Biology subjects

Derhourhi, M.

Publications and source records attributed to Derhourhi, M..

5 recordsLinked to original sources

bioRxiv2025

Flagellin-mediated TLR5 activation enhances innate immune responses in healthy and diseased human airway epithelium

Bacterial pneumonia poses a significant challenge to public health, often leading to antibiotic treatment failure. Enhancing innate immunity represents a promising adjunctive strategy to conventional antibiotic therapy. Bacterial flagellin, a Toll-like receptor 5 (TLR5) agonist, has been shown to stimulate innate immune defenses when delivered via the respiratory route, demonstrating efficacy in both preventing and treating bacterial pneumonia in murine models. This protective effect is primarily mediated through TLR5-driven activation of airway epithelial cells. This study aimed to characterize the immunomodulatory effects of flagellin on human primary respiratory epithelium. Using the MucilAir air-liquid interface model and RNA sequencing, we demonstrated that apical administration of flagellin induced robust immune responses in airway epithelium derived from healthy individuals, as well as patients with chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). TLR5-mediated epithelial signaling triggered key immune-related pathways, including cytokine production, leukocyte chemotaxis, neutrophil recruitment, and antimicrobial defense, with strong commonalities across healthy and diseased airway epithelia. Furthermore, we demonstrated that flagellin effectively activated epithelial immune responses even in the presence of the bacteria Pseudomonas aeruginosa or Streptococcus pneumoniae. However, epithelial activation alone was insufficient to directly limit bacterial colonization or replication, highlighting the potential role of epithelial-immune cell interactions in achieving effective bacterial clearance. These findings support TLR5 activation as a promising therapeutic strategy to enhance host defense mechanisms and improve treatment outcomes for bacterial pneumonia in both healthy individuals and patients with COPD or CF.

immunology↗

bioRxiv2025

Deep learning models reading clinical data and liver omics strongly distinguish NASH from steatosis and suggest new genes involved in liver disease severity

Background & AimsMetabolic dysfunction-associated steatotic liver disease (MASLD, previously NAFLD) is a frequent co-morbidity of obesity and diabetes, with prevalence increasing worldwide in all age groups and both sexes. Only early stages of the disease are fully reversible. Recognising liver disease stages and elucidating the molecular underpinning of their progression are thus medically important. We developed a deep learning model to recognise simple steatosis from steatohepatitis combining liver transcriptomics, epigenetics, and clinical data. MethodsWe used clinical data, liver gene expression and liver DNA methylation gathered from 300 patients with obesity of the ABOS cohort (80 without NAFLD, 137 with simple steatosis, 83 with steatohepatitis). We selected non-redundant clinical variables, gene expressions and CpGs methylation levels most associated with severity using unsupervised approaches. We designed a multi-module, multi-layer perceptron to predict patients liver status. We trained five model instances on independent training/test sets and combined the predictions. ResultsWe used a score based on gene expression/DNA methylation and relevant principal component analysis (PCA) loadings to select 200 genes and 260 CpG methylations. Models trained on the three modalities reached an AUC of 0.945 overall on a validation set with accuracies above 81% for simple steatosis and 88% for NASH, outperforming any other machine learning model so far. We retrieved patient clusters previously found using clinical variables in the latent space of our clinical data module, but not in the gene expression and DNA methylation modules. While all three modules are needed to reach the best prediction accuracy in all classes, the gene expression module had the most impact on the decision. Independent models weighted gene expression inputs similarly, shining light on their importance. The most impactful genes were linked to immune responses and extracellular matrix. However, many of those genes were previously unassociated with steatotic liver disease onset or progression. ConclusionsA multi-omics deep-learning model can recognise steatohepatitis from simple liver steatosis with an AUC of 0.945 and identify new genes potentially involved in NAFLD progression. Gene expressions profiles predicting disease severity are largely different from those specific of clinical variable clusters. Impact and implicationsThis study suggests that clinical variables are not sufficient to recognise the severity of steatotic liver disease with high accuracy, but model efficiency increases when used together with liver epigenetics and transcriptomics.

molecular biology↗

bioRxiv2022

The HDAC inhibitor trichostatin A impairs pancreatic β-cell function through an epigenome-wide reprogramming

ObjectiveThe pancreatic islets of Langerhans contain distinct cell subtypes including insulin-producing {beta} cells. Although their cell-specific gene expression pattern defines their identity, the underlying molecular network driving this transcriptional specificity is not fully understood. Among the numerous transcriptional regulators, histone deacetylases (HDAC) enzymes are potent chromatin modifiers which directly regulate gene expression through deacetylation of lysine residues within specific histone proteins. The precise molecular mechanisms underlying HDAC effects on cellular plasticity and {beta}-cell identity are currently unknown. MethodsThe pharmacological inhibition of HDAC activity by trichostatin A (TSA) was studied in the mouse Min6 and human EndocBH1 cell lines, as well as primary mouse sorted {beta} cells and human pancreatic islets. The molecular and functional effects of treating these complementary {beta}-cell models with TSA was explored at the epigenomic and transcriptomic level through next-generation sequencing of chromatin immunoprecipitation (ChIP) assays (ChIP-seq) and RNA sequencing (RNA-seq) experiments, respectively. ResultsWe showed that TSA alters insulin secretion associated with {beta}-cell specific transcriptome programming in both mouse and human {beta}-cell lines, as well as on human pancreatic islets. We also demonstrated that this alternative {beta}-cell transcriptional program in response to HDAC inhibition is related to an epigenome-wide remodeling at both promoters and enhancers. ConclusionsTaken together, our data indicate that full HDAC activity is required to safeguard the epigenome, to protect against loss of {beta}-cell identity with unsuitable expression of genes associated with alternative cell fates.

genomics↗

bioRxiv2021

Excessive fetal growth affects HSC quiescence maintenance through epigenetic programming of EGR1 transcriptional network

Excessive fetal growth is associated with DNA methylation alterations in human hematopoietic stem and progenitor cells (HSPC), but their functional impact remains elusive. We implemented an integrative analysis combining single-cell epigenomics, single-cell transcriptomics, and in vitro analyses to functionally link DNA methylation changes to putative alterations of HSPC functions. We showed in hematopoietic stem cells (HSC) from large for gestational age neonates that both DNA hypermethylation and chromatin rearrangement target a specific network of transcription factors known to sustain stem cell quiescence. In parallel, we found a decrease expression of key genes regulating HSC differentiation including EGR1, KLF2, SOCS3, and JUNB. Our functional analyses showed that this epigenetic programming was associated with a decreased ability for HSCs to stay quiescent. Taken together, our multimodal approach using single-cell (epi)genomics showed that human fetal overgrowth affects hematopoietic stem cells quiescence maintenance via epigenetic programming.

genomics↗

bioRxiv2020

Pancreatic β-cell specific loss of E2f1 impairs insulin secretion and β-cell identity through the epigenetic repression of non β-cell programs

The loss of pancreatic {beta}-cell identity emerges as an important feature of type 2 diabetes development, but the molecular mechanisms are still elusive. Here, we explore the cell-autonomous role of the cell cycle regulator and transcription factor E2F1 in the maintenance of {beta}-cell identity and insulin secretion. We show that the {beta}-cell-specific loss of E2f1 function in mice triggers glucose intolerance associated with defective insulin secretion, an altered -to-{beta}-cell ratio, a downregulation of many {beta}-cell genes and a concomitant increase of non-{beta}-cell markers. Mechanistically, the epigenomic profiling of non-beta cell upregulated gene promoters identified an enrichment of bivalent H3K4me3/H3K27me3 or H3K27me3 marks. Conversely, downregulated genes were enriched in active chromatin H3K4me3 and H3K27ac histone marks. We find that histone deacetylase inhibitors modulate E2F1 transcriptional and epigenomic signatures associated with these {beta}-cell dysfunctions. Finally, the pharmacological inhibition of E2F transcriptional activity in human islets also impairs insulin secretion and the expression of {beta}-cell identity genes. Our data suggest that E2F1 is critical for maintaining {beta}-cell identity through a sustained repression of non {beta}-cell transcriptional programs.

physiology↗