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Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests
Published on: September 6, 2024
Toward a Pathway-Driven Clinical-Molecular Framework for Classifying Autism Spectrum Disorders
Catherine A Ziats1, Owen M Rennert1, Mark N Ziats2
1National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland.
Background:
The current classification system of neurodevelopmental disorders is based on clinical criteria; however, this method alone fails to incorporate what is now known about genomic similarities and differences between closely related clinical neurodevelopmental disorders. Here we present an alternative clinical molecular classification system of neurodevelopmental disorders based on shared molecular and cellular pathways, using syndromes with autistic features as examples.
Methods:
Using the Online Mendelian Inheritance in Man database, we identified 83 syndromes that had "autism" as a feature of disease, which in combination were associated with 69 autism disease-causing genes. Using annotation terms generated from the DAVID annotation tool, we grouped each gene and its associated autism syndrome into three biological pathways: ion transport, cellular synaptic function, and transcriptional regulation.
Results:
The majority of the autism syndromes we analyzed (54 of 83) enriched for processes related to transcriptional regulation and were associated with more non-neurologic symptoms and co-morbid psychiatric disease when compared with the other two pathways studied. Disorders with disrupted cellular synaptic function had significantly more motor-related symptoms when compared with the other groups of disorders.
Conclusion:
Our pathway-based classification system identified unique clinical characteristics within each group that may help guide clinical diagnosis, prognosis, and treatment. These results suggest that shifting current clinical classification of autism disorders toward molecularly driven, pathway-related diagnostic groups such as this may more precisely guide clinical decision making and may be informative for future clinical trial and drug development approaches.
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