Biology subjects

VandenBosch, L. S.

Publications and source records attributed to VandenBosch, L. S..

3 recordsLinked to original sources

bioRxiv2025

Machine Learning Prediction of Non-Coding Variant Impact in Cell-Class-Specific Human Retinal Cis-Regulatory Elements

Non-coding variants in cis-regulatory elements such as promoters and enhancers contribute to inherited retinal diseases (IRDs), however, characterizing the functional impact of most regulatory variants remains challenging. To improve identification of variants of interest, we implemented machine learning using a gapped k-mer support vector machine approach trained on single nucleus ATAC-seq data from specific cell classes of the adult and developing human retina. We developed 18 distinct ML models to predict the impact of non-coding variants on 39,437 cell-class-specific regulatory elements. These models demonstrate accuracy over 90% and a high degree of cell class specificity. Variant Impact Prediction (VIP) scores highlight specific sequences within candidate CREs, including putative transcription factor (TF) binding motifs, that are predicted to alter CRE function if mutated. Correlations to massively parallel reporter assays support the predictive value of VIP scores to model single nucleotide variants and indels in a cell-class-specific manner. These analyses demonstrate the capacity for single nucleus epigenomic data to predict the impact of non-coding sequence variants and allow for rapid prioritization of patient variants for further functional analysis.

genomics↗

bioRxiv2021

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal Cis-Regulatory Elements

PurposePrior studies demonstrate the significance of specific cis-regulatory variants in retinal disease, however determining the functional impact of regulatory variants remains a major challenge. In this study, we utilize a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis-regulatory variants. MethodsWe used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence putative cis-regulatory elements. 80% of these elements were used to train a machine learning model utilizing a gapped k-mer support vector machine-based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all potential single nucleotide variants within cis-regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay (MPRA) data. ResultsWe generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants (SNVs) were scored. Variants with negative impact scores correlated with reduced population allele frequency, higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively-parallel reporter expression. ConclusionsWe demonstrated the utility of human retinal epigenomic data to train a machine learning model for the purpose of predicting the impact of non-coding regulatory sequence variants. Our model accurately scored sequences and predicted putative transcription factor binding motifs. This approach has the potential to expedite the characterization of pathogenic non-coding sequence variants in the context of unexplained retinal disease.

genomics↗

bioRxiv2019

Cis-Regulatory Accessibility Directs Muller Glial Development and Regenerative Capacity

Diseases and damage to the retina lead to losses in retinal neurons and eventual visual impairment. Although the mammalian retina has no inherent regenerative capabilities, fish have robust regeneration from Muller glia (MG). Recently, we have shown that driving expression of Ascl1 in adult mouse MG stimulates neurogenesis similar to fish regeneration. The regeneration observed in the mouse is limited in the variety of neurons that can be derived from MG; Ascl1-expressing MG primarily generate bipolar cells. To better understand the limits of MG-based regeneration in mouse retinas, we used ATAC- and RNA-seq to compare newborn progenitors with MG. Our analysis demonstrated striking similarities between MG and progenitors, with losses in regulatory motifs for neurogenesis genes. Young MG were found to have intermediate expression profiles and accessible DNA, which is mirrored in the ability of Ascl1 to direct bipolar neurogenesis in young MG. When comparing what makes bipolar and photoreceptor cells distinct from glial cells, we find that bipolar-specific accessible regions are more frequently linked to bHLH motifs and Ascl1 binding, indicating that Ascl1 preferentially binds to bipolar regions. Overall, our analysis indicates a loss of neurogenic gene expression and motif accessibility during glial maturation that may prevent efficient reprogramming.

developmental biology↗