<p>The overall hypothesis tested in this proposed food safety research program is that variation in L. monocytogenes abundance over space and time can be substantially predicted by remotely-sensed data. An essential initial step to see this research is to validate methods for estimating L. monocytogenes abundance and to gather preliminary data on the impact of local meteorological variation on the abundance of L. monocytogenes in microcosm samples and in the field. The main objective of this seed grant project is to gather preliminary data about the abundance and mobility of L. monocytogenes in environmental reservoirs in support of geographic information systems based models to predict the dispersal of L. monocytogenes through the food system from farm to fork. </p>
<p>Objective 1 - Evaluate the efficacy of quantitative real-time PCR to quantify L. monocytogenes abundance in inoculated environmental samples from produce fields. </p>
<p>Objective 2 - Quantify the impact of precipitation events on the abundance and prevalence of L. monocytogenes under controlled conditions in soils from produce fields. Since this award has been recommended for complete funding over its two year term, I anticipate that all objectives are accomplishable within the original project timeline starting at the new proposed start date of September 15th, 2013.</p>
<p>NON-TECHNICAL SUMMARY:<br/> Our ability to predict and prevent foodborne disease outbreaks associated with produce is currently limited by a lack of models of foodborne pathogen transmission at any stage along the food production chain from farm to fork. Listeria monocytogenes, in particular, exemplifies the complexities of surveillance and prevention of foodborne disease because of its dual roles as a soil saprophyte and a cause of severe, and often fatal, foodborne illness. L. monocytogenes can persist in numerous environmental reservoirs including wildlife and domestic animals, soil and waterways. In order to develop on-farm HACCP and GAPs guidance to prevent L. monocytogenes contamination of fresh fruits and vegetables, the environmental processes linking natural L. monocytogenes populations to produce commodities in cultivation fields must be understood. This project
builds on the foundation of a geographic information systems (GIS) enabled environmental model to predict L. monocytogenes prevalence in croplands. Two controlled laboratory experiments will measure the abundance of L. monocytogenes in environmental samples from produce farms, and use statistical techniques to quantify the impacts of environmental sample type and of precipitation on the population size and viability of L. monocytogenes. This work seeks to advance the quantitative risk modeling in fruit and vegetable farms by: i) validating the usefulness of quantitative real-time PCR to enumerate viable L. monocytogenes genomes in the pre-harvest environment, and ii) testing a hypothesis, arising from our recently published environmental model, that recent precipitation increases the abundance of L. monocytogenes in produce farm soils. Produce growers, food safety auditors and food
safety researchers will benefit from advances in knowledge about our ability to accurately quantify and predict changes in the population size and mobility of this foodborne pathogen in produce cultivation environments.
<p>APPROACH:<br/> Methods for Obj. 1. Specificity and sensitivity of quantitative polymerase chain reaction detection of Listeria monocytogenes in produce farm samples will be evaluated using controlled experiments. Varyingquantitiesof live (i.e., potentially virulent)and dead Listeria monocytogenes will be inoculated into drag swab, topsoil, vegetable tissue and water samples. The samples will be incubated so that the liveinocula can integrate into the sample. One challenge in quantification of foodborne pathogens in environmental samples is that qPCR and other molecular methods are not inherently capable of differentiating DNA from live Listeria monocytogenes from DNA from dead Listeria monocytogenes. Since DNA from dead Listeria monocytogenes can still be detected by qPCR assays, propidium monoazide (PMA)will be added to the environmental samples.This DNA binding dye
will crosslink with the DNA from dead cells to prevent its detection by qPCR.PMA cannot enter living bacteria, so cannot bind DNA in live Listeria monocytogenes cells. The bacteria from the samples will then be harvested and DNA will be extracted from the inoculated samples and uninoculated controls. An analysis of covariance model will be applied to quantify the signal to noise ratio associated with quantification of Listeria monocytogenes in different sample types from produce farm environments and to also characterize the noise introduced by the presence of dead Listeria monocytogenes cells in the samples. Methods for Obj. 2. The impact of precipitation on the population size (i.e. abundance) of Listeria monocytogenes in soils will be tested in microcosms of soil from produce farms using both qPCR and cultivation-based enumeration methods. Soil microcosms will be inoculated with
Listeria monocytogenes on day 0 of the experiment and the abundance of Listeria monocytogenes in the sample will be tracked for 80 days. Soil will be held at a constant temperature but exposed to three different simulated rainfall regimes using a spray application chamber. The control group will receive no simulated precipitation at all. The second group will receive 10 mm of simulated rainfall per week and the third will receive a single rainfall event followed by no simulated precipitation for the remainder of the experiment. The chance in abundance of live L. monocytogenes will be estimated using quantitative PCR with PMA treatment. The change in apparent prevelence in the samples will be evaluated by standard cultivation methods for detecting Listeria monocytogenes that use buffered enrichment broth followed by genetic confirmation of Listeria monocytogenes isolation. Evaluation of
research. The research will be considered successful when data have been obtained and published addressing two main objectives: i) accurate and reproducible measures of the signal and noise associated with PMA-qPCR based quantification of L. monocytogenes in produce farm samples and ii) quantification of the impact of rainfall on the abundance of L. monocytogenes in topsoils from produce farms with a corresponding advance in our understanding of how rainfall impacts the abundance and mobility of this pathogen when it inhabits soils in close proximity to produce commodities.