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TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies

ABSTRACT: In spite of its central role in biology and disease, protein turnover is a largely understudied aspect of most proteomic studies due to the complexity of computational workflows that analyze in-vivo turnover rates. To address this need, we developed a new computational tool, TurnoveR, to accurately calculate protein turnover rates from mass spectrometric analysis of metabolic labeling experiments in Skyline, a free and open-source proteomics software platform. TurnoveR is a straightforward graphical interface that enables seamless integration of protein turnover analysis into a traditional proteomics workflow in Skyline, allowing users to take advantage of the advanced and flexible data visualization and curation features built into the software. The TurnoveR computational pipeline performs critical steps to determine protein turnover rates, including isotopologue demultiplexing, precursor-pool correction, statistical analysis, and generation of data reports and visualizations. This workflow is compatible with many mass spectrometric platforms and re-capitulates turnover rates and differential changes in turnover rates between treatment groups calculated in previous studies. We expect that the addition of TurnoveR to the widely used Skyline proteomics software will facilitate wider utilization of protein turnover analysis in highly relevant biological models, including aging, neurodegeneration, and skeletal muscle atrophy.

INSTRUMENT(S): LTQ Orbitrap Velos, TripleTOF 6600

ORGANISM(S): Mus Musculus (ncbitaxon:10090)

SUBMITTER: Birgit Schilling

PROVIDER: MSV000088893 | MassIVE | Tue Feb 22 09:56:00 GMT 2022

SECONDARY ACCESSION(S): PXD031832

REPOSITORIES: MassIVE

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Project description:Age-related skeletal muscle atrophy is a debilitating condition that has significant negative impacts on health and quality of life. Despite broad clinical impact, the molecular basis of age-related skeletal muscle atrophy is not well understood. Here, we determined protein turnover rates in skeletal muscle of 22-month-old control mice and 22-month-old muscle-specific ATF4 knockout (ATF4 mKO) mice, which are partially resistant to age-related muscle atrophy. All samples were analyzed by DDA TripleTOF 6600 mass spectrometer for downstream analysis of protein turnover. Quantitative MS1 peak areas were extracted from DDA raw files in the Skyline-Daily software platform. ProteinPilot search results were imported into the Skyline software to build a spectral library, followed by import of raw MS files for extraction of chromatographic peak areas. A custom skyline report containing all peptide and protein characteristics, annotations, and quantitative information including isotopologue peak areas was exported and used for downstream analysis and calculation of protein turnover rates in R using in-house R scripts (TurnoveR tool). Precursor-pool corrected protein turnover rates were calculated using the same approach employed in previous studies using the Topograph software platform. For calculation of protein abundance changes, DIA acquisitions from six samples (3 ATF4 KO and 3 WT samples) were quantitatively processed using Spectronaut v14. Analysis of protein turnover indicates that most proteins with altered turnover rates in ATF4 mKO muscles have decreased half-lives and are components of the mitochondrion. These proteins include subunits of the ATP Synthase (Atp5b, Atp5o), Ubiquinol-Cytochrome C Reductase (Uqcrc1, Uqcrh) and Cytochrome C Oxidase (Cox6b1) complexes, among others. These findings implicate increased rates of mitochondrial protein turnover as a mechanism that underlies, at least in part, the protection from age-induced muscle atrophy in ATF4 mKO mice.

Project description:Measuring the properties of endogenous cell proteins, such as expression level, subcellular localization and turnover rates, on a whole proteome level remains a major challenge in the post-genome era. Quantitative methods for measuring mRNA expression do not reliably predict corresponding protein levels and provide little or no information on other protein properties. Here we describe a combined pulse-labelling, spatial proteomics and data analysis strategy to characterize the expression, localization, synthesis, degradation and turnover rates of endogenously expressed, untagged human proteins in different subcellular compartments. Using quantitative mass spectrometry and SILAC, a total of 80,098 peptides from 8,041 HeLa proteins were quantified, and their spatial distribution between the cytoplasm, nucleus and nucleolus determined and visualised using specialised software tools developed in PepTracker. Using information from ion intensities and rates of change in isotope ratios, protein abundance levels and protein synthesis, degradation and turnover rates were calculated for the whole cell and for the respective cytoplasmic, nuclear and nucleolar compartments. Expression levels of endogenous HeLa proteins varied by up to seven orders of magnitude. The average turnover rate for HeLa proteins was approximately 20 hours. Turnover rate did not correlate with either molecular weight or net charge, but did correlate with abundance, with highly abundant proteins showing longer than average half-lives. Fast turnover proteins had overall a higher frequency of PEST motifs than slow turnover proteins but no general correlation was observed between amino or carboxy terminal amino acid identities and turnover rates. A subset of proteins was identified that exist in pools with different turnover rates depending on their subcellular localization. This strongly correlated with subunits of large, multi-protein complexes, suggesting a general mechanism whereby their assembly is controlled in a different subcellular location to their main site of function.

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TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies

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