Biology subjects

Vazquez, J.

Publications and source records attributed to Vazquez, J..

4 recordsLinked to original sources

bioRxiv2018

ECM deposition is driven by caveolin1-dependent regulation of exosomal biogenesis and cargo sorting

The composition and physical properties of the extracellular matrix (ECM) critically influence tumour cell behaviour, and ECM deposition and remodelling by stromal fibroblast populations is therefore pivotal for tumour progression. The molecular mechanisms by which stromal and tumour cell populations regulate ECM layering are poorly understood. Tumour-stroma interaction is critically dependent on cell-cell communication mediated by exosomes, small vesicles secreted by most cell types and generated within multivesicular bodies (MVBs). Here, we show that caveolin-1 (Cav1), an essential regulator of stromal remodelling and tumour cell fate, plays a central role in modulating both exosome biogenesis and exosomal protein cargo sorting through cholesterol-dependent mechanisms. Quantitative proteomics profiling revealed that a major share of Cav1-dependent exosomal cargoes are compsed of ECM proteins, one of the most important components being tenascin-C (TnC). Comparative functional assays demonstrated that Cav1 is required for fibroblast-derived exosomes to depose ECM and promote tumour cell invasiveness. Exosomes purified from Cav1WT cells, but not those from Cav1-null cells, were able to nucleate distant stromal niches in different organs in vivo. These findings suggest a key role for Cav1 as a cholesterol rheostat in MVBs, and seems to determine ECM deposition by eliciting ECM component sorting into specific exosome pools. These results, together with previous work, support a model in which Cav1 is a central regulatory hub for tumour-stroma interactions through a novel exosome-dependent ECM deposition mechanism.

cell biology↗

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

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↗

bioRxiv2017

SQANTI: extensive characterization of long read transcript sequences for quality control in full-length transcriptome identification and quantification

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in very well annotated organisms as mice and humans. Nonetheless, there is a need for studies and tools that characterize these novel isoforms. Here we present SQANTI, an automated pipeline for the classification of long-read transcripts that computes 47 descriptors that can be used to assess the quality of the data and of the preprocessing pipelines. We applied SQANTI to a neuronal mouse transcriptome using PacBio long reads and illustrate how the tool is effective in readily describing the composition of and characterizing the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach, and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, result more frequently in novel ORFs than novel UTRs and are enriched in both general metabolic and neural specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases we find that alternative isoforms are elusive to proteogenomics detection and are variable in protein changes with respect to the principal isoform of their genes. SQANTI allows the user to maximize the analytical outcome of long read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes. SQANTI is available at https://bitbucket.org/ConesaLab/sqanti.

bioinformatics↗