Biology subjects

Lopez-Dominguez, R.

Publications and source records attributed to Lopez-Dominguez, R..

4 recordsLinked to original sources

bioRxiv2025

Microenvironment-specific modulation of macrophage function and tumour progression by ADAMTS1 through Syndecan-4 shedding

Recent studies have emphasized the role of ADAMTS proteases in inflammation and immunity, particularly in the context of tumour progression. Since inflammatory cells can either constrain or promote tumour growth, understanding how ADAMTS proteases influence immune cell behaviour is crucial. Using syngeneic tumour models-B16F1 melanoma and Lewis Lung Carcinoma (LLC)-in Adamts1 knockout (Ats1-KO) mice, we observed model-specific outcomes underscoring the complexity of ADAMTS1 function in cancer. Transcriptomic and functional analyses revealed broad alterations in the matrisome and immune-related pathways across both tumour types. Strikingly, while tumour progression was impaired in B16F1-derived tumours, the LLC model-characterized by a stronger myeloid component-showed no dependency on Adamts1. To investigate this apparent resistance, we experimentally depleted macrophages and uncovered a profound functional defect in this population in Ats1-KO mice. In vitro assays confirmed reduced macrophage phagocytic activity. Mechanistically, we identified the transmembrane heparan sulfate proteoglycan syndecan-4 (SDC4), a known ADAMTS1 substrate, as a key mediator of this activity. Together, these findings reveal a previously unrecognized ADAMTS1-SDC4 axis that links extracellular matrix remodelling to macrophage phagocytosis, ultimately shaping tumour cell clearance and tumour progression.

cancer biology↗

bioRxiv2024

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics

Recent advances in single-cell RNA-Seq (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes, such as diseases, at the level of individual cells. The analysis of the resulting data poses significant challenges due to their sparsity and large volume, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications. However, standard classification schemas are often not suitable for scRNA-Seq due to its unique characteristics, and new algorithms are required to effectively analyze and classify samples at the single-cell level. In this article, we introduce singleDeep, an end-to-end pipeline that streamlines the analysis of scRNA-Seq data training deep neural networks, enabling robust prediction and characterization of sample phenotypes. To validate the effectiveness of singleDeep, we applied it to make predictions on scRNA-Seq datasets from different conditions, including systemic lupus erythematosus and Alzheimers disease. Our results demonstrate strong diagnostic performance, validated both internally and externally. Moreover, compared with traditional machine learning methods applied to pseudobulk data, singleDeep consistently outperformed these approaches. In addition to prediction accuracy, singleDeep provides valuable insights into cell types and gene importance estimation for phenotypic characterization. This functionality provided additional and valuable information in our use cases. For instance, we corroborated that some interferon signature genes are consistently relevant for autoimmunity across all immune cell types in lupus. On the other hand, we discovered that genes linked to dementia have relevant roles in specific brain cell populations, such as APOE in astrocytes.

bioinformatics↗

bioRxiv2021

GeneCodis 4: Expanding the modular enrichment analysis to regulatory elements

GeneCodis is a web-based tool for functional enrichment analysis that allows researchers to integrate different sources of annotations. It extracts sets of significant concurrent annotations and assigns a statistical score to evaluate those that are significantly enriched in the input list. Since its first release in 2007, it has been widely employed to analyze lists of genes in order to interpret the underlying biological mechanisms. Here we present GeneCodis 4, a new release that expands the functional analysis provided by the application, to accept regulatory elements, including lists of, transcription factors, CpG sites and miRNAs. It also incorporates new annotation databases and improved interactive visualizations functionalities to explore results. GeneCodis 4 is freely available at https://genecodis.genyo.es.

bioinformatics↗

bioRxiv2020

A comprehensive and centralized database for exploring omics data in Autoimmune Diseases

Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Here, we present ADEx (https://adex.genyo.es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

bioinformatics↗