Phylodynamics of Shiga Toxin-Producing Escherichia coli from Local Sources

本地产志贺毒素大肠杆菌的系统动力学

• 批准号: 10616754
• 负责人: Gillian Tarr
• 金额: $ 13.76万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-02 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10616754
• 关键词: Accounting; Age; Animals; Area; Bioinformatics; Canada; Case Mixes; Case Study; Cattle; Characteristics; Child; Classification; Collaborations; Data; Deer; Detection; Development; Disease; Disease Outbreaks; Environment; Environmental Pollution; Epidemiology; Escherichia coli; Escherichia coli EHEC; Escherichia coli Infections; Escherichia coli O157:H7; Exposure to; Farm; Food; Food Safety; Food Supply; Food production; Future; Genes; Genomics; Genotype; Goals; Health; Human; Infection; Intervention; Investigation; Knowledge; Linear Models; Location; Maintenance; Mentorship; Meta-Analysis; Mexico; Milk; Minnesota; Modeling; National Institute of Allergy and Infectious Disease; Outcome; Pathway interactions; Persons; Phylogenetic Analysis; Phylogeny; Physical environment; Population; Population Density; Population Sizes; Production; Proxy; Public Health; Recording of previous events; Reporting; Research; Risk; Risk Factors; Route; Ruminants; Sheep; Shiga Toxin; Social Environment; Source; Structure; System; Testing; Training; Travel; Trees; Universities; Virulence Factors; Visit; Water; Weather; Work; density; design; disorder control; disorder risk; epidemiological model; food desert; foodborne pathogen; innovation; insight; migration; pathogen; population migration; public health intervention; rural environment; segregation; sex; skills; source localization; stem; transmission process

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.

项目总结/文摘