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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
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.
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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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