Biology subjects

Carmona-Saez, P.

Publications and source records attributed to Carmona-Saez, P..

12 recordsLinked to original sources

bioRxiv2026

A transcriptional patient map of systemic lupus erythematosus reveals disease-related multicellular immune programs conserved between blood and kidney

Systemic lupus erythematosus (SLE) shows marked clinical and molecular heterogeneity, yet patient stratification often relies on gene expression signatures lacking multicellular context. Here we construct a transcriptional patient map of SLE by analyzing 1,167 total samples (783 SLE, 384 healthy controls) across different resolutions, including single-cell and bulk blood as well as spatially resolved kidney tissue transcriptomes. Using an unsupervised approach we inferred patient-level transcriptomic immune programs from two independent single-cell RNA sequencing cohorts of peripheral blood mononuclear cells (PBMCs), capturing both differences between SLE and health as well as within-SLE heterogeneity. Specifically, we identified four conserved programs comprising two multicellular inflammatory programs driven by interferon and TNF/NFkB activity across immune cells, and two cell type-specific programs reflecting CD8 T cell cytotoxicity and a CD4 T cell naive-to-effector state. Functional analysis of these programs revealed a rewiring of both cell-to-cell interactions and task allocation across cell types during disease activation. In addition, mapping these programs onto an external longitudinal blood transcriptomic cohort predicted flare risk and identified candidate blood protein biomarkers detectable by proteomics. Finally, we showed that these blood programs were enriched in immune-infiltrated glomerular regions from kidney biopsies of individuals with lupus nephritis using spatially resolved transcriptomic data, thereby linking systemic immune programs to local tissue pathology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/721379v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1e75f00org.highwire.dtl.DTLVardef@10e0a63org.highwire.dtl.DTLVardef@cc194forg.highwire.dtl.DTLVardef@191b4d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

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↗

bioRxiv2022

Polycomb repression during S/G2 phases restrain initiation of cell differentiation to the G1 phase of the cell cycle

The potential of pluripotent cells to respond to developmental cues and trigger cell differentiation is enhanced during the G1 phase of the cell cycle, but the molecular mechanisms involved are poorly understood. Variations in polycomb activity during interphase progression have been hypothesized to regulate the cell-cycle-phase-dependent transcriptional activation of differentiation genes during lineage transition in pluripotent cells. Here, we asked whether the Polycomb Repressive Complex 1 (PRC1) modulates the ability of mouse embryonic stem cells (ESCs) to differentially respond to developmental cues depending on the phase of the cell cycle in which they are found. We discovered that recruitment of PRC1 complexes and their associated molecular functions, ubiquitination of H2AK119 and three-dimensional chromatin interactions, are enhanced during S and G2 phases compared to the G1 phase. In agreement with the accumulation of PRC1 at target promoters upon G1 phase exit, cells in S and G2 phases show firmer transcriptional repression of developmental regulator genes that is drastically perturbed upon genetic ablation of the PRC1 catalytic subunit Ring1b. Importantly, depletion of Ring1b during retinoic acid stimulation interferes with the preference of mESCs to induce the transcriptional activation of differentiation genes in G1 phase. We propose that incremental enrolment of polycomb repressive activity during interphase progression reduces the tendency of cells to respond to developmental cues during S and G2 phases, facilitating activation of cell differentiation in the G1 phase of the pluripotent cell cycle.

molecular biology↗

bioRxiv2022

EZH2 endorses cell plasticity to carcinoma cells facilitating mesenchymal to epithelial transition and tumour colonization

Reversible transition between the epithelial and mesenchymal states are key aspects of carcinoma cell dissemination and the metastatic disease, and thus, characterizing the molecular basis of the epithelial to mesenchymal transition (EMT) is crucial to find druggable targets and more effective therapeutic approaches in cancer. Emerging studies suggest that epigenetic regulators might endorse cancer cells with the cell plasticity required to conduct dynamic changes in cell state during EMT. However, epigenetic mechanisms involved remain mostly unknown. Polycomb Repressive Complexes (PRCs) proteins are well-established epigenetic regulators of development and stem cell differentiation, but their role in different cancer systems is inconsistent and sometimes paradoxical. In this study, we have analysed the role of the PRC2 protein EZH2 in lung carcinoma cells. We found that besides its described role in CDKN2A-dependent cell proliferation, EZH2 upholds the epithelial state of cancer cells by repressing the transcription of hundreds of mesenchymal genes. Chemical inhibition or genetic removal of EZH2 promotes the residence of cancer cells in the mesenchymal state during reversible epithelial-mesenchymal transition. In fitting, analysis of human patient samples and tumour xenograft models indicate that EZH2 is required to efficiently repress mesenchymal genes and facilitate tumour colonization in vivo. Overall, this study discloses a novel role of PRC2 as a master regulator of EMT in carcinoma cells. This finding has important implications for the design of therapies based on EZH2 inhibitors in human cancer patients.

cancer biology↗

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

DREIMT: a drug repositioning database and prioritization tool for immunomodulation.

MotivationDrug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. ResultsDREIMT is a new hypothesis-generation web tool which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4,960 drug profiles and ~2,6K immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables. Availabilityhttp://www.dreimt.org Contactfalshahrour@cnio.es; ggomez@cnio.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↗

bioRxiv2020

Polycomb regulation is coupled to cell cycle transition in pluripotent stem cells

When self-renewing pluripotent cells receive a differentiation signal, ongoing cell duplication needs to be coordinated with entry into a differentiation program. Accordingly, transcriptional activation of lineage specifiers genes and cell differentiation is confined to the G1-phase of the cell cycle by unknown mechanisms. We found that Polycomb repressive complex 2 (PRC2) subunits are differentially recruited to lineage specifier gene promoters across cell cycle in mouse embryonic stem cells (mESCs). Jarid2 and the catalytic subunit Ezh2 are dramatically accumulated at target promoters during S and G2, while the transcriptionally activating subunits EPOP and EloB are enriched during G1. Importantly, fluctuations in the recruitment of PRC2 subunits promote changes in RNA synthesis and RNA polymerase II binding that are compromised in Jarid2 -/- mESCs. Overall, we show that differential recruitment of PRC2 subunits across cell cycle enables the establishment of a chromatin state that facilitates the induction of cell differentiation in G1.

molecular biology↗

bioRxiv2019

Direct detection of circulating microRNA-122 using dynamic chemical labelling with single molecule detection overcomes stability and isomiR challenges for biomarker qualification

Circulating microRNAs are biomarkers reported to be stable and translational across species. miR-122 (miR-122-5p) is a hepatocyte-specific microRNA biomarker for drug-induced liver injury (DILI). Our objective was to develop an extraction-free and amplification-free detection method for measuring miR-122 that has translational utility in context of DILI. We developed a single molecule dynamic chemical labelling (DCL) assay based on miR-122 hybridization to an abasic peptide nucleic acid probe that contained a reactive amine instead of a nucleotide at a specific position in the sequence. The single molecule DCL assay specifically measured miR-122 directly from 10 {micro}L of serum or plasma without any extraction steps, with a fit-for-purpose limit of detection of 1.32 pM. In 192 human serum samples, DCL accurately identified patients at risk of DILI (area under ROC curve 0.98 (95%CI 0.96-1), P<0.0001). The miR-122 assay also quantified liver injury in rats and dogs. When DCL beads were added to serum, the miR-122 signal was stabilised (no loss of signal after 14 days at room temperature). By contrast, there was substantial degradation of miR-122 in the absence of beads ({approx}60% lost in 1 day). RNA sequencing demonstrated the presence of multiple miR-122 isomiRs with DILI that were at low concentration or not present in healthy patient serum. Sample degradation over time produced more isomiRs, particularly rapidly with DILI. PCR was inaccurate when analysing miR-122 isomiRs, whereas the DCL assay demonstrated accurate quantification. In summary, the DCL assay can accurately measure miR-122 directly from serum and plasma to diagnose liver injury in humans and other species, and can overcome important microRNA biomarker analytical and biological challenges.

pharmacology and toxicology↗

bioRxiv2019

Placental DNA methylation signatures of maternal smoking during pregnancy and potential impacts on fetal growth

Maternal smoking during pregnancy (MSDP) contributes to poor birth outcomes, in part through disrupted placental functions, which may be reflected in the placental epigenome. We meta-analyzed the associations between MSDP and placental DNA methylation (DNAm) and between DNAm and birth outcomes within the Pregnancy And Childhood Epigenetics (PACE) consortium (7 studies, N=1700, 344 with any MSDP). We identified 1224 CpGs that were associated with MSDP, of which 341 associated with birth outcomes and 141 associated with gene expression. Only 6 of these CpGs were consistent with the findings from a prior meta-analysis of cord blood DNAm, demonstrating substantial tissue-specific responses to MSDP. The placental MSDP associated CpGs were enriched for growth-factor signaling, hormone activity, inflammation, and vascularization, which play important roles in placental function. We demonstrate links between placental DNAm, MSDP and poor birth outcomes, which may better inform the mechanisms through which MSDP impacts placental function and fetal growth.

genomics↗

bioRxiv2018

mCSEA: Detecting subtle differentially methylated regions

MotivationThe identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.\n\nResultsWe present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify differentially methylated regions from Illumina 450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets, we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify differentially methylated regions not detected by other methods.\n\nAvailabilitymCSEA is freely available from the Bioconductor repository.\n\nContactpedro.carmona@genyo.es

bioinformatics↗