Intergenerational arsenic exposure on the mouse epigenome and metabolic physiology.
Mathia L ColwellNicole M WannerAmanda RezabekChristopher D FaulkPublished in: Environmental and molecular mutagenesis (2023)
Inorganic arsenic (iAs) is one of the largest toxic exposures to impact humanity worldwide. Exposure to iAs during pregnancy may disrupt the proper remodeling of the epigenome of F1 developing offspring and potentially their F2 grand-offspring via disruption of fetal primordial germ cells (PGCs). There is a limited understanding between the correlation of disease phenotype and methylation profile within offspring of both generations and whether it persists to adulthood. Our study aims to understand the intergenerational effects of in utero iAs exposure on the epigenetic profile and onset of disease phenotypes within F1 and F2 adult offspring, despite the lifelong absence of direct arsenic exposure within these generations. We exposed F0 female mice (C57BL6/J) to the following doses of iAs in drinking water 2 weeks before pregnancy until the birth of the F1 offspring: 1, 10, 245, and 2300 ppb. We found sex- and dose-specific changes in weight and body composition that persist from early time to adulthood within both generations. Fasting blood glucose challenge suggests iAs exposure causes dysregulation of glucose metabolism, revealing generational, exposure, and sex-specific differences. Toward understanding the mechanism, genome-wide DNA methylation data highlights exposure-specific patterns in liver, finding dysregulation within genes associated with cancer, T2D, and obesity. We also identified regions containing persistently differentially methylated CpG sites between F1 and F2 generations. Our results indicate the F1 developing embryos and their PGCs, which will result in F2 progeny, retain epigenetic damage established during the prenatal period and are associated with adult metabolic dysfunction.
Keyphrases
- dna methylation
- drinking water
- genome wide
- body composition
- blood glucose
- high fat diet
- gene expression
- insulin resistance
- physical activity
- blood pressure
- metabolic syndrome
- machine learning
- bone mineral density
- pregnant women
- health risk assessment
- induced apoptosis
- copy number
- resistance training
- air pollution
- big data
- squamous cell
- health risk
- deep learning
- cell death
- postmenopausal women