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 Nitrospira 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.