Dataset Information

Metagenomics and Faecal Metabolomics Integrative Analysis towards the Impaired Glucose Regulation and Type 2 Diabetes in Uyghur-Related Omics.

ABSTRACT: Objective:Gut microbiota and their metabolites play an important role in the development of type 2 diabetes mellitus (T2DM). This research was designed to study the relationship between gut microbiota and faecal metabolites of Uyghur newly onset T2DM and impaired glucose regulation (IGR) patients. Materials and Methods:A total of 60 different glycemic Uyghur subjects were enrolled and divided into T2DM, IGR, and normal glucose tolerance (NGT) groups. Metagenomics and LC-MS-based untargeted faecal metabolomics were employed. Correlations between bacterial composition and faecal metabolomics were evaluated. Results:We discovered that the composition and diversity of gut microbiota in newly onset T2DM and IGR were different from those in NGT. The ?-diversity was higher in NGT than in T2DM and IGR; ?-diversity analysis revealed apparent differences in the bacterial community structures between patients with T2DM, IGR, and NGT. LC-MS faecal metabolomics analysis discovered different metabolomics features in the three groups. Alchornoic acid, PE (14?:?0/20?:?3), PI, L-tyrosine, LysoPC (15?:?0), protorifamycin I, pimelic acid, epothilone A, 7-dehydro-desmosterol, L-lysine, LysoPC (14?:?1), and teasterone are the most significant differential enriched metabolites. Most of the differential enriched metabolites were involved in metabolic processes, including carbohydrate metabolism, starch and sucrose metabolism, phenylpropanoid biosynthesis, and biosynthesis of amino acids. Procrustes analysis and correlation analysis identified correlations between gut microbiota and faecal metabolites. Matricin was positively correlated with Bacteroides and negatively correlated with Actinobacteria; protorifamycin I was negatively correlated with Actinobacteria; epothilone A was negatively correlated with Actinobacteria and positively correlated with Firmicutes; PA was positively correlated with Bacteroides and negatively correlated with Firmicutes; and cristacarpin was positively correlated with Actinobacteria; however, this correlation relationship does not imply causality. Conclusions:This study used joint metagenomics and metabolomics analyses to elucidate the relationship between gut microbiota and faecal metabolites in different glycemic groups, and the result suggested that metabolic disorders and gut microbiota dysbiosis occurred in Uyghur T2DM and IGR. The results provide a theoretical basis for studying the pathological mechanism for further research.

SUBMITTER: Nuli R

PROVIDER: S-EPMC6885810 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Metagenomics and Faecal Metabolomics Integrative Analysis towards the Impaired Glucose Regulation and Type 2 Diabetes in Uyghur-Related Omics.

Nuli Rebiya R Azhati Jureti J Cai Junxiu J Kadeer Aizhatiguli A Zhang Bing B Mohemaiti Patamu P

Journal of diabetes research 20191118

<h4>Objective</h4>Gut microbiota and their metabolites play an important role in the development of type 2 diabetes mellitus (T2DM). This research was designed to study the relationship between gut microbiota and faecal metabolites of Uyghur newly onset T2DM and impaired glucose regulation (IGR) patients.<h4>Materials and methods</h4>A total of 60 different glycemic Uyghur subjects were enrolled and divided into T2DM, IGR, and normal glucose tolerance (NGT) groups. Metagenomics and LC-MS-based u ...[more]

PMID: 31828159

Similar Datasets

Project description:Xiaoqu, one of three traditional jiuqu in China, is a saccharifying and fermenting agent used in Xiaoqu jiu brewing, with different ingredient compositions and preparation techniques used in various regions. The yield and quality of Xiaoqu jiu are significantly affected by the metabolites and microbiota of Xiaoqu; however, the associated relationship remains poorly understood. This study aimed to analyze this relationship in three typical traditional Xiaoqu from the Guizhou province in China. The non-volatile metabolites of Xiaoqu were detected using gas chromatography time-of-flight mass spectrometry, whereas the classification and metabolic potential of the microbiota were investigated using metagenomic sequencing. Results show that Firmicutes, Proteobacteria, and Actinobacteria represent the dominant bacterial phyla, with Lactobacillus, Bacillus, Acinetobacter, Leuconostoc, and Weissella found to be the dominant bacterial genera. Meanwhile, Ascomycota, Mucoromycota, and Basidiomycota are the dominant fungal phyla with Aspergillus, Saccharomyces, Pichia, Rhizopus, and Phycomyces being the predominant fungal genera. Functional annotation of the microbiota revealed a major association with metabolism of carbohydrates, cofactors, and vitamins, as well as amino acids. A total of 39 significantly different metabolites (SDMs) were identified that are involved in 47 metabolic pathways, primarily that of starch and sucrose; glycine, serine, and threonine; glyoxylate and dicarboxylate; pyruvate; as well as biosynthesis of pantothenate and CoA. Further, based on Spearman's correlation analysis, Aspergillus, Saccharomyces, Lactobacillus, Acetobacter, Weissella, Pantoea, Desmospora, and Bacillus are closely correlated with production of physicochemical indexes and SDMs. Moreover, the metabolic network generated for the breakdown of substrates and formation of SDMs in Xiaoqu was found to primarily center on the metabolism of carbohydrates and the tricarboxylic acid cycle. These results provide insights into the functional microorganisms and metabolic patterns present in traditional Guizhou Xiaoqu and might guide researchers in the production of stable and efficient Xiaoqu in the future.

Dataset Information

Metagenomics and Faecal Metabolomics Integrative Analysis towards the Impaired Glucose Regulation and Type 2 Diabetes in Uyghur-Related Omics.

Publications

Metagenomics and Faecal Metabolomics Integrative Analysis towards the Impaired Glucose Regulation and Type 2 Diabetes in Uyghur-Related Omics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets