Classifying mild traumatic brain injuries with functional network analysis

Dash Pramod,John Redell,F. Anthony San Lucas,Yin Liu

doi:10.1186/s12918-018-0645-z

Abstract

BackgroundTraumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. TBI is a result of an external force damaging brain tissue, accompanied by delayed pathogenic events which aggravate the injury. Molecular responses to different mild TBI subtypes have not been well characterized. TBI subtype classification is an important step towards the development and application of novel treatments. The computational systems biology approach is proved to be a promising tool in biomarker discovery for central nervous system injury.ResultsIn this study, we have performed a network-based analysis on gene expression profiles to identify functional gene subnetworks. The gene expression profiles were obtained from two experimental models of injury in rats: the controlled cortical impact and the fluid percussion injury. Our method integrates protein interaction information with gene expression profiles to identify subnetworks of genes as biomarkers. We have demonstrated that the selected gene subnetworks are more accurate to classify the heterogeneous responses to different injury models, compared to conventional analysis using individual marker genes selected without network information.ConclusionsThe systems approach can lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks will have important prognostic functions for patients who sustain mild TBIs.

Highlights

Traumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging
In the cases of mild TBI (mTBI) classification, if we only examined the differences in the expression levels of individual genes across different mTBI models and neglected the genes that are not associated with a TBI subtype at a significance threshold, we would fail to account for the complexities and redundancies that arise from gene interactions inherent to the mTBI responses
Using the features drawn from different gene sets, we trained Support Vector Machine (SVM) based on the simulated gene expression data from four-fifth of samples, and we tested the performance of the learned feature weights on the remaining one-fifth of Conclusions We have aimed to improve the identification of biomarkers that can distinguish two different classes of TBI in rodent animal models: the mild Controlled Cortical Impact and the mild Fluid Percussion Injury, representing focal and diffuse TBIs, respectively

Summary

Introduction

Traumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. We have utilized two common experimental models of injury in this study: the mild controlled cortical impact (mCCI) model that causes a focal injury, and the mild fluid percussion injury (mFPI) model that causes a more diffuse brain injury. Both injury models qualitatively recapitulate a number of functional deficits and pathological responses exhibited in human TBI cases. We employ a systems approach to improving the identification of biomarkers that can distinguish there two models These biomarkers, if successfully identified, could be used to better guide treatments to mTBI patients, and more optimistically they could be potential targets of novel treatments

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