Comprehensive characterization of protein–protein interactions perturbed by disease mutations

Cheng, Feixiong; Zhao, Junfei; Wang, Yang; Lu, Weiqiang; Liu, Zehui; Zhou, Yadi; Martin, William R.; Wang, Ruisheng; Huang, Jin; Hao, Tong; Yue, Hong; Ma, Jing; Hou, Yuan; Castrillon, Jessica A.; Fang, Jiansong; Lathia, Justin D.; Keri, Ruth A.; Lightstone, Felice C.; Antman, Elliott Marshall; Rabadan, Raul; Hill, David E.; Eng, Charis; Vidal, Marc; Loscalzo, Joseph

doi:10.1038/s41588-020-00774-y

Comprehensive characterization of protein–protein interactions perturbed by disease mutations

Journal Article · Mon Feb 08 00:00:00 EST 2021 · Nature Genetics

DOI:https://doi.org/10.1038/s41588-020-00774-y· OSTI ID:1887014

^[1]; Zhao, Junfei ^[2]; Wang, Yang ^[3]; ^[4]; Liu, Zehui ^[5]; Zhou, Yadi ^[6]; ^[6]; Wang, Ruisheng ^[7]; Huang, Jin ^[5]; ^[3]; ^[3]; Ma, Jing ^[3]; Hou, Yuan ^[6]; Castrillon, Jessica A. ^[6]; Fang, Jiansong ^[6]; Lathia, Justin D. ^[8]; Keri, Ruth A. ^[8]; ^[9]; ^[7]; ^[2] more »

Cleveland Clinic, Cleveland, OH (United States); Case Western Reserve Univ., Cleveland, OH (United States)
Columbia Univ., New York, NY (United States)
Dana-Farber Cancer Institute, Boston, MA (United States); Harvard Medical School, Boston, MA (United States)
East China Normal Univ. (ECNU), Shanghai (China)
East China Univ. of Science and Technology, Shanghai (China)
Cleveland Clinic, Cleveland, OH (United States)
Brigham and Women's Hospital (Harvard Medical School), Boston, MA (United States)
Case Western Reserve Univ., Cleveland, OH (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein–protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. Further, we experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: America Heart Association; National Institutes of Health (NIH); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1887014

Report Number(s):: LLNL-JRNL-797982; 999853

Journal Information:: Nature Genetics, Journal Name: Nature Genetics Journal Issue: 3 Vol. 53; ISSN 1061-4036

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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