Bonzon-Kulichenko, E. | Search bioRxiv

bioRxiv2018

Comprehensive quantification of the modified proteome reveals oxidative heart damage in mitochondrial heteroplasmy

Post-translational modifications hugely increase the functional diversity of proteomes. Recent algorithms based on ultratolerant database searching are forging a path to unbiased analysis of peptide modifications by shotgun mass spectrometry. However, these approaches identify only half of the modified forms potentially detectable and do not map the modified residue. Moreover, tools for the quantitative analysis of peptide modifications are currently lacking. Here, we present a suite of algorithms that allow comprehensive identification of detectable modifications, pinpoint the modified residues, and enable their quantitative analysis through an integrated statistical model. These developments were used to characterize the impact of mitochondrial heteroplasmy on the proteome and on the modified peptidome in several tissues from 12-week old mice. Our results reveal that heteroplasmy mainly affects cardiac tissue, inducing oxidative damage to proteins of the oxidative phosphorylation system, and provide a molecular mechanism that explains the structural and functional alterations produced in heart mitochondria.\n\nHighlightsO_LIIdentifies all protein modifications detectable by mass spectrometry\nC_LIO_LILocates the modified site with 85% accuracy\nC_LIO_LIIntegrates quantitative analysis of the proteome and the modified peptidome\nC_LIO_LIReveals that mtDNA heteroplasmy causes oxidative damage in heart OXPHOS proteins\nC_LI

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bioRxiv2017

QuiXoT: quantification and statistics of high-throughput proteomics by stable isotope labelling

AbstractIn most software tools for quantification of mass spectrometry-based proteomics by stable isotope labelling (SIL), there is a recurrent disconnection between the use of a statistical model and convenient data visualisation to check correct data modelling. Most of them lack a robust statistical framework, using models which do not account for the major difficulties in proteomics, such as the unbalanced peptide-protein distribution, undersampling, or the correct separation of sources of variance. This makes especially difficult the interpretation of quantitative proteomics experiments. Here we present QuiXoT, an extensively tested quantification and statistics open source software based on a robust and extensively validated statistical model, the WSPP (weighted spectrum, peptide, and protein). Its associated software package allows the user to visually represent and inspect results at all modelled levels (scan, peptide and protein) on routine bases. It is applicable to practically any SIL method (SILAC, iTRAQ, and 18O among others) or MS instrument.

bioinformatics↗