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中文摘要
翻译
描述(由申请人提供):在抗生素耐药细菌如何出现和传播以及有利于其成功的生态因素方面,我们的知识存在根本差距。这一差距的持续存在是制定更好的抗生素管理战略和针对人群(无论是宿主还是细菌)的一个重要问题,在那里它们将产生最大的效果。这个应用程序的长期目标是开发这样的策略。追踪抗性的出现,包括最终导致进化死胡同的不成功变异,是识别与成功相关的谱系和特征的有力方法。本申请的目的是研究在实施针对这种病原体的绝大多数耐药菌株的有效疫苗后,肺炎球菌耐药性的重新出现。核心假设是,新出现的耐药菌株虽然罕见,但在接种疫苗之前就已经存在,随着它们变得越来越普遍,它们将在大量使用抗生素的地区占主导地位。申请人根据在其实验室收集的初步数据和对以前引入疫苗后的数据的审查,提出了这一假设。该建议的基本原理是,接种疫苗提供了一个观察耐药性出现的机会,并将其与出现耐药性的环境特征和新出现的菌株本身的特性联系起来。该假设将有三个具体目标:1)确定新出现的抗性克隆的起源及其与已有种群的关系;2)明确与不同地区出现抗药性相关的生态因素;3)定义成功克隆的基因组特性。在第一个目标下,细菌将通过全基因组测序来表征,该方法已经在PI的实验室中使用,将用于确定疫苗前肺炎球菌种群与合作者从2900多万人中取样的耐药菌株之间的关系。第二个目标将探讨不同地区之间耐药性流行程度的差异,以及存在的确切菌株,是否可以用抗生素使用的差异或其他因素来最好地解释,使用的方法是由co-I开发的。第三个目标将检查目标1中收集的基因组,以测试能够解释菌株成功或失败的特性。预期这些将反映重组的历史,将现有的遗传物质重新组合成新的组合,PI先前曾涉及该病原体耐药性的传播。这种方法的创新之处在于,它结合了生态和基因组分析,并强调在耐药性出现时对其进行观察。这项研究意义重大,因为它有望垂直推进对肺炎球菌和其他病原体耐药流行病学的理解,并提出通过仔细的抗生素管理来限制耐药的方法。
英文摘要
DESCRIPTION (provided by applicant): There is a fundamental gap in our knowledge of how antibiotic resistant bacteria emerge and spread, and the ecological factors that favor their success. The continued existence of this gap is an important problem in devising better strategies of antibiotic stewardship and targeting them at the population - either host or bacteria - where they will have the greatest effect. The long-term goal of this application is the development of such strategies. Tracking resistance as it emerges, including ultimately unsuccessful variants that lead to evolutionary dead-ends, is a powerful way of identifying lineages and features associated with success. The objective of this application is to study the re-emergence of resistance in the pneumococcus following implementation of an effective vaccine that targets the great majority of resistant strains of this pathogen. The central hypothesis is that emerging resistant strains will have been present, though rare, before vaccination, and as they become more common they will come to predominate in regions with a large amount of antibiotic use. The applicants have formulated this hypothesis on the basis of preliminary data collected in their laboratories, and examination of data following previous vaccine introductions. The rationale for the proposal is that vaccination offers an opportunity to observe the emergence of resistance, and relate it both to features of the environments in which it emerges and the properties of the emerging strains themselves. The hypothesis will be addressed with three specific aims: 1) Define the origins of emerging resistant clones and their relationship to pre-existing populations; 2) Define ecological factors associated with the emergence of resistance in different regions; and 3) Define genomic properties of successful clones. Under the first aim, bacteria will be characterized by whole genome sequencing, with methods already in use in the PI's laboratory, will be used to determine the relationship between the prevaccine pneumococcal population, and resistant strains sampled by collaborators from a population of more than 29 million persons. The second aim will ask whether differences between regions in the prevalence of resistance, and the exact strains present, are best explained by differences in antibiotic use or other factors, applying methods developed by the co-I. The third aim will examine the genomes gathered in aim 1, to test for properties that could explain the success or failure of strains. The expectation is that these will reflect a history of recombination, which shuffles existing genetic material into novel combinations and which the PI has previously implicated in the spread of resistance in this pathogen. This approach is innovative in its combination of ecological and genomic analyses, and its emphasis on observing resistance as it emerges. The research is significant because it is expected to vertically advance understanding of the epidemiology of resistance in pneumococcus and other pathogens, and suggest means by which it may be limited through careful antibiotic stewardship.
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Casual, Statistical and Mathematical Modeling with Serologic Data
  • 批准号:
    10852367
  • 项目类别:
  • 资助金额:
    $115.19万
  • 财政年份:
    2020
  • 负责人:
    William Hanage
  • 依托单位:
Casual, Statistical and Mathematical Modeling with Serologic Data
  • 批准号:
    10264480
  • 项目类别:
  • 资助金额:
    $169.51万
  • 财政年份:
    2020
  • 负责人:
    William Hanage
  • 依托单位:
Deep sequencing of pathogens to precisely define transmission networks using rare variants
  • 批准号:
    10196948
  • 项目类别:
  • 资助金额:
    $55.45万
  • 财政年份:
    2017
  • 负责人:
    William Hanage
  • 依托单位:
Deep sequencing of pathogens to precisely define transmission networks using rare variants
  • 批准号:
    9382280
  • 项目类别:
  • 资助金额:
    $67.36万
  • 财政年份:
    2017
  • 负责人:
    William Hanage
  • 依托单位:
海外基金