Identification of Genetic Risk Factors Involved in Autism Spectrum Disorders

Background: There have been multiple genes, risk alleles, and copy number variants implicated in the pathology of Autism Spectrum Disorders (ASD). Yet, currently established genetic causes account for only a small percentage of the cases, and a genetic diagnosis for this disorder is not yet possible. Thus, advancing our understanding of ASD requires transcriptomic data, which considers both genetic information and genome function. Gene expression changes resulting from environmental influences can be monitored in the blood. However, a less invasive method of sample collection is of foremost importance considering that the participants of this study are young children. Buccal cells are easily collected with minimal discomfort and present an alternative sample material for biomarker testing. Susceptibility genes and common molecular pathways dysregulated in ASD have been investigated with the application of microarray technology.

Objectives: Our study aimed to identify diagnostic biomarkers for dysregulated metabolic or signaling pathways that may provide insight into the pathology of ASD. Our study also determined if total RNA isolated from buccal mucosa may be used as an alternative tissue source to determine relative gene expression.

Methods: Buccal cell RNA was used for comparing gene expression levels between typically developing children and children with an ASD diagnosis. Total RNA was isolated from cells, reverse transcribed, and amplified. Illumina Human HT-12 v4 Expression BeadChip arrays were used for global gene expression studies. P values were corrected using Benjamini and Hochberg False Discovery Rate method. The Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources was used for additional functional annotation. Statistically significant functions were determined using the Fisher Exact test.

Results: Analyses of significant differentially regulated genes revealed numerous genes that were unanimously upregulated or downregulated among the affected children relative to the typically developing children. The list of differentially regulated genes was enriched with molecules associated with inflammation, regulation of transcription, axonogenesis, and circadian rhythm.

Conclusions: This study demonstrates a non-invasive technique for quantification of human gene expression signatures permitting gene-environment interactions. Our results suggest that RNA from buccal cells can be used to detect differential gene expression between typically developing and affected children. The results support the notion that various genetic factors underlie the development of ASD, and that these factors can be converging at, or diverging from, central networks. The findings of this study enabled the identification of genetic biomarkers underlying brain development and other physiological symptoms associated with ASD.