AI Tool Helps Link BRSK1 Variants to Neurodevelopmental Disorder

Researchers have utilized artificial intelligence and genetic studies to link changes in the BRSK1 gene with a complex neurodevelopmental disorder affecting multiple families. The study, published in the American Journal of Human Genetics, reveals that reduced activity of this gene may disrupt normal nervous system development. A child enrolled in the Texome Project, which offers genetic testing to those with rare, undiagnosed conditions in Texas, served as the starting point for the investigation.

Standard genome analysis did not identify a cause for the child's symptoms, prompting researchers to employ AI-MARRVEL, an AI-based tool that prioritized candidate disease variants based on genomic and clinical data. This analysis pointed to a rare change in the BRSK1 gene. Further investigation in fruit fly studies identified nine additional affected individuals, bringing the total to 10 people across seven unrelated families.

These individuals exhibited developmental delays and varied phenotypes, including anxiety, ADHD, autism, and seizures, with severity and symptoms differing among relatives carrying the same variant. Researchers then examined how these variants affected Drosophila melanogaster, discovering that the affected individuals showed movement difficulties, heightened sensitivity to stressors leading to seizure-like behavior, increased vulnerability to heat-induced paralysis, and shorter lifespans.

These findings suggest that the BRSK1 gene is crucial for normal nervous system function. Introducing normal human BRSK1 largely corrected the behavioral and neurological defects observed in the flies, although three specific variants found in affected individuals only partially rescued these effects. The study concludes that reduced BRSK1 activity rather than its complete absence results in the observed symptoms, potentially disrupting the cellular machinery necessary for healthy brain development and neuronal communication.

This work not only enhances understanding of genetic causes of neurodevelopmental disorders but also demonstrates the effectiveness of integrating AI-driven gene discovery with experimental studies in model organisms to uncover rare diseases and their underlying mechanisms.