Baseline pattern activation of TBI associated genes in healthy brain

Research Objectives: To develop a baseline expression profile for TBI associates-genes.

Design: We used hypergeometric test which compares gene expression patterns between expressed candidate genes to annotated background genes for each brain region.

Setting: Gene expression datasets were downloaded from the Allen Institute of Brain Sciences which provides publicly available data from the human brain. The genes that were used from the analysis were ACE, APOE, ANKK1, BCL2, BDNF, COMT, DRD2, IL-6, MME, NOS3, PARP1, TP53 which have been reported to play a crucial role on the pathophysiology and the outcome following moderate to severe TBI.

Participants: We used the adult samples from the 5 development stages dataset from Allen Brain Atlas (ABA) microarray data. The donors in ABA include four female and five male healthy individuals between the ages 18 to 40 years with no known neuropsychiatric or neuropathological history.

Interventions: Not applicable

Main Outcome Measures: In order to define the brain areas in which the expression of the genes of interest were significant, we performed an enrichment analysis with the help of ABA Enrichment package in R language. Lastly, we used the CerebroViz to visualize the expression data findings from the enrichment analysis.

Results: The enrichment analysis has shown that there are 16 statistically significant brain areas where the candidate genes during the adult life are significantly expressed. The most significant area for expression enrichment of the candidate genes was the striatum with the minimum FWER 0.03. Some of the major gene players in the upregulated striatum cluster were APOE followed by DRD2 and PARP1.

Conclusions: This research project provides evidence of how TBI associated genes are expressed in healthy adult brains. For future research, it would be interesting to replicate the present research’s approach in patients with TBI and compare the expression patterns with the healthy brains. These kinds of frameworks can aid the development of personalized medicine-based approaches which can be done once the genetic underpinnings of medical conditions are understood.

Author(s) Disclosures: The authors declare no conflict of interest.