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.

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.Electronic supplementary materialThe online version of this article (10.1007/s00204-018-2178-z) contains supplementary material, which is available to authorized users.

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-a (TNFa)-and interferon-g (IFNg)-producing T cells. Upon combined treatment, tumor cell proliferation was significantly decreased compared with single treated and untreated tumors. Furthermore, we showed that TNFa strongly enhanced cisplatin-induced apoptotic tumor cell death in a JNKdependent 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 TNFa 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 TNFa-induced enhanced cisplatin-mediated killing of tumor cells, together resulting in superior tumor eradication.

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.

T-cell acute lymphoblastic leukemia (T-ALL) frequently involves aberrant expression of TAL1 (T-cell acute lymphocytic leukemia 1) and LMO2, oncogenic members of the TAL1 transcriptional complex. Transcriptional activity of the TAL1-complex is thought to have a pivotal role in the transformation of thymocytes and is associated with a differentiation block and self-renewal. The transcription factor Forkhead Box P3 (FOXP3) was recently described to be expressed in a variety of malignancies including T-ALL. Here we show that increased FOXP3 levels negatively correlate with expression of genes regulated by the oncogenic TAL1-complex in human T-ALL patient samples as well as a T-ALL cell line ectopically expressing FOXP3. In these cells, FOXP3 expression results in altered regulation of cell cycle progression and reduced cell viability. Finally, we demonstrate that FOXP3 binds LMO2 in vitro, resulting in decreased interaction between LMO2 and TAL1, providing a molecular mechanism for FOXP3-mediated transcriptional modulation in T-ALL. Collectively, our findings provide initial evidence for a novel role of FOXP3 as a tumor suppressor in T-ALL through modulation of TAL1 transcriptional activity.

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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.

Background & Aims: One of the early key events of drug-induced liver injury (DILI) is the activation of adaptive stress responses, a cellular mechanism to overcome stress. Given the diversity of DILI outcomes and lack in understanding of population variability, we mapped the inter-individual variability in stress response activation to improve DILI prediction. Approach & Results: High-throughput transcriptome analysis of over 8,000 samples was performed in primary human hepatocytes of 50 individuals upon 8 to 24 h exposure to broad concentration ranges of stress inducers: tunicamycin to induce the unfolded protein response (UPR), diethyl maleate for the oxidative stress response, cisplatin for the DNA damage response and TNFα for NF-κB signalling. This allowed investigation of the inter-individual variability in concentration-dependent stress response activation, where the average of benchmark concentrations (BMCs) had a maximum difference of 864, 13, 13 and 259-fold between different hepatocytes for UPR, oxidative stress, DNA damage and NF-κB signalling-related genes, respectively. Hepatocytes from patients with liver disease resulted in less stress response activation. Using a population mixed-effect framework, the distribution of BMCs and maximum fold change were modelled, allowing simulation of smaller or larger PHH panel sizes. Small panel sizes systematically under-estimated the variance and resulted in low probabilities in estimating the correct variance for the human population. Moreover, estimated toxicodynamic variability factors were up to 2-fold higher than the standard uncertainty factor of 101/2 to account for population variability during risk assessment, exemplifying the need of data-driven variability factors. Conclusions: Overall, by combining high-throughput transcriptome analysis and population modelling, improved understanding of variability in stress response activation across the human population could be established, thereby contributing towards improved prediction of DILI.