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基于网络评估恶性疟原虫代谢药物靶点相对于人类肝脏代谢的选择性。

Network-based assessment of the selectivity of metabolic drug targets in Plasmodium falciparum with respect to human liver metabolism.

作者信息

Bazzani Susanna, Hoppe Andreas, Holzhütter Hermann-Georg

机构信息

Institut für Biochemie, Charite-Universitätsmedizin, Berlin, Germany.

出版信息

BMC Syst Biol. 2012 Aug 31;6:118. doi: 10.1186/1752-0509-6-118.

Abstract

BACKGROUND

The search for new drug targets for antibiotics against Plasmodium falciparum, a major cause of human deaths, is a pressing scientific issue, as multiple resistance strains spread rapidly. Metabolic network-based analyses may help to identify those parasite's essential enzymes whose homologous counterparts in the human host cells are either absent, non-essential or relatively less essential.

RESULTS

Using the well-curated metabolic networks PlasmoNet of the parasite Plasmodium falciparum and HepatoNet1 of the human hepatocyte, the selectivity of 48 experimental antimalarial drug targets was analyzed. Applying in silico gene deletions, 24 of these drug targets were found to be perfectly selective, in that they were essential for the parasite but non-essential for the human cell. The selectivity of a subset of enzymes, that were essential in both models, was evaluated with the reduced fitness concept. It was, then, possible to quantify the reduction in functional fitness of the two networks under the progressive inhibition of the same enzymatic activity. Overall, this in silico analysis provided a selectivity ranking that was in line with numerous in vivo and in vitro observations.

CONCLUSIONS

Genome-scale models can be useful to depict and quantify the effects of enzymatic inhibitions on the impaired production of biomass components. From the perspective of a host-pathogen metabolic interaction, an estimation of the drug targets-induced consequences can be beneficial for the development of a selective anti-parasitic drug.

摘要

背景

寻找针对恶性疟原虫(人类死亡的主要原因之一)的抗生素新药物靶点是一个紧迫的科学问题,因为多重耐药菌株迅速传播。基于代谢网络的分析可能有助于识别那些在人类宿主细胞中不存在同源对应物、非必需或相对不太必需的寄生虫必需酶。

结果

利用精心策划的恶性疟原虫代谢网络PlasmoNet和人类肝细胞代谢网络HepatoNet1,分析了48个实验性抗疟药物靶点的选择性。通过计算机模拟基因缺失,发现其中24个药物靶点具有完美的选择性,即它们对寄生虫是必需的,但对人类细胞是非必需的。利用降低的适应性概念评估了在两个模型中都必需的一部分酶的选择性。然后,有可能量化在相同酶活性的逐步抑制下两个网络功能适应性的降低。总体而言,这种计算机模拟分析提供了一个与众多体内和体外观察结果一致的选择性排名。

结论

基因组规模模型可用于描述和量化酶抑制对生物量成分受损产生的影响。从宿主 - 病原体代谢相互作用的角度来看,估计药物靶点诱导的后果可能有利于开发选择性抗寄生虫药物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec6e/3543272/90314d751024/1752-0509-6-118-1.jpg

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