Role prediction of Gram-negative species in the resistome of raw cow's milk

Dynamic Interplay between Microbiota Shifts and Differential Metabolites during Dairy Processing and Storage

Due to the intricate complexity of the original microbiota, residual heat-resistant enzymes, and chemical components, identifying the essential factors that affect dairy quality using traditional methods is challenging. In this study, raw milk, pasteurized milk, and ultra-heat-treated (UHT) milk samples were collectively analyzed using metagenomic next-generation sequencing (mNGS), high-throughput liquid chromatography-mass spectrometry (LC-MS), and gas chromatography-mass spectrometry (GC-MS). The results revealed that raw milk and its corresponding heated dairy products exhibited different trends in terms of microbiota shifts and metabolite changes during storage. Via the analysis of differences in microbiota and correlation analysis of the microorganisms present in differential metabolites in refrigerated pasteurized milk, the top three differential microorganisms with increased abundance, Microbacterium (p < 0.01), unclassified Actinomycetia class (p < 0.05), and Micrococcus (p < 0.01), were detected; these were highly correlated with certain metabolites in pasteurized milk (r > 0.8). This indicated that these genera were the main proliferating microorganisms and were the primary genera involved in the metabolism of pasteurized milk during refrigeration-based storage. Microorganisms with decreased abundance were classified into two categories based on correlation analysis with certain metabolites. It was speculated that the heat-resistant enzyme system of a group of microorganisms with high correlation (r > 0.8), such as Pseudomonas and Acinetobacter, was the main factor causing milk spoilage and that the group with lower correlation (r < 0.3) had a lower impact on the storage process of pasteurized dairy products. By comparing the metabolic pathway results based on metagenomic and metabolite annotation, it was proposed that protein degradation may be associated with microbial growth, whereas lipid degradation may be linked to raw milk's initial heat-resistant enzymes. By leveraging the synergy of metagenomics and metabolomics, the interacting factors determining the quality evolution of dairy products were systematically investigated, providing a novel perspective for controlling dairy processing and storage effectively.

Monitoring Changes in the Antimicrobial-Resistance Gene Set (ARG) of Raw Milk and Dairy Products in a Cattle Farm, from Production to Consumption

Raw milk and dairy products can serve as potential vectors for transmissible bacterial, viral and protozoal diseases, alongside harboring antimicrobial-resistance genes. This study monitors the changes in the antimicrobial-resistance gene pool in raw milk and cheese, from farm to consumer, utilizing next-generation sequencing. Five parallel sampling runs were conducted to assess the resistance gene pool, as well as phage or plasmid carriage and potential mobility. In terms of taxonomic composition, in raw milk the Firmicutes phylum made up 41%, while the Proteobacteria phylum accounted for 58%. In fresh cheese, this ratio shifted to 93% Firmicutes and 7% Proteobacteria. In matured cheese, the composition was 79% Firmicutes and 21% Proteobacteria. In total, 112 antimicrobial-resistance genes were identified. While a notable reduction in the resistance gene pool was observed in the freshly made raw cheese compared to the raw milk samples, a significant growth in the resistance gene pool occurred after one month of maturation, surpassing the initial gene frequency. Notably, the presence of extended-spectrum beta-lactamase (ESBL) genes, such as OXA-662 (100% coverage, 99.3% identity) and OXA-309 (97.1% coverage, 96.2% identity), raised concerns; these genes have a major public health relevance. In total, nineteen such genes belonging to nine gene families (ACT, CMY, EC, ORN, OXA, OXY, PLA, RAHN, TER) have been identified. The largest number of resistance genes were identified against fluoroquinolone drugs, which determined efflux pumps predominantly. Our findings underscore the importance of monitoring gene pool variations throughout the product pathway and the potential for horizontal gene transfer in raw products. We advocate the adoption of a new approach to food safety investigations, incorporating next-generation sequencing techniques.

Dynamics of microbiota and antimicrobial resistance in on-farm dairy processing plants using metagenomic and culture-dependent approaches

On-farm dairy processing plants, which are situated close to farms and larger dairy processing facilities, face unique challenges in maintaining environmental hygiene. This can impact various stages of dairy processing. These plants operate on smaller scales and use Low-Temperature-Long-Time (LTLT) pasteurization, making them more susceptible to microbial contamination through direct and indirect contact. Antimicrobial-resistant bacteria found on dairy farms pose risks to human health by potentially transferring resistance via dairy products. Our study aimed to investigate microbial distribution and antimicrobial resistance at four key stages: the farm, pre-pasteurization, post-pasteurization, and processing environments. We assessed microbial distribution by quantifying indicator bacteria and conducting metagenomic analysis. Antimicrobial resistance was examined by identifying resistance phenotypes and detecting resistance genes in bacterial isolates and metagenomes. Our results showed that the indicator bacteria were detected at all stages of on-farm dairy processing. We observed a significant reduction in aerobic microbes and coliforms post-pasteurization. However, contamination of the final dairy products increased, suggesting potential cross-contamination during post-pasteurization. Metagenomic analysis revealed that Pseudomonas, a representative psychrotrophic bacterium, was predominant in both the farm (24.1 %) and pre-pasteurization (65.9 %) stages, indicating microbial transfer from the farms to the processing plants. Post-pasteurization, Pseudomonas and other psychrotrophs like Acinetobacter and Enterobacteriaceae remained dominant. Core microbiota analysis identified 74 genera in total, including 13 psychrotrophic bacteria, across all stages. Of the 59 strains isolated from these plants, 49 were psychrotrophic. Antimicrobial resistance analysis showed that 74.6 % (44/59) of isolates were resistant to at least one antibiotic, with cefoxitin-, ampicillin-, amoxicillin-, and ticarcillin-resistant bacteria present at all stages. Identical antimicrobial resistance patterns were observed in isolates from serial stages of the same farm and season, suggesting bacterial transmission across stages. Additionally, 27.1 % (16/59) of isolates carried plasmid-mediated resistance genes, which were also detected in the metagenomes of non-isolated samples, indicating potential antimicrobial resistance gene transmission and their presence in uncultured bacteria. These findings reveal the persistence of antimicrobial-resistant psychrotrophic bacteria in on-farm dairy processing plants, which pose potential health risks via dairy consumption. Our study underscores the importance of both culture-dependent and culture-independent methods to fully understand their distribution and impact.

Seasonal and geographical impact on the Irish raw milk microbiota correlates with chemical composition and climatic variables

Season and location have previously been shown to be associated with differences in the microbiota of raw milk, especially in milk from pasture-based systems. Here, we further advance research in this area by examining differences in the raw milk microbiota from several locations across Ireland over 12 months, and by investigating microbiota associations with climatic variables and chemical composition. Shotgun metagenomic sequencing was used to investigate the microbiota of raw milk collected from nine locations (n = 241). Concurrent chemical analysis of the protein, fat, lactose, total solids, nonprotein nitrogen contents, and titratable acidity (TA) of the same raw milk were performed. Although the raw milk microbiota was highly diverse, a core microbiota was found, with Pseudomonas_E, Lactococcus, Acinetobacter, and Leuconostoc present in all samples. Microbiota diversity significantly differed by season and location, with differences in seasonality and geography corresponding to 11.8% and 10.5% of the variation in the microbiota. Functional and antibiotic resistance profiles also varied across season and location. The analysis of other metadata revealed additional interactions, such as an association between mean daily air and grass temperatures with the abundance of spoilage taxa like Pseudomonas species. Correlations were identified between pathogenic, mastitis-related species, fat content, and the number of sun hours, suggesting a seasonal effect. Ultimately, this study expands our understanding of the interconnected nature of the microbiota, environment/climate variables, and chemical composition of raw milk and provides evidence of a season- and location-specific microbiota.

IMPORTANCE

The microbiota of raw milk is influenced by many factors that encourage or prevent the introduction and growth of both beneficial and undesirable microorganisms. The seasonal and geographical impacts on the microbial communities of raw milk have been previously seen, but the relationships with environmental factors and the chemical composition has yet to be investigated. In this year-long study, we found that while raw milk is highly diverse, a core microbiota was detected for Irish raw milk, with strong evidence of seasonal and geographical influence. We also found associations between groups of microorganisms, environmental factors, and milk composition, which expand current knowledge on the relationships between microbial and chemical composition and the climate. These results provide evidence for the development of a tool to allow for the prediction of raw milk quality and safety.

Metagenomics reveals the temporal dynamics of the rumen resistome and microbiome in goat kids

BACKGROUND

The gut microbiome of domestic animals carries antibiotic resistance genes (ARGs) which can be transmitted to the environment and humans, resulting in challenges of antibiotic resistance. Although it has been reported that the rumen microbiome of ruminants may be a reservoir of ARGs, the factors affecting the temporal dynamics of the rumen resistome are still unclear. Here, we collected rumen content samples of goats at 1, 7, 14, 28, 42, 56, 70, and 84 days of age, analyzed their microbiome and resistome profiles using metagenomics, and assessed the temporal dynamics of the rumen resistome in goats at the early stage of life under a conventional feeding system.

RESULTS

In our results, the rumen resistome of goat kids contained ARGs to 41 classes, and the richness of ARGs decreased with age. Four antibiotic compound types of ARGs, including drugs, biocides, metals, and multi-compounds, were found during milk feeding, while only drug types of ARGs were observed after supplementation with starter feed. The specific ARGs for each age and their temporal dynamics were characterized, and the network inference model revealed that the interactions among ARGs were related to age. A strong correlation between the profiles of rumen resistome and microbiome was found using Procrustes analysis. Ruminal Escherichia coli within Proteobacteria phylum was the main carrier of ARGs in goats consuming colostrum, while Prevotella ruminicola and Fibrobacter succinogenes associated with cellulose degradation were the carriers of ARGs after starter supplementation. Milk consumption was likely a source of rumen ARGs, and the changes in the rumen resistome with age were correlated with the microbiome modulation by starter supplementation.

CONCLUSIONS

Our data revealed that the temporal dynamics of the rumen resistome are associated with the microbiome, and the reservoir of ARGs in the rumen during early life is likely related to age and diet. It may be a feasible strategy to reduce the rumen and its downstream dissemination of ARGs in ruminants through early-life dietary intervention. Video Abstract.

The Antibiotic Resistome and Its Association with Bacterial Communities in Raw Camel Milk from Altay Xinjiang

Raw camel milk is generally contaminated with varied microbiota, including antibiotic-resistant bacteria (ARB), that can act as a potential pathway for the spread of antibiotic resistance genes (ARGs). In this study, high-throughput quantitative PCR and 16S rRNA gene-based Illumine sequencing data were used to establish a comprehensive understanding of the antibiotic resistome and its relationship with the bacterial community in Bactrian camel milk from Xinjiang. A total of 136 ARGs and up to 1.33 × 108 total ARG copies per gram were identified, which predominantly encode resistance to β-lactamas and multidrugs. The ARGs' profiles were mainly explained by interactions between the bacteria community and physicochemical indicators (77.9%). Network analysis suggested that most ARGs exhibited co-occurrence with Corynebacterium, Leuconostoc and MGEs. Overall, raw camel milk serves as a reservoir for ARGs, which may aggravate the spread of ARGs through vertical and horizontal gene transfer in the food chain.

A step forward for Shiga toxin-producing Escherichia coli identification and characterization in raw milk using long-read metagenomics

Shiga toxin-producing Escherichia coli (STEC) are a cause of severe human illness and are frequently associated with haemolytic uraemic syndrome (HUS) in children. It remains difficult to identify virulence factors for STEC that absolutely predict the potential to cause human disease. In addition to the Shiga-toxin (stx genes), many additional factors have been reported, such as intimin (eae gene), which is clearly an aggravating factor for developing HUS. Current STEC detection methods classically rely on real-time PCR (qPCR) to detect the presence of the key virulence markers (stx and eae). Although qPCR gives an insight into the presence of these virulence markers, it is not appropriate for confirming their presence in the same strain. Therefore, isolation steps are necessary to confirm STEC viability and characterize STEC genomes. While STEC isolation is laborious and time-consuming, metagenomics has the potential to accelerate the STEC characterization process in an isolation-free manner. Recently, short-read sequencing metagenomics have been applied for this purpose, but assembly quality and contiguity suffer from the high proportion of mobile genetic elements occurring in STEC strains. To circumvent this problem, we used long-read sequencing metagenomics for identifying eae-positive STEC strains using raw cow's milk as a causative matrix for STEC food-borne outbreaks. By comparing enrichment conditions, optimizing library preparation for MinION sequencing and generating an easy-to-use STEC characterization pipeline, the direct identification of an eae-positive STEC strain was successful after enrichment of artificially contaminated raw cow's milk samples at a contamination level as low as 5 c.f.u. ml^-1. Our newly developed method combines optimized enrichment conditions of STEC in raw milk in combination with a complete STEC analysis pipeline from long-read sequencing metagenomics data. This study shows the potential of the innovative methodology for characterizing STEC strains from complex matrices. Further developments will nonetheless be necessary for this method to be applied in STEC surveillance.

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.

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