Omics approaches in food safety: fulfilling the promise?

Salmonella enterica serovar Braenderup shows clade-specific source associations and a high proportion of molecular epidemiological clustering

Salmonella enterica serovar Braenderup (S. enterica ser. Braenderup) is an important clinical serovar in the United States. This serovar was reported by the CDC in 2017 as the fifth most common Salmonella enterica serovar associated with outbreaks in the United States, which have been linked to both fresh produce and food animal products. The goals of this study were to compare the relatedness of human clinical isolates from southeastern USA (Tennessee (n = 106), Kentucky (n = 48), Virginia (n = 252), South Carolina (n = 109), Georgia (n = 159), Alabama (n = 8), Arkansas (n = 26), and Louisiana (n = 91)) and global clinical (n = 5,153) and nonclinical (n = 1,053) isolates obtained from the NCBI. Additionally, we also examined the population structure of S. enterica ser. Braenderup strains (n = 3,131) on EnteroBase and found that all the strains of this serovar are associated with a single cgMLST eBurst group (ceBG 185), confirming that this serovar is monophyletic. We divided the S. enterica ser. Braenderup population into two clades (Clade I and Clade II) and one clade group (Clade Group III). The composition of distinct environmental isolates in the clades differed: Clade I was significantly associated with produce (90.7%; P < 0.0001) and water, soil, and sediment (76.9%; P < 0.0001), and Clade II was significantly associated with poultry environments (62.8%; P < 0.0001). The clade-specific gene associations (e.g., Clade I-associated competence proteins and cytochrome_c_asm protein and Clade II-associated heme-exporter protein and dimethyl sulfoxide [DMSO] reductase-encoding genes) provide potential insights into possible mechanisms driving environmental adaptation and host-pathogen interaction. Phylogenetic analyses identified 218 molecular epidemiological clusters in the current study, which represented a greater proportion of potentially outbreak-related isolates than previously estimated.

IMPORTANCE

This study provides insights into the genomic diversity of S. enterica ser. Braenderup by revealing distinct clade-specific source attribution patterns and showing that a greater proportion of isolates were associated with epidemiological clusters based on the genomic relatedness than previously estimated. Specifically, we analyzed the diversity of human clinical isolates from southeastern USA and compared them with the global clinical and nonclinical isolates. Our analysis showed different clades of S. enterica ser. Braenderup linked to different environments, providing insights on the potential source of human sporadic infection and outbreaks. These findings can enhance public health surveillance and response strategies targeting S. enterica serovar Braenderup by expanding our understanding of potential transmission pathways and the genomic diversity of clinical and environmental isolates.

Relationship between microorganisms and milk metabolites during quality changes in refrigerated raw milk: A metagenomic and metabolomic exploration

Although cold storage at 4 °C can effectively prolong the shelf life of raw milk, it cannot prevent its eventual spoilage. In this study, we analyzed the main physicochemical and microbial indexes of raw milk stored at 4 °C for 6 days. The changes in microbial profiles and milk metabolites and their relationship during refrigeration were also explored. Metagenomic analysis performed using the Illumina Hiseq Xten sequencing platform revealed that the dominant genera in raw milk evolved from Acinetobacter, Streptococcus, Staphylococcus, and Anaplasma to Flavobacterium, Pseudomonas, and Lactococcus during cold storage. Using the UHPLC-Q-TOF MS method, 77 significantly different metabolites (p < 0.05) were identified, among which lipids were the most abundant (37). The most significant metabolic changes largely occurred at 3-4 days of refrigeration, coinciding with the rapid increase in dominant psychrotrophic bacteria. Subsequently, correlation analysis demonstrated that these lipid-related metabolites were significantly associated with Acinetobacter, Flavobacterium, and Pseudomonas. Both macro indicators and microanalysis indicated that the key stage of quality changes in raw milk was 3-4 days. Thus, this stage can be targeted for the quality control of raw milk.

Advanced data analytics and "omics" techniques to control enteric foodborne pathogens

Enteric pathogens, particularly bacterial pathogens, are associated with millions of cases of foodborne illness in the U.S. and worldwide, necessitating the identification and development of methods to control and minimize their impact on public health. Predictive modeling and quantitative microbial risk assessment are two such methods that analyze data on microbial behavior, particularly as a response to changes in the food matrix, to predict and control the presence and prevalence of these pathogens in food. However, a number of these bacterial enteric pathogens, including Escherichia coli, Listeria monocytogenes, and Salmonella enterica, have inherent genetic and phenotypic differences among their subtypes and variants. This has led to an increasing reliance on "omics" technologies, including genomics, proteomics, transcriptomics, and metabolomics, to identify and characterize pathogenic microorganisms and their behavior in food systems. With this exponential increase in available data on these enteric pathogens, comes a need for the development of novel strategies to analyze this data. Advanced data analysis/analytics is a means to extract value from these large data sources, and is considered the core of data processing. In the past few years, advanced data analytics methods such as machine learning and artificial intelligence have been increasingly used to extract meaningful, actionable knowledge from these data sources to help mitigate food safety issues caused by enteric pathogens. This chapter reviews the latest in research into the use of advanced data analytics, particularly machine learning, to analyze "omics" data of enteric bacterial pathogens, and identifies potential future uses of these techniques in mitigating the risk of these pathogens on public health.

Use of whole genome sequencing for surveillance and control of foodborne diseases: status quo and quo vadis

Improvements in sequencing quality, availability, speed and costs results in an increased presence of genomics in infectious disease applications. Nevertheless, there are still hurdles in regard to the optimal use of WGS for public health purposes. Here, we discuss the current state ("status quo") and future directions ("quo vadis") based on literature regarding the use of genomics in surveillance, hazard characterization and source attribution of foodborne pathogens. The future directions include the application of new techniques, such as machine learning and network approaches that may overcome the current shortcomings. These include the use of fixed genomic distances in cluster delineation, disentangling similarity or lack thereof in source attribution, and difficulties ascertaining function in hazard characterization. Although, the aforementioned methods can relatively easily be applied technically, an overarching challenge is the inference and biological/epidemiological interpretation of these large amounts of high-resolution data. Understanding the context in terms of bacterial isolate and host diversity allows to assess the level of representativeness in regard to sources and isolates in the dataset, which in turn defines the level of certainty associated with defining clusters, sources and risks. This also marks the importance of metadata (clinical, epidemiological, and biological) when using genomics for public health purposes.

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.

Food Safety-Transcriptomics and Proteomics

Food safety is a critical aspect of public health and involves the handling, preparation, and storage of food to avoid contamination and foodborne illnesses [...].

Wholegenome sequencing as the gold standard approach for control of Listeria monocytogenes in the food chain

Listeria monocytogenes has been implicated in numerous outbreaks and related deaths of listeriosis. In food production, L. monocytogenes occurs in raw food material and above all, through postprocessing contamination. The use of next-generation sequencing technologies such as whole-genome sequencing (WGS) facilitates foodborne outbreak investigations, pathogen source tracking and tracing geographic distributions of different clonal complexes, routine microbiological/epidemiological surveillance of listeriosis, and quantitative microbial risk assessment. WGS can also be used to predict various genetic traits related to virulence, stress, or antimicrobial resistance, which can be of great benefit for improving food safety management as well as public health.

Metataxonomic sequencing to assess microbial safety of Turkish white cheeses

High-throughput sequencing has provided a way to monitor the large diversity of microorganisms in fermented foods that have complex microbiota. Up to date, many kinds of cheese have been characterized with the metataxonomic approach, but the safety of unpacked Turkish white cheeses, which are widely consumed in Turkey, has not been assessed. In this study, fifteen unpacked white cheeses sold in public bazaars in Ankara province have been collected and subjected to microbial enumeration as well as physicochemical analysis. Five white cheeses, which have relatively the highest foodborne pathogens, out of fifteen white cheeses, have been analyzed by next-generation sequencing and metataxonomic analysis. According to the results, abundant families were Lactobacillaceae, Oceanospirillaceae, Enterococcaceae, Pseudomonadaceae, and Vibrionaceae. Staphylococcus aureus, E. coli, and Salmonella, which are indicators of bad hygiene and sanitation conditions, were found in cheeses. In conclusion, culture-independent methods such as metataxonomic can be important to evaluate the safety of foods.

Metagenomics Approaches for Improving Food Safety: A Review

ABSTRACT

Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole genome sequencing. This approach is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, next-generation sequencing is still anchored in the traditional laboratory practice of the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding and shotgun and long-read metagenomics, are part of the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed alongside current limitations and knowledge gaps and new opportunities arising from the use of this technology.

HIGHLIGHTS

Microbiota Analysis and Microbiological Hazard Assessment in Chinese Chive (Allium tuberosum Rottler) Depending on Retail Types

Chinese chive (Allium tuberosum Rottler) has potential risks associated with pathogenic bacterial contamination as it is usually consumed raw. In this study, we investigated the microbiota of Chinese chives purchased from traditional markets and grocery stores in March (Spring) and June (Summer) 2017. Differences in bacterial diversity were observed, and the microbial composition varied across sampling times and sites. In June, potential pathogenic genera, such as Escherichia, Enterobacter, and Pantoea, accounted for a high proportion of the microbiota in samples purchased from the traditional market. A large number of pathogenic bacteria (Acinetobacter lwoffii, Bacillus cereus, Klebsiella pneumoniae, and Serratia marcescens) were detected in the June samples at a relatively high rate. In addition, the influence of the washing treatment on Chinese chive microbiota was analyzed. After storage at 26°C, the washing treatment accelerated the growth of enterohemorrhagic Escherichia coli (EHEC) because it caused dynamic shifts in Chinese chive indigenous microbiota. These results expand our knowledge of the microbiota in Chinese chives and provide data for the prediction and prevention of food-borne illnesses.

Recent Advancements in the Technologies Detecting Food Spoiling Agents

To match the current life-style, there is a huge demand and market for the processed food whose manufacturing requires multiple steps. The mounting demand increases the pressure on the producers and the regulatory bodies to provide sensitive, facile, and cost-effective methods to safeguard consumers' health. In the multistep process of food processing, there are several chances that the food-spoiling microbes or contaminants could enter the supply chain. In this contest, there is a dire necessity to comprehend, implement, and monitor the levels of contaminants by utilizing various available methods, such as single-cell droplet microfluidic system, DNA biosensor, nanobiosensor, smartphone-based biosensor, aptasensor, and DNA microarray-based methods. The current review focuses on the advancements in these methods for the detection of food-borne contaminants and pathogens.

Detection of Campylobacter jejuni from Fresh Produce: Comparison of Culture- and PCR-based Techniques, and Metagenomic Approach for Analyses of the Microbiome before and after Enrichment

ABSTRACT

In this study, we compared the efficiency of culture-based methods with or without membrane filtration, real-time PCR, and digital droplet PCR (ddPCR) for the detection of Campylobacter in fresh produce. Alfalfa sprouts, clover sprouts, coleslaw, and lettuce salad spiked with Campylobacter jejuni were enriched in Bolton broth for 48 h, and enrichment cultures were either directly inoculated onto modified charcoal-cefoperazone-deoxycholate agar or applied on membrane filters placed on the surface of plating media. In parallel, 2-mL Bolton broth cultures were taken to extract DNA for real-time PCR and ddPCR assays and bacterial community analysis. A developed primer set for ddPCR and real-time PCR was evaluated for its inclusivity and exclusivity using pure culture of C. jejuni and non-C. jejuni strains, respectively. In pure culture, the primer set reacted only with C. jejuni strains and showed negative reaction to non-C. jejuni strains. There was no significant difference (P > 0.05) in the detection efficiency of positive Campylobacter isolates from coleslaw and lettuce salad using four detection methods. However, for sprout samples, the detection efficiency of the culture method was significantly (P < 0.05) lower than those of the two PCR assays and the filtration method. The analysis also revealed the presence of Pseudomonas and Acinetobacter as the most prevalent competing microbiota in enriched culture and only Acinetobacter on agar plates in the selective culture step.

HIGHLIGHTS

A multi-platform metabolomics approach to identify possible biomarkers for human faecal contamination in Greenshell™ mussels (Perna canaliculus)

Bivalve molluscs have the potential to bioaccumulate microbial pathogens including noroviruses from aquatic environments and as such, there is a need for a rapid and cheap in-situ method for their detection. Here, we characterise the tissue-specific response of New Zealand Greenshell™ mussels (Perna canaliculus) to faecal contamination from two different sources (municipal sewage and human faeces). This is done with the view to identify potential biomarkers that could be further developed into low cost, rapid and sensitive in-situ biosensors for human faecal contamination detection of mussels in growing areas. Tissue-specific metabolic profiles from gills, haemolymph and digestive glands were analysed using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). Clear differentiation of metabolic profiles was observed among treatments in each tissue type. Overall, energy pathways such as glycolysis, citrate cycle and oxidative phosphorylation were downregulated across the three mussel tissues studied following simulated contamination events. Conversely, considerable sterol upregulation in the gills was observed after exposure to contamination. Additionally, free pools of nucleotide phosphates and the antioxidant glutathione declined considerably post-exposure to contamination in gills. These results provide important insights into the tissue-specific metabolic effects of human faecal contamination in mussels. This study demonstrates the utility of metabolomics as a tool for identifying potential biomarkers in mussels.

Foodborne pathogens in the omics era

Outbreaks and deaths related to Foodborne Diseases (FBD) occur constantly in the world, as a result of the consumption of contaminated foodstuffs with pathogens such as Listeria monocytogenes, Escherichia coli, Staphylococcus aureus, Salmonella spp, Clostridium spp. and Campylobacter spp. The purpose of this review is to discuss the main omic techniques applied in foodborne pathogen and to demonstrate their functionalities through the food chain and to guarantee the food safety. The main techniques presented are genomic, transcriptomic, secretomic, proteomic, and metabolomic, which together, in the field of food and nutrition, are known as "Foodomics." This review had highlighted the potential of omics to integrate variables that contribute to food safety and to enable us to understand their application on foodborne diseases. The appropriate use of these techniques had driven the definition of critical parameters to achieve successful results in the improvement of consumers health, costs and to obtain safe and high-quality products.

Ciguatera Mini Review: 21st Century Environmental Challenges and the Interdisciplinary Research Efforts Rising to Meet Them

Globally, the livelihoods of over a billion people are affected by changes to marine ecosystems, both structurally and systematically. Resources and ecosystem services, provided by the marine environment, contribute nutrition, income, and health benefits for communities. One threat to these securities is ciguatera poisoning; worldwide, the most commonly reported non-bacterial seafood-related illness. Ciguatera is caused by the consumption of (primarily) finfish contaminated with ciguatoxins, potent neurotoxins produced by benthic single-cell microalgae. When consumed, ciguatoxins are biotransformed and can bioaccumulate throughout the food-web via complex pathways. Ciguatera-derived food insecurity is particularly extreme for small island-nations, where fear of intoxication can lead to fishing restrictions by region, species, or size. Exacerbating these complexities are anthropogenic or natural changes occurring in global marine habitats, e.g., climate change, greenhouse-gas induced physical oceanic changes, overfishing, invasive species, and even the international seafood trade. Here we provide an overview of the challenges and opportunities of the 21st century regarding the many facets of ciguatera, including the complex nature of this illness, the biological/environmental factors affecting the causative organisms, their toxins, vectors, detection methods, human-health oriented responses, and ultimately an outlook towards the future. Ciguatera research efforts face many social and environmental challenges this century. However, several future-oriented goals are within reach, including digital solutions for seafood supply chains, identifying novel compounds and methods with the potential for advanced diagnostics, treatments, and prediction capabilities. The advances described herein provide confidence that the tools are now available to answer many of the remaining questions surrounding ciguatera and therefore protection measures can become more accurate and routine.

Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review

Metabolomics is a high precision analytical approach to obtaining detailed information of varieties of metabolites produced in biological systems, including foods. This study reviews the use of metabolomic approaches such as liquid chromatography mass spectrometry (LCMS), gas chromatography mass spectrometry (GC-MS), matrix assisted laser desorption /ionization tandem time of flight mass spectrometry (MALDI-TOF-MS) and nuclear magnetic resonance (NMR) for investigating the presence of foodborne pathogens and their metabolites. Pathogenic fungi and their notable metabolites (mycotoxins) have been studied more extensively using metabolomics as compared to bacteria, necessitating further studies in this regard. Nevertheless, such identified fungal and bacteria metabolites could be used as biomarkers for a more rapid detection of these pathogens in food. Other important compounds detected through metabolomics could also be correlated to functionality of these pathogenic strains, determined by the composition of the foods in which they exist, thereby providing insights into their metabolism. Considering the prevalence of these food pathogens, metabolomics still has potentials in the determination of food-borne pathogenic microorganisms especially for the determination of pathogenic bacteria toxins and is expected to generate research interests for further studies and applications.

Characterization of Shiga toxin-producing Escherichia coli isolated from Cattle and Sheep in Xinjiang province, China, using whole-genome sequencing

Shiga toxin-producing Escherichia coli (STEC) is an important food-borne pathogen capable of causing severe gastrointestinal diseases in humans. Cattle and sheep are the natural reservoir hosts of STEC strains. Previously, we isolated 56 STEC strains from anal and carcass swab samples of cattle and sheep in farms and slaughterhouses. In this study, we performed whole-genome sequencing of these isolates and determined their serotypes, virulence profiles, sequence types (STs) and genetic relationships. Our results showed that the 56 isolates belong to 20 different STs, 29 O:H serotypes and 8 stx subtype combinations. The highly prevalent serotypes for bovine and ovine isolates were O8:H25 and O87:H16, respectively. Five serotypes of cattle or sheep isolates are novel. The majority (63%) of cattle isolates contain stx1 + stx2, subtyped into stx1a, stx2a and stx2c. In contrast, most of the sheep isolates contain stx1 only, primarily subtyped into stx1a and stx1c. None of the isolates tested eae-positive, but virulence factors such as ehxA and espP were present with variable prevalence rates. The prevalence of saa (19.6%) and espP (12.5%) in cattle isolates is much higher than that in sheep isolates, whereas that of subA (34%), katP (14.3%) and ireA (28.6%) in sheep isolates is considerably higher than that in cattle isolates. Core-genome SNP analysis revealed that the majority of isolates could be clustered based on their serotypes or STs, whereas some clustering is associated with more than one ST or serotype. Five sheep isolates (4 belonging to ST675 and serotype O76:H19 and 1 belonging to ST25 and serotype O128:H2) share STs, serotypes and stx profiles with two hemolytic uremic syndrome-associated enterohemorrhagic E. coli (HUSEC) isolates; a cattle isolate belonging to the same ST as HUSEC isolate HUSEC001 contains all the nine virulence genes tested. These data suggest a potential of the six isolates for causing severe human infections. Collectively, we described the characteristics of cattle and sheep STEC isolates from Xinjiang, China, which may be utilized in comparative studies of other geographic regions and sources of isolation, and for surveillance as well.

Precision long-read metagenomics sequencing for food safety by detection and assembly of Shiga toxin-producing Escherichia coli in irrigation water

Shiga toxin-producing Escherichia coli (STEC) contamination of agricultural water might be an important factor to recent foodborne illness and outbreaks involving leafy greens. Closed bacterial genomes from whole genome sequencing play an important role in source tracking. We aimed to determine the limits of detection and classification of STECs by qPCR and nanopore sequencing using 24 hour enriched irrigation water artificially contaminated with E. coli O157:H7 (EDL933). We determined the limit of STEC detection by qPCR to be 30 CFU/reaction, which is equivalent to 105 CFU/ml in the enrichment. By using Oxford Nanopore's EPI2ME WIMP workflow and de novo assembly with Flye followed by taxon classification with a k-mer analysis software (Kraken2), E. coli O157:H7 could be detected at 103 CFU/ml (68 reads) and a complete fragmented E. coli O157:H7 metagenome-assembled genome (MAG) was obtained at 105-108 CFU/ml. Using a custom script to extract the E. coli reads, a completely closed MAG was obtained at 107-108 CFU/ml and a complete, fragmented MAG was obtained at 105-106 CFU/ml. In silico virulence detection for E. coli MAGs for 105-108 CFU/ml showed that the virulotype was indistinguishable from the spiked E. coli O157:H7 strain. We further identified the bacterial species in the un-spiked enrichment, including antimicrobial resistance genes, which could have important implications to food safety. We propose this workflow provides proof of concept for faster detection and complete genomic characterization of STECs from a complex microbial sample compared to current reporting protocols and could be applied to determine the limit of detection and assembly of other foodborne bacterial pathogens.

Quality of Dietary Supplements Containing Plant-Derived Ingredients Reconsidered by Microbiological Approach

Dietary supplements cover a wide range of products, the most popular are those containing plant-based ingredients. Supplements are consumed by consumers of all ages as well as by both healthy and sick people. The lack of unified regulation in this sector increases the probability that supplements are poor chemical and microbiological quality and can be dangerous for patients. The aim of this paper is to highlight selected issues associated with the microbiological quality of dietary supplements containing plant materials. We focus on the most recent reports referring to bacterial and fungal contaminations as well as the presence of mycotoxins. Dietary supplements containing plant ingredients commonly show a variety of microbial contaminants, which might be crucial for consumer safety. They often contain microorganisms potentially pathogenic to humans. Metabolites produced by microorganisms may pose a threat to the health of consumers. Because of that, in this review, we emphasize the risk that may be associated with the lack of appropriate studies of the quality of the supplements.

Mining whole genome sequence data to efficiently attribute individuals to source populations

Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.

The sources and transmission routes of microbial populations throughout a meat processing facility

Microbial food spoilage is responsible for a considerable amount of waste and can cause food-borne diseases in humans, particularly in immunocompromised individuals and children. Therefore, preventing microbial food spoilage is a major concern for health authorities, regulators, consumers, and the food industry. However, the contamination of food products is difficult to control because there are several potential sources during production, processing, storage, distribution, and consumption, where microorganisms come in contact with the product. Here, we use high-throughput full-length 16S rRNA gene sequencing to provide insights into bacterial community structure throughout a pork-processing plant. Specifically, we investigated what proportion of bacteria on meat are presumptively not animal-associated and are therefore transferred during cutting via personnel, equipment, machines, or the slaughter environment. We then created a facility-specific transmission map of bacterial flow, which predicted previously unknown sources of bacterial contamination. This allowed us to pinpoint specific taxa to particular environmental sources and provide the facility with essential information for targeted disinfection. For example, Moraxella spp., a prominent meat spoilage organism, which was one of the most abundant amplicon sequence variants (ASVs) detected on the meat, was most likely transferred from the gloves of employees, a railing at the classification step, and the polishing tunnel whips. Our results suggest that high-throughput full-length 16S rRNA gene sequencing has great potential in food monitoring applications.

Temporal Dynamics of Salmonella enterica subsp. enterica Serovar Agona Isolates From a Recurrent Multistate Outbreak

The largest outbreak of Salmonella Agona in the United States occurred in 1998. It affected more than 400 patients and was linked to toasted oat cereal. Ten years later, a similar outbreak occurred with the same outbreak strain linked to the same production facility. In this study, whole-genome sequence (WGS) data from a set of 46 Salmonella Agona including five isolates associated with the 1998 outbreak and 25 isolates associated with the 2008 outbreak were analyzed. From each outbreak one isolate was sequenced on the Pacific Biosciences RS II Sequencer to determine the complete genome sequence. We reconstructed a phylogenetic hypothesis of the samples using a reference-based method for identifying variable sites. Using Single Nucleotide Polymorphism (SNP) analyses, we were able to distinguish and separate Salmonella Agona isolates from both outbreaks with only a mean of eight SNP differences between them. The phylogeny illustrates that the 2008 outbreak involves direct descendants from the 1998 outbreak rather than a second independent contamination event. Based on these results, there is evidence supporting the persistence of Salmonella over time in food processing facilities and highlights the need for consistent monitoring and control of organisms in the supply chain to minimize the risk of successive outbreaks.

Addressing Learning Needs on the Use of Metagenomics in Antimicrobial Resistance Surveillance

One Health surveillance of antimicrobial resistance (AMR) depends on a harmonized method for detection of AMR. Metagenomics-based surveillance offers the possibility to compare resistomes within and between different target populations. Its potential to be embedded into policy in the future calls for a timely and integrated knowledge dissemination strategy. We developed a blended training (e-learning and a workshop) on the use of metagenomics in surveillance of pathogens and AMR. The objectives were to highlight the potential of metagenomics in the context of integrated surveillance, to demonstrate its applicability through hands-on training and to raise awareness to bias factors. The target participants included staff of competent authorities responsible for AMR monitoring and academic staff. The training was organized in modules covering the workflow, requirements, benefits and challenges of surveillance by metagenomics. The training had 41 participants. The face-to-face workshop was essential to understand the expectations of the participants about the transition to metagenomics-based surveillance. After revision of the e-learning, we released it as a Massive Open Online Course (MOOC), now available at https://www.coursera.org/learn/metagenomics. This course has run in more than 20 sessions, with more than 3,000 learners enrolled, from more than 120 countries. Blended learning and MOOCs are useful tools to deliver knowledge globally and across disciplines. The released MOOC can be a reference knowledge source for international players in the application of metagenomics in surveillance.

Translating 'big data': better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry

Recent technological advances has led to the generation, storage, and sharing of colossal sets of information ('big data'), and the expansion of 'omics' in science. To date, genomics/metagenomics, transcriptomics, proteomics, and metabolomics are arguably the most ground breaking approaches in food and public safety. Here we review some of the recent studies of foodborne pathogens (Campylobacter spp., Salmonella spp., and Escherichia coli) in poultry using big data. Genomic/metagenomic approaches have reveal the importance of the gut microbiota in health and disease. They have also been used to identify, monitor, and understand the epidemiology of antibiotic-resistance mechanisms and provide concrete evidence about the role of poultry in human infections. Transcriptomics studies have increased our understanding of the pathophysiology and immunopathology of foodborne pathogens in poultry and have led to the identification of host-resistance mechanisms. Proteomic/metabolomic approaches have aided in identifying biomarkers and the rapid detection of low levels of foodborne pathogens. Overall, 'omics' approaches complement each other and may provide, at least in part, a solution to our current food-safety issues by facilitating the development of new rapid diagnostics, therapeutic drugs, and vaccines to control foodborne pathogens in poultry. However, at this time most 'omics' approaches still remain underutilized due to their high cost and the high level of technical skills required.

Genome-Wide Profiling of Enterotoxigenic Staphylococcus aureus Strains Used for the Production of Naturally Contaminated Cheeses

Staphylococcus aureus is a major human pathogen and an important cause of livestock infections. More than 20 staphylococcal enterotoxins with emetic activity can be produced by specific strains responsible for staphylococcal food poisoning, one of the most common food-borne diseases. Whole genome sequencing provides a comprehensive view of the genome structure and gene content that have largely been applied in outbreak investigations and genomic comparisons. In this study, six enterotoxigenic S. aureus strains were characterised using a combination of molecular, phenotypical and computational methods. The genomes were analysed for the presence of virulence factors (VFs), where we identified 110 genes and classified them into five categories: adherence (n = 31), exoenzymes (n = 28), genes involved in host immune system evasion (n = 7); iron uptake regulatory system (n = 8); secretion machinery factors and toxins' genes (n = 36), and 39 genes coding for transcriptional regulators related to staphylococcal VFs. Each group of VFs revealed correlations among the six enterotoxigenic strains, and further analysis revealed their accessory genomic content, including mobile genetic elements. The plasmids pLUH02 and pSK67 were detected in the strain ProNaCC1 and ProNaCC7, respectively, carrying out the genes sed, ser, and selj. The genes carried out by prophages were detected in the strain ProNaCC2 (see), ProNaCC4, and ProNaCC7 (both positive for sea). The strain ProNaCC5 resulted positive for the genes seg, sei, sem, sen, seo grouped in an exotoxin gene cluster, and the strain ProNaCC6 resulted positive for seh, a transposon-associated gene. The six strains were used for the production of naturally contaminated cheeses which were tested with the European Screening Method for staphylococcal enterotoxins. The results obtained from the analysis of toxins produced in cheese, combined with the genomic features represent a portrait of the strains that can be used for the production of staphylococcal enterotoxin-positive cheese as reference material.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.

Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks

Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.

Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions

Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared with 0.37 bits for the SNP-threshold method and 0.84 bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.

Investing in Food Safety for Developing Countries: Opportunities and Challenges in Applying Whole-Genome Sequencing for Food Safety Management

Whole-genome sequencing (WGS) has become a significant tool in investigating foodborne disease outbreaks and some countries have incorporated WGS into national food control systems. However, WGS poses technical challenges that deter developing countries from incorporating it into their food safety management system. A rapid scoping review was conducted, followed by a focus group session, to understand the current situation regarding the use of WGS for foodborne disease surveillance and food monitoring at the global level and identify key limiting factors for developing countries in adopting WGS for their food control systems. The results showed that some developed nations routinely use WGS in their food surveillance systems resulting in more precise understanding of the causes of outbreaks. In developing nations, knowledge of WGS exists in the academic/research sectors; however, there is limited understanding at the government level regarding the usefulness of WGS for food safety regulatory activities. Thus, incorporation of WGS is extremely limited in most developing nations. While some countries lack the capacity to collect and analyze the data generated from WGS, the most significant technical gap in most developing countries is in data interpretation using bioinformatics. The gaps in knowledge and capacities between developed and developing nations regarding use of WGS likely introduce an inequality in international food trade, and thus, relevant international organizations, as well as the countries that are already proficient in the use of WGS, have significant roles in assisting developing nations to be able to fully benefit from the technology and its applications in food safety management.

Current and Emerging Technologies for the Detection of Norovirus from Shellfish

Reports of norovirus infections associated with the consumption of contaminated bivalve molluscan shellfish negatively impact both consumers and commercial shellfish operators. Current virus recovery and PCR detection methods can be expensive and time consuming. Due to the lack of rapid, user-friendly and onsite/infield methods, it has been difficult to establish an effective virus monitoring regime that is able to identify contamination points across the production line (i.e., farm-to-plate) to ensure shellfish quality. The focus of this review is to evaluate current norovirus detection methods and discuss emerging approaches. Recent advances in omics-based detection approaches have the potential to identify novel biomarkers that can be incorporated into rapid detection kits for onsite use. Furthermore, some omics techniques have the potential to simultaneously detect multiple enteric viruses that cause human disease. Other emerging technologies discussed include microfluidic, aptamer and biosensor-based detection methods developed to detect norovirus with high sensitivity from a simple matrix. Many of these approaches have the potential to be developed as user-friendly onsite detection kits with minimal costs. However, more collaborative efforts on research and development will be required to commercialize such products. Once developed, these emerging technologies could provide a way forward that minimizes public health risks associated with shellfish consumption.

Virulence gene profiles and phylogeny of Shiga toxin-positive Escherichia coli strains isolated from FDA regulated foods during 2010-2017

Illnesses caused by Shiga toxin-producing Escherichia coli (STECs) can be life threatening, such as hemolytic uremic syndrome (HUS). The STECs most frequently identified by USDA's Microbiological Data Program (MDP) carried toxin gene subtypes stx1a and/or stx2a. Here we described the genome sequences of 331 STECs isolated from foods regulated by the FDA 2010-2017, and determined their genomic identity, serotype, sequence type, virulence potential, and prevalence of antimicrobial resistance. Isolates were selected from the MDP archive, routine food testing by FDA field labs (ORA), and food testing by a contract company. Only 276 (83%) strains were confirmed as STECs by in silico analysis. Foods from which STECs were recovered included cilantro (6%), spinach (25%), lettuce (11%), and flour (9%). Phylogenetic analysis using core genome MLST revealed these STEC genomes were highly variable, with some clustering associated with ST types and serotypes. We detected 95 different sequence types (ST); several ST were previously associated with HUS: ST21 and ST29 (O26:H11), ST11 (O157:H7), ST33 (O91:H14), ST17 (O103:H2), and ST16 (O111:H-). in silico virulome analyses showed ~ 51% of these strains were potentially pathogenic [besides stx gene they also carried eae (25%) or 26% saa (26%)]. Virulence gene prevalence was also determined: stx1 only (19%); stx2 only (66%); and stx1/sxt2 (15%). Our data form a new WGS dataset that can be used to support food safety investigations and monitor the recurrence/emergence of E. coli in foods.

Detection of Foodborne Pathogens Using Proteomics and Metabolomics-Based Approaches

Considering the short shelf-life of certain food products such as red meat, there is a need for rapid and cost-effective methods for pathogen detection. Routine pathogen testing in food laboratories mostly relies on conventional microbiological methods which involve the use of multiple selective culture media and long incubation periods, often taking up to 7 days for confirmed identifications. The current study investigated the application of omics-based approaches, proteomics using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToF MS) and metabolomics using gas chromatography-mass spectrometry (GC-MS), for detection of three red meat pathogens - Listeria monocytogenes, Salmonella enterica and Escherichia coli O157:H7. Species-level identification was achieved within 18 h for S. enterica and E. coli O157:H7 and 30 h for L. monocytogenes using MALDI-ToF MS analysis. For the metabolomics approach, metabolites were extracted directly from selective enrichment broth samples containing spiked meat samples (obviating the need for culturing on solid media) and data obtained using GC-MS were analyzed using chemometric methods. Putative biomarkers relating to L. monocytogenes, S. enterica and E. coli O157:H7 were observed within 24, 18, and 12 h, respectively, of inoculating meat samples. Many of the identified metabolites were sugars, fatty acids, amino acids, nucleosides and organic acids. Secondary metabolites such as cadaverine, hydroxymelatonin and 3,4-dihydroxymadelic acid were also observed. The results obtained in this study will assist in the future development of rapid diagnostic tests for these important foodborne pathogens.

Current Status and Future Opportunities of Omics Tools in Mycotoxin Research

Mycotoxins are toxic secondary metabolites of low molecular weight produced by filamentous fungi, such as Aspergillus, Fusarium, and Penicillium spp. Mycotoxins are natural contaminants of agricultural commodities and their prevalence may increase due to global warming. Dangerous mycotoxins cause a variety of health problems not only for humans, but also for animals. For instance, they possess carcinogenic, immunosuppressive, hepatotoxic, nephrotoxic, and neurotoxic effects. Hence, various approaches have been used to assess and control mycotoxin contamination. Significant challenges still exist because of the complex heterogeneous nature of food composition. The potential of combined omics approaches such as metabolomics, genomics, transcriptomics, and proteomics would contribute to our understanding about pathogen fungal crosstalk as well as strengthen our ability to identify, isolate, and characterise mycotoxins pre and post-harvest. Multi-omics approaches along with advanced analytical tools and chemometrics provide a complete annotation of such metabolites produced before/during the contamination of crops. We have assessed the merits of these individual and combined omics approaches and their promising applications to mitigate the issue of mycotoxin contamination. The data included in this review focus on aflatoxin, ochratoxin, and patulin and would be useful as benchmark information for future research.

Design Elements of Listeria Environmental Monitoring Programs in Food Processing Facilities: A Scoping Review of Research and Guidance Materials

Occurrence of Listeria monocytogenes (Lm), the causative agent of listeriosis, in food processing facilities presents considerable challenges to food producers and food safety authorities. Design of an effective, risk-based environmental monitoring (EM) program is essential for finding and eliminating Lm from the processing environment to prevent product contamination. A scoping review was conducted to collate and synthesize available research and guidance materials on Listeria EM in food processing facilities. An exhaustive search was performed to identify all available research, industry and regulatory documents, and search results were screened for relevance based on eligibility criteria. After screening, 198 references were subjected to an in-depth review and categorized according to objectives for conducting Listeria sampling in food processing facilities and food sector. Mapping of the literature revealed research and guidance gaps by food sector, as fresh produce was the focus in only 10 references, compared to 72 on meat, 52 on fish and seafood, and 50 on dairy. Review of reported practices and guidance highlighted key design elements of EM, including the number, location, timing and frequency of sampling, as well as methods of detection and confirmation, and record-keeping. While utilization of molecular subtyping methods is a trend that will continue to advance understanding of Listeria contamination risks, improved study design and reporting standards by researchers will be essential to assist the food industry optimize their EM design and decision-making. The comprehensive collection of documents identified and synthesized in this review aids continued efforts to minimize the risk of Lm contaminated foods.

Serotype Diversity and Antimicrobial Resistance among Salmonella enterica Isolates from Patients at an Equine Referral Hospital

Although Salmonella enterica can produce life-threatening colitis in horses, certain serotypes are more commonly associated with clinical disease. Our aim was to evaluate the proportional morbidity attributed to different serotypes, as well as the phenotypic and genotypic antimicrobial resistance (AMR) of Salmonella isolates from patients at an equine referral hospital in the southern United States. A total of 255 Salmonella isolates was obtained from clinical samples of patients admitted to the hospital between 2007 and 2015. Phenotypic resistance to 14 antibiotics surveilled by the U.S. National Antimicrobial Resistance Monitoring System was determined using a commercially available panel. Whole-genome sequencing was used to identify serotypes and genotypic AMR. The most common serotypes were Salmonella enterica serotype Newport (18%), Salmonella enterica serotype Anatum (15.2%), and Salmonella enterica serotype Braenderup (11.8%). Most (n = 219) of the isolates were pansusceptible, while 25 were multidrug resistant (≥3 antimicrobial classes). Genes encoding beta-lactam resistance, such as bla_CMY-2, bla_SHV-12, bla_CTX-M-27, and bla_TEM-1B, were detected. The qnrB2 and aac(6')-Ib-cr genes were present in isolates with reduced susceptibility to ciprofloxacin. Genes encoding resistance to gentamicin (aph(3')-Ia, aac(6')-IIc), streptomycin (strA and strB), sulfonamides (sul1), trimethoprim (dfrA), phenicols (catA), tetracyclines [tet(A) and tet(E)], and macrolides [ere(A)] were also identified. The main predicted incompatibility plasmid type was I1 (10%). Core genome-based analyses revealed phylogenetic associations between isolates of common serotypes. The presence of AMR Salmonella in equine patients increases the risk of unsuccessful treatment and causes concern for potential zoonotic transmission to attending veterinary personnel, animal caretakers, and horse owners. Understanding the epidemiology of Salmonella in horses admitted to referral hospitals is important for the prevention, control, and treatment of salmonellosis.IMPORTANCE In horses, salmonellosis is a leading cause of life-threatening colitis. At veterinary teaching hospitals, nosocomial outbreaks can increase the risk of zoonotic transmission, lead to restrictions on admissions, impact hospital reputation, and interrupt educational activities. The antimicrobials most often used in horses are included in the 5th revision of the World Health Organization's list of critically important antimicrobials for human medicine. Recent studies have demonstrated a trend of increasing bacterial resistance to drugs commonly used to treat Salmonella infections. In this study, we identify temporal trends in the distribution of Salmonella serotypes and their mechanisms of antimicrobial resistance; furthermore, we are able to determine the likely origin of several temporal clusters of infection by using whole-genome sequencing. These data can be used to focus strategies to better contain the dissemination and enhance the mitigation of Salmonella infections and to provide evidence-based policies and guidelines to steward antimicrobial use in veterinary medicine.

Microbiological profile of chicken carcasses: A comparative analysis using shotgun metagenomic sequencing

In the last few years metagenomic and 16S rRNA sequencing have completly changed the microbiological investigations of food products. In this preliminary study, the microbiological profile of chicken carcasses collected from animals fed with different diets were tested by using shotgun metagenomic sequencing. A total of 15 carcasses have been collected at the slaughetrhouse at the end of the refrigeration tunnel from chickens reared for 35 days and fed with a control diet (n=5), a diet supplemented with 1500 FTU/kg of commercial phytase (n=5) and a diet supplemented with 1500 FTU/kg of commercial phytase and 3g/kg of inositol (n=5). Ten grams of neck and breast skin were obtained from each carcass and submited to total DNA extraction by using the DNeasy Blood & Tissue Kit (Qiagen). Sequencing libraries have been prepared by using the Nextera XT DNA Library Preparation Kit (Illumina) and sequenced in a HiScanSQ (Illumina) at 100 bp in paired ends. A number of sequences ranging between 5 and 9 million was obtained for each sample. Sequence analysis showed that Proteobacteria and Firmicutes represented more than 98% of whole bacterial populations associated to carcass skin in all groups but their abundances were different between groups. Moraxellaceae and other degradative bacteria showed a significantly higher abundance in the control compared to the treated groups. Furthermore, Clostridium perfringens showed a relative frequency of abundance significantly higher in the group fed with phytase and Salmonella enterica in the group fed with phytase plus inositol. The results of this preliminary study showed that metagenome sequencing is suitable to investigate and monitor carcass microbiota in order to detect specific pathogenic and/or degradative populations.

Unbiased Strain-Typing of Arbovirus Directly from Mosquitoes Using Nanopore Sequencing: A Field-forward Biosurveillance Protocol

The future of infectious disease surveillance and outbreak response is trending towards smaller hand-held solutions for point-of-need pathogen detection. Here, samples of Culex cedecei mosquitoes collected in Southern Florida, USA were tested for Venezuelan Equine Encephalitis Virus (VEEV), a previously-weaponized arthropod-borne RNA-virus capable of causing acute and fatal encephalitis in animal and human hosts. A single 20-mosquito pool tested positive for VEEV by quantitative reverse transcription polymerase chain reaction (RT-qPCR) on the Biomeme two3. The virus-positive sample was subjected to unbiased metatranscriptome sequencing on the Oxford Nanopore MinION and shown to contain Everglades Virus (EVEV), an alphavirus in the VEEV serocomplex. Our results demonstrate, for the first time, the use of unbiased sequence-based detection and subtyping of a high-consequence biothreat pathogen directly from an environmental sample using field-forward protocols. The development and validation of methods designed for field-based diagnostic metagenomics and pathogen discovery, such as those suitable for use in mobile “pocket laboratories”, will address a growing demand for public health teams to carry out their mission where it is most urgent: at the point-of-need.

Application of molecular tools to elucidate the microbiota of seafood

The aim of this review is to present the methodologies currently applied to identify microbiota and pathogens transmitted to humans through seafood consumption, focusing on molecular techniques and pointing out their importance, advantages, disadvantages and applicability. Knowledge of available techniques allows researchers to identify which technique best fits their expectations. With such discernment, it will be possible to infer which disadvantages will be present and, therefore, not interfering with the final result. Two methodologies can be employed for this purpose, dependent and independent cultures. However, the dependent culture has certain limitations that can be solved through the independent cultivation techniques, such as PCR, PFGE and NGS, especially through the sequencing of the 16S rRNA region, providing a complete view of microbial diversity. These have revolutionized microbiological knowledge, mainly because they allow for the identification of uncultivable micro-organisms, which represent a substantial portion of total micro-organisms, making it possible to elucidate not yet described taxa which may display pathogenic potential, besides quantifying microbial communities, microbiota genetics, translated proteins and produced metabolites. In addition, transcriptomic and metabolomic techniques also allow for the evaluation of possible impacts that microbial communities may create in their environment, as well as the determination of potential pathogenicity to humans.