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.

• Klíčová slova
• Biostatistics, Dose-response modeling, Ecotoxicogenomics, Mixture toxicity, Myriophyllum, Zebrafish embryo,
• MeSH
• dánio pruhované růst a vývoj   metabolismus   MeSH
• ekosystém * MeSH
• embryo nesavčí účinky léků   metabolismus   MeSH
• genomika * MeSH
• látky znečišťující životní prostředí toxicita   MeSH
• lineární modely MeSH
• metabolomika * MeSH
• rychlé screeningové testy MeSH
• sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů MeSH
• tetrachlorethylen toxicita   MeSH
• transkriptom účinky léků   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• látky znečišťující životní prostředí MeSH
• tetrachlorethylen MeSH

The compositions of bacterial groundwater communities of three sites contaminated with chlorinated ethenes were analyzed by pyrosequencing their 16S rRNA genes. For each location, the entire and the active bacterial populations were characterized by independent molecular analysis of the community DNA and RNA. The sites were selected to cover a broad range of different environmental conditions and contamination levels, with tetrachloroethene (PCE) and trichloroethene (TCE) being the primary contaminants. Before sampling the biomass, a long-term monitoring of the polluted locations revealed high concentrations of cis-1,2-dichloroethene (cDCE) and vinyl chloride (VC), which are toxic by-products of the incomplete bacterial degradation of PCE and TCE. The applied pyrosequencing technique enabled known dechlorinators to be identified at a very low detection level (<0.25%) without compromising the detailed analysis of the entire bacterial community of these sites. The study revealed that only a few species dominated the bacterial communities, with Albidiferax ferrireducens being the only highly prominent member found at all three sites. Only a limited number of OTUs with abundances of up to 1% and high sequence identities to known dechlorinating microorganisms were retrieved from the RNA pools of the two highly contaminated sites. The dechlorinating consortium was likely to be comprised of cDCE-assimilating bacteria (Polaromonas spp.), anaerobic organohalide respirers (mainly Geobacter spp.), and Burkholderia spp. involved in cometabolic dechlorination processes, together with methylotrophs (Methylobacter spp.). The deep sequencing results suggest that the indigenous dechlorinating consortia present at the investigated sites can be used as a starting point for future bioremediation activities by stimulating their anaerobic and aerobic chloroethene degradation capacities (i.e. reductive dechlorination, and metabolic and cometabolic oxidation).

• MeSH
• Bacteria účinky léků   genetika   metabolismus   MeSH
• biodegradace MeSH
• chemické látky znečišťující vodu toxicita   MeSH
• DNA genetika   metabolismus   MeSH
• monitorování životního prostředí MeSH
• podzemní voda mikrobiologie   MeSH
• polymerázová řetězová reakce MeSH
• RNA ribozomální 16S genetika   metabolismus   MeSH
• RNA genetika   metabolismus   MeSH
• společenstvo MeSH
• tetrachlorethylen toxicita   MeSH
• trichlorethylen toxicita   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH
• Názvy látek
• chemické látky znečišťující vodu MeSH
• DNA MeSH
• RNA ribozomální 16S MeSH
• RNA MeSH
• tetrachlorethylen MeSH
• trichlorethylen MeSH