Differences in TCDD-elicited gene expression profiles in human HepG2, mouse Hepa1c1c7 and rat H4IIE hepatoma cells

<p>Abstract</p> <p>Background</p> <p>2,3,7,8-Tetrachlorodibenzo-<it>p</it>-dioxin (TCDD) is an environmental contaminant that elicits a broad spectrum of toxic effects in a species-specific manner. Current risk assessment practices routinely extrapolate results from <it>in vivo </it>and <it>in vitro </it>rodent models to assess human risk. In order to further investigate the species-specific responses elicited by TCDD, temporal gene expression responses in human HepG2, mouse Hepa1c1c7 and rat H4IIE cells were compared.</p> <p>Results</p> <p>Microarray analysis identified a core set of conserved gene expression responses across species consistent with the role of AhR in mediating adaptive metabolic responses. However, significant species-specific as well as species-divergent responses were identified. Computational analysis of the regulatory regions of species-specific and -divergent responses suggests that dioxin response elements (DREs) are involved. These results are consistent with <it>in vivo </it>rat vs. mouse species-specific differential gene expression, and more comprehensive comparative DRE searches.</p> <p>Conclusions</p> <p>Comparative analysis of human HepG2, mouse Hepa1c1c7 and rat H4IIE TCDD-elicited gene expression responses is consistent with <it>in vivo </it>rat-mouse comparative gene expression studies, and more comprehensive comparative DRE searches, suggesting that AhR-mediated gene expression is species-specific.</p

In vivo – in vitro toxicogenomic comparison of TCDD-elicited gene expression in Hepa1c1c7 mouse hepatoma cells and C57BL/6 hepatic tissue

BACKGROUND: In vitro systems have inherent limitations in their ability to model whole organism gene responses, which must be identified and appropriately considered when developing predictive biomarkers of in vivo toxicity. Systematic comparison of in vitro and in vivo temporal gene expression profiles were conducted to assess the ability of Hepa1c1c7 mouse hepatoma cells to model hepatic responses in C57BL/6 mice following treatment with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). RESULTS: Gene expression analysis and functional gene annotation indicate that Hepa1c1c7 cells appropriately modeled the induction of xenobiotic metabolism genes in vivo. However, responses associated with cell cycle progression and proliferation were unique to Hepa1c1c7 cells, consistent with the cell cycle arrest effects of TCDD on rapidly dividing cells. In contrast, lipid metabolism and immune responses, representative of whole organism effects in vivo, were not replicated in Hepa1c1c7 cells. CONCLUSION: These results identified inherent differences in TCDD-mediated gene expression responses between these models and highlighted the limitations of in vitro systems in modeling whole organism responses, and additionally identified potential predictive biomarkers of toxicity

Comparative temporal and dose-dependent morphological and transcriptional uterine effects elicited by tamoxifen and ethynylestradiol in immature, ovariectomized mice

<p>Abstract</p> <p>Background</p> <p>Uterine temporal and dose-dependent histopathologic, morphometric and gene expression responses to the selective estrogen receptor modulator tamoxifen (TAM) were comprehensively examined to further elucidate its estrogen receptor-mediated effects. These results were systematically compared to the effects elicited by the potent estrogen receptor ligand 17α-ethynylestradiol (EE) to identify pathways similarly and uniquely modified by each compound.</p> <p>Results</p> <p>Three daily doses of 100 μg/kg TAM elicited a dose-dependent increase in uterine wet weight (UWW) in immature, ovariectomized C57BL/6 mice at 72 hrs with concurrent increases in luminal epithelial cell height (LECH), luminal circumference and glandular epithelial tubule number. Significant UWW and LECH increases were detected at 24 hrs after a single dose of 100 μg/kg TAM. cDNA microarray analysis identified 2235 differentially expressed genes following a single dose of 100 μg/kg TAM at 2, 4, 8, 12, 18 and 24 hrs, and at 72 hrs after three daily doses (3 × 24 hrs). Functional annotation of differentially expressed genes was associated with cell growth and proliferation, cytoskeletal organization, extracellular matrix modification, nucleotide synthesis, DNA replication, protein synthesis and turnover, lipid metabolism, glycolysis and immunological responses as is expected from the uterotrophic response. Comparative analysis of TAM and EE treatments identified 1209 common, differentially expressed genes, the majority of which exhibited similar profiles despite a temporal delay in TAM elicited responses. However, several conserved and treatment specific responses were identified that are consistent with proliferation (Fos, Cdkn1a, Anapc1), and water imbibition (Slc30a3, Slc30a5) responses elicited by EE.</p> <p>Conclusion</p> <p>Overall, TAM and EE share similar gene expression profiles. However, TAM responses exhibit lower efficacy, while responses unique to EE are consistent with the physiological differences elicited between compounds.</p

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials

A paradigm shift is taking place in risk assessment to replace animal models, reduce the number of economic resources, and refine the methodologies to test the growing number of chemicals and nanomaterials. Therefore, approaches such as transcriptomics, proteomics, and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. One promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of adverse outcome pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level toward an adverse effect outcome after exposure to a chemical. However, the implementation of omics-based approaches in the AOPs and their acceptance by the risk assessment community is still a challenge. Because the existing modules in the main repository for AOPs, the AOP Knowledge Base (AOP-KB), do not currently allow the integration of omics technologies, additional tools are required for omics-based data analysis and visualization. Here we show how WikiPathways can serve as a supportive tool to make omics data interoperable with the AOP-Wiki, part of the AOP-KB. Manual matching of key events (KEs) indicated that 67% could be linked with molecular pathways. Automatic connection through linkage of identifiers between the databases showed that only 30% of AOP-Wiki chemicals were found on WikiPathways. More loose linkage through gene names in KE and Key Event Relationships descriptions gave an overlap of 70 and 71%, respectively. This shows many opportunities to create more direct connections, for example with extended ontology annotations, improving its interoperability. This interoperability allows the needed integration of omics data linked to the molecular pathways with AOPs. A new AOP Portal on WikiPathways is presented to allow the community of AOP developers to collaborate and populate the molecular pathways that underlie the KEs of AOP-Wiki. We conclude that the integration of WikiPathways and AOP-Wiki will improve risk assessment because omics data will be linked directly to KEs and therefore allow the comprehensive understanding and description of AOPs. To make this assessment reproducible and valid, major changes are needed in both WikiPathways and AOP-Wiki

Tamoxifen-elicited uterotrophy: cross-species and cross-ligand analysis of the gene expression program

<p>Abstract</p> <p>Background</p> <p>Tamoxifen (TAM) is a well characterized breast cancer drug and selective estrogen receptor modulator (SERM) which also has been associated with a small increase in risk for uterine cancers. TAM's partial agonist activation of estrogen receptor has been characterized for specific gene promoters but not at the genomic level <it>in vivo</it>.Furthermore, reducing uncertainties associated with cross-species extrapolations of pharmaco- and toxicogenomic data remains a formidable challenge.</p> <p>Results</p> <p>A comparative ligand and species analysis approach was conducted to systematically assess the physiological, morphological and uterine gene expression alterations elicited across time by TAM and ethynylestradiol (EE) in immature ovariectomized Sprague-Dawley rats and C57BL/6 mice. Differential gene expression was evaluated using custom cDNA microarrays, and the data was compared to identify conserved and divergent responses. 902 genes were differentially regulated in all four studies, 398 of which exhibit identical temporal expression patterns.</p> <p>Conclusion</p> <p>Comparative analysis of EE and TAM differentially expressed gene lists suggest TAM regulates no unique uterine genes that are conserved in the rat and mouse. This demonstrates that the partial agonist activities of TAM extend to molecular targets in regulating only a subset of EE-responsive genes. Ligand-conserved, species-divergent expression of carbonic anhydrase 2 was observed in the microarray data and confirmed by real time PCR. The identification of comparable temporal phenotypic responses linked to related gene expression profiles demonstrates that systematic comparative genomic assessments can elucidate important conserved and divergent mechanisms in rodent estrogen signalling during uterine proliferation.</p

AOP: An R Package For Sufficient Causal Analysis in Pathway-based Screening of Drugs and Chemicals for Adversity

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharmacology, toxicology and risk assessment. By identifying these sufficient causal key events, we have fewer events to monitor for a pathway, thereby decreasing assay costs and time, while maximizing the value of the information. I have developed the "aop" package which uses back-door analysis of causal networks to identify these minimal sets of key events that are sufficient for making causal predictions. Availability and Implementation: The source for the aop package is available online at Github at https://github.com/DataSciBurgoon/aop and can be installed using the R devtools package. The aop package runs within the R statistical environment. The package has functions that can take pathways (as directed graphs) formatted as a Cytoscape JSON file as input, or pathways can be represented as directed graphs using the R/Bioconductor "graph" package. The "aop" package has functions that can perform backdoor analysis to identify the minimal set of key events for making causal predictions. Contact: [email protected]</jats:p

The AOPOntology: A Semantic Artificial Intelligence Tool for Predictive Toxicology

AbstractIntroductionToxicology needs artificial intelligence tools that can automate the prediction of toxicity. Today we are at an interesting nexus. We have thousands of chemicals in the environment that lack regulatory thresholds for determining risk. New high throughput in vitro testing methods are becoming available to test these chemicals. Causal Adverse Outcome Pathway Networks (CAOPN) are emerging that will allow us to make predictions based on perturbations of specific key events within the network. The AOPOntology was developed as infrastructure for this nexus, providing the ability to model and marry the data from the in vitro tests for the thousands of chemicals and place them within the CAOPN framework to facilitate adverse outcome predictions.Materials and MethodsThe AOPN is a functional specialized ontology that creates classes that model biological pathways and CAOPNs. Adverse outcome predictions are based on mathematical determinations of key events that are sufficient to infer adverse outcomes will occur, or biological information. These sufficiency relationships are captured in the AOPOntology and used by the semantic reasoners to make predictions.ResultsThe AOPOntology version 1.0 architecture is in place, and a CAOPN for steatosis demonstrates how causal network theory is used to make predictions. The AOPOntology is available at https://github.com/DataSciBurgoon/aop-ontology.DiscussionThe AOPOntology is a knowledge base for CAOPNs that one can use to make predictions about a chemical’s potential toxicity using in vitro high throughput and other assays.ConclusionsUsing CAOPNs and causal network theory one is able to predict potential toxicity for chemicals using in vitro high throughput and various high content screens.</jats:sec