High-throughput sequencing-based characterisation of fermented foods and their impacts on host gut microbiota

Fermentation has been practised worldwide for millennia as a method to preserve or enhance foods, and, today, fermented foods remain a significant component in the human diet. Additionally, these foods are becoming increasingly popular since numerous health benefits have been ascribed to them, and thus it is necessary to (1) optimise their production, (2) assess their safety, and (3) determine the mechanisms by which they confer these effects. In this thesis, we examine if high-throughput sequencing technologies, particularly shotgun metagenomics, can address these needs. In Chapters 3 and 4, we show that shotgun metagenomics, when used alongside metabolomics, can be applied to understand the ways in which the microbiota influences flavour development in fermented foods. In Chapter 5, we report that shotgun metagenomics can accurately, and rapidly, detect pathogenic strains in fermented foods. In Chapter 6, we demonstrate that the choice of bioinformatics tools has a significant impact on shotgun metagenomic analysis of fermented foods. Finally, in Chapter 7, we provide evidence that a traditional fermented food modulates the gut microbiota in mice, while simultaneously reducing anxious-like behaviours in the animals. Overall, this thesis highlights that high-throughput sequencing is an invaluable tool for studying fermented foods. We illustrate that the technology not only expands our knowledge on the roles played by microorganisms during food fermentations, but it can also be used to ensure food safety or even investigate the ways in which these foods affect the host. Thus, high-throughput sequencing can bridge the gap between traditional food microbiology and health

Kefir ameliorates specific microbiota-gut-brain axis impairments in a mouse model relevant to autism spectrum disorder.

Abstract Autism spectrum disorder (ASD) is one of the most severe developmental disorders, affecting on average 1 in 150 children worldwide. There is a great need for more effective strategies to improve quality of life in ASD subjects. The gut microbiome has emerged as a potential therapeutic target in ASD. A novel modulator of the gut microbiome, the traditionally fermented milk drink kefir, has recently been shown to modulate the microbiota and decrease repetitive behaviour, one of the hallmarks of ASD, in mice. As such, we hypothesized that kefir could ameliorate behavioural deficits in a mouse model relevant to ASD; the BTBR T+ Itpr3tf/J mouse strain. To this end, adult mice were administered either kefir (UK4) or a milk control for three weeks as treatment lead-in, after which they were assessed for their behavioural phenotype using a battery of tests. In addition, we assessed systemic immunity by flow cytometry and the gut microbiome using shotgun metagenomic sequencing. We found that indeed kefir decreased repetitive behaviour in this mouse model. Furthermore, kefir prolonged stress-induced increases in corticosterone 60 min post-stress, which was accompanied by an ameliorated innate immune response as measured by LY6Chi monocyte levels. In addition, kefir increased the levels of anti-inflammatory Treg cells in mesenteric lymph nodes (MLNs). Kefir also increased the relative abundance of Lachnospiraceae bacterium A2, which correlated with reduced repetitive behaviour and increased Treg cells in MLNs. Functionally, kefir modulated various predicted gut microbial pathways, including the gut-brain module S-Adenosylmethionine (SAM) synthesis, as well as L-valine biosynthesis and pyruvate fermentation to isobutanol, which all correlated with repetitive behaviour. Taken together our data show that kefir modulates peripheral immunoregulation, can ameliorate specific ASD behavioural dysfunctions and modulates selective aspects of the composition and function of the gut microbiome, indicating that kefir supplementation might prove a viable strategy in improving quality of life in ASD subjects

Testing the transferability of a coarse-grained model to intrinsically disordered proteins

The intermediate-resolution coarse-grained protein model PLUM [T. Bereau and M. Deserno, J. Chem. Phys., 2009, 130, 235106] is used to simulate small systems of intrinsically disordered proteins involved in biomineralisation. With minor adjustments to reduce bias toward stable secondary structure, the model generates conformational ensembles conforming to structural predictions from atomistic simulation. Without additional structural information as input, the model distinguishes regions of the chain by predicted degree of disorder, manifestation of structure, and involvement in chain dimerisation. The model is also able to distinguish dimerisation behaviour between one intrinsically disordered peptide and a closely related mutant. We contrast this against the poor ability of PLUM to model the S1 quartz-binding peptide

Species classifier choice is a key consideration when analysing low-complexity food microbiome data

peer-reviewedBackground 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, R2 = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R2 = 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.This research was funded by Science Foundation Ireland in the form of a centre grant (APC Microbiome Institute grant number SFI/12/RC/2273). Research in the Cotter laboratory is also funded by Science Foundation Ireland through the PI award “Obesibiotics” (11/PI/1137). Orla O’Sullivan is funded by Science Foundation Ireland through a Starting Investigator Research Grant award (13/SIRG/2160)

Bioinformatic approaches for studying the microbiome of fermented food

peer-reviewedHigh-throughput DNA sequencing-based approaches continue to revolutionise our understanding of microbial ecosystems, including those associated with fermented foods. Metagenomic and metatranscriptomic approaches are state-of-the-art biological profiling methods and are employed to investigate a wide variety of characteristics of microbial communities, such as taxonomic membership, gene content and the range and level at which these genes are expressed. Individual groups and consortia of researchers are utilising these approaches to produce increasingly large and complex datasets, representing vast populations of microorganisms. There is a corresponding requirement for the development and application of appropriate bioinformatic tools and pipelines to interpret this data. This review critically analyses the tools and pipelines that have been used or that could be applied to the analysis of metagenomic and metatranscriptomic data from fermented foods. In addition, we critically analyse a number of studies of fermented foods in which these tools have previously been applied, to highlight the insights that these approaches can provide.European Union’s Horizon 2020 research and innovation programm

Microbial Succession and Flavor Production in the Fermented Dairy Beverage Kefir

peer-reviewedKefir is a putatively health-promoting dairy beverage that is produced when a kefir grain, consisting of a consortium of microorganisms, is added to milk to initiate a natural fermentation. Here, a detailed analysis was carried out to determine how the microbial population, gene content, and flavor of three kefirs from distinct geographic locations change over the course of 24-h fermentations. Metagenomic sequencing revealed that Lactobacillus kefiranofaciens was the dominant bacterial species in kefir during early stages of fermentations but that Leuconostoc mesenteroides became more prevalent in later stages. This pattern is consistent with an observation that genes involved in aromatic amino acid biosynthesis were absent from L. kefiranofaciens but were present in L. mesenteroides. Additionally, these shifts in the microbial community structure, and associated pathways, corresponded to changes in the levels of volatile compounds. Specifically, Acetobacter spp. correlated with acetic acid; Lactobacillus spp. correlated with carboxylic acids, esters and ketones; Leuconostoc spp. correlated with acetic acid and 2,3-butanedione; and Saccharomyces spp. correlated with esters. The correlation data suggest a causal relationship between microbial taxa and flavor that is supported by observations that addition of L. kefiranofaciens NCFB 2797 increased the levels of esters and ketones whereas addition of L. mesenteroides 213M0 increased the levels of acetic acid and 2,3-butanedione. Finally, we detected genes associated with probiotic functionalities in the kefir microbiome. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein and can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods. IMPORTANCE Traditional fermented foods represent relatively low-complexity microbial environments that can be used as model microbial communities to understand how microbes interact in natural environments. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein. In the process, the link between individual species, and associated pathways, with flavor compounds is revealed and several genes that could be responsible for the purported gut health-associated benefits of consuming kefir are identified. Ultimately, in addition to providing an important fundamental insight into microbial interactions, this information can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods

Meta-analysis of cheese microbiomes highlights contributions to multiple aspects of quality

peer-reviewedA detailed understanding of the cheese microbiome is key to the optimization of flavour, appearance, quality and safety. Accordingly, we conducted a high-resolution meta-analysis of cheese microbiomes and corresponding volatilomes. Using 77 new samples from 55 artisanal cheeses from 27 Irish producers combined with 107 publicly available cheese metagenomes, we recovered 328 metagenome-assembled genomes, including 47 putative new species that could influence taste or colour through the secretion of volatiles or biosynthesis of pigments. Additionally, from a subset of samples, we found that differences in the abundances of strains corresponded with levels of volatiles. Genes encoding bacteriocins and other antimicrobials, such as pseudoalterin, were common, potentially contributing to the control of undesirable microorganisms. Although antibiotic-resistance genes were detected, evidence suggested they are not of major concern with respect to dissemination to other microbiomes. Phages, a potential cause of fermentation failure, were abundant and evidence for phage-mediated gene transfer was detected. The anti-phage defence mechanism CRISPR was widespread and analysis thereof, and of anti-CRISPR proteins, revealed a complex interaction between phages and bacteria. Overall, our results provide new and substantial technological and ecological insights into the cheese microbiome that can be applied to further improve cheese production.Science Foundation Irelan