Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America.
Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America.
PLoS Comput Biol. 2022 Feb 23;18(2):e1009870. doi: 10.1371/journal.pcbi.1009870. eCollection 2022 Feb.
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.
原生动物寄生虫引起多种具有全球重大影响的疾病。这些生物体的发病机制和生物学研究受到经济和实验限制的限制。因此，通常将对一种寄生虫的研究推断为另一种寄生虫的知识，跨越和在属内。体外或体内模型系统经常用于增强实验可操作性，但这些系统通常使用与临床相关病原体相关但又不同的物种。寄生虫物种之间功能差异的特征仅限于事后或单一目标研究，限制了这种推断方法的实用性。为了解决这一挑战并广泛推动寄生虫学研究，我们对 192 个基因组进行了功能比较分析，这些基因组代表了所有高质量的、公开可用的原生动物寄生虫基因组，包括疟原虫、弓形虫、隐孢子虫、内阿米巴、锥虫、利什曼原虫、贾第虫和其他物种。我们生成了一个针对真核生物优化的自动化代谢网络重建管道。这些代谢网络重建作为每个寄生虫的生化知识库，使寄生虫之间的代谢行为能够进行定性和定量比较。我们确定了基因必需性和途径利用的潜在差异，以促进实验结果的比较，并发现系统发育并不是代谢相似性的唯一预测因素。这个知识库代表了病原体和真核生物最大的基因组规模代谢模型集合；有了这个资源，我们可以预测特定物种的功能，将实验结果置于上下文中，并优化对难养物种的实验系统的选择。
