Characteristics of bacterial and yeast microbiomes in spontaneous and mixed-fermentation beer and cider.

The production of experimental beer and cider products has increased, worldwide. The complex microbiomes found in these beverages affect their organoleptic qualities and chemical compositions and can have diverse impacts on human health. The total diversity of a microbiome can be elucidated through the use of high-throughput sequencing and comprehensive data analysis tools. We analysed the bacterial and yeast microbiomes found in mixed and spontaneously fermented beers (n = 14) and unpasteurised apple ciders (n = 6), using high-throughput 16S rRNA and internal transcribed spacer (ITS) sequencing. The ratio of bacteria to yeast was measured using quantitative polymerase chain reaction (qPCR), and short-chain organic acids were analysed using high-performance liquid chromatography (HPLC). An upgraded version of the Knomics-Biota system was used to analyse the data. The microbiomes included both starter microorganisms and those that originate from the production environment and the raw materials. In addition to the common Saccharomyces and Brettanomyces, the yeast diversity included many non-conventional species. The bacterial community in beer was dominated by Lactobacillus species, whereas these communities were more diverse in cider. Lactobacillus acetotolerans was prevalent in wild ales, whereas Candida ethanolica was prevalent in cask-matured beverages. We observed complex patterns of subspecies-level yeast diversity across beer styles, breweries, and countries. Our study represents an exploratory analysis of non-conventional beer and cider microbiomes and metabolomes, which contributes information necessary to develop improved quality control processes and may drive innovative product development in experimental and artisanal brewing.

Metabolic Phenotypes as Potential Biomarkers for Linking Gut Microbiome With Inflammatory Bowel Diseases.

The gut microbiome is of utmost importance to human health. While a healthy microbiome can be represented by a variety of structures, its functional capacity appears to be more important. Gene content of the community can be assessed by "shotgun" metagenomics, but this approach is still too expensive. High-throughput amplicon-based surveys are a method of choice for large-scale surveys of links between microbiome, diseases, and diet, but the algorithms for predicting functional composition need to be improved to achieve good precision. Here we show how feature engineering based on microbial phenotypes, an advanced method for functional prediction from 16S rRNA sequencing data, improves identification of alterations of the gut microbiome linked to the disease. We processed a large collection of published gut microbial datasets of inflammatory bowel disease (IBD) patients to derive their community phenotype indices (CPI)-high-precision semiquantitative profiles aggregating metabolic potential of the community members based on genome-wide metabolic reconstructions. The list of selected metabolic functions included metabolism of short-chain fatty acids, vitamins, and carbohydrates. The machine-learning approach based on microbial phenotypes allows us to distinguish the microbiome profiles of healthy controls from patients with Crohn's disease and from ones with ulcerative colitis. The classifiers were comparable in quality to conventional taxonomy-based classifiers but provided new findings giving insights into possible mechanisms of pathogenesis. Feature-wise partial dependence plot (PDP) analysis of contribution to the classification result revealed a diversity of patterns. These observations suggest a constructive basis for defining functional homeostasis of the healthy human gut microbiome. The developed features are promising interpretable candidate biomarkers for assessing microbiome contribution to disease risk for the purposes of personalized medicine and clinical trials.

Knomics-Biota - a system for exploratory analysis of human gut microbiota data.

BACKGROUND: Metagenomic surveys of human microbiota are becoming increasingly widespread in academic research as well as in food and pharmaceutical industries and clinical context. Intuitive tools for investigating experimental data are of high interest to researchers. RESULTS: Knomics-Biota is a web-based resource for exploratory analysis of human gut metagenomes. Users can generate and share analytical reports corresponding to common experimental schemes (like case-control study or paired comparison). Interactive visualizations and statistical analysis are provided in association with the external factors and in the context of thousands of publicly available datasets arranged into thematic collections. The web-service is available at https://biota.knomics.ru. CONCLUSIONS: Knomics-Biota web service is a comprehensive tool for interactive metagenomic data analysis.

Microbiome Responses to an Uncontrolled Short-Term Diet Intervention in the Frame of the Citizen Science Project.

Personalized nutrition is of increasing interest to individuals actively monitoring their health. The relations between the duration of diet intervention and the effects on gut microbiota have yet to be elucidated. Here we examined the associations of short-term dietary changes, long-term dietary habits and lifestyle with gut microbiota. Stool samples from 248 citizen-science volunteers were collected before and after a self-reported 2-week personalized diet intervention, then analyzed using 16S rRNA sequencing. Considerable correlations between long-term dietary habits and gut community structure were detected. A higher intake of vegetables and fruits was associated with increased levels of butyrate-producing Clostridiales and higher community richness. A paired comparison of the metagenomes before and after the 2-week intervention showed that even a brief, uncontrolled intervention produced profound changes in community structure: resulting in decreased levels of Bacteroidaceae, Porphyromonadaceae and Rikenellaceae families and decreased alpha-diversity coupled with an increase of Methanobrevibacter, Bifidobacterium, Clostridium and butyrate-producing Lachnospiraceae- as well as the prevalence of a permatype (a bootstrapping-based variation of enterotype) associated with a higher diversity of diet. The response of microbiota to the intervention was dependent on the initial microbiota state. These findings pave the way for the development of an individualized diet.