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

Area of Science:

• Environmental microbiology
• Biotechnology
• Molecular ecology

Background:

• Wastewater treatment plant (WWTP) microbial communities exhibit significant variability in substrate biotransformation rates.
• Taxonomic composition differences are potential predictors of these biotransformation rate variations.

Purpose of the Study:

• To develop a novel framework for predicting biotransformation rates based on bacterial 16S rRNA sequence abundances.
• To identify specific bacterial taxa that act as biomarkers for WWTP microbial activity.

Main Methods:

• Collected samples from ten WWTPs with diverse operational metrics.
• Measured in situ ammonia biotransformation rate constants.
• Analyzed 16S rRNA sequences and developed multivariate models linking sequence abundance to biotransformation rates.
• Extended the framework to predict micropollutant biotransformation rates.

Main Results:

• Over 80% of model scenarios yielded significant predictions of ammonia biotransformation rates using 16S rRNA sequences.
• Nitrosomonas and Nitrospira groups were consistently identified as key predictors for ammonia biotransformation.
• Specific phylogenetic groups were identified as robust biomarkers for the biotransformation of isoproturon, propachlor, ranitidine, and venlafaxine.

Conclusions:

• A predictive framework linking microbial taxonomy to biotransformation rates in WWTPs was successfully established.
• Identified bacterial groups serve as reliable biomarkers for predicting WWTP microbial community activity and efficiency.
• This approach advances tools for predicting WWTP performance based on microbial community composition.