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.

中文翻译：

---

TinderMIX：毒物基因组学数据的时间-剂量集成建模。


背景组学技术已广泛应用于毒理学研究，以研究不同物质对暴露的生物系统的影响。经典的毒理学研究包括测试化合物在不同剂量水平和不同时间点的作用。主要挑战在于确定与剂量和时间点相关的基因改变模式。大多数现有的毒物基因组学数据分析方法允许在每个时间点单独研究暴露（或治疗）后的分子变化。然而，这种分析无法识别剂量反应的动态（时间依赖性）事件。结果我们提出了 TinderMIX，这是一种同时模拟时间和剂量对转录组的影响的方法，以研究响应暴露而产生的分子改变的过程。 TinderMIX从基因对数倍数变化出发，对每个基因拟合不同的综合时间和剂量模型，选择最优模型，并计算其时间和剂量效应图；然后应用用户选择的阈值来识别每个图上的响应区域并验证基因是否表现出动态（时间依赖性）和剂量依赖性响应；最终，根据整合的时间和剂量出发点对响应基因进行标记。结论 为了展示 TinderMIX 方法，我们分析了 Open TG-GATEs 数据集中的 2 种药物，即环孢素 A 和硫代乙酰胺。我们首先确定了每种药物的动态剂量依赖性作用机制并进行了比较。我们的分析强调，不同的时间和剂量积分出发点概括了化合物的毒性潜力及其动态剂量依赖性作用机制。