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

Abstract

1 Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology 2 of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are 3 frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in 4 vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species 5 related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences 6 among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation 7 approach. To address this challenge and to accelerate parasitology research broadly, we present a functional 8 comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite 9 genome including Plasmodium, 10 and other species. We generated an automated metabolic network reconstruction pipeline optimized for 11 eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each 12 parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified 13 putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental 14 findings. This knowledgebase represents the largest collection of genome-scale metabolic models for both 15 pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize 16 experimental results, and optimize selection of experimental systems for fastidious species. 42hindering drug development, such as resistance to genetic modification. For example, Plasmodium falciparum 43 (malaria) was considered refractory to genetic modification until recently (Ghorbal et al. 2014; Lee and Fidock 44 2014). Entamoeba histolytica (diarrheal disease) has also been refractory to efficient genetic manipulation, and the genomes of Leishmania develop significant aneuploidy under selective pressure (Downing et al. 2011; Sterkers Although these challenges may be circumvented with new technology, the use of clinical samples, and reductionist 1 approaches, little data exist relative to that which is available for most bacterial pathogens. Without adequate 2 profiling data (genome-wide essentiality, growth profiling in diverse environmental conditions, etc.), we do not 3 have the knowledge to rationally identify novel drug targets. Untargeted and unbiased screens of chemical 4 compounds for antiparasitic effects have proven useful (if the parasite can be cultured, e.g. (Jumani et al. 2018; 5 Chao et al. 2018; Love et al. 2017; Subramanian et al. 2018; Lucantoni et al. 2013)), but this approach provides 6 little information about mechanism of action or mechanisms of resistance development. Typical approaches to 7 study drug resistance, such as evolving resistance to identify mutations in a drug's putative target, are not poss...

Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

“…Phenotyping individual organisms in high throughput is a promising approach toward improving GEM curation; however, it is not always feasible (e.g., obligate intracellular organisms) or may be experimentally challenging to set up (e.g., organisms which have no known, chemically defined culture conditions) ( 36 , 50 ). Furthermore, when studies are scaled to the complexity of most natural microbiomes, it becomes infeasible to test the performance of each individual organism in well-controlled monocultures.…”

PhenoMapping: A protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models

“…FBA can be performed to a certain extent in such GEMs. Draft GEMs could be generated automatically with available systematic construction pipelines, as recently shown with paraDIGM ( Carey et al., 2019 ).…”