Benchmark Concentrations for Untargeted Metabolomics Versus Transcriptomics for Liver Injury Compounds in In Vitro Liver Models.

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.

A Unidirectional 96-Well Fluidic Culture Platform for Upstream Cell Dosing with Subsequent Downstream Nonlinear and Ascending Exposure Gradients for Real-Time and Cell-Based Toxicity Screening Environments.

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.

Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data.

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.

High-throughput confocal imaging of differentiated 3D liver-like spheroid cellular stress response reporters for identification of drug-induced liver injury liability.

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.

Organotypic 3D HepaRG Liver Model for Assessment of Drug-Induced Cholestasis.

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.

The Power of Resolution: Contextualized Understanding of Biological Responses to Liver Injury Chemicals Using High-throughput Transcriptomics and Benchmark Concentration Modeling.

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.

From the Cover: Three-Dimensional (3D) HepaRG Spheroid Model With Physiologically Relevant Xenobiotic Metabolism Competence and Hepatocyte Functionality for Liver Toxicity Screening.

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 invitro 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 invitro 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 50-100× 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.

A 3D in vitro model of differentiated HepG2 cell spheroids with improved liver-like properties for repeated dose high-throughput toxicity studies.

Immortalized hepatocyte cell lines show only a weak resemblance to primary hepatocytes in terms of gene expression and function, limiting their value in predicting drug-induced liver injury (DILI). Furthermore, primary hepatocytes cultured on two-dimensional tissue culture plastic surfaces rapidly dedifferentiate losing their hepatocyte functions and metabolic competence. We have developed a three-dimensional in vitro model using extracellular matrix-based hydrogel for long-term culture of the human hepatoma cell line HepG2. HepG2 cells cultured in this model stop proliferating, self-organize and differentiate to form multiple polarized spheroids. These spheroids re-acquire lost hepatocyte functions such as storage of glycogen, transport of bile salts and the formation of structures resembling bile canaliculi. HepG2 spheroids also show increased expression of albumin, urea, xenobiotic transcription factors, phase I and II drug metabolism enzymes and transporters. Consistent with this, cytochrome P450-mediated metabolism is significantly higher in HepG2 spheroids compared to monolayer cultures. This highly differentiated phenotype can be maintained in 384-well microtiter plates for at least 28 days. Toxicity assessment studies with this model showed an increased sensitivity in identifying hepatotoxic compounds with repeated dosing regimens. This simple and robust high-throughput-compatible methodology may have potential for use in toxicity screening assays and mechanistic studies and may represent an alternative to animal models for studying DILI.