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 ( 2 H 2 O) 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 largescale biomolecular investigations of human disease mechanisms with a temporal perspective.

Rationale Mitochondrial protein homeostasis is an essential component of the functions and oxidative stress responses of the heart. Objective To determine the specificity and efficiency of proteome turnover of the cardiac mitochondria by endogenous and exogenous proteolytic mechanisms. Methods and Results Proteolytic degradation of the murine cardiac mitochondria was assessed using two-dimensional differential gel electrophoresis (2D-DIGE) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Mitochondrial proteases demonstrated a substrate preference for basic protein variants, indicating a possible recognition mechanism based on protein modifications. Endogenous mitochondrial proteases and the cytosolic 20S proteasome exhibited different substrate specificities. Conclusions The cardiac mitochondrial proteome contains low amounts of proteases and is remarkably stable in isolation. Oxidative damage lowers the proteolytic capacity of cardiac mitochondria and reduces substrate availability for mitochondrial proteases. The 20S proteasome preferentially degrades specific substrates in the mitochondria and may contribute to cardiac mitochondrial proteostasis.

The innovations in mass spectrometry-based investigations in proteome biology enable systematic characterization of molecular details in pathophysiological phenotypes. However, the process of delineating large-scale raw proteomic datasets into a biological context requires high-throughput data acquisition and processing. A spectral library search engine makes use of previously annotated experimental spectra as references for subsequent spectral analyses. This workflow delivers many advantages, including elevated analytical efficiency and specificity as well as reduced demands in computational capacity. In this study, we created a spectral matching engine to address challenges commonly associated with a library search workflow. Particularly, an improved sliding dot product algorithm, that is robust to systematic drifts of mass measurement in spectra, is introduced. Furthermore, a noise management protocol distinguishes spectra correlation attributed from noise and peptide fragments. It enables elevated separation between target spectral matches and false matches, thereby suppressing the possibility of propagating inaccurate peptide annotations from library spectra to query spectra. Moreover, preservation of original spectra also accommodates user contributions to further enhance the quality of the library. Collectively, this search engine supports reproducible data analyses using curated references, thereby broadening the accessibility of proteomics resources to biomedical investigators.

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