BACKGROUND: Omics technologies have been widely applied in toxicology studies to investigate the effects of different substances on exposed biological systems. A classical toxicogenomic study consists in testing the effects of a compound at different dose levels and different time points. The main challenge consists in identifying the gene alteration patterns that are correlated to doses and time points. The majority of existing methods for toxicogenomics data analysis allow the study of the molecular alteration after the exposure (or treatment) at each time point individually. However, this kind of analysis cannot identify dynamic (time-dependent) events of dose responsiveness. RESULTS: We propose TinderMIX, an approach that simultaneously models the effects of time and dose on the transcriptome to investigate the course of molecular alterations exerted in response to the exposure. Starting from gene log fold-change, TinderMIX fits different integrated time and dose models to each gene, selects the optimal one, and computes its time and dose effect map; then a user-selected threshold is applied to identify the responsive area on each map and verify whether the gene shows a dynamic (time-dependent) and dose-dependent response; eventually, responsive genes are labelled according to the integrated time and dose point of departure. CONCLUSIONS: To showcase the TinderMIX method, we analysed 2 drugs from the Open TG-GATEs dataset, namely, cyclosporin A and thioacetamide. We first identified the dynamic dose-dependent mechanism of action of each drug and compared them. Our analysis highlights that different time- and dose-integrated point of departure recapitulates the toxicity potential of the compounds as well as their dynamic dose-dependent mechanism of action.
BACKGROUND: Omics technologies have been widely applied in toxicology studies to investigate the effects of different substances on exposed biological systems. A classical toxicogenomic study consists in testing the effects of a compound at different dose levels and different time points. The main challenge consists in identifying the gene alteration patterns that are correlated to doses and time points. The majority of existing methods for toxicogenomics data analysis allow the study of the molecular alteration after the exposure (or treatment) at each time point individually. However, this kind of analysis cannot identify dynamic (time-dependent) events of dose responsiveness. RESULTS: We propose TinderMIX, an approach that simultaneously models the effects of time and dose on the transcriptome to investigate the course of molecular alterations exerted in response to the exposure. Starting from gene log fold-change, TinderMIXfits different integrated time and dose models to each gene, selects the optimal one, and computes its time and dose effect map; then a user-selected threshold is applied to identify the responsive area on each map and verify whether the gene shows a dynamic (time-dependent) and dose-dependent response; eventually, responsive genes are labelled according to the integrated time and dose point of departure. CONCLUSIONS: To showcase the TinderMIX method, we analysed 2 drugs from the Open TG-GATEs dataset, namely, cyclosporin A and thioacetamide. We first identified the dynamic dose-dependent mechanism of action of each drug and compared them. Our analysis highlights that different time- and dose-integrated point of departure recapitulates the toxicity potential of the compounds as well as their dynamic dose-dependent mechanism of action.
Authors: Rafael A Irizarry; Benjamin M Bolstad; Francois Collin; Leslie M Cope; Bridget Hobbs; Terence P Speed Journal: Nucleic Acids Res Date: 2003-02-15 Impact factor: 16.971
Authors: K Oniki; K Morita; T Watanabe; A Kajiwara; K Otake; K Nakagawa; Y Sasaki; Y Ogata; J Saruwatari Journal: Nutr Diabetes Date: 2016-05-23 Impact factor: 5.097
Authors: Angela Serra; Michele Fratello; Antonio Federico; Ravi Ojha; Riccardo Provenzani; Ervin Tasnadi; Luca Cattelani; Giusy Del Giudice; Pia A S Kinaret; Laura A Saarimäki; Alisa Pavel; Suvi Kuivanen; Vincenzo Cerullo; Olli Vapalahti; Peter Horvath; Antonio Di Lieto; Jari Yli-Kauhaluoma; Giuseppe Balistreri; Dario Greco Journal: Brief Bioinform Date: 2022-01-17 Impact factor: 11.622