Prospective modeling and estimating the epidemiologically informative match rate within large foodborne pathogen genomic databases.

OBJECTIVES: Much has been written about the utility of genomic databases to public health. Within food safety these databases contain data from two types of isolates-those from patients (i.e., clinical) and those from non-clinical sources (e.g., a food manufacturing environment). A genetic match between isolates from these sources represents a signal of interest. We investigate the match rate within three large genomic databases (Listeria monocytogenes, Escherichia coli, and Salmonella) and the smaller Cronobacter database; the databases are part of the Pathogen Detection project at NCBI (National Center for Biotechnology Information). RESULTS: Currently, the match rate of clinical isolates to non-clinical isolates is 33% for L. monocytogenes, 46% for Salmonella, and 7% for E. coli. These match rates are associated with several database features including the diversity of the organism, the database size, and the proportion of non-clinical BioSamples. Modeling match rate via logistic regression showed relatively good performance. Our prediction model illustrates the importance of populating databases with non-clinical isolates to better identify a match for clinical samples. Such information should help public health officials prioritize surveillance strategies and show the critical need to populate fledgling databases (e.g., Cronobacter sakazakii).

SARS-CoV-2 wastewater variant surveillance: pandemic response leveraging FDA's GenomeTrakr network.

Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA's genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses. Version-controlled protocols for each step of the process were developed and published on protocols.io. A custom data analysis tool and a publicly accessible dashboard were built to facilitate real-time visualization of the collected data, focusing on the relative abundance of SARS-CoV-2 variants and sub-lineages across different samples and sites throughout the project. From September 2021 through June 2023, a total of 3,389 wastewater samples were collected, with 2,517 undergoing sequencing and submission to NCBI under the umbrella BioProject, PRJNA757291. Sequence data were released with explicit quality control (QC) tags on all sequence records, communicating our confidence in the quality of data. Variant analysis revealed wide circulation of Delta in the fall of 2021 and captured the sweep of Omicron and subsequent diversification of this lineage through the end of the sampling period. This project successfully achieved two important goals for the FDA's GenomeTrakr program: first, contributing timely genomic data for the SARS-CoV-2 pandemic response, and second, establishing both capacity and best practices for culture-independent, population-level environmental surveillance for other pathogens of interest to the FDA. IMPORTANCE: This paper serves two primary objectives. First, it summarizes the genomic and contextual data collected during a Covid-19 pandemic response project, which utilized the FDA's laboratory network, traditionally employed for sequencing foodborne pathogens, for sequencing SARS-CoV-2 from wastewater samples. Second, it outlines best practices for gathering and organizing population-level next generation sequencing (NGS) data collected for culture-free, surveillance of pathogens sourced from environmental samples.

Application of quasimetagenomics methods to define microbial diversity and subtype Listeria monocytogenes in dairy and seafood production facilities.

IMPORTANCE: In developed countries, the human diet is predominated by food commodities, which have been manufactured, processed, and stored in a food production facility. Little is known about the application of metagenomic sequencing approaches for detecting foodborne pathogens, such as L. monocytogenes, and characterizing microbial diversity in food production ecosystems. In this work, we investigated the utility of 16S rRNA amplicon and quasimetagenomic sequencing for the taxonomic and phylogenetic classification of Listeria culture enrichments of environmental swabs collected from dairy and seafood production facilities. We demonstrated that single-nucleotide polymorphism (SNP) analyses of L. monocytogenes metagenome-assembled genomes (MAGs) from quasimetagenomic data sets can achieve similar resolution as culture isolate whole-genome sequencing. To further understand the impact of genome coverage on MAG SNP cluster resolution, an in silico downsampling approach was employed to reduce the percentage of target pathogen sequence reads, providing an initial estimate of required MAG coverage for subtyping resolution of L. monocytogenes.

Multinational Outbreak of Listeria monocytogenes Infections Linked to Enoki Mushrooms Imported from the Republic of Korea 2016-2020.

Keeping the global food supply safe necessitates international collaborations between countries. Health and regulatory agencies routinely communicate during foodborne illness outbreaks, allowing partners to share investigational evidence. A 2016-2020 outbreak of Listeria monocytogenes infections linked to imported enoki mushrooms required a multinational collaborative investigation among the United States, Canada, Australia, and France. Ultimately, this outbreak included 48 ill people, 36 in the United States and 12 in Canada, and was linked to enoki mushrooms sourced from one manufacturer located in the Republic of Korea. Epidemiologic, laboratory, and traceback evidence led to multiple regulatory actions, including extensive voluntary recalls by three firms in the United States and one firm in Canada. In the United States and Canada, the Korean manufacturer was placed on import alert while other international partners provided information about their respective investigations and advised the public not to eat the recalled enoki mushrooms. The breadth of the geographic distribution of this outbreak emphasizes the global reach of the food industry. This investigation provides a powerful example of the impact of national and international coordination of efforts to respond to foodborne illness outbreaks and protect consumers. It also demonstrates the importance of fast international data sharing and collaboration in identifying and stopping foodborne outbreaks in the global community. Additionally, it is a meaningful example of the importance of food sampling, testing, and integration of sequencing results into surveillance databases.

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.

Assessment of plasmids for relating the 2020 Salmonella enterica serovar Newport onion outbreak to farms implicated by the outbreak investigation.

BACKGROUND: The Salmonella enterica serovar Newport red onion outbreak of 2020 was the largest foodborne outbreak of Salmonella in over a decade. The epidemiological investigation suggested two farms as the likely source of contamination. However, single nucleotide polymorphism (SNP) analysis of the whole genome sequencing data showed that none of the Salmonella isolates collected from the farm regions were linked to the clinical isolates-preventing the use of phylogenetics in source identification. Here, we explored an alternative method for analyzing the whole genome sequencing data driven by the hypothesis that if the outbreak strain had come from the farm regions, then the clinical isolates would disproportionately contain plasmids found in isolates from the farm regions due to horizontal transfer. RESULTS: SNP analysis confirmed that the clinical isolates formed a single, nearly-clonal clade with evidence for ancestry in California going back a decade. The clinical clade had a large core genome (4,399 genes) and a large and sparsely distributed accessory genome (2,577 genes, at least 64% on plasmids). At least 20 plasmid types occurred in the clinical clade, more than were found in the literature for Salmonella Newport. A small number of plasmids, 14 from 13 clinical isolates and 17 from 8 farm isolates, were found to be highly similar (> 95% identical)-indicating they might be related by horizontal transfer. Phylogenetic analysis was unable to determine the geographic origin, isolation source, or time of transfer of the plasmids, likely due to their promiscuous and transient nature. However, our resampling analysis suggested that observing a similar number and combination of highly similar plasmids in random samples of environmental Salmonella enterica within the NCBI Pathogen Detection database was unlikely, supporting a connection between the outbreak strain and the farms implicated by the epidemiological investigation. CONCLUSION: Horizontally transferred plasmids provided evidence for a connection between clinical isolates and the farms implicated as the source of the outbreak. Our case study suggests that such analyses might add a new dimension to source tracking investigations, but highlights the need for detailed and accurate metadata, more extensive environmental sampling, and a better understanding of plasmid molecular evolution.

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples.

Background: The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). Methods: We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Results: Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). Conclusion: This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

Transient and resident pathogens: Intra-facility genetic diversity of Listeria monocytogenes and Salmonella from food production environments.

Food production facilities are often routinely tested over time for the presence of foodborne pathogens (e.g., Listeria monocytogenes or Salmonella enterica subsp. enterica). Strains detected in a single sampling event can be classified as transient; positive findings of the same strain across multiple sampling events can be classified as resident pathogens. We analyzed whole-genome sequence (WGS) data from 4,758 isolates (L. monocytogenes = 3,685; Salmonella = 1,073) from environmental samples taken by FDA from 536 U.S. facilities. Our primary objective was to determine the frequency of transient or resident pathogens within food production facilities. Strains were defined as isolates from the same facility that are less than 50 SNP (single-nucleotide polymorphisms) different from one another. Resident pathogens were defined as strains that had more than one isolate collected >59 days apart and from the same facility. We found 1,076 strains (median = 1 and maximum = 21 strains per facility); 180 were resident pathogens, 659 were transient, and 237 came from facilities that had only been sampled once. As a result, 21% of strains (180/ 839) from facilities with positive findings and that were sampled multiple times were found to be resident pathogens; nearly 1 in 4 (23%) of L. monocytogenes strains were found to be resident pathogens compared to 1 in 6 (16%) of Salmonella strains. Our results emphasize the critical importance of preventing the colonization of food production environments by foodborne pathogens, since when colonization does occur, there is an appreciable chance it will become a resident pathogen that presents an ongoing potential to contaminate product.

Using Evolutionary Analyses to Refine Whole-Genome Sequence Match Criteria.

Whole-genome sequence databases continue to grow. Collection times between samples are also growing, providing both a challenge for comparing recently collected sequence data to historical samples and an opportunity for evolutionary analyses that can be used to refine match criteria. We measured evolutionary rates for 22 Salmonella enterica serotypes. Based upon these measurements, we propose using an evolutionary rate of 1.97 single-nucleotide polymorphisms (SNPs) per year when determining whether genome sequences match.

Genomic analysis of Listeria monocytogenes from US food processing environments reveals a high prevalence of QAC efflux genes but limited evidence of their contribution to environmental persistence.

BACKGROUND: Quaternary ammonium compound (QAC) efflux genes increase the minimum inhibitory concentration of Listeria monocytogenes (Lm) to benzalkonium chloride sanitizer, but the contribution of these genes to persistence in food processing environments is unclear. The goal of this study was to leverage genomic data and associated metadata for 4969 Lm isolates collected between 1999 and 2019 to: (1) evaluate the prevalence of QAC efflux genes among Lm isolates from diverse US food processors, (2) use comparative genomic analyses to assess confounding factors, such as clonal complex identity and stress tolerance genotypes, and (3) identify patterns in QAC efflux gene gain and loss among persistent clones within specific facilities over time. RESULTS: The QAC efflux gene cassette bcrABC was present in nearly half (46%) of all isolates. QAC efflux gene prevalence among isolates was associated with clonal complex (𝛘2 < 0.001) and clonal complex was associated with the facility type (𝛘2 < 0.001). Consequently, changes in the prevalence of QAC efflux genes within individual facilities were generally attributable to changes in the prevalence of specific clonal complexes. Additionally, a GWAS and targeted BLAST search revealed that clonal complexes with a high prevalence of QAC efflux genes commonly possessed other stress tolerance genes. For example, a high prevalence of bcrABC in a clonal complex was significantly associated with the presence of the SSI-1 gene cluster (p < 0.05). QAC efflux gene gain and loss were both observed among persistent populations of Lm in individual facilities, suggesting a limited direct role for QAC efflux genes as predictors of persistence. CONCLUSION: This study suggests that although there is evidence that QAC efflux genes are part of a suite of adaptations common among Lm isolated from some food production environments, these genes may be neither sufficient nor necessary to enhance persistence. This is a crucial distinction for decision making in the food industry. For example, changes to sanitizer regimen targeting QAC tolerance would not address other contributing genetic or non-genetic factors, such as equipment hygienic design which physically mediates sanitizer exposure.

Genomic evidence of environmental and resident Salmonella Senftenberg and Montevideo contamination in the pistachio supply-chain.

Pistachios have been implicated in two salmonellosis outbreaks and multiple recalls in the U.S. This study performed an in-depth retrospective data analysis of Salmonella associated with pistachios as well as a storage study to evaluate the survivability of Salmonella on inoculated inshell pistachios to further understand the genetics and microbiological dynamics of this commodity-pathogen pair. The retrospective data analysis on isolates associated with pistachios was performed utilizing short-read and long-read sequencing technologies. The sequence data were analyzed using two methods: the FDA's Center for Food Safety and Applied Nutrition Single Nucleotide Polymorphism (SNP) analysis and Whole Genome Multilocus Sequence Typing (wgMLST). The year-long storage study evaluated the survival of five strains of Salmonella on pistachios stored at 25 °C at 35% and 54% relative humidity (RH). Our results demonstrate: i) evidence of persistent Salmonella Senftenberg and Salmonella Montevideo strains in pistachio environments, some of which may be due to clonal resident strains and some of which may be due to preharvest contamination; ii) presence of the Copper Homeostasis and Silver Resistance Island (CHASRI) in Salmonella Senftenberg and Montevideo strains in the pistachio supply chain; and iii) the use of metagenomic analysis is a novel tool for determining the composition of serovar survival in a cocktail inoculated storage study.

Microbiome Population Dynamics of Cold-Smoked Sockeye Salmon during Refrigerated Storage and after Culture Enrichment.

ABSTRACT: Cold-smoked salmon is a ready-to-eat seafood product of high commercial importance. The processing and storage steps facilitate the introduction, growth, and persistence of foodborne pathogens and spoilage bacteria. The growth of commensal bacteria during storage and once the product is opened also influence the quality and safety of cold-smoked salmon. Here we investigated the microbial community through targeted 16S rRNA gene and shotgun metagenomic sequencing as means to better understand the interactions among bacteria in cold-smoked salmon. Cold-smoked salmon samples were tested over 30 days of aerobic storage at 4°C and cultured at each time point in a buffered Listeria enrichment broth (BLEB) commonly used to detect Listeria in foods. The microbiomes were composed of Firmicutes and Proteobacteria, namely, Carnobacterium, Brochothrix, Pseudomonas, Serratia, and Psychrobacter. Pseudomonas species were the most diverse species, with 181 taxa identified. In addition, we identified potential homologs to 10 classes of bacteriocins in microbiomes of cold-smoked salmon stored at 4°C and corresponding BLEB culture enrichments. The findings presented here contribute to our understanding of microbiome population dynamics in cold-smoked salmon, including changes in bacterial taxa during aerobic cold storage and after culture enrichment. This may facilitate improvements to pathogen detection and quality preservation of this food.

Evaluation of Virulence Determinants Using Whole-Genome Sequencing and Phenotypic Biofilm Analysis of Outbreak-Linked Staphylococcus aureus Isolates.

Biofilms are a frequent cause of food contamination of potentially pathogenic bacteria, such as Staphylococcus aureus. Given its vast role in human disease, the possible impact of biofilm-producing S. aureus isolates in a food processing environment is evident. Sixty-nine S. aureus isolates collected from one firm following multiple staphylococcal food poisoning outbreak investigations were utilized for this analysis. Strain evaluations were performed to establish virulence determinants and the evolutionary relationships using data generated by shotgun whole-genome sequencing (WGS), along with end point polymerase chain reaction (PCR) and in vitro phenotypic assessments. S. aureus isolates were grouped into six well-supported clades in the phylogenetic tree, with the relationships within the clades indicating a strong degree of clonal structure. Our analysis identified four major sequence types 47.8% ST1, 31.9% ST45, 7.2% ST5, and 7.2% ST30 and two major spa types 47.8% t127 and 29.0% t3783. Extrapolated staphylococcal enterotoxin (SE) analysis found that all isolates were positive for at least 1 of the 23 SEs and/or SE-like toxin genes. Enterotoxigenic assessments found that 93% of the isolates expressed a classical SE(A-E). SE gene concurrence was observed at 96.2%, based on PCR and WGS results. In total, 46 gene targets were distinguished. This included genes that encode for adhesion and biofilm synthesis such as clfA, clfB, bbp, ebpS, ica, bap and agr. Our evaluation found agr group III to be the most prevalent at 55%, followed by 35% for agr group I. All isolates harbored the complete intercellular adhesion operon that is recognized to contain genes responsible for the adhesion step of biofilm formation by encoding proteins involved in the syntheses of the biofilm matrix. Phenotypic characterization of biofilm formation was evaluated three times, with each test completed in triplicate and accomplished utilizing the microtiter plate method and Congo red agar (CRA). The microtiter plate results indicated moderate to high biofilm formation for 96% of the isolates, with 4% exhibiting weak to no biofilm development. CRA results yielded all positive to intermediate results. The potential to inadvertently transfer pathogenic bacteria from the environment into food products creates challenges to any firm and may result in adulterated food.

A Series of Papaya-Associated Salmonella Illness Outbreak Investigations in 2017 and 2019: A Focus on Traceback, Laboratory, and Collaborative Efforts.

ABSTRACT: In 2017 and 2019, five outbreaks of infections from multiple strains of Salmonella linked to the consumption of whole, fresh Maradol papayas were reported in the United States, resulting in 325 ill persons. Traceback, laboratory, and epidemiologic evidence indicated papayas as the likely vehicle for each of these outbreaks and identified the source of papayas. State and U.S. Food and Drug Administration (FDA) laboratories recovered Salmonella from papaya samples from various points of distribution, including at import entry, and conducted serotyping, pulsed-field gel electrophoresis, and phylogenetic analyses of whole genome sequencing data. Federal and state partners led traceback investigations to determine the source of papayas. Four different suppliers of papayas were linked by traceback and laboratory results to five separate outbreaks of Salmonella infections associated with papayas. In 2017, multiple states tested papaya samples collected at retail, and Maryland and Virginia investigators recovered strains of Salmonella associated with one outbreak. FDA collected 183 papaya samples in 2017, and 11 samples yielded 62 isolates of Salmonella. Eleven serotypes of Salmonella were recovered from FDA papaya samples, and nine serotypes were closely related genetically by pulsed-field gel electrophoresis and whole genome sequencing to clinical isolates of four outbreaks, including the outbreak associated with positive state sample results. Four farms in Mexico were identified, and their names were released to the general public, retailers, and foreign authorities. In 2019, FDA collected 119 papaya samples, three of which yielded Salmonella; none yielded the 2019 outbreak strain. Investigators determined that papayas of interest had been sourced from a single farm in Campeche, Mexico, through traceback. This information was used to protect public health through public guidance, recalls, and import alerts and helped FDA collaborate with Mexican regulatory partners to enhance the food safety requirements for papayas imported from Mexico.

Interpretative Labor and the Bane of Nonstandardized Metadata in Public Health Surveillance and Food Safety.

Open-source DNA sequence databases have long been touted as beneficial to public health, including the facilitation of earlier detection and response to infectious disease outbreaks. Of critical importance to harnessing these benefits is the metadata that describe general and other domain-specific attributes (eg, collection location, isolate type) of a sample. Unlike the sequence data, metadata are often incomplete and lack adherence to an international standard. Here, we describe the problem posed by such variable and incomplete metadata in terms of interpretative labor costs (the time and energy necessary to make sense of the signal in the genetic data) and the impact such metadata have on foodborne outbreak detection and response. Improving the quality of sequence-associated metadata would allow for earlier detection of emerging food safety hazards and allow faster response to foodborne outbreaks.

AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence.

Antimicrobial resistance (AMR) is a significant public health threat. With the rise of affordable whole genome sequencing, in silico approaches to assessing AMR gene content can be used to detect known resistance mechanisms and potentially identify novel mechanisms. To enable accurate assessment of AMR gene content, as part of a multi-agency collaboration, NCBI developed a comprehensive AMR gene database, the Bacterial Antimicrobial Resistance Reference Gene Database and the AMR gene detection tool AMRFinder. Here, we describe the expansion of the Reference Gene Database, now called the Reference Gene Catalog, to include putative acid, biocide, metal, stress resistance genes, in addition to virulence genes and species-specific point mutations. Genes and point mutations are classified by broad functions, as well as more detailed functions. As we have expanded both the functional repertoire of identified genes and functionality, NCBI released a new version of AMRFinder, known as AMRFinderPlus. This new tool allows users the option to utilize only the core set of AMR elements, or include stress response and virulence genes, too. AMRFinderPlus can detect acquired genes and point mutations in both protein and nucleotide sequence. In addition, the evidence used to identify the gene has been expanded to include whether nucleotide or protein sequence was used, its location in the contig, and presence of an internal stop codon. These database improvements and functional expansions will enable increased precision in identifying AMR genes, linking AMR genotypes and phenotypes, and determining possible relationships between AMR, virulence, and stress response.

Draft Genome Sequences of 62 Staphylococcus aureus Isolates Associated with Four Foodborne Outbreaks in the United States.

Staphylococcus aureus bacteria are ranked among the top five foodborne pathogens in the United States. Here, we report the draft genome sequences of 62 S. aureus isolates that originated from the manufacturing environment of an Illinois bakery and were associated with outbreaks between 2010 and 2011 in the United States.

Investigating the Distribution of Strains of Erwinia amylovora and Streptomycin Resistance in Apple Orchards in New York Using Clustered Regularly Interspaced Short Palindromic Repeat Profiles: A 6-Year Follow-Up.

Fire blight, caused by the bacterium Erwinia amylovora, is one of the most important diseases of apple. The antibiotic streptomycin is routinely used in the commercial apple industries of New York (NY) and New England to manage the disease. In 2002 and again, from 2011 to 2014, outbreaks of streptomycin resistance (SmR) were reported and investigated in NY. Motivated by new grower reports of control failures, we conducted a follow-up investigation of the distribution of SmR and E. amylovora strains for major apple production regions of NY over the last 6 years (2015 to 2020). Characterization of clustered regularly interspaced short palindromic repeat (CRISPR) profiles revealed that a few "cosmopolitan" strains were widely prevalent across regions, whereas many other "resident" strains were confined to one location. In addition, we uncovered novel CRISPR profile diversity in all investigated regions. SmR E. amylovora was detected only in a small area spanning two counties from 2017 to 2020 and was always associated with one CRISPR profile (41:23:38), which matched the profile of SmR E. amylovora, discovered in 2002. This suggests the original SmR E. amylovora was never fully eradicated and went undetected because of several seasons of low disease pressure in this region. Investigation of several representative isolates under controlled greenhouse conditions indicated significant differences in aggressiveness on 'Gala' apples. Potential implications of strain differences include the propensity of strains to become distributed across wide geographic regions and associated resistance management practices. Results from this work will directly influence sustainable fire blight management recommendations for commercial apple industries in NY state and other regions.

Evaluation of Avocados as a Possible Source of Listeria monocytogenes Infections, United States, 2016 to 2019.

ABSTRACT: Outbreaks of Listeria monocytogenes infections have historically been associated with contaminated deli meats, but recent outbreaks have been linked to produce. To date, avocados have not been identified as the source of any outbreaks of L. monocytogenes infections in the United States, but avocado samples have yielded strains that were closely related genetically to clinical L. monocytogenes isolates. To determine whether avocados have been a source of listeriosis, we conducted a retrospective review of epidemiological data for clinical isolates that were genetically related to isolates from avocados. Using a national database, we identified clusters containing clinical and at least one avocado isolate. We then selected clusters based upon isolation dates, cluster and composition size, and available food history data. For each cluster, we assessed (i) whether avocado consumption was higher among case patients in the cluster than among those with sporadic illnesses and (ii) whether the only food isolates within the cluster were from avocados. If both conditions were met, the link was considered "likely," if one condition was met the link was considered "possible," and if neither condition was met evidence was "limited." Five of 15 clusters met the criteria for assessment. Of these, two were classified as having "limited" evidence for a link to avocados, two as "possible," and one as "likely." For the cluster considered "likely," avocado consumption was significantly higher among case patients in the cluster compared with sporadic illnesses (odds ratio, 8.5; 95% confidence interval, 1.5 to 86.5). We identified three clusters that were likely or possibly linked to avocados, which suggests that avocados could be a source of listeriosis in the United States. Messaging on safe handling might be warranted for groups at higher risk, but further research is first needed to better characterize the ecology of pathogens on avocados and the likelihood of internalization of L. monocytogenes.