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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/总结 Wellcome Trust 估计每年因微生物致病而死亡的人数为 700,000 人。这个数字是如果抗菌素耐药性的上升问题仍未得到解决，预计未来十年将迅速增加。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都会引发特定压力的传统观念，揭示了沟通强调探测系统级细胞生理学重要性的细菌反应之间利用多维表型。尽管已经进行了许多尝试来表征细胞对抗生素的反应综合规模，大多数这些研究都存在测量体积的显着缺点 population-level responses.由于大多数耐药突变体是经过选择性筛选后占主导地位的亚群抗生素瓶颈已被应用，无法解释单细胞行为的批量测量不捕获对抗生素应激的整个反应范围。我将利用两项关键技术发展：1）高通量成像和图像分析管道，以及 2) 模型生物体中必需基因敲除的 CRISPR 干扰文库大肠杆菌回答有关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批量测量相结合应激反应记者绘制大肠杆菌在富氧和缺氧条件下的反应动态。我将结合形态参数和应激反应信息来构建丰富的景观表型分析可用于识别新型抗生素的靶标、预测拮抗作用组合疗法，并探讨通路之间的基本联系。为了研究分子网络架构背后的机制，我将采用 CRISPRi 遗传工具来改变药物靶点表达和药物流出。我的首要目标是消除药物发现和药物研发的关键瓶颈给药方法——识别抗生素的细胞靶标，其作用机制未知从压力的角度来看，组合疗法的作用和预测可提高疗效响应激活。这项研究应通过快速靶向加速抗生素发现管道鉴定，同时还有助于深入了解细菌生理学，以指导未来的研究广泛的生物体。