Potency Ranking of Per- and Polyfluoroalkyl Substances Using High-Throughput Transcriptomic Analysis of Human Liver Spheroids.
Anthony J F ReardonAndrea Rowan-CarrollStephen S FergusonKaren LeingartnerRemi GagneByron KuoAndrew WilliamsLuigi LorussoJulie A Bourdon-LacombeRichard CarrierIvy MoffatCarole L YaukElla AtlasPublished in: Toxicological sciences : an official journal of the Society of Toxicology (2022)
Per- and polyfluoroalkyl substances (PFAS) are some of the most prominent organic contaminants in human blood. Although the toxicological implications of human exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) are well established, data on lesser-understood PFAS are limited. New approach methodologies (NAMs) that apply bioinformatic tools to high-throughput data are being increasingly considered to inform risk assessment for data-poor chemicals. The aim of this study was to compare the potencies (ie, benchmark concentrations: BMCs) of PFAS in primary human liver microtissues (3D spheroids) using high-throughput transcriptional profiling. Gene expression changes were measured using TempO-seq, a templated, multiplexed RNA-sequencing platform. Spheroids were exposed for 1 or 10 days to increasing concentrations of 23 PFAS in 3 subgroups: carboxylates (PFCAs), sulfonates (PFSAs), and fluorotelomers and sulfonamides. PFCAs and PFSAs exhibited trends toward increased transcriptional potency with carbon chain-length. Specifically, longer-chain compounds (7-10 carbons) were more likely to induce changes in gene expression and have lower transcriptional BMCs. The combined high-throughput transcriptomic and bioinformatic analyses support the capability of NAMs to efficiently assess the effects of PFAS in liver microtissues. The data enable potency ranking of PFAS for human liver cell spheroid cytotoxicity and transcriptional changes, and assessment of in vitro transcriptomic points of departure. These data improve our understanding of the possible health effects of PFAS and will be used to inform read-across for human health risk assessment.
Keyphrases
- single cell
- high throughput
- gene expression
- rna seq
- electronic health record
- endothelial cells
- big data
- risk assessment
- drinking water
- health risk assessment
- transcription factor
- dna methylation
- heavy metals
- stem cells
- public health
- induced pluripotent stem cells
- pluripotent stem cells
- healthcare
- mental health
- data analysis
- bone marrow
- oxidative stress
- artificial intelligence
- mass spectrometry