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      Unexpected role of interferon-γ in regulating neuronal connectivity and social behavior

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          Abstract

          Immune dysfunction is commonly associated with several neurological and mental disorders. Although the mechanisms by which peripheral immunity may influence neuronal function are largely unknown, recent findings implicate meningeal immunity influencing behavior, such as spatial learning and memory 1 . Here we show that meningeal immunity is also critical for social behavior; mice deficient in adaptive immunity exhibit social deficits and hyper-connectivity of fronto-cortical brain regions. Associations between rodent transcriptomes from brain and cellular transcriptomes in response to T cell–derived cytokines suggest a strong interaction between social behavior and interferon-gamma (IFN-γ) driven responses. Concordantly, we demonstrate that inhibitory neurons respond to IFN-γ and increase GABAergic currents in projection neurons, suggesting that IFN-γ is a molecular link between meningeal immunity and neural circuits recruited for social behavior. Meta-analysis on the transcriptomes of a range of organisms revealed that rodents, fish, and flies elevate IFN-γ/JAK-STAT–dependent gene signatures in a social context, suggesting that the IFN-γ signaling pathway could mediate a co-evolutionary link between social/aggregation behavior and an efficient anti-pathogen response. This study implicates adaptive immune dysfunction, in particular IFN-γ, in disorders characterized by social dysfunction and suggests a co-evolutionary link between social behavior and an anti-pathogen immune response driven by IFN-γ signaling.

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          Most cited references 70

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                9 June 2016
                21 July 2016
                21 January 2017
                : 535
                : 7612
                : 425-429
                Affiliations
                [1 ]Center for Brain Immunology and Glia, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
                [2 ]Department of Neuroscience, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
                [3 ]Department of Pharmacology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
                [4 ]Neuroscience Graduate Program, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
                [5 ]Medical Scientist Training Program, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
                [6 ]Department of Radiology and Medical Imaging, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
                [7 ]Department of Neurosurgery, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
                [8 ]Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA 01655, USA
                [9 ]Department of Public Health Sciences, School of Medicine University of Virginia, Charlottesville, VA 22908, USA
                [10 ]Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01655, USA
                Author notes
                [* ]Correspondence to: A.J.F. ( ajf5v@ 123456virginia.edu ), V.L. ( Vladimir.Litvak@ 123456umassmed.edu ), or J.K. ( kipnis@ 123456virginia.edu ); Tel: +1 434-982-3858, Fax: +1 434-982-4380

                J.K. and V.L. are co-senior authors

                Article
                NIHMS793134
                10.1038/nature18626
                4961620
                27409813

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