Modeling omics dose-response at the pathway level with DoseRider
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 imp...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001266 |
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| author | Pablo Monfort-Lanzas Johanna M. Gostner Hubert Hackl |
| author_facet | Pablo Monfort-Lanzas Johanna M. Gostner Hubert Hackl |
| author_sort | Pablo Monfort-Lanzas |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8cd4175e04f44d3fbc1fa9d8df9b5af7 |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-8cd4175e04f44d3fbc1fa9d8df9b5af72025-08-20T03:05:07ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01271440144810.1016/j.csbj.2025.04.004Modeling omics dose-response at the pathway level with DoseRiderPablo Monfort-Lanzas0Johanna M. Gostner1Hubert Hackl2Institute of Medical Biochemistry, Biocenter, Medical University Innsbruck, 6020 Innsbruck, Austria; Institute of Bioinformatics, Biocenter, Medical University Innsbruck, 6020 Innsbruck, AustriaInstitute of Medical Biochemistry, Biocenter, Medical University Innsbruck, 6020 Innsbruck, Austria; Corresponding authors.Institute of Bioinformatics, Biocenter, Medical University Innsbruck, 6020 Innsbruck, Austria; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2001037025001266Benchmark doseDose-response modelingMixed modelsMulti-omicsSystem biologyTrend change dose |
| spellingShingle | Pablo Monfort-Lanzas Johanna M. Gostner Hubert Hackl Modeling omics dose-response at the pathway level with DoseRider Computational and Structural Biotechnology Journal Benchmark dose Dose-response modeling Mixed models Multi-omics System biology Trend change dose |
| title | Modeling omics dose-response at the pathway level with DoseRider |
| title_full | Modeling omics dose-response at the pathway level with DoseRider |
| title_fullStr | Modeling omics dose-response at the pathway level with DoseRider |
| title_full_unstemmed | Modeling omics dose-response at the pathway level with DoseRider |
| title_short | Modeling omics dose-response at the pathway level with DoseRider |
| title_sort | modeling omics dose response at the pathway level with doserider |
| topic | Benchmark dose Dose-response modeling Mixed models Multi-omics System biology Trend change dose |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025001266 |
| work_keys_str_mv | AT pablomonfortlanzas modelingomicsdoseresponseatthepathwaylevelwithdoserider AT johannamgostner modelingomicsdoseresponseatthepathwaylevelwithdoserider AT huberthackl modelingomicsdoseresponseatthepathwaylevelwithdoserider |