Biological organisms carry a rich potential for removing toxins from our environment, but identifying suitable candidates and improving them remain challenging. We explore the use of computational tools to discover strains and enzymes that detoxify harmful compounds. In particular, we will focus on mycotoxins—fungi-produced toxins that contaminate food and feed—and biological enzymes that are capable of rendering them less harmful. We discuss the use of established and novel computational tools to complement existing empirical data in three directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and improving the performance of detoxifying enzymes. We hope to create a synergistic conversation between researchers in computational biology and those in the bioremediation field. We showcase open bioremediation questions where computational researchers can contribute and highlight relevant existing and emerging computational tools that could benefit bioremediation researchers.

No abstract

Bacterial detoxification of mycotoxins has the potential to offer a low-cost solution to ensure that feed and food commodities contaminated by fungal growth become safe to consume. Among bacteria, Rhodococcus species are of particular interest because they can be metabolically versatile, non-pathogenic, and environment-friendly. However, the native response of Rhodococcus environmental isolates appears inadequate for current detoxification needs. By analyzing the detoxification of aflatoxin by two Rhodococcus species: R. pyridinivorans and R. erythropolis, we examine important features of the dynamics that could guide future optimization of bacterial detoxification. Our results for Rhodococcus species suggest that detoxification happens through a regulated process of secreting extracellular enzymes. We show that enzyme fatigue in the presence of the toxin determines the lifetime of the enzyme and limits the overall detoxification performance of these species. Additionally, we show that the regulation of enzyme production can be both species- and environment-dependent. Overall, our quantitative approach reveals that enzyme fatigue is a major determinant of overall detoxification and needs to be accounted for in assessing the performance of detoxification by live cells or cell-free filtrates.

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.

Biological organisms carry a rich potential for removing toxins from our environment, but the search to identify suitable candidates remains challenging. We survey and explore the use of computational tools to discover and optimize the detoxification of harmful compounds. In particular, we will focus on mycotoxins—fungi-produced toxins that contaminate food and feed—and biological enzymes that are capable of rendering them less harmful. We discuss the use of computational tools to complement existing empirical data in three main directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and optimizing the performance of enzymes with detoxification capability.