Toxicogenomics directory of chemically exposed human hepatocytes

Chronic metabolic stress drives developmental programs and loss of tissue functions in non-transformed liver that mirror tumor states and stratify survival

Under chronic stress, cells must balance competing demands between cellular survival and tissue function. In metabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD/NASH), hepatocytes cooperate with structural and immune cells to perform crucial metabolic, synthetic, and detoxification functions despite nutrient imbalances. While prior work has emphasized stress-induced drivers of cell death, the dynamic adaptations of surviving cells and their functional repercussions remain unclear. Namely, we do not know which pathways and programs define cellular responses, what regulatory factors mediate (mal)adaptations, and how this aberrant activity connects to tissue-scale dysfunction and long-term disease outcomes. Here, by applying longitudinal single-cell multi -omics to a mouse model of chronic metabolic stress and extending to human cohorts, we show that stress drives survival-linked tradeoffs and metabolic rewiring, manifesting as shifts towards development-associated states in non-transformed hepatocytes with accompanying decreases in their professional functionality. Diet-induced adaptations occur significantly prior to tumorigenesis but parallel tumorigenesis-induced phenotypes and predict worsened human cancer survival. Through the development of a multi -omic computational gene regulatory inference framework and human in vitro and mouse in vivo genetic perturbations, we validate transcriptional (RELB, SOX4) and metabolic (HMGCS2) mediators that co-regulate and couple the balance between developmental state and hepatocyte functional identity programming. Our work defines cellular features of liver adaptation to chronic stress as well as their links to long-term disease outcomes and cancer hallmarks, unifying diverse axes of cellular dysfunction around core causal mechanisms.

SULT2B1-CS-DOCK2 axis regulates effector T-cell exhaustion in HCC microenvironment

BACKGROUND AND AIMS

HCC is a malignant disease. Compared with tyrosine kinase inhibitors (the classical therapy), immune checkpoint inhibitors are more effective in the treatment of HCC, despite their limited efficacy. Among these restricted factors, exhaustion of tumor-infiltrated lymphocytes, especially CD8 + T cells, is a core event. We aimed to determine the key factors contributing to CD8 + T-cell infiltration in HCC and investigate the underlying mechanisms.

APPROACH AND RESULTS

Using machine learning and multiplex immunohistochemistry analysis, we showed that dedicator of cytokinesis protein 2 (DOCK2) was a potential indicator of infiltrated CD8 + T cells in HCC. Using RNA sequencing, flow cytometry analysis, and mouse HCC models, we demonstrated that DOCK2 inactivation accounted for infiltrated CD8 + T-cell exhaustion in tumors. Using quasi-targeted metabolomics, mass spectrum, and mass cytometry by time of flight analysis, we found that cholesterol sulfate synthesized by sulfotransferase 2B1 in tumor cells suppressed DOCK2 enzymatic activity of T cells. Through virtual screening, molecular docking simulation, and experiments validation, we demonstrated that tolazamide reversed DOCK2 inactivation-mediated CD8 + T-cell exhaustion and enhanced anti-programmed death-ligand 1 antibody+apatinib immunotherapeutic effects on HCC.

CONCLUSIONS

This study indicates that DOCK2 controls CD8 + T-cell infiltration in HCC, and cholesterol sulfate synthesized by sulfotransferase 2B1 in tumor cells promotes effector T-cell exhaustion. The findings suggest that the usage of conventional drugs affects immunotherapy efficacy in HCC patients.

Capturing time-dependent activation of genes and stress-response pathways using transcriptomics in iPSC-derived renal proximal tubule cells

Transcriptomic analysis is a powerful method in the utilization of New Approach Methods (NAMs) for identifying mechanisms of toxicity and application to hazard characterization. With this regard, mapping toxicological events to time of exposure would be helpful to characterize early events. Here, we investigated time-dependent changes in gene expression levels in iPSC-derived renal proximal tubular-like cells (PTL) treated with five diverse compounds using TempO-Seq transcriptomics with the aims to evaluate the application of PTL for toxicity prediction and to report on temporal effects for the activation of cellular stress response pathways. PTL were treated with either 50 μM amiodarone, 10 μM sodium arsenate, 5 nM rotenone, or 300 nM tunicamycin over a temporal time course between 1 and 24 h. The TGFβ-type I receptor kinase inhibitor GW788388 (1 μM) was used as a negative control. Pathway analysis revealed the induction of key stress-response pathways, including Nrf2 oxidative stress response, unfolding protein response, and metal stress response. Early response genes per pathway were identified much earlier than 24 h and included HMOX1, ATF3, DDIT3, and several MT1 isotypes. GW788388 did not induce any genes within the stress response pathways above, but showed deregulation of genes involved in TGFβ inhibition, including downregulation of CYP24A1 and SERPINE1 and upregulation of WT1. This study highlights the application of iPSC-derived renal cells for prediction of cellular toxicity and sheds new light on the temporal and early effects of key genes that are involved in cellular stress response pathways.

Use of transcriptomics in hazard identification and next generation risk assessment: A case study with clothianidin

Toxicological risk assessment is essential in the evaluation and authorization of different classes of chemical substances. Genotoxicity and mutagenicity testing are of highest priority and rely on established in vitro systems with bacterial and mammalian cells, sometimes followed by in vivo testing using rodent animal models. Transcriptomic approaches have recently also shown their value to determine transcript signatures specific for genotoxicity. Here, we studied how transcriptomic data, in combination with in vitro tests with human cells, can be used for the identification of genotoxic properties of test compounds. To this end, we used liver samples from a 28-day oral toxicity study in rats with the pesticidal active substances imazalil, thiacloprid, and clothianidin, a neonicotinoid-type insecticide with, amongst others, known hepatotoxic properties. Transcriptomic results were bioinformatically evaluated and pointed towards a genotoxic potential of clothianidin. In vitro Comet and γH2AX assays in human HepaRG hepatoma cells, complemented by in silico analyses of mutagenicity, were conducted as follow-up experiments to check if the genotoxicity alert from the transcriptomic study is in line with results from a battery of guideline genotoxicity studies. Our results illustrate the combined use of toxicogenomics, classic toxicological data and new approach methods in risk assessment. By means of a weight-of-evidence decision, we conclude that clothianidin does most likely not pose genotoxic risks to humans.

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Analysis of clothianidin genotoxicity in silico, in vitro and in vivo.

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Application of a toxicogenomics approach to analyze genotoxicity.

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Weight-of-evidence decision supports classification as “non-genotoxic”.

Comprehensive Analysis of Aldehyde Dehydrogenases (ALDHs) and Its Significant Role in Hepatocellular Carcinoma

Oxidative DNA damage is closely related to the occurrence and progression of cancer. Oxidative stress plays an important role in alcohol-induced hepatocellular carcinoma (HCC). Aldehyde dehydrogenase (ALDH) is a family of enzymes that plays an essential role in the reducing oxidative damage. However, how ALDHs family affects alcohol-related HCC remains obscure. We aimed to explore the correlation between the differential expression of ALDHs in patients with HCC and pathological features, as well as the relationship between ALDHs and prognosis, and finally analyze the possible mechanism of ALDHs in targeted therapy of HCC. The data of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. This research explored the expression and prognostic values of ALDHs in HCC using Oncomine, UALCAN, Human Protein Atlas, cBioPortal, Kaplan-Meier plotter, GeneMANIA, Tumor Immune Estimation Resource, GEPIA databases, and WebGestalt. Low mRNA and protein expressions of ALDHs were found to be significantly associated with tumor grade and clinical cancer stages in HCC patients. In particular, the loss of ALDH expression is more obvious in Asians, and its effect on prognosis is far more significant than that in the White race. Our findings play an important role in the study of prognostic markers and anti-liver cancer therapeutic targets for the members of the ALDHs family, especially in patients with liver cancer in Asia.

A read-across case study on chronic toxicity of branched carboxylic acids (1): Integration of mechanistic evidence from new approach methodologies (NAMs) to explore a common mode of action

This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

Epigenomic and transcriptional profiling identifies impaired glyoxylate detoxification in NAFLD as a risk factor for hyperoxaluria

Epigenetic modifications (e.g. DNA methylation) in NAFLD and their contribution to disease progression and extrahepatic complications are poorly explored. Here, we use an integrated epigenome and transcriptome analysis of mouse NAFLD hepatocytes and identify alterations in glyoxylate metabolism, a pathway relevant in kidney damage via oxalate release-a harmful waste product and kidney stone-promoting factor. Downregulation and hypermethylation of alanine-glyoxylate aminotransferase (Agxt), which detoxifies glyoxylate, preventing excessive oxalate accumulation, is accompanied by increased oxalate formation after metabolism of the precursor hydroxyproline. Viral-mediated Agxt transfer or inhibiting hydroxyproline catabolism rescues excessive oxalate release. In human steatotic hepatocytes, AGXT is also downregulated and hypermethylated, and in NAFLD adolescents, steatosis severity correlates with urinary oxalate excretion. Thus, this work identifies a reduced capacity of the steatotic liver to detoxify glyoxylate, triggering elevated oxalate, and provides a mechanistic explanation for the increased risk of kidney stones and chronic kidney disease in NAFLD patients.

Contextualization of causal regulatory networks from toxicogenomics data applied to drug-induced liver injury

In recent years, network-based methods have become an attractive analytical approach for toxicogenomics studies. They can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach depicts the mechanisms of regulation that connect upstream regulators in signaling networks to their downstream gene targets. In this work, we applied CARNIVAL, a causal network contextualisation tool, to infer upstream signaling networks deregulated in drug-induced liver injury (DILI) from gene expression microarray data from the TG-GATEs database. We focussed on six compounds that induce observable histopathologies linked to DILI from repeated dosing experiments in rats. We compared responses in vitro and in vivo to identify potential cross-platform concordances in rats as well as network preservations between rat and human. Our results showed similarities of enriched pathways and network motifs between compounds. These pathways and motifs induced the same pathology in rats but not in humans. In particular, the causal interactions "LCK activates SOCS3, which in turn inhibits TFDP1" was commonly identified as a regulatory path among the fibrosis-inducing compounds. This potential pathology-inducing regulation illustrates the value of our approach to generate hypotheses that can be further validated experimentally.

Unconjugated p-cresol activates macrophage macropinocytosis leading to increased LDL uptake

Patients with chronic kidney disease (CKD) and end-stage renal disease suffer from increased cardiovascular events and cardiac mortality. Prior studies have demonstrated that a portion of this enhanced risk can be attributed to the accumulation of microbiota-derived toxic metabolites, with most studies focusing on the sulfonated form of p-cresol (PCS). However, unconjugated p-cresol (uPC) itself was never assessed due to rapid and extensive first-pass metabolism that results in negligible serum concentrations of uPC. These reports thus failed to consider the host exposure to uPC prior to hepatic metabolism. In the current study, not only did we measure the effect of altering the intestinal microbiota on lipid accumulation in coronary arteries, but we also examined macrophage lipid uptake and handling pathways in response to uPC. We found that atherosclerosis-prone mice fed a high-fat diet exhibited significantly higher coronary artery lipid deposits upon receiving fecal material from CKD mice. Furthermore, treatment with uPC increased total cholesterol, triglycerides, and hepatic and aortic fatty deposits in non-CKD mice. Studies employing an in vitro macrophage model demonstrated that uPC exposure increased apoptosis whereas PCS did not. Additionally, uPC exhibited higher potency than PCS to stimulate LDL uptake and only uPC induced endocytosis- and pinocytosis-related genes. Pharmacological inhibition of varying cholesterol influx and efflux systems indicated that uPC increased macrophage LDL uptake by activating macropinocytosis. Overall, these findings indicate that uPC itself had a distinct effect on macrophage biology that might have contributed to increased cardiovascular risk in patients with CKD.

Transcriptomic identification of HBx-associated hub genes in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies around the world. Among the risk factors involved in liver carcinogenesis, hepatitis B virus (HBV) X protein (HBx) is considered to be a key regulator in hepatocarcinogenesis. Whether HBx promotes or protects against HCC remains controversial, therefore exploring new HBx-associated genes is still important.

METHODS

HBx was overexpressed in HepG2, HepG2.2.15 and SMMC-7721 cell lines, primary mouse hepatocytes and livers of C57BL/6N mice. High-throughput RNA sequencing profiling of HepG2 cells with HBx overexpression and related differentially-expressed genes (DEGs), pathway enrichment analysis, protein-protein interaction networks (PPIs), overlapping analysis were conducted. In addition, Gene Expression Omnibus (GEO) and proteomic datasets of HBV-positive HCC datasets were used to verify the expression and prognosis of selected DEGs. Finally, we also evaluated the known oncogenic role of HBx by oncogenic array analysis.

RESULTS

A total of 523 DEGs were obtained from HBx-overexpressing HepG2 cells. Twelve DEGs were identified and validated in cells transiently transfected with HBx and three datasets of HBV-positive HCC transcription profiles. In addition, using the Kaplan-Meier plotter database, the expression levels of the twelve different genes were further analyzed to predict patient outcomes.

CONCLUSION

Among the 12 identified HBx-associated hub genes, HBV-positive HCC patients expressing ARG1 and TAT showed a good overall survival (OS) and relapse-free survival (RFS). Thus, ARG1 and TAT expression could be potential prognostic markers.

Relevance of In Vitro Transcriptomics for In Vivo Mode of Action Assessment

Recently, we reported an in vitro toxicogenomics comparison approach to categorize chemical substances according to similarities in their proposed toxicological modes of action. Use of such an approach for regulatory purposes requires, among others, insight into the extent of biological concordance between in vitro and in vivo findings. To that end, we applied the comparison approach to transcriptomics data from the Open TG-GATEs database for 137 substances with diverging modes of action and evaluated the outcomes obtained for rat primary hepatocytes and for rat liver. The results showed that a relatively small number of matches observed in vitro were also observed in vivo, whereas quite a large number of matches between substances were found to be relevant solely in vivo or in vitro. The latter could not be explained by physicochemical properties, leading to insufficient bioavailability or poor water solubility. Nevertheless, pathway analyses indicated that for relevant matches the mechanisms perturbed in vitro are consistent with those perturbed in vivo. These findings support the utility of the comparison approach as tool in mechanism-based risk assessment.

Comparing in vitro human liver models to in vivo human liver using RNA-Seq

The liver plays an important role in xenobiotic metabolism and represents a primary target for toxic substances. Many different in vitro cell models have been developed in the past decades. In this study, we used RNA-sequencing (RNA-Seq) to analyze the following human in vitro liver cell models in comparison to human liver tissue: cancer-derived cell lines (HepG2, HepaRG 3D), induced pluripotent stem cell-derived hepatocyte-like cells (iPSC-HLCs), cancerous human liver-derived assays (hPCLiS, human precision cut liver slices), non-cancerous human liver-derived assays (PHH, primary human hepatocytes) and 3D liver microtissues. First, using CellNet, we analyzed whether these liver in vitro cell models were indeed classified as liver, based on their baseline expression profile and gene regulatory networks (GRN). More comprehensive analyses using non-differentially expressed genes (non-DEGs) and differential transcript usage (DTU) were applied to assess the coverage for important liver pathways. Through different analyses, we noticed that 3D liver microtissues exhibited a high similarity with in vivo liver, in terms of CellNet (C/T score: 0.98), non-DEGs (10,363) and pathway coverage (highest for 19 out of 20 liver specific pathways shown) at the beginning of the incubation period (0 h) followed by a decrease during long-term incubation for 168 and 336 h. PHH also showed a high degree of similarity with human liver tissue and allowed stable conditions for a short-term cultivation period of 24 h. Using the same metrics, HepG2 cells illustrated the lowest similarity (C/T: 0.51, non-DEGs: 5623, and pathways coverage: least for 7 out of 20) with human liver tissue. The HepG2 are widely used in hepatotoxicity studies, however, due to their lower similarity, they should be used with caution. HepaRG models, iPSC-HLCs, and hPCLiS ranged clearly behind microtissues and PHH but showed higher similarity to human liver tissue than HepG2 cells. In conclusion, this study offers a resource of RNA-Seq data of several biological replicates of human liver cell models in vitro compared to human liver tissue.

MicroRNA hsa-miR-1301-3p Regulates Human ADH6, ALDH5A1 and ALDH8A1 in the Ethanol-Acetaldehyde-Acetate Metabolic Pathway

Alcohol dehydrogenases (ADHs) and aldehyde dehydrogenases (ALDHs) are vital enzymes involved in the metabolism of a variety of alcohols. Differences in the expression and enzymatic activity of human ADHs and ALDHs correlate with individual variability in metabolizing alcohols and drugs and in the susceptibility to alcoholic liver disease. MicroRNAs (miRNAs) function as epigenetic modulators to regulate the expression of drug-metabolizing enzymes. To characterize miRNAs that target ADHs and ALDHs in human liver cells, we carried out a systematic bioinformatics analysis to analyze free energies of the interaction between miRNAs and their cognate sequences in ADH and ALDH transcripts and then calculated expression correlations between miRNAs and their targeting ADH and ALDH genes using a public data base. Candidate miRNAs were selected to evaluate bioinformatic predictions using a series of biochemical assays. Our results showed that 11 miRNAs have the potential to modulate the expression of two ADH and seven ALDH genes in the human liver. We found that hsa-miR-1301-3p suppressed the expression of ADH6, ALDH5A1, and ALDH8A1 in liver cells and blocked their induction by ethanol. In summary, our results revealed that hsa-miR-1301-3p plays an important role in ethanol metabolism by regulating ADH and ALDH gene expression. SIGNIFICANCE STATEMENT: Systematic bioinformatics analysis showed that 11 microRNAs might play regulatory roles in the expression of two alcohol dehydrogenase (ADH) and seven aldehyde dehydrogenase (ALDH) genes in the human liver. Experimental evidences proved that hsa-miR-1301-3p suppressed the expression of ADH6, ALDH5A1, and ALDH8A1 in liver cells and decreased their inducibility by ethanol.

Bioinformatics analysis reveals novel core genes associated with nonalcoholic fatty liver disease and nonalcoholic steatohepatitis

Nonalcoholic fatty liver disease (NAFLD) is the most frequent liver disease and associated with a wide spectrum of hepatic disorders ranging from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). NASH is projected to become the most common indication for liver transplantation, and the annual incidence rate of NASH-related HCC is 5.29 cases per 1000 person-years. Owing to the epidemics of NAFLD and the unclear mechanism of NAFLD progression, it is important to elucidate the underlying NAFLD mechanisms in detail. NASH is mainly caused by the development of NAFL Therefore, it is also of great significance to understand the mechanism of progression from NAFL to NASH. Gene expression chip data for NAFLD and NASH were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between NAFLD and normal controls (called DEGs for NAFLD), as well as between NASH and normal tissue (called DEGs for NASH-Normal), and between NASH and NAFL tissue (called DEGs for NASH-NAFL). For DEGs for the NAFLD group, key genes were identified by studying the form of intersection. Potential functions of DEGs for NASH were then analyzed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A protein-protein interaction network (PPI) was constructed using the STRING database. A total of 249 DEGs and one key gene for NAFLD were identified. For NASH-Normal, 514 DEGs and 11 hub genes were identified, three of which were closely related to the survival analysis of HCC, and potentially closely related to progression from NASH to HCC. One key gene for NASH-NAFL (AKR1B10) was identified. These genes appear to mediate the molecular mechanism underlying NAFLD and may be promising biomarkers for the presence of NASH.

Enhanced activation of human NK cells by drug-exposed hepatocytes

Drug-induced liver injury (DILI) represents one of the major causes why drugs have to be withdrawn from the market. In this study, we describe a new interaction between drug-exposed hepatocytes and natural killer (NK) cells. In a previous genome-wide expression analysis of primary human hepatocytes that had been exposed to clinically relevant concentrations of 148 drugs, we found that several activating ligands for NK cell receptors were regulated by various drugs (e.g., valproic acid, ketoconazole, promethazine, isoniazid). Especially expression of the activating NKG2D ligands (MICA, MICB and ULBPs) and the NKp30 ligand B7-H6 were upregulated in primary human hepatocytes upon exposure to many different drugs. Using the human hepatocyte cell lines Huh7 and HepG2, we confirmed that protein levels of activating NK cell ligands were elevated after drug exposure. Hepatocyte cell lines or primary human hepatocytes co-cultivated with NK cells caused enhanced NK cell activation after pretreatment with drugs at in vivo relevant concentrations compared to solvent controls. Enhanced NK cell activation was evident by increased cytotoxicity against hepatocytes and interferon (IFN)-γ production. NK cell activation could be blocked by specific antibodies against activating NK cell receptors. These data support the hypothesis that NK cells can modulate drug-induced liver injury by direct interaction with hepatocytes resulting in cytotoxicity and IFN-γ production.

Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances

The first in vitro tests for developmental toxicity made use of rodent cells. Newer teratology tests, e.g. developed during the ESNATS project, use human cells and measure mechanistic endpoints (such as transcriptome changes). However, the toxicological implications of mechanistic parameters are hard to judge, without functional/morphological endpoints. To address this issue, we developed a new version of the human stem cell-based test STOP-tox_(UKN). For this purpose, the capacity of the cells to self-organize to neural rosettes was assessed as functional endpoint: pluripotent stem cells were allowed to differentiate into neuroepithelial cells for 6 days in the presence or absence of toxicants. Then, both transcriptome changes were measured (standard STOP-tox_(UKN)) and cells were allowed to form rosettes. After optimization of staining methods, an imaging algorithm for rosette quantification was implemented and used for an automated rosette formation assay (RoFA). Neural tube toxicants (like valproic acid), which are known to disturb human development at stages when rosette-forming cells are present, were used as positive controls. Established toxicants led to distinctly different tissue organization and differentiation stages. RoFA outcome and transcript changes largely correlated concerning (1) the concentration-dependence, (2) the time dependence, and (3) the set of positive hits identified amongst 24 potential toxicants. Using such comparative data, a prediction model for the RoFA was developed. The comparative analysis was also used to identify gene dysregulations that are particularly predictive for disturbed rosette formation. This 'RoFA predictor gene set' may be used for a simplified and less costly setup of the STOP-tox_(UKN) assay.

Comprehensive Evaluation of Organotypic and Microphysiological Liver Models for Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major concern for the pharmaceutical industry and constitutes one of the most important reasons for the termination of promising drug development projects. Reliable prediction of DILI liability in preclinical stages is difficult, as current experimental model systems do not accurately reflect the molecular phenotype and functionality of the human liver. As a result, multiple drugs that passed preclinical safety evaluations failed due to liver toxicity in clinical trials or postmarketing stages in recent years. To improve the selection of molecules that are taken forward into the clinics, the development of more predictive in vitro systems that enable high-throughput screening of hepatotoxic liabilities and allow for investigative studies into DILI mechanisms has gained growing interest. Specifically, it became increasingly clear that the choice of cell types and culture method both constitute important parameters that affect the predictive power of test systems. In this review, we present current 3D culture paradigms for hepatotoxicity tests and critically evaluate their utility and performance for DILI prediction. In addition, we highlight possibilities of these emerging platforms for mechanistic evaluations of selected drug candidates and present current research directions towards the further improvement of preclinical liver safety tests. We conclude that organotypic and microphysiological liver systems have provided an important step towards more reliable DILI prediction. Furthermore, we expect that the increasing availability of comprehensive benchmarking studies will facilitate model dissemination that might eventually result in their regulatory acceptance.

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (C_max) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC₁₀) yielded better metrics than higher toxicity thresholds (EC₅₀); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of C_max were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC₁₀ and C_max as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity.

Assessment of stem cell differentiation based on genome-wide expression profiles

In recent years, protocols have been established to differentiate stem and precursor cells into more mature cell types. However, progress in this field has been hampered by difficulties to assess the differentiation status of stem cell-derived cells in an unbiased manner. Here, we present an analysis pipeline based on published data and methods to quantify the degree of differentiation and to identify transcriptional control factors explaining differences from the intended target cells or tissues. The pipeline requires RNA-Seq or gene array data of the stem cell starting population, derived 'mature' cells and primary target cells or tissue. It consists of a principal component analysis to represent global expression changes and to identify possible problems of the dataset that require special attention, such as: batch effects; clustering techniques to identify gene groups with similar features; over-representation analysis to characterize biological motifs and transcriptional control factors of the identified gene clusters; and metagenes as well as gene regulatory networks for quantitative cell-type assessment and identification of influential transcription factors. Possibilities and limitations of the analysis pipeline are illustrated using the example of human embryonic stem cell and human induced pluripotent cells to generate 'hepatocyte-like cells'. The pipeline quantifies the degree of incomplete differentiation as well as remaining stemness and identifies unwanted features, such as colon- and fibroblast-associated gene clusters that are absent in real hepatocytes but typically induced by currently available differentiation protocols. Finally, transcription factors responsible for incomplete and unwanted differentiation are identified. The proposed method is widely applicable and allows an unbiased and quantitative assessment of stem cell-derived cells.This article is part of the theme issue 'Designer human tissue: coming to a lab near you'.