Integrated omics dissection of proteome dynamics during cardiac remodeling View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-01-09

AUTHORS

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, Peipei Ping

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. More... »

PAGES

120

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-017-02467-3

DOI

http://dx.doi.org/10.1038/s41467-017-02467-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100179003

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29317621


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33 schema:description 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.
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40 abundance data
41 approach
42 biological models
43 candidates
44 cardiac hypertrophy
45 cardiac remodeling
46 correlation
47 data
48 data types
49 discovery
50 disease gene candidates
51 disease pathogenesis
52 disease protein
53 dissection
54 diverse pathways
55 dynamics
56 experiments
57 gain
58 gene candidates
59 genetic strains
60 hypertrophy
61 important insights
62 inclusion
63 insights
64 integration
65 investigation
66 landscape
67 measurements
68 mice
69 model
70 mouse model
71 multi-omics investigations
72 omics approaches
73 omics data types
74 omics experiments
75 pathogenesis
76 pathological cardiac hypertrophy
77 pathway
78 post-transcriptional regulation
79 protein
80 protein abundance
81 protein abundance data
82 protein turnover
83 protein turnover data
84 protein turnover measurements
85 proteome
86 proteome dynamics
87 regulation
88 remodeling
89 results
90 signature discovery
91 signatures
92 steady-state transcripts
93 strains
94 transcript abundance
95 transcripts
96 turnover
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98 turnover measurements
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