The rates at which wastewater treatment plant (WWTP) microbial communities biotransform micropollutants can differ by orders of magnitude among WWTP communities. Differences in the taxonomic composition among WWTP communities may explain differences in the rates of some types of biotransformations. A novel framework for establishing biologically meaningful relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates will be presented. Ten WWTPs and ten micropollutants that undergo different types of biotransformations were selected for this study. Ammonia and micropollutant biotransformation rate constants were measured in each WWTP community. Total RNA was isolated from samples and 16S rRNA sequence reads were analyzed. Multivariate models were constructed between the measured abundances of 16S rRNA sequence reads and the biotransformation rate constants. Specific model scenarios were designed to explore the effects of model regularization, model linearity and non-linearity, and aggregation of 16S rRNA sequences into operational taxonomic units as a function of the sequence dessimilarity threshold (SDT). For ammonia biotransformation rate constants, a large percentage of model scenarios resulted in well-performing and significant models at specific intermediate SDTs. The 16S rRNA sequences consistently selected into the well-performing and significant models at those SDTs were classified as <i>Nitrosomonas</i> and <i>Nitrospira</i> groups, validating the biological relevance of the constructed models. We then applied the framework to the micropollutant biotransformation rate constants. We thereby identified phylogenetic groups whose abundance can predict the biotransformation rates for isoproturon, propachlor, ranitidine, and venlafaxine biotransformations. However, taxonomic composition could not robustly predict the differences in biotransformation rate constants measured among the WWTP communities for the remaining six micropollutants.  This observation could be explained following an analysis of how constrained a particular biotransformation is among microbial taxa.  The developed framework is an important step towards developing tools to predict micropollutant biotransformation types based on taxonomic composition.

Establishing predictive relationships between specific bacterial 16S rRNA sequences and micropollutant biotransformation rates

249th National Meeting (2015)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.