Systems biology approach to elucidate complex metabolic dependencies in the evolution of antibiotic resistance

项目摘要 我们提出了一种系统生物学的方法来研究新陈代谢和突现之间的联系。 人类两种主要病原菌铜绿假单胞菌和铜绿假单胞菌的耐药性 金黄色葡萄球菌，在生理相关环境的背景下。两个最严重的 对AMR、铜绿假单胞菌和金黄色葡萄球菌的威胁导致30,000多人和30多万人耐药感染 在美国，分别为每年[4]。这个R01应用程序的目标是构建计算 耐药和敏感铜绿假单胞菌和金黄色葡萄球菌的代谢网络模型 通过实验验证代谢基因和代谢途径的模型预测 抵抗。为了对抗机会性和院内感染，已开发出用于临床的抗菌药。 使用；然而，随着每一种新的抗菌剂的引入，上述病原体很快就产生了抗药性 [12，13]。因此，迫切需要新的方法来限制AMR在这些地区的出现和扩大 物种。重点介绍革兰氏阴性杆菌(铜绿假单胞菌)和革兰氏阳性菌(金黄色葡萄球菌) 细菌，我们将比较支持抗菌素耐药性的代谢功能，以评估是否 被观察到的关键代谢基因和途径是有机体或更一般的机制所独有的 新陈代谢适应能力。我们假设，表型分析、计算建模和 对临床耐药菌株的分析将揭示细菌的代谢过程 是抗菌素耐药微生物生长的中心，可以被调节以选择不利于生长的微生物 具有抗菌素耐药性的人群。我们实验室和其他实验室的数据最近发现，抗菌剂- 耐药铜绿假单胞菌和金黄色葡萄球菌表现出系统水平的新陈代谢差异[2，14-16]。此外， 从实验收集的数据将使：将基因表达数据与新陈代谢数据相结合的新方法 复杂环境的网络模型，更好地解释新陈代谢不确定性的集成方法 网络重建和机器学习方法，以描绘与AMR相关的代谢状态 在临床分离株中。拟议工作的意义在于对本质的机械性理解 基因、反应和底物偏好是AMR发生的基础，在两个临床上很重要 病原体。此外，将对临床分离株的代谢状态进行表征，以支持 对AMR发展过程中代谢依赖性的系统研究。这项工作的知识差距 Will Address是新陈代谢与AMR发展之间的机制纽带。随着成功的 实施拟议的项目，我们将确定几个潜在的治疗靶点在临床上具有重要意义 病原体，并为这种计算模型驱动的方法如何 适用于其他抗菌素耐药病原体。

Project Summary We propose a systems biology approach to investigate the connection between metabolism and the emergence of antimicrobial resistance (AMR) in two prominent human pathogens, Pseudomonas aeruginosa and Staphylococcus aureus, within the context of physiologically-relevant environments. Two of the most serious threats for AMR, P. aeruginosa and S. aureus cause more than 30,000 and 300,000 drug-resistant infections per year in the United States, respectively [4]. The objective of this R01 application is to construct computational metabolic network models of antimicrobial-resistant and -sensitive P. aeruginosa and S. aureus and to experimentally validate model predictions of metabolic genes and pathways supporting their evolution towards resistance. To combat opportunistic and nosocomial infections, antimicrobials have been developed for clinical use; yet with every new antimicrobial introduced, the aforementioned pathogens have quickly evolved resistance [12, 13]. Therefore, new approaches are urgently needed to limit emergence and expansion of AMR in these species. With a focus in this application on Gram-negative (P. aeruginosa) and Gram-positive (S. aureus) bacteria, we will compare metabolic functions supportive of antimicrobial resistance to evaluate whether the metabolic genes and pathways observed to be critical are unique to an organism or a more general mechanism of metabolic adaptation. We posit that a combination of phenotyping, computational modeling, and analysis of clinical antimicrobial-resistant strains will reveal bacterial metabolic processes that are central to the growth of antimicrobial-resistant microbes and can be modulated to select against growth of antimicrobial-resistant populations. Data from our lab and others have recently found that antimicrobial- resistant P. aeruginosa and S. aureus display systems-level differences in metabolism [2, 14-16]. Furthermore, data collected from experiments will enable: novel approaches to integrate gene expression data with metabolic network models for complex environments, ensemble methods that better account for uncertainty in metabolic network reconstructions, and machine learning methods to delineate metabolic states that correlate with AMR in clinical isolates. The significance of the proposed work lies in the mechanistic understanding of essential genes, reactions, and substrate preferences that underlie the development of AMR in two clinically important pathogens. Further, metabolic states will be characterized in clinical isolates to support the relevance of the systematic interrogation of metabolic dependencies in the development of AMR. The knowledge gap this work will address is the mechanistic link between metabolism and the development of AMR. With the successful implementation of the proposed project, we will identify several potential therapeutic targets in clinically important pathogens as well as establish a framework for how such computational model-driven approaches can be applied to other antimicrobial resistant pathogens.