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      Dietary Advanced Glycation End Products Shift the Gut Microbiota Composition and Induce Insulin Resistance in Mice

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          Abstract

          Objective

          This study aimed to explore the associations between gut microbiota characteristics and glycometabolic profiles in mice fed diets high in advanced glycation end products (AGEs).

          Methods

          C57BL/6 mice were exposed to a heat-treated diet or exogenous AGEs for 24 weeks, and glucose metabolism was assessed via the intraperitoneal glucose-tolerance test (IPGTT). Serum AGE and lipopolysaccharide-binding protein (LBP) levels were quantified using ELISA kits. 16S rDNA sequencing was performed to analyze the changes in gut microbiota according to α- and β-diversity. Key operational taxonomic units (OTUs) were evaluated, and co-abundance groups (CAGs) were delineated using weighted correlation network analysis. Associations between CAGs and clinical parameters were analyzed using Spearman correlation; predictive functional analysis of gut microbiota was performed using Kyoto Encyclopedia of Genes and Genomes data.

          Results

          We identified significant increases in fasting blood glucose (FBG) and fasting insulin levels, as well as homeostatic model assessment insulin resistance (HOMA-IR) and glucose area under the receiver operating characteristic curve from IPGTT, in the high-AGE diet groups relative to controls at week 24. Serum AGE and LBP levels were elevated, and the α- and β-diversity of gut microbiota reduced in high-AGE diet groups. We identified 92 key OTUs that clustered into six CAGs, revealing positive correlations between CAG2/3/5 and insulin levels and mice weight and negative correlations between CAG1/3/4/5 and AGE, FBG, and LBP levels and HOMA-IR in mice fed high-AGE diets. We observed a reduced abundance of butyrate-producing bacteria, including Bacteroidales_S24-7, Ruminococcaceae, and Lachnospiraceae, in mice fed high-AGE diets, with pathway analysis of gut microbiota revealing significantly enriched fructose and mannose metabolism.

          Conclusion

          High-AGE diets altered the gut microbiota composition and structure, and induced insulin resistance in mice. In the pathogenesis of insulin resistance, the loss of butyrate-producing bacteria might impair the colonic epithelial barrier, thereby triggering chronic low-grade inflammation.

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          Most cited references44

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          UPARSE: highly accurate OTU sequences from microbial amplicon reads.

          Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.
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            Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

            Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
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              Microbial interactions: from networks to models.

              Metagenomics and 16S pyrosequencing have enabled the study of ecosystem structure and dynamics to great depth and accuracy. Co-occurrence and correlation patterns found in these data sets are increasingly used for the prediction of species interactions in environments ranging from the oceans to the human microbiome. In addition, parallelized co-culture assays and combinatorial labelling experiments allow high-throughput discovery of cooperative and competitive relationships between species. In this Review, we describe how these techniques are opening the way towards global ecosystem network prediction and the development of ecosystem-wide dynamic models.
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                Author and article information

                Journal
                Diabetes Metab Syndr Obes
                Diabetes Metab Syndr Obes
                dmso
                Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy
                Dove
                1178-7007
                15 February 2022
                2022
                : 15
                : 427-437
                Affiliations
                [1 ]Department of Endocrinology and Metabolism, First Affiliated Hospital of Nanchang University , Nanchang, 330006, People’s Republic of China
                [2 ]Jiangxi Clinical Research Center for Endocrine and Metabolic Disease , Nanchang, 330006, People’s Republic of China
                [3 ]Jiangxi Branch of National Clinical Research Center for Metabolic Disease , Nanchang, 330006, People's Republic of China
                [4 ]Department of Medical Genetics and Cell biology, Medical College of Nanchang University , Nanchang, 330006, People’s Republic of China
                Author notes
                Correspondence: Jixiong Xu, Email Jixiong.Xu@ncu.edu.cn
                [*]

                These authors contributed equally to this work

                Article
                346411
                10.2147/DMSO.S346411
                8857970
                35210793
                d8cc30b0-d145-48e4-a1ac-4b9dbef6aea6
                © 2022 Wang et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 27 October 2021
                : 22 January 2022
                Page count
                Figures: 5, References: 44, Pages: 11
                Funding
                Funded by: National Natural Science Funds of China;
                Funded by: Natural Science Foundation of Jiangxi Province;
                This study was supported by grants from the National Natural Science Funds of China [grant no. 81760168] and Natural Science Foundation of Jiangxi Province [grant no. 20192BAB205031].
                Categories
                Original Research

                Endocrinology & Diabetes
                diet,advanced glycation end products,gut microbiota,insulin resistance

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