Detection of Novel hub-methylated differentially expressed genes in pregnant women with gestational diabetes mellitus via WGCNA of epigenome-wide and transcriptome-wide profiling

Abstract

Objectives: Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder that adversely affects pregnant women and their growing fetuses. Evidence suggests that genetic and epigenetic modifications, such as DNA methylation, may contribute to the disease phenotype. This study aimed to identify GDM-related hub-methylated genes involved in GDM pathogenesis.

Methods: RNA-seq transcriptomic-wide data (GSE203346) and microarray epigenomic-wide data (GSE106099) were obtained from the Gene Expression Omnibus. Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression (DEGs) analysis were performed on the RNA-seq data using the "R" packages "WGCNA" and "DESeq2," respectively. Differentially methylated genes (DMGs) were identified using the "limma" package.

Results: WGCNA identified 18 modules, with only two modules [MEyellow r = -0.32; P = 0.042 and MEmagenta r = -0.32; P = 0.041] showing significant inverse correlations with GDM and one module [MEblue r = 0.35; P = 0.026], showing a direct correlation. Following intersecting the hub genes from WGCNA, DEGs and DMGs, six hub genes were identified as hypomethylated and highly expressed (UCKL1, SHANK2, GDPD5, CMYA5, ESRRG, NOS3), while two genes (DPYSL3 and FTH1) were hypermethylated and showed low expression. Gene set enrichment analysis revealed that the GDM-related hub DMGs were mainly enriched in pathways related to ferroptosis, VEGF signaling, and arginine and proline metabolism.

Conclusion: This multi-omics study identified eight novel GDM-related hub DMGs in placental tissue from GDM cases, suggesting their potential involvement in GDM pathogenesis. Further study is needed.

Keywords: DNA-methylation; RNA-seq; epigenetics; gene expression; gestational diabetes mellitus; weighted gene co-expression network analysis.

Figure 1

Study flowchart

Figure 2

(a) Cluster dendrogram for 41 samples. (b) Showing the screening for soft threshold power for WGCNA. Above: scale-free fit index analysis for different power thresholds. Below: Calculation of mean connectivity corresponding for the different threshold power. (c) Cluster dendrogram of all expressed genes. (d) Module-disease correlation heatmap. (e) Relative expression levels of UCKL1, SHANK2, GDPD5, CMYA5, ESRRG, and NOS3 genes were significantly increased in GDM cases compared to controls. (f) Relative expression levels of DPYSL3 and FTH1genes were significantly lower in GDM cases compared to controls

Figure 3

(a) GO Enrichment analysis of genes in blue module. (b) GO Enrichment analysis of genes in yellow module. (c) GO Enrichment analysis of genes in magenta module. GO: Gene ontology

Figure 4

(a) Gene set Enrichment Analysis scores in BP: Biological process; CC: Cellular component; MF: Molecular function for the UCKL1, NOS3, and FTH1 hub genes. (b) Kyoto Encyclopedia of Genes and Genomes pathways enrichment scores for the UCKL1, NOS3, and FTH1 hub genes