Metagenomics Approaches for Improving Food Safety: A Review

A Metagenomic Survey of Virological Hazards in Market-Ready Oysters

Viral contamination of bivalve molluscs, such as oysters, is a well-recognized food safety risk. The aim of this study was to assess virological hazards in market-ready oysters on the Dutch market. Non-targeted metagenome analysis was first performed on norovirus spiked-in samples showing linear and sensitive detection of norovirus GI.2 and GII.4 down to 14 and 5 genome copies per reaction, respectively. Subsequently, metagenomic measurements were performed to detect vertebrate viral genomes present in 24 undepurated B-area samples and 144 market-ready oyster samples taken in November up to and including February of the years 2015-2021. Genome sequences from fifteen viral species were identified in market-ready oysters which are associated with infections in humans and were detected above the genomic coverage threshold (5%) applied. Among these, the two genera from the Caliciviridae family, norovirus and sapovirus were detected at high prevalence (44 and 30%). Additionally, adeno-associated dependoparvovirus A and B as well as Aichi virus A and B (ribo)nucleic acids were detected (42, 33, 6, and 11%). Nucleic acids from virus species in oysters included potentially hazardous Picobirnavirus, Anellovirus, and multiple Circoviridae and Genomoviridae species. By integrating metagenome analysis into the monitoring process, researchers, food producers and regulatory bodies can gain valuable insights into the viral communities present in the food chain. This allows for the detection of potential pathogenic hazards at an early stage, providing an opportunity for tailored monitoring programs and targeted interventions to maintain the sanitary quality of the production area and safeguard public health.

Ecological insights into the microbiology of food using metagenomics and its potential surveillance applications

A diverse array of micro-organisms can be found on food, including those that are pathogenic or resistant to antimicrobial drugs. Metagenomics involves extracting and sequencing the DNA of all micro-organisms on a sample, and here, we used a combination of culture and culture-independent approaches to investigate the microbial ecology of food to assess the potential application of metagenomics for the microbial surveillance of food. We cultured common foodborne pathogens and other organisms including Escherichia coli, Klebsiella/Raoultella spp., Salmonella spp. and Vibrio spp. from five different food commodities and compared their genomes to the microbial communities obtained by metagenomic sequencing following host (food) DNA depletion. The microbial populations of retail food were found to be predominated by psychrotrophic bacteria, driven by the cool temperatures in which the food products are stored. Pathogens accounted for a small percentage of the food metagenome compared to the psychrotrophic bacteria, and cultured pathogens were inconsistently identified in the metagenome data. The microbial composition of food varied amongst different commodities, and metagenomics was able to classify the taxonomic origin of 59% of antimicrobial resistance genes (ARGs) found on food to the genus level, but it was unclear what percentage of ARGs were associated with mobile genetic elements and thus transferable to other bacteria. Metagenomics may be used to survey the ARG burden, composition and carriage on foods to which consumers are exposed. However, food metagenomics, even after depleting host DNA, inconsistently identifies pathogens without enrichment or further bait capture.

Bioinformatic Pipeline for Profiling Foodborne Bacterial Ecology and Resistome from Short-Read Metagenomics

Next-generation sequencing revolutionized food safety management these last years providing access to a huge quantity of valuable data to identify, characterize, and monitor bacterial pathogens on the food chain. Shotgun metagenomics emerged as a particularly promising approach as it enables in-depth taxonomic profiling and functional investigation of food microbial communities. In this chapter, we provide a comprehensive step-by-step bioinformatical workflow to characterize bacterial ecology and resistome composition from metagenomic short-reads obtained by shotgun sequencing.

The Microbial Diversity and Traceability Analysis of Raw Milk from Buffalo Farms at Different Management Ranks in Guangxi Province

Farm management has a significant impact on microbial composition and may affect the quality of raw buffalo milk. This study involved a diversity analysis and traceability of the microbial communities in raw buffalo milk from three buffalo farms at different management ranks in Guangxi Province, China. The microbial composition of the raw buffalo milk and its environmental sources were investigated using 16S rRNA gene sequencing and bioinformatics analysis. The results demonstrated that different management ranks significantly influenced microbial composition in milk, with the primary sources of contamination varying across farms. The env.OPS_17 was the predominant differential bacterium in farm rank A, whereas Enterobacteriaceae, Aerococcaceae, and Planococcaceae were dominant in farm rank B. The Fast Expectation-Maximization for Microbial Source Tracking model revealed that while the sources of microbial contamination varied across farms at different management ranks, the teat and teat liner consistently emerged as the primary sources of microbial contamination in raw buffalo milk. This study provides important insights into how different farm management ranks affect the microbial composition of raw buffalo milk, highlighting the importance of improved management practices during milk production, particularly in cleaning the milking equipment and farm environment, as these are key factors in ensuring the quality and safety of raw buffalo milk.

Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world

Foodborne illnesses pose a substantial health and economic burden, presenting challenges in prevention due to the diverse microbial hazards that can enter and spread within food systems. Various factors, including natural, political and commercial drivers, influence food production and distribution. The risks of foodborne illness will continue to evolve in step with these drivers and with changes to food systems. For example, climate impacts on water availability for agriculture, changes in food sustainability targets and evolving customer preferences can all have an impact on the ecology of foodborne pathogens and the agrifood niches that can carry microorganisms. Whole-genome and metagenome sequencing, combined with microbial surveillance schemes and insights from the food system, can provide authorities and businesses with transformative information to address risks and implement new food safety interventions across the food chain. In this Review, we describe how genome-based approaches have advanced our understanding of the evolution and spread of enduring bacterial foodborne hazards as well as their role in identifying emerging foodborne hazards. Furthermore, foodborne hazards exist in complex microbial communities across the entire food chain, and consideration of these co-existing organisms is essential to understanding the entire ecology supporting pathogen persistence and transmission in an evolving food system.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Comprehensive identification of pathogenic microbes and antimicrobial resistance genes in food products using nanopore sequencing-based metagenomics

Foodborne pathogens, particularly antimicrobial-resistant (AMR) bacteria, remain a significant threat to global health. Given the limitations of conventional culture-based approaches, which are limited in scope and time-consuming, metagenomic sequencing of food products emerges as a promising solution. This method provides a fast and comprehensive way to detect the presence of pathogenic microbes and antimicrobial resistance genes (ARGs). Notably, nanopore long-read sequencing provides more accurate bacterial taxonomic classification in comparison to short-read sequencing. Here, we revealed the impact of food types and attributes (origin, retail place, and food processing methods) on microbial communities and the AMR profile using nanopore metagenomic sequencing. We analyzed a total of 260 food products, including raw meat, sashimi, and ready-to-eat (RTE) vegetables. Clostridium botulinum, Acinetobacter baumannii, and Vibrio parahaemolyticus were identified as the top three foodborne pathogens in raw meat and sashimi. Importantly, even with low pathogen abundance, higher percentages of samples containing carbapenem and cephalosporin resistance genes were identified in chicken and RTE vegetables, respectively. In parallel, our results demonstrated that fresh, peeled, and minced foods exhibited higher levels of pathogenic bacteria. In conclusion, this comprehensive study offers invaluable data that can contribute to food safety assessments and serve as a basis for quality indicators.

Inter-facility characterization of bacteria in seafood processing plants: Exploring potential reservoirs of spoilage organisms and the resistome

A study was conducted in fish processing facilities to investigate the microbial composition, microbial metabolic potential, and distribution of antibiotic resistance genes. Whole metagenomic sequencing was used to analyze microbial communities from different processing rooms, operators and fish products. Taxonomic analyses identified the genera Pseudomonas and Psychrobacter as the most prevalent bacteria. A Principal Component Analysis revealed a distinct separation between fish product and environmental samples, as well as differences between fish product samples from companies processing either Gadidae or Salmonidae fish. Some particular bacterial genera and species were associated with specific processing rooms and operators. Metabolic analysis of metagenome assembled genomes demonstrated variations in microbiota metabolic profiles of microbiota across rooms and fish products. The study also examined the presence of antibiotic-resistance genes in fish processing environments, contributing to the understanding of microbial dynamics, metabolic potential, and implications for fish spoilage.

Analysis of Human Milk Microbiota in Northern Greece by Comparative 16S rRNA Sequencing vs. Local Dairy Animals

Milk is a biological fluid with a dynamic composition of micronutrients and bioactive molecules that serves as a vital nutrient source for infants. Milk composition is affected by multiple factors, including genetics, geographical location, environmental conditions, lactation phase, and maternal nutrition, and plays a key role in dictating its microbiome. This study addresses a less-explored aspect, comparing the microbial communities in human breast milk with those in mature milk from species that are used for milk consumption. Since mature animal milk is used as a supplement for both the infant (formula) and the child/adolescent, our main aim was to identify shared microbial communities in colostrum and mature human milk. Using 16S rRNA metagenomic sequencing, we focused on characterizing the milk microbiota in the Northern Greek population by identifying shared microbial communities across samples and comparing the relative abundance of prevalent genera. We analyzed ten human milk samples (from five mothers), with five collected three days postpartum (colostrum) and five collected thirty to forty days postpartum (mature milk) from corresponding mothers. To perform an interspecies comparison of human milk microbiota, we analyzed five goat and five bovine milk samples from a local dairy industry, collected fifty to seventy days after birth. Alpha diversity analysis indicated moderate diversity and stability in bovine milk, high richness in goat milk, and constrained diversity in breast milk. Beta diversity analysis revealed significant distinctions among mammalian species, emphasizing both presence/absence and abundance-based clustering. Despite noticeable differences, shared microbial components underscore fundamental aspects across all mammalian species, highlighting the presence of a core microbiota predominantly comprising the Proteobacteria, Firmicutes, and Actinobacteriota phyla. At the genus level, Acinetobacter, Gemella, and Sphingobium exhibit significant higher abundance in human milk compared to bovine and goat milk, while Pseudomonas and Atopostipes are more prevalent in animal milk. Our comparative analysis revealed differences and commonalities in the microbial communities of various mammalian milks and unraveled the existence of a common fundamental milk core microbiome. We thus revealed both species-specific and conserved microbial communities in human, bovine, and goat milk. The existence of a common core microbiome with conserved differences between colostrum and mature human milk underscores fundamental similarities in the microbiota of milk across mammalian species, which could offer valuable implications for optimizing the nutritional quality and safety of dairy products as well as supplements for infant health.

Advances in aptamer-based biosensors for monitoring foodborne pathogens

Biosensors are analytical devices for detecting a wide range of targets, including cells, proteins, DNA, enzymes, and chemical and biological compounds. They mostly rely on using bioprobes with a high binding affinity to the target for specific detection. However, low specificity and effectiveness of the conventional biosensors has led to the search for novel materials, that can specifically detect biomolecules. Aptamers are a group of single-stranded DNA or RNA oligonucleotides, that can bind to their targets with high specificity and serve as effective bioprobes for developing aptamer-based biosensors. Aptamers have a shorter production time, high stability, compared to traditional bioprobes, and possess ability to develop them for specific target molecules for tailored applications. Thus, various aptasensing approaches, including electrochemical, optical, surface plasmon resonance and chip-dependent approaches, have been investigated in recent times for various biological targets, including foodborne pathogens. Hence, this article is an overview of various conventional foodborne pathogen detection methods, their limitations and the ability of aptamer-based biosensors to overcome those limitations and replace them. In addition, the current status and advances in aptamer-based biosensors for the detection of foodborne pathogens to ensure food safety were also discussed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-023-05889-8.

A metagenomics-based workflow for the detection and genomic characterization of GBS in raw freshwater fish

The unexpected foodborne outbreak in Singapore in 2015 has accentuated Group B Streptococcus (GBS, Streptococcus agalactiae) sequence type 283 as an emerging foodborne pathogen transmitted via the consumption of contaminated raw freshwater fish. Isolation-based workflows utilizing conventional microbiological and whole-genome sequencing methods are commonly used to support biosurveillance efforts critical for the control management of this emerging foodborne pathogen. However, these isolation-based workflows tend to have relatively long turnaround times that hamper a timely response for implementing risk mitigation. To address this gap, we have developed a metagenomics-based workflow for the simultaneous detection and genomic characterization of GBS in raw freshwater fish. Notably, our validation results showed that this metagenomics-based workflow could achieve comparable accuracy and potentially better detection limits while halving the turnaround time (from 2 weeks to 5 days) relative to an isolation-based workflow. The metagenomics-based workflow was also successfully adapted for use on a portable long-read nanopore sequencer, demonstrating its potential applicability for real-time point-of-need testing. Using GBS in freshwater fish as an example, this work represents a proof-of-concept study that supports the feasibility and validity of metagenomics as a rapid and accurate test methodology for the detection and genomic characterization of foodborne pathogens in complex food matrices.

IMPORTANCE

The need for a rapid and accurate food microbiological testing method is apparent for a timely and effective foodborne outbreak response. This is particularly relevant for emerging foodborne pathogens such as Group B Streptococcus (GBS) whose associated food safety risk might be undercharacterized. By using GBS in raw freshwater fish as a case example, this study describes the development of a metagenomics-based workflow for rapid food microbiological safety testing and surveillance. This study can inform as a working model for various foodborne pathogens in other complex food matrices, paving the way for future methodological development of metagenomics for food microbiological safety testing.

Foodborne Pathogen Dynamics in Meat and Meat Analogues Analysed Using Traditional Microbiology and Metagenomic Sequencing

Meat analogues play an increasing role in meeting global nutritional needs. However, while it is well known that meat possesses inherent characteristics that create favourable conditions for the growth of various pathogenic bacteria, much less is known about meat analogues. This study aimed to compare the growth and survival of Escherichia coli HEHA16, Listeria monocytogenes, Salmonella enterica Typhi, Cronobacter sakazakii, and a cocktail of these bacteria in sterile juices from minced chicken, pig, and beef, as well as pea-based and soy-based minced meat. Traditional microbiology and next-generation sequencing of those metagenomes were employed to analyse the pathogen variability, abundance, and survival after an incubation period. Our findings show that all the meat juices provided favourable conditions for the growth and proliferation of the studied bacteria, with the exception of E. coli HEHA16, which showed lower survival rates in the chicken matrix. Meat analogue juice mainly supported L. monocytogenes survival, with C. sakazakii survival supported to a lesser extent. A correlation was observed between the traditional culturing and metagenomic analysis results, suggesting that further work is needed to compare these technologies in foodborne setups. Our results indicate that plant-based meats could serve as vectors for the transmission of certain, but likely not all, foodborne pathogens, using two accurate detection methods. This warrants the need for additional research to better understand and characterise their safety implications, including their potential association with additional pathogens.

Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples

Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.

Aggregative Soil Sampling Using Boot Covers Compared to Soil Grabs From Commercial Romaine Fields Shows Similar Indicator Organism and Microbial Community Recoveries

Aggregative boot cover sampling may be a more representative, practical, and powerful method for preharvest produce soil testing than grab sampling because boot covers aggregate soil from larger areas. Our study tests if boot cover sampling results reflect quality and safety indicator organisms and community diversity of grab sampling. We collected soil samples from commercial romaine lettuce fields spanning 5060 m2 using boot covers (n = 28, m = 1.1 ± 0.4 g; wearing boot covers and walking along the path), composite grabs (n = 28, m = 231 ± 24 g; consisting of 60 grabs of 3-5 g each), and high-resolution grabs (n = 72, m = 56 ± 4 g; taking one sample per stratum). Means and standard deviations of log-transformed aerobic plate counts (APCs) were 7.0 ± 0.3, 7.1 ± 0.2, and 7.3 ± 0.2 log(CFU/g) for boot covers, composite grabs, and high-resolution grabs, respectively. APCs did not show biologically meaningful differences between sample types. Boot covers recovered on average 0.6 log(CFU/g) more total coliforms than both grabs (p < 0.001) where means and standard deviations of log-transformed counts were 3.2 ± 1.0, 2.6 ± 0.6, and 2.6 ± 1.0 log(CFU/g) for boot covers, composite grabs, and high-resolution grabs, respectively. There were no generic E. coli detected in any sample by enumeration methods with LODs of 1.3-2.1 log(CFU/g) for boot covers and 0.5 log(CFU/g) for both grabs. By 16S rRNA sequencing, community species diversity (alpha diversity) was not significantly different within collection methods. While communities differed (p < 0.001) between soil sampling methods (beta diversity), variance in microbial communities was not significantly different. Of the 28 phyla and 297 genera detected, 25 phyla (89%) and 258 genera (87%) were found by all methods. Overall, aggregative boot cover sampling is similar to both grab methods for recovering quality and safety indicator organisms and representative microbiomes. This justifies future work testing aggregative soil sampling for foodborne pathogen detection.

Cultured Meat Safety Research Priorities: Regulatory and Governmental Perspectives

As with every new technology, safety demonstration is a critical component of bringing products to market and gaining public acceptance for cultured meat and seafood. This manuscript develops research priorities from the findings of a series of interviews and workshops with governmental scientists and regulators from food safety agencies in fifteen jurisdictions globally. The interviews and workshops aimed to identify the key safety questions and priority areas of research. Participants raised questions about which aspects of cultured meat and seafood production are novel, and the implications of the paucity of public information on the topic. Novel parameters and targets may require the development of new analytical methods or adaptation and validation of existing ones, including for a diversity of product types and processes. Participants emphasized that data sharing of these efforts would be valuable, similar to those already developed and used in the food and pharmaceutical fields. Contributions to such databases from the private and public sectors would speed general understanding as well as efforts to make evaluations more efficient. In turn, these resources, combined with transparent risk assessment, will be critical elements of building consumer trust in cultured meat and seafood products.

Foodborne Microbial Communities as Potential Reservoirs of Antimicrobial Resistance Genes for Pathogens: A Critical Review of the Recent Literature

Antimicrobial resistance (AMR) is a global and increasing threat to human health. Several genetic determinants of AMR are found in environmental reservoirs, including bacteria naturally associated with widely consumed fermented foods. Through the food chain, these bacteria can reach the gut, where horizontal gene transfer (HGT) can occur within the complex and populated microbial environment. Numerous studies on this topic have been published over the past decades, but a conclusive picture of the potential impact of the non-pathogenic foodborne microbial reservoir on the spread of AMR to human pathogens has not yet emerged. This review critically evaluates a comprehensive list of recent experimental studies reporting the isolation of AMR bacteria associated with fermented foods, focusing on those reporting HGT events, which represent the main driver of AMR spread within and between different bacterial communities. Overall, our analysis points to the methodological heterogeneity as a major weakness impairing determination or a causal relation between the presence of AMR determinants within the foodborne microbial reservoir and their transmission to human pathogens. The aim is therefore to highlight the main gaps and needs to better standardize future studies addressing the potential role of non-pathogenic bacteria in the spread of AMR.

Use of a taxon-specific reference database for accurate metagenomics-based pathogen detection of Listeria monocytogenes in turkey deli meat and spinach

BACKGROUND

The reliability of culture-independent pathogen detection in foods using metagenomics is contingent on the quality and composition of the reference database. The inclusion of microbial sequences from a diverse representation of taxonomies in universal reference databases is recommended to maximize classification precision for pathogen detection. However, these sizable databases have high memory requirements that may be out of reach for some users. In this study, we aimed to assess the performance of a foodborne pathogen (FBP)-specific reference database (taxon-specific) relative to a universal reference database (taxon-agnostic). We tested our FBP-specific reference database's performance for detecting Listeria monocytogenes in two complex food matrices-ready-to-eat (RTE) turkey deli meat and prepackaged spinach-using three popular read-based DNA-to-DNA metagenomic classifiers: Centrifuge, Kraken 2 and KrakenUniq.

RESULTS

In silico host sequence removal led to substantially fewer false positive (FP) classifications and higher classification precision in RTE turkey deli meat datasets using the FBP-specific reference database. No considerable improvement in classification precision was observed following host filtering for prepackaged spinach datasets and was likely a consequence of a higher microbe-to-host sequence ratio. All datasets classified with Centrifuge using the FBP-specific reference database had the lowest classification precision compared to Kraken 2 or KrakenUniq. When a confidence-scoring threshold was applied, a nearly equivalent precision to the universal reference database was achieved for Kraken 2 and KrakenUniq. Recall was high for both reference databases across all datasets and classifiers. Substantially fewer computational resources were required for metagenomics-based detection of L. monocytogenes using the FBP-specific reference database, especially when combined with Kraken 2.

CONCLUSIONS

A universal (taxon-agnostic) reference database is not essential for accurate and reliable metagenomics-based pathogen detection of L. monocytogenes in complex food matrices. Equivalent classification performance can be achieved using a taxon-specific reference database when the appropriate quality control measures, classification software, and analysis parameters are applied. This approach is less computationally demanding and more attainable for the broader scientific and food safety communities.

The power, potential, benefits, and challenges of implementing high-throughput sequencing in food safety systems

The development and application of modern sequencing technologies have led to many new improvements in food safety and public health. With unprecedented resolution and big data, high-throughput sequencing (HTS) has enabled food safety specialists to sequence marker genes, whole genomes, and transcriptomes of microorganisms almost in real-time. These data reveal not only the identity of a pathogen or an organism of interest in the food supply but its virulence potential and functional characteristics. HTS of amplicons, allow better characterization of the microbial communities associated with food and the environment. New and powerful bioinformatics tools, algorithms, and machine learning allow for development of new models to predict and tackle important events such as foodborne disease outbreaks. Despite its potential, the integration of HTS into current food safety systems is far from complete. Government agencies have embraced this new technology, and use it for disease diagnostics, food safety inspections, and outbreak investigations. However, adoption and application of HTS by the food industry have been comparatively slow, sporadic, and fragmented. Incorporation of HTS by food manufacturers in their food safety programs could reinforce the design and verification of effectiveness of control measures by providing greater insight into the characteristics, origin, relatedness, and evolution of microorganisms in our foods and environment. Here, we discuss this new technology, its power, and potential. A brief history of implementation by public health agencies is presented, as are the benefits and challenges for the food industry, and its future in the context of food safety.