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      Dietary and Pharmacologic Manipulations of Host Lipids and Their Interaction With the Gut Microbiome in Non-human Primates

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          Abstract

          The gut microbiome influences nutrient processing as well as host physiology. Plasma lipid levels have been associated with the microbiome, although the underlying mechanisms are largely unknown, and the effects of dietary lipids on the gut microbiome in humans are not well-studied. We used a compilation of four studies utilizing non-human primates ( Chlorocebus aethiops and Macaca fascicularis) with treatments that manipulated plasma lipid levels using dietary and pharmacological techniques, and characterized the microbiome using 16S rDNA. High-fat diets significantly reduced alpha diversity (Shannon) and the Firmicutes/Bacteroidetes ratio compared to chow diets, even when the diets had different compositions and were applied in different orders. When analyzed for differential abundance using DESeq2, Bulleidia, Clostridium, Ruminococcus, Eubacterium, Coprocacillus, Lachnospira, Blautia, Coprococcus , and Oscillospira were greater in both chow diets while Succinivibrio, Collinsella, Streptococcus, and Lactococcus were greater in both high-fat diets (oleic blend or lard fat source). Dietary cholesterol levels did not affect the microbiome and neither did alterations of plasma lipid levels through treatments of miR-33 antisense oligonucleotide (anti-miR-33), Niemann–Pick C1-Like 1 (NPC1L1) antisense oligonucleotide (ASO), and inducible degrader of LDLR (IDOL) ASO. However, a liver X receptor (LXR) agonist shifted the microbiome and decreased bile acid levels. Fifteen genera increased with the LXR agonist, while seven genera decreased. Pseudomonas increased on the LXR agonist and was negatively correlated to deoxycholic acid, cholic acid, and total bile acids while Ruminococcus was positively correlated with taurolithocholic acid and taurodeoxycholic acid. Seven of the nine bile acids identified in the feces significantly decreased due to the LXR agonist, and total bile acids (nmol/g) was reduced by 62%. These results indicate that plasma lipid levels have, at most, a modest effect on the microbiome, whereas bile acids, derived in part from plasma lipids, are likely responsible for the indirect relationship between lipid levels and the microbiome.

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          QIIME allows analysis of high-throughput community sequencing data.

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            phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

            Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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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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                Author and article information

                Contributors
                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                26 August 2021
                2021
                : 8
                : 646710
                Affiliations
                [1] 1Departments of Medicine, Microbiology and Human Genetics, University of California, Los Angeles , Los Angeles, CA, United States
                [2] 2Department of Medicine, Division of Cardiology, University of California, Los Angeles , Los Angeles, CA, United States
                [3] 3Cardiovascular Research Center, University of Kentucky , Lexington, KY, United States
                [4] 4Cardiovascular and Metabolic Diseases, Novartis Institutes for Biomedical Research , Cambridge, MA, United States
                [5] 5Department of Physiology, School of Basic Medical Sciences, Shandong University , Jinan, China
                [6] 6Ionis Pharmaceuticals , Carlsbad, CA, United States
                [7] 7Department of Physiology, University of Kentucky , Lexington, KY, United States
                Author notes

                Edited by: Arash Haghikia, Charité Universitätsmedizin Berlin, Germany

                Reviewed by: Padraig Strappe, Central Queensland University, Australia; Lin Jia, The University of Texas at Dallas, United States

                *Correspondence: Aldons J. Lusis jlusis@ 123456mednet.ucla.edu

                This article was submitted to Hematology, a section of the journal Frontiers in Medicine

                Article
                10.3389/fmed.2021.646710
                8426918
                34513856
                9723fb84-9235-40a3-8416-453f19681513
                Copyright © 2021 Lang, Sedgeman, Cai, Layne, Wang, Pan, Lee, Temel and Lusis.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 December 2020
                : 03 August 2021
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 73, Pages: 17, Words: 11920
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: DK117850
                Award ID: HL088528
                Award ID: HL111932
                Award ID: HL144651
                Award ID: T32 DK007789
                Categories
                Medicine
                Original Research

                microbiome,primate,lipids,high-fat diet,bile acids
                microbiome, primate, lipids, high-fat diet, bile acids

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