MassIVE MSV000092407

Partial Public PXD043694

ProteasomeID: quantitative mapping of proteasome interactomes and substrates for in vitro and in vivo studies

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Description

Proteasomes are essential molecular machines responsible for the degradation of proteins in eukaryotic cells. Altered proteasome activity has been linked to neurodegeneration, auto-immune disorders and cancer. Despite the relevance for human disease and drug development, no method currently exists to monitor proteasome composition and interactions in vivo in animal models. To fill this gap, we developed a strategy based on tagging of proteasomes with promiscuous biotin ligases and generated a new mouse model enabling the quantification of proteasome interactions by mass spectrometry. We show that biotin ligases can be incorporated in fully assembled proteasomes without negative impact on their activity. We demonstrate the utility of our method by identifying novel proteasome-interacting proteins, charting interactomes across mouse organs, and showing that proximity-labeling enables the identification of both endogenous and small molecule-induced proteasome substrates. [doi:10.25345/C5CJ87W7D] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: proteasome, BioID, mass spectrometry, protein degradation, proximity labeling, mouse model

Contact

Principal Investigators:
(in alphabetical order) 		Alessandro Ori, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany
Submitting User: ProteomicsCF_FLI
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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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