Quantitative proteome‐based systematic identification of SIRT7 substrates |
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Authors: | Ming Tang Zhongyi Cheng Tingting Li Haiying Wang Wei‐Guo Zhu |
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Institution: | 1. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, P. R. China;2. Department of Biochemistry and Molecular Biology, School of Medicine, Shenzhen University, Shenzhen, P. R. China;3. Jingjie PTM Biolab (Hangzhou) Co. Ltd, Hangzhou, P. R. China;4. Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, P. R. ChinaAdditional corresponding authors:Haiying Wang, E‐mail: Tingting Li, E‐mail:;5. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, P. R. ChinaAdditional corresponding authors:Haiying Wang, E‐mail: Tingting Li, E‐mail:;6. Peking University‐Tsinghua University Center for Life Sciences, Beijing, P. R. China |
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Abstract: | SIRT7 is a class III histone deacetylase that is involved in numerous cellular processes. Only six substrates of SIRT7 have been reported thus far, so we aimed to systematically identify SIRT7 substrates using stable‐isotope labeling with amino acids in cell culture (SILAC) coupled with quantitative mass spectrometry (MS). Using SIRT7+/+ and SIRT7 ?/? mouse embryonic fibroblasts as our model system, we identified and quantified 1493 acetylation sites in 789 proteins, of which 261 acetylation sites in 176 proteins showed ≥2‐fold change in acetylation state between SIRT7?/? and SIRT7+/+ cells. These proteins were considered putative SIRT7 substrates and were carried forward for further analysis. We then validated the predictive efficiency of the SILAC–MS experiment by assessing substrate acetylation status in vitro in six predicted proteins. We also performed a bioinformatic analysis of the MS data, which indicated that many of the putative protein substrates were involved in metabolic processes. Finally, we expanded our list of candidate substrates by performing a bioinformatics‐based prediction analysis of putative SIRT7 substrates, using our list of putative substrates as a positive training set, and again validated a subset of the proteins in vitro. In summary, we have generated a comprehensive list of SIRT7 candidate substrates. |
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Keywords: | Bioinformatics Quantitative proteomics SIRT7 Substrates Systematic |
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