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      Sex-specific microglia state in the Neuroligin-4 knock-out mouse model of autism spectrum disorder

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          Abstract

          Neuroligin-4 (NLGN4) loss-of-function mutations are associated with monogenic heritable autism spectrum disorder (ASD) and cause alterations in both synaptic and behavioral phenotypes. Microglia, the resident CNS macrophages, are implicated in ASD development and progression. Here we studied the impact of NLGN4 loss in a mouse model, focusing on microglia phenotype and function in both male and female mice. NLGN4 depletion caused lower microglia density, less ramified morphology, reduced response to injury and purinergic signaling specifically in the hippocampal CA3 region predominantly in male mice. Proteomic analysis revealed disrupted energy metabolism in male microglia and provided further evidence for sexual dimorphism in the ASD associated microglial phenotype. In addition, we observed impaired gamma oscillations in a sex-dependent manner. Lastly, estradiol application in male NLGN4 −/− mice restored the altered microglial phenotype and function. Together, these results indicate that loss of NLGN4 affects not only neuronal network activity, but also changes the microglia state in a sex-dependent manner that could be targeted by estradiol treatment.

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          Most cited references71

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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              MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

              Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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                Author and article information

                Journal
                8800478
                1990
                Brain Behav Immun
                Brain Behav Immun
                Brain, behavior, and immunity
                0889-1591
                1090-2139
                5 May 2023
                July 2023
                29 March 2023
                10 July 2023
                : 111
                : 61-75
                Affiliations
                [a ]Cellular Neuroscience, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
                [b ]Department of Neurobiology, Harvard Medical School, Boston, USA
                [c ]Department of Ophthalmology, Charité – Universitätsmedizin Berlin, Germany
                [d ]Proteomics Platform, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute of Health, Berlin, Germany
                [e ]Institute of Neurophysiology, Charité – Universitätsmedizin, Berlin, Germany
                [f ]Department of Microbiology, Oslo University Hospital, Oslo, Norway
                [g ]Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
                [h ]Univ. Bordeaux, INRA, Bordeaux INP, NutriNeuro, Bordeaux, France
                [i ]Molecular Immunotherapy, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute of Health, Berlin, Germany
                [j ]Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
                [k ]Evergrande Center for Immunologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Germany
                [l ]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
                [m ]Psychoneuroimmunology, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
                Author notes
                [* ]Corresponding author at: Psychoneuroimmunology, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association, Robert-Rössle-Str. 10, 13125 Berlin, Germany and Charitè-Universitätsmedizin, Neuroimmunology and Experimental Ophthalmology, Augustenburger Platz 1, 13353 Berlin, Germany. susanne.wolf@ 123456charite.de , susanne.wolf@ 123456mdc-berlin.de (S.A. Wolf).
                Article
                NIHMS1892973
                10.1016/j.bbi.2023.03.023
                10330133
                37001827
                8309bbfe-f4d0-48de-83b0-5622e6978773

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

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                Categories
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                Neurosciences
                microglia,purinergic signaling,autism spectrum disorder,gamma oscillation,sex difference

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