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      Microbes modulate sympathetic neurons via a gut-brain circuit

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          Abstract

          Gut-brain connections monitor the intestinal tissue and its microbial and dietary content 1 , regulating both intestinal physiological functions such as nutrient absorption and motility 2, 3 , and brain–wired feeding behaviour 2 . It is therefore plausible that circuits exist to detect gut microbes and relay this information to central nervous system (CNS) areas that, in turn, regulate gut physiology 4 . We characterized the influence of the microbiota on enteric–associated neurons (EAN) by combining gnotobiotic mouse models with transcriptomics, circuit–tracing methods, and functional manipulations. We found that the gut microbiome modulates gut–extrinsic sympathetic neurons; while microbiota depletion led to increased cFos expression, colonization of germ-free mice with short-chain fatty acid–producing bacteria suppressed cFos expression in the gut sympathetic ganglia. Chemogenetic manipulations, translational profiling, and anterograde tracing identified a subset of distal intestine-projecting vagal neurons positioned to play an afferent role in microbiota–mediated modulation of gut sympathetic neurons. Retrograde polysynaptic neuronal tracing from the intestinal wall identified brainstem sensory nuclei activated during microbial depletion, as well as efferent sympathetic premotor glutamatergic neurons that regulate gastrointestinal transit. These results reveal microbiota–dependent control of gut extrinsic sympathetic activation through a gut-brain circuit.

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          Most cited references46

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          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                3 April 2020
                08 July 2020
                July 2020
                08 January 2021
                : 583
                : 7816
                : 441-446
                Affiliations
                [1 ]Laboratory of Mucosal Immunology, The Rockefeller University, New York, NY, USA
                [2 ]Laboratory of Molecular Genetics, Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA.
                [3 ]Laboratory of Neurophysiology and Behavior, The Rockefeller University, New York, NY, USA
                [4 ]Fishberg Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [5 ]Department of Microbiology and Immunology, Keio University School of Medicine, Tokyo, Japan
                [6 ]Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
                Author notes
                [‡]

                These authors contributed equally to this work.

                Author Contributions

                P.A.M. initiated, designed, performed and analysed the research, helped with supervision of the research and wrote the manuscript. M.S. performed brain AdipoClear and ClearMap analysis, provided guidance on brain experiments, analysed data, and helped with manuscript preparation. F.M. performed experiments, analysed data, and helped with figure and manuscript preparation. P.W. performed stereotaxic brain injections, provided technical advice, and reviewed the manuscript. Z.K. performed experiments, analysed research, and reviewed the manuscript. A.I. performed stereotaxic brain injections of the NTS and AP, provided technical advice, and reviewed the manuscript. K.P. performed i.c.v. and stereotaxic viral injection experiments. T.B.R.C. gave guidance on bioinformatic approaches, wrote analysis scripts, and performed 16S sequencing analysis. I.d.A, W.H., and M.P. gave expert advice and technical support with vagal nerve experiments including nerve recordings. K.H. and M.F. provided the Clostridium consortium and performed faecal SFB SCFA measurements. A.R. extracted, ran and validated the quantification SCFA quantifications. A.J.P. developed, validated and analysed the SCFA quantifications. J. R.C. supervised and analysed SCFA quantifications. J.d.M. performed electrophysiology experiments and analysed the data. D.M. initiated, designed and supervised the research, and wrote the manuscript.

                Article
                NIHMS1581734
                10.1038/s41586-020-2474-7
                7367767
                32641826
                0139a4b4-7ead-4fe5-b5de-f594000fd968

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