Lineage-dependent effects of aryl hydrocarbon receptor agonists contribute to liver tumorigenesis

Rodent cancer bioassays indicate that the aryl hydrocarbon receptor (AHR) agonist, 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD), causes increases in both hepatocytic and cholangiocytic tumors. Effects of AHR activation have been evaluated on rodent hepatic stem cells (rHpSCs) versus their descendants, hepatoblasts (rHBs), two lineage stages of multipotent, hepatic precursors with overlapping but also distinct phenotypic traits. This was made possible by defining the first successful culture conditions for ex vivo maintenance of rHpScs consisting of a substratum of hyaluronans and Kubota's medium (KM), a serum-free medium designed for endodermal stem/progenitor cells. Supplementation of KM with leukemia inhibitory factor elicited lineage restriction to rHBs. Cultures were treated with various AHR agonists including TCDD, 6-formylindolo-[3,2-b]carbazole (FICZ), and 3-3'-diindolylmethane (DIM) and then analyzed with a combination of immunocytochemistry, gene expression, and high-content image analysis. The AHR agonists increased proliferation of rHpSCs at concentrations producing a persistent AHR activation as indicated by induction of Cyp1a1. By contrast, treatment with TCDD resulted in a rapid loss of viability of rHBs, even though the culture conditions, in the absence of the agonists, were permissive for survival and expansion of rHBs. The effects were not observed with FICZ and at lower concentrations of DIM. Conclusion: Our findings are consistent with a lineage-dependent mode of action for AHR agonists in rodent liver tumorigenesis through selective expansion of rHpSCs in combination with a toxicity-induced loss of viability of rHBs. These lineage-dependent effects correlate with increased frequency of liver tumors. (Hepatology 2015;61:548-560

A Qualitative Modeling Approach for Whole Genome Prediction Using High-Throughput Toxicogenomics Data and Pathway-Based Validation

Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model was trained and validated using a large scale toxicogenomics database with gene expression data from exposure to heterogeneous chemicals from a wide range of classes (the Open TG-GATEs data base). The method of predictor selection was designed to allow high fidelity gene prediction from any pre-existing gene expression data set, regardless of animal species or data measurement platform. Predictive qualitative models were developed with this TG-GATES data that contained gene expression data of human primary hepatocytes with over 941 samples covering 158 compounds. A sequential forward search-based greedy algorithm, combining different fitting approaches and machine learning techniques, was used to find an optimal set of surrogate genes that predicted differential expression changes of the remaining genome. We then used pathway enrichment of up-regulated and down-regulated genes to assess the ability of a limited gene set to determine relevant patterns of tissue response. In addition, we compared prediction performance using the surrogate genes found from our greedy algorithm (referred to as the SV2000) with the landmark genes provided by existing technologies such as L1000 (Genometry) and S1500 (Tox21), finding better predictive performance for the SV2000. The ability of these predictive algorithms to predict pathway level responses is a positive step toward incorporating mode of action (MOA) analysis into the high throughput prioritization and testing of the large number of chemicals in need of safety evaluation

A cellular genetics approach identifies gene-drug interactions and pinpoints drug toxicity pathway nodes

New approaches to toxicity testing have incorporated high-throughput screening across a broad-range of in vitro assays to identify potential key events in response to chemical or drug treatment. To date, these approaches have primarily utilized repurposed drug discovery assays. In this study, we describe an approach that combines in vitro screening with genetic approaches for the experimental identification of genes and pathways involved in chemical or drug toxicity. Primary embryonic fibroblasts isolated from 32 genetically-characterized inbred mouse strains were treated in concentration-response format with 65 compounds, including pharmaceutical drugs, environmental chemicals, and compounds with known modes-of-action. Integrated cellular responses were measured at 24 and 72 h using high-content imaging and included cell loss, membrane permeability, mitochondrial function, and apoptosis. Genetic association analysis of cross-strain differences in the cellular responses resulted in a collection of candidate loci potentially underlying the variable strain response to each chemical. As a demonstration of the approach, one candidate gene involved in rotenone sensitivity, Cybb, was experimentally validated in vitro and in vivo. Pathway analysis on the combined list of candidate loci across all chemicals identified a number of over-connected nodes that may serve as core regulatory points in toxicity pathways

A core outcome set for future endometriosis research : an international consensus development study

Objective: To develop a core outcome set for endometriosis. Design: Consensus development study. Setting: International. Population: One hundred and sixteen healthcare professionals, 31 researchers and 206 patient representatives. Methods: Modified Delphi method and modified nominal group technique. Results: The final core outcome set includes three core outcomes for trials evaluating potential treatments for pain and other symptoms associated with endometriosis: overall pain; improvement in the most troublesome symptom; and quality of life. In addition, eight core outcomes for trials evaluating potential treatments for infertility associated with endometriosis were identified: viable intrauterine pregnancy confirmed by ultrasound; pregnancy loss, including ectopic pregnancy, miscarriage, stillbirth and termination of pregnancy; live birth; time to pregnancy leading to live birth; gestational age at delivery; birthweight; neonatal mortality; and major congenital abnormalities. Two core outcomes applicable to all trials were also identified: adverse events and patient satisfaction with treatment. Conclusions: Using robust consensus science methods, healthcare professionals, researchers and women with endometriosis have developed a core outcome set to standardise outcome selection, collection and reporting across future randomised controlled trials and systematic reviews evaluating potential treatments for endometriosis. Tweetable abstract: @coreoutcomes for future #endometriosis research have been developed @jamesmnduffy

A core outcome set for future endometriosis research: an international consensus development study

OBJECTIVE: To develop a core outcome set for endometriosis. DESIGN: Consensus development study. SETTING: International. POPULATION: One hundred and sixteen healthcare professionals, 31 researchers and 206 patient representatives. METHODS: Modified Delphi method and modified nominal group technique. RESULTS: The final core outcome set includes three core outcomes for trials evaluating potential treatments for pain and other symptoms associated with endometriosis: overall pain; improvement in the most troublesome symptom; and quality of life. In addition, eight core outcomes for trials evaluating potential treatments for infertility associated with endometriosis were identified: viable intrauterine pregnancy confirmed by ultrasound; pregnancy loss, including ectopic pregnancy, miscarriage, stillbirth and termination of pregnancy; live birth; time to pregnancy leading to live birth; gestational age at delivery; birthweight; neonatal mortality; and major congenital abnormalities. Two core outcomes applicable to all trials were also identified: adverse events and patient satisfaction with treatment. CONCLUSIONS: Using robust consensus science methods, healthcare professionals, researchers and women with endometriosis have developed a core outcome set to standardise outcome selection, collection and reporting across future randomised controlled trials and systematic reviews evaluating potential treatments for endometriosis. TWEETABLE ABSTRACT: @coreoutcomes for future #endometriosis research have been developed @jamesmnduffy