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      Ketogenic diet enhances neurovascular function with altered gut microbiome in young healthy mice

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          Abstract

          Neurovascular integrity, including cerebral blood flow (CBF) and blood-brain barrier (BBB) function, plays a major role in determining cognitive capability. Recent studies suggest that neurovascular integrity could be regulated by the gut microbiome. The purpose of the study was to identify if ketogenic diet (KD) intervention would alter gut microbiome and enhance neurovascular functions, and thus reduce risk for neurodegeneration in young healthy mice (12–14 weeks old). Here we show that with 16 weeks of KD, mice had significant increases in CBF and P-glycoprotein transports on BBB to facilitate clearance of amyloid-beta, a hallmark of Alzheimer’s disease (AD). These neurovascular enhancements were associated with reduced mechanistic target of rapamycin (mTOR) and increased endothelial nitric oxide synthase (eNOS) protein expressions. KD also increased the relative abundance of putatively beneficial gut microbiota ( Akkermansia muciniphila and Lactobacillus), and reduced that of putatively pro-inflammatory taxa ( Desulfovibrio and Turicibacter). We also observed that KD reduced blood glucose levels and body weight, and increased blood ketone levels, which might be associated with gut microbiome alteration. Our findings suggest that KD intervention started in the early stage may enhance brain vascular function, increase beneficial gut microbiota, improve metabolic profile, and reduce risk for AD.

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          QIIME allows analysis of high-throughput community sequencing data.

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            Search and clustering orders of magnitude faster than BLAST.

            Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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              An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea

              Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a ‘taxonomy to tree' approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/.
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                Author and article information

                Contributors
                ailing.lin@uky.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 April 2018
                27 April 2018
                2018
                : 8
                : 6670
                Affiliations
                [1 ]ISNI 0000 0004 1936 8438, GRID grid.266539.d, Sanders-Brown Center on Aging, , University of Kentucky, ; Lexington, KY 40536 USA
                [2 ]ISNI 0000 0001 2175 0319, GRID grid.185648.6, Research Resources Center, , University of Illinois at Chicago, ; Chicago, IL 60612 USA
                [3 ]ISNI 0000 0004 1936 8438, GRID grid.266539.d, Deparment of Pharmacology and Nutritional Science, , University of Kentucky, ; Lexington, KY 40536 USA
                [4 ]ISNI 0000 0004 1936 8438, GRID grid.266539.d, Department of Molecular and Cellular Biochemistry, , University of Kentucky, ; Lexington, KY 40536 USA
                [5 ]ISNI 0000 0004 1936 8438, GRID grid.266539.d, Department of Pharmaceutical Sciences, , University of Kentucky, ; Lexington, KY 40536 USA
                [6 ]ISNI 0000 0004 1936 8438, GRID grid.266539.d, F. Joseph Halcomb III, M.D. Department of Biomedical Engineering, , University of Kentucky, ; Lexington, KY 40536 USA
                Author information
                http://orcid.org/0000-0003-2781-359X
                Article
                25190
                10.1038/s41598-018-25190-5
                5923270
                29703936
                3da9f89f-0d13-443b-8350-76d052d7b7a4
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 September 2017
                : 17 April 2018
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