2D development. Next-generation RNA-sequencing was performed for 84 livers of diabetic and non-diabetic mice of 41 different CC lines (both sexes) following 12 weeks on high-fat diet (42% fat). Data analysis revealed significant variations of hepatic gene expression in diabetic versus non-diabetic mice with significant sex effect, where 601 genes were differentially expressed (DE) in overall population (males and females), 718 genes in female mice, and 599 genes in male mice. Top prioritized DE candidate genes were Lepr, Ins2, Mb, Ckm, Mrap2, and Ckmt2 for the overall population; for females-only group were Hdc, Serpina12, Socs1, Socs2, and Mb, while for males-only group were Serpine1, Mb, Ren1, Slc4a1, and Atp2a1. Data analysis for sex differences revealed 193 DE genes in health (Top: Lepr, Cav1, Socs2, Abcg2, and Col5a3), and 389 genes DE between diabetic females versus males (Top: Lepr, Clps, Ins2, Cav1, and Mrap2). Furthermore, integrating gene expression results with previously published QTL, we identified significant variants mapped at chromosomes at positions 36–49 Mb, 62–71 Mb, and 79–99 Mb, on chromosomes 9, 11, and 12, respectively. Our findings emphasize the complexity of T2D development and that significantly controlled by host complex genetic factors. As well, we demonstrate the significant sex differences between males and females during health and increasing to extent levels during disease/diabetes. Altogether, opening the venue for further studies targets the discovery of effective sex-specific and personalized preventions and therapies.

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All raw data and statistical results are available upon request from the authors.

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Acknowledgements

Sequencing and primary data quality control was performed at the Genomics Unit of the Centre for Genomic Regulation (CRG) in Barcelona, Spain, with the support of European Sequencing and Genotyping Infrastructure (ESGI) Consortium. We thank Tel-Aviv University for core funding and technical support.

Funding

This work was supported by the Hendrech and Eiran Gotwert Fund for studying diabetes, Wellcome Trust grants 085906/Z/08/Z, 075491/Z/04, Wellcome Trust core funding Grant 090532/Z/09/Z, and core funding by Tel-Aviv University. The work leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement n° 262055 (ESGI).

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Authors and Affiliations

  1. Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv, 69978, Tel Aviv, Israel

    H. J. Abu-Toamih Atamni & Fuad A. Iraqi

  2. Institute of Biology, Medicinal Chemistry & Biotechnology, National Hellenic Research Foundation, Athens, Greece

    G. Kontogianni, I. Binenbaum & A. Chatziioannou

  3. Department of Biology, University of Patras, Patras, Greece

    I. Binenbaum

  4. Department of Genetics, University College of London, London, UK

    R. Mott

  5. Centre for Genomic Regulation (CRG), Barcelona, Spain

    H. Himmelbauer

  6. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    H. Himmelbauer

  7. University of Natural Resources and Life Sciences (BOKU), Vienna, Austria

    H. Himmelbauer

  8. Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany

    H. Lehrach

  9. e-NIOS Applications PC, 17671, Kallithea, Greece

    A. Chatziioannou

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  1. H. J. Abu-Toamih Atamni
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Correspondence to Fuad A. Iraqi.

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Abu-Toamih Atamni, H.J., Kontogianni, G., Binenbaum, I. et al. Hepatic gene expression variations in response to high-fat diet-induced impaired glucose tolerance using RNAseq analysis in collaborative cross mouse population. Mamm Genome 30, 260–275 (2019). https://doi.org/10.1007/s00335-019-09816-1

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