Metabolic Network Reconstruction for the Pan-Genome: A Scalable Method to Get High-Quality Metabolic Models across the Tree of Life
LEGACY
2018
5th Conference on Constraint-Based Reconstruction and Analysis (COBRA 2018)
Poster Session
Poster Session
Sunday, October 14, 2018 - 6:00pm to 7:00pm
Background
Genome-scale metabolic models (GEM's) are a valuable tool used to study the metabolism of organisms with metabolic simulations, but they also provide a scaffold for 'omics data integration. The drop in cost for genome sequencing has lead to an exponential increase in the diversity of sequenced genomes, but the number of curated GEM's has not kept pace. This gap hinders our ability to study physiology across the tree of life. Furthermore, our review of published GEM's has found that metabolic models contain significant commission and omission errors in central metabolism. To address these quantity and quality GEM issues, we propose open and transparent efforts to curate the pan-genome, pan-reaction, and pan-metabolome of larger groups of organisms by research communities, rather than for a single species. We outline our approach for budding yeasts.
Method
We created a consensus metabolic network from 13 fungi/yeasts GEM's spanning seven species. We added additional reactions to the network to account for growth substrates and non-natural enzyme activities in the fungal literature that were not present in biochemical databases. Reactions were annotated with ortholog ID's from AYbRAH, our fungal ortholog database, to generate 33 fungi/yeasts GEM's from the subkingdom Dikarya.
Results
The fungal pan-GEM contains 2224 reactions, 2696 metabolites, 1543 orthologs, and 10 compartments. The gene coverage in the species models created from our pan-GEM is higher than published GEM's. Pathways not previously included in published GEM's, such as degradation of alkanes and aromatics, are captured using our approach.
Conclusion
Maintaining a metabolic network reconstruction annotated with a curated ortholog database at a higher pan-genome level improves the quality and quantity of GEM's. Capturing accurate ortholog-reaction-protein associations minimizes commission and omission errors, and the ability to scale GEM's to more branches of the tree of life.