Elucidating Interactions within Photoautotroph-Methanotroph Cocultures at Both Systems and Molecular Levels

Abstract

In the US, the biogas produced from anaerobic digestion (AD) of industrial, municipal, and agricultural waste streams has a great potential as a renewable feedstock. To tap into the immense biogas potential while reducing greenhouse gas (GHG) emissions, effective biotechnologies such as methanotroph-photoautotroph (M-P) coculture that can operate at ambient pressure and temperature without requiring biogas cleaning/upgrading are urgently needed. Although it has been recognized that microbial communities offer a number of advantages over monocultures, the utilization of microbial communities for biotechnological applications has been limited. This work aims to help change that by developing effective experimental and computational tools to enable the exploration of interactions in microbial communities. This study investigates and addresses the following major challenges in developing an M-P coculture-based biotechnology: (1) lack of experimental and computational tools to efficiently and accurately characterize the coculture in real-time; (2) lack of tools and methodologies in determining largely unknown interspecies dependencies and their contributions among the species; (3) lack of understanding and modeling strategies to quantitatively characterize the highly complex dynamics of the coculture cells and their metabolic interactions over time, which hinders the design and scale-up of M-P photobioreactors, as well as the optimization and control of operating conditions. In this work, the advantages of the M-P coculture over sequential photoautotroph and methanotroph single cultures for biogas conversion are investigated. An experimental-computational protocol is proposed for fast, easy, and accurate quantitative characterization of M-P cocultures. A semi-structured kinetic model is proposed that can accurately predict coculture growth under a wide range of cultivation conditions. A genome-scale metabolic network model (GEM) is employed to develop steady-state M-P coculture models, to postulate potential molecular interactions responsible for the enhanced growth observed in the coculture. The kinetic model and coculture GEM model were utilized to develop a dynamic GEM model that can determine the metabolic flux profile and contribution of mutualistic interactions over time.