Multilayer View of Pathogenic SNVs in Human Interactome through In Silico Edgetic Profiling. Issue 18 (14th September 2018)

• Record Type: Journal Article
• Authors: Cui, Hongzhu; Zhao, Nan; Korkin, Dmitry
• Is Part Of: Journal of molecular biology. Volume 430:Issue 18(2018)Part A
• Page Start: 2974
• Page End: 2992
• Publication Date: 2018-09-14
• Subjects: protein–protein interaction -- network perturbation -- edgotype -- edgetic profiling -- non-synonymous SNVs; SNVs single-nucleotide variants -- PPI protein–protein interaction -- T2DM type 2 diabetes mellitus -- GWAS Genome-wide association studies; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2018.07.012
• Languages: English
• ISSNs: 0022-2836

Abstract

Abstract: Non-synonymous mutations linked to the complex diseases often have a global impact on a biological system, affecting large biomolecular networks and pathways. However, the magnitude of the mutation-driven effects on the macromolecular network is yet to be fully explored. In this work, we present a systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring, "edgetic, " effects on the human interactome. Our in silico analysis highlights the intrinsic differences and important similarities between the pathogenic single-nucleotide variants (SNVs) and frameshift mutations. We show that pathogenic SNVs are more likely to cause gene pleiotropy than pathogenic frameshift mutations and are enriched on the protein interaction interfaces. Functional profiling of SNVs indicates widespread disruption of the protein–protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction-disrupting mutations in type 2 diabetes mellitus and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the patient-specific genetic variation. The current and future in silico edgotyping tools present a cheap and fast solution to deal with the rapidly growing data sets of discovered mutations. Graphical abstract: Highlights: Non-synonymous mutations linked to the complex diseases have a global impact on the interactome. First large-scale in silico edgetic profiling of disease mutations Edgetic profiling covers 669 genes carrying 3401 mutations and involved in 1491 interactions. New insights into molecular mechanisms in type 2 diabetes mellitus and cancer