Biology subjects

Kular, L.

Publications and source records attributed to Kular, L..

5 recordsLinked to original sources

bioRxiv2026

Epigenomic profiling of cerebrospinal fluid cells identifies immune regulatory alterations and implicates protocadherins in multiple sclerosis

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS), where DNA methylation may play a role by connecting genetic and environmental risk factors. We performed whole-genome DNA methylation profiling of cerebrospinal fluid (CSF) cells from relapsing-remitting MS patients and matched controls, identifying 2,710 differentially methylated positions (DMPs) and 4,330 regions (DMRs). These changes were enriched in immune signaling, adhesion and migration processes, and were accompanied by corresponding RNA expression changes. MS-associated methylation changes enriched in the cohesin chromatin regulation pathway mapped to enhancers of T helper 17 (Th17) cells, whereas in other T cell types they were mapping to bivalent enhancers and repressed chromatin. Notably, this pathway comprised multiple Protocadherin (PCDH) genes, typically expressed in neuronal cells, that displayed consistent methylation and expression changes in CSF cells. Expression of shared intracellular domain of PCDH{gamma} cluster proteins was confirmed in peripheral blood T cells by flow cytometry as well as expression of PCDH{gamma} cluster genes in memory CD4+ T cell subsets. Moreover, co-expression analysis suggests a role of PCDH genes in aryl hydrocarbon receptor (AHR) signaling. In summary, DNA methylation changes in CSF resident cells reflect dysregulated T cell activation and migration in MS and suggest a novel role of protocadherin molecules in MS pathogenesis.

immunology↗

bioRxiv2026

Targeted DNA methylation editing in vivo

The number of epigenome-wide association studies linking CpG DNA methylation with disease, traits and exposures, continues to rise. Despite the rapid development of epigenome editing tools, establishing causation remains challenging, particularly in vivo. In this study, we developed and characterized three Cre-dependent CRISPR-based mouse lines that enable locus-specific DNA methylation deposition by either constitutive or inducible dCas9-DNMT3A expression. We demonstrate robust highly locus-specific DNA methylation deposition at MHC class II (H2-Ab1) and interleukin 6 (Il6) genes in bone marrow-derived myeloid cells ex vivo. Moreover, neuron-specific methylation targeting resulted in reduced cannabinoid receptor 1 (Cnr1) expression in striatal neurons in vivo. Notably, we demonstrate that the causal effect of DNA methylation on gene expression is locus-dependent, reinforcing the necessity of such editing tools for detailed understanding of the role of DNA methylation and for addressing the causality of disease-associated CpGs.

molecular biology↗

bioRxiv2026

Whole-genome profiling of native 5-hydroxymethylation in human neurons with long-read sequencing

The 5-hydroxymethylcytosine (5hmC) modification of DNA is particularly prevalent in neurons and thereby a hallmark of the brains epigenetic landscape. While 5mC DNA methylation is a well-known player in genome stability and transcriptional regulation, the role of 5hmC remains largely unknown. Here, we used long-read Oxford Nanopore Technology (ONT) to profile whole-genome, native 5mC and 5hmC levels in sorted neuronal nuclei samples from human post-mortem brain tissue. We applied different models for DNA modification calling and compared with array-based 5mC and 5hmC levels derived from the same samples, demonstrating high sample-wise correlations. Annotation across genomic and regulatory features, as well as chromatin states, generated by the International Human Epigenome Consortium, revealed high levels of 5hmC in introns, actively transcribed genes and (distal) enhancers. Pathway analysis of genes with high levels of 5hmC (> 60%) were enriched in neuron-related terms, with functional variety when stratifying across chromatin states. Analysis of transcription factor motifs in highly methylated regions, demonstrated 5hmC- and 5mC-specific enrichment affecting downstream regulatory networks. Altogether, our study demonstrates the potential of ONT to characterize whole-genome, native 5hmC and 5mC DNA modifications in human neurons, specifically highlighting the enrichment of 5hmC in actively transcribed regions and enhancers in the human brain.

neuroscience↗

bioRxiv2025

Systematic comparison of dCas9-based DNA methylation epimodifiers over time indicates efficient on-target and widespread off-target effects

CRISPR/dCas9-based epigenome editing systems, including DNA methylation epimodifiers, have greatly advanced molecular functional studies revolutionizing their precision and applicability. Despite their promise, challenges such as the magnitude and stability of the on-target editing and unwanted off-target effects underscore the need for improved tool characterization and design. We systematically compared specific targeting of the BACH2 gene promoter and genome-wide off-target effects of available and novel dCas9-based DNA methylation editing tools over time. We demonstrate that multimerization of the catalytic domain of DNA methyltransferase 3A enhances editing potency but also induces widespread, early methylation deposition at low-to-medium methylated promoter-related regions with specific gRNAs and, interestingly, also with non-targeting gRNAs. A small fraction of the methylation changes associated with transcriptional dysregulation and mapped predominantly to bivalent chromatin associating both with transcriptional repression and activation. Additionally, specific non-targeting control gRNA caused pervasive and long-lasting methylation-independent transcriptional alterations particularly in genes linked to RNA and energy metabolism. CRISPRoff emerged as the most efficient tool for stable targeting of the BACH2 promoter, with fewer and less stable off-target effects compared to other epimodifiers but with persistent transcriptome alterations. Our findings highlight the delicate balance between potency and specificity of epigenome editing and provide critical insights into the design and application of future tools to improve their precision and minimize unintended consequences.

molecular biology↗

bioRxiv2024

GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis

BackgroundGene-Set Analysis (GSA) is commonly used to analyze high-throughput experiments. However, GSA cannot readily disentangle clusters or pathways due to redundancies in upstream knowledge bases, which hinders comprehensive exploration and interpretation of biological findings. To address this challenge, we developed GeneSetCluster, an R package designed to summarize and integrate GSA results. Over time, we and users as well identified limitations in the original version, such as difficulties in managing redundancies across multiple gene-sets, large computational times, and its lack of accessibility for users without programming expertise. ResultsWe present GeneSetCluster 2.0, a comprehensive upgrade that delivers methodological, computational, interpretative, and user-experience enhancements. Methodologically, GeneSetCluster 2.0 introduces a novel approach to address duplicated gene-sets and implements a seriation-based clustering algorithm that reorders results, aiding pattern identification. Computationally, the package is optimized for parallel processing, significantly reducing execution time. GeneSetCluster 2.0 enhances cluster annotations by associating clusters with relevant tissues and biological processes to improve biological interpretation, particularly for human and mouse data. To broaden accessibility, we have developed a user-friendly web application enabling non-programmers to use it. This version also ensures seamless integration between the R package, catering to users with programming expertise, and the web application for broader audiences. We evaluated the updates in a single-cell RNA public dataset. ConclusionGeneSetCluster 2.0 offers substantial improvements over its predecessor. Furthermore, by bridging the gap between bioinformaticians and clinicians in multidisciplinary teams, GeneSetCluster 2.0 facilitates collaborative research. The R package and web application, along with detailed installation and usage guides, are available on GitHub (https://github.com/TranslationalBioinformaticsUnit/GeneSetCluster2.0), and the web application can be accessed at https://translationalbio.shinyapps.io/genesetcluster/.

bioinformatics↗