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中文摘要
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描述(由申请人提供):我们对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的认识存在根本性差距。这一差距的持续存在是设计更好的抗生素管理策略并将其靶向人群(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。本申请的长期目标是制定此类战略。在抗性出现时对其进行跟踪,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一种强有力的方法。本申请的目的是研究在实施针对该病原体的绝大多数耐药菌株的有效疫苗后,肺炎球菌中的耐药性重新出现。核心假设是,在接种疫苗之前,新出现的耐药菌株虽然罕见,但已经存在,随着它们变得越来越常见,它们将在大量使用抗生素的地区占主导地位。申请人根据其实验室收集的初步数据以及既往疫苗引入后的数据检查制定了该假设。该提议的基本原理是,接种疫苗提供了一个观察耐药性出现的机会,并将其与耐药性出现的环境特征和新出现的菌株本身的特性联系起来。该假说将解决三个具体目标:1)定义新出现的抗性克隆的起源及其与先前存在的种群的关系; 2)定义与不同地区抗性出现相关的生态因素; 3)定义成功克隆的基因组特性。根据第一个目标,细菌将通过全基因组测序进行表征,PI实验室已经使用的方法将用于确定疫苗接种前肺炎球菌群体与合作者从超过2900万人的人群中采样的耐药菌株之间的关系。第二个目标将询问,应用co-I开发的方法,是否可以通过抗生素使用或其他因素的差异来最好地解释地区之间耐药流行率的差异以及存在的确切菌株。第三个目标将检验目标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,其将现有的遗传物质改组成新的组合,并且PI先前已经暗示了该病原体中抗性的传播。这种方法是创新的,它结合了生态和基因组分析,并强调在抗性出现时对其进行观察。这项研究意义重大,因为它有望纵向推进对肺炎球菌和其他病原体耐药性流行病学的理解,并提出通过谨慎的抗生素管理来限制耐药性的方法。
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