Protein turnover7/29/2023 Despite successful applications of dynamic SILAC in vitro in bacterial 24, 25, yeast 26, and cultured mammalian cells 20, 27– 29, protein turnover in freely-growing cultured cells does not recapitulate protein turnover in animals in vivo 8, 30. A common strategy is to introduce synthesized, isotope-tagged amino acids into cultured cells, such as in dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) experiments that measure the time lapse required to fully label cellular proteins in culture 22, 23. In contrast to steady-state protein abundance, which may be quantified directly in mass spectrometry (MS) by spectral intensity 16, 17 or sampling frequency 18, 19, protein turnover rates cannot be predicted from steady-state data 20, requiring instead methods that can distinguish old and new protein molecules in mass spectra 11, 21. With recent progresses in shotgun proteomics, methodologies began to reach the sophistication and throughput required to understand turnover dynamics on a proteome scale. The history of protein dynamics traces back to 1935, when Schoenheimer and Rittenberg synthesized the first isotopologs of biological molecules to demonstrate the continuous renewal of proteins throughout life 14, 15. The present dataset was collected using a technology platform we recently developed, which overcame several technical challenges in quantifying individual protein turnover rates on a proteome scale. To promote data reusability, we describe four example use cases where this dataset may be re-analyzed to support basic research, translational investigation, omics data integration, and kinetic modeling. Key proteins in mitochondria and metabolic pathways are encompassed, in addition to contractile machineries and sarcolemmal signaling proteins. Proteins with quantified dynamics belong to over 10 major cellular compartments and over 200 known pathways. The dataset contains over 1.92 million data points in protein isotope labeling kinetics, culminating as the in vivo turnover rates of 3,228 cardiac proteins and the expression levels of 8,064 proteins. We report here a large dataset of protein turnover dynamics in the heart of six common genetic strains of mice, acquired under both normal and hypertrophic conditions. However, large-scale protein dynamics datasets have remained scarce, due to the specialized technologies necessary to measure turnover of individual proteins on a global scale. Protein dynamics data are therefore sought to better describe homeostatic processes and enhance the utility of phenotyping-by-omics approaches. Because proteostatic events often trigger zero net change in protein abundance but instead alter protein temporal dynamics 11– 13, they typically elude conventional experiments that measure only the steady-state abundance of proteins. In the heart, decreased proteolytic capacity and accumulating proteotoxcity have been shown to directly exacerbate outcomes in cardiac infarcts, hypertrophy, and failure 9, 10. Recent studies have associated proteostatic disruptions causatively to an expanding list of disorders, including cystic fibrosis, neurodegenerations, and cardiovascular diseases 3– 8. Regulation of proteome integrity requires chaperone-assisted folding of unfolded proteins, dissolution of misfolded aggregates, proteolytic removal of proteins, and other concerted proteostatic processes 1, 2. Cellular proteomes are under constant insults.
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