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

细菌应激反应网络的表型分析：用于表征和预测抗生素靶点和相互作用的变革框架

基本信息

批准号：
9898254
负责人：
Manohary Rajendram
金额：
$ 2.83万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2020-08-21
项目状态：
已结题

项目摘要

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.

项目摘要/摘要惠康信任估计，由于微生物发病机理的死亡人数为700,000/年。这个数字是如果抗菌素耐药性的兴起尚未得到解决，预计在未来十年将迅速增加。作为了解抗生素抗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，受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的传统概念，引起了特定的压力，揭示了交流在细菌反应之间强调了探测系统级的细胞生理学的重要性和利用多维表型。尽管已经尝试了许多尝试表征细胞对抗生素的反应综合规模，大多数研究都遭受了衡量大量的巨大灾难人口级反应。因为大多数抗性突变体是一个子群，在选择性之后主导已经应用了抗生素瓶颈，无法解释单细胞行为的批量测量捕获整个对抗生素应激的反应。我将利用两个关键的技术发展：1）高通量成像和图像分析管道和2）模型有机体中基本基因敲低的CRIS PR干扰库大肠杆菌回答有关细菌对抗生素的基本问题。我建议使用高通量显微镜和基于板块的荧光量的大量测量的组合应力反应记者在富氧和缺氧条件下绘制大肠杆菌中的响应动态。我将结合形态学参数和压力响应信息，以建立丰富的景观可以用来鉴定新抗生素靶标的表型分析，预测拮抗作用组合疗法，并探测途径之间的基本接线。研究分子网络体系结构的基础机制，我将使用CRISPRI遗传工具来改变药品目标表达和药物流出。我的总体目标是消除药物发现和毒品中的关键瓶颈管理方法 - 鉴定具有未知机制的抗生素的细胞靶标从应力点起作用和预测组合疗法和提高效率的组合疗法 - 响应激活。这项研究应通过快速目标加速抗生素发现管道识别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生物。