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.