Project 2: Response to Treatment

摘要-项目2 结核分枝杆菌对个别药物和方案的反应及其与治疗结果的关系仍然存在 变化无常且难以理解。我们的前提是细菌网络及其调控的知识 控制措施构成了通向新靶点和治疗策略的强大但未被探索的窗口。 这个项目的主要目标是识别与菌株无关的和特定菌株相关的细胞网络。 结核分枝杆菌中不同的药物反应及其调节。在目标1中，该项目将利用精心挑选的 临床药物敏感结核分枝杆菌分离株对治疗表现出不同的反应，以表征基因， 通过接触重要的抗结核病药物揭示转录和代谢差异，然后进行MAP 条件特异性耐药表型的这些变化。我们将对每一种菌株进行详细的 分析包括转录组学、代谢组学和基于调节器的基因筛查对FEAR的反应 在促进耐受性的条件下使用抗生素。在目标2中，我们将使用来自目标1的数据 建立和完善监管网络模型，阐明共同的和特定菌株的结核分枝杆菌策略，以 颠覆毒品行动。这些模型将通过迭代测试模型驱动的预测来改进 一系列多组学分析和扰动，包括更有针对性的实验，如靶向 蛋白质相互作用研究、细菌细胞分选和固相时间推移显微镜。最终，在目标3中 我们将测试在AIMS 1和2中生成的药物反应网络模型预测临床的程度 治疗结果，并确定干扰适应性药物反应网络状态的潜在策略 以提高化疗的疗效。我们将进一步测试已确定的药物反应的相关性 针对人类治疗失败的结核分枝杆菌分离株的针对性研究中的网络。这个项目的结果是 将识别和验证与不同药物相关的特定细胞网络 结核分枝杆菌的反应及其调控。

Abstract – Project 2 The responses of Mtb to individual drugs and regimens and their relationship to treatment outcome remains variable and poorly understood. Our premise is that knowledge of bacterial networks and their regulatory controls constitute a powerful but underexplored window to novel targets and treatment strategies. The major goal of this project is to identify strain-independent and strain-specific cellular networks associated with varying drug responses in Mtb, and their regulation. In Aim 1 this project will utilize carefully selected clinical drug-sensitive Mtb isolates exhibiting varying responses to treatment to characterize the genetic, transcriptional, and metabolic differences revealed by exposure to important anti-TB drugs, and then to map those changes to condition-specific drug tolerance phenotypes. We will subject each strain to detailed analyses including transcriptomics, metabolomics, and regulator-based genetic screens in response to front line antibiotics and in conditions that promote tolerance. In Aim 2 we will employ the data from Aim 1 to build and refine regulatory network models that elucidate both common and strain-specific Mtb strategies to subvert drug action. These models will be refined by testing model driven predictions through an iterative series of multi-omic analyses and perturbations including more focused experiments such as targeted protein interaction studies, bacterial cell sorting and solid-phase time-lapse microscopy. Ultimately, in Aim 3 we will test the extent to which the drug-response network models generated in Aims 1 and 2 predict clinical treatment outcomes, and identify potential strategies to interfere with adaptive drug-response network states to improve the efficacy of chemotherapy. We will further test the relevance of identified drug response networks in targeted studies of Mtb isolates from treatment failures in humans. The outcome of this project will be the identification and validation of the specific cellular networks associated with varying drug responses in Mtb and their regulation.