Biology subjects

Sanchez-Taltavull, D.

Publications and source records attributed to Sanchez-Taltavull, D..

8 recordsLinked to original sources

bioRxiv2026

Stress-induced vtRNA1-1 modulates redox homeostasis and ferroptosis susceptibility in hepatocellular carcinoma cells

Ferroptosis is a unique form of regulated cell death characterized by iron-dependent lipid peroxidation. Although the molecular details of ferroptosis regulation have been widely explored, the contributions of short non-coding RNAs (ncRNAs) to ferroptosis regulation, other than miRNAs remain poorly understood. Here, we identified vault RNA1-1 (vtRNA1-1) as a previously unrecognized short ncRNA regulator of ferroptosis in hepatocellular carcinoma (HCC) cells. vtRNA1-1 expression was upregulated by ferroptosis inducers and exhibited strong negative correlation with ferroptosis sensitivity, thus protecting cells from ferroptosis. vtRNA1-1 levels were elevated in selected ferroptosis-resistant cells, while its depletion reversed the phenotype thus resensitizing these cells to ferroptosis. These findings suggested a contribution of vtRNA1-1 to both intrinsic and acquired ferroptosis resistance. Mechanistically, we uncovered that increased oxidative stress, which potentiates lipid peroxidation, specifically induced expression of the vtRNA1-1 paralog in an NF-{kappa}B dependent manner. Elevated vtRNA1-1 levels suppressed NF-{kappa}B-mediated pro-oxidant gene expression, thereby limiting reactive oxygen species (ROS) accumulation and alleviating oxidative stress. Taken together, oxidative stress-inducible vtRNA1-1 governs redox balance by forming a reciprocal regulatory loop with NF-{kappa}B and this loop determines ferroptosis susceptibility by adjusting basal ROS levels. Our findings provide unprecedented insights into the regulation of redox homeostasis in HCC cells mediated by a short ncRNA and uncovered vtRNA1-1 as a potential therapeutic target for overcoming ferroptosis resistance in liver cancer.

molecular biology↗

bioRxiv2024

Released Bacterial ATP Shapes Local and Systemic Inflammation during Abdominal Sepsis

Sepsis causes millions of deaths per year worldwide and is a current global health priority declared by the WHO. Sepsis-related deaths are a result of dysregulated inflammatory immune responses indicating the need to develop strategies to target inflammation. An important mediator of inflammation is extracellular adenosine triphosphate (ATP) that is secreted by inflamed host cells and tissues, and also by bacteria in a strain-specific and growth phase-dependent manner. Here, we investigated the mechanisms by which bacteria release ATP. Using genetic mutant strains of Escherichia coli (E. coli), we demonstrate that ATP release is dependent on ATP synthase within the inner bacterial membrane. In addition, impaired integrity of the outer bacterial membrane and bacterial death notably contribute to ATP release. In a mouse model of abdominal sepsis, local effects of bacterial ATP were analysed using a transformed E. coli bearing an arabinose-inducible periplasmic apyrase hydrolyzing ATP to be released. Abrogating bacterial ATP release shows that bacterial ATP suppresses local immune responses, resulting in reduced neutrophil counts and impaired survival. In addition, bacterial ATP has systemic effects via its transport in outer membrane vesicles (OMV). ATP-loaded OMV are quickly distributed throughout the body and upregulated expression of genes activating degranulation in neutrophils, potentially contributing to the exacerbation of sepsis severity. This study reveals mechanisms of bacterial ATP release and its local and systemic roles in sepsis pathogenesis.

microbiology↗

bioRxiv2021

Single cell chronoatlas of regenerating mouse livers reveals early Kupffer cell proliferation

The liver is exemplar to study tissue regeneration due to its inherent ability of repair and regrowth. It replaces its lost or injured tissue by the proliferation, interaction and temporal coordination of multiple residential cell types. Until now we lacked a detailed description of the specific contributions of each cell type to the regenerative process, and therefore analyzed mouse livers 0, 3, 6, and 24 hours following two-thirds partial hepatectomy (PHx) by single cell RNA-sequencing (scRNA-seq) and mass cytometry. Our resulting genome wide temporal atlas contains the time dependent transcriptional changes in hepatocytes, endothelial cells, bone marrow-derived macrophages (BMDM) and Kupffer cells. In addition, it describes the cell specific contribution of mitogenic growth factors from biliary epithelial, endothelial and stellate cells as well as chemokines and cytokines from BMDM and granulocytes. And interestingly, Kupffer cells as opposed to hepatocytes emerged as the first cell to proliferate presenting a new dynamic in the liver following PHx. Here, we provide a robust data set at cellular resolution to uncover new elements and revisit current dogmas on the mechanisms underlying liver regeneration. To facilitate access to the data, we have launched the portal www.phxatlas.ch in which the scRNA-seq data can be visualized.

developmental biology↗

bioRxiv2019

Absolute quantification of transcription factors reveals principles of gene regulation in erythropoiesis

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). Yet, despite years of studies we still do not know the protein copy number of TFs in the nucleus. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first Gene Regulatory Network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that in the nucleus, corepressors are dramatically more abundant than coactivators at the protein, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

systems biology↗

bioRxiv2019

Compartmentalization of intestinal bacteria by hepatic ILC3s prevents infections after surgery

Infections after surgical interventions are assumed to be caused by contamination. We show by analyzing multicentric data of 6561 patients that surgical infections as well as sepsis had a predominantly enteric microbial signature irrespective of the type of surgery, suggesting failure of intestinal bacterial compartmentalization. In mice, we reveal that hepatic surgery induced dysregulation of intestinal and hepatic type 3 innate lymphoid cells (ILC3s) and intestinal leakage resulting in enteric bacterial translocation via lymphatic vessels. In the absence of hepatic ILC3s, inflammasome activation and the induction of antimicrobial peptide encoding genes, bacteria colonized remote systemic organs and impaired surgical outcomes. Conversely, mammalian-microbial commensalism is required for the education of host immunity to ensure optimal hepatic healing responses. In fact, microbial-derived products were sufficient for the induction of proliferative transcriptional networks in the mouse liver, as illustrated by serum transfer experiments, mass spectrometry and RNA expression analysis, indicating that the balanced exposure of the host to commensals is essential for recovery. This study reveals the intestinal origin of microbes causing complications after surgical interventions and highlights host protective mechanisms of controlled commensalism that prevent infections.\n\nOne Sentence SummaryIntestinal bacteria cause surgical infections

immunology↗

bioRxiv2019

Bayesian Correlation is a robust similarity measure for single cell RNA-seq data

Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq.\n\nRecently, a Bayesian correlation scheme, that assigns low similarity to genes that have low confidence expression estimates, has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering 3 ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression.\n\nWe demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population.\n\nWe conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.

bioinformatics↗

bioRxiv2018

Connexin-43 dependent ATP release mediates macrophage activation during peritonitis

Peritonitis is the consequence of bacterial spillage into a sterile environment by gastrointestinal hollow-organ perforation that may lead to fulminant sepsis. Outcome of peritonitis-induced sepsis critically depends on macrophage activation by extracellular ATP release and associated para- and autocrine signaling via purinergic receptors. Mechanisms that mediate and control ATP release, however, are poorly understood. Here we show that TLR-2 and -4 agonists trigger ATP release via Connexin-43 (CX43) hemichannels in peritoneal macrophages leading to poor survival during sepsis. In humans, CX43 expression was upregulated on macrophages isolated from the peritoneal cavity in patients with intraperitoneal infection but not in healthy controls. Using a murine caecal ligation and puncture (CLP) model, we identified increased CX43 expression in activated infiltrating peritoneal, hepatic and pulmonary macrophages. Conditional MAC-CX43 KO Lyz2cre/creCx43flox/flox mice were developed to specifically assess the CX43 impact in macrophages. Both macrophage-specific CX43 deletion (using Lyz2cre/creCx43flox/flox mice) or pharmacological CX43 blockade were associated with reduced cytokine secretion by macrophages in response to LPS and CLP. This was ultimately resulting in increased survival in Lyz2cre/creCx43flox/flox mice and after pharmacological blockade. Specific inhibition of the purinergic receptor P2RY1 abrogated CX43 elicited cytokine responses. In conclusion, inhibition of autocrine ATP signaling via CX43 on macrophages and P2RY1 improves sepsis outcome in experimental peritonitis.\n\nBrief SummaryConnexin-43-mediated ATP release from macrophages in response to TLR-4 and -2 agonists modulates autocrine activation of macrophages in a P2Y1-dependent manner, ultimately determining sepsis survival.

immunology↗

bioRxiv2017

Uncovering Robust Patterns of MicroRNA Co-Expression across Cancers using Bayesian Relevance Networks

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing--with its unique statistical properties--became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca/Software.html.

bioinformatics↗