Table_2_Post-translational Modification Crosstalk and Hotspots in Sirtuin Interactors Implicated in Cardiovascular Diseases.XLSX

Sirtuins are protein deacetylases that play a protective role in cardiovascular diseases (CVDs), as well as many other diseases. Absence of sirtuins can lead to hyperacetylation of both nuclear and mitochondrial proteins leading to metabolic dysregulation. The protein post-translational modifications (PTMs) are known to crosstalk among each other to bring about complex phenotypic outcomes. Various PTM types such as acetylation, ubiquitination, and phosphorylation, and so on, drive transcriptional regulation and metabolism, but such crosstalks are poorly understood. We integrated protein–protein interactions (PPI) and PTMs from several databases to integrate information on 1,251 sirtuin-interacting proteins, of which 544 are associated with cardiac diseases. Based on the ∼100,000 PTM sites obtained for sirtuin interactors, we observed that the frequency of PTM sites (83 per protein), as well as PTM types (five per protein), is higher than the global average for human proteome. We found that ∼60–70% PTM sites fall into ordered regions. Approximately 83% of the sirtuin interactors contained at least one competitive crosstalk (in situ) site, with half of the sites occurring in CVD-associated proteins. A large proportion of identified crosstalk sites were observed for acetylation and ubiquitination competition. We identified 614 proteins containing PTM hotspots (≥5 PTM sites) and 133 proteins containing crosstalk hotspots (≥3 crosstalk sites). We observed that a large proportion of disease-associated sequence variants were found in PTM motifs of CVD proteins. We identified seven proteins (TP53, LMNA, MAPT, ATP2A2, NCL, APEX1, and HIST1H3A) containing disease-associated variants in PTM and crosstalk hotspots. This is the first comprehensive bioinformatics analysis on sirtuin interactors with respect to PTMs and their crosstalks. This study forms a platform for generating interesting hypotheses that can be tested for a deeper mechanistic understanding gained or derived from big-data analytics.

缢蛏蛋白去乙酰化酶在心血管疾病（CVDs）以及其他多种疾病中扮演着保护性的角色。缢蛏蛋白的缺失可能导致核和线粒体蛋白过度乙酰化，进而引起代谢失调。蛋白质翻译后修饰（PTMs）之间相互交联，从而产生复杂的表型结果。诸如乙酰化、泛素化、磷酸化等多种PTM类型，驱动转录调控和代谢，但此类交联机制理解尚浅。本研究整合了来自多个数据库的蛋白质-蛋白质相互作用（PPI）和PTMs信息，涵盖了1,251个与缢蛏蛋白相互作用的蛋白，其中544个与心脏疾病相关。基于从缢蛏相互作用蛋白中获得的约10万个PTM位点，我们发现PTM位点的频率（每蛋白83个）以及PTM类型（每蛋白5种）均高于人类蛋白组的全局平均值。我们观察到，约60-70%的PTM位点集中在有序区域。大约83%的缢蛏相互作用蛋白含有至少一个竞争性交联（原位）位点，其中一半位于与CVD相关的蛋白中。在识别的交联位点中，大量涉及乙酰化和泛素化竞争。我们鉴定出含有PTM热点（≥5个PTM位点）的614个蛋白和含有交联热点（≥3个交联位点）的133个蛋白。我们发现，大量与疾病相关的序列变异出现在CVD蛋白的PTM基序中。我们鉴定出7种蛋白（TP53、LMNA、MAPT、ATP2A2、NCL、APEX1和HIST1H3A）含有与PTM和交联热点相关的疾病相关变异。这是关于PTMs及其交联的缢蛏相互作用蛋白的首个全面生物信息学分析。本研究为从大数据分析中获得或推断的更深入的机制理解，形成了一个生成有趣假设的平台。