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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.

SUBMITTER: Tremblay BL

PROVIDER: S-EPMC6628241 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

Tremblay Bénédicte L BL Guénard Frédéric F Lamarche Benoît B Pérusse Louis L Vohl Marie-Claude MC

Nutrients 20190604 6

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 ...[more]

PMID: 31167428

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Project description:Lipoprotein (a) (Lp(a)) is considered a genetic factor for cardiovascular disease playing an important role in atherogenesis and thrombosis, but the evidence about its association with sleep duration is controversial. We evaluated the relation between self-reported sleep duration and Lp(a). Among 1600 participants of the population-based sample, we selected 1427 subjects without previously known cardiovascular events, who answered the questions about their sleep duration; had valid lipid profile results (total cholesterol, low- and high-density lipoproteins, Lp(a), apolipoprotein AI (ApoAI), ApoB, and ApoB/ApoAI); and did not take lipid-lowering drugs (mean age 46 ± 12 years). We performed a structured interview, which included questions about lifestyle, medical history, complaints, and sleep duration (How long have you been sleeping per night during the last month?). Sleep duration was classified as follows: <6 h/night-short, 6-9 h/night-normal, and ≥10 h/night-long. Overall, 73 respondents (5.2%) were short-sleepers and 69 (4.8%) long-sleepers. Males were slightly more prevalent among short-sleepers. The groups matched by age, body mass index, blood pressure, diabetes mellitus, and hypertension rate. Short-sleepers had lower rates of high total cholesterol (≥5.0 mmol/L), lower Lp(a) levels and lower rates of increased Lp(a) ≥0.5 g/L, and higher insulin and insulin resistance (assessed by the homeostatic model assessment for insulin resistance (HOMA-IR)). ApoAI, ApoB, their ratio, and other lab tests were similar in the groups. The multinomial logistic regression demonstrated that only the short sleep duration was independently (odds ratio (OR) 0.29, 95% confidence interval (CI) (0.09-0.91), p = 0.033) associated with Lp(a) (χ2 = 41.58, p = 0.003). Other influencing factors were smoking and HOMA-IR. Such an association was not found for long-sleepers. In conclusion, a short-sleep duration is associated with Lp(a). The latter might mediate the higher insulin resistance and higher cardiometabolic risks in short-sleepers.

Project description:BackgroundAberrant methylation plays an essential role in early cancer development. In this study, we investigated methylation patterns in lung squamous cell carcinoma (LUSC) and matched non-tumor tissue and plasma samples to evaluate the potential of these patterns in the diagnosis of LUSC.MethodsThe study group included 49 patients with stage I-III LUSC. We collected resected tumor tissue, paired peritumoral tissue, distant normal tissue, and corresponding plasma samples. A bespoke lung cancer bisulfite sequencing panel was used to profile the methylation level. Another 48 healthy volunteers provided control plasma samples.ResultsPeritumoral and distant normal tissues presented similar methylation signatures, distinct from those in tumor tissue samples. A comparison of methylation profiles led to the identification of 871 tumor-specific differentially methylated blocks, including 847 hypermethylated and 24 hypomethylated blocks (adjusted P value <0.05). All top-ranked blocks were tumor-related. Tissue samples were analyzed for field cancerization to identify progressively aggravating aberrant methylations during tumor initiation and development. The analysis revealed that 221 blocks presented a stepwise increase in methylation levels, while seven blocks presented a stepwise decrease in methylation pattern as the sampling drew nearer to the tumor. The malignant contaminated ratio (MCR) confirmed the presence of distinct methylation patterns between tumor and peritumoral tissue samples. We then constructed a diagnostic panel using a combined diagnostic score of cell-free DNA (cfDNA) that showed high sensitivity and specificity. The healthy controls had a significantly lower combined diagnostic score (cd-score) than LUSC patients. Additionally, based on the methylation profiles, LUSC could be classified into two subgroups, C1 and C2. The methylation profile of the C2 group was not distinct from the healthy controls, which had a significantly lower cd-score than did the C1 group.ConclusionsLUSC-specific methylation patterns could potentially discriminate between peritumoral tissue, distant normal tumor tissue, and tumor tissues. This preliminary study also supported the potential utility of cfDNA methylation analysis in diagnosing LUSC.

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

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

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