An integrated meta-omics approach for identifying candidate organic micropollutant degraders in complex microbial communities.

Biotransformation is a significant determinant of the fate of organic micropollutants (OMPs) in natural and engineered environments. Here we propose a genome-resolved metatranscriptomics approach for the identification of candidate OMP-transforming microorganisms based on positive relations between biotransformation rate constants and the activity of metagenome-assembled genomes (MAGs). To demonstrate the approach, we used five nitrifier-rich batch cultures, first validating with ammonia (a macropollutant) before applying it to atenolol (an OMP). As expected, the biotransformation rate constant of ammonia was correlated with the activity of an ammonia-oxidizing bacterium, namely Nitrosomonas europaea; it was not correlated with the activity of other bacteria, including several ammonia oxidizers. Additionally, the biotransformation rate constant of ammonia was correlated with the transcript relative abundance of the ammonia monooxygenase (AMO) expressed by N. europaea but not with the transcript abundance of AMO at the community level. The biotransformation rate constant of atenolol was correlated with the activity of four MAGs representing three heterotrophic genera: Terrimonas, Flavobacterium, and Zeimonas. It was not correlated with the total transcript relative abundance of any member of a comprehensive set of amidohydrolases, which are predicted to transform this drug. By contrast, it was correlated with the expression of the amidohydrolase asparagine synthase (AsnB) identified in the Terrimonas and Flavobacterium MAGs. In summary, we present a novel association-based method for investigating biotransformation processes robust to variability in enzyme reaction kinetics with implications for OMP control.