Integration of temporal single cell cellular stress response activity with logic-ODE modeling reveals activation of ATF4-CHOP axis as a critical predictor of drug-induced liver injury

Human iPSC-derived liver co-culture spheroids to model liver fibrosis

The lack of adequate humanin vitromodels that recapitulate the cellular composition and response of the human liver to injury hampers the development of anti-fibrotic drugs. The goal of this study was to develop a human spheroid culture model to study liver fibrosis by using induced pluripotent stem cell (iPSC)-derived liver cells. iPSCs were independently differentiated towards hepatoblasts (iHepatoblasts), hepatic stellate cells (iHSCs), endothelial cells (iECs) and macrophages (iMΦ), before assembly into free floating spheroids by culturing cells in 96-well U-bottom plates and orbital shaking for up to 21 days to allow further maturation. Through transcriptome analysis, we show further maturation of iECs and iMΦ, the differentiation of the iHepatoblasts towards hepatocyte-like cells (iHeps) and the inactivation of the iHSCs by the end of the 3D culture. Moreover, these cultures display a similar expression of cell-specific marker genes (CYP3A4, PDGFRβ, CD31andCD68) and sensitivity to hepatotoxicity as spheroids made using freshly isolated primary human liver cells. Furthermore, we show the functionality of the iHeps and the iHSCs by mimicking liver fibrosis through iHep-induced iHSC activation, using acetaminophen. In conclusion, we have established a reproducible human iPSC-derived liver culture model that can be used to mimic fibrosisin vitroas a replacement of primary human liver derived 3D models. The model can be used to investigate pathways involved in fibrosis development and to identify new targets for chronic liver disease therapy.

Searching for LINCS to Stress: Using Text Mining to Automate Reference Chemical Curation

Adaptive stress response pathways (SRPs) restore cellular homeostasis following perturbation but may activate terminal outcomes like apoptosis, autophagy, or cellular senescence if disruption exceeds critical thresholds. Because SRPs hold the key to vital cellular tipping points, they are targeted for therapeutic interventions and assessed as biomarkers of toxicity. Hence, we are developing a public database of chemicals that perturb SRPs to enable new data-driven tools to improve public health. Here, we report on the automated text-mining pipeline we used to build and curate the first version of this database. We started with 100 reference SRP chemicals gathered from published biomarker studies to bootstrap the database. Second, we used information retrieval to find co-occurrences of reference chemicals with SRP terms in PubMed abstracts and determined pairwise mutual information thresholds to filter biologically relevant relationships. Third, we applied these thresholds to find 1206 putative SRP perturbagens within thousands of substances in the Library of Integrated Network-Based Cellular Signatures (LINCS). To assign SRP activity to LINCS chemicals, domain experts had to manually review at least three publications for each of 1206 chemicals out of 181,805 total abstracts. To accomplish this efficiently, we implemented a machine learning approach to predict SRP classifications from texts to prioritize abstracts. In 5-fold cross-validation testing with a corpus derived from the 100 reference chemicals, artificial neural networks performed the best (F1-macro = 0.678) and prioritized 2479/181,805 abstracts for expert review, which resulted in 457 chemicals annotated with SRP activities. An independent analysis of enriched mechanisms of action and chemical use class supported the text-mined chemical associations (p < 0.05): heat shock inducers were linked with HSP90 and DNA damage inducers to topoisomerase inhibition. This database will enable novel applications of LINCS data to evaluate SRP activities and to further develop tools for biomedical information extraction from the literature.

The integrated stress response-related expression of CHOP due to mitochondrial toxicity is a warning sign for DILI liability

BACKGROUND AND AIMS

Drug-induced liver injury (DILI) is one of the most frequent reasons for failure of drugs in clinical trials or market withdrawal. Early assessment of DILI risk remains a major challenge during drug development. Here, we present a mechanism-based weight-of-evidence approach able to identify certain candidate compounds with DILI liabilities due to mitochondrial toxicity.

METHODS

A total of 1587 FDA-approved drugs and 378 kinase inhibitors were screened for cellular stress response activation associated with DILI using an imaging-based HepG2 BAC-GFP reporter platform including the integrated stress response (CHOP), DNA damage response (P21) and oxidative stress response (SRXN1).

RESULTS

In total 389, 219 and 104 drugs were able to induce CHOP-GFP, P21-GFP and SRXN1-GFP expression at 50 μM respectively. Concentration response analysis identified 154 FDA-approved drugs as critical CHOP-GFP inducers. Based on predicted and observed (pre-)clinical DILI liabilities of these drugs, nine antimycotic drugs (e.g. butoconazole, miconazole, tioconazole) and 13 central nervous system (CNS) agents (e.g. duloxetine, fluoxetine) were selected for transcriptomic evaluation using whole-genome RNA-sequencing of primary human hepatocytes. Gene network analysis uncovered mitochondrial processes, NRF2 signalling and xenobiotic metabolism as most affected by the antimycotic drugs and CNS agents. Both the selected antimycotics and CNS agents caused impairment of mitochondrial oxygen consumption in both HepG2 and primary human hepatocytes.

CONCLUSIONS

Together, the results suggest that early pre-clinical screening for CHOP expression could indicate liability of mitochondrial toxicity in the context of DILI, and, therefore, could serve as an important warning signal to consider during decision-making in drug development.

A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury

Drug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been broadly performed. Gene coregulation network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improve mechanistic interpretation of toxicogenomic data. Here we performed an extensive concentration time course response-toxicogenomic study in the HepG2 cell line exposed to 20 DILI compounds, 7 reference compounds for stress response pathways, and 10 agonists for cytokines and growth factor receptors. We performed whole transcriptome targeted RNA sequencing to more than 500 conditions and applied weighted gene coregulated network analysis to the transcriptomics data followed by the identification of gene coregulated networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress response gene coregulated networks. We correlated gene coregulated networks with cell death onset and causal relationships of 67 critical target genes of these modules with the onset of cell death was evaluated using RNA interference screening. We identified GTPBP2, HSPA1B, IRF1, SIRT1, and TSC22D3 as essential modulators of DILI compound-induced cell death. These genes were also induced by DILI compounds in PHH. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI.

Application of high throughput in vitro metabolomics for hepatotoxicity mode of action characterization and mechanistic-anchored point of departure derivation: a case study with nitrofurantoin

Omics techniques have been increasingly recognized as promising tools for Next Generation Risk Assessment. Targeted metabolomics offer the advantage of providing readily interpretable mechanistic information about perturbed biological pathways. In this study, a high-throughput LC-MS/MS-based broad targeted metabolomics system was applied to study nitrofurantoin metabolic dynamics over time and concentration and to provide a mechanistic-anchored approach for point of departure (PoD) derivation. Upon nitrofurantoin exposure at five concentrations (7.5 µM, 15 µM, 20 µM, 30 µM and 120 µM) and four time points (3, 6, 24 and 48 h), the intracellular metabolome of HepG2 cells was evaluated. In total, 256 uniquely identified metabolites were measured, annotated, and allocated in 13 different metabolite classes. Principal component analysis (PCA) and univariate statistical analysis showed clear metabolome-based time and concentration effects. Mechanistic information evidenced the differential activation of cellular pathways indicative of early adaptive and hepatotoxic response. At low concentrations, effects were seen mainly in the energy and lipid metabolism, in the mid concentration range, the activation of the antioxidant cellular response was evidenced by increased levels of glutathione (GSH) and metabolites from the de novo GSH synthesis pathway. At the highest concentrations, the depletion of GSH, together with alternations reflective of mitochondrial impairments, were indicative of a hepatotoxic response. Finally, a metabolomics-based PoD was derived by multivariate PCA using the whole set of measured metabolites. This approach allows using the entire dataset and derive PoD that can be mechanistically anchored to established key events. Our results show the suitability of high throughput targeted metabolomics to investigate mechanisms of hepatoxicity and derive point of departures that can be linked to existing adverse outcome pathways and contribute to the development of new ones.

Chemically cross-linked hydrogels from repetitive protein arrays

Biomaterials for tissue regeneration must mimic the biophysical properties of the native physiological environment. A protein engineering approach allows the generation of protein hydrogels with specific and customised biophysical properties designed to suit a particular physiological environment. Herein, repetitive engineered proteins were successfully designed to form covalent molecular networks with defined physical characteristics able to sustain cell phenotype. Our hydrogel design was made possible by the incorporation of the SpyTag (ST) peptide and multiple repetitive units of the SpyCatcher (SC) protein that spontaneously formed covalent crosslinks upon mixing. Changing the ratios of the protein building blocks (ST:SC), allowed the viscoelastic properties and gelation speeds of the hydrogels to be altered and controlled. The physical properties of the hydrogels could readily be altered further to suit different environments by tuning the key features in the repetitive protein sequence. The resulting hydrogels were designed with a view to allow cell attachment and encapsulation of liver derived cells. Biocompatibility of the hydrogels was assayed using a HepG2 cell line constitutively expressing GFP. The cells remained viable and continued to express GFP whilst attached or encapsulated within the hydrogel. Our results demonstrate how this genetically encoded approach using repetitive proteins could be applied to bridge engineering biology with nanotechnology creating a level of biomaterial customisation previously inaccessible.

Oridonin ameliorates acetaminophen-induced acute liver injury through ATF4/PGC-1α pathway

Acetaminophen (APAP) overdose-induced acute liver injury (ALI) causes hepatocyte cell death, oxidative stress, and inflammation. Oridonin (Ori), a covalent NLRP3-inflammasome inhibitor, ameliorates APAP-induced ALI through an unclear molecular mechanism. This study found that Ori decreased hepatic cytochrome P450 2E1 level and increased glutathione content to prevent APAP metabolism, and then reduced the necrotic area, improved liver function, and inhibited APAP-induced proinflammatory cytokines and oxidative stress. Ori also decreased activating transcription factor 4 (ATF4) protein levels and increased peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) to reduce APAP-induced endoplasmic reticulum stress activation and mitochondrial dysfunction. Furthermore, western blot and luciferase assay found that ATF4 inhibited transcription in the PGC-1α promoter -507 to -495 region to reduce PGC-1α levels, while ATF4 knockdown neutralized the hepatoprotective effect of Ori. Molecular docking showed that Ori bound to ATF4's amino acid residue glutamate 302 through 6, 7, and 18 hydroxyl bands. Our findings demonstrated that Ori prevented metabolic activation of APAP and further inhibited the ATF4/PGC-1α pathway to alleviate APAP overdose-induced hepatic toxicity, which illuminated its potential therapeutic effects on ALI.

Highlights of the drug-induced liver injury literature for 2021

INTRODUCTION

In 2021, over 3,000 articles on Drug-Induced Liver Injury (DILI) were published, nearly doubling the annual number compared to 2011. This review selected DILI articles from 2021 we felt held the greatest interest and clinical relevance.

AREAS COVERED

A literature search was conducted using PubMed between 1 March 2021 and 28 February 2022. 86 articles were included. This review discusses new and established cases of hepatotoxins, including new FDA approvals and COVID-19 therapeutics. Developments in biomarkers and causality assessment methods are discussed. Updates from registries are also explored.

EXPERT OPINION

DILI diagnosis and prognostication remain challenging. Roussel Uclaf Causality Assessment Method (RUCAM) is the best option for determining causality and has been increasingly accepted by clinicians. Revised Electronic Causality Assessment Method (RECAM) may be more user-friendly and accurate but requires further validation. Quantitative systems pharmacology methods, such as DILIsym, are increasingly used to predict hepatotoxicity. Oncotherapeutic agents represent many newly approved and described causes of DILI. Such hepatotoxicity is deemed acceptable relative to the benefit these drugs offer. Drugs developed for non-life-threatening disorders may not show a favorable benefit-to-risk ratio and will be more difficult to approve. As the COVID-19 landscape evolves, its effect on DILI deserves further investigation.

Identification of proteome markers for drug-induced liver injury in zebrafish embryos

The zebrafish embryo (ZFE) is a promising alternative non-rodent model in toxicology, and initial studies suggested its applicability in detecting hepatic responses related to drug-induced liver injury (DILI). Here, we hypothesize that detailed analysis of underlying mechanisms of hepatotoxicity in ZFE contributes to the improved identification of hepatotoxic properties of compounds and to the reduction of rodents used for hepatotoxicity assessment. ZFEs were exposed to nine reference hepatotoxicants, targeted at induction of steatosis, cholestasis, and necrosis, and effects compared with negative controls. Protein profiles of the individual compounds were generated using LC-MS/MS. We identified differentially expressed proteins and pathways, but as these showed considerable overlap, phenotype-specific responses could not be distinguished. This led us to identify a set of common hepatotoxicity marker proteins. At the pathway level, these were mainly associated with cellular adaptive stress-responses, whereas single proteins could be linked to common hepatotoxicity-associated processes. Applying several stringency criteria to our proteomics data as well as information from other data sources resulted in a set of potential robust protein markers, notably Igf2bp1, Cox5ba, Ahnak, Itih3b.2, Psma6b, Srsf3a, Ces2b, Ces2a, Tdo2b, and Anxa1c, for the detection of adverse responses.

Model-based translation of DNA damage signaling dynamics across cell types

Interindividual variability in DNA damage response (DDR) dynamics may evoke differences in susceptibility to cancer. However, pathway dynamics are often studied in cell lines as alternative to primary cells, disregarding variability. To compare DDR dynamics in the cell line HepG2 with primary human hepatocytes (PHHs), we developed a HepG2-based computational model that describes the dynamics of DDR regulator p53 and targets MDM2, p21 and BTG2. We used this model to generate simulations of virtual PHHs and compared the results to those for PHH donor samples. Correlations between baseline p53 and p21 or BTG2 mRNA expression in the absence and presence of DNA damage for HepG2-derived virtual samples matched the moderately positive correlations observed for 50 PHH donor samples, but not the negative correlations between p53 and its inhibitor MDM2. Model parameter manipulation that affected p53 or MDM2 dynamics was not sufficient to accurately explain the negative correlation between these genes. Thus, extrapolation from HepG2 to PHH can be done for some DDR elements, yet our analysis also reveals a knowledge gap within p53 pathway regulation, which makes such extrapolation inaccurate for the regulator MDM2. This illustrates the relevance of studying pathway dynamics in addition to gene expression comparisons to allow reliable translation of cellular responses from cell lines to primary cells. Overall, with our approach we show that dynamical modeling can be used to improve our understanding of the sources of interindividual variability of pathway dynamics.

Susceptibility to develop cancer varies among people, partially due to differences in genetic background. Ideally, healthy human-derived cells are used to investigate intracellular signaling pathways and their interindividual variability contributing to cancer susceptibility. Because cells from healthy human tissue are difficult to obtain and culture for periods longer than a few days, cell lines are often used as substitute. However, it is unclear to what extent signaling dynamics in cell lines represent dynamics in healthy human tissue. We asked whether we could reproduce interindividual variability in DNA damage response gene expression in a set of 50 human liver cell donors. Therefore, we built a mathematical model that simulates temporal expression dynamics of the DNA damage response in the HepG2 liver cell line upon chemical activation and used the simulations to create virtual donors. Our virtual donors displayed similar relations between genes as the samples from human donors, provided that we adjusted the strength of specific molecular interactions. Thus, our approach can be used to examine the applicability of widely used cell systems to healthy human tissue in terms of their dynamic responses.

Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of “first activation” as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat-dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time-concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time-concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://anikaliu.shinyapps.io/dili_cascades), which allows users to query events of interest in more detail.

Understanding mechanisms from systems-scale biological data is of great relevance in toxicology as well as drug discovery; however how to generate causal hypotheses instead of correlations is by no means clear. In this work, we study the conserved temporal order of events and present an automatable framework to quantify and characterize time concordance across a large set of time-series. We apply this concept to events derived from time-resolved gene expression and histopathology from the TG-GATEs in vivo liver data as a case study. We were able to recover known events involved in the pathogenesis of Drug-Induced Liver Injury (DILI), and identify potentially novel pathway and transcription factors (TFs) which precede adverse histopathology. As complementary sources of evidence for causality, we additionally show how time concordance and prior knowledge on plausible interactions between TFs can be combined to derive causal hypotheses on the TFs’ mode of regulation and interaction partners. Overall, the results derived in our case study can serve as valuable hypothesis-free starting points for the development of Adverse Outcome Pathways for DILI, and demonstrate that our approach provides a novel angle to prioritize mechanistically relevant events.