Computational approaches for analysing and engineering micropollutant degradation in microbial communities

Abstract

The presence of micropollutants in wastewater is problematic, as many micropollutants exert negative ecological and toxicological effects in their environment. A well-known effect of micropollutants is the feminisation of aquatic wildlife by environmental estrogens, a proportion of which enter water courses from municipal sources via wastewater treatment plants (WWTPs). While WWTPs remove some micropollutants, they are not designed to do so. Given that WWTPs already have high operating costs (both financially and energetically), there is a need for novel approaches to micropollutant removal that are both cost-effective and environmentally sustainable. One proposed approach is to use enzymes to degrade micropollutants, which requires an understanding of metabolic pathways for the desired micropollutant, and a strategy for deploying the enzymes in the environment. Although tools exist to assist with metabolic pathway prediction and enzyme discovery, there are currently no computational approaches that are able to identify enzymes from a user’s collection of proteins (given a query compound and expected change to that query compound). To address this research gap, we developed EnSeP, a data-driven, transformation-specific approach to enzyme discovery. Using EnSeP, we then identified candidate enzymes involved in estradiol degradation. Recent advances in synthetic biology mean that deploying a single synthetic construct in multiple microorganisms is feasible. In the context of micropollutant metabolism, this means that a biodegradative pathway could be introduced into multiple organisms in a community simultaneously, providing more opportunities for the construct (and its functionality) to persist in the population long-term. However, current design tools have not yet been adapted for multiple organism applications. To address this research gap, we developed an evolutionary algorithm (EA) that optimises a single coding sequence (CDS) for multiple hosts. Finally, based on insights from developing the EA, we developed an improved version of the single-organism CDS optimisation algorithm that the EA is based on.