课题基金 / 基金详情

Computational models identifying within-host evolution of pathogens using high-throughput DNA sequencing

Computational models identifying within-host evolution of pathogens using high-throughput DNA sequencing
使用高通量 DNA 测序识别病原体宿主内进化的计算模型
批准号:
RGPIN-2017-04860
负责人:
Long, Quan
金额:
$1.75万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

项目摘要

项目成果

Long, Quan的其他基金

相似基金

相关文献

中文摘要
翻译
抗微生物药物耐药性(AMR)是对动物的全球性威胁。然而,对于感染重要动物(如肉牛和奶牛)的病原体,人们对其发生和传播知之甚少。每次感染都代表了对抗菌素具有不同敏感性的多种病原体的种群。当宿主群体在药物治疗的选择性压力下结合免疫反应进化时,通常会发生抗菌素耐药性。下一代DNA测序(NGS)技术的巨大飞跃使我们能够在不需要在实验室(即未经培养)中培养病原体的情况下对宿主内的病原体种群进行测序,从而为加速表征宿主内AMR的进化和传播提供了材料。然而,缺乏合适的计算模型阻碍了这项有前途的研究。******首先,在未培养的样本中,收集多个菌株的病原体DNA并在池中一起测序。为了在个体水平上获取基因组信息,需要新的计算方法来估计菌株或单倍型的身份和频率。这是池测序中许多关键分析的基本技术障碍。******其次,假设克服了上述技术障碍,为了科学地检查选择中的基因和菌株,需要在宿主内病原体进化的背景下扩展为多细胞生物开发的标准群体遗传模型。例如,我们应该使用什么作为菌株或单倍型多样性的中性(没有选择)期望,以及我们如何最好地估计祖先的等位基因,宿主获得的病原体的基础菌株?******我的长期目标是通过对宿主内和宿主间进化的基因组分析来了解AMR的机制,并最终为其在农业中的预防和控制做出贡献。在NSERC发现基金的支持下,我的第一个短期目标是开发新的计算模型来解决所描述的方法论挑战。这为我的第二个短期目标铺平了道路,即通过确定宿主内选择的遗传基础,对阿尔伯塔省感染牛的密螺旋体和大肠杆菌的AMR研究有益。******从根本上说,提出的工作增加了病原体进化的标准理论模型,以更好地解释宿主环境。实际上,鉴定产生抗菌素耐药性的基因的能力将与加拿大的农业实践有关
英文摘要
Antimicrobial resistance (AMR) is a global threat to animals. However, its occurrence and transmission are poorly understood for pathogens infecting important animals, e.g. beef and dairy cattle. Each infection represents a population of multiple strains of a pathogen with different sensitivities to antimicrobials. Often AMR occurs when this within-host population evolves under the selective pressure of drug treatments in conjunction with immune responses. Huge leaps in next-generation DNA sequencing (NGS) technology allow us to sequence within-host populations of a pathogen without the need to grow them in the laboratory (i.e., uncultured), offering materials to speed up the characterization of within-host evolution and transmission of AMR. However, the lack of suitable computational models blocks this promising research. ******First, in the uncultured sample, pathogen DNA of multiple strains is collected and sequenced together in pools. To access genomic information at individual-level, novel computational methods are needed to estimate the identity and frequency of strains or haplotypes. This is a fundamental technical roadblock to many critical analyses in pooled sequencing. ******Second, assuming the above technical barriers are conquered, to scientifically examine genes and strains under selection, standard population genetic models developed for multicellular organisms need to be extended in the context of within-host pathogen evolution. For instance, what should we use as a neutral (without selection) expectation of strain or haplotype diversity, and how do we best estimate alleles of ancestors, the founding strains of the pathogen acquired by a host? ******My long-term goal is to understand the mechanism of AMR by genomic analysis of within- and between-host evolution and ultimately contribute to its prevention and control in agriculture. Supported by this NSERC Discovery Grant, my first short-term goal is to develop novel computational models to resolve the methodological challenges described. This paves the way for my second short-term goal to benefit the research of AMR of Treponema spp. and E. coli that infect cattle in Alberta, by identifying genetic underpinnings of within-host selection.******Fundamentally, the proposed work augments the standard theoretical models of pathogen evolution to better account for within-host environments. Practically, the ability to identify genes that confer AMR will be relevant to agricultural practice in Canada.**
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Computational models identifying within-host evolution of pathogens using high-throughput DNA sequencing
  • 批准号:
    RGPIN-2017-04860
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.5万
  • 财政年份:
    2022
  • 负责人:
    Long, Quan
  • 依托单位:
Computational models identifying within-host evolution of pathogens using high-throughput DNA sequencing
  • 批准号:
    RGPIN-2017-04860
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.75万
  • 财政年份:
    2021
  • 负责人:
    Long, Quan
  • 依托单位:
Computational models identifying within-host evolution of pathogens using high-throughput DNA sequencing
  • 批准号:
    RGPIN-2017-04860
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.75万
  • 财政年份:
    2020
  • 负责人:
    Long, Quan
  • 依托单位:
Computational models identifying within-host evolution of pathogens using high-throughput DNA sequencing
  • 批准号:
    RGPIN-2017-04860
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.75万
  • 财政年份:
    2019
  • 负责人:
    Long, Quan
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
河北南部地区灰霾的来源和形成机制研究
  • 批准号:
    41105105
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    25.0万元
  • 批准年份:
    2011
  • 负责人:
    王丽涛
  • 依托单位:
保险风险模型、投资组合及相关课题研究
  • 批准号:
    10971157
  • 项目类别:
    面上项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2009
  • 负责人:
    胡亦钧
  • 依托单位:
RKTG对ERK信号通路的调控和肿瘤生成的影响