Natan Basisty Natan Basisty, Ph.D., is a tenure-track investigator and PI of Translational Geroproteomics Unit at the National Institute on Aging (NIH). He received his Ph.D. in Pathology and B.S. in Biochemistry from the University of Washington, where he investigated the role of protein turnover in mammalian aging and longevity using combinations of metabolic labeling, LC-MS/MS, and software tools. In 2015, he joined The Buck Institute for Research on Aging, where he did his postdoctoral fellowship in the lab of Dr. Birgit Schilling. There he developed and applied proteomic approaches to understand aging processes and age-related diseases, including the development and application of computational pipelines like TurnoveR with the goal of expanding the scope of analyses that can be performed in Skyline.

Analysis of Protein Turnover Rates in Skyline with the TurnoveR External Tool

Introduction: Loss of protein homeostasis is a hallmark of aging and age-related diseases, including neurodegeneration, sarcopenia, and diabetes. While disruptions of the cellular protein turnover machinery, such as autophagy or proteasome dysfunction, are often associated with pathologies, changes in these processes do not necessarily predict changes in protein turnover rates. Therefore, methods to measure the turnover rates of proteins directly, rather than surrogate measurements of translation and degradation machinery, are critically needed to accurately examine the stability of the proteome in vivo. Additionally, while the measurement of protein turnover relevant in many biological settings, protein turnover studies remain computationally difficult for most scientists. Versatile computational tools on widely accessible, open-source platforms, such as Skyline, are needed to make this approach more user-friendly.

Methods: To perform in vivo estimation of protein turnover rates in mice, we analyzed data from two completely independent experiments aimed at determining protein turnover rates in mouse liver and skeletal muscle to study the effects of calorie restriction and sarcopenia, respectively. In both studies, mice were metabolically labeled in a time course with deuterated leucine supplemented in the diet. Samples from both experiments were homogenized, trypsin digested, and desalted for mass spectrometric analysis. Liver samples were analyzed by data-dependent acquisitions (DDA) on an Orbitrap Velos mass spectrometer, and muscle samples were analyzed by DDA on a TripleTOF 6600. The novel TurnoveR tool calculated protein half-lives and demonstrated that the results are consistent with previous reports and reproduce key results from prior studies.

Results: Here, we have developed TurnoveR and integrated it as ‘external tool’ directly into Skyline for the accurate calculation of protein turnover rates from mass spectrometry analysis of metabolic labeling experiments. Following the specification of a filter and label setting in a simple GUI, TurnoveR reads data directly from a Skyline document and runs a computational pipeline that incorporates critical computational steps associated with accurate calculation of protein turnover rates including deconvolution of overlapping isotope envelopes between unlabeled and labeled peaks, calculation of relative isotope enrichment of the amino acid precursor pool, turnover regressions, statistical comparisons between treatment groups, and visualization of data. Using data generated from metabolic labeling of mice with heavy leucine, we demonstrate how this tool enables the calculation of protein turnover rates entirely within a Skyline workspace using raw data acquired on multiple mass spectrometric platforms. We re-analyze data in calorie restricted and ad libitum-fed mice to show that this approach recapitulates turnover rates and differential changes in turnover between treatment groups calculated in previous studies using previously established tools. Visualizations and statistical reports generated by TurnoveR confirm that calorie restricted mice show 13% less newly synthesized protein globally compared to control mice after 20 days of labeling (p = 2.09 e-20) and have slower turnover of previously reported key mitochondrial proteins such as Echs1 (p = 0.01), Mdh2 (p < 0.0001), and Got2 (p < 0.0001). The calculated fractions of all proteins that were newly synthesized were consistent with previously reported values generated by the Topograph tool (r = 0.91). We anticipate that the addition of this external tool to the widely used Skyline proteomics software will facilitate wider utilization of metabolic labeling and protein turnover analysis in highly relevant biological models, including aging, neurodegeneration, and skeletal muscle atrophy.