Year: 2023
Type: article
Abstract: Humans and animals are regularly exposed to compounds that may have adverse effects on health. The Toxicity Forecaster (ToxCast) program was developed to use high throughput screening assays to quickly screen chemicals by measuring their effects on many biological end points. Many of these assays test for effects on cellular receptors and transcription factors (TFs), under the assumption that a toxicant may perturb normal signaling pathways in the cell. We hypothesized that we could reconstruct the intermediate proteins in these pathways that may be directly or indirectly affected by the toxicant, potentially revealing important physiological processes not yet tested for many chemicals. We integrate data from ToxCast with a human protein interactome to build toxicant signaling networks that contain physical and signaling protein interactions that may be affected as a result of toxicant exposure. To build these networks, we developed the EdgeLinker algorithm, which efficiently finds short paths in the interactome that connect the receptors to TFs for each toxicant. We performed multiple evaluations and found evidence suggesting that these signaling networks capture biologically relevant effects of toxicants. To aid in dissemination and interpretation, interactive visualizations of these networks are available at http://graphspace.org.
Source: Chemical Research in Toxicology
Institution: Virginia Tech
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $5,000
Funders: National Science Foundation, U.S. Department of Energy, U.S. Department of Agriculture, Virginia Polytechnic Institute and State University, National Institute of Food and Agriculture, Office of Science, Division of Graduate Education, Division of Chemical, Bioengineering, Environmental, and Transport Systems, Division of Molecular and Cellular Biosciences, Division of Biological Infrastructure, Biological and Environmental Research
Awards: G1325154587, G2237619613, G2288509925, Collaborative Research: ABI Innovation: Automated Prioritization and Design of Experiments to Validate and Improve Mathematical Models of Molecular Regulatory Systems, G541598085, G5435506357, G585293913, UNS: Integrated Tissue Engineering: A Gastrointestinal-Liver Platform to Investigate the Metabolism of Chemicals, Collarborative Research: A systems approach to understanding signaling networks in host-microbiome-parasite interactions
OpenAlex ID: W4384820747