Integrating Genome-Resolved Metagenomics with Trait-Based Process Modeling to Determine Biokinetics of Distinct Nitrifying Communities within Activated Sludge.

Conventional bioprocess models for wastewater treatment are based on aggregated bulk biomass concentrations and do not incorporate microbial physiological diversity. Such a broad aggregation of microbial functional groups can fail to predict ecosystem dynamics when high levels of physiological diversity exist within trophic guilds. For instance, functional diversity among nitrite-oxidizing bacteria (NOB) can obfuscate engineering strategies for their out-selection in activated sludge (AS), which is desirable to promote energy-efficient nitrogen removal. Here, we hypothesized that different NOB populations within AS can have different physiological traits that drive process performance, which we tested by estimating biokinetic growth parameters using a combination of highly replicated respirometry, genome-resolved metagenomics, and process modeling. A lab-scale AS reactor subjected to a selective pressure for over 90 days experienced resilience of NOB activity. We recovered three coexisting population genomes belonging to two sublineages, which exhibited distinct growth strategies and underwent a compositional shift following the selective pressure. A trait-based process model calibrated at the NOB genus level better predicted nitrite accumulation than a conventional process model calibrated at the NOB guild level. This work demonstrates that trait-based modeling can be leveraged to improve our prediction, control, and design of functionally diverse microbiomes driving key environmental biotechnologies.