Abstract 14080: Post-Transcriptional Regulation of Cardiac Protein Expression in Hypertrophy
Introduction: Whether gene expression is predominantly regulated by transcriptional or post-transcriptional processes is often debated. Pathological cardiac hypertrophy is associated with the dysregulation of protein homeostasis, but how this may affect the cardiac protein expression profile has not been examined.
Hypothesis: We hypothesize that the rates of protein turnover contribute to cardiac hypertrophy by acting as a post-transcriptional regulation of cardiac protein expression profile.
Methods: We integrated stable isotope mass spectrometry, computational modeling and a systems genetics model to generate a large dataset on protein dynamics in six inbred mouse strains showing contrasting susceptibilities to cardiac hypertrophy. To identify candidate disease markers, we performed regression modeling of proteome parameters over phenotypic responses. A meta-analysis on existing transcriptomics datasets was performed to compare implicated disease genes from transcript and protein data.
Results: We analyzed the turnover and abundance of 3,228 proteins in cardiac hypertrophy, encompassing 201 cellular pathways. The data were acquired from 1,404 mass spectrometry experiments. The data shows evidence that functionally-related proteins are co-regulated in turnover, with the turnover rate of glycolytic enzymes positively correlated with the severity of hypertrophy (r > 0.58), whereas the turnover of fatty acid oxidation pathways is negatively correlated (r < -0.66). Moreover, when co-analyzed with up to 30 sets of transcriptomic profiling data, we find that the inclusion of protein turnover information consistently leads to ~30% gain in power to discover candidate disease genes. Known hypertrophy markers (e.g., ANXA2, ANXA3, FHL1) exhibit co-directional changes on transcriptional and translational levels, but disease-specific changes of additional marker candidates are revealed only when protein turnover data were included.
Conclusions: We demonstrate that protein dynamics information provides independent and complementary insights into the regulation of gene expression in the diseased heart, and can avail in-depth elucidation of disease drivers and mechanisms.
Author Disclosures: E. Lau: None. Q. Cao: None. D.C. Ng: None. B.J. Bleakley: None. M.P. Lam: None. P. Ping: None.
- © 2016 by American Heart Association, Inc.