Dataset Information

Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning.

ABSTRACT: The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.

SUBMITTER: Galkin F

PROVIDER: S-EPMC7298543 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning.

Galkin Fedor F Mamoshina Polina P Aliper Alex A Putin Evgeny E Moskalev Vladimir V Gladyshev Vadim N VN Zhavoronkov Alex A

iScience 20200523 6

The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural ...[more]

PMID: 32534441

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Project description:BackgroundBile salt hydrolase plays an important role in bile acid-mediated signaling pathways, which regulate lipid absorption, glucose metabolism, and energy homeostasis. Several reports suggest that changes in the composition of bile acids are found in many diseases caused by dysbacteriosis.ResultsHere, we present the taxonomic identification of bile salt hydrolase (BSH) in human microbiota and elucidate the abundance and activity differences of various bacterial BSH among 11 different populations from six continents. For the first time, we revealed that bile salt hydrolase protein sequences (BSHs) are distributed in 591 intestinal bacterial strains within 117 genera in human microbiota, and 27.52% of these bacterial strains containing BSH paralogs. Significant variations are observed in BSH distribution patterns among different populations. Based on phylogenetic analysis, we reclassified these BSHs into eight phylotypes and investigated the abundance patterns of these phylotypes among different populations. From the inspection of enzyme activity among different BSH phylotypes, BSH-T3 showed the highest enzyme activity and is only found in Lactobaclillus. The phylotypes of BSH-T5 and BSH-T6 mainly from Bacteroides with high percentage of paralogs exhibit different enzyme activity and deconjugation activity. Furthermore, we found that there were significant differences between healthy individuals and patients with atherosclerosis and diabetes in some phylotypes of BSHs though the correlations were pleiotropic.ConclusionThis study revealed the taxonomic and abundance profiling of BSH in human gut microbiome and provided a phylogenetic-based system to assess BSHs activity by classifying the target sequence into specific phylotype. Furthermore, the present work disclosed the variation patterns of BSHs among different populations of geographical regions and health/disease cohorts, which is essential to understand the role of BSH in the development and progression of related diseases.

Dataset Information

Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning.

Publications

Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning.

Similar Datasets

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