High-throughput transcriptomics toxicity assessment of eleven data-poor bisphenol A alternatives.
Marc A BealMelanie C CoughlanAndrée NunnikhovenMatthew GagnéTara S Barton-MaclarenLauren M BradfordAndrea Rowan-CarrollAndrew WilliamsMatthew J MeierPublished in: Environmental pollution (Barking, Essex : 1987) (2024)
Bisphenol A (BPA), a widely used chemical in the production of plastics and epoxy resins, has garnered significant attention due to its association with adverse health effects, particularly its endocrine-disrupting properties. Regulatory measures aimed at reducing human exposure to BPA have led to a proliferation of alternative chemicals used in various consumer and industrial products. While these alternatives serve to reduce BPA exposure, concerns have arisen regarding their safety and potential toxicity as regrettable substitutes. Previous efforts have demonstrated that in vitro high-throughput transcriptomics (HTTr) studies can be used to assess the endocrine-disrupting potential of BPA alternatives, and this strategy produces transcriptomic points-of-departure (tPODs) that are protective of human health when compared to the PODs from traditional rodent studies. In this study, we used in vitro HTTr to assess the potential for toxicity of eleven data-poor legacy chemicals sharing structural similarities to BPA. Human breast cancer MCF-7 cells were exposed to BPA and 11 alternatives at concentrations ranging from 0.1 to 25 μM to assess toxicity. Analysis of global transcriptomic changes and a previously characterized estrogen receptor alpha (ERα) transcriptomic biomarker signature revealed that 9 of 11 chemicals altered gene expression relative to controls. One of the chemicals (2,4'-Bisphenol A) activated the ERα biomarker at the same concentration as BPA (i.e., 4,4'-BPA) but was deemed to be more potent as it induced global transcriptomic changes at lower concentrations. These results address data gaps in support of ongoing screening assessments to identify BPA alternatives with hazard potential and help to identify potential candidates that may serve as safer alternatives.
Keyphrases
- single cell
- human health
- high throughput
- estrogen receptor
- gene expression
- risk assessment
- rna seq
- oxidative stress
- endothelial cells
- electronic health record
- induced apoptosis
- breast cancer cells
- signaling pathway
- healthcare
- health information
- heavy metals
- working memory
- wastewater treatment
- deep learning
- endoplasmic reticulum stress
- data analysis