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      Contribution of uremic dysbiosis to insulin resistance and sarcopenia

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          Abstract

          Background

          Chronic kidney disease (CKD) leads to insulin resistance (IR) and sarcopenia, which are associated with a high mortality risk in CKD patients; however, their pathophysiologies remain unclear. Recently, alterations in gut microbiota have been reported to be associated with CKD. We aimed to determine whether uremic dysbiosis contributes to CKD-associated IR and sarcopenia.

          Methods

          CKD was induced in specific pathogen-free mice via an adenine-containing diet; control animals were fed a normal diet. Fecal microbiota transplantation (FMT) was performed by oral gavage in healthy germ-free mice using cecal bacterial samples obtained from either control mice (control-FMT) or CKD mice (CKD-FMT). Vehicle mice were gavaged with sterile phosphate-buffered saline. Two weeks after inoculation, mice phenotypes, including IR and sarcopenia, were evaluated.

          Results

          IR and sarcopenia were evident in CKD mice compared with control mice. These features were reproduced in CKD-FMT mice compared with control-FMT and vehicle mice with attenuated insulin-induced signal transduction and mitochondrial dysfunction in skeletal muscles. Intestinal tight junction protein expression and adipocyte sizes were lower in CKD-FMT mice than in control-FMT mice. Furthermore, CKD-FMT mice showed systemic microinflammation, increased concentrations of serum uremic solutes, fecal bacterial fermentation products and elevated lipid content in skeletal muscle. The differences in gut microbiota between CKD and control mice were mostly consistent between CKD-FMT and control-FMT mice.

          Conclusions

          Uremic dysbiosis induces IR and sarcopenia, leaky gut and lipodystrophy.

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          Most cited references56

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          QIIME allows analysis of high-throughput community sequencing data.

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            Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

            The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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              Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

              The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Nephrology Dialysis Transplantation
                Oxford University Press (OUP)
                0931-0509
                1460-2385
                September 01 2020
                September 01 2020
                June 14 2020
                September 01 2020
                September 01 2020
                June 14 2020
                : 35
                : 9
                : 1501-1517
                Affiliations
                [1 ]Department of Internal Medicine, Division of Endocrinology, Metabolism and Nephrology, Keio University School of Medicine, Tokyo, Japan
                [2 ]AMED-CREST, Japan Agency for Medical Research and Development, Tokyo, Japan
                [3 ]Department of Applied Biological Science, Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan
                [4 ]Institute of Physiology, University of Zurich, Zurich, Switzerland
                Article
                10.1093/ndt/gfaa076
                32535631
                d1b0f1c1-620e-4f1f-a070-d01ecd5390d4
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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