Biology subjects

Sigalova, O. M.

Publications and source records attributed to Sigalova, O. M..

3 recordsLinked to original sources

bioRxiv2026

Modeling cis-regulatory variation in human brain enhancers across a large Parkinson's Disease cohort

Genome-wide association studies (GWAS) have linked more than a hundred non-coding genomic loci to Parkinsons disease (PD) risk. Deciphering their functional impact on gene regulation requires cell type-aware modeling approaches to assess the effects of sequence variation on enhancer function and target gene expression. To address this challenge, we generated a comprehensive matched dataset from 190 human donors (115 controls and 75 PD), comprising long-read whole-genome sequencing alongside single nucleus multiome atlases (snATAC-seq and snRNA-seq for 3.1 and 1.1 million nuclei respectively) of the anterior cingulate cortex and substantia nigra. By integrating chromatin accessibility quantitative trait loci (caQTL), DNA methylation QTL (meQTL), and allele-specific chromatin accessibility (ASCA), we identified 53,841 high-confidence cis-acting genetic variants that modulate cell type-specific enhancer accessibility in one or both brain regions. We further demonstrate that sequence-to-function models can accurately predict the impact of these variants directly from the genomic sequence. Novel explainability approaches allowed stratifying these variants according to their regulatory function, with the majority disrupting specific transcription factor binding sites in a cell type specific manner. Integrating these "enhancer variants" (EV) with eQTL mapping and gene locus modeling linked a subset of EVs to their target genes. Finally, we applied these models to prioritize regulatory variants at known PD GWAS loci, bypassing statistical limitations in rare disease-relevant populations like dopaminergic neurons. All together, we establish a unique resource and new sequence modeling strategies to interpret functional non-coding variation in the human brain.

genomics↗

bioRxiv2024

Contextualising transcription factor binding during embryogenesis using natural sequence variation

Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F1 crosses with extensive genetic diversity to profile allele-specific binding of four TFs at several embryonic time-points, using Drosophila as a model. Using a combined haplotype test, we identified 9-18% of TF bound regions impacted by genetic variation. By expanding WASP (a tool for allele-specific read mapping) to examine INDELs, we increased detection of allele imbalanced (AI) peaks by 30-50%. This fine-grained mutagenesis could reconstruct functionalized binding motifs of all factors. To prioritise potential causal variants, we trained a convolutional neural network (Basenji) to predict TF binding from DNA sequence. The model could accurately predict experimental AI for strong effect variants, providing a mechanistic interpretation for how genetic variation impacted TF binding. This revealed unexpected relationships between TFs, including potential cooperative pairs, and mechanisms of tissue specific recruitment of the ubiquitous factor CTCF.

genetics↗

bioRxiv2020

Predictive features of gene expression variation reveal a mechanistic link between expression variation and differential expression

For most biological processes, organisms must respond to extrinsic cues, while maintaining essential gene expression programs. Although studied extensively in single cells, it is still unclear how variation is controlled in multicellular organisms. Here, we used a machine-learning approach to identify genomic features that are predictive of genes with high versus low variation in their expression across individuals, using bulk data to remove stochastic cell-to-cell variation. Using embryonic gene expression across 75 Drosophila isogenic lines, we identify features predictive of expression variation, while controlling for expression level. Genes with low variation fall into two classes, indicating they employ different mechanisms to maintain a robust expression. In contrast, genes with high variation seem to lack both types of stabilizing mechanisms. Applying the framework to human tissues from GTEx revealed similar predictive features, indicating that promoter architecture is an ancient mechanism to control expression variation. Remarkably, expression variation features could also predict differential expression upon stress in both Drosophila and human. Differential gene expression signatures may therefore be partially explained by genetically encoded gene-specific features, unrelated to the studied treatment.

genomics↗