Comprehensive characterization of protein–protein interactions perturbed by disease mutations
Abstract
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.
- Authors:
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[1];
[4];
[6];
[3];
[3];
[9];
[7];
[2]
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- 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)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); National Institutes of Health (NIH); America Heart Association
- OSTI Identifier:
- 1887014
- Report Number(s):
- LLNL-JRNL-797982
Journal ID: ISSN 1061-4036; 999853
- Grant/Contract Number:
- AC52-07NA27344; K99 HL138272; R00 HL138272; R01AG066707; U01 HG007690; P50 GM107618; U54 HL119145; P50 HG004233; U41 HG001715; D700382; TC02274.0
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Genetics
- Additional Journal Information:
- Journal Volume: 53; Journal Issue: 3; Journal ID: ISSN 1061-4036
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
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, and Loscalzo, Joseph. Comprehensive characterization of protein–protein interactions perturbed by disease mutations. United States: N. p., 2021.
Web. doi:10.1038/s41588-020-00774-y.
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. Comprehensive characterization of protein–protein interactions perturbed by disease mutations. United States. https://doi.org/10.1038/s41588-020-00774-y
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, and Loscalzo, Joseph. Mon .
"Comprehensive characterization of protein–protein interactions perturbed by disease mutations". United States. https://doi.org/10.1038/s41588-020-00774-y. https://www.osti.gov/servlets/purl/1887014.
@article{osti_1887014,
title = {Comprehensive characterization of protein–protein interactions perturbed by disease mutations},
author = {Cheng, Feixiong and Zhao, Junfei and Wang, Yang and Lu, Weiqiang and Liu, Zehui and Zhou, Yadi and Martin, William R. and Wang, Ruisheng and Huang, Jin and Hao, Tong and Yue, Hong and Ma, Jing and Hou, Yuan and Castrillon, Jessica A. and Fang, Jiansong and Lathia, Justin D. and Keri, Ruth A. and Lightstone, Felice C. and Antman, Elliott Marshall and Rabadan, Raul and Hill, David E. and Eng, Charis and Vidal, Marc and Loscalzo, Joseph},
abstractNote = {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.},
doi = {10.1038/s41588-020-00774-y},
journal = {Nature Genetics},
number = 3,
volume = 53,
place = {United States},
year = {Mon Feb 08 00:00:00 EST 2021},
month = {Mon Feb 08 00:00:00 EST 2021}
}
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