Prediction of Possible Biomarkers and Novel Pathways Conferring Risk to Post-Traumatic Stress Disorder

Abstract

Post-traumatic stress disorder is one of the common mental ailments that is triggered by exposure to traumatic events. Till date, the molecular factors conferring risk to the development of PTSD is not well understood. In this study, we have conducted a meta-analysis followed by hierarchical clustering and functional enrichment, to uncover the potential molecular networks and critical genes which play an important role in PTSD. Two datasets of expression profiles from Peripheral Blood Mononuclear Cells from 62 control samples and 63 PTSD samples were included in our study. In PTSD samples of GSE860 dataset, we identified 26 genes informative when compared with Post-deploy PTSD condition and 58 genes informative when compared with Pre-deploy and Post-deploy PTSD of GSE63878 dataset. We conducted the meta-analysis using Fisher, roP, Stouffer, AW, SR, PR and RP methods in MetaDE package. Results from the rOP method of MetaDE package showed that among these genes, the following showed significant changes including, OR2B6, SOX21, MOBP, IL15, PTPRK, PPBPP2 and SEC14L5. Gene ontology analysis revealed enrichment of these significant PTSD-related genes for cell proliferation, DNA damage and repair (p-value ≤ 0.05). Furthermore, interaction network analysis was performed on these 7 significant genes. This analysis revealed highly connected functional interaction networks with two candidate genes, IL15 and SEC14L5 highly enriched in networks. Overall, from these results, we concluded that these genes can be recommended as some of the potential targets for PTSD.

Fig 1. Normalization and differential expression analysis of datasets.

(A,B) represents the box-whisker plot of the samples in the GSE860 and GSE63878 datasets. The middle line of each box-and-Whisker plot represents the median expression level in each sample of the dataset (C) represents the Venn diagram showing the control samples from the datasets used for our study. The result showed 6696 genes commonly modulated in control (red) and pre-(blue), post-deploy (green) control samples of both GSE860 and GSE63878 datasets (D) represents four-set Venn diagram showing the probes/genes dysregulated between samples of the datasets GSE860 and GSE63878.

Fig 2. Hierarchical clustering of differential expressed genes in the datasets.

(A) Represents PTSD_GSE860 (yellow) vs Post-deploy PTSD_GSE63878 (red) condition (B) Represents PTSD_GSE860 (red) vs Pre-deploy PTSD_GSE63878 (orange) vs Post-deploy PTSD_GSE63878 (red) condition. All the probes were clustered based on normalized signal intensity ratios. Each row represented a single gene; each column represents the samples in each condition with an average linkage of expression.

Fig 3. Circos plot and Gene Ontology (GO) enrichment analysis at a P value ≤ 0.05.

(A, C) Shows the expression of the up-regulated informative genes in relation to the corresponding datasets (B, D) shows the expression of the up-regulated informative genes in PTSD_GSE860 vs Post-deploy PTSD_GSE63878 condition and PTSD_GSE860 vs Pre- vs Post-deploy PTSD_GSE63878 condition. Each connection between a gene and the condition represents the absolute fold change (E, F) Represents the gene ontology in PTSD_GSE860 vs Post-deploy PTSD_GSE63878 condition and PTSD_GSE860 vs Pre- vs Post-deploy PTSD_GSE63878 condition.

Fig 4. Interaction network analysis of datasets.

(A, B, C, D, E) represents interaction network of PTSD_GSE860 vs Post-deploy PTSD_GSE63878 condition (F, G) represents interaction network of PTSD_GSE860 vs Pre- vs Post-deploy PTSD_GSE63878 condition. In G, L solid lines represent direct interactions and dashed lines represent indirect interactions. In B, C, D, E, G nodes represent the genes and the edges represent their corresponding interactions.