Phylodynamics of Shiga Toxin-Producing Escherichia coli from Local Sources

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.