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      Gut microbiota and plasma metabolites associated with diabetes in women with, or at high risk for, HIV infection

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          Abstract

          Background

          Gut microbiota alteration has been implicated in HIV infection and metabolic disorders. The relationship between gut microbiota and diabetes has rarely been studied in HIV-infected individuals, who have excess risk of metabolic disorders.

          Methods

          Our study during 2015–2016 enrolled predominantly African Americans and Hispanics in the Women's Interagency HIV Study. We studied 28 women with long-standing HIV infection under antiretroviral therapy and 20 HIV-uninfected, but at high risk of infection, women (16 HIV+ and 6 HIV- with diabetes). Fecal samples were analyzed by sequencing prokaryotic16S rRNA gene. Plasma metabolomics profiling was performed by liquid chromatography-tandem mass spectrometry.

          Findings

          No significant differences in bacterial α- or β-diversity were observed by diabetes or HIV serostatus (all P > .1). Relative abundances of four genera ( Finegoldia, Anaerococcus, Sneathia, and Adlercreutzia) were lower in women with diabetes compared to those without diabetes (all P < .01). In women with diabetes, plasma levels of several metabolites in tryptophan catabolism (e,g., kynurenine/tryptophan ratio), branched-chain amino acid and proline metabolism pathways were higher, while glycerophospholipids were lower (all P < .05). Results were generally consistent between HIV-infected and HIV-uninfected women, and no significant modification effects by HIV serostatus were observed (all P interaction  > 0.05). Anaerococcus, known to produce butyrate which is involved in anti-inflammation and glucose metabolism, showed an inverse correlation with kynurenine/tryptophan ratio ( r = −0.38, P < .01).

          Interpretation

          Among women with or at high risk for HIV infection, diabetes is associated with gut microbiota and plasma metabolite alteration, including depletion of butyrate-producing bacterial population along with higher tryptophan catabolism.

          Fund

          NHLBI (K01HL129892, R01HL140976) and FMF.

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          Most cited references29

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              Pathview: an R/Bioconductor package for pathway-based data integration and visualization

              Summary: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. Availability: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. Contact: luo_weijun@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                23 October 2018
                November 2018
                23 October 2018
                : 37
                : 392-400
                Affiliations
                [a ]Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
                [b ]Department of Biology, Long Island University, Brooklyn, NY, USA
                [c ]Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
                [d ]Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
                [e ]Department of Microbial Pathogens and Immunity, Rush Medical College, Chicago, IL, USA
                [f ]Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, USA
                [g ]Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
                [h ]Department of Microbiology & Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
                Author notes
                [* ]Corresponding author at: Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States qibin.qi@ 123456einstein.yu.edu
                Article
                S2352-3964(18)30455-9
                10.1016/j.ebiom.2018.10.037
                6286648
                30366816
                e130218f-3c05-4231-ad8a-15b8712c366f
                © 2018 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 17 August 2018
                : 11 October 2018
                : 12 October 2018
                Categories
                Research paper

                gut microbiota,metabolite,diabetes,hiv
                gut microbiota, metabolite, diabetes, hiv

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