UC San Diego

Abstract Background Small molecule ligands often have multiple effects on the transcriptional program of a cell: they trigger a receptor specific response and additional, indirect responses ("side effects"). Distinguishing those responses is important for understanding side effects of drugs and for elucidating molecular mechanisms of toxic chemicals. Results We explored this problem by exposing cells to the environmental contaminant benzo-[a]-pyrene (B[a]P). B[a]P exposure activates the aryl hydrocarbon receptor (Ahr) and causes toxic stress resulting in transcriptional changes that are not regulated through Ahr. We sought to distinguish these two types of responses based on a time course of expression changes measured after B[a]P exposure. Using Random Forest machine learning we classified 81 primary Ahr responders and 1,308 genes regulated as side effects. Subsequent weighted clustering gave further insight into the connection between expression pattern, mode of regulation, and biological function. Finally, the accuracy of the predictions was supported through extensive experimental validation. Conclusion Using a combination of machine learning followed by extensive experimental validation, we have further expanded the known catalog of genes regulated by the environmentally sensitive transcription factor Ahr. More broadly, this study presents a strategy for distinguishing receptor-dependent responses and side effects based on expression time courses.