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Systematic analysis of the intersection of disease mutations with protein modifications

机译：系统分析疾病突变与蛋白质修饰的交集

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Perturbed posttranslational modification (PTM) landscapes commonly cause pathological phenotypes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors allowing the identification of spontaneous cancer-driving mutations, while Uniprot and dbSNP manage genetic disease-associated variants in the human population. PhosphoSitePlus (PSP) is the most comprehensive resource for studying experimentally observed PTM sites and the only repository with daily updates on functional annotations for many of these sites. To elucidate altered PTM landscapes on a large scale, we integrated disease-associated mutations from TCGA, Uniprot, and dbSNP with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus. Characterization of somatic variants revealed oncoprotein-like mutation profiles of U2AF1, PGM5, and several other proteins, showing alteration patterns similar to germline mutations. The union of all datasets uncovered previously unknown losses and gains of PTM events in diseases unevenly distributed across different PTM types. Focusing on phosphorylation, our DeltaScansite workflow predicted perturbed signaling networks consistent with calculations by the machine learning method MIMP. We discovered oncoprotein-like profiles in TCGA and mutations that presumably modify protein function by impacting PTM sites directly or by rewiring upstream regulation. The resulting datasets are enriched with functional annotations from PhosphoSitePlus and present a unique resource for potential biomarkers or disease drivers.
机译：扰动的翻译后修饰（PTM）格局通常会导致病理表型。癌症基因组图谱（TCGA）项目对数千种肿瘤进行了分析，从而可以识别自发的癌症驱动突变，而Uniprot和dbSNP管理着人类中与遗传疾病相关的变异。 PhosphoSitePlus（PSP）是研究实验观察到的PTM站点的最全面资源，并且是唯一一个每日对许多这些站点的功能注释进行更新的存储库。为了大规模阐明PTM的变化，我们将TCGA，Uniprot和dbSNP中与疾病相关的突变与PhosphoSitePlus的PTM位点进行了整合。我们分别对每个数据集进行了表征，将体细胞与种系突变进行了比较，并分析了与疾病变异直接相交的PTM位点。为了评估突变在磷酸位点侧翼区域中的影响，我们开发了DeltaScansite，该产品线比较了Scansite对野生型和突变序列的预测。在PhosphoSitePlus中还可以看到疾病突变。体细胞变异的表征揭示了U2AF1，PGM5和其他几种蛋白质的癌蛋白样突变谱，显示出与种系突变相似的变化模式。所有数据集的并集揭示了以前未知的疾病中PTM事件的损失和收益，这些疾病在不同PTM类型之间分布不均。专注于磷酸化，我们的DeltaScansite工作流程预测了与通过机器学习方法MIMP计算得出的扰动信号网络。我们在TCGA中发现了癌蛋白样特征，并且发现突变可能通过直接影响PTM位点或通过重新连接上游调控来修饰蛋白质功能。得到的数据集充斥着PhosphoSitePlus的功能注释，并为潜在的生物标记或疾病驱动器提供了独特的资源。

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