Biology subjects

Pirvan, L.

Publications and source records attributed to Pirvan, L..

5 recordsLinked to original sources

bioRxiv2025

A non-catalytic role for MLL2 in controlling chromatin organisation and mobility during the priming of pluripotent cells for differentiation

How the chromatin regulator MLL2 (KMT2B) influences cell differentiation remains poorly understood. MLL2 is the main histone 3 lysine 4 (H3K4) trimethyltransferase acting at bivalent promoters in embryonic stem cells (ESCs) and is required for ESCs to differentiate into neuroectoderm. We show here that this requirement occurs during exit from naive pluripotency, days before neuroectoderm differentiation is impaired. Although MLL2 knockout has only a subtle effect on transcription during exit, reducing the expression of a few important neuroectodermal transcription factors, it substantially remodels chromatin architecture, disrupting 3D chromatin loops associated with bivalent promoters. The enzymatic activity of MLL2 is not needed for stabilising these loops or for neuroectoderm differentiation. This non-catalytic function of MLL2 in stabilising 3D chromatin loops has implications for lineage specification. Because MLL2 shares features with all four MLLs, chromatin tethering, rather than H3K4 methylation, may represent the primary function of MLL proteins during lineage commitment.

cell biology↗

bioRxiv2024

Integrative single-cell analysis of neural stem/progenitor cells reveals epigenetically dysregulated interferon response in progressive multiple sclerosis

Progressive multiple sclerosis (PMS) is characterized by a primary smouldering pathological disease process associated with a superimposed inflammatory activity. Cellular and molecular processes sustaining the pathobiology of PMS remain to be identified. We previously discovered senescence signatures in neural stem/progenitor cells (NSCs) from people with PMS. Applying direct reprogramming to generate directly induced NSCs (iNSCs) from somatic fibroblasts, we retain epigenetic information and observe hypomethylation of genes associated with lipid metabolic processes and IFN signalling only in PMS lines. Single-cell/nucleus transcriptomic and epigenetic profiling reveal an inflammatory, senescent-like, IFN-responsive radial glia (RG)-like cell subcluster mainly in PMS iNSCs that is driven by IFN-associated transcription factors. Lastly, we identify a population of senescent, IFN-responsive, disease-associated RG-like cells (DARGs) in the PMS brain that share pseudotime trajectories with iNSCs in vitro. We describe the existence of a non-neurogenic, dysfunctional DARG population that has the potential to fuel smouldering inflammation in PMS.

neuroscience↗

bioRxiv2024

Increased Cholesterol Synthesis Drives Neurotoxicity in Patient Stem Cell-Derived Model of Multiple Sclerosis

Senescent neural progenitor cells have been identified in brain lesions of people with progressive multiple sclerosis (PMS). However, their role in disease pathobiology and contribution to the lesion environment remains unclear. By establishing directly induced neural stem/progenitor cell (iNSC) lines from PMS patient fibroblasts, we studied their senescent phenotype in vitro. Senescence was strongly associated with inflammatory signaling, hypermetabolism, and the senescence associated secretory phenotype (SASP). PMS-derived iNSCs displayed increased glucose-dependent fatty acid and cholesterol synthesis, which resulted in the accumulation of cholesteryl ester-enriched lipid droplets. An HMG-CoA reductase-mediated lipogenic state was found to induce secretion of the SASP in PMS iNSC conditioned media via transcriptional regulation by cholesterol-dependent transcription factors. SASP from PMS iNSCs induced neurotoxicity. Chemical targeting of HMG-CoA reductase using the cholesterol-lowering drug simvastatin (SV) prevented SASP release and resulting neurotoxicity. Our findings suggest a disease-associated, cholesterol-related, hypermetabolic phenotype of PMS iNSCs that leads to neurotoxic signaling and is rescuable pharmacologically.

neuroscience↗

bioRxiv2020

Identifying regulatory and spatial genomic architectural elements using cell type independent machine and deep learning models

Chromosome conformation capture methods such as Hi-C enables mapping of genome-wide chromatin interactions and is a promising technology to understand the role of spatial chromatin organisation in gene regulation. However, the generation and analysis of these data sets at high resolutions remain technically challenging and costly. We developed a machine and deep learning approach to predict functionally important, highly interacting chromatin regions (HICR) and topologically associated domain (TAD) boundaries independent of Hi-C data in both normal physiological states and pathological conditions such as cancer. This approach utilises gradient boosted trees and convolutional neural networks trained on both Hi-C and histone modification epigenomic data from three different cell types. Given only epigenomic modification data these models are able to predict chromatin interactions and TAD boundaries with high accuracy. We demonstrate that our models are transferable across cell types, indicating that combinatorial histone mark signatures may be universal predictors for highly interacting chromatin regions and spatial chromatin architecture elements.

genomics↗

bioRxiv2020

Pangaea: A modular and extensible collection of tools for mining context dependent gene relationships from the biomedical literature

MotivationPangaea is a scalable and extensible command line interface (CLI) software that integrates gene-relationship detection features to extract context-dependent structured gene-gene and gene-term relationships from the biomedical literature. It provides computational methods to identify biological relationships between a collection of genes and can be used to search and extract different types of contextual relationships amongst genes. ResultsWe implemented a CLI-based software for downloading PubMed articles and extracting gene relationships from abstracts using natural language processing methods. In terms of scalability, the software was designed to support the retrieval and processing of millions of articles whilst minimising memory requirements and optimising for parallel processing on multiple CPU cores. To allow extensibility, the tool permits the use of contextual custom-made models for the text processing parts, and the output is serialised as JSON objects to allow flexible post-processing workflows. AvailabilityThe software is available online at: https://github.com/ss-lab-cancerunit/pangaea

bioinformatics↗