High-throughput concentration-response analysis for omics
datasets

J 2015

High-throughput concentration-response analysis for omics datasets

SMETANOVÁ, Soňa; Janet RIEDL; Dimitar ZITZKAT; Rolf ALTENBURGER; Wibke BUSCH et. al.

Základní údaje

Originální název

High-throughput concentration-response analysis for omics datasets

Autoři

SMETANOVÁ, Soňa (203 Česká republika, domácí); Janet RIEDL (276 Německo); Dimitar ZITZKAT (276 Německo); Rolf ALTENBURGER (276 Německo, garant) a Wibke BUSCH (276 Německo)

Vydání

Toxicol. Environ. Chem. HOBOKEN (USA), Elsevier Science, 2015, 0730-7268

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30304 Public and environmental health

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.763

Kód RIV

RIV/00216224:14310/15:00086675

Organizační jednotka

Přírodovědecká fakulta

DOI

https://doi.org/10.1002/etc.3025

UT WoS

000360500600031

EID Scopus

2-s2.0-84940450006

Klíčová slova anglicky

Ecotoxicogenomics; Biostatistics; Dose-response modeling; Mixture toxicity; Zebrafish embryo; Myriophyllum

Štítky

AKR, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 3. 2020 11:21, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

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.

Návaznosti

LO1214, projekt VaV

Název: Centrum pro výzkum toxických látek v prostředí (Akronym: RECETOX)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Centrum pro výzkum toxických látek v prostředí

603437, interní kód MU

Název: SOLUTIONS - Solutions for present and future emerging pollutants in land and water resources management (Akronym: SOLUTIONS)

Investor: Evropská unie, SOLUTIONS - Solutions for present and future emerging pollutants in land and water resources management, Spolupráce

Citovat

SMETANOVÁ, Soňa; Janet RIEDL; Dimitar ZITZKAT; Rolf ALTENBURGER a Wibke BUSCH. High-throughput concentration-response analysis for omics datasets. Toxicol. Environ. Chem. HOBOKEN (USA): Elsevier Science, 2015, roč. 34, č. 9, s. 2167-2180. ISSN 0730-7268. Dostupné z: https://doi.org/10.1002/etc.3025.

@article{1336259,
   author = {Smetanová, Soňa and Riedl, Janet and Zitzkat, Dimitar and Altenburger, Rolf and Busch, Wibke},
   article_location = {HOBOKEN (USA)},
   article_number = {9},
   doi = {https://doi.org/10.1002/etc.3025},
   keywords = {Ecotoxicogenomics; Biostatistics; Dose-response modeling; Mixture toxicity; Zebrafish embryo; Myriophyllum},
   language = {eng},
   issn = {0730-7268},
   journal = {Toxicol. Environ. Chem.},
   title = {High-throughput concentration-response analysis for omics datasets},
   url = {http://onlinelibrary.wiley.com/doi/10.1002/etc.3025/abstract?systemMessage=Wiley+Online+Library+will+be+unavailable+on+Saturday+27th+February+from+09:00-14:00+GMT+/+04:00-09:00+EST+/+17:00-22:00+SGT+for+essential+maintenance.++Apologies+for+the+inconveni},
   volume = {34},
   year = {2015}
}

TY  - JOUR
ID  - 1336259
AU  - Smetanová, Soňa - Riedl, Janet - Zitzkat, Dimitar - Altenburger, Rolf - Busch, Wibke
PY  - 2015
TI  - High-throughput concentration-response analysis for omics datasets
JF  - Toxicol. Environ. Chem.
VL  - 34
IS  - 9
SP  - 2167-2180
EP  - 2167-2180
PB  - Elsevier Science
SN  - 07307268
KW  - Ecotoxicogenomics
KW  - Biostatistics
KW  - Dose-response modeling
KW  - Mixture toxicity
KW  - Zebrafish embryo
KW  - Myriophyllum
UR  - http://onlinelibrary.wiley.com/doi/10.1002/etc.3025/abstract?systemMessage=Wiley+Online+Library+will+be+unavailable+on+Saturday+27th+February+from+09:00-14:00+GMT+/+04:00-09:00+EST+/+17:00-22:00+SGT+for+essential+maintenance.++Apologies+for+the+inconveni
L2  - http://onlinelibrary.wiley.com/doi/10.1002/etc.3025/abstract?systemMessage=Wiley+Online+Library+will+be+unavailable+on+Saturday+27th+February+from+09:00-14:00+GMT+/+04:00-09:00+EST+/+17:00-22:00+SGT+for+essential+maintenance.++Apologies+for+the+inconveni
N2  - 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.
ER  -

SMETANOVÁ, Soňa; Janet RIEDL; Dimitar ZITZKAT; Rolf ALTENBURGER a Wibke BUSCH. High-throughput concentration-response analysis for omics datasets. \textit{Toxicol. Environ. Chem.}. HOBOKEN (USA): Elsevier Science, 2015, roč.~34, č.~9, s.~2167-2180. ISSN~0730-7268. Dostupné z: https://doi.org/10.1002/etc.3025.

Přehled o publikaci