Project PXD002054

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Bank Vole Protein Turnover Study C4PR_LIV


We have used dietary administration of stable isotope labelled lysine to assess protein turnover rates for proteins from four tissues in the bank vole, Myodes glareolus. The annotated genome for this species is not available, so protein identification was attained through cross-species matching to the mouse. For proteins for which confident identifications were derived, the pattern of lysine incorporation over 40d was used to define the rate of synthesis of individual proteins in the four tissues. The data were heavily filtered to retain a very high quality data-set of turnover rates for 1088 proteins. Comparative analysis of the four tissues revealed different median rates of degradation (kidney: 0.099 per day; liver 0.136 per day; heart, 0.054 per day and skeletal muscle, 0.035 per day). These data were compared with protein degradation rates from other studies on intact animals or from cells in culture.

Sample Processing Protocol

10 Bank Voles were heavy isotopically-labelled to a maximum RIA of 0.5 in a 'dynamic-SILAC' experimental configuration, by dietary administration of stable isotope labelled lysine (+6Da). 2 animals were sacrificed after 1 day of labelling, 2 after 5 days, 2 after 12 days, 2 after 25 days and, finally, 2 after 40 days labelling. Whole tissue preparations from each animal were homogenised and protein extracted and solubilised using Rapigest, prior to being digested in solution using Trypsin. Tryptic peptides were analysed by LC-MSMS on a q-Exactive instrument after separation over a 95 min linear gradient of 3.8 – 40 % buffer B at 300 nl per min flow rate, on an Easy-Spray PepMap, 15 cm C18 column. The MS instrument was operated in data-dependent mode with survey scans acquired at a resolution of 70,000 at m/z 200. Up to the top 10 most abundant isotope patterns with charge states +2, +3 and/or +4 from the survey scan were selected for fragmentation by HCD.

Data Processing Protocol

Thermo raw MS data files from each tissue and duplicate animal over the labelling trajectory were analyzed using the Progenesis-QI software package from Nonlinear Dynamics/WAters. Aligned, detected peptide features in each raw data file were quantified and each peak list, on a tissue by tissue basis (n = 10 in each data-set) per experiment, were merged and searched using Mascot (version 2.4.1) against a UniProt Mouse database. The search parameters allowed for a single trypsin missed cleavage and carbamidomethylation of cysteine residues as a fixed modification. Oxidation of methionine and [13C6] lysine were allowed as variable modifications. A peptide tolerance of 10ppm was set, with an MS/MS tolerance of 0.01Da. Protein identifications were based on both arginine- and lysine-terminated tryptic peptide matches, but only lysine-terminated peptides carried the dynamic stable isotope labeling pattern for turnover measurement. To improve the quality of the search results and allow determination of false discovery rate (FDR), automatic decoy database searches were performed in Mascot. An FDR cut-off of 1% at the peptide level was applied to the database searches; only those lysine-containing peptide matches that satisfied these criteria were imported into Progenesis-QI to match feature-derived peptide quantitation(s) with identification. All other (lower quality) peptide data were ignored. To enable the feature-level quantitation performed by Progenesis-QI to be used in turnover calculations, we used Proteolabels, a development version of the Progenesis post-processor (PPP). Proteolabels searches the Progenesis-QI feature table to recover cognate pairs of heavy and light features that co-exist in the retention time dimension of aligned 2D LC-MS/MS maps, separated by the mass shift in m/z space due to incorporation of one or more [13C6]lysine residues. In house generated scripts were created for post-processing of Proteolabels output files. The scripts, written to separate mono- and multi-labelled peptides, parse peptide feature data and aggregate to protein-level, were written in Perl. To obtain protein-level turnover data, RIAt values for peptides belonging to the same protein were grouped together and fitted using the nls() package in R. The resultant kinetic profiles were plotted with 95% confidence intervals, using ggplot2.


Dean Hammond, University of Liverpool
Robert John Beynon, University of Liverpool ( lab head )

Submission Date


Publication Date



    Hammond DE, Claydon AJ, Simpson DM, Edward D, Stockley P, Hurst JL, Beynon RJ. Proteome dynamics: tissue variation in the kinetics of proteostasis in intact animals. Mol Cell Proteomics. 2016 Feb 1. pii: mcp.M115.053488 PubMed: 26839000


Showing 1 - 6 of 6 results
# Accession Title Proteins Peptides Unique Peptides Spectra Identified Spectra View in Reactome
1 49915 no assay title provided (mzIdentML) 1072 9879 3039 76648 8885
2 49916 no assay title provided (mzIdentML) 898 9847 3062 89957 8459
3 49917 no assay title provided (mzIdentML) 942 13582 3356 61958 11191
4 49918 no assay title provided (mzIdentML) 1101 13033 3902 90236 11485
5 49919 no assay title provided (mzIdentML) 281 5556 2603 21383 3823
6 49920 no assay title provided (mzIdentML) 753 4609 2564 19585 3785