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Identification of genetic elements in metabolism by high-throughput mouse phenotyping
Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773596/ https://www.ncbi.nlm.nih.gov/pubmed/29348434 http://dx.doi.org/10.1038/s41467-017-01995-2 |
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author | Rozman, Jan Rathkolb, Birgit Oestereicher, Manuela A. Schütt, Christine Ravindranath, Aakash Chavan Leuchtenberger, Stefanie Sharma, Sapna Kistler, Martin Willershäuser, Monja Brommage, Robert Meehan, Terrence F. Mason, Jeremy Haselimashhadi, Hamed Hough, Tertius Mallon, Ann-Marie Wells, Sara Santos, Luis Lelliott, Christopher J. White, Jacqueline K. Sorg, Tania Champy, Marie-France Bower, Lynette R. Reynolds, Corey L. Flenniken, Ann M. Murray, Stephen A. Nutter, Lauryl M. J. Svenson, Karen L. West, David Tocchini-Valentini, Glauco P. Beaudet, Arthur L. Bosch, Fatima Braun, Robert B. Dobbie, Michael S. Gao, Xiang Herault, Yann Moshiri, Ala Moore, Bret A. Kent Lloyd, K. C. McKerlie, Colin Masuya, Hiroshi Tanaka, Nobuhiko Flicek, Paul Parkinson, Helen E. Sedlacek, Radislav Seong, Je Kyung Wang, Chi-Kuang Leo Moore, Mark Brown, Steve D. Tschöp, Matthias H. Wurst, Wolfgang Klingenspor, Martin Wolf, Eckhard Beckers, Johannes Machicao, Fausto Peter, Andreas Staiger, Harald Häring, Hans-Ulrich Grallert, Harald Campillos, Monica Maier, Holger Fuchs, Helmut Gailus-Durner, Valerie Werner, Thomas Hrabe de Angelis, Martin |
author_facet | Rozman, Jan Rathkolb, Birgit Oestereicher, Manuela A. Schütt, Christine Ravindranath, Aakash Chavan Leuchtenberger, Stefanie Sharma, Sapna Kistler, Martin Willershäuser, Monja Brommage, Robert Meehan, Terrence F. Mason, Jeremy Haselimashhadi, Hamed Hough, Tertius Mallon, Ann-Marie Wells, Sara Santos, Luis Lelliott, Christopher J. White, Jacqueline K. Sorg, Tania Champy, Marie-France Bower, Lynette R. Reynolds, Corey L. Flenniken, Ann M. Murray, Stephen A. Nutter, Lauryl M. J. Svenson, Karen L. West, David Tocchini-Valentini, Glauco P. Beaudet, Arthur L. Bosch, Fatima Braun, Robert B. Dobbie, Michael S. Gao, Xiang Herault, Yann Moshiri, Ala Moore, Bret A. Kent Lloyd, K. C. McKerlie, Colin Masuya, Hiroshi Tanaka, Nobuhiko Flicek, Paul Parkinson, Helen E. Sedlacek, Radislav Seong, Je Kyung Wang, Chi-Kuang Leo Moore, Mark Brown, Steve D. Tschöp, Matthias H. Wurst, Wolfgang Klingenspor, Martin Wolf, Eckhard Beckers, Johannes Machicao, Fausto Peter, Andreas Staiger, Harald Häring, Hans-Ulrich Grallert, Harald Campillos, Monica Maier, Holger Fuchs, Helmut Gailus-Durner, Valerie Werner, Thomas Hrabe de Angelis, Martin |
author_sort | Rozman, Jan |
collection | PubMed |
description | Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome. |
format | Online Article Text |
id | pubmed-5773596 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57735962018-01-23 Identification of genetic elements in metabolism by high-throughput mouse phenotyping Rozman, Jan Rathkolb, Birgit Oestereicher, Manuela A. Schütt, Christine Ravindranath, Aakash Chavan Leuchtenberger, Stefanie Sharma, Sapna Kistler, Martin Willershäuser, Monja Brommage, Robert Meehan, Terrence F. Mason, Jeremy Haselimashhadi, Hamed Hough, Tertius Mallon, Ann-Marie Wells, Sara Santos, Luis Lelliott, Christopher J. White, Jacqueline K. Sorg, Tania Champy, Marie-France Bower, Lynette R. Reynolds, Corey L. Flenniken, Ann M. Murray, Stephen A. Nutter, Lauryl M. J. Svenson, Karen L. West, David Tocchini-Valentini, Glauco P. Beaudet, Arthur L. Bosch, Fatima Braun, Robert B. Dobbie, Michael S. Gao, Xiang Herault, Yann Moshiri, Ala Moore, Bret A. Kent Lloyd, K. C. McKerlie, Colin Masuya, Hiroshi Tanaka, Nobuhiko Flicek, Paul Parkinson, Helen E. Sedlacek, Radislav Seong, Je Kyung Wang, Chi-Kuang Leo Moore, Mark Brown, Steve D. Tschöp, Matthias H. Wurst, Wolfgang Klingenspor, Martin Wolf, Eckhard Beckers, Johannes Machicao, Fausto Peter, Andreas Staiger, Harald Häring, Hans-Ulrich Grallert, Harald Campillos, Monica Maier, Holger Fuchs, Helmut Gailus-Durner, Valerie Werner, Thomas Hrabe de Angelis, Martin Nat Commun Article Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome. Nature Publishing Group UK 2018-01-18 /pmc/articles/PMC5773596/ /pubmed/29348434 http://dx.doi.org/10.1038/s41467-017-01995-2 Text en © The Author(s) 2017 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Rozman, Jan Rathkolb, Birgit Oestereicher, Manuela A. Schütt, Christine Ravindranath, Aakash Chavan Leuchtenberger, Stefanie Sharma, Sapna Kistler, Martin Willershäuser, Monja Brommage, Robert Meehan, Terrence F. Mason, Jeremy Haselimashhadi, Hamed Hough, Tertius Mallon, Ann-Marie Wells, Sara Santos, Luis Lelliott, Christopher J. White, Jacqueline K. Sorg, Tania Champy, Marie-France Bower, Lynette R. Reynolds, Corey L. Flenniken, Ann M. Murray, Stephen A. Nutter, Lauryl M. J. Svenson, Karen L. West, David Tocchini-Valentini, Glauco P. Beaudet, Arthur L. Bosch, Fatima Braun, Robert B. Dobbie, Michael S. Gao, Xiang Herault, Yann Moshiri, Ala Moore, Bret A. Kent Lloyd, K. C. McKerlie, Colin Masuya, Hiroshi Tanaka, Nobuhiko Flicek, Paul Parkinson, Helen E. Sedlacek, Radislav Seong, Je Kyung Wang, Chi-Kuang Leo Moore, Mark Brown, Steve D. Tschöp, Matthias H. Wurst, Wolfgang Klingenspor, Martin Wolf, Eckhard Beckers, Johannes Machicao, Fausto Peter, Andreas Staiger, Harald Häring, Hans-Ulrich Grallert, Harald Campillos, Monica Maier, Holger Fuchs, Helmut Gailus-Durner, Valerie Werner, Thomas Hrabe de Angelis, Martin Identification of genetic elements in metabolism by high-throughput mouse phenotyping |
title | Identification of genetic elements in metabolism by high-throughput mouse phenotyping |
title_full | Identification of genetic elements in metabolism by high-throughput mouse phenotyping |
title_fullStr | Identification of genetic elements in metabolism by high-throughput mouse phenotyping |
title_full_unstemmed | Identification of genetic elements in metabolism by high-throughput mouse phenotyping |
title_short | Identification of genetic elements in metabolism by high-throughput mouse phenotyping |
title_sort | identification of genetic elements in metabolism by high-throughput mouse phenotyping |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773596/ https://www.ncbi.nlm.nih.gov/pubmed/29348434 http://dx.doi.org/10.1038/s41467-017-01995-2 |
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