Phylodynamics of Shiga Toxin-Producing Escherichia coli from Local Sources

项目摘要/摘要 产生志贺毒素的大肠杆菌(STEC)的本地来源极大地增加了疾病风险； 然而，由于无法区分本地病例和非本地病例，因此无法充分描述本地病例的特征。 传输系统。这项研究的长期目标是开发有针对性的公共卫生干预措施 用系统流行病学阐明STEC维持和发展的途径和机制 变速箱。为了实现这一目标，目前研究的总体目标是确定 与本地STEC传播相关的病原体、宿主和环境。中心假设是 本地感染的STEC病例与从外地感染的STEC病例有显著不同 这个案子是当地的。中心假设将通过追求三个具体目标来检验：1)区分和 确定本地传播的STEC菌株的特征，包括O157和非O157，2)确定宿主特征 与获取本地与非本地STEC菌株相关，以及3)识别环境特征 与本地传播有关。在目标1中，将使用结构化的聚合体系统动力学模型来 建立从明尼苏达州卫生局报告的病例中分离的STEC菌株的系统发育 (MDH)自2016年以来，与MN外分离并可在NCBI上获得的菌株进行了比较。推断出的位置 树节点将用于将STEC菌株分类为本地或非本地。将使用广义线性模型来 将应变特性集成到树中，并确定它们对局部MN STEC有效的影响 人口规模和迁移率。第二个目标将评估寄主特征的关联， 包括年龄、性别和潜在暴露，本地与非本地STEC。在目标3中，两者之间的联系 将估计物理和社会环境的特征以及当地的STEC传播 考虑到了空间相关性。为了实现这些目标，PI吉莉安·塔尔博士将获得先进的 生物信息学和系统动力学建模方面的培训。塔尔博士还将加强她对 在食品生产和分销系统方面，她的研究和管理技能得到了进一步发展。带着一个长长的 食品安全研究的历史和与MDH的合作，明尼苏达大学提供最佳 为本研究提供了良好的环境。导师团队在生物信息学方面拥有专业知识，并应用于 系统发育建模，包括STEC和食品系统主题专家。拟议的研究是 在申请人的意见中，具有创新性，因为它将1)表征本地传输系统，而不是 限制隔离暴发或使用代用品，如最近的旅行，以及2)使用结构化聚合器 尚未在任何可比疾病系统中应用于此目的的模型。区分本地 输入性病例的传播和当地宿主、病原体和环境特征的鉴定 传输是一个重要的贡献，因为它使特定的假设得以开发和检验 对于当地的水库和传播路径，然后可以通过量身定做的干预措施来确定目标。

PROJECT SUMMARY/ABSTRACT Local sources of Shiga toxin-producing Escherichia coli (STEC) contribute significantly to disease risk; however, inability to differentiate local from non-local cases has precluded full characterization of local transmission systems. The long-term goal of this research is to develop targeted public health interventions using systems epidemiology to elucidate the pathways and mechanisms of STEC maintenance and transmission. In pursuit of this goal, the overall objective of the current study is to identify characteristics of pathogen, host, and environment associated with local STEC transmission. The central hypothesis is that STEC cases infected from local sources are significantly different than those infected by strains from outside the case’s local area. The central hypothesis will be tested by pursuing three specific aims: 1) differentiate and characterize locally transmitted STEC strains, both O157 and non-O157, 2) identify host characteristics associated with acquiring local vs. non-local STEC strains, and 3) identify environmental characteristics associated with local transmission. In aim 1, a structured coalescent phylodynamic model will be used to generate a phylogeny of STEC strains isolated from cases reported to the Minnesota Department of Health (MDH) since 2016 compared to strains isolated outside MN and available on NCBI. The inferred location of tree nodes will be used to classify STEC strains as local or non-local. A generalized linear model will be used to integrate strain characteristics into the tree and determine their influence on the local MN STEC effective population size and migration rates. The second aim will assess the association of host characteristics, including age, sex, and potential exposures, with local vs. non-local STEC. In aim 3, the association between characteristics of the physical and social environment and local STEC transmission will be estimated accounting for spatial correlation. To accomplish these aims, PI Dr. Gillian Tarr will obtain advanced training in bioinformatics and phylodynamic modeling. Dr. Tarr will also enhance her knowledge of food production and distribution systems and further develop her research management skills. With a long history of food safety research and collaboration with MDH, the University of Minnesota provides the optimal environment for this research. The mentorship team has expertise in bioinformatics and applied phylogenetic modeling and includes STEC and food systems subject matter experts. The proposed research is innovative, in the applicants’ opinions, because it will 1) characterize local transmission systems without restriction to isolated outbreaks or use of proxies such as recent travel, and 2) employ a structured coalescent model that has not been applied for this purpose in any comparable disease system. Differentiating local transmission from imported cases and identifying the host, pathogen, and environment characteristics of local transmission is a significant contribution, because it enables specific hypotheses to be developed and tested for local reservoirs and transmission pathways, which can then be targeted by tailored interventions.