Maternal and neonatal one-carbon metabolites and the epigenome-wide infant response

Metaboepigenetic regulation of gene expression in obesity and insulin resistance

INTRODUCTION

Variation in DNA methylation (DNAm) in adipose tissue is associated with the pathogenesis of obesity and insulin resistance. The activity of enzymes involved in altering DNAm levels is dependent on several metabolite cofactors.

OBJECTIVES

To understand the role of metabolites as mechanistic regulators of epigenetic marks, we tested the association between selected plasma metabolites and DNAm levels in the adipose tissue of African Americans.

METHODS

In the AAGMEx cohort (N = 256), plasma levels of metabolites were measured by untargeted liquid chromatography-mass spectrometry; adipose tissue DNAm and transcript levels were measured by reduced representation bisulfite sequencing, and expression microarray, respectively.

RESULTS

Among the 21 one-carbon metabolism pathway metabolites evaluated, six were associated with gluco-metabolic traits (PFDR < 0.05, for BMI, SI, or Matsuda index) in AAGMEx. Methylation levels of 196, 116, and 180 CpG-sites were associated (P < 0.0001) with S-adenosylhomocysteine (SAH), cystine, and hypotaurine, respectively. Cis-expression quantitative trait methylation (cis eQTM) analyses suggested the role of metabolite-level-associated CpG sites in regulating the expression of adipose tissue transcripts, including genes in G-protein coupled receptor signaling pathway. Plasma SAH level-associated CpG sites chr19:3403712 and chr19:3403735 were also associated with the expression of G-protein subunit alpha 15 (GNA15) in adipose. The expression of GNA15 was significantly correlated with BMI (β = 1.87, P = 1.9 × 10-16) and SI (β = -1.61, P = 2.49 × 10-5).

CONCLUSION

Our study suggests that a subset of metabolites modulates the methylation levels of CpG sites in specific loci and, in turn, regulates the expression of transcripts involved in obesity and insulin resistance.

Combined Exposures and Mixtures Research: An Enduring NIEHS Priority

BACKGROUND

The National Institute of Environmental Health Sciences (NIEHS) continues to prioritize research to better understand the health effects resulting from exposure to mixtures of chemical and nonchemical stressors. Mixtures research activities over the last decade were informed by expert input during the development and deliberations of the 2011 NIEHS Workshop "Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects." NIEHS mixtures research efforts since then have focused on key themes including a) prioritizing mixtures for study, b) translating mixtures data from in vitro and in vivo studies, c) developing cross-disciplinary collaborations, d) informing component-based and whole-mixture assessment approaches, e) developing sufficient similarity methods to compare across complex mixtures, f) using systems-based approaches to evaluate mixtures, and g) focusing on management and integration of mixtures-related data.

OBJECTIVES

We aimed to describe NIEHS driven research on mixtures and combined exposures over the last decade and present areas for future attention.

RESULTS

Intramural and extramural mixtures research projects have incorporated a diverse array of chemicals (e.g., polycyclic aromatic hydrocarbons, botanicals, personal care products, wildfire emissions) and nonchemical stressors (e.g., socioeconomic factors, social adversity) and have focused on many diseases (e.g., breast cancer, atherosclerosis, immune disruption). We have made significant progress in certain areas, such as developing statistical methods for evaluating multiple chemical associations in epidemiology and building translational mixtures projects that include both in vitro and in vivo models.

DISCUSSION

Moving forward, additional work is needed to improve mixtures data integration, elucidate interactions between chemical and nonchemical stressors, and resolve the geospatial and temporal nature of mixture exposures. Continued mixtures research will be critical to informing cumulative impact assessments and addressing complex challenges, such as environmental justice and climate change. https://doi.org/10.1289/EHP14340.

An industrialized diet as a determinant of methylation in the 1F region of the NR3C1 gene promoter

Background

Dietary composition can modify gene expression, favoring the development of chronic diseases via epigenetic mechanisms.

Objective

Our study aimed to investigate the relationship between dietary patterns and NR3C1 gene methylation in users of the Brazilian Public Unified Health System (SUS).

Methods

We recruited 250 adult volunteers and evaluated their socioeconomic status, psychosocial characteristics, lifestyle, and anthropometrics. Peripheral blood was collected and evaluated for cortisol levels, glycemia, lipid profile, and insulin resistance; methylation of CpGs 40-47 of the 1F region of the NR3C1 gene was also measured. Factors associated with degree of methylation were evaluated using generalized linear models (p < 0.05). Lifestyle variables and health variables were included as confounding factors.

Results

The findings of our cross-sectional study indicated an association between NR3C1 DNA methylation and intake of processed foods. We also observed relevant associations of average NR3C1 DNA across the segment analyzed, methylation in component 1 (40-43), and methylation in component 2 (44-47) with a pattern of consumption of industrialized products in relation to BMI, serum cortisol levels, and lipid profile. These results may indicate a relationship between methylation and metabolic changes related to the stress response.

Conclusion

These findings suggest an association of methylation and metabolic alterations with stress response. In addition, the present study highlights the significant role of diet quality as a stress-inducing factor that influences NR3C1 methylation. This relationship is further linked to changes in psychosocial factors, lifestyle choices, and cardiometabolic variables, including glucose levels, insulin resistance, and hyperlipidemia.

Maternal methyl donor supplementation: A potential therapy for metabolic disorder in offspring

The prevalences of diabetes mellitus and obesity are increasing yearly and has become a serious social burden. In addition to genetic factors, environmental factors in early life development are critical in influencing the prevalence of metabolic disorders in offspring. A growing body of evidence suggests the critical role of early methyl donor intervention in offspring health. Emerging studies have shown that methyl donors can influence offspring metabolism through epigenetic modifications and changing metabolism-related genes. In this review, we focus on the role of folic acid, betaine, vitamin B12, methionine, and choline in protecting against metabolic disorders in offspring. To address the current evidence on the potential role of maternal methyl donors, we summarize clinical studies as well as experimental animal models that support the impact of maternal methyl donors on offspring metabolism and discuss the mechanisms of action that may bring about these positive effects. Given the worldwide prevalence of metabolic disorders, these findings could be utilized in clinical practice, in which methyl donor supplementation in the early life years may reverse metabolic disorders in offspring and block the harmful intergenerational effect.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.