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ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the La...

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Autores principales: Imbert, Alyssa, Rompais, Magali, Selloum, Mohammed, Castelli, Florence, Mouton-Barbosa, Emmanuelle, Brandolini-Bunlon, Marion, Chu-Van, Emeline, Joly, Charlotte, Hirschler, Aurélie, Roger, Pierrick, Burger, Thomas, Leblanc, Sophie, Sorg, Tania, Ouzia, Sadia, Vandenbrouck, Yves, Médigue, Claudine, Junot, Christophe, Ferro, Myriam, Pujos-Guillot, Estelle, de Peredo, Anne Gonzalez, Fenaille, François, Carapito, Christine, Herault, Yann, Thévenot, Etienne A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642540/
https://www.ncbi.nlm.nih.gov/pubmed/34862403
http://dx.doi.org/10.1038/s41597-021-01095-3
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author Imbert, Alyssa
Rompais, Magali
Selloum, Mohammed
Castelli, Florence
Mouton-Barbosa, Emmanuelle
Brandolini-Bunlon, Marion
Chu-Van, Emeline
Joly, Charlotte
Hirschler, Aurélie
Roger, Pierrick
Burger, Thomas
Leblanc, Sophie
Sorg, Tania
Ouzia, Sadia
Vandenbrouck, Yves
Médigue, Claudine
Junot, Christophe
Ferro, Myriam
Pujos-Guillot, Estelle
de Peredo, Anne Gonzalez
Fenaille, François
Carapito, Christine
Herault, Yann
Thévenot, Etienne A.
author_facet Imbert, Alyssa
Rompais, Magali
Selloum, Mohammed
Castelli, Florence
Mouton-Barbosa, Emmanuelle
Brandolini-Bunlon, Marion
Chu-Van, Emeline
Joly, Charlotte
Hirschler, Aurélie
Roger, Pierrick
Burger, Thomas
Leblanc, Sophie
Sorg, Tania
Ouzia, Sadia
Vandenbrouck, Yves
Médigue, Claudine
Junot, Christophe
Ferro, Myriam
Pujos-Guillot, Estelle
de Peredo, Anne Gonzalez
Fenaille, François
Carapito, Christine
Herault, Yann
Thévenot, Etienne A.
author_sort Imbert, Alyssa
collection PubMed
description Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.
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spelling pubmed-86425402021-12-15 ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis Imbert, Alyssa Rompais, Magali Selloum, Mohammed Castelli, Florence Mouton-Barbosa, Emmanuelle Brandolini-Bunlon, Marion Chu-Van, Emeline Joly, Charlotte Hirschler, Aurélie Roger, Pierrick Burger, Thomas Leblanc, Sophie Sorg, Tania Ouzia, Sadia Vandenbrouck, Yves Médigue, Claudine Junot, Christophe Ferro, Myriam Pujos-Guillot, Estelle de Peredo, Anne Gonzalez Fenaille, François Carapito, Christine Herault, Yann Thévenot, Etienne A. Sci Data Data Descriptor Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration. Nature Publishing Group UK 2021-12-03 /pmc/articles/PMC8642540/ /pubmed/34862403 http://dx.doi.org/10.1038/s41597-021-01095-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Imbert, Alyssa
Rompais, Magali
Selloum, Mohammed
Castelli, Florence
Mouton-Barbosa, Emmanuelle
Brandolini-Bunlon, Marion
Chu-Van, Emeline
Joly, Charlotte
Hirschler, Aurélie
Roger, Pierrick
Burger, Thomas
Leblanc, Sophie
Sorg, Tania
Ouzia, Sadia
Vandenbrouck, Yves
Médigue, Claudine
Junot, Christophe
Ferro, Myriam
Pujos-Guillot, Estelle
de Peredo, Anne Gonzalez
Fenaille, François
Carapito, Christine
Herault, Yann
Thévenot, Etienne A.
ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis
title ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis
title_full ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis
title_fullStr ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis
title_full_unstemmed ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis
title_short ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis
title_sort prometis, deep phenotyping of mouse models by combined proteomics and metabolomics analysis
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642540/
https://www.ncbi.nlm.nih.gov/pubmed/34862403
http://dx.doi.org/10.1038/s41597-021-01095-3
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