Potency Ranking of Per- and Polyfluoroalkyl Substances Using High-Throughput Transcriptomic Analysis of Human Liver Spheroids

Abstract

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 (i.e., 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 three 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 to 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.

A Transformative Vision for an Omics-Based Regulatory Chemical Testing Paradigm

“…A logic framework ( Figure 1 ) is proposed that explores the feasibility and future development of transcriptomic methods to refine and replace the current apical endpoint-based regulatory toxicity testing and risk assessment paradigm. It is important to note that the discussions were focused on risk assessment applications, not screening and prioritization, where some of these methods are already in use ( Harrill et al , 2019 ; Krewski et al , 2020 ; Reardon et al , 2021 ; Wang et al , 2018 ). The threshold for establishing confidence and fitness for purpose is lower for the application of this approach in screening and prioritization as compared with risk assessment ( Parish et al , 2020 ).…”

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line

“…Similarly, the 25th rank-ordered feature might suffer from a similar problem; however, it at least offers a more stable POD. It should be noted that the choice of the 25th rankordered feature is subjective, and has previously only been explored in transcriptomics studies (Reardon et al 2021). The approach used to derive the BMC value for each time point and assay corresponds to the 1st rank-ordered unannotated feature.…”

“…To address the effect of sampling time points (i.e., exposure time) on BMC values obtained from highthroughput in vitro metabolomics data, it was necessary to initially investigate approaches for selecting the most robust metabolic features for POD derivation. The transcriptomics community has explored this topic in depth by evaluating several approaches to select groups of genes and/or molecular pathways to derive PODs (Farmahin et al 2017;Ramaiahgari et al 2019;Reardon et al 2021); however, some of these methods (e.g., pathway analysis) require robust annotation and identification of the 'features' being measured (i.e., metabolites in this study), which remains a challenge for untargeted metabolomics workflows (Nash and Dunn 2019). Given that the current dataset was obtained using nESI-DIMS (i.e., lacking chromatographic separation), only putative annotations of metabolites and lipids based upon accurate m/z of full scan data (MS 1 data) were possible.…”

“…The three methods assessed here were (1) the 1st rankordered unannotated feature (i.e., the feature with the lowest BMC value), (2) the 1st rank-ordered putatively annotated feature (with putative annotation conducted using the Hep-aRG-specific library of polar metabolites and lipids), and (3) the 25th rank-ordered feature previously proposed for derivation of PODs using transcriptomics datasets (Reardon et al 2021). These approaches were evaluated for the four chemicals to which the HepaRG cell line was exposed over 24 h, and are referred to in Fig.…”

New approach methodologies: A quantitative in vitro to in vivo extrapolation case study with PFASs

“…For the case of PFASs, in vitro toxicity readouts to build such NAMs may be obtained from studies with human hepatocytes or liver cell lines, considering their potential for causing hepatotoxicity and the liver playing a fundamental role in the regulation of cholesterol and lipid homeostasis. Exposure of human HepaRG liver cells ( Behr et al, 2020 ; Louisse et al, 2020 ) (also Louisse et al, submitted ), and human primary hepatocyte spheroids ( Reardon et al, 2021 ; Rowan-Carroll et al, 2021 ), to various PFASs was shown to induce a downregulation of several genes related to the cholesterol biosynthesis pathway, cholesterol uptake from the liver and SREBP 2 signaling. Additionally, PFOS and PFOA were shown to strongly decrease bile acid synthesis, the main catabolic product of cholesterol ( Behr et al, 2020 ) and induce a concentration-dependent increase in TG accumulation in HepaRG cells ( Louisse et al, 2020 ) ( Louisse et al, submitted ).…”