Updated adverse outcome pathway network for chemical-induced liver steatosis
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ORGANISM(S): Homo sapiens (human)
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PROVIDER: S-ONTX23 | biostudies-other |
REPOSITORIES: biostudies-other
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| S-ONTX24 | biostudies-other
Project description:Here, we present an in vitro test battery to analyze chemicals for their potential to induce liver triglyceride accumulation, a hallmark of liver steatosis. We describe steps for using HepG2 and HepaRG human hepatoma cells in conjunction with a combination of several in vitro assays covering the different molecular initiating events and key events of the respective adverse outcome pathway. This protocol is suitable for assessing single substance effects as well as mixtures allowing their classification as steatotic or non-steatotic. For complete details on the use and execution of this protocol, please refer to Luckert et al. (2018),1 Lichtenstein et al. (2020),2 and Knebel et al. (2019).3.
| S-EPMC10463250 | biostudies-literature
Project description:Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.
| S-EPMC7397752 | biostudies-literature
Project description:Risk assessors use liver endpoints in rodent toxicology studies to assess the safety of chemical exposures. Yet, rodent endpoints may not accurately reflect human responses. For this reason and others, human-based invitro models are being developed and anchored to adverse outcome pathways to better predict adverse human health outcomes. Here, a networked adverse outcome pathway-guided selection of biology-based assays for lipid uptake, lipid efflux, fatty acid oxidation, and lipid accumulation were developed. These assays were evaluated in a metabolically competent human hepatocyte cell model (HepaRG) exposed to compounds known to cause steatosis (amiodarone, cyclosporine A, and T0901317) or activate lipid metabolism pathways (troglitazone, Wyeth-14,643, and 22(R)-hydroxycholesterol). All of the chemicals activated at least one assay, however, only T0901317 and cyclosporin A dose-dependently increased lipid accumulation. T0901317 and cyclosporin A increased fatty acid uptake, decreased lipid efflux (inferred from apolipoprotein B100 levels), and increased fatty acid synthase protein levels. Using this biologically-based evaluation of key events regulating hepatic lipid levels, we demonstrated dysregulation of compensatory pathways that normally balance hepatic lipid levels. This approach may provide biological plausibility and data needed to increase confidence in linking invitro-based measurements to chemical effects on adverse human health outcomes.
| S-EPMC8130650 | biostudies-literature
Project description:Toxicological responses to stressors are more complex than the simple one-biological-perturbation to one-adverse-outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid in the understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present study introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using 2 example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses (or previously undefined emergent patterns of response) are introduced. Along with a companion article (part I), these concepts set the stage for the development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. The present study addresses one of the major themes identified through a Society of Environmental Toxicology and Chemistry Horizon Scanning effort focused on advancing the AOP framework. Environ Toxicol Chem 2018;37:1734-1748. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.
| S-EPMC6010347 | biostudies-literature
Project description:BackgroundIn light of the vulnerability of the developing brain, mixture risk assessment (MRA) for the evaluation of developmental neurotoxicity (DNT) should be implemented, since infants and children are co-exposed to more than one chemical at a time. One possible approach to tackle MRA could be to cluster DNT chemicals in a mixture on the basis of their mode of action (MoA) into 'similar' and 'dissimilar', but still contributing to the same adverse outcome, and anchor DNT assays to common key events (CKEs) identified in DNT-specific adverse outcome pathways (AOPs). Moreover, the use of human in vitro models, such as induced pluripotent stem cell (hiPSC)-derived neuronal and glial cultures would enable mechanistic understanding of chemically-induced adverse effects, avoiding species extrapolation.MethodsHiPSC-derived neural progenitors differentiated into mixed cultures of neurons and astrocytes were used to assess the effects of acute (3?days) and repeated dose (14?days) treatments with single chemicals and in mixtures belonging to different classes (i.e., lead(II) chloride and methylmercury chloride (heavy metals), chlorpyrifos (pesticide), bisphenol A (organic compound and endocrine disrupter), valproic acid (drug), and PCB138 (persistent organic pollutant and endocrine disrupter), which are associated with cognitive deficits, including learning and memory impairment in children. Selected chemicals were grouped based on their mode of action (MoA) into 'similar' and 'dissimilar' MoA compounds and their effects on synaptogenesis, neurite outgrowth, and brain derived neurotrophic factor (BDNF) protein levels, identified as CKEs in currently available AOPs relevant to DNT, were evaluated by immunocytochemistry and high content imaging analysis.ResultsChemicals working through similar MoA (i.e., alterations of BDNF levels), at non-cytotoxic (IC20/100), very low toxic (IC5), or moderately toxic (IC20) concentrations, induce DNT effects in mixtures, as shown by increased number of neurons, impairment of neurite outgrowth and synaptogenesis (the most sensitive endpoint as confirmed by mathematical modelling) and increase of BDNF levels, to a certain extent reproducing autism-like cellular changes observed in the brain of autistic children.ConclusionsOur findings suggest that the use of human iPSC-derived mixed neuronal/glial cultures applied to a battery of assays anchored to key events of an AOP network represents a valuable approach to identify mixtures of chemicals with potential to cause learning and memory impairment in children.
| S-EPMC7038628 | biostudies-literature
Project description:The comprehensive understanding of the precise mode of action and/or adverse outcome pathway (MoA/AOP) of chemicals has become a key step toward the development of a new generation of predictive toxicology tools. One of the challenges of this process is to test the feasibility of the molecular modelling approaches to explore key molecular initiating events (MIE) within the integrated strategy of MoA/AOP characterisation. The description of MoAs leading to toxicity and liver damage has been the focus of much interest. Growing evidence underlines liver PPAR? ligand-dependent activation as a key MIE in the elicitation of liver steatosis. Synthetic PPAR? full agonists are of special concern, since they may trigger a number of adverse effects not observed with partial agonists. In this study, molecular modelling was performed based on the PPAR? complexes with full agonists extracted from the Protein Data Bank. The receptor binding pocket was analysed, and the specific ligand-receptor interactions were identified for the most active ligands. A pharmacophore model was derived, and the most important pharmacophore features were outlined and characterised in relation to their specific role for PPAR? activation. The results are useful for the characterisation of the chemical space of PPAR? full agonists and could facilitate the development of preliminary filtering rules for the effective virtual ligand screening of compounds with PPAR? full agonistic activity.
| S-EPMC4057697 | biostudies-literature
Project description:Exposure to chemicals contributes to the development and progression of fatty liver, or steatosis, a process characterized by abnormal accumulation of lipids within liver cells. However, lack of knowledge on how chemicals cause steatosis has prevented any large-scale assessment of the 80,000+ chemicals in current use. To address this gap, we mined a large, publicly available toxicogenomic dataset associated with 18 known steatogenic chemicals to assess responses across assays (in vitro and in vivo) and species (i.e., rats and humans). We identified genes that were differentially expressed (DEGs) in rat in vivo, rat in vitro, and human in vitro studies in which rats or in vitro primary cell lines were exposed to the chemicals at different doses and durations. Using these DEGs, we performed pathway enrichment analysis, analyzed the molecular initiating events (MIEs) of the steatosis adverse outcome pathway (AOP), and predicted metabolite changes using metabolic network analysis. Genes indicative of oxidative stress were among the DEGs most frequently observed in the rat in vivo studies. Nox4, a pro-fibrotic gene, was down-regulated across these chemical exposure conditions. We identified eight genes (Cyp1a1, Egr1, Ccnb1, Gdf15, Cdk1, Pdk4, Ccna2, and Ns5atp9) and one pathway (retinol metabolism), associated with steatogenic chemicals and whose response was conserved across the three in vitro and in vivo systems. Similarly, we found the predicted metabolite changes, such as increases of saturated and unsaturated fatty acids, conserved across the three systems. Analysis of the target genes associated with the MIEs of the current steatosis AOP did not provide a clear association between these 18 chemicals and the MIEs, underlining the multi-factorial nature of this disease. Notably, our overall analysis implicated mitochondrial toxicity as an important and overlooked MIE for chemical-induced steatosis. The integrated toxicogenomics approach to identify genes, pathways, and metabolites based on known steatogenic chemicals, provide an important mean to assess development of AOPs and gauging the relevance of new testing strategies.
| S-EPMC6813744 | biostudies-literature
Project description:The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147-164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
| S-EPMC7820971 | biostudies-literature
Project description:We used microarrays to provide a transcriptomic signature of different types of cholestasis evoked by 3 different drugs and obstructive surgery Adverse outcome pathways (AOPs) have been recently introduced as tools to map the mechanisms underlying toxic events relevant for chemical risk assessment. AOPs particularly depict the linkage between a molecular initiating event and an adverse outcome through a number of intermediate key events. An AOP has been previously introduced for cholestatic liver injury. The objective of this study was to test the robustness of this AOP for different types of cholestatic insult and the in vitro to in vivo extrapolation. For this purpose, in vitro samples from human hepatoma HepaRG cell cultures were exposed to cholestatic drugs (i.e. intrahepatic cholestasis), while in vivo samples were obtained from livers of cholestatic mice (i.e. extrahepatic cholestasis). The occurrence of cholestasis in vitro was confirmed through analysis of bile transporter functionality and bile acid analysis. Transcriptomic analysis revealed inflammation and oxidative stress as key events in both types of cholestatic liver injury. Major transcriptional differences between intrahepatic and extrahepatic cholestatic liver insults were observed at the level of cell death and metabolism. Novel key events identified by pathway analysis included endoplasmic reticulum stress in intrahepatic cholestasis, and autophagy and necroptosis in both intrahepatic as extrahepatic cholestasis. This study demonstrates that AOPs constitute dynamic tools that should be frequently updated with new input information.
2020-06-16 | GSE152494 | GEO