A framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates

The rates at which wastewater treatment plant (WWTP) microbial communities biotransform specific substrates can differ by orders of magnitude among WWTP communities. Differences in taxonomic compositions among WWTP communities may predict differences in the rates of some types of biotransformations. In this work, we present a novel framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates. We selected ten WWTPs with substantial variation in their environmental and operational metrics and measured the in situ ammonia biotransformation rate constants in nine of them. We isolated total RNA from samples from each WWTP and analyzed 16S rRNA sequence reads. We then developed multivariate models between the measured abundances of specific bacterial 16S rRNA sequence reads and the ammonia biotransformation rate constants. We constructed model scenarios that systematically explored the effects of model regularization, model linearity and non-linearity, and aggregation of 16S rRNA sequences into operational taxonomic units (OTUs) as a function of sequence dissimilarity threshold (SDT). A large percentage (greater than 80%) of model scenarios resulted in well-performing and significant models at intermediate SDTs of 0.13-0.14 and 0.26. The 16S rRNA sequences consistently selected into the well-performing and significant models at those SDTs were classified as Nitrosomonas and Nitrospira groups. We then extend the framework by applying it to the biotransformation rate constants of ten micropollutants measured in batch reactors seeded with the ten WWTP communities. We identified phylogenetic groups that were robustly selected into all well-performing and significant models constructed with biotransformation rates of isoproturon, propachlor, ranitidine, and venlafaxine. These phylogenetic groups can be used as predictive biomarkers of WWTP microbial community activity towards these specific micropollutants. This work is an important step towards developing tools to predict biotransformation rates in WWTPs based on taxonomic composition.