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Abstract Background Numerous studies have highlighted the impact of bacterial communities on the quality and safety of raw ewe milk-derived cheeses. Despite reported differences in the microbiota among cheese types and even producers, to the best of our knowledge, no study has comprehensively assessed all potential microbial sources and their contributions to any raw ewe milk-derived cheese, which could suppose great potential for benefits from research in this area. Here, using the Protected Designation of Origin Idiazabal cheese as an example, the impact of the environment and practices of artisanal dairies (including herd feed, teat skin, dairy surfaces, and ingredients) on the microbiomes of the associated raw milk, whey, and derived cheeses was examined through shotgun metagenomic sequencing. Results The results revealed diverse microbial ecosystems across sample types, comprising more than 1300 bacterial genera and 3400 species. SourceTracker analysis revealed commercial feed and teat skin as major contributors to the raw milk microbiota (45.6% and 33.5%, respectively), being a source of, for example, Lactococcus and Pantoea, along with rennet contributing to the composition of whey and cheese (17.4% and 41.0%, respectively), including taxa such as Streptococcus, Pseudomonas_E or Lactobacillus_H. Functional analysis linked microbial niches to cheese quality- and safety-related metabolic pathways, with brine and food contact surfaces being most relevant, related to genera like Brevibacterium, Methylobacterium, or Halomonas. With respect to the virulome (virulence-associated gene profile), in addition to whey and cheese, commercial feed and grass were the main reservoirs (related to, e.g., Brevibacillus_B or CAG-196). Similarly, grass, teat skin, or rennet were the main contributors of antimicrobial resistance genes (e.g., Bact-11 or Bacteriodes_B). In terms of cheese aroma and texture, apart from the microbiome of the cheese itself, brine, grass, and food contact surfaces were key reservoirs for hydrolase-encoding genes, originating from, for example, Lactococcus, Lactobacillus, Listeria or Chromohalobacter. Furthermore, over 300 metagenomic assembled genomes (MAGs) were generated, including 60 high-quality MAGs, yielding 28 novel putative species from several genera, e.g., Citricoccus, Corynebacterium, or Dietzia. Conclusion This study emphasizes the role of the artisanal dairy environments in determining cheese microbiota and, consequently, quality and safety. Video Abstract

Abstract Background Artisanal cheeses usually contain a highly diverse microbial community which can significantly impact their quality and safety. Here, we describe a detailed longitudinal study assessing the impact of ripening in three natural caves on the microbiome and resistome succession across three different producers of Cabrales blue-veined cheese. Results Both the producer and cave in which cheeses were ripened significantly influenced the cheese microbiome. Lactococcus and the former Lactobacillus genus, among other taxa, showed high abundance in cheeses at initial stages of ripening, either coming from the raw material, starter culture used, and/or the environment of processing plants. Along cheese ripening in caves, these taxa were displaced by other bacteria, such as Tetragenococcus, Corynebacterium, Brevibacterium, Yaniella, and Staphylococcus, predominantly originating from cave environments (mainly food contact surfaces), as demonstrated by source-tracking analysis, strain analysis at read level, and the characterization of 613 metagenome-assembled genomes. The high abundance of Tetragenococcus koreensis and Tetragenococcus halophilus detected in cheese has not been found previously in cheese metagenomes. Furthermore, Tetragenococcus showed a high level of horizontal gene transfer with other members of the cheese microbiome, mainly with Lactococcus and Staphylococcus, involving genes related to carbohydrate metabolism functions. The resistome analysis revealed that raw milk and the associated processing environments are a rich reservoir of antimicrobial resistance determinants, mainly associated with resistance to aminoglycosides, tetracyclines, and β-lactam antibiotics and harbored by aerobic gram-negative bacteria of high relevance from a safety point of view, such as Escherichia coli, Salmonella enterica, Acinetobacter, and Klebsiella pneumoniae, and that the displacement of most raw milk-associated taxa by cave-associated taxa during ripening gave rise to a significant decrease in the load of ARGs and, therefore, to a safer end product. Conclusion Overall, the cave environments represented an important source of non-starter microorganisms which may play a relevant role in the quality and safety of the end products. Among them, we have identified novel taxa and taxa not previously regarded as being dominant components of the cheese microbiome (Tetragenococcus spp.), providing very valuable information for the authentication of this protected designation of origin artisanal cheese. Video Abstract

Abstract Background The global threat of antimicrobial resistance (AMR) is a One Health problem impacted by antimicrobial use (AMU) for human and livestock applications. Extensive Iberian swine production is based on a more sustainable and eco-friendly management system, providing an excellent opportunity to evaluate how sustained differences in AMU impact the resistome, not only in the animals but also on the farm environment. Here, we evaluate the resistome footprint of an extensive pig farming system, maintained for decades, as compared to that of industrialized intensive pig farming by analyzing 105 fecal, environmental and slurry metagenomes from 38 farms. Results Our results evidence a significantly higher abundance of antimicrobial resistance genes (ARGs) on intensive farms and a link between AMU and AMR to certain antimicrobial classes. We observed differences in the resistome across sample types, with a higher richness and dispersion of ARGs within environmental samples than on those from feces or slurry. Indeed, a deeper analysis revealed that differences among the three sample types were defined by taxa-ARGs associations. Interestingly, mobilome analyses revealed that the observed AMR differences between intensive and extensive farms could be linked to differences in the abundance of mobile genetic elements (MGEs). Thus, while there were no differences in the abundance of chromosomal-associated ARGs between intensive and extensive herds, a significantly higher abundance of integrons in the environment and plasmids, regardless of the sample type, was detected on intensive farms. Conclusions Overall, this study shows how AMU, production system, and sample type influence, mainly through MGEs, the profile and dispersion of ARGs in pig production. Video Abstract

Abstract Background Mounting evidence suggests a role for the gut microbiota in modulating brain physiology and behaviour, through bi-directional communication, along the gut-brain axis. As such, the gut microbiota represents a potential therapeutic target for influencing centrally mediated events and host behaviour. It is thus notable that the fermented milk beverage kefir has recently been shown to modulate the composition of the gut microbiota in mice. It is unclear whether kefirs have differential effects on microbiota-gut-brain axis and whether they can modulate host behaviour per se. Methods To address this, two distinct kefirs (Fr1 and UK4), or unfermented milk control, were administered to mice that underwent a battery of tests to characterise their behavioural phenotype. In addition, shotgun metagenomic sequencing of ileal, caecal and faecal matter was performed, as was faecal metabolome analysis. Finally, systemic immunity measures and gut serotonin levels were assessed. Statistical analyses were performed by ANOVA followed by Dunnett's post hoc test or Kruskal-Wallis test followed by Mann-Whitney U test. Results Fr1 ameliorated the stress-induced decrease in serotonergic signalling in the colon and reward-seeking behaviour in the saccharin preference test. On the other hand, UK4 decreased repetitive behaviour and ameliorated stress-induced deficits in reward-seeking behaviour. Furthermore, UK4 increased fear-dependent contextual memory, yet decreased milk gavage-induced improvements in long-term spatial learning. In the peripheral immune system, UK4 increased the prevalence of Treg cells and interleukin 10 levels, whereas Fr1 ameliorated the milk gavage stress-induced elevation in neutrophil levels and CXCL1 levels. Analysis of the gut microbiota revealed that both kefirs significantly changed the composition and functional capacity of the host microbiota, where specific bacterial species were changed in a kefir-dependent manner. Furthermore, both kefirs increased the capacity of the gut microbiota to produce GABA, which was linked to an increased prevalence in Lactobacillus reuteri. Conclusions Altogether, these data show that kefir can signal through the microbiota-gut-immune-brain axis and modulate host behaviour. In addition, different kefirs may direct the microbiota toward distinct immunological and behavioural modulatory effects. These results indicate that kefir can positively modulate specific aspects of the microbiota-gut-brain axis and support the broadening of the definition of psychobiotic to include kefir fermented foods. Video abstract.

Abstract Background A westernized diet comprising a high caloric intake from animal fats is known to influence the development of pathological inflammatory conditions. However, there has been relatively little focus upon the implications of such diets for the progression of infectious disease. Here, we investigated the influence of a high-fat (HF) diet upon parameters that influence Listeria monocytogenes infection in mice. Results We determined that short-term administration of a HF diet increases the number of goblet cells, a known binding site for the pathogen, in the gut and also induces profound changes to the microbiota and promotes a pro-inflammatory gene expression profile in the host. Host physiological changes were concordant with significantly increased susceptibility to oral L. monocytogenes infection in mice fed a HF diet relative to low fat (LF)- or chow-fed animals. Prior to Listeria infection, short-term consumption of HF diet elevated levels of Firmicutes including Coprococcus, Butyricicoccus, Turicibacter and Clostridium XIVa species. During active infection with L. monocytogenes, microbiota changes were further exaggerated but host inflammatory responses were significantly downregulated relative to Listeria-infected LF- or chow-fed groups, suggestive of a profound tempering of the host response influenced by infection in the context of a HF diet. The effects of diet were seen beyond the gut, as a HF diet also increased the sensitivity of mice to systemic infection and altered gene expression profiles in the liver. Conclusions We adopted a systems approach to identify the effects of HF diet upon L. monocytogenes infection through analysis of host responses and microbiota changes (both pre- and post-infection). Overall, the results indicate that short-term consumption of a westernized diet has the capacity to significantly alter host susceptibility to L. monocytogenes infection concomitant with changes to the host physiological landscape. The findings suggest that diet should be a consideration when developing models that reflect human infectious disease.

Abstract Background Despite the relevance of viral populations, our knowledge of (bacterio) phage populations, i.e., the phageome, suffers from the absence of a “gold standard” protocol for viral DNA extraction with associated in silico sequence processing analyses. To overcome this apparent hiatus, we present here a comprehensive performance evaluation of various protocols and propose an optimized pipeline that covers DNA extraction, sequencing, and bioinformatic analysis of phageome data. Results Five widely used protocols for viral DNA extraction from fecal samples were tested for their performance in removal of non-viral DNA. Moreover, we developed a novel bioinformatic platform, METAnnotatorX, for metagenomic dataset analysis. This in silico tool facilitates a range of read- and assembly-based analyses, including taxonomic profiling using an iterative multi-database pipeline, classification of contigs at genus and species level, as well as functional characterizations of reads and assembled data. Performances of METAnnotatorX were assessed through investigation of seven mother-newborn pairs, leading to the identification of shared phage genotypes, of which two were genomically decoded and characterized. METAnnotatorX was furthermore employed to evaluate a protocol for the identification of contaminant non-viral DNA in sequenced datasets and was exploited to determine the amount of metagenomic data needed for robust evaluation of human adult-derived (fecal) phageomes. Conclusions Results obtained in this study demonstrate that a comprehensive pipeline for analysis of phageomes will be pivotal for future explorations of the ecology of phages in the gut environment as well as for understanding their impact on the physiology and bacterial community kinetics as players of dysbiosis and homeostasis in the gut microbiota.

Abstract Background The use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads. Results Compositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R 2 = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R 2 = 0.127). The relative abundances predicted by the classifiers, apart from MetaPhlAn2, were apparently biased by reference genome sizes. Additionally, we observed varying false-positive rates among the classifiers. MetaPhlAn2 had the lowest false-positive rate, whereas SLIMM had the greatest false-positive rate. Strain-level analysis results were also similar across platforms. Each platform correctly identified the strains present in the mock community, but accuracy was improved slightly with greater sequencing depth. Notably, PanPhlAn detected the dominant strains in each kefir sample above 500,000 reads per sample. Again, the outputs from functional profiling analysis using SUPER-FOCUS were generally accordant between the platforms at different sequencing depths. Finally, and expectedly, metagenome assembly completeness was significantly lower on the MiSeq than either on the NextSeq (p = 0.03) or the Proton (p = 0.011), and it improved with increased sequencing depth. Conclusions Our results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did.