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.

Enabling novel paradigms: a biological questions-based approach to human chemical hazard and drug safety assessment.

Throughput needs, costs of time and resources, and concerns about the use of animals in hazard and safety assessment studies are fueling a growing interest in adopting new approach methodologies for use in product development and risk assessment. However, current efforts to define "next-generation risk assessment" vary considerably across commercial and regulatory sectors, and an a priori definition of the biological scope of data needed to assess hazards is generally lacking. We propose that the absence of clearly defined questions that can be answered during hazard assessment is the primary barrier to the generation of a paradigm flexible enough to be used across varying product development and approval decision contexts. Herein, we propose a biological questions-based approach (BQBA) for hazard and safety assessment to facilitate fit-for-purpose method selection and more efficient evidence-based decision-making. The key pillars of this novel approach are bioavailability, bioactivity, adversity, and susceptibility. This BQBA is compared with current hazard approaches and is applied in scenarios of varying pathobiological understanding and/or regulatory testing requirements. To further define the paradigm and key questions that allow better prediction and characterization of human health hazard, a multidisciplinary collaboration among stakeholder groups should be initiated.

A systems approach reveals species differences in hepatic stress response capacity.

To minimize the occurrence of unexpected toxicities in early phase preclinical studies of new drugs, it is vital to understand fundamental similarities and differences between preclinical species and humans. Species differences in sensitivity to acetaminophen (APAP) liver injury have been related to differences in the fraction of the drug that is bioactivated to the reactive metabolite N-acetyl-p-benzoquinoneimine (NAPQI). We have used physiologically based pharmacokinetic modeling to identify oral doses of APAP (300 and 1000 mg/kg in mice and rats, respectively) yielding similar hepatic burdens of NAPQI to enable the comparison of temporal liver tissue responses under conditions of equivalent chemical insult. Despite pharmacokinetic and biochemical verification of the equivalent NAPQI insult, serum biomarker and tissue histopathology analyses revealed that mice still exhibited a greater degree of liver injury than rats. Transcriptomic and proteomic analyses highlighted the stronger activation of stress response pathways (including the Nrf2 oxidative stress response and autophagy) in the livers of rats, indicative of a more robust transcriptional adaptation to the equivalent insult. Components of these pathways were also found to be expressed at a higher basal level in the livers of rats compared with both mice and humans. Our findings exemplify a systems approach to understanding differential species sensitivity to hepatotoxicity. Multiomics analysis indicated that rats possess a greater basal and adaptive capacity for hepatic stress responses than mice and humans, with important implications for species selection and human translation in the safety testing of new drug candidates associated with reactive metabolite formation.

Identifying multiscale translational safety biomarkers using a network-based systems approach.

Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4-dependent stress response and potential early safety signals.

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.

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.

Drug-induced liver injury (DILI) is the most prevalent adversity encountered in drug development and clinical settings leading to urgent needs to understand the underlying mechanisms. In this study, we have systematically investigated the dynamics of the activation of cellular stress response pathways and cell death outcomes upon exposure of a panel of liver toxicants using live cell imaging of fluorescent reporter cell lines. We established a comprehensive temporal dynamic response profile of a large set of BAC-GFP HepG2 cell lines representing the following components of stress signaling: i) unfolded protein response (UPR) [ATF4, XBP1, BIP and CHOP]; ii) oxidative stress [NRF2, SRXN1, HMOX1]; iii) DNA damage [P53, P21, BTG2, MDM2]; and iv) NF-κB pathway [A20, ICAM1]. We quantified the single cell GFP expression as a surrogate for endogenous protein expression using live cell imaging over > 60 h upon exposure to 14 DILI compounds at multiple concentrations. Using logic-based ordinary differential equation (Logic-ODE), we modelled the dynamic profiles of the different stress responses and extracted specific descriptors potentially predicting the progressive outcomes. We identified the activation of ATF4-CHOP axis of the UPR as the key pathway showing the highest correlation with cell death upon DILI compound perturbation. Knocking down main components of the UPR provided partial protection from compound-induced cytotoxicity, indicating a complex interplay among UPR components as well as other stress pathways. Our results suggest that a systematic analysis of the temporal dynamics of ATF4-CHOP axis activation can support the identification of DILI risk for new candidate drugs.

A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays.

Chemical-induced liver cancer occurs in rodents through well-characterized adverse outcome pathways. We hypothesized that measurement of the 6 most common molecular initiating events (MIEs) in liver cancer adverse outcome pathways in short-term assays using only gene expression will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in 2-year bioassays. We tested this hypothesis using transcript data from a rat liver microarray compendium consisting of 2013 comparisons of 146 chemicals administered at doses with previously established effects on rat liver tumor induction. Five MIEs were measured using previously characterized gene expression biomarkers composed of gene sets predictive for genotoxicity and activation of 1 or more xenobiotic receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Because chronic injury can be important in tumorigenesis, we also developed a biomarker for cytotoxicity that had a 96% balanced accuracy. Characterization of the genes in each biomarker set using the unsupervised TXG-MAP network model demonstrated that the genes were associated with distinct functional coexpression modules. Using the Toxicological Priority Index to rank chemicals based on their ability to activate the MIEs showed that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Balanced accuracies using thresholds derived from either TG-GATES or DrugMatrix data sets to predict tumorigenicity in independent sets of chemicals were up to 93%. These results show that a MIE-directed approach using only gene expression biomarkers could be used in short-term assays to identify chemicals and their doses that cause tumors.

Inflammation-associated suppression of metabolic gene networks in acute and chronic liver disease.

Inflammation has been recognized as essential for restorative regeneration. Here, we analyzed the sequential processes during onset of liver injury and subsequent regeneration based on time-resolved transcriptional regulatory networks (TRNs) to understand the relationship between inflammation, mature organ function, and regeneration. Genome-wide expression and TRN analysis were performed time dependently in mouse liver after acute injury by CCl4 (2 h, 8 h, 1, 2, 4, 6, 8, 16 days), as well as lipopolysaccharide (LPS, 24 h) and compared to publicly available data after tunicamycin exposure (mouse, 6 h), hepatocellular carcinoma (HCC, mouse), and human chronic liver disease (non-alcoholic fatty liver, HBV infection and HCC). Spatiotemporal investigation differentiated lobular zones for signaling and transcription factor expression. Acute CCl4 intoxication induced expression of gene clusters enriched for inflammation and stress signaling that peaked between 2 and 24 h, accompanied by a decrease of mature liver functions, particularly metabolic genes. Metabolism decreased not only in pericentral hepatocytes that underwent CCl4-induced necrosis, but extended to the surviving periportal hepatocytes. Proliferation and tissue restorative TRNs occurred only later reaching a maximum at 48 h. The same upstream regulators (e.g. inhibited RXR function) were implicated in increased inflammation and suppressed metabolism. The concomitant inflammation/metabolism TRN occurred similarly after acute LPS and tunicamycin challenges, in chronic mouse models and also in human liver diseases. Downregulation of metabolic genes occurs concomitantly to induce inflammation-associated genes as an early response and appears to be initiated by similar upstream regulators in acute and chronic liver diseases in humans and mice. In the acute setting, proliferation and restorative regeneration associated TRNs peak only later when metabolism is already suppressed.

A Novel Open Access Web Portal for Integrating Mechanistic and Toxicogenomic Study Results.

Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as gene set enrichment analysis, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the 2 approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for "hands-on" computer programming experience, the selection of 1 or more analysis methods (eg pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogeomics, an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements "best-practice" methods in computational biology. New study results are compared with over 4000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.

Characterisation of the NRF2 transcriptional network and its response to chemical insult in primary human hepatocytes: implications for prediction of drug-induced liver injury.

The transcription factor NRF2, governed by its repressor KEAP1, protects cells against oxidative stress. There is interest in modelling the NRF2 response to improve the prediction of clinical toxicities such as drug-induced liver injury (DILI). However, very little is known about the makeup of the NRF2 transcriptional network and its response to chemical perturbation in primary human hepatocytes (PHH), which are often used as a translational model for investigating DILI. Here, microarray analysis identified 108 transcripts (including several putative novel NRF2-regulated genes) that were both downregulated by siRNA targeting NRF2 and upregulated by siRNA targeting KEAP1 in PHH. Applying weighted gene co-expression network analysis (WGCNA) to transcriptomic data from the Open TG-GATES toxicogenomics repository (representing PHH exposed to 158 compounds) revealed four co-expressed gene sets or 'modules' enriched for these and other NRF2-associated genes. By classifying the 158 TG-GATES compounds based on published evidence, and employing the four modules as network perturbation metrics, we found that the activation of NRF2 is a very good indicator of the intrinsic biochemical reactivity of a compound (i.e. its propensity to cause direct chemical stress), with relatively high sensitivity, specificity, accuracy and positive/negative predictive values. We also found that NRF2 activation has lower sensitivity for the prediction of clinical DILI risk, although relatively high specificity and positive predictive values indicate that false positive detection rates are likely to be low in this setting. Underpinned by our comprehensive analysis, activation of the NRF2 network is one of several mechanism-based components that can be incorporated into holistic systems toxicology models to improve mechanistic understanding and preclinical prediction of DILI in man.

Function of inhibitor of Bruton's tyrosine kinase isoform α (IBTKα) in nonalcoholic steatohepatitis links autophagy and the unfolded protein response.

Nonalcoholic fatty liver disease (steatosis) is the most prevalent liver disease in the Western world. One of the advanced pathologies is nonalcoholic steatohepatitis (NASH), which is associated with induction of the unfolded protein response (UPR) and disruption of autophagic flux. However, the mechanisms by which these processes contribute to the pathogenesis of human diseases are unclear. Herein, we identify the α isoform of the inhibitor of Bruton's tyrosine kinase (IBTKα) as a member of the UPR, whose expression is preferentially translated during endoplasmic reticulum (ER) stress. We found that IBTKα is located in the ER and associates with proteins LC3b, SEC16A, and SEC31A and plays a previously unrecognized role in phagophore initiation from ER exit sites. Depletion of IBTKα helps prevent accumulation of autophagosome intermediates stemming from exposure to saturated free fatty acids and rescues hepatocytes from death. Of note, induction of IBTKα and the UPR, along with inhibition of autophagic flux, was associated with progression from steatosis to NASH in liver biopsies. These results indicate a function for IBTKα in NASH that links autophagy with activation of the UPR.

Test systems in drug discovery for hazard identification and risk assessment of human drug-induced liver injury.

INTRODUCTION: The liver is an important target for drug-induced toxicities. Early detection of hepatotoxic drugs requires use of well-characterized test systems, yet current knowledge, gaps and limitations of tests employed remains an important issue for drug development. Areas Covered: The current state of the science, understanding and application of test systems in use for the detection of drug-induced cytotoxicity, mitochondrial toxicity, cholestasis and inflammation is summarized. The test systems highlighted herein cover mostly in vitro and some in vivo models and endpoint measurements used in the assessment of small molecule toxic liabilities. Opportunities for research efforts in areas necessitating the development of specific tests and improved mechanistic understanding are highlighted. Expert Opinion: Use of in vitro test systems for safety optimization will remain a core activity in drug discovery. Substantial inroads have been made with a number of assays established for human Drug-induced Liver Injury. There nevertheless remain significant gaps with a need for improved in vitro tools and novel tests to address specific mechanisms of human Drug-Induced Liver Injury. Progress in these areas will necessitate not only models fit for application, but also mechanistic understanding of how chemical insult on the liver occurs in order to identify translational and quantifiable readouts for decision-making.

Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes.

The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived from the models would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), human primary hepatocytes (HPH) from TG, and mouse liver/HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal lower concordance for all methods. We observe that the baseline state of untreated cultured cells relative to untreated rat liver shows striking similarity with toxicant-exposed cells in vivo, indicating that gross systems level perturbation in the underlying networks in culture may contribute to the low concordance.

CHOP links endoplasmic reticulum stress to NF-κB activation in the pathogenesis of nonalcoholic steatohepatitis.

Free fatty acid induction of inflammation and cell death is an important feature of nonalcoholic steatohepatitis (NASH) and has been associated with disruption of the endoplasmic reticulum and activation of the unfolded protein response (UPR). After chronic UPR activation, the transcription factor CHOP (GADD153/DDIT3) triggers cell death; however, the mechanisms linking the UPR or CHOP to hepatoceullular injury and inflammation in the pathogenesis of NASH are not well understood. Using HepG2 and primary human hepatocytes, we found that CHOP induces cell death and inflammatory responses after saturated free fatty acid exposure by activating NF-κB through a pathway involving IRAK2 expression, resulting in secretion of cytokines IL-8 and TNFα directly from hepatocytes. TNFα facilitates hepatocyte death upon exposure to saturated free fatty acids, and secretion of both IL-8 and TNFα contribute to inflammation. Of interest, CHOP/NF-κB signaling is not conserved in primary rodent hepatocytes. Our studies suggest that CHOP plays a vital role in the pathophysiology of NASH by induction of secreted factors that trigger inflammation and hepatocellular death via a signaling pathway specific to human hepatocytes.

Xenobiotic perturbation of ER stress and the unfolded protein response.

The proper folding, assembly, and maintenance of cellular proteins is a highly regulated process and is critical for cellular homeostasis. Multiple cellular compartments have adapted their own systems to ensure proper protein folding, and quality control mechanisms are in place to manage stress due to the accumulation of unfolded proteins. When the accumulation of unfolded proteins exceeds the capacity to restore homeostasis, these systems can result in a cell death response. Unfolded protein accumulation in the endoplasmic reticulum (ER) leads to ER stress with activation of the unfolded protein response (UPR) governed by the activating transcription factor 6 (ATF6), inositol requiring enzyme-1 (IRE1), and PKR-like endoplasmic reticulum kinase (PERK) signaling pathways. Many xenobiotics have been shown to influence ER stress and UPR signaling with either pro-survival or pro-death features. The ultimate outcome is dependent on many factors including the mechanism of action of the xenobiotic, concentration of xenobiotic, duration of exposure (acute vs. chronic), cell type affected, nutrient levels, oxidative stress, state of differentiation, and others. Assessing perturbations in activation or inhibition of ER stress and UPR signaling pathways are likely to be informative parameters to measure when analyzing mechanisms of action of xenobiotic-induced toxicity.

Relating molecular properties and in vitro assay results to in vivo drug disposition and toxicity outcomes.

A primary goal of lead optimization is to identify compounds with improved absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. A number of reports have linked computed molecular properties to desirable in vivo ADMET outcomes, but a significant limitation of these analyses is the failure to control statistically for possible covariates. We examine the relationship between molecular properties and in vitro surrogate assays vs in vivo properties within 173 chemical series from a database of 3773 compounds with rodent pharmacokinetic and toxicology data. This approach identifies the following pairs of surrogates as most predictive among those examined: rat primary hepatocyte (RPH) cytolethality/volume of distribution (V(d)) for in vivo toxicology outcomes, scaled microsome metabolism/calculated logP for in vivo unbound clearance, and calculated logD/kinetic aqueous solubility for thermodynamic solubility. The impact of common functional group substitutions is examined and provides insights for compound design.

Human and environmental health challenges for the next decade (2010–2020).

The public health and environmental communities will face many challenges during the next decade. To identify significant issues that might be addressed as part of the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) scientific portfolio, an expert group of key government, academic, and industry scientists from around the world were assembled in 2009 to map the current and future landscape of scientific and regulatory challenges. The value of the scientific mapping exercise was the development of a tool which HESI, individual companies, research institutions, government agencies, and regulatory authorities can use to anticipate key challenges, place them into context, and thus strategically refine and expand scientific project portfolios into the future.

Toxicogenomics and cancer risk assessment: a framework for key event analysis and dose-response assessment for nongenotoxic carcinogens.

In order to determine a threshold for nongenotoxic carcinogens, the traditional risk assessment approach has been to identify a mode of action (MOA) with a nonlinear dose-response. The dose-response for one or more key event(s) linked to the MOA for carcinogenicity allows a point of departure (POD) to be selected from the most sensitive effect dose or no-effect dose. However, this can be challenging because multiple MOAs and key events may exist for carcinogenicity and oftentimes extensive research is required to elucidate the MOA. In the present study, a microarray analysis was conducted to determine if a POD could be identified following short-term oral rat exposure with two nongenotoxic rodent carcinogens, fenofibrate and methapyrilene, using a benchmark dose analysis of genes aggregated in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) biological processes, which likely encompass key event(s) for carcinogenicity. The gene expression response for fenofibrate given to rats for 2days was consistent with its MOA and known key events linked to PPARα activation. The temporal response from daily dosing with methapyrilene demonstrated biological complexity with waves of pathways/biological processes occurring over 1, 3, and 7days; nonetheless, the benchmark dose values were consistent over time. When comparing the dose-response of toxicogenomic data to tumorigenesis or precursor events, the toxicogenomics POD was slightly below any effect level. Our results suggest that toxicogenomic analysis using short-term studies can be used to identify a threshold for nongenotoxic carcinogens based on evaluation of potential key event(s) which then can be used within a risk assessment framework.

The future of drug safety testing: expanding the view and narrowing the focus.

Drug safety remains a high profile issue at a time when the cost and time required to develop a new drug are at an all time high. Balancing risk against the expected clinical benefit is the primary purpose of preclinical and clinical testing. We offer an expanded view on the application of predictive strategies and technologies to early safety decisions and suggestions to narrow the focus for improving preclinical safety testing to the problems that contribute most to adverse drug reactions.