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Nat Commun. 2018 Jan 9;9(1):120. doi: 10.1038/s41467-017-02467-3.

Integrated omics dissection of proteome dynamics during cardiac remodeling.

Lau E1,2,3, Cao Q1,2,4, Lam MPY1,2,5, Wang J1,2, Ng DCM1,2, Bleakley BJ1,2, Lee JM1,2, Liem DA1,2, Wang D1,2, Hermjakob H1,6, Ping P7,8,9,10.

Author information

1
NIH BD2K Center of Excellence in Biomedical Computing, Los Angeles, CA, 90095, USA.
2
Department of Physiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.
3
Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA.
4
Shanghai Institute of Cardiovascular Diseases, Zhongshan Hosptial, Fudan University, Shanghai, 200032, China.
5
Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
6
Molecular Systems Cluster, European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
7
NIH BD2K Center of Excellence in Biomedical Computing, Los Angeles, CA, 90095, USA. pping38@g.ucla.edu.
8
Department of Physiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA. pping38@g.ucla.edu.
9
Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA. pping38@g.ucla.edu.
10
Department of Bioinformatics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA. pping38@g.ucla.edu.

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.

PMID:
29317621
PMCID:
PMC5760723
DOI:
10.1038/s41467-017-02467-3
[Indexed for MEDLINE]
Free PMC Article

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