High-throughput concentration-response analysis for omics datasets

SMETANOVÁ, Soňa, Janet RIEDL, Dimitar ZITZKAT, Rolf ALTENBURGER and Wibke BUSCH. High-throughput concentration-response analysis for omics datasets. Toxicol. Environ. Chem. HOBOKEN (USA): Elsevier Science, 2015, vol. 34, No 9, p. 2167-2180. ISSN 0730-7268. Available from: https://dx.doi.org/10.1002/etc.3025.

Basic information

• Original name: High-throughput concentration-response analysis for omics datasets
• Authors: SMETANOVÁ, Soňa (203 Czech Republic, belonging to the institution), Janet RIEDL (276 Germany), Dimitar ZITZKAT (276 Germany), Rolf ALTENBURGER (276 Germany, guarantor) and Wibke BUSCH (276 Germany).
• Edition: Toxicol. Environ. Chem. HOBOKEN (USA), Elsevier Science, 2015, 0730-7268.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 30304 Public and environmental health
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 2.763
• RIV identification code: RIV/00216224:14310/15:00086675
• Organization unit: Faculty of Science
• Doi: http://dx.doi.org/10.1002/etc.3025
• UT WoS: 000360500600031
• Keywords in English: Ecotoxicogenomics; Biostatistics; Dose-response modeling; Mixture toxicity; Zebrafish embryo; Myriophyllum
• Tags: AKR, rivok
• Tags: International impact, Reviewed

Abstract

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.

Links

• LO1214, research and development project: Name: Centrum pro výzkum toxických látek v prostředí (Acronym: RECETOX)

Investor: Ministry of Education, Youth and Sports of the CR

• 603437, interní kód MU: Name: SOLUTIONS - Solutions for present and future emerging pollutants in land and water resources management (Acronym: SOLUTIONS)

Investor: European Union, Cooperation