Browsing by Author "Murphy, Claire M."

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    Effect of pesticide application on Salmonella survival on inoculated tomato leaves
    Gu, Ganyu; Murphy, Claire M.; Hamilton, Alexis M.; Zheng, Jie; Nou, Xiangwu; Rideout, Steven L.; Strawn, Laura K. (Wiley, 2023-02)
    Outbreaks of Salmonellosis have been traced to contaminated tomato. The produce production environment poses a risk for Salmonella contamination; however, little is known about the effects of pest management practices on Salmonella during production. The study objective was to evaluate pesticide application on the inactivation of Salmonella on tomato leaves. Thirty greenhouse-grown tomato plants were inoculated with S. enterica serovars Newport or Typhimurium. Inoculation was performed by dipping tomato leaves in an 8-log CFU/mL Salmonella suspension with 0.025% (vol/vol) Silwet L-77 surfactant for 30 s, for a starting concentration of 6–7 log CFU/mL. Plants were treated with one of four pesticides, each with a different mode of action [acibenzolar- S-methyl, copper-hydroxide, peroxyacetic acid (PAA), and streptomycin]. Pesticides were applied at manufacturers' labeled rate for plant disease management with water as a control treatment. Salmonella was enumerated at 0.125 (3 h), 2, 6, and 9 days post-inoculation (dpi), and counts log-transformed. Growth of Salmonella was not observed. At 2 dpi, PAA and streptomycin significantly reduced surface Salmonella concentrations of inoculated tomato leaves (0.7 and 0.6-log CFU/g, respectively; p ≤ 0.05), while significant Salmonella log reduction occurred in the ground tomato leaves after copper hydroxide treatment (0.8-log CFU/g; p ≤ 0.05), compared to the control. No significant differences in Salmonella populations on tomato leaf surface and in ground leaves were observed from 2 to 9 dpi, regardless of pesticide application. These findings suggest single in-field pesticide applications may not be an effective mitigation strategy in limiting potential Salmonella contamination. Future research, including multiple in-field pesticide applications, or pesticide use in combination with other mitigation strategies, may offer intriguing management practices to limit possible preharvest contamination.
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    Factors Associated With E. coli Levels in and Salmonella Contamination of Agricultural Water Differed Between North and South Florida Waterways
    Murphy, Claire M.; Strawn, Laura K.; Chapin, Travis K.; McEgan, Rachel; Gopidi, Sweeya; Friedrich, Loretta; Goodridge, Lawrence D.; Weller, Daniel L.; Schneider, Keith R.; Danyluk, Michelle D. (Frontiers, 2022-02-02)
    The microbial quality of agricultural water is often assessed using fecal indicator bacteria (FIB) and physicochemical parameters. The presence, direction, and strength of associations between microbial and physicochemical parameters, and the presence of human pathogens in surface water vary across space (e.g., region) and time. This study was undertaken to understand these associations in two produce-growing regions in Florida, USA, and to examine the pathogen ecology in waterways used for produce production. The relationship between Salmonella presence, and microbial and physicochemical water quality; as well as weather and land use factors were evaluated. Water samples were collected from six sites in North Florida (N = 72 samples) and eight sites in South Florida (N = 96 samples) over 12 sampling months. Land use around each sampling site was characterized, and weather and water quality data were collected at each sampling. Salmonella, generic Escherichia coli, total coliform, and aerobic plate count bacteria populations were enumerated in each sample. Univariable and multivariable regression models were then developed to characterize associations between microbial water quality (i.e., E. coli levels and Salmonella presence), and water quality, weather, and land use factors separately for North and South Florida. The E. coli and total coliforms mean concentrations (log(10) MPN/100 mL) were 1.8 +/- 0.6 and >3.0 +/- 0.4 in North and 1.3 +/- 0.6 and >3.3 +/- 0.2 in South Florida waterways, respectively. While Salmonella was detected in 23.6% (17/72) of North Florida and 28.1% (27/96) of South Florida samples, the concentration ranged between <0.48 and 1.4 log(10) MPN/100 mL in North Florida, and E. coli levels, and if a sample was Salmonella-positive. The factors associated with Salmonella presence and log(10) E. coli levels in North Florida differed from those in South Florida; no factors retrained in multivariable regression models were the same for the North and South Florida models. The differences in associations between regions highlight the complexity of understanding pathogen ecology in freshwater environments and suggest substantial differences between intra-state regions in risk factors for Salmonella contamination of agricultural water.
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    Land Use, Weather, and Water Quality Factors Associated With Fecal Contamination of Northeastern Streams That Span an Urban-Rural Gradient
    Weller, Daniel L.; Murphy, Claire M.; Johnson, Stephanie; Green, Hyatt; Michalenko, Edward M.; Love, Tanzy, M.T.; Strawn, Laura K. (Frontiers, 2022-02)
    Fecal contamination of surface water has been associated with multiple enteric disease outbreaks and food recalls. Thus, it is important to understand factors associated with fecal contamination of agricultural water sources. Since fecal indicator bacteria (FIB) were used to monitor surface water for potential fecal contamination, the purpose of the present study was to characterize associations between environmental factors, and (i) FIB (E. coli, Enterococcus, and coliform) levels, and (ii) host-specific fecal marker detection. This study used data collected from 224 sites along 3 waterways, which spanned an urban-rural gradient around Syracuse, New York. Between 2008 and 2017, 2,816 water samples were collected, and E. coli, Enterococcus, and/or coliform concentrations were enumerated. Thirty-one samples were also tested for human and ruminant microbial source-tracking markers. Water quality (e.g., turbidity, nitrate) and weather data were also collected for each site. Univariable Bayesian regression was used to characterize the relationship between each microbial target and land use, water quality, and weather factor. For eachmodel, probability of direction and region of practical equivalence overlap (ROPE) were calculated to characterize the association’s direction and strength, respectively. While levels of different FIB were not correlated with each other, FIB levels were associated with environmental conditions. Specifically, FIB levels were also positively associated with temperature, nutrient and sediment levels. Log10 E. coli levels increased by 0.20 (CI = 0.11, 0.31) and log10 Enterococcus levels increased by 0.68 (CI=0.08, 1.24) for each log10 increase in salinity and nitrate, respectively. These findings may indicate that similar processes drove microbial, sediment, and nutrient contamination of the sampled watersheds. While fecal contamination was strongly associated with land use, the direction of association varied between FIBs and the buffer distance used to calculate land use metrics. E. coli levels and human marker detection were positively associated with percent pasture cover within 122, 366, and 1,098mof the sampling site, while Enterococcus and coliform levels were only associated with pasture cover within 1,098m (not 122 or 366m). Ruminant markers were positively associated with pasture cover within 122m, but not 366 or 1,098m. These findings highlight the importance of considering (i) adjacent land use (and associated non-point sources of contamination) when developing strategies for managing fecal hazards associated in agricultural and recreational water, and (ii) spatial scale (e.g., 122 vs. 1,098m) when developing these strategies.
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    Salmonella Prevalence Is Strongly Associated with Spatial Factors while Listeria monocytogenes Prevalence Is Strongly Associated with Temporal Factors on Virginia Produce Farms
    Murphy, Claire M.; Weller, Daniel L.; Strawn, Laura K. (American Society for Microbiology, 2023-02-02)
    The heterogeneity of produce production environments complicates the development of universal strategies for managing preharvest produce safety risks. Understanding pathogen ecology in different produce-growing regions is important for developing targeted mitigation strategies. This study aimed to identify environmental and spatiotemporal factors associated with isolating Salmonella and Listeria from environmental samples collected from 10 Virginia produce farms. Soil (n = 400), drag swab (n = 400), and irrigation water (n = 120) samples were tested for Salmonella and Listeria, and results were confirmed by PCR. Salmonella serovar and Listeria species were identified by the Kauffmann-White-Le Minor scheme and partial sigB sequencing, respectively. Conditional forest analysis and Bayesian mixed models were used to characterize associations between environmental factors and the likelihood of isolating Salmonella, Listeria monocytogenes (LM), and other targets (e.g., Listeria spp. and Salmonella enterica serovar Newport). Surrogate trees were used to visualize hierarchical associations identified by the forest analyses. Salmonella and LM prevalence was 5.3% (49/920) and 2.3% (21/920), respectively. The likelihood of isolating Salmonella was highest in water samples collected from the Eastern Shore of Virginia with a dew point of >9.4°C. The likelihood of isolating LM was highest in water samples collected in winter from sites where <36% of the land use within 122 m was forest wetland cover. Conditional forest results were consistent with the mixed models, which also found that the likelihood of detecting Salmonella and LM differed between sample type, region, and season. These findings identified factors that increased the likelihood of isolating Salmonella- and LM-positive samples in produce production environments and support preharvest mitigation strategies on a regional scale. IMPORTANCE This study sought to examine different growing regions across the state of Virginia and to determine how factors associated with pathogen prevalence may differ between regions. Spatial and temporal data were modeled to identify factors associated with an increased pathogen likelihood in various on-farm sources. The findings of the study show that prevalence of Salmonella and L. monocytogenes is low overall in the produce preharvest environment but does vary by space (e.g., region in Virginia) and time (e.g., season), and the likelihood of pathogen-positive samples is influenced by different spatial and temporal factors. Therefore, the results support regional or scale-dependent food safety standards and guidance documents for controlling hazards to minimize risk. This study also suggests that water source assessments are important tools for developing monitoring programs and mitigation measures, as spatiotemporal factors differ on a regional scale.
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    Spatial Versus Nonspatial Variance in Fecal Indicator Bacteria Differs Within and Between Ponds
    Murphy, Claire M.; Weller, Daniel L.; Ovissipour, Reza; Boyer, Renee R.; Strawn, Laura K. (Elsevier, 2023-03)
    Surface water environments are inherently heterogenous, and little is known about variation in microbial water quality between locations. This study sought to understand how microbial water quality differs within and between Virginia ponds. Grab samples were collected twice per week from 30 sampling sites across nine Virginia ponds (n = 600). Samples (100 mL) were enumerated for total coliform (TC) and Escherichia coli (EC) levels, and physicochemical, weather, and environmental data were collected. Bayesian models of coregionalization were used to quantify the variance in TC and EC levels attributable to spatial (e.g., site, pond) versus nonspatial (e.g., date, pH) sources. Mixed-effects Bayesian regressions and conditional inference trees were used to characterize relationships between data and TC or EC levels. Analyses were performed separately for each pond with ≥3 sampling sites (5 intrapond) while one interpond model was developed using data from all sampling sites and all ponds. More variance in TC levels were attributable to spatial opposed to nonspatial sources for the interpond model (variance ratio [VR] = 1.55) while intrapond models were pond dependent (VR: 0.65–18.89). For EC levels, more variance was attributable to spatial sources in the interpond model (VR = 1.62), compared to all intrapond models (VR < 1.0) suggesting that more variance is attributable to nonspatial factors within individual ponds and spatial factors when multiple ponds are considered. Within each pond, TC and EC levels were spatially independent for sites 56–87 m apart, indicating that different sites within the same pond represent different water quality for risk management. Rainfall was positively and pH negatively associated with TC and EC levels in both inter- and intrapond models. For all other factors, the direction and strength of associations varied. Factors driving microbial dynamics in ponds appear to be pond-specific and differ depending on the spatial scale considered.