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      Cerebral small vessel disease burden is associated with decreased abundance of gut Barnesiella intestinihominis bacterium in the Framingham Heart Study

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          Abstract

          A bidirectional communication exists between the brain and the gut, in which the gut microbiota influences cognitive function and vice-versa. Gut dysbiosis has been linked to several diseases, including Alzheimer's disease and related dementias (ADRD). However, the relationship between gut dysbiosis and markers of cerebral small vessel disease (cSVD), a major contributor to ADRD, is unknown. In this cross-sectional study, we examined the connection between the gut microbiome, cognitive, and neuroimaging markers of cSVD in the Framingham Heart Study (FHS). Markers of cSVD included white matter hyperintensities (WMH), peak width of skeletonized mean diffusivity (PSMD), and executive function (EF), estimated as the difference between the trail-making tests B and A. We included 972 FHS participants with MRI scans, neurocognitive measures, and stool samples and quantified the gut microbiota composition using 16S rRNA sequencing. We used multivariable association and differential abundance analyses adjusting for age, sex, BMI, and education level to estimate the association between gut microbiota and WMH, PSMD, and EF measures. Our results suggest an increased abundance of Pseudobutyrivibrio and Ruminococcus genera was associated with lower WMH and PSMD ( p values < 0.001), as well as better executive function ( p values < 0.01). In addition, in both differential and multivariable analyses, we found that the gram-negative bacterium Barnesiella intestinihominis was strongly associated with markers indicating a higher cSVD burden. Finally, functional analyses using PICRUSt implicated various KEGG pathways, including microbial quorum sensing, AMP/GMP-activated protein kinase, phenylpyruvate, and β-hydroxybutyrate production previously associated with cognitive performance and dementia. Our study provides important insights into the association between the gut microbiome and cSVD, but further studies are needed to replicate the findings.

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          Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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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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              Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms

              DNA sequencing continues to decrease in cost with the Illumina HiSeq2000 generating up to 600 Gb of paired-end 100 base reads in a ten-day run. Here we present a protocol for community amplicon sequencing on the HiSeq2000 and MiSeq Illumina platforms, and apply that protocol to sequence 24 microbial communities from host-associated and free-living environments. A critical question as more sequencing platforms become available is whether biological conclusions derived on one platform are consistent with what would be derived on a different platform. We show that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.
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                Author and article information

                Contributors
                fongang@uthscsa.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                21 August 2023
                21 August 2023
                2023
                : 13
                : 13622
                Affiliations
                [1 ]GRID grid.267309.9, ISNI 0000 0001 0629 5880, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, , University of Texas Health Science Center at San Antonio, ; San Antonio, TX USA
                [2 ]GRID grid.267309.9, ISNI 0000 0001 0629 5880, Department of Biochemistry and Structural Biology, , University of Texas Health Science Center at San Antonio, ; San Antonio, TX USA
                [3 ]GRID grid.267309.9, ISNI 0000 0001 0629 5880, Department of Population Health Sciences, , University of Texas Health Science Center at San Antonio, ; San Antonio, TX USA
                [4 ]GRID grid.267309.9, ISNI 0000 0001 0629 5880, Department of Medicine, , University of Texas Health Science Center at San Antonio, ; San Antonio, TX USA
                [5 ]GRID grid.510954.c, ISNI 0000 0004 0444 3861, Framingham Heart Study, ; Framingham, MA USA
                [6 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Department of Biostatistics, , Boston University School of Public Health, ; Boston, MA USA
                [7 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Department of Neurology, , Boston University School of Medicine, ; Boston, MA USA
                [8 ]GRID grid.267309.9, ISNI 0000 0001 0629 5880, Department of Neurology, , University of Texas Health Science Center at San Antonio, ; San Antonio, TX USA
                [9 ]GRID grid.449717.8, ISNI 0000 0004 5374 269X, Department of Human Genetics, South Texas Diabetes and Obesity Institute, , The University of Texas Rio Grande Valley School of Medicine, ; Brownsville, TX USA
                [10 ]GRID grid.449717.8, ISNI 0000 0004 5374 269X, Department of Neurosciences and Department of Human Genetics, , University of Texas Rio Grande Valley School of Medicine, ; Brownsville, TX USA
                [11 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, Department of Neurology, Alzheimer’s Disease Center, , University of California, Davis, ; Sacramento, CA USA
                [12 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Department of Medicine, Section of Cardiovascular Medicine, Boston Medical Center, , Boston University School of Medicine, ; Boston, MA USA
                [13 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Department of Medicine, Section of Preventive Medicine and Epidemiology, , Boston University School of Medicine, ; Boston, MA USA
                [14 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Department of Epidemiology, , Boston University School of Public Health, ; Boston, MA USA
                [15 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Boston University’s Center for Computing and Data Sciences, ; Boston, MA USA
                [16 ]GRID grid.215352.2, ISNI 0000000121845633, The University of Texas School of Public Health in San Antonio, ; San Antonio, TX USA
                [17 ]GRID grid.468222.8, The Long School of Medicine, , University of Texas Health Science Center, ; San Antonio, TX USA
                Article
                40872
                10.1038/s41598-023-40872-5
                10442369
                37604954
                4e5f3895-48d8-49b9-8328-ef766cdda706
                © Springer Nature Limited 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 February 2023
                : 17 August 2023
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                © Springer Nature Limited 2023

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                neurological disorders,blood-brain barrier,cognitive ageing,cognitive neuroscience

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