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Deubiquitylating enzymes (DUBs) are a large group of proteases that regulate ubiquitin-dependent metabolic pathways by cleaving ubiquitin-protein bonds. Here we present a global study aimed at elucidating the effects DUBs have on protein abundance changes in eukaryotic cells. To this end we compare wild-type Saccharomyces cerevisiae to 20 DUB knock-out strains using quantitative proteomics to measure proteome-wide expression of isotope labeled proteins, and analyze the data in the context of known transcription-factor regulatory networks. Overall we find that protein abundances differ widely between individual deletion strains, demonstrating that removing just a single component from the complex ubiquitin system causes major changes in cellular protein expression. The outcome of our analysis confirms many of the known biological roles for characterized DUBs such as Ubp3p and Ubp8p, and we demonstrate that Sec28p is a novel Ubp3p substrate. In addition we find strong associations for several uncharacterized DUBs providing clues for their possible cellular roles. Hierarchical clustering of all deletion strains reveals pronounced similarities between various DUBs, which corroborate current DUB knowledge and uncover novel functional aspects for uncharacterized DUBs. Observations in our analysis support that DUBs induce both direct and indirect effects on protein abundances.
The availability of the results of high-throughput analyses coming from 'omic' technologies has been one of the major driving forces of pathway biology. Analytical pathway biology strives to design a 'pathway search engine', where the input is the 'omic' data and the output is the list of activated or dominant pathways in a given sample. Here we describe the first attempt to design and validate such a pathway search engine using as input expression proteomics data. The engine represents a specific workflow in computational tools developed originally for mRNA analysis (BMC Bioinformatics 2006, 7 (Suppl 2), S13). Using our own datasets as well as data from recent proteomics literature we demonstrate that different dominant pathways (EGF, TGF(beta), stress, and Fas pathways) can be correctly identified even from limited datasets. Pathway search engines can find application in a variety of proteomics-related fields, from fundamental molecular biology to search for novel types of disease biomarkers.
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