In silico prediction of metabolic trait robustness in microbial cells

Abstract

In industrial applications, microbial strains undergo notable biomass expansion subjecting them to Darwinian selection. The consequent adaptive evolution may threaten the often tediously developed desired traits of the strains, such as flavor or platform chemical production. Yet, it remains unresolved how to predict the evolutionary trait robustness. Here, we propose TRAEV (Trait Robustness Against EVolution), a computational framework for in silico prediction of evolutionary trajectories and robustness of desired metabolic traits in application environments. TRAEV uses constraint-based metabolic model simulations to predict environment-dependent trait-fitness dependencies and Monte Carlo-based perturbation analysis to account for the stochasticity of adaptive evolution. First, TRAEV predicts the immediate phenotypic adaptation to the new environment, and then, the evolutionary trajectories of fitness and desired trait by sampling enzyme usage changes. From the predicted trajectories, trait robustness is quantified as two scores: Robustness Score (RS) and Trade-off Score (TS). RS and TS are the mean of a normalized desired trait and the mean of the product of normalized changes in the desired trait and in fitness, respectively, over intermediate metabolic states along the evolutionary trajectory. We validated TRAEV by demonstrating that it predicted the relative robustness of heterologous pigmentation of genetically engineered Saccharomyces cerevisiae strains in synthetic defined chemical environments aligned with experimental observations. We then further applied TRAEV to predictively assess the robustness of desired aroma generation trait in a wine must environment by multiple S. cerevisiae strains if they were developed via a laboratory selection process. Thus, we showed how TRAEV predictions could guide such strain development. TRAEV can be integrated into computational strain design workflows across microbial strains, metabolic traits, and application environments. Ultimately, model-predicted evolutionary robustness of desired traits can guide both strain and process development and help avoiding production losses and enhancing the economic attractiveness of industrial applications using microbial cells. Author summaryMicrobial cells developed to express desired traits by genetic engineering or selection processes are used in a wide range of applications, from biotechnological chemical production to food and beverage fermentations. However, when the cells replicate in the application environment the traits are subject to Darwinian selection and consequent adaptive evolution. As a result, desired traits can be rapidly lost. To mitigate such undesired evolutionary changes, we developed TRAEV (Trait Robustness Against EVolution), a computational framework for in silico assessment of evolutionary trajectories and robustness of desired traits in application environments by integrating constraint-based modeling and Monte Carlo-based perturbation analysis methods. We demonstrate the usability of TRAEV by validating its predictions with experimental data on the robustness of heterologous pigmentation of engineered Saccharomyces cerevisiae strains, and applying it to predict the robustness of aroma generation in wine must by multiple S. cerevisiae strains if they were developed by laboratory selection in specific conditions. As being applicable across microbial strains, metabolic traits, and application environments, we believe that TRAEV can help to avoid production losses, and thus, contribute to the development of economically attractive industrial applications using microbial cells.