Monfort-Lanzas Pablo, Gostner Johanna M, Hackl Hubert
Institute of Medical Biochemistry, Biocenter, Medical University Innsbruck, 6020 Innsbruck, Austria.
Institute of Bioinformatics, Biocenter, Medical University Innsbruck, 6020 Innsbruck, Austria.
Comput Struct Biotechnol J. 2025 Apr 3;27:1440-1448. doi: 10.1016/j.csbj.2025.04.004. eCollection 2025.
The generation of omics data sets has become an important approach in modern pharmacological and toxicological research as it can provide mechanistic and quantitative information on a large scale. Analyses of these data frequently revealed a non-linear dose-response relationship underscoring the importance of the modeling process to infer biological exposure limits. A number of tools have been developed for dose-response modeling and various thresholds have been defined as a quantitative representation of the effect of a substance, such as effective concentrations or benchmark doses (BMD). Here we present DoseRider an easy-to-use web application and a companion R package for linear and non-linear dose-response modeling and assessment of BMD at the level of biological pathways or signatures using generalized mixed effect models. This approach allows to analyze custom or provided multi-omics data such as RNA sequencing or metabolomics data and its annotation of a collection of pathways and gene sets from various species. Moreover, we introduce the concept of the trend change doses (TCDs) as a numerical descriptor of effects derived from complex dose-response curves. The usability of DoseRider was demonstrated by analyses of RNA sequencing data of bisphenol AF (BPAF) treatment of a human breast cancer cell line (MCF-7) at 8 different concentrations using gene sets for chemical and genetic perturbations (MSigDB). The BMD for BPAF and a set of genes upregulated by estrogen in breast cancer was 0.2 µM (95 %-CI 0.1-0.5 µM) and the lowest TCD (TCD1) was 0.003 µM (95 %-CI 0.0006-0.01 µM). The comprehensive presentation of the results underlines the suitability of the system for pharmacogenomics, toxicogenomics, and applications beyond.
组学数据集的生成已成为现代药理学和毒理学研究中的一种重要方法，因为它可以大规模提供机制和定量信息。对这些数据的分析经常揭示出非线性剂量反应关系，突出了建模过程对推断生物暴露限值的重要性。已经开发了许多用于剂量反应建模的工具，并定义了各种阈值作为物质效应的定量表示，如有效浓度或基准剂量（BMD）。在此，我们展示了DoseRider，这是一个易于使用的网络应用程序以及一个配套的R包，用于使用广义混合效应模型在生物途径或特征水平进行线性和非线性剂量反应建模以及BMD评估。这种方法允许分析自定义或提供的多组学数据，如RNA测序或代谢组学数据，以及对来自各种物种的途径和基因集集合的注释。此外，我们引入了趋势变化剂量（TCD）的概念，作为从复杂剂量反应曲线得出的效应的数值描述符。通过使用化学和基因扰动基因集（MSigDB）分析双酚AF（BPAF）在8种不同浓度下对人乳腺癌细胞系（MCF-7）的RNA测序数据，证明了DoseRider的可用性。BPAF和一组在乳腺癌中被雌激素上调的基因的BMD为0.2µM（95%置信区间0.1 - 0.5µM），最低TCD（TCD1）为0.003µM（95%置信区间0.0006 - 0.01µM）。结果的全面呈现强调了该系统在药物基因组学、毒理基因组学及其他领域的适用性。
