Identifying Windows of susceptibility by temporal Gene Analysis

No Thumbnail Available
Authors
Bennett, Kristin P.
Brown, Elisabeth
De Los Santos, Hannah
Poegel, Matt
Kiehl, Thomas
Patton, Evan
Norris, Spencer
Temple, Sally
Erickson, John S.
McGuinness, Deborah L.
Issue Date
2019-02-01
Type
Language
Keywords
Research Projects
Organizational Units
Journal Issue
Alternative Title
Abstract
Increased understanding of developmental disorders of the brain has shown that genetic mutations, environmental toxins and biological insults typically act during developmental windows of susceptibility. Identifying these vulnerable periods is a necessary and vital step for safeguarding women and their fetuses against disease causing agents during pregnancy and for developing timely interventions and treatments for neurodevelopmental disorders. We analyzed developmental time-course gene expression data derived from human pluripotent stem cells, with disease association, pathway, and protein interaction databases to identify windows of disease susceptibility during development and the time periods for productive interventions. The results are displayed as interactive Susceptibility Windows Ontological Transcriptome (SWOT) Clocks illustrating disease susceptibility over developmental time. Using this method, we determine the likely windows of susceptibility for multiple neurological disorders using known disease associated genes and genes derived from RNA-sequencing studies including autism spectrum disorder, schizophrenia, and Zika virus induced microcephaly. SWOT clocks provide a valuable tool for integrating data from multiple databases in a developmental context with data generated from next-generation sequencing to help identify windows of susceptibility.
Description
Full Citation
Bennett, K.P., Brown, E.M., Santos, H.D.l. et al. Identifying Windows of Susceptibility by Temporal Gene Analysis. Sci Rep 9, 2740 (2019). https://doi.org/10.1038/s41598-019-39318-8
Publisher
Scientific Reports
Terms of Use
Journal
Volume
Issue
PubMed ID
DOI
ISSN
EISSN