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.

项目概要 抗生素耐药性是 21 世纪全球公共卫生面临的最严重的细菌威胁之一。 无论抗生素的类别或作用机制如何，它们都具有显着的进化耐药性的能力。 不幸的是，药物发现的步伐远远落后于新出现的耐药性的快速发展。 迫切需要了解细菌如何在多种长度尺度上对抗生素做出反应，范围 从分子水平到整个微生物群落的规模，希望能够延长药效 此外，了解抗生素的作用也至关重要。 最近的许多研究表明，耐药性决定因素在微生物群体中进化和传播。 细胞群落对抗生素的反应通常是一种集体现象，这表明 分子机制的详细表征虽然很重要，但可能不足以预测 在这项提案中，我们概述了一个综合研究计划。 旨在了解细菌群落中的集体相互作用如何影响动态和 抗生素耐药性在多个长度尺度上的进化，范围从大规模行为 如果成功，我们的工作将在空间上扩展群落到生物膜的单细胞结构。 揭示微生物生态学新的定量原理，为测试提供实验模型系统 进化理论的预测，并阐明群落动态在塑造细菌中的作用 这些结果将丰富我们对细胞合作如何的理解。 在空间复杂的环境中传播，对基础进化生物学具有潜在影响 以及其他与健康相关的领域，例如癌症生物学，其中多细胞协调可能 从长远来看，这些发现甚至可能为针对疾病的创新疗法打开大门。 细菌感染中的群落结构不稳定。