Research gaps and priorities for quantitative microbial risk assessment (QMRA).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Development of a novel machine learning-based weighted modeling approach to incorporate Salmonella enterica heterogeneity on a genetic scale in a dose-response modeling framework.

Estimating microbial dose-response is an important aspect of a food safety risk assessment. In recent years, there has been considerable interest to advance these models with potential incorporation of gene expression data. The aim of this study was to develop a novel machine learning model that considers the weights of expression of Salmonella genes that could be associated with illness, given exposure, in hosts. Here, an elastic net-based weighted Poisson regression method was proposed to identify Salmonella enterica genes that could be significantly associated with the illness response, irrespective of serovar. The best-fit elastic net model was obtained by 10-fold cross-validation. The best-fit elastic net model identified 33 gene expression-dose interaction terms that added to the predictability of the model. Of these, nine genes associated with Salmonella metabolism and virulence were found to be significant by the best-fit Poisson regression model (p < 0.05). This method could improve or redefine dose-response relationships for illness from relative proportions of significant genes from a microbial genetic dataset, which would help in refining endpoint and risk estimations.

Low prevalence of viable Toxoplasma gondii in fresh, unfrozen American pasture-raised pork and lamb from retail meat stores in the United States.

In a national survey of fresh, unfrozen, American pasture-raised lamb and pork, the prevalence of viable Toxoplasma gondii was determined in 1500 samples selected by random multistage sampling (750 pork, 750 lamb) obtained from 250 retail meat stores from 10 major geographic areas in the USA. Each sample consisted of a minimum of 500g of meat purchased from the retail meat case. To detect viable T. gondii, 50g meat samples of each of 1500 samples were bioassayed in mice. Viable T. gondii was isolated from 2 of 750 lamb samples (unweighted: 0.19%, 0.00-0.46%; weighted: 0.04%, 0.00-0.11%) and 1 of 750 pork samples (unweighted: 0.12%, 0.00-0.37%; weighted: 0.18%, 0.00-0.53%) samples. Overall, the prevalence of viable T. gondii in these retail meats was very low. Nevertheless, consumers, especially pregnant women, should be aware that they can acquire T. gondii infection from ingestion of undercooked meat. Cooking meat to an internal temperature of 66°C kills T. gondii.

Age-Associated Distribution of Antimicrobial-Resistant Salmonella enterica and Escherichia coli Isolated from Dairy Herds in Pennsylvania, 2013-2015.

Antimicrobial resistance has become a major global public health concern, and agricultural operations are often implicated as a source of resistant bacteria. This study characterized the prevalence of antimicrobial-resistant Salmonella enterica and Escherichia coli from a total of 443 manure composite samples from preweaned calves, postweaned calves, dry cows, and lactating cows from 80 dairy operations in Pennsylvania. A total of 1095 S. enterica and 2370 E. coli isolates were screened and tested for resistance to 14 antimicrobials on the National Antimicrobial Resistance Monitoring System Gram-negative (NARMS GN) panel. Salmonellae were isolated from 67% of dairy operations, and 99% of the isolates were pan-susceptible. Salmonella were isolated more frequently from lactating and dry cow samples than from pre- and postweaned calf samples. Overall, the most prevalent serotypes were Cerro, Montevideo, Kentucky, and Newport. E. coli were isolated from all the manure composite samples, and isolates were commonly resistant to tetracyclines, sulfonamides, and aminoglycosides. Resistance was detected more frequently in the E. coli isolates from pre- and postweaned calf samples than in isolates from dry and lactating cow samples (p < 0.05). Multidrug-resistant E. coli (i.e., resistant to >3 antimicrobial classes) were isolated from 66 farms (83%) with significantly greater prevalence in preweaned calves (p < 0.05) than in the older age groups. The blaCTX-M and blaCMY genes were detected in the cephalosporin-resistant E. coli from 4% and 35% of the farms, respectively. These findings indicate that dairy animals, especially the calf population, serve as significant reservoirs for antimicrobial-resistant bacteria. Additional research on the colonization and persistence of resistant E. coli in calves is warranted to identify potential avenues for mitigation.

A System Model for Understanding the Role of Animal Feces as a Route of Contamination of Leafy Greens before Harvest.

The majority of foodborne outbreaks in the United States associated with the consumption of leafy greens contaminated with Escherichia coli O157:H7 have been reported during the period of July to November. A dynamic system model consisting of subsystems and inputs to the system (soil, irrigation, cattle, wild pig, and rainfall) simulating a hypothetical farm was developed. The model assumed two crops of lettuce in a year and simulated planting, irrigation, harvesting, ground preparation for the new crop, contamination of soil and plants, and survival of E. coli O157:H7. As predicted by the baseline model for crops harvested in different months from conventional fields, an estimated 13 out of 257 (5.05%) first crops harvested in July would have at least one plant with at least 1 CFU of E. coli O157:H7. Predictions indicate that no first crops would be contaminated with at least 1 CFU of E. coli O157:H7 for other months (April to June). The maximum E. coli O157:H7 concentration in a plant was higher in the second crop (27.10 CFU) than in the first crop (9.82 CFU). For the second crop, the probabilities of having at least one plant with at least 1 CFU of E. coli O157:H7 in a crop were predicted as 15/228 (6.6%), 5/333 (1.5%), 14/324 (4.3%), and 6/115 (5.2%) in August, September, October, and November, respectively. For organic fields, the probabilities of having at least one plant with ≥1 CFU of E. coli O157:H7 in a crop (3.45%) were predicted to be higher than those for the conventional fields (2.15%). IMPORTANCE: This study is the first attempt toward developing a mathematical system model to understand the pathway of E. coli O157:H7 in the production of leafy greens. Results of the presented system model indicate that the seasonality of outbreaks of E. coli O157:H7-associated contamination of leafy greens was in good agreement with the prevalence of this pathogen in cattle and wild pig feces in a major leafy greens-producing region in California. On the basis of comparisons among the results of different scenarios, it can be recommended that the concentration of E. coli O157:H7 in leafy greens can be reduced considerably if contamination of soil with wild pig and cattle feces is mitigated.

Prevalence and genetic characterization of Toxoplasma gondii in free-range chickens from grocery stores and farms in Maryland, Ohio and Massachusetts, USA.

Chickens are considered important in the epidemiology of Toxoplasma gondii. Chicken hearts (n = 1185) obtained from grocery stores were tested for T. gondii infection. Antibodies to T. gondii were assayed in fluid removed from the heart cavity using the modified agglutination test (MAT) at 1:5, 1:25, and 1:100 dilutions. MAT antibodies were detected in 222 hearts at 1:5 dilution and 8 hearts at 1:25 dilution, but none were positive at 1:100 dilution. Seropositive (n = 230, 19.4%) chicken hearts were bioassayed in mice and seronegative (n = 157) chickens were bioassayed in cats. Viable T. gondii was not isolated from any hearts by bioassays in mice. The 2 cats fed 60 and 97 hearts did not excrete T. gondii oocysts. The results indicate a low prevalence of viable T. gondii in chickens from grocery stores. Molecular typing of 23 archived T. gondii strains isolated from free-range chickens from Ohio and Massachusetts using the 10 PCR-RFLP markers including SAG1, SAG2 (5'-3'SAG2 and altSAG2), SAG3, BTUB, GRA6, c22-8, c29-2, L358, PK1, and Apico revealed that seven were ToxoDB PCR-RFLP genotype #1, 11 were genotype #2, one was genotype #3, three were genotype #170, and one was mixed genotype. These results indicate that the clonal genotypes #1 (type II), #2 (type III), and #3 (type II variant) are common in free-range chickens.

Transmission of Bacterial Zoonotic Pathogens between Pets and Humans: The Role of Pet Food.

Recent Salmonella outbreaks associated with dry pet food and treats raised the level of concern for these products as vehicle of pathogen exposure for both pets and their owners. The need to characterize the microbiological and risk profiles of this class of products is currently not supported by sufficient specific data. This systematic review summarizes existing data on the main variables needed to support an ingredients-to-consumer quantitative risk model to (1) describe the microbial ecology of bacterial pathogens in the dry pet food production chain, (2) estimate pet exposure to pathogens through dry food consumption, and (3) assess human exposure and illness incidence due to contact with pet food and pets in the household. Risk models populated with the data here summarized will provide a tool to quantitatively address the emerging public health concerns associated with pet food and the effectiveness of mitigation measures. Results of such models can provide a basis for improvements in production processes, risk communication to consumers, and regulatory action.

Modeling the long-term kinetics of Salmonella survival on dry pet food.

Due to multiple outbreaks and large-scale product recalls, Salmonella has emerged as a priority pathogen in dry pet food and treats. However, little data are available to quantify risks posed by these classes of products to both pets and their owners. Specifically, the kinetics of Salmonella survival on complex pet food matrices are not available. This study measured the long-term kinetics of Salmonella survival on a dry pet food under storage conditions commonly encountered during production, retail, and in households (aw < 0.60, 23 °C). A Salmonella enterica cocktail of 12 strains isolated from dry pet foods and treats was used to inoculate commercial dry dog food. Salmonella was enumerated on non-selective (BHI) and selective (XLD and BS) media. Results at 570 days indicated an initial relatively rapid decline (up to 54 days), followed by a much slower extended decline phase. The Weibull model provided a satisfactory fit for time series of Log-transformed Salmonella counts from all three media (δ: mean 4.65 day/Log (CFU/g); p: mean 0.364 on BHI). This study provides a survival model that can be applied in quantitative risk assessment models.

Development of Dose-Response Models to Predict the Relationship for Human Toxoplasma gondii Infection Associated with Meat Consumption.

Toxoplasma gondii is a protozoan parasite that is responsible for approximately 24% of deaths attributed to foodborne pathogens in the United States. It is thought that a substantial portion of human T. gondii infections is acquired through the consumption of meats. The dose-response relationship for human exposures to T. gondii-infected meat is unknown because no human data are available. The goal of this study was to develop and validate dose-response models based on animal studies, and to compute scaling factors so that animal-derived models can predict T. gondii infection in humans. Relevant studies in literature were collected and appropriate studies were selected based on animal species, stage, genotype of T. gondii, and route of infection. Data were pooled and fitted to four sigmoidal-shaped mathematical models, and model parameters were estimated using maximum likelihood estimation. Data from a mouse study were selected to develop the dose-response relationship. Exponential and beta-Poisson models, which predicted similar responses, were selected as reasonable dose-response models based on their simplicity, biological plausibility, and goodness fit. A confidence interval of the parameter was determined by constructing 10,000 bootstrap samples. Scaling factors were computed by matching the predicted infection cases with the epidemiological data. Mouse-derived models were validated against data for the dose-infection relationship in rats. A human dose-response model was developed as P (d) = 1-exp (-0.0015 × 0.005 × d) or P (d) = 1-(1 + d × 0.003 / 582.414)(-1.479) . Both models predict the human response after consuming T. gondii-infected meats, and provide an enhanced risk characterization in a quantitative microbial risk assessment model for this pathogen.

A Systematic Meta-Analysis of Toxoplasma gondii Prevalence in Food Animals in the United States.

Toxoplasma gondii is a widely distributed protozoan parasite. The Centers for Disease Control and Prevention reported that T. gondii is one of three pathogens (along with Salmonella and Listeria), that together account for >70% of all deaths due to foodborne illness in the United States. Food animals are reservoirs for T. gondii and act as one of the sources for parasite transmission to humans. Based on limited population-based data, the Food and Agriculture Organization/World Health Organization estimated that approximately 22% of human T. gondii infections are meatborne. The objective of the current study was to conduct a systematic meta-analysis to provide a precise estimation of T. gondii infection prevalence in food animals produced in the United States. Four databases were searched to collect eligible studies. Prevalence was estimated in six animal categories (confinement-raised market pigs, confinement-raised sows, non-confinement-raised pigs, lamb, goats, and non-confinement-raised chickens) by a quality-effects model. A wide variation in prevalence was observed in each animal category. Animals raised outdoors or that have outdoor access had a higher prevalence as compared with animals raised indoors. T. gondii prevalence in non-confinement-raised pigs ranked the highest (31.0%) followed by goats (30.7%), non-confinement-raised chickens (24.1%), lambs (22.0%), confinement-raised sows (16.7%), and confinement-raised market pigs (5.6%). These results indicate that T. gondii-infected animals are a food safety concern. The computed prevalence can be used as an important input in quantitative microbial risk assessment models to further predict public health burden.

Dynamics of Escherichia coli Virulence Factors in Dairy Herds and Farm Environments in a Longitudinal Study in the United States.

Pathogenic Escherichia coli or its associated virulence factors have been frequently detected in dairy cow manure, milk, and dairy farm environments. However, it is unclear what the long-term dynamics of E. coli virulence factors are and which farm compartments act as reservoirs. This study assessed the occurrence and dynamics of four E. coli virulence factors (eae, stx1, stx2, and the gamma allele of the tir gene [γ-tir]) on three U.S. dairy farms. Fecal, manure, water, feed, milk, and milk filter samples were collected from 2004 to 2012. Virulence factors were measured by postenrichment quantitative PCR (qPCR). All factors were detected in most compartments on all farms. Fecal and manure samples showed the highest prevalence, up to 53% for stx and 21% for γ-tir in fecal samples and up to 84% for stx and 44% for γ-tir in manure. Prevalence was low in milk (up to 1.9% for stx and 0.7% for γ-tir). However, 35% of milk filters were positive for stx and 20% were positive for γ-tir. All factors were detected in feed and water. Factor prevalence and levels, expressed as qPCR cycle threshold categories, fluctuated significantly over time, with no clear seasonal signal independent from year-to-year variability. Levels were correlated between fecal and manure samples, and in some cases autocorrelated, but not between manure and milk filters. Shiga toxins were nearly ubiquitous, and 10 to 18% of the lactating cows were potential shedders of E. coli O157 at least once during their time in the herds. E. coli virulence factors appear to persist in many areas of the farms and therefore contribute to transmission dynamics.

A machine learning approach to identifying Salmonella stress response genes in isolates from poultry processing.

We explored the potential of machine learning to identify significant genes associated with Salmonella stress response during poultry processing using whole genome sequencing (WGS) data. The Salmonella isolates (n = 177) used in this study were obtained from various chicken sources (skin before chiller, chicken carcass before chiller, frozen chicken, and post-chill chicken carcass). Six machine learning algorithms (random forest, neural network, cost-sensitive learning, logit boost, and support vector machine linear and radial kernels) were trained on Salmonella WGS data, and model fit was assessed using standard evaluation metrics such as the area under the receiver operating characteristic (AUROC) curve and confusion matrix statistics. All models achieved high performances based on the AUROC metric, with logit boost showing the best performance with an AUROC score of 0.904, sensitivity of 0.889, and specificity of 0.920. The significant genes identified included ybtX, which encodes a Yersiniabactin-associated zinc transporter, and the transferase-encoding genes yccK and thiS. Additionally, genes coding for cold (cspA, cspD, and cspE) and heat shock (rpoH and rpoE) responses were identified. Other significant genes included those involved in lipopolysaccharide biosynthesis (irp1, waaD, rfc, and rfbX), DNA repair and replication (traI), biofilm formation (ccdA and fyuA), and cellular metabolism (irtA).

Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions.

Vibrio parahaemolyticus and Vibrio vulnificus are bacteria with a significant public health impact. Identifying factors impacting their presence and concentrations in food sources could enable the identification of significant risk factors and prevent incidences of foodborne illness. In recent years, machine learning has shown promise in modeling microbial presence based on prevalent external and internal variables, such as environmental variables and gene presence/absence, respectively, particularly with the generation and availability of large amounts and diverse sources of data. Such analyses can prove useful in predicting microbial behavior in food systems, particularly under the influence of the constant changes in environmental variables. In this study, we tested the efficacy of six machine learning regression models (random forest, support vector machine, elastic net, neural network, k-nearest neighbors, and extreme gradient boosting) in predicting the relationship between environmental variables and total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater and oysters. In general, environmental variables were found to be reliable predictors of total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater, and pathogenic V. parahaemolyticus in oysters (Acceptable Prediction Zone >70 %) when analyzed using our machine learning models. SHapley Additive exPlanations, which was used to identify variables influencing Vibrio concentrations, identified chlorophyll a content, seawater salinity, seawater temperature, and turbidity as influential variables. It is important to note that different strains were differentially impacted by the same environmental variable, indicating the need for further research to study the causes and potential mechanisms of these variations. In conclusion, environmental variables could be important predictors of Vibrio growth and behavior in seafood. Moreover, the models developed in this study could prove invaluable in assessing and managing the risks associated with V. parahaemolyticus and V. vulnificus, particularly in the face of a changing environment.

Linking microbial contamination to food spoilage and food waste: the role of smart packaging, spoilage risk assessments, and date labeling.

Ensuring a safe and adequate food supply is a cornerstone of human health and food security. However, a significant portion of the food produced for human consumption is wasted annually on a global scale. Reducing harvest and postharvest food waste, waste during food processing, as well as food waste at the consumer level, have been key objectives of improving and maintaining sustainability. These issues can range from damage during processing, handling, and transport, to the use of inappropriate or outdated systems, and storage and packaging-related issues. Microbial growth and (cross)contamination during harvest, processing, and packaging, which causes spoilage and safety issues in both fresh and packaged foods, is an overarching issue contributing to food waste. Microbial causes of food spoilage are typically bacterial or fungal in nature and can impact fresh, processed, and packaged foods. Moreover, spoilage can be influenced by the intrinsic factors of the food (water activity, pH), initial load of the microorganism and its interaction with the surrounding microflora, and external factors such as temperature abuse and food acidity, among others. Considering this multifaceted nature of the food system and the factors driving microbial spoilage, there is an immediate need for the use of novel approaches to predict and potentially prevent the occurrence of such spoilage to minimize food waste at the harvest, post-harvest, processing, and consumer levels. Quantitative microbial spoilage risk assessment (QMSRA) is a predictive framework that analyzes information on microbial behavior under the various conditions encountered within the food ecosystem, while employing a probabilistic approach to account for uncertainty and variability. Widespread adoption of the QMSRA approach could help in predicting and preventing the occurrence of spoilage along the food chain. Alternatively, the use of advanced packaging technologies would serve as a direct prevention strategy, potentially minimizing (cross)contamination and assuring the safe handling of foods, in order to reduce food waste at the post-harvest and retail stages. Finally, increasing transparency and consumer knowledge regarding food date labels, which typically are indicators of food quality rather than food safety, could also contribute to reduced food waste at the consumer level. The objective of this review is to highlight the impact of microbial spoilage and (cross)contamination events on food loss and waste. The review also discusses some novel methods to mitigate food spoilage and food loss and waste, and ensure the quality and safety of our food supply.

Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends.

Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The best-fit Elastic Net model (α = 0.50; λ = 2.18) identified 53 significant gene features. The final multi-variable Poisson regression model (χ2 = 5748.22; pseudo R2 = 0.669; probability > χ2 = 0) identified 127 significant predictor terms (p < 0.10), comprising 45 gene-only predictors, average temperature, average precipitation, and average snowfall, and 79 gene-meteorological interaction terms. The significant genes ranged in functionality from cellular signaling and transport, virulence, metabolism, and stress response, and included gene variables not considered as significant by the baseline model. This study presents a holistic approach towards evaluating multiple data sources (such as genomic and environmental data) to predict outbreak scale, which could help in revising the estimates for human health risk.

A Machine Learning Model for Food Source Attribution of Listeria monocytogenes.

Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.

Exploring the predictive capability of advanced machine learning in identifying severe disease phenotype in Salmonella enterica.

The past few years have seen a significant increase in availability of whole genome sequencing information, allowing for its incorporation in predictive modeling for foodborne pathogens to account for inter- and intra-species differences in their virulence. However, this is hindered by the inability of traditional statistical methods to analyze such large amounts of data compared to the number of observations/isolates. In this study, we have explored the applicability of machine learning (ML) models to predict the disease outcome, while identifying features that exert a significant effect on the prediction. This study was conducted on Salmonella enterica, a major foodborne pathogen with considerable inter- and intra-serovar variation. WGS of isolates obtained from various sources (i.e., human, chicken, and swine) were used as input in four machine learning models (logistic regression with ridge, random forest, support vector machine, and AdaBoost) to classify isolates based on disease severity (extraintestinal vs. gastrointestinal) in the host. The predictive performances of all models were tested with and without Elastic Net regularization to combat dimensionality issues. Elastic Net-regularized logistic regression model showed the best area under the receiver operating characteristic curve (AUC-ROC; 0.86) and outcome prediction accuracy (0.76). Additionally, genes coding for transcriptional regulation, acidic, oxidative, and anaerobic stress response, and antibiotic resistance were found to be significant predictors of disease severity. These genes, which were significantly associated with each outcome, could possibly be input in amended, gene-expression-specific predictive models to estimate virulence pattern-specific effect of Salmonella and other foodborne pathogens on human health.

Molecular epidemiology of Mycobacterium avium subsp. paratuberculosis in a longitudinal study of three dairy herds.

The objective of this study was to evaluate whether cows that were low shedders of Mycobacterium avium subsp. paratuberculosis were passively shedding or truly infected with M. avium subsp. paratuberculosis. We also investigated whether it is possible that these M. avium subsp. paratuberculosis-infected animals could have been infected as adults by contemporary high-shedding animals (supershedders). The M. avium subsp. paratuberculosis isolates were obtained from a longitudinal study of three dairy herds in the northeastern United States. Isolates were selected from fecal samples and tissues at slaughter from all animals that were culture positive at the same time that supershedders were present in the herds. Shedding levels (CFU of M. avium subsp. paratuberculosis/g of feces) for the animals at each culture-positive occasion were determined. Using a multilocus short-sequence-repeat technique, we found 15 different strains of M. avium subsp. paratuberculosis from a total of 142 isolates analyzed. Results indicated herd-specific infection patterns; there was a clonal infection in herd C, with 89% of isolates from animals sharing the same strain, whereas herds A and B showed several different strains infecting the animals at the same time. Tissues from 80% of cows with at least one positive fecal culture (other than supershedders) were culture positive, indicating a true M. avium subsp. paratuberculosis infection. The results of M. avium subsp. paratuberculosis strain typing and observed shedding levels showed that at least 50% of low shedders have the same strain as that of a contemporary supershedder. Results of this study suggest that in a dairy herd, more of the low-shedding cows are truly infected with M. avium subsp. paratuberculosis than are passively shedding M. avium subsp. paratuberculosis. The sharing of strains between low shedders and the contemporary supershedders suggests that low shedders may have been infected by environmental exposure of M. avium subsp. paratuberculosis.

Increased in vitro adherence and on-farm persistence of predominant and persistent Listeria monocytogenes strains in the milking system.

Dairy farms are a reservoir for Listeria monocytogenes, and the reduction of this pathogen at the farm level is important for reducing human exposure. The objectives of this research were to study the diversity of L. monocytogenes strains on a single dairy farm, assess strain dynamics within the farm, identify potential sources of L. monocytogenes in bulk tank milk and milk filters, and assess the adherence abilities of representative strains. A total of 248 L. monocytogenes isolates were analyzed by pulsed-field gel electrophoresis (PFGE). Combined AscI and ApaI restriction analysis yielded 40 PFGE types (strains). The most predominant strains were T (28.6%), D (22.6%), and F (14.9%). A high level of heterogeneity of strains among isolates from fecal (Simpson's index of diversity [SID] = 0.96) and environmental (SID = 0.96) samples was observed. A higher homogeneity of strains was observed among isolates from milk filters (SID = 0.71) and bulk tank milk (SID = 0.65). Six of 17 L. monocytogenes isolates (35.3%) were classified in an in vitro assay as having a "low adherence ability," 9 (52.9%) were classified as having a "medium adherence ability," and 2 (11.8%) were classified as having a "high adherence ability." The L. monocytogenes strains that were predominant and persistent showed significantly better adherence than did strains that were only sporadic, predominant, or persistent (P = 0.0006). Our results suggest that the milking system was exposed to several L. monocytogenes strains from different sources. Only 3 strains, however, were successful in persisting within the milking system, suggesting that some strains are more suitable to that particular ecological environment than others.