Network Analysis of the Potential Role of DNA Methylation in the Relationship between Plasma Carotenoids and Lipid Profile

Abstract

Variability in plasma carotenoids may be attributable to several factors including genetic variants and lipid profile. Until now, the impact of DNA methylation on this variability has not been widely studied. Weighted gene correlation network analysis (WGCNA) is a systems biology method used for finding gene clusters (modules) with highly correlated methylation levels and for relating them to phenotypic traits. The objective of the present study was to examine the role of DNA methylation in the relationship between plasma total carotenoid concentrations and lipid profile using WGCNA in 48 healthy subjects. Genome-wide DNA methylation levels of 20,687 out of 472,245 CpG sites in blood leukocytes were associated with total carotenoid concentrations. Using WGCNA, nine co-methylation modules were identified. A total of 2734 hub genes (17 unique top hub genes) were potentially related to lipid profile. This study provides evidence for the potential implications of gene co-methylation in the relationship between plasma carotenoids and lipid profile. Further studies and validation of the hub genes are needed.

Epigenetic stratification of head and neck cancer survivors reveals differences in lycopene levels, alcohol consumption, and methylation of immune regulatory genes

“…Inflammatory and bioactive compounds also interfere with epigenetic processes [3,4]. Previous studies have found that carotenoid intake and plasma concentration are associated with DNA methylation in blood leukocytes, especially surrounding inflammatory genes [5,6]. This is of particular interest, considering that uncontrolled chronic inflammation can induce tumorigenesis via the growth factor activity of cytokines as well as persistent production of reactive oxygen species [7,8].…”

CENPL, ISG20L2, LSM4 , MRPL3 Are Four Novel Hub Genes and May Serve as Diagnostic and Prognostic Markers in Breast Cancer

“…Among a wide range of different bioinformatics tools, weighted gene co-expression network analysis WGCNA algorithm is the most commonly used method for gene co-expression network research. By constructing co-expression gene modules and associating external information, the key gene modules and potential hub genes can be identi ed [4][5][6] . In general, hub genes show high connectivity in the gene coexpression network, which often located in the upstream of the gene regulatory network and play a predominant role in the gene network coordination 7,8 .…”

MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Network, Inspired by Capsule Networks

“…[29][30][31][32][33] More recent work has called for a greater understanding of the implications of DNAm-DNAm interactions through the incorporation of Gaussian Graphical Models, Canonical Correlation Analysis, and module discovery through weighted gene co-methylation networks. [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] There is growing support for the use of novel deep learning methods to aggregate, group, and select CpGs by their local context (e.g., genes) in an effort to connect and interpret the data with clinical outcomes. [51][52][53] Incorporation of prior biological knowledge not only improves the transparency and interpretability of the modeling approach but also reduces noise while increasing signal by meaningfully pruning redundant relationships between predictors.…”