ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.
Fiche publication
Date publication
décembre 2021
Journal
Scientific data
Auteurs
Membres identifiés du Cancéropôle Est :
Dr HERAULT Yann, Dr SORG Tania, Dr CARAPITO Christine
Tous les auteurs :
Imbert A, Rompais M, Selloum M, Castelli F, Mouton-Barbosa E, Brandolini-Bunlon M, Chu-Van E, Joly C, Hirschler A, Roger P, Burger T, Leblanc S, Sorg T, Ouzia S, Vandenbrouck Y, Médigue C, Junot C, Ferro M, Pujos-Guillot E, de Peredo AG, Fenaille F, Carapito C, Herault Y, Thévenot EA
Lien Pubmed
Résumé
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.
Référence
Sci Data. 2021 Dec 3;8(1):311