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Biology subjects

Donnelly, D.

Publications and source records attributed to Donnelly, D..

2 recordsLinked to original sources

New pathogenic variants and insights into pathogenic mechanisms in GRK1-related Oguchi disease.

PurposeBiallelic mutations in G-Protein coupled receptor kinase 1 (GRK1) cause Oguchi disease, a rare subtype of congenital stationary night blindness (CSNB). The purpose of this study was to identify pathogenic GRK1 variants and use in-depth bioinformatic analyses to evaluate how their impact on protein structure could lead to pathogenicity. MethodsPatients genomic DNA was sequenced by whole genome, whole exome or focused exome sequencing. Pathogenic variants, published and novel, were compared to nondisease associated missense variants. The impact of GRK1 missense variants at the protein level were then predicted using a series of computational tools. ResultsWe identified eleven previously unpublished cases with biallelic pathogenic GRK1 variants, including seven novel variants, and reviewed all GRK1 pathogenic variants. Further structure-based scoring revealed a hotspot for missense variants in the kinase domain. Additionally, to aid future clinical interpretation, we identified the bioinformatics tools best able to differentiate pathogenic from non-pathogenic variants. ConclusionWe identified new GRK1 pathogenic variants in Oguchi disease patients and investigated how disease-causing variants may impede protein function, giving new insights into the mechanisms of pathogenicity. All pathogenic GRK1 variants described to date have been collated into a Leiden Open Variation Database (http://dna2.leeds.ac.uk/GRK1_LOVD/genes/GRK1).

genetics↗

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Image-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.

pathology↗