Microarray-based gene expression analysis of an animal model for closed head injury

Abstract

Objective: Traumatic brain injury (TBI) is a major cause of death and disability in both children and the elderly. Mortality from TBI is said account for 1-2% of all deaths. One-third to one-half of all traumatic deaths is due to head injury. Of those who survive, the majority is left with significant disabilities, including 3% who remain in a vegetative state and only approximately 30% who make a good recovery. Microarray studies and other genomic techniques facilitate the discovery of new targets for the treatment of diseases, which aids in drug development, immunotherapeutics and gene therapy. Gene expression profiling or microarray analysis enables the measurement of thousands of genes in a single RNA sample.

Methods: In this study, adult Wistar-albino rats underwent TBI using a trauma device. Brain tissues and blood samples were taken for gene expression at 1, 12 and 48 h post-trauma and were then analysed via microarray. Total RNA was isolated using an RNeasy Mini Kit (QIAGEN-Sample & Assay Technologies, Hilden, Germany) and tested using a 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). Overall changes in gene expression were evaluated using Agilent Whole Rat Genome 4 × 44 K oligonucleotide arrays and analysed with GeneSpring (GeneSpring 6.1, Silicon Genetics, Redwood City, CA) software. Only genes with a signal-to-noise ratio of above 2 in the experiments were included in the statistical analysis.

Results: ANOVA (p<0.05) was performed to identify differentially expressed probe sets. Additional filtering (minimum 2-fold change) was applied to extract the most differentially expressed genes based on the study groups (Control vs. 1st hour, Control vs. 12th hour, Control vs. 48th hour). Differentially expressed genes were detected via microarray analysis. A gene interaction-based network investigation of the genes that were identified via traditional microarray data analysis describes a significantly relevant gene network that includes the C1ql2, Cbnl, Sdc1, Bdnf, MMP9, and Cd47 genes, which were differentially expressed compared with the controls.

Conclusions: In this study, we will review the current understanding of the genetic susceptibility of TBI with microarrays. Our results highlight the importance of genes that control the response of the brain to injury as well as the suitability of microarrays for identifying specific targets for further study.