Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production

Occurrence of recreational water quality monitoring general fecal indicator bacteria and fecal source identification genetic markers in gray seal scat

The number of gray seals (Halichoerus grypus) observed along the United States Northwest Atlantic region has been increasing for decades. These colonial animals often haul-out on beaches seasonally in numbers ranging from a few individuals to several thousands. While these larger aggregations are an important part of gray seal behavior, there is public concern that haul-outs could lead to large amounts of fecal waste in recreational areas, potentially resulting in beach closures. Yet, data to confirm whether these animals contribute to beach closures is lacking and minimal information is available on the occurrence of key water quality monitoring genetic markers in gray seal scat. This study evaluates the concentration of E. coli (EC23S857), enterococci (Entero1a), and fecal Bacteroidetes (GenBac3) as well as six fecal source identification genetic markers (HF183/BacR287, HumM2, CPQ_056, Rum2Bac, DG3, and GFD) measured by qPCR in 48 wild gray seal scat samples collected from two haul-out areas in Cape Cod (Massachusetts, U.S.A.). Findings indicate that FIB genetic markers are shed in gray seal scat at significantly different concentrations with the Entero1a genetic marker exhibiting the lowest average concentration (-0.73 log10 estimated mean copies per nanogram of DNA). In addition, systematic testing of scat samples demonstrated that qPCR assays targeting host-associated genetic markers indicative of human, ruminant, and canine fecal pollution sources remain highly specific in waters frequented by gray seals (>97 % specificity).

Prevalence of STEC virulence markers and Salmonella as a function of abiotic factors in agricultural water in the southeastern United States

Fresh produce can be contaminated by enteric pathogens throughout crop production, including through contact with contaminated agricultural water. The most common outbreaks and recalls in fresh produce are due to contamination by Salmonella enterica and Shiga toxin-producing E. coli (STEC). Thus, the objectives of this study were to investigate the prevalence of markers for STEC (wzy, hly, fliC, eaeA, rfbE, stx-I, stx-II) and Salmonella (invA) in surface water sources (n = 8) from produce farms in Southwest Georgia and to determine correlations among the prevalence of virulence markers for STEC, water nutrient profile, and environmental factors. Water samples (500 mL) from eight irrigation ponds were collected from February to December 2021 (n = 88). Polymerase chain reaction (PCR) was used to screen for Salmonella and STEC genes, and Salmonella samples were confirmed by culture-based methods. Positive samples for Salmonella were further serotyped. Particularly, Salmonella was detected in 6/88 (6.81%) water samples from all ponds, and the following 4 serotypes were detected: Saintpaul 3/6 (50%), Montevideo 1/6 (16.66%), Mississippi 1/6 (16.66%), and Bareilly 1/6 (16.66%). Salmonella isolates were only found in the summer months (May-Aug.). The most prevalent STEC genes were hly 77/88 (87.50%) and stx-I 75/88 (85.22%), followed by fliC 54/88 (61.63%), stx-II 41/88 (46.59%), rfbE 31/88 (35.22%), and eaeA 28/88 (31.81%). The wzy gene was not detected in any of the samples. Based on a logistic regression analysis, the odds of codetection for STEC virulence markers (stx-I, stx-II, and eaeA) were negatively correlated with calcium and relative humidity (p < 0.05). A conditional forest analysis was performed to assess predictive performance (AUC = 0.921), and the top predictors included humidity, nitrate, calcium, and solar radiation. Overall, information from this research adds to a growing body of knowledge regarding the risk that surface water sources pose to produce grown in subtropical environmental conditions and emphasizes the importance of understanding the use of abiotic factors as a holistic approach to understanding the microbial quality of water.

Shiga Toxin-Producing Escherichia coli O157:H7 Illness Outbreak Associated with Untreated, Pressurized, Municipal Irrigation Water - Utah, 2023

During July-September 2023, an outbreak of Shiga toxin-producing Escherichia coli O157:H7 illness among children in city A, Utah, caused 13 confirmed illnesses; seven patients were hospitalized, including two with hemolytic uremic syndrome. Local, state, and federal public health partners investigating the outbreak linked the illnesses to untreated, pressurized, municipal irrigation water (UPMIW) exposure in city A; 12 of 13 ill children reported playing in or drinking UPMIW. Clinical isolates were genetically highly related to one another and to environmental isolates from multiple locations within city A's UPMIW system. Microbial source tracking, a method to indicate possible contamination sources, identified birds and ruminants as potential sources of fecal contamination of UPMIW. Public health and city A officials issued multiple press releases regarding the outbreak reminding residents that UPMIW is not intended for drinking or recreation. Public education and UPMIW management and operations interventions, including assessing and mitigating potential contamination sources, covering UPMIW sources and reservoirs, indicating UPMIW lines and spigots with a designated color, and providing conspicuous signage to communicate risk and intended use might help prevent future UPMIW-associated illnesses.

Factors Associated with the Prevalence of Salmonella, Generic Escherichia coli, and Coliforms in Florida's Agricultural Soils

Limited data exist on the environmental factors that impact pathogen prevalence in the soil. The prevalence of foodborne pathogens, Salmonella and Listeria monocytogenes, and the prevalence and concentration of generic E. coli in Florida's agricultural soils were evaluated to understand the potential risk of microbial contamination at the preharvest level. For all organisms but L. monocytogenes, a longitudinal field study was performed in three geographically distributed agricultural areas across Florida. At each location, 20 unique 5 by 5 m field sampling sites were selected, and soil was collected and evaluated for Salmonella presence (25 g) and E. coli and coliform concentrations (5 g). Complementary data collected from October 2021 to April 2022 included: weather; adjacent land use; soil properties, including macro- and micro-nutrients; and field management practices. The overall Salmonella and generic E. coli prevalence was 0.418% (1/239) and 11.3% (27/239), respectively; with mean E. coli concentrations in positive samples of 1.56 log CFU/g. Farm A had the highest prevalence of generic E. coli, 22.8% (18/79); followed by Farm B, 10% (8/80); and Farm C 1.25% (1/80). A significant relationship (p < 0.05) was observed between generic E. coli and coliforms, and farm and sampling trip. Variation in the prevalence of generic E. coli and changes in coliform concentrations between farms suggest environmental factors (e.g. soil properties) at the three farms were different. While Salmonella was only detected once, generic E. coli was detected in Florida soils throughout the duration of the growing season meaning activities that limit contact between soil and horticultural crops should continue to be emphasized. Samples collected during an independent sampling trip were evaluated for L. monocytogenes, which was not detected. The influence of local environmental factors on the prevalence of indicator organisms in the soil presents a unique challenge when evaluating the applicability of more global models to predict pathogen prevalence in preharvest produce environments.

Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety

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 meta-analyses. 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 quantified 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 differences 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.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

Scale and detection method impacted Salmonella prevalence and diversity in ponds

Site-specific approaches for managing food safety hazards in agricultural water require an understanding of foodborne pathogen ecology. This study identified factors associated with Salmonella contamination in Virginia ponds. Grab samples (250 mL, N = 600) were collected from 30 sites across nine ponds. Culture- and culture-independent (CIDT)-based methods were used to detect Salmonella in each sample. Salmonella isolated by culture-based methods were serotyped by Kauffman-White classification. Environmental data were collected for each sample. McNemar's χ2 was used to determine if Salmonella detection differed by testing method. Separate mixed effect models were used to identify environmental factors associated with culture and CIDT-based Salmonella detection. Separate models were built for each pond, and for all ponds combined. Salmonella detection differed significantly (p < 0.001) between CIDT (31 %; 183/600)- and culture (13 %; 77/600)-based methods. Culture-based methods yielded 11 different serovars. All cultured Salmonella samples were confirmed by CIDT; 42.1 % of CIDT Salmonella-positive samples could be cultured. Associations between environmental factors and Salmonella detection also varied substantially by pond and detection method. In the all-pond model, associations were observed for five factors (total coliforms, Escherichia coli, air temperature, UV, rain) for both culture- and CIDT-based Salmonella detection. Rain prior to sampling (24 h) increased odds of Salmonella detection for culture (OR = 5.09) and CIDT (OR = 3.62) in the all-pond model. When all the pond data were used, models masked associations at the individual pond level, as there were noticeable differences between ponds and the odds of isolating Salmonella by environmental factors. Ponds were within a 187-ha area in this study, emphasizing water management needs to be individualized (i.e., assess hazards/risks by pond). Results also highlight detection methods and scale strongly affect observed water quality and should be considered when developing monitoring programs to develop guidance for growers.

A perspective on data sharing in digital food safety systems

In this age of data, digital tools are widely promoted as having tremendous potential for enhancing food safety. However, the potential of these digital tools depends on the availability and quality of data, and a number of obstacles need to be overcome to achieve the goal of digitally enabled "smarter food safety" approaches. One key obstacle is that participants in the food system and in food safety often lack the willingness to share data, due to fears of data abuse, bad publicity, liability, and the need to keep certain data (e.g., human illness data) confidential. As these multifaceted concerns lead to tension between data utility and privacy, the solutions to these challenges need to be multifaceted. This review outlines the data needs in digital food safety systems, exemplified in different data categories and model types, and key concerns associated with sharing of food safety data, including confidentiality and privacy of shared data. To address the data privacy issue a combination of innovative strategies to protect privacy as well as legal protection against data abuse need to be pursued. Existing solutions for maximizing data utility, while not compromising data privacy, are discussed, most notably differential privacy and federated learning.

Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data

AIMS

While fecal indicator bacteria (FIB) testing is used to monitor surface water for potential health hazards, observed variation in FIB levels may depend on the scale of analysis (SOA). Two decades of citizen science data, coupled with random effects models, were used to quantify the variance in FIB levels attributable to spatial versus temporal factors.

METHODS AND RESULTS

Separately, Bayesian models were used to quantify the ratio of spatial to non-spatial variance in FIB levels and identify associations between environmental factors and FIB levels. Separate analyses were performed for three SOA: waterway, watershed, and statewide. As SOA increased (from waterway to watershed to statewide models), variance attributable to spatial sources generally increased and variance attributable to temporal sources generally decreased. While relationships between FIB levels and environmental factors, such as flow conditions (base versus stormflow), were constant across SOA, the effect of land cover was highly dependent on SOA and consistently smaller than the effect of stormwater infrastructure (e.g. outfalls).

CONCLUSIONS

This study demonstrates the importance of SOA when developing water quality monitoring programs or designing future studies to inform water management.

Understanding potential cattle contribution to leafy green outbreaks: A scoping review of the literature and public health reports

Recently, multiple reports from regulatory agencies have linked leafy green outbreaks to nearby or adjacent cattle operations. While they have made logical explanations for this phenomenon, the reports and data should be summarized to determine if the association was based on empirical data, epidemiological association, or speculation. Therefore, this scoping review aims to gather data on the mechanisms of transmission for pathogens from livestock to produce, identify if direct evidence linking the two entities exists, and identify any knowledge gaps in the scientific literature and public health reports. Eight databases were searched systematically and 27 eligible primary research products, which focus on produce safety concerning proximity to livestock, provided empirical or epidemiological association and described mechanisms of transmission, qualitatively or quantitatively were retained. Fifteen public health reports were also covered. Results from the scientific articles provided evidence that proximity to livestock might be a risk factor; however, most lack quantitative data on the relative contribution of different pathways for contamination. Public health reports mainly indicate livestock presence as a possible source and encourage further research. Although the collected information regarding the proximity of cattle is a concern, data gaps indicate that more studies should be conducted to determine the relative contribution of different mechanisms of contamination and generate quantitative data to inform food safety risk analyses, regarding leafy greens produced nearby livestock areas.

Comparative genomics unveils extensive genomic variation between populations of Listeria species in natural and food-associated environments

Comprehending bacterial genomic variation linked to distinct environments can yield novel insights into mechanisms underlying differential adaptation and transmission of microbes across environments. Gaining such insights is particularly crucial for pathogens as it benefits public health surveillance. However, the understanding of bacterial genomic variation is limited by a scarcity of investigations in genomic variation coupled with different ecological contexts. To address this limitation, we focused on Listeria, an important bacterial genus for food safety that includes the human pathogen L. monocytogenes, and analyzed a large-scale genomic dataset collected by us from natural and food-associated environments across the United States. Through comparative genomics analyses on 449 isolates from the soil and 390 isolates from agricultural water and produce processing facilities representing L. monocytogenes, L. seeligeri, L. innocua, and L. welshimeri, we find that the genomic profiles strongly differ by environments within each species. This is supported by the environment-associated subclades and differential presence of plasmids, stress islands, and accessory genes involved in cell envelope biogenesis and carbohydrate transport and metabolism. Core genomes of Listeria species are also strongly associated with environments and can accurately predict isolation sources at the lineage level in L. monocytogenes using machine learning. We find that the large environment-associated genomic variation in Listeria appears to be jointly driven by soil property, climate, land use, and accompanying bacterial species, chiefly representing Actinobacteria and Proteobacteria. Collectively, our data suggest that populations of Listeria species have genetically adapted to different environments, which may limit their transmission from natural to food-associated environments.

Risk factors associated with the prevalence of Shiga-toxin-producing Escherichia coli in manured soils on certified organic farms in four regions of the USA

Introduction

Biological soil amendments of animal origin (BSAAO), including untreated amendments are often used to improve soil fertility and are particularly important in organic agriculture. However, application of untreated manure on cropland can potentially introduce foodborne pathogens into the soil and onto produce. Certified organic farms follow the USDA National Organic Program (NOP) standards that stipulate a 90- or 120-day interval between application of untreated manure and crop harvest, depending on whether the edible portion of the crop directly contacts the soil. This time-interval metric is based on environmental factors and does not consider a multitude of factors that might affect the survival of the main pathogens of concern. The objective of this study was to assess predictors for the prevalence of Shiga-toxin-producing Escherichia coli (non-O157 STEC) in soils amended with untreated manure on USDA-NOP certified farms.

Methods

A longitudinal, multi-regional study was conducted on 19 farms in four USA regions for two growing seasons (2017–2018). Untreated manure (cattle, horse, and poultry), soil, and irrigation water samples were collected and enrichment cultured for non-O157 STEC. Mixed effects logistic regression models were used to analyze the predictors of non-O157 STEC in the soil up to 180 days post-manure application.

Results and discussion

Results show that farm management practices (previous use with livestock, presence of animal feces on the field, season of manure application) and soil characteristics (presence of generic E. coli in the soil, soil moisture, sodium) increased the odds of STEC-positive soil samples. Manure application method and snowfall decreased the odds of detecting STEC in the soil. Time-variant predictors (year and sampling day) affected the presence of STEC. This study shows that a single metric, such as the time interval between application of untreated manure and crop harvest, may not be sufficient to reduce the food safety risks from untreated manure, and additional environmental and farm-management practices should also be considered. These findings are of particular importance because they provide multi-regional baseline data relating to current NOP wait-time standards. They can therefore contribute to the development of strategies to reduce pathogen persistence that may contribute to contamination of fresh produce typically eaten raw from NOP-certified farms using untreated manure.

Salmonella Prevalence Is Strongly Associated with Spatial Factors while Listeria monocytogenes Prevalence Is Strongly Associated with Temporal Factors on Virginia Produce Farms

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.

Soil Collected from a Single Great Smoky Mountains Trail Contains a Diversity of Listeria monocytogenes and Listeria spp

Listeria monocytogenes, a foodborne pathogen, and other Listeria spp. are present in natural environments. Isolating and characterizing strains from natural reservoirs can provide insight into the prevalence and diversity of Listeria spp. in these environments, elucidate their contribution to contamination of agricultural and food processing environments and food products, and lead to the discovery of novel species. In this study, we evaluated the diversity of Listeria spp. isolated from soil in a small region of the Great Smoky Mountains National Park, the most biodiverse national park in the U.S. National Park system. Of the 17 Listeria isolates recovered, whole-genome sequencing revealed that 14 were distinct strains. The strains represented a diversity of Listeria species (L. monocytogenes [n = 9], L. cossartiae subsp. cossartiae [n = 1], L. marthii [n = 1], L. booriae [n = 1], and a potentially novel Listeria sp. [n = 2]), as well as a diversity of sequence types based on multilocus sequence typing (MLST) and core genome MLST, including many novel designations. The isolates were not closely related (≥99.99% average nucleotide identity) to any isolates in public databases (NCBI, PATRIC), which also indicated novelty. The Listeria samples isolated in this study were collected from high-elevation sites near a creek that ultimately leads to the Mississippi River; thus, Listeria present in this natural environment could potentially travel downstream to a large region that includes portions of nine southeastern and midwestern U.S. states. This study provides insight into the diversity of Listeria spp. in the Great Smoky Mountains and indicates that this environment is a reservoir of novel Listeria spp. IMPORTANCE Listeria monocytogenes is a foodborne pathogen that can cause serious systemic illness that, although rare, usually results in hospitalization and has a relatively high mortality rate compared to other foodborne pathogens. Identification of novel and diverse Listeria spp. can provide insights into the genomic evolution, ecology, and evolution and variance of pathogenicity of this genus, especially in natural environments. Comparing L. monocytogenes and Listeria spp. isolates from natural environments, such as those recovered in this study, to contamination and/or outbreak strains may provide more information about the original natural sources of these strains and the pathways and mechanisms that lead to contamination of food products and agricultural or food processing environments.

A longitudinal study to examine the influence of farming practices and environmental factors on pathogen prevalence using structural equation modeling

The contamination of fresh produce with foodborne pathogens has been an on-going concern with outbreaks linked to these commodities. Evaluation of farm practices, such as use of manure, irrigation water source, and other factors that could influence pathogen prevalence in the farming environment could lead to improved mitigation strategies to reduce the potential for contamination events. Soil, water, manure, and compost were sampled from farms in Ohio and Georgia to identify the prevalence of Salmonella, Listeria monocytogenes (Lm), Campylobacter, and Shiga-toxin-producing Escherichia coli (STEC), as well as Arcobacter, an emerging human pathogen. This study investigated agricultural practices to determine which influenced pathogen prevalence, i.e., the percent positive samples. These efforts identified a low prevalence of Salmonella, STEC, and Campylobacter in soil and water (< 10%), preventing statistical modeling of these pathogens. However, Lm and Arcobacter were found in soil (13 and 7%, respectively), manure (49 and 32%, respectively), and water samples (18 and 39%, respectively) at a comparatively higher prevalence, suggesting different dynamics are involved in their survival in the farm environment. Lm and Arcobacter prevalence data, soil chemical characteristics, as well as farm practices and weather, were analyzed using structural equation modeling to identify which factors play a role, directly or indirectly, on the prevalence of these pathogens. These analyses identified an association between pathogen prevalence and weather, as well as biological soil amendments of animal origin. Increasing air temperature increased Arcobacter and decreased Lm. Lm prevalence was found to be inversely correlated with the use of surface water for irrigation, despite a high Lm prevalence in surface water suggesting other factors may play a role. Furthermore, Lm prevalence increased when the microbiome's Simpson's Diversity Index decreased, which occurred as soil fertility increased, leading to an indirect positive effect for soil fertility on Lm prevalence. These results suggest that pathogen, environment, and farm management practices, in addition to produce commodities, all need to be considered when developing mitigation strategies. The prevalence of Arcobacter and Lm versus the other pathogens suggests that multiple mitigation strategies may need to be employed to control these pathogens.

Structural Equation Models Suggest That On-Farm Noncrop Vegetation Removal Is Not Associated with Improved Food Safety Outcomes but Is Linked to Impaired Water Quality

While growers have reported pressures to minimize wildlife intrusion into produce fields through noncrop vegetation (NCV) removal, NCV provides key ecosystem services. To model food safety and environmental tradeoffs associated with NCV removal, published and publicly available food safety and water quality data from the Northeastern United States were obtained. Because data on NCV removal are not widely available, forest-wetland cover was used as a proxy, consistent with previous studies. Structural equation models (SEMs) were used to quantify the effect of forest-wetland cover on (i) food safety outcomes (e.g., detecting pathogens in soil) and (ii) water quality (e.g., nutrient levels). Based on the SEMs, NCV was not associated with or had a protective effect on food safety outcomes (more NCV was associated with a reduced likelihood of pathogen detection). The probabilities of detecting Listeria spp. in soil (effect estimate [EE] = -0.17; P = 0.005) and enterohemorrhagic Escherichia coli in stream samples (EE = -0.27; P < 0.001) were negatively associated with the amount of NCV surrounding the sampling site. Larger amounts of NCV were also associated with lower nutrient, salinity, and sediment levels, and higher dissolved oxygen levels. Total phosphorous levels were negatively associated with the amount of NCV in the upstream watershed (EE = -0.27; P < 0.001). Similar negative associations (P < 0.05) were observed for other physicochemical parameters, such as nitrate (EE = -0.38). Our findings suggest that NCV should not be considered an inherent produce safety risk or result in farm audit demerits. This study also provides a framework for evaluating environmental tradeoffs associated with using specific preharvest food safety strategies. IMPORTANCE Currently, on-farm food safety decisions are typically made independently of conservation considerations, often with detrimental impacts on agroecosystems. Comanaging agricultural environments to simultaneously meet conservation and food safety aims is complicated because farms are closely linked to surrounding environments, and management decisions can have unexpected environmental, economic, and food safety consequences. Thus, there is a need for research on the conservation and food safety tradeoffs associated with implementing specific preharvest food safety practices. Understanding these tradeoffs is critical for developing adaptive comanagement strategies and ensuring the short- and long-term safety, sustainability, and profitability of agricultural systems. This study quantifies tradeoffs and synergies between food safety and environmental aims, and outlines a framework for modeling tradeoffs and synergies between management aims that can be used to support future comanagement research.

Asymptomatic Carriage of Listeria monocytogenes by Animals and Humans and Its Impact on the Food Chain

Humans and animals can become asymptomatic carriers of Listeria monocytogenes and introduce the pathogen into their environment with their feces. In turn, this environmental contamination can become the source of food- and feed-borne illnesses in humans and animals, with the food production chain representing a continuum between the farm environment and human populations that are susceptible to listeriosis. Here, we update a review from 2012 and summarize the current knowledge on the asymptomatic carrier statuses in humans and animals. The data on fecal shedding by species with an impact on the food chain are summarized, and the ways by which asymptomatic carriers contribute to the risk of listeriosis in humans and animals are reviewed.

The fate of antibiotic resistance genes, microbial community, and potential pathogens in the maricultural sediment by live seaweeds and oxytetracycline

Three common seaweeds including Ulva fasciata, Codium cylindricum and Ishige okamurai were used for the remediation of maricultural wastewater and sediment in the presence/absence of trace level of oxytetracycline (OTC) in lab-scale experiments. Higher NO₃^--N and PO₄^3--P removal rates were achieved due to the presence of seaweeds, and trace OTC also had a positive effect on NO₃^--N removal. A slight variation of 2.10-2.15% were observed in the total relative abundances of antibiotic resistance genes (ARGs) of different sediment samples after one-month operation. However, the variation of ARGs profiles by the co-existence of different seaweeds and OTC was in the descending order of Ishige okamurai > Codium cylindricum > Ulva fasciata, which was in accordance with the variation of microbial hosts at genus level. The abundance of dominant tetracycline resistance genes promoted by the co-existence of different seaweeds and OTC in compared with the presence of single seaweed or OTC via metagenomic sequencing and qPCR analysis, and the co-existence of Ishige okamurai and OTC exhibited the largest impact. The potential pathogens were more sensitive to the co-existence of seaweed and OTC than single seaweeds. Meanwhile, a variety of ARGs were enriched in the pathogens, and the dominant pathogenic bacteria of Vibrio had 133 Vibrio species with 28 subtypes of ARGs. The variation of ARGs profiles in the sediment were strongly related with the dominant phyla Proteobacteria, Actinobacteria, Firmicutes, Planctomycetes and Cyanobacteria. Besides, Nitrate level exhibited more significant effect on ∑ARGs, ARGs resistant to vancomycin and streptogramin_a, while phosphate level exhibited more positively significant effect on ARGs resistant to fosmidomycin, ATFBT and cephalosporin.

Environmental and anthropogenic factors associated with the likelihood of detecting Salmonella in agricultural watersheds

Surface water is one of the primary sources of irrigation water for produce production; therefore, its contamination by foodborne pathogens, such as Salmonella, may substantially impact public health. In this study, we determined the presence of Salmonella in surface water and characterized the relationship between Salmonella detection and environmental and anthropogenic factors. From April 2019 to February 2020, 120 samples from 30 sites were collected monthly in four watersheds located in two different central Chile agricultural regions (N = 1080). Water samples from rivers, canals, streams, and ponds linked to each watershed were obtained. Surface water (10 L) was filtrated in situ, and samples were analyzed for the presence of Salmonella. Salmonella was detected every month in all watersheds, with a mean detection percentage of 28% (0%-90%) across sampling sites, regardless of the season. Overall, similar detection percentages were observed for both regions: 29.1% for Metropolitan and 27.0% for Maule. Salmonella was most often detected in summer (39.8% of all summer samples tested positive) and least often in winter (14.4% of winter samples). Random forest analysis showed that season, water source, and month, followed by latitude and river, were the most influential factors associated with Salmonella detection. The influences of water pH and temperature (categorized as environmental factors) and factors associated with human activity (categorized as anthropogenic factors) registered at the sampling site were weakly or not associated with Salmonella detection. In conclusion, Salmonella was detected in surface water potentially used for irrigation, and its presence was linked to season and water source factors. Interventions are necessary to prevent contamination of produce, such as water treatment before irrigation.

Humans and Hoofed Livestock Are the Main Sources of Fecal Contamination of Rivers Used for Crop Irrigation: A Microbial Source Tracking Approach

Freshwater bodies receive waste, feces, and fecal microorganisms from agricultural, urban, and natural activities. In this study, the probable sources of fecal contamination were determined. Also, antibiotic resistant bacteria (ARB) were detected in the two main rivers of central Chile. Surface water samples were collected from 12 sampling sites in the Maipo (n = 8) and Maule Rivers (n = 4) every 3 months, from August 2017 until April 2019. To determine the fecal contamination level, fecal coliforms were quantified using the most probable number (MPN) method and the source of fecal contamination was determined by Microbial Source Tracking (MST) using the Cryptosporidium and Giardia genotyping method. Separately, to determine if antimicrobial resistance bacteria (AMB) were present in the rivers, Escherichia coli and environmental bacteria were isolated, and the antibiotic susceptibility profile was determined. Fecal coliform levels in the Maule and Maipo Rivers ranged between 1 and 130 MPN/100-ml, and 2 and 30,000 MPN/100-ml, respectively. Based on the MST results using Cryptosporidium and Giardia host-specific species, human, cattle, birds, and/or dogs hosts were the probable sources of fecal contamination in both rivers, with human and cattle host-specific species being more frequently detected. Conditional tree analysis indicated that coliform levels were significantly associated with the river system (Maipo versus Maule), land use, and season. Fecal coliform levels were significantly (p < 0.006) higher at urban and agricultural sites than at sites immediately downstream of treatment centers, livestock areas, or natural areas. Three out of eight (37.5%) E. coli isolates presented a multidrug-resistance (MDR) phenotype. Similarly, 6.6% (117/1768) and 5.1% (44/863) of environmental isolates, in Maipo and Maule River showed and MDR phenotype. Efforts to reduce fecal discharge into these rivers should thus focus on agriculture and urban land uses as these areas were contributing the most and more frequently to fecal contamination into the rivers, while human and cattle fecal discharges were identified as the most likely source of this fecal contamination by the MST approach. This information can be used to design better mitigation strategies, thereby reducing the burden of waterborne diseases and AMR in Central Chile.

Prevalence and Clonal Diversity of over 1,200 Listeria monocytogenes Isolates Collected from Public Access Waters near Produce Production Areas on the Central California Coast during 2011 to 2016

A 5-year survey of public access surface waters in an agricultural region of the Central California Coast was done to assess the prevalence of the foodborne pathogen Listeria monocytogenes. In nature, L. monocytogenes lives as a saprophyte in soil and water, which are reservoirs for contamination of preharvest produce. Moore swabs were deployed biweekly in lakes, ponds, streams, and rivers during 2011 to 2016. L. monocytogenes was recovered in 1,224 of 2,922 samples, resulting in 41.9% prevalence. Multiple subtypes were isolated from 97 samples, resulting in 1,323 L. monocytogenes isolates. Prevalence was higher in winter and spring and after rain events in some waterways. Over 84% of the isolates were serotype 4b. Whole-genome sequencing was done on 1,248 isolates, and in silico multilocus sequence typing revealed 74 different sequence types (STs) and 39 clonal complexes (CCs). The clones most isolated, CC639, CC183, and CC1, made up 27%, 19%, and 13%, respectively, of the sequenced isolates. Other types were CC663, CC6, CC842, CC4, CC2, CC5, and CC217. All sequenced isolates contained intact copies of core L. monocytogenes virulence genes, and pathogenicity islands LIPI-3 and LIPI-4 were identified in 73% and 63%, respectively, of the sequenced isolates. The virulence factor internalin A was predicted to be intact in all but four isolates, while genes important for sanitizer and heavy metal resistance were found in <5% of the isolates. These waters are not used for crop irrigation directly, but they are available to wildlife and can flood fields during heavy rains. IMPORTANCE Listeria monocytogenes serotype 4b and 1/2a strains are implicated in most listeriosis, and hypervirulent listeriosis stems from strains containing pathogenicity islands LIPI-3 and LIPI-4. The waters and sediments in the Central California Coast agricultural region contain widespread and diverse L. monocytogenes populations, and all the isolates contain intact virulence genes. Emerging clones CC183 and CC639 were the most abundant clones, and major clones CC1, CC4, and CC6 were well represented. CC183 was responsible for three produce-related outbreaks in the last 7 years. Most of the isolates in the survey differ from those of lesser virulence that are often isolated from foods and food processing plants because they contain genes encoding an intact virulence factor, internalin A, and most did not contain genes for sanitizer and heavy metal resistance. This isolate collection is important for understanding L. monocytogenes populations in agricultural and natural regions.

Occurrence of Fecal Bacteria and Zoonotic Pathogens in Different Water Bodies: Supporting Water Quality Management

Water contaminated with microbiological and chemical constituents can cause a variety of diseases. Water bodies may become contaminated by wild and domestic animal feces, agricultural runoff or sewage, and are often overlooked as a reservoir and source of human infection by pathogenic microorganisms. The objectives of this study were to evaluate the presence of the zoonotic pathogens, Salmonella spp. and Listeria monocytogenes, in various water bodies located in urban and rural areas in the north of Portugal. Water samples were collected from six sites, including natural and artificial ponds, in two different time periods. Several water quality physicochemical parameters, as well as fecal indicator bacteria, were evaluated. High levels of total coliforms (>1.78 log CFU/100 mL) were detected in all samples, and substantial numbers of Enterococcus (>2.32 log CFU/100 mL) were detected in two ponds located in a city park and in an urban garden. Escherichia coli counts ranged from undetectable to 2.76 log CFU/100 mL. Salmonella spp. was isolated from two sites, the city park and the natural pond, while L. monocytogenes was isolated from three sites: the city garden, the natural pond and the artificial pond, both in the rural area. These data show that artificial and natural ponds are a reservoir of fecal indicator bacteria and enteric and zoonotic pathogens. This may impact the potential risks of human infections by potential contaminants during recreational activities, being important for assessing the water quality for strategic management of these areas.

WGS analysis of Listeria monocytogenes from rural, urban, and farm environments in Norway: Genetic diversity, persistence, and relation to clinical and food isolates

Listeria monocytogenes is a ubiquitous environmental bacterium associated with a wide variety of natural and human-made environments, such as soil, vegetation, livestock, food processing environments, and urban areas. It is also among the deadliest foodborne pathogens, and knowledge about its presence and diversity in potential sources is crucial to effectively track and control it in the food chain. Isolation of L. monocytogenes from various rural and urban environments showed higher prevalence in agricultural and urban developments than in forest or mountain areas, and that detection was positively associated with rainfall. Whole-genome sequencing (WGS) was performed for the collected isolates and for L. monocytogenes from Norwegian dairy farms and slugs (218 isolates in total). The data were compared to available data sets from clinical and food-associated sources in Norway collected within the last decade. Multiple examples of clusters of isolates with 0 to 8 whole-genome multilocus sequence typing (wgMLST) allelic differences were collected over time in the same location, demonstrating persistence of L. monocytogenes in natural, urban, and farm environments. Furthermore, several clusters with 6 to 20 wgMLST allelic differences containing isolates collected across different locations, times, and habitats were identified, including nine clusters harboring clinical isolates. The most ubiquitous clones found in soil and other natural and animal ecosystems (CC91, CC11, and CC37) were distinct from clones predominating among both clinical (CC7, CC121, and CC1) and food (CC9, CC121, CC7, and CC8) isolates. The analyses indicated that ST91 was more prevalent in Norway than other countries and revealed a high proportion of the hypovirulent ST121 among Norwegian clinical cases.

IMPORTANCEListeria monocytogenes is a deadly foodborne pathogen that is widespread in the environment. For effective management, both public health authorities and food producers need reliable tools for source tracking, surveillance, and risk assessment. For this, whole-genome sequencing (WGS) is regarded as the present and future gold standard. In the current study, we use WGS to show that L. monocytogenes can persist for months and years in natural, urban, and dairy farm environments. Notably, clusters of almost identical isolates, with genetic distances within the thresholds often suggested for defining an outbreak cluster, can be collected from geographically and temporally unrelated sources. The work highlights the need for a greater knowledge of the genetic relationships between clinical isolates and isolates of L. monocytogenes from a wide range of environments, including natural, urban, agricultural, livestock, food production, and food processing environments, to correctly interpret and use results from WGS analyses.

Prediction of E. coli Concentrations in Agricultural Pond Waters: Application and Comparison of Machine Learning Algorithms

The microbial quality of irrigation water is an important issue as the use of contaminated waters has been linked to several foodborne outbreaks. To expedite microbial water quality determinations, many researchers estimate concentrations of the microbial contamination indicator Escherichia coli (E. coli) from the concentrations of physiochemical water quality parameters. However, these relationships are often non-linear and exhibit changes above or below certain threshold values. Machine learning (ML) algorithms have been shown to make accurate predictions in datasets with complex relationships. The purpose of this work was to evaluate several ML models for the prediction of E. coli in agricultural pond waters. Two ponds in Maryland were monitored from 2016 to 2018 during the irrigation season. E. coli concentrations along with 12 other water quality parameters were measured in water samples. The resulting datasets were used to predict E. coli using stochastic gradient boosting (SGB) machines, random forest (RF), support vector machines (SVM), and k-nearest neighbor (kNN) algorithms. The RF model provided the lowest RMSE value for predicted E. coli concentrations in both ponds in individual years and over consecutive years in almost all cases. For individual years, the RMSE of the predicted E. coli concentrations (log10 CFU 100 ml-1) ranged from 0.244 to 0.346 and 0.304 to 0.418 for Pond 1 and 2, respectively. For the 3-year datasets, these values were 0.334 and 0.381 for Pond 1 and 2, respectively. In most cases there was no significant difference (P > 0.05) between the RMSE of RF and other ML models when these RMSE were treated as statistics derived from 10-fold cross-validation performed with five repeats. Important E. coli predictors were turbidity, dissolved organic matter content, specific conductance, chlorophyll concentration, and temperature. Model predictive performance did not significantly differ when 5 predictors were used vs. 8 or 12, indicating that more tedious and costly measurements provide no substantial improvement in the predictive accuracy of the evaluated algorithms.

Salmonella enterica Serovar Diversity, Distribution, and Prevalence in Public Access Waters from a Central California Coastal Leafy Green Growing Region during 2011 - 2016

Prevalence and serovar diversity of Salmonella enterica was measured during a five-year survey of surface waters in a 500 mi^2 agricultural region of the Central California Coast. Rivers, streams, lakes, and ponds were sampled bimonthly resulting in 2,979 samples. Overall prevalence was 56.4% with higher levels detected in Spring than in Fall. Small, but significant, differences in prevalence were detected based on sample locations. Detection of Salmonella was correlated positively with both significant rain events and, in some environments, levels of generic Escherichia coli. Analysis of 1,936 isolates revealed significant serovar diversity, with 91 different serovars detected. The most common isolated serovars were S. enterica subsp. enterica serovars I 6,8:d:- (406 isolates, 21.0%, and potentially monophasic Salmonella Muenchen), Give (334 isolates, 17.3%), Muenchen (158 isolates, 8.2%), Typhimurium (227 isolates, 11.7%), Oranienburg (106 isolates, 5.5%), and Montevideo (78 isolates, 4%). Sixteen of the 24 most common serovars detected in the region are among the serovars reported to cause the most human salmonellosis in the United States. Some of the serovars were associated with location and seasonal bias. Analysis of XbaI Pulsed Field Gel Electrophoresis (PFGE) patterns of strains of serovars Typhimurium, Oranienburg, and Montevideo showed significant intra-serovar diversity. PFGE pulsotypes were identified in the region for multiple years of the survey, indicating persistence or regular re-introduction to the region. Importance Non-typhoidal Salmonella is the among the leading causes of bacterial foodborne illness and increasing numbers of outbreaks and recalls are due to contaminated produce. High prevalence and 91 different serovars were detected in this leafy green growing region. Seventeen serovars that cause most of the human salmonellosis in the United States were detected, with 16 of those serovars detected in multiple locations and multiple years of the 5-year survey. Understanding the widespread prevalence and diversity of Salmonella in the region will assist in promoting food safety practices and intervention methods for growers and regulators.

Levels of Salmonella enterica and Listeria monocytogenes in Alternative Irrigation Water Vary Based on Water Source on the Eastern Shore of Maryland

Irrigation water sources have been shown to harbor foodborne pathogens and could contribute to the outbreak of foodborne illness related to consumption of contaminated produce. Determining the probability of and the degree to which these irrigation water sources contain these pathogens is paramount. The purpose of this study was to determine the prevalence of Salmonella enterica and Listeria monocytogenes in alternative irrigation water sources. Water samples (n = 188) were collected over 2 years (2016 to 2018) from 2 reclaimed water plants, 3 nontidal freshwater rivers, and 1 tidal brackish river on Maryland's Eastern Shore (ESM). Samples were collected by filtration using modified Moore swabs (MMS) and analyzed by culture methods. Pathogen levels were quantified using a modified most probable number (MPN) procedure with three different volumes (10 liters, 1 liter, and 0.1 liter). Overall, 65% (122/188) and 40% (76/188) of water samples were positive for S. enterica and L. monocytogenes, respectively. For both pathogens, MPN values ranged from 0.015 to 11 MPN/liter. Pathogen levels (MPN/liter) were significantly (P < 0.05) greater for the nontidal freshwater river sites and the tidal brackish river site than the reclaimed water sites. L. monocytogenes levels in water varied based on season. Detection of S. enterica was more likely with 10-liter filtration compared to 0.1-liter filtration. The physicochemical factors measured attributed only 6.4% of the constrained variance to the levels of both pathogens. This study shows clear variations in S. enterica and L. monocytogenes levels in irrigation water sources on ESM. IMPORTANCE In the last several decades, Maryland's Eastern Shore has seen significant declines in groundwater levels. While this area is not currently experiencing drought conditions or water scarcity, this research represents a proactive approach. Efforts, to investigate the levels of pathogenic bacteria and the microbial quality of alternative irrigation water are important for sustainable irrigation practices into the future. This research will be used to determine the suitability of alternative irrigation water sources for use in fresh produce irrigation to conserve groundwater.

Isolation of Ciprofloxacin and Ceftazidime-Resistant Enterobacterales From Vegetables and River Water Is Strongly Associated With the Season and the Sample Type

The dissemination of antibiotic-resistant bacteria (ARB) from water used for crop irrigation to vegetables is poorly studied. During a year, five farmer markets in a city in Central Chile were visited, and 478 vegetable samples (parsleys, corianders, celeries, lettuces, chards, and beets) were collected. Simultaneously, 32 water samples were collected from two rivers which are used to irrigate the vegetables produced in the area. Resistant Enterobacterales were isolated and identified. Colistin resistance gene mcr-1 and extended spectrum β-lactamases (ESBL) were molecularly detected. The association of environmental factors was evaluated, with the outcomes being the presence of Enterobacterales resistant to four antibiotic families and the presence of multidrug resistance (MDR) phenotypes. Parsley, coriander, and celery showed the highest prevalence of resistant Enterobacterales (41.9% for ciprofloxacin and 18.5% for ceftazidime). A total of 155 isolates were obtained, including Escherichia coli (n=109), Citrobacter sp. (n=20), Enterobacter cloacae complex (n=8), Klebsiella pneumoniae (n=8), and Klebsiella aerogenes (n=1). Resistance to ampicillin (63.2%) and ciprofloxacin (74.2%) was most frequently found; 34.5% of the isolates showed resistance to third-generation cephalosporins, and the MDR phenotype represented 51.6% of the isolates. In two E. coli isolates (1.29%), the gene mcr-1 was found and ESBL genes were found in 23/62 isolates (37%), with bla_CTX-M being the most frequently found in 20 isolates (32%). Resistant Enterobacterales isolated during the rainy season were less likely to be MDR as compared to the dry season. Understanding environmental associations represent the first step toward an improved understanding of the public health impact of ARB in vegetables and water.

Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination

Aquatic fecal contamination poses human health risks by introducing pathogens in water that may be used for recreation, consumption, or agriculture. Identifying fecal contaminant sources, as well as the factors that affect their transport, storage, and decay, is essential for protecting human health. However, identifying these factors is often difficult when using fecal indicator bacteria (FIB) because FIB levels in surface water are often the product of multiple contaminant sources. In contrast, microbial source-tracking (MST) techniques allow not only the identification of predominant contaminant sources but also the quantification of factors affecting the transport, storage, and decay of fecal contaminants from specific hosts. We visited 68 streams in the Finger Lakes region of Upstate New York, United States, between April and October 2018 and collected water quality data (i.e., Escherichia coli, MST markers, and physical–chemical parameters) and weather and land-use data, as well as data on other stream features (e.g., stream bed composition), to identify factors that were associated with fecal contamination at a regional scale. We then applied both generalized linear mixed models and conditional inference trees to identify factors and combinations of factors that were significantly associated with human and ruminant fecal contamination. We found that human contaminants were more likely to be identified when the developed area within the 60 m stream buffer exceeded 3.4%, the total developed area in the watershed exceeded 41%, or if stormwater outfalls were present immediately upstream of the sampling site. When these features were not present, human MST markers were more likely to be found when rainfall during the preceding day exceeded 1.5 cm. The presence of upstream campgrounds was also significantly associated with human MST marker detection. In addition to rainfall and water quality parameters associated with rainfall (e.g., turbidity), the minimum distance to upstream cattle operations, the proportion of the 60 m buffer used for cropland, and the presence of submerged aquatic vegetation at the sampling site were all associated based on univariable regression with elevated levels of ruminant markers. The identification of specific features associated with host-specific fecal contaminants may support the development of broader recommendations or policies aimed at reducing levels of aquatic fecal contamination.

Free-Living Aquatic Turtles as Sentinels of Salmonella spp. for Water Bodies

Reptile-associated human salmonellosis cases have increased recently in the United States. It is not uncommon to find healthy chelonians shedding Salmonella enterica. The rate and frequency of bacterial shedding are not fully understood, and most studies have focused on captive vs. free-living chelonians and often in relation to an outbreak. Their ecology and significance as sentinels are important to understanding Salmonella transmission. In 2012-2013, Salmonella prevalence was determined for free-living aquatic turtles in man-made ponds in Clarke and Oconee Counties, in northern Georgia (USA) and the correlation between species, basking ecology, demographics (age/sex), season, or landcover with prevalence was assessed. The genetic relatedness between turtle and archived, human isolates, as well as, other archived animal and water isolates reported from this study area was examined. Salmonella was isolated from 45 of 194 turtles (23.2%, range 14-100%) across six species. Prevalence was higher in juveniles (36%) than adults (20%), higher in females (33%) than males (18%), and higher in bottom-dwelling species (31%; common and loggerhead musk turtles, common snapping turtles) than basking species (15%; sliders, painted turtles). Salmonella prevalence decreased as forest cover, canopy cover, and distance from roads increased. Prevalence was also higher in low-density, residential areas that have 20-49% impervious surface. A total of 9 different serovars of two subspecies were isolated including 3 S. enterica subsp. arizonae and 44 S. enterica subsp. enterica (two turtles had two serotypes isolated from each). Among the S. enterica serovars, Montevideo (n = 13) and Rubislaw (n = 11) were predominant. Salmonella serovars Muenchen, Newport, Mississippi, Inverness, Brazil, and Paratyphi B. var L(+) tartrate positive (Java) were also isolated. Importantly, 85% of the turtle isolates matched pulsed-field gel electrophoresis patterns of human isolates, including those reported from Georgia. Collectively, these results suggest that turtles accumulate Salmonella present in water bodies, and they may be effective sentinels of environmental contamination. Ultimately, the Salmonella prevalence rates in wild aquatic turtles, especially those strains shared with humans, highlight a significant public health concern.

Nationwide genomic atlas of soil-dwelling Listeria reveals effects of selection and population ecology on pangenome evolution

Natural bacterial populations can display enormous genomic diversity, primarily in the form of gene content variation caused by the frequent exchange of DNA with the local environment. However, the ecological drivers of genomic variability and the role of selection remain controversial. Here, we address this gap by developing a nationwide atlas of 1,854 Listeria isolates, collected systematically from soils across the contiguous United States. We found that Listeria was present across a wide range of environmental parameters, being mainly controlled by soil moisture, molybdenum and salinity concentrations. Whole-genome data from 594 representative strains allowed us to decompose Listeria diversity into 12 phylogroups, each with large differences in habitat breadth and endemism. 'Cosmopolitan' phylogroups, prevalent across many different habitats, had more open pangenomes and displayed weaker linkage disequilibrium, reflecting higher rates of gene gain and loss, and allele exchange than phylogroups with narrow habitat ranges. Cosmopolitan phylogroups also had a large fraction of genes affected by positive selection. The effect of positive selection was more pronounced in the phylogroup-specific core genome, suggesting that lineage-specific core genes are important drivers of adaptation. These results indicate that genome flexibility and recombination are the consequence of selection to survive in variable environments.

The effect of vegetation barriers at reducing the transmission of Salmonella and Escherichia coli from animal operations to fresh produce

Due to the recent outbreaks of Salmonella and Escherichia coli in fresh produce in the United States, the transfer of foodborne pathogens between animal feeding operations and fresh produce continues to be a considerable risk. The purpose of this study was to determine if the establishment of a vegetation barrier (VB) on small-scale sustainable farms could prevent the transmission of Salmonella and E. coli to nearby fresh produce fields. A 5-layer VB (31 × 49 m) was constructed between a dairy farm, a poultry farm, and a nearby produce field. Fresh produce (i.e., romaine lettuce and tomato), animal feces, and environmental (i.e., air, soil, and barrier) samples were collected for 15 months from 2018 to 2019. Four replicates of soil and fresh produce samples were taken from three plots located 10 m, 61 m, and 122 m away from the respective animal locations and processed for Salmonella and E. coli. Air and vegetative strip samples were sampled at 15-day intervals. Multiple colonies were processed from each positive sample, and a total of 143 positive Salmonella (n = 15) and E. coli (n = 128) isolates were retrieved from the soil, produce, air, and fecal samples. Interestingly, 18.2% of the Salmonella and E. coli isolates (n = 26) were recovered from fresh produce (n = 9) samples. Surprisingly, Salmonella isolates (n = 9) were only found in fecal (n = 3) samples collected from the dairy pasture. Data analysis suggests that the VB is an effective tool at reducing the transmission of E. coli and Salmonella from animal farms to fresh produce fields. However, based on phenotypic and genotypic testing, it is clear that fecal samples from animal farms are not the only source of pathogen contamination. This indicates that the environment (e.g., soil and wind), as well as the initial setup of the farm (e.g., proximity to service roads and produce plot placement), can contribute to the contamination of fresh produce. Our study recommends the need for more effective bioremediation and prevention control measures to use in conjunction with VBs to reduce pathogen transmission.

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

Since E. coli is considered a fecal indicator in surface water, government water quality standards and industry guidance often rely on E. coli monitoring to identify when there is an increased risk of pathogen contamination of water used for produce production (e.g., for irrigation). However, studies have indicated that E. coli testing can present an economic burden to growers and that time lags between sampling and obtaining results may reduce the utility of these data. Models that predict E. coli levels in agricultural water may provide a mechanism for overcoming these obstacles. Thus, this proof-of-concept study uses previously published datasets to train, test, and compare E. coli predictive models using multiple algorithms and performance measures. Since the collection of different feature data carries specific costs for growers, predictive performance was compared for models built using different feature types [geospatial, water quality, stream traits, and/or weather features]. Model performance was assessed against baseline regression models. Model performance varied considerably with root-mean-squared errors and Kendall's Tau ranging between 0.37 and 1.03, and 0.07 and 0.55, respectively. Overall, models that included turbidity, rain, and temperature outperformed all other models regardless of the algorithm used. Turbidity and weather factors were also found to drive model accuracy even when other feature types were included in the model. These findings confirm previous conclusions that machine learning models may be useful for predicting when, where, and at what level E. coli (and associated hazards) are likely to be present in preharvest agricultural water sources. This study also identifies specific algorithm-predictor combinations that should be the foci of future efforts to develop deployable models (i.e., models that can be used to guide on-farm decision-making and risk mitigation). When deploying E. coli predictive models in the field, it is important to note that past research indicates an inconsistent relationship between E. coli levels and foodborne pathogen presence. Thus, models that predict E. coli levels in agricultural water may be useful for assessing fecal contamination status and ensuring compliance with regulations but should not be used to assess the risk that specific pathogens of concern (e.g., Salmonella, Listeria) are present.

Co-quantification of crAssphage increases confidence in wastewater-based epidemiology for SARS-CoV-2 in low prevalence areas

Wastewater surveillance of SARS-CoV-2 RNA is increasingly being incorporated into public health efforts to respond to the COVID-19 pandemic. In order to obtain the maximum benefit from these efforts, approaches to wastewater monitoring need to be rapid, sensitive, and relatable to relevant epidemiological parameters. In this study, we present an ultracentrifugation-based method for the concentration of SARS-CoV-2 wastewater RNA and use crAssphage, a bacteriophage specific to the human gut, to help account for RNA loss during transit in the wastewater system and sample processing. With these methods, we were able to detect, and sometimes quantify, SARS-CoV-2 RNA from 20 mL wastewater samples within as little as 4.5 hours. Using known concentrations of bovine coronavirus RNA and deactivated SARS-CoV-2, we estimate recovery rates of approximately 7-12% of viral RNA using our method. Results from 24 sewersheds across Upstate New York during the spring and summer of 2020 suggested that stronger signals of SARS-CoV-2 RNA from wastewater may be indicative of greater COVID-19 incidence in the represented service area approximately one week in advance. SARS-CoV-2 wastewater RNA was quantifiable in some service areas with daily positives tests of less than 1 per 10,000 people or when weekly positive test rates within a sewershed were as low as 1.7%. crAssphage DNA concentrations were significantly lower during periods of high flow in almost all areas studied. After accounting for flow rate and population served, crAssphage levels per capita were estimated to be about 1.35 × 1011 and 2.42 × 108 genome copies per day for DNA and RNA, respectively. A negative relationship between per capita crAssphage RNA and service area size was also observed likely reflecting degradation of RNA over long transit times. Our results reinforce the potential for wastewater surveillance to be used as a tool to supplement understanding of infectious disease transmission obtained by traditional testing and highlight the potential for crAssphage co-detection to improve interpretations of wastewater surveillance data.

Strain, Soil-Type, Irrigation Regimen, and Poultry Litter Influence Salmonella Survival and Die-off in Agricultural Soils

The use of untreated biological soil amendments of animal origin (BSAAO) have been identified as one potential mechanism for the dissemination and persistence of Salmonella in the produce growing environment. Data on factors influencing Salmonella concentration in amended soils are therefore needed. The objectives here were to (i) compare die-off between 12 Salmonella strains following inoculation in amended soil and (ii) characterize any significant effects associated with soil-type, irrigation regimen, and amendment on Salmonella survival and die-off. Three greenhouse trials were performed using a randomized complete block design. Each strain (~4 log CFU/g) was homogenized with amended or non-amended sandy-loam or clay-loam soil. Salmonella levels were enumerated in 25 g samples 0, 0.167 (4 h), 1, 2, 4, 7, 10, 14, 21, 28, 56, 84, 112, 168, 210, 252, and 336 days post-inoculation (dpi), or until two consecutive samples were enrichment negative. Regression analysis was performed between strain, soil-type, irrigation, and (i) time to last detect (survival) and (ii) concentration at each time-point (die-off rate). Similar effects of strain, irrigation, soil-type, and amendment were identified using the survival and die-off models. Strain explained up to 18% of the variance in survival, and up to 19% of variance in die-off rate. On average Salmonella survived for 129 days in amended soils, however, Salmonella survived, on average, 30 days longer in clay-loam soils than sandy-loam soils [95% Confidence interval (CI) = 45, 15], with survival time ranging from 84 to 210 days for the individual strains during daily irrigation. When strain-specific associations were investigated using regression trees, S. Javiana and S. Saintpaul were found to survive longer in sandy-loam soil, whereas most of the other strains survived longer in clay-loam soil. Salmonella also survived, on average, 128 days longer when irrigated weekly, compared to daily (CI = 101, 154), and 89 days longer in amended soils, than non-amended soils (CI = 61, 116). Overall, this study provides insight into Salmonella survival following contamination of field soils by BSAAO. Specifically, Salmonella survival may be strain-specific as affected by both soil characteristics and management practices. These data can assist in risk assessment and strain selection for use in challenge and validation studies.

Small Produce Farm Environments Can Harbor Diverse Listeria monocytogenes and Listeria spp. Populations

ABSTRACT

A comprehensive understanding of foodborne pathogen diversity in preharvest environments is necessary to effectively track pathogens on farms and identify sources of produce contamination. As such, this study aimed to characterize Listeria diversity in wildlife feces and agricultural water collected from a New York state produce farm over a growing season. Water samples were collected from a pond (n = 80) and a stream (n = 52). Fecal samples (n = 77) were opportunistically collected from areas <5 m from the water sources; all samples were collected from a <0.5-km2 area. Overall, 86 (41%) and 50 (24%) of 209 samples were positive for Listeria monocytogenes and Listeria spp. (excluding L. monocytogenes), respectively. For each positive sample, one L. monocytogenes or Listeria spp. isolate was speciated by sequencing the sigB gene, thereby allowing for additional characterization based on the sigB allelic type. The 86 L. monocytogenes and 50 Listeria spp. isolates represented 8 and 23 different allelic types, respectively. A subset of L. monocytogenes isolates (n = 44) from pond water and pond-adjacent feces (representing an ∼5,000-m2 area) were further characterized by pulsed-field gel electrophoresis (PFGE); these 44 isolates represented 22 PFGE types, which is indicative of considerable diversity at a small spatial scale. Ten PFGE types were isolated more than once, suggesting persistence or reintroduction of PFGE types in this area. Given the small spatial scale, the prevalence of L. monocytogenes and Listeria spp., as well as the considerable diversity among isolates, suggests traceback investigations may be challenging. For example, traceback of finished product or processing facility contamination with specific subtypes to preharvest sources may require collection of large sample sets and characterization of a considerable number of isolates. Our data also support the adage "absence of evidence does not equal evidence of absence" as applies to L. monocytogenes traceback efforts at the preharvest level.

HIGHLIGHTS

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers’ ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (N = 181 samples) and 2018 (N = 191 samples) to train and test models that predict the likelihood of detecting Salmonella and pathogenic E. coli markers (eaeA, stx) in agricultural water. Four types of features were used to train the models: microbial, physicochemical, spatial and weather. “Full models” were built using all four features types, while “nested models” were built using between one and three types. Twenty learners were used to develop separate full models for each pathogen. Separately, to assess information gain associated with using different feature types, six learners were randomly selected and used to develop nine, nested models each. Performance measures for each model were then calculated and compared against baseline models where E. coli concentration was the sole covariate. In the methods, we outline the advantages and disadvantages of each learner. Overall, full models built using ensemble (e.g., Node Harvest) and “black-box” (e.g., SVMs) learners out-performed full models built using more interpretable learners (e.g., tree- and rule-based learners) for both outcomes. However, nested eaeA-stx models built using interpretable learners and microbial data performed almost as well as these full models. While none of the nested Salmonella models performed as well as the full models, nested models built using spatial data consistently out-performed models that excluded spatial data. These findings demonstrate that machine learning approaches can be used to predict when and where pathogens are likely to be present in agricultural water. This study serves as a proof-of-concept that can be built upon once larger datasets become available and provides guidance on the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).

Total Coliform and Generic E. coli Levels, and Salmonella Presence in Eight Experimental Aquaponics and Hydroponics Systems: A Brief Report Highlighting Exploratory Data

Although many studies have investigated foodborne pathogen prevalence in conventional produce production environments, relatively few have investigated prevalence in aquaponics and hydroponics systems. This study sought to address this knowledge gap by enumerating total coliform and generic E. coli levels, and testing for Salmonella presence in circulating water samples collected from five hydroponic systems and three aquaponic systems (No. of samples = 79). While total coliform levels ranged between 6.3 Most Probable Number (MPN)/100-mL and the upper limit of detection (2496 MPN/100-mL), only three samples had detectable levels of E. coli and no samples had detectable levels of Salmonella. Of the three E. coli positive samples, two samples had just one MPN of E. coli/100-mL while the third had 53.9 MPN of E. coli/100-mL. While the sample size reported here was small and site selection was not randomized, this study adds key data on the microbial quality of aquaponics and hydroponics systems to the literature. Moreover, these data suggest that contamination in these systems occurs at relatively low-levels, and that future studies are needed to more fully explore when and how microbial contamination of aquaponics and hydroponic systems is likely to occur.

The Composition of Microbial Communities in Six Streams, and Its Association With Environmental Conditions, and Foodborne Pathogen Isolation

Surface water used for produce production is a potential source of pre-harvest contamination with foodborne pathogens. Decisions on how to mitigate food safety risks associated with pre-harvest water use currently rely on generic Escherichia coli-based water quality tests, although multiple studies have suggested that E. coli levels are not a suitable indicator of the food safety risks under all relevant environmental conditions. Hence, improved understanding of spatiotemporal variability in surface water microbiota composition is needed to facilitate identification of alternative or supplementary indicators that co-occur with pathogens. To this end, we aimed to characterize the composition of bacterial and fungal communities in the sediment and water fractions of 68 agricultural water samples collected from six New York streams. We investigated potential associations between the composition of microbial communities, environmental factors and Salmonella and/or Listeria monocytogenes isolation. We found significantly different composition of fungal and bacterial communities among sampled streams and among water fractions of collected samples. This indicates that geography and the amount of sediment in a collected water sample may affect its microbial composition, which was further supported by identified associations between the flow rate, turbidity, pH and conductivity, and microbial community composition. Lastly, we identified specific microbial families that were weakly associated with the presence of Salmonella or Listeria monocytogenes, however, further studies on samples from additional streams are needed to assess whether identified families may be used as indicators of pathogen presence.

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between Escherichia coli levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds Ratio [OR] = 2.18), and eaeA-stx codetection (OR = 6.49) was significantly greater for MS compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also showed that eaeA-stx codetection in AZ (OR = 50.2) and NY (OR = 18.4), and Salmonella detection in AZ (OR = 4.4) were significantly associated with E. coli levels, while Salmonella detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.