Assessing bioactivity-exposure profiles of fruit and vegetable extracts in the BioMAP profiling system.

The ToxCast program has generated in vitro screening data on over a thousand chemicals to assess potential disruption of important biological processes and assist in hazard identification and chemical testing prioritization. Few results have been reported for complex mixtures. To extend these ToxCast efforts to mixtures, we tested extracts from 30 organically grown fruits and vegetables in concentration-response in the BioMAP® assays. BioMAP systems use human primary cells primed with endogenous pathway activators to identify phenotypic perturbations related to proliferation, inflammation, immunomodulation, and tissue remodeling. Clustering of bioactivity profiles revealed separation of these produce extracts and ToxCast chemicals. Produce extracts elicited 87 assay endpoint responses per item compared to 20 per item for ToxCast chemicals. On a molar basis, the produce extracts were 10 to 50-fold less potent and when constrained to the maximum testing concentration of the ToxCast chemicals, the produce extracts did not show activity in as many assay endpoints. Using intake adjusted measures of dose, the bioactivity potential was higher for produce extracts than for agrichemicals, as expected based on the comparatively small amounts of agrichemical residues present on conventionally grown produce. The evaluation of BioMAP readouts and the dose responses for produce extracts showed qualitative and quantitative differences from results with single chemicals, highlighting challenges in the interpretation of bioactivity data and dose-response from complex mixtures.

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.

An in vitro approach for prioritization and evaluation of chemical effects on glucocorticoid receptor mediated adipogenesis.

Rising obesity rates worldwide have socio-economic ramifications. While genetics, diet, and lack of exercise are major contributors to obesity, environmental factors may enhance susceptibility through disruption of hormone homeostasis and metabolic processes. The obesogen hypothesis contends that chemical exposure early in development may enhance adipocyte differentiation, thereby increasing the number of adipocytes and predisposing for obesity and metabolic disease. We previously developed a primary human adipose stem cell (hASC) assay to evaluate the effect of environmental chemicals on PPARG-dependent adipogenesis. Here, the assay was modified to determine the effects of chemicals on the glucocorticoid receptor (GR) pathway. In differentiation cocktail lacking the glucocorticoid agonist dexamethasone (DEX), hASCs do not differentiate into adipocytes. In the presence of GR agonists, adipocyte maturation was observed using phenotypic makers for lipid accumulation, adipokine secretion, and expression of key genes. To evaluate the role of environmental compounds on adipocyte differentiation, progenitor cells were treated with 19 prioritized compounds previously identified by ToxPi as having GR-dependent bioactivity, and multiplexed assays were used to confirm a GR-dependent mode of action. Five chemicals were found to be strong agonists. The assay was also modified to evaluate GR-antagonists, and 8/10 of the hypothesized antagonists inhibited adipogenesis. The in vitro bioactivity data was put into context with extrapolated human steady state concentrations (Css) and clinical exposure data (Cmax). These data support using a human adipose-derived stem cell differentiation assay to test the potential of chemicals to alter human GR-dependent adipogenesis.

Identifying genes that mediate anthracyline toxicity in immune cells.

The role of the immune system in response to chemotherapeutic agents remains elusive. The interpatient variability observed in immune and chemotherapeutic cytotoxic responses is likely, at least in part, due to complex genetic differences. Through the use of a panel of genetically diverse mouse inbred strains, we developed a drug screening platform aimed at identifying genes underlying these chemotherapeutic cytotoxic effects on immune cells. Using genome-wide association studies (GWAS), we identified four genome-wide significant quantitative trait loci (QTL) that contributed to the sensitivity of doxorubicin and idarubicin in immune cells. Of particular interest, a locus on chromosome 16 was significantly associated with cell viability following idarubicin administration (p = 5.01 × 10(-8)). Within this QTL lies App, which encodes amyloid beta precursor protein. Comparison of dose-response curves verified that T-cells in App knockout mice were more sensitive to idarubicin than those of C57BL/6J control mice (p < 0.05). In conclusion, the cellular screening approach coupled with GWAS led to the identification and subsequent validation of a gene involved in T-cell viability after idarubicin treatment. Previous studies have suggested a role for App in in vitro and in vivo cytotoxicity to anticancer agents; the overexpression of App enhances resistance, while the knockdown of this gene is deleterious to cell viability. Further investigations should include performing mechanistic studies, validating additional genes from the GWAS, including Ppfia1 and Ppfibp1, and ultimately translating the findings to in vivo and human studies.

Immune cell-based screening assay for response to anticancer agents: applications in pharmacogenomics.

BACKGROUND: Interpatient variability in immune and chemotherapeutic cytotoxic responses is likely due to complex genetic differences and is difficult to ascertain in humans. Through the use of a panel of genetically diverse mouse inbred strains, we developed a drug screening platform aimed at examining interstrain differences in viability on normal, noncancerous immune cells following chemotherapeutic cytotoxic insult. Drug effects were investigated by comparing selective chemotherapeutic agents, such as BEZ-235 and selumetinib, against conventional cytotoxic agents targeting multiple pathways, including doxorubicin and idarubicin. METHODS: Splenocytes were isolated from 36 isogenic strains of mice using standard procedures. Of note, the splenocytes were not stimulated to avoid attributing responses to pathways involved with cellular stimulation rather than toxicity. Cells were incubated with compounds on a nine-point logarithmic dosing scale ranging from 15 nM to 100 µM (37°C, 5% CO2). At 4 hours posttreatment, cells were labeled with antibodies and physiological indicator dyes and fixed with 4% paraformaldehyde. Cellular phenotypes (eg, viability) were collected and analyzed using flow cytometry. Dose-response curves with response normalized to the zero dose as a function of log concentration were generated using GraphPad Prism 6. RESULTS: Phenotypes were quantified using flow cytometry, yielding interstrain variation for measured endpoints in different immune cells. The flow cytometry assays produced over 16,000 data points that were used to generate dose-response curves. The more targeted agents, BEZ-235 and selumetinib, were less toxic to immune cells than the anthracycline agents. The calculated heritability for the viability of immune cells was higher with anthracyclines than the novel agents, making them better suited for downstream genetic analysis. CONCLUSION: Using this approach, we identify cell lines of variable sensitivity to chemotherapeutic agents and aim to identify robust, replicable endpoints of cellular response to drugs that provide the starting point for identifying candidate genes and cellular toxicity pathways for future validation in human studies.

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

UNLABELLED: 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.

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.

Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments.

Relative to microarrays, RNA-seq has been reported to offer higher precision estimates of transcript abundance, a greater dynamic range, and detection of novel transcripts. However, previous comparisons of the 2 technologies have not covered dose-response experiments that are relevant to toxicology. Male F344 rats were exposed for 13 weeks to 5 doses of bromobenzene, and liver gene expression was measured using both microarrays and RNA-seq. Multiple normalization methods were evaluated for each technology, and gene expression changes were statistically analyzed using both analysis of variance and benchmark dose (BMD). Fold-change values were highly correlated between the 2 technologies, whereas the -log p values showed lower correlation. RNA-seq detected fewer statistically significant genes at lower doses, but more significant genes based on fold change except when a negative binomial transformation was applied. Overlap in genes significant by both p value and fold change was approximately 30%-40%. Random sampling of the RNA-seq data showed an equivalent number of differentially expressed genes compared with microarrays at ~5 million reads. Quantitative RT-PCR of differentially expressed genes uniquely identified by each technology showed a high degree of confirmation when both fold change and p value were considered. The mean dose-response expression of each gene was highly correlated between technologies, whereas estimates of sample variability and gene-based BMD values showed lower correlation. Differences in BMD estimates and statistical significance may be due, in part, to differences in the dynamic range of each technology and the degree to which normalization corrects genes at either end of the scale.

Genetic testing and cancer risk management recommendations by physicians for at-risk relatives.

PURPOSE: Sequence-based cancer susceptibility testing results are described as negative, deleterious mutation or variant of uncertain significance. We studied the impact of different types of test results on clinical decision making. METHODS: Practicing physicians from five specialties in Texas completed an online case-based survey (n = 225). Respondents were asked to make genetic testing and management recommendations for healthy at-risk relatives of patients with cancer. RESULTS: When the patient carried a deleterious BRCA1 mutation or variant of uncertain significance, 98% and 82% of physicians, respectively, recommended testing of at-risk relatives (P < 0.0001). In both situations, comprehensive BRCA1/2 analysis was selected most with a corresponding 9-fold increase in unnecessary genetic testing costs. There was no difference in physicians with (n = 81) or without (n = 144) prior BRCA1/2 testing experience (P = 0.3869). Cancer risk management recommendations were most intense for the relative with a deleterious mutation compared with variant of uncertain significance, negative, or no testing with 63%, 13%, 5%, and 2%, respectively, recommending oophorectomy (P < 0.0001). CONCLUSIONS: Independent of experience, or specialty, physicians chose more comprehensive testing for healthy relatives than current guidelines recommend. In contrast, management decisions demonstrated the uncertainty associated with a variant of uncertain significance. Utilization of genetic professionals and education of physicians on family-centered genetic testing may improve efficacy and substantially reduce costs.