Computer-assisted approaches to identify functional gene networks involved in traumatic brain injury

Abstract

Traumatic brain injury (TBI), a leading cause of death and disability in industrialized countries, is a result of an external force damaging brain tissue, accompanied with delayed pathogenic events which aggravate the injury. Molecular responses to TBI have not been well characterized, leaving molecular classification of TBI cases difficult. TBI subtype classification is an important step towards the development and selective application of novel treatments. To improve TBI classification, we have performed a network-based computational analysis on gene expression profiles from entire rat genome to identify functional gene subnetworks. The gene expression profiles are obtained from two experimental models of injury in rats: the controlled cortical impact (CCI; a focal brain injury) and fluid percussion injury (FPI; a diffuse brain injury). We demonstrate that the analysis of gene subnetworks is more suitable to classify the heterogeneous responses to different TBI models, compared to conventional analysis using an individual gene list. We therefore believe that effectively incorporating gene expression profiles into protein interaction information can improve the identification of functional subnetworks involved in TBI. The systems approach could lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks could have important prognostic functions for patients who sustain mild TBIs.