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Salmonella accounts for approximately 50% of produce-associated outbreaks in the United States, several of which have been traced back to contamination in the produce production environment. To quantify Salmonella diversity and aid in identification of Salmonella contamination sources, we characterized Salmonella isolates from two geographically diverse produce-growing regions in the United States. Initially, we characterized the Salmonella serotype and subtype diversity associated with 1,677 samples collected from 33 produce farms in New York State (NYS). Among these 1,677 samples, 74 were Salmonella positive, yielding 80 unique isolates (from 147 total isolates), which represented 14 serovars and 23 different pulsed-field gel electrophoresis (PFGE) types. To explore regional Salmonella diversity associated with production environments, we collected a smaller set of samples (n = 65) from South Florida (SFL) production environments and compared the Salmonella diversity associated with these samples with the diversity found among NYS production environments. Among these 65 samples, 23 were Salmonella positive, yielding 32 unique isolates (from 81 total isolates), which represented 11 serovars and 17 different PFGE types. The most common serovars isolated in NYS were Salmonella enterica serovars Newport, Cerro, and Thompson, while common serovars isolated in SFL were Salmonella serovars Saphra and Newport and S. enterica subsp. diarizonae serovar 50:r:z. High PFGE type diversity (Simpson's diversity index, 0.90 ± 0.02) was observed among Salmonella isolates across both regions; only three PFGE types were shared between the two regions. The probability of three or fewer shared PFGE types was <0.000001; therefore, Salmonella isolates were considerably different between the two sampled regions. These findings suggest the potential for PFGE-based source tracking of Salmonella in production environments. [ABSTRACT FROM AUTHOR]

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform metaanalyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quant ified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological difference s between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies. [ABSTRACT FROM AUTHOR]

Pond irrigation water comprises a major pathway of pathogenic bacteria to fresh produce. Current regulatory methods have been shown to be ineffective in assessing this risk when variability of bacterial concentrations is large. This paper proposes using mechanistic modeling of bacterial transport as a way to identify improved strategies for mitigating this risk pathway. If the mechanistic model is successfully tested against observed data, global sensitivity analysis (GSA) can identify important mechanisms to inform alternative, preventive bacterial control practices. Model development favored parsimony and prediction of peak bacterial concentration events. Data from two highly variable surface water irrigation ponds showed that the model performance was similar or superior to that of existing pathogen transport models, with a Nash-Sutcliffe efficiency of 0.48 and 0.18 for the two ponds. GSA quantified bacterial sourcing and hydrology as the most important processes driving pond bacterial contamination events. Model analysis has two main implications for improved regulatory methods: that peak concentration events are associated with runoff-producing rainfall events and that intercepting bacterial runoff transport may be the best option to prevent bacterial contamination of surface water irrigation ponds and thus fresh produce. This research suggests the need for temporal management strategies. IMPORTANCE Preventive management of agricultural waters requires understanding of the drivers of bacterial contamination events. We propose mechanistic modeling as a way forward to understand and predict such events and have developed and tested a parsimonious model for rain-driven surface runoff contributing to generic Escherichia coli contamination of irrigation ponds in Central Florida. While the model was able to predict the timing of peak events reasonably well, the highly variable magnitude of the peaks was less well predicted. This indicates the need to collect more data on the fecal contamination inputs of these ponds and the use of mechanistic modeling and global sensitivity analysis to identify the most important data needs. [ABSTRACT FROM AUTHOR]

Recurrent outbreaks of bacterial gastroenteritis linked to the consumption of fresh fruits and vegetables highlight the paucity of understanding of the ecology of Salmonella enterica under crop production and postharvest conditions. These gaps in knowledge are due, at least in part, to the lack of suitable surrogate organisms for studies for which biosafety level 2 is problematic. Therefore, we constructed and validated an avirulent strain of Salmonella enterica serovar Typhimurium. The strain lacks major Salmonella pathogenicity islands SPI-1, SPI-2, SPI-3, SPI-4, and SPI-5 as well as the virulence plasmid pSLT. Deletions and the absence of genomic rearrangements were confirmed by genomic sequencing, and the surrogate behaved like the parental wildtype strain on selective media. A loss-of-function (phoN) selective marker allowed the differentiation of this strain from wildtype strains on a medium containing a chromogenic substrate for alkaline phosphatase. Lack of virulence was confirmed by oral infection of female BALB/c mice. The strain persisted in tomatoes, cantaloupes, leafy greens, and soil with the same kinetics as the parental wild-type and selected outbreak strains, and it reached similar final population levels. The responses of this strain to heat treatment and disinfectants were similar to those of the wild type, supporting its potential as a surrogate for future studies on the ecology and survival of Salmonella in production and processing environments. [ABSTRACT FROM AUTHOR]

Irrigation water has been implicated as a likely source of produce contamination by Salmonella enterica. Therefore, the distribution of S. enterica was surveyed monthly in irrigation ponds (n = 10) located within a prime agricultural region in southern Georgia and northern Florida. All ponds and 28.2% of all samples (n = 635) were positive for Salmonella, with an overall geometric mean concentration (0.26 most probable number [MPN]/liter) that was relatively low compared to prior reports for rivers in this region. Salmonella peaks were seasonal; the levels correlated with increased temperature and rainfall (P<0.05). The numbers and occurrence were significantly higher in water (0.32 MPN/liter and 37% of samples) than in sediment (0.22 MPN/liter and 17% of samples) but did not vary with depth. Representative isolates (n = 185) from different ponds, sample types, and seasons were examined for resistance to 15 different antibiotics; most strains were resistant to streptomycin (98.9%), while 20% were multidrug resistant (MDR) for 2 to 6 antibiotics. DiversiLab repetitive extragenic palindromic-element sequence-based PCR (rep-PCR) revealed genetic diversity and showed 43 genotypes among 191 isolates, as defined by>95% similarity. The genotypes did not partition by pond, season, or sample type. Genetic similarity to known serotypes indicated Hadar, Montevideo, and Newport as the most prevalent. All ponds achieved the current safety standards for generic Escherichia coli in agricultural water, and regression modeling showed that the E. coli level was a significant predictor for the probability of Salmonella occurrence. However, persistent populations of Salmonella were widely distributed in irrigation ponds, and the associated risks for produce contamination and subsequent human exposure are unknown, supporting continued surveillance of this pathogen in agricultural settings. [ABSTRACT FROM AUTHOR]

Identification of Salmonella serotypes is important for understanding the environmental diversity of the genus Salmonella. This study evaluates the diversity of Salmonella isolates recovered from 165 of 202 Central Florida surface water samples and investigates whether the serotype of the environmental Salmonella isolates can be predicted by a previously published multiplex PCR assay (S. Kim, J. G. Frye, J. Hu, P. J. Fedorka-Cray, R. Gautom, and D. S. Boyle, J. Clin. Microbiol. 44:3608-3615,2006, http://dx .doi.org/10.1128/JCM.00701-06). Multiplex PCR was performed on 562 Salmonella isolates (as many as 36 isolates per water sample) to predict serotypes. Kauffmann-White serogrouping was used to confirm multiplex PCR pattern groupings before isolates were serotyped, analyzed by pulsed-field gel electrophoresis, and assayed for antimicrobial susceptibility. In 41.2% of the Salmonella-positive water samples, all Salmonella isolates had identical multiplex PCR patterns; in the remaining 58.8%, two or more multiplex PCR patterns were identified. Within each sample, isolates with matching multiplex PCR patterns had matching serogroups. The multiplex patterns o f495 isolates (88.1%) did not match any previously reported pattern. The remaining 68 isolates matched reported patterns but did not match the serotypes for those patterns. The use of the multiplex PCR allowed the number of isolates requiring further analysis to be reduced to 223. Thirty-three Salmonella enterica serotypes were identified; the most frequent included serotypes Muenchen, Rubislaw, Anatum, Gaminara, and IV_50:z4,z23:-. A majority (141/223) of Salmonella isolates clustered into one genotypic group. Salmonella isolates in Central Florida surface waters are serotypically, genotypically, and phenotypically (in terms of antimicrobial susceptibility) diverse. While isolates could be grouped as different or potentially the same using multiplex PCR, the multiplex PCR pattern did not predict the Salmonella serotype. [ABSTRACT FROM AUTHOR]

Coliforms, Escherichia coli, and various physicochemical water characteristics have been suggested as indicators of microbial water quality or index organisms for pathogen populations. The relationship between the presence and/or concentration of Salmonella and biological, physical, or chemical indicators in Central Florida surface water samples over 12 consecutive months was explored. Samples were taken monthly for 12 months from 18 locations throughout Central Florida (n = 202). Air and water temperature, pH, oxidation-reduction potential (ORP), turbidity, and conductivity were measured. Weather data were obtained from nearby weather stations. Aerobic plate counts and most probable numbers (MPN) for Salmonella, E. coli, and coliforms were performed. Weak linear relationships existed between biological indicators (E. coli/coliforms) and Salmonella levels (R² < 0.1) and between physicochemical indicators and Salmonella levels (R² < 0.1). The average rainfall (previous day, week, and month) before sampling did not correlate well with bacterial levels. Logistic regression analysis showed that E. coli concentration can predict the probability of enumerating selected Salmonella levels. The lack of good correlations between biological indicators and Salmonella levels and between physicochemical indicators and Salmonella levels shows that the relationship between pathogens and indicators is complex. However, Escherichia coli provides a reasonable way to predict Salmonella levels in Central Florida surface water through logistic regression. [ABSTRACT FROM AUTHOR]