Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets.
Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets.
复制标题
DOI：
10.1186/1752-0509-1-26
复制
发表时间：
2007-06-08
影响因子：
--
通讯作者：
Palsson, Bernhard O
中科院分区：
文献类型：
--
作者：
Jamshidi, Neema;Palsson, Bernhard O
结核分枝杆菌仍然是第三世界的主要病原体，据最新估计，每年导致近200万人死亡。即使在工业化国家，结核病多药耐药(MDR)菌株的出现也需要开发更多的治疗药物。许多用于治疗结核病的药物都是针对代谢酶的。基因组规模的模型可以用于分析、发现和作为假设生成工具，这有望帮助Rational药物开发过程。这些模型需要能够从大数据集中吸收数据并对其进行分析。我们完成了结核分枝杆菌H37Rv代谢网络的自下而上的重建。这种在硅细菌中具有功能的iNJ661包含661个基因和939个反应，可以产生许多结核病特有的复杂化合物，如霉菌酸和支原体。我们在不同介质上的硅胶中培养这种细菌，在多个高通量数据集的背景下分析模型，最后我们通过计算硬耦合反应(HCR)集，即由于质量守恒和连通性限制而被迫一致操作的反应组，以“无偏”的方式分析网络。尽管我们在不同的介质中观察到的生长率与实验观察相当(倍增时间从大约12到24小时不等)，但基因重要性与实验数据的比较不那么令人鼓舞(通常约为55%)。结果经常相互矛盾的原因是多方面的，包括不同条件下基因表达的差异性以及缺乏完整的生物学知识。体外和体内以及体内和体外结果之间的一些不一致突出了值得进一步实验研究的特定基因座。最后，通过在已知结核病治疗药物靶点的背景下考虑HCR集合，我们提出了新的替代但相同的药物靶点。
Mycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them. We completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints. Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.