Paired metagenomic and chemical evaluation of aflatoxin-contaminated dog kibble.

Introduction: Identification of chemical toxins from complex or highly processed foods can present 'needle in the haystack' challenges for chemists. Metagenomic data can be used to guide chemical toxicity evaluations by providing DNA-based description of the wholistic composition (eukaryotic, bacterial, protozoal, viral, and antimicrobial resistance) of foods suspected to harbor toxins, allergens, or pathogens. This type of information can focus chemistry-based diagnostics, improve hazard characterization and risk assessment, and address data gaps. Additionally, there is increasing recognition that simultaneously co-occurring mycotoxins, either from single or multiple species, can impact dietary toxicity exposure. Metagenomic data provides a way to address data gaps related to co-occurrence of multiple fungal species. Methods: Paired metagenomic and chemical data were used to evaluate aflatoxin-contaminated kibble with known levels of specific mycotoxins. Kibble was ground to a fine powder for both chemical and molecular analyses. Chemical analyses were performed with Liquid Chromatography Mass Spectrometry (LCMS) and according to the AOAC Official method 2005.08: Aflatoxins in Corn, Raw Peanuts, and Peanut Butter using Liquid Chromatography with Post-Column Photochemical Derivatization. Metagenomes were created from DNA extracted from ground kibble and sequenced on an Illumina NextSeq 2000 with an average sequence depth of 180 million reads per replicate. Results and discussion: Metagenomic data demonstrated that the abundance of DNA from putative aflatoxigenic Aspergillus spp. correlated with the levels of aflatoxin quantified by LCMS. Metagenomic data also identified an expansive range of co-occurring fungal taxa which may produce additional mycotoxins. DNA data paired with chemical data provides a novel modality to address current data gaps surrounding dietary mycotoxin exposure, toxigenic fungal taxonomy, and mycotoxins of emerging concern.

Advancing metagenome-assembled genome-based pathogen identification: unraveling the power of long-read assembly algorithms in Oxford Nanopore sequencing.

Oxford Nanopore sequencing is one of the high-throughput sequencing technologies that facilitates the reconstruction of metagenome-assembled genomes (MAGs). This study aimed to assess the potential of long-read assembly algorithms in Oxford Nanopore sequencing to enhance the MAG-based identification of bacterial pathogens using both simulated and mock communities. Simulated communities were generated to mimic those on fresh spinach and in surface water. Long reads were produced using R9.4.1+SQK-LSK109 and R10.4 + SQK-LSK112, with 0.5, 1, and 2 million reads. The simulated bacterial communities included multidrug-resistant Salmonella enterica serotypes Heidelberg, Montevideo, and Typhimurium in the fresh spinach community individually or in combination, as well as multidrug-resistant Pseudomonas aeruginosa in the surface water community. Real data sets of the ZymoBIOMICS HMW DNA Standard were also studied. A bioinformatic pipeline (MAGenie, freely available at https://github.com/jackchen129/MAGenie) that combines metagenome assembly, taxonomic classification, and sequence extraction was developed to reconstruct draft MAGs from metagenome assemblies. Five assemblers were evaluated based on a series of genomic analyses. Overall, Flye outperformed the other assemblers, followed by Shasta, Raven, and Unicycler, while Canu performed least effectively. In some instances, the extracted sequences resulted in draft MAGs and provided the locations and structures of antimicrobial resistance genes and mobile genetic elements. Our study showcases the viability of utilizing the extracted sequences for precise phylogenetic inference, as demonstrated by the consistent alignment of phylogenetic topology between the reference genome and the extracted sequences. R9.4.1+SQK-LSK109 was more effective in most cases than R10.4+SQK-LSK112, and greater sequencing depths generally led to more accurate results.IMPORTANCEBy examining diverse bacterial communities, particularly those housing multiple Salmonella enterica serotypes, this study holds significance in uncovering the potential of long-read assembly algorithms to improve metagenome-assembled genome (MAG)-based pathogen identification through Oxford Nanopore sequencing. Our research demonstrates that long-read assembly stands out as a promising avenue for boosting precision in MAG-based pathogen identification, thus advancing the development of more robust surveillance measures. The findings also support ongoing endeavors to fine-tune a bioinformatic pipeline for accurate pathogen identification within complex metagenomic samples.

Metagenomic survey of antimicrobial resistance (AMR) in Maryland surface waters differentiated by high and low human impact.

Here, we examine surface waters as a modality to better understand baseline antimicrobial resistance (AMR) across the environment to supplement existing AMR monitoring in pathogens associated with humans, foods, and animals. Data from metagenomic and quasimetagenomic (shotgun sequenced enrichments) are used to describe AMR in Maryland surface waters from high and low human impact classifications.

Evaluation of universal preenrichment broth and comparison of rapid molecular methods for the detection of Salmonella from spent sprout irrigation water (SSIW).

Sprouts and spent sprout irrigation water (SSIW) present unique challenges for the development of a Salmonella detection method in food matrices. This study aimed to compare universal preenrichment broth (UPB) and lactose broth (LB) as preenrichment media for cultural and rapid screening methods and to compare their abilities to recover Salmonella in SSIW samples from different sprout varieties (i.e., alfalfa, broccoli, and mung bean sprouts). The associated co-enriched microbiota with different sprout varieties using different preenrichment media were also examined using a quasi-metagenomic approach. The performance of media and detection methods was compared using the relative level of detection (RLOD) value, as recommended by ISO 16140-2:2016. The level of detection (LOD) for Salmonella culture method with UPB was similar to that with LB in low aerobic plate count (APC) background samples (the relative LOD, i.e., RLOD, was nearly 1 after adjusting for the effects of SSIW variety and serovar), but significantly lower than that with LB in high APC background samples (RLOD = 0.32). The LOD for Salmonella with selected rapid methods was comparable to each other (RLOD from 0.97 to 1.50) and to the culture method (RLOD from 0.69 to 1.03), and no significant difference was detected between preenrichment broths in low APC background samples with RLOD values between 0.76 and 1.04. In samples with a high APC background, however, a drastic difference in LOD was observed between methods and between preenrichment broths for each method. The RLOD ranged from 0.03 to 0.32 when UPB was compared to LB as preenrichment broth. The composition and relative abundance (RA) of co-enriched microbiota was affected by multiple factors including food matrices, preenrichment media and Salmonella contamination. Altogether, this study validated UPB as a better preenrichment broth than LB for the detection of Salmonella enterica from SSIW. This study also suggested UPB may also be an optimal preenrichment medium for rapid screening methods when APC level is high. The observation of potential exclusion of Salmonella in preenrichment through the overgrowth of competitive microflora from the quasi-metagenomic study provided novel information that may be used to further optimize preenrichment formulations.

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.

Precision metagenomics sequencing for food safety: hybrid assembly of Shiga toxin-producing Escherichia coli in enriched agricultural water.

Culture-independent metagenomic sequencing of enriched agricultural water could expedite the detection and virulotyping of Shiga toxin-producing Escherichia coli (STEC). We previously determined the limits of a complete, closed metagenome-assembled genome (MAG) assembly and of a complete, fragmented MAG assembly for O157:H7 in enriched agricultural water using long reads (Oxford Nanopore Technologies, Oxford), which were 107 and 105 CFU/ml, respectively. However, the nanopore assemblies did not have enough accuracy to be used in Single Nucleotide Polymorphism (SNP) phylogenies and cannot be used for the precise identification of an outbreak STEC strain. The present study aimed to determine the limits of detection and assembly for STECs in enriched agricultural water by Illumina MiSeq sequencing technology alone, followed by establishing the limit of hybrid assembly with nanopore long-read sequencing using three different hybrid assemblers (SPAdes, Unicycler, and OPERA-MS). We also aimed to generate a genome with enough accuracy to be used in a SNP phylogeny. The classification of MiSeq and nanopore sequencing identified the same highly abundant species. Using the totality of the MiSeq output and a precision metagenomics approach in which the E. coli reads are binned before assembly, the limit of detection and assembly of STECs by MiSeq were determined to be 105 and 107 CFU/ml, respectively. While a complete, closed MAG could not be generated at any concentration, a complete, fragmented MAG was produced using the SPAdes assembler with an STEC concentration of at least 107 CFU/ml. At this concentration, hybrid assembled contigs aligned to the nanopore-assembled genome could be accurately placed in a neighbor-joining tree. The MiSeq limit of detection and assembly was less sensitive than nanopore sequencing, which was likely due to factors including the small starting material (50 vs. 1 µg) and the dilution of the library loaded on the cartridge. This pilot study demonstrates that MiSeq sequencing requires higher coverage in precision metagenomic samples; however, with sufficient concentration, STECs can be characterized and phylogeny can be accurately determined.

bettercallsal: better calling of Salmonella serotypes from enrichment cultures using shotgun metagenomic profiling and its application in an outbreak setting.

Most current Salmonella subtyping analyses rely on whole genome sequencing (WGS), which focuses on the high-resolution analysis of single genomes or multiple single genomes from the isolated colonies on microbiological agar plates. In this study, we introduce bioinformatics innovations for a metagenomic outbreak response workflow that accurately identifies multiple Salmonella serovars at the same time. bettercallsal is one of the first analysis tools to identify multiple Salmonella enterica serotypes from metagenomic or quasi-metagenomic datasets with high accuracy, allowing these isolate-independent methods to be incorporated into surveillance and root cause investigations. It was tested on an in silico benchmark dataset comprising 29 unique Salmonella serovars, 46 non-Salmonella bacterial genomes, and 10 viral genomes at varying read depths and on previously well-characterized and sequenced non-selective primary and selective enrichments of papaya and peach samples from separate outbreak investigations that resulted in the identification of multiple Salmonella serovars using traditional isolate culturing and WGS as well as nucleic acid assays. Analyses were also conducted on these datasets using a custom-built k-mer tool, SeqSero2, and Kallisto to compare serotype calling to bettercallsal. The in silico dataset analyzed with bettercallsal achieved the maximum precision, recall, and accuracy of 100, 83, and 94%, respectively. In the papaya outbreak samples, bettercallsal identified the presence of multiple serovars in agreement with the Luminex® xMAP assay results and also identified more serovars per sample, as evidenced by NCBI SNP clustering. In peach outbreak samples, bettercallsal identified two serovars in concordance with k-mer analysis and the Luminex xMAP assay. The genome hit reported by bettercallsal clustered with the chicken isolate genome, as reported by the FDA peach outbreak investigation from sequenced isolates (WGS). Overall, bettercallsal outperformed k-mer, Seqsero2, and Kallisto in identifying multiple serovars from enrichment cultures using shotgun metagenomic sequencing.

Conventional tobacco products harbor unique and heterogenous microbiomes.

While an increasing number of studies have evaluated tobacco microbiomes, comparative microbiome analyses across diverse tobacco products are non-existent. Moreover, to our knowledge, no previous studies have characterized the metabolically-active (live) fraction of tobacco bacterial communities and compared them across products. To address these knowledge gaps, we compared bacterial communities across four commercial products (cigarettes, little cigars, cigarillos and hookah) and one research cigarette product. After total DNA extraction (n = 414) from all samples, the V3V4 region of the 16S rRNA gene was sequenced on the Illumina HiSeq platform. To identify metabolically-active bacterial communities within these products, we applied a coupled 5-bromo-2'-deoxyuridine labeling and sequencing approach to a subset of samples (n = 56). Each tobacco product was characterized by its signature microbiome, along with a shared microbiome across all tobacco products consisting of Pseudomonas aeruginosa, P. putida, P. alcaligenes, Bacillus subtilis, and Klebsiella pneumoniae. Comparing across products (using Linear discriminant analysis Effect Size (LEfSe)), a significantly higher (p < 0.05) relative abundance of Klebsiella and Acinetobacter was observed in commercial cigarettes, while a higher relative abundance of Pseudomonas and Pantoea was observed in research cigarettes. Methylorubrum and Paenibacillus were higher in hookah, and Brevibacillus, Lactobacillus, Bacillus, Lysinibacillus, and Staphylococcus were higher in little cigars and cigarillos. Across all products, the majority of the metabolically-active bacterial communities belonged to the genus Pseudomonas, followed by several genera within the Firmicutes phylum (Bacillus, Terribacillus, and Oceanobacillus). Identification of some metabolically-active pathogens such as Bacillus cereus and Haemophilus parainfluenzae in commercial products is of concern because of the potential for these microorganisms to be transferred to users' respiratory tracts via mainstream smoke. Future work is warranted to evaluate the potential impact of these tobacco bacterial communities on users' oral and lung microbiomes, which play such an important role on the spectrum from health to disease.

Variations in Bacterial Communities and Antibiotic Resistance Genes Across Diverse Recycled and Surface Water Irrigation Sources in the Mid-Atlantic and Southwest United States: A CONSERVE Two-Year Field Study.

Reduced availability of agricultural water has spurred increased interest in using recycled irrigation water for U.S. food crop production. However, there are significant knowledge gaps concerning the microbiological quality of these water sources. To address these gaps, we used 16S rRNA gene and metagenomic sequencing to characterize taxonomic and functional variations (e.g., antimicrobial resistance) in bacterial communities across diverse recycled and surface water irrigation sources. We collected 1 L water samples (n = 410) between 2016 and 2018 from the Mid-Atlantic (12 sites) and Southwest (10 sites) U.S. Samples were filtered, and DNA was extracted. The V3-V4 regions of the 16S rRNA gene were then PCR amplified and sequenced. Metagenomic sequencing was also performed to characterize antibiotic, metal, and biocide resistance genes. Bacterial alpha and beta diversities were significantly different (p < 0.001) across water types and seasons. Pathogenic bacteria, such as Salmonella enterica, Staphylococcus aureus, and Aeromonas hydrophilia were observed across sample types. The most common antibiotic resistance genes identified coded against macrolides/lincosamides/streptogramins, aminoglycosides, rifampin and elfamycins, and their read counts fluctuated across seasons. We also observed multi-metal and multi-biocide resistance across all water types. To our knowledge, this is the most comprehensive longitudinal study to date of U.S. recycled water and surface water used for irrigation. Our findings improve understanding of the potential differences in the risk of exposure to bacterial pathogens and antibiotic resistance genes originating from diverse irrigation water sources across seasons and U.S. regions.

Use of Whole Genome Sequencing by the Federal Interagency Collaboration for Genomics for Food and Feed Safety in the United States.

ABSTRACT: 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.

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.

Microbiomes of commercially-available pine nuts and sesame seeds.

Metagenomic analysis of food is becoming more routine and can provide important information pertaining to the shelf life potential and the safety of these products. However, less information is available on the microbiomes associated with low water activity foods. Pine nuts and sesame seeds, and food products which contain these ingredients, have been associated with recalls due to contamination with bacterial foodborne pathogens. The objective of this study was to identify the microbial community of pine nuts and sesame seeds using targeted 16S rRNA sequencing technology. Ten different brands of each seed type were assessed, and core microbiomes were determined. A total of 21 and 16 unique taxa with proportional abundances >1% in at least one brand were identified in the pine nuts and sesame seeds, respectively. Members of the core pine nut microbiome included the genera Alishewanella, Aminivibrio, Mycoplasma, Streptococcus, and unassigned OTUs in the families of Desulfobacteraceae and Xanthomonadaceae. For sesame seeds, the core microbiome included Aminivibrio, Chryseolina, Okibacterium, and unassigned OTUs in the family Flavobacteriaceae. The microbiomes of these seeds revealed that these products are dominated by environmental bacterial genera commonly isolated from soil, water, and plants; bacterial genera containing species known as commensal organisms were also identified. Understanding these microbiomes can aid in the risk assessment of these products by identifying food spoilage potential and community members which may co-enrich with foodborne bacterial pathogens.

Evaluating the accuracy of Listeria monocytogenes assemblies from quasimetagenomic samples using long and short reads.

BACKGROUND: Whole genome sequencing of cultured pathogens is the state of the art public health response for the bioinformatic source tracking of illness outbreaks. Quasimetagenomics can substantially reduce the amount of culturing needed before a high quality genome can be recovered. Highly accurate short read data is analyzed for single nucleotide polymorphisms and multi-locus sequence types to differentiate strains but cannot span many genomic repeats, resulting in highly fragmented assemblies. Long reads can span repeats, resulting in much more contiguous assemblies, but have lower accuracy than short reads. RESULTS: We evaluated the accuracy of Listeria monocytogenes assemblies from enrichments (quasimetagenomes) of naturally-contaminated ice cream using long read (Oxford Nanopore) and short read (Illumina) sequencing data. Accuracy of ten assembly approaches, over a range of sequencing depths, was evaluated by comparing sequence similarity of genes in assemblies to a complete reference genome. Long read assemblies reconstructed a circularized genome as well as a 71 kbp plasmid after 24 h of enrichment; however, high error rates prevented high fidelity gene assembly, even at 150X depth of coverage. Short read assemblies accurately reconstructed the core genes after 28 h of enrichment but produced highly fragmented genomes. Hybrid approaches demonstrated promising results but had biases based upon the initial assembly strategy. Short read assemblies scaffolded with long reads accurately assembled the core genes after just 24 h of enrichment, but were highly fragmented. Long read assemblies polished with short reads reconstructed a circularized genome and plasmid and assembled all the genes after 24 h enrichment but with less fidelity for the core genes than the short read assemblies. CONCLUSION: The integration of long and short read sequencing of quasimetagenomes expedited the reconstruction of a high quality pathogen genome compared to either platform alone. A new and more complete level of information about genome structure, gene order and mobile elements can be added to the public health response by incorporating long read analyses with the standard short read WGS outbreak response.

Metataxonomic Profiling of Native and Starter Microbiota During Ripening of Gouda Cheese Made With Listeria monocytogenes-Contaminated Unpasteurized Milk.

Unpasteurized milk is used to produce aged artisanal cheeses, which presents a safety concern due to possible contamination with foodborne pathogens, especially Listeria monocytogenes. The objective of this study was to examine the composition of the bacterial community in unpasteurized milk used to prepare Gouda cheese artificially contaminated with L. monocytogenes (~1 log CFU/ml) and assess the community dynamics and their potential interaction with L. monocytogenes during a 90-day ripening period using targeted 16S rRNA sequencing. The diversity of bacterial taxa in three batches of unpasteurized milk was not significantly different, and the microbiomes were dominated by species of Lactococcus, Streptomyces, Staphylococcus, and Pseudomonas. The highest relative abundances were observed for Pseudomonas fluorescens (31.84-78.80%) and unidentified operational taxonomic units (OTUs) of Pseudomonas (7.56-45.27%). After manufacture, both with and without L. monocytogenes-contaminated unpasteurized milk, Gouda cheese was dominated by starter culture bacteria (including Lactococcus lactis subsp. cremoris, lactis, lactis bv. diacetylactis, and Streptococcus thermophilus), in addition to unassigned members in the taxa L. lactis and Streptococcus. During ripening there was an overall decrease in L. lactis abundance and an increase in the number of taxa with relative abundances >0.1%. After 90-day ripening, a total of 82 and 81 taxa were identified in the Gouda cheese with and without L. monocytogenes, respectively. Of the identified taxa after ripening, 31 (Gouda cheese with L. monocytogenes) and 56 (Gouda cheese without L. monocytogenes) taxa had relative abundances >0.1%; 31 were shared between the two types of Gouda cheese, and 25 were unique to the Gouda cheese without added L. monocytogenes. No unique taxa were identified in the Gouda cheese with the added L. monocytogenes. This study provides information on the dynamics of the bacterial community in Gouda cheese during ripening, both with and without the addition of L. monocytogenes.

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.

Water Microbiota from a Commercial Alfalfa Sprout Crop over 4 Days of Growth.

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.

HPLC-UV, Metabarcoding and Genome Skims of Botanical Dietary Supplements: A Case Study in Echinacea.

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.

Taxonomic and Functional Shifts in the Sprout Spent Irrigation Water Microbiome in Response to Salmonella Contamination of Alfalfa Seeds.

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.

Using full chloroplast genomes of 'red' and 'yellow' Bixa orellana (achiote) for kmer based identification and phylogenetic inference.

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.

Metabolically-active bacteria in reclaimed water and ponds revealed using bromodeoxyuridine DNA labeling coupled with 16S rRNA and shotgun sequencing.

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 5­bromo­2'-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.