ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.
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ABSTRACT:
SUBMITTER: Imbert A
PROVIDER: S-EPMC8642540 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:<p>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.</p><p><br></p><p><strong>Data availability:</strong></p><p>The <strong>proteomic</strong> data generated in this study have been deposited in <strong>PRIDE</strong> repository under accession code <a href='https://www.ebi.ac.uk/pride/archive/projects/PXD028416' rel='noopener noreferrer' target='_blank'><strong>PXD028416</strong></a> for the <strong>liver proteome</strong> and <a href='https://www.ebi.ac.uk/pride/archive/projects/PXD028550' rel='noopener noreferrer' target='_blank'><strong>PXD028550</strong></a> for the <strong>plasma proteome</strong> [<a href='https://www.ebi.ac.uk/pride' rel='noopener noreferrer' target='_blank'>https://www.ebi.ac.uk/pride</a>].</p>
2021-09-01 | MTBLS1903 | MetaboLights
Project description:We develop new tools enabling high-throughput and combined proteomics and metabolomics data analysis. The tools are validated on two knockout mice models (LAT (Linker For Activation Of T-Cells) and Mx2 KO mice) generated and phenotyped by the PHENOMIN infrastructure.
2021-11-18 | PXD028416 | Pride
Project description:The ProMetIS dataset corresponds to the proteomic and metabolomic analysis of mouse lines lacking the LAT (linker for activation of T cells; OMIM: 602354) or MX2 gene (MX dynamin-like GTPase 2; OMIM: 147890), as well as the control (WT) line. Besides its role in T-cell receptor (TCR) signaling (Roncagalli et al., 2010), LAT has been shown to be involved in neurodevelopmental diseases (Loviglio et al., 2017). The systematic generation of mouse models is part of the functional characterization of the genome by the IMPC consortium (Brown et al., 2018). This characterization is currently based on a battery of animal phenotypic tests (anatomy, behaviour, histology, haematology, physiology), the results of which feed the IMPC database (https://www.mousephenotype.org). In particular, gene knockouts associated to metabolic diseases have been identified (Rozman et al., 2018). To further characterize these models, global molecular approaches are required, such as the metabolomics analysis of plasma samples which has been described recently (Barupal et al., 2019). The originality of ProMetIS is to provide, in addition to the clinical data, the proteomics and metabolomics analysis of the LAT, MX2 and WT mouse models in liver and plasma. ProMetIS provides access to unique molecular functional information on the LAT and MX2 genes. In addition, the dataset has been generated by the four national infrastructures for mouse phenogenomics (PHENOMIN-ICS), proteomics (ProFI), metabolomics (MetaboHUB) and bioinformatics (IFB) for optimal quality, reproducibility and accessibility. The protocols and computational workflows provided here can be easily extended to a larger number of individuals and tissues. Finally, ProMetIS will be of great interest to develop and evaluate bioinformatics and biostatistical methods for the processing and integration of proteomic and metabolomic data.
2021-11-11 | PXD028550 | Pride
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