Chemical Inhibitors to Define an Essential M. Tuberculosis Signaling Network

描述(由申请人提供)：针对RFA-AI-11-004，本申请提出了一项高度协作的研究计划，使用小分子化学抑制剂作为探针来表征结核分枝杆菌的生理过程，这些过程受基本的丝氨酸/苏氨酸蛋白激酶PKNA和PKNB调节。这些激酶本身都是候选的药物靶点，并调节下游蛋白质和途径，这些下游蛋白和途径也将成为药物开发的潜在靶点。PI和合作研究人员将汇集转录学、磷蛋白组学、脂类组学、代谢组学和计算生物学方面的最先进专业知识，目标是实现对PnuA/PnuB信号网络的系统水平的了解。除了这种多平台的“组学”方法，该提案的一个重要优势是在这项研究中使用了这些激酶的新的高效化学抑制剂作为PKNOA/PKNOB调节网络的探针。这项研究既提出了无偏见的、广泛的方法来确定这些激酶调控的过程，也提出了有针对性的方法，重点研究了两条重要的细胞被膜生物合成途径，大量数据表明这两条途径对PKNA和PKNB具有调节作用：肽聚糖合成和霉菌酸合成。虽然每个研究平台都会产生数据，让人们直接了解PKNA和PKNB的功能，但这种方法的另一个关键组成部分是使用计算方法来对整合不同类型实验数据的网络进行建模。这项研究的结果将提供系统水平的洞察力，以了解PKNOA/PKNOB信号通路及其调控的过程。我们期望这些知识将证实这些激酶作为结核分枝杆菌药物开发的优秀靶点，并确定受调控的蛋白和过程，这些蛋白和过程也可能是抗结核药物开发的有吸引力的靶点。

DESCRIPTION (provided by applicant): In response to RFA-AI-11-004, this application proposes a highly collaborative research plan to use small molecule chemical inhibitors as probes to characterize the physiologic processes in Mycobacterium tuberculosis that are regulated by the essential Serine/Threonine protein kinases PknA and PknB. These kinases are both candidate drug targets themselves, and regulate downstream proteins and pathways that will also be potential targets for drug development. The PI and co-investigators will bring together state of the art expertise in transcriptomics, phosphoproteomics, lipidomics, metabolomics and computational biology with the goal of achieving a systems-level understanding of the PknA/PknB signaling network. In addition to this multi-platform "omics" approach, an important strength of the proposal is the use in this research of novel highly potent chemical inhibitors of these kinases as probes for PknA/PknB-regulated networks. The research proposes both unbiased, broad approaches to identify processes regulated by these kinases, and targeted approaches focused on two essential cell envelope biosynthetic pathways for which substantial data indicates a regulatory role for PknA and PknB: peptidoglycan synthesis and mycolic acid synthesis. While each research platform will generate data that will provide direct insight into the functions of PknA and PknB, another key component of this approach is to use computational methods to model networks that integrate the different types of experimental data. The results of this research will provide systems-level insights into the PknA/PknB signaling pathways and the processes that they regulate. We expect this knowledge will validate these kinases as excellent targets for M. tuberculosis drug development and identify proteins and processes regulated PknA and PknB that may also be attractive targets for anti-tuberculosis drug development.