Zhang Chaohua, Zhai Zichao, Tang Ming, Cheng Zhongyi, Li Tingting, Wang Haiying, Zhu Wei-Guo
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.
Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, P. R. China.
Proteomics. 2017 Jul;17(13-14). doi: 10.1002/pmic.201600395.
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.
SIRT7是一种III类组蛋白脱乙酰酶，参与众多细胞过程。迄今为止，仅报道了六种SIRT7的底物，因此我们旨在使用细胞培养中的氨基酸稳定同位素标记（SILAC）结合定量质谱（MS）来系统地鉴定SIRT7的底物。以SIRT7和SIRT7小鼠胚胎成纤维细胞作为我们的模型系统，我们鉴定并定量了789种蛋白质中的1493个乙酰化位点，其中176种蛋白质中的261个乙酰化位点在SIRT7和SIRT7细胞之间的乙酰化状态变化≥2倍。这些蛋白质被视为假定的SIRT7底物，并进行进一步分析。然后，我们通过评估六种预测蛋白质在体外的底物乙酰化状态，验证了SILAC-MS实验的预测效率。我们还对MS数据进行了生物信息学分析，结果表明许多假定的蛋白质底物参与了代谢过程。最后，我们以假定的底物列表作为阳性训练集，通过对假定的SIRT7底物进行基于生物信息学的预测分析，扩展了我们的候选底物列表，并再次在体外验证了一部分蛋白质。总之，我们生成了一份全面的SIRT7候选底物列表。
