1. Sequential Fractionation Strategy Identifies Three Missing Proteins in the Mitochondrial Proteome of Commonly Used Cell Lines.
- Author
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Ronci M, Pieroni L, Greco V, Scotti L, Marini F, Carregari VC, Cunsolo V, Foti S, Aceto A, and Urbani A
- Subjects
- Cell Line, Chemical Fractionation, Databases, Protein, Humans, Mass Spectrometry methods, Membrane Proteins analysis, Methyltransferases analysis, Neoplasm Proteins analysis, Proteomics methods, RGS Proteins analysis, Mitochondria chemistry, Mitochondrial Proteins analysis, Proteome analysis
- Abstract
Mitochondria are undeniably the cell powerhouse, directly affecting cell survival and fate. Growing evidence suggest that mitochondrial protein repertoire affects metabolic activity and plays an important role in determining cell proliferation/differentiation or quiescence shift. Consequently, the bioenergetic status of a cell is associated with the quality and abundance of the mitochondrial populations and proteomes. Mitochondrial morphology changes in the development of different cellular functions associated with metabolic switches. It is therefore reasonable to speculate that different cell lines do contain different mitochondrial-associated proteins, and the investigation of these pools may well represent a source for mining missing proteins (MPs). A very effective approach to increase the number of IDs through mass spectrometry consists of reducing the complexity of the biological samples by fractionation. The present study aims at investigating the mitochondrial proteome of five phenotypically different cell lines, possibly expressing some of the MPs, through an enrichment-fractionation approach at the organelle and protein level. We demonstrate a substantial increase in the proteome coverage, which, in turn, increases the likelihood of detecting low abundant proteins, often falling in the category of MPs, and resulting, for the present study, in the identification of METTL12, FAM163A, and RGS13. All MS data have been deposited to the MassIVE data repository ( https://massive.ucsd.edu ) with the data set identifier MSV000082409 and PXD010446.
- Published
- 2018
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