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.

The resident microbiome in food industries may impact on food quality and safety. In particular, microbes residing on surfaces in dairy industries may actively participate in cheese fermentation and ripening and contribute to the typical flavor and texture. In this work, we carried out an extensive microbiome mapping in 73 cheese-making industries producing different types of cheeses (fresh, medium and long ripened) and located in 4 European countries. We sequenced and analyzed metagenomes from cheese samples, raw materials and environmental swabs collected from both food contact and non-food contact surfaces, as well as operators’ hands and aprons. Dairy plants were shown to harbor a very complex microbiome, characterized by high prevalence of genes potentially involved in flavor development, probiotic activities, and resistance to gastro-intestinal transit, suggesting that these microbes may potentially be transferred to the human gut microbiome. More than 6100 high-quality Metagenome Assembled Genomes (MAGs) were reconstructed, including MAGs from several Lactic Acid Bacteria species and putative new species. Although microbial pathogens were not prevalent, we found several MAGs harboring genes related to antibiotic resistance, highlighting that dairy industry surfaces represent a potential hotspot for antimicrobial resistance (AR) spreading along the food chain. Finally, we identified facility-specific strains that can represent clear microbial signatures of different cheesemaking facilities, suggesting an interesting potential of microbiome tracking for the traceability of cheese origin.

The microbiota of raw milk is affected by many factors that can control or promote the introduction of undesirable microorganisms. Chlorine-based cleaning agents have been commonly used due to their effectiveness in controlling undesirable microorganisms, but they have been associated with the formation of chlorine residues that are detrimental to product quality and may impact consumer health.

Klebsiella pneumoniae is a bacterium of public health importance due to its association with antimicrobial resistance (AMR) and its role as a major cause of both hospital- and community-acquired infections. While Klebsiella species have been detected in foods, our understanding of their diversity and the potential risks they pose from food is limited. This study aims to comprehensively evaluate the Klebsiella species population and their contribution to the burden of AMR, virulence, and heavy metal tolerance from diverse food samples. We generated short-read sequence data for 570 Klebsiella isolates recovered from 361 food samples, including leafy greens, pork, prawns, chicken, salmon, and shellfish. Genome analysis showed that eleven unique Klebsiella species were present across food commodities, with K. pneumoniae being the most common (28.3 %); food-derived genomes were intermingled with publicly available Klebsiella genomes isolated from human clinical infections. We detected critical AMR genes, bla_CTX-M-15, bla_CTX-M-27, bla_SHV-70, and bla_DHA-1, in K. pneumoniae (n = 8) and K. quasipneumoniae (n = 6) from prawns. Additionally, we identified 46 virulent K. pneumoniae and K. quasipneumoniae isolates from domestic and imported food, including two hypervirulent K. pneumoniae isolates from domestic pork samples. Notably, a K. planticola isolate from salmon exhibited the hypermucoviscosity phenotype. AMR and virulence genes on plasmid contigs were widespread across different Klebsiella species, while chromosome-linked genes were mostly species-specific. These results highlight that food can harbour a range of Klebsiella species with resistance and virulence genes typically found in clinical settings, underscoring the need for monitoring foodborne Klebsiella as a potential risk to human health.

This study provides a thorough analysis of methodology from swab sample storage to DNA extraction and bioinformatics analysis for both 16S rRNA and shotgun metagenomic sequencing of buccal and rectal swabs with Oxford Nanopore Technologies (ONT). It makes several extensive comparisons at each step of the wet-lab process (swab type, storage, sampling conditions and DNA extraction methods), with good elaboration on the effects on the microbiome of each choice. The in-depth evaluation of bioinformatic workflows not only examines the more commonly used tools in ONT analysis, but it also goes further by assessing different parameters/thresholds and classifiers, providing interesting results that add value to the analysis of ONT reads. The authors also offer an optimised workflow, which would is beneficial for studies that utilise swabs for sample collection or for the processing of other low-biomass samples, and a bioinformatic pipeline that can be downloaded for ease of adoption by other researchers. Compared to other previous studies, more complex mock communities and more extensive comparisons were made across both the wet-lab and bioinformatic analyses, which makes this paper very useful to the research community, particularly those working with or intending to use ONT sequencing.

Artisanal raw milk cheeses harbour complex microbial communities that drive cheese making and shape sensory quality. Previous work on Idiazabal cheese identified rennet as a major microbial source, although all reservoirs contributed to varying degrees. However, their impact in terms of enzyme-encoding genes related to technological quality of cheese remained unexplored. Building on that, this study draws on metagenome-assembled genomes (MAGs) from cheeses and dairy environments to comprehensively identify enzyme-encoding genes involved in key biochemical processes. In cheese MAGs (Lacticaseibacillus paracasei), protease-encoding genes were dominated by ATP-dependent metalloproteases (M41), carbohydrate-active enzyme-encoding genes (CAZymes) by glycoside hydrolases (GH) and glycosyltransferases (GT), while esterase, lipase, and related-enzyme-encoding genes were restricted to sparse ‘GDXG’, type-B and esterase D families. Dairy environments emerged as major reservoirs of enzyme-encoding genes, with notable differences among sample types (p ≤ 0.001). The richest sources of protease-encoding genes were grass (610 genes), linked primarily to Pantoea agglomerans, and rennet (318), mainly related to Basfia sp. and Moraxella sp., dominated by metalloproteases (M23, M38) and serine proteases (S15). The largest reservoirs of CAZyme-encoding genes were food contact surfaces (1550), associated mainly with Salinisphaera sp. and Dietzia sp., and rennet (1505), related to, e.g., Bacteroides pyogenes, Alloprevotella sp., and Lentilactobacillus buchneri. Food contact surfaces were also the richest source of esterase, lipase and related-enzyme-encoding genes (1209), mainly linked to Dietzia sp., Corynebacterium sp., and Brevibacterium aurantiacum. Similarly, aroma-related enzyme-encoding genes (e.g., oppA, pepA, GH13, esterase D) were consistently detected in environmental matrices. These results provide novel insights into dairy microbiomes as functional reservoirs of aroma precursors, revealing their relevance for artisanal PDO cheese production and future biotechnological applications.