Genome-wide profiling of DNA methylation and gene expression identifies candidate genes for human diabetic neuropathy

Abstract

Background: Diabetic peripheral neuropathy (DPN) is the most common complication of type 2 diabetes (T2D). Although the cellular and molecular mechanisms of DPN are poorly understood, we and others have shown that altered gene expression and DNA methylation are implicated in disease pathogenesis. However, how DNA methylation might functionally impact gene expression and contribute to nerve damage remains unclear. Here, we analyzed genome-wide transcriptomic and methylomic profiles of sural nerves from T2D patients with DPN. Results: Unbiased clustering of transcriptomics data separated samples into groups, which correlated with HbA1c levels. Accordingly, we found 998 differentially expressed genes (DEGs) and 929 differentially methylated genes (DMGs) between the groups with the highest and lowest HbA1c levels. Functional enrichment analysis revealed that DEGs and DMGs were enriched for pathways known to play a role in DPN, including those related to the immune system, extracellular matrix (ECM), and axon guidance. To understand the interaction between the transcriptome and methylome in DPN, we performed an integrated analysis of the overlapping genes between DEGs and DMGs. Integrated functional and network analysis identified genes and pathways modulating functions such as immune response, ECM regulation, and PI3K-Akt signaling. Conclusion: These results suggest for the first time that DNA methylation is a mechanism regulating gene expression in DPN. Overall, DPN patients with high HbA1c have distinct alterations in sural nerve DNA methylome and transcriptome, suggesting that optimal glycemic control in DPN patients is an important factor in maintaining epigenetic homeostasis and nerve function.

Epigenetic Regulation of Processes Related to High Level of Fibroblast Growth Factor 21 in Obese Subjects

“…Additionally, we demonstrated differentially expressed miRNAs known to target and inversely regulate the FGFR1 and VEGFA genes. As DNA methylation is reversible and depends on environmental factors, there is the potential to influence the methylation status of key genes by nutrition and a healthy lifestyle to prevent obesity-related complications [ 62 , 63 , 64 , 65 ]. These findings give rise for further studies on detailed dietary factors and targeted DNA methylation editing therapies that may regulate revealed genes.…”

Genome‑wide profiling of DNA methylation and gene expression unravel the epigenetic landscape in diabetes-related hypothyroidism

“…DNA methylation in CpG-rich promoter regions is linked to gene repression and DNA methylation within gene bodies is associated with gene activation [ 22 ]. In a study of diabetic peripheral neuropathy (DPN), the authors found that DNA methylation and gene expression profile of sural nerves in DPN patients with high HbA1c had significant changes, suggesting that the interaction between DNA methylation and gene expression participated in the pathological mechanism in DPN [ 23 ]. In this study, RNA sequencing and reduced representation bisulfite sequencing (RRBS) were performed in Nthy-ori3-1 cells cultured in high-glucose medium and normal medium, respectively.…”

Multi-omics integration strategies for animal epigenetic studies

“…A common method of integration is to use gene overlap of multiple omics data. There are three ways of gene overlap: 1) Entire overlap between the identified differentially methylated genes (DMGs) and differentially expressed genes (DEGs), 2) overlap based on methylation and expression levels, 3) overlap between DMGs and DEGs based on gene components [83][84][85]. The entire overlapping method is a simple and intuitive method for finding the relationship by using the overlapping genes of the entire DMGs and DEGs.…”

“…In addition to the gene superposition methods, various databases and methods are used for direct (physical) and indirect (functional) analyses [86][87][88]. The most common is to build a PPI network, a gene interaction network that identifies sets of genes that can interact with each other, using the STRING database [84,89]. Using the transcription factor database, we can construct networks that provide the orientation and relationship to the target gene along with the motif enrichment analysis [90].…”