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      Age, brain region, and gene dosage-differential transcriptomic changes in Shank3-mutant mice

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          Abstract

          Shank3 is an abundant excitatory postsynaptic scaffolding protein implicated in various neurodevelopmental disorders, including autism spectrum disorder (ASD), Phelan-McDermid syndrome, intellectual disability, and schizophrenia. Shank3-mutant mice show various molecular, synaptic, and behavioral deficits, but little is known about how transcriptomic phenotypes vary across different ages, brain regions, and gene dosages. Here, we report transcriptomic patterns in the forebrains of juvenile and adult homozygous Shank3-mutant mice that lack exons 14–16 and also the prefrontal, hippocampal, and striatal transcriptomes in adult heterozygous and homozygous Shank3-mutant mice. The juvenile and adult mutant transcriptomes show patterns opposite from and similar to those observed in ASD (termed reverse-ASD and ASD-like patterns), respectively. The juvenile transcriptomic changes accompany synaptic upregulations and ribosomal and mitochondrial downregulations, whereas the adult transcriptome show opposite changes. The prefrontal, hippocampal, and striatal transcriptomes show differential changes in ASD-related gene expressions and biological functions associated with synapse, ribosome, mitochondria, and spliceosome. These patterns also differ across heterozygous and homozygous Shank3-mutant mice. These results suggest age, brain region, and gene dosage-differential transcriptomic changes in Shank3-mutant mice.

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          Most cited references65

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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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            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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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Contributors
                Journal
                Front Mol Neurosci
                Front Mol Neurosci
                Front. Mol. Neurosci.
                Frontiers in Molecular Neuroscience
                Frontiers Media S.A.
                1662-5099
                12 October 2022
                2022
                : 15
                : 1017512
                Affiliations
                [1] 1Center for Synaptic Brain Dysfunctions, Institute for Basic Science (IBS) , Daejeon, South Korea
                [2] 2Division of National Supercomputing, Korea Institute of Science and Technology Information (KISTI) , Daejeon, South Korea
                [3] 3Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST) , Daejeon, South Korea
                Author notes

                Edited by: Markus Wöhr, KU Leuven, Belgium

                Reviewed by: Michael J. Schmeisser, Johannes Gutenberg University Mainz, Germany; Gerhard Schratt, ETH Zürich, Switzerland

                *Correspondence: Eunjoon Kim, kime@ 123456kaist.ac.kr

                These authors have contributed equally to this work

                This article was submitted to Brain Disease Mechanisms, a section of the journal Frontiers in Molecular Neuroscience

                Article
                10.3389/fnmol.2022.1017512
                9597470
                36311023
                17c0d5c1-f1ca-4fb0-a699-88926fd8108a
                Copyright © 2022 Yoo, Yoo, Kang and Kim.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 August 2022
                : 15 September 2022
                Page count
                Figures: 7, Tables: 0, Equations: 0, References: 66, Pages: 16, Words: 8166
                Funding
                Funded by: Institute for Basic Science, doi 10.13039/501100010446;
                Award ID: IBS-R002-D1 to E.K.
                Funded by: Korea Institute of Science and Technology Information, doi 10.13039/501100003708;
                Award ID: K-19-L02-C07-S01 to H.K
                Categories
                Neuroscience
                Original Research

                Neurosciences
                autism spectrum disorder,shank3,age,cortex,hippocampus,striatum,gene dosage,rna-seq
                Neurosciences
                autism spectrum disorder, shank3, age, cortex, hippocampus, striatum, gene dosage, rna-seq

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