<p>Phase 1: Determine the best method (most reliable, ease of use, low false positive rate) for E.coli detection in irrigation waters based on the comparison of three methods currently available for the detection of E.coli in irrigation waters. Determine influence of temperature and salinity (and other environmental factors) on false positive rates of these three methods for accurate E.coli detection in irrigation waters.</p>
<p>Phase 2: Develop an exposure scenario (model) for E .coli in irrigation waters taking into consideration the type of irrigation method, the irrigated crop, the transfer rate of E.coli to the crop, and the E.coli survival post irrigation. Estimate the risk of illness from ingestion of various levels of E.coli from the proposed irrigation scenarios. Develop a simple, user friendly guideline (program or graph) for estimating risk of infection from the different irrigation scenarios (e.g., different levels of E.coli deposited, different crops irrigated). These guidelines will be compared to risks associated with the current guideline of 126 CFU/100 mL.</p>
<p>NON-TECHNICAL SUMMARY:<br/> The goals of this project are to evaluate currently available detection methods for the accurate assessment of Escherichia coli contamination in irrigation waters and provide guidance for interpretation of results through a revised risk based E.coli standard. Currently, there is concern that the false positive rate of E.coli detection may be high in these waters giving false indications of the level of risk from enteric pathogens. This may result in unnecessary costly interventions as well as inaccurate perception of risk among consumers. We propose evaluating three different commercial systems for E.coli detection in irrigation waters and assessing false positive rates by use of molecular technologies. As a secondary objective to evaluating E.coli as a reliable indicator, we propose using a Quantitative Microbial Risk Assessment (QMRA) to
assess the use of E.coli as an accurate indicator of food safety risk using data collected in the first stage of this project coupled with existing information found in the scientific literature. Ultimately this work will offer recommendations towards the most reliable methods to be used by the produce industry to assess irrigation water contamination as well as a scientific risk-based E.coli guideline that growers can use to protect public health.
<br/>Field Sampling: Samples will be collected in three different agricultural areas: the University of Arizona research farms (Maricopa Agricultural Center-MAC and Yuma Agricultural Center-YAC) in Maricopa and Yuma, AZ, respectively, and the Imperial Valley (CA). These three locations represent a significant portion of winter leafy green production in the United States. Sample collection will take place during the winter growing season and additional select times of year to assess the effects of temperature, salinity and other environmental factors (e. g. sunlight intensity, precipitation). MAC samples will be collected by Dr. Rock, while Dr. Bright will collect the YAC and Imperial Valley samples during a minimum of eight sampling trips to these areas. Sampling will be focused in the winter growing season in order to assess conditions most frequently
encountered produce growers. A total of 150 samples will be collected at each of the 3 locations for a total of 450 samples. On each sampling date, grab samples of water will also be collected from the irrigation system during scheduled irrigation events by reducing the water velocity manually at the irrigation control box and directing the irrigation spray into sterile bottles. Following collection, water samples will be placed on ice for transport to the laboratory. Sampling sites will be determined based on relative distance up-stream from the irrigation practice and each production field. This will be done in collaboration with University of Arizona faculty at YAC and MAC and cooperating grower partners.
<br/>Laboratory Analysis: In the laboratory, water samples will be divided equally for testing by the three above methods for enumeration of E.coli. Methods 1 and 3 (MI agar and m-Coli
Blue) involve sample processing using the membrane filter technique, which is widely accepted and approved as a procedure for monitoring water microbial quality in many countries (Rompré et al., 2002; USEPA, 2002a). Water samples will be filtered in three dilutions (100, 10, and 1.0 mL) through Whatman gridded filters (0.45 µm pore size, 152 mm diameter) (Whatman International Ltd., Kent, UK). For method 1 filters will be placed onto 60 mm Petri plates with prepared MI Agar with cefsulodin added at 5 mg L-1 153 to inhibit growth of Gram positive organisms and selected non-coliform Gram-negatives (e.g., Pseudomonas spp.). Plates are incubated at 37º C for 24-36 h before counting dark blue colonies presumptive for E.coli. Water samples aliquots for method (2), will be processed according to manufacturer instructions and quanti-trays will be read after 18 hours incubation
at 37º C. Only wells fluorescing ""blue"" color will be selected for false positive confirmation.For method (3), the filters will be placed in a Petri plate containing an absorbent pad soaked in m-ColiBlue broth. Petri plate samples will be inverted and incubated at 35º C for 24 hrs. Following incubation colonies showing a blue color will be selected as presumptive for E.coli. m-ColiBlue24 broth has been approved by the EPA for monitoring drinking water and can also be used to detect coliforms and E.coli in other types of water (e.g., bottled, surface, ground, well) (US EPA, 40 CFR Parts 141, 143). Identification of False Positive Organisms: Microorganisms that test presumptively positive for E.coli on each of the three methods mentioned above but do not produce the 106-bp DNA fragment using PCR will be selected for sequencing for identification. Following regrowth in trypticase
soy broth, the bacterial pellet will be used as a template for PCR 197 utilizing universal bacterial 63f [5'-CAG GCC TAA CAC ATG CAA GTC-3'] and 1387r [5'-198 ACG GGC GGT GTG TAC AAG-3'] primers (Marchesi et al., 1998). The resulting 1325-bp amplicons will be sequenced in both directions using an automated ABI Prism 377 DNA Sequencer (Applied Biosystems, Carlsbad, CA). Retrieved sequences will be aligned with completed bacterial genomes entered into the NCBI-BLAST database (NCBI-BLAST, 2010) to identify the organisms most closely aligned to the amplicons from false positive isolates. A minimum of 97% agreement with genomic sequences deposited in the database will be required to confirm the identity of each isolate. Development of risk scenarios: The probability of illness from exposure to different concentrations of E.coli in recreational waters is known (Cabelli, 1989).
The observed illness may be caused by a variety of pathogens including bacteria, viruses and protozoa. However, the relationship between E.coli and pathogens causing human illness can be used to estimate probability of illness from different levels of exposure to E.coli. A certain amount of uncertainty exists in this approach because the concentrations and types of pathogens in irrigation waters may be different than those in bathing waters. However, many of the irrigation waters in the Western United States originate from reservoirs and/or rivers where recreation is common. The QMRA will be dependent on knowledge of the level of E .coli ingested on the produce by the consumer, determined from research reports showing transfer rates of E.coli from irrigation to the produce and its survival on the produce. This is dependent on both the type of irrigation method and type of produce. This
information is available from the literature (Petterson et al., 2001) and our own studies (Stine et. al. 2005a; 2005b; Stine et al. submitted for publication). We have used a similar approach to estimate risks from produce by Salmonella and hepatitis A virus (Stine 2005a). This ""event tree"" approach has also been used by Gale (2003) to estimate risk from pathogen contamination of food crops. The scenario will be developed for various irrigation delivery systems (e.g., drip, furrow) and will focus on impacts to leafy greens, but may include additional crops (melons, carrots, peppers) depending upon the availability of data). Only surface contamination of the produce will be considered in the QMRA. While the industry is concerned with internalization of pathogens in produce, risk from such events will not be considered in this study. However, the QMRA scenarios developed in the current
study could certainly be updated to include microbial internalization as more data becomes available. Estimate the risk of illness from ingestion of various levels of E.coli from the irrigation scenarios: Since the yearly consumption various produce in the United States is known, the yearly risk of illness from can be determined (Stine et al., 2005a).This can be modeled for each type of irrigation method and crop, and the information will be incorporated into graphics and/or simple programs to allow estimates of risk based on different scenarios. Usingcollected data we will estimate the level of risk from the E .coli and pathogens in the same water. While, we do not expect a direct correlation, we anticipate that these results will demonstrate that the relationship between E.coli and enteric pathogens can be conservatively estimated within a range in water quality found in irrigation
waters. Monte Carlo simulations between likely and observed values will aid in identifying the relationships between the observed values and environmental parameters in the modeled scenarios, and how these relationships affect model outcomes and overall determination of risk. The simulations will also identify which parameters are most critical in determination of overall risk (Haas et al, 1991). The resulting output can be used to identify values of exposure or risk corresponding to a specified probability, say the 50th percentile or 95 percentile.</p>