A Mixture of Endocrine Disrupting Chemicals Associated with Lower Birth Weight in Children Induces Adipogenesis and DNA Methylation Changes in Human Mesenchymal Stem Cells.
Polina LizunkovaElin EngdahlGábor BorbélyChris GenningsChristian H LindhCarl-Gustaf BornehagJoëlle RüeggPublished in: International journal of molecular sciences (2022)
Endocrine Disrupting Chemicals (EDCs) are man-made compounds that alter functions of the endocrine system. Environmental mixtures of EDCs might have adverse effects on human health, even though their individual concentrations are below regulatory levels of concerns. However, studies identifying and experimentally testing adverse effects of real-life mixtures are scarce. In this study, we aimed at evaluating an epidemiologically identified EDC mixture in an experimental setting to delineate its cellular and epigenetic effects. The mixture was established using data from the Swedish Environmental Longitudinal Mother and child Asthma and allergy (SELMA) study where it was associated with lower birth weight, an early marker for prenatal metabolic programming. This mixture was then tested for its ability to change metabolic programming of human mesenchymal stem cells. In these cells, we assessed if the mixture induced adipogenesis and genome-wide DNA methylation changes. The mixture increased lipid droplet accumulation already at concentrations corresponding to levels measured in the pregnant women of the SELMA study. Furthermore, we identified differentially methylated regions in genes important for adipogenesis and thermogenesis. This study shows that a mixture reflecting human real-life exposure can induce molecular and cellular changes during development that could underlie adverse outcomes.
Keyphrases
- dna methylation
- genome wide
- pregnant women
- mesenchymal stem cells
- birth weight
- human health
- endothelial cells
- gene expression
- risk assessment
- stem cells
- chronic obstructive pulmonary disease
- emergency department
- gestational age
- type diabetes
- bone marrow
- machine learning
- young adults
- cross sectional
- cell proliferation
- ionic liquid
- transcription factor
- climate change
- high glucose
- cell death
- cystic fibrosis
- preterm birth
- single cell
- endoplasmic reticulum stress
- allergic rhinitis
- data analysis