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      Rescuing Over-activated Microglia Restores Cognitive Performance in Juvenile Animals of the Dp(16) Mouse Model of Down Syndrome

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          Summary

          Microglia are brain-resident immune cells and regulate mechanisms essential for cognitive functions. Down syndrome (DS), the most frequent cause of genetic intellectual disability, is caused by a supernumerary chromosome 21, containing also genes related to the immune system. In the hippocampus of the Dp(16) mouse model of DS and DS individuals, we found activated microglia, as assessed by their morphology; activation markers; and, for DS mice, electrophysiological profile. Accordingly, we found increased pro-inflammatory cytokine levels and altered interferon signaling in Dp(16) hippocampi. DS mice also showed decreased spine density and activity of hippocampal neurons and hippocampus-dependent cognitive behavioral deficits. Depletion of defective microglia or treatment with a commonly used anti-inflammatory drug rescued the neuronal spine and activity impairments and cognitive deficits in juvenile Dp(16) mice. Our results suggest an involvement of microglia in Dp(16)-mouse cognitive deficits and identify a new potential therapeutic approach for cognitive disabilities in DS individuals.

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          Highlights

          • DS mice display microglia alterations and cognitive impairment

          • Depletion of microglia rescues cognitive impairment in DS mice

          • Acetaminophen treatment rescues microglia and cognitive impairments in DS mice

          • Brain samples of DS people recapitulate microglia alterations observed in DS mice

          Abstract

          Pinto, Morelli et al. identify a critical role for activated microglia in cognitive impairments of Down syndrome mouse models that can be ameliorated by either depleting microglia or using anti-inflammatory drugs to reduce microglia activation. In this work, microglia activation is also revealed in brains of people with Down syndrome.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Contributors
                Journal
                Neuron
                Neuron
                Neuron
                Cell Press
                0896-6273
                1097-4199
                09 December 2020
                09 December 2020
                : 108
                : 5
                : 887-904.e12
                Affiliations
                [1 ]BIO@SNS, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy
                [2 ]Brain Development and Disease Laboratory, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
                [3 ]Core Facilities - Clinical Proteomics and Metabolomics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
                [4 ]Cellular Biology Department, University of Valencia, Valencia, Spain
                [5 ]Electron Microscopy Facility, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
                [6 ]Dulbecco Telethon Institute, Rome, Italy
                Author notes
                []Corresponding author laura.cancedda@ 123456iit.it
                [7]

                These authors contributed equally

                [8]

                Senior author

                [9]

                Lead Contact

                Article
                S0896-6273(20)30710-8
                10.1016/j.neuron.2020.09.010
                7736620
                33027640
                06daecc7-f26b-43d4-a613-a99d7b8ea43a
                © 2020 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
                : 15 January 2020
                : 16 July 2020
                : 4 September 2020
                Categories
                Article

                Neurosciences
                down syndrome,neurodevelopmental disorders,microglia,dp(16),cognitive defects,acetaminophen,dendritic spines,neuroinflammation,ts65dn,proteomics

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