Tackling Malaria Resistance with an Integrated Modeling/Experimental Approach

 描述(申请人提供)：全世界每年有近一百万人死于疟疾。恶性疟原虫是这种疾病最致命形式的病原体，在使用抗疟疾药物治疗时，很容易产生抗药性。实验室培育的耐药株使我们能够研究这些寄生虫在药物挑战期间是如何生存的，并有助于提供更成功的治疗策略。临床和实验室研究都表明，耐药寄生虫在其基因组中有特定的变化，导致目标蛋白过量(通过基因组扩增)或物理阻止药物结合(通过突变)。由于生物体内的新陈代谢与所有细胞过程高度相关，与突变靶标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。其中一些代谢变化可能很小，没有被注意到，而另一些可能会带来意想不到的好处，比如对其他药物的交叉耐药性。这项提议描述了一种多方面的方法来发现并阻止这些“提高”寄生虫生存的代谢转变。利用现有的实验系统，在两种临床相关的抗疟疾药物之间观察到交叉耐药，我们将：1)利用代谢组学数据构建生物上准确的代谢网络，在耐药产生前后重建寄生虫，以确定耐药状态的代谢依赖；2)通过计算和经验询问这些关键途径，以确定它们的取消是否可以防止恶性疟原虫耐药。考虑到这项工作涉及致病有机体，这对实验操作是困难的，计算模型有助于将假设和实验设计集中在一起，并降低风险。此外，由于我们使用的是经过验证的实验系统，并且该方法利用了本提案中涉及的联合PI的专业知识，因此我们处于有利地位，可以快速建立有效的计算和实验方法的管道。一旦实现了这一点，研究就可以扩大到发现其他临床相关的抗疟疾药物的代谢调节剂。最终目标将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。以一般应激反应为目标将限制耐药性的发展，并具有广泛的影响。一种有希望的“耐药性阻滞剂”可以与相关的抗疟疾药物一起使用，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.