Biology subjects

Jorge, I.

Publications and source records attributed to Jorge, I..

5 recordsLinked to original sources

bioRxiv2025

Integrative multi-layer workflow for quantitative analysis of post-translational modifications

Novel algorithms based on ultratolerant database searching have paved the way for comprehensive analysis of all possible post-translational modifications (PTM) that can be detected by mass spectrometry-based proteomics, obviating their prior knowledge. These tools together with novel quantitative statistical models allow hypothesis-free approaches to study the role and impact of PTM on biological systems. However, interpretation of this information from a pathophysiological perspective is challenging due to the huge amounts of PTM data, the existence of chemical, structural, and statistical artifacts and the lack of dedicated tools for their analysis. Here we propose a novel integrative workflow that automatically captures several layers of PTM-related information, including variations in trypsin efficiency, zonal changes, specific PTM changes and hypermodified regions, allowing advanced control of artefacts and coherent and comprehensive interpretation of PTM data. We show the performance of the new workflow by reanalyzing proteomics data from animal models of mitochondrial heteroplasmy and ischemia/reperfusion, revealing relevant PTM information not previously detectable, including consistent detection of novel oxidative modifications in Met and Cys residues from raw proteomics data. The workflow is available through the application PTM-compass.

bioinformatics↗

bioRxiv2024

Protein aggregation capture assisted profiling of the thiol redox proteome

Oxidative damage is critical in various diseases, including cardiovascular and neurological conditions. Thiol redox reactions, acting as oxidative stress sensors, influence protein structure and function. Redox proteomics based on differential alkylation of reduced and oxidized Cys forms using mass spectrometry enables comprehensive analysis of thiol redox status in cells and tissues. We introduce PACREDOX, an innovative redox proteomics approach based on the Protein Aggregation Capture (PAC) protocol and we demonstrate its compatibility with library free data-independent acquisition (DIA). PACREDOX reduces preparation time and costs compared to traditional methods, such as FASILOX, while maintaining thiol and proteome coverage. To enable library-free DIA, we corrected in silico spectral libraries in DIA-NN using experimental retention time data from beta-methylthiol-modified peptides. PACREDOX with DIA quantified 4,000 protein groups and [~]45,000 modified peptides in myocardial tissue from a porcine model of atrial fibrillation, including over 8,000 cysteine-containing peptides, 30% of which were reversibly oxidized. Benchmarking PACREDOX and DIA against FASILOX in a myocardial infarction model reflects the potential and efficiency of this methodology to study oxidative damage. Overall, PACREDOX offers a high-throughput, cost-effective strategy for thiol redox proteome analysis, compatible with label-free quantitative workflows.

systems biology↗

bioRxiv2024

In-gel protein digestion using acidic methanol produces a highly selective methylation of glutamic 1 acid residues.

Mass-tolerant open search methods allow the high-throughput analysis of modified peptides by mass spectrometry. These techniques have paved the way to unbiased analysis of post-translational modifications (PTMs) in biological contexts, as well as of chemical modifications produced during the manipulation of protein samples. In this work, we have analyzed in-depth a wide variety of samples of different biological origin, including cells, extracellular vesicles, secretomes, centrosomes and tissue preparations, using Comet-ReCom, a recently improved version of the open search engine Comet-PTM. Our results demonstrate that glutamic acid residues undergo intensive methyl esterification when protein digestion is performed using in-gel techniques, but not using gel-free approaches. This effect was highly specific to Glu and was not found for other methylable residues such as Asp.

systems biology↗

bioRxiv2023

iSanXoT: a Standalone Application for the Integrative Analysis of Mass Spectrometry-Based Quantitative Proteomics Data

Numerous bioinformatics tools currently exist to perform quantitative analysis of proteomics experiments. The majority of these tools apply diverse statistical models to assign a quantitative protein value from the mass-spectrometry information. Here we present iSanXoT, a standalone application that allows integrative analysis of quantitative proteomics data. iSanXoT processes relative abundances between MS signals and integrates them sequentially to upper levels using our previously published Generic Integration Algorithm (GIA). iSanXoT offers unique capabilities that complement conventional quantitative softwares, including statistical weighting and independent modeling of error distributions in each integration, aggregation of technical or biological replicates, quantification of posttranslational modifications or analysis of coordinated protein behavior. iSanXoT is a standalone, user-friendly application which accepts output from widespread proteomics pipelines and enables free construction of quantification workflows and fully customizable reports than can be reused across different projects or shared among users. Diverse integrative workflows constructed using GIA for the analysis of high-throughput quantitative proteomics experiments have been successfully applied in numerous publications. iSanXoT has been tested with the main operating systems. Download links for the corresponding distributions are available at https://github.com/CNIC-Proteomics/iSanXoT/releases.

bioinformatics↗

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↗