Integrated omics dissection of proteome dynamics during cardiac remodeling

Edward Lau, Quan Cao, Maggie P. Y. Lam, Jie Wang, Dominic C. M. Ng, Brian J. Bleakley, Jessica M. Lee, David A. Liem, Ding Wang, Henning Hermjakob and Peipei Ping ()
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Edward Lau: NIH BD2K Center of Excellence in Biomedical Computing
Quan Cao: NIH BD2K Center of Excellence in Biomedical Computing
Maggie P. Y. Lam: NIH BD2K Center of Excellence in Biomedical Computing
Jie Wang: NIH BD2K Center of Excellence in Biomedical Computing
Dominic C. M. Ng: NIH BD2K Center of Excellence in Biomedical Computing
Brian J. Bleakley: NIH BD2K Center of Excellence in Biomedical Computing
Jessica M. Lee: NIH BD2K Center of Excellence in Biomedical Computing
David A. Liem: NIH BD2K Center of Excellence in Biomedical Computing
Ding Wang: NIH BD2K Center of Excellence in Biomedical Computing
Henning Hermjakob: NIH BD2K Center of Excellence in Biomedical Computing
Peipei Ping: NIH BD2K Center of Excellence in Biomedical Computing

Nature Communications, 2018, vol. 9, issue 1, 1-14

Abstract: Abstract Transcript abundance and protein abundance show modest correlation in many biological models, but how this impacts disease signature discovery in omics experiments is rarely explored. Here we report an integrated omics approach, incorporating measurements of transcript abundance, protein abundance, and protein turnover to map the landscape of proteome remodeling in a mouse model of pathological cardiac hypertrophy. Analyzing the hypertrophy signatures that are reproducibly discovered from each omics data type across six genetic strains of mice, we find that the integration of transcript abundance, protein abundance, and protein turnover data leads to 75% gain in discovered disease gene candidates. Moreover, the inclusion of protein turnover measurements allows discovery of post-transcriptional regulations across diverse pathways, and implicates distinct disease proteins not found in steady-state transcript and protein abundance data. Our results suggest that multi-omics investigations of proteome dynamics provide important insights into disease pathogenesis in vivo.

Date: 2018
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DOI: 10.1038/s41467-017-02467-3

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