Biology subjects

Abassi, N.

Publications and source records attributed to Abassi, N..

3 recordsLinked to original sources

bioRxiv2025

KDM6B inhibition modulates monocyte activation and alleviates IMQ-psoriasis skin inflammation

Inflammatory monocytes are increasingly recognized as key amplifiers of psoriasis, yet the epigenetic drivers of their pathogenic signature remain unclear. Here, we demonstrate that the histone demethylase KDM6B is markedly upregulated and catalytically active in classical monocytes during the imiquimod (IMQ)-induced psoriasis model. This is associated with reduced levels of the repressive histone mark H3K27me3, an epigenetic modification linked to chromatin compaction and transcriptional silencing, at the Il1b, Tnf, Pgam1, Pgk1, and Aldoa promoters, together with an enhanced inflammatory and glycolytic gene signature. Pharmacological blockade of KDM6B after disease onset using GSK-J4, a cell-permeable prodrug that is intracellularly converted to the active KDM6B inhibitor GSK-J1, restores H3K27me3 at inflammatory and metabolic loci, suppresses Il1b/Tnf transcription, normalizes bioenergetic profiles, and reduces monocyte and neutrophil recruitment to the inflamed skin. Single-cell transcriptomic profiling further reveals that KDM6B inhibition represses cytokine-mediated signaling, glycolysis, and chemotaxis pathways in monocytes, yet enriches antigen presentation modules, consistent with a shift toward a homeostatic, antigen-presenting surveillance program in myeloid cells and a Treg-supportive milieu. Collectively, our data identify KDM6B as an epigenetic-metabolic switch that sustains monocyte-driven inflammation in the IMQ-induced psoriasis model. Importantly, we provide preclinical evidence that targeting KDM6B can reduce maladaptive inflammatory response even in progressed diseases. These findings propose KDM6B inhibitors as a promising adjunct to current biologics for psoriasis and other myeloid-driven autoinflammatory disorders. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/682023v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@efef15org.highwire.dtl.DTLVardef@395983org.highwire.dtl.DTLVardef@123e832org.highwire.dtl.DTLVardef@615f84_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

GeDi: Simplifying Gene Set Distances for Enhanced Omics Interpretation in R/Bioconductor

BackgroundFunctional enrichment analysis is a standard component in many omics data analysis workflows, supported by a variety of methods and algorithms. However, despite their utility and wide application, these methods often return the results as an extensive and redundant list of gene sets, impeding interpretation and hypothesis generation. Moreover, network based information can provide additional biological context through functional interaction data, yet this is often overlooked by existing tools. ResultsWe developed GeDi, an R/Bioconductor package designed to streamline and standardize the interpretation of functional enrichment results. GeDi aggregates gene sets into biologically meaningful clusters using a suite of gene set distance metrics and clustering algorithms, aimed to reduce redundancy and improve clarity. GeDi also enables the integration of protein-protein interaction (PPI) data, through the implementation of a weighted distance metric, providing a richer biological context by capturing functional connectivity between pathways and their components. The package offers visualizations, aggregation, and automated reporting, and is available as both a stand-alone R-package and an interactive Shiny application. ConclusionGeDi facilitates clearer, faster interpretation of enrichment results by combining clustering and network context. Application to a public RNA-seq dataset revealed coherent biological themes, supporting both experimental and computational research. GeDi is freely available in the Bioconductor project under the MIT license (https://bioconductor.org/packages/GeDi), and a demo instance is accessible on the Shiny server (http://shiny.imbei.uni-mainz.de:3838/GeDi).

bioinformatics↗

bioRxiv2024

NeuroVar: An Open-source Tool for Gene Expression and Variation Data Visualization for Biomarkers of Neurological Diseases

BackgroundThe expanding availability of large-scale genomic data and the growing interest in uncovering gene-disease associations call for efficient tools to visualize and evaluate gene expression and genetic variation data. MethodologyData collection involved filtering biomarkers related to multiple neurological diseases from the ClinGen database. We developed a comprehensive pipeline that was implemented as an interactive Shiny application and a standalone desktop application. ResultsNeuroVar is a tool for visualizing genetic variation (single nucleotide polymorphisms and insertions/deletions) and gene expression profiles of biomarkers of neurological diseases. ConclusionThe tool provides a user-friendly graphical user interface to visualize genomic data and is freely accessible on the projects GitHub repository (https://github.com/omicscodeathon/neurovar).

bioinformatics↗