Identifying Windows of Susceptibility by Temporal Gene Analysis

Thomas R Kiehl,John Erickson,Nathan C Boles,Matthew Poegel,Sally Temple,Kristin P Bennett,Hannah De Los Santos,Spencer Norris,Deborah L Mcguinness,Evan W Patton,Elisabeth M Brown

doi:10.1038/s41598-019-39318-8

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.

Highlights

Temporal analysis of expression data has focused on identifying genes that change over time and using clustering approaches to group genes by temporal profile together
By integrating expression data with multiple databases and constructing a powerful visualization tool, we have developed a method to reveal potential windows of susceptibility to a variety of environmental insults leading to neurodevelopmental disorders
Susceptibility Windows Ontological Transcriptome (SWOT) clocks generated from multiple studies and the SFARI autism candidate genes pointed to either the Neural Differentiation (ND), Cortical Specification (CS), or Upper Layers generation (UL) stages of development as WOS

Summary

Introduction

Temporal analysis of expression data has focused on identifying genes that change over time and using clustering approaches to group genes by temporal profile together These clusters are used in a variety of enrichment tests in combination with biological databases such as Gene Ontology (GO) to assign biological meaning to each cluster. Current bioinformatic tools such as DAVID5, DOSE6, and other enrichment tools or algorithms such as goseq[7] can be utilized to find enrichment of ontological terms or pathways associated with a gene list These tools in combination with disease-gene databases, such as OMIM, have been used in a wide variety of studies to try and translate gene expression changes into etiological mechanisms underlying disease. We utilized expression data covering a time-course of human cerebral cortex development from hPSCs20 and identified putative periods of vulnerability for a variety of diseases that impact neurodevelopment, including autism spectrum disorder, schizophrenia and ZIKV-virus-induced microcephaly

Methods

Results

Conclusion