Comparative analyses of parasites with a comprehensive database of geno-scale metabolic models

Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species. Author summaryComparative genomics and phylogeny-based assumptions are useful approaches to generate predictions about cellular behavior for data-poor organisms, such as unculturable but clinically-relevant pathogens. Computational approaches, including metabolic modeling, can accelerate such comparisons. Genome-scale metabolic network models serve as a knowledgebase for an organism and enable rigorous and quantitative comparisons of disparate and sparse data, such as genomics and biochemical data, within and across species. Here, we generated a pipeline to create metabolic network models for 192 genomes from protozoan parasites, including the malaria parasite and organisms that cause diarrhea, African sleeping sickness, and leishmaniasis. Importantly, this pipeline was developed to propagate manual curation efforts from one model to others as manual curation remains the field's 'gold standard' for high-quality networks. We compare metabolic behavior across parasites to contextualize experimental results and compare metabolism. We identify which organisms are metabolically similar for the purpose of identifying experimental model systems and find that both metabolic niche and phylogeny influence metabolic similarity.