Biology subjects

Nunez-Carpintero, I.

Publications and source records attributed to Nunez-Carpintero, I..

4 recordsLinked to original sources

bioRxiv2025

A conserved transcriptional backbone and rewiring of gene-regulatory networks in activated human CD4⁺ T cells

CD4+ T cells are components of the adaptive immune system with a plethora of subtype-specific functions. In order to further dissect the activation and differentiation regulatory program(s) of individual CD4+ T cell subsets, we performed an in vitro activation and differentiation of human primary naive CD4+ T cells towards Th1, Th2, Th17 and Treg subtypes followed by the single-cell RNA-seq and ATAC-seq (multiome) analysis. Resulting multiome data were used for constructing the subtype-specific gene regulatory networks, which were next assessed for their differences/similarities among the subtypes. Surprisingly, a conserved set of 8 "backbone" transcription factors (TFs) was identified as highly central in all subtypes, however, with unique differentiation-driven rewiring tendency. Subtype-specific "driver" TFs were identified in the case of Th1-Th1_17-Th17 lineage (EOMES, HLF), naive Tregs (ESR1, DACH1), and memory Tregs (SOX13). Finally, we applied community detection algorithms to identify potential non-obvious groups of genes that regulate diverse molecular functions within the differentiated subtypes, linked to the backbone TFs. Our atlas aims at providing a high resolution understanding of the gene regulatory networks and their rewiring in human primary CD4+ T cells, upon activation and differentiation.

immunology↗

bioRxiv2025

Sex-specific transcriptome similarity networks elucidate comorbidity relationships

Humans present sex-driven biological differences. Consequently, the prevalence of analyzing specific diseases and comorbidities differs between the sexes, directly impacting patients management and treatment. Despite its relevance and the growing evidence of said differences across numerous diseases (with 4,370 PubMed results published within the past year), knowledge at the comorbidity level remains limited. In fact, to date, no study has attempted to identify the biological processes altered differently in women and men, promoting differences in comorbidities. To shed light on this problem, we analyze expression data for more than 100 diseases from public repositories, analyzing each sex independently. We calculate similarities between differential expression profiles by disease pairs and find that 13-16% of transcriptomically similar disease pairs are sex-specific. By comparing these results with epidemiological evidence, we recapitulate 53-60% of known comorbidities distinctly described for men and women, finding sex-specific transcriptomic similarities between sex-specific comorbid diseases. The analysis of shared underlying pathways shows that diseases can co-occur in men and women by altering alternative biological processes. Finally, we identify different drugs differentially associated with comorbid diseases depending on patients sex, highlighting the need to consider this relevant variable in the administration of drugs due to their possible influence on comorbidities.

bioinformatics↗

bioRxiv2024

Exploring the Boundaries of Medulloblastoma Subgroups with Synthetic Data Generation

Medulloblastoma is a childhood brain tumor traditionally classified into four molecular subgroups. Recent evidence suggests that Groups 3 and 4 represent a biological continuum rather than distinct entities, a paradigm shift with significant implications for understanding disease biology and treatment strategies. Nevertheless, assessing this hypothesis is challenging mainly due to data scarcity. In this study, we analyze the largest available transcriptomics dataset to provide compelling evidence for the existence of an intermediate subgroup between Groups 3 and 4, characterized by distinct molecular features. To overcome limitations posed by data scarcity, we employ synthetic data generation using a Variational Autoencoder and apply explainability techniques to identify key relationships between gene expression and disease subgroups. Furthermore, by incorporating Machine Learning Fairness approaches, we demonstrate that overlooking this intermediate subgroup can result in treatment disparities. Our findings are further supported by both existing and newly proposed studies using diverse datasets and methodologies, including graphbased analyses and multi-scale simulations, underscoring the robustness and reproducibility of our results. This study demonstrates the potential of synthetic data generation to refine rare disease subtyping and advance our understanding of the underlying biological mechanisms. Keywords: Medulloblastoma, pediatric cancer, representation learning, autoencoder, synthetic data

bioinformatics↗

bioRxiv2023

Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes

Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network analysis of protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. We explore this in more detail for one of the proteins not previously associated with the NMJ, USH2A. Loss of the zebrafish USH2A ortholog revealed some effects on early movement and gross NMJ morphology. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.

bioinformatics↗