Partner-assisted artificial selection of a secondary function for efficient bioremediation

Microbial enzymes have a broad potential to address many current needs, such as detoxification of harmful toxins and waste, but their native performance often does not match specific applications of interest. In attempting to evolve strains for a specific need, one challenge is that our functions of interest may not confer a fitness effect on the producer. As a result, a conventional selection scheme cannot be used to improve such secondary functions. We propose an alternative approach, partner-assisted artificial selection (PAAS), in which an assisting population acts as an intermediate to create a feedback from the function of interest to the fitness of the producer. We use a simplified model to examine how well and under what conditions such a scheme leads to improved enzymatic function, focusing on degradation of a toxin as a case example. We find that selection for improved growth in this scheme successfully leads to improved degradation performance, even in the presence of other sources of stochasticity. We find that standard selection considerations apply in PAAS: a more restrictive bottleneck leads to stronger selection but adds uncertainty. We also examine how much stochasticity in other traits can be tolerated in PAAS. Our findings offer a roadmap for successful implementation of PAAS to evolve improved functions of interest such as detoxification of harmful compounds.