Targeted metabolic profiling rapidly differentiates Escherichia coli and Staphylococcus aureus at species and strain level

Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

Utilizing the Food-Pathogen Metabolome to Putatively Identify Biomarkers for the Detection of Shiga Toxin-Producing E. coli (STEC) from Spinach

Shiga toxigenic E. coli (STEC) are an important cause of foodborne disease globally with many outbreaks linked to the consumption of contaminated foods such as leafy greens. Existing methods for STEC detection and isolation are time-consuming. Rapid methods may assist in preventing contaminated products from reaching consumers. This proof-of-concept study aimed to determine if a metabolomics approach could be used to detect STEC contamination in spinach. Using untargeted metabolic profiling, the bacterial pellets and supernatants arising from bacterial and inoculated spinach enrichments were investigated for the presence of unique metabolites that enabled categorization of three E. coli risk groups. A total of 109 and 471 metabolite features were identified in bacterial and inoculated spinach enrichments, respectively. Supervised OPLS-DA analysis demonstrated clear discrimination between bacterial enrichments containing different risk groups. Further analysis of the spinach enrichments determined that pathogen risk groups 1 and 2 could be easily discriminated from the other groups, though some clustering of risk groups 1 and 2 was observed, likely representing their genomic similarity. Biomarker discovery identified metabolites that were significantly associated with risk groups and may be appropriate targets for potential biosensor development. This study has confirmed that metabolomics can be used to identify the presence of pathogenic E. coli likely to be implicated in human disease.

Recent advances in the application of metabolomics for food safety control and food quality analyses

As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.

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.

Escherichia coli Clonobiome: Assessing the Strain Diversity in Feces and Urine by Deep Amplicon Sequencing

While microbiome studies have focused on diversity at the species level or higher, bacterial species in microbiomes are represented by different, often multiple, strains. These strains could be clonally and phenotypically very different, making assessment of strain content vital to a full understanding of microbiome function. This is especially important with respect to antibiotic-resistant strains, the clonal spread of which may be dependent on competition between them and susceptible strains from the same species. The pandemic, multidrug-resistant, and highly pathogenic Escherichia coli subclone ST131-H30 (H30) is of special interest, as it has already been found persisting in the gut and bladder in healthy people. In order to rapidly assess E. coli clonal diversity, we developed a novel method based on deep sequencing of two loci used for sequence typing, along with an algorithm for analysis of the resulting data. Using this method, we assessed fecal and urinary samples from healthy women carrying H30 and were able to uncover considerable diversity, including strains with frequencies at <1% of the E. coli population. We also found that, even in the absence of antibiotic use, H30 could completely dominate the gut and, especially, urine of healthy carriers. Our study offers a novel tool for assessing a species' clonal diversity (clonobiome) within the microbiome, which could be useful in studying the population structure and dynamics of multidrug-resistant and/or highly pathogenic strains in their natural environments.IMPORTANCE Bacterial species in the microbiome are often represented by multiple genetically and phenotypically different strains, making insight into subspecies diversity critical to a full understanding of the microbiome, especially with respect to opportunistic pathogens. However, methods allowing efficient high-throughput clonal typing are not currently available. This study combines a conventional E. coli typing method with deep amplicon sequencing to allow analysis of many samples concurrently. While our method was developed for E. coli, it may be adapted for other species, allowing microbiome researchers to assess clonal strain diversity in natural samples. Since assessment of subspecies diversity is particularly important for understanding the spread of antibiotic resistance, we applied our method to the study of a pandemic multidrug-resistant E. coli clone. The results we present suggest that this clone could be highly competitive in healthy carriers and that the mechanisms of colonization by such clones need to be studied.

Identification of Putative Biomarkers Specific to Foodborne Pathogens Using Metabolomics

Metabolomics is one of the more recently developed "omics" that measures low molecular weight (typically < 1500 Da) compounds in biological samples. Metabolomics has been widely explored in environmental, clinical, and industrial biotechnology applications. However, its application to the area of food safety has been limited but preliminary work has demonstrated its value. This chapter describes an untargeted (nontargeted) metabolomics workflow using gas chromatography coupled to mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for extraction of polar metabolites from media, analyzing the extracts using GC-MS and, finally, chemometric data analysis using the software "SIMCA" to identify potential pathogen-specific biomarkers.

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.