Biology subjects

Weykopf, G.

Publications and source records attributed to Weykopf, G..

4 recordsLinked to original sources

bioRxiv2026

Loop extrusion dynamics and cooperative activation independently regulate enhancer-driven transcription

How enhancers transmit their activation signals to target promoters is a fundamental unanswered question. Gene activation by distal enhancers is affected by the three-dimensional organisation of chromatin driven by cohesin-mediated loop extrusion, but how the dynamic loop extrusion cycle shapes transcriptional outcomes is not fully understood. In addition, genes are typically regulated by multiple enhancers and cases of cooperativity between enhancers have been observed that depend on the relative position of enhancers and gene. The reason for such context-dependent cooperativity is unknown, and whether it is linked to loop extrusion has not been explored. Here, we use synthetic gene activation to show that NIPBL depletion, which decreases loop extrusion rate, reduces long-range activation in a dose-dependent manner. In contrast, WAPL depletion, which reduces cohesin turnover, can both increase and decrease distal gene activation depending on the presence of adjacent CTCF binding sites. These effects on gene activation correlate with the impact on chromatin contacts. Depletion of the co-activator BRD4 also affects long-range activation, but not by changing chromatin contacts. Synthetic activation from two locations results in super-additive responses, which can be predicted by an independently determined response model. These predictions hold for dual activation from proximal and/or distal sites, and with perturbed NIPBL, WAPL, or BRD4. Distal activation therefore appears to be equivalent in nature to proximal activation, and context-dependent cooperativity can arise simply from different level of activation inputs operating on a non-linear response function.

molecular biology↗

bioRxiv2026

Identifying severe COVID-19 risk variants modulating enhancer reporter activity in lung cells

Common genetic variants contribute to risk for complex human diseases. However, despite thousands of associations, variants modulating disease risk and their functional impact remain largely unknown. This includes SARS-CoV-2 infection, where outcomes range from asymptomatic to fatal. Most host risk variants associated with COVID-19 disease, identified through genome wide association studies, are located in the non-coding genome and may function by altering gene expression in disease-relevant cells and tissues. To address this at scale, we tested >4800 severe COVID-19-associated variants to determine the impact of individual variants and variant combinations on regulatory activity using Self-Transcribing Active Regulatory Region sequencing, a massively-parallel reporter assay, in a lung epithelial cell line (A549). We identify 166 variants within active sequences, of which 29 modulate activity allele-specifically. Evaluating variant combinations, we observe both additive and non-additive effects on regulatory activity. We employ state-of-the-art deep learning models to interpret allele-specific variant effects on regulatory activity and endogenous genomic features. Our work provides a set of prioritised severe COVID-19-associated variants that modulate regulatory activity in lung epithelial cells, candidate transcription factors, and candidate target genes with potential to be disease modifying.

molecular biology↗

bioRxiv2025

Disease-associated genetic variants can cause mutations in tissue-specific protein isoforms

Genetic variants can cause protein-coding mutations that result in disease. Variants are typically interpreted using the reference transcript for a gene. However, most human multi-exon genes encode alternative isoforms. Here, we show that coding exons in alternative isoforms harbour more population variants than exons of reference isoforms, consistent with their reduced evolutionary constraint, and that these variants are more likely to cause nonsynonymous coding mutations. Common and rare disease-associated variants mapping to alternative transcripts can lead to amino acid substitutions predicted to be structurally damaging in the corresponding protein isoform. The alternative transcripts to which disease-associated variants map demonstrate high tissue-specific expression, with many unannotated in reference human genomes, revealed only by long-read RNA-sequencing. As an example, we report an unannotated alternative transcript of the inflammasome regulator DPP9 that is lung epithelium-specific and which harbours a common genetic variant associated with severe COVID-19 and lung fibrosis. The variant causes a p.Leu8Pro missense mutation in an alternative first exon, predicted to disrupt the encoded alpha helix. These findings highlight the importance of considering alternative isoforms, their tissue-specific expression, and full-length transcripts in variant interpretation, with implications for uncovering underappreciated mechanisms of both common and rare disease.

genomics↗

bioRxiv2023

DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants

Genome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, these highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritisation of variants is commonly based on functional genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyse six markers of active regulatory elements for their ability to identify functional variants. We benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA as markers for functional variants. This signature provides high precision, trading-off low recall, thus substantially reducing candidate variant sets to prioritise variants for functional validation. We present this as a framework called FINDER - Functional SNV IdeNtification using DNase footprints and Enhancer RNA, and demonstrate its utility to prioritise variants using leukocyte count trait and analyse variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritising variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.

genomics↗