Correlation of shiga toxin gene frequency with commonly used microbial indicators of recreational water quality

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).

Relationships between Microbial Indicators and Pathogens in Recreational Water Settings

Fecal pollution of recreational waters can cause scenic blight and pose a threat to public health, resulting in beach advisories and closures. Fecal indicator bacteria (total and fecal coliforms, Escherichia coli, and enterococci), and alternative indicators of fecal pollution (Clostridium perfringens and bacteriophages) are routinely used in the assessment of sanitary quality of recreational waters. However, fecal indicator bacteria (FIB), and alternative indicators are found in the gastrointestinal tract of humans, and many other animals and therefore are considered general indicators of fecal pollution. As such, there is room for improvement in terms of their use for informing risk assessment and remediation strategies. Microbial source tracking (MST) genetic markers are closely associated with animal hosts and are used to identify fecal pollution sources. In this review, we examine 73 papers generated over 40 years that reported the relationship between at least one indicator and one pathogen group or species. Nearly half of the reports did not include statistical analysis, while the remainder were almost equally split between those that observed statistically significant relationships and those that did not. Statistical significance was reported less frequently in marine and brackish waters compared to freshwater, and the number of statistically significant relationships was considerably higher in freshwater (p < 0.0001). Overall, significant relationships were more commonly reported between FIB and pathogenic bacteria or protozoa, compared to pathogenic viruses (p: 0.0022–0.0005), and this was more pronounced in freshwater compared to marine. Statistically significant relationships were typically noted following wet weather events and at sites known to be impacted by recent fecal pollution. Among the studies that reported frequency of detection, FIB were detected most consistently, followed by alternative indicators. MST markers and the three pathogen groups were detected least frequently. This trend was mirrored by reported concentrations for each group of organisms (FIB > alternative indicators > MST markers > pathogens). Thus, while FIB, alternative indicators, and MST markers continue to be suitable indicators of fecal pollution, their relationship with waterborne pathogens, particularly viruses, is tenuous at best and influenced by many different factors such as frequency of detection, variable shedding rates, differential fate and transport characteristics, as well as a broad range of site-specific factors such as the potential for the presence of a complex mixture of multiple sources of fecal contamination and pathogens.

Microbial quality of agricultural water in Central Florida

The microbial quality of water that comes into the edible portion of produce is believed to directly relate to the safety of produce, and metrics describing indicator organisms are commonly used to ensure safety. The US FDA Produce Safety Rule (PSR) sets very specific microbiological water quality metrics for agricultural water that contacts the harvestable portion of produce. Validation of these metrics for agricultural water is essential for produce safety. Water samples (500 mL) from six agricultural ponds were collected during the 2012/2013 and 2013/2014 growing seasons (46 and 44 samples respectively, 540 from all ponds). Microbial indicator populations (total coliforms, generic Escherichia coli, and enterococci) were enumerated, environmental variables (temperature, pH, conductivity, redox potential, and turbidity) measured, and pathogen presence evaluated by PCR. Salmonella isolates were serotyped and analyzed by pulsed-field gel electrophoresis. Following rain events, coliforms increased up to 4.2 log MPN/100 mL. Populations of coliforms and enterococci ranged from 2 to 8 and 1 to 5 log MPN/100 mL, respectively. Microbial indicators did not correlate with environmental variables, except pH (P<0.0001). The invA gene (Salmonella) was detected in 26/540 (4.8%) samples, in all ponds and growing seasons, and 14 serotypes detected. Six STEC genes were detected in samples: hly (83.3%), fliC (51.8%), eaeA (17.4%), rfbE (17.4%), stx-I (32.6%), stx-II (9.4%). While all ponds met the PSR requirements, at least one virulence gene from Salmonella (invA-4.8%) or STEC (stx-I-32.6%, stx-II-9.4%) was detected in each pond. Water quality for tested agricultural ponds, below recommended standards, did not guarantee the absence of pathogens. Investigating the relationships among physicochemical attributes, environmental factors, indicator microorganisms, and pathogen presence allows researchers to have a greater understanding of contamination risks from agricultural surface waters in the field.

Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches

Quantitative assessment of bacterial pathogens, their geographic variability, and distribution in various matrices at Great Lakes beaches are limited. Quantitative PCR (qPCR) was used to test for genes from E. coli O157:H7 (eaeO157), shiga-toxin producing E. coli (stx2), Campylobacter jejuni (mapA), Shigella spp. (ipaH), and a Salmonella enterica-specific (SE) DNA sequence at seven Great Lakes beaches, in algae, water, and sediment. Overall, detection frequencies were mapA>stx2>ipaH>SE>eaeO157. Results were highly variable among beaches and matrices; some correlations with environmental conditions were observed for mapA, stx2, and ipaH detections. Beach seasonal mean mapA abundance in water was correlated with beach seasonal mean log10 E. coli concentration. At one beach, stx2 gene abundance was positively correlated with concurrent daily E. coli concentrations. Concentration distributions for stx2, ipaH, and mapA within algae, sediment, and water were statistically different (Non-Detect and Data Analysis in R). Assuming 10, 50, or 100% of gene copies represented viable and presumably infective cells, a quantitative microbial risk assessment tool developed by Michigan State University indicated a moderate probability of illness for Campylobacter jejuni at the study beaches, especially where recreational water quality criteria were exceeded. Pathogen gene quantification may be useful for beach water quality management.

The microcosm mediates the persistence of shiga toxin-producing Escherichia coli in freshwater ecosystems

Water is a major route for infection of humans by exotoxin-producing bacteria, including Shiga toxin-producing Escherichia coli (STEC). While STEC has the potential to be present in nearly every type of water source, its distribution is sporadic, and an understanding of factors that govern its emergence and persistence within water is lacking. In this study, we examined the influence of microbe content on STEC persistence in freshwater. We found that depletion of microbes in the water leads to a considerable increase in the persistence of STEC, an effect that can be mitigated by adding grazing protists to the water. STEC strains appear to be more resistant to the impact of grazing protists than E. coli strains that lack the Shiga toxin (stx) gene. Our results demonstrate that the microcosm can dramatically influence the persistence of STEC in aquatic ecosystems and that the overall impact by microbes on STEC strains is fundamentally different from that of non-STEC strains of bacteria. Overall, these results provide insight into why STEC and possibly other exotoxin-producing bacterial pathogens display such variability in abundance, distribution, and persistence in aquatic ecosystems.

The occurrence of subtilase-cytotoxin-encoding genes in environmental Escherichia coli isolated from a Northern California estuary

The presence of subtilase-cytotoxin-encoding genes was determined in 397 environmental Escherichia coli strains isolated from water, suspended solids, and sediments sampled from different hydrological and environmental conditions in a California estuary. A total of 7 strains (1.76%) were found to harbor subtilase-cytotoxin-encoding genes. Using primers targeting subA only, we generated PCR amplicons from 2 strains; while using primers targeting the 3′ end of SubA downstream to the 5′ end of SubB, amplicons of 232 bp were generated from 5 additional strains. The 556 bp subA sequences were almost identical to that in the subtilase-cytotoxin-positive strain ED 591 (98%), while subAB sequences of 2 non-Shiga-toxigenic strains revealed 100% similarity with the Shiga-toxigenic E. coli O113:H21 strain 98NK2 that was isolated from an outbreak of hemolytic uremic syndrome. Additionally, the serogroup O113:H21 was present in this collection of environmental E. coli, and it was found to harbor stx2d, hra1 that encodes the heat resistant agglutinin 1, and a subAB sequence similar to that in the non-Shiga-toxigenic E. coli subtilase cytotoxin strain ED 591. To further understand potential health risks posed by strains encoding SubAB, future epidemiological studies should consider screening isolates for subAB regardless of the presence of Shiga-toxin-encoding genes.

Contamination level and location of recreational freshwater influence the ability to predict Escherichia coli concentration by qPCR targeting Bacteroides

Fecal bacteria are common microbial contaminants in freshwater with the potential to cause human illness. Detection of these microbes have traditionally relied on microbial plating to enumerate colonies of fecal indicator bacteria (FIB) such as Escherichia coli (E. coli), which can take 24 h or longer to complete. Quantitative PCR (qPCR) is a rapid and sensitive method for detection of FIB in recreational water that could compliment or potentially substitute for microbial plating. In this study, we have isolated DNA from the beach water on the shoreline at three different locations of Lake Erie and subjected these samples to qPCR to examine the relative abundance of Bacteroides. These values were compared to colony forming units (CFU) of E. coli. The resultant linear regressions between these different measurements of microbe concentration were used to determine the efficacy of qPCR targeting Bacteroides at predicting E. coli concentrations that are relevant for decision making by recreational water managers. Our findings indicate that the ability of Bacteroides to serve as an early predictive tool for E. coli CFU concentration depends on sample location and level of bacterial contamination, but can be used in some cases to supplement recreational water quality measurement and consequential management.

Human-specific E.coli single nucleotide polymorphism (SNP) genotypes detected in a South East Queensland waterway, Australia

The World Health Organization recommends that the majority of water monitoring laboratories in the world test for E. coli daily since thermotolerant coliforms and E. coli are key indicators for risk assessment of recreational waters. Recently, we developed a new SNP method for typing E. coli strains, by which human-specific genotypes were identified. Here, we report the presence of these previously described specific SNP profiles in environmental water, sourced from the Coomera River, located in South East Queensland, Australia, over a period of two years. This study tested for the presence of human-specific E. coli to ascertain whether hydrologic and anthropogenic activity plays a key role in the pollution of the investigated watershed or whether the pollution is from other sources. We found six human-specific SNP profiles and one animal-specific SNP profile consistently across sampling sites and times. We have demonstrated that our SNP genotyping method is able to rapidly identify and characterize human- and animal-specific E. coli isolates in water sources.

Shiga toxin: expression, distribution, and its role in the environment

In this review, we highlight recent work that has increased our understanding of the production and distribution of Shiga toxin in the environment. Specifically, we review studies that offer an expanded view of environmental reservoirs for Shiga toxin producing microbes in terrestrial and aquatic ecosystems. We then relate the abundance of Shiga toxin in the environment to work that demonstrates that the genetic mechanisms underlying the production of Shiga toxin genes are modified and embellished beyond the classical microbial gene regulatory paradigms in a manner that apparently "fine tunes" the trigger to modulate the amount of toxin produced. Last, we highlight several recent studies examining microbe/protist interactions that postulate an answer to the outstanding question of why microbes might harbor and express Shiga toxin genes in the environment.

Detection of stx and stx genes in Pennsylvanian white-tailed deer

Shiga toxin-producing E. coli carrying the stx(1) and/or stx(2) genes can cause multi-symptomatic illness in humans. A variety of terrestrial and aquatic environmental reservoirs of stx have been described. Culture based detection of microbes in deer species have found a low percentage of samples that have tested positive for Stx-producing microbes, suggesting that while deer may contain these microbes, their overall abundance in deer is low. In this study, quantitative PCR (qPCR) was utilized to test for the presence of stx genes in white-tailed deer fecal matter in western Pennsylvania. In this culture independent screening, nearly half of the samples tested positive for the stx(2) gene, with a bias towards samples that were concentrated with stx(2). This study, while limited in scope, suggests that deer may be a greater reservoir for stx than was previously thought.

Impact of urbanization and agriculture on the occurrence of bacterial pathogens and stx genes in coastal waterbodies of central California

Fecal pollution enters coastal waters through multiple routes, many of which originate from land-based activities. Runoff from pervious and impervious land surfaces transports pollutants from land to sea and can cause impairment of coastal ocean waters. To understand how land use practices and water characteristics influence concentrations of fecal indicator bacteria (FIB) and pathogens in natural waters, fourteen coastal streams, rivers, and tidal lagoons, surrounded by variable land use and animal densities, were sampled every six weeks over two years (2008 & 2009). Fecal indicator bacteria (FIB; Escherichia coli and Enterococci) and Salmonella concentrations, the occurrence of Bacteroidales human, ruminant, and pig-specific fecal markers, E. coli O157:H7, and Shiga toxin (stx) genes present in E. coli, were measured. In addition, environmental and climatic variables (e.g., temperature, salinity, rainfall), as well as human and livestock population densities and land cover were quantified. Concentrations of FIB and Salmonella were correlated with each other, but the occurrence of host-specific Bacteroidales markers did not correlate with FIB or pathogens. FIB and Salmonella concentrations, as well as the occurrence of E. coli harboring stx genes, were positively associated with the fraction of the surrounding subwatershed that was urban, while the occurrence of E. coli O157:H7 was positively associated with the agricultural fraction. FIB and Salmonella concentrations were negatively correlated to salinity and temperature, and positively correlated to rainfall. Areal loading rates of FIB, Salmonella and E. coli O157:H7 to the coastal ocean were calculated for stream and river sites and varied with land cover, salinity, temperature, and rainfall. Results suggest that FIB and pathogen concentrations are influenced, in part, by their flux from the land, which is exacerbated during rainfall; once waterborne, bacterial persistence is affected by water temperature and salinity.