Phylodynamics of Shiga Toxin-Producing Escherichia coli from Local Sources

项目总结/摘要 产滋贺毒素大肠杆菌（STEC）的本地来源显著增加了疾病风险; 然而，由于无法区分当地和非当地情况， 传输系统。这项研究的长期目标是制定有针对性的公共卫生干预措施 使用系统流行病学来阐明STEC维持的途径和机制， 传输为了实现这一目标，目前研究的总体目标是确定 病原体、宿主和环境与当地STEC传播有关。核心假设是， 本地来源感染的STEC病例与外来菌株感染的病例有显著差异 这是当地的案件。中心假设将通过追求三个具体目标进行测试：1）区分和 表征局部传播的STEC菌株，包括O 157和非O 157，2）识别宿主特征 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，将使用结构化聚结聚合动力学模型来 从明尼苏达州卫生部报告的病例中分离出STEC菌株， (MDH)自2016年以来，与MN以外分离的菌株和NCBI上的菌株进行了比较。的推断的位置 树节点将用于将STEC菌株分类为局部或非局部。将使用广义线性模型来 将应变特征整合到树中，并确定它们对局部MN STEC有效性的影响 人口规模和移民率。第二个目标是评估宿主特征之间的关联， 包括年龄、性别和潜在暴露，本地与非本地STEC。在目标3中， 将估计物理和社会环境以及当地STEC传播的特征 考虑到空间相关性。为了实现这些目标，PI Gillian Tarr博士将获得高级 生物信息学和生物动力学建模方面的培训。塔尔博士还将提高她的知识， 食品生产和分配系统，并进一步发展她的研究管理技能。悠久 食品安全研究的历史和与MDH的合作，明尼苏达大学提供了最佳的 为这项研究提供了环境。导师团队拥有生物信息学方面的专业知识， 系统发育建模，包括STEC和食品系统主题专家。拟议的研究是 在申请人看来，这是创新的，因为它将1）表征本地传输系统， 限制孤立的爆发或使用代理，如最近的旅行，和2）采用结构化的聚结 该模型尚未在任何可比疾病系统中用于此目的。区分局部 输入性病例的传播，并确定当地的宿主、病原体和环境特征， 传播是一个重要的贡献，因为它使特定的假设得以发展和检验 为当地的水库和传输路径，然后可以有针对性的干预措施。

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.