Aqueous film-forming foams (AFFFs) are complex product mixtures that often contain per- and polyfluorinated alkyl substances (PFAS) to enhance fire suppression and protect firefighters. However, PFAS have been associated with a range of adverse health effects (e.g., liver and thyroid disease and cancer), and innovative approach methods to better understand their toxicity potential and identify safer alternatives are needed. In this study, we investigated a set of 30 substances (e.g., AFFF, PFAS, and clinical drugs) using differentiated cultures of human hepatocytes (HepaRG, 2D), high-throughput transcriptomics, deep learning of cell morphology images, and liver enzyme leakage assays with benchmark dose analysis to (1) predict the potency ranges for human liver injury, (2) delineate gene- and pathway-level transcriptomic points-of-departure for molecular hazard characterization and prioritization, (3) characterize human hepatocellular response similarities to inform regulatory read-across efforts, and (4) introduce an innovative approach to translate mechanistic hepatocellular response data to predict the potency ranges for PFAS-induced hepatomegaly in vivo. Collectively, these data fill important mechanistic knowledge gaps with PFAS/AFFF and represent a scalable platform to address the thousands of PFAS in commerce for greener chemistries and next-generation risk assessments.

Renal proximal tubule epithelial cells (RPTECs) are a primary site for kidney injury. We created two RPTEC lines from CD-1 mice immortalized with hTERT (human telomerase reverse transcriptase) or SV40 LgT antigen (Simian Virus 40 Large T antigen). Our hypothesis was that low-level, repeated exposure to subcytotoxic levels of 0.25–2.5 μM cisplatin (CisPt) or 12.5–100 μM aflatoxin B1 (AFB1) would activate distinctive genes and pathways in these two differently immortalized cell lines. RNA-seq showed only LgT cells responded to AFB1 with 1139 differentially expressed genes (DEGs) at 72 h. The data suggested that AFB1 had direct nephrotoxic properties on the LgT cells. However, both the cell lines responded to 2.5 μM CisPt from 3 to 96 h expressing 2000–5000 total DEGs. For CisPt, the findings indicated a coordinated transcriptional program of injury signals and repair from the expression of immune receptors with cytokine and chemokine secretion for leukocyte recruitment; robust expression of synaptic and substrate adhesion molecules (SAMs) facilitating the expression of neural and hormonal receptors, ion channels/transporters, and trophic factors; and the expression of nephrogenesis transcription factors. Pathway analysis supported the concept of a renal repair transcriptome. In summary, these cell lines provide in vitro models for the improved understanding of repeated renal injury and repair mechanisms. High-throughput screening against toxicant libraries should provide a wider perspective of their capabilities in nephrotoxicity.

High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-consuming manual process, we have developed a method that uses deep learning to automatically classify digital assay images. We have trained a convolutional neural network (CNN) to perform binary and multi-class classification. The binary classifier binned assay images into healthy (comparable to untreated controls) and altered (not comparable to untreated-control) classes with >98% accuracy; the multi-class classifier assigned "Healthy," "Intermediate" and "Altered" labels to assay images with >95% accuracy. Our dataset comprised high-resolution assay images from primary human hepatocytes and undifferentiated (proliferating) and differentiated 2D cultures of HepaRG cells. In this study we have focused on testing and fine-tuning various CNN architectures, including ResNet 34, 50 and 101. To visualize regions in the images that the CNN model used for classification, we employed Class Activation Maps (CAM). This allowed us to better understand the inner workings of the neural network and led to additional optimizations of the algorithm. The results indicate a strong correspondence between dosage and classifier-predicted scores, suggesting that these scores might be useful in further characterizing benchmark dose. Together, these results clearly demonstrate that deep-learning based automated image classification of cell morphology changes upon chemical-induced stress can yield highly accurate and reproducible assessments of cytotoxicity across a variety of cell types.

Introduction: Because of the importance to create in vitro screening tools that better mimic in vivo models, for exposure responses to drugs or toxicants, reproducible and adaptable culture platforms must evolve as approaches to replicate functions that are native to human organ systems. The Stairstep Waterfall (SsWaterfall) Fluidic Culture System is a unidirectional, multiwell, gravity-driven, cell culture system with micro-channels connecting 12 wells in each row (8-row replicates). Materials and Methods: The construct allows for the one-way flow of medium, parent and metabolite compounds, and the cellular signaling between connected culture wells while simultaneously operating as a cascading flow and discretized nonlinear dosing device. Initial cell seeding in SsWaterfall mimics traditional static plate protocols but thereafter functions with controlled flow and ramping concentration versus time exposure environments. Results: To investigate the utility of a microfluidic system for predicting drug efficacy and toxicity, we first delineate device design, fabrication, and characterization of a disposable dosing and gradient-exposure platform. We start with detailed characterizations by demarcating various features of the device, including low nonspecific binding, wettability, biocompatibility with multiple cell types, intra-well and inter-well flow, and efficient auto-mixing properties of dose compounds added into the platform. Discussion: We demonstrate the device utility using an example in sequential testing-screening drug toxicity and efficacy across wide-ranging inducible exposures, 0 → IC100, featuring real-time assessments. Conclusion: The integrated auto-gradient technology, gravity flow with stairstep pathways, offers end-users an easy and quick alternative to evaluate broad-ranging toxicity of new compound entities (e.g., pharmaceutical, environmental, agricultural, cosmetic) as opposed to traditional/arduous manual drug dilutions and/or expensive robotic technology.

Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.

A 5-day in vivo rat model was evaluated as an approach to estimate chemical exposures that may pose minimal risk by comparing benchmark dose (BMD) values for transcriptional changes in the liver and kidney to BMD values for toxicological endpoints from traditional toxicity studies. Eighteen chemicals, most having been tested by the National Toxicology Program in 2-year bioassays, were evaluated. Some of these chemicals are potent hepatotoxicants (eg, DE71, PFOA, and furan) in rodents, some exhibit toxicity but have minimal hepatic effects (eg, acrylamide and alpha,beta-thujone), and some exhibit little overt toxicity (eg, ginseng and milk thistle extract) based on traditional toxicological evaluations. Male Sprague Dawley rats were exposed once daily for 5 consecutive days by oral gavage to 8-10 dose levels for each chemical. Liver and kidney were collected 24 h after the final exposure and total RNA was assayed using high-throughput transcriptomics (HTT) with the rat S1500(+) platform. HTT data were analyzed using BMD Express 2 to determine transcriptional gene set BMD values. BMDS was used to determine BMD values for histopathological effects from chronic or subchronic toxicity studies. For many of the chemicals, the lowest transcriptional BMDs from the 5-day assays were within a factor of 5 of the lowest histopathological BMDs from the toxicity studies. These data suggest that using HTT in a 5-day in vivo rat model provides reasonable estimates of BMD values for traditional apical endpoints. This approach may be useful to prioritize chemicals for further testing while providing actionable data in a timely and cost-effective manner.

Cholestasis remains a major challenge in drug-induced liver injury, and therefore warrants identification of chemical entities that may lead to cholestasis. Recent advances in cell culture methods enable 3D spheroid models to remain viable for much longer periods of time than conventional sandwich cultures of primary human hepatocytes while maintaining native tissue-like functionality, such as drug metabolism activity, receptor signaling functionality, and physiological relevance. These spheroid models enable us to study repeated exposure effects associated with chemicals and their metabolites that may ultimately progress to cholestasis and liver injury. HepaRG cells cultured as spheroids are viable for more than 4 weeks with cytochrome P450 enzymatic activities comparable to ranges observed in freshly isolated/cryopreserved suspensions of primary human hepatocytes. HepaRG spheroids form bile canalicular structures with potential application as a model to study biliary excretion processes and intrahepatic obstruction of bile flow, leading to hepatocellular damage and death. In this chapter, we describe methods to culture 3D spheroids of HepaRG cells with extensive bile canalicular structures/networks, image transport of bile acid (cholyl-lysyl-fluorescein) to the bile canaliculi, and measure cholestatic drug-induced cytotoxicity.

Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.

Adaptive stress response pathways play a key role in the switch between adaptation and adversity, and are important in drug-induced liver injury. Previously, we have established an HepG2 fluorescent protein reporter platform to monitor adaptive stress response activation following drug treatment. HepG2 cells are often used in high-throughput primary toxicity screening, but metabolizing capacity in these cells is low and repeated dose toxicity testing inherently difficult. Here, we applied our bacterial artificial chromosome-based GFP reporter cell lines representing Nrf2 activation (Srxn1-GFP and NQO1-GFP), unfolded protein response (BiP-GFP and Chop-GFP), and DNA damage response (p21-GFP and Btg2-GFP) as long-term differentiated 3D liver-like spheroid cultures. All HepG2 GFP reporter lines differentiated into 3D spheroids similar to wild-type HepG2 cells. We systematically optimized the automated imaging and quantification of GFP reporter activity in individual spheroids using high-throughput confocal microscopy with a reference set of DILI compounds that activate these three stress response pathways at the transcriptional level in primary human hepatocytes. A panel of 33 compounds with established DILI liability was further tested in these six 3D GFP reporters in single 48 h treatment or 6 day daily repeated treatment. Strongest stress response activation was observed after 6-day repeated treatment, with the BiP and Srxn1-GFP reporters being most responsive and identified particular severe-DILI-onset compounds. Compounds that showed no GFP reporter activation in two-dimensional (2D) monolayer demonstrated GFP reporter stress response activation in 3D spheroids. Our data indicate that the application of BAC-GFP HepG2 cellular stress reporters in differentiated 3D spheroids is a promising strategy for mechanism-based identification of compounds with liability for DILI.

Botanical dietary supplements are complex mixtures with numerous potential sources of variation along the supply chain from raw plant material to the market. Approaches for determining sufficient similarity (ie, complex mixture read-across) may be required to extrapolate efficacy or safety data from a tested sample to other products containing the botanical ingredient(s) of interest. In this work, screening-level approaches for generating both chemical and biological-response profiles were used to evaluate the similarity of black cohosh (Actaea racemosa) and Echinacea purpurea samples to well-characterized National Toxicology Program (NTP) test articles. Data from nontargeted chemical analyses and gene expression of toxicologically important hepatic receptor pathways (aryl hydrocarbon receptor [AhR], constitutive androstane receptor [CAR], pregnane X receptor [PXR], farnesoid X receptor [FXR], and peroxisome proliferator-activated receptor alpha [PPARα]) in primary human hepatocyte cultures were used to determine similarity through hierarchical clustering. Although there were differences in chemical profiles across black cohosh samples, these differences were not reflected in the biological-response profiles. These findings highlight the complexity of biological-response dynamics that may not be reflected in chemical composition profiles. Thus, biological-response data could be used as the primary basis for determining similarity among black cohosh samples. Samples of E. purpurea displayed better correlation in similarity across chemical and biological-response measures. The general approaches described herein can be applied to complex mixtures with unidentified active constituents to determine when data from a tested mixture (eg, NTP test article) can be used for hazard identification of sufficiently similar mixtures, with the knowledge of toxicological targets informing assay selection when possible.

The use of three-dimensional (3-D) in vitro culture systems (spheroids, organoids) in biomolecular and drug discovery research has become increasingly popular. The popularity is due, in part, to a diminished reliance on animal bioassays and a desire to develop physiologically relevant cell culture systems that simulate the in vivo tissue microenvironment. Most evaluations of 3-D cultures are by confocal microscopy and high-content imaging; however, these technologies do not allow for detailed cellular morphologic assessments or permit basic hematoxylin and eosin histologic evaluations. There are few studies that have reported detailed processes for preparing 3-D cultures for paraffin embedding and subsequent use for histochemical or immunohistochemical staining. In an attempt to do so, we have developed a protocol to paraffin-embed human liver spheroids that can be sectioned with a microtome and mounted onto glass slides for routine histochemical and immunohistochemical staining and light microscopic evaluations.

Background The rat genome was sequenced in 2004 with the aim to improve human health altered by disease and environmental influences through gene discovery and animal model validation. Here, we report development and testing of a probe set for whole exome sequencing (WES) to detect sequence variants in exons and UTRs of the rat genome. Using an in-silico approach, we designed probes targeting the rat exome and compared captured mutations in cancer-related genes from four chemically induced rat tumor cell lines (C6, FAT7, DSL-6A/C1, NBTII) to validated cancer genes in the human database, Catalogue of Somatic Mutations in Cancer (COSMIC) as well as normal rat DNA. Paired, fresh frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) liver tissue from naive rats were sequenced to confirm known dbSNP variants and identify any additional variants. Results Informatics analysis of available gene annotation from rat RGSC6.0/rn6 RefSeq and Ensembl transcripts provided 223,636 unique exons representing a total of 26,365 unique genes and untranslated regions. Using this annotation and the Rn6 reference genome, an in-silico probe design generated 826,878 probe sequences of which 94.2% were uniquely aligned to the rat genome without mismatches. Further informatics analysis revealed 25,249 genes (95.8%) covered by at least one probe and 23,603 genes (93.5%) had every exon covered by one or more probes. We report high performance metrics from exome sequencing of our probe set and Sanger validation of annotated, highly relevant, cancer gene mutations as cataloged in the human COSMIC database, in addition to several exonic variants in cancer-related genes. Conclusions An in-silico probe set was designed to enrich the rat exome from isolated DNA. The platform was tested on rat tumor cell lines and normal FF and FFPE liver tissue. The method effectively captured target exome regions in the test DNA samples with exceptional sensitivity and specificity to obtain reliable sequencing data representing variants that are likely chemically induced somatic mutations. Genomic discovery conducted by means of high throughput WES queries should benefit investigators in discovering rat genomic variants in disease etiology and in furthering human translational research.

Table S2. PCR forward and reverse primer pairs for Sanger sequencing. (XLSX 10 kb)

Effective prediction of human responses to chemical and drug exposure is of critical importance in environmental toxicology research and drug development. While significant progress has been made to address this challenge using in vitro liver models, these approaches often fail due to inadequate tissue model functionality. Herein, we describe the development, optimization, and characterization of a novel three-dimensional ( 3D) spheroid model using differentiated HepaRG cells that achieve and maintain physiologically relevant levels of xenobiotic metabolism( CYP1A2, CYP2B6, and CYP3A4/5). This in vitro model maintains a stable phenotype over multiple weeks in both 96- and 384-well formats, supports highly reproducible tissue-like architectures and models pharmacologically- and environmentally important hepatic receptor pathways ( ie AhR, CAR, and PXR) analogous to primary human hepatocyte cultures. HepaRG spheroid cultures use 50-100x fewer cells than conventional two dimensional cultures, and enable the identification of metabolically activated toxicants. Spheroid size, time in culture and culture media composition were important factors affecting basal levels of xenobiotic metabolism and liver enzyme inducibility with activators of hepatic receptors AhR, CAR and PXR. Repeated exposure studies showed higher sensitivity than traditional 2D cultures in identifying compounds that cause liver injury and metabolism-dependent toxicity. This platform combines the well-documented impact of 3D culture configuration for improved tissue functionality and longevity with the requisite throughput and repeatability needed for year-over-year toxicology screening.

Adsorption onto granular activated carbon (GAC) is an effective treatment step for the removal of organic micropollutants (OMP) from wastewater or drinking water. Different test methods are commonly used for evaluating the capability of a GAC depending on the raw material and activation method. Several of these methods have been tested with one GAC and different size fractions obtained by grinding and sieving: batch experiments with two distinct modes of agitation (magnetic stirring and horizontal agitation), laboratory-scale adsorbers (LSA), rapid small-scale column tests (RSSCT) and differential column batch reactors (DCBR). It was found that magnetically stirred batches and DCBR are well suited for minimizing the effects of film diffusion. However, magnetic stirring reduces the particle size especially for large grains and long experimental periods. For fixed-bed adsorbers (LSA and RSSCT), it was shown that increasing the contact time of the water with the GAC at high throughputs can significantly improve the removal of OMP even after 100,000 bed volumes. However, permeability losses in RSSCT even with tap water have to be considered as a severe drawback. Correlations have been established between the loading achieved in a batch after 24 h and the carbon usage rate (CUR) at which the same loading was reached in an RSSCT. As expected, RSSCT removed OMP more efficiently than batch experiments at the same CUR.

301 Received August 22, 2016; Accepted November 1, 2016; Epub November 14, 2016; https://doi.org/10.14573/altex.1608221 systems for accurate and cost-effective prediction of human health outcomes. Development of increasingly sophisticated in vitro tissue models has accelerated in recent years, driven by the recognition that conventional two-dimensional (2D) cell culture formats do not adequately recapitulate the 3D arrangements of cells and extracellular matrix of tissues and

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.