Systems biology-based identification of Mycobacterium tuberculosis persistence genes in mouse lungs.
Systems biology-based identification of Mycobacterium tuberculosis persistence genes in mouse lungs.
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DOI：
10.1128/mbio.01066-13
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发表时间：
2014-02-18
期刊：
影响因子：
6.4
通讯作者：
Bader JS
中科院分区：
文献类型：
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作者：
Dutta NK;Bandyopadhyay N;Veeramani B;Lamichhane G;Karakousis PC;Bader JS
鉴定结核分枝杆菌持续基因对开发新药以缩短结核病治疗时间具有重要意义。我们开发了计算算法来预测结核分枝杆菌在小鼠肺部长期存活所需的基因。作为输入，我们使用高通量结核分枝杆菌突变文库筛选数据，分枝杆菌在小鼠和巨噬细胞中的全局转录谱，以及功能相互作用网络。我们选择了57个独特的、基因定义的突变体（18个先前测试过，39个未测试过）来评估这种方法在小鼠结核感染模型中的预测能力。我们观察到，相对于随机选择的突变池，预测的结核分枝杆菌在小鼠肺中持久性所需的基因组富集了6倍。我们的结果也使我们能够重新分类几个基因所需的结核分枝杆菌在体内的持久性。最后，新的结果暗示了额外的高优先级候选基因的测试。计算预测的实验验证证明了这种系统生物学方法在阐明结核分枝杆菌持久性基因方面的力量。结核分枝杆菌是结核病（TB）的病原体，其遗传库使其能够在面对宿主免疫反应时持续存在。这种持久性基因的鉴定可以揭示新的药物靶点，阐明生物体逃避免疫系统和抵抗药物的机制。基因筛选共鉴定出31种持久性基因，但迄今为止，在约4000种结核分枝杆菌基因中，只有15%进行了实验测试。在本文中，作为蛮力实验筛选的替代方案，我们描述了通过将已知示例与不断增长的生物网络数据库相结合来预测新的持久性基因的计算方法。实验测试表明，这些预测是高度准确的，验证了计算方法，并提供了关于结核分枝杆菌在宿主组织中持久性的新信息。使用新的实验结果作为额外的输入，突出了需要测试的额外基因。我们的方法可以扩展到其他数据类型和目标生物，以表征与此和其他传染病相关的宿主-病原体相互作用。
Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes. Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000 M. tuberculosis genes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information about M. tuberculosis persistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.
影响因子：
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作者：
Veeramani B;Bader JS
通讯作者：
Bader JS