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      Neuron-specific transcriptomic signatures indicate neuroinflammation and altered neuronal activity in ASD temporal cortex

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          Significance

          We present a comprehensive assessment of neuronal cell-type-specific gene expression and alternative splicing changes in ASD cortex, directly comparing RNA-seq results from bulk tissue with isolated neurons. We observe strong signatures of cell stress and neural-immune/inflammatory pathway activation present within ASD neurons—a signal that is typically attributed to astrocyte/microglial populations. Our findings also provide further evidence for the hypothesized imbalance of excitatory to inhibitory neuronal activity in the brains of individuals with ASD. Moreover, we find that the transcriptomic architecture of ASD interacts substantially with age, thus revealing windows of opportunity for treatments that target specific molecular pathology.

          Abstract

          Autism spectrum disorder (ASD) is a highly heterogeneous disorder, yet transcriptomic profiling of bulk brain tissue has identified substantial convergence among dysregulated genes and pathways in ASD. However, this approach lacks cell-specific resolution. We performed comprehensive transcriptomic analyses on bulk tissue and laser-capture microdissected (LCM) neurons from 59 postmortem human brains (27 ASD and 32 controls) in the superior temporal gyrus (STG) of individuals ranging from 2 to 73 years of age. In bulk tissue, synaptic signaling, heat shock protein-related pathways, and RNA splicing were significantly altered in ASD. There was age-dependent dysregulation of genes involved in gamma aminobutyric acid (GABA) ( GAD1 and GAD2) and glutamate ( SLC38A1) signaling pathways. In LCM neurons, AP-1-mediated neuroinflammation and insulin/IGF-1 signaling pathways were upregulated in ASD, while mitochondrial function, ribosome, and spliceosome components were downregulated. GABA synthesizing enzymes GAD1 and GAD2 were both downregulated in ASD neurons. Mechanistic modeling suggested a direct link between inflammation and ASD in neurons, and prioritized inflammation-associated genes for future study. Alterations in small nucleolar RNAs (snoRNAs) associated with splicing events suggested interplay between snoRNA dysregulation and splicing disruption in neurons of individuals with ASD. Our findings supported the fundamental hypothesis of altered neuronal communication in ASD, demonstrated that inflammation was elevated at least in part in ASD neurons, and may reveal windows of opportunity for biotherapeutics to target the trajectory of gene expression and clinical manifestation of ASD throughout the human lifespan.

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          Most cited references85

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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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            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
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                2 March 2023
                7 March 2023
                2 March 2023
                : 120
                : 10
                : e2206758120
                Affiliations
                [1] aDepartment of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California , Los Angeles, CA 90095
                [2] bDepartment of Human Genetics, David Geffen School of Medicine, University of California , Los Angeles, CA 90095
                [3] cSemel Institute for Neuroscience and Human Behavior, University of California , Los Angeles, CA 90095
                [4] dDepartment of Psychiatry and Behavioral Sciences, School of Medicine, University of California, Davis , Sacramento, CA 95817
                [5] eUniversity of California, Davis, MIND Institute , Sacramento, CA 95817
                [6] fDepartment of Neurology, University of California, Davis, School of Medicine , Sacramento, CA 95817
                [7] gDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104
                [8] hDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104
                [9] iLifespan Brain Institute, Penn Med and the Children’s Hospital of Philadelphia , Philadelphia, PA 19104
                Author notes

                Edited by Huda Akil, University of Michigan-Ann Arbor, Ann Arbor, MI; received July 12, 2022; accepted December 28, 2022

                2M.J.G., B.S., and C.M.S. contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-0690-6949
                https://orcid.org/0000-0001-5800-5128
                https://orcid.org/0000-0002-5440-8816
                Article
                202206758
                10.1073/pnas.2206758120
                10013873
                36862688
                7d469e2e-512d-43e7-be4f-8bdb313c24e6
                Copyright © 2023 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 12 July 2022
                : 28 December 2022
                Page count
                Pages: 11, Words: 6529
                Funding
                Funded by: HHS | NIH | National Institute of Mental Health (NIMH), FundRef 100000025;
                Award ID: MH108909
                Award Recipient : Boryana Stamova Award Recipient : Cynthia M Schumann
                Categories
                dataset, Dataset
                research-article, Research Article
                neuro, Neuroscience
                424
                Biological Sciences
                Neuroscience

                asd,transcriptome,neuron-specific
                asd, transcriptome, neuron-specific

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