Tackling Malaria Resistance with an Integrated Modeling/Experimental Approach

 描述（由申请人提供）：疟疾每年在世界各地造成近一百万人死亡。恶性疟原虫是这种疾病最致命形式的病原体，在用抗疟药物治疗时很容易产生抗药性。人工饲养的耐药品系使我们能够研究这些寄生虫在药物挑战期间如何生存，并有助于为更成功的治疗策略提供信息。临床和实验室研究都表明，耐药寄生虫的基因组发生了特定的变化，导致过量的靶蛋白（通过基因组扩增）或药物结合的物理阻止（通过突变）。由于生物体内的代谢与所有细胞过程高度相关，因此与突变靶点相互作用的途径也必须进行调整，以支持寄生虫继续茁壮成长的能力。其中一些代谢变化可能是轻微的，不被注意到，而另一些可能会带来意想不到的好处，如对其他药物的交叉耐药性。该提案描述了一种多方面的方法来发现，然后阻止这些代谢的变化，“提高”寄生虫的生存。使用现有的实验系统，其中观察到两种临床相关抗疟药之间的交叉耐药性，我们将：1）采用代谢组学数据来构建寄生虫在产生抗性之前和之后的生物学上精确的代谢网络重建，以鉴定抗性状态的代谢依赖性，以及通过计算和经验研究这些关键途径以确定它们的废除是否可以防止恶性疟原虫抗性。鉴于这项工作涉及致病生物体，这是难治性的实验操作，计算模型有助于集中假设和实验设计，并降低风险。此外，由于我们正在使用一个经过验证的实验系统，并且该方法利用了该提案中涉及的co-PI的专业知识，因此我们能够快速构建一个有效的计算和实验方法的管道。一旦实现这一点，研究可以扩大到发现其他临床相关的抗疟药的代谢调节剂。最终目标是汇编各种实验系统的结果，并确定寄生虫在压力下生存所需的途径。针对一般的压力反应将限制耐药性的发展，并具有广泛的影响。一种有希望的“抗药性阻断剂”可以与相关的抗疟药物一起使用，以延长其效果并减少全球疟疾死亡人数。

 DESCRIPTION (provided by applicant): Malaria is responsible for almost a million deaths per year across the world. Plasmodium falciparum, the causative agent of the most deadly form of this disease, can readily develop resistance when treated with antimalarial drugs. Laboratory-reared resistant lines allow us to study how these parasites survive during drug challenge and help to inform more successful treatment strategies. Both clinical and laboratory investigations reveal that resistant parasites have specific changes in their genome that lead to excess amounts of the target protein (through genomic amplification) or the physical prevention of drug binding (through mutations). Because metabolism within an organism is highly interconnected with all cellular processes, pathways that interact with the mutated target must also make adjustments to support the parasite's continued ability to thrive. Some of these metabolic shifts may be minor and go unnoticed while others could confer unexpected benefits such as cross-resistance to additional drugs. This proposal describes a multifaceted approach to uncover and then impede these metabolic shifts that "enhance" parasite survival. Using an existing experimental system in which cross-resistance between two clinically-relevant antimalarials was observed, we will: 1) employ metabolomics data to construct a biologically accurate metabolic network reconstruction of the parasite before and after the generation of resistance in order identify metabolic dependencies of the resistant state and 2) computationally and empirically interrogate these critical pathways to determine if their abrogation can prevent P. falciparum resistance. Given that this work involves the disease-causing organism, which is refractory to experimental manipulation, computational models help to focus hypotheses and experimental design and mitigate risk. Additionally, since we are working with a validated experimental system and the approach takes advantage of expertise of the co-PIs involved in this proposal, we are well-positioned to rapidly build a pipeline of effective computational and experimental approaches. Once this is achieved, investigations can expand to uncover metabolic modulators of other clinically-relevant antimalarials. The ultimate goal will be to compile results from varios experimental systems and identify pathways that are required for parasite survival under stress. Targeting the general stress response would limit the development of resistance and have wide- reaching implications. A promising "resistance blocker" could be administered alongside relevant antimalarial drugs to prolong their effects and decrease worldwide malaria deaths.