The goal of our project was to implement a pipeline for the systematic, computationally-driven study and optimization of microbial interactions and their effect on lignocellulose degradation and biofuel production. We specifically sought to design and construct artificial microbial consortia that could collectively degrade lignocellulose from plant biomass, and produce precursors of energy-rich biofuels. This project fits into the bigger picture goal of helping identify a sustainable strategy for the production of energy-rich biofuels that would satisfy the existing energy constraints and demand of our society. Based on the observation that complex natural microbial communities tend to be metabolically efficient and ecologically robust, we pursued the study of a microbial system in which the desired engineering function is achieved through division of labor across multiple microbial species. Our approach was aimed at bypassing the complexity of natural communities by establishing a rational approach to design small synthetic microbial consortia. Towards this goal, we combined multiple approaches, including computer modeling of ecosystem-level microbial metabolism, mass spectrometry of metabolites, genetic engineering, and experimental evolution. The microbial production of biofuels from lignocellulose is a complex, multi-step process. Microbial consortia are an ideal approach to consolidated bioprocessing: a community of microorganisms performs a wide variety of functions more efficiently and is more resilient to environmental perturbations than a microbial monoculture. Each organism we chose for this project addresses a specific challenge: lignin degradation (Pseudomonas putida)
(hemi)cellulose degradation (Cellulomonas fimi)
lignin degradation product demethoxylation (Methylobacterium spp)
generation of biofuel lipid precursors (Yarrowia lipolytica). These organisms are genetically tractable, aerobic, and have been used in biotechnological applications. Throughout the project, we have used mass spectrometry to characterize and measure the metabolic inputs and outputs of each of these consortium members, providing valuable information for model refinement, and enabling the establishment of metabolism-mediated interactions. In addition to lignocellulose degradation, we have started addressing the challenge of removing metabolites (e.g. formaldehyde) produced by the demethoxylation of lignin monomers, which can otherwise inhibit microbial growth due to their toxicity. On the computational side, we have implemented genome-scale models for all consortium members, based on KBase reconstructions and literature curation, and we studied small consortia and their properties. Overall, our project has identified a complex landscape of interactions types and metabolic processes relevant to community-level functions, illustrating the challenges and opportunities of microbial community engineering for the transformation of biomass into bioproducts.