Mapping Interindividual Variability of Toxicodynamics Using High-Throughput Transcriptomics and Primary Human Hepatocytes from Fifty Donors.

BACKGROUND: Understanding the variability across the human population with respect to toxicodynamic responses after exposure to chemicals, such as environmental toxicants or drugs, is essential to define safety factors for risk assessment to protect the entire population. Activation of cellular stress response pathways are early adverse outcome pathway (AOP) key events of chemical-induced toxicity and would elucidate the estimation of population variability of toxicodynamic responses. OBJECTIVES: We aimed to map the variability in cellular stress response activation in a large panel of primary human hepatocyte (PHH) donors to aid in the quantification of toxicodynamic interindividual variability to derive safety uncertainty factors. METHODS: High-throughput transcriptomics of over 8,000 samples in total was performed covering a panel of 50 individual PHH donors upon 8 to 24 h exposure to broad concentration ranges of four different toxicological relevant stimuli: tunicamycin for the unfolded protein response (UPR), diethyl maleate for the oxidative stress response (OSR), cisplatin for the DNA damage response (DDR), and tumor necrosis factor alpha (TNFα) for NF-κB signaling. Using a population mixed-effect framework, the distribution of benchmark concentrations (BMCs) and maximum fold change were modeled to evaluate the influence of PHH donor panel size on the correct estimation of interindividual variability for the various stimuli. RESULTS: Transcriptome mapping allowed the investigation of the interindividual variability in concentration-dependent stress response activation, where the average of BMCs had a maximum difference of 864-, 13-, 13-, and 259-fold between different PHHs for UPR, OSR, DDR, and NF-κB signaling-related genes, respectively. Population modeling revealed that small PHH panel sizes systematically underestimated the variance and gave low probabilities in estimating the correct human population variance. Estimated toxicodynamic variability factors of stress response activation in PHHs based on this dataset ranged between 1.6 and 6.3. DISCUSSION: Overall, by combining high-throughput transcriptomics and population modeling, improved understanding of interindividual variability in chemical-induced activation of toxicity relevant stress pathways across the human population using a large panel of plated cryopreserved PHHs was established, thereby contributing toward increasing the confidence of in vitro-based prediction of adverse responses, in particular hepatotoxicity. https://doi.org/10.1289/EHP11891.

Systematic transcriptome-based comparison of cellular adaptive stress response activation networks in hepatic stem cell-derived progeny and primary human hepatocytes.

Various adaptive cellular stress response pathways are critical in the pathophysiology of liver disease and drug-induced liver injury. Human-induced pluripotent stem cell (hiPSC)-derived hepatocyte-like cells (HLCs) provide a promising tool to study cellular stress response pathways, but in this context there is limited insight on how HLCs compare to other in vitro liver models. Here, we systematically compared the transcriptomic profiles upon chemical activation in HLCs, hiPSC, primary human hepatocytes (PHH) and HepG2 liver cancer cells. We used targeted RNA-sequencing to map concentration transcriptional response using benchmark concentration modeling for the various stress responses in the different test systems. We found that HLCs are very sensitive towards oxidative stress and inflammation conditions as corresponding genes were activated at over 3 fold lower concentrations of the corresponding pathway inducing compounds as compared to PHH. PHH were the most sensitive model when studying UPR related effects. Due to the non-proliferative nature of PHH and HLCs, these do not pose a good/sensitive model to pick up DNA damage responses, while hiPSC and HepG2 were more sensitive in these conditions. We envision that this study contributes to a better understanding on how HLCs can contribute to the assessment of cell physiological stress response activation to predict hepatotoxic events.

Utility of Extrapolating Human S1500+ Genes to the Whole Transcriptome: Tunicamycin Case Study.

The TempO-Seq S1500+ platform(s), now available for human, mouse, rat, and zebrafish, measures a discrete number of genes that are representative of biological and pathway co-regulation across the entire genome in a given species. While measurement of these genes alone provides a direct assessment of gene expression activity, extrapolating expression values to the whole transcriptome (~26 000 genes in humans) can estimate measurements of non-measured genes of interest and increases the power of pathway analysis algorithms by using a larger background gene expression space. Here, we use data from primary hepatocytes of 54 donors that were treated with the endoplasmic reticulum (ER) stress inducer tunicamycin and then measured on the human S1500+ platform containing ~3000 representative genes. Measurements for the S1500+ genes were then used to extrapolate expression values for the remaining human transcriptome. As a case study of the improved downstream analysis achieved by extrapolation, the "measured only" and "whole transcriptome" (measured + extrapolated) gene sets were compared. Extrapolation increased the number of significant genes by 49%, bringing to the forefront many that are known to be associated with tunicamycin exposure. The extrapolation procedure also correctly identified established tunicamycin-related functional pathways reflected by coordinated changes in interrelated genes while maintaining the sample variability observed from the "measured only" genes. Extrapolation improved the gene- and pathway-level biological interpretations for a variety of downstream applications, including differential expression analysis, gene set enrichment pathway analysis, DAVID keyword analysis, Ingenuity Pathway Analysis, and NextBio correlated compound analysis. The extrapolated data highlight the role of metabolism/metabolic pathways, the ER, immune response, and the unfolded protein response, each of which are key activities associated with tunicamycin exposure that were unrepresented or underrepresented in one or more of the analyses of the original "measured only" dataset. Furthermore, the inclusion of the extrapolated genes raised "tunicamycin" from third to first upstream regulator in Ingenuity Pathway Analysis and from sixth to second most correlated compound in NextBio analysis. Therefore, our case study suggests an approach to extend and enhance data from the S1500+ platform for improved insight into biological mechanisms and functional outcomes of diseases, drugs, and other perturbations.

The hepatotoxic fluoroquinolone trovafloxacin disturbs TNF- and LPS-induced p65 nuclear translocation in vivo and in vitro.

Idiosyncratic drug-induced liver injury (IDILI) is a severe disease that cannot be detected during drug development. It has been shown that hepatotoxicity of some compounds associated with IDILI becomes apparent when these are combined in vivo and in vitro with LPS or TNF. Among these compounds trovafloxacin (TVX) induced apoptosis in the liver and increased pro-inflammatory cytokines in mice exposed to LPS/TNF. The hepatocyte survival and the cytokine release after TNF/LPS stimulation relies on a pulsatile activation of NF-κB. We set out to evaluate the dynamic activation of NF-κB in response to TVX + TNF or LPS models, both in mouse and human cells. Remarkably, TVX prolonged the first translocation of NF-κB induced by TNF both in vivo and in vitro. The prolonged p65 translocation caused by TVX was associated with an increased phosphorylation of IKK and MAPKs and accumulation of inhibitors of NF-κB such as IκBα and A20 in HepG2. Coherently, TVX suppressed further TNF-induced NF-κB translocations in HepG2 leading to decreased transcription of ICAM-1 and inhibitors of apoptosis. TVX prolonged LPS-induced NF-κB translocation in RAW264.7 macrophages increasing the secretion of TNF. In summary, this study presents new, relevant insights into the mechanism of TVX-induced liver injury underlining the resemblance between mouse and human models. In this study we convincingly show that regularly used toxicity models provide a coherent view of relevant pathways for IDILI. We propose that assessment of the kinetics of activation of NF-κB and MAPKs is an appropriate tool for the identification of hepatotoxic compounds during drug development.

Model-based identification of TNFα-induced IKKß-mediated and IκBα-mediated regulation of NFκB signal transduction as a tool to quantify the impact of drug-induced liver injury compounds.

Drug-induced liver injury (DILI) has become a major problem for patients and for clinicians, academics and the pharmaceutical industry. To date, existing hepatotoxicity test systems are only poorly predictive and the underlying mechanisms are still unclear. One of the factors known to amplify hepatotoxicity is the tumor necrosis factor alpha (TNFα), especially due to its synergy with commonly used drugs such as diclofenac. However, the exact mechanism of how diclofenac in combination with TNFα induces liver injury remains elusive. Here, we combined time-resolved immunoblotting and live-cell imaging data of HepG2 cells and primary human hepatocytes (PHH) with dynamic pathway modeling using ordinary differential equations (ODEs) to describe the complex structure of TNFα-induced NFκB signal transduction and integrated the perturbations of the pathway caused by diclofenac. The resulting mathematical model was used to systematically identify parameters affected by diclofenac. These analyses showed that more than one regulatory module of TNFα-induced NFκB signal transduction is affected by diclofenac, suggesting that hepatotoxicity is the integrated consequence of multiple changes in hepatocytes and that multiple factors define toxicity thresholds. Applying our mathematical modeling approach to other DILI-causing compounds representing different putative DILI mechanism classes enabled us to quantify their impact on pathway activation, highlighting the potential of the dynamic pathway model as a quantitative tool for the analysis of DILI compounds.

Vaccine-induced tumor necrosis factor-producing T cells synergize with cisplatin to promote tumor cell death.

PURPOSE: Cancer immunotherapy, such as vaccination, is an increasingly successful treatment modality, but its interaction with chemotherapy remains largely undefined. Therefore, we explored the mechanism of synergy between vaccination with synthetic long peptides (SLP) of human papillomavirus type 16 (HPV16) and cisplatin in a preclinical tumor model for HPV16. EXPERIMENTAL DESIGN: SLP vaccination in this preclinical tumor model allowed the elucidation of novel mechanisms of synergy between chemo- and immunotherapy. By analyzing the tumor immune infiltrate, we focused on the local intratumoral effects of chemotherapy, vaccination, or the combination. RESULTS: Of several chemotherapeutic agents, cisplatin synergized best with SLP vaccination in tumor eradication, without requirement for the maximum-tolerated dose (MTD). Upon SLP vaccination, tumors were highly infiltrated with HPV-specific, tumor necrosis factor-α (TNFα)- and interferon-γ (IFNγ)-producing T cells. Upon combined treatment, tumor cell proliferation was significantly decreased compared with single treated and untreated tumors. Furthermore, we showed that TNFα strongly enhanced cisplatin-induced apoptotic tumor cell death in a JNK-dependent manner. This is consistent with upregulation of proapoptotic molecules and with enhanced cell death in vivo upon combined SLP vaccination and cisplatin treatment. In vivo neutralization of TNFα significantly reduced the antitumor responses induced by the combined treatment. CONCLUSION: Taken together, our data show that peptide vaccination with cisplatin treatment leads to decreased tumor cell proliferation and TNFα-induced enhanced cisplatin-mediated killing of tumor cells, together resulting in superior tumor eradication.

Quantitative high content imaging of cellular adaptive stress response pathways in toxicity for chemical safety assessment.

Over the past decade, major leaps forward have been made on the mechanistic understanding and identification of adaptive stress response landscapes underlying toxic insult using transcriptomics approaches. However, for predictive purposes of adverse outcome several major limitations in these approaches exist. First, the limited number of samples that can be analyzed reduces the in depth analysis of concentration-time course relationships for toxic stress responses. Second these transcriptomics analysis have been based on the whole cell population, thereby inevitably preventing single cell analysis. Third, transcriptomics is based on the transcript level, totally ignoring (post)translational regulation. We believe these limitations are circumvented with the application of high content analysis of relevant toxicant-induced adaptive stress signaling pathways using bacterial artificial chromosome (BAC) green fluorescent protein (GFP) reporter cell-based assays. The goal is to establish a platform that incorporates all adaptive stress pathways that are relevant for toxicity, with a focus on drug-induced liver injury. In addition, cellular stress responses typically follow cell perturbations at the subcellular organelle level. Therefore, we complement our reporter line panel with reporters for specific organelle morphometry and function. Here, we review the approaches of high content imaging of cellular adaptive stress responses to chemicals and the application in the mechanistic understanding and prediction of chemical toxicity at a systems toxicology level.