Dataset Information

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

ABSTRACT:

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.

Data availability:

The proteomic data generated in this study have been deposited in PRIDE repository under accession code PXD028416 for the liver proteome and PXD028550 for the plasma proteome [https://www.ebi.ac.uk/pride].

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase, Liquid Chromatography MS - negative - hilic

SUBMITTER: Etienne A. Thevenot

PROVIDER: MTBLS1903 | MetaboLights | 2021-09-01

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	MTBLS1903	Other
	FILES	Other
	a_MTBLS1903_LC-MS_negative_hilic_metabolite_profiling_liver.txt	Txt
	a_MTBLS1903_LC-MS_negative_hilic_metabolite_profiling_plasma.txt	Txt
	a_MTBLS1903_LC-MS_negative_reverse-phase-acquity_metabolite_profiling_plasma.txt	Txt

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Publications

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

Imbert Alyssa A Rompais Magali M Selloum Mohammed M Castelli Florence F Mouton-Barbosa Emmanuelle E Brandolini-Bunlon Marion M Chu-Van Emeline E Joly Charlotte C Hirschler Aurélie A Roger Pierrick P Burger Thomas T Leblanc Sophie S Sorg Tania T Ouzia Sadia S Vandenbrouck Yves Y Médigue Claudine C Junot Christophe C Ferro Myriam M Pujos-Guillot Estelle E de Peredo Anne Gonzalez AG Fenaille François F Carapito Christine C Herault Yann Y Thévenot Etienne A EA

Scientific data 20211203 1

PMID: 34862403

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