Per- and Polyfluoroalkyl Substances (PFAS) in mixtures show additive effects on transcriptomic points of departure in human liver spheroids

Per- and polyfluoroalkyl substances (PFAS) are a wide range of chemicals that are used in a variety of consumer and industrial products leading to direct human exposure. Many PFAS are chemically non-reactive and persistent in the environment, resulting in additional exposure from water, soil, and dietary intake. While some PFAS have documented negative health effects, data on simultaneous exposures to multiple PFAS (PFAS mixtures) are inadequate for making informed decisions for risk assessment. The current study leverages data from previous work in our group using Templated Oligo-Sequencing (TempO-Seq™) for high-throughput transcriptomic analysis of PFAS-exposed primary human liver cell spheroids; herein, we determine the transcriptomic potency of PFAS in mixtures. Gene expression data from single PFAS and mixture exposures of liver cell spheroids were subject to benchmark concentration (BMC) analysis. We used the 25th lowest gene BMC as the point of departure to compare the potencies of single PFAS to PFAS mixtures of varying complexity and composition. Specifically, the empirical potency of eight PFAS mixtures were compared to predicted mixture potencies calculated using the principal of concentration addition (i.e., dose addition) in which mixture component potencies are summed by proportion to predict mixture potency. In this study, for most mixtures, empirical mixture potencies were comparable to potencies calculated through concentration addition. This work supports that the effects of PFAS mixtures on gene expression largely follow the concentration addition predicted response and suggests that effects of these individual PFAS in mixtures are not strongly synergistic or antagonistic.

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

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.

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

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.

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

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.

High-Throughput Transcriptomic Analysis of Human Primary Hepatocyte Spheroids Exposed to Per- and Polyfluoroalkyl Substances as a Platform for Relative Potency Characterization

Per- and poly-fluoroalkyl substances (PFAS) are widely found in the environment because of their extensive use and persistence. Although several PFAS are well studied, most lack toxicity data to inform human health hazard and risk assessment. This study focused on 4 model PFAS: perfluorooctanoic acid (PFOA; 8 carbon), perfluorobutane sulfonate (PFBS; 4 carbon), perfluorooctane sulfonate (PFOS; 8 carbon), and perfluorodecane sulfonate (PFDS; 10 carbon). Human primary liver cell spheroids (pooled from 10 donors) were exposed to 10 concentrations of each PFAS and analyzed at 4 time points. The approach aimed to: (1) identify gene expression changes mediated by the PFAS, (2) identify similarities in biological responses, (3) compare PFAS potency through benchmark concentration analysis, and (4) derive bioactivity exposure ratios (ratio of the concentration at which biological responses occur, relative to daily human exposure). All PFAS induced transcriptional changes in cholesterol biosynthesis and lipid metabolism pathways, and predicted PPARα activation. PFOS exhibited the most transcriptional activity and had a highly similar gene expression profile to PFDS. PFBS induced the least transcriptional changes and the highest benchmark concentration (ie, was the least potent). The data indicate that these PFAS may have common molecular targets and toxicities, but that PFOS and PFDS are the most similar. The transcriptomic bioactivity exposure ratios derived here for PFOA and PFOS were comparable to those derived using rodent apical endpoints in risk assessments. These data provide a baseline level of toxicity for comparison with other known PFAS using this testing strategy.

Comparison of toxicogenomics and traditional approaches to inform mode of action and points of departure in human health risk assessment of benzo[a]pyrene in drinking water

Toxicogenomics is proposed to be a useful tool in human health risk assessment. However, a systematic comparison of traditional risk assessment approaches with those applying toxicogenomics has never been done. We conducted a case study to evaluate the utility of toxicogenomics in the risk assessment of benzo[a]pyrene (BaP), a well-studied carcinogen, for drinking water exposures. Our study was intended to compare methodologies, not to evaluate drinking water safety. We compared traditional (RA1), genomics-informed (RA2) and genomics-only (RA3) approaches. RA2 and RA3 applied toxicogenomics data from human cell cultures and mice exposed to BaP to determine if these data could provide insight into BaP's mode of action (MOA) and derive tissue-specific points of departure (POD). Our global gene expression analysis supported that BaP is genotoxic in mice and allowed the development of a detailed MOA. Toxicogenomics analysis in human lymphoblastoid TK6 cells demonstrated a high degree of consistency in perturbed pathways with animal tissues. Quantitatively, the PODs for traditional and transcriptional approaches were similar (liver 1.2 vs. 1.0 mg/kg-bw/day; lungs 0.8 vs. 3.7 mg/kg-bw/day; forestomach 0.5 vs. 7.4 mg/kg-bw/day). RA3, which applied toxicogenomics in the absence of apical toxicology data, demonstrates that this approach provides useful information in data-poor situations. Overall, our study supports the use of toxicogenomics as a relatively fast and cost-effective tool for hazard identification, preliminary evaluation of potential carcinogens, and carcinogenic potency, in addition to identifying current limitations and practical questions for future work.

Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells

The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion article, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or nongenotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and nongenotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4 hr and collected 0 hr, 4 hr, and 20 hr postexposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24 hr. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as nongenotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24 hr, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.

Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells

The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion article, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or nongenotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and nongenotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4 hr and collected 0 hr, 4 hr, and 20 hr postexposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24 hr. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as nongenotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24 hr, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.

Preparation of archival formalin-fixed paraffin-embedded mouse liver samples for use with the Agilent gene expression microarray platform

INTRODUCTION

Tissue samples are routinely formalin-fixed and paraffin-embedded (FFPE) for long term preservation. Gene expression analysis of archival FFPE tissues may advance knowledge of the molecular perturbations contributing to disease. However, formalin causes extensive degradation of RNA.

METHODS

We compared RNA quality/yield from FFPE samples using six commercial FFPE RNA extraction kits. In addition we compared four DNA microarray protocols for the Agilent 8×60K platform using 16year old FFPE mouse liver samples treated with phenobarbital or vehicle.

RESULTS

Despite low quality RNA, archival phenobarbital samples exhibited strong induction of the positive control genes Cyp2b9 and Cyp2b10 by quantitative real-time PCR (qPCR). We tested one- and two-color microarray designs and evaluated the effects of increasing the amount of hybridized cDNA. Canonical gene responders to phenobarbital were measurably induced under each experimental condition. Increasing the amount of labeled cDNA did not improve the overall signal intensity. One-color experiments yielded larger fold changes than two-color and the number of differentially expressed genes varied between protocols. Gene expression changes were validated by qPCR and literature searches. Individual protocols exhibited high rates of false positives; however, pathway analysis revealed that nine of the top ten canonical pathways were consistent across experiments. Genes that were differentially expressed in more than one experiment were more likely to be validated. Thus, we recommend that experiments on FFPE samples be done in duplicate to reduce false positives.

DISCUSSION

In this analysis of archival FFPE samples we were able to identify pathways that are consistent with phenobarbital's mechanism of action. Therefore, we conclude that FFPE samples can be used for meaningful microarray gene expression analyses.