Spatiotemporal Dynamics of Collective Antibiotic Resistance in Microbial Communities

微生物群落集体抗生素耐药性的时空动态

• 批准号: 9553824
• 负责人: Kevin Wood
• 金额: $ 31.2万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9553824
• 关键词: Achievement; Antibiotic Resistance; Antibiotics; Architecture; Bacteria; Bacterial Infections; Behavior; Biological Models; Biology; Cancer Biology; Cells; Communities; Complex; Disease; Ecology; Environment; Evolution; Experimental Models; Health; Innovative Therapy; Length; Mathematics; Medical; Microbial Biofilms; Molecular; Pharmaceutical Preparations; Physics; Population; Public Health; Research; Resistance; Role; Science; Shapes; Spatial Behavior; Structure; Testing; Work; bacterial community; drug discovery; drug resistant pathogen; experimental study; microbial; microbial community; pathogenic bacteria; programs; response; spatiotemporal; theories; tool

Project Summary Antibiotic resistance is among the most serious threats to global public health in the 21st century. Bacteria have a remarkable ability to evolve resistance to antibiotics, regardless of their class or mechanism of action. Unfortunately, the pace of drug discovery lags far behind the rapid pace of emerging resistance. As a result, there is an urgent need to understand how bacteria respond to antibiotics on multiple length scales, ranging from the molecular level to the scale of entire microbial communities, in hopes of prolonging the efficacy of current drugs or uncovering new potential therapies. In addition, it is critical to understand how antibiotic resistance determinants evolve and spread in microbial populations. A number of recent studies suggest that the response of cellular communities to antibiotics is often a collective phenomenon, indicating that a detailed characterization of molecular mechanisms—while essential—may not be sufficient to predict large-scale microbial behavior and evolution. In this proposal, we outline an integrated research program aimed at understanding how collective interactions in bacterial communities impact the dynamics and evolution of antibiotic resistance across multiple length scales, ranging from the large-scale behavior of spatially extended communities to the single-cell architecture of biofilms. If successful, our work will uncover new quantitative principles of microbial ecology, provide experimental model systems for testing predictions of evolutionary theory, and clarify the role of community dynamics in shaping the bacterial response to antibiotics. These results will enrich our understanding of how cellular cooperation is disseminated in spatially complex environments, with potential implications for basic evolutionary biology as well as other health-related fields, such as cancer biology, where multi-cellular coordination may underlie disease. In the long term, the findings may even open the door to innovative therapies aimed at destabilizing community structure in bacterial infections.

项目摘要 抗生素耐药性是对21世纪全球公共卫生最严重的威胁之一。细菌 无论其类别或作用机理如何，都具有出色的发展抗生素抗性的能力。 不幸的是，药物发现的空间远远落后于新兴耐药性的快速步伐。因此， 迫切需要了解细菌如何在多个长度上对抗生素反应，范围 从分子水平到整个微生物群落的规模，希望延长效率 当前药物或发现新的潜在疗法。另外，了解如何了解抗生素 抗性决定了微生物种群中的进化和扩散。最近的许多研究表明 细胞群落对抗生素的反应通常是一种集体现象，表明A 分子机制的详细表征 - 虽然必不可少 - 可能不足以预测 大规模微生物行为和进化。在此建议中，我们概述了一项综合研究计划 旨在了解细菌群落中的集体互动如何影响动态和 抗生素耐药性在多个长度尺度上的演变，从大规模行为 空间扩展社区到生物膜的单细胞体系结构。如果成功，我们的工作将会 发现微生物生态学的新定量原理，为测试提供实验模型系统 预测进化论，并阐明社区动态在塑造细菌中的作用 对抗生素的反应。这些结果将丰富我们对蜂窝合作的理解 在空间复杂的环境中传播，对基本进化生物学有潜在的影响 以及其他与健康相关的领域，例如癌症生物学，多细胞协调可能 基础疾病。从长远来看，这些发现甚至可能打开了针对创新疗法的大门 在细菌感染中破坏社区结构。