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.

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BackgroundRisk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015.Methods and findingsWe develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population. ToPs discovers these specific clusters and the specific predictive model that performs best for each cluster. In comparison with existing clinical risk scoring methods and state-of-the-art machine learning methods, our method provides significant improvements in survival predictions, both post- and pre-cardiac transplantation. For instance: in terms of 3-month survival post-transplantation, our method achieves AUC of 0.660; the best clinical risk scoring method (RSS) achieves 0.587. In terms of 3-year survival/mortality predictions post-transplantation (in comparison to RSS), holding specificity at 80.0%, our algorithm correctly predicts survival for 2,442 (14.0%) more patients (of 17,441 who actually survived); holding sensitivity at 80.0%, our algorithm correctly predicts mortality for 694 (13.0%) more patients (of 5,339 who did not survive). ToPs achieves similar improvements for other time horizons and for predictions pre-transplantation. ToPs discovers the most relevant features (covariates), uses available features to best advantage, and can adapt to changes in clinical practice.ConclusionsWe show that, in comparison with existing clinical risk-scoring methods and other machine learning methods, ToPs significantly improves survival predictions both post- and pre-cardiac transplantation. ToPs provides a more accurate, personalized approach to survival prediction that can benefit patients, clinicians, and policymakers in making clinical decisions and setting clinical policy. Because survival prediction is widely used in clinical decision-making across diseases and clinical specialties, the implications of our methods are far-reaching.

Background. We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. The present study aimed to examine the stability of machine-learning algorithms in new biopsies, compare 3AA vs. 4AA algorithms, assess supervised binary classifiers trained on histologic or molecular diagnoses, create a report combining many scores into an ensemble of estimates, and examine possible automated sign-outs. Methods. We studied 889 EMBs from 454 transplant recipients at eight centers: the initial cohort (N=331) and a new cohort (N=558). Published 3AA algorithms derived in cohort 331 were tested in cohort 558; the 3AA and 4AA models were compared; and supervised binary classifiers were created. Results. Algorithms derived in cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (AUCs>0.87) better than histologic rejection (AUCs<0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by one expert showed highly significant agreement with histology (p<0.001), but with many discrepancies as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated sign-outs. Conclusions. Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states.

Purpose High-throughput quantification of human protein turnover via in vivo administration of deuterium oxide (2H2O) is a powerful new approach to examine potential disease mechanisms. Its immediate clinical translation is contingent upon characterizations of the safety and hemodynamic effects of in vivo administration of 2H2O to human subjects. Experimental design We recruited 10 healthy human subjects with a broad demographic variety to evaluate the safety, feasibility, efficacy, and reproducibility of 2H2O intake for studying protein dynamics. We designed a protocol where each subject orally consumed weight-adjusted doses of 70% 2H2O daily for 14 days to enrich body water and proteins with deuterium. Plasma proteome dynamics was measured using a high-resolution MS method we recently developed. Results This protocol was successfully applied in 10 human subjects to characterize the endogenous turnover rates of 542 human plasma proteins, the largest such human dataset to-date. Throughout the study, we did not detect physiological effects or signs of discomfort from 2H2O consumption. Conclusions and clinical relevance Our investigation supports the utility of a 2H2O intake protocol that is safe, accessible, and effective for clinical investigations of large-scale human protein turnover dynamics. This workflow shows promising clinical translational value for examining plasma protein dynamics in human diseases.

Pre-existing psychiatric illness was associated with an increased risk of recurrent TC. No significant association was noted between pre-existing psychiatric illness and survival.

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