Genome-Scale Modeling Specifies the Metabolic Capabilities of Rhizophagus irregularis

Rhizophagus irregularis is one of the most extensively studied arbuscular mycorrhizal fungi (AMF) that forms symbioses with and improves the performance of many crops. Lack of transformation protocol for R. irregularis renders it challenging to investigate molecular mechanisms that shape the physiology and interactions of this AMF with plants. Here, we used all published genomics, transcriptomics, and metabolomics resources to gain insights into the metabolic functionalities of R. irregularis by reconstructing its high-quality genome-scale metabolic network that considers enzyme constraints. Extensive validation tests with the enzyme-constrained metabolic model demonstrated that it can be used to (i) accurately predict increased growth of R. irregularis on myristate with minimal medium; (ii) integrate enzyme abundances and carbon source concentrations that yield growth predictions with high and significant Spearman correlation ([Formula: see text] = 0.74) to measured hyphal dry weight; and (iii) simulate growth rate increases with tighter association of this AMF with the host plant across three fungal structures. Based on the validated model and system-level analyses that integrate data from transcriptomics studies, we predicted that differences in flux distributions between intraradical mycelium and arbuscles are linked to changes in amino acid and cofactor biosynthesis. Therefore, our results demonstrated that the enzyme-constrained metabolic model can be employed to pinpoint mechanisms driving developmental and physiological responses of R. irregularis to different environmental cues. In conclusion, this model can serve as a template for other AMF and paves the way to identify metabolic engineering strategies to modulate fungal metabolic traits that directly affect plant performance. IMPORTANCE Mounting evidence points to the benefits of the symbiotic interactions between the arbuscular mycorrhiza fungus Rhizophagus irregularis and crops; however, the molecular mechanisms underlying the physiological responses of this fungus to different host plants and environments remain largely unknown. We present a manually curated, enzyme-constrained, genome-scale metabolic model of R. irregularis that can accurately predict experimentally observed phenotypes. We show that this high-quality model provides an entry point into better understanding the metabolic and physiological responses of this fungus to changing environments due to the availability of different nutrients. The model can be used to design metabolic engineering strategies to tailor R. irregularis metabolism toward improving the performance of host plants.