Impact of Environmentally Relevant Concentrations of Bisphenol A (BPA) on the Gene Expression Profile in an In Vitro Model of the Normal Human Ovary.
Aeman ZahraRachel KerslakeIoannis KyrouHarpal S RandevaCristina SisuEmmanouil KarterisPublished in: International journal of molecular sciences (2022)
Endocrine-disrupting chemicals (EDCs), including the xenoestrogen Bisphenol A (BPA), can interfere with hormonal signalling. Despite increasing reports of adverse health effects associated with exposure to EDCs, there are limited data on the effect of BPA in normal human ovaries. In this paper, we present a detailed analysis of the transcriptomic landscape in normal Human Epithelial Ovarian Cells (HOSEpiC) treated with BPA (10 and 100 nM). Gene expression profiles were determined using high-throughput RNA sequencing, followed by functional analyses using bioinformatics tools. In total, 272 and 454 differentially expressed genes (DEGs) were identified in 10 and 100 nM BPA-treated HOSEpiCs, respectively, compared to untreated controls. Biological pathways included mRNA surveillance pathways, oocyte meiosis, cellular senescence, and transcriptional misregulation in cancer. BPA exposure has a considerable impact on 10 genes: ANAPC2 , AURKA , CDK1 , CCNA2 , CCNB1 , PLK1 , BUB1 , KIF22 , PDE3B , and CCNB3 , which are also associated with progesterone-mediated oocyte maturation pathways. Future studies should further explore the effects of BPA and its metabolites in the ovaries in health and disease, making use of validated in vitro and in vivo models to generate data that will address existing knowledge gaps in basic biology, hazard characterisation, and risk assessment associated with the use of xenoestrogens such as BPA.
Keyphrases
- endothelial cells
- genome wide
- single cell
- risk assessment
- high throughput
- healthcare
- public health
- induced pluripotent stem cells
- pluripotent stem cells
- genome wide identification
- emergency department
- copy number
- photodynamic therapy
- signaling pathway
- human health
- cell death
- oxidative stress
- heavy metals
- cell cycle
- big data
- papillary thyroid
- metabolic syndrome
- machine learning
- cell cycle arrest
- newly diagnosed
- binding protein
- case control
- health information
- adverse drug
- polycystic ovary syndrome
- climate change
- heat shock protein
- pi k akt