BACKGROUND\nOBJECTIVES\nMETHODS\nRESULTS\nDISCUSSION\nUnderstanding the variability across the human population with respect to toxicodynamic responses after exposure to chemicals, such as... Show moreBACKGROUND\nOBJECTIVES\nMETHODS\nRESULTS\nDISCUSSION\nUnderstanding 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.\nWe 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.\n 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.\n 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.\nOverall, 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. Show less
Braak, B. ter; Niemeijer, M.; Boon, R.; Parmentier, C.; Baze , A.; Richert. L.; ... ; Water, B. van de 2021
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... Show moreVarious 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. Show less
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... Show moreThe 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. Show less
