Bilateral gene interaction hierarchy analysis of the cell death gene response emphasizes the significance of cell cycle genes following unilateral traumatic brain injury

Abstract

BackgroundDelayed or secondary cell death that is caused by a cascade of cellular and molecular processes initiated by traumatic brain injury (TBI) may be reduced or prevented if an effective neuroprotective strategy is employed. Microarray and subsequent bioinformatic analyses were used to determine which genes, pathways and networks were significantly altered 24 h after unilateral TBI in the rat. Ipsilateral hemi-brain, the corresponding contralateral hemi-brain, and naïve (control) brain tissue were used for microarray analysis.ResultsIngenuity Pathway Analysis showed cell death and survival (CD) to be a top molecular and cellular function associated with TBI on both sides of the brain. One major finding was that the overall gene expression pattern suggested an increase in CD genes in ipsilateral brain tissue and suppression of CD genes contralateral to the injury which may indicate an endogenous protective mechanism. We created networks of genes of interest (GOI) and ranked the genes by the number of direct connections each had in the GOI networks, creating gene interaction hierarchies (GIHs). Cell cycle was determined from the resultant GIHs to be a significant molecular and cellular function in post-TBI CD gene response.ConclusionsCell cycle and apoptosis signalling genes that were highly ranked in the GIHs and exhibited either the inverse ipsilateral/contralateral expression pattern or contralateral suppression were identified and included STAT3, CCND1, CCND2, and BAX. Additional exploration into the remote suppression of CD genes may provide insight into neuroprotective mechanisms that could be used to develop therapies to prevent cell death following TBI.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2412-0) contains supplementary material, which is available to authorized users.

Epitranscriptomic profiling of N6-methyladenosine-related RNA methylation in rat cerebral cortex following traumatic brain injury

“…The mechanism involved in secondary injury is more complicated [3][4][5]. It includes delayed cell death (such as the newly discovered ferroptosis [6]), brain edema, metabolic defects [7], and blood-brain barrier damage caused by biochemical and physiological changes after trauma [8].…”

Plasma Exosome-derived MicroRNAs as Novel Biomarkers of Traumatic Brain Injury in Rats

“…Although severe TBI is one of the main causes of death or disability in patients, mild or moderate TBI is a leading cause of long-term disability including seizures and emotional and behavioural issues [2]. The damage to the cranium and the intracranial contents caused by a TBI can be divided into the primary injury and delayed secondary injury, which is more profound and includes axonal shearing [3], neuroinflammation [4,5], neurochemical changes [6,7], brain edema [8], vascular injury [9], cell apoptosis [10] and mitochondrial dysfunction [11].…”

Older Age Results in Differential Gene Expression after Mild Traumatic Brain Injury and Is Linked to Imaging Differences at Acute Follow-up

“…A plausible reason for this is that whereas preclinical studies are able to examine neuronal gene-activity changes clinical studies are restricted to the used of primarily peripheral blood. An example of this is a recent study in a rat model of TBI which showed that brain tissue-related changes in gene-activity were related to cell death and survival gene pathways ( White et al, 2016 ), as well as inflammation gene-pathways ( Sabir et al, 2015 ). Within preclinical studies, there are both studies using in vitro and in vivo induced TBI models, with a very interesting study that compared both models using a genome-wide approach to have differential gene expression related to genes that direct the function of amyloid precursor protein to the recycling pathway by direct binding and away from amyloid beta producing enzymes, in both TBI models ( Lamprecht et al, 2016 ).…”