Phenotypic profiling of bacterial stress response networks: A transformative framework for characterizing and predicting antibiotic targets and interactions

项目摘要/摘要 Wellcome Trust估计每年因微生物致病而死亡的人数为70万人。这个数字 如果抗生素耐药性的上升仍然得不到解决，预计在未来十年内将迅速增加。作为 作为了解抗生素耐药性出现机制的第一步，最近的研究探索了 从整体细胞的角度来看抗生素影响的生物过程。这些研究的结果 挑战了每种抗生素引起特定压力的传统观念，揭示了沟通 细菌的反应强调了探测系统水平细胞生理学的重要性， 利用多维表型。 虽然已经进行了许多尝试来表征细胞对抗生素的反应，但在某些情况下， 在全面的范围内，这些研究中的大多数都存在测量体积的显著缺点 人口层面的回应。由于大多数抗性突变体是一个亚群体， 抗生素的瓶颈已经应用，批量测量，不能考虑单细胞的行为， 捕捉抗生素应激的整个反应谱。 我将利用两个关键的技术发展：1）高通量成像和图像分析 流水线，和2）在模式生物中的必需基因敲低的CRISPR干扰文库 大肠杆菌来回答关于细菌对抗生素反应的基本问题。我建议使用 高通量显微镜和基于酶标仪的荧光定量测定的组合 应激反应报告者绘制E.大肠杆菌在富氧和缺氧条件下。我 将联合收割机形态参数和应激反应信息相结合，为 表型分析可用于鉴定新型抗生素的靶点， 组合疗法，并探索途径之间的基本布线。为了研究 基于网络架构的机制，我将采用CRISPRi遗传工具来改变药物靶点， 表达和药物外排。我的首要目标是消除药物发现和药物治疗中的一个关键瓶颈。 给药途径-确定抗生素的细胞靶点， 从压力的Vantage角度来看，具有改善功效的组合疗法的作用和预测- 反应激活这项研究应该通过快速靶向加速抗生素发现管道 鉴定，同时也有助于深入了解细菌生理学，以指导未来的研究 各种各样的有机体。

Project Abstract/Summary The Wellcome Trust estimates the death toll due to microbial pathogenesis to be 700,000/year. This number is expected to rapidly increase in the next decade if the rise of antimicrobial resistance remains unaddressed. As a first step to understanding the mechanisms of antibiotic resistance emergence, recent studies have explored the biological processes affected by antibiotics from a holistic cellular perspective. Results from these studies have challenged the traditional notion of each antibiotic eliciting a specific stress, revealing communication between bacterial responses that highlight the importance of probing systems-level cellular physiology and exploiting multi-dimensional phenotypes. Although many attempts have been made to characterize cellular response to antibiotics on a comprehensive scale, most of these studies suffer from the significant disadvantage of measuring bulk population-level responses. As most resistant mutants are a sub-population that dominates after selective antibiotic bottlenecks have been applied, bulk measurements that fail to account for single-cell behavior do not capture the entire spectrum of responses to antibiotic stress. I will leverage two key technological developments: 1) a high-throughput imaging and image analysis pipeline, and 2) a CRISPR interference library of essential gene knockdowns in the model organism Escherichia coli to answer fundamental questions about the bacterial response to antibiotics. I propose to use a combination of high-throughput microscopy and plate reader-based bulk measurements of fluorescent stress-response reporters to map response dynamics in E. coli under both oxygen-rich and anoxic conditions. I will combine morphological parameters and stress response information to build a rich landscape for phenotypic profiling that can be utilized to identify targets of novel antibiotics, predict antagonism in combinatorial therapies, and probe the fundamental wiring between pathways. To investigate the molecular mechanisms underlying the network architecture, I will employ CRISPRi genetic tools to alter drug-target expression and drug efflux. My overarching goal is to eliminate a key bottleneck in drug discovery and drug administration approaches–the identification of cellular targets for antibiotics with unknown mechanisms of action and prediction of combinatorial therapeutics with improved efficacy from the vantage point of stress- response activation. This study should accelerate the antibiotic discovery pipeline through rapid target identification while also contributing deep understanding of bacterial physiology to guide future research across a wide range of organisms.