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TinderMIX: Time-dose integrated modelling of toxicogenomics data
Angela Serra1,2, Michele Fratello1,2, Giusy Del Giudice1,2
1Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland.
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.
Insights
TinderMIX models time and dose effects on the transcriptome, identifying dynamic, dose-dependent gene alterations in toxicology. This approach reveals how molecular changes evolve over time and with varying compound exposure levels.
Area of Science:
- Toxicogenomics
- Computational Biology
- Transcriptomics
Background:
- Omics technologies are crucial for toxicology, but analyzing dose and time effects simultaneously is challenging.
- Current methods often analyze molecular alterations at single time points, missing dynamic, dose-dependent responses.
- Identifying gene alteration patterns correlated with both dose and time is a key challenge in toxicogenomic studies.
Purpose of the Study:
- To propose TinderMIX, a novel approach for simultaneously modeling time and dose effects on the transcriptome.
- To investigate the dynamic course of molecular alterations in response to substance exposure.
- To identify genes exhibiting time-dependent and dose-dependent responses.
Main Methods:
- TinderMIX fits integrated time and dose models to gene log fold-changes.
- It selects the optimal model for each gene and computes a time and dose effect map.
- A user-defined threshold identifies responsive genes and their point of departure.
Main Results:
- TinderMIX was applied to analyze cyclosporin A and thioacetamide from the Open TG-GATEs dataset.
- The method identified dynamic, dose-dependent mechanisms of action for both drugs.
- Analysis highlighted the recapitulation of toxicity potential and dynamic mechanisms via integrated time and dose departure points.
Conclusions:
- TinderMIX effectively models the dynamic interplay of time and dose on gene expression.
- The approach provides insights into compound-specific toxicity mechanisms.
- Integrated time and dose analysis enhances understanding of toxicological responses.
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