Systems genetics of artemisinin resistance

摘要 使用人源化小鼠进行疟疾实验性遗传杂交的能力提供了令人兴奋的 恶性疟原虫的遗传分析的机会。系统遗传学方法可以补充 由于RNA的转录、翻译成蛋白质以及这些蛋白质的代谢，这些努力提供了 连接基因型和表现型的关键中间步骤。我们将描述转录， 来自三个遗传杂交的亲本寄生虫和后代的蛋白质组学和代谢组学特征 核心A（Experimental Genetic Crosses）为了生产所需的材料，我们将成长， 在整个寄生虫生命周期中以6小时的间隔收获紧密同步的寄生虫培养物（2个重复）， mRNA、蛋白质和代谢物的提取：RNAseq、蛋白质组学和代谢物组学表征将 由Core C（基因组学）进行。生成的系统数据集将用于询问应用程序和 关于寄生虫生物学的基本问题：（一）我们假设收集的“组学”数据将提供关键的 深入了解抗性机制、抗性中涉及的代谢网络和抗性成本。 连锁作图框架中的系统遗传分析将显示其他位点如何影响青蒿素 抗性，以及kelch 13等位基因在观察到的抗性水平上变化的原因。(ii)我们假设 从模式生物研究中产生的系统生物学范式不太适合疟疾。恶性疟原虫是 从根本上不同于模式生物，在整个细胞中有及时的mRNA产生级联反应。 生命周期和少数编码转录因子。我们将进行基因组范围的基因组数量性状分析， 研究基因、mRNA、蛋白质和代谢物之间的相互作用和反馈， 测试这个假设。这些分析将探索蛋白质丰度被调节的水平， SNP、微卫星和拷贝数变异在驱动调控进化中的相对重要性。这些 分析将涉及与核心B（数据集成和分析核心）和RP 02（药物 抗性分析和QTL作图）。RNAseq、蛋白质组学和代谢组学数据将 提供给疟疾研究界使用。

ABSTRACT The ability to conduct experimental genetic crosses in malaria using humanized mice provides exciting opportunities for genetic analysis of Plasmodium falciparum. Systems genetics approaches can complement these efforts, because transcription of RNA, translation into proteins and metabolism of these proteins, provide the critical intermediate steps that link genotype and phenotype. We will characterize the transcriptional, proteomic and metabolomic signatures of both parental parasites and progeny from three genetic crosses generated by Core A (Experimental Genetic Crosses). To generate the material needed, we will grow and harvest tightly synchronized parasite cultures (2 replicates) at 6 hr intervals throughout the parasites lifecycle for extraction of mRNA, proteins and metabolites: RNAseq, proteomic and metabolomic characterization will then be conducted by Core C (Genomics). The systems datasets generated will be used to ask both applied and fundamental questions about parasite biology: (i) We hypothesis that `omic' data collected will provide key insights into the mechanism of resistance, the metabolic networks involved in resistance and cost of resistance. Systems genetic analyses in a linkage mapping framework will show how additional loci impact artemisinin resistance, and the reasons that kelch13 alleles vary in level of resistance observed. (ii) we hypothesize that the systems biology paradigms emerging from studies of model organism will fit poorly for malaria. P. falciparum is fundamentally different from models organism in having just-in-time cascades of mRNA production across the lifecycle and few encoded transcription factors. We will conduct genome wide QTL analyses of mRNA, proteins and metabolites to investigate the interplay and feedback between genes, mRNA, proteins and metabolites to test this hypothesis. These analyses will explore the level at which protein abundance is regulated, and the relative importance of SNPs, microsatellites and copy number variation in driving regulatory evolution. These analyses will involve strong collaboration with Core B (Data Integration and Analysis Core) and RP02 (Drug Resistance Profiling and QTL mapping) in Notre Dame. The RNAseq, proteomics and metabolomics data will be made publically available for use by the malaria research community.