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      Negative feedback control of neuronal activity by microglia

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          Abstract

          Microglia, the brain’s resident macrophages, help to regulate brain function by removing dying neurons, pruning non-functional synapses, and producing ligands that support neuronal survival 1 . Here we show that microglia are also critical modulators of neuronal activity and associated behavioural responses in mice. Microglia respond to neuronal activation by suppressing neuronal activity, and ablation of microglia amplifies and synchronizes the activity of neurons, leading to seizures. Suppression of neuronal activation by microglia occurs in a highly region-specific fashion and depends on the ability of microglia to sense and catabolize extracellular ATP, which is released upon neuronal activation by neurons and astrocytes. ATP triggers the recruitment of microglial protrusions and is converted by the microglial ATP/ADP hydrolysing ectoenzyme CD39 into AMP; AMP is then converted into adenosine by CD73, which is expressed on microglia as well as other brain cells. Microglial sensing of ATP, the ensuing microglia-dependent production of adenosine, and the adenosine-mediated suppression of neuronal responses via the adenosine receptor A 1R are essential for the regulation of neuronal activity and animal behaviour. Our findings suggest that this microglia-driven negative feedback mechanism operates similarly to inhibitory neurons and is essential for protecting the brain from excessive activation in health and disease.

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          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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            HTSeq—a Python framework to work with high-throughput sequencing data

            Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                11 September 2020
                30 September 2020
                October 2020
                30 March 2021
                : 586
                : 7829
                : 417-423
                Affiliations
                [1 ]Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [2 ]Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [3 ]Center for Glial Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [4 ]Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [5 ]Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
                [6 ]Department of Anatomy and Molecular Cell Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan
                [7 ]Division of System Neuroscience, Kobe University Graduate School of Medicine, Kobe, Japan
                [8 ]Department of Pharmacology, University of Minnesota, Minneapolis, MN, USA
                [9 ]Center for Brain Immunology and Glia, Department of Neuroscience, University of Virginia, Charlottesville, VA, USA
                [10 ]Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
                [11 ]Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
                [12 ]Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
                [13 ]Department of Genetics and Genomic Sciences, Icahn Institute of Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [14 ]Department of Biochemistry and Structural Biology, University of Texas Health Science Center, San Antonio, TX, USA
                [15 ]Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
                [16 ]Department of Anesthesia, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
                [17 ]Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
                [18 ]Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
                [19 ]Vanderbilt Center for Addiction Research, Vanderbilt University, Nashville, TN, USA
                [20 ]Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
                [21 ]Department of Psychiatry and Behavioral Sciences, Vanderbilt University, Nashville, TN, USA
                [22 ]Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
                [23 ]The Broad Institute of MIT and Harvard, Cambridge, MA, USA
                Author notes

                Contributions

                A.S. and A.B. conceived and designed the study. A.B. did molecular, behavioral, FACS and imaging experiments. H.J.S. did primary neuronal culture, microglia isolation, microglia culture, FACS and Axion microelectrode array experiments. P.A. did in vivo TRAP experiments. A.B., X.C., A.N., V.G., and A.S. designed two-photon imaging which were performed by X.C. and A.N. A.K. built the customized two-photon system. A.B., A.I., H.W., and A.S. designed the two-photon imaging of microglial protrusions that were performed by A.I. A.T.C. and R.S. performed single nuclei 10X sequencing. Y.W. analyzed single nuclei 10X sequencing data. Y.E.L. analyzed bulk RNA-Seq data from TRAP experiments. A.S., D.J.S., and S.M.G. designed experiments to measure neuronal excitability that were conducted by S.M.G. A.B., M.I., P.J.K., and A.S. designed experiment to measure sEPSCs that were conducted by M.I. A.S. and A.B. designed and P.H. performed molecular and imaging experiments. C.L. and W.G.J. conducted the HPLC analysis. M.G.K. and E.S.C. conducted the microdialysis experiments. A.B., J.O.U., and U.B.E. conducted seizure susceptibility experiments on P2ry12 −/− mice. S.C.R. generated Cd39 fl/fl mice. J.X.J. generated Csf1 fl/fl mice. M.C. generated Il34 fl/fl mice. M.W. and F.J.Q. generated Cd39 fl/flCx3cr1 CreErt2/+(Jung) mice and A.B., M.W., F.J.Q., and A.S. designed behavioral experiments. A.S. and A.B. wrote the manuscript. All authors discussed results, and provided input and edits on the manuscript.

                Correspondence to Anne Schaefer ( anne.schaefer@ 123456mssm.edu ).
                Article
                NIHMS1624874
                10.1038/s41586-020-2777-8
                7577179
                32999463
                86f35eb4-3b70-4829-a0a4-450959babc4d

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