Rapid detection of Escherichia coli in dairy milk using static headspace-comprehensive two-dimensional gas chromatography.

A new approach is introduced for rapid and reliable bacteria detection in food. Namely, static headspace-comprehensive two-dimensional gas chromatography (HS-GC × GC) with backflushing. The introduced approach provides fast detection of Escherichia coli (E. coli) in enriched ultra-high-temperature processed (UHT) dairy milk. The presence of E. coli may be indicated by detecting microbial volatile organic compounds emanating from test solutions inoculated with E. coli. In the present investigation, HS-GC × GC analysis is preceded by conventional enrichment in nutrient broth and inoculated samples are clearly discernable from controls following as little as 15 h sample enrichment. Headspace equilibration for 28 min followed by an 8 min GC × GC analysis of enriched test solutions reduces time-to-response by approximately one full day compared to conventional culture-based methods. The presence of ethanol, 1-propanol, and acetaldehyde may be used as a putative marker of E. coli contamination in milk and the introduced approach is able to detect single-cell initial bacterial load. Faster, reliable detection of pathogens and/or spoilage microbes in food products is desirable for the food industry. The described approach has great potential to complement the conventional workflow and be utilised for rapid microbial screening of foodstuff.

Species-Level Discrimination of Psychrotrophic Pathogenic and Spoilage Gram-Negative Raw Milk Isolates Using a Combined MALDI-TOF MS Proteomics-Bioinformatics-based Approach.

Identification of psychrotrophic pathogenic and spoilage Gram-negative bacteria using rapid and reliable techniques is important in commercial milk processing, as these bacteria can produce heat-resistant proteases and act as postprocessing contaminants in pasteurized milk. Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is a proven technology for identification of bacteria in food, however, may require optimization for identification of pathogenic and spoilage bacteria in milk and dairy products. The current study evaluated the effects of various culture conditions and sample preparation methods on assigning of raw milk isolates to the species level by MALDI-TOF MS. The results indicated that culture media, incubation conditions (temperature and time), and sample preparation significantly affected the identification rates of bacteria to the species level. Nevertheless, the development of spectral libraries of isolates grown on different media using a web tool for hierarchical clustering of peptide mass spectra (SPECLUST) followed by a ribosomal protein based bioinformatics approach significantly enhanced the assigning of bacteria, with at least one unique candidate biomarker peak identified for each species. Phyloproteomic relationships based on spectral profiles were compared to phylogenetic analysis using 16S rRNA gene sequences and demonstrated similar clustering patterns with significant discriminatory power. Thus, with appropriate optimization, MALDI-TOF MS is a valuable tool for species-level discrimination of pathogenic and milk spoilage bacteria.

Comparing the identification of Clostridium spp. by two Matrix-Assisted Laser Desorption Ionization-Time of Flight (MALDI-TOF) mass spectrometry platforms to 16S rRNA PCR sequencing as a reference standard: a detailed analysis of age of culture and sample preparation.

We compared the identification of Clostridium species using mass spectrometry by two different Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) platforms (Bruker MS and Vitek MS) against 16S rRNA sequencing as the reference standard. We then examined the impact of different sample preparations and (on one of those platforms) age of bacterial colonial growth on the performance of the MALDI-TOF MS systems. We identified 10 different species amongst the 52 isolates by 16S rRNA sequencing, with Clostridium perfringens the most prevalent (n=30). Spectrometric analysis using Vitek MS correctly speciated 47/52 (90.4%) isolates and was not affected by the sample preparation used. Performance of the Bruker MS was dependent on sample preparation with correct speciation obtained for 36 of 52 (69.2%) isolates tested using the Direct Transfer [DT] protocol, but all 52 (100%) isolates were correctly speciated using either an Extended Direct Transfer [EDT] or a Full Formic Extraction [EX] protocol. We then examined the effect of bacterial colonial growth age on the performance of Bruker MS and found substantial agreement in speciation using DT (Kappa=0.62, 95% CI: 0.46-0.75), almost perfect agreement for EDT (Kappa=0.94, 95% CI: 0.86-1.00) and exact agreement for EX (Kappa=1.00) between different days.

Unravelling the gut-lung axis: insights into microbiome interactions and Traditional Indian Medicine's perspective on optimal health.

The microbiome of the human gut is a complex assemblage of microorganisms that are in a symbiotic relationship with one another and profoundly influence every aspect of human health. According to converging evidence, the human gut is a nodal point for the physiological performance matrixes of the vital organs on several axes (i.e. gut-brain, gut-lung, etc). As a result of COVID-19, the importance of gut-lung dysbiosis (balance or imbalance) has been realised. In view of this, it is of utmost importance to develop a comprehensive understanding of the microbiome, as well as its dysbiosis. In this review, we provide an overview of the gut-lung axial microbiome and its importance in maintaining optimal health. Human populations have successfully adapted to geophysical conditions through traditional dietary practices from around the world. In this context, a section has been devoted to the traditional Indian system of medicine and its theories and practices regarding the maintenance of optimally customized gut health.

Plasma Metabolic and Lipidomic Fingerprinting of Individuals with Increased Intestinal Permeability.

The dual-sugar intestinal permeability test is a commonly used test to assess changes in gut barrier function. However, it does not identify functional changes and the exact mechanism of damage caused by the increased intestinal permeability. This study aims to explore the application of untargeted metabolomics and lipidomics to identify markers of increased intestinal permeability. Fifty fasting male participants (18-50 years) attended a single visit to conduct the following procedures: assessment of anthropometric measures, assessment of gastrointestinal symptoms, intestinal permeability test, and assessment of blood samples 90 min post-administration of the intestinal permeability test. Rhamnose and lactulose were analysed using gas chromatography-mass spectrometry (GC-MS). Untargeted polar metabolites and lipidomics were assessed by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF MS). There was an elevated lactulose/rhamnose ratio in 27 subjects, indicating increased permeability compared to the remaining 23 control subjects. There were no significant differences between groups in characteristics such as age, body mass index (BMI), weight, height, and waist conference. Fourteen metabolites from the targeted metabolomics data were identified as statistically significant in the plasma samples from intestinal permeability subjects. The untargeted metabolomics and lipidomics analyses yielded fifteen and fifty-one statistically significant features, respectively. Individuals with slightly elevated intestinal permeability had altered energy, nucleotide, and amino acid metabolism, in addition to increased glutamine levels. Whether these biomarkers may be used to predict the early onset of leaky gut warrants further investigation.

MALDI-ToF MS: A Rapid Methodology for Identifying and Subtyping Listeria monocytogenes.

Listeria monocytogenes is a major food-borne pathogen and causative agent of a fatal disease, listeriosis. Stringent regulatory guidelines and zero tolerance policy toward this bacterium necessitate rapid, accurate, and reliable methods of identification and subtyping. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) has recently become a method of choice for routine identification of pathogens in clinical settings and has largely replaced biochemical assays. Identification relies on well-curated databases such as SARAMIS. Extensive use of SARAMIS to generate consensus mass spectra, in conjunction with statistical analysis, such as partial least square-discriminant analysis and hierarchical cluster analysis, is useful in subtyping bacteria. While MALDI-ToF MS has been extensively used for pathogen detection, its application in bacterial subtyping has been limited. The protocol describes a MALDI-ToF MS workflow as a single tool for simultaneous identification and subtyping of L. monocytogenes directly from solid culture medium.

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.

Consumer Acceptance of Brown and White Rice Varieties.

Rice is consumed as a staple food by more than half of the world's population. Due to a higher fibre and micronutrient content, brown rice is more nutritious than white rice, but the consumption of brown rice is significantly lower than that of white rice, primarily due to sensory attributes. Therefore, the present research aimed to identify the sensory attributes which drive liking of Australian-grown brown and white rice varieties. Participants (n = 139) tasted and scored (9-point hedonic scale) their liking (i.e., overall liking, aroma, colour and texture) of brown and white rice types of Jasmine (Kyeema), Low GI (Doongara), and Medium grain rice (Amaroo). In addition, participants scored aroma, colour, hardness, fluffiness, stickiness, and chewiness, on Just About Right Scales. A within-subjects crossover design with randomised order (William's Latin Square design) was used with six repeated samples for liking and Just About Right scales. Penalty analyses were applied to determine the relative influence of perception of sensory attributes on consumer liking of the rice varieties. Across all varieties, white rice was liked more than brown rice due to the texture and colour, and Jasmine rice was preferred over Low GI and Medium Grain. Rice texture (hardness and chewiness) was the most important sensory attribute among all rice varieties and aroma was important for driving of liking between white rice varieties.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome.

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

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.

Comparison of identification systems for psychrotrophic bacteria isolated from raw bovine milk.

Psychrotrophic bacteria in raw milk produce heat-resistant extracellular proteases, resulting in spoilage and shelf-life reduction of ultrahigh temperature treated milk and milk products. Controlling of these spoilage microbes requires rapid and reliable identification systems for screening of raw milk. This study aimed to compare commercial bacterial identification systems with a genetic method (considered as the 'gold standard' method) for the identification of heat-resistant protease producing bacteria in raw milk. Five bacterial identification systems were compared based on typability, discrimination power (i.e. Simpson's Index of Diversity), reproducibility and speed of analysis. The accuracy of 16S rRNA gene sequencing, Biolog, MALDI-TOF MS, API, and Microbact for the identification of Gram negative bacilli at the species level was 100.0%, 86.8%, 63.2%, 60.5% and 57.9%, respectively. The Gram positive bacilli were identified by 16S rRNA gene sequencing, Biolog, MALDI-TOF MS, and API with accuracies at the species level of 100.0%, 85.0%, 95.0% and 90.0%, respectively. The 16S rRNA gene sequencing and phylogenetic analysis discriminated Pseudomonas fluorescens, Pseudomonas syringae, Hafnia alvei, Bacillus cereus, Bacillus pumilus and Bacillus licheniformis to the subspecies level. The Simpson's Index of Diversity scores were 0.966, 0.711, 0.496, 0.472, and 0.140, for 16S rRNA gene sequencing, Biolog, MALDI-TOF MS, API and Microbact, respectively. Limited reference profiles in the databases of Biolog, MALDI-TOF MS, API and Microbact systems reduced their accuracy in bacterial identification, compared to 16S rRNA gene sequencing. The rapidity of each assay is in the following order; MALDI-TOF MS>16S rRNA gene sequencing>Biolog>Microbact>API. The reproducibility of the assays is in the order of 16S rRNA gene sequencing>API>Microbact>MALDI-TOF MS>Biolog. Thus, 16S rRNA gene sequencing appears to be the most reliable and robust system for the identification of dairy spoilage bacteria. The Biolog system is suitable for the identification of Gram negative spoilage bacteria, while MALDI-TOF MS and API systems are suitable for the identification of Gram positive spoilage bacteria isolated from raw milk. The commercial systems used in this study have been developed and extensively used for the identification of clinical microbes but only a limited number of studies used those systems to identify the environmental microorganisms that often contaminate raw milk. Therefore, comparison of those systems for the identification of spoilage microbes in raw milk would provide better understanding of their suitability for routine dairy microbiology and more extensive dairy research.