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Precise and rapid identification ofSalmonellaserotypes from suspect food matrices is critical for successful source attribution of illness outbreaks (Scallan et al., 2011). Currently, close to 3% of U.S. foodborneSalmonellaoutbreaks have been attributed to multiple Salmonellaserotypes (2.85%, 2000 - 2020) (CDC, 2022). Recent foodborne outbreaks that have been attributed to multipleSalmonellaserotypes force us to question whether these are rare events or if previous methods simply did not have the throughput to provide an accurate picture of the complex ecology that is connected to outbreak etiologies. (Hassan et al., 2019; FDA, 2021; Whitney et al., 2021).Anin-silicobenchmark dataset, comprising 29 uniqueSalmonella, 46 non-Salmonellabacterial and 10 viral genomes, was generated with varying read depths. For outbreak samples, analysis was performed on previously sequenced pre-enrichments and selective enrichments of papayas and peaches (fruits and leaves) that led to the identification of multiple serovars. Data analyses was performed using a custom-builtk-mertool, SeqSero2, Kallisto and bettercallsal.Thein-silicodataset analyzed with bettercallsal had accuracy, recall and specificity of 95%, 96% and 98 % respectively. In the papaya outbreak samples, bettercallsal identified multiple serovar presence in concordance with Bioplex assay results and the genome hits assigned to the samples areSalmonellaisolates from the papaya outbreak as evident by NCBI SNP cluster information. In peach outbreak samples, bettercallsal identified both the serovars (Alachua and Gaminara) in concordance withk-mer analysis and the Luminex xMap assay. bettercallsal outperformedk-mer, Kallisto and Seqsero2 in identifying multiple serovars from enrichment cultures using shotgun metagenomics sequencing.MostSalmonellasubtyping work has relied upon WGS methods which focuses on the high-resolution analysis of single genomes, or multiple single genomes picked from colonies on agar. Here we introduce laboratory and bioinformatics innovations for a metagenomic outbreak response workflow that accurately identifies multipleSalmonellaserovars at the same time in a much higher throughput approach. bettercallsal is one of the first analysis tools that can potentially identify multipleSalmonellaspp. serotypes from a metagenomic or quasi-metagenomic datasets with accuracy and can provide early insights into the etiology of the sample.

This multiagency report developed by the Interagency Collaboration for Genomics for Food and Feed Safety provides an overview of the use of and transition to whole genome sequencing (WGS) technology for detection and characterization of pathogens transmitted commonly by food and for identification of their sources. We describe foodborne pathogen analysis, investigation, and harmonization efforts among the following federal agencies: National Institutes of Health; Department of Health and Human Services, Centers for Disease Control and Prevention (CDC) and U.S. Food and Drug Administration (FDA); and the U.S. Department of Agriculture, Food Safety and Inspection Service, Agricultural Research Service, and Animal and Plant Health Inspection Service. We describe single nucleotide polymorphism, core-genome, and whole genome multilocus sequence typing data analysis methods as used in the PulseNet (CDC) and GenomeTrakr (FDA) networks, underscoring the complementary nature of the results for linking genetically related foodborne pathogens during outbreak investigations while allowing flexibility to meet the specific needs of Interagency Collaboration partners. We highlight how we apply WGS to pathogen characterization (virulence and antimicrobial resistance profiles) and source attribution efforts and increase transparency by making the sequences and other data publicly available through the National Center for Biotechnology Information. We also highlight the impact of current trends in the use of culture-independent diagnostic tests for human diagnostic testing on analytical approaches related to food safety and what is next for the use of WGS in the area of food safety.

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.

Microorganisms frequently colonize surfaces and equipment within food production facilities.Listeria monocytogenesis a ubiquitous foodborne pathogen widely distributed in food production environments and is the target of numerous control and prevention procedures. Detection ofL. monocytogenesin a food production setting requires culture dependent methods, but the complex dynamics of bacterial interactions within these environments and their impact on pathogen detection remains largely unexplored. To address this challenge, we applied both 16S rRNA and shotgun quasimetagenomic (enriched microbiome) sequencing of swab culture enrichments from seafood and dairy production environments. Utilizing 16S rRNA amplicon sequencing, we observed variability between samples taken from different production facilities and a distinctive microbiome for each environment. With shotgun quasimetagenomic sequencing, we were able to assembleL. monocytogenesmetagenome assembled genomes (MAGs) and compare these MAGSs to their previously sequenced whole genome sequencing (WGS) assemblies, which resulted in two polyphyletic clades (lineages I and II). Using these same datasets together within silicodownsampling to produce a titration series of proportional abundances ofL. monocytogenes, we were able to begin to establish limits forListeriadetection and subtyping using shotgun quasimetagenomics. This study contributes to the understanding of microbial diversity within food production environments and presents insights into how many reads or relative abundance is needed in a metagenome sequencing dataset to detect, subtype, and source track at a SNP level, as well as providing an important foundation for utilizing metagenomics to mitigate unfavorable occurrences along the farm to fork continuum.IMPORTANCEIn developed countries, the human diet is predominantly food commodities, which have been manufactured, processed, and stored in a food production facility. It is well known that the pathogenListeria monocytogenesis frequently isolated from food production facilities and can cause serious illness to susceptible populations. Multistate outbreaks ofL. monocytogenesover the last 10 years have been attributed to food commodities manufactured and processed in production facilities, especially those dealing with dairy products such as cheese and ice cream. A myriad of recalls due to possibleL. monocytogenescontamination have also been issued for seafood commodities originating from production facilities. It is critical to public health that the means of growth, survival and spread ofListeriain food production ecosystems is investigated with developing technologies, such as 16S rRNA and quasimetagenomic sequencing, to aid in the development of effective control methods.

The use of DNA-based methods to authenticate botanical dietary supplements has been vigorously debated for a variety of reasons. More comparisons of DNA-based and chemical methods are needed, and concordant evaluation of orthogonal approaches on the same products will provide data to better understand the strengths and weaknesses of both approaches. The overall application of DNA-based methods is already firmly integrated into a wide array of continually modernizing stand alone and complementary authentication protocols. Recently, the use of full-length chloroplast genome sequences provided enhanced discriminatory capacity for closely related species of Echinacea compared to traditional DNA barcoding approaches (matK and rbcL). Here, two next-generation sequencing approaches were used: (1) genome skimming and (2) PCR amplicon (metabarcoding). The two genetic approaches were then combined with HPLC-UV to evaluate 20 commercially available dietary supplements of Echinacea representing “finished” products. The trade-offs involved in different DNA approaches were discussed, with a focus on how DNA methods support existing, accepted chemical methods. In most of the products (19/20), HPLC-UV suggested the presence of Echinacea spp. While metabarcoding was not useful with this genus and instead only resolved 7 products to the family level, genome skimming was able to resolve to species (9) or genus (1) with the 10/20 products where it was successful. Additional ingredients that HPLC-UV was unable to identify were also found in four products along with the relative sequence proportion of the constituents. Additionally, genome skimming was able to identify one product that was a different Echinacea species entirely.

The National Antimicrobial Resistance Monitoring System (NARMS) has monitored antimicrobial resistance (AMR) associated with pathogens of humans and animals since 1996. In alignment with One Health strategic planning, NARMS is currently exploring the inclusion of surface waters as an environmental modality for monitoring AMR. From a One Health perspective, surface waters function as key environmental integrators between humans, animals, agriculture, and the environment. Surface waters however, due to their dilute nature present a unique challenge for monitoring critically important antimicrobial resistance. Selective enrichments from water paired with genomic sequencing effectively describe AMR for single genomes but do not provide data to describe a broader environmental resistome. Metagenomic data effectively describe a broad range of AMR from certain matrices however, depth of coverage is usually insufficient to describe clinically significant AMR from aquatic matrices. Thus, the coupling of biological enrichments of surface water with shotgun NGS sequencing has been shown to greatly enhance the capacity to report an expansive profile of clinically significant antimicrobial resistance genes. Here we demonstrate, using water samples from distinct sites (a creek in close proximity to a hospital and a reservoir used for recreation and municipal water), that the AMR portfolio provided by enriched (quasimetagenomic) data is capable of describing almost 30% of NARMS surveillance targets contrasted to only 1% by metagenomic data. Additionally, the quasimetagenomic data supported reporting of statistically significant (P< 0.05) differential abundance of specific AMR genes between sites. A single time-point for two sites is a small pilot, but the robust results describing critically important AMR determinants from each water source, provide proof of concept that quasimetagenomics can be applied to aquatic AMR surveillance efforts for local, national, and global monitoring.

Background Full chloroplast genomes provide high resolution taxonomic discrimination between closely related plant species and are quickly replacing single and multi-locus barcoding regions as reference materials of choice for DNA based taxonomic annotation of plants. Bixa orellana, commonly known as “achiote” and “annatto” is a plant used for both human and animal foods and was thus identified for full chloroplast sequencing for the Center for Veterinary Medicine (CVM) Complete Chloroplast Animal Feed database. This work was conducted in collaboration with the Instituto de Medicina Tradicional (IMET) in Iquitos, Peru. There is a wide range of color variation in pods of Bixa orellana for which genetic loci that distinguish phenotypes have not yet been identified. Here we apply whole chloroplast genome sequencing of “red” and “yellow” individuals of Bixa orellana to provide high quality reference genomes to support kmer database development for use identifying this plant from complex mixtures using shotgun data. Additionally, we describe chloroplast gene content, synteny and phylogeny, and identify an indel and snp that may be associated with seed pod color. Results Fully assembled chloroplast genomes were produced for both red and yellow Bixa orellana accessions (158,918 and 158,823 bp respectively). Synteny and gene content was identical to the only other previously reported full chloroplast genome of Bixa orellana (NC_041550). We observed a 17 base pair deletion at position 58,399–58,415 in both accessions, relative to NC_041550 and a 6 bp deletion at position 75,531–75,526 and a snp at position 86,493 in red Bixa orellana. Conclusions Our data provide high quality reference genomes of individuals of red and yellow Bixa orellana to support kmer based identity markers for use with shotgun sequencing approaches for rapid, precise identification of Bixa orellana from complex mixtures. Kmer based phylogeny of full chloroplast genomes supports monophylly of Bixaceae consistent with alignment based approaches. A potentially discriminatory indel and snp were identified that may be correlated with the red phenotype.

Understanding weather-related drivers of crop plant-microbiome relationships is important for food security and food safety in the face of a changing climate. Cucumber and tomato are commercially important commodities that are susceptible to plant disease and have been implicated in foodborne disease outbreaks. To investigate the influence of precipitation on plant-associated microbiomes, epiphytically associated bacterial communities of cucumber and tomato samples were profiled by 16 S rRNA gene sequencing (V1-V3) in the days surrounding two rain events over a 17-day period. Following rain, α (within-sample) diversity measured on cucumber and tomato fruit surfaces, but not tomato leaf surfaces, increased significantly and remained elevated for several days. Bacterial β (between-sample) diversity on cucumber and tomato fruit responded to precipitation. In the cucumber fruit surface (carpoplane), notable shifts in the families Xanthomonadaceae, Oxalobacteriaceae, Sphingobacteriaceae and Comamonadaceae were detected following precipitation. In the tomato carpoplane, shifts were detected in the families Enterobacteriaceae and Xanthomonadaceae following the first rain event, and in the Pseudomonadaceae and Oxalobacteriaceae following the second rain event. Few taxonomic shifts were detected in the tomato leaf surface (phylloplane). Exploring rain-induced shifts in plant microbiomes is highly relevant to crop protection, food safety and agroecology, and can aid in devising ways to enhance crop resilience to stresses and climate fluctuations.

Background The more quickly bacterial pathogens responsible for foodborne illness outbreaks can be linked to a vehicle of transmission or a source, the more illnesses can be prevented. Whole genome sequencing (WGS) based approaches to source tracking have greatly increased the speed and resolution with which public health response can pinpoint the vehicle and source of outbreaks. Traditionally, WGS approaches have focused on the culture of an individual isolate before proceeding to DNA extraction and sequencing. For Listeria monocytogenes (Lm), generation of an individual isolate for sequencing typically takes about 6 days. Here we demonstrate that a hybrid, “quasimetagenomic” approach ie; direct sequencing of microbiological enrichments (first step in pathogen detection and recovery) can provide high resolution source tracking sequence data, 5 days earlier than response that focuses on culture and sequencing of an individual isolate. This expedited approach could save lives, prevent illnesses and potentially minimize unnecessary destruction of food. Methods Naturally contaminated ice cream (from a 2015 outbreak) was enriched to recover Listeria monocytogenes following protocols outlined in the Bacteriological Analytic Manual (BAM). DNA from enriching microbiota was extracted and sequenced at incremental time-points during the first 48 h of pre-enrichment using the Illumina MiSeq platform (2 by 250), to evaluate genomic coverage of target pathogen, Listeria monocytogenes. Results Quasimetagenomic sequence data acquired from hour 20 were sufficient to discern whether or not Lm strain/s were part of the ongoing outbreak or not. Genomic data from hours 24, 28, 32, 36, 40, 44, and 48 of pre-enrichments all provided identical phylogenetic source tracking utility to the WGS of individual isolates (which require an additional 5 days to culture). Conclusions The speed of this approach (more than twice as fast as current methods) has the potential to reduce the number of illnesses associated with any given outbreak by as many as 75% percent of total cases and potentially with continued optimization of the entire chain of response, contribute to minimized food waste.

BackgroundMicrobial eukaryotes are nearly ubiquitous in microbiomes on Earth and contribute to many integral ecological functions. Metagenomics is a proven tool for studying the microbial diversity, functions, and ecology of microbiomes, but has been underutilized for microeukaryotes due to the computational challenges they present. For taxonomic classification, the use of a eukaryotic marker gene database can improve the computational efficiency, precision and sensitivity. However, state-of-the-art tools which use marker gene databases implement universal thresholds for classification rather than dynamically learning the thresholds from the database structure, impacting the accuracy of the classification process.ResultsHere we introduce taxaTarget, a method for the taxonomic classification of microeukaryotes in metagenomic data. Using a database of eukaryotic marker genes and a supervised learning approach for training, we learned the discriminatory power and classification thresholds for each 20 amino acid region of each marker gene in our database. This approach provided improved sensitivity and precision compared to other state-of-the-art approaches, with rapid runtimes and low memory usage. Additionally, taxaTarget was better able to detect the presence of multiple closely related species as well as species with no representative sequences in the database. One of the greatest challenges faced during the development of taxaTarget was the general sparsity of available sequences for microeukaryotes. Several algorithms were implemented, including threshold padding, which effectively handled the missing training data and reduced classification errors. Using taxaTarget on metagenomes from human fecal microbiomes, a broader range of genera were detected, including multiple parasites that the other tested tools missed.ConclusionData-driven methods for learning classification thresholds from the structure of an input database can provide granular information about the discriminatory power of the sequences and improve the sensitivity and precision of classification. These methods will help facilitate a more comprehensive analysis of metagenomic data and expand our knowledge about the diverse eukaryotes in microbial communities.

Wild birds are common reservoirs of Salmonella enterica. Wild birds carrying resistant S. enterica may pose a risk to public health as they can spread the resistant bacteria across large spatial scales within a short time. Here, we whole-genome sequenced 375 S. enterica strains from wild birds collected in 41 U.S. states during 1978-2019 to examine bacterial resistance to antibiotics and heavy metals. We found that Typhimurium was the dominant S. enterica serovar, accounting for 68.3% (256/375) of the bird isolates. Furthermore, the proportions of the isolates identified as multi-antimicrobial resistant (multi-AMR: resistant to at least three antimicrobial classes) or multi-heavy metal resistant (multi-HMR: resistant to at least three heavy metals) were both 1.87% (7/375). Interestingly, all the multi-resistant S. enterica (n = 12) were isolated from water birds or raptors; none of them was isolated from songbirds. Plasmid profiling demonstrated that 75% (9/12) of the multi-resistant strains carried resistance plasmids. Our study indicates that wild birds do not serve as important reservoirs of multi-resistant S. enterica strains. Nonetheless, continuous surveillance for bacterial resistance in wild birds is necessary because the multi-resistant isolates identified in this study also showed close genetic relatedness with those from humans and domestic animals.

The backflush procedure pushes out all the cells and particles that were captured during the pumping procedure. It is most easily carried out in the lab with established (pump holding equipment) or ad hoc installation of pump, flushing material , filter and a >500 sterile vessel to collect the backflush.

QIAGEN DNeasy PowerWater SOP Purpose: For the isolation of genomic DNA from filter water samples, inculding turbid water Introduction: The DNeasy PowerWater Kit can isolate genomic DNA from a variety of filtered water samples. Utilizing Inhibitor Removal Technology® (IRT), even water containing heavy amounts of contaminants can be processed to provide DNA of high quality and yield. The DNeasy PowerWater Kit can isolate DNA equally well from any commonly used type of filter membrane. Purified DNA is ready to use in a final elution volume of 100 μl. Principle & Procedure: The DNeasy PowerWater Kit starts with the filtration of a water sample onto a filter membrane. Filter membranes may be user supplied. The membrane is then added to our special 5 ml bead beating tube containing a unique bead mix. Rapid and thorough lysis occurs through vortexing in a specially formulated lysis buffer that enhances the isolation of microorganisms from filter membranes. After the protein and inhibitor removal steps, total genomic DNA is captured on an MB Spin Column. High-quality DNA is then washed and eluted from the MB Spin Column membrane for use in downstream applications including PCR and qPCR.

Dead end ultrafiltration is the most basic form of filtration. In this case a Rexeed #25S Filter is used with a peristaltic pump to pull volumes from 10L to 100 L through nanometer sized pores ( approximately 30 kDa). This size selection is perfect for capture of viruses, bacteria, protists and even larger particles and organisms. The large collection volumes made possible by this method have great value if there is a need to examine rare members of a community such as pathogens.

Irrigation water, particularly if applied overhead, could be an important source of bacterial contamination to fresh produce. The colonization, survival, and proliferation of exogenous bacterial pathogens can be strongly influenced by the produce microbiota. In this study, spinach grown in an organic field was irrigated with ground water and potential alternative irrigation water including reclaimed wastewater, and urban runoff water, over a period of 2 weeks. Water and spinach samples were collected before and after irrigation for bacterial plate count, qPCR, and community profiling using 16S rDNA high-throughput sequencing analyses. The average bacterial population densities on spinach (6.50 ± 0.04 log CFU/g, 7.40 ± 0.10 log 16S copies/g) were significantly higher than those in irrigation water (3.61 ± 0.12 log CFU/ml, 4.94 ± 0.13 log 16S copies/ml). The composition and relative abundance of spinach microbiomes varied with different types of irrigation waters; however, the most abundant microbial taxa on spinach were not significantly affected by the irrigation with different types of water. Shigella, Salmonella, Listeria, Campylobacter spp., and pathogenic Escherichia coli were not detected in this study. This study provides information on the microbial ecology of diverse bacterial communities on spinach surface after irrigation by different types of water, which can benefit future studies on the interaction of microbes on produce, and the prevention of foodborne pathogens and plant disease.

Fire blight, caused by the bacterium

Despite recent advances in Salmonella-sprout research, little is known about the relationship between Salmonella and the sprout microbiome during sprouting. Sprout spent irrigation water (SSIW) provides an informative representation of the total microbiome of this primarily aquaponic crop. This study was designed to characterize the function and taxonomy of the most actively transcribed genes in SSIW from Salmonella enterica serovar Cubana-contaminated alfalfa seeds throughout the sprouting process. Genomic DNA and total RNA from SSIW was collected at regular intervals and sequenced using Illumina MiSeq and NextSeq platforms. Nucleic acid data were annotated using four different pipelines. Both metagenomic and metatranscriptomic analyses revealed a diverse and highly dynamic SSIW microbiome. A “core” SSIW microbiome comprised Klebsiella, Enterobacter, Pantoea, and Cronobacter. The impact, however, of Salmonella contamination on alfalfa seeds influenced SSIW microbial community dynamics not only structurally but also functionally. Changes in genes associated with metabolism, genetic information processing, environmental information processing, and cellular processes were abundant and time dependent. At time points of 24 h, 48 h, and 96 h, totals of 541, 723, and 424 S. Cubana genes, respectively, were transcribed at either higher or lower levels than at 0 h in SSIW during sprouting. An array of S. Cubana genes (107) were induced at all three time points, including genes involved in biofilm formation and modulation, stress responses, and virulence and tolerance to antimicrobials. Taken together, these findings expand our understanding of the effect of Salmonella seed contamination on the sprout crop microbiome and metabolome. IMPORTANCE Interactions of human enteric pathogens like Salmonella with plants and plant microbiomes remain to be elucidated. The rapid development of next-generation sequencing technologies provides powerful tools enabling investigation of such interactions from broader and deeper perspectives. Using metagenomic and metatranscriptomic approaches, this study identified not only changes in microbiome structure of SSIW associated with sprouting but also changes in the gene expression patterns related to the sprouting process in response to Salmonella contamination of alfalfa seeds. This study advances our knowledge on Salmonella-plant (i.e., sprout) interaction.

Sprouts have been implicated in numerous foodborne illness outbreaks. To better understand baseline microbial profiles of irrigation water and subsequent spent irrigation water of alfalfa sprouts, DNA from water was extracted, sequenced, and annotated with CosmosID and a custom pipeline to provide bacterial, fungal, protist, and antimicrobial resistance gene profiles.

Background: Rapid pathogen detection is essential for an effective public health response. The Illumina MiSeq short read sequencer has been the workhorse of many whole genome sequencing source tracking programs. However, short reads cannot span many bacterial genomic repeats, resulting in highly fragmented assemblies. Long read sequencing can span the repeats resulting in the complete assembly of bacterial genomes. With the advancing throughput and accuracy of inexpensive long read sequencing data, it is important to evaluate this resource for potential inclusion in rapid response pathogen identification protocols.Results: Here we sought to maximize the utility of long read (GridIon, Oxford Nanopore) and short read (MiSeq, Illumina) sequencing data for the identification of Listeria monocytogenes from naturally contaminated ice cream. Aliquots from the temporal enrichment (quasimetagenomes) of L. monocytogenes were sequenced and assembled with 10 different assembly approaches. Long read assembly tools generated genome-length contigs and a circularized 71 kbp putative L. monocytogenes plasmid; however, the high sequencing error rate prevented strain typing at even 150X depth of coverage. Short read assemblies provided accurate core gene strain typing after 28 hours of enrichment but were too fragmented for typing the full gene set and reconstructing a circularized genome and plasmid. Hybrid approaches demonstrated the most promising results but were biased by assembly strategy. Short read assemblies scaffolded with long reads were able to accurately strain type L. monocytogenes after just 24 hours of enrichment, but were still too fragmented for sequence typing the full gene set. Long read assemblies polished with short reads recovered genome-length contigs, the full complement of genes, and a circularized plasmid after 24 hours enrichment; however they could not consistently achieve the core gene strain typing accuracy of short read or short read hybrid assembly approaches. Conclusion: These analyses demonstrate that strategic integration and optimization of microbiological (quasimetagenomic), molecular (integrated long and short reads), and bioinformatic approaches (diverse assembly strategies) can greatly improve our ability to quickly and accurately identify pathogens.

BackgroundSeeds from Bixa orellana, commonly known as “achiote” and “annatto” produce bixin and norbixin apocarotenoids which impart bright red and orange colors that have been used for thousands of years for food, medicine and body painting by indigenous Americans, and by Europeans for ~ 500 years as food coloring, especially for cheeses. Use of Bixa colorants continues to grow as synthetic dyes come under increased scrutiny for toxicity to human and environmental systems. There is a wide range of color variation in pods of Bixa orellana for which genetic loci that delineate phenotypes have not yet been identified. Whole chloroplast genomes and raw genome skims provide a wide variety of genetic markers that can be used for identification purposes as well as phylogenetic inference of broad scale evolutionary relationships. Here we apply whole chloroplast genome sequencing of “red” and “yellow” individuals of Bixa orellana for phylogenetic analyses to explore the position of Bixaceae relative to other families within the Malvales as well as to underpin future work that may delineate diverse color phenotypes.ResultsFully assembled chloroplast genomes were produced for both red and yellow Bixa orellana accessions (158,918 and 158,823 bp respectively). Synteny and gene content was identical to the only other previously reported full chloroplast genome of Bixa orellana (NC_041550). We observed a 17 base pair deletion at position 58399-58415 in both of our accessions, relative to NC_041550 and a 6 base pair deletion at position 75531-75526 in the accession of “red” Bixa. A phylogeny based on alignment free kmer distance metrics was used to confirm monophylly of Bixa accessions, and to place Bixaceae relative to other families within the Malvales.ConclusionsOur data support Bixaceae as sister to Malvaceae and identified several potentially diagnostic insertion-deletion mutations that may with future work, reliably distinguish between red and yellow phenotypes. In addition to utility for phylogenic questions and development of identity markers, we demonstrate that chloroplast genomes can be used in conjunction with modern bioinformatic search tools (kmer based) to provide rapid and precise identification of Bixa orellana for Next Generation Sequencing approaches to natural product authentication.

Understanding the complex microbiota of agricultural irrigation water is vital to multiple sectors of sustainable agriculture and public health. To date, microbiome characterization methods have provided comprehensive profiles of aquatic microbiotas, but have not described which taxa are likely metabolically-active. Here, we combined 5‑bromo‑2′-deoxyuridine (BrdU) labeling with 16S rRNA and shotgun sequencing to identify metabolically-active bacteria in reclaimed and agricultural pond water samples (n = 28) recovered from the Mid-Atlantic United States between March 2017 and January 2018. BrdU-treated samples were significantly less diverse (alpha diversity) compared to non-BrdU-treated samples. The most abundant taxa in the metabolically-active fraction of water samples (BrdU-treated samples) were unclassified Actinobacteria, Flavobacterium spp., Pseudomonas spp. and Aeromonas spp. Interestingly, we also observed that antimicrobial resistance and virulence gene profiles seemed to be more diverse and more abundant in non-BrdU-treated water samples compared to BrdU-treated samples. These findings raise the possibility that these genes may be associated more with relic (inactive) DNA present in the tested water types rather than viable, metabolically-active microorganisms. Our study demonstrates that the coupled use of BrdU labeling and sequencing can enhance understanding of the metabolically-active fraction of bacterial communities in alternative irrigation water sources. Agricultural pond and reclaimed waters are vital to the future of sustainable agriculture, and thus, the full understanding of the pathogenic potential of these waters is important to guide mitigation strategies that ensure appropriate water quality for intended purposes.

Background The microbiome of cheese is diverse, even within a variety. The metagenomics of cheese is dependent on a vast array of biotic and abiotic factors. Biotic factors include the population of microbiota and their resulting cellular metabolism. Abiotic factors, including the pH, water activity, fat, salt, and moisture content of the cheese matrix, as well as environmental conditions (temperature, humidity, and location of aging), influence the biotic factors. This study assessed the metagenomics of commercial Gouda cheese prepared using pasteurized or unpasteurized cow milk or pasteurized goat milk via 16S rDNA sequencing. Results Results were analyzed and compared based on milk pasteurization and source, spatial variability (core, outer, and under the rind), and length of aging (2–4 up to 12–18 months). The dominant organisms in the Gouda cheeses, based on percentage of sequence reads identified at the family or genus levels, were Bacillaceae, Lactococcus, Lactobacillus, Streptococcus, and Staphylococcus. More genus- or family-level (e.g. Bacillaceae) identifications were observed in the Gouda cheeses prepared with unpasteurized cow milk (120) compared with those prepared with pasteurized cow milk (92). When assessing influence of spatial variability on the metagenomics of the cheese, more pronounced differences in bacterial genera were observed in the samples taken under the rind; Brachybacterium, Pseudoalteromonas, Yersinia, Klebsiella, and Weissella were only detected in these samples. Lastly, the aging length of the cheese greatly influenced the number of organisms observed. Twenty-seven additional genus-level identifications were observed in Gouda cheese aged for 12–18 months compared with cheese only aged 2–4 months. Conclusions Collectively, the results of this study are important in determining the typical microbiota associated with Gouda cheese and how the microbiome plays a role in safety and quality.

Aims The effect of insect exclusion via netting on bacterial microbiota associated with field-grown tomato fruit and flowers was evaluated. Methods and results Amplicon-based bacterial community profiling from insect-exposed plants and plants wrapped in nylon mosquito netting was conducted on total DNA extracted from tomato flower and mature unripe fruit washes. The V1-V3 region of the 16S rRNA gene was sequenced using Illumina MiSeq and analysed using qiime ver. 1.8. The carposphere supported significantly more phylogenetic diversity (PD) compared to the anthosphere, as measured by operational taxonomic unit richness (P = 0·001) and Faith's PD (P = 0·004). Flowers and fruit hosted distinct bacterial community structures (R2 = 0·27, P = 0·001), with specific taxonomic differences in taxa that included the Xanthomonadaceae (higher in flowers), and the Pseudomonadaceae, Methylobacteriaceae and Rhizobiales (higher in fruit) (FDR-P 0·10). However, less variation between samples was observed among flowers (~50% less, P = 0·004) and green fruit (~10% less, P = 0·038) collected from netted than non-netted plants. Conclusion Insects may introduce or augment variability in bacterial diversity associated with tomato flowers and potentially green fruit surfaces. Significance and impact of the study This work contributes to knowledge on microbiome dynamics of the tomato holobiont. Deciphering drivers of bacterial diversity and community structure of fruit crops could reveal processes important to agricultural management, such as competitive exclusion of pathogens and priming of plant defense mechanisms.

Precise, species-level identification of plants in foods and dietary supplements is difficult. While the use of DNA barcoding regions (short regions of DNA with diagnostic utility) has been effective for many inquiries, it is not always a robust approach for closely related species, especially in highly processed products. The use of fully sequenced chloroplast genomes, as an alternative to short diagnostic barcoding regions, has demonstrated utility for closely related species. The U. S. Food and Drug Administration (FDA) has also developed species-specific DNA-based assays targeting plant species of interest by utilizing chloroplast genome sequences. Here, we introduce a repository of complete chloroplast genome sequences called GenomeTrakrCP, which will be publicly available at the National Center for Biotechnology Information (NCBI). Target species for inclusion are plants found in foods and dietary supplements, toxin producers, common contaminants and adulterants, and their close relatives. Publicly available data will include annotated assemblies, raw sequencing data, and voucher information with each NCBI accession associated with an authenticated reference herbarium specimen. To date, 40 complete chloroplast genomes have been deposited in GenomeTrakrCP (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA325670/), and this will be expanded in the future.

Background The more quickly bacterial pathogens responsible for foodborne illness outbreaks can be traced to their source, the more illnesses can be prevented and the less unnecessary destruction of food will occur. Whole genome sequencing (WGS) based approaches to source tracking have greatly increased the speed and resolution with which public health response can pinpoint the source of outbreaks. Traditionally, WGS approaches have focused on the culture of an individual isolate before proceeding to DNA extraction and sequencing. For Listeria monocytogenes (Lm), generation of an individual isolate for sequencing typically takes about 6 days. Here we demonstrate that a hybrid, “quasimetagenomic” approach ie; direct sequencing of microbiological enrichment (first step in pathogen detection and recovery) can provide high resolution source tracking sequence data, five days earlier than response that focuses on culture and sequencing of an individual isolate.Methods Naturally contaminated ice cream (from a 2015 outbreak) was enriched to recover Listeria monocytogenes following protocols outlined in the Bacteriological Analytic Manual (BAM). DNA from enriching microbiota was extracted and sequenced at incremental time-points during the first 48 hours of pre-enrichment using the Illumina MiSeq platform (2 by 250), to evaluate genomic coverage of target pathogen, Listeria monocytogenes.Results Quasimetagenomic sequence data acquired from hour 20 were sufficient to discern whether or not Lm strain/s were part of the ongoing outbreak or not. Genomic data from hours 24, 28, 32, 36, 40, 44, and 48 of pre-enrichments all provided identical phylogenetic source tracking utility to the WGS of individual isolates (which require an additional 5 days to culture). Conclusions The speed of this approach (more than twice as fast as current methods) has the potential to reduce the number of illnesses associated with any given outbreak by as many as 75% percent of total cases and additionally, reduce the unnecessary waste of food that has been detained for investigation and due to lengthy response time, could not be deemed safe before spoilage.

Background Salmonella enterica possess several iron acquisition systems, encoded on the chromosome and plasmids. Recently, we demonstrated that incompatibility group (Inc) FIB plasmid-encoded iron acquisition systems (Sit and aerobactin) likely play an important role in persistence of Salmonella in human intestinal epithelial cells (Caco-2). In this study, we sought to determine global transcriptome analyses of S. enterica in iron-rich (IR) and iron-depleted (ID) growth conditions. Results The number of differentially-expressed genes were substantially higher for recipient (SE819) (n = 966) and transconjugant (TC) (n = 945) compared to the wild type (WT) (SE163A) (n = 110) strain in ID as compared to IR growth conditions. Several virulence-associated factors including T3SS, flagellin, cold-shock protein (cspE), and regulatory genes were upregulated in TC in ID compared to IR conditions. Whereas, IS1 and acrR/tetR transposases located on the IncFIB plasmid, ferritin and several regulatory genes were downregulated in TC in ID conditions. Enterobactin transporter (entS), iron ABC transporter (fepCD), colicin transporter, IncFIB-encoded enolase, cyclic di-GMP regulator (cdgR) and other regulatory genes of the WT strain were upregulated in ID compared to IR conditions. Conversely, ferritin, ferrous iron transport protein A (feoA), IncFIB-encoded IS1 and acrR/tetR transposases and ArtA toxin of WT were downregulated in ID conditions. SDS-PAGE coupled with LC-MS/MS analyses revealed that siderophore receptor proteins such as chromosomally-encoded IroN and, IncFIB-encoded IutA were upregulated in WT and TC in ID growth conditions. Both chromosome and IncFIB plasmid-encoded SitA was overexpressed in WT, but not in TC or recipient in ID conditions. Increased expression of flagellin was detected in recipient and TC, but not in WT in ID conditions. Conclusion Iron concentrations in growth media influenced differential gene expressions both at transcriptional and translational levels, including genes encoded on the IncFIB plasmid. Limited iron availability within the host may promote pathogenic Salmonella to differentially express subsets of genes encoded by chromosome and/or plasmids, facilitating establishment of successful infection. Electronic supplementary material The online version of this article (10.1186/s12864-019-5768-0) contains supplementary material, which is available to authorized users.

This chapter takes a brief look at the new frontier of microbiological food safety, nutrition, and immunology. With the use of metagenomic profiles of food and food ecologies, we can now better describe risk factors for contamination by human pathogens along the farm-to-fork continuum. We have also begun to organize data that may also improve our understanding of the more comprehensive prebiotic, probiotic, and antibiotic impact of food on the human microbiomes and how this may be relevant to nutrition, immunology, and chronic disease. We describe case studies with fresh produce such as tomatoes, sprouts, and leafy greens and highlight new approaches to pathogen detection using quasimetagenomics.

Indigenous bacterial populations in fresh-cut produce processing facilities can have a profound effect on the survival and proliferation of inadvertently contaminating foodborne pathogens. In this study, environmental samples were collected from a variety of Zone 3 sites in a processing plant before and after daily routine sanitation. Viable mesophilic aerobic bacteria population was evaluated using both culturing method and quantitative real-time PCR (qPCR) after propidium monoazide treatment. Zone 3 surface microbiota were analyzed using 16S rRNA gene amplicon sequencing with the Qiime2 bioinformatic pipeline. Over 8000 bacterial species across 4 major phyla were identified in Zone 3 microbiomes in the processing facility. Overall, effective bacterial reduction was observed at the sampling sites on the production floor, while sanitation effect on peripheral surfaces was less evident. Effective sanitation resulted in both quantitative and qualitive shifts of Zone 3 microbiota. Several species were highly abundant at multiple sample sites for both winter and summer samplings. Based on the spatial and temporal distribution of the most abundant species, a Zone 3 core microbiome in the processing facility was tentatively described to included Cupriavidus sp., Pseudomonas sp., Ralstonia sp., Arthrobacter psychrolactophdus, Pseudomonas vemnii, Stenotrophomonas sp., and an unknown species of the family Enterobacteriaceae. USDA-NIFA Specialty Crop Research Initiative [2016-51181-25403]; Chinese Scholarship CouncilChina Scholarship Council Authors thank the unnamed commercial fresh-cut processor for access to the facility for sampling. This project is partially supported by a research grant from USDA-NIFA Specialty Crop Research Initiative, Award No. 2016-51181-25403. LW was a visiting graduate student at ARS and was funded by Chinese Scholarship Council for Oversea Studies. Public domain – authored by a U.S. government employee

Due to the intimate association between plants and their microbial symbionts, an examination of the influence of agricultural practices on phytobiome structure and diversity could foster a more comprehensive understanding of plant health and produce safety. Indeed, the impact of upstream crop producti006Fn practices cannot be overstated in their role in assuring an abundant and safe food supply. To assess whether fertilizer type impacted rhizosphere and phyllosphere bacterial communities associating with tomato plants, the bacterial microbiome of tomato cv. 'BHN602' grown in soils amended with fresh poultry litter, commercially available sterilized poultry litter pellets, vermicompost or synthetic fertilizer was described. Culture independent DNA was extracted from bulk and rhizosphere soils, and washes of tomato blossoms and ripe fruit. PCR amplicons of hypervariable regions of the 16S rRNA gene were sequenced and profiled using the QIIME pipeline. Bulk and rhizosphere soil, and blossom and fruit surfaces all supported distinct bacterial communities according to principal coordinate analysis and ANOSIM (R=0.87, p=0.001 in year 1; R=0.93, p=0.001 in year 2). Use of microbiologically diverse organic fertilizers generally did not influence bacterial diversity, community structure or relative abundance of specific taxa on any plant organ surface. However, statistically significant differences in sand and silt contents of soil (p

Summary Salmonella is a diverse genus of Gram‐negative bacilli and a major foodborne pathogen responsible for more than a million illnesses annually in the United States alone. Rapid, reliable detection and identification of this pathogen in food and environmental sources is key to safeguarding the food supply. Traditional microbiological culture techniques have been the ‘gold standard’ for State and Federal regulators. Unfortunately, the time to result is too long to effectively monitor foodstuffs, especially those with very short shelf lives. Advances in traditional microbiology and molecular biology over the past 25 years have greatly improved the speed at which this pathogen is detected. Nonetheless, food and environmental samples possess a distinctive set of challenges for these newer, more rapid methodologies. Furthermore, more detailed identification and subtyping strategies still rely heavily on the availability of a pure isolate. However, major shifts in DNA sequencing technologies are meeting this challenge by advancing the detection, identification and subtyping of Salmonella towards a culture‐independent diagnostic framework. This review will focus on current approaches and state‐of‐the‐art next‐generation advances in the detection, identification and subtyping of Salmonella from food and environmental sources.

Fresh produce, as a known or suspected source of multiple foodborne outbreaks, harbors large populations of diverse microorganisms, which are partially released into wash water during processing. However, the dynamics of bacterial communities in wash water during produce processing is poorly understood. In this study, we investigated the effect of chlorine (FC) and peracetic acid (PAA) on the microbiome dynamics in spinach and romaine lettuce rinse water. Treatments with increasing concentrations of sanitizers resulted in convergence of distinct microbiomes. The resultant sanitizer resistant microbiome showed dominant presence by Bacillus sp., Arthrobacter psychrolactophilus, Cupriavidus sp., and Ralstonia sp. Most of the FC and PAA resistant bacteria isolated from spinach and lettuce rinse water after sanitation were gram positive spore forming species including Bacillus, Paenibacillus, and Brevibacillus spp., while several PAA resistant Pseudomonas spp. were also isolated from lettuce rinse water. Inoculation of foodborne pathogens altered the microbiome shift in spinach rinse water under PAA treatment, but not in lettuce rinse water or FC treated samples. These inoculated foodborne pathogens were not isolated among the sanitizer resistant strains.

Food microbiome composition impacts food safety and quality. The resident microbiota of many food products is influenced throughout the farm to fork continuum by farming practices, environmental factors, and food manufacturing and processing procedures. Currently, most food microbiology studies rely on culture-dependent methods to identify bacteria. However, advances in high-throughput DNA sequencing technologies have enabled the use of targeted 16S rRNA gene sequencing to profile complex microbial communities including non-culturable members. In this study we used 16S rRNA gene sequencing to assess the microbiome profiles of plant and animal derived foods collected at two points in the manufacturing process; post-harvest/pre-retail (cilantro) and retail (cilantro, masala spice mixes, cucumbers, mung bean sprouts, and smoked salmon). Our findings revealed microbiome profiles, unique to each food, that were influenced by the moisture content (dry spices, fresh produce), packaging methods, such as modified atmospheric packaging (mung bean sprouts and smoked salmon), and manufacturing stage (cilantro prior to retail and at retail). The masala spice mixes and cucumbers were comprised mainly of Proteobacteria, Firmicutes, and Actinobacteria. Cilantro microbiome profiles consisted mainly of Proteobacteria, followed by Bacteroidetes, and low levels of Firmicutes and Actinobacteria. The two brands of mung bean sprouts and the three smoked salmon samples differed from one another in their microbiome composition, each predominated by either by Firmicutes or Proteobacteria. These data demonstrate diverse and highly variable resident microbial communities across food products, which is informative in the context of food safety, and spoilage where indigenous bacteria could hamper pathogen detection, and limit shelf life.

The Maryland blue crab ( Callinectes sapidus ) is a treasured food, especially in areas surrounding the Chesapeake Bay. It has huge economic impact on commerce, and thus, understanding the bacterial, fungal, and viral constituents of its microbiome provides valuable information to safely manage aquaculture, handling, and cooking of this valuable commodity.

Aquaculture plays an increasingly important role in the growing demand for seafood. Hatchery production of oyster larvae is an integral component of oyster farming, providing single seed for off-bottom farming or larvae for setting on larger substrates for on-bottom farming. Larvae from certain tanks in an established aquaculture enterprise were dying from an unknown etiologic agent. A metagenomic approach was used to examine oyster larvae and water from larval tanks with high and low survival rates to evaluate the epidemiological efficacy of this approach.

The bacterial microbiome of flour recalled for possible Escherichia coli O121 contamination was characterized before (hour 0) and after (hour 24) enrichment using shotgun sequencing. At hour 0, Staphylococcus (46.8 to 66.5%) and Pantoea (12.6 to 21.0%) bacteria were dominant. At hour 24, Enterobacter (28.7 to 70.9%) and Klebsiella (25.6 to 68.6%) bacteria dominated, and Escherichia coli ranged from 0.3 to 17.9%.

Describing baseline microbiota associated with agricultural commodities in the field is an important step towards improving our understanding of a wide range of important objectives from plant pathology and horticultural sustainability, to food safety. Environmental pressures on plants (wind, dust, drought, water, temperature) vary by geography and characterizing the impact of these variable pressures on phyllosphere microbiota will contribute to improved stewardship of fresh produce for both plant and human health. A higher resolution understanding of the incidence of human pathogens on food plants and co-occurring phytobiota using metagenomic approaches (metagenome tracking) may contribute to improved source attribution and risk assessment in cases where human pathogens become introduced to agro-ecologies. Between 1990 and 2007, as many as 1990 culture-confirmed Salmonella illnesses were linked to tomatoes from as many as 12 multistate outbreaks (Bell et al., 2012; Bell et al., 2015; Bennett et al., 2014; CDC, 2004; CDC, 2007; Greene et al., 2005a; Gruszynski et al., 2014). When possible, source attribution for these incidents revealed a biogeographic trend, most events were associated with eastern growing regions. To improve our understanding of potential biogeographically linked trends in contamination of tomatoes by Salmonella, we profiled microbiota from the surfaces of tomatoes from Virginia, Maryland, North Carolina and California. Bacterial profiles from California tomatoes were completely different than those of Maryland, Virginia and North Carolina (which were highly similar to each other). A statistically significant enrichment of Firmicutes taxa was observed in California phytobiota compared to the three eastern states. Rhizobiaceae, Sphingobacteriaceae and Xanthobacteraceae were the most abundant bacterial families associated with tomatoes grown in eastern states. These baseline metagenomic profiles of phyllosphere microbiota may contribute to improved understanding of how certain ecologies provide supportive resources for human pathogens on plants and how components of certain agro-ecologies may play a role in the introduction of human pathogens to plants. © 2018 We would like to acknowledge the work of Dr. Rebecca Bell, Dr. Jie Zheng, Dr. Sarah Allard, Erik Burrows, Charles Wang, Alex Enurah, Dr. Marc Allard, Dr. Steven Musser and all other agricultural scientists at the Virginia Tech Agricultural Research and Extension Center in Painter, VA who supported the “Team Tomato” research initiatives.

Background. Using a novel combination of whole-genome sequencing (WGS) analysis and geographic metadata, we traced the origins of Salmonella Bareilly isolates collected in 2012 during a widespread food-borne outbreak in the United States associated with scraped tuna imported from India. Methods. Using next-generation sequencing, we sequenced the complete genome of 100 Salmonella Bareilly isolates obtained from patients who consumed contaminated product, from natural sources, and from unrelated historically and geographically disparate foods. Pathogen genomes were linked to geography by projecting the phylogeny on a virtual globe and produced a transmission network. Results. Phylogenetic analysis of WGS data revealed a common origin for outbreak strains, indicating that patients in Maryland and New York were infected from sources originating at a facility in India. Conclusions. These data represent the first report fully integrating WGS analysis with geographic mapping and a novel use of transmission networks. Results showed that WGS vastly improves our ability to delimit the scope and source of bacterial food-borne contamination events. Furthermore, these findings reinforce the extraordinary utility that WGS brings to global outbreak investigation as a greatly enhanced approach to protecting the human food supply chain as well as public health in general.