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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:The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.

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.

Project description:UnlabelledBovine parvovirus (BPV), the causative agent of respiratory and gastrointestinal disease in cows, is the type member of the Bocaparvovirus genus of the Parvoviridae family. Toward efforts to obtain a template for the development of vaccines and small-molecule inhibitors for this pathogen, the structure of the BPV capsid, assembled from the major capsid viral protein 2 (VP2), was determined using X-ray crystallography as well as cryo-electron microscopy and three-dimensional image reconstruction (cryo-reconstruction) to 3.2- and 8.8-Å resolutions, respectively. The VP2 region ordered in the crystal structure, from residues 39 to 536, conserves the parvoviral eight-stranded jellyroll motif and an αA helix. The BPV capsid displays common parvovirus features: a channel at and depressions surrounding the 5-fold axes and protrusions surrounding the 3-fold axes. However, rather than a depression centered at the 2-fold axes, a raised surface loop divides this feature in BPV. Additional observed density in the capsid interior in the cryo-reconstructed map, compared to the crystal structure, is interpreted as 10 additional N-terminal residues, residues 29 to 38, that radially extend the channel under the 5-fold axis, as observed for human bocavirus 1 (HBoV1). Surface loops of various lengths and conformations extend from the core jellyroll motif of VP2. These loops confer the unique surface topology of the BPV capsid, making it strikingly different from HBoV1 as well as the type members of other Parvovirinae genera for which structures have been determined. For the type members, regions structurally analogous to those decorating the BPV capsid surface serve as determinants of receptor recognition, tissue and host tropism, pathogenicity, and antigenicity.ImportanceBovine parvovirus (BPV), identified in the 1960s in diarrheic calves, is the type member of the Bocaparvovirus genus of the nonenveloped, single-stranded DNA (ssDNA) Parvoviridae family. The recent isolation of human bocaparvoviruses from children with severe respiratory and gastrointestinal infections has generated interest in understanding the life cycle and pathogenesis of these emerging viruses. We have determined the high-resolution structure of the BPV capsid assembled from its predominant capsid protein VP2, known to be involved in a myriad of functions during host cell entry, pathogenesis, and antigenicity for other members of the Parvovirinae. Our results show the conservation of the core secondary structural elements and the location of the N-terminal residues for the known bocaparvovirus capsid structures. However, surface loops with high variability in sequence and conformation give BPV a unique capsid surface topology. Similar analogous regions in other Parvovirinae type members are important as determinants of receptor recognition, tissue and host tropism, pathogenicity, and antigenicity.

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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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