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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.

ABSTRACT: 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).

SUBMITTER: Weller DL

PROVIDER: S-EPMC8009603 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:There is a need for science-based tools to (i) help manage microbial produce safety hazards associated with preharvest surface water use, and (ii) facilitate comanagement of agroecosystems for competing stakeholder aims. To develop these tools an improved understanding of foodborne pathogen ecology in freshwater systems is needed. The purpose of this study was to identify (i) sources of potential food safety hazards, and (ii) combinations of factors associated with an increased likelihood of pathogen contamination of agricultural water Sixty-eight streams were sampled between April and October 2018 (196 samples). At each sampling event separate 10-L grab samples (GS) were collected and tested for Listeria, Salmonella, and the stx and eaeA genes. A 1-L GS was also collected and used for Escherichia coli enumeration and detection of four host-associated fecal source-tracking markers (FST). Regression analysis was used to identify individual factors that were significantly associated with pathogen detection. We found that eaeA-stx codetection [Odds Ratio (OR) = 4.2; 95% Confidence Interval (CI) = 1.3, 13.4] and Salmonella isolation (OR = 1.8; CI = 0.9, 3.5) were strongly associated with detection of ruminant and human FST markers, respectively, while Listeria spp. (excluding Listeria monocytogenes) was negatively associated with log10 E. coli levels (OR = 0.50; CI = 0.26, 0.96). L. monocytogenes isolation was not associated with the detection of any fecal indicators. This observation supports the current understanding that, unlike enteric pathogens, Listeria is not fecally-associated and instead originates from other environmental sources. Separately, conditional inference trees were used to identify scenarios associated with an elevated or reduced risk of pathogen contamination. Interestingly, while the likelihood of isolating L. monocytogenes appears to be driven by complex interactions between environmental factors, the likelihood of Salmonella isolation and eaeA-stx codetection were driven by physicochemical water quality (e.g., dissolved oxygen) and temperature, respectively. Overall, these models identify environmental conditions associated with an enhanced risk of pathogen presence in agricultural water (e.g., rain events were associated with L. monocytogenes isolation from samples collected downstream of dairy farms; P = 0.002). The information presented here will enable growers to comanage their operations to mitigate the produce safety risks associated with preharvest surface water use.

Project description:Podocytes are an important part of the glomerular filtration barrier and the key player in the development of proteinuria, which is an early feature of complement mediated renal diseases. Complement factors are mainly liver-born and present in circulation. Nevertheless, there is a growing body of evidence for additional sites of complement protein synthesis, including various cell types in the kidney. We hypothesized that podocytes are able to produce complement components and contribute to the local balance of complement activation and regulation. To investigate the relevant balance between inhibiting and activating sides, our studies focused on complement factor H (CFH), an important complement regulator, and on C3, the early key component for complement activation. We characterized human cultured podocytes for the expression and secretion of activating and regulating complement factors, and analyzed the secretion pathway and functional activity. We studied glomerular CFH and C3 expression in puromycin aminonucleoside (PAN) -treated rats, a model for proteinuria, and the physiological mRNA-expression of both factors in murine kidneys. We found, that C3 and CFH were expressed in cultured podocytes and expression levels differed from those in cultivated glomerular endothelial cells. The process of secretion in podocytes was stimulated with interferon gamma and located in the Golgi apparatus. Cultured podocytes could initiate the complement cascade by the splitting of C3, which can be shown by the generation of C3a, a functional C3 split product. C3 contributed to external complement activation. Podocyte-secreted CFH, in conjunction with factor I, was able to split C3b. Podocytes derived from a patient with a CFH mutation displayed impaired cell surface complement regulation. CFH and C3 were synthesized in podocytes of healthy C57Bl/6-mice and were upregulated in podocytes of PAN treated rats. These data show that podocytes produce functionally active complement components, and could therefore influence the local glomerular complement activation and regulation. This modulating effect should therefore be considered in all diseases where glomerular complement activation occurs. Furthermore, our data indicate a potential novel role of podocytes in the innate immune system.

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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.

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