Adopting Integrated Bioinformatics and Systems Biology Approaches to Pinpoint the COVID-19 Patients' Risk Factors That Uplift the Onset of Posttraumatic Stress Disorder

Abstract

Owing to the recent emergence of COVID-19, there is a lack of published research and clinical recommendations for posttraumatic stress disorder (PTSD) risk factors in patients who contracted or received treatment for the virus. This research aims to identify potential molecular targets to inform therapeutic strategies for this patient population. RNA sequence data for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and PTSD (from the National Center for Biotechnology Information [NCBI]) were processed using the GREIN database. Protein-protein interaction (PPI) networks, pathway enrichment analyses, miRNA interactions, gene regulatory network (GRN) studies, and identification of linked drugs, chemicals, and diseases were conducted using STRING, DAVID, Enrichr, Metascape, ShinyGO, and NetworkAnalyst v3.0. Our analysis identified 15 potentially unique hub proteins within significantly enriched pathways, including PSMB9, MX1, HLA-DOB, HLA-DRA, IFIT3, OASL, RSAD2, and so on, filtered from a pool of 201 common differentially expressed genes (DEGs). Gene ontology (GO) terms and metabolic pathway analyses revealed the significance of the extracellular region, extracellular space, extracellular exosome, adaptive immune system, and interleukin (IL)-18 signaling pathways. In addition, we discovered several miRNAs (hsa-mir-124-3p, hsa-mir-146a-5p, hsa-mir-148b-3p, and hsa-mir-21-3p), transcription factors (TF) (WRNIP1, FOXC1, GATA2, CREB1, and RELA), a potentially repurposable drug carfilzomib and chemicals (tetrachlorodibenzodioxin, estradiol, arsenic trioxide, and valproic acid) that could regulate the expression levels of hub proteins at both the transcription and posttranscription stages. Our investigations have identified several potential therapeutic targets that elucidate the probability that victims of COVID-19 experience PTSD. However, they require further exploration through clinical and pharmacological studies to explain their efficacy in preventing PTSD in COVID-19 patients.

Keywords: COVID-19; drug discovery; hub protein; miRNA; molecular pathway; ontological function; posttraumatic stress disorder (PTSD); transcription factor.

Figure 1.

Schematic representation of the whole work; starting with collecting the data, taking it through different stages of analysis, and finally trying to provide the clinical specialists with multidirectional data to work with.

Figure 2.

Venn diagram and heatmap of the DEGs and common DEGs. Venn diagrams: (A) common DEGs between PTSD and COVID-19, (B) DEGs with logFC > 1 and (C) logFC < −1; Heatmaps: (D) DEGs with logFC > 1 and (E) logFC < −1.

Figure 3.

Physical interlinkages between over-expressed proteins. The nodes at the center of each circular-shaped structure represent highly prioritized genes, on the other hand, lines interconnecting them (edges) illustrate the interaction intensities among different genes.

Figure 4.

Identification of hub genes using several CytoHubba algorithms. The red-to-yellow hue inclination defines the ranks from high to low.

Figure 5.

Bubble plot representing the enrichments of the significant GO terms.

Figure 6.

Bubble plot representing the enrichments of the significant pathways.

Figure 7.

The chord diagram that illustrates the fold changes of the unique hub genes against the highly enriched GO terms.

Figure 8.

The chord diagram that illustrates the fold changes of the unique hub genes against the highly enriched pathways.

Figure 9.

Gene-miRNA interaction network using (A) miRTarBase, (B) TarBase databases and TF-gene interaction network using, (C) ENCODE, and (D) JASPAR databases.

Figure 10.

(A) Protein-drug interactions using Drug Bank v5.0 database, (B) protein-chemical interactions using the CTD database, and (C) disease-gene association using DisGeNET database.