Quantitative Real-Time Analysis of Differentially Expressed Genes in Peripheral Blood Samples of Hypertension Patients

Abstract

Hypertension (HTN) is considered one of the most important and well-established reasons for cardiovascular abnormalities, strokes, and premature mortality globally. This study was designed to explore possible differentially expressed genes (DEGs) that contribute to the pathophysiology of hypertension. To identify the DEGs of HTN, we investigated 22 publicly available cDNA Affymetrix datasets using an integrated system-level framework. Gene Ontology (GO), pathway enrichment, and transcriptional factors were analyzed to reveal biological information. From 50 DEGs, we ranked 7 hypertension-related genes (p-value < 0.05): ADM, ANGPTL4, USP8, EDN, NFIL3, MSR1, and CEBPD. The enriched terms revealed significant functional roles of HIF-1-α transcription; endothelin; GPCR-binding ligand; and signaling pathways of EGF, PIk3, and ARF6. SP1 (66.7%), KLF7 (33.3%), and STAT1 (16.7%) are transcriptional factors associated with the regulatory mechanism. The expression profiles of these DEGs as verified by qPCR showed 3-times higher fold changes (2-ΔΔCt) in ADM, ANGPTL4, USP8, and EDN1 genes compared to control, while CEBPD, MSR1 and NFIL3 were downregulated. The aberrant expression of these genes is associated with the pathophysiological development and cardiovascular abnormalities. This study will help to modulate the therapeutic strategies of hypertension.

Keywords: cDNA datasets; differentially expressed genes; enrichment analysis; expression profiling; hypertension; qPCR.

Figure 1

Normalization of differentially expressed genes. The figure shows a density plot of the standard deviation of the intensities across arrays on the y-axis versus the rank of their mean on the x-axis. The red dots, connected by lines, show the running median of the standard deviation. After normalization and transformation to a logarithm(-like) scale, one typically expects the red line to be approximately horizontal; that is, to show no substantial trend. In some cases, a hump on the right hand of the x-axis can be observed, which is symptomatic of the saturation of the intensities.

Figure 2

Gene Ontology (GO) analysis of differentially expressed genes. The GO analysis indicates important molecular functions and includes the top-ranked GO categories classified according to the levels of differentially expressed genes enriched in the three major classifications.

Figure 3

Integrative genome pathway remodeling study was used to map the possible mechanisms of the DEGs.

Figure 4

(A) Transcriptional factors of hypertension-related DEGS, identifying SP1, KLF7, STAT1, DBX2, PRRX1, and other regulatory factors. (B) Motif analysis highlighting a significant number of functional motifs associated with important biological functions.

Figure 5

Mutation analysis of hypertension-related DEGs indicating post-translational modifications with significant cutoff parameters. It highlights the significant disordered regions of the proteins with a pathophysiological role in disease development.

Figure 6

Protein product co-expression network analysis using STRING database version 11.0. The protein network was calculated based on the neighborhood score with higher confidence (confidence score > 0.99). Nodes represent proteins and edges indicate interactions. The co-expression scores based on RNA expression patterns and protein co-regulation were studied through the STRING database and annotated keywords (FDR < 0.05) were observed.

Figure 7

(a) Aberrant expression levels of differentially expressed genes in hypertension cases and controls based on the fold changes of gene expression. (b) The qRT-PCR array validation of the differentially expressed genes. The plot graph shows the correlation between the expression levels of hypertension-related DEGs measured by array analysis and expression levels measured by individual qRT-PCR. The −ΔΔCt method was applied for this analysis.

Figure 8

Hierarchical cluster analysis heatmap indicating expression profiles of differentially expressed genes (ADM, ANGPTL4, USP8, EDN1, NFIL3, MSR1, and CEBPD). The columns in the figure represent the samples and the rows represent the differentially expressed genes.