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Protein kinetic signatures of the remodeling heart following isoproterenol stimulation
Maggie P.Y. Lam, … , Mario C. Deng, Peipei Ping
Maggie P.Y. Lam, … , Mario C. Deng, Peipei Ping
Published March 10, 2014
Citation Information: J Clin Invest. 2014;124(4):1734-1744. https://doi.org/10.1172/JCI73787.
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Technical Advance Cardiology

Protein kinetic signatures of the remodeling heart following isoproterenol stimulation

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Abstract

Protein temporal dynamics play a critical role in time-dimensional pathophysiological processes, including the gradual cardiac remodeling that occurs in early-stage heart failure. Methods for quantitative assessments of protein kinetics are lacking, and despite knowledge gained from single-protein studies, integrative views of the coordinated behavior of multiple proteins in cardiac remodeling are scarce. Here, we developed a workflow that integrates deuterium oxide (2H2O) labeling, high-resolution mass spectrometry (MS), and custom computational methods to systematically interrogate in vivo protein turnover. Using this workflow, we characterized the in vivo turnover kinetics of 2,964 proteins in a mouse model of β-adrenergic–induced cardiac remodeling. The data provided a quantitative and longitudinal view of cardiac remodeling at the molecular level, revealing widespread kinetic regulations in calcium signaling, metabolism, proteostasis, and mitochondrial dynamics. We translated the workflow to human studies, creating a reference dataset of 496 plasma protein turnover rates from 4 healthy adults. The approach is applicable to short, minimal label enrichment and can be performed on as little as a single biopsy, thereby overcoming critical obstacles to clinical investigations. The protein turnover quantitation experiments and computational workflow described here should be widely applicable to large-scale biomolecular investigations of human disease mechanisms with a temporal perspective.

Authors

Maggie P.Y. Lam, Ding Wang, Edward Lau, David A. Liem, Allen K. Kim, Dominic C.M. Ng, Xiangbo Liang, Brian J. Bleakley, Chenguang Liu, Jason D. Tabaraki, Martin Cadeiras, Yibin Wang, Mario C. Deng, Peipei Ping

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