InterPred: a webtool to predict chemical autofluorescence and luminescence interference.

High-throughput screening (HTS) research programs for drug development or chemical hazard assessment are designed to screen thousands of molecules across hundreds of biological targets or pathways. Most HTS platforms use fluorescence and luminescence technologies, representing more than 70% of the assays in the US Tox21 research consortium. These technologies are subject to interferent signals largely explained by chemicals interacting with light spectrum. This phenomenon results in up to 5-10% of false positive results, depending on the chemical library used. Here, we present the InterPred webserver (version 1.0), a platform to predict such interference chemicals based on the first large-scale chemical screening effort to directly characterize chemical-assay interference, using assays in the Tox21 portfolio specifically designed to measure autofluorescence and luciferase inhibition. InterPred combines 17 quantitative structure activity relationship (QSAR) models built using optimized machine learning techniques and allows users to predict the probability that a new chemical will interfere with different combinations of cellular and technology conditions. InterPred models have been applied to the entire Distributed Structure-Searchable Toxicity (DSSTox) Database (∼800,000 chemicals). The InterPred webserver is available at https://sandbox.ntp.niehs.nih.gov/interferences/.

BMDExpress 2: enhanced transcriptomic dose-response analysis workflow.

SUMMARY: A new version (version 2) of the genomic dose-response analysis software, BMDExpress, has been created. The software addresses the increasing use of transcriptomic dose-response data in toxicology, drug design, risk assessment and translational research. In this new version, we have implemented additional statistical filtering options (e.g. Williams' trend test), curve fitting models, Linux and Macintosh compatibility and support for additional transcriptomic platforms with up-to-date gene annotations. Furthermore, we have implemented extensive data visualizations, on-the-fly data filtering, and a batch-wise analysis workflow. We have also significantly re-engineered the code base to reflect contemporary software engineering practices and streamline future development. The first version of BMDExpress was developed in 2007 to meet an unmet demand for easy-to-use transcriptomic dose-response analysis software. Since its original release, however, transcriptomic platforms, technologies, pathway annotations and quantitative methods for data analysis have undergone a large change necessitating a significant re-development of BMDExpress. To that end, as of 2016, the National Toxicology Program assumed stewardship of BMDExpress. The result is a modernized and updated BMDExpress 2 that addresses the needs of the growing toxicogenomics user community. AVAILABILITY AND IMPLEMENTATION: BMDExpress 2 is available at https://github.com/auerbachs/BMDExpress-2/releases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential Receptor Tyrosine Kinase Phosphorylation in the Uterus of Rats Following Developmental Exposure to Tetrabromobisphenol A.

Tetrabromobisphenol A (TBBPA) is a brominated flame retardant that induces endometrial adenocarcinoma and other uterine tumors in Wistar Han rats; however, early molecular events or biomarkers of TBBPA exposure remain unknown. We investigated the effects of TBBPA on growth factor receptor activation (phospho-RTK) in uteri of rats following early-life exposures. Pregnant Wistar Han rats were exposed to TBBPA (0, 0.1, 25, 250 mg/kg/day) via oral gavage on gestation day 6 through weaning of pups (PND 21). Pups were exposed in utero, through lactation, and by daily gavage from PND 22 to PND 90. Uterine horns were collected (at PND 21, PND 33, PND 90) and formalin-fixed or frozen for histologic, immunohistochemical, phospho-RTK arrays, or western blot analysis. At PND 21, the phosphor-RTKs, FGFR2, FGFR3, TRKC and EPHA1 were significantly increased at different treatment concentrations. Several phospho-RTKs were also significantly overexpressed at PND 33 which included epithelial growth factor receptor (EGFR), Fibroblast Growth Factor Receptor 3-4 (FGFR2, FGFR3, FGFR4), insulin-like growth factor receptor 1 (IGF1R), INSR, AXL, MERTK, PDGFRa and b, RET, Tyrosine Kinase with Immunoglobulin Like and EGF Like Domains 1 and 2 (TIE1; TIE2), TRKA, VEGFR2 and 3, and EPHA1 at different dose treatments. EGFR, an RTK overexpressed in endometrial cancer in women, remained significantly increased for all treatment groups at PND 90. Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2) and IGF1R were overexpressed at PND 33 and remained increased through PND 90, although ERBB2 was statistically significant at PND 90. The phospho-RTKs, FGFR3, AXL, DTK, HGFR, TRKC, VEGFR1 and EPHB2 and 4 were also statistically significant at PND 90 at different dose treatments. The downstream effector, phospho-MAPK44/42 was also increased in uteri of treated rats. Our findings show RTKs are dysregulated following early life TBBPA exposures and their sustained activation may contribute to TBBPA-induced uterine tumors observed in rats later in life.

The Power of Resolution: Contextualized Understanding of Biological Responses to Liver Injury Chemicals Using High-throughput Transcriptomics and Benchmark Concentration Modeling.

Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.