Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85

Abstract

Fibrobacter succinogenes is a cellulolytic predominant bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the Automatic Reconstruction of metabolic models (AuReMe) workspace. The reconstruction was based on genome annotation, 5 templates-based orthology methods, gap-filling and manual curation. The metabolic network of F. succinogenes S85 comprises 1565 reactions with 77% linked to 1317 genes, 1586 unique metabolites and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for computation of Elementary Flux Modes (EFMs). A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the Root mean squared error of 19%. Resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step towards the integration of omics microbial information into predictive models of the rumen metabolism.