👤 Vera Rogiers

This study employs animal-free Next Generation Risk Assessment (NGRA) principles to evaluate the safety of repeated dermal exposure to 2.5% (w/w) HC Yellow No. 13 (HCY13) hair dye. As multiple in silico tools consistently flagged hepatotoxic potential, likely due to HCY13's trifluoromethyl group, which is known to interfere with hepatic lipid metabolism, liver steatosis was chosen as the primary mode of action for evaluation. AOP-guided in vitro tests were conducted, exposing human stem cell-derived hepatic cells to varying HCY13 concentrations over 72 h. The expression of 11 lipid metabolism-related marker genes (AHR, PPARA, LXRA, APOB, ACOX1, CPT1A, FASN, SCD1, DGAT2, CD36, and PPARG) and triglyceride accumulation, a phenotypic hallmark of steatosis, were measured. PROAST software was used to calculate in vitro Points of Departure (PoD Show less

Non-alcoholic steatohepatitis (NASH) is a life-threatening stage of non-alcoholic fatty liver disease (NAFLD) for which no drugs have been approved. We have previously shown that human-derived hepatic in vitro models can be used to mimic key cellular mechanisms involved in the progression of NASH. In the present study, we first characterize the transcriptome of multiple in vitro NASH models. Subsequently, we investigate how elafibranor, which is a peroxisome proliferator-activated receptor (PPAR)-α/δ agonist that has recently failed a phase 3 clinical trial as a potential anti-NASH compound, modulates the transcriptome of these models. Finally, we compare the elafibranor-induced gene expression modulation to transcriptome data of patients with improved/resolved NAFLD/NASH upon bariatric surgery, which is the only proven clinical NASH therapy. Human whole genome microarrays were used for the transcriptomics evaluation of hepatic in vitro models. Comparison to publicly available clinical datasets was conducted using multiple bioinformatic application tools. Primary human hepatocytes (PHH), HepaRG, and human skin stem cell-derived hepatic progenitors (hSKP-HPC) exposed to NASH-inducing triggers exhibit up to 35% overlap with datasets of liver samples from NASH patients. Exposure of the in vitro NASH models to elafibranor partially reversed the transcriptional modulations, predicting an inhibition of toll-like receptor (TLR)-2/4/9-mediated inflammatory responses, NFκB-signaling, hepatic fibrosis, and leukocyte migration. These transcriptomic changes were also observed in the datasets of liver samples of patients with resolved NASH. Peroxisome Proliferator Activated Receptor Alpha (PPARA), PPARG Coactivator 1 Alpha (PPARGC1A), and Sirtuin 1 (SIRT1) were identified as the major common upstream regulators upon exposure to elafibranor. Analysis of the downstream mechanistic networks further revealed that angiopoietin Like 4 (ANGPTL4), pyruvate dehydrogenase kinase 4 (PDK4), and perilipin 2 (PLIN2), which are involved in the promotion of hepatic lipid accumulation, were also commonly upregulated by elafibranor in all in vitro NASH models. Contrarily, these genes were not upregulated in liver samples of patients with resolved NASH. Transcriptomics comparison between in vitro NASH models exposed to elafibranor and clinical datasets of NAFLD patients after bariatric surgery reveals commonly modulated anti-inflammatory responses, but discordant modulations of key factors in lipid metabolism. This discordant adverse effect of elafibranor deserves further investigation when assessing PPAR-α/δ agonism as a potential anti-NASH therapy. Show less

A long-term goal of numerous research projects is to identify biomarkers for in vitro systems predicting toxicity in vivo. Often, transcriptomics data are used to identify candidates for further evaluation. However, a systematic directory summarizing key features of chemically influenced genes in human hepatocytes is not yet available. To bridge this gap, we used the Open TG-GATES database with Affymetrix files of cultivated human hepatocytes incubated with chemicals, further sets of gene array data with hepatocytes from human donors generated in this study, and publicly available genome-wide datasets of human liver tissue from patients with non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular cancer (HCC). After a curation procedure, expression data of 143 chemicals were included into a comprehensive biostatistical analysis. The results are summarized in the publicly available toxicotranscriptomics directory ( http://wiki.toxbank.net/toxicogenomics-map/ ) which provides information for all genes whether they are up- or downregulated by chemicals and, if yes, by which compounds. The directory also informs about the following key features of chemically influenced genes: (1) Stereotypical stress response. When chemicals induce strong expression alterations, this usually includes a complex but highly reproducible pattern named 'stereotypical response.' On the other hand, more specific expression responses exist that are induced only by individual compounds or small numbers of compounds. The directory differentiates if the gene is part of the stereotypical stress response or if it represents a more specific reaction. (2) Liver disease-associated genes. Approximately 20 % of the genes influenced by chemicals are up- or downregulated, also in liver disease. Liver disease genes deregulated in cirrhosis, HCC, and NASH that overlap with genes of the aforementioned stereotypical chemical stress response include CYP3A7, normally expressed in fetal liver; the phase II metabolizing enzyme SULT1C2; ALDH8A1, known to generate the ligand of RXR, one of the master regulators of gene expression in the liver; and several genes involved in normal liver functions: CPS1, PCK1, SLC2A2, CYP8B1, CYP4A11, ABCA8, and ADH4. (3) Unstable baseline genes. The process of isolating and the cultivation of hepatocytes was sufficient to induce some stress leading to alterations in the expression of genes, the so-called unstable baseline genes. (4) Biological function. Although more than 2,000 genes are transcriptionally influenced by chemicals, they can be assigned to a relatively small group of biological functions, including energy and lipid metabolism, inflammation and immune response, protein modification, endogenous and xenobiotic metabolism, cytoskeletal organization, stress response, and DNA repair. In conclusion, the introduced toxicotranscriptomics directory offers a basis for a rationale choice of candidate genes for biomarker evaluation studies and represents an easy to use source of background information on chemically influenced genes. Show less