Deconvolution of Transcriptional Networks in Post-Traumatic Stress Disorder Uncovers Master Regulators Driving Innate Immune System Function

Abolfazl Doostparast Torshizi,Kai Wang

doi:10.1038/s41598-017-15221-y

Abstract

Post-Traumatic Stress Disorder (PTSD) is a psychiatric disorder that develops in individuals experiencing a shocking incident, but the underlying disease susceptibility gene networks remain poorly understood. Breen et al. conducted a Weighted Gene Co-expression Network Analysis on PTSD, and identified a dysregulated innate immune module associated with PTSD development. To further identify the Master Regulators (MRs) driving the network function, here we deconvoluted the transcriptional networks on the same datasets using ARACNe (Algorithm for Reconstruction of Accurate Cellular Networks) followed by protein activity analysis. We successfully identified several MRs including SOX3, TNFAIP3, TRAFD1, POU3F3, STAT2, and PML that govern the expression of a large collection of genes. Transcription factor binding site enrichment analysis verified the binding of these MRs to their predicted targets. Notably, the sub-networks regulated by TNFAIP3, TRAFD1 and PML are involved in innate immune response, suggesting that these MRs may correlate with the innate immune module identified by Breen et al. These findings were replicated in an independent dataset generated on expression microarrays. In conclusion, our analysis corroborated previous findings that innate immunity may be involved in the progression of PTSD, yet also identified candidate MRs driving the disease progression in the innate immunity pathways.

Highlights

Under investigation in order to establish more effective predictive models[11,12,13,14,15,16,17,18,19]
Our observations indicate that the identified Master Regulators (MRs) regulate downstream targets that are enriched for innate immune responses which have been reported by Breen et al to be hyper-activated in Post-Traumatic Stress Disorder (PTSD), but through a different type of network analysis technique
We performed a reverse-engineering of the transcriptional network on PTSD, using the ARACNe algorithm on three gene expression datasets

Summary

Introduction

Under investigation in order to establish more effective predictive models[11,12,13,14,15,16,17,18,19]. Organization of gene expression profile data into functionally meaningful information in the context of PTSD has not been addressed in the literature This challenge, known as “reverse engineering” of cellular networks, has opened new windows to generate cellular networks in the form of graphs to overcome the common difficulties in the area of genetic networks. We present the successful reverse engineering of transcriptional regulatory networks to identify the Master Regulators (MRs) governing cellular processes in patients suffering from PTSD. Reverse engineer the transcriptional networks from RNA-seq data based on an information theoretic approach to identify hub genes that may regulate a large blanket of downstream genes. Infer protein activity of hub Transcription Factors (TFs) known as MRs to discover the candidate regulatory drivers of PTSD signatures

Objectives

Methods

Results

Conclusion