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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 references 56

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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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              Is Open Access

              UCHIME improves sensitivity and speed of chimera detection

              Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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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
                © 2020

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