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.

BackgroundMetagenomic 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.ResultsKnomics-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.ConclusionsKnomics-Biota web service is a comprehensive tool for interactive metagenomic data analysis.Electronic supplementary materialThe online version of this article (10.1186/s13040-018-0187-3) contains supplementary material, which is available to authorized users.

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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.

Summary: 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 exploration of experimental data are of high interest to researchers. Knomics-Biota is a Web-based resource for exploratory analysis of human gut metagenomes. Users can generate analytical reports that correspond to common experimental schemes (like case-control study or paired comparison). Statistical analysis and visualizations of microbiota composition are provided in association with the external factors and in the context of thousands of publicly available datasets. Availability and Implementation: The Web-service is available at https:/