Analysis of gene expression profile for identification of novel gene signatures during dengue infection

Abstract

Background: Dengue is a major arthropod-borne viral disease spreading rapidly across the globe. The absence of vaccines and inadequate vector control measures leads to further expansion of dengue in many regions globally. Hence, the identification of genes involved in the pathogenesis of dengue will help to understand the molecular basis of the disease and the genes responsible for the disease progression.

Methods: In the present study, a meta-analysis was carried out using dengue gene expression data obtained from Gene Expression Omnibus repository. The differentially expressed genes such as CCNB1 and CCNB2 (G2/mitotic-specific cyclin-B2 and B1) were upregulated in dengue fever to control (DF-CO) and severe dengue (dengue hemorrhagic fever [DHF]) to control (DHF-CO) were identified as key genes for controlling the major pathways (cell cycle, oocyte meiosis, p53 signaling pathway, cellular senescence and progesterone-mediated oocyte maturation). Similarly, interferon alpha-inducible (IFI27) genes, type-I and type-III interferon (IFN) signaling genes (STAT1 and STAT2), B cell activation and survival genes (TNFSF13B, TNFRSF17) and toll like receptor (TLR7) genes were differentially up activated during DF-CO and DHF-CO. Followed by, Cytoscape was used to identify the immune system process and topological analysis.

Results: The results showed that the top differentially expressed genes under the statistical significance p <0.001, which is majorly involved in Kyoto Encyclopedia of Genes and Genomes orthology K05868 and K21770 with gene names CCNB1 and CCNB2. In addition to this, the immune system profile showed up-regulation of IL12A, CXCR3, TNFSF13B, IFI27, TNFRSF17, STAT, STAT2, and TLR7 genes in DF-CO and DHF-CO act as immunological signatures for inducing the immune response towards dengue infection.

Conclusions: The current study could aid in understanding of molecular pathogenesis, genes and corresponding pathway upon dengue infection, and could facilitate for identification of novel drug targets and prognostic markers.

Keywords: Data analysis; Dengue fever; Gene expression; Gene signatures; Microarray; Severe dengue.

Fig. 1

Box plots for gene expression data of each sample (A) before normalization (B) after normalization of the dengue samples.

Fig. 2

Venn showed (up and down) regulation of genes between the groups (DF-CO, SD-CO, CP-DF, and CP-DHF). CP, convalescent patients; DF-CO, dengue fever to control; DHF, dengue hemorrhagic fever; SD, severe dengue.

Fig. 3

Volcano plots demonstrating an overview of DEGs. The plot compared the DEGs between DF-CO, SD-CO, CP-DF, and CP-SD groups. The down-regulated genes are on the left side of the plot (0–6) and up-regulated are on the right side of the plot (0–6). CP, convalescent patients; DEGs, differentially expressed genes; DF-CO, dengue fever to control; SD, severe dengue.

Fig. 4

Hierarchical cluster analysis of top 200 DEGs (up-regulated and down-regulated) between severe dengue and control groups. Hierarchical cluster analysis between other clinical groups was shown in Supplemental Figure S6 (A-F). DEGs, differentially expressed genes.

Fig. 5

Gene ontology (GO) enrichment analysis showing most enriched GO terms are biological processes and molecular function of SD-CO (other groups are shown in Supplemental Figs. S7 A-M). The x-axis represents the number of DEGs enriched terms. Y-axis represents the GO terms. CO, control; DEGs, differentially expressed genes. SD, severe dengue.

Fig. 6

STRING generated interaction network between commonly identified up and down-regulated DEGs genes in 4 group comparisons. STRING, Search Tool for the Retrieval of Interacting Genes.

Fig. 7

Cytoscape immune response pathway network of significantly over-represented Immune system process gene ontology transcriptome and proteome profiling by ClueGo for DEGs. DEGs, differentially expressed genes.