Dataset Information

Discovering Targets of Non-enzymatic Acylation by Thioester Reactivity Profiling.

ABSTRACT: Non-enzymatic protein modification driven by thioester reactivity is thought to play a major role in the establishment of cellular lysine acylation. However, the specific protein targets of this process are largely unknown. Here we report an experimental strategy to investigate non-enzymatic acylation in cells. Specifically, we develop a chemoproteomic method that separates thioester reactivity from enzymatic utilization, allowing selective enrichment of non-enzymatic acylation targets. Applying this method to cancer cell lines identifies numerous candidate targets of non-enzymatic acylation, including several enzymes in lower glycolysis. Functional studies highlight malonyl-CoA as a reactive thioester metabolite that can modify and inhibit glycolytic enzyme activity. Finally, we show that synthetic thioesters can be used as novel reagents to probe non-enzymatic acylation in living cells. Our studies provide new insights into the targets and drivers of non-enzymatic acylation, and demonstrate the utility of reactivity-based methods to experimentally investigate this phenomenon in biology and disease.

SUBMITTER: Kulkarni RA

PROVIDER: S-EPMC5864104 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Discovering Targets of Non-enzymatic Acylation by Thioester Reactivity Profiling.

Kulkarni Rhushikesh A RA Worth Andrew J AJ Zengeya Thomas T TT Shrimp Jonathan H JH Garlick Julie M JM Roberts Allison M AM Montgomery David C DC Sourbier Carole C Gibbs Benjamin K BK Mesaros Clementina C Tsai Yien Che YC Das Sudipto S Chan King C KC Zhou Ming M Andresson Thorkell T Weissman Allan M AM Linehan W Marston WM Blair Ian A IA Snyder Nathaniel W NW Meier Jordan L JL

Cell chemical biology 20170202 2

Non-enzymatic protein modification driven by thioester reactivity is thought to play a major role in the establishment of cellular lysine acylation. However, the specific protein targets of this process are largely unknown. Here we report an experimental strategy to investigate non-enzymatic acylation in cells. Specifically, we develop a chemoproteomic method that separates thioester reactivity from enzymatic utilization, allowing selective enrichment of non-enzymatic acylation targets. Applying ...[more]

PMID: 28163016

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Project description:BackgroundAn important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties.Principal findings(1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4). (2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter.ConclusionPostprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite.

Dataset Information

Discovering Targets of Non-enzymatic Acylation by Thioester Reactivity Profiling.

Publications

Discovering Targets of Non-enzymatic Acylation by Thioester Reactivity Profiling.

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