Kim Hyun Uk, Kim Tae Yong, Lee Sang Yup
Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea.
Mol Biosyst. 2010 Feb;6(2):339-48. doi: 10.1039/b916446d. Epub 2009 Dec 8.
Acinetobacter baumannii has emerged as a new clinical threat to human health, particularly to ill patients in the hospital environment. Current lack of effective clinical solutions to treat this pathogen urges us to carry out systems-level studies that could contribute to the development of an effective therapy. Here we report the development of a strategy for identifying drug targets by combined genome-scale metabolic network and essentiality analyses. First, a genome-scale metabolic network of A. baumannii AYE, a drug-resistant strain, was reconstructed based on its genome annotation data, and biochemical knowledge from literatures and databases. In order to evaluate the performance of the in silico model, constraints-based flux analysis was carried out with appropriate constraints. Simulations were performed from both reaction (gene)- and metabolite-centric perspectives, each of which identifies essential genes/reactions and metabolites critical to the cell growth. The gene/reaction essentiality enables validation of the model and its comparative study with other known organisms' models. The metabolite essentiality approach was undertaken to predict essential metabolites that are critical to the cell growth. The EMFilter, a framework that filters initially predicted essential metabolites to find the most effective ones as drug targets, was also developed. EMFilter considers metabolite types, number of total and consuming reaction linkage with essential metabolites, and presence of essential metabolites and their relevant enzymes in human metabolism. Final drug target candidates obtained by this system framework are presented along with implications of this approach.
鲍曼不动杆菌已成为对人类健康的一种新的临床威胁，尤其是对医院环境中的患病患者。目前缺乏治疗这种病原体的有效临床解决方案，这促使我们开展系统层面的研究，以促进有效治疗方法的开发。在此，我们报告一种通过联合基因组规模代谢网络和必需性分析来鉴定药物靶点的策略。首先，基于鲍曼不动杆菌AYE（一种耐药菌株）的基因组注释数据、文献和数据库中的生化知识，重建了其基因组规模代谢网络。为了评估计算机模型的性能，采用适当的约束条件进行基于约束的通量分析。从反应（基因）和代谢物两个角度进行模拟，每个角度都能识别对细胞生长至关重要的必需基因/反应和代谢物。基因/反应必需性能够验证模型，并与其他已知生物体的模型进行比较研究。采用代谢物必需性方法来预测对细胞生长至关重要的必需代谢物。还开发了EMFilter，这是一个筛选最初预测的必需代谢物以找到最有效的作为药物靶点的框架。EMFilter考虑代谢物类型、与必需代谢物的总反应和消耗反应连接数，以及人类代谢中必需代谢物及其相关酶的存在情况。展示了通过该系统框架获得的最终药物靶点候选物以及这种方法的意义。
