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PubMed2024

SCAR-6 elncRNA locus epigenetically regulates PROZ and modulates coagulation and vascular function.

In this study, we characterize a novel lncRNA-producing gene locus that we name Syntenic Cardiovascular Conserved Region-Associated lncRNA-6 (scar-6) and functionally validate its role in coagulation and cardiovascular function. A 12-bp deletion of the scar-6 locus in zebrafish (scar-6gib007Δ12/Δ12) results in cranial hemorrhage and vascular permeability. Overexpression, knockdown and rescue with the scar-6 lncRNA modulates hemostasis in zebrafish. Molecular investigation reveals that the scar-6 lncRNA acts as an enhancer lncRNA (elncRNA), and controls the expression of prozb, an inhibitor of factor Xa, through an enhancer element in the scar-6 locus. The scar-6 locus suppresses loop formation between prozb and scar-6 sequences, which might be facilitated by the methylation of CpG islands via the prdm14-PRC2 complex whose binding to the locus might be stabilized by the scar-6 elncRNA transcript. Binding of prdm14 to the scar-6 locus is impaired in scar-6gib007Δ12/Δ12 zebrafish. Finally, activation of the PAR2 receptor in scar-6gib007Δ12/Δ12 zebrafish triggers NF-κB-mediated endothelial cell activation, leading to vascular dysfunction and hemorrhage. We present evidence that the scar-6 locus plays a role in regulating the expression of the coagulation cascade gene prozb and maintains vascular homeostasis.

Animals↗

PubMed2024

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.

BACKGROUND: Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset. METHODS: We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson's Disease. RESULTS: Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings. CONCLUSION: We have created a ready to deploy tool, that can classify variants linked with Wilson's disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.

Hepatolenticular Degeneration↗