Comparative phylogenetic analysis and transcriptomic profiling of Dengue (DENV-3 genotype I) outbreak in 2021 in Bangladesh

Abstract

Background: The next-generation sequencing (NGS) technology facilitates in-depth study of host-pathogen metatranscriptome. We, therefore, implicated phylodynamic and transcriptomic approaches through NGS technology to know/understand the dengue virus (DENV) origin and host response with dengue fever.

Methods: In this study, blood serum RNA was extracted from 21 dengue patients and 3 healthy individuals. Total transcriptomic data were analyzed for phylogenetic, phylodynamic, differential express gene (DEG), and gene ontology (GO) using respective bioinformatics tools.

Results: The viral genome sequence revealed dengue viral genome size ranges 10647 to 10707 nucleotide. Phylogenetic and phylodynamic analysis showed that the 2021 epidemic isolates were DENV-3 genotype-I and maintained as a new clade in compared to 2019 epidemic. Transcriptome analysis showed a total of 2686 genes were DEG in dengue patients compared to control with a q-value < 0.05. DESeq2 plot counts function of the top 24 genes with the smallest q-values of differential gene expression of RNA-seq data showed that 11 genes were upregulated, whereas 13 genes were downregulated. GO analysis showed a significant upregulation (p = < 0.001) in a process of multicellular organismal, nervous system, sensory perception of chemical stimulus, and G protein-coupled receptor signaling pathways in the dengue patients. However, there were a significant downregulation (p = < 0.001) of intracellular component, cellular anatomical entity, and protein-containing complex in dengue patients. Most importantly, there was a significant increase of a class of immunoregulatory proteins in dengue patients in compared to the controls, with increased GO of immune system process. In addition, upregulation of toll receptor (TLR) signaling pathways were found in dengue patients. These TLR pathways were particularly involved for the activation of innate system coupled with adaptive immune system that probably involved the rapid elimination of dengue virus infected cells. These differentially expressed genes could be further investigated for target based prophylactic interventions for dengue.

Conclusion: This is a first report describing DENV complete genomic features and differentially expressed genes in patients in Bangladesh. These genes may have diagnostic and therapeutic values for dengue infection. Continual genomic surveillance is required to further investigate the shift in dominant genotypes in relation to viral pathogenesis.

Supplementary Information: The online version contains supplementary material available at 10.1186/s12985-023-02030-1.

Fig. 1

Phylodynamic analysis showed clad shift in DENV3 epidemic in Bangladesh. Bayesian Inference analysis compared all the sequences used to construct a phylogenetic tree. Bars in the internal nodes indicates 95% HPD intervals. Bangladeshi DENV-3 sequences distributed in two clades characterized as genotype I for DENV3 epidemic of 2017 and 2021, and the previous epidemics in genotype II that contains isolates from 2009, 2008, 2006, 2002, and 2000. The neighbor-joining tree was reconstructed using Tamura-Nei model with Gamma distributed rates among sites. 1000 bootstrap replications were used to test the phylogeny

Fig. 2

Neighbor joining (NJ) phylogenetic tree of dengue whole genome and envelope protein E. (A) A neighbor joining (NJ) phylogenetic tree with a bootstrap value of 1000 and the Jukes-Cantor substitution model were used to construct the whole viral genome and (B) the envelope protein E phylogenetic tree of 95 DENV-3 strains. The DENV-3 genotype I strain sequenced in this study is shown in red. To root the tree, the full sequences of DENV-3 genotypes II, III, and V were employed

Fig. 3

Dengue positive and negative groups were subjected to principal component analysis (PCA). The variance stabilizing transformation in DESeq2 was used to identify principal component 1 (PC1) and principal component 2 (PC2). The percentage of variance represents the amount of variance explained by PC1 and PC2

Fig. 4

Venn diagram of transcriptome response of dengue positive and negative groups. Venn diagram represents the number of unique and shared genes between dengue positive and negative patients. The total number of genes discovered in each category is shown in parentheses

Fig. 5

Heatmap of dengue positive and negative cases transcriptome. The genes were analyzed using hierarchical clustering and heatmap, with < 25 genes filtered off every row. In the heatmap, positive and negative groupings create distinct clusters

Fig. 6

Volcano plots of differentially expressed genes (DEGs) on DEseq2 analysis. The default log₂FC cut-off is >|2|, and the default p-value cut-off is 0.01. The ash color represents non-significant genes; the green color represents genes that are outside the range of log2FC is -2 to 2 but statistically non-significant (p-value ≥ 0.01); the blue color represents genes that are statistically significant (p-value < 0.01) but outside the range of log2FC is -2 to 2; and the red color represents genes that are statistically and log2FC significant. NS: Non-Significant; FC: Fold Change

Fig. 7

The top 24 DEGs based on p-value adjusted (padj 2.48E-06) were displayed. The Y-axis shows the normalized gene counts, while the X-axis shows the groupings (negative and positive). Details of the analysis can be found in Supplementary Data 1

Fig. 8

Pathway enrichment and GO phrase analysis for negative and positive groups found in PANTHER Functional classification viewed in gene list analyses. The X-axis of the stack plot shows the GO term, and the Y-axis depicts the proportions of genes for each GO term in each group. The positive group is represented by orange, while the negative group is represented by green. Details of the analysis can be found in Supplementary Data 2