Cilantro microbiome before and after nonselective pre-enrichment for Salmonella using 16S rRNA and metagenomic sequencing

Exploring animal food microbiomes and resistomes via 16S rRNA gene amplicon sequencing and shotgun metagenomics

As a diverse and complex food matrix, the animal food microbiota and repertoire of antimicrobial resistance (AMR) genes remain to be better understood. In this study, 16S rRNA gene amplicon sequencing and shotgun metagenomics were applied to three types of animal food samples (cattle feed, dry dog food, and poultry feed). ZymoBIOMICS mock microbial community was used for workflow optimization including DNA extraction kits and bead-beating conditions. The four DNA extraction kits (AllPrep PowerViral DNA/RNA Kit, DNeasy Blood & Tissue Kit, DNeasy PowerSoil Kit, and ZymoBIOMICS DNA Miniprep Kit) were compared in animal food as well as the use of peptide nucleic acid blockers for 16S rRNA gene amplicon sequencing. Distinct microbial community profiles were generated, which varied by animal food type and DNA extraction kit. Employing peptide nucleic acid blockers prior to 16S rRNA gene amplicon sequencing was comparable with post-sequencing in silico filtering at removing chloroplast and mitochondrial sequences. There was a good agreement between 16S rRNA gene amplicon sequencing and shotgun metagenomics on community profiles in animal feed data sets; however, they differed in taxonomic resolution, with the latter superior at resolving at the species level. Although the overall prevalence of AMR genes was low, resistome analysis of animal feed data sets by shotgun metagenomics revealed 10 AMR gene/protein families, including beta-lactamases, erythromycin/lincomycin/pristinamycin/tylosin, fosfomycin, phenicol, and quinolone. Future expansion of microbiome and resistome profiling in animal food will help better understand the bacterial and AMR gene diversity in these commodities and help guide pathogen control and AMR prevention efforts.IMPORTANCEWith the growing interest and application of metagenomics in understanding the structure/composition and function of diverse microbial communities along the One Health continuum, this study represents one of the first attempts to employ these advanced sequencing technologies to characterize the microbiota and AMR genes in animal food. We unraveled the effects of DNA extraction kits on sample analysis by 16S rRNA gene amplicon sequencing and showed similar efficacies of two strategies at removing chloroplast and mitochondrial reads. The in-depth analysis using shotgun metagenomics shed light on the community compositions and the presence of an array of AMR genes in animal food. This exploration of microbiomes and resistomes in representative animal food samples by both sequencing approaches laid important groundwork for future metagenomic investigations to gain a better understanding of the baseline/core microbiomes and associated AMR functions in these diverse commodities and help guide pathogen control and AMR prevention efforts.

Peptide nucleic acid (PNA) clamps reduce amplification of host chloroplast and mitochondria rRNA gene sequences and increase detected diversity in 16S rRNA gene profiling analysis of oak-associated microbiota

BACKGROUND

Acquiring representative bacterial 16S rRNA gene community profiles in plant microbiome studies can be challenging due to the excessive co-amplification of host chloroplast and mitochondrial rRNA gene sequences that reduce counts of plant-associated bacterial sequences. Peptide Nucleic Acid (PNA) clamps prevent this by blocking PCR primer binding or binding within the amplified region of non-target DNA to stop the function of DNA polymerase. Here, we applied a universal chloroplast (p)PNA clamp and a newly designed mitochondria (m)PNA clamp to minimise host chloroplast and mitochondria amplification in 16S rRNA gene amplicon profiles of leaf, bark and root tissue of two oak species (Quercus robur and Q. petraea).

RESULTS

Adding PNA clamps to PCR led to an overall reduction of host chloroplast and mitochondrial 16S rRNA gene sequences of 79%, 46% and 99% in leaf, bark and root tissues, respectively. This resulted in an average increase in bacterial sequencing reads of 72%, 35%, and 17% in leaf, bark, and root tissue, respectively. Moreover, the bacterial diversity in the leaf and bark increased, with the number of ASVs rising by 105 in the leaf samples and 218 in the bark samples, respectively. In root tissues, where host oak chloroplast and mitochondria contamination were low, alpha and beta diversity did not change, suggesting the PNA clamps did not bias the bacterial community.

CONCLUSION

In conclusion, this study shows that PNA clamps can effectively reduce host chloroplast and mitochondria PCR amplification and improve assessment of the detected bacterial diversity in Quercus petraea and Quercus robur bacterial 16S rRNA gene sequencing studies.

Limit of detection of Salmonella ser. Enteritidis using culture-based versus culture-independent diagnostic approaches

To prevent the spread of foodborne illnesses, the presence of pathogens in the food chain is monitored by government agencies and food producers. The culture-based methods currently employed are sensitive but time- and labor-intensive, leading to increasing interest in exploring culture-independent diagnostic tests (CIDTs) for pathogen detection. However, few studies quantify the relative sensitivity and reliability of these CIDTs compared to current approaches. To address this issue, we conducted a comparison of the limit of detection (LOD50) for Salmonella between a culture-based method and three CIDTs: qPCR (targeting invA and stn), metabarcode (16S) sequencing, and shotgun metagenomic sequencing. Samples of chicken feed and chicken caecal contents were spiked with S. serovar Enteritidis and subjected to culture- and DNA-based detection methods. To explore the impact of non-selective enrichment on LOD50, all samples underwent both immediate DNA extraction and overnight enrichment prior to gDNA extraction. In addition to this spike-in experiment, feed and caecal samples acquired from the field were tested with culturing, qPCR, and metabarcoding. In general, LOD50 was comparable between qPCR and shotgun sequencing methods. Overnight microbiological enrichment resulted in an improvement in LOD50 with up to a three-log decrease. However, Salmonella reads were detected in some unspiked feed samples, suggesting false-positive detection of Salmonella. In addition, the LOD50 in feeds was three logs lower than in caecal contents, underscoring the impact of background microbiota on Salmonella detection using all methods.

IMPORTANCE

The appeal of culture-independent diagnostic tests (CIDTs) is increased speed with lowered cost, as well as the potential to detect multiple pathogen species in a single analysis and to monitor other areas of concern such as antimicrobial resistance genes or virulence factors. This study provides quantitative data on the sensitivity of CIDTs relative to current approaches, which is essential for determining the feasibility of implementing these methods in pathogen surveillance programs.

Aggregative Swab Sampling Method for Romaine Lettuce Show Similar Quality and Safety Indicators and Microbial Profiles Compared to Composite Produce Leaf Samples in a Pilot Study

Composite produce leaf samples from commercial production rarely test positive for pathogens, potentially due to low pathogen prevalence or the relatively small number of plants sampled. Aggregative sampling may offer a more representative alternative. This pilot study investigated whether aggregative swab samples performed similarly to produce leaf samples in their ability to recover quality indicators (APCs and coliforms), detect Escherichia coli, and recover representative microbial profiles. Aggregative swabs of the outer leaves of romaine plants (n = 12) and composite samples consisting of various grabs of produce leaves (n = 14) were collected from 60 by 28 ft sections of a one-acre commercial romaine lettuce field. Aerobic plate counts were 9.17 ± 0.43 and 9.21 ± 0.42 log(CFU/g) for produce leaf samples and swabs, respectively. Means and variance were not significantly different (p = 0.38 and p = 0.92, respectively). Coliform recoveries were 3.80 ± 0.76 and 4.19 ± 1.15 log(CFU/g) for produce leaf and swabs, respectively. Means and variances were not significantly different (p = 0.30 and p = 0.16, respectively). Swabs detected generic E. coli in 8 of 12 samples, more often than produce leaf samples (3 of 14 positive, Fisher's p = 0.045). Full-length 16S rRNA microbial profiling revealed that swab and produce leaf samples did not show significantly different alpha diversities (p = 0.75) and had many of the most prevalent bacterial taxa in common and in similar abundances. These data suggest that aggregative swabs perform similarly to, if not better than, produce leaf samples in recovering indicators of quality (aerobic and coliform bacteria) and food safety (E. coli), justifying further method development and validation.

A mica filter enables bacterial enrichment from large volumes of natural water for sensitive monitoring of pathogens by nanopore sequencing

Nanopore sequencing is extremely promising for the high-throughput detection of pathogenic bacteria in natural water; these bacteria may be transmitted to humans and cause waterborne infectious diseases. However, the concentration of pathogenic bacteria in natural water is too low to be detected directly by nanopore sequencing. Herein, we developed a mica filter to enrich over 85% of bacteria from > 10 L of natural water in 100 min, which led to a 102-fold improvement in the assay limits of the MinION sequencer for assessing pathogenic bacteria. Correspondingly, the sequencing time of S. Typhi detection at a concentration as low as 105 CFU/L was reduced from traditional 48 h to 3 h. The bacterial adsorption followed pseudo-first-order kinetics and the successful adsorption of bacteria to the mica filter was confirmed by scanning electron microscopy and Fourier infrared spectroscopy et al. The mica filter remained applicable to a range of water samples whose quality parameters were within the EPA standard limits for freshwater water. The mica filter is thus an effective tool for the sensitive and rapid monitoring of pathogenic bacteria by nanopore sequencing, which can provide timely alerts for waterborne transmission events.

Bacterial enrichment prior to third-generation metagenomic sequencing improves detection of BRD pathogens and genetic determinants of antimicrobial resistance in feedlot cattle

Introduction

Bovine respiratory disease (BRD) is one of the most important animal health problems in the beef industry. While bacterial culture and antimicrobial susceptibility testing have been used for diagnostic testing, the common practice of examining one isolate per species does not fully reflect the bacterial population in the sample. In contrast, a recent study with metagenomic sequencing of nasal swabs from feedlot cattle is promising in terms of bacterial pathogen identification and detection of antimicrobial resistance genes (ARGs). However, the sensitivity of metagenomic sequencing was impeded by the high proportion of host biomass in the nasal swab samples.

Methods

This pilot study employed a non-selective bacterial enrichment step before nucleic acid extraction to increase the relative proportion of bacterial DNA for sequencing.

Results

Non-selective bacterial enrichment increased the proportion of bacteria relative to host sequence data, allowing increased detection of BRD pathogens compared with unenriched samples. This process also allowed for enhanced detection of ARGs with species-level resolution, including detection of ARGs for bacterial species of interest that were not targeted for culture and susceptibility testing. The long-read sequencing approach enabled ARG detection on individual bacterial reads without the need for assembly. Metagenomics following non-selective bacterial enrichment resulted in substantial agreement for four of six comparisons with culture for respiratory bacteria and substantial or better correlation with qPCR. Comparison between isolate susceptibility results and detection of ARGs was best for macrolide ARGs in Mannheimia haemolytica reads but was also substantial for sulfonamide ARGs within M. haemolytica and Pasteurella multocida reads and tetracycline ARGs in Histophilus somni reads.

Discussion

By increasing the proportion of bacterial DNA relative to host DNA through non-selective enrichment, we demonstrated a corresponding increase in the proportion of sequencing data identifying BRD-associated pathogens and ARGs in deep nasopharyngeal swabs from feedlot cattle using long-read metagenomic sequencing. This method shows promise as a detection strategy for BRD pathogens and ARGs and strikes a balance between processing time, input costs, and generation of on-target data. This approach could serve as a valuable tool to inform antimicrobial management for BRD and support antimicrobial stewardship.

Microbiome-based classification models for fresh produce safety and quality evaluation

Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k-mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food.

IMPORTANCE

Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k-mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.

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.

A novel culture-enriched metagenomic sequencing strategy effectively guarantee the microbial safety of drinking water by uncovering the low abundance pathogens

Assessing the presence of waterborne pathogens and antibiotic resistance genes (ARGs) is crucial for managing the environmental quality of drinking water sources. However, detecting low abundance pathogens in such settings is challenging. In this study, a workflow was developed to enrich for broad spectrum pathogens from drinking water samples. A mock community was used to evaluate the effectiveness of various enrichment broths in detecting low-abundance pathogens. Monthly metagenomic surveillance was conducted in a drinking water source from May to September 2021, and water samples were subjected to five enrichment procedures for 6 h to recover the majority of waterborne bacterial pathogens. Oxford Nanopore Technology (ONT) was used for metagenomic sequencing of enriched samples to obtain high-quality pathogen genomes. The results showed that selective enrichment significantly increased the proportions of targeted bacterial pathogens. Compared to direct metagenomic sequencing of untreated water samples, targeted enrichment followed by ONT sequencing significantly improved the detection of waterborne pathogens and the quality of metagenome-assembled genomes (MAGs). Eighty-six high-quality MAGs, including 70 pathogen MAGs, were obtained from ONT sequencing, while only 12 MAGs representing 10 species were obtained from direct metagenomic sequencing of untreated water samples. In addition, ONT sequencing improved the recovery of mobile genetic elements and the accuracy of phylogenetic analysis. This study highlights the urgent need for efficient methodologies to detect and manage microbial risks in drinking water sources. The developed workflow provides a cost-effective approach for environmental management of drinking water sources with microbial risks. The study also uncovered pathogens that were not detected by traditional methods, thereby advancing microbial risk management of drinking water sources.

16S microbiome analysis of microbial communities in distribution centers handling fresh produce

Little is known about the microbial communities found in distribution centers (DCs), especially in those storing and handling food. As many foodborne bacteria are known to establish residence in food facilities, it is reasonable to assume that DCs handling foods are also susceptible to pathogen colonization. To investigate the microbial communities within DCs, 16S amplicon sequencing was completed on 317 environmental surface sponge swabs collected in DCs (n = 18) across the United States. An additional 317 swabs were collected in parallel to determine if any viable Listeria species were also present at each sampling site. There were significant differences in median diversity measures (observed, Shannon, and Chao1) across individual DCs, and top genera across all reads were Carnobacterium_A, Psychrobacter, Pseudomonas_E, Leaf454, and Staphylococcus based on taxonomic classifications using the Genome Taxonomy Database. Of the 39 16S samples containing Listeria ASVs, four of these samples had corresponding Listeria positive microbiological samples. Data indicated a predominance of ASVs identified as cold-tolerant bacteria in environmental samples collected in DCs. Differential abundance analysis identified Carnobacterium_A, Psychrobacter, and Pseudomonas_E present at a significantly greater abundance in Listeria positive microbiological compared to those negative for Listeria. Additionally, microbiome composition varied significantly across groupings within variables (e.g., DC, season, general sampling location).

Genetic diversity of enterotoxigenic Bacillus cereus strains in coriander in southwestern Mexico

Background

Coriander, like other leafy green vegetables, is available all year round and is commonly consumed raw in Mexico as in other countries in the preparation of street or homemade food. Bacillus cereus (B. cereus) is a microorganism that can reach coriander because it is usually found in the soil and in some regions the vegetables are irrigated with polluted water. Therefore, the aim of this study was to determinate the presence of B. cereus in coriander used for human consumption in southwestern Mexico and determine the toxigenic profile, biofilm production, genes associated with the production of biofilms, sporulation rates, enzymatic profile, psychotropic properties, and genetic diversity of B. cereus.

Methods

Fresh coriander samples were collected from several vegetable retailers in different markets, microbiological analysis was performed. Molecular identification, genes related to the production of biofilm, and toxin gene profiling of B. cereus isolates were determined by PCR. The biofilm formation was measured by performing a crystal violet assay. The genetic diversity of B. cereus strains was determined by PCR of repetitive elements using oligonucleotide (GTG) 5.

Results

We found a frequency of B. cereus in vegetables was 20% (13/65). In this study, no strains with genes for the HBL toxin were found. In the case of genes related to biofilms, the frequency was low for sipW [5.8%, (1/17)] and tasA [11.7%, (2/17)]. B. cereus strains produce a low amount of biofilm with sporulation rates around 80%. As for genetic diversity, we observed that strains isolated from the same market, but different vegetable retailers are grouped into clusters. In the coriander marketed in southwestern Mexico, were found B. cereus strains with genes associated with the production of diarrheal toxins. Together, these results show actual information about the state of art of B. cereus strains circulating in the southwestern of Mexico.

Microbial Antagonism in Food-Enrichment Culture: Inhibition of Shiga Toxin-Producing Escherichia coli and Shigella Species

Bacterial pathogens, such as Shiga toxin-producing Escherichia coli (STEC) and Shigella spp., are important causes of foodborne illness internationally. Recovery of these organisms from foods is critical for food safety investigations to support attribution of illnesses to specific food commodities; however, isolation of bacterial cultures can be challenging. Methods for the isolation of STEC and Shigella spp. from foods typically require enrichment to amplify target organisms to detectable levels. Yet, during enrichment, target organisms can be outcompeted by other bacteria in food matrices due to faster growth rates, or through production of antimicrobial agents such as bacteriocins or bacteriophages. The purpose of this study was to evaluate the occurrence of Shigella and STEC inhibitors produced by food microbiota. The production of antimicrobial compounds in cell-free extracts from 200 bacterial strains and 332 food-enrichment broths was assessed. Cell-free extracts produced by 23 (11.5%) of the strains tested inhibited growth of at least one of the five Shigella and seven STEC indicator strains used in this study. Of the 332 enrichment broths tested, cell-free extracts from 25 (7.5%) samples inhibited growth of at least one of the indicator strains tested. Inhibition was most commonly associated with E. coli recovered from meat products. Most of the inhibiting compounds were determined to be proteinaceous (34 of the 48 positive samples, 71%; including 17 strains, 17 foods) based on inactivation by proteolytic enzymes, indicating presence of bacteriocins. The cell-free extracts from 13 samples (27%, eight strains, five foods) were determined to contain bacteriophages based on the observation of plaques in diluted extracts and/or resistance to proteolytic enzymes. These results indicate that the production of inhibitors by food microbiota may be an important challenge for the recovery of foodborne pathogens, particularly for Shigella sonnei. The performance of enrichment media for recovery of Shigella and STEC could be improved by mitigating the impact of inhibitors produced by food microbiota during the enrichment process.

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.

HIGHLIGHTS

Salmonella Genomics in Public Health and Food Safety

The species Salmonella enterica comprises over 2,600 serovars, many of which are known to be intracellular pathogens of mammals, birds, and reptiles. It is now apparent that Salmonella is a highly adapted environmental microbe and can readily persist in a number of environmental niches, including water, soil, and various plant (including produce) species. Much of what is known about the evolution and diversity of nontyphoidal Salmonella serovars (NTS) in the environment is the result of the rise of the genomics era in enteric microbiology. There are over 340,000 Salmonella genomes available in public databases. This extraordinary breadth of genomic diversity now available for the species, coupled with widespread availability and affordability of whole-genome sequencing (WGS) instrumentation, has transformed the way in which we detect, differentiate, and characterize Salmonella enterica strains in a timely way. Not only have WGS data afforded a detailed and global examination of the molecular epidemiological movement of Salmonella from diverse environmental reservoirs into human and animal hosts, but they have also allowed considerable consolidation of the diagnostic effort required to test for various phenotypes important to the characterization of Salmonella. For example, drug resistance, serovar, virulence determinants, and other genome-based attributes can all be discerned using a genome sequence. Finally, genomic analysis, in conjunction with functional and phenotypic approaches, is beginning to provide new insights into the precise adaptive changes that permit persistence of NTS in so many diverse and challenging environmental niches.

Analysis of Bacterial Communities by 16S rRNA Gene Sequencing in a Melon-Producing Agro-environment

Cantaloupe melons, which have been responsible of an increasing number of foodborne disease outbreaks, may become contaminated with microbial pathogens during production. However, little information is available on the microbial populations in the cantaloupe farm environment. The purpose of this work was to characterize the bacterial communities present on cantaloupe farms. Fruit, soil, and harvester hand rinsates were collected from two Mexican cantaloupe farms, each visited three times. Microbiome analysis was performed by sequencing 16sRNA and analyzed using qiime2 software. Correlations were determined between sample type and microbial populations. The α and β diversity analysis identified 2777 sequences across all samples. The soil samples had the highest number and diversity of unique species (from 130 to 1329 OTUs); cantaloupe (from 112 to 205 OTUs), and hands (from 67 to 151 OTUs) had similar diversity. Collectively, Proteobacteria was the most abundant phyla (from 42 to 95%), followed by Firmicutes (1-47%), Actinobacteria (< 1 to 23%), and Bacteroidetes (< 1 to 4.8%). The most abundant genera were Acinetobacter (20-58%), Pseudomonas (14.5%), Erwinia (13%), and Exiguobacterium (6.3%). Genera with potential to be pathogenic included Bacillus (4%), Salmonella (0.85%), Escherichia-Shigella (0.38%), Staphylococcus (0.32%), Listeria (0.29%), Clostridium (0.28%), and Cronobacter (0.27%), which were found at lower frequencies. This study provides information on the cantaloupe production microbiome, which can inform future research into critical food safety issues such as antimicrobial resistance, virulence, and genomic epidemiology.

An investigation into the anaerobic spoilage microbiota of beef carcass and rump steak cuts using high- throughput sequencing

The presence of anaerobic microflora on fresh beef carcass and rump steaks, which may contribute to meat spoilage, was explored in this study. A total of 120 carcass and 120 rump steak swabs were collected immediately after slaughtering and boning, respectively from five meat plants, anaerobically incubated and enriched at 4°C for 3 weeks. This was followed by DNA extraction and 16S rRNA amplicon sequencing using the Illumina MiSeq, with subsequent bioinformatics analysis. The enriched microbiota of the samples was classified and grouped into 149 operational taxonomic units (OTUs). The microbiota recovered from both sample types consisted mainly of Carnobacterium, with an average relative abundance of 28.4% and 32.8% in beef carcasses and beef rump steaks, respectively. This was followed by Streptococcus, Serratia, Lactococcus, Enterococcus, Escherichia-Shigella, Raoultella and Aeromonas ranging from 1.5 to 20% and 0.1 to 29.8% in enriched carcasses and rump steak swabs, respectively. Trichococcus, Bacteroides, Dysgomonas, Providencia, Paraclostridium and Proteus were also present ranging from 0 to 0.8% on carcass and 0 to 1.8% on rump steak swabs, respectively. Alpha and beta diversity measurements showed limited diversity between the two sample types, but some differences between samples from the beef plants investigated were evident. This study highlights the presence of potential spoilage bacteria, mainly anaerobic genera on and between carcass and rump steaks, as an indication of contamination on and between these samples.

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.

Direct identification and molecular characterization of zoonotic hazards in raw milk by metagenomics using Brucella as a model pathogen

Metagenomics is a valuable diagnostic tool for enhancing microbial food safety because (i) it enables the untargeted detection of pathogens, (ii) it is fast since primary isolation of micro-organisms is not required, and (iii) it has high discriminatory power allowing for a detailed molecular characterization of pathogens. For shotgun metagenomics, total nucleic acids (NAs) are isolated from complex samples such as foodstuff. Along with microbial NAs, high amounts of matrix NAs are extracted that might outcompete microbial NAs during next-generation sequencing and compromise sensitivity for the detection of low abundance micro-organisms. Sensitive laboratory methods are indispensable for detecting highly pathogenic foodborne bacteria like Brucella spp., because a low infectious dose is sufficient to cause human disease through the consumption of contaminated dairy or meat products. In our study, we applied shotgun metagenomic sequencing for the identification and characterization of Brucella spp. in artificially and naturally contaminated raw milk from various ruminant species. With the depletion of eukaryotic cells prior to DNA extraction, Brucella was detectable at 10 bacterial cells ml⁻¹, while at the same time microbiological culture and isolation of the fastidious bacteria commonly failed. Moreover, we were able to retrieve the genotype of a Brucella isolate from a metagenomic dataset, indicating the potential of metagenomics for outbreak investigations using SNPs and core-genome multilocus sequence typing (cgMLST). To improve diagnostic applications, we developed a new bioinformatics approach for strain prediction based on SNPs to identify the correct species and define a certain strain with only low numbers of genus-specific reads per sample. This pipeline turned out to be more sensitive and specific than Mash Screen. In raw milk samples, we simultaneously detected numerous other zoonotic pathogens, antimicrobial resistance genes and virulence factors. Our study showed that metagenomics is a highly sensitive tool for biological risk assessment of foodstuffs, particularly when pathogen isolation is hazardous or challenging.

Comparison between cage and free-range egg production on microbial composition, diversity and the presence of Salmonella enterica

The microbial composition of the food production environment plays an important role in food safety and quality. This study employed both 16 S rRNA gene sequencing technology and culture-based techniques to investigate the bacterial microbiota of an egg production facility comprising of both free-range and conventional cage housing systems. The study also aimed to detect the presence of Salmonella enterica and determine whether its presence was positively or negatively associated with other taxa. Our findings revealed that microbiota profiles of free-range and cage houses differ considerably in relation to the relative abundance and diversity with a number of taxa unique to each system and to individual sampling sites within sheds. Core to each housing system were known inhabitants of the poultry gastrointestinal tracts, Romboutsia and Turicibacter, as well as common spoilage bacteria. Generally, free-range samples contained fewer taxa and were dominated by Staphylococcus equorum, differentiating them from the cage samples. Salmonella enterica was significantly associated with the presence of a taxa belonging to the Carnobacteriaceae family. The results of this study demonstrate that the diversity and composition of the microbiota is highly variable across egg layer housing systems, which could have implications for productivity, food safety and spoilage.

Accurate and sensitive detection of Salmonella in foods by engineered bacteriophages

Salmonella is a major causative agent of foodborne illness and rapid identification of this pathogen is essential to prevent disease. Currently most assays require high bacterial burdens or prolonged enrichment to achieve acceptable performance. A reduction in testing time without loss of sensitivity is critical to allow food processors to safely decrease product holding time. To meet this need, a method was developed to detect Salmonella using luciferase reporter bacteriophages. Bacteriophages were engineered to express NanoLuc, a novel optimized luciferase originating from the deep-sea shrimp Oplophorus gracilirostris. NanoLuc-expressing bacteriophages had a limit of detection of 10-100 CFU per mL in culture without enrichment. Luciferase reporters demonstrated a broad host range covering all Salmonella species with one reporter detecting 99.3% of 269 inclusivity strains. Cross-reactivity was limited and only observed with other members of the Enterobacteriaceae family. In food matrix studies, a cocktail of engineered bacteriophages accurately detected 1 CFU in either 25 g of ground turkey with a 7 h enrichment or 100 g of powdered infant formula with a 16 h enrichment. Use of the NanoLuc reporter assay described herein resulted in a considerable reduction in enrichment time without a loss of sensitivity.

Optimization of a Method for the Concentration of Genetic Material in Bacterial and Fungal Communities on Fresh Apple Peel Surfaces

Apples are the most consumed fruit in the United States and have recently been shown to exhibit some vulnerability to contamination across the supply chain. It is unclear what role a fruit microbiome analysis may serve in future food safety programs interested in understanding changes in the product and the processing environment. Ultimately, sample integrity is key if any of these approaches are to be employed; low microbial loads on apple surfaces, the inability to sample the entire surface, and inefficiency of removal may act as barriers to achieving high-quality DNA. As such, the objective of this study was to identify a reproducible method to concentrate and quantify bacterial and fungal DNA from fresh apple surfaces. Five methods were evaluated: two variations of wash solutions for bath sonication, wash filtration, epidermis excision, and surface swabbing. Epidermis excision returned the highest mean DNA quantities, followed by the sonicated washes and wash filtration. Surface swabbing was consistently below the limit of detection. Based on the quantity of host DNA contamination in surface excision, the sonicated wash solution containing a surfactant presents the greatest opportunity for consistent, high-yielding DNA recovery from the entire apple surface.

Strain-Level Metagenomic Data Analysis of Enriched In Vitro and In Silico Spiked Food Samples: Paving the Way towards a Culture-Free Foodborne Outbreak Investigation Using STEC as a Case Study

Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample.

Salmonella enterica and Escherichia coli in Wheat Flour: Detection and Serotyping by a Quasimetagenomic Approach Assisted by Magnetic Capture, Multiple-Displacement Amplification, and Real-Time Sequencing

Food safety is a new area for novel applications of metagenomics analysis, which not only can detect and subtype foodborne pathogens in a single workflow but may also produce additional information with in-depth analysis capabilities. In this study, we applied a quasimetagenomic approach by combining short-term enrichment, immunomagnetic separation (IMS), multiple-displacement amplification (MDA), and nanopore sequencing real-time analysis for simultaneous detection of Salmonella and Escherichia coli in wheat flour. Tryptic soy broth was selected for the 12-h enrichment of samples at 42°C. Enrichments were subjected to IMS using beads capable of capturing both Salmonella and E. coli MDA was performed on harvested beads, and amplified DNA fragments were subjected to DNA library preparation for sequencing. Sequencing was performed on a portable device with real-time basecalling adaptability, and resulting sequences were subjected to two parallel pipelines for further analysis. After 1 h of sequencing, the quasimetagenomic approach could detect all targets inoculated at approximately 1 CFU/g flour to the species level. Discriminatory power was determined by simultaneous detection of dual inoculums of Salmonella and E. coli, absence of detection in control samples, and consistency in microbial flora composition of the same flour samples over several rounds of experiments. The total turnaround time for detection was approximately 20 h. Longer sequencing for up to 15 h enabled serotyping for many of the samples with more than 99% genome coverage, which could be subjected to other appropriate genetic analysis pipelines in less than a total of 36 h.IMPORTANCE Enterohemorrhagic Escherichia coli (EHEC) and Salmonella are of serious concern in low-moisture foods, including wheat flour and its related products, causing illnesses, outbreaks, and recalls. The development of advanced detection methods based on molecular principles of analysis is essential to incorporate into interventions intended to reduce the risk from these pathogens. In this work, a quasimetagenomic method based on real-time sequencing analysis and assisted by magnetic capture and DNA amplification was developed. This protocol is capable of detecting multiple Salmonella and/or E. coli organisms in the sample within less than a day, and it can also generate sufficient whole-genome sequences of the target organisms suitable for subsequent bioinformatics analysis. Multiplex detection and identification were accomplished in less than 20 h and additional whole-genome analyses of different nature were attained within 36 h, in contrast to the several days required in previous sequencing pipelines.

Phylogenetic analysis revealed that Salmonella Typhimurium ST313 isolated from humans and food in Brazil presented a high genomic similarity

Salmonella Typhimurium sequence type 313 (S. Typhimurium ST313) has caused invasive disease mainly in sub-Saharan Africa. In Brazil, ST313 strains have been recently described, and there is a lack of studies that assessed by whole genome sequencing (WGS)-the relationship of these strains. The aims of this work were to study the phylogenetic relationship of 70 S. Typhimurium genomes comparing strains of ST313 (n = 9) isolated from humans and food in Brazil among themselves, with other STs isolated in this country (n = 31) and in other parts of the globe (n = 30) by 16S rRNA sequences, the Gegenees software, whole genome multilocus sequence typing (wgMLST), and average nucleotide identity (ANI) for the genomes of ST313. Additionally, pangenome analysis was performed to verify the heterogeneity of these genomes. The phylogenetic analyses showed that the ST313 genomes were very similar among themselves. However, the ST313 genomes were usually clustered more distantly to other STs of strains isolated in Brazil and in other parts of the world. By pangenome calculation, the core genome was 2,880 CDSs and 4,171 CDSs singletons for all the 70 S. Typhimurium genomes studied. Considering the 10 ST313 genomes analyzed the core genome was 4,112 CDSs and 76 CDSs singletons. In conclusion, the ST313 genomes from Brazil showed a high similarity among them which information might eventually help in the development of vaccines and antibiotics. The pangenome analysis showed that the S. Typhimurium genomes studied presented an open pangenome, but specifically tending to become close for the ST313 strains.

Next-generation sequencing as a screening tool for foodborne pathogens in fresh produce

Next generation sequencing (NGS) approaches are increasingly applied to tracing microbial contaminants entering the food chain due to NGS' untargeted nature and ability to investigate non-culturable (and/or difficult to culture) organisms while yielding genomic information about the microbiota. So far, a plethora of microbes has been shown to be associated with fresh produce, but few studies have utilised NGS to identify contamination with human pathogens. This study aims to establish the limit of detection (LoD) for Salmonella and phage MS2 (a Norovirus surrogate) contamination of fresh produce employing NGS approaches on the Illumina MiSeq: 16S amplicon-sequencing, and RNA-seq, using ScriptSeq (Illumina) and NEBNext (New England BioLabs) kits. ScriptSeq proved the most sensitive approach; delivering an LoD of 10⁴ CFU reaction^-1 (Colony Forming Units) for Salmonella and 10⁵ PFU reaction^-1 (Plaque Forming Units) for phage MS2. Use of the NEBNext kit resulted in detection of Salmonella at 10⁶ CFU reaction^-1 and phage MS2 at 10⁷ PFU reaction^-1. 16S amplicon-sequencing yielded a similar LoD of 10⁵ CFU reaction^-1 for Salmonella but could not detect MS2. The tested NGS methodologies, in combination with bioinformatics approaches applied, proved less sensitive than conventional microbial detection approaches.

Metagenomics of pasteurized and unpasteurized gouda cheese using targeted 16S rDNA sequencing

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.

Microbiomes Associated With Foods From Plant and Animal Sources

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.

Presence and Persistence of Salmonella in Water: The Impact on Microbial Quality of Water and Food Safety

Salmonella ranks high among the pathogens causing foodborne disease outbreaks. According to the Centers for Disease Control and Prevention, Salmonella contributed to about 53.4% of all foodborne disease outbreaks from 2006 to 2017, and approximately 32.7% of these foodborne Salmonella outbreaks were associated with consumption of produce. Trace-back investigations have suggested that irrigation water may be a source of Salmonella contamination of produce and a vehicle for transmission. Presence and persistence of Salmonella have been reported in surface waters such as rivers, lakes, and ponds, while ground water in general offers better microbial quality for irrigation. To date, culture methods are still the gold standard for detection, isolation and identification of Salmonella in foods and water. In addition to culture, other methods for the detection of Salmonella in water include most probable number, immunoassay, and PCR. The U.S. Food and Drug Administration (FDA) issued the Produce Safety Rule (PSR) in January 2013 based on the Food Safety Modernization Act (FSMA), which calls for more efforts toward enhancing and improving approaches for the prevention of foodborne outbreaks. In the PSR, agricultural water is defined as water used for in a way that is intended to, or likely to, contact covered produce, such as spray, wash, or irrigation. In summary, Salmonella is frequently present in surface water, an important source of water for irrigation. An increasing evidence indicates irrigation water as a source (or a vehicle) for transmission of Salmonella. This pathogen can survive in aquatic environments by a number of mechanisms, including entry into the viable but nonculturable (VBNC) state and/or residing within free-living protozoa. As such, assurance of microbial quality of irrigation water is critical to curtail the produce-related foodborne outbreaks and thus enhance the food safety. In this review, we will discuss the presence and persistence of Salmonella in water and the mechanisms Salmonella uses to persist in the aquatic environment, particularly irrigation water, to better understand the impact on the microbial quality of water and food safety due to the presence of Salmonella in the water environment.

The Present and Future of Whole Genome Sequencing (WGS) and Whole Metagenome Sequencing (WMS) for Surveillance of Antimicrobial Resistant Microorganisms and Antimicrobial Resistance Genes across the Food Chain

Antimicrobial resistance (AMR) surveillance is a critical step within risk assessment schemes, as it is the basis for informing global strategies, monitoring the effectiveness of public health interventions, and detecting new trends and emerging threats linked to food. Surveillance of AMR is currently based on the isolation of indicator microorganisms and the phenotypic characterization of clinical, environmental and food strains isolated. However, this approach provides very limited information on the mechanisms driving AMR or on the presence or spread of AMR genes throughout the food chain. Whole-genome sequencing (WGS) of bacterial pathogens has shown potential for epidemiological surveillance, outbreak detection, and infection control. In addition, whole metagenome sequencing (WMS) allows for the culture-independent analysis of complex microbial communities, providing useful information on AMR genes occurrence. Both technologies can assist the tracking of AMR genes and mobile genetic elements, providing the necessary information for the implementation of quantitative risk assessments and allowing for the identification of hotspots and routes of transmission of AMR across the food chain. This review article summarizes the information currently available on the use of WGS and WMS for surveillance of AMR in foodborne pathogenic bacteria and food-related samples and discusses future needs that will have to be considered for the routine implementation of these next-generation sequencing methodologies with this aim. In particular, methodological constraints that impede the use at a global scale of these high-throughput sequencing (HTS) technologies are identified, and the standardization of methods and protocols is suggested as a measure to upgrade HTS-based AMR surveillance schemes.

Quasimetagenomics-Based and Real-Time-Sequencing-Aided Detection and Subtyping of Salmonella enterica from Food Samples

Metagenomics analysis of food samples promises isolation-independent detection and subtyping of foodborne bacterial pathogens in a single workflow. The selective concentration of Salmonella genomic DNA by immunomagnetic separation (IMS) and multiple displacement amplification (MDA) shortened the time for culture enrichment of Salmonella-spiked raw chicken breast samples by over 12 h while permitting serotyping and high-fidelity single nucleotide polymorphism (SNP) typing of the pathogen using short shotgun sequencing reads. The herein-termed quasimetagenomics approach was evaluated on Salmonella-spiked lettuce and black peppercorn samples as well as retail chicken parts naturally contaminated with different serotypes of Salmonella Culture enrichment of between 8 and 24 h was required for detecting and subtyping naturally occurring Salmonella from unspiked chicken parts compared with 4- to 12-h culture enrichment when Salmonella-spiked food samples were analyzed, indicating the likely need for longer culture enrichment to revive low levels of stressed or injured Salmonella cells in food. A further acceleration of the workflow was achieved by real-time nanopore sequencing. After 1.5 h of analysis on a potable sequencer, sufficient data were generated from sequencing the IMS-MDA products of a cultured-enriched lettuce sample to enable serotyping and robust phylogenetic placement of the inoculated isolate.IMPORTANCE Both culture enrichment and next-generation sequencing remain time-consuming processes for food testing, whereas rapid methods for pathogen detection are widely available. Our study demonstrated a substantial acceleration of these processes by the use of immunomagnetic separation (IMS) with multiple displacement amplification (MDA) and real-time nanopore sequencing. In one example, the combined use of the two methods delivered a less than 24-h turnaround time from the collection of a Salmonella-contaminated lettuce sample to the phylogenetic identification of the pathogen. An improved efficiency such as this is important for further expanding the use of whole-genome and metagenomics sequencing in the microbial analysis of food. Our results suggest the potential of the quasimetagenomics approach in areas where rapid detection and subtyping of foodborne pathogens are important, such as for foodborne outbreak response and the precision tracking and monitoring of foodborne pathogens in production environments and supply chains.

High Throughput Sequencing for Detection of Foodborne Pathogens

High-throughput sequencing (HTS) is becoming the state-of-the-art technology for typing of microbial isolates, especially in clinical samples. Yet, its application is still in its infancy for monitoring and outbreak investigations of foods. Here we review the published literature, covering not only bacterial but also viral and Eukaryote food pathogens, to assess the status and potential of HTS implementation to inform stakeholders, improve food safety and reduce outbreak impacts. The developments in sequencing technology and bioinformatics have outpaced the capacity to analyze and interpret the sequence data. The influence of sample processing, nucleic acid extraction and purification, harmonized protocols for generation and interpretation of data, and properly annotated and curated reference databases including non-pathogenic "natural" strains are other major obstacles to the realization of the full potential of HTS in analytical food surveillance, epidemiological and outbreak investigations, and in complementing preventive approaches for the control and management of foodborne pathogens. Despite significant obstacles, the achieved progress in capacity and broadening of the application range over the last decade is impressive and unprecedented, as illustrated with the chosen examples from the literature. Large consortia, often with broad international participation, are making coordinated efforts to cope with many of the mentioned obstacles. Further rapid progress can therefore be prospected for the next decade.

A Review on the Applications of Next Generation Sequencing Technologies as Applied to Food-Related Microbiome Studies

The development of next generation sequencing (NGS) techniques has enabled researchers to study and understand the world of microorganisms from broader and deeper perspectives. The contemporary advances in DNA sequencing technologies have not only enabled finer characterization of bacterial genomes but also provided deeper taxonomic identification of complex microbiomes which in its genomic essence is the combined genetic material of the microorganisms inhabiting an environment, whether the environment be a particular body econiche (e.g., human intestinal contents) or a food manufacturing facility econiche (e.g., floor drain). To date, 16S rDNA sequencing, metagenomics and metatranscriptomics are the three basic sequencing strategies used in the taxonomic identification and characterization of food-related microbiomes. These sequencing strategies have used different NGS platforms for DNA and RNA sequence identification. Traditionally, 16S rDNA sequencing has played a key role in understanding the taxonomic composition of a food-related microbiome. Recently, metagenomic approaches have resulted in improved understanding of a microbiome by providing a species-level/strain-level characterization. Further, metatranscriptomic approaches have contributed to the functional characterization of the complex interactions between different microbial communities within a single microbiome. Many studies have highlighted the use of NGS techniques in investigating the microbiome of fermented foods. However, the utilization of NGS techniques in studying the microbiome of non-fermented foods are limited. This review provides a brief overview of the advances in DNA sequencing chemistries as the technology progressed from first, next and third generations and highlights how NGS provided a deeper understanding of food-related microbiomes with special focus on non-fermented foods.

High-Resolution Microbiome Profiling for Detection and Tracking of Salmonella enterica

16S rRNA community profiling continues to be a useful tool to study microbiome composition and dynamics, in part due to advances in next generation sequencing technology that translate into reductions in cost. Reliable taxonomic identification to the species-level, however, remains difficult, especially for short-read sequencing platforms, due to incomplete coverage of the 16S rRNA gene. This is especially true for Salmonella enterica, which is often found as a low abundant member of the microbial community, and is often found in combination with several other closely related enteric species. Here, we report on the evaluation and application of Resphera Insight, an ultra-high resolution taxonomic assignment algorithm for 16S rRNA sequences to the species level. The analytical pipeline achieved 99.7% sensitivity to correctly identify S. enterica from WGS datasets extracted from the FDA GenomeTrakr Bioproject, while demonstrating 99.9% specificity over other Enterobacteriaceae members. From low-diversity and low-complexity samples, namely ice cream, the algorithm achieved 100% specificity and sensitivity for Salmonella detection. As demonstrated using cilantro and chili powder, for highly complex and diverse samples, especially those that contain closely related species, the detection threshold will likely have to be adjusted higher to account for misidentifications. We also demonstrate the utility of this approach to detect Salmonella in the clinical setting, in this case, bloodborne infections.

Metagenomics: The Next Culture-Independent Game Changer

A trend towards the abandonment of obtaining pure culture isolates in frontline laboratories is at a crossroads with the ability of public health agencies to perform their basic mandate of foodborne disease surveillance and response. The implementation of culture-independent diagnostic tests (CIDTs) including nucleic acid and antigen-based assays for acute gastroenteritis is leaving public health agencies without laboratory evidence to link clinical cases to each other and to food or environmental substances. This limits the efficacy of public health epidemiology and surveillance as well as outbreak detection and investigation. Foodborne outbreaks have the potential to remain undetected or have insufficient evidence to support source attribution and may inadvertently increase the incidence of foodborne diseases. Next-generation sequencing of pure culture isolates in clinical microbiology laboratories has the potential to revolutionize the fields of food safety and public health. Metagenomics and other 'omics' disciplines could provide the solution to a cultureless future in clinical microbiology, food safety and public health. Data mining of information obtained from metagenomics assays can be particularly useful for the identification of clinical causative agents or foodborne contamination, detection of AMR and/or virulence factors, in addition to providing high-resolution subtyping data. Thus, metagenomics assays may provide a universal test for clinical diagnostics, foodborne pathogen detection, subtyping and investigation. This information has the potential to reform the field of enteric disease diagnostics and surveillance and also infectious diseases as a whole. The aim of this review will be to present the current state of CIDTs in diagnostic and public health laboratories as they relate to foodborne illness and food safety. Moreover, we will also discuss the diagnostic and subtyping utility and concomitant bias limitations of metagenomics and comparable detection techniques in clinical microbiology, food and public health laboratories. Early advances in the discipline of metagenomics, however, have indicated noteworthy challenges. Through forthcoming improvements in sequencing technology and analytical pipelines among others, we anticipate that within the next decade, detection and characterization of pathogens via metagenomics-based workflows will be implemented in routine usage in diagnostic and public health laboratories.

High temporal variability in airborne bacterial diversity and abundance inside single-family residences

Our homes are microbial habitats, and although the amounts and types of bacteria in indoor air have been shown to vary substantially across residences, temporal variability within homes has rarely been characterized. Here, we sought to quantify the temporal variability in the amounts and types of airborne bacteria in homes, and what factors drive this variability. We collected filter samples of indoor and outdoor air in 15 homes over 1 year (approximately eight time points per home, two per season), and we used culture-independent DNA sequencing approaches to characterize bacterial community composition. Significant differences in indoor air community composition were observed both between homes and within each home over time. Indoor and outdoor air community compositions were not significantly correlated, suggesting that indoor and outdoor air communities are decoupled. Indoor air communities from the same home were often just as different at adjacent time points as they were across larger temporal distances, and temporal variation correlated with changes in environmental conditions, including temperature and relative humidity. Although all homes had highly variable indoor air communities, homes with the most temporally variable communities had more stable, lower average microbial loads than homes with less variable communities.

Early Recovery of Salmonella from Food Using a 6-Hour Non-selective Pre-enrichment and Reformulation of Tetrathionate Broth

Culture based methods are commonly employed to detect pathogens in food and environmental samples. These methods are time consuming and complex, requiring multiple non-selective and selective enrichment broths, and usually take at least 1 week to recover and identify pathogens. Improving pathogen detection in foods is a primary goal for regulatory agencies and industry. Salmonella detection in food relies on a series of culture steps in broth formulations optimized to resuscitate Salmonella and reduce the abundance of competitive bacteria. Examples of non-selective pre-enrichment broths used to isolate Salmonella from food include Lactose, Universal Pre-enrichment, BPW, and Trypticase Soy broths. Tetrathionate (TT) and Rappaport-Vassiliadis (RV) broths are employed after a 24-h non-selective enrichment to select for Salmonella and hamper the growth of competitive bacteria. In this study, we tested a new formulation of TT broth that lacks brilliant green dye and has lower levels of TT . We employed this TT broth formulation in conjunction with a 6-h non-selective pre-enrichment period and determined that Salmonella recovery was possible one day earlier than standard food culture methods. We tested the shortened culture method in different non-selective enrichment broths, enumerated Salmonella in the non-selective enrichments, and used 16S rRNA gene sequencing to determine the proportional abundances of Salmonella in the TT and RV selective enrichments. Together these data revealed that a 6-h non-selective pre-enrichment reduces the levels of competitive bacteria inoculated into the selective TT and RV broths, enabling the recovery of Salmonella 1 day earlier than standard culture enrichment methods.

Strain-Level Discrimination of Shiga Toxin-Producing Escherichia coli in Spinach Using Metagenomic Sequencing

Consumption of fresh bagged spinach contaminated with Shiga toxin-producing Escherichia coli (STEC) has led to severe illness and death; however current culture-based methods to detect foodborne STEC are time consuming. Since not all STEC strains are considered pathogenic to humans, it is crucial to incorporate virulence characterization of STEC in the detection method. In this study, we assess the comprehensiveness of utilizing a shotgun metagenomics approach for detection and strain-level identification by spiking spinach with a variety of genomically disparate STEC strains at a low contamination level of 0.1 CFU/g. Molecular serotyping, virulence gene characterization, microbial community analysis, and E. coli core gene single nucleotide polymorphism (SNP) analysis were performed on metagenomic sequence data from enriched samples. It was determined from bacterial community analysis that E. coli, which was classified at the phylogroup level, was a major component of the population in most samples. However, in over half the samples, molecular serotyping revealed the presence of indigenous E. coli which also contributed to the percent abundance of E. coli. Despite the presence of additional E. coli strains, the serotype and virulence genes of the spiked STEC, including correct Shiga toxin subtype, were detected in 94% of the samples with a total number of reads per sample averaging 2.4 million. Variation in STEC abundance and/or detection was observed in replicate spiked samples, indicating an effect from the indigenous microbiota during enrichment. SNP analysis of the metagenomic data correctly placed the spiked STEC in a phylogeny of related strains in cases where the indigenous E. coli did not predominate in the enriched sample. Also, for these samples, our analysis demonstrates that strain-level phylogenetic resolution is possible using shotgun metagenomic data for determining the genomic relatedness of a contaminating STEC strain to other closely related E. coli.

Enrichment dynamics of Listeria monocytogenes and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak

Background

Microbiota that co-enrich during efforts to recover pathogens from foodborne outbreaks interfere with efficient detection and recovery. Here, dynamics of co-enriching microbiota during recovery of Listeria monocytogenes from naturally contaminated ice cream samples linked to an outbreak are described for three different initial enrichment formulations used by the Food and Drug Administration (FDA), the International Organization of Standardization (ISO), and the United States Department of Agriculture (USDA). Enrichment cultures were analyzed using DNA extraction and sequencing from samples taken every 4 h throughout 48 h of enrichment. Resphera Insight and CosmosID analysis tools were employed for high-resolution profiling of 16S rRNA amplicons and whole genome shotgun data, respectively.

Results

During enrichment, other bacterial taxa were identified, including Anoxybacillus, Geobacillus, Serratia, Pseudomonas, Erwinia, and Streptococcus spp. Surprisingly, incidence of L. monocytogenes was proportionally greater at hour 0 than when tested 4, 8, and 12 h later with all three enrichment schemes. The corresponding increase in Anoxybacillus and Geobacillus spp.indicated these taxa co-enriched in competition with L. monocytogenes during early enrichment hours. L. monocytogenes became dominant after 24 h in all three enrichments. DNA sequences obtained from shotgun metagenomic data of Listeria monocytogenes at 48 h were assembled to produce a consensus draft genome which appeared to have a similar tracking utility to pure culture isolates of L. monocytogenes.

Conclusions

All three methods performed equally well for enrichment of Listeria monocytogenes. The observation of potential competitive exclusion of L. mono by Anoxybacillus and Geobacillus in early enrichment hours provided novel information that may be used to further optimize enrichment formulations. Application of Resphera Insight for high-resolution analysis of 16S amplicon sequences accurately identified L. monocytogenes. Both shotgun and 16S rRNA data supported the presence of three slightly variable genomes of L. monocytogenes. Moreover, the draft assembly of a consensus genome of L. monocytogenes from shotgun metagenomic data demonstrated the potential utility of this approach to expedite trace-back of outbreak-associated strains, although further validation will be needed to confirm this utility.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-016-0894-1) contains supplementary material, which is available to authorized users.

Mungo bean sprout microbiome and changes associated with culture based enrichment protocols used in detection of Gram-negative foodborne pathogens

BACKGROUND

Fresh sprouted seeds have been associated with a number of large outbreaks caused by Salmonella and Shiga toxin-producing E. coli. However, the high number of commensal bacteria found on sprouted seeds hampers the detection of these pathogens. Knowledge about the composition of the sprout microbiome is limited. In this study, the microbiome of mungo bean sprouts and the impact of buffered peptone water (BPW) and Enterobacteriaceae enrichment broth (EE-broth)-based enrichment protocols on this microbiome were investigated.

RESULTS

Assessments based on aerobic mesophilic colony counts showed similar increases in mungo bean sprout background flora levels independent of the enrichment protocol used. 16S rRNA sequencing revealed a mungo bean sprout microbiome dominated by Proteobacteria and Bacteroidetes. EE-broth enrichment of such samples preserved and increased Proteobacteria dominance while reducing Bacteroidetes and Firmicutes relative abundances. BPW enrichment, however, increased Firmicutes relative abundance while decreasing Proteobacteria and Bacteroidetes levels. Both enrichments also lead to various genus level changes within the Protobacteria and Firmicutes phyla.

CONCLUSIONS

New insights into the microbiome associated with mungo bean sprout and how it is influenced through BPW and EE-broth-based enrichment strategies used for detecting Gram-negative pathogens were generated. BPW enrichment leads to Firmicutes and Proteobacteria dominance, whereas EE-broth enrichment preserves Proteobacteria dominance in the mungo bean sprout samples. By increasing the relative abundance of Firmicutes, BPW also increases the abundance of Gram-positive organisms including some that might inhibit recovery of Gram-negative pathogens. The use of EE-broth, although preserving and increasing the dominance of Proteobacteria, can also hamper the detection of lowly abundant Gram-negative target pathogens due to outgrowth of such organisms by the highly abundant non-target Proteobacteria genera comprising the mungo bean sprout associated background flora.

Recent and emerging innovations in Salmonella detection: a food and environmental perspective

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.