Community Research and Development Information Service - CORDIS


PROduCTS Report Summary

Project ID: 614768
Funded under: FP7-IDEAS-ERC
Country: Switzerland

Periodic Report Summary 2 - PRODUCTS (Predicting environment-specific biotransformation of chemical contaminants)

PROduCTS seeks to improve our ability to predict rates and products of microbial biotransformation for a broad variety of chemical contaminants accurately. This is essential for chemical risk management but also in the context of contaminated site remediation or the development of green chemical alternatives. To achieve this goal, research in PROduCTS is focused on explaining observed variability in biotransformation rates and pathways through the metabolic potential of the involved environmental microbial communities. The central hypothesis of this research is that reactions rather than substrate structures should become the core element of biotransformation prediction because similar biotransformation reactions are expected to be catalyzed by similar enzymes. Therefore, these reactions should show a similar dependency on environmental conditions and the metabolic potential of the communities shaped by those conditions.
Several results in working towards this goal have been achieved so far. First, has been developed as a central resource for conveniently storing and predicting contaminant biotransformation pathway and rate information. In this new environment we have encoded the Eawag-Soil data package, containing regulatory soil degradation information (half-lives, pathways, and metadata on study conditions) for 282 pesticides, including 1619 compounds and 4716 biotransformation half-lives. A chemical space analysis has shown that these data significantly increased coverage towards polar, multifunctional contaminants and should therefore facilitate the development of more accurate biotransformation prediction models for many classes of chemicals of current concern for water resources. Eawag-Soil is further unique in that it contains both reaction and half-life information collected under different, yet consistent experimental conditions. Ongoing research therefore groups chemicals in Eawag-Soil into classes of biotransformation reactions and explicitly tests the hypothesis that chemicals undergoing the same dominant biotransformation reaction show similar dependencies on study conditions.
Second, experimental biotransformation studies have been designed to further test the central hypothesis and simultaneously identify classes of enzymes potentially responsible for catalyzing relevant types of contaminant biotransformation reactions. A “contaminant test battery” has been developed with ~80 chemicals that cover ~10 expected major reactions and four types of bioreactor experiments (activated sludge or urine-fed) have been run with the contaminant test battery. These experiments have demonstrated largely consistent behavior between compounds undergoing the same biotransformation reaction across microbial communities adapted to different conditions (e.g., different sludge age or redox conditions) with stronger dependencies observed for oxidative transformations and less dependencies for substitution reactions. Through inhibitor experiments we could reject the hypothesis that nitrifying bacteria were mostly responsible for improved oxidative biotransformation. Rather, conditions with little readily available carbon and favoring slow-growing organisms were identified as fostering oxidative biotransformation reactions.
Based on these findings and theoretical associations between observed reactions and genes as derived from mining of enzyme databases, ongoing research seeks associations between the observed trends in biotransformation reactions and taxonomic and gene expression data for our studied microbial communities to identify plausible candidate enzymes involved in contaminant biotransformation. This information, in turn, will feed back into our framework for biodegradation prediction to render it more community or environmental condition-specific and hence more accurate.

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